EPA/600/R-15/066
SLFPA Environmental Protection External Review Draft
VLI ** Agency November 2015
www.epa.gov/isa
Integrated Science Assessment for
Sulfur Oxides—Health Criteria
(External Review Draft)
November 2015
National Center for Environmental Assessment—RTP Division
Office of Research and Development
U.S. Environmental Protection Agency
Research Triangle Park, NC
-------
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.
November 2015
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CONTENTS
INTEGRATED SCIENCE ASSESSMENT TEAM FOR SULFUR OXIDES—HEALTH CRITERIAxiv
AUTHORS, CONTRIBUTORS, AND REVIEWERS xvi
CLEAN AIR SCIENTIFIC ADVISORY COMMITTEE SULFUR OXIDES NAAQS REVIEW PANEL
xx
ACRONYMS AND ABBREVIATIONS xxii
PREFACE xxix
Legislative Requirements for the Review of the National Ambient Air Quality Standards xxix
Overview and History of the Reviews of the Primary National Ambient Air Quality Standard for
Sulfur Dioxide xxxi
Table I History of the primary National Ambient Air Quality Standards for sulfur
dioxide since 1971. xxxii
References for Preface xxxvi
EXECUTIVE SUMMARY xxxvii
Purpose and Scope of the Integrated Science Assessment xxxvii
Sources and Human Exposure to Sulfur Dioxide xxxviii
Dosimetry and Mode of Action of Inhaled Sulfur Dioxide xli
Health Effects of Sulfur Dioxide Exposure xlii
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. xliv
Sulfur Dioxide Exposure and Respiratory Effects xliv
Sulfur Dioxide Exposure and Other Health Effects xlv
Policy-Relevant Considerations for Health Effects Associated with Sulfur Dioxide Exposure xlvi
References for Executive Summary xlviii
CHAPTER 1 SUMMARY OF THE INTEGRATED SCIENCE ASSESSMENT 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 1-5
1.4 From Emissions Sources to Exposure to Sulfur Dioxide 1-6
1.4.1 Emission Sources and Distribution of Ambient Concentrations 1-6
1.4.2 Assessment of Human Exposure 1-8
1.5 Dosimetry and Mode of Action of Sulfur Dioxide 1-11
1.5.1 Dosimetry of Inhaled Sulfur Dioxide 1-12
1.5.2 Mode of Action of Inhaled Sulfur Dioxide 1-13
1.6 Health Effects of Sulfur Dioxide 1-14
1.6.1 Respiratory Effects 1-15
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CONTENTS (Continued)
1.6.2 Health Effects beyond the Respiratory System 1-17
Table 1-1 Key evidence contributing to causal determinations for sulfur dioxide (SO2)
exposure and health effects evaluated in the current draft Integrated Science
Assessment (ISA) for Sulfur Oxides. 1-21
1.7 Policy-Relevant Considerations 1-24
1.7.1 Durations and Lag Structure of Sulfur Dioxide Exposure Associated with Health Effects 1-24
1.7.2 Concentration-Response Relationships and Thresholds 1-24
1.7.3 Regional Heterogeneity in Effect Estimates 1-25
1.7.4 Public Health Significance 1-26
1.8 Conclusions 1-28
References for Chapter 1 1-29
CHAPTER 2 ATMOSPHERIC CHEMISTRY AND AMBIENT CONCENTRATIONS OF
SULFUR OXIDES 2-1
2.1 Introduction 2-1
2.2 Sources of Sulfur Dioxide 2-1
2.2.1 U.S. Anthropogenic Versus Natural Sources 2-2
Figure 2-1 Sulfur dioxide emissions by sector in tons, annually (U.S. EPA National
Emissions Inventory, 2011). 2-3
2.2.2 Sources by Category and Geographic Distribution 2-3
Figure 2-2 Distribution of electric power generating unit (EGU)-derived sulfur dioxide
emissions across the U.S., based on the 2011 National Emissions Inventory. 2-4
Figure 2-3(A) Distribution of sulfur dioxide (SO2) emissions produced by
(A) industrial cement production, and (B) industrial chemical and allied
products manufacturing. 2-5
Figure 2-3(B) Distribution of sulfur dioxide (SO2) emissions produced by
(A) industrial cement production, and (B) industrial chemical and allied
products manufacturing. 2-6
2.2.3 Sources by Facility 2-7
Figure 2-4 Geographic distribution of (A) major continental U.S. sulfur dioxide (SO2)
emitting facilities, with (B) an enlargement of the Midwest states, including the
Ohio River Valley, where a large number of these sources are concentrated
(2011 National Emissions Inventory). 2-8
2.2.4 U.S. Emission Trends 2-9
Figure 2-5 National sulfur dioxide (SO2) emissions trends, by major emissions sector. 2-9
Table 2-1 Summary of 2013 EPA sulfur dioxide trends data for the major emissions
sectors shown in Figure 2-5. 2-10
2.2.5 Natural Sources 2-10
Figure 2-6 The global sulfur cycle, showing estimated fluxes (Tg/yr [S]) for the most
important sulfur-containing compounds. 2-11
Figure 2-7 Image derived from data collected by the Atmospheric Infrared Sounder
(AIRS) instrument aboard the NASA Aqua satellite during the July 12-20,
2008 eruption of the Okmok Volcano. 2-12
Figure 2-8 Geographic location of volcanoes and other geologically active sites within the
continental U.S. 2-13
Figure 2-9 NASA/Ozone Monitoring Instrument (OMI) image of the KTIauea sulfur dioxide
(SO2) plume during its March 20-27, 2008 eruption. 2-14
2.2.6 Indirect Sources 2-16
Table 2-2 Global sulfide emissions in Gg(S)/yr. 2-16
2.3 Atmospheric Chemistry and Fate 2-18
2.3.1 Sulfur Oxide Species 2-18
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CONTENTS (Continued)
Table 2-3 Atmospheric lifetimes of sulfur dioxide and reduced sulfur species with respect
to reaction with hydroxy! (OH), nitrate (NO3), and chlorine (CI) radicals. 2-19
2.3.2 Gas Phase Oxidation of Sulfur Dioxide 2-20
2.3.3 Aqueous Oxidation of Sulfur Dioxide 2-21
Figure 2-10 The effect ofpH on the rates of aqueous-phase S(IV) oxidation by various
oxidants. 2-23
2.4 Measurement Methods 2-24
2.4.1 Federal Reference and Equivalency Methods 2-24
Table 2-4 Performance specifications for sulfur dioxide based in 40 Code of Federal
Regulations Part 53, Subpart B. 2-25
2.4.2 Positive and Negative Interferences 2-26
2.4.3 Other Sulfur Dioxide Measurements 2-27
2.4.4 Ambient Sampling Network Design 2-29
Figure 2-11 Routinely operating sulfur dioxide monitoring networks. 2-30
2.5 Environmental Concentrations 2-34
2.5.1 Sulfur Dioxide Metrics and Averaging Time 2-34
Table 2-5 Summary of sulfur dioxide (SO2) metrics and averaging times. 2-35
2.5.2 Spatial Variability 2-35
Table 2-6 Summary of sulfur dioxide data sets originating from the Air Quality System
database. 2-36
Table 2-7 National statistics of sulfur dioxide concentrations (parts per billion) from Air
Quality System monitoring sites during 2010-2012. 2-37
Figure 2-12 Map of 99th percentile of daily 1-h max sulfur dioxide (SO2) concentration
reported at Air Quality System monitoring sites during 2010-2012. 2-38
Figure 2-13 Map of 99th percentile of daily 24-h avg sulfur dioxide (SO2) concentration
reported at Air Quality System monitoring sites during 2010-2012. 2-39
Figure 2-14 Map of Cleveland, OH Core-based Statistical Area (CBSA). 2-41
Figure 2-15 Map of Pittsburgh, PA Core-based Statistical Area (CBSA). 2-42
Figure 2-16 Map of New York City, NY Core-based Statistical Area (CBSA). 2-43
Figure 2-17 Map of St Louis, MO-IL Core-based Statistical Area (CBSA). 2-44
Figure 2-18 Map of Houston, TX Core-based Statistical Area (CBSA). 2-45
Figure 2-19 Map of Payson/Phoenix, AZ Core-based Statistical Areas (CBSAs) (hereafter
referred to as Payson/Phoenix Metropolitan focus area). 2-46
Table 2-8 1-Hour max sulfur dioxide concentration distribution by AQS monitor in six
core-based statistical area/metropolitan focus areas. 2-47
Figure 2-20 Pairwise monitor correlations of 24-hour average sulfur dioxide versus
distance between monitor pairs in six core-based statistical area/metropolitan
focus area, 2010-2012. 2-50
Figure 2-21 Histogram of pairwise correlations of 24-hour average sulfur dioxide data in
six core-based statistical area/metropolitan focus areas, 2010-2012 data. 2-51
Figure 2-22 Pairwise monitor correlations of hourly 5-minute maximum data versus
distance between monitors in six core-based statistical area/metropolitan
focus areas, 2010-2012. 2-52
Figure 2-23 Histogram of pairwise correlations of hourly 5-minute maximum sulfur dioxide
data in six core-based statistical area/metropolitan focus areas, 2010-2012
data. 2-53
2.5.3 Temporal Variability 2-55
Figure 2-24 National sulfur dioxide air quality trend (1990-2012), based on 163 sites,
showing a 72% decrease in the national average. 2-55
Figure 2-25 Sulfur dioxide month-to-month variability based on 1-hour maximum
concentrations at Air Quality System sites in each core-based statistical area. 2-57
Figure 2-26 Diet variability based on 1-hour average sulfur dioxide concentrations. 2-59
Figure 2-27 Diet trend based on hourly 5-minute maximum data in the six core-based
statistical area focus areas. 2-60
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CONTENTS (Continued)
Table 2-9 Five-minute sulfur dioxide concentrations by AQS monitor in select
core-based statistical area/metropolitan focus areas. 2-63
Figure 2-28 Scatterplot of hourly 5-minute maximum versus 1-hour average sulfur dioxide
concentrations by core-based statistical area/metropolitan focus area. 2-68
Figure 2-29 Time-series and frequency distribution of hourly 5-minute maximum sulfur
dioxide (SO2) concentrations from four "high concentration" monitors in the
Cleveland core-based statistical area. 2-71
Figure 2-30 Time-series and frequency distribution of hourly 5-minute, maximum sulfur
dioxide (SO2) concentrations from three "high concentration" monitors in the
Pittsburgh core-based statistical area. 2-72
Figure 2-31 Time-series and frequency distribution of hourly 5-minute, maximum sulfur
dioxide (SO2) concentrations from 2 "high concentration" monitors in the
Payson/Phoenix, AZ core-based statistical area/metropolitan focus area. 2-73
Table 2-10 Pearson correlation coefficient and peak-to-mean ratio for maximum sulfur
dioxide concentrations in core-based statistical areas. 2-74
Table 2-11 Number of hours (percent hours) which hourly 5-minute maximum sulfur
dioxide concentrations are above health benchmark levels during 2010-2012. 2-75
2.5.4 Background Concentrations 2-75
Figure 2-32 Annual mean model-predicted concentrations of sulfur dioxide (parts per
billion) calculated using the MOZART three-dimensional, chemistry-transport
model. 2-77
Figure 2-33 Daily maximum 1-hour sulfur dioxide (SO2) concentrations measured at
(a) Hilo, HI and (b) Pahala, HI. 2-79
Figure 2-34 Average 24-hour ambient sulfur dioxide concentrations during low and high
(volcanic gas) vog-exposure study periods (November 26, 2007 to June 6,
2008) for Ka'u District, located downwind of KJlauea Volcano. 2-80
2.5.5 Copollutant Correlations 2-80
Figure 2-35 Distribution of Pearson correlation coefficients for comparison of daily 24-hour
average sulfur dioxide from the year-round data set with colocated National
Ambient Air Quality Standards pollutants (and PM2.5 S) from Air Quality
System during 2010-2012. 2-81
Figure 2-36 Distribution of Pearson correlation coefficients for comparison of daily 1-hour
maximum sulfur dioxide from the year-round data set with colocated National
Ambient Air Quality Standards pollutants (and PM2.5 S) from Air Quality
System during 2010-2012. 2-82
Figure 2-37 Distribution of Pearson correlation coefficients for comparison of daily 24-hour
average sulfur dioxide stratified by season with colocated National Ambient Air
Quality Standards pollutants (and PM2.5 S) from Air Quality System during
2010-2012. 2-83
Figure 2-38 Distribution of Pearson correlation coefficients for comparison of daily 1-hour
maximum sulfur dioxide stratified by season with colocated National Ambient
Air Quality Standards pollutants (and PM2.5 S) from Air Quality System during
2010-2012. 2-84
2.6 Atmospheric Modeling 2-85
2.6.1 Dispersion Modeling 2-85
2.7 Summary 2-90
References For Chapter 2 2-93
CHAPTER 3 EXPOSURE TO AMBIENT SULFUR DIOXIDE 3-1
3.1 Introduction 3-1
3.2 Methodological Considerations for Use of Exposure Data 3-1
3.2.1 Measurements 3-1
3.2.2 Modeling 3-4
Table 3-1 Characteristics of exposure modeling approaches. 3-5
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CONTENTS (Continued)
3.2.3 Choice of Exposure Metrics in Epidemiologic Studies 3-16
Table 3-2 Summary of exposure assignment methods, their typical use in sulfur dioxide
epidemiologic studies, and related errors and uncertainties. 3-16
Table 3-3 Exposure data for epidemiologic studies using modeling for exposure
estimation. 3-18
3.3 Exposure Assessment and Epidemiologic Inference 3-26
3.3.1 Conceptual Model of Total Personal Exposure 3-26
3.3.2 Relationships between Personal Exposure and Ambient Concentration 3-28
Table 3-4 Relationships between indoor and outdoor sulfur dioxide concentration. 3-30
3.3.3 Factors Contributing to Error in Estimating Exposure to Ambient Sulfur Dioxide 3-37
Figure 3-1 Distribution of time that National Human Activity Pattern Survey respondents
spent in 10 microenvironments based on smoothed 1-minute diary data. 3-38
Table 3-5 Mean fraction of time spent in outdoor locations by various age groups in the
National Human Activity Pattern Survey study. 3-39
Table 3-6 Mean ventilation rates (Uminute) at different activity levels for different age
groups. 3-40
Figure 3-2 Map of the Cleveland, OH core-based statistical area including National
Emissions Inventory facility locations, urban sulfur dioxide monitor locations,
and distance from 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. 3-44
Table 3-7 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. 3-45
Figure 3-3 Map of the Pittsburgh, PA core-based statistical area including National
Emissions Inventory facility locations, urban sulfur dioxide monitor locations,
and distance from 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.
The inset map shows National Emissions Inventory facilities located to the
southeast of the highly urbanized areas. 3-46
Table 3-8 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. 3-47
3.3.4 Confounding 3-49
Table 3-9 Synthesis of sulfur dioxide ambient-ambient copollutant correlations from
short-term and long-term epidemiology studies. 3-52
3.3.5 Implications for Epidemiologic Studies of Different Designs 3-58
Table 3-10 The influence of exposure metrics on error in health effect estimates. 3-62
3.4 Summary and Conclusions 3-67
References for Chapter 3 3-71
CHAPTER 4 DOSIMETRY AND MODE OF ACTION 4-1
4.1 Introduction 4-1
4.2 Dosimetry of Inhaled Sulfur Dioxide 4-1
4.2.1 Chemistry 4-2
4.2.2 Absorption 4-2
4.2.3 Distribution 4-5
4.2.4 Metabolism 4-7
4.2.5 Elimination 4-8
4.2.6 Sources and Levels of Exogenous and Endogenous Sulfite 4-9
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CONTENTS (Continued)
4.3 Mode of Action of Inhaled Sulfur Dioxide
4-11
4.3.1
4.3.2
4.3.3
4.3.4
4.3.5
4.3.6
Activation of Neural Reflexes
Injury to Airway Mucosa
Modulation of Airway Responsiveness and Allergic lnflammation_
Transduction of Extrapulmonary Effects
Role of Endogenous Sulfur Dioxide/Sulfite
Mode of Action Framework
Figure 4-1
Figure 4-2
Figure 4-3
Summary of evidence for the mode of action linking short-term exposure to
sulfur dioxide and respiratory effects.
Summary of evidence for the mode of action linking long-term exposure to
sulfur dioxide and respiratory effects.
Summary of evidence for the mode of action linking exposure to sulfur dioxide
and extrapulmonary effects.
References for Chapter 4
.4-12
.4-16
.4-17
.4-21
.4-23
.4-23
4-24
4-26
4-27
4-29
CHAPTER 5
INTEGRATED HEALTH EFFECTS OF EXPOSURE TO SULFUR OXIDES
5.1 Introduction
5.1.1 Scope of the Chapter
5.1.2 Evidence Evaluation and Integration to Form Causal Determinations
5.1.3 Summary
5.2 Respiratory Morbidity _
5.2.1 Short-Term Exposure
Table 5-1
Table 5-2
Figure 5-1
Table 5-3
Table 5-4
Table 5-5
Table 5-6
Table 5-7
Table 5-8
Figure 5-2
Distribution of individual airway sensitivity to SO2. The cumulative percentage
of subjects is plotted as a function of PC(S02), which is the concentration of
SO2 that provoked a 100% increase in specific airway resistance compared to
clean air.
Percent change in post- versus pre-exposure measures of FEV1 relative to
clean air control after 5-10-minute exposures to SO2 during exercise.
Percent change in post- versus pre-exposure measures of specific airway
resistance (sRaw) relative to clean air control after 5-10-minute exposures to
SO2 during exercise.
Summary of recent panel studies examining associations between SO2
concentrations and lung function among adults with asthma.
Summary of recent epidemiologic studies examining associations between
SO2 concentrations and lung function among children with asthma.
Study-specific details from controlled human exposure studies of respiratory
symptoms.
Summary of recent epidemiologic studies examining associations between
SO2 concentrations and respiratory symptoms among children with asthma.
Percent increase in asthma hospital admissions and ED 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-hour average or 40-ppb
increase in 1-hour maximum sulfur dioxide concentrations.
Table 5-9
Table 5-10
Corresponding risk estmates for studies presented in Figure 5-2.
Study-specific details and mean and upper percentile concentrations from
asthma hospital admission and ED visit studies conducted in the U. S. and
Canada and evaluated in the 2008 SOx ISA and studies published since the
2008 SOx ISA.
.5-1
. 5-1
. 5-1
. 5-2
_ 5-4
. 5-5
5-5
Study-specific details from controlled human exposure studies of individuals
with asthma.
Percentage of asthmatic adults in controlled human exposure studies
experiencing S02-induced decrements in lung function and respiratory
symptoms.
5-8
5-14
5-16
5-18
5-19
5-22
5-24
5-29
5-35
5-39
5-40
5-43
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CONTENTS (Continued)
Figure 5-3
Figure 5-4
Table 5-11
Table 5-12
Figure 5-5
Locally weighted scatterpiot smoothing concentration-response estimates
(solid line) and twice-standard error estimates (dashed lines) from generalized
additive models 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 SO2 concentrations in the Atlanta, GA area.
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 inter-quartile range increase in each pollutant at lag 0-2 days. Inter-quartile
range for 1-hour maximum SO2 concentrations = 10.51 ppb.
Summary of recent epidemiologic studies examining associations between
SO2 concentrations and airway inflammation and oxidative stress.
Study-specific details from animal toxicological studies of subclinical effects
underlying asthma.
Percent increase in chronic obstructive pulmonary disease hospital
admissions and ED visits from U. S. and Canadian studies evaluated in the
2008 SOx Integrated Science Assessment (ISA) and recent studies in all-year
analyses for a 10-ppb increase in 24-hour average or 40-ppb increase in
1-hour maximum SO2concentrations. Note: Black circles = U.S. and
Canadian studies evaluated in the 2008 SOx ISA; red circles = recent chronic
obstructive pulmonary disease hospital admission and ED visit studies, a =
study evaluated in the 2008 SOx ISA.
Table 5-13
Table 5-14
Corresponding risk estimates for studies presented in Figure 5-5.
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.
Table 5-15 Study-specific details and mean and upper percentile concentrations from
respiratory infection hospital admission and emergency deparment visit
studies conducted in the U.S. and Canada and evaluated in the 2008 SOx ISA
and studies published since the 2008 SOx ISA.
Figure 5-6
Table 5-16
Figure 5-7
Table 5-17
Table 5-18
Table 5-19
Table 5-20
Table 5-21
Table 5-22
Table 5-23
Percent increase in respiratory infection hospital admissions and ED visits
from U.S. and Canadian studies evaluated in the 2008 SOx Integrated
Science Assessment (ISA) and recent studies in all-year and seasonal
analyses for a 10-ppb increase in 24-hour average or 40-ppb increase in
1-hour maximum SO2concentrations. Note: Black circles = U.S. and
Canadian studies evaluated in the 2008 SOx ISA; red circles = recent
respiratory infection hospital admissions and ED visits studies.
Corresponding risk estimates for studies presented in Figure 5-6.
Corresponding risk estimates for studies presented in Figure 5-7.
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.
Study-specific details from controlled human exposure studies of lung function
and respiratory symptoms in healthy adults.
Summary of recent epidemiologic studies examining associations between
SO2 concentrations and lung function among adults.
Summary of recent epidemiologic studies examining associations between
SO2 concentrations and lung function among children.
Study-specific details from animal toxicological studies of lung function.
Summary of recent epidemiologic studies examining associations between
SO2 concentrations and respiratory symptoms among adults.
Table 5-24 Summary of recent epidemiologic studies examining associations between
SO2 concentrations and respiratory symptoms among children.
5-58
5-60
5-64
5-67
5-73
5-74
5-75
5-82
Percent increase in respiratory disease hospital admissions and ED 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-hour average or
40-ppb increase in 1-hour maximum SO2 concentrations.
5-89
5-89
5-91
5-92
_ 5-94
5-103
5-104
5-108
5-115
5-117
5-118
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CONTENTS (Continued)
Table 5-25 Study-specific details from animal toxicological studies of airway
responsiveness.
Table 5-26
Figure 5-8
Study-specific details from animal toxicological studies of subclinical effects. _
Percent increase in chronic obstructive pulmonary disease (COPD) mortality
associated with a 10 /jg/m3 (3.62 ppb) increase in 24-hour average SO2
concentrations at various single and multiday lags.
Figure 5-9
City-specific concentration-response curves for short-term SO2 exposures and
daily chronic obstructive pulmonary disease (COPD) mortality in four Chinese
cities.
Table 5-27 Summary of evidence for a causal relationship between short-term SO2
exposure and respiratory effects.
5.2.2 Long-Term Exposure
Table 5-28 Summary of recent epidemiologic studies examining associations between
SO2 concentrations and the development of asthma.
Study-specific details from animal toxicological studies.
Summary of recent epidemiologic studies examining associations between
SO2 concentrations and lung function.
Table 5-29
Table 5-30
Table 5-31
Summary of evidence for a suggestive of, but not sufficient to infer, a causal
relationship between long-term SO2 exposure and respiratory effects.
5.3 Cardiovascular Effects
5.3.1 Short-Term Exposure
Table 5-32 Mean and upper percentile concentrations of sulfur dioxide from ischemic
heart disease hospital admission and emergency department visit studies._
Figure 5-10 Results of studies of short-term sulfur dioxide exposure and hospital
admissions for ischemic heart disease.
Table 5-33 Corresponding risk estimates for hospital admissions for ischemic heart
disease for studies presented in Figure 5-10.
Table 5-34
Table 5-35
Epidemiologic studies of arrhythmia and cardiac arrest.
Mean and upper percentile concentrations of sulfur dioxide from
cerebrovascular disease and stroke-related hospital admission and
emergency department visit studies.
Figure 5-11 Results of studies of short-term sulfur dioxide exposure and hospital
admissions for cerebrovascular disease and stroke.
Table 5-36 Corresponding risk estimates for hospital admissions or emergency
department visits for cerebrovascular disease and stroke for studies presented
in Figure 5-11.
Table 5-37 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.
Table 5-38 Mean and upper percentile concentrations of sulfur dioxide from
cardiovascular-related hospital admission and emergency department visit
studies.
Figure 5-12 Studies of hospital admissions and emergency department visits for all
cardiovascular disease (CVD).
Table 5-39 Corresponding relative risk (95% CI) for hospital admissions and emergency
department visits for all CVD for studies presented in Figure 5-12.
Figure 5-13 Percent increase in stroke mortality associated with a 10jjg/m3 (3.62 ppb)
increase in SO2 concentrations using different lag structures.
Figure 5-14 Pooled concentration-response curves for SO2 and daily stroke mortality in
eight Chinese cities for a 10jjg/m3 (3.62 ppb) increase in 24-hour average
concentrations at lag 0-1 day. Note: The black line represents the mean
estimate and the dotted lines are 95% confidence intervals.
Table 5-40 Epidemiologic studies of biomarkers of cardiovascular effects.
5-124
5-128
5-132
5-134
5-135
5-140
5-143
5-154
5-156
5-170
5-173
5-173
5-176
5-178
5-178
5-184
5-187
5-189
5-190
5-195
5-200
5-203
5-204
5-211
5-212
5-219
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CONTENTS (Continued)
Table 5-41 Summary of evidence, which is suggestive of, but not sufficient to infer, a
causal relationship between short-term SO2 exposure and cardiovascular
effects. 5-226
5.3.2 Long-Term Exposure 5-228
Table 5-42 Epidemiologic studies of long-term exposure to SO2 and effects on the
cardiovascular system. 5-231
Table 5-43 Summary of evidence, which is inadequate to infer a causal relationship
between long-term sulfur dioxide exposure and cardiovascular effects. 5-238
5.4 Reproductive and Developmental Effects 5-240
5.4.1 Introduction 5-240
Table 5-44 Key reproductive and developmental epidemiologic studies for SO2. 5-242
Table 5-45 Study specific details from animal toxicological studies of the reproductive and
developmental effects of sulfur dioxide 5-254
5.4.2 Summary and Causal Determination 5-255
Table 5-46 Summary of evidence supporting suggestive of a causal relationship between
SO2 exposure and reproductive and developmental effects. 5-258
5.5 Mortality 5-260
5.5.1 Short-Term Mortality 5-260
Table 5-47 Air quality characteristics of multicity studies and meta-analyses evaluated in
the 2008 SOx ISA and recently published multicity studies and meta-analyses. 5-262
Figure 5-15 Percent increase in total mortality from multi-city studies and meta-analyses
evaluated in the 2008 SOx Integrated Science Assessment (black circles) and
recently published multi-city studies (red circles) for a 10-ppb increase in
24-hour average SO2 concentrations. 5-265
Table 5-48 Corresponding excess risk estimates for Figure 5-15. 5-266
Figure 5-16 Percent increase in total, cardiovascular, and respiratory mortality from multi-
city studies evaluated in the 2008 SOx Integrated Science Assessment (black
circles) and recently published multi-city studies (red circles) for a 10-ppb
increase in 24-hour average SO2 concentrations. 5-268
Table 5-49 Corresponding excess risk estimates for Figure 5-16. 5-269
Table 5-50 Percent increase in total, cardiovascular, and respiratory mortality for a 10-ppb
increase in 24-hour average sulfur dioxide concentrations at lag 0-1 in single
and copollutant models. 5-272
Figure 5-17 Percent increase in total, cardiovascular, and respiratory mortality associated
with a 10jjg/m3 (3.62 ppb) increase in 24-hour average SO2 concentrations,
lag 0-1, in single and copollutants models in Public Health and Air Pollution in
Asia cities. 5-273
Figure 5-18 Percent increase in daily mortality associated with a 10 /jg/m3 (3.62 ppb)
increase in 24-hour average SO2 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. 5-275
Figure 5-19 Percent increase in total mortality associated with a 10 /jg/m3 (3.62 ppb)
increase in 24-hour average SO2 concentrations at lag 0-1 in Public Health
and Air Pollution in Asia cities, using different degrees of freedom/year for
time trend. 5-276
Figure 5-20 Percent increase in daily mortality associated with a 10ug/m3 (3.62 ppb)
increase in 24-hour average SO2 concentrations, using various lag structures
for S02in the China Air Pollution and Health Effects Study cities, 1996-2008. _ 5-279
Figure 5-21 Percent increase in total mortality associated with a 10 /jg/m3 (3.62 ppb)
increase in 24-hour average SO2 concentrations for different lag structures in
individual Public Health and Air Pollution in Asia cities and in combined four
city analyses. BK = Bangkok; HK = Hong Kong; SH = Shanghai;
WH = Wuhan. 5-280
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CONTENTS (Continued)
Figure 5-22 Flexible ambient concentration-response relationship between short-term SO2
(ppb) exposure (24-hour average concentrations) and total mortality at lag 1.
Note: Pointwise means and 95% confidence intervals adjusted for size of the
bootstrap sample (d = 4). 5-282
Figure 5-23 Concentration-response curves for total mortality (degrees of freedom = 3) for
SO2 in each of the four Public Health and Air Pollution in Asia cities. Note:
x-axis is the average of lag 0-1 24-hour average SO2 concentrations (jjg/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 inter-quartile range of SO2 concentrations within each city, while
the thin vertical bar represents the World Health Organization guideline of
20 ijg/m3 for a 24-hour averaging time of SO2. 5-283
Table 5-51 Summary of evidence, which is suggestive of, but not sufficient to infer, a
causal relationship between short-term SO2 exposure and total mortality. 5-287
5.5.2 Long-Term Mortality 5-288
Table 5-52 Summary of studies of long-term exposure and mortality. 5-289
Figure 5-24 Relative risks (95% CI) of sulfur dioxide-associated total mortality. Effect
estimates are standardized per 5-ppb increase in sulfur dioxide
concentrations. 5-302
Table 5-53 Corresponding risk estimates for Figure 5-24. 5-302
Figure 5-25 Relative risks (95% CI) of sulfur dioxide-associated cause-specific mortality.
Effect estimates are standardized per 5-ppb increase in sulfur dioxide
concentrations. 5-303
Table 5-54 Corresponding risk estimates for Figure 5-25. 5-304
Table 5-55 Summary of evidence, which is suggestive of, but not sufficient to infer, a
causal relationship between long-term SO2 exposure and total mortality. 5-306
5.6 Cancer 5-308
5.6.1 Introduction 5-308
5.6.2 Genotoxicity and Mutagenicity 5-314
5.6.3 Summary and Causal Determination 5-315
Table 5-56 Summary of evidence, which is suggestive of, but not sufficient to infer, a
causal relationship between long-term SO2 exposure and cancer. 5-316
Annex for Chapter 5: Evaluation of Studies on Health Effects of Sufur Oxides 5-318
Table A-1 Scientific considerations for evaluating the strength of inference from studies
on the health effects of sulfur oxides. 5-318
References for Chapter 5 5-324
CHAPTER 6 POPULATIONS AND LIFESTAGES POTENTIALLY AT 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 forAt-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/Conditions 6-3
Table 6-2 Prevalence of respiratory diseases, cardiovascular diseases, diabetes, and
obesity among adults by age and region in the U. S. in 2012. 6-4
6.3.1 Asthma 6-5
Table 6-3 Controlled human exposure and animal toxicology studies evaluating
pre-existing asthma and sulfur dioxide exposure. 6-6
Table 6-4 Epidemiologic studies evaluating pre-existing asthma. 6-8
6.3.2 Cardiovascular Disease 6-9
Table 6-5 Epidemiologic studies evaluating pre-existing cardiovascular disease and
sulfur dioxide exposure. 6-9
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CONTENTS (Continued)
Table 6-6 Controlled human exposure evaluating pre-existing cardiovascular disease
and sulfur dioxide exposure. 6-10
6.3.3 Diabetes6-10
Table 6-7 Epidemiologic studies evaluating pre-existing diabetes and sulfur dioxide
exposure. 6-11
6.3.4 Obesity 6-11
Table 6-8 Epidemiologic studies evaluating pre-existing obesity and sulfur dioxide
exposure. 6-12
6.4 Genetic Factors 6-14
Table 6-9 Epidemiologic studies evaluating genetic factors and sulfur dioxide exposure. 6-15
6.5 Sociodemographic Factors 6-15
6.5.1 Lifestage 6-15
Table 6-10 Epidemiologic studies evaluating childhood lifestage and sulfur dioxide
exposure. 6-17
Table 6-11 Epidemiologic studies evaluating older adult lifestage and sulfur dioxide
exposure. 6-19
6.5.2 Sex 6-23
Table 6-12 Epidemiologic studies evaluating effect modification by sex and sulfur dioxide
exposure. 6-24
6.5.3 Socioeconomic Status 6-32
Table 6-13 Epidemiologic studies evaluating socioeconomic status and sulfur dioxide
exposure. 6-33
6.5.4 Race/Ethnicity 6-34
Table 6-14 Epidemiologic studies evaluating race/ethnicity and sulfur dioxide exposure. 6-35
6.6 Behavioral and Other Factors 6-35
6.6.1 Smoking 6-35
Table 6-15 Epidemiologic studies evaluating smoking status and sulfur dioxide exposure. 6-37
6.7 Conclusions 6-38
Table 6-16 Summary of evidence for potential increased SO2 exposure and increased risk
of SC>2-related health effects. 6-39
References for Chapter 6 6-41
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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 (Acting 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 (Co-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. Lisa Vinikoor-Imler (Co-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
November 2015
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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. Elizabeth Oesterling Owens—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
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
Mr. Ryan Jones—National Center for Environmental Assessment, Office of Research and
Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Mr. Richard N. Wilson—National Center for Environmental Assessment, Office of Research
and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
November 2015
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AUTHORS, CONTRIBUTORS, AND REVIEWERS
Authors
Dr. Tom Long (Co-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. Lisa Vinikoor-Imler (Co-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
November 2015
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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. 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 Thurston—Department of Environmental Medicine, New York University
School of Medicine, Tuxedo, NY
Dr. Gregory Wellenius—Department of Community Health (Epidemiology Section), Brown
University, Providence, RI
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
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
November 2015
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Ms. Kaylyn Gootman—Curriculum for the Environment and Ecology, University of North
Carolina, Chapel Hill, 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
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
Mr. Doug Solomon—Office of Air Quality Planning and Standards, Office of Air and
Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC
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
November 2015
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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. 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
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. 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
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. 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
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
November 2015
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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
EPA Administrator
** Members of the statutory CASAC appointed by the EPA Administrator
** immediate Past CASAC Chair
November 2015
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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, (veow.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
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
Acronym/
Abbreviation Meaning
P beta
BAL bronchoalveolar lavage
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;
C1 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
CBSA core-based statistical area
CCN cloud condensation nuclei
CDC Centers for Disease Control and
Prevention
CFR Code of Federal Regulations
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Acronym/
Abbreviation
Meaning
cGMP
cyclic guanosine
monophosphate
CHsSH
methyl mercaptan
CH3-S-CH3
dimethyl sulfide
CH3-S-S-CH3
dimethyl disulfide
(CH3)2SO
dimethyl sulfoxide
CH3SO3H
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
DBP
diastolic blood pressure
DC
District of Columbia
DEcCBP
diesel exhaust particle extract-
coated carbon black particles
DEP
diesel exhaust particles
Acronym/
Abbreviation
Meaning
df
degrees of freedom
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 50% 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 new 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 systems
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 new Hong Kong
HO2 hydroperoxyl radical
HR hazard ratio(s); heart rate
HRV heart rate variability
HS hemorrhagic stroke
HS03~ 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 intra-class 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
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Acronym/
Abbreviation
Meaning
ISAAC
International Study of Asthma
and Allergies in Children
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; rats
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 ofLF and IFF 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
mu; micro
|xg/m3
micrograms per cubic meter
m
meter
M
male
MA
Massachusetts
Ml
Month 1
M2
Month 2
M3
Month 3
Acronym/
Abbreviation
Meaning
M12
average of Ml & M2
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
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Acronym/
Abbreviation
Meaning
N
population number
N2
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 Bloi
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
Acronym/
Abbreviation Meaning
OH hydroxide; Ohio
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% cut-point of
10 ± 0.5 |xm aerodynamic
diameter (the 50% cut point
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% cut-point of 10 |xm
aerodynamic diameter and a
lower 50% cut-point of 2.5 |xm
aerodynamic diameter (the 50%
cut point 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% cut-point of
2.5 |xm aerodynamic diameter
(the 50%o cut point 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
s
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
sulfate
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 Teragrams
Thl T helper 1
Th2 T- helper 2
HA 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
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. 20051.
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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 (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), EPA's task is to establish standards that are neither more nor less
stringent than necessary for these purposes. In so doing, EPA may not consider the costs
of implementing the standards.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
appropriate ...." Section 109(d)(2) requires that an independent scientific review
committee "shall complete a review of the criteria ... and the national primary and
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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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.
SO2 is the indicator for gaseous sulfur oxides. EPA considers the term sulfur oxides to
refer to all forms of oxidized sulfur including multiple gaseous species (e.g., SO2, sulfur
trioxide (SO3)] and particulate species (e.g., sulfates). The review of the primary SO2
NAAQS focuses on evaluating the health effects associated with exposure to the gaseous
sulfur oxides, particularly SO2 because other gaseous sulfur oxide species are not present
in ambient air at concentrations significant for human exposures (see Chapter 2). The
atmospheric chemistry, exposure, and health effects associated with sulfur compounds
present in particulate matter (PM) were most recently considered in the EPA's review of
the NAAQS for PM. The welfare effects associated with sulfur oxides are being
considered in a separate assessment as part of the review of the secondary NAAQS for
nitrogen dioxide and SO2 (U.S. EPA. 2013c).
The 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 EPA
promulgated NAAQS for sulfur oxides in 1971, establishing the indicator as SO2 [36 FR
8186; (U.S. EPA. 1971)1. The 1971 primary standards were set at 365 |ig/nr' [equal to
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.nsf/WebCASAC/CommitteesandMembership70peiiDocument.
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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/nr1 (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; h = hour; ppb = parts per billion; S02 = suflur dioxide; yr = year.
aThe initial level of the 24-h S02 standard was 365 |jg/m3 which is equal to 0.14 parts per million or 140 ppb. The initial level of
the annual S02 standard was 80 |jg/m3 which is equal to 0.03 parts per million 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 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. EPA. 1982b). In 1986, a second addendum was published presenting
newly available evidence from epidemiologic and controlled human exposure studies
(U.S. EPA. 1986a). In 1988, the EPA published a proposed decision not to revise the
existing standards (53 FR 14926). However, the 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.
As a result of public comments on the 1988 proposal and other post-proposal
developments, the EPA published a second proposal on November 15, 1994 (59 FR
58958). The 1994 reproposal was based in part on a supplement to the second addendum
of the criteria document, which evaluated new findings on short-term SO2 exposures in
asthmatics (U.S. EPA. 1994V As in the 1988 proposal, the EPA proposed to retain the
existing 24-hour and annual standards. The EPA also solicited comment on three
1 Note that 0.14 ppm is equivalent to 140 parts per billion (ppb) and 0.03 ppm is equivalent to 30 ppb.
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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, one expected exceedance; and (3) augmenting
implementation of existing standards by focusing on those sources or source types likely
to produce high 5-minute peak concentrations of SO2.
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 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 asthmatics. As a result, the
Administrator judged that 5-minute peak SO2 levels 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 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 EPA had failed to adequately
explain its determination that no revision to the SO2 NAAQS was appropriate and
remanded the decision back to EPA for further explanation.1 Specifically, the court found
that EPA had failed to provide adequate rationale to support the Agency judgment that
exposures to 5-minute peaks 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 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 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.
1 See American LungAss'n v. EPA, 134 F. 3d 388 (D.C. Cir. 1998).
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The last review of the air quality criteria for sulfur oxides (health criteria) and the
primary SO2 standard was initiated in May 2006 (71 FR 28023).12 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 Integrated Science Assessment for Sulfur Oxides—Health Criteria
(U.S. EP A. 2008b). multiple drafts of which received review by CASAC and the public.
The EPA also conducted quantitative human risk and exposure assessments, after
consulting 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. 200%). multiple drafts of which were reviewed by
CASAC and the public.
On June 22, 2010, the 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 EPA
established a new 1-hour SO2 standard at a level of 75 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 minutes to 24 hours. More
specifically, 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 reported in epidemiologic studies of mostly 1 and 24 hours
(e.g., respiratory-related emergency department visits and hospitalizations). In the last
review, the EPA also revoked the then-existing 24-hour and annual primary standards
based largely on the recognition that a 1-hour standard at 75 ppb would have the effect of
maintaining 24-hour and annual SO2 concentrations generally well below the levels of
those 24-hour and annual NAAQS. 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
1 Documents related to reviews completed in 2010 and 1996 are available at:
http://www.epa.gOv/ttn/naaqs/standards/so2/s so2 indcx.html.
2 The 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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respiratory effects in exercising asthmatics—also satisfied the remand by the D.C. Circuit
in 1998.
As mentioned above, in the last review EPA placed considerable weight on substantially
limiting health effects associated with 5-minute peak SO2 concentrations. Thus, as part of
the final rulemaking, the 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. 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 peaks of
concern.
After publication of the final rule, a number of industry groups and states filed petitions
for review arguing that the 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 Lie v. EPA, 133 S. Ct. 983 (Jan. 22, 2013).
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References for Preface
CAA (Clean Air Act). (1990a). Clean Air Act, as amended by Pub. L. No. 101-549, section 108: Air quality
criteria and control techniques, 42 USC 7408. http://www.law.cornell.edu/uscode/text/42/7408
CAA (Clean Air Act). (1990b). Clean Air Act, as amended by Pub. L. No. 101-549, section 109: National
primary and secondary ambient air quality standards, 42 USC 7409.
http://www.epa. gov/air/caa/title 1 ,html#ia
CAA (Clean Air Act). (2005). Clean Air Act, section 302: Definitions, 42 USC 7602.
http://www.gpo.gOv/fdsYs/pkg/USCODE-2005-title42/pdf/USCODE-2005-title42-chap85-subchapIII-
sec7602.pdf
HEW (U.S. Department of Health, Education and Welfare). (1969). Air quality criteria for sulfur oxides.
Washington, DC: National Air Pollution Control Administration.
U.S. EPA (U.S. Environmental Protection Agency). (1971). National primary and secondary ambient air quality
standards. Fed Reg 36: 8186-8201.
U.S. EPA (U.S. Environmental Protection Agency). (1982a). Air quality criteria for particulate matter and sulfur
oxides (final, 1982) [EPA Report]. (EPA 600/8-82/029a). Research Triangle Park: Environmental Criteria
and Assessment Office, http://cfpub.epa. gov/ncea/cfm/recordisplav.cfm?deid=46205
U.S. EPA (U.S. Environmental Protection Agency). (1982b). Air quality criteria for particulate matter and sulfur
oxides, volume I addendum [EPA Report]. (EPA-600/8-82-029a). Research Triangle Park. NC:
Environmental Criteria and Assessment Office.
U.S. EPA (U.S. Environmental Protection Agency). (1986a). Air quality criteria for particulate matter and sulfur
oxides (1982): assessment of newly available health effects information, 2nd addendum. (EPA/600/8-
86/020F). Washington, DC: Office of Health and Environmental Assessment.
http://nepis.epa.gov/exe/ZvPURL.cgi?Dockev=30001FM5.txt
U.S. EPA (U.S. Environmental Protection Agency). (1994). Supplement to the second addendum (1986) to air
quality criteria for particulate matter and sulfur oxides (1982): Assessment of new findings on sulfur dioxide
acute exposure health effects in asthmatic individuals [EPA Report]. (EPA/600/FP-93/002). Research
Triangle Park, NC: Environmental Criteria and Assessment Office.
http://ncpis.epa.gov/E.\c/ZvPURL.cgi?Dockcv=30002382.t.\t
U.S. EPA (U.S. Environmental Protection Agency). (2007a). Integrated plan for review of the primary national
ambient air quality standards for sulfur oxides [EPA Report]. Washington, DC.
http://www.epa.gov/ttn/naaas/standards/so2/data/so2 review plan final 10-09-07.txlf
U.S. EPA (U.S. Environmental Protection Agency). (2007b). Sulfur dioxide health assessment plan: Scope and
methods for exposure and risk assessment draft [EPA Report]. Washington, DC: Office of Air Quality
Planning and Standards.
http://www.epa.gov/ttn/naaas/standards/so2/data/20Q71113 hcalthasscssmciitplan.pdf
U.S. EPA (U.S. Environmental Protection Agency). (2008b). Integrated science assessment for sulfur oxides:
Health criteria [EPA Report]. (EPA/600/R-08/047F). Research Triangle Park, NC: U.S. Environmental
Protection Agency, National Center for Environmental Assessment.
http://cfpub.epa.gov/ncea/cfm/recordisplav.cfm?deid=198843
U.S. EPA (U.S. Environmental Protection Agency). (2009b). Risk and exposure assessment to support the
review of the S02 primary national ambient air quality standards: Final report [EPA Report]. (EPA-452/R-
09-007). Washington, DC: Office of Air Quality Planning and Standards.
http://www.epa.gov/ttn/naaas/standards/so2/data/200908SQ2REAFinalReport.pdf
U.S. EPA (U.S. Environmental Protection Agency). (2013c). Notice of workshop and call for information on
integrated science assessment for oxides of nitrogen and oxides of sulfur. Fed Reg 78: 53452-53454.
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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) NAAQS for sulfur
dioxide (SO2).2 In 2010, the U.S. Environmental Protection Agency (EPA) established a
new 1-hour standard at a level of 75 parts per billion (ppb) SO2 based on the 3-year
average of the 99th percentile of each year's 1-hour daily maximum (max) concentrations
(75 FR 35520).3 The 1-hour standard was established to protect against a broad range of
respiratory effects associated with short-term exposures (i.e., 5-minute to 24-hour) in
potential at-risk populations such as people with asthma. The 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 based largely on the recognition that
a 1-hour standard at 75 ppb would effectively maintain 24-hour and annual SO2
concentrations well below the then-current NAAQS and thus, these standards would
provide little additional public health protection. In light of considerable weight being
placed on health effects associated with 5-minute peak SO2 concentrations, the 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.4
This ISA updates the 2008 ISA for Sulfur Oxides (U.S. EPA. 2008b) with studies and
reports published from January 2008 through April 2015. EPA conducted in-depth
searches to identify peer-reviewed literature on relevant topics such as health effects,
atmospheric chemistry, ambient concentrations, and exposure. Subject-area experts and
the public were also able to recommend studies and reports during a kick-off workshop
held at the EPA in June 2013. To fully describe the state of available science, EPA also
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. 2015e). www.epa.gov/isa/isa.
2 This ISA evaluates the health effects of gaseous sulfur oxides, of which only SO2 is present in the atmosphere at
relevant concentrations. Particulate sulfur oxides 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. 2009aTI.
The welfare effects of sulfur oxides 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. 2013c).
3 The legislative requirements and history of the SO2 NAAQS are described in detail in the Preface to this ISA.
4 In this ISA, the blue electronic links can be used to navigate to cited chapters, sections, tables, figures, and studies.
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brought forward the most relevant studies from previous assessments to include in this
ISA.
As in the 2008 ISA, this ISA determines the causality of relationships with health effects
only for SO2 (Chapter 5); other gaseous SOx species are not included, as SO2 is the only
gaseous sulfur oxide species that is relevant for public health in ambient air and the
health literature is focused on SO2. The ISA aims to characterize the independent health
effects of SO2, not its role as a marker for a broader mixture of pollutants in the ambient
air. 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 subsequent biological
mechanisms that may be affected (Chapter 4). Further, the ISA aims to characterize the
independent effect of SO2 on health effects (Chapter 5). The ISA also informs
policy-relevant issues (Section 1.7). 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 factors that affect exposure to ambient SO2 and the
ability to understand factors that add uncertainty in estimating exposure, such as spatial
variability in SO2 concentrations, joint exposure to other pollutants, and uncharacterized
time-activity patterns.
Emissions of SO2 have declined by approximately 70% for all major sources since 1990,
as a consequence of several federal 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 (coal-fired boilers), emitting 4,500,000 tons of SO2
annually, according to the 2011 National Emissions Inventory (Section 2.2). Beyond the
rate at which a source emits the pollutant, important variables that determine the
concentration of SO2 downwind of a source and/or at monitoring locations include the
photochemical removal processes occurring in the emissions plume and local
meteorology, including wind, atmospheric stability, humidity, and cloud/fog cover.
On a nationwide basis, the average daily 1-hour maximum SO2 concentration reported
during 2010-2012 is 9 ppb (Section 2.5). However, the 99th percentile of daily maximum
SO2 concentrations can approach 75 ppb at some monitors located near large
anthropogenic or natural sources, e.g., volcanoes. Similarly, new 5-minute data
demonstrate that most hourly 5-minute maximum concentrations are well below the
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short-term health benchmark level of 200 ppb (i.e., the lowest level where lung function
decrements were reported in controlled human exposure studies of individuals with
asthma engaged in exercise) although on some occasions (99th percentile and above)
concentrations can be greater than 200 ppb near anthropogenic sources such as EGUs.
Correlations between ambient SO2 and copollutants tend to vary across location, study,
and SO2 averaging time (Section 2.5.5). Median daily SO2 correlations with particulate
matter, nitrogen dioxide (NO2), and carbon monoxide (CO) range from 0.2-0.4 for
2010-2012, while the median daily copollutant correlation of SO2 with ozone (O3) is 0.1
(Figure 2-35V Daily SO2 copollutant correlations for all pollutants can be greater than 0.7
on rare occasions.
Dispersion models can be used to estimate SO2 concentrations in locations where
monitoring is not practical or sufficient (Section 2.6. IV 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. The widely-used dispersion model
American Meteorological Society/U.S. EPA Regulatory Model (AERMOD) is designed
to simulate hourly concentrations which can then be averaged to yield longer-term
concentrations. Multiple evaluations of AERMOD's performance against field study
databases over averaging times from 1 hour to 1 year have indicated that the model is
relatively unbiased in estimating upper-percentile 1-hour concentration values.
Uncertainties in model predictions are influenced by uncertainties in model input data,
particularly emissions and meteorological conditions (e.g., wind).
Multiple techniques can be used to assign exposure for epidemiologic studies, including
evaluation of data from central site monitoring, personal SO2 monitoring, and various
modeling approaches (Section 3.2). 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 provide a
continuous record of SO2 concentrations over many years, but they do not fully capture
the relatively high spatial variability in SO2 across an urban area. Exposure error tends to
attenuate health effect estimates in time-series epidemiologic studies for SO2 measured
by central site monitors. For long-term studies, bias of the health effect estimate may
occur in either direction. In all study types, use of central site monitors is expected to
widen confidence intervals of the health effect estimate. Personal SO2 monitors can
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capture the study participant's 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 hour or less.
The time and expense involved to deploy personal monitors makes them more suitable
for panel epidemiologic studies. Models can be used to estimate exposure for individuals
and large populations when personal exposure measurements are unavailable. Modeling
approaches include estimation of concentration surfaces, estimation of time-activity
patterns, and microenvironment-based models combining 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 the particular application.
Exposure measurement error, which refers to the bias and uncertainty associated with
using exposure metrics to represent the actual exposure of an individual or population,
can be an important contributor to variability in epidemiologic study results
(Section 3.3.3). Several exposure-related factors, including time-activity patterns, spatial
and temporal variability of SO2 concentrations, and proximity of individuals and
populations to central-site monitors, contribute to error in estimating exposure to ambient
SO2. Activity patterns vary both among and within individuals, resulting in
corresponding variations in exposure across a population and over time. Spatial and
temporal variability in SO2 concentrations can contribute to exposure error in
epidemiologic studies, whether they rely on central-site monitor data or concentration
modeling for exposure assessment. SO2 has low to moderate spatial correlations between
ambient monitors across urban geographic scales; thus, using central-site monitor data for
epidemiologic exposure assessment introduces exposure error into the resulting effect
estimate.
Exposure error can bias epidemiologic associations between ambient pollutant
concentrations and health outcomes and tends to widen confidence intervals around those
estimates (Section 3.3.5V The importance of exposure error varies with study design and
is dependent on the spatial and temporal aspects of the design. For time-series studies,
more bias (generally toward the null) would be anticipated for the health effect estimate
when using a central site monitor to estimate SO2 exposure, and more variability in the
health effect estimate would be expected when using more spatially resolved exposure
metrics such as a population-weighted average. For cohort studies of long-term exposure
to SO2, spatial variability in SO2 concentrations across the study area could lead to
positive or negative bias in the health effect estimate when fixed site monitors are used to
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estimate exposure and the distribution of the true exposure data differs from the
distribution captured by the monitoring network. In all study types, use of central-site
monitors is expected to widen confidence intervals.
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 in providing
biological plausibility for linking SO2 exposure with observed health effects.
SO2 is readily absorbed in the nasal passages of both humans and laboratory animals
under resting conditions (Section 4.2). 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. Due to their increased amount of
oral breathing, children and individuals with asthma or allergic rhinitis may be expected
to have greater SO2 penetration into the lower respiratory tract than healthy adults.
Children may also be expected to have a greater intake dose of SO2 per body mass than
adults.
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, which is rapidly excreted
through the urine in proportion 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.
Although inhaled SO2 produces sulfite that is distributed through the circulation, sulfite
levels in the body are predominately influenced by endogenous production and by
ingestion of sulfite in food (Section 4.2.6). Endogenous sulfite from the catabolism of
ingested sulfur-containing amino acids far exceeds exogenous sulfite from ingestion of
food additives for both adults and young children. 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 (i.e., 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.
SO2 exposure results in increased airway resistance due to bronchoconstriction in healthy
adults and in adults with asthma (Section 4.3). as demonstrated in controlled human
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exposure studies. In healthy adults, this response occurs primarily as a result of activation
of neural reflexes mediated by pathways involving the vagus nerve. However, in adults
with asthma, evidence indicates that the response is only partially due to neural reflexes
and that inflammatory mediators also play an important role. Enhancement of allergic
inflammation has been observed in adults with asthma who were exposed acutely to SO2.
Animal toxicological studies in both naive and allergic animal models provide further
evidence for allergic sensitization and enhanced allergic inflammatory responses, which
may enhance airway hyperresponsiveness (AHR) and promote bronchoconstriction in
response to a trigger. Thus, allergic inflammation and AHR may also link short-term SO2
exposure to the epidemiologic outcome of asthma exacerbation.
For long-term SO2 exposure, the initiating event in the development of respiratory effects
is the recurrent or prolonged redox stress due to the formation of reactive products in the
epithelial lining fluid. 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, which together are characteristics of asthma. The resulting outcome may
be new asthma onset, which presents as an asthma exacerbation that leads to
physician-diagnosed asthma.
Although there is some evidence that SO2 inhalation results in extrapulmonary effects,
there is uncertainty regarding the mode of action underlying these responses. Evidence
from controlled human exposure studies points to SO2 exposure-induced
activation/sensitization of neural reflexes possibly leading to altered heart rate or heart
rate variability. Evidence also points to transport of sulfite into the circulation. Sulfite is
highly reactive and may be responsible for redox stress 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.
Health Effects of Sulfur Dioxide Exposure
In this ISA, information on SO2 exposure and health effects from controlled human
exposure, epidemiologic, and toxicological studies is integrated to form conclusions
about the causal nature of relationships between SO2 exposure and health effects. Health
effects examined in relation to the full range of SO2 concentrations relevant to ambient
conditions are considered. Based on peak concentrations (Section 2.5) and the ISA
definition that ambient-relevant exposures be within one to two orders of magnitude of
current conditions (Preamble (U.S. EPA. 2015e). Section 5c), SO2 concentrations up to
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2,000 ppb1 are defined to be ambient-relevant. A consistent and transparent framework
(Preamble (U.S. EPA. 2015e). 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 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
The conclusions presented in Table ES-1 are informed by recent findings and whether
these recent findings, integrated with information from the 2008 ISA for Sulfur Oxides
(U.S. EPA. 2008b). support a change in conclusion. Important considerations include
judgments of error and uncertainty in the collective body of available studies; the
coherence of findings integrated across controlled human exposure, epidemiologic, and
toxicological studies demonstrating an independent effect of SO2 exposure and potential
underlying biological mechanisms; consistency in epidemiologic evidence across various
methods used to estimate SO2 exposure; and examination in epidemiologic studies of the
potential influence of factors that could bias associations observed with SO2 exposure.
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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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 Determination13
2008 ISA
Current Draft ISA
Respiratory effects-
Short-term exposure
Section 5.2.1. Table 5-27
Causal relationship
Causal relationship
Respiratory effects-
Long-term exposure
Section 5.2.2. Table 5-31
Inadequate to infer the presence or
absence of a causal relationship
Suggestive but not sufficient to infer a
causal relationship
Cardiovascular effects-
Short-term exposure
Section 5.3.1. Table 5-41
Inadequate to infer the presence or
absence of a causal relationship
Suggestive but not sufficient to infer a
causal relationship
Cardiovascular effects-
Long-term exposure
Section 5.3.2. Table 5-43
Not included
Inadequate to infer the presence or
absence of a causal relationship
Reproductive and
developmental effects0
Section 5.4, Table 5-46
Inadequate to infer the presence or
absence of a causal relationship
Suggestive but not sufficient to infer a
causal relationship
Total mortality-
Short-term exposure
Section 5.5.1. Table 5-51
Suggestive but not sufficient to infer a
causal relationship
Suggestive but not sufficient to infer a
causal relationship
Total mortality-
Long-term exposure
Section 5.5.2, Table 5-55
Inadequate to infer the presence or
absence of a causal relationship
Suggestive but not sufficient to infer a
causal relationship
Cancer-
Long-term exposure
Section 5.6. Table 5-56
Inadequate to infer the presence or
absence of a causal relationship
Suggestive but not sufficient to infer a
causal relationship
ISA = integrated Science Assessment.
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.
bSince the 2008 ISA for Sulfur Oxides, the phrasing of causal determinations has changed slightly, and the weight of evidence
that describes each level in the hierarchy of the causal framework has been more explicitly characterized.
Reproductive and developmental effects studies consider a wide range of exposure durations.
Sulfur Dioxide Exposure and Respiratory Effects
1 The strongest evidence indicates that there is a causal relationship between short-term
2 SO2 exposure and respiratory effects, particularly in individuals with asthma, which is
3 consistent with the conclusions of the 2008 SOx ISA. This determination is based on the
4 consistency of findings within disciplines, coherence among multiple lines of evidence,
5 and biological plausibility for effects specifically related to asthma exacerbation. The
6 evidence for this conclusion comes primarily from controlled human exposure studies
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available at the time of the 2008 SOx ISA that showed lung function decrements and
respiratory symptoms in adults 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). Epidemiologic evidence is also supportive of a causal relationship,
including additional studies that add to the evidence provided by the 2008 SOx ISA.
Studies of asthma hospital admissions and emergency department visits report positive
associations with short-term SO2 exposures that are generally unchanged in copollutant
models. There is also some supporting evidence for positive associations between
short-term SO2 exposures and respiratory symptoms among children with asthma.
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.
For long-term SO2 exposure and respiratory effects the evidence is suggestive of, but not
sufficient to infer, a causal relationship. The combined evidence from a limited number
of recent longitudinal epidemiologic studies and animal toxicological evidence for the
development of an asthma-like phenotype support this causal determination, but overall
the evidence is limited. 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.
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 catabolism of endogenous sulfur-containing amino acids.
For short-term and long-term SO2 exposure, evidence is suggestive of but not sufficient
to infer a causal relationship with total mortality, reproductive and developmental effects,
and cancer (Table ES-1). For cardiovascular effects, the evidence for short-term SO2
exposure is also suggestive of a causal relationship, but for long-term exposure the
evidence is inadequate to infer the presence or absence of a causal relationship.
These conclusions are similar to those made in the 2008 SOx ISA, although in several
cases, the causal determination has changed from inadequate to suggestive due to a
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limited body of new evidence that suggests a relationship between SO2 exposure and
effects but does not reduce important uncertainties present during the last review.
Policy-Relevant Considerations for Health Effects Associated
with Sulfur Dioxide Exposure
The primary SO2 NAAQS are based on 1-hour daily max concentrations (3-year average
of each year's 99th percentile) set to protect against respiratory morbidity associated with
short-term SO2 exposures (Section 1.1). Controlled human exposure studies have
reported respiratory effects after exposures of 5-10 minutes. Consistent associations
between SO2 concentrations and asthma hospital admissions and emergency department
visits that are generally unchanged in copollutant models have been demonstrated in
epidemiologic studies using daily exposure metrics (24-hour average and 1-hour 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, which is also observed in the limited number of epidemiologic studies that
examined lag structures and reported associations within the first few days of exposure.
Results from controlled human exposure studies of respiratory morbidity indicate wide
interindividual variability in response to SO2 exposures, with peak (5 to 10 minutes)
exposures at levels as low as 200-300 ppb eliciting respiratory responses in some
individuals with asthma. A clear increase was observed in the magnitude of respiratory
effects with increasing exposure concentrations between 200 and 1,000 ppb during
5-10 minutes SO2 exposures. That is, both the number of affected individuals with
asthma and the severity of the response increased as SO2 concentrations increased. There
is limited epidemiologic research on concentration-response functions relating SO2
concentrations to respiratory health morbidity, but there is no epidemiologic evidence to
support a deviation from linearity or the occurrence of a population-level threshold
concentration below which health effects are not observed.
SO2 concentrations are highly spatially heterogeneous, with SO2 concentrations at some
monitors possibly not highly correlated with the community average concentration. 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 they rely on central-site
monitor data or concentration modeling for exposure assessment.
Consistent with the findings of the 2008 SOx ISA, this ISA concludes that there is
adequate evidence that people with asthma are at increased risk for SCh-related health
effects compared with those without asthma. This conclusion is based on the evidence for
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1 short-term SO2 exposure and respiratory effects (specifically lung function decrements),
2 for which a causal relationship has been determined. There is also evidence suggestive of
3 increased risk of SCh-related health effects for children and older adults relative to other
4 life stages.
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References for Executive Summary
U.S. EPA (U.S. Environmental Protection Agency). (2008b). Integrated science assessment for sulfur oxides:
Health criteria [EPA Report]. (EPA/600/R-08/047F). Research Triangle Park, NC: U.S. Environmental
Protection Agency, National Center for Environmental Assessment.
http://cfpub.epa. gov/ncea/cfm/recordisplav.cfm?deid= 198843
U.S. EPA (U.S. Environmental Protection Agency). (2009a). Integrated science assessment for particulate matter
[EPA Report]. (EPA/600/R-08/139F). Research Triangle Park, NC: U.S. Environmental Protection Agency,
National Center for Environmental Assessment.
http://cfpub.epa. gov/ncea/cfm/recordisplav.cfm?deid=216546
U.S. EPA (U.S. Environmental Protection Agency). (2013c). Notice of workshop and call for information on
integrated science assessment for oxides of nitrogen and oxides of sulfur. Fed Reg 78: 53452-53454.
U.S. EPA (U.S. Environmental Protection Agency). (2015e). Preamble to the Integrated Science Assessments
[EPA Report]. (EPA/600/R-15/067). Research Triangle Park, NC: National Center for Environmental
Assessment, Office of Research and Development.
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CHAPTER 1 SUMMARY OF THE INTEGRATED
SCIENCE ASSESSMENT
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 criteria for a
broad category of gaseous 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 Standards (NAAQS) for sulfur dioxide (SO2). Gaseous SOx include SO2, sulfur
trioxide (SO3), and their various reaction products (Section 2.3). There also are
particulate species of SOx (e.g., sulfate) that are being considered in the current review of
the NAAQS for particulate matter (PM) and were evaluated in the 2009 ISA for PM
(U.S. EPA. 2009a'). The welfare effects of SOx 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. 2013c).
This ISA evaluates relevant scientific literature published since the 2008 ISA for Sulfur
Oxides (U.S. EPA. 2008b). 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. 1986a. 1982b).
Thus, this ISA updates the state of the science that was available for the 2008 ISA, which
informed decisions on the primary SO2 NAAQS in the review completed in 2010. In
2010, the U.S. Environmental Protection Agency (EPA) established a new 1-hour (h)
standard at a level of 75 parts per billion (ppb) SO2 based on the 3-year (yr) average (avg)
of the 99th percentile of each year's 1-hour daily maximum (max) concentrations.2 The
1-hour standard was established to protect against a broad range of respiratory effects
associated with short-term (i.e., 5-minutes to 24-hours) exposures in potential at-risk
populations such as people with asthma. The 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 a 1-hour standard at
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. 201561.
2 The legislative requirements and history of the SO2 NAAQS are described in detail in the Preface to this ISA.
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75 ppb would effectively maintain 24-hour and annual SO2 concentrations well below the
then-current NAAQS. In light of considerable weight being placed on health effects
associated with 5-minute peak SO2 concentrations, the 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. 2010c).
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. 2014b). To address these
questions and update the scientific judgments in the 2008 SOx ISA, 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 life stages potentially with
increased risk of health effects related to exposure to SOx.
Sulfur dioxide is the most prevalent species of gaseous SOx in the atmosphere, with other
species not present at concentrations relevant for human exposures. Most studies on the
health effects of gaseous SOx focus on SO2; effects of other gaseous species are
considered as information is available. In evaluating the health evidence, this ISA
considers possible influences of other atmospheric pollutants, including interactions of
SO2 with other co-occurring pollutants such as PM, NOx, carbon monoxide (CO), and
ozone (O3).
In addressing policy-relevant questions, this ISA aims to characterize the independent
health effects of SO2, not its role as a marker for a broader mixture of pollutants in the
ambient air. 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.
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1.2 Process for Developing Integrated Science Assessments
EPA uses a structured and transparent process for evaluating scientific information and
determining the causality of relationships between air pollution exposures and health
effects [see Preamble (U.S. EPA. 2015eYI. 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 gaseous SOx and
potential relationships with health effects. Relevant studies include those examining
atmospheric chemistry, spatial and temporal trends, and exposure assessment, as well as
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.
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. 2013c). Thereafter, EPA routinely
conducted literature searches to identify relevant peer-reviewed studies published since
the previous ISA (i.e., from January 2008 through April 2015). Multiple search methods
were used [Preamble (U.S. EPA. 2015e). Section 2] including searches in databases such
as PubMed and Web of Science. Subject-area experts and the public were also able to
recommend studies and reports during a kick-off workshop held at the EPA in June 2013.
EPA identified 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
(http://hero.epa.gov/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.
Health effects were considered for evaluation in this ISA if they were examined in
previous EPA assessments for SOx or multiple recent studies. Literature 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. 2015f). Literature
searches have also identified a few recently published toxicological studies on
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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. 2015gYI.
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 toxicological
studies were conducted in animal models consistent with studies of other health endpoints
and generally focused on nonspecific preclinical outcomes (e.g. oxidative stress, protein
expression). The epidemiologic studies were conducted in geographic areas and
populations for which associations between SO2 and other health effects have been
demonstrated. Thus, the exclusion of these studies does not exclude the assessment of
particular geographic locations, potential at-risk lifestages or populations, or range of
ambient concentrations of SOx.
The Preamble to the Integrated Science Assessments (U.S. EPA. 2015e) 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 characterize quantitative relationships between SO2 and health
effects, examine exposure metrics that well represent the variability in concentrations in
the study area, consider the potential influence of other air pollutants and factors
correlated with SO2, examine potential at-risk populations and lifestages, or 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 that are defined in this ISA to be relevant to
ambient exposures. Based on peak ambient concentrations (Section 2.5) and the ISA
definition that ambient-relevant exposures be within one to two orders of magnitude of
current levels, 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, land use regression (LUR) models, and personal exposure monitors. 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
'The 2,000-ppb upper limit applies largely to animal toxicological studies but also a few controlled human
exposure studies.
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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 (U.S. EPA. 2015eYI 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 where 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 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.
1.3 Organization of the Integrated Science Assessment
This ISA comprises the Preface (legislative requirements and history of the primary SO2
NAAQS), Executive Summary, and six chapters. The general process for developing an
ISA is described in a companion document, Preamble to the Integrated Science
Assessments (U.S. EPA. 2015e). 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.
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
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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
SCh-related health effects (Section 1.7V A key consideration in the health effects
assessment is the extent to which evidence indicates that SO2 exposure independently
causes health effects rather than SO2 only serving as a marker for effects due to a broader
mixture of air pollutants. To that end, this chapter draws upon information about the
sources, distribution, and exposure to ambient SO2 (Section 3.3.5) 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 these factors 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
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
Because of its historically high atmospheric concentrations and the locations of its
sources with respect to human populations, SO2 is the gaseous sulfur oxide chemical
species of greatest importance to public health. Emissions of SO2 have declined by
approximately 70% for all major sources since 1990 as a consequence 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
(coal-fired boilers), emitting 4,500,000 tons of SO2 annually, according to the 2011
National Emissions Inventory (NEI) (Section 2.2).
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Beyond the rate at which a source emits the pollutant, the important variables that
determine the concentration of SO2 downwind of the source are the photochemical
removal processes occurring in the emissions plume and local meteorology, including
wind, atmospheric stability, humidity, and cloud/fog cover. 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 in areas with high concentrations of Criegee precursors, i.e. small 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 EPA monitoring network as a result of the new
1-hour primary NAAQS standard promulgated in 2010. 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 5-minute
data in light of health effects evidence on respiratory effects among exercising
individuals with asthma following a 5-10 minute exposure to SO2. Since the release of
the 2008 SOx ISA (U.S. EPA. 2008b). there are more than 400 monitoring sites across
the U.S. reporting 5-minute data. Analysis of environmental concentrations of SO2 data
reported in Chapter 2 reflect the monitoring network changes, particularly the analysis of
the recent 5-minute data.
On a nationwide basis, the average daily 1-hour maximum SO2 reported during
2010-2012 is 9 ppb (Section 2.5). However, peak concentrations (99th percentile) of
daily maximum SO2 concentrations can approach 75 ppb at some monitors located near
large anthropogenic or natural sources, e.g., volcanoes. Similarly, new 5-minute data
demonstrate that most hourly 5-minute maximum concentrations are well below the
short-term health benchmark levels of 200 ppb (i.e., the lowest level where lung function
decrements were reported in controlled human exposure studies of individuals with
asthma engaged in exercise, see Section 5.2.1.2). although on some occasions
(99th percentile and above) concentrations can be greater than 200 ppb at some monitors
near anthropogenic sources such as EGUs.
SO2 concentrations are highly variable across urban spatial scales, exhibiting moderate to
poor correlations between SO2 measured at different monitors across a metropolitan area.
This high degree of urban spatial variability may not be fully captured by central site
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monitoring estimates, and thus has implications for the interpretation of human exposure
and health effects data (Sections 2.5.2.2 and 3.3.3.2).
Sulfur dioxide correlations with copollutants tend to vary across location, study and SO2
averaging time (Section 2.5.5). Median daily SO2 correlations with PM, nitrogen dioxide
(NO2), and CO range from 0.2-0.4 for 2010-2012, while the median daily copollutant
correlation of SO2 with O3 is 0.1 (Figure 2-35). Daily SO2 copollutant correlations for all
pollutants can be greater than 0.7 on rare occasions.
Dispersion models can be used to estimate SO2 concentrations in locations where
monitoring is not practical or sufficient (Section 2.6.1). 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 human exposure to SO2 in epidemiologic studies (Section 3.2.2.1. Chapter 5).
The widely-used dispersion model American Meteorological Society/U.S. EPA
Regulatory Model (AERMOD) is designed to simulate hourly concentrations which can
then be averaged to yield longer-term concentrations. Multiple evaluations of
AERMOD's performance against field study databases over averaging times from 1 hour
to 1 year have indicated that the model is relatively unbiased in estimating
upper-percentile 1-hour concentration values. 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 various
modeling approaches (Section 3.2). Each has strengths and limitations, as summarized in
Table 3-2. 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 participant's activity-related exposure across different microenvironments, but
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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 makes them more suitable for panel epidemiologic
studies. Models can be used to estimate exposure for individuals and large populations
when personal exposure measurements are unavailable. Modeling approaches include
estimation of concentration surfaces, estimation of time-activity patterns, and
microenvironment-based models combining air quality data with time-activity patterns.
Strengths and limitations of these approaches are summarized in Table 3-1. 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 public buildings. 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 (Table 3-4). 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 concentrations were relatively high (>0.75),
suggesting that variations in outdoor concentration are driving indoor concentrations.
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 SOx ISA (U.S. EP A. 2008b'). 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. 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.
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
(Section 3.3.3V Activity patterns vary both among and within individuals, resulting in
corresponding variations in exposure across a population and overtime. Variation in SO2
concentrations among various microenvironments means that the amount of time spent in
each location, as well as exertion level, will influence an individual's exposure to
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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 introduce error into population-averaged exposure estimates.
Spatial and temporal variability in SO2 concentrations can contribute to exposure error in
epidemiologic studies. SO2 has low to moderate spatial correlations among ambient
monitors 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 monitors that do not capture all of the spatial variability within a city. 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 monitors may influence how well people's exposure
is represented by measurements at the monitors, although factors other than distance play
an important role as well. While many SO2 monitors are located near dense population
centers, other monitors are located near sources and may not fully represent SO2
concentrations experienced by populations in epidemiologic studies. Use of these
near-source monitors introduces exposure error into health effect estimates, although this
error can be mitigated by using average concentrations across multiple monitors 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 to moderate
correlations with other daily NAAQS pollutant concentrations at collocated monitors
(Figure 2-35). However, a wide range of copollutant correlations is observed at different
monitoring sites, from moderately negative to moderately positive. In studies where daily
SO2 correlations with NO2 and CO were observed to be high, it is possible the data may
have been collected before recent rulemaking to reduce sulfur content in diesel fuel
(66 FR 5002). 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 contribute to variability in epidemiologic study results by biasing
effect estimates toward or away from the null and widening confidence intervals
(Section 3.3.5). The magnitude of 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
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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. Low
spatial correlations between central site monitors also contribute to exposure error in
time-series studies, potentially biasing the health effect estimate towards the null and
widening the confidence intervals around the health effect estimate. For long-term
studies, bias of the health effect estimate may occur in either direction depending on
whether the monitor is over- or under-estimating 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.
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, ambient and nonambient concentration differences, 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 concentration or exposure estimates using various approaches offer an
alternative 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.
1.5 Dosimetry and Mode of Action of Sulfur Dioxide
This ISA summarizes information on the dosimetry of inhaled SO2, including the
processes of absorption, distribution, metabolism, and inhalation, 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.
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1.5.1 Dosimetry of Inhaled Sulfur Dioxide
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
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 the physiochemical properties 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 SOx ISA, with most studies conducted prior to the 1982 AQCD (U.S.
EPA. 1982a) and the 1986 Second Addendum (U.S. EPA. 1986b).
Because SO2 is highly soluble in water, it is readily absorbed in the nasal passages of
both humans and laboratory animals under resting conditions. 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. 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. Due to
their increased amount of oral breathing, individuals with asthma or allergic rhinitis and
children may be expected to have greater SO2 penetration into the lower respiratory tract
than healthy 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,
the majority of SC>2-derived products in the body at any given time following exposure
are found in the respiratory tract and may be detected there for up to a week following
inhalation. 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. 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. 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. The primary endogenous contribution of sulfite is from the
catabolism of sulfur-containing amino acids (namely, cysteine and methionine).
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Endogenous sulfite from 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)]. 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 Chapter 4 of this ISA updates the
basic concepts derived from the SO2 literature presented in the 1982 AQCD (U.S. EPA.
1982a) and the 2008 SOx ISA (U.S. EPA. 2008b) 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 neural reflexes, (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 or sensitization of neural reflexes,
release of inflammatory mediators, and modulation of allergic inflammation or
sensitization. These key events may collectively lead to several endpoints, including
bronchoconstriction and airway hyperresponsiveness (AHR). Bronchoconstriction is
characteristic of an asthma attack, and AHR often leads to bronchoconstriction in
response to a trigger. These pathways may be linked to the epidemiologic outcome of
asthma exacerbation.
SO2 exposure results in increased airway resistance due to bronchoconstriction in healthy
adults and in adults with asthma, as demonstrated in controlled human exposure studies.
In healthy adults, this response occurs primarily as a result of activation of neural reflexes
mediated by cholinergic parasympathetic pathways involving the vagus nerve. However,
in adults with asthma, evidence indicates that the response is only partially due to neural
reflexes 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
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the vagus nerve, although noncholinergic mechanisms may also be involved.
Enhancement of allergic inflammation (i.e., leukotriene-mediated increases in numbers of
sputum eosinophils) has been observed in adults with asthma who were exposed acutely
to SO2. Animal toxicological studies in both naive and allergic animal models provide
further evidence for allergic sensitization and enhanced allergic inflammatory responses,
which 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.
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. Evidence for this mode of action comes from studies in both naive and
allergic experimental animals, which demonstrate enhanced allergic responses and
pathologic changes following exposure to SO2 over several weeks. 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.
Although there is some evidence that SO2 inhalation results in extrapulmonary effects,
there is uncertainty regarding the mode of action underlying these responses. 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 into the
circulation. Sulfite is highly reactive and may be responsible for redox stress (possibly
through autooxidation 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.
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
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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
Respiratory Effects Associated with Short-term Exposure to Sulfur Dioxide
The strongest 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 SOx ISA (U.S. EPA. 2008b). 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 5-27).
The evidence for this conclusion comes primarily from controlled human exposure
studies included in the 2008 SOx ISA (U.S. EPA. 2008b) that showed lung function
decrements and respiratory symptoms in adult individuals with asthma exposed to SO2
for 5-10 minutes under increased ventilation conditions; no new controlled human
exposure studies have been conducted to 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 second (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 the asthmatic population
(-5-30%) has also been observed 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 mild asthmatics, 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, they are more limited in reserve to deal with an insult compared with
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individuals with mild asthma; therefore, the impact of SCh-induced decrements in lung
function is greater in asthmatics than healthy adults.
These findings are consistent with the current understanding of dosimetry and modes of
action (Section 1.5). Due to their increased contribution 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 or sensitization of neural reflexes, 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 SOx ISA. Studies of
asthma hospital admissions and emergency department (ED) visits report positive
associations with short-term SO2 exposures that are generally unchanged in copollutant
models involving PM and other criteria pollutants when examining all ages, children (i.e.,
<18 years of age) and older adults (i.e., 65 years of age and older) (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). Due to their increased contribution of oral breathing, children may be
expected to have greater SO2 penetration into the lower respiratory tract than healthy
adults. Children may also be expected to have a greater intake dose of SO2 per body mass
than adults. 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 5.2.1.7).
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 (Sections 5.2.1.3. 5.2.1.4. 5.2.1.5. and
5.2.1.6). The limited and inconsistent evidence for these nonasthma-related respiratory
effects does not contribute heavily to the causal determination.
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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. There is a very 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 is coherent with limited animal toxicological evidence
of allergic sensitization, airway remodeling, and enhanced airway responsiveness, which
are key events (or endpoints) in the mode of action for the development of asthma. 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 exposure and the
development of asthma. Details of the causal determination are provided in Table 5-31.
1.6.2 Health Effects beyond the Respiratory System
Cardiovascular Effects Associated with Short-Term Exposure to Sulfur
Dioxide
Overall, the available evidence is suggestive of, but not sufficient to infer, a causal
relationship between short-term exposure to SO2 and cardiovascular health effects
(Table 5-41). This conclusion represents a change from the 2008 ISA for Sulfur Oxides
that concluded "the evidence as a whole is inadequate to infer a causal
relationship" (U.S. EPA. 2008b). The revised causal determination is based on new
evidence that supports the potential for independent associations of SO2 with
cardiovascular effects after adjusting for some pollutants, recognizing that uncertainties
still remain regarding the potential for SO2 to serve as an indicator for other pollutants
and the lack of support for biologically plausible mechanisms for cardiovascular effects
from the limited, inconsistent experimental evidence available.
The primary evidence comes from epidemiologic studies of adults that generally
demonstrate an association between short-term exposure to SO2 and a myocardial
infarction (MI). This conclusion is supported by epidemiologic studies reporting
S02-associated hospitalizations and ED visits for MI, ischemic heart disease (IHD), and
aggregated cardiovascular disease (CVD), ST-segment alterations, and mortality from
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cardiovascular disease. There is a lack of experimental studies investigating related
clinical outcomes in order to evaluate the coherence across disciplines. However
dosimetric studies (Section 4.2) show that following absorption in the respiratory tract,
SCh-derived products are widely distributed throughout the body and have been observed
in the blood and urine within 5 minutes of starting an SO2 exposure. Some animal
toxicological studies have demonstrated oxidative injury to blood or other tissues and/or
inflammation and other effects in tissues distal to the absorption site. The current
understanding of modes of action (Section 4.3) is that exposure to SO2 may potentially
result in effects outside the respiratory tract via activation of neural reflexes or mediated
by circulating sulfite. 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 an MI. Evidence
for other cardiovascular and related metabolic effects is inconclusive.
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 5-43). 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 a lack of experimental evidence to provide
coherence or biological plausibility for an independent effect of long-term exposure to
SO2 on cardiovascular health.
Reproductive and Developmental Effects
Overall the evidence is suggestive of, but not sufficient to infer, a causal relationship
between exposure to SO2 and reproductive and developmental outcomes (Table 5-46).
The 2008 SOx ISA (U.S. EPA. 2008b) concluded the evidence was inadequate to infer
the presence or absence of a causal relationship with reproductive and developmental
effects.
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,
there are a number of uncertainties associated with the observed relationship between
exposure to SO2 and birth outcomes, such as timing of exposure windows, exposure
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error, and spatial and temporal heterogeneity. Few studies have examined other health
outcomes, such as fertility, effects on pregnancy (e.g., preeclampsia, gestational
diabetes), and developmental effects, and there is little coherence or consistency among
epidemiologic and toxicological studies for these outcomes. Although there are few
toxicological studies at relevant dose ranges of SO2, many studies provide supportive
evidence for health outcomes following SO2 exposure. Many uncertainties remain when
evaluating the evidence for these health endpoints, including exposure measurement error
and the potential for copollutant confounding; therefore, the evidence is suggestive of,
but not sufficient to infer, a causal relationship between exposure to SO2 and reproductive
and developmental outcomes.
Total Mortality Associated with Short-Term Exposure to Sulfur Dioxide
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 but not sufficient to infer a causal relationship
(Table 5-51). This conclusion is consistent with that reached in the 2008 SOx ISA (U.S.
EPA. 2008b'). 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 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, 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, no biological
mechanism that could lead to mortality as a result of short-term SO2 exposures has been
characterized.
Total Mortality Associated with Long-Term Exposure to Sulfur Dioxide
The overall evidence is suggestive of, but not sufficient to infer, a causal relationship
between long-term exposure to SO2 and total mortality among adults (Table 5-55). The
recent evidence is generally consistent with the evidence in the 2008 SOx ISA (U.S.
EPA. 2008b). The most 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
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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.
Cancer
The overall evidence for long-term SO2 exposure and cancer is suggestive but not
sufficient to infer a causal relationship (Table 5-56). The 2008 SOx ISA concluded that
the evidence was inadequate to infer a causal relationship (U.S. EPA. 2008b). 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 (SO2) exposure and health
effects evaluated in the current draft Integrated Science Assessment (ISA) for Sulfur Oxides.
Health Effect Category3 and Causal Determination13
SO2 Concentrations
Associated with Effects
Respiratory Effects and Short-term Exposure (Section 5.2.1): Causal relationship
No change in causal determination from 2008 ISA; new evidence consistent with prior determination
Key evidence
(Table 5-27)
Strongest evidence is for effects on asthma exacerbation. There is consistent evidence from multiple,
high-quality controlled human exposure studies showing 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, in studies of all ages, 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 or sensitization of neural reflexes and/or inflammation leading to
bronchoconstriction and allergic inflammation leading to increased airway responsiveness, which are
key events or endpoints in the proposed mode of action linking short-term SO2 exposure and asthma
exacerbation.
Overall studies' means:
Controlled human
exposure studies of
decreased lung function:
400-600 ppb, with
responses observed in
some asthmatics at
200 ppb
Controlled human
exposure studies of
increased respiratory
symptoms: 600-1,000 ppb
Epidemiologic studies: 1-h
max: 9.6-10.8 ppb
24-h avg: 1.03-36.9 ppb
Respiratory Effects and Lonq-term Exposure (Section 5.2.2): Suqaestive but not sufficient to infer a causal relationship
Change in causal determination from 2008 ISA (inadequate to infer a causal relationship) due to new, but limited, evidence.
Key evidence0
(Table 5-31)
Evidence from epidemiologic studies is generally supportive but not entirely consistent for increases in
asthma incidence and prevalence related to SO2 exposure. The limited animal toxicological evidence
provides coherence and biological plausibility. There is some evidence for a mode of action involving
inflammation, allergic sensitization, AHR, and airway remodeling.
Overall epidemiologic
studies' mean (SD):
4.0 (3.4) ppb and
1.98 (0.97) ppb
Animal toxicological
studies: 2,000 ppb
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Table 1-1 (Continued): Key evidence contributing to causal determinations for sulfur dioxide (SO2) exposure and
health effects evaluated in the current draft Integrated Science Assessment (ISA) for
Sulfur Oxides.
Health Effect Category3 and Causal Determination13
SO2 Concentrations
Associated with Effects
Cardiovascular Effects and Short-term Exposure (Section 5.3.1) Suggestive but not sufficient to infer a causal relationship
Change in causal determination from 2008 ISA (inadequate to infer a causal relationship) due to new, but limited, evidence.
Key evidence0
(Table 5-41)
There is generally supportive but not entirely consistent evidence of increased hospital admissions and ED visits
among adults for IHD, Ml, and all CVD, coherence with ST-segment depression in adults with pre-existing
coronary heart disease, and increased risk of cardiovascular mortality. There is a lack of 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.
Overall epidemiologic
studies' 24-h avg:
1.2-30.2 ppb
Cardiovascular Effects and Long-term Exposure (Section 5.3.2) Inadeguate to infer a causal relationship
Not included in 2008 ISA
Key evidence0
(Table 5-43)
Results of epidemiologic studies of long-term SO2 concentrations and Ml, CVD, and stroke events are
limited and inconsistent. There is limited coherence with evidence for cardiovascular mortality and
weak evidence to identify key events in a mode of action linking long-term SO2 exposure and
cardiovascular effects.
Overall epidemiologic
studies' means:
1.3-1.72 ppb
Reproductive and Developmental Effects and Exposure (Section 5.4) Suggestive but not sufficient to infer a causal relationship
Change in causal determination from 2008 ISA (inadequate to infer a causal relationship) due to new, but limited, evidence.
Key evidence0
(Table 5-46)
Consistent positive associations are observed with near-birth exposures to SO2 and preterm birth.
Although limited evidence is available, positive associations are also reported for fetal growth metrics,
birth weight, and infant and fetal mortality. There is a lack of evidence from epidemiologic studies to
support an association of SO2 exposure with detrimental effects on fertility or pregnancy. Limited
evidence is available for an understanding of key reproductive and developmental events in mode of
action.
Overall epidemiologic
studies' means:
1.9-13.2 ppb
Total Mortality and Short-term Exposure (Section 5.5.1) Suggestive but not sufficient to infer a causal relationship
No change in causal determination from 2008 ISA; new evidence consistent with prior determination
Key evidence0
(Table 5-51)
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 SOs-related respiratory mortality.
Overall epidemiologic
studies' mean 24-h avgs:
U.S., Canada, South
America, Europe:
0.4-28.2e ppb
Asia:
0.7->200 ppb
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Table 1-1 (Continued): Key evidence contributing to causal determinations for sulfur dioxide (SO2) exposure and
health effects evaluated in the current draft Integrated Science Assessment (ISA) for
Sulfur Oxides.
Health Effect Category3 and Causal Determination13
SO2 Concentrations
Associated with Effects
Total Mortality and Lonq-term Exposure (Section 5.5.2) Suqaestive but not sufficient to infer a causal relationship
Change in causal determination from 2008 ISA (inadequate to infer a causal relationship) due to new, but limited, evidence.
Key evidence0 Some epidemiologic studies report positive associations, but results are not entirely consistent with
(Table 5-55) some studies reporting null associations. Additionally there is no evidence for associations between
long-term respiratory or cardiovascular health effects to support an association with mortality from
these causes.
Overall epidemiologic
studies' ambient means:
1.6-24.0 ppb
Cancer and Lonq-term Exposure (Section 5.6) Suqaestive but not sufficient to infer a causal relationship
Change in causal determination from 2008 ISA (inadequate to infer a causal relationship) due to new, but limited, evidence.
Key evidence0 Among a small body of evidence, some epidemiologic studies report associations in lung cancer and
(Table 5-56) 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
studies' means:
1.49-27.87 ppb.
Toxicological studies:
5,000, 10,700, 21,400,
32,100 ppb
AHR = airway hyperresponsiveness; CO = carbon monoxide; CVD = cardiovascular disease; ED = emergency department; IHD = ischemic heart disease; ISA = Integrated Science
Assessment; Ml = myocardial infarction; NAAQS = National Ambient Air Quality Standards; PM25 = particulate matter with an aerodynamic diameter less than or equal to a nominal
2.5 |jm; PM10 = particulate matter with an aerodynamic diameter less than or equal to a nominal 10 |jm; S02 = sulfur dioxide.
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.
bSince the completion of the 2008 ISA for Sulfur Oxides, the phrasing of causal determinations has changed slightly, and the weight of evidence that describes each level in the
hierarchy of the causal framework has been more explicitly characterized.
cUncertainties 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 NAAQS 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 (U.S. EPA. 20156) and Section LI, 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
The primary SO2 NAAQS is based on the 99th percentile of 1-hour daily maximum
concentrations averaged over 3 years, set to protect against respiratory morbidity
associated with short-term SO2 exposures (Section 1.1). Controlled human exposure
studies have examined effects after 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-hour average and 1-hour daily
maximum), 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 the concentration-response relationship aids in quantifying
the public health impact of SO2 exposure. A key issue is whether the relationship is linear
across the full range of ambient concentrations or whether there are deviations from
linearity at and below the levels of the current 1-hour standard of 75 ppb. Additionally,
there is the question of whether a threshold might exist, which would indicate an ambient
concentration below which adverse health outcomes are not elicited. Lack of a threshold
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implies that exposure to even the lowest measured ambient SO2 concentrations has the
potential to cause harm.
Results from controlled human exposure studies indicate wide interindividual variability
in response to SO2 exposures, with peak (5 to 10 minutes) exposures at levels as low as
200-300 ppb eliciting lung function decrements in some individuals with asthma. A clear
increase in the magnitude of respiratory effects was observed with increasing exposure
concentrations between 200 and 1,000 ppb during 5-10 minute SO2 exposures. There is
limited epidemiologic research on concentration-response functions relating SO2
concentrations to respiratory health morbidity, but overall there is no reason to conclude a
deviation from linearity or the appearance of a population-level threshold.
1.7.3 Regional Heterogeneity in Effect Estimates
The 2008 SOx ISA discussed spatial variability in SO2 concentrations and its impact on
effect estimates from epidemiologic studies. Intermonitor correlations ranged from very
low to very high values, suggesting that SO2 concentrations at some monitors 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 monitors
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 they rely on central-site monitor data or concentration
modeling for exposure assessment. SO2 has low to moderate spatial correlations between
ambient monitors 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 intra-urban
concentration and exposure differences 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 concentrations to evaluate health effects. Low inter-monitor
correlations 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.
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 ("ATS. 2000).
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 the 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.
At-risk populations or lifestages can be characterized by specific biological,
sociodemographic, or behavioral factors, among others. Since the 2008 SOx ISA (U.S.
EPA. 2008b). EPA has used a framework for drawing conclusions about the role of such
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factors in modifying risk of health effects of air pollution exposure (Table III of the
Preamble (U.S. EPA. 2015eY). 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 (Table 6-16). which is consistent with the findings of the 2008 SOx ISA
(U.S. EP A. 2008b). 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 5.2.1.8). There are a limited number of
epidemiologic studies evaluating SCh-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. 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. Although the 2008 SOx ISA (U.S. EPA. 2008b) 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 SCh-associated respiratory outcomes reported mixed results.
For adults, recent evidence generally found similar associations for SCh-related
respiratory outcomes or mortality across age groups, though those over 75 years were
more consistently at increased risk. In addition, there was a lack of toxicological evidence
regarding the effect of lifestage on respiratory responses to SO2 to support observations
made across epidemiologic studies that evaluated lifestage.
Summary of Public Health Significance of Health Effects Related to Sulfur
Dioxide Exposure
The public health significance of SC>2-related health effects is indicated by many lines of
evidence. SO2 exposure is linked to health effects that are clearly adverse such as ED
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visits and hospital admissions for asthma and asthma exacerbation. The public health
significance of SCh-related health effects is also indicated by the evidence for increased
risk among people with asthma, and the suggestive evidence for increased risk among
children and older adults. The roles of co-occurring risk factors or combined higher SO2
exposure and health risk within a population in influencing risk of SC>2-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 can translate into a large
number of people affected by SO2 and thus magnify the public health impact of ambient
SO2 exposure.
1.8 Conclusions
In summary, studies since the 2008 SOx ISA have supported the conclusion of that ISA
(U.S. EPA. 2008b) that there is a causal relationship between short-term SO2 exposure
and respiratory effects. This determination is based on the consistency of findings within
disciplines, coherence among multiple lines of evidence, and biological plausibility
indicating that there is a causal relationship between short-term SO2 exposure and
respiratory effects in individuals with asthma. 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. 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. Other health
effects, including cardiovascular morbidity, reproductive and developmental effects,
mortality, and cancer had causal determinations of either "suggestive but not sufficient to
infer a causal relationship" or "inadequate to infer a causal relationship" with short-
and/or long-term SO2 exposure. Additionally, among various populations and lifestages,
there is adequate evidence that people with asthma, and suggestive evidence that children
and older adults, are at increased risk for S02-related health effects. The large proportions
of children and older adults in the U.S. population and the high prevalence of asthma in
children can 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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References for Chapter 1
ATS (American Thoracic Society). (2000). What constitutes an adverse health effect of air pollution? Am J
Respir Crit Care Med 161: 665-673. http://dx.doi.Org/10.1164/airccm.161.2.ats4-00
CAA (Clean Air Act). (1990a). Clean Air Act, as amended by Pub. L. No. 101-549, section 108: Air quality
criteria and control techniques, 42 USC 7408. http://www.law.cornell.edu/uscode/text/42/7408
U.S. EPA (U.S. Environmental Protection Agency). (1982a). Air quality criteria for particulate matter and sulfur
oxides (final, 1982) [EPA Report]. (EPA 600/8-82/029a). Research Triangle Park: Environmental Criteria
and Assessment Office, http://cfpub.epa. gov/ncea/cfm/recordisplav. cfm?deid=46205
U.S. EPA (U.S. Environmental Protection Agency). (1982b). Air quality criteria for particulate matter and sulfur
oxides, volume I addendum [EPA Report]. (EPA-600/8-82-029a). Research Triangle Park. NC:
Environmental Criteria and Assessment Office.
U.S. EPA (U.S. Environmental Protection Agency). (1986a). Air quality criteria for particulate matter and sulfur
oxides (1982): assessment of newly available health effects information, 2nd addendum. (EPA/600/8-
86/020F). Washington, DC: Office of Health and Environmental Assessment.
http://nepis.epa.gov/exe/ZvPURL.cgi?Dockev=30001FM5.txt
U.S. EPA (U.S. Environmental Protection Agency). (1986b). Second addendum to air quality criteria for
particulate matter and sulfur oxides (1982): Assessment of newly available health effects information [EPA
Report]. (EPA/600/8-86/020F). Research Triangle Park, NC: Environmental Criteria and Assessment Office.
U.S. EPA (U.S. Environmental Protection Agency). (2008b). Integrated science assessment for sulfur oxides:
Health criteria [EPA Report]. (EPA/600/R-08/047F). Research Triangle Park, NC: U.S. Environmental
Protection Agency, National Center for Environmental Assessment.
http://cfpub.cpa.gov/ncca/cfm/rccordisplav.cfm7deidH98843
U.S. EPA (U.S. Environmental Protection Agency). (2009a). Integrated science assessment for particulate matter
[EPA Report]. (EPA/600/R-08/139F). Research Triangle Park, NC: U.S. Environmental Protection Agency,
National Center for Environmental Assessment.
http://cfpub.epa.gov/ncca/cfm/rccordisplav.cfm?deid=216546
U.S. EPA (U.S. Environmental Protection Agency). (2010c). Primary national ambient air quality standards for
nitrogen dioxide; final rule. Fed Reg 75: 6474.
U.S. EPA (U.S. Environmental Protection Agency). (2013c). Notice of workshop and call for information on
integrated science assessment for oxides of nitrogen and oxides of sulfur. Fed Reg 78: 53452-53454.
U.S. EPA (U.S. Environmental Protection Agency). (2014b). Integrated review plan for the primary national
ambient air quality standard for sulfur dioxide [EPA Report]. (EPA-452/R-14-007). Research Triangle Park,
NC: U.S. Environmental Protection Agency, National Center for Environmental Assessment.
http://www.epa. gov/ttn/naaas/standards/so2/data/2Q 141028so2rcviewplan.pdf
U.S. EPA (U.S. Environmental Protection Agency). (2015e). Preamble to the Integrated Science Assessments
[EPA Report]. (EPA/600/R-15/067). Research Triangle Park, NC: National Center for Environmental
Assessment, Office of Research and Development.
U.S. EPA (U.S. Environmental Protection Agency). (2015f). Table 5S-1. Summary of epidemiologic studies of
S02 exposure and other morbidity effects (i.e., eye irritation, effects on the nervous and gastrointestinal
systems).
U.S. EPA (U.S. Environmental Protection Agency). (2015g). Table 5S-2. Study-specific details of experimental
studies of S02 exposure and other morbidity effects (i.e., hematological and nervous system effects).
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WHO (World Health Organization). (1948). Preamble to the Constitution of the World Health Organization as
adopted by the International Health Conference, New York, 19-22 June, 1946. In Constitution of the World
Health Organization (pp. 2). Geneva, Switzerland.
http://whalibdoc.who.int/hist/official records/constitution.pdf
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CHAPTER 2 ATMOSPHERIC CHEMISTRY AND
AMBIENT CONCENTRATIONS OF
SULFUR OXIDES
2.1 Introduction
This chapter provides concepts and findings relating to source emissions, atmospheric
chemistry and fate, measurement methods, environmental concentrations, atmospheric
modeling of gas-phase SOx. It is intended as a prologue for detailed discussions on health
effects evidence in the subsequent chapters, and as a source of information to help
interpret that evidence in the context of relevant ambient concentrations.
2.2 Sources of Sulfur Dioxide
Sulfur dioxide is the most important of the gas-phase sulfur oxides for both atmospheric
chemistry and health effects. SOx was initially defined to include disulfur monoxide
(S2O), SO3, and gas-phase H2SO4, but none of these species is present in the atmosphere
in concentrations significant for human exposures. Therefore, this section focuses on
sources of SO2. Additional gas-phase sulfur oxides important for atmospheric chemistry
and fate are described in detail in Section 23..
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 fire (wildfires as
well as controlled burns) are the main natural sources of primary SO2. Industrial chemical
production and natural biological activity (plants, fungi and prokaryotes) serve as the
sources of reduced sulfur compounds that oxidize in the atmosphere to produce
secondary SO2.
This section briefly describes the main U.S. anthropogenic and natural sources of SO2
emissions. Values 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.
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2.2.1 U.S. Anthropogenic Versus Natural Sources
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
metallosulfur minerals) and in its 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). However, with the exception of volcanic and
other geologic emissions, naturally occurring SO2 is largely derived from the oxidation of
sulfides emitted by low flux "area" sources, such as the oceans and moist soils.
