vvEPA
United States September 2007
|ng™mental Protection EPA/600/R-07/108A
Annexes for the
Integrated Science Assessment
for Sulfur Oxides -
Health Criteria
(First External Review Draft)
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EPA/600/R-07/108A
September 2007
Annexes for the
Integrated Science Assessment
for Sulfur Oxides - Health Criteria
National Center for Environmental Assessment-RTF Division
Office of Research and Development
U.S. Environmental Protection Agency
Research Triangle Park, NC
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DISCLAIMER
This document is a first external review draft being released for review purposes only and
does not constitute U.S. Environmental Protection Agency policy. Mention of trade names or
commercial products does not constitute endorsement or recommendation for use.
PREFACE
Legislative Requirements
Two sections of the Clean Air Act (CAA) govern the establishment and revision of the
national ambient air quality standards (NAAQS). Section 108 (U.S. Code, 2003a) directs the
Administrator to identify and list "air pollutants" that "in his judgment, may reasonably be
anticipated to endanger public health and welfare" and whose "presence ... in the ambient air
results from numerous or diverse mobile or stationary sources" and to issue air quality criteria
for those that are listed. Air quality criteria are intended to "accurately reflect the latest scientific
knowledge useful in indicating the kind and extent of identifiable effects on public health or
welfare which may be expected from the presence of [a] pollutant in ambient air ...."
Section 109 (U.S. Code, 2003b) directs the Administrator to propose and promulgate
"primary" and "secondary" NAAQS for pollutants listed under section 108. Section 109(b)(l)
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 required to protect the public welfare from any known
or anticipated adverse effects associated with the presence of [the] pollutant in the ambient air."2
The 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" [U.S. Senate (1970)].
Welfare effects as defined in section 302(h) [U.S. Code, 2005] include, but are 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."
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The requirement that primary standards include an adequate margin of safety was
intended to address uncertainties associated with inconclusive scientific and technical
information available at the time of standard setting. It was also intended to provide a reasonable
degree of protection against hazards that research has not yet identified. See Lead Industries
Association v. EPA, 647 F.2d 1130, 1154 (D.C. Cir 1980), cert, denied. 449 U.S. 1042 (1980);
American Petroleum Institute v. Costle, 665 F.2d 1176, 1186 (D.C. Cir. 1981), cert, denied.
455 U.S. 1034 (1982). Both kinds of uncertainties are components of the risk associated with
pollution at levels below those at which human health effects can be said to occur with
reasonable scientific certainty. Thus, in selecting primary standards that include an adequate
margin of safety, the Administrator is seeking not only to prevent pollution levels that have been
demonstrated to be harmful but also to prevent lower pollutant levels that may pose an
unacceptable risk of harm, even if the risk is not precisely identified as to nature or degree.
In selecting a margin of safety, the U.S. Environmental Protection Agency (EPA)
considers such factors as the nature and severity of the health effects involved, the size of
sensitive population(s) at risk, and the kind and degree of the uncertainties that must be
addressed. The selection of any particular approach to providing an adequate margin of safety is
a policy choice left specifically to the Administrator's judgment. See Lead Industries
Association v. EPA, supra, 647 F.2d at 1161-62.
In setting standards that are "requisite" to protect public health and welfare, as provided
in section 109(b), EPA's task is to establish standards that are neither more nor less stringent
than necessary for these purposes. In so doing, EPA may not consider the costs of implementing
the standards. See generally Whitman v. American Trucking Associations, 531 U.S. 457,
465-472, 475-76 (2001).
Section 109(d)(l) requires that "not later than December 31, 1980, and at 5-year
intervals thereafter, the Administrator shall complete a thorough review of the criteria
published under section 108 and the national ambient air quality standards ... and shall make
such revisions in such criteria and standards and promulgate such new standards as may be
appropriate ...." Section 109(d)(2) requires that an independent scientific review committee
"shall complete a review of the criteria ... and the national primary and secondary ambient air
quality standards ... and shall recommend to the Administrator any new ... standards and
revisions of existing criteria and standards as may be appropriate ...." Since the early 1980s, this
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independent review function has been performed by the Clean Air Scientific Advisory
Committee (CASAC) of EPA's Science Advisory Board.
History of Reviews of the Primary NAAQS for Sulfur Dioxide
On April 30, 1971, the EPA promulgated primary NAAQS for sulfur dioxide (SO2).
These primary standards, which were based on the findings outlined in the original 1969 Air
Quality Criteria (hereafter "AQCD") for Sulfur Oxides (U.S. DHEW, 1969), were set at
0.14 parts per million (ppm) averaged over a 24-hour period, not to be exceeded more than
once per year, and 0.030 ppm annual arithmetic mean. In 1982, EPA published the AQCD for
Particulate Matter (PM) and Sulfur Oxides along with an addendum of newly published
controlled human exposure studies (U.S. Environmental Protection Agency, 1982), which
updated the scientific criteria upon which the initial standards were based. In 1986, a second
addendum was published presenting newly available evidence from epidemiologic and
controlled human exposure studies (U.S. Environmental Protection Agency, 1986). In 1988,
EPA reviewed and revised the health criteria upon which the SO2 standards were based. As a
result of that review, EPA published a proposed decision not to revise the existing standards
(Federal Register, 1988). However, EPA specifically requested public comment on the
alternative of revising the current standards and adding a new 1-h primary standard of 0.4 ppm.
As a result of public comments on the 1988 proposal and other post-proposal
developments, EPA published a second proposal on November 15, 1994 (Federal Register,
1994). The 1994 re-proposal was based in part on a supplement to the second addendum of the
criteria document, which evaluated new findings on short-term SO2 exposures in asthmatics
(U.S. Environmental Protection Agency, 1994). As in the 1988 proposal, EPA proposed to
retain the existing 24-h and annual standards. The EPA also solicited comment on three
regulatory alternatives to further reduce the health risk posed by exposure to high 5-min peaks of
SO2 if additional protection were judged to be necessary. The three alternatives included (1)
revising the existing primary SO2 NAAQS by adding a new 5-min standard of 0.60 ppm SO2; (2)
establishing a new regulatory program under section 303 of the Act to supplement protection
provided by the existing NAAQS, with a trigger level of 0.60 ppm SO2; and (3) augmenting
implementation of existing standards by focusing on those sources or source types likely to
produce high 5-min peak concentrations of SO2. On May 22, 1996, EPA's final decision, that
revisions of the NAAQS for sulfur oxides were not appropriate at that time, was announced in
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the Federal Register (Federal Register, 1996). In that decision, EPA announced an intention to
propose guidance, under section 303 of the Act, to assist states in responding to short-term peak
levels of SO2.
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Annexes for the Integrated Science Assessment
for Sulfur Oxides - Health Criteria
ANNEX CHAPTERS
AXl. CHAPTER 1 ANNEX-INTRODUCTION AX1-1
AX2. CHAPTER 2 ANNEX - ATMOSPHERIC CHEMISTRY OF
NITROGEN AND SULFUR OXIDES AX2-1
AX3. CHAPTER 3 ANNEX - A FRAMEWORK FOR MODELING HUMAN
EXPOSURES TO SO2 AND RELATED AIR POLLUTANTS AX3-1
AX4. CHAPTER 4 ANNEX - TOXICOLOGICAL STUDIES OF THE
HEALTH EFFECTS OF SULFUR OXIDES AX4-1
AX5. CHAPTER 5 ANNEX - EPIDEMIOLOGICAL STUDIES OF HUMAN
HEALTH EFFECTS ASSOCIATED WITH EXPOSURE TO AMBIENT
SULFUR OXIDES AX5-1
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ANNEX TABLE OF CONTENTS
Page
List of Tables ix
List of Figures xi
Authors, Contributors, and Reviewers xiii
U.S. Environmental Protection Agency Project Team xviii
U.S. Environmental Protection Agency Science Advisory Board (SAB)
Staff Office Clean Air Scientific Advisory Committee (CASAC) xxi
Abbreviations and Acronyms xxiv
AX1. CHAPTER 1 ANNEX-INTRODUCTION AX1-1
AX1.1 LEGISLATIVE REQUIREMENTS AX1-2
AX1.2 HISTORY OF REVIEWS OF THE PRIMARY NAAQS
FORSO2 AX1-3
AX2. CHAPTER 2 ANNEX - ATMOSPHERIC CHEMISTRY OF
NITROGEN AND SULFUR OXIDES AX2-1
AX2.1 INTRODUCTION AX2-1
AX2.2 CHEMISTRY OF NITROGEN OXIDES IN THE
TROPOSPHERE AX2-2
AX2.2.1 Basic Chemistry AX2-2
AX2.2.2 Nonlinear Relations between Nitrogen Oxide
Concentrations and Ozone Formation AX2-9
AX2.2.3 Multiphase Chemistry Involving NOX AX2-12
AX2.3 CHEMISTRY OF SULFUR OXIDES IN THE
TROPOSPHERE AX2-24
AX2.4 MECHANISMS FOR THE AQUEOUS PHASE
FORMATION OF SULFATE AND NITRATE AX2-28
AX2.5 TRANSPORT OF NITROGEN AND SULFUR OXIDES IN
THE ATMOSPHERE AX2-31
AX2.6 SOURCES AND EMISSIONS OF NITROGEN OXIDES,
AMMONIA, AND SULFUR DIOXIDE AX2-35
AX2.6.1 Interactions of Nitrogen Oxides with
the Biosphere AX2-35
AX2.6.2 Emissions of NOX, NH3, and SO2 AX2-48
AX2.6.3 Field Studies Evaluating Emissions Inventories AX2-56
AX2.7 METHODS USED TO CALCULATE CONCENTRATIONS
OF NITROGEN OXIDES AND THEIR CHEMICAL
INTERACTIONS IN THE ATMOSPHERE AX2-58
AX2.7.1 Chemistry-Transport Models AX2-59
AX2.7.2 CTM Evaluation AX2-73
AX2.8 SAMPLING AND ANALYSIS OF NITROGEN
AND SULFUR OXIDES AX2-86
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ANNEX TABLE OF CONTENTS
(cont'd)
rage
AX2.8.1 Availability and Accuracy of Ambient
Measurements forNOy AX2-86
AX2.8.2 Measurements of HNO3 AX2-93
AX2.8.3 Techniques for Measuring Other NOy Species AX2-95
AX2.8.4 Remote Sensing of Tropospheric NO2 Columns
for Surface NOX Emissions and Surface NO2
Concentrations AX2-96
AX2.8.5 SAMPLING AND ANALYSIS FOR SO2 AX2-98
AX2.8.6 Sampling and Analysis for Sulfate, Nitrate,
and Ammonium AX2-101
AX2.9 POLICY RELEVANT BACKGROUND CONCENTRATIONS
OF NITROGEN AND SULFUR OXIDES AX2-110
AX2.10 REFERENCES AX2-129
AX3. CHAPTER 3 ANNEX - A FRAMEWORK FOR MODELING HUMAN
EXPOSURES TO SO2 AND RELATED AIR POLLUTANTS AX3-1
AX3.1 INTRODUCTION: CONCEPTS, TERMINOLOGY,
AND OVERALL SUMMARY AX3-1
AX3.2 POPULATION EXPOSURE MODELS: THEIR
EVOLUTION AND CURRENT STATUS AX3-7
AX3.3 CHARACTERIZATION OF AMBIENT CONCENTRATIONS
OF SO2 AND RELATED AIR POLLUTANTS AX3-10
AX3.4 CHARACTERIZATION OF MICROENVIRONMENTAL
CONCENTRATIONS AX3-11
AX3.4.1 Characterization of Activity Events AX3-13
AX3.4.2 Characterization of Inhalation Intake and Uptake AX3-13
AX3.5 CONCLUDING COMMENTS AX3-14
AX3.6 REFERENCES AX3-17
AX4. CHAPTER 4 ANNEX - TOXICOLOGICAL STUDIES OF THE
HEALTH EFFECTS OF SULFUR OXIDES AX4-1
AX4.1 REFERENCES AX4-66
AX5. CHAPTER 5 ANNEX - EPIDEMIOLOGICAL STUDIES OF HUMAN
HEALTH EFFECTS ASSOCIATED WITH EXPOSURE TO AMBIENT
SULFUR OXIDES AX5-1
AX5.1 REFERENCES AX5-236
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ANNEX LIST OF TABLES
Page
AX2.3-1. Atmospheric Lifetimes of Sulfur Dioxide and Reduced Sulfur Species
With Respect to Reaction With OH, NO3, and Cl Radicals AX2-119
AX2.4-la. Relative Contributions of Various Reactions to the Total S(IV)
Oxidation Rate within a Sunlit Cloud, 10 Minutes after
Cloud Formation AX2-120
AX2.4-lb. Relative Contributions of Various Gas and Aqueous Phase Reactions
to Aqueous Nitrate Formation within a Sunlit Cloud, 10 Minutes
after Cloud Formation AX2-121
AX2.6-1. Emissions of Nitrogen Oxides, Ammonia, and Sulfur Dioxide
in the United States in 2002 AX2-122
AX2.8-1. Satellite Instruments Used to Retrieve Tropospheric NO2 Columns AX2-128
AX3.2-1. The Essential Attributes of the pNEM, HAPEM, APEX, SHEDS,
andMENTOR-lA AX3-16
AX4-1. Physiological Effects of SO2 Exposure AX4-2
AX4-2. Inflammatory Responses Following SO2 Exposure AX4-4
AX4-3. Effects of SO2 Exposure on Hypersensitivity/Allergic Reactions AX4-5
AX4-4. Effects of SO2 Exposure on Host Lung Defenses AX4-7
AX4-5. Effects of SO2 Exposure on Cardiovascular Endpoints AX4-8
AX4-6. Nervous System—Neurophysiology and Biochemistry Effects of
SO2 and Derivatives AX4-11
AX4-7. Reproductive and Developmental Effects of SO2 AX4-18
AX4-8. Hematological Effects of SO2 AX4-20
AX4-9. Endocrine System Effects of SO2 AX4-22
AX4-10. Effects of SO2 Exposure on Respiratory System Morphology AX4-23
AX4-11. Carcinogenic Effects of SO2 AX4-24
AX4-12. Respiratory System Biochemistry Effects of SO2 AX4-27
AX4-13. Respiratory System Effects of SO2 in Disease Models AX4-31
AX4-14. Effects of Mixtures Containing SO2 and Ozone AX4-32
AX4-15. Effects of SO2 Layered on Metallic or Carbonaceous Particles AX4-3 5
AX4-16. Effects of SO2 and Sulfate Mixtures AX4-45
AX4-17. Effects of Actual or Simulated Air Pollution Mixtures AX4-48
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ANNEX LIST OF TABLES
(cont'd)
Page
AX4-18. Effects of Meteorological Conditions on SO2 Effects AX4-52
AX4-19. In Vitro or Ex Vivo Respiratory System Effects of SO2 and
Metabolites AX4-55
AX4-20. Genotoxic Effects of SO2 and Metabolite AX4-57
AX4-21. Liver and Gastrointestinal Effects of SO2 AX4-61
AX4-22. Renal Effects of SO2 AX4-64
AX4-23. Lymphatic System Effects of SO2 and SO2 Mixture AX4-65
AX5-1. Associations of Short-term Exposure to Sulfur Dioxide with
Respiratory Morbidity in Field/Panel Studies AX5-2
AX5.2. Associations of Short-Term Exposure to Sulfur Dioxide with
Emergency Department Visits and Hospital Admissions
for Respiratory Diseases AX5-45
AX5.3. Associations of Short-Term Exposure to Sulfur Dioxide with
Cardiovascular Morbidity in Field/Panel Studies AX5-134
AX5.4. Associations of Short-Term Exposure to Sulfur Dioxide with
Emergency Department Visits and Hospital Admissions
for Cardiovascular Diseases AX5-146
AX5.5. Associations of Short-Term Exposure to Sulfur Dioxide on Mortality AX5-174
AX5.6. Associations of Long-Term Exposure to Sulfur Dioxide with
Respiratory Morbidity AX5-197
AX5.7. Associations of Long-Term Exposure to Sulfur Dioxide with
Incidence of Cancer AX5-214
AX5.8. Associations of Long-Term Exposure to Sulfur Dioxide with
Prenatal and Neonatal Outcomes AX5-216
AX5.9. Associations of Long-Term Exposure to Sulfur Dioxide
with Mortality AX5-229
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ANNEX LIST OF FIGURES
Page
AX2.1-1. Schematic diagram of the cycle of reactive nitrogen species in
the atmosphere AX2-3
AX2.2-1. Measured values of 63 and NOZ (NOy-NOx) during the afternoon at rural
sites in the eastern United States (gray circles) and in urban areas and
urban plumes associated with Nashville, TN (gray dashes), Paris, FR
(black diamonds) and Los Angeles, CA (X's) AX2-12
AX2.2-2. Structures of nitro-polycyclic aromatic hydrocarbons AX2-16
AX2.2-3. Formation of 2-nitropyrene (2NP) from the reaction of OH with
gaseous pyrene (PY) AX2-17
AX2.3-1. Transformations of sulfur compounds in the atmosphere AX2-26
AX2.4-1. Comparison of aqueous-phase oxidation paths AX2-29
AX2.6-1. Diel cycles of median concentrations (upper panels) and fluxes
(lower panels) for the Northwest clean sector, left panels) and Southwest
(polluted sector, right panels) wind sectors at Harvard Forest,
April-November, 2000, for NO, NO2, and O3/10. NO and O3 were
sampled at a height of 29m, andNO2at22m AX2-43
AX2.6-2. Simple NOX photochemical canopy model outputs AX2-44
AX2.6-3. Hourly (dots) and median nightly (pluses) NO2 flux vs. concentration,
with results of least-squares fit on the hourly data (curve) AX2-45
AX2.6-4. Averaged profiles at Harvard Forest give some evidence of some
NO2 input near the canopy top from light-mediated ambient reactions,
or emission from open stomates AX2-46
AX2.6-5. Summer (June-August) 2000 median concentrations (upper panels),
fractions of NOy (middle panels), and fluxes (lower panels) of NOy
and component species separated by wind direction (Northwest on the
left and Southwest on the right) AX2-47
AX2.7-1. Scatter plot of total nitrate (HNOs plus aerosol nitrate) wet deposition
(mg(N)m 2yr^) of the mean model versus measurements for the
North American Deposition Program (NADP) network AX2-70
AX2.7-2. Same as Figure AX2.7-1 but for sulfate wet deposition (mg(S)m"2yr-1).... AX2-71
AX2.7-3a,b. Impact of model uncertainty on control strategy predictions for
O3 for two days (August 1 Oa and 1 Ib, 1992) in Atlanta, GA AX2-76
AX2.7-4. Ozone isopleths (ppb) as a function of the average emission rate for
NOX and VOC (1012 molec. cm"2 s"1) in zero dimensional box
model calculations AX2-77
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ANNEX LIST OF FIGURES
(cont'd)
age
AX2.7-5a. Time series for measured gas-phase species in comparison with results
from a photochemical model AX2-78
AX2.7-5b. Time series for measured gas-phase species in comparison with results
from a photochemical model AX2-79
AX2.7-6. Correlations for O3 versus NOZ (NOy-NOx) in ppb from chemical
transport models for the northeast corridor, Lake Michigan, Nashville,
the San Joaquin Valley, and Los Angeles AX2-81
AX2.7-7a,b. Evaluation of model versus measured O3 versus NOy for two model
scenarios for Atlanta AX2-83
AX2.7-8a,b. Evaluation of model versus: (a) measured O3 versus NOZ and (b) O3
versus the sum 2H2O2 + NOZ for Nashville, TN AX2-84
AX2.7-9. Time series of concentrations of RO2, HO2, and OH radicals, local O3
photochemical production rate and concentrations of NOX from
measurements made during BERLIOZ AX2-85
AX2.8-1. Tropospheric NO2 columns (molecules NO2/ cm2) retrieved from the
SCIAMACHY satellite instrument for 2004-2005 AX2-97
AX2.9-1. Annual mean concentrations of NO2 (ppbv) in surface air over the
United States in the present-day (upper panel) and policy relevant
background (middle panel) MOZART-2 simulations AX2-112
AX2.9-2. Same as Figure AX2.9-1 but for SO2 concentrations AX2-113
AX2.9-3. Same as for Figure AX2.9-1 but for wet and dry deposition of
HNO3, NH4NO3, NOX, HO2NO2, and organic nitrates (mg N m~V ^ AX2-114
AX2.9-4. Same as Figure AX2.9-1 but for SOX deposition (SO2 + SO4)
(mgSnrV1) AX2-115
AX2.9-5. July mean soil NO emissions (upper panels; 1 x 10 9 molecules cm"2 s1)
and surface PRB NOX concentrations (lower panels; pptv) over the
United States from MOZART-2 (left) and GEOS-Chem (right) model
simulations in which anthropogenic O3 precursor emissions were
set to zero in North America AX2-116
AX3.1-1. Schematic description of a general framework identifying the processes
(steps or components) involved in assessing inhalation exposures and
doses for individuals and populations AX3-4
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Authors, Contributors, and Reviewers
Authors
Dr. Jee Young Kim (SOX Team Leader)—National Center for Environmental Assessment (B243-
01), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Jeffrey Arnold—National Center for Environmental Assessment (B243-01), U.S.
Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. James S. Brown—National Center for Environmental Assessment (B243-01),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Douglas Bryant—Intrinsik Science, 1900 Minnesota Court, Mississauga, Ontario L8S IPS
Dr. Ila Cote—National Center for Environmental Assessment (B243-01), U.S. Environmental
Protection Agency, Research Triangle Park, NC 27711
Dr. Arlene Fiore—Geophysical Fluid Dynamics Laboratory/National Oceanographic &
Atmospheric Administration, 201 Forrestal Rd., Princeton, NJ 08542-0308
Dr. Panos Georgopoulos—Computational Chemodynamics Laboratory, EOHSI Room 308, 170
Frelinghuysen Road, Piscataway, New Jersey 08854
Dr. Brett Grover—National Exposure Research Laboratory (D205-03), U.S. Environmental
Protection Agency, Research Triangle Park, NC 27711
Dr. Vic Hasselblad—Duke University Medical Center, Box 17969, Durham, NC 27715
Dr. Larry Horowitz—Geophysical Fluid Dynamics Lab oratory/National Oceanographic &
Atmospheric Administration, Princeton University Forrestal Campus, 201 Forrestal Road,
Princeton, NJ 08540-5063
Dr. Annette lanucci—Sciences International, 1800 Diagonal Road, Suite 500, Alexandria, VA
22314
Dr. Kazuhiko Ito—New York University School of Medicine, 57 Old Forge Road, Tuxedo, NY
10987
Dr. Doug Johns—National Center for Environmental Assessment (B243-01),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Ellen Kirrane—National Center for Environmental Assessment (B243-01),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
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Authors, Contributors, and Reviewers
(cont'd)
Authors
Dr. Jane Koenig—University of Washington, Department of Environmental and Occupational
Health Sciences, Box 357234, Seattle, WA 98195-7234
Dr. Thomas Long—National Center for Environmental Assessment (B243-01), U.S.
Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Thomas Luben—National Center for Environmental Assessment (B243-01), U.S.
Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Therese Mar—University of Washington, Department of Environmental and Occupational
Health Sciences, Box 357234, Seattle, WA 98195-7234
Dr. Qingyu Meng—National Center for Environmental Assessment (B243-01), U.S.
Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Anu Mudipalli—National Center for Environmental Assessment (B243-01), U.S.
Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Joseph Pinto—National Center for Environmental Assessment (B243-01), U.S.
Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Mary Ross—National Center for Environmental Assessment (B243-01), U.S. Environmental
Protection Agency, Research Triangle Park, NC 27711
Dr. David Svendsgaard—National Center for Environmental Assessment (B243-01), U.S.
Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Lori White—National Center for Environmental Assessment (B243-01), U.S. Environmental
Protection Agency, Research Triangle Park, NC 27711
Dr. William Wilson—National Center for Environmental Assessment (B243-01), U.S.
Environmental Protection Agency, Research Triangle Park, NC 27711
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Authors, Contributors, and Reviewers
(cont'd)
Contributors
Dr. Dale Allen, University of Maryland, College Park, MD
Ms. Louise Camalier, U.S. EPA, OAQPS, Research Triangle Park, NC
Dr. Russell Dickerson, University of Maryland, College Park, MD
Dr. Tina Fan, EOHSI/UMDNJ, Piscataway, NJ
Dr. William Keene, University of Virginia, Charlottesville, VA
Dr. Randall Martin, Dalhousie University, Halifax, Nova Scotia
Dr. Maria Morandi, University of Texas, Houston, TX
Dr. William Munger, Harvard University, Cambridge, MA
Mr. Charles Piety, University of Maryland, College Park, MD
Dr. Sandy Sillman, University of Michigan, Ann Arbor, MI
Dr. Helen Suh, Harvard University, Boston, MA
Dr. Charles Wechsler, EOHSI/UMDNJ, Piscataway, NJ
Dr. Clifford Weisel, EOHSI/UMDNJ, Piscataway, NJ
Dr. Jim Zhang, EOHSI/UMDNJ, Piscataway, NJ
Reviewers
Dr. Tina Bahadori—American Chemistry Council, 1300 Wilson Boulevard, Arlington, VA
22209
Dr. Tim Benner—Office of Science Policy, Office of Research and Development, Washington,
DC 20004
Dr. Daniel Costa—National Program Director for Air, Office of Research and Development,
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
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Authors, Contributors, and Reviewers
(cont'd)
Reviewers
Dr. Robert Devlin—National Health and Environmental Effects Research Laboratory, Office of
Research and Development, U.S. Environmental Protection Agency, Chapel Hill, NC
Dr. Judy Graham—American Chemistry Council, LRI, 1300 Wilson Boulevard, Arlington, VA
22209
Dr. Stephen Graham—Office of Air Quality Planning and Standards (C504-06), Office of Air
and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Ms. Beth Hassett-Sipple—Office of Air Quality Planning and Standards (C504-06), Office of
Air and Radiation, U.S. Environmental Protection Agency (C504-06), Research Triangle Park,
NC27711
Dr. Gary Hatch—National Health and Environmental Effects Research Laboratory, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
27711
Dr. Scott Jenkins—Office of Air Quality Planning and Standards (C504-06), Office of Air and
Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. David Kryak—National Exposure Research Laboratory, Office of Research and
Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Mr. John Langstaff—Office of Air Quality Planning and Standards (C504-06), Office of Air and
Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Morton Lippmann—NYU School of Medicine, 57 Old Forge Road, Tuxedo, NY 10987
Dr. Karen Martin—Office of Air Quality Planning and Standards (C504-06), Office of Air and
Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. William McDonnell—William F. McDonnell Consulting, 1207 Hillview Road, Chapel Hill,
NC27514
Dr. Dave McKee—Office of Air Quality Planning and Standards (C504-06), Office of Air and
Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Lucas Neas—National Health and Environmental Effects Research Laboratory, Office of
Research and Development, U.S. Environmental Protection Agency, Chapel Hill, NC 27711
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Authors, Contributors, and Reviewers
(cont'd)
Reviewers
Dr. Russell Owen—National Health and Environmental Effects Research Laboratory, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
27711
Dr. Haluk Ozkaynak—National Exposure Research Laboratory, Office of Research and
Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Jennifer Peel—Colorado State University, 1681 Campus Delivery, Fort Collins, CO 80523-
1681
Mr. Harvey Richmond—Office of Air Quality Planning and Standards (C504-06), Office of Air
and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Mr. Steven Silverman—Office of General Counsel, U.S. Environmental Protection Agency,
Washington, DC 20460
Dr. Michael Stewart—Office of Air Quality Planning and Standards (C504-06), Office of Air
and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Ms. Susan Stone—Office of Air Quality Planning and Standards (C504-06), Office of Air and
Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Ms. Chris Trent—Office of Air Quality Planning and Standards (C504-06), Office of Air and
Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. John Vandenberg—National Center for Environmental Assessment (B243-01), Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
27711
Dr. Alan Vette—National Exposure Research Laboratory, Office of Research and Development,
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Mr. Ron Williams—National Exposure Research Laboratory, Office of Research and
Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
September 2007 xvii DRAFT-DO NOT QUOTE OR CITE
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U.S. Environmental Protection Agency Project Team
for Development of Integrated Scientific Assessment
for Sulfur Oxide
Executive Direction
Dr. Ila Cote (Acting Director)—National Center for Environmental Assessment-RTF Division,
(B243-01), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Scientific Staff
Dr. Jee Young Kim (SOX Team Leader)—National Center for Environmental Assessment
(B243-01), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Jeff Arnold—National Center for Environmental Assessment (B243-01),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. James S. Brown—National Center for Environmental Assessment (B243-01),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Ellen Kirrane—National Center for Environmental Assessment (B243-01),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Dennis Kotchmar—National Center for Environmental Assessment (B243-01),
Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Tom Long—National Center for Environmental Assessment (B243-01),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Thomas Luben—National Center for Environmental Assessment (B243-01),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Quingyu Meng—National Center for Environmental Assessment (B243-01),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Joseph Pinto—National Center for Environmental Assessment (B243-01),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Paul Reinhart—National Center for Environmental Assessment (B243-01),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
September 2007 xviii DRAFT-DO NOT QUOTE OR CITE
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U.S. Environmental Protection Agency Project Team
for Development of Integrated Scientific Assessment
for Sulfur Oxide
(cont'd)
Scientific Staff
(cont'd)
Dr. Mary Ross—National Center for Environmental Assessment (B243-01),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. David Svendsgaard—National Center for Environmental Assessment (B243-01),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Lori White—National Center for Environmental Assessment (B243-01),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. William Wilson—National Center for Environmental Assessment (B243-01),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Technical Support Staff
Ms. Emily R. Lee—Management Analyst, National Center for Environmental Assessment
(B243-01), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Ms. Christine Searles—Management Analyst, National Center for Environmental Assessment
(B243-01), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Ms. Debra Walsh—Program Analyst, National Center for Environmental Assessment (B243-01),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Mr. Richard Wilson—Clerk, National Center for Environmental Assessment (B243-01),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Document Production Staff
Ms. Barbra H. Schwartz—Task Order Manager, Computer Sciences Corporation,
2803 Slater Road, Suite 220, Morrisville, NC 27560
Mr. John A. Bennett—Technical Information Specialist, Library Associates of Maryland,
11820 Parklawn Drive, Suite 400, Rockville, MD 20852
September 2007 xix DRAFT-DO NOT QUOTE OR CITE
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U.S. Environmental Protection Agency Project Team
for Development of Integrated Scientific Assessment
for Sulfur Oxide
(cont'd)
Document Production Staff
(cont'd)
Mrs. Melissa Cesar—Publication/Graphics Specialist, Computer Sciences Corporation,
2803 Slater Road, Suite 220, Morrisville, NC 27560
Mrs. Rebecca Early—Publication/Graphics Specialist, TekSystems, 1201 Edwards Mill Road,
Suite 201, Raleigh, NC 27607
Mr. Eric Ellis—Records Management Technician, InfoPro, Inc., 8200 Greensboro Drive, Suite
1450, McLean, VA 22102
Ms. Stephanie Harper—Publication/Graphics Specialist, TekSystems, 1201 Edwards Mill Road,
Suite 201, Raleigh, NC 27607
Ms. Sandra L. Hughey—Technical Information Specialist, Library Associates of Maryland,
11820 Parklawn Drive, Suite 400, Rockville, MD 20852
Dr. Barbara Liljequist—Technical Editor, Computer Sciences Corporation, 2803 Slater Road,
Suite 220, Morrisville, NC 27560
Ms. Molly Windsor—Graphic Artist, Computer Sciences Corporation, 2803 Slater Road,
Suite 220, Morrisville, NC 27560
September 2007 xx DRAFT-DO NOT QUOTE OR CITE
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U.S. Environmental Protection Agency
Science Advisory Board (SAB)
Staff Office Clean Air Scientific Advisory Committee (CASAC)
CASAC NOX and SOX Primary NAAQS Review Panel
Chair
Dr. Rogene Henderson*, Scientist Emeritus, Lovelace Respiratory Research Institute,
Albuquerque, NM
Members
Mr. Ed Avol, Professor, Preventive Medicine, Keck School of Medicine, University of Southern
California, Los Angeles, CA
Dr. John R. Balmes, Professor, Department of Medicine, Division of Occupational and
Environmental Medicine, University of California, San Francisco, CA
Dr. Ellis Cowling*, University Distinguished Professor At-Large, North Carolina State
University, Colleges of Natural Resources and Agriculture and Life Sciences, North Carolina
State University, Raleigh, NC
Dr. James D. Crapo [M.D.]*, Professor, Department of Medicine, National Jewish Medical and
Research Center, Denver, CO
Dr. Douglas Crawford-Brown*, Director, Carolina Environmental Program; Professor,
Environmental Sciences and Engineering; and Professor, Public Policy, Department of
Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel
Hill, NC
Dr. Terry Gordon, Professor, Environmental Medicine, NYU School of Medicine, Tuxedo, NY
Dr. Dale Hattis, Research Professor, Center for Technology, Environment, and Development,
George Perkins Marsh Institute, Clark University, Worcester, MA
Dr. Patrick Kinney, Associate Professor, Department of Environmental Health Sciences,
Mailman School of Public Health, Columbia University, New York, NY
Dr. Steven Kleeberger, Professor, Laboratory Chief, Laboratory of Respiratory Biology,
NIH/NIEHS, Research Triangle Park, NC
Dr Timothy Larson, Professor, Department of Civil and Environmental Engineering, University
of Washington, Seattle, WA
September 2007 xxi DRAFT-DO NOT QUOTE OR CITE
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U.S. Environmental Protection Agency
Science Advisory Board (SAB)
Staff Office Clean Air Scientific Advisory Committee (CASAC)
CASAC NOX and SOX Primary NAAQS Review Panel
(cont'd)
Members
(cont'd)
Dr. Kent Pinkerton, Professor, Regents of the University of California, Center for Health and
the Environment, University of California, Davis, CA
Mr. Richard L. Poirot*, Environmental Analyst, Air Pollution Control Division, Department of
Environmental Conservation, Vermont Agency of Natural Resources, Waterbury, VT
Dr. Edward Postlethwait, Professor and Chair, Department of Environmental Health Sciences,
School of Public Health, University of Alabama at Birmingham, Birmingham, AL
Dr. Armistead (Ted) Russell*, Georgia Power Distinguished Professor of Environmental
Engineering, Environmental Engineering Group, School of Civil and Environmental
Engineering, Georgia Institute of Technology, Atlanta, GA
Dr. Richard Schlesinger, Associate Dean, Department of Biology, Dyson College, Pace
University, New York, NY
Dr. Christian Seigneur, Vice President, Atmospheric and Environmental Research, Inc., San
Ramon, CA
Dr. Elizabeth A. (Lianne) Sheppard, Research Professor, Biostatistics and Environmental &
Occupational Health Sciences, Public Health and Community Medicine, University of
Washington, Seattle, WA
Dr. Frank Speizer [M.D.]*, Edward Kass Professor of Medicine, Channing Laboratory,
Harvard Medical School, Boston, MA
Dr. George Thurston, Associate Professor, Environmental Medicine, NYU School of Medicine,
New York University, Tuxedo, NY
Dr. James Ultman, Professor, Chemical Engineering, Bioengineering Program, Pennsylvania
State University, University Park, PA
Dr. Ronald Wyzga, Technical Executive, Air Quality Health and Risk, Electric Power Research
Institute, P.O. Box 10412, Palo Alto, CA
September 2007 xxii DRAFT-DO NOT QUOTE OR CITE
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U.S. Environmental Protection Agency
Science Advisory Board (SAB)
Staff Office Clean Air Scientific Advisory Committee (CASAC)
CASAC NOX and SOX Primary NAAQS Review Panel
(cont'd)
SCIENCE ADVISORY BOARD STAFF
Mr. Fred Butterfield, CASAC Designated Federal Officer, 1200 Pennsylvania Avenue, N.W.,
Washington, DC, 20460, Phone: 202-343-9994, Fax: 202-233-0643 fbutterfield.fred@epa.gov)
(Physical/Courier/FedEx Address: Fred A. Butterfield, III, EPA Science Advisory Board Staff
Office (Mail Code 1400F), Woodies Building, 1025 F Street, N.W., Room 3604, Washington,
DC 20004, Telephone: 202-343-9994)
* Members of the statutory Clean Air Scientific Advisory Committee (CASAC) appointed by the EPA
Administrator
September 2007 xxiii DRAFT-DO NOT QUOTE OR CITE
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ACCENT
ADS
AHH
AHR
AIRPEX
AIRQUIS
ALT
AM
AMF
AMI
AMMN
ANOVA
AP
API
AOR
APEX
APHEA
APIMS
AQCD
AQEG
ARIC
ARIMA
ARR
ATP
ATTILA
asl
AST
P
BAL
B[or]P
BC
BERLIOZ
BHPN
BHR
BME
BMI
Abbreviations and Acronyms
European Union project Atmospheric Composition Change: the
European Network of Excellence
annular denuder system
aryl hydrocarbon hydroxylase
airways hyperreactiveness
Air Pollution Exposure (model)
Air Quality Information System (model)
alanine-amino-transferase
alveolar or pulmonary macrophages
air mass factor
acute myocardial infarction
7V-nitroso-acetoxymethylmethylamine
analysis of variance
alkaline phosphatase
air pollution index
adjusted odds ratio
Air Pollution Exposure (model)
Air Pollution on Health: a European Approach (study)
atmospheric pressure ionization mass spectrometer
Air Quality Criteria Document
Air Quality Expert Group
Atherosclerosis Risk in Communities (study)
Autoregressive Integrated Moving Average (model)
arrhythmia
adenosine triphosphate
type of Lagrangian model
above sea level
aspartate-amino-transferase
beta; slope
bronchoalveolar lavage
benzo[a]pyrene
black carbon
Berlin Ozone Experiment
7V-bis(2-hydroxypropyl)nitrosamine
bronchial hyperresponsiveness
Bayesian Maxim Eutropy
body mass index
September 2007
xxiv
DRAFT-DO NOT QUOTE OR CITE
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bpm
Br
Br
BrO
bw
C
CA
CAMP
CAMx
CAT
CB4, CB-IV
CCN
CD
Cd
CEPEX
CFD
CG
CH4
C5H8
CHAD
CH3-CHO
CH3CH(O)OONO2
CH3C(O)O
CH3-C(0)02,
CH3-C(O)OO
CHF
CH2I2
Choi
CH3OOH
(CH3)2S, CH3.S-CH3
CH3-S-H
(CH3)2SO
CH3SO3H
CH3-S-S-CH3
CI
CIMS
beats per minute
bromine
bromine ion
bromine oxide
body weight
carbon or carbon black particles
chromosome aberrations
Childhood Asthma Management Program
Comprehensive Air-Quality Model
catalase
Carbon Bond 4 (chemical mechanism)
cyanomethylidyne radical
criteria document
cadmium
Central Equatorial Pacific Experiment
Computational Fluid Dynamics
cloud-to-ground (flash)
methane
ethene
ethane
isoprene
Consolidated Human Activities Database
acetaldehyde
peroxyacetyl nitrate
peroxyacetyl radical
acetyl peroxy, peroxyacetyl
congestive heart failure
diiodomethane
cholesterol
methyl hydroperoxide
dimethylsulfide
methyl mercaptan
dimethylsulfoxide
methanesulfonic acid
dimethyl disulfide
confidence interval
chemical ionization mass spectroscopy
September 2007
xxv
DRAFT-DO NOT QUOTE OR CITE
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CL
Cl
cr
CLRD
CMAQ
CMD
CO
CoH
COPD
CP
Cr
CS2
CTM
Cu
CVD
CYP
Dae
DEcCBP
DEN
DEP
DEP+C
dG
DL
DMBA
DMS
DMSO
DNA
DNS
DOAS
EC
ECG
ED
EDMAS
EDXRF
EE
EIB
EMECAM
EPA
chemiluminescence
chorine
chorine ion
chronic lower respiratory disease
Community Multiscale Air Quality (model)
count median diameter
carbon monoxide
coefficient of haze
chronic obstructive pulmonary disease
coarse paniculate
chromium
carbon disulfide
chemistry transport model
copper
cardiovascular disease
cytochrome P450
aerodynamic diameter
diesel exhaust particulates extract-coated carbon black particles
diethylnitrosamine
diesel exhaust particles
diesel exhaust particle extract adsorbed to C
2'-deoxyguanosine
detection limit
7, 12-dimethylbenzanthracene
dimethylsulfide
dimethylsulfoxide
deoxyribonucleic acid
Direct Numerical Simulation
differential optical absorption spectroscopy
elemental carbon
el ectrocardi ogram
emergency department
Exposure and Dose Modeling and Analysis System
energy dispersive X-ray fluorescence
energy expenditure
exercise-linked bronchial reactivity
Spanish Multicentre Study on Air Pollution
U.S. Environmental Protection Agency
September 2007
xxvi
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ER
ESR
F344
Fe
FEM
FEVi
FHLC
FL
FLEXPART
FP
FPD
FRM
FTIR
FVC
FW2
yN205
GAM
GCE
GC/ECD
GCS
GEE
GEOS-Chem
GEOS-1 DAS
GFED
GIS
GLM
GMP
GOME
GP
GPx
GRed
GSD
GSH
GSH-Px
GSSG
GSSO3H
emergency room
electron spin resonance (spectroscopy)
Fischer 344 (rat)
iron
forced expiratory flow between 25 and 75% of vital capacity
Federal Equivalent Method
forced expiratory volume in 1 second
fetal hamster lung cells
fluoranthene
type of Lagrangian model
fine paniculate
flame photometric detection
Federal Reference Method
Fourier Transform Infrared Spectroscopy
forced vital capacity
black carbon soot model
uptake coefficient for N2O5
Generalized Additive Model(s)
Goddard Cumulus Ensemble (model)
gas chromatography-electron capture detection
y-glutamylcysteine synthetase
Generalized Estimating Equations
three-dimensional model of atmospheric composition driven by
assimilated Goddard Earth Orbiting System observations
NASA Goddard Earth Orbiting System Data Assimilation System
Global Fire Emissions Database
Geographic Information System
Generalized Linear Model(s)
guanosine-3',5'-monophosphate
Global Ozone Monitoring Instrument
general practitioner physician
glutathione peroxidase
glutathione reductase
geometric standard deviation
glutathione
glutathione peroxidase
oxidized glutathione; glutathione disulfide
glutathione S-sulfonate
September 2007
xxvn
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GST
GT
3H
H+
HA
HAPEM
HCHO
HC1
HC
HCOO
HEADS
HEI
HES
HF
Hg
HNO3
HNO4
HO2
H202
HOBr
HOC1
HONO, HNO2
H02N02
HOONO
HOX
HOX
HP
HRV
HS
H2S
HSO3
H2S04
hv
HVA-/Ca
I
IARC
fflEM
1C
glutathione S-transferase (e.g., GSTM1, GSTP1, GSTT1)
y-glutamyltransferase
hydrogen-3 radionuclide; tritium
hydrogen ion
hospital admissions
Hazardous Air Pollutant Exposure Model
formaldehyde
hydrochloric acid
hydrocarbon
formate
Harvard-EPA Annular Denuder System
Health Effects Institute
hospital episode statistics
high frequency
mercury
nitric acid
pernitric acid
hydroperoxyl; hydroperoxy radical
hydrogen peroxide
hypobromous acid
hypochlorous acid
nitrous acid
peroxynitric acid
pernitrous acid
hypohalous acid
oxides of hydrogen
hydrolyzed protein
heart rate variability
hemorrhagic stroke
hydrogen sulfide
hydrogen sulfite
sulfuric acid
solar ultraviolet photon
high-voltage activated calcium currents
iodine
International Agency for Research on Cancer
Individual Based Exposure Models
intracloud (flash); ion chromatography
September 2007
xxvin
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ICARTT
ICD, ICD9
Ig
fflD
IIASA
IMPROVE
INDOEX
INO3
INTEX-NA
IO
IPCC-AR4
IPCC-TAR
IQR
IS
IUGR
JPL
Ka
KH
85Kr
Kw
LEW
LDH
LES
LF
LFHFR
LIF
LOESS
LP
LRD
LRI
LRS
LWC
M
MAD
MAP
MAQSIP
International Consortium for Atmospheric Research on Transport and
Transformation
International Classification of Disease, 9th Revision
immunoglobulin (e.g., IgA, IgE, IgG)
ischemic heart disease
International Institute for Applied Systems Analysis
Interagency Monitoring of Protected Visual Environments
Indian Ocean Experiment
iodine nitrate
NASA Intercontinental Chemical Transport Experiment - North
America
iodine oxide
Intergovernmental Panel on Climate Change-Fourth Assessment
Report
Intergovernmental Panel on Climate Change-Third Assessment Report
interquartile range
ischemic stroke
intrauterine growth retardation
Jet Propulsion Laboratory
acid dissociation constant in M
Henry's Law constant in M atm"1
krypton-85 radionuclide
ion product of water
low birth weight
lacticate dehydrogenase
Large Eddy Simulation
low frequency
low frequency/high frequency ratio
laser-induced fluorescence
locally estimated smoothing splines
long-path
lower respiratory disease
lower respiratory illness
lower respiratory symptoms
liquid water content
air molecule
median aerodynamic diameter
mean arterial pressure
Multiscale Air Quality Simulation Platform
September 2007
XXIX
DRAFT-DO NOT QUOTE OR CITE
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MAX
MBL
MCM
MDA
MEF50
MEM
MENTOR-1A
MET
MgO
MI
MIESR
MM5
MMAD
MMEF
MN
MNPCE
Mo
MOBILE6
MONICA
MOZART-2
MPAN
MPP
mRNA
MSA
15N
N
N, n
N2
NA
NAAQS
NaCl
Na2CO3
NADP
NaHCO3
NARSTO
NASA
NBS
multi axis
marine boundary layer
master chemical mechanism
malondialdehyde
maximal midexpiratory flow at 50% of forced vital capacity
model ensemble mean
Modeling Environment for Total Risk for One-Atmosphere studies
metabolic equivalent of work
magnesium oxide
myocardial infarction
matrix isolation electron spin resonance (spectroscopy)
National Center for Atmospheric Research/Penn State Mesoscale
Model
mass median aerodynamic diameter
maximal midexpiratory flow
micronuclei
micrenucleated polychromatic erythrocytes
molybdenum
Highway Vehicle Emission Factor Model
Monitoring Trend and Determinants in Cardiovascular Disease
(registry)
(model)
peroxymethacryloyl nitrate; peroxy-methacrylic nitric anhydride
multi-phase process
messenger ribonucleic acid
metropolitan statistical area
nitrogen-15 radionuclide
nitrogen
number of observations
molecular nitrogen, nitrogen gas
not available
National Ambient Air Quality Standards
sodium chloride
sodium carbonate
National Atmospheric Deposition Program
sodium bicarbonate
North American Regional Strategy for Atmospheric Ozone
National Aeronautics and Space Administration
National Bureau of Standards
September 2007
XXX
DRAFT-DO NOT QUOTE OR CITE
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NCAR
NCICAS
NDMA
NDMA-D
NMBzA
NEM
NEM/pNEM
NERL
NF
NH2
NH3
NH4+
NH4C1
NH4NO3
(NH4)2SO4
NIST
NMHC
NMOC
NN
NO
NO2
NO2+
NO2
N03
NO3
N205
NOX
NOy
NOZ
NP
NPAHs
NR
NRC
NS
NSA
nss
NTRMs
National Center for Atmospheric Research
National Cooperative Inner-City Asthma Study
7V-nitrosodimethylamine
7V-nitrosodimethylamine demethylase
7V-nitrosomethylbenzylamine
National Ambient Exposure Model
National Ambient Exposure Model and Probabilistic National
Exposure Model
National Exposure Research Laboratory
nitrofluoranthene (e.g., 3- or 8-nitrofluoranthene)
amino
ammonia
ammonium ion
ammonium chloride
ammonium nitrate?
ammonium sulfate
National Institute of Standards and Technology
nonmethane hydrocarbon
nonmethane organic compound
nitronaphthalene (e.g., 1- or 2-nitronaphthalene)
nitric oxide
nitrogen dioxide
nitronium ion
nitrite
nitrate (radical)
nitrate
dinitrogen pentoxide
nitrogen oxides; oxides of nitrogen
sum of NOX and NOZ; odd nitrogen species
oxides of nitrogen and nitrates; difference between NOy and NOX
nitropyrene (e.g., 1- or 2-nitropyrene)
nitro polycyclic aromatic hydrocarbons
not reported; data not relevant
National Research Council
nonsignificant
nitrosating agent
non-sea-salt
NIST Traceable Reference Materials
September 2007
XXXI
DRAFT-DO NOT QUOTE OR CITE
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16Q
02
03
OAQPS
OC
OCS
OC'D)
OH
OHC
8-OHdG
OMI
0(3P)
OPE
OPSIS
OR
OSPM
P,P
PAHs
PAMS, PAMs
PAN
Pb
PBEM
PCA
PCE
PE
PEC
PEF
PEFR
PERI
P(HN03)
PIH
PIXE
PKA
PKI
PL
PM
PM2.5
oxygen-16 radionuclide
molecular oxygen
ozone
Office of Air Quality Planning and Standards
organic carbon
carbonyl sulfide
electronically excited oxygen atom
hydroxyl radical
oxygenated hydrocarbons
8-hydroxy-2'-deoxyguanosine
Ozone Monitoring Instrument
ground-state oxygen atom
ozone production efficiency
Open Path Ambient Air Monitoring Systems for SO2
odds ratio
Danish Operational Street Pollution Model
probability value
polycyclic aromatic hydrocarbons
Photochemical Aerometric Monitoring System
peroxyacetyl nitrate; peroxyacyl nitrate
lead
Population Based Exposure Models
principal component analysis
polychromatic erythrocytes
parameter estimates
pulmonary endocrine cells
peak expiratory flow
peak expiratory flow rate
peripheral vascular and cerebrovascular disease
particulate nitrate
primary intracerebral hemorrhage
particle induced X-ray emission
cyclic AMP-dependent protein kinase A
synthetic peptide inhibitor of PKA
phospholipids
particulate matter
particulate matter with 50% upper cut point aerodynamic diameter of
2.5 jim for sample collection; surrogate for fine PM
September 2007
xxxn
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PMiQ-2.5
PM13
PM-CAMx
PMN
PNC
PNN50
PMT
pNEM
P(03)
POM
ppb
ppbv
ppm
PPN
ppt
pptv
PRB
Pt
PSA
psi
PTEP
PTFE
PY
r
R2
RACM
RADM
RANS
RBC
RDBMS
REHEX
RH
RMR
r-MSSD
particulate matter with 50% upper cut point aerodynamic diameter of
10 |im for sample collection
particulate matter with 10 |im as upper cut point aerodynamic diameter
and 2.5 jim as lower cut point for sample collection; surrogate for
thoracic coarse PM (does not include fine PM)
particulate matter with 50% upper cut point aerodynamic diameter of
13 jim for sample collection
Particulate Matter Comprehensive Air Quality Model with Extensions
polymorphonuclear leukocytes
particle number concentration
percentage of differences between adjacent NN intervals
photomultiplier tube
Probabilistic National Exposure Model
ozone precursor
particulate organic matter
parts per billion
parts per billion by volume
parts per million
peroxypropionyl nitrate; peroxypropionic nitric anhydride
parts per trillion
parts per trillion by volume
policy relevant background
platinum
particle strong acidity
pounds per square inch
PMio Technical Enhancement Program
polytetrafluoroethylene (Teflon)
pyrene
correlation coefficient
coefficient of determination
Regional Air Chemistry Mechanism
Regional Acid Deposition Model
Reynolds Averaged Numerical Simulation
red blood cell or erythrocyte
Relational Database Management Systems
Regional Human Exposure Model
relative humidity
resting metabolic rate
root mean square of successive differences in R-R intervals.
September 2007
xxxin
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RO2
RONO2
ROONO2, RO2NO2
RR
34
S2*
S20
SCAQS
SCE
SCIAMACHY
SCOS97
SD
SDNN
SE
SEPs
SGV
SHEDS
SHS
SIDS
SMOKE
SO
SO2
S02*
SO3
SO32
SO42
SOD
SONEX
SOX
SPF
SPM
SQCA
SRM
SSO
STE
organic peroxyl; organic peroxy
organic nitrate
peroxy nitrate
relative risk
sulfur-34 radionuclide
sulfur
sulfide
electronically excited sulfur molecules
disulfur monoxide
Southern California Air Quality Study
sister chromatid exchange
Scanning Imaging Absorption Spectrometer for Atmospheric
Chartography
1997 Southern California Ozone Study
standard deviation
standard deviation of normal R-R intervals
standard error
somatosensory-evoked potentials
subgrid variability
Simulation of Human Exposure and Dose System
sub arachnoid hemorrhagic stroke
sudden infant death syndrome
Spare-Matrix Operator Kernel Emissions (system)
sulfur monoxide
sulfur dioxide
electronically excited sulfur dioxide molecules
sulfur trioxide
sulfite ion
sulfate ion
superoxide dismutase
Subsonic Assessment Ozone and Nitrogen Oxides Experiment
oxides of sulfur
specific pathogen free
suspended particulate matter
squamous cell carcinoma
standard reference material; suspended particulate matter extract
seabuckthorn seed oil
stratospheric-tropospheric exchange
September 2007
xxxiv
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STEP
STN
STPD
STRF
SV40
T
t
TEARS
TC
TOLAS
Tg
TIA
TOC
TOR
TP
TPLIF
TRS
TSDS
TSP
TSPM
TTFMS
TTX
TTX-R
TTX-S
UBRE
U-EPX
UMD-CTM
URD
URI
URS
UV
V
vd
VE
VEPs
VOC
W
WHO
Stratospheric-Tropospheric-Exchange Proj ect
Speciation Trends Network
standard temperature and pressure, dry
Spatio-Temporal Random Field (theory)
simian virus 40
tau; atmospheric lifetime
t statistic
thiobarbituric acid-reactive substances
total carbon
tunable-diode laser absorption spectroscopy
teragram
transient ischemic attack
potassium channel transient outward currents
thermal-optical reflectance
total paniculate
two-photon laser-induced fluorescence
total reduced sulfur
treatment, storage, or disposal facilities
total suspended particles
total suspended paniculate matter
two-tone frequency-modulated spectroscopy
tetrodotoxin
tetrodotoxin-resi stant
tetrodotoxin-sensitive
unbiased risk estimator
urinary eosinophil protein
University of Maryland Chemical Transport Model
upper respiratory disease
upper respiratory illness
upper respiratory symptoms
ultraviolet
vanadium
deposition velocity
total ventilation rate
visual-evoked potentials
volatile organic compound
tungsten
World Health Organization
September 2007
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XRF X-ray fluorescence
Zn zinc
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i AX1. CHAPTER 1 ANNEX-INTRODUCTION
2
O
4 The draft Annexes are prepared in support of the draft Integrated Science Assessment for
5 Sulfur Oxides - Health Criteria (EPA/600/R-07/108). The Integrated Science Assessment (ISA)
6 presents a concise synthesis of the most policy-relevant science to form the scientific foundation
7 for the review of the primary (health-based) national ambient air quality standards (NAAQS) for
8 sulfur dioxide (802). This series of Annexes provide more extensive and detailed summaries of
9 the most pertinent scientific literature. The Annexes identify, evaluate, and summarize scientific
10 research in the areas of atmospheric sciences, air quality analyses, exposure assessment,
11 dosimetry, controlled human exposure studies, toxicology, and epidemiology, focusing on
12 studies relevant to the review of the primary NAAQS.
13 These draft Annexes are organized by scientific study areas and include research that is
14 relevant to the key policy questions discussed previously to provide an evidence base supporting
15 the development of the ISA, risk, and exposure assessments. In this Annex 1, we provide
16 legislative background and history of previous reviews of the NAAQS for sulfur oxides. In
17 Annex 2, we present evidence related to the physical and chemical processes controlling the
18 production, destruction, and levels of sulfur oxides in the atmosphere, including both oxidized
19 and reduced species. Annex 3 presents information on environmental concentrations, patterns,
20 and human exposure to ambient sulfur oxides; however, most information relates to SC>2. Annex
21 4 presents results from toxicological studies as well as information on dosimetry of sulfur oxides.
22 Annex 5 discusses evidence from epidemiologic studies. These Annexes include more detailed
23 information on health or exposure studies that is summarized in tabular form, as well as more
24 extensive discussion of atmospheric chemistry, source, exposure, and dosimetry information.
25 Annex tables for health studies are generally organized to include information about
26 (1) concentrations of sulfur oxides levels or doses and exposure times, (2) description of study
27 methods employed, (3) results and comments, and (4) quantitative outcomes for sulfur oxides
28 measures.
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1 AX1.1 LEGISLATIVE REQUIREMENTS
2 Two sections of the Clean Air Act (CAA) govern the establishment and revision of the
3 NAAQS. Section 108 (42 U.S.C. 7408) directs the Administrator to identify and list "air
4 pollutants" that "in his judgment, may reasonably be anticipated to endanger public health and
5 welfare" and whose "presence ... in the ambient air results from numerous or diverse mobile or
6 stationary sources" and to issue air quality criteria for those that are listed. Air quality criteria
7 are intended to "accurately reflect the latest scientific knowledge useful in indicating the kind
8 and extent of identifiable effects on public health or welfare which may be expected from the
9 presence of [a] pollutant in ambient air ... ."
10 Section 109 (42 U.S.C. 7409) directs the Administrator to propose and promulgate
11 "primary" and "secondary" NAAQS for pollutants listed under section 108. Section 109(b)(l)
12 defines a primary standard as one "the attainment and maintenance of which in the judgment of
13 the Administrator, based on such criteria and allowing an adequate margin of safety, are requisite
14 to protect the public health."1 A secondary standard, as defined in section 109(b)(2), must
15 "specify a level of air quality the attainment and maintenance of which, in the judgment of the
16 Administrator, based on such criteria, is required to protect the public welfare from any known
17 or anticipated adverse effects associated with the presence of [the] pollutant in the ambient air."2
18 The requirement that primary standards include an adequate margin of safety was
19 intended to address uncertainties associated with inconclusive scientific and technical
20 information available at the time of standard setting. It was also intended to provide a reasonable
21 degree of protection against hazards that research has not yet identified. See Lead Industries
22 Association v. EPA, 647 F.2d 1130, 1154 (D.C. Cir 1980), cert, denied, 449 U.S. 1042 (1980);
23 American Petroleum Institute v. Costle, 665 F.2d 1176, 1186 (D.C. Cir. 1981), cert, denied, 455
24 U.S. 1034(1982). Both kinds of uncertainties are components of the risk associated with
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
Welfare effects as defined in section 302(h) [42 U.S.C. 7602(h)] include, but are not limited to, "effects
on soils, water, crops, vegetation, 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."
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1 pollution at levels below those at which human health effects can be said to occur with
2 reasonable scientific certainty. Thus, in selecting primary standards that include an adequate
3 margin of safety, the Administrator is seeking not only to prevent pollution levels that have been
4 demonstrated to be harmful but also to prevent lower pollutant levels that may pose an
5 unacceptable risk of harm, even if the risk is not precisely identified as to nature or degree.
6 In selecting a margin of safety, the EPA considers such factors as the nature and severity
7 of the health effects involved, the size of sensitive population(s) at risk, and the kind and degree
8 of the uncertainties that must be addressed. The selection of any particular approach to
9 providing an adequate margin of safety is a policy choice left specifically to the Administrator's
10 judgment. See Lead Industries Association v. EPA, supra, 647 F.2d at 1161-62.
11 In setting standards that are "requisite" to protect public health and welfare, as provided
12 in section 109(b), EPA's task is to establish standards that are neither more nor less stringent
13 than necessary for these purposes. In so doing, EPA may not consider the costs of implementing
14 the standards. See generally Whitman v. American Trucking Associations, 531 U.S. 457, 465-
15 472,475-76(2001).
16 Section 109(d)(l) requires that "not later than December 31, 1980, and at 5-year
17 intervals thereafter, the Administrator shall complete a thorough review of the criteria
18 published under section 108 and the national ambient air quality standards . . . and shall make
19 such revisions in such criteria and standards and promulgate such new standards as may be
20 appropriate . . . ." Section 109(d)(2) requires that an independent scientific review committee
21 "shall complete a review of the criteria . . . and the national primary and secondary ambient air
22 quality standards . . . and shall recommend to the Administrator any new . . . standards and
23 revisions of existing criteria and standards as may be appropriate . . . ." Since the early 1980s,
24 this independent review function has been performed by the Clean Air Scientific Advisory
25 Committee (CASAC) of EPA's Science Advisory Board.
26
27
28 AX1.2 HISTORY OF REVIEWS OF THE PRIMARY NAAQS FOR SO2
29 On April 30, 1971, the EPA promulgated primary NAAQS for SO2. These primary
30 standards, which were based on the findings outlined in the original 1969 Air Quality Criteria for
31 Sulfur Oxides, were set at 0.14 parts per million (ppm) averaged over a 24-hour period, not to be
32 exceeded more than once per year, and 0.030 ppm annual arithmetic mean. In 1982, EPA
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1 published the Air Quality Criteria for Particulate Matter and Sulfur Oxides along with an
2 addendum of newly published controlled human exposure studies, which updated the scientific
3 criteria upon which the initial standards were based (EPA, 1982). In 1986, a second addendum
4 was published presenting newly available evidence from epidemiologic and controlled human
5 exposure studies (EPA, 1986). In 1988, EPA reviewed and revised the health criteria upon
6 which the SC>2 standards were based. As a result of that review, EPA published a proposed
7 decision not to revise the existing standards (53 FR 14926). However, EPA specifically
8 requested public comment on the alternative of revising the current standards and adding a new
9 1 -hour primary standard of 0.4 ppm.
10 As a result of public comments on the 1988 proposal and other post-proposal
11 developments, EPA published a second proposal on November 15, 1994 (59 FR 58958). The
12 1994 re-proposal was based in part on a supplement to the second addendum of the criteria
13 document, which evaluated new findings on short-term SC>2 exposures in asthmatics (EPA,
14 1994a). As in the 1988 proposal, EPA proposed to retain the existing 24-hour and annual
15 standards. The EPA also solicited comment on three regulatory alternatives to further reduce the
16 health risk posed by exposure to high 5-minute peaks of 862 if additional protection were judged
17 to be necessary. The three alternatives included: 1) Revising the existing primary 862 NAAQS
18 by adding a new 5-minute standard of 0.60 ppm 862; 2) establishing a new regulatory program
19 under section 303 of the Act to supplement protection provided by the existing NAAQS, with a
20 trigger level of 0.60 ppm SO2, one expected exceedance; and 3) augmenting implementation of
21 existing standards by focusing on those sources or source types likely to produce high 5-minute
22 peak concentrations of SC>2. On May 22, 1996, EPA's final decision, that revisions of the
23 NAAQS for sulfur oxides were not appropriate at that time, was announced in the Federal
24 Register. In that decision, EPA announced an intention to propose guidance, under section
25 303 of the Act, to assist states in responding to short-term peak levels of 862. The basis for the
26 decision, and subsequent litigation, is discussed below in Chapter 3.
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i AX2. CHAPTER 2 ANNEX-ATMOSPHERIC
2 CHEMISTRY OF NITROGEN AND SULFUR OXIDES
3
4
5 AX2.1 INTRODUCTION
6 Nitrogen oxides (NOX) along with volatile organic compounds (VOCs) including
7 anthropogenic and biogenic hydrocarbons, aldehydes, etc. and carbon monoxide (CO) serve as
8 precursors in the formation of ozone (03) and other elements of photochemical smog. Nitrogen
9 oxides are defined here as nitric oxide (NO) and nitrogen dioxide (NO2), the latter of which is a
10 U.S. Environmental Protection Agency (EPA) Criteria Air Pollutant; similarly, oxides of sulfur
11 (SOX) are defined here to be sulfur monoxide (SO), sulfur dioxide (S02), the largest component
12 of SOX and also a EPA Criteria Air Pollutant, and sulfur trioxide (SO3). S03 rapidly reacts with
13 water vapor to form H2S04, and only S02 is present in the atmosphere at detectable levels.
14 Nitrogen dioxide is an oxidant and can further react to form other photochemical
15 oxidants, in particular the organic nitrates, including peroxy acetyl nitrates (PAN) and higher
16 PAN analogues. It can also react with toxic compounds such as polycyclic aromatic
17 hydrocarbons (PAHs) to form nitro-PAHs, which may be even more toxic than the precursors.
18 Nitrogen dioxide together with sulfur dioxide (S02), another EPA criteria air pollutant, can be
19 oxidized to the strong mineral acids, nitric acid (HN03) and sulfuric acid (H2S04), which
20 contribute to the acidity of cloud, fog, and rainwater, and can form ambient particles.
21 The role of NOX in 03 formation was reviewed in Chapter 2 (Section 2.2) of the latest
22 AQCD for Ozone and Other Photochemical Oxidants (U.S. Environmental Protection Agency,
23 2006 CD06), and in numerous texts (e.g., Seinfeld and Pandis, 1998; Jacob, 2000; Jacobson,
24 2002). Mechanisms for transporting 03 precursors, the factors controlling the efficiency of 03
25 production from NOX, methods for calculating 03 from its precursors, and methods for
26 measuring NOX were all reviewed in Section 2.6 of CD06. The main points from those
27 discussions in CD06 and updates, based on new materials will be presented here. Ammonia
28 (NH3) is included here because its oxidation can be a source of NOX, and it is a precursor for
29 ammonium ions (NH4+), which play a key role in neutralizing acidity in ambient particles and in
30 cloud, fog, and rain water. Ammonia is also involved in the ternary nucleation of new particles,
31 and it reacts with gaseous HN03 to form ammonium nitrate (NH4N03), which is a major
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1 constituent of ambient Particulate Matter (PM) in many areas. Ammonia is also involved in over
2 nitrification of aqueous and terrestrial ecosystems and participates in the N cascade (Galloway
3 et al., 2003)
4 The atmospheric chemistry of NOX is discussed in Section AX2.2, and of SOz in Section
5 AX2.3. Mechanisms for the formation of aqueous-phase sulfate (S042~) and nitrate (N(V) are
6 reviewed in Section AX2.4. Sources and emissions of NOX, NHs, and SOz are discussed in
7 Section AX2.5. Modeling methods used to calculate the atmospheric chemistry, transport, and
8 fate of NOX and S02 and their oxidation products are presented in Section AX2.6. Measurement
9 techniques for the nitrogen-containing compounds and for SOz, nitrates, sulfates, and ammonium
10 ion are discussed in Section AX2.8. Estimates of policy-relevant background concentrations of
11 NOX and SOX are given in Section AX2.9. An overall review of key points in this chapter is
12 given in Section AX2.11.
13 The overall chemistry of reactive nitrogen compounds in the atmosphere is summarized
14 in Figure AX2.1-1 and is described in greater detail in the following sections. Nitrogen oxides
15 are emitted primarily as NO with smaller quantities of N02. Emissions of NOX are spatially
16 distributed vertically with some occurring at or near ground level and others aloft as indicated in
17 Figure AX2.1-1. Because of atmospheric chemical reactions, the relative abundance of different
18 compounds contributed by different sources varies with location. Both anthropogenic and
19 natural (biogenic) processes emit NOX. In addition to gas phase reactions, multiphase processes
20 are important for forming aerosol-phase pollutants, including aerosol N0s~.
21
22
23 AX2.2 CHEMISTRY OF NITROGEN OXIDES IN THE TROPOSPHERE
24
25 AX2.2.1 Basic Chemistry
26 There is a rapid photochemical cycle in the troposphere that involves photolysis of N02
27 by solar UV-A radiation to yield NO and a ground-state oxygen atom, 0(3P):
28
hv ^N0 + 0(3P),
29 This ground-state oxygen atom can then combine with molecular oxygen (Oz) to form Os; and,
30 colliding with any molecule from the surrounding air (M = Nz, Oz, etc), the newly formed Os
31 molecule, transfers excess energy and is stabilized:
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Long range transport to remote
regions at low temperatures
V
HMO
NH
nitro-PAHs
Urt R-C=C-RW
NO, —^ RONC)
nitrosamines,
nitro-phenols,
quinones, etc.
deposition
emissions
Figure AX2.1-1.
Schematic diagram of the cycle of reactive nitrogen species in the
atmosphere. MPP refers to multi-phase process; hv to a photon of
solar energy.
2
3
4
6
7
8
9
0(3P)
03 + M,
(AX2 2)
where M = Nz, Oz. Reaction AX2-2 is the only significant reaction forming Os in the
troposphere.
NO and 03 react to reform N02:
, (AX23)
Reaction AX2-3 is responsible for 03 decreases and N02 increases found near sources of NO
(e.g., highways) , especially at night when the actinic flux is 0. Oxidation of reactive VOCs leads
to the formation of reactive radical species that allow the conversion of NO to N02 without the
participation of Os (as in Reaction AX2-3):
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,.„ HO,,RO,
1 N0 * * > 2' (AX2-4)
2 Ozone, therefore, can accumulate as N02 photolyzes as in Reaction AX2-1, followed by
3 Reaction AX2-2. Specific mechanisms for the oxidation of a number of VOCs were discussed in
4 the 03 AQCD (U.S. Environmental Protection Agency, 2006).
5 It is often convenient to speak about families of chemical species defined in terms of
6 members that interconvert rapidly among themselves on time scales that are shorter than those
7 for formation or destruction of the family as a whole. For example, an "odd oxygen" (Ox) family
8 can be defined as
9 0, = I(0(JP) + OC D) + 03 + NO y)
10 In much the same way, NOX is sometimes referred to as "odd nitrogen". Hence, we see that
1 1 production of Ox occurs by the schematic Reaction AX2-4, and that the sequence of reactions
12 given by reactions AX2-1 through AX2-3 represents no net production of Ox. Definitions of
13 species families and methods for constructing families are discussed in Jacobson (1999) and
14 references therein. Other families that include nitrogen- containing species (and which will be
1 5 referred to later in this chapter) include:
24
16
17 One can then see that production of Ox occurs by the schematic Reaction AX2-4, and that the
18 sequence of reactions given by reactions AX2-1 through AX2-3 represents no net production of
19 Ox. Definitions of species families and methods for constructing families are discussed in
20 Jacobson (1999) and references therein. Other families that include nitrogen- containing species,
21 and which will be referred to later in this chapter, are: (which is the sum of the products of the
22 oxidation of NOX)
NOZ = X (HNO3 + HNO4 + NO3 + 2NO2O5 + PAN(CH3CHO - OO - NO2) + other
23 organic ni trades + halogen nitrates + particulate nitrate)',
NOy = NOX + NOZ + HONO;
andNHx ^
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The reaction of N02 with 03 leads to the formation of N03~ radical,
N03 + hv ^N0 + 02(}0%}
2
3 However, because the NOs radical photolyzes rapidly (lifetime of ~ 5 s during the
4 photochemically most active period of the day around local solar noon (Atkinson et al., 1992a),
(AX2 6a)
6 2 (90%} (AX2-6b)
7 its concentration remains low during daylight hours, but can increase after sunset to nighttime
8 concentrations of <5 x 107 to 1 x 1010 molecules cm"3 (<2 to 430 parts per trillion (ppt)) over
9 continental areas influenced by anthropogenic emissions of NOX (Atkinson et al., 1986). At
10 night, NOs, rather than the hydroxyl radical (OH), is the primary oxidant in the system.
1 1 Nitrate radicals can combine with N02 to form dinitrogen pentoxide (N20s):
12
13 and N20s both photolyzes and thermally decomposes back to N02 and N03 during the day;
14 however, N205 concentrations ([N205]) can accumulate during the night to parts per billion (ppb)
1 5 levels in polluted urban atmospheres.
16 The tropospheric chemical removal processes for NOX include reaction of N02 with the
17 OH radical and hydrolysis of N20s in aqueous aerosol solutions if there is no organic coating.
18 Both of these reactions produce HNOs.
19 23 (AX2.8)
20 2s—
21 The gas-phase reaction of the OH radical with N02 (Reaction AX2-8) initiates one of the
22 major and ultimate removal processes for NOX in the troposphere. This reaction removes OH
23 and N02 radicals and competes with hydrocarbons for OH radicals in areas characterized by high
24 NOX concentrations, such as urban centers (see Section AX2.2.2). The timescale (T) for
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1 conversion of NOX to HN03 in the planetary boundary layer at 40 N latitude ranges from about
2 4 hours in July to about 16 hours in January. The corresponding range in T at 25 N latitude is
3 between 4 and 5 hours, while at 50 N latitude, HN03 T ranges from about 4 to 20 hours (Martin
4 et al., 2003). In addition to gas-phase HNOs, Golden and Smith (2000) have shown on the basis
5 of theoretical studies that pernitrous acid (HOONO) is also produced by the reaction of NOz and
6 OH radicals. However, this channel of production most likely represents a minor yield
7 (approximately 15% at the surface) (Jet Propulsion Laboratory, 2003). Pernitrous acid will also
8 thermally decompose and can photolyze. Gas-phase HNOs formed from Reaction AX2-8
9 undergoes wet and dry deposition to the surface, and uptake by ambient aerosol particles.
10 Reaction AX2-8 limits NOX T to a range of hours to days.
1 1 In addition to the uptake of HN03 on particles and in cloud drops, it photolyzes and
1 2 reacts with OH radicals via
13 3 2 (AX2_1Q)
14 and
15 HN03 + OH^NOi + H20. (AX2 n)
16 The lifetime of HNOs with respect to these two reactions is long enough for HNOs to act as a
17 reservoir species for NOX during long-range transport, contributing in this way to N02 levels and
18 to Os formation in areas remote from the source region of the NOX that formed this HNOs.
19 Geyer and Platt (2002) concluded that Reaction AX2-9 constituted about 10% of the
20 removal of NOX at a site near Berlin, Germany during spring and summer. However, other
21 studies found a larger contribution to HNOs production from Reaction AX2-9. Dentener and
22 Crutzen (1993) estimated 20% in summer and 80% of HNOs production in winter is from
23 Reaction AX2-9. Tonnesen and Dennis (2000) found between 16 to 31% of summer HNOs
24 production was from Reaction AX2-9. The contribution of Reaction AX2-9 to HNOs formation
25 is highly uncertain during both winter and summer. The importance of Reaction AX2-9 could be
26 much higher during winter than during summer because of the much lower concentration of OH
27 radicals and the enhanced stability of N20s due to lower temperatures and less sunlight. Note
28 that Reaction AX2-9 proceeds as a heterogeneous reaction. Recent work in the northeastern
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1 United States indicates that this reaction is proceeds at a faster rate in power plant plumes than in
2 urban plumes (Brown et al., 2006a,b; Frost et al., 2006).
3 OH radicals also can react with NO to produce nitrous acid (HONO or HN02) :
4 ,_ (AX212)
5 In the daytime, HN02 is rapidly photolyzed back to the original reactants:
6 2 . (AX2_13)
7 Reaction AX2-12 is, however, a negligible source of HONO, which is formed mainly by
8 multiphase processes (see Section AX2.2.3). At night, heterogeneous reactions of N02 in
9 aerosols or at the earth's surface result in accumulation of HONO (Lammel and Cape, 1996;
10 Jacob, 2000; Sakamaki et al., 1983; Pitts et al., 1984; Svensson et al., 1987; Jenkin et al., 1988;
1 1 Lammel and Perner, 1988; Notholt et al., 1992a,b). Harris et al. (1982) and Zhang et al. (2006)
1 2 (e.g.) suggested that photolysis of this HN02 at sunrise could provide an important early-
13 morning source of OH radicals to drive Os formation
14 Hydroperoxy (H02) radicals can react with N02 to produce pernitric acid (HN04):
15 H02 + N02+M^HNO^M (AX2.M)
16 which then can thermally decompose and photolyze back to its original reactants. The acids
17 formed in these gas-phase reactions are all water soluble. Hence, they can be incorporated into
18 cloud drops and in the aqueous phase of particles.
19 Although the lifetimes of HN04 and N20s are short (minutes to hours) during typical
20 summer conditions, they can be much longer at the lower temperatures and darkness found
21 during the polar night. Under these conditions, species such as PAN, HN03, HN04, and N20s
22 serve as NOX reservoirs that can liberate N02 upon the return of sunlight during the polar spring.
23 A broad range of organic nitrogen compounds can be directly emitted by combustion sources or
24 formed in the atmosphere from NOX emissions. Organic nitrogen compounds include the PANs,
25 nitrosamines, nitro-PAHs, and the more recently identified nitrated organics in the quinone
26 family. Oxidation of VOCs produces organic peroxy radicals (R02), as discussed in the latest
27 AQCD for Ozone and Other Photochemical Oxidants (U.S. Environmental Protection Agency,
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1 2006). Reaction of R02 radicals with NO and N02 produces organic nitrates (RON02) and
2 peroxynitrates (R02N02):
(AX2-15)
DD _i_ KID M i P/O A//O
4 ^+ N°2 >R02N02 (AX2-16)
5 Reaction (AX2-15) is a minor branch for the reaction of R02 with NO. The major branch
6 produces RO and N02, as discussed in the next section; however, the organic nitrate yield
7 increases with carbon number (Atkinson, 2000).
8 The most important of these organic nitrates is PAN, the dominant member of the
9 broader family of peroxyacylnitrates which includes peroxypropionyl nitrate (PPN) of
10 anthropogenic origin and peroxymethacrylic nitrate (MPAN) produced from isoprene oxidation.
11 The PANs are formed by the combination reaction of acetyl peroxy radicals with N02:
12 CH3C(O)-OO + NO2 -> CH3C(O}OONO2
13 where the acetyl peroxy radicals are formed mainly during the oxidation of ethane (C2H6).
14 Acetaldehyde (CH3CHO) is formed as an intermediate species during the oxidation of ethane.
15 Acetaldehyde can be photolyzed or react with OH radicals to yield acetyl radicals:
16 CH3-C(0)H + hv -> CH3-C(0) + H (AX2-18)
17 CH3-C(0)H + OH^ CH3-C(0) + H2O (AX2-19)
18 Acetyl radicals then react with 02 to yield acetyl peroxy radicals
19 CHr-C(0) + 02 + M -> CH3C(0)-00 + M (AX2-20)
20 However, acetyl peroxy radicals will react with NO in areas of high NO concentrations
2i CH3(CO)-OO + NO^ CH3(CO)-O + NO2
22 and the acetyl-oxy radicals will then decompose
23 CH3(CO)-0 -> CH3 + C02
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1 Thus, the formation of PAN is favored at conditions of high ratios of NOz to NO, which are most
2 typically found under low NOX conditions. The PANs both thermally decompose and photolyze
3 back to their reactants on timescales of a few hours during warm sunlit conditions, with lifetimes
4 with respect to thermal decomposition ranging from ~1 hour at 298 K to -2.5 days at 273 K, up
5 to several weeks at 250 K. Thus, they can provide an effective sink of NOX at cold temperatures
6 and high solar zenith angles, allowing release of NOz as air masses warm, in particular by
7 subsidence. The PANs are also removed by uptake to vegetation (Sparks et al., 2003;
8 Teklemariam and Sparks, 2004).
9 The organic nitrates may react further, depending on the functionality of the R group, but
10 they will typically not return NOX and can therefore be viewed mainly as a permanent sink for
11 NOX, as alkyl nitrates are sparingly soluble and will photolyze. This sink is usually small
12 compared to HNOs formation, but the formation of isoprene nitrates may be a significant sink for
13 NOX in the United States in summer (Liang et al., 1998).
14 The peroxynitrates produced by (1-16) are thermally unstable and most have very short
15 lifetimes (less than a few minutes) owing to thermal decomposition back to the original
16 reactants. They are thus not effective sinks of NOX.
17
18 AX2.2.2 Nonlinear Relations between Nitrogen Oxide Concentrations and
19 Ozone Formation
20 Ozone is unlike some other species whose rates of formation vary directly with the
21 emissions of their precursors in that Os production (P(0s)) changes nonlinearly with the
22 concentrations of its precursors. At the low NOX concentrations found in most environments,
23 ranging from remote continental areas to rural and suburban areas downwind of urban centers,
24 the net production of Os increases with increasing NOX. At the high NOX concentrations found in
25 downtown metropolitan areas, especially near busy streets and roadways, and in power plant
26 plumes, there is net destruction of 03 by (titration) reaction with NO. Between these two
27 regimes is a transition stage in which Os shows only a weak dependence on NOX concentrations.
28 In the high NOX regime, N02 scavenges OH radicals which would otherwise oxidize VOCs to
29 produce peroxy radicals, which in turn would oxidize NO to NOz. In the low NOX regime, VOV
30 (VOC) oxidation generates, or at least does not consume, free radicals, and Os production varies
31 directly with NOX. Sometimes the terms 'VOC-limited' and 'N0x-limited' are used to describe
32 these two regimes. However, there are difficulties with this usage because: (1) VOC
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1 measurements are not as abundant as they are for NOX, (2) rate coefficients for reaction of
2 individual VOCs with free radicals vary over an extremely wide range, and (3) consideration is
3 not given to CO nor to reactions that can produce free radicals without invoking VOCs. The
4 terms N0x-limited and N0x-saturated (used by, e.g., Jaegle et al., 2001) will be used wherever
5 possible to describe these two regimes more adequately. However, the terminology used in
6 original articles will also be kept. The chemistry of OH radicals, which are responsible for
7 initiating the oxidation of hydrocarbons, shows behavior similar to that for Os with respect to
8 NOX concentrations (Hameed et al., 1979; Pinto et al., 1993; Poppe et al., 1993; Zimmerman and
9 Poppe, 1993). These considerations introduce a high degree of uncertainty into attempts to relate
10 changes in 03 concentrations to emissions of precursors. It should also be noted at the outset that
11 in a N0x-limited (or N0x-sensitive) regime, Os formation is not insensitive to radical production
12 or the flux of solar UV photons, just that Os formation is more sensitive to NOX. For example,
13 global tropospheric Os is sensitive to the concentration of CH4 even though the troposphere is
14 predominantly N0x-limited.
15 Various analytical techniques have been proposed that use ambient NOX and VOC
16 measurements to derive information about 03 production and 03-NOX-VOC sensitivity.
17 Previously (e.g., National Research Council, 1991), it was suggested that 03 formation in
18 individual urban areas could be understood in terms of measurements of ambient NOX and VOC
19 concentrations during the early morning. In this approach, the ratio of summed (unweighted by
20 chemical reactivity) VOC to NOX concentrations is used to determine whether conditions are
21 N0x-sensitive or VOC sensitive. This technique is inadequate to characterize Os formation
22 because it omits many factors recognized as important for P(0s), including: the effect of
23 biogenic VOCs (which are not present in urban centers during early morning); important
24 individual differences in the ability of VOCs to generate free radicals, rather than just from total
25 VOC concentration and other differences in Os-forming potential for individual VOCs (Carter,
26 1995); the effect of multiday transport; and general changes in photochemistry as air moves
27 downwind from urban areas (Milford et al., 1994).
28 Jacob et al. (1995) used a combination of field measurements and a chemical transport
29 model (CTM) to show that the formation of Os changed from N0x-limited to N0x-saturated as
30 the season changed from summer to fall at a monitoring site in Shenandoah National Park, VA.
31 Photochemical production of Os generally occurs together with production of various other
September 2007 AX2-10 DRAFT-DO NOT QUOTE OR CITE
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1 species including HN03, organic nitrates, and hydrogen peroxide (H202). The relative rates of
2 P(03) and the production of other species varies depending on photochemical conditions, and can
3 be used to provide information about 03-precursor sensitivity.
4 There are no hard and fast rules governing the levels of NOX at which the transition from
5 NOx-limited to N0x-saturated conditions occurs. The transition between these two regimes is
6 highly spatially and temporally dependent. In the upper troposphere, responses to NOX additions
7 from commercial aircraft have been found which are very similar to these in the lower
8 troposphere (Bruhl et al., 2000). Bruhl et al. (2000) found that the NOX levels for 03 production
9 versus loss are highly sensitive to the radical sources included in model calculations. They found
10 that inclusion of only CH4 and CO oxidation leads to a decrease in net 03 production in the
11 North Atlantic flight corridor due to NO emissions from aircraft. However, the additional
12 inclusion of acetone photolysis was found to shift the maximum in 03 production to higher NOX
13 mixing ratios, thereby reducing or eliminating areas in which 03 production rates decreased due
14 to aircraft emissions.
15 Trainer et al. (1993) suggested that the slope of the regression line between 03 and
16 summed NOX oxidation products (NOZ, equal to the difference between measured total reactive
17 nitrogen, N0y, and NOX) can be used to estimate the rate of P(03) per NOX (also known as the 03
18 production efficiency, or OPE). Ryerson et al. (1998, 2001) used measured correlations between
19 03 and NOZ to identify different rates of 03 production in plumes from large point sources.
20 Sillman (1995) and Sillman and He (2002) identified several secondary reaction products
21 that show different correlation patterns for N0x-limited conditions and N0x-saturated conditions.
22 The most important correlations are for 03 versus N0y, 03 versus NOZ, 03 versus HN03, and
23 H202 versus HN03. The correlations between 03 and N0y, and 03 and NOZ are especially
24 important because measurements of N0y and NOX are widely available. Measured 03 versus
25 NOZ (Figure AX2.2-1) shows distinctly different patterns in different locations. In rural areas
26 and in urban areas such as Nashville, TN, 03 shows a strong correlation with NOZ and a
27 relatively steep slope to the regression line. By contrast, in Los Angeles 03 also increases with
28 NOZ, but the rate of increase of 03 with NOZ is lower and the 03 concentrations for a given NOZ
29 value are generally lower.
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1 The difference between N0x-limited and N0x-saturated regimes is also reflected in
2 measurements of H202. Formation of H202 takes place by self-reaction of photochemically-
3 generated H02 radicals, so that there is large seasonal variation of H202 concentrations, and
250 -i
Q.
Q.
£ 100
Figure AX2.2-1.
x
X
X
X
X X
10 20
NOZ {ppb)
X
XX ^
' .
30
40
Measured values of O3 and NOZ (NOy-NOx) during the afternoon at
rural sites in the eastern United States (gray circles) and in urban
areas and urban plumes associated with Nashville, TN (gray dashes),
Paris, FR (black diamonds) and Los Angeles, CA (X's)
4 values in excess of 1 ppb are mainly limited to the summer months when photochemistry is more
5 active (Kleinman, 1991). Hydrogen peroxide is produced in abundance only when Os is
6 produced under N0x-limited conditions. When the photochemistry is N0x-saturated, much less
7 H202 is produced. In addition, increasing NOX tends to slow the formation of H202 under NOX-
8 limited conditions. Differences between these two regimes are also related to the preferential
9 formation of sulfate during summer and to the inhibition of sulfate and hydrogen peroxide during
10 winter (Stein and Lamb, 2003). Measurements in the rural eastern United States (Jacob et al.,
11 1995), at Nashville (Sillman et al., 1998), and at Los Angeles (Sakugawa and Kaplan, 1989)
12 show large differences in H202 concentrations likely due to differences in NOX availability at
13 these locations.
14
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1 AX2.2.3 Multiphase Chemistry Involving NOX
2 Recent laboratory studies on sulfate and organic aerosols indicate that the reaction
3 probability yN205 is in the range of 0.01 to 0.05 (Kane et al., 2001; Hallquist et al., 2003;
4 Thornton et al., 2003). Tie et al. (2003) found that a value of 0.04 in their global model gave the
5 best simulation of observed NOX concentrations over the Arctic in winter.
6 Using aircraft measurements over the northeastern United States., Brown et al. (2006b)
7 found that the uptake coefficient for NzOs, yNzOs, on the surfaces of particles depends strongly
8 on their sulfate content. They found that yNzOs was highest (0.017) in regions where the aerosol
9 sulfate concentration was highest and lower elsewhere (<0.0016). This result contrasts with that
10 of Dentener and Crutzen (1993) who concluded that yN2Os would be independent of aerosol
11 composition, based on a value for yN2Os of 0.1, implying that the heterogeneous hydrolysis of
12 N205 would be saturated for typical ambient aerosol surface areas. The importance of this
13 reaction to tropospheric chemistry depends on the value of yNzOs. If it is 0.01 or lower, there
14 may be difficulty in explaining the loss of N0y and the formation of aerosol nitrate, especially
15 during winter. A decrease in NzOs slows down the removal of NOX by leaving more NOz
16 available for reaction and thus increases Os production. Based on the consistency between
17 measurements of N0y partitioning and gas-phase models, Jacob (2000) considers it unlikely that
18 HNOs is recycled to NOX in the lower troposphere in significant concentrations. However, only
19 one of the reviewed studies (Schultz et al., 2000) was conducted in the marine troposphere and
20 none was conducted in the MBL. An investigation over the equatorial Pacific reported
21 discrepancies between observations and theory (Singh et al., 1996) which might be explained by
22 HNOs recycling. It is important to recognize that both Schultz et al. (2000) and Singh et al.
23 (1996) involved aircraft sampling at altitude which, in the MBL, can significantly under-
24 represent sea salt aerosols and thus most total NOs (defined to be HNOs + NOs") and large
25 fractions of N0y in marine air (e.g., Huebert et al., 1996). Consequently, some caution is
26 warranted when interpreting constituent ratios and N0y budgets based on such data.
27 Recent work in the Arctic has quantified significant photochemical recycling of NOs" to
28 NOX and attendant perturbations of OH chemistry in snow (Honrath et al., 2000; Dibb et al.,
29 2002; Domine and Shepson, 2002) which suggest the possibility that similar multiphase
30 pathways could occur in aerosols. As mentioned above, NOs" is photolytically reduced to NOz
31 (Zafiriou and True, 1979) in acidic sea salt solutions (Anastasio et al., 1999). Further photolytic
September 2007 AX2-13 DRAFT-DO NOT QUOTE OR CITE
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1 reduction of N02 to NO (Zafariou and True, 1979) could provide a possible mechanism for
2 HNOs recycling. Early experiments reported production of NOX during the irradiation of
3 artificial seawater concentrates containing N03~ (Petriconi and Papee, 1972). Based on the
4 above, HNOs recycling in sea salt aerosols is potentially important and warrants further
5 investigation. Other possible recycling pathways involving highly acidic aerosol solutions and
6 soot are reviewed by Jacob (2000).
7 Stemmler et al. (2006) studied the photosensitized reduction of N02 to HONO on humic
8 acid films using radiation in the UV-A through the visible spectral regions. They also found
9 evidence for reduction occurring in the dark, reactions which may occur involving surfaces
10 containing partly oxidized aromatic structures. For example, Simpson et al. (2006) found that
11 aromatic compounds constituted -20% of organic films coating windows in downtown Toronto.
12 They calculated production rates of HONO that are compatible with observations of high HONO
13 levels in a variety of environments. The photolysis of HONO formed this way could account for
14 up to 60% of the integrated source of OH radicals in the inner planetary boundary layer. A
15 combination of high N02 levels and surfaces of soil and buildings and other man-made structures
16 exposed to diesel exhaust would then be conducive to HONO formation and, hence, to high
17 [OH] (Xuetal., 2006).
18 Ammann et al. (1998) reported the efficient conversion of N02 to HONO on fresh soot
19 particles in the presence of water. They suggest that interaction between N02 and soot particles
20 may account for high mixing ratios of HONO observed in urban environments. Conversion of
21 N02 to HONO and subsequent photolysis and HONO to NO + OH would constitute a NOX-
22 catalyzed Os sink involving snow. High concentrations of HONO can lead to the rapid growth in
23 OH concentrations shortly after sunrise, giving a "jump start" to photochemical smog formation.
24 Prolonged exposure to ambient oxidizing agents appears to deactivate this process. Broske et al.
25 (2003) studied the interaction of N02 on secondary organic aerosols and concluded that the
26 uptake coefficients were too low for this reaction to be an important source of HONO in the
27 troposphere.
28 Choi and Leu (1998) evaluated the interactions of HN03 on model black carbon soot
29 (FW2), graphite, hexane, and kerosene soot. They found that HNOs decomposed to N02 and
30 H20 at higher HNOs surface coverages, i.e., P(HNOs) > = 10~4 Torr. None of the soot models
31 used were reactive at low HNOs coverages, at P(HNOs) = 5 x 10~7 Torr or at temperatures below
September 2007 AX2-14 DRAFT-DO NOT QUOTE OR CITE
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1 220 K. They conclude that it is unlikely that aircraft soot in the upper troposphere/lower
2 stratosphere reduces HN03.
3 Heterogeneous production on soot at night is believed to be the mechanism by which
4 HONO accumulates to provide an early morning source of HOX in high NOX environments
5 (Harrison et al., 1996; Jacob, 2000). HONO has been frequently observed to accumulate to
6 levels of several ppb overnight, and this has been attributed to soot chemistry (Harris et al., 1982;
7 Calvert et al., 1994; Jacob, 2000).
8 Longfellow et al. (1999) observed the formation of HONO when methane, propane,
9 hexane, and kerosene soots were exposed to N02. They suggested that this reaction may account
10 for some part of the unexplained high levels of HONO observed in urban areas. They comment
11 that without details about the surface area, porosity, and amount of soot available for this
12 reaction, reactive uptake values cannot be estimated reliably. They comment that soot and N02
13 are produced in close proximity during combustion, and that large quantities of HONO have
14 been observed in aircraft plumes.
15 Saathoff et al. (2001) studied the heterogeneous loss of N02, HN03, N03/N205,
16 H02/H02N02 on soot aerosol using a large aerosol chamber. Reaction periods of up to several
17 days were monitored and results used to fit a detailed model. Saathoff et al. derived reaction
18 probabilities at 294 K and 50% RH for N02, N03, H02, and H02N02 deposition to soot; HN03
19 reduction to N02; and N20s hydrolysis. When these probabilities were included in
20 photochemical box model calculations of a 4-day smog event, the only noteworthy influence of
21 soot was a 10% reduction in the second day 03 maximum, for a soot loading of 20 pg rrf3, i.e.,
22 roughly a factor of 10 times observed black carbon loadings seen in United States urban areas,
23 even during air pollution episodes.
24 Munoz and Rossi (2002) conducted Knudsen cell studies of HN03 uptake on black and
25 grey decane soot produced in lean and rich flames, respectively. They observed HONO as the
26 main species released following HN03 uptake on grey soot, and NO and traces of N02 from
27 black soot. They conclude that these reactions would only have relevance in special situations in
28 urban settings where soot and HN03 are present in high concentrations simultaneously.
29
30 Formation ofNitro PAHs
31 Nitro-polycyclic aromatic hydrocarbons (nitro-PAHs) (see Figure AX2.2-2 for some
32 example nitro-PAHs) are generated from incomplete combustion processes through electrophilic
September 2007 AX2-15 DRAFT-DO NOT QUOTE OR CITE
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2-nitronaphthalene 9-nitroanthracene 2-nitrofluoranthene 6-nitrobenzo(a)pyrene
Figure AX2.2-2. Structures of nitro-polycyclic aromatic hydrocarbons.
1 reactions of poly cyclic aromatic hydrocarbons (PAHs) in the presence of NO 2 (International
2 Agency for Research on Cancer [IARC] ,1989; World Health Organization [WHO], 2003).
3 Among combustion sources, diesel emissions have been identified as the major source of nitro-
4 PAHs in ambient air (Bezabeh et al, 2003; Gibson, 1983; Schuetzle, 1983; Tokiwa and Ohnishi,
5 1986). Direct emissions of NPAHs in PM vary with type of fuel, vehicle maintenance, and
6 ambient conditions (Zielinska et al., 2004).
7 In addition to being directly emitted, nitro-PAHs can also be formed from both gaseous
8 and heterogeneous reactions of PAHs with gaseous nitrogenous pollutants in the atmosphere
9 (Arey et al., 1986; Arey et al., 1989, Arey, 1998; Perrini, 2005; Pitts, 1987; Sasaki et al., 1997;
10 Zielinska et al., 1989). Different isomers of nitro-PAHs are formed through different formation
11 processes. For example, the most abundant nitro-PAH in diesel particles is 1-nitropyene (1NP),
12 followed by 3-nitrofluoranthene (3NF) and 8-nitrofluoranthene (8NF) (Bezabeh et al., 2003;
13 Gibson, 1983; Schuetzle, 1983; Tokiwa and Ohnishi, 1986). However, in ambient particulate
14 organic matter (POM), 2-nitrofluoranthene (2NF) is the dominant compound, followed by 1NP
15 and 2-nitropyrene (2NP) (Arey et al., 1989; Bamford et al., 2003; Reisen and Arey, 2005;
16 Zielinska et al., 1989), although 2NF and 2NP are not directly emitted from primary combustion
17 emissions. The reaction mechanisms for the different nitro-PAH formation processes have been
18 well documented and are presented in Figure AX2.2-2.
19 The dominant process for the formation of nitro-PAHs in the atmosphere is gas-phase
20 reaction of PAHs with OH radicals in the presence of NOX (Arey et al., 1986, Arey, 1998;
21 Atkinson and Arey, 1994; Ramdahl et al., 1986; Sasaki et al., 1997). Hydroxyl radicals can be
22 generated photochemically or at night through ozone-alkene reactions, (Finlayson-Pitts and Pitts,
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1 2000). The postulated reaction mechanism of OH with PAHs involves the addition of OH at the
2 site of highest electron density of the aromatic ring, for example, the 1-position for pyrene (PY)
3 and the 3-position for fluoranthene (FL). This reaction is followed by the addition of N02 to the
4 OH-PAH adduct and elimination of water to form the nitroarenes (Figure AX2.2-3, Arey et al.,
5 1986; Aktinson et al., 1990; Pitts, 1987). After formation, nitro-PAHs with low vapor pressures
6 (such as 2NF and 2NP) immediately migrate to particles under ambient conditions (Fan et al.,
7 1995; Feilberg et al., 1999). The second order rate-constants for the reactions of OH with most
8 PAHs range from 10~10 to 10~12 cm3moleonVy * at 298 K with the yields ranging from -0.06 to
9 -5% (Atkinson and Arey, 1994). 2NF and 2NP have been found as the most abundant nitro-
10 PAHs formed via reactions of OH with gaseous PY and FL, respectively in ambient air.
Figure AX2.2-3.
OH
|NO£
2NP
Formation of 2-nitropyrene (2NP) from the reaction of OH with
gaseous pyrene (PY).
11 The second important process for the formation of nitro-PAHs in the atmosphere is the
12 nitration of PAHs by N0s~ in the presence of NOX at night (Atkinson et al., 1990; Atkinson and
13 Arey, 1994; Sasaki et al., 1997). Nitrate radicals can be generated by reaction of ozone (03) with
14 NOz in the atmosphere by Reaction AX2-5:
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(AX2-5)
2 Similar to the mechanism of OH reactions with PAHs, N03 initially adds to the PAH ring
3 to form an N03-PAH adduct, followed by loss of HN03 to form nitro-PAHs (Atkinson et al.,
4 1990; Atkinson and Arey, 1994; Sasaki et al., 1997). For example, in the mixture of naphthalene
5 and NzOs-NOs-NOz, the major products formed through the N03 reaction are 1- and 2-nitro-
6 naphthalene (INN and 2NN) (Atkinson et al., 1990; Feilberg et al., 1999; Sasaki et al., 1997).