Conversely, anthropogenic emissions of sulfur are primarily in the form of SO2, emerging
from point sources and in quantities that may substantially affect local and regional air
quality. The largest SCh-emitting sector within the U.S. is electricity generation based on
coal combustion. The mass of emissions produced by coal-fired electric generating units
(EGUs) exceeds those produced by the next largest sector (coal-fired boilers) by nearly a
factor of 10. Figure 2-1 provides a sector comparison according to annual emissions rates
found in the EPA 2011 National Emissions Inventory.
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5,000;
4,500;
4,000;
3,500;
3,000;
2,500;
2,000;
1,500;
1,000;
500,
000
000
000
000
000
000
000
000
000
000
ro
o
ro
o
CD
C
CD
-C
"o
ro
GO
<
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2.000 Kilometers --. I
I 1 1 1 1
0 1.250 2,500 Kilometers
SO2 Tons Emitted from
EGU Facilities
~ 100-1,000
• 1,000-10,000
# 10,000-100,000
0100,000- 150,000
~ U S Counties
Counties with ambient SO21-hr
concentrations that exceed 75 ppb
1 i
75 150 Kilometers
^ {>
0 125 250 Kilometers
Figure 2-2 Distribution of electric power generating unit (EGU)-derived sulfur
dioxide emissions across the U.S., based on the 2011 National
Emissions Inventory.
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(A)
Figure 2-3(A) Distribution of sulfur dioxide (SO2) emissions produced by
(A) industrial cement production, and (B) industrial chemical and
allied products manufacturing.
SO2 Tons Emitted by
Industrial Cement Sources
~ 100—1,000
• 1,000—10,000
§ 10,000—100,000
% 100,000—150,000
~ U S Counties
Counties with ambient SO21-hr
concentrations that exceed 75 ppb
I i i i I
0 1,250 2.500 Kilometeis
I 1 1
0 125 250 Kilometeis
0 75 150 Kilometeis
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(B)
Figure 2-3(8) Distribution of sulfur dioxide (SO2) emissions produced by
(A) industrial cement production, and (B) industrial chemical and
allied products manufacturing.
2,000 Kilometers
SO2 Tons Emitted by Industrial
Chemical Manufacturing
Sources
• 100—1,000
• 1,000—10,000
f 10,000-100,000
f 100,000—150,000
I I U S Counties
, Counties with ambient SO; 1-hr
concentrations thai exceed 75 ppb
I 1 1 1 1
0 1,250 2 500 Kilometers
I 1 1
0 125 250 Kilometers
I 1 1
0 75 150 Kilometers
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2.2.3 Sources by Facility
The preceding sections have shown SO2 emissions by sector and source category, but
these classifications are not especially helpful for assessing the size of complex point
sources that may include more than one S02-emitting process. The Clean Air Act coined
the term "major emitting facility" for more complex sources, defining these by the total
potential emissions of the criteria pollutant (100 tons per year or greater for SO2). In
addition to fossil fuel-fired steam electricity plants, example facilities include coal
cleaning plants, kraft pulp mills, Portland Cement plants, iron and steel mill plants,
sulfuric acid plants, petroleum refineries, and chemical processing plants. Figure 2-4
shows the geographic distribution of major continental U.S. SO2 emitting facilities, with
an enlargement of the Midwest states including the Ohio River Valley, where a large
number of these SCh-emitting sources are located. All of the counties registering SO2
concentrations above the current NAAQS level (75 ppb) are shown in association with
sources with substantial SO2 emissions.
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(A)
Source: 2011 National Emissions Inventory
(B)
Figure 2-4 Geographic distribution of (A) major continental U.S. sulfur
dioxide (SO2) emitting facilities, with (B) an enlargement of the
Midwest states, including the Ohio River Valley, where a large
number of these sources are concentrated (2011 National
Emissions Inventory).
Facilities Emitting 1,000-150,000
in NEI 2011
Tons of S02
Facilities SO? Emissions {tons
1003-5000
5001 - 50000
50001 - 150000
U.S. Counties
2010 Population Density (persona per ml1)
0.0-1.0
1 1 - 20 0
| 20 1 -87 3
| 67 4 - 5000
| 500.1 -2000.0
| 20O0 1 - 70000 0
Counties with SOj concentrations above the NAAQS
Facilities in Midwest United States Emitting 1,000-150,000 Tons of S02
in NEI 2011
Facilities
80, Emissions (tons)
• 1003 - 5000
• 5001 -50000
• 50001 - 150000
U.S. Counties
2010 Population Density (persons per mi1)
0.0-1.0
1 1-200
¦I 20 1 • 87 3
¦I 67 4-500 0
HH5001 -20000
^¦20001-700000
CounUes vwtsi SOj eoocenirat»c»s above the NAAQS
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2.2.4 U.S. Emission Trends
1 Anthropogenic missions of SO2 in the U.S. have shown dramatic declines since the 1990
2 amendments to the Clean Air Act were enacted. Figure 2-5 shows the trend in SO2
3 emissions since 1990 in relation to the timeline over which the Clean Air Act control
4 programs [Acid Rain Program (ARP), NOx Budget Program (NBP), and Clean Air
5 Interstate Rule (CAIR)] were implemented. Table 2-1 gives the annual SO2 emissions,
6 percentage of the U.S. SO2 emissions budget, and change in emissions rate since 2003 for
7 the important emissions sectors listed in Figure 2-5.
CAIR
ri~t-1
-------
Table 2-1 Summary of 2013 EPA sulfur dioxide trends data for the major
emissions sectors shown in Figure 2-5.
Source Type
Kilotons (2013)
Percentage of
Total
Percent Change
Since 2003
Electric Generating Units (all fuel types)
3,257
63
-68
Industrial (all fuel types)
763
15
-57
Chemical and allied product manufacturing
224
4
-61
Metal processing
126
2
-51
Petroleum and related industries
145
3
-28
Industrial processes
116
2
-51
Solvent utilization
186
4
-45
Highway vehicles
17
0.3
-37
Miscellaneous (fires, dust)
29
0.6
-88
2.2.5 Natural Sources
2.2.5.1 The Global Sulfur Cycle
1 The total budget for sulfur, in all its forms, at Earth's surface is on the order of 101CI Tg
2 (S) (Schlesinger. 1997V The sulfur cycle, summarized in Figure 2-6. comprises the many
3 chemical and biological processes that continuously interconvert the element among its
4 four main oxidation states (-2, 0, +4, +6). The reduced form of sulfur is present in the
5 environment in hydrogen sulfide, hydrogen disulfide, and a number of organic
6 compounds. Oxidized sulfur is present primarily as SO2 and sulfate (SO42 ).
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so2,so42-
From continents S02r SO|~
From oceans SOj~
75
SO-
SO?"
DMS
so2
DMS
OCS
CS
DMS
"Water \
weathering
SO„
Waste^
waters
I 90
From deposition
River runoff
225
Note: Aeolian and volcanic emissions are highly uncertain and, therefore, not included. (1 Tg = 1.1 x 106 tons)
Source: Brimblecombe (2003).
CS2 = carbon disulfide; DMS = dimethyl sulfide; OCS = carbonyl sulfide; S02 = sulfur dioxide; S042" = sulfate ion.
Figure 2-6 The global sulfur cycle, showing estimated fluxes (Tg/yr [S]) for
the most important sulfur-containing compounds.
Nonbiological, natural sources of directly emitted atmosphenc SO2 include volcanoes
and wildfire. With the exception of volcanoes, natural sources of reduced sulfur that
subsequently oxidize in the atmosphere to form SO2 are largely biological in nature.
Under anaerobic conditions, various species of plants, fungi, and prokaryotes convert
oxidized sulfur into its reduced forms (Madman MT. 2006). Photosynthetic green and
purple bacteria, and some chemolithotrophs oxidize sulfides to form elemental sulfur.
Some species oxidize elemental sulfur to form sulfate and SO2; others reduce elemental
sulfur to sulfides (dissimilative sulfur reduction), while others are capable of reducing
sulfate all the way down to sulfide (dissimilative sulfate reduction).
2.2.5.2 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),
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hydrochloric acid, chlorine and others (Simpson et al.. 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 are therefore difficult to estimate (Simpson et al.. 1999).
The western United States 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 eaipted once or twice per year, on average over the past 100 years with impacts on
local communities (Power. 2013). Figure 2-7 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. 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 features at 4 and 7.3 urn. which
allowed Prata et al. (2010 to calculate the total mass of SO2 emitted during the eruption
as 0.29 ± 0.01 Tg.
Okmok AIRS 7.3 pm Cumulative S02 12-20 July, 2008
5.0
70
170
4.5
4.0
-180
-50
^ 3.5
-60
50
2.5
-150
-140
1.0
¦rto
0.5
Processed by NILU [fred.prata@nilg.no]
Source: NASA (2008a).
Figure 2-7 Image derived from data collected by the Atmospheric Infrared
Sounder (AIRS) instrument aboard the NASA Aqua satellite
during the July 12-20, 2008 eruption of the Okmok Volcano.
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The line of volcanoes that begins with the Aleutian Islands in Alaska, and extends south
and east through the states of Washington, Oregon, California, Arizona and New Mexico,
with outlying geologically active sites in Idaho (Craters of the Moon) and Wyoming
(Yellowstone). Figure 2-8 shows the geographic location and activity potential for these
sites within the continental United States.
Bellingham
Mount Shasta A A Medicine Lake
Lassen Peak A
A Glacier Peak
* WASHINGTON •! SPokarie
A Mount Rainier
Mount St. Helens Mount Adams
A ftlountHood
Seat
Great Falls
MONTANA
Billings
Port and
,m. A Mount Jefferson
Three Sisters .
• ~« Bend
Eugene . „ I
A Newberry Crater
A Yellowstone
A Craters of the Moon
A Crater Lake
DREGOf
Boise
Casper
vVYOMIN
Pocate o
Cheyenne
Salt Lake City
UTAH
Denver
COLORADO
Clear Lake A
NEVADA
Sacramento
\
A\Long Valley Caldera
CALIFORNIA
Coso A
San Francis
Las Vegas
Santa Fe
A San Francisco Field
Albuquerque
A Bandera Field
Volcano active during
past 2,000 years
Other potentially active
Los Angeles
ARIZONA
Phoenix
volcanic areas
NEW M E a I
0 100 ZOO Kilometers
i—1
Tucson
100 miles
SUSGS
Topinfo, USGS/CVO, 1999, Modified froirt: Bntntiey, 1994, Volcanoes of the United States: USGS Genera interest Publication
Source: USGS (1999).
Figure 2-8
Geographic location of volcanoes and other geologically active
sites within the continental U.S.
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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, KTlauea, located on the Big Island of Hawaii. KTlauea 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 2-9 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 due west, away from major populated areas.
Aura/OMI - Average column for 20080320-20080327
-162 -160 -158 -156
-160 -158
S02 column [DU]
t <3 1
T^*\
\*0>
: 1
<2 ^
1
t ! 1
9
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Source: NASA (2008b).
Note: DU = Dobson Units which are 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.
Figure 2-9 NASA/Ozone Monitoring Instrument (OMI) image of the KTlauea
sulfur dioxide (SO2) plume during its March 20-27, 2008 eruption.
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In another study, using SO2 column densities derived from GOME-2 satellite
measurements for the period 2007-2012, Beirle et al. (2013) determined Kilauea's
monthly mean SO2 emission rates and effective SO2 lifetimes. For the March through
November 2008 period, the authors reported the effective SO2 lifetime as 1-2 days and
Kilauea's SO2 emission rates as 9-21 kiloton/day.
Several modeling studies have been undertaken to estimate the global emissions of sulfur
by volcanoes, arriving at an estimated range for SO2 emissions of 109-288 Gmols/year
(7-18.5 Tg/year) (Chin et al.. 2000; Feichter et al.. 1996; Pham et al.. 1996; Langner and
Rodhe. 1991).
2.2.5.3 Wildfires
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
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 for the U.S. at 0.16 Tg in the year 2000. Canadian fires emitted 0.11 Tg, and
Mexican fires emitted 0.05 Tg of SChforthe same period. However, wildfire emissions
do vary from year to year. The 2011 NEI (Version 1) estimate for wildfire emissions is
105,228 short tons (0.095 Tg). Emissions estimates for SO2 derived from global
modeling studies of wildfire range between 72-91 Gmols/year (4.6-5.8 Tg/year [SO2])
(Chin et al.. 2000; Feichter et al.. 1996; Pham et al.. 1996; Langner and Rodhe. 1991).
Projected increases in wildfire frequency and intensity under warming climate conditions
imply increasing wildfire-related SO2 emissions. However, estimates of future
wildfire-related SO2 emissions are highly uncertain, due to deficiencies in the available
science 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 estimates for agricultural and prescribed
burning emissions at 26,965 and 85,087 short tons (0.025 and 0.077 Tg), respectively.
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2.2.6 Indirect Sources
1 Sulfides, including H2S, carbonyl sulfide (OCS), carbon disulfide (CS2),
2 methylmercaptan (CH3SH), dimethyl sulfide (DMS), and dimethyl disulfide (DMDS), are
3 emitted from energy production, industrial activities, agriculture, and various ecosystems,
4 especially coastal wetland systems, inland soils and oceans. In addition to SO2, volcanoes
5 release sulfides, i.e. OCS and CS2. As described in Section 2.3. all of these gases, with
6 the exception of OCS, have short atmospheric lifetimes, given their high rates of reaction
7 with hydroxyl radical and nitrate radical (NO3) with SO2 as a reaction product. Table 2-2
8 provides a list of the natural and anthropogenic sources of the five main organosulfides.
9 Dimethyl sulfide is particularly important, both for the large role it plays as a source of
10 atmospheric sulfur and for its role in initiating the formation of marine clouds.
Table 2-2 Global sulfide emissions in Gg(S)/yr.
Sources
OCS
cs2
CHsSH
DMS
DMDS
Seawater and marshes
317
243
4,738
28,187
213
Vegetation and soils
70
1,735
3,470
868
Volcanoes
11
17
Atmospheric oxidation
463
Biomass burning (all types)
46
1.84
6
119
Pulp and paper industry
97.2
78.5
1,680
1,462
273
Rayon/cellulosics manufacture
1,060
138
95.4
Manure
330
660
660
Paddy fields
0.38
26.9
0.76
25
0.57
Pigment industry
74
205
Food processing and waste
0.63
3.97
28.9
Gas industry
0.7
4.8
0.84
0.1
Wastewater
0.034
1.03
65
5.6
27
Aluminum industry
88
4
Coal combustion
16.3
0.33
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Table 2-2 (Continued): Global sulfide emissions in Gg(S)/yr.
Sources
OCS
CS2
CHsSH
DMS
DMDS
Coke production
9
14
Biofuel combustion
46.8
1.9
Vehicles
6
0.3
Shipping
30
1.5
Tire wear
1.7
2.3
Tire combustion
0.003
0.00006
Landfill
0.079
0.19
0.34
0.26
0.008
Brick making
0.03
Total global sources
1,208
1,728
8,692
33,916
2,190
1 Tg = 103 Gg; CH3SH = methylmercaptan; CS2 = carbon disulfide; DMDS = dimethyl disulfide;
DMS = dimethylsulfide; OCS = carbonyl sulfide.
Adapted from Lee and Brimblecombe (2015).
2.2.6.1 Dimethyl Sulfide
Dimethyl sulfide has significant anthropogenic sources (pulp and paper production,
agricultural operations), but these are dwarfed by the quantity emitted by natural
biological activity. Dimethyl sulfide originates with the breakdown of dimethyl
sulfoniopropionate, a metabolite of methionine, produced by marine organisms living in
upwelling or coastal zones and by anaerobic bacteria in marshes and estuaries. Dimethyl
sulfide is the main source of cloud condensation nuclei (CCN) in ocean environments,
due to its rapid oxidation by OH and NO3 radicals to form SO2, followed by SO4 2 . the
main component of CCN. Accurate estimates of DMS production at the ocean surface are
a critical input into the cloud models needed for calculating the radiative balance of the
atmosphere, [e.g., for climate studies, but are difficult to achieve. Lee and Brimblecombe
(2015)1 provide a literature-derived global estimate of DMS emissions from seawater and
marshland of 28 Tg (S)/yr (878 Gmols/year). Older estimates for seawater DMS
emissions cover a wide range from 172 to 681 Gmols/year (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).
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2.3
Atmospheric Chemistry and Fate
2.3.1 Sulfur Oxide Species
The four known monomeric sulfur oxides are sulfur monoxide (SO), SO2, SO3, and S2O.
SO can be formed by photolysis of SO2 at wavelengths in the ultraviolet range (less than
220 nm), above the stratospheric ozone layer (Pinto et al.. 1989). SO3 can be emitted
from power plants and factories, but it reacts within seconds with water (H2O) in the
stacks or immediately after release into the atmosphere to form H2SO4. Of the four
species, only SO2 is present at concentrations significant for chemistry in the troposphere,
boundary layer, and for human exposures.
Sulfur dioxide is primarily emitted directly from pollutant sources, but is also produced
by the photochemical oxidation of reduced sulfur compounds such as dimethyl sulfide
(DMS, CH3-S-CH3), H2S, CS2, OCS, methyl mercaptan (CH3-SH), and dimethyl disulfide
(DMDS, CH3-S-S-CH3), which are all mainly biogenic in origin.
Table 2-3 lists the atmospheric lifetimes (r) of reduced sulfur species with respect to
reaction with various oxidants. Except for OCS, a compound that is relatively inert in the
troposphere and is mainly removed by photolysis in the stratosphere, all of these species
are lost primarily by reaction with OH and NO3 radicals.
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Table 2-3 Atmospheric lifetimes of sulfur dioxide and reduced sulfur species
with respect to reaction with hydroxyl (OH), nitrate (NO3), and
chlorine (CI) radicals.
OH
no3
CI
Compound
/cx 1012a
T
k x 1012
T
k x 1012
T
SO2
1.6
7.2 days
<7 x icr9
NR
NA
CH3-S-CH3
6.7
2.3 days
1.1
1.1 h
530
29 days
H2S
4.7
2.2 days
<8 x icr4
NR
74
157 days
CS2
1.2
9.6 days
<4 x 10"4
>116 days
< 4 x 10"3
NR
OCS
1.9 x 10"3
17 yr
<1 x icr4
>1.3 yr
<1 x icr4
NR
CHs-S-H
33
8.4 h
0.89
1.2 h
200
58 days
CH3-S-S-CH3
230
1.2 h
0.53
2.1 h
NA
CH3-S-CH3 = dimethyl sulfide; CH3-S-H = methyl mercaptan; CH3-S-S-CH3 = dimethyl disulfide; CI = chlorine radical; CS2 = carbon
disulfide; H2S = hydrogen sulfide; k =reaction rate constant; NA = not available; N03 = nitrate radical; NR = not reported;
OCS = carbonyl sulfide; OH = hydroxyl radical; S02 = sulfur dioxide.
aThe units of k vary depending on the order, i.e., first, second, tertiary, of the reaction.
Reaction with NO3 radicals at night most likely represents the major loss process for
DMS and methyl mercaptan. However, the mechanisms for the oxidation of DMS are not
completely understood. Initial attack by NO3 and OH radicals involves H atom
abstraction, with a smaller branch leading to OH addition to the S atom. The smaller OH
addition branch increases in importance as temperatures decrease, becoming the major
pathway below temperatures of 285 K (12°C) (Ravishankara et al.. 1997). The adduct
may either decompose to form methane sulfonic acid (MSA) or undergo further reactions
in the main pathway to yield dimethyl sulfoxide (Barnes et al. 1994). Following H atom
abstraction from DMS, the main reaction products include MSA and SO2. Oxidation of
DMS by bromine oxide (BrO) produces dimethyl sulfoxide | (CH^SOI (Toumi. 1994;
Barnes etal.. 1991). and oxidation by atomic chlorine leads to formation of SO2 (Keene
et al.. 1998). (CH3)2SO and SO2 are precursors for methanesulfonic acid (CH3SO3H) and
H2SO4, respectively. In the marine boundary layer (MBL), virtually all H2SO4 and
CH3SO3H vapor condenses onto existing aerosols or cloud droplets, thereby contributing
to aerosol growth and acidification. Based on global-scale, three-dimensional modeling,
Long et al. (2013) suggested that reaction of BrO with DMS could add approximately
40% to the DMS loss rate.
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The ratio of MSA to SO2 is strongly temperature dependent, varying from about 0.1 over
tropical waters to about 0.4 over Antarctic waters (Seinfeld and Pandis. 1998V Excess
sulfate (i.e., over that expected from the sulfate in sea water) in marine aerosol is related
mainly to the production of SO2 from the oxidation of DMS. Sulfur dioxide can be
oxidized either in the gas phase or, because it is soluble, in the aqueous phase in cloud
drops.
2.3.2 Gas Phase Oxidation of Sulfur Dioxide
Sulfur exists in its S(IV) oxidation state in SO2. In the atmosphere, SO2 is oxidized
further to form SO3, taking the sulfur atom from the S(IV) to S(VI) oxidation state. The
gas-phase oxidation of SO2 by OH involves two steps. The first step takes SO2 to bisulfite
ion (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).
As indicated in Table 2-3. the rate coefficient for the reaction between SO2 and NO3 is
too low to be of importance as an oxidation mechanism. The same is true for the reaction
between SO2 and the hydroperoxyl (HO2) radical (Sander et al.. 2011). Rate coefficients
for the reaction of stabilized Criegee intermediates with SO2 span a wide range from an
upper limit of 4 x 10 cm3/second (Johnson et al.. 2001) to approximately 3.5 x 10~n
(Liu et al.. 2014b) or 3.9 x 1011 cmVsecond (Welz et al.. 2012). Earlier investigations
[e.g., (U.S. EPA. 1985; Atkinson and Llovd. 1984)1. which were largely indirect
measurements, reported much lower values, 7 x 10 14 and 7 x 10 12 cm '/sccond.
respectively. Although some of the more recent determinations report rate coefficients
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greater than 3 * 10 11 cm3/second, Berndt et al. (2012) derived a range of (0.9 to
7.7) x 10~13 cmVsecond. well within the range of those given by Atkinson and Llovd
(1984) and U.S. EPA (1985).
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, in particular biogenic alkenes, such as terpenoids
emitted by trees.
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.
SOs + H2O -> H2SO4
Equation 2-4
Because H2SO4 is extremely water soluble, it will be removed rapidly by dissolution into
the aqueous phase of aerosol particles and cloud droplets.
2.3.3 Aqueous Oxidation of Sulfur Dioxide
The major sulfur-containing species in clouds are the HSO3 and SO,2 (sulfite) ions that
form when SO2 dissolves in cloud droplets and subsequently reacts with water. Both exist
in the S(IV) oxidation state, which readily oxidizes in the presence of particle 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 copper 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., (Jacobson. 2002; Jacob. 1999; Seinfeld and Pandis.
1998)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 can be summarized as follows
(Jacobson. 2002):
Dissolution of SO2 occurs first:
SO2 (g) <=> SO2 (aq)
Equation 2-5
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Followed by the formation and dissociation of sulfiirous acid (H2SO3):
S02(aq) + H20(1) <=> H2SO3 <=> H+ + HSO3- ^ 2H+ + SOs2"
Equation 2-6
In the pH range commonly found in rainwater (2 to 6), H2O2 will oxidize HSO , to
sulfate ion (SO42 ):
HSO3-+ 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 2-10. 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 vapor (NH3) readily dissolves in acidic cloud drops to form
ammonium ion (NH4+). 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.
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 (Hoppel and
Caffrev. 2005; Zhang and Mi Hero. 1991).
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u>
5
"NO.
,-12
rU
I"'8
0
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6
PH
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 |jM; [Mn(ll),aq)] = 0.3 |jM.
Source: Seinfeld and Pandis (1998).
Figure 2-10 The effect of pH on the rates of aqueous-phase S(IV) oxidation by
various oxidants.
1 In the same way that SO2 is removed from the gas-phase by dissolution into cloud
2 droplets, it can be removed by depositing (dry deposition) onto wet surfaces. Scavenging
3 by rain (wet deposition) serves as another removal route. Modeling studies have shown
4 that slightly more than half of SO2 in both models is lost by gas- and aqueous-phase
5 oxidation, with the remainder of SO2 loss accounted for by wet and dry deposition (Long
6 et al.. 2013; Liu et al.. 2012a).
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2.4 Measurement Methods
This section discusses the federal reference and equivalency methods used for NAAQS
compliance as well as the state, local, and tribal monitoring networks across the U.S.
2.4.1 Federal Reference and Equivalency Methods
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, a federal equivalent method (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. The UVF method was added as a FRM as a result of the new 1-hour SO2 primary
NAAQS established in 2010 (75 FR 35520 June 22, 2010). 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 is still sound and 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 some energy, first, by collision with
another gas molecule and, then, emits a photon of light at a longer wavelength, to return
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, 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 nanometers [nm]) to enter the
optical chamber. The light passing through the source bandwidth filter is collimated using
a UV lens and passes through the optical chamber, where it is detected on the opposite
side of the chamber by the reference detector. A photomultiplier tube (PMT) is offset
from and placed perpendicular to the light path to detect the SO2 fluorescence. Because
the SO2 fluorescence at about 330 nm is different from its excitation wavelength, an
optical bandwidth filter is placed in front of the PMT to filter out any stray light from the
UV lamp. A lens is located between the filter and the PMT to focus the fluorescence onto
the active area of the detector and optimize the fluorescence signal. A particulate filter is
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also placed after the sample inlet to prevent damage, malfunction, and interference from
particles in the sampled air.
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 2-4. 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 detectable limit for a routine, automated SO2 analyzer is
required to be 0.002 ppm (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. These new trace-level instruments have detection limits of
0.2 ppb or lower.
Table 2-4 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
Total, all interferents
± 0.005 ppm (5 ppb)
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
80% of upper range limit
2.0%
2.0%
ppb = parts per billion; ppm = parts per million.
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2.4.2 Positive and Negative Interferences
The UVF method has a number of positive and negative interferences. The most frequent
source of positive interference is other gases that fluoresce at the same wavelength as
SO2. The most common gases include volatile organic compounds (e.g., xylenes,
benzene, toluene, etc.), and the polycyclic aromatic hydrocarbons (PAHs;
e.g., naphthalene). To reduce this source of positive interference, high-sensitivity SO2
analyzers are equipped with scrubbers or "kickers" to remove these compounds from the
air stream 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,
m-xylene. /^-xylene, m-cthyltolucne. 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 cc/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
photomultiplier tube (PMT) is specifically designed to prevent detection of NO
fluorescence at the PMT. Care must be exercised when using multicomponent calibration
gases containing both NO and SO2, so that the NO rejection ratio of the SO2 analyzer is
sufficient to prevent NO interference.
The most common source of positive bias 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 PMT 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 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
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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
dryer system to remove moisture from the sample gas before it reaches the particulate
filter.
2.4.3 Other 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
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 seconds.
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 detection limit of 0.5 ppb. Both the tunable and
nontunable instruments have low limits of detection (<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 the
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measurement of SO2. At an absorption wavelength of 308 nm, the CRDS had a detection
limit of 3.5 ppb, which was lower 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 UVF methods (a few seconds vs. 80 seconds). 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 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. 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 do not directly measure pollutant concentration but rather
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) SO2 absorption occurs at shorter wavelengths that coincide with stronger
ozone absorption so that only large SO2 sources can be observed 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 and smelters), and newly
constructed coal-burning facilities with high, uncontrolled SO2 emissions (Bovnard et al..
2014; McC'ormick et al.. 2014; Streets et al.. 2014; Clarisse et al.. 2012; Fioletov et al..
2011; Nowlan et al.. 2011; Bobrowski et al.. 2010; Li et al.. 2010; Khokhar et al.. 2008;
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Cam et al.. 2007). Remote sensing techniques have potential use in targeted applications
(e.g., near-source monitoring), and validation of these methods against ground-based and
aircraft measurements is ongoing.
2.4.4 Ambient Sampling Network Design
Compliance with NAAQS is primarily carried out through the State and Local Air
Monitoring Stations (SLAMS) network, although modeling may also be used to
characterize air quality for implementation purposes (75 FR 35520). There are
approximately 400 monitors reporting 1-hour SO2 concentrations to EPA's Air Quality
System (AQS). In addition to their use in compliance evaluations, some of these monitors
function as central site monitors 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) or all twelve 5-minute average SO2 concentrations within the hour. The
number of monitors reporting 5-minute continuous SO2 concentrations increased
dramatically from 2 to 195 monitors in 2009 and 2012, respectively. The SLAMS
network includes the NCore monitoring network, which began January 1, 2011 and
consists of about 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 that data with respect to the NAAQS. The Clean Air
Status and Trends Network also measures ambient SO2; however, these data are not used
for NAAQS compliance purposes. This network provides weekly averages of total sulfur
(dry SO2, dry sulfate, and wet sulfate) in about 90 sites located in or near rural locations
to assess long-term trends in acidic deposition due to emission reduction programs.
Figure 2-11 shows the locations of these monitoring networks across the U.S.
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N
• *
W
*§
395
790
1,580 Kilometers
NCORE, 5 min
NCORE, 1 hr |
SLAMS, 5 min
SLAMS, 1 hr
state
Figure 2-11 Routinely operating sulfur dioxide monitoring networks.
2.4.4.1 Minimum Monitoring Requirements
1 The minimum monitoring requirements for the SLAMS network are outlined in 40 CFR
2 Part 58, Appendix D. SO2 monitors in SLAMS sites represent four main spatial scales:
3 (1) microscale—areas in close proximity, up to 100 meters from a SO2 point and area
4 source, (2) middle scale—areas up to several city blocks, with dimensions of about 100 to
5 500 meters, (3) neighborhood scale—areas with dimensions of 0.5 to 4 km, and (4) urban
6 scale—urban areas with dimension of 4 to 50 km. Microscale, middle-scale, and
7 neighborhood-scale monitors are used to determine maximum hourly SO2 concentrations
8 because these monitors are close to stationary point and area sources, whereas
9 neighborhood- and urban-scale monitors are used as central site monitors to characterize
10 population exposures and trends, including in epidemiologic studies (Section 3.2.1).
3,000 Kilometers
75 150 Kilometers
0 125 250 Kilometers
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Urban-scale monitors 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 areas of tens to hundreds of kilometers,
which are 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, and peak concentrations
normally occur near the source of origin. Prior to the revised SO2 primary NAAQS in
2010, 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.
2009c'). To address this deficiency, 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
monitors in a given CBSA. 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 persons-tons per
year. A minimum of three SO2 monitors 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 monitors is required.
Lastly, a minimum of one SO2 monitor 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 monitors in a given state, which is based on the state's contribution to the national SO2
emissions inventory. This requirement was designed to offer some flexibility in monitor
placement, either inside or outside of a CBSA, independent of the PWEI criteria.
Additionally, all monitors in the network must be placed at locations where maximum
peak hourly SO2 concentrations are anticipated. Monitors 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 monitor is located in a CBSA that
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meets the aforementioned requirements based on the PWEI criteria, that monitor can
count towards the minimum monitoring requirements.
2.4.4.2
Siting Criteria for Probe and Monitoring Path
A number of criteria for probe and monitoring path siting are required for SLAMS and
NCore sites as specified in 40 CFR Part 58, Subpart G, Appendix E. These criteria are
discussed below.
The probe, or at least 80% of the monitoring path, must be located between 2 and 15 m
above ground level (AGL) for all SO2 monitoring sites. Additionally, the probe, or at
least 90% of the monitoring path, must be positioned at least 1 m (vertically or
horizontally) from any supporting structure, walls, parapets, penthouses, etc., and away
from dusty or dirty areas. If the probe, or a significant portion of the monitoring path, is
located near the side of a building, it should be located on the windward side relative to
the prevailing wind direction during the season of highest concentration potential for the
SO2 measurement.
Local minor sources of a primary pollutant such as SO2 can heighten concentrations of
that particular pollutant at a monitoring site. If the site objective is to investigate local
primary pollutant emissions, then the site should be located where the spatial and
temporal variability in these emissions can be captured. This type of monitoring site
would likely be the microscale type. If a monitoring site is to be used to determine air
quality over a much larger area, such as a neighborhood or city, a monitoring agency
should avoid placing a monitor probe, path, or inlet near local, minor sources. The plume
from the local minor sources should not be allowed to inappropriately influence the air
quality data collected. To reduce these potential interferences, the probe, or at least 90%
of the monitoring path, must be placed away from furnace flues, incineration flues, or
other minor sources of SO2. The separation distance should take into account the heights
of the flues, type of waste or fuel burned, and the sulfur content of the fuel.
2.4.4.3
Horizontal and Vertical Placement
2.4.4.4
Spacing from Minor Sources
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2.4.4.5
Spacing from Obstructions
Buildings and other obstacles can scavenge SO2 and restrict airflow for any pollutant. To
avoid this interference, the probe, inlet, or at least 90% of the monitoring path must (1) be
located away from obstacles at a distance of at least twice the height of the obstacle and
(2) have unrestricted airflow in an arc of at least 180° and the arc must include the
predominant wind direction for the season of highest SO2 concentrations. An exemption
can be made for measurements taken in street canyons or at near-source sites where
buildings and other structures are unavoidable.
Special consideration must be made for the use of open-path analyzers as they are
inherently more sensitive to certain types of interferences or optical obstructions
(e.g., trees, brush, buildings, plumes, dust, rain, particles, fog, snow, obstructions that
may be moved by wind, human activity, growth of vegetation, etc.). Any temporary
obstructions that are of sufficient density to obscure the light beam will affect the ability
of the open-path analyzer to measure SO2 concentrations continuously. Temporary, but
significant, obscuration of especially longer measurement paths could occur because
certain meteorological conditions (e.g., heavy fog, rain, snow) and/or because aerosol
levels are of sufficient density to prevent the analyzer's light transmission. Measures can
be implemented to compensate for these obstructions (e.g., shorter path lengths, higher
light source intensity) and ensure data recovery during periods when greatest primary
pollutant potential could be compromised.
Trees can provide surfaces for SO2 adsorption or reactions and surfaces for particle
deposition. Trees can also act as obstructions if they are located between the air pollutant
sources or source areas and the monitoring site, and if they have sufficient height and leaf
canopy density to interfere with normal airflow around the probe, inlet, or monitoring
path. To reduce possible interference, the probe, inlet, or at least 90% of the monitoring
path must be at least 10 meters or further from the drip line of trees. For microscale sites,
trees or shrubs should not be located between the probe and the source under
investigation, such as a roadway or a stationary source.
2.4.4.6
Spacing from Trees
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2.5 Environmental Concentrations
This section provides an overview of SO2 ambient and background concentrations as well
as copollutant correlations with SO2. SO2 data discussed in this section were obtained
from the AQS, EPA's repository for detailed air pollution data that is subject to quality
control and assurance procedures. 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.
Background SO2 concentrations from natural sources are subsequently discussed in
Section 2.5.4. Lastly, temporal correlations between SO2 and other NAAQS copollutants
are discussed in Section 2.5.5.
2.5.1 Sulfur Dioxide Metrics and Averaging Time
Different metrics are used to represent ambient SO2 concentrations for epidemiologic
analysis and NAAQS compliance. As discussed in Section 2.4.4. hourly and 5-minute
concentration data are routinely reported to 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 2-5 provides
information on how different SO2 metrics are derived. Two common daily metrics are the
daily average SO2 concentration (i.e., 24-hour average) and the daily 1-hour maximum
(1-hour maximum) SO2 concentration. Hourly metrics include (1) the 5-minute maximum
concentration reported during a given hour and (2) the 1-hour average concentration. The
averaging time used to construct SO2 metrics can represent different exposure scenarios
or windows. For example, while average daily metrics (24-hour average) represent
overall concentration during a given day, metrics derived using maximum concentration
statistics (i.e., 1-hour maximum or 5-minute maximum) provide insight to peak ambient
concentration levels 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 averaging time when
comparing the magnitude and range of ambient concentrations related to different
metrics.
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19
Table 2-5 Summary of sulfur dioxide (SO2) metrics and averaging times.
Metric
Averaging Time
Averaging Time Description
24-h avg
Daily
Daily mean of 1-h avg SO2 concentrations
1-h max
Daily
Maximum 1-h SO2 concentration reported
during the day
1-h avg
Hourly
Hourly mean of 5-min SO2 concentrations
5-min max
Hourly
Maximum 5-min SO2 concentration
reported during 1 h
Avg = average; max
= maximum; S02 = sulfur dioxide.
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.
2.5.2.1 Nationwide Spatial Variability
In the previous Integrated Science Assessment of Sulfur Oxides (U.S. EPA. 2008b). daily
(24-hour average, 1-hour maximum) and hourly (1-hour average) SO2 concentrations
measured at AQS monitoring sites during 2003-2005 were reported. Nationwide
statistics of 5-minute maximum SO2 data were not reported in the previous assessment
due to a lack of monitors reporting such data. From 2003-2005, nationwide, central
statistics (mean and median) of daily 1-hour maximum and 24-hour average 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 daily 1-hour maximum concentrations (99th percentile: 116 ppb). In
addition, concentrations of 1-hour average SO2 exhibited low mean concentrations
(4 ppb), with 99th percentile values near 34 ppb. Relatively high concentrations were
typically observed at sites near major stationary anthropogenic sources (e.g., electric
generating units).
Similar statistics were computed for more recent AQS SO2 monitoring data reported
during 2010-2012. AQS SO2 data used to compute national statistics meet the data
quality and completeness criteria listed in Table 2-6. Based on these criteria, statistics
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were computed for data from a total of 309 monitors across the U.S. for 5-minute
maximum SO2 concentrations and for data from a total of 337 monitors for the remaining
daily (1-hour maximum, 24-hour average) and hourly (1-hour average) SO2 metrics.
Table 2-6 Summary of sulfur dioxide data sets originating from the Air Quality
System database.
Years
2010-2012
Months
January-December
Except during 2010
Completeness criteria
75% of hours in day
75% of days in calendar quarter
All 4 quarters of the yr
Number of monitors meeting completeness criteria
309 monitors reporting 5-min data
(2010-2012)
337 monitors reporting 1-h data
(2010-2012)
Five-minute data is only available for 3rd and 4th quarter during 2010.
As expressed in Table 2-1. more recent, nationwide concentrations are similar, but
slightly lower than concentrations reported in the 2008 SOx ISA (U.S. EPA. 2008b). For
all daily (24-hour average, 1-hour maximum) and hourly (5-minute maximum, 1-hour
average) metrics, mean and median statistics are below 15 ppb, while SO2 concentrations
in the upper range of the distribution (99th percentile) cover a wide range of values and
can be greater than the primary NAAQS level of 75 ppb. Across all metrics, large
differences are observed between mean and 99th percentile concentrations, particularly
among SO2 metrics representing maximum, daily, and hourly concentrations (1-hour
maximum, 5-minute maximum). Such large differences between mean and 99th
percentile values are consistent with the highly variable nature of SO2, which is
characterized by periodic peak concentrations superimposed on a relatively low
background concentration.
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Table 2-7 National statistics of sulfur dioxide concentrations (parts per billion)
from Air Quality System monitoring sites during 2010-2012.
Year
N of Obs
Mean
5
10
25
50
75
90
95
98
99
AQS
Max IDa
5-min max
2010
955,660
3
0
0
0
1
3
7
11
24
41
51390006
2011
2,555,841
3
0
0
0
1
3
6
10
19
32
490110004
2012
2,557,263
2
0
0
0
1
2
5
8
16
27
40071001
2010-2012
6,068,764
3
0
0
0
1
2
5
9
19
31
51390006
1-h avg
2010
2,844,021
3
0
0
0
1
2
5
8
15
24
150010007
2011
2,845,106
2
0
0
0
1
2
4
7
12
20
150010007
2012
2,840,900
2
0
0
0
1
2
4
6
11
18
150010007
2010-2012
8,530,027
2
0
0
0
1
2
4
7
13
21
150010007
1-h max
2010
120,845
11
0
0
1
3
7
18
31
67
125
150010007
2011
121,000
8
0
0
1
3
7
15
27
55
88
150010007
2012
120,991
8
0
0
1
2
5
13
25
56
105
150010007
2010-2012
362,836
9
0
0
1
3
6
15
28
59
105
150010007
24-h avg
2010
120,845
3
0
0
0
1
3
5
8
15
25
150010007
2011
121,000
2
0
0
0
1
2
5
7
12
18
150010007
2012
120,991
2
0
0
0
1
2
4
6
12
22
150010007
2010-2012
36,836
2
0
0
0
1
2
5
7
13
21
150010007
AQS = Air Quality System; avg = average; max = maximum; N = population number; Obs = observations.
aAQS site ID number reporting the highest 3-year concentration across the U.S.
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1 Focusing on the distribution of daily 1-hour maximum SO2 concentrations, the absolute
2 highest value is greater than 7,000 ppb (7,131 ppb). This extremely high value is reported
3 at a site near an active volcano in Hawaii (Table 2-7. Figure 2-12). Other sites reporting
4 relatively high 99th percentile, 1-hour maximum values (greater than 200 ppb) occur at
5 sites near industrial or combustion sources in Tennessee, New Hampshire, Arizona,
6 Indiana, and Louisiana. However, as shown in the nationwide map in Figure 2-12. the
7 majority of monitoring sites across the U.S. report 99th percentile, 1-hour maximum
8 concentrations below the primary NAAQS level of 75 ppb.
1,600 Kilometers
Monitor 1-hr Daily Max S02
2010 - 2012, 99th Percentile
• <75 ppb
75-200 ppb
201 -400 ppb
• 401 - 600 ppb
0 125 250 Kilometers
3,000 Kilometers
I " 1
0 85 170 Kilometers
• > 600 ppb
Figure 2-12 Map of 99th percentile of daily 1-h max sulfur dioxide (SO2)
concentration reported at Air Quality System monitoring sites
during 2010-2012.
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1
2
3
4
5
6
7
8
9
10
On a national scale, the east-to-west gradient in SO2 emissions discussed in Section 2.2
corresponds to the higher SO2 concentrations observed in the eastern portion of the
contiguous U.S. (Figures 2-12 and 2-13). The highest SO2 concentrations are reported in
the Ohio River Valley, adjacent to many industrial and power plant facilities. In this area,
several monitors in eastern Ohio and western Pennsylvania report 99th percentile, 1-hour
maximum concentrations above 75 ppb. Lower SO2 concentrations are generally reported
in the western U.S., where only a small subset of monitors report 1-hour maximum
concentrations above 75 ppb. These low concentrations reflect fewer SO2 sources in the
western U.S. in comparison to the eastern U.S., where the vast majority of high-emitting
SO2 sources are located (Figure 2-4).
Monitor 24-hr Daily Average S02
2010 -2012, 99th Percentile
• <13 ppb
13-45 ppb
46- 108 ppb
• >108 ppb
1 1
0 125 250 Kilometers
I 1 1
0 1,500 3,000 Kilometers
i i i i i i i
400 800 1,600 Kilometers
Figure 2-13 Map of 99th percentile of daily 24-h avg sulfur dioxide (SO2)
concentration reported at Air Quality System monitoring sites
during 2010-2012.
November 2015
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21
22
23
24
25
26
27
2.5.2.2
Urban Spatial Variability
Air quality measurements from centrally located, urban monitors 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 large
geographical scales. This variation may in turn introduce exposure misclassification and
error into a health study (Section 3.3.3.2). The degree of exposure error associated with
central site estimates strongly depends on the spatial variability of the pollutant of
concern. When utilizing central site estimates for exposure assessment, pollutants with
high spatial variability are subject to more exposure error than pollutants that are spatially
homogenous across an urban area (Goldman et al.. 2010).
To examine the potential for exposure error (due to spatial variability) in health studies,
SO2 spatial variability was characterized in six CBS A/metropolitan focus areas:
Cleveland, OH; Pittsburgh, PA; New York City, NY; St. Louis, MO; Houston, TX; and
Payson/Phoenix, AZ. These focus areas were selected based on (1) their relevance to
current health studies (i.e., areas with peer-reviewed, epidemiologic analysis), (2) high
monitor density (four or more monitors located within area boundaries), and (3) the
presence of several diverse SO2 sources within a given CBS A/metropolitan focus area
boundary.
Maps of individual CBS A/metropolitan focus areas indicating monitor and point source
locations are presented in Figures 2-14-2-19. For each map, CBSA, county, and city
limit boundaries are included to provide spatial orientation. As shown by the maps, up to
10 SO2 monitoring sites are located in individual CBS A/metropolitan focus areas, with
most areas having less than six monitors. Monitors in each CBS A/metropolitan focus
area are located within various distances of SO2 sources. Around each monitor, buffer
zones up to 15 km are marked to indicate nearby sources. Due to the relatively short
atmospheric lifetime of SO2 (Table 2-3). monitors within 15 km of large point sources
(e.g., electric generating units, industrial sources, copper smelting facilities, shipping
ports) are expected to experience the greatest impact from source emissions.
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0 10 20 40 Kilometers
Distance to Urban S02
C NEI Facility
• Urban SO2 Monitor
| | 5 km
~ 10 km
| | 15 km
Cleveland City Border
~~I Cleveland CBSA Counties
Figure 2-14 Map of Cleveland, OH Core-based Statistical Area (CBSA).
November 2015
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Distance from S02 Monitor
c NEI Facility
• Urban SO2 Monitor
5 km
10 km
15 km
Pittsburgh City Border
Pittsburgh CBSA Counties
0 15 30 60 Kilometers
Figure 2-15
November 2015
Map of Pittsburgh, PA Core-based Statistical Area (CBSA).
2-42 DRAFT: Do Not Cite or Quote
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Figure 2-16 Map of New York City, NY Core-based Statistical Area (CBSA).
November 2015
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0 15 30 60 Kilometers
Distance to Urban S02 Monitors
O Emissions_St_louis
# Urban S02Monitor
I | 5 km
~ 10 km
I | 15 km
St. Louis City Border
St. Louis CBSA Counties
Figure 2-17 Map of St Louis, MO-IL Core-based Statistical Area (CBSA).
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80 Kilometers Distance to Urban S02 Monitors
NEI Facility
Urban SO2Monitor
5 km
10km
15 km
Houston City Border
Houston CBSA counties
Figure 2-18
November 2015
Map of Houston, TX Core-based Statistical Area (CBSA).
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1
2
3
4
5
6
7
8
9
10
11
w
Distance to Urban SO2
O NEI Facility
9 Urban SO2
| 5 km
n 10 km
15 km
25
50
100 Kilometers
PaysonTown Limit
Phoenix City Limit
Payson CBSA Counties
Phoenix CBSA Counties
Figure 2-19 Map of Payson/Phoenix, AZ Core-based Statistical Areas (CBSAs)
(hereafter referred to as Payson/Phoenix Metropolitan focus area).
Table 2-8 displays the distribution of daily 1-hour maximum SO2 concentrations and
monitor type (standard vs. trace level monitor) reported at individual AQS monitors in
the six CBS A/metropolitan focus area. Concentrations reported at these sites are similar
to nationwide SO2 concentrations discussed earlier in this section (Section 5.2.1). For all
but one individual monitor, median concentrations are below 15 ppb. The one exception
was the monitor in Payson/Phoenix CBS A/metropolitan focus area for which the median
concentration was 50 ppb. This particular monitor (Site D in Payson/Phoenix, AZ) is
located within 1 km of a copper smelting plant with markedly high annual SO2 emissions
(greater than 21,000 tons of SC^/year,
http: //www. epa.gov/ttnchie 1 /net/2011 inventory .html).
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Table 2-8 1-Hour max sulfur dioxide concentration distribution by AQS monitor
in six core-based statistical area/metropolitan focus areas.
AQS
Site Label Monitor ID
Mean
Min
10
25
50
75
90
99
Max
Monitor Type
Cleveland-Elyria-Mentor, OH
A 390350065
6
0
0
0
1
6
20
64
138
Standard
B 390350060
17
0
3
6
13
23
40
73
128
Standard
C 390850003
10
0
4
6
8
12
17
34
113
Standard
D 390350038
17
0
2
5
11
24
40
80
117
Standard
E 390850007
28
1
4
5
10
36
86
152
238
Standard
F 390350045
6
0
0
0
3
6
15
59
106
Standard
Pittsburgh, PA
A 421255001
8
0
4
5
7
9
13
22
46
Standard
B 420030064
24
0
4
7
15
31
55
140
450
Standard
C 421250005
7
0
0
2
5
9
15
39
70
Standard
D 420030067
6
0
1
2
4
8
13
30
108
Standard
E 420030002
7
0
1
2
5
9
15
45
97
Standard
F 420070005
22
0
2
5
12
26
53
145
350
Standard
G 420030010
8
0
2
4
6
9
15
29
57
Standard
H 420070002
11
0
2
4
7
13
21
61
187
Standard
I 420030008
6
0
2
3
5
8
11
27
55
Trace
New York-Northern New Jersey-Long Island, NY-NJ-PA
A 360050133
9
0
2
4
7
12
20
40
64
Standard
B 340130003
4
0
1
2
3
6
9
17
33
Trace
C 340170006
8
0
2
4
7
11
15
26
45
Standard
D 340171002
4
0
1
2
3
6
9
19
25
Standard
E 340273001
3
0
0
1
1
4
8
23
67
Standard
F 340390003
3
0
0
1
2
3
6
11
22
Standard
G 340390004
6
0
0
2
4
8
13
32
59
Standard
November 2015
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Table 2-8 (Continued): 1-Hour max sulfur dioxide concentration distribution by
AQS monitor in six core-based statistical area/metropolitan
focus areas
AQS
Site Label Monitor ID
Mean
Min
10
25
50
75
90
99
Max
Monitor Type
H 360590005
4
0
1
1
3
5
9
23
50
Standard
1 360790005
2
0
0
1
1
2
4
10
26
Standard
J 360810124
6
0
1
2
4
7
12
26
64
Trace
St. Louis, MO-IL
A 171170002
2
0
1
1
2
3
5
12
23
Standard
B 171191010
6
0
1
2
3
7
15
39
69
Standard
C 171193007
8
0
1
2
5
10
17
42
106
Standard
D 171630010
5
0
1
2
4
7
13
24
44
Standard
E 295100085
*
*
*
*
*
*
*
*
*
Trace
F 295100086
9
0
2
3
7
13
20
47
76
Standard
Houston-Sugar Land-Baytown, TX
A 481670005
5
0
1
2
3
7
12
34
58
Standard
B 482010051
2
0
0
0
1
2
7
19
73
Standard
C 482010062
3
0
0
0
1
4
8
19
56
Standard
D 482010416
5
0
0
1
2
6
13
32
60
Standard
E 482011035
5
0
0
0
2
6
14
38
75
Standard
F 482011039
*
*
*
*
*
*
*
*
*
Trace
G 482011050
3
0
0
1
2
4
7
14
22
Standard
Payson/Phoenix CBSA
A 40133002
3
0
1
2
3
4
6
9
12
Standard
B 40139812
*
*
*
*
*
*
*
*
*
Standard
C 40070009
1
0
2
2
8
40
84
213
1501
Standard
D 40071001
65
0
8
25
50
86
135
295
1501
Trace
AQS= Air Quality System; CBSA = core-based statistical area; max = maximum; min = minimum.
*1-h max S02 concentrations not reported at monitor.
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9
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18
19
20
21
22
More substantial monitor-to-monitor differences are observed in the 99th percentile of
SO2 concentrations. Across these monitors, 99th percentile concentrations range from 9
to 295 ppb, with the majority of sites exhibiting 99th percentile concentrations below
50 ppb. Relatively high 99th percentile concentrations are generally reported at monitors
within 5 km of a major SO2 point source, particularly in Pittsburgh, PA and
Payson-Phoenix, AZ. This trend is 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 electric generation (Cloughertv et al..
2013: Wheeler etal.. 2008).
To evaluate the extent of SO2 spatial variability over urban geographical scales,
concentrations were correlated between monitor pairs in each of the six
CBS A/metropolitan focus areas. Pairwise monitor comparisons were evaluated using
Pearson correlations to estimate the degree to which concentrations at two different
monitoring locations follow similar temporal trends. Across the six CBS A/metropolitan
focus areas, Pearson correlations range from 0 to 0.9. Correlations close to 1 represent
strong correspondence between pairwise monitor concentrations, while values close to
0 represent poor correspondence between monitor values.
Figure 2-20 shows scatterplots of pairwise monitor correlations of 24-hour average SO2
concentrations versus distance between monitor pairs. The distribution of pairwise
correlations for each CBS A/metropolitan focus area is presented as histograms in
Figures 2-21. 2-22. and 2-23 show similar information on spatial correlations but focus
on the comparison among hourly 5-minute maximum concentrations.
November 2015
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Figure 2-20
1-0-|
I i i i i 1 i i i i i 1 i i i i 1 i i i i 1
0.8-
Cleveland
0.6-
•
0.4-
• ~
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•••* .8
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11111111 1111111111111
0 10 40 80 120
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'j Q I I I I I I I I I I I I I I I I I I I I I
0.8-
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0.0 | i i i i i i i i i i| riiii ii i i
1 0 I i i i i I i i i i i I i i i i I i i i i I
Pittsburgh
0 10 40 80 120
'j Q _ I I I I I I
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1 Q I ' ¦ ¦ ' I ' ' ' ' ¦ I ' ' ' ' 1 ¦ ' ¦ ¦ I ¦
08_ Payson/Phoenix
0.6-
0.4-
0.2- •
o.o
| i i i i | i i i ii | ii i i | i i i i | i
0 10 40 80 120 0 10 40 80 120
Distance (km)
Pairwise monitor correlations of 24-hour average sulfur dioxide
versus distance between monitor pairs in six core-based
statistical area/metropolitan focus area, 2010-2012.
November 2015
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g. 6-1
0 4
cr 2
£
0-1
0.0
0.2
0.4
0.6
Cleveland
0.8
1.0
o
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8" 4
£ 0-I
0.0
0.2
0.4
0.6
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0.8
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o
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0 8 _ Pittsburgh
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| i i i i | i i i i i | i i i i | i i i i | i
0 10 40 80 120
I I I I | I I I II | I I I I | I I I I | I
0 10 40 80 120
1.0-1
1 i i i i 1 i i i i i 1 i i i i 1 i i i i 1 i
r °i
1 i i i i 1 i i i i i 1 i i i i 1 i i i i 1 i
0.8-
Houston
0.8-
Payson/Phoenix
0.6-
0.6-
0.4-
• & •
0.4-
•
0.2-
0.2-
0.0-
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0.0-
•
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I I I I | I I I I I | I I I I | I I I I | I
0 10
40 80 120
Distance (km)
| i i i i | i i i i i | i i i i | i i it
0 10 40 80 120
Figure 2-22 Pairwise monitor correlations of hourly 5-minute maximum data
versus distance between monitors in six core-based statistical
area/metropolitan focus areas, 2010-2012.
November 2015
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6-
c
-
0
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4-
cr
CD
/ -
fi-
Cleveland
o.o
0.2
0.4
0.6
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4 -
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0.0
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0.4
New York City
0.6
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n
n-
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0.8
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Houston
Payson/Phoenix
0.4 0.6
Spatial Correlation
Figure 2-23 Histogram of pairwise correlations of hourly 5-minute maximum
sulfur dioxide data in six core-based statistical area/metropolitan
focus areas, 2010-2012 data.
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Pairwise comparisons in Figure 2-20 demonstrate that daily 24-hour average SO2
concentrations are highly variable across urban spatial scales. In every
CBS A/metropolitan focus area, low to moderate pairwise correlations of daily 24-hour
average SO2 values are observed, with the majority of Pearson correlations below 0.6
(Figure 2-20). In general, correlations tend to decrease with distance. Even within
relatively short distances (up to 15 km), most pairwise correlations are low and decay
rapidly, reflecting the variable nature of ambient SO2 across urban spatial scales primarily
due to its short atmospheric residence time and the episodic nature of emissions discussed
earlier in this chapter (Chapter 2).
In comparison, hourly 5-minute maximum SO2 values show a similar, but enhanced level
of spatial variability across urban spatial scales (Figure 2-22). In most cases, pairwise
correlations of hourly 5-minute maximum values are generally lower (less than 0.4) and
decline more dramatically with distance than pairwise correlations of daily 24-hour
average concentrations. Greater spatial variability in hourly 5-minute maximum values
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-hour average 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. Comparatively, pairwise
correlations in New York City, NY are generally high and uniform across tens of
kilometers, demonstrating relatively good agreement between pairwise SO2
concentrations despite dramatic changes in distance between monitors. Stronger pairwise
correlations in New York City, NY may be directly related to low background SO2
concentrations due to fewer large SO2 sources within the CBS A/metropolitan focus area
boundaries. Such low concentrations may be less variable over spatial and temporal
scales than higher concentrations. On the other hand, spatial variations in Cleveland, OH
and St. Louis, MO may be amplified because emissions from local sources impact
various locations within these cities differentially.
In summary, SO2 concentrations vary substantially across urban spatial scales as
evidenced by poor to moderate pairwise monitor correlations observed in SO2 data in six
CBS A/metropolitan focus areas. Greater spatial heterogeneity tends to occur in cities
impacted by a wide variety of local SO2 sources rather than areas with few SO2 sources,
characterized by low concentrations (New York City, NY). Additionally, metrics
representing maximum SO2 concentrations (5-minute maximum, 1-hour maximum)
generally exhibit more spatial heterogeneity than daily average metrics (24-hour
average). Given the high degree of spatial variability of daily or hourly SO2
concentrations across metropolitan areas, exposure assessment based on central site
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monitors is subject to some degree of uncertainty, which may introduce health effect
biases in epidemiologic analyses (Section 3.3.5).
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
temporal trends are discussed, spanning long-term temporal trends on an annual basis to
short-term trends on a subhourly basis. The variability in ambient concentrations is
discussed in the context of short-term and long-term exposure and health effects analyses.
Trends in SO2 concentrations reported at AQS monitoring sites across the U.S. from 1990
to 2012 are shown in Figure 2-24. The white line shows the mean annual values. The
upper and lower borders of the blue (shaded) areas represent the 10th and 90th percentile
values, respectively. Information on trends at individual, local air monitoring sites can be
found at http://www.epa.gov/air/airtrends/sulfiir.html (U.S. EPA. 2012b).
0 "i—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—
11111111112222222222222
99999999990000000000000
999999999900000000001 11
01 2345678901 23456789012
1990 to 2012 : 72% decrease in National Average
The white line shows the mean values and the upper and lower borders of the blue (shaded) areas represent the 10th and 90th
percentile values.
Data reported by the Environmental Protection Agency Air Quality Trends Network.
Figure 2-24 National sulfur dioxide air quality trend (1990-2012), based on
163 sites, showing a 72% decrease in the national average.
2.5.3.1
Long-Term Trends
350
300-
.Q
Cl
National Standard
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The steady decline in SO2 concentrations over the past 25 years is largely attributed to
emissions reductions at electric utilities due to the Acid Rain Program under the Clean
Air Act Amendments of 1990 | Section 2.2.41. The goal of this program was to reduce
power plant SO2 emissions by 10 million tons from 1980 levels. Reductions in SO2
emissions from the Acid Rain Program commenced in 1996 and continued into the
2000s, resulting in dramatic decreases in total, nationwide SO2 emissions and
concentrations. Additional environmental regulations on sulfur content in diesel fuel for
mobile sources resulted in further reductions in ambient SO2 over the past decade. From
1990-2012, the annual 99th percentile average of daily 1-hour maximum SO2
concentration has decreased by 72% 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 (AL, FL,
GA, MS), 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%/year) reported at monitoring sites across these four states.
2.5.3.2 Seasonal Trends
In the 2008 SOx ISA (U.S. EPA. 2008b). month-to-month trends for SO2 were observed
across a number of metropolitan areas; however, seasonal profiles varied by location.
Some cities, such as Steubenville, PA 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 daily 1-hour maximum concentrations
(2010-2012) is shown for the six CBS A/metropolitan focus areas introduced earlier in
this chapter (Section 2.5.2.2). Figure 2-25 displays the range of SO2 concentrations
reported at all monitors within each CBS A/metropolitan focus area. For every month,
median concentrations are displayed by black lines inside the box, and mean
concentrations are displayed by red markers. The interquartile range (IQR) (25th to 75th
percentile range) is outlined by the box, and 5th and 95th percentiles are shown by the
top and bottom whiskers of the box, respectively.
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75
-Q
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25
Cleveland
-| 1 1—
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20
a. 15
§10
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Pittsburgh
St. Louis
25
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"§-15
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O 10
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~n ~
~ ~ ~
J FMAMJ JASOND
Hour
200
150
100
50
0
Payson/Phoenix
~ ~
~n ~ !~
~ ~
J FMAMJ JASOND
Hour
Figure 2-25 Sulfur dioxide month-to-month variability based on 1-hour
maximum concentrations at Air Quality System sites in each
core-based statistical area.
1 Recent data indicate that daily 1-hour maximum SO2 data vary across seasons, especially
2 in the upper end of monthly SO2 concentrations. Across CBS A/metropolitan focus areas,
3 median concentrations (50th percentile: black line) tend to exhibit little variability
4 throughout the year, while large variations are observed in the upper range (greater than
5 75th percentile) of SO2 values. Notably, mean monthly SO2 concentrations are higher and
6 more variable than median values, indicating that mean concentrations tend to be skewed
7 right by extremely high ambient concentrations that infrequently occur.
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Recent data also further demonstrate that seasonal profiles vary by location. While each
CBS A/metropolitan focus area exhibits some extent of seasonal variation, no distinct
seasonal profile was observed across all areas. For example, summertime maxima in
daily 1 -hour maximum SO2 are evident in Cleveland, OH and Gila County, AZ
corresponding to CBS A/metropolitan focus areas with the highest SO2 concentrations.
Alternatively, New York City, NY and Houston, TX show clear wintertime maxima. The
remaining CBS A/metropolitan focus areas, Pittsburgh, PA and St. Louis, MO exhibit
both summer and winter peaks.
Month-to-month variations in SO2 concentrations appear to be related to different sources
within each location and, to some extent, the atmospheric chemistry of SO2. For all
CBS A/metropolitan focus areas, wintertime and summertime SO2 enhancements likely
correspond to higher power plant emissions due to increased demands for heating and
cooling of residential/commercial buildings during seasons with extreme temperatures.
Summertime minima, observed in some CBS A/metropolitan focus areas (New York City,
NY, and Houston, TX), may correspond to more rapid oxidation of SO2 to SO42 by
photochemically derived atmospheric oxidants that are more prevalent during the
summer. Other seasonal variations are likely due to SO2 sources specific to a given
location. The difference in seasonality among these cities suggest that SO2 can be
substantially variable across local and regional scales.
2.5.3.3 Diel Variability
The 2008 SOx ISA (U.S. EPA. 2008b) explored nationwide diel patterns in SO2
concentrations 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 as it expands throughout the day due to
convective mixing.
Diel patterns were investigated in the six CBS A/metropolitan focus areas using more
recent hourly average (1-hour average) and maximum (5-minute maximum) SO2 data.
Figures 2-26 and 2-27 show variations in 1-hour average and hourly 5-minute maximum
SO2 concentrations in the six focus areas. For every hour, median concentrations are
displayed as black lines inside the box, and the mean concentrations are displayed as 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.
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-Q
Q.
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30
20
10
Fmrart
Cleveland
EE
25
20
15
10
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0
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_Q
Q.
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New York City
20
15
10
St. Louis
15
S-10
Q.
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w 5
Houston
ggSssssi
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Hour
18:00
60
40
20
jyyym
Payson/Phoenix
^ y 3,
0:00
6:00
12:00
Hour
18:00
Figure 2-26 Diel variability based on 1 -hour average sulfur dioxide
concentrations.
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Cleveland
_Q
Q.
Q.
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pnmmnnfl-
nnmm
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TV
fflP
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ihi
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S-10
Q.
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0:00
6:00
12:00
Hour
18:00
60
40
20
0:00
Payson/Phoenix
6:00
12:00
Hour
18:00
Figure 2-27 Diel trend based on hourly 5-minute maximum data in the six
core-based statistical area focus areas.
1 Consistent with the nationwide diel patterns reported in the 2008 SOx ISA (U.S. EPA.
2 2008b). SO2 concentrations in the six CBS A/metropolitan focus areas are generally low
3 during nighttime and approach maxima values during daytime hours (Figures 2-26 and
4 2-27). The timing of daytime maxima, however, varies by location. In New York City,
5 NY and Payson/Phoenix, AZ daytime maxima occur during early morning hours (6:00 to
6 9:00 a.m. LST), whereas SO2 tends to peak later in the morning or in some cases
7 early/midafternoon in the remaining urban areas (Cleveland, OH; Pittsburgh, PA; St.
8 Louis, MO; Houston, TX).
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The timing and duration of daytime SO2 peaks in the six focus areas are likely a result of
a combination of source emissions and meteorological parameters. Similar to conclusions
in the 2008 SOx ISA, higher daytime SO2 likely reflects 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 in New York City, NY may be related to an increase in mobile source emissions
from diesel vehicles during morning rush hour when traffic activity is high and
atmospheric conditions are stable. Stable atmospheric conditions tend to trap atmospheric
pollution near the ground, resulting in an overall increase in ground-level pollution. This
result is consistent with Wheeler et al. (2008) who found that a large portion of SO2
variability in Ontario, Canada, can be explained by diesel truck traffic.
Notably, SO2 concentrations are all well below primary NAAQS levels during all hours
of the day in every CBS A/metropolitan focus area. In each location, median hourly
maxima (5-minute maximum) and average (1-hour average) concentrations are roughly
5 ppb. Even when examining the upper end of the distribution (90th percentile) of hourly
5-minute maximum values, concentrations are for the most part below 15 ppb. However,
concentrations (1-hour average) above primary NAAQS levels of 75 ppb are reported at
some sites and generally comprise the highest concentrations (99th percentile and above)
reported at a given monitoring sites.
2.5.3.4 Sulfur Dioxide 5-Minute Data
The previous EPA NAAQS review concluded that short-term exposure (5-10 minutes) to
SO2 above 200 ppb can cause lung function decrements among exercising asthmatics,
with the severity of the effect increasing with concentration (U.S. EPA. 2008b). Based on
these findings, state monitoring networks have been expanding over the past 5 years in an
effort to characterize hourly 5-minute maximum concentrations and to understand the
extent to which these maximum values approach health-relevant levels. Under the recent
monitoring guidelines, states currently report subhourly concentrations in the form of
either (1) a 5-minute maximum concentration reported every hour or (2) twelve 5-minute
average concentrations reported every hour. These 5-minute metrics are used to estimate
the range of ambient concentrations and potential community exposures occurring on
short time scales. In this section, subhourly concentrations are evaluated to understand
the distribution of 5-minute concentrations observed in the six CBS A/metropolitan focus
areas introduced in Section 2.5.2.2.
Over the past decade, the number of AQS monitoring sites reporting 5-minute SO2
concentrations has significantly increased. During 1997-2007, a total of 95 monitoring
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sites periodically reported hourly 5-minute maximum concentrations. Of these 95 sites,
only 16 sites posted all twelve 5-minute average concentrations per hour, limiting the
amount of information reported on 5-minute data in the 2008 SOx ISA (U.S. EPA.
2008b). To date, approximately 309 sites report 5-minute data, including urban sites
within CBSAs, sites near city centers, sites near major SO2 sources, and sites in rural
areas. Figure 2-11 displays a map of the AQS 5-minute SO2 monitoring network during
2010-2012.
In the 2008 SOx ISA, analyses were conducted on hourly 5-minute maximum data in
16 metropolitan focus areas to understand SO2 variability over short time scales (minutes
to hours). In these 16 metropolitan areas, concentrations of hourly 5-minute maximum
data ranged from 0 ppb to approximately 4,000 ppb, with median concentrations below
10 ppb. The 99th percentile of hourly 5-minute maximum SO2 reported at all 16 locations
was below benchmark levels of 200 ppb; however, maximum concentrations at several
monitors often reported values above 200 ppb. While strong agreement between hourly
5-minute maximum and 1-hour average metrics was observed (Pearson r > 0.87 in
16 cities), the magnitude of hourly 5-minute maximum concentrations was not often
captured by 1-hour average values.
Similar analyses on hourly 5-minute maximum concentrations were performed on more
recent data reported at individual monitors in the six CBS A/metropolitan focus areas
introduced earlier in this chapter (Cleveland, OH; Pittsburgh, PA; New York City, NY;
Houston, TX; St Louis, MO; and Payson/Phoenix, AZ). Table 2-9 shows the range in
5-minute maximum SO2 concentrations reported at individual monitors within the six
CBS A/metropolitan focus areas during 2010-2012. In general, median 5-minute
maximum concentrations are below 5 ppb, while maximum concentrations range from
32 to 2,001 ppb.
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Table 2-9 Five-minute sulfur dioxide concentrations by AQS monitor in select core-based statistical
area/metropolitan focus areas.
Site Label AQS Monitor ID
N
Mean
Min
25
50
75
90
99
Max
Monitor Type
Cleveland-Elyria-Mentor, OH
A 390350065
20,424
4
0
0
1
5
10
39
241
Standard
B 390350060
19,694
8
0
2
4
8
18
62
266
Standard
C 390850003
21,025
5
0
2
4
6
9
23
183
Standard
D 390350038
20,496
7
0
1
2
7
19
57
206
Standard
E 390850007
20,740
12
0
1
3
5
18
171
522
Standard
F 390350045
20,422
2
0
0
1
2
4
25
285
Standard
Pittsburgh, PA
A 421255001
19,776
3
0
0
2
3
6
19
228
Standard
B 420030064
25,826
10
0
2
4
10
23
81
704
Standard
C 421250005
19,825
5
0
3
4
6
8
15
68
Standard
D 420030067
26,112
2
0
0
1
3
6
17
390
Standard
E 420030002
25,416
3
0
0
1
3
8
28
505
Standard
F 420070005
23,278
9
0
1
4
8
19
87
889
Standard
G 420030010
18,883
4
0
2
3
5
8
16
128
Standard
H 420070002
19,880
5
0
2
3
5
9
28
202
Standard
I 420030008
22,938
3
0
1
2
4
6
15
94
Trace
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Table 2-9 (Continued): Five-minute sulfur dioxide concentrations by AQS monitor in select core-based statistical
area/metropolitan focus areas.
Site Label AQS Monitor ID
N
Mean
Min
25
50
75
90
99
Max
Monitor Type
New York-Northern New Jersey-Long Island, NY-NJ-PA
A 360050133
17,300
5
0
2
3
7
12
27
68
Standard
B 340130003
19,684
2
0
1
1
3
5
13
32
Trace
C 340170006
18,332
5
0
1
3
6
12
26
83
Standard
D 340171002
20,073
2
0
1
2
3
5
14
64
Standard
E 340273001
20,024
2
0
1
1
2
3
11
94
Standard
F 340390003
18,088
2
0
1
1
2
4
8
33
Standard
G 340390004
20,183
3
0
1
1
3
7
27
288
Standard
H 360590005
15,833
2
0
1
1
2
4
15
73
Standard
I 360790005
15,811
1
0
1
1
1
2
6
91
Standard
J 360810124
15,893
3
0
1
2
4
8
19
90
Trace
St. Louis, MO-IL
A 171170002
19,522
1
0
1
1
1
2
5
65
Standard
B 171191010
19,088
2
0
1
2
4
19
112
Standard
C 171193007
18,924
3
0
1
1
3
6
27
112
Standard
D 171630010
19,132
2
0
1
1
3
5
17
96
Standard
E 295100085
16,335
4
0
1
2
4
9
34
154
Trace
F 295100086
17,003
3
0
1
2
4
8
24
241
Standard
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Table 2-9 (Continued): Five-minute sulfur dioxide concentrations by AQS monitor in select core-based statistical
area/metropolitan focus areas.
Site Label AQS Monitor ID
N
Mean
Min
25
50
75
90
99
Max
Monitor Type
Houston-Sugar Land-Baytown, TX
A 481670005
20,099
2
0
1
1
3
6
17
80
Standard
B 482010051
19,408
1
0
0
0
0
1
10
92
C 482010062
20,322
1
0
0
1
1
3
12
81
Standard
D 482010416
20,139
2
0
0
1
2
4
23
103
Standard
E 482011035
20,232
2
0
0
0
1
4
27
206
F 482011039
16,954
1
0
0
0
1
3
14
99
Trace
G 482011050
19,694
2
0
1
2
3
4
11
41
Standard
Payson/Phoenix CBSA
A 40133002
21,297
2
0
1
2
2
4
7
29
B 40139812
17,129
2
0
1
2
3
4
8
91
Standard
C 40070009
20,357
8
0
2
3
5
18
101
422
Standard
D 40071001
21,448
26
0
1
3
18
70
301
2,001
Trace
AQS = Air Quality System; CBSA = core-based statistical area; max = maximum; min = minimum; N = population number.
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35
36
37
Table 2-10 shows the range in temporal correlations between collocated, hourly
5-minute maximum and 1-hour average SO2 measurements reported at these monitors
within the six CBS A/metropolitan focus areas. Similar to results in the 2008 SOx ISA
(U.S. EPA. 2008b). 5-minute maximum concentrations tend to correlate well with 1-hour
average metrics, suggesting that 1-hour average metrics, in most cases, adequately
represent changes in 5-minute maximum data overtime. However, 5-minute maximum
values tend to be higher than 1-hour average concentrations.
The hourly 5-minute maximum to 1-hour average SO2 relationship was further evaluated
at these same monitors in the six CBS A/metropolitan focus areas to identify the
magnitude and location of hourly 5-minute maximum SO2 values approaching levels
relevant to human health impacts (greater than 200 ppb). This relationship also informs
the degree to which 5-minute maximum values are higher than 1-hour average
concentrations across different types of monitoring sites (i.e., sites near major sources vs.
sites far downwind of sources).
To evaluate the 5-minute maximum 1-hour average relationship across different monitor
types, individual monitors were classified into three categories based on the highest (99th
percentile) daily 1-hour maximum SO2 concentration. The categories consisted of "low
concentration," "moderate concentration," and "high concentration" monitors. This
classification approach was employed to systematically distinguish between near-source
monitors reporting relatively high SO2 concentrations versus monitors located far
downwind from major sources that typically report low concentrations. As demonstrated
in Table 2-8 (Section 2.5.2.2). the distribution of the 99th percentile of 1-hour maximum
SO2 concentrations reported at these monitors [population number (N) = 42 monitors]
range from 9 to 295 ppb. Monitors with relatively high 99th percentile concentrations
(upper quartile of all monitors) were classified as "high concentration" monitors, while
monitors with moderate (IQR of all monitors) or low (lower quartile of all monitors)
concentrations were classified as "moderate concentration" and "low concentration"
monitors, respectively. Of the 42 monitors examined, the majority (N = 24) were
classified as "moderate concentration" and were characteristic of urban, central site
monitors. "High concentration" and "low concentration" sites were primarily
representative of near-source and background monitors, respectively. However, not all
monitors within 15 km of a point source report high SO2 values. These select monitors
near sources were instead classified as "moderate concentration" or "low concentration"
sites.
Out of nine monitors classified as "high concentration" sites, the majority
(N = 5 monitors) were located in Cleveland, OH, which has a substantial number of
power plant and industrial SO2 sources. The remaining four "high concentration"
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8
9
10
11
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14
monitors were located near large stationary sources in Pittsburgh, PA and Gila County,
AZ. Notably, no monitors in St. Louis, MO or Houston, TX were classified as "high
concentration" monitors even although both metropolitan areas contain a number of SO2
sources, including power plants, chemical manufacturing facilities, shipping ports. The
lack of "high concentration" monitors in St Louis, MO and Houston, TX indicate
relatively lower SO2 concentrations, which may reflect more widespread implementation
of control technologies on point sources or meteorological factors reducing overall
ambient SO2 levels.
Scatterplots of collocated hourly 5-minute maximum and 1-hour average measurements
are displayed by CBS A/metropolitan focus area in Figure 2-28. Each scatterplot indicates
data reported at "high," "moderate," and "low" concentration monitors. Furthermore, a
variety of peak-to-mean ratios (PMRs) are displayed on each plot to further evaluate the
difference in magnitude between hourly 5-minute maximum and 1-hour average
concentrations.
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800
§ 600-1
~ 400 -=
_i I I l
3:1 ' 2:1 ~
' ^
^ *
1:1
200-i
Cleveland
1—i—i—i—i—|—i—i—i—r~
0 200 400
800-
600-j
400 4
200-I
0
l I l I l l l I I I l l l l l l l I l l l l I I I l
v2:1 '
_3:1~7 /
/ V ~ 1:1
/
/ •.
Pittsburgh
II I! II I I | II I I II II | II I I I II I |
0 200 400 600
E
c
E
I
LO
300-
I 200
100-
0-
_L
7 /
3:1 / 2:1 /
1:1-
'z,
New York City
100
200
300
200
100-
0-
-I I I I I I I « ¦
T^A~r
.3:1/ -/
-1:1-
St Louis
1—1—¦—¦—¦—I—1—1 1 1
0 100 200
E
c
E
I
LO
300
I 200-
100
0-
_j 1 1 1 1 1 1 1—
17717-
' , —1:1—"
V'
Houston
]—1 r—1 1 1 1 1 1 1 r
100 200
1-hr avg
600 i
400
200 -3
0
I I I I I i I I I 1 I I I I I I I
.3:1 '• '
. / 2:1 /
./ ./
-1:1—^
•rr-
Payson/Phoenix
| I I I I I I I I | I I I I I I I I |
0 200 400
1-hr avg
• Low Cone Sites • Moderate Cone Sites • High Cone Sites
Within each focus area, individual monitoring sites are displayed by marker color ("Low Cone" = green, "Moderate Cone" = red,
"High Cone" = blue). Peak-to-Mean ratios (PMR) are displayed on each scatter plot as 1:1 (hourly 5-max = 1-h avg), 2:1 (hourly
5-minute maximum is 2 times higher than 1-h avg), and 3:1 (hourly 5-minute maximum is 3X times higher than 1-h avg).
Cone = concentration.
Figure 2-28 Scatterplot of hourly 5-minute maximum versus 1-hour average
sulfur dioxide concentrations by core-based statistical
area/metropolitan focus area.
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37
PMRs have been used extensively in the previous SO2 NAAQS review to evaluate the
distribution of hourly 5-minute maximum concentrations corresponding to a given 1-hour
SO2 concentration (U.S. EPA. 2009b). PMRs are determined by dividing the hourly
5-minute maximum concentration over the 1-hour average concentration. Using this
approach, a PMR of 1 demonstrates that hourly 5-minute maximum and 1-hour average
concentrations are equivalent. Increasing PMR values (up to a maximum value of 12 in
this case) indicate that hourly 5-minute maximum values are increasingly higher than
1-hour average concentrations. For example, a PMR of 2 (shown as 2:1 on Figure 2-28)
indicates that hourly 5-minute maximum values are 2 times higher than 1-hour average
concentrations, while a value of 3 (shown as 3:1 on Figure 2-28) corresponds to hourly
5-minute maximum peaks 3 times higher than 1-hour average concentrations. PMR
values 1 (1:1) through 3 (3:1) are displayed in Figure 2-28.
As demonstrated by the scatterplots in Figure 2-28. the majority of hourly 5-minute
maximum SO2 concentrations fall between a PMR value of 1 and 3, indicating that
hourly 5-minute maximum SO2 concentrations are generally between 1 and 3 times
higher than 1-hour average concentrations. On occasion, hourly 5-minute maximum
concentrations can be more than 3 times higher than 1-hour average concentrations, and
in rare cases, can be up to 12 times higher, particularly when SO2 concentrations are low
(less than 25 ppb). However, at such low concentrations, 5-minute maximum SO2 values
very rarely approach health-relevant concentrations of 200 ppb (i.e., the lowest level
where lung function decrements were reported in controlled human exposure studies of
individuals with asthma engaged in exercise, see Section 5.2.1.2).
Of all data reported at these sites during 2010-2012, only a small portion of hourly
5-minute data is greater than 200 ppb. As shown in Figure 2-28. these high values
primarily occur at the nine "high concentration" sites located in Cleveland, OH;
Pittsburgh, PA; and Payson/Phoenix, AZ. At these nine "high concentration" sites, most
hourly 5-minute maximum values above 200 ppb occur when 1-hour average
concentrations are greater than the primary NAAQS level of 75 ppb. On rare occasions,
hourly 5-minute maximum concentrations above 200 ppb correspond to 1-hour average
concentrations below 75 ppb. For example, in Cleveland, less than 10% of hourly
5-minute maximum data above 200 ppb approximately 10 hours over a 3 year period)
correspond to 1-hour average concentrations below primary NAAQS levels. These results
emphasize that 1-hour average concentrations at or below 75 ppb, for the most part,
represent hourly 5-minute maximum values below 200 ppb.
To understand how often 5-minute maximum values exceed 200 ppb, Figures 2-29-2-31
display time-series and frequency analysis of hourly 5-minute maximum concentrations
reported at "high concentration" sites by CBS A/metropolitan focus area during
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1 2010-2012. As demonstrated by these figures, most hourly 5-minute maximum
2 concentrations above 200 ppb fall between 200 ppb and 400 ppb, with far fewer values
3 above 400 ppb. Among these "high concentration" monitors, the absolute highest hourly
4 5-minute maximum concentration of 889 ppb is reported at a site in Pittsburgh, PA
5 located within 10 km of three large electric-generating and industrial point sources. Other
6 monitors reporting hourly 5-minute maximum concentrations above 400 ppb are located
7 in Cleveland, OH and Payson/Phoenix, AZ and correspond to monitors located near a
8 large power plant (Cleveland, OH) or a copper smelting facility (Gila County, AZ).
November 2015
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600-
500-
n
400-
Q.
300-
o
CO
200-
100-
0-
J I I I
Site A
1 i I r
1/1/2010 1/1/2011 1/1/2012
Time (LST)
I i I i
.Q
a.
a.
O
(f)
600 -
Site B
500 -
400 -
300-
200 -
1/1/2010 1/1/2011 1/1/2012
Time (LST)
600-
500-
n
400-
n
u.
300-
O
C/)
200-
100-
0-
Site D
1/1/2010 1/1/2011 1/1/2012
Time (LST)
j i i L
-Q
Q.
a.
CM
o
C/3
600-
Site E
500 -
400 -
300 -
200-
100-
1/1/2010 1/1/2011 1/1/2012
Time (LST)
c
D
O
O
10 -1
n 1 r
200 400 600
S02 (ppb)
c
D
O
O
10
200
400 600
S02 (ppb)
c
3
O
O
10 -1
200 400 600
S02 (ppb)
c
23
O
a
10 -1
"i 1 r
200 400 600
S02 (ppb)
800 1000
800 1000
800 1000
800 1000
Figure 2-29 Time-series and frequency distribution of hourly 5-minute
maximum sulfur dioxide (SO2) concentrations from four "high
concentration" monitors in the Cleveland core-based statistical
area.
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1000-
Site B
800 -
600 -
200 -
i 1 r
1/1/2010 1/1/2011 1/1/2012
Time (LST)
J I I I L
1000 -
Site F
800 -
600 -
400 -
200 -
1 1 T
1/1/2010 1/1/2011 1/1/2012
Time (LST)
1000 -
Site H
800 -
400 -
200 -
"I 1 T
1/1/2010 1/1/2011 1/1/2012
Time (LST)
o
O
200 400 600
S02 (ppb)
i r
800 1000
o
o
n
200 400 600
S02 (ppb)
J I L
800 1000
o
o
200 400 600 800 1000
S02 (ppb)
Figure 2-30 Time-series and frequency distribution of hourly 5-minute,
maximum sulfur dioxide (SO2) concentrations from three "high
concentration" monitors in the Pittsburgh core-based statistical
area.
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2
3
4
5
6
7
8
9
10
1000 -
800 -
¦Q
Cl
600 -
Cl
fsl
o
tn
400 -
200 -
0 -
-Q
Q_
Cl
O
CO
1000 -
800 -
600-
400-
200 -
0-
1/1/2010
* 9 m # •# , •
1 1 1 <
1/1/2011 1/1/2012
Time (LST)
J I L
Site D
"I i 1 r
1/1/2010 1/1/2011 1/1/2012
Time (LST)
CZ
Z5
O
O
c
o
O
n
400 600
S02 (ppb)
J L
S02 (ppb)
1000
1000
Figure 2-31 Time-series and frequency distribution of hourly 5-minute,
maximum sulfur dioxide (SO2) concentrations from 2 "high
concentration" monitors in the Payson/Phoenix, AZ core-based
statistical area/metropolitan focus area.
Table 2-11 presents the percent of 5-minute maximum values (during 2010-2012) above
200 ppb at "high concentration" sites in Cleveland, OH; Pittsburgh, PA; and
Payson/Phoenix, AZ. The number of hourly 5-minute maximum SO2 values above
200 ppb varies dramatically by site. For the most part, sites closer to major point sources
more frequently report hourly 5-minute peak values above health benchmark levels. This
trend is particularly evident in Pittsburgh, PA where up to 44 hourly 5-minute maximum
concentrations are above 200 ppb at Site F (located within 10 km of three major point
sources). Conversely, only a single 5-minute maximum concentration value was greater
than 200 ppb at Site E, located in the urban center. Furthermore, although health-relevant
5-minute peaks (200 ppb, 400 ppb, and 600 ppb) are recorded at every "high
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1
2
concentration" site, they generally comprise only a small fraction of total data (up to 2%)
over the 3-year sampling period (2010-2012).
Table 2-10 Pearson correlation coefficient and peak-to-mean ratio for maximum
sulfur dioxide concentrations in core-based statistical areas.
CBSA
N monitors
Correlation Coefficient
Mean PMRa
Median PMRa
Cleveland, OH
6
0.87-0.94
1.5
1.2
Pittsburgh, PA
9
0.92-0.97
1.4
1.2
New York City, NY
10
0.87-0.98
1.6
1.4
St Louis, MO
6 (only 5 with both
measurements)
0.82-0.91
1.8
1.7
Houston, TX
6
0.89-0.95
2.0
1.5
Payson, AZ
4 (only 3 with
measurements)
0.84-0.93
1.7
1.4
CBSA = core-based statistical area; N = population number.
aPeak-to-mean ratio (PMR) = 5 min max/1-h average.
3 In summary, hourly 5-minute maximum concentrations above 200 ppb can be expected to
4 occur on rare occasions at some, but not all, monitors located within close proximity to
5 sources. For example, in analyses within six CBS A/metropolitan focus areas, most peak
6 values above 200 ppb are observed at sites classified as "high concentration" monitors,
7 generally corresponding to near-source monitors. However, peak values may not always
8 be detected by near-source monitors, due to meteorological effects or implementation of
9 strategies to reduce local impacts (e.g., effective stack height on power plants).
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Table 2-11 Number of hours (percent hours) which hourly 5-minute maximum
sulfur dioxide concentrations are above health benchmark levels
during 2010-2012.
CBSA
Site ID
Site
Label3
>200 ppb
>400 ppb
>600 ppb
Cleveland, OH
360350065
A
3 (0.01%)
0 (0%)
0 (0%)
390350060
B
4 (0.02%)
0 (0%)
0 (0%)
390350038
D
1 (0.005%)
0 (0%)
0 (0%)
390850007
E
115 (0.6%)
6 (0.003%)
0 (0%)
390350045
F
2 (0.009%)
0 (0%)
0 (0%)
Pittsburgh, PA
420030064
B
24 (0.09%)
9 (0.03%)
2 (0.007%)
420070002
E
1 (0.005%)
0 (0%)
0 (0%)
420070005
F
44 (0.1%)
5 (0.02%)
2 (0.007%)
Payson, AZ
40070009
C
14 (0.07%)
1 (0.005%)
0 (0%)
40071001 D 17(2%) 4(0.4%) 0(0%)
CBSA = core-based statistical area; ppb = parts per billion.
aLabel on CBSA maps [Cleveland (Figure 2-14). Pittsburgh (Figure 2-15). and Payson, AZ (Figure 2-19)1
2.5.4 Background Concentrations
1 An understanding of the sources and contributions of background SO2 to SO2
2 concentrations in the U.S. is potentially useful in reviewing the SO2 NAAQS, especially
3 related to days at the upper end of the distribution of SO2 concentrations. In the context
4 of a review of the NAAQS, it is useful to define background SO2 concentrations in a way
5 that distinguishes among concentrations that result from precursor emissions that are
6 relatively less controllable from those that are relatively more controllable through U.S.
7 policies.
8 In previous NAAQS reviews, a specific definition of background concentrations was
9 used and referred to as policy-relevant background (PRB). In those previous reviews,
10 PRB concentrations were defined by EPA as those concentrations that would occur in the
11 U.S. in the absence of anthropogenic emissions in continental North America, defined
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here as the U.S., Canada, and Mexico. In the current round of reviews, other definitions
are possible depending on the pollutant under consideration.
Contributions to background concentrations include natural emissions of SO2 and
photochemical reactions involving reduced sulfur compounds of natural origin, as well as
their long-range transport from outside of North America from any source. As an
example, transport of SO2 from Eurasia across the Pacific Ocean or the Arctic Ocean
would carry background SO2 into the U.S. Section 2.2.5.1 contains a schematic diagram
(Figure 2-6) showing the major photochemical processes involved in the sulfur cycle,
including natural sources of reduced sulfur species from anaerobic microbial activity in
wetlands and volcanic activity. Volcanoes and wildfires are the major natural sources of
SO2. Biogenic emissions from agricultural activities are not considered in the formation
of PRB concentrations.
Figure 2-32. which is taken from the 2008 SOx ISA (U.S. EPA. 2008b). shows global
scale three-dimensional model simulations for annual mean SO2 concentrations in surface
air including all sources (both anthropogenic and natural), or the "base case" (top panel);
the background simulation (middle panel); and the percentage contribution of the
background to the total base case SO2 (bottom panel). Results shown in Figure 2-32 are
for the meteorological year 2001. Maximum concentrations in the base case simulation,
greater than 5 ppb, occur along the Ohio River Valley (upper panel). Background SO2
concentrations are orders of magnitude smaller, below 0.01 per billion (ppb) over much
of the U.S. (middle panel). Maximum PRB concentrations of SO2 are 0.03 ppb. In the
U.S. Northwest, there are geothermal sources of SO2 responsible for 70 to 80% of the
background SO2 concentration; even so, absolute SO2 concentrations are still of the order
of ~2 ppb or less. In these simulations, background contributes less than 1% to
present-day SO2 concentrations in surface air (bottom panel) with the exception of the
West Coast where volcanic SO2 emissions cause high background contributions.
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Total
120°W
100°W
S0°W
< 0.01 1.21 2.41 3.60 4.80
Background
6.00
ppb
120°W
100°W
ao°W
< 0.001 0.006
0.01 f
0.015 0.020
0.025
ppb
Percent Background Contribution
b |i
latniv
100°W
1 5 10
Source: NOAA Geophysical Fluid Dynamics Laboratory.
Figure 2-32 Annual mean model-predicted concentrations of sulfur dioxide
(parts per billion) calculated using the MOZART
three-dimensional, chemistry-transport model.
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Satellite-borne instruments have mapped major SO2 sources globally and have obtained
data showing intercontinental transport. Fioletov et al. (2013) identified a number of
"hot-spots" for continuous SO2 emissions, both anthropogenic and volcanic. Major
industrial sources in the Northern Hemisphere are found (e.g., in China, Russia, the
United States, the Gulf of Mexico and Saudi Arabia). Major volcanic sources include:
Kilauea, Hawaii and Anahatan in the Marianas. Clarisse etal. (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.
When estimating background concentrations, it is instructive to consider measurements
of SO2 at relatively remote monitoring sites (i.e., sites located in sparsely populated areas
not subject to obvious local sources of pollution). Berresheim et al. (1995) used a type of
APIMS at Cheeka Peak, WA (48.30EN 124.62EW, 480 m asl) in April 1991 during a
field study for DMS oxidation products. SO2 concentrations ranged between 20 and
40 ppt. Thornton et al. (2002) have also used an APIMS with an isotopically labeled
internal standard to determine background SO2 levels. SO2 concentrations of 25 to 40 ppt
were observed in northwestern Nebraska in October, 1999 at 150 m above ground using
the National Center for Atmospheric Research's C-130 research aircraft. These data are
comparable to remote central South Pacific convective boundary layer SO2 data
(Thornton et al.. 1999).
As noted earlier, volcanic sources of SO2 in the U.S. are found in the Pacific Northwest,
Alaska, and Hawaii. The greatest potential domestic effects from volcanic SO2 occur on
the island of Hawaii. Nearly continuous venting of SO2 from Mauna Loa and Kilauea
produces SO2 in high concentrations that can affect populated areas on the island.
Figure 2-33a shows the time series for daily 1-hour maximum SO2 concentrations at Hilo,
HI, (population of approximately 40,000) which is located about 50 km northeast of
Kilauea. Figure 2-33b shows the same time series at Pahala (population -1,300) which is
located about 30 km southeast of Kilauea. As demonstrated by these figures, daily 1-hour
maximum levels can reach levels greater than 1,000 ppb. In addition to these two sites,
other communities scattered throughout the southern half of the island are exposed to
such high SO2 levels as indicated in Figure 2-34 (Longo et al.. 2010).
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>
J2
a.
a.
c
o
c
0)
u
C
o
o
(/)
1800
1600
1400
1200
1000
800
600
400
200
Daily MAX 1 -hr S02 at Hilo, HI
i lilill l.ill..l ¦
-I .ill- in
1/1/2008 1/1/2009 1/1/2010 1/1/2011 1/1/2012 1/1/2013
Date
1800
Daily MAX 1-h S02 at Pahala; HI
1600
14 OC
x'V \1 v'V
^ ^ ^
V \V V "V V
Date
Figure 2-33 Daily maximum 1-hour sulfur dioxide (SO2) concentrations
measured at (a) Hilo, HI and (b) Pahala, HI.
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300
250
§
200
_Q
CD
Cl
CO
±3
c
0)
o
c
O
CO
150
100
50
Low Exposure Period
High Exposure Period
~
c ~
"H. t
E ~ ~
hi
t •*
i
C/3 ~
~ ~ ~
~ ~ ~
#
~
~
~ ~
%~ ~~ .
~
~ ~ ~ *
; * ~ ,
. ~% ~ . %* * *
•
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
For copollutant correlation analysis, colocated air quality data reported within the EPA
AQS repository system during 2010-2012 were used. Air quality data met the 75% data
completeness criteria presented earlier in this chapter in Table 2-6. Daily air quality
metrics representing either maximum or average concentration values were used. Pearson
correlations were utilized to evaluate temporal correlations among SO2 and NAAQS
copollutants. In addition, correlations between SO2 and PM2.5 sulfur were examined
because PM2.5 sulfur serves as a proxy for SO2 oxidation products (i.e., sulfate) and may
have confounding effects on SO2 health outcomes. Correlations were also stratified by
season to determine whether copollutant confounding varied by time of year.
Figures 2-35 and 2-36 display the distribution of correlations between NAAQS
copollutants and SO2 daily metrics (24-hour average, 1-hour maximum). In these
boxplots, the interquartile range of collocated copollutant correlations are expressed in
the box median values are reported by the red line within the box; and mean values are
presented as a green star. The 10th and 90th percentile of correlations are shown by the
bottom and top whiskers, respectively. Outlier copollutant correlations are presented by
black markers.
CO Max
CO Avg
N02 Max
N02 Avg
03 Max
F'M25 S Avg
PM25 Avg
PM10 Avg
Note: Shown are the median (red line), mean (green star), and inner-quartile range (box), 5th and 96th percentile (whiskers) and
extremes (black circles)
Figure 2-35 Distribution of Pearson correlation coefficients for comparison of
daily 24-hour average sulfur dioxide from the year-round data set
with colocated National Ambient Air Quality Standards pollutants
(and PM2.5 S) from Air Quality System during 2010-2012.
» • mm-
" •
i
•
•
•• i«H
** • mm
1*
p • • • •
1
1—
*
¦
—r -
1—
—f..
mm . |
*
-^mmmm . •
••
M • • •
1
HI
>
•
i
-1.0 -0.3 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
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9
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14
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17
CO Max
CO Avg
N02 Max -
N02 Avg
03 Max
PM25 S Avg
PM25 Avg
PM10 Avg
Note: Shown are the median (red line), mean (green star), and inner-quartile range (box), 5th and 96th percentile (whiskers) and
extremes (black circles)
Figure 2-36 Distribution of Pearson correlation coefficients for comparison of
daily 1-hour maximum sulfur dioxide from the year-round data set
with colocated National Ambient Air Quality Standards pollutants
(and PM2.5 S) from Air Quality System during 2010-2012.
# ##
• • •
• *«m|-
-+
•• • ••
H"
-1.0 -0,8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
Pearson Correlation Coefficients
While 24-hour average SO2 exhibits a wide range of correlations with NAAQS
copollutants, correlations are generally low to moderate, with all median Pearson
correlations below 0.4 (Figure 2-35). The lowest correlations are observed between SO2
and O3, with median Pearson correlations below 0.1. Slightly higher correlations are
observed between SO2 and other primary NAAQS pollutants (NO2 and CO), with median
correlations between 0.3 and 0.4. On occasion, correlations close to 1 or below 0 are
observed, but only occur at a few outlier monitoring sites. Comparatively, copollutant
correlations of daily 1-hour maximum SO2 in Figure 2-36 are similar, but slightly lower
than copollutant correlations based on SO2 24-hour average values in Figure 2-35. The
range of Pearson correlations between daily 1-hour maximum SO2 and NAAQS
pollutants is below 0.3, with the exception of NO2, which exhibits median correlations
slightly above 0.3.
Correlations between SO2 and NAAQS copollutants demonstrate very little variability
across seasons (Figures 2-37 and 2-38). All median and average copollutant correlations
are below 0.4 across every season. The only substantial seasonal difference in SO2
correlations occurs during the winter, when SO2 exhibits lower negative correlations with
O3 (median winter correlations = -0.1). The lower wintertime SO2-O3 correlation could
November 2015
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1 be directly linked to relatively low O3 concentrations during this time of year due to less
2 photochemical O3 production. At such low ambient levels that are presumably near the
3 instrument detection limit, O3 measurements may be subject to substantial measurement
4 error, which may lead to poor correlations between O3 and other pollutants, including
5 S02.
Winter
Spring
CO Max
CO Avg
M02 Max
N02 Avg
03 Max
PM25 S Avg
PM25 Avg
PM10 Avg
• • .4
*
—• •
• —f
f
j....
• • • mm
•b
«i
— *
r"" #
•
•
•
•t
1
-f~—
*
-f •
• +
—H
—h
—
*
-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
Summer
N02 Max
N02 Avg
03 Max
PM25 S Avg
PM25 Avg
PM10 Avg
-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
—f-nih+~
-¦h-:
* Mi>M
- 4—f •
-H-QEbfc
-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
Note: Shown are the median (red line), mean (green star), and inner-quartile range (box), 5th and 96th percentile (whiskers) and
extremes (black circles).
Figure 2-37 Distribution of Pearson correlation coefficients for comparison of
daily 24-hour average sulfur dioxide stratified by season with
colocated National Ambient Air Quality Standards pollutants (and
PM2.5 S) from Air Quality System during 2010-2012.
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Winter
CO Max
CO Avg
N02 Max
N02 Avg
03 Max
PM25 S Avg
PM25 Avg
PM10 Avg
CO Max
CO Avg
NQ2 Max
N02 Avg
03 Max
PM25 S Avg
PM25 Avg
PM10 Avg
H—GO—H
-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
Summer
-+•-
-4-cr
--h-d
—mn-f"
• -
HKEhH-'
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 daily 24-hour average
Spring
-pFf--
¦HUD—b
Z>-h
• -HXhh-
• H—OH—I—
- —i—m—
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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 —
~ 1
Zh-h-
—[—~~—I—
-¦i—or
• -f—D~h4**
-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 daily 24-hour average
Note: Shown are the median (red line), mean (green star), and inner-quartile range (box), 5th and 96th percentile (whiskers) and
extremes (black circles).