7 2NF and 4NP were reported as the primary products of the gas-phase reactions of FL and PY
8 with N03 radical, respectively (Atkinson et al., 1990; Atkinson and Arey, 1994).
9 The reaction with N03 is of minor importance in the daytime because N03 radical is not
10 stable in sunlight. In addition, given the rapid reactions of NO with N03 and with 03 in the
11 atmosphere (Finlayson-Pitts and Pitts 2000), concentrations of N03 at ground level are low
12 during daytime. However, at night, concentrations of N03 radicals formed in polluted ambient
13 air are expected to increase. According to Atkinson et al. (1991), the average N03 concentration
14 is about 20 ppt in the lower troposphere at night and can be as high as 430 ppt. It is also worth
15 noting that significant N03 radical concentrations are found at elevated altitudes where 03 is
16 high but NO is low (Reissell and Arey, 2001; Stutz et al., 2004). When N03 reaches high
17 concentrations, the formation of nitro-PAHs by the reaction of gaseous PAHs with N03 may be
18 of environmental significance. At 10~17 - 10~18 cm3 molecule" V1, the rate constants of N03
19 with most PAHs are several orders of magnitude lower than those of OH with the same PAHs;
20 however, the yields of nitro-PAHs from N03 reactions are generally much higher than those of
21 OH reactions. For example, the yields of 1-NN and 2NF are 0.3% and 3%, respectively from
22 OH reactions, but the yields are 17% and 24% for these two compounds generated from the N03
23 radical reactions (Atkinson and Arey, 1994). Therefore, formation of nitro-PAHs via reactions
24 of N03 at nighttime under certain circumstances can be significant.
25 The third process of nitro-PAH formation in the atmosphere is nitration of PAHs by
26 N02/N205 in the presence of trace amounts of HN03 (HN03) in both gas and particle phases.
27 This mechanism could be operative throughout the day and night (Pitts et al., 1983, 1985a,b;
28 Grosjean et al., 1983; Ramdahl et al., 1984; Kamens et al., 1990). The formation of nitro-
29 fluoranthenes was observed when adsorbed FL was exposed to gaseous NzOs, and the
30 distribution of product NF isomers was 3- > 8- > 7- > 1- NF (Pitts et al., 1985a,b). The proposed
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1 mechanism for this reaction was an ionic electrophilic nitration by nitronium ion (N02+). It was
2 speculated that N20s became ionized prior to the reaction with FL (Zielinska et al, 1986). Only
3 1NP was observed for the reaction of PY with N205 on filters (Pitts et al., 1985b). Compared to
4 the reactions of OH and NOs, nitration of PAHs by NOz/NzOs is less important.
5 Measurements of nitro-PAHs in ambient air provide evidence for the proposed reaction
6 mechanism, i.e. the reactions of OH and NOs radicals with PAHs are the major sources of
7 nitro-PAHs (Bamford and Baker, 2003; Reisen and Arey, 2005; and references therein). 2NF is
8 a ubiquitous component of ambient POM, much higher than 1NP, itself a marker of combustion
9 sources. Nitro-PAH isomer ratios show strong seasonality. For instance, the mean ratios of
10 2NF/1NP were higher in summer than in winter (Bamford et al., 2003; Reisen and Arey, 2005),
11 indicating that reactions of OH and NOs with FL are the major sources of nitro-PAHs in ambient
12 air in summer. The ratio of 2NF/1NP was lower in winter than in summer because of lower OH
13 concentrations and, therefore, less production of 2NF via atmospheric reactions. A ratio of
14 1NP/2NF greater than 1 was observed in locations with major contributions from vehicle
15 emissions (Dimashki et al., 2000; Feilberg et al., 2001). In addition, the ratio of 2NF/2NP was
16 also used to evaluate the contribution of OH and N03 initiated reactions to the ambient nitro-
17 PAHs (Bamford et al., 2003; Reisen and Arey, 2005).
18 The concentrations for most nitro-PAHs found in ambient air are much lower than
19 1 pg/m3, except NNs, 1NP, and 2NF, which can be present at several pg/m3. These levels are
20 much lower (~2 to -1000 times lower) than their parent PAHs. However, nitro-PAHs are much
21 more toxic than PAHs (Durant et al., 1996; Grosovsky et al., 1999; Salmeen et al., 1982; Tokiwa
22 et al., 1998; Tokiwa and Ohnishi, 1986). Moreover, most nitro-PAHs are present in particles
23 with a mass median diameter <0.1 pm.
24 Esteve et al. (2006) examined the reaction of gas-phase N02 and OH radicals with
25 various PAHs adsorbed onto model diesel particulate matter (SRM 1650a, NIST). Using pseudo
26 second order rate coefficients, they derived lifetimes for conversion of the particle-bound PAHs
27 to nitro-PAHs of a few days (for typical urban N02 levels of 20 ppb). They also found that the
28 rates of reaction of OH with the PAHs were about four orders of magnitude larger than for the
29 reactions involving NOz. However, since the concentrations of NOz used above are more than
30 four orders of magnitude larger than those for OH (106-107/cm3), they concluded that the
31 pathway involving NOz is expected to be favored over that involving OH radicals. Consistent
September 2007 AX2-19 DRAFT-DO NOT QUOTE OR CITE
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1 with the importance of the gas-phase formation of NPAHS, both the mutagenic potency of PM
2 and the content of NPAHs in PM vary by particle size, and are higher in the submicron size
3 range (Xu and Lee, 2000; Kawanaka et al, 2004).
4 The major loss process of nitro-PAHs is photodecomposition (Fan et al., 1996; Feilberg
5 et al., 1999; Feilberg and Nielsen, 2001), with lifetimes on the order of hours. However, lacking
6 direct UV light sources indoors, nitro-PAHs are expected have a longer lifetimes (days) indoors
7 than outdoors; and may therefore pose increased health risks. Many nitro-PAHs are semi- or
8 nonvolatile organic compounds. As stated above, indoor environments have much greater
9 surface areas than outdoors. Thus, it is expected that gas/particle distribution of nitro-PAHs
10 indoors will be different from those in ambient air. A significant portion of nitro-PAHs will
11 probably be adsorbed by indoor surfaces, such as carpets, leading to different potential exposure
12 pathways to nitro-PAHs in indoor environments. The special characteristics of indoor
13 environments, which can affect the indoor chemistry and potential exposure pathways
14 significantly, should be taken into consideration when conducting exposure studies of nitro-
15 PAHs.
16 Reaction with OH and NOs radicals is a major mechanism for removing gas-phase PAHs,
17 with OH radical initiated reactions predominating depending on season (Vione et al., 2004;
18 Bamford et al., 2003). Particle-bound PAH reactions occur but tend to be slower.
19 Nitronaphthalenes tend to remain in the vapor phase, but because phase partitioning depends on
20 ambient temperature, in very cold regions these species can condense (Castells et al., 2003)
21 while the higher molecular weight PAHs such as the nitroanthracenes, nitrophenantrenes and
22 nitrofluoranthenes condense in and on PM (Ciganek et al., 2004; Cecinato, 2003).
23
24 Multiphase Chemical Processes Involving Nitrogen Oxides and Halogens
25 Four decades of observational data on Os in the troposphere have revealed numerous
26 anomalies not easily explained by gas-phase HOX-NOX photochemistry. The best-known
27 example is the dramatic depletion of ground-level 03 during polar sunrise due to multiphase
28 catalytic cycles involving inorganic Br and Cl radicals (Barrie et al., 1988; Martinez et al., 1999;
29 Foster et al., 2001). Other examples of anomalies in tropospheric 03 at lower latitudes include
30 low levels of 03 (<10 ppbv) in the marine boundary layer (MBL) and overlying free troposphere
31 (FT) at times over large portions of the tropical Pacific (Kley et al., 1996), as well as post-sunrise
32 Os depletions over the western subtropical Pacific Ocean (Nagao et al., 1999), the temperate
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15
1 Southern Ocean (Galbally et al, 2000), and the tropical Indian Ocean (Dickerson et al, 1999).
2 The observed Os depletions in near-surface marine air are generally consistent with the model-
3 predicted volatilization of Br2, BrCl, and C12 from sea salt aerosols through autocatalytic halogen
4 "activation" mechanisms (e.g., Vogt et al., 1996; von Glasow et al., 2002a) involving these
5 aqueous phase reactions.
6 HOBr + Br- + H+ -> Br2 + H2O
7 HOCL + Br~ + //+-» BrCl + H2O
8 HOC1 + Cl- + H* -> C12 + H20 (AX2_25)
9 In polluted marine regions at night, the heterogeneous reaction
1Q N205+C1-^C1N02+N03- (AX2_26)
1 1 may also be important (Finlayson-Pitts et al., 1989; Behnke et al., 1997; Erickson et al., 1999).
12 Diatomic bromine, BrCl, Clz, and CINOz volatilize and photolyze in sunlight to produce atomic
13 Br and Cl. The acidification of sea salt aerosol via incorporation of HN03 (and other acids)
14 leads to the volatilization of HC1 (Erickson et al., 1999), e.g.
HN03+C1-->HCH-N03
1 6 and the corresponding shift in phase partitioning can accelerate the deposition flux to the surface
17 of total N03 (Russell et al., 2003; Fischer et al., 2006). However, Pryor and Sorensen (2000)
18 have shown that the dominant form of nitrate deposition is a complex function of wind speed. In
19 polluted coastal regions where HC1 from Reaction 35 often exceeds 1 ppbv, significant
20 additional atomic Cl~ is produced via:
21 ffCl + OH -> Cl + H20 (AX2_28)
22 (Singh and Kasting, 1988; Keene et al., 2007). Following production, Br and Cl atoms
23 catalytically destroy Os via:
24 X + O3^>XO + O2 (AX2-29)
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(AX2_3Q)
(AX2_31)
3 where (X = Br and Cl).
4 Formation of Br and Cl nitrates via
5 2-3 (AX2.32)
6 and the subsequent reaction of XNOs with sea salt and sulfate aerosols via
7 XNO3 + H2O -» HOX +H+ + NO3-
8 and:
9 1 (AX2_34)
10 (where Y = Cl, Br, or I) accelerates the conversion of NOX to particulate N0s~ and thereby
1 1 contributes indirectly to net Os destruction (Sander et al., 1999; Vogt et al., 1999, Pszenny et al.,
1 2 2004) . Most XNOs reacts via reaction 34 on sea salt whereas reaction 33 is more important on
13 sulfate aerosols. Partitioning of HC1 on sulfate aerosols following Henry's Law provides Cl~ for
14 reaction 34 to form BrCl. Product N03~ from both reactions AX2-33 and AX2-34 partitions
15 with the gas-phase HNOs following Henry's Law. Because most aerosol size fractions in the
16 MBL are near equilibrium with respect to HN03 (Erickson et al., 1999; Keene et al., 2004), both
17 sulfate and sea salt aerosol can sustain the catalytic removal of NOX and re-activation of Cl and
18 Br with no detectable change in composition. The photolytic reduction of N0s~ in sea salt
19 aerosol solutions recycles NOX to the gas phase (Pszenny et al., 2004). Halogen chemistry also
20 impacts 03 indirectly by altering OH/H02 ratios (XO + H02 -> HOX + 02 -> OH + X) (e.g.,
21 Stutz et al., 1999; Bloss et al., 2005).
22 In addition to Os destruction via reaction AX2-37, atomic Cl oxidizes hydrocarbons
23 (HCs) primarily via hydrogen abstraction to form HC1 vapor and organz products (Jobson et al.,
24 1994; Pszenny et al., 2006). The enhanced supply of odd-H radicals from HC oxidation leads to
25 net Os production in the presence of sufficient NOX (Pszenny et al., 1993). Available evidence
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1 suggests that Cl radical chemistry may be a significant net source for 03 in polluted
2 coastal/urban air (e.g., Tanaka et al., 2003; Finley and Saltzman, 2006).
3 An analogous autocatalyic 03 destruction cycle involving multiphase iodine chemistry
4 also operates in the marine atmosphere (Alicke et al., 1999, Vogt et al., 1999; McFiggans et al.,
5 2000; Ashworth et al., 2002). In this case, the primary source of I is believed to be either
6 photolysis of ClH^Iz, other I-containing gases (Carpenter et al., 1999; Carpenter, 2003), and/or
7 perhaps lz (McFiggans et al., 2004; Saiz-Lopez and Plane, 2004; McFiggans, 2005) emitted by
8 micro-and macro flora. Sea salt and sulfate aerosols provide substrates for multiphase reactions
9 that sustain the catalytic I-IO cycle. The 10 radical has been measured by long-path (LP) and/or
10 multi axis (MAX) differential optical absorption spectroscopy (DOAS) at Mace Head, Ireland;
11 Tenerife, Canary Islands; Cape Grim, Tasmania; and coastal New England, USA; having
12 average daytime levels of about 1 ppt with maxima up to 7 ppt (e.g., Allan et al., 2000; Pikelnaya
13 et al., 2006). Modeling suggests that up to 13% per day of Os in marine air may be destroyed via
14 multiphase iodine chemistry (McFiggans et al., 2000). The reaction of 10 with NOz followed by
15 uptake of INOs into aerosols (analogous to Reactions AX2-9 through AX2-11) accelerates the
16 conversion of NOX to participate N03~ and thereby contributes to net 03 destruction. The
17 reaction 10 + NO —»• I + N02 also influences NOX cycling.
18 Most of the above studies have focused on halogen-radical chemistry and related
19 influences on NOX cycling in coastal and urban air. However, available evidence suggests that
20 similar chemical transformations proceed in other halogen-rich tropospheric regimes. For
21 example, Cl, Br, and/or I oxides have been measured at significant concentrations in near-surface
22 air over the Dead Sea, Israel, the Great Salt Lake, Utah (e.g., Hebestreit et al., 1999; Stutz et al.,
23 1999, 2002; Zingler and Platt, 2005), and the Salar de Uyuni salt pan in the Andes mountains
24 (U. Platt, unpublished data, 2006); high column densities of halogenated compounds have also
25 been observed from satellites over the northern Caspian Sea (Wagner et al., 2001; Hollwedel
26 et al., 2004). The primary source of reactive halogens in these regions is thought to be from
27 activation along the lives of that in reactions in AX2-23 through AX2-25 involving concentrated
28 salt deposits on surface evaporite pans. High concentrations of BrO have also been measured in
29 volcanic plumes (Bobrowski et al., 2003, Gerlach, 2004). Although virtually unexplored, the
30 substantial emissions of inorganic halogens during biomass burning (Lobert et al., 1999; Keene
31 et al., 2006) and in association with crustal dust (Keene et al., 1999; Sander et al., 2003) may
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1 also support active halogen-radical chemistry and related transformations involving NOX
2 downwind of sources. Finally, observations from satellites, balloons, and aircraft indicate that
3 BrO is present in the free troposphere at levels sufficient to significantly influence
4 photochemistry (e.g., von Glasow et al., 2004).
5
6
7 AX2.3 CHEMISTRY OF SULFUR OXIDES IN THE TROPOSPHERE
8 The four known monomeric sulfur oxides are sulfur monoxide (SO), sulfur dioxide
9 (S02), sulfur trioxide (S03), and disulfur monoxide (S20). SO can be formed by photolysis of
10 S02 at wavelengths less than 220 nm, and so could only be found in the middle and upper
11 stratosphere (Pinto et al., 1989). SOs can be emitted from the stacks of power plants and
1 2 factories however, it reacts extremely rapidly with H20 in the stacks or immediately after release
13 into the atmosphere to form H2S04. Of the four species, only S02 is present at concentrations
14 significant for atmospheric chemistry and human exposures.
1 5 Sulfur dioxide can be oxidized either in the gas phase, or, because it is soluble, in the
16 aqueous phase in cloud drops. The gas-phase oxidation of S02 proceeds through the reaction
1 7 SO2 + OH+M^ HSO3 + M
18 followed by
19 3 23 2 (AX2_36)
20 SO 3 + H20 -> H2S04 (AX2-37)
21 Since H2S04 is extremely soluble, it will be removed rapidly by transfer to the aqueous phase of
22 aerosol particles and cloud drops. Rate coefficients for reaction of S02 with H02 or N03 are too
23 low to be significant (JPL, 2003) .
24 S02 is chiefly but not exclusively primary in origin; it is also produced by the
25 photochemical oxidation of reduced sulfur compounds such as dimethyl sulfide (CH3-S-CH3) ,
26 hydrogen sulfide (H2S), carbon disulfide (CS2), carbonyl sulfide (OCS), methyl mercaptan
27 (CHs-S-H), and dimethyl disulfide (CHs-S-S-CHs) which are all mainly biogenic in origin.
28 Their sources are discussed in Section AX2.5. Table AX2.3-1 lists the atmospheric lifetimes of
29 reduced sulfur species with respect to reaction with various oxidants. Except for OCS, which is
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1 lost mainly by photolysis (x~6 months), all of these species are lost mainly by reaction with OH
2 and N03 radicals. Because OCS is relatively long-lived in the troposphere, it can be transported
3 upwards into the stratosphere. Crutzen (1976) proposed that its oxidation serves as the major
4 source of sulfate in the stratospheric aerosol layer sometimes referred to the "Junge layer,"
5 (Junge et al., 1961) during periods when volcanic plumes do not reach the stratosphere.
6 However, the flux of OCS into the stratosphere is probably not sufficient to maintain this
7 stratospheric aerosol layer. Myhre et al. (2004) propose instead that S02 transported upwards
8 from the troposphere is the most likely source, have become the upward flux of OCS is too small
9 to sustain observed sulfate loadings in the Junge layer. In addition, insitu measurements of the
10 isotopic composition of sulfur do not match those of OCS (Leung et al., 2002). Reaction with
11 NOs radicals at night most likely represents the major loss process for dimethyl sulfide and
12 methyl mercaptan. The mechanisms for the oxidation of DMS are still not completely
13 understood. Initial attack by NOs and OH radicals involves H atom abstraction, with a smaller
14 branch leading to OH addition to the S atom. The OH addition branch increases in importance as
15 temperatures decrease and becoming the major pathway below temperatures of 285 K
16 (Ravishankara, 1997). The adduct may either decompose to form methane sulfonic acid (MSA),
17 or undergo further reactions in the main pathway, to yield dimethyl sulfoxide (Barnes et al.,
18 1991). Following H atom abstraction from DMS, the main reaction products include MSA and
19 S02. The ratio of MSA to S02 is strongly temperature dependent, varying from about 0.1 in
20 tropical waters to about 0.4 in Antarctic waters (Seinfeld and Pandis, 1998). Excess sulfate (over
21 that expected from the sulfate in seawater) in marine aerosol is related mainly to the production
22 of SOz from the oxidation of DMS. Transformations among atmospheric sulfur compounds are
23 summarized in Figure AX2.3-1.
September 2007 AX2-25 DRAFT-DO NOT QUOTE OR CITE
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hv, O
so
OH
Tropopause
Figure AX2.3-1. Transformations of sulfur compounds in the atmosphere.
Source: Adapted from Berresheim et al. (1995).
1 Multiphase Chemical Processes Involving Sulfur Oxides and Halogens
2 Chemical transformations involving inorganic halogenated compounds effect changes in
3 the multiphase cycling of sulfur oxides in ways analogous to their effects on NOX. Oxidation of
4 dimethylsulfide (CH3)2S by BrO produces dimethylsulfoxide (CH3)2SO (Barnes et al., 1991 ;
5 Toumi, 1994), and oxidation by atomic chloride leads to formation of SOz (Keene et al., 1996).
6 (CHs^SO and SOz are precursors for methanesulfonic acid (CHsSOsH) and IH^SCM. In the MBL,
7 virtually all IH^SCM and CHsSOsH vapor condenses onto existing aerosols or cloud droplet, which
8 subsequently evaporate, thereby contributing to aerosol growth and acidification. Unlike
9 CH3S03H, H2S04 also has the potential to produce new particles (Korhonen et al., 1999; Kumala
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1 et al., 2000), which in marine regions is thought to occur primarily in the free troposphere. Saiz-
2 Lopez et al. (2004) estimated that observed levels of BrO at Mace Head would oxidize (CH3)2S
3 about six times faster than OH and thereby substantially increase production rates of H2S04 and
4 other condensible S species in the MBL. Sulfur dioxide is also scavenged by deliquesced
5 aerosols and oxidized to H2S04 in the aqueous phase by several strongly pH-dependent pathways
6 (Chameides and Stelson, 1992; Vogt et al., 1996; Keene et al., 1998). Model calculations
7 indicate that oxidation of S(IV) by Os dominates in fresh, alkaline sea salt aerosols, whereas
8 oxidation by hypohalous acids (primarily HOC1) dominates in moderately acidic solutions.
9 Additional particulate non-sea salt (nss) S042~ is generated by S02 oxidation in cloud droplets
10 (Clegg and Toumi, 1998). Ion-balance calculations indicate that most nss S042~ in short-lived
11 (two to 48 hours) sea salt size fractions accumulates in acidic aerosol solutions and/or in acidic
12 aerosols processed through clouds (e.g., Keene et al., 2004). The production, cycling, and
13 associated radiative effects of S-containing aerosols in marine and coastal air are regulated in
14 part by chemical transformations involving inorganic halogens (von Glasow et al., 2002b).
15 These transformations include: dry-deposition fluxes of nss S042~ in marine air dominated,
16 naturally, by the sea salt size fractions (Huebert et al., 1996; Turekian et al., 2001); HC1 phase
17 partitioning that regulates sea salt pH and associated pH-dependent pathways for S(IV) oxidation
18 (Keene et al., 2002; Pszenny et al., 2004); and potentially important oxidative reactions with
19 reactive halogens for (CH3)2S and S(IV). However, both the absolute magnitudes and relative
20 importance of these processes in MBL S cycling are poorly understood.
21 Iodine chemistry has been linked to ultrafine particle bursts at Mace Head (O'Dowd
22 et al., 1999, 2002). Observed bursts coincide with the elevated concentrations of 10 and are
23 characterized by particle concentrations increasing from background levels to up to
24 300,000 cm"3 on a time scale of seconds to minutes. This newly identified source of marine
25 aerosol would provide additional aerosol surface area for condensation of sulfur oxides and
26 thereby presumably diminish the potential for nucleation pathways involving H2S04. However,
27 a subsequent investigation in polluted air along the New England, USA coast found no
28 correlation between periods of nanoparticle growth and corresponding concentrations of I oxides
29 (Russell et al., 2006). The potential importance of I chemistry in aerosol nucleation and its
30 associated influence on sulfur cycling remain highly uncertain.
31
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1 AX2.4 MECHANISMS FOR THE AQUEOUS PHASE FORMATION OF
2 SULFATE AND NITRATE
3 The major species containing sulfur in clouds are HS03~ and S032~, which are derived
4 from the dissolution of S02 in water and are referred to as S(IV); and HSO,f and S042~, which
5 are referred to as S(VI). The major species capable of oxidizing S(IV) to S(VI) in cloud water
6 are 03, peroxides (either H202 or organic peroxides), OH radicals, and ions of transition metals
7 such as Fe and Cu that can catalyze the oxidation of S(IV) to S(VI) by 02.
8 The basic mechanism of the aqueous phase oxidation of S02 has long been studied and
9 can be found in numerous texts on atmospheric chemistry, e.g., Seinfeld and Pandis (1998),
10 Jacob (2000), and Jacobson (2002). The steps involved in the aqueous phase oxidation of S02
11 can be summarized as follows (Jacobson, 2002):
12 Dissolution of S02
13 SO2(g) <=> SO2(aq) (AX2-38)
14 The formation and dissociation of H2S03
15 SO2(aq) + H2O(aq) <^ H2SO3 <^> H++ HSOf O 2H+
16 In the pH range commonly found in rainwater (2 to 6), the most important reaction converting
17 S (IV) to S (VI) is
lg HSO3~ + H2O2 + //+<=> SO/' + H2O + 2H+
19 as S032~ is much less abundant than HS03~.
20 Major pathways for the aqueous phase oxidation of S(IV) to S(VI) as a function of pH are
21 shown in Figure AX2.4-1. For pH up to about 5.3, H202 is seen to be the dominant oxidant;
22 above 5.3, 03, followed by Fe(III) becomes dominant. Higher pHs are expected to be found
23 mainly in marine aerosols. However, in marine aerosols, the chloride-catalyzed oxidation of
24 S(IV) may be more important (Zhang and Millero, 1991; Hoppel and Caffrey, 2005). Because
25 NH4+ is so effective in controlling acidity, it affects the rate of oxidation of S(IV) to S(VI) and
26 the rate of dissolution of S02 in particles and cloud drops.
September 2007 AX2-28 DRAFT-DO NOT QUOTE OR CITE
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Figure AX2.4-1.
(A
10-'
10
,-10
t3
\
53 io-12
10
,-14
10
-16
10
,-18
I I
I I
H2°2
0 1
234
PH
Comparison of aqueous-phase oxidation paths. The rate of
conversion of S(IV) to S(VI) is shown as a function of pH. Conditions
assumed are: [SO2(g)] = 5 ppb; [NO2(g)] = 1 ppb; [H2O2(g)] = 1 ppb;
[O3(g>] = 50 ppb; [Fe(III)(aq)] = 0.3 uM; [Mn(II)(aq)] = 0.3 uM.
Source: Seinfeld and Pandis (1998).
1 Nitrogen dioxide is also taken up in cloud drops and can be oxidized to N0s~, although it
2 is much less soluble than SOz and this pathway is of minor importance. Instead, the uptake of
3 more highly soluble nitrogen- containing acids initiates aqueous-phase chemistry of NOs
4 formation.
5 Warneck (1999) constructed a box model describing the chemistry of the oxidation of
6 S02 and N02 including the interactions of N and S species and minor processes in sunlit cumulus
7 clouds. The relative contributions of different reactions to the oxidation of S(IV) species to
September 2007
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1 S(VI) and N02 to N03 10 minutes after cloud formation are given in Tables AX2.4-la and
2 AX2.4-lb. The two columns show the relative contributions with and without transition metal
3 ions. As can be seen from Table AX2.4-la, S02 within a cloud (gas + cloud drops) is oxidized
4 mainly by H202 in the aqueous phase, while and the gas-phase oxidation by OH radicals is small
5 by comparison. A much smaller contribution in the aqueous phase is made by methyl
6 hydroperoxide (CH3OOH) because it is formed mainly in the gas phase and its Henry's Law
7 constant is several orders of magnitude smaller that of H202. After H202, HN04 is the major
8 contributor to S(IV) oxidation. The contribution from the gas phase oxidation of S02 to be small
9 by comparison to the aqueous -phase reactions given above.
10 In contrast to the oxidation of S02, Table AX2.4-lb shows that the oxidation of N02
11 occurs mainly in the gas phase within clouds, implying that the gas phase oxidation of N02 by
12 OH radicals predominates. Clouds occupy about 15%, on average, of the volume of the
13 troposphere.
14 The values shown in Tables AX2.4-la and AX2.4-lb indicate that only about 20% of
15 S02 is oxidized in the gas phase, but about 90% of N02 is oxidized in the gas phase. Thus, S02
16 is oxidized mainly by aqueous-phase reactions, but N02 is oxidized mainly by gas phase
17 reactions.
18 Multiphase Chemical Processes Involving Sulfur Oxides and Ammonia
19 The phase partitioning of NH3 with deliquesced aerosol solutions is controlled primarily
20 by the thermodynamic properties of the system expressed as follows:
KH Kb
2! NH3g <-> [NH3aq] <-> [NH4+] + K
22
23 where KH and Kb are the temperature-dependent Henry's Law and dissociation constants
24 (62 M atrrf1) (1.8 x 10~5 M), respectively, for NH3, and Kw is the ion product of water (1.0 x
25 10~14 M) (Chameides, 1984). It is evident that for a given amount of NHX (NH3 + particulate
26 NH4+) in the system, increasing aqueous concentrations of particulate H+ will shift the
27 partitioning of NH3 towards the condensed phase. Consequently, under the more polluted
28 conditions characterized by higher concentrations of acidic sulfate aerosol, ratios of gaseous NH3
29 to particulate NH4+ decrease (Smith et al., 2007). It also follows that in marine air, where
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1 aerosol acidity varies substantially as a function of particle size, NH3 partitions preferentially to
2 the more acidic sub-pm size fractions (e.g., Keene et al., 2004; Smith et al., 2007).
3 Because the dry-deposition velocity of gaseous NH3 to the surface is substantially greater
4 than that for the sub-pm, sulfate aerosol size factions with which most particulate NH4+ is
5 associated, dry-deposition fluxes of total NH3 are dominated by the gas phase fraction (Russell
6 et al., 2003; Smith et al., 2007). Consequently, partitioning with highly acidic sulfate aerosols
7 effectively increases the atmospheric lifetime of total NH3 against dry deposition. This shift has
8 important consequences for NH3 cycling and potential ecological effects. In coastal New
9 England during summer, air transported from rural eastern Canada contains relatively low
10 concentrations of particulate non-sea salt (nss) S042~ and total NH3 (Smith et al., 2007). Under
11 these conditions, the roughly equal partitioning of total NH3 between the gas and particulate
12 phases sustains substantial dry-deposition fluxes of total NH3 to the coastal ocean (median of
13 10.7 pmol rrf2 day"1). In contrast, heavily polluted air transported from the industrialized
14 midwestern United States contains concentrations of nss S042~ and total NH3 that are, about a
15 factory of 3 greater, based on median values. Under these conditions, most total NH3 (>85%)
16 partitions to the highly acidic sulfate aerosol size fractions and, consequently, the median dry-
17 deposition flux of total NH3 is 30% lower than that under the cleaner northerly flow regime. The
18 relatively longer atmospheric lifetime of total NH3 against dry deposition under more polluted
19 conditions implies that, on average, total NH3 would accumulate to higher atmospheric
20 concentrations under these conditions and also be subject to atmospheric transport over longer
21 distances. Consequently, the importance NHX of removal via wet deposition would also increase.
22 Because of the inherently sporadic character of precipitation, we might expect by greater
23 heterogeneity in NH3 deposition fields and any potential responses by sensitive ecosystems
24 downwind of major S-emission regions.
25
26
27 AX2.5 TRANSPORT OF NITROGEN AND SULFUR OXIDES IN
28 THE ATMOSPHERE
29 Major episodes of high 03 concentrations in the eastern United Sates and in Europe are
30 associated with slow moving high-pressure systems. High-pressure systems during the warmer
31 seasons are associated with subsidence, resulting in warm, generally cloudless conditions with
32 light winds. The subsidence results in stable conditions near the surface, which inhibit or reduce
September 2007 AX2-31 DRAFT-DO NOT QUOTE OR CITE
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1 the vertical mixing of 03 precursors (NOX, VOCs, and CO). Photochemical activity is enhanced
2 because of higher temperatures and the availability of sunlight. However, it is becoming
3 increasingly apparent that transport of 03 and NOX and VOC from distant sources can provide
4 significant contributions to local [Os] even in areas where there is substantial photochemical
5 production. There are a number of transport phenomena occurring either in the upper boundary
6 layer or in the free troposphere which can contribute to high Os values at the surface. These
7 phenomena include stratospheric-tropospheric exchange (STE), deep and shallow convection,
8 low-level jets, and the so-called "conveyor belts" that serve to characterize flows around frontal
9 systems.
10
11 Convective Transport
12 Crutzen and Gidel (1983), Gidel (1983), and Chatfield and Crutzen (1984) hypothesized
13 that convective clouds played an important role in rapid atmospheric vertical transport of trace
14 species and first tested simple parameterizations of convective transport in atmospheric chemical
15 models. At nearly the same time, evidence was shown of venting the boundary layer by shallow,
16 fair weather cumulus clouds (e.g., Greenhut et al., 1984; Greenhut, 1986). Field experiments
17 were conducted in 1985 which resulted in verification of the hypothesis that deep convective
18 clouds are instrumental in atmospheric transport of trace constituents (Dickerson et al., 1987).
19 Once pollutants are lofted to the middle and upper troposphere, they typically have a much
20 longer chemical lifetime and with the generally stronger winds at these altitudes, they can be
21 transported large distances from their source regions. Transport of NOX from the boundary layer
22 to the upper troposphere by convection tends to dilute the higher in the boundary layer
23 concentrations and extend the NOX lifetime from less than 24 hours to several days.
24 Photochemical reactions occur during this long-range transport. Pickering et al. (1990)
25 demonstrated that venting of boundary layer NOX by convective clouds (both shallow and deep)
26 causes enhanced Os production in the free troposphere. The dilution of NOX at the surface can
27 often increase Os production efficiency. Therefore, convection aids in the transformation of
28 local pollution into a contribution to global atmospheric pollution. Downdrafts within
29 thunderstorms tend to bring air with less NOX from the middle troposphere into the boundary
30 layer. Lightning produces NO which is directly injected chiefly into the middle and upper
31 troposphere. The total global production of NO by lightning remains uncertain, but is on the
32 order of 10% of the total.
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1 Observations of the Effects ofConvective Transport
2 The first unequivocal observations of deep convective transport of boundary layer
3 pollutants to the upper troposphere were documented by Dickerson et al. (1987).
4 Instrumentation aboard three research aircraft measured CO, Os, NO, NOX, N0y, and
5 hydrocarbons in the vicinity of an active mesoscale convective system near the
6 Oklahoma/Arkansas border during the 1985 PRE-STORM experiment. Anvil penetrations about
7 two hours after maturity found greatly enhanced mixing ratios inside the cloud of all of the
8 aforementioned species compared with outside it. Nitric oxide mixing ratios in the anvil
9 averaged 3 to 4 ppbv, with individual 3-min observations reaching 6 ppbv; boundary layer NOX
10 was typically 1.5 ppbv or less outside the cloud. Therefore, the anvil observations represent a
11 mixture of boundary layer NOX and NOX contributed by lightning. Luke et al. (1992)
12 summarized the air chemistry data from all 18 flights during PRE-STORM by categorizing each
13 case according to synoptic flow patterns. Storms in the maritime tropical flow regime
14 transported large amounts of CO, Os, and N0y into the upper troposphere with the
15 midtroposphere remaining relatively clean. During frontal passages a combination of stratiform
16 and convective clouds mixed pollutants more uniformly into the middle and upper levels.
17 Prather and Jacob (1997) and Jaegle et al. (1997) noted that precursors of HOX are also
18 transported to the upper troposphere by deep convection, in addition to primary pollutants (e.g.,
19 NOX, CO, VOCs). The HOX precursors of most importance are water vapor, HCHO, H202,
20 CHsOOH, and acetone. The hydroperoxyl radical is critical for oxidizing NO to N02 in the Os
21 production process as described above.
22 Over remote marine areas, the effects of deep convection on trace gas distributions differ
23 from those over moderately polluted continental regions. Chemical measurements taken by the
24 NASA ER-2 aircraft during the Stratosphere-Troposphere Exchange Project (STEP) off the
25 northern coast of Australia show the influence of very deep convective events. Between 14.5
26 and 16.5 km on the February 2-3, 1987 flight, chemical profiles that included pronounced
27 maxima in CO, water vapor, and CCN, and minima of N0y, and Os (Pickering et al., 1993).
28 Trajectory analysis showed that these air parcels likely were transported from convective cells
29 800-900 km upstream. Very low marine boundary layer mixing ratios of N0y and Os in this
30 remote region were apparently transported upward in the convection. A similar result was noted
31 in CEPEX (Central Equatorial Pacific Experiment; Kley et al., 1996) and in INDOEX (Indian
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1 Ocean Experiment) (deLaat et al, 1999) where a series of ozonesonde ascents showed very low
2 upper tropospheric 03 following deep convection. It is likely that similar transport of low-ozone
3 tropical marine boundary layer air to the upper troposphere occurs in thunderstorms along the
4 east coast of Florida. Deep convection occurs frequently over the tropical Pacific. Low-ozone
5 and low-NOx convective outflow likely will descend in the subsidence region of the subtropical
6 eastern Pacific, leading to some of the cleanest air that arrives at the west coast of the United
7 States.
8 The discussion above relates to the effects of specific convective events. Observations
9 have also been conducted by NASA aircraft in survey mode, in which the regional effects of
10 many convective events can be measured. The SONEX (Subsonic Assessment Ozone and
11 Nitrogen Oxides Experiment) field program in 1997 conducted primarily upper tropospheric
12 measurements over the North Atlantic. The regional effects of convection over North America
13 and the Western Atlantic on upper tropospheric NOX were pronounced (Crawford et al., 2000;
14 Allen et al., 2000). A discussion of the results of model calculations of convection and its effects
15 can be found in Section AX2.7.
16
17 Effects on Photolysis Rates and Wet Scavenging
18 Thunderstorm clouds are optically very thick, and, therefore, have major effects on
19 radiative fluxes and photolysis rates. Madronich (1987) provided modeling estimates of the
20 effects of clouds of various optical depths on photolysis rates. In the upper portion of a
21 thunderstorm anvil, photolysis is likely to be enhanced by a factor of 2 or more due to multiple
22 reflections off the ice crystals. In the lower portion and beneath the cloud, photolysis is
23 substantially decreased. With enhanced photolysis rates, the NO/NOz ratio in the upper
24 troposphere is driven to larger values than under clear-sky conditions. Existing experimental
25 evidence seems to confirm, at least qualitatively these model results (Kelley et al., 1994).
26 Thunderstorm updraft regions, which contain copious amounts of water, are regions
27 where efficient scavenging of soluble species can occur (Balkanski et al., 1993). Nitrogen
28 dioxide itself is not very soluble and therefore wet scavenging is not a major removal process for
29 it. However, a major NOX reservoir species, HN03 is extremely soluble. Very few direct field
30 measurements of the amounts of specific trace gases that are scavenged in storms are available.
31 Pickering et al. (2001) used a combination of model estimates of soluble species that did not
32 include wet scavenging and observations of these species from the upper tropospheric outflow
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1 region of a major line of convection observed near Fiji. Over 90% of the and in the outflow air
2 appeared to have been removed by the storm. About 50% of CH3OOH and about 80% of HCHO
3 had been lost.
4 Convective processes and small-scale turbulence transport pollutants both upward and
5 downward throughout the planetary boundary layer and the free troposphere. Ozone and its
6 precursors (NOX, CO, and VOCs) can be transported vertically by convection into upper part of
7 the mixed layer on one day, then transported overnight as a layer of elevated mixing ratios,
8 perhaps by a nocturnal low-level jet, and then entrained into a growing convective boundary
9 layer downwind and brought back to the surface.
10 Because NO and N02 are only slightly soluble, they can be transported over longer
11 distances in the gas phase than can more soluble species which can be depleted by deposition to
12 moist surfaces, or taken up more readily on aqueous surfaces of particles. During transport, they
13 can be transformed into reservoir species such as HNOs, PANs, and NzOs. These species can
14 then contribute to local NOX concentrations in remote areas. For example, it is now well
15 established that PAN decomposition provides a major source of NOX in the remote troposphere
16 (Staudt et al., 2003). PAN decomposition in subsiding air masses from Asia over the eastern
17 Pacific could make an important contribution to 03 and NOX enhancement in the United States
18 (Kotchenruther et al., 2001; Hudman et al., 2004). Further details about mechanisms for
19 transporting ozone and its precursors were described at length in CD06.
20
21
22 AX2.6 SOURCES AND EMISSIONS OF NITROGEN OXIDES,
23 AMMONIA, AND SULFUR DIOXIDE
24 All three of the species listed in the title to this section have both natural and
25 anthropogenic sources. In Section AX2.6.1, interactions of NOX with the terrestrial biosphere are
26 discussed. Because of the tight coupling between processes linking emissions and deposition,
27 they are discussed together. In Section AX2.6.2, emissions of NOX, NH3, and S02 are discussed.
28 Field studies evaluating emissions inventories are discussed in Section AX2.6.3.
29
30 AX2.6.1 Interactions of Nitrogen Oxides with the Biosphere
31 Nitrogen oxides affect vegetated ecosystems, and in turn the atmospheric chemistry of
32 NOX is influenced by vegetation. Extensive research on nitrogen inputs from the atmosphere to
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1 forests was conducted in the 1980s as part of the Integrated Forest Study, and is summarized by
2 Johnson and Lindberg (1992). The following sections discuss sources of NOX from soil,
3 deposition of NOX to foliage, reactions with biogenic hydrocarbons, and ecological effects of
4 nitrogen deposition.
5
6 NOX Sources
1
8 Soil NO
9 Nitric oxide NO from soil metabolism is the dominant, but not exclusive, source of
10 nitrogen oxides from the biosphere to the atmosphere. As noted below, our understanding of
11 N02 exchange with vegetation suggests that there should be emission of N02 from foliage when
12 ambient concentrations are less than about 1 ppb. However, Lerdau et al. (2000) have pointed
13 out that present understanding of the global distribution of NOX is not consistent with a large
14 source that would be expected in remote forests if N02 emission was important when
15 atmospheric concentrations were below the compensation point.
16 The pathways for nitrification and denitrification include two gas-phase intermediates,
17 NO and NzO, some of which can escape. While N20 is of interest for its greenhouse gas
18 potential and role in stratospheric chemistry it is not considered among the reactive nitrogen
19 oxides important for urban and regional air quality and will not be discussed further.
20 Temperature and soil moisture are critical factors that control the rates of reaction and
21 importantly the partitioning between NO and N20 which depend on oxygen levels: in flooded
22 soils where oxygen levels are low, N20 is the dominant soil nitrogen gas; as soil dries, allowing
23 more 02 to diffuse, NO emissions increase. In very dry soils microbial activity is inhibited and
24 emissions of both N20 and NO decrease. Nitrogen metabolism in soil is strongly dependent on
25 the substrate concentrations. Where nitrogen is limiting, nitrogen is efficiently retained and little
26 gaseous nitrogen is released. Where nitrogen is in excess of demand, gaseous nitrogen emissions
27 increase; consequently, soil NO emissions are highest in fertilized agriculture and tropical soils
28 (Davidson and Kingerlee, 1997; Williams et al., 1992).
29
30 Sinks
31 Several reactive nitrogen are species are deposited to vegetation, among them, HN03,
32 N02, PAN, and organic nitrates.
September 2007 AX2-36 DRAFT-DO NOT QUOTE OR CITE
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1 HNO3
2 Deposition of HN03 appears to be relatively simple. Field observations based on
3 concentration gradients and recently using eddy covariance demonstrate rapid deposition that
4 approaches the aerodynamic limit (as constrained by atmospheric turbulence) in the Wesely
5 (1989) formulation based on analogy to resistance. Surface resistance for HNOs uptake by
6 vegetation is negligible. Deposition rates are independent of leaf area or stomatal conductance,
7 implying that deposition occurs to branches, soil, and leaf cuticle as well as internal leaf surfaces.
8 Deposition velocities (Vd) typically exceed 1 cm s^and exhibit a daily pattern controlled
9 by turbulence characteristics: midday maximum and lower values at night when there is stable
10 boundary layer.
11
12 Deposition of NO 2
13 Nitrogen dioxide interaction with vegetation is more complex. Application of 15N-
14 labeled Nitrogen Dioxide demonstrates that Nitrogen Dioxide is absorbed and metabolized by
15 foliage (Siegwolf et al., 2001; Mocker et al., 1998; Segschneider et al., 1995; Weber et al.,
16 1995). Exposure to N02 induces nitrate reductase (Weber et al., 1995, 1998), a necessary
17 enzyme for assimilating oxidized nitrogen. Understanding of NOz interactions with foliage is
18 largely based on leaf cuvette and growth chamber studies, which expose foliage or whole plants
19 to controlled levels of NOz and measure the fraction of NOz removed from the chamber air. A
20 key finding is that the fit of NOz flux to NOz concentration, has a non-zero intercept, implying a
21 compensation point or internal concentration. In studies at very low N02 concentrations
22 emission from foliage is observed (Teklemariam and Sparks, 2006). Evidence for a
23 compensation point is not solely based on the fitted intercept. Nitrogen dioxide uptake rate to
24 foliage is clearly related to stomatal conductance. Internal resistance is variable, and may be
25 associated with concentrations of reactive species such as ascorbate in the plant tissue that react
26 with NOz (Teklemariam and Sparks, 2006). Foliar NOz emissions show some dependence on
27 nitrogen content (Teklemariam and Sparks, 2006). Internal NOz appears to derive from plant
28 nitrogen metabolism.
29 Two approaches to modeling N02 uptake by vegetation are the resistance-in-series
30 analogy which considers flux (F) as the product of concentration (C) and Vd, where is related to
31 the sum of aerodynamic, boundary layer, and internal resistances (Ra, Rt>, and Rc; positive fluxes
32 are from atmosphere to foliage)
September 2007 AX2-37 DRAFT-DO NOT QUOTE OR CITE
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1 F=C¥d (AX2-42)
2 yd = (Ra + Rb + Rcr! (AX2-43)
3 Ra and Rb and controlled by turbulence in the mixed layer; Rc is dependent on
4 characteristics of the foliage and other elements of the soil, and may be viewed as 2 combination
5 of resistance internal to the foliage and external on the cuticle, soils, and bark. This approach is
6 amenable to predicting deposition in regional air quality models (Wesely, 1989). Typically, the
7 NOz, Vd is less than that for Os, due to the surface's generally higher resistance to NOz uptake,
8 consistent with NCVs lower reactivity.
9 Alternatively, NOz exchange with foliage can be modeled from a physiological viewpoint
10 where the flux from the leaf is related to the stomatal conductance and a concentration gradient
1 1 between the ambient air and interstitial air in the leaf. This approach best describes results for
12 exchange with individual foliage elements, and is expressed per unit leaf (needle) area. While
13 this approach provides linkage to leaf physiology, it is not straightforward to scale up from the
14 leaf to ecosystem scale :
15 sa-i (AX2-44)
16 This model implicitly associates the compensation point with a finite internal
17 concentration. Typically observed compensation points are around 1 ppb. Finite values of
18 internal NOz concentration are consistent with metabolic pathways that include oxides of
19 nitrogen. In this formulation, the uptake will be linear with N02 concentration, which is
20 typically observed with foliar chamber studies.
21 Several studies have shown the UV dependence of NOz emission, which implies some
22 photo-induced surface reactions that release N02. Rather than model this as a UV-dependent
23 internal concentration, it would be more realistic to add an additional term to account for
24 emission that is dependent on light levels and other surface characteristics:
25 sa- S (AX2-45)
26 The mechanisms for surface emission are discussed below. Measurement of NOz flux is
27 confounded by the rapid interconversion of NO, NOz, and Os (Gao et al., 1991).
28
September 2007 AX2-38 DRAFT-DO NOT QUOTE OR CITE
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1 PAN Deposition
2 Peroxyacetyl nitrate is phytotoxic, so clearly it is absorbed at the leaf. Observations
3 based on inference from concentration gradients and rates of decline at night (Shepson et al.,
4 1992; Schrmipf et al., 1996) and leaf chamber studies (Teklemariam and Sparks, 2004) have
5 indicated that PAN uptake is slower than that of Os; however, recent work in coniferous canopy
6 with direct eddy covariance PAN flux measurements indicated a Vd more similar to that of Os.
7 Uptake of PAN is under stomatal control, has a non-zero deposition at night, and is influenced by
8 leaf wetness (Turnipseed et al., 2006). On the other hand, flux measurements determined by
9 gradient methods over a grass surface showed a Vd closer to 0.1 cm s'1, with large uncertainty
10 (Doskey et al., 2004). A factor of 10 uncertainty remains in Vd 0.1-1 cm s"1 giving a range.
11 Whether the discrepancies are methodological or indicate intrinsic differences between different
12 vegetation is unknown. Uptake of PAN is smaller than its thermal decomposition in all cases.
13
14 Organic Nitrates
15 The biosphere also interacts with NOX through hydrocarbon emissions and their
16 subsequent reactions to form multi-functional organic nitrates. Isoprene nitrates are an important
17 class of these. Isoprene reacts with OH to form a radical that adds N02 to form a hydroxyalkyl
18 nitrate. The combination of hydroxyl and nitrate functional group makes these compounds
19 especially soluble with low vapor pressures; they likely deposit rapidly (Shepson et al., 1996;
20 Treves et al., 2000). Many other unsaturated hydrocarbons react by analogous routes.
21 Observations at Harvard Forest show a substantial fraction of total N0y not accounted for by
22 NO, NOz and PAN, which is attributed to the organic nitrates (Horii et al., 2006, Munger et al.,
23 1998). Furthermore, the total N0y flux exceeds the sum of HNOs, NOX, and PAN, which implies
24 that the organic nitrates are a substantial fraction of nitrogen deposition. Other observations that
25 show evidence of hydoxyalkyl nitrates include those of Grossenbacher et al. (2001) and Day
26 etal. (2003).
27 Formation of the hydroxyalkyl nitrates occurs after VOC + OH reaction. In some sense,
28 this mechanism is just an alternate pathway for OH to react with NOX to form a rapidly
29 depositing species. If VOC were not present, OH would be available to react with NOz when it
30 is present instead to form HNOs.
31
September 2007 AX2-39 DRAFT-DO NOT QUOTE OR CITE
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1 HONO
2 Nitrous acid formation on vegetative surfaces at night has long been observed based on
3 measurements of positive gradients (Harrison and Kitto, 1994). Surface reactions of N02
4 enhanced by moisture were proposed to explain these results. Production was evident at sites
5 with high ambient N02; at low concentration, uptake of HONO exceeded the source.
6 Daytime observations of HONO when rapid photolysis is expected to deplete ambient
7 concentrations to very low levels implies a substantial source of photo-induced HONO formation
8 at a variety of forested sites where measurements have been made. Estimated source strengths
9 are 200-1800 pptv hr1 in the surface layer (Zhou et al., 2002a, 2003), which is about 20 times
10 faster than all nighttime sources. Nitrous acid sources could be important to OH/H02 budgets as
11 HONO is rapidly photolyzed by sunlight to OH and NO. Additional evidence of light-dependent
12 reactions to produce HONO comes from discovery of a HONO artifact in pyrex sample inlet
13 lines exposed to ambient light. Either covering the inlet or washing it eliminated the HONO
14 formation (Zhou et al., 2002b). Similar reactions might serve to explain observations of UV-
15 dependent production of NOX in empty foliar cuvettes that had been exposed to ambient air (Hari
16 et al., 2003; Raivonen et al., 2003).
17 Production of HONO in the dark is currently believed to occur via a heterogeneous
18 reaction involving N02 on wet surfaces (Jenkin et al., 1988; Pitts et al., 1984; He et al., 2006;
19 Sakamaki et al., 1983), and it is proposed that the mechanism has first-order dependence in both
20 N02 and H20 (Kleffmann et al., 1998; Svensson et al., 1987) despite the stoichiometry.
21 However, the molecular pathway of the mechanism is still under debate. Jenkin et al. (1988)
22 postulated a H20-N02 water complex reacting with gas phase N02 to produce HONO, which is
23 inconsistent with the formation of an N204 intermediate leading to HONO as proposed by
24 Finlayson-Pitts et al. (2003). Another uncertainty is whether the reaction forming HONO is
25 dependent on water vapor (Svensson et al., 1987; Stutz et al., 2004) or water adsorbed on
26 surfaces (Kleffmann et al., 1998). Furthermore, the composition of the surface and the available
27 amount of surface or surface-to-volume ratio can significantly influence the HONO production
28 rates (Kaiser and Wu, 1977; Kleffmann et al., 1998; Svensson et al., 1987), which may explain
29 the difference in the rates observed between laboratory and atmospheric measurements.
30 There is no consensus on a chemical mechanism for photo-induced HONO production.
31 Photolysis of HNOs or N0s~ absorbed on ice or in surface water films has been proposed
September 2007 AX2-40 DRAFT-DO NOT QUOTE OR CITE
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1 (Honrath et al., 2002; Ramazan et al., 2004; Zhou et al., 2001, 2003). Alternative pathways
2 include N02 interaction with organic surfaces such as humic substances (George et al., 2005;
3 Stemmler et al., 2006). Note that either N03~ photolysis or heterogeneous reaction of N02 are
4 routes for recycling deposited nitrogen oxides back to the atmosphere in an active form. Nitrate
5 photolysis would return nitrogen that heretofore was considered irreversibly deposited, surface
6 reactions between N02 and water films or organic molecules would decrease the effectiveness of
7 observed N02 deposition if the HONO were re-emitted.
8
9 Fast Homogeneous Reactions
10 Inferences from observations at Blodgett Forest (Cohen et al. in prep) suggest that
11 radicals from 03 + VOC react with NOX in the canopy to produce HN03 and organic nitrates
12 among other species. This mechanism would contribute to canopy retention of soil NO emission
13 in forests with high VOC possibly more effectively than the NO to N02 conversion and foliar
14 uptake of N02 that has been proposed to reduce the amount of soil NO that escapes to the supra-
15 canopy atmosphere (Jacob and Bakwin, 1991).
16
17 Some NO 2 and HNOs Flux Data from Harvard Forest
18
19 Observations from TDL Measurements of NO 2
20 Harvard Forest is a rural site in central Massachusetts, where ambient NOX, N0y, and
21 other pollutant concentrations and fluxes of total N0y have been measured since 1990 (Munger
22 et al., 1996). An intensive study in 2000 utilized a Tunable Diode Laser Absorption
23 Spectrometer (TDLAS) to measure N02 and HNOs. TDLAS has an inherently fast response, and
24 for species such as N02 and HN03 with well-characterized spectra it provides an absolute and
25 specific measurement. Absolute concentrations of HNOs were measured, and the flux inferred
26 based on the dry deposition inferential method that uses momentum flux measurements to
27 compute a deposition velocity and derives an inferred flux (Wesely and Hicks, 1977; Hicks et al.,
28 1987). Direct eddy covariance calculations for HNOs were not possible because the atmospheric
29 variations were attenuated by interaction with the inlet walls despite very short residence time
30 and use of fluorinated silane coatings to make the inlet walls more hydrophobic. Nitrogen Oxide
31 response was adequate to allow both concentration and eddy covariance flux determination.
32 Simultaneously, NO and N0y eddy covariance fluxes were determined with two separate 03
September 2007 AX2-41 DRAFT-DO NOT QUOTE OR CITE
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1 chemiluminescence detectors, one equipped with a H2-gold catalyst at the inlet to convert all
2 reactive nitrogen compounds to NO. Additionally, the measurements include concentration
3 gradients for NO, N02, and Os over several annual cycles to examine their vertical profiles in the
4 forest canopy.
5 Overall, the results show typical N02 concentrations of 1 ppb under clean-air conditions
6 and mean concentrations up to 3 ppb at night and 1 ppb during daytime for polluted conditions.
7 Net positive fluxes (emission) of N02 were evident in the daytime and negative fluxes
8 (deposition) were observed at night (Figure AX2.6-1). Nitric oxide fluxes were negative during
9 the daytime and near zero at night.
10 In part the opposite NO and N02 fluxes are simply consequences of variable N0/N02
11 distributions responding to vertical gradients in light intensity and Os concentration, which
12 resulted in no net flux of NOX (Gao et al., 1993). In the Harvard Forest situation, the NO and
13 N02 measurements were not at the same height above the canopy, and the resulting differences
14 derive at least in part from the gradient in flux magnitude between the two inlets
15 (Figure AX2.6-2).
16 At night, when NO concentrations are near 0 due to titration by ambient 03 there is not a
17 flux of NO to offset N02 fluxes. Nighttime data consistently show N02 deposition (Figure
18 AX2.6-3), which increases with increasing N02 concentrations. Concentrations above 10 ppb
19 were rare at this site, but the few high N02 observations suggest a nonlinear dependence on
20 concentration. The data fit a model with Vd of -0.08 plus an enhancement term that was second
21 order in N02 concentration. The second order term implies that N02 deposition rates to
22 vegetation in polluted urban sites would be considerably larger than what was observed at this
23 rural site.
24 After accounting for the N0-N02 null cycle the net NOX flux could be derived. Overall,
25 there was a net deposition of NOX during the night and essentially zero flux in the day, with large
26 variability in the magnitude and sign of individual flux observations (Figure AX2.6-3). For the
27 periods with [N02] > 2 ppb, deposition was always observed. These canopy-scale field
28 observations are consistent with a finite compensation point for N02 in the canopy that offsets
29 foliar uptake or even reverses it when concentrations are especially low. At concentrations
30 above the compensation point, NOX is absorbed by the canopy. Examination of concentration
31 profiles corroborates the flux measurements (Figure AX2.6-4). During daytime for low-NOx
September 2007 AX2-42 DRAFT-DO NOT QUOTE OR CITE
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NW
SW
o
E
u
i
o
o--,-•,-,-
JC
ty ?
E
I "
—' -2
X
_3
|| _,4
[NO]
[NOJ
[OJ/10
t^~t.*:i~T
F NO
FO/10
luT
-+-
12
Hours
18
6 12 18
Hours
Figure AX2.6-1.
Diel cycles of median concentrations (upper panels) and fluxes (lower
panels) for the Northwest clean sector, left panels) and Southwest
(polluted sector, right panels) wind sectors at Harvard Forest, April-
November, 2000, for NO, NO2, and O3/10. NO and O3 were sampled
at a height of 29 m, and NO2 at 22 m. Vertical bars indicate 25th and
27th quartiles for NO and NO2 measurements. NO2 concentration
and nighttime deposition are enhanced under southwesterly
conditions, as are O3 and the morning NO maximum.
Source: Horiietal. (2004).
September 2007
AX2-43
DRAFT-DO NOT QUOTE OR CITE
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Simple Model
100
80
, 60
D5
"5
40
20
0
Ii
NO
NO,
NO ' \ NO.
Figure AX2.6-2.
0.0 0.2 0.4 0.6 0.8 1.0-2 -1 0 1 2
Concentration (nmol mol"1) Flux (nmol mol"1 cm s"1)
Simple NOX photochemical canopy model outputs. Left panel,
concentrations of NO (dashed) and NO2 (solid); right, fluxes of NO
(dashed) and NO2 (solid). Symbols indicate measurement heights for
NO (29m) and NO2 (22m) at Harvard Forest. The model solves the
continuity equation for NO concentration at 200 levels, d/dz(-
Kc(dNO/dz)) = PNO - LNO, where PNO = [NO]/tl, LNO = [NO]/t2,
and zero net deposition or emission of NOX is allowed. NOX (NO +
NO2) is normalized to Ippb. tl = 70s in this example. Due to the
measurement height difference, observed upward NO2 flux due to
photochemical cycling alone should be substantially larger than
observed downward NO flux attributable to the same process.
Source: Horii (2002).
September 2007
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DRAFT-DO NOT QUOTE OR CITE
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FNO2 (night) = F0+ V0 [NO2] + a [NO2]2
5-
o
_
E
"o
E
LL.
cT
-10-
-15-
-20-
Figure AX2.6-3.
Hourly Data (fitted) •
Nightly Medians +
Vf= -0.08 ±0.03 (cms"')
a = -0,013 ±0.001 (nmor1 mol cm s~1)
R2=0.63
I
0
10 15 20
[NOJ (nmol mol'1)
25
30
Hourly (dots) and median nightly (pluses) NOi flux vs. concentration,
with results of least-squares fit on the hourly data (curve). The flux is
expressed in units of concentration times velocity (nmol mol'1 cm s'1)
in order to simplify the interpretation of the coefficients in the least-
squares fit. Pressure and temperature corrections have been taken
into account in the conversion from density to mixing ratio.
Source: Horiietal. (2004).
1 conditions, there is a local maximum in the concentration profile near the top of the canopy
2 where Os has a local minimum, which is consistent with foliar emission or light-dependent
3 production of NOX in the upper canopy. Depletion is evident for both NOX and 03 near the forest
4 floor. Air reaching the ground has passed through the canopy where uptake is efficient and the
5 vertical exchange rates near the ground are slow. At night, the profiles generally decrease with
6 decreasing height above the ground, showing only uptake. At higher concentrations, the daytime
7 NOX concentrations are nearly constant through the canopy; no emission is evident from the
8 sunlit leaves.
September 2007
AX2-45
DRAFT-DO NOT QUOTE OR CITE
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NOv PROFILES
Canopy
Top
30--
25--
0J
5 -"
NO2 //NOX
xo
xo
Night Low NOx
o
-O^
25"
5 --
Night High NOx
0.70 0.75 0.80 0.85 15 20 25 30
3,4 3.8 4.2 4.6 14 16 18 20 22 24
Canopy
Top —
25--
20
.5?
"5
NOV
Day Low NOx
25 t x
o -•'
Day High NOx o
060 0.65 0.70 075 0.80 28 30 32 34 36 4.2 4.4 4.6 4.8 5.0 22 23 24 25 26 27 28
Concentration (nmol mol"1)
Concentration (nmol mol"1)
Figure AX2.6-4. Averaged profiles at Harvard Forest give some evidence of some
input near the canopy top from light-mediated ambient reactions, or
emission from open stomates.
Source: Horiietal. (2004).
1 Figure AX2.6-5 compares observed fluxes of all the observed species. The measured
2 NOX and estimated PAN fluxes are small relative to the observed total N0y flux. In clean air,
3 HN03 accounts for nearly all the N0y flux and the sum of all measured species is about equal to
September 2007
AX2-46
DRAFT-DO NOT QUOTE OR CITE
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Summer 2000
NW
SW
12--
u
o
o
o 10
I 8
O
E 6
--NO
NOx+HNO3+PAN
NO*
4
2
0
1.2 -
\
c
g
"•§ 0.4
££
LL
0.0 --
-5
"o
E -15
-25
^
15
20
Figure AX2.6-5.
Summer (June-August) 2000 median concentrations (upper panels),
fractions of NOy (middle panels), and fluxes (lower panels) of NOy and
component species separated by wind direction (Northwest on the left
and Southwest on the right). Vertical lines in the flux panels show
25th and 75th quartiles of F(NOy) and F(HNO3); negative fluxes
represent deposition; F(NOX) is derived from eddy covariance F(NO)
and F(NO2) measurements (corrected for photochemical cycling),
F(HNC>3) is inferred, and F(NOy) was measured by eddy covariance.
The sum of NOX, HNOs, and PAN accounts for all of the NOy
concentration and flux for Northwesterly (unpolluted background)
flows, whereas up to 50% of NOy and F(NOy) under Southwesterly
flows are in the form of reactive nitrogen species whose fluxes are not
measured or estimated here.
Source: Horiietal. (2006).
September 2007
AX2-47
DRAFT-DO NOT QUOTE OR CITE
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1 the N0y concentration. However, in polluted conditions, unmeasured species are up to 25% of
2 the N0y, and HN03 fluxes cannot account for all the total N0y flux observed. Likely these
3 unmeasured N0y species are hydroxyalkyl nitrates and similar compounds and are rapidly
4 deposited. Although NOz uptake may be important to the plant, because it is an input directly to
5 the interior of foliage that can be used immediately in plant metabolism, it is evidently not a
6 significant part of overall nitrogen deposition to rural sites. The deposition of HNOs and
7 multifunctional organic nitrates are the largest elements of the nitrogen dry deposition budget.
8 Two key areas of remaining uncertainty are the production of HONO over vegetation and the
9 role of very reactive biogenic VOCs. HONO is important because its photolysis is a source of
10 OH radicals, and its formation may represent an unrecognized mechanism to regenerate
11 photochemically active NOX from nitrate that had been considered terminally removed from the
12 atmosphere.
13
14 Ecosystem Effects
15 In addition to the contribution to precipitation acidity, atmospheric nitrogen oxides have
16 ecological effects. Total loading by both and wet and dry deposition is the relevant metric for
17 considering ecosystem impacts. At low inputs, nitrogen deposition adds essential nutrients to
18 terrestrial ecosystems. Most temperate forests are nitrogen limited; thus the inputs stimulate
19 growth. Anthropogenic nitrogen may influence some plant species different and alter the
20 distribution of plant species (cf. Wedin and Tilman, 1996). At high nitrogen loading, where
21 nitrogen inputs exceed nutrient requirements, deleterious effects including forest decline
22 associated with 'nitrogen saturation' are seen (Aber at al., 1998; Driscoll et al., 2003). In aquatic
23 ecosystems, however, nitrogen is may or may not be limiting, but in brackish waters atmospheric
24 deposition of anthropogenic nitrogen is suspected of contributing to eutrophication of some
25 coastal waters and lakes (see Bergstrom and Jansson, 2006; Castro and Driscoll, 2002).
26
27 AX2.6.2 Emissions of NOX, NH3, and SO2
28
29 Emissions ofNOx
30 Estimated annual emissions of NOX, NHs, and SOz for 2002 (U.S. Environmental
31 Protection Agency, 2006) are shown in Table AX2.6-1. Methods for estimating emissions of
32 criteria pollutants, quality assurance procedures, and examples of emissions calculated by using
September 2007 AX2-48 DRAFT-DO NOT QUOTE OR CITE
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1 data are given in U.S. Environmental Protection Agency (1999). Discussions of uncertainties in
2 current emissions inventories and strategies for improving them can be found in NARSTO
3 (2005).
4 As can be seen from the table, combustion by stationary sources, such as electrical
5 utilities and various industries, accounts for roughly half of total anthropogenic emissions of
6 NOX. Mobile sources account for the other half, with highway vehicles representing the major
7 mobile source component. Approximately half the mobile source emissions are contributed by
8 diesel engines, the remainder are emitted by gasoline-fueled vehicles and other sources.
9 Emissions of NOX associated with combustion arise from contributions from both fuel
10 nitrogen and atmospheric nitrogen. Combustion zone temperatures greater than about 1300 K
11 are required to fix atmospheric N2:
12 »2^2^<-™ (AX2-46)
13 Otherwise, NO can be formed from fuel N according to this reaction:
H CaHbOcNd + 02 -> xC02 + yH20 + zNO (AX2-47)
In addition to NO formation by the schematic reactions given above, some N02 and CO
15 are also formed depending on temperatures, concentrations of OH and H02 radicals and 02
16 levels. Fuel nitrogen is highly variable in fossil fuels, ranging from 0.5 to 2.0 percent by weight
17 (wt %) in coal to 0.05% in light distillates (e.g., diesel fuel), to 1.5 wt % in heavy fuel oils (UK
18 AQEG, 2004). The ratio of N02 to NOX in primary emissions ranges from 3 to 5 % from
19 gasoline engines, 5 to 12% from heavy-duty diesel trucks, 5 to 10% from vehicles fueled by
20 compressed natural gas and from 5 to 10% from stationary sources. In addition to NOX, motor
21 vehicles also emit HONO, with ratios of HONO to NOX ranging from 0.3% in the Caldecott
22 Tunnel, San Francisco Bay (Kirchstetter and Harley, 1996) to 0.5 to 1.0% in studies in the
23 United Kingdom (UK AQEG, 2004). The N02 to NOX ratios in emissions from turbine jet
24 engines are as high as 32 to 35 % during taxi and takeoff (CD93). Sawyer et al. (2000) have
25 reviewed the factors associated with NOX emissions by mobile sources. Marine transport
26 represents a minor source of NOX, but it constitutes a larger source in the EU where it is expected
27 to represent about two-thirds of land-based sources (UK AQEG, 2004).
September 2007 AX2-49 DRAFT-DO NOT QUOTE OR CITE
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1 NOX Emissions from Natural Sources (Soil, Wild Fires, and Lightning)
2
3 Soil
4 Emission rates of NO from cultivated soil depend mainly on fertilization levels and soil
5 temperature. About 60% of the total NOX emitted by soils occurs in the central corn belt of the
6 United States. The oxidation of NHs, emitted mainly by livestock and soils, leads to the
7 formation of NO, also NH4+ and N0s~ fertilizers lead to NO emissions from soils. Estimates of
8 emissions from natural sources are less certain than those from anthropogenic sources. On a
9 global scale, the contribution of soil emissions to the oxidized nitrogen budget is on the order of
10 10% (van Aardenne et al., 2001; Finlayson-Pitts and Pitts, 2000; Seinfeld and Pandis, 1998), but
11 NOX emissions from fertilized fields are highly variable. Soil NO emissions can be estimated
12 from the fraction of the applied fertilizer nitrogen emitted as NOX, but the flux varies strongly
13 with land use and temperature. Estimated globally averaged fractional applied nitrogen loss as
14 NO varies from 0.3% (Skiba et al., 1997) to 2.5% (Yienger and Levy, 1995). Variability within
15 biomes to which fertilizer is applied, such as shortgrass versus tallgrass prairie, accounts for a
16 factor of three in uncertainty (Williams et al., 1992; Yienger and Levy, 1995; Davidson and
17 Kingerlee, 1997).
18 The local contribution can be much greater than the global average, particularly in
19 summer and especially where corn is grown extensively. Williams et al. (1992) estimated that
20 contributions to NO budgets from soils in Illinois are about 26% of the emissions from industrial
21 and commercial processes in that State. In Iowa, Kansas, Minnesota, Nebraska, and South
22 Dakota, all states with smaller human populations, soil emissions may dominate the NO budget.
23 Conversion of NHs to NOs (nitrification) in aerobic soils appears to be the dominant pathway to
24 NO. The mass and chemical form of nitrogen (reduced or oxidized) applied to soils, the
25 vegetative cover, temperature, soil moisture, and agricultural practices such as tillage all
26 influence the amount of fertilizer nitrogen released as NO.
27 Emissions of NO from soils peak in summer when Os formation is also at a maximum.
28 An NRC panel report (NRC, 2002) outlined the role of agriculture in emissions of air pollutants
29 including NO and NH3. That report recommends immediate implementation of best
30 management practices to control these emissions, and further research to quantify the magnitude
31 of emissions and the impact of agriculture on air quality. Civerolo and Dickerson (1998) report
September 2007 AX2-50 DRAFT-DO NOT QUOTE OR CITE
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1 that use of the no-till cultivation technique on a fertilized cornfield in Maryland reduced NO
2 emissions by a factor of seven.
3
4 NOxfrom Biomass Burning
5 During biomass burning, nitrogen is derived mainly from fuel nitrogen and not from
6 atmospheric Nz, since temperatures required to fix atmospheric Nz are likely to be found only in
7 the flaming crowns of the most intense boreal forest fires. Nitrogen is present mainly in plants as
8 amino (NHz) groups in amino acids. During combustion, nitrogen is released mainly in
9 unidentified forms, presumably as N2, with very little remaining in fuel ash. Apart from N2, the
10 most abundant species in biomass burning plumes is NO. Emissions of NO account for only
11 about 10 to 20% relative to fuel N (Lobert et al., 1991). Other species such as N02, nitriles,
12 ammonia, and other nitrogen compounds account for a similar amount. Emissions of NOX are
13 about 0.2 to 0.3% relative to total biomass burned (e.g., Andreae, 1991; Radke et al., 1991).
14 Westerling et al. (2006) have noted that the frequency and intensity of wildfires in the western
15 United States have increased substantially since 1970.
16
17 Lightning Production of NO
18 Annual global production of NO by lightning is the most uncertain source of reactive
19 nitrogen. In the last decade, literature values of the global average production rate range from
20 2 to 20 Tg N per year. However, the most likely range is from 3 to 8 Tg N per year, because the
21 majority of the recent estimates fall in this range. The large uncertainty stems from several
22 factors: (1) a large range of NO production rates per meter of flash length (as much as two orders
23 of magnitude); (2) the open question of whether cloud-to-ground (CG) flashes and intracloud
24 flashes (1C) produce substantially different amounts of NO; (3) the global flash rate; and (4) the
25 ratio of the number of 1C flashes to the number of CG flashes. Estimates of the amount of NO
26 produced per flash have been made based on theoretical considerations (e.g., Price et al., 1997),
27 laboratory experiments (e.g., Wang et al., 1998); field experiments (e.g., Stith et al., 1999;
28 Huntrieser et al., 2002, 2007) and through a combination of cloud-resolving model simulations,
29 observed lightning flash rates, and anvil measurements of NO (e.g., DeCaria et al., 2000, 2005;
30 Ott et al., 2007). The latter method was also used by Pickering et al. (1998), who showed that
31 only ~5 to 20% of the total NO produced by lightning in a given storms exists in the boundary
32 layer at the end of a thunderstorm. Therefore, the direct contribution to boundary layer 03
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1 production by lightning NO is thought to be small. However, lightning NO production can
2 contribute substantially to 03 production in the middle and upper troposphere. DeCaria et al.
3 (2005) estimated that up to 10 ppbv of ozone was produced in the upper troposphere in the first
4 24 hours following a Colorado thunderstorm due to the injection of lightning NO. A series of
5 midlatitude and subtropical thunderstorm events have been simulated with the model of DeCaria
6 et al. (2005), and the derived NO production per CG flash averaged 500 moles/flash while
7 average production per 1C flash was 425 moles/flash (Ott et al., 2006).
8 A major uncertainty in mesoscale and global chemical transport models is the
9 parameterization of lightning flash rates. Model variables such as cloud top height, convective
10 precipitation rate, and upward cloud mass flux have been used to estimate flash rates. Allen and
11 Pickering (2002) have evaluated these methods against observed flash rates from satellite, and
12 examined the effects on ozone production using each method.
13
14 Uses of Satellite Data to Derive Emissions
15 Satellite data have been shown to be useful for optimizing estimates of emissions of NOz.
16 (Leue et al., 2001; Martin et al., 2003; Jaegle et al., 2005). Satellite-borne instruments such as
17 GOME (Global Ozone Monitoring Experiment; Martin et al., 2003; and references therein) and
18 SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Chartography;
19 Bovensmann et al., 1999) retrieve tropospheric columns of NOz, which can then be combined
20 with model-derived chemical lifetimes of NOX to yield emissions of NOX.
21 Top-down inference of NOX emission inventory from the satellite observations of NOz
22 columns by mass balance requires at minimum three pieces of information: the retrieved
23 tropospheric NOz column, the ratio of tropospheric NOX to NOz columns, and the NOX lifetime
24 against loss to stable reservoirs. A photochemical model has been used to provide information
25 on the latter two pieces of information. The method is generally applied exclusively to land
26 surface emissions, excluding lightning. Tropospheric N02 columns are insensitive to lightning
27 NOX emissions since most of the lightning NOX in the upper troposphere is present as NO at the
28 local time of the satellite measurements (Ridley et al., 1996), owing to the slower reactions of
29 NO with 03 there.
30 Jaegle et al. (2005) applied additional information on the spatial distribution of emissions
31 and on fire activity to partition NOX emissions into sources from fossil fuel combustion, soils,
32 and biomass burning. Global a posteriori estimates of soil NOX emissions are 68% larger than
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1 the a priori estimates. Large increases are found for the agricultural region of the western United
2 States during summer, increasing total U.S. soil NOX emissions by a factor of 2 to 0.9 Tg N yr"1.
3 Bertram et al. (2005) found clear signals in the SCIAMACHY observations of short intense NOX
4 pulses following springtime fertilizer application and subsequent precipitation over agricultural
5 regions of the western United States. For the agricultural region in North-Central Montana, they
6 calculate a yearly SCIAMACHY top-down estimate that is 60% higher than a commonly used
7 model of soil NOX emissions by Yienger and Levy (1995).
8 Martin et al. (2006) retrieved tropospheric nitrogen dioxide (N02) columns for
9 May 2004 to April 2005 from the SCIAMACHY satellite instrument to derive top-down NOX
10 emissions estimates via inverse modeling with a global chemical transport model (GEOS-Chem).
11 The top-down emissions were combined with a priori information from a bottom-up emission
12 inventory with error weighting to achieve an improved a posteriori estimate of the global
13 distribution of surface NOX emissions. Their a posteriori inventory improves the GEOS-Chem
14 simulation of NOX, PAN, and HNOs with respect to airborne in situ measurements over and
15 downwind of New York City. Their a posteriori inventory shows lower NOX emissions from the
16 Ohio River valley during summer than during winter, reflecting recent controls on NOX
17 emissions from electric utilities. Their a posteriori inventory is highly consistent (R2 = 0.82,
18 bias = 3%) with the NEI99 inventory for the United States. In contrast, their a posteriori
19 inventory is 68% larger than a recent inventory by Streets et al. (2003) for East Asia for the year
20 2000.
21
22 Emissions ofNH3
23 Emissions of NHs show a strikingly different pattern from those of NOX. Three-way
24 catalysts used in motor vehicles emit small amounts of NHs as a byproduct during the reduction
25 of NOX. Stationary combustion sources make only a small contribution to emissions of NHs
26 because efficient combustion favors formation of NOX and, NH3 from combustion is produced
27 mainly by inefficient, low temperature fuel combustion. For these reasons, most emissions of
28 NHs arise from fertilized soils and from livestock.
29 The initial step in the oxidation of atmospheric NHs to NO is by reaction with OH
30 radicals. However, the lifetime of NH3 from this pathway is sufficiently long (-1-2 months
31 using typical OH values 1-2 x 106/cm3) that it is a small sink compared to uptake of NHs by
32 cloud drops, dry deposition, and aerosol particles. Thus, the gas-phase oxidation of NHs makes a
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1 very small contribution as a source of NO. Holland et al. (2005) estimated wet and dry
2 deposition of NHX, based on measurements over the continental United States, and found that
3 emissions of NH3 in the National Emissions Inventory are perhaps underestimated by about a
4 factor of two to three. Reasons for this imbalance include under-representation of deposition
5 monitoring sites in populated areas and the neglect of off-shore transport in their estimate. The
6 use of fixed deposition velocities that do not reflect local conditions at the time of measurement
7 introduces additional uncertainty into their estimates of dry deposition.
8
9 Emissions ofSO2
10 As can be seen from Table AX2.6-1, emissions of S02 are due mainly to the combustion
11 of fossil fuels by electrical utilities and industry. Transportation related sources make only a
12 minor contribution. As a result, most S02 emissions originate from point sources. Since sulfur
13 is a volatile component of fuels, it is almost quantitatively released during combustion and
14 emissions can be calculated on the basis of the sulfur content of fuels to greater accuracy than for
15 other pollutants such as NOX or primary PM.
16 The major natural sources of S02 are volcanoes and biomass burning and DMS oxidation
17 over the oceans. S02 constitutes a relatively minor fraction (0.005% by volume) of volcanic
18 emissions (Holland, 1978). The ratio of H2S to S02 is highly variable in volcanic gases. It is
19 typically much less than one, as in the Mt. Saint Helen's eruption (Turco et al., 1983). However,
20 in addition to being degassed from magma, H2S can be produced if ground waters, especially
21 those containing organic matter, come into contact with volcanic gases. In this case, the ratio of
22 H2S to S02 can be greater than one. H2S produced this way would more likely be emitted
23 through side vents than through eruption columns (Pinto et al., 1989). Primary particulate sulfate
24 is a component of marine aerosol and is also produced by wind erosion of surface soils.
25 Volcanic sources of S02 are limited to the Pacific Northwest, Alaska, and Hawaii. Since
26 1980, the Mount St. Helens volcano in the Washington Cascade Range (46.20 N, 122.18 W,
27 summit 2549 m asl) has been a variable source of S02. Its major effects came in the explosive
28 eruptions of 1980, which primarily affected the northern part of the mountainous western half of
29 the United States. The Augustine volcano near the mouth of the Cook Inlet in southwestern
30 Alaska (59.363 N, 153.43 W, summit 1252 m asl) has had variable S02 emission since its last
31 major eruptions in 1986. Volcanoes in the Kamchatka peninsula of eastern region of Siberian
32 Russia do not significantly effect surface S02 concentrations in northwestern North America.
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1 The most serious effects in the United States from volcanic S02 occurs on the island of Hawaii.
2 Nearly continuous venting of S02 from Mauna Loa and Kilauea produces S02 in such large
3 amounts that > 100 km downwind of the island S02 concentrations can exceed 30 ppbv
4 (Thornton and Bandy, 1993). Depending on wind direction, the west coast of Hawaii (Kona
5 region) has had significant deleterious effects from S02 and acidic sulfate aerosols for the past
6 decade.
7 Emissions of S02 from burning vegetation are generally in the range of 1 to 2% of the
8 biomass burned (see e.g., Levine et al., 1999). Sulfur is bound in amino acids in vegetation.
9 This organically bound sulfur is released during combustion. However, unlike nitrogen, about
10 half of the sulfur initially present in vegetation is found in the ash (Delmas, 1982). Gaseous
11 emissions are mainly in the form of S02 with much smaller amounts of H2S and OCS. The ratio
12 of gaseous nitrogen to sulfur emissions is about 14, very close to their ratio in plant tissue
13 (Andreae, 1991). The ratio of reduced nitrogen and sulfur species such as NHs and H2S to their
14 more oxidized forms, such as NO and S02, increases from flaming to smoldering phases of
15 combustion, as emissions of reduced species are favored by lower temperatures and 02 reduced
16 availability.
17 Emissions of reduced sulfur species are associated typically with marine organisms living
18 either in pelagic or coastal zones and with anaerobic bacteria in marshes and estuaries.
19 Mechanisms for their oxidation were discussed in Section AX2.2. Emissions of dimethyl sulfide
20 (DMS) from marine plankton represent the largest single source of reduced sulfur species to the
21 atmosphere (e.g., Berresheim et al., 1995). Other sources such as wetlands and terrestrial plants
22 and soils probably account for less than 5% of the DMS global flux, with most of this coming
23 from wetlands.
24 The coastal and wetland sources of DMS have a dormant period in the fall/winter from
25 senescence of plant growth. Marshes die back in fall and winter, so dimethyl sulfide emissions
26 from them are lower, reduced light levels in winter at mid to high latitudes reduce cut
27 phytoplankton growth which also tends to reduce DMS emissions. Western coasts at mid to high
28 latitudes have reduced levels of the light that drive photochemical production and oxidation of
29 DMS. Freezing at mid and high latitudes affects the release of biogenic sulfur gases, particularly
30 in the nutrient-rich regions around Alaska. Transport of S02 from regions of biomass burning
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1 seems to be limited by heterogeneous losses that accompany convective processes that ventilate
2 the surface layer and the lower boundary layer (Thornton et al., 1996, TRACE-P data archive).
3 However, it should be noted that reduced sulfur species are also produced by industry.
4 For example, DMS is used in petroleum refining and in petrochemical production processes to
5 control the formation of coke and carbon monoxide. In addition, it is used to control dusting in
6 steel mills. It is also used in a range of organic syntheses. It also has a use as a food flavoring
7 component. It can also be oxidized by natural or artificial means to dimethyl sulfoxide (DMSO),
8 which has several important solvent properties.
9
10 AX2.6.3 Field Studies Evaluating Emissions Inventories
11 Comparisons of emissions model predictions with observations have been performed in a
12 number of environments. A number of studies of ratios of concentrations of CO to NOX and
13 NMOC to NOX during the early 1990s in tunnels and ambient air (summarized in Air Quality
14 Criteria for Carbon Monoxide (U.S. Environmental Protection Agency, 2000)) indicated that
15 emissions of CO and NMOC were systematically underestimated in emissions inventories.
16 However, the results of more recent studies have been mixed in this regard, with many studies
17 showing agreement to within ±50% (U.S. Environmental Protection Agency, 2000).
18 Improvements in many areas have resulted from the process of emissions model development,
19 evaluation, and further refinement. It should be remembered that the conclusions from these
20 reconciliation studies depend on the assumption that NOX emissions are predicted correctly by
21 emissions factor models. Roadside remote sensing data indicate that over 50% of NMHC and
22 CO emissions are produced by less than about 10% of the vehicles (Stedman et al., 1991). These
23 "super-emitters" are typically poorly maintained vehicles. Vehicles of any age engaged in off-
24 cycle operations (e.g., rapid accelerations) emit much more than if operated in normal driving
25 modes. Bishop and Stedman (1996) found that the most important variables governing CO
26 emissions are fleet age and owner maintenance.
27 Emissions inventories for North America can be evaluated by comparison to measured
28 long-term trends and or ratios of pollutants in ambient air. A decadal field study of ambient CO
29 at a rural site in the Eastern United States (Hallock-Waters et al., 1999) indicates a downward
30 trend consistent with the downward trend in estimated emissions over the period 1988 to 1999
31 (U.S. Environmental Protection Agency, 1997), even when a global downward trend is
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1 accounted for. Measurements at two urban areas in the United States confirmed the decrease in
2 CO emissions (Parrish et al., 2002). That study also indicated that the ratio of CO to NOX
3 emissions decreased by almost a factor of three over 12 years (such a downward trend was noted
4 in AQCD 96). Emissions estimates (U.S. Environmental Protection Agency, 1997) indicate a
5 much smaller decrease in this ratio, suggesting that NOX emissions from mobile sources may be
6 underestimated and/or increasing. Parrish et al. (2002) conclude that Os photochemistry in U.S.
7 urban areas may have become more N0x-limited over the past decade.
8 Pokharel et al. (2002) employed remotely sensed emissions from on-road vehicles and
9 fuel use data to estimate emissions in Denver. Their calculations indicate a continual decrease in
10 CO, HC, and NO emissions from mobile sources over the 6-year study period. Inventories based
11 on the ambient data were 30 to 70% lower for CO, 40% higher for HC, and 40 to 80% lower for
12 NO than those predicted by the MOBILE6 model.
13 Stehr et al. (2000) reported simultaneous measurements of CO, SOz, and N0y at an East
14 Coast site. By taking advantage of the nature of mobile sources (they emit NOX and CO but little
15 S02) and power plants (they emit NOX and S02 but little CO), the authors evaluated emissions
16 estimates for the eastern United States. Results indicated that coal combustion contributes 25 to
17 35% of the total NOX emissions in rough agreement with emissions inventories (U.S.
18 Environmental Protection Agency, 1997).
19 Parrish et al. (1998) and Parrish and Fehsenfeld (2000) proposed methods to derive
20 emission rates by examining measured ambient ratios among individual VOC, NOX and N0y.
21 There is typically a strong correlation among measured values for these species because emission
22 sources are geographically collocated, even when individual sources are different. Correlations
23 can be used to derive emissions ratios between species, including adjustments for the impact of
24 photochemical aging. Investigations of this type include correlations between CO and N0y (e.g.,
25 Parrish et al., 1991), between individual VOC species and N0y (Goldan et al., 1995, 1997, 2000)
26 and between various individual VOC (Goldan et al., 1995, 1997; McKeen and Liu, 1993;
27 McKeen et al., 1996). Buhr et al. (1992) derived emission estimates from principal component
28 analysis (PCA) and other statistical methods. Many of these studies are summarized in Trainer
29 et al. (2000), Parrish et al. (1998), and Parrish and Fehsenfeld (2000). Goldstein and Schade
30 (2000) also used species correlations to identify the relative impacts of anthropogenic and
31 biogenic emissions. Chang et al. (1996, 1997) and Mendoza-Dominguez and Russell (2000,
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1 2001) used the more quantative technique of inverse modeling to derive emission rates, in
2 conjunction with results from chemistry-transport models.
3
4
5 AX2.7 METHODS USED TO CALCULATE CONCENTRATIONS OF
6 NITROGEN OXIDES AND THEIR CHEMICAL
7 INTERACTIONS IN THE ATMOSPHERE
8 Atmospheric chemistry and transport models are the major tools used to calculate the
9 relations among Os, other oxidants, and their precursors, the transport and transformation of air
10 toxics, the production of secondary organic aerosol, the evolution of the particle size distribution,
11 and the production and deposition of pollutants affecting ecosystems. Chemical transport
12 models are driven by emissions inventories for primary species such as the precursors for Os and
13 PM and by meterological fields produced by other numerical models. Emissions of precursor
14 compounds can be divided into anthropogenic and natural source categories. Natural sources can
15 be further divided into biotic (vegetation, microbes, animals) and abiotic (biomass burning,
16 lightning) categories. However, the distinction between natural sources and anthropogenic
17 sources is often difficult to make as human activities affect directly, or indirectly, emissions from
18 what would have been considered natural sources during the preindustrial era. Emissions from
19 plants and animals used in agriculture have been referred to as anthropogenic or natural in
20 different applications. Wildfire emissions may be considered to be natural, except that forest
21 management practices may have led to the buildup of fuels on the forest floor, thereby altering
22 the frequency and severity of forest fires. Needed meteorological quantities such as winds and
23 temperatures are taken from operational analyses, reanalyses, or circulation models. In most
24 cases, these are off-line analyses, i.e., they are not modified by radiatively active species such as
25 03 and particles generated by the model.
26 A brief overview of atmospheric chemistry-transport models is given in Section AX2.7.1.
27 A discussion of emissions inventories of precursors used by these models is given in Section
28 AX2.7.2. Uncertainties in emissions estimates have also been discussed in Air Quality Criteria
29 for Particulate Matter (U.S. Environmental Protection Agency, 2004). Chemistry-transport
30 model evaluation and an evaluation of the reliability of emissions inventories are presented in
31 Section AX2.7.4.
32
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1 AX2.7.1 Chemistry-Transport Models
2 Atmospheric CTMs have been developed for application over a wide range of spatial
3 scales ranging from neighborhood to global. Regional scale CTMs are used: 1) to obtain better
4 understanding of the processes controlling the formation, transport, and destruction of gas-and
5 particle-phase criteria and hazardous air pollutants; 2) to understand the relations between Os
6 concentrations and concentrations of its precursors such as NOX and VOCs, the factors leading to
7 acid deposition, and hence to possible damage to ecosystems; and 3) to understand relations
8 among the concentration patterns of various pollutants that may exert adverse health effects.
9 Chemistry Transport Models are also used for determining control strategies for 03 precursors.
10 However, this application has met with varying degrees of success because of the highly
11 nonlinear relations between Os and emissions of its precursors, and uncertainties in emissions,
12 parameterizations of transport, and chemical production and loss terms. Uncertainties in
13 meteorological variables and emissions can be large enough to lead to significant errors in
14 developing control strategies (e.g., Russell and Dennis, 2000; Sillman et al., 1995).
15 Global scale CTMs are used to address issues associated with climate change,
16 stratospheric ozone depletion, and to provide boundary conditions for regional scale models.
17 CTMs include mathematical (and often simplified) descriptions of atmospheric transport, the
18 transfer of solar radiation through the atmosphere, chemical reactions, and removal to the surface
19 by turbulent motions and precipitation for pollutants emitted into the model domain. Their upper
20 boundaries extend anywhere from the top of the mixing layer to the mesopause (about 80 km in
21 height), to obtain more realistic boundary conditions for problems involving stratospheric
22 dynamics. There is a trade-off between the size of the modeling domain and the grid resolution
23 used in the CTM that is imposed by computational resources.
24 There are two major formulations of CTMs in current use. In the first approach, grid-
25 based, or Eulerian, air quality models, the region to be modeled (the modeling domain) is
26 subdivided into a three-dimensional array of grid cells. Spatial derivatives in the species
27 continuity equations are cast in finite-difference there are also some finite-element models, but
28 not many applications form over this grid, and a system of equations for the concentrations of all
29 the chemical species in the model are solved numerically at each grid point. Time dependent
30 continuity (mass conservation) equations are solved for each species including terms for
31 transport, chemical production and destruction, and emissions and deposition (if relevant), in
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1 each cell. Chemical processes are simulated with ordinary differential equations, and transport
2 processes are simulated with partial differential equations. Because of a number of factors such
3 as the different time scales inherent in different processes, the coupled, nonlinear nature of the
4 chemical process terms, and computer storage limitations, all of the terms in the equations are
5 not solved simultaneously in three dimensions. Instead, operator splitting, in which terms in the
6 continuity equation involving individual processes are solved sequentially, is used. In the second
7 CTM formulation, trajectory or Lagrangian models, a large number of hypothetical air parcels
8 are specified as following wind trajectories. In these models, the original system of partial
9 differential equations is transformed into a system of ordinary differential equations.
10 A less common approach is to use a hybrid Lagrangian/Eulerian model, in which certain
11 aspects of atmospheric chemistry and transport are treated with a Lagrangian approach and
12 others are treaded in an Eulerian manner (e.g., Stein et al., 2000). Each approach has its their
13 advantages and disadvantages. The Eulerian approach is more general in that it includes
14 processes that mix air parcels and allows integrations to be carried out for long periods during
15 which individual air parcels lose their identity. There are, however, techniques for including the
16 effects of mixing in Lagrangian models such as FLEXPART (e.g., Zanis et al., 2003), ATTILA
17 (Reithmeier and Sausen, 2002), and CLaMS (McKenna et al., 2002).
18
19 Regional Scale Chemistry Transport Models
20 Major modeling efforts within the U.S. Environmental Protection Agency center on the
21 Community Multiscale Air Quality modeling system (CMAQ, Byun and Ching, 1999; Byun and
22 Schere, 2006). A number of other modeling platforms using Lagrangian and Eulerian
23 frameworks have been reviewed in the 96 AQCD for Os (U.S. Environmental Protection
24 Agency, 1997), and in Russell and Dennis (2000). The capabilities of a number of CTMs
25 designed to study local- and regional-scale air pollution problems are summarized by Russell and
26 Dennis (2000). Evaluations of the performance of CMAQ are given in Arnold et al. (2003), Eder
27 and Y (2005), Appel et al. (2005), and Fuentes and Raftery (2005). The domain of CMAQ can
28 extend from several hundred km to the hemispherical scale. In addition, both of these classes of
29 models allow the resolution of the calculations over specified areas to vary. CMAQ is most
30 often driven by the MM5 mesoscale meteorological model (Seaman, 2000), though it may be
31 driven by other meteorological models (e.g., RAMS). Simulations of Os episodes over regional
32 domains have been performed with a horizontal resolution as low as 1 km, and smaller
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1 calculations over limited domains have been accomplished at even finer scales. However,
2 simulations at such high resolutions require better parameterizations of meteorological processes
3 such as boundary layer fluxes, deep convection and clouds (Seaman, 2000), and finer-scale
4 emissions. Finer spatial resolution is necessary to resolve features such as urban heat island
5 circulations; sea, bay, and land breezes; mountain and valley breezes, and the nocturnal low-level
6 jet.
7 The most common approach to setting up the horizontal domain is to nest a finer grid
8 within a larger domain of coarser resolution. However, there are other strategies such as the
9 stretched grid (e.g., Fox-Rabinovitz et al., 2002) and the adaptive grid. In a stretched grid, the
10 grid's resolution continuously varies throughout the domain, thereby eliminating any potential
11 problems with the sudden change from one resolution to another at the boundary. Caution
12 should be exercised in using such a formulation, because certain parameterizations that are valid
13 on a relatively coarse grid scale (such as convection) may not be valid on finer scales. Adaptive
14 grids are not fixed at the start of the simulation, but instead adapt to the needs of the simulation
15 as it evolves (e.g., Hansen et al., 1994). They have the advantage that they can resolve processes
16 at relevant spatial scales. However, they can be very slow if the situation to be modeled is
17 complex. Additionally, if adaptive grids are used for separate meteorological, emissions, and
18 photochemical models, there is no reason a priori why the resolution of each grid should match,
19 and the gains realized from increased resolution in one model will be wasted in the transition to
20 another model. The use of finer horizontal resolution in CTMs will necessitate finer-scale
21 inventories of land use and better knowledge of the exact paths of roads, locations of factories,
22 and, in general, better methods for locating sources and estimating their emissions.
23 The vertical resolution of these CTMs is variable, and usually configured to have higher
24 resolution near the surface and decreasing aloft. Because the height of the boundary layer is of
25 critical importance in simulations of air quality, improved resolution of the boundary layer height
26 would likely improve air quality simulations. Additionally, current CTMs do not adequately
27 resolve fine scale features such as the nocturnal low-level jet in part because little is known about
28 the nighttime boundary layer.
29 CTMs require time-dependent, three-dimensional wind fields for the period of
30 simulation. The winds may be either generated by a model using initial fields alone or with four-
31 dimensional data assimilation to improve the model's performance, fields (i.e., model equations
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1 can be updated periodically or "nudged", to bring results into agreement with observations.
2 Modeling efforts typically focus on simulations of several days' duration, the typical time scale
3 for individual 03 episodes, but there have been several attempts at modeling longer periods. For
4 example, Kasibhatla and Chameides (2000) simulated a four-month period from May to
5 September of 1995 using MAQSIP. The current trend in modeling applications is towards
6 annual simulations. This trend is driven in part by the need to better understand observations of
7 periods of high wintertime PM (e.g., Blanchard et al., 2002) and the need to simulate Os episodes
8 occurring outside of summer.
9 Chemical kinetics mechanisms (a set of chemical reactions) representing the important
10 reactions occurring in the atmosphere are used in CTMs to estimate the rates of chemical
11 formation and destruction of each pollutant simulated as a function of time. Unfortunately,
12 chemical mechanisms that explicitly treat the reactions of each individual reactive species are too
13 computationally demanding to be incorporated into CTMs. For example, a master chemical
14 mechanism includes approximately 10,500 reactions involving 3603 chemical species (Derwent
15 et al., 2001). Instead, "lumped" mechanisms, that group compounds of similar chemistry
16 together, are used. The chemical mechanisms used in existing photochemical Os models contain
17 significant uncertainties that may limit the accuracy of their predictions; the accuracy of each of
18 these mechanisms is also limited by missing chemistry. Because of different approaches to the
19 lumping of organic compounds into surrogate groups, chemical mechanisms can produce
20 somewhat different results under similar conditions. The CB-IV chemical mechanism (Gery
21 et al., 1989), the RADM II mechanism (Stockwell et al., 1990), the SAPRC (e.g., Wang et al.,
22 2000a,b; Carter, 1990) and the RACM mechanisms can be used in CMAQ. Jimenez et al. (2003)
23 provide brief descriptions of the features of the main mechanisms in use and they compared
24 concentrations of several key species predicted by seven chemical mechanisms in a box model
25 simulation over 24 h. The average deviation from the average of all mechanism predictions for
26 03 and NO over the daylight period was less than 20%, and was 10% for N02 for all
27 mechanisms. However, much larger deviations were found for HNOs, PAN, HOz, H-zOz, CzFu,
28 and C5H8 (isoprene). An analysis for OH radicals was not presented. The large deviations
29 shown for most species imply differences between the calculated lifetimes of atmospheric
30 species and the assignment of model simulations to either N0x-limited or radical quantity limited
31 regimes between mechanisms. Gross and Stockwell (2003) found small differences between
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1 mechanisms for clean conditions, with differences becoming more significant for polluted
2 conditions, especially for NOz and organic peroxy radicals. They caution modelers to consider
3 carefully the mechanisms they are using. Faraji et al. (2006) found differences of 40% in peak
4 Ih Os in the Houston-Galveston-Brazoria area between simulations using SAPRAC and CB4.
5 They attributed differences in predicted Os concentrations to differences in the mechanisms of
6 oxidation of aromatic hydrocarbons.