Figure 2-38 Distribution of Pearson correlation coefficients for comparison of
daily 1-hour maximum sulfur dioxide stratified by season with
colocated National Ambient Air Quality Standards pollutants (and
PM2.5 S) from Air Quality System during 2010-2012.
1 Overall, daily and hourly SO2 metrics generally exhibit low to moderate correlations with
2 other collocated NAAQS copollutants at AQS monitors, exhibiting median Pearson
3 correlations around 0.2-0.4. However, given that a small subset of monitors report
4 relatively strong copollutant correlations, confounding may need to be considered on a
5 study-by-study basis.
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2.6 Atmospheric Modeling
This section discusses various modelling techniques to estimate ambient concentrations
of SO2. Different types of models are discussed in terms of their capabilities, strengths
and limitations.
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. Using equations that represent the physical and chemical atmospheric
processes that govern dispersal and fate, they 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 with 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-hour
average concentrations associated with major sources often contain extreme gradients,
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 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 is 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 the following: steady-state (emissions and
meteorology); Gaussian-based formulations [e.g., AERMOD, (Cimorelli et al.. 2005)1;
Lagrangian models [e.g., SCIPUFF, (Svkes et al.. 2007); HYSPLIT, (Draxler. 1999);
(NOAA. 2014)1. that are useful when emissions and meteorological conditions are
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variable over the modeling increment; and Eulerian photochemical grid-based models
[e.g., Community Multiscale Air Quality (CMAQ), (Bvun and Schere. 2006)1. where
chemical processes are explicitly handled and modeling resolution ranges from about one
to tens of kilometers. Additionally, there are stochastic or statistical approaches using, for
example, Monte Carlo techniques (Hannaetal.. 1982) or those using simple regression
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 were available.
It is not uncommon for the terms "dispersion model" and "Gaussian model" to be
associated with each other to the exclusion of other types of ATD models. For primary
pollutants such as SO2, dispersion models used within the United States for applications
such as determination of compliance with standards and determination of primary
pollutant impacts from new or proposed sources are most commonly of a steady-state
Gaussian form (U.S. EPA. 2010a). The same is true for these types of analyses in other
countries. For example, ADMS (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 where 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 meters of
the source. Near field or near-to-the-source dispersion is the real strength of steady state
modeling.
In addition to compliance and new source analyses, dispersion models and particularly
Gaussian models have been used in support of environmental health studies where the
temporal and spatial distribution of concentrations are needed at a resolution beyond that
of typical grid models such as CMAQ or that of available monitoring networks
(Ozkavnak et al.. 2013; Vette et al.. 2013). In these studies, models are applied in
combination with monitoring data either separately where the monitoring establishes
background or by statistically blending the modeling and monitoring together.
While there are several dispersion models recommended by the EPA (U.S. EPA. 2013d)
for specialized applications involving SO2 (e.g., BLP for aluminum reduction plants;
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CALPUFF for Class I applications in complex flow), AERMOD is the workhorse.
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
consideration for the source characteristics of point, area, and volume source types. In
convective conditions, where dispersion provides for 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). In light wind conditions, the model simulates a meandering plume and has
turbulence-based lower limits on the transport wind speed.
AERMOD and models like it are designed to simulate concentrations on an hourly
increment. Longer term concentrations are obtained by averaging the 1-hour values.
While the model may be appropriate in some cases for averages less than 1 hour (should
the input data be available), model evaluations have focused on averaging periods of
1 hour and greater [e.g., Perry et al. (2005)1. 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'). This, however, can be challenging especially for light
winds and long transport distances. AERMOD is also somewhat 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.. 2012V In urban areas, AERMOD uses a simple 4-hour half-life
assumption for reducing SO2 in the plume with travel time. 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 relatively close to the source, chemistry is not expected to play a significant role
in determining compliance with primary standards.
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
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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 concentrations and measured concentrations. Monitored data
(within sampling error) represents actual realizations of events while modeling estimates
represent ensemble mean values (Rao. 2005). Based on a study comparing a variety of
models (including Gaussian) to a number of tracer field study results, Hannaetal. (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%. He points out that these levels of model to monitor differences 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 value will likely differ, sometimes
significantly, because of 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
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). Therefore, for evaluations of a model's
ability to simulate high concentrations within the modeling domain, it is important to
include an analysis of modeled and monitored concentration distributions.
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 square 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) that also provided for the estimation of confidence
limits on the values computed. Chang and Hanna (2004) also discussed exploratory
analysis methods of plotting and analyzing the modeled and measured values. They also
pointed out that the most useful model evaluation studies are those that examine a
number of models with a number of field studies.
The intended use of a model and the objective of a model evaluation guide the selection
of evaluation criteria. For models intended for application to compliance assessments
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(e.g., related to the 1-hour 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 26
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.
At the time of its inclusion into the EPA Guideline on Air Quality Models (U.S. EPA.
2005b), AERMOD's performance was evaluated against seventeen field-study databases
(Perry et al.. 2005) over averaging times from 1 hour to 1 year. 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-hour average RHC to monitored RHC to range 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-hour average ratios ranged from 1.0 to 1.35 (i.e., a slight tendency to
over-predict the high concentrations). Examination of quantile-quantile plots supported
the findings that the model was capturing the upper end of the 1 and 3-hour average
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 for
AERMOD was 0.77 and for ADMS was 0.80, each a small under-prediction. 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 over-predict (with ADMS
less biased). Unlike the ratio of maximum values, MG is a measure of performance over
the entire distribution of concentrations.
In a recent evaluation involving a study of tracer emissions and measurements along a
four-lane freeway in California (Heist et al.. 2013). AERMOD and ADMS displayed a
slight average under-prediction (FB, of 0.13 and 0.09, respectively with FB = 0
representing an unbiased model). While there is expected scatter in these time and space
pairings of model to observations, over 75% of the estimates for both models were within
a factor of two. Finally, Hurlev (2006) evaluated AERMOD and two Australian models
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against seven field studies and found no database against which AERMOD performed
poorly.
With the adoption of the new 1-hour SO2 and NO2 standards, 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 Modeling
Conference on Air Quality in 2012 (U.S. EPA. 2012a). Among them were concerns about
simulations in stable conditions with light and meandering winds, modeling of emissions
from haul roads, plume chemistry and building downwash. Research in many of these
areas is underway. 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 reducing model uncertainty and expanding the applicability. Model
evaluations over a wide range of conditions have demonstrated the skill that dispersion
models possess and the value they provide in estimating hourly averaged concentrations.
2.7 Summary
Of the several reactive sulfur oxide chemical species, only SO2 is of importance to U.S.
air quality, due to its historically high atmospheric concentrations and the locations of its
sources with respect to human populations. As a consequence of several U.S. air quality
regulatory programs, emissions of SO2 have declined by approximately 70% for all major
sources since 1990. Coal-fired electric generating units (EGUs) remain the dominant
source by nearly an order of magnitude above the next highest source (coal-fired boilers),
emitting 4,500,000 tons of SO2 annually, according to the 2011 NEI.
Beyond the strength 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 and local meteorology. The primary gas phase
photochemical SO2 oxidation mechanism requires the hydroxyl radical. Another
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., small
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.
The atmospheric photochemical SO2 oxidation processes, coupled with variable
meteorological conditions, including wind, atmospheric stability, humidity, and cloud/fog
cover, influence observed SO2 concentrations at monitoring locations.
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Dispersion models can be used to estimate SO2 concentrations in locations where
monitoring is not practical or sufficient (Section 2.6.1V 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 human exposure to SO2 in epidemiologic studies (Section 3.2.2.1. Chapter 5).
The widely-used dispersion model AERMOD is designed to simulate hourly
concentrations which can then be averaged to yield longer-term concentrations. Multiple
evaluations of AERMOD's performance against field study databases over averaging
times from 1 hour to 1 year have indicated that the model is relatively unbiased in
estimating upper-percentile 1-hour concentration values. Uncertainties in model
predictions are influenced by uncertainties in model input data, particularly emissions and
meteorological conditions (e.g., wind).
Changes were undertaken to the existing EPA monitoring network as a result of the new
1-hour primary NAAQS standard promulgated in 2010. 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 asthmatics following a
5-10 minute exposure of SO2 above 200 ppb (Section 5.2.1.2). Since the publication of
the 2008 SOx ISA (U.S. EPA. 2008b). there are more than 400 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 daily 1-hour maximum SO2 concentration reported
during 2010-2012 is 9 ppb. However, peak concentrations (99th percentile) of daily
maximum SO2 concentrations can approach 75 ppb at some monitors located near large
anthropogenic or natural sources (e.g., volcanoes). Similarly, new 5-minute data
demonstrate that most hourly 5-minute maximum concentrations are well below the
short-term health benchmark levels of 200 ppb, and under rare occasions (99th percentile
and above) can be greater than 200 ppb at some monitors near anthropogenic sources
such as EGUs.
Given the relatively short atmospheric lifetime of SO2, urban spatial variability was
emphasized in this chapter. SO2 is highly variable across urban spatial scales, exhibiting
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moderate to poor correlations between SO2 measured at different monitors across a
metropolitan area. This high degree of urban spatial variability may not be fully captured
by central site monitoring estimates; thus, it has implications for the interpretation of
human exposure and health effects data.
Sulfur dioxide correlations with copollutants tend to vary across location, study and SO2
averaging time. Daily SO2 correlations with other NAAQS copollutants are generally
moderate to low. Median daily SO2 correlations with PM, NO2, and CO range from
0.2-0.4 for 2010-2012, while the median daily copollutant correlation of SO2 with O3 is
0.1. Daily SO2 copollutant correlations for all pollutants can be greater than 0.7 on rare
occasions. Given that a small subset of monitors report relatively strong copollutant
correlations, the potential for SO2 copollutant confounding may need to be considered on
a study-by-study basis.
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CHAPTER 3 EXPOSURE TO AMBIENT SULFUR
DIOXIDE
3.1 Introduction
The 2008 SOx ISA (U.S. EPA. 2008b) evaluated SO2 concentrations and exposure
assessment in multiple microenvironments, presented methods for estimating personal
and population exposure via monitoring and modeling, analyzed relationships between
personal exposure and ambient concentrations, and discussed the implications of using
ambient SO2 concentrations to estimate exposure in epidemiologic studies. As discussed
previously, this chapter will focus on SO2 because other gaseous SOx species are not
present in the atmosphere in concentrations significant for human exposures. This chapter
summarizes that information and presents new information regarding exposure to
ambient SO2. Specific 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.
3.2 Methodological Considerations for Use of Exposure Data
This section describes techniques that have been used to measure microenvironmental
concentrations of SO2 for estimating personal SO2 exposures and the results of the studies
using those techniques. Previous studies from the 2008 SOx ISA (U.S. EPA. 2008b) are
described along with newer studies that evaluate indoor-outdoor concentration
relationships, associations between personal exposure and ambient monitor
concentration, and exposure to multiple copollutants in conjunction with SO2. Tables are
provided to summarize important study results.
3.2.1 Measurements
3.2.1.1 Central Site Monitoring
Central site monitors are primarily used to determine whether attainment goals are met
under the Clean Air Act. However, central site monitoring data are also often used in
epidemiologic studies to represent exposure to SO2, as discussed previously in
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Section 2.5.2.2 and in this chapter in Section 3.3.5. Methods and uncertainties regarding
measurements made by central site monitors are described in Section 2A. Various uses of
these data are possible depending on the design of the epidemiologic study. Short-term
(e.g., daily) 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 emissions of SO2 (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. Section 3.3.3.2
discusses exposure error due to spatial variability in SO2 concentration and the potential
influence of that error on epidemiologic effect estimates. Briefly, SO2 has moderate to
high spatial variability within an urban area, resulting in some level of exposure error for
individuals not living near a monitor. This exposure error typically attenuates effect
estimates in time-series studies, while bias may occur in either direction for effect
estimates in long-term studies. This topic will be discussed further in Section 3.3.5.
Widening of confidence intervals due to this exposure error is generally expected for all
study types.
Central site monitoring is also subject to instrument biases and uncertainties that
introduce error into the measurement, as described in detail in Section 2.4.2 and
Section 3.3.3.4. Ultraviolet fluorescence (UVF) detection of SO2 has a high detection
limit relative to ambient levels, potentially introducing uncertainty into exposure
estimates. UVF detection is subject to positive biases in concentration measurements due
to stray light in the optical measurement chamber and fluorescence at or near the same
wavelength as SO2 by volatile organic compounds (VOCs), PAHs, other aromatic
hydrocarbons, and NO. Relative humidity can cause negative biases in concentration
measurements and can cause suppression of fluorescence. The effect of positive or
negative biases depends on epidemiologic study design, as described further in
Section 3.3.5.
3.2.1.2 Personal Monitoring Techniques
As described in the 2008 SOx ISA (U.S. EPA. 2008b). both active and passive samplers
have been used to measure personal exposure to SO2. The Harvard-EPA annular denuder
system is an active sampler initially developed to measure particles and acid gases
simultaneously (Braueret al.. 1989; Koutrakis et al.. 1988V The system draws air at
10 L/minute past an impactor to remove particles and then through an annular denuder
coated with sodium carbonate to trap SO2 and other acid gases. The denuder is 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
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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
exposure.
An emerging area in environmental monitoring is the development of small, low-cost
sensors and sensing platforms suitable for use by the general public. For example, Al-Ali
et al. (2010) set up a mobile data acquisition system consisting of a commercial sensor
array and global positioning system (GPS) unit interfaced via modem to a stationary
server with the capability to improve spatiotemporal resolution. Additionally, several
different designs have been proposed in recent years for sensors using nanomaterials or
light-based detection. For example, microelectrodes using nanomaterials, such as a film
of single-walled carbon nanotubes (Zhang et al.. 2013b; Cai et al.. 2012; Yao et al..
2011). a CdS semiconductor sensor (Fu. 2013). a zinc phthalocyanine film (Jaisutti and
Osotchan. 2012). ZnO nanorod flowers (Peng et al.. 2013). graphene (Ren et al.. 2012).
and a palladium polymer (Meka et al.. 2008). reduce noise from ionization of interferents
with shorter sensing times and hence improved sensitivity and specificity. New designs,
such as that of Zhang et al. (2013b). in which a triple carbon nanotube sensor with two
electric fields in opposite direction acts to reduce spurious charge, enhance sensitivity of
the detectors. Light absorption techniques have been developed based on the absorption
of SO2 in the ultraviolet spectrum at 286 nm; because the absorption differs from that of
NO2 (at 403 nm in the visible/ultraviolet range), these detectors can attain improved
specificity using a light-emitting diode (LED) (Degner et al.. 2010; Hawe et al.. 2008) or
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laser (Simeonsson et al.. 2012; Gondal and Dastageer. 2008) light source. Moreover, the
LED designs have achieved detection limits of 1 ppm (Degner et al.. 2010) and 15 ppm
(Hawe et al.. 2008). while the laser-based sensors have detection limits of 0.5 ppb
(Simeonsson et al.. 2012) and 4 ppb (Gondal and Dastageer. 2008). These devices are
also limited by their sensitivity to water vapor, both because water vapor can react with
SO2 to form sulfites and sulfates and because it can adsorb to the sensor to change its
resistance (Rubasinghege and Grassian. 2013). These devices are generally in early
stages of development and not now commercially available. Until sensors achieve both
data quality and cost objectives, they will not be suitable for widespread use.
3.2.2 Modeling
Because existing ambient SO2 monitors may not be sited in locations to capture peak
1-hour concentrations (75 FR 35520), the implementation program for the 2010 primary
SO2 NAAQS allows for air quality modeling to be used to characterize air quality, and
for such air quality information to be used in the process for informing final designation
decisions. The SO2 NAAQS is currently the only criteria pollutant standard for which
modeling is used to characterize air quality for the purpose of the area designation
process. Computational models of various designs can also be used to estimate exposure
of individuals and populations when personal exposure measurements are unavailable or
for separating personal exposure to ambient SO2 from total personal exposures. This
section describes several modeling approaches used for exposure assessments, including
(1) estimating concentration as a surrogate for exposure, (2) estimating time-activity
patterns, and (3) modeling of building air exchange rates (AERs) and
microenvironmental exposure. The strengths and limitations for several specific exposure
modeling methods are summarized in Table 3-1. The remainder of this section describes
each modeling approach.
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Table 3-1 Characteristics of exposure modeling approaches.
Model Type
Model
Description
Strengths
Limitations
Proximity to
SPM
Exposures are estimated
Few input data required
Does not consider
sources
from distance of receptor
emission rate and
from source
duration, atmospheric
chemistry, and physics
EWPM
Exposures are estimated
Considers emission rate
Does not consider
from distance of receptor
and duration
atmospheric chemistry
to pollution source,
and physics
emission rate, and
duration
Land use
Measured
High spatial resolution
Does not account for
regression
concentrations are
atmospheric chemistry
(LUR)
regressed on local
and physics, limited
variables (e.g., land use
generalizability,
factors), and the
moderate resources
resulting model is used
needed
to estimate
concentrations at
specific locations
Local outdoor
Spatial
Measured
High spatial resolution, few
Does not fully capture
concentration
interpolation
concentrations are
input data needed
spatial variability
(e.g., nearest
interpolated to estimate
among monitors
monitor, inverse
concentration surfaces
distance
across regions
weighting,
kriging)
Chemistry-
Grid-based
Accounts for atmospheric
Limited grid cell
transport
concentrations are
chemistry and physics
resolution (i.e., grid cell
(e.g., CMAQ)
estimated from
length scale is typically
emissions, meteorology,
4-36 km and much
and atmospheric
larger than plume
chemistry and physics
width),
resource-intensive,
does not account for
local emissions sources
Gaussian plume Concentrations at
dispersion
(e.g.,
AERMOD)
specific locations are
estimated from
emissions, meteorology,
and atmospheric physics
High spatial and temporal
resolution, accounts for
atmospheric physics from
local emission sources
Resource intensive,
very limited
representation of
atmospheric chemistry
or background
concentrations
Time-location
patterns of
people
Micro-
environment
classifier based
on personal
sensors (e.g.,
MicroTrac)
Personal sensor data
(e.g., GPS, temperature,
light) are used to
estimate time people
spend in various
microenvironments
(e.g., indoors and
outdoors at home)
Accounts for time spent in
different microenvironments
with different concentrations
Input data from
personal sensors
(e.g., GPS) is required
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Table 3-1 (Continued): Characteristics of exposure modeling approaches.
Model Type Model
Description
Strengths
Limitations
Micro- Population
environment- (e.g., APEX,
based exposure SHEDS)
Estimates distributions of
microenvironmental
concentrations,
exposures, and doses
for populations
(e.g., census tracts)
based on air quality
data, demographic
variables, and activity
patterns
Accounts for variability of
exposures across large
populations, accounts for
different concentrations in
different microenvironments,
accounts for location-activity
information
Input data from outdoor
concentrations is
required, does not
estimate exposures for
individuals
AERMOD = American Meteorological Society/U.S. EPA Regulatory Model; APEX = Air Pollution Exposure model;
CMAQ = Community Multiscale Air Quality; EWPM = emission-weighted proximity model; GPS = global positioning system;
LUR = land use regression; SHEDS = Stochastic Human Exposure and Dose Simulation; SPM = source proximity model.
3.2.2.1 Estimation of Concentration as an Exposure Surrogate
Models can be used to predict the outdoor concentration of SO2 across geographic
regions (e.g., concentration surfaces) 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 actual human exposure to SO2. This method
does not estimate exposures directly because time-activity patterns and indoor
concentrations at various microenvironments are not considered. However, local outdoor
concentration models can improve exposure assessment by their ability to estimate
concentrations at locations among monitors. Approaches described include distance to
SO2 source, dispersion models, chemistry-transport models, and LUR. These models can
be applied at urban, regional, or national scales to estimate daily, or longer, average
concentrations. Short-term (e.g., daily) estimates are needed for acute exposure
assessments, whereas long-term (e.g., annual) estimates can be used for chronic exposure
assessments. This discussion will focus on modeled concentrations used for exposure
assessment studies.
Source Proximity Models
Source proximity models (SPMs) provide a simple method to estimate human exposure
to air pollution. These models calculate the distance from receptors (e.g., homes, schools)
to a source of pollution (e.g., industrial facilities, roads). It is assumed that concentration,
as a surrogate for exposure, is some function of distance from the source. SO2 from a
point source is thought to disperse as a meandering plume, such that average
concentration decreases with distance from the source (Section 2.6.1). Exposure
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assessments based on SPMs assume that higher exposures occur at locations closer to
emission sources. These models do not necessarily account for the effect of stack height
to limit SO2 concentrations in the immediate vicinity of the point source. For example,
Burstvn et al. (2008) modeled SO2 concentration as a function of distance within 2 km
and 50 km buffers of gas plants and oil wells. The study authors used the natural log of
the distance to the -2/3 power for each SO2 point source to reflect the inverse
relationship between SO2 concentration and distance to source. In another epidemiologic
model, proximity to source was treated as a Boolean variable as a proxy for high and
moderate SO2 exposure (C'ambra et al.. 2011). Likewise, Liu et al. (2012b) computed
relative risk of respiratory disease using zip code 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 SO2 concentration and found no statistically
significant decrease in SO2 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 large 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) 1. In addition, while SPMs can be used to associate distance to sources
with health effects, their ability to determine health risk under various exposure scenarios
is limited.
To improve the accuracy of SPMs, an emission-weighted proximity model (EWPM) was
developed that considers the emission rate and duration of each pollutant source, in
addition to the distance from source. Zou et al. (2009b) evaluated the SPM and EWPM to
estimate SO2 concentrations in Dallas and Ellis counties, Texas. Normalized exposure
estimates based on SPM and EWPM were compared to normalized measurements at
three monitoring sites. Similarly, Zou (2010) compared SPM and EWPM to a kriged
representation of SO2 concentrations and concentrations estimated by the AERMOD
dispersion model. In both studies, the EWPM estimates agreed more closely to the
observed concentrations than the SPM estimates. Epidemiologic estimates of risk also
were in closer agreement for EWPM and AERMOD compared with SPM (Zou et al..
2011). In addition, surface maps of EWPM and SPM exposure estimates across two
counties showed that with SPM, exposure risks are 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 concentrations that
individuals were exposed to across these regions.
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Land Use Regression Models
LUR fits a multiple linear regression model of concentration based on land use data and
then applies that model to locations without monitors as an attempt to increase
heterogeneity in the spatial resolution of the concentration field compared with other
methods, such as central site monitoring (Marshall et al.. 2008). The spatial variability in
estimated ambient concentrations captured by the LUR model can be used for large
health studies because it provides variability in exposure estimates across the study
population. Recently, it has been implemented to examine local-scale concentration
estimates for PM, NO2, and other pollutants across the United States (Novotnv et al..
2011; Hart et al.. 2009) and Canada (Hvstad et al.. 2011). Although LUR is more
typically employed for NO2, LUR has also been used to study spatial variability in SO2
concentration [e.g., Atari et al. (2008)1. Models are typically calibrated using data from
passive sampler measurements and several predictor variables, such as land use, road
length, population density, and proximity to areas of high concentrations (e.g., point
sources). Given that most passive 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. Hence, LUR is commonly
used to estimate air pollution exposure in long-term epidemiologic studies. 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 SO2
concentration in a city is represented by the LUR.
LUR has been applied to estimate exposures to industrial SO2 sources. Atari et al. (2008)
developed an LUR model to predict SO2 concentrations in Sarnia, Ontario, Canada. 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 SO2 concentration for model training were collected with passive
samplers at 39 locations across the city for 2 weeks in the fall of 2005. The in-sample
coefficient of determination (found by comparing the model with the measurements used
to train the model) for the LUR model fit to the measurements was If = 0.66. An out-of-
sample coefficient of determination was calculated to cross-validate the model with
measurements that were not used to train the model. The coefficient ranged from
R2 = 0.62 to If = 0.73, and the root-mean-square error (RMSE) of the out-of-sample
predictions were 0.3 to 1 ppb. The SO2 validation produced a wider range of errors and
lower out-of-sample If compared with LUR simulations for NO2; Atari et al. (2008)
attributed this moderate validation to a skewed SO2 concentration distribution compared
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with the concentration distribution of NO2. These LUR results were then used by Atari et
al. (2009) to correlate modeled concentrations with individual and community
perceptions of odor, by Oiamo and Luginaah (2013) to study whether males and females
are affected differently by SO2 exposure, and by Oiamo etal. (2011) to investigate the
relationship between air pollution exposure and access to a general practitioner.
Kanaroglou et al. (2013) used a spatial autocorrelation LUR model to estimate SO2
concentrations 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 SO2
concentration (p < 0.001, RMSE = 1.24).
LUR has also been applied to predict SO2 exposures in the vicinity of urban sources.
Cloughertv et al. (2013) modeled concentrations of SO2, NO2, PM2 5, and black carbon
(BC) across New York City. SO2 concentration was predicted by the reference site mean
(partial R2 = 0.35), number of oil-burning units (partial If = 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. These findings were thought to reflect the presence of large combustion boilers
in Manhattan and western Bronx, where SO2 concentrations were predicted to be highest
because sulfur content in residential heating fuel is high. SO2 concentration was not
influenced by vehicle traffic, unlike the other air pollutants studied. Beelen et al. (2007)
modeled SO2, NO2, NO, and black smoke (BS) 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 still contained sulfur. 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. The Beelen et al. (2007) study was applied in a longitudinal cohort study
of vascular damage among young adults (Section 5.3.2.5) (Lenters et al.. 2010). Wheeler
et al. (2008) applied LUR for a study of air pollutant 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 SO2 concentration with
in-sample R2 = 0.69 and out-of-sample If = 0.65. Wheeler et al. (2008) also evaluated
LUR performance for predicting SO2 concentration across seasons by comparing the
LUR results with measurements for a study of air pollutant exposure in Windsor, Ontario.
They found that correlation of summer SO2 predictions with those from other seasons
was lower, suggesting that photochemistry might not be well represented in the LUR
model.
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Inverse Distance Weighting
Inverse distance weighting (IDW), in which concentration at a receptor point is
calculated as the weighted average of concentration measured at monitoring locations,
has been used to estimate exposure with 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 Maclntvre et
al. (2011) estimated exposure to SO2 and other industrial pollutants within 10 km of point
sources using an IDW sum of 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 SO2 concentration
surface. The results from IDW were correlated with the other three methods
(r = 0.88-0.97), and the mean estimated SO2 concentration estimated with IDW was
within 10% of the mean computed with the other methods. However, Neupane et al.
(2010) estimated the SO2 concentration surface using both bicubic spline functions 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 splines
produced a lower mean and larger IQR compared with IDW; 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 concentration data set.
Gaussian Plume Dispersion Models
Gaussian dispersion models can be applied to estimate human exposure to SO2. A
detailed description of Gaussian dispersion modeling, along with strengths and
limitations for modeling SO2 concentrations, can be found in Section 2.6. Zou etal.
(2009c) developed a modeling system to spatially estimate source-specific population
exposure to ambient SO2 across Dallas County in Texas. A hybrid dispersion modeling
approach was used to predict SO2 concentrations at a fine spatial resolution by combining
modeled air pollution concentrations with population distributions. This hybrid method
included air dispersion modeling (AERMOD) and kriging interpolation to produce an
ambient SO2 concentration grid map (100 m x 100 m) that was used to estimate
population exposures. The AERMOD simulation included three SO2 source
classifications (industrial, vehicle, and industrial/vehicle). A population density map was
generated at the block level based on 2000 census data and converted to a grid map
(100 m x 100 m) to match the spatial resolution of the ambient SO2 concentration grid
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map. The population exposure was estimated by multiplying the 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 population exposure estimates
were moderately correlated with vehicle sources (r = 0.440) and weakly with industrial
sources (r = 0.069); this study used emissions data from the year 2000, prior to the
ultra-low sulfur diesel fuel regulations. This population exposure modeling system
provides a potential method to develop exposure metrics for health studies, urban
planning, and mitigation strategies.
Lagrangian particle modeling has also been employed to model SOx concentrations from
specific sources (Ancona et al.. 2015). The Lagrangian particle model represents the
pollutant of interest as a group of nonreactive, massless particles and tracks their
positions over space based on simulated mean and turbulent wind components (Tinarelli
etal.. 1994). Note that Ancona et al. (2015) called the pollutant "SOx" throughout the
paper. Given that the simulated particles were nonreactive, "SOx" in this case can be
considered a marker of the emission source representing some combination of directly
emitted SO2 and sulfate formed in the atmosphere (Section 2.3). The wind velocity at
every location in the domain is simulated as the sum of (1) a mean three-dimensional
wind using a meteorological model and (2) a turbulence component modeled as the
product of observed dispersion parameters and a random number generated at each time
step (Gariazzo et al.. 2004). Then, each pollutant particle's position is updated at each
30-minute time step as the sum of its original position and the product of wind velocity at
that position and time step. Gariazzo et al. (2004) compared the SPRAY Lagrangian
particle model against SO2 observations at a measurement station and observed
reasonable agreement, although the observations seemed to lag the modeled SO2
concentration at times.
Chemical Transport Models
Chemical transport models (CTMs), such as the CMAQ model, can be used to estimate
SO2 exposures when measurement data are unavailable or not available for portions of a
study area. CTMs, such as CMAQ, are deterministic of chemical transport that account
for physical processes including advection, dispersion, diffusion, gas-phase reaction, and
mixing while following the constraint of mass conservation (Bvun and Schere. 2006).
These models provide regional concentration estimates and are typically run with surface
grid resolutions of 4, 12, or 36 km. Temporal resolution of CTMs can be as fine as
1 hour, although larger temporal aggregation often occurs for the purpose of maintaining
reasonable data file size. Lipfert et al. (2009) estimated SO2 concentration based on the
CMAQ model for use as an exposure surrogate. The SO2 concentrations were estimated
with a 36-km by 36-km grid across the contiguous United States. The modeled SO2
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concentrations were used 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
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
SO2 but are more commonly used in long-term exposure studies. These models are used
to compute interactions among 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
primary species such as SO2, NO2, NH3, VOCs, and primary PM, and by meteorological
fields produced by other numerical prediction models. Values for meteorological state
variables such as winds and temperatures are taken from operational analyses, reanalyses,
or weather circulation models. In most cases, these are off-line meteorological analyses,
meaning that they are not modified by radiatively active species generated by the air
quality model. Work to integrate meteorology and chemistry was done in the mid-1990s
by Lu et al. (1997a) and Lu et al. (1997b) and references therein, although limits to
computing power prevented their widespread application. More recently, new integrated
models of meteorology and chemistry are available; see, for example, Binkowski et al.
(2007) and the Weather Research and Forecast model with chemistry (WRF Chem)
(http://ruc.noaa.gov/wrf/WG 11/). Given observed biases in the CTMs [e.g., U.S. EPA
(2008a) I. much attention has been given to bias correction of these models for application
in exposure assessment. Chen etal. (2014a) evaluated CMAQ results for several
pollutants and found that SO2 was underpredicted by roughly a factor of two, but this
problem was largely ameliorated through bias correction techniques.
Biases in SO2 concentrations predicted by CTMs can occur as a result of error in model
representation of atmospheric processes converting SO2 to H2SO4. For example,
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
sulfate (Alexander et al.. 2009). Therefore, when using CMAQ for estimation of exposure
to SO2, attention must be given to the version of the program so that any inherent biases
are understood.
CMAQ has been used to explore vertical emissions of SOx, NOx, and PM10 from power
plants, industrial combustion, and other industrial processes with stack heights that varied
by facility. Guevara et al. (2014) modeled the vertical concentration distribution of these
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emissions in Spain and found that the distribution varied with source. The lower
atmosphere was partitioned into 11 layers spanning 1,250 m AGL. Within the layer
closest to the ground (0-39 m AGL) where people are exposed, negligible SO2
concentrations were modeled for the largest emissions sources (power plants emitted
53% of SOx and refineries emitted 20% of SOx in the study area). The highest modeled
ground layer SO2 emissions were for the paper and pulp industries, industrial boilers, and
nonferrous metallurgy, which contributed between <1 to 3% of emissions. One major
limitation of CTMs for estimating SO2 concentrations in exposure assessment 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
SO2 concentrations can be underestimated along the plume path. Baldasano et al. (2014)
recognized this limitation and merged HYSPLIT with a CTM simulation of SO2 and
PM10 transport in the vicinity of a refinery. HYSPLIT models dispersion of pollutants
such as 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. Similarly, Karamchandani et al. (2010) coupled a plume-in-grid model with
CTM that treats dispersion as a Gaussian process whose parameters are set using
micrometeorological conditions. Inclusion of subgrid-scale modeling enables fine-scale
calculation of the SO2 plume such that maximum concentration, and potentially
maximum exposures, can be estimated by the model suite (Baldasano et al.. 2014).
3.2.2.2 Time-Activity Models
The time people spend in various microenvironments (ME) is a critical aspect of
exposure assessment. Future improvements in SO2 exposure assessment are anticipated
by accounting for time spent in different MEs with different SO2 concentrations.
Exposure models can account for variations in time spent by people in different locations
by using time-weighted pollutant concentrations in each ME. For population-level
exposure assessments, exposure models rely on databases of time-activity diary data from
other studies, such as the Consolidated Human Activity Database (CHAD) (see
Section 3.3.3.1) (U.S. EPA. 2014a; McCurdv et al.. 2000). For individual exposure
assessments, diaries from the study participants can be used. However, diaries have
limitations, including burden on participants, inaccuracies due to recall and reporting
errors, and missing data.
To address the limitations of diaries, 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 MEs is limited due to large (potentially thousands of samples per
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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 MEs (e.g., wooden structures have no substantial
indoor/outdoor differences in satellite signal strength). To address these limitations,
automated ME 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 MEs (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 ME 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) .With a high percentage of the U.S. population using GPS-enabled smartphones,
large sets of GPS data collected with low participant burden could be classified in various
MEs by MicroTrac to increase the sample size and update the older diary data in the
time-activity databases (e.g., CHAD), which are used for population-level exposure
assessments (U.S. EPA. 2014a; McC'urdv et al.. 2000).
3.2.2.3 Models of Building Air Exchange Rates and Microenvironmental
Exposures
Models of Building Air Exchange Rates
The AER, which is the airflow into and out of a building, influences the rate of entry of
ambient SO2 and removal of nonambient SO2. Because people living in the United States
spend an average of 87% of their time within enclosed buildings (klepeis et al.. 2001).
the AER is a critical parameter for air pollution exposure models, such as Air Pollutants
Exposure Model (APEX) and Stochastic Human Exposure and Dose Simulation
(SHEDS), discussed below under Microenvironmental Exposure Models.
AER models can reduce the uncertainty of exposure models by accounting for various
factors, including the physical driving forces of the airflows (e.g., pressure differences
across the building envelope from wind, indoor-outdoor temperature differences, and
mechanical ventilation), building characteristics (e.g., local wind sheltering, tightness of
the building envelope), and occupant behavior (e.g., opening windows, operating
outdoor-vented fans, thermostat temperature setting during heating and cooling seasons).
Therefore, substantial spatial and temporal AER variations can occur due to temporal and
geographical differences in weather conditions, building characteristics, and occupant
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behavior. The resulting spatial-temporal variations in exposure may help explain any
possible differences in epidemiologic associations between ambient SO2 concentrations
and health effects in different U.S. communities (Breen et al.. 2014b).
Microenvironmental Exposure Models
Microenvironmental exposure models can account for the variations in the time people
spend in different locations by using time-weighted pollutant concentrations in each
microenvironment (e.g., outdoors; indoors at home, school, workplace; in-vehicle).
Models such as SHEDS and APEX are not used for exposure assessment in
epidemiologic studies, but they are described here because they are 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
state of the science for stochastic population exposure models has not changed
substantially since the 2008 ISA for Oxides of Nitrogen, as described in detail in the 2008
NOx ISA Annex 3.6 (U.S. EPA. 2008a).
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). These models simulate the movement of individuals across time and
space and their exposure to air pollutants in various microenvironments. The inputs
required for population exposure models include outdoor pollutant data, indoor-outdoor
pollutant ratios for mass-balance indoor air quality models to estimate indoor
microenvironmental concentrations, population demographic databases (e.g., U.S.
census), and human time-activity pattern databases (e.g., CHAD) to determine the time
spent and the activity performed in different microenvironments (McCurdv et al.. 2000).
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3.2.3 Choice of Exposure Metrics in Epidemiologic Studies
Epidemiologic studies use a variety of methods to assign exposure. Study design, data
availability, and research objectives are all important factors for epidemiologists when
selecting an exposure assessment method. Common methods for assigning exposure from
monitoring data include using a single fixed-site monitor to represent population
exposure, averaging concentrations from multiple monitors, and selecting the closest
monitor. Investigators may also use statistical adjustment methods, such as trimming
extreme values, to prepare the concentration data set. Epidemiologic study design
influences the relevance and utility of exposure metrics. Table 3-2 summarizes various
exposure metrics used in SO2 epidemiologic studies, appropriate applications for the
metrics, and errors and uncertainties that may be associated with the metrics. Table 3-3
lists relevant exposure information for the epidemiologic studies found in Chapter 5
where models were used to estimate exposure.
Table 3-2 Summary of exposure assignment methods, their typical use in
sulfur dioxide epidemiologic studies, and related errors and
uncertainties.
Exposure Assignment
Method Epidemiologic Application Errors and Uncertainties
Central site monitors Short-term community
time-series exposure of a
population within a city
Correlation between true outdoor concentrations and
outdoor measurement typically decreases with
increasing distance from the monitor (Section 3.3.5).
potentially leading to decreased precision and bias
towards the null
Long-term exposure to compare Potential for exposure bias and reduced precision if
populations among multiple the monitor site does not correspond to the location of
cities exposed population (Section 3.3.5)
Passive monitors Short-term panel (e.g., personal High detection limit potentially leads to reduced
or residential samples) within a precision (Section 3.2.1.2)
city
Long-term exposure within a Potential for exposure bias and reduced precision
city or among multiple cities (Section 3.3.5)
SPM and EWPM Long-term exposure within a Potential for exposure bias and reduced precision if
city or among multiple cities or concentration at a receptor location is higher or lower
regions than the average over the area of the circle formed
around the source with radius equal to the distance
between the source and receptor (Section 3.2.2.1)
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Table 3-2 (Continued): Summary of exposure assignment methods, their typical
use in sulfur dioxide epidemiologic studies, and related
errors and uncertainties.
Exposure Assignment
Method Epidemiologic Application Errors and Uncertainties
LUR model Long-term exposure, usually Potential for exposure bias and reduced precision if
across a city but sometimes grid is not finely resolved (Section 3.2.2.1)
among multiple cities Potential for bias and reduced precision if the model
is misspecified or applied to a location different from
where the model was fit (Section 3.3.5)
IDW and kriging Long-term exposure within a
city
Potential for negative bias and reduced precision if
sources are not captured or overly smoothed
(Section 3.2.2.1)
Gaussian plume Long-term exposure within a
dispersion modeling city
Potential for exposure bias where the dispersion
model does not capture boundary conditions and
resulting fluid dynamics well (e.g., in large cities with
urban topography affecting dispersion)
(Section 3.2.2.1)
CTM Long-term exposure, sometimes Potential for exposure bias and reduced precision
within a city but more typically when grid cells are too large to capture spatial
across a larger region variability of exposures (Section 3.2.2.1)
Microenvironmental model Panel studies Potential for exposure bias and reduced precision
when the modeled distributions of ambient
concentration, indoor-outdoor pollutant ratios, and
time-activity patterns differ from the true distributions
(Section 3.2.2.3)
CTM = chemical transport model; EWPM = emission-weighted proximity model; IDW = inverse distance-weighting; LUR = land use
regression; SPM = source proximity model.
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Table 3-3 Exposure data for epidemiologic studies using modeling for exposure estimation.
Exposure Concentration
Time Averaging Spatial Scale Exposure Model Validation Summary Stats
Reference Location Population Period Time of Exposure Model Type Resolution Metric units = ppb
Longitudinal cohort
Darrow et al.
(2011)
Iw et al. (2008)
Atlanta, GA 406,627 full-term 1994-
births 2004
1 h daily Urban
max
averaged
over
28 days
during first
trimester,
gestational
day 196 to
birth during
third
trimester
Population-
weighted
average
NR
NR
First mo
gestation: mean
(SD): 11 (3.4),
IQR: 4; third
trimester: mean
(SD): 9.5 (2.0),
IQR: 3
Atkinson et al.
U.K.
836,557 adults
2003-
1 yr
National
Dispersion 1 km * 1 km
R2 =
Mean (SD): 1.5
(2013)
ages 40-89 yr
2007
model
0.23-0.45
(0.80), IQR: 0.84
Beelen et al.
Netherlands
114,378 adults
1986-
1 yr
Regional,
Sum of NR
R= .34,
Mean (SD): 5.2
(2008a)
ages 55-69 yr
1997
urban, local
regional,
RMSE = 1.23
(2.0)
Beelen et al.
urban, and
(NLCS-AIR
(2007)
local models;
method)
regional SO2
modeled with
IDW, urban
and local SO2
modeled with
LUR
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Table 3-3 (Continued): Exposure data for epidemiologic studies using modeling for exposure estimation.
Exposure
Concentration
Time Averaging
Spatial Scale
Exposure
Model
Validation
Summary Stats
Reference
Location
Population
Period Time
of Exposure
Model Type
Resolution
Metric
units = ppb
Brunekreef et al.
Netherlands
120,852 adults
1986- 1 yr
Regional,
Sum of
NR
R2 = 0.35,
Mean (SD): 5.2
(2009)
ages 55-69 yr
1997
urban, local
regional,
RMSE = 3.23
(2.0)
Beelen et al.
urban, and
(2007)
local models;
regional SO2
modeled with
IDW, urban
and local SO2
modeled with
LUR
Carev et al.
U.K.
835,607 adults
2003- 1 yr
National
Dispersion
1 km x 1 km
R2 = 0-0.39
Mean (SD): 1.5
(2013)
ages 40-89 yr
2007
model
(0.80), IQR: 0.8
Hart et al. (2011)
U.S.
53,814 men ages
1985- 1 yr
National
Generalized
NR
Not given for
Mean (SD): 4.8
Hart et al. (2009)
15-84 yr working
2000
additive model
SO2, but
(2.9), IQR: 4
in the trucking
using spatial
favorable
industry
smoothing
exposure
and GIS-
method
based
comparison
covariates
with IDW for
PM10 and NO2
Lipfert et al.
U.S.
70,000 U.S. male
1976- 1 yr
National
CMAQ-
36 km x 36 km
Compared
Raw mean (SD):
(2009)
veterans by
2001
MADRID-APT
model to
1.9 (1.8);
mortality period
chemical
AIRS data;
subject-weighted
transport with
unweighted
mean (SD): 4.3
reactive
r= 0.26-0.29;
(3.1); high traffic
plume-in-grid
weighted r =
subject mean
model
0.36-0.39
(SD): 6.4 (3.2)
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Table 3-3 (Continued): Exposure data for epidemiologic studies using modeling for exposure estimation.
Time Averaging Spatial Scale Exposure Model
Reference Location Population Period Time of Exposure Model Type Resolution
Exposure
Validation
Metric
Concentration
Summary Stats
units = ppb
Nafstad et al.
(2004)
Gram et al.
(2003)
Oslo, 16,209 males ages 1972- 1 yr
Norway 40-49 yr 1998
Urban
AirQUIS
dispersion
model ran for
1979 and
1995, then
projected for
other yr based
on changes in
point source
and traffic
emissions
NR
NR
5-yr median
average (range):
3.6 (0.076-21)
Wood et al.
U.K.
399 adults; mean
1997- 1 yr
Regional
Dispersion
1 km x 1 km
Relative error
Mean (SD): 1.6
(2010)
age 51.1 yr
2006
model with
typically
(0.1)
Stedman and
weighted
within 50% for
Kent (2008)
regression to
O3 (not
incorporate
reported for
sources
SO2), daily
mean
r= 0.43; daily
max r= 0.36
Nishimura et al.
U.S.
3,343 Latino and
2006- 1 yr
National
IDW
NR
NR
Mean (SD): 4.0
(2013)
(Chicago,
977 African-
2011
(3.4)
Bronx,
American
Houston,
participants ages
San
8-21 yr
Francisco
Bay Area)
Portnov et al.
Greater
3,922 school
2006- 1 yr
Regional
(1) IDW,
NR
Similar
Mean (SD): 5.4
(2012)
Haifa Area,
children mean age
2008
(2) kriging
epidemiologic
(1.3)
Israel
10.2 yr
model results
with IDW and
kriging
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Table 3-3 (Continued): Exposure data for epidemiologic studies using modeling for exposure estimation.
Exposure
Concentration
Time
Averaging
Spatial Scale
Exposure
Model
Validation
Summary Stats
Reference
Location
Population
Period
Time
of Exposure
Model Type
Resolution
Metric
units = ppb
Clark et al.
South-
3,482 children
1999-
Duration of
Regional
IDW
NR
7% difference
Mean (SD)
(2010)
western
classified as
2003
pregnancy
between
(in utero): 2.0
Brauer et al.
British
asthma cases,
plus first yr
mean SO2
(0.95); mean
(2008)
Columbia,
33,919 classified
of life
when
(SD) (first yr): 2.1
Canada
as nonasthma, and
comparing
(1.0)
17,410 random
IDW with
controls, 1999 and
nearest
2000 births, mean
monitor
age at follow up
48 ± 7 mo
Panasevich et al.
Stockholm,
1,028 men,
1992-
1 yr, 5 yr,
Urban
Dispersion
Four layers of
Reported
Mean (last 1 yr):
(2009)
Sweden
508 women ages
1994
and 30 yr
model coupled different
modeled
1.1, mean (last
Bellander et al.
45-70 yr
with street
resolution applied
concentration
5 yr): 1.8, mean
(2001)
canyon model
to countryside
within 20% of
(last 30 yr): 9.9
for central city
area
measure-
(2 km x 2 km),
ments for the
regional area
same model
(500 m x 500 m),
for NO2 (not
urban area
reported for
(100 m x 100 m),
SO2)
and inner-city
area
(25 m x 25 m)
Case-cohort
Beelen et al.
The
120,852 adults
1987-
1 yr
Regional,
Sum of
NR
In-sample
Mean (SD):
(2008b)
Netherlands
ages 55-69 yr in
1006
urban, local
regional,
R2 = 0.56
5.2 (1.9)
Beelen et al.
204 municipalities
urban, and
(2007)
local models;
regional SO2
modeled with
IDW, urban
and local SO2
modeled with
LUR
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Table 3-3 (Continued): Exposure data for epidemiologic studies using modeling for exposure estimation.
Exposure Concentration
Time Averaging Spatial Scale Exposure Model Validation Summary Stats
Reference Location Population Period Time of Exposure Model Type Resolution Metric units = ppb
Case-control
Johnson et al. Edmonton, All patients who 2003- 1 yr, 5 yr, Urban IDW NR NR Mean (IQR):
(2010) Canada presented with 2007 and 30 yr 1.3(0.1)
stroke to 1 of 11
emergency
department sites in
and around
Edmonton
Rosenlund et al.
(2006)
Bellander et al.
(2001)
Stockholm,
Sweden
1,397 first-time Ml
cases 45 to 70 yr,
1,870 controls
1992- 30 yr Regional Dispersion Three layers of
1994 model coupled different
with street resolution applied
canyon model to
for central city regional/country-
side area
(500 m x 500 m),
urban area
(100 m x 100 m),
and inner-city
area
(25 m x 25 m)
Reported Cases median
modeled (range): 9.7
concentration (2.6-18);
within 20% of controls median
measure- (range): 9.4
mentsforthe (2.7-18)
same model
for NO2
Prospective cohort
Lipsett et al. California 124,614 female 1996- 1 mo Regional IDW 250 m * 250 m NR Mean (IQR): 1.7
(2011) California public 2004 (0.43)
school teachers
ages 20-80 yr
Case-crossover
Smarqiassi et al. Montreal,
(2009) Canada
3,470 children
ages 2-4 yr
1996-
2004
1 yr
Neighborhood
AERMOD
dispersion
model
3,469 receptor
locations
Relative error Mean (SD): 4.3
<50%, daily (2.9)
mean
r= 0.43; daily
max r= 0.36
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Table 3-3 (Continued): Exposure data for epidemiologic studies using modeling for exposure estimation.
Exposure
Concentration
Time
Averaging Spatial Scale
Exposure
Model
Validation
Summary Stats
Reference
Location Population Period
Time of Exposure
Model Type
Resolution
Metric
units = ppb
Cross-sectional
Son etal. (2010)
Ulsan, 2,102 participants 2003-
1 day Urban
(1) average
NR
Mean SO2
Average across
South Korea ages 7-97 yr 2007
across
calculation
monitors mean
monitors,
within 16%
(SD): 8.6 (4.1);
(2) nearest
across
nearest monitor
monitor,
models
mean (SD): 7.3
(3) IDW,
(5.9);
(4) kriging
IDW mean (SD):
8.4 (5.3); kriging
mean (SD): 8.3
(4.4)
Deaer et al.
Montreal, 842 children ages 2006
1 yr Neighborhood
AERMOD
3,469 receptor
Relative error
Mean (SD): 1.8
(2012)
Canada 6-12 mo
dispersion
locations
<50%, daily
(1.2)
Smaraiassi et al.
model
mean
(2009)
r= 0.43; daily
max r= 0.36
Forbes et al.
England, 32,712 adults ages 1995,
1 yr National
Dispersion
1 km x 1 km
Compared to
1995 median
(2009c)
U.K. 16 yr and older 1996,
model
national
(IQR): 3.5 (2.9);
Forbes et al.
1997,
network:
1996 median
(2009b)
2001
R2 = 0.45,
(IQR): 3.5 (2.9);
relative
1997 median
error = 20%;
(IQR): 3.5 (2.7);
compared to
2001 median
verification
(IQR): 1.5 (1.0)
sites,
R2 = 0.56,
relative
error = 6.0%
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Table 3-3 (Continued): Exposure data for epidemiologic studies using modeling for exposure estimation.
Exposure
Concentration
Time
Averaging
Spatial Scale
Exposure
Model
Validation
Summary Stats
Reference
Location
Population
Period
Time
of Exposure
Model Type
Resolution
Metric
units = ppb
Forbes et al.
England,
36,350 residences
1994,
1 yr
National
Dispersion
1 km x 1 km
Compared to
1994 median
(2009b)
U.K.
1998,
model
national
(IQR): 3.6 (3.2);
2003
network:
1998 median
R2 = 0.45,
(IQR): 2.4 (2.3);
relative
2003 median
error = 20%;
(IQR): 1.6 (1.1)
compared to
verification
sites,
R2 = 0.56,
relative
error = 6.0%
Forbes et al.
England,
19,000 adults ages 1994,
1 yr
National
Dispersion
1 km x 1 km
Compared to
1994 median
(2009a)
U.K.
45 yr and older
1998,
model
national
(IQR): 3.6 (3.2);
Forbes et al.
2003
network:
1998 median
(2009b)
R2 = 0.45,
(IQR): 2.4 (2.3);
relative
2003 median
error = 20%;
(IQR): 1.6 (1.1)
compared to
verification
sites,
R2 = 0.56,
relative
error = 6.0%
Raae et al.
France
328 adults
1991-
1 yr
Urban
(1) Closest
4 km x 4 km
r> 0.73 for all
Mean (SD): 8.1
(2009)
(Paris, Lyon,
diagnosed with
1995
monitor,
comparisons
(3.3)
Marseille,
asthma with
(2) multi-
Montpellier,
episodes of
variate
Grenoble)
breathlessness/
geostatistical
wheezing and
models
asthma attack
compared with
within 12 mo
IDWor
univariate
kriging
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Table 3-3 (Continued): Exposure data for epidemiologic studies using modeling for exposure estimation.
Exposure
Concentration
Time
Averaging
Spatial Scale
Exposure
Model
Validation
Summary Stats
Reference
Location
Population
Period
Time
of Exposure
Model Type
Resolution
Metric
units = ppb
Wana et al.
Brisbane,
51,233 deaths
1996-
1 yr
Regional
IDW
NR
NR
Mean 1 h daily
(2009)
Australia
2004
average of
max: 5.4
1 h daily
max
Gorai et al.
New York
19.3 million
2005-
1 yr
Regional
Kriging
NR
MSE = -0.01,
2005 mean (SD):
(2014)
State
residents
2007
average of
RMSSE =
8.5 (2.9); 2006
1 h daily
0.85
mean (SD): 6.9
max
(2.3); 2007 mean
(SD): 7.2 (2.4)
Penard-Morand
France
9,615 children;
1999-
3 yr
Urban
STREET
NR
72% of model
Mean (minimum
et al. (2010)
(Bordeaux,
mean age 10.4 yr
2000
dispersion
estimates
to maximum
Penard-Morand
Clermont-
model
within 15% of
across six
et al. (2006)
Ferrand,
measure-
French cities):
Creteil,
ments, 100%
1.6-5.0
Marseille,
within 50% of
Strasbourg,
measure-
Reims)
ments.
Sahsuvaroqlu et Hamilton, 1,467 children age 1994- 3 yr Urban IDW, kriging NR NR Mean: 5.8
al. (2009) Canada 6-7 yr and 13-14 1995
yr
AERMOD = American Meteorological Society/U.S. EPA Regulatory Model; AIRS = Aerometric Information Retrieval System; CMAQ = Community Multiscale Air Quality;
GIS = geographic information systems; IDW = inverse distance weighting; IQR = interquartile range; LUR = land use regression; MSE = mean standardized error; NLCS-AIR =
Netherlands Cohort Study on Diet and Cancer—air pollution mortality study; N02 = nitrogen dioxide; NR = not reported; 03 = ozone; RMSE = root-mean-square error;
RMSSE = root-mean-square standardized error; SD = standard deviation; S02 = sulfur dioxide.
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3.3 Exposure Assessment and Epidemiologic Inference
This section describes exposure assessment issues related to the use of exposure estimates
in epidemiologic studies that may influence or introduce error into the observed health
effect estimate.
3.3.1 Conceptual Model of Total Personal Exposure
A theoretical model of personal exposure is presented to highlight measurable quantities
and the uncertainties that exist in this framework. 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:
f
r-* f ss 1 ,
Ef — | 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'n;i) origin of the form:
£t = Ea + Ena
Equation 3-2
Although indoor combustion of sulfur-containing fuels, particularly kerosene, is a
nonambient source of SO2 (see Section 3.3.2). these sources are specific to individuals
and may not be important sources of population exposure. This assessment 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 microenvironments ("U.S. EPA. 2006; Wilson et
al.. 2000):
Ea = 2/oCo + ^/i^inf.i^o.i
Equation 3-3
where/= fraction of the relevant time period (equivalent to dt in Equation 3-1); subscript
o = index of outdoor microenvironments; subscript i = index of indoor
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microenvironments; subscript o,i = index of outdoor microenvironments adjacent to a
given indoor microenvironment; and /'mi; = infiltration factor for indoor
microenvironment Equation 3-3 is subject to the constraint If0 + = 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:
Pa
Finf = J^+k)
Equation 3-4
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 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:
17 — (f 1 Yf p \r
r-a - \Jo + Annnf.ijt-a
Equation 3-5
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 3.3.5 with
respect to implications for interpreting epidemiologic studies. Epidemiologic studies
often 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:
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Equation 3-6
Combining Equations 3-5 and 3^6 yields:
« = fo + 2/iFinf.i
Equation 3-7
where a varies between 0 and 1. 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 rir. Time-activity
data and corresponding estimates of Fmf 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 Fmf,
which can vary with building and meteorology-related air exchange characteristics. 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, LUR models, receptor models, fine-scale CTMs, or
some combination of these techniques. These techniques are described in Section 3.2.2.
3.3.2 Relationships between Personal Exposure and Ambient Concentration
Several factors influence the relationship between personal SO2 exposure and ambient
concentration. Due to the lack of indoor SO2 sources, and the fact that ambient SO2 tends
to deposit on surfaces after it penetrates into enclosed microenvironments, indoor SO2
concentrations are highly dependent on air exchange rate and therefore vary widely in
different microenvironments. People spending the bulk of their time indoors has a
substantial impact on personal exposure. Personal exposures are often much lower than
ambient concentrations; for example, Brown et al. (2009) reported the mean winter
personal exposure in Boston to be 1.8 ppb, while the ambient concentration was 11.3 ppb.
Both personal exposure and ambient concentration was lower in summer, with mean
values of-0.2 and 3.6 ppb, respectively. The negative mean value reflects the large
fraction of personal exposure samples below the detection limit (99% in summer, 93% in
winter).
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3.3.2.1
Indoor-Outdoor Relationships
A number of studies from the U.S., Canada, Europe, and Asia summarized in the 2008
SOx ISA (U.S. EPA. 2008b). as well as a few new studies conducted outside the U.S.,
have characterized the relationship between outdoor and indoor SO2 concentrations.
Ratios and slopes of the indoor concentration versus the concentration immediately
outside the indoor microenvironment show an extremely wide range, from near zero to
near unity (Table 3-4). One of the most detailed older studies 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 (Patterson and Eatough.
2000). Studies conducted since the 2008 SOx ISA (U.S. EPA. 2008b) have focused on
public buildings and show generally similar results to older studies.
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), personal activities affecting air exchange rates, indoor
deposition of SO2, and analytical capabilities. When reported, correlations between
indoor and outdoor concentrations were relatively high (>0.75), suggesting that variations
in outdoor concentration are driving indoor concentrations (Table 3-4). These high
correlations were observed across seasons and geographic locations. This is supported by
the relative lack of indoor sources of SO2. The main indoor source is combustion of
sulfur-containing fuels, such as kerosene, which is generally considered an emergency or
supplemental source of heat in the United States. Triche et al. (2005) measured SO2
concentrations in homes where secondary heating sources (fireplaces, kerosene heaters,
gas space heaters, and wood stoves) were used and found elevated concentrations only
when kerosene heaters were used. Median indoor SO2 concentrations were 6.4 ppb during
kerosene heater use, compared with 0.22 ppb at other times. For other criteria pollutants,
nonambient sources can be an important contributor to total personal exposure. Because
there are relatively few indoor sources of SO2, personal SO2 exposure is expected to be
dominated by ambient SO2 in outdoor microenvironments and in indoor
microenvironments with high air exchange rates (e.g., with open windows).
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Table 3-4 Relationships between indoor and outdoor sulfur dioxide concentration.
Years/
Study
Sample
Micro-
Concentration/
Study
Location
Season
Population
Design
Duration Ratio3
Correlation
Environment
Comment
Detection limit (ppb)
Brauer et
Boston, MA
Jul-Aug
NR
Pooled
24 h 0.23
NR
Home
Concent-
Mean (LOD)
al. (1989)
1988
ration and
Indoor
ratio
0.6 (0.25)
estimated
from Figure 2
Outdoor
3 (0.77)
Brauer et Boston, MA Jul-Aug NR
al. (1991)
Pooled
24 h
GM: 0.39
GSD: 1.57
Slope (SE):
0.55 (0.04)
March
(late
winter)
In: 24 h GM: 0.05
Out: 48 h GSD: 1.71
Slope (SE):
0.12 (0.02)
0.94
Home
0.85
Intercept
nonsignificant
at p < 0.01
Mean (SD)
Indoor
1.47 (1.52)
Range:
-------
Table 3-4 (Continued): Relationships between indoor and outdoor sulfur dioxide concentration.
Years/ Study Sample
Study Location Season Population Design Duration
Ratio3
Micro-
Correlation Environment
Comment
Concentration/
Detection limit (ppb)
Chan et al. Taipei, May 1992 Non-
(1994) Taiwan asthmatics
Pooled
12 h
GM: 0.24
GSD: 2.46
NR
Home
l-O
regression
slope
nonsignificant
at p < 0.05
Mean (SD)
Indoor
2.5 (1.9)
Outdoor
7.6 (4.6)
LOD: NR
Jan-Apr Children
1993 with asthma
24 h
GM: 0.23
GSD: 2.30
Slope (SE):
0.77 (0.13)
Intercept
(SE):
-1.02 (0.28)
0.55
Mean (SD)
Indoor
2.4 (2.9)
Outdoor
8.2 (4.6)
LOD: NR
Chao Hong Kong,
(2001) China
May-Jun NR
1997
Pooled 48 h
1.01
SD: 0.78
Range
0.25-3.0
NR
Apartment
Units had
high AER
(mean
5.4 h"1,
median
2.7 h"1)
Two of 10
units had I/O
ratio >1
Some units
reported
incense
burning
Mean (SD)
Indoor
2.4 (0.84)
Range: 1.0-4.0
Outdoor
3.1 (1.5)
Range: 1.0-6.0
LOD: NR
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Table 3-4 (Continued): Relationships between indoor and outdoor sulfur dioxide concentration.
Study
Location
Years/
Season
Population
Study
Design
Sample
Duration
Ratio3
Correlation
Micro-
Environment
Comment
Concentration/
Detection limit (ppb)
Godoi et
Curitiba,
Nov/Dec
Children
Pooled
2 weeks
0.7
NR
Urban school
Ratio of
Mean
al. (2013)
Brazil
2009,
June
2010,
May-Oct
2011
indoor and
outdoor
means
Indoor
0.70
Outdoor
1.0
LOD: NR
1.0
Suburban
school
Indoor
0.34
Outdoor
0.34
Kindzierski
and
Sembaluk
(2001)
Boyle,
Alberta,
Canada
Late fall
NR
Pooled
7 days
0.13b
Range
0.05-0.52
<0.41
Single-family
dwellings
Replaced
values below
LOD with
0.1 ppb
Median (Range)
Indoor
0.2
(0.1-0.9)
Outdoor
1.6
(1.4-2.1)
Sherwood
0.13b
Indoor
Park,
Alberta,
Canada
Range
0.08-0.4
0.5
(0.3-2.0)
Outdoor
3.8
(3.1-5.0)
LOD: 0.13
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Table 3-4 (Continued): Relationships between indoor and outdoor sulfur dioxide concentration.
Years/ Study Sample
Study Location Season Population Design Duration
Micro- Concentration/
Ratio3 Correlation Environment Comment Detection limit (ppb)
Lee et al. Hong Kong Oct NR
(1999) 1996-Mar
1997
Pooled 20 min Slope: 0.87
Intercept:
0.0002
0.60
0.93
0.75 Public spaces Smoking
occurred in
some
locations
Car Park
Library
Range:
Indoor
3-12
Outdoor
3-9
LOD: 1
0.95
Restaurant
1.10
Recreation
Place
1.00
Sport Center
0.78
Shopping
Mall
Li and
Essex, U.K. Jun-Jul NR
Pooled 24 h
0.22
0.84
University
Mean (SD)
Harrison
1989
Range
buildings
Indoor
(1990)
0.1-0.4
1.1 (NR)
Slope0 (SE):
Outdoor
0.14 (0.03)
5.7 (NR)
Intercept0
LOD: 0.014
(SE): 0.72
Fraction above LOD:
(0.46)
100%
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Table 3-4 (Continued): Relationships between indoor and outdoor sulfur dioxide concentration.
Years/ Study Sample Micro- Concentration/
Study Location Season Population Design Duration Ratio3 Correlation Environment Comment Detection limit (ppb)
Lopez-
Prague,
Jul 2009- All age
Longi-
1 mo
Mean (SD)
NR
Historic
No heating or
Range*
Aparicio et
Czech
Mar 2010 groups
tudinal
0.49 (0.16)
Library
air
Indoor
al. (2011)
Republic
cohort
Range
conditioning
0.8-2
0.25-0.74
Ratio: Indoor
Outdoor
mean/
1-7
outdoor mean
*Estimated from Figure 2
LOD: 0.04
Patterson
Lindon, UT
Jan-Feb Children
Longi-
10 h
Slope (SE):
0.79
School
Mean (SD)
and
1997 (during
tudinal
daytime
0.023 (.004)
Outdoor
Eatouah
school
cohort
Intercept
0.93 (0.34)
(2000)
hours)
(SE): 0.018
(.006)
Children
14 h
0.030 (.003)
0.91
Outdoor
nighttime
0.002 (.002)
1.4 (0.17)
Children
All
0.027 (.002)
0.85
Outdoor
0.008 (.003)
0.49 (0.12)
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Table 3-4 (Continued): Relationships between indoor and outdoor sulfur dioxide concentration.
Years/
Study
Sample
Micro-
Concentration/
Study
Location
Season
Population
Design
Duration
Ratio3
Correlation
Environment
Comment
Detection limit (ppb)
Spenaler
Portage, Wl
May
NR
Pooled
1 yr
0.67
NR
Residences
Ratio of
Range*
et al.
1977-
indoor and
Indoor
(1979)
Topeka, KS
Apr1978
0.50
outdoor
annual
0.4-8.4
Outdoor
Kingston, TN
0.08
24-h, 6th-day
0.8-20
samples
LOD: NR
Watertown,
0.33
Ratios
MA
reported in
Kindzierski
St. Louis,
0.31
and
MO
Ranaanathan
(2006)
Steubenville,
0.39
OH
Stock et al.
Houston, TX
Aug-Oct
NR
Pooled
1 h
0.55
NR
Residence
Ratio: Indoor
Mean (SD)
(1985)
2001
mean/
Indoor
outdoor mean
2.8 (5.0)
Outdoor
5.1 (5.3)
LOD: NR
AER = air exchange rate; GM = geometric mean; GSD = geometric standard deviation; LOD = limit of detection; NR = not reported; ppb = parts per billion; SD = standard deviation;
SE = standard error
aMean value unless otherwise indicated.
bMedian.
Calculated from Table 1 of Li and Harrison (1990).
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3.3.2.2
Personal-Ambient Relationships
As described in the 2008 SOx ISA (U.S. EPA. 2008b). three main study designs are used
to evaluate personal-ambient relationships: (1) longitudinal cohort, (2) pooled, and
(3) community-averaged time-series exposure (typically assessed using daily average
measurements). Longitudinal studies include measurements made on multiple days for
each subject. Thus, they describe the temporal variation in daily personal exposure and
ambient concentration for the same subject, and the correlation can differ among study
subjects based on activity pattern and other factors. This provides the distribution of
correlations for each subject across a study population. Such a study design may be
informative for panel epidemiologic studies because variation in longitudinal correlation
represent interpersonal variations in ambient exposure. Studies using this design for the
health effects of SO2 exposure have reported a wide variation in 24-hour correlations
among subjects, ranging from -0.75 to 0.70, with a median of 0.00-0.10 (Sarnat et al..
2005; Sarnat et al.. 2001; Sarnat et al.. 2000). However, >95% of personal samples were
below the personal monitor detection limit (2-6 ppb), meaning that the reported
correlations include substantial noise in the personal exposure measurement. This tends
to obscure the true relationship between personal exposure and ambient SO2
concentrations.
Pooled studies include one or a few measurements per subject, with different subjects
studied on different days, and a regression calculated across all subject-days in the study.
Studies using this design for SO2 found personal-ambient slopes of 0.03-0.13 for 24-hour
samples (Sarnat et al.. 2006; Braueretal.. 1989). Correlations varied across the studies,
again due at least in part to detection limit issues. The Brauer et al. (1989) study reported
an R2 value of 0.43 (r = 0.66) in Boston, with all personal samples above the detection
limit of 0.19 ppb. Lower correlations were reported by Sarnat et al. (2006); for
Steubenville, OH in the fall, R2 was 0.15 (r = 0.39) with 31.6 % of personal samples
below the detection limit of 3.8 ppb; while in summer, R2 was 0.00, with 53.5% of the
personal samples below the detection limit of 5.5 ppb.
For the community time-series study design, exposures and ambient concentrations are
averaged across subjects for each day and used to calculate the correlation between the
daily average exposure and daily average ambient concentration, which is informative for
community time-series epidemiologic study designs that evaluate associations between
community average concentration and health outcomes. However, no community time-
series SO2 exposure studies reporting personal-ambient correlations have been identified.
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Looking across study designs, when nearly all of the personal samples are below the
MDL, negligible correlation can be observed in part due to large uncertainties in the
measurements. However, when the bulk of the personal samples are above the MDL,
personal exposure is moderately correlated with ambient concentration.
3.3.3 Factors Contributing to Error in Estimating Exposure to Ambient Sulfur
Dioxide
In this section, parameters are discussed that are relevant to estimating SO2 exposure but
are not themselves direct measures of exposure. The use of SO2 measurements from
central ambient monitoring sites is the most common method for assigning exposure in
epidemiologic studies. However, fixed-site measurements do not account for the effects
of spatial variation in SO2 concentration, ambient and nonambient concentration
differences, and varying activity patterns on personal exposures (Brown et al.. 2009;
Zeger et al.. 2000). Inter-individual variability in exposure error across a population will
be minimal when (1) SO2 concentrations are uniform across the region; (2) personal
activity patterns are similar across the population; and (3) housing characteristics, such as
air exchange rate and indoor reaction rate, are constant over the study area. To the extent
that these factors vary by location and population, there will be errors in the magnitude of
total exposure based solely on ambient monitoring data. Time-location-activity patterns
have a substantial influence on exposure and dose by determining an individual's extent
and duration of exposure. Omission of this information can lead to exposure error. Spatial
and temporal variability in SO2 concentrations can contribute to exposure error in
epidemiologic studies, whether they rely on central site monitor data or concentration
modeling for exposure assessment. Proximity of populations to ambient monitors may
also influence how well the populations' exposure is represented by measurements at the
monitors.
3.3.3.1 Activity Patterns
The activity pattern of individuals is an important determinant of their exposure.
Variation in SO2 concentrations among various microenvironments means that the
amount of time spent in each location, as well as the level of activity, 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.
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Activity patterns vary both among and within individuals, resulting in corresponding
variations in exposure across a population and overtime. Large-scale human activity
databases, such as CHAD (McCurdv et al.. 2000). which includes the National Human
Activity Pattern Survey (NHAPS) (Klepeis et al.. 2001) data together with other activity
study results, have been designed to characterize exposure patterns among much larger
population subsets than can be examined during individual panel studies. The complex
human activity patterns across the population (all ages) are illustrated in Figure 3-1 from
(Klepeis et al.. 2001). which is presented to illustrate the diversity of daily activities
among the entire population as well as the proportion of time spent in each
microenvironment.
100
Oilier Outdoor
Residence-Indoors
Residence-Outdoors
Inside Vehicle .
j^////////////,
Near Vehicle
(Outdoors)
Bar Restaurant
ex
School-'
Public Bids!
i
3 3
R
re
a
3h
re
ooooooooooooooooooooooooo
ooooooooooooooooooooooooo
-h r-i en »n ^6 CC 6\ O »-h rn -rt vc r-" do o\ o c\ i
Time of Day
Source: Reprinted with permission of Nature Publishing Group (Klepeis et al.. 2001).
Figure 3-1 Distribution of time that National Human Activity Pattern Survey
respondents spent in 10 microenvironments based on smoothed
1-minute diary data.
Time spent in different locations has also been found to vary by age. Table 3-5
summarizes 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 outdoors, while the school age
population spent the most time outdoors. NHAPS respondents aged 65 years and over
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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 comparison of children (mostly less than age 8 years), adults 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 et al..
201 lb). Increased time spent outdoors performing vigorous physical activity is consistent
with evidence from the 2008 SOx ISA (U.S. EPA. 2008b') suggesting that younger age
groups are more at risk for SC>2-related health effects.
Table 3-5 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)
Together with location, exertion level is an important determinant of exposure. Table 3-6
summarizes ventilation rates for different age groups at several levels of activity as
presented in Table 6-2 of the EPA's Exposure Factors Handbook (U.S. EPA. 201IV
Most of the age-related variability is seen for moderate and high intensity activities,
except for individuals under 1 year. For moderate intensity, ventilation rate increases with
age through childhood and adulthood until age 61, after which a moderate decrease is
observed. Ventilation rate is most variable for high intensity activities. Children aged 1 to
<11 years have ventilation rates of approximately 40 L/minute, while children aged
11+ years and adults have ventilation rates of approximately 50 L/minute. The peak is
observed for the 51 to <61 age group, at 53 L/minute, with lower ventilation rates for
older adults.
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Table 3-6 Mean ventilation rates (L/minute) at different activity levels for
different age groups.
Age Group (yr)
Sleep or Nap
Sedentary/
Passive
Light Intensity
Moderate
Intensity
High Intensity
Birth to <1
3.0
3.1
7.6
14
26
1 to <2
4.5
4.7
12
21
38
2 to <3
4.6
4.8
12
21
39
3 to <6
4.3
4.5
11
21
37
6 to <11
4.5
4.8
11
22
42
11 to <16
5.0
5.4
13
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49
16 to <21
4.9
5.3
12
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21 to <31
4.3
4.2
12
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31 to <41
4.6
4.3
12
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49
41 to <51
5.0
4.8
13
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52
51 to <61
5.2
5.0
13
29
53
61 to <71
5.2
4.9
12
26
47
71 to <81
5.3
5.0
12
25
47
81 +
5.2
4.9
12
25
48
Source: Data from Exposure Factors Handbook (U.S. EPA. 2011).
A dramatic increase in ventilation rate occurs as exercise intensity increases. For children
and adults <31 years, high intensity activities result in nearly double the ventilation rate
for moderate activity, which itself is nearly double the rate for light activity. Children
have other important differences in ventilation compared to adults. As discussed in
Chapter 4, children tend to have a greater oral breathing contribution than adults, and
they breathe at higher minute ventilations relative to their lung volumes. Both of these
factors tend to increase dose normalized to lung surface area.
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
age, gender, employment, and lifestyle-dependent factors. These differences may
manifest as higher mean 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
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activities [the intra-class 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
between-person differences. The ICC value might be different for other population
groups.
The EPA's National Exposure Research Laboratory has consolidated many of the most
important human activity databases into one comprehensive database called CHAD. The
current version of CHAD contains data from nineteen human-activity-pattern studies
(including NHAPS), which were conducted between 1982 and 1998 and evaluated to
obtain over 33,000 person-days of 24-hour human activities in CHAD (McCurdv et al.
2000). The surveys include probability-based recall studies conducted by EPA and the
California Air Resources Board, as well as real-time diary studies conducted in individual
U.S. metropolitan areas using both probability-based and volunteer subject panels. All
ages of both genders are represented in CHAD. The data for each subject consist of 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.
3.3.3.2 Spatial and Temporal Variability
Spatial and temporal variability in SO2 concentrations can contribute to exposure error in
epidemiologic studies, whether they rely on central site monitor data or concentration
modeling for exposure assessment. Spatial variability in the magnitude of concentrations
may affect cross-sectional and large-scale cohort studies by undermining the assumption
that intra-urban concentration and exposure differences are less important than
inter-urban differences. Low inter-monitor correlations contribute to exposure error in
time-series studies, including bias toward the null and increased confidence intervals.
The 2008 SOx ISA (U.S. EPA. 2008b) discussed spatial variability in 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 SO2 concentrations at some monitors 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
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urban area. Factors contributing to differences among monitors include presence of point
sources, proximity to point sources, terrain features, and uncertainty regarding the
measurement of low SO2 concentrations. Low correlation between a specific monitor and
the community average concentration will tend to bias an effect estimate toward the null.
Several recent studies have evaluated the impact of SO2 spatial variability on
epidemiologic effect estimates. Strickland et al. (2011) reported a relatively low
chi-square goodness-of-fit statistic for SO2 compared with other primary and secondary
criteria pollutants in Atlanta, GA. The authors attributed this poor fit to spatial
heterogeneity in SO2 concentrations and the inability of a central site monitor to capture
SO2 plume touch downs in other parts of the city. The chi-squared statistic moderately
increased when average 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, especially for effect estimates based on a
standardized increment rather than the IQR. This difference is due in part to the spatial
heterogeneity of SO2. The higher concentration reported at the central site monitor is not
reflected in the urban averages, resulting in null bias (see Section 5.2.1.2). while spatial
variability is partially accounted for in the IQR. The different exposure assignment
approaches only altered the magnitude, not direction, of observed associations.
High spatial and temporal variability leading to a null-biased effect estimate was also
observed in Atlanta by Goldman et al. (2010). 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 error than primary pollutants such
as CO and SO2, which tend to have higher spatial variability than secondary pollutants.
Goldman et al. (2010) simulated error related to assuming the central site monitor
represents exposure at a receptor's location. The study authors computed a semivariance
term over distance to the central site monitor to estimate error at a distance from the
monitor. The observed 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. 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) found that the
simulated bias decreased for SO2 when using unweighted, population-weighted, and
area-weighted averages of concentrations from multiple monitors. Similarly,
epidemiologic studies in the United States (Kumar. 2012; Morello-Frosch et al.. 2010)
and Australia Jalaludin et al. (2007). found higher associations between SO2 and birth
outcomes when the analysis was restricted to mothers matched with an SO2 monitor
within 3-5 km of their residence, suggesting bias towards the null remained in the wider
spatial averages used in the base case (Section 5.4).
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Because SO2 concentrations can have high spatial variability, exposure estimates may
have less error for populations living close to a fixed-site monitor. Figure 3-2 illustrates
the location of 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 SO2 monitors,
some of the highest density census block groups are located some distance from central
site monitors. This is also illustrated by Table 3-7. which indicates that approximately
one-third of the population in various age groups lives more than 15 km from an SO2
monitor. For the Pittsburgh CBSA (Figure 3-3). 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
an SO2 monitor (Table 3-8). 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.
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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
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 3-2 Map of the Cleveland, OH core-based statistical area including
National Emissions Inventory facility locations, urban sulfur
dioxide monitor locations, and distance from 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 3-7 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.
Total Population
Within 1 km
Within 5 km
Within 10 km
Within 15 km
Population
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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—1—
12.5
I
25
Distance to NEI Facilities
® NEI Facility
U 5 km
J 10 km
I I 15 km
41 Urban SO:- Monitor
2011 ACS Population Per Sq Km
Based on Pittsburgh CBSA Block Groups
0-391
392 - 958
H 959- 1684
1685 - 2665
| 2666 -4462
4463 -8146
T 1 1 1
50 Kilometers
Im)
J«L V J /
' Wr
I « « i | i i i |
0 5 10 20 Kilcmetms
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 3-3 Map of the Pittsburgh, PA core-based statistical area including
National Emissions Inventory facility locations, urban sulfur
dioxide monitor locations, and distance from 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. The
inset map shows National Emissions Inventory facilities located
to the southeast of the highly urbanized areas.
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Table 3-8 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).
Fewer studies have considered temporal variability. On a seasonal basis, Jalaludin et al.
(2007) reported that the association between SO2 concentration and preterm birth in
Sydney, Australia was much higher for conception during autumn (6.489, 95%
CI = 4.365-9.648) than in winter (0.826, 95% CI = 0.759-0.898), with winter and spring
showing similar effect estimates (1.323, 95% CI = 1.027-1.704; 1.287, 95%
CI = 0.955-1.734). However, the study authors noted that other potentially important
factors in birth outcomes also vary seasonally, such as outdoor activity, Vitamin D levels,
and concentrations of other pollutants. A study in Canada suggests that an exposure
metric based on a single year can represent exposure over a multidecade period. The
authors compared exposure assessment methods for long-term SO2 exposure and found
that the annual average concentration in the census tract of a subject's residence during
1980 and 1994 was well correlated (Pearson R = 0.83and 0.85 for all subjects,
respectively) with a 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 etal.
(2011) also found that coverage of the study population reduced from 40% for the
fixed-time exposure assignments, to 31% when averaging the two methods, to 29% when
assigning exposures based on census subdivision, suggesting that improved spatial and
temporal resolution in long-term studies may come at the expense of data completeness.
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3.3.3.3
Variability in Infiltration and Building Ventilation
Given that people spend the majority of their time indoors, building air exchange rates
influence exposure to ambient SO2, as indicated by Equation 3-4. Lopez-Aparicio et al.
(2011) measured 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 P = 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 study of SO2 infiltration. It is difficult to find an
analog with other gases that follow similar infiltration patterns to SO2. While O3 also has
no indoor sources, peak values occur during the summer when photochemical activity is
highest. NO2 has indoor sources from cooking and other combustion. Nonetheless,
factors influencing infiltration of other gases may influence SO2 infiltration as well. In a
study of NO2 infiltration, Meng et al. (2012) found that high air exchange rate (>1.3 air
changes per hour), no central air conditioning, use and nonuse of window fans, and
presence of old carpeting were determinants of a for NO2 in summer; none of these
factors were determinants of a for NO2 in winter. In a study of O3 infiltration and related
health effects, Chen etal. (2012a) found that the only influential factor was window
opening.
3.3.3.4 Method Detection Limit, Instrument Accuracy, and Instrument
Precision
Personal exposure is moderately correlated with ambient SO2 concentration when
personal samples are above the MDL, although the magnitude of personal exposures is
often much lower than the magnitude of ambient concentrations (Section 3.3.2).
Moderate correlation between personal exposure and ambient concentration indicates that
concentration-based exposure metrics are capturing the variability in exposure 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. This results in a
high fraction (>90%) of personal samples below the detection limit and are thus unable to
provide a signal that could correlate with variations in ambient concentration. Low
correlations 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. Data below detection limits are less of an issue
for ratios of personal exposure to ambient concentration, for which a low personal sample
value likely represents a true low exposure and thus appropriately leads to a low ratio.
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Low ratios result from low penetration and high deposition of SO2 in indoor
microenvironments where people spend most of their time.
Instrument error occurs when the SO2 measurements are subject to interferences that
cause biases or noise leading to error in estimating exposure. FRM or FEM SO2
measurements are subject to positive bias from detection of interfering compounds. See
Section 2.4.2 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) investigated instrument precision error at locations where ambient
central site monitors were collocated. Instrument precision error increased with
increasing concentration for the central site monitors. When instrument error and
concentration are correlated, error in the exposure estimates will be larger in locations
with more prevalent or stronger sources or at times when SO2 emissions are higher for a
given location. For example, the magnitude of the instrument error would be expected to
be largest at times of day when SO2 emissions are 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) study. 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, instrument error would not be expected to influence
the exposure metric on a daily basis. When comparing health effect estimates among
cities for a long-term SO2 exposure epidemiologic study, differences in instrument error
among cities could lead to biased exposure estimates, given the reliance on differences in
exposure magnitude to study spatial contrasts. Section 3.3.5 describes the influence of
instrument error and high MDL on exposure error and health effect estimates for
community time-series (Section 3.3.5.1). longitudinal cohort (Section 3.3.5.2). and panel
(Section 3.3.5.3) epidemiologic studies.
3.3.4 Confounding
To assess the independent health effects of SO2 in an epidemiologic study, it is necessary
to identify (1) which copollutants (e.g., NO2, PM2 5, UFP, BC) are potential confounders
of the health effect-SCh relationship so that their correlation with SO2 can be tested and,
if needed, accounted for in the epidemiologic model; (2) 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 (3) the spatial correlation structure across multiple pollutants,
if the epidemiologic study design is for long-term exposure (Bateson et al. 2007).
Additionally, confounding can also vary by the health endpoint studied.
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For monitors that do show high correlations, copollutant epidemiologic models may be
appropriate to adjust the effect estimate for each pollutant for confounding by the other
pollutant (Tolbert et al.. 2007). As discussed in the 2010 CO ISA (U.S. EPA. 2010b).
copollutant 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. However, collinearity potentially affects model performance when highly
correlated pollutants are modeled simultaneously, and differences in the spatial
distribution of SO2 and the copollutants may also complicate model interpretation
(Grvparis et al.. 2007) (Section 5.1.2.1). Because SO2 exhibits a relatively high degree of
exposure error compared with other criteria pollutants, copollutant models in which the
SO2 effect estimate remains robust provide additional evidence for an independent health
effect of SO2.
This section considers temporal copollutant correlations and how relationships among
copollutants may change in space. Temporal copollutant correlations are computed from
the time series of concentrations for two different collocated pollutants. Temporal
correlations are informative for epidemiologic studies of short-term exposure when the
sampling interval is a month or less for each of the copollutants. Temporal correlations
are informative for epidemiologic studies of long-term exposures when sampling
intervals are months to years. 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 SO2 over different temporal and spatial resolutions.
3.3.4.1 Temporal Relationships among Ambient Sulfur Dioxide and
Copollutant Exposures
Short-Term Temporal Correlations
As discussed in Section 2.5.5. daily concentrations of ambient SO2 generally exhibit low
to moderate correlations with other daily NAAQS pollutant concentrations at collocated
monitors (Figure 2-35). However, a wide range of copollutant correlations is observed at
different monitoring sites, from moderately negative to moderately positive (Table 3-9).
This indicates 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 recent rulemaking on use of ultra-low sulfur diesel fuel (66 FR 5002).
Some of the studies cited in this ISA may precede 2006 and 2007, when the new sulfur
standards took effect for highway vehicles and heavy duty vehicles, respectively. This
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change may contribute to wider variation in correlation between SO2 and copollutants
presented in this section. Note that potential for confounding also varies by health
endpoint.
Exposure studies have examined correlations between ambient SO2 and ambient or
personal copollutants, generally reporting low or moderate correlations. Spearman
correlations between daily ambient concentrations of SO2 and ambient copollutants
reported in Baltimore were generally near zero, although a moderate correlation (0.41)
was observed for O3 (Sarnat et al.. 2001). Pearson correlations reported in an exposure
study conducted in Steubenville, OH between ambient SO2 and ambient PM2 5, sulfate,
and elemental carbon (EC) were higher in the fall (0.47-0.58) than in the spring
(0.1-0.22) (Sarnat et al.. 2006). although it is difficult to generalize from this single
result, particularly because correlations between ambient SO2 concentration and personal
PM2 5, sulfate, and elemental carbon were similar in both seasons (r = 0.1-0.3).
Correlations between daily ambient SO2 concentration and personal exposure to PM2 5
were found to vary widely between subjects in both Baltimore and Boston (Sarnat et al..
2005). Both moderately positive (>0.5) and moderately negative (<-0.5) Spearman
correlations were reported, with a median correlation near zero. The Sarnat et al. (2001)
study in Steubenville, OH reported a Pearson correlation of 0.3 between ambient SO2
concentration and personal PM2 5 exposure. Taken together, this evidence suggests that
correlations between copollutant exposure and ambient SO2 concentration vary among
individuals, and thus the potential for copollutant confounding cannot be ruled out.
Long-Term Correlations
Long-term epidemiologic studies that have reported copollutant correlations are also
listed in Table 3-9. Similar to daily correlations, no clear pattern is apparent for
correlation of ambient SO2 concentration with any of the other copollutants. A wide
range of correlations has been reported among the copollutants, including moderately
negative and moderately to strongly positive. Because confounding may occur even when
correlations are low, no clear conclusion can be drawn regarding the potential for
confounding of long-term SO2 epidemiologic estimates by copollutants.
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Table 3-9 Synthesis of sulfur dioxide ambient-ambient copollutant correlations from short-term and long-term
epidemiology studies.
Study
Duration
Location
Correlation
Measure
CO
o3
no2
PM2.5
PM10 NOx TSP
Short Term Studies
Dales et al. (2006)
1 day
11 Canadian cities
Pearson
-0.41 to
0.13
0.20 to
0.67
-0.09 to
0.61
Faiz et al. (2013)
1 day
New Jersey
Pearson
0.49
0.51
0.31
Leem et al. (2006)
1 day
Incheon, South Korea
Pearson
0.31
0.54
0.13
Liu et al. (2003)
1 day
Vancouver, Canada
Pearson
0.64
-0.35
0.61
Liu et al. (2007)
1 day
Canada
Pearson
0.21
-0.30
0.34
0.44
Peel et al. (2011)
1 day
Atlanta, GA
Spearman
0.39
-0.11
0.31
0.20
0.21
Pereira et al. (1998)
1 day
Sao Paulo, Brazil
Pearson
0.34
0.15
0.41
0.74
Saaiv et al. (2005)
1 day
Pennsylvania
Pearson
0.46
Zhao et al. (2011)
1 day
Guangzhou, China
Pearson
0.84
0.75
Lee et al. (2011a)
7 days
Allegheny County, PA
Pearson
0.30
0.10
0.40
0.20
0.10
Long term studies
Clouahertv et al. (2013)
2 weeks
5-boroughs of New York
City
Pearson
0.51
0.70
Darrow et al. (2009)
4 weeks
5 counties in Atlanta, GA
Spearman
0.44
-0.32
0.37
0.12
-0.17
Le et al. (2012)
1 mo
Detroit
NR
0.35
0.27
0.11
Darrow et al. (2011)
1st mo
5 counties in Atlanta, GA
Spearman
0.44
-0.27
0.32
-0.07
-0.18
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Table 3-9 (Continued): Synthesis of sulfur dioxide ambient copollutant correlations from short-term and
long-term epidemiology studies.
Study
Duration
Location
Correlation
Measure CO
o3
no2
PM2.5
PM10
NOx TSP
Slama et al. (2013)
M1-M1
Teplice District, Czech
¦ Republic
NR
-0.69
0.73
0.79
M1-M2
-0.69
0.39
0.41
M1-M12
-0.74
0.64
0.70
M2-M1
-0.47
0.58
0.54
M2-M2
-0.70
0.72
0.80
M2-M12
-0.63
0.71
0.75
M12-M1
-0.65
0.74
0.74
M12-M2
-0.78
0.63
0.68
M12-M12
-0.77
0.77
0.82
Strickland et al. (2009)
5 weeks
Atlanta, GA
Spearman 0.23
0.30
0.39
0.41
Woodruff etal. (2008)
2 mo
United States
Spearman 0.27
-0.22
0.21
0.00
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Table 3-9 (Continued): Synthesis of sulfur dioxide ambient copollutant correlations from short-term and
long-term epidemiology studies.
Study
Duration
Location
Correlation
Measure
CO
o3
no2
PM2.5
PM10
NOx
TSP
Bobak (2000)
T1-T1
Czech Republic
NR
0.53
0.71
T1-T2
0.19
0.17
T1-T3
-0.05
-0.39
T2-T1
0.33
0.09
T2-T2
0.62
0.68
T2-T3
0.26
0.16
T3-T1
0.04
-0.22
T3-T2
0.30
0.13
T3-T3
0.63 0.73
Darrow et al. (2011)
T3
0.61
-0.50
0.39
-0.18
-0.30
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Table 3-9 (Continued): Synthesis of sulfur dioxide ambient copollutant correlations from short-term and
long-term epidemiology studies.
Study
Duration
Location
Correlation
Measure
CO
o3
no2
PM2.5
PM10
NOx TSP
Faiz etal. (2012)
T1-T1
New Jersey
Pearson
0.40
0.30
0.07
T1-T2
0.25
0.10
0.15
T1-T3
0.21
0.05
0.41
T1-P
0.39
0.16
0.33
T2-T1
0.37
0.26
-0.07
T2-T2
0.43
0.31
0.11
T2-T3
0.26
0.10
0.17
T2-P
0.47
0.23
0.13
T3-T1
0.20
0.08
0.40
T3-T2
0.38
0.27
-0.04
T3-T3
0.43
0.32
0.14
T3-P
0.45
0.23
0.30
P-T1
0.35
0.30
0.17
P-T2
0.37
0.30
0.10
P-T3
0.32
0.21
0.33
P
0.47
0.28
0.33
Geeretal. (2012)
T1-T3
Texas
Pearson
0.61
-0.23
-0.30
0.05
-0.07
Hwana and Jaakkola (2008)
T1
Taiwan
NR
0.24
0.23
0.50
0.45
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Table 3-9 (Continued): Synthesis of sulfur dioxide ambient copollutant correlations from short-term and
long-term epidemiology studies.
Study
Duration
Correlation
Location Measure
CO
O3 NO2
PM2.5 PM10 NOx TSP
Lee et al. (2003)
T1
South Korea NR
0.79
0.78
T2
0.86
0.82
T3
0.86
0.85
Lee et al. (2012)
T1
Allegheny Co, PA Pearson
0.30
-0.60 0.30
-0.30 -0.30
Lin et al. (2014)
T1
Taiwan Pearson
0.31
0.18 0.51
0.54
Lipfert et al. (2000b)
1 yr
United States NR
-0.15
0.04
Rich et al. (2009)
T1-T1
New Jersey Pearson
0.22
0.16
0.17
T1-T2
0.18
-0.05
0.04
T1-T3
0.02
-0.08
0.37
T2-T1
0.12
0.06
-0.11
T2-T2
0.25
0.12
0.17
T2-T3
0.21
-0.06
0.04
T3-T1
0.21
-0.03
0.33
T3-T2
0.24
0.16
-0.02
T3-T3
0.38
0.17
0.19
Xu et al. (2014)
T1
Florida Pearson
0.29
-0.36 0.15
0.20
T2
0.25
-0.39 0.08
0.13
P
0.18
-0.28 0.06
0.10
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Table 3-9 (Continued): Synthesis of sulfur dioxide ambient copollutant correlations from short-term and
long-term epidemiology studies.
Study
Duration
Location
Correlation
Measure
CO
o3
no2
PM2.5
PM10
NOx TSP
Yana et al. (2003a)
T1-T1
Taiwan
NR
0.46
T1-T2
0.11
T1-T3
-0.06
T2-T1
0.32
T2-T2
0.45
T2-T3
0.08
T3-T1
0.00
T3-T2
0.31
T3-T3
0.45
Ebisu and Bell (2012)
P
Northeast and
Mid-Atlantic states
Pearson
-0.35 to
0.87
-0.76 to
0.66
-0.16 to
0.89
-0.50 to
0.77
-0.61 to
0.63
Hwana et al. (2011)
P
Taiwan
NR
0.15
0.13
0.41
0.53
Ha et al. (2001)
2 yr
Seoul, South Korea
Pearson
0.83
0.70
0.67
CO = carbon monoxide; M1 = 1st mo of unprotected intercourse; M2 = 2nd mo of unprotected intercourse; M12 = average of M1 & M2 (2 mo total); N02 = nitrogen dioxide;
NOx = the sum of nitric oxide and N02; NR = not reported; 03 = ozone; P = entire pregnancy; 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;TSP = total suspended particulates; T1 = 1st Trimester; T2 = 2nd trimester;
T3 = 3rd trimester; T1-T1 = correlation between 1st trimester S02 and copollutants.
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3.3.4.2
Spatial Variability 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.
3.3.5 Implications for Epidemiologic Studies of Different Designs
Exposure measurement error, which refers to the bias and uncertainty associated with
using concentration metrics to represent the actual exposure of an individual or
population, 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 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., Delfino et al.
(1996)1. Panel studies may also apply a microenvironmental model to represent exposure
to an air pollutant. 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.
Estimates of SO2 exposures are subject to errors that can vary in nature, as described in
Section 3.3.3. Classical error is defined as error scattered around the true personal
exposure and independent of the measured exposure. 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 is defined as error scattered around the exposure
surrogate (in most cases, the central site monitor measurement) and independent of the
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true value (Goldman etal.. 2011; Reeves et al.. 1998). Pure Berkson error is not expected
to bias the health effect estimate.
Definitions for Berkson-like and classical-like errors were developed for modeled
exposures. These errors depend on how exposure metrics are averaged across space.
Szpiro etal. (2011) defined Berkson-like and classical-like errors as errors sharing some
characteristics with Berkson and classical errors, respectively, but with some differences.
Specifically, Berkson-like errors occur when the modeled exposure does not capture all
of the variability in the true exposure. Berkson-like errors increase the variability around
the health effect estimate in a manner similar to pure Berkson error, but Berkson-like
errors are spatially correlated and not independent of predicted exposures, unlike pure
Berkson errors. Szpiro and Paciorek (2013) simulated Berkson-like errors' influence on
health effect estimates. For the case simulated where spatial variability in the exposure
estimates from measurements exceeded the spatial variability in the true exposures
(which were modeled to be uniform), the health effect estimates were biased away from
the null. For the case simulated where covariates were included in the health model but
not 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.
Classical-like errors can add variability to predicted exposures and can bias health effect
estimates in a manner similar to pure classical errors, but they differ from pure classical
errors in that the variability in estimated exposures is also not independent across space.
Exposure error can bias epidemiologic associations between ambient pollutant
concentrations and health outcomes and tends to widen confidence intervals around those
estimates (Sheppard et al.. 2005; Zeger et al.. 2000). The importance of exposure error
varies with study design and is dependent on the spatial and temporal aspects of the
design. Other 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.
3.3.5.1 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 3.3.1). Community time-series epidemiologic studies capturing the
exposures and health outcomes of a large cohort frequently use the ambient concentration
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at a central site monitor (Cacsm) as a surrogate for Ea in an epidemiologic model (Wilson
et al.. 2000). At times, an average of central site monitored concentrations is used for the
A', surrogate. For studies involving thousands of participants, it is not feasible to measure
personal exposures. Moreover, for community time-series epidemiology studies of
short-term exposure, the temporal variability in concentration is of primary importance to
relate to variability in the health effect estimate (Zcgcr et 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 SO2 concentrations across the study area
could attenuate an epidemiologic health effect estimate if the exposures are not correlated
in time with CLCsm 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. The relative risk (RR) per ppm was
negatively biased in the case of classical error (1-hour daily max SO2: -1.3%) and
negligibly positively biased in the case of Berkson error (1-hour daily max
SO2: 0.0042%). The 95% confidence interval range for RR per ppm was wider for
Berkson error (1-hour daily max SO2: 0.028) compared with classical error (1-hour daily
max SO2: 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 or outdoor
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 with distance from the monitoring site. The average of the
calculated random term was added to a central site monitoring time series (considered in
this study to be the base case) to estimate population exposure to SO2 subject to spatial
error. For the analysis with temporal autocorrelation considered, RR per ppm for
1-hour daily max SO2 dropped slightly to 1.0045 (95% CI: 1.0023, 1.0065) when it was
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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-hour daily max SO2. The results of Goldman et al. (2010) suggest that
spatial exposure error from use of central site monitoring data results in biasing the health
effect estimate towards the null, but the magnitude of the change in effect was small.
In a another study analyzing the influence of spatiotemporal variability among ambient or
outdoor 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 "true" ambient
concentrations of SO2 and other air pollutant measures. 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
measurements obtained directly from monitors at those sites. Geostatistical-simulated
concentrations were considered to be "true" 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, (3) performing a population-weighted
average across all monitoring sites, (4) performing an area-weighted average across all
monitoring sites, and (5) population-weighted averaging of the geostatistical simulation
(see Table 3-10). Goldman et al. (2012) observed that the exposure error was somewhat
correlated with both the measured and true values, reflecting both Berkson and classical
error components. For the central site monitor, the exposure errors were somewhat
inversely correlated with the true value but had relatively higher positive correlation with
the measured value. For the other exposure estimation methods, the exposure errors were
inversely correlated with the true value, while having positive but lower magnitude
correlation with the measured value. 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 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.
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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Table 3-10 The influence of exposure metrics on error in health effect estimates.
Exposure Estimation Approach
Bias [(Z-Z*)/Z]a
R2(Z, Z*)b
R[(Z-F), Z*]c
R[(Z-F), Zjf
so2
Central site monitor
0.76
0.13
-0.40
0.74
Unweighted average
0.45
0.16
-0.73
0.35
Population-weighted average
0.36
0.18
-0.79
0.25
Area-weighted average
0.15
0.18
-0.88
0.08
Geostatistical model—
population-weighted average
N/A
0.28
-0.86
-0.0003
N/A = not applicable; S02 = sulfur dioxide.
Note: Model errors were based on comparisons between measured data and simulated data at several monitoring sites. Errors
were estimated for a single central site monitor, various monitor averages, and values computed from a geostatistical model. Z
denotes the measured concentration, and Z* denotes the true concentration, considered here to be from the geostatistical model.
Bias in the exposure metric is given as the proportion of error between the measurement and true value to the measurement.
aData provided by the authors for Figure 5 of Goldman et al. (2012).
bData provided by the authors of Figure 4 of Goldman et al. (2012).