7 CMAQ and other CTMs (e.g., PM-CAMx) incorporate processes and interactions of
8 aerosol-phase chemistry (Mebust et al., 2003). There have also been several attempts to study
9 the feedbacks of chemistry on atmospheric dynamics using meteorological models, like MM5
10 (e.g., Grell et al., 2000; Liu et al., 2001a; Lu et al., 1997; Park et al., 2001). This coupling is
11 necessary to simulate accurately feedbacks such as may be caused by the heavy aerosol loading
12 found in forest fire plumes (Lu et al., 1997; Park et al., 2001), or in heavily polluted areas.
13 Photolysis rates in CMAQ can now be calculated interactively with model produced Os, NOz,
14 and aerosol fields (Binkowski et al., 2007).
15 Spatial and temporal characterizations of anthropogenic and biogenic precursor emissions
16 must be specified as inputs to a CTM. Emissions inventories have been compiled on grids of
17 varying resolution for many hydrocarbons, aldehydes, ketones, CO, NHs, and NOX. Emissions
18 inventories for many species require the application of some algorithm for calculating the
19 dependence of emissions on physical variables such as temperature and to convert the
20 inventories into formatted emission files required by a CTM. For example, preprocessing of
21 emissions data for CMAQ is done by the SMOKE (Spare-Matrix Operator Kernel Emissions)
22 system. For many species, information concerning the temporal variability of emissions is
23 lacking, so long-term (e.g., annual or Os-season) averages are used in short-term, episodic
24 simulations. Annual emissions estimates are often modified by the emissions model to produce
25 emissions more characteristic of the time of day and season. Significant errors in emissions can
26 occur if an inappropriate time dependence or a default profile is used. Additional complexity
27 arises in model calculations because different chemical mechanisms are based on different
28 species, and inventories constructed for use with another mechanism must be adjusted to reflect
29 these differences. This problem also complicates comparisons of the outputs of these models
30 because one chemical mechanism may produce some species not present in another mechanism
31 yet neither may agree with the measurements.
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1 In addition to wet deposition, dry deposition (the removal of chemical species from the
2 atmosphere by interaction with ground-level surfaces) is an important removal process for
3 pollutants on both urban and regional scales and must be included in CTMs. The general
4 approach used in most models is the resistance in series method, in which where dry deposition
5 is parameterized with a Vd, which is represented as Vd = (ra + rb + r^1 where ra, it,, and rc
6 represent the resistance due to atmospheric turbulence, transport in the fluid sublayer very near
7 the elements of surface such as leaves or soil, and the resistance to uptake of the surface itself.
8 This approach works for a range of substances, although it is inappropriate for species with
9 substantial emissions from the surface or for species whose deposition to the surface depends on
10 its concentration at the surface itself. The approach is also modified somewhat for aerosols: the
11 terms rb and rc are replaced with a surface Vd to account for gravitational settling. In their
12 review, Wesley and Hicks (2000) point out several shortcomings of current knowledge of dry
13 deposition. Among those shortcomings are difficulties in representing dry deposition over
14 varying terrain where horizontal advection plays a significant role in determining the magnitude
15 of ra and difficulties in adequately determining a Vd for extremely stable conditions such as those
16 occurring at night (e.g., Mahrt, 1998). Under the best of conditions, when a model is exercised
17 over a relatively small area where dry deposition measurements have been made, models still
18 commonly show uncertainties at least as large as ±30% (e.g., Massman et al., 1994; Brook et al.,
19 1996; Padro, 1996). Wesely and Hicks (2000) state that an important result of these comparisons
20 is that the current level of sophistication of most dry deposition models is relatively low, and that
21 deposition estimates therefore must rely heavily on empirical data. Still larger uncertainties exist
22 when the surface features in the built environment are not well known or when the surface
23 comprises a patchwork of different surface types, as is common in the eastern United States.
24 The initial conditions, i.e., the concentration fields of all species computed by a model,
25 and the boundary conditions, i.e., the concentrations of species along the horizontal and upper
26 boundaries of the model domain throughout the simulation must be specified at the beginning of
27 the simulation. It would be best to specify initial and boundary conditions according to
28 observations. However, data for vertical profiles of most species of interest are sparse. The
29 results of model simulations over larger, preferably global, domains can also be used. As may be
30 expected, the influence of boundary conditions depends on the lifetime of the species under
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1 consideration and the time scales for transport from the boundaries to the interior of the model
2 domain (Liu etal.,2001b).
3 Each of the model components described above has an associated uncertainty, and the
4 relative importance of these uncertainties varies with the modeling application. The largest
5 errors in photochemical modeling are still thought to arise from the meteorological and
6 emissions inputs to the model (Russell and Dennis, 2000). Within the model itself, horizontal
7 advection algorithms are still thought to be significant source of uncertainty (e.g., Chock and
8 Winkler, 1994), though more recently, those errors are thought to have been reduced (e.g.,
9 Odman et al., 1996). There are also indications that problems with mass conservation continue
10 to be present in photochemical and meteorological models (e.g., Odman and Russell, 1999);
11 these can result in significant simulation errors. The effects of errors in initial conditions can be
12 minimized by including several days "spin-up" time in a simulation to allow the model to be
13 driven by emitted species before the simulation of the period of interest begins.
14 While the effects of poorly specified boundary conditions propagate through the model's
15 domain, the effects of these errors remain undetermined. Because many meteorological
16 processes occur on spatial scales which are smaller than the model grid spacing (either
17 horizontally or vertically) and thus are not calculated explicitly, parameterizations of these
18 processes must be used and these introduce additional uncertainty.
19 Uncertainty also arises in modeling the chemistry of Os formation because it is highly
20 nonlinear with respect to NOX concentrations. Thus, the volume of the grid cell into which
21 emissions are injected is important because the nature of Os chemistry (i.e., Os production or
22 titration) depends in a complicated way on the concentrations of the precursors and the OH
23 radical as noted earlier. The use of ever-finer grid spacing allows regions of Os titration to be
24 more clearly separated from regions of Os production. The use of grid spacing fine enough to
25 resolve the chemistry in individual power-plant plumes is too demanding of computer resources
26 for this to be attempted in most simulations. Instead, parameterizations of the effects of sub-
27 grid-scale processes such as these must be developed; otherwise serious errors can result if
28 emissions are allowed to mix through an excessively large grid volume before the chemistry step
29 in a model calculation is performed. In light of the significant differences between atmospheric
30 chemistry taking place inside and outside of a power plant plume (e.g., Ryerson et al., 1998 and
31 Sillman, 2000), inclusion of a separate, meteorological module for treating large, tight plumes is
September 2007 AX2-65 DRAFT-DO NOT QUOTE OR CITE
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1 necessary. Because the photochemistry of 03 and many other atmospheric species is nonlinear,
2 emissions correctly modeled in a tight plume may be incorrectly modeled in a more dilute plume.
3 Fortunately, it appears that the chemical mechanism used to follow a plume's development need
4 not be as detailed as that used to simulate the rest of the domain, as the inorganic reactions are
5 the most important in the plume see (e.g., Kumar and Russell, 1996). The need to include
6 explicitly plume-in-grid chemistry only down to the level of the smallest grid disappears if one
7 uses the adaptive grid approach mentioned previously, though such grids are more
8 computationally intensive. The differences in simulations are significant because they can lead
9 to significant differences in the calculated sensitivity of 03 to its precursors (e.g., Sillman et al.,
10 1995).
11 Because the chemical production and loss terms in the continuity equations for individual
12 species are coupled, the chemical calculations must be performed iteratively until calculated
13 concentrations converge to within some preset criterion. The number of iterations and the
14 convergence criteria chosen also can introduce error.
15
16 Global Scale CJMs
17 The importance of global transport of Os and Os precursors and their contribution to
18 regional 03 levels in the United States is slowly becoming apparent. There are presently on the
19 order of 20 three-dimensional global models that have been developed by various groups to
20 address problems in tropospheric chemistry. These models resolve synoptic meteorology,
21 Os-NOx-CO-hydrocarbon photochemistry, have parameterizations for wet and dry deposition,
22 and parameterize sub-grid scale vertical mixing processes such as convection. Global models
23 have proven useful for testing and advancing scientific understanding beyond what is possible
24 with observations alone. For example, they can calculate quantities of interest that cannot be
25 measured directly, such as the export of pollution from one continent to the global atmosphere or
26 the response of the atmosphere to future perturbations to anthropogenic emissions.
27 Global simulations are typically conducted at a horizontal resolution of about 200 km2.
28 Simulations of the effects of transport from long-range transport link multiple horizontal
29 resolutions from the global to the local scale. Finer resolution will only improve scientific
30 understanding to the extent that the governing processes are more accurately described at that
31 scale. Consequently, there is a critical need for observations at the appropriate scales to evaluate
32 the scientific understanding represented by the models.
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1 During the recent IPCC-AR4 tropospheric chemistry study coordinated by the European
2 Union project Atmospheric Composition Change: the European Network of excellence
3 (ACCENT), 26 atmospheric CTMs were used to estimate the impacts of three emissions
4 scenarios on global atmospheric composition, climate, and air quality in 2030 (Dentener et al.,
5 2006a). All models were required to use anthropogenic emissions developed at IIASA (Dentener
6 et al., 2005) and GFED version 1 biomass burning emissions (van der Werf et al., 2003) as
7 described in Stevenson et al. (2006). The base simulations from these models were evaluated
8 against a suite of present-day observations. Most relevant to this assessment report are the
9 evaluations with ozone and N02, and for nitrogen and sulfur deposition (Stevenson et al., 2006;
10 van Noije et al., 2006; Dentener et al., 2006a), which are summarized briefly below.
11 An analysis of the standard deviation of zonal mean and tropospheric column Os reveals
12 large inter-model variability in the tropopause region and throughout the polar troposphere,
13 likely reflecting differences in model tropopause levels and the associated stratospheric injection
14 of Os to the troposphere (Stevenson et al., 2006). Ozone distributions in the tropics also exhibit
15 large standard deviations (-30%), particularly as compared to the mid-latitudes (-20%),
16 indicating larger uncertainties in the processes that influence ozone in the tropics: deep tropical
17 convection, lightning NOX, isoprene emissions and chemistry, and biomass burning emissions
18 (Stevenson etal., 2006).
19 Stevenson et al., (2006) found that the model ensemble mean (MEM) typically captures
20 the observed seasonal cycles to within one standard deviation. The largest discrepancies
21 between the MEM and observations include: (1) an underestimate of the amplitude of the
22 seasonal cycle at 30°-90°N with a 10 ppbv overestimate of winter ozone, possibly due to the lack
23 of a seasonal cycle in anthropogenic emissions or to shortcomings in the stratospheric influx of
24 Os, and (2) an overestimate of Os throughout the northern tropics. However, the MEM was
25 found to capture the observed seasonal cycles in the Southern Hemisphere, suggesting that the
26 models adequately represent biomass burning and natural emissions.
27 The mean present-day global ozone budget across the current generation of CTMs differs
28 substantially from that reported in the IPCC TAR, with a 50% increase in the mean chemical
29 production (to 5100 Tg Os yr"1), a 30% increase in the chemical and deposition loss terms (to
30 4650 and 1000 Tg Os yr"1, respectively) and a 30% decrease in the mean stratospheric input flux
31 (to 550 Tg Os yr"1) (Stevenson et al., 2006). The larger chemical terms as compared to the IPCC
September 2007 AX2-67 DRAFT-DO NOT QUOTE OR CITE
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1 TAR are attributed mainly to higher NOX (as well as an equatorward shift in distribution) and
2 isoprene emissions, although more detailed NMHC schemes and/or improved representations of
3 photolysis, convection, and stratospheric-tropospheric exchange may also contribute (Stevenson
4 etal.,2006).
5 A subset of 17 of the 26 models used in the Stevenson et al. (2006) study was used to
6 compare with three retrievals of NOz columns from the GOME instrument (van Noije et al.,
7 2006) for the year 2000. The higher resolution models reproduce the observed patterns better,
8 and the correlation among simulated and retrieved columns improved for all models when
9 simulated values are smoothed to a 5° x 5° grid, implying that the models do not accurately
10 reproduce the small-scale features of N02 (van Noije et al., 2006). Van Noije et al. (2006)
11 suggest that variability in simulated N02 columns may reflect a model differences in OH
12 distributions and the resulting NOX lifetimes, as well as differences in vertical mixing which
13 strongly affect partitioning between NO and NOz. Overall, the models tend to underestimate
14 concentrations in the retrievals in industrial regions (including the eastern United States) and
15 overestimate them in biomass burning regions (van Noije et al., 2006).
16 Over the eastern United States, and industrial regions more generally, the spread in
17 absolute column abundances is generally larger among the retrievals than among the models,
18 with the discrepancy among the retrievals particularly pronounced in winter (van Noije et al.,
19 2006), suggesting that the models are biased low, or that the European retrievals may be biased
20 high as the Dalhousie/SAO retrieval is closer to the model estimates. The lack of seasonal
21 variability in fossil fuel combustion emissions may contribute to a wintertime model
22 underestimate (van Noije et al., 2006) that is manifested most strongly over Asia. In biomass
23 burning regions, the models generally reproduce the timing of the seasonal cycle of the
24 retrievals, but tend to overestimate the seasonal cycle amplitude, partly due to lower values in the
25 wet season, which may reflect an underestimate in wet season soil NO emissions (van Noije
26 et al., 2006; Jaegle et al., 2004, 2005).
27
28 Deposition in Global CTMs
29 Both wet and dry deposition are highly parameterized in global CTMs. While all current
30 models implement resistance schemes for dry deposition, the generated Vd generated from
31 different models can vary highly across terrains (Stevenson et al., 2006). The accuracy of wet
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1 deposition in global CTMs is tied to spatial and temporal distribution of model precipitation and
2 the treatment of chemical scavenging. Dentener et al. (2006b) compared wet deposition across
3 23 models with available measurements around the globe. Figures AX2.7-1 and AX2.7-2 below
4 extract the results of a comparison of the 23-model mean versus observations from Dentener
5 et al. (2006b) over the eastern United States for nitrate and sulfate deposition, respectively. The
6 mean model results are strongly correlated with the observations (r > 0.8), and usually capture
7 the magnitude of wet deposition to within a factor of 2 over the eastern United States (Dentener
8 et al., 2006b). Dentener et al. (2006b) conclude that 60-70% of the participating models capture
9 the measurements to within 50% in regions with quality controlled observations. This study then
10 identified world regions receiving >1000 mg (N) rrf2 yr"1 (the "critical load") and found that
11 20% of the natural vegetation (non-agricultural) in the United States is exposed to nitrogen
12 deposition in excess of the critical load threshold (Dentener et al., 2006b).
13
14 Modeling the Effects of Convection
15 The effects of deep convection can be simulated using cloud-resolving models, or in
16 regional or global models in which the convection is parameterized. The Goddard Cumulus
17 Ensemble (GCE) model (Tao and Simpson, 1993) has been used by Pickering et al. (1991;
18 1992a,b; 1993; 1996), Scala et al. (1990) and Stenchikov et al. (1996) in the analysis of
19 convective transport of trace gases. The cloud model is nonhydrostatic and contains a detailed
20 representation of cloud microphysical processes. Two- and three-dimensional versions of the
21 model have been applied in transport analyses. The initial conditions for the model are usually
22 from a sounding of temperature, water vapor and winds representative of the region of storm
23 development. Model-generated wind fields can be used to perform air parcel trajectory analyses
24 and tracer advection calculations.
25 Such methods were used by Pickering et al. (1992b) to examine transport of urban
26 plumes by deep convection. Transport of an Oklahoma City plume by the 10-11 June 1985
27 PRE-STORM squall line was simulated with the 2-D GCE model. This major squall line passed
28 over the Oklahoma City metropolitan area, as well as more rural areas to the north. Chemical
29 observations ahead of the squall line were conducted by the PRE-STORM aircraft. In this event,
30 forward trajectories from the boundary layer at the leading edge of the storm showed that almost
31 75% of the low-level inflow was transported to altitudes exceeding 8 km. Over 35% of
September 2007 AX2-69 DRAFT-DO NOT QUOTE OR CITE
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600
Figure AX2.7-1.
400
T3
O
200
Ave, model: 227 Ave, Mea, 195 r; 0,82 n = 226
2 param. fit: y = 51.1 + 0.90x
1 param. fit: y = 1,Q8x
Percentage within ± 50%: 74.8
200 400
Measurement
600
Scatter plot of total nitrate (HNOs plus aerosol nitrate) wet deposition
(mg(N)m 2yr-1) of the mean model versus measurements for the
North American Deposition Program (NADP) network. Dashed lines
indicate factor of 2. The gray line is the result of a linear regression
fitting through 0.
Source: Dentener et al. (2006b).
1 the air parcels reached altitudes over 12 km. Tracer transport calculations were performed for
2 CO, NOX, 03, and hydrocarbons. Rural boundary layer NOX was only 0.9 ppbv, whereas the
3 urban plume contained about 3 ppbv. In the rural case, mixing ratios of 0.6 ppbv were
4 transported up to 11 km. Cleaner air descended at the rear of the storm lowering NOX at the
5 surface from 0.9 to 0.5 ppbv. In the urban plume, mixing ratios in the updraft core reached
6 1 ppbv between 14 and 15 km. At the surface, the main downdraft lowered the NOX mixing
7 ratios from 3 to 0.7 ppbv.
8
September 2007
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1000
800 -
o>
T3
O
600 -
400 -
200
Ave. model: 383 Ave. Meas: 322 r: 0.87 n = 226
2 param fit: y = 114.0 + 0.77x
1 param fit: y = 1.00x
percentage within ± 50%: 66.0
0
200 400 600
Measurement
800
1000
2
Figure AX2.7-2. Same as Figure AX2.7-1 but for sulfate wet deposition (mg(S)m yr ).
Source: Dentener et al. (2006b).
1 Regional chemical transport models have been used for applications such as simulations
2 of photochemical Os production, acid deposition, and fine PM. Walcek et al. (1990) included a
3 parameterization of cloud-scale aqueous chemistry, scavenging, and vertical mixing in the
4 chemistry model of Chang et al. (1987). The vertical distribution of cloud microphysical
5 properties and the amount of sub-cloud-layer air lifted to each cloud layer are determined using a
6 simple entrainment hypothesis (Walcek and Taylor, 1986). Vertically integrated 03 formation
7 rates over the northeast U. S. were enhanced by -50% when the in-cloud vertical motions were
8 included in the model.
9 Wang et al. (1996) simulated the 10-11 June 1985 PRE-STORM squall line with the
10 NCAR/Penn State Mesoscale Model (MM5; Grell et al., 1994; Dudhia, 1993). Convection was
11 parameterized as a sub-grid-scale process in MM5 using the Kain Fritsch (1993) scheme. Mass
September 2007
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1 fluxes and detrainment profiles from the convective parameterization were used along with the
2 3-D wind fields in CO tracer transport calculations for this convective event.
3 Convective transport in global chemistry and transport models is treated as a sub-grid-
4 scale process that is parameterized typically using cloud mass flux information from a general
5 circulation model or global data assimilation system. While GCMs can provide data only for a
6 "typical" year, data assimilation systems can provide "real" day-by-day meteorological
7 conditions, such that CTM output can be compared directly with observations of trace gases.
8 The NASA Goddard Earth Observing System Data Assimilation System (GEOS-1 DAS and
9 successor systems; Schubert et al., 1993; Bloom et al., 1996; Bloom et al., 2005) provides
10 archived global data sets for the period 1980 to present, at 2° x 2.5° or better resolution with
11 20 layers or more in the vertical. Deep convection is parameterized with the Relaxed
12 Arakawa-Schubert scheme (Moorthi and Suarez, 1992) in GEOS-1 and GEOS-3 and with the
13 Zhang and McFarlane (1995) scheme in GEOS-4. Pickering et al. (1995) showed that the cloud
14 mass fluxes from GEOS-1 DAS are reasonable for the 10-11 June 1985 PRE-STORM squall line
15 based on comparisons with the GCE model (cloud-resolving model) simulations of the same
16 storm. In addition, the GEOS-1 DAS cloud mass fluxes compared favorably with the regional
17 estimates of convective transport for the central U. S. presented by Thompson et al. (1994).
18 However, Allen et al. (1997) have shown that the GEOS-1 DAS overestimates the amount and
19 frequency of convection in the tropics and underestimates the convective activity over
20 midlatitude marine storm tracks.
21 Global models with parameterized convection and lightning have been run to examine
22 the roles of these processes over North America. Lightning contributed 23% of upper
23 tropospheric N0y over the SONEX region according to the UMD-CTM modeling analysis of
24 Allen et al. (2000). During the summer of 2004 the NASA Intercontinental Chemical Transport
25 Experiment - North America (INTEX-NA) was conducted primarily over the eastern two-thirds
26 of the United States, as a part of the International Consortium for Atmospheric Research on
27 Transport and Transformation (ICARTT). Deep convection was prevalent over this region
28 during the experimental period. Cooper et al. (2006) used a particle dispersion model simulation
29 for NOX to show that 69-84% of the upper tropospheric Os enhancement over the region in
30 Summer 2004 was due to lightning NOX. The remainder of the enhancement was due to
31 convective transport of Os from the boundary layer or other sources of NOX. Hudman et al.
September 2007 AX2-72 DRAFT-DO NOT QUOTE OR CITE
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1 (2007) used a GEOS-Chem model simulation to show that lightning was the dominant source of
2 upper tropospheric NOX over this region during this period. Approximately 15% of North
3 American boundary layer NOX emissions were shown to have been vented to the free troposphere
4 over this region based on both the observations and the model.
5
6 AX2.7.2 CTM Evaluation
7 The comparison of model predictions with ambient measurements represents a critical
8 task for establishing the accuracy of photochemical models and evaluating their ability to serve
9 as the basis for making effective control strategy decisions. The evaluation of a model's
10 performance, or its adequacy to perform the tasks for which it was designed can only be
11 conducted within the context of measurement errors and artifacts. Not only are there analytical
12 problems, but there are also problems in assessing the representativeness of monitors at ground
13 level for comparison with model values which represent typically an average over the volume of
14 a grid box.
15 Evaluations of CMAQ are given in Arnold et al. (2003) and Fuentes and Raftery (2005).
16 Discrepancies between model predictions and observations can be used to point out gaps in
17 current understanding of atmospheric chemistry and to spur improvements in parameterizations
18 of atmospheric chemical and physical processes. Model evaluation does not merely involve a
19 straightforward comparison between model predictions and the concentration field of the
20 pollutant of interest. Such comparisons may not be meaningful because it is difficult to
21 determine if agreement between model predictions and observations truly represents an accurate
22 treatment of physical and chemical processes in the CTM or the effects of compensating errors in
23 complex model routines. Ideally, each of the model components (emissions inventories,
24 chemical mechanism, meteorological driver) should be evaluated individually. However, this is
25 rarely done in practice.
26 Chemical transport models for Os formation at the urban/regional scale have traditionally
27 been evaluated based on their ability to simulate correctly Os. A series of performance statistics
28 that measure the success of individual model simulations to represent the observed distribution
29 of ambient 03, as represented by a network of surface measurements at the urban scale were
30 recommended by the EPA (U.S. Environmental Protection Agency, 1991; see also Russell and
31 Dennis, 2000). These statistics consist of the following:
September 2007 AX2-73 DRAFT-DO NOT QUOTE OR CITE
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1 • Unpaired peak 03 concentration within a metropolitan region (typically for a
2 single day) .
3 • Normalized bias equal to the summed difference between model and measured
4 hourly concentrations divided by the sum of measured hourly concentrations.
5 • Normalized gross error, equal to the summed unsigned (absolute value) difference
6 between model and measured hourly concentrations divided by the sum of
7 measured hourly concentrations.
8
9 Unpaired peak prediction accuracy, Au;
{-p\X> t Jmax ~ *- o\x < * )max ^Jnnn/
A,.- — - - *100%,
' '
10 Co(x''/max (AX2-48)
11 Normalized bias, D;
1 N {Cn(jr;,/)-C,,(*;,0}
D = — X ; , ,/ = 1,24,
12 ^'=/ Co(-M) (AX2-49)
13 Gross error, Ed (for hourly observed values of 03 >60ppb)
14 N i=! co(xi-fi (AX2-50)
15 The following performance criteria for regulatory models were recommended in U.S.
16 Environmental Protection Agency (1991): unpaired peak Os to within ±15% or ±20%;
17 normalized bias within ± 5% to ± 15%; and normalized gross error less than 30% to 35%, but
18 only when Os the concentration >60 ppb. This can lead to difficulties in evaluating model
19 performance since nighttime and diurnal cycles are ignored. A major problem with this method
20 of model evaluation is that it does not provide any information about the accuracy of Os-
21 precursor relations predicted by the model. The process of Os formation is sufficiently complex
22 that models can predict Os correctly without necessarily representing the Os formation process
23 properly. If the Os formation process is incorrect, then the modeled source-receptor relations
24 will also be incorrect.
September 2007 AX2-74 DRAFT-DO NOT QUOTE OR CITE
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1 Studies by Sillman et al. (1995, 2003), Reynolds et al. (1996), and Pierce et al. (1998)
2 have identified instances in which different model scenarios can be created with very different
3 Os-precursor sensitivity, but without significant differences in the predicted 03 fields.
4 Figures AX2.7-3a,b provides an example. Referring to the Os-NOx-VOC isopleth plot
5 (Figure AX2.7-4), it can be seen that similar Os concentrations can be found for photochemical
6 conditions that have very different sensitivity to NOX and VOCs.
7 Global-scale CTMs have generally been evaluated by comparison with measurements for
8 a wide array of species, rather than just for 03 (e.g., Wang et al., 1998; Emmons et al., 2000; Bey
9 et al., 2001; Hess, 2001; Fiore et al., 2002). These have included evaluation of major primary
10 species (NOX, CO, and selected VOCs) and an array of secondary species (HN03, PAN, H202)
11 that are often formed concurrently with Os. Models for urban and regional Os have also been
12 evaluated against a broader ensemble of measurements in a few cases, often associated with
13 measurement intensives (e.g., Jacobson et al., 1996; Lu et al., 1997; Sillman et al., 1998). The
14 results of a comparison between observed and computed concentrations from Jacobson et al.
15 (1996) for the Los Angeles Basin are shown in Figures AX2.7-5a,b.
16 The highest concentrations of primary species usually occur in close proximity to
17 emission sources (typically in urban centers) and at times when dispersion rates are low. The
18 diurnal cycle includes high concentrations at night, with maxima during the morning rush hour,
19 and low concentrations during the afternoon (Figure AX2.7-5a). The afternoon minima are
20 driven by the much greater rate of vertical mixing at that time. Primary species also show a
21 seasonal maximum during winter, and are often high during fog episodes in winter when vertical
22 mixing, is suppressed. By contrast, secondary species such as Os are typically highest during the
23 afternoon (the time of greatest photochemical activity), on sunny days and during summer.
24 During these conditions, concentrations of primary species may be relatively low. Strong
25 correlations between primary and secondary species are generally observed only in downwind
26 rural areas where all anthropogenic species are simultaneously elevated. The difference in the
27 diurnal cycles of primary species (CO, NOX, and ethane) and secondary species (03, PAN, and
28 HCHO) is evident in Figure AX2.7-5b.
29 Models for urban and regional chemistry have been evaluated less extensively than
30 global-scale models in part because the urban/regional context presents a number of difficult
September 2007 AX2-75 DRAFT-DO NOT QUOTE OR CITE
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.Q
Q.
U
3
K.
CO
20
•3 10
o
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>*
100 120 140 160
Ozone(ppb)
-x
*
180
200
35
^ 30
13
0.
-S
c
25
20
15
10
0
x
XX
X
X
X
X
100 120 140 160
Ozone (ppb)
180
200
Figure AX2.7-3a,b. Impact of model uncertainty on control strategy predictions for Os for
two days (August lOa and lib, 1992) in Atlanta, GA. The figures
show the predicted reduction in peak Os resulting from 35%
reductions in anthropogenic VOC emissions (crosses) and from 35%
reductions in NOX (solid circles) in a series of model scenarios with
varying base case emissions, wind fields, and mixed layer heights.
Source: Results are plotted from tabulated values published in Sillman et al. (1995, 1997).
September 2007
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9 0.1
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a.
n
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1.0 3.16 10.0 31.6 100.0
VOC Emission Rate (1012 molec. cm-2 s-1)
Figure AX2.7-4. Ozone isopleths (ppb) as a function of the average emission rate for
NOX and VOC (1012 molec. cm"2 s *) in zero dimensional box model
calculations. The isopleths (solid lines) represent conditions during
the afternoon following 3-day calculations with a constant emission
rate, at the hour corresponding to maximum Os. The ridge line
(shown by solid circles) lies in the transition from NOx-saturated to
NOx-limited conditions.
September 2007
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0.15
0.10
0.05
0.00
6
5
4
3
2
1
0
Figure AX2.7-5a.
Reseda
o3(g)
Predicted
Observed
o
16 24 32 40 48 56 64 72
Hour After First Midnight
Reseda
NOX (g)
Predicted
Observed
16 24 32 40 48 56 64 72
Hour After First Midnight
Riverside
C0(g)
Predicted
Observed
l.l.l
16 24 32 40 48 56 64 72
Hour After First Midnight
Time series for measured gas-phase species in comparison with results
from a photochemical model. The dashed lines represent
measurements, and solid lines represent model predictions (in parts
per million, ppmv) for August 26-28,1988 at sites in southern
California. The horizontal axis represents hours past midnight,
August 25. Results represent Os and NOX at Reseda and CO at
Riverside.
Source: Jacobson et al. (1996).
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0,060
| 0.050
Q.
3 0.040
o
'• 0.030
0) 0.020
s °-010
0.000
0.030
| 0.025
a
3 0.020
O
*j 0.015
a:
en 0.010
~ 0.005
0.000
0.020
S*
E 0.016
a.
3
'Q' 0.012
*•*
ro
OC QQQQ
0>
!E 0.004
2
0.000
Claremont
Ethane (g)
—— Predicted
O Observed
16 24 32 40 48 56 64 72
Hour After First Midnight
Claremont
Formaldehyde (g)
Predicted
O Observed
I
0 8 16 24 32 40 48 56 64 72
Hour After First Midnight
Los Angeles
PAN (g)
Predicted
Observed
16 24 32 40 48 56
Hour After First Midnight
64
72
Figure AX2.7-5b.
Time series for measured gas-phase species in comparison with results
from a photochemical model. The circles represent measurements,
and solid lines represent model predictions (in parts per million,
ppmv) for August 26-28,1988 at sites in southern California. The
horizontal axis represents hours past midnight, August 25. Results
represent ethane and formaldehyde at Claremont and PAN at Los
Angeles.
Source: Jacobson et al. (1996).
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1 challenges. Global-scale models typically represent continental-scale events and can be
2 evaluated effectively against a sparse network of measurements. By contrast, urban/regional
3 models are critically dependent on the accuracy of local emission inventories and event-specific
4 meteorology, and must be evaluated separately for each urban area that is represented.
5 The evaluation of urban/regional models is also limited by the availability of data.
6 Measured NOX and speciated VOC concentrations are widely available through the EPA PAMs
7 network, but questions have been raised about the accuracy of those measurements and the data
8 have not yet been analyzed thoroughly. Evaluation of urban/regional models versus
9 measurements has generally relied on results from a limited number of field studies in the United
10 States. Short-term, research-grade measurements for species relevant to 03 formation, including
11 VOCs, NOX, PAN, HN03, and H202 are also available at selected rural and remote sites (e.g.,
12 Daum et al., 1990, 1996; Martin et al., 1997; Young et al., 1997; Thompson et al., 2000; Hoell
13 et al., 1997, 1999; Fehsenfeld et al., 1996a; Emmons et al., 2000; Hess, 2001; Carroll et al.,
14 2001). The equivalent measurements are available for some polluted rural sites in the eastern
15 United States, but only at a few urban locations (Meagher et al., 1998; Hiibler et al., 1998;
16 Kleinman et al., 2000, 2001; Fast et al., 2002; new SCAQS-need reference). Extensive
17 measurements have also been made in Vancouver (Steyn et al., 1997) and in several European
18 cities (Staffelbach et al., 1997; Prevot et al., 1997, Dommen et al., 1999; Geyer et al., 2001;
19 Thielman et al., 2001; Martilli et al., 2002; Vautard et al., 2002).
20 The results of straightforward comparisons between observed and predicted
21 concentrations of Os can be misleading because of compensating errors, although this possibility
22 is diminished when a number of species are compared. Ideally, each of the main modules of a
23 CTM system (for example, the meteorological model and the chemistry and radiative transfer
24 routines) should be evaluated separately. However, this is rarely done in practice. To better
25 indicate how well physical and chemical processes are being represented in the model,
26 comparisons of relations between concentrations measured in the field and concentrations
27 predicted by the model can be made. These comparisons could involve ratios and correlations
28 between species. For example, correlation coefficients could be calculated between primary
29 species as a means of evaluating the accuracy of emission inventories or between secondary
30 species as a means of evaluating the treatment of photochemistry in the model. In addition,
31 spatial relations involving individual species (correlations, gradients) can also be used as a means
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1 of evaluating the accuracy of transport parameterizations. Sillman and He (2002) examined
2 differences in correlation patterns between Os and NOZ in Los Angeles, CA, Nashville, TN, and
3 various sites in the rural United States. Model calculations (Figure AX2.7-6) show differences in
4 correlation patterns associated with differences in the sensitivity of Os to NOX and VOCs.
5 Primarily N0x-sensitive (N0x-limited) areas in models show a strong correlation between Os and
6 NOZ with a relatively steep slope, while primarily VOC-sensitive (N0x-saturated) areas in
7 models show lower Os for a given NOZ and a lower Os-N0z slope. They found that differences
8 found in measured data ensembles were matched by predictions from chemical transport models.
250
0
Figure AX2.7-6.
20
NOZ (ppb)
Correlations for Os versus NOZ (NOy-NOx) in ppb from chemical
transport models for the northeast corridor, Lake Michigan,
Nashville, the San Joaquin Valley, and Los Angeles. Each location is
classified as NOx-limited or NOx-sensitive (circles), NOx-saturated or
VOC-sensitive (crosses), mixed or with near-zero sensitivity (squares),
and dominated by NOX titration (asterisks) based on the model
response to reduced NOX and VOC.
Source: Sillman and He (2002).
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1 Measurements in rural areas in the eastern United States show differences in the pattern
2 of correlations for 03 versus NOZ between summer and autumn (Jacob et al., 1995; Hirsch et al.,
3 1996), corresponding to the transition from N0x-limited to N0x-saturated patterns, a feature
4 which is also matched by CTMs.
5 The difference in correlations between secondary species in N0x-limited to NOX-
6 saturated environments can also be used to evaluate the accuracy of model predictions in
7 individual applications. Figures AX2.7-7a and AX2.7-7b show results for two different model
8 scenarios for Atlanta. As shown in the figures, the first model scenario predicts an urban plume
9 with high N0y and 03 formation apparently suppressed by high N0y. Measurements show much
10 lower N0y in the Atlanta plume. This error was especially significant because the model
11 locations sensitive to NOX. The second model scenario (with primarily N0x-sensitive
12 conditions) shows much better agreement with measured values. Figure AX2.7-8a,b shows
13 model-measurement comparisons for secondary species in Nashville, showing better agreement
14 with measured with conditions. Greater confidence in the predictions made by CTMs will be
15 gained by the application of techniques such as these on a more routine basis.
16 The ability of chemical mechanisms to calculate the concentrations of free radicals under
17 atmospheric conditions was tested in the Berlin Ozone Experiment, BERLIOZ (Volz-Thomas
18 et al., 2003) during July and early August at a site located about 50 km NW of Berlin. (This
19 location was chosen because 03 episodes in central Europe are often associated with SE winds.)
20 Concentrations of major compounds such as 03, hydrocarbons, etc., were fixed at
21 observed values. In this regard, the protocol used in this evaluation is an example of an
22 observationally high N0y were not sensitive to NOX, while locations with lower N0y were
23 primarily based method. Figure AX2.7-9 compares the concentrations of ROz, HOz, and OH
24 radicals predicted by RACM and MCM with observations made by the laser-induced
25 fluorescence (LIF) technique and by matrix isolation ESR spectroscopy (MIESR). Also shown
26 are the production rates of 03 calculated using radical concentrations predicted by the
27 mechanisms and those obtained by measurements, and measurements of NOX concentrations. As
28 can be seen, there is good agreement between measurements of R02, H02, OH, radicals with
29 values predicted by both mechanisms at high concentrations of NOX (>10 ppb). However, at
30 lower NOX concentrations, both mechanisms substantially overestimate OH concentrations and
September 2007 AX2-82 DRAFT-DO NOT QUOTE OR CITE
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200
10
20
NOy (PP^
30
40
10
20
NOy (ppb)
30
40
Figure AX2.7-7a,b. Evaluation of model versus measured Os versus NOy for two model
scenarios for Atlanta. The model values are classified as NOX- limited
(circles), NOx-saturated (crosses), or mixed or with low sensitivity to
NOX (squares). Diamonds represent aircraft measurements.
Source: Sillman et al. (1997).
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.Q
Q.
n
O
10
20
NOZ (ppb)
30
40
10 20 30
2H2O2 + NOZ (ppb)
40
Figure AX2.7-8a,b. Evaluation of model versus: (a) measured Os versus NOZ and (b) Os
versus the sum 2H2O2 + NOZ for Nashville, TN. The model values are
classified as NOx-limited (gray circles), NOx-saturated (X's), mixed or
near-zero sensitivity (squares), or dominated by NOX titration (filled
circles). Diamonds represent aircraft measurements.
Source: Sillman et al. (1998).
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*?
E
o
to
O
CM
O
sr
o
09
O
CM
O
I
o
-------
1 moderately overestimate H02 concentrations. Agreement between models and measurements is
2 generally better for organic peroxy radicals, although the MCM appears to overestimate their
3 concentrations somewhat. In general, the mechanisms reproduced the H02 to OH and R02 to
4 OH ratios better than the individual measurements. The production of Os was found to increase
5 linearly with NO (for NO < 0.3 ppb) and to decrease with NO (for NO > 0.5 ppb).
6 OH and H02 concentrations measured during the PM2.5 Technology Assessment and
7 Characterization Study conducted at Queens College in New York City in the summer of 2001
8 were also compared with those predicted by RACM (Ren et al., 2003). The ratio of observed to
9 predicted H02 concentrations over a diurnal cycle was 1.24 and the ratio of observed to predicted
10 OH concentrations was about 1.10 during the day, but the mechanism significantly
11 underestimated OH concentrations during the night.
12
13
14 AX2.8 SAMPLING AND ANALYSIS OF NITROGEN AND
15 SULFUR OXIDES
16
17 AX2.8.1 Availability and Accuracy of Ambient Measurements for NOy
18 Section AX2.8.1-AX2.8.4 focus on current methods and on promising new technologies,
19 but no attempt is made here to cover the extensive development of these methods or of methods
20 such as wet chemical techniques, no longer in widespread use. More detailed discussions of
21 these methods may be found elsewhere (U.S. Environmental Protection Agency, 1993, 1996).
22 McClenny (2000), Parrish and Fehsenfeld (2000), and Clemitshaw (2004) reviewed methods for
23 measuring NOX and N0y compounds. Discussions in Sections 2.8.1-2.8.4 center on
24 chemiluminescence and optical Federal Reference and Equivalent Methods (FRM and FEM,
25 respectively).
26 The use of methods such as observationally based methods or source apportionment
27 models, either as stand-alone methods or as a basis for evaluating chemical transport models, is
28 often limited by the availability and accuracy of measurements. Measured NOX and speciated
29 VOC concentrations are widely available in the United States through the PAMS network.
30 However, challenges have been raised about both the accuracy of the measurements and their
31 applicability.
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1 The PAMs network currently includes measured NO and NOX. However, Cardelino and
2 Chameides (2000) reported that measured NO during the afternoon was frequently at or below
3 the detection limit of the instruments (1 ppb), even in large metropolitan regions (Washington,
4 DC; Houston, TX; New York, NY). Nitric dioxide measurements are made with commercial
5 chemilluminescent detectors with hot molybdenum converters. However, these measurements
6 typically include a wide variety of other reactive N species, such as organic nitrates in addition to
7 NOX, and cannot be interpreted as a "pure" NOX measurement (see summary in Parrish and
8 Fehsenfeld, 2000). Detection of these species can be considered an interference or a cross
9 sensitivity useful for understanding the chemistry of the air.
10 Total reactive nitrogen (N0y) is included in the PAMS network only at a few sites. The
11 possible expansion of PAMS to include more widespread N0y measurements has been suggested
12 (McClenny, 2000). N0y measurements are also planned for inclusion in the NCore network
13 (U.S. Environmental Protection Agency, 2005). A major issue to be considered when measuring
14 NOX and N0y is the possibility that HNOs, a major component of N0y, is sometimes lost in inlet
15 tubes and not measured (Luke et al., 1998; Parrish and Fehsenfeld, 2000). This problem is
16 especially critical if measured N0y is used to identify N0x-limited versus N0x-saturated
17 conditions. The problem is substantially alleviated although not necessarily completely solved
18 by using much shorter inlets on N0y monitors than on NOX monitors and by the use of surfaces
19 less likely to take up HN03. The correlation between 03 and N0y differs for N0x-limited versus
20 N0x-saturated locations, but this difference is driven primarily by differences in the ratio of Os to
21 HNOs. If HNOs were omitted from the N0y measurements, then the measurements would
22 represent a biased estimate and their use would be problematic.
23
24 AX2.8.1.1 Calibration Standards
25 Calibration gas standards of NO, in Nz (certified at concentrations of approximately 5 to
26 40 ppm) are obtainable from the Standard Reference Material (SRM) Program of the National
27 Institute of Standards and Technology (NIST), formerly the National Bureau of Standards
28 (NBS), in Gaithersburg, MD. These SRMs are supplied as compressed gas mixtures at about
29 135 bar (1900 psi) in high-pressure aluminum cylinders containing 800 L of gas at standard
30 temperature and pressure, dry (STPD) National Bureau of Standards, 1975; Guenther et al.,
31 1996). Each cylinder is supplied with a certificate stating concentration and uncertainty. The
32 concentrations are certified to be accurate to ±1 percent relative to the stated values. Because of
September 2007 AX2-87 DRAFT-DO NOT QUOTE OR CITE
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1 the resources required for their certification, SRMs are not intended for use as daily working
2 standards, but rather as primary standards against which transfer standards can be calibrated.
3 Transfer stand-alone calibration gas standards of NO in N2 (at the concentrations
4 indicated above) are obtainable from specialty gas companies. Information as to whether a
5 company supplies such mixtures is obtainable from the company, or from the SRM Program of
6 NIST. These NIST Traceable Reference Materials (NTRMs) are purchased directly from
7 industry and are supplied as compressed gas mixtures at approximately 135 bar (1900 psi) in
8 high-pressure aluminum cylinders containing 4,000 L of gas at STPD. Each cylinder is supplied
9 with a certificate stating concentration and uncertainty. The concentrations are certified to be
10 accurate to within ±1 percent of the stated values (Guenther et al., 1996). Additional details can
11 be found in the previous AQCD for Os (U.S. Environmental Protection Agency, 1996).
12
13 AX2.8.1.2 Measurement of Nitric Oxide
14
15 Gas-phase Chemiluminescence (CL) Methods
16 Nitric oxide can be measured reliably using the principle of gas-phase
17 chemiluminescence induced by the reaction of NO with Os at low pressure. Modern commercial
18 NOX analyzers have sufficient sensitivity and specificity for adequate measurement in urban and
19 many rural locations (U.S. Environmental Protection Agency, 1993, 1996, 2006). Research
20 grade CL instruments have been compared under realistic field conditions to spectroscopic
21 instruments, and the results indicate that both methods are reliable (at concentrations relevant to
22 smog studies) to better than 15 percent with 95 percent confidence. Response times are on the
23 order of 1 minute. For measurements meaningful for understanding Os formation, emissions
24 modeling, and N deposition, special care must be taken to zero and calibrate the instrument
25 frequently. A chemical zero, obtained by reacting the NO up-stream and out of view of the
26 photomultiplier tube, is preferred because it accounts for interferences such as light emitting
27 reactions with unsaturated hydrocarbons. Calibration should be performed with NTRM-of
28 compressed NO in N2. Standard additions of NO at the inlet will account for NO loss or
29 conversion to N02 in the lines. In summary, CL methods, when operated carefully in an
30 appropriate manner, can be suitable for measuring or monitoring NO (e.g., Crosley, 1996).
31
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1 Spectroscopic Methods for Nitric Oxide
2 Nitric oxide has also been successfully measured in ambient air with direct Spectroscopic
3 methods; these include two-photon laser-induced fluorescence (TPLIF), tunable diode laser
4 absorption spectroscopy (TDLAS), and two-tone frequency-modulated spectroscopy (TTFMS).
5 These were reviewed thoroughly in the previous AQCD and will be only briefly summarized
6 here. The Spectroscopic methods demonstrate excellent sensitivity and selectivity for NO with
7 detection limits on the order of 10 ppt for integration times of 1 min. Spectroscopic methods
8 compare well with the CL method for NO in controlled laboratory air, ambient air, and heavily
9 polluted air (e.g., Walega et al., 1984; Gregory et al., 1990; Kireev et al., 1999). These
10 Spectroscopic methods remain in the research arena due to their complexity, size, and cost, but
11 are essential for demonstrating that CL methods are reliable for monitoring NO concentrations
12 involved in Os formation—from around 20 ppt to several hundred of ppb.
13 Atmospheric pressure laser ionization followed by mass spectroscopy has also been
14 deployed for detection of NO and N02. Garnica et al. (2000) describe a technique involving
15 selective excitation at one wavelength followed by ionization at a second wavelength. They
16 report good selectivity and detection limits well below 1 ppb. The practicality of the instrument
17 for ambient monitoring, however, has yet to be demonstrated.
18
19 AX2.8.1.3 Measurements of Nitrogen Dioxide
20
21 Gas-Phase Chemiluminescence Methods
22 Reduction of N02 to NO, on the surface of a heated (to 300 to 400 °C) molybdenum
23 oxide substrate followed by detection of the chemiluminescence produced during the reaction of
24 NO with Os at low pressure as described earlier for measurement of NO serves as the basis of the
25 FRM for measurement of ambient N02. However, the substrate used in the reduction of N02 to
26 NO is not specific to N02; hence the chemiluminescence analyzers are subject to interference
27 nitrogen oxides other than N02 produced by oxidized N0y compounds, or NOZ. Thus, this
28 technique will overestimate N02 concentrations particularly in areas downwind of sources of NO
29 and N02 as NOX is oxidized to NOZ in the form of PANs and other organic nitrates, and HNOs
30 and HN04. Many of these compounds are reduced at the catalyst with nearly the same efficiency
31 as N02. Interferences have also been found from a wide range of other compounds as described
32 in the latest AQCD for N02.
September 2007 AX2-89 DRAFT-DO NOT QUOTE OR CITE
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1 Other Methods
2 Nitrogen dioxide can be selectively converted to NO by photolysis. For example,
3 (Ryerson et al., 2000) developed a gas-phase chemiluminescence method using a photolytic
4 converter based on a Hg lamp with increased radiant intensity in the region of peak N02
5 photolysis (350 to 400 nm) and producing conversion efficiencies of 70% or more in less than
6 Is. Metal halide lamps with conversion efficiency of about 50% and accuracy on the order of
7 20% (Nakamura, et al., 2003) have been used. Because the converter produces little radiation at
8 wavelengths less than 350 nm, interferences from HNOs and PAN are minimal. Alternative
9 methods to photolytic reduction followed by CL are desirable to test the reliability of this widely
10 used technique. Any method based on a conversion to measured species presents potential for
11 interference a problem. Several atmospheric species, PAN and HOzNOz for example, dissociate
12 to N02 at higher temperatures.
13 Laser induced fluorescence for N02 detection involves excitation of atmospheric N02
14 with laser light emitted at wavelengths too long to induce photolysis. The resulting excited
15 molecules relax in a photoemissive mode and the fluorescing photons are counted. Because
16 collisions would rapidly quench the electronically excited N02, the reactions are conducted at
17 low pressure. Matsumi et al. (2001) describe a comparison of LIF with a photofragmentation
18 chemiluminescence instrument. The LIF system involves excitation at 440 nm with a multiple
19 laser system. They report sensitivity of 30 ppt in 10 s and good agreement between the two
20 methods under laboratory conditions at mixing ratios up to 1.0 ppb. This high-sensitivity LIF
21 system has yet to undergo long-term field tests. Cleary et al. (2002) describe field tests of a
22 system that uses continuous, supersonic expansion followed by excitation at 640 nm with a
23 commercial cw external-cavity tunable diode laser. More recently, LIF has been successfully
24 used to detect N02 with accuracy of about 15% and detections limits well below 1 ppb. When
25 coupled with thermal dissociation, the technique also measures peroxy nitrates such as PAN,
26 alkyl nitrates, HN04 and HN03 (Cohen, 1999; Day et al., 2002; Farmer et al., 2006; Perez et al.,
27 2007; Thornton et al., 2003). This instrument can have very fast sampling rates be fast (>1 Hz)
28 and shows good correlation with chemiluminescent techniques, but remains a research-grade
29 device.
30 Nitrogen Dioxide can be detected by differential optical absorption spectroscopy (DOAS)
31 in an open, long-path system by measuring narrow band absorption features over a background
September 2007 AX2-90 DRAFT-DO NOT QUOTE OR CITE
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1 of broad band extinction (e.g., Stutz et al, 2000; Kim and Kim, 2001). A DOAS system
2 manufactured by OPSIS is designated as a Federal Equivalent Method for measuring N02.
3 DOAS systems can also be configured to measure NO, HONO, and N03 radicals. Typical
4 detection limits are 0.2 to 0.3 ppbv for NO, 0.05 to 0.1 ppbv for N02, 0.05 to 0.1 ppbv for
5 HONO, and 0.001 to 0.002 ppbv for N03, at path lengths of 0.2, 5, 5, and 10 km, respectively.
6 The obvious advantage compared to fixed point measurements is that concentrations relevant to
7 a much larger area are obtained, especially if multiple targets are used. At the same time, any
8 microenvironmental artifacts are minimized over the long path integration. A major limitation in
9 this technique had involved inadequate knowledge of absorption cross sections. Harder et al.
10 (1997) conducted an experiment in rural Colorado involving simultaneous measurements of N02
11 by DOAS and by photolysis followed by chemiluminescence. They found differences of as
12 much as 110% in clean air from the west, but for N02 mixing ratios in excess of 300 ppt, the two
13 methods agreed to better than 10%. Stutz (2000) cites two intercomparisons of note. Nitric
14 oxide was measured by DOAS, by photolysis of N02 followed by chemiluminescence, and by
15 LIF during July 1999 as part of the SOS in Nashville, TN. On average, the three methods agreed
16 to within 2%, with some larger differences likely caused by spatial variability over the DOAS
17 path. In another study in Europe, and a multi-reflection set-up over a 15 km path, negated the
18 problem of spatial averaging here agreement with the chemiluminescence detector following
19 photolytic conversion was excellent (slope = 1.006 ± 0.005; intercept = 0.036 ± 0.019; r = 0.99)
20 over a concentration range from about 0.2 to 20 ppbv.
21 Nitric oxide can also be detected from space with DOAS-like UV spectroscopy
22 techniques (Kim et al., 2006; Ma et al., 2006). These measurements appear to track well with
23 emissions estimates and can be a useful indicator of column content as well as for identifying hot
24 spots in sources. See also Richter et al., 2005. Leigh (2006) report on a DOAS method that uses
25 the sun as a light source and compares well with an in situ chemiluminescence detector in an
26 urban environment.
27 Chemiluminescence on the surface of liquid Luminol has also been used for measurement
28 of N02 (Gaffney et al., 1998; Kelly et al., 1990; Marley et al., 2004; Nikitas et al., 1997; Wendel
29 etal., 1983). This technique is sensitive and linear, and more specific than hot MoOx. Luminol
30 does not emit light when exposed to NHOs or alkyl nitrates, but does react with PAN. This
31 interference can be removed by chromatographic separation prior to detection and the resulting
September 2007 AX2-91 DRAFT-DO NOT QUOTE OR CITE
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1 measurement compares well with more specific techniques for moderate to high (> 1 ppb) mixing
2 ratios of N02.
3 Several tunable diode laser spectroscopy techniques have been used successfully for N02
4 detection (Eisele et al., 2003; Osthoff et al., 2006). These devices remain research grade
5 instruments, not yet practical for urban monitoring.
6
7 Measurements of Total Oxidized Nitrogen Species, NOy
8 Gold catalyzed CO, or H2 reduction or as conversion on hot molybdenum oxide catalyst
9 have been used to reduce N0y to NO before then detection by chemiluminescence (Fehsenfeld
10 et al., 1987; Crosley, 1996). Both techniques offer generally reliable measurements, with
11 response times on the order of 60 s and a linear dynamic range demonstrated in field
12 intercomparisons from about 10 ppt to 10's of ppb. Under certain conditions, HCN, NH3, RN02,
13 and CH3CN can be converted to NO, but at normal concentrations and humidity these are minor
14 interferences. Thermal decomposition followed by LIF has also been used for N0y detection, as
15 described above. In field comparisons, instruments based on these two principles generally
16 showed good agreement (Day et al., 2002). The experimental uncertainty is estimated to be of
17 15-30%.
18
19 AX2.8.1.4 Monitoring for NOi Compliance Versus Monitoring for Ozone Formation
20 Regulatory measurements of N02 have been focused on demonstrating compliance with
21 the NAAQS for N02. Today, few locations violate that standard, but N02 and related N0y
22 compounds remain among the most important atmospheric trace gases to measure and
23 understand. Commercial instruments for NO/NOX detection are generally constructed with an
24 internal converter for reduction of N02 to NO, and generate a signal referred to as NOX. These
25 converters, generally constructed of molybdenum oxides (MoOx), reduce not only N02 but also
26 most other N0y species. Unfortunately, with an internal converter, the instruments may not give
27 a faithful indication of N0y either—reactive species such as HNOs will adhere to the walls of the
28 inlet system. Most recently, commercial vendors such as Thermo Environmental (Franklin, MA)
29 have offered N0/N0y detectors with external Mo converters. If such instruments are calibrated
30 through the inlet with a reactive nitrogen species such as propyl nitrate, they give accurate
31 measurements of total N0y, suitable for evaluation of photochemical models. (Crosley, 1996;
32 Fehsenfeld et al., 1987; Nunnermacker et al., 1998; Rodgers and Davis, 1989). Under conditions
September 2007 AX2-92 DRAFT-DO NOT QUOTE OR CITE
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1 of fresh emissions, such as in urban areas during the rush hour, N0y ~ NOX and these monitors
2 can be used for testing emissions inventories (Dickerson, et al., 1995; Parrish, 2006). The State
3 of Maryland for example is making these true N0y measurements at the Piney Run site in the
4 western part of the state. These data produced at this site can be more reliably compared to the
5 output of CMAQ and other chemical transport models.
6
7 Summary of Methods for Measuring NO 2
8 A variety of techniques exist for reliable monitoring of atmospheric NOz and related
9 reactive nitrogen species. For demonstration of compliance with the NAAQS for N02,
10 commercial chemiluminescence instruments are adequate. For certain conditions, luminol
11 chemiluminescence is adequate. Precise measurements of NOz can be made with research grade
12 instruments such as LIF and TDLS. For path-integrated concentration determinations UV
13 spectroscopic methods provide useful information. Commercial NOX instruments are sensitive to
14 other N0y species, but do not measure N0y quantitatatively. N0y instruments with external
15 converters offer measurements more useful for comparison to chemical transport model
16 calculations.
17
18 AX2.8.2 Measurements of HNO3
19 Accurate measurement of HNOs, has presented a long-standing analytical challenge to
20 the atmospheric chemistry community. In this context, it is useful to consider the major factors
21 that control HNOs partitioning between the gas and deliquesced-particulate phases in ambient
22 air. In equation form,
K K
23 HN03g <-£*> [HN03ai/] <^>[H+] + [NO3~] (AX2_51)
24 where KH is the Henry's Law constant in M atrrf * and Ka is the acid dissociation constant in M.
25 Thus, the primary controls on HN03 phase partitioning are its thermodynamic properties
26 (KH, Ka, and associated temperature corrections), aerosol liquid water content (LWC), solution
27 pH, and kinetics. Aerosol LWC and pH are controlled by the relative mix of different acids and
28 bases in the system, hygroscopic properties of condensed compounds, and meteorological
29 conditions (RH, temperature, and pressure). It is evident from relationship AX2-51 that, in the
30 presence of chemically distinct aerosols of varying acidities (e.g., super-pm predominantly sea
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1 salt and sub-pm predominantly S aerosol), HN03 will partition preferentially with the less-acidic
2 particles; and this is consistent with observations (e.g., Huebert et al., 1996; Keene and Savoie,
3 1998; Keene et al., 2002). Kinetics are controlled by atmospheric concentrations of HN03 vapor
4 and particulate N03~ and the size distribution and corresponding atmospheric lifetimes of
5 particles against deposition. Sub-pm diameter aerosols typically equilibrate with the gas phase
6 in seconds to minutes while super-um aerosols require hours to a day or more (e.g., Meng and
7 Seinfeld, 1996; Erickson et al., 1999). Consequently, smaller aerosol size fractions are typically
8 close to thermodynamic equilibrium with respect to HN03 whereas larger size fractions (for
9 which atmospheric lifetimes against deposition range from hours to a few days) are often
10 undersaturated (e.g., Erickson et al., 1999; Keene and Savioe, 1998).
11 Many sampling techniques for HN03 (e.g., annular denuder, standard filterpack and mist-
12 chamber samplers) employ upstream prefilters to remove particulate species from sample air.
13 However, when chemically distinct aerosols with different pHs (e.g., sea salt and S aerosols) mix
14 together on a bulk filter, the acidity of the bulk mixture will be greater than that of the less acidic
15 aerosols with which most N03~ is associated. This change in pH may cause the bulk mix to be
16 supersaturated with respect to HN03 leading to volatilization and, thus, positive measurement
17 bias in HN03 sampled downstream. Alternatively, when undersaturated super-pm size fractions
18 (e.g., sea salt) accumulate on a bulk filter and chemically interact over time with HN03 in the
19 sample air stream, scavenging may lead to negative bias in HN03 sampled downstream.
20 Because the magnitude of both effects will vary as functions of the overall composition and
21 thermodynamic state of the multiphase system, the combined influence can cause net positive or
22 net negative measurement bias in resulting data. Pressure drops across particle filters can also
23 lead to artifact volatilization and associated positive bias in HN03 measured downstream.
24 Widely used methods for measuring HN03 include standard filterpacks configured with
25 nylon or alkaline-impregnated filters (e.g., Goldan et al., 1983; Bardwell et al., 1990), annular
26 denuders (EPA Method IP-9), and standard mist chambers (Talbot et al., 1990). Samples from
27 these instruments are typically analyzed by ion chromatography. Intercomparisons of these
28 measurement techniques (e.g., Hering et al., 1988; Tanner et al., 1989; Talbot et al., 1990) report
29 differences on the order of a factor of two or more.
30 More recently, sensitive HN03 measurements based on the principle of Chemical
31 lonization Mass Spectroscopy (CIMS) have been reported (e.g., Huey et al., 1998; Mauldin
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1 et al., 1998; Furutani and Akimoto, 2002; Neuman et al, 2002). CMS relies on selective
2 formation of ions such as SiFs^-HNOs or HSO^-HNOs followed by detection via mass
3 spectroscopy. Two CMS techniques and a filter pack technique were intercompared in Boulder,
4 CO (Fehsenfeld et al., 1998). Results indicated agreement to within 15% between the two CMS
5 instruments and between the CMS and filterpack methods under relatively clean conditions with
6 HNOs mixing ratios between 50 and 400 pptv. In more polluted air, the filterpack technique
7 generally yielded higher values than the CMS suggesting that interactions between chemically
8 distinct particles on bulk filters is a more important source of bias in polluted continental air.
9 Differences were also greater at lower temperature when particulate N03~ corresponded to
10 relatively greater fractions of total N03~.
11
12 AX2.8.3 Techniques for Measuring Other NOy Species
13 Methods for sampling and analysis of alkyl nitrates in the atmosphere have been
14 reviewed by Parrish and Fehsenfeld (2000). Peroxyacetylnitrate, PPN, and MPAN are typically
15 measured using a chromatograph followed by electron capture detectors or GC/ECD (e.g.,
16 Gaffney et al., 1998), although other techniques such as FTIR could also be used. Field
17 measurements are made using GC/ECD with a total uncertainty of ± 5 pptv +15% (Roberts
18 etal.,1998).
19 In the IMPROVE network and in the EPA's speciation network, particulate nitrate in the
20 PM2.5 size range is typically collected on nylon filters downstream of annular denuders coated
21 with a basic solution capable of removing acidic gases such as HN03, HN02, and S02. Filter
22 extracts are then analyzed by ion chromatography (1C) for nitrate, sulfate, and chloride. Nitrite
23 ions are also measured by this technique but their concentrations are almost always beneath
24 detection limits. However, both of these networks measure nitrate only in the PM2.5 fraction.
25 Because of interactions with more highly acidic components on filter surfaces, there could be
26 volatilization of nitrate in PMio samples. These effects are minimized if separate aerosol size
27 fractions are collected, i.e., the more acidic PM2.s and the more alkaline PMio-2.5 as in a
28 dichotomous sampler or multistage impactor.
29
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1 AX2.8.4 Remote Sensing of Tropospheric NO2 Columns for Surface NOX
2 Emissions and Surface NO2 Concentrations
3 Table AX2.8-1 contains an overview of the three satellite instruments that are used
4 retrieve tropospheric N02 columns from measurements of solar backscatter. All three
5 instruments are in polar sun-synchronous orbits with global measurements in the late morning
6 and early afternoon. The spatial resolution of the measurement from SCIAMACHY is 7 times
7 better than that from GOME (Ozone Monitoring Instrument), and that from OMI (Ozone
8 Monitoring Instrument) is 40 times better than that from GOME.
9 Figure AX2.8-1 shows tropospheric N02 columns retrieved from SCIAMACHY.
10 Pronounced enhancements are evident over major urban and industrial emissions. The high
11 degree of spatial heterogeneity over the southwestern United States provides empirical evidence
12 that most of the tropospheric NO2 column is concentrated in the lower troposphere.
13 Tropospheric N02 columns are more sensitive to NOX in the lower troposphere than in the upper
14 troposphere (Martin et al., 2002). This sensitivity to NOX in the lower troposphere is due to the
15 factor of 25 decrease in the N02/N0 ratio from the surface to the upper troposphere (Bradshaw
16 et al., 1999) that is driven by the temperature dependence of the NO + 03 reaction. Martin et al.
17 (2004a) integrated in situ airborne measurements of N02 and found that during summer the
18 lower mixed layer contains 75% of the tropospheric N02 column over Houston and Nashville.
19 However, it should be noted that these measurements are also sensitive to surface albedo and
20 aerosol loading.
21 The retrieval involves three steps: (1) determining total N02 line-of-sight (slant) columns
22 by spectral fitting of solar backscatter measurements, (2) removing the stratospheric columns by
23 using data from remote regions where the tropospheric contribution to the column is small, and
24 (3) applying an air mass factor (AMF) for the scattering atmosphere to convert tropospheric slant
25 columns into vertical columns. The retrieval uncertainty is determined by (1) and (2) over
26 remote regions where there is little tropospheric N02, and by (3) over regions in regions of
27 elevated tropospheric N02 (Martin et al., 2002; Boersma et al., 2004).
28 The paucity of in situ N02 measurements motivates the inference of surface N02
29 concentrations from satellite measurements of tropospheric N02 columns. This prospect would
30 take advantage of the greater sensitivity of tropospheric N02 columns to NOX in the lower
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0
1
8
10
Figure AX2.8-1. Tropospheric NOi columns (molecules NOi/ cm ) retrieved from the
SCIAMACHY satellite instrument for 2004-2005.
Source: Martin et al. (2006).
1 troposphere than in the upper troposphere as discussed earlier. Tropospheric N02 columns show
2 a strong correlation with in situ N02 measurements in northern Italy (Ordonez et al., 2006).
3 Quantitative calculation of surface N02 concentrations from a tropospheric N02 column
4 would require information on the relative vertical profile. Comparison of vertical profiles of
5 N02 in a chemical transport model (GEOS-Chem) versus in situ measurements over and
6 downwind of North America shows a high degree of consistency (Martin et al., 2004b; Martin
7 et al., 2006), suggesting that chemical transport models could be used to infer the relationship
8 between surface N02 concentrations and satellite observations of the tropospheric N02 column.
9 However, the satellites carrying the spectrometer (GOME/SCIAMACHY/OMI) are in
10 near polar, sun-synchronous orbits. As a result, these measurements are made only once per day,
11 typically between about 10:00 to 11:00 a.m. or 1 p.m. local time, during a brief overflight. Thus
12 the utility of these measurements is limited as they would likely miss short-term features.
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1 AX2.8.5 SAMPLING AND ANALYSIS FOR SO2
2 Currently, ambient S02 is measured using instruments based on pulsed fluorescence. The
3 UV fluorescence monitoring method for atmospheric S02 was developed to improve upon the
4 flame photometric detection (FPD) method for S02, which in turn had displaced the
5 pararosaniline wet chemical method for SOz measurement. The pararosaniline method is still the
6 FRM for atmospheric SOz, but is rarely used because of its complexity and slow response, even
7 in its automated forms. Both the UV fluorescence and FPD methods are designated as FEMs by
8 the EPA, but UV fluorescence has largely supplanted the FPD approach because of the UV
9 method's inherent linearity, sensitivity, and the absence of consumables, such as the hydrogen
10 gas needed for the FPD method.
11 Basically, S02 molecules absorb ultraviolet (UV) light at one wavelength and emit UV
12 light at longer wavelengths. This process is known as fluorescence, and involves the excitation
13 of the SOz molecule to a higher energy (singlet) electronic state. Once excited, the molecule
14 decays non-radiatively to a lower energy electronic state from which it then decays to the
15 original, or ground, electronic state by emitting a photon of light at a longer wavelength (i.e.,
16 lower energy) than the original, incident photon. The process can be summarized by the
17 following equations:
18 ow^nvj ^ow2 (AX2-52)
19 S02*-»S02 +hv2 (AX2-53)
20 where SO2* represents the excited state of SOz, h vi, and h v2 represent the energy of the
21 excitation and fluorescence photons, respectively, and hv2
-------
1 optical bandwidth filter is placed in front of the PMT to filter out any stray light from the UV
2 lamp. A lens is located between the filter and the PMT to focus the fluorescence onto the active
3 area of the detector and optimize the fluorescence signal. The Detection Limit (DL) for a non-
4 trace level S02 analyzer is 10 parts per billion (ppb) (Code of Federal Regulations, Volume 40,
5 Part 53.23c). The S02 measurement method is subject to both positive and negative interference.
6
7 Sources of Positive Interference
8 The most common source of interference is from other gases that fluoresce in a similar
9 fashion to S02 when exposed to far UV radiation. The most significant of these are polycyclic
10 aromatic hydrocarbons (PAHs); of which naphthalene is a prominent example. Xylene is
11 another hydrocarbon that can cause interference.
12 Such compounds absorb UV photons and fluoresce in the region of the S02 fluorescence.
13 Consequently, any such aromatic hydrocarbons that are in the optical chamber can act as a
14 positive interference. To remove this source of interference, the high sensitivity S02 analyzers,
15 such as those to be used in the NCore network (U.S. Environmental Protection Agency, 2005),
16 have hydrocarbon scrubbers to remove these compounds from the sample stream before the
17 sample air enters the optical chamber.
18 Another potential source of positive interference is nitric oxide (NO). NO fluoresces in a
19 spectral region that is close to the S02 fluorescence. However, in high sensitivity S02 analyzers,
20 the bandpass filter in front of the PMT is designed to prevent NO fluorescence from reaching the
21 PMT and being detected. Care must be exercised when using multicomponent calibration gases
22 containing both NO and S02 that the NO rejection ratio of the S02 analyzer is sufficient to
23 prevent NO interference. The most common source of positive bias (as constrasted with positive
24 spectral interference) in high-sensitivity S02 monitoring is stray light reaching the optical
25 chamber. Since S02 can be electronically excited by a broad range of UV wavelengths, any
26 stray light with an appropriate wavelength that enters the optical chamber can excite S02 in the
27 sample and increase the fluorescence signal.
28 Furthermore, stray light at the wavelength of the S02 fluorescence that enters the optical
29 chamber may impinge on the PMT and increase the fluorescence signal. Several design features
30 are incorporated to minimize the stray light that enters the chamber. These features include the
31 use of light filters, dark surfaces, and opaque tubing to prevent light from entering the chamber.
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1 Luke (1997) reported the positive artifacts of a modified pulsed fluorescence detector
2 generated by the co-existence of NO, CS2, and a number of highly fluorescent aromatic
3 hydrocarbons such as benzene, toluene, o-xylene, m-xylene, p-xylene, m-ethyltoluene,
4 ethylbenzene, and 1,2,4-trimethylbenzene. The positive artifacts could be reduced by using a
5 hydrocarbon "kicker" membrane. At a flow rate of 300 standard cc min"1 and a pressure drop of
6 645 torr across the kicker, the interference from ppm levels of many aromatic hydrocarbons was
7 eliminated entirely.
8 Nicks and Benner (2001) described a sensitive S02 chemiluminescence detector, which
9 was based on a differential measurement where response from ambient S02 is determined by the
10 difference between air containing S02 and air scrubbed of S02 where both air samples contain
11 other detectable sulfur species, and the positive artifact could also be reduced through this way.
12
13 Sources of Negative Interference
14 Nonradiative deactivation (quenching) of excited S02 molecules can occur from
15 collisions with common molecules in air, including nitrogen, oxygen, and water. During
16 collisional quenching, the excited S02 molecule transfers energy, kinetically allowing the S02
17 molecule to return to the original lower energy state without emitting a photon. Collisional
18 quenching results in a decrease in the S02 fluorescence and results in the underestimation of S02
19 concentration in the air sample. The concentrations of nitrogen and oxygen are constant in the
20 ambient air, so quenching from those species at a surface site is also constant, but the water
21 vapor content of air can vary. Luke (1997) reported that the response of the detector could be
22 reduced by about 7% and 15% at water vapor mixing ratios of 1 and 1.5 mole percent
23 (RH = 35 to 50% at 20-25 °C and 1 atm for a modified pulsed fluorescence detector (Thermo
24 Environmental Instruments, Model 43s). Condensation of water vapor in sampling lines must be
25 avoided, as it can absorb S02 from the sample air. The simplest approach to avoid condensation
26 is to heat sampling lines to a temperature above the expected dew point, and within a few
27 degrees of the controlled optical bench temperature. At very high S02 concentrations, reactions
28 between electronically excited S02 and ground state S02 to form S03 and SO might occur
29 (Calvert et al., 1978). However, this possibility has not been examined.
30
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1 Other Techniques for Measuring SO 2
2 A more sensitive S02 measurement method than the UV-fluorescence method was
3 reported by Thornton et al. (2002). Thornton et al (2002) reported an atmospheric pressure
4 ionization mass spectrometer. The high measurement precision and instrument sensitivity were
5 achieved by adding isotopically labeled SOz (34S1602) continuously to the manifold as an internal
6 standard. Field studies showed that the method precision was better than 10% and the limit of
7 detection was less than 1 pptv for a sampling interval of Is.
8 Sulfur Dioxide can be measured by LIF at around 220 nm (Matsumi et al., (2005).
9 Because the laser wavelength is alternately tuned to an S02 absorption peak at 220.6 and bottom
10 at 220.2 nm, and the difference signal at the two wavelengths is used to extract the S02
11 concentration, the technique eliminates interference from either absorption or fluorescence by
12 other species and has high sensitivity (5 pptv in 60 sec). Sulfur Dioxide can also be measured by
13 the same DOAS instrument that can measure NOz.
14 Photoacoutsic techniques have been employed for S02 detection, but they generally have
15 detection limits suitable only for source monitoring (Gondal, 1997; Gondal and Mastromarino,
16 2001).
17 Chemical Ionization Mass Spectroscopy (CIMS) utilizes ionization via chemical
18 reactions in the gas phase to determine an unknown sample's mass spectrum and identity. High
19 sensitivity (10 ppt or better) has been achieved with uncertainty of ~ 15% when a charcoal
20 scrubber is used for zeroing and the sensitivity is measured with isotopically labeled 34S02
21 (Hanke et al., 2003; Huey et al., 2004; Hennigan et al., 2006).
22
23 AX2.8.6 Sampling and Analysis for Sulfate, Nitrate, and Ammonium
24
25 Sampling Artifacts
26 Sulfate, nitrate, and ammonium are commonly present in PM2.5. Most PM2.5 samplers
27 have a size-separation device to separate particles so that only those particles approximately
28 2.5 pm or less are collected on the sample filter. Air is drawn through the sample filter at a
29 controlled flow rate by a pump located downstream of the sample filter. The systems have two
30 critical flow rate components for the capture of fine particulate: (1) the flow of air through the
31 sampler must be at a flow rate that ensures that the size cut at 2.5 pm occurs; and (2) the flow
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1 rate must be optimized to capture the desired amount of particulate loading with respect to the
2 analytical method detection limits.
3 When using the system described above to collect sulfate, nitrate and particulate
4 ammonium, sampling artifacts can occur because of: (1) positive sampling artifact for sulfate,
5 nitrate, and particulate ammonium due to chemical reaction; and (2) negative sampling artifact
6 for nitrate and ammonium due to the decomposition and evaporation.
7
8 Sampling and Analysis Techniques
9
10 Denuder-Filter Based Sampling and Analysis Techniques for Sulfate, Nitrate, and Ammonium
11 There are two major PM speciation ambient air-monitoring networks in the United States:
12 the Speciation Trend Network (STN), and the Interagency Monitoring of Protected Visual
13 Environments (IMPROVE) network. The current STN samplers include three filters: (1) Teflon
14 for equilibrated mass and elemental analysis including elemental sulfur; (2) a HNOs denuded
15 nylon filter for ion analysis including NOs and S04, (3) a quartz-fiber filter for elemental and
16 organic carbon. The IMPROVE sampler, which collects two 24-h samples per week,
17 simultaneously collects one sample of PMio on a Teflon filter, and three samples of PM2.5 on
18 Teflon, nylon, and quartz filters. PM2.5 mass concentrations are determined gravimetrically from
19 the PM2.s Teflon filter sample. The PM2.s Teflon filter sample is also used to determine
20 concentrations of selected elements. The PM2.s nylon filter sample, which is preceded by a
21 denuder to remove acidic gases, is analyzed to determine nitrate and sulfate aerosol
22 concentrations. Finally, the PM2.5 quartz filter sample is analyzed for OC and EC using the
23 thermal-optical reflectance (TOR) method. The STN and the IMPROVE networks represent a
24 major advance in the measurement of nitrate, because the combination of a denuder (coated with
25 either Na2COs or MgO) to remove HNOs vapor and a Nylon filter to adsorb HNOs vapor
26 volatilizing from the collected ammonium nitrate particles overcomes the loss of nitrate from
27 Teflon filters.
28 The extent to which sampling artifacts for particulate NH3+ have been adequately
29 addressed in the current networks is not clear. Recently, new denuder-filter sampling systems
30 have been developed to measure sulfate, nitrate, and ammonium with an adequate correction of
31 ammonium sampling artifacts. The denuder-filter system, Chembcomb Model 3500 speciation
32 sampling cartridge developed by Rupprecht & Patashnick Co, Inc. could be used to collect
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1 nitrate, sulfate, and ammonium simultaneously. The sampling system contains a single-nozzle
2 size-selective inlet, two honeycomb denuders, the aerosol filter and two backup filters (Keck and
3 Wittmaack, 2005). The first denuder in the system is coated with 0.5% sodium carbonate and
4 1% glycerol and collects acid gases such as HCL, SOz, HONO, and HNOs. The second denuder
5 is coated with 0.5% phosphoric acid in methanol for collecting NHs. Backup filters collect the
6 gases behind denuded filters. The backup filters are coated with the same solutions as the
7 denuders. A similar system based on the same principle was applied by Possanzini et al. (1999).
8 The system contains two NaCl-coated annular denuders followed by other two denuders coated
9 with NaCOs/glycerol and citric acid, respectively. This configuration was adopted to remove
10 HN03 quantitatively on the first NaCl denuder. The third and forth denuder remove S02 and
11 NH3, respectively. A polyethylene cyclone and a two-stage filter holder containing three filters
12 is placed downstream of the denuders. Aerosol fine particles are collected on a Teflon
13 membrane. A backup nylon filter and a subsequent citric acid impregnated filter paper collect
14 dissociation products (HNOs and NHs) of ammonium nitrate evaporated from the filtered
15 particulate matter.
16 Several traditional and new methods could be used to quantify elemental S collected on
17 filters: energy dispersive X-ray fluorescence, synchrotron induced X-ray fluorescence, proton
18 induced X-ray emission (PIXE), total reflection X-ray fluorescence, and scanning electron
19 microscopy. Energy dispersive X-ray fluorescence (EDXRF) (Method 10-3.3, U.S.
20 Environmental Protection Agency, 1997; see 2004 PM CD for details) and PIXE are the most
21 commonly used methods. Since sample filters often contain very small amounts of particle
22 deposits, preference is given to methods that can accommodate small sample sizes and require
23 little or no sample preparation or operator time after the samples are placed into the analyzer. X-
24 ray fluorescence (XRF) meets these needs and leaves the sample intact after analysis so it can be
25 submitted for additional examinations by other methods as needed. To obtain the greatest
26 efficiency and sensitivity, XRF typically places the filters in a vacuum which may cause volatile
27 compounds (nitrates and organics) to evaporate. As a result, species that can volatilize such as
28 ammonium nitrate and certain organic compounds can be lost during the analysis. The effects of
29 this volatilization are important if the PTFE filter is to be subjected to subsequent analyses of
30 volatile species.
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1 Polyatomic ions such as sulfate, nitrate, and ammonium are quantified by methods such
2 as ion chromatography (1C) (an alternative method commonly used for ammonium analysis is
3 automated colorimetry). All ion analysis methods require a fraction of the filter to be extracted
4 in deionized distilled water for sulfate and NaCOs/NaHCOs solution for nitrate and then filtered
5 to remove insoluble residues prior to analysis. The extraction volume should be as small as
6 possible to avoid over-diluting the solution and inhibiting the detection of the desired
7 constituents at levels typical of those found in ambient PM2.s samples. During analysis, the
8 sample extract passes through an ion-exchange column which separates the ions in time for
9 individual quantification, usually by an electroconductivity detector. The ions are identified by
10 their elution/retention times and are quantified by the conductivity peak area or peak height.
11 In a side-by-side comparison of two of the major aerosol monitoring techniques (Hains
12 et al., 2007), PM2.5 mass and major contributing species were well correlated among the different
13 methods with r-values in excess of 0.8. Agreement for mass, sulfate, OC, TC, and ammonium
14 was good while that for nitrate and BC was weaker. Based on reported uncertainties, however,
15 even daily concentrations of PM2.s mass and major contributing species were often significantly
16 different at the 95% confidence level. Greater values of PM2.5 mass and individual species were
17 generally reported from Speciation Trends Network methods than from the Desert Research
18 Institute Sequential Filter Samplers. These differences can only be partially accounted for by
19 known random errors. The authors concluded that the current uncertainty estimates used in the
20 STN network may underestimate the actual uncertainty.
21
22 Positive Sampling Artifacts
23 The reaction of S02 (and other acid gases) with basic sites on glass fiber filters or with
24 basic coarse particles on the filter leads to the formation of sulfate (or other nonvolatile salts,
25 e.g., nitrate, chloride). These positive artifacts lead to the overestimation of total mass, and
26 sulfate, and probably also nitrate concentrations. These problems were largely overcome by
27 changing to quartz fiber or Teflon filters and by the separate collection of PM2.5. However, the
28 possible reaction of acidic gases with basic coarse particles remains a possibility, especially with
29 PMio and PMio-2.s measurements. These positive artifacts could be effectively eliminated by
30 removing acidic gases in the sampling line with denuders coated with NaCl or Na2COs.
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1 Positive sampling artifacts also occur during measurement of particulate NH4. The
2 reaction of NHs with acidic particles (e.g. 2NHs + IH^SC^ - (NH^ZSC^), either during sampling
3 or during transportation, storage, and equilibration could lead to an overestimation of particulate
4 NH4 concentrations. Techniques have been developed to overcome this problem: using a
5 denuder to remove NHs during sampling and to protect the collected PM from NHs (Suh et al.,
6 1992, 1994; Brauer et al., 1991; Koutrakis et al., 1988a,b; Keck and Wittmaack, 2006;
7 Possanzini et al., 1999; Winberry et al., 1999). Hydrogen fluoride, citric acid, and phosphorous
8 acids have been used as coating materials for the NH3 denuder. Positive artifacts for particulate
9 NH4 can also be observed during sample handling due to contamination. No chemical analysis
10 method, no matter how accurate or precise, can adequately represent atmospheric concentrations
11 if the filters to which these methods are applied are improperly handled. Ammonia is emitted
12 directly from human sweat, breath and smoking. It can then react with acidic aerosols on the
13 filter to form ammonium sulfate, ammonium bisulfate and ammonium nitrate if the filter was not
14 properly handled (Sutton et al., 2000). Therefore, it is important to keep filters away from
15 ammonia sources, such as human breath, to minimize neutralization of the acidic compounds.
16 Also, when filters are handled, preferably in a glove box, the analyst should wear gloves that are
17 antistatic and powder-free to act as an effective contamination barrier.
18
19 Negative Sampling Artifact
20 Although sulfate is relatively stable on a Teflon filter, it is now well known that
21 volatilization losses of particulate nitrates occur during sampling.
22 For nitrate, the effect on the accuracy of atmospheric particulate measurements from
23 these volatilization losses is more significant for PM2.5 than for PMio. The FRM for PM2.5 will
24 likely suffer a loss of nitrates similar to that experienced with other simple filter collection
25 systems. Sampling artifacts resulting from the loss of particulate nitrates represents a significant
26 problem in areas such as southern California that experience high loadings of nitrates. Hering
27 and Cass (1999) discussed errors in PM2.s mass measurements due to the volatilization of
28 particulate nitrate. They examined data from two field measurement campaigns that were
29 conducted in southern California: (1) the Southern California Air Quality Study (SCAQS)
30 (Lawson, 1990) and (2) the 1986 CalTech study (Solomon et al., 1992). In both these studies,
31 side-by-side sampling of PM2.5 was conducted. One sampler collected particles directly onto a
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1 Teflon filter. The second sampler consisted of a denuder to remove gaseous HN03 followed by
2 a nylon filter that absorbed the HN03 as it evaporated from NITXN03. In both studies, the
3 denuder consisted of MgO-coated glass tubes (Appel et al., 1981). Fine particulate nitrate
4 collected on the Teflon filter was compared to fine particulate nitrate collected on the denuded
5 nylon filter. In both studies, the PM2.5 mass lost because of ammonium nitrate volatilization
6 represented a significant fraction of the total PM2.5 mass. The fraction of mass lost was higher
7 during summer than during fall (17% versus 9% during the SCAQS study, and 21% versus 13%
8 during the CalTech study). In regard to percentage loss of nitrate, as opposed to percentage loss
9 of mass discussed above, Hering and Cass (1999) found that the amount of nitrate remaining on
10 the Teflon filter samples was on average 28% lower than that on the denuded nylon filters.
11 Hering and Cass (1999) also analyzed these data by extending the evaporative model
12 developed by Zhang and McMurry (1987). The extended model used by Hering and Cass (1999)
13 takes into account the dissociation of collected particulate ammonium nitrate on Teflon filters
14 into HNOs and NHs via three mechanisms: (1) the scrubbing of HNOs and NHs in the sampler
15 inlet (John et al. (1988) showed that clean PMio inlet surfaces serve as an effective denuder for
16 HN03); (2) the heating of the filter substrate above ambient temperature by sampling; and (3) the
17 pressure drop across the Teflon filter. For the sampling systems modeled, the flow-induced
18 pressure drop was measured to be less than 0.02 atm, and the corresponding change in vapor
19 pressure was 2%, so losses driven by pressure drop were not considered to be significant in this
20 work. Losses from Teflon filters were found to be higher during the summer than during the
21 winter, higher during the day compared to night, and reasonably consistent with modeled
22 predictions.
23 Finally, during the SCAQS (Lawson, 1990) study, particulate samples also were collected
24 using a Berner impactor and greased Tedlar substrates in size ranges from 0.05 to 10 pm in
25 aerodynamic diameter. The Berner impactor PM2.5 nitrate values were much closer to those
26 from the denuded nylon filter than those from the Teflon filter, the impactor nitrate values being
27 -2% lower than the nylon filter nitrate for the fall measurements and -7% lower for the summer
28 measurements. When the impactor collection was compared to the Teflon filter collection for a
29 nonvolatile species (sulfate), the results were in agreement. Chang et al. (2000) discuss reasons
30 for reduced loss of nitrate from impactors.
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1 Brook and Dann (1999) observed much higher nitrate losses during a study in which they
2 measured particulate nitrate in Windsor and Hamilton, Ontario, Canada, by three techniques:
3 (1) a single Teflon filter in a dichotomous sampler, (2) the Teflon filter in an annular denuder
4 system (ADS), and (3) total nitrate including both the Teflon filter and the nylon back-up filter
5 from the ADS. The Teflon filter from the dichotomous sampler averaged only 13% of the total
6 nitrate, whereas the Teflon filter from the ADS averaged 46% of the total nitrate. The authors
7 concluded that considerable nitrate was lost from the dichotomous sampler filters during
8 handling, which included weighing and X-ray fluorescence (XRF) measurement in a vacuum.
9 Kim et al. (1999) also examined nitrate-sampling artifacts by comparing denuded and
10 non-denuded quartz and nylon filters during the PMio Technical Enhancement Program (PTEP)
11 in the South Coast Air Basin of California. They observed negative nitrate artifacts (losses) for
12 most measurements; however, for a significant number of measurements, they observed positive
13 nitrate artifacts. Kim et al. (1999) pointed out that random measurement errors make it difficult
14 to measure true amounts of nitrate loss.
15 Diffusion denuder samplers, developed primarily to measure particle strong acidity
16 (Koutrakis et al., 1988b, 1992), also can be used to study nitrate volatilization. Such techniques
17 were used to measure loss of particulate nitrate from Teflon filters in seven U.S. cities (Babich
18 et al., 2000). Measurements were made with two versions of the Harvard-EPA Annular Denuder
19 System (HEADS). HN03 vapor was removed by a Na2C03-coated denuder. Particulate nitrate
20 was the sum of nonvolatile nitrate collected on a Teflon filter and volatized nitrate collected on a
21 NazCOs-coated filter downstream of the Teflon filter (full HEADS) or on a Nylon filter
22 downstream of the Teflon filter (Nylon HEADS). It was found that the full HEADS (using a
23 Na2C03 filter) consistently underestimated the total particulate nitrate by approximately 20%
24 compared to the nylon HEADS. Babich et al. (2000) found significant nitrate losses in
25 Riverside, CA; Philadelphia, PA; and Boston, MA, but not in Bakersfield, CA; Chicago, IL;
26 Dallas, TX; or Phoenix, AZ, where measurements were made only during the winter. Tsai and
27 Huang (1995) used a diffusion denuder to study the positive and negative artifacts on glass and
28 quartz filters. They found positive artifacts attributed to S02 and HN03 reaction with basic sites
29 on glass fibers and basic particles and negative artifacts attributed to loss of HNOs and HC1 due
30 to volatilization of N^NOs and NH4C1 and reaction of these species with acid sulfates.
September 2007 AX2-107 DRAFT-DO NOT QUOTE OR CITE
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1 Volatile compounds can also leave the filter after sampling and prior to filter weighing or
2 chemical analysis. Losses of NOs, NH4, and Cl from glass and quartz-fiber filters that were
3 stored in unsealed containers at ambient air temperatures for 2 to 4 weeks prior to analysis
4 exceeded 50 percent (Witz et al., 1990). Storing filters in sealed containers and under
5 refrigeration will minimize these losses.
6 Negative sampling artifacts due to decomposition and volatilization are also significant
7 for particulate ammonium. Ammonium particulates, especially NH4 NS nitrate NH4 Cl are very
8 sensitive to some environmental factors, such as temperature, relative humidity, acidity of
9 aerosols, as well as to filter type (Spurny, 1999; Keck and Wittmaack, 2005). Any change in
10 these parameters during the sampling period influences the position of the equilibrium between
11 the particle phase and the gas phase. Keck and Wittmaack (2005) observed that at temperatures
12 below OC, acetate-nitrate, quartz fiber, and Teflon filters could properly collect particulate NH4
13 NHs and Cl. At temperature above OC, the salts were lost from quartz fiber and Teflon filters,
14 more so the higher the temperature and with no significant difference between quartz fiber and
15 Teflon filters. The salts were lost completely from denuded quartz fiber filters above about 20C,
16 and from non-undenuded quartz fiber and Teflon filters above about 25C. It is anticipated that
17 current sampling techniques underestimate NH4 concentrations due to the volatilization of NH4,
18 but fine particle mass contains many acidic compounds and consequently, a fraction of
19 volatilized NH4 (in the form of NHs) can be retained on a PTFE filter by reaction with the acid
20 compounds. Therefore, it is reasonable to assume that NH4 loss will be less than the nitrate loss.
21 Techniques have been applied to particulate ammonium sampling to correct particulate
22 ammonium concentrations due to evaporation: a backup filter coated with hydrofluoric acids,
23 citric acid, or phosphorous acids, is usually introduced to absorb the evaporated ammonium (as
24 ammonia); the total ammonium concentration is the sum of the particle phase ammonium
25 collected on the Teflon filter and the ammonia concentration collected on the backup filter.
26
27 Other Measurement Techniques
28
29 Nitrate
30 An integrated collection and vaporization cell was developed by Stolzenburg and Hering
31 (2000) that provides automated, 10-min resolution monitoring of fine-particulate nitrate. In this
32 system, particles are collected by a humidified impaction process and analyzed in place by flash
September 2007 AX2-108 DRAFT-DO NOT QUOTE OR CITE
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1 vaporization and chemiluminescent detection of the evolved NOX. In field tests in which the
2 system was collocated with two FRM samplers, the automated nitrate sampler results followed
3 the results from the FRM, but were offset lower. The system also was collocated with a HEADS
4 and a SASS speciation sampler (MetOne Instruments). In all these tests, the automated sampler
5 was well correlated to other samplers with slopes near 1 (ranging from 0.95 for the FRM to 1.06
6 for the HEADS) and correlation coefficients ranging from 0.94 to 0.996. During the Northern
7 Front Range Air Quality Study in Colorado (Watson et al., 1998), the automated nitrate monitor
8 captured the 12-min variability in fine-particle nitrate concentrations with a precision of
9 approximately ±0.5 pg/m3 (Chow et al., 1998). A comparison with denuded filter
10 measurements followed by ion chromatographic (1C) analysis (Chow and Watson, 1999) showed
11 agreement within ± 0.6 pg/m3 for most of the measurements, but exhibited a discrepancy of a
12 factor of two for the elevated nitrate periods. More recent intercomparisons took place during
13 the 1997 Southern California Ozone Study (SCOS97) in Riverside, CA. Comparisons with
14 14 days of 24-h denuder-filter sampling gave a correlation coefficient of R2 = 0.87 and showed
15 no significant bias (i.e., the regression slope is not significantly different from 1). As currently
16 configured, the system has a detection limit of 0.7 pg/m3 and a precision of 0.2 pg/m3.
17
18 Sulfate
19 Continuous methods for the quantification of aerosol sulfur compounds first remove
20 gaseous sulfur (e.g., S02, H2S) from the sample stream by a diffusion tube denuder followed by
21 the analysis of particulate sulfur (Cobourn et al., 1978; Durham et al., 1978; Huntzicker et al.,
22 1978; Mueller and Collins, 1980; Tanner et al., 1980). Another approach is to measure total
23 sulfur and gaseous sulfur separately by alternately removing particles from the sample stream.
24 Particulate sulfur is obtained as the difference between the total and gaseous sulfur (Kittelson
25 et al., 1978). The total sulfur content is measured by a flame photometric detector (FPD) by
26 introducing the sampling stream into a fuel-rich, hydrogen-air flame (e.g., Stevens et al., 1969;
27 Farwell and Rasmussen, 1976) that reduces sulfur compounds and measures the intensity of the
28 chemiluminescence from electronically excited sulfur molecules (S2*). Because the formation
29 of S2* requires two sulfur atoms, the intensity of the chemiluminescence is theoretically
30 proportional to the square of the concentration of molecules that contain a single sulfur atom.
31 In practice, the exponent is between 1 and 2 and depends on the sulfur compound being analyzed
32 (Dagnall et al., 1967; Stevens et al., 1971). Calibrations are performed using both particles and
September 2007 AX2-109 DRAFT-DO NOT QUOTE OR CITE
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1 gases as standards. The FPD can also be replaced by a chemiluminescent reaction with ozone
2 that minimizes the potential for interference and provides a faster response time (Benner and
3 Stedman, 1989, 1990). Capabilities added to the basic system include in situ thermal analysis
4 and sulfuric acid speciation (Cobourn et al., 1978; Huntzicker et al., 1978; Tanner et al., 1980;
5 Cobourn and Husar, 1982). Sensitivities for particulate sulfur as low as 0.1 pg/m3, with time
6 resolution ranging from 1 to 30 min, have been reported. Continuous measurements of
7 particulate sulfur content have also been obtained by on-line XRF analysis with resolution of
8 30 min or less (Jaklevic et al., 1981). During a field-intercomparison study of five different
9 sulfur instruments, Camp et al. (1982) reported four out of five FPD systems agreed to within
10 ±5% during a 1-week sampling period.
11
12
13 AX2.9 POLICY RELEVANT BACKGROUND CONCENTRATIONS OF
14 NITROGEN AND SULFUR OXIDES
15 Background concentrations of nitrogen and sulfur oxides used for purposes of informing
16 decisions about NAAQS are referred to as Policy Relevant Background (PRB) concentrations.
17 Policy Relevant Background concentrations are those concentrations that would occur in the
18 United States in the absence of anthropogenic emissions in continental North America (defined
19 here as the United States, Canada, and Mexico). Policy Relevant Background concentrations
20 include contributions from natural sources everywhere in the world and from anthropogenic
21 sources outside these three countries. Background levels so defined facilitate separation of
22 pollution levels that can be controlled by U.S. regulations (or through international agreements
23 with neighboring countries) from levels that are generally uncontrollable by the United States.
24 EPA assesses risks to human health and environmental effects from NOz and SOz levels in
25 excess of PRB concentrations.
26 Contributions to PRB concentrations include natural emissions of NOz, SOz, and
27 photochemical reactions involving natural emissions of reduced nitrogen and sulfur compounds,
28 as well as their long-range transport from outside North America. Natural sources of N02 and its
29 precursors include biogenic emissions, wildfires, lightning, and the stratosphere. Natural sources
30 of reduced nitrogen compounds, mainly NHs, include biogenic emissions and wildfires. Natural
31 sources of reduced sulfur species include anaerobic microbial activity in wetlands and volcanic
32 activity. Volcanos and biomass burning are the major natural source of SOz. Biogenic
September 2007 AX2-110 DRAFT-DO NOT QUOTE OR CITE
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1 emissions from agricultural activities are not considered in the formation of PRB concentrations.
2 Discussions of the sources and estimates of emissions are given in Section AX2.6.2.
3
4 Analysis of PRB Contribution to Nitrogen and Sulfur oxide Concentrations and Deposition
5 over the United States
6 The MOZART-2 global model of tropospheric chemistry (Horowitz et al., 2003) is used
7 to diagnose the PRB contribution to nitrogen and sulfur oxide concentrations, as well as to total
8 (wet plus dry) deposition. The model setup for the present-day simulation has been published in
9 a series of papers from a recent model intercomparison (Dentener et al., 2006a,b; Shindell et al.,
10 2006; Stevenson et al., 2006; van Noije et al., 2006). MOZART-2 is driven by National Center
11 for Environmental Prediction meteorological fields and IIASA 2000 emissions at a resolution of
12 1.9° x 1.9° with 28 sigma levels in the vertical, and it includes gas- and aerosol phase chemistry.
13 Results shown in Figures AX2.9-1 to AX2.9-5 are for the meteorological year 2001. Note that
14 color images are available on the web. An additional "policy relevant background" simulation
15 was conducted in which continental North American anthropogenic emissions were set to zero.
16 We first examine the role of PRB in contributing to N02 and S02 concentrations in
17 surface air. Figure AX2.9-1 shows the annual mean N02 concentrations in surface air in the base
18 case simulation (top panel) and the PRB simulation (middle panel), along with the percentage
19 contribution of the background to the total base case N02 (bottom panel). Maximum
20 concentrations in the base case simulation occur along the Ohio River Valley and in the
21 Los Angeles basin. While present-day concentrations are often above 5 ppbv, PRB is less than
22 300 pptv over most of the continental United States, and less than 100 pptv in the eastern United
23 States. The distribution of PRB (middle panel of Figure AX2.9-1) largely reflects the
24 distribution of soil NO emissions, with some local enhancements due to biomass burning such as
25 is seen in western Montana. In the northeastern United States, where present-day N02
26 concentrations are highest, PRB contributes <1% to the total.
27 The spatial pattern of present-day S02 concentrations over the United States is similar to
28 that of N02, with highest concentrations (>5 ppbv) along the Ohio River valley (upper panel
29 Figure AX2.9-2). Background S02 concentrations are orders of magnitude smaller, below
30 10 pptv over much of the United States (middle panel of Figure AX2.9-2). Maximum PRB
31 concentrations of S02 are 30 ppt. In the Northwest where there are geothermal sources of S02,
32 the contribution of PRB to total S02 is 70 to 80%. However, with the exception of the West
September 2007 AX2-111 DRAFT-DO NOT QUOTE OR CITE
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Total
100°W
ao°w
Background
25°N
120°W
100°W
ao°w
Percent Background Contribution
Figure AX2.9-1.
Annual mean concentrations of NOi (ppbv) in surface air over the
United States in the present-day (upper panel) and policy relevant
background (middle panel) MOZART-2 simulations. The bottom
panel shows the percentage contribution of the background to the
present-day concentrations. Please see text for details.
September 2007
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Total
iao°W
so°w
Background
iao°w
Percent Background Contribution
120°W
ioo°w
so°w
Figure AX2.9-2. Same as Figure AX2.9-1 but for SO2 concentrations.
September 2007
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Total
Background
120°W
100°W
Percent Bacl^round Contribution
Figure AX2.9-3. Same as for Figure AX2.9-1 but for wet and dry deposition of HNO:
-2 -K
'3?
NH4NO3, NOX, HO2NO2, and organic nitrates (mg N m y ).
September 2007
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Total
lao'W
IQCfW
Background
Percent Background Contribution
13D°W
100°W
Figure AX2.9-4. Same as Figure AX2.9-1 but for SOX deposition (SO2 + SO4)
(mg S m V1).
September 2007
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MOZART-2 SOIL NO.
GEOS-Chem SOIL NO.
50°N
45°N
40°N
35°N
30°N
25°N
S3°N
45°N
40°N
35°N
30°N
1ZO°W
1QO°W
SO°W
130°W
100CW
50°N
40°N
35°N
30°N
25°N
MOZART-2 Surface NOx JUL
12D°W
100°W
so°w
50°N
45°N
40°N
35°N
30°N
Z5°N
GEOS-Chem Surface NO..
120°W
100°W
ao°w
Figure AX2.9-5.
July mean soil NO emissions (upper panels; 1 x 10 9 molecules cm 2 s1)
and surface PRB NOX concentrations (lower panels; pptv) over the
United States from MOZART-2 (left) and GEOS-Chem (right) model
simulations in which anthropogenic Os precursor emissions were set
to zero in North America.
1 Coast where volcanic SOz emissions enhance PRB concentrations, the PRB contributes <1% to
2 present-day S02 concentrations in surface air (bottom panel Figure AX2.9-2).
3 The spatial pattern of N0y (defined here as HN03, NH4N03, NOX, H02N02, and organic
4 nitrates) wet and dry deposition is shown in Figure AX2.9-3. Figure AX2.9-3 (upper panel)
5 shows that highest values are found in the eastern United States in and downwind of the Ohio
6 River Valley. The pattern of nitrogen deposition in the PRB simulation (Figure AX2.9-3, middle
7 panel), however, shows maximum deposition centered over Texas and in the Gulf Coast region,
8 reflecting a combination of nitrogen emissions from lightning in the Gulf region, biomass
9 burning in the Southeast, and from microbial activity in soils (maximum in central Texas and
September 2007
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1 Oklahoma). The bottom panel of Figure AX2.9-3 shows that the PRB contribution to nitrogen
2 deposition is less than 20% over the eastern United States, and typically less than 50% in the
3 western United States where N0y deposition is low (25-50 mg N rrf2 yr"1).
4 Present-day SOX (SOz + SC^ =) deposition is largest in the Ohio River Valley, likely due
5 to coal-burning power plants in that region, while background deposition is typically at least an
6 order of magnitude smaller (Figure AX2.9-4). Over the eastern United States, the background
7 contribution to SOX deposition is <10%, and it is even smaller (<1%) where present-day SOX
8 deposition is highest. The contribution of PRB to sulfate deposition is highest in the western
9 United States (>20%) because of geothermal sources of S02 and oxidation of dimethyl sulfide in
10 the surface of the eastern Pacific.
11 Thus far, the discussion has focused on results from the MOZART-2 tropospheric
12 chemistry model. In Figure AX2.9-5, results from MOZART-2 are compared with those from
13 another tropospheric chemistry model, GEOS-Chem (Bey et al., 2001), which was previously
14 used to diagnose PRB Os (Fiore et al., 2003; U.S. Environmental Protection Agency, 2006). In
15 both models, the surface PRB NOX concentrations tend to mirror the distribution of soil NO
16 emissions, which are highest in the Midwest. The higher soil NO emissions in GEOS-Chem (by
17 nearly a factor of 2) as compared to MOZART-2 reflect different assumptions regarding the
18 contribution to soil NO emissions largely through fertilizer, since GEOS-Chem total soil NO
19 emissions are actually higher than MOZART-2 (0.07 versus 0.11 Tg N) over the United States in
20 July. Even with the larger PRB soil NO emissions, surface NOX concentrations in GEOS-Chem
21 are typically below 500 pptv.
22 It is instructive to also consider measurements of SOz at relatively remote monitoring
23 sites, i.e., site located in sparsely populate areas not subject to obvious local sources of pollution.
24 Berresheim et al. (1993) used a type of atmospheric pressure ionization mass spectrometer
25 (APIMS) at Cheeka Peak, WA (48.30N 124.62W, 480 m asl), in April 1991 during a field study
26 for DMS oxidation products. Sulfur Dioxide concentrations ranged between 20 and 40 pptv.
27 Thornton et al. (2002) have also used an APIMS with an isotopically labeled internal standard to
28 determine background S02 levels. S02 concentrations of 25 to 40 pptv were observed in
29 northwestern Nebraska in October 1999 at 150m above ground using the NCAR C-130
30 (Thornton, unpublished data). These data are comparable to remote central south Pacific
31 convective boundary layer SOz (Thornton et al., 1999).
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1 Volcanic sources of S02 in the UNITED STATES are limited to the Pacific Northwest,
2 Alaska, and Hawaii. Since 1980 the Mt. St. Helens volcano in Washington Cascade Range
3 (46.20 N, 122.18 W, summit 2549 m asl) has been a variable source of S02. Its major impact
4 came in the explosive eruptions of 1980, which primarily affected the northern part of the
5 mountain west of the UNITED STATES. The Augustine volcano near the mouth of the Cook
6 Inlet in southwestern Alaska (59.363 N, 153.43 W, summit 1252 m asl) has had S02 emissions
7 of varying extents since its last major eruptions in 1986. Volcanoes in the Kamchatka peninsula
8 of eastern region of Siberian Russia do not particularly impact the surface concentrations in the
9 northwestern NA. The most serious impact in the United States from volcanic S02 occurs on the
10 island of Hawaii. Nearly continuous venting of S02 from Mauna Loa and Kilauea produce S02
11 in such large amounts so that > 100 km downwind of the island S02 concentrations can exceed
12 30 ppbv (Thornton and Bandy, 1993). Depending on the wind direction the west coast of Hawaii
13 (Kona region) has had significant impacts from S02 and acidic sulfate aerosols for the past
14 decade. Indeed, S02 levels in Volcanoes National Park, HI exceeded the 3-h and the 24-h
15 NAAQS in 2004 -2005. The area's design value is 0.6 ppm for the 3-h, and 0.19 ppm for the
16 24-h NAAQS (U.S. Environmental Protection Agency, 2006).
17 Overall, the background contribution to nitrogen and sulfur oxides over the United States
18 is relatively small, except for S02 in areas where there is volcanic activity.
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TABLE AX2.3-1. ATMOSPHERIC LIFETIMES OF SULFUR DIOXIDE AND
REDUCED SULFUR SPECIES WITH RESPECT TO REACTION WITH OH, NO3,
AND CL RADICALS
OH NO3 Cl
Compound
SO2
CH3-S-CH3
H2S
CS2
DCS
CH3-S-H
CH3-S-S-CH3
k x 1012
1.6
5.0
4.7
1.2
0.0019
33
230
T
7.2d
2.3d
2.2 d
9.6 d
17y
8.4 h
1.2 h
k x 1012
NA
1.0
NA
<0.0004
<0.0001
0.89
0.53
T
1.1 h
>116d
> 1-3 y
1.2 h
2.1 h
k x 1012
NA
400
74
<0.004
<0.0001
200
NA
T
29 d
157 d
NR
NR
58 d
Notes:
NA = Reaction rate coefficient not available. NR = Rate coefficient too low to be relevant as an atmospheric loss mechanism.
Rate coefficients were calculated at 298 K and 1 atmosphere.
y = year, d = day. h = hour. OH = 1 x 106/cm3; N03 = 2.5 x 108/cm3; Cl = 1 x 103/cm3.
1 Rate coefficients were taken from JPL Chemical Kinetics Evaluation No. 14 (JPL, 2003).
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TABLE AX2.4-la. RELATIVE CONTRIBUTIONS OF VARIOUS REACTIONS TO
THE TOTAL S(IV) OXIDATION RATE WITHIN A SUNLIT CLOUD, 10 MINUTES
AFTER CLOUD FORMATION
Reaction % of Totala % of Totalb
Gas Phase
OH + S02 3.5 3.1
Aqueous Phase
03 + HS03" 0.6 0.7
03 + S032~ 7.0 8.2
H202 + S03" 78.4 82.1
CH3OOH + HS03" 0.1 0.1
HN04 + HS03~ 9.0 4.4
HOONO + HS03" <0.1 <0.1
HS05~ + HS03~ 1.2 <0.1
HS05~ + Fe2+ 0.6
a In the absence of transition metals.
b In the presence of iron and copper ions.
Source: Adapted from Warneck (1999).
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TABLE AX2.4-lb. RELATIVE CONTRIBUTIONS OF VARIOUS GAS AND
AQUEOUS PHASE REACTIONS TO AQUEOUS NITRATE FORMATION WITHIN
A SUNLIT CLOUD, 10 MINUTES AFTER CLOUD FORMATION
Reaction % of Totala % of Total b
Gas Phase
OH + N02 + M 57.7 67.4
Aqueous Phase
N205g + H20 8.1 11.2
N03 + Cr <0.1 0.1
N03 + HSO;f 0.7 1.0
N03 + HCOO~ 0.6 0.8
HN04 + HS03~ 31.9 20.5
HOONO + NOs" 0.8 <0.1
03 + N02"
-------
TABLE AX2.6-1. EMISSIONS OF NITROGEN OXIDES, AMMONIA, AND SULFUR
DIOXIDE IN THE UNITED STATES IN 2002
2002 Emissions (Tg/year)
Source Category
TOTAL ALL SOURCES
FUEL COMBUSTION TOTAL
FUEL COMB. ELEC. UTIL.
Coal
Bituminous
Subbituminous
anthracite & lignite
Other
Oil
Residual
Distillate
Gas
Natural
Process
Other
Internal Combustion
FUEL COMBUSTION INDUSTRIAL
Coal
Bituminous
Subbituminous
anthracite & lignite
Other
Oil
Residual
Distillate
Other
Gas
Natural
Process
Other
Other
wood/bark waste
liquid waste
Other
Internal Combustion
NO,1
23.19
9.11
5.16
4.50
2.90
1.42
0.18
<0.01
0.14
0.13
0.01
0.30
0.29
0.01
0.05
0.17
3.15
0.49
0.25
0.07
0.04
0.13
0.19
0.09
0.09
0.01
1.16
0.92
0.24
<0.01
0.16
0.11
0.01
0.04
1.15
NH3
4.08
0.02
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
S02
16.87
14.47
11.31
10.70
8.04
2.14
0.51
0.38
0.36
0.01
0.01
0.21
0.01
2.53
1.26
0.70
0.10
0.13
0.33
0.59
0.40
0.16
0.02
0.52
0.15
0.01
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TABLE AX2.6-1 (cont'd). EMISSIONS OF NITROGEN OXIDES, AMMONIA, AND
SULFUR DIOXIDE IN THE UNITED STATES IN 2002
2002 Emissions (Tg/year)
FUEL COMB. OTHER
Commercial/Institutional Coal
Commercial/Institutional Oil
Commercial/Institutional Gas
Misc. Fuel Comb. (Except Residential)
Residential Wood
Residential Other
distillate oil
bituminous/subbituminous coal
Other
INDUSTRIAL PROCESSES TOTAL
CHEMICAL & ALLIED PRODUCT MFC
Organic Chemical Mfg
Inorganic Chemical Mfg
sulfur compounds
Other
Polymer & Resin Mfg
Agricultural Chemical Mfg
ammonium nitrate/urea mfg.
Other
Paint, Varnish, Lacquer, Enamel Mfg
Pharmaceutical Mfg
Other Chemical Mfg
METALS PROCESSING
Non-Ferrous Metals Processing
Copper
Lead
Zinc
Other
Ferrous Metals Processing
Metals Processing NEC
NO,1
0.80
0.04
0.08
0.25
0.03
0.03
0.36
0.06
0.26
0.04
1.10
0.12
0.02
0.01
<0.01
0.05
0.00
0.00
0.03
0.09
0.01
0.07
0.01
NH3
<0.01
<0.01
<0.01
<0.01
<0.01
0.21
0.02
<0.01
<0.01
<0.01
0.02
<0.01
0.02
<0.01
<0.01
<0.01
<0.01
<0.01
S02
0.63
0.16
0.28
0.02
0.01
<0.01
0.16
0.15
<0.01
<0.01
1.54
0.36
0.01
0.18
0.17
0.02
<0.01
0.05
0.00
0.00
0.12
0.30
0.17
0.04
0.07
0.01
<0.01
0.11
0.02
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TABLE AX2.6-1 (cont'd). EMISSIONS OF NITROGEN OXIDES, AMMONIA, AND
SULFUR DIOXIDE IN THE UNITED STATES IN 2002
2002 Emissions (Tg/year)
PETROLEUM & RELATED INDUSTRIES
Oil & Gas Production
natural gas
Other
Petroleum Refineries & Related Industries
fluid catalytic cracking units
Other
Asphalt Manufacturing
OTHER INDUSTRIAL PROCESSES
Agriculture, Food, & Kindred Products
Textiles, Leather, & Apparel Products
Wood, Pulp & Paper, & Publishing Products
Rubber & Miscellaneous Plastic Products
Mineral Products
cement mfg
glass mfg
Other
Machinery Products
Electronic Equipment
Transportation Equipment
Miscellaneous Industrial Processes
SOLVENT UTILIZATION
Degreasing
Graphic Arts
Dry Cleaning
Surface Coating
Other Industrial
Nonindustrial
Solvent Utilization NEC
NO,1
0.16
0.07
0.05
0.04
0.54
0.01
<0.01
0.09
<0.01
0.42
0.24
0.01
0.10
<0.01
<0.01
<0.01
0.01
0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
NH3
<0.01
<0.01
<0.01
<0.01
<0.01
0.05
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
0.05
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
S02
0.38
0.11
0.11
0.01
0.26
0.16
0.07
0.01
0.46
0.01
<0.01
0.10
<0.01
0.33
0.19
0.09
<0.01
<0.01
<0.01
0.02
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
September 2007 AX2-124 DRAFT-DO NOT QUOTE OR CITE
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TABLE AX2.6-1 (cont'd). EMISSIONS OF NITROGEN OXIDES, AMMONIA, AND
SULFUR DIOXIDE IN THE UNITED STATES IN 2002
2002 Emissions (Tg/year)
STORAGE & TRANSPORT
Bulk Terminals & Plants
Petroleum & Petroleum Product Storage
Petroleum & Petroleum Product Transport
Service Stations: Stage II
Organic Chemical Storage
Organic Chemical Transport
Inorganic Chemical Storage
Inorganic Chemical Transport
Bulk Materials Storage
WASTE DISPOSAL & RECYCLING
Incineration
Industrial
Other
Open Burning
Industrial
land clearing debris
Other
POTW
Industrial Waste Water
TSDF
Landfills
Industrial
Other
Other
NO,1
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
0.01
<0.01
<0.01
0.01
0.17
0.06
0.10
<0.01
<0.01
<0.01
<0.01
<0.01
NH3
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
0.14
<0.01
<0.01
0.14
<0.01
<0.01
<0.01
<0.01
S02
0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
0.03
0.02
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
September 2007
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TABLE AX2.6-1 (cont'd). EMISSIONS OF NITROGEN OXIDES, AMMONIA, AND
SULFUR DIOXIDE IN THE UNITED STATES IN 2002
2002 Emissions (Tg/year)
TRANSPORTATION TOTAL
HIGHWAY VEHICLES
Light-Duty Gas Vehicles & Motorcycles
light-duty gas vehicles
Motorcycles
Light-Duty Gas Trucks
light-duty gas trucks 1
light-duty gas trucks 2
Heavy-Duty Gas Vehicles
Diesels
heavy-duty diesel vehicles
light-duty diesel trucks
light-duty diesel vehicles
OFF-HIGHWAY
Non-Road Gasoline
Recreational
Construction
Industrial
lawn & garden
Farm
light commercial
Logging
airport service
railway maintenance
recreational marine vessels
Non-Road Diesel
Recreational
Construction
Industrial
lawn & garden
Farm
light commercial
Logging
airport service
railway maintenance
recreational marine vessels
NOX 1 NH3
12.58 0.32
8.09 0.32
2.38 0.20
2.36
0.02
1.54 0.10
1.07
0.47
0.44 <0.01
3.73 <0.01
3.71
0.01
0.01
4.49 <0.01
0.23 <0.01
0.01
0.01
0.01
0.10
0.01
0.04
<0.01
<0.01
<0.01
0.05
1.76 <0.01
0.00
0.84
0.15
0.05
0.57
0.08
0.02
0.01
<0.01
0.03
S02
0.76
0.30
0.10
0.10
0.00
0.07
0.05
0.02
0.01
0.12
0.46
0.01
0.22
September 2007
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TABLE AX2.6-1 (cont'd). EMISSIONS OF NITROGEN OXIDES, AMMONIA, AND
SULFUR DIOXIDE IN THE UNITED STATES IN 2002
2002 Emissions (Tg/year)
NOX
NH,
SO,
Aircraft
Marine Vessels
Diesel
residual oil
Other
Railroads
Other
liquefied petroleum gas
compressed natural gas
MISCELLANEOUS
Agriculture & Forestry
agricultural crops
agricultural livestock
Other Combustion
Health Services
Cooling Towers
Fugitive Dust
Other
Natural Sources
0.09
1.11
1.11
0.98
0.32
0.29
0.04
0.39
<0.01
<0.01
3.53
3.45
<0.01
2.66
0.08
0.01
0.18
0.05
0.00
0.10
<0.01
0.10
3.10
0.03
1 Emissions are expressed in terms of N02.
2 Estimate based on Guenther et al. (2000).
Source: U.S. Environmental Protection Agency (2006).
September 2007
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TABLE AX2.8-1. SATELLITE INSTRUMENTS USED TO RETRIEVE
TROPOSPHERIC NO2 COLUMNS.
Instrument
GOME
SCIAMACHY
OMI
Coverage
1995-2002
2002-
2004-
Typical U.S.
Measurement Time
10:30-11:30 AM
10:00-11:00 AM
12:45-1:45 PM
Typical
Resolution
(km)
320x40
30x60
13x24
Return Time
(days)1
3
6
1
Instrument
Overview
Burrows et al.
(1999)
Bovensmann
etal. (1999)
Levelt et al.
(2006)
Return time is reported here for cloud free conditions. Note that due to precession of the satellite's orbit, return measurements are close to
but not made over the same location. In practice, clouds decrease observation frequency by a factor of 2.
September 2007
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1 AX2.10 REFERENCES
3 Aber, J.; McDowell, W.; Nadelhoffer, K.; Magill, A.; Berntson, G.; Kamakea, M.; McNulty, S.;
4 Currie, W.; Rustad, L.; Fernandez, I. (1998) Nitrogen saturation in temperate forest
5 ecosystems. BioScience 48: 921-934.
6 Alicke, B.; Hebestreit, K.; Stutz, J.; Platt, U. (1999) Iodine oxide in the marine boundary layer.
7 Nature (London, U.K.) 397: 572-573.
8 Allan, B. J.; McFiggans, G.; Plane, J. M. C.; Coe, H. (2000) Observations of iodine monoxide in
9 the remote marine boundary layer. J. Geophys. Res. [Atmos.] 105: 14,363-14,369.
10 Allen, D. J.; Pickering, K. E. (2002) Evaluation of lightning flash rate parameterizations for use
11 in a global chemical transport model. J. Geophys. Res. [Atmos.] 107(D23):
12 10.1029/2002JD002066.
13 Allen, D. J.; Pickering, K. E.; Molod, A. (1997) An evaluation of deep convective mixing in the
14 Goddard Chemical Transport Model using ISCCP cloud parameters. J. Geophys. Res.
15 [Atmos.] 102: 25,467-25,476.
16 Allen, D.; Pickering, K.; Stenchikov, G.; Thompson, A.; Kondo, Y. (2000) A three-dimensional
17 total odd nitrogen (N0y) simulation during SONEX using a stretched-grid chemical
18 transport model. J. Geophys. Res. [Atmos.] 105: 3851-3876.
19 Ammann, M.; Kalberer, M.; Jost, D. T.; Tobler, L.; Rossler, E.; Piguet, D.; Gaggeler, H. W.;
20 Baltensperger, U. (1998) Heterogeneous production of nitrous acid on soot in polluted air
21 masses. Nature (London) 395: 157-160.
22 Anastasio, C.; Newberg, J. T.; Williams, D. K.; Chu, G. B.; Matthew, B. M. (1999)
23 Photoformation of hydroxyl radical in sea salt particles. EOS Trans. Am. Geophys. Union
24 80: F147.
25 Andreae, M. 0. (1991) Biomass burning: its history, use, and distribution and its impact on
26 environmental quality and global climate. In: Levine, J. S., ed. Global biomass burning:
27 atmospheric, climatic, and biospheric implications. Cambridge, MA: MIT Press; pp. 1-
28 21.
29 Appel, B. R.; Tokiwa, Y.; Haik, M. (1981) Sampling of nitrates in ambient air. Atmos. Environ.
30 15: 283-289.
31 Appel, K. W.; Gilliland, A.; Eder, B. (2005) An operational evaluation of the 2005 release of
32 models-3 CMAQ version 4.5. Washington, DC: National Oceanic and Atmospheric
33 Administration (NOAA) Air Resources Laboratory, Atmospheric Sciences Modeling
34 Division.
35 Arey, J. (1998) Atmospheric reactions of PAHs including formation of nitroarenes. In: Neilson,
36 A. N., vol. ed. Anthropogenic Compounds. V. 3, Part I. PAHs and Related Compounds -
37 Chemistry. New York, NY: Springer-Verlag; pp. 347-385. (Hutzinger, 0., ed. The
38 Handbook of Environmental Chemistry series).
September 2007 AX2-129 DRAFT-DO NOT QUOTE OR CITE
-------
1 Arey, J.; Zielinska, B.; Atkinson, R.; Winer, A. M.; Ramdahl, T.; Pitts, J. N., Jr. (1986) The
2 formation of nitro-PAH from the gas-phase reactions of fluoranthene and pyrene with the
3 OH radical in the presence of NOX. Atmos. Environ. 20: 2339-2345.
4 Arey, J.; Atkinson, R.; Zielinska, B.; McElroy, P. A. (1989) Diurnal concentrations of volatile
5 polycyclic aromatic hydrocarbons and nitroarenes during a photochemical air pollution
6 episode in Glendora, California. Environ. Sci. Technol. 23: 321-327.
7 Arnold, J. R.; Dennis, R. L.; Tonnesen, G. S. (2003) Diagnostic evaluation of numerical air
8 quality models with specialized ambient observations: testing the Community Multiscale
9 Air Quality modeling system (CMAQ) at selected SOS 95 ground sites. Atmos. Environ.
10 37:1185-1198.
11 Ashworth, S. H.; Allan, B. J.; Plane, J. M. C. (2002) High resolution spectroscopy of the 010
12 radical: implications for the ozone-depleting potential of iodine. Geophys. Res. Lett. 29:
13 10.1029/GL013851.
14 Atkinson, R. (1991) Kinetics and mechanisms of the gas-phase reactions of the NOs radical with
15 organic compounds. J. Phys. Chem. Ref. Data 20: 459-507.
16 Atkinson, R. (2000) Atmospheric chemistry of VOCs and NOX. Atmos. Environ. 34: 2063-2101.
17 Atkinson, R.; Arey, J. (1994) Atmospheric chemistry of gas-phase polycyclic aromatic
18 hydrocarbons: formation of atmospheric mutagens. Environ. Health Perspect. 102(suppl.
19 4): 117-126.
20 Atkinson, R.; Winer, A. M.; Pitts, J. N., Jr. (1986) Estimation of night-time NzOs concentrations
21 from ambient NOz and NOs radical concentrations and the role of NzOs in night-time
22 chemistry. Atmos. Environ. 20: 331-339.
23 Atkinson, R.; Arey, J.; Zielinska, B.; Aschmann, S. M. (1990) Kinetics and nitro-products of the
24 gas-phase OH and N03 radical-initiated reactions of naphthalene-ds, fluoranthene-dw and
25 pyrene. Int. J. Chem. Kinet. 22: 999-1014.
26 Atkinson, R.; Baulch, D. L.; Cox, R. A.; Hampson, R. F., Jr.; Kerr, J. A.; Troe, J. (1992a)
27 Evaluated kinetic and photochemical data for atmospheric chemistry: supplement IV,
28 IUPAC Subcommittee on Gas Kinetic Data Evaluation for Atmospheric Chemistry. J.
29 Phys. Chem. Ref. Data 21: 1125-1568.
30 Babich, P.; Davey, M.; Allen, G.; Koutrakis, P. (2000) Method comparisons for particulate
31 nitrate, elemental carbon, and PM2.5 mass in seven U.S. cities. J. Air Waste Manage.
32 Assoc. 50: 1095-1105.
33 Balkanski, Y. J.; Jacob, D. J.; Gardener, G. M.; Graustein, W. C.; Turekian, K. K. (1993)
34 Transport and residence times of tropospheric aerosols inferred from a global three-
35 dimensional simulation of 210Pb. J. Geophys. Res. [Atmos.] 98: 20,573-20,586.
36 Bamford, H. A.; Baker, J. E. (2003) Nitro-polycyclic aromatic hydrocarbon concentrations and
37 sources in urban and suburban atmospheres of the mid-Atlantic region. Atmos. Environ.
38 37: 2077-2091.
39 Bamford, H. A.; Bezabeh, D. Z.; Schantz, M. M.; Wise, S. A.; Baker, J. E. (2003) Determination
40 and comparison of nitrated-polycyclic aromatic hydrocarbons measured in air and diesel
41 particulate reference materials. Chemosphere 50: 575-587.
September 2007 AX2-130 DRAFT-DO NOT QUOTE OR CITE
-------
1 Bandy, A.; Maroulis, P. (1980) Impact of recent measurements of OCS, CS2, and S02 in
2 background air on the global sulfur cycle. In: Shriner, D. S.; Richmond, C. R.; Lindberg,
3 S. E., eds. Atmospheric sulfur deposition: environmental impact and health effects:
4 proceedings of the 2nd life sciences symposium, potential environmental and health
5 consequences of atmospheric sulfur deposition; October, 1979; Gatlinburg, Tennessee.
6 Ann Arbor, MI: Ann Arbor Science Publishers; pp. 55-63.
7 Bardwell, C. A.; Maben, J. R.; Hurt, J. A.; Keene, W. C.; Galloway, J. N.; Boatman, J. F.;
8 Wellman, D. L. (1990) A technique using high-flow, dichotomous filter packs for
9 measuring major atmospheric chemical constituents. Global Biogeochem. Cycles 4: 151-
10 163.
11 Barnes, I.; Bastian, V.; Becker, K. H.; Overath, R. D. (1991) Kinetic studies of the reactions of
12 10, BrO and CIO with Dimethylsulfide. Int. J. Chem. Kinet. 23: 579-591.
13 Barrie, L. A.; Bottenheim, J. W.; Schnell, R. C.; Crutzen, P. J.; Rasmussen, R. A. (1988) Ozone
14 destruction and photochemical reactions at polar sunrise in the lower Arctic atmosphere.
15 Nature (London, U.K.) 334: 138-141.
16 Behnke, W.; George, C.; Sheer, V.; Zetzsch, C. (1997) Production and decay of C1N02 from the
17 reaction of gaseous N20s with NaCl solution: bulk and aerosol experiments. J. Geophys.
18 Res. [Atmos.] 102: 3795-3804.
19 Benner, R. L.; Stedman, D. H. (1989) Universal sulfur detection by chemiluminescence. Anal.
20 Chem. 61: 1268-1271.
21 Benner, R. L.; Stedman, D. H. (1990) Field evaluation of the sulfur chemiluminescence detector.
22 Environ. Sci. Technol. 24: 1592-1596.
23 Bergstrom, A.-K.; Jansson, M. (2006) Atmospheric nitrogen deposition has caused nitrogen
24 enrichment and eutrophication of lakes in the northern hemisphere. Glob. Change Biol.
25 12:635-643.
26 Berkowitz, C. M.; Zaveri, R. A.; Bian, X.; Zhong, S.; Disselkamp, R. S.; Laulainen, N. S.;
27 Chapman, E. G. (2001) Aircraft observations of aerosols, Os and NO^ in a nighttime
28 urban plume. Atmos. Environ. 35: 2395-2404.
29 Berresheim, H.; Eisele, F. L.; Tanner, D. J.; Mclnnes, L. M.; Ramsey-Bell, D. C.; Covert, D. S.
30 (1993) Atmospheric sulfur chemistry and cloud condensation nuclei (CCN)
31 concentrations over the northeastern Pacific coast. J. Geophys. Res. [Atmos.] 98: 12,701-
32 12,711.
33 Berresheim, H.; Wine, P. H.; Davis, D. D. (1995) Sulfur in the atmosphere. In: Singh, H. B., ed.
34 Composition, chemistry, and climate of the atmosphere. New York, NY: Van Nostrand
35 Reinhold; pp. 251-307.
36 Bertram, T. H.; Heckel, A.; Richter, A.; Burrows, J. R.; Cohen, R. C. (2005) Satellite
37 measurements of daily variations in soil NO* emissions. Geophys. Res. Lett. 32(L24812):
38 10.1029/2005GL024640.
39 Bey, I.; Jacob, D. J.; Yantosca, R. M.; Logan, J. A.; Field, B.; Fiore, A. M.; Li, Q.; Liu, H.;
40 Mickley, L. J.; Schultz, M. G. (2001) Global modeling of tropospheric chemistry with
September 2007 AX2-131 DRAFT-DO NOT QUOTE OR CITE
-------
1 assimilated meteorology: model description and evaluation. J. Geophys. Res. [Atmos.]
2 106: 23,073-23,095.
3 Bezabeh, D. Z.; Bamford, H. A.; Schantz, M. M.; Wise, S. A. (2003) Determination of nitrated
4 polycyclic aromatic hydrocarbons in diesel particulate-related standard reference
5 materials by using gas chromatography/mass spectrometry with negative ion chemical
6 ionization. Anal. Bioanal. Chem. 375: 381-388.
7 Binkowski, F. S.; Arunachalam, S.; Adelman, Z.; Pinto, J. P. (2007) Examining photolysis rates
8 with a prototype online photolysis module in CMAQ. J. Appl. Meteorol. Climatol.:
9 submitted.
10 Bishop, G. A.; Stedman, D. H. (1996) Measuring the emissions of passing cars. Ace. Chem. Res.
11 29:489-495.
12 Blanchard, P.; Brook, J. R.; Brazil, P. (2002) Chemical characterization of the organic fraction of
13 atmospheric aerosol at two sites in Ontario, Canada. J. Geophys. Res. [Atmos.]
14 107(D21): 10.1029/2001JD000627.
15 Bloom, S. C.; Takacs, L. L.; Da Silva, A. M.; Ledvina, D. (1996) Data assimilation using
16 incremental analysis updates. Mon. Weather Rev. 124: 1256-1271.
17 Bloom, S. C.; da Silva, A.; Dee, D.; Bosilovich, M.; Chern, J.-D.; Pawson, S.; Schubert, S.;
18 Sienkiewicz, M.; Stajner, L; Tan, W.-W.; Wu, M.-L. (2005) Documentation and
19 validation of the Goddard Earth Observing System (GEOS) Data Assimilation System-
20 version 4. Technical Report Series on Global Modeling and Data Assimilation 104606,
21 26.
22 Bloss, W. J.; Lee, J. D.; Johnson, G. P.; Sommariva, R.; Heard, D. E.; Saiz-Lopez, A.; Plane, J.
23 M. C.; McFiggans, G.; Coe, H.; Flynn, M.; Williams, P.; Rickard, A. R.; Fleming, Z. L.
24 (2005) Impact of halogen monoxide chemistry upon boundary layer OH and H02
25 concentrations at a coastal site. Geophys. Res. Lett. 32: 10.1029/2004GL022084.
26 Bobrowski, N.; Honninger, G.; Galle, B.; Platt, U. (2003) Detection of bromine monoxide in a
27 volcanic plume. Nature (London, U.K.) 423: 273-276.
28 Boersma, K. F.; Eskes, H. J.; Brinksma, E. J. (2004) Error analysis for tropospheric NOz retrieval
29 from space. J. Geophys. Res. [Atmos.] 109(D04311): 10.1029/2003JD003962.
30 Bovensmann, H.; Burrows, J. P.; Buchwitz, M.; Frerick, J.; Noel, S.; Rozanov, V. V. (1999)
31 SCIAMACHY: mission objectives and mesurement modes. J. Atmos. Sci. 56: 127-150.
32 Bradshaw, J.; Davis, D.; Crawford, J.; Chen, G.; Shetter, R.; Miiller, M.; Gregory, G.; Sachse,
33 G.; Blake, D.; Heikes, B.; Singh, H.; Mastromarino, J.; Sandholm, S. (1999)
34 Photofragmentation two-photon laser-induced fluorescence detection of N02 and NO:
35 comparison of measurements with model results based on airborne observations during
36 PEM-Tropics A. Geophys. Res. Lett. 26: 471-474.
37 Brauer, M.; Koutrakis, P.; Keeler, G. J.; Spengler, J. D. (1991) Indoor and outdoor
38 concentrations of inorganic acidic aerosols and gases. J. Air Waste Manage. Assoc. 41:
39 171-181.
40 Brook, J. R.; Dann, T. F. (1999) Contribution of nitrate and carbonaceous species to PM2.5
41 observed in Canadian cities. J. Air Waste Manage. Assoc. 49: 193-199.
September 2007 AX2-132 DRAFT-DO NOT QUOTE OR CITE
-------
1 Brook, J. R.; Sirois, A.; Clarke, J. F. (1996) Comparison of dry deposition velocities for S02,
2 HN03, and S042 estimated with two inferential models. Water Air Soil Pollut. 87: 205-
3 218.
4 Broske, R.; Kleffmann, J.; Wiesen, P. (2003) Heterogeneous conversion of NOz on secondary
5 organic aerosol surfaces: a possible source of nitrous acid (HONO) in the atmosphere?
6 Atmos. Chem. Phys. 3: 469-474.
7 Brown, S. S.; Neuman, J. A.; Ryerson, T. B.; Trainer, M.; Dube, W. P.; Holloway, J. S.;
8 Warneke, C.; De Gouw, J. A.; Donnelly, S. G.; Atlas, E.; Matthew, B.; Middlebrook, A.
9 M.; Peltier, R.; Weber, R. J.; Stohl, A.; Meagher, J. F.; Fehsenfeld, F. C.; Ravishankara,
10 A. R. (2006a) Nocturnal odd-oxygen budget and its implications for ozone loss in the
11 lower troposphere. Geophys. Res. Lett. 33(L08801): 10.1029/2006GL025900.
12 Brown, S. S.; Ryerson, T. B.; Wollny, A. G.; Brock, C. A.; Peltier, R.; Sullivan, A. P.; Weber, R.
13 J.; Dube, W. P.; Trainer, M.; Meagher, J. F.; Fehsenfeld, F. C.; Ravishankara, A. R.
14 (2006b) Variability in nocturnal nitrogen oxide processing and its role in regional air
15 quality. Science (Washington, DC, U.S.) 311: 67-70.
16 Briihl, C.; Poschl, U.; Crutzen, P. J.; Steil, B. (2000) Acetone and PAN in the upper troposphere:
17 impact on ozone production from aircraft emissions. Atmos. Environ. 34: 3931-3938.
18 Buhr, M. P.; Trainer, M.; Parrish, D. D.; Sievers, R. E.; Fehsenfeld, F. C. (1992) Assessment of
19 pollutant emission inventories by principal component analysis of ambient air
20 measurements. Geophys. Res. Lett. 19: 1009-1012.
21 Burrows, J. P.; Weber, M.; Buchwitz, M.; Rozanov, V.; Ladstatter-WeiBenmayer, A.; Richter,
22 A.; DeBeek, R.; Hoogen, R.; Bramstedt, K.; Eichmann, K.-U.; Eisinger, M. (1999) The
23 Global Ozone Monitoring Experiment (GOME): mission concept and first scientific
24 results. J. Atmos. Sci. 56: 151-175.
25 Byun, D. W.; Ching, J. K. S., eds. (1999) Science algorithms of the EPA models-3 community
26 multiscale air quality model (CMAQ) modeling system. Washington, DC: U.S.
27 Environmental Protection Agency, Office of Research and Development; EPA/600/R-
28 99/030. Available: http://www.epa.gov/asmdnerl/CMAQ/CMAQscienceDoc.html [4
29 October, 2006].
30 Byun, D. W.; Schere, K. L. (2006) Review of the governing equations, computational
31 algorithms, and other components of the models-3 community multiscale air quality
32 (CMAQ) modeling system. Appl. Mech. Rev. 59: 51-77.
33 Calvert, J. G.; Su, F.; Bottenheim, J. W.; Strausz, 0. P. (1978) Mechanism of the homogeneous
34 oxidation of sulfur dioxide in the troposphere. In: Sulfur in the atmosphere: proceedings
35 of the international symposium; September 1977; Dubrovnik, Yugoslavia. Atmos.
36 Environ. 12: 197-226.
37 Calvert, J. G.; Yarwood, G.; Dunker, A. M. (1994) An evaluation of the mechanism of nitrous
38 acid formation in the urban atmosphere. Res. Chem. Intermed. 20: 463-502.
39 Camp, D. C.; Stevens, R. K.; Cobourn, W. G.; Husar, R. B.; Collins, J. F.; Huntzicker, J. J.;
40 Husar, J. D.; Jaklevic, J. M.; McKenzie, R. L.; Tanner, R. L.; Tesch, J. W. (1982)
41 Intercomparison of concentration results from fine particle sulfur monitors. Atmos.
42 Environ. 16: 911-916.
September 2007 AX2-133 DRAFT-DO NOT QUOTE OR CITE
-------
1 Cardelino, C. A.; Chameides, W. L. (2000) The application of data from photochemical
2 assessment monitoring stations to the observation-based model. Atmos. Environ. 34:
3 2325-2332.
4 Carpenter, L. J. (2003) Iodine in the marine boundary layer. Chem. Rev. (Washington, DC, U.S.)
5 103:4953-4962.
6 Carpenter, L. J.; Sturges, W. T.; Penkett, S. A.; Liss, P. S.; Alicke, B.; Hebestreit, K.; Platt, U.
7 (1999) Short-lived alkyl iodides and bromides at Mace Head, Ireland: links to biogenic
8 sources and halogen oxide production. J. Geophys. Res. [Atmos.] 104: 1679-1689.
9 Carroll, M. A.; Bertman, S. B.; Shepson, P. B. (2001) Overview of the program for research on
10 oxidants, photochemistry, emissions and transport (PROPHET) summer 1998
11 measurements intensive. J. Geophys. Res. [Atmos.] 106: 24,275-24,288.
12 Carter, W. P. L. (1990) A detailed mechanism for the gas-phase atmospheric reactions of organic
13 compounds. Atmos. Environ. Part A 24: 481-518.
14 Carter, W. P. L. (1995) Computer modeling of environmental chamber studies of maximum
15 incremental reactivities of volatile organic compounds. Atmos. Environ. 29: 2513.
16 Castells, P.; Santos, F. J.; Gaiceran, M. T. (2003) Development of a sequential supercritical fluid
17 extraction method for the analysis of nitrated an oxygenated derivatives of polycyclic
18 aromatic hydrocarbons in urban aerosols. J. Chromatogr. A 1010: 141-151.
19 Castro, M. S.; Driscoll, C. T. (2002) Atmospheric nitrogen deposition has caused nitrogen
20 enrichment and eutrophication of lakes in the northern hemisphere. Environ. Sci.
21 Technol. 36: 3242-3249.
22 Cecinato, A. (2003) Nitrated polynuclear aromatic hydrocarbons in ambient air in Italy. A brief
23 overview. J. Separation Sci. 26: 402-408.
24 Chameides, W. L. (1984) The photochemistry of a remote marine stratiform cloud. J. Geophys.
25 Res. [Atmos.] 89: 4739-4755.
26 Chameides, W. L.; Stelson, A. W. (1992) Aqueous-phase chemical processes in deliquescent
27 sea-salt aerosols: a mechanism that couples the atmospheric cycles of S and sea salt. J.
28 Geophys. Res. [Atmos.] 97: 20,565-20,580.
29 Chang, J. S.; Brost, R. A.; Isaken, I. S. A.; Madronich, S.; Middleton, P.; Stockwell, W. R.;
30 Walcek, C. J. (1987) A three-dimensional Eulerian acid deposition model: physical
31 concepts and formulation. J. Geophys. Res. [Atmos.] 92: 14,681-14,700.
32 Chang, M. E.; Hartley, D. E.; Cardelino, C.; Chang, W.-L. (1996) Inverse modeling of biogenic
33 emissions. Geophys. Res. Lett. 23: 3007-3010.
34 Chang, M. E.; Hartley, D. E.; Cardelino, C.; Hass-Laursen, D.; Chang, W. L. (1997) On using
35 inverse methods for resolving emissions with large spatial inhomogeneities. J. Geophys.
36 Res. [Atmos.] 102: 16,023-16,036.
37 Chang, M. C.; Sioutas, C.; Kim, S.; Gong, H., Jr.; Linn, W. S. (2000) Reduction of nitrate losses
38 from filter and impactor samplers by means of concentration enrichment. Atmos.
39 Environ. 34: 85-98.
September 2007 AX2-134 DRAFT-DO NOT QUOTE OR CITE
-------
1 Chatfield, R. B.; Crutzen, P. J. (1984) Sulfur dioxide in remote oceanic air: cloud transport of
2 reactive precursors. J. Geophys. Res. [Atmos.] 89: 7111-7132.
3 Ching, J. K. S.; Byun, D.; Young, J.; Binkowski, F. S.; Pleim, J.; Roselle, S.; Godowitch, J.;
4 Benjey, W.; Gipson, G. (1998) Science features in Models-3 Community Multiscale Air
5 Quality System. In: Preprints of the 10th Joint AMS/AWMA Conference on Applications
6 of Air Pollution Meteorology; Phoenix, AZ. Pittsburgh, PA: Air & Waste Management
7 Association.
8 Chock, D. P.; Winkler, S. L. (1994) A comparison of advection algorithms coupled with
9 chemistry. Atmos. Environ. 28: 2659-2675.
10 Choi, W.; Leu, M.-T. (1998) Nitric acid uptake and decomposition on black carbon (soot)
11 surfaces: its implications for the upper troposphere and lower stratosphere. J. Phys.
12 Chem. A 102: 7618-7630.
13 Choi, Y.-J.; Calabrese, R. V.; Ehrman, S. H.; Dickerson, R. R.; Stehr, J. W. (2006) A combined
14 approach for the evaluation of a volatile organic compound emissions inventory. J. Air
15 Waste Manage Assoc. 56: 169-178.
16 Chow, J. C.; Watson, J. G. (1999) Ion chromatography in elemental analysis of airborne
17 particles. In: Landsberger, S.; Creatchman, M., eds. Elemental analysis of airborne
18 particles. Amsterdam, The Netherlands: Gordon and Breach Science Publishers; pp. 97-
19 137. (Vo-Dinh, T., ed. Advances in environmental industrial and process control
20 technologies: v. 1).
21 Chow, J. C.; Zielinska, B.; Watson, J. G.; Fujita, E. M.; Richards, H. W.; Neff, W. D.; Dietrich,
22 D.; Hering, S. V. (1998) Northern Front Range Air Quality Study. Volume A: Ambient
23 measurements. Fort Collins, CO: Colorado State University, Cooperative Institute for
24 Research in the Atmosphere. Available: http://www.nfraqs.colostate.edu/dri.html [4
25 February 2002].
26 Chuang, J. C.; Mack, G. A.; Kuhlman, M. R.; Wilson, N. K. (1991) Polycyclic aromatic
27 hydrocarbons and their derivatives in indoor and outdoor air in an eight-home study.
28 Atmos. Environ. Part B 25: 369-380.
29 Ciganek, M.; Neca, J.; Adamec, V.; Janosek, J.; Machala, M. (2004) A combined chemical and
30 bioassay analysis of traffic-emitted polycyclic aromatic hydrocarbons. Sci. Total Environ.
31 334-334(spec. issue): 141-148.
32 Civerolo, K. L.; Dickerson, R. R. (1998) Nitric oxide soil emissions from tilled and untilled
33 cornfields. Agric. For. Meteorol. 90: 307-311.
34 Cleary, P. A.; Wooldridge, P. J.; Cohen, R. C. (2002) Laser-induced fluorescence detection of
35 atmospheric NOz with a commercial diode laser and a supersonic expansion. Appl.
36 Optics 41: 6950-6956.
37 Clegg, N. A.; Toumi, R. (1998) Non-sea-salt-sulphate formation in sea-salt aerosol. J. Geophys.
38 Res. [Atmos.] 103: 31,095-31,31,102.
39 Clemitshaw, K. C. (2004) A review of instrumentation and measurement techniques for ground-
40 based and airborne field studies of gas-phase tropospheric chemistry. Crit. Rev. Environ.
41 Sci. Technol. 34: 1-108.
September 2007 AX2-135 DRAFT-DO NOT QUOTE OR CITE
-------
1 Cobourn, W. G.; Husar, R. B. (1982) Diurnal and seasonal patterns of particulate sulfur and
2 sulfuric acid in St. Louis, July 1977-June 1978. Atmos. Environ. 16: 1441-1450.
3 Cobourn, W. G.; Husar, R. B.; Husar, J. D. (1978) Continuous in situ monitoring of ambient
4 particulate sulfur using flame photometry and thermal analysis. In: Husar, R. B.; Lodge,
5 J. P., Jr.; Moore, D. J., eds. Sulfur in the atmosphere: proceedings of the international
6 symposium; September 1977; Dubrovnik, Yugoslavia. Atmos. Environ. 12: 89-98.
7 Cohen, R. C. (1999) Laser-induced fluorescence detection of atmospheric N02 at parts per
8 trillion mixing ratios: implications for nitrogen oxide photochemistry in the stratosphere
9 and troposphere [abstract]. Abstr. Pap. Am. Chem. Soc. (Phys.) 218(Pt. 2, Aug. 22): 262.
10 Cooper, 0. R.; Stohl, A.; Trainer, M.; Thompson, A. M.; Witte, J. C.; Oltmans, S. J.; Morris, G.;
11 Pickering, K. E.; Crawford, J. H.; Chen, G.; Cohen, R. C.; Bertram, T. H.; Wooldridge,
12 P.; Perring, A.; Brune, W.; Merrill, J.; Moody, J. L.; Tarasick, D.; Nedelec, P.; Forbes,
13 G.; Newchurch, M.; Schmidlin, F.; Johnson, B. J.; Turquety, S.; Baughcum, S. L.; Ren,
14 X.; Fehsenfeld, F. C.; Meagher, J. F.; Spichtinger, N.; Brown, C. C.; McKeen, S. A.;
15 McDermid, I. S.; Leblanc, T. (2006) Large upper tropospheric ozone enhancements
16 above mid-latitude North America during summer: in situ evidence from the IONS and
17 MOZAIC ozone measurement network. J. Geophys. Res. [Atmos.]
18 111 (D24S05): 10.1029/2006JD007306.
19 Crawford, J.; Davis, D.; Olson, J.; Chen, G.; Liu, S.; Fuelberg, H.; Hannan, J.; Kondo, Y.;
20 Anderson, B.; Gregory, G.; Sachse, G.; Talbot, R.; Viggiano, A.; Heikes, B.; Snow, J.;
21 Singh, H.; Blake, D. (2000) Evolution and chemical consequences of lightning-produced
22 NO* observed in the North Atlantic upper troposphere. J. Geophys. Res. [Atmos.] 105:
23 19,795-19,809.
24 Crosley, D. R. (1996) N0y blue ribbon panel. J. Geophys. Res. [Atmos.] 101: 2049-2052.
25 Crutzen, P. J. (1976) Upper limits on atmospheric ozone reductions following increased
26 application of fixed nitrogen to the soil. Geophys. Res. Lett. 3: 169-172.
27 Crutzen, P. J.; Gidel, L. T. (1983) A two-dimensional photochemical model of the atmosphere. 2.
28 The tropospheric budgets of the anthropogenic chlorocarbons, CO, CH4, CHsCl and the
29 effect of various NOX sources on tropospheric ozone. J. Geophys. Res. [Atmos.] 88:
30 6641-6661.
31 Dagnall, R. M.; Thompson, K. C.; West, T. S. (1967) Molecular-emission in cool flames. Part I.
32 the behaviour of sulphur species in a hydrogen-nitrogen diffusion flame and in a shielded
33 air-hydrogen flame. Analyst 92: 506-512.
34 Daum, P. H.; Kleinman, L. L; Hills, A. J.; Lazrus, A. L.; Leslie, A. C. D.; Busness, K.; Boatman,
35 J. (1990) Measurement and interpretation of concentrations of H202 and related species
36 in the upper midwest during summer. J. Geophys. Res. [Atmos.] 95: 9857-9871.
37 Daum, P. H.; Kleinman, L. L; Newman, L.; Luke, W. T.; Weinstein-Lloyd, J.; Berkowitz, C. M.;
38 Busness, K. M. (1996) Chemical and physical properties of plumes of anthropogenic
39 pollutants transported over the North Atlantic during the North Atlantic Regional
40 Experiment. J. Geophys. Res. [Atmos.] 101: 29,029-29,042.
41 Davidson, E. A.; Kingerlee, W. (1997) A global inventory of nitric oxide emissions from soils.
42 Nutr. Cycling Agroecosyst. 48: 37-50.
September 2007 AX2-136 DRAFT-DO NOT QUOTE OR CITE
-------
1 Davis, D.; Chen, G.; Bandy, A.; Thornton, D.; Eisele, F.; Mauldin, L.; Tanner, D.; Lenschow,
2 D.; Fuelberg, H.; Huebert, B.; Heath, J.; Clarke, A.; Blake, D. (1999) Dimethyl sulfide
3 oxidation in the equatorial Pacific: comparison of model simulations with field
4 observations for DMS, S02, H2S04(g), MSA(g), MS and NSS. J. Geophys. Res. [Atmos.]
5 104: 5765-5784.
6 Day, D. A.; Wooldridge, P. J.; Dillon, M. B.; Thornton, J. A.; Cohen, R. C. (2002) A thermal
7 dissociation laser-induced fluorescence instrument for in situ detection of N02, peroxy
8 nitrates, alkyl nitrates, and HN03. J. Geophys. Res. [Atmos.] 107(D6):
9 10.1029/2001JD000779.
10 Day, D. A.; Dillon, M. B.; Woolridge, P. J.; Thornton, J. A.; Rosen, R. S.; Wood, E. C.; Cohen,
11 R. C. (2003) On alkyl nitrates, 03, and the "missing N0y". J. Geophys. Res. [Atmos.]
12 108: 10.1029/2003JD003685.
13 DeCaria, A. J.; Pickering, K. E.; Stenchikov, G. L.; Scala, J. R.; Stith, J. L.; Dye, J. E.; Ridley, B.
14 A.; Laroche, P. (2000) A cloud-scale model study of lightning-generated NOX in an
15 individual thunderstorm during STERAO-A. J. Geophys. Res. [Atmos.] 105: 11,601-
16 11,616.
17 DeCaria, A. J.; Pickering, K. E.; Stenchikov, G. L.; Ott, L. E. (2005) Lightning-generated NOX
18 and its impact on tropospheric ozone production: a three-dimensional modeling study of a
19 stratosphere-troposphere experiment: radiation, aerosols and ozone (STERAO-A)
20 thunderstorm. J. Geophys. Res. [Atmos.] 110(014303): 10.1029/2004JD005556.
21 De Laat, A. T. J.; Zachariasse, M.; Roelofs, G. J.; Van Velthoven, P.; Dickerson, R. R.; Rhoads,
22 K. P.; Oltmans, Oltmans, S. J.; Lelieveld, J. (1999) Tropospheric 03 distribution over the
23 Indian Ocean during spring 1995 evaluated with a chemistry-climate model. J. Geophys.
24 Res. [Atmos.] 104: 13,881-13,893.
25 Delmas, R. (1982) On the emissions of carbon, nitrogen and sulfur to the atmosphere during
26 bushfires in intertropical savannah zones. Geophys. Res. Lett. 9: 761-764.
27 Dentener, F. J.; Crutzen, P. J. (1993) Reaction of N20s on tropospheric aerosols: impact on the
28 global distributions of NOX, 03, and OH. J. Geophys. Res. [Atmos.] 98: 7149-7163.
29 Dentener, F.; Stevenson, D.; Cofala, J.; Mechler, R.; Amann, M.; Bergamaschi, P.; Raes, F.;
30 Derwent, R. (2005) The impact of air pollutant and methane emission controls on
31 tropospheric ozone and radiative forcing: CTM calculations for the period 1990-2030.
32 Atmos. Chem. Phys. 5: 1731-1755.
33 Dentener, F.; Stevenson, D.; Ellingsen, K.; Van Noije, T.; Schultz, M.; Amann, M.; Atherton, C.;
34 Bell, N.; Bergmann, D.; Bey, L; Bouwman, L.; Butler, T.; Cofala, J.; Collins, B.; Drevet,
35 J.; Doherty, R.; Eickhout, B.; Eskes, H.; Fiore, A.; Gauss, M.; Hauglustaine, D.;
36 Horowitz, L.; Isaksen, I. S. A.; Josse, B.; Lawrence, M.; Krol, M.; Lamarque, J. F.;
37 Montanaro, V.; Miiller, J. F.; Peuch, V. H.; Pitari, G.; Pyle, J.; Rast, S.; Rodriguez, J.;
38 Sanderson, M.; Savage, N. H.; Shindell, D.; Strahan, S.; Szopa, S.; Sudo, K.; Van
39 Dingenen, R.; Wild, 0.; Zeng, G. (2006a) The global atmospheric environment for the
40 next generation. Environ. Sci. Technol. 40: 3586-3594.
41 Dentener, F.; Drevet, J.; Lamarque, J. F.; Bey, L; Eickhout, B.; Fiore, A. M.; Hauglustaine, D.;
42 Horowitz, L. W.; Krol, M.; Kulshrestha, U. C.; Lawrence, M.; Galy-Lacaux, C.; Rast, S.;
September 2007 AX2-137 DRAFT-DO NOT QUOTE OR CITE
-------
1 Shindell, D.; Stevenson, D.; Van Noije, T.; Atherton, C.; Bell, N.; Bergman, D.; Butler,
2 T.; Cofala, J.; Collins, B.; Doherty, R.; Ellingsen, K.; Galloway, J.; Gauss, M.;
3 Montanaro, V.; Miiller, J. F.; Pitari, G.; Rodriguez, J.; Sanderson, M.; Strahan, S.;
4 Schultz, M.; Sudo, K.; Szopa, S.; Wild, 0. (2006b) Nitrogen and sulfur deposition on
5 regional and global scales: a multi-model evaluation. Global Biogeochem. Cycles:
6 20(GB4003): 10.1029/2005GB002672.
7 Derwent, R. G.; Collins, W. J.; Johnson, C. E.; Stevenson, D. S. (2001) Transient behaviour of
8 tropospheric ozone precursors in a global 3-D CTM and their indirect greenhouse effects.
9 Climatic Change 49: 463-487.
10 Dibb, J. E.; Arsenault, M.; Peterson, M. C. (2002) Fast nitrogen oxide photochemistry in
11 Summit, Greenland snow. Atmos. Environ. 36: 2501-2511.
12 Dickerson, R. R.; Huffman, G. J.; Luke, W. T.; Nunnermacker, L. J.; Pickering, K. E.; Leslie, A.
13 C. D.; Lindsey, C. G.; Slinn, W. G. N.; Kelly, T. J.; Daum, P. H.; Delany, A. C.;
14 Greenberg, J. P.; Zimmerman, P. R.; Boatman, J. F.; Ray, J. D.; Stedman, D. H. (1987)
15 Thunderstorms: an important mechanism in the transport of air pollutants. Science
16 (Washington, DC) 235: 460-465.
17 Dickerson, R. R.; Doddridge, B. G.; Kelley, P.; Rhoads, K. P. (1995) Large-scale pollution of the
18 atmosphere over the remote Atlantic Ocean: evidence from Bermuda. J. Geophys. Res.
19 [Atmos.] 100: 8945-8952.
20 Dickerson, R. R.; Rhoads, K. P.; Carsey, T. P.; Oltmans, S. J.; Burrows, J. P.; Crutzen, P. J.
21 (1999) Ozone in the remote marine boundary layer: a possible role for halogens. J.
22 Geophys. Res. [Atmos.] 104: 21,385-21,395.
23 Dimashki, M.; Harrad, S.; Harrison, R. M. (2000) Concentrations and phase distribution of nitro-
24 PAH in the Queensway road tunnel in Birmingham, United Kingdom. Polycyclic
25 Aromat. Cmpd. 20: 205-223.
26 Domine, F.; Shepson, P. B. (2002) Air-snow interactions and atmospheric chemistry. Science
27 (Washington, DC, U.S.) 297: 1506-1510.
28 Dommen, J.; Prevot, A. S. H.; Hering, A. M.; Staffelbach, T.; Kok, G. L.; Schillawski, R. D.
29 (1999) Photochemical production and aging of an urban air mass. J. Geophys. Res.
30 [Atmos.] 104: 5493-5506.
31 Doskey, P. V.; Kotamarthi, V. R.; Fukui, Y.; Cook, D. R.; Breitbeil, F. W., Ill; Wesely, M. L.
32 (2004) Air-surface exchange of peroxyacetyl nitrate at a grassland site. J. Geophys. Res.
33 [Atmos.] 109(010310): 10.1029/2004JD004533.
34 Driscoll, C. T.; Whitall, D.; Aber, J.; Boyer, E.; Castro, M.; Cronan, C.; Goodale, C. L.;
35 Groffman, P.; Hopkinson, C.; lambert, K.; Lawrence, G.; Ollinger, S. (2003) Nitrogen
36 pollution in the northeastern United States: sources, effects, and management options.
37 BioScience 53: 357-374.
38 Dudhia, J. (1993) A nonhydrostatic version of the Penn State-NCAR mesoscale model:
39 validation tests and simulation of an Atlantic cyclone and cold front. Mon. Weather Rev.
40 121: 1493-1513.
September 2007 AX2-138 DRAFT-DO NOT QUOTE OR CITE
-------
1 Durant, J. L.; Busby, W. R, Jr.; Lafleur, A. L.; Penman, B. W.; Crespi, C. L. (1996) Human cell
2 mutagenicity of oxygenated, nitrated and unsubstituted polycyclic aromatic hydrocarbons
3 associated with urban aerosols. Mutat. Res. 371: 123-157.
4 Durham, J. L.; Wilson, W. E.; Bailey, E. B. (1978) Application of an SCVdenuder for
5 continuous measurement of sulfur in submicrometric aerosols. Atmos. Environ. 12: 883-
6 886.
7 Eder, B.; Yu, S. (2005) A performance evaluation of the 2004 release of Models-3 CMAQ.
8 Atmos. Environ. 40: 4811-4824.
9 Eisele, F. L.; Mauldin, L.; Cantrell, C.; Zondlo, M.; Apel, E.; Fried, A.; Walega, J.; Shetter, R.;
10 Lefer, B.; Flocke, F.; Weinheimer, A.; Avery, M.; Vay, S.; Sachse, G.; Podolske, J.;
11 Diskin, G.; Barrick, J. D.; Singh, H. B.; Brune, W.; Harder, H.; Martinez, M.; Bandy, A.;
12 Thornton, D.; Heikes, B.; Kondo, Y.; Riemer, D.; Sandholm, S.; Tan, D.; Talbot, R.;
13 Dibb, J. (2003) Summary of measurement intercomparisons during TRACE-P. J.
14 Geophys. Res. [Atmos.] 108(D20): 10.1029/2002JD003167.
15 Emmons, L. K.; Hauglustaine, D. A.; Miiller, J.-R; Carroll, M. A.; Brasseur, G. P.; Brunner, D.;
16 Staehelin, J.; Thouret, V.; Marenco, A. (2000) Data composites of airborne observations
17 of tropospheric ozone and its precursors. J. Geophys. Res. [Atmos.] 105: 20,497-20,538.
18 Erickson, D. J., Ill; Seuzaret, C.; Keene, W. C.; Gong, S. L. (1999) A general circulation model
19 based calculation of HC1 and CIN02 production from sea-salt dechlorination: reactive
20 chlorine emissions inventory. J. Geophys. Res. [Atmos.] 104: 8347-8372.
21 Esteve, W.; Budzinski, H.; Villenave, E. (2006) Relative rate constants for the heterogeneous
22 reactions of NOz and OH radicals with polycyclic aromatic hydrocarbons adsorbed on
23 carbonaceous particles. Part 2: PAHs adsorbed on diesel particulate exhaust SRM 1650a.
24 Atmos. Environ. 40: 201-211.
25 Fan, Z.; Chen, D.; Birla, P.; Kamens, R. M. (1995) Modeling of nitro-polycyclic aromatic
26 hydrocarbon formation and decay in the atmosphere. Atmos. Environ. 29: 1171-1181.
27 Fan, Z.; Kamens, R. M.; Hu, J.; Zhang, J.; McDow, S. (1996) Photostability of nitro-polycyclic
28 aromatic hydrocarbons on combustion soot particles in sunlight. Environ. Sci. Technol.
29 30: 1358-1364.
30 Faraji, M.; et al. (2005) Comparison of the carbon bond and SAPRC photochemical mechanisms
31 event to southeast Texas. Houston, TX: Texas Environmental Research Consortium
32 (TERC); project report H12.8HRB.
33 Farmer, D. K.; Wooldridge, P. J.; Cohen, R. C. (2006) Application of thermal-dissociation laser
34 induced fluorescence (TD-LIF) to measurement of HNOs, Ealkyl nitrates, Eperoxy
35 nitrates, and NOz fluxes using eddy covariance. Atmos. Chem. Phys. 6: 3471-3486.
36 Farwell, S. 0.; Rasmussen, R. A. (1976) Limitations of the FPD and ECD in atmospheric
37 analysis: a review. J. Chromatogr. Sci. 14: 224-234.
38 Fast, J. D.; Zaveri, R. A.; Bian, R. X.; Chapman, E. G.; Easter, R. C. (2002) Effect of regional-
39 scale transport on oxidants in the vicinity of Philadelphia during the 1999 NE-OPS Field
40 Campaign. J. Geophys. Res. [Atmos.] 107(016): 10.1029/2001JD000980.
41 Fehsenfeld and Parrish (2000)
September 2007 AX2-139 DRAFT-DO NOT QUOTE OR CITE
-------
1 Fehsenfeld, F. C.; Dickerson, R. R.; Hiibler, G.; Luke, W. T.; Nunnermacker, L. J.; Williams, E.
2 J.; Roberts, J. M.; Calvert, J. G.; Curran, C. M.; Delany, A. C.; Eubank, C. S.; Fahey, D.
3 W.; Fried, A.; Gandrud, B. W.; Langford, A. 0.; Murphy, P. C.; Norton, R. B.; Pickering,
4 K. E.; Ridley, B. A. (1987) A ground-based intercomparison of NO, NOX, and N0y
5 measurement techniques. J. Geophys. Res. [Atmos.] 92: 14,710-14,722.
6 Fehsenfeld, F. C.; Trainer, M.; Parrish, D. D.; Volz-Thomas, A.; Penkett, S. (1996) North
7 Atlantic Regional Experiment (NARE) 1993 summer intensive: foreword. J. Geophys.
8 Res. [Atmos.] 101: 28,869-28,875.
9 Fehsenfeld, F. C.; Huey, L. G.; Sueper, D. T.; Norton, R. B.; Williams, E. J.; Eisele, F. L.;
10 Mauldin, R. L., Ill; Tanner, D. J. (1998) Ground-based intercomparison of nitric acid
11 measurement techniques. J. Geophys. Res. [Atmos.] 103: 3343-3353.
12 Feilberg, A.; Kamens, R. M.; Strommen, M. R.; Nielsen, T. (1999) Modeling the formation,
13 decay, and partitioning of semivolatile nitro-polycyclic aromatic hydrocarbons
14 (nitronaphthalenes) in the atmosphere. Atmos. Environ. 33: 1231-1243.
15 Feilberg, A.; Nielsen, T. (2001) Photodegradation of nitro-PAHs in viscous organic media used
16 as models of organic aerosols. Environ. Sci. Technol. 35: 108-113.
17 Feilberg, A.; Poulsen, M. W. B.; Nielsen, T.; Skov, B. (2001) Occurrence and sources of
18 particulate nitro-polycyclic aromatic hydrocarbons in ambient air in Denmark. Atmos.
19 Environ. 35: 353-366.
20 Finlayson-Pitts, B. J.; Pitts, J. N., Jr. (2000) Chemistry of the upper and lower atmosphere:
21 theory, experiments and applications. San Diego, CA: Academic Press.
22 Finlayson-Pitts, B. J.; Ezell, M. J.; Pitts, J. N., Jr. (1989) Formation of chemically active chlorine
23 compounds by reactions of atmospheric NaCl particles with gaseous N20s and C10N02.
24 Nature (London) 337: 241-244.
25 Finlayson-Pitts, B. J.; Wingen, L. M.; Sumner, A. L.; Syomin, D.; Ramazan, K. A. (2003) The
26 heterogeneous hydrolysis of N02 in laboratory system and in outdoor and indoor
27 atmospheres: an integrated mechanism. Phys. Chem. Chem. Phys. 5: 223-242.
28 Finley, B. D.; Saltzman, E. S. (2006) Measurement of C12 in coastal urban air. Geophys. Res.
29 Lett. 33(LI 1809): 10.1029/2006GL025799.
30 Fiore, A. M.; Jacob, D. J.; Field, B. D.; Streets, D. G.; Fernandes, S. D.; Jang, C. (2002) Linking
31 ozone pollution and climate change: the case for controlling methane. Geophys. Res.
32 Lett. 29(19): 10.1029/2002GL015601.
33 Fiore, A.; Jacob, D. J.; Liu, H.; Yantosca, R. M.; Fairlie, T. D.; Li, Q. (2003) Variability in
34 surface ozone background over the United States: implications for air quality policy. J.
35 Geophys. Res. [Atmos.] 108(D24): 10.1029/2003JD003855.
36 Fischer, E.; Pszenny, A.; Keene, W.; Maben, J.; Smith, A.; Stohl, A.; Talbot, R. (2006) Nitric
37 acid phase partitioning and cycling in the New England coastal atmosphere. J. Geophys.
38 Res. [Atmos.] 111(D23S09): 10.1029/2006JD007328.
39 Foster, K. L.; Plastridge, R. A.; Bottenheim, J. W.; Shepson, P. B.; Finlayson-Pitts, B. J.; Spicer,
40 C. W. (2001) The role of Br2 and BrCl in surface ozone destruction at polar sunrise.
41 Science (Washington, DC, U.S.) 291: 471-474.
September 2007 AX2-140 DRAFT-DO NOT QUOTE OR CITE
-------
1 Fox-Rabinovitz, M. S.; Takacs, L. L.; Govindaraju, R. C. (2002) A variable-resolution stretched-
2 grid general circulation model and data assimilation system with multiple areas of
3 interest: studying the anomalous regional climate events of 1998. J. Geophys. Res.
4 [Atmos.] 107(024): 10.1029/2002JD002177.
5 Frost, G. J.; McKeen, S. A.; Trainer, M.; Ryerson, T. B.; Neuman, J. A.; Roberts, J. M.;
6 Swanson, A.; Holloway, J. S.; Sueper, D. T.; Fortin, T.; Parrish, D. D.; Fehsenfeld, F. C.;
7 Flocke, F.; Peckham, S. E.; Grell, G. A.; Kowal, D.; Cartwright, J.; Auerbach, N.;
8 Habermann, T. (2006) Effects of changing power plant NOX emissions on ozone in the
9 eastern United States: proof of concept. J. Geophys. Res. [Atmos.] Ill (D 12306):
10 10.1029/2005JD006354.
11 Fuentes, M.; Raftery, A. E. (2005) Model evaluation and spatial interpolation by Bayesian
12 combination of observations with outputs from numerical models. Biometrics 61: 36-45.
13 Furutani, H.; Akimoto, H. (2002) Development and characterization of a fast measurement
14 system for gas-phase nitric acid with a chemical ionization mass spectrometer in the
15 marine boundary layer. J. Geophys. Res. [Atmos.] 107(D2): 10.1029/2000JD000269.
16 Gaffney, J. S.; Bornick, R. M.; Chen, Y.-H.; Marley, N. A. (1998) Capillary gas
17 chromatographic analysis of nitrogen dioxide and pans with luminol chemiluminescent
18 detection. Atmos. Environ. 32: 1445-1454.
19 Galbally, I. E.; Bentley, S. T.; Meyer, C. P. (2000) Mid-latitude marine boundary-layer ozone
20 destruction at visible sunrise observed at Cape Grim, Tasmania, 41°S. Geophys. Res.
21 Lett. 27: 3841-3844.
22 Galloway, J. N.; Aber, J. D.; Erisman, J. W.; Seitzinger, S. P.; Howarth, R. W.; Cowling, E. B.;
23 Cosby, B. J. (2003) The nitrogen cascade. BioScience 53: 341-356.
24 Gao, W.; Wesely, M. L.; Lee, I. Y. (1991) A numerical study of the effects of air chemistry on
25 fluxes of NO, N02, and 03 near the surface. J. Geophys. Res. 96: 18,761-18,769.
26 Gao, W.; Wesely, M. L.; Doskey, P. V. (1993) Numerical modeling of the turbulent diffusion
27 and chemistry of NOX, Os, isoprene, and other reactive trace gases in and above a forest
28 canopy. J. Geophys. Res. [Atmos.] 98: 18339-18353.
29 Garnica, R. M.; Appel, M. F.; Eagan, L.; McKeachie, J. R.; Benter, T. (2000) A REMPI method
30 for the ultrasensitive detection of NO and N02 using atmospheric pressure laser
31 ionization mass spectrometry. Anal. Chem. 72: 5639-5646.
32 George, C.; Strekowski, R. S.; Kleffmann, J.; Stemmler, K.; Ammann, M. (2005) Photoenhanced
33 uptake of gaseous N02 on solid organic compounds: a photochemical source of HONO?
34 Faraday Discuss. 130: 195-210.
35 Gerlach, T. M. (2004) Volcanic sources of tropospheric ozone-depleting trace gases. Geochem.
36 Geophys. Geosys. 5: 10.1029/2004GC000747.
37 Gery, M. W.; Whitten, G. Z.; Killus, J. P.; Dodge, M. C. (1989) A photochemical kinetics
38 mechanism for urban and regional scale computer modeling. J. Geophys. Res. [Atmos.]
39 94: 12,925-12,956.
September 2007 AX2-141 DRAFT-DO NOT QUOTE OR CITE
-------
1 Geyer, A.; Platt, U. (2002) Temperature dependence of the N03 loss frequency: A new indicator
2 for the contribution of N03 to the oxidation of monoterpenes and NOX removal in the
3 atmosphere. J. Geophys. Res. [Atmos.] 107(D20): 10.1029/2001JD001215.
4 Geyer, A.; Alicke, B.; Konrad, S.; Schmitz, T.; Stutz, J.; Platt, U. (2001) Chemistry and
5 oxidation capacity of the nitrate radical in the continental boundary layer near Berlin. J.
6 Geophys. Res. [Atmos.] 106: 8013-8025.
7 Gibson, T. L. (1983) Sources of direct-acting nitroarene mutagens in airborne particulate matter.
8 Mutat. Res. 122: 115-121.
9 Gidel, L. T. (1983) Cumulus cloud transport of transient tracers. J. Geophys. Res. [Atmos.] 88:
10 6587-6599.
11 Goldan, P. D.; Kuster, W. C.; Albritton, D. L.; Fehsenfeld, F. C.; Connell, P. S.; Norton, R. B.;
12 Huebert, B. J. (1983) Calibration and tests of the filter-collection method for measuring
13 clean-air, ambient levels of nitric acid. Atmos. Environ. 17: 1355-1364.
14 Goldan, P. D.; Trainer, M.; Kuster, W. C.; Parrish, D. D.; Carpenter, J.; Roberts, J. M.; Yee, J.
15 E.; Fehsenfeld, F. C. (1995) Measurements of hydrocarbons, oxygenated hydrocarbons,
16 carbon monoxide, and nitrogen oxides in an urban basin in Colorado: implications for
17 emission inventories. J. Geophys. Res. [Atmos.] 100: 22,771-22,783.
18 Goldan, P. D.; Kuster, W. C.; Fehsenfeld, F. C. (1997) Nonmethane hydrocarbon measurements
19 during the tropospheric OH photochemistry experiment. J. Geophys. Res. [Atmos.] 102:
20 6315-6324.
21 Goldan, P. D.; Parrish, D. D.; Kuster, W. C.; Trainer, M.; McKeen, S. A.; Holloway, J.; Jobson,
22 B. T.; Sueper, D. T.; Fehsenfeld, F. C. (2000) Airborne measurements of isoprene, CO,
23 and anthropogenic hydrocarbons and their implications. J. Geophys. Res. [Atmos.] 105:
24 9091-9105.
25 Golden, D. M.; Smith, G. P. (2000) Reaction of OH + N02 + M: a new view. J. Phys. Chem. A
26 104:3991-3997.
27 Goldstein, A. H.; Schade, G. W. (2000) Quantifying biogenic and anthropogenic contributions to
28 acetone mixing ratios in a rural environment. Atmos. Environ. 34: 4997-5006.
29 Gondal, M. A. (1997) Laser photoacoustic spectrometer for remote monitoring of atmospheric
30 pollutants. Appl. Opt. 36: 3195-3201.
31 Gondal, M. A.; Mastromarino, J. (2001) Pulsed laser photoacoustic detection of S02 near 225.7
32 nm. Appl. Opt. 40: 2010-2016.
33 Greenhut, G. K. (1986) Transport of ozone between boundary layer and cloud layer by cumulus
34 clouds. J. Geophys. Res. [Atmos.] 91: 8613-8622.
35 Greenhut, G. K.; Ching, J. K. S.; Pearson, R., Jr.; Repoff, T. P. (1984) Transport of ozone by
36 turbulence and clouds in an urban boundary layer. J. Geophys. Res. [Atmos.] 89: 4757-
37 4766.
38 Gregory, G. L.; Hoell, J. M., Jr.; Torres, A. L.; Carroll, M. A.; Ridley, B. A.; Rodgers, M. 0.;
39 Bradshaw, J.; Sandholm, S.; Davis, D. D. (1990) An intercomparison of airborne nitric
40 oxide measurements: a second opportunity. J. Geophys. Res. [Atmos.] 95: 10,129-10,138.
September 2007 AX2-142 DRAFT-DO NOT QUOTE OR CITE
-------
1 Grell, G. A.; Dudhia, J.; Stauffer, D. R. (1994) Description of the fifth-generation Penn
2 State/NCAR mesoscale model (MM5). Boulder, CO: National Center for Atmospheric
3 Research, Mesoscale and Microscale Meteorology Division; report no. NCAR/TN-
4 398+STR. Available from: NTIS, Springfield, VA; PB95-206348.
5 Grell, G. A.; Emeis, S.; Stockwell, W. R.; Schoenemeyer, T.; Forkel, R.; Michalakes, J.; Knoche,
6 R.; Seidl, W. (2000) Application of a multiscale, coupled MM5/chemistry model to the
7 complex terrain of the VOTALP valley campaign. Atmos. Environ. 34: 1435-1453.
8 Grosjean, D.; Fung, K.; Harrison, J. (1983) Interactions of polycyclic aromatic hydrocarbons
9 with atmospheric pollutants. Environ. Sci. Technol. 17: 673-679.
10 Grosovsky, A. J.; Sasaki, J. C.; Arey, J.; Eastmond, D. A.; Parks, K. K.; Atkinson, R. (1999)
11 Evaluation of the potential health effects of the atmospheric reaction products of
12 polycyclic aromatic hydrocarbons. Cambridge, MA: Health Effects Institute; research
13 report no. 84.
14 Gross, A.; Stockwell, W. R. (2003) Comparison of the EMEP, RADM2 and RACM
15 mechanisms. J. Atmos. Chem. 44: 151-170.
16 Grossenbacher, J. W.; Couch, T.; Shepson, P. B.; Thornberry, T.; Witmer-Rich, M.; Carroll, M.
17 A.; Faloona, I.; Tan, D.; Brune, W.; Ostling, K.; Bertman, S. (2001) Measurements of
18 isoprene nitrates above a forest canopy. J. Geophys. Res. [Atmos.] 106: 24,429-24,438.
19 Guenther, F. R.; Dorko, W. D.; Miller, W. R.; Rhoderick, G. C. (1996) The NIST traceable
20 reference material program for gas standards. Washington, DC: U.S. Department of
21 Commerce, National Institute of Standards and Technology; NIST special publication
22 260-126.
23 Hains, J. C.; Chen, L.-W. A.; Taubman, B. F.; Doddridge, B. G.; Dickerson, R. R. (2007) A side-
24 by-side comparison of filter-based PM2.s measurements at a suburban site: a closure
25 study. Atmos. Environ. 41: 6167-6184.
26 Hallock-Waters, K. A.; Doddridge, B. G.; Dickerson, R. R.; Spitzer, S.; Ray, J. D. (1999) Carbon
27 monoxide in the U.S. mid-Atlantic troposphere: evidence for a decreasing trend.
28 Geophys. Res. Lett. 26: 2861-2864.
29 Hallquist, M.; Stewart, D. J.; Stephenson, S. K.; Cox, R. A. (2003) Hydrolysis of N205 on sub-
30 micron sulfate aerosols. Phys. Chem. Chem. Phys. 5: 3453-3463.
31 Hameed, S.; Pinto, J. P.; Stewart, R. W. (1979) Sensitivity of the predicted CO-OH-CH4
32 perturbation to tropospheric NOX concentrations. J. Geophys. Res. C: Oceans Atmos. 84:
33 763-768.
34 Hanke, M.; Umann, B.; Uecker, J.; Arnold, F.; Bunz, H. (2003) Atmospheric measurements of
35 gas-phase HN03 and S02 using chemical ionization mass spectrometry during the
36 MINATROC field campaign 2000 on Monte Cimone. Atmos. Chem. Phys. 3: 417-436.
37 Hansen, K.; Draaijers, G. P. J.; Ivens, W. P. M. F.; Gundersen, P.; van Leeuwen, N. F. M. (1994)
38 Concentration variations in rain and canopy throughfall collected sequentially during
39 individual rain events. Atmos. Environ. 28: 3195-3205.
40 Harder, J. W.; Williams, E. J.; Baumann, K.; Fehsenfeld, F. C. (1997) Ground-based comparison
41 of N02, H20, and Os measured by long-path and in situ techniques during the 1993
September 2007 AX2-143 DRAFT-DO NOT QUOTE OR CITE
-------
1 Tropospheric OH Photochemistry Experiment. J. Geophys. Res. [Atmos.] 102: 6227-
2 6243.
3 Hari, P.; Raivonen, M.; Vesala, T.; Munger, J. W.; Pilegaard, K.; Kulmala, M. (2003) Ultraviolet
4 light and leaf emission of NOX. Nature 422: 134.
5 Harris, G. W.; Carter, W. P. L.; Winer, A. M.; Pitts, J. N., Jr.; Platt, U.; Perner, D. (1982)
6 Observations of nitrous acid in the Los Angeles atmosphere and implications for
7 predictions of ozone-precursor relationships. Environ. Sci. Technol. 16: 414-419.
8 Harrison, R. M.; Kitto, A.-M. N. (1994) Evidence for a surface source of atmospheric nitrous
9 acid. Atmos. Environ. 28: 1089-1094.
10 Harrison, R. M.; Peak, J. D.; Collins, G. M. (1996) Tropospheric cycle of nitrous acid. J.
11 Geophys. Res. [Atmos.] 101: 14,429-14,439.
12 He, Y.; Zhou, X.; Hou, J.; Gao, H.; Bertman, S. B. (2006) Importance of dew in controlling the
13 air-surface exchange of HONO in rural forested environments. Geophys. Res. Lett. 33:
14 10.1029/2005GL024348.
15 Hebestreit, K.; Stutz, J.; Rosen, D.; Matveiv, V.; Peleg, M.; Luria, M.; Platt, U. (1999) DOAS
16 measurements of tropospheric bromine oxide in mid-latitudes. Science (Washington, DC)
17 283: 55-57.
18 Hennigan, C. J.; Sandholm, S.; Kim, S.; Stickel, R. E.; Huey, L. G.; Weber, R. J. (2006)
19 Influence of Ohio River valley emissions on fine particle sulfate measured from aircraft
20 over large regions of the eastern United States and Canada during INTEX-NA. J.
21 Geophys. Res. [Atmos.] 111(D24S04): 10.1029/2006JD007282.
22 Hering, S.; Cass, G. (1999) The magnitude of bias in the measurement of PM2.s arising from
23 volatilization of particulate nitrate from Teflon filters. J. Air Waste Manage. Assoc. 49:
24 725-733.
25 Hering, S. V.; Lawson, D. R.; Allegrini, L; Febo, A.; Perrino, C.; Possanzini, M.; Sickles, J. E.,
26 II; Anlauf, K. G.; Wiebe, A.; Appel, B. R.; John, W.; Ondo, J.; Wall, S.; Braman, R. S.;
27 Sutton, R.; Cass, G. R.; Solomon, P. A.; Eatough, D. J.; Eatough, N. L.; Ellis, E. C.;
28 Grosjean, D.; Hicks, B. B.; Womack, J. D.; Horrocks, J.; Knapp, K. T.; Ellestad, T. G.;
29 Paur, R. J.; Mitchell, W. J.; Pleasant, M.; Peake, E.; MacLean, A.; Pierson, W. R.;
30 Brachaczek, W.; Schiff, H. I.; Mackay, G. L; Spicer, C. W.; Stedman, D. H.; Winer, A.
31 M.; Biermann, H. W.; Tuazon, E. C. (1988) The nitric acid Shootout: field comparison of
32 measurement methods. Atmos. Environ. 22: 1519-1539.
33 Hess, P. G. (2001) Model and measurement analysis of springtime transport and chemistry of the
34 Pacific Basin. J. Geophys. Res. [Atmos.] 106: 12,689-12,717.
35 Hicks, B. B.; Baldocchi, D. D.; Meyers, T. P.; Hosker, R. P., Jr.; Matt, D. R. (1987) A
36 preliminary multiple resistance routine for deriving dry deposition velocities from
37 measured quantities. Water Air Soil Pollut. 36: 311-330.
38 Hirsch, A. I.; Munger, J. W.; Jacob, D. J.; Horowitz, L. W.; Goldstein, A. H. (1996) Seasonal
39 variation of the ozone production efficiency per unit NOX at Harvard Forest,
40 Massachusetts. J. Geophys. Res. [Atmos.] 101: 12,659-12,666.
September 2007 AX2-144 DRAFT-DO NOT QUOTE OR CITE
-------
1 Hoell, J. M.; Davis, D. D.; Liu, S. C.; Newell, R. E.; Akimoto, H.; McNeal, R. J.; Bendura, R. J.
2 (1997) The Pacific exploratory mission-west phase B: February-March, 1994. J.
3 Geophys. Res. [Atmos.] 102: 28,223-28,239.
4 Hoell, J. M.; Davis, D. D.; Jacob, D. J.; Rodgers, M. 0.; Newell, R. E.; Fuelberg, H. E.; McNeal,
5 R. J.; Raper, J. L.; Bendura, R. J. (1999) Pacific exploratory mission in the tropical
6 Pacific: PEM-Tropics A, August-September 1996. J. Geophys. Res. [Atmos.] 104: 5567-
7 5583.
8 Holland, H. D. (1978) The chemistry of the atmosphere and oceans. New York, NY: Wiley.
9 Holland, E. A.; Braswell, B. H.; Sulzman, J.; Lamarque, J.-F. (2005) Nitrogen deposition onto
10 the United States and Western Europe: synthesis of observations and models. Ecol. Appl.
11 15:38-57.
12 Hollwedel, J.; Wenig, M.; Beirle, S.; Kraus, S.; Kiihl, S.; Wilms-Grabe, W.; Platt, U.; Wagner,
13 T. (2004) Year-to-year variations of spring time polar tropospheric BrO as seen by
14 GOME. Adv. Space Res. 34: 804-808.
15 Honrath, R. E.; Guo, S.; Peterson, M. C.; Dziobak, M. P.; Dibb, J. E.; Arsenault, M. A. (2000)
16 Photochemical production of gas phase NOX from ice crystal NOs. J. Geophys. Res.
17 [Atmos.] 105: 24,183-24,190.
18 Honrath, R. E.; Lu, Y.; Peterson, M. C.; Dibb, J. E.; Arsenault, M. A.; Cullen, N. J.; Steffen, K.
19 (2002) Vertical fluxes of NOX, HONO, and HN03 above the snowpack at Summit,
20 Greenland. Atmos. Environ. 36: 2629-2640.
21 Hoppel, W. A.; Caffrey, P. F. (2005) Oxidation of S(IV) in sea-salt aerosol at high pH: ozone
22 versus aerobic reaction. J. Geophys. Res. [Atmos.] 110(D23202):
23 10.1029/2005JD006239.
24 Horii, C. V. (2002) Tropospheric reactive nitrogen speciation, deposition, and chemistry at
25 Harvard Forest [dissertation]. Cambridge, MA: Harvard University.
26 Horii, C. V.; Munger, J. W.; Wofsy, S. C.; Zahniser, M.; Nelson, D.; McManus, J. B. (2004)
27 Fluxes of nitrogen oxides over a temperate deciduous forest. J. Geophys. Res. [Atmos.]
28 109(008305): 10.1029/2003JD004326.
29 Horii, C.; Munger, J. W.; Wofsy, S.; Zahniser, M.; Nelson, D.; McManus, J. B. (2006)
30 Atmospheric reactive nitrogen concentrations and flux budgets at a northeastern U.S
31 forest site. Agric. For. Meteorol. 136: 159-174.
32 Horowitz, L. W.; Walters, S.; Mauzerall, D. L.; Emmons, L. K.; Rasch, P. J.; Granier, C.; Tie,
33 X.; Lamarque, J.-F.; Schultz, M. G.; Tyndall, G. S.; Orlando, J. J.; Brasseur, G. P. (2003)
34 A global simulation of tropospheric ozone and related tracers: description and evaluation
35 of MOZART, version 2. J. Geophys. Res. [Atmos.] 108(D24): 10.1029/2002JD002853.
36 Hiibler, G.; Alvarez, R., II; Daum, P.; Dennis, R.; Gillani, N.; Kleinman, L.; Luke, W.; Meagher,
37 J.; Rider, D.; Trainer, M.; Valente, R. (1998) An overview of the airborne activities
38 during the Southern Oxidants Study (SOS) 1995 Nashville/Middle Tennessee ozone
39 study. J. Geophys. Res. [Atmos.] 103: 22,245-22,259.
40 Hudman, R. C.; Jacob, D. J.; Cooper, 0. R.; Evans, M. J.; Heald, C. L.; Park, R. J.; Fehsenfeld,
41 F.; Flocke, F.; Holloway, J.; Hiibler, G.; Kita, K.; Koike, M.; Kondo, Y.; Neuman, A.;
September 2007 AX2-145 DRAFT-DO NOT QUOTE OR CITE
-------
1 Nowak, J.; Oltmans, S.; Parrish, D.; Roberts, J. M.; Ryerson, T. (2004) Ozone production
2 in transpacific Asian pollution plumes and implications for ozone air quality in
3 California. J. Geophys. Res. [Atmos.] 109(D23): 10.1029/2004JD004974.
4 Hudman, R. C.; Jacob, D. J.; Turquety, S.; Leibensperger, E. M.; Murray, L. T.; Wu, S.;
5 Gilliland, A. B.; Avery, M.; Bertram, T. H.; Brune, W.; Cohen, R. C.; Dibb, J. E.; Flocke,
6 F. M.; Fried, A.; Holloway, J.; Neuman, J. A.; Orville, R.; Perring, A.; Ren, X.; Ryerson,
7 T. B.; Sachse, G. W.; Singh, H. B.; Swanson, A.; Wooldridge, P. J. (2007) Surface and
8 lightning sources of nitrogen oxides in the United States: magnitudes, chemical
9 evolution, and outflow. J. Geophys. Res. [Atmos.] 112(D12S05):
10 10.1029/2006JD007912.
11 Huebert, B. J.; Zhuang, L.; Howell, S.; Noone, K.; Noone, B. (1996) Sulfate, nitrate,
12 methanesulfonate, chloride, ammonium, and sodium measurements from ship, island, and
13 aircraft during the Atlantic Stratocumulus Transition Experiment/Marine Aerosol Gas
14 Exchange. J. Geophys. Res. [Atmos.] 101: 4413-4423.
15 Huey, L. G.; Dunlea, E. J.; Lovejoy, E. R.; Hanson, D. R.; Norton, R. B.; Fehsenfeld, F. C.;
16 Howard, C. J. (1998) Fast time response measurements of HNOs in air with a chemical
17 ionization mass spectrometer. J. Geophys. Res. [Atmos.] 103: 3355-3360.
18 Huey, L. G.; Tanner, D. J.; Slusher, D. L.; Dibb, J. E.; Arimoto, R.; Chen, G.; Davis, D.; Buhr,
19 M. P.; Nowak, J. B.; Mauldin, R. L., Ill; Eisele, F. L.; Kosciuch, E. (2004) CMS
20 measurements of HN03 and S02 at the South Pole during ISCAT 2000. Atmos. Environ.
21 38:5411-5421.
22 Huntrieser, H.; Feigl, C.; Schlager, H.; Schroder, F.; Gerbig, C.; Van Velthoven, P.; Flat0y, F.;
23 Thery, C.; Petzold, A.; Holler, H.; Schumann, U. (2002) Airborne measurements of NOX,
24 tracer species, and small particles during the European Lightning Nitrogen Oxides
25 Experiment. J. Geophys. Res. [Atmos.] 107(D11): 10.1029/2000JD000209.
26 Huntzicker, J. J.; Hoffman, R. S.; Ling, C.-S. (1978) Continuous measurement and speciation of
27 sulfur-containing aerosols by flame photometry. Atmos. Environ. 12: 83-88.
28 International Agency for Research on Cancer (IARC). (1989) Diesel and gasoline engine
29 exhausts and some nitroarenes. Lyon, France: International Agency for Research on
30 Cancer. (IARC monographs on the evaluation of carcinogenic risks to humans: v. 46).
31 Jacob, D. J. (2000) Heterogeneous chemistry and tropospheric ozone. Atmos. Environ. 34: 2131-
32 2159.
33 Jacob, D. J.; Bakwin, P. S. (1991) Cycling of NOX in tropical forest canopies. In: Rogers, J. E.;
34 Whitman, W. B., eds. Microbial production and consumption of greenhouse gases.
35 Washington, DC: American Society of Microbiology; pp. 237-253.
36 Jacob, D. J.; Horowitz, L. W.; Munger, J. W.; Heikes, B. G.; Dickerson, R. R.; Artz, R. S.;
37 Keene, W. C. (1995) Seasonal transition from NOX- to hydrocarbon-limited conditions
38 for ozone production over the eastern United States in September. J. Geophys. Res.
39 [Atmos.] 100: 9315-9324.
40 Jacobson, M. Z. (1999) Isolating nitrated and aromatic aerosols and nitrated aromatic gases as
41 sources of ultraviolet light absorption. J. Geophys. Res. [Atmos.] 104: 3527-3542.
September 2007 AX2-146 DRAFT-DO NOT QUOTE OR CITE
-------
1 Jacobson, M. Z. (2002) Atmospheric pollution: history, science, and regulation. New York, NY:
2 Cambridge University Press.
3 Jacobson, M. Z.; Lu, R.; Turco, R. P.; Toon, 0. P. (1996) Development and application of a new
4 air pollution modeling system-Part I: gas-phase simulations. Atmos. Environ. 30: 1939-
5 1963.
6 Jaegle, L.; Jacob, D. J.; Wennberg, P. 0.; Spivakovsky, C. M.; Hanisco, T. F.; Lanzendorf, E. J.;
7 Hintsa, E. J.; Fahey, D. W.; Keim, E. R.; Proffitt, M. H.; Atlas, E. L.; Flocke, F.;
8 Schauffler, S.; McElroy, C. T.; Midwinter, C.; Pfister, L.; Wilson, J. C. (1997) Observed
9 OH and H02 in the upper troposphere suggests a major source from convective injection
10 of peroxides. Geophys. Res. Lett. 24: 3181-3184.
11 Jaegle, L.; Jacob, D. J.; Brune, W. H.; Wennberg, P. 0. (2001) Chemistry of HOX radicals in the
12 upper troposphere. Atmos. Environ. 35: 469-489.
13 Jaegle, L.; Martin, R. V.; Chance, K.; Steinberger, L.; Kurosu, T. P.; Jacob, D. J.; Modi, A. I.;
14 Yoboue, V.; Sigha-Nkamdjou, L.; Galy-Lacaux, C. (2004) Satellite mapping of rain-
15 induced nitric oxide emissions from soils. J. Geophys. Res. [Atmos.] 109(D21):
16 10.1029/2004JD004787.
17 Jaegle, L.; Steinberger, L.; Martin , R. V.; Chance, K. (2005) Global partitioning of NOX sources
18 using satellite observations: Relative roles of fossil fuel combustion, biomass burning and
19 soil emissions. Faraday Discuss. 130: 407-423.
20 Jaklevic, J. M.; Loo, B. W.; Fujita, T. Y. (1981) Automatic particulate sulfur measurements with
21 a dichotomous sampler and on-line x-ray fluorescence analysis. Environ. Sci. Technol.
22 15:687-690.
23 Jenkin, M. E.; Cox, R. A.; Williams, D. J. (1988) Laboratory studies of the kinetics of formation
24 of nitrous acid from the thermal reaction of nitrogen dioxide and water vapour. Atmos.
25 Environ. 22: 487-498.
26 Jenkin, M. E.; Saunders, S. M.; Pilling, M. J. (1997) The tropospheric degradation of volatile
27 organic compounds: a protocol for mechanism development. Atmos. Environ. 31:81-
28 104.
29 Jet Propulsion Laboratory. (2003) Chemical kinetics and photochemical data for use in
30 atmospheric studies. Pasadena, CA: California Institute of Technology; JPL publication
31 no. 02-25. Available: http://jpldataeval.jpl.nasa.gov/pdf/JPL_02-25_l_intro_rev0.pdf (18
32 December, 2003).
33 Jimenez, P.; Baldasano, J. M.; Dabdub, D. (2003) Comparison of photochemical mechanisms for
34 air quality modeling. Atmos. Environ. 37: 4179-4194.
35 Jobson, B. T.; Niki, H.; Yokouchi, Y.; Bottenheim, J.; Hopper, F.; Leaitch, R. (1994)
36 Measurements of Cz-Ce hydrocarbons during the Polar Sunrise 1992 Experiment:
37 evidence for Cl atom and Br atom chemistry. J. Geophys. Res. [Atmos.] 99: 25,355-
38 25,368.
39 John, W.; Wall, S. M.; Ondo, J. L. (1988) A new method for nitric acid and nitrate aerosol
40 measurement using the dichotomous sampler. Atmos. Environ. 22: 1627-1635.
September 2007 AX2-147 DRAFT-DO NOT QUOTE OR CITE
-------
1 Johnson, D. W.; Lindberg, S. E., eds. (1992) Atmospheric deposition and forest nutrient cycling:
2 a synthesis of the integrated forest study. New York, NY: Springer-Verlag, Inc. (Billings,
3 W. D.; Golley, F.; Lange, 0. L.; Olson, J. S.; Remmert, H., eds. Ecological studies:
4 analysis and synthesis: v. 91).
5 Junge, C. E.; Chagnon, C. W.;; Manson, J. E. (1961) Stratospheric aerosols. J. Meteorol. 18: 81-
6 108.
7 Kain, J. S.; Fritsch, J. M. (1993) Convective parameterization in mesoscale models: the Kain-
8 Fritsch scheme. In: Emanuel, K. A.; Raymond, D. J., eds. The Representation of Cumulus
9 Convection in Numerical Models. Boston, MA: American Meteorological Society; pp.
10 165-170. (Meteorological Monographs, v. 24, no. 46).
11 Kaiser, E. W.; Wu, C. H. (1977) A kinetic study of the gas phase formation and decomposition
12 reactions of nitrous acid. J. Phys. Chem. 81: 1701-1706.
13 Kamens, R. M.; Guo, J.; Guo, Z.; McDow, S. R. (1990) Polynuclear aromatic hydrocarbon
14 degradation by heterogenous reactions with NzOs on atmospheric particles. Atmos.
15 Environ. Part A 24: 1161-1173.
16 Kane, S. M.; Caloz, F.; Leu, M.-T. (2001) Heterogeneous uptake of gaseous NzOs by (NH4)2S04,
17 NH4HS04, and H2S04 aerosols. J. Phys. Chem. A 105: 6465-6470.
18 Kasibhatla, P.; Chameides, W. L. (2000) Seasonal modeling of regional ozone pollution in the
19 eastern United States. Geophys. Res. Lett. 27: 1415-1418.
20 Kawanaka, Y.; Matsumoto, E.; Sakamoto, K.; Wang, N.; Yun, S. J. (2004) Size distributions of
21 mutagenic compounds and mutagenicity in atmospheric particulate matter collected with
22 a low-pressure cascade impactor. Atmos. Environ. 38: 2125-2132.
23 Keck, L.; Wittmaack, K. (2005) Effect of filter type and temperature on volatilisation losses from
24 ammonium salts in aerosol matter. Atmos. Environ. 39: 4093-4100.
25 Keck, L.; Wittmaack, K. (2006) Simplified approach to measuring semivolatile inorganic
26 particulate matter using a denuded cellulose filter without backup filters. Atmos. Environ.
27 40:7106-7114.
28 Keene, W. C.; Savoie, D. L. (1998) The pH of deliquesced sea-salt aerosol in polluted marine
29 air. Geophys. Res. Lett. 25: 2181-2194.
30 Keene, W. C.; Jacob, D. J.; Fan, S.-M. (1996) Reactive chlorine: a potential sink for
31 dimethylsulfide and hydrocarbons in the marine boundary layer. Atmos. Environ. 30(6):
o o • • • •
32 i-m.
33 Keene, W. C.; Sander, R.; Pszenny, A. A. P.; Vogt, R.; Crutzen, P. J.; Galloway, J. N. (1998)
34 Aerosol pH in the marine boundary layer: a review and model evaluation. J. Aerosol Sci.
35 29:339-356.
36 Keene, W. C.; Khalil, M. A. K.; Erickson, D. J., Ill; McCulloch, A.; Graedel, T. E.; Lobert, J.
37 M.; Aucott, M. L.; Gong, S. L.; Harper, D. B.; Kleiman, G.; Midgley, P.; Moore, R. M.;
38 Seuzaret, C.; Sturges, W. T.; Benkovitz, C. M.; Koropalov, V.; Barrie, L. A.; Li, Y. F.
39 (1999) Composite global emissions of reactive chlorine from anthropogenic and natural
40 sources: reactive chlorine emissions inventory. J. Geophys. Res. [Atmos.] 104(D7):
41 8429-8440.