°Pearson correlation.
Source: Goldman et al. (2012).
In addition to the effect of the correlations and ratios themselves, spatial variation in their
values 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 metrics compared with the central site monitor and more classical error
for the central site monitor estimate compared with the other exposure assessment
techniques. Hence, more bias would be anticipated for the health effect estimate
calculated from the central site monitor, and more variability would be expected for the
health effect estimate calculated with the more spatially resolved methods. Differences in
the magnitude of exposure 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
(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
assessment methods but was more pronounced for the central site measurement 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 3.3.3.4 describes the influence of high MDL on the relationship between
measured ambient SO2 concentrations and personal exposure to ambient SO2. When
measurements are above detection limit, 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 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 (Zcgcr 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 3.3.3.4 also describes the influence of instrument accuracy and precision on the
relationship between ambient SO2 concentrations and personal exposure to ambient SO2.
Exposure measurement error related to instrument precision has a smaller effect on health
effect estimates in time-series studies compared with error related to spatial gradients in
the concentration because instrument precision would not be expected to modify the
ability of the instruments to respond to changes in 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 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
time series to simulate population estimates for ambient air concentrations subject to
instrument precision error in 1,000 Monte Carlo simulations. Very little changes in the
risk ratios were observed for 1-hour daily max SO2. For 1-hour daily max SO2
concentration, the RR per ppm of SO2 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 3.3.2 nonambient sources of SO2 are rare. Even in
microenvironments where nonambient exposure is substantial, such as in a room with a
kerosene heater, such exposure is unlikely to be temporally correlated with ambient SO2
exposure (Wilson and Suh. 1997). and therefore would not affect epidemiologic
associations between ambient SO2 and a health effect in a time-series study. In
simulations of a nonreactive pollutant, Sheppard et al. (2005) concluded that nonambient
exposure does not influence the health outcome effect estimate if ambient and
nonambient concentrations are independent. Because personal exposure to ambient SO2
is some fraction of the ambient concentration, it should be noted that effect estimates
calculated based on personal exposure rather than ambient concentration will be
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positively biased in proportion to the ratio of ambient concentration to ambient exposure,
and daily fluctuations in this ratio can widen the confidence intervals in the ambient
concentration effect estimate. Uncorrelated nonambient exposure will not bias the effect
estimate but may also widen the confidence intervals (Sheppard et al.. 2005; Wilson and
Suh. 1997).
3.3.5.2 Long-Term Cohort Studies
For cohort epidemiologic studies of long-term human exposure to SO2, where the
difference in the magnitude of the concentration is of most interest, if Ca,Csm is used as a
surrogate for A' ,, 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 SO2 concentrations across the study area could lead to bias in the health effect estimate
if CacSm is not representative of A',. This could occur, for example, if the study participants
are clustered in a location where their SO2 exposure is higher or lower than the exposure
estimated at a modeled or measurement site. CLCsm may be an acceptable surrogate for A'„
if the central site monitor is located in proximity to the study participants and the SO2
source (e.g., near the plume touch-down of a power plant) and spatial variability of the
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 or dedicated
measurements means that investigators must rely on fixed-site monitoring data or model
estimates. These concentrations may be used directly, averaged across counties or other
geographic areas, or used to construct geospatial or regression models to assign
concentrations to unmonitored locations. The number of long-term studies of SO2
exposure studies that permit evaluation of the relationship between long-term average
SO2 concentrations and personal or population exposure is limited, and the value of
short-term exposure data for evaluating long-term concentration exposure 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
exposures 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, as described in Section 3.3.3.2. However, lower
correlation between long-term exposures and ambient concentration could occur if
important exposure determinants change over a period of several years, including activity
pattern and residential air exchange rate.
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Minimization of error in the exposure estimate does not always minimize error in the
health effect estimate. Szpiro etal. (2011) evaluated bias and uncertainty of the health
effect estimate obtained when using correctly specified and misspecified exposure
simulation conditions. For comparison purposes, correct specification was considered to
be the use of three spatial prediction variables, and misspecification implied unmeasured
error in the model. LUR calculations were used to simulate exposure; the misspecified
model omitted a geographic covariate in the LUR. Szpiro et al. (2011) also reduced the
amount of variability in the third covariate in simulating the monitoring network data in
an additional set of simulations. Prediction accuracy of the exposure estimate was higher
for the correctly specified model compared with the misspecified model. However, the
health effect estimate was more variable for the correctly specified model compared with
the misspecified model when the variability in the exposure covariate in the monitoring
data decreased. The results of Szpiro et al. (2011) suggest that use of more accurately
defined exposure metrics in a cohort study does not necessarily improve health effect
estimates, and the influence of the refined metrics depends on the relative variability of
the exposure covariates. The Szpiro etal. (2011) simulations were for a generic air
pollutant but are relevant to SO2.
Error correction is a relatively new approach to estimate the correct standard error and to
potentially correct for bias in longitudinal cohort studies. Using this approach, Szpiro and
Paciorek (2013) established that two conditions must hold for the health effect estimate to
be predicted correctly: the exposure estimates from monitors must come from the same
underlying distribution as the true exposures, and the health effect model includes all
covariates relevant to the population. Szpiro and Paciorek (2013) performed several
simulations to investigate what happens when these conditions are violated. In one set of
simulations, the distribution of the exposure was varied. When the assigned exposure
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
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 improved the model prediction, even
when the true model contained several degrees of freedom (df). 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. Sections 2.4.1 and
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3.2.1 describe how the presence of copollutants can cause SO2 concentrations measured
using central site monitors to be overestimated and how high relative humidity can cause
SO2 measurements to be underestimated. Relative humidity would not be expected to
vary greatly within a city. However, local 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 estimate 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 exposures.
3.3.5.3 Panel Studies
Panel or small-scale cohort studies involving dozens of individuals may use more
individualized concentration measurements, such as personal exposures, residential
fixed-site indoor or outdoor measurements, or concentration data from local
study-specific monitors. Modeled concentrations are not typically used as exposure
surrogates in panel epidemiologic studies. Probabilistic, distribution-based approaches
are not designed to estimate exposures for specific individuals, such as might be needed
for panel epidemiologic studies. Another main disadvantage of the modeling approach is
that the results of modeling exposure assessment must be compared to an independent set
of measured exposure levels (Klepeis. 1999). In addition, resource-intensive development
of validated and representative model inputs is required, such as human activity patterns,
distributions of air exchange rate, and deposition rate. Therefore, modeled exposures are
used much less frequently in panel epidemiologic studies.
Section 3.3.3.4 describes the influence of high method detection limit on the relationship
between measured ambient SO2 concentrations and personal exposure to 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, as discussed. 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-based effect estimate or response
function relative to the ambient concentration-based response function (see
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Equation 3-6). However, if the ratio is approximately constant over time, the strength of
the statistical association would be similar for concentration- and exposure-based effect
estimates (Sheppard. 2005; Sheppard et al.. 2005).
3.4 Summary and Conclusions
The 2008 SOx ISA (U.S. EPA. 2008b) evaluated SO2 concentrations and exposures in
multiple microenvironments, discussed methods for estimating personal and population
exposure 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 in epidemiologic studies. Key findings were that
indoor concentrations and personal exposures 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 concentrations and indoor or personal
exposures. 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 estimates on epidemiologic
study results, high spatial variability of SO2 concentrations across an urban area results in
highly variable correlations among urban SO2 monitors. Low correlations between
individual monitor concentrations and the community average 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. 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 estimates in
epidemiologic studies. Multiple techniques can be used to assign exposure for
epidemiologic studies, including the use of central site monitor concentrations, personal
SO2 monitors, and various types of models. Each has strengths and limitations, as
summarized in Table 3-2. Central site monitors 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, 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 under-estimating
exposure for the population of interest. In all study types, use of central site monitors is
expected to widen confidence intervals. Personal SO2 monitors are a direct measure of
exposure, but low ambient SO2 concentrations often result in a substantial fraction of the
samples below the method detection limit for averaging times of 24 hours or less.
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Personal monitors also provide a relatively limited data set, making them more suitable
for panel epidemiologic studies.
Computational models can be used for exposure assessments of 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 SO2 concentrations measured at several
monitors. Other spatial interpolation techniques, such as kriging, also require SO2
concentrations from several monitors and apply more complex mathematical functions to
interpolate among monitors. LUR regresses measured concentrations on local variables
and then uses the resulting model to predict concentrations across a study area or at the
locations of specific receptors. As such, LUR enables higher spatial resolution of
predicted SO2 concentrations and requires more detailed input data compared with IDW
and LUR. Mechanistic models, such as dispersion models and CTMs, simulate the
transport and dispersion of SO2, and in the case of CTMs, the atmospheric chemistry. The
strength of mechanistic models is increased accuracy of the 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 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 (Table 3-4). 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 concentrations were relatively high (>0.75), suggesting that
variations in outdoor concentration are driving indoor concentrations. The bulk of the
evidence for personal-ambient SO2 relationships was available at the time of the previous
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ISA and showed a wide range of correlations between ambient concentration and
personal exposure, in part due to a large fraction of samples below the MDL in several
studies. 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.
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. Variation in SO2 concentrations
among various microenvironments means that the amount of time spent in each location,
as well as the level of exertion, will 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
exposure estimates.
Spatial and temporal variability in SO2 concentrations can contribute to exposure error in
epidemiologic studies, whether they rely on central site monitor data or concentration
modeling for exposure assessment. SO2 has low to moderate spatial correlations between
ambient monitors 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 undermining the assumption that
intra-urban concentration and exposure differences 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 concentrations to evaluate health effects.
Proximity of populations to ambient monitors may influence how well people's exposure
is represented by measurements at the monitors, although factors other than distance play
an important role as well. While many SO2 monitors are located near dense population
centers, other monitors are located near sources and may not fully represent SO2
concentrations experienced by populations in epidemiologic studies. Use of these near-
source monitors introduces exposure error into health effect estimates, although this error
can be mitigated by using average concentrations across multiple monitors 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 to moderate
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correlations with other daily NAAQS pollutant concentrations at collocated monitors
(Figure 2-35). However, a wide range of copollutant correlations is observed at different
monitoring sites, from moderately negative to moderately positive. In studies where daily
SO2 correlations with NO2 and CO were observed to be high, it is possible the data may
have been collected before recent rulemaking to reduce sulfur content in diesel fuel
(66 FR 5002). 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 contribute to variability in epidemiologic study results by biasing
effect estimates toward or away from the null and widening confidence intervals. The
importance of 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. Low spatial correlations
between central site monitors also contribute to exposure error in time-series studies,
potentially biasing the health effect estimate towards the null and widening the
confidence intervals around the health effect estimate. For long-term studies, bias of the
health effect estimate may occur in either direction depending on whether the monitor is
over- or under-estimating 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. 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, ambient and non-ambient concentration differences, and varying
activity patterns on personal exposure to SO2. Personal exposure measurements, such as
those made in panel epidemiologic studies, provide specific exposure estimates that may
more accurately reflect spatial variability, but sample size is often small and only a
limited set of health outcomes can be studied. Modeled concentration or exposure
estimates using various approaches offer an alternative to measurements, with the
advantage of estimating exposures 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 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.
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CHAPTER 4 DOSIMETRY AND MODE OF ACTION
4.1 Introduction
Chapter 4 begins with a discussion of the dosimetry of inhaled SO2 (Section 4.2). This
includes consideration of the chemical properties of SO2 and the processes of absorption,
distribution, metabolism, and elimination, followed by a brief discussion of the sources
and levels of exogenous and endogenous sulfite. The biological pathways that potentially
underlie health effects are described in the subsequent section, Modes of Action of
Inhaled SO2 (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.
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. 2008b). 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 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
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toxicodynamics. The discussion of dosimetry will conclude with a consideration of other
sources of SCh-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. Henry's law shows that the
amount of SO2 in the aqueous phase is directly proportional to the partial pressure or
concentration of SO2 in the gas phase. Although the solubility of most gases in the ELF is
not known, the Henry's law constant is known for many gases in water, and for SO2, it is
0.047 (mole/L) air/(mole/L) water at 37°C and 1 atmosphere (Hales and Sutter. 1973).
For comparison, Henry's law constant for O3 is 6.4 (mole/L) air/(mole/L) water 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. 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 (HSO3) anions, and sulfite ( SOf") 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).
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.
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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 (2013b) I.
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 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 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. The nasal absorption of
SO2 (1 ppm) was effectively 100% at 3.5 L/minute and 96.8% at 35 L/minute. 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
35S02 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.
The above studies indicate that the nasal passages remove SO2 more efficiently than the
oral pathway. With increasing physical activity, there is an increase in ventilatory rate
and a shift from nasal to oronasal breathing (Niinimaa et al.. 1981). Children and adults
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with asthma might be expected to have greater SO2 penetration into the lower respiratory
tract compared to healthy adults, due to differences in route of breathing. Children tend to
have a greater oral breathing contribution than adults at rest and during exercise (Bennett
et al.. 2008; Becquemin et al.. 1999). Chadha et al. (1987) found that most (11 of 12)
patients with asthma or allergic rhinitis also breathe oronasally at rest. In conjunction
with the shift in route of breathing, the pattern of SO2 absorption shifts from the upper
airways to the tracheobronchial airways.
The dose rate to the lower airways of children compared to adults is increased further
because children breathe at higher ventilation rates relative to their body mass than
adults. 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 adults (25-45 years of age; 0.266 m3/kg-day), ventilation rates normalized
to body mass are increased 1.5 times in normal-weight children (7-10 years of age;
0.388 m3/kg-day) and doubled in normal-weight infants (0.22-0.5 years of age;
0.534 m3/kg-day). These ventilation rates normalized to body mass should not be
confused with median daily ventilation rates which are 2.41, 7.34, and 10.8 L/minute in
infants (0.22-0.5 years of age), children (7-10 years of age), and adults (25-45 years of
age), respectively.
Although daily inhalation rates normalized to body weight (m3/kg-day) are decreased in
overweight individuals compared to those of normal weight, the absolute ventilation rates
(m3/day) are increased (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.
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%. 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
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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 and individuals with
asthma may be expected to have greater SO2 penetration into the lower respiratory tract
than healthy adults. Children may also be expected to have a greater intake dose of SO2
per body mass than adults.
4.2.3 Distribution
Once inhaled, SO2 is absorbed in the respiratory tract and S02-derived products are
widely distributed throughout the body, as was demonstrated in early studies using
radiolabeled 35S02. Although rapid extrapulmonary distribution of S02-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.. I960. 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 35S02 inhalation exposures. 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
35S02 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
35S02 exposure concentration, about 6% of the retained 35S 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 of35 S 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 35S02 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 S02-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
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transforms to sulfite/bisulfite at physiologic pH. Sulfite can diffuse across cell
membranes, and bisulfite can react with disulfide bonds (R1S-S-R2) to form thiols
(Ri-SH) and S-sulfonates (W2-S-SO5 ') 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 significant biologic
effects. Secreted airway mucins contain many disulfide bonds, and breaking these bonds
might alter their function and thereby alter mucociliary clearance.
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.. 1981). 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. Recently, a subacute study measured sulfite plus
S-sulfonate content of the lung, liver, and brain of mice exposed to 5, 10, and 20 ppm
SO2 for 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).
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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. 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 SCh-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 its concentration 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, intracellular 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. 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 significant lowering of the lethal dose for
intraperitoneally injected bisulfite. A deficiency in sulfite oxidase activity may lead to
toxicity even in the 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
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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.
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.. 1959). 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
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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 (1QM. 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.
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.
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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 SC>2-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 S02 to systemic sulfite levels
varies with age, activity level, and S02 concentration. Using median and 97.5th
percentile daily ventilation rates from Brochu etal. (2011). adults (25-45 years of age)
are estimated to receive 0.004 and 0.006 mg S02 per kg body mass, respectively, from a
full day exposure to 5 parts per billion (ppb) S02. As an upper-bound estimate for
ambient exposure in most locations, a full-day exposure to 75 ppb S02 (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 S02 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
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; Balchum 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
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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 SCh-derived products accumulate and are retained within the
respiratory tract; SC>2-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. 2008b) and
introduces the recent relevant literature. While this section highlights findings of studies
published since the 2008 SOx ISA (U.S. EPA. 2008b). 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 1.1) 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
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.
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SO2 is a highly reactive antioxidant gas. At physiologic pH, its hydrated forms include
sulfiirous 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 the dosimetry section, 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 neural reflexes, (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 Neural Reflexes
SO2 is classified as a sensory irritant in the mouse, guinea pig, rat, and human (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
has been demonstrated in humans breathing SO2 gas through the nose. Furthermore,
de sensitization of this response occurs with repeated exposure. Most sensory irritants,
including SO2, also cause bronchoconstriction, but at concentrations higher than those
stimulating nerve endings in the nose. These higher concentrations lead to greater
penetration of the gas in the respiratory tract.
SO2 is also classified as a pulmonary 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,
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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,
vago-vagal reflexes, 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 humans and cats resulted in a
decreased response to SO2 in the second exposure compared with the first. This
desensitization response mirrors that observed for decreased respiratory rate when SO2
exposure is restricted to the upper respiratory tract.
Early experiments demonstrated that SCh-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 SCh-exposed tracheotomized cats
(bypassing the nose). Histamine was proposed to play a role in SC>2-induced
bronchoconstriction (U.S. EPA. 1982a). but this hypothesis remains unconfirmed.
Hydrogen ions, sulfurous acid, sulfite, and bisulfite are all putative mediators of the
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 SCh-induced
effects. In two studies utilizing bilateral vagotomy, vagal afferents were found to mediate
the immediate ventilatory responses to SO2 (Wang et al.. 1996). but not the prolonged
bronchoconstrictor response (Barthelemv et al.. 1988). Other studies showed that atropine
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failed to block S02-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 SCh-induced bronchoconstriction are not
entirely understood. In nonasthmatics, 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
asthmatics, 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; Linn 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 SO;-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 asthmatics. 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 (Harries et al.. 1981). providing an
alternative mechanism for the reduction in SC>2-induced bronchoconstriction observed.
It has been proposed that inflammation contributes to the enhanced sensitivity to SO2
seen in asthmatics by altering autonomic responses (Tunnicliffe et al.. 2001). enhancing
mediator release (Tan et al.. 1982). and/or sensitizing C-flbers and rapidly adapting
receptors (Lee and Widdicombe. 2001). Whether local axon reflexes also play a role in
SCh-induced bronchoconstriction in asthmatics 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-flber-mediated neurogenic
inflammation may be unimportant in humans (Groneberg et al.. 2004; Widdicombe.
2003; Widdicombe and Lee. 2001).
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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 (Ranguclova 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
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
neural reflexes. 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 asthmatics.
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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-Hcimsoth 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 (Tunnicliffc 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 have generally been observed following chronic exposure to
SO2 concentrations of 10 ppm and higher (U.S. EPA. 2008b). Inflammatory changes have
been noted in some animal models following subacute exposure to 5-100 ppm SO2 (U.S.
EPA. 2008b). 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).
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 hours (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, which is involved in inflammation, and for bax (B-cell lymphoma
2-like protein 4), which is 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
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activity, the respiratory tract may be more resistant than the heart to the effects of inhaled
S02.
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. 2008b). However, in an animal model of allergic airway disease, SO2 exposure
enhanced airway responsiveness. In this study, sheep previously sensitized and
challenged with Ascaris suum extract were exposed to 5 ppm SO2 for 4 hours (Abraham
et al.. 1981). Airway responsiveness to carbachol was increased 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
(Devalia et al.. 1994a). However, following exposure to the two pollutants in
combination, subjects demonstrated an increase response to the inhaled allergen. Rusznak
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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
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 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 enhanced the effects of ovalbumin on levels of interferon
gamma (IFN-y) (decreased) and IL-4 (increased) in BALF and on IgE levels in serum
(increased). Because levels of IL-4 are 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
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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.
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
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 (2008b). 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 antiovalbumin
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
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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.
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 SCh-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.. 2011). 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
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sulfite treatment in house dust mite-challenged mice. In addition, interleukin-5 (IL-5)
levels, indicative of a Th2 response, and the ratio of II -5/1 FN-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, AHRto house dust
mite allergen occurred in mild allergic asthmatics 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.. 1994a). 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 etal.. 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 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
asthmatics. 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
S02.
4.3.4 Transduction of Extrapulmonary Effects
As described in the 2008 SOx ISA (U.S. EPA. 2008b). 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 asthmatics (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 (Woerman and
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Mendelowitz. 2013b) ("Woerman and Mendelowitz. 2013a). Whether these responses
were due to activation of neural reflexes or some other mechanism is not known.
Numerous studies over several decades have reported other extrapulmonary effects of
inhaled SO2 (U.S. EPA. 2008b). Most of these occur at concentrations far higher than
those measured in ambient air. Studies 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. 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 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 SC>2-induced lipid peroxidation in erythrocytes (Etlik et al..
1995). Additionally, a recent study reporting mitochondrial biogenesis in the brains of
rats exposed to SO2 for several weeks (Oin et al.. 2012) suggests that SO2 exposure
induces an adaptive response to oxidative stress in mitochondria of tissues distal to the
absorption site. Other recent studies report altered markers of brain inflammation and
synaptic plasticity following several weeks to months of exposure to 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 neural reflexes or mediated by circulating sulfite. A few studies employing
concentrations of 2 ppm SO2 or less have demonstrated effects that are consistent with
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sulfite-mediated redox stress, such 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 higher concentrations of SO2.
4.3.5 Role of Endogenous Sulfur Dioxide/Sulfite
Endogenous SCh/sulfite is a product of normal metabolism of sulfur-containing amino
acids (e.g., cysteine and methionine) (Hwang et al.. 2011). 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
(Hwang etal.. 2011). 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, decreased blood pressure (BP) and vascular remodeling in
hypertensive animals, and cytoprotective effects in myocardial ischemia-reperfusion
injury (Hwang et al.. 2011). Effects were in many cases concentration dependent. In vivo
studies generally were conducted using 5 ppm and higher concentrations of SO2 (or
sulfite/bisulfite) (Hwang et al.. 2011). In summary, endogenous SO2 is a newly
recognized gasotransmitter that may play important roles in cardiovascular and other
systems.
4.3.6 Mode of Action Framework
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
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from short-term and long-term exposures to SO2 (Chapter 5) are summarized as a part of
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 4-1 depicts the mode of action for respiratory effects due to short-term exposure to
S02.
so,
Legend
C 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
Note: S02 = sulfur dioxide. 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 4-1 Summary of evidence for the mode of action linking short-term
exposure to sulfur dioxide and respiratory effects.
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 neural reflexes,
release of inflammatory mediators, and modulation of allergic inflammation or
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sensitization. These key events may collectively lead to several endpoints, including
bronchoconstriction and AHR. Bronchoconstriction is characteristic of an asthma attack,
and AHR often leads to bronchoconstriction in response to a trigger. 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 healthy adults, this response occurred
primarily as a result of activation of neural reflexes mediated by cholinergic
parasympathetic pathways involving the vagus nerve. However, in adults with asthma,
evidence indicates that the response is only partially due to neural reflexes and that
inflammatory mediators such as histamine and leukotrienes also play an important role.
Activation of neural reflexes results in effects that are frequently measured in studies of
human occupational exposure to SO2. These effects 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 effects are not a part of the mode
of action described here. 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-fiber 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 acutely to SO2 (i.e., leukotriene-mediated increases in numbers of sputum
eosinophils). In an animal model of allergic airway disease, repeated exposure to 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, numbers of BALF
eosinophils, and serum IgE levels in this same model. In newborn allergic animals
exposed repeatedly to SO2, enhanced allergic inflammation was found, as was evidence
of AHR and airway remodeling. Other studies demonstrated that repeated exposure of
naive animals to SO2 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 (Th 2 polarization and allergen specific
IgE levels) and exacerbated allergic inflammatory responses (histopathology) in animals
subsequently sensitized and challenged with allergen. These changes in allergic
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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 4-2 depicts the mode of action for respiratory effects due to long-term exposure to
S02.
Allergic
sensitization
SO,
Legend
Pollutant
¦ Key Events
¦ Endpoints
¦ Outcomes
Recurrent
redox stress
Airway
inflammation
Airway
remodeling
Airway
hyperresponsiveness
trigger
New onset asthma/
Asthma exacerbation
Note: S02 = sulfur dioxide. 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 4-2 Summary of evidence for the mode of action linking long-term
exposure to sulfur dioxide and respiratory effects.
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.
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Evidence for this mode of action comes from studies in both naive and allergic
experimental animals. Exposure of naive newborn animals to SC^for several weeks
resulted in hyperemia in lung parenchyma, inflammation in the airways, and Th2
polarization, 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
over several days led to pathologic changes, including inflammatory cell influx. Th2
polarization and airway inflammation may set the stage for AHR. In addition, short-term
SO2 exposure 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 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 4-3 depicts the mode of action for extrapulmonary effects due to short-term or
long-term exposure to SO2.
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
Note: S02 = sulfur dioxide. 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 4-3 Summary of evidence for the mode of action linking exposure to
sulfur dioxide and extrapulmonary effects.
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Although SO2 inhalation results in extrapulmonary effects, there is uncertainty regarding
the mode of action underlying these responses. Evidence from controlled human
exposure studies points to SO2 exposure-induced activation/sensitization of neural
reflexes 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. Sulfite is
highly reactive and may be responsible for redox stress (possibly through autooxidation
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
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 all forms of oxidized sulfur including multiple
gaseous (e.g., SO2, SO3) and particulate species (e.g., sulfates), this chapter focuses on
evaluating the health effects associated with exposure to the gaseous sulfur oxides,
particularly SO2 As discussed in Section 2.1. gaseous sulfur oxide species other than SO2
are not present in ambient air at concentrations that are significant for human exposures.
Additionally, particulate species of sulfur oxides (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).
Sections within this chapter comprise evaluations of the epidemiologic, controlled human
exposure, and animal toxicological evidence of SCh-related respiratory (Section 5.2).
cardiovascular (Section 5.3). reproductive and developmental (Section 5.4. 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. 2015f) and 5S-2 (U.S. EPA. 2015g). Exposures including peak
(i.e., 5-10 minutes), short-term (i.e., up to 1 month), and long-term (i.e., more than
1 month to years) exposures are evaluated in the chapter. Sections for respiratory,
cardiovascular, and mortality effects are divided into subsections describing the evidence
for short- (inclusive of peak) and long-term exposures. The evidence for reproductive and
developmental and cancer effects is considered within one long-term exposure section,
although time-windows of exposure are 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 ISA (U.S. EPA. 2015e)l.
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, MI) that comprise a continuum of
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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
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 5.2.1.8V 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 SCh-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 ISA (U.S. EPA. 20156*) (Section 5.a), causal
determinations were informed through 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 to Chapter 5 of the ISA and are consistent with current best
practices employed in other approaches for reporting or evaluating health science data.1
1 For example, National Toxicology Program Office of Health Assessment and Translation approach (Roonev et al..
20141. Integrated Risk Information System Preamble (U.S. EPA. 201361. ToxRTool (Klimischetal.. 19971.
STROBE guidelines (von Elm et al.. 20071. Animals in Research: Reporting In Vivo Experiments guidelines
(Kilkenny et al.. 20101
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Additionally, these aspects are compatible with published EPA guidelines related to
cancer, neurotoxicity, reproductive toxicity, and developmental toxicity (U.S. EPA.
2005a. 1998. 1996. 1991).
The aspects described in the Annex 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 (U.S. EPA. 2015e).
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 modelled 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 3.3.4). Correlations of short-term SO2
concentrations with other NAAQS pollutants are generally low to moderate, but may
vary by location (Section 3.3.4.1). 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 based on considering the strength of inference from individual
studies and on integrating multiple lines of evidence. As detailed in the Preamble (U.S.
EPA. 2015c). 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.
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Controlled human exposure and animal toxicological studies can provide 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-hour averages, effect estimates were
scaled to a 10-ppb increase for SO2. For 1-hour daily maximum, 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
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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strength of inference from studies comprising the evidence base, and integrate multiple
lines of evidence, to characterize relationships between sulfur oxides and various health
effects.
5.2 Respiratory Morbidity
5.2.1 Short-Term Exposure
5.2.1.1 Introduction
The 2008 ISA for Sulfur Oxides (U.S. EPA. 2008b) concluded that there is a causal
relationship between respiratory morbidity 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 peak exposures of 5-10 minutes in exercising adults
with asthma.
The available epidemiologic studies consistently observed a relationship between
short-term SO2 exposure and respiratory effects in locations with ambient concentrations
below the previous 24-hour average NAAQS level of 140 ppb. Evidence was strongest
for increased respiratory symptoms in children and for respiratory-related hospital
admissions and ED visits, especially in children. However, most studies did not
adequately assess potential confounding by copollutants.
The current review brings forth additional studies, mostly epidemiologic, that add to the
evidence provided by the previous ISA and AQCD (U.S. EPA. 2008b. 1982a).
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.
New studies from all disciplines along with key studies from the previous reviews are
integrated in the following sections. These sections are organized by respiratory outcome
group (e.g., asthma exacerbation, respiratory infection, etc.) with a separate section for
general population studies in order to clearly characterize differences in the weight of
evidence and the extent of coherence among disciplines and related outcomes.
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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., FEVi and sRaw), increased symptoms (wheezing, cough, shortness of breath, etc.),
and AHR, as well as some subclinical effects such as inflammation.
As detailed in the previous 2008 ISA for Sulfur Oxides (U.S. EPA. 2008b). controlled
human exposure studies reported respiratory effects (i.e., respiratory symptoms and
decreased lung function) after short-term peak exposures, defined here as exposures from
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. The majority of
the controlled human exposure studies evaluating the respiratory effects of SO2 in healthy
adults demonstrated respiratory effects such as increased airway resistance and decreased
FEVi following exposures to concentrations >1.0-5.0 ppm. While children may be
especially susceptible to the respiratory effects of SO2 due to dosimetric considerations
(Section 4.2.2). there are no available controlled human exposure studies in children
under 12.
The evidence from controlled human exposure studies was supported by numerous
epidemiologic studies reporting an association between ambient SO2 exposures and
increased respiratory symptoms in children. The 2008 SOx ISA also noted that few
epidemiologic studies were performed that examined the effects of ambient SO2 exposure
on respiratory symptoms in adults; these studies had generally inconsistent findings. Most
studies did not adequately assess potential confounding by copollutants. In addition, the
2008 SOx ISA reported that respiratory morbidity, in the form of respiratory-related
hospital admissions and ED visits, including those for asthma, was generally positively
associated with short-term SO2 exposures, with associations often larger in magnitude
among children.
The 2008 SOx ISA (U.S. EPA. 2008b) provided limited evidence for a relationship
between SO2 concentrations and AHR, allergic responses, and inflammation in
individuals with asthma, supported by intervention studies and animal models of allergic
airway disease. Children and adults with atopy and asthma were found to be at greater
risk of effects, such as respiratory symptoms, lung function decrements, and AHR, in
association with SO2 exposure.
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Consistent with the body of evidence presented in the 2008 SOx ISA, recent studies
corroborate these respiratory effects related to short-term SO2 exposure in individuals
with asthma. Most of the recent evidence comes from epidemiologic studies that build
upon the evidence for ambient SCh-associated increases in respiratory hospital
admissions and ED visits and respiratory symptoms among children. In addition, there
are a few new animal toxicological studies. No new controlled human exposure studies in
individuals with asthma have been published since the last review: however there are a
few new controlled human exposure studies in individuals without asthma
(Section 5.2.1.6).
Lung Function Changes in Populations with Asthma
The 2008 SOx ISA (U.S. EPA. 2008b) 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. Previous controlled human exposure
studies also demonstrated a subset of individuals (i.e., responders) within this population
that are particularly sensitive to the effects of SO2. Epidemiologic evidence of lung
function decrements in association with SO2 concentrations consisted of a limited number
of short-term studies among adults. These studies found some associations between SO2
concentration and lung function but were limited by potential copollutants confounding.
There was 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. It is observed in free-breathing controlled human
exposure studies after approximately 5-10-minute exposures (defined here as peak
exposures) and can occur at concentrations as low as 0.2 ppm in exercising individuals,
with more consistent decrements seen at 0.4 ppm (U.S. EPA. 2008b). In contrast, healthy
adults are relatively insensitive to the respiratory effects of SO2 below 1 ppm
(Section 5.2.1.6). 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 that reaches the airways (Section 4.2.2). Generally speaking, the
majority of controlled human exposure studies are conducted in adults, given the ethical
considerations for exposing children to air pollutants, and thus provide limited
information about children's responses. Characteristics of controlled exposure studies in
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individuals with asthma are summarized in Table 5-1. Controlled exposure studies
individuals without asthma are discussed in Section 5.2.1.6.
Table 5-1 Study-specific details from controlled human exposure studies of
individuals with asthma.
Study
Disease Status3; n;
Sex; Age
(mean ± SD)
Exposure Details (Concentration;
Duration)
Outcomes
Examined
Balmes et al. (1987)
Asthma; n = 8; 6 M, 3 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 etal. (1983)
Asthma; n = 10; 8 M,
2 F
(22-36 yr)
0 or 0.5 ppm SO2 for 5 min with exercise
750 kilopond m/min
sRaw
Bethel etal. (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 etal. (1985)
Asthma; n = 19; 16 M,
3 F (22-46 yr)
0 or 0.25 ppm SO2 for 5 min during heavy
exercise [bicycle, 750 (n = 19) or 1,000
(n = 9) kg m/min]
sRaw
Gona et al. (1995)
Asthma; n = 14; 12 M,
2 F (18-50 yr)
0 or 0.5, 1.0 ppm SO2 with light, medium,
and heavy exercise (average ventilation 30,
36, and 43 L/min) for 10 min
sRaw, FEV1
symptoms
psychophysical
(stamina) changes
Gona et al. (1996)
Asthma; n = 10; 2 M,
8 F (19-49 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)
FEV1
symptoms
Gona etal. (2001)
Asthma; n = 12; 2 M,
10 F (20-48 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/day for 3 days)
sRaw
FEV1
symptoms
eosinophil counts in
induced sputum
Horstman etal. (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/m2 x min)
(2) 2 ppm SO2 for 10 min with exercise
(treadmill, 21 L/m2xmin)
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 Status3; n;
Sex; Age
(mean ± SD)
Exposure Details (Concentration; Outcomes
Duration) Examined
Jorres and Maanussen Asthma; n = 14; 10 M,
(1990) 4 F (34 ± 14 yr)
0 or 0.25 ppm NO2, or 0.5 ppm SO2 at rest
followed by challenge with 0.75 ppm SO2
during voluntary eucapnic hyperpnea.
Ventilation increased in 15 L/min steps,
each lasting 3 min
sRaw
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,
Vmax50, Vmax75,
symptoms
Koeniq et al. (1981)
Asthma; n = 8; 6 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 30 min exposure via mouthpiece
at rest followed by 10 min exercise on a
treadmill (sixfold increase in min vent)
FEV1, RT, FRC,
Vmax50, Vmax75,
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 sixfold
increase in Ve)
(2) 0.5 ppm SO2 + 1mg/m3 NaCI via a face
mask with no nose clip with exercise
conditions the same as above
FEV1, RT, FRC,
Vmax50, Vmax75,
symptoms
Koeniq et al.
(1987)
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 and 20 min
prior pretreatment (0 or 180 |jg albuterol)
FEV1, RT, FRC
symptoms
Koeniq et al.
(1988)
Asthma w/EIB; n = 8;
2 M, 6 F (13-17 yr)
1.0 ppm SO210 min (mouthpiece, treadmill,
35 L/min) with pretreatment (0, 20, 40,
60 mg cromolyn) 20 min prior
FEV1
RT
Koeniq et al.
(1992)
Asthma; n = 8; 2 M, 6 F
(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
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)
FEV1, RT, FRC,
V max50
symptoms
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 hyperventilation (20 L/min) for
4 min sequential exposures with
pretreatment with zafirlukast (0 or 20 mg) 2
or 10 h earlier
sRaw
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Table 5-1 (Continued): Study-specific details from controlled human exposure
studies of individuals with asthma.
Study
Disease Status3; n;
Sex; Age
(mean ± SD)
Exposure Details (Concentration;
Duration)
Outcomes
Examined
Linnetal. (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,
and FEV1,
symptoms
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)
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, 17, 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
Linnetal. (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
Linnetal. (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; 16 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.FEVi,
MMFR
symptoms
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Table 5-1 (Continued): Study-specific details from controlled human exposure
studies of individuals with asthma.
Study
Disease Status3; n;
Sex; Age
(mean ± SD)
Exposure Details (Concentration; Outcomes
Duration) 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
Exposures were repeated so total of eight
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-day after, and
1-week after-
symptom score,
self-rated activity
Immediately after
exposure—bronch-
ial reactivity
percentage change
in FEV 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 h
post, 1 week 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 (0, 20, or 200 mg)
sRaw
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Table 5-1 (Continued): Study-specific details from controlled human exposure
studies of individuals with asthma.
Study
Disease Status3; n;
Sex; Age
(mean ± SD)
Exposure Details (Concentration; Outcomes
Duration) Examined
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
Roger et al. (1985)
Asthma; n = 28; 28 M
(19-33 yr)
75 min Raw; sRaw; FVC,
0, 0.25, 0.5, or 1.0 ppm SO2 FEVl> FEp25-75,
Three 10 min periods of exercise 42.4 L/min ppp™*' '=E'=50,
Rubinstein et al.
(1990)
Asthma; n = 9; 5 M, 4 F 0 or 0.3 ppm NO2 during exercise followed sRaw, FVC, FEV1,
(23-34 yr) by challenge with 0.25 to 4.0 ppm SO2, in single-breath
doubling dose increments, for 4 min each nitrogen test
until sRaw increased by 8 U
Sheppard et al. (1983) Asthma; n = 8; 4 M, 4 F 0.5 ppm SO2 for 3 min eucapnic hyperpnea sRaw, symptoms
(22-36 yr)
Trenqa et al. (1999)
Asthma; n = 47; 14 M,
33 F (18-39 yr)
0.5 ppm SO2 for 10 min during moderate
exercise
Pulmonary function
tests (FEV1, FVC,
FEV1/FVC, PEF,
FEF25-75)
symptoms ratings
Trenqa 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 FVC, FEV1,
exercise (treadmill) FEF25-75, PEF,
symptoms
Tunnicliffe et al. (2003) Asthma; n = 12
Healthy; n = 12
0 or 0.2 ppm SO2 at rest
Symptoms, FEV1,
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; FEVt = forced expiratory volume in 1 second; FVC = forced vital capacity; FEF25-75% = forced
expiratory flow at 25-75% of forced vital capacity; FEF50% = forced expiratory flow at 50% of forced vital capacity; FEF75o/o = 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; N02 = nitrogen dioxide; 03 = ozone; PEF = peak expiratory flow; PEFR = peak
expiratory flow rates; ppm = parts per million; Raw = airway reistance; rH = relative humidity; RT = total respiratory resistance;
SD = standard deviation; sGAW = specific airway conductance; sRaw = specific airway resistance; S02 = sulfur dioxide;
VE = minute volume; Vmax = maximal flow of expired vital capacity.
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1
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8
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21
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28
29
30
Several investigators (Linnetal.. 1990; Linn et al.. 1988; Linn et al.. 1987; Bethel et al..
1985; I inn et al.. 1984a; I inn et al.. 1983b) demonstrated >100% increase in sRaw or
>15% decrease in FEVi after 5-10-minute exposures to low concentrations
(0.2-0.3 ppm) of SO2 in exercising adults with asthma, with effects being more
pronounced following 5-10-minute exposures to 0.4-0.6 ppm SO2 (Linn et al.. 1990;
Magnussen et al.. 1990; I inn et al.. 1988; I inn et al.. 1987; Roger et al.. 1985; I inn et al .
1983b).
SCh-induced bronchoconstriction occurs rapidly and is transient. Investigators have
shown bronchoconstriction to occur in as little as 2 minutes from the start of exposure in
exercising adults with asthma (Horstman et al.. 1988; Balmes et al.. 1987; Sheppard et
al.. 1983). However, when exposure to SO2 occurs during 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; I inn et al.. 1987; I inn et al.. 1984c). I inn 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; 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; Linn et al.. 1984b).
Responders versus nonresponders to SO2. It is 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 asthmatic adults in controlled human exposure
studies experiencing S02-induced decrements in lung function and
respiratory symptoms.
SO2 Exposure Ventil- ,
Cone Duration No. ation
(ppm) (min) Subj (L/min)
Cumulative Percentage of
Responders (Number of Subjects)1
sRaw
>100% t >200% Is >300% t
FEV1
Lung
Func
>15% nU >20% >30% <4/
Study
Respiratory
Symptoms:
Supporting
Studies
0.2 5
10
10
0.25 5
5
10
0.3
10
10
10
10
23
-48
sRaw 9% (2)2
Linn etal. (1983b)
40
-40
sRaw 5% (2)
Linn et al. (1987)3
40
-40
FEVi 13% (5) 5% (2) 3% (1) Linn et al. (1987)
19
-50-60 sRaw 32% (6) 16% (3) 0
-80-90 sRaw 22% (2) 0
Bethel etal. (1985)
Bethel etal. (1985)
28
-40
sRaw 4% (1) 0
Roger et al. (1985)
20
-50
sRaw 10% (2) 5% (1) 5%(1) Linn et al. (1988V
21
-50
sRaw 33% (7) 10% (2) 0
Linn et al.
1990)4
Limited
¦ evidence of
SC>2-induced
¦ increases in
respiratory
¦ symptoms in
some
asthmatics:
(Linn et al.
.(1990); Linn
etal. (1988);
. Linn et al.
(1987):
. Schachter et
al. (1984):
. Linn et al.
20
-50
FEVi 15% (3) 0
0
Linn et al.
1988)
21
-50
FEVi 24% (5) 14% (3) 10% (2) Linn et al.
1990)
(1983b))
0.4
0.5
5
10
10
5
10
10
23
-48
sRaw 13% (3) 4% (1) 0
Linn et al.
1983b)
40
-40
sRaw 25% (10) 8% (3) 3%(1)
Linn et al.
1987)
40
-40
FEVi 30% (12) 25% (10) 13% (5) Linn et al. (1987)
10
-50-60 sRaw 60% (6) 40% (4) 20% (2) Bethel et al. (1983)
28
-40
sRaw 18% (5) 4% (1) 4% (1) Roger et al. (1985)
45
-30
sRaw 36% (16) 16% (7) 13% (6) Magnussen et al. (1990)6
Stronger
¦ evidence
with some
¦ statistically
significant
¦ increases in
respiratory
¦ symptoms:
Balmes et
¦ al. (1987)6.
Gong et al.
(1995)
(Linn et al.
(1987): Linn
et al.
(1983b))
Roger et al.
(1985)
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Table 5-2 (Continued): Percentage of asthmatic adults in controlled human
exposure studies experiencing S02-induced decrements in
lung function and respiratory symptoms.
Cumulative Percentage of
Responders (Number of Subjects)1
sRaw
>100% t >200% Is >300% t
FEVi Respiratory
SO2 Exposure Ventil Symptoms:
Cone Duration No. ation Lung Supporting
(ppm) (min) Subj (L/min) Func >15% nU >20% si/ >30% nU Study Studies
LO
CD
O
23
-48
sRaw
39% (9)
26% (6)
17% (4)
Linn et al.
(1983b)
10
40
-40
sRaw
35% (14)
28% (11)
18% (7)
Linn et al.
(1987)
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
FEV1
53% (21)
48% (19)
23% (9)
Linn et al.
(1987)
10
20
-50
FEV1
55% (11)
55% (11)
5% (1)
Linn et al.
(1988)
10
21
-50
FEV1
43% (9)
38% (8)
14% (3)
Linn et al.
(1990)
1.0 10
28
-40
sRaw
50% (14)
25% (7)
14% (4)
Roaeretal. (1985)5
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)
10 10-40 sRaw 60% (6) 20% (2) 0 Kehrl et al. (1987)
Cone = concentration; FE\A| = forced expiratory volume in 1 second; func = function ppm = parts per million; sRaw = specific
airway resistance; S02 = sulfur dioxide; subj = subject.
1Data presented from all references from which individual data were available. Percentage of individuals who experienced greater
than or equal to a 100, 200, or 300% increase in specific airway resistance (sRaw), or a 15, 20, or 30% decrease in FEV-i. Lung
function decrements are adjusted for effects of exercise in clean air (calculated as the difference between the percent change
relative to baseline with exercise/S02 and the percent change relative to baseline with exercise/clean air).
2Numbers in parenthesis represent the number of subjects experiencing the indicated effect.
3Responses of mild and moderate asthmatics reported in Linn et al. (1987) have been combined. Data reported only for the first
10 min period of exercise in the first round of exposures.
4Analysis includes data from only mild Linn et al. (1988) and moderate Linn et al. (1990) asthmatics who were not receiving
supplemental medication.
5One 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.
1 Horstman et al. (1986) reported that individuals required different concentrations of SO2
2 to produce a doubling of sRaw (>100%) compared to clean air exposure [provocative
3 concentration of SO2, PC(SC>2)] (Figure 5-1). This study described the distribution of
4 individual bronchial sensitivity to SO2, measured by sRaw, in 27 subjects with asthma.
5 Individuals were exposed to concentrations of SO2 between 0 and 2 ppm for 10 minutes
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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 pm to elicit a
response. These data demonstrate substantial inter-individual variability in sensitivity to
the bronchoconstrictive effects of SO2 in exercising adults with asthma.
100-
— 75-
X
X
50.
25-
X
X
X
X
1 1 1 r™
0.5 0.75 1.0 2.0
PC(S02) (ppm)
0.25
5.0
10.0
Note: Each data point represents the PC(S02) for an individual subject. PC(S02) = provocative concentration of S02.
Source: Horstman et al. (1986).
Figure 5-1 Distribution of individual airway sensitivity to SO2. The cumulative
percentage of subjects is plotted as a function of PC(S02), which
is the concentration of SO2 that provoked a 100% increase in
specific airway resistance compared to clean air.
Further 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
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5-10 minutes with elevated ventilation rates (Linn et al.. 1990; Linn et al.. 1988; Linn et
al.. 1987; Roger etal.. 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.]
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
S02.
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). Of the
56 individuals included in the Goodman et al. (2015) analysis, eight individuals (14%)
were identified as sensitive to the effects of SO2. 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. Additionally, the statistical assumption of homoscedasticity, as noted in the
analysis by Johns et al. (2010). was not met for their linear regression determination of
individual subject slopes of response versus concentration in the Goodman et al. (2015)
study. For these statistical reasons, no further consideration is given to the Goodman et
al. (2015) study.
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Table 5-3 Percent change in post- versus pre-exposure measures of FEVi
relative to clean air control after 5-10-minute exposures to SO2
during exercise.
FEV1
so2
Concentration
ppm
Number of
Exposures
95% Confidence Limits
% 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
0.005 a b
0.4
37
-17.4
-21.3
-13.6
<0.001 ab
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 second; ppm = parts per million; S02 = sulfur dioxide.
A 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 (sRaw) relative to clean air control after
5-10-minute exposures to SO2 during exercise.
sRaw
SO2
Number of
Exposures
95% Confidence Limits
concentration
ppm
% 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 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.
(1983b). Linn et al. (1987). Linn et al. (1988). Linn et al. (1990). and Roger et al. (1985).
indicates significance at 0.05 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, while the moderate/severe group consisted of adults that
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, it is likely that this moderate/severe group would be classified as
moderate by today's classification standards (Johns et al.. 2010; Reddel. 2009). Linn et
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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. 2008b). Medications evaluated include
short-acting and long-acting beta-adrenergic bronchodilators (Gong et al.. 1996; Linn et
al.. 1990; Linn 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 do not completely eliminate these
effects.
Asthma symptoms are difficult to control in severe asthmatics due to inadequate drug
therapy or poor compliance among those who are on regular medication Rabe et al.
(2004). Individuals with mild asthma are less likely to use asthma medication than those
with a more severe diagnosis (Q'Byrne. 2007; Rabe et al.. 2004). Therefore, it is
reasonable to conclude that individuals with asthma exposed to SO2 are at high risk of
experiencing adverse respiratory effects and proper therapies may not be accessible
during exposure.
Adolescents. There is evidence that adolescents (ages 12-18 years) with asthma or atopic
symptoms are responsive to coexposures of SO2 and sodium chloride (NaCl) droplet
aerosol (Koenig et al.. 1992; Koenig et al.. 1990; Koenig et al.. 1988; Koenig et al.. 1987;
Koenig et al.. 1983. 1981; Koenig et al.. 1980). Exposure concentrations in these
controlled human exposure studies ranged from 0.1 to 1.0 ppm SO2. Koenig et al. (1983)
observed that the average lung function changes ranged from 8-47% in exercising
adolescents (12 to 16 years old) with asthma after a 10-minute exposure to 0.5 ppm
SO2 + 1 mg/m3 NaCl droplet aerosol. 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.
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Mixtures effects. The health effects of SO2 can be potentially modified by its interaction
with other pollutants during or prior to exposure. A few controlled human exposure
studies have examined the interactive effects of O3 and SChboth sequentially and in
combination. Exercising adolescents with asthma exposed to 0.1 ppm SO2 for 15 minutes
after a 45 minutes exposure to 0.12 ppm O3 had a significant decrease (8%) in FEVi (8%)
(p < 0.05), a significant increase in 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 (Koenig 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 SC>2-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.
Epidemiologic Studies
Adults. The 2008 SOx ISA (U.S. EPA. 2008b) evaluated a limited number of studies
focusing on short-term SO2 exposures and changes in lung function among adults. These
studies found some associations between SO2 concentration and lung function but were
potentially limited by copollutant confounding. One study suggested that elderly adults
with both atopy and asthma were at greater risk of changes in lung function in association
with short-term SO2 exposure (Boezen et al.. 2005). Recent studies, listed in Table 5-5
and summarized below, were consistent with the 2008 SOx ISA (U.S. EPA. 2008b).
finding some positive associations. However, these were for various lags and treatment
groups with no overall consistency among the studies. Copollutant confounding was
evaluated in some studies and a few of the observed associations were relatively
unchanged with their inclusion.
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Table 5-5 Summary of recent panel studies examining associations between
SO2 concentrations and lung function among adults with asthma.
Study
Location and Study Population
Years and N Measure of SO2
MeanS02 and
Upper
Concentration
Level Adjusted Effect Estimate
Qian et al.
(2009b)
United States
1997-1999
Patients (12-65 yr)
with persistent
asthma were
recruited from six
university-based
ambulatory care
centers as part of
the
NHLBI-sponsored
Salmeterol Off
Corticosteroids
Study
N = 154
Fixed site
monitors; 24-h
mean SO2
concentrations
Mean (SD): 4.8
(3.9) ppb
75th percentile:
6.2 ppb
Max: 31.5 ppb
Change (95% CI) in PEF (L/min)
per 10-ppb increase in mean SO2
Lag 0
Lag 1
Lag 2
-0.12 (-2.96, 2.71)
-2.15 (-4.97, 0.68)
-0.65 (-3.45, 2.14)
Lag 0-2: -1.93 (-5.56, 1.70)
Canova et al. Random sample of Fixed site Mean (SD): 1.36 Quantitative effect estimates for
(2010) asthmatic residents monitors; 24-h (1.09) ppb SO2 not reported
Padua, Italy (15-44 yr) mean SO2 Max: 4.88 ppb
n — An concentrations
2004-2005 N _ 4U
Wiwatanadate
and
Liwsrisakun
(2011)
Chiang Mai,
Thailand
2005-2006
Asthmatics
(13-78 yr) who
experienced
symptoms within the
year prior and lived
within 10 km of the
air monitoring station
N = 121
Fixed site
monitors; 24-h
mean SO2
concentrations
SO2
Mean (SD): 1.73
(0.62) ppb
90th percentile:
2.42 ppb
Max: 3.89 ppb
Change in evening PEFR (95%
CI) per 10-ppb increase in SO2
Lag 4: 9.1 (3.8, 14.4)
Lag 6: 6.4 (1.0, 11.8)
Change in average PEFR (95%
CI) per 10-ppb increase in SO2
Lag 4: 5.0 (0.4, 9.6)
Lag 6: 5.7 (1.0, 10.3)
*Note: quantitative estimates only
provided for lags 2, 4, and 6 when
results were "statistically
significant"
CI = confidence interval; NHLBI = National Heart, Lung, and Blood Institute; PEF = peak expiratory flow; PEFR = peak expiratory
flow rates.
1 A study in the United States assessed airway obstruction effects of ambient air pollutants,
2 considering daily self-measured peak expiratory flow (PEF) from patients with persistent
3 asthma during the 16 weeks of active treatment in the Salmeterol Off Corticosteroids
4 Study trial (Qian et al.. 200%). The three therapies were an inhaled corticosteroid
5 (triamcinolone acetonide), an inhaled long-acting beta-agonist (salmeterol xinafoate), and
6 placebo. The participants were nonsmokers aged 12 through 63 years. Using the
7 U.S. EPA AIRS database, the central site air pollution monitors closest to the ZIP code
8 centroid of the participants' home addresses were identified. The effect estimates in each
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medication group were obtained from the main effect and pollutant*medication group
interaction models. Inverse associations were found between PEF and SO2
concentrations, but only in the triamcinolone group. No association between SO2
concentration and PEF was observed in the placebo group, and no association was
observed in the salmeterol group with the exception of a positive association for the lag
averaged over 0-2 days. None of the other pollutants (NO2, PM10, O3) demonstrated an
association with PEF among those in the triamcinolone group. NO2 at lag 0 and PM10 at
lag 2 were associated with PEF in the salmeterol and placebo groups, respectively. In the
two-pollutant models, SO2 associations with PEF in the triamcinolone group were
relatively unchanged with inclusion of PM10, O3, or NO2 in the models. The only
correlation coefficient reported for SO2 was with NO2 (correlation coefficient 0.58);
correlations with the other copollutants are likely to be low-moderate (Section 3.3.4. IV
Although attenuation of the effect estimates is possible due to the use of central site
monitors, this is likely to affect results from all treatments to the same extent. Overall,
this study indicates that an association appears to be present between SO2 concentration
and PEF in this study, but only among one of the treatment groups.
Studies of SO2 concentrations and lung function among adults asthmatics have also been
conducted in Italy and Thailand, but neither examined associations stratified by therapies
as was done by Qian et al. (2009b) (described above). Canova et al. (2010) tested the
effects of exposure to air pollutants on lung function in a panel of adult asthmatics that
was followed for five 30-day periods during 2 years in Italy. Despite point estimates in
the inverse direction, overall null associations were observed for morning and evening
PEF and FEV1. However, in this study population of mainly moderate-to-severe
asthmatics (in which two-thirds of the patients used corticosteroid medications), no
analysis excluding subjects with steroid use was attempted due to the small sample size
(n = 40) although control for corticosteroid use was included in the models. Thus, the
small study size, lack of supervised lung function measurements, and inability to stratify
by medication use may have diminished the ability to find associations in this study. Null
associations were noted for PM10 and NO2; an inverse association was observed between
CO and PEF but not FEVi. Copollutant models were not assessed for SO2, but SO2 was
correlated with PM10 (correlation coefficient 0.509), NO2 (correlation coefficient 0.535),
and CO (correlation coefficient 0.499). Wiwatanadate and Liwsrisakun (2011) assessed
the effects of air pollutants on the peak expiratory flow rates (PEFR) among asthmatic
patients aged 13-78 years in Thailand. No association was reported between SO2 and
morning PEFR. SO2 was positively associated with evening PEFR and daily average
PEFR using lag Days 4 and 6. In multipollutant models adjusting for PM2 5 or PM10 and
NO2, as well as O3 in the models for evening PEFR, the association with lag Day 4
remained. The association with change in PEFR was present only for lag Day 6 and did
not persist in the multipollutant model adjusting for PM10, CO, and NO2. SO2 was
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somewhat correlated with CO (correlation coefficient 0.38), PMio (correlation coefficient
0.23), and NO2 (correlation coefficient 0.23) but not O3 (correlation coefficient -0.04) or
PM2 5 (correlation coefficient -0.07). In summary, some associations were observed
between SO2 concentration and measures of lung function but no consistency was found
among the studies.
Children. The 2008 SOx ISA (U.S. EPA. 2008b) presented multiple studies of SO2
concentration and lung function among children with asthma, but overall the results were
inconsistent. Similarly, recent studies published since the 2008 SOx ISA, described
below and in Table 5-6. have also reported inconsistent findings for the association
between SO2 concentrations and lung function measures among children with asthma.
However, some of these studies did report positive associations that were relatively
unchanged with the inclusion of other pollutants.
Table 5-6 Summary of recent epidemiologic studies examining associations
between SO2 concentrations and lung function among children with
asthma.
Mean SO2 and
Study Upper
Location and Study Study Population Measure of Concentration
Years Design and N SO2 Level Adjusted Effect Estimate
O'Connor et Panel Children (5-12 yr)
al. (2008) study living in low-income
census tracts in big
cities across the
United States with
persistent asthma
and atopy, who made
up the Inner-City
Asthma Study Cohort
N = 861
Fixed site
monitors; 24-
mean SO2
concen-
trations
Mean and upper
level concentration
values NR. The
authors stated
"SO2
concentrations
were well below
the 24-h average
NAAQS"
Estimated change (95% CI)
per 10-ppb increase in
5-day average
concentration SO2
FEV1, % predicted
-1.29 (-2.04, -0.54)
PEFR, % predicted
-1.73 (-2.49, -0.96)
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Table 5-6 (Continued): Summary of recent epidemiologic studies examining
associations between S02Concentrations and lung
function among children with asthma.
Study
Location and
Years
Study
Design
Study Population
and N
Measure of
SO2
Mean SO2 and
Upper
Concentration
Level
Adjusted Effect Estimate
Dales et al. Longit- School children
(2009) udinal (9-14 yr) with asthma
Canada repeated and living in a home
2005 measures with no cigarette
smoke
N = 182
Fixed site
monitors; 24-h
mean SO2
concen-
trations
Mean (SD): 6.0
(4.8) ppb
75th percentile:
8.8 ppb
Percent change (95% CI) in
bedtime FEV1 per 10-ppb
increase in SO2
0-12 h averaging time:
-0.17 (-0.98, 0.65)
12-24 h averaging time:
0.00 (-0.92, 0.93)
0-24 h averaging time:
-0.14 (-1.03, 0.76)
Percent change (95% CI) in
morning FEV1 per 10-ppb
increase in SO2O-8 h
averaging time: 0.63 (-0.28,
1.55)
Diurnal change (95% CI) in
FEV1 per6.5-ppb increase
in SO2
0-12 h averaging time:
-1.41 (-2.73, -0.08)
Liu et al.
(2009b)
Canada
2005
Longit- School children
udinal (9-14 yr) with asthma
repeated from a nonsmoking
measures household
N = 182
Fixed site
monitors; 24-h
mean SO2
concent-
rations
Median 1-day
average: 4.5 ppb
95th percentile:
15.5 ppb
Median 2-day
average: 5.0 ppb
95th percentile:
13.0 ppb
Median 3-day
average: 5.6 ppb
95th percentile:
13.8 ppb
Lag 1 [percent change
(95% CI) per 10-ppb
increase in SO2]
FEV1
0.2 (-1.7, 2.0)
FEF25 -75%
-1.4 (-4.7, 2.1)
2-day average (percent
change (95% CI) per 10-
ppb increase in SO2)
FEV1
-0.2 (-2.0, 1.7)
-4.2 (-10.0, 1.9)FEF25-75%
-3.2 (-8.2, 2.1)
3-day average (percent
change (95% CI) per 10-
ppb increase in SO2)
FEV1 -0.6 (-3.5, 2.5)
FEF25 -75%
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Table 5-6 (Continued): Summary of recent epidemiologic studies examining
associations between S02Concentrations and lung
function among children with asthma.
Study
Location and
Years
Study
Design
Study Population
and N
Measure of
SO2
Mean SO2 and
Upper
Concentration
Level
Adjusted Effect Estimate
Wiwatanadate
and
Trakultivakorn
(2010)
Chiang Mai,
Thailand
2005-2006
Panel Children (4-11 yr)
study with asthma who
experienced
symptoms within the
previous year and
lived within 25 km of
the air monitoring
station
N = 31
Fixed site
monitors; 24-h
mean SO2
concentrations
SO2 mean (SD):
1.73 (0.62) ppb
90th percentile:
2.42 ppb
Max: 3.89 ppb
Estimated change in
morning PEFR (95% CI) per
10-ppb increase in SO2
Lag 1: 7.1 (-10.7, 24.8)
Lag 2: -8.0 (-26.1, 10.1)
Lag 3: -8.9 (-26.7, 8.9)
Lag 4: -13.9 (-31.4, 3.6)
Lag 5: -0.8 (-18.2, 16.7)
Lag 6: -3.0 (-20.5, 14.4)
Estimated change in
evening PEFR (95% CI) per
10-ppb increase in SO2
Lag 0
Lag 1
Lag 2
14.7)Lag 3:
3.6)
Lag 4
Lag 5
Lag 6
-8.1 (-25.3, 9.2)
3.0 (-14.2, 20.3)
-2.5 (-19.7,
13.4 (-30.4,
¦1)
-21.2 (-38.3, -4.
7.5 (-9.8, 24.8)
-7.7 (-25.0, 9.6)
Estimated change in
average PEFR (95% CI) per
10-ppb increase in SO2
Lag 0
Lag 1
Lag 2
Lag 3
Lag 4
Lag 5
Lag 6
-0.3 (-15.0, -14.5)
7.9 (-7.0, 22.8)
-2.3 (-17.7, 13.1)
-9.1 (-24.0, 5.7)
-17.5 (-32.2, -2.8)
4.9 (-9.9, 19.7)
-8.6 (-23.4, 6.2)
Estimated change in
APEFR (95% CI) per 10-
ppb increase in SO2
Lag 0
Lag 1
Lag 2
Lag 3
Lag 4
Lag 5
Lag 6
1.4 (-4.7,7.4)
-1.7 (-7.9, 4.6)
2.5 (-3.9, 8.8)
5.3 (-0.9, 11.5)
-7.3 (-13.3, -1.2)
-3.7 (-9.8, 2.5)
1.6 (-4.5, 7.8)
CI = confidence interval; FEF25-75% = forced expiratory flow at 25-75% of forced vital capacity; FE\A = forced expiratory volume in
1 second; N = population number; NAAQS = National Ambient Air Quality Standards; NR = not reported; PEFR = peak expiratory
flow rates; ppb = parts per billion; SD = standard deviation; S02 = sulfur dioxide.
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O'Connor et al. (2008) investigated associations between fluctuations in outdoor air
pollution and lung function among inner-city children with asthma. They considered data
in seven U.S. urban communities from children with persistent asthma, who performed
2-week periods of twice-daily pulmonary function testing every 6 months for 2 years, and
utilized daily air pollution measurements from the EPA AIRS database. In
single-pollutant models, higher 5-day average concentrations of SO2 were associated with
lower lung function measured by FEV1 and PEFR. No association was reported using a
1-day average. Associations were also observed for increased PM2 5 and NO2
concentrations, but not CO or O3 concentrations. The correlation coefficients between
SO2 and other air pollutants were 0.37 for PM2 5, -0.43 for O3, 0.59 for NO2, and 0.32 for
CO. SO2 was not included in multipollutant models. In Canada, Dales et al. (2009)
investigated the acute effects of air pollution on lung function among children with
asthma who lived in cigarette smoke-free homes. They recorded morning and evening
FEVi for 28 consecutive days. SO2 concentration was associated with diurnal changes in
FEVi, but SO2 concentration was not associated with morning or evening FEVi. Diurnal
changes were also observed for NO2 and PM2 5 but not for maximum O3 concentrations
(correlation coefficients for these pollutants and SO2 were 0.31, 0.43, and -0.02,
respectively). In copollutant models, the association between SO2 and diurnal changes in
FEViwas relatively unchanged with adjustment of O3, NO2, and PM2 5. Using the same
study population, Liu et al. (2009b) reported that no association was observed between
SO2 concentration and FEVi or FEF25-75% = forced expiratory flow at 25-75% of forced
vital capacity (FEF25-75%). Low to moderate correlations were observed between SO2 and
NO2 (correlation coefficient 0.18) and PM25 (correlation coefficient 0.56), but not O3
(correlation coefficient -0.02). In copollutant models, results were similar to those of
single pollutant models when adjusted by NO2 or O3, but slight increases in the point
estimate were observed when adjusted for PM2 5. Associations did not vary by
corticosteroid use. Other pollutants (NO2, O3, PM2 5) were associated with lung function
outcomes. Wiwatanadate and Trakultivakorn (2010) examined the association between
daily air pollution concentrations and PEFRs among children with asthma in Thailand.
SO2 concentrations (lag Day 4) were inversely associated with evening PEFR, change in
PEFR, and daily average PEFR. No association was present between SO2 concentrations
and morning PEFR. No associations were present between PM2 5, PM10, CO, or NO2 and
PEFR values, but an inverse association was observed with O3 concentration and average
daily PEFR. In a two-pollutant model for daily average PEFR, the association with SO2
was relatively unchanged with the inclusion of O3. No other copollutant models were
described. SO2 was somewhat correlated with PM10 (correlation coefficient 0.23), CO
(correlation coefficient 0.38), and NO2 (correlation coefficient 0.23). No correlation was
observed between SO2 and PM2 5 (correlation coefficient -0.07) or O3 (correlation
coefficient -0.04). In summary, studies performed among children with asthma have
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reported some associations between SO2 concentration and lung function but overall
results are inconsistent.
Summary of Lung Function Changes
Controlled human exposure studies provide strong evidence for SC>2-induced lung
function decrements in adults with asthma under increased ventilation conditions.
Short-term peak exposures of 5-10 minutes to 0.2-0.3 ppm SO2 resulted in
approximately 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). Exposure of exercising individuals with asthma to 5-10-minute peak
exposures of SO2 at concentrations >0.4 ppm results in moderate or greater decrements in
lung function, in terms of FEV1 and sRaw, in approximately 20-60% of tested
individuals in these studies.
Both older and more recent epidemiologic studies among adults and children with asthma
demonstrate some positive associations between SO2 concentrations and lung function,
but there is no overall consistency among the studies. These studies all utilized 24-hour
averages derived from fixed site monitors. SO2 concentration is highly variable in space,
which could lead to reduced correlation of the SO2 measured at central site monitors with
the true SO2 exposure. For time-series epidemiologic studies, this typically leads to
attenuation and uncertainty of the health effect estimate (Section 3.3.5.1). Studies are
limited by potential exposure measurement error and potential copollutant confounding.
However, some of the studies in adults and children reported positive associations that
were relatively unchanged with the inclusion of other pollutants.
Respiratory Symptoms in Populations with Asthma
The 2008 SOx ISA (U.S. EPA. 2008b) 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. A limited number of epidemiologic
studies evaluated the relationship between SO2 concentrations and respiratory symptoms
among adults with asthma or other chronic respiratory symptoms (U.S. EPA. 2008b).
While some of these studies reported positive associations, most did not examine
copollutant confounding. Evidence for a relationship between SO2 concentrations and
respiratory symptoms was stronger in children with asthma or other chronic respiratory
symptoms. Some studies found that atopic adults and children were also at greater risk of
respiratory symptoms in association with SO2 exposure. Recent epidemiologic studies
report inconsistent findings.
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Controlled Human Exposure Studies
The 2008 SOx ISA and the Supplement to the Second Addendum (1986) described
several studies that evaluated respiratory symptoms in individuals with asthma following
controlled human exposures to SO2 (U.S. EPA. 2008b. 1994) (Tables 5-2 and 5-7).
Incidence or severity of respiratory symptoms (i.e., cough, chest tightness, throat
irritation) were shown to increase with increasing concentrations of SO2 between 0.2 and
0.6 ppm in individuals with asthma when exercising, with symptom category scores
reaching statistical significance at concentrations >0.4 ppm SO2. These studies are briefly
described below.
Table 5-7 Study-specific details from controlled human exposure studies of
respiratory symptoms.
Study
Disease Status3; n;
Sex; Age
(mean ± SD)
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/day for 3 days)
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
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Table 5-7 (Continued): Study-specific details from controlled human exposure
studies of respiratory symptoms.
Study
Disease Status3; n;
Sex; Age
(mean ± SD)
Exposure Details (Concentration;
Duration)
Time of Symptom
Assessment
Koenia et al. (1981)
Asthma;
n = 8; 6 M,
(14-18 yr)
2 F;
0 or 1 ppm SO2 with 1 mg/m3 of NaCI Before, during, and
droplet aerosol, 1 mg/m3 NaCI droplet immediately after
aerosol for 30 min exposure via mouthpiece exposure
at rest followed by 10 min exercise on a
treadmill (sixfold increase in Ve)
Koeniq et al. (1983)
Phase 1:
Asthma with EIB;
n = 9; 6 M, 3 F;
(12-16 yr)
Phase 2:
Asthma with EIB;
n = 7 (Sex NR);
(12-16 yr)
Phase 1:
1 g/m3 of NaCI droplet aerosol, 1 ppm SO2
1 mg/m3 NaCI, 0.5 ppm SO2 + 1mg/m3 NaCI
for 30 min exposure via mouthpiece at rest
followed by 10 min exercise on treadmill
(five- to sixfold increase in Ve)
Phase 2:
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)
Before and
immediately after
exposure
Koenia et al. (1987)
Asthma with EIB;
n = 10; 3 M, 7 F;
(13-17 yr)
0 or 0.75 ppm SO2 (mouthpiece) with Before and
exercise (33.7 L/min) for 10 min and 20 min immediately after
prior pretreatment (0 or 180 |jg albuterol) pretreatment and
exposure
Koenia et al. (1992)
Asthma;
n = 8; 2 M, 6 F;
(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
Before and
immediately after
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)
Before and
immediately after
exposure
Linn et al. (1983b)
Asthma;
n = 23; 13 M, 10 F;
(23.3 ± 4.4 yr)
0, 0.2, 0.4, or 0.6 ppm SO2 with low
humidity or high humidity for 10 min with
exercise (bicycle, 5 min 50 L/min)
0 or 0.6 ppm SO2 with warm air or cold air
with exercise (bicycle, 50 L/min, ~5 min)
Before and
immediately after
exposure
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, 17, and -6°C,
rH 80% with exercise (bicycle, 50 L/min,
~5 min)
Before, during,
immediately after,
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
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Table 5-7 (Continued): Study-specific details from controlled human exposure
studies of respiratory symptoms.
Disease Status3; n;
Sex; Age
Exposure Details (Concentration;
Time of Symptom
Study
(mean ± SD)
Duration)
Assessment
Linnetal. (1984b)
Asthma;
Phase 1:
Phase 1:
Phase 1 (Pilot)
0, 0.2, 0.4, or 0.6 ppm SO2 at 5°C, 50, and
before and
n = 8; 4 M, 4 F;
85% rH with exercise (5 min, 50 L/min)
immediately after
(24.5 ± 3.9 yr)
Phase 2:
exposure
Phase 2
0 and 0.6 ppm SO2 at 5° and 22°C, 85% rH
Phase 2:
n = 19 M, 5 F;
with exercise (5 min, 50 L/min)
before, immediately
(24.0 ± 4.3 yr)
after, 1 day after,
and 1 week after
exposure
Linnetal. (1985b)
Asthma;
0 or 0.6 ppm SO2 at 21 and 38°C, 20 and
Before, immediately
n = 22; 13 M, 9 F;
80% rH with exercise (~5 min, 50 L/min)
after, and 24 h after
(23.5 ± 4.0 yr)
exposure
Linn et al. (1985a)
Asthma with COPD;
0, 0.4, or 0.8 ppm SO2 for 1 h with exercise
Before, during,
n = 24; 15 M, 9 F;
(2 x 15 min, bicycle, 18 L/min)
immediately after,
(60 yr;
24 h after, and
Range: 49-68 yr)
7 days after
exposure
Linnetal. (1987)
Healthy;
0, 0.2, 0.4, or 0.6 ppm SO2 for 1 h with
Before and during
n = 24; 15 M, 9 F;
exercise (3 * 10-min, bicycle, -40 L/min)
exposure (after first
(18-37 yr)
exercise and after
Atopic (sensitive to
last exercise)
common airborne
allergens but not
asthmatic);
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)
Linnetal. (1988)
Asthma;
Three pretreatment groups
Before, immediately
n = 20; 13 M, 7 F;
(1) metaproterenol sulfate, (2) placebo
after, 10 min,
(28 ± 5 yr)
(3) no treatment
30 min, 60 min,
0,0.3, and 0.6 ppm SO2 for 10 min with
120 min, 24 h, and
exercise (bike, 50 L/min)
1 week after
exposure
Linnetal. (1990)
Asthma;
0, 0.3, or 0.6 ppm SO210 min with exercise
Before exposure,
n = 21; 6 M, 15 F;
(50 L/min)
after pretreatment,
(34.8 ± 8.9 yr)
(1) low medication use (2) normal (3) high
immediately after,
usual medication supplemented by inhaled
30 min after, and
metaproterenol before exposure
60 min after
exposure
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Table 5-7 (Continued): Study-specific details from controlled human exposure
studies of respiratory symptoms.
Study
Disease Status3; n;
Sex; Age
(mean ± SD)
Exposure Details (Concentration;
Duration)
Time of Symptom
Assessment
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 ppm
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.
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 little as 3 minutes to 0.5 ppm SO2 via mouthpiece during
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eucapnic hyperpnea (Ve= 0 L/minute), where seven out of eight individuals with asthma
developed respiratory symptoms (Balmes et al.. 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.
Collectively, these studies report SC>2-induced respiratory symptoms in exercising adults
with asthma exposed to 0.2-0.6 ppm SO2, with the most consistent evidence from
exposures ranging from 0.4-0.6 ppm SO2 (Table 5-2).
Epidemiologic Studies
Adults. The 2008 SOx ISA (U.S. EPA. 2008b) included studies of respiratory symptoms
among adults with asthma and other chronic respiratory symptoms. While some studies
observed positive associations in this population, overall the results were inconsistent.
One study suggested that elderly adults with both atopy and asthma were at greater risk
of respiratory symptoms in association with short-term SO2 exposure (Boezen et al..
2005).
Since the previous review, a study has reported mixed findings related to the timing of
symptoms in association with SO2 concentrations among adults with asthma.
Wiwatanadate and Liwsrisakiin (2011) assessed the effects of air pollutants on respiratory
symptoms among asthmatic patients aged 13-78 years in Thailand. Daily air pollutant
concentrations were measured by a monitor from the Ministry of National Resources and
Environment in the city's center. Although study participants were required to live within
10 km of this air monitor, the correlation between SO2 at the central site monitor and a
receptor located several km away may be low (Section 3.3.5.1). possibly biasing the
effect estimate downwards and underestimating the magnitude of the effect. The mean
SO2 concentration during the study period was 1.73 ppb (SD 0.62 ppb) with a 90th
percentile of 2.42 ppb and a maximum of 3.89 ppb. SO2 concentrations were inversely
associated with daytime asthma symptoms [OR 0.341 (95% CI 0.123, 0.945) per 10 ppb]
but positively associated with nighttime asthma symptoms [OR 4.374 (95% CI 1.495,
12.799) per 10 ppb], although neither of these associations remained after adjustment for
other pollutants. Other pollutants (PM2 5, PM10, and O3) also demonstrated this inverse
association with daytime symptoms, and NO2 (lag 5) and PMiomax (lag 5) were
positively related with nighttime symptoms. SO2 was somewhat correlated with CO,
PM10, and NO2 (correlation coefficients 0.38, 0.23, and 0.23, respectively), but not O3 or
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PM2 5 (correlation coefficients -0.04 and -0.07, respectively). This recent study does not
offer strong evidence of an association between SO2 concentration and respiratory
symptoms among adults and adds to the inconsistency observed among studies reported
in the 2008 SOx ISA.
Children. The 2008 SOx ISA (U.S. EPA. 2008b) stated that, "...epidemiologic studies
provided evidence for an association between ambient SO2 exposures and increased
respiratory symptoms in children, particularly those with asthma or chronic respiratory
symptoms." One study suggested that children with both atopy and asthma were at
greater risk of respiratory symptoms in association with short-term SO2 exposure
(Boezen et al.. 1999). Recent studies among children with asthma reported some positive
associations between SO2 concentrations and certain respiratory symptoms, although
inconsistencies were observed. Concentrations of recent studies were either not reported
or were lower than some of the highest quality studies included in the 2008 SOx ISA
(U.S. EPA. 2008b). These recent studies are detailed below and in Table 5-8.
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Table 5-8 Summary of recent epidemiologic studies examining associations
between SO2 concentrations and respiratory symptoms among
children with asthma.
Study Location Study Population Measure of
and Years and N SO2
Mean SO2
and Upper
Concentration
Level Adjusted Effect Estimate
O'Connor et al.
(2008)
United States
1998-2001
Inner-City Asthma
Study Cohort:
children (5-12 yr)
living in low-income
census tracts in big
cities across the
U.S. with persistent
asthma and atopy,
N = 861
Fixed site
monitors; 24-h
mean SO2
concentrations
Mean and upper
level concentration
values not reported.
The authors stated
"SO2 concentrations
were well below the
24-h average
NAAQS"
RR (95% CI) for asthma-related
symptoms per 10-ppb increase
in 5-day average concentration
SO2
Wheeze-cough,
days/2 weeks
1.05 (0.89, 1.23)
Nighttime asthma,
days/2 weeks
1.11 (0.91, 1.36)
Slow play,
days/2 weeks
1.06 (0.88, 1.27)
Missed school,
>1 day/2 weeks vs.
0 days/2 weeks
1.10 (0.82, 1.49)
(Spira-Cohen
(2013); Spira-
Cohen et al. (2011))
United States
2002-2005
Children (10-12 yr)
with asthma
attending fifth grade
at one of four
elementary schools
N =40
Fixed site
monitors; 24-h
mean SO2
concentrations
NR
RR (95% CI) per 10-ppb
increase in peak morning SO2
Cough
1.12 (1.05, 1.21)
Wheeze
1.16 (1.04, 1.30)
Shortness of breath
1.10 (0.98, 1.23)
Dales et al. (2009)
Canada
2005
School children
(9-14 yr) with
asthma and living in
a home with no
cigarette smoke.
N = 182
Fixed site
monitors; 24-h
mean SO2
concentrations
SO2 mean (SD):
6.0 (4.8) ppb
75th percentile:
8.8 ppb
OR (95% CI) for chest tightness
for daily SO2 concentrations of
>8.8 ppb vs. <2.3 ppb
1.30 (1.06, 1.58)
*Note: authors stated that
difficulty breathing, cough, and
wheeze were not "significant at
p < 0.05"; no quantitative results
were provided
CI = confidence interval; N = population number; NAAQS = National Ambient Air Quality Standards; NR = not reported; OR = odds
ratio; ppb = parts per billion; RR = relative risk; SD = standard deviation; S02 = sulfur dioxide.
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Recent studies in the U.S. have reported some associations between SO2 concentrations
and respiratory symptoms, but the SO2 concentrations were not reported, making
comparisons and interpretations difficult. O'Connor et al. (2008) investigated associations
between fluctuations in outdoor air pollution and asthma morbidity among inner-city
children with asthma using data collected in seven U.S. urban communities in
conjunction with the Inner-City Asthma Study. Central site air pollution measurements
were obtained from EPA's AIRS. The authors noted SO2 concentrations were "well
below the 24-hour average National Ambient Air Quality Standards," but exact
concentrations were not given. When examining wheeze-cough, nighttime asthma, slow
play, and missed school, no positive associations with SO2 concentrations were observed.
Associations were observed with other pollutants, such as CO. SO2 was not included in
any multipollutant models of respiratory symptoms, and the correlation coefficients
between SO2 and other air pollutants were 0.37 for PM2 5, -0.43 for O3, 0.59 for NO2, and
0.32 for CO. Another study in the United States examined the associations of respiratory
health symptoms with increased exposure to air pollution among inner-city children with
asthma (Spira-Cohen. 2013; Spira-Cohen et al.. 2011). Gaseous pollutants were measured
using mobile air monitors located adjacent to the children's schools. Peak morning SO2
concentrations were associated with increased risk of both cough and wheeze, but not
with shortness of breath; however, mean concentrations are not reported. Associations
were also present for EC and O3 concentrations, but not PM2 5 nor NO2 concentrations.
No correlation coefficients among the pollutants were reported, and the only mention of
copollutant models with SO2 is in a copollutant model with EC. Thus, recent studies in
the United States have reported inconsistency in associations between SO2 concentration
and respiratory symptoms among children with asthma. It is difficult to place these
studies in the context of what is known, as no mean SO2 concentrations are reported.
A study in Windsor, Ontario studied respiratory symptoms among children who were
asthmatic and lived in a home without cigarette smoke (Dales et al.. 2009). The highest
category of SO2 concentration (>8.8 ppb) was positively associated with chest tightness
compared to the lowest category of SO2 concentration (<2.3 ppb). SO2 concentrations
were not associated with breathing difficulty, cough, or wheeze. No other pollutants
[NO2, PM2.5, O3 concentrations (correlation coefficients with SO2: 0.31, 0.43, and -0.02,
respectively)] were associated with any respiratory symptoms and copollutant models for
respiratory symptoms were not performed. This study demonstrated a positive association
between SO2 concentrations and chest tightness but no other respiratory symptoms.
Summary of Respiratory Symptoms
Controlled human exposure studies provide strong evidence for the effects of SO2
exposure on respiratory symptoms in adults with asthma under increased ventilation
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conditions. Short-term peak exposures of 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 ranging from 0.4-0.6 ppm SO2.
Both older epidemiologic studies and a more recent epidemiology study among adults
with asthma demonstrate some positive associations between SO2 concentrations and
respiratory symptoms, but there is no overall consistency among the studies.
Epidemiologic studies among children with asthma provide stronger evidence for
respiratory symptoms in relation to SO2 concentrations. However, findings of recent
studies are more inconsistent than those of older ones among children. A potential reason
for the more inconsistent findings is that concentrations of recent studies were either not
reported or were lower than some of the highest quality studies included in the 2008 SOx
ISA (U.S. EPA. 2008b).
Airway Responsiveness
The term "airway responsiveness" refers to the ability of the airways to narrow in
response to constrictor stimuli. A characteristic feature of individuals with asthma is an
increased sensitivity of their airways to respond to this type of stimuli (i.e., AHR). The
2008 SOx ISA (U.S. EPA. 2008b) provided limited evidence for a relationship between
SO2 concentrations and AHR in asthmatics and in animal models of allergic airway
disease. Only a few studies have evaluated this endpoint, and only one new toxicological
study is available for review since the last ISA. The following section details the
information from all lines of evidence. Additional support for the relationship between
allergic responses and AHR is found in Section 4.3.3 (Mode of Action) and
Section 5.2.1.2 (Subclinical Effects Underlying Asthma). Furthermore, evidence that
repeated SO2 exposure may induce AHR and allergic responses in naive animals is found
in Section 5.2.1.6.
As described in the 2008 SOx ISA (U.S. EPA. 2008b). two epidemiologic studies provide
evidence for a relationship between SO2 concentrations and AHR. One study, conducted
in Norway, reported an association between SO2 concentration in the previous 24 hours
and AHR among children with atopy (Sovseth et al.. 1995). The other study found an
association between SO2 concentration and AHR among adult asthmatics in England
(Taggart et al.. 1996).
The evidence from controlled human exposure studies consists of two studies in adults
with asthma. Both of these studies provide evidence for AHR 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 did not affect airway responsiveness to house dust mite allergen immediately after a
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6-hour exposure at rest. Because volunteers were exposed at rest, it is unlikely that a high
enough concentration of SO2 or NO2 reached the airways to cause an effect. However,
following exposure to the two pollutants in combination, volunteers demonstrated an
increase response to inhaled allergen (Devalia et al.. 1994b). Rusznak et al. (1996)
confirmed these results in a similar study and found that AHR to dust mites persisted up
to 48-hour post-exposure. These results provide further evidence that SO2 may elicit
effects beyond the short time period typically associated with this pollutant.
Animal toxicological studies in several species have shown that a single exposure to SO2
at a concentration of up to 10 ppm failed to increase airway responsiveness to a challenge
agent (U.S. EPA. 2008b) (Section 5.2.1.6). While the majority of these experiments were
conducted in naive animals, one study compared effects in naive and allergic animals.
Animal models of allergic airway disease share many of the phenotypes associated with
asthma. In this study, naive sheep and sheep previously sensitized and challenged with
Ascaris suum extract were exposed to 5 ppm SO2 for 4 hours (Abraham et al.. 1981).
Airway responsiveness to carbachol was increased 24 hours, but not immediately, after
SO2 exposure in allergic sheep but not in the naive sheep. These results suggest that the
context of allergic inflammation may confer a greater sensitivity to SO2 effects.
Furthermore, the results suggest that AHR may require some time to develop. A recent
animal toxicological study in a different model of allergic airway disease also
demonstrated AHR following SO2 exposure (Song et al.. 2012). In this study, newborn
rats were previously sensitized and challenged with ovalbumin and repeatedly exposed to
2 ppm SO2 (4 hours/day, 28 days). Results are described below because AHR developed
in the context of allergic inflammation.
Summary of Airway Responsiveness
In summary, a few studies provide evidence for a relationship between exposure to SO2
and AHR. This includes a limited number of older epidemiologic studies and a recent
toxicological study involving repeated exposure to SO2 in an animal model of allergic
airway disease.
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. As summarized in
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1 Section 5.2.1. the 2008 SOx ISA (U.S. EPA. 2008b) included the first thorough
2 evaluation of respiratory morbidity in the form of respiratory-related hospital admissions
3 and ED visits, including asthma. These studies reported generally positive associations
4 with short-term SO2 exposures, with associations often larger in magnitude for children
5 (see Figure 5-2. Table 5-9). Additionally, SO2 associations with asthma hospital
6 admissions and ED visits were found to remain generally robust in copollutant models
7 with PM, NO2, and O3.
Study
Sonetal. (2013)
Sonetal. (2013)
Lin et al. (2004)
Samoli et al. (2011 )a
Sheppard et al. (1999; 2003)
Son et al. (2013)a
Son et al. (2013)a,b
Wilson et al. (2005)
Ito et al. (2007)
Peel et al. (2005)
AT SDR (2006)
Stieb et al. (2009)
Villeneuve et al. (2007)
Wilson et al. (2005)
Jalaludin et al. (2008)
Li et al. (2011)
Strickland et al. (2010)
Jaffe et al. (2003)
Wilson et al. (2005)
Location
8 South Korean cities
8 South Korean cities
Bronx County, NY
Athens, Greece
Seattle, WA. .
8 South Korean cities
8 South Korean cities
Portland, ME
Manchester, NH
New York, NY
Atlanta, GA
Bronx, NY
Manhattan, NY
7 Canadian cities
Edmonton, Canada
Portland, ME
Manchester, NH
Syndey, Australia
Detroit, MI
Atlanta, GA
3 Ohio cities
Portland, ME
Manchester, NH
Portland, ME
Manchester, NH
Age
All
0-14
0-14
0-14
5-14
<65
75+
All
0-14
75+
All
All
All
All
All
All
All
>2
0-14
0-14
1-14
5-34
15-64
15-64
65+
65+
0-3
NR
0
0
0-3
0-3
0-3
0-3
0
0
0-1
0-2
0-4
0-4
2
0-4
0
0
0-1
0-4c
0-4d
0-2
NR
0
0
0
0
Hospital Admissions
10.0 20.0
0 Increase
Note: a = results were presented for four seasons; however the summer and winter estimates represented the largest and smallest
estimates across seasons; b = this estimate is for allergic disease, which includes asthma; c = time-series results;
d = case-crossover results. Black circles = U.S. and Canadian studies evaluated in the 2008 SOx Integraged Science Assessment
(ISA); red circles = recent asthma hospital admissions and emergency department (ED) visits studies. Circle = all-year;
diamond = warm/summer months; square = cool/winter months.
Figure 5-2 Percent increase in asthma hospital admissions and ED 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-hour average or 40-ppb increase in 1-hour
maximum sulfur dioxide concentrations.
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Table 5-9 Corresponding risk estmates for studies presented in Figure 5-2.
Study
Location
Age
(yrs)
Avg Time
Season
Lag (days)
% Increase
(95% CI)
Hospital Admissions
Son etal. (2013)a
Eight South
Korean cities
All
24-h avg
All
0
5.3 (-0.4, 13.0)
Summer
19.1 (-18.3, 73.9)
Winter
-3.4 (-13.8, 8.5)
Son etal. (2013)
Eight South
Korean cities
0-14
24-h avg
All
0
5.6 (-3.7, 16.0)
Lin et al. (2004V
Bronx County,
NY
0-14
24-h avg
All
NR
19.0 (11.0, 29.0)
Samoli et al. (2011)a
Athens, Greece
0-14
24-h avg
All
0
16.5 (2.3, 32.6)
Summer
46.6 (-13.8, 149.3)
Winter
20.2 (0.7, 43.5)
Samoli etal. (2011)
Athens, Greece
5-14
24-h avg
All
0
18.0 (-5.6, 47.5)
(Sheppard (2003):
Sheppard et al.
(1999))b
Copenhagen,
Denmark
0-18
24-h avg
All
0-4
2.1 (-4.0, 6.2)
Son etal. (2013)
Eight South
Korean cities
75+
24-h avg
All
0-3
6.7 (-4.1, 18.7)
Son etal. (2013)ac
Eight South
Korean cities
All
24-h avg
All
0-3
3.1 (-3.7, 10.7)
Summer
4.9 (-4.1, 14.9)
Winter
8.2 (-3.7, 21.9)
ED Visits
Wilson et al. (2005)b
Portland, ME
All
24-h avg
All
0
11.0 (2.0, 20.0)
Manchester, NH
6.0 (-4.0, 17.0)
I to et al. (2007)b
New York, NY
All
24-h avg
All
0-1
8.9 (4.9, 13.0)
Warm
35.9 (22.2, 51.2)
Cold
8.5 (4.8, 12.4)
Peel et al. (2005)b
Atlanta, GA
All
1-h max
All
0-2
0.2 (-3.2, 3.4)
ATSDR (2006)b
Bronx, NY
All
24-h avg
All
0-4
10.0 (5.0, 15.0)
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Table 5-9 (Continued): Corresponding risk estimates for studies presented in
Figure 5-2.
Study
Location
Age
(yrs)
Avg Time
Season
Lag (days)
% Increase
(95% CI)
Manhattan, NY
-1.0 (-11.0, 11.0)
Stieb et al. (2009)
Seven Canadian
cities
All
24-h avg
All
2
-2.1 (-5.4, 1.4)
Villeneuve et al.
(2007)
Edmonton,
Canada
>2
24-h avg
All
0-4
-15.7 (-21.5, -6.5)
Warm
-6.5 (-21.5, 6.8)
Cold
-21.5 (-29.6, -9.7)
Wilson et al. (2005)b
Portland, ME
0-14
24-h avg
All
0
11.0 (2.0, 20.0)
Manchester, NH
6.0 (-4.0, 17.0)
Jalaludin et al.
(2008)
Sydney,
Australia
1-14
24-h avg
All
0-1
29.7 (14.7, 46.5)
Warm
6.4 (-8.4, 25.0)
Cold
20.3 (1.4, 42.3)
Li et al. (2011)
Detroit, Ml
2-18
24-h avg
All
0-4d
20.5 (8.9, 33.2)
0-4e
22.8 (12.6, 33.7)
Strickland et al.
(2010)
Atlanta, GA
5-17
1-h max
All
0-2
4.2 (-2.1, 10.8)
Warm
10.8 (0.7, 21.7)
Cold
0.3 (-7.4, 9.0)
Jaffe et al. (2003)b
Three Ohio cities
5-34
24-h avg
Summer
NR
6.1 (0.5, 11.5)
Wilson et al. (2005)b
Portland, ME
15-64
24-h avg
All
0
12.0 (1.0, 23.0)
Manchester, NH
3.0 (-8.0, 16.0)
Portland, ME
65+
12.0 (-15.0, 47.0)
Manchester, NH
12.0 (-29.0, 75.0)
avg = average; CI = confidence interval; ED = emergency department; NR = not reported.
aResults were presented for four seasons; the summer and winter estimates represented the largest and smallest estimates for
each season.
bStudies evaluated in the 2008 SOx Integrated Science Assessment (ISA).
This estimate is for allergic disease, which includes asthma.
dTime-series analysis results.
eCase-crossover analysis results.
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
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 often only a small percentage of respiratory-related ED visits will be admitted to
the hospital. Therefore, ED visits may represent potentially less serious, but more
common, outcomes. Additionally, it is important to note that when focusing on children
(i.e., defined age ranges <18 years of age) in the evaluation of asthma hospital admissions
and ED visit studies, the results should be viewed with caution if they include children
<5 years of age in the study population because of the difficulty in reliably diagnosing
asthma within this age range (NAEPP. 2007).
For each of the studies evaluated in this section, Table 5-11 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
focus of this evaluation because they were conducted in small single-cities, encompassed
a short study duration, had insufficient sample size, and/or did not examine potential
copollutant confounding. The full list of these studies, as well as study specific details,
can be found in Supplemental Table 5S-3 (U.S. EPA. 201510.
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Table 5-10 Study-specific details and mean and upper percentile concentrations
from asthma hospital admission and ED 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
Hospital Admissions
Lin et al. (2004)a
Bronx
County, NY,
U.S.
(1991-1993)
Avg of SO2
concentrations
from two
monitoring sites
24-h avg
Cases: 16.8
Controls: 15.6
NR
NR
(Sheppard (2003):
Sheppard et al.
f 1999Va
Seattle, WA,
U.S.
(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
Two-pollutant
models
examined:
none
Son et al. (2013)
Eight South
Korean cities
(2003-2008)
Avg of hourly
ambient SO2
concentrations
from monitors in
each city
24-h avg
CO
i
CM
CO
NR
Correlation (r):
PM10: 0.5
o3: -0.1
NO2: 0.6
CO: 0.6
Two-pollutant
models
examined:
none
(Samoli 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
Two-pollutant
models
examined:
PM10, SO2,
NO2, O3
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Table 5-10 (Continued): Study-specific details and mean and upper percentile
concentrations from asthma hospital admission and ED
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)a
Cincinnati,
When more than
24-h avg
Cincinnati: 13.7
Max:
Correlations (r)
Cleveland,
one monitoring
Cleveland:
Cincinnati: 50
(Range across
and
station operating
15.0
Cleveland: 64
cities)
Columbus,
in a day, monitor
Columbus: 4.2
Columbus: 22
NO2: 0.07-0.28
OH, U.S.
reporting
Os: 0.14-0.26
(1991-1996)
highest 24-h avg
SO2
PM10:
concentration
0.29-0.42
used
Two-pollutant
models
examined:
none
Ito et al. (2007)a
New York,
Average SO2
24-h avg
7.8
75th: 10
Correlations
NY, U.S.
concentrations
95th: 17
(r): NR
(1999-2002)
across
Two-pollutant
19 monitors
models
examined:
PM2.5, NO2, O3,
CO
ATSDR (2006)a
Bronx, NY,
SO2 concentra-
24-h avg
Manhattan: 12
NR
Correlations
U.S.
tions from one
Bronx: 11
(r):
Manhattan,
monitor in Bronx
Bronx:
NY, U.S.
and one in
Os: -0.49
(1999-2000)
Manhattan
NO2: 0.50
PM2.5: 0.39
Max PM10:
0.0.34
Manhattan:
03: -0.40
N02: 0.47
PM2.5: 0.26
PM10: 0.24
Two-pollutant
models: O3,
FRM and Max
PM2.5, NO2
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Table 5-10 (Continued): Study-specific details and mean and upper percentile
concentrations from asthma hospital admission and ED
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)a
Atlanta, GA,
U.S.
(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
PM2.5OC: 0.18
PM2.5 EC: 0.20
Oxygenated
HCs: 0.14
Os: 0.19
CO: 0.26
NO2: 0.34
Two-pollutant
models: none
Wilson et al.
(2005)a
Portland,
ME, U.S.
Manchester,
NH, U.S.
(1996-2000)
SO2 concentra-
tions from one
monitor in each
city
24-h avg
Portland: 11.1
Manchester:
16.5
NR
Correlation (r)
(Range across
cities):
Os: 0.05-0.24
Two-pollutant
models
examined:
none
Stieb et al. (2009) Seven
Canadian
cities
(1992-2003)
Average SO2 24-h avg 2.6-10.0
concentrations
across all
monitors in each
city. Number of
SO2 monitors in
each city ranged
from 1-11.
75th: 3.3-13.4 Correlations (r)
only reported
by city and
season
Two-pollutant
models: none
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Table 5-10 (Continued): Study-specific details and mean and upper percentile
concentrations from asthma hospital admission and ED
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
Strickland et al.
Atlanta, GA,
Combined daily
1-h max
All-year: 10.8a
NR
Correlations
(2010)
U.S.
SO2 concentra-
Warm
(r): NR
(1993-2004)
tions across
(May-Oct):
Two-pollutant
monitors using
9.6a
models: none
population-
Cold
weighting
(Nov-Apr):
12.0a
Li et al. (2011)
Detroit, Ml,
Average of SO2
24-h avg
3.8
75th: 5.1
Correlations
U.S.
concentrations
Max: 27.3
(r), range
(2004-2006)
across two
across
monitors in
monitors:
Detroit
CO: 0.17-0.31
metropolitan
PM2.5:
area that
0.40-0.53
measure SO2
NO2: 0.42-0.55
Two-pollutant
models: none
Villeneuve et al.
Edmonton,
Average of SO2
24-h avg
Summer
Summer
Correlations
(2007)
Canada
concentrations
(Apr-Sep)
75th: 3.0
(r): NR
(1992-2002)
across three
50th: 2.0
Winter
Two-pollutant
monitoring
Winter
75th: 4.0
models: NR
stations
(Oct-Mar)
50th: 3.0
Jalaludin et al.
Sydney,
Average of SO2
24-h avg
All-year: 1.07
Max
Correlations
(2008)
Australia
concentrations
Warm: 1.03
All-year: 4.1
(r): (warm,
(1997-2001)
across
Cold: 1.1
Warm: 4.1
cold)
14 monitoring
Cold: 3.9
PM10: 0.37,
stations
0.46
PM2.5: 0.27,
0.46
Os: 0.45, -0.04
CO: 0.46, 0.51
NO2: 0.52, 0.56
Two-pollutant
models: PM10,
PM2.5, O3, CO,
NO2
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Table 5-10 (Continued): Study-specific details and mean and upper percentile
concentrations from asthma hospital admission and ED
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
(Orazzo et al.
(2009))
Six Italian
cities
(1996-2002)
Average of SO2
concentrations
across all
monitors in each
city
24-h avg
All-year:
2.1-8.1
Warm
(Apr-Sep):
1.3-9.0
Cold
(Oct-Mar):
2.6-7.3
NR
Correlations
(r): NR
Two-pollutant
models: none
Smaraiassi et al.
(2009)
Montreal,
Canada
(1996-2004)
SO2 concentra- 24-h avg Regional: 4.3 75th:
NR
tions measured
at two
monitoring sites
east and
southwest of the
refinery
At-home
estimates of
daily exposure
by estimating
SO2 concentra-
tions at centroid
of residential
postal codes
using AERMOD
East: 6.9
Southwest: 4.4
AERMOD:
East + South-
west: 3.0
East: 3.7
Southwest: 2.4
Regional: 5.3
East: 9.2
Southwest: 5.9
AERMOD:
East + South-
west: 4.3
East: 5.5
Southwest: 3.0
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Table 5-10 (Continued): Study-specific details and mean and upper percentile
concentrations from asthma hospital admission and ED
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
Winquist et al.
(2014)
Atlanta, GA,
U.S.