September 2007 AX2-148 DRAFT-DO NOT QUOTE OR CITE
-------
1 Keene, W. C.; Pszenny, A. A. P.; Maben, J. R.; Sander, R. (2002) Variation of marine aerosol
2 acidity with particle size. Geophys. Res. Lett. 29(7): 10.1029/2001GLO13881.
3 Keene, W. C.; Pszenny, A. A. P.; Maben, J. R.; Stevenson, E.; Wall, A. (2004) Closure
4 evaluation of size-resolved aerosol pH in the New England coastal atmosphere during
5 summer. J. Geophys. Res. [Atmos.] 109(D23307): 10.1029/2004JD004801.
6 Keene, W. C.; Lobert, R. M.; Crutzen, P. J.; Maben, J. R.; Scharffe, D. H.; Landmann, T.; Hely,
7 C.; Brain, C. (2006) Emissions of major gaseous and particulate species during
8 experimental burns of southern African biomass. J. Geophys. Res. [Atmos.]
9 111(004301): 10.1029/2005JD006319.
10 Keene, W. C.; Stutz, J.; Pszenny, A. A. P.; Maben, J. R.; Fischer, E.; Smith, A. M.; Von Glasow,
11 R.; Pechtl, S.; Sive, B. C.; Varner, R. K. (2007) Inorganic chlorine and bromine in coastal
12 New England air during summer. J. Geophys. Res. [Atmos.]:
13 doi:10.1029/2006JD007689, in press.
14 Kelly, T. J.; Spicer, C. W.; Ward, G. F. (1990) An assessment of the luminol chemiluminescence
15 technique for measurement of N02 in ambient air. Atmos. Environ. Part A 24: 2397-
16 2403.
17 Kim, K.-H.; Kim, M.-Y. (2001) Comparison of an open path differential optical absorption
18 spectroscopy system and a conventional in situ monitoring system on the basis of long-
19 term measurements of S02, N02, and 03. Atmos. Environ. 35: 4059-4072.
20 Kim, B. M.; Lester, J.; Tisopulos, L.; Zeldin, M. D. (1999) Nitrate artifacts during PM2.5
21 sampling in the South Coast Air Basin of California. J. Air Waste Manage. Assoc.
22 49(special issue): PM142-153.
23 Kim, S.-W.; Heckel, A.; McKeen, S. A.; Frost, G. J.; Hsie, E.-Y.; Trainer, M. K.; Richter, A.;
24 Burrows, J. P.; Peckham, S. E.; Grell, G. A. (2006) Satellite-observed U.S. power plant
25 NOX emission reductions and their impact on air quality. Geophys. Res. Lett.
26 33(L22812): 10.1029/2006GL027749.
27 Kirchstetter, T. W.; Harley, R. A. (1996) Measurement of nitrous acid in motor vehicle exhaust.
28 Environ. Sci. Technol. 30: 2843-2849.
29 Kireev, S. V.; Shnyrev, S. L.; Zhiganov, A. A. (1999) A laser fluorimeter for NO and N02 in
30 atmosphere. Instrum. Exp. Tech. 42: 701-703.
31 Kittelson, D. B.; McKenzie, R.; Vermeersch, M.; Dorman, F.; Pui, D.; Linne, M.; Liu, B.;
32 Whitby, K. (1978) Total sulfur aerosol concentration with an electrostatically pulsed
33 flame photometric detector system. Atmos. Environ. 12: 105-111.
34 Kleffmann, J.; Becker, K. H.; Wiesen, P. (1998) Heterogeneous N02 conversion processes on
35 acid surfaces: possible atmospheric implications. Atmos. Environ. 32: 2721-2729.
36 Kleinman, L. I. (1991) Seasonal dependence of boundary-layer peroxide concentration: the low
37 and high NO* regimes. J. Geophys. Res. [Atmos.] 96: 20,721-20,733.
38 Kleinman, L. I.; Daum, P. H.; Imre, D. G.; Lee, J. H.; Lee, Y.-N.; Nunnermacker, L. J.;
39 Springston, S. R.; Weinstein-Lloyd, J.; Newman, L. (2000) Ozone production in the New
40 York City urban plume. J. Geophys. Res. [Atmos.] 105: 14,495-14,511.
September 2007 AX2-149 DRAFT-DO NOT QUOTE OR CITE
-------
1 Kleinman, L. I.; Daum, P. H.; Lee, Y.-N.; Nunnermacker, L. J.; Springston, S. R.; Weinstein-
2 Lloyd, J.; Rudolph, J. (2001) Sensitivity of ozone production rate to ozone precursors.
3 Geophys. Res. Lett. 28: 2903-2906.
4 Kley, D.; Crutzen, P. J.; Smit, H. G. J.; Vomel, H.; Oltmans, S. J.; Grassl, H.; Ramanathan, V.
5 (1996) Observations of near-zero ozone concentrations over the convective Pacific:
6 effects on air chemistry. Science (Washington, DC) 274: 230-233.
7 Korhonen, P.; Kulmala, M.; Laaksonen, A.; Viisanen, Y.; McGraw, R.; Seinfeld, J. H. (1999)
8 Ternary nucleation of H2S04, NH3, and H20 in the atmosphere. J. Geophys. Res.
9 [Atmos.] 104: 26,349-26,353.
10 Kotchenruther, R. A.; Jaffe, D. A.; Jaegle, L. (2001) Ozone photochemistry and the role of
11 peroxyacetyl nitrate in the springtime northeastern Pacific troposphere: results from the
12 Photochemical Ozone Budget of the Eastern North Pacific Atmosphere (PHOBEA)
13 campaign.!. Geophys. Res. [Atmos.] 106: 28,731-28,742.
14 Koutrakis, P.; Wolfson, J. M.; Slater, J. L.; Brauer, M.; Spengler, J. D.; Stevens, R. K.; Stone, C.
15 L. (1988a) Evaluation of an annular denuder/filter pack system to collect acidic aerosols
16 and gases. Environ. Sci. Technol. 22: 1463-1468.
17 Koutrakis, P.; Wolfson, J. M.; Spengler, J. D. (1988b) An improved method for measuring
18 aerosol strong acidity: results from a nine-month study in St. Louis, Missouri and
19 Kingston, Tennessee. Atmos. Environ. 22: 157-162.
20 Koutrakis, P.; Thompson, K. M.; Wolfson, J. M.; Spengler, J. D.; Keeler, G. J.; Slater, J. L.
21 (1992) Determination of aerosol strong acidity losses due to interactions of collected
22 particles: results from laboratory and field studies. Atmos. Environ. Part A 26: 987-995.
23 Kulmala, M.; Pirjola, L.; Makela, J. M. (2000) Stable sulphate clusters as a source of new
24 atmospheric particles. Nature (London, U.K.) 404: 66-69.
25 Kumar, N.; Russell, A. G. (1996) Development of a computationally efficient, reactive subgrid-
26 scale plume model and the impact in the northeastern United States using increasing
27 levels of chemical detail. J. Geophys. Res. [Atmos.] 101: 16,737-16,744.
28 Lammel, G.; Cape, J. N. (1996) Nitrous acid and nitrite in the atmosphere. Chem. Soc. Rev. 25:
29 361-369.
30 Lammel, G.; Perner, D. (1988) The atmospheric aerosol as a source of nitrous acid in the
31 polluted atmosphere. J. Aerosol Sci. 19: 1199-1202.
32 Lawson, D. R. (1990) The Southern California Air Quality Study. J. Air Waste Manage. Assoc.
33 40: 156-165.
34 Leigh, R. J. (2006) Concurrent multiaxis differential optical absorption spectroscopy system for
35 the measurement of tropospheric nitrogen dioxide. Appl. Opt. 45: 7504-7518.
36 Lerdau, M. T.; Munger, J. W.; Jacob, D. J. (2000) The N02 flux conundrum. Science 289: 2291,
37 2293.
38 Leue, C.; Wenig, M.; Wagner, T.; Klimm, 0.; Platt, U.; Jahne, B. (2001) Quantitative analysis of
39 NOx emissions from Global Ozone Monitoring Experiment satellite image sequences. J.
40 Geophys. Res. [Atmos.] 106(D6): 5493-5505.
September 2007 AX2-150 DRAFT-DO NOT QUOTE OR CITE
-------
1 Leung, F.-Y. T.; Colussi, A. J.; Hoffmann, M. R. (2002) Isotopic fractionation of carbonyl
2 sulfide in the atmosphere: implications for the source of background stratospheric sulfate
3 aerosol. Geophys. Res. Lett. 29: 10.1029/2001GL013955.
4 Levine, J. S.; Bobbe, T.; Ray, N.; Witt, R. G.; Singh, A. (1999) Wildland fires and the
5 environment: a global synthesis. Nairobi, Kenya: United Nations Environment
6 Programme (UNEP), Division of Environmental Information, Assessment an Early
7 Warning (DEIA&EW); report no. UNEP/DEIAEW/TR.99.1. Available:
8 http://www.na.unep.net/publications/wildfire.pdf [17 August, 2007].
9 Levelt, P. F.; Yen Den Oord, G. H. J.; Dobber, M. R.; Malkki, A.; Visser, H.; De Vries, J.;
10 Stammes, P.; Lundell, J. 0. V.; Saari, H. (2006) The ozone monitoring instrument. IEEE
11 Trans. Geosci. Remote Sens. 44: 1093-1101.
12 Liang, J.; Horowitz, L. W.; Jacob, D. J.; Wang, Y.; Fiore, A. M.; Logan, J. A.; Gardner, G. M.;
13 Munger, J. W. (1998) Seasonal budgets of reactive nitrogen species and ozone over the
14 United States, and export fluxes to the global atmosphere. J. Geophys. Res. [Atmos.] 103:
15 13,435-13,450.
16 Liu, X. H.; Hegg, D. A.; Stoelinga, M. T. (2001a) Numerical simulation of new particle
17 formation over the northwest Atlantic using the MM5 mesoscale model coupled with
18 sulfur chemistry. J. Geophys. Res. [Atmos.] 106: 9697-9715.
19 Liu, H.; Jacob, D. J.; Bey, I.; Yantosca, R. M. (2001) Constraints from 210Pb and 7Be on wet
20 deposition and transport in a global three-dimensional chemical tracer model driven by
21 assimilated meteorological fields. J. Geophys. Res. [Atmos.] 106: 12,109-12,128.
22 Lobert, J. M.; Scharffe, D. H.; Hao, W.-M.; Kuhlbusch, T. A.; Seuwen, R.; Warneck, P.;
23 Crutzen, P. J. (1991) Experimental evaluation of biomass burning emissions: nitrogen
24 and carbon containing compounds. In: Levine, J. S., ed. Global biomass burning:
25 atmospheric, climatic, and biospheric implications. Cambridge, MA: MIT Press; pp. 289-
26 304.
27 Lobert, J. M.; Keene, W. C.; Logan, J. A.; Yevich, R. (1999) Global chlorine emissions from
28 biomass burning: Reactive Chlorine Emissions Inventory. J. Geophys. Res. [Atmos.]
29 104(07): 8373-8389.
30 Longfellow, C. A.; Ravishankara, A. R.; Hanson, D. R. (1999) Reactive uptake on hydrocarbon
31 soot: focus on N02. J. Geophys. Res. [Atmos.] 104: 13,833-13,840.
32 Lu, R.; Turco, R. P.; Jacobson, M. Z. (1997) An integrated air pollution modeling system for
33 urban and regional scales: 1. Structure and performance. J. Geophys. Res. [Atmos.] 102:
34 6063-6079.
35 Luke, W. T. (1997) Evaluation of a commercial pulsed fluorescence detector for the
36 measurement of low-level S02 concentrations during the Gas-Phase Sulfur
37 Intercomparison Experiment. J. Geophys. Res. [Atmos.] 102: 16,255-16,265.
38 Luke, W. T.; Dickerson, R. R.; Ryan, W. F.; Pickering, K. E.; Nunnermacker, L. J. (1992)
39 Tropospheric chemistry over the lower Great Plains of the United States. 2. Trace gas
40 profiles and distributions. J. Geophys. Res. [Atmos.] 97: 20,647-20,670.
September 2007 AX2-151 DRAFT-DO NOT QUOTE OR CITE
-------
1 Luke, W. T.; Watson, T. B.; Olszyna, K. J.; Gunter, R. L.; McMillen, R. T.; Wellman, D. L.;
2 Wilkison, S. W. (1998) A comparison of airborne and surface trace gas measurements
3 during the Southern Oxidants Study (SOS). J. Geophys. Res. [Atmos.] 103: 22,317-
4 22,337.
5 Ma, J.; Richter, A.; Burrows, J. P.; NiiB, H.; Van Aardenne, J. A. (2006) Comparison of model-
6 simulated tropospheric N02 over China with GOME-satellite data. Atmos. Environ. 40:
7 593-604.
8 Madronich, S. (1987) Photodissociation in the atmosphere. 1. Actinic flux and the effects of
9 ground reflections and clouds. J. Geophys. Res. [Atmos.] 92: 9740-9752.
10 Mahrt, L. (1998) Stratified atmospheric boundary layers and breakdown of models. Theor.
11 Comput. Fluid Dyn. 11: 263-279.
12 Marley, N. A.; Gaffney, J. S.; White, R. V.; Rodriguez-Cuadra, L.; Herndon, S. E.; Dunlea, E.;
13 Volkamer, R. M.; Molina, L. T.; Molina, M. J. (2004) Fast gas chromatography with
14 luminol chemiluminescence detection for the simultaneous determination of nitrogen
15 dioxide and peroxyacetyl nitrate in the atmosphere. Rev. Sci. Instrum. 75: 4595-4605.
16 Martilli, A.; Neftel, A.; Favaro, G.; Kirchner, F.; Sillman, S.; Clappier, A. (2002) Simulation of
17 the ozone formation in the northern part of the Po Valley. J. Geophys. Res. [Atmos.]
18 107(022): 10.1029/2001JD000534.
19 Martin, D.; Tsivou, M.; Bonsang, B.; Abonnel, C.; Carsey, T.; Springer-Young, M.; Pszenny, A.;
20 Suhre, K. (1997) Hydrogen peroxide in the marine atmospheric boundary layer during the
21 Atlantic Stratocumulus Transition Experiment/Marine Aerosol and Gas Exchange
22 experiment in the eastern subtropical North Atlantic. J. Geophys. Res. [Atmos.] 102:
23 6003-6015.
24 Martin, R. V.; Chance, K.; Jacob, D. J.; Kurosu, T. P.; Spurr, R. J. D.; Bucsela, E.; Gleason, J. F.;
25 Palmer, P. I.; Bey, I.; Fiore, A. M.; Li, Q.; Yantosca, R. M.; Koelemeijer, R. B. A. (2002)
26 An improved retrieval of tropospheric nitrogen dioxide from GOME. J. Geophys. Res.
27 [Atmos.] 107(D20): 10.1029/2001D001027.
28 Martin, R. V.; Jacob, D. J.; Chance, K. V.; Kurosu, T. P.; Palmer, P. L; Evans, M. J. (2003)
29 Global inventory of nitrogen oxide emissions constrained by space-based observations of
30 N02 columns. J. Geophys. Res. [Atmos.] 108(D17): 10.1029/2003JD003453.
31 Martin, R. V.; Fiore, A. M.; Van Donkelaar, A. (2004a) Space-based diagnosis of surface ozone
32 sensitivity to anthropogenic emissions. Geophys. Res. Lett. 31 (L06120):
33 10.1029/2004GL019416.
34 Martin, R. V.; Parrish, D. D.; Ryerson, T. B.; Nicks, D. K., Jr.; Chance, K.; Kurosu, T. P.; Jacob,
35 D. J.; Sturges, E. D.; Fried, A.; Wert, B. P. (2004b) Evaluation of GOME satellite
36 measurements of trophospheric N02 and HCHO using regional data from aircraft
37 campaigns in the southeastern United States. J. Geophys. Res. [Atmos.] 109(D24307):
38 10.1029/2004JD004869.
39 Martin, R. V.; Sioris, C. E.; Chance, K.; Ryerson, T. B.; Bertram, T. H.; Wooldridge, P. J.;
40 Cohen, R. C.; Neuman, J. A.; Swanson, A.; Flocke, F. M. (2006) Evaluation of space-
41 based constraints on nitrogen oxide emissions with regional aircraft measurements over
September 2007 AX2-152 DRAFT-DO NOT QUOTE OR CITE
-------
1 and downwind of eastern North America. J. Geophys. Res. [Atmos.] 111 (D15308):
2 10.1029/2005JD006680.
3 Martinez, M.; Arnold, T.; Perner, D. (1999) The role of bromine and chlorine chemistry for
4 arctic ozone depletion events in Ny-Alesund and comparison with model calculations.
5 Ann. Geophys. 17: 941-956.
6 Massman, W. J.; Pederson, J.; Delany, A.; Grantz, D.; Denhartog, G.; Neumann, H. H.; Oncley,
7 S. P.; Pearson, R.; Shaw, R. H. (1994) An evaluation of the regional acid deposition
8 model surface module for ozone uptake at 3 sites in the San-Joaquin valley of California.
9 J. Geophys. Res. [Atmos.] 99: 8281-8294.
10 Matsumi, Y.; Murakami, S.-L; Kono, M.; Takahashi, K.; Koike, M.; Kondo, Y. (2001) High-
11 sensitivity instrument for measuring atmospheric NOz. Anal. Chem. 73: 5485-5493.
12 Matsumi, Y.; Shigemori, H.; Takahashi, K. (2005) Laser-induced fluorescence instrument for
13 measuring atmospheric SOz. Atmos. Environ. 39: 3177-3185.
14 Mauldin, R. L., Ill; Tanner, D. J.; Eisele, F. L. (1998) A new chemical ionization mass
15 spectrometer technique for the fast measurement of gas phase nitric acid in the
16 atmosphere.J. Geophys. Res. [Atmos.] 103: 3361-3367.
17 McClenny, W. A., ed. (2000) Recommended methods for ambient air monitoring of NO, NOz,
18 N0y, and individual NOZ species. Research Triangle Park, NC: U.S. Environmental
19 Protection Agency, National Exposure Research Laboratory; report no. EPA/600/R-
20 01/005.
21 McFiggans, G. (2005) Marine aerosols and iodine emissions. Nature (London, U.K.) 433(7026):
22 E13.
23 McFiggans, G.; Plane, J. M. C.; Allan, B.; Carpenter, L. J.; Coe, H.; O'Dowd, C. (2000) A
24 modeling study of iodine chemistry in the marine boundary layer. J. Geophys. Res.
25 [Atmos.] 105: 14,371-14,385.
26 McFiggans, G.; Coe, H.; Burgess, R.; Allan, J.; Cubison, M.; Alfarra, M. R.; Saunders, R.; Saiz-
27 Lopez, A.; Plane, J. M. C.; Wevill, D.; Carpenter, L. J.; Rickard, A. R.; Monks, P. S.
28 (2004) Direct evidence for coastal iodine particles from Laminaria macroalgae - linkage
29 to emissions of molecular iodine. Atmos. Chem. Phys. 4: 701-713.
30 McKeen, S. A.; Liu, S. C. (1993) Hydrocarbon ratios and photochemical history of air masses.
31 Geophys. Res. Lett. 20: 2363-2366.
32 McKeen, S. A.; Liu, S. C.; Hsie, E.-Y.; Lin, X.; Bradshaw, J. D.; Smyth, S.; Gregory, G. L.;
33 Blake, D. R. (1996) Hydrocarbon ratios during PEM-WEST A: a model perspective. J.
34 Geophys. Res. [Atmos.] 101: 2087-2109.
35 McKenna, D. S.; Konopka, P.; GrooB, J.-U.; Giinther, G.; Miiller, R.; Spang, R.; Offermann, D.;
36 Orsolini, Y. (2002) A new chemical Lagrangian model of the stratosphere (CLaMS) 1.
37 formulation of advection and mixing. J. Geophys. Res. [Atmos.] 107(D16):
38 10.1029/2000JD000114.
39 Meagher, J. F.; Cowling, E. B.; Fehsenfeld, F. C.; Parkhurst, W. J. (1998) Ozone formation and
40 transport in southeastern United States: overview of the SOS Nashville/Middle Tennessee
41 Ozone Study. J. Geophys. Res. [Atmos.] 103: 22,213-22,223.
September 2007 AX2-153 DRAFT-DO NOT QUOTE OR CITE
-------
1 Mebust, M. R.; Eder, B. K.; Binkowski, F. S.; Roselle, S. J. (2003) Models-3 community
2 multiscale air quality (CMAQ) model aerosol component. 2. Model evaluation. J.
3 Geophys. Res. [Atmos.] 108(D6): 10.1029/2001JD001410.
4 Mendoza-Dominguez, A.; Russell, A. G. (2000) Iterative inverse modeling and direct sensitivity
5 analysis of a photochemical air quality model. Environ. Sci. Technol. 3: 4974-4981.
6 Mendoza-Dominguez, A.; Russell, A. G. (2001) Estimation of emission adjustments from the
7 application of four-dimensional data assimilation to photochemical air quality modeling.
8 Atmos. Environ. 35: 2879-2894.
9 Meng, Z.; Seinfeld, J. H. (1996) Time scales to achieve atmospheric gas-aerosol equilibrium for
10 volatile species. Atmos. Environ. 30: 2889-2900.
11 Mocker, D.; Jung, K.; Forstel, H.; Schuurmann, G. (1998) Isotopic and enzymatic investigations
12 into the assimilation and effect of N02 on C3 and C4 plants. J. Appl. Bot. 72: 186-190.
13 Moorthi, S.; Suarez, M. J. (1992) Relaxed Arakawa-Schubert: a parameterization of moist
14 convection for general circulation models. Mon. Weather Rev. 120: 978-1002.
15 Mueller, P. K.; Collins, J. F. (1980) Development of a particulate sulfate analyzer. Palo Alto,
16 CA: Electric Power Research Institute; report no. P-1382F. Available from: NTIS,
17 Springfield, VA; EPRI-EA-1492.
18 Munger, J. W.; Wofsy, S. C.; Bakwin, P. S.; Fan, S.-M.; Goulden, M. L.; Daube, B. C.;
19 Goldstein, A. H. (1996) Atmospheric deposition of reactive nitrogen oxides and ozone in
20 a temperate deciduous forest and a subarctic woodland. 1. Measurements and
21 mechanisms.J. Geophys. Res. [Atmos.] 101: 12639-12657.
22 Munger, J. W.; Fan, S.-M.; Bakwin, P. S.; Goulden, M. L.; Goldstein, A. H.; Colman, A. S.;
23 Wofsy, S. C. (1998) Regional budgets of nitrogen oxides from continental sources:
24 variations of rates for oxidation and deposition with season and distance from source
25 regions. J. Geophys. Res. [Atmos.] 103: 8355-8368.
26 Munoz, M. S. S.; Rossi, M. J. (2002) Heterogeneous reactions of HNOs with flame soot
27 generated under different combustion conditions. Reaction mechanism and kinetics.
28 Phys. Chem. Chem. Phys. 4: 5110-5118.
29 Myhre, G.; Berglen, T. F.; Myhre, C. E.; Isaksen, I. S. A. (2004) The radiative effect of the
30 anthropogenic influence on the stratospheric sulfate aerosol layer. Tellus Ser. B 56: 294-
31 299.
32 Nagao, L; Matsumoto, K.; Tanaka, H. (1999) Sunrise ozone destruction found in the sub-tropical
33 marine boundary layer. Geophys. Res. Lett. 26: 3377-3380.
34 Nakamura, K.; Kondo, Y.; Chen, G.; Crawford, J. H.; Takegawa, N.; Koike, M.; Kita, K.;
35 Miyazaki, Y.; Shetter, R. E.; Lefer, B. L.; Avery, M.; Matsumoto, J. (2003) Measurement
36 of NOz by the photolysis conversion technique during the Transport and Chemical
37 Evolution Over the Pacific (TRACE-P) campaign. J. Geophys. Res. [Atmos.] 108(D24):
38 10.1029/2003JD003712.
39 NARSTO. (2005) Improving emission inventories for effective air quality management across
40 North America. A NARSTO assessment. Pasco, WA: The NARSTO Emission Inventory
September 2007 AX2-154 DRAFT-DO NOT QUOTE OR CITE
-------
1 Assessment Team; report no. NARSTO-05-001. Available:
2 http://www.narsto.org/section.src?SID=8 [18 April, 2006].
3 National Bureau of Standards. (1975) Catalog of NBS standard reference materials, 1975-76
4 edition. Washington, DC: U.S. Department of Commerce, National Bureau of Standards;
5 NBS special publication no. 260.
6 National Research Council. (1991) Rethinking the ozone problem in urban and regional air
7 pollution. Washington, DC: National Academy Press. Available:
8 http://www.nap.edu/books/0309046319/html/ [26 March, 2004].
9 National Research Council. (2002) Estimating the public health benefits of proposed air
10 pollution regulations. Washington, DC: National Academy of Sciences. Available at:
11 http://books.nap.edu/books/0309086094/html/index.html [7 November, 2002].
12 Neuman, J. A.; Huey, L. G.; Dissly, R. W.; Fehsenfeld, F. C.; Flocke, F.; Holecek, J. C.;
13 Holloway, J. S.; Hiibler, G.; Jakoubek, R.; Nicks, D. K., Jr.; Parrish, D. D.; Ryerson, T.
14 B.; Sueper, D. T.; Weinheimer, A. J. (2002) Fast-response airborne in-situ measurements
15 of HN03 during the Texas 2000 Air Quality Study. J. Geophys. Res. [Atmos.] 107:
16 10.1029/2001JD001437.
17 Nicks, D. K.; Benner, R. L. (2001) Subminute measurements of S02 and low parts per trillion by
18 volume mixing ratios in the atmosphere. J. Geophys. Res. [Atmos.] 106: 27,769-27,776.
19 Nikitas, C.; Clemitshaw, K. C.; Oram, D. E.; Penkett, S. A. (1997) Measurements of PAN in the
20 polluted boundary layer and free troposphere using a luminol-N02 detector combined
21 with a thermal converter. J. Atmos. Chem. 28: 339-359.
22 Notholt, J.; Hjorth, J.; Raes, F. (1992a) Formation of HN02 on aerosol surfaces during foggy
23 periods in the presence of NO and N02. Atmos. Environ. Part A 26: 211-217.
24 Notholt, J.; Hjorth, J.; Raes, F.; Schrems, 0. (1992b) Simultaneous long path field measurements
25 of HN02, CH20 and aerosol. Ber. Bunsen Ges. Phys. Chem. 96: 290-293.
26 Nunnermacker, L. J.; Imre, D.; Daum, P. Ft.; Kleinman, L.; Lee, Y.-N.; Lee, J. Ft.; Springston,
27 S. R.; Newman, L.; Weinstein-Lloyd, J.; Luke, W. T.; Banta, R.; Alvarez, R.; Senff, C.;
28 Sillman, S.; Holdren, M.; Keigley, G. W.; Zhou, X. (1998) Characterization of the
29 Nashville urban plume on July 3 and July 18, 1995. J. Geophys. Res. [Atmos.] 103:
30 28,129-28,148.
31 Odman, M. T.; Ingram, C. I. (1996) Multiscale air quality simulation platform (MAQSIP):
32 source code documentation and validation. Research Triangle Park, NC: MCNC;
33 technical report ENV-96TR002-vl.0. Available:
34 http://www.ce.gatech.edu/~todman/maqsip.pdf (9 September 2003).
35 Odman, M. T.; Russell, A. G. (1999) Mass conservative coupling of non-hydrostatic
36 meteorological models with air quality models. In: Crying, S.-E.; Batchvarova, E., eds.
37 Air Pollution Modeling and its Application XIII. New York, NY: Plenum Press.
38 O'Dowd, C.; McFiggans, G.; Creasey, D. J.; Pirjola, L.; Hoell, C.; Smith, M. Ft.; Allan, B. J.;
39 Plane, J. M. C.; Heard, D. E.; Lee, J. D.; Pilling, M. J.; Kulmala, M. (1999) On the
40 photochemical production of new particles in the coastal boundary layer. Geophys. Res.
41 Lett. 26: 1707-1710.
September 2007 AX2-155 DRAFT-DO NOT QUOTE OR CITE
-------
1 O'Dowd, C. D.; Jimenez, J. L.; Bahreini, R.; Flagan, R. C.; Seinfeld, J. H.; Hameri, K.; Pirjola,
2 L.; Kulmala, M.; Jennings, S. G.; Hoffman, T. (2002) Marine aerosol formation from
3 biogenic iodine emissions. Nature (London, U.K.) 417: 632-636.
4 Ordonez, C.; Richter, A.; Steinbacher, M.; Zellweger, C.; NiiB, H.; Burrows, J. P.; Prevot, A. S.
5 H. (2006) Comparison of 7 years of satellite-borne and ground-based tropospheric NOz
6 measurements around Milan, Italy. J. Geophys. Res. [Atmos.] 111 (D05310):
7 10.1029/2005JD006305.
8 Osthoff, H. D.; Brown, S. S.; Ryerson, T. B.; Fortin, T. J.; Lerner, B. M.; Williams, E. J.;
9 Pettersson, A.; Baynard, T.; Dube, W. P.; Ciciora, S. J.; Ravishankara, A. R. (2006)
10 Measurement of atmospheric N02 by pulsed cavity ring-down spectroscopy. J. Geophys.
11 Res. [Atmos.] 111(D12): 10.1029/2005JD006942.
12 Ott, L. E.; et al. (2006) Production of lightning NOX and its vertical distribution calculated from
13 3-D cloud-scale chemical transport model simulations of observed midlatitude and
14 subtropical thunderstorms. J. Geophys. Res. [Atmos.]: in press.
15 Ott, L. E.; Pickering, K. E.; Stenchikov, G. L.; Huntrieser, H.; Schumann, U. (2007) Effects of
16 lightning NOX production during the July 21 European Lightning Nitrogen Oxides Project
17 storm studied with a three-dimensional cloud-scale chemical transport model. J.
18 Geophys. Res. [Atmos.] 112(D05307): 10.1029/2006JD007365.
19 Padro, J. (1996) Summary of ozone dry deposition velocity measurements and model estimates
20 over vineyard, cotton, grass and deciduous forest in summer. Atmos. Environ. 30: 2363-
21 2369.
22 Park, R. J.; Stenchikov, G. L.; Pickering; Dickerson, R. R.; Allen, D. J.; Kondragunta, S. (2001)
23 Regional air pollution and its radiative forcing: studies with a single column chemical
24 and radiation transport model. J. Geophys. Res. [Atmos.] 106: 28,751-28,770.
25 Parrish, D. D. (2006) Critical evaluation of US on-road vehicle emission inventories. Atmos.
26 Environ. 40: 2288-2300.
27 Parrish, D. D.; Fehsenfeld, F. C. (2000) Methods for gas-phase measurements of ozone, ozone
28 precursors and aerosol precursors. Atmos. Environ. 34: 1921-1957.
29 Parrish, D. D.; Trainer, M.; Buhr, M. P.; Watkins, B. A.; Fehsenfeld, F. C. (1991) Carbon
30 monoxide concentrations and their relation to concentrations of total reactive oxidized
31 nitrogen at two rural U.S. sites. J. Geophys. Res. [Atmos.] 96: 9309-9320.
32 Parrish, D. D.; Trainer, M.; Young, V.; Goldan, P. D.; Kuster, W. C.; Jobson, B. T.; Fehsenfeld,
33 F. C.; Lonneman, W. A.; Zika, R. D.; Farmer, C. T.; Riemer, D. D.; Rodgers, M. 0.
34 (1998) Internal consistency tests for evaluation of measurements of anthropogenic
35 hydrocarbons in the troposphere. J. Geophys. Res. [Atmos.] 103: 22,339-22,359.
36 Parrish, D. D.; Trainer, M.; Hereid, D.; Williams, E. J.; Olszyna, K. J.; Harley, R. A.; Meagher,
37 J. F.; Fehsenfeld, F. C. (2002) Decadal change in carbon monoxide to nitrogen oxide
38 ratio in U.S. vehicular emissions J. Geophys. Res. [Atmos.] 107(D13):
39 10.1029/2001JD000720.
September 2007 AX2-156 DRAFT-DO NOT QUOTE OR CITE
-------
1 Perez, I.; Wooldridge, P. J.; Cohen, R. C. (2007) Laboratory evaluation of a novel thermal
2 dissociation chemiluminescence method for in situ detection of nitrous acid. Atmos.
3 Environ. 41: 3993-4001.
4 Perrini, G.; Tomasello, M.; Librando, V.; Minniti, Z. (2005) Nitrated polycyclic aromatic
5 hydrocarbons in the environment: formation, occurrences and analysis. Ann. Chim. 95:
6 567-577.
7 Petriconi, G. L.; Papee, H. M. (1972) On the photolytic separation of halogens from sea water
8 concentrates. Water Air Soil Pollut. 1: 117-131.
9 Pickering, K. E.; Thompson, A. M.; Dickerson, R. R.; Luke, W. T.; McNamara, D. P.;
10 Greenberg, J. P.; Zimmerman, P. R. (1990) Model calculations of tropospheric ozone
11 production potential following observed convective events. J. Geophys. Res. [Atmos.]
12 95: 14,049-14,062.
13 Pickering, K. E.; Thompson, A. M.; Scala, J. R.; Tao, W.-K.; Simpson, J.; Garstang, M. (1991)
14 Photochemical ozone production in tropical squall line convection during NASA Global
15 Tropospheric Experiment/Amazon Boundary Layer Experiment 2A. J. Geophys. Res.
16 [Atmos.] 96:3099-3114.
17 Pickering, K. E.; Thompson, A. M.; Scala, J. R.; Tao, W.-K.; Simpson, J. (1992a) Ozone
18 production potential following convective redistribution of biomass burning emissions. J.
19 Atmos. Chem. 14: 297-313.
20 Pickering, K. E.; Thompson, A. M.; Scala, J. R.; Tao, W.-K.; Dickerson, R. R.; Simpson, J.
21 (1992b) Free tropospheric ozone production following entrainment of urban plumes into
22 deep convection. J. Geophys. Res. [Atmos.] 97: 17,985-18,000.
23 Pickering, K. E.; Thompson, A. M.; Tao, W.-K.; Kucsera, T. L. (1993) Upper tropospheric ozone
24 production following mesoscale convection during STEP/EMEX. J. Geophys. Res.
25 [Atmos.] 98: 8737-8749.
26 Pickering, K. E.; Thompson, A. M.; Tao, W.-K.; Rood, R. B.; McNamara, D. P.; Molod, A. M.
27 (1995) Vertical transport by convective clouds: comparisons of three modeling
28 approaches. Geophys. Res. Lett. 22: 1089-1092.
29 Pickering, K. E.; Thompson, A. M.; Wang, Y.; Tao, W.-K.; McNamara, D. P.; Kirchhoff, W. J.
30 H.; Heikes, B. G.; Sachse, G. W.; Bradshaw, J. D.; Gregory, G. L.; Blake, D. R. (1996)
31 Convective transport of biomass burning emissions over Brazil during TRACE A. J.
32 Geophys. Res. [Atmos.] 101: 23,993-24,012.
33 Pickering, K. E.; Wang, Y.; Tao, W.-K.; Price, C.; Miiller, J.-F. (1998) Vertical distributions of
34 lightning NO* for use in regional and global chemical transport models. J. Geophys. Res.
35 [Atmos.] 103: 31,203-31,216.
36 Pickering, K. E.; Thompson, A. M.; Kim, H.; DeCaria, A. J.; Pfister, L.; Kucsera, T. L.; Witte, J.
37 C.; Avery, M. A.; Blake, D. R.; Crawford, J. H.; Heikers, B. G.; Sachse, G. W.;
38 Sandholm, S. T.; Talbot, R. W. (2001) Trace gas transport and scavenging in PEM-
39 Tropics B South Pacific Convergence Zone convection. J. Geophys. Res. [Atmos.] 106:
40 32,591-32,602.
September 2007 AX2-157 DRAFT-DO NOT QUOTE OR CITE
-------
1 Pierce, T; Geron, C.; Bender, L.; Dennis, R.; Tonnesen, G.; Guenther, A. (1998) Influence of
2 increased isoprene emissions on regional ozone modeling. J. Geophys. Res. [Atmos.]
3 103:25,611-25,629.
4 Pikelnaya, 0.; Hurlock, S. C.; Trick, S.; Stutz, J. (2006) Measurements of reactive iodine species
5 on the Isles of Shoals, Gulf of Maine. J. Geophys. Res. [Atmos.]: submitted.
6 Pinto, J. P.; Turco, R. P.; Toon, 0. B. (1989) Self-limiting physical and chemical effects in
7 volcanic eruption clouds. J. Geophys. Res. [Atmos.] 94: 11,165-11,174.
8 Pinto, J. P.; Bruhl, C.; Thompson, A. M. (1993) The current and future envirionmental role of
9 atmospheric methane. In: Khalil, M. A. K., ed. Atmospheric methane sources, sinks, and
10 role in global change, p. 514-531. (NATO ASI Series, v. 113).
11 Pitts, J. N., Jr. (1983) Formation and fate of gaseous and particulate mutagens and carcinogens in
12 real and simulated atmospheres. Environ. Health Perspect. 47: 115-140.
13 Pitts, J. N., Jr. (1987) Nitration of gaseous polycyclic aromatic hydrocarbons in simulated and
14 ambient urban atmospheres: a source of mutagenic nitroarenes. Atmos. Environ. 21:
15 2531-2547.
16 Pitts, J. N., Jr.; Biermann, H. W.; Atkinson, R.; Winer, A. M. (1984) Atmospheric implications
17 of simultaneous nighttime measurements of N03 radicals and HONO. Geophys. Res.
18 Lett. 11:557-560.
19 Pitts, J. N., Jr.; Atkinson, R.; Sweetman, J. A.; Zielinska, B. (1985a) The gas-phase reaction of
20 naphthalene with NzOs to form nitronaphthalenes. Atmos. Environ. 19: 701-705.
21 Pitts, J. N., Jr.; Sweetman, J. A.; Zielinska, B.; Atkinson, R.; Winer, A. M.; Harger, W. P.
22 (1985b) Formation of nitroarenes from the reaction of polycyclic aromatic hydrocarbons
23 with dinitrogen pentoxide. Environ. Sci. Technol. 19: 1115-1121.
24 Platt (2006) Unpublished Data.
25 Pokharel, S. S.; Bishop, G. A.; Stedman, D. H. (2002) An on-road motor vehicle emissions
26 inventory for Denver: an efficient alternative to modeling. Atmos. Environ. 36: 5177-
27 5184.
28 Poppe, D.; Wallasch, M.; Zimmermann, J. (1993) The dependence of the concentration of OH on
29 its precursors under moderately polluted conditions: a model study. J. Atmos. Chem. 16:
30 61-78.
31 Possanzini, M.; De Santis, F.; Di Palo, V. (1999) Measurements of nitric acid and ammonium
32 salts in lower Bavaria. Atmos. Environ. 33: 3597-3602.
33 Prather, M. J.; Jacob, D. J. (1997) A persistent imbalance in HOX and NOX photochemistry in the
34 upper troposphere driven by deep tropical convection. Geophys. Res. Lett. 24: 3189-
35 3192.
36 Price, C.; Penner, J.; Prather, M. (1997) NOX from lightning: 1. Global distribution based on
37 lightning physics. J. Geophys. Res. [Atmos.] 102: 5929-5941.
38 Pszenny, A. A. P.; Keene, W. C.; Jacob, D. J.; Fan, S.; Maben, J. R.; Zetwo, M. P.; Springer-
39 Young, M.; Galloway, J. N. (1993) Evidence of inorganic chlorine gases other than
40 hydrogen chloride in marine surface air. Geophys. Res. Lett. 20: 699-702.
September 2007 AX2-158 DRAFT-DO NOT QUOTE OR CITE
-------
1 Pszenny, A. A. P.; Moldanova, J.; Keene, W. C.; Sander, R.; Maben, J. R.; Martinez, M.;
2 Crutzen, P. J.; Perner, D.; Prinn, R. G. (2004) Halogen cycling and aerosol pH in the
3 Hawaiian marine boundary layer. Atmos. Chem. Phys. 4: 147-168.
4 Pszenny, A. A. P.; Fischer, E. V.; Russo, R. S.; Sive, B. C.; Varner, R. K. (2006) Estimates of Cl
5 atom concentrations and hydrocarbon kinetic reactivity in surface air at Appledore Island,
6 Maine (USA), during International Consortium for Atmospheric Research on Transport
7 and Transformation/Chemistry of Halogens at the Isles of Shoals. J. Geophys. Res.
8 [Atmos.] 112(D10S13): 10.1029/2006JD007725.
9 Radke, L. F.; Hegg, D. A.; Hobbs, P. V.; Nance, J. D.; Lyons, J. H.; Laursen, K. K.; Weiss, R.
10 E.; Riggan, P. J.; Ward, D. E. (1991) Particulate and trace gas emissions from large
11 biomass fires in North America. In: Levine, J. S., ed. Global biomass burning:
12 atmospheric, climatic, and biospheric implications. Cambridge, MA: MIT Press; pp. 209-
13 224.
14 Raivonen, M.; Keronen, P.; Vesala, T.; Kulmala, M.; Hari, P. (2003) Measuring shoot-level NOX
15 flux in field conditions: the role of blank chambers. Boreal Environ. Res. 8: 445-455.
16 Ramazan, K. A.; Syomin, D.; Finlayson-Pitts, B. J. (2004) The photochemical production of
17 HONO during the heterogeneous hydrolysis of NOz. Phys. Chem. Chem. Phys. 6: 3836-
18 3843.
19 Ramdahl, T.; Schjoldager, J.; Currie, L. A.; Hanssen, J. E.; M011er, M.; Klouda, G. A.; Alfheim,
20 I. (1984) Ambient impact of residential wood combustion in Elverum, Norway. Sci. Total
21 Environ. 36: 81-90.
22 Ramdahl, T.; Zielinska, B.; Arey, J.; Atkinson, R.; Winer, A. M.; Pitts, J. N., Jr. (1986)
23 Ubiquitous occurrence of 2-nitrofluoranthene and 2-nitropyrene in air. Nature (London)
24 321:425-427.
25 Ravishankara, A. R. (1997) Heterogeneous and multiphase chemistry in the troposphere. Science
26 (Washington, DC) 276: 1058-1065.
27 Reisen, F.; Arey, J. (2005) Atmospheric reactions influence seasonal PAH and nitro-PAH
28 concentrations in the Los Angeles basin. Environ. Sci. Technol. 39: 64-73.
29 Reissell, A.; Arey, J. (2001) Biogenic volatile organic compounds at Azusa and elevated sites
30 during the 1997 Southern California Ozone Study. J. Geophys. Res. [Atmos.] 106: 1607-
31 1621.
32 Reithmeier, C.; Sausen, R. (2002) ATTILA: atmospheric tracer transport in a Lagrangian model.
33 Tellus 54B: 278-299.
34 Ren, X. R.; Harder, H.; Martinez, M.; Lesher, R. L.; Oliger, A.; Shirley, T.; Adams, J.; Simpas,
35 J. B.; Brune, W. H. (2003) HOX concentrations and OH reactivity observations in New
36 York City during PMTACS-NY2001. Atmos. Environ. 37: 3627-3637.
37 Reynolds, S.; Michaels, H.; Roth, P.; Tesche, T. W.; McNally, D.; Gardner, L.; Yarwood, G.
38 (1996) Alternative base cases in photochemical modeling: their construction, role, and
39 value. Atmos. Environ. 30: 1977-1988.
40 Richter, A.; Burrows, J. P.; NiiB, H.; Granier, C.; Niemeier, U. (2005) Increase in tropospheric
41 nitrogen dioxide over China observed from space. Nature (London, U.K.) 437: 129-132.
September 2007 AX2-159 DRAFT-DO NOT QUOTE OR CITE
-------
1 Ridley, B. A.; Dye, J. E.; Walega, J. G.; Zheng, J.; Grahek, F. E.; Rison, W. (1996) On the
2 production of active nitrogen by thunderstorms over New Mexico. J. Geophys. Res.
3 [Atmos.] 101: 20,985-21,005.
4 Roberts, J. M.; Williams, J.; Baumann, K.; Buhr, M. P.; Goldan, P. D.; Holloway, J.; Hiibler, G.;
5 Kuster, W. C.; McKeen, S. A.; Ryerson, T. B.; Trainer, M.; Williams, E. J.; Fehsenfeld,
6 F. C.; Bertman, S. B.; Nouaime, G.; Seaver, C.; Grodzinsky, G.; Rodgers, M.; Young, V.
7 L. (1998) Measurements of PAN, PPN, and MPAN made during the 1994 and 1995
8 Nashville Intensives of the Southern Oxidant Study: implications for regional ozone
9 production from biogenic hydrocarbons. J. Geophys. Res. [Atmos.] 103: 22,473-22,490.
10 Rodgers, M. 0.; Davis, D. D. (1989) A UV-photofragmentation/laser-induced fluorescence
11 sensor for the atmospheric detection of HONO. Environ. Sci. Technol. 23: 1106-1112.
12 Russell, A.; Dennis, R. (2000) NARSTO critical review of photochemical models and modeling.
13 Atmos. Environ. 34: 2283-2324.
14 Russell, K. M.; Keene, W. C.; Maben, J. R.; Galloway, J. N.; Moody, J. L. (2003) Phase-
15 partitioning and dry deposition of atmospheric nitrogen at the mid-Atlantic U.S. coast. J.
16 Geophys. Res. [Atmos.] 108(D21): 10.1029/2003JD003736.
17 Ryerson, T. B.; Buhr, M. P.; Frost, G. J.; Goldan, P. D.; Holloway, J. S.; Hiibler, G.; Jobson, B.
18 T.; Kuster, W. C.; McKeen, S. A.; Parrish, D. D.; Roberts, J. M.; Sueper, D. T.; Trainer,
19 M.; Williams, J.; Fehsenfeld, F. C. (1998) Emissions lifetimes and ozone formation in
20 power plant plumes. J. Geophys. Res. [Atmos.] 103: 22,569-22,583.
21 Ryerson, T. B.; Williams, E J.; Fehsenfeld, F. C. (2000) An efficient photolysis system for fast-
22 response N02 measurements. J. Geophys. Res. [Atmos.] 105: 26,447-26,461.
23 Ryerson, T. B.; Trainer, M.; Holloway, J. S.; Parrish, D. D.; Huey, L. G.; Sueper, D. T.; Frost, G.
24 J.; Donnelly, S. G.; Schauffler, S.; Atlas, E. L.; Kuster, W. C.; Goldan, P. D.; Hiibler, G.;
25 Meagher, J. F.; Fehsenfeld, F. C. (2001) Observations of ozone formation in power plant
26 plumes and implications for ozone control strategies. Science (Washington, DC) 292:
27 719-723.
28 Saathoff, H.; Naumann, K. H.; Riemer, N.; Kamm, S.; Mohler, 0.; Schurath, U.; Vogel, H.;
29 Vogel, B. (2001) The loss of N02, HN03, N03/N205, and H02/HOON02 on soot aerosol:
30 a chamber and modeling study. Geophys. Res. Lett. 28: 1957-1960.
31 Saiz-Lopez, A.; Plane, J. M. C. (2004) Novel iodine chemistry in the marine boundary layer.
32 Geophys. Res. Lett. 31(L04112): 10.1029/2003GL019215.
33 Saiz-Lopez, A.; Plane, J. M. C.; Shillito, J. A. (2004) Bromine oxide in the mid-latitude marine
34 boundary layer. Geophys. Res. Lett. 31(L03111): 10.1029/2003GL018956.
35 Sakamaki, F.; Hatakeyama, S.; Akimoto, H. (1983) Formation of nitrous acid and nitric oxide in
36 the heterogeneous dark reaction of nitrogen dioxide and water vapor in a smog chamber.
37 Int. J. Chem. Kinet. 15: 1013-1029.
38 Sakugawa, H.; Kaplan, I. R. (1989) H202 and Os in the atmosphere of Los Angeles and its
39 vicinity: factors controlling their formation and their role as oxidants of S02. J. Geophys.
40 Res. [Atmos.] 94: 12,957-12,973.
September 2007 AX2-160 DRAFT-DO NOT QUOTE OR CITE
-------
1 Salmeen, I.; Durisin, A. M.; Prater, T. J.; Riley, T.; Schuetzle, D. (1982) Contribution of 1-
2 nitropyrene to direct-acting Ames assay mutagenicities of diesel particulate extracts.
3 Mutat. Res. 104: 17-23.
4 Sander, R.; Rudich, Y.; Von Glasow, R.; Crutzen, P. J. (1999) The role of BrN03 in marine
5 tropospheric chemistry: a model study. Geophys. Res. Lett. 26: 2858-2860.
6 Sander, R.; Keene, W. C.; Pszenny, A. A. P.; Arimoto, R. Ayers, G. P.; Chainey, J. M.; Crutzen,
7 P. J.; Duce, R. A.; Huebert, B. J.; Maenhaut, W.; Turekian, V. C.; Van Dingenan, R.
8 (2003) Inorganic bromine in the marine boundary layer: a critical review. Atmos. Chem.
9 Phys. 3: 1301-1336.
10 Sasaki, J. C.; Arey, J.; Eastmond, D. A.; Parks, K. K.; Grosovsky, A. J. (1997) Genotoxicity
11 induced in human lymphoblasts by atmospheric reaction products of naphthalene and
12 phenanthrene. Mutat. Res. 393: 23-35.
13 Sawyer, R. F.; Harley, R. A.; Cadle, S. H.; Norbeck, J. M.; Slott, R.; Bravo, H. A. (2000) Mobile
14 sources critical review: 1998 NARSTO assessment. Atmos. Environ. 34: 2161-2181.
15 Scala, J. M.; Garstang, M.; Tao, W.-K.; Pickering, K. E.; Thompson, A. M.; Simpson, J.;
16 Kirchhoff, V. W. J. H.; Browell, E. V.; Sachse, G. W.; Torres. A. L.; Gregory, G. L.;
17 Rasmussen, R. W.; Khalil, M. A. K. (1990) Cloud draft structure and trace gas transport.
18 J. Geophys. Res. [Atmos.] 95: 17,015-17,030.
19 Schrimpf, W.; Lienaerts, K.; Miiller, K. P.; Rudolph, J.; Neubert, R.; SchiiBler, W.; Levin, I.
20 (1996) Dry deposition of peroxyacetyl nitrate (PAN): determination of its deposition
21 velocity at night from measurements of the atmospheric PAN and 222radon concentration
22 gradient. Geophys. Res. Lett. 23: 3599-3602.
23 Schubert, S. D.; Rood, R. B.; Pfaendtner, J. (1993) An assimilated dataset for earth science
24 applications. Bull. Am. Meteorol. Soc. 74: 2331-2342.
25 Schuetzle, D. (1983) Sampling of vehicle emissions for chemical analysis and biological testing.
26 Environ. Health Perspect. 47: 65-80.
27 Schultz, M. G.; Jacob, D. J.; Bradshaw, J. D.; Sandholm, S. T.; Dibb, J. E.; Talbot, R. W.; Singh,
28 H. B. (2000) Chemical NOX budget in the upper troposphere over the tropical South
29 Pacific. J. Geophys. Res. [Atmos.] 105: 6669-6679.
30 Seaman, N. L. (2000) Meteorological modeling for air quality assessments. Atmos. Environ. 34:
31 2231-2259.
32 Segschneider, H.-J.; Wildt, J.; Forstel, H. (1995) Uptake of 15N02 by sunflower (Helianthus-
33 annuus) during exposures in light and darkness: quantities, relationship to stomatal
34 aperture and incorporation into different nitrogen pools within the plant. New Phytol.
35 131:109-119.
36 Seinfeld, J. H.; Pandis, S. N. (1998) Atmospheric chemistry and physics: from air pollution to
37 climate change. New York, NY: John Wiley & Sons, Inc.
38 Shepson, P. B.; Bottenheim, J. W.; Hastie, D. R.; Venkatram, A. (1992) Determination of the
39 relative ozone and PAN deposition velocities at night. Geophys. Res. Lett. 19: 1121-
40 1124.
September 2007 AX2-161 DRAFT-DO NOT QUOTE OR CITE
-------
1 Shepson, P. B.; Mackay, E.; Muthuramu, K. (1996) Henry's Law constants and removal
2 processes for several atmospheric p-hydroxy alkyl nitrates. Environ. Sci. Technol. 30:
3 3618-3623.
4 Shindell, D. T.; Faluvegi, G.; Stevenson, D. S.; Krol, M. C.; Emmons, L. K.; Lamarque, J.-F.;
5 Petron, G.; Dentener, F. J.; Ellingsen, K.; Schultz, M. G.; Wild, 0.; Amann, M.;
6 Atherton, C. S.; Bergmann, D. J.; Bey, I.; Butler, T.; Cofala, J.; Collins, W. J.; Derwent,
7 R. G.; Doherty, R. M.; Drevet, J.; Eskes, H. J.; Fiore, A. M.; Gauss, M.; Hauglustaine, D.
8 A.; Horowitz, L. W.; Isaksen, I. S. A.; Lawrence, M. G.; Montanaro, V.; Muller, J.-F.;
9 Pitari, G.; Prather, M. J.; Pyle, J. A.; Rast, S.; Rodriguez, J. M.; Sanderson, M. G.;
10 Savage, N. H.; Strahan, S. E.; Sudo, K.; Szopa, S.; Unger, N.; Van Noije, T. P. C.; Zeng,
11 G. (2006) Multimodel simulations of carbon monoxide: comparison with observations
12 and projected near-future changes. J. Geophys. Res. [Atmos.] 111 (D 19306):
13 10.1029/2006JD007100.
14 Siegwolf, R. T. W.; Matyssek, R.; Sauer, M.; Maurer, S.; Giinthardt-Goerg, M. S.; Schmutz, P.;
15 Bucher, J. B. (2001) Stable isotope analysis reveals differential effects of soil nitrogen
16 and nitrogen dioxide on the water use efficiency in hybrid poplar leaves. New Phytol.
17 149: 233-246.
18 Sillman, S. (1995) The use of N0y, H202 and HN03 as indicators for ozone-NOx-hydrocarbon
19 sensitivity in urban locations. J. Geophys. Res. [Atmos.] 100: 14,175-14,188.
20 Sillman, S. (2000) Ozone production efficiency and loss of NOx in power plant plumes:
21 photochemical model and interpretation of measurements in Tennessee. J. Geophys. Res.
22 [Atmos.] 105: 9189-9202.
23 Sillman, S.; He, D.-Y. (2002) Some theoretical results concerning 03-NOX-VOC chemistry and
24 NOX-VOC indicators. J. Geophys. Res. [Atmos.] 107: 10.1029/2001JD001123.
25 Sillman, S.; Al-Wali, K. L; Marsik, F. J.; Nowacki, P.; Samson, P. J.; Rodgers, M. 0.; Garland,
26 L. J.; Martinez, J. E.; Stoneking, C.; Imhoff, R.; Lee, J.-H.; Newman, L.; Weinstein-
27 Lloyd, J.; Aneja, V. P. (1995) Photochemistry of ozone formation in Atlanta, GA—
28 models and measurements. Atmos. Environ. 29: 3055-3066.
29 Sillman, S.; He, D.; Cardelino, C.; Imhoff, R. E. (1997) The use of photochemical indicators to
30 evaluate ozone-NOx-hydrocarbon sensitivity: case studies from Atlanta, New York, and
31 Los Angeles. J. Air Waste Manage. Assoc. 47: 1030-1040.
32 Sillman, S.; He, D.; Pippin, M. R.; Daum, P. H.; Imre, D. G.; Kleinman, L. I.; Lee, J. H.;
33 Weinstein-Lloyd, J. (1998) Model correlations for ozone, reactive nitrogen, and
34 peroxides for Nashville in comparison with measurements: implications for Os-N0x-
35 hydrocarbon chemistry. J. Geophys. Res. [Atmos.] 103: 22,629-22,644.
36 Sillman, S.; Vautard, R.; Menut, L.; Kley, D. (2003) 03-NOX-VOC sensitivity and NOX-VOC
37 indicators in Paris: results from models and atmospheric pollution over the Paris area
38 (ESQUIF) measurements. J. Geophys. Res. [Atmos.] 108: 10.1029/2002JD001561.
39 Simpson, A. J.; Lam, B.; Diamond, M. L.; Donaldson, D. J.; Lefebvre, B. A.; Moser, A. Q.;
40 Williams, A. J.; Larin, N.; Kvasha, M. P. (2006) Assessing the organic composition of
41 urban surface films using nuclear magnetic resonance spectroscopy. Chemosphere 63:
42 142-152.
September 2007 AX2-162 DRAFT-DO NOT QUOTE OR CITE
-------
1 Singh, H. B.; Kasting, J. F. (1988) Chlorine-hydrocarbon photochemistry in the marine
2 troposphere and lower stratosphere. J. Atmos. Chem. 7: 261-285.
3 Singh, H. B.; Herlth, D.; Kolyer, R.; Salas, L.; Bradshaw, J. D.; Sandholm, S. T.; Davis, D. D.;
4 Crawford, J.; Kondo, Y.; Koike, M.; Talbot, R.; Gregory, G. L.; Sachse, G. W.; Browell,
5 E.; Blake, D. R.; Rowland, F. S.; Newell, R.; Merrill, J.; Heikes, B.; Liu, S. C.; Crutzen,
6 P. J.; Kanakidou, M. (1996) Reactive nitrogen and ozone over the western Pacific:
7 distribution, partitioning, and sources. J. Geophys. Res. [Atmos.] 101: 1793-1808.
8 Skiba, U.; Fowler, D.; Smith, K. A. (1997) Nitric oxide emissions from agricultural soils in
9 temperate and tropical climates: sources, controls and mitigation options. Nutr. Cycling
10 Agroecosyst. 48: 139-153.
11 Smith, A. M.; Keene, W. C.; Maben, J. R.; Pszenny, A. A. P.; Fischer, E.; Stohl, A. (2007)
12 Ammonia sources, transport, transformation, and deposition in coastal New England
13 during summer. J. Geophys. Res. [Atmos.] 112(D10S08): 10.1029/2006JD007574.
14 Solomon, P. A.; Salmon, L. G.; Fall, T.; Cass, G. R. (1992) Spatial and temporal distribution of
15 atmospheric nitric acid and particulate nitrate concentrations in the Los Angeles area.
16 Environ. Sci. Technol. 26: 1596-1601.
17 Sparks, J. P.; Roberts, J. M.; Monson, R. K. (2003) The uptake of gaseous organic nitrogen by
18 leaves: a significant global nitrogen transfer process. Geophys. Res. Lett. 30(23):
19 10.1029/2003GL018578.
20 Spurny (1999)
21 Staffelbach, T.; Neftel, A.; Blatter, A.; Gut, A.; Fahrni, M.; Stahelin, J.; Prevot, A.; Hering, A.;
22 Lehning, M.; Neininger, B.; Baumle, M.; Kok, G. L.; Dommen, J.; Hutterli, M.; Anklin,
23 M. (1997) Photochemical oxidant formation over southern Switzerland 1. results from
24 summer 1994. J. Geophys. Res. [Atmos.] 102: 23,345-23,362.
25 Staudt, A. C.; Jacob, D. J.; Ravetta, F.; Logan, J. A.; Bachiochi, D.; Krishnamurti, T. N.;
26 Sandholm, S. T.; Ridley, B. A.; Singh, H. B.; Talbot, B. (2003) Sources and chemistry of
27 nitrogen oxides over the tropical Pacific. J. Geophys. Res. [Atmos.] 108(8239):
28 10.1029/2002JD002139.
29 Stedman, D. H.; Bishop, G.; Peterson, J. E.; Guenther, P. L. (1991) On-road CO remote sensing
30 in the Los Angeles Basin: final report. Sacramento, CA: California Air Resources Board,
31 ARE Contract No. A932-189.
32 Stehr, J. W.; Dickerson, R. R.; Hallock-Waters, K. A.; Doddridge, B. G.; Kirk, D. (2000)
33 Observations of N0y , CO, and S02 and the origin of reactive nitrogen in the eastern
34 United States. J. Geophys. Res. [Atmos.] 105: 3553-3563.
35 Stein, A. F.; Lamb, D. (2003) Empirical evidence for the low- and high-NOx photochemical
36 regimes of sulfate and nitrate formation. Atmos. Environ. 37: 3615-3625.
37 Stein, A. F.; Lamb, D.; Draxler, R. R. (2000) Incorporation of detailed chemistry into a three-
38 dimensional Lagrangian-Eulerian hybrid model: application to regional tropospheric
39 ozone. Atmos. Environ. 34: 4361-4372.
September 2007 AX2-163 DRAFT-DO NOT QUOTE OR CITE
-------
1 Stemmler, K.; Ammann, M.; Bonders, C.; Kleffmann, J.; George, C. (2006) Photosensitized
2 reduction of nitrogen dioxide on humic acid as a source of nitrous acid. Nature 440: 195-
3 198.
4 Stenchikov, G.; Dickerson, R.; Pickering, K.; Ellis, W. Jr.; Doddridge, B.; Kondragunta, S.;
5 Poulida, 0.; Scala, J.; Tao, W.-K. (1996) Stratosphere-troposphere exchange in a
6 midlatitude mesoscale convective complex. 2. Numerical simulation. J. Geophys. Res.
7 [Atmos.] 101: 6837-6851.
8 Stevens, R. K.; O'Keeffe, A. E.; Ortman, G. C. (1969) Absolute calibration of a flame
9 photometric detector to volatile sulfur compounds at sub-part-per-million levels. Environ.
10 Sci. Technol. 3: 652-655.
11 Stevens, R. K.; Mulik, J. D.; O'Keefe, A. E.; Krost, K. J. (1971) Gas chromatography of reactive
12 sulfur gases in air at the parts-per-billion level. Anal. Chem. 43: 827-831.
13 Stevenson, D.; Dentener, F. J.; Schultz, M. G.; Ellingsen, K.; Van Noije, T. P. C.; Wild, 0.;
14 Zeng, G.; Amann, M.; Atherton, C. S.; Bell, N.; Bergmann, D. J.; Bey, I.; Butler, T.;
15 Cofala, J.; Collins, W. J.; Derwent, R. G.; Doherty, R. M.; Drevet, J.; Eskes, H. J.; Fiore,
16 A. M.; Gauss, M.; Hauglustaine, D. A.; Horowitz, L. W.; Isaksen, I. S. A.; Krol, M. C.;
17 Lamarque, J.-F.; Lawrence, M. G.; Montanaro, V.; Miiller, J.-F.; Pitari, G.; Prather, M. J.;
18 Pyle, J. A.; Rast, S.; Rodriguez, J. M.; Sanderson, M. G. (2006) Multimodel ensemble
19 simulations of present-day and near-future tropospheric ozone. J. Geophys. Res. [Atmos.]
20 111(008301): 10.1029/2005JD006338.
21 Steyn, D. J.; Bottenheim, J. W.; Thomson, R. B. (1997) Overview of tropospheric ozone in the
22 Lower Fraser Valley, and the Pacific '93 field study. Atmos. Environ. 31: 2025-2036.
23 Stith, J.; Dye, J.; Ridley, B.; Laroche, P.; Defer, E.; Baumann, K.; Hiibler, G.; Zerr, R.;
24 Venticinque, M. (1999) NO signatures from lightning flashes. J. Geophys. Res. [Atmos.]
25 101: 16081-16089.
26 Stockwell, W. R.; Middleton, P.; Chang, J. S.; Tang, X. (1990) The second generation Regional
27 Acid Deposition Model chemical mechanism for regional air quality modeling. J.
28 Geophys. Res. [Atmos.] 95: 16,343-16,367.
29 Stockwell, W. R.; Kirchner, F.; Kuhn, M.; Seefeld, S. (1997) A new mechanism for regional
30 atmospheric chemistry modeling. J. Geophys. Res. [Atmos.] 102: 25,847-25,879.
31 Stolzenburg, M. R.; Hering, S. V. (2000) Method for the automated measurement of fine particle
32 nitrate in the atmosphere. Environ. Sci. Technol. 34: 907-914.
33 Streets, D.; Bond, T. C.; Carmichael, G. R.; Fernandes, S. D.; Fu, Q.; He, D.; Klimont, Z.;
34 Nelson, S. M.; Tsai, N. Y.; Wang, M. Q.; Woo, J.-H.; Yarber, K. F. (2003) An inventory
35 of gaseous and primary aerosol emissions in Asia in the year 2000. J. Geophys. Res.
36 [Atmos.] 108(021): 10.1029/2002JD003093.
37 Stutz (2000)
38 Stutz, J.; Hebestreit, K.; Alicke, B.; Platt, U. (1999) Chemistry of halogen ozides in the
39 troposphere: comparison of model calculations with recent field data. J. Atmos. Chem.
40 34:65-85.
September 2007 AX2-164 DRAFT-DO NOT QUOTE OR CITE
-------
1 Stutz, J.; Ackermann, R.; Fast, J. D.; Barrie, L. (2002) Atmospheric reactive chlorine and
2 bromine at the Great Salt Lake, Utah. Geophys. Res. Lett. 29: 10.1029/2002GL014812.
3 Stutz, J.; Alicke, B.; Ackermann, R.; Geyer, A.; Wang, S.; White, A. B.; Williams, E. J.; Spicer,
4 C. W.; Fast, J. D. (2004) Relative humidity dependence of HONO chemistry in urban
5 areas. J. Geophys. Res. [Atmos.] 109: 10.1029/2003JD004135.
6 Stutz, J.; Alicke, B.; Ackermann, R.; Geyer, A.; White, A.; Williams, E. (2004) Vertical profiles
7 of NOs, NzOs, Os, and NOX in the nocturnal boundary layer: 1. Observations during the
8 Texas Air Quality Study 2000. J. Geophys. Res. [Atmos.] 1 09 (D 12306):
9 10.1029/2003JD004209.
10 Suh, H. H.; Spengler, J. D.; Koutrakis, P. (1992) Personal exposures to acid aerosols and
11 ammonia. Environ. Sci. Technol. 26: 2507-2517.
12 Suh, H. H.; Koutrakis, P.; Spengler, J. D. (1994) The relationship between airborne acidity and
13 ammonia in indoor environments. J. Exposure Anal. Environ. Epidemiol. 4: 1-23.
14 Sutton, M. A.; Dragosits, U.; Tang, Y. S.; Fowler, D. (2000) Ammonia emissions from non-
15 agricultural sources in the UK. Atmos. Environ. 34: 855-869.
16 Svensson, R.; Ljungstroem, E.; Lindqvist, 0. (1987) Kinetics of the reaction between nitrogen
17 dioxide and water vapour. Atmos. Environ. 21: 1529-1539.
18 Talbot, R. W.; Vijgen, A. S.; Harriss, R. C. (1990) Measuring tropospheric HN03: problems and
19 prospects for nylon filter and mist chamber techniques. J. Geophys. Res. [Atmos.] 95:
20 7553-7561.
21 Tanaka, P. L.; Riemer, D. D.; Chang, S.; Yarwood, G.; McDonald-Buller, E. C.; Apel, E. C.;
22 Orlando, J. J.; Silva, P. J.; Jimenez, J. L.; Canagaratna, M. R.; Neece, J. D.; Mullins, C.
23 B.; Allen, D. T. (2003) Direct evidence for chlorine-enhanced urban ozone formation in
24 Houston, Texas. Atmos. Environ. 37: 1393-1400.
25 Tanner, R. L.; D'Ottavio, T.; Garber, R.; Newman, L. (1980) Determination of ambient aerosol
26 sulfur using a continuous flame photometric detection system. I. Sampling system for
27 aerosol sulfate and sulfuric acid. Atmos. Environ. 14: 121-127.
28 Tao, W.-K.; Simpson, J. (1993) The Goddard Cumulus Ensemble Model. Part I: model
29 description. Terr. Atmos. Oceanic Sci. (TAO) 4: 35-71.
30 Teklemariam, T. A.; Sparks, J. P. (2004) Gaseous fluxes of peroxyacetyl nitrate (PAN) into plant
31 leaves. Plant Cell Environ. 27: 1149-1158.
32 Teklemariam, T. A.; Sparks, J. P. (2006) Leaf fluxes of NO and NOz in four herbaceous plant
33 species: the role of ascorbic acid. Atmos. Environ. 40: 2235-2244.
34 Thielman, A.; Prevot, A. S. H.; Griiebler, F. C.; Staelhelin, J. (2001) Empirical ozone isopleths
35 as a tool to identify ozone production regimes. Geophys. Res. Lett. 28: 2369-2372.
36 Thompson, A. M.; Pickering, K. E.; Dickerson, R. R.; Ellis, W. G., Jr.; Jacob, D. J.; Scala, J. R.;
37 Tao, W.-K.; McNamara, D. P.; Simpson, J. (1994) Convective-transport over the central
38 United States and its role in regional CO and Os budgets. J. Geophys. Res. [Atmos.] 99:
39 18,703-18,711.
September 2007 AX2-165 DRAFT-DO NOT QUOTE OR CITE
-------
1 Thompson, A. M.; Singh, H. B.; Schlager, H. (2000) Subsonic assessment ozone and nitrogen
2 oxide experiment (SONEX) and pollution from aircraft emissions in the North Atlantic
3 Flight Corridor (POLINAT 2). J. Geophys. Res. [Atmos.] 105: 3595-3603.
4 Thornton [Unpublished data]
5 Thornton, D. C.; Bandy, A. R. (1993) Sulfur dioxide and dimethyl sulfide in the central Pacific
6 troposphere. J. Atmos. Chem. 17: 1-13.
7 Thornton, D. C.; Bandy, A. R.; Blomquist, B. W.; Anderson, B. E. (1996) Impact of
8 anthropogenic and biogenic sources and sinks on carbonyl sulfide in the North Pacific
9 troposphere. J. Geophys. Res. [Atmos.] 101: 1873-1881.
10 Thornton, D. C.; Bandy, A. R.; Blomquist, B. W.; Driedger, A. R.; Wade, T. P. (1999) Sulfur
11 dioxide distribution over the Pacific Ocean 1991-1996. J. Geophys. Res. [Atmos.] 104:
12 5845-5854.
13 Thornton, D. C.; Bandy, A. R.; Tu, F. H.; Blomquist, B. W.; Mitchell, G. M.; Nadler, W.;
14 Lenschow, D. H. (2002) Fast airborne sulfur dioxide measurements by atmospheric
15 pressure ionization mass spectrometry (APIMS). J. Geophys. Res. [Atmos.] 107(D22):
16 10.1029/2002JD02289.
17 Thornton, J. A.; Braban, C. F.; Abbatt, J. P. D. (2003) N205 hydrolysis on sub-micron organic
18 aerosol: the effect of relative humidity, particle phase, and particle size. Phys. Chem.
19 Chem. Phys. 5: 4593-4603.
20 Tie, X. X.; Emmons, L.; Horowitz, L.; Brasseur, G.; Ridley, ,B.; Atlas, E.; Stround, C.; Hess, P.;
21 Klonecki, A.; Madronich, S.; Talbot, R.; Dibb, J. (2003) Effect of sulfate aerosol on
22 tropospheric NOX and ozone budgets: model simulations and TOPSE evidence. J.
23 Geophys. Res. [Atmos.] 108(D4): 10.1029/2001JDOO1508.
24 Tokiwa, H.; Ohnishi, Y. (1986) Mutagenicity and carcinogenicity of nitroarenes and their
25 sources in the environment. Crit. Rev. Toxicol. 17: 23-60.
26 Tokiwa, H.; Nakanishi, Y.; Sera, N.; Hara, N.; Inuzuka, S. (1998) Analysis of environmental
27 carcinogens associated with the incidence of lung cancer. Toxicol. Lett. 99: 33-41.
28 Tonnesen, G. S.; Dennis, R. L. (2000) Analysis of radical propagation efficiency to assess ozone
29 sensitivity to hydrocarbons and NOX: 2. Long-lived species as indicators of ozone
30 concentration sensitivity. J. Geophys. Res. [Atmos.] 105: 9227-9241.
31 Toumi, R. (1994) BrO as a sink for dimethylsulfide in the marine atmosphere. Geophys. Res.
32 Lett. 21: 117-120.
33 Trainer, M.; Parrish, D. D.; Buhr, M. P.; Norton, R. B.; Fehsenfeld, F. C.; Anlauf, K. G.;
34 Bottenheim, J. W.; Tang, Y. Z.; Wiebe, H. A.; Roberts, J. M.; Tanner, R. L.; Newman,
35 L.; Bowersox, V. C.; Meagher, J. F.; Olszyna, K. J.; Rodgers, M. 0.; Wang, T.;
36 Berresheim, H.; Demerjian, K. L.; Roychowdhury, U. K. (1993) Correlation of ozone
37 with N0y in photochemically aged air. J. Geophys. Res. [Atmos.] 98: 2917-2925.
38 Trainer, M.; Parrish, D. D.; Golday, P. D.; Roberts, J.; Fehsenfeld, F. C. (2000) Review of
39 observation-based analysis of the regional factors influencing ozone concentrations.
40 Atmos. Environ. 34: 2045-2061.
September 2007 AX2-166 DRAFT-DO NOT QUOTE OR CITE
-------
1 Treves, K.; Shragina, L.; Rudich, Y. (2000) Henry's Law constants of some P-, y-, and 5-hydroxy
2 alkyl nitrates of atmospheric interest. Environ. Sci. Technol. 34: 1197-1203.
3 Tsai, C.-J.; Huang, H.-Y. (1995) Atmospheric aerosol sampling by an annular denuder system
4 and a high-volume PMio sampler. Environ. Int. 21: 283-291.
5 Turco, R. P.; Toon, 0. B.; Whitten, R. C.; Hamill, P.; Keesee, R. G. (1983) The 1980 eruptions
6 of Mount St. Helens: physical and chemical processes in the stratospheric clouds. J.
7 Geophys. Res. C: Oceans Atmos. 88: 5299-5319.
8 Turekian, V. C.; Macko, S. A.; Keene, W. C. (2001) Application of stable sulfur isotopes to
9 differentiate sources of size-resolved particulate sulfate in polluted marine air at Bermuda
10 during spring. Geophys. Res. Lett. 28: 1491-1494.
11 Turnipseed, A. A.; Huey, L. G.; Nemitz, E.; Stickel, R.; Higgs, J.; Tanner, D. J.; Slusher, D. L.;
12 Sparks, J. P.; Flocke, F.; Guenther, A. (2006) Eddy covariance fluxes of peroxyacetyl
13 nitrates (PANs) and N0y to a coniferous forest. J. Geophys. Res. [Atmos.] 111 (D09304):
14 10.1029/2005JD006631.
15 United Kingdom Air Quality Expert Group (U.K. AQEG). (2004) Nitrogen dioxide in the United
16 Kingdom. London, United Kingdom: Department for Environment, Food and Rural
17 Affairs. Available:
18 http://www.defra.gov.uk/environment/airquality/panels/aqeg/index.htm [12 April, 2007].
19 U.S. Environmental Protection Agency. (1991) Guideline for regulatory application of the urban
20 airshed model. Research Triangle Park, NC: Office of Air Quality Planning and
21 Standards; report no. EPA-450/4-91-013. Available from: NTIS, Springfield, VA; PB92-
22 108760.
23 U.S. Environmental Protection Agency. (1993) Ambient air quality surveillance. Final rule. F. R.
24 (February 12): 8452-8475.
25 U.S. Environmental Protection Agency. (1996) Air quality criteria for ozone and related
26 photochemical oxidants. Research Triangle Park, NC: Office of Research and
27 Development; report nos. EPA/600/AP-93/004aF-cF. 3v. Available from: NTIS,
28 Springfield, VA; PB96-185582, PB96-185590, and PB96-185608. Available:
29 http ://cfpub 2. epa.gov/ncea/.
30 U.S. Environmental Protection Agency. (1997) National air pollutant emission trends 1990-1996.
31 Research Triangle Park, NC: Office of Air Quality Planning and Standards; report no.
32 EPA/454/R-97/011.
33 U.S. Environmental Protection Agency. (1999) Getting started: emissions inventory methods for
34 PM-2.5. Research Triangle Park, NC: Office of Air Quality Planning and Standards,
35 Emission Factor and Inventory Group; EIIP document series - volume IX. Available:
36 http://www.epa.gov/ttn/chief/eiip/techreport/volume09/index.html [7 May 2003].
37 U.S. Environmental Protection Agency. (2000) Air quality criteria for carbon monoxide.
38 Research Triangle Park, NC: National Center for Environmental Assessment; report no.
39 EPA/600/P-99/001F. Available: http://www.epa.gov/ncea/pdfs/coaqcd.pdf [19 April
40 2007].
September 2007 AX2-167 DRAFT-DO NOT QUOTE OR CITE
-------
1 U.S. Environmental Protection Agency. (2004) Air quality criteria for particulate matter.
2 Research Triangle Park, NC: National Center for Environmental Assessment; report no.
3 EPA/600/P-99/002aF-bF. 2v. Available: http://cfpub.epa.gov/ncea/ [9 November, 2004].
4 U.S. Environmental Protection Agency. (2005) Technical assistance document (TAD) for
5 precursor gas measurements in the NCore multi-pollutant monitoring network. Version 4.
6 Research Triangle Park, NC: Office of Air Quality Planning and Standards; report no.
7 EPA-454/R-05-003.
8 U.S. Environmental Protection Agency. (2006a) Air quality criteria for ozone and related
9 photochemical oxidants. Research Triangle Park, NC: National Center for Environmental
10 Assessment; report no. EPA/600/R-05/004aF-cF. 3v. Available:
11 http://cfpub.epa.gov/ncea/ [24 March, 2006].
12 U.S. Environmental Protection Agency. (2006b) Technology Transfer Network clearinghouse for
13 inventories and emission factors. Available:
14 http://www.epa.gov/ttn/chief/net/2002inventory.html [19 September, 2007].
15 Van Aardenne, J. A.; Dentener, F. J.; Olivier, J. G. J.; Klein Goldewijk, C. G. M.; Lelieveld, J.
16 (2001) A 1° x 1° resolution data set of historical anthropogenic trace gas emissions for
17 the period 1980-1990. Global Biogeochem. Cycles 15: 909-928.
18 Van der Werf, G. R.; Randerson, J. T.; Collatz, J.; Giglio, L. (2003) Carbon emissions from fires
19 in tropical and subtropical ecosystems. Glob. Change Biol. 9: 547-562.
20 Van Noije, T. P. C.; Eskes, H. J.; Dentener, F. J.; Stevenson, D. S.; Ellingsen, K.; Schultz, M. G.;
21 Wild, 0.; Amann, M.; Atherton, C. S.; Bergmann, D. J.; Bey, I.; Boersma, K. F.; Butler,
22 T.; Cofala, J.; Drevet, J.; Fiore, A. M.; Gauss, M.; Hauglustaine, D. A.; Horowitz, L. W.;
23 Isaksen, I. S. A.; Krol, M. C.; Lamarque, J.-F.; Lawrence, M. G.; Martin, R. V.;
24 Montanaro, V.; Miiller, J.-F.; Pitari, G.; Prather, M. J.; Pyle, J. A.; Richter, A.;
25 Rodriguez, J. M.; Savage, N. H.; Strahan, S. E.; Sudo, K.; Szopa, S.; Van Roozendael, M.
26 (2006) Multi-model ensemble simulations of tropospheric N02 compared with GOME
27 retrievals for the year 2000. Atmos. Chem. Phys. Discuss. 6: 2965-3047.
28 Vautard, R.; Martin, D.; Beekman, M.; Drobinski, P.; Friedrich, R.; Jaubertie, A.; Kley, D.;
29 Lattuati, M.; Moral, P.; Neininger, B.; Theloke, J. (2002) Paris emission inventory
30 diagnostics from the ESQUIF airborne measurements and a chemistry transport model. J.
31 Geophys. Res. [Atmos.] 108(D17): 10.1029/2002JD002797.
32 Vione, D.; Barra, S.; De Gennaro, G.; De Rienzo, M.; Gilardoni, S.; Perrone, M. G.; Pozzoli, L.
33 (2004) Polycyclic aromatic hydrocarbons in the atmosphere: monitoring, sources, sinks
34 and fate. II: sinks and fate. Ann. Chim. 94: 257-268.
35 Vogt, R.; Crutzen, P. J.; Sander, R. (1996) A mechanism for halogen release from sea-salt
36 aerosol in the remote marine boundary layer. Nature (London, U.K.) 383: 327-330.
37 Vogt, R.; Sander, R.; Von Glasow, R.; Crutzen, P. J. (1999) Iodine chemistry and its role in
38 halogen activation and ozone loss in the marine boundary layer: a model study. J. Atmos.
39 Chem. 32: 375-395.
40 Volz-Thomas, A.; Geiss, H.; Hofzumahaus, A.; Becker, K.-H. (2003) Introduction to special
41 section: photochemistry experiment in BERLIOZ. J. Geophys. Res. [Atmos.] 108(D4):
42 10.1029/JD002029.
September 2007 AX2-168 DRAFT-DO NOT QUOTE OR CITE
-------
1 Von Glasow, R.; Sander, R.; Bott, A.; Crutzen, P. J. (2002a) Modeling halogen chemistry in the
2 marine boundary layer. 1. Cloud-free MBL. J. Geophys. Res. [Atmos.] 107(D17):
3 10.1029/2001JD000942.
4 Von Glasow, Sander, R.; Bott, A.; Crutzen, P. J. (2002b) Modeling halogen chemistry in the
5 marine boundary layer. 2. Interactions with sulfur and cloud-covered MBL. J. Geophys.
6 Res. [Atmos.] 107(D17): 10.1029/2001JD000943.
7 Von Glasow, R.; Von Kuhlmann, R.; Lawrence, M. G.; Platt, U.; Crutzen, P. J. (2004) Impact of
8 reactive bromine chemistry in the troposphere. Atmos. Chem. Phys. 4: 2481-2497.
9 Wagner, T.; Leue, C.; Wenig, M.; Pfeilsticker, K.; Platt, U. (2001) Spatial and temporal
10 distribution of enhanced boundary layer BrO concentrations measured by the GOME
11 instrument aboard ERS-2. J. Geophys. Res. [Atmos.] 106: 24,225-24,235.
12 Walcek, C. J.; Taylor, G. R. (1986) A theoretical method for computing vertical distributions of
13 acidity and sulfate production within cumulus clouds. J. Atmos. Sci. 43: 339-355.
14 Walcek, C. J.; Stockwell, W. R.; Chang, J. S. (1990) Theoretical estimates of the dynamic,
15 radiative and chemical effects of clouds on tropospheric trace gases. Atmos. Res. 25: 53-
16 69.
17 Walega, J. G.; Stedman, D. H.; Shetter, R. E.; Mackay, G. I.; Iguchi, T.; Schiff, H. I. (1984)
18 Comparison of a chemiluminescent and a tunable diode laser absorption technique for the
19 measurement of nitrogen oxide, nitrogen dioxide, and nitric acid. Environ. Sci. Technol.
20 18:823-826.
21 Wang, Y.; Tao, W.-K.; Pickering, K. E.; Thompson, A. M.; Kain, J. S.; Adler, R. F.; Simpson, J.;
22 Keehn, P. R.; Lai, G. S. (1996) Mesoscale model simulations of TRACE A and
23 preliminary regional experiment for storm-scale operational and research meteorology
24 convective systems and associated tracer transport. J. Geophys. Res. [Atmos.] 101:
25 24,013-24,027.
26 Wang, Y.; DeSilva, A. W.; Goldenbaum, G. C.; Dickerson, R. R. (1998) Nitric oxide production
27 by simulated lightning: dependence on current, energy, and pressure. J. Geophys. Res.
28 [Atmos.] 103: 19,149-19,159.
29 Wang, L. H.; Milford, J. B.; Carter, W. P. L. (2000a) Reactivity estimates for aromatic
30 compounds. Part I: uncertainty in chamber-derived parameters. Atmos. Environ. 34:
31 4337-4348.
32 Wang, L. H.; Milford, J. B.; Carter, W. P. L. (2000b) Reactivity estimates for aromatic
33 compounds. Part 2. uncertainty in incremental reactivities. Atmos. Environ. 4349-4360.
34 Warneck, P. (1999) The relative importance of various pathways for the oxidation of sulfur
35 dioxide and nitrogen dioxide in sunlit continental fair weather clouds. Phys. Chem.
36 Chem. Phys. 1:5471-5483.
37 Watson, J. G.; Fujita, E. M.; Chow, J. C.; Zielinska, B.; Richards, L. W.; Neff, W.; Dietrich, D.
38 (1998) Northern front range air quality study. Final report. Fort Collins, CO: Colorado
39 State University, Cooperative Institute for Research in the Atmosphere. Available:
40 http://www.nfraqs.colostate.edu/index2.html (16 Jan 2002).
September 2007 AX2-169 DRAFT-DO NOT QUOTE OR CITE
-------
1 Weber, P.; NuBbaum, S.; Fuhrer, J.; Gfeller, H.; Schlunegger, U. P.; Brunold, C.; Rennenberg,
2 H. (1995) Uptake of atmospheric 15N02 and its incorporation into free amino-acids in
3 wheat (Triticum-aestivum). Physiol. Plant. 94: 71-77.
4 Weber, P.; Thoene, B.; Rennenberg, H. (1998) Absorption of atmospheric N02 by spruce (Picea
5 abies) trees. III. Interaction with nitrate reductase activity in the needles and phloem
6 transport. Bot. Acta 111: 377-382.
7 Wedin, D. A.; Tilman, D. (1996) Influence of nitrogen loading and species composition on the
8 carbon balance of grasslands. Science (Washington, DC) 274: 1720-1723.
9 Wendel, G. J.; Stedman, D. H.; Cantrell, C. A.; Damrauer, L. (1983) Luminol-based nitrogen
10 dioxide detector. Anal. Chem. 55: 937-940.
11 Wesely, M. L. (1989) Parameterization of surface resistances to gaseous dry deposition in
12 regional-scale numerical models. Atmos. Environ. 23: 1293-1304.
13 Wesely, M. L.; Hicks, B. B. (1977) Some factors that affect the deposition rates of sulfur dioxide
14 and similar gases on vegetation. J. Air Pollut. Control Assoc. 27: 1110-1116.
15 Wesely, M. L.; Hicks, B. B. (2000) A review of the current status of knowledge on dry
16 deposition. Atmos. Environ. 34: 2261-2282.
17 Westerling, A. L.; Hildalgo, H. G.; Cayan, D. R.; Swetnam, T. W. (2006) Warming and earlier
18 spring increase western U.S. forest wildfire activity. Science (Washington, DC, U.S.)
19 313: 940-943.
20 Williams, E. J.; Guenther, A.; Fehsenfeld, F. C. (1992) An inventory of nitric oxide emissions
21 from soils in the United States. J. Geophys. Res. [Atmos.] 97: 7511-7519.
22 Winberry, W. T., Jr.; Ellestad, T.; Stevens, B. (1999) Compendium method 10-4.2:
23 determination of reactive acidic and basic gases and strong acidity of atmospheric fine
24 particles (< 2.5 ^im). Compendium of methods for the determination of inorganic
25 compounds in ambient air. Cincinnati, OH: U.S. Environmental Protection Agency,
26 Center for Environmental Research Information; report no. EPA/625/R-96/010a.
27 Available: http://www.epa.gov/ttn/amtic/files/ambient/inorganic/mthd-4-2.pdf [2 May,
28 2007].
29 Witz, S.; Eden, R. W.; Wadley, M. W.; Dunwoody, C.; Papa, R.; Torre, K. J. (1990) Rapid loss
30 of particulate nitrate, chloride and ammonium on quartz fiber filters during storage. J. Air
31 Waste Manage. Assoc. 40: 53-61.
32 World Health Organization (WHO). (2003) Nitrogenated polycyclic aromatic hydrocarbons.
33 Geneva, Switzerland: World Health Organization. (Environmental Health Criteria 229).
34 Xu, J. H.; Lee, F. S. C. (2000) Quantification of nitrated polynuclear aromatic hydrocarbons in
35 atmospheric particulate matter. Anal. Chim. Acta 416: 111-115.
36 Xuetal. (2006) [P.AX2-14]
37 Yienger, J. J.; Levy, H., II. (1995) Empirical model of global soil-biogenic NOX emissions. J.
38 Geophys. Res. [Atmos.] 100: 11,447-11,464.
September 2007 AX2-170 DRAFT-DO NOT QUOTE OR CITE
-------
1 Young, V. L.; Kieser, B. N.; Chen, S. P.; Niki, H. (1997) Seasonal trends and local influences on
2 nonmethane hydrocarbon concentrations in the Canadian boreal forest. J. Geophys. Res.
3 [Atmos.] 102: 5913-5918.
4 Zafiriou, 0. C.; True, M. B. (1979) Nitrate photolysis in seawater by sunlight. Mar. Chem. 8: 33-
5 42.
6 Zanis, P.; Trickl, T.; Stohl, A.; Wernli, H.; Cooper, 0.; Zerefos, C.; Gaeggeler, H.; Schnabel, C.;
7 Tobler, L.; Kubik, P. W.; Priller, A.; Scheel, H. E.; Ranter, H. J.; Cristofanelli, P.;
8 Forster, C.; James, P.; Gerasopoulos, E.; Delcloo, A.; Papayannis, A.; Claude, H. (2003)
9 Forecast, observation and modelling of a deep stratospheric intrusion event over Europe.
10 Atmos. Chem. Phys. 3: 763-777.
11 Zhang, G. J.; McFarlane, N. A. (1995) Sensitivity of climate simulations to the parameterization
12 of cumulus convection in the Canadian Climate Centre general circulation model. Atmos.
13 Ocean 33: 407-446.
14 Zhang, X. Q.; McMurry, P. H. (1987) Theoretical analysis of evaporative losses from impactor
15 and filter deposits. Atmos. Environ. 21: 1779-1789.
16 Zhang, J.-Z.; Millero, F. J. (1991) The rate of sulfite oxidation in seawater. Geochim.
17 Cosmochim. Acta 55: 677-685.
18 Zhang et al. (2006) [P. AX2-7]
19 Zhou, X.; Beine, H. J.; Honrath, R. E.; Fuentes, J. D.; Simpson, W.; Shepson, P. B.; Bottenheim,
20 J. W. (2001) Snowpack photochemical production of HONO: a major source of OH in
21 the Arctic boundary layer in springtime. Geophys. Res. Lett. 28: 4087-4090.
22 Zhou, X.; Civerolo, K.; Dai, H. (2002a) Summertime nitrous acid chemistry in the atmospheric
23 boundary layer at a rural site in New York state. J. Geophys. Res. [Atmos.] 107(D21):
24 10.1029/2001JD001539.
25 Zhou, X. L.; He, Y.; Huang, G.; Thornberry, T. D.; Carroll, M. A.; Bertman, S. B. (2002b)
26 Photochemical production of nitrous acid on glass sample manifold surface. Geophys.
27 Res. Lett. 29(1681): 10.1029/2002GL015080.
28 Zhou, X.; Gao, H.; He, Y.; Huang, G. (2003) Nitric acid photolysis on surfaces in low-NOx
29 environments: significant atmospheric implications. Geophys. Res. Lett. 30:
30 10.1029/2003GL018620.
31 Zielinska, B.; Arey, J.; Atkinson, R.; Ramdahl, T.; Winer, A. M.; Pitts, J. N., Jr. (1986) Reaction
32 of dinitrogen pentoxide with fluoranthene. J. Am. Chem. Soc. 108: 4126-4132.
33 Zielinska, B.; Arey, J.; Atkinson, R.; Winer, A. M. (1989) The nitroarenes of molecular weight
34 247 in ambient particulate samples collected in southern California. Atmos. Environ. 23:
35 223-229.
36 Zielinska, B.; Sagebiel, J.; McDonald, J. D.; Whitney, K.; Lawson, D. R. (2004) Emission rates
37 and comparative chemical composition from selected in-use diesel and gasoline-fueled
38 vehicles. J. Air Waste Manage. Assoc. 54: 1138-1150.
39 Zimmermann, J.; Poppe, D. (1993) Nonlinear chemical couplings in the tropospheric NOX—HOX
40 gas phase chemistry. J. Atmos. Chem. 17: 141-155.
September 2007 AX2-171 DRAFT-DO NOT QUOTE OR CITE
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1 Zingler, J.; Platt, U. (2005) Iodine oxide in the Dead Sea Valley: evidence for inorganic sources
2 of boundary layer 10. J. Geophys. Res. [Atmos.] 110(D07307): 10.1029/2004JD004993.
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i AX3. CHAPTER 3 ANNEX - A FRAMEWORK FOR
2 MODELING HUMAN EXPOSURES TO SO2 AND
3 RELATED AIR POLLUTANTS
4
5
6 AX3.1 INTRODUCTION: CONCEPTS, TERMINOLOGY, AND
7 OVERALL SUMMARY
8 Predictive (or prognostic) exposure modeling studies1, specifically focusing on 862,
9 could not be identified in the literature, though, often, statistical (diagnostic) analyses have been
10 reported using data obtained in various field exposure studies. However, existing prognostic
11 modeling systems for the assessment of inhalation exposures can in principle be directly applied
12 to, or adapted for, SO2 studies; specifically, such systems include APEX, SHEDS, and
13 MENTOR-1 A, to be discussed in the following sections. Nevertheless, it should be mentioned
14 that such applications will be constrained by data limitations, such as the degree of ambient
15 concentration characterization (e.g., concentrations at the local level) and quantitative
16 information on indoor sources and sinks.
17 Predictive models of human exposure to ambient air pollutants such as 862 can be
18 classified and differentiated based upon a variety of attributes. For example, exposure models
19 can be classified as:
20 • models of potential (typically maximum) outdoor exposure versus models of actual
21 exposures (the latter including locally modified microenvironmental exposures, both
22 outdoor and indoor),
23 • Population Based Exposure Models (PBEM) versus Individual Based Exposure Models
24 (ffiEM),
25 • deterministic versus probabilistic (or statistical) exposure models,
26 • observation-driven versus mechanistic air quality models (see Section AX3.4 for
27 discussions about the construction, uses and limitations of this class of mathematical
28 models.
1 i.e. assessments that start from emissions and demographic information and explicitly consider the physical and
chemical processes of environmental and microenvironmental transport and fate, in conjunction with human
activities, to estimate inhalation intake and uptake.
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1 Some points should be made regarding terminology and essential concepts in exposure
2 modeling, before proceeding to the overview of specific developments reported in the current
3 research literature:
4 First, it must be understood that there is significant variation in the definitions of many of
5 the terms used in the exposure modeling literature; indeed, the science of exposure modeling is a
6 rapidly evolving field and the development of a standard and commonly accepted terminology is
7 an ongoing process (see, e.g., WHO, 2004).
8 Second, it should also be mentioned that, very often, procedures that are called exposure
9 modeling, exposure estimation, etc. in the scientific literature, may in fact refer to only a sub-set
10 of the complete set of steps or components required for a comprehensive exposure assessment.
11 For example, certain self-identified exposure modeling studies focus solely on refining the sub-
12 regional or local spatio-temporal dynamics of pollutant concentrations (starting from raw data
13 representing monitor observations or regional grid-based model estimates). Though not
14 exposure studies per se, such efforts have value and are included in the discussion of the next
15 sub-section, as they provide potentially useful tools that can be used in a complete exposure
16 assessment. On the other hand, formulations that are self-identified as exposure models but
17 actually focus only on ambient air quality predictions, such as chemistry-transport models, are
18 not included in the discussion that follows.
19 Third, the process of modeling human exposures to ambient pollutants (traditionally
20 focused on ozone) is very often identified explicitly with population-based modeling, while
21 models describing the specific mechanisms affecting the exposure of an actual individual (at
22 specific locations) to an air contaminant (or to a group of co-occurring gas and/or aerosol phase
23 pollutants) are usually associated with studies focusing specifically on indoor air chemistry
24 modeling.
25 Finally, fourth, the concept of microenvironments, introduced in earlier sections of this
26 document, should be clarified further, as it is critical in developing procedures for exposure
27 modeling. In the past, microenvironments have typically been defined as individual or aggregate
28 locations (and sometimes even as activities taking place within a location) where a homogeneous
29 concentration of the pollutant is encountered. Thus a microenvironment has often been
30 identified with an ideal (i.e. perfectly mixed) compartment of classical compartmental modeling.
31 More recent and general definitions view the microenvironment as a control volume, either
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1 indoors or outdoors, that can be fully characterized by a set of either mechanistic or
2 phenomenological governing equations, when appropriate parameters are available, given
3 necessary initial and boundary conditions. The boundary conditions typically would reflect
4 interactions with ambient air and with other microenvironments. The parameterizations of the
5 governing equations generally include the information on attributes of sources and sinks within
6 each microenvironment. This type of general definition allows for the concentration within a
7 microenvironment to be non-homogeneous (non-uniform), provided its spatial profile and
8 mixing properties can be fully predicted or characterized. By adopting this definition, the
9 number of microenvironments used in a study is kept manageable, but variability in
10 concentrations in each of the microenvironments can still be taken into account.
11 Microenvironments typically used to determine exposure include indoor residential
12 microenvironments, other indoor locations (typically occupational microenvironments), outdoors
13 near roadways, other outdoor locations, and in-vehicles. Outdoor locations near roadways are
14 segregated from other outdoor locations (and can be further classified into street canyons,
15 vicinities of intersections, etc.) because emissions from automobiles alter local concentrations
16 significantly compared to background outdoor levels. Indoor residential microenvironments
17 (kitchen, bedroom, living room, etc. or aggregate home microenvironment) are typically
18 separated from other indoor locations because of the time spent there and potential differences
19 between the residential environment and the work/public environment.
20 Once the actual individual and relevant activities and locations (for Individual Based
21 Modeling), or the sample population and associated spatial (geographical) domain (for
22 Population Based Modeling) have been defined along with the temporal framework of the
23 analysis (time period and resolution), the comprehensive modeling of individual/population
24 exposure to SO2 (and related pollutants) will in general require seven steps (or components, as
25 some of them do not have to be performed in sequence) that are listed below. This list represents
26 a composite based on approaches and frameworks described in the literature over the last twenty -
27 five years (Ott, 1982; Ott, 1985; Lioy, 1990; U.S. Environmental Protection Agency, 1992;
28 Georgopoulos and Lioy, 1994; U.S. Environmental Protection Agency, 1997; Price et al., 2003;
29 Georgopoulos et al., 2005; WHO, 2005; U.S. Environmental Protection Agency, 2006a;
30 Georgopoulos and Lioy, 2006) as well on the structure of various inhalation exposure models
31 (NEM/pNEM, HAPEM, SHEDS, REHEX, EDMAS, MENTOR, ORAMUS, APEX, AIRPEX,
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1 AIRQUIS, etc., to be discussed in the following section) that have been used in the past or in
2 current studies to specifically assess inhalation exposures. Figure AX3.1-1, adapted from
3 Georgopoulos et al. (2005), schematically depicts the sequence of steps involved that are
4 summarized here (and further discussed in the following sub-sections).
i.a. Emissions: NEI (NET, NTI), state; Processing with SMOKE,
EMS-HAP, MOBILE/MOVES, NONROAD, FAAED, BEIS, etc.
i.b. Meteorology: NWS, NCDC; Modeling MMS, RAMS, CALMET
i.e. Land Use/ Land Cover, Topography: NLDC (USGS), etc.