(1998-2004)
Population-
weighted
average of SO2
concentrations
1-h max
Warm
(May-Oct):
Cold (Nov-
April): 10.8
8.3
75th:
Warm: 11.4
Cold: 14.6
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
Two-pollutant
models: none
Outpatient and Physician Visits
(Burra etal. (2009))
Toronto,
Canada
(1992-2001)
Average of SO2
concentrations
across six
monitors
1-h max 9.7
75th: 12.0
95th: 35.0
Max: 62.0
Correlations
(r): NR
Two-pollutant
models: none
Sinclair et al. (2010) Atlanta, GA, SO2 concentra- 1-h max 1998-2000: NR Correlations
U.S. tions collected 19.3 (r): NR
(1998-2002) as part of 2000-2002: Two-pollutant
AIRES at 17 6 models: none
SEARCH 1998-2002'
Jefferson street ^3
site
AERMOD = American Meteorological Society/U.S. EPA Regulatory Model; AIRES = Aerosol Research Inhalation Epidemiology
Study; avg = average; CO = carbon monoxide; EC = elemental carbon; ED = emergency department; FRM = federal reference
method; HCs = hydrocarbons; ISA = Integrated Science Assessment; N02 = nitrogen dioxide; NR = not reported; 03 = ozone;
OC = organic carbon; PM = particulate matter; ppb = parts per billion; SEARCH = Southeast Aerosol Research Characterization;
S02 = sulfur dioxide; UFP = ultrafine particle.
aStudies evaluated in the 2008 SOx ISA.
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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, but were limited to studies of
individual cities, which are sometimes subject to publication bias (Figure 5-2).
Additionally, there have been a relative lack of studies that 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
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-hour average 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 to moderately
correlated with other pollutants examined in the study, with the highest correlation with
N02 (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-hour average 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 5-2).
Emergency Department Visits
The majority of studies, both recent and those evaluated in the 2008 SOx ISA, that have
examined the association between short-term SO2 exposures and 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 and evidence of no association in other locations
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(Figure 5-2). Additionally, there was limited evidence for potential seasonal differences
in SO2 associations with asthma ED visits. Similar to 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 focused on either children or the entire population. (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). that examined all respiratory ED visits. However, unlike Tolbert et
al. (2007). which used a single-site centrally located monitor, (Strickland et al. (2010))
used population-weighting, as a more refined exposure assignment approach, to combine
daily pollutant concentrations across monitors. As discussed in Section 3.3.5.1. a study
by Goldman et al. (2012) demonstrates that the bias in health effect estimates decreases
from 76% to 36% when using population-weighted averages instead of a central site
monitor when assigning exposure. 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-hour maximum 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 was weakly
correlated 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-hour average SO2
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 (see Chapter 5). Jalaludin et al. (2008) examined single day
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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-hour average 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.
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, Alberta,
Canada, respectively, did not observe evidence of a positive association between
short-term SO2 exposures and asthma ED visits (Figure 5-2). 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-hour average 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
2.1 to 4.3%, respectively, for a 10-ppb increase in 24-hour average SO2 concentrations.
Within this study, copollutant analyses or correlations with other pollutants were not
presented.
Smargiassi et al. (2009) also informed upon 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 the case of
Smargiassi et al. (2009) because the study focuses on asthma hospital admissions and ED
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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 individuals of older age where 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, Canada 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-hour maximum 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-hour average 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 fixed site
monitor located far from a point source may not adequately capture population exposures
for residences of interest located closer to that source (see Section 3.3.3.2). 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 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, Canada,
Burra et al. (2009) examined asthma physician visits among patients aged 1-17 and
18-64 years in a 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.
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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 5-10. 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-hour average SO2 concentrations]. Across the eight cities, mean 24-hour
average 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 etal. (2011) in a study conducted in Athens, Greece. Samoli et
al. (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-hour average 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
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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-hour maximum SO2
concentrations], with no evidence of an association during the winter [0.4% (95% CI:
-7.5, 9.0)]. 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.
Although there is some evidence for larger associations during the summer, studies
conducted by Villeneuve et al. (2007) in Edmonton, Canada 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 5-2).
Overall, the results of Samoli etal. (2011). Son et al. (2013). and Strickland et al. (2010)
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 resulting in some
health outcome. Recent studies examined multiple single- and multiday lags in an attempt
to identify whether there is a specific exposure window for SO2 that resulted in the
strongest association with 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, through
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analyses of 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-hour average 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-hour average SO2
concentrations] and 1 day [16.1% (95% CI: 5.1, 26.5)]. This is further reflected in the
largest SO2 association being observed for the multiday lag of 0-1 days [29.7% (95% CI:
14.7, 46.5)].
The aforementioned studies support a rather immediate effect of SO2 on asthma hospital
admissions and ED visits, specifically within the first few days after exposure
(i.e., 0-3 days). Controlled human exposure studies demonstrate the rapid onset of
bronchoconstriction (i.e., minutes) in exercising asthmatics exposed to SO2. In addition,
one study in asthmatics demonstrated that a 10-minute exposure to SO2 led to an allergic
inflammatory response a few hours later. Studies in allergic animals, which share many
of the phenotypic features of asthma, show that repeated SO2 exposure may exacerbate
allergic inflammation over the course of several hours to days. These latter effects are
associated with AHR and may lead to bronchoconstriction in response to a trigger
(e.g., an allergen).
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 examining 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
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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 depicting the
largest association.
Exposure Assignment
Questions often arise in air pollution epidemiologic studies with regard to the method
used to assign exposure (see Section 3.2.3). 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. Within this
study, 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 3.3.3.2). 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-hour maximum 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-hour maximum 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-hour
maximum 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 (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 S02-pediatric asthma ED visit relationship using different
analytical approaches.
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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 indicating
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 5-3). Collectively, these analyses do not provide evidence of a threshold.
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Sulfur Dioxide Warm Season
° II II I I II
5 10 15 20
Concentration (ppb)
Source: Reprinted with permission of the American Thoracic Society. The American Journal of Respiratory and Critical Care
Medicine is an official journal of the American Thoracic Society. Strickland et al. (2010).
Figure 5-3 Locally weighted scatterplot smoothing concentration-response
estimates (solid line) and twice-standard error estimates (dashed
lines) from generalized additive models 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 SO2 concentrations in the Atlanta, GA area.
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 (~91st 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
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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-hour average 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 at concentrations greater than 8 ppb as reflected by this value
representing the ~91st percentile of SO2 concentrations.
Sulfur Dioxide within the Muitipoilutant 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 makes answering
this question difficult and primarily limited to examining copollutant models. A recent
study conducted by Winquist et al. (2014) using pediatric asthma ED visits data from
Atlanta assessed whether specific mixtures are more strongly associated with health
effects compared to others. Although the primary objective of this type of study 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 muitipoilutant 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 muitipoilutant 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 5-4).
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Source: (Winauist et ai.. 2014)
Figure 5-4 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 inter-quartile range increase in
each pollutant at lag 0-2 days. Inter-quartile range for 1-hour maximum SO2
concentrations = 10.51 ppb.
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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, in the warm season, joint effect models reported consistent positive
associations with pediatric asthma ED visits. 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 found to be relatively similar. Overall, the results during the cold
season were more variable. The results of Winquist et al. (2014) suggest that SO2 alone
and in combination with other pollutants is associated with asthma ED visits, but also
highlights the difficulty in separating out the independent effect of a pollutant that is part
of a mixture where multiple pollutants are often highly correlated.
Summary of Asthma Hospital Admission and Emergency Department
Visit Studies
Recent studies that examined the association between short-term SO2 exposure and
asthma hospital admissions and ED visits report generally positive associations in studies
examining all ages, children (i.e., <18 years of age), and older adults (i.e., 65 years of age
and older) (Figure 5-2). The pattern of associations observed across studies focusing on
all ages as well as age stratified analyses is consistent with those studies evaluated in the
2008 SOx ISA. It is important to note that these studies rely on central site monitors and
SO2 generally has low to moderate spatial correlations across urban geographical scales,
which could contribute to some degree of exposure error (Section 333.2). Across asthma
hospital admission and ED visit studies that evaluated the lag structure of associations,
the most consistent evidence indicated that associations were largest in magnitude for
multiday lags that encompassed the first few days after exposure (i.e., average of 0-2 and
0-3 day lags). 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 to moderately correlated with other pollutants
examined, which is supported by analyses of NAAQS pollutants at collocated monitors
(Section 3.3.4.IV Evidence from these studies is consistent with those studies evaluated
in the 2008 SOx ISA and adds to the body of evidence indicating that S02-asthma
hospital admission and ED visit associations remain relatively unchanged 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
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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 5.2.1.5. 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 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). 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
SCh-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 evidence
of a linear, no-threshold relationship between short-term SO2 exposures and asthma ED
visits (Li etal.. 2011; Strickland et al.. 2010).
Subclinical Effects Underlying Asthma
Airway inflammation is a key subclinical effect in the pathogenesis of asthma. It consists
of both acute and chronic responses, and involves the orchestrated inter-play 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.
The 2008 SOx ISA (U.S. EPA. 2008b) 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. These studies are discussed below
along with a limited number of recent studies.
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Controlled Human Exposure Studies
Airway inflammation following peak 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 airway 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 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 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 4.3.3).
Epidemiologic Studies
As discussed in the 2008 SOx ISA (U.S. EPA. 2008b). a study among atopic children
found an association between SO2 concentration and capillary blood eosinophil number
that was consistent with recruitment of eosinophils to the airways (Sovseth et al.. 1995).
The 2008 SOx ISA (U.S. EPA. 2008b) also included an epidemiologic study that
evaluated inflammation (measured by eNO) and reported no association with SO2
concentration. Similarly, a recent study of eNO and SO2 concentration reported no
association (Qian et al.. 2009a). However, a recent study performed among children with
asthma did indicate that oxidative stress may occur with increased SO2 concentrations,
but no association was reported with fractional exhaled nitric oxide (FeNO) (Liu et al..
2009b). These recent studies are listed in Table 5-11 and described below.
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Table 5-11 Summary of recent epidemiologic studies examining associations between SO2 concentrations and
airway inflammation and oxidative stress.
Mean SO2 and Upper
Study Study Design Study Population and N Measure of SO2 Concentration Level Adjusted Effect Estimate
Liu et al.
Longitudinal
School children (9-14 yr)
Fixed site monitors; 24-h Median 1-day
Lag 1 [percent change (95% CI) per
(2009b)
repeated measures
with asthma from a
mean SO2 concentrations average: 4.5 ppb
10-ppb increase in SO2]
Canada 2005
nonsmoking household
95th percentile:
FeNO
N = 182
15.5 ppb
2.5 (-13.9, 22.0)
Median 2-day
TBARS
average: 5.0 ppb
22.5 (-2.8, 54.3)
95th percentile:
8-lsoprostane
13.0 ppb
-3.7 (-17.4, 12.4)
2-day avg [percent change (95% CI)
Median 3-day
per 10-ppb increase in SO2]
average: 5.6 ppb
FeNO
95th percentile:
10.8 (-16.0, 46.1)
13.8 ppb
TBARS
71.1 (17.6, 149.0)
8-lsoprostane
17.4 (-9.1, 51.6)
3-day avg [percent change (95% CI)
per 10-ppb increase in SO2]
FEV1
-0.6 (-3.5, 2.5)
FEF25-75%
-4.2 (-10.0, 1.9)
FeNO
3.2 (-24.5, 41.0)
TBARS
143.8 (51.0, 293.7)
8-lsoprostane
-0.6 (-28.2, 37.8)
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Table 5-11 (Continued): Summary of recent epidemiologic studies examining associations between SO2
concentrations and airway inflammation and oxidative stress.
Study
Study Design Study Population and N
Measure of SO2
Mean SO2 and Upper
Concentration Level
Adjusted Effect Estimate
Qian et al.
(2009a)
United States
1997-1999
Clinical trial/panel
study
Patients (12-65 yr) with
persistent asthma were
recruited from six university-
based ambulatory care
centers as part of the
NHLBI-sponsored
Salmeterol Off
Corticosteroids Study.
N = 119
Fixed site monitors; 24-h
mean SO2 concentrations
Mean (SD): 5.3
(4.4) ppb
75th percentile:
7.6 ppb
Max: 27.2 ppb
Change (95% CI) in eNO (ppb) per
10-ppb increase in mean SO2
Lag 0
Lag
Lag 2
Lag 3
0.09 (-0.07, 0.25)
0.07 (-0.09, 0.23)
-0.02 (-0.15, 0.11)
0.01 (-0.13, 0.15)
Lag 0-3: 0.07 (-0.12, 0.26)
CI = confidence interval; eNO = exhaled nitric oxide; FEF25-75% = forced expiratory flow at 25-75% of forced vital capacity; FeNO = fractional exhaled nitric oxide; FE\A = forced
expiratory volume in 1 second; N = population number; NHLBI = National Heart, Lung, and Blood Institute; ppb = parts per billion; S02 = sulfur dioxide; TBARS = thiobabituric acid
reactive substances (species).
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Qian et al. (2009a) compared airway inflammation in response to air pollution among a
cohort of nonsmoking asthmatics. The participants were randomized into three treatment
groups: inhaled corticosteroid (triamcinolone acetonide), inhaled long-acting
(32-adrenergic agonist (salmeterol xinafoate), and a placebo. Participant's residential
address was used to apply central site air monitoring data from the EPA AQS database.
No association was reported between SO2 concentrations and eNO in any of the treatment
groups in both single and copollutant models (models controlled for PM10, O3, or NO2;
correlations between pollutants not provided). Associations were observed with other
pollutants (positive associations with NO2 and PM10 and inverse with O3). To summarize,
this study of individuals with asthma reported no association between SO2 concentration
and eNO.
A study of children with asthma used daily monitoring values from Environment
Canada's National Air Pollution Surveillance Network to examine the association
between air pollution concentrations and FeNO, oxidative stress [measured by
thiobarbituric acid reactive substances (TBARS) and 8-isoprostane], and IL-6 (Liu et al..
2009b). No association was observed between SO2 concentration and FeNO. Increased
SO2 concentration was associated with increased TBARS (lag 0, 2-day average, and
3-day average) and 8-isoprostane (lag 0). Associations did not vary by corticosteroid use.
Other pollutants (NO2, PM2.5) were associated with TBARS but not with 8-isoprostane.
The majority of IL-6 levels were under the limit of detection and therefore associations
were not analyzed. SO2 was correlated with NO2 and PM2 5 but not O3 (correlation
coefficients of 0.18, 0.56, and -0.02, respectively). The results for SO2 concentrations
and FeNO and TBARS were not different when copollutants (NO2, O3, or PM2.5) were
included in the models. 8-isoprostane was not examined in copollutant models. Overall,
this study reported null associations between SO2 concentrations and FeNO, but a
positive association between SO2 concentrations and measures of oxidative stress among
children with asthma.
Animal Toxicological Studies
The 2008 SOx ISA (U.S. EPA. 2008b) discussed several studies that investigated the
effects of repeated exposure to SO2 on allergic inflammatory responses. While one study
failed to demonstrate inflammation following a single subacute exposure to 1 ppm SO2
(U.S. EPA. 2008b). other studies found that repeated SO2 exposure enhanced the
development of an allergic phenotype and altered physiologic responses in naive animals.
Studies demonstrating effects of repeated SO2 exposures in naive rats are described
below in Section 5.2.1.6. Studies demonstrating effects of repeated SO2 exposure in
models of allergic airway disease are listed in Table 5-12 and described here.
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Table 5-12 Study-specific details from animal toxicological studies of
subclinical effects underlying asthma.
Study
Species (Strain); n; Sex;
Lifestage/Age
(mean ± SD)
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 days
followed by:
(1) Challenge with 1%
ovalbumin aerosol for 30 min
for 7 days beginning at
15 days,
(2) Exposure to 2 ppm SO2
for 1 h/day for 7 days, or
(3) SO2 exposure followed by
ovalbumin aerosol challenge
for 7 days
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;
180-200g
Sensitization by i.p. injection
of 100 mg ovalbumin followed
by booster injection of 10 mg
ovalbumin after 7 days
followed by:
(1) Challenge with
1% ovalbumin aerosol for
30 min for 7 days beginning
at 15 days,
(2) Exposure to 2 ppm SO2
for 1 h/day for 7 days, or
(3) SO2 exposure followed by
ovalbumin aerosol challenge
for 7 days
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);
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 days
followed by:
(1) Challenge with
1% ovalbumin aerosol for
30 min for 7 days beginning
at 15 days,
(2) Exposure to 2 ppm SO2
for 1 h/day for 7 days, or
(3) SO2 exposure followed by
ovalbumin aerosol challenge
for 7 days
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 5-12 (Continued): Study-specific details from animal toxicological studies
of subclinical effects underlying asthma.
Study
Species (Strain); n; Sex;
Lifestage/Age
(mean ± SD)
Exposure Details
(Concentration; Duration)
Endpoints Examined
Song etal. (2012)
Rats (Sprague-Dawley);
n = 10/group; M;
4 week-old neonates
Sensitization by i.p. injection
of 10 mg ovalbumin followed
by booster injection of 10 mg
ovalbumin after 7 days
followed by:
(1) Challenge with
1% ovalbumin aerosol for
30 min daily for 4 weeks
beginning at 15 days, and/or
(2)Exposure to 2 ppm SO2 for
4 h/day for 4 weeks beginning
at 15 days
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
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 days
followed by:
(1) Challenge with
1% ovalbumin aerosol for
30 min for 7 days beginning
at 15 days,
(2) Exposure to 2 ppm SO2
for 1 h/day for 7 days, or
(3) SO2 exposure followed by
ovalbumin aerosol challenge
for 7 days
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
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 ;IFN-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;
MCh = methacholine; 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.
1 Repeated exposure to SO2 promoted an allergic phenotype when ovalbumin sensitization
2 and challenge preceded SO2 exposure. As described in the 2008 SOx ISA (U.S. EPA.
3 2008b). Li et al. (2007) demonstrated that rats, which were first sensitized and challenged
4 with ovalbumin and subsequently exposed to 2 ppm SO2 for 1 hour/day for 7 days, had
5 an increased number of inflammatory cells in BALF and an enhanced histopathological
6 response compared with those treated with ovalbumin or SO2 alone. Similarly, ICAM-1,
7 a protein involved in regulating inflammation, and MUC5AC, a mucin protein, were
8 upregulated in lungs and trachea to a greater extent in rats treated with ovalbumin and
9 SO2 than in those treated with ovalbumin or SO2 alone. A follow up study involving the
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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
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 which 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 which 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.
Two other follow-up studies by the same laboratory examined the effects of inhaled SO2
on the asthma-related genes EGF, EGFR and 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 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.
Another new toxicological study evaluated the effects of repeated SO2 exposure on the
development of an allergic phenotype and AHR (Song et al.. 2012). In this study, both
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naive newborn rats and newborn rats that were sensitized and challenged with ovalbumin
were exposed to SO2. Effects in naive rats are described below in Section 5.2.1.6. Effects
in allergic rats are described here. Exposure of ovalbumin-treated newborn rats to SO2
(2 ppm, 4 hours/day for 28 days) resulted in a greater enhancement of lavage fluid IL-4
and an increase in serum IL-4 levels compared with ovalbumin alone. IL-4 is a Th2
cytokine associated with allergic responses; an increase in the ratio of Th2 to Thl
cytokines indicates Th2 polarization, a key step in allergic sensitization. In addition, SO2
exposure led to increased airway responsiveness and airway remodeling, as indicated by
increased content of airway smooth muscle, in the ovalbumin-treated rats. Stiffness and
contractility of airway smooth muscle was assessed in vitro using cells from
experimentally treated animals. In airway smooth muscle cells from ovalbumin-treated
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 enhanced allergic inflammation, AHR and airway remodeling in
SCh-exposed ovalbumin-treated rats. Further, this study suggests that airway remodeling
may contribute to AHR in newborn allergic animals following prolonged exposure to
S02.
Summary of Subclinical Effects
The available evidence supports a relationship between short-term exposure to SO2 and
allergic responses related to asthma. This includes findings of eosinophilic inflammation
in asthmatics exposed acutely to SO2 and findings of an association between SO2
exposure and changes in blood eosinophils in atopic children. In addition, enhanced
inflammation and allergic responses were demonstrated in animals made allergic to
ovalbumin and exposed repeatedly to SO2. One study suggests that airway remodeling
may contribute to AHR in newborn allergic animals following prolonged exposure to
SO2. Recent epidemiologic studies did not specifically evaluate indices of allergic
inflammation and found no evidence for a relationship with a nonspecific indicator of
inflammation (eNO), although there is some evidence for a relationship between SO2
exposure and markers of oxidative stress.
5.2.1.3 Chronic Obstructive Pulmonary Disease Exacerbation
COPD is a type of lung disease characterized by deterioration of lung tissue and airflow
limitation. Clinical symptoms demonstrating exacerbations of COPD include decrements
in lung function and/or symptoms (dyspnea, sputum changes, nasal discharge/congestion,
wheeze/tight chest, or upper respiratory symptoms). Severe exacerbations can lead to
hospital admissions or ED visits.
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Lung Function and Respiratory Symptoms
Only one study investigating the relationship between short-term SO2 exposure and
exacerbation of COPD was described in the 2008 SOx ISA (U.S. EPA. 2008b). This
controlled human exposure study found no evidence of a relationship. A recent
epidemiologic study found no association between SO2 concentration and worsening
COPD symptoms.
Controlled Human Exposure Studies
The relationship between individuals with COPD and S02-induced respiratory health
effects has been examined in only one controlled human exposure study. Linn et al.
(1985a) reported no significant effect on lung function following 15-minute exposures to
SO2 at concentrations of 0.4 and 0.8 ppm in a group of older adults with
physician-diagnosed COPD. Although it appears that older adults with COPD are less
sensitive to SO2, the authors suggested that the lack of response may be explained by
several factors. One apparent difference in this study and those conducted in individuals
with asthma is very low levels of exercise (VE = 18 L/minute), which effectively lowers
the dose delivered to the lungs (Section 4.2.2).
Epidemiologic Studies
In a recent study, Peacock et al. (2011) investigated air pollution's effect on respiratory
symptoms and lung function among adult patients with moderate to severe COPD in east
London, England. Outdoor air pollution exposure (lag 1 day) was obtained from local
central site monitoring stations from the U.K. National Air Quality Information Archive.
Twenty-four-hour means were calculated from hourly measures of SO2. The year-round
mean was 7.5 ppb (SD 6.3 ppb) and the 75th percentile was 9.3 ppb. The mean was
higher in the autumn/winter (9.8 ppb) compared to the spring/summer (5.5 ppb) but the
maximum concentration was highest in the spring/summer (74 ppb; 42 ppb for
autumn/winter). No association was observed between SO2 concentration and worsening
COPD symptoms (dyspnea, sputum changes, nasal discharge/congestion, wheeze/tight
chest, or upper respiratory symptoms) or changes in lung function [PEF, FEVi, forced
vital capacity (FVC)]. The odds ratios for a 10 ppb change in SO2 for each of the
symptoms measures were as follows, dyspnoea: 0.96 (95% CI 0.82, 1.13), sputum
changes: 1.08 (95% CI 0.89, 1.32), nasal discharge/congestion: 1.12 (95% CI 0.94, 1.32),
wheeze/tight chest: 1.02 (95% CI 0.87, 1.20), and upper respiratory symptoms: 0.91
(95% CI 0.75, 1.12). For lung function measures, the change in estimate associated with a
10 ppb change in SO2 concentration was 0.31 (95% CI -0.10, 0.72) for PEF, -0.35 (95%
CI -3.86, 3.16) for FEVi, and -3.35 (95% CI -11.92, 5.22) for FVC. No association was
detected for respiratory symptoms or lung function among other pollutants (NO2, O3,
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PMio, BS, correlation coefficients with SO2 not provided). No associations were
demonstrated between SO2 concentrations and COPD exacerbations or symptomatic fall
in PEF (PM10 and BS were positively associated with the latter). Copollutant models
were not examined for SO2. This study does not support a relationship between SO2
concentration and COPD symptoms or lung function among adult patients with COPD.
Summary of Lung Function and Respiratory Symptoms
Very few studies have examined a relationship between SO2 concentrations and lung
function or respiratory symptoms among individuals with COPD. The available evidence
is not supportive of such a relationship.
Hospital Admissions and Emergency Department Visits for Chronic
Obstructive Pulmonary Disease
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
admissions or ED visits (Figure 5-5. Table 5-13). Recent studies add to the initial
evidence, which generally indicates no association between short-term SO2 exposures
and COPD 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 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-3 (U.S.
EPA. 201510.
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Study
Location
Age
Lag
Hospital Admissions
Qiuetal. (2013)
Hong Kong
All
0-3
1
1
:•
i
i
Wong et al. (2009)
Hong Kong
All
0-1
i
i
i
i
65+
0-1
i
i
i
i
; ED Visits
l
I
l
Peel et al. (2005) Atlanta, GA All 0-2 —
I
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Stieb et al. (2009) 7 Canadian cities All 1 —#-i—
I
I
Arbex et al. (2009) Sao Paulo, Brazil 40+ 0-3 DL ! • ~
I
I
I
I 1 1 1 1 1 1 1 1
-10.0 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0
% Increase
ED = emergency department
Figure 5-5 Percent increase in chronic obstructive pulmonary disease
hospital admissions and ED visits from U.S. and Canadian
studies evaluated in the 2008 SOx Integrated Science Assessment
(ISA) and recent studies in all-year analyses for a 10-ppb increase
in 24-hour average or 40-ppb increase in 1-hour maximum SO2
concentrations. Note: Black circles = U.S. and Canadian studies
evaluated in the 2008 SOx ISA; red circles = recent chronic
obstructive pulmonary disease hospital admission and ED visit
studies, a = study evaluated in the 2008 SOx ISA.
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Table 5-13 Corresponding risk estimates for studies presented in Figure 5-5.
Study
Location
Age
Avg Time
Season
Lag
% Increase
(95% CI)
Hospital Admissions
Qiu etal. (2013a)
Hong Kong,
China
All
24-h avg
All
0-3
1.6 (0.1, 3.1)
Wona et al. (2009)
Hong Kong,
China
All
24-h avg
All
0-1
0.8 (-1.5, 3.1)
65+
0.5 (-2.0, 3.0)
ED Visits
Peel et al. (2005)a
Atlanta, GA
All
1-h max
All
0-2
3.2 (-3.0, 10.0)
Stieb et al. (2009)
Seven Canadian
cities
All
24-h avg
All
1
-2.7 (-8.4, 3.6)
Arbex et al. (2009)
Sao Paulo, Brazil
40+
24-h avg
All
0-3 DL
49.4 (4.1, 113.7)
Avg = average; CI = confidence interval; DL = distributor lag; ED = emergency department; ISA = Integrated Science Assessment.
aStudies evaluated in the 2008 SOx ISA.
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Table 5-14 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
Hospital Admissions
(Qiu et al. (2013a); Hong Kong, Average of SO2 24-h avg 7.4 NR Correlations
Ko et al. (2007a)) China concentrations (r):
(1998—2007) from q3- q 173
10 monitoring T x x
stations TW°"P°'™nt
models: PM10
Wong 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
Correlations
(r): NR
Two-pollutant
models: none
ED Visits
Peel et al. (2005)a
Atlanta, GA
(1993-2000)
Average of SO2
concentrations
across 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
Two-pollutant
models: none
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Table 5-14 (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
(Stieb et al. (2009))
Seven
Average SO2
24-h avg
2.6-10.0
75th: 3.3-13.4
Correlations
Canadian
concentrations
(r) only
cities
across all
reported by
(1992-2003)
monitors in each
city and
city. Number of
season.
SO2 monitors in
Two-pollutant
each city ranged
models: none
from 1-11.
(Arbex et al. (2009))
Sao Paulo,
Average of SO2
24-h avg
5.3
75th: 6.6
Correlations
Brazil
concentrations
Max: 16.4
(r):
(2001-2003)
across
PM10: 0.77
13 monitoring
NO2: 0.63
stations
CO: 0.52
Two-pollutant
models: none
CO = carbon monoxide; EC = elemental carbon; ED = emergency department; ISA = Integrated Science Assessment; NR = not
reported; 03 = ozone; OC =organic carbon; N02 = nitrogen dioxide; PM = particulate matter; ppb = parts per billion; r= correlation
coefficient; S02 = sulfur dioxide; UFP - ultrafine particle.
aStudies evaluated in the 2008 SOx ISA.
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 across studies
there was inconsistent evidence of an association. Although recent studies continued to
assess 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 for a 10-ppb
increase in 24-hour average SO2 concentrations in analyses of both all ages [0.8% (95%
CI: -1.5, 3.1)] and individuals overthe 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. (2013a) 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. (2013a)
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 a multiday 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-hour average SO2 concentrations. The
magnitude of the SO2 association was found to differ between Qiu et al. (2013a) 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 ISA for SOx 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, consistent with the asthma ED
visits results, Stieb et al. (2009) did not find any evidence of associations between
24-hour average 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-hour average of ED visits versus 3-hour average
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-hour average 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
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observed in other locations, SO2 was highly correlated with PM10 (r = 0.77) and less
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, Oiu etal. (2013a) examined potential seasonal differences in
associations by the traditional approach of stratifying by season, but also by examining
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-hour average 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)]. Oiu et al. (2013a) 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-hour average SO2 concentrations, Oiu et al. (2013a) 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 play
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-hour average SO2
concentrations]. The results from Oiu et al. (2013a) 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 they 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. (2013a) 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 days) 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 day.
Collectively, the results of Oiu et al. (2013a) 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 Hospital
Admission and Emergency Department Visit Studies
To date, a relatively limited number of studies have examined the association between
short-term SO2 exposures and COPD hospital admissions and ED visits, and these studies
have reported inconsistent evidence of an association (Figure 5-5). Additionally, it is
important to note that these studies rely on central site monitors and SO2 generally has
low to moderate spatial correlations across urban geographical scales, which could
contribute to some degree of exposure error (Section 3.3.3.2). Although limited in
number, studies that examined potential seasonal patterns of associations and the lag
structure of associations indicate a potential combined effect of both temperature and
humidity on SO2-COPD hospital admission associations, specifically cool temperatures
and low humidity (Oiu et al.. 2013a). and provide initial evidence of an immediate effect
within the first few days after exposure (i.e., 0-3 days). An examination of potential
factors that may modify the SO2-COPD hospital admission or ED visits relationship finds
potential differences by lifestage, sex, and influenza intensity (see Chapter 6. However,
similar to studies that have examined other respiratory-related hospital admissions and
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ED visits, studies of COPD hospital admissions and ED visits have not conducted
extensive analyses to examine potential copollutant confounding. Overall, the limited
number of studies that have examined associations between short-term SO2 exposures
and COPD hospital admissions and ED visits complicate the ability to assess whether
there is an independent effect of short-term SO2 concentrations on COPD hospital
admissions or ED visits.
5.2.1.4 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. 2008b).
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. Recent contributions to the
evidence are limited to epidemiologic studies.
Hospital Admissions and Emergency Department Visits for Respiratory
Infections
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. For each of the studies evaluated in this section, Table 5-15 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
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1 studies, as well as study specific details, can be found in Supplemental Table 5S-3 (U.S.
2 EPA. 201510.
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Table 5-15 Study-specific details and mean and upper percentile concentrations from respiratory infection
hospital admission and emergency deparment 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
(ICD9/10)
Exposure
Assignment
Metric
Mean Concentra-
tion (ppb)
Upper Percentile
of
Concentrations
ppb
Copollutants
Examined
Hospital Admissions
HEI (2012)
Mehta et al.
(2013)
Ho Chi Minh City,
Vietnam
(2003-2005)
Acute lower
respiratory
infection (J13-16,
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
Two-pollutant
models: NO2,
PM10, O3
Seaala 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
Two-pollutant
models: none
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Table 5-15 (Continued): Study-specific details and mean and upper percentile concentrations from respiratory
infection hospital admission and emergency deparment 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
of
Study
Location (years)
Type of Visit
(ICD9/10)
Exposure
Assignment Metric
Mean Concentra- Concentrations
tion (ppb) ppb
Copollutants
Examined
ED Visits
Peel et al. (2005)a
Atlanta, GA
Pneumonia
Average of SO2 1-h max
16.5 90th: 39.0
Correlations (r):
(1993-2000)
(480-486)
concentrations
from monitors for
several monitoring
networks
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
Two-pollutant
models: none
Stieb et al. (2009) Seven Canadian
Respiratory
Average SO2 24-h avg
2.6-10.0
75th: 3.3-13.4
Correlations (r)
cities (1992-2003)
infection (464,
466, 480-487)
concentrations
across all monitors
in each city.
Number of SO2
monitors in each
city ranged from
1-11.
only reported by
city and season.
Two-pollutant
models: none
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Table 5-15 (Continued): Study-specific details and mean and upper percentile concentrations from respiratory
infection hospital admission and emergency deparment 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
(ICD9/10)
Exposure
Assignment
Metric
Mean Concentra-
tion (ppb)
Upper Percentile
of
Concentrations
ppb
Copollutants
Examined
Seaala 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
Two-pollutant
models: none
Zemek et al.
(2010)
Edmonton,
Canada
(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
Two-pollutant
models: none
Outpatient and Physician Visits
Sinclair 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
Two-pollutant
models: none
AIRES = Aerosol Research Inhalation Epidemiology Study; avg = average; BS = black smoke; CO = carbon monoxide; EC = elemental carbon; ED = emergency department;
ICD = International Classification of Diseases; ISA = Integrated Science Assessment; N02 = nitrogen dioxide; 03 = ozone; OC = organic carbon; PM = particulate matter; NR = not
reported; ppb = parts per billion; r= correlation coefficient; SEARCH = Southeast Aerosol Research Characterization; S02 = sulfur dioxide; UFP = ultrafine particle.
aStudy evaluated in 2008 SOx ISA.
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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-hour average SO2 concentrations]. A larger association was observed in the
time-series analysis (HEI. 2012) (Figure 5-6. Table 5-16). 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-hour average SO2 concentrations].
In another study that also examined respiratory infections (i.e., bronchiolitis) in children,
Segala 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
(Segala 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-hour average SO2 concentrations [34.8% (95% CI: 19.5, 47.8)] with a similar risk
estimate observed for the 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
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examined the same respiratory infection outcomes (Figure 5-6). 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-hour average 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 day. However, Sega la et al.
(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-hour average 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, Alberta, Canada, 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 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, Sega 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
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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-hour average
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-hour average SO2 concentrations, lag 1-6 day average], 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-hour average SO2concentrations, lag 1-6 day average].
Additionally, Zemek et al. (2010) in the study of otitis media ED visits in Alberta,
Canada 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 to the cold months, (October-March), -4.3% (95% CI: -16.30, 9.0) at lag 4
for a 10-ppb increase in 24-hour average SO2 concentrations.
Nonhospital Admissions or Emergency Department Visit Respiratory
Infection Studies
Two epidemiologic studies performed among general populations of school children
examined respiratory infection as one of the outcomes. These studies reported
inconsistent findings. Linares et al. (2010) examined children in Mexico where the mean
24-hour average SO2 concentration ranged from 8.8 to 13.6 ppb depending on the season
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and monitor. A positive association was observed between SO2 concentration and acute
respiratory tract infection [OR 1.14 (95% CI 1.07, 1.22) per 10 ppb], but no association
was present for acute respiratory tract infection hospitalization [OR 1.00 (95% CI 0.98,
1.02) per 10 ppb]. Zhao et al. (2008) evaluated children in China where mean ambient
SO2 concentrations were much higher (272.1 ppb) but reported no association with
respiratory infection [OR 0.99 (95% CI 0.98, 1.01) per 10 ppb].
Summary of Respiratory Infection Studies
Overall, a relatively limited number of studies have examined the association between
short-term SO2 exposures and respiratory infection hospital admissions and ED visits,
specifically with respect to the respiratory infection outcomes examined. Additionally
evidence from the few studies that focused on respiratory infections in school children is
inconsistent and does not add to understanding the relationship between short-term SO2
exposures and respiratory infections (Linares et al.. 2010; Zhao et al. 2008). Overall, the
hospital admission and ED visits studies provide initial evidence of a positive association,
but the lack of multiple studies examining the same respiratory infection outcome
complicates the interpretation of the collective body of evidence, specifically because the
etiology of upper and lower respiratory infections are vastly different (Figure 5-6).
Additionally, these studies rely on central site monitors, and SO2 generally has low to
moderate spatial correlations across urban geographical scales, which could contribute to
some degree of exposure error (Section 3.3.3.2). Similar to studies that have examined
other respiratory-related hospital admissions and ED visits, studies of respiratory
infection hospital admissions and ED visits have not conducted extensive analyses to
examine potential copollutant confounding. Although the SO2 correlations with other
pollutants were high in some locations outside of the U.S., an examination of correlations
between NAAQS pollutants at collocated monitors in the U.S. has demonstrated that SO2
is low to moderately correlated with other pollutants (Section 3.3.4.1). An examination of
potential factors that could modify the S02-respiratory infection hospital admission or
ED visit association finds potential differences in SO2 risk estimates by SES with
inconsistent evidence for differences in risk estimates by sex (see Chapter 6).
Additionally, the relatively small number of studies has resulted in inadequate assessment
of issues such as potential seasonal differences in associations or the lag structure of
associations.
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Study Location Age
HEI (2012); Mehta etal. (2013) Ho Chi Minh, Vietnam 28 days - 5 years
28 days - 5 years
Segala et al. (2008)
Peel et al. (2005)
Stieb et al. (2009)
Segala et al. (2008)
Peel et al. (2005)
Zemek et al. (2010)
Paris, France
Atlanta, GA
7 Canadian cities
Paris, France
Atlanta, GA
Edmonton, Canada
< 3
All
All
< 3
All
1-3
Lag
l-6a
l-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
¦ ~
Pneumonia
Otitis media
-20.0 -10.0
10.0 20.0
30.0 40.0
ED = emergency department.
Figure 5-6 Percent increase in respiratory infection hospital admissions and
ED visits from U.S. and Canadian studies evaluated in the 2008
SOx Integrated Science Assessment (ISA) and recent studies in
all-year and seasonal analyses for a 10-ppb increase in 24-hour
average or 40-ppb increase in 1-hour maximum SO2
concentrations. Note: Black circles = U.S. and Canadian studies
evaluated in the 2008 SOx ISA; red circles = recent respiratory
infection hospital admissions and ED visits studies.
Table 5-16 Corresponding risk estimates for studies presented in Figure 5-6.
Study
Location
Age
Avg Time Season
Lag
% Increase
(95% CI)
Hospital Admissions
Respiratory Infection
HEI (2012): Mehta etal.
(2013)
Ho Chi Minh,
Vietnam
28 days
5
24-h avg All
1 - 6b
13.6 (2.2, 26.4)
years
1 - 6C
7.0 (-3.9, 19.1)
Bronchiolitis
Seaala et al. (2008)
Paris, France
< 3
24-h avg Winter
O
I
O"
31.6 (13.7, 51.2)
l
0
34.8 (19.5, 47.8)
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Table 5-16 (Continued): Corresponding risk estimates for studies presented in
Figure 5-6.
Study
Location
Age
Avg Time
Season
Lag
% Increase
(95% CI)
ED Visits
Respiratory Infection
Peel et al. (2005)a
Atlanta, GA
All
1-h max
All
0-2
2.0 (-0.4, 4.9)
Stieb et al. (2009)
Seven Canadian
cities
All
24-h avg
All
2
1.2 (-2.5, 5.2)
Bronchiolitis
Seaala et al. (2008)
Paris, France
< 3
24-h avg
Winter
O
I
O"
34.7 (25.5, 44.5)
l
0
16.6 (8.1, 25.5)
Pneumonia
Peel et al. (2005)a
Atlanta, GA
All
1-h max
All
0-2
0.6 (-3.2, 4.7)
Otitis Media
All
0.0 (-8.4, 13.7)
Zemeketal. (2010)
Edmonton,
Canada
1 -3
24-h avg
Warm
4
9.0 (-8.4, 34.2)
Cold
-4.3 (-16.3, 9.0)
Avg = average; CI = confidence interval; ED = emergency department; ISA = Integrated Science Assessment.
aStudies evaluated in the 2008 SOx ISA.
b Time-series analysis.
0 Case-crossover analysis.
5.2.1.5 Respiratory Disease Hospital Admissions and Emergency
Department Visits
1 In addition to individual respiratory conditions, epidemiologic studies examined
2 respiratory effects as an aggregate of multiple respiratory conditions (e.g., asthma,
3 COPD, respiratory infections). Epidemiologic studies examining the association between
4 short-term SO2 exposures and respiratory-related hospital admissions or ED visits,
5 including those discussed earlier in this chapter, were not available until after the
6 completion of the 1986 Supplement to the Second Addendum of the 1982 SOx AQCD
7 (U.S. EPA. 1994). Therefore, the 2008 ISA for SOx (U.S. EPA. 2008b) included the first
8 thorough evaluation of respiratory morbidity in the form of respiratory-related hospital
9 admissions and ED visits. Of the studies evaluated, the majority consisted of single-city,
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1 time-series studies that primarily examined all respiratory disease or asthma hospital
2 admissions or ED visits with a more limited number of studies examining other
3 respiratory outcomes, as discussed in previous sections. The studies that examined all
4 respiratory disease hospital admissions and ED visits generally reported positive
5 associations (see Figure 5-7. Table 5-17). These associations were found to remain
6 generally positive with some evidence of an attenuation of the association in models with
7 gaseous pollutants (i.e., NO2 and O3) and particulate matter (U.S. EPA. 2008b).
Study
Location
Age
Lag
Cakmak et al. (2006)
10 Canadian cities
All
2.6
Son et al. (2013)
8 South Korean cities
All
0-3
Atkinson et al. (2012)
Meta-analysis (Asia)
All
NR
Burnett et al. (1997)
Toronto, CAN
All
0-3
Wong 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
Son et al. (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
Wong et al. (2009)
Hong Kong
65+
0-1
Fung et al. (2006)
Vancouver, CAN
65+
0-6
Son et al. (2013)
8 South Korean cities
75+
0-3
Wong et al. (2009)a
Hong Kong
All
0-1
0-14
0-1
Wilson et al. (2005)
Peel et al. (2005)
Tolbert et al. (2007)
Wilson et al. (2005)
Portland, ME
Manchester, NH
Atlanta, GA
Atlanta, GA
Portland, ME
Manchester, NH
Portland, ME
Manchester, NH
Portland, ME
Manchester, NH
All
All
All
All
0-14
0-14
15-64
15-64
65+
65+
0
0
0-2
0-2
0
0
0
0
0
0
Hospital Admissions
+
ED Visits
10 20 30
% Increase
Note: Black circles = U.S. and Canadian studies evaluated in the 2008 SOx Integrated Science Assessment (ISA); red
circles = recent respiratory infection hospital admissions and emergency department (ED) visits studies;
a = Wong et al. (2009) also presented results for acute respiratory disease hospital admissions, which is a subset of total respiratory
hospital admissions.
Figure 5-7 Percent increase in respiratory disease hospital admissions and
ED 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-hour average or 40-ppb increase in 1-hour
maximum SO2 concentrations.
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Table 5-17 Corresponding risk estimates for studies presented in Figure 5-7.
Study
Location
Age
Avg Time
Season
Lag
% Increase
(95% CI)
Hospital Admissions
Cakmak et al. (2006)
10 Canadian
cities
All
24-h avg
All
2.6
2.4 (1.1, 4.0)
Son et al. (2013)
Eight South
Korean cities
All
24-h avg
All
0-3
5.6 (1.4, 10.0)
Atkinson et al. (2012)
Meta-analysis
(Asia)
All
24-h avg
All
NR
1.3 (-0.4, 3.2)
Burnett et al. (1997)a
Toronto, Canada
All
1-h max
Summer
0-3
38.4 (13.1, 69.2)
Wona et al. (2009)
Hong Kong,
China
All
24-h avg
All
0-1
0.8 (-0.6, 2.3)
Dales et al. (2006)a
11 Canadian
cities
0-27
days
24-h avg
All
2
5.5 (2.8, 8.3)
Yana et al. (2003b)a
Vancouver,
Canada
<3
24-h avg
All
2
3.0 (-6.0, 15.0)
Son et al. (2013)
Eight South
Korean cities
0-14
24-h avg
All
0-3
4.2 (-1.0, 9.6)
Schwartz (1995)a
Tacoma, WA
65+
24-h avg
All
0
3.0 (1.0, 6.0)
Yana et al. (2003b)a
Vancouver,
Canada
65+
24-h avg
All
0
6.0 (0.0, 12.0)
Schwartz (1995)a
New Haven, CT
65+
24-h avg
All
2
2.0 (1.0, 3.0)
Schwartz et al. (1996)a
Cleveland, OH
65+
24-h avg
All
0-1
0.8 (-0.3, 1.5)
Wona et al. (2009)
Hong Kong,
China
65+
24-h avg
All
0-1
1.0 (-0.8, 2.8)
Funa et al. (2006)a
Vancouver,
Canada
65+
24-h avg
All
0-6
13.0 (1.0, 26.0)
Son et al. (2013)
Eight South
Korean cities
75+
24-h avg
All
0-3
8.2 (-0.7, 17.9)
Wona et al. (2009'b
Hong Kong,
China
All
24-h avg
All
0-1
-2.0 (-4.4, 0.4)
0-14
-1.6 (-4.1, 1.0)
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Table 5-17 (Continued): Corresponding risk estimates for studies presented in
Figure 5-7.
Study
Location
Age
Avg Time
Season
Lag
% Increase
(95% CI)
ED Visits
Wilson et al. (2005)a
Portland, ME
All
24-h avg
All
0
7.0 (3.0, 12.0)
Manchester, NH
1.0 (-3.0, 5.0)
Peel et al. (2005)a
Atlanta, GA
All
1-h max
All
0-2
1.6 (-0.6, 3.8)
Tolbert et al. (2007)a
Atlanta, GA
All
1-h max
All
0-2
0.8 (-0.7, 2.3)
Wilson et al. (2005)a
Portland, ME
0-14
24-h avg
All
0
-4.0 (-11.0, 4.0)
Manchester, NH
0.0 (-8.0, 8.0)
Portland, ME
15-64
9.0 (5.0, 14.0)
Manchester, NH
1.0 (-3.0, 5.0)
Portland, ME
65+
16.0 (7.0, 26.0)
Manchester, NH
7.0 (-5.0, 21.0)
Avg = average; CI = confidence interval; ED = emergency department; ISA = Integrated Science Assessment; NR = not reported.
aStudies evaluated in the 2008 SOx ISA.
bWong et al. (2009) also presented results for acute respiratory disease hospital admissions, which is a subset of total respiratory
hospital admissions.
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 5-18
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-3 (U.S. EPA. 2015h).
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Table 5-18 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.
Mean Upper Percentile
Location
Exposure
Concentration of Concentrations
Copollutants
Study
years
Assignment
Metric
PPb) PPb)
Examined
Hospital Admissions
Cakmak et al.
10 Canadian
Average of
24-h
4.6 Max: 14-75
Correlations (r):
(20061®
cities
SO2
avg
NR
(1993-2000)
concentrations
Two-pollutant
across all
models
monitors in
examined: none
each city
Dales et al. (2006)a
11 Canadian
Average of
24-h
4.3a 95th: 3.5-23.5
Correlations (r):
cities
SO2
avg
PM10: -0.09 to
(1986-2000)
concentrations
0.61
across all
Os: -0.41 to
monitors in
0.13
each city
NO2: 0.20 to
0.67
CO: 0.19 to
0.66
Two-pollutant
models
examined: none
Burnett et al.
Toronto,
Average of 1-h max
7.9
75th:
11
Correlations (r):
(1997)a
Canada
SO2
95th:
18
H+: 0.45
(1992-1994)
concentrations
Max:
26
S04: 0.42
from four to six
PM10: 0.55
monitors
during the
PM2.5: 0.49
course of the
PM10-2.5: 0.44
study
COH: 0.50
Os: 0.18
NO2: 0.46
CO: 0.37
Two-pollutant
models
examined:
COH, PM10,
PM10-2.5, PM2.5
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Table 5-18 (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.
Mean
Upper Percentile
Location
Exposure
Concentration
of Concentrations
Copollutants
Study
years
Assignment
Metric
PPb)
PPb)
Examined
Funa et al. (2006)a
Vancouver,
Average of
24-h
3.46
Max: 12.5
Correlations (r):
Canada
SO2
avg
CO: 0.61
(1995-1999)
concentrations
COH: 0.65
across all
Os: -0.35
monitors within
Vancouver
NO2: 0.57
PM10: 0.61
PM2.5: 0.42
PM10-2.5: 0.57
Two-pollutant
models
examined: none
Schwartz (1995)a
New Haven,
Average of
24-h
New Haven:
New Haven:
Correlations (r):
CT
SO2
avg
29.8
75th: 38.2
NR
Tacoma,
concentrations
Tacoma: 11.5
90th: 60.7
Two-pollutant
WA
across all
Tacoma:
models
(1988-1990)
monitors in
75th: 21.4
examined:
each city
90th: 28.2
PM10, O3
Schwartz et al.
Cleveland,
Average of
24-h
35.0
75th: 45.0
Correlations (r):
(1996)a
OH
SO2
avg
90th: 61.0
NR
(1988-1990)
concentrations
Two-pollutant
across all
models
monitors
examined: none
Yana et al.
Vancouver,
Average of
24-h
4.8
75th: 6.3
Correlation (r):
(2003b)a
Canada
SO2
avg
Max: 24.0
Os: -0.37
(1986-1998)
concentrations
Two-pollutant
across four
models
monitors
examined: O3
Son et al. (2013)
Eight South
Average of
24-h
3.2-7.3
NR
Correlation (r):
Korean
hourly ambient
avg
PM10: 0.5
cities
SO2
Os: -0.1
(2003-2008)
concentrations
NO2: 0.6
from monitors
Two-pollutant
in each city
models
examined: none
Atkinson et al.
Meta-
NR
24-h
NR
NR
Correlation (r):
(2012)
analysis
avg
NR
(Asia)
Two-pollutant
(1980-2007)
models
examined: none
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Table 5-18 (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.
Study
Location
years
Exposure
Assignment
Metric
Mean
Concentration
PPb)
Upper Percentile
of Concentrations
PPb)
Copollutants
Examined
Wong 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
Two-pollutant
models
examined: none
ED Visits
Peel et al. (2005)a
Atlanta, GA
(1993-2000)
Average of 1-h max
16.5
90th: 39.0
Correlations (r):
SO2
PM2.5: 0.17
concentrations
PM10: 0.20
from monitors
PM10-2.5: 0.21
for several
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
Two-pollutant
models: none
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Table 5-18 (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.
Mean
Upper Percentile
Location
Exposure
Concentration
of Concentrations
Copollutants
Study
years
Assignment
Metric ppb)
ppb)
Examined
Tolbert et al.
Atlanta, GA
Average of
1-h max 14.9
75th: 20.0
Correlations (r):
(2007)a
(1993-2004)
SO2
concentrations
90th: 35.0
PM10: 0.21
Os: 0.21
from monitors
NO2: 0.36
for several
monitoring
CO: 0.28
networks
PM10-2.5: 0.16
PM2.5: 0.17
PM2.5 SO4: 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
Two-pollutant
models
examined: none
Wilson et al.
Portland,
SO2
24-h Portland: 11.1
NR
Correlation (r):
(20051®
ME
concentrations
av9 Manchester:
Portland
Manchester,
from a central
16.5
Os: 0.05
NH
site monitor
Manchester
(1996-2000)
Os: 0.01
Two-pollutant
models
examined: none
CO = carbon monoxide; COH = coefficient of haze; EC = elemental carbon; ED = emergency department; H+ = hydrogen ion;
ISA = Integrated Science Assessment; OC = organic carbon; N02 = nitrogen dioxide; NR = not reported; 03 = ozone;
PM = particulate matter; ppb = parts per billion; r= correlation coefficient; S02 = sulfur dioxide; S04 = sulfate; TC = total
hydrocarbon; UFP = ultrafine particle.
aStudies evaluated in the 2008 SOx ISA.
Hospital Admissions
1 A recent multicity study conducted in Korea (Son et al.. 2013) and a single-city study
2 conducted in Hong Kong (Wong et al.. 2009) provide additional insight into the
3 relationship between short-term SO2 exposures and hospital admissions for all respiratory
4 diseases.
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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-hour average 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 are observed and the magnitude of the association (Figure 5-7).
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 5-7). 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-hour average 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-hour average SO2 concentrations at lag 0-1]).
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-hour
average 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
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-hour
average SO2 concentrations. However, Atkinson et al. (2012) found some evidence of
publication bias for associations between SO2 and respiratory hospital admissions.
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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 (Figure 5-7. Table 5-17). 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.
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
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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-hour average 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-hour average 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-hour average 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-hour
average 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 Hospital Admission and Emergency Department Visit Studies
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 5-7). Note that these studies rely on central site
monitors and SO2 generally has low to moderate spatial correlations across urban
geographical scales, which could contribute to some degree of exposure error
(Section 3.3.3.2V These recent studies provide some insight 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
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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 5-7)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.
Although SO2 correlations with other pollutants in some studies could be considered
high, an examination of correlations between NAAQS pollutants at collocated monitors
in the U.S. has demonstrated that SO2 is low to moderately correlated (Section 3.3.4.1).
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.
5.2.1.6 Respiratory Effects in General Populations and Healthy Individuals
The 2008 SOx ISA (U.S. EPA. 2008b) reported respiratory effects of SO2 in general
populations and healthy individuals but did not make specific conclusions about causal
relationships between SO2 exposure and health effects in these groups. Respiratory
effects were demonstrated in healthy individuals following SO2 exposures >1.0 ppm in
controlled human exposure studies. Epidemiologic evidence was insufficient to conclude
an association between SO2 and lung function or respiratory symptoms in general
populations of children or adults. However, animal toxicological studies demonstrated
bronchoconstriction after a single SO2 exposure and increased airway responsiveness and
inflammation after repeated SO2 exposures.
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
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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. 2008b). Limited epidemiologic studies described in the
2008 SOx ISA (U.S. EPA. 2008b) provided insufficient evidence to draw conclusions
about decrements in lung function and SO2 exposure in general populations. More recent
epidemiologic studies report some positive associations in general populations of adults
and children, although evidence is inconsistent. Animal toxicological studies
demonstrated SO2 exposure-induced effects on lung function at concentrations of 2 ppm
and lower (U.S. EPA. 2008b).
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 S02-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 5-19. Andersen et al. (1974) reported that exposures of up to 6 hours to 1.0 ppm
SO2 in resting healthy adults induced a constriction in the upper airways as evidenced by
decreases in FEF25 75 and to a lesser extent FEVi. Another human exposure study (van
Thriel et al.. 2010) reported that healthy subjects 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,
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1 lung function measurements in this study were not performed between 40-100 minutes
2 after exercise and more sensitive measures such as shallow rapid respiration or FEF25-75
3 were not reported.
4 The interaction of SO2 exposure with O3 was reported in two studies. Hazucha and Bates
5 (1975) demonstrated that a combined 2 hours exposure to low concentrations of O3
6 (0.37 ppm) and SO2 (0.37 ppm) has a greater effect on lung function than exposure to
7 either agent alone in exercising adults. However using a similar study design, Bedi et al.
8 (1979) did not observe a synergistic effect.
Table 5-19 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 SO2 for 6 h at
Nasal mucociliary flow
al. (1974)
20-28 yr
rest
Area of the nasal airway
Airway resistance (FEV1, FEF25-75%)
Nasal removal of SO2
Discomfort level symptoms
Linn et al.
Healthy; n = 24;
15 M,
0, 0.2, 0.4, or 0.6 ppm SO2
Lung function measure pre-exposure,
(1987)
9 F; 18-37 yr
1 h exposures
-15 min and -55 min into exposure
3 x 10-min exercise (bicycle)
sRaw, FVC, FEV1, peak expiratory flow
periods -40 L/min
rate, maximal mid expiratory flow rate
Exposures were repeated for a
Continuously-EKG
total of eight
Midway-HR
Before, during, 1-day after, and 1 week
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;
8 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)
75W) two times during each
session
Tunnicliffe et
Asthma; n = 12
0 or 0.2 ppm SO2 at rest
Symptoms, FEV1, FVC, MMEF, exhaled
al. (2003)
Healthy; n = 12
NO, ascorbic and uric acid in nasal
lavage fluid
van Thriel et
Healthy; n = 16;
8 M,
0, 0.5, 1.0, or 2.0 SO2 for 4 h
Symptoms, FEV1
al. (2010)
8 F; M: 28.4 ± 3.9 yr,
with exercise for 15 min (bicycle,
F: 24.3 ± 5.2 yr
75W) 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 second; 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.
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Epidemiologic Studies
Adults. Evidence of an association between ambient SO2 concentrations and lung
function in adults without asthma or chronic respiratory symptoms was limited in the
2008 SOx ISA (U.S. EPA. 2008b). Recent studies have reported some positive
associations although the overall evidence is inconsistent. These recent studies are
summarized in the following text and Table 5-20.
Table 5-20 Summary of recent epidemiologic studies examining associations
between SO2 concentrations and lung function among adults.
Mean SO2 and
Study, Study Upper
Location, Study Population Concentration
and Years Design and N Measure of SO2 Level Adjusted Effect Estimate
Dales et al.
(2013)
Ontario,
Canada
Cross-
over
Healthy,
nonsmoking
volunteers
(mean age
24.2 yr) not
living in a town
bordering a
steel plant
N = 61
Fixed site monitors;
10-h mean SO2
concentrations
Neighborhood near
steel mill: 7.76
(13.21) ppb
College campus:
1.59 ppb (4.18) ppb
Percent change per 10 ppb
(0-2 days)
FEV1
-0.50 (-1.04, 0.05)
FVC
-0.45 (-1.09, 0.19)
FEV1/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
-2.19 (-4.16, -0.18)
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Table 5-20 (Continued): Summary of recent epidemiologic studies examining
associations between S02Concentrations and lung
function among adults.
Mean SO2 and
Study, Study Upper
Location, Study Population Concentration
and Years Design and N Measure of SO2 Level Adjusted Effect Estimate
Steinvil et Cross-
al. (2009) sectional
Tel Aviv,
Israel
2002-2007
Healthy adult
(mean 43 yr),
nonsmokers
enrolled in the
residing within
11 km of
monitor
N = 2,380
Fixed site monitors;
24-h mean SO2
concentrations
SO2
Mean (SD): 2.8
(1.2) ppb
75th percentile:
3.4 ppb
Max: 9.4 ppb
Change in FEV1, expected for
a 10-ppb increase in SO2
Lag 0
Lag 1
Lag 2
Lag 3
Lag 4
Lag 5
Lag 6
Lag 7
93 (-90, 277)
67 (-117, 250)
-60 (-243, 123)
-267 (-460, -73)
-207 (-387, -27)
-300 (-487, -113)
-247 (-427, -67)
-173 (-353, 7)
7-day avg: -447 (-750, -143)
Change in FVC, expected for
a 10-ppb increase in SO2
Lag 0: 53 (-167, 273)
Lag 1: 80 (-143, 303)
Lag 2: -13 (-237, 210)
Lag 3: -313 (-550, -77)
Lag 4: -300 (-517, -83)
Lag 5: -373 (-600, -147)
Lag 6: -327 (-543, -110)
Lag 7: -227 (-447, -7)
7-day avg: -560 (-927, -193)
Change in FEV1/FVC,
expected for a 1.5-ppb
increase in SO2
Lag 0
Lag 1
Lag 2
Lag 3
Lag 4
Lag 5
Lag 6
Lag 7
247 (-10, 503)
60 (-203, 323)
-100 (-363, 163)
20 (-260, 300)
153 (-50, 357)
133 (-137, 403)
120 (-140, 380)
107 (13, 200)
7-day avg: 220 (-217, 657)
Min et al.
(2008a)
South
Korea
2006
Cross-
sectional
Volunteers
(20-86 yr) with
no serious
medical
conditions
N = 867
Fixed site monitors;
1-h mean SO2
concentrations
6 ppb
Quantitative effects estimates
not reported
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Table 5-20 (Continued): Summary of recent epidemiologic studies examining
associations between S02Concentrations and lung
function among adults.
Study,
Location,
and Years
Study
Design
Study
Population
and N
Measure of SO2
Mean SO2 and
Upper
Concentration
Level
Adjusted Effect Estimate
Son et al.
(2010)
South
Korea
2003-2007
Cross-
sectional
Participants
(7-97 yr) were
recruited from
a cohort study
of residents
near the Ulsan
petrochemical
complex.
N = 2,102
Fixed site monitors;
24-h mean SO2
concentrations
Average across all
monitors
Mean (SD): 8.60
(4.13) ppb
75th percentile:
10.46 ppb
Max: 23.80 ppb
Nearest Monitor
Mean (SD): 7.25
(5.92) ppb
75th percentile:
9.50 ppb
Max: 34.21 ppb
IDW
Mean (SD): 8.35
(5.26) ppb
75th percentile:
10.81 ppb
Max: 29.06 ppb
Kriging
Mean (SD):8.29
(4.41) ppb
75th percentile:
9.62 ppb
Max: 24.78 ppb
Percent change (95% CI) in
predicted FVC per 10-ppb
increase in SO2 (Lag 0-2)
Average across all monitors
-6.96 (-9.04, -4.82)
Nearest monitor
-5.61 (-7.35, -3.85)
IDW
-5.31 (-7.07, -3.53)
Kriging
-6.19 (-8.16, -4.18)
Percent change (95% CI) in
predicted FEV1 per 10-ppb
increase in SO2 (Lag 2)
Average across all monitors
-0.15 (-0.89, 0.58)
Nearest monitor
0.35 (-0.21, 0.92)
IDW
0.31 (-0.32, 0.95)
Kriging
-0.08 (-0.76, 0.60)
avg = average ; CI = confidence interval; FEF25-75% = forced expiratory flow at 25-75% of forced vital capacity; FE\A = forced
expiratory volume in 1 second; FVC = forced vital capacity ; IDW = inverse distance weighting; N = population number; ppb = parts
per billion; SD = standard deviation; S02 = sulfur dioxide.
Some studies have reported associations between SO2 concentration and lung function
among healthy adults. A cross-over study in Canada recruited healthy, nonsmoking
participants who were assigned to spend five consecutive 8-hour days outside at a
neighborhood bordering a steel plant and a college campus, with a wash-out period
between the two periods (Dales et al.. 2013). Air monitors at each location measured
ambient air pollutants. SO2 concentration was inversely associated with multiple
measures of lung function (e.g., FEF25-75, total lung capacity, residual volume). Other
pollutants examined in the study (NO2, PM2 5, and UFP) were also inversely associated
with measures of lung function. Concentrations of O3 demonstrated some associations,
but these were fewer than for the other pollutants. No correlation coefficients or
copollutant models were provided. Similarly, inverse associations between SO2
concentrations and lung function (FEVi and FVC for lags of at least 3 days) were
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observed among healthy, nonsmoking adults in the Tel Aviv Sourasky Medical Center
Inflammation Survey (Steinvil et al.. 2009). Some inverse associations were also
observed for NO2 and CO but not PM10. Positive associations were reported for O3 and
both FEVi and FVC. None of the air pollutants were associated with the FEVi/FVC ratio
with the exception of CO and O3 on lag Day 7. Although highly correlated with NO2
(correlation coefficient 0.70), the inverse association between SO2 concentration and
FEVi and FVC were relatively unchanged when adjusted for NO2, CO (correlation
coefficient -0.25), and O3 (correlation coefficient 0.62) in copollutant models.
Studies in Korea examined the associations between air pollution and lung function using
general populations regardless of health status. Son et al. (2010) investigated how
different methods of estimating exposure may influence health effect estimates in a case
study of FEVi and FVC for cohort subjects in Korea chosen based on residence near a
petrochemical complex. Age of study participants ranged from 7 to 97 years, with a mean
age of 45 years. Measurements from Korea's Department of Environment's air monitors
were used to estimate individual-level exposures by averaging across values from all
monitors, selecting the value from the nearest monitor, weighting by inverse distance,
and kriging. Cross-validation indicated that kriging provided the smallest range of
estimated exposures. An inverse association was observed between FVC, but not FEVi,
and SO2 exposure estimated using all four methods. The same was true of the other
pollutants, with the exception of O3, which was inversely related to both FVC and FEVi.
Copollutant models were run for pollutant pairs that were not highly correlated. Inclusion
of CO or O3 in the models with SO2 did not affect its association with forced expiratory
volume (FEV). The association between SO2 and FVC did change with adjustment for O3
concentration, but the association remained when CO concentration was included in the
model. Health effect estimates were generally higher using exposures based on averaging
across all monitors or kriging than for exposures based on nearest monitor or IDW
exposure. Another study of adults in Korea examined the relationship between SO2
concentration and lung function by smoking status (Min et al.. 2008a'). One hour lags
were examined over a 48-hour period for SO2 measured from an air monitor. The study
population consisted of individuals with no serious medical problems, although having
asthma was not an exclusion factor and instead was controlled for in the analysis
(proportion of participants with asthma not provided). No association was observed
between SO2 concentration and FVC or FEVi, among nonsmokers, but among smokers,
there was an inverse association starting around 6 hours and lasting until about 20 hours.
From the figures presented in the paper, it is difficult to determine whether confidence
intervals overlap for the two groups, but the difference appears to be greatest around
11 hours. No other pollutants were examined in this study. In summary, studies from
Korea have had mixed findings regarding the association between SO2 concentration and
lung function.
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1 Children. The 2008 SOx ISA (U.S. EPA. 2008b) stated that there was insufficient
2 evidence to conclude that an association was present between SO2 and lung function
3 among children. Summarized below and in Table 5-21 are recent studies published since
4 the previous review that examine SO2 concentrations and lung function in general
5 populations of children. None of these studies were performed in the U.S. or Canada.
6 General populations of children include both healthy children and those with pre-existing
7 disease. Although overall the studies report inconsistent results, some studies report
8 findings that are supportive of an association between SO2 concentration and lung
9 function in children.
Table 5-21 Summary of recent epidemiologic studies examining associations
between SO2 concentrations and lung function among children.
Study,
Location,
and Years
Study
Design
Study
Population
and N
Measure of
SO2
Mean SO2 and
Upper
Concentration
Level
Adjusted Effect Estimate
Mean (SD)
Change (95% CI) per 10-ppb
Spring
increase in SO2
School 1:
FVC
11.9 (0.4) ppb
-0.0649 (-0.1297,
-0.0001)
School 2:
FEV1
9.1 (0.4) ppb
-0.0076 (-0.0106,
-0.0046)
PEF
Summer
-0.0270 (-0.0539,
0.0000)
School 1:
FEV1/FVC
12.3 (0.3) ppb
-0.0728 (-0.1805,
0.0349)
School 2:
8.8 (0.5) ppb
Fall
School 1:
10.4 (0.3) ppb
School 2:
10.2 (0.6) ppb
Linares et
al. (2010)
Salamanca,
Mexico
2004-2005
Longitud-
inal
repeated
measures
Children
(6-14 yr) from
two schools.
N = 464
Fixed site
monitors; 24-h
mean SO2
concentrations
Winter
School 1: 9.9 (0.2)
ppb
School 2: 13.6(0.7)
ppb
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Table 5-21 (Continued): Summary of recent epidemiologic studies examining
associations between S02Concentrations and lung
function among children.
Mean SO2 and
Study,
Study
Upper
Location,
Study
Population
Measure of
Concentration
and Years
Design
and N
SO2
Level
Adjusted Effect Estimate
Correia-
Panel
School
Fixed site
Mean (SD):
Estimated change (95% CI) in PEF
Deur et al.
study
children
monitors; 24-h
8.78 (3.27) ppb
(L/min) per 0.38-ppb increase in
(2012)
(9-11 yr)
mean SO2
75th percentile:
SO2
Brazil
N = 96
concentrations
11.2 ppb
2-h avg
2004
90th percentile:
13.1 ppb
-2.11 (-3.82, -0.39)
24-h avg
-0.79 (-3.03, 1.45)
Estimated change (95% CI) in
FEV1 (L) per 0.38-ppb increase in
SO2
2-h avg
0.00 (0.00, 0.00)
24-h avg
-0.026 (-0.053, 0.000)
Note: authors stated no negative
association was demonstrated
between SO2 concentrations and
PEF during lag times of 3-, 5-, 7-,
and 10-day moving avgs;
quantitative results were not
provided
Estimated change (95% CI) in PEF
(L/min) per 10-ppb increase in SO2
Lag 1: -0.73 (-2.46, 0.99)
Lag 2: -0.99 (-2.60, 0.61)
Lag 3: 0.34 (-1.13, 1.81)
2-day avg; -1.81 (-3.78, 0.17)
3-day avg: -1.47 (-3.39, 0.46)
Castro et al.
(2009)
Brazil
2004
Panel
study
Random
selection of
children
(6-15 yr) from
low-income
families
attending one
public school
in a potentially
high air
pollution area.
N = 118
Fixed site
monitors; 24-h
mean SO2
concentrations
Mean (SD):
7.10 (6.83) ppb
90th percentile:
15.5 ppb
Max: 36.6 ppb
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Table 5-21 (Continued): Summary of recent epidemiologic studies examining
associations between S02Concentrations and lung
function among children.
Mean SO2 and
Study,
Study
Upper
Location,
Study
Population
Measure of
Concentration
and Years
Design
and N
so2
Level
Adjusted Effect Estimate
Altua et al.
Cross-
Students
Fixed site
Summer period
OR (95% CI) per 10-ppb increase
(2013)
sectional
(9-13 yr) from
monitors;
Suburban schools:
in SO2
Eskisehir,
public primary
weekly mean
mean (SD): 8.5
Summer
Turkey
schools,
located in
(1) suburban
(2) urban, or
(3) urban-
traffic regions
N = 1,880
SO2
(3.1) ppb
Impaired lung function
2008-2009
concentrations
Max: 16.1 ppb Urban
Girls: 1.22 (0.72, 2.09)
Schools:
Mean (SD): 10.2
(3.9) ppb
Max: 16.4 ppb
Urban-traffic
Boys: 0.83 (0.47, 1.45)
Winter
Impaired lung function
Girls: 1.00 (0.76, 1.32)
Boys: 0.83 (0.61, 1.11)
schools:
Mean (SD): 6.3
(2.1) ppb
Max: 8.9 ppb
Winter period
Suburban schools:
mean (SD): 21.1
(6.3) ppb
Max: 28.9 ppb
Urban schools:
mean (SD): 29.1
(7.2) ppb
Max: 44.2 ppb
Urban-traffic
schools:
mean (SD): 22.0
(7.0) ppb
Max: 32.7 ppb
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Table 5-21 (Continued): Summary of recent epidemiologic studies examining
associations between S02Concentrations and lung
function among children.
Study,
Location,
and Years
Study
Design
Study
Population
and N
Measure of
SO2
Mean SO2 and
Upper
Concentration
Level
Adjusted Effect Estimate
Altuq et al.
(2014)
Eskisehir,
Turkey
Cross-
sectional
4th and 5th
grade
students
(9-13 yr) from
public primary
Schools
located in
(1) suburban,
(2) urban, or
(3) urban-
traffic regions.
N = 605
Fixed site
monitors;
weekly mean
SO2
concentrations
Suburban schools:
Mean (SD): 21.1
(6.3) ppb
Max: 28.9 ppb
Urban schools:
mean (SD): 29.1
(7.2) ppb
max: 44.2 ppb
Urban-traffic
schools:
mean (SD): 22.0
(7.0) ppb
Max: 32.7 ppb
OR (95% CI) per 10-ppb increase
in SO2
Subjects with upper respiratory
tract symptoms
FeNO
1.05 (0.97, 1.14)
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)
Subjects without upper respiratory
tract symptoms
FeNO
0.97 (0.87, 1.09)
FVC
1.00 (0.97, 1.03)
FEV1
1.00 (0.97, 1.03)
PEF
1.00 (0.97, 1.03)
MMEF
1.00 (0.92, 1.08)
Chang et al.
(2012)
Taipei
Taiwan
1996-1997
Cross-
sectional
School
children
(12-16 yr)
were
randomly
selected from
87 junior high
schools in
5 districts.
(23.8%
<14 yr,
33.4% = 14 yr,
42.8% >14 yr)
N = 2,919
Fixed site Six-day median: Estimated change (95% CI) in FVC
monitors; 4-h, 2.6 ppb (mL) per 10-ppb increase in SO2
10-h mean 75th percentile: |_ag 0
SO2 5.2 ppb Avg: 5.5 (-28.6, 39.6)
concentrations Peak:6.3 (-18.9, 31.5)
Lag 1
Avg: -129.0 (-207.0, -50.9)
Peak: -89.6 (-135.1, -44.1)
Lag 2
Avg: 32.8 (-36.6, 102.2)
Peak: 28.8 (-5.2, 62.8)
Estimated change (95% CI) in
FEV1 (mL) per 10-ppb increase in
SO2
Lag 0
Avg: 0.4 (-32.2, 33.0)
Peak: 3.6 (-20.8, 27.9)
Lag 1
Avg: -117.3 (-193.0, -41.6)
Peak: -84.8 (-129.0, -40.6)
Lag 2
Avg: 21.2 (-47.0, 89.4)
Peak: 25.4 (-7.9, 58.6)
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Table 5-21 (Continued): Summary of recent epidemiologic studies examining
associations between S02Concentrations and lung
function among children.
Mean SO2 and
Study,
Study
Upper
Location,
Study
Population
Measure of
Concentration
and Years
Design
and N
SO2
Level
Adjusted Effect Estimate
Amadeo et
Cross-
School
Fixed site
Mean (SD): 1.79
(3 (95% CI) per 10-ppb increase in
al. (2015)
sectional
children
monitors; max
(1.41) ppb
SO2 averaged over previous
Guadeloupe
randomly
daily 1-h
Max: 4.85 ppb
2 weeks
2008-2009
selected from
mean SO2
PEF: 30.9 (-69.4, 131.0)
seven
concentrations
Change in PEF: 3.7 (-24.6, 32.0)
elementary
schools
(8-13 yr)
N = 506
Reddv et al.
Longitud-
Indigenous
Fixed site
Mean (SD): 5.8
Percent change in intra-day
(2012)
inal
African
monitors; 24-h
(0.2) ppb
variability of FEVi (95% CI) per
South Africa
repeated
children from
mean SO2
Max: 40.8 ppb
10-ppb increase in SO2
2004-2005
measures
seven primary
concentrations
Lag 1: 1.62 (-0.03, 3.30)
schools
(9-11 yr).
N = 129
Lag 2: 0.27 (-1.28, 1.83)
Lag 3: -0.79 (-2.40, 0.85)
Lag 4: -0.09 (-1.92, 1.77)
Lag 5: -0.08 (-1.64, 1.50)
5-day avg: 0.95 (-3.05, 5.11)
avg = average; CI = confidence interval; FeNO = fractional exhaled nitric oxide; FEVi = forced expiratory volume in
1 second; FVC = forced vital capacity; MMEF = maximum midexpiratory flow; N = population number; OR = odds
ratio; PEF = peak expiratory flow; ppb = parts per billion; SD = standard deviation; SO2 = sulfur dioxide.
Linares et al. (2010) performed a longitudinal repeated-measures study examining air
pollution exposure and lung function among school-aged children in Mexico. SO2
concentrations were inversely associated with FVC, FEVi, and PEF, but not FEVi/FVC.
When stratified by sex, the precision decreased and associations were no longer
statistically significant. The point estimates were lower among girls but became positive
among boys. Additionally, the associations between FEVi/FVC were positive among
both boys and girls. Results were similar in two-pollutant models controlling for O3 or
PM10. Associations were also observed between other air pollutants (O3, NO2, and PM10)
and lung function, with some of the associations remaining in two-pollutant models
stratified by sex. No correlation coefficients between the air pollutants were provided.
Similarly, in Brazil a study of school-aged children examined the associations between
air pollution and lung function and found an association for SO2 (Correia-Deur et al..
2012). SO2 concentration was inversely associated with PEF and FEVi when using
shorter lag times (2-and 24-hour), but not with longer moving averages (3-, 5-, 7-, and
10-day). Other pollutants were associated with all time periods (PM10, NO2, and O3).
Results for two-pollutant models with SO2 were not provided, but SO2 was positively
correlated with CO, PM10, NO, and NO2 (r = 0.62, 0.75, 0.87, 0.60, respectively) but not
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with O3 (r = 0.07). When stratifying by categories of allergic sensitization, the association
between SO2 concentration and PEF became null among all categories, possibly due to
decreased precision. Associations remained for some of the other pollutants. Chang et al.
(2012) assessed the association between air pollutants exposure and the lung function of
junior high school students in Taiwan and reported an inverse association between SO2
concentration and lung function. SO2 concentrations with 1 day lags were inversely
associated with FVC and FEVi. No associations were present with 0 or 2 day lags.
Similar associations were detected for O3, CO, PM10, and NO2 concentrations.
Correlation coefficients and copollutant models were not provided. Other studies did not
report an association between SO2 concentrations and lung function. Castro et al. (2009)
examined lung function among school children in another study from Brazil. In this
study, no association was observed between SO2 concentrations and PEF, but there was a
large amount of missing data for SO2 concentrations that could have affected the results.
PM10 and NO2 concentrations were inversely associated with lung function, while O3
concentration showed a positive association and CO demonstrated no association.
Correlation coefficients between the pollutants were not provided, and the study did not
utilize copollutant models. A study in Turkey observed no association between SO2
concentration and impaired lung function over two-week periods in either the summer or
winter period (Altug et al.. 2013). This persisted in models adjusted for O3 and NO2.
Associations with NO2 and O3 concentrations also demonstrated no associations with
lung function, with the exception of summer O3 concentrations among girls. Correlation
coefficients were not provided, but in two-pollutant models among girls, the lack of
association with lung function remained when SO2 was adjusted for O3 or NO2. A
subpopulation of this study was re-examined (Altug et al.. 2014). The association with
lung function was assessed by stratifying the population into children who reported upper
respiratory tract symptoms and those who did not. No association was observed between
SO2 concentrations and lung function in either group. Similarly, no associations were
observed with NO2 or O3 concentrations, with the exception of a slight inverse
association between O3 and PEF among those without upper respiratory symptoms.
Correlation coefficients with SO2 were 0.486 and 0.395 for NO2 and O3, respectively. No
copollutant models were provided. A study in Guadeloupe also reported no associations
between SO2 concentrations and lung function (baseline PEF and after exercise) for any
of the time windows examined (lag 0 through 2-week average) (Amadeo et al.. 2015).
This study examined the potential for an interaction by asthma status in models using the
2-week average of SO2 concentrations, but none were observed. No associations were
found between lung function and O3 or NO2, but associations were noted for PM10. No
correlations or copollutants models were provided. In summary, although some of these
studies reported inverse associations between SO2 concentration and lung function, many
did not and overall the findings are inconsistent among the studies.
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South African school children were included in a study of air pollution and lung function
that explored differences by glutathione S-transferase mu 1 (GSTM1) and glutathione
S-transferase Pi 1 (GSTP1) genes (Reddv et al.. 2012). Overall, null associations were
observed between SO2 concentrations and intra-day variability of FEV1. However, when
stratifying by GSTM1 genes, a positive association between SO2 concentration averaged
over 5 days, and FEVi intra-day variability was observed among GSTM1 positive
children. The association remained null among GSTM1 null children. Conversely,
associations with PM10 and NO2 were null overall but demonstrated inverse relationships
with FEVi among GSTM1 positive individuals. When stratifying the population by
GSTP1 genotypes, positive associations were observed for GSTP1 adenine-adenine and
guanine-guanine genotype (AA+GG) children at lag Days 1 and 3, with results for
children with GSTP1 AA genotypes being null. NO2 concentrations were positively
associated with FEVi among GSTP1 AA genotypes and inversely associated with GSTP1
AA+GG genotypes during lag Days 1, 2, and 3, while PM10 concentrations were
inversely associated with FEVi among GSTP1 AA genotypes and positively associated
with GSTP1 AA+GG genotypes during lag Days 2 and 3. Correlation coefficients with
SO2 were not provided and no copollutant models were examined. This study suggests
that the association between SO2 concentration and lung function may vary by genotype
but the overall evidence base regarding modification of S02-related health effects by
genotype is limited (see Section 6.4).
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. 2008b). 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. 2008b. 1982a). The 2008 SOx ISA (U.S. EPA. 2008b)
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 5-22. 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,
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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.
Table 5-22 Study-specific details from animal toxicological studies of lung
function.
Study
Species (Strain); n; Sex;
Lifestage/Age
(mean ± SD)
Exposure Details
(Concentration; Duration)
Endpoints Examined
Amdur et al. (1983)
Hartley guinea pig; male;
age NR; 200-300 g;
n = 8-23/group
~1 ppm (2.62 mg/m3)
only for 1 h
head Endpoints examined during
exposure and up to 1 h
post-exposure.
Lung function—pulmonary
resistance, dynamic
compliance, breathing
frequency, tidal volume, and
minute volume
Conner et al. (1985)
Hartley guinea pig; male;
age NR; 250-320 g;
n < 18/group/time point
1 ppm (2.62 mg/m3); nose
only for 3 h/day for 6 days
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; sex NR; adult;
mean 2.0 kg;
n = 5-9/group; rabbits
were mechanically
ventilated
0.5 ppm (1.3 mg/m3) for
45 min; intra-tracheal
Endpoints examined 5 min
before and up to 1 h
post-exposure.
Lung function—pulmonary
resistance
CO = carbon monoxide; n = sample size; NR = not reported; SD = standard deviation.
Summary of Lung Function in General Populations and Healthy
Individuals
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
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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. Some
epidemiologic studies reported associations between SO2 exposure and decreases in lung
function among general populations of children and adults; however, the evidence is
limited and inconsistent. The epidemiologic studies utilized concentration estimates
derived from fixed site monitors, which could have issues with exposure measurement
error due to spatial variability (Section 3.3.3.2). One study that explored modeling the
information from monitors using averages, inverse distance weighting, and kriging
reported similar results across the methods.
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. 2008b) 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. There was limited evidence for an association between SO2 concentrations
and respiratory symptoms among adults and children without asthma. A few recent
epidemiologic studies have become available for review; studies among children report
mixed results.
Controlled Human Exposure Studies
Controlled human exposure studies examining respiratory symptoms in healthy
individuals exposed to SO2 are summarized in Table 5-19. 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). However, Linn et al.
(1984a) reported significantly greater clinical scores 1 week post exposure to
2 consecutive days of 0.6 ppm SO2 exposure for 6 hours.
Epidemiologic Studies
Adults. There was limited evidence in the 2008 SOx ISA (U.S. EPA. 2008b) for an
association between SO2 concentrations and respiratory symptoms among adults without
asthma or other respiratory conditions. Since the previous review, the evidence regarding
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an association between SO2 concentrations and respiratory symptoms among adults
without asthma continues to be limited and is described below and in Table 5-23.
Table 5-23 Summary of recent epidemiologic studies examining associations
between SO2 concentrations and respiratory symptoms among
adults.
Mean SO2 and
Study Upper
Location Study Study Population Measure of Concentration
and Years Design and N SO2 Level Adjusted Effect Estimate
Ishiqami et
al. (2008)
Japan
2005
Cross- Healthy volunteers Fixed site
sectional (>15 yr) working on monitors;
an active volcanic 1-h mean
island after and 1-h max
evacuation order SO2
was lifted concentratio
N = 611 ns
Mean SO2
levels at
monitoring sites
ranged from 0 to
3,550 ppb
Max range
varied by
locations from
3,790 to
10,320 ppb
NR
Goldberg
et al.
(2009)
Canada
2002-2003
Panel
study
Congestive heart Fixed site Mean: 4.50 ppb Mean difference (95% CI) for
failure patients
(50-85 yr) with
limitations in
physical
functioning and an
ejection fraction
<35%
N = 31
monitors;
24-h mean
SO2
concentratio
ns
Max- 25 1 ppb shortness of breath at night per
10 ppb SO2
Lag 0
Lag 1
Lag 2
1.21,1.85)
-1.80, 1.32)
0.32 (-
-0.24
0.68
(-0.84, 2.20)
Lag 3: 0.20
(-1.31, 1.72)
Lag 4: -0.58
(-2.08, 0.91)
Lag 0-2: 0.79 (-1.88, 3.46)
CI = confidence interval.
A study on the Japanese island of Miyakejima, with an active volcano, examined health
effects among healthy adults on the island after the evacuation order was lifted (Tshigami
et al.. 2008). No associations were observed between symptoms and hourly mean SO2
levels <100 ppb and hourly maximum SO2 levels <2,900 ppb. At concentrations above
these, associations were observed with cough, scratchy throat, sore throat, and
breathlessness. When examining rate ratios by sex, higher rates were observed among
women compared to men. No associations were found for nasal congestion, eye pain, or
skin itching among men or women. No other air pollutants were examined in this study.
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A study of patients with congestive heart failure (CHF) in Canada examined the
association between air pollution concentrations and shortness of breath at night
(Goldberg et al.. 2009). No association was observed with SO2 concentrations. None of
the other pollutants examined were associated with shortness of breath at night, including
CO (correlation coefficient with SO2: 0.53), NO2 (correlation coefficient with SO2: 0.59),
O3 (correlation coefficient with SO2: -0.09), and PM2 5 (correlation coefficient with SO2
0.50). Copollutant models were not examined.
Overall, there continues to be limited studies among the general population exposed to
average ambient levels that examine the association between SO2 concentration and
respiratory symptoms among adults.
Children. Evidence of an association between SO2 concentration and respiratory
symptoms among children without asthma or in the general population was limited in the
2008 SOx ISA (U.S. EPA. 2008b). although some studies did report positive associations.
Recent studies also demonstrate some positive associations with the overall evidence
being mixed among various outcome measures. None of the recent studies were
conducted in the U.S. or Canada. Details of these recent studies are described below and
in Table 5-24.
Table 5-24 Summary of recent epidemiologic studies examining associations
between SO2 concentrations and respiratory symptoms among
children.
Study, Study
Location, Study Population Measure of Mean SO2 and Upper Adjusted Effect
and Years Design and N SO2 Concentration Level Estimate
Linares et Longitudinal Children
al. (2010)
Salamanca,
Mexico
2004-2005
repeated
measures
(6-14 yr) from
two schools
N = 464
Fixed site
monitors; 24-h
mean SO2
concentrations
Mean (SD)
Spring
School 1:
School 2:
Summer
School 1:
School 2:
Fall
School 1:
School 2:
Winter
School 1:
School 2:
11.9 (0.4) ppb
9.1 (0.4) ppb
12.3 (0.3) ppb
8.8 (0.5) ppb
10.4 (0.3) ppb
10.2 (0.6) ppb
9.9 (0.2) ppb
13.6 (0.7) ppb
OR (95% CI) per 10-ppb
increase in SO2
Wheezing
1.0567 (1.0047, 1.1114)
Rhinorrhea
0.9815 (0.9171, 1.0504)
Eczema
0.9919 (0.9574, 1.0277)
Dyspnea
1.0216 (0.9722, 1.0735)
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Table 5-24 (Continued): Summary of recent epidemiologic studies examining
associations between SO2 concentrations and respiratory
symptoms among children.
Study, Study
Location, Study Population Measure of Mean SO2 and Upper Adjusted Effect
and Years Design and N SO2 Concentration Level Estimate
Altuq et al. Cross-
(2014) sectional
Eskisehir,
Turkey
2009
4th and 5th Fixed site
grade students monitors;
from public weekly mean
primary SO2
schools in concentrations
(1) suburban,
(2) urban, or
(3) urban-traffic
regions
N = 605
Suburban schools:
Mean (SD): 21.1 (6.3) ppb
Max: 28.9 ppb
Urban schools:
Mean (SD): 29.1 (7.2) ppb
Max: 44.2 ppb
Urban-traffic schools:
Mean (SD): 22.0 (7.0) ppb
Max: 32.7 ppb
OR (95% CI) per 10-ppb
increase in SO2
Cold in the last 7 days
0.74 (0.58, 0.94)
Cold at the moment
0.92 (0.67, 1.27)
Complaints of the throat
in last 7 days
0.83 (0.59, 1.15)
Complaints of the throat
at the moment
1.03 (0.72, 1.47)
Runny nose in the last
7 days
0.95 (0.74, 1.22)
Runny nose at the
moment
0.92 (0.69, 1.23)
Shortness of breath or
wheeze in the last 7 days
1.72 (1.05, 2.81)
Medication for shortness
of breath or wheeze in the
last 7 days
1.44 (0.69, 2.99)
Shortness of breath or
wheeze today
1.79 (0.90, 3.58)
Medication for shortness
of breath or wheeze today
0.74 (0.16, 3.33)
Moon et al.
(2009)
South
Korea
2003
Cross-
sectional
Random
selection of
primary school
children
(<13 yr)
located near
air pollution
monitoring
stations
N = 696
Fixed site
monitors; 24-h
mean SO2
concentrations
Mean concentrations NR
(majority 24-h avgs were
<20 ppb and the max was
38 ppb)
OR (95% CI) per 10-ppb
increase in SO2
Lower respiratory
symptoms
LagO: 1.003
(0.931, 1.082)
Upper respiratory
symptoms
LagO: 1.112
(1.034, 1.196)
Irritation symptoms
moving avg 0-1: 1.010
(0.918, 1.112)
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Table 5-24 (Continued): Summary of recent epidemiologic studies examining
associations between SO2 concentrations and respiratory
symptoms among children.
Study, Study
Location, Study Population Measure of Mean SO2 and Upper Adjusted Effect
and Years Design and N SO2 Concentration Level Estimate
Zhao et al.
(2008)
Taiyuan
City, China
2004
Cross- Students Fixed site
sectional (11—15 yr) monitors;
from randomly weekly mean
selected junior SO2
high schools concentrations
N = 1,993 (indoor
concentrations
measured in
multiple
classrooms;
outdoor
measured
outside of the
school)
Indoor
Mean (SD):
101.1 (53.1) ppb
Max: 244.7 ppb
Outdoor
Mean (SD):
272.1 (72.3) ppb
Max: 387.5 ppb
OR (95% CI) per 10-ppb
increase in indoor SO2
Cumulative asthma
1.03 (0.96, 1.12)
Wheeze
1.04 (1.01, 1.08)
Daytime attacks of
breathlessness
1.02 (0.99, 1.04)
Nocturnal attacks of
breathlessness
1.07 (1.01, 1.13)
Furry pet or pollen allergy
1.03 (0.98, 1.08)
OR (95% CI) per 10-ppb
increase in outdoor SO2
Cumulative asthma
0.97 (0.92, 1.03)
Wheeze
1.01 (0.98, 1.04)
Daytime attacks of
breathlessness
0.99 (0.97, 1.01)
Nocturnal attacks of
breathlessness
1.01 (0.96, 1.06)
Furry pet or pollen
Farhat et Panel study Cystic fibrosis Fixed site
al. (2014)
Sao Paulo,
Brazil
2006-2007
patients
(median 8.9 yr;
range 0-15+)
at Children's
Institute,
Clinics Hospital
(University of
Sao Paulo)
were invited to
enroll.
N = 103
monitors; 24-h
mean SO2
concentrations
SO2
Mean (SD): 3.78
(1.55) ppb
75th percentile: 4.62 ppb
Max: 9.59 ppb
RR (95% CI) of
respiratory exacerbation
of cystic fibrosis per
10-ppb increase in SO2
Lag 0
Lag 1
Lag 2
Lag 3
Lag 4
Lag 5
Lag 6
0.91 (0.07,11.04)
1.51 (0.08,
7.58 (0.31,
0.52 (0.03,
0.31 (0.02,
1.38 (0.11,
3.24 (0.22,
28.03)
187.41)
9.67)
4.68)
17.19)
46.98)
avgs = averages; CI = confidence interval; N = population number; NR = not reported; OR = odds ratio; ppb = parts per billion;
RR = relative risk; SD = standard deviation; S02 = sulfur dioxide.
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A study of children in Mexico used a longitudinal repeated measures design to examine
the association between respiratory symptoms and air pollution measurements (Linares et
al.. 2010). SO2 concentrations varied by season and school location. Positive associations
were observed between SO2 concentrations and wheezing. Positive associations were also
detected for these respiratory symptoms and O3 and PM10, butnotNCh concentrations.
No associations between SO2 concentrations and rhinorrhea, eczema, or dyspnea were
present. Neither correlation coefficients between the pollutants nor copollutant models
between air pollutants and respiratory symptoms were provided. A study in Turkey
examined the association between air pollution and respiratory symptoms both in the
prior 7 days and at that moment/today and also reported a positive association with
wheezing (Altug et al.. 2014). SO2 concentrations were positively associated with an
attack of shortness of breath or wheeze in the last 7 days, but not the use of medications
for shortness of breath or wheeze. An inverse association was observed between SO2
concentrations and report of a cold in the past week. No association between SO2
concentrations and other respiratory tract complaints (complaints of the throat and runny
nose) were observed. The correlation coefficients between SO2 and NO2 and SO2 and O3
were 0.486 and 0.395, respectively. NO2 concentrations were not associated with any
respiratory tract complaints and O3 concentrations were positively associated with having
a cold and having a runny nose at the moment. No copollutant models were provided. In
summary, these studies reported positive associations between SO2 concentration and
some respiratory symptoms, especially wheezing.
Conversely, Moon et al. (2009) used epidemiologic surveillance data in Korea to
investigate the relationship between air pollution and respiratory symptoms among
children and reported no association between SO2 concentrations and lower respiratory
symptoms (cough, phlegm, wheezing), both overall and when cities were examined
individually. SO2 concentration was positively associated with upper respiratory
symptoms (runny nose, sneezing), although when examining the associations by city, not
all cities demonstrated this positive association and one city even had an inverse
association. A positive association between SO2 concentration and allergic symptoms
(irritated eyes, itching skin) was demonstrated in a couple of cities, but this association
was not observed in the overall analysis. In overall analyses, NO2 was positively
associated with allergic symptoms, whereas O3 was inversely related to allergic
symptoms. CO was positively associated with lower respiratory, upper respiratory, and
allergic symptoms. PM10 was not associated with any symptoms in the overall analyses.
Correlations coefficients between the pollutants were not provided and only
single-pollutant models were utilized. Null associations between SO2 concentrations and
respiratory symptoms were also observed in a study in China (Zhao et al.. 2008). Mean
outdoor SO2 concentrations were reported as 272.1 ppb, but 3 of the 10 samplers were
completely saturated and assigned the saturation concentration, although the actual
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concentration of SO2 could have been higher. No associations were observed between
1-week SO2 concentrations and cumulative asthma, wheeze/whistling in the chest,
daytime attacks of breathlessness, nocturnal attacks of breathlessness, or pet/pollen
allergy. Some associations were noted between measured concentrations of SO2 indoors
and respiratory symptoms. Indoor and outdoor SO2 concentrations were correlated
(/> value < 0.01). Although correlation was seen among some of the indoor pollutants,
none of the outdoor pollutants were correlated (correlation coefficients not provided).
Additionally, none of the outdoor concentrations ofNC>2 or O3 were associated with
respiratory symptoms, and copollutant models with outdoor air pollutants were not
performed. Some associations were observed with outdoor and indoor concentrations of
formaldhyde, as well as some indoor concentrations of NO2 and O3. Overall, studies in
Asia did not observe associations between SO2 concentration and wheezing or other
lower respiratory symptoms.
A study of respiratory exacerbations among children with cystic fibrosis examined
whether risk was associated with air pollution (Farhat et al. 2014V Lag Days 0-6 were
examined and SO2 concentrations were not observed to be associated with cystic fibrosis
exacerbations. Similarly, no associations were detected for CO, NO2, or PM10, although a
positive association was found for O3 and a 2-day lag. Correlation coefficients between
SO2 and the other pollutants were 0.37 for O3, 0.56 for CO, 0.57 for NO2, and 0.70 for
PM10. Results of copollutant models with SO2 were not reported.
Summary of Respiratory Symptoms in General Populations and Healthy
Individuals
There is limited evidence for an association between SO2 concentrations and respiratory
symptoms in general populations or among individuals without asthma or other
respiratory conditions. Controlled human exposure studies of healthy adults exposed to
up to 2 ppm SO2 generally did not find increases in respiratory symptoms. While
epidemiologic studies in adults are generally not supportive of a relationship between
SO2 concentrations and respiratory symptoms, some studies in children report positive
associations, further supporting the hypothesis that children may be more sensitive to
SO2. The epidemiologic studies utilized pollutant concentrations derived from fixed site
monitors, which could have issues with exposure measurement error due to spatial
variability (Section 3.3.3.2).
Airway Responsiveness in General Populations and Healthy Individuals
The term "airway responsiveness" refers to the ability of the airways to narrow in
response to constrictor stimuli. Studies that examined airway responsiveness in relation to
short-term SO2 exposures are limited to animal toxicological studies in control, or naive,
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animals. In several studies, the effects of SO2 exposure on airway responsiveness was
examined in parallel in allergic animals. Results in allergic animals are discussed in
Section 5.2.1.2.
The 2008 SOx ISA (U.S. EPA. 2008b) 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
following 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
(Amdur et al.. 1988).
However, two toxicological studies (Park et al.. 2001) (Riedel et al.. 1988) described in
the 2008 SOx ISA, and one more recent one (Song et al. 2012). provide evidence that
repeated SO2 exposure in naive animals leads to the development of increased airway
responsiveness (AHR). These studies, and others, are summarized in Table 5-25 and are
described below because the development of AHR occurred in conjunction with the
development of an allergic phenotype following repeated SO2 exposure.
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Table 5-25 Study-specific details from animal toxicological studies of airway
responsiveness.
Study
Species (strain); n; Sex;
Lifestage/Age
(mean ± SD)
Exposure Details
(Concentration; Duration)
Endpoints Examined
Amduret al. (1988)
Guinea pig; n = 8
1 ppm for 1 h
Endpoints examined 2 h
following exposure
Airway responsiveness to
acetylcholine
Riedeletal. (1988)
Guinea pigs (Perlbright-
White); n = 5-14; M; age
NR; 300-350 g;
0.1, 4.3, and 16.6 ppm whole
body; 8 h/day for 5 days
Animals were sensitized to
ovalbumin (ovalbumin
aerosol) on the last 3 days of
exposure
Bronchial provocation every
other day with aerosolized
0.1% ovalbumin began at
1 week after the last
exposure to SO2 and
continued for 14 days
4 groups:
Control
0.1 ppm SO2
4.3 ppm SO2
16.6 ppm SO2
Bronchial obstruction
determined by examination of
the respiratory loop
measured by whole-body
plethysmography in
spontaneously breathing
animals after each bronchial
provocation.
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/day
for 5 days
Animals were sensitized to
ovalbumin (0.1% ovalbumin
aerosol) on the last 3 days of
exposure
Bronchial challenge with
1% ovalbumin aerosol
occurred at 1 week after the
last exposure to SO2
4 groups:
Control
Ovalbumin
Bronchial
obstruction—measurement of
Penh by whole-body
plethysmography
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Table 5-25 (Continued): Study-specific details from animal toxicological studies
of airway responsiveness.
Species (strain); n; Sex;
Lifestage/Age Exposure Details
Study (mean ± SD) (Concentration; Duration) Endpoints Examined
Song et al. (2012) Rats (Sprague-Dawley);
N = 40; n = 10 per
exposure group; M;
4-week old neonates
BAL—IL-4, IFN-y
Serum—IL-4, IFN-y
Lung—histopathology
In vitro culture of ASM cells
from experimentally treated
animals—stiffness and
contractility
SO2
Ovalbumin/SC>2
Exposure to 2 ppm SO2 for
4 h/day for 4 weeks beginning
at 15 days
Endpoints examined 24 h
after challenge
Lung function—whole-body
plethysmography (MCh
challenge)
ASM = airway smooth muscle; BAL = bronchoalveolar lavage; IFN-y = interferon gamma; IL-4 = interleukin-4; M = male;
MCh = methacholine; n = sample size; NR = not reported; Penh = enhanced pause; ppm = parts per million; SD = standard
deviation; S02 = sulfur dioxide.