*
Estimate background ;
levels of air pollutants j
through I
a. multivariate spatio- |
temporai analysis of |
monitor data j
b. emissions-based air ^^
quality modeling \
(with regional, j
grid-based models: 1
ModelS-3/ CMAQ, CAM* j
and REMSAD) I
* A
J . Estimate local outdoor
> - pollutant levels that
characterize Hie ambient air of
an administrative unit (such as
census tract) or a conveniently
defined grid through
a. spatiotemporal statistical
analysis of monitor data
b. application of urban scale
model at high resolution
c. subgrld (e.g. plume-in-grfd)
modeling
d. data/ model assimilation
-u; Develop database of ]
- :' individual subjects \
attributes (residence ft I
work location, housing >
characteristics, age, \
gender, race, income, etc.) j-
a. collect study-specific ]"*
information j
b. supplement with \
available relevant local, j
regional, and national ]
demographic j
information j
1 Develop activity event
"*> {or exposure event)
sequences for each individual
of the study for the exposure
period
a. collect study-specific
information
b. supplement with other
available data
c. organize time-activity
database in format
compatible with CHAD
t t
/ Study-speciflc survey / Study-specific survey
(also US Census, (or default from
US Housing Survey) CHAD, NHAPS)
^
s
— *
ii.a. Emissions: EMS-HAP
ii.b. Local Meteorology — Local
Effects; RAMS, FLUENT
*
Estimate levels and
... temporal profiles of
pollutants in various
microenvironments (streets,
residences, offices, restaurants,
vehicles, etc.) through
a. regression of observational
data
b. simple linear mass balance
c. lumped (nonlinear) uE
gas/aerosol chemistry models
d« combined chemistry 8t CFD
(DNS, LES, RANS) models
*
Calculate appropriate
Inhalation rates for the
members of the sample
population, combining the
physiological attributes of the
study subjects and the
activities pursued during the
individual exposure events
t
/ ICRP and Other
Physiological & METS
Databases
— — \
1
^
• i
Calculate ™|P Biologically
exposures/ i based
intakes target tissue
dose modeling
A
> *
j
Figure AX3.1-1.
Schematic description of a general framework identifying the
processes (steps or components) involved in assessing inhalation
exposures and doses for individuals and populations. In general
terms, existing comprehensive exposure modeling systems such as
SHEDS, APEX, and MENTOR-1A follow this framework.
Source: Figure adapted with modifications from Georgopoulos et al. (2005).
5
6
9
10
1. Estimation of the background or ambient levels of both 862 and related
pollutants. This is done through either (or a combination of):
a. multivariate spatio-temporal analysis of fixed monitor data, or
b. emissions-based, photochemical, air quality modeling (typically with a
regional, grid-based model such as Models-3/CMAQ or CAMx) applied in
a coarse resolution mode.
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1 2. Estimation of local outdoor pollutant levels of both 862 and related pollutants.
2 These levels could typically characterize the ambient air of either an
3 administrative unit (such as a census tract, a municipality, a county, etc.) or a
4 conveniently defined grid cell of an urban scale air quality model. Again, this
5 may involve either (or a combination of):
6 a. spatio-temporal statistical analysis of monitor data, or
7 b. application of an urban multi-scale, grid based model (such as CMAQ or
8 CAMx) at its highest resolution (typically around 2-4 km), or
9 c. correction of the estimates of the regional model using some scheme that
10 adjusts for observations and/or for subgrid chemistry and mixing
11 processes.
12
13 3. Characterization of relevant attributes of the individuals or populations under
14 study (residence and work locations, occupation, housing data, income, education,
15 age, gender, race, weight, and other physiological characteristics). For Population
16 Based Exposure Modeling (PBEM) one can either:
17 a. select a fixed-size sample population of virtual individuals in a way that
18 statistically reproduces essential demographics (age, gender, race,
19 occupation, income, education) of the administrative population unit used
20 in the assessment (e.g., a sample of 500 people is typically used to
21 represent the demographics of a given census tract, whereas a sample of
22 about 10,000 may be needed to represent the demographics of a county),
23 or
24 b. divide the population-of interest into a set of cohorts representing selected
25 subpopulations where the cohort is defined by characteristics known to
26 influence exposure.
27
28 4. Development of activity event (or exposure event) sequences for each member of
29 the sample population (actual or virtual) or for each cohort for the exposure
30 period. This could utilize:
31 a. study-specific information, if available
32 b. existing databases based on composites of questionnaire information from
33 past studies
34 c. time-activity databases, typically in a format compatible with U.S.
35 Environmental Protection Agency's Consolidated Human Activity
36 Database (CHAD - McCurdy et al., 2000)
37
38 5. Estimation of levels and temporal profiles of both SC>2 and related pollutants in
39 various outdoor and indoor microenvironments such as street canyons, roadway
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1 intersections, parks, residences, offices, restaurants, vehicles, etc. This is done
2 through either:
3 a. linear regression of available observational data sets,
4 b. simple mass balance models (with linear transformation and sinks) over
5 the volume (or a portion of the volume) of the microenvironment,
6 c. lumped (nonlinear) gas or gas/aerosol chemistry models, or
7 d. detailed combined chemistry and Computational Fluid Dynamics
8 modeling.
9
10 6. Calculation of appropriate inhalation rates for the members of the sample
11 population, combining the physiological attributes of the (actual or virtual) study
12 subjects and the activities pursued during the individual exposure events.
13
14 7. Calculation of target tissue dose through biologically based modeling estimation
15 (specifically, respiratory dosimetry modeling in the case of SC>2 and related
16 reactive pollutants) if sufficient information is available.
17
18 Implementation of the above framework for comprehensive exposure modeling has
19 benefited significantly from recent advances and expanded availability of computational
20 technologies such as Relational Database Management Systems (RDBMS) and Geographic
21 Information Systems (GIS) (Purushothaman and Georgopoulos, 1997, 1999a,b; Georgopoulos
22 et al., 2005).
23 In fact, only relatively recently comprehensive, predictive, inhalation exposure modeling
24 studies for ozone, PM, and various air toxics, have attempted to address/incorporate all the
25 components of the general framework described here. In practice, the majority of past exposure
26 modeling studies have either incorporated only subsets of these components or treated some of
27 them in a simplified manner, often focusing on the importance of specific factors affecting
28 exposure. Of course, depending on the objective of a particular modeling study, implementation
29 of only a limited number of steps may be necessary. For example, in a regulatory setting, when
30 comparing the relative effectiveness of emission control strategies, the focus can be on expected
31 changes in ambient levels (corresponding to those observed at NAAQS monitors) in relation to
32 the density of nearby populations. The outdoor levels of pollutants, in conjunction with basic
33 demographic information, can thus be used to calculate upper bounds of population exposures
34 associated with ambient air (as opposed to total exposures that would include contributions from
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1 indoor sources) useful in comparing alternative control strategies. Though the metrics derived
2 would not be quantitative indicators of actual human exposures, they can serve as surrogates of
3 population exposures associated with outdoor air, and thus aid in regulatory decision making
4 concerning pollutant standards and in studying the efficacy of emission control strategies. This
5 approach has been used in studies performing comparative evaluations of regional and local
6 emissions reduction strategies in the eastern United States (e.g., Purushothaman and
7 Georgopoulos, 1997; Georgopoulos et al., 1997a; Foley et al., 2003).
8
9
10 AX3.2 POPULATION EXPOSURE MODELS: THEIR EVOLUTION
11 AND CURRENT STATUS
12 Existing comprehensive inhalation exposure models consider the trajectories of
13 individual human subjects (actual or virtual), or of appropriately defined cohorts, in space and
14 time as sequences of exposure events. In these sequences, each event is defined by time, a
15 geographic location, a microenvironment, and the activity of the subject. U.S. Environmental
16 Protection Agency offices (OAQPS and NERL) have supported the most comprehensive efforts
17 in developing models implementing this general concept (see, e.g., Johnson, 2002), and these
18 efforts have resulted in the NEM/pNEM (National Exposure Model and Probabilistic National
19 Exposure Model - Whitfield et al., 1997), HAPEM (Hazardous Air Pollutant Exposure Model -
20 Rosenbaum, 2005), SHEDS (Simulation of Human Exposure and Dose System - Burke et al.,
21 2001), APEX (Air Pollutants Exposure model - U.S. Environmental Protection Agency,
22 2006b,c), and MENTOR (Modeling Environment for Total Risk studies - Georgopoulos et al.,
23 2005; Georgopoulos and Lioy, 2006) families of models. European efforts have produced some
24 formulations with similar general attributes as the above U.S. models but, generally, involving
25 simplifications in some of their components. Examples of European models addressing
26 exposures to photochemical oxidants (specifically ozone) include the AirPEx (Air Pollution
27 Exposure) model (Freijer et al., 1998), which basically replicates the pNEM approach and has
28 been applied to the Netherlands, and the AirQUIS (Air Quality Information System) model
29 (Clench-Aas et al., 1999).
30 The NEM/pNEM, SHEDS, APEX, and MENTOR-1A (MENTOR for One-Atmosphere
31 studies) families of models provide exposure estimates defined by concentration and breathing
32 rate for each individual exposure event, and then average these estimates over periods typically
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1 ranging from one h to one year. These models allow simulation of certain aspects of the
2 variability and uncertainty in the principal factors affecting exposure. An alternative approach is
3 taken by the HAPEM family of models that typically provide annual average exposure estimates
4 based on the quantity of time spent per year in each combination of geographic locations and
5 microenvironments. The NEM, SHEDS, APEX, and MENTOR-type models are therefore
6 expected to be more appropriate for pollutants with complex chemistry such as SC>2, and could
7 provide useful information for enhancing related health assessments.
8
9 More specifically, regarding the consideration of population demographics and activity
10 patterns:
11 1. pNEM divides the population of interest into representative cohorts based on the
12 combinations of demographic characteristics (age, gender, and employment),
13 home/work district, residential cooking fuel and replicate number, and then
14 assigns an activity diary record from CHAD (Consolidated Human Activities
15 Database) to each cohort according to demographic characteristic, season, day-
16 type (weekday/weekend) and temperature.
17 2. HAPEM6 divides the population of interest into demographic groups based on
18 age, gender and race, and then for each demographic group/day-type
19 (weekday/weekend) combination, selects multiple activity patterns randomly
20 (with replacement) from CHAD and combines them to find the averaged annual
21 time allocations for group members in each census tract for different day types.
22 3. SHEDS, APEX, and MENTOR-1A generate population demographic files, which
23 contain a user-defined number of person records for each census tract of the
24 population based on proportions of characteristic variables (age, gender,
25 employment, and housing) obtained from the population of interest, and then
26 assign a matching activity diary record from CHAD to each individual record of
27 the population based on the characteristic variables. It should be mentioned that,
28 in the formulations of these models, workers may commute from one census tract
29 to another census tract for work. So, with the specification of commuting
30 patterns, the variation of exposure concentrations due to commuting between
31 different census tracts can be captured.
32
33 The essential attributes of the pNEM, HAPEM, APEX, SHEDS, and MENTOR-1A
34 models are summarized in Table AX3.2-1.
35 The conceptual approach originated by the SHEDS models was modified and expanded
36 for use in the development of MENTOR-1 A (Modeling Environment for Total Risk - One
37 Atmosphere). Flexibility was incorporated into this modeling system, such as the option of
38 including detailed indoor chemistry and other relevant microenvironmental processes, and
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1 providing interactive linking with CHAD for consistent definition of population characteristics
2 and activity events (Georgopoulos et al., 2005).
3 NEM/pNEM implementations have been extensively applied to ozone studies in the
4 1980s and 1990s. The historical evolution of the pNEM family of models of OAQPS started
5 with the introduction of the first NEM model in the 1980s (Biller et al., 1981). The first such
6 implementations of pNEM/Os in the 1980s used a regression-based relationship to estimate
7 indoor ozone concentrations from outdoor concentrations. The second generation of pNEM/Os
8 was developed in 1992 and included a simple mass balance model to estimate indoor ozone
9 concentrations. A report by Johnson et al. (2000) describes this version of pNEM/Os and
10 summarizes the results of an initial application of the model to 10 cities. Subsequent
11 enhancements to pNEM/O3 and its input databases included revisions to the methods used to
12 estimate equivalent ventilation rates, to determine commuting patterns, and to adjust ambient
13 ozone levels to simulate attainment of proposed NAAQS. During the mid-1990s, the
14 Environmental Protection Agency applied updated versions of pNEM/Os to three different
15 population groups in selected cities: (1) the general population of urban residents, (2) outdoor
16 workers, and (3) children who tend to spend more time outdoors than the average child. This
17 version of pNEM/Os used a revised probabilistic mass balance model to determine ozone
18 concentrations over one-h periods in indoor and in-vehicle microenvironments (Johnson, 2001).
19 In recent years, pNEM has been replaced by (or "evolved to") the Air Pollution Exposure
20 Model (APEX). APEX differs from earlier pNEM models in that the probabilistic features of the
21 model are incorporated into a Monte Carlo framework (Langstaff, 2007; U.S. Environmental
22 Protection Agency, 2006b,c). Like SHEDS and MENTOR-1 A, instead of dividing the
23 population-of-interest into a set of cohorts, APEX generates individuals as if they were being
24 randomly sampled from the population. APEX provides each generated individual with a
25 demographic profile that specifies values for all parameters required by the model. The values
26 are selected from distributions and databases that are specific to the age, gender, and other
27 specifications stated in the demographic profile. The Environmental Protection Agency has
28 applied APEX to the study of exposures to ozone and other criteria pollutants; APEX can be
29 modified and used for the estimation of SC>2 exposures, if required.
30 Reconfiguration of APEX for use with SC>2 or other pollutants would require significant
31 literature review, data analysis, and modeling efforts. Necessary steps include determining
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1 spatial scope and resolution of the model; generating input files for activity data, air quality and
2 temperature data; and developing definitions for microenvironments and pollutant-
3 microenvironment modeling parameters (penetration and proximity factors, indoor source
4 emissions rates, decay rates, etc.) (ICF Consulting, 2005). To take full advantage of the
5 probabilistic capabilities of APEX, distributions of model input parameters should be used
6 wherever possible.
7
8
9 AX3.3 CHARACTERIZATION OF AMBIENT CONCENTRATIONS OF
10 SO2 AND RELATED AIR POLLUTANTS
11 As mentioned earlier, background and regional outdoor concentrations of pollutants over
12 a study domain may be estimated through emissions-based mechanistic modeling, through
13 ambient data based modeling, or through a combination of both. Emissions-based models
14 calculate the spatio-temporal fields of the pollutant concentrations using precursor emissions and
15 meteorological conditions as inputs and using numerical representations of transformation
16 reactions to drive outputs. The ambient data based models typically calculate spatial or spatio-
17 temporal distributions of the pollutant through the use of interpolation schemes, based on either
18 deterministic or stochastic models for allocating monitor station observations to the nodes of a
19 virtual regular grid covering the region of interest. The geostatistical technique of kriging
20 provides various standard procedures for generating an interpolated spatial distribution for a
21 given time, from data at a set of discrete points. Kriging approaches were evaluated by
22 Georgopoulos et al. (Georgopoulos et al., 1997b) in relation to the calculation of local ambient
23 ozone concentrations for exposure assessment purposes, using either monitor observations or
24 regional/urban photochemical model outputs. It was found that kriging is severely limited by the
25 nonstationary character of the concentration patterns of reactive pollutants; so the advantages this
26 method has in other fields of geophysics do not apply here. The above study showed that the
27 appropriate semivariograms had to be hour-specific, complicating the automated reapplication of
28 any purely spatial interpolation over an extended time period.
29 Spatio-temporal distributions of pollutant concentrations such as ozone, PM, and various
30 air toxics have alternatively been obtained using methods of the Spatio-Temporal Random Field
31 (STRF) theory (Christakos and Vyas, 1998a,b). The STRF approach interpolates monitor data in
32 both space and time simultaneously. This method can thus analyze information on temporal
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1 trends which cannot be incorporated directly in purely spatial interpolation methods such as
2 standard kriging. Furthermore, the STRF method can optimize the use of data which are not
3 uniformly sampled in either space or time. STRF was further extended within the Bayesian
4 Maximum Entropy (BME) framework and applied to ozone interpolation studies (Christakos and
5 Hristopulos, 1998; Christakos and Kolovos, 1999; Christakos, 2000). It should be noted that
6 these studies formulate an over-arching scheme for linking air quality with population dose and
7 health effects; however, they are limited by the fact that they do not include any
8 microenvironmental effects. MENTOR has incorporated STRF/BME methods as one of the
9 steps for performing a comprehensive analysis of exposure to ozone and PM (Georgopoulos
10 etal.,2005).
11 The issue of subgrid variability (SGV) from the perspective of interpreting and evaluating
12 the outcomes of grid-based, multiscale, photochemical air quality simulation models is discussed
13 in Ching et al. (2006), who suggest a framework that can provide for qualitative judgments on
14 model performance based on comparing observations to the grid predictions and its SGV
15 distribution. From the perspective of Population Exposure Modeling, the most feasible/practical
16 approach for treating subgrid variability of local concentrations is probably through 1) the
17 identification and proper characterization of an adequate number of outdoor microenvironments
18 (potentially related to different types of land use within the urban area as well as to proximity to
19 different types of roadways) and 2) then, concentrations in these microenvironments will have to
20 be adjusted from the corresponding local background ambient concentrations through either
21 regression of empirical data or various types of local atmospheric dispersion/transformation
22 models. This is discussed further in the next subsection.
23
24
25 AX3.4 CHARACTERIZATION OF MICROENVIRONMENTAL
26 CONCENTRATIONS
27 Once the background and local ambient spatio-temporal concentration patterns have been
28 derived, microenvironments that can represent either outdoor or indoor settings when individuals
29 come in contact with the contaminant of concern (e.g., 802) must be characterized. This process
30 can involve modeling of various local sources and sinks, and interrelationships between ambient
31 and microenvironmental concentration levels. Three general approaches have been used in the
32 past to model microenvironmental concentrations:
September 2007 AX3-11 DRAFT-DO NOT QUOTE OR CITE
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1 • Empirical (typically linear regression) fitting of data from studies relating ambient/local
2 and microenvironmental concentration levels to develop analytical relationships.
3 • Parameterized mass balance modeling over, or within, the volume of the
4 microenvironment. This type of modeling has ranged from very simple formulations, i.e.
5 from models assuming ideal (homogeneous) mixing within the microenvironment (or
6 specified portions of it) and only linear physicochemical transformations (including
7 sources and sinks), to models incorporating analytical solutions of idealized dispersion
8 formulations (such as Gaussian plumes), to models that take into account aspects of
9 complex multiphase chemical and physical interactions and nonidealities in mixing.
10 • Detailed Computational Fluid Dynamics (CFD) modeling of the outdoor or indoor
11 microenvironment, employing either a Direct Numerical Simulation (DNS) approach, a
12 Reynolds Averaged Numerical Simulation (RANS) approach, or a Large Eddy
13 Simulation (LES) approach, the latter typically for outdoor situations (see, e.g., Milner
14 et al., 2005; Chang and Meroney, 2003; Chang, 2006).
15
16 Parameterized mass balance modeling is the approach currently preferred for exposure
17 modeling for populations. As discussed earlier, the simplest microenvironmental setting
18 corresponds to a homogeneously mixed compartment, in contact with possibly both
19 outdoor/local environments as well as other microenvironments. The air quality of this idealized
20 microenvironment is affected mainly by the following processes:
21 a. Transport processes: These can include advection/convection and dispersion that
22 are affected by local processes and obstacles such as vehicle induced turbulence,
23 street canyons, building structures, etc.
24 b. Sources and sinks: These can include local outdoor emissions, indoor emissions,
25 surface deposition, etc.
26 c. Transformation processes: These can include local outdoor as well as indoor gas
27 and aerosol phase chemistry, such as formation of secondary organic and
28 inorganic aerosols.
29
30 Exposure modeling also requires information on activity patterns to determine time spent
31 in various microenvironments and estimates of inhalation rates to characterize dose. The next
32 two subsections describe recent work done in these areas.
33
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1 AX3.4.1 Characterization of Activity Events
2 An important development in inhalation exposure modeling has been the consolidation of
3 existing information on activity event sequences in the Consolidated Human Activity Database
4 (CHAD) (McCurdy, 2000; McCurdy et al., 2000). Indeed, most recent exposure models are
5 designed (or have been re-designed) to obtain such information from CHAD which incorporates
6 24-h time/activity data developed from numerous surveys. The surveys include probability-
7 based recall studies conducted by Environmental Protection Agency and the California Air
8 Resources Board, as well as real-time diary studies conducted in individual U.S. metropolitan
9 areas using both probability-based and volunteer subject panels. All ages of both genders are
10 represented in CHAD. The data for each subject consist of one or more days of sequential
11 activities, in which each activity is defined by start time, duration, activity type (140 categories),
12 and microenvironment classification (110 categories). Activities vary from one min to one h in
13 duration, with longer activities being subdivided into clock-hour durations to facilitate exposure
14 modeling. A distribution of values for the ratio of oxygen uptake rate to body mass (referred to
15 as metabolic equivalents or METs) is provided for each activity type listed in CHAD. The forms
16 and parameters of these distributions were determined through an extensive review of the
17 exercise and nutrition literature. The primary source of distributional data was Ainsworth et al.
18 (1993), a compendium developed specifically to facilitate the coding of physical activities and to
19 promote comparability across studies.
20
21 AX3.4.2 Characterization of Inhalation Intake and Uptake
22 Use of the information in CHAD provides a rational way for incorporating realistic
23 intakes into exposure models by linking inhalation rates to activity information. As mentioned
24 earlier, each cohort of the pNEM-type models, or each (virtual or actual) individual of the
25 SHEDS, MENTOR, APEX, and HAPEM4 models, is assigned an exposure event sequence
26 derived from activity diary data. Each exposure event is typically defined by a start time, a
27 duration, assignments to a geographic location and microenvironment, and an indication of
28 activity level. The most recent versions of the above models have defined activity levels using
29 the activity classification coding scheme incorporated into CHAD. A probabilistic module
30 within these models converts the activity classification code of each exposure event to an energy
31 expenditure rate, which in turn is converted into an estimate of oxygen uptake rate. The oxygen
September 2007 AX3-13 DRAFT-DO NOT QUOTE OR CITE
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1 uptake rate is then converted into an estimate of total ventilation rate (yE)5 expressed in liters
2 min"1. Johnson (2001) reviewed briefly the physiological principles incorporated into the
3 algorithms used in pNEM to convert each activity classification code to an oxygen uptake rate
4 and describes the additional steps required to convert oxygen uptake to VE.
5 McCurdy (1997a,b, 2000) has recommended that the ventilation rate should be estimated
6 as a function of energy expenditure rate. The energy expended by an individual during a
7 particular activity can be expressed as EE = (MET)(RMR) in which EE is the average energy
8 expenditure rate (kcal min"1) during the activity and RMR is the resting metabolic rate of the
9 individual expressed in terms of number of energy units expended per unit of time (kcal min"1).
10 MET (the metabolic equivalent of tasks) is a ratio specific to the activity and is dimensionless. If
11 RMR is specified for an individual, then the above equation requires only an activity-specific
12 estimate of MET to produce an estimate of the energy expenditure rate for a given activity.
13 McCurdy et al. (2000) developed distributions of MET for the activity classifications appearing
14 in the CHAD database.
15
16
17 AX3.5 CONCLUDING COMMENTS
18 An issue that should be mentioned in closing is that of evaluating comprehensive
19 prognostic exposure modeling studies, for either individuals or populations, with field data.
20 Although databases that would be adequate for performing a comprehensive evaluation are not
21 expected to be available any time soon, there have been a number of studies, reviewed in earlier
22 sections of this Chapter, that can be used to start building the necessary information base. Some
23 of these studies report field observations of personal, indoor, and outdoor levels and have also
24 developed simple semi-empirical personal exposure models that were parameterized using the
25 observational data and regression techniques.
26 In conclusion, though existing inhalation exposure modeling systems have evolved
27 considerably in recent years, limitations of available modeling methods and data in relation to
28 potential 862 studies should be taken into account. Existing prognostic modeling systems for
29 inhalation exposure can in principle be directly applied to, or adapted for, 862 studies; APEX,
30 SHEDS, and MENTOR-1A are candidates. However, such applications would be constrained by
September 2007 AX3-14 DRAFT-DO NOT QUOTE OR CITE
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1 data limitations such as ambient characterization at the local scale and by lack of quantitative
2 information for indoor sources and sinks.
September 2007 AX3-15 DRAFT-DO NOT QUOTE OR CITE
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TABLE AX3.2-1. THE ESSENTIAL ATTRIBUTES OF THE PNEM, HAPEM, APEX,
SHEDS, AND MENTOR-1A
Exposure Estimate
Characterization of
the High-End
Exposures
Typical Spatial
Scale/Resolution
Temporal
Scale/Resolution
Population Activity
Patterns Assembly
Microenvironment
Concentration
Estimation
Microenvironmental
(ME) Factors
Specification of
Indoor Source
Emissions
Commuting Patterns
Exposure Routes
Potential Dose
Calculation
Physiologically
Based Dose
Variability/
Uncertainty
pNEM
Hourly
averaged
Yes
Urban
areas/Census
tract level
A yr/one h
Top-down
approach
Non-steady-
state and
steady-state
mass balance
equations (hard-
coded)
Random
samples from
probability
distributions
Yes (gas-stove,
tobacco
smoking)
Yes
Inhalation
Yes
No
Yes
HAPEM
Annual averaged
No
Ranging from
urban to national/
Census tract
level
A yr/one h
Top-down
approach
Linear
relationship
method (hard-
coded)
Random samples
from probability
distributions
Available; set to
zero in HAPEM6
Yes
Inhalation
No
No
No
APEX
Hourly averaged
Yes
Urban
area/Census tract
level
A yr/one h
Bottom-up
"person-
oriented"
approach
Non-steady-state
mass balance and
linear regression
(flexibility of
selecting
algorithms)
Random samples
from probability
distributions
Yes (multiple
sources defined
by the user)
Yes
Inhalation
Yes
No
Yes
SHEDS
Activity event
based
Yes
Urban
areas/Census
tract level
A yr/event based
Bottom-up
"person-
oriented"
approach
Steady -state
mass balance
equation
(residential) and
linear regression
(non-residential)
(hard-coded)
Random samples
from probability
distributions
Yes (gas-stove,
tobacco smoking,
other sources)
Yes
Inhalation
Yes
Yes
Yes
MENTOR-1A
Activity event
based
Yes
Multiscale/
Census tract level
A yr/activity event
based time step
Bottom-up
"person-oriented"
approach
Non-steady-state
mass balance
equation with
indoor air
chemistry module
or regression
methods
(flexibility of
selecting
algorithms)
Random samples
from probability
distributions
Yes (multiple
sources defined by
the user)
Yes
Multiple
(optional)
Yes
Yes
Yes (Various
"Tools")
September 2007
AX3-16
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1 AX3.6 REFERENCES
3 Biller, W. F.; Feagans, T. B.; Johnson, T. R.; Duggan, G. M.; Paul, R. A.; McCurdy, T.; Thomas,
4 H. C. (1981) A general model for estimating exposure associated with alternative
5 NAAQS. Presented at: 74th annual meeting of the Air Pollution Control Association;
6 June; Philadelphia, PA. Pittsburgh, PA: Air Pollution Control Association; paper no. 81-
7 18.4.
8 Burke, J. M.; Zufall, M. J.; Ozkaynak, H. (2001) A population exposure model for particulate
9 matter: case study results for PM2.s in Philadelphia, PA. J. Exposure Anal. Environ.
10 Epidemiol. 11: 470-489.
11 Chang, C.-H. (2006) Computational fluid dynamics simulation of concentration distributions
12 from a point source in the urban street canyons. J. Aerospace Eng. 19: 80-86.
13 Chang, C.-H.; Meroney, R. N. (2003) Concentration and flow distributions in urban street
14 canyons: wind tunnel and computational data. J. Wind Eng. Ind. Aerodyn. 91: 1141-
15 1154.
16 Ching, J.; Herwehe, J.; Swall, J. (2006) On joint deterministic grid modeling and sub-grid
17 variability conceptual framework for model evaluation. Atmos. Environ. 40: 4935-4945.
18 Christakos, G. (2000) Modern spatiotemporal geostatistics. New York, NY: Oxford University
19 Press. (International Association for Mathematical Geology; studies in mathematical
20 geology: 6).
21 Christakos, G.; Hristopulos, D. T. (1998) Spatiotemporal environmental health modelling. A
22 tractatus stochasticus. Boston, MA: Kluwer Academic Publishers.
23 Christakos, G.; Vyas, V. M. (1998a) A composite space/time approach to studying ozone
24 distribution over eastern United States. Atmos. Environ. 32: 2845-2857.
25 Christakos, G.; Vyas, V. M. (1998b) A novel method for studying population health impacts of
26 spatiotemporal ozone distribution. Soc. Sci. Med. 47: 1051-1066.
27 Christakos, G.; Kolovos, A. (1999) A study of the spatiotemporal health impacts of ozone
28 exposure. J. Exposure Anal. Environ. Epidemiol. 9: 322-335.
29 Clench-Aas, J.; Bartonova, A.; B0hler, T.; Granskei, K. E.; Sivertsen, B.; Larssen, S. (1999) Air
30 pollution exposure monitoring and estimating. Part I. Integrated air quality monitoring
31 system. J. Environ. Monit. 1: 313-319.
32 Foley, G. J.; Georgopoulos, P. G.; Lioy, P. J. (2003) Accountability within new ozone standards.
33 Environ. Sci. Technol. 37: 392A-399A.
34 Freijer, J. I; Bloemen, H. J. T.; de Loos, S.; Marra, M.; Rombout, P. J. A.; Steentjes, G. M.; Van
35 Veen, M. P. (1998) Modelling exposure of the Dutch population to air pollution. J.
36 Hazard. Mater. 61: 107-114.
37 Georgopoulos, P. G.; Lioy, P. J. (1994) Conceptual and theoretical aspects of human exposure
38 and dose assessment. J. Exposure Anal. Environ. Epidemiol. 4: 253-285.
September 2007 AX3-17 DRAFT-DO NOT QUOTE OR CITE
-------
1 Georgopoulos, P. G.; Lioy, P. J. (2006) From theoretical aspects of human exposure and dose
2 assessment to computational model implementation: the Modeling ENvironment for
3 TOtal Risk studies (MENTOR). J. Toxicol. Environ. Health Part B 9: 457-483.
4 Georgopoulos, P. G.; Arunachalam, S.; Wang, S. (1997a) Alternative metrics for assessing the
5 relative effectiveness of NOX and VOC emission reductions in controlling ground-level
6 ozone. J. Air Waste Manage. Assoc. 47: 838-850.
7 Georgopoulos, P. G.; Purushothaman, V.; Chiou, R. (1997b) Comparative evaluation of methods
8 for estimating potential human exposure to ozone: photochemical modeling and ambient
9 monitoring. J. Exposure Anal. Environ. Epidemiol. 7: 191-215.
10 Georgopoulos, P. G.; Wang, S.-W.; Vyas, V. M.; Sun, Q.; Burke, J.; Vedantham, R.; McCurdy,
11 T.; Ozkaynak, H. (2005) A source-to-dose assessment of population exposures to fine
12 PM and ozone in Philadelphia, PA, during a summer 1999 episode. J. Exposure Anal.
13 Environ. Epidemiol. 15: 439-457.
14 ICF Consulting. (2005) Decision points for consulting APEX for air toxics exposure
15 assessments. Research Triangle Park, NC: U.S. Environmental Protection Agency, Office
16 of Air Quality Planning and Standards. Available:
17 http ://www.epa.gov/ttnmain l/fera/data/apex/APEXDecisionPoints_l 21205 .pdf [ 19
18 September, 2007].
19 Johnson, T. (2001) A guide to selected algorithms, distributions, and databases used in exposure
20 models developed by the Office of Air Quality Planning and Standards (DRAFT).
21 Research Triangle Park, NC: U.S. Environmental Protection Agency, CERM report
22 TR01.
23 Johnson, T. (2002) A guide to selected algorithms, distributions, and databases used in exposure
24 models developed by the Office of Air Quality Planning and Standards [revised draft].
25 Research Triangle Park, NC: U.S. Environmental Protection Agency, Office of Research
26 and Development; EPA grant no. CR827033. Available:
27 http://www.epa.gov/ttn/fera/data/human/report052202.pdf [6 March, 2007].
28 Johnson, T.; Long, T.; Ollison, W. (2000) Prediction of hourly microenvironmental
29 concentrations of fine particles based on measurements obtained from the Baltimore
30 scripted activity study. J. Exposure Anal. Environ. Epidemiol. 10: 403-411.
31 Langstaff, J. E. (2007) Analysis of uncertainty in ozone population exposure modeling [technical
32 memorandum]. Research Triangle Park, NC: U.S. Environmental Protection Agency,
33 Office of Air Quality Planning and Standards.
34 Lioy, P. J. (1990) Assessing total human exposure to contaminants: a multidisciplinary approach.
35 Environ. Sci. Technol. 24: 938-945.
36 McCurdy, T. (1997a) Human activities that may lead to high inhaled intake doses in children
37 aged 6-13. Environ. Toxicol. Pharmacol. 4: 251-260.
38 McCurdy, T. (1997b) Modeling the dose profile in human exposure assessments: ozone as an
39 example. Rev. Toxicol. 1: 3-23.
40 McCurdy, T. (2000) Conceptual basis for multi-route intake dose modeling using an energy
41 expenditure approach. J. Exposure Anal. Environ. Epidemiol. 10: 86-97.
September 2007 AX3-18 DRAFT-DO NOT QUOTE OR CITE
-------
1 McCurdy, T.; Glen, G.; Smith, L.; Lakkadi, Y. (2000) The National Exposure Research
2 Laboratory's Consolidated Human Activity Database. J. Exposure Anal. Environ.
3 Epidemiol. 10: 566-578.
4 Milner, J. T.; Dimitroulopoulou, C.; ApSimon, H. (2005) Indoor concentrations in buildings
5 from sources outdoors. In: Atmospheric Dispersion Modelling Liaison Committee
6 Annual Report 2004-2005, Annex B. Available: http:www.admlc.org.uk/ar04-05.htm [6
7 March, 2007].
8 Ott, W. R. (1982) Concepts of human exposure to air pollution. Environ. Int. 7: 179-196.
9 Ott, W. R. (1985) Total human exposure: an emerging science focuses on humans as receptors of
10 environmental pollution. Environ. Sci. Technol. 19: 880-886.
11 Price, P. S.; Chaisson, C. F.; Koontz, M.; Wilkes, C.; Ryan, B.; Macintosh, D.; Georgopoulos, P.
12 G. (2003) Construction of a comprehensive chemical exposure framework using person
13 oriented modeling. Prepared for: The Exposure Technical Implementation Panel,
14 American Chemistry Council; contract #1338. Annandale, VA: The LifeLine Group.
15 Available: http://www.thelifelinegroup.org/lifeline/docs.htm [6 March, 2007].
16 Purushothaman, V.; Georgopoulos, P. G. (1997) Computational tools to aid the estimation and
17 visualization of potential human exposure to ozone. In: Proceedings of the Air & Waste
18 Management Association Specialty Conference on Computing in Environmental
19 Resource Management; December, 1996; Research Triangle Park, NC. Pittsburgh, PA:
20 A&WMA; VIP-68; pp. 17-28.
21 Purushothaman, V.; Georgopoulos, P. G. (1999a) Evaluation of regional emissions control
22 strategies for ozone utilizing population exposure metrics: a new GIS-based approach
23 applied to the July 1995 episode over the OTAG domain. Piscataway, NJ: Environmental
24 and Occupational Health Sciences Institute, Ozone Research Center; ORC technical
25 report, ORC-TR99-02.
26 Purushothaman, V.; Georgopoulos, P. G. (1999b) Integrating photochemical modeling,
27 geostatistical techniques and geographical information systems for ozone exposure
28 assessment. Piscataway, NJ: Environmental and Occupational Health Sciences Institute,
29 Ozone Research Center; ORC technical report, ORC-TR99-01.
30 Rosenbaum, A. (2005) The HAPEMS user's guide - hazardous air pollutant exposure model,
31 version 5. Prepared for: U.S. Environmental Protection Agency. Research Triangle Park,
32 NC: ICF Consulting.
33 U.S. Environmental Protection Agency. (1992) Final guidelines for exposure assessment.
34 Research Triangle Park, NC: National Center for Environmental Assessment; EPA/600Z-
35 92/001.
36 U.S. Environmental Protection Agency. (1997) Exposure factors handbook. Washington, DC:
37 Office of Research and Development, National Center for Environmental Assessment;
38 report nos. EPA/600/P-95/002Fa-c.
39 U.S. Environmental Protection Agency. (2006a) Air quality criteria for ozone and related
40 photochemical oxidants. Research Triangle Park, NC: National Center for Environmental
41 Assessment; report no. EPA/600/R-05/004aF-cF. 3v. Available:
42 http://cfpub.epa.gov/ncea/ [24 March, 2006].
September 2007 AX3-19 DRAFT-DO NOT QUOTE OR CITE
-------
1 U.S. Environmental Protection Agency. (2006b) Total risk integrated methodology (TRIM) air
2 pollutants exposure model documentation (TREVI.Expo/APEX, version 4). Volume I:
3 User's guide. Research Triangle Park, NC: U.S. Environmental Protection Agency.
4 Available: http://www.epa.gov/ttn/fera/data/apex/APEX4UsersGuideJuly2006.pdf [7
5 March, 2007].
6 U.S. Environmental Protection Agency. (2006c) Total risk integrated methodology (TRIM) air
7 pollutants exposure model documentation (TREVI.Expo/APEX, version 4). Volume II:
8 technical support document. Research Triangle Park, NC: U.S. Environmental Protection
9 Agency. Available: http://www.epa.gov/ttn/fera/data/apex/APEX4TSDJuly2006.pdff7
10 March, 2007].
11 Whitfield, R. G.; Richmond, H. M.; Johnson, T. R. (1997) Overview of ozone human exposure
12 and health risk analyses used in the U.S. EPA's review of the ozone air quality standard.
13 In: Proceedings of the U.S.-Dutch International Symposium on Air Pollution in the 21st
14 Century; April; Noordwijk, The Netherlands. Argonne, IL: Argonne National Laboratory;
15 ANL/DIS/CP-92660. Available: http://www.osti.gov/dublincore/gpo/servlets/purl/9748-
16 Nh3avx/webviewable/9748.pdf (22 July 2003).
17 World Health Organization (WHO). (2004) IPCS risk assessment terminology. Part 2: IPCS
18 glossary of key exposure assessment terminology. Geneva, Switzerland: IPCS
19 Harmonization Project document no. 1. Available:
20 http://www.who.int/ipcs/methods/harmonization/areas/ipcsterminologypartsland2.pdf
21 [19 April, 2007].
22 World Health Organization (WHO). (2005) Principles of characterizing and applying human
23 exposure models. Geneva, Switzerland: World Health Organization. (IPCS
24 harmonization project document; no. 3). Available:
25 http://whqlibdoc.who.int/publications/2005/9241563117_eng.pdf [7 March, 2007].
26
27
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AX4. CHAPTER 4 ANNEX - TOXICOLOGICAL
STUDIES OF THE HEALTH EFFECTS
OF SULFUR OXIDES
September 2007 AX4-1 DRAFT-DO NOT QUOTE OR CITE
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TABLE AX4-1. PHYSIOLOGICAL EFFECTS OF SO2 EXPOSURE
to
o
o
X
i±
to
o
o
2
o
H
O
c
o
H
W
O
^
O
HH
H
W
Concentration Duration
Acute and Subacute Exposures
~1 ppm 1 h
(2.62 mg/m3);
head only
1 ppm 3 h/day for 6 days;
(2.62 mg/m3); animals evaluated for up
nose only to 48 h following
exposure; exposures
occurred in a furnace
5 ppm 45 min
(13.1 mg/m3);
apperantly
intratracheal
5 ppm 2 h/day for 13 wks
(13.1 mg/m3);
whole body
Species
Hartley guinea pig,
male, age not
reported, 200-300 g,
n = 8-23/group
Hartley guinea pig,
male, age not
reported, 250-320 g,
n= <18 group/time
point
Rabbit, sex not
reported, adult, mean
2.0 kg,
n = 5-9/group;
rabbits were
mechanically
ventilated
New Zealand White
rabbit, male and
female, 1 -day -old,
weight not reported,
n = 3-4/group,
immunized against
Alternaria tennis
Effects
An 1 1% increase in pulmonary resistance and 12% decrease in
dynamic compliance were observed. Neither effect persisted into
the 1-h period following exposure. No effects were observed for
breathing frequency, tidal volume, or min volume.
No effect was observed on residual volume, functional reserve
capacity, vital capacity, total lung capacity, respiratory
frequency, tidal volume, pulmonary resistance, or pulmonary
compliance at 1 or 48 h after the last exposure.
Bivagotomy had no effect on SO2-induced increases in lung
resistance (54% increase before vagotomy and 56% increase after
vagotomy). Reflex bronchoconstrictive response to
phenyldiguanide (intravenously administered) was eliminated by
exposure to SO2 but SO2 had no effect on lung resistance induced
by intravenously -administered histamine. The study authors
concluded that (1) vagal reflex is not responsible for
SO2-induced increase in lung resistance and (2) transient
alteration in tracheobronchial wall following SO2 exposure
may have reduced accessibility of airway nervous receptors
to phenyldiguanide.
No effects on lung resistance, dynamic compliance,
transpulmonary pressure, tidal volume, respiration rate, or min
volume.
Reference
Amdur et al.
(1983)
Conner et al.
(1985)
Barthelemy
etal. (1988)
Douglas et al.
(1994)
-------
TABLE AX4-1 (cont'd). PHYSIOLOGICAL EFFECTS OF SO2 EXPOSURE
Concentration
Duration
Species
Effects
Reference
to
o
o
Subchronic and Chronic Exposure
15 or 50 ppm
(39.3 or
131mg/m3);
intratracheal
exposure
2 h/day, 4 or 5 days/wk,
for 5 mos (low dose
group) or 10-11 mos
(high dose group);
study authors stated
that physiological
changes were observed
within 5 mos; there was
a 7-9 mo recovery
period
Mongrel dogs, adult,
sex not reported,
10-20 kg;
n= 3-4/group
(3 hyperresponsive,
3 hyporesponsive,
and 1 avg
responsive)
At 15 ppm, there was no clinical evidence of bronchitis; pulmonary
resistance increased by 35-38% in 2 of 3 dogs, and dynamic lung
compliance decreased in 1 of 3 dogs, but the physiological changes were
not significant for the group as a whole. At 50 ppm, cough and mucous
hypersecretion were observed; the symptoms ceased during the recovery
period. Pulmonary resistance increased by 56% during the treatment
period and an additional 28% during the recovery period for a total
increase of 99%; dynamic lung compliance decreased in 2 of 4 dogs and
increased in 1 of 4 dogs during treatment but there were no significant
changes in the group as a whole. Study authors considered 15 ppm to
be the lower limit of exposure that failed to produce physiological
changes.
Scanlon
etal.
(1987)
X
O
o
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
1 ppm
(2.62 mg/m3);
whole body
5 h/day, 5 days/wk for
4 mos
Sprague-Dawley rat,
male, young adult,
initial weight not
reported,
n= 12-15/data point
Physiological tests were conducted in anesthetized animals; many of the
tests were conducted while the rat was allowed to breathe spontaneously
and during paralysis. SO2 exposure resulted in an 11% decrease in
residual volume (only during paralysis) and reduced quasistatic
compliance (only examined in paralyzed animals). Study authors noted
that because residual volume was only decreased in paralyzed rats
and the magnitude of effect was very small, it may have been due to
chance. Quasistatic compliance values were observed to be very
high in controls and may have accounted for the effect in the
treatment group.
Smith et al.
(1989)
-------
TABLE AX4-2. INFLAMMATORY RESPONSES FOLLOWING SO2 EXPOSURE
to
o
o
X
O
o
2
o
H
o
c
o
H
W
O
^
O
HH
H
W
Concentration Duration
Acute/Subacute/Subchronic
10 ppm (26.2 mg/m3); 4 h
nose only
14, 28, or 56 mg/m3; 4 h/day for 7 days
(5.35, 10.7, or
21.4 ppm); whole body
Species
Outbred Swiss
mouse, female, age
and weight not
reported, n = 107
experimental value
Kunming albino
mouse, male, age
not reported,
18-22 g,
n = 10/group
Effects
No evidence was seen of inflammatory response in terms
of total cell number, lymphocyte/polymorphonuclear
leukocytes differentials, or total protein level taken from
BAL fluid.
In lung tissue, in vivo SO2 exposure (low, mid
concentrations) significantly elevated levels of the
pro-inflammatory cytokines interleukin-6 and tumor
necrosis factor-a, but did not affect levels of the
anti-inflammatory cytokine transforming growth
factor-pi. In serum, the only effect observed was a
low-dose elevation of tumor necrosis factor- a.
Reference
Clarke et al.
(2000)
Meng et al.
(2005a)
5, 50, or 100 ppm (13.1,
131, or 262 mg/m3);
whole body
5 h/day for 7-28 days
Wistar rat, male,
7 wks old, weight
not reported,
n = 4-5/treatment
group, 8 controls
No lung injury was observed and evidence of
inflammatory response was only observed in the 100 ppm
group. A 4-fold increase in BAL fluid leukocyte numbers
was observed in the 100 ppm group at day 14; the increase
lessened at days 21 and 28 but remained higher than
controls. The number of macrophages in BAL fluid was
increased at day 28 in the 100 ppm group. Neutrophil
numbers were 120 times higher than controls at day 14 in
the 100 ppm group but returned to normal by day 21.
Blood neutrophils were depleted in rats exposed to
50 ppm on days 7-21 but were increased in rats exposed to
5 ppm (significant) and 100 ppm (non-significant) at day
14. Lung epithelial permeability was not affected.
Langley-Evans
etal. (1996)
BAL = bronchoalveolar lavage
-------
TABLE AX4-3. EFFECTS OF SO2 EXPOSURE ON HYPERSENSITIVITY/ALLERGIC REACTIONS
Concentration
Duration
Species
Effects
Reference
to
o
o
X
O
o
2
o
H
O
c
o
H
W
O
^
O
HH
H
W
Antigen Sensitization/Allergic Reactions - Acute/Subacute
5ppm(13.1 mg/m);
head only
0.1 ppm
(0.26 mg/m3); whole
body; with and
without exposure to
ovalbumin
0.1, 4.3, or 16.6 ppm
(0,0.26, 11.3, or
43.5 mg/m3); whole
body; animals were
sensitized to
ovalbumin on the last
3 days of exposure.
4h
5 h/day for 5 days.
8 h/day for 5 days
Sheep, sex and age not
reported, mean weight
38 ± 7 kg, n = 7/group
Dunkin-Hartley guinea
pig, male, age not
reported, 250-350 g,
n = 7-12/group
Perlbright-White
Guinea pig, female,
age not reported,
300-350 g, n= 5 or
6/group (14 controls)
Acute exposure to 5 ppm SO2 did not produce significant airway Abraham et al.
changes (pulmonary resistance, static compliance, dynamic (1981)
compliance, tidal volume, breathing frequency) in either normal
or allergic (sensitized to Ascaris suum antigen) sheep, nor
increase airway reactivity (measured as pulmonary resistance
increase after aerosolized carbachol provocation) in normal
sheep. However, 5 ppm SO2 did significantly increase airway
reactivity in allergic sheep, which have antigen-induced
airway responses similar to humans with allergic airway
disease, and thus may model airway responses to SO2 in a
sensitive human subpopulation.
After bronchial challenge, the ovalbumin/SO2-exposed group had Park et al.
significantly increased enhanced pause (indicator of airway (2001)
obstruction) and eosinophil counts in B AL fluids than all other
groups, including the SO2 group. The bronchial and lung tissue
of this group showed infiltration of inflammatory cells,
bronchiolar epithelial damage, and mucus and cell plug in the
lumen. Study authors concluded that low level SO2 may
enhance the development of ovalbumin-induced asthmatic
reactions in guinea pigs.
Bronchial provocation with ovalbumin was conducted every Riedel et al.
other day for 2 wks, starting at 1 wk after the last exposure. (1988)
Numbers of animals displaying symptoms of bronchial
obstruction after ovalbumin provocation was increased in all SO2
groups compared to air-exposed groups. Anti-ovalbumin
antibodies (IgG total and IgGl) were increased in BAL fluid and
serum of SO2-exposed compared to air-exposed controls, with
statistical significance obtained for IgG total in BAL fluid at
>4.3 ppm SO2 and in serum at all SO2 concentrations. Results
indicate that in this model, subacute exposure to even low
concentrations of SO2 can potentiate allergic sensitization of
the airway.
-------
TABLE AX4-3 (cont'd). EFFECTS OF SO2 EXPOSURE ON HYPERSENSITIVITY/ALLERGIC REACTIONS
Concentration
Duration
Species
Effects
Reference
to
o
o
Antigen Sensitization/Allergic Reactions - Subchronic
4 h/day, 5 days/wk, 6
wks
5 ppm
(13.1mg/m3);
whole body;
sensitized with
Candida albicans
on day 1 and wk 4
General Bronchial Reactivity Studies - Acute
5 ppm
(13.1mg/m3);
whole body
2h
Hartley guinea pig, male, Respiratory challenge with Candida albicans was conducted
. __ _, ™« _ 2 wks ^gj. ^ last exposure At 15 h after challenge
increased number of SO2-exposed animals displayed
prolonged expiration, inspiration, or both. Study authors
concluded that exposure to SO2 increased dyspneic
symptoms.
Hartley guinea pig, male,
age not reported, -200 g,
n = 12/group
New Zealand White
rabbit, sex not reported,
apparently 3 mos old,
2.2-3. 1 kg, n=6/group
No effect on airway responsiveness to inhaled histamine, as
measured by provocation concentrations of histamine
required to increase pulmonary resistance by 50% and
decrease dynamic compliance by 35%.
Kitabatake
etal. (1992,
1995)
Douglas et al.
(1994)
X
O
O
2
O
H
O
c
o
H
W
O
V
O
HH
H
W
10 or 30 ppm (26.2
or 78.6 mg/m3);
intratracheal
5 min; a second exposure
was conducted 20 days
later, after exposure to
the antiallergic drug
General Bronchial Reactivity Studies - Chronic
15 or 50 ppm
(39.3 or
131 mg/m3);
intratracheal
2 h/day, 4 or 5 days/wk
for 5 mos (low dose
group) or 10-11 mos
(high dose group); study
authors stated that
physiological changes
were observed within 5
mos; there was a 7-9 mo
recovery period.
Mongrel dogs, male and
female, age and weight
not reported;
n = 5-15/group
Mongrel dogs, adult, sex
not reported, 10-20 kg;
n= 3-4/group
(3 hyperresponsive,
3 hyporesponsive, and
1 avg responsive)
No effect was observed at 10 ppm. At 30 ppm Lewis and
hyperresponsiveness and hypersensitivity to aerosolized Kirchner
methacholine and 5-hydroxytryptamine was observed for up (1984)
to 24 h following exposure. Twenty days later, pretreatment
with aerosolized 4% Wy-41,195 or disodium cromoglycate
(antiallergic drugs) at high doses lessened the
methacholine-induced hypersensitivity observed after
exposure to 30 ppm SO2. The calculations used to determine
hyperresponsive and hyperreactivity were not clear.
Bronchial reactivity in response to inhaled histamine or Scanlon et al.
methacholine was not affected in either treatment group, as (1987)
determined by the concentration of histamine or
methacholine required to double pulmonary resistance or the
concentrations required to decrease dynamic compliance by
65% (ED65).
BAL = bronchoalveolar lavage
IgG = immunoglobulin
-------
September 2007
Concentration
Clearance - Subchronic
5 ppm (13.1 mg/m3);
TABLE AX4-4.
Duration
2 h/day, 5 days/wk
EFFECTS OF SO2 EXPOSURE ON HOST LUNG DEFENSES
Species
F344/Crl rat, male and
Effects
There was no effect on pulmonary clearance of
Reference
Wolff etal. (1989)
weight not reported,
n = 6/sex/group
1.0 uM).
Immune Responses - Acute/Subacute
10 ppm (26.2 mg/m);
nose only
4h
Specific pathogen-free
white Swiss mice, female,
5 wks old, 20-23 g,
n = 5/group
No effect was observed on in situ
Fc-receptor-mediated phagocytosis of sheep red blood
cells by AM, which was assessed 3 days after
exposure to SO2.
Jakab etal. (1996)
X
O
O
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
10 ppm (26.2 mg/m3)
SO2; nose only
10 ppm (26.2 mg/m );
whole body
4 h Outbred Swiss mouse,
female, age and weight
not specified,
n = 10/experimental value
24 h, 1 wk, 2 wks, or OF^ mice, female, age
3 wks not reported, mean 20.6 g,
n = 768 (32/group)
No effect on in situ AM phagocytosis (data not
shown) or on intrapulmonary bactericidal activity
toward Staphylococcus aureus.
Respiratory challenge withKlebsiellapneumoniae
resulted in increased mortality and decreased survival
time in the 1, 2, and 3 wk SO2 exposure groups
compared to controls. Differences did not correlate
with exposure length.
Clarke et al. (2000)
Azoulay-Dupuis
etal. (1982)
AM = alveolar or pulmonary macrophages
MAD = median aerodynamic diameter
MMAD = mass median aerodynamic diameter
-------
TABLE AX4-5. EFFECTS OF SO2 EXPOSURE ON CARDIOVASCULAR ENDPOINTS
to
o
o
X
i±
oo
o
o
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
Concentration Duration
In Vitro Exposure
Bisulfite/sulfite, Not reported
1:3 molar/molar,
10 uM
Bisulfite/sulfite, Not reported
1:3 molar/molar,
10 uM
Species
Ventricular myocytes
isolated from Wistar
rats, adult, 200-300 g,
n=8
Ventricular myocytes
isolated from Wistar
rats, adult, 200-300 g,
Effects
Effects of the 10 uM bisulfite/sulfite mixture on sodium
current included a shift of steady state inactivation curve to a
more positive potential, a shift of the time-dependent recovery
from inactivation curve to the left, accelerated recovery, and
shortened inactivation and activation time constants. It was
concluded that the bisulfite/sulfite mixture stimulated cardiac
sodium channels.
Effects of the 10 uM bisulfite/sulfite mixture on
voltage-dependent L-type calcium currents included a shift of
steady-state activation and inactivation to more positive
Reference
Nie and Meng
(2005)
Nie and Meng
(2006)
n=;
Acute/Subacute Exposure
1.0, 2.5, or 5 ppm
(2.62, 6.55, or
13.1 mg/m3) in
cold dry air;
apparently
intratracheal
In pre-exposure period:
15-min exposure to warm
humid air, 10-min
exposure to cold dry air,
and 15-min exposure to
warm humid air. In
exposure period: 10-min
exposures to each SO2
concentration or cold dry
air were preceded and
folio wed by 15-min
exposures to warm humid
air.
Duncan-Hartley
guinea pigs, male,
age and weight not
reported,
n= 7-12/group,
mechanically
ventilated; animals
were hyperventilated
during cold air and
SO2 exposure to
simulate exercise
potentials, accelerated recovery from inactivation, and
shortened fast and slow time inactivation constants. Study
authors stated that their results suggested the possibility
cardiac injury following SO2 inhalation.
Arterial blood pressure increased transiently during exposure
to 5 ppm SO2 in cold dry air. No analyses were done to
determine the effects on blood pressure were caused by
exposure to cold air or SO2.
Halinen et al.
(2000a)
-------
TABLE AX4-5 (cont'd). EFFECTS OF SO2 EXPOSURE ON CARDIOVASCULAR ENDPOINTS
Concentration
Duration
Species
Effects
Reference
to
o
o
X
O
o
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
Acute/Subacute Exposure
1 ppm (2.62 mg/m3) in
cold dry air; apparently
intratracheal
1 ppm (2.62 mg/m3);
nose only
10, 20, or 40 ppm (26.2,
52.4, or 105 mg/m3);
whole body
10, 20, or 40 ppm (26.2,
52.4, or 104.8 mg/m3);
whole body
5, 50, or 100 ppm (13.1,
131, or 262 mg/m3);
whole body
22, 56, or 112 mg/m3
(8.4, 21, or 43 ppm);
whole body
60 min Duncan-Hartley guinea pigs,
male, age and weight not
reported, n = 8-9/group,
mechanically ventilated;
animals were hyperventilated
during cold air and SO2
exposure to simulate exercise
4 h F344 rat, male, 18 mos old,
weight not reported, n = 20
(crossover design)
6 h Wistar rat, male, 7-8 wks old,
180-200 g;n= 10/group
6 h/day Wistar rat, male, 7-8 wks old,
for 7 days 180-200 g; n = 10/group
5 h/day Wistar rat, male, 7 wks old,
for weight not reported,
7-28 days n = 4-5/treatment group,
8 controls
6 h/day Kunming albino mice, male
for 7 days and female, 5 wks old,
19±2g, n = 10/sex/group
Blood pressure and heart rate increased similarly with exposure Halinen et al.
to cold dry air or SO2 in cold dry air. Blood pressure generally (2000b)
increased during the first 10-20 min of exposure and remained
steady from that point forward. The increase in heart rate was
gradual. No analyses were done to determine if the effects on
blood pressure were caused by exposure to cold air or SO2.
SO2 exposure had no effect on spontaneous arrythmia frequency Nadziejko et al.
in aged rats. Study authors urged caution in the (2004)
interpretation of effects because occurrence of arrhythmias in
aged rats was sporadic and variable from day to day.
A dose-related decrease in blood pressure was observed at Meng et al.
> 20 ppm. (2003b)
Dose-related decreases in blood pressure were observed on Meng et al.
exposure day 3 in the 10 ppm group, exposure days 2-6 in the (2003b)
20 ppm group, and all exposure days in the 40 ppm group. The
study authors noted possible adaptive mechanism in the low
but not the high dose group.
GSH was depleted in the heart at 5 and 100 ppm. At 50 ppm, Langley-Evans
GSH level decreased in heart at 7 days and returned to normal by et al. (1996)
14 days. No effects were observed for other GSH-related
enzymes. Injury and inflammation were not assessed in heart, but
assessment in lung revealed no effect.
Changes observed in heart (concentrations of effect) included: Meng et al.
lower SOD activity in males and females (> 8.4 ppm), higher (2003a)
TEARS level in males and females (>8.4 ppm), lower GPx
activity in males (8.4 and 21 ppm; also 43 ppm according to text)
and lower GSH level in males (43 ppm). Study authors
concluded that SO2 induced oxidative damage in hearts of
mice.
-------
% TABLE AX4-5 (cont'd). EFFECTS OF SO2 EXPOSURE ON CARDIOVASCULAR ENDPOINTS
"C ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^=^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^=
r+
g Concentration Duration Species Effects Reference
cr
^ Acute/Subacute Exposure
to
o 22, 64, or 148 mg/m3 6 h/day for 7 days Kunming-strain GSH, GST, and glucose-6-phosphate dehydrogenase activities Wu and Meng
(8.4, 24.4, or mice, male, age not were decreased in the heart at 148 mg/m3. (2003)
56.5 ppm); whole reported, 18-20 g,
body n = 10/group
GPx = glutathione peroxidase
GSD = geometric standard deviation
GSH = glutathione
GST = glutathione-S-transferase
MMAD = mass median aerodynamic diameter
SOD = superoxide dismutase
TEARS = thiobarbituric acid-reactive substances
O
o
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
-------
TABLE AX4-6. NERVOUS SYSTEM—NEUROPHYSIOLOGY AND BIOCHEMISTRY EFFECTS
OF SO2 AND DERIVATIVES
Concentration
Duration
Species
Effects
Reference
to
o
o
>
O
O
2
O
H
O
c
o
H
W
O
V
O
HH
H
W
In Vitro/Ex Vivo
1, 10, 50, or 100 uM
SO2 derivatives (1:3,
NaHSO3 to Na2SO3)
Not specified
1 or 10 uM SO2
derivatives
(l:3,NaHSO3to
Na2S03)
2-4 min
Wistar rat, sex not reported,
6-12 days old, weight and
number not reported; typical
observations made on
60 isolated hippocampal neurons
per concentration
Wistar rat, both sexes,
10-15 days old, weight and
number not reported; n = 6-13
isolated dorsal root ganglion
neurons avgd per endpoint
Exposure to SO2 derivatives (sulfite, bisulfite) reversibly Du and Meng
increased the amplitude of potassium channel TOCs in a (2004a)
dose-dependent and voltage-dependent manner. Compared
to controls, 10 uM SO2 shifted inactivation of
depolarization toward more positive potentials without
significantly affecting the activation process. By
increasing maximal TOC conductance and delaying
TOC inactivation, micromolar concentrations of SO2
derivatives may increase the excitability of
hippocampal neurons and thus contribute to the
enhanced neuronal activity associated with SO2
intoxication.
Maximum sodium current amplitudes for both TTX-S and Du and Meng
TTX-R channels were increased by exposure to SO2 (2004b)
derivatives (10 or 1 uM, respectively), with amplitudes
diminished at more negative evoking potentials and
enhanced at less negative or positive potentials. SO2
derivatives (a) slowed both current activation and
inactivation for both types of sodium channels; (b) shifted
activation currents to more positive potentials, increasing
threshold voltages for action potential generation and
contributing to reduced neuron excitability; and (c) caused
even larger counteracting positive shifts in inactivation
voltages tending to increase dorsal root ganglion neuron
excitability. On balance, the data suggest micromolar
concentrations of sulfite/bisulfite can increase the
excitability of dorsal root ganglion neurons, providing a
basis for SO2-associated neurotoxicity.
-------
TABLE AX4-6 (cont'd). NERVOUS SYSTEM—NEUROPHYSIOLOGY AND BIOCHEMISTRY EFFECTS
OF SO2 AND DERIVATIVES
Concentration
Duration
Species
Effects
Reference
to
o
o
to
O
o
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
In Vitro/Ex Vivo
0.01, 0.1,0.5, or luM
SO2 derivatives (1:3,
NaHSO3 to Na2SO3)
Not specified, but
brief ("added to the
external solution just
before each
experiment")
Wistar rat, both sexes,
10-15 days old, weight and
number not reported;
n = 6-15 isolated dorsal root
ganglion neurons avgd per
endpoint
Acute/Subacute/Subchronic Exposure
22, 64, or 148 mg/m3
(8.4, 24.4, or
56.5 ppm); whole body
6 h/day for 7 days
Kunming-strain mice, male,
age not reported, 18-20 g,
n = 10/group
In isolated dorsal root ganglion neurons, SO2 derivatives Du and
increased HVA-/Ca amplitudes in a concentration- and Meng
depolarizing voltage-dependent manner (EC50 was ~0.4 uM) (2006)
by altering Ca channel properties. This effect was partially
reversible by SO2 derivative washout, and was
PKI-inhibitable, indicating involvement of PKA and
secondary messengers. Additionally, exposure caused a
positive shift in reversal potential. SO2 derivatives also
delayed activation and inactivation of Ca channels, but the
latter was more pronounced, thus overall prolonging action
potential duration and increasing HVA-/Ca. Exposure also
slowed the fast component and accelerated the slow
component of recovery from Ca channel inactivation. Thus,
<1 uM sulfite/bisulfite caused prolonged opening and
altered properties of Ca channels, elevated HVA-/Ca, and
abnormal Ca signaling with neuronal cell injury.
Authors speculate these effects may correlate to SO2
inhalation toxicity, perhaps leading to abnormal
regulation via peripheral neuron Ca channels of
nociceptive impulse transmission.
Decreased glutathione, glucose-6-phosphate dehydrogenase, Wu and
and GST activities were observed in the brain at 64 and Meng
148 mg/m3. (2003)
-------
TABLE AX4-6 (cont'd). NERVOUS SYSTEM—NEUROPHYSIOLOGY AND BIOCHEMISTRY EFFECTS
OF SO2 AND DERIVATIVES
Concentration
Duration
Species
Effects
Reference
to
o
o
O
o
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
10 ppm (26.2 mg/m );
whole body
Ih/dayfor21or
24 days
Guinea pig, sex not
reported, adult, 250-500 g,
n = 12/group (6/subgroup)
10 ppm (26.2 mg/m3);
whole body
1 h/day for
30 days
Charles Foster rat, male,
adult, 150-200 g,
n = 12/group (6/subgroup)
The effects of SO2 exposure on lipid profiles, lipid peroxidation Haider
and lipase activity in three regions of the brain (cerebral et al.
hemisphere, CH; cerebellum, CB; brain stem, BS) were (1981)
examined. Significant (p < 0.001-0.05) findings include
reductions in total lipids (CH, BS; also CB, but nonsignificant)
and free fatty acids (CH, CB, BS). PL were elevated in CH, but
reduced in CB; Choi was elevated in CH, but reduced in CB and
BS; and esterified fatty acids were elevated in CB, but reduced in
CH and BS. Levels of malonaldehyde and lipase activity were
elevated in all regions. Results indicate that subacute brief
exposures to SO2 can lead to degradation of brain lipids, with
the exact nature of the lipid alterations dependent upon brain
region.
The effects of SO2 exposure on lipid profiles, lipid peroxidation Haider
and lipase activity in three regions of the brain (cerebral et al.
hemisphere, CH; cerebellum, CB; brain stem, BS) were (1982)
examined. Significant (p < 0.001-0.05) findings include
reductions in total lipids (CH, BS, CB), while PL were elevated
only in CB. Choi was elevated in CH and CB, but not BS; and
gangliosides were elevated in CB and BS, but reduced in CH.
Lipid peroxidation (malonaldehyde formation) was elevated in
whole brain and all regions (although nonsignificantly in BS), as
was lipase activity in CH, the only tissue examined. Despite
regional differences in PL and Choi changes, Choi/PL ratios were
elevated in all three brain regions (again nonsignificantly in BS).
Results are somewhat different than those seen in guinea pig
(Haider et al., 1981), but again suggest that subacute brief
exposures to SO2 can lead to degradation of brain lipids, with
the exact nature of the lipid alterations dependent upon brain
region.
-------
TABLE AX4-6 (cont'd). NERVOUS SYSTEM—NEUROPHYSIOLOGY AND BIOCHEMISTRY EFFECTS
OF SO2 AND DERIVATIVES
Concentration
Duration
Species
Effects
Reference
to
o
o
>
O
O
2
O
H
O
c
o
H
W
O
V
O
HH
H
W
10 ppm (26.2 mg/m )
SO2 alternated with
20 ppm (14.7 mg/m3)
H2S; whole body
1 h/day for 30 days
(alternating SO2 or
H2S)
Guinea pig, sex and age
not reported, 250-400 g,
n = 18/group in 2 groups
(6/group in some
subgroups)
10 ppm (26.2 mg/m3)
(± iv alloxan to induce
experimental type 1
diabetes); whole body
1 h/day, 7 days/wk
for 6 wks
Swiss albino rat, male,
3 mos old, weight not
reported, n = 10/group in
4 groups
The effects of alternating SO2 + H2S exposure on lipid profiles, lipid Haider and
peroxidation and lipase activity in four regions of the brain (cerebral Hasan
hemisphere, CH; basal ganglia, BG; cerebellum, CB; brain stem, (1984)
BS) and in the spinal cord (SC) were examined. Significant
(p < 0.001-0.05) findings include reductions in total lipids and Choi,
and elevated lipid peroxidation (malonaldehyde formation) and
lipase activity, in all brain regions and SC. Choi/PL ratios were also
reduced in all tissues (but nonsignificantly in BG and CB). For
other parameters (PL, free fatty acids, esterified fatty acids, and
gangliosides), changes were observed in most tissues but were
region-specific. Results indicate that subacute brief, alternating
exposures to SO2 or H2S lead to degradation of brain lipids,
again with the exact nature of the lipid alterations dependent
upon brain/spinal cord region. Additionally, some of the effects
observed for this mixture vary from those seen with SO2 alone
(Haider et al., 1981,1982).
In retina tissue, exposure elevated SOD activity and reduced GPx Agar et al.
and catalase activities. TEARS were elevated only in non-diabetic (2000)
rats exposed to SO2. In brain tissue, exposure elevated SOD and
reduced GPx activities in both non-diabetics and diabetics, while
catalase activities were not affected; TEARS were elevated in both
non-diabetics and diabetics. With respect to VEPs, exposure
prolonged latencies in 4 of 5 VEP components in non-diabetics and
5 of 5 in diabetics, while reducing virtually all peak-to-peak
amplitudes in non-diabetics and diabetics. For many endpoints, SO2
effects were additive to those resulting from the induced diabetic
condition. In summary, brain and retinal anti-oxidant and lipid
peroxidation status, as well as neuro-visual performance were
affected by subchronic exposure to brief periods of 10 ppm SO2,
and these effects were exacerbated by a diabetic condition.
-------
TABLE AX4-6 (cont'd). NERVOUS SYSTEM—NEUROPHYSIOLOGY AND BIOCHEMISTRY EFFECTS
OF SO2 AND DERIVATIVES
Concentration
Duration
Species
Effects
Reference
to
o
o
>
O
O
2
O
H
O
c
o
H
W
O
V
O
HH
H
W
Subchronic/Chronic Exposure
10 ppm (26.2 mg/m )
(± iv alloxan to induce
experimental type 1
diabetes); whole body
1 h/day,
7 days/wk for
6 wks
Rat, male, 3 mos old,
weight not reported,
n = 10/group in 4 groups
10 ppm (26.2 mg/m3);
whole body
1 h/day,
7 days/wk for
6 wks
Swiss albino rat, male, 3,
12, or 24 mos old, weight
not reported, n = 10/group
in 6 groups
In brain tissue, SO2 exposure elevated SOD and reduced GPx Kiiciikatay
activities in both non-diabetics and diabetics, while catalase et al.
activities were not affected; TEARS were elevated in both (2003)
non-diabetics and diabetics. With respect to afferent peripheral
nerve pathways (SEPs), exposure prolonged latencies in 4 of 4
SEP components in both non-diabetics and diabetics; also altered
were some inter-peak latencies (non-diabetics and diabetics) and
some peak-to-peak amplitudes (non-diabetics only). In some
cases, SO2 effects were additive to those resulting from the
induced diabetic condition. In summary, brain anti-oxidant
and lipid peroxidation status, as well as afferent peripheral
nerve pathways, were affected by subchronic exposure to
10 ppm SO2, and these effects were exacerbated by a diabetic
condition. Authors suggest that SO2 exposure could
potentiate the incidence and/or severity of diabetes.
Effects of aging ± SO2 exposure on levels of lipid peroxidation Yargicoglu
(TEARS), antioxidant enzyme status (catalase, GPx, SOD), and et al.
afferent peripheral nerve pathways (SEPs) were monitored in the (1999)
brain of young (Y, 3 mo), middle-aged (M, 12 mo) and old (O,
24 mo) rats. In addition to age-related changes, SO2 exposure
significantly (p < 0.0001-0.02) elevated TEARS and SOD, while
reducing GPx (Y, M, O); catalase levels were not affected. Of
4 monitored SEP component peaks, SO2 significantly
(p < 0.01-0.05) prolonged latencies in groups Y (4/4) and M
(1/4), but not in O (0/4). Peak-to-peak amplitudes were
decreased in Y, (2/3) and increased in M (1/3), but not affected in
O (0/3). Taken together, these data indicate that subchronic
exposure to brief periods of 10 ppm SO2 can impact afferent
peripheral nerve pathways and the lipid peroxidation and
antioxidant enzyme status of the brain.
-------
TABLE AX4-6 (cont'd). NERVOUS SYSTEM—NEUROPHYSIOLOGY AND BIOCHEMISTRY EFFECTS
OF SO2 AND DERIVATIVES
Concentration
Duration
Species
Effects
Reference
to
o
o
O
O
2
O
H
O
c
o
H
W
O
V
O
HH
H
W
Subchronic/Chronic Exposure
10 ppm
(26.2 mg/m3);
whole body
1 h/day,
7 days/wk
for 6 wks
Swiss albino rat, male, 3, 12,
or 24 mos old, weight not
reported, n = 10/group in
6 groups
Neurodevelopment/Neurobehavior
32 or 65 ppm
(83.8 or
170 mg/m3);
whole body
5, 12, or 30 ppm
(13.1, 31.4, or
78.6 mg/m3);
whole body
Gestation
day 7-18
Near
continuous
(80% of
time)
exposure
from 9 days
before
mating
through the
12-14thday
of pregnancy
CD-I mouse dams were
exposed; numbers of dams
exposed and offspring
evaluated not indicated
CD-I mouse, adult male and
female parental animals were
exposed (n = 10/group/sex)
and male and female offspring
(n = 8 litters/group, fostered by
unexposed dams at birth) were
evaluated at 2-18 days of age;
adult male offspring also
evaluated (n = 8/group)
Effects of aging ± SO2 exposure on levels of lipid peroxidation Kilic
(TEARS), antioxidant enzyme status (catalase, GPx, SOD), and visual (2003)
system function (VEPs) were monitored in the brain and eye (retina and
lens) of young (Y, 3 mo), middle-aged (M, 12 mo) and old (O, 24 mo)
rats. In addition to age-related changes, SO2 exposure significantly
(p < 0.0001-0.04) elevated TEARS in brain and lens (Y, M, O), and in
retina (Y); reduced GPx in brain (Y) and lens (Y, M, O); reduced
catalase in retina (Y, M, O); and elevated SOD in brain (Y, M), retina
(Y, M, O) and lens (M, O). Of 5 monitored VEP component peaks, SO2
prolonged latencies in groups Y (4/5), M (3/5) and O (1/5). Taken
together, these data indicate that subchronic exposure to brief
periods of 10 ppm SO2 can impact the visual system and the lipid
peroxidation and antioxidant enzyme status of the brain and eye.
Righting and negative geotaxis reflexes were delayed at both Singh
concentrations. (1989)
Offspring: No effects observed for birth weight, postnatal body weight Petruzzi
gain, somatic and neurobehavioral development (e.g., eyelid and ear et al.
opening, incisor eruption, and reflex development); no postnatal (1996)
developmental data were shown by study authors. No effects observed
in passive avoidance testing of adult males. Adults: Observation of
behavior outside the exposure chamber on exposure days 3, 6, and
9 revealed dose-related increases in digging and decreases in grooming
by females in the 30 ppm group on exposure day 9; non-dose related
increases were observed for crossing and wall rearing by females in the
30 ppm group on exposure day 9. Observance of behaviors in 2 breeding
pairs/group in the 12 and 30 ppm groups revealed increased rearing and
social interaction in the 30 ppm group shortly after the start of exposure,
followed by return to baseline levels; effects were generally of greater
magnitude in males.
-------
TABLE AX4-6 (cont'd). NERVOUS SYSTEM—NEUROPHYSIOLOGY AND BIOCHEMISTRY EFFECTS
OF SO2 AND DERIVATIVES
Concentration
Duration
Species
Effects
Reference
to
o
o
Neurodevelopment/Neurobehavior
5, 12, or 30 ppm Near continuous (90% CD-I mouse, adult male and
(13.1, 31.4, or
78.6 mg/m3);
whole body
of time) exposure
from 9 days before
mating through the
14th day of pregnancy
female parental animals were
exposed and adult male
offspring (fostered by
unexposed dams at birth) were
evaluated at -120 days of age,
n= 11-12 offspring/group
In 20-min encounters with unexposed males, prenatally-exposed
males compared to controls displayed (dose(s) of effect, time of
testing effect observed) increased duration of serf grooming
(5 ppm, 15-20 min), decreased frequency and duration of tail
rattling (>5 ppm at 5-10 min and 12 ppm at 10-15 min), and
decreased duration of defensive postures (> 12 ppm, 0-5 min).
Study authors also noted a non-significant decrease in freezing
(apparently at all dose levels) and non-significant increases in
social exploration (apparently at all doses) and rearing
(apparently at > 12 ppm).
Fiore et al.
(1998)
CAT = catalase
Choi = cholesterol
GPx = Se-dependent glutathione peroxidase
GST = glutathione-S-transferase
HVA-/ca = high-voltage activated calcium currents
PKA = cyclic AMP-dependent protein kinase A
PKI = synthetic peptide inhibitor of PKA
PL = phospholipids
SEPs = somatosensory-evoked potentials
SOD = Cu,Zn-superoxide dismutase
TEARS = thiobarbituric acid-reactive substances
TOC = potassium channel transient outward currents
TTX = tetrodotoxin
TTX-R = tetrodotoxin-resistant
TTX-S = tetrodotoxin-sensitive
VEPs = visual-evoked potentials
O
o
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
-------
TABLE AX4-7. REPRODUCTIVE AND DEVELOPMENTAL EFFECTS OF SO2
Concentration ppm
Duration
Species
Effects
Reference
to
o
o
oo
O
o
2
o
H
O
c
o
H
W
O
^
O
HH
H
W
Reproductive Organ Effects - Subacute/Subchronic
22, 56, orll2mg/m3
(8.4, 21, or 43 ppm);
whole body
6 h/day for 7 days
10 or 30 ppm
(26.2 or 78.6 mg/m3);
whole body
6 h/day,
~5 days/wk for
21 wks (total of
99 days)
Developmental/Reproductive
32 or 65 ppm
(83.8 or 170 mg/m3);
whole body
5, 12, or 30 ppm
(13.1, 31.4, or
78.6 mg/m3); whole
body
Gestation day
7-18
Near continuous
(80% of time)
exposure from
9 days before
mating through
the 12-14thday
of pregnancy
Kunming albino mice, male,
5 wks old, 19 ± 2 g,
n = 10/group
Sprague-Dawley CD rat,
male, 8 wks old, weight not
reported, n = 70/group in
3 groups (inhalation series)
CD-I mouse dams were
exposed; numbers of dams
exposed and offspring
evaluated not indicated
CD-I mouse, adult male
and female parental animals
were exposed
(n = 10/group/sex) and male
and female offspring
(n = 8 litters/group, fostered
by unexposed dams at birth)
were evaluated at 2-18 days
of age; adult male offspring
also evaluated (n = 8/group)
Changes observed in mouse testes (concentrations of
effects) included decreased activities of SOD (43 ppm,
possibly at 21 ppm according to text) and GPx
(>21 ppm), increased catalase activity (8.4 and 21 ppm),
decreased GSH level (>21 ppm), and increased TEARS
levels (>8.4 ppm). The study authors concluded that
SO2 can induce oxidative damage in testes of mice.
No significant (p < 0.05) effect on testes histopathology
was found, although there was a very slight and probably
biologically insignificant increase in relative testes
weight. (0.61 ± 0.02 vs. 0.56 ± 0.02, %body weight).
No significant effects were observed for number of live
pups born/litter. Pup birth weight was lower at 65 ppm.
Righting and negative geotaxis reflexes were delayed at
both concentrations.
Decreased food and water intake were observed in
parental males and females of the 12 and 30 ppm groups
at the start of mating (exposure days 9-13). No effects
observed for mating or successful pregnancies. There
were no effects on litter sizes, sex ratio, or neonatal
mortality (data not shown by study authors). No effects
observed for birth weight, postnatal body weight gain,
somatic and neurobehavioral development (e.g., eyelid
and ear opening, incisor eruption, and reflex
development); no postnatal developmental data were
shown by study authors. No effects observed in passive
avoidance testing of adult males.
Meng and Bai (2004)
Gunnison et al.
(1987)
Singh (1989)
Petruzzi et al. (1996)
-------
TABLE AX4-7 (cont'd). REPRODUCTIVE AND DEVELOPMENTAL EFFECTS OF SO2
Concentration ppm
Duration
Species
Effects
Reference
to
o
o
Developmental/Reproductive
5, 12, or 30 ppm
(13.1, 31.4, or
78.6 mg/m3); whole
body
5 ppm
(13.1 mg/m3);
whole body
Near continuous
(90% of time)
exposure from
9 days before
mating through
the 14th day of
pregnancy
2 h/day for
13wks
CD-I mouse, adult male and
female parental animals were
exposed and adult male
offspring (fostered by
unexposed dams at birth) were
evaluated at -120 days of age,
n= 11-12 offspring/group
New Zealand White rabbit,
male and female,
n = 3-4/group, 1-day-old,
immunized against Alternaria
tenuis
In 20-min encounters with unexposed males,
prenatally-exposed males compared to controls
displayed (dose(s) of effect, time of testing effect
observed) increased duration of self grooming (5 ppm,
15-20 min), decreased frequency and duration of tail
rattling (>5 ppm at 5-10 min and 12 ppm at 10-15 min),
and decreased duration of defensive postures
(> 12 ppm,0-5 min). Study authors also noted a
non-significant decrease in freezing (apparently at all
dose levels) and non-significant increases in social
exploration (apparently at all doses) and rearing
(apparently at > 12 ppm).
Following subchronic exposure beginning in the
neonatal period, there were no effects on lung resistance,
dynamic compliance, transpulmonary pressure, tidal
volume, respiration rate, or min volume.
Fioreetal. (1998)
Douglas etal. (1994)
GPx = glutathione peroxidase
GSH = glutathione
SOD = superoxide dismutase
TEARS = thiobarbituric acid-reactive substances
O
o
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
-------
TABLE AX4-8. HEMATOLOGICAL EFFECTS OF SO2
Concentration
Duration
Species
Effects
Reference
to
o
o
to
o
O
O
2
O
H
O
c
o
H
W
O
^
O
HH
H
W
Acute/Subacute Exposure
0.87 ppm 24 h
(2.36 mg/m3);
whole body
Subchronic Exposure
286 mg/m3 5 h/day
(100 ppm); whole for
body. The units 28 days
were initially
reported as ug/m3
but were corrected
per correspondence
with the study
author.
Swiss Albino rat,
male, age not
reported, 250-300;
n=51, 50
Wistar rat, male,
7 wks old, weight
not reported,
n=4-16
Effects of SO2 exposure included increased hematocrit, sulfhemoglobin and Baskurt (1988)
osmotic fragility and decreased whole blood and packed cell viscosities. RBC
number, hemoglobin, mean corpuscular volume, mean corpuscular hemoglobin
concentration, and plasma viscosity were not significantly altered.
Dams were fed diets containing casein at 180 [control], 120, 90, or 60 g/kg Langley-Evans
during pregnancy and their offspring were exposed to air or SO2 as adults. In et al. (1997);
blood of offspring, SO2 exposure significantly reduced the numbers of Langley Evans
circulating total leukocytes and lymphocytes in the 180 and 120 g/kg dietary (2007)
groups; neutrophils numbers were not affected in any group. GSH levels in the
180 and 60 g/kg (but not the two intermediate) dietary groups were reduced by
SO2 exposure. This study provides information for an extremely high
concentration level but is being acknowledged here with the unit corrected
to verify that a low-concentration level study was not missed.
10 ppm
(26.2 mg/m3);
whole body
10 ppm
(26.2 mg/m3);
whole body
10 ppm
(26.2 mg/m3);
whole body
1 h/day
for
30 days
1 h/day,
7 days/
wkfor
6 wks
1 h/day
for
45 days
Guinea pig, sex and
age not reported,
250-450 g,
n = 12/group
Swiss Albino rat,
male, 3 mos old,
weight not reported,
n = 10 per group in
4 groups
Rat, sex and age not
reported, 214-222 g,
n = 6-8 per group
SO2 exposure resulted in RBC membrane lipoperoxidation (elevated levels of
malonyldialdehyde) and other oxidative damage (elevated osmotic fragility
ratios and levels of methemoglobin and sulfhemoglobin). All these effects
were significantly (p < 0.05) mitigated by injections of Vitamin E+C three
times per wk.
RBC parameters were monitored in non-diabetic rats, non-diabetic rats
exposed to SO2, alloxan-induced diabetic rats, and diabetic rats exposed to
SO2. In both non-diabetic and diabetic rats exposed to SO2, levels of GPx,
catalase, GSH, GST, and TEARS were elevated in RBC while those of SOD
were reduced.
SO2 exposure significantly elevated levels of methemoglobin, sulfhemoglobin
and malonyldialdehyde, the latter of which was substantially reversed by
Vitamin E+C treatment. RBC osmotic fragility was increased by SO2, and
again partially mitigated by Vitamin E+C. SO2 elevated RBC, white blood
cell, hemoglobin and hematocrit values, but not mean corpuscular volume,
mean corpuscular hemoglobin or mean corpuscular hemoglobin concentration.
Vitamin E+C exposure did not affect these parameters.
Etlik et al.
(1995)
Agar et al.
(2000)
Etlik et al.
(1997)
-------
TABLE AX4-8 (cont'd). HEMATOLOGICAL EFFECTS OF SO2
Concentration
Duration
Species
Effects
Reference
to
o
o
Acute/Subacute Exposure
10 ppm (26.2 mg/m3); 1 h/day, 7 days/wk
whole body for 8 wks
10 ppm (26.2 mg/m3); 1 h/day, 7 days/wk
whole body for 6 wks
Swiss-Albino rat, male,
2.5-3.0 mos old, weight
not reported, n = 30
(14 controls, 16 treated)
Albino rat, male, 3, 12,
and 24 mos old, mean
weight 213-448 g,
n = 10/group
Decreased Cu,Zn- SOD activity, increased GPx and GST Gumus.lu et al.
activity, and increased TEARS formation were observed in (1998)
RBC of treated rats. No significant effect on
glucose-6-phosphate dehydrogenase or catalase levels was
observed.
Enzyme and GSH activity (GPx, catalase, GSH, and GST) Yargicoglu et al.
were increased and copper-zinc SOD activity was decreased (2001)
inRBCs of all experimental groups compared to controls.
RBCs in older rats had lower levels of all antioxidants
enzymes and increased TEARS activity compared to
younger rats.
to
GSH = glutathione
GPx = glutathione peroxidase
GST = glutathione-S-transferase
HP = hydrolyzed protein
RBC = red blood cell or erythrocyte
SOD = superoxide dismutase
TEARS = thiobarbituric acid-reactive substance
O
o
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
-------
to
o
o
TABLE AX4-9. ENDOCRINE SYSTEM EFFECTS OF SO2
Concentration
5 or 10ppm(13.1 or
26.2 mg/m3); whole
body
Duration
24 h/day for
15 days
Species
Sprague-Dawley CD rat, male, age
not reported, 250-275 g,
n = 9/subgroup in 9 subgroups
Effects
Subjects were rats fed standard diet (normal) or high
cholesterol diet, and rats with streptozotocin-induced
diabetes fed standard diet. In diabetic rats, there was no
effect on glucose levels. Exposure to > 5 ppm lowered
plasma insulin level in normal and hypercholesterolemic
diet groups, but elevated it (non-significantly) in diabetic
rats. In each rat model, inhalation of SO2 at levels
without overt effects affected insulin levels. Specific
effects varied according to diet or diabetes.
Reference
Lovati et al.
(1996)
10 ppm (26.2 mg/m3);
whole body
1 h/day,
7 days/wk for
6 wks
Swiss Albino rat, male, 3 mos old,
weight not reported, n = 10/group
Effects were compared in non-diabetic rats and rats with Agar et al.
alloxan induced diabetes. SO2 increased blood glucose (2000)
in diabetic and non-diabetic rats.
to
to
10 ppm (26.2 mg/m3);
whole body
1 h/day,
7 days/wk for
6 wks
Rat, male, 3 mos old, weight not
reported, n = 10/group in 4 groups
Effects were compared in normal rats and rats with Kiicukatay et al.
alloxan induced diabetes. SO2 elevated blood glucose (2003)
levels in both non-diabetics and diabetics.
O
O
2
O
H
O
c
o
H
W
O
V
O
HH
H
W
-------
TABLE AX4-10. EFFECTS OF SO2 EXPOSURE ON RESPIRATORY SYSTEM MORPHOLOGY
Concentration
Duration
Species
Effects
Reference
to
o
o
Acute/Subacute Exposure
1 ppm (2.6 mg/m );
nose only
3 h/day for 6 days;
animals evaluated for
up to 72 h following
exposure
Hartley guinea pig, male,
age not reported,
250-320 g,
n = 14/group/time point
In combined group of SO2 exposed animals and furnace gas Conner et al.
controls, no alveolar lesions were observed. (1985)
Subchronic/Chronic Exposure
to
5 ppm (13 mg/m );
nose only
1 ppm
(2.62 mg/m3);
whole body
2 h/day, 5 days/wk for
4 wks
5 h/day, 5 days/wk for
4 or 8 mos; half the
animals in the 8-mo
group were allowed to
recover for 3 mos.
F344/Crl rat, male and
female, 10-11 wks old,
weight not reported,
n = 3/sex/group
Sprague-Dawley rat,
male, young adult, initial
weight not reported,
n= 12-15/data point
No nasal or pulmonary lesions.
At 4 mos of SO2 exposure, increases were observed for
incidence of bronchial epithelial hyperplasia (80 vs. 40% in
controls) and numbers of nonciliated epithelial cells (31.1 vs.
27.7% in controls); neither effect persisted past 4 mos of
exposure.
Wolff etal.
(1989)
Smith et al.
(1989)
O
o
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
-------
TABLE AX4-11. CARCINOGENIC EFFECTS OF SO2
Concentration
Duration
Species
Effects
Reference
to
o
o
to
O
o
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
Pulmonary Effects - SO?
0, 10, or 30 ppm (0, 26.2,
or 78.6 mg/m3) SO2
(whole body) ± 1 mg
B[a]P
0, 100 or 400 ppm W, or
[400 ppm W + 40 ppm
Mo] in a low-Mo diet,
±B[a]P (See Effects
column) ±B[a]P
SO2: 21 wk, 5 day/wk
(minus holidays),
6 h/day
High W, low Mo diet:
21 wk, 7 day/wk
B[a]P: 15 wk, once
per wk starting wk 4
Rat,
Sprague-Dawley,
male, 9 wk old,
~315-340 g,
n = 20-74/group
Purpose was to investigate carcinogenic/cocarcinogenic effects of
SO2 inhalation or dietary-induced high levels of systemic
sulfite/bisulfite in conjunction with trachea! installation of B[a]P.
High drinking water levels of W in conjunction with low-Mo feed
induce sulfite oxidase deficiency in rats, and thus high systemic
levels of sulfite and bisulfite (at 0, 100 or 400 ppm W, mean plasma
sulfite was 0, 0 or 44 uM, while mean tracheal sulfite + bisulfite
was 33, 69 or 550 nmol/g wet wt). Mortality in B[a]P groups
(-50% after -380-430 d) was due almost exclusively to SQCA of
the respiratory tract; survival rate was excellent for other groups
(-50% mortality after -620-700 d). Results indicate no SQCA
was induced in any of the SO2 inhalation or systemic
sulfite + bisulfite groups, nor were incidences in the B[a]P
groups enhanced by such coexposures. This lack of
cocarcinogenicity does not support the hypothesis that SO2
exposure could elevate systemic sulfite/bisulfite, generating
GSSO3H, which would inhibit GST and reduce intracellular
GSH, thus interfering with a major detoxication pathway for
B[a]P and enhancing its carcinogenicity. Authors note that due
to the high incidence of animals with tumors in the B[a]P only
groups (65/72 and 63/72), cocarcinogenicity of SO2 or
sulfite + bisulfite could only have been demonstrated by
shortening of tumor induction time and/or increased rate of
SQCA appearance—neither were observed.
Gunnison
etal.
(1988)
-------
TABLE AX4-11 (cont'd). CARCINOGENIC EFFECTS OF SO2
Concentration
Duration
Species
Effects
Reference
to
o
o
to
O
o
2
o
H
O
c
o
H
W
O
^
O
HH
H
W
Pulmonary Effects - SO2
0, 0.2 mL C, or
{0.2 mL
DEP+C ± [4 ppm
(10.48 mg/m3)SO2 or
6ppm(11.28mg/m3)
NO2 or 4 ppm
SO2 + 6ppmNO2]};
whole body
[Note: 0.2 mL
CBP = Img; 0.2 mL
DEcCBP = 1 mg
CBP + 2.5mgDEP)]
0, C, or {25 mg
SPM+C ± [4 ppm
(10.48 mg/m3)SO2 or
6ppm(11.28mg/m3)
NO2 or 4 ppm
SO2 + 6ppmNO2]};
whole body
SO2 and/or NO2:
10 mo, 16 h/day
CBP or DEcCBP:
4 wk, once/wk by
intratracheal infusion
Rat, SPF F344/M,
male, 6 wk old, wt
not reported,
n = 23-30 per group
in 6 groups
SO2 and/or NO2:
11 mo, 16 h/day
C ± SPM: 4 wk,
once/wk by
intratracheal injection
Rat, SPF Fisher 344,
male, 5 wk old, wt
not reported, n = 5
per group in 6 groups
Purpose was to study effects of DEP on rat lung tumorigenesis Ohyama et al.
and possible tumor promoting effects of SO2 or NO2 singly or (1999)
together. Alveolar hyperplasia and adenoma were significantly
(p < 0.01-0.05) increased over controls in the CBP group, but not
the DEcCBP group. This was ascribed to induction of alveolitis
and AM infiltration (a tumor response specific to rat and of
questionable relevance to humans) in the former group, but
apparently prevented by DEP in the latter. Alveolar
bronchiolization near small hyaline masses of deposited DEcCBP
was observed in all DEcCBP groups, the masses presumably
allowing long-term exposure to DEP extracts by contacted
alveolar epithelium. DNA adducts were found only in the three
gas-exposed groups. Discounting the CBA group, elevated
alveolar hyperplasia was seen only in the DEcCBP + NO2 group,
and elevated incidences of alveolar adenoma in the
DEcCBP + SO2 and particularly the DEcCBP + NO2 groups;
neither effect was observed with coexposure to both gases—
speculated by the authors to perhaps result from inhibition of the
stronger NO2 promotion by HSO3~. Thus, SO2 appears to have
weaker capacity than NO2 for promoting tumor induction
(and perhaps genotoxicity) by DEP extract, and may
antagonize such effects by NO2 during coexposure of the
gases.
Purpose was to study effects of Tokyo air SPM, with or without Ito et al.
coexposure to SO2 or NO2 or their combination, on the (1997)
development of proliferative lesions of PEC. PEC hyperplasia
was significantly (p < 05) increased by exposure to SPM, but
coexposure to either gas or their mixture was without
additional effect. No PEC papillomas were observed in
control groups, while a few were seen in the SPM groups,
irrespective of gas coexposures. Thus, SO2 demonstrated no
tumor promotion or cocarcinogenic properties. [Study did
not describe the nature of the carbon (C) used.]
-------
TABLE AX4-11 (cont'd). CARCINOGENIC EFFECTS OF SO2
to
o
o
to
O
O
2
O
H
O
c
o
H
W
O
^
O
HH
H
W
Concentration
Pulmonary Effects
0 or [10 ppm
(26.2 mg/m3)
SO2 + 5 ppm
(9.4 mg/m3)
NO2] ± [3 or
6 mg/kg bw of
DEN]; exposure
to gases whole
body
Duration
-SO2
SO2 + NO2: 6,
10.5, 15, or 18 mo,
5 day/wk, 19 h/day
DEN: once by
s.c. injection,
~2 wk after the
start of inhalation
exposure
Species
Hamster, Syrian
golden, both
sexes, 10 wk old,
bw not reported,
n = 40/sex per
each of
12 exposure
groups
Effects
The principle focus of this large study was to examine whether two inhaled
diesel-exhaust emission preparations (± particulates) could enhance the
tumorigenesis of injected DEN. An ancillary aim was to see whether inhalation
of the irritant SO2 + NO2 mixture could cause similar enhancement of DEN
tumorigenicity. Gas mixture exposure did not affect bw gain, but slightly
shortened survival times (although significantly only for females). Apart from
effects attributed to DEN, serial sacrifices showed progressive increases in
ciliated trachea! cell aberrations and in number of trachea! mucosal cells. In the
lung, gas mixture-related effects were limited to a progressing alveolar lesion
involving lining with bronchiolar epithelium and the presence of some
pigment-containing AM, and to a mild, diffuse thickening of the alveolar septa.
SO2 + NO2 exposure did not by itself elevate tumor rate in the upper
respiratory tract, nor did it enhance increases induced by DEN. Thus the
mixture appeared to have no tumor inducing or promoting effects.
Reference
Heinrich
etal. (1989)
Nonpulmonary Effects - SO?
0 or 6 ppm (0 or
15.72 mg/m3)
SO2, ± 0.2 ppm
(600 ug/m3);
whole body;
NDMA
20 mo, 5 day/wk,
4 h/day
Rat,
Sprague-Dawley,
female, age and
wt not reported,
n = 36 per group
in 4 relevant
groups
This is a preliminary report for observations after 20 mo (800 h inhalation in
200 exposures, with calculated inhaled cumulative doses of 77 mg SO2 and
2-3 mg NDMA per rat). The effects of NDMA ± SO2 inhalation were studied.
Group mortality was as follows: control (3/36), SO2 (5/36), NDMA (4/36),
NDMA + SO2 (7/36). The only tumors observed were nasal: control (0), SO2
(0), NDMA (1), NDMA + SO2 (3). No observable parameters, including
body wt gain, were affected by the additional SO2 exposure; assessment of
tumor incidence effects could not yet be performed.
Klein et al.
(1989)
Dae = aerodynamic diameter
AM = alveolar macrophage
B[a]P = benzo[a]pyrene
BHPN = N-bis(2-hydroxypropyl) nitrosamine
C = carbon or carbon black particles
DEN = diethylnitrosamine
DEP+C = DEP extract-coated C
DEcCBP = DEP extract coated carbon black particles
DEP = diesel exhaust particles
GSH = glutathione
GSSO3H = glutathione S-sulfonate
GST = glutathione-S-transferase
NDMA = N-nitroso-dimethylamine
Mo = molybdenum
PEC = pulmonary endocrine cells
SPM = suspended particulate matter extract
SPF = specific pathogen free
SQCA = squamous cell carcinoma
W = tungsten
-------
TABLE AX4-12. RESPIRATORY SYSTEM BIOCHEMISTRY EFFECTS OF SO2
Concentration
Duration
Species
Effects
Reference
to
o
o
Oxidation and Antioxidant Defenses -
Subacute/Subchronic Exposure
22, 56, orll2mg/m3
(8.4, 21, or 43 ppm);
whole body
6 h per day for 7 days
Kunming albino mice, male and
female, 5 wks old, 19 ± 2 g,
n = 10/sex/group
to
Changes observed in lung tissue
(concentrations of effect) included higher
SOD activity in males (8.4 ppm) and
females (8.4 and 21 ppm), lower SOD
activity in males (21 and 43 ppm) and
females (43 ppm), increased GPx activity in
males and females (8.4 ppm), decreased
GPx activity in males and females
(>21 ppm), decreased catalase activity in
males (43 ppm), decreased reduced GSH
level in males and females (>8.4 ppm),
increased TEARS level in males (>8.4 ppm)
and females >21 ppm). Study authors
concluded that sulfur dioxide induced
oxidative damage in lungs of mice.