Subclinical Respiratory Effects in Healthy Individuals
Controlled Human Exposure Studies
Airway 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.
A recent controlled human exposure study examined eNO and other biomarkers of
airway 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 significant changes in eNO; leukotriene B4, prostaglandin E2, and 8-iso-prostaglandin
F2 alpha in EBC; and substance P, interleukin-8 (IL-8), and brain derived neurotrophic
factor in NALF after exposures compared to air.
Epidemiologic Studies
Since the 2008 SOx ISA (U.S. EPA. 2008b). recent studies have examined the
association between SO2 concentration and biomarkers among children and young adults
in Beijing before, during, and after the 2008 Olympics. A study of elementary school
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children reported lower concentrations of SO2 BC, and PM2 5 during the Olympics
compared to concentrations prior to the Olympics (Lin et al.. 2015). Associations were
observed between SO2 concentration and 8-oxo-7,8-dihydro-2'-deoxyguanosine and
malondialdehyde. These two biomarkers were also associated with concentrations of the
other pollutants. The associations with SO2 concentrations generally appeared to be null
with the inclusion of copollutants. Additionally, associations were examined stratified by
sex and by asthma status, but the results were similar between the respective groups.
Nonsmoking, healthy young adults (ages 19-33 years) participated in a study that
reported the mean 24-hour average SO2 concentration measured on the roof of the study
hospital to be 6.07 ppb (SD 4.01 ppb) (Rov et al.. 2014). While pollutants and biomarkers
are grouped together in this study, some individual results for SO2 demonstrated
associations with biomarkers of pulmonary inflammation/oxidative stress and biomarkers
of systemic inflammation/oxidative stress. These results were the same as demonstrated
for other pollutants [CO, EC, NO2, organic carbon (OC), sulfate, PM2 5], although
associations were not as strong for models of systemic inflammation/oxidative stress. In
summary, limited evidence is available to demonstrate an independent association
between SO2 concentrations and oxidative stress markers among the available studies.
Animal Toxicological Studies
The 2008 SOx ISA (U.S. EPA. 2008b) 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
are summarized in Table 5-26. 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 antiovalbumin 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.
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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.
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, (Li et al.. 2007) (Li et
al.. 2014) 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.
Several other indicators of inflammation and immune response were not changed by
exposure to SO2 alone.
Since the 2008 SOx ISA (U.S. EPA. 2008b). one new toxicological study evaluated the
effects of repeated SO2 exposure on the development of an allergic phenotype and AHR
(Song et al.. 2012). In this study, both naive newborn rats and rats sensitized and
challenged with ovalbumin were exposed to SO2. Effects in newborn rats sensitized and
challenged with ovalbumin are described above in Section 5.2.1.2. Exposure of naive rats
to SO2 (2 ppm, 4 hours/day for 28 days) resulted in hyperemia in lung parenchyma and
inflammation in the airways. In addition, SO2 exposure altered cytokine levels in a way
that suggested a shift in Thl/Th2 balance away from Thl and towards Th2. This is
known as Th2 polarization and is one of the steps involved in allergic sensitization. In
naive animals exposed to SO2, levels of IL-4, indicative of a Th2 response, were
increased and levels of IFN-y, indicative of a Thl response, were decreased in BALF.
This study provides additional evidence that repeated exposure to SO2 promoted allergic
sensitization in naive newborn animals.
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Summary of Subclinical Respiratory Effects
In summary, there is limited evidence for inflammatory and other subclinical respiratory
effects following short-term exposure to SO2, primarily from animal toxicological studies
involving allergen sensitization. In a recent controlled human exposure study, biomarkers
of inflammation were unchanged after a single exposure of exercising individuals to
2 ppm SO2. Recent epidemiologic studies provide limited evidence of an independent
association between SO2 concentrations and oxidative stress markers. 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, SO2 exposure
of guinea pigs to 0.1 ppm SO2 enhanced allergic sensitization, allergic inflammatory
responses, and airway responsiveness to that allergen. In newborn rats, repeated exposure
to 2 ppm SO2 resulted in Th2 polarization and airway inflammation.
Table 5-26 Study-specific details from animal toxicological studies of
subclinical effects.
Study
Species (strain); n; Sex;
Lifestage/Age
(mean ± SD)
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/day for
1-5 days
BAL performed each day.
BALF—total and differential
cell counts
Riedeletal. (1988)
Guinea pigs (Perlbright-
White); n = 5-14/group;
M; age NR; 300-350 g;
0.1, 4.3, and 16.6 ppm whole
body; 8 h/day for 5 days
Animals were sensitized to
ovalbumin (ovalbumin
aerosol) on the last 3 days of
exposure
Bronchial provocation every
other day with 0.1%
ovalbumin aerosol began at
1 week after the last
exposure to SO2 and
continued for 14 days
Four groups:
Control
0.1 ppm SO2
4.3 ppm SO2
16.6 ppm SO2
Endpoints examined 48 h
after the last provocation.
Serum—anti IgG levels
BALF—anti IgG levels
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Table 5-26 (Continued): Study-specific details from animal toxicological studies
of subclinical effects.
Species (strain); n; Sex;
Lifestage/Age
Exposure Details
Study
(mean ± SD)
(Concentration; Duration)
Endpoints Examined
Park etal. (2001)
Guinea pigs (Dunkin-
0.1 ppm whole body; 5 h/day
Endpoints examined 24 h
Hartley); n = 7-12/group;
for 5 days
after the bronchial challenge.
M; age NR; 250-350 g;
Animals were sensitized to
BALF—differential cell counts
ovalbumin (0.1% ovalbumin
cells
aerosol) on the last 3 days of
Lung and bronchial
exposure
tissue—histopathology
Bronchial challenge with 1%
ovalbumin aerosol occurred
at 1 week after the last
exposure to SO2
Four groups:
Control
Ovalbumin
SO2
0valbumin/S02
2 ppm SO2 for 1 h/day for Endpoints examined 24 h
7 days 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
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 ASM cells
from experimentally treated
animals—stiffness and
contractility
Li etal. (2007) Rats (Wistar);
n = 6/group; M; age NR
Song etal. (2012)
Rats (Sprague-Dawley);
n = 40, n = 10 per
exposure group; M;
4-week old neonates
Sensitization by i.p. injection
of 10 mg OVA followed by
booster injection of 10 mg
OVA after 7 days
Challenge with 1% OVA
aerosol for 30 min daily for
4 weeks beginning at 15 days
Exposure to 2 ppm SO2 for
4 h/day for 4 weeks beginning
at 15 days
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Table 5-26 (Continued): Study-specific details from animal toxicological studies
of subclinical effects.
Study
Species (strain); n; Sex;
Lifestage/Age
(mean ± SD)
Exposure Details
(Concentration; Duration) Endpoints Examined
Li etal. (2014)
Rats (Wistar);
2 ppm SO2 for 1 h/day for
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
n = 6/group; M; age NR; 7 days
180-200 g
ASM = airway smooth muscle; 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; kBa = nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha;
i.p. = intraperitoneal; M = male; MCh = methacholine; MUC5AC = mucin 5AC glycoprotein; n = sample size; NFkB = nuclear factor
kappa-light-chain-enhancer of activated B cells; NR = not reported; OVA = ovalbumin; ppm = parts per million; SD = standard
deviation; S02 = sulfur dioxide
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 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 5-16).
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 with regard to 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,
5.2.1.7
Respiratory Mortality
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3.17) for a 10-ppb increase in 24-hour average SO2 concentrations at lag 0-1 days] and
NO2 [1.16% (95% CI: -0.03, 2.37) for a 10-ppb increase in 24-hour average SO2
concentrations at lag 0-1], 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 5-17).
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-hour average
SO2 concentrations at lag 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 PM10, -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. Additionally, it is important to note that the
aforementioned studies relied on central site monitors for estimating exposure. SO2 is
more spatially variable than other pollutants as reflected in the generally low to moderate
spatial correlations across urban geographical scales (Section 3.3.3.2); therefore, the
attenuation in SO2 associations may be a reflection of the different degree of exposure
error across pollutants. This possibility is further supported by an analysis of correlations
between NAAQS pollutants at collocated monitors in the U.S., which demonstrated that
SO2 is low to moderately correlated with other pollutants (Section 3.3.4.1). Overall, the
studies that examined the potential confounding effects of copollutants on the
S02-respiratory mortality relationship report 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.
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%
4.0
3.5
£ 3.0
s
| 2.5
-0.5 A
A 1 j ) 4 fi S 7 Al Ii4 or
-1.0 sn?
Source: Adapted from Menq et al. (2013).
Figure 5-8 Percent increase in chronic obstructive pulmonary disease
(COPD) mortality associated with a 10 |jg/m3 (3.62 ppb) increase
in 24-hour average SO2 concentrations at various single and
multiday lags.
Of the studies evaluated only Bellini et al. (2007). a multicity study conducted in Italy,
examined potential seasonal differences in the SC^-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-hour
average 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 by Bellini et al. (2007) for total and cardiovascular mortality. However, it
remains unclear whether this seasonal pattern of SC^-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
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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 5-8). It should be noted 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-hour average
SO2 concentrations] is similar to that reported in Meng et al. (2013) for COPD [3.7%
(95% CI: 2.4,4.9)].
Meng et al. (2013) also examined the shape of the SO2-COPD mortality C-R relationship.
To examine the assumption of linearity the authors modeled the relationship between air
pollution exposures and COPD mortality using a natural spline with 3 df. Meng et al.
(2013) then computed the difference between the deviance of the linear and spline
models to assess whether there was evidence of nonlinearity in the SO2-COPD
relationship. As depicted in Figure 5-9. the authors found no evidence that the spline
model resulted in a better fit of the S02-mortality relationship compared to the linear
model. However, the authors did not present confidence intervals for each of the C-R
curves, which complicates the interpretation of the results.
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Bdjng
Shwngta
Guaitgitau
Han^orq
0 fid 100 1®
502 concert ration at lag Q1 day
Source: Adapted from Meng et al. (2013).
Figure 5-9 City-specific concentration-response curves for short-term SO2
exposures and daily chronic obstructive pulmonary disease
(COPD) mortality in four Chinese cities.
Overall, recent multi-city 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 day; and that there is a linear, no threshold
C-R relationship, respectively.
5.2.1.8 Summary and Causal Determination
Strong evidence indicates that there is a causal relationship between short-term SO2
exposure and respiratory morbidity, particularly for respiratory effects in the at-risk
population of individuals with asthma. This determination is based on the consistency of
S02-induced bronchoconstriction in exercising individuals with asthma in controlled
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human studies, coherence of respiratory effects among multiple lines of evidence, and
biological plausibility for effects specifically related to asthma exacerbation. There is
some support for other SCh-related respiratory effects, including exacerbation of COPD
in individuals with COPD; respiratory infection and aggregated respiratory conditions,
particularly in children; and respiratory mortality in the general population. However, 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 consistent with the conclusions of the 2008
SOx ISA (U.S. EPA. 2008b). The evidence for this conclusion was heavily based on
controlled human exposure studies that showed lung function decrements and respiratory
symptoms in adults 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). Numerous epidemiologic studies evaluated in the previous review
reported associations between short-term SO2 exposure and respiratory health effects,
ranging from respiratory symptoms to respiratory-related ED visits and hospital
admissions. The evidence for a causal relationship is detailed below using the framework
described in the Preamble (U.S. EPA. 20156). 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 related to the causal framework is presented in
Table 5-27.
Table 5-27 Summary of evidence for a causal relationship between short-term
SO2 exposure and respiratory effects.
Rationale for Causal
Determination3
Key Evidence13
Key References'3
SO2
Concentrations
Associated with
Effects0
Asthma Exacerbation
Consistent evidence
from multiple,
high-quality controlled
human exposure
studies
Decreased lung function following peak
exposures of 5-10 min in exercising
individuals with asthma
Section 5.2.1.2
Table 5-2
400-600 ppb
Increased respiratory symptoms following
peak exposure of 5-10 min in exercising
individuals with asthma
Section 5.2.1.2
Table 5-2
600-1,000 ppb
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Table 5-27 (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
Consistent evidence
from multiple,
high-quality
epidemiologic studies
at relevant SO2
Increase in asthma hosDital admissions and Section 5.2.1.2
ED visits in single- and multicity studies, in
studies of all ages, children and older adults
1-h max:
9.6-10.8 ppb
24-h avg:
1.03-36.9 ppb
concentrations
Some supporting epidemiologic evidence of Section 5.2.1.2
associations with respiratory symptoms
among children with asthma
2.2-21.7 ppb
Uncertainty regarding
exposure
measurement error
Exposure assessments in epidemiologic
studies of short-term SO2 exposure using
central site monitors may not capture spatial
variability of SO2
Section 3.3.3.2
Uncertainty regarding
potential copollutants
confounding
SO2 associations in copollutant models
remained positive, and generally relatively
unchanged. Some studies show attenuation
of the association in models with NO2 and
PM.
Generally SO2 is low to moderately
correlated with other NAAQS pollutants at
collocated monitors.
Section 5.2.1.2
Section 3.3.4.1
Limited and supportive Association with AHR and blood eosinophils Sovseth et al. (1995)
evidence for allergic
inflammation, airway
remodeling, and AHR
among children with atopy
Increased airway eosinophils in asthmatics
exposed to SO2
Repeated exposure of allergic animals
enhanced inflammation, allergic
inflammation, airway remodeling, and airway
responsiveness
Gong et al. (2001)
Li et al. (2007)
Li et al. (2014)
Song et al. (2012)
Last 24-h median
7.9 ppb
750 ppb
2,000 ppb
2,000 ppb
Evidence for key
events in proposed
mode of action
Neural reflexes and/or inflammation lead to
bronchoconstriction
Allergic inflammation leads to increased
airway responsiveness
Section 4.3.6
Evidence for Other Respiratory Effects
Limited and
inconsistent evidence
forCOPD, respiratory
infection, respiratory
diseases hospital
admissions and ED
visits, and respiratory
effects in general
populations and
healthy individuals
Sections 5.2.1.3. 5.2.1.4.
5.2.1.5. 5.2.1.6
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Table 5-27 (Continued): Summary of evidence for a causal relationship between
short-term sulfur dioxide exposure and respiratory
effects.
so2
Concentrations
Rationale for Causal
Associated with
Determination3
Key Evidence13
Key References'3
Effects0
Evidence for Respiratory Mortality
Consistent
Increases in respiratory mortality in multicity
Sections 5.2.1.7 and
Mean 24-h avg:
epidemiologic
studies conducted in the United States,
5.5.1.3
U.S., Canada,
evidence from multiple,
Canada, Europe, and Asia
Fiaures 5-8 and 5-16
Europe:
high-quality studies at
0.4-28.2e ppb
relevant SO2
Asia:
concentrations
0.7->200 ppb
Table 5-47
Uncertainty regarding
The magnitude of SO2 associations
Section 5.2.1.7
potential confounding
remained positive, but was reduced in
Section 3.3.4.1
by copollutants
copollutant models with PM10 and NO2. No
studies examined copollutant models with
PM2 5. The reduction of SO2 associations,
specifically in models with NO2 suggest
potential copollutant confounding, but
studies were limited to areas with relatively
high SO2 concentrations, complicating the
interpretation of whether SO2 is
independently associated with total mortality.
Uncertainty regarding
Studies that examine the association
(Section 3.3.3.2)
exposure
between short-term SO2 exposures and
measurement error
mortality rely on central site monitors.
AHR = airway hyperresponsiveness; COPD = chronic obstructive pulmonary disease; ED = emergency department;
NAAQS = National Ambient Air Quality Standards; N02 = nitrogen dioxide; PM = particulate matter; ppb = parts per billion;
S02 = sulfur dioxide.
aBased on aspects considered in judgments of causality and weight of evidence in causal framework in Tables I and II of the
Preamble (U.S. EPA. 2015e).
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).
Evidence for Asthma Exacerbations
1 A causal relationship between short-term SO2 exposure and respiratory effects is
2 primarily based on evidence from controlled human exposure studies of respiratory
3 effects in adults with asthma. These studies consistently demonstrated that the majority of
4 individuals with asthma experience a moderate or greater, defined as a >100% increase
5 sRaw or >15% decrease in FEVi, decrement in lung function frequently accompanied by
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respiratory symptoms, following peak exposures of 5-10 minutes with elevated
ventilation rates at concentrations of 0.4-0.6 ppm (Johns et al.. 2010; I inn et al.. 1990;
Linn et al.. 1988; Balmes et al.. 1987; Linn et al.. 1987; Horstman et al.. 1986; Linn et al..
1983b). A small fraction of the asthmatic population (-5-30%) has also been observed to
have decrements in lung function at lower SO2 concentrations (0.2-0.3 ppm) (Johns etal..
2010; I inn et al.. 1990; I inn et al.. 1988; I inn et al.. 1987; Bethel et al.. 1985). 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). While
SCh-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.
Epidemiologic evidence also supports a causal relationship. Studies of asthma hospital
admissions and ED visits report positive associations with short-term SO2 exposures
when examining all ages, children (i.e., <18 years of age) and older adults (i.e., 65 years
of age and older) (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). 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 between
respiratory morbidity and respiratory mortality.
Most epidemiologic studies indicating associations between short-term SO2 exposures
and asthma exacerbation assign exposure using SO2 concentrations measured at central
site monitors. The use of central site monitors to assign exposure may introduce exposure
measurement error if the spatial variability in SO2 concentrations is not captured. SO2 has
low to moderate spatial correlations across urban geographical scales (Pearson r < 0.4 for
5-minute maximum within an hour, r < 0.6 for 24-hour average). Additional uncertainty
exists regarding potential copollutant confounding. While some epidemiologic studies
reported that associations were relatively unchanged with the inclusion of copollutants in
the model, others either failed to examine copollutants or were not robust to their
inclusion. However, these inconsistencies could reflect differences in exposure
measurement error for SO2 compared to NOx, CO, PM, and O3 when pollutants are
simultaneously included in a model.
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There is limited, but supportive evidence for a relationship between short-term SO2
exposure and AHR, airway inflammation, and airway remodeling. Sovseth et al. (1995)
demonstrated an association between ambient concentrations of SO2 and AHR among
atopic children. An associations between SO2 concentrations and blood eosinophils was
also observed in this study, suggesting possible recruitment of eosinophils to airways.
Controlled human exposure and animal toxicological studies provide coherence and
biological plausibility for these relationships. 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 asthmatics (Section 4.2.1). Further support for
an important role of airway inflammation, as well as for increased airway responsiveness
and remodeling, 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; Song et
al.. 2012: Li etal.. 2007).
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 5-7).
suggesting that the respiratory effects of short-term SO2 exposure may extend beyond
exacerbation of asthma. However, from the limited data available for other respiratory
conditions, there is uncertainty about relationships with SO2 and these outcomes because
of inconsistency among disciplines and/or lack of biological plausibility. Where
epidemiologic associations were found, the studies evaluated were limited by the lack of
copollutant analyses, which would support an independent effect of SO2 on respiratory
disease hospital admissions and ED visits. For COPD exacerbation, evidence from
controlled human exposure and epidemiologic studies suggests no association between
SO2 exposure and respiratory effects, while evidence of hospital admissions and ED
visits was limited and inconsistent. Although there is limited evidence for positive
associations between short-term SO2 exposures and hospital admissions and ED visits
due to respiratory infections, the lack of controlled human exposure, animal
toxicological, and other epidemiologic studies examining specific outcomes, along with
the lack of multiple studies examining the same respiratory infection outcome
complicates the interpretation of the collective body of evidence. Controlled human
exposure studies in healthy individuals provide evidence for transient decreases in lung
function at concentrations >1 ppm SO2 under exercising or forced oral breathing
condition with no evidence for increased respiratory symptoms. Recent epidemiologic
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studies have reported some positive associations at relevant concentrations, but the
overall evidence is inconsistent and limited among adults without asthma.
Conclusion
Multiple lines of evidence support a causal relationship between short-term SO2 exposure
and asthma exacerbations in individuals with asthma. This determination is primarily
based on respiratory effects observed in controlled human exposure studies in adults with
asthma. Epidemiologic studies of asthma hospital admissions and ED visits provide
strong support for this conclusion. Limited, but supportive evidence for a relationship
between short-term SO2 exposure and AHR, airway inflammation, and airway
remodeling is provided by controlled human exposure, epidemiological, and
toxicological studies. While some evidence exists for associations between SO2 exposure
and COPD exacerbation in individuals with COPD and respiratory effects including
respiratory infection, aggregated respiratory conditions, and respiratory mortality in the
general population, there is inconsistency within disciplines and outcomes and
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.
5.2.2 Long-Term Exposure
The 2008 SOx ISA (U.S. EPA. 2008b) reviewed the epidemiologic and toxicological
evidence of a relationship between 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 respiratory outcomes (i.e., 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-4 (U.S. EPA. 2015i). Animal toxicological
studies of the effects of long-term exposure to SO2, which were reviewed in the 2008
SOx ISA (U.S. EPA. 2008b). 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.
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Both older and more recent epidemiologic and toxicological studies that evaluate the
relationship between long-term SO2 exposure and the development of asthma
(Section 5.2.2.1). reduced lung function and development in children (Section 5.2.2.2).
and other respiratory outcomes (Section 5.2.2.3). including symptoms and markers of
respiratory allergy and asthma severity, chronic bronchitis, and respiratory infection are
discussed below. Recent longitudinal cohort studies of asthma incidence (Nishimura et
al.. 2013; Clark et al.. 2010) provide some of the strongest results in the evidence base,
but uncertainties related to exposure estimates based on IDW-concentrations (see
Section 3.2.2.1) may limit the inferences that can be made. The majority of the 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. EP A. 2008b) 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 two pollutant analysis. The 2008 SOx ISA (U.S. EPA. 2008b) 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
animal toxicological study that is discussed in this review found effects of subchronic
exposure to SO2 on airway responsiveness, airway remodeling, and allergic
inflammation.
5.2.2.1 Development of Asthma
Epidemiologic Studies
Asthma is a chronic disease characterized by inflammation, AHR, and airway
remodeling. In characterizing the epidemiologic evidence for a relationship between
long-term SO2 exposure and asthma, longitudinal studies of asthma incidence and
cross-sectional studies of asthma prevalence in children are evaluated. The studies
considered in the 2008 SOx ISA (U.S. EPA. 2008b) were limited to those with
cross-sectional designs [Supplemental Table 5S-4 (U.S. EPA. 2015i)l. The majority of
these studies reported positive associations of long-term SO2 exposure with asthma
prevalence.
Recent longitudinal studies of asthma incidence add to this evidence base. In a large
multicity study (N = 4,320), Nishimura et al. (2013) observed that SO2 exposures during
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the first year of life were not associated with asthma incidence [0.95 (95% CI: 0.59-1.47)
per 5 ppb change]. However, SO2 exposure during the first 3 years of life was associated
with asthma incidence [OR= .16 (0.73-1.84) per 5 ppb SO2]. Pollutant concentrations
were estimated using the IDW average of the four fixed site monitors within 50 km of the
subject's residence. The cities examined included Chicago, IL, Bronx, NY, Houston, TX,
San Francisco Bay Area, CA, and Puerto Rico. In a study of the British Columbia Birth
Cohort (n = 2,801), Clark et al. (2010) used IDW estimate-based concentrations from the
three closest fixed site 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 during pregnancy and first year of life.
Conducted in Southwest British Columbia, there were 14 SO2 stations 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. The use of questionnaires in both 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 (see Section 3.2.2.1). 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 in both studies were observed with
NO2 concentration. Correlations between pollutant concentrations were not reported by
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.
Several recent studies presented in Table 5-28 also examine the association of long-term
exposure to SO2 with the prevalence of asthma in cross-sectional studies. While these
studies are less informative, most (Liu et al.. 2014a; Dong et al.. 2013c; Dong etal..
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. An example of a recent source study, Amster et al. (2014). reported an
adjusted association between SO2 as an ambient measure but not for "power plant event
or source" exposure and asthma prevalence, COPD, and shortness of breath. The source
approach yielded wider 95% CI than the event approach for SO2. These studies are
consistent with similar studies in the 2008 SOx ISA (U.S. EPA. 2008b). Neither these
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1 recent cross-sectional studies of asthma prevalence nor the recent longitudinal studies of
2 asthma incidence attempt to address the potential for copollutant confounding by
3 conducting a two-pollutant analysis. Thus, within the recent epidemiologic evidence
4 base, no new studies reduce the uncertainty related to whether the effect was from SO2 or
5 another pollutant. However, the studies of asthma incidence address the temporality of
6 exposure and response and are supportive of this relationship.
Table 5-28 Summary of recent epidemiologic studies examining associations
between SO2 concentrations and the development of asthma.
Study, Location, Exposure Pollutant
and Years Population Assessment Correlations Comment Results
Longitudinal Asthma Incidence Studies
Clark et al.
(2010).
Incident asthma
Southwest British
Columbia,
Canada
Births from
1999-2000
British Columbia
Birth Cohort
(mean age at
follow up 48 mo,
SD-7 mo)
N = 2,801
SO2, NO2, CO, NR
NO2 effect largest Adjusted
PM10, black carbon
observed using
SO2/IDW per
SO2 estimated
LUR
5 ppb first year
using IDW levels
exposure OR
Covariate-adjusted
(95% CI) -1.47
conditional logistic
(1.30-1.89)
regression
Covariate
adjustment:
native status,
breast-feeding,
maternal
smoking, income
quartile, birth
weight, and
gestational length
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Table 5-28 (Continued): Summary of recent epidemiologic studies examining
associations between SO2 concentrations and the
development of asthma.
Study, Location,
and Years
Population
Exposure
Assessment
Pollutant
Correlations
Comment
Results
Nishimura et al.
(2013)
Incident asthma
multicity study
Includes:
Chicago, IL;
Bronx, NY;
Houston, TX;
San Francisco
Bay Area, CA;
and Puerto Rico
2006-2011
GALA II and
SAGE II cohorts
(Latinos and
African
Americans
8-21 yr)
N = 4,320
SO2, NO2, O3,
PM10, PM2.5
ambient air
pollution annually
averaged
Measurements
obtained from fixed
monitors.
Exposures for the
first 3 yr of life
were estimated
based on
residential histories
using an inverse
distance-squared
weighted average
from the four
closest monitors
within 50 km of
residence. SO2
overall mean (SD)
ppb 4.0 (3.4)
Adjusted logistic
regression models
NR
Early life NO2
exposure was
associated with
childhood asthma
5 ppb change in
SO2O.95
(0.59-1.47)
First 3 yr of life
SO2 exposures
multicity analyses
OR (95% CI) for
5 ppb change in
SO21.16
(0.74-1.84)
Covariate
adjustment: age,
sex, ethnicity,
and composite
Cross-Sectional Asthma Prevalence Studies
Akinbami et al.
(2010)
Metropolitan
areas, United
States
2001-2004
National Health
Interview Survey,
children (3-17 yr)
N = 34,073.
SO2, NO2, O3,
PM2.5 and PM10.
SO212 mo
average by county
Median 3.0 ppb,
IQR 1.7-4.8 ppb
Exposure
estimated with a
single pollutant
logistic regression
SO2-N2: 0.25
SO2-O3: -0.38
SO2-PM2.5: 0.12
SO2-PM10: -0.15
The adjusted
current asthma
was strongest for
ozone
SO2 per 5-ppb
increase positive
for both current
asthma and
asthma attack but
CI spanned well
below 1. Current
asthma adjusted
4th
quartile OR 1.15
(0.26-5.01).
Covariate
adjustment: age,
sex,
race/ethnicity,
and adult smoker
in household,
single parent,
highest level of
parental
education,
poverty status,
and region of
residence
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Table 5-28 (Continued): Summary of recent epidemiologic studies examining
associations between SO2 concentrations and the
development of asthma.
Study, Location,
and Years
Population
Exposure
Assessment
Pollutant
Correlations
Comment
Results
Altua et al.
(2013)
Prevalence of
asthma
symptoms and
lung function
Eskisehir, Turkey
Jan 2008-Mar
2009
ISAAC
questionnaire
School children
(9-13 yr)
N = 1,880
Summer (May
27-Jun 13, 2008)
and winter (Feb
27-Mar 13, 2009)
seasons
SO2, NO2, O3 by
passive sampling
SO2 range of
sampled regions-
mean (SD)
Summer
16.5(9.3-23.4) to
26.7(10.9-42.9);
Winter
55.2(18.4-75.8) to
76.2 (55.5-115.9)
|jg/m3
Exposure
estimated from
measurements
taken in the child's
primary school
garden
SO2-O3: -0.395
(winter)
SO2-NO2: 0.486
(winter)
Association
between ozone
and impaired
lung function only
for girls in the
summer season
Potentially
confounding
variables
included:
responder, sex,
age, parental
smoking habits,
coal or wood
stove use,
maximal parental
education of
family,
domestic pets,
and mold in the
home
No associations
found for SO2,
some positive
some negative
Amster et al.
(2014)
Prevalence of
asthma, COPD,
and related
symptoms
Hadera, Israel
area
2003 to 2004
Adults in the
ECRHS cohort;
cross-sectional
prevalence
design
N = 2,244
SO2 exposures at
the residence were
determined for an
8 yr avg from
20 monitoring sites
based on kriging in
relation to
emissions from the
power plant.
Annual SO2 mean
(SD) for total,
power plant
source, and power
plant event in ppb
were respectively:
2.52 (0.32);
6.22(2.03); and
16.55 (12.10)
"Source
approach"
correlated with
the "event
approach" for
SO2 (Pearson
correlation
coefficient
r= 0.66) but not
for NOx
(r= -0.07)
Association
between NOx
and SO2
exposure
Estimates for
both the "source
approach"
(Pearson
correlation
coefficient
r= 0.62) and the
"event approach"
(Pearson
correlation
coefficient
r= 0.97)
Prevalence of
asthma and
history of
shortness of
breath were
statistically
associated with
total (power plant
and nonpower
plant)
exposures to
SO2. Both source
and event
approaches of
estimating the
power
plant-specific
exposure to SO2
were not
statistically
associated with
the outcomes of
interest. The
"source
approach"
yielded much
wider 95% CI
than the "event
approach"
For the adjusted
model for asthma
prevalence for a
5-ppb increase
for total
exposure: 24.11
(1.61, 362.59);
for power plant
event 1.05 (0.95,
1.16);
and for power
plant source 1.47
(0.95, 2.19),
adjusted for age,
sex, smoking
history, housing
density, proximity
to major
highways, and
level of education
Two pollutant
models with SO2
and NOx did not
change the
results
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Table 5-28 (Continued): Summary of recent epidemiologic studies examining
associations between SO2 concentrations and the
development of asthma.
Study, Location,
and Years
Population
Exposure
Assessment
Pollutant
Correlations
Comment
Results
Arnedo-Pena et
al. (2009)
Prevalence of
recent symptoms
of asthma
Seven centers
(Asturias,
Barcelona,
Bilbao,
Cartagena, LA
Coruna, Madrid,
and Valencia),
Spain
2002-2003
ISAAC
questionnaire
School children
(6-7 yr)
N = 20,455
SO2, CO, NO2,
TSP-SO2
monitoring stations
annual
concentration
mean (SD)—12.4
(4.6) |jg/m3
Cross-sectional,
covariate-adjusted
multivariate logistic
regression
SO2-CO: 0.6203
SO2-NO2:
-0.5505
SO2-TSP:
-0.1615
Other pollutants
not as strongly or
inverse
associations
Recent severe
asthma-adjusted
OR (95% CI)
1.34 (1.01-1.78)
between Level 1
and 3
Covariate
adjustment: sex,
use of
paracetamol,
maternal
smoking, elder
siblings cooking
with electricity or
gas, temperature
and humidity
Deqer et al.
(2012)
Prevalence of
asthma
Montreal,
Quebec, Canada
2006
ISAAC
questionnaire
Children
(6 mo-12 yr
N = 821
Yearly ambient
SO2 levels from
refinery stack
emissions were
estimated at the
locations of the
centra id
coordinates of the
six-digit postal
code using
dispersion
modeling to
determine
residential
exposure
estimates
Yearly SO2 level
|jg/m3 mean (SD)
active
asthma—4.75
(3.24); poor
asthma control
group—5.37 (3.50)
Cross-sectional
covariate-adjusted
log-binomial
regression model
NR
No other
pollutants
considered
PR adjusted—
active asthma
1.44 (95% CI
0.84 to 2.48);
poor asthma
control 1.39 (1.00
to 1.94)
Covariate
adjustment:
child's age, sex,
parental history
of atopy and
tobacco smoke
exposure at
home
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Table 5-28 (Continued): Summary of recent epidemiologic studies examining
associations between SO2 concentrations and the
development of asthma.
Study, Location,
and Years
Population
Exposure
Assessment
Pollutant
Correlations
Comment
Results
Dona et al.
(2013b)
Asthma
symptoms
25 districts of
seven cites in
Northeast China
2006-2008
Children
(2-14 yr)
Three body
weight
categories;
normal weight,
overweight, and
obese defined by
BMI
N = 30,056
Ambient SO2, NR
The association
PM10, NO2, O3
between each
measured at
pollutants
municipal air
concentrations
pollution
and the studies
monitoring stations
respiratory
Annual SO2 mean
symptoms and
(range) 50.3
asthma was
(20-80) |jg/m3
consistently
Three-yr mean SO2
stronger among
children with a
concentrations
status of
used as surrogate
BMI >85% than
of long term
those with normal
exposure
weight
Cross-sectional
mixed logistic
regression model
Doctor-diagnosed
asthma in
combined
overweight and
obese population
SO2IQR
5ppb-OR (95%
CI) 1.24
(1.13-1.35)
Interaction
between
overweight and
obese with SO2
p-value = 0.011
Covariate
adjustment: age,
sex, breast
feeding habits
family history of
atopy, passive
smoking
exposure, study
district and
parental
education level
Dong et al.
(2013c)
Asthma
symptoms
25 districts of
seven cites in
Northeast China
2008-2009
Children
(2-14 yr)
Two breast
feeding groups;
mainly breastfed
for greater than
3 mo and not
mainly breast-fed
for greater than
3 mo
N = 31,049
Ambient SO2, NR
Association of air
PM10, NO2, O3
pollution with
measured at
respiratory
municipal air
conditions was
pollution
modified by
monitoring stations
breastfeeding
taken 2006-2008
Breastfeeding is
Annual SO2 mean
associated with
(range) 50.3
smaller
(20-80) |jg/m3
associations
Three-yr mean SO2
between air
concentrations
pollution and
used as surrogate
respiratory
of long-term
conditions in
exposure
children but not
Cross-sectional
for doctor-
mixed logistic
diagnosed
regression model
asthma
Doctor-diagnosed
asthma in
breastfed
population SO2
IQR 5 ppb OR
95% CI 1.11
(1.04-1.19)
Breastfeeding
status test for
interaction
p= 0.70
Covariate
adjustment: age,
sex,
parental
education,
obesity, family
history of atopy,
low birth weight,
home coal use,
home pets,
district, passive
smoking
exposure, and
area of residence
per person
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Table 5-28 (Continued): Summary of recent epidemiologic studies examining
associations between SO2 concentrations and the
development of asthma.
Study, Location,
and Years
Population
Exposure
Assessment
Pollutant
Correlations
Comment
Results
Gorai et al.
(2014)
Asthma
emergency
department visit
rate and asthma
discharge rate
New York State,
United States
2005-2007
Department of
Health Asthma
Surveillance
summary report
Asthma hospital
discharges visits
for 2005, 2006,
and 2007
respectively:
39,927, 40,205,
and 37,950
Asthma ED visits
for 2005, 2006,
and 2007
respectively:
59,572, 164,116,
and 161,200
Estimated PM2.5,
SO2, and O3
concentrations at
centroids of
counties using GIS
kriging
SO2 mean (SD)
ppb for 2005,
2006, and 2007
respectively: 8.46
(2.88), 6.92 (2.31),
and 7.18 (2.38)
Pearson two-tailed
correlation analysis
SO2-O3: -0.759
(2005)
SO2-O3: -0.716
(2006)
SO2-O3: -0.741
(2007)
SO2-PM2.5: 0.868
(2005)
SO2-PM2.5: 0.922
(2006)
SO2-PM2.5: 0.794
(2007)
A negative
association
between asthma
rate and O3
observed
Asthma
prevalence
among the New
York residents
was associated
with exposure to
PM2.5 followed by
SO2
Correlation
coefficients
asthma hospital
discharges and
SO2 for 2005,
2006, and 2007
respectively:
0.52, 0.38, and
0.41
Correlation
coefficients
asthma ED visits
and SO2 for
2005, 2006, and
2007
respectively:
0.46, 0.31, and
0.13
Kara et al. (2013)
Asthma cases
Nigde, Turkey
2006-2010
Asthma hospital
admissions
determined from
the hospital
automated
diagnosis system
(captures >80%
of city patients)
Ambient SO2 and
PM10 were
obtained from the
continuous
emissions
monitoring system.
Vehicular SO2
emissions were
estimated using
motor vehicle data
9.3% of the daily
average SO2
concentrations
were above
60 |jg/m3
Parametric
statistical analysis
and Mann-Kendall
nonparametric
evaluation
SO2-PM10: 0.045
SO2-O3: -0.36
SO2-NO2: 0.42
SO2-CO: 0.24
PM10 and SO2
reported to effect
asthma cases in
Nigde
Total cases of
asthma were
dependent on
ambient SO2
concentration
Pearson
correlation
coefficient
between ambient
SO2 and total
monthly asthma
cases = 0.4869.
Statistically
significant at 99%
confidence
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Table 5-28 (Continued): Summary of recent epidemiologic studies examining
associations between SO2 concentrations and the
development of asthma.
Study, Location,
and Years
Population
Exposure
Assessment
Pollutant
Correlations
Comment
Results
Liao etal. (2011)
Asthma hospital
admission rates
and fluctuations
in virus
respiratory tract
infections
Taiwan
2001-2008
Asthma
admission rate
per 100,000 total
population
extracted from
the National
Health Insurance
Research
Database
SO2, PM10, NO2,
CO, and O3. Major
air monitoring
stations in Taipei
(five stations) and
Kaohsiung (four
stations)
SO2 annual means
SO2 for Kaohsiung
~8 ppb and ~4 ppb
for Taipei
Probabilistic risk
assessment based
on a DFA to predict
future respiratory
virus and air
pollutant
associated asthma
incidence
Linear and
nonlinear
autoregression
models
PM10: 0.045
Os: -0.360
NO2: 0.420
CO: 0.236
No significant
correlation found
between asthma
admission and
PM10, O3, NO2,
and CO. The
DFA-based risk
model can
describe the
multiple triggers
related to asthma
admissions. Frev
and Suki (2008)
suggest that the
fluctuation
analysis
approach can be
used to identify
the dynamic
patterns of
clinical symptoms
of complex
chronic diseases
The association
among influenza
(r = 0.80,
p < 0.05) and
SO2 level
(r = 0.73,
p < 0.05) and
asthma
admission rate
was observed to
be strong
Liu etal. (2014a)
Prevalence of
respiratory
symptoms and
diagnosed
asthma
China
2006-2008
23,326 Chinese
children aged 6
to 13 yr were
evaluated using
the ATS
respiratory
questionnaire in
a cross-sectional
study using a
two-stage
hierarchical
model with
logistic and
ecologic model
analyses
Three-year
(2006-2008)
average SO2
concentration,
mean (SD): 50.3
(16.8) |jg/m3
Ranges: PM10
(79-171 pg/m3),
SO2
(20-80 |jg/m3), and
O3 (34-89 pg/m3
calculated from
monitoring stations
in each of the
25 districts located
near schools and
near the students'
homes
NO2 with O3
(0.66) and SO2
(0.52) tended to
be relatively low
across the
25 districts, with
a higher
correlation
between PM10
and SO2 (0.78),
and between
PM10 and O3
(0.74)
Two-pollutant
models were not
possible due to
the high
correlation
between
pollutants; unable
to control for
weather factors
(e.g.,
temperature or
humidity).
Adjusted OR for
diagnosed
asthma was 1.14
(95% CI,
1.09-1.19) per
5-ppb increase in
SO2
Adjusted for age,
sex, house type,
smoking, parental
atopic disease,
breastfeeding,
proximity to main
roads and
factories
Pan et al. (2010) Children
Asthma
prevalence
18 districts of
six cities in
Liaoning
Province,
northern China
1997-2000
(3-12 yr)
N = 11,860
SO2, TSP, NO2.
SO2 monitored
within 1 km of
elementary school
in each district,
annual mean SO2
(SD) 64 (42) pg/m3
Cross-sectional
two-stage
regression
SO2-TSP: 0.889
SO2-NO2: 0.577
Larger effects
between cities
than within
reflecting wider
between-city air
gradient. Three
pollutant analysis
OR's for SO2
decreased
For IQR of 5 ppb
for SO2 OR (95%
CI) current
asthma—1.09
(1.05,1.15)
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Table 5-28 (Continued): Summary of recent epidemiologic studies examining
associations between SO2 concentrations and the
development of asthma.
Study, Location, Exposure Pollutant
and Years Population Assessment Correlations Comment Results
Penard-Morand
etal. (2010)
Prevalence of
asthma and
allergies
French Cities
(Bordeaux,
Clermont-
Ferrand, Creteil,
Marseille, Reims,
and Strasbourg)
Mar 1999-Oct
2000
ISAAC
questionnaire
Children
(9-11 yr)
N =6,683
Cross-sectional
generalized
estimating
equation
adjusted for
potential
confounders
SO2, benzene,
PM10, NO2, CO.
Mean SO2 range
across the six
cites—mean
(minimum-
maximum) |jg/m3
4.1 (3.1-6.7) to
13.2 (10.7-16.4)
Exposure
estimated using
SO2 concentrations
at the school
calculated with a
validated
dispersion model
that integrates
background air
pollution, traffic
emissions,
topography and
meteorology
SC>2-benzene:
0.70
SO2-VOC: 0.54
SO2-CO: 0.60
SO2-NO2: 0.58
S02-NOx: 0.51
SO2-PM10: 0.70
The most robust
associations
were found for
PM10 and
benzene
SO2 OR (95% CI)
adjusted for
lifetime asthma
for IQR (5 ppb)
1.83 (1.31-2.51)
Covariate
adjustment: age,
sex, older
siblings, family
history of allergy,
parental
education,
mother's ethnic
origin, and
potential sources
of indoor pollution
at home
(smoking; mould
or dampness;
natural gas used
for heating,
cooking, or
water-heater; and
pets)
Portnov et al.
(2012)
Asthma
prevalence
Northern Israel
2006-2008
Clalit Health
Services
Database
School children
(6-14 yr) [mean
age 10.2 yr (SD
2.6 yr)]
N = 3,922
Binary logistic
regression
performed
separately for the
seven individual
townships
covered. BMA
implemented
SO2, PM10. SO2 SO2-PM10: 0.322
PM10 effects
SO2 asthma
measured at
observed
prevalence IDW
14 monitoring
OR (95%
stations. The
CI)—0.99
average values of
(0.89-1.10) BMA
SO2 were
approach
interpolated by
estimates the
kriging providing
posterior
continuous
probability for an
surfaces and IDW.
SO2 effect to be
GIS mapping used
only 2.8%
home addresses
strengthening
SO2 mean (SD) 5.4
the standard
(1.3) ppb
logistic
regression
analysis that SO2
should not be
added to the
model when
PM10 is included
Covariate
adjustment: sex,
age, proximity to
main roads, town
or residence, and
families SES
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Table 5-28 (Continued): Summary of recent epidemiologic studies examining
associations between SO2 concentrations and the
development of asthma.
Study, Location, Exposure Pollutant
and Years Population Assessment Correlations Comment Results
Sahsuvaroalu et
al. (2009);
Asthma
prevalence
Hamilton,
Canada
1994-1995
The ISAAC
Phase I
questionnaire
Children [6-7 yr
and 13-14 yr
(pre and post-
pubescent)]
N~1,467
SO2, ozone, PM
NO2. SO2 Thiessen
polygons, bicubic
spline, and IDW
interpolation
techniques were
used to estimate
exposure
SO2 3 yr
average = 5.82 ppb
Adjusted logistic
regressions
S02-N0x: -0.165
(Thiessen)
SO2-NO2: 0.442
(SO2 Thiessen;
NO2 Kriged)
SO2-NO2: 0.237
(SO2 Thiessen;
NO2 LUR)
The most robust
effects were
observed in NO2
LUR models in
girls for asthma
without hay fever
Per 5-ppb
increase in SO2
(Thiessen)
controlling for
confounding,
strongest effect,
regression
coefficent
between
nonallergic
(without hay
fever) asthma
and SO2 in the
older children
Exp(B) = 1.25)
(1.02-1.53). All
other SO2 effects
were positive but
CI spanned
below 1.00 such
as all
children 1.09
(0.99-1.20).
Covariate
adjustment:
neighborhood
proxies for
income, dwelling
value, female
smoking
ATS = American Thoracic Society; BMA = Bayesian Model Averaging; BMI = body mass index; CI = confidence interval;
CO = carbon monoxide; COPD = chronic obstructive pulmonary disease; DFA = Detrended Fluctuation Analysis;
ECRHS = European Community Respiratory Health Survey; ED = emergency department; Exp(B) = odds ratio of bivariate
associations; GALA II = Genes-environments and Admixture in Latino Americans; GIS = geographic information systems;
IDW = inverse distance weighting; IQR = interquartile range; ISAAC = International Study of Asthma and Alerrgies in Children;
LUR = land use regression; N = population number; N2 = nitrogen; N02 = nitrogen dioxide; NR = not reported; 03 = ozone;
OR = odds ratio; PM = particulate matter; ppb = parts per billion; PR = prevalence ratio; r = correlation coefficient; SD = standard
deviation; SES = socioeconomic status; S02 = sulfur dioxide; TSP = total suspended solids; VOC = volatile organic compound.
1 Additional epidemiologic evidence for a link between long-term exposure to SO2 and the
2 development of asthma may come from intervention studies. Physicians, in part, diagnose
3 asthma based on the occurrence or exacerbation of asthma symptoms such as cough and
4 wheeze, and the level of bronchial hyperreactivity (BHR) in the subjects. Decline in such
5 symptoms and BHR in relation to a decline of a pollutant level may support a relationship
6 between asthma development and exposure to pollutants such as SO2. In an intervention
7 study discussed in the 2008 SOx ISA (U.S. EPA. 2008b). Peters et al. (1996) observed
8 decreases in respiratory symptoms, including any wheeze or asthmatic symptoms,
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wheezing, and cough and sore throat, in association with decreases in SO2 concentrations
due to a government restriction of sulfur content of fuels. In a related study, Wong et al.
(1998) examined the effect of the same government intervention in Hong Kong that
restricted sulfur content of fuels from July 1990 onwards on BHR in children aged 9-12
who were nonwheezing and nonasthmatic 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. (1996) 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/nr1 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.
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. 2008b). Study characteristics are summarized
in Table 5-29. 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.
No studies on airway responsiveness or pulmonary inflammatory responses to long-term
exposure to SO2 concentrations of 2 ppm and lower were discussed in the 2008 SOx ISA
(U.S. EPA. 2008b). One new animal toxicological study of subchronic SO2 exposure has
become available since the last review. This study involves newborn rats and is discussed
above in Sections 5.2.1.2. and 5.2.1.6. Key findings are also discussed here; study
characteristics are summarized in Table 5-29. Song et al. (2012) found that airway
responsiveness was enhanced in a model of allergic airways disease using rats that were
first sensitized and challenged with ovalbumin and then exposed to 2 ppm SO2 for
4 hours/day for 28 days. Airway responsiveness was not changed with exposure to SO2
alone in naive rats. However, Song et al. (2012) observed hyperemia in the lung
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
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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/S02 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, 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.
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Table 5-29 Study-specific details from animal toxicological studies.
Study
Species (strain); n;
Sex; Lifestage/Age
(mean ± SD)
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/day,
5 days/week for 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 week old neonates
Sensitization by i.p. injection
of 10 mg ovalbumin followed
by booster injection of 10 mg
ovalbumin after 7 days
Challenge with 1% ovalbumin
aerosol for 30 min daily for
4 weeks beginning at 15 days
Exposure to 2 ppm SO2 for
4 h/day for 4 weeks beginning
at 15 days
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; MCh = methacholine;
n = sample size; N2 = nitrogen; NOx = the sum of nitric oxide and nitrogen dioxide; ppm = parts per million; SD = standard
deviation; S02 = sulfur dioxide.
Summary of Development of Asthma
1 Recent epidemiologic evidence from a limited number of longitudinal studies report
2 associations between asthma incidence among children and long-term SO2 exposures.
3 Additional supportive evidence for a link between long-term SO2 exposure and the
4 development of asthma is provided by cross-sectional studies of asthma prevalence. The
5 longitudinal studies help reduce the uncertainty associated with the temporality of
6 exposure and response that is inherent in cross-sectional study designs. This evidence is
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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
strengthens the link between long-term exposure to SO2 and the development of asthma
in children.
5.2.2.2 Lung Function
Epidemiologic Studies
As discussed in the 2008 SOx ISA (U.S. EPA. 2008b). earlier cross-sectional studies
(Dockerv et al.. 1989; Schwartz. 1989) found no association between long-term SO2
exposure and lung function in children in the United States. 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 (Jedrychowski etal.. 1999) found lung function effects 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-4 (U.S. EPA. 2015i).
Recent studies in children and adults add to this evidence base (Table 5-30). In a
cross-sectional, longitudinal repeated-measures study of children, Linares et al. (2010)
reported a decline in FEVi 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 lb) 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 United Kingdom study of
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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, Ontario using a cross-sectional prevalence
design.
In summary, the 2008 SOx ISA (U.S. EPA. 2008b) concluded that the available evidence
from the few epidemiologic studies was inadequate to infer that lung function effects
occur as a result of long-term exposure to SO2 at ambient concentrations. The recent
studies add to the database evaluating this relationship. The majority of the recent studies
and earlier studies used cross-sectional designs. Some studies took into account
potentially confounding covariates. The designs used in most of the recent studies
(i.e., ecological, cross-sectional) limit the possible inferences about the causality of the
relationship between long-term SO2 exposure and lung function. The evidence does not
include studies evaluating concentration-responses. The one study conducting a
two-pollutant analysis found attenuation of the effect. Thus, recent studies do not add
information that changes conclusions made in the 2008 SOx ISA (U.S. EPA. 2008b).
Table 5-30 Summary of recent epidemiologic studies examining associations
between SO2 concentrations and lung function.
Pollutant
Study
Population
Exposure
Correlations
Comment
Result
Dales et al.
In 2,328 elementary
Annual average
Wheeler et al.
eNO was
Adjusted
(2008)
school children
levels of SO2, NO2,
(2008) provides
associated
associations
Examined
9-11 yr of age in a
and PIVte.swere
Spearman
with
between 5 ppb SO2
eNO, FEVi
cross-sectional
estimated by LUR at
correlation
roadway
and percent
and FVC
prevalence study
the postal-code
coefficients: for
measures.
predicted FEVi,
Windsor,
Ontario,
Canada
level. The mean and
SO2 and NO2,
percent predicted
IQR for SO2 was
benzene, and
FVC and ln[eNO]x
5.39 ppb and 0.94
toluene of 0.85,
103 are respectively:
ppb. (95th percentile:
0.82, and 0.61,
-5.45 (-16.6 to 5.7);
2004-2005
6.92 ppb)
respectively
-1.55 (-7.15 to
4.05); and 3.0
(-273.0 to 274.0).
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Table 5-30 (Continued): Summary of recent epidemiologic studies examining
associations between SO2 concentrations and lung
function.
Study
Population
Exposure
Pollutant
Correlations
Comment
Result
Forbes et al.
(2009c).
Lung function
England
1995, 1996,
1997, and
2001
Health Survey for
England
Adults from white
ethnic groups (>16 yr)
N = 32,712
households
SO2, PM10, NO2,
and O3 for each
1 km2 using air
dispersion models.
Exposure estimated
based on the model
value in the
participant's post
code
Median SO2 (IQR) in
|jg/m3 for the 4 yr
respectively 9.3
(7.5); 9.2 (7.6); 9.2
(7.2); and 3.8 (2.7)
Cross-sectional,
multilevel linear
regression
SO2-PM10: 0.34
(1995)
SO2-PM10: 0.17
(1996)
SO2-PM10: 0.13
(1997)
SO2-PM10: 0.17
(2001)
SO2-O3: -0.29
(1995)
SO2-O3: -0.36
(1996)
SO2-O3: -0.30
(1997)
SO2-O3: -0.16
(2001)
SO2-NO2:
(1995)
SO2-NO2:
(1996)
SO2-NO2:
(1997)
SO2-NO2:
(2001)
Effects also
seen for
PM10 and
NO2 but not
for O3
0.31
0.31
0.29
0.1«
SO2 yr-specific
estimates were
pooled using fixed
effects
meta-analysis
difference (mL) (95%
CI): FEV1 -28.82
(-47.16, -9.17) per
5 ppb adjusted
Covariate
adjustment: age,
sex, height, social
class of head of
household, smoking,
and region
Iwasawa et al.
(2010)
Miyakejima
Island, Japan
near the
volcano Mt.
Oyama
Feb 2005-Nov
2006
Miyake children [mean
age (SD)—10.7 yr
(4.4 yr)]
N = 141
SO2 monitored at NR
Percent FVC and
seven sampling
FEV1 in
points of the
hypersusceptible
residential areas.
children were
Mean SO2
significantly reduced
concentration from
in November 2006
February 2005 to
compared to
November 2006 was
February 2006
31 ppb; range 19 to
(p = 0.047, 0.027)
45 ppb across areas
although no
Inhabitant areas
reduction observed
were classified into
in normosusceptible
one lower-SChand
children.
three higher.SC>2
Covariate
areas to gauge
adjustment: sex,
exposure.
age, residential
area, and
hypersusceptiveness
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Table 5-30 (Continued): Summary of recent epidemiologic studies examining
associations between SO2 concentrations and lung
function.
Study
Population
Exposure
Pollutant
Correlations
Comment
Result
Lee et al.
(2011b)
Lung function
14 Taiwanese
communities
2005-2007
Taiwan Children
Health Study; Children
of Han Chinese ethnic
origin (12-13 yr)
N = 3,957
SO2, CO, NO2, O3,
PM10, PM2.5.-air
monitoring stations
in each community
SC>2-chronic monthly
average over the
time course of the
study—mean (SD)
4.68 (2.20) ppb;
subchronic average
July-September
2007 3.90 (1.48)
Cross-sectional,
linear regression
models
NR
Found
greater
effects for
NO2 and CO
SO2 chronic all
subjects FEV1 (95%
CI) changes in mL
per IQR 5 ppb:
-28.91 (-115.65,
58.04)
Covariate
adjustment: age,
sex, height, weight,
parental education,
mother smoking
during pregnancy,
dog at home, and
visible mould
Linares et al.
(2010)
Lung function
and respiratory
symptoms
Salamanca,
Mexico
Mar2004-Feb
2005
ISAAC questionnaire
Children attending two
primary schools
(6-14 yr). School 1
1,100 m from
petrochemical zone.
School 2 was 7,300 m
away.
SO2, O3, NO2, PM10 NR
The two schools
were located within
2 km from one of the
three monitoring
stations.
3-mo levels of SO2
varied by season
and school (23 to
36 |jg/m3)
Cross-sectional,
longitudinal
repeated measures
study for pulmonary
function generalized
linear mixed models
for respiratory
symptoms multilevel
logistic models
Frequency
of
respiratory
symptoms
higher in the
school
closer to the
major
stationary
air pollution
sources. PM
effects were
the most
consistent
factor. For
SO2,
outcomes in
one- and
two-pollutant
analysis of
PM10 and O3
by sex were
attenuated.
Per 5 ppb
S02;FEVi—adjusted
beta coefficient
-0.004 (-0.005,
0.002)
Covariate
adjustment: height,
BMI, sex, age, fossil
fuel, passive
smoking and
clustering by child
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Table 5-30 (Continued): Summary of recent epidemiologic studies examining
associations between SO2 concentrations and lung
function.
Study
Population
Exposure
Pollutant
Correlations
Comment
Result
Rusconi et al.
(2011)
Lung function
Sarroch and
Burcei, Italy
Jan 2007-Jun
2007
Schoolchildren
(6-14 yr)
Sarroch/petrochemical
area; Burcei/reference
area
Sarroch
N =275
Burcei
N =214
SO2, NO2, benzene
assessed by
passive
dosimeters from
15 May to 7 Jun
1 week before
children
examinations
SChmean: Sarroch
18.1 |jg/m3; Burcei
3.6 |jg/m3
Ecological
cross-sectional
study, generalized
linear models
Estimated logistic
regression model
coefficients reported
as prevalence ratios
NR
Found an
increase of
markers of
bronchial
inflammation
and
oxidative
damage in
children
living near
an oil
refinery as
compared to
those in a
nonpolluted
area
Decrease in FEVi
reported comparing
the two towns.
Covariate
adjustment: age,
sex, parental history
of asthma, parental
education, passive
smoking, and damp
in child's bedroom
Tanaka et al.
Officially
(2013)
acknowledged victims
Chronic
of pollution-related
bronchitis,
illnesses (>65 yr)
asthma, or
N = 563
emphysema
Respiratory symptom
Kurashiki and
questionnaire and
Okayama,
yearly spirometry
Japan
examinations
2000-2009
Mean daily NR
Reduction of
High pollutant
i concentrations of
air pollution
concentrations
SO2 concentrations
levels may
around 1970
were determined at
have
resulted in
21 points in
reduced
decreases in
Kurashiki starting in
respiratory
respiratory function
1965.
disease
and increased
After 1974, SO2
related to air
respiratory
levels decreased
pollution.
symptoms. Changes
below 40 ppb.
from 2000 to 2009
were in the normal
Regression
coefficients
calculated using
range and were
probably due to
normal aging
simple linear
regression. Mean
annual changes in
respiratory function
were compared
between subjects
with and without
worsening of
dyspnea.
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Table 5-30 (Continued): Summary of recent epidemiologic studies examining
associations between SO2 concentrations and lung
function.
Study
Population
Exposure
Pollutant
Correlations
Comment
Result
Turnovska and
Marinov
(2009)
Lung function
Dimitrovgrad,
Bulgaria
1985-2003
Children (10 yr).
N = 122
Three groups: (1) born
and lived in heavy
pollution (n = 60);
(2) born and lived
after the abrupt drop
in pollution but
pregnancy was during
heavy exposure
(n = 39); (3) born after
the drop, lived in lower
pollution levels and
mothers had
pregnancy in this
environment (n = 23)
SO2, TSP, NO2,
h2s, hf
Average annual
mean (SE) |jg/m3
SO2 dropped from
120 (21) to 30 (4)
from 1985 to 2003.
Similar drop in TSP
but no other
pollutants.
Regression model
No
differences
in
respiratory
diseases
observed.
The
combined
impact of
additively
acting
pollutants
was
suggested
as important
such as
NO2 + SO2
The highest values
of FEV1 percent
predicted are found
among the children
in the third group
and the lowest
values are in the
children in the first
group
Covariate
adjustment: sex,
home heating fuel
type, passive
smoking, mother's
education, and
birthweight included
Wood et al.
(2010)
Lung function
United
Kingdom
1997-2006
Patients with
a1-antitrypsin
deficiency, chronic
obstructive pulmonary
disease, from the U.K.
national registry for
a1-antitrypsin
deficiency
Mean age (SEM)
51.09 (0.70) yr
N = 399
SO2, NO2, PM10 NR
High PM10
Annual means with
exposure
validated dispersion
predicted
model
more rapid
decline of
Dispersion kernel
FEV1
approaches and
weighted regression
analysis were used
to map pollutant
levels on a 1 x 1 km
grid. Exposure
estimated
from map based on
patient's address
Mean(SE)per year
of decline SO2 in
|jg/m3: 4.12 (0.17)
Generalized
estimating
equations
SO2 change in FEV1
(mL/yr) mean (95%
CI) per increase of
5 ppb -39.3 (-91.7
to 13.1)
Covariate
adjustment: age,
sex, smoke
exposure, level of
occupational risk,
and baseline lung
function
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Table 5-30 (Continued): Summary of recent epidemiologic studies examining
associations between SO2 concentrations and lung
function.
Study
Population
Exposure
Pollutant
Correlations
Comment
Result
Yoaev-Baaaio
etal. (2010)
Lung function
near a
coal-fired
power plant
Hadera
district, Israel
1996 and 1999
School children
(2nd-5th grade)
N = 1,181
Three groups:
healthy, chest
symptoms, and
pulmonary disease
SO2 and NO2 from
12 monitoring
stations
"Event"
approach—half-an-
hour concentrations
of NOx and SO2 that
simultaneously
exceeded
predefined air
pollution levels were
multiplied by the
average
concentrations
during the air
pollution events and
summed up from
1996 to 1999
Study area divided
into three air
pollution zones: low
pollution
(NOx x SO2
<312 ppm); medium
pollution
(312 < NOx x SO2
<2,640 ppm), and
high pollution
(NOx x SO2
>2,640 ppm)
Exposure estimated
at child's home
Analysis of variance,
multiple regression
analysis
NR
The greatest
effect was
on children
with chest
symptoms.
Change in
FEV1—the effect of
the NOx x SO2
interaction term on
children's pulmonary
function test
performance
appears to be
negative and highly
significant in most of
the models implying
that increasing air
pollution levels
(ppm) to have a
significant and
negative effect on
the children's
pulmonary function
growth
Covariate
adjustment: height,
age, sex, parental
education, passive
smoking, housing
density, length of
residency in study
area, and proximity
to the main road
BMI = body mass index; CI = confidence interval; CO = carbon monoxide; eNO = exhaled nitric oxide; FE\A| = forced expiratory
volume in 1 second; FVC = forced vital capacity; H2S = Hydrogen sulfide; HF = high frequency; IQR = interquartile range;
ISAAC = International Study of Asthma and Allergies in Children; In = natural logarithm; LUR = land use regression; N = population
number; N02 = nitrogen dioxide; NOx = the sum of nitric oxide and N02; NR = not reported; 03 = ozone; PM = particulate matter;
ppb = parts per billion; SD = standard deviation; SE = standard error; SEM = standard error of the mean; S02 = sulfur dioxide;
TSP = total suspended solids.
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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. 2008b). Study characteristics are summarized
in Table 5-29. 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 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 of no association between long-
term SO2 exposure and lung function in children made in the 2008 SOx ISA (U.S. EPA.
2008b).
5.2.2.3 Other Respiratory Outcomes
Severity of Asthma Symptoms
Section 5.2.2.1 discussed studies on the development of asthma (i.e., asthma incidence).
However, two studies focused on the relationship between long-term SO2 exposure and
the prevalence of asthma symptoms [Supplemental Table 5S-5 (U.S. EPA. 2015i)l. Deger
et al. (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
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35
36
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 of age and >6 years of age) 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%). 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. The observed
associations between asthma severity and air pollution support the notion that air
pollutants may increase asthma severity but the uncertainty related to these effects
potentially being influenced by short-term exposure is a possibility that needs to be
examined.
Respiratory Symptoms
In the 2008 SOx ISA (U.S. EPA. 2008b). studies examining an array of respiratory
symptoms related to SO2 exposure are presented in in Supplemental Table 5S-5 (U.S.
EPA. 2015i; Ware et al.. 1986; Chapman et al.. 1985; Dodge et al.. 1985V 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 cross-sectional studies of long-term SO2 exposure estimated at fixed
site monitors from volcano emissions in Japan and Hawaii were conducted as shown in
Supplemental Table 5S-5 (U.S. EPA. 2015i). Iwasawa et al. (2009) and Iwasawa et al.
(2010) 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 SO2 exposure.
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). In other cross-section studies
the prevalence of respiratory symptoms was positively associated with long-term
exposure to SO2 (Altug et al.. 2013; Pan et al.. 2010; Amedo-Pena et al.. 2009; Rage et
al.. 2009; Pino et al.. 2004). Although limited by their cross-sectional design, these
volcano emission and other studies suggest a potential relationship between long-term
SO2 exposure and the prevalence of respiratory symptoms.
Several studies examine the prevalence of various markers for respiratory allergies
including IgE antibodies, rhinitis, eczema, sensitization to pollen, and hay fever related to
long-term SO2 exposure in cross-sectional studies (Bhattacharvva and Shapiro. 2010;
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Penard-Morand et al.. 2010; Parker et al.. 2009; Nordling et al.. 2008). Positive results
are observed for children using various indicators of allergy. Further, a very weak
relationship was found Dales et al. (2008) between long-term SO2 exposure and eNO, an
indicator of inflammation.
In summary, some studies examining associations between long-term exposure to SO2
and respiratory symptoms provide support for a potential relationship between SO2
exposure and the severity of asthma among asthmatics. Another set of studies provide
support for a potential relationship between long-term SO2 exposure and respiratory
symptoms among children and adults living near active volcanoes. Furthermore, there is
some evidence for a potential relationship between long-term SO2 exposure and
indicators or respiratory allergies and inflammation among children.
Chronic Bronchitis and Chronic Obstructive Pulmonary Disease
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. 2008b). 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-5 (U.S. EPA. 2015i)
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-5 (U.S. EPA. 2015j).
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 R2 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.
Respiratory Infection
Studies also examine 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-5 (U.S. EPA. 2015i)l. The strongest
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associations were observed with NO2 and CO concentrations. Maclntyre et al. (2011)
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 United States
National Health Interview Survey of 126,060 children age 3-6 years. Lu et al. (2014)
observed that the prevalence of pneumonia in children 3 to 6 years old was related to
long-term SO2 exposure. 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-year average 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). 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. 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. 2008b). 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 Other Respiratory Outcomes
A limited number of cross-sectional analyses of prevalence demonstrate increases in
respiratory symptoms among children in relation to long-term SO2 exposure.
Associations were observed with SO2 concentrations estimated from central site
monitors. These studies are supportive of the development of asthma; however they may
also reflect other respiratory conditions. Evidence for prevalence of bronchitis and/or
respiratory infections consists of generally positive associations found in cross-sectional
studies. In other cross-sectional studies, limited findings suggest associations between
long-term SO2 exposure and respiratory infection. 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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5.2.2.4
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. Table 5-54. Figure 5-25). 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 not adequate for
study of 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
mortality among adults.
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. 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.
2008b).
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 1 of the Preamble to this ISA (U.S. EPA. 2015e) and is
presented in Table 5-31.
5.2.2.5
Summary and Causal Determination
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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 (Clark et al.. 2010); (Nishimura et al.. 2013)
(Section 5.2.2.1). Results are fairly consistent between these studies with one based on
several different locations across the United States and the other on a large area in
Canada; both with a large number of participants. Uncertainties and the potential for
measurement error related to the use of IDW in these studies may limit inferences that
can be made (Section 3.2.2.1V 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.
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 to help reduce this
uncertainty. No studies of asthma incidence or prevalence evaluated copollutant models
to address copollutant confounding, making it difficult to evaluate the independent effect
of SO2 within the epidemiologic evidence base. 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 compared to those for SO2 (Clark et
al.. 2010); (Nishimura et al.. 2013). 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 SC>2-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, important steps in allergic
sensitization (Song et al.. 2012) (Section 5.2.2.1). 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 5.2.1.6). Furthermore, enhancement of allergic sensitization and other
inflammatory responses were observed along with AHR in guinea pigs exposed
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repeatedly to SO2 for several days and subsequently sensitized and challenged with an
allergen (Section 5.2.1.6). Similarly, SO2 exposure enhanced airway inflammation in rats
previously sensitized with an allergen (Section 5.2.1.2).
Epidemiologic evidence from a few long-term studies provides a link between SO2
exposure and respiratory allergies among children. 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.
Evidence for Lung Function
Several studies evaluated the relationship between long-term SO2 exposure and
decrements in lung function (Section 5.2.2.2). 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 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. 2008b) 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 Other Respiratory Outcomes
Other respiratory outcomes related to long-term SO2 exposure are discussed in
Section 5.2.2.3. A limited number of cross-sectional analyses of prevalence demonstrate
increases in respiratory symptoms among children in relation to long-term SO2 exposure.
Associations were observed with SO2 concentrations estimated from central sites. These
studies are supportive of the development of asthma; however they may also reflect other
respiratory conditions. A limited number of 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.
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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 5.2.2.4). 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. The strongest evidence of
long-term SC>2-related respiratory effects is in morbidity studies among children.
However, there is no evidence for a relationship between long-term exposure to SO2 and
respiratory mortality in children.
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 5-31). 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 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.
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Table 5-31 Summary of evidence for a suggestive of, but not sufficient to infer, a
causal relationship between long-term SO2 exposure and respiratory
effects.
Rationale for Causal
Determination3
Key Evidence13
Key References'3
so2
Concentrations
Associated with
Effects0
Asthma Development
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. 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
Cross-sectional studies of asthma
prevalence among children provide
support, although there is uncertainty
regarding the temporal sequence between
exposure and the development of asthma
Section 5.2.2.1
Supporting evidence for respiratory
symptoms among children in
cross-sectional studies
Section 5.2.2.3
Supporting evidence for markers of
respiratory allergies among children in
cross-sectional studies
Section 5.2.2.3
Uncertainty regarding Use of IDW in asthma incidence studies Section 3.2.2.1
potential for and fixed monitoring sites in
measurement error in cross-sectional studies
exposure estimates
Uncertainty regarding No copollutant models analyzed in asthma Section 3.3.4
potential confounding incidence studies
by copollutants
Limited animal
Evidence for Th2 polarization and airwav Sona et al. (2012)
2,000 ppb
toxicological evidence
inflammation following repeated exposure
provides coherence
of naive newborn rats for 28 days
and biological
Evidence for enhanced inflammation,
plausibility
airway remodeling and AHR following
repeated exposure of allergic newborn
rats for 28 days
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Table 5-31 (Continued): Summary of evidence for a suggestive of, but not
sufficient to infer, a causal relationship between
long-term SO2 exposure and respiratory effects.
Rationale for Causal
Determination3
Key Evidence13
Key References'3
SO2
Concentrations
Associated with
Effects0
Coherence with
evidence from
short-term animal
toxicological studies
Evidence for inflammation and
morphologic responses indicative of
airway remodeling following repeated
exposures of naive rats over several days
Li et al. (2007)
2,000 ppb
Evidence for enhancement of allergic Riedel et al. (1988) 100 ppb
sensitization, 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
Evidence for enhanced inflammation and Li et al. (2007) 2,000 ppb
allergic responses in rats previously y et al (2014)
sensitized 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
Lung Function
Epidemiologic
evidence of
decrements in lung
function among
children from quality
studies but uncertainty
regarding SO2
independent effects
In two cohort studies, associations Jedrvchowski et al. (1999)
inconsistent with adjustment for PM and Frischeretal (1999)
by season
Inconsistent results from cross-sectional
studies
Dockerv et al. (1989)
Schwartz (1989)
Ackermann-Liebrich et al.
(1997)
Frve et al. (2003)
Other Respiratory Outcomes
Limited epidemiologic Generally positive associations in Section 5.2.2.3
evidence for cross-sectional studies; fixed site
respiratory symptoms monitors
and markers of
respiratory allergies
among children but
uncertainty regarding
SO2 independent
effects
Limited epidemiologic Section 5.2.2.3
evidence for chronic
bronchitis but
uncertainty regarding
SO2 independent
effects
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Table 5-31 (Continued): Summary of evidence for a suggestive of, but not
sufficient to infer, a causal relationship between
long-term SO2 exposure and respiratory effects.
Rationale for Causal
Determination3
Key Evidence13
Key References'3
SO2
Concentrations
Associated with
Effects0
Limited evidence for
respiratory infection,
primarily in children;
uncertainty regarding
SO2 independent
effects
Limited animal
toxicological evidence
Generally positive associations in
cross-sectional studies; fixed site monitors
Section 5.2.2.3
Lack of evidence for
key events in proposed
mode of action
Changes in specific host defense
mechanisms but no evidence of greater
infect ivity
Respiratory Mortality
Epidemiologic studies
report generally
consistent, positive
associations with
respiratory mortality
Small, positive associations between
long-term exposure to SO2 and respiratory
mortality in several cohorts, even after
adjustment for common potential
confounders
Hart etal. (2011)
4.8
Nafstad et al. (2004)
3.6
Elliott etal. (2007)
12.2-41.4
Cao et al. (2011)
27.7
Carev etal. (2013)
1.5
Dona etal. (2012)
23.9
Katanoda etal. (2011)
2.4-19.0
No coherence between No evidence for a relationship between
respiratory morbidity in long-term exposure and respiratory
children and mortality among children to support the
respiratory mortality in observed associations with respiratory
adults morbidity among children
Section 5.5.2.5
AHR = airway hyperresponsiveness; IDW = inverse distance weighting; PM = particulate matter; ppb = parts per billion;
SD = standard deviation; S02 = sulfur dioxide.
aBased on aspects considered in judgments of causality and weight of evidence in causal framework in Tables I and II of the
Preamble (U.S. EPA. 2015e).
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 (for experimental studies, <2,000 ppb).
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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. 2008b) 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 to SO2 in humans,
animals, and cells. 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. 2008b).
When considered with the evidence reviewed in to 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, and the lack of mechanistic evidence to describe
a role for SO2 in the manifestation of cardiovascular diseases, including key events in the
proposed mode of action, and potential confounding by copollutants. The majority of the
recent evidence is from epidemiologic studies, which suggest that exposure to SO2 may
result in the triggering of MI. To clearly characterize the evidence underlying causality,
the discussion of the evidence is organized into groups of related outcomes
(e.g., ischemic heart disease and myocardial infarction, arrhythmia, and cardiac arrest).
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.
The previous ISA included a small number of animal toxicological and controlled human
exposure studies that examined cardiovascular effects from short-term exposure to SO2.
Since the 2008 ISA for Sulfur Oxides (U.S. EPA. 2008b). no controlled human exposure
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studies and one animal toxicological study have investigated the effects of short-term SO2
exposure on the cardiovascular system. Study details for the animal toxicological and
controlled human exposure studies from the current and past review that evaluated
cardiovascular effects of short-term SO2 exposures of less than 2,000 ppb are
summarized in Supplemental Table 5S-3 (U.S. EPA. 2015h). Some studies using higher
(5,000-20,000 ppb) inhaled concentrations of SO2 reported measurable changes in the
concentrations of sulfite and sulfonates in plasma and tissues. A recent report in mice
exposed to 5,000-20,000 ppb SO2 for 7 days found an increase in sulfite + sulfonate
levels in lung, heart, and brain compared to controls (Meng et al.. 2005b). At ambient
relevant concentrations these changes would be expected to be far less. The literature
discussing the distribution and metabolism of sulfite is discussed in Section 4.2.4.
5.3.1.2 Myocardial Infarction and Ischemic Heart Disease
Several lines of evidence are discussed in support of a 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 where IHD is evaluated will include any patients diagnosed with an 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. 2008b') 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 (Table 5-33. and Figure 5-10). 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 5-32. 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, all of the studies in this section use central site monitors to estimate
ambient SO2 exposure, which may result in exposure error due to time-activity patterns
and spatial variation of SO2 (Section 3.3.3). This exposure error is likely to lead to
attenuation and loss of precision of the effect estimates (Section 3.3.5.1).
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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-hour average 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.
One prominent study from the previous 2008 ISA for Sulfur Oxides (U.S. EPA. 2008b)
was a study conducted in 14 cities across Spain, which 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-hour average SO2 on the same day]. The inclusion of
PM2 5 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.
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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
10-ppb increase in SO2 on the previous day (Stieb et al.. 2009). Most (Qiu et al.. 2013b;
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 3.3.3.2).
Table 5-32 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
tBhaskaran et al.
15 conurbations
Central site 1-h max
Mean: 1.9
75th: 3.4
(2011)
in England and
monitor from each
Wales
conurbation
(2003-2006)
(aggregated when
more than one
monitor)
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 24-h avg
Takashima County
(20 km)
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.I
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
tChenq et al.
(2009)
Kaohsiung,
Taiwan
(1996-2006)
Average across
six monitoring
stations
24-h avg Mean: 9.33
75th: 11.69
Max: 31.26
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Table 5-32 (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
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
tTsai et al. Taipei, Taiwan Average across 24-h avg Mean: 3.94 75th: 5.01
(2012) (1999-2009) six monitoring Max: 12.7
stations
tQiu et al. Hong Kong, Average across 24-h avg Mean: 7.4 NR
(2013b) China 14 monitoring
(1998,2007) 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
ISA = Integrated Science Assessment; NR = not reported; S02 = sulfur dioxide.
fStudies published since the 2008 ISA for Sulfur Oxides.
Note: Studies are listed in the order that they are discussed in the text.
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Study
Bhaskaran etal. (2011)
Milojevic et al. (2014)
Turin et al. (2012)
BaUester et al. (2006)
Rich et al. (2010)
Cheng et al. (2009)
Hsieh et al. (2010)
Steib et aL (2009)
Cendon et aL (2006)
Thach et al. (2010)
Tsai et al (2013)
Qui et al. (2013)
Bell et al. (2008)
Outcome
MI
MI
MI
IHD
MI
MI
MI
Ml/Angina
MI
IHD
MI
IHD
IHD
Lag
1-6 h
0-4
1
0-1
0
0-2
0-2
0-2
0-2
1
0-7
0-1
0-2
0-2
0-3
0-3
0-3
0-3
Notes
I
~
>25° C
<25° C
>23° C
<23° C
Warm Days
w/o Arrhythmia
Cool Days
w/o Arrhythmia
All Year
Warm Days
Cool Days
All Monitors
City Monitors
Correlated Monitors
I*
I •
• I
0.5 1 1.5
Risk or Odds Ratio (95% CI)
Note: Studies in red are recent studies. Studies in black were included in the 2008 Integrated Science Assesment (ISA) for Sulfur
Oxides. Relative risks are standardized to a 10-ppb or 40-ppb increase in S02for 24-hour average and 1-hour max metrics,
respectively.
Figure 5-10 Results of studies of short-term sulfur dioxide exposure and
hospital admissions for ischemic heart disease.
Table 5-33 Corresponding risk estimates for hospital admissions for ischemic
heart disease for studies presented in Figure 5-10.
Study
Location
Health
Effect
Risk or Odds Ratio3
(95% CI)
Copollutant Examination13
tBhaskaran et al.
(2011)
15 conurbations Ml
in England and
Wales
Lag 1-6 h:
1.00 (0.79, 1.27)
No copollutant models examined
SO2 correlations:
Os: -0.14; PM10: 0.26; NO2: 0.31;
CO: 0.30
tMiloievic et al. (2014)
15 conurbations Ml
in England and
Wales
Lag 0-4:
1.04 (1.00, 1.09)
No copollutant models examined
No correlations provided
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Table 5-33 (Continued): Corresponding risk estimates for hospital admissions for
ischemic heart disease for studies presented in
Figure 5-10.
Study
Location
Health
Effect
Risk or Odds Ratio3
(95% CI)
Copollutant Examination13
tTurin et al. (2012)
Takashima
Ml
Lag 1:
No copollutant models examined
County, Japan
0.96 (0.56, 1.67)
SO2 correlations: SPM: 0.54; NO2:
0.23
Ballester et al. (2006)
14 Spanish
cities
IHD
Lag 0-1:
1.05 (1.01, 1.08)
SO2: attenuated after adjustment
for CO or NO2, and lessened in
magnitude, but more precise with
adjustment for TSP or O3
Copollutants: PM10 and NO2
attenuated but still positive after
SO2 adjustment. CO and O3
robust to SO2 adjustment, BS and
TSP less precise after SO2
adjustment
SO2 correlations:
BS: 0.24; NO2: 0.46; CO: 0.51; O3:
-0.03; TSP: 0.31; PM10: 0.46
SO2: attenuated and no longer
positive after PM2.5 adjustment
Copollutants: PM2.5 robust to SO2
adjustment
SO2 correlations:
Os: -0.32; PM2.5: 0.44; NO2: 0.56;
CO: 0.42
SO2: associations attenuated and
no longer positive after adjustment
for PM10, NO2, or CO. Robust to
O3 adjustment
Copollutants: PM10, NO2, CO, and
O3 associations robust to
adjustment for SO2
SO2 correlations:
Os: -0.09; PM10: 0.33; NO2: 0.53;
CO: 0.52
SO2: associations attenuated and
no longer positive after adjustment
for PM10, NO2, CO, or O3
Copollutants: PM10, NO2, CO, and
O3 associations robust to
adjustment for SO2
SO2 correlations:
Os: -0.01; PM10: 0.51; NO2: 0.51;
CO: 0.47
tRich et al. (2010) New Jersey Ml Lag 0:
1.05 (0.84, 1.29)
tChenq et al. (2009) Kaohsiung, Ml >25°C
Taiwan Lag 0-2:
1.06 (0.94, 1.16)
<25°C
Lag 0-2:
1.18 (1.02, 1.40)
tHsieh et al. (2010) Taipei, Taiwan Ml >23°C
Lag 0-2:
1.12 (0.96, 1.29)
<23°C
Lag 0-2:
1.00 (0.89, 1.16)
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Table 5-33 (Continued): Corresponding risk estimates for hospital admissions for
ischemic heart disease for studies presented in
Figure 5-10.
Health or Odds Ratio3
Study Location Effect (95% CI) Copollutant Examination13
tStieb et al. (2009) Seven Ml/ Lag 1: No copollutant models examined
Canadian cities Angina 1.04 (1.00,1.08) Warm season SO2 correlations
(Pearson r):
NO2: 0.05 to 0.69; CO: -0.06 to
0.75; Os: -0.24 to 0.21; PM2.5:
0.01 to 0.55; PM10: 0.25 to 0.60
Cool season SO2 correlations:
NO2: 0.23 to 0.64; CO: 0.00 to
0.71; Os: -0.18 to -0.52; PM2.5:
0.01 to 0.67; PM10: 0.23 to 0.64
Cendon et al. (2006)
Sao Paulo,
Brazil
Ml
Lag 0-7:
1.21 (1.05, 1.32)
No copollutant models examined
SO2 correlations:
Os: 0.31; PM10: 0.77; NO2: 0.70
tThach et al. (2010)
Hong Kong,
China
IHD
Lag 0-1:
1.05 (1.02, 1.07)
No copollutant models examined
No correlations provided
tTsaietal. (2012)
Taipei, Taiwan
Ml
Lag 0-2
Warm days
w/o arrhythmia:
1.18 (1.00, 1.38)
Cool days
w/o arrhythmia:
0.92 (0.77, 1.04)
No copollutant models examined
SO2 correlations (Pearson r):
Os: 0.06; PM10: 0.52; NO2: 0.48;
CO: 0.29
tQiu et al. (2013b)
Hong Kong,
China
IHD
Lag 0-3
All yr:
1.06 (1.04, 1.07)
Warm:
1.03 (1.00, 1.05)
Cool:
1.13 (1.10, 1.16)
No copollutant models examined
No correlations provided
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14
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16
Table 5-33 (Continued): Corresponding risk estimates for hospital admissions for
ischemic heart disease for studies presented in
Figure 5-10.
Health or Odds Ratio3
Study Location Effect (95% CI) Copollutant Examination13
tBell et al. (2008) Taipei, Taiwan IHD Lag 0-3 No copollutant models examined
All Taipei monitors: No correlations provided
1.04 (0.95, 1.14)
City monitors only:
1.06 (0.95, 1.17)
Correlated monitors:
1.06 (0.96, 1.18)
BS = black smoke; CI = confidence interval; CO = carbon monoxide; IHD = ischemic heart disease; ISA = Integrated Science
Assessment; Ml = myocardial infarction; N02 = nitrogen dioxide; 03 = ozone; PM = particulate matter; r= correlation coefficient;
S02 = sulfur dioxide; SPM = suspended particulate matter; TSP = total suspended particulate.
fStudies published since the 2008 ISA for Sulfur Oxides.
Note: Studies are listed in the order that they are discussed in the text. All Lag times are in days, unless otherwise noted.
aEffect estimates are standardized to a 10-ppb or 40-ppb increase in S02 24-h avg and 1-h max metrics, respectively.
bRelevant relative risks for copollutant models can be found in Supplemental Figures 5S-1 (U.S. EPA. 20153). 5S-2 (U.S. EPA.
2015b). and 5S-3 (U.S. EPA. 2015c) and corresponding Supplemental Tables 5S-7 (U.S. EPA. 20151). 5S-8 (U.S. EPA. 2015m)
and 5S-9 (U.S. EPA. 2015n).
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. However, exposure to SO2 was assigned using fixed
site monitors, which have limitations in capturing spatial variation in SO2
(Section 3.3.3.2). These limitations are likely to lead to attenuation and loss of precision
of the effect estimates (Section 3.3.5.1).
Summary of Ischemic Heart Disease and Myocardial Infarction
In summary, evidence from epidemiologic studies suggests that there may be an
association between ambient SO2 concentrations and rates of hospital admissions or ED
visits for MI or ischemic heart diseases in single-pollutant models, but this association
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23
24
25
26
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28
29
30
31
32
33
34
35
may be at least partly due to 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. All of the studies in this section used fixed site
monitors to measure ambient SO2, which have noted limitations in capturing spatial
variation in SO2, which typically leads to attenuation and loss of precision in the effect
estimates (Section 3.3.3.2). No experimental studies have been conducted to evaluate
measures of ischemic heart disease or MI following short-term SO2 exposure. Overall,
there is limited, but generally consistent, evidence to suggest that short-term exposure to
SO2 in adults may lead to increases in ischemic heart disease and MI from epidemiologic
studies of hospital admissions and ED visits and ST-segment changes.
5.3.1.3 Arrhythmias and Cardiac Arrest
The 2008 ISA for Sulfur Oxides (U.S. EPA. 2008b) 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 5-34). 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
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29
multicity study in Canada (Stieb et al.. 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.
The majority of out-of-hospital cardiac arrests (OHCA) are due to cardiac arrhythmias.
Dennekamp et al. (2010) considered the association between ambient pollutants and
OHCA among 8,434 cases identified through the Victorian Cardiac Arrest Registry in
Melbourne, Australia and found null and/or imprecise associations (e.g., wide 95% CIs)
between SO2 concentrations and risk of OHCA. A similar approach was used by
Silverman etal. (2010) using data from 8,216 OHCAs in New York City. Quantitative
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.
One animal toxicological study evaluated arrhythmia frequency in rats following
short-term SO2 exposure and reported no significant changes in spontaneous arrhythmias
(irregular, delayed, or premature beats) Nadzieiko et al. (2004).
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 3.3.3.2). One
toxicological study also found no evidence for arrhythmias following short-term SO2
exposure.
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Table 5-34 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)
tMetzaer et al.
Atlanta, GA
1-h max: 15.5
Central
All tachyarrhythmic events (OR); year
(2007)
1993-2002
90th percentile:
monitor
round
(n = 518)
36
Lag 0: 1.00 (0.94, 1.08)
Max: 149
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.
London, U.K.
24-h avg: 1.03
Citywide avg
ICD activations (OR);
(2010)
1995-2003
75th percentile:
Lag 01: 1.35 (0.75, 2.41)
[n = 705
1.15
Lag 05: 1.71 (0.69, 4.27)
(5,462 device
Max: 2.67
Correlations: PM10: 0.48, PM25: 0.42, BS:
activations)]
0.35, SO42-: 0.19, PNC: 0.29, NO2: 0.60,
NO: 0.44, NOx: 0.49, O3: -0.36
tLinketal. (2013)
Boston, MA
2006-2010
[n = 176
(328 atrial
fibrillation
episodes
>30 sec)]
24-h avg: 3.2
75th percentile: 4
Citywide avg ICD activations (percent change);
Lag 1: 32.0 (-48.5, 336.2)
Correlations: CO:-0.06 to 0.75, NO2: 0.05
to 0.69, Os: -0.52 to -0.18, PM10: 0.27 to
0.55, PM2.5: 0.01 to 0.67
tStieb et al. (2009)
Seven
Canadian
cities
1992-2003
(n = 45,160 ED
visits)
24-h avg: 2.6 to
10 across cities
75th percentile:
3.3 to 13.4 across
cities
Citywide avg Dysrhythmia ED visits (percent change);
for each city Lag 0:-1.4 (-6.0, 3.4)
Lag 1: 0.8 (-6.4, 8.6)
Lag 2: -5.0 (-9.2, -0.6)
Correlations: PM1O: 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
tDennekamp et al.
Melbourne,
24-h avg: 0.49
Central
OHCA (percent change);
(2010)
Australia
75th percentile:
monitor
Lag 0: -10.0 (-40.3, 64.0)
2003-2006
0.76
Lag 1: 6.9 (-34.9, 75.6)
(n = 8,434)
Lag 2: 0.8 (-39.0, 66.7)
Lag 01: -0.7 (-34.9, 75.6)
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Table 5-34 (Continued): 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)
tSilverman et al.
New York City,
24-h avg: 6.3
Citywide avg No quantitative results; results presented
(2010)
NY
(median)
graphically. Null association between
2003-2006
75th percentile:
OHCA and year-round SO2
(n = 8,216)
9.6
concentrations. OHCA positively but
95th percentile:
18
imprecisely (i.e., wide 95% CI) associated
with ambient SO2 during the warm season
tStranev et al.
Perth,
1-h avg: 0.4
Nearest
OHCA (OR);
(2014)
Australia
2000-2010
(n = 8,551)
(median)
75th percentile:
0.9
95th: 3.5
monitor
Lag 0: 0.91 (0.71, 1.17)
tRosenthal et al.
(2013)
Helsinki,
Finland
1998-2006
(n = 2,134)
24-h avg: 1.5
Citywide avg OHCA (OR);
Lag 0
Lag 1
Lag 2
Lag 3
0.93 (0.58,
0.68 (0.42,
1.08 (0.68,
1.00 (0.63,
1.44)
1.08)
1.66)
1.55)
Lag 03: 0.86 (0.42, 1.55)
CI = confidence interval; ED = emergency department; ICD = implantable cardioverter defibrillators;
ISA = Integrated Science Assessment; n = sample size; NO = nitric oxide; NO2 = nitrogen dioxide; NOx = the sum of
NO and NO2; O3 = ozone; OHCA = out-of-hospital cardiac arrhthymias; OR = odds ratio; SO2 = sulfur dioxide.
fStudies published since the 2008 ISA for Sulfur Oxides.
All Lag times are in days, unless otherwise noted.
aEffect estimates are standardized to a 10-ppb or40-ppb increase in SO2 concentration for 24- h avg and 1-h max
metrics, respectively.
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). Eleven additional studies are now available for consideration
(study details and results presented in Tables 5-35. 5-36. and Figure 5-11). In Edmonton,
Canada, 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, Canada, 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:
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1.00, 3.10)]. Chen et al. (2014b) also observed an association between SO2 and ischemic
stroke at longer lags in Edmonton, Canada. 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-hour average 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-hour average SO2 at lag 2] in Lanzhou, a heavily polluted city in China with a high
observed mean daily concentration of SO2 over the 5 year study period (30.19 ppb). 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 no association was seen with other types of stroke. However, the
95% CI for the hemorrhagic stroke association was wide, indicating an imprecise
association and co-pollutant 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, Canada. 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 5-35 Mean and upper percentile concentrations of sulfur dioxide from
cerebrovascular disease and stroke-related hospital admission and
emergency department visit studies.
Mean/Median
Upper Percentile of
Location
Exposure
Concentration
Concentrations
Study
years
Assignment
Metric
PPb
PPb
tZhena et al.
Lanzhou, China
Average across
24-h avg
Mean: 30.19
75th: 40.46
(2013)
(2001-2005)
four monitoring
Max: 141.60
stations
tThach et al. Hong Kong, Average across 24-h avg Mean: 6.79 NR
(2010) 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,
Canada
(1992-2002)
Average across
three monitoring
stations
24-h avg
Mean:
2.6
NR
tSzvszkowicz et al.
(2012a)
Vancouver,
Canada
(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:
Max:
2.67
22.52
tChen et al.
(2014b)
Edmonton,
Canada
(1998-2002)
Average across
three monitoring
stations
1-h avg
Mean:
2.0
95th:
6.7
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Table 5-35 (Continued): 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
tVilleneuve et al.
Edmonton,
Average across
24-h avg
Mean: 1.5
75th: 1.9
(2012)
Canada
three monitoring
(2003-2009)
stations
tCosta
Nascimento et al.
(2012)
Sao Paulo, Brazil
(2007-2008)
Central site
monitor
24-h avg
NR
NR
avg = average; ISA = Integrated Science Assessment; NR = not reported; ppb = parts per billion.
fStudies published since the 2008 ISA for Sulfur Oxides.
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Study
Outcome
Lag
Notes
Zheng et aL (2013)
Cerebrovascular Disease
0-3
Thach et aL (2010)
Stroke
0-1
Bell et al. (2008)
Cerebrovascular Disease
0-3
All Monitors
0-3
City Monitors
0-3
Correlated Monitors
Turin et aL (2012)
Stroke
0
Cerebral Infarction
0
Intracerebral Hemorrhage
0
Subarachnoid Hemorrhage
0
Henrotin et al. (2007)
Ischemic Stroke
1
Hemorrhagic Stroke
1
Szyszkowicz et al. (2008)
Ischemic Stroke
1
65-100 years; Cold Season
Ischemic Stroke
1
65-100 years; Female
Ischemic Stroke
1
65-100 years; Warm Season;
Szyszkowicz et al. (2012)
Ischemic Stroke
3
Mechtouff et al. (2012)
Ischemic Stroke
NR
Chen et al. (2014)
Acute Ischemic Stroke
1-24 h
Acute Ischemic Stroke
25-28 h
ViDeneuve etal. (2012)
Stroke
0-2
All Year
Ischemic Stroke
0-2
Warm Days
Transient Ischemic Stroke
0-2
Cool Days
Nascimento et aL (2012)
Stroke
0
?
4-
-1-
4
P
P
0.5 1 1.5 2 2.5
Risk or Odds Ratio (95% CI)
Note: Studies in red are recent studies not included in the 2008 Integrated Science Assessment (ISA) for Sulfur Oxides. Effect
estimates are standardized to a 10-ppb increase in sulfur dioxide 24-hour average metric, but not standardized for other metrics
(e.g.. Chen et al.. 2014b).
Figure 5-11 Results of studies of short-term sulfur dioxide exposure and
hospital admissions for cerebrovascular disease and stroke.
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Table 5-36 Corresponding risk estimates for hospital admissions or emergency
department visits for cerebrovascular disease and stroke for studies
presented in Figure 5-11.
Risk or Odds
Location Ratio3
Study Years Health Effect (95% CI) Copollutant Examination13
tZhenq et al. Lanzhou, Cerebrovascular Lag 0-3: SO2: robust after adjustment for
(2013) China disease 1.017 (1.005,1.029) PM10 or NO2 in copollutant models
2001-2005 Copollutants: NO2 association
attenuated in magnitude and
precision, but still positive after
adjustment for SO2
SO2 correlations:
NO2: 0.64; PM10: 0.62
tThach et al.
Hong Kong,
Stroke
Lag 0-1:
No copollutant models examined
(2010)
China
0.996 (0.979, 1.012)
No correlations provided
1996-2002
tBell et al. (2008)
Taipei,
Cerebrovascular
Lag 0-3
No copollutant models examined
Taiwan
disease
All Taipei monitors:
No correlations provided
1995-2002
0.997 (0.930, 1.068)
City monitors only:
0.972 (0.898, 1.053)
Correlated monitors:
0.979 (0.907, 1.056)
tTurin et al.
Takashima
Stroke
Lag 0
No copollutant models examined
(2012)
County,
Cerebral infarction
All stroke:
SO2 correlations:
Japan
Hemorrhagic stroke
1.00 (0.79, 1.30)
Suspended PM: 0.54;
1988-2004
Cerebral infarction:
NO2: 0.23
0.79 (0.59, 1.09)
Intra-cerebral
hemorrhage:
1.74 (1.00, 3.18)
Subarachnoid
hemorrhage:
1.42 (0.62, 3.41)
Henrotin et al.
Dijon,
Ischemic stroke
Lag 1
No copollutant models examined
(2007)
France
Hemorrhagic stroke
Ischemic stroke:
No correlations provided
1994-2004
0.94 (0.68, 1.31)
Hemorrhagic stroke:
1.04 (0.76, 1.42)
tSzvszkowicz
Edmonton,
Ischemic stroke
Lag 1
No copollutant models examined
(2008)
Canada
Adults 65-100 yr old
No correlations provided
1992-2002
Cold season:
1.21 (1.02, 1.43)
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Table 5-36 (Continued): Corresponding risk estimates for hospital admissions or
emergency department visits for cerebrovascular disease
and stroke for studies presented in Figure 5-11.
Risk or Odds
Location Ratio3
Study Years Health Effect (95% CI) Copollutant Examination13
Females:
1.22 (1.02, 1.46)
Males and warm
season: 1.46 (1.12,
2.22)
SO2: robust to adjustment for O3 in
a copollutant model, and
attenuated, although still positive
after adjustment for CO
Copollutants: O3 association robust
to adjustment for SO2 in a
copollutant model; CO association
attenuated, although still positive
after adjustment for SO2
No correlations provided
tMechtouff et al.
(2012)
Lyon, France
2006-2007
Ischemic stroke
Lag NR:
0.91 (0.41, 1.98)
No copollutant models examined
No correlations provided
tChen et al.
(2014b)
Edmonton,
Canada
1998-2002
Acute ischemic
stroke
Lag 1-24 h:
1.03 (0.99, 1.06)
Lag 25-48 h:
1.03 (0.99, 1.07)
No copollutant models examined
SO2 correlations:
NO2: 0.18; Os: -0.02; PM10: 0.14;
PM2.5: 0.15
tVilleneuve et al.
(2012)
Edmonton,
Canada
2003-2009
Stroke
Ischemic stroke
Transient ischemic
stroke
Lag 0-2
All stroke:
1.28 (0.92, 1.90)
Ischemic stroke:
1.39 (0.78, 2.39)
Transient ischemic
stroke: 1.00 (0.55,
1.90)
Copollutants: O3, CO, NO2, and
PM2.5 associations with ischemic
stroke in the warm season robust to
adjustment for SO2
No correlations provided
tCosta
Nascimento et al.
(2012)
Sao Paulo,
Brazil
2007-2008
Stroke
Lag 0:
1.078 (1.000, 1.165)
No copollutant models examined
SO2 correlations:
Os: 0.26; PM10: 0.48
CI = confidence interval; CO = carbon monoxide; ISA = Integrated Science Assessment; N02 = nitrogen dioxide; NR = not
reported; 03 = ozone; PM = particulate matter; r = correlation coefficient; S02 = sulfur dioxide.
fStudies published since the 2008 ISA for Sulfur Oxides.
All Lag times are in days, unless otherwise noted.
aEffect estimates are standardized to a 10-ppb increase in S02 24-h avg metric, but not standardized for other metrics (e.g.. Chen
et al.. 2014b).
bRelevant relative risks for copollutant models can be found in Supplemental Figures 5S-1 (U.S. EPA. 20153). 5S-2 (U.S. EPA.
2015b). and 5S-3 (U.S. EPA. 2015c) and corresponding Supplemental Tables 5S-7 (U.S. EPA. 20151). 5S-8 (U.S. EPA. 2015m)
and 5S-9 (U.S. EPA. 2015n).
tSzvszkowicz et Vancouver, Ischemic stroke Lag 3:
al. (2012a) Canada 2.09 (1.23,3.52)
1999-2003
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5.3.1.5
Blood Pressure and Hypertension
Based on the data available at the time, the 2008 ISA for Sulfur Oxides (U.S. EPA.