Meng et al. (2003a)
O
O
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
22, 64, or 148 mg/m3
(8.4, 24.4, or 56.5 ppm);
whole body
6 h/day for 7 days
Kunming-strain mice, male, age
not reported, 18-20 g,
n = 10/group
Glucose-6-phosphate dehydrogenase and
GST activity were decreased in lung at
64 and 148 mg/m3. Lung GSH levels were
reduced in the 22 and 148 mg/m3 exposure
groups. Administration of buckthorn seed
oil increased GST and decreased TEARS
activity compared to mice exposed to
42 mg/m3 SO2 alone.
Wu and Meng
(2003)
-------
TABLE AX4-12 (cont'd). RESPIRATORY SYSTEM BIOCHEMISTRY EFFECTS OF SO2
Concentration
Duration
Species
Effects
Reference
to
o
o
to
oo
Oxidation and Antioxidant Defenses -
Subacute/Subchronic Exposure
5, 50, or 100 ppm
(13.1, 131, or
262 mg/m3);
whole body
5 h/day for
7-28 days
Wistar rat, male, 7 wks old,
weight not reported,
n = 4-5/treatment group,
8 controls
In the 5 and 100 ppm groups, GSH in BAL fluid
decreased at 7 days and increased at 21 days; at 28 days
GSH returned to normal in the 5 ppm group and further
increased in the 100 ppm group. GSH was depleted in the
lung, at 5 and 100 ppm but not at 50 ppm. With respect
to GSH-related enzymes, exposure to 5 ppm lowered
GCS, GPx, GST, and GRed activity in the lung. Effects
in the 100 ppm group were similar to the 5 ppm group,
except that lung GPx was not reduced. Exposure to
50 ppm did not affect lung GST, but reduced the number
of inflammatory cells in circulation and decreased GCS,
GPx, GRed, and GT in the lung. Study authors
concluded that sulfitolysis of glutathione disulphide
occurs in vivo during SO2 exposure and that SO2 is a
potent glutathione depleting agent, even in the absence
of pulmonary injury.
Langley-Evans et al.
(1996)
O
O
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
10 ppm
(26.2 mg/m3);
whole body
1 h/day,
7 days/wk for
6 wks
Swiss albino rat, male, 3, 12,
or 24 mos old, 210-450 g,
n = 9-1 I/group in 6 groups
Effects of age on SO2-induced oxidative effects in lung
tissue were observed in young (3-mo-old), middle aged
(12-mo-old), and old (24-mo old) rats. SO2 exposure
significantly elevated TEARS, SOD, GPx, and GST in all
age groups; reduced catalase in young and middle-aged
rats, but did not affect catalase in old rats. In rats not
exposed to SO2, SOD, GPx and GST increased with age
and catalase decreased with age. General observations in
SO2-exposed animals were increases in SOD, GPx, and
TEARS with age. The authors of the AQCD toxicology
chapter noted that while lipid peroxidation increased with
age, relative TEARS increases in response to SO2 were
inversely correlated with age (i.e., largest percent increase
seen in young rats).
Gumus.lu et al.
(2001)
-------
TABLE AX4-12 (cont'd). RESPIRATORY SYSTEM BIOCHEMISTRY EFFECTS OF SO2
Concentration
Duration
Species
Effects
Reference
to
o
o
to
VO
Oxidation and Antioxidant Defenses -
Subacute/Subchronic Exposure
286 mg/m3 (-101 ppmby
study author calculations);
whole body
Note: The study
mistakenly listed units of
jig/m3 and it was verified
with the study authors
that the units should
have been listed as
mg/m3.
5 h/day for 28 days
Wistar rat, male, 7 wks
old, weight not
reported, n = 4-16
This study explored the effects of maternal diet protein
restriction during gestation on offspring lung enzyme
responses after SO2 exposure in adulthood. Adult
offspring representing different maternal dietary
concentrations of casein (180 [control], 120, 90 or
60 g/kg) were exposed either to air or SO2. GSH levels
in B AL fluid and the lung were not affected either by
maternal diet or SO2 exposure. In the lung GRed and
GT were not affected by SO2 in any maternal diet group;
GPx was reduced only in the 120 g/kg maternal diet
group; GCS was elevated in the 180 and 60 g/kg groups;
and GST was reduced in the 180, 120 and 90 g/kg
groups (to the level seen in both the air- and
SO2-exposed 60 g/kg maternal diet groups). This study
does not provide information relevant to ambient
exposures, but is being mentioned in this table to
record that a low-concentration level study was not
overlooked.
Langley-Evans
etal. (1997);
Langley Evans
et al. (2007)
O
O
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
-------
TABLE AX4-12 (cont'd). RESPIRATORY SYSTEM BIOCHEMISTRY EFFECTS OF SO2
Concentration
Duration
Species
Effects
Reference
to
o
o
Differential Gene Expression
Subacute Exposure
14, 28, or 56 mg/m3
(5.35, 10.70, or
21.40ppm);
whole body
6 h/day for 7 days
Wistar Rat, male, age not
reported, 180-200 g,
n = 6/group in 4 groups
Repeated acute exposure caused significant (p < 0.001-0.05)
concentration-dependent reductions in enzyme activities and
gene expression in the lung for both CYP1A1 and CP1A2.
Effects were seen at the mid and high concentrations, but not
the low. Authors speculate that underlying mechanisms may
involve oxidative stress and/or cytokine release, and may
represent an adaptive response to minimize cell damage.
Qin and Meng
(2005)
14, 28, or 56 mg/m3
(5.35, 10.70, or
21.40ppm);
whole body
6 h/day for 7 days
Wistar rat, male, age not
reported, 180-200 g,
n = 6/group in 4 groups
SO2 exposure caused significant concentration-dependent
changes in the mRNA (mid and high concentrations) and
protein expression (all concentrations in lung, but statistical
significance not indicated) of apoptosis-related genes:
increases for box andp53 apoptosis-promoting genes, and
decreases for the apoptosis-repressing gene bcl-2.
Caspase-3 activity (occurring early in apoptosis process) was
also increased at the mid and high concentration.
Bai and Meng
(2005a)
O
O
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
BAL = bronchoalveolar lavage
CYP = Cytochrome P450
GCS = y-glutamylcysteine synthetase
GSH = glutathione
GT = y-glutamyl transpeptidase
GST = glutathione S-transferase
GPx = glutathione peroxidase
GRed = glutathione reductase
SOD = superoxide dismutase
TEARS = thiobarbituric acid-reactive substances
-------
TABLE AX4-13. RESPIRATORY SYSTEM EFFECTS OF SO2 IN DISEASE MODELS
Concentration
Duration
Species
Effects
Reference
to
o
o
>
I ppm
(2.62 mg/m3);
whole body
5 h/day, 5 days/wk for
4 or 8 mos; half the
animals in the 8-mo
group were allowed to
recover for 3 mos.
Sprague-Dawley rat,
male, young adult,
initial weight not
reported,
n= 12-15/data point
Respiratory system exposure effects on "normal" and
emphysema-like lungs (elastase induced) were assessed by
morphological (e.g., histopathology and morphometry) and
physiological (e.g., lung function and volume measured during
spontaneous breathing and paralysis) endpoints. At 4 mos of
SO2 exposure, bronchial alveolar hyperplasia was increased in
normal animals, but reduced in elastase-treated animals, and
numbers of nonciliated epithelial cells were increased (by
12%) in normal but not elastase-treated animals; neither
morphological observation persisted past 4 mos of exposure.
Physiological tests conducted at 4 mos of exposure revealed
decreased residual volume and quasistatic compliance in
normal SO2-exposed animals during paralyses, and decreased
residual volume/total lung capacity ratio during spontaneous
breathing and decreased nitrogen washout slope during
paralysis in elastase-treated, SO2-exposed animals. After
8 mos of exposure, lung volume and incidence of alveolar
emphysema were elevated by SO2 only in the elastase-treated
animals; those effects were not observed in the recovery
period. Authors concluded that elastase-induced
emphysema persisted but obscured rather than enhanced
SO2 effects. It was indicated that the model lacked tar
residues typically found in the lungs of smokers.
Smith et al.
(1989)
O
O
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
-------
TABLE AX4-14. EFFECTS OF MIXTURES CONTAINING SO2 AND OZONE
to
o
o
Concentration
SO2 (ppm) Ozone (ppm)
Acute/Subacute Exposure
3 ppm 0.3
(7.9 mg/m3);
head only
Duration Species
5 h/day for 3 days Sheep, sex not
reported, adult,
23 -50 kg, n = 6
Endpoints
Tracheal mucus
velocity
Ciliary beat
frequency
Interaction
Decreased by 40% immediately
after exposure and 25% at 24 h
postexposure to the mixture of the
2 compounds. The effects of
either compound alone were not
reported.
No effect
Reference
Abraham
etal.
(1986)
Chronic/Subchronic Exposure
to
O
O
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
13.2 mg/m3
(5.0 ppm) in
addition to
1.04 mg/m3
ammonium
sulfate; whole
body
0.2 mg/m3
(0.10 ppm)
5 h/day,
5 days/wk for
up to 103 days
GDI mice, female,
3-4 wks old, weight not
reported,
n = 360/group total
(14-154/group in each
assay)
Mortality rate after
Streptococcus
aerosol challenge
Alveolar
macrophage
bactericidal activity
towards inhaled
K. pneumoniae
Counts, viability,
and ATP levels in
cells obtained by
pulmonary lavage
Increased in groups exposed to Aranyi
ozone alone and mixture of ozone, et al.
SO2, and ammonium sulfate. (1983)
Increased trend (non-significant)
in ozone group but significantly
increased in mixture group.
No effect of either treatment
-------
TABLE AX4-14 (cont'd). EFFECTS OF MIXTURES CONTAINING SO2 AND OZONE
Concentration
to
o
o
o
o
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
S02 (ppm)
1 ppm
(2.62 mg/m3);
whole body
Ozone (ppm)
1 ppm in
addition to
3 ppm
trans-2-butene
Duration
23 h/day,
7 days/wk, for
4 wks
Species Endpoints
Golden hamster, male, age Lung volumes
not reported, -105 g,
n = 14 or 15/group; mild
emphysema was induced
in some animals by
intratracheal
administration of elastase
Interaction
End expiratory volume, residual
volume, total lung capacity and
vital capacity were unaffected in
the mixture versus air exposure
group in normal or emphysematous
hamsters.
Reference
Raub et al.
(1983)
Respiratory system
compliance
Distribution of
ventilation (N2
washout slope)
Diffusion capacity
for carbon
monoxide
Unaffected in the mixture versus air
exposure group in normal or
emphysematous hamsters.
The N2 slope decreased in the
mixture versus air exposure group
in both normal and emphysematous
hamsters.
Significantly increased in the
mixture versus air-exposed normal
animals. Although the text reported
an increase in the mixture versus
air-exposed emphysematous
animals, Figure 3 of the study
indicated that the effect was very
small and did not obtain statistical
significance. Significantly lower in
emphysematous versus normal
hamsters exposed to the mixture.
The authors noted a significant
interaction between exposure to
the mixture and emphysema.
-------
to
o
o
TABLE AX4-14 (cont'd). EFFECTS OF MIXTURES CONTAINING SO2 AND OZONE
Concentration
SO2 (ppm) Ozone (ppm)
Duration
Species
Endpoints
Interaction
Reference
Histopathology Inflammatory lesions were found
in the lungs of emphysematous
hamsters exposed to air or the
mixture. Hyperplasia incidence
was higher in emphysema
hamsters exposed to the mixture
versus air. Inflammatory lesions
were similar in emphysematous
hamsters exposed to air or the
mixture. Data were not shown for
histopathology data.
Overall author Animals with impaired lung
conclusion function may have decreased
capacity to compensate for the
pulmonary insult caused by
exposure to a complex pollutant
mixture.
O
O
2
O
H
O
c
o
H
W
O
V
O
HH
H
W
-------
TABLE AX4-15. EFFECTS OF SO2 LAYERED ON METALLIC OR CARBONACEOUS PARTICLES
to
o
o
O
O
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
Concentration
SO,
Metal (mg/m3)
Duration
Species
Endpoints
Interaction
Reference
~ 1 ppm
(2.6 mg/m3);
whole body
Zinc oxide: 0.8, 3 h
2.7, or 6.0 mg/m3
(0.05 uM projected
area diameter,
GSD 2.0) (sulfate,
sulfite, and sulfur
trioxide detected)
Hartley guinea Vital capacity
pig, male, age not
reported,
240-300 g,
n = 7-16/group
No effect with exposure to 7.8 mg/m3 zinc
oxide alone and 2.7 mg/m3 zinc oxide in
combination with SO2, but decreased with
exposure to 0.8 and 6.0 mg/m3 zinc oxide in
combination with SC>2.
Lam et al.
(1982)
7.8
Total lung capacity
Diffusion capacity
for carbon monoxide
and ratio of
diffusion capacity
for carbon monoxide
to total lung capacity
or alveolar volume.
Alveolar volume
No effect with exposure to 7.8 mg/m3 zinc
oxide alone, but decreased with exposure to
6.0 mg/m3 zinc oxide in combination with
S02.
No effect with exposure to 7.8 mg/m3 zinc
oxide alone, but decreased with exposure to
2.7 and 6.0 mg/m3 zinc oxide in
combination with SO?.
No effect with exposure to 7.8 mg/m3 zinc
oxide alone, but decreased with exposure to
6.0 mg/m3 zinc oxide in combination with
S02.
~1 ppm 0
(2.6 mg/m3);
head only
1 h Hartley guinea
pig, male, age not
reported,
200-300 g,
n = 8-23/group
Pulmonary function SO2 exposure alone resulted in an 1 1%
increase in resistance and 12% decrease in
compliance.
Amdur
etal.
(1983)
-------
TABLE AX4-15 (cont'd). EFFECTS OF SO2 LAYERED ON METALLIC OR CARBONACEOUS PARTICLES
Concentration
to
o
o
SO,
Metal (mg/m3)
Species Endpoints
Interaction
Reference
Concentration
O
O
2
O
H
O
c
o
H
W
O
^
O
HH
H
W
~1 ppm
(2.6 mg/m3)
~1 ppm
(2.6 mg/m3)
~1 ppm
(2.6 mg/m3)
~1 ppm
(2.6 mg/m3)
Zinc oxide: -1-2 (0.05 uM
projected area diameter,
GSD 2.0); mixed at 24 °C
and 30% RH
-1-2; mixed at 24 °C and
30% RH
Pulmonary function
Pulmonary function
-1-2; mixed at 480 °C and
30% RH
Pulmonary function
-1-2; mixed at 480 °C and
80% RH with addition of
water vapor downstream
-1-2; mixed at 480 °C and
30% RH with addition of
water vapor during
mixing.
Pulmonary function
Pulmonary function
Zinc oxide exposure alone resulted in
a 9% decrease in compliance that
persisted 1 h after exposure.
A 12% decrease in compliance and
decreased tidal volume that persisted
1 h after exposure, and decreased min
volume. There was no evidence of
new compound formation. Study
authors concluded that effects on
tidal volume and min volume
mostly likely represented an
additive effect.
A 12% decrease in compliance and
decreased tidal volume that persisted
1 h after exposure and a 12% increase
in resistance and decreased min
volume. There was no evidence of
new compound formation.
A 13% decrease in compliance that
persisted 1 h after exposure and a
29% increase in resistance. Sulfite
formation was observed.
A 19% increase in resistance that
persisted 1 h after exposure,
decreased tidal volume immediately
after exposure, and a 26% decrease in
compliance 1 h after exposure.
Sulfate, sulfite, and sulfur trioxide
formation was observed.
-------
TABLE AX4-15 (cont'd). EFFECTS OF SO2 LAYERED ON METALLIC OR CARBONACEOUS PARTICLES
Concentration
to
o
o
O
o
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
SO,
Metal (mg/m3) Duration
Species
Endpoints
Interaction
Reference
1.10-1.25 ppm Copperoxide: Ih
(2.9-3.3 mg/m3); 1.16-2.70
head only (O.I uM)
Hartley guinea pig, Pulmonary
male, age not resistance
reported, 275-375 g;
n = 8/group
Increased 32-47% during exposure
and at 1 and 2 h postexposure when
SO2 and copper oxide were mixed at
37 °C, a condition that resulted in
formation of 0.36 umol/m3 sulfite
on the copper oxide particles. No
effect was observed with the
compounds were mixed at 141 1 °C,
a condition that led to the formation
of sulfate on the copper oxide
particles.
Chen et al.
(1991)
1.02 ppm
(2.7 mg/m3);
head only
1.10 ppm
(2.9 mg/m3)
1.08 ppm
(2.8 mg/m3)
Zinc oxide: 0
(0.05 uM median
diameter, GSD
2.0)
2.76
Ih
Hartley guinea pig,
male, age not
reported, 290-410 g,
n = 6-9/group
Dynamic lung
compliance
Baseline pulmonary
resistance at 2 h
following exposure
Airway
hyperresponsiveness
to acetylcholine
No effect when mixed under
conditions that led to the formation
of either sulfate or sulfite on
particles.
No effect in any group.
No effect with exposure to SO2 or
zinc oxide alone; compared to
furnace controls (3% argon).
Hyperresponsiveness increased in
both groups exposed to
SO2-layered zinc oxide particles.
Chen et al.
(1992)
0.87
2.34
-------
TABLE AX4-15 (cont'd). EFFECTS OF SO2 LAYERED ON METALLIC OR CARBONACEOUS PARTICLES
to
o
o
oo
o
o
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
Concentration
SO2 Metal (mg/m3)
10 ppm 0
(26.2 mg/m3);
nose only
Duration Species
4 h Swiss mice,
female, 5 wks
old, 20-23 g,
n = 5/group
Endpoints
AM Fc-receptor
mediated phagocytosis
of sheep red blood cells
at 3 days after exposure
Interaction
Dose-dependent reductions in AM
phagocytosis were observed at each
concentration of SO2 mixed with carbon
black aerosol at 85% relative humidity,
Reference
Jakab et al.
(1996)
5 ppm
(13.1 mg/m3)
10 ppm
(26.2 mg/m3)
Carbon black:
10 mg/m3 (0.3 uM,
GSD 2.7)
10 mg/m3 (formed
6 ug sulfate at 85%
humidity)
10 mg/m3 (formed
13.7 ug sulfate at
85% humidity)
the only conditions under which SO2
significantly chemisorbed to carbon black
aerosol and oxidized to sulfate. AM
phagocytic activity was reduced
somewhat immediately after exposure
(Day 0), was minimal on Days 1 and 3,
began increasing on Day 7, and was fully
recovered by Day 14. No effects were
observed with exposure to SO2 or carbon
black alone. The data indicate that
environmentally relevant respirable
carbon particles can act as effective
vectors for delivering toxic amounts of
acid SO42 to distal parts of the lung.
-------
TABLE AX4-15 (cont'd). EFFECTS OF SO2 LAYERED ON METALLIC OR CARBONACEOUS PARTICLES
to
o
o
VO
O
O
2
O
H
O
c
o
H
W
O
^
O
HH
H
W
Concentration
S02
20ppm
(52.4 mg/m3)
10 ppm
(26.2 mg/m3);
nose only
Metal (mg/m3)
10 mg/m3 (formed
48.7 ug sulfate at
85% humidity)
0
Duration
4 h once or
for 4, 5, or
6 days
Species
Outbred Swiss
mouse, female, age
and weight not
Endpoints
Inflammatory
response after a
single 4-h exposure
Interaction
There was no effect on total cell
number, lymphocyte/PMN differentials,
or total protein levels in B AL fluid in
Reference
Clarke
etal.
(2000)
10 ppm
(26.2 mg/m3)
Carbon black:
10 mg/m3 (10%
humidity)
Carbon black:
10 mg/m3 in 85%
humidity to
generate 8 ug/m3
acid sulfate
Carbon black:
10 mg/m3 in 10%
humidity to
generate 41 ug/m3
acid sulfate
specified, n = 10 or
12 per
experimental
value.
AM Fc-mediated
phagocytosis after a
single 4-h exposure
Intrapulmonary
bactericidal activity
toward
Staphylococcus
aureus
any group.
Suppressed by acid sulfate coated
particles (at -140 ug/m3) at 1, 3, and
7 days postexposure; values returned to
normal by Day 14.
Decreased by a single 4-h exposure to
sulfate coated particles (at -140 ug/m3)
at 1 and 3 days postexposure, with
recovery by Day 7. Suppression was
also observed after 5 and 6 days of
repeated exposure to -20 ug/m3 sulfate
coated particles a condition more
relevant to potential ambient human
exposures.
-------
TABLE AX4-15 (cont'd). EFFECTS OF SO2 LAYERED ON METALLIC OR CARBONACEOUS PARTICLES
to
o
o
Concentration
S02
10 ppm
(26.2 mg/m3)
1 ppm
(2.62 mg/m3)
1 ppm
(2.6 mg/m3);
nose only
Metal (mg/m3) Duration
Carbon black:
10 mg/m3 in 85%
humidity to
generate 137 ug/m3
acid sulfate
Carbon black:
1 mg/m3 in 85%
humidity to
generate
20 ug/m3acid
sulfate:
Zinc oxide: 3 h/day for 6 days;
6 (0.05 uM animals evaluated
projected area for up to 72 h
diameter, GSD 2.0) following exposure
Species Endpoints Interaction
Hartley guinea Right lung to No effect by SO2. Increased for 48 h in
pig, male, age body weight group exposed to SO2-layered zinc oxide.
not reported, ratio
250-320 g,
n = 5-18/group/
time point
Reference
Conner
etal.
(1985)
O
o
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
-------
TABLE AX4-15 (cont'd). EFFECTS OF SO2 LAYERED ON METALLIC OR CARBONACEOUS PARTICLES
Concentration
to
o
o
SO,
Metal (mg/m3) Duration Species
Endpoints
Interaction
Reference
>
-k
1 ppm 0
(2.6 mg/m3)
Right lung wet to
dry weight ratio
Lung morphology
Trachea! secretory
cell concentration.
Epithelial
permeability
DNA synthesis
(3H-tymidien
uptake) terminal
bronchial cells
Lung volumes
No effect by SO2. Increased at 1 h after exposure in
SO2-layered zinc oxide group.
No lesions were observed in the SO2 group. In the group
exposed to SO2-layered zinc oxide, there was increased
incidence of alveolar duct inflammation consisting of
interstitial cellular infiltrate, increased numbers of
macrophages, and replacement of squamous alveolar
epithelium with cuboidal cells. Frequency and severity of
lesions were greatest immediately following exposure and by
72 h following exposure, lesions were mild and infrequent.
No effects with either exposure scenario.
No effects with either exposure scenario.
Unaffected by SO2. Increased at 24 and 72 h after exposure to
zinc oxide/SO2.
Unaffected by SO2 exposure. Functional reserve capacity,
o
o
0
H
O
o
H
W
O
O
H
hi
Diffusion capacity
for carbon
monoxide
Alveolar volume
Pulmonary
mechanics
viuii ^ajjci^iiy , oiiu luicii lung ^ajjd^iiy wcic uc^icascu iiuiii
1 to 72 h following exposure to zinc oxide/SO2.
Unaffected by SO2 exposure. Decreased by -40-50% from
1 to 24 h following exposure to zinc oxide/SO2.
Unaffected by SO2 exposure. Decreased by -10% from 1 to
24 h following exposure to zinc oxide/SO2.
Respiratory frequency, tidal volume, pulmonary resistance,
and pulmonary compliance were unaffected by either
exposure.
-------
TABLE AX4-15 (cont'd). EFFECTS OF SO2 LAYERED ON METALLIC OR CARBONACEOUS PARTICLES
Concentration
to
o
o
SO,
Metal (mg/m3)
Duration
Species
Endpoints
Interaction
Reference
to
O
o
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
1 ppm
(2.6 mg/m3)
Author
conclusion
1 ppm
(2.6 mg/m3);
head only
Zinc oxide: 1 or 2.5
(0.05 uM CMD, GSD 2.0)
Sulfate was generated at
7 and 11 ug/m3 at each
respective dose; sulfuric
acid level was reported at
21 and 33 ug/m3 at each
respective dose.
3 h/day for
5 days
Guinea pig, sex, Pulmonary
age, and weight not diffusing
reported, capacity
n = 8-9/group
Ih
Bronchial
sensitivity to
acetylcholine
Changes were identical to those
reported in a previous study in
which guinea pigs were exposed to
zinc oxide alone. Sulfur
compounds deposited on the
surface are less important than the
zinc oxide particle.
No effect with exposure to 1 ppm
SC>2 or 2.5 mg/m3 zinc oxide alone
(data not shown by study authors).
Significant and dose related
decreases on exposure days 4 and
5 at 7 ug/m3 sulfate (20% less than
control) and days 2-5 at 11 ug/m3
sulfate (up to 40% less than control).
No effect of 1 ppm SO2 or 2.8 mg/m3
zinc oxide alone. Increased with SO2
administered in combination with
either zinc oxide dose. The study
authors noted that responses were
similar to those produced by
200 jig/m3 sulfuric acid of similar
particle size, thus indicating the
importance of surface layer.
Amdur
etal.
(1988)
-------
TABLE AX4-15 (cont'd). EFFECTS OF SO2 LAYERED ON METALLIC OR CARBONACEOUS PARTICLES
to
o
o
O
O
2
o
H
O
c
o
H
W
O
^
O
HH
H
W
Concentration
S02
5 ppm
(13 mg/m3);
nose only
Metal (mg/m3)
22 mg/m3 gallium
oxide (0.2 uM
volume median
Duration
2 h/day for 4 days,
followed by 2 days
without exposure,
Species
Fischer-344, male
and female,
18-19 wks old,
Endpoints
Tracheal and
large airways
morphology
Interaction
No effects observed with coexposure
to gallium oxide and SO2.
Reference
Shami et al.
(1985)
diameter, GSD not
reported), with and
without addition of
7 mg/m3
benzo(a)pyrene
followed by 5 more
days of exposure;
animals were evaluated
for up to 28 days
following exposure
weight not reported,
n = 2/sex/group at
each evaluation time
period
Pulmonary
morphology
Cell
proliferation
(3H-thymidine
intake) in
trachea and
large airways
Increase numbers of non-ciliated
cells in terminal bronchial epithelium
was observed in the SO2/gallium
oxide/benzo(a)pyrene group. Mild
peribronchial and perivascular
mononuclear inflammatory cell
infiltrate and small hyperplastic
epithelial cells in alveoli, and
alveolar septal hypertrophy was
observed in the SO2/gallium oxide
group, with and without
benzo(a)pyrene exposure; effects
were more prominent with
benzo(a)pyrene exposure.
In the SO2/gallium oxide group:
increased on days 1 and 14; basal
cells were primarily labeled. In the
SO2/gallium oxide/benzo(a)pyrene
group: increased on day 8.
-------
TABLE AX4-15 (cont'd). EFFECTS OF SO2 LAYERED ON METALLIC OR CARBONACEOUS PARTICLES
Concentration
to
o
o
SO,
Metal (mg/m3)
Duration
Species
Endpoints
Interaction
Reference
5 ppm
(13 mg/m3);
nose only
Cell proliferation
(3H-thymidine intake)
in terminal
bronchioles
Types of
3H-thymidine-labeled
cells in the alveolar
region
In the SO2/gallium oxide group: increased
on day 14; Clara cells were primarily
labeled. In the SO2/gallium
oxide/benzo(a)pyrene group: increased
on day 11.
In the SO2/gallium oxide group: type II
cells were primarily labeled in the
alveolar region through 14 days of
exposure.
In the SO2/gallium oxide/benzo(a)pyrene
group: labeling was increased in Type II,
Type I, and endothelial cells on day 8.
O
O
2
O
H
O
c
o
H
W
O
V
O
HH
H
W
5 ppm
(13 mg/m3);
nose only
5 ppm
(13 mg/m3)
Gallium oxide:
27 mg/m3
(-0.20 uM
MMD, GSD
-1.5-2), with and
without
7.5 mg/m3 of
1-nitropyrene and
benzo[a]pyrene
0
2 h/day,
5 days/wk for
4 wks
F344/Crl rat, male
and female,
10-11 wks old,
weight not
reported,
n = 6/sex/group
Pulmonary particle
clearance
No effect was observed with exposure to
SO2 alone; clearance was slowed only by
gallium oxide, with or without
coexposure to SO2 or the other
compounds; SO2 in combination with the
polyaromatic hydrocarbons had no effect
on clearance rate. Study authors
concluded that toxicity was dominated by
gallium oxide.
Wolff etal.
(1989)
BAL = bronchoalveolar lavage fluid
CMD = count median diameter
GSD = geometric standard deviation
MMAD = mass median aerodynamic diameter
MMD = mass median diameter
RH = relative humidity
-------
TABLE AX4-16. EFFECTS OF SO2 AND SULFATE MIXTURES
to
o
o
O
O
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
Concentration
SO2 Sulfate (mg/m3)
Acute
5 ppm Sulfate aerosol 1.5
(13.1 mg/m3); (0.5 uMMMAD,
nose only GSD 1.6)
Chronic/Subchronic
1 ppm 0
(2.62 mg/m3);
whole body
Duration
4h
5 h/day,
5 days/wk
for 4 or
8 mos; half
the animals
in the 8-mo
group were
Species
Sprague Dawley
rat, male, age not
reported, -200 g,
n = 8/group
Sprague-Dawley
rat, male, young
adult, initial
weight not
reported,
n= 12-15/data
point
Endpoints
Lung clearance of
radiolabeled tracer
particles.
Morphological
observations at
4 mos exposure in
"normal" rats
Interaction
No significant effect was observed
with the mixture of the two
compounds at 80-85% humidity.
Bronchiolar epithelial hyperplasia
and increased numbers of
non-ciliated epithelial cells were
observed in rats exposed to either
compound alone but coexposure to
both compounds did not magnify the
effects. An increase in alveolar
Reference
Mannix et al.
(1982)
Smith et al.
(1989)
allowed to
recover for
3 mos.
1 ppm
(2.62 mg/m3)
(NH4)2S04:
0.5 mg/m3
(MMAD = 0.42-0.44
± 0.04 urn, GSD
2.2-2.6)
0.5 mg/m3
Morphological
observations at
4 mos exposure in
rats treated with
elastase to induce an
emphysema-like
condition
Morphological
observations at
8 mos exposure in
"normal" rats
chord length was observed in the
(NH4)2SO4 group and no further
changes were observed with
coexposure to SO2.
Bronchiolar epithelial hyperplasia
was decreased in groups exposed to
either compound alone or the
mixture of the two compounds.
A decrease in alveolar chord length
was observed in the (NH4)2SO4
group and no further changes were
observed with coexposure to SO2.
An increase in non-ciliated epithelial
cells and alveolar birefringence (an
indication of alveolar interstitial
fibrosis) was observed only in the
group exposed to (NH4)2SO4.
-------
TABLE AX4-16 (cont'd). EFFECTS OF SO2 AND SULFATE MIXTURES
Concentration
to
o
o
SO,
Sulfate (mg/m3) Duration
Species
Endpoints
Interaction
Reference
O
O
2
O
H
O
c
o
H
W
O
^
O
HH
H
W
Chronic/Subchronic
Morphological
observations at
8 mos exposure in
rats treated with
elastase
Morphological
observations at
12 mos exposure in
normal rats
Morphological
observations at
12 mos exposure in
rats treated with
elastase
Lung function
effects at 4 mos
exposure in normal
rats
Lung function
effects at 4 mos
exposure in
elastase-treated
rats
An increase in lung volume per body
weight and emphysema incidence was
observed in groups treated with either
compound alone or in combination;
alveolar chord length was increased only
in the group exposed to the mixture of
compounds.
Increased alveolar chord length was
observed only in the (NH4)2SO4 group.
In increase in absolute lung volume was
observed only in the group treated with
the mixture of both compounds.
A decrease in residual volume was
observed in the SO2 group and decreased
quasistatic compliance was observed in
the SO2 group and in the (NH4)2SO4
group, but the effects were not observed
with the mixture.
Ratio of residual volume/total lung
capacity and N2 washout was decreased
in the SO2 group and in the (NH4)2SO4
group, but the effects were not observed
with the mixture.
-------
TABLE AX4-16 (cont'd). EFFECTS OF SO2 AND SULFATE MIXTURES
to
o
o
Concentration
S02
Sulfate (mg/m3) Duration
Species
Endpoints
Interaction
Reference
Chronic/Subchronic
Overall In general, pollutant effects were
conclusions minimal and transient, and
appeared obscured or repressed
in elastase-treated groups;
(NH4)2SO4 was more bioactive
than SO2, with little evidence of
mixture additivity (in several
instances, effects seen with one or
both pollutants individually were
not seen with the mixture).
GSD = geometric standard deviation
MMAD = mass median aerodynamic diameter
O
o
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
-------
TABLE AX4-17. EFFECTS OF ACTUAL OR SIMULATED AIR POLLUTION MIXTURES
to
o
o
Concentration
Exposed Group
Control Group
Duration
Species
Endpoints
Effect
Reference
oo
O
o
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
Acute/Subactute
Air pollutant mixture at full
concentration (tested in two
studies): 0.35 ppm ozone,
1.3 ppm nitrogen dioxide,
2.5 ppm (6.6 mg/m3) SO2,
10 ug/m3 manganese sulfate,
500 ug/m3 ferric sulfate,
500 ug/m3 ammonium sulfate,
500 ug/m3 carbon aerosol. The
mixture was also tested at l/i and
YA concentrations. For aerosols
MMAD = 0.3-0.48 uM with
GSD = 2.6-4.6. Nose-only
exposure Compounds formed
included sulfate, nitrate,
hydrogen ion, and nitric acid.
Clean air
4h
Sprague-Dawl
ey rat, male,
age not
reported,
240-280 g,
n = 6-9/group
Breathing
pattern
Histopathology
Mucociliary
clearance
Effect of full concentration mixture
in two studies: increased breathing
frequency, trend or significant
decrease in tidal volume, decreased
or unaffected oxygen consumption,
and increased or unaffected
ventilation equivalent for oxygen.
Effect of half concentration
mixture: increased min ventilation.
Quarter concentration: no
significant effects.
Full concentration: Area of type 1
parenchyma! lung lesions were
increased in 1 of 2 experiments and
area of type 2 parenchymal lung
lesions were increased in both
experiments. Effects were
equivalent to those observed with
ozone exposure alone.
Half and quarter concentrations:
No effects.
No effect on early or late clearance
of 85Kr-labeled polystyrene
particles.
Mautz et al.
(1988)
-------
TABLE AX4-17 (cont'd). EFFECTS OF ACTUAL OR SIMULATED AIR POLLUTION MIXTURES
Concentration
to
o
o
Exposed Group
Control Group Duration
Species
Endpoints
Effect
Reference
O
O
2
O
H
O
c
o
H
W
O
^
O
HH
H
W
Acute/Subacute
2.55 ppm (6.7 mg/m )
SO2, 0.3 ppm ozone,
1.2 ppm nitrogen oxide,
150 ug/m3 ferric oxide,
130 ug/m3 nitric acid,
2.0 uM/m3 hydrogen
ion, and 500 ug/m3
total Fe3+, Mn2+, and
NH42+ combined; nose
only
Purified air
4 h/day for
7 or
21 days
Sprague-Dawle
y rat, male, age
not reported,
200-225 g,
n= 5-13/group/
time period
Nasal epithelial injury (measured by
tritiated thymidine uptake).
Bronchoalveolar epithelial permeability
to 99mTc-diethylenetriaminepentaacetate.
Nasal mucosal permeability to
99mTc-diethylenetriaminepentaacetate.
Macrophage rosette formation.
No effect at any
concentration.
No effect at either
time period.
No effect at either
time period.
Rosette formation
was decreased
(indicating damage
to Fc receptors) for
up to 4 days after
the 7-or 21-day
exposure;
magnitude of effect
was greater
following the
21-day exposure.
By day 4 after
exposure, numbers
began increasing
and by day 7 were
equivalent to
control values.
Phalen and
Kleinman
(1987)
-------
TABLE AX4-17 (cont'd). EFFECTS OF ACTUAL OR SIMULATED AIR POLLUTION MIXTURES
Concentration
to
o
o
Exposed Group
Control Group Duration
Species
Endpoints
Effect
Reference
O
O
2
O
H
O
c
o
H
W
O
V
O
HH
H
W
Acute/Subacute
Macrophage
phagocytic
activity
Subchronic/Chronic
Urban air: Sao Paulo, mean
levels of air pollutants
measured 200 m from the
police station where rats
were kept: 29.05 ug/m3
(0.011ppm)SO2; 1.25ppm
carbon monoxide,
ll.OSppb ozone,
35.18 ug/m3 particulates.
Rural air: Atibaia,
an agricultural
town 50 km from
Sao Paulo was
considered the
control; air
pollutant levels
were not measured.
6 mos Wistar rat,
male, 2 mos old,
weight not
reported,
n = 14-30/group
Death
Respiratory
mechanics
Mucus properties
Bronchoalveolar
lavage
In rats exposed for 7 days, decreased
activity was observed for 2 days
following exposure. No effects were
observed after the 21-day exposure
period.
37 of 69 rats housed in Sao Paulo died Saldiva
before the end of the study and etal.
autopsy of 10 animals identified (1992)
pneumonia as the cause of death; 10 of
56 animals housed in Atibaia died.
Nasal resistance was higher in animals
housed in Atibaia. No differences
were observed for pulmonary
resistance or dynamic lung elastance.
In animals from Sao Paulo trachea!
mucus output was lower, relative
speed of tracheal mucus (as assessed
by frog palate assay) was slower, ratio
between viscosity and elasticity was
higher for nasal mucus, and rigidity of
tracheal mucus was increased.
In lavage fluid from animals housed in
Sao Paulo, there were increased
numbers of cells, lymphocytes, and
polymorphonuclear cells.
-------
TABLE AX4-17 (cont'd). EFFECTS OF ACTUAL OR SIMULATED AIR POLLUTION MIXTURES
Concentration
to
o
o
Exposed Group
Control Group
Duration
Species
Endpoints
Effect
Reference
>
O
O
2
O
H
O
c
o
H
W
O
V
O
HH
H
W
Subchronic/Chronic
Histochemical
evaluation
Ultrastructural
studies
Hyperplasia was observed in
respiratory epithelium of rats
housed in Sao Paulo.
Animals housed in Sao Paulo
had a higher frequency of cilia
abnormalities including
composite cilia, microtubular
defect, vesiculation, and
decreased microvelocity of
luminal membrane.
Urban air: See description
for Saldiva etal. (1992)
Urban air: Sao Paulo, levels
of air pollutants measured
were: -8-50 ug/m3
(0.003-0.019 ppm) SO2,
—0.1-0.45 ppm nitrogen
dioxide, -4.8-7 ppm carbon
monoxide, and
-50-120 ug/m3 paniculate
matter.
Rural air: See
description for
Saldiva et al.
(1992)
Rural air:
Atibaia, an
agricultural town
50 km from Sao
Paulo was
considered the
control; air
pollutant levels
were not
measured.
6 mos
Four groups of rats
were housed:
3 mos in Sao
Paulo, 3 mos in
Sao Paulo followed
by 3 mos in
Atibaia, 3 mos in
Atibaia, or 6 mos
at Atibaia.
Rats were Nasal passage
from the same pathology
cohort as
Saldiva et al.
(1992);
n= 15/group
Wistar rats, Lung
male, responsiveness to
1.0-1.5 mos methacholine
old, weight not
reported,
n = 30/group
Rats housed in Sao Paulo had
increased nasal epithelium
volume, larger amounts of
mucosubstances stored in
epithelium, and more acidic
mucus secretions in lamina
propria glands.
Increased respiratory system
elastance resulting from
increased sensitivity to
methacholine in rats housed in
Sao Paulo for 3 mos compared
to all the other groups. No
exposure-related effects were
observed for respiratory system
resistance.
Lemos et al.
(1994)
Pereira et al.
(1995)
-------
TABLE AX4-18. EFFECTS OF METEOROLOGICAL CONDITIONS ON SO2 EFFECTS
to
o
o
to
O
O
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
Concentration
S02(ppm)
0.5 or 5 ppm (1.31
or 13.1 mg/m3);
apparently
intratracheal
1.0, 2.5, or 5 ppm
(2.62, 6.55, or
13.1 mg/m3);
apparently
intratracheal
Condition
Drop in air
temperature
from 3 8 °C to
15 °C
Drop in
intratracheal
temperatures
from -35.5 °C
to -27 °C
Duration
45 min
In pre-exposure period:
15-min exposure to
warm humid air,
10-min exposure to
cold dry air, and
15-min exposure to
warm humid air. In the
SO2 exposure period:
10-min exposures to
Species
Rabbit, sex not
reported, adult,
mean 2.0 kg,
n= 5-10/group;
animals were
mechanically
ventilated.
Duncan-Hartley
guinea pigs, male,
age and weight not
reported,
n= 7-12/group,
mechanically
ventilated; animals
were
hyperventilated
Endpoints Interaction
Lung Exposure to cool air for 20 min
resistance resulted in a -54% mean increase in
lung resistance. Exposure to SO2 for
20 min increased lung resistance by
16% at 0.5 ppm and 50% at 5 ppm.
The difference in lung resistance
from warm to cold air was halved
(27%) by exposure to 0.5 ppm and
was not significant at 5 ppm. The
study authors concluded that
transient alteration in
tracheobronchial wall following
SO2 exposure may have reduced
accessibility of airway nervous
receptors to cold air.
Peak Percent decreases were significantly
expiratory greater with exposures to SO2 in dry
flow air at concentrations of 1.0 ppm
(-32.7%) and 2.5 ppm (-35.6%) than
with exposure to cold dry air (-27%);
decrease at 5 ppm SO2 in cold dry air
(-25.3%) was similar to that with
cold dry air. The effects did not
persist following exposures.
Reference
Barthelemy
etal. (1988)
Halinen et al.
(2000a)
each SO2 concentration
in cold dry air or with
cold dry air alone were
preceded and followed
by 15-min exposures to
warm humid air.
during cold air and
SO2 exposure to
simulate exercise.
-------
TABLE AX4-18 (cont'd). EFFECTS OF METEOROLOGICAL CONDITIONS ON SO2 EFFECTS
Concentration
to
o
o
SO2 (ppm) Condition Duration
Species
Endpoints
Interaction
Reference
O
o
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
Bronchoalveolar
lavage
Tidal volume Percent decreases were significantly
greater with exposure to SO2 in cold
dry air at concentrations of 1.0 ppm
(-22.4%) and 2.5 ppm (-28.3%) than
with exposure to cold dry air
(-18.1%); decrease at 5 ppm SO2 in
cold dry air (-17.8%) was similar to
that of cold dry air. The effects did
not persist following exposures.
The clean dry air group had
significantly more macrophages,
lymphocytes, and increased protein
concentration in lavage than the
warm humid air control. The cold
dry air + SO2 group had fewer
macrophages than the clean dry air
group and higher protein
concentration than the unexposed
controls.
Histopathology Increased incidence of eosinophilic
infiltration within and below tracheal
epithelium with exposure to cold dry
air or SO2 in cold dry air.
-------
TABLE AX4-18 (cont'd). EFFECTS OF METEOROLOGICAL CONDITIONS ON SO2 EFFECTS
Concentration
to
o
o
O
O
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
SO2 (ppm) Condition Duration
Species
Endpoints
Interaction
Reference
1 ppm
(2.62 mg/m3);
apparently
intratracheal
Drop in 60 min
intratracheal
temperatures
from -37 °C
to -26 °C
Duncan-Hartley Peak expiratory
guinea pigs, male, flow
age and weight not
reported,
n = 8-9/group,
mechanically
ventilated; animals
were hyperventilated
during cold air and
SO2 exposure to
simulate exercise.
Non-significant decreases compared Halinen et al.
to baseline (4.5-10.8%) at 10 and (2000b)
20 min of exposure to cold dry air.
With exposure to SO2 in cold dry air:
decreased significantly (11.4%, i.e.,
bronchoconstriction) compared to
baseline at 10 min of exposure but
recovered from 20 to 60 min of
exposure. The effect with SO2
exposure was not statistically
significant compared to that of cold
dry air alone.
Tidal volume
Bronchoalveolar
lavage
Histopathology
General
conclusions
Decreased from baseline throughout
most of the exposure period with cold
dry air or SO2 in cold dry air;
response with SO2 was more shallow
than that of cold dry air alone, but
statistical significance compared to
cold dry air was obtained only at
60 min of exposure.
Decreased neutrophil numbers in the
SO2 group compared to the warm
humid air group but no significant
difference compared to the cold dry
air group.
No effect in lung or tracheobronchial
airways.
Functional effects on the lower
respiratory tract were weaker than in
the previous study with 10-min
exposures (Halinen et al., 2000a).
GSD = geometric standard deviation
MMAD = mass median aerodynamic density
-------
to
o
o
TABLE AX4-19. IN VITRO OR EX VIVO RESPIRATORY SYSTEM EFFECTS OF SO2
AND METABOLITES
Concentration
Duration
Species
Effects
Reference
O
O
2
O
H
O
c
o
H
W
O
V
O
HH
H
W
In Vitro—Primary/Nonprimary
0,5,10,20, 30, or
50ppm(0, 13.1,
26.2, 52.4, or
131mg/m3)SO2
Ih
0,0.1,2, 20, or
40 mM (0, 4, 80,
800, or 1600 ug/mL)
min - 96 h
Fauve de Bourgogne
rabbits, 1 mo old,
tracheal epithelium
explants
Rat, Sprague-Dawley,
200-250g; sex, age,
and n not reported;
lung cells and liver
cells.
Human lung-derived
cell line, A549
Relative to control cultures, cell viability was not
reduced at 5 and 10 ppm, but was at 30 ppm (-70%)
and 50 ppm (-60%). Ciliary beat frequency was
significantly reduced (p < 0.05) at 10-30 ppm, and
was correlated with swollen mitochondria and
depletion of cellular ATP, as well as with blebbing of
ciliated or microvilli-covered cells and with
aggregation and flattening of cilia.
This study focused on intracellular covalent reactions
of sulfite with primarily proteinaceous sulfhydryl
compounds in cells isolated from rat lung and rat
liver (for some comparative purposes), as well as in
the human lung-derived cell line, A549. Sulfitolysis
of protein disulfide bonds results in formation of
cysteine S-sulfonate, and sulfitolysis of GSSG in
formation of GSSO3H. The latter was formed in
dose-dependent fashion upon the addition of sulfite to
A549 cells. In addition to fibronectin and albumin,
this study identified a third sulfite-binding protein in
rat lung cytosol. GSSO3H was shown to be a potent
competitive inhibitor of GST in rat lung, liver and
A549 cells. Results suggest that SO2 could affect
the detoxication of PAHs and other xenobiotics via
formation of GSSO3H and subsequent inhibition
of GST and enzymatic conjugation of GSH with
reactive electrophiles.
Blanquart et al. (1995)
Menzeletal. (1986)
-------
TABLE AX4-19 (cont'd). IN VITRO OR EX VIVO RESPIRATORY SYSTEM EFFECTS OF SO2
AND METABOLITES
Concentration
Duration
Species
Effects
Reference
to
o
o
O
O
2
O
H
O
c
o
H
W
O
V
O
HH
H
W
Ex Vivo
7.5, 15,22.5, 30, or
37.5 mg/m3 (2.9, 5.7,
8.6, 11.5, or
14.3 ppm); ex vivo
expsoure of trachea
30 min
Guinea pig, sex, age, and
weight not reported,
n = 4-8/group
2.5,5.0,7.5, 10.0, or
12.5 ppm (6.6, 13.1,
19.7, 26.2, or 32.8
mg/m3); ex vivo
exposure of trachea
30 min
Guinea pig, sex, age, and
weight not reported,
n = 4-7/group
No remarkable morphologic abnormalities in the trachea! Riechelmann
mucociliary system of the 2.9 ppm group, though slight et al. (1995)
vacuolization, rare membrane blebs, and slightly widened
intercellular spaces were observed. Abnormalities in the
5.7 and 8.6 ppm groups were similar and included loosened
contact to the basal membrane, extensive intracellular
edema and vacuolization, swollen mitochondria, polypoid
extrusions and huge blebs in the cell membrane and ciliary
membrane, widened intercellular space, and disrupted tight
junctions. Additional abnormalities in the 11.5 and
14.3 ppm groups included marked epithelial sloughing,
occasionally disrupted cell membranes and microtubules,
and frequently disrupted ciliary membranes. Tracheal
mucociliary activity was significantly decreased in all
exposure groups (from 8.7 ± 1.0 Hz [controls] to 4.0 ±1.1,
3.4 ± 2.7, 1.8 ± 2.2, 1.5 ± 1.8, and 2.0 ± 1.2 Hz in the 7.5,
15, 22.5, 30, and 37.5 mg/m3 groups, respectively).
63% decrease in tracheal mucociliary activity at 2.5 ppm Knorst et al.
with dose-dependent decrease to 81% at 7.5 ppm; higher (1994)
concentrations did not further decrease mucociliary activity.
Ciliary beat frequency decreased by 45% at 5.0 ppm with
dose-dependent decrease to 72% at 12.5 ppm. All
reductions are relative to baseline values; no effect on
controls for either parameter.
GSH = glutathione
GSSG = glutathione disulfide
GSSO3H = glutathione S-sulfonate
GST = glutathione-S-transferase
PAH = polycyclic aromatic hydrocarbons
AM = alveolar macrophages
BAL = bronchoalveolar lavage
-------
TABLE AX4-20. GENOTOXIC EFFECTS OF SO2 AND METABOLITE
Concentration
Duration
Species/System
Effects
Reference
to
o
o
O
O
2
O
H
O
c
o
H
W
O
^
O
HH
H
W
"Point Mutation"1
In Vitro
0 or 50 ppm (131 mg/m3) 48 h
SO2 or the equivalent
agar concentration of
SO32~, 15 ug/ml)
Cytogenetic and DNA Damage2
In Vitro
0, 20, 50 or 200 ppm (0, 1-24 h
52.4, 13 lor 524 mg/m3)
S02;
0, 0.1, 0.2 or 0.4 mM
S032~
0 or 2.5 umol HSCV per
microtiter plate well
0, 0.1, 0.2 or 0.4 mM
SO42~
0 or 10 umol MgSO4 per
tube
Rat, Sprague-Dawley,
female, liver enzyme
preparations
Hamster, Syrian
golden, fetal lung
cells (FHLC,
gestational Day 15)
Rat, Sprague-Dawley,
male, age not
reported, ~200g,
hepatocytes
Chinese hamster
ovary cell line
transformed by SV40,
CO60 cells
Precinorm U (human
serum standard)
In vitro induction of reverse mutation in cultures of S. typhimurium strain
TA98 was not affected by incubating the bacterial-B(a)P-liver S9 enzyme
activation system in the presence of SO2/sulfite. An ancillary finding from
the 0 ug B(a)P control exposures is that SO2/sulfite itself did not appear
mutagenic.
Toxicity and genotoxicity of SO2, suffite/bisulfite and sulfate (also
NO2/NOX) were variously assessed in several in vitro test systems. It was
noted that medium pH remained stable at [SO2] <200 ppm. Precinorm
LDH activity was substantially inhibited by 50 ppm SO2 after 1-3 h, and
by 0.1 mM sulfite ion almost immediately, but not by 0.1 mM sulfate ion;
AST was modestly inhibited after 5 h by 200 ppm SO2; other monitored
enzymes were not affected. While trypan blue exclusion was not affected,
SO2 cytotoxicity to FHLC was demonstrated at 20 ppm by reduced plating
efficiency; at 50 ppm, enzyme activity leaked into culture medium was
reduced only for AP and especially LDH (not other enzymes). 200 ppm
SO2 did not induce DNA damage (single-strand breaks) by itself in either
FHLC or rat hepatocytes, but did somewhat reduce that induced by
AMMN. In hepatocytes, incubation with MgSO4 also caused a small
reduction in AMMN-induced DNA damage. A 1-h exposure to 200 ppm
SO2 did not induce selective amplification of SV40 DNA in CO60 cells,
nor affect that induced by DMBA or B[a]P. However, while also not
affecting induction by DMBA or B[a]P, HSCV added directly to the
medium for 24 h did induce SV40 DNA amplification on its own—authors
appear to suggest this might result from arrest of cells in mid-S phase,
which leads to DNA amplification. Thus, principal findings include
inhibition of LDH by SO2 or sulfite that could impair the cellular
energy system; such an impairment could be responsible (possibly
along with SO42~ conjugation of reactive intermediates) for the
observed inhibition of AMMN-induced DNA damage by SO2.
Further, SO2 does not appear by itself to induce DNA damage.
Pool-Zobel
et al. (1990)
Pool et al.
(1988a)
-------
TABLE AX4-20 (cont'd). GENOTOXIC EFFECTS OF SO2 AND METABOLITE
Concentration
Duration
Species/System
Effects
Reference
to
o
o
oo
O
o
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
Cytogenetic and DNA Damage2
In Vitro
3 mM SO32~
5 mM SO3 (as Na2SO3)
Cytogenetic and DNA Damage2
Acute/Subacute Exposure
0 mg/m3 (0 ppm) SO2 (+ 0 or 8
mg/kg bw SSO) or 28 mg/m3
(10.7 ppm) SO2 (+ 0, 2, 4, 6 or 8
mg/kg bw SSO); whole body
0, 14, 28, 56, or 84 mg/m3 (0,
5.35, 10.7, 21.4, or 32.1 ppm)
SO2; whole body
40 min
(test tube reactions)
1.5 h
(test tube reaction)
± SSO ip on Days
l-3;thenSO2for
5 day (Days 4-8),
6h/day
7 day, 4 h/day
dGorDNA
dG
Kunming mouse, male
and female, ~6 wk
old, 20-25 g,
n = 6/sex/conc.
Kunming mouse, male
and female, ~6 wk
old, 20-25 g,
n = 10/sex/conc.
Test tube reaction mixtures that caused sulfite to oxidize Shi and
to sulfur trioxide radical (SO3~) resulted in the Mao (1994)
hydroxylation of dG (8-OHdG) and the generation of
DNA double strand breaks.
Test tube reaction of sulfite ion with H2O2 shown to Shi(1994)
generate OH radicals capable of hydroxylating dG to the
DNA damage marker, 8-OHdG. Furthermore,
incubation of sulfite with nitrite or various transition
metal ions was shown to generate sulfur trioxide anion
radical (SO3~).
Subacute inhalation of 28 mg/m3 SO2 induced a Ruan et al.
significant (p < 0.001) 10-fold increase in mouse bone (2003)
marrow MNPCE, which was partially mitigated in
dose-dependent fashion by pretreatment with SSO, a
complex natural anti-oxidant substance. SO2
exposure also resulted in organ:bw ratios that increased
for liver and kidney, decreased for lung and spleen, and
remained unchanged for heart. Such ratio changes were
largely mitigated by SSO pretreatment.
In vivo exposure caused significantly (p < 0.01-0.001) Meng et al.
increased frequencies of bone marrow MNPCE similarly (2002)
in both sexes at all concentrations in a dose-dependent
manner, and with only minimal cytotoxicity at the
3 highest concentrations. The level of MNPCE (%) even
at the low SO2 cone, was triple that of the control value.
Thus, subacute inhalation of SO2 at noncytotoxic
concentrations (though still notably higher than most
human exposures) was clastogenic in mice.
-------
TABLE AX4-20 (cont'd). GENOTOXIC EFFECTS OF SO2 AND METABOLITE
Concentration
Duration
Species/System
Effects
Reference
to
o
o
O
O
2
O
H
O
c
o
H
W
O
V
O
HH
H
W
Cytogenetic and DNA Damage
Acute/Subacute Exposure
0, 14, 28, 56, or
84 mg/m3 (0, 5.35,
10.7, 21.4, or
32.1ppm)SO2;
whole body
7 day, 6 h/day
Kunming mouse, male
and female, ~5 wk old,
18-20 g,
n = 6/sex/conc.
0 or 50 ppm
(131 mg/m3) SO2
2wk,
7 day/wk,
24 h/day
Rat, Sprague-Dawley,
female, 4 mo old, wt
not reported, n = 5 per
group
Following in vivo exposure to SO2, it was shown by the single cell gel
electrophoresis (comet) assay that such exposure induced significant
(p <.001-.05) dose-dependent DNA damage (presumed mostly to be
single-strand breaks and alkali-labile sites) in cells isolated from brain,
lung, liver, intestine, kidney, spleen, and testicle, as well as in
lymphocytes, and beginning at the lowest concentration (except male
intestine—lowest response at 28 mg/m3). Results demonstrate that SO2,
can cause systemic DNA damage in many organs, not just the lung.
Authors note that potential occupational exposures and the fact that
the obligate nose-breathing mouse removes ~95% of inhaled SO2 in its
nasal passages make this experimental concentration range relevant
to possible human exposures.
Assessments were conducted on isolated primary lung and liver cells, or
on blood serum. In vivo SO2 exposure did not affect viability (trypan blue
exclusion) of cells either immediately after isolation or after 1 h incubation
with 1% DMSO (used for enzyme leakage assays). In contrast to controls,
hepatocytes from SO2-exposed rats released no LDH activity into
DMSO-medium after 1 h, and AST activity was reduced. Other enzyme
(AP, ALT, GT) activity releases were not affected in lung cells, and none
were in hepatocytes. In blood serum, the only effect was a marked
increase in LDH activity. The only significant (p < 0.001-0.01) exposure
effects on lung or liver activities (in x 9000 g supernatants of cell
homogenates) of xenobiotic metabolizing enzymes (AHH, NDMA-D,
GST) were elevated NDMA-D in the liver and reduced GST in the lung.
Single-strand DNA breakage induced by three nitroso compounds
(AMMN, NDMA, NMBzA) was reduced in hepatocytes from
SO2-exposed rats. Authors discuss possible mechanisms for the observed
effects, and note they are similar to in vitro effects reported elsewhere (see
above, Pool etal., 1988a).
Meng et al.
(2005b)
Pool et al.
(1988b)
-------
TABLE AX4-20 (cont'd). GENOTOXIC EFFECTS OF SO2 AND METABOLITE
Concentration
Duration
Species/System
Effects
Reference
to
o
o
O
O
2
O
H
O
c
o
H
W
O
V
O
HH
H
W
Cytogenetic and DNA Damage2
Subchronic/chronic
0, 0.2 mL C, or
{0.2 mL DEP+C ± [4 ppm
(10.48 mg/m3) SO2 or 6 ppm
(11.28 mg/m3) NO2 or 4 ppm
SO2 + 6 ppm NO2]}; whole body
[Note: 0.2 mL C = 1 mg; 0.2 mL
DEcCBP=lmgC + 2.5mg
DEP)]
SO2 and/or NO2:
10 mo, 16 h/day
Cor DEP+C:
4 wk, once/wk
by intratracheal
infusion
Rat, SPF F344/M,
male, 6 wk old, wt
not reported,
n= 23-30 per group
in 6 groups
Purpose was to study effects of DEP on rat lung tumorigenesis
and possible tumor promoting effects of SO2 or NO2 singly or
together. [See Table AX4-17 for tumor-related effects.] DEP
extract-DNA adducts were found only in the three
gas-exposed groups. Chromatograms revealed two different
adducts, one of which appears somewhat more abundant with
SO2 coexposure, the other substantially more so with NO2;
combined coexposure of both gases with DEP+C produced an
adduct chromatogram appearing to be a composite of those for
the individual gases. Thus, SO2 and NO2 appear capable of
promoting the genotoxicity of DEP extract, though
perhaps not in identical fashion.
Ohyama
etal.
(1999)
'Encompasses classical mutant selection assays based upon growth conditions under which mutants (or prototrophic revertants), but not the wild type (or auxotrophic) population treated with the test
agent, can successfully grow (e.g., "Ames test", CHO/HGRPT or mouse lymphoma L5178Y/TK mammalian cell systems, various yeast and Drosophila systems, etc.); while most viable mutation events
detected in these assays are typically "point" mutations (DNA base substitutions, small deletions or frameshifts, etc.), some may involve larger losses/rearrangements of genetic material.
2Encompasses CA, induction of MN or SCE, aneuploidy/polyploidy, DNA adduct and crosslink formation, DNA strand breakage, etc.
AHH = aryl hydrocarbon hydroxylase
AP = alkaline phosphatase
AMMN = N-nitroso-acetoxymethylmethylamine
ALT = alanine-amino-transferase
AST = aspartate-amino-transferase
B[a]P = benzo[a]pyrene
bw = body weight
C = carbon or carbon black particles
CA = chromosome aberrations
DEcCBP = DEP extract coated carbon black particles
DEP+C = diesel exhaust particle extract adsorbed to C
DMBA = 7, 12-dimethylbenzanthracene
DMSO = dimethyl sulfoxide
dG = 2'-deoxyguanosine
8-OHdG = 8-hydroxy-2'-deoxyguanosine
FHLC = fetal hamster lung cells
GT = y-glutamyltransferase
GST = glutathione-S-transferase;
LDH = lactate dehydrogenase
MN = micronuclei
MNPCE = micronucleated PCE
NDMA = N-nitrosodimethylamine
NDMA-D = N-nitrosodimethylamine demethylase
NMBzA = N-nitrosomethylbenzylamine
PCE = polychromatic erythrocytes
SSO = seabuckthorn seed oil
SCE = sister chromatid exchanges
SV40 = simian virus 40
-------
TABLE AX4-21. LIVER AND GASTROINTESTINAL EFFECTS OF SO2
Concentration
Duration
Species
Effects
Reference
to
o
o
O
O
2
O
H
O
c
o
H
W
O
^
O
HH
H
W
Subacute/Subchronic Exposure
22, 56, orll2mg/m3
(7.86, 20, or 40 ppm
per author
conversion); whole
body
22, 64, or 148 mg/m3
(8.4, 24.4, or
56.5 ppm); whole
body
14, 28, or 56 mg/m3
(5.35, 10.70, or
21.40 ppm); whole
body
14, 28, or 56 mg/m3
(5.35, 10.70,
or 21.40 ppm); whole
body
6 h/day for 7 days
Kunming albino mouse,
male and female, 5 wks
old, 19 ± 2 g,
n = 6/sex/subgroup
6 h/day for 7 days Kunming-strain mice,
male, age not reported,
18-20 g,n=10/group
6 h/day for 7 days Wistar rat, male, age not
reported, 180-200 g,
n = 6/group in 4 groups
6 h/day for 7 days Wistar rat, male, age not
reported, 180-200 g,
n = 6/group in 4 groups
Effects observed in stomach (concentration of effect) included: Meng et al.
increase in SOD activity (7.86 ppm, males only) and TEARS level (2003c)
(>7.86 ppm) and decreases in SOD (>20 ppm, males only) and GPx
activities (>20 ppm, males only) and GSH level (40 ppm). Effects
observed in intestine were increases in catalase activity (>20 ppm in
males, 40 ppm in females) and TEARS level (>20 ppm) and
decreases in SOD (>7.86 ppm) and GPx (>20 ppm) activities and
GSH level(>7.86 ppm).
No effects were observed in the liver at 22 or 64 mg/m3. GST and Wu and
glucose-6-phosphate dehydrogenase activities and GSH level were Meng
decreased at 148 mg/m3. (2003)
Significant and concentration-dependent changes in mRNA (mid and Bai and
high concentrations) and protein expression (all concentrations) Meng
included increases for bax and p53 apoptosis-promoting genes, and (2005b)
decrease for bcl-2 apoptosis-repressing gene. Authors speculated
potential impact on human apoptosis-deficient diseases.
SO2 caused significant concentration-dependent reductions in liver Qin and
enzyme activities and gene expression for CYP1A1 and CYP1A2. Meng
Effects were seen at the mid and high concentrations (only high for (2005)
CYP1A1 enzyme activity), but not the low. Authors speculate that
underlying mechanisms may involve oxidative stress and/or cytokine
release, and may represent an adaptive response to minimize cell
damage.
-------
TABLE AX4-21 (cont'd). LIVER AND GASTROINTESTINAL EFFECTS OF SO2
Concentration
Duration
Species
Effects
Reference
to
o
o
to
O
o
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
Subacute/Subchronic Exposure
5 or 10ppm(13.1 or
26.2 mg/m3); whole
body
5, 50, or 100 ppm
(13.1, 131, or
262 mg/m3); whole
body
286 mg/m3
(100 ppm); whole
body Units were
incorrectly reported as
ug/m3 in the study but
were corrected
according to
information provided
by study author
24 h/day for
15 days
5 h/day for
7-28 days
5 h/day for
28 days
Sprague-Dawley CD rat,
male, age not reported,
250-275 g, n = 9/subgroup
Wistar rat, male, 7 wks
old, weight not reported,
n = 4-5/treatment group,
8 controls
Wistar rat, male, 7 wks
old, weight not reported,
n=4-16
Subjects were rats fed standard diet (normal) or high cholesterol Lovati et al.
diet, and rats with streptozotocin-induced diabetes fed standard (1996)
diet. SO2 (> 5 ppm) elevated plasma triglycerides in normal and
hypercholesterolemic groups, while 10 ppm lowered plasma high
density lipoprotein cholesterol in hypercholesterolemic rats. In
diabetic rats, 10 ppm SO2 lowered triglycerides and free fatty
acids without affecting high density lipoprotein cholesterol or total
cholesterol. In the liver, SO2 elevated triglycerides in normal and
hypercholesterolemic groups (at 10 ppm), but lowered it in diabetic
rats (at >5 ppm); esterified cholesterol was elevated in normal rats
(at 10 ppm), but lowered in diabetic rats (at > 5ppm), and free
cholesterol was unchanged in all groups. In normal rats,
triglycerides secretion rate was inhibited by 10 ppm SO2. SO2
caused several changes in plasma apolipoprotein composition in
normal and hypercholesterolemic groups, but not in diabetic rats.
Leukotriene parameters were not affected. Thus, in each rat
model, inhalation of SO2 at levels without overt effects affected
plasma and tissue lipid content. Specific effects varied
according to diet or diabetes.
GSH was depleted in the liver at 5 and 100 ppm but not at 50 ppm. Langley-Evans
With respect to GSH-related enzymes, exposure to 5 ppm et al. (1996)
decreased GRed and GST activity in the liver. Exposure to 50 ppm
did not affect liver GST, but decreased liver GRed and GPx.
Adult rats exposed to air or SO2 were born to dams fed diets with Langley-Evans
varying casein contents (180 [control], 120, 90 or 60 g/kg) during et al. (1997);
gestation. In the liver, SO2 exposure elevated GSH level in the Langley-Evans
120 g/kg dietary group but lowered it in the 60 g/kg dietary group. 2007
SO2 did not affect liver GST in any group. SO2 increased GCS
levels in the 180 and 90 g/kg groups, GPx in the 60 g/kg group,
and GRed in the 120 and 90 g/kg groups. This study provides
information for an extremely high concentration level but is being
acknowledged here with the unit corrected to verify that a
low-concentration level study was not missed.
-------
TABLE AX4-21 (cont'd). LIVER AND GASTROINTESTINAL EFFECTS OF SO2
to
o
o
Concentration Duration
Subacute/Subchronic Exposure
10 or 30 ppm (26.2 or 6 h/day,
78.6 mg/m3); whole ~5 days/wk for
body 21 wks (total of
99 days)
Species
Sprague-Dawley CD
rat, male, 8 wks old,
weight not reported,
n = 70/group in
3 groups (inhalation
series)
Effects
No effects on relative liver weight or histopathology were found.
Reference
Gunnison
etal.
(1987)
10 ppm (26.2 mg/m3);
whole body
1 h/day, 7 days/wk
for 6 wks
10 ppm (26.2 mg/m3);
whole body
1 h/day, 7 days/wk
for 6 wks
Swiss Albino rat, male,
3 mos old, weight not
reported, n = 10/group
Rat, male, 3 mos old,
weight not reported,
n = 10/group in 4
groups
Effects were compared in non-diabetic rats, non-diabetic rats Agar et al.
exposed to SO2, alloxan-induced diabetic rats, and diabetic rats (2000)
exposed to SO2. SO2 increased blood glucose in all groups, but did
not affect total cholesterol, high density lipoprotein cholesterol, low
density lipoprotein cholesterol, very low density lipoprotein
cholesterol, or triglyceride levels in either normal or diabetic rats.
Effects compared in normal rats and rats with alloxan induced Kuciikatay
diabetes. Among the significant effects observed, SO2 exposure et al.
enhanced the body weight loss seen in the diabetic group, but did (2003)
not affect body weight gain in the control group. SO2 elevated blood
glucose levels in both controls and diabetics, but lowered
triglycerides only in diabetics. Cholesterol parameters were not
affected.
O
O
2
O
H
O
c
o
H
W
O
V
O
HH
H
W
GST = glutathione S-transferase
GT = y-glutamyltranspeptidase
SOD = Cu,Zn-superoxide dismutase
TEARS = thiobarbituric acid-reactive substances
-------
TABLE AX4-22. RENAL EFFECTS OF SO2
to
o
o
O
O
2
O
H
O
c
o
H
W
O
V
O
HH
H
W
Concentration
22, 64, or 148 mg/m3 (8.4,
24.4, or 56.5 ppm)
Duration
6 h/day for 7 days
Species
Kunming-strain mice,
male, age not reported,
18-20 g, n= 10/group
Effects
GST was decreased in the kidney at 64 and
148 mg/m3 and glucose-6-phosphate dehydrogenase
activity was decreased at 148 mg/m3. Kidney GSH
levels were reduced at all exposure levels.
Reference
Wu and Meng
(2003)
5, 50, or 100 ppm (13.1, 131, 5 h/day for 7-28 days Wistar rat, male, 7 wks GSH was depleted in the kidney in the 5 and 100 ppm Langley-Evans
or262mg/m3) old, weight not reported, groups but not in the 50 ppm group. No effects were etal. (1996)
n = 4-5/treatment group, observed for other GSH-related enzymes.
8 controls
-------
TABLE AX4-23. LYMPHATIC SYSTEM EFFECTS OF SO2 AND SO2 MIXTURE
Concentration
Duration
Species
Effects
Reference
to
o
o
Subchronic/Chronic Exposure
1 ppm (2.62 mg/m3);
whole body
13.2 mg/m3 (5.0 ppm)
SO2+1.04 mg/m3
ammonium
sulfate + 0.2 mg/m3
(0.10 ppm) ozone;
whole body
5 h/day, 5 days/wk
for 4 mos.
5 h/day, 5 days/wk
for up to 103 days
Sprague-Dawley rat, male,
young adult, initial weight
not reported, n= 12-15/data
point
CD1 mice, female, 3-4 wks
old, weight not reported,
n = 360/group total
(14-154/group in each assay)
No significant effects were reported for spleen weight or
mitogen-induced activation of peripheral blood lymphocytes
or spleen cells (data not shown by study authors).
Cytostasis of MBL-2 leukemia target cells by peritoneal
macrophage was increased in groups exposed to ozone alone
or a mixture of the three compounds but was significantly
higher with the mixture than with ozone alone at a
macrophage:target cell ratio of 10:1; no significant effects
were observed with macrophage:target cell ratio of 20:1.
A reduction in splenic lymphocyte blastogenesis in response
to phytohemagglutinin and concanavalin A occurred after
exposure to ozone alone, but increased response occurred
after exposure to the mixture; no response to alloantigen
occurred after exposure to ozone alone but increased
response occurred after exposure to mixture; there were no
effects on S. typhosa lipopolysaccharide with either exposure
scenario.
Smith et al.
(1989)
Aranyi
etal.
(1983)
O
O
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
B(a)P = benzo(a)pyrene
GCS = y-glutamylcysteine synthetase
GPx = glutathione peroxidase
GRed = glutathione reductase
GSH = glutathione
GSH = glutathione
GST = glutathione-S-transferase
GPx = glutathione peroxidase
GSH = glutathione
SOD = superoxide dismutase
TEARS = thiobarbituric acid-reactive substances
GST = glutathione S-transferase
GT = y-glutamyltranspeptidase
SOD = Cu,Zn-superoxide dismutase
TEARS = thiobarbituric acid-reactive substances
-------
1 AX4.1 REFERENCES
2
3 Abraham, W. M.; Oliver, W., Jr.; Welker, M. 1; King, M. M.; Wanner, A.; Sackner, M. A.
4 (1981) Differences in airway reactivity in normal and allergic sheep after exposure to
5 sulfur dioxide. J. Appl. Physiol. 51: 1651-1656.
6 Abraham, W. M.; Sielczak, M. W.; Delehunt, J. C.; Marchette, B.; Wanner, A. (1986)
7 Impairment of tracheal mucociliary clearance but not ciliary beat frequency by a
8 combination of low level ozone and sulfur dioxide in sheep. Eur. J. Respir. Dis. 68: 114-
9 120.
10 Agar, A.; Kii9iikatay, V.; Yargi9oglu, P.; Aktekin, B.; Kipmen-Korgun, S.; Gumus.lu, D.;
11 Apaydin, C. (2000) The effect of sulfur dioxide inhalation on visual evoked potentials,
12 antioxidant status, and lipid peroxidation in alloxan-induced diabetic rats. Arch. Environ.
13 Contam. Toxicol. 39: 257-264.
14 Amdur, M. O.; McCarthy, J. F.; Gill, M. W. (1983) Effect of mixing conditions on irritant
15 potency of zinc oxide and sulfur dioxide. Am. Ind. Hyg. Assoc. J. 44: 7-13.
16 Amdur, M. O.; Chen, L. C.; Guty, J.; Lam, H. F.; Miller, P. D. (1988) Speciation and pulmonary
17 effects of acidic SOX formed on the surface of ultrafine zinc oxide aerosols. Atmos.
18 Environ. 22: 557-560.
19 Aranyi, C.; Vana, S. C.; Thomas, P. T.; Bradof, J. N.; Fenters, J. D.; Graham, J. A.; Miller, F. J.
20 (1983) Effects of subchronic exposure to a mixture of Os, SC>2, and (NH/t^SC^ on host
21 defenses of mice. J. Toxicol. Environ. Health 12: 55-71.
22 Azoulay-Dupuis, E.; Bouley, G.; Blayo, M. C. (1982) Effects of sulfur dioxide on resistance to
23 bacterial infection in mice. Environ. Res. 29: 312-319.
24 Bai, J.; Meng, Z. (2005a) Effects of sulfur dioxide on apoptosis-related gene expressions in lungs
25 from rats. Regul. Toxicol. Pharmacol. 43: 272-279.
26 Bai, J.; Meng, Z. (2005b) Expression of apoptosis-related genes in livers from rats exposed to
27 sulfur dioxide. Toxicology 216: 253-260.
28 Barthelemy, P.; Badier, M.; Jammes, Y. (1988) Interaction between SC>2 and cold-induced
29 bronchospasm in anesthetized rabbits. Respir. Physiol. 71: 1-10.
30 Baskurt, O. K. (1988) Acute hematologic and hemorheologic effects of sulfur dioxide inhalation.
31 Arch. Environ. Health 43: 344-348.
32 Blanquart, C.; Giuliani, I.; Houcine, O.; Jeulin, C.; Guennou, C.; Marano, F. (1995) In vitro
33 exposure of rabbit tracheal epithelium to SO2: effects on morphology and ciliary beating.
34 Toxicol. in Vitro 9: 123-132.
35 Chen, L. C.; Peoples, S. M.; Amdur, M. O. (1991) Pulmonary effects of sulfur oxides on the
36 surface of copper oxide aerosol. Am. Ind. Hyg. Assoc. J. 52: 187-191.
37 Chen, L. C.; Miller, P. D.; Amdur, M. O.; Gordon, T. (1992) Airway hyperresponsiveness in
38 guinea pigs exposed to acid-coated ultrafine particles. J. Toxicol. Environ. Health 35:
39 165-174.
September 2007 AX4-66 DRAFT-DO NOT QUOTE OR CITE
-------
1 Clarke, R. W.; Antonini, J. M.; Hemenway, D. R.; Frank, R.; Kleeberger, S. R.; Jakab, G. J.
2 (2000) Inhaled particle-bound sulfate: effects on pulmonary inflammatory responses and
3 alveolar macrophage function. Inhalation Toxicol. 12: 169-186.
4 Conner, M. W.; Lam, H. F.; Rogers, A. E.; Fitzgerald, S.; Amdur, M. O. (1985) Lung injury in
5 guinea pigs caused by multiple exposures to submicron zinc oxide mixed with sulfur
6 dioxide in a humidified furnace. J. Toxicol. Environ. Health 16: 101-114.
7 Douglas, G. J.; Price, J. F.; Page, C. P. (1994) A method for the long-term exposure of rabbits to
8 environmental pollutant gases. Eur. Respir. J. 7: 1516-1526.
9 Du, Z.; Meng, Z. (2004a) Effects of derivatives of sulfur dioxide on transient outward potassium
10 currents in acutely isolated hippocampal neurons. Food Chem. Toxicol. 42: 1211-1216.
11 Du, Z.; Meng, Z. (2004b) Modulation of sodium currents in rat dorsal root ganglion neurons by
12 sulfur dioxide derivatives. Brain Res. 1010: 127-133.
13 Du, Z.; Meng, Z. (2006) Sulfur dioxide derivatives modulation of high-threshold calcium
14 currents in rat dorsal root ganglion neurons. Neurosci. Lett. 405: 147-152.
15 Etlik, O.; Tomur, A.; Kutman, M. N.; Yoriikan, S.; Duman, O. (1995) The effects of sulfur
16 dioxide inhalation and antioxidant vitamins on red blood cell lipoperoxidation. Environ.
17 Res. 71:25-28.
18 Etlik, O.; Tomur, A.; Tuncer, M.; Ridvanagaoglu, A. Y.; Anda9, O. (1997) Protective effect of
19 antioxidant vitamins on red blood cell lipoperoxidation induced by 862 inhalation. J.
20 Basic Clin. Physiol. Pharmacol. 8: 31-43.
21 Fiore, M.; Petruzzi, S.; Dell'Omo, G.; Alleva, E. (1998) Prenatal sulfur dioxide exposure induces
22 changes in the behavior of adult male mice during agonistic encounters. Neurotoxicol.
23 Teratol. 20: 543-548.
24 Gumus.lu, S.; Akbas, H.; Alicigiizel, Y.; Agar, A.; Kii9iikatay, V.; Yargi9oglu, P. (1998) Effects
25 of sulfur dioxide inhalation on antioxidant enzyme activities in rat erythrocytes. Ind.
26 Health 36: 70-73.
27 Gumus.lu, S.; Bilmen, S.; Korgun, D. K.; Yargi9oglu, P.; Agar, A. (2001) Age-related changes in
28 antioxidant enzyme activities and lipid peroxidation in lungs of control and sulfur dioxide
29 exposed rats. Free Radical Res. 34: 621-627.
30 Gunnison, A. F.; Benton, A. W. (1971) Sulfur dioxide: sulfite. Interaction with mammalian
31 serum and plasma. Arch. Environ. Health 22: 381-388.
32 Gunnison, A. F.; Sellakumar, A.; Currie, D.; Snyder, E. A. (1987) Distribution, metabolism and
33 toxicity of inhaled sulfur dioxide and endogenously generated sulfite in the respiratory
34 tract of normal and sulfite oxidase-deficient rats. J. Toxicol. Environ. Health 21: 141-162.
35 Gunnison, A. F.; Sellakumar, A.; Snyder, E. A.; Currie, D. (1988) The effect of inhaled sulfur
36 dioxide and systemic sulfite on the induction of lung carcinoma in rats by
37 benzo[a]pyrene. Environ. Res. 46: 59-73.
38 Haider, S. S.; Hasan, M. (1984) Neurochemical changes by inhalation of environmental
39 pollutants sulfur dioxide and hydrogen sulfide: degradation of total lipids, elevation of
September 2007 AX4-67 DRAFT-DO NOT QUOTE OR CITE
-------
1 lipid peroxidation and enzyme activity in discrete regions of the guinea pig brain and
2 spinal cord. Ind. Health 22: 23-31.
3 Haider, S. S.; Hasan, M.; Hasan, S. N.; Khan, S. R.; Ali, S. F. (1981) Regional effects of sulfur
4 dioxide exposure on the guinea pig brain lipids, lipid peroxidation and lipase activity.
5 Neurotoxicology 2: 443-450.
6 Haider, S. S.; Hasan, M.; Khan, N. H. (1982) Air pollutant sulfur dioxide-induced alterations on
7 the levels of lipids, lipid peroxidation and lipase activity in various regions of the rat
8 brain. ActaPharmacol. Toxicol. 51: 45-50.
9 Halinen, A. I.; Salonen, R. O.; Pennanen, A. S.; Kosma, V. M. (2000a) Combined respiratory
10 effects of cold air with SO2 or NO2 in repeated 10-minute exposures of hyperventilating
11 guinea pigs. Inhalation Toxicol. 12: 671-691.
12 Halinen, A. I; Salonen, R. O.; Pennanen, A. S. (2000b) Combined respiratory effects of cold air
13 with SO2 or NO2 in single 1-hour exposures of hyperventilating guinea pigs. Inhalation
14 Toxicol. 12: 693-713.
15 Heinrich, U.; Mohr, U.; Fuhst, R.; Brockmeyer, C. (1989) Investigation of a potential
16 cotumorigenic effect of the dioxides of nitrogen and sulfur, and of diesel-engine exhaust,
17 on the respiratory tract of Syrian golden hamsters. Cambridge, MA: Health Effects
18 Institute; research report no. 26. Available from: NTIS, Springfield, VA; PB90-111147.
19 Ito, T.; Ohyama, K.-L; Kusano, T.; Usuda, Y.; Nozawa, A.; Hayashi, H.; Ohji, H.; Kitamura, H.;
20 Kanisawa, M. (1997) Pulmonary endocrine cell hyperplasia and papilloma in rats induced
21 by intratracheal injections of extract from particulate air pollutants. Exp. Toxicol. Pathol.
22 49: 65-70.
23 Jakab, G. 1; Clarke, R. W.; Hemenway, D. R.; Longphre, M. V.; Kleeberger, S. R.; Frank, R.
24 (1996) Inhalation of acid coated carbon black particles impairs alveolar macrophage
25 phagocytosis. Toxicol. Lett. 88: 243-248.
26 Kilic, D. (2003) The effects of ageing and sulfur dioxide inhalation exposure on visual-evoked
27 potentials, antioxidant enzyme systems, and lipid-peroxidation levels of the brain and
28 eye. Neurotoxicol. Teratol. 25: 587-598.
29 Kitabatake, M.; Yoshida, K.; Kasama, K.; Murase, S.; Yuan, P. F.; Manjurul, H.; Yamauchi, T.
30 (1992) Procedure for evaluating changes in respiratory symptoms of experimentally
31 asthma-induced guinea pigs by a personal computer. J. Toxicol. Environ. Health 37: 265-
32 275.
33 Kitabatake, M.; Yamamoto, H.; Yuan, P. F.; Manjurul, H.; Murase, S.; Yamauchi, T. (1995)
34 Effects of exposure to NO2 or SO2 on bronchopulmonary reaction induced by Candida
35 albicam in guinea pigs. J. Toxicol. Environ. Health 45: 75-82.
36 Klein, R. G.; Janowsky, I.; Schmezer, P.; Hermann, R.; Spiegelhalder, B.; Zeller, W. J.; Pool, B.
37 L. (1989) Effect of long-term inhalation ofW-nitroso-dimethylamine (NDMA) and
38 SO2/NOX in rats. Exp. Pathol. 37: 273-280.
39 Knorst, M. M.; Kienast, K.; Riechelmann, H.; Muller-Quernheim, J.; Ferlinz, R. (1994) Effect of
40 sulfur dioxide on mucociliary activity and ciliary beat frequency in guinea pig trachea.
41 Int. Arch. Occup. Environ. Health 65: 325-328.
September 2007 AX4-68 DRAFT-DO NOT QUOTE OR CITE
-------
1 Kii9iikatay, V.; Agar, A.; Yargi9oglu, P.; Gumus.lu, S.; Aktekin, B. (2003) Changes in
2 somatosensory evoked potentials, lipid peroxidation, and antioxidant enzymes in
3 experimental diabetes: effect of sulfur dioxide. Arch. Environ. Health 58: 14-22.
4 Lam, H. F.; Peisch, R.; Amdur, M. O. (1982) Changes in lung volumes and diffusing capacity in
5 guinea pigs exposed to a combination of sulfur dioxide and submicron zinc oxide mixed
6 in a humidified furnace. Toxicol. Appl. Pharmacol. 66: 427-433.
7 Langley-Evans, S. C.; Phillips, G. J.; Jackson, A. A. (1996) Sulphur dioxide: a potent glutathione
8 depleting agent. Comp. Biochem. Physiol. Part C: Pharmacol. Toxicol. Endocrinol. 114:
9 89-98.
10 Langley-Evans, S. C.; Phillips, G. J.; Jackson, A. A. (1997) Fetal exposure to low protein
11 maternal diet alters the susceptibility of young adult rats to sulfur dioxide-induced lung
12 injury. J. Nutr. 127: 202-209.
13 Langley-Evans, S. (2007) [Letter to Annette lannucci on sulfur oxides concentrations].
14 Loughborough, United Kingdom: University of Nottingham; January 30.
15 Lemos, M.; Lichtenfels, A. J. F. C.; Amaro, E., Jr.; Macchione, M.; Martins, M. A.; King, M.;
16 Bohm, G. M.; Saldiva, P. H. N. (1994) Quantitative pathology of nasal passages in rats
17 exposed to urban levels of air pollution. Environ. Res. 66: 87-95.
18 Lewis, A. J.; Kirchner, T. (1984) Modulation of sulfur dioxide-induced airways
19 hyperresponsiveness in the conscious dog. Int. Arch. Allergy Appl. Immunol. 75: 188-
20 190.
21 Lovati, M. R.; Manzoni, C.; Daldossi, M.; Spolti, S.; Sirtori, C. R. (1996) Effects of sub-chronic
22 exposure to SO2 on lipid and carbohydrate metabolism in rats. Arch. Toxicol. 70: 164-
23 173.
24 Mannix, R. C.; Phalen, R. F.; Kenoyer, J. L.; Crocker, T. T. (1982) Effect of sulfur dioxide-
25 sulfate exposure on rat respiratory tract clearance. Am. Ind. Hyg. Assoc. J. 43: 679-685.
26 Mautz, W. J.; Kleinman, M. T.; Finlayson-Pitts, B. (1988) Respiratory effects of acid containing
27 multicomponent pollutant atmospheres. Prepared for: California Air Resources Board.
28 Irvine, CA: University of California, Department of Community and Environmental
29 Medicine, Air Pollution Health Effects Laboratory.
30 Meng, Z.; Bai, W. (2004) Oxidation damage of sulfur dioxide on testicles of mice. Environ. Res.
31 96:298-304.
32 Meng, Z.; Zhang, B.; Ruan, A.; Sang, N.; Zhang, J. (2002) Micronuclei induced by sulfur
33 dioxide inhalation in mouse bone-marrow cells in vivo. Inhalation Toxicol. 14: 303-309.
34 Meng, Z.; Qin, G.; Zhang, B.; Geng, H.; Bai, Q.; Bai, W.; Liu, C. (2003a) Oxidative damage of
35 sulfur dioxide inhalation on lungs and hearts of mice. Environ. Res. 93: 285-292.
36 Meng, Z.; Geng, H.; Bai, J.; Yan, G. (2003b) Blood pressure of rats lowered by sulfur dioxide
37 and its derivatives. Inhalation Toxicol. 15: 951-959.
38 Meng, Z.; Zhang, B.; Bai, J.; Geng, H.; Liu, C. (2003c) Oxidative damage of sulfur dioxide
39 inhalation on stomachs and intestines of mice. Inhalation Toxicol. 15: 397-410.
September 2007 AX4-69 DRAFT-DO NOT QUOTE OR CITE
-------
1 Meng, Z.; Liu, Y.; Wu, D. (2005a) Effect of sulfur dioxide inhalation on cytokine levels in lungs
2 and serum of mice. Inhalation Toxicol. 17: 303-307.
3 Meng, Z.; Qin, G.; Zhang, B. (2005b) DNA damage in mice treated with sulfur dioxide by
4 inhalation. Environ. Mol. Mutagen. 46: 150-155.
5 Menzel, D. B.; Keller, D. A.; Leung, K.-H. (1986) Covalent reactions in the toxicity of SC>2 and
6 sulfite. In: Kocsis, J. J.; Jollow, D. J.; Witmer, C. M.; Nelson, J. O.; Snyder, R., eds.
7 Biological reactive intermediates III: mechanisms of action in animal models and human
8 disease. New York, NY: Plenum Press; pp. 477-492. (Advances in experimental
9 medicine and biology: v. 197).
10 Nadziejko, C.; Fang, K.; Narciso, S.; Zhong, M.; Su, W. C.; Gordon, T.; Nadas, A.; Chen, L. C.
11 (2004) Effect of particulate and gaseous pollutants on spontaneous arrhythmias in aged
12 rats. Inhalation Toxicol. 16: 373-380.
13 Me, A.; Meng, Z. (2005) Study of the interaction of sulfur dioxide derivative with cardiac
14 sodium channel. Biochim. Biophys. Acta 1718: 67-73.
15 Me, A.; Meng, Z. (2006) Modulation of L-type calcium current in rat cardiac myocytes by sulfur
16 dioxide derivatives. Food Chem. Toxicol. 44: 355-363.
17 Ohyama, K.; Ito, T.; Kanisawa, M. (1999) The roles of diesel exhaust particle extracts and the
18 promotive effects of NC>2 and/or SC>2 exposure on rat lung tumorigenesis. Cancer Lett.
19 139: 189-197.
20 Park, J.-K.; Kim, Y.-K.; Lee, S.-R.; Cho, S.-H.; Min, K.-U.; Kim, Y.-Y. (2001) Repeated
21 exposure to low levels of sulfur dioxide (802) enhances the development of ovalbumin-
22 induced asthmatic reactions in guinea pigs. Ann. Allergy Asthma Immunol. 86: 62-67'.
23 Pereira, P. M.; Saldiva, P. H. M; Sakae, R. S.; Bohm, G. M.; Martins, M. A. (1995) Urban levels
24 of air pollution increase lung responsiveness in rats. Environ. Res. 69: 96-101.
25 Petruzzi, S.; Dell'Omo, G.; Fiore, M.; Chiarotti, F.; Bignami, G.; Alleva, E. (1996) Behavioural
26 disturbances in adult CD-I mice and absence of effects on their offspring upon SO2
27 exposure. Arch. Toxicol. 70: 757-766.
28 Phalen, R. F.; Kleinman, M. T. (1987) 21-day exposure to mixed air pollutants: effects on lung
29 airways and macrophages. Prepared for: California Air Resources Board. Irvine, CA:
30 University of California, Department of Community and Environmental Medicine, Air
31 Pollution Health Effects Laboratory.
32 Pool, B. L.; Janowsky, L; Klein, P.; Klein, R. G.; Schmezer, P.; Vogt-Leucht, G.; Zeller, W. J.
33 (1988a) Effects of SC>2 or NOX on toxic and genotoxic properties of chemical
34 carcinogens. I. In vitro studies. Carcinogenesis 9: 1237-1245.
35 Pool, B. L.; Brendler, S.; Klein, R. G.; Monarca, S.; Pasquini, R.; Schmezer, P.; Zeller, W. J.
36 (1988b) Effects of 862 or NOX on toxic and genotoxic properties of chemical
37 carcinogens. II. Short term in vivo studies. Carcinogenesis 9: 1247-1252.
38 Pool-Zobel, B. L.; Schmezer, P.; Zeller, W. J.; Klein, R. G. (1990) In vitro and ex vivo effects of
39 the air pollutants 862 and NOX on benzo(a)pyrene activating enzymes of the rat liver.
40 Exp. Pathol. 39: 207-212.
September 2007 AX4-70 DRAFT-DO NOT QUOTE OR CITE
-------
1 Qin, G.; Meng, Z. (2005) Effect of sulfur dioxide inhalation on CYP1 Al and CYP1A2 in rat
2 liver and lung. Toxicol. Lett. 160: 34-42.
3 Raub, J. A.; Miller, F. I; Graham, J. A.; Gardner, D. E.; O'Neil, J. J. (1983) Pulmonary function
4 in normal and elastase-treated hamsters exposed to a complex mixture of olefm-ozone-
5 sulfur dioxide reaction products. Environ. Res. 31: 302-310.
6 Riechelmann, H.; Maurer, J.; Kienast, K.; Hafner, B.; Mann, W. J. (1995) Respiratory epithelium
7 exposure to sulfur dioxide—functional and ultrastructural alterations. Laryngoscope 105:
8 295-299.
9 Riedel, F.; Kramer, M.; Scheibenbogen, C.; Rieger, C. H. L. (1988) Effects of SC>2 exposure on
10 allergic sensitization in the guinea pig. J. Allergy Clin. Immunol. 82: 527-534.
11 Ruan, A.; Min, H.; Meng, Z.; Lii, Z. (2003) Protective effects of seabuck thorn seed oil on mouse
12 injury induced by sulfur dioxide inhalation. Inhalation Toxicol. 15: 1053-1058.
13 Saldiva, P. H. N.; King, M.; Delmonte, V. L. C.; Macchione, M.; Parada, M. A. C.; Daliberto, M.
14 L.; Sakae, R. S.; Criado, P. M. P.; Silveira, P. L. P.; Zin, W. A.; Bohm, G. M. (1992)
15 Respiratory alterations due to urban air pollution: an experimental study in rats. Environ.
16 Res. 57: 19-33.
17 Scanlon, P. D.; Seltzer, J.; Ingram, R. H., Jr.; Reid, L.; Drazen, J. M. (1987) Chronic exposure to
18 sulfur dioxide: physiologic and histologic evaluation of dogs exposed to 50 or 15 ppm.
19 Am. Rev. Respir. Dis. 135: 831-839.
20 Shami, S. G; Wolff, R. K.; Hahn, F. F.; Brooks, A. L.; Griffith, W. C. (1985) Early cytokinetic
21 and morphological response of rat lungs to inhaled benzo(a)pyrene, gallium oxide, and
22 SO2. Environ. Res. 37: 12-25.
23 Shi, X. (1994) Generation of S(V and OH radicals in SOs2" reactions with inorganic
24 environmental pollutants and its implications to SOs2" toxicity. J. Inorg. Biochem. 56:
25 155-165.
26 Shi, X.; Mao, Y. (1994) 8-hydroxy-2'-deoxyguanosine formation and DNA damage induced by
27 sulfur trioxide anion radicals. Biochem. Biophys. Res. Commun. 205: 141-147.
28 Singh, J. (1989) Neonatal development altered by maternal sulfur dioxide exposure.
29 NeuroToxicology 10: 523-527.
30 Smith, L. G.; Busch, R. H.; Buschbom, R. L.; Cannon, W. C.; Loscutoff, S. M.; Morris. J. E.
31 (1989) Effects of sulfur dioxide or ammonium sulfate exposure, alone or combined, for 4
32 or 8 months on normal and elastase-impaired rats. Environ. Res. 49: 60-78.
33 Wolff, R. K.; Griffith, W. C.; Henderson, R. F.; Hahn, F. F.; Harkema, J. R.; Rebar, A. H.;
34 Eidson, A. F.; McClellan, R. O. (1989) Effects of repeated inhalation exposures to 1-
35 nitropyrene, benzo(a)pyrene, Ga2Os particles, and SC>2 alone and in combinations on
36 particle clearance, bronchoalveolar lavage fluid composition, and histopathology. J.
37 Toxicol. Environ. Health 27: 123-138.
38 Wu, D.; Meng, Z. (2003) Effect of sulfur dioxide inhalation on the glutathione redox system in
39 mice and protective role of sea buckthorn seed oil. Arch. Environ. Contam. Toxicol. 45:
40 423-428.
September 2007 AX4-71 DRAFT-DO NOT QUOTE OR CITE
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1 Yargi90glu, A.; Agar, S.; Gumus.lu, S.; Bilmen, S.; Oguz, Y. (1999) Age-related alterations in
2 antioxidant enzymes, lipid peroxide levels, and somatosensory-evoked potentials: effect
3 of sulfur dioxide. Arch. Environ. Contam. Toxicol. 37: 554-560.
4 Yargi90glu, P.; Gumus.luoriob, S.; Agar, A.; Korgun, D. K.; Kii9iikatay, V. (2001) Effect of
5 sulfur dioxide inhalation on erythrocyte antioxidant status, food intake, and lipid
6 peroxidation during aging. Arch. Environ. Health 56: 53-57.
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AX5. CHAPTER 5 ANNEX - EPIDEMIOLOGICAL
STUDIES OF HUMAN HEALTH EFFECTS ASSOCIATED
WITH EXPOSURE TO AMBIENT SULFUR OXIDES
September 2007 AX5-1 DRAFT-DO NOT QUOTE OR CITE
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
to
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o
>
X
H
6
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HH
H
W
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean SO2 Levels Copollutants Considered
Findings &
Interpretation
Effect Estimates
(95% CI)
UNITED STATES (cont'd)
Schildcrout et al. (2006)
Albuquerque, NM;
Baltimore MD;
Boston MA;
Denver, CO;
San Diego, CA;
Seattle, WA;
St. Louis, MO;
Toronto, Ontario, Canada
Novl993-Septl995
Meta-analysis of 8 panel
studies with 990 children of
the Childhood Asthma
Management Program
(CAMP), during the 22-mo
prerandomization phase to
investigate effects of
criteria pollutants on
asthma exacerbations (daily
symptoms and use of
rescue inhalers). Poisson
regression and logistic
regression models used in
analyses. Within city
models controlled for day
of wk, ethnicity, annual
family income, flexible
functions of age and log-
transformed sensitivity to
the methacholine challenge
using natural splines with
knots fixed at 25th, 50th,
and 75th percentiles. Also
controlled for confounding
due to seasonal factors. All
city-specific estimates
included in calculations of
study-wide effects except
Albuquerque where SO2
data were not collected.
24-h avg SO2:
Median (10th, 25th, 75th,
90th percentile):
Albuquerque: NA
Baltimore: 6.7 ppb
(3.2,4.7,9.8,14.2)
Boston: 5.8 ppb
(2.7,3.7,9.1,14.1)
Denver: 4.4 ppb
(1.2,2.5,6.7,9.5)
San Diego: 2.2 ppb
(1.2,1.7,3.1,4.4)
Seattle: 6.0 ppb
(3.7,4.7,7.5,9.5)
St. Louis: 7.4 ppb
(3.9,5.3,10.7,13.6)
Toronto: 2.5 ppb
(0.2,1.0,4.8,8.8)
O3(-0.03
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
to
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W
Reference, Study
Location, & Period
Outcomes, Design,
& Methods Mean SO2 Levels
Findings &
Copollutants Considered Interpretation
Effect Estimates
(95% CI)
UNITED STATES (cont'd)
Schwartz etal. (1994)
Watertown, MA
(Apr-Aug 1985);
Kingston-Harriman, TN
(Apr-Aug 1986);
St. Louis, MO;
(Apr-Aug 1986);
Steubenville, OH;
(Apr-Aug 1987);
Portage, WI;
(Apr-Aug 1987);
Topeka, KS
(Apr-Aug 1988)
Longitudinal study of 1 ,844 24-h mean SO2 :
children in grades 2-5 from Median: 4.1 ppb
the Six Cities Study to IQR: 1 .4, 8.2
examine the effects of PM Max' 819
and SOX on respiratory
health. Daily diaries
completed by parents,
recording symptoms, such
as cough, chest pain,
phlegm, wheeze, sore
throat, and fever. Logistic
regression models adjusting
for aurocorrelation were
used for the analysis. To
examine possible non-
linearity in the relationship,
smooth functions of the air
pollution variables were fit
using GAM and the
significance of the
deviation from linearity
was tested.
O3 (r = -0.09) SO2 associated with
NO2 (r = 0. 5 1 ) incidence of cough and
PM10 (r = 0.53) lower respiratory
PM (r = 0 55) symptoms. Local smooth
PM2" sulfur (r = 0.50) sho^ed mf ease,d co^h
TT+ / _ Q 93 \ incidence for only above a
4-day avg of 20 ppb (less
than 5% of data). Test for
nonlinearity was
significant (p = 0.002).
No increase in incidence
of lower respiratory
symptoms was seen until
24-h avg SO2
concentrations exceeded
22 ppb.
ORs for cough and lower
respiratory symptoms
related to were
substantially reduced after
adjustment for PM10,
suggesting the SO2
associations might be
confounded by particles.
OR for cough incidence
associated with 10-ppb
increase in 4-day avg SO2
concentration:
Single-pollutant model:
1.15(1.02,1.31)
SO2 with PM10 model:
1.08(0.93,1.25)
SO2 with O3 model'
1.15 (1.01, 1.31)
SO2 with NO2 model:
i no (r\ 0/1 i "?ru
1 AJy (U.V^t, 1 . J\))
OR for lower respiratory
symptoms associated with
10-ppb increase in 24-h
avg SO2 concentration:
Single-pollutant model:
1.28(1.13,1.46)
SO2 with PM10 model:
Not presented. Stated as
not statistically
significant.
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
to
o
o
>
X
H
6
o
o
H
O
o
H
W
O
O
HH
H
W
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean SO2 Levels
Copollutants
Considered
Findings &
Interpretation
Effect Estimates
(95% CI)
UNITED STATES (cont'd)
Delfino et al. (2003)
Los Angeles, CA
Novl999-Jan2000
Panel study of 22 Hispanic
children with asthma aged
10 to 16 yrs. Participants
performed twice-daily PEF
measurements and filled out
symptom diaries. Analyses
of symptoms conducted using
GEE with exchangeable
correlation. Linear mixed
model used for PEF analyses.
GEE models controlled for
respiratory infections (data
available for 20 subjects) and
temperature.
l-hmaxSO2:
7.0 ppb (SD 4.0)
IQR: 4.0
8-h max SO2:
4. 6 ppb (SD 3.0)
IQR: 2.5
O3(r=-0.19)
N02(r=0.89)
CO (r = 0.69)
PM10
(r=0.73)
EC (r= 0.87)
OC(r = 0.83)
VOCs
V V_/v_.o
NoneoftheVOCsor
gaseous pollutants
associated with PEF.
Current-day, but not
previous-day, SO2
concentrations
associated with symptom
score >1 and>2.
OR for symptom score
>1 per IQR increase in
S02:
1-hmax SO2:
LagO: 1.31
(1.10,1.55)
Lagl: 1.11
(0.91,1.36)
8-h max SO2:
LagO: 1.23
(1.06,1.41)
T I 111
Lagl: 1.11
(0.97, 1.28)
OR for symptom
score >2 per IQR
increase in SO2:
1-hmax SO2:
LagO: 1.37
(0.87,2.18)
Lagl: 0.76
(0.35, 1.64)
8-h max SO2:
LagO: 1.36
(1.08, 1.71)
Lagl: 0.91
(0.51, 1.60)
Neasetal. (1995)
Uniontown, PA
Summer 1990
Panel study of 83 fourth and
fifth grade students in
Uniontown, Pennsylvania.
Participants reported twice-
daily PEF and the presence of
cold, cough, or wheeze.
During the summer of 1990,
there were 3,582 child-days.
PEF analyzed with
autoregressive linear
regression model that
included a separate intercept
for evening measurements,
trend, temperature and 12-h
avg air pollutant
concentration, weighted by
the number of hours child
spent outdoors during the
previous 12 h.
12-h avg SO2:
10.2 ppb
Max: 44.9
IQR: 11.1
Daytime 12-h avg
SO2 (8 a.m.-8 p.m.):
14.5 ppb
Overnight 12-h avg
SO2(8p.m.-8a.m.):
5.9 ppb
PM10
PM25
03
total sulfate
particles
particle-strong
acidity
(r = 0.44)
Incidence of new
evening cough episodes
significantly associated
with the preceding
daytime 12-h avg SO2.
Mean deviation in PEF
not associated with SO2.
Effects associated with 10-ppb increase in
12-h avg SO2:
Change in mean deviation in PEF:
-0.63 L/min(-1.33, 0.07)
OR for evening cough: 1.19 (1.00, 1.42)
Concentration weighted by proportion of
hours spent outdoors during prior 12 h:
Change in mean deviation in PEF:
-1.25 L/min (-2.75, 0.25)
OR for evening cough: 1.53 (1.07,2.20)
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Copollutants
Mean SO2 Levels Considered
Findings &
Interpretation
Effect Estimates
(95% CI)
UNITED STATES (cont'd)
Newhouse et al. (2004)
Tulsa, OK
Sep-Oct 2000
Panel study of 24 patients aged 9 to 64 yrs
with physician-diagnosed asthma. Subjects
performed twice-daily PEF (morning and
evening) measurements, and recorded
medications taken and symptoms. Simple
linear regression, forward stepwise multiple
regression and correlation analysis
performed. Multiple regression analyses
used to develop predictive models for other
environmental factors. Analyses produced
complex models with different predictor
variables for each symptom.
24-h avg SO2: PM25
0.01 ppm CO
Range: 0.00,0.02 O3
pollen
fungal spores
Of the atmospheric
pollutants, avg and max
O3 were most significant
factors that influenced
symptoms. Quantitative
results not provided for
S02.
Avg or max SO2 found to
be negative predictors of
asthma in subgroup
analyses of women and
Not quantitatively useful.
Ross et al. (2002) Panel study of 59 asthmatic subjects aged
East Moline, IL 5 to 49 yrs. Analysis based on 40 subjects,
May-Oct 1994 due to withdrawal or failure to provide
requested health data. Study assessed the
effect of single and combined exposures to
air pollutants and airborne allergens on PEF,
symptom scores and medication use
frequency. Multivariate linear-regression
models with 1 st order autoregression used
for analysis of daily means of mean -
standardized PEF, symptom scores and
asthma medication use; logistic regression
used for dichotomized data for symptom
score and medication use, log-linear models
for log-transformed symptom scores and
medication use frequency.
24-h avg SO2:
3.4ppb(SD3.1)
Median: 2.8
IQR: 2.4
Range: 0,27.3
PM10
03
NO2
pollen
fungi
nonsmokers and rhinitis in
all patients. Avg SO2 also
negative predictor of
evening PEF.
No associations observed
with SO2.
No effect estimates provided.
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
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Reference, Study
Location, & Period
Outcomes, Design,
& Methods Mean SO2 Levels
Copollutants Findings &
Considered Interpretation
Effect Estimates
(95% CI)
EUROPE
Boezenetal. (1998)
Amsterdam and
Meppel, the
Netherlands
winter of 1993-1994
Panels study of 1 89 adults (48 to 24-h avg SO2
73 yrs) with and without chronic
respiratory symptoms in urban and Urban
rural areas to investigate whether Mean: 11.8ug/m3
bronchial hyperresponsiveness and Range: 2.7,33.5
PEF variability can be used to
identify subjects who are susceptible Rural
to air pollution. Spirometry and Mean: 8.2
methacholine challenge were Range: 0.8,41.5
performed and subjects with a fall in
FEY] of 20% or greater were
considered BHR. Subjects
performed twice-daily peak flow for
3 mos. A subject's basal PEF
variability was calculated over an
8-day period with low air pollution.
PEF variability was expressed as
(highest PEF-lowest PEF/mean) or
amplitude % mean (ampl%mean)
PEF. After calculation of the daily
PEF variability, the number of days
where the ampl% mean was greater
than 5% was determined. This
resulted in two groups of subjects;
those with ampli%mean PEF of 5%
or less every day in the 8-day period,
and those with an ampl%mean PEF
greater than 5% on at least 1 day.
Effects of air pollutants on
prevalence of symptoms assessed
with logistic regression models that
adjusted for autocorrelation of the
residuals, daily min temp, time trend
and weekends/holidays.
PM10 No association
BS between SO2 and
NO2 respiratory symptoms
in subjects with no
BHR, BHR at < cum
2.0 methacholine or
BHR at < cum
1 .0 methacholine. In
subjects with
ampl%mean PEF > 5%
and those with
ampli%mean
PEF > 5% for > 33%
of days, SO2 was
associated with the
prevalence of phlegm.
Odds ratio (per 40 ug/m3 SO2)
Subjects with no BHR
URS: 0.86(0.73,1.03)
LRS: 1.15(0.90,1.46)
Cough: 1.01 (0.84,1.21)
Phlegm: 1.01(0.86,1.20)
BHR at < cum 2.0
Methacholine
URS: 1.11(0.78,1.56)
LRS: 1.03(0.72,1.47)
Cough: 0.89(0.66,1.19)
Phlegm: 1.03(0.78,1.37)
BHR at < 1.0
Methacholine
URS: 1.02(0.65,1.61)
LRS: 0.96(0.63,1.47)
Cough: 0.96(0.64,1.44)
Phlegm: 1.00(0.68,1.46)
AmpP/omeanPEF <5%
URS: 0.82(0.62,1.08)
LRS: 1.38(0.93,2.03)
Cough: 0.72(0.52,0.98)
Phlegm: 0.79(0.59,1.05)
AmpP/omean PEF > 5%
URS: 1.04(0.88,1.23)
LRS: 1.14(0.96,1.36)
Cough: 1.07(0.90,1.26)
Phlegm: 1.23(1.05,1.43)
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean SO2 Levels
Copollutants
Considered
Findings &
Interpretation
Effect Estimates
(95% CI)
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EUROPE (cont'd)
Boezenetal. (1998)
(cont'd)
Ampl%mean PEF > 5%, >33% of
days
URS: 1.10(0.85,1.41)
LRS: 1.14(0.91,1.42)
Cough: 1.14(0.89,1.47)
Phlegm: 1.36(1.14,1.63)
X
10% morning PEF decrease
LagO: 1.09(0.89,1.34)
Lagl: 1.00(0.81,1.23)
>10% evening PEF decrease
LagO: 1.060.86,1.30)
Lagl: 0.83(0.68,1.02)
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean SO2 Levels
Copollutants
Considered
Findings &
Interpretation
Effect Estimates
(95% CI)
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Boezenetal. (1999)
(cont'd)
NO BHR and low IgE (n = 167)
LRS
LagO: 1.12(0.76,1.66)
Lagl: 0.61(0.39,0.94)
URS
LagO: 1.01(0.89,1.13)
Lagl: 1.08(0.96,1.22)
>10 morning PEF decrease
LagO: 1.02(0.89,1.16)
Lagl: 1.00(0.87,1.15)
>10% evening PEF decrease
LagO: 1.10(0.97,1.25)
Lagl: 1.06(0.93,1.21)
With BHR and low IgE (n = 67)
LRS
LagO: 0.72(0.41,1.28)
Lagl: 1.03(0.56,1.91)
URS
LagO: 0.82(0.62,1.09)
Lagl: 0.84(0.64,1.12)
>10% morning PEF decrease
LagO: 0.74(0.51,1.07)
Lagl: 0.96(0.67,1.37)
>10% evening PEF decrease
LagO: 1.23(0.88,1.73)
Lagl: 1.32(0.96,1.82)
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean SO2 Levels
Copollutants
Considered
Findings &
Interpretation
Effect Estimates
(95% CI)
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Boezenetal. (1999)
(cont'd)
With BHR and low IgE (n = 67)
LRS
LagO: 0.72(0.41,1.28)
Lagl: 1.03(0.56,1.91)
URS
LagO: 0.82(0.62,1.09)
Lagl: 0.84(0.64,1.12)
>10% morning PEF decrease
LagO: 0.74(0.51,1.07)
Lagl: 0.96(0.67,1.37)
>10% evening PEF decrease
LagO: 1.23(0.88,1.73)
Lagl: 1.32(0.96,1.82)
No BHR and high IgE (n = 104)
LRS
LagO: 1.44(1.17,1.77)
Lagl: 1.28(1.00,1.64)
Lag 2: (1.38(1.08,1.77)
5-day mean: 2.49(1.54,4.04)
URS
LagO: 0.98(0.84,1.14)
Lagl: 1.010.87,1.18)
>10% morning PEF decrease
LagO: 0.92(0.79,1.08)
Lagl: 1.03(0.89,1.21)
>10% evening PEF decrease
LagO: 1.00(0.85,1.17)
Lagl: 1.05(0.90,1.23)
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
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Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean SO2 Levels
Copollutants Findings &
Considered Interpretation
Effect Estimates
(95% CI)
EUROPE (cont'd)
Boezen et al. (2005)
Meppel, Nunspeet,
Amsterdam, The
Netherlands
two winters 1993-1995
Panel study of 327 elderly patients
(50 to 70 yrs) to determine
susceptibility to air pollution by
airway hyperresponsiveness (AHR),
high total immunoglobulin (IgE), and
sex. Methacholine challenges were
performed and subjects with greater
than or equal to 20% fall in FEVj after
inhalation of up to 2.0 mg
methacholine were considered AHR+.
Subjects with total serum IgE > 20
kU/L were defined as high total IgE
(IgE+). Twice daily PEF
measurements and daily symptoms
recorded for 3 mos. Data analysis
performed using logistic regression
with modeling of first-order
autocorrelation in the residuals that
adjusted for daily minimum
temperature, time trend,
weekend/holidays and influenza
incident for the rural and urban areas
and the two winters separately.
Subjects were classified as IgE+
AHR+, IgE+ AHR- , IgE- AHR+ or
IgE- AHR+. Examined effects of
pollutants on the same day, Lag 1, Lag
2 and the 5-day mean concentration of
Lag 0 to Lag 4 preceding that day.
Groups that had effect estimates for
PMio, BS, SO2, and NO2 that were
outside the 95% CI of the effect
estimates for the AHR-/IgE- (control
group) were considered to have
increased susceptibility to air
pollution.
24-h mean SO2
(ug/m3) in winter
Winter 1993/1 994
Urban:
Mean: 11.8 ug/m3
Median: 10.2
Range: 2.7,33.5
Rural:
Mean: 8.2
Median: 4.4
Range: 0.8,41.5
Winter 1994/1 995
Urban:
Mean: 8.3
Median: 7.4
Range: 0.6,24.4
Rural:
Mean: 4.3
Median: .7
Range: 0.5, 17.0
PM10 No consistent
BS associations between
NO2 the prevalence of LRS
or > 10% fall in
evening PEF and air
pollution in any of the
four groups. In the
AHR+/IgE group, the
prevalence of URS
was associated with
SO2 at 1 day Lag, and
the prevalence of
>10% fall in morning
PEF with SO2 at Lag
l,Lag2 and 5-day
mean (avg of Lag 0 to
Lag 4). For females
who were AHR+/IgE+,
the prevalence of
> 10% fall in PEF was
associated with SO2
Lag 1 , Lag 2 and 5-day
mean. In subjects with
AHR-/IgE+ the
prevalence of URS
was associated with
SO2 the previous day
and the mean of Lag 0
to Lag 4. The effect
estimate was outside
the 95% CI of
Odds ratio (per 10 ug/m3 SO2)
AHR-/IgE-
URS
LagO: 0.99(0.93,1.05)
Lagl: 1.02(0.97,1.08)
Cough:
LagO: 1.03(0.98,1.08)
Lagl: 0.97(0.93,1.02)
>10% fall in morning PEF
Lagl: 1.00(0.92,1.08)
AHR-/IgE+
URS LagO: 0.98(0.92,1.03)
Lagl: 1.07(1.01,1.12)
5-day mean 1.15 (1.02, 1.29),
OR outside 95% CI of control group
Cough:
LagO: 1.01(0.95,1.07)
Lagl: 1.02(0.96,1.08)
>10 % fall in morning PEF
Lagl: 1.00(0.92,1.08)
AHR+/IgE-
LagO: 1.05(0.94,1.17)
Lagl: 1.07(0.96,1.19)
Cough:
LagO: 1.03(0.95,1.12)
Lagl: 1.01(0.93,1.09)
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean SO2 Levels
Copollutants
Considered
Findings &
Interpretation
Effect Estimates
(95% CI)
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Boezen et al. (2005)
(cont'd)
The estimate for the
control group
AHR-/IgE-. No
consistent positive
associations found
between prevalences
ofURS, cough or
>10% fall in morning
PEF and air pollutants
in subjects with
AHR+/IgE- or
AHR-/IgE-. Based
on results of the study,
authors conclude that
subjects with
AHR+/IgE+ were the
most responsive to air
pollution.
>10 % fall in morning PEF
Lagl: 0.99(0.87,1.12)
5-day mean: 0.78(0.61,0.98),
OR outside 95% CI of control group
AHR+/IgE+
LagO: 1.06(0.97,1.15)
Lagl: 1.13(1.05,1.23)
Cough:
LagO: 1.02(0.94,1.11)
Lagl: 1.02(0.94,1.10)
>10 % fall in morning PEF
Lagl: 0.99(0.87,1.12)
AHR+/IgE+
URS
LagO: 1.06(0.97,1.15)
Lagl: 1.13(1.05,1.23),
OR outside 95% CI of control group
Cough:
LagO: 1.02(0.94,1.11)
Lagl: 1.02(0.94,1.10)
>10 % fall in morning PEF
Lagl: 1.15(1.04,1.27),
OR outside 95% CI of control group
Lag 2: 1.18(1.07,1.30),
OR outside 95% CI of control group
5-day mean : 1.26 (1.07, 1.49),
OR outside 95% CI of control group
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean SO2 Levels
Copollutants
Considered
Findings &
Interpretation
Effect Estimates
(95% CI)
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X
Cuijpersetal. (1994)
Maastricht, the
Netherlands
Nov-Dec 1990
(baseline)
Aug 8-16
(smog episode)
The effects of exposure to summer
smog on respiratory health were
studied in 535 children (age
unspecified). During a smog
episode, 212 children were randomly
chosen to be reexamined for lung
function and symptoms. Only 112 of
the children had adequately
completed summer questionnaires
and were used for the symptom
analysis. Lung function
measurements made with forced
oscillation technique were available
for 212 children and valid spirometry
was available for 208 children.
Corrected baseline lung function
compared using paired t test and
difference in the prevalence in
symptoms during baseline and
episode compared.
24-h avg SO2
Baseline 55 |ig/m3
Summer episode
23 ng/m
N02
BS
03
PM10
Acid aerosol
IT
Small decrements in
FEV] and FEF25_75 found
in the 212 children
during the episode
compared to baseline.
However, there was also
a significant decrease in
resistance parameters.
No increases observed in
the prevalence of acute
respiratory symptoms.
Change in lung function and
impedance between baseline and
smog episode:
FEVj: -0.032 L (SD 0.226),
p< = 0.05
FEF25.75: -0.086 L/s (SD 0.415),
p< = 0.01
Resistance at 8 Hz: - 0.47 cm H2O
(L/s)
(SD1.17),p< = 0.05
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Hoek and Brunekreff
(1995)
Deurne and
Enkhuizen, The
Netherlands
Mar-Jul 1989
Panel study of 300 children
(7-11 yrs) to examine the effects of
photochemical air pollution on acute
respiratory symptoms. Occurrence
of respiratory symptoms recorded by
parents in daily diary. Symptoms
included cough, shortness of breath,
upper and lower respiratory
symptoms, throat and eye irritation,
headache and nausea. Association of
symptom prevalence and incidence
assessed using first order
autoregressive, logistic regression
model.
Daily concentration
ofSO2<43|ig/m3
03
PM10
SO42
Same day concentrations
ofSO2andNO2not
associated with symptom
prevalence.
No effect estimates for SO2
provided
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean SO2 Levels
Copollutants Findings &
Considered Interpretation
Effect Estimates
(95% CI)
EUROPE (cont'd)
Koppetal. (1999)
Two towns in Black
Forest, Germany
Villingen and
Freudenstadt
Mar-Octl994
Panel study of 170 children
(median age 9. 1 yrs) to
investigate nasal inflammation
and subsequent adaptation after
ambient ozone exposures. Nasal
lavage was sampled over 1 1 time
points, and skin prick tests
performed. Nasal lavage samples
were analyzed for eosinophil
cationic protein, albumen, and
leukocytes as markers of nasal
inflammation. To avoid
confounding with allergens, the
study population was restricted to
only children with no positive
reaction to any of the tested
inhalant allergens. GEE used in
analysis.
Mean SO2 (mg/m3)
Villingen
Mean: 3
5%: 0
95%: 9
Freudenstadt
Mean: 3
5%: 0
95%: 9
O3, NO2, TSP, PM10 Results for only O3.
Authors noted that
since there were very
low concentrations of
NOX and SO2, the
confounding effects of
these components in
ambient air were
negligible.
Eosinophil cationic
protein and leukocyte
levels peaked after the
first increase in
ambient ozone levels.
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
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Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean SO2 Levels
Copollutants Findings &
Considered Interpretation
Effect Estimates
(95% CI)
EUROPE (cont'd)
Peters etal. (1996)
Erfurt and Weimar,
former German
Democratic Republic;
Sokolov, Czech
Republic
Sept 1990 to June
1992
Panel study of 102 adult
(32 to 80 yrs) and 155 children
(7 to 15 yrs) with asthma from
the former German Democratic
Republic and Czech Republic to
investigate the acute effects of
winter type air pollution on
symptoms, medication intake and
PEF. Used regression analyses
and distributed Lag models.
Winter 1990/1 991
Erfurt
Mean: 125 ug/m3,
Max: 564 ug/m3,
IQR: 113 ug/m3
Weimar
Mean: 236 ug/m3,
Max: 1018 ug/m3,
IQR: 207 ug/m3
Sokolov
Mean: 90 ug/m3,
Max: 492 ug/m3,
IQR: 94 ug/m3
Winter 199 1/1 992
Tnrfnrf
HIILU I
Mean: 96 ug/m3,
Max: 462 ug/m3,
IQR: 80 ug/m3
Weimar
Mean: 153 ug/m ,
Max: 794 ug/m3,
IQR: 130 ug/m3
Sokolov
Mean: 71 ug/m3,
Max: 383 ug/m3,
IQR: 66 ug/m3
TSP, 5-day mean
PM10, concentration of SO2
SO4j associated with PEF
PSA (particle strong and symptoms in
acidity) children (combined
analysis from former
German Democratic
Republic and Czech
Republic).
Correlation coefficient
between SO2 and TSP
in Erfurt was r = 0.8,
0.9 during both winters
and in Weimar during
the first winter.
Correlation with TSP
in Sokolov and in
Weimar during the
second winter was
r= 0.4, 0.5.
Combined analysis for children
Change in PEF
Concurrent day 0.18 (-0.44, 0.09) per
133 ug/m3
5-day mean -.90 (-1.35, -0.46) per
128 ug/m3
Change in symptom score
Concurrent day -0.1 (-5.9, 5. 7) per
133 ug/m3
5-day mean 14.7 (0.8, 28.6) per
128 ug/m3
Combined analysis for adults
Change in PEF
Concurrent day -0.20 (-0.53, 0.12) per
133 ug/m3
5-day mean -0.28 (-0.72, 0.16) per
128 ug/m3
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean SO2 Levels
Copollutants
Considered
Findings &
Interpretation
Effect Estimates
(95% CI)
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Pinter etal. (1996)
Tata Area, Hungary
winter mos between
Dec 1993-Mar 1994
Longitudinal (children <14 yrs)
and cross-sectional study (9 to
11 yrs) to examine air pollution
and respiratory morbidity in
children. In the longitudinal
prospective study, respiratory
morbidity was evaluated daily
and on a weekly basis. In cross-
sectional study, anthropometric
parameters, physical status, pulse
and blood pressure, lung function
parameters, eosinophils in the
nasal smear, hematological
characteristics and urinary
excretion of some metabolites
were examine and measured.
Anova and linear regression used
in analysis.
Mean SO2 exceeded
the limit of yearly
avg 150 ug/m3
Daily peaks reached
as high as 450 ug/m3
No specific values
given
NO,
Significant correlation
between SO2 levels and
acute daily respiratory
morbidity, but no
correlation with
weekly incidence.
Authors stated that in
the cross-sectional
study, almost all health
parameters were
impaired but no results
were shown.
Results only provided in graph. No
p-values provided
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
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Reference, Study
Location, & Period
Outcomes, Design, Copollutants
& Methods Mean SO2 Levels Considered
Findings &
Interpretation
Effect Estimates
(95% CI)
EUROPE (cont'd)
Roemeretal. (1993)
Wageningen and
Bennekom,
Netherlands
Panel of 73 children (mean age Daily concentrations NO2
9.3 yrs, range 6 to 12 yrs) with of SO2 shown in PM10
chronic respiratory symptoms to graph gg
investigate effects of winter air
pollution on lung function, Highest 24-h avg
symptoms and medication use. concentration SO2:
Subjects performed twice-daily 105 ng/m3
PEF measurements, largest of
three PEF readings used in
regression analysis. Both
incidence and prevalence of
symptoms analyzed, using
logistic regression.
Positive association
between incidence of
phlegm and runny nose
with SO2 on the same
day. Significant
association also found
between evening PEF
and SO2 on, the same
day, previous day and
1 wk (avg of same day
and 6 days before).
The use of
bronchodilators also
associated with SO2.
Correlation with
copollutants:
N02: r=0.26
PM10: r = 0.65
BS: r = 0.63
Mean of individual regression
coefficient
Morning PEF
Same day: -0.021(0.024)
Lagl: -0.024(0.031)
Wk: -0.50(0.069)
Evening PEF
Same day: -0.048 (0.018) p< 0.05
Lagl: -0.039 (0.021) p< 0.10
Wk: -0.110(0.055)p<0.05
Prevalence of symptoms
(per 50 ng/m3 SO2)
Asthma attack
Same day: 0.008(0.012)
Lagl: 0.016(0.011)
1 wk: 0.058(0. 027) p< 0.05
Wheeze
Same day: 0.033 (0.17) p< 0.10
Lagl: 0.042 (0.016) p< 0.05
Wk: 0.069 (0.032) p< 0.05
Waken with symptoms
Same: day 0.033 (0.019) p< 0.10
Lagl: 0.032 (0.018) p< 0.10
Wk: 0.058(0.045)
Shortness of breath
Same: day 0.029 (0.016) p< 0.10
Lagl: 0.016(0.015)
Wk: 0.044(0.035)
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean SO2 Levels
Copollutants
Considered
Findings & Interpretation
Effect Estimates
(95% CI)
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Roemeretal. (1993)
(cont'd)
Roemeretal. (1998)
14 European Centers:
Umea, Sweden;
Malmo, Sweden;
Kuopi, Finland;
Oslo, Norway;
Amsterdam, The
Netherlands;
Berlin, Germany;
Katowice, Poland;
Cracow, Poland;
Teplice, Czech
Republic;
Prague, Czech
Republic; Budapest,
Hungary;
Pisa,Italy;
Athens, Greece
Winter 1993-1994
Multicenter panel study of the
acute effects of air pollution on
respiratory health of
2010 children (aged 6 to 12 yrs)
with chronic respiratory
symptoms. Results from
individual centers were reported
by Kotesovec et al. (1998),
Kalandidi et al. (1998), Haluszka
etal. (1998),Forsbergetal.
(1998), Clench-Aas etal. (1998),
and Beyer etal. (1998).
Calculated effect estimates of air
pollution on PEF or the daily
prevalence of respiratory
symptoms and bronchodilator use
from the panel-specific effect
estimates
Range: -2.7 |ig/m3
(Umea, urban),
113.9ng/m3
(Prague, urban)
PM10,
BS
No clear associations between
PMio, BS, SO2j or NO2 and
morning PEF, evening PEF,
prevalence of respiratory
symptoms, or bronchodilator
use could be detected.
Previous day PM10 was
negatively associated with
evening PEF, but only in
locations where BS was high
compared to PM10
concentrations.
No consistent differences in
effect estimates between
subgroups based on urban
versus suburban,
geographical location or
mean levels of PM10, BS,
SO2, andNO2.
Cough
Same day 0.018 (0.025)
Lagl: 0.012(0.023)
Wk 0.072 (0.066)
Runny nose
Same day 0.070 (0.026) p < 0.05
Lagl: -0.11(0.025)
Wk 0.153 (0.074) p< 0.05
Phlegm
Same day 0.011(0.022)
Lagl: 0.014(0.020)
Wk-0.005 (0.056)
Combined effect estimates with 95%
CI of air pollution on PEF
Morning
LagO: 0.2 (-0.2, 0.6)
Lagl: 0.2 (-0.2, 0.6)
Lag 2: 0.6(0.2,1.0)
7-day mean 0.6 (-1.3, 2.5)
Afternoon
LagO: 0.1 (-0.3, 0.5)
Lagl: 0.0 (-0.4, 0.4)
Lag 2: 0.1 (-0.4, 0.6)
7-day mean 0.2 (-0.5, 0.9)
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods Mean SO2 Levels
Copollutants Findings &
Considered Interpretation
Effect Estimates
(95% CI)
EUROPE (cont'd)
Taggartetal. (1996)
Runcorn and Widnes
in NW England
Jul-Sep 1993
Panel study of 38 nonsmoking 24-h avg SO2
asthma subjects (18 to 70 yrs) to Max: 103.7 ug/m
investigate the relationship
between asthmatic bronchial
hyperresponsiveness and
pulmonary function (PEF, FEVi,
FVC) and summertime ambient
air pollution. Used univariate
nested (hierarchical) analysis of
variance to test hypothesis that
BHR or spirometry
measurements varied with air
pollution levels. Analysis was
limited to within- subject variation
of(BHR,FEVl5orFVC).
NO2 No association
O3 between SO2 and FEVj
smoke or FVC.
Changes in BHR
correlated significantly
with changes in 24-h
mean SO2, NO2, and
smoke.
Correlation with
copollutants:
O3: r= 0.13
NO2: r — 0.65
Smoke: r=0.48
Percentage change in BHR per 10 ug/m3 SO2
24-h mean SO2 -6.3 % (- 13.6, 0.6)
48-h mean -2.9 % (- 12.8, 8.2)
24-h Lag 7.4% (-4. 5, 20. 8)
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
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Reference, Study Outcomes, Design, Copollutants Findings &
Location, & Period & Methods Mean SO2 Levels Considered Interpretation
Effect Estimates
(95% CI)
EUROPE (cont'd)
Ward et al. (2002) Panel study of 162 children 24-h avg SO2 NO2
Birmingham and (9 yrs at time of enrollment) O3
Sandwell, England from two inner city Winter: PM10
Jan-Mar 1997 locations to investigate the Jan 13-Mar 10, jj+
May -Jul 1997 association between 1997 pi-
ambient acid species with Median: 5. 4 ppb „„,
PEF and symptoms. Daily Range: 2, 1 8 ppb
symptoms and twice-daily HJNO3
"MTT
peak flow measurements Summer'
were recorded over 8 wk May 19- July 14, NH/t
periods in the summer and 1997 NO3
winter. 39 of the children Median: 4.7 ppb SO42~
reported wheezing in the Range: 2, 10 ppb
past 12 mos. Linear
regression used for PEF and
logistic regression for
symptoms.
In the summer,
changes in
morning PEF were
associated with
SO2 at 3-days lag
and the 7-day
mean SO2.
Prevalence of
cough associated
with SO2 on the
same day.
In the winter SO2
was only
associated with
symptom of
feeling ill on the
same day.
24-h avg SO2 (per 4.0 ppb in winter; per 2.2 ppb in summer)
Data also available for 3-,4-,
Change in PEF (L/min)
Morning- Lag 0-day
Winter
-0.60 (-2.51, 1.32)
Summer
0.91 (-0.95, 2.78)
Afternoon- Lag 0-day
Winter
-0.32 (-2.71, 2.04)
Wummer
-0.89 (-2. 61, 0.83)
Odds ratio for symptoms
Cough-Lag 0-day
Winter
0.92(0.81,1.05)
Summer
1.08(1.02,1.15)
Ill-Lag 0-day
Winter
1.09(1.01,1.18)
Summer
1.05(0.96,1.14)
Shortness of breath-
Lag 0-day
Winter
1.02(0.93,1.13)
Summer
0.98(0.87,1.10)
and 7-day Lag
Morning- Lag 1-day
Winter
0.08 (-1.67,1. 86)
Summer
0.29 (-1.56, 2.14)
Afternoon- Lag 1-day
Winter
-0.88 (-2.87, 1.10)
Summer
-0.02 (-1.68, 1.65)
Cough-Lag 1-day
Winter
1.00(0.87,1.15)
Summer
1.04(0.97,1.11)
Ill-Lag 1-day
Winter
1.03(0.95,1.11)
Summer
1.02(0.94,1.12)
Shortness of breath-
Lag 1-day
Winter
1.00(0.90,1.09)
Summer
1.00(0.89,1.13)
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study Outcomes, Design, Mean SO2 Copollutants Findings & Effect Estimates
Location, & Period & Methods Levels Considered Interpretation (95% CI)
EUROPE (cont'd)
Ward et al. (2002)
(cont'd)
Wake at night with cough-
Lag 0 day
Winter
1.00(0.91,1.10)
Summer
1.00(0.87,1.14)
Wheeze- Lag 0 day
Winter
0.96, (0.85, 1.07)
Summer
1.05(0.92,1.19)
Wake at night with cough-
Lag 1 day
Winter
1.05(0.96,1.15)
Summer
1.02(0.89,1.16)
Wheeze-Lag 1 day
Winter
0.96(0.86,1.07)
Summer
1.00(0.88,1.13
Summer change in PEF 2.7 (1.03, 4.38)
per2.2ppbSO2
Lag 3 days (p < 0.05)
Summer change in PEF 6.83 (0.98, 12.69)
per2.2ppbSO2
Lag 0-6 days (p < 0.05)
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
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Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean SO2 Copollutants Findings &
Levels Considered Interpretation
Effect Estimates
(95% CI)
EUROPE (cont'd)
van der Zee etal. (1999)
Netherlands, 3 winters
from 1992 to 1995
Rotterdam and
Bodegrven/Reeuwijk
(1992-1993)
Amsterdam and Meppel
(1993-1994)
Amsterdam and
Nunspeet (1994-1995)
Panel study of
633 children (aged 7 to
1 1 yrs) with and without
chronic respiratory
symptoms, living in
urban and nonurban
areas in the Netherlands.
Volunteers measured
daily PEF and reported
the occurrence of
respiratory symptoms
and bronchodilator use
in a diary. Association
between air pollution
and decrements in PEF,
symptoms and
bronchodilator use
evaluated with logistic
regression models that
adjusted for first order
autocorrelation, min
daily temperature, day of
wk, time trend,
incidence of influenza
and influenza-like
illness.
Median and max PM10 The correlation between
24-h mean Black smoke SO2 and PM varied from
concentration Sulfate 0.5 to 0.8 during first
(ug/m3) N02
1992-1993
Urban 23 (152);
Nonurban 8.9
(43)
1993-1994
Urban 1 1 (34);
Nonurban 5.0
(42)
1994-1995
Urban 6.0 (24);
Nonurban 3.6
(17)
two winters. Correlation
with NO2 about 0.50.
In the urban areas, SO2
was associated with
>10% decrements in
evening PEF, LRS and
use of bronchodilator in
children with symptoms.
Most consistent
associations found with
PMio, BS, and sulfate.
No association found
between SO2 and
prevalence of URS,
cough, phlegm, and
>10% decrements in
morning PEF. In the
nonurban areas, no
associations found with
SO2. In children without
symptoms, no consistent
associations with SO2.
Authors concluded that
children with symptoms
are more susceptible to
particulate air pollution
effects and that use of
medication for asthma
did not prevent the
adverse effects of PM in
children with symptoms.
Odds ratio (per 40 ug/m3 SO2)
Children with symptoms
Urban areas
Evening PEF
LagO: 1.32(0.96,1.80)
Lagl: 0.83(0.60,1.14)
Lag 2: 1.67(1.28,2.19)
Symptoms of lower
respiratory tract
LagO: 1.35(1.01,1.79)
Lagl: 1.23(0.93,1.64)
Symptoms of upper
respiratory tract
LagO: 0.97(0.82,1.14)
Lagl: 1.10(0.94,1.28)
Cough
LagO: 0.90(0.77,1.05)
Lagl: 1.12(0.96,1.30)
Use of bronchodilator
LagO: 0.92(0.72,1.18)
Lagl: 1.45(1.13,1.86)
Nonurban areas
Evening PEF
LagO: 1.20(0.91,1
Lagl: 0.89(0.68,1
Symptoms of lower
respiratory tract
Lag 0: 0.91 (0.69, 1
Lagl: 0.91(0.69,1
Symptoms of upper
respiratory
LagO: 0.94(0.81,1
Lagl: 0.97(0.83,1
5-day mean:
0.67 (0.47, 0.94)
Cough
LagO: 1.08(0.94,1
Lagl: 0.98(0.85,1
.58)
.17)
.19)
.22)
.09)
.13)
.23)
.12)
Use of bronchodilator
Lag 0: 0.86 (0.59, 1
Lagl: 1.18(0.80,1
.25)
.74)
-------
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^=
3 Reference, Study Outcomes, Design, Mean SO2 Copollutants Findings & Effect Estimates
n> Location, & Period & Methods Levels Considered Interpretation (95% CI)
K> EUROPE (cont'd)
O van der Zee etal. (1999) Odds ratio (per 40 ng/m3 SO2)
(cont'd) Children without symptoms
Urban areas Nonurban areas
Evening PEF Evening PEF
LagO: 1.13(0.88,1.47) LagO: 1.10(0.87,1.39)
Lagl: 1.16(0.90,1.50) Lagl: 1.07(0.85,1.35)
URS URS
LagO: 0.92(0.76,1.11) LagO: 1.07(0.92,1.25)
Lagl: 1.10(0.91,1.34) Lagl: 0.85(0.72,1.00)
Lag 2: 0.83(0.70,0.99)
Cough Cough
^ LagO: 0.93(0.78, 1.11) Lag 0: 0.86 (0.76, 0.97)
X Lag 1: 1.02 (0.84, 1.23) Lag 1: 0.95 (0.83, 1.08)
to
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
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Reference, Study Outcomes, Design,
Location, & Period & Methods
Mean SO2 Copollutants
Levels Considered Findings & Interpretation
Effect Estimates
(95% CI)
EUROPE (cont'd)
van der Zee (2000) Panel study of 489 adults
Netherlands, (aged 50 to 70 yrs) with
3 winters from and without chronic
1992 to 1995 respiratory symptoms,
Rotterdam living in urban and
1992-1993 nonurban areas in the
Netherlands. Volunteers
measured daily PEF and
reported the occurrence of
respiratory symptoms and
bronchodilator use in a
diary. Association
between air pollution and
decrements in PEF,
symptoms and
bronchodilator use
evaluated with logistic
regression models that
adjusted for first order
autocorrelation, min daily
temperature, day of wk,
time trend, incidence of
influenza and influenza-
like illness.
Median PM10
(max) cone BS
Sulfate
1992/1993: NO2
Urban 25
(61) ue/m3
V / r^fo
1993/1994
Urban 11
(34) NQ/rn
\ / r-& ?
Nonurban
5.0
(42) ug/m3
1994/1995
Urban 6.0
(24),
Nonurban
1 f.
J.D
(17) ug/m3
Among symptomatic adults
living in urban areas, the
prevalence of >20%
decrement in morning PEF
was associated with SO2.
Moreover, there were no
associations found with
prevalence of
bronchodilator use, LRS,
>10% decrement in
morning PEF and >10% and
>20% decrement in evening
PEF.
In the nonurban areas, there
was no consistent
association between air
pollution and respiratory
health. In the
nonsymptomatic adults, no
consistent associations
observed between health
effects and air pollutants,
but a significant and
positive association was
observed with URS in the
nonurban area at 1 day Lag.
Range of Spearman
correlation coefficients
between 24-h avg cone SO2
and copollutants :
PM10: 0.31,0.78
BS: 0.21,0.75
Sulfate: 0.29,0.69
NO2: 0.47,0.51
Odds ratio (per 40 ug/m3 SO2)
symptomatic adults
In urban areas In nonurban areas
> 1 0% decline in PEF > 1 0 % decline in PEF
Morning
LagO: 0.86(0.60, 1.23)
Lagl: 0.97(0.68,1.39)
>20% decline in PEF
Morning
LagO: 1.33(0.66,2.71)
Lagl: 1.98(1.03-3.79)
LRS
LagO: 1.01(0.84,1.20)
Lagl: .97(0.82,1.16)
5-day mean:
0.71 (95% CI: 0.53 to
0.95)
URS
LagO: 1.15(0.97,1.37)
Lagl: 1.06(0.90,1.26)
Bronchodilator use
LagO: 1.09(0.93,1.28)
Lagl: 1.05(0.89,1.24)
Lag 2: 0.85(0.72,0.99)
Morning
LagO: 79(0.48,1.29)
Lagl: 1.08(0.68,1.72)
>20% decline in PEF
Morning
LagO: 0.79(0.22,2.88)
Lagl: 71(0.13,4.02)
LRS
LagO: 1.11(0.94,1.30)
Lagl: 1.04(0.88,1.22)
URS
LagO: 0.97(0.79,1.20)
Lagl: 1.20(0.98,1.47)
Bronchodilator use
LagO: 1.04(0.91,1.18)
Lagl: 1.08(0.95,1.22)
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study Location,
& Period
Outcomes, Design,
& Methods
Mean SO2
Levels
Copollutants
Considered
Findings &
Interpretation
Effect Estimates
(95% CI)
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van der Zee (2000) (cont'd)
Nonsymptomatic adults
Urban areas Nonurban areas
> 10% decline in PEF
Morning
LagO: 0.77(0.39,1.52)
Lagl: 0.94(0.51,1.73)
URS
LagO: 1.10(0.81,1.48)
Lagl: 1.23(0.92,1.65)
> 10% decline in PEF
Morning
LagO: 2.12(0.98,4.62)
Lagl: 0.87(0.38,1.99)
Lag 2: 0.13(0.04,0.36)
5-day mean:
0.03 (0.00, 0.24)
URS
LagO: 0.73(0.49,1.07)
Lagl: 1.71(1.18,2.46)
Lag 2: 0.65(0.44,0.97)
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean SO2 Levels
Copollutants
Considered
Findings &
Interpretation
Effect Estimates
(95% CI)
EUROPE (cont'd)
Desqueyroux et al.
(2002)
Paris, France
Nov 1995-Nov 1996
Panel study of 60 patients with
moderate to severe physician-
diagnosed asthma (mean age
55 yrs). Asthma attacks were
noted by physician at each
consultation (regular or
emergency). Asthmatic
attacks defined as need to
increase twofold the dose of
beta2 agonist.
24-h avg SO2
Summer 7 (5) ug/m3
Range: 2,27
Winter 19 (12) ug/m3
Range: 3, 81
PM10
N02
03
No association
between asthma
attacks and SO2 for
any Lag or season.
Mean 24-h SO2 (per 10 ug/m3)
OR on incident of asthma attacks
Lagl: day 0.98 (0.76, 1.27)
Lag 2: day 0.92 (0.72, 1.19)
Lag 3: day 1.01(0.82,1.23)
Lag 4: day 1.01(0.86,1.19)
Lag 5: day 1.05(0.85,1.29)
Cumulative exposure mean (-1 to -5 days)
Forsberg et al. (1993) Panel study of 31 asthmatic 24-h avg SO2 (ug/m3) NO2, BS
Pitea, Northern
Sweden
March to April
patients (9 to 71 yrs) to assess
relationship between daily
occurrence of asthma
symptoms and fluctuations in
air pollution and
meteorological conditions.
Subjects recorded symptoms
(shortness of breath,
wheezing, cough, and phlegm)
for 14 consecutive days.
Mean: 5.7
Range: 1.3, 12.9
No significant
association observed
withSO2. Positive
association between
severe shortness of
breath and black
smoke.
Correlation with
copollutants:
NO2: r=0.24
BS: r = 0.70
0.99(0.76,1.30)
Regression coefficient and 90% CI
Subjects with shortness of breath (n = 28)
0.0345 (-0.49, 0.118)
Subjects with 5 or more incident episodes of
severe shortness of breath (n = 10)
-0.0266 (-0.140, 0.087)
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
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Reference, Study Outcomes, Design, Copollutants Findings &
Location, & Period & Methods Mean SO2 Levels Considered Interpretation
Effect Estimates
(95% CI)
EUROPE (cont'd)
Higginsetal. (1995) Panel study of 75 patients with Maximum 24-h SO2 O3 The amplitude % mean
United Kingdom physician diagnosed asthma or 117ug/m3
chronic bronchitis (mean age
50, range 18to82yrs)to
determine if air pollution
affects respiratory function
and symptoms. Subjects
asked to keep symptom
records and perform PEF for
28 days. PEF values recorded
every 2 h beginning at 02.00
our each day. Methacholine
challenge performed on each
subjects. Those with PM20
FEVj of<12.25 umol were
considered as methacholine
reactors. PEF variability was
calculated as the amplitude %
mean: (highest-lowest PEF
value/mean) x 100. 75 patients
had PEF records,
65 completed symptom
questionnaires.
NO2 was significantly
associated with
increasing levels of
SO2, on the same day
for all subjects and
among reactors. Mean
daily PEF and
minimum PEF
associated with SO2
among reactors only.
Significant
associations also
observed with wheeze
and SO2 on the same
day, at 24-h Lag, and
48-h Lag for all
subjects and meta-
choline reactors; and
with bronchodilator
use for all subjects at
24-h Lag.
Regression coefficient per 10 ug/m SO2
All subjects
Mean PEF (L/min)
SciinG dciy
-0.021 (0.031)
24-h Lag
0.003 (0.033)
48-h Lag
0.021 (0.032)
Minimum
PEF(L/min)
Same day
-0.062(0.039)
24-h Lag
-0.048(0.041)
48-h Lag
-0.001 (0.040)
Reactors
Mean PEF (1/min)
SciiTiG dciy
-0.087(0.054)
24-h Lag
-0.44(0.058)
48-h Lag
0.012(0.057)
Minimum
PEF(L/min)
Same day
-0.168(0.071)
24-h Lag
-0.078(0.076)
48-h Lag
-0.026(0.075)
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
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Reference, Study Outcomes, Design, Copollutants Findings & Effect Estimates
Location, & Period & Methods Mean SO2 Levels Considered Interpretation (95% CI)
EUROPE (cont'd)
Higgins et al. (1995) Amplitude
(cont'd) (% mean)
Same day:
0.167(0.072)
24-h Lag 0.191 (0.76)
48-h Lag 0.022 (0.075)
Wheeze
Same day:
1.14(1.03,1.26)
24-h Lag
1.22(1.09,1.37)
48-h Lag
1.14(1.02,1.27)
Dyspnoea
Same day:
1.03(0.94,1.14)
24-h Lag
1.07(0.96,1.18)
48-h Lag
0.94(0.85,1.05)
Cough
Same day:
1.03(0.95,1.12)
24-h Lag
1.04(0.95,1.13)
48-h Lag
1.02(0.94,1.12)
Amplitude
(% mean)
Same day:
0.157(0.120)
24-h Lag 0.083 (0.127)
48-h Lag 0.005 (0.126)
Wheeze
Same day:
1.26(1.08,1.47)
24-h Lag
1.57(1.30,1.89)
48-h Lag
1.24(1.06,1.45)
Dyspnoea
Same day:
1.04(0.90,1.20)
24-h Lag
1.17(1.00,1.37)
48-h Lag
1.03(0.89,1.20)
Cough
Same day:
1.09(0.96,1.24)
24-h Lag
1.05(0.91,1.20)
48-h Lag
1.00(0.87,1.15)
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
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Reference, Study Outcomes, Design, Copollutants Findings & Effect Estimates
Location, & Period & Methods Mean SO2 Levels Considered Interpretation (95% CI)
EUROPE (cont'd)
Higgins et al. (1995) Throat symptoms
(cont'd) Same day:
1.01(0.92,1.11)
24-h Lag
1.00(0.91,1.10)
48-h Lag
0.96(0.87,1.06)
Eye symptoms
Same day:
1.08(0.97,1.20)
24-h Lag
1.11(0.99,1.24)
48-h Lag
1.10(0.99,1.21)
Bronchodilator use
Same day
1.11(0.97,1.26)
24-h Lag
1.16(1.01,1.34)
48-h Lag
1.12(0.98,1.27)
Throat symptoms
Same day:
1.06(0.92,1.21)
24-h Lag
1.06(0.91,1.23)
48-h Lag
1.01(0.87,1.17)
Eye symptoms
Same day:
1.19(1.01,1.40)
24-h Lag
1.21 (1.01,1.45)
48-h Lag
1.08(0.91,1.28)
Bronchodilator use
Same day
1.18(0.99,1.42)
24-h Lag
1.23(1.02,1.50)
48-h Lag
1.31 (1.09,1.58)
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
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Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean SO2 Levels
Copollutants
Considered
Findings & Interpretation
Effect Estimates
(95% CI)
EUROPE (cont'd)
Hoek and Brunekreff
(1993)
Wageningen,
The Netherlands
Panel study of 112 children
(7 to 12 yrs, non-urban) to
assess effects of winter air
pollution pulmonary function
and respiratory symptoms.
Parents filled out symptom
diary that was turned in every
2 wks. Pulmonary function
test performed by technician
every 3 wks. Additional
pulmonary function tests
performed when SO2 was
predicted to be higher than
125 ug/m3 orNO2 >90 ug/m3.
Daily concentrations
presented in graph;
Highest 24-h avg
cone SO2: 105 ug/m3
(air pollution
episode)
o, BS, NO2 During the winter episode,
pulmonary function of
schoolchildren was significantly
lower than baseline. Significant
negative associations between SO2
and FVC, FEVj and MMEF. No
significant associations found with
prevalence of respiratory
symptoms. Authors noted that it is
not clear which components of
episode mix responsible for
association and that the
concentrations of acid aerosol and
SO2 were too low for direct effects
to be likely. SO2 moderately
correlated with PM10 (r = 0.69)
and black smoke (r = 0.63) but not
NO2 (r = 0.28).
Mean of individual regression
slopes and SE
FVC
Same day -0.55 (0.10), p < 0.05
Lagl: -0.74 (0.15) p< 0.05
1 wk-0.94 (0.20) p< 0.05
FEVj
Same day -0.51 (0.09) p < 0.05
Lagl: -0.21 (-0.63) p< 0.05
1 wk-0.78 (0.18) p< 0.05
PEF
Same day-0.64 (-0.44)
Lagl: -0.21(0.63)
1 wk-0.34 (0.81) p< 0.05
MMEF
Same day-0.54 (0.20)
Lagl: -0.40(0.29)
Iwk-0.61 (0.37)
Prevalence of acute respiratory
symptoms
regression coefficient from time-
series model and SE
Cough
Same day 0.02(0.18)
Lagl: -0.14(0.19)
Iwk 0.13 (0.76)
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean SO2 Levels
Copollutants
Considered
Findings & Interpretation
Effect Estimates
(95% CI)
to
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EUROPE (cont'd)
Hoek and Brunekreff
(1993) (cont'd)
Upper respiratory symptoms
Same day 0.12 (0.16)
Lagl: -0.02(0.17)
1 wk -0.24 (0.76)
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Lagorio et al. (2006)
May 24 to June 24,
1999 and Nov 18 to
Dec 22, 1999
Rome, Italy
Panel study of 29 patients with
either COPD (n = 11, mean
age 67 yrs), asthma (n = 11,
mean age 33 yrs) or ischemic
heart disease (n = 7, mean age
63 yrs) to evaluate whether
daily levels of air pollutants
have a measurable impact on
lung function in adults with
preexisting lung or heart
disease.
24-h mean SO2
Spring mean 4.7
SD1.8
Winter mean 7. 9
SD2.2
Overall mean 6.4
SD2.6
PM2.5
PM10-2.5
PM10
CD
Cr
FE
NI
PB
PT
V
Zn
N02
CO
03
Because avg 24-h concentrations
of SO2 were low and showed little
variability, SO2 was not
considered in the analysis
Correlation with copollutants:
PM25: r=0.34
PM10.2.5: r= -0.16
PM10: r = 0.21
NO2: r=0.01
03: r=-0.61
CO: r=0.65
Lower respiratory symptoms
Same day 0.06 (0.26)
Lagl: -0.11(0.29)
1 wk -0.54 (0.92)
Any respiratory symptoms
Same day 0.01 (0.13)
Lagl: -0.03(0.13)
1 wk -0.11(0.60)
No data available
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean SO2 Levels
Copollutants
Considered
Findings & Interpretation
Effect Estimates
(95% CI)
to
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EUROPE (cont'd)
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Neukirchetal. (1998)
Paris, France
Novl5, 1992 to
May 9, 1993
Panel study of
40 nonsmoking, mild to
moderate asthmatics (16 to
70 yrs, mean 46) to examine
the short-term effects of
winter air pollution in asthma
symptoms and three daily
peak flow measurements.
Patients were followed for
23wks. Used GEE models
that controlled for
autocorrelation of responses,
weather, and time trends.
Analysis conducted on entire
study population and for
subgroup of subjects who took
inhaled B2 agonists as needed.
Assessed air pollution effect
on both incident and
prevalence of symptoms,
Z-transformed morning PEF
and daily PEF variability.
24-h avg SO2
Mean: 21.7
(13.5) ug/m3
Range: 4.4, 83.8
NO2, PM13, Significant effects on
Black smoke incidence and prevalence of
symptoms. Effects at Lag
days 3-6 and weekly avg
exposures. Based on group
avgPEFof4071/min,a
50 ug/m increase SO2 caused
a maximum decrease in
morning PEF of 5.5%.
Correlation with copollutants:
NO2: r=0.54
PM13: r = 0.83
BS: r = 0.89
24-h avg SO2 (per 50 ug/mj)
Odds ratio: all subjects
Incident episodes:
Wheeze:
Lag 5: 1.66(1.01,2.70)
Nocturnal cough:
Lag 3: 1.60(0.98,2.62)
Lag 4: 1.71(0.86,3.40)
Lag 6: 1.72(1.16,2.55)
Respiratory infections:
Lag 3: 3.14(1.30,7.59)
Lag 4: 2.70(1.36,5.37)
Lag 5: 2.79(0.95,8.21)
Wk: 8.52(1.20,60.5)
Odds ratio: all subjects
Prevalent episodes:
Wheeze:
Lag 5: 1.35(1.01,1.81)
Lag 6: 1.39(1.04,1.87)
Wk: 1.64(0.91,2.94)
Nocturnal cough:
Lag 6: 1.34(1.00,1.79)
Shortness of breath:
Wk: 1.56(1.06,2.32)
Respiratory infections:
Lag 4: 2.40(1.33,4.33)
Lag 5: 2.72(1.67,4.44)
Lag 6: 2.94(1.80,4.79)
Wk: 6.30(1.31,30.2)
-------
TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean SO2
Levels
Copollutants
Considered
Findings & Interpretation
Effect Estimates
(95% CI)
to
o
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EUROPE (cont'd)
Neukirchetal. (1998)
(cont'd)
Odds ratio: Subjects taking B2 agonists
Incident episodes:
Asthma attacks:
Lag 6: 2.19(0.91,5.29)
Wheeze:
Lag 5: 1.84(1.13,3.00)
Nocturnal cough:
>
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Lag 3: 2.41(1.47,3.93
Lag 4: 2.35(0.88,6.26)
Lag 6: 1.86(1.14,3.04)
Odds ratio: Subjects taking B2 agonists
Prevalent episodes:
Asthma attacks:
Lag 5: 1.88(0.95,3.73)
Lag 6: 2.82(1.57,5.07)
Wheeze:
Lag 5: 1.51(1.02,2.23)
Lag 6: 1.57(1.06,2.32)
Nocturnal cough:
Lag 3: 1.73(1.06,2.82)
Lag 4: 2.28(1.27,4.11)
Lag 5: 1.91(1.17,3.12)
Lag 6: 1.91(1.17,3.12)
-------
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^=^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^=
3 Reference, Study Outcomes, Design, Mean SO2 Copollutants Effect Estimates
n> Location, & Period & Methods Levels Considered Findings & Interpretation (95% CI)
K> EUROPE (cont'd)
O Neukirchetal. (1998) Shortness of breath:
(cont'd) Lag 4: 1.81(1.22,2.67)
Lag 5: 1.65(1.11,2.44)
Lag 6: 1.61(1.20,2.16)
Wk: 3.03(1.26,7.33)
Regression coefficients of the effects and
SE (per 1 |ig/m3)
Z-transformed morning PEF
Lag 5: -0.450 (0.138) p = 0.001
Lag 6: -0.337 (0.164) p = 0.03
PEF daily variability
> Lag 2: 0.025 (0.013) p = 0.05
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean SO2
Levels
Copollutants
Considered
Findings & Interpretation
Effect Estimates
(95% CI)
to
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EUROPE (cont'd)
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Peacock et al. (2003)
Southern England
Nov 1,1996 to Feb 14,
1997
Panel study of 177 children
(mean age 10.7 yrs, range 7 to
13) from three schools (two
urban and 1 rural location) to
investigate effects of winter
air pollution on respiratory
function. Children were
followed for 13 wks. Used
two sources of air pollution in
the rural area, one that was
"locally validated" and the
other "nationally validated".
24-h avg SO2
(Ppb)
Rural
(nationally
validated)
Mean 5.1(4.7)
Range 0.0, 35.6
Rural
(locally
validated)
Mean 5.4 (5.1)
Range 0.0, 39.1
Urban 1
Mean 6.0 (6.0)
Range 0.5, 32.5
03
N02
PM10
S04
No statistically significant
association between winter SO2
andPEFR,
0.70% decline in PEFR for a
10-ppb increase in the five-day
mean concentration of SO2
(community monitor)
24-h avg SO2
change in PEF per 1 ppb SO2 - community
monitor
LagO: 0.05 (-0.05, 0.16)
Lagl: -0.04 (-0.13, 0.06)
Lag 2: -0.08 (-0.19, 0.04)
Mean (0-4) -0.23 (-0.65, 0.18)
Change in PEF per 1 ppb SO2 - local
LagO: -0.01 (-0.10, 0.07)
Lagl: 0.02 (-0.05, 0.10)
Lag 2: -0.09 (-0.18, 0.01)
Mean (0-4) -0.09 (-0.25, 0.07)
Odds of 20% decrement in PEF below
the median-all children
Lag 00.987 (0.958, 1.017)
Lagl 1.007(0.986,1.030)
Lag 2 0.992 (0.963, 1.023)
Mean (0-4) 0.972 (0.887, 1.066)
Odds of 20% decrement in PEF below
the median-wheezy children
Lag 00.981 (0.925, 1.041)
Lag 10.999 (0.957, 1.042)
Lag 2 0.995 (0.939 1.054)
Mean (0-4) 1.019 (0.890 to 1.167)
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean SO2
Levels
Copollutants
Considered
Findings & Interpretation
Effect Estimates
(95% CI)
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PonkaA. (1990)
Helsinki, Finland 1991
Survey study to compare
weekly changes in ambient
SO2, NO2, and temperature
and the incidence of
respiratory diseases, and
absenteeism for children in
day-care centers and schools
and for adults in the work
place during a 1-yr period
(1987).
Mean weekly
concentration of
S02 (ug/m3)
Mean: 21.1
SD= 11.7
Median: 17.0
Range: 9,61.5
Mean of daily
max
Mean: 53
SD = 20.8
Median: 48
Range: 25.9,
130.3
N02
Mean SO2 concentration
correlated with the incidences
of URI and tonsillitis reported
from health centers. SO2 also
correlated with absenteeism
due to febrile illness among
children in day care centers and
adults. When comparing
incidences during the low and
high levels of SO2, the number
of cases of URI and tonsillitis
reported from health centers
increased as well as
absenteeism. After
standardization for temperature,
the only difference that was
statistically significant was the
occurrence of URI diagnosed at
health centers. Frequency of
URI was 15% higher during
high levels of SO2 compared to
low.
Statistical significance (p) of product
moment correlation coefficients
(correlation coefficient) between SO2 and
respiratory disease and absenteeism
Respiratory tract infections diagnosed at
health centers:
URI SO2 arithmetic mean p < 0.001
(0.553)
SO2 mean of daily maximums: p = 0.0012
(0.437)
Tonsillitis
Arithmetic mean: 0.0098 (0.355)
Mean of daily maximums: NS
Absenteeism due to febrile illness:
Day care centers
SO2 arithmetic mean: p = 0.012 (0.404)
Mean of daily maximums: p = 0.048
(0.323)
School children
SO2 arithmetic mean: NS
Mean of daily maximums:
NS
Adults
SO2 arithmetic mean: p < 0.0001 (0.644)
Mean of daily maximums: p < 0.0001
(0.604)
No significant correlation between SO2 and
URI, tonsillitis, otitis, or LRI in day care
center children
-------
TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean SO2
Levels
Copollutants
Considered
Findings & Interpretation
Effect Estimates
(95% CI)
to
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EUROPE (cont'd)
X
oo
PonkaA. (1990)
(cont'd)
Statistical significance of weekly
frequency of respiratory tract disease and
absenteeism during low and high levels of
S02:
Respiratory infections diagnosed at health
centers: URI
SO2 arithmetic mean: p< 0.001
Mean of daily max: p = 0.0005
Tonsillitis
SO2 arithmetic mean: 0.0351
SO mean of daily max: NS
Absenteeism due to febrile illness
Day care center children: p = 0.0256
School children: p = 0.0014
Adults: p = 0.0005
H
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
to
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Reference, Study
Location, & Period
Outcomes, Design, Copollutants Findings &
& Methods Mean SO2 Levels Considered Interpretation
Effect Estimates
(95% CI)
EUROPE (cont'd)
Segalaetal. (1998)
Paris, France
Novl5, 1992 to
May 9, 1993
Panel study of 84 children (7 to SO2 mean (SD): NO2
1 5 yrs) with physician 21.7 (13.5) ng/m3 PM13
diagnosed asthma to examine gg
the effects of winter air Range' (44
pollution on childhood asthma. gg g\ ue/m3
For 25 wks, parents recorded
the presence or absence of
asthma attacks, upper or lower
respiratory infections with
fever, the use of supplementary
inhaled B2 agonist, the severity
of symptoms (wheeze,
nocturnal cough and shortness
of breath). Children also
recorded PEF three times a day.
GEE models adjusted for age,
sex, weather and time trend.
Investigated effects of SO2 at
0 to 6 day Lags.
SO2 associated with
both incident and
prevalent episodes
of asthma, use of
supplementary beta
2 agonist, incident
episodes of
nocturnal cough,
prevalent episodes
of shortness of
breath and
respiratory
infection.
Correlation with
copollutants:
MM • r — H ^A
1NW2. I — U.J^I
T>TV /r n A "3
PM13: r- 0.43
Re. r — n QQ
IJO. I — U.O:7
OR per 50 ug/m3 SO2
(Only effects at 0 and 1-days Lag shown below unless
statistically significant)
Incident episodes:
Mild asthmatics (n = 43)
Asthma'
LagO: OR 2.86 (1.31, 6.27)
Lagl: 2.45(1.01,5.92)
Wheeze:
LagO: 1.47(0.90,2.41)
Lagl: 1.27(0.48,3.38)
Nocturnal cough:
Lag 3: 1.93(1.18,3.15)
Lag 4: 2.12(1.43,3.13)
Respiratory infections
Lagl: 1.52(0.38,5.98)
Prevalent episodes
Mild asthmatics (n = 43)
Asthma'
LagO: 1.71(1.15,2.53)
Lagl: 1.55(0.86,2.78)
Wheeze:
Lag 4: 1.48(0.90,2.41)
Shortness of breath :
Lagl: 1.36(0.92,2.01)
Lag 2: 1.45(0.98,2.14)
Lag 3: 1.52(1.03,2.25)
Lag 4: 1.51(1.02,2.24)
Respiratory infections:
LagO: 1.58(0.72,3.46)
Lagl: 1.91(0.79,4.62)
Lag 2: 2.13(0.97,4.67)
Lag 3: 2.09(1.05,4.15)
Lag 4: 2.05(1.14,3.68)
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean SO2
Levels
Copollutants
Considered
Findings &
Interpretation
Effect Estimates
(95% CI)
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EUROPE (cont'd)
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Segalaetal. (1998)
(cont'd)
Beta2 agonist: Beta2 agonist:
Lag 4: 1.63 Lag 4: 2.02(1.02,4.01)
(1.00,2.66) Lag 5: 1.96(0.99,3.88)
Moderate asthmatics (n = 41)
Statistically significant (only) prevalent
episodes:
Beta2 agonist:
LagO: 3.67(1.25,10.8)
Lagl: 4.60(2.10,10.1)
Lag 2: 7.01(3.53,13.9)
Lag 3: 4.74(1.96,11.5)
Timonen and Panel study of 169 children (7 to 12 yrs)
Pekkanen (1 997) with asthma or cough symptoms living in
Kuopio, Finland urban and suburban areas of Kuopio,
1 994 Finland to determine association between
air pollution and respiratory health. In
the urban areas there were 39 asthmatics
and 46 with cough only; in the suburban
areas there were 35 asthmatics and 49
with cough who were included in the
final analysis. Twice daily PEF and daily
symptoms were recorded for 3 mos. First
order autoregressive models used to
assess associations between air pollutants
and PEF and logistic regression models
used for symptom prevalences and
incidences. Analysis conducted on daily
mean PEF deviations. Mean morning or
evening PEF calculated for each child
was subtracted from the daily value of
morning or evening PEF. The daily
deviations were then Avgd to obtain daily
mean PEF deviation for morning or
evening PEF.
Avg daily PM10
SO2 (ng/m3) BS
NO2
Urban area:
Mean: 6.0
25th
percentile: 2.6
50th
percentile: 3.6
75th
percentile:
7.1
Max: 32
Among children with
cough only, morning and
evening deviations in
PEF in the urban panel
was negatively
associated with SO2.
SO2 was also associated
with an increase in the
incidence of URS in
children with cough only
in the urban area. When
excluding the three
highest SO2 days, these
effects were no longer
statistically significant.
No associations found
between SO2 and
morning or evening PEF
or respiratory symptoms
in children with cough
only in the suburban
panel.
Correlation coefficient with SO2:
PM10r=0.21
BSr = 0.20
N02 r = 0.22
Regression coefficient
S02)
Morning PEF
deviations
Children with cough
alone
LagO: -0.229
(0.608)
Lag 1: -1.38(0.564)
Lag 2: -0.683
(0 523)
V -^ ±*~* f
4-day mean:
-1.28(0.633)
(SE)(perlO|ig/m3
Evening PEF
deviations
Children with
cough alone
LagO: -1.84
(0.673)
Lag 1: -0.144
(0.711)
Lag 2: -0.291
(0613)
V /
4-day mean:
-0.878(0.868)
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study Outcomes, Design,
Location, & Period & Methods
Mean SO2 Copollutants Findings &
Levels Considered Interpretation
Effect Estimates
(95% CI)
EUROPE (cont'd)
Timonen and
Pekkanen(1997)
(cont'd)
Correlation with
copollutants (urban
area):
PM10: r = 0.21
BS: r = 0.20
N02: r=0.22
Asthmatic
LagO: 0.198(0.804)
Lagl: 0.382(0.789)
Lag 2: 0.648(0.715)
4 day mean:
1.39(1.14)
Odds ratio (per
10 ng/m3)
URS
Lagl: 1.46(1.07,
2.00)
Lag 2: 1.46(1.14,
1.87)
4-day mean: 1.55
(1.08,2.24)
Asthmatics
LagO: 1.28(0.711)
Lagl: 0.575(0.727)
Lag 2: 0.819(0.642)
4-day mean:
1.34(1.05)
Odds ratio when
excluded 3 highest
SO2 days (no 95% CI
provided, but effects
were not significant)
Lagl: 1.13
Lag 2: 1.46
4-day mean: 1.12
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Copollutants
Mean SO2 Levels Considered
Findings &
Interpretation
Effect Estimates
(95% CI)
LATIN AMERICA
Pino et al. (2004)
Santiago, Chile
1995-1997
Cohort study of 492 infants recruited at
4 mos of age and followed through the
first yr of life to determine the association
between air pollution on wheezing
bronchitis.
Mean concentration PM2 5
ofSO2(ppb) NO2
Mean: 11.6
SD=8.1
Median: 10.0
No consistent
association was found
between the 24-h avg
SO2 and risk of
wheezing bronchitis.
However, after a
7-day lag, a 10-ppb
increase in the 24-h
avg SO2 was
associated with a 2 1 %
increase in risk of
wheezing bronchitis.
Increase in wheezing bronchitis
(95%CI)perlOppbSO2
21% (8, 39%)
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Romieuetal. (1996)
Mexico City, Mexico
April-Jul 1991
Nov 1991-Feb 1992
Panel study of 71 mildly asthmatic
children (5 to 13 yrs) to assess the
relationship between air pollution and
childhood asthma exacerbation. Children
measured PEF three times daily and
recorded daily symptoms and medication
use. Examined both incidence and
prevalence of symptoms. Lower
respiratory symptoms, cough, phlegm,
wheeze, and/or difficulty breathing.
24-h avg SO2 (ppm)
Mean: 0.09
SD = 0.05
Range: 0.02,0.20
03
PM10
PM2.5
NO2
SO2 concentrations
were not related to
changes in PEF or
respiratory symptoms
Change in PEF per 10-ppb
increase in SO2
0.26 (-0.35, 0.88,1.01) L/min
Odds ratio per 10 ppb SO2
Coughing: 0.96(0.92,1.01)
Lower respiratory symptoms:
0.97(0.94,1.01)
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean SO2 Levels
Copollutants
Considered
Findings &
Interpretation
Effect Estimates
(95% CI)
ASIA
Chen etal. (1999)
Three towns in Taiwan:
Sanchun, Taihsi, Linyuan
May 1995-Jan 1996
Cross-sectional panel study of 895
children (8 to 13 yrs) to evaluate the
short-term effect of ambient air pollution
on pulmonary function. Single and
multipollutant models adjusted for sex,
height, BMI, community, temperature,
and rainfall. Examined 1, 2, and 7-day
lag effects.
Peak concentrations
ofS02
Range: 0, 72.4 ppb
Day-time avg and
1-day lag
SO2 correlated with
PM10 (r = 0.68)
SO2 correlated with
NO2(r = 0.71)
CO
N03
PM10
NO2
Daytime peak SO2 at 2
days lag significantly
associated with FVC
using the single-
pollutant model.
Association also
observed with NO2
and CO with FVC.
No PM10 effects.
Only O3 effects
significant in
multipollutant models.
Change in FVC (mL) daytime
avg SO2
Lag 1: -3.18(1.80)
Lag 2: -2.70(1.49)
Lag 7: 0.61 (2.59)
Daytime peak SO2
Lag 1: -0.91 (0.73)
Lag 2: -1.27 (0.59), p< 0.05
Lag 7: -1.05(1.29)
Change in FEV! (mL)
daytime avg SO2
Lag 1: -1.95(1.69)
Lag 2: -1.12(1.41)
Lag 7: -1.48(2.44)
Daytime peak SO2
Lag 1: -0.57(0.68)
Lag 2: -0.64(0.56)
Lag 7: -1.96(1.22)
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Xuetal. (1991)
Beijing, China
Three areas: industrial,
residential and suburban
(control)
August 1986
Cross sectional survey of 1140 adults
(40 to 69 yrs) who had never smoked
living in three areas of Beijing, to
determine respiratory health effects of
indoor and outdoor air pollution. A
trained interviewer obtained pulmonary
function measurements and determined
history of chest illnesses, respiratory
symptoms, cigarette smoking,
occupational exposure, residential history,
education level and type of fuel used for
cooking and heating.
Annual mean
concentration of SO2
(ug/m3)
Residential: 128
Industrial: 57
Suburban: 18
TSPM
An inverse linear
association found
between Ln outdoor
SO2 and FEVj and
FVC after adjusting
for age, height and
Regression estimate and standard
error
per Ln SO2 (ug/m3)
Height-adjusted FEVj (mL):
-35.6(17.3)
Height-adjusted FVC (mL):
-131.4(18.8)
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TABLE AX5.1 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Copollutants
Mean SO2 Levels Considered
Findings &
Interpretation
Effect Estimates
(95% CI)
ASIA (cont'd)
Park et al. (2002)
Seoul, Korea
Mar 2, 1996 to
Dec 22, 1999
Time series analysis of school
absenteeism due to illness and air
pollution in one elementary school in
Seoul. School located in area with heavy
traffic. Avg enrollment in 1996 was
1,264.
24-h avg SO2 PM10
N02
Mean:9.19ppb CO
SD=4.61 o
Range: 2.68,28.11 3
SO2 correlated with
CO (r = 0.67)
SO2, PM10j and O3
associated with illness
related school
absenteeism. SP2 and
O3 are protective for
non-illness related
absences.
Relative risk per IQR SO2
(5.68ppb)
Total absences:
1.03(1.02,1.05)
Non-illness related absences:
0.95(0.92,0.99)
Illness related absences:
1.09(1.07,1.12)
2-pollutant model with O3:
1.10(1.08,1.13)
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Park et al. (2005a)
Korea
March to June 2002
Panel study of 69 patients (16 to 75 yrs)
diagnosed with asthma by bronchial
challenge or by bronchodilator response.
Patients recorded twice-daily PE,
symptoms at the end of each day (cough,
wheeze, chest tightness, shortness of
breath, sputum changes and the next
morning, night awakenings). During the
study period, 14 Asian dust days were
identified. GEE and generalized additive
Poisson regression model used in
analysis.
Daily avg SO2
Control days:
0.0069 (0.0019) ppm
Dust days:
0.0052 (0.0010) ppm
PM10
N02
CO
03
During the dust days, Relative risk based on Poisson
SO2 levels were
significantly lower
compared to control
days. SO2 had no
significant effect on
PEF variability or
night symptoms.
log-linear regression analysis
PEF variability (>20%)
0.76(0.37,1.56)
Night symptoms:
0.98(0.59,1.51)
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TABLE AX5.2. ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
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Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
UNITED STATES
Jaffe et al. (2003)
3 cities, Ohio, United
States (Cleveland,
Columbus, Cincinnati)
Period of Study:
7/91-6/96
ED Visits
Outcome (ICD9):
Asthma (493)
Age Groups
Analyzed:
5-34
Study design: Time
series
N: 4,416
Statistical analyses:
Poisson regression
using a standard
GAM approach
Covariates: City, day
ofwk, wk, yr,
minimum
temperature, overall
trend, dispersion
parameter
Season: June to Aug
only
Dose-response
investigated: Yes
Statistical package:
NR
Lag: 0-3 days
24-h avg:
Cincinnati: 35.9(25.1)
ug/m3
Range: 1.7, 132
Cleveland: 39.2(25.3)
ug/m3
Range: 2.6, 167
Columbus: 11.1
(8.5) ug/m3
Range: 0,56.8
Cincinnati:
PM2.5;r = 0.31
NO2; r = 0.07
O3;r=0.14
Cleveland:
PM2 5; r = 0.29
N02; r = 0.28
O3; r = 0.26
Columbus:
PM2.5; r = 0.22
wn • r — NT?
1NW2, 1 — IN IX
O3; r = 0.42
Wide confidence intervals
for data from Cleveland
and Columbus make these
data not significant and
unstable. Only data for
Cincinnati was considered
statistically significant and
demonstrated a
concentration response
function that was positive.
No multipollutant models
were utilized.
Increment: 50 ug/m
Cincinnati: 35% [9, 21] lag 2
Cleveland: 6% [-7, 21] lag 2
Columbus: 26% [-25, 213] lag 3
All cities: 12% [1,23]
Attributable risk from SO2
increment:
Cincinnati: 4.2%
Cleveland: 0.66%
Columbus: 2.94%
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels & Copollutants
Monitoring Stations & Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
UNITED STATES (cont'd)
Moolgavkar* et al.
(1997)
United States:
Minneapolis-St. Paul;
Birmingham
Period of Study:
1986-1991
Hospital Admissions
Outcomes (ICD 9
codes): COPD including
asthma
(490-496), Pneumonia
(480-487)
Age groups analyzed:
65+
Study design: Time
series
Statistical analyses:
Semi-parametric Poisson
regression, GAM
Covariates: day of wk,
season, temporal trends,
temperature
Statistical package:
SPlus
Lag: 0-3 days
SO2 24-h avg (ppb):
Minneapolis:
PM2.5; r = 0.08
Minneapolis:
Mean: 4.82
10th: 1.9
25th: 2.66
50th: 4.02
75th: 6.0
90th: 8.5
Birmingham:
Mean: 6.58
10th: 2.2
25th: 3.7
50th: 6.0
75th: 8.6
90th: 11.6
NO2; r :
CO;r =
O3;r =
= 0.09
0.07
-0.12
Birmingham:
PM2.5;r = 0.17
CO; r= 0.16
03;r =
0.02
SO2 with NO2 and PM2 5
were associated with
hospital admissions.
Evidence of mixture
effects was found. No
single-pollutant was
more important than the
other for respiratory
admissions. Each
pollutant was associated
with admissions except
CO.
Consideration of four
pollutants together
showed the strongest
association with ozone.
No pollutant other than
O3 was stable in its
association with hospital
admissions.
Increment: 3.5 ppb
Sum of Pneumonia and COPD
1.6% [-0.1, 3. 3] lag 2
Pneumonia Only
Minneapolis:
65+ 0.9% [-1.1, 2.9] lag 2 20 df
0.5% [-1.5, 2.5]
lag2130dfS
No effects were reported
for Birmingham.
Positive results were
only observed in
Minneapolis.
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels & Copollutants
Monitoring Stations & Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
UNITED STATES (cont'd)
Moolgavkar (2000)
Reanalysis (2003)
Multicity, United States:
Chicago, Los Angeles,
Maricopa County,
(Phoenix)
Period of Study:
1987-1995
Hospital Admissions
Outcomes (ICD 9 codes):
COPD including asthma
(490-496)
Age groups analyzed:
0-19,20-64,65+
(LA only)
Study design: Time series
Statistical analyses:
Poisson regression, GAM
Covariates: Day of wk,
temporal trends,
temperature, relative
humidity
Lag: 0-5 days
Chicago:
Median: 6 ppb
25th: 4
75th: 8
Range: 0.5,36
Los Angeles:
Median: 2 ppb
25th' 1
75th: 4
Range: 0, 16
Maricopa:
Median: 2 ppb
25th: 0.5
75th: 4
Range: 0, 14
Chicago:
PM2.5; r = 0.42
CO; r= 0.35
NO2; r = 0.44
03;r=0.01
Los Angeles:
PM2.5; r = 0.42
PM • r = o 41
CO; r= 0.78
NO2; r = 0.74
03; r= -0.21
Maricopa:
PM2.5;r = 0.11
CO; r= 0.53
NO2; r = 0.02
O3;r=-0.37
In Los Angeles there
was a significant
association with and
hospital admissions
for COPD.
SO2 may be acting as
a surrogate for other
pollutants since
heterogeneous
responses found in
different cities are
inconsistent with a
cause-effect model.
Increment:
COPD, >65
Chicago lag
LA lag 0: 2
LA lag 0: 1
GAM- 100
LA lag 0: 1
NS-100
10 ppb
yrs
0: 4.87(t=3.18)GAM-100
.84 (t= 13.32) GAM-30
.80(1=9.60)
.78 ( t = 7.72)
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
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Reference, Study Outcomes, Design,
Location, & Period & Methods
Mean Levels & Monitoring Copollutants Method, Findings,
Stations & Correlations Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
UNITED STATES (cont'd)
Schwartz (1995)
New Haven, CT
Tacoma, WA
United States
Period of Study:
1988-1990
Hospital Admissions
Outcomes
(ICD9codes): All
respiratory admissions
(460-519)
Age groups analyzed:
>65
Study design:
Time series
Ni "3 A *7n
: 13,470
Statistical analyses:
Poisson regression, log
linear regression using
GLM and GAM
Covariates: dewpoint,
temp, long-term trends,
days of wk
Statistical package:
S- Plus
T n i
Lag: 0-1
24-h avg PM2.5
New Haven O3
Mean 78 ug/m3 (29.8 ppb)
10th: 23
25th: 35
50th: 78
75th: 100
90th: 159
Tacoma 44 ug/m3 (16.8 ppb)
10th: 15
25th: 26
50th: 40
75th: 56
90th: 74
In New Haven, risk
associated with SO2
was not affected by
inclusion of PM2 5 in
the model and the
effect of PM2 5 was
not strongly affected
by inclusion of SO2.
This suggests that in
New Haven, SO2 and
PM2 5 acted
independently.
In Tacoma
2-pollutant model
analysis showed risk
associated with SO2
was attenuated by
PM25. This
suggested risks
associated with SO2
and PM2 5 were not
independent.
Possible SO2 acts as a
surrogate for PM2 5 in
this city.
Increment: 50 ug/m3
New Haven, CT
RR= 1.03 [CI 1.02,1.
p< 0.001
or 18. 8 ppb
05], lag 0-1
2-pollutant model with PM2 5:
RR = 1.04 [CI 1.02, 1.06] p < 0.001
Tacoma, WA
RR = 1.06 [CI 1.01, 1.12], lag 0-1
p>0.02
2-pollutant model with PM2 5:
RR = 0.99 [CI 0.93, 1.06] p > 0.5
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH EMERGENCY
DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
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W
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels & Copollutants
Monitoring Stations & Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
UNITED STATES (cont'd)
Wilson et al. (2005)
Multicity, United
States
(Portland, ME and
Manchester, NH)
Period of Study: 1996-
2000 (Manchester)
1998-2000 (Portland)
ED Visits
Outcomes
(ICD9codes): All
respiratory
(460-519);
Asthma (493)
Age groups analyzed:
0-14yrs; 15-64yrs;
>65 yrs
Study design: Time
series
Statistical analyses:
Multiple regression
analysis standard GAM
with more stringent
criteria parameters
Covariates:
Time-trend, season,
influenza, temperature,
humidity, precipitation
Stat package:
S-Plus
Lag: 0-2
SO2 1-h max: Mean, (SD) O3
(Ppb) PM2.5
Portland
Allyr: 11.1(9.1)
Winter: 17.1 (12.0)
Spring: 10.0(7.1)
Summer: 9.1 (8.0)
Fall: 9.7(7.1)
Manchester
Allyr: 16.5(14.7)
Winter: 25.7(15.8)
Spring: 14.8(12.0)
Summer: 10.6(15.1)
Fall: 14.6(11.1)
Elevated levels of
SO2 were positively
associated with
elevated respiratory
and asthmatic ER
visits. The
significance of these
relationships is not
sensitive to analytic
or smoothing
techniques.
Increment: 6.3 ppb (IQR) for Portland; IQR
for Manchester
Portland:
All respiratory
All ages RR 1.05 [1.02, 1.07] lag 0
0-14 yrs RR 0.98 [0.93, 1.02] lag 0
15-64 yrs RR 1.06 [1.03, 1.09] lag 0
>65 yrs RR 1.10 [1.05, 1.15] lag 0
Asthma
All ages RR 1.06 [1.01, 1.12] lag 2
0-14 yrs RR 1.03 [0.93, 1.15] lag 2
15-64 yrs 1.07 [1.01, 1.15] lag 2
>65 yrs RR 1 .07 [0.90, 1 .26] lag 2
Manchester:
All respiratory
All ages RR 1.01 [0.99, 1.02] lag 0
0-14 yrs RR 1.00 [0.96, 1.04] lag 0
15-64 yrs RR 1.00 [0.98, 1.03] lag 0
>65 yrs RR 1.04 [0.97, 1.11] lag 0
Asthma
All ages RR 1.03 [0.98, 1.09] lag 2
0-14 yrs RR 1.11 [0.98, 1.25] lag 2
15-64 yrs 1.02 [0.96, 1.08] lag 2
>65 yrs RR 1 .06 [0.83, 1 .36] lag 2
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design, & Mean Levels &
Methods Monitoring Stations
Copollutants Method, Findings,
& Correlations Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
UNITED STATES (cont'd)
Gwynn* et al. (2000)
Buffalo, NY
United States
Period of Study:
1988-1990
Days: 1,090
Hospital Admissions 24-h avg SO2 (ppb):
Outcomes (ICD 9 Min: 1.63
codes): Respiratory 25th: 8.4
admissions: Acute Mean: 12.2
bronchitis/bronchiolitis 75th- 154
(466); Pneumonia M'. 37y
(480-4860); COPD and
Asthma
(490-493, 496)
Age groups analyzed:
6
Study design:
Time series
N: 24,
Statistical analyses:
Poisson regression
with GLM and GAM
Covariates: season,
day of wk, holiday,
temperature, relative
humidity
Lag: 0-3 days
If r = 0.06 Significant
SO42~ r = 0. 1 9 associations observed
PM2 5 r = 0. 1 9 between several
O r = 0 02 pollutants and
TVTQ - o % various health-effect
rr>2 -nil outcomes make it
V_/\J r U. 1 1 Jm£~£- 1j_ j_
COHr-019 difficult to
COM r- 0.1 9 discriminate the
influence of a single-
pollutant. This is
likely the a result of
the relatively high
intercorrelations
among the various
pollutants, as well as
the possible
interactive role of
several pollutants in
the reported
associations.
Increment: 25. 5, 7.0 ppb
(Max-Mean; IQR)
SO2 alone:
Max-Mean RR 1.096 (t = 3.05) lag 0
IQR RR1. 025 (t= 3.05) lag 0
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels & Copollutants Method, Findings,
Monitoring Stations & Correlations Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
UNITED STATES (cont'd)
Lin et al. (2004a)
New York (Bronx
County), United States
Period of Study:
6/1991-12/1993
Hospital Admissions
Outcomes (ICD 9
codes): Asthma (493)
Age groups analyzed: 0-
14
Study design:
Case-control
N: 2,629 cases; 2,236
controls
Statistical analyses:
logistic regression
Covariates: Race and
ethnicity, age, gender,
season
Statistical package:
Lag: 0,1,2,3,0-3
Cases:
24-havg: 16.78 ppb
50th: 13.72
Range: 2.88,66.35
Controls:
24-havg: 15. 57 ppb
50th: 13.08
Range: 2.88,66.35
Quartile Concentrations
(ppb) :
Ql : 2.88,8.37
Q2: 9.37, 13.38
Q3: 13.5,20.91
Q4: 20.21,66.35
Odds ratios for risk
of hospitalization for
asthma increased
with each quartile of
SO2 concentration.
Lag 1,2, or 3 all
showed a
concentration
response that was
positive for odds
ratio as each quartile
was compared to the
total exposure group
(trend p> 0.001).
Quartile (24-h avg)
Q2 OR 1.26 lag 3
Q3 OR 1.45 lag 3
Q4 OR 2. 16 [1.77, 2.65] lag 3
Quartile (1-h max)
Q4 OR 1.86 [1.52, 2.27] lag 3
For a 4-ppb increase in SO2
(24-h avg)
RR 1.07 [1.04, 1.11]
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design, Mean Levels & Copollutants Method, Findings,
& Methods Monitoring Stations & Correlations Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
UNITED STATES (cont'd)
Michaud et al. (2004)
Hilo, Hawaii
2/21/1997-5/31/2001
ED Visits 1-hmax: PMi
Outcomes (ICD 9 1 .92 (12.2) ppb
codes): COPD(490- Range: 0.0,447
496); Asthma (493,
495); bronchitis (490, 24 h avg-
491), other COPD L97 (7.12)ppb
(492,494,496) ^ Q ^ 5
Age groups analyzed:
All
Study design: Time
series
Statistical analyses:
Exponential
regression models
Covariates: temporal
variables, day of wk,
meteorology
Stat package: Stata,
SAS
Lag: 0,1, 2,3 days
The lack of organic
carbon shows the pure
SO2 effect
uncontaminated by
vehicle emissions.
Asthma is associated
with Vog, but Vog is not
a major cause of asthma
in Hawaii. The strongest
association was with the
mo oftheyr.
Admission for asthma
and respiratory
conditions was higher in
the winter compared to
the summer, based on
admission per day
(observational- not
statistical analysis).
Increment: 10 ppb
COPD
RR 1.04 [0.99, 1.09] lag
RR 1.04 [1.00, 1.09] lag
RR 1.07 [1.03, 1.11] lag
Asthma
RR1.01 [1.00, 1.10] lag
RR 1.02 [1.03, 1.12] lag
RR 1.02 [1.03, 1.12] lag
Bronchitis
RR1.01 [0.93, 1.13] lag
RR 0.99 [0.88, 1.05] lag
RR1.01 [1.00, 1.14] lag
Other COPD
RR 1.00 [0.78, 1.23] lag
RR 0.96 [0.62, 1.11] lag
RR 0.98 [0.75, 1.16] lag
;1
;2
;3
;i
;2
;3
;i
;2
;3
;i
;2
;3
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O
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH EMERGENCY
DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
UNITED STATES (cont'd)
Peel et al. (2005)
Atlanta, GA, United
States
Period of Study:
1/93-8/2000
ED Visits
Outcome(s) (ICD 9):
All respiratory (460-6,
477, 480-6, 480-6, 490-
3, 496); Asthma (493);
COPD(491-2,496);
Pneumonia (480-486);
Upper Respiratory
Infection (460-6, 477)
Age groups analyzed:
All
Study design: Time
series
1-hmax: 16.5
(17.1) ppb
10th%: 2.0
90th%: 39.0
03
N02
CO
PM2.5
Evaluated
multipollutant models
(data not shown)
Estimates from
distributed lag
models (0-13 days)
tend to be higher than
for 3-day moving
avg. Positive
associations for URI
and COPD with SO2
were noted for
unconstrained lags
(0-1 3 days) that
covered the previous
two weeks of
Increment: 20 ppb
All respiratory
RR 1.008 [0.997, 1.019] lag 0-2, 3-day
moving avg
Upper Respiratory Infection (URI)
RR 1.010 [0.998, 1.024] lag 0-2, 3-day
moving avg
Asthma
All: 1.001 [0.984, 1.017] lag 0-2, 3-day
moving avg
Pneumonia
N: 484,830
# of Hospitals: 31
Statistical analyses:
Poisson Regression,
GEE, GLM, and GAM
(data not shown for
GAM)
Covariates: Day of wk,
hospital entry/exit,
holidays, time trend;
season, temperature, dew
point temperature
Statistical package:
SAS, S-Plus
Lag: 0 to 7 days. 3 day
moving avgs.
exposure.
RR 1.003 [0.984, 1.023] lag 0-2, 3-day
moving avg
COPD
RR 1.016 [0.985, 1.049] lag 0-2, 3-day
moving avg
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
to
o
o
UNITED STATES (cont'd)
>
X
Schwartz etal. (1996)
Cleveland, OH
Period of Study:
1988-1990
Hospital Admissions
Outcomes (ICD 9
codes): All respiratory
disease
Age groups analyzed:
>65
Study design: Time
series
Statistical analyses:
Poisson regression
Covariates: Season,
temperature, day of wk
Statistical package:
Lag: 0-1
24-h avg: 35 ppb
10th: 13
25th: 20
50th: 31
75th: 45
90th: 61
PM2.5
03
Significant
associations were
seen for PM2 5 and
O3, with somewhat
weaker evidence for
S02.
Increment: 100 ug/m
RR 1.03 [0.99, 1.06] lag 0-1
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
to
o
o
UNITED STATES (cont'd)
>
X
Sheppard et al. (1999)
Reanalysis (2003)
Seattle, WA, United
States
Period of Study:
1987-1994
Hospital Admissions
Outcomes (ICD 9 codes):
Asthma (493)
Age groups analyzed: <65
Study design: Time series
N: 7,837
# of hospitals: 23
Statistical analyses: Poisson
regression with adjustment
for auto-correlation.
Covariates:
Statistical package: S-Plus
Lag: 0,1,2,3
24-h avg: 8 ppb
IQR: 5 ppb
10th:
25th:
50th:
75th:
90th:
3.0
5.0
8.0
10.0
13.0
PM2.5;r = 0.31
PM2.5; r = 0.22
O3;r=0.07
CO; r= 0.24
Sources of SO2
adjacent or near to
monitoring site. Low
concentrations. No
association with SO2
for asthma but
positive association
for appendicitis.
Increment: 5 ppb (IQR)
GAM with stricter criteria:
1.0% [-2.0, 3.0] lag 0
GLM with natural spline
smoothing:
0.0% [-3.0, 4.0] lag 0
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
to
o
o
CANADA
>
X
H
6
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O
o
H
W
O
O
HH
H
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Bates etal. (1990)
Vancouver Region, BC,
Canada
Period of Study:
7/1/1984-10/31/1986
ED Visits
Outcome(s) (ICD 9): Asthma
(493);
Pneumonia (480-486);
Chronic bronchitis
(491,492,496);
Other respiratory (466)
Age groups analyzed:
All; 15-60
Study design:
# of Hospitals: 9
Statistical analyses: Pearson
correlation coefficients were
calculated between asthma
visits and environmental
variables
Season:
Warm (May-Oct);
Cool (Nov-Apr)
Covariates: NR
Statistical package: NR
Lag: 0,1,2
May-Oct
SO2l-hmax:
Range: 0.0137,0.0151
ppm
Nov-Apr
Range: 0.012,
0.0164 ppm
Number of stations: 11
May-Oct.
03; r = 0.23
NO2; r = 0.67
CoH; r = 0.34
SO4; r = 0.46
Nov-Apr
03; r = 0.47
NO2; r = 0.61
CoH; r = 0.64
S04; r = 0.54
SO2 effects depend
on the season. In the
summer a rise in
ambient SO2 levels
was seen to coincide
with a rise in
respiratory related
hospital admissions.
Correlation Coefficients:
Warm Season (May-Oct)
Asthma (15-60 yrs)
r= 0.118 lag 0 p<0.01
r= 0.139 lag 1
Respiratory (15-60 yrs)
r= 0.134 lag 0 p< 0.001
r = 0.164 lag 1 p< 0.001
Cool Season (Nov-Apr)
Respiratory
1-14 yrs
r= 0.205 lag 0 p< 0.001
r= 0.234 lag 1 p< 0.001
r= 0.234 lag 2 p< 0.001
15-60 yrs
r= 0.180 lag 0 p< 0.001
r= 0.214 lag 1 p< 0.001
r= 0.215 lag 2 p< 0.001
>61 yrs
r= 0.257 lag 0 p< 0.001
r= 0.308 lag 1 p< 0.001
r= 0.307 lag 2 p< 0.001
Asthma (>61 yrs)
r = 0.125 lag 0 p< 0.001
r = 0.149 lag 1 p< 0.001
r= 0.148 lag 2 p< 0.001
Total ER admissions (>61 yrs)
r =0.13 lag 1 p<0.01
r= 0.13 lag 2 p<0.01
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
to
o
o
CANADA (cont'd)
>
X
Burnett etal. (1997a)
16 cities
Canada
Period of Study:
4/1981-12/1991
Days: 3,927
Hospital Admissions
Outcomes (ICD 9 codes): All
respiratory admissions
(466, 480-6, 490-4, 496)
Study design: Time series
N: 720,519
# of hospitals: 134
Statistical analyses: random
effects relative risk regression
model
Covariates: Long-term trend,
season, day of wk, hospital,
Statistical package: NR
Lag: 0, 1,2 day
l-hmaxSO2(ppb)
Mean: 14.4
SD = 22.2
25th: 3
50th: 10
75th: 19
95th: 45
99th: 97
O3r=0.04
CO
N02
COH
Control of SO2
reduced but did not
eliminate the ozone
association with
respiratory hospital
admissions.
Increment: lOppb
Single-pollutant
SO2 and respiratory admissions,
p = 0.134
Multipollutant model (adjusted for CO, O3,
NO2, COH, dew point):
RR 1.0055 [0.9982, 1.0128] lag 0
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15
Study design: Time series
N: 1,163
1-h max SO2 (ppb)
Mean: 38.1
Range: 0,390
95th 110
O3; r = 0.04
NO2;r=-0.03
SO42~; r = 0.23
TSP;r=0.16
SO2 did not affect the Increment: NR
rate of asthma ED
visits when O3 was SO2 + O3: (3 = - 0.0030 (0.0027) lag 0
included in the
model.
# of Hospitals: 2
Statistical analyses: SAS
NLIN (Equivalent to Poisson
GEE)
Covariates: Day of wk, long-
term trends,
Season: Summers only
(May-Sep)
Dose-response investigated?:
Yes
Statistical package: SAS
Lag: 0-3 days
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
CANADA (cont'd)
Stieb* et al. (2000)
Saint John, New
Brunswick, Canada
Period of Study:
Retrospective:
7/92-6/94
Prospective:
7/94-3/96
ED Visits
Outcome(s): Asthma; COPD;
Respiratory infection
(bronchitis, bronchiolitis,
croup, pneumonia);
All respiratory ICD9 codes:
NR
Age groups analyzed: All
Study design: Time series
N: 19,821
Statistical analyses: Poisson
regression, GAM
Covariates: Day of wk,
selected weather variables in
each model
Seasons: All yr, summer only
Dose-response investigated:
VV,o
i es
Statistical package: S-Plus
Lag: all yr = 0; summer
only = 0-3
24-h avg:
Annual mean: 6.7
(5.6) ppb
95th: 18.0
Max: 60.0
Warm season mean: 7.6
(5 .2) ppb
95th: 18.0
Max: 29.0
1-hmax:
Annual mean: 23.8(21.0)
,
ppD
95th: 62.0
Max: 161.0
Warm season mean