2008b) concluded that the overall evidence was insufficient to conclude that SO2 has an
effect on blood pressure. Recent evidence provides limited and inconsistent evidence for
changes in blood pressure associated with short-term exposure to SO2.
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. (2013a) 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. (2013a) and Rich et al. (2012) observed a positive association between 24-hour
average SO2 and systolic blood pressure, but an inverse association between 24-hour
average 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. (2013a).
A pair of cross-sectional studies also reported contrasting 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%
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23
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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, there were also three new studies
examining ED visits for hypertension. In Beijing, Guo et al. (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-hour average SO2 on the same day. The association was attenuated, but still positive,
in a copollutant model adjusting for PM10 [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-hour average 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-6 (U.S. EPA.
2015k). 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 intra-tracheally 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 if
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 co-pollutant 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
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25
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such, the current evidence does not support the presence of an association between
ambient SO2 and blood pressure.
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 or insulin deficiency 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-hour average 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.
2008b) were inconsistent with regard to the association between ambient SO2
concentrations and hospital admissions or ED visits for heart failure. Three additional
studies are now available, including a multicity study of seven Canadian cities (Stiebet
al.. 2009). Stieb et al. (2009) observed an imprecise association (i.e., wide 95% CI)
between 24-hour average 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-hour average 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 5-37 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 5-37 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.
Location
Mean
Type of Visit
Concentration Upper Percentile of
Study
(Years)
(ICD9/10)
Metric
ppb Concentrations ppb
United States
Gwvnn et al.
Buffalo and
Hospital admissions:
24-h avg
12.2 Range: 1.63, 37.7
(2000)
Rochester, NY
Circulatory (401-405,
(1988-1990)
410-417)
tlto et al.
New York City,
Hypertensive diseases
24-h avg
7.4
(2011)
NY
(402, 111); Ml (410,
(2000-2006)
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 5-37 (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
(ICD9/10)
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
Metzqer et al. Atlanta, GA ED visits: 1-h max: 11.0 (median) 10th—90th range: 2.0 to
(2004) (1993-2000) IHD (410-414); acute 39
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
(2004) (1997-2001)
ED visits
Heart (410-414,
425-429)
24-h avg 1.92 (all hourly Max: 447 (all hourly
measurements) measurements)
Moolaavkar
(2003)
Moolaavkar
(2000)
Cook County,
IL; Los
Angeles
County, CA;
Maricopa
County, AZ
(1987-1995)
Hospital admissions:
CVD (390-429);
cerebrovascular
disease (430-448)
24-h avg
Cook: 6
(median)
Los Angeles:
(median)
Maricopa:
2 (median)
Cook: Max: 36
Los Angeles: max: 16
Maricopa: max: 14
Morris et al.
(1995)
Los Angeles,
CA; Chicago,
IL;
Philadelphia,
PA; New York
City, NY;
Detroit, Ml;
Houston, TX;
Milwaukee, Wl
(1986-1989)
Hospital admissions:
CHF (428)
1-h max Los Angeles: NR
0.010 (0.005)
Chicago: 0.025
(0.011)
Philadelphia:
0.029 (0.015)
New York City:
0.032 (0.015)
Detroit: 0.025
(0.013)
Houston: 0.018
(0.009)
Milwaukee:
0.017 (0.013)
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Table 5-37 (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.
Location
Mean
Type of Visit
Concentration
Upper Percentile of
Study
(Years)
(ICD9/10) Metric
PPb
Concentrations 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: IHD 24-h avg 25.4 90th: 44.0
Morris (1995) (1986-1989) (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
Range: 1.0, 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
Birmingham,
Hospital admissions:
24-h avg
6.22 (median)
90th: 16.17
al. (2005b)
AL; Chicago,
ischemic stroke,
IL; Cleveland,
primary diagnosis of
OH; Detroit,
acute but ill-defined
Ml;
cerebrovascular
Minneapolis,
disease or occlusion of
MN; New
the cerebral arteries;
Haven, CT;
HS, primary diagnosis
Pittsburgh,
of intra-cerebral
PA; Seattle,
hemorrhage. (ICD
WA
codes not provided)
(1986-1999)
tRich et al. New Jersey
(201°) (2004-2006)
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Table 5-37 (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.
Location
Mean
Type of Visit
Concentration
Upper Percentile of
Study
(Years)
(ICD9/10)
Metric
PPb
Concentrations ppb
Wellenius et
Allegheny
Hospital admissions:
24-h avg
14.78 (9.88)
95th: 33.93
al. (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 dysrhythmias
Etobicoke,
(427); heart failure
North York,
(428); all cardiac
Scarborough,
(410-414, 427, 428)
Toronto, York),
Canada
(1992-1994)
Burnett et al.
Metropolitan
IHD (410-414); cardiac
24-h avg
5.35
Max: 57
(1999)
Toronto (East
dysrhythmias (427);
York,
CHF (428); all cardiac
Etobicoke,
(410-414, 427, 428)
North York,
Scarborough,
Toronto,
York), Canada
(1980-1994)
Funa et al.
Windsor,
CHF (428), IHD
1-h max
27.5 (16.5)
Max: 129
(2005)
Ontario,
(410-414),
Canada
dysrhythmias (427)
(1995-2000)
and all cardiac
Stieb et al.
Saint John,
ED visits:
24-h avg
6.7 (5.6)
95th: 18
(2000)
New
angina pectoris, Ml,
Max: 60
Brunswick,
dysrhythmia/conduction
Canada
disturbance, CHF, all
(1992-1996)
cardiac
tSzvszkowicz
Edmonton,
ED visits:
24-h avg
2.6
NR
(2008)
Canada
acute ischemic stroke
(1992-2002)
(434 and 436)
tSzvszkowicz
Vancouver,
ED visits (discharge
24-h avg
2.5
NR
et al. (2012a)
Canada
diagnosis):
(1999-2003)
Transient ischemic
attack, cerebrovascular
incident, seizure
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23
Table 5-37 (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
(ICD9/10) Metric
Mean
Concentration Upper Percentile of
ppb Concentrations ppb
tSzvszkowicz
etal. (2012b)
Edmonton,
Canada
(1992-2002)
ED visits: hypertension 24 h avg
(401.9)
2.6
Max: 16.3
Villeneuve et
al. (2006)
Edmonton,
Canada
(1992-2002)
ED visits: 24-h avg
stroke
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; ISA = Integrated Science Assessment;
Ml = myocardial infarction; NR = not reported; ppb = parts per billion; 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. 2008b) 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 study and other studies in this section are presented in Tables 5-38. 5-39.
and Figure 5-121. 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. 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-hour average 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. A study in New York City (Ito etal.. 2011) observed an association
between SO2 concentrations that was stronger and more precise in the warm season [OR:
1.026 (95% CI: 1.021, 1.031) per 10-ppb increase in 24-hour average SO2] than in the
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cold season [OR: 1.018 (95% CI: 0.998, 1.049)]. Two studies in Sao Paolo, Brazil (Filho
et al.. 2008; Martins et al.. 2006) also found associations in single pollutant models (no
quantitative results; results presented graphically). Another study found an increase in the
risk of daily hospital per IQR increase in 24-hour average SO2 in the heavily polluted city
of Lanzhou, China (Zheng et al.. 2013). However, this association was less clinically
relevant when standardized to a 10-ppb increase in 24-hour average SO2.
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, all of the
studies in this section used fixed site monitors to measure ambient SO2, which have noted
limitations in capturing spatial variation in SO2, which generally lead to attenuation and
loss of precision of the effect estimates (Sections 3.3.3.2 and 3.3.5.1).
Table 5-38 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 et al. (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, Canada
(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)
Fixed site
monitors in each
city
24-h avg
Median: 1.9-8.0
across cities
90th: 5.3-29.4
across cities
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Table 5-38 (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
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
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
tGuo 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
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Table 5-38 (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
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
avg = average; ISA = Integrated Science Assessment; NR = not reported; ppb = parts per billion; S02 = sulfur dioxide.
fStudies published since the 2008 ISA for Sulfur Oxides.
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Study
Outcome
Lag
Ito etal. (2011)
CVD
0
Metzger et al. (2004)
CVD
0-2
Moolgavkar et al, (2003)
CVD
0
Schwartz et al. (1997)
CVD
0-2
Burnett et al. (1997)
CVD
0-3
Sunyer et al. (2003)
CVD
0-1
Ballester et al. (2006)
CVD
0-1
Atkinson et al. (1999)
CVD
0
Poloniecki etal, (1997)
CVD
1
Anderson et al. (2001)
CVD
0-1
Ballester et al. (2001)
CVD
2
Llorca et al. (2005)
CVD
0
Zheng et al. (2013)
CVD
0-3
Guo et al. (2009)
CVD
0
Chen et al. (2010b)
CVD
5
Wong et al. (1999)
CVD
0-1
Chang et al. (2005)
CVD
0-2
Jalaludin et al. (2006)
CVD
0
Petroeschevsky et al. (2001)
CVD
0
0
1
Notes
Warm Season
Cold Season
All Ages
65+ Years Old
All Ages
0-64 Years Old
65+ Years Old
All Ages
65+ Years Old
>20° C
<20° C
All Aaes
15-64 Years Old
65+ Years Old
I
0.85 0.95 1.05 1.15 1.25
Risk or Odds Ratio (95% CI)
Note: red indicates new studies since the 2008 SOx Integrated Science Assessment (ISA).
Figure 5-12 Studies of hospital admissions and emergency department visits
for all cardiovascular disease (CVD).
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Table 5-39 Corresponding relative risk (95% CI) for hospital admissions and
emergency department visits for all CVD for studies presented in
Figure 5-12.
Study
Location
Risk or Odds Ratio3
95% CI
Copollutant Examination13
tlto et al. (2011)
New York City, NY
Lag 0
Warm season:
1.026 (1.021, 1.031)
Cold season:
1.018 (0.998, 1.49)
No copollutant models examined
Warm season: PM2.5: 0.66; EC:
0.60; OC: 0.71; SCU2": 0.44; NOs":
0.71; NO2: 0.68; CO: 0.50
Cold season: PM2.5: 0.57; EC: 0.53;
OC: 0.52; SO42": 0.53; NOs": 0.43;
NO2: 0.67; CO: 0.34
Metzaer et al. (2004)
Atlanta, GA
Lag 0-2:
1.014 (0.985, 1.044)
No copollutant models examined
SO2 correlations:
PM10: 0.20; Os: 0.19; NO2: 0.34; CO:
0.26;PM2.5: 0.17; UFP: 0.24
Moolaavkar (2003)
Los Angeles, CA
Lag 0:
1.137 (1.083, 1.190)
No copollutant models examined
No correlations provided
Schwartz (1997)
Tuscon, AZ
Lag 0-2:
1.036 (0.999, 1.075)
No copollutant models examined
SO2 correlations:
PM10: 0.10; NO2: 0.48; O3: -0.27;
CO: 0.40
Burnett et al. (1997)
Toronto, Canada
Lag 0-3:
1.238 (1.055, 1.452)
No copollutant models examined
SO2 correlations:
Os: 0.18; NO2: 0.46; CO: 0.37
Sunver et al. (2003) Seven European Lag 0-1 No copollutant models examined
cities All ages: No correlations provided
1.019 (1.008, 1.029)
65+ yr old:
1.019 (1.007, 1.031)
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Table 5-39 (Continued): Corresponding relative risk (95% CI) for hospital
admissions and emergency department visits for all CVD
for studies presented in Figure 5-12.
Study
Location
Risk or Odds Ratio3
95% CI
Copollutant Examination13
Ballester et al. (2006)
14 Spanish cities
Lag 0-1:
1.036 (1.004, 1.067)
SO2: attenuated after adjustment for
CO or NO2, and lessened in
magnitude, but more precise with
adjustment for TSP or O3
Copollutants: PM10 and NO2
attenuated but still positive after SO2
adjustment. CO and O3 robust to
SO2 adjustment, BS and TSP less
precise after SO2 adjustment
SO2 correlations:
BS: 0.24; PM10: 0.46; TSP: 0.31;
NO2: 0.46; CO: 0.51; O3: -0.03
Atkinson et al. (1999) London, England
Lag 0
All ages:
1.023 (1.003, 1.043)
0-64 yr old:
1.036 (1.004, 1.069)
65+ yr old:
1.025 (1.002, 1.049)
No copollutant models examined
No correlations provided
Poloniecki et al. (1997) London, England
Lag 1:
1.013 (1.003, 1.023)
No copollutant models examined
No correlations provided
Anderson et al. (2001) Birmingham,
England
Lag 0-1:
0.995 (0.974, 1.017)
No copollutant models examined
SO2 correlations:
PM10: 0.55; PM2.5: 0.52; PM2.5-10:
0.31; BS: 0.50; NO2: 0.52; O3: -0.22;
CO: 0.49
Ballester et al. (2001)
Valencia, Spain
Lag 2:
1.082 (1.011, 1.158)
SO2: slightly attenuated, but still
positive after adjustment for BS;
robust to adjustment for CO
Copollutants: BS attenuated but still
positive after SO2 adjustment. CO
attenuated and null after SO2
adjustment
SO2 correlations
(Pearson r):
BS: 0.63; NO2:
-0.35
0.22; CO: 0.74; 03:
Llorca et al. (2005)
Torrelavega, Spain Lag 0:
0.984 (0.956, 1.012)
No copollutant models examined
SO2 correlations: NO2: 0.59; TSP:
-0.40
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Table 5-39 (Continued): Corresponding relative risk (95% CI) for hospital
admissions and emergency department visits for all CVD
for studies presented in Figure 5-12.
Study
Location
Risk or Odds Ratio3
95% CI
Copollutant Examination13
tZhenq et al. (2013)
Lanzhou, China
Lag 0-3:
1.007 (0.999, 1.015)
SO2: small, precise association
robust to NO2 adjustment;
attenuated and null after adjustment
for PM10
Copollutants: PM10 and NO2 robust
to SO2 adjustment
SO2 correlations:
NO2: 0.64; PM10: 0.62
tGuo et al. (2009)
Beijing, China
Lag 0
1.037 (1.011, 1.056)
SO2: association robust to NO2 or
PM2.5 adjustment
SO2 correlations:
NO2: 0.53; PM2.5: 0.42
tChen et al. (2010b)
Shanghai, China
Lag 5:
1.017 (1.008, 1.025)
SO2: association robust to NO2 or
PM10 adjustment
Copollutants: PM10 attenuated and
no longer positive after SO2
adjustment. NO2 attenuated in
strength and precision, but still
positive
SO2 correlations:
NO2: 0.76; PM10: 0.72
Wong et al. (1999)
Hong Kong, China
Lag 0-1
All ages:
1.043 (1.016, 1.070)
65+ yr old:
1.056 (1.027, 1.087)
No copollutant models examined
No correlations provided
Chang et al. (2005)
Taipei, Taiwan
Lag 0-2
>20°C:
0.885 (0.798, 0.982)
<20°C:
1.056 (0.876, 1.272)
No association between SO2 and
CVD hospitalizations. Copollutant
models did not change results
No correlations provided
Jalaludin et al. (2006)
Sydney, Australia
Lag 0
65+ yr old:
1.193 (1.033, 1.377)
SO2: attenuated, but still positive
after adjustment for BS, PM2.5, NO2,
or CO; robust to adjustment for O3 or
PM10
Copollutants: PM10 attenuated but
still positive after SO2 adjustment.
BS, PM2.5, NO2, and CO robust to
SO2 adjustment
SO2 correlations:
BS: 0.21; PM10: 0.37; PM2.5: 0.27;
Os: 0.45; NO2: 0.52; CO: 0.46
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Table 5-39 (Continued): Corresponding relative risk (95% CI) for hospital
admissions and emergency department visits for all CVD
for studies presented in Figure 5-12.
Risk or Odds Ratio3
Study Location 95% CI Copollutant Examination13
Petroeschevskv et al. Brisbane, Australia Lag 0: No copollutant models examined
(2001) All ages No correlations provided
1.028 (0.987, 1.070)
Lag 0:
15-64 yr old
1.081 (1.010, 1.157
Lag 1:
65+ yr old
1.038 (0.988, 1.091)
BS = black smoke; CI = confidence interval; CO = carbon monoxide; CVD = cardiovascular disease; EC = elemental carbon;
ISA = Integrated Science Assessment; PM = particulate matter; 03 = ozone; OC = organic carbon; S042" = sulfate; N02 = nitrogen
dioxide; N03~ = nitrate; r = correlation coefficient; S02 = sulfur dioxide; TSP = total suspended particulates; UFP = ultrafine
particle.
fStudies published since the 2008 ISA for Sulfur Oxides.
aEffect estimates are standardized to a 10-ppb or 40-ppb increase in S02 24-h avg and 1-h max metrics, respectively.
bRelevant relative risks for copollutant models can be found in Supplemental Figures 5S-1 (U.S. EPA. 20153). 5S-2 (U.S. EPA.
2015b). and 5S-3 (U.S. EPA. 2015c) and corresponding Supplemental Tables 5S-7 (U.S. EPA. 20151). 5S-8 (U.S. EPA. 2015m)
and 5S-9 (U.S. EPA. 2015n).
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. Across studies, there was evidence that the magnitude of the
SCh-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 (Section5.5.1.3. Figure 5-16). The associations between short-term
SO2 concentrations and cardiovascular mortality are further supported by studies focusing
on stroke mortality (Yang etal. 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 5-16). 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 et al. (2014b) reporting a 2.5% increase in stroke mortality (95% CI: 1.8, 3.1)
for a 10-ppb increase in 24-hour average SO2 concentrations in a meta-analysis of
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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 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 5-47).
Previous studies evaluated in and prior to the 2008 SOx ISA, which 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 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-hour average 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 etal. (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 5.5.1.4). 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 SC>2-cardiovascular 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 5-17). In
an analysis of stroke mortality in eight of the CAPES cities, Chen et al. (2013) reported a
similar pattern of associations as 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-hour
average 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 N02, -80% reduction [0.0% (95%
CI: -1.8, 1.9)]. Additionally, it is important to note that the aforementioned studies rely
on central site monitors for estimating exposure. SO2 is more spatially variable than other
pollutants as reflected in the generally low to moderate spatial correlations across urban
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geographical scales (Section 3.3.3.2); therefore, the attenuation in SO2 associations may
be a reflection of the different degree of exposure error across pollutants
(Section 3.3.5.1). Overall, the studies that examined potential copollutant confounding on
the SCh-cardiovascular mortality relationship report results consist with what has been
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..
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, 2.9% increase, which is
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,
which found when examining single-day lags of 0 to 3 days, the S02-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) report rather poor
monitor-to-monitor temporal correlations for SO2, which would indicate potential
exposure error and subsequently attenuation and imprecision in the risk estimate
(Sections 3.3.3.2. 3.3.5.1). Sacks et al. (2012) provide additional support to the limited
evidence indicating differences in the seasonal pattern of S02-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 S02-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 it remains unclear if the seasonal pattern of
S02-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.
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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.
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 5-13 there is evidence of a steady decline in the SC>2-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 5-18). as well as the PAPA study (Kan et al.. 2010b) (Figure 5-19). 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.
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2.5 r
2.0
1.5
1 10
2
3 0.5
>.
.ti
R3
n
•E 0.0
B
o
£ -0.5
-1.0
01 04
Adapted from Chen et al. (2013).
Figure 5-13 Percent increase in stroke mortality associated with a 10 |jg/m3
(3.62 ppb) increase in SO2 concentrations using different lag
structures.
1 Chen et al. (2013) also examined the shape of the SC^-stroke mortality C-R relationship.
2 To examine the assumption of linearity, the authors fit both a linear and spline model to
3 the SCh-stroke mortality relationship. Chen et al. (2013) then computed the deviance
4 between the two models to determine if there was any evidence of nonlinearity. An
5 examination of the deviance did not indicate that the spline model improved the overall
6 fit of the SC>2-stroke mortality relationship (Figure 5-14).
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Adapted from Chen et al. (2013).
Figure 5-14 Pooled concentration-response curves for SO2 and daily stroke
mortality in eight Chinese cities for a 10 |jg/m3 (3.62 ppb) increase
in 24-hour average concentrations at lag 0-1 day. Note: The black
line represents the mean estimate and the dotted lines are 95%
confidence intervals.
Overall, recent multi-city 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
the world. A few studies examined potential seasonal patterns in associations, and found
initial evidence of larger S02-cardiovascualr 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 associations: remain
robust when alternating the df used to control for seasonality; associations are larger and
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more precise within the first few days after exposure in the range of 0 and 1 day; and that
there is a linear, no threshold C-R relationship, respectively.
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 (Task Force of the
European Society of Cardiology and the North American Society of Pacing and
Electrophysiologv. 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) (Task Force of the European Society of Cardiology and
the North American Society of Pacing and Electrophysiologv. 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 etal.. 1994).
Epidemiology
Six 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%)
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change in LF per 10-ppb increase in 24-hour average 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 inter-quartile 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
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. (2013a) 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.
(2013a) 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
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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, or HF
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-6. (U.S. EPA. 2015k)
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, Routledge 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 SC>2-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)
reported a reduction in SDNN, rMSSD, percentage of successive RR interval differences
exceeding 50 ms (pNN50), 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
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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 and largely inconsistent.
QT-lnterval 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. 2008b) 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-hour average SO2] (Henneberger et al.. 2005). There was little variability between
daily measured SO2 concentrations, therefore 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. Contrary to what the limited
findings of these studies may suggest, epidemiologic and experimental evidence is not
suggestive of an association between SO2 exposure and arrhythmias (Section 5.3.1.3).
Insulin Resistance
There were no epidemiologic studies of diabetes or insulin deficiency available for the
2008 ISA for Sulfur Oxides. Two recent studies reported contrasting findings regarding
short-term associations between air pollutants and measures of insulin resistance, which
plays a key role 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
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(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-hour average 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.
Conversely, kelishadi et al. (2009) reported a lack of an association between
24-hour average SO2 and insulin resistance in a study of 374 Iranian children aged
10-18 years. Both of the recent studies relied on central site monitoring for exposure
estimation, and neither evaluated potential confounding by other pollutants.
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.
Blood Markers 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. 2008b) (Table 5-40).
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.
Epidemiologic Studies
The epidemiologic data available for review by the 2008 ISA for Sulfur Oxides (U.S.
EPA. 2008b') did not suggest a consistent link between SO2 and blood markers of
cardiovascular risk, including markers of inflammation and coagulation. Results from
more recent studies continue to be inconsistent. Dubowskv et al. (2006) investigated
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 5-40). Similarly, during the Beijing Olympics, SO2 was inversely associated with
white blood cell counts, although positively associated with fibrinogen (Zhang et al..
2013a). 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. 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
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1 women (Lee etal.. 2011a). and one of 38 male patients with chronic pulmonary disease
2 (Hildebrandt et al.. 2009) observed null associations between SO2 and CRP. In a
3 cross-sectional analysis of 3,659 participants in Tel-Aviv, Steinvil et al. (2008) observed
4 inconsistent and/or imprecise associations between SO2 and CRP, white blood cells, or
5 fibrinogen among men and women. Observed associations were both positive and
6 negative depending on the length of the lags, making interpretation of the results difficult.
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Table 5-40 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,
(2010) Canada
1999-2003
(n = 45)
tLee et al. (2011a) Allegheny
County, PA
1997-2001
(n = 1,696)
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
7-day avg: 8.4 Citywide avg
75th percentile: 10.1
Max: 25.4
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-day
moving average SO2 concentrations.
Null association between SO2 and
fibrinogen
Correlations: CO: 0.43, NO2: 0.44,
Os: -0.19, PM2.5: 0.45
No quantitative results presented.
"...SO2... associations (with CRP)
were negligible for both the entire
population and nonsmokers only."
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 5-40 (Continued): Epidemiologic studies of biomarkers of cardiovascular
effects.
Location and Mean and Upper „ „ A A A „
Years Concentration SO2 Exposure Selected Effect Estimates
Study (sample size) (ppb) Assessment (95% CI)
tHildebrandt et al. Erfurt, 24-havg:1.35 Central site No quantitative results presented.
(2009) Germany Max: 14.2 "No significant associations"
2001-2002 between SO2 and inflammatory
(fibrinogen, E-selectin) or
coagulation (D-dimer, prothrombin)
markers.
(n = 38)
Baccarelli et al. Lombardia,
(2007a) Italy
1995-2005
(n = 1,218)
24-h avg median: Citywide avg
2.4
75th percentile: 4.5
Max: 96.7
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. Lombardia,
(2007b) Italy
1995-2005
(n = 1,213)
24-h avg Median: Citywide avg
2.4
75th percentile: 4.5
Max: 96.7
Homocysteine difference, fasting
(percent change)
Lag 24 h: 0.2 (-6.3, 6.7)
Lag 0-6 days: 0.2 (-4.3, 4.7)
Homocysteine difference,
postmethionine-load (percent
change)
Lag 24 h: 2.6 (-3.2, 8.6)
Lag 0-6 days: 2.6 (-1.5, 6.7)
Wellenius et al. Boston, MA 24-h avg: 4.8 Citywide avg No quantitative results presented.
(2007) 2002-2003 N° significant associations were
_ observed between (NO2) and B-type
'n ~ ' natriuretic peptide levels at any of
the lags examined."
tGoldbera et al. Montreal,
(2008) Canada
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. Augsburg,
(2011) Germany
2003-2004
(n = 200)
24-h avg: 1.15 Central site
75th percentile:
1.26
Max: 2.4
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, Os: -0.45.
tZhanq et al. Beijing, China 24-h avg
(2013a) Jun-Oct 2008 Before: 7.45
During: 2.97
After: 6.81
(n = 125)
Central site No quantitative results; results
presented graphically. Positive
association between SO2 and
fibrinogen (lag 6). Inverse
association between SO2 and WBC
count (lag 5).
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Table 5-40 (Continued): Epidemiologic studies of biomarkers of cardiovascular
effects.
Location and
Years
Study (sample size)
tKhafaie et al. Pune City,
(2013) India
2005-2007
(n = 1,392)
Mean and Upper
Concentration SO2 Exposure
(ppb) Assessment
24-h avg: 8.3 City wide avg
Selected Effect Estimates3
(95% CI)
No quantitative results; results
presented graphically. SChwas
associated with increases in CRP at
lags 0, 1, 2, 4, 5, 0-7, 0-14, and
0-30.
avg = average; AT = atascadero; CI = confidence interval; CRP = C-reactive protein; IL-6 = interleukin-;6;
Lp-PLA2 = lipoprotein-associated phospholipase A;2n = sample size; ISA = Integrated Science Assessment; mg/cL = milligrams
per distributed lag; NO = nitric oxide; N02 = nitrogen dioxide; NR = not reported; PNC = particle number concentration; ppb = parts
per billion; 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.
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 blood markers of cardiovascular risk following
SO2 exposure. Study characteristics are summarized in Supplemental Table 5S-6 (U.S.
EPA. 2015k). 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 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
(Baskurt. 1988). These results indicate a systemic effect of inhaled SO2 and are consistent
with an oxidative injury to red blood cells.
Summary of Subclinical Effects Underlying Cardiovascular Disease
There is inconsistent evidence regarding any potential link between SO2 and other
circulating markers of cardiovascular risk. Studies of markers of inflammation in
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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 suggestive of, but not sufficient to infer, a causal
relationship between short-term exposure to SO2 and cardiovascular health effects.
Associations of short-term exposure to SO2 with the triggering of an MI have been
observed in epidemiologic studies. Epidemiologic studies have also reported
S02-associated hospitalizations and ED visits for MI, IHD, and aggregated CVD,
ST-segment alterations, and mortality from cardiovascular disease. In general, studies
used fixed-site monitors to measure ambient SO2 concentrations This approach has noted
limitations in capturing spatial variation in SO2 and typically leads to attenuation and loss
of precision of the effect estimates (Sections 3.3.3.2 and 3.3.5.1). There is also
uncertainty regarding the influence of confounding by copollutants. Experimental studies
examining the direct effect of SO2 exposure on cardiovascular outcomes, which would
allow a complete evaluation of coherence across disciplines, are lacking. The limited
evidence from the available experimental studies is inconsistent and does not demonstrate
potentially biologically plausible mechanisms for cardiovascular effects.
This conclusion represents a change from the 2008 ISA for Sulfur Oxides that concluded
the "the evidence as a whole is inadequate to infer a causal relationship" (U.S. EPA.
2008b). Specifically, the epidemiologic and experimental studies available at the time of
the last review were inconsistent, lacked coherence across and within disciplines, and
were limited by inadequate control for potential confounding. Despite some positive
findings, a limited number of controlled human exposure and epidemiologic studies
reviewed in the 2008 ISA for Sulfur Oxides provided inconsistent evidence to support an
effect of SO2 on the autonomic nervous system. Some epidemiologic studies found
positive associations between ambient SO2 concentrations and risk of hospital admissions
or ED visits for all cardiovascular diseases. However, it was unclear at that time whether
these results supported a direct effect of short-term SO2 exposure on cardiovascular
morbidity or were confounded by other correlated pollutants. Recent epidemiologic
studies have further evaluated this uncertainty using copollutant models and comparing
associations of SO2 with those of other criteria pollutants. While the recently reviewed
studies provide some evidence for independent associations of SO2 with cardiovascular
effects after adjusting for some pollutants, uncertainties still remain regarding the
independent effect of SO2 after adjustment for copollutants [Supplemental
Figures 5S-1-5S-3 (U.S. EPA. 2015a. b, c) and corresponding Supplemental Tables 5S-
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7-5S-9 (U.S. EPA. 20151. m, n)|. Moreover, there continues to be a lack of experimental
evidence in coherence with the epidemiologic studies to strengthen the inference of
causality for SCh-related cardiovascular effects, including MI. Although spillover of
sulfite into the circulation could possibly lead to redox stress and inflammation, there is
no evidence that this occurs at relevant concentrations (Section 4.3). Thus, the limited
and inconsistent mechanistic evidence, including key events in the proposed mode of
action, fails to describe a role for SO2 in the triggering of cardiovascular diseases; an
uncertainty that remains from the 2008 ISA for Sulfur Oxides.
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 KU.S. EPA. 20156*). Tables I and II]. The key evidence, supporting or
contradicting, as it relates to the causal framework is summarized in Table 5-41. The
causal determination between short-term SO2 exposure and cardiovascular effects is
primarily based on the evidence for effects related to triggering an MI. The evaluation of
evidence supporting the occurrence of an MI includes hospital admissions and ED visits
for IHD or MI and ST-segment amplitude changes. Time-series studies of adults in the
general population generally report seasonal or year-round associations between 24-hour
average and 1-hour maximum SO2 concentrations and hospital admissions and ED visits
for IHD and MI in single-pollutant models (Figure 5-10. Section 5.3.1.2). Although the
majority of the reported relative risks were above 1.0, the risk estimates ranged from 0.92
to 1.21 per 10-ppb increase in SO2, depending on whether stratified analyses were
conducted. The small number of epidemiologic studies based on clinical data report
inconsistent evidence regarding associations between ambient SO2 concentrations and
risk of MI. However, one of the studies reviewed that observed a null association was
likely underpowered to detect an association of the expected magnitude. Once
hospitalized, ST-segment decreases are considered a nonspecific marker of myocardial
ischemia. A single study reported an association between short-term SO2 exposure and
ST-segment changes in patients with a history of coronary heart disease that generally
remained unchanged after additional control for PM2 5 and BC in copollutant models
(Chuang et al.. 2008).
The evidence for IHD and MI hospital admissions and ED visits is coherent with the
positive associations reported in epidemiologic studies of short-term SO2 exposure and
stroke 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.
Controlled human exposure and animal toxicological studies have reported limited and
inconsistent results for effects on the cardiovascular system, including heart rate, HRV,
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arrhythmia frequency, blood pressure, and biomarkers of cardiovascular risk. Studies
have not evaluated SO2 exposure and measures of atherosclerotic plaque instability or
rupture that could provide coherence with epidemiologic studies reporting associations
with triggering an MI. Additionally, experimental studies do not provide convincing
evidence to support a plausible biological mechanism leading to cardiovascular effects
such as triggering an MI following SO2 exposure. There is the potential that
cardiovascular effects following SO2 exposure could be mediated through activation of
neural reflexes or oxidative stress (Section 4.3.1); however, uncertainty remains.
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
moderately to highly correlated with SO2. Generally, SO2 has low to moderate
correlations with other NAAQS pollutants with the highest correlations for primary
pollutants (i.e., CO and NO2) (Section 3.3.4.1V 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-1, (U.S. EPA. 2015a)l. N02 [Figure 5S-2, (U.S. EPA.
2015b)], or other correlated pollutants [Figure 5S-3; (U.S. EPA. 2015cYI 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
N02and 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 CVD
outcomes or if SO2 is a marker for the effects of another correlated pollutant or mix of
pollutants.
There is inconclusive evidence from epidemiologic, controlled human exposure, and
animal toxicological studies for other cardiovascular effects from short-term exposure to
SO2. Studies of patients with implantable cardioverter defibrillators, hospital admissions
for arrhythmias, and out-of-hospital cardiac arrest do not provide evidence for a
relationship between ambient SO2 concentrations and arrhythmias (Section 5.3.1.3).
Epidemiologic and experimental studies provide inconsistent evidence for a potential
association between ambient SO2 concentrations and risk of cerebrovascular disease and
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stroke (Section 5.3.1.4) and increased BP (Section 5.3.1.5). Other outcomes have an
insufficient quantity of studies to evaluate the effects, including venous
thromboembolism (Section 5.3.1.6) and heart failure (Section 5.3.1.7).
In conclusion, epidemiologic evidence from multiple studies at relevant SO2
concentrations is suggestive of, but not sufficient to infer, a causal relationship between
short-term SO2 exposure and cardiovascular health effects. The strongest evidence
supporting this determination comes from epidemiologic studies of varying quality that
are generally supportive of an association between ambient SO2 and triggering an MI.
This evidence is supported by findings from epidemiologic studies of cardiovascular and
stroke mortality. The use fixed-site monitors to measure ambient SO2 in the
epidemiologic studies, has limitations in capturing spatial variation in SO2, which
generally lead to attenuation and loss of precision of the effect estimates. Additionally,
the majority of the studies did not include analyses to determine whether SO2 is
independently associated with these cardiovascular outcomes; thus, uncertainty remains
regarding copollutant confounding. Generally, there is a lack of experimental studies in
human and animal studies evaluating exposure to SO2; their findings are inconsistent and
do not provide evidence to support the epidemiologic studies and the lack of studies.
Thus, the combined evidence from epidemiologic and experimental studies is suggestive
of, but not sufficient to infer, a causal relationship between short-term SO2 exposure and
cardiovascular effects.
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Table 5-41 Summary of evidence, which is suggestive of, but not sufficient to
infer, a causal relationship between short-term SO2 exposure and
cardiovascular effects.
Rationale for Causal
Determination3
Key Evidence13
Key References'3
SO2 Concentrations
Associated with
Effects0
Triggering a Myocardial Infarction
Generally supportive
but not entirely
consistent evidence
from multiple,
high-quality
epidemiologic studies
at relevant SO2
concentrations
Increases in hospital admissions and
ED visits for IHD and Ml in adults in
multiple studies, including multicity
studies, in diverse locations
Section 5.3.1.2
24-h avg: 1.2-15.6 ppb
Increases in hospital admissions and
ED visits for all CVD in adults in
multiple studies, including multicity
studies, in diverse locations
Section 5.3.1.8
24-h avg: 1.9-30.2 ppb
Coherence with ST-segment
depression in adults with pre-existing
coronary heart disease in
association with SO2
Chuana et al. (2008)
24-h avg: 4.6 ppb
(median)
Consistent evidence for increased
risk of cardiovascular mortality in
adults applying differing model
specifications in diverse locations
Section 5.3.1.9
Uncertainty regarding
potential confounding
by copollutants
A number of studies report
associations with ED visits and
hospital admissions were attenuated
after adjustment with CO, NO2, or
PM10
Supplemental Figures
5S-1, 5S-2, and 5S-3 (U.S.
EPA. 2015a. b. c)
Uncertainty regarding
exposure
measurement error
Majority of evidence from time-series
studies that rely on exposure
estimates from central site monitors
Sections 3.3.3.2 and
3.3.5.1
Uncertainty due to
lack of coherence with
other lines of evidence
Lack of evidence from epidemiologic
panel studies and experimental
studies for clinical cardiovascular
effects
Lack of evidence to
identify key events in
the proposed mode of
action
Lack of mechanistic evidence for key
events leading to extrapulmonary
effects
Section 4.3
Limited and inconsistent evidence of
increased systemic inflammation in
epidemiologic studies
Section 5.3.1.10
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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 SO2 exposure and cardiovascular effects.
Rationale for Causal
Determination3 Key Evidence13
Key References'3
SO2 Concentrations
Associated with
Effects0
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
for an association between SO2 and
risk of cerebrovascular disease and
stroke, and increased blood
pressure and hypertension
Sections 5.3.1.4 and
5.3.1.5
Insufficient quantity of studies
evaluating decompensation of heart
failure and venous thrombosis and
pulmonary embolism
Sections 5.3.1.6 and
5.3.1.7
Inconsistent evidence for changes in
HR and HRV in controlled human
exposure and epidemiologic studies
Tunnicliffe et al. (2001)
Routledae et al. (2006)
Section 5.3.1.10
200 ppb, 1 h at rest
(humans)
Some evidence to Some evidence for activation of Section 4.3.1
identify key events in neural reflexes in humans leading to Fiaure 4-3
the proposed mode of altered HRV
action
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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 SO2 exposure and cardiovascular effects.
Rationale for Causal
Determination3 Key Evidence13
Key References'3
SO2 Concentrations
Associated with
Effects0
Cardiovascular Mortality
Consistent
epidemiologic
evidence but
uncertainty regarding
SO2 independent
effect
Multicity studies consistently observe Section 5.3.1.9
associations with cardiovascular
mortality, including stroke with 24-h
avg SO2 at lags primarily of
0-1 days.
Results based on SO2 averaged
across central site monitors.
24-h avg: 2.5-38.2
Chen etal. (2012b)
Chen etal. (2013)
Kan etal. (2010b)
Bellini etal. (2007)
Atkinson et al. (2012)
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.
Uncertainty due to
limited coherence with
cardiovascular
morbidity evidence
Generally supportive, but not entirely
consistent epidemiologic evidence
for 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
avg = average; 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; ppb = parts per
billion; PM = particulate matter; S02 = sulfur dioxide; ST-segment = segment of the electrocardiograph between the end of the
S wave and beginning of the T wave.
aBased on aspects considered in judgments of causality and weight of evidence in causal framework in TablesJ_and M of the
Preamble (U.S. EPA. 2015e).
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.
5.3.2 Long-Term Exposure
5.3.2.1 Introduction
1 Studies of the effects of long-term exposure to SO2 on the cardiovascular system were not
2 available for inclusion in the 1982 AQCD (U.S. EPA. 1982a). The 2008 ISA for Sulfur
3 Oxides (U.S. EPA. 2008b)(U.S. EPA. 2008) reviewed a limited body of toxicological and
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epidemiologic studies published through 2006 and concluded that the available evidence
was inadequate to determine a causal relationship between the effects of long-term
exposure to SO2 on cardiovascular health. New studies do not change this conclusion.
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
postmenopausal 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 3.3.5.2). Consequently, the extent to which this study supports
an independent effect of SO2 on the cardiovascular system is limited.
Experimental animal studies with long-term exposures below 5,000 ppb were not
available for inclusion in the 2008 ISA for Sulfur Oxides. Although a small number of
studies using exposures above 5,000 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. Several recent epidemiologic studies of the
association of long term SO2 exposure with preclinical and clinical cardiovascular
outcomes add to the available body of evidence. These studies do not change the
conclusion from the 2008 ISA for Sulfur Oxides. 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 SO2. To clearly characterize the evidence underlying causality, the discussion
of the evidence is organized into groups of related outcomes (e.g., ischemic heart disease
and myocardial infarction, cerebrovascular disease and stroke). Evidence for subclinical
effects (e.g., blood biomarkers of cardiovascular effects) of long-term exposure to SO2
are discussed in Section 5.3.2.5. and serve to inform biological plausibility across
multiple clinical cardiovascular events and outcomes.
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5.3.2.2
Myocardial Infarction and Ischemic Heart Disease
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 5-42). However, uncertainty remains regarding the influence of
exposure measurement error on the effect estimates observed in epidemiologic studies
(Section 3.3.3.2) and the ability of these studies to distinguish the independent effect of
long-term SO2 exposure from the effect of correlated copollutant exposures
(Section 3.3.4).
Lipsett etal. (2011) analyzed the association of incident MI with long-term exposure to
SO2, other gases (NO2, CO, O3) and PM. These authors studied a cohort of California
public school teachers aged 20-80 years old (n = 124,614). Each participant's geocoded
residential address was linked to pollutant surfaces that were determined by IDW
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 forthe urban/regional SO2. An increased risk of 1.20 (1.02, 1.41) was
observed per 10 (.ig/nr1 per PM2 5. An imprecise association between SO2 and incident MI
was observed (see Table 5-42). Fewer observations were available forthe 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).
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Table 5-42 Epidemiologic studies of long-term exposure to SO2 and effects on
the cardiovascular system.
Study
Cohort, Location, Exposure
and Study Period Mean (ppb) Assessment
Effect Estimates (95% CI)
Lipsett et al. California Teachers SO2
(2011) Study Cohort IQR: 0.43
N=124,614 Mean: 1.72
California, U.S.
Jun1996-
Dec 2005
Geocoded residential
address linked to
pollutant 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
Ml incidence
SO2: HR 1.97 (0.07, 60)
Stroke incidence
SO2: HR 6.21 (0.4, 88)
per 5 ppb SO2
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
Atkinson et al.
(2013)
National GP
Patient Cohort
England
2003
IQR: 0,83
Mean (SD):
1.47
Annual average SO2
concentration for 2002
at a 1 by 1 km
resolution derived from
dispersion models and
linked to residential
post codes
Correlation of SO2 with:
NO2, r= 0.86
Ml incidence
HR: 1.34 (1.13, 1.50)
Stroke incidence
HR: 1.13 (1.00, 1.34)
Arrhythmia incidence
HR: 1.13 (1.00, 1.27)
Heart failure incidence
HR: 1.27 (1.06, 1.59)
per 5 ppb
Covariates: age, sex, smoking
BMI, diabetes, hypertension, and
index of multiple deprivation
Copollutant adjustment: none
Rosenlund et al.
(2006)
n = 1,397 cases
and 1,870 controls
SHEEP cohort
Stockholm,
Sweden
1992-1994
Cases
Dispersion models to
Med: 9.6
estimate SO2 from
5th—95th:
heating at residential
2.6-18.2
address. Residential
Controls
history available for
Med: 9.3
30 yr exposure estimate
5th—95th:
Correlation of 30 yr SO2
7.7-17.5
with:
30 yr NO2, 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
Copollutant adjustment: none
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Table 5-42 (Continued): Epidemiologic studies of long-term exposure to SO2 and
effects on the cardiovascular system.
Cohort, Location,
Exposure
Study
and Study Period
Mean (ppb)
Assessment
Effect Estimates (95% CI)
Miller et al.
WHI Cohort
NR
Annual avg (2000):
Cardiovascular events
(2007)
United States
nearest monitor to
HR: 1.07 (0.95, 1.20)
1994-1998
residence zip code
centroid
per 5 ppb
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
Dona et al.
N =24,845
Mean: 20
3-yr avg (2006-2008)
Stroke
(2013a)
Random selection
Med: 18
SO2 concentration for
OR: 1.21 (1.01, 1.46)
(18-74 yr) from
IQR: 7.5
each district
per 5 ppb
households in
Correlations between
CHD, Ml, orCHF
33 communities in
PM10, ozone, and SO2
OR: 1.18 (0.86, 1.66) per 5 ppb
11 districts of
characterized as "high"
Note: associations stronger
northeastern China
NO2, r= 0.38
Os, r= 0.87
PM10, r= 0.70
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
Dona et al.
n = 9,354
Mean: 18.
4-yr avg concentration
Hypertension in males:
(2014)
Children (5-17 yr)
SD: 20
for one central site
OR 1.17( 1.08, 1.27)
Seven cities
monitor within 1 km of
Hypertension in females:
northeastern China
participant's home
OR 1.19 (1.10, 1.28)
2012-2013
Correlations NR
per 5 ppb
Diastolic blood pressure (all
children)
0.43 (0.26, 0.61)
SBP (all children
0.71 (0.50, 0.91)
per 5 ppb
Covariates adjustment: age, sex,
BMI, parental education, low birth
weight, premature birth, income,
passive smoking exposure, home
coal use, exercise time, area
residence per person, family
history of hypertension, and
district
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Table 5-42 (Continued): Epidemiologic studies of long-term exposure to SO2 and
effects on the cardiovascular system.
Study
Cohort, Location, Exposure
and Study Period Mean (ppb) Assessment
Effect Estimates (95% CI)
Johnson et al. Edmonton, Canada SO2
(201°) Jan 2003- Mean: 1.3
Dec 2007
IDW average monitor
SO2 concentration
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
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
BMI = body mass index; CHF = congestive heart failure; CHD = coronary heart disease; 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; med = median; Ml = myocardial infarction;
N = population number; N02 = nitrogen dioxide; non-HS = non-hemhorragic stroke; NR = not reported; Q1 = 1st quartile; Q2 = 2nd
quartile; Q3 = 3rd quartile; Q4 = 4th quartile; Q5 = 5th quartile; OR = odds ratio; 03 = ozone; PM = particulate matter; ppb = parts
per billion; r= correlation coefficient; RR = relative risk; SBP = systolic blood pressure; SD = standard deviation;
SES = socioeconomic status; SHEEP = Stockholm Heart Epidemiology Programme; S02 = sulfur dioxide; TIA = transient ischemic
attack;WHI = Women's Health Initiative.
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 I / I 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 away from the null (Section 3.3.5.2). Associations of other pollutants
(i.e., PM10, NO2 ozone) with MI in this study were also observed.
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-year average 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 observed. Panasevich
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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. Weak or inverse associations of cardiovascular and
ischemic heart disease were reported in a study relying on a particle dispersion model to
estimate SOx emissions (gaseous and particulate component) from a refinery (Ancona et
al.. 2015). Null associations with PMio, which was highly correlated with SOx (r = 0.81)
in this study, were also observed.
Overall, these epidemiologic data do not provide support for an association of long-term
SO2 exposure with MI. Correlations between SO2 concentration and other pollutants are
generally moderate to high introducing uncertainty regarding the independent effect of
SO2 on the cardiovascular system. Further, the exposure assessment may be subject to
some degree of error depending on the method (Sections 3.3.3.2).
5.3.2.3 Cerebrovascular Diseases and Stroke
Lipsctt et al. (2011) evaluated the association of incident stroke with long-term exposure
to SO2, other gases (NO2, NOx, CO, O3) and PM (Table 5-42). The authors observed an
imprecise, although positive association between SO2 and incident stroke. Point estimates
for the association of other pollutants (PM10, PM2.5, NO2, NOx and O3) 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. 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, Canada (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).
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 and
in Table 5-42. 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 were observed for the associations of
NO2 and PM10 with which moderate correlations with SO2 were reported. No association
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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).
Blood Pressure and Hypertension
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
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-year average SO2 concentration. Associations of hypertension with
the other pollutants examined (i.e. PM10, Ozone, CO, NO2) were also reported in these
studies.
5.3.2.4 Cardiovascular Mortality
The recent evidence for associations between long-term SO2 exposure and total mortality
is generally consistent with the evidence in the 2008 ISA for Sulfur Oxides
(Section 5.5.2). Several studies report associations between long-term SO2 exposure and
cardiovascular mortality (Figure 5-25); 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 3.3.4) and
uncertainties remain regarding the influence of exposure measurement error
(Sections 3.3.3.2 and 3.3.5.2). Together, these uncertainties limit the interpretation of the
causal nature of the associations observed in the available epidemiologic studies of
long-term mortality.
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5.3.2.5
Subclinical Effects Underlying Cardiovascular Diseases
Markers of Cardiovascular Disease Risk
In an analysis of the Atherosclerosis Risk in Young Adults study, which is a prospective
cohort study (Lcnters et al.. 2010). no association of SO2 concentration with carotid
intima-media thickness (cIMT) was observed; however, weak imprecise increases in
pulse wave velocity and augmentation index were observed in association with SO2
concentration. Other pollutants examined (NO2, PM2.5, black smoke) were not associated
cIMT although associations between NO2 concentration and pulse wave
velocity/augmentation index were observed. SO2 concentration at the home 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).
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-year average 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).
Markers of Inotropic Change in the Rodent Heart
Ion channels in the heart including the adenosine triphosphate (ATP)-sensitive potassium
(Katp) channels are important for cardiac force conduction and heart rate.
Pathophysiologically, the Katp channels are thought to play an important role in
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myocardial ischemia reperfusion injury and ischemic preconditioning (Backx. 2008).
These ion channels were examined in adult male mice that had been exposed to 1.24 and
2.48 ppm SO2 for 4 hours/day for 30 days and the two Katp channel subunits,
sulfonylurea receptor 2A (SUR2A) and the inward-rectifier potassium ion channel Kir6.2,
in rat hearts were shown to have significantly higher message (mRNA) levels after SO2
exposure versus air control males (Zhang et al.. 2014). Protein levels of these subunits
reflected the same trend, albeit not significant. Earlier studies are consistent with these
findings (Zhang and Meng. 2012; Nie and Meng. 2005; Du and Meng. 2004). Message
levels of cardiac ion channels, which contribute to inotropic potential of rat hearts, is
significantly altered by exposure to inhaled SO2.
Overall, there is no consistent positive trend in the associations observed between SO2
and markers of cardiovascular risk, most notably 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.
5.3.2.6 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. This
conclusion is consistent with the conclusion of the 2008 ISA for Sulfur Oxides (U.S.
EPA. 2008b). The available evidence examining the relationship between long-term
exposure to SO2 and cardiovascular effects was evaluated using the framework described
in Tables I and II of the Preamble (U.S. EPA. 2015e). The key evidence, supporting or
contradicting, as it relates to the causal framework is summarized in Table 5-43.
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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
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 Lipsett et al. (2011)
with Ml, CVD events, or
stroke events Atkinson et al. (2013)
Miller etal. (2007)
1.72 ppb (mean)
1.47 ppb (mean)
NR
Null/inverse associations Rosenlund et al. (2006) 9.6 ppb (med)
observed with Ml and stroke
Johnson et al. (2010)
1.3 ppb (mean)
Limited coherence with
evidence for cardiovascular
mortality
No consistent positive trend
observed in studies of
cardiovascular mortality
Section 5.3.2.4
Uncertainty due to
confounding by correlated
pollutants
Correlations of SO2 with CO Table 5-42
and NO2 vary by location but
are generally moderate to
high.
Uncertainty due to exposure
measurement error
Central site monitors may
not capture spatial variability
of SO2 concentrations.
SO2 estimates from
dispersion model show poor
to moderate agreement with
measured concentrations.
Miller etal. (2007)
Section 3.3.3.2
Atkinson et al. (2013)
Forbes et al. (2009a)
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
Backx (2008)
Section 5.3.2.5
1.24 or 2.48 ppm SO2
4 h/day for 30 days
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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 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
systemic inflammation
(e.g. cIMT, IL-6, CRP) in
epidemiologic studies
cIMT = carotid intima-media thickness; CO = carbon monoxide; CRP = C-reactive protein; CVD = cardiovascular disease;
IL-6 = interleukin-;6; med = median; Ml = myocardial infarction; N02 = nitrogen dioxide; NR = not reported; ppb = parts per billion;
S02 = sulful dioxide.
aBased on aspects considered in judgments of causality and weight of evidence in causal framework in Tables I and M of the
Preamble (U.S. EPA. 2015e).
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.
Although a number of epidemiologic studies report positive associations between
long-term exposure to SO2 concentrations and cardiovascular disease and stroke
(Section 5.3.2.5). the evidence for any one endpoint is limited and inconsistent. The
animal toxicological literature shows evidence for negative inotropic changes in the
rodent heart after chronic SO2 exposure. Exposure measurement error is an uncertainty in
the interpretation of the evidence. As discussed in Section 3.3.3.2. short-term metrics of
SO2 concentration typically have low to moderate spatial correlations across urban
geographical scales and thus, studies using central site monitors for exposure assessment
are subject to some degree of exposure error. Dispersion models generally capture SO2
variability on near-source spatial scales (up to tens of km), but are subject to uncertainty
under specific meteorological conditions (Section 3.2.2.1). There is additional uncertainty
regarding the potential for copollutant confounding (Section 3.3.4). Primary pollutants
such as NO2 and CO typically show moderate to high correlations with SO2 (Table 5-42)
and there is a lack of experimental evidence to provide coherence or biological
plausibility for an independent effect of SO2 on cardiovascular health. In conclusion, 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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5.4
Reproductive and Developmental Effects
5.4.1 Introduction
The body of literature characterizing the reproductive health effects of exposure to SO2
has grown considerably since the 2008 SOx ISA (U.S. EPA. 2008b). with over 40 recent
epidemiologic studies. However, the number of studies for any particular outcome
remains limited. Among the recent epidemiologic studies, outcomes of fetal growth
(e.g., small for gestational age, preterm birth, and birth weight) predominate. Several new
studies of congenital anomalies have been added 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.
At present, one of the challenges to reproductive health research is selecting the
appropriate exposure windows to study, as biological modes of action leading to adverse
reproductive outcomes are not well understood. While some outcomes (e.g., cardiac birth
defects) have a known risk period, many outcomes do not, or have multiple possibilities
for risk periods and modes of action. Due to this, many epidemiologic studies will
examine multiple exposure windows, including both long-term (months to years;
i.e., trimesters or entire pregnancy) and short-term (days to weeks; i.e., days or weeks
immediately preceding birth) periods. Animal toxicological studies will investigate
short-term air pollution exposure windows that are equivalent to human pregnancy in
lifestage but not in absolute time (e.g., entire pregnancy of a rodent is typically
18-24 days). In order to characterize the weight of evidence for the effects of SO2 on
reproductive and developmental effects in a consistent, cohesive, and integrated manner,
results from both short-term and long-term exposure periods are included in this section
and are identified accordingly in the text and tables throughout this section.
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), but also 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.
Epidemiologic studies included in the 2008 SOx ISA (U.S. EPA. 2008b) examined
impacts on reproductive outcomes including: preterm birth; birth weight; intra-uterine
growth retardation; birth defects; infant mortality; and neonatal respiratory
hospitalizations. Possible modes of action follow those proposed for other air pollutants
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including: oxidative stress, systemic inflammation, vascular dysfunction, and impaired
immune function. While positive associations were observed in the previous SOx ISA
(U.S. EPA. 2008b). 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.
In toxicological research, only a single study has been published at relevant exposure
levels (2,000 ppb or lower) for this ISA. This study investigated reproductive changes in
exposed females and their offspring, altered birth outcomes, and developmental effects.
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: the difficulty in assessing exposure
as most studies use existing monitoring networks to estimate individual exposure to
ambient air pollution, the need for detailed exposure data and potential residential
movement of mothers during pregnancy, 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 limited evidence on the
physiological modes of action for these effects (Ritz and Wilhelm. 2008; Slama et al..
2008). 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.
An ongoing limitation of many air pollution studies is adjustment for copollutants; in
studies of reproductive and developmental outcomes, copollutants often are not adjusted
for. Three recent studies across reproductive and developmental health outcomes
examine effects of SO2 adjusted for copollutants (Faiz et al.. 2013; Slama et al.. 2013; Le
et al.. 2012). No clear trends are observed in copollutant models. 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 difficult.
Overall, the number of studies examining associations between exposure to ambient SO2
and reproductive and developmental outcomes has increased substantially since
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publication of the 2008 ISA for Sulfur Oxides, yet evidence for an association with
individual outcomes remains relatively limited.
Table 5-44 Key reproductive and developmental epidemiologic studies for SO2.
Study
Location
sample size
Mean SO2
PPb
Exposure
Assessment
Selected Effect Estimates*
95% CI
Fetal Growth
IUGR (those with birth weight falls
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: 1.07 (1.00, 1.14)
T2: 0.98 (0.91, 1.04)
T3: 1.03 (0.96, 1.10)
Brauer et al.
Vancouver, Canada
5.3
Inverse distance
SGA (those with birth weights below
(2008)
(n = 70,249)
weighting of three
the 10th percentile of the cohort,
closest monitors
stratified by sex, for each week of
within 50 km, 14 SO2
gestation)
monitors
EP: 1.02 (1.00, 1.03)
Rich et al. (2009)
New Jersey, U.S.
T1: 5.7
Nearest monitor
VSGA (growth ratio <0.75)
(n = 178)
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)
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: ref
Q2: 1.18 (0.92, 1.51)
Q3: 1.01 (0.83, 1.23)
Q4: 1.05 (0.87, 1.28)
T2, adjusted for CO, NO2, and PM10
Q1: ref
Q2: 1.30 (1.01, 1.69)
Q3: 1.12 (0.91, 1.37)
Q4: 1.11 (0.90, 1.36)
T3, adjusted for CO, NO2, and PM10
Q1: ref
Q2: 1.17 (0.94, 1.45)
Q3: 1.24 (1.02, 1.50)
Q4: 1.31 (1.06, 1.60)
Liu et al. (2003) Vancouver, Canada 4.9 Monitors at census
(n = 229 085) subdivision level
Le et al. (2012) Detroit, Ml, U.S. 5.8 Nearest monitor (zip
(n = 112 609) code within 4 km of
one of three monitors)
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Table 5-44 (Continued): Key reproductive and developmental epidemiologic
studies for SO2.
Study
Location
sample size
Mean SO2
PPb
Exposure
Assessment
Selected Effect Estimates*
95% CI
Preterm Birth
Liu et al. (2003)
Vancouver, Canada
(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 U.S.
(n = 187,997)
7.9
Monitors at county
level
Last 6 weeks: 1.05 (1.00, 1.10)
3 day lag: 1.02 (0.99, 1.05)
Zhao et al. (2011)
Guangzhou, China
(n = 7,836 preterm
births)
20
City average from
monitors
Same day: 1.04 (1.02, 1.06)
1 day lag: 1.01 (0.99, 1.04)
2 day lag: 1.02 (0.99, 1.04)
3 day lag: 1.02 (0.99, 1.04)
Low Birth Weight
Ha et al. (2001)
Seoul, South Korea
(n = 276,763)
T1: 13
T3: 12
Monitors averaged to
city
T1: 1.05 (1.02, 1.08)
T1, adjusted forT3: 1.06 (0.98,
T3: 0.96 (0.92, 0.99)
T3, adjusted forT1: 1.02 (0.94,
1.13)
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, Canada
(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,
Canada
(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)
Morello-Frosch et
al. (2010)
California, U.S.
(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)
Ebisu 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)
Kumar (2012)
Chicago, IL, U.S.
(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)
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Table 5-44 (Continued): Key reproductive and developmental epidemiologic
studies for SO2.
Study
Location
sample size
Mean SO2 Exposure
ppb Assessment
Selected Effect Estimates*
95% CI
Birth Weight
Ag
Darrow et al.
(2011)
Distributed lag,
1-h max SO2
Atlanta, GA, U.S.
(n =400,556)
M1: 10.7 Population weighted
T3: 9.5 spatial model based
on monitors, five
county area, 1-h max
M1: 0.625 (-2.625, 3.875)
T3: -6.500 (-12.500, -0.667)
Non-Hispanic white
T3: -8.667 (-15.333, -2.000)
Non-Hispanic black
T3: -3.167 (-9.833, 3.667)
Hispanic
T3: -9.5 (-19.000, -0.167)
Geer et al. (2012)
Texas, U.S.
2.3
County average from
EP: -15.594 (-25.344, -5.844)
(n = 1,548,904)
monitors
Fetal and Infant Mortality
Hwana 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)
Faiz et al. (2012)
New Jersey, U.S.
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)
Faiz et al. (2013)
New Jersey, U.S.
5.8
Nearest monitor
2 day 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.
United States
3 (median)
Monitors, averaged to
All causes
(2008)
(n = 6,639 cases)
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 5-44 (Continued): Key reproductive and developmental epidemiologic
studies for SO2.
Study
Location
sample size
Mean SO2 Exposure
ppb Assessment
Selected Effect Estimates*
95% CI
Developmental
Dales et al. (2006) Atlanta, Georgia, 4.3 Monitors, averaged to Neonatal hospitalization for
U.S. city respiratory disease
(n = 8,586 cases) 2 day 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)
Clark et al. (2010) British Columbia, 2
Canada
(n = 3,482 cases)
Inverse distance Asthma
weighting 3 nearest EP: 1.45 (1.28, 1.84)
monitors (of 14) within 1st year of life: 1.45(1.28, 1.84)
50 km
CI = confidence interval; CO = carbon monoxide; EP = entire pregnancy; IUGR = intrauterine growth restriction; M1 = month 1;
M2 = month 2; M3 = month ;3; n = sample size; N02 = nitrogen dioxide; 03 = ozone; PM = particulate matter; 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.
•Relative risk per 5 ppb change in S02, unless otherwise noted
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5.4.1.1
Fertility, Reproduction, and Pregnancy
Infertility affects approximately 11% of all women ages 15-44 in the United States
(Chandra et al.. 2013). and can have negative psychological impacts and affect quality of
life; 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 often
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).
Three recent studies have examined the effects of SO2 on measures of fertility; all use
different populations and outcomes and observed null effects for SO2 exposures. One
study examined semen quality parameters in a cohort of men from Chongqing, China and
observed decreases in normal morphology with increases in SO2 exposure; however, all
other quality metrics showed null associations (Zhou et al.. 2014V 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. Three recent studies examined increased blood pressure during pregnancy or
5.4.1.2
Effects on Reproduction (Fertility) and Pregnancy
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pre-eclampsia. The studies in an Alleghany County, PA population found no associations
between SO2 exposure during the first trimester and changes in late pregnancy blood
pressure (Lee et al.. 2012); however, a study in Florida observed increased hypertension
with higher SO2 exposure during the 1st trimester (Xu et al.. 2014). A small Iranian study
found no association between pre-eclampsia and SO2 above versus below median
concentrations (Nahidi et al. 2014). In other pregnancy-related outcomes, no associations
were observed in the Alleghany County, PA population for short-term near birth
exposures and C-reactive protein, an inflammatory biomarker linked to increased risk of
preterm birth (Lee etal.. 2011a). Increases in SO2 exposure during the preconception
period and the 1st trimester were associated with increased odds of gestational diabetes
mellitus (Robledo et al.. 2015).
No recent animal studies evaluating fertility and pregnancy were identified. An older
study in laboratory animals exposed to sulfur dioxide 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, significant alterations in stages of the
estrus cycle were seen including significant decreases in duration of diestrus and
metastrus. During the 2nd and 3rd months 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, 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 study in this area is limited, currently SO2 exposures appear to have no association
with fertility, or effects on pregnancy. Studies are summarized in Supplemental
Table 5S-11 (U.S. EPA. 2015o).
5.4.1.3 Birth Outcomes
Fetal growth
Fetal growth can be difficult to quantify; typically, small-for-gestational age (SGA) or
intrauterine 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).
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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. Another issue is that SGA/IUGR is based only on weight distribution at
birth, so only infants born are accounted for, fetal weight of continuing pregnancies is not
considered, although those fetuses are part of the population at risk (Ritz and Wilhelm.
2008). 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 although may
be updated with time (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 single recent study conducted in Australia examined
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]. Three Canadian studies
using the traditional definition of SGA/IUGR had mixed results. In Vancouver
populations, increases in ORs were observed with entire pregnancy exposures (Brauer et
al.. 2008) and with 1st month and 1st trimester exposures (Liu et al.. 2003). Whereas in a
study over Calgary, Edmonton, and Montreal, Liu et al. (2007) found lowered ORs with
exposures in Months 1 to 5 of pregnancy and no associations in Months 6 to 9. Of the
two recent studies in the United States, Le et al. (2012) observed generally null
associations for 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. 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.
No recent animal studies evaluating fertility and pregnancy were identified.
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In summary, there is some evidence for increased odds of fetal growth restriction with
exposure to SO2 during pregnancy, but 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 rather than peak
concentrations. Studies examining the association between SO2 and fetal growth can be
found in Supplemental Table 5S-12 (U.S. EPA. 2015p).
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; IQM. 2007; Gilbert et al.. 2003). PTB is
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. Studies in Europe
and Asia report increased ORs/RRs of PTB with exposures across pregnancy, although
not consistently between studies (Zhao etal.. 2011; Leem et al.. 2006; Bobak. 2000; Xu
et al.. 1995). However, mean SO2 concentrations and exposure contrasts are high for
these studies. In the more recent study, a 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. In the United States and Canada, older
studies of SO2 and PTB in Pennsylvania (Sagiv et al.. 2005) and Vancouver (Liu et al..
2003) found increased ORs with near-birth exposures I Sagiv et al. (2005): 6 weeks
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pre-birth 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 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). A recent time-series study in Atlanta, GA observed null associations
for both 1st month and near-birth exposures using 1-hour maximum SO2 [exposure
during last week of pregnancy RR per 5-ppb increase = 0.99 (0.98, 1.01)] (Darrow et al..
2009). Finally, a cross-sectional study of PTB across the U.S. reported only that SO2
showed "nonsignificant" effects with PTB for exposures during the month of birth
(Trasande et al.. 2013).
No recent animal studies evaluating birth outcomes 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-hour maximum SO2
found no associations for PTB. Studies are characterized in Supplemental Table 5S-13
(U.S. EPA. 2015a).
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 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).
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 (Yorifuji et al.. 2015; Ebisu and Bell. 2012; Kumar.
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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 et al.. 2008; Bell et al..
2007).
Studies examining continuous birth weight (Ag) in the United States have inconsistent
results. In a northeast population, Bell et al. (2007) observed no association with change
in birth weight for entire pregnancy exposure [-2.71 lg (-13.253g, 7.83 lg) 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 [—15.594g (-25.344g, -5.844g)] (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-hour maximum 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 outcomes were identified.
In summary, LBW may be associated with SO2, while evidence for an association with
change in birth weight is inconsistent. Studies for both LBW and change in birth weight
can be found in Supplemental Table 5S-14 (U.S. EPA. 2015r).
Litter Size
No recent animal studies evaluating birth outcomes were identified. In laboratory animals
from an older study, exposure to sulfur dioxide has been shown to affect 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 5-45).
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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 United States. 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; Gil boa et al.. 2005). decreased (Dadvand et al.. 201 la. 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 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 et al.. 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 outcomes 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-15 (U.S. EPA. 2015s).
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
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
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associations between stillbirth and increasing SO2 exposure (Green et al.. 2015). 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 Enkhmaa et al. (2014)"s
study, other pollutants also showed very strong correlations and were highly correlated
with one another.
No recent animal studies evaluating birth outcomes were identified.
In summary, although few in number, studies of fetal mortality and SO2 show elevated
associations for both short and long-term exposures. Studies are characterized in
Supplemental Table 5S-16 (U.S. EPA. 2015t).
Infant Mortality
Studies of infant mortality and SO2 are limited in number. In a study across the U.S.
Woodruff et al. (2008) observed increased ORs for respiratory-related post-neonatal
infant mortality with long-term (2 month) exposure increases in county-level SO2
concentrations [OR = 1.09 (0.89, 1.36) per 5-ppb increase]. This held 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
timing of exposure was unclear Son et al. (2008). Studies are characterized in
Supplemental Table 5S-16 (U.S. EPA. 2015t).
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5.4.1.4
Developmental Outcomes
Respiratory Outcomes
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, gaseous copollutants and PM10
confidence intervals were very large, but effect estimates remained elevated.
Hospitalizations due to respiratory causes are covered in Section 5.2.1.5.
Two recent studies examined asthma onset in association with early life exposure to SO2.
Clark et al. (2010) observed elevated ORs for asthma with SO2 exposure in pregnancy
and the 1st year of life. While Nishimura et al. (2013) observed elevated ORs for asthma
with SO2 exposure in the 1st 3 years of life, but not the 1st year of life alone. Asthma
onset is covered in further detail in Section 5.2.1.2.
In summary, there is some evidence for an association between pregnancy and early life
exposure to SO2 and respiratory health effects after birth, although evidence is limited
and exposure windows are uncertain. Key studies are summarized in Table 5-44.
Table 5-45 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 days
Estrus cyclicity duration (F0 and F1),
Rat
litter size, offspring growth (body
weight)
ppm = parts per million; SO2 = sulfur dioxide.
Other Developmental Effects
Studies examining other developmental exposures are limited in number. A single recent
study has examined SO2 exposure with apnea and bradycardia in a vulnerable
subpopulation of infants in Atlanta, and found no association for either health outcome
(Peel et al.. 2011). In an older study from the animal toxicology literature, adult female
albino rats were exposed to either 0.057 ppm or 1.5 ppm SO2 by inhalation, 12 hours/day
for 72 days (Mamatsashvili. 1970b). Changes in offspring growth or body weight over
time were reported with 1.5 ppm exposure.
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5.4.2 Summary and Causal Determination
Overall the evidence is suggestive of, but not sufficient to infer, a causal relationship
between exposure to SO2 and reproductive and developmental outcomes. The 2008 ISA
for Sulfur Oxides 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 35 recent studies, examining the relationship between
exposure to SO2 and reproductive and developmental effects was evaluated using the
framework described in the ISA Preamble (U.S. EPA. 2015e). The key evidence as it
relates to the causal framework is summarized in Table 5-46.
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 (Lc et al..
2012; Zhao et al.. 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,
there are a number of uncertainties connected with the associations observed between
exposure to SO2 and birth outcomes.
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)
(Le et al.. 2012; Zhao et al.. 2011; Sagiv et al.. 2005; Liu et al.. 2003).
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 (Chapter 3). 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.
Again, 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
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context of the greater air pollution mixture, and of those that do, no clear trends for the
effects of adjustment emerge (Faiz et al.. 2013; Slama et al.. 2013; Le et al. 2012).
There is also little information on potential modes of action of SO2 on reproductive
outcomes at relevant exposure levels for this ISA I 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., preeclampsia, 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
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, even if the literature
was more extensive.
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 reproductive toxicity based on evidence from laboratory animal
studies and epidemiologic studies. However, 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.
Other U.S. regulatory agencies have addressed the reproductive and developmental
toxicology of sulfur dioxide. Although the body of evidence is relatively small, multiple
high-quality (Chapter 5 Annex) studies observe association between SO2 and birth
outcomes. The strongest evidence is for near-birth exposures and preterm birth. Preterm
birth is a marker for immediate fetal underdevelopment, and is linked to many later
health outcomes, including: infant mortality, infant rehospitalizations,
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neurodevelopmental problems, and growth issues. The associations with preterm birth
and near-birth SO2 exposures are coherent with the strongest evidence for SO2, which
indicates short-term effects. Other reproductive health outcomes also show positive
associations with SO2; however they may lack consistency in timing of exposure or in
definition of outcome, and as such present weaker evidence for causal association.
Although there is only a single toxicological study at relevant dose ranges of SO2, this
study offers supportive evidence for health outcomes of altered menstrual function with
prolonged estrus cycles in exposed rodents that return to normal cycling after a SO2
exposure is stopped (Mamatsasln ili. 1970a). Many uncertainties remain when evaluating
the evidence for these health endpoints; therefore, the evidence is suggestive of, but not
sufficient to infer, a causal relationship between exposure to SO2 and reproductive and
developmental outcomes.
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Table 5-46 Summary of evidence supporting suggestive of a causal relationship
between SO2 exposure and reproductive and developmental effects.
Rationale for Causal
Determination3
Key Evidence13
Key References'3
SO2 Concentrations
Associated with Effects0
Overall Reproductive and Developmental Effects—Suggestive of, but Not Sufficient to Infer, a Causal
Relationship
Evidence from multiple
epidemiologic studies of
preterm birth is generally
supportive but uncertainties
remain
Consistent positive
associations observed with
near-birth exposures to
SO2 and preterm birth after
adjustment for common
potential confounders.
Associations not evaluated
in copollutant models.
Liu et al. (2003) (Le et al.
(2012); Saqiv etal. (2005))
Section 5.4.1.3
Mean: 4.9 ppb
Mean: 5.8 ppb
Mean: 7.9 ppb
Limited and inconsistent
epidemiologic evidence for
other birth outcomes
Several studies show
positive associations with
fetal growth metrics,
although definitions vary
across studies, and timing
of exposure is inconsistent.
Section 5.4.1.3
Means: 4.9-5.8 ppb
Several high quality studies
show associations between
SO2 exposure and low birth
weight or change in birth
weight. Timing of exposure
is inconsistent across
studies. Only one study
uses 1-h max for exposure
determination.
Section 5.4.1.3
Means: 2.1-13.2 ppb
Limited and inconsistent
epidemiologic evidence for
associations with various
birth defects
Section 5.4.1.3
Reported means: 1.9-6
Limited number of studies
of SO2 and fetal death,
positive associations
observed across studies,
although timing of
exposure and outcome
definitions are inconsistent
Limited evidence for an
association with SO2 in
respiratory related infant
mortality
Section 5.4.1.3
Mean: 5.7 ppb
Mean: 5.8 ppb
Mean: 5.9 ppb
Mean: 3 ppb
Limited evidence for
positive associations
between prenatal/early life
exposures and childhood
respiratory outcomes
Section 5.4.1.4
Means: 2-4.3 ppb
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Table 5-46 (Continued): Summary of evidence supporting suggestive of a causal
relationship between SO2 exposure and reproductive and
developmental effects.
Rationale for Causal
Determination3 Key Evidence13
Limited evidence for key
events in proposed mode
of action
Lack of evidence from
epidemiologic studies to
support an association of
SO2 exposure with
detrimental effects on
fertility or pregnancy
Uncertainty regarding
potential confounding by
copollutants
Uncertainty regarding
exposure measurement
error
Uncertainty regarding
exposure timing for specific
outcomes
Altered menstrual function,
fetal growth, and birth
weight outcomes with
impaired postnatal growth
in in utero exposed pups
Limited adjustment for
copollutants, with no clear
directionality or trends for
effect estimate shifts after
adjustment
Associations of exposure to
SO2 at particular windows
during pregnancy are
inconsistent between
studies and across
outcomes.
Key References'3
Mamatsashvili (1970a)
(Faiz et al. (2013): Slama et
al. (2013): Le et al. (2012))
SO2 Concentrations
Associated with Effects0
57 or 1,427 ppb
A limited number of studies Section 5.4.1.1 Mean 8.4-59 ppb
on fertility and pregnancy
outcomes show no
associations with SO2.
Central site monitors Chapter 3
subject to some degree of
exposure error. Spatial and
temporal heterogeneity
may introduce exposure
error in long-term effects.
ISA = Integrated Science Assessment; ppb = parts per billion; S02 = sulfur dioxide.
aBased on aspects considered in judgments of causality and weight of evidence in causal framework in the ISA Preamble (U.S.
EPA. 2015e).
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).
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5.5 Mortality
5.5.1 Short-Term Mortality
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.
In the 2008 SOx ISA (U.S. EP A. 2008b). 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-hour average SO2
concentrations. These associations were primarily observed at mean 24-hour average 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. EP A. 2008b).
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 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
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SCh-mortality risk estimates, respectively (katsouvanni et al.. 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.
5.5.1.2 Evaluation of Short-Term Sulfur Dioxide Exposure and Mortality
Studies
Since the completion of the 2008 SOx ISA (U.S. EPA. 2008b). 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. 2008b). 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
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.. 2008)
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 (2013b)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. 2008b). 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 S02-related mortality, or address a limitation
or uncertainty in the S02-mortality relationship not represented in multicity studies. The
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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-17
(U.S. EPA. 2015u). Overall this section evaluates studies that examined the association
between short-term SO2 exposure and mortality, and addresses key limitations and
uncertainties in the SCh-mortality relationship that were evident at the completion of the
2008 SOx ISA (U.S. EP A. 2008b'). Specifically, this section evaluates whether there is
evidence of: confounding (i.e., copollutants and seasonal/temporal), effect modification
(i.e., sources of heterogeneity in risk estimates across cities or within a population), and
seasonal heterogeneity in SCh-mortality associations. Additionally, the section assesses
the SCh-mortality C-R relationship and related issues, such as the lag structure of
associations.
5.5.1.3 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. 2008b). Although only a small number of multicity studies have
been conducted since the completion of the 2008 SOx ISA, these studies, as well as a
meta-analysis of studies conducted in Asia (Atkinson et al.. 2012). build upon and
provide additional evidence for an association between short-term SO2 exposure and total
mortality (Figure 5-15; Table 5-48). Air quality characteristics and study specific details
for the studies evaluated in this section are provided in Table 5-47.
Table 5-47 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.
(20031®
72 U.S. cities
(NMMAPS)b
1987-
1994
Total
24-h avg
0.4-14.2
...
Burnett et al. 12 Canadian 1981- Total 24-h avg 0.9-9.6
(2004)a cities 1999 Cardiovascular
Respiratory
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Table 5-47 (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)
Moolaavkar et al.
(2013)
85 U.S. cities
(NMMAPS)f
1987-
2000
Total
24-h avg
—
—
Europe
Katsouvanni et al.
(1997)a
12 European
cities
(APHEA-1)
1980-
1992
Total
24-h avg
5.0-28.2°
90th:
17.2-111.8
Biaaeri et al. (2005)a
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)a
Netherlands
1986-
1994
Total
Cardiovascular
Respiratory
24-h avg
3.5-5.6
—
Beralind et al. (2009)
Five European
cities9
1992-
2002
Total
24-h avg
1.0-1 6h
—
Bellini et al. (2007)
15 Italian
cities
(MISA-2)
1996-
2002
Total
Cardiovascular
Respiratory
24-h avg
—
—
Asia
Kan et al. (2010b):
Wona et al. (2008):
Wona et al. (2010)
Four Asian
cities (PAPA)
1996-
2004'
Total
Cardiovascular
Respiratory
24-h avg
5.0-17.1
75th: 6.0-21.5
Max: 23.4-71.7
Chen et al. (2012b)
17 Chinese
cities
(CAPES)
1996-
2010J
Total
Cardiovascular
Respiratory
24-h avg
6.1-38.2
75th: 6.5-56.1
Max:
25.2-298.5
Chen et al. (2013)
Eight Chinese
cities
1996-
200&
Stroke
24-h avg
6.1-32.1
...
Mena et al. (2013)
Four Chinese
cities
1996-
2008'
COPD
24-h avg
6.8-19.1
...
Meta-analyses
Stieb et al. (2003)a
Meta-analysis
1958-
1999d
Total
24-h avg
0.7-75.2
...
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Table 5-47 (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)
HEI (2004)a Meta-analysis 1980- Total 24-h avg ~10->200
(South Korea, 2003e
China,
Taiwan, India,
Singapore,
Thailand,
Japan)
Atkinson et al. Meta-analysis 1980- Total 24-h avg
(2012) (Asia) 2007k Cardiovascular
Respiratory
COPD
Shah et al. (2015) Meta-analysis 1948-Jan Stroke NR 6.2C Max: 30.2
2014
Yang et al. (2014b) Meta-analysis 1996- Stroke
(Asia, Europe, 2013
and North
America)
24-h avg Asia: 11.4° 75th: Asia: 18.6
Europe: 5.2C Europe: 2.3
North America: North America:
4.2C 7.6
APHEA = Air Pollution and Health: A European Approach study; avg = average; 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; ppb = parts per billion; SOx = Sulfur Oxides.
aMulticity studies and meta-analyses evaluated in the 2008 SOx ISA.
bOf the 90 cities included in the NMMAPS analysis only 72 had S02 data.
°Median concentration.
dThe mortality time-series of studies included in the meta-analysis spanned these years.
eStudies included within this meta-analysis were published during this time period.
'Of the 108 cities included in the analyses using NMMAPS data only 85 had S02 data.
gS02 data was not available for Barcelona, therefore the S02 results only encompass four cities.
hMedian 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.
kYr defined represent the yr in which studies were published that were included in the meta-analysis.
'Study period varied from 2 to 7 yr. Hong Kong was the only city that had air quality data prior to 2001.
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Study
Location
Age
Lag
Dominici etal. (2003)
72 U.S. cities (NMMAPS)
All
1
Burnett et al. (2004)
12 Canadian cities
All
0-2
Katsouyanni etal. (1997)
12 European cities (APHEA1)
All
Variable (0-3 days)
Biggeri et al. (2005)
8 Italian cities (MISA-1)
All
0-1
Hoek et al. (2003)
Netherlands
All
0-6
Stieb et al. (2002)
Meta-analysis (Worldwide)
All
Variable
HEI (2004)a
Meta-analysis (Asia)
All
Variable
Moolgavkar et al. (2013)
85 U.S. cities (NMMAPS)
All
1
Berglind et al. (2009)b
5 European cities
35-74
0-1
Bellini et al. (2007)
15 Italian cities (MISA-2)
All
0-1
Chen etal. (2012)
17 Chinese cities (CAPES)
All
0-1
Kan et al. (2010)c
4 Asian cities (PAPA)
All
0-1
Atkinson et al. (2012)
Meta-analysis (Asia)
All
Variable
—•-
-5.0
0.0
5.0
10.0
% Increase
Note: a = Meta-analysis of Asian cities: South Korea, China, Hong Kong, Taipei, India, Singapore, Thailand, Japan; b = Study was of Ml survivors therefore only included individuals
35+; c = Kan et al. (201 Ob) reported results that were also found in (Wong et al.. 2010: Wong et al. (2008)').
Figure 5-15 Percent increase in total mortality from multi-city studies and meta-analyses evaluated in the
2008 SOx Integrated Science Assessment (black circles) and recently published multi-city studies
(red circles) for a 10-ppb increase in 24-hour average SO2 concentrations.
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Table 5-48 Corresponding excess risk estimates for Figure 5-15.
Study
Location
Age
Lag
Averaging Time
% Increase
(95% CI)
Studies Evaluated in 2008 ISA for Sulfur Oxides
Dominici et al.
(2003)
72 U.S. cities
(NMMAPS)
All
1
24-h avg
0.60
(0.26, 0.95)
Burnett et al.
(2004)
12 Canadian
cities
All
0-2
24-h avg
0.74
(0.29, 1.19)
Katsouvanni et al.
(1997)
12 European
cities (APHEA-1)
All
Variable
(0-3 days)
24-h avg
1.14
(0.88, 1.39)
Biaaeri et al.
(2005)
Eight Italian cities
(MISA-1)
All
0-1
24-h avg
4.14
(1.05, 7.33)
Hoek (2003)
Netherlands
All
0-6
24-h avg
1.78
(0.86, 2.70)
Stieb et al. (2002)
Meta-analysis
All
Variable
24-h avg
0.95
(0.64, 1.27)
HEI (2004)
Meta-analysis
All
Variable
24-h avg
1.49
(0.86, 2.13)
Recent Multicity Studies
Moolaavkar et al.
(2013)
85 U.S. cities
All
1
24-h avg
1.5
(1.1, 1.7)
Beralind et al.
(2009)
Five European
cities
35-74
0-1
24-h avg
1.2
(-25.7, 37.7)
Bellini et al.
(2007)
15 Italian cities
(MISA-2)
All
0-1
24-h avg
1.6
(-1.0, 4.2)
Chen et al.
(2012b)
17 Chinese cities
(CAPES)
All
0-1
24-h avg
2.0
(1.2, 2.7)
Kan et al.
(2010b)a
Four Asian cities
(PAPA)
All
0-1
24-h avg
2.6
(2.0, 3.3)
Atkinson et al.
(2012)
Meta-analysis
(Asia)
All
Variable
24-h avg
1.8
(1.1, 2.5)
APHEA = Air Pollution and Health: A European Approach study; avg = average; CAPES = China Air Pollution and Health Effects
Study; CI = confidence interval; 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; PAPA = Public Health and Air Pollution in
Asia.
aThese results were also presented in Wong et al. (2008) and Wong et al. (2010).
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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 5-16). However, a study conducted in the Netherlands by Hock (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 5-16 and corresponding Table 5-49 lend
additional support to the body of evidence indicating SCh-induced respiratory effects
presented in the 2008 SOx ISA, as well as Section 5^2 of this ISA. Unlike the results
reported in Hock (2003). recent studies do not provide evidence indicating associations
larger in magnitude for SCh-related cardiovascular mortality compared to other mortality
outcomes.
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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)
Bellini et al. (2007)
Atkinson et al. (2012)
Chen et al. (2012)
Chen et al. (2013)
Meng et al. (2013)
Kan et al. (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
Variable
0-1
0-1
-6 -4 -2 0
2 4 6 8 10 12 14 16 18 20
% Increase
Note: Total mortality = circle; cardiovascular-related mortality = triangle; and respiratory-related mortality = diamond, a = Only five of the seven cities included in Katsouyanni et al.
(1997) 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 (CAPES)
cities that had stroke data; c = Meng et al. (2013) was not part of CAPES, but the four cities included had data for the same years as the CAPES study; d = These results were also
presented in Wong et al. (2008) and Wong et al. (2010).
Figure 5-16 Percent increase in total, cardiovascular, and respiratory mortality from multi-city studies
evaluated in the 2008 SOx Integrated Science Assessment (black circles) and recently published
multi-city studies (red circles) for a 10-ppb increase in 24-hour average SO2 concentrations.
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Table 5-49 Corresponding excess risk estimates for Figure 5-16.
Study
Location
Age
Lag
Averaging
Time
Mortality
% Increase
(95% CI)
Studies Evaluated in 2008 SOx ISA
Katsouvanni et al.
(1997)
Seven Western
European cities
(APHEA-1)
All
Variable
(0-3 days)
24-h avg
Total
2.0 (1.2, 2.8)
Zmirou et al. (1998)
Five Western
European cities
(APHEA-1)3
All
Cardiovascular
Respiratory
2.3 (0.9, 3.7)
2.8 (1.7, 4.0)
Katsouvanni et al.
(1997)
Five Central
European cities
(APHEA-1)
All
Variable
(0-3 days)
24-h avg
Total
0.5 (-0.4, 1.4)
Zmirou et al. (1998)
Five Central
European cities
(APHEA-1)
All
Cardiovascular
Respiratory
0.6 (0.0, 1.1)
0.6 (-1.1, 2.3)
Biaaeri et al. (2005)
Eight Italian cities
(MISA-1)
All
0-1
24-h avg
Total
Cardiovascular
Respiratory
4.1 (1.1, 7.3)
4.9 (0.4, 9.7)
7.4 (-3.6, 19.6)
Hoek (2003)
Netherlands
All
0-6
24-h avg
Total
Cardiovascular
Heart failure
Thrombosis-
related
COPD
Pneumonia
1.8 (0.9, 2.7)
2.7 (1.3, 4.1)
7.1 (2.6, 11.7)
9.6 (3.1, 16.6)
3.6 (-0.3, 7.7)
6.6 (1.2, 12.2)
Recent Multicity Studies
Bellini et al. (2007)
15 Italian cities
(MISA-2)
All
0-1
24-h avg
Total
Cardiovascular
Respiratory
1.6 (-1.0, 4.2)
2.9 (-1.6, 8.4)
4.1 (-5.7, 14.7)
Atkinson et al. (2012)
Meta-analysis
(Asia)
All
Variable
24-h avg
Total
Cardiovascular
Respiratory
COPD
1.8 (1.1, 2.5)
2.5 (0.8, 4.2)
2.6 (1.6, 3.7)
4.6 (0.3, 9.0)
Chen et al. (2012b)
17 Chinese cities
(CAPES)
17 Chinese cities
(CAPES)
All
0-1
24-h avg
Total
Cardiovascular
Stroke
Respiratory
COPD
2.0 (1.2, 2.7)
2.2 (1.2, 3.1)
2.3 (1.4, 3.2)
3.3 (2.1, 4.6)
3.7 (2.4, 4.9)
Chen et al. (2012b)
Eight Chinese
cities (CAPES)b
17 Chinese cities
(CAPES)
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Table 5-49 (Continued): Corresponding excess risk estimates for Figure 5-16.
Study
Location
Age Lag
Averaging
Time
Mortality
% Increase
(95% CI)
Mena et al. (2013)
Four Chinese
cities0
Kan et al. (2010b)d
Four Asian cities
(PAPA)
All 0-1
24-h avg
Total
Cardiovascular
Respiratory
2.6 (2.0, 3.3)
2.9 (1.9, 3.9)
3.9 (2.2, 5.5)
APHEA-1 = Air Pollution and Health: A European Approach study; avg = average; CAPES = China Air Pollution and Health Effects
Study; CI = confidence interval; COPD = chronic obstructive pulmonary disease; ISA = Integrated Science Assessment;
MISA = Meta-analysis of the Italian studies on short-term effects of air pollution; PAPA = Public Health and Air Pollution in Asia.
aOnly five of the seven cities included in Katsouvanni et al. (1997) had cause-specific mortality data and were included in the
analysis.
bChen et al. (2012b) examined stroke only in the CAPES cities that had stroke data.
cMeng et al. (2013) was not considered an analysis based out of CAPES, but the four cities included had data for the same yr as
was included in CAPES.
dThese results were also presented in Wong et al. (2008) and Wong et al. (2010).
5.5.1.4 Potential Confounding of the Sulfur Dioxide-Mortality Relationship
A limitation of the studies evaluated in the 2008 SOx ISA, was the relatively limited
analyses of the potential confounding effects of copollutants on the SCh-mortality
relationship (U.S. EPA. 2008b). 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.
Modeling Approaches to Control for Copollutant Confounding
In the 2008 SOx ISA (U.S. EPA. 2008b). 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 study (APHEA-1) study, Hock (2003) in the Netherlands, and Burnett et al.
(2004) in 12 Canadian cities. Copollutant models in these studies focused on the effect of
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PMio, BS or NO2 on the SCh-mortality relationship. The SO2 mortality risk estimate was
found to either increase (Hoek. 2003) or slightly attenuate (Dominici et al.. 2003;
Katsouvanni etal. 1997) in models with BS or PMm; 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.
2008b). the evaluation of copollutant confounding on the S02-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-hour
average 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
SCh-mortality risk estimates was not conducted. The results of Moolgavkar et al. (2013)
provide additional support for an S02-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
PM10, on the S02-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 and highly correlated with
PM10 (r = 0.49) and NO2 (r = 0.65), respectively. The results from copollutant models
(Table 5-50) indicate that SO2 mortality associations within these cities may be
confounded by PM10 and NO2. Although SO2 risk estimates remained positive, they were
attenuated by approximately 39-54% in models with PM10 and 65-79% in models with
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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
found a similar reduction in SO2 risk estimates in models with PM10 and NO2.
Table 5-50 Percent increase in total, cardiovascular, and respiratory mortality
for a 10-ppb increase in 24-hour average sulfur dioxide
concentrations at lag 0-1 in single and copollutant models.
Copollutant
Total Mortality
% Increase (95% CI)
Cardiovascular Mortality
% Increase (95% CI)
Respiratory Mortality
% Increase (95% CI)
S02
—
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; PM = particulate matter.
Source: Adapted from Chen et al. (2012b).
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 5-17).
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, SO2 mortality risk estimates were attenuated, and in many
cases null in copollutant models with NO2. However, only in Shanghai and Wuhan was
SO2 found to be highly correlated with NO2 (r = 0.64 and 0.76, respectively). Similarly,
SO2 was also found to be highly correlated with PM10 in Shanghai and Wuhan, 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.
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Total Mortality
BK
HK SH
ji i|i
Cardiovascular Mortality
6 -
4 .
2
0
-2 -
-4 .
-6
SC1C2C3 SC1C2C3 SC1C2C3 SC1C2C3
BK
HK
SH
WH
Respiratory Mortality
BK
HK SH
1t|T ijli
WH
S C1C2C3 S C1C2C3 S C1C2C3 S C1C2C3
S C1C2C3 S C1C2C3 SC1C2C3 S C1C2C3
BK = Bangkok; HK = Hong Kong; SH = Shanghai; WH = Wuhan.
S = single-pollutant model; C1 = sulfur dioxide + nitrogen dioxide; C2 = sulfur dioxide + PM10; C3 = sulfur dioxide + ozone.
Figure adapted from Kan et al. (2010b).
Figure 5-17 Percent increase in total, cardiovascular, and respiratory mortality associated with a 10 |jg/m3
(3.62 ppb) increase in 24-hour average SO2 concentrations, lag 0-1, in single and copollutants
models in Public Health and Air Pollution in Asia cities.
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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 PMio, 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. It is
important to note that the aforementioned studies rely on central site monitors for
estimating exposure to SO2. SO2 is more spatially variable than other pollutants as
reflected in the generally low to moderate spatial correlations across urban geographical
scales (Section 3.3.3.2); therefore, the attenuation in SO2 associations may be a reflection
of the different degree of exposure error across pollutants. This is further supported by an
analysis of correlations between NAAQS pollutants at collocated monitors in the U.S.,
which demonstrated that SO2 is low to moderately correlated with other pollutants
(Section 3.3.4.1). 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
Weather
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 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
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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.
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 5-18).
2.0 ,
£•
® 1.5 -
o
E
¦S 1.0 -
S
ra
D
b 0.5 -
4
6
8
10
4
6
8
10
4
6
8
10
Total mortality Cardiovascular mortality Respiratory mortality
Source: (Chen et al.. 2012b).
Figure 5-18 Percent increase in daily mortality associated with a 10 |jg/m3
(3.62 ppb) increase in 24-hour average SO2 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,
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or 12 df per year, the authors reported relatively similar SCh-mortality risk estimates
across cities. However, as depicted in Figure 5-18. and in some cities in Figure 5-19.
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.
i { M 1
1 1
DF per year
Source: (Kan et al.. 20106).
Figure 5-19 Percent increase in total mortality associated with a 10 |jg/m3
(3.62 ppb) increase in 24-hour average SO2 concentrations at lag
0-1 in Public Health and Air Pollution in Asia cities, using
different degrees of freedom/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-hour average 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 have 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.
5.5.1.5 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. 2008b). 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. (2008))! conducted
extensive analyses of potential effect measure modifiers of the S02-mortality relationship
as detailed in Chapter 6. These studies provided limited evidence for potential differences
in the risk of S02-related mortality by lifestage, sex, and socioeconomic status (SES).
Season
A limited number of studies have examined whether there is evidence of seasonal
differences 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.
Since the completion of the 2008 SOx ISA, only a few recent studies have examined
whether there are seasonal differences in S02-mortality associations and these studies
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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 S02-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-hour average SO2
concentrations. Overall, the limited number of studies that conducted seasonal analyses
reported initial evidence indicating larger SCh-mortality associations during the summer
season.
5.5.1.6 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
S02-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 5-20. the authors found
evidence of immediate SO2 effects on mortality that slowly declined over time with the
multiday lag of 0-1 days exhibiting the largest risk estimate across mortality outcomes.
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2.0 -i
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4 -
BK
HK
SH
WH
Fixed-
effect
Combined
Random-
effect
Combined
3 -
2 -
1 -
0 --
-1 -
0 0-1 0-4
0 0-1 0-4
0 0-1 0-4
0 0-1 0-4
0 0-1 0-4
0 0-1 0-4
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-hour average SO2 concentrations for
different lag structures in individual Public Health and Air
Pollution in Asia cities and in combined four city analyses.
BK = Bangkok; HK = Hong Kong; SH = Shanghai; WH = Wuhan.
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-hour average 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 approximately Lag Day 10, which is consistent with the results from Chen
et al. (2012b) (Figure 5-20). 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.
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
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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. 2008b). 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 SCh-mortality C-R
relationship and whether a threshold exists. These studies have conducted analyses
focusing on the combined C-R relationship across multiple cities, or an evaluation of
single-city C-R relationships in the context of a multicity study.
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 5-22. 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
0.04
oc
cc
ra
o
0.02
-0.02
0 10 20 30 40 50 BO
Lag-1 S02
Source: Moolaavkar et al. (2013).
Figure 5-22 Flexible ambient concentration-response relationship between
short-term SO2 (ppb) exposure (24-hour average concentrations)
and total mortality at lag 1. Note: Pointwise means and 95%
confidence intervals adjusted for size of the bootstrap sample
(d = 4).
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 SCh-mortality relationship was assessed by applying a natural spline
smoother with 3 df to 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 5-23). 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
-0.1-
LU
10 20 30 40 50
S02concentration (iig/'m3)
Shanghai
II 1
50 100 150
S02 concentration (^g/m3)
Source: (Wong et a I.. 2008).
0.3
0.2
011J
0.0
-o.i
Hong Kong
-0.1
D 20 40 60 80 100
S02 concentration (ng/nV3)
W.han
1
50 100 150
S02 concentration lug/nt3)
Figure 5-23 Concentration-response curves for total mortality (degrees of
freedom = 3) for SO2 in each of the four Public Health and Air
Pollution in Asia cities. Note: x-axis is the average of lag 0-1
24-hour average SO2 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 inter-quartile range of SO2 concentrations within
each city, while the thin vertical bar represents the World Health
Organization guideline of 20 pg/m3 for a 24-hour averaging time of
SO2.
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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 Sections 5.2.1.7 (respiratory mortality) and
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 (Figures 5-9 and 5-14).
Collectively across studies, specifically within the range of SO2 concentrations where the
data density is highest, evidence supports a linear, no threshold relationship between
short-term SO2 concentrations and mortality. Although, some differences in the shape of
the curve were observed on a city-to-city basis, these results are consistent with what has
been reported for other criteria air pollutants.
5.5.1.7 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. 2008b). 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
copollutant models with NO2 and PM10. This section describes the evaluation of evidence
for total mortality, with respect to the causal determination for short-term exposures to
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SO2 using the framework described in Table II of the Preamble (U.S. EPA. 20156). The
key evidence, as it relates to the causal framework, is summarized in Table 5-51.
Collectively, the evidence from recent multicity studies of short-term SO2 exposures and
mortality consistently demonstrate positive SCh-mortality associations in single-pollutant
models. Although SCh-mortality associations remain positive in copollutant models with
PM10 and NChthey are often attenuated to a large degree, questioning the independent
effect of SO2 on mortality. However, SO2 is more spatially variable than other pollutants
as reflected in the generally low to moderate spatial correlations across urban
geographical scales (Section 3.3.3.2); therefore, the attenuation in SO2 associations in
copollutant models may be a reflection of the different degree of exposure error across
pollutants (Section 3.3.5.1). 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 S02-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
SCh-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 S02-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 SO2 -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. However, 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).
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Those studies that examined the lag structure of associations for the SCh-mortality
relationship generally observed that there is evidence of an immediate effect (i.e., lag 0 to
1 day) 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 studies
of cause-specific mortality as detailed in Sections 5.2.1.7 (respiratory mortality) and
5.3.1.9 (cardiovascular).
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. 2012). For
cardiovascular effects the evidence is suggestive of, but not sufficient 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 generally supportive, but not entirely consistent
evidence for ischemic events such as triggering a myocardial infarction. Additionally,
there is inconclusive epidemiologic and experimental evidence for other cardiovascular
endpoints. 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 provides limited coherence and biological plausibility for
S02-related 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 peak (generally 5-10-minute) 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 central site monitors to assign exposure. Therefore, the
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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 3.3.3.2). 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 central site monitors in capturing exposure to SO2,
and the uncertainty in the biological mechanism that could lead to SC>2-induced mortality
(Section 3.3.5.1V 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.
Table 5-51 Summary of evidence, which is suggestive of, but not sufficient to
infer, a causal relationship between short-term SO2 exposure and
total mortality.
SO2
Concentrations
Rationale for Causal
Associated with
Determination3
Key Evidence13
Key References'3
Effects0
Consistent epidemiologic
Increases in mortality in multicity studies
Section 5.5.1.3
Mean 24-h avg:
evidence from multiple,
conducted in the U.S., Canada, Europe, and
Fiaure 5-15
U.S., Canada,
high quality studies at
Asia
South America,
relevant SO2
Europe:
concentrations
0.4-28.2e ppb
Asia:
0.7->200 ppb
Table 5-47
Uncertainty regarding
The magnitude of SO2 associations remained
Section 5.5.1.4
potential confounding by
positive, but were reduced in copollutant
Section 3.3.4.1
copollutants
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.
Uncertainty regarding
U.S. Studies that examine the association
Section 3.3.3.2
exposure measurement
between short-term SO2 exposures and
Section 3.3.5.1
error
mortality rely on central site monitors and
SO2 generally has low to moderate spatial
correlations across urban geographical
scales.
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Table 5-51 (Continued): Summary of evidence, which is suggestive of, but not
sufficient to infer, a causal relationship between
short-term SO2 exposure and total mortality.
Rationale for Causal
Determination3
Key Evidence13
Key References'3
SO2
Concentrations
Associated with
Effects0
Uncertainty due to limited Generally supportive, but not entirely
coherence and biological consistent epidemiologic evidence for
plausibility with
cardiovascular and
respiratory morbidity
evidence
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 SC>2-related cardiovascular mortality,
which comprises -35% of total mortalityd.
Section 5.3.1.11
Table 5-41
Consistent evidence of asthma exacerbations Section 5.2.1.8
from controlled human exposure studies Table 5-27
demonstrating respiratory effects
(i.e. respiratory symptoms and decreased
lung function) in response to peak SO2
exposures, generally 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 SCb-related respiratory
mortality, which comprises -8% of total
mortalityd.
avg = average; ED = emergency department; NAAQS = National Ambient Air Quality Standards; N02 = nitrogen dioxide;
PM = particulate matter; ppb = parts per billion; S02 = sulfur dioxide.
aBased on aspects considered in judgments of causality and weight of evidence in causal framework in Tables I and II of the
Preamble (U.S. EPA. 2015e).
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).
5.5.2 Long-Term Mortality
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
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relationship" (U.S. EPA. 2008b). 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. A brief
summary of the studies included in this section can be found in Table 5-52.
Table 5-52 Summary of studies of long-term exposure and mortality.
Correlation Selected Effect
Location Mean SO2 with Other Estimates (95%
Study (years) (ppb) Exposure Assessment Pollutants Cl)a
Hart et al. (2011) United States 4.8 Annual average All cause:
(302- exposures based on 1.09 (1.03,1.15)
1985-2000- residential address from Respiratory
follow-up- model using spatial 1 10 (0 89 1 35)
1985-2000) smoothing and GIS-based
covariates; current „
calendar year and °'93 (0'71 ¦ 1 22)
long-term average from Lung cancer:
1985-2000 1.11 (0.98,1.27)
Krewski et al. (2000) United States HSC:
HSC:
(SO2:
1977-1985;
follow-up:
1974-1991)
ACS:
(SO2: 1980;
follow-up:
1982-1989)
1.6-24.0
ACS: 9.3
HSC: mean levels from
central site monitors
ACS: City-specific annual
mean
HSC:
PM2.5: 0.85
SO4: 0.85
NO2: 0.84
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)
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Table 5-52 (Continued): Summary of studies of long-term exposure and mortality.
Correlation
Selected Effect
Location
Mean SO2
with Other
Estimates (95%
Study
(years)
(ppb) Exposure Assessment
Pollutants
Cl)a
Pope et al. (2002)b United States 6.7-9.7
(SO2:
1982-1998;
follow-up:
1982-1998)
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)
All cause:
1.03 (1.02, 1.05)
Lipfert et al. (2009)
United States 4.3
(SO2: 1999;
follow-up:
1976-2001)
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
Subject-
weighted:
EC: 0.68
NOx: 0.65
S042" : 0.79
All cause:
1.02 (1.01, 1.03)
Krewski et al. (2009)
United States 9.6
(SO2: 1980;
follow-up:
1982-2000)
City-specific annual mean
All cause:
1.02 (1.02, 1.03)
Lung cancer:
1.00 (0.98, 1.02)
Lipfert et al. (2006a)1
United States
(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)b
United States
5.6
ZIP-code level monthly
Mean
All cause:
(SO2:
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)
S04: 0.68
1.00 (0.88, 1.14)
When
Lung cancer:
exceeding
Men:
100 ppb (O3)
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 etal. (2008b)b
Netherlands 5.2
(SO2: SD: 1.9
1976-1985,
1987-1996;
follow-up:
1987-1996)
IDW to regional
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)
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Table 5-52 (Continued): Summary of studies of long-term exposure and mortality.
Correlation
Selected Effect
Location
Mean SO2
with Other
Estimates (95%
Study
(years)
(ppb) Exposure Assessment
Pollutants
Cl)a
Nafstad et al. (2004)b Norway
(SO2:
1974-1995;
follow-up:
1972-1998)
3.6 Model results (per square
kilometer) for some
yr/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)b 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)
Carey et al. (2013)
England
(SO2: 2002;
follow-up:
2003-2007)
1.5 Annual mean for 1-km PM10: 0.45
SD: 0.8 grid cells from air NO2: 0.37
IQR: 0.8 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)
Ancona 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)
Cao et al. (2011)
China
(SO2:
1991-2000;
follow-up:
1991-2000)
27.7 Annual average by linking
fixed site monitoring data
with residential ZIP code
All cause:
1.02 (1.02, 1.03)
CVD:
1.02 (1.00, 1.03)
Respiratory:
1.04 (1.02, 1.06)
Lung cancer:
1.06 (1.03, 1.08)
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Table 5-52 (Continued): Summary of studies of long-term exposure and mortality.
Correlation
Selected Effect
Location
Mean SO2
with Other
Estimates (95%
Study
(years)
(PPb)
Exposure Assessment
Pollutants
Cl)a
Dona et al. (2012)
China
23.9
1-yr average of from five
Respiratory:
(SO2:
SD: 5.7
fixed site monitors
1.05 (0.96, 1.16)
1998-2009;
follow-up:
1998-2009)
Zhana et al. (2011)
Shenyang,
23.9
1-yr average and yearly
All cause:
China
deviations in each of five
0.93 (0.90, 0.99)
(SO2:
monitoring stations
1998-2009;
calculated from 24-h avg
follow-up:
1998-2009)
Katanoda 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)1
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)
Bennett 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)
Wang et al. (2009)
Brisbane,
Australia
(SO2:
1996-2004;
follow-up:
1996-2004)
5.4 1-h max from
13 monitoring stations
aggregated to annual
means used with IDW
Cardiopulmonary:
1.26 (1.03, 1.54)
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Table 5-52 (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
Wanqetal. (2014a)
China
(SO2:
2004-2010;
life table:
2010)
46.31 Annual average across
monitoring stations in
85 city regions
Life expectancy: 10-
|jg/m3 increase in
SO2 correlated with
0.28-0.47 yr
decrease in life
expectancy
ACS = American Cancer Society; AER = Atmospheric and Environmental Research; BS = black smoke; CI = condifence interval;
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; ISA = Integrated Science Assessment; NO = nitric oxide; N02 = nitrogen dioxide; NOx = the
sum of NO and N02; 03 = ozone; OC = organic carbon; PM = particulate matter; SD = standard deviation; S02 = sulfur dioxide;
S04 = sulfate; SOx = oxides of sulfur; SPM = suspended particulate matter; TSP = total suspended solids.
aEffect estimates are standardized per 5-ppb increase in S02 concentrations.
"Included in 2008 SOx ISA.
°Effect estimate per 2.88 |jg/m3 increase in SOxconcentration (as reported by author in original publication).
5.5.2.1 United States 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; Lipfcrt 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) utilizes 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
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).
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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 reanalyses 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
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.
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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%
were current smokers (81% of the cohort had been smokers at one time). PM25, 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 EPA's Speciation Trends Network
(STN) data, which collected chemical components of PM2 5. They analyzed the STN data
for year 2002, again using county-level averages. PM2 5 and gaseous pollutants data for
1999 through 2001 were also analyzed. As in the previous 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 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, N02,NOx, EC)
in these analyses, makes it difficult to discern whether these associations are truly
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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
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
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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.
Carey 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
dispersion model for SO2. Ancona et al. (2015) used a Lagrangian particle dispersion
model (see Section 3.2.2.1 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
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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 utilized 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
Three recent cohort studies have been conducted in China to examine the association
between long-term exposure to SO2 and mortality (Dong et al.. 2012; Cao etal.. 2011;
Zhang etal.. 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 5-52). Cao et al. (2011) observed
generally modest positive associations with all-cause, respiratory and lung cancer
mortality. 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
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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 United States 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
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 populations,
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.
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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 cardio-respiratory 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 of Evidence on the Effect of Long-Term Exposure on
Mortality
Figure 5-24 and Table 5-53 present 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 United States, 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 observed by C'arev 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.
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1 Figure 5-25 and Table 5-54 present the cause-specific mortality effect estimates
2 associated with long-term exposure to SO2. The overall range of effects spans 0.93 to
3 4.40 per 5-ppb increase in the annual (or longer period) average SO2 concentration.
4 Generally, there was a trend toward more positive associations for respiratory and lung
5 cancer mortality compared to cardiopulmonary, cardiovascular, and other causes of
6 death. Specifically, recent studies examining respiratory mortality provide some evidence
7 that this cause of death may be more consistently associated with long-term exposure to
8 SO2 than other causes of death. This is consistent with both the short- and long-term
9 exposure to SO2 that are associated with respiratory effects.
Study
Location
Mean
(PPb)
Notes
Hart etal. 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 etal. 2005
France
3.0-8.2
Carey et al. 2013
England
1.5
Elliott et al. 2007
Great Britain
12.2-41.4
Cao et al. 2011
Eastern China
27.7
Zhang et al. 2011
Shenyang, China
23.9
0.8 0.9 1 1.1 1.2
Relative Risk (95% CI)
1.3
1.4
CI = confidence interval; HSC = Harvard Six Cities Study; ACS = American Cancer Society Study.
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Figure 5-24 Relative risks (95% CI) of sulfur dioxide-associated total mortality.
Effect estimates are standardized per 5-ppb increase in sulfur
dioxide concentrations.
Table 5-53 Corresponding risk estimates for Figure 5-24.
Study
Location
Notes
Relative Risk3 (95% CI)
Hartetal. (2011)
United States
Men
1.09 (1.03, 1.15)
Krewski et al. (2000)
United States
HSC
1.05 (1.02, 1.09)
Krewski et al. (2000)
United States
ACS
1.06 (1.05, 1.07)
Pope et al. (2002)
United States
ACS
1.03 (1.02, 1.05)
Krewski et al. (2009)
United States
ACS
1.02 (1.02, 1.03)
Lipfert et al. (2006a)
United States
Men
0.99 (0.97, 1.01)
LiDfert et al. (2009)
United States
Men
1.02 (1.01, 1.03)
Abbev et al. (1999)
United States
Men
1.07 (0.92, 1.25)
Abbev et al. (1999)
United States
Women
1.00 (0.88, 1.14)
Nafstad et al. (2004)
Norway
Men
0.97 (0.95, 1.01)
Beelen et al. (2008b)
Netherlands
Case-cohort
0.94 (0.80, 1.10)
Filleul et al. (2005)
France
1.01 (0.99, 1.04)
Carev et al. (2013)
England
1.26 (1.19, 1.34)
Elliott et al. (2007)
Great Britain
1.02 (1.02, 1.02)
Cao et al. (2011)
Eastern China
1.02 (1.02, 1.03)
Zhana et al. (2011)
Shenyang, China
0.93 (0.90, 0.99)
ACS = American Cancer Society; CI = confidence interval; HSC = Harvard Six Cities.
aEffect estimates are standardized to a 5-ppb increase in S02 concentration.
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Study
Hart etal. 2011
Nafstad et al. 2004
Beelen et al. 2008
Elliott etal. 2007
Cao et al. 2011
Carey et al. 2013
Dong et al. 2012
Katanoda et al. 2011
Hart etal. 2011
Katanoda et al. 2011
Katanoda et al. 2011
Hart etal. 2011
Krewski et al. 2000
Krewski et al. 2009
Abbey et al. 1999
Abbey et al. 1999
Nafstad et al. 2004
Beelen et al. 2008
Filleul etal. 2005
Carey et al. 2013
Elliott etal. 2007
Cao et al. 2011
Katanoda et al. 2011
Krewski et al. 2000
Krewski et al. 2009
Abbey et al. 1999
Abbey et al. 1999
Filleul etal. 2005
Elliott etal. 2007
Wang et al. 2009
Hart etal. 2011
Beelen et al. 2008
Elliott etal. 2007
Cao et al. 2011
Zhang et al. 2011
Hart etal. 2011
Krewski et al. 2009
Nafstad et al. 2004
Carey et al. 2013
Nafstad et al. 2004
Zhang et al. 2011
Beelen et al. 2008
Elliott etal. 2007
Krewski et al. 2009
Location
USA
Norway
The Netherlands
Great Britain
Eastern China
England
China
Japan
USA
Japan
Japan
USA
USA
USA
USA
USA
Norway
The Netherlands
France
England
Great Britain
Eastern China
Japan
USA
USA
USA
USA
France
Great Britain
Mean
(ppb)
4.8
3.6
5.2
12.2-41.4
27.7
1.5
23.9
2.4-19.0
4.8
2.4-19.0
2.4-19.0
4.8
9.6
5.6
5.6
3.6
5.2
1.5
12.2-41.4
27.7
2.4-19.0
9.6
5.6
5.6
Brisbane, Australia 5.4
USA
The Netherlands
Great Britain
Eastern China
Shenyang, China
USA
USA
Norway
England
Norway
Shenyang, China
The Netherlands
Great Britain
USA
Notes
Men
Men
COPD - Men-
COPD
Pneumonia
Men
HSC
ACS
Men
Women
Men
12.2-41.4
4.8
5.2
12.2-41.4
27.7
23.9
4.8
9.6
3.6
1.5
3.6
23.9
5.2
12.2-41.4
9.6
HSC
ACS
Men
Women
Men
IHD - Men
IHD
IHD-Men —i
Circulatory
Cerebrovascular -Men-
Cerebrovascular —•
Respiratory
Lung Cancer
2.52 (1.34, 4.77)
4.40 2.34,8.33
Ca rdiopulmonary
Cardiovascular
Other
0.6 0.8 1 1.2 1.4
Relative Risk (95% CI)
1.6
1.8
CI = confidence interval; COPD = chronic obstructive pulmonary disease; HSC = Harvard Six Cities Study; ACS = American Cancer
Society Study; IHD = ischemic heart disease
Figure 5-25 Relative risks (95% CI) of sulfur dioxide-associated cause-specific
mortality. Effect estimates are standardized per 5-ppb increase in
sulfur dioxide concentrations.
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Table 5-54 Corresponding risk estimates for Figure 5-25.
Study
Location
Notes
Relative Risk3
(95% CI)
Respiratory
Hartetal. (2011)
United States
Men
1.10
0.89, 1.35)
Nafstad et al. (2004)
Norway
Men
1.04
0.91, 1.19)
Beelen et al. (2008b)
Netherlands
0.92
0.64, 1.31)
Elliott et al. (2007)
Great Britain
1.06
1.06, 1.07)
Cao et al. (2011)
Eastern China
1.04
1.02, 1.06)
Carev et al. (2013)
England
1.67
1.42, 1.97)
Dona et al. (2012)
China
1.05
0.96, 1.16)
Katanoda et al. (2011)
Japan
1.20
1.15, 1.24)
Hartetal. (2011)
United States
COPD—men
0.93
0.71, 1.22)
Katanoda et al. (2011)
Japan
COPD
1.15
0.94, 1.41)
Katanoda et al. (2011)
Japan
Pneumonia
1.20
1.16, 1.25)
Lung Cancer
Hartetal. (2011)
United States
Men
1.11
0.98, 1.11)
Krewski et al. (2000)
United States
HSC
1.03
0.91, 1.16)
Krewski et al. (2009)
United States
ACS
1.00
0.98, 1.02)
Abbev et al. (1999)
United States
Men
2.52
1.34, 4.77)
Abbev et al. (1999)
United States
Women
4.40
2.34, 8.33)
Nafstad et al. (2004)
Norway
Men
1.00
0.91, 1.11)
Beelen et al. (2008b)
Netherlands
0.99
0.73, 1.35)
Filleul et al. (2005)
France
0.99
0.90, 1.09}
Carev et al. (2013)
England
1.34
1.06, 1.58)
Elliott et al. (2007)
Great Britain
1.00
0.99, 1.01)
Cao et al. (2011)
Eastern China
1.06
1.03, 1.08)
Katanoda et al. (2011)
Japan
1.12
1.03, 1.22)
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Table 5-54 (Continued): Corresponding risk estimates for Figure 5-25.
Study
Location
Notes
Relative Risk3
(95% CI)
Cardiopulmonary
Krewski et al. (2000)
United States
HSC
1.05
1.00, 1.10}
Krewski et al. (2009)
United States
ACS
1.02
1.01, 1.03}
Abbev et al. (1999)
United States
Men
1.01
0.83, 1.25}
Abbev et al. (1999)
United States
Women
1.03
0.87, 1.21)
Filleul et al. (2005)
France
0.96
0.90, 1.03)
Elliott et al. (2007)
Great Britain
1.03
1.02, 1.03)
Wana et al. (2009)
Brisbane, Australia
1.26
1.03, 1.54}
Cardiovascular
Hart et al. (2011)
United States
Men
1.08
0.99, 1.19}
Beelen et al. (2008b)
Netherlands
0.92
0.75. 1.12)
Elliott et al. (2007)
Great Britain
1.01
1.01, 1.02)
Cao et al. (2011)
Eastern China
1.04
1.03, 1.05)
Zhana et al. (2011)
Shenyang, China
0.95
0.90, 1.01)
Hart et al. (2011)
United States
IHD—men
1.08
0.96, 1.21)
Krewski et al. (2009)
United States
IHD
1.04
1.02, 1.05)
Nafstad et al. (2004)
Norway
IHD—men
0.93
0.88, 0.99)
Carev et al. (2013)
England
Circulatory
1.26
1.19, 1.42)
Nafstad et al. (2004)
Norway
Cerebrovascular
1.03
0.91, 1.16)
Zhana et al. (2011)
Shenyang, China
Cerebrovascular
0.93
0.87, 1.00)
Other
Beelen et al. (2008b)
Netherlands
0.95
0.81, 1.12)
Elliott et al. (2007)
Great Britain
1.02
1.01, 1.02)
Krewski et al. (2009)
United States
1.02
1.02, 1.03)
ACS = American Cancer Society; CI = confidence interval; COPD = chronic obstructive pulmonary disease; HSC = Harvard Six
Cities; IHD = ischemic heart disease.
aEffect estimates are standardized to a 5-ppb increase in S02 concentration.
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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, 2008 SOx ISA noted 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.
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
Preamble (U.S. EPA. 2015eV The key evidence as it relates to the causal framework is
summarized in Table 5-55. The overall evidence is suggestive of, but not sufficient to
infer, a causal relationship between long-term exposure to SO2 and total mortality among
adults. The strongest evidence supporting this conclusion comes from increased
consistency in the results of cohort studies that evaluate respiratory and total mortality.
Table 5-55 Summary of evidence, which is suggestive of, but not sufficient to
infer, a causal relationship between long-term SO2 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
Krewski et al. (2009)
Jerrett et al. (2003)
Krewski et al. (2000)
City-specific annual
¦ mean: 9.3-9.6 ppb
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Table 5-55 (Continued): Summary of evidence, which is suggestive of, but not
sufficient to infer, a causal relationship between long-
term SO2 exposure and total mortality.
Rationale for
Causal
Determination3
Key Evidence13
Key References'3
SO2 Concentrations
Associated with
Effects0
Lipfert et al. (2009)
County-level mean from
air quality model: 4.3 ppb
Recent cohort studies in the United
States observe increases in total mortality
and mortality due to lung cancer and
cardiovascular and respiratory disease,
but exposure assessment and statistical
methods were not adequate for study of
SO2.
Hart et al. (2011)
Annual average at
residential address from
model: 4.8 ppb
Some
epidemiologic
studies report no
associations
No association observed in European
cohort studies for total, respiratory, or
cardiovascular mortality
Beelen et al. (2008b)
IDWto 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 to
high.
Table 5-52
Uncertainty
regarding how
exposure
measurement error
may influence the
results
SO2 has low to moderate 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 3.3.3.2
No evidence for long-term exposure and
respiratory health effects in adults to
support the observed associations with
respiratory mortality
Section 5.2.2.4
No coherence with
evidence for
respiratory and
cardiovascular
morbidity
No evidence for long-term exposure and
cardiovascular health effects in adults to
support the observed associations with
cardiovascular mortality
Section 5.3.2.4
ACS = American Cancer Society; HSC = Harvard Six Cities; IDW = inverse distance weighting; ppb = parts per billion; S02 = sulfur
dioxide.
aBased on aspects considered in judgments of causality and weight of evidence in causal framework in Tables I and II of the
Preamble (U.S. EPA. 2015e).
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).
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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. 2008b). 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 and laboratory studies of mutagenicity or genotoxicity. The 2008 SOx ISA
summarized the literature on SO2 concentrations and lung cancer as "inconclusive" (U.S.
EPA. 2008b). Multiple studies across the United States and Europe investigated the
relationship between SO2 concentrations and lung cancer incidence and mortality. Many
studies reported no association present, 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-18 (U.S. EPA. 2015vYI and
genotoxic/mutagenic [Table 5S-19 (U.S. EPA. 2015w)l literature. The animal toxicology
literature of SO2 exposure is dominated by studies of SO2 acting 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.
5.6.1.1 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 were 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
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relatively unchanged when adjusting for PMio. 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 association was apparent 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.
Studies in the United States have reported inconsistent findings for the association
between SO2 concentrations and lung cancer mortality. 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 (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.
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also demonstrated a positive association between SO2 concentration and lung cancer
mortality (Carey et al. 2013V 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, like
studies conducted in the United States examining SO2 concentrations and cancer
mortality, recent studies performed in Asia and Europe also had inconsistent findings.
Finally, a study in Italy used a Lagrangian dispersion model to estimate SOx
concentrations as a marker for refinery plant emissions (exposure information in
Section 3.2.2.1 (Ancona et al.. 2015). 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 PM10, 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.
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 a significant increase in the number of lung tumors
subgrouped as (1) adenomas and (2) primary carcinomas versus controls. Males also had
a nonsignificant increase 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
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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
et al.. 1997). Adult male rats were exposed to SO2 for 11 months, 16 hours/day ± SPM
for 4 weeks, once/week by intra-tracheal injection. Thus, 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 (Ohvama et al.. 1999). Adult male rats were intra-tracheally 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. Thus, SO2 acted as a tumor
promoted 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 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
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c-jun with SO2 and B[a]P coexposure was observed in rodent lungs (Oin and Meng.
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) significantly downregulated expression of
tumor suppressor genes p 16 and myc, and increased expression of oncogenes c-myc,
H-ras, and p53. Others have reported that SO2 exposure alone could induce p53
expression in rats (Bai and Meng. 2005).
5.6.1.2 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. 2008b).
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). No association was observed among men or
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 association
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 ofNCh 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-18, (U.S. EPA. 2015v). 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 estimated
using a Lagrangian dispersion model reported no association between SOx concentration
and bladder cancer mortality or hospitalizations among men or women (Ancona et al..
2015). However, results of this study are difficult to interpret because of unknown
validity of the model (see Section 3.2.2.1) and high correlation with PM10 and H2S.
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5.6.1.3
Incidence of Other Cancers
Recent studies of SO2 concentrations and other cancer types have also been published
since the 2008 ISA for Sulfur Oxides (U.S. EPA. 2008b). but provided limited
information on associations with SO2. An ecological study in southern France also
investigated the relationships between S02and hospitalizations for breast cancer, acute
leukemia, myeloma, and non-Hodgkin's lymphoma (Pascal et al.. 2013). No associations
were observed in sex-stratified analyses among men or 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
utilized 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 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.
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.
Similar to studies of SO2 concentrations and lung cancer in the previous ISA (U.S. EPA.
2008b). 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
5.6.1.4
Summary
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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.
5.6.2 Genotoxicity and Mutagenicity
Multiple studies of genotoxicity or mutagenesis with SO2 in vivo or in vitro exposure
have been reported in the literature and are detailed below in Supplemental Table 5S-19
(U.S. EPA. 2015\v).
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 days, 6 hours/day) induced a significant increase in
MNPCE with this effect attenuated by exogenous antioxidant SSO pretreatment (Ruanet
al.. 2003V
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).
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 SO2 in vivo or in vitro exposure
have been reported in the literature and are summarized in Supplemental Table 5S-19
(U.S. EPA. 2015w). Mixed results of genotoxicity or mutagenicity have been reported
after SO2 exposure including positive associations with SO2 inhalation exposure in the
mouse MN assay.
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5.6.3 Summary and Causal Determination
The overall evidence for long-term SO2 exposure and cancer is suggestive of, but not
sufficient to infer, a causal relationship. This conclusion is based on evidence from some
epidemiologic studies, as well as some evidence within the 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. EPA. 2008b). Recent studies include evidence on lung cancer as
well as new types of cancer, evaluating both incidence and mortality. All available
evidence for cancer due to long-term SO2 concentrations was evaluated using the
framework described in Table II of the Preamble (U.S. EPA. 2015e). The key evidence as
it relates to the causal framework is summarized in Table 5-56.
Some of the epidemiologic studies provide support for the suggestive relationship
between SO2 concentrations and cancer. Although some studies of SO2 concentrations
and lung cancer mortality have reported null results, other studies have reported positive
associations. Some of these studies with positive associations were relatively unchanged
with the inclusion of various cofounders and copollutants. Cohort studies have reported
no association 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.
Animal toxicological studies employing SO2 exposure with other known carcinogens
provide further supporting evidence, showing that inhaled SO2 can increase tumor load in
laboratory rodents. Nonetheless, toxicological data provide no clear evidence of SO2
acting as a complete carcinogen and not all epidemiologic studies report positive
associations.
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
parti culate-induced tumorigenesis.
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American Conference of Governmental Industrial Hygienists has rated sulfur dioxide as
A4. 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.
However, in some animal toxicological models SO2 may act as a tumor promoter.
Genotoxic and mutagenic studies with SO2 have mixed results. Some studies with
coexposure to other known carcinogens demonstrated that inhaled SO2 can increase
tumor burden in rodents. Collectively, while some studies observed no associations, the
evidence from several toxicological and epidemiologic studies is suggestive of, but not
sufficient to infer, a causal relationship between long-term exposure to SO2 and cancer
incidence and mortality.
Table 5-56 Summary of evidence, which is suggestive of, but not sufficient to
infer, a causal relationship between long-term SO2 exposure and
cancer.
Rationale for Causal
Determination3
Key Evidence13
Key References'3
SO2 Concentrations Associated
with Effects0
Among a small body
of evidence, some
epidemiologic studies
show an association.
Increases in lung cancer and
bladder cancer mortality in
studies conducted in the
United States, Europe, and
Asia.
Section 5.6.1
Means varied with studies of lung
cancer mortality including areas
estimating mean concentrations
of SO2 as low as 1.49 ppb to as
high as 27.87 ppb. Associations
observed with bladder cancer
mortality at levels as low as
4.39-6.09 ppb.
Uncertainty due to Central site monitors used in Section 3.3.3.2
exposure cancer studies may not
measurement error capture spatial variability of
SO2 concentrations
Uncertainty due to Correlations of SO2 with other Section 3.3.4.1
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.
Peacock and Spence 500,000 ppb
(1967)
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Table 5-56 (Continued): Summary of evidence, which is suggestive of, but not
sufficient to infer, a causal relationship between long-
term SO2 exposure and cancer.
Rationale for Causal
Determination3
Key Evidence13
Key References'3
SO2 Concentrations Associated
with Effects0
Uncertainty due to Studies in a
limited coherence with tumor-susceptible mouse
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 5.6.1.1
Mena et al. (2002).
5,000, 10,700, 21,400,
Some evidence to
identify key events
within the MOA from
mutagenesis and
genotoxicity
Mixed evidence of
mutagenicity and genotoxicity
formation in animal cells
exposed to SO2
Ruan et al. (2003),
Pool et al. (1988b)
Section 5.6.2
32,100 ppb
ppb = parts per billion; MOA = mode of action; N02 = nitrogen dioxide; S02 = sulfur dioxide.
aBased on aspects considered in judgments of causality and weight of evidence in causal framework in Tables I and II of the
Preamble (U.S. EPA. 2015e).
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
Sufur Oxides
Table A-1 Scientific considerations for evaluating the strength of inference
from studies on the health effects of sulfur oxides.
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.
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 report 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.a 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.
Exposure Assessment or Assignment
Controlled Human Exposure:
For this assessment, the focus will be on studies that utilize SO2 concentrations less than or equal to 2 ppm
(Section 12). Studies that utilize 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 peak exposures, defined here as exposures from 5-10 minutes, to 0.2-0.6 ppm SO2, were
emphasized (Section 1.2).
Animal Toxicology:
For this assessment, the focus will be on studies that utilize SO2 concentrations less than or equal to 2,000 ppb
(Section 12). Studies that utilize 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 (Sections 3.3.2 and 3.3.3.1). 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 3.3.5.1).
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.
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 report, 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.
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 2.5.5), ranging from negative to strong correlations, making evaluation of copollutant
confounding necessary on a study-specific, rather than a general, basis.
Other Potential Confounding Factorse
Controlled Human Exposure:
Preference is given to studies utilizing 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 utilizing 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.
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
t-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; ppb = parts per billion; ppm = parts per million; SES = socioeconomic status; S02 = sulfur dioxide.
aToren et al. (1993): (Muraia 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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studies of S02 exposure and other morbidity effects (i.e., hematological and nervous system effects).
U.S. EPA (U.S. Environmental Protection Agency). (2015h). Table 5S-3. Summary of additional respiratory
hospital admission and emergency department visit studies.
U.S. EPA (U.S. Environmental Protection Agency). (2015i). Table 5S-4. Summary of epidemiologic studies of
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U.S. EPA (U.S. Environmental Protection Agency). (2015j). Table 5S-5. Summary of cross-sectional
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U.S. EPA (U.S. Environmental Protection Agency). (2015k). Table 5S-6. Study-specific details of experimental
studies of S02 and cardiovascular effects.
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ambient sulfur dioxide for hospital admissions for cardiovascular disease in studies conducting copollutants
models with CO or 03 presented in Figure 5S-1.
U.S. EPA (U.S. Environmental Protection Agency). (2015m). Table 5S-8. Corresponding risk estimates of
ambient sulfur dioxide for hospital admissions for cardiovascular disease in studies conducting copollutants
models with N02 presented in Figure 5S-2.
U.S. EPA (U.S. Environmental Protection Agency). (2015n). Table 5S-9. Corresponding risk estimates of
ambient sulfur dioxide for hospital admissions for cardiovascular disease in studies conducting copollutants
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U.S. EPA (U.S. Environmental Protection Agency). (2015o). Table 5S-11. Summary of epidemiologic studies of
exposure to sulfur dioxide and fertility/pregnancy effects.
U.S. EPA (U.S. Environmental Protection Agency). (2015p). Table 5S-12. Summary of epidemiologic studies of
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exposure to sulfur dioxide and preterm birth.
U.S. EPA (U.S. Environmental Protection Agency). (2015r). Table 5S-14. Summary of epidemiologic studies of
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CHAPTER 6 POPULATIONS AND LIFESTAGES
POTENTIALLY AT 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 detrimental 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 ISA (U.S. EPA. 20156)1. 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 ISA (U.S.
EPA. 2015eYI. 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 ISA (U.S. EPA. 2015e). 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, could be at increased risk of an air
pollutant-related health effect via multiple avenues. As discussed in the Preamble (U.S.
EPA. 2015c). there are many avenues by which risk may be modified, including intrinsic
or extrinsic factors, differences in internal dose, or differences in exposure to air pollutant
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
SCh-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 SCh-related health effect due to a
combination of multiple factors [e.g., residential location and socioeconomic status
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(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 ISA (U.S. EPA. 2015eVI.
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 (U.S. EPA. 2015e). 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. EPA. 2008b). Based on the approach developed in
previous ISAs (U.S. EPA. 2015d. 2013a. b), evidence is integrated across scientific
disciplines, across health effects, and where available, with information on exposure and
dosimetry (Chapters 3 and 4). 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 SO2-
related health effect.
As discussed in the Preamble (U.S. EPA. 2015e). 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,
those that include stratified analyses to compare populations or lifestages exposed to
similar air pollutant concentrations within the same study design provide the most
relevant evidence with consideration of their strengths and limitations. Other
epidemiologic studies that do not stratify results but instead examine a specific
population or lifestage can provide further evidence, particularly when these studies are
similar enough to allow comparison. Experimental studies in human subjects or animal
models that focus on factors, such as genetic background or health status, are also
important lines of evidence to evaluate because they inform the independent effects of
SO2 as well as coherence and biological plausibility of effects observed in epidemiologic
studies. Additionally, studies examining whether factors may result in differential
exposure to SO2 and subsequent increased risk of S02-related health effects are also
included.
The objective of this chapter is to identify, evaluate and characterize the overall
confidence that various factors may increase the risk of an S02-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
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include pre-existing disease (Section 6.3). genetic background (Section 6.4).
sociodemographic (Section 6.5). and behavioral and other factors (see Section 6.6). The
classifications of evidence are characterized in Table 6-1. and a summary of the
characterization of the evidence for each factor considered is presented in Section 6/7.
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.
6.3 Pre-Existing Disease/Conditions
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.
2008b) 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 by pre-existing disease, most
focused on asthma (Section 6.3.1). though other studies examined effect modification by
pre-existing CVD (Section 6.3.2). diabetes (Section 6.3.3). and obesity (Section 6.3.4).
Table 6-2 presents the prevalence of these 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
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diseases, including various cardiovascular diseases, indicates the potential public health
impact, and thus, the importance of characterizing the risk of SCh-related health effects
for affected populations.
Table 6-2 Prevalence of respiratory diseases, cardiovascular diseases,
diabetes, and obesity among adults by age and region in the U.S. in
2012.
Adults
(18+)
Age (%)a
Region (%)b
Chronic N (in
Disease/Condition thousands)
18-44
45-64
65
-74
75+
North-
east
Midwest
South
West
All (N, in 234,921
thousands)
111,034
82,038
23,760
18,089
42,760
53,378
85,578
53,205
Selected respiratory diseases
Asthma0 18,719
8.1
8.4
7.8
6.0
9.2
8.1
7.3
7.8
COPD—chronic 8,658
bronchitis
2.5
4.7
4.9
5.2
3.2
4.4
3.9
2.4
COPD— 4,108
emphysema
0.3
2.3
4.7
4.7
1.3
2.0
1.9
1.0
Selected cardiovascular diseases/conditions
All heart disease 26,561
3.8
12.1
24.4
36.9
10.0
11.6
11.6
9.3
Coronary heart 15,281
disease
0.9
7.1
16.2
25.8
5.3
6.5
7.0
5.1
Hypertension 59,830
8.3
33.7
52.3
59.2
21.4
24.1
26.6
21.5
Stroke 6,370
0.6
2.8
6.3
10.7
1.8
2.5
3.0
2.5
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Table 6-2 (Continued): Prevalence of respiratory diseases, cardiovascular
diseases, and diabetes among adults by age and region in
the U.S. in 2010.
Adults
(18+)
Age (%)a
Region (%)b
Chronic
Disease/Condition
N (in
thousands)
18-44
45-64 65
-74
75+
North-
east
Midwest
South
West
Metabolic disorders/conditions
Diabetes
21,391
2.4
12.7
21.1
19.8
7.6
8.4
10.0
7.3
Obesity (BMI
>30 kg/m2)
64,117
26
33.7
29.7
18
25.1
29.9
29.9
25.2
Overweight (BMI
25-30 kg/m2)
78,455
31.4
36.8
40.7
38.6
34.3
34.1
34.2
35.3
BMI = body mass index; COPD = chronic obstructive pulmonary disease.
Percentage of individual adults 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).
°Asthma 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.
6.3.1 Asthma
Approximately 8.0% of adults and 9.3% 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. Based on evidence from the 2008 ISA for Sulfur Oxides (U.S.
EPA. 2008b) 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 (Sections 5.2.1.2 and 5.2.1.8). This is nearly the same body of
evidence evaluated in the 2008 SOx ISA (U.S. EPA. 2008b). which also concluded that
individuals with asthma were more sensitive to exposures to ambient SO2. 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 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
adolescents adults
(14-18 yr) (21-55 yr)
f Decrements in n = 9 adolescents
Vmax75 and Vmax50
Decrements in
sRaw and FEVi
1 ppm Koenia et
SO2 + 1 mg/m3 al. (1980)
NaCI droplet,
1 mg/m3 NaCI
droplet for
60 min at rest
Asthma Healthy
(atopic)
Lung function
(sRaw)
Mild asthma
Moderate/
severe
asthma
n = 4 normal,
21 atopic;
mild asthma
n = 16
moderate/
severe asthma
n =24
0.2, 0.4,
0.6 ppm SO2
for 1 h with
exercise;
Exposures
were repeated
eight times
Linn et al.
(1987)
Asthma Healthy
(atopic)
Lung function
(FEV1)
Mild asthma
Moderate/
severe
asthma
Asthma Healthy
(atopic)
Mild asthma
Moderate/
severe
asthma
Respiratory
symptoms during
' exposure
Asthma
Healthy
f Lung function
n = 46 bronchial
0.5 ppm SO2
Maanussen
(sRaw)
asthma, 12 healthy
for 10 min tidal
et al.
breathing,
(1990)
10 min of
isocapnic
hyperventilation
(30 L/min);
Histamine
challenge
Asthma
Healthy
Lung function
n = 12 asthma,
0.2 ppm SO2
Tunnicliffe
(FEV1, FVC,
12 healthy
for 1 h at rest
et al.
MMEF)
(2003)
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Table 6 3 (Continued): Controlled human exposure 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
Mild asthma
Healthy
-
Heart rate
n =24
0.2 ppm SO2
Tunnicliffe
n = 12,
n = 12,
HRV
for 1 h at rest;
et al.
34.5 yr
35.7 yr
ECG during
(2001)
exposure
Asthmatic rat
model (OVA
sensitization)
Normal rats
T
T
AHR (methach-
oline)
IL-4 in BALF
IFN-y in BALF
Rats (Sprague-
Dawley), n = 10
males/group
(4 weeks)
2 ppm SO2 for
4 h/day for
4 weeks
beginning at
15 days
Sona et al.
(2012)
f Airway smooth
muscle cell
stiffness (in vitro)
f Airway smooth
muscle cell
contractility
(in vitro)
AHR = airway hyperresponsiveness; BALF = bronchoalveolar lavage fluid; ECG = electrocardiogram; FE\A| = forced expiratory
volume in 1 second; FVC = forced vital capacity; HRV = heart rate variability; IFN-y = interferon gamma; IL-4 = interleukin 4;
MMEF = maximum midexpiraotry flow; NaCI = sodium chloride; OVA = ovalbumin; sRAW = specific airway resistance;
Vmax5o = maximal expiratory flow rate at 50%; Vmax75 = maximal expiratory flow rate at 75%.
aUp facing arrow 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 indicates that the effect of S02
is smaller in the group with the factor evaluated than in the reference group. A dash indicates no 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.
Across experimental evidence, subjects with asthma consistently have greater decrements
in lung function with SO2 exposure than those without asthma. Controlled human
exposure studies have evaluated respiratory outcomes at SO2 concentrations ranging from
0.2 ppm 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, mild asthmatic, atopic asthmatic, and moderate/severe asthmatic
individuals 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 subjects with asthma relative to healthy
controls when exposed at rest (Tunnicliffe et al.. 2003; Koenig et al.. 1980). It is
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important to note though that these studies were limited by exposure design and small
sample sizes. In addition to controlled human exposure studies, a long-term study
conducted in ovalbumin (OVA)-sensitized rats as an asthmatic 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, only two studies included stratification by asthma
status but did not find differences for short-term exposure to ambient SO2 with respiratory
outcomes (Table 6-4) (Amadeo et al. 2015; Lin et al.. 2015). However, evidence
presented in Section 5.2.1.2 demonstrates consistent positive associations between
ambient SO2 concentrations and asthma-related hospitalizations and ED visits. In
addition, some evidence from recent panel studies and older studies reviewed in the 2008
ISA for Sulfur Oxides (U.S. EPA. 2008b) indicates that children with asthma experience
respiratory symptoms associated with exposure to ambient SO2.
In conclusion, there is consistent evidence from controlled human exposure studies and
animal toxicology studies demonstrating decrements in lung function with SO2
exposures. In addition, there is clear biological plausibility (Section 4.3) supporting the
observed effects as well as epidemiologic evidence suggesting individuals with asthma
experience respiratory symptoms associated with exposure to ambient SO2. Overall, there
is adequate evidence from experimental studies to conclude that people with pre-existing
asthma are at increased risk of S02-induced respiratory effects.
Table 6-4 Epidemiologic studies evaluating pre-existing asthma.
Factor Reference
Evaluated Category
Direction of
Effect
Modification3
Outcome
Study
Population
Study Details Study
Short-term exposure
Asthmatic Nonasthmatic - Lung function (PEF) n = 506 Guadeloupe
n = 16 6% n = 83 4% elementary school (French West
children ages Indies)
8-13 yr December
2008-
December
2009
Beijing, China Lin et al
June 2007- (2015)
September
2008
n = sample size; PEF = peak expiratory flow; yr = year.
aA dash indicates no difference in S02-related health effect between groups.
Amadeo
et al.
(2015)
Asthmatic Nonasthmatic - Oxidative stress n = 36 elementary
n = 8 n = 28 (8-oxo-7,8-dihydro- school children
2 -deoxyguano-sine (fourth grade,
and malondial- mean age 10.6 yr)
dehyde)
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6.3.2 Cardiovascular Disease
Cardiovascular disease is the primary cause of death in the U.S., and approximately 12%
of adults report a diagnosis of heart disease ITable 6-2; (Schiller et al.. 2012)1. The
evidence on SCh-related health effects in individuals with pre-existing cardiovascular
disease evaluated in the 2008 ISA for Sulfur Oxides (U.S. EPA. 2008b) was found to be
limited and inconsistent. Recent evidence reviewed in this ISA adds two epidemiologic
studies evaluating effects of SO2 exposure on individuals with pre-existing cardiovascular
disease, but does not provide any more clarity regarding whether or not these individuals
may be at greater risk for SC>2-related health effects compared to individuals without
cardiovascular disease (Table 6-5).
Table 6-5 Epidemiologic studies evaluating pre-existing cardiovascular
disease and sulfur dioxide exposure.
Factor
Evaluated
Reference
Category
Direction of
Effect
Modification3
Outcome
Study
Population
Study Details
Study
Short-term exposure
Hypertension
n = 40% visits
No
hypertension
n = 60% visits
—
Hospital
admissions,
myocardial
infarction
n =27,563
admissions
Taipei, Taiwan
1999-2009
Tsai et al.
(2012)
CHF
n = 15% visits
No CHF
n = 85% visits
"
Cardiac
arrhythmia
n = 11% visits
No cardiac
arrhythmia
n = 89% visits
Cardiovascular
disease
n = 535
No cardiova-
scular
disease
n = 956
T
Hospital
admissions,
stroke
n = 1,491
admissions for
stroke
Tehran, Iran
2004
Nabavi et al.
(2012)
Hypertension No
n = 955 hypertension
n = 536
CHF = congestive heart failure; n = sample size.
aUp facing arrow indicates that the effect of S02 is greater (e.g., larger forced expiratory volume in 1 second decrement, larger
increase in airway inflammation) in the group with the factor evaluated than in the reference group. Down facing arrow indicates
that the effect of S02 is smaller in the group with the factor evaluated than in the reference group. A dash indicates no difference in
S02-related health effect between groups.
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19
Tsai et al. (2012) and Nabavi et al. (2012) reported weak associations between short-term
ambient SO2 exposure and hospital admissions for myocardial infarction and stroke,
respectively, and those associations remained weak for individuals with various
pre-existing cardiovascular conditions. Similarly, Routledge et al. (2006) compared
cardiovascular outcomes in older adults with and without pre-existing cardiovascular
disease during exposure to 200 ppb SO2 and found that only individuals without
pre-existing disease had significant responses to SO2 (Table 6-6). Additionally, biological
plausibility for the outcomes examined remains unclear (Section A.B.C).
Table 6-6 Controlled human exposure evaluating pre-existing cardiovascular
disease and sulfur dioxide exposure.
Factor Reference Direction of
Evaluated Category Effect3 Outcome
Study
Population/
Animal Model
Study Details
Study
Older adults Healthy
with older
pre-existing adults
cardiovascular n = 20
disease
n =20
Heart rate
Adults 5-75 yr 200 ppb SO2 for Routledge et al.
Decrements
in HRV
1 h at rest;
ECG 4 h after
exposure
(2006)
Blood
pressure
ECG = electrocardiogram; HRV = heart rate variability.
aA dash indicates that S02 was not observed to induce an effect in the group with cardiovascular disease evaluated relative to the
reference group. An up-facing arrow indicates that effect measured after S02 exposure was reduced in the group of healthy, older
adults and exposure did not affect response in older adults with cardiovascular disease. Down facing arrow indicates that the
effect of S02 is smaller in the group.
Overall, the limited and inconsistent evidence and lack of biological plausibility is
inadequate to determine if individuals with pre-existing cardiovascular disease may be at
increased risk for S02-related health outcomes.
6.3.3 Diabetes
Diabetes mellitus is a group of diseases characterized by high blood glucose levels which
affected an estimated 20 million Americans in 2012 or approximately 8.6% of the adult
population (Blackwell et al.. 2014). High blood glucose levels have adverse effects on the
cardiovascular system, and diabetes and cardiovascular disease are linked by common
risk factors such as hypertension and obesity. These relationships provide support for
diabetes influencing the risk of cardiovascular disease; however, diabetes has not
consistently been observed to modify epidemiologic associations in studies of short-term
SO2 exposure and cardiovascular effects (Table 6-7). Filho et al. (2008) found stronger
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associations between SO2 exposures and emergency department visits for hypertension or
ischemic heart disease among individuals with diabetes compared to those without, but
no difference by diabetic status was observed in studies examining SCh-related hospital
admissions for MI (Tsai et al.. 2012; Filho et al.. 2008). Huang et al. (2012) found
SCh-associated decrements in HRV in a panel study of individuals with cardiovascular
disease, and decrements were greater in individuals with diabetes compared to those
without; however, the sample sizes were small. Given the limited number of studies
evaluating diabetes as a risk factor, the evidence is inadequate to determine whether
people with diabetes are at increased risk for SCh-associated health outcomes.
Table 6-7 Epidemiologic studies evaluating pre-existing diabetes and sulfur
dioxide exposure.
Factor
Evaluated
Reference
Category
Direction of
Effect
Modification3
Outcome
Study
Population
Study Details
Study
Short-term exposure
Diabetes
n = 700 ED
visits
No diabetes
n =44,300
ED visits
T
ED visits for
hypertension
and cardiac
ischemic
disease
N =45,000
ED visits
Sao Paulo
Hospital, Brazil
January 2001-
July 2003
Filho et al.
(2008)
Diabetes
n = 29.6%
No diabetes
n = 70.4%
Hospital
admissions,
myocardial
infarction
N =27,563
admissions
Taipei, Taiwan
1999-2009
Tsai et al.
(2012)
Diabetes
n = 9
No diabetes
n = 31
T
HRV
decrements
(SDNN)
N = 40 with CVD
Mean age 66 yr
Beijing, China
2008
Huana et al.
(2012)
CVD = cardiovascular disease; ED = emergency department; HRV = heart rate variability; SDNN = standard deviation of normal
RR intervals.
aUp facing arrow indicates that the effect of S02 is greater (e.g., larger risk of ED visit, larger decrement in HRV) in the group with
the factor evaluated than in the reference group. Down facing arrow indicates that the effect of S02 is smaller in the group with the
factor evaluated than in the reference group. A dash indicates no difference in S02-related health effect between groups.
6.3.4 Obesity
In the U.S., obesity is defined as a BMI of 30 kg/m2 or greater, with a BMI between 25
and 30 kg/m2 indicating an overweight individual. It is a public health issue of increasing
importance as obesity rates in adults have continually increased over several decades in
the U.S., reaching an estimated 28% in 2012 (Blackwell et al.. 2014). Furthermore,
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34.5% of adults in the U.S. are considered overweight while only 28.9% are at a healthy
weight (BMI 18.5-25 kg/m2) (Blackwell et al.. 2014). Obesity or high BMI could
increase the risk of SC>2-related health effects through multiple mechanisms, including
persistent low-grade inflammation, and may act in combination with other risk factors
such as poor diet or chronic disease that commonly occur with obesity.
The 2008 ISA for Sulfur Oxides (U.S. EPA. 2008b) did not evaluate obesity as a
potential factor that could increase the risk of S02-related health effects, but recent
literature includes obesity or BMI as a potential effect measure modifier (Table 6-8).
Across cardiovascular outcomes, including decrements in heart rate variability, blood
pressure, and levels of lipoprotein-associated phospholipase 2 (an indicator of vascular
inflammation), associations with short-term exposure to ambient S02were not
consistently greater in obese or overweight individuals compared to those of healthy
weight (Sun et al.. 2015; Huang et al.. 2012; Briiske etal.. 2011; Longo et al.. 2008).
Studies of long-term SO2 exposure examined associations with cardiovascular mortality,
heart failure, and respiratory mortality, but reported mixed results (Atkinson et al.. 2013;
Dong et al.. 2013b; Dong et al.. 2012; Cao et al.. 2011; Zhang etal.. 2011). In addition,
there is a lack of biological plausibility and uncertainty in the causality of cardiovascular
effects related to SO2 exposure (Sections 4.2.6. 5.3.1.11. and 5.3.2.6). Thus, the limited
and inconsistent evidence is inadequate to determine whether obese or overweight
individuals are at greater risk for S02-associated health outcomes than nonobese
individuals.
Table 6-8 Epidemiologic studies evaluating pre-existing obesity and sulfur
dioxide exposure.
Factor Evaluated
Reference
Category
Direction of
Effect
Modification3
Outcome
Study
Population
Study
Details
Study
Short-term exposure
High BMI (>25)
n = 16
Low BMI
(<25)
n =24
1
HRV
decrements
(SDNN)
n =40
nonsmoking
adults with CVD
Mean age: 66 yr
Beijing,
China
2007-2008
Huana et
al. (2012)
High BMI (25
-------
Table 6-8 (Continued): Epidemiologic studies evaluating pre-existing obesity and
sulfur dioxide exposure.
Factor Evaluated
Reference
Category
Direction of
Effect
Modification3
Outcome
Study
Population
Study
Details
Study
High BMI (>28)b
Low BMI
(<25)
(kg/m2)b
Blood pressure,
systolic
n = 335 adults
>20 yr, residing
downwind from
volcano
Hawaii
April-June
2004
Lonqo et
al. (2008)
High BMI (>25)
Low BMI
-
Lipoprotein-
n = 200 post-MI
Augsburg,
Bruske et
n = 168
(—25)
associated
patients
Germany
al. (2011)
n = 31
phospholipase
Mean age:
May 2003-
A2 in plasma
61,9±9.0 yr
March 2004
(marker for
vascular
inflammation
related to
atherosclerosis)
Long-term exposure
High BMI (>30)
Lower BMI
1
Heart failure
n = 12,851
England
Atkinson
n =2,570
(25-30)
Ages 40-89 yr in
2003-2007
et al.
n =4,194
2003
(2013)
High BMI (>25)
Low BMI
-
Cardiovascular
n = 9,941 subject
Shenyang,
Zhana et
n = 73
(<18.5)
mortality
s >25 yr at study
China
al. (2011)
n =21
enrollment and
1998-2009
living at
residence >10 yr;
256 cardiovasc-
ular deaths
High BMI (>25)b
Low BMI
T
Cardiovascular
n = 70,947 study
China
Cao et al.
(<25)b
mortality
participants,
1991-2000
(2011)
8,319 deaths
High BMI (>25)
Low BMI
T
Respiratory
n = 9,441 reside
Shenyang,
Dona et
n = 10
(<18.5)
mortality
nts >35 yr living
China
al. (2012)
n = 12
at residence
1998-2009
>10 yr; 72 deaths
due to
respiratory
disease
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Table 6-8 (Continued): Epidemiologic studies evaluating pre-existing obesity and
sulfur dioxide exposure.
Direction of
Reference
Effect
Study
Study
Factor Evaluated
Category
Modification3
Outcome
Population
Details
Study
Overweight/obese
Normal
T
Doctor-diagnose
n = 30,056
Seven
Dona et
children
weight
d asthma
children ages
northeastern
al.
n = 7,937
children
¦ 2-14 yr; weight
cities study,
(2013b)
n =22,119
T
Respiratory
categories
Liaoning
symptoms
determined
Provence,
(cough, phlegm,
according to
northeast
wheeze)
CDC standards
China
2006-2008
BMI = body mass index in kg/m2; CDC = Centers for Disease Control and Prevention; CVD = cardiovascular disease;
HRV = heart rate variability.
aUp facing arrow indicates that the effect of S02 is greater (e.g., larger change in ventricular repolarization) in the group with
the factor evaluated than in the reference group. Down facing arrow indicates that the effect of S02 is smaller in the group with
the evaluated factor than in the reference group. A dash indicates no difference in S02-related health effect between groups.
bSample size not reported.
6.4 Genetic Factors
Genetic variation in the human population is known to contribute to numerous diseases
and differential physiologic responses. The 2008 SOx ISA (U.S. EPA. 2008b) 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. Only one recently conducted study reviewed in this ISA examined effect
measure modification by genotype I(Reddv et al.. 2012); Table 6-91 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
Ilel05Val genotype are associated with reduced antioxidant enzyme function; however,
effect measure modification of these genotypes on SC^-associated intra-day variability of
FEVi showed conflicting results. Despite biological plausibility, the limited evidence
base is inadequate to determine whether genetic background contributes to increased risk
for SC>2-associated health outcomes.
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7
8
9
10
11
12
Table 6-9 Epidemiologic studies evaluating genetic factors and sulfur dioxide
exposure.
Factor Direction of
Evaluated/Gene Reference Effect Study
Function Category Modification3 Outcome Population Study Details Study
GSTM1 null
n = 33
Null oxidant
metabolizing
capacity
GSTM1
positive
n = 89
Lung function
(FEV-i.intraday
variability)
n = 129
indigenous
African children,
9-11 yr
Durban, South
Africa
2004-2005
Reddv et al.
(2012)
GSTP lle/Val + GSTP1 f
Val/Val (AG + lle/lle (AA)
GG) n = 21
n = 91
Reduced oxidant
metabolizing
capacity
(Val/Val)
AA = adenine-adenine genotype; AG = adenine-guanine genotype; FEVi = forced expiratory volume in 1 second; GG = guanine-
guanine genotype; GSTM1 = glutathione s-transferase mu 1; GSTP = glutathione s-transferase P; GSTP1 = glutathione S-
transferase pi 1; lie = isoleucine; Val = valine.
aUp facing arrow indicates that the effect of S02 is greater (e.g., larger change in ventricular repolarization) in the group with the
factor evaluated than in the reference group. Down facing arrow indicates that the effect of S02 is smaller in the group with the
evaluated factor than in the reference group. A dash indicates no difference in S02-related health effect between groups.
6.5 Sociodemographic Factors
6.5.1 Lifestage
The 2008 ISA for Sulfur Oxides (U.S. EPA. 2008b) 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 and/or
physiological characteristics that are associated with development and growth (U.S. EPA.
2014c). 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, it is plausible to consider children to
have intrinsic risk for respiratory effects due to potential perturbations in normal lung
development. 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 (Table 6-2). which
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21
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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. The following sections present the
evidence comparing lifestages from the recent literature, which builds on the evidence
presented in the 2008 SOx ISA (U.S. EPA. 2008b).
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. supports the public health significance of characterizing the risk of
SCh-related health effects among children, especially because there is a causal
relationship between ambient SO2 exposure and lung function decrements in individuals
with asthma, which affects approximately 10.5-11% of children 5 years and older. The
2008 ISA for Sulfur Oxides (U.S. EPA. 2008b) presented evidence demonstrating
increased risk of S02-related respiratory outcomes in children compared to adults;
however, recent evidence is not entirely consistent with the discussion presented
previously (Table 6-10). Although Son et al. (2013) found children (0-14 years) to be at
greater risk for S02-related asthma hospital admissions, several studies did not observe
differences between children and adults when examining associations of ambient SO2 and
asthma hospitalizations or emergency department visits (Samoli et al.. 2011; Ko et al..
2007b; Villeneuve et al.. 2007). Jalaludin et al. (2008) compared associations of
short-term SO2 exposure and respiratory-related ED visits among different age groups of
children and found those of ages 1-4 years to have greater associations than those of ages
10-14 years. However, Dong et al. (2013c) and Nishimura et al. (2013) did not find
age-related differences among children for SCh-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.
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Table 6-10 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
Childhood
All ages
1
Hospital
14 hospitals
Hong Kong,
Wona et al.
Ages 0-14 yr
n = 104.9/day
admissions for
China
(2009)
n = 60.1/day
acute
1996-2002
respiratory
distress
Childhood
Adulthood
-
Asthma
15 hospitals
Hong Kong,
Ko et al.
Ages 0-14 yr
Ages 15-65 yr
hospital
n =69,176
China
(2007b)
n =23,596
n = 21,204
admissions
admissions
2000-2005
Childhood
Adulthood
T
Asthma
Database
Eight South
Son et al.
Ages 0-14 yr
Ages 15-64 yr
hospital
accounting for
Korean cities
(2013)
n = 8.7/day
n = 4.3/day
admissions
48% of Korean
2003-2008
population
n = 19/day
Childhood
Childhood
_
Asthma
Three main
Athens,
Samoli et al.
Ages 0-4 yr
Ages 5-14 yr
hospital
children's
Greece
(2011)
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
Ages 2-4 yr
Ages 5-14 yr
visits
servicing more
Canada
al. (2007)
n = 7,247
n = 13,145
than 80% of
1992-2002
the
metropolitan
area
n = 57,192
visits
Childhood
Childhood
T
Respiratory-
Daily number
Sydney,
Jalaludin et al.
Ages 1-4 yr
Ages 10-14 yr
related ED
of ED visits in
Australia
(2008)
n = 109/day
n = 25/day
visits
metropolitan
1997-2001
Sydney from
the New South
Wales Health
Department
n = 174/day
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Table 6-10 (Continued): Epidemiologic studies evaluating childhood lifestage and
sulfur dioxide exposure.
Direction of
Factor
Reference
Effect
Study
Evaluated
Category
Modification3
Outcome
Population
Study Details
Study
Long-term exposure
Childhood
Childhood
-
Doctor-
n = 31,049
Seven
Dona et al.
Ages 2-5 yr
Ages 6-14 yr
diagnosed
Children
northeastern
(2013c)
n = 7,508
n = 23,541
asthma
Ages 2-14 yr
cities study,
Liaoning
T
Respiratory
Provence,
symptoms
northeast
(cough,
China
phlegm,
2008-2009
current
wheeze)
First year of
First three
-
Physician-
n =4,320
Chicago, IL;
Nishimura et
life exposure
years of life
diagnosed
GALA II and
Bronx, NY;
al. (2013)
n =2,876
exposure
asthma plus
SAGE II
Houston, TX;
n = 2,512
two or more
cohorts
San Francisco
symptoms of
(Latinos and
Bay Area, CA
coughing,
African-
and Puerto
wheezing or
Americans
Rico
shortness of
ages 8-21 yr)
2006-2011
breath
Younger
Older children
1
Non-allergic
n -1,467
Hamilton,
Sahsuvaroalu
children
Ages 13-14 yr
asthma
Children
Canada
et al. (2009)
Ages 6-7 yr
n = 549
grades one
1994-1995
n = 918
(ages 6-7 yr)
and eight
(ages 13-14
yr)
ED = emergency department; GALA II = Genes-environments and Admixture in Latino Americans; SAGE II = Study of African
Americans, Asthma, Genes and Environments.
aUp facing arrow indicates that the effect of S02 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 S02 is smaller in the group with the factor
evaluated than in the reference group. A dash indicates no difference in S02-related health effect between groups.
1 Overall, the combined evidence from the previous and current ISA examining respiratory
2 outcomes across lifestages is only suggestive of increased risk in children, given the
3 inconsistencies across epidemiologic studies and limited toxicological evidence to inform
4 plausibility.
6.5.1.2 Older Adults
5 According to the 2008 National Population Projections issued by the U.S. Census
6 Bureau, approximately 12.9% of the U.S. population is age 65 years or older, and by
7 2030, this fraction is estimated to grow to 20% (Vincent and Velkoff. 2010). Thus, this
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lifestage represents a substantial proportion of the U.S. population that is potentially at
increased risk for health effects related to SO2 exposure.
The 2008 ISA for Sulfur Oxides (U.S. EPA. 2008b) 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 mortality, respiratory, or cardiovascular
effects. Recently published studies evaluating risk in older adults compared to younger
adults are shown in Table 6-11. and generally support conclusions from the 2008 ISA for
Sulfur Oxides (U.S. EPA. 2008b). 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 respiratory admissions or ED visits in adults greater than 65 years of age
reported mixed results compared to the earlier literature (Son et al.. 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 years and older than 75 years)
to have larger decrements in lung function compared to adults aged 16-44.
Table 6-11 Epidemiologic studies evaluating older adult lifestage and sulfur
dioxide exposure.
Factor
Evaluated
Reference
Category
Direction of
Effect
Modification3
Outcome
Study
Population
Study Details
Study
Short-term exposure
Older
adulthood
Ages >65 yr
n =24,916
Younger
adulthood
Ages 15-65 yr
n =21,204
Asthma
hospital
admissions
15 hospitals
n =69,176
admissions
Hong Kong,
China
2000-2005
Ko et al.
(2007b)
Older
adulthood
Ages 65-74 yr
n =4,705
Younger
adulthood
Ages 15-64 yr
n = 32,815
Asthma ED
visits
Five hospitals
n = 57,912 visits
Edmonton,
Canada
1992-2002
Villeneuve
et al. (2007)
Older
adulthood
Ages >75 yr
n = 1,855
T
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Table 6-11 (Continued) Epidemiologic studies evaluating older adult lifestage and
sulfur dioxide exposure.
Factor
Evaluated
Reference
Category
Direction of
Effect
Modification3
Outcome
Study
Population
Study Details Study
Older
adulthood
Ages >65 yr
n = 789
Younger
adulthood
Ages 40-64 yr
n = 980
COPDED
visits
Sao Paulo
Hospital, daily
records for
patients >40 yr
n = 1,769
Sao Paulo,
Brazil
2001-2003
Arbex et al.
(2009)
Older Younger
adulthood adulthood
Ages 65-74 yr Ages 15-64 yr
n = 5.8/day n = 8.8/day
Older
adulthood
Ages >75 yr
n = 5.8/day
Younger
adulthood
Ages 15-64 yr
n = 8.8/day
Asthma and Hospital
allergic
disease
hospital
admissions
admission
database
accounting for
48% of Korean
population
n = 37.7/day
Eight South
Korean cities
2003-2008
Son et al.
(2013)
Older
adulthood
Ages >65 yr
n = 59.6
Older
adulthood
Ages >65 yr
n = 138.5
Older
adulthood
Ages >65 yr
n = 130.8
All ages
n = 91.5
COPD hospital
admissions
14 hospitals
Hong Kong,
China
1996-2002
Wong et al.
(2009)
All ages
n =270.3
Respiratory
disease
hospital
admissions
All ages
n =203.5
Cardiovascular
hospital
admissions
Older
Younger
Ischemic
Five hospitals
Edmonton,
Szvszkowicz
adulthood
adulthood
stroke (ED
from the
Canada
(2008)
Ages
Ages 20-64 yr
visit)
metropolitan
1992-2002
65-100 yr
n =2,873
area
n = 8,008
n = 10,881
Older
Younger
Blood
n = 335 adults
Big Island,
Lonao et al.
adulthood
adulthood
pressure,
>20 yr, residing
Hawaii
(2008)
Ages >65 yr
Ages <65 yr
systolic
downwind from
n = 87
n =248
volcano for
>7 yr
Older
Younger
Diabetes
Santiago, Chile
Dales et al.
adulthood
adulthood
(hospital-
2001-2008
(2012)
Ages 65-74 yr
Ages <64 yr
izations)
Older
-
adulthood
Ages 75-84 yr
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Table 6-11 (Continued) Epidemiologic studies evaluating older adult lifestage and
sulfur dioxide exposure.
Factor
Evaluated
Reference
Category
Direction of
Effect
Modification3
Outcome
Study
Population
Study Details Study
Older
adulthood
Ages >85 yr
Ministry of
Health statistics
for
hospitalizations
for acute
serious
complications of
diabetes
Older
adulthood
Ages >65 yrb
Adulthood,
childhood
Ages 5-64 yrb
Total mortality
Data from
Municipal
Centers for
Disease Control
and Prevention
17 Chinese cities Chen et al.
(2012c)
Older
adulthood
Ages >75 yr
All ages
(>65 yr)
Total mortality
Data from the
Ministry of
Public Health,
Bangkok; the
Census and
Statistic
Department,
Hong Kong; the
Shanghai
Municipal
Center of
Disease Control
and Prevention,
Shanghai; and
the Wuhan
Centre for
Disease
Prevention and
Control, Wuhan
Centre for
Disease
Prevention and
Control, Wuhan
Bangkok,
Thailand; Hong
Kong, Shanghai,
and Wuhan,
China1996-2004
Wong et al.
(2008)
Long-term exposure
Older
adulthood
Ages >65 yr
n = 2,234
Younger
adulthood
Ages <55 yr
n = 18,698
Blood
pressure
(hyper-
tension)
Older
adulthood
Ages 55-64 yr
n = 3,913
Younger
adulthood
Ages <55 yr
n = 18,698
n = 24,845
subject from
different
households at
residence for
>5 yr
Shenyang,
Anshan and
Jinzhou, China
2006-2008
Dong et al.
(2013d)
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Table 6-11 (Continued) Epidemiologic studies evaluating older adult lifestage and
sulfur dioxide exposure.
Direction of
Factor Reference Effect Study
Evaluated Category Modification3 Outcome Population
Study Details Study
Older
adulthood
Ages >40 yr
n = 1,404
Younger
adulthood
Ages <40 yr
n = 45
Stroke
(hospital
admissions)
n = 1,491
patients
admitted with
the diagnosis of
stroke in eight
referral
hospitals in
Tehran
Tehran, Iran
2004
Nabavi et al.
(2012)
Older
Younger
T
Cerebro-
Records
Lanzhou, China
Zhena et al.
adulthood
adulthood
vascular
obtained from
2001-2005
(2013)
Ages >65 yr
Ages <65 yr
disease
four largest
n = 5.50
admissions/day
n = 4.15
admissions/day
(hospital
admissions)
hospitals
Older
adulthood
Ages 65-89 yr
n -2,213
Younger
adulthood
Ages 40-64 yr
n = 10,638
Heart failure
(hospital
admissions)
n = 12,851
Ages 40-89 yr
at baseline
England
2003-2007
Atkinson et
al. (2013)
Older
Younger
1
Respiratory
n = 9,441
Shenyang,
Dona et al.
adulthood
adulthood
mortality
residents >35 yr
China
(2012)
Ages >60 yr
Ages <60 yr
living at
1998-2009
residence
>10 yr;
72 deaths due
to respiratory
disease
Older
Younger
All-cause
n = 420,776
Great Britain
Elliott et al.
adulthood
adulthood
mortality
deaths
1982-1986,
(2007)
Ages >65 yr
Ages 30-65 yr
1986-1990,
1990-1994,
1994-1998
Older
Younger
Cardio-
n = 9,941
Shenyang,
Zhana et al.
adulthood
adulthood
vascular
subjects
China
(2011)
Ages >60 yr
Ages <60 yr
mortality
>25 years at
1998-2009
n =4,061
n = 5,880
study enrollment
and living at
residence
>10 yr;
256 cardio-
vascular deaths
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19
20
Table 6-11 (Continued) Epidemiologic studies evaluating older adult lifestage and
sulfur dioxide exposure.
Direction of
Factor Reference Effect Study
Evaluated Category Modification3 Outcome Population Study Details Study
Older Younger
adulthood adulthood
Ages 45-74 yrb Ages 16-44 yrb
Older Younger
adulthood adulthood
Ages >75 yrb Ages 16-44 yrb
f Decrements in n = 32,712
lung function households.
(FEV-i) adults from
white ethnic
groups (>16
Health Survey Forbes et al.
for England; (2009c)
Lung function in
England
yr) 1995, 1996,
1997, and 2001
COPD = chronic obstructive pulmonary disease; ED = emergency department; FE\A| = forced expiratory volume in 1 second.
aUp facing arrow indicates that the effect of S02 is greater (e.g., larger risk of hospital admission, larger decrement in heart rate
vatiability) in the group with the factor evaluated than in the reference group. Down facing arrow indicates that the effect of S02 is
smaller in the group with the factor evaluated than in the reference group. A dash indicates no difference in S02-related health
effect between groups.
bSample size not reported.
Other recent studies comparing results in older and younger adults evaluated
cardiovascular outcomes and mortality and generally found associations between
outcomes and short or long-term SO2 exposures to be the same (Atkinson et al. 2013;
Dong et al.. 2013d; Dales et al. 2012; Nabavi et al.. 2012; Zhang et al.. 2011; Longo et
al.. 2008; Szvszkowicz. 2008; Elliott et al.. 2007). However, Chen et al. (2012c) and
Wong et al. (2008) both 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.
Taken together, the collective evidence builds on conclusions from the previous ISA and
is suggestive that older adults may be at increased risk for SCh-related health effects.
Although the evidence from cardiovascular studies is generally the same in comparisons
of age, there is uncertainty in the relationship between ambient SO2 and cardiovascular
outcomes in general. The evidence from the current and previous ISA related to
respiratory hospitalizations and ED visits as well as mortality, consistently suggest that
older adults, particularly those older than 75 years, may be at increased risk for
SCh-related health effects.
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 (How den and Mover.
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2011). However, the distribution varies by age, with a greater prevalence of females
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.
There are a number of studies evaluating sex-based differences in SCh-associated health
effects, details for which are shown in Table 6-12. 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 men compared to
women. No differences were found between men and women for COPD ED visits (Arbex
et al.. 2009). In children, SCh-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 significantly stronger associations between ambient SO2
exposure and asthma hospital admissions.
Table 6-12 Epidemiologic studies evaluating effect modification by sex and
sulfur dioxide exposure.
Factor
Evaluated
Reference
Category
Direction of
Effect
Modification
Outcome
Study
Population
Study
Details
Study
Short-term exposure
Female
Male
T
Respiratory
Healthy adult
Miyakejima
Ishiaami et
20% person h
80% person h
symptoms (cough,
volunteers
Island,
al. (2008)
scratchy throat, sore
working on an
Japan
throat,
active volcanic
2005
breathlessness)
island after the
evacuation
order was lifted
n = 955
Female
Male
_
Lung function (FEV1)
Elementary
Windsor,
Dales et al.
n = 39
n = 114
school children
Ontario,
(2009)
with asthma
Canada
(no cigarette
October-
smoking in
December
home)
2005
n = 182
children (ages
9-14 yr)
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Table 6 12 (Continued): Epidemiologic studies evaluating effect modification by
sex and sulfur dioxide exposure.
Factor
Evaluated
Reference
Category
Direction of
Effect
Modification
Outcome
Study
Population
Study
Details
Study
Female
n =235
Male
n =229
Lung function (FEV-i, Children
FVC, PEF,
FEVi/FVC)
recruited from
two schools
with different
roadway
proximity
n =464
(6-14 yr)
Salamanca, Linares et al.
Mexico (2010)
2004-2005
Female
n = 794
Male
n = 875
COPD ED visits
Sao Paulo
Hospital, daily
records for
patients >
40 yr
n = 1,769
Sao Paulo,
Brazil
2001-2003
Arbex et al.
(2009)
Female
n = 7.4
admissions/
day
Male
n = 8
admissions/
day
Female
n = 7.1
admissions/
day
Male
n = 8
admissions/
day
Asthma hospital
admissions
Allergic disease
hospital admissions
Database
accounting for
48% of Korean
population
¦ n = 19/day
Eight South Son et al.
Korean cities (2013)
2003-2008
Femaleb
Maleb
Asthma hospital
admissions
Three main
children's
hospitals—
approximately
85% of
pediatric beds
of metropolitan
area of Athens
n = 3,601
Athens,
Greece
2001-2004
Samoli et al.
(2011)
Female
n = 36
Male
n = 164
Lipoprotein-
associated
phospholipase A2 in
plasma (marker for
vascular
inflammation related
to atherosclerosis)
Post-MI
patients
Mean age:
61,9±9.0 yr
n = 200
Augsburg,
Germany
May 2003-
March 2004
Bruske et al.
(2011)
Female
n =24
Male
n = 16
Reductions in HRV
(SDNN)
n = 40
nonsmoking
adults with
CVD
Mean age:
65.6 yr
Beijing,
China
2007-2008
Huang et al.
(2012)
November 2015
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Table 6 12 (Continued): Epidemiologic studies evaluating effect modification by
sex and sulfur dioxide exposure.
Direction of
Effect
Factor
Reference
Modification
Study
Study
Evaluated
Category
a
Outcome
Population
Details
Study
Female
Male
T
CHF, Cardiovascular
Healthy older
Sao Paulo,
Martins et al.
n = 22.04
n =22.39
disease (measured
adults (>64 yr)
Brazil
(2006)
admissions/da
admissions/da
by hospital
1996-2001
y
y
admissions)
Female
Male
T
Ischemic stroke (ED
Five hospitals
Edmonton,
Szvszkowicz
n = 5,250
n = 5,680
visit)
from the
Canada
(2008)
metropolitan
1992-2002
area
n = 10,881
Femaleb
Maleb
Diabetes
Ministry of
Santiago,
Dales et al.
(hospitalizations)
Health
Chile
(2012)
statistics for
2001-2008
hospitalizations
for acute
serious
complications
of diabetes
Long-term exposure
Carotid intima-media n = 745 Utrecht, Lenters et al.
thickness (preclinical Ages 26-30 yr Netherlands (2010)
atherosclerosis) 1999-2000
| Pulse wave index
(measure of arterial
stiffness, marker for
CHD)
Female Male
53% 47%
Female
Male
| Blood pressure
n = 24,845
Shenyang,
Dona et al.
n = 12,184
n = 12,661
(hypertension)
subject from
Anshan, and
(2013d)
different
Jinzhou,
households at
China
residence for
2006-2008
>5 yr
Mean age:
41.7 ± 13.7 yr
Female
Male
| Incident
n = 24,845
Shenyang,
Dona et al.
n = 12,184
n = 12,661
cardiovascular
subject from
Anshan, and
(2013a)
disease (hospital
different
Jinzhou,
admissions)
households at
China
¦ residence for
2006-2008
| Incident stroke
>5 yr
(hospital admissions)
Mean age:
41.7± 13.7 yr
Female
Male
Hospital admissions,
n = 1,491
Tehran, Iran
Nabavi et al.
n = 727
n = 764
stroke
admissions for
2004
(2012)
stroke
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Table 6 12 (Continued): Epidemiologic studies evaluating effect modification by
sex and sulfur dioxide exposure.
Factor
Evaluated
Reference
Category
Direction of
Effect
Modification
Outcome
Study
Population
Study
Details
Study
Female
n =6,139
Male
n =6,712
Heart failure n = 12,851 England Atkinson et
(hospital admissions) Ages 40-89 yr 2003-2007 al. (2013)
in 2003
Female
n = 3.71
admissions/
day
Male
n = 5.94
admissions/
day
Cerebrovascular
disease (hospital
admissions)
Records Lanzhou, Zheng et al.
obtained from China (2013)
four largest 2001-2005
hospitals
Female
43.5%
Male
56.5%
Cardiovascular
mortality
n = 70,947
study
participants;
8,319 deaths
China
1991-2000
Cao et al.
(2011)
Female
n =69
Male
n = 187
Cardiovascular
mortality
n = 9,941
subjects >25 yr
at study
enrollment and
living at
residence
>10 yr;
256 cardio-
vascular
deaths
Shenyang,
China
1998-2009
Zhang et al.
(2011)
Female
n = 18
Male
n = 54
Respiratory mortality
n = 9,441
residents
>35 yr living at
residence
>10 yr;
72 deaths due
to respiratory
cause
Shenyang,
China
1998-2009
Dong et al.
(2012)
Femaleb
Maleb
Hospitalizations for Hospitalization Etang-de- Pascal et al.
cancer (lung,
myeloma, and
non-Hodgkin
lymphoma)
Hospitalizations
(acute leukemia)
records
obtained from
database used
in public and
private
hospitals
Berre,
France
2004-2007
(2013)
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Table 6 12 (Continued): Epidemiologic studies evaluating effect modification by
sex and sulfur dioxide exposure.
Direction of
Effect
Factor
Reference
Modification
Study
Study
Evaluated
Category
a
Outcome
Population
Details
Study
Femaleb
Maleb
_
Cancer incidence
Records
Haifa, Israel
Eitan et al.
(bladder, lung,
obtained from
1995-1999
(2010)
non-Hodgkin
national
lymphoma)
database;
patients had to
reside in
Jewish
communities in
study area
>10 yr
n = 1,452 case
s
Femaleb
Maleb
Decrements in lung
N = 32,712
Health
Forbes et al.
function (FEV-i)
households
Survey for
(2009c)
adults from
England.
white ethnic
Lung
groups (>16 yr)
function in
England
1995, 1996,
1997, and
2001
Female
Male
_
Decrements in lung
n = 3,957
Taiwan
Lee et al.
n = 1,968
n = 1,989
function (FEV-i, FVC,
seventh grade
Children
(2011b)
MMEF, PEFR)
children
Health Study
Ages 12-13 yr
2005-2007
from
14 Taiwanese
communities
Female
Male
_
Decrements in lung
n = 464
Salamanca,
Linares et al.
n =235
n =229
function (FVC, FEV-i,
children from
Mexico
(2010)
PEF, FEVi/FVC)
two schools
March 2004-
Ages 6-14 yr
February
2005
Female
Male
_
Decrements in lung
n = 399 COPD
United
Wood et al.
n = 155
n =244
function (FEV-i)
patients with
Kingdom
(2010)
severe
1997-2006
a-1-antitrypsin
deficiency
Female
Male
Impaired lung
n = 1,880
Eskisehir,
Altua et al.
n = 731
n =649
function (FEV-i, FVC,
students
Turkey
(2013)
PEF, MMEF)
Ages 9-13 yr
January
(summer)
2008-March
900Q
zuuy
Female
Male
Impaired lung
n = 588
n = 530
function (FEV-i, FVC,
PEF, MMEF) (winter)
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Table 6 12 (Continued): Epidemiologic studies evaluating effect modification by
sex and sulfur dioxide exposure.
Direction of
Effect
Factor
Reference
Modification
Study
Study
Evaluated
Category
a
Outcome
Population
Details
Study
Femaleb
Maleb
T
Asthma diagnosis
n = 20,364
Southwester
Clark et al.
children
n British
(2010)
diagnosed with
Columbia
asthma at up
Births from
to 3-4 yr of
1999-2000
age, mean age
at follow up
48 ± 7 mo
Femaleb
Maleb
-
Respiratory
n = 31,049
Seven
Dona et al.
symptoms (cough,
children
northeastern
(2013c)
phlegm, current
Ages 2-14 yr
cities study,
wheeze) and doctor-
Liaoning
diagnosed asthma
Provence,
northeast
China
2008-2009
Female
Male
-
Physician-diagnosed
n = 4,320
Chicago, IL;
Nishimura et
n = 2,826
n =2,561
asthma plus two or
GALA II and
Bronx, NY;
al. (2013)
more symptoms of
SAGE II
Houston, TX;
coughing, wheezing,
cohorts
San
or shortness of
(Latinos and
Francisco
breath
African-
Bay Area,
Americans
CA and
ages 8-21 yr)
Puerto Rico
2006-2011
Female
Male
Lifetime asthma
n =6,683
French Six
Penard-
n =2,458
n = 2,449
- children
Cities study
Morand et al.
-
Past year asthma
Ages 9-11 yr
(Bordeaux,
(2010)
- (mean
Clermont-
Exercise-induced
age = 10.4 yr)
Ferrand,
asthma
Creteil,
Marseille,
Strasbourg,
Reims)
March 1999-
October
2000
Female
Male
-
Nonallergic asthma
n -1,467
Hamilton,
Sahsuvaroal
n = 729
n = 738
children grades
Canada
u et al.
one (ages 6-7
1994-1995
(2009)
yr) and eight
(ages 13-14
yr)
Female
Male
_
Oxidative stress
n = 36
China
Lin et al.
n = 19
n = 17
(8-oxo-7,8-dihydro-
elementary
June 2007-
(2015)
2-deoxyguanosine
school children
September
and
(fourth grade,
2008
malondialdehyde)
mean age
10.6 yr)
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Table 6 12 (Continued): Epidemiologic studies evaluating effect modification by
sex and sulfur dioxide exposure.
Direction of
Effect
Factor
Reference
Modification
Study
Study
Evaluated
Category
a
Outcome
Population
Details
Study
Female
Male
-
Hypertension and
n = 9,354
Seven
Dona et al.
n =4,583
n =4,771
arterial blood
elementary
Northeastern
(2014)
pressure (diastolic
and middle
Cities study,
and systolic)
school children
Liaoning
Ages 5-17 yr
Provence,
northeast
China
2012-2013
Female
Male
T
Autonomic
N = 53 adults
Shanghai,
Sun et al.
n =27
n =26
dysfunction—change
ages 51-68 yr
China April,
(2015)
s in heart rate
with Type-2
June, and
variability (changes
diabetes or
September
in SDNN)
impaired
2010
glucose
tolerance
Female
Male
-
Mortality
n = 85,559
Rome
Ancona et al.
n =44,181
n =41,378
Natural causes,
Ages 5-106 yr
Longitudinal
(2015)
cardiovascular
Study,
diseases, cancers
suburb of
(stomach, colon and
Rome (Italy)
rectum, liver,
2001-2010
pancreas, lung,
bladder, kidney,
brain, lymphatic, and
hematopoietic tissue)
T
Mortality
Cancer
(larynx)
1
Mortality
Respiratory diseases
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Table 6 12 (Continued): Epidemiologic studies evaluating effect modification by
sex and sulfur dioxide exposure.
Factor
Evaluated
Reference
Category
Direction of
Effect
Modification
Outcome
Study
Population
Study
Details
Study
Female Male - Hospital admissions N = 85,559 Rome Ancona et al.
N = 44,181 N= 41,378 All causes, Ages 5-106 yr Longitudinal (2015)
cardiovascular Study,
diseases, and cancer suburb of
(stomach, colon and Rome (Italy)
rectum, pancreas, 2001-2010
lung, bladder,
kidney, brain,
lymphatic, and
hematopoietic tissue)
Hospital admissions
Cancer
(liver, larynx)
Hospital admissions
Respiratory diseases
CHD = coronary heart disease; CHF = congestive heart failure; COPD = chronic obstructive pulmonary disease;
CVD = cardiovascular disease; ED = emergency department; FE\A| = forced expiratory volume in 1 second; FVC = forced vital
capacity; GALA II = Genes-environments and Admixture in Latino Americans; HRV = heart rate variability; Ml = myocardial
infarction; MMEF = maximum midexpiratory flow; PEF = peak expiratory flow; PEFR = peak expiratory flow rate; SDNN = standard
deviation of RR intervals.
aUp facing arrow indicates that the effect of S02 is greater (e.g., larger risk of hospital admission, larger decrement in HRV) in the
group with the factor evaluated than in the reference group. Down facing arrow indicates that the effect of S02 is smaller in the
group with the factor evaluated than in the reference group. A dash indicates no difference in S02-related health effect between
groups.
bSample size not reported.
Cardiovascular outcomes associated with both short- and long-term SO2 exposures also
evaluated sex-specific differences, though results varied across studies. In short- and
long-term studies of SO2 exposure, there was no consistent pattern of risk for men or
women for the various subclinical markers of cardiovascular outcomes that were
examined, including lipo-protein-associated phospholipase A2, heart rate variability,
carotid intima-media thickness, pulse wave index, and blood pressure (Dong et al.
2013d; Huang et al.. 2012; Briiske etal.. 2011; Lenters et al.. 2010). There was some
evidence that females had stronger associations for cardiovascular hospitalizations or ED
visits than men relative to short-term SO2 levels (Szvszkowicz. 2008; Martins et al.
2006). but long-term exposures resulted in the same or smaller associations for hospital
admissions for cardiovascular codes when comparing females to males (Dong etal..
2013a; Zheng et al.. 2013; Nabavi et al.. 2012). Evidence for cardiovascular-specific
mortality in long-term studies did not indicate consistent sex-specific differences (Cao et
al.. 2011; Zhang etal.. 2011). which is also true for respiratory mortality (Dong et al..
2012) and cancer outcomes (Pascal et al.. 2013; Eitan et al.. 2010).
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The collective body of evidence does not clearly indicate that SCh-related health effects
differ between males and females. Several different outcomes were evaluated in studies
looking at effect modification by sex, including a wide range of cardiovascular outcomes
in short- and long-term studies. Due to the wide range of outcomes examined and
inconsistent results demonstrated across studies, the evidence is inadequate to determine
whether males or females may be at increased risk for SCh-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.
Persons with lower SES have been generally 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 this population's risk to
SCh-related health effects. 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 challenge the
synthesis of findings, which is also complicated by variations in definitions of SES across
countries based on population demographics, bureaucracy, and the local economy, which
can contribute to varying degrees of deprivation or inequities. 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.
Although there is evidence demonstrating differential exposures to air pollution based on
SES, no studies have investigated SCh-specific exposure. However, a handful of studies
in this ISA evaluated effect modification by income and education on SCh-associated
health outcomes (Table 6-13). The majority of evidence on effect modification is for
mortality, but results across studies are inconsistent. Chen et al. (2012c) found low
education to increase risk for mortality with short-term SO2 exposure, but in a study of
long-term SO2 exposure, Krewski et al. (2009) did not find low education to increase risk
for mortality. Dong et al. (2012) and Zhang et al. (2011) looked at associations between
long-term SO2 and respiratory and cardiovascular mortality, respectively, in the same
cohort and generally did not find low SES (education and income) to increase risk, with
the exception of an increase in risk of respiratory mortality for low education, although
only a small number of deaths in the study were due to respiratory causes.
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Table 6-13 Epidemiologic studies evaluating socioeconomic status and sulfur
dioxide exposure.
Factor
Evaluated
Reference
Category
Direction of
Effect
Modification3
Outcome
Study Population
Study
Details
Study
Short-term exposure
Low education
(illiterate/
primary
school)b
High education
(middle school
and above)b
T
Total mortality
Data from
Municipal Centers
for Disease
Control and
Prevention
17 Chinese
cities
Chen et al.
(2012c)
Long-term exposure
Low education
n = 13%
Medium or high
education
n = 87%
T
Carotid intima-
media thickness
(preclinical
atherosclerosis)
n = 745
Ages 26-30 yr
Utrecht,
Netherlands
1999-2000
Lenters et
al. (2010)
Pulse wave
index (measure
of arterial
stiffness, marker
for CHD)
Lowest
deprivation
index
n =2,109
Highest
deprivation
index
n = 2,327
Heart failure
(hospital
admissions)
n = 12,851
Ages 40-89 yr in
2003
England
2003-2007
Atkinson et
al. (2013)
Low education
(< grade 12)
n = 4,026
High education
(> grade 12)
n = 76,685
Mortality (all
cause, lung
cancer,
cardiopulmonary)
American Cancer
Society cohort
United
States
1982-2000
Krewski et
al. (2009)
Low education
n = 76.4%
High education
n = 23.6%
Cardiovascular
mortality
N = 70,947 study
participants;
8,319 deaths
China
1991-2000
Cao et al.
(2011)
Low income
(<200
RMB/mo)
n =48
High income
(>800
RMB/mo)
n = 75
Cardiovascular
mortality
n = 9,941 subjects
>25 yr at study
enrollment and
living at residence
>10 yr; 256
Shenyang,
China
1998-2009
Zhana et al.
(2011)
Low education
n = 183
High education
n = 72
-
cardiovascular
deaths
Low income
(<200
RMB/mo)
n = 14
High income
(>800
RMB/mo)
n =20
Respiratory
mortality
Shenyang,
China
1998-2009
Dona et al.
(2012)
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17
18
19
20
21
22
Table 6-13 (Continued): Epidemiologic studies evaluating socioeconomic status
and sulfur dioxide exposure.
Factor
Evaluated
Reference
Category
Direction of
Effect
Modification3
Outcome
Study Population
Study
Details
Study
Low education High education
n = 47 n = 25
n = 9,441
residents >35 yr
living at residence
>10 yr; 72 deaths
due to respiratory
disease
CHD = coronary heart disease; RMB = renminbi.
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 S02 is smaller in the group with the factor
evaluated than in the reference group. A dash indicates no difference in S02-related health effect between groups.
Lenters et al. (2010) and Atkinson et al. (2013) evaluated SCh-associated cardiovascular
outcomes with long-term exposure but did not report consistent evidence related to effect
modification by education level or a deprivation index, respectively.
Overall, the evidence for effect modification by SES is inconsistent for a limited number
of health outcomes. In addition, various SES factors were used across studies that were
conducted in a wide variety of countries. Due to these limitations and complexities, the
evidence is inadequate to determine whether low SES increases risk for SCh-related
health effects.
6.5.4 Race/Ethnicity
Based on the 2010 census, 63.7% of the U.S. population identified themselves as
non-Hispanic whites, 12.6% reported their race as non-Hispanic black, and 16.3%
reported being Hispanic (Humes et al.. 2011). Race and ethnicity are complex factors that
are often closely correlated with other factors including particular genetics, diet, and
socioeconomic status. Therefore, race and ethnicity may influence any potential
differences in SC>2-related health effects through both intrinsic and extrinsic mechanisms.
Despite our understanding of disproportionate health effects experienced across race or
ethnicity, only two epidemiologic studies evaluating associations between long-term
ambient SO2 exposure and birth weight decrements have considered effect modification
by race (Table 6-14). Neither Bell et al. (2007) nor Darrow etal. (2011) found any
indication of greater SC>2-associated decrements in birth weight for mothers of black or
Hispanic infants compared to white infants. In addition, there is uncertainty regarding the
relationship between ambient SO2 exposures and birth outcomes in the general
population, including associations with birth weight (Section 5.4.1.3). Overall, this
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limited evidence is inadequate to determine whether individuals of a certain race or
ethnicity are at increased risk for SCh-associated health effects.
Table 6-14 Epidemiologic studies evaluating race/ethnicity and sulfur dioxide
exposure.
Factor
Evaluated
Reference
Category
Direction of
Effect
Modification3
Outcome
Study
Population
Study Details
Study
Long-term exposure
Black
maternal
race
White
maternal
race
-
Birth weight
decrements
n = 358,504
births (all
registered births)
Massachusetts,
Connecticut
1999-2002
Bell et al.
(2007)
n = 10.7%
n = 83.4%
Hispanic
maternal
race
White
maternal
race
-
Birth weight
decrements
N = 406,627
full-term,
singleton births
Atlanta, GA
1994-2004
Darrow et al.
(2011)
n = 14.3%
n = 45.2%
Non-
Hispanic
black
maternal
race
n = 40.5%
aUp facing arrow indicates that the effect of S02 is greater (e.g., larger risk of hospital admission, larger decrement in birth weight) in
the group with the factor evaluated than in the reference group. Down facing arrow indicates that the effect of S02 is smaller in the
group with the factor evaluated than in the reference group. A dash indicates no difference in S02-related health effect between
groups.
6.6 Behavioral and Other Factors
6.6.1 Smoking
3 Smoking is a common behavior as indicated by the 2010 National Health Interview
4 Survey which estimated that within the U.S. adult population approximately 19.2% of
5 individuals report being current smokers and 21.5% report being a former smoker
6 (Schiller et al.. 2012). Smoking is a we 11-documented risk factor for many diseases, but it
7 is unclear whether smoking exacerbates health effects associated with air pollutant
8 exposures, including SO2.
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24
Of the epidemiologic studies included in this ISA that evaluate effect modification by
smoking status, the majority focused on cardiovascular outcomes (Table 6-15). Min et al.
(2009) and Lenters et al. (2010) found stronger associations between subclinical
cardiovascular outcomes with short- or long-term SO2 exposures, respectively, in
individuals who smoke compared to those that do not. Further, Atkinson et al. (2013)
found current or former smoking to increase risk for SCh-associated hospital admissions
for heart failure; however, Cao etal. (2011) and Zhang etal. (2011) did not find
increased risk for cardiovascular mortality associated with long-term ambient SO2
exposures in individuals with smoking.
Dong et al. (2012) and Forbes et al. (2009c) were the only other studies that investigated
effect modification in respiratory endpoints by smoking status. Dong et al. (2012) found
that of the few number of respiratory deaths included in their retrospective cohort study,
associations with long-term ambient SO2 were only present with smoking. 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 no smoking; however, former
smoking did appear to increase risk in this study.
Overall, the collective evidence is suggestive of increased risk for S02-associated health
effects in individuals who smoke, particularly for cardiovascular outcomes. There is
consistency among cohort studies showing stronger associations between subclinical
cardiovascular outcomes and SO2 exposure with smoking, which is strengthened by
evidence for hospital admissions. There are limitations in that evidence does not show
that associations with cardiovascular mortality are modified by smoking status in addition
to the uncertainties related to the overall strength of the relationship between SO2 and
cardiovascular effects (Sections 5.3.1.11 and 5.3.2.6).
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Table 6-15 Epidemiologic studies evaluating smoking status and sulfur dioxide
exposure.
Direction of
Factor Reference Effect Study
Evaluated Category Modification3 Outcome Population Study Details Study
Short-term exposure
Smoking
No smoking
f Reductions in
n = 1,023 adults
Taein Island,
Min et al.
n =256
n = 767
HRV (SDNN, LF,
Korea
(2009)
HF)
Long-term exposure
Current
Never
f Carotid
n = 745
Utrecht,
Lenters et al.
smoking
smoking
intima-media
Ages 26-30 yr
Netherlands
(2010)
n = 31%
n = 55%
thickness
1999-2000
(preclinical
atherosclerosis)
Pulse wave index
(measure of
arterial stiffness,
marker for CHD)
Current or
Never
f Heart failure
n = 12,851
England
Atkinson et al.
former
smoking
(hospital
Ages 40-89 yr in
2003-2007
(2013)
smoking
n = 5,329
admissions)
2003
n =2,310
Current
Never
| Cardiovascular
N = 70,947 study
China
Cao et al.
smoking
smoking
mortality
participants;
1991-2000
(2011)
n = 37.1%
n = 58.2%
8,319 deaths
Current
Never
- Cardiovascular
n = 9,941
Shenyang, China
Zhana et al.
smoking
smoking
mortality
subjects >25 yr
1998-2009
(2011)
n =2,850
n =4,359
at study
enrollment and
living at
residence >10 yr;
256
cardiovascular
deaths
Smoking
Smoking
f Respiratory
n = 9,441
Shenyang, China
Dona et al.
(yes)
(no)
mortality
residents >35 yr
1998-2009
(2012)
n = 37
n = 35
living at
residence >10 yr;
72 deaths due to
respiratory cause
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Table 6-15 (Continued) Epidemiologic studies evaluating smoking status and
sulfur dioxide exposure
Factor Reference
Evaluated Category
Direction of
Effect
Modification3
Outcome
Study
Population
Study Details Study
Current Never
smokingb smokingb
Former Never
smokingb smokingb
Decrements in
lung function
¦ (FEVi)
n = 32,712
households.
Adults from white
ethnic groups
(>16 yr)
Health Survey for
England. Lung
function in
England
1995, 1996,
1997, and 2001
Forbes et al.
(2009c)
CHD = coronary heart disease; FE\A| = forced expiratory volume in 1 second; HF = high frequency; HRV = heart rate variability;
LF = low frequency; SDNN = standard deviation of RR intervals.
aUp facing arrow indicates that the effect of S02 is greater (e.g., larger risk hypertension) in the group with the factor evaluated than in
the reference group. Down facing arrow indicates that the effect of S02 is smaller in the group with the factor evaluated than in the
reference group. A dash indicates no difference in S02-related health effect between groups.
6.7 Conclusions
1 This chapter characterized factors that may result in populations and lifestages being at
2 increased risk for SCh-related health effects (Table 6-16). The evaluation of each factor
3 focused on the consistency, coherence, and biological plausibility of evidence integrated
4 across scientific disciplines, specifically, epidemiologic, controlled human exposure, and
5 toxicological studies using the weight-of-evidence approach detailed in Table 6-1. In
6 evaluating and integrating evidence related to at-risk factors, it is important to consider
7 additional information including that related to exposure, dosimetry, modes of action,
8 and/or the independence of relationships of SO2 exposure with health effects; however,
9 this information was particularly limited for literature on ambient SO2.
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1
2
3
4
5
6
7
8
9
10
11
Table 6-16 Summary of evidence for potential increased SO2 exposure and
increased risk of S02-related health effects.
Evidence
Classification
Factor Evaluated
Rationale for Classification
Adequate
evidence
Asthma (Section 6.3.1)
Consistent evidence for increased risk for SC>2-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
Children (Section 6.5.1.1)
Older adults (Section 6.5.1.2)
Smokina (Section 6.6.1)
Each factor: evidence for increased risk provided in
previous ISA; lack of toxicology studies
Children: mixed results in studies for
respiratory-related outcomes
Older adults: mixed results in studies for
respiratory-related outcomes and mortality
Smoking: evidence primarily for cardiovascular
morbidity
Inadequate
evidence
Pre-existing cardiovascular disease
(Section 6.3.2)
Pre-existina diabetes (Section 6.3.3)
Pre-existinq obesitv (Section 6.3.4)
Genetic background (Section 6.4)
Sex (Section 6.5.2)
Socioeconomic status (Section 6.5.3)
Race/ethnicitv (Section 6.5.4)
Epidemiologic findings inconsistently show differences
in S02-related health effects, show no difference, or
are limited in quantity.
Findings based primarily on cardiovascular effects,
diabetes, birth outcomes, and mortality. 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.
Consistent with observations made in the 2008 ISA for Sulfur Oxides (U.S. EPA. 2008b).
there is adequate evidence to conclude that people with asthma are at increased risk for
SCh-related health effects. The majority of evidence was presented in the previous ISA as
well, 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 5.2.1.8V 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 5.2.1.2V
Further support for increased risk in individuals with asthma is provided by biological
plausibility drawn from modes of action.
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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
Evidence for age-related risk of SCh-related respiratory effects is suggestive of increased
risk for children and older adults. Although the 2008 ISA for Sulfur Oxides (U.S. EPA.
2008b) 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 evidence generally found similar associations for S02-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.
The evidence is suggestive that smoking increases risk for SC>2-associated health effects,
particularly cardiovascular outcomes. Cohort and hospital admissions studies show
stronger associations between cardiovascular outcomes and SO2 with smoking, although
uncertainties remain regarding the overall strength of the relationship between SO2 and
cardiovascular effects in addition to inconsistencies related to respiratory outcomes
associated with long-term SO2 exposure.
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 SC>2-related health effects. Generally, there were a limited
number of studies available to inform risk for individuals with pre-existing
cardiovascular disease, diabetes, or obesity as well as those evaluating SES, genetic
background, and race/ethnicity. 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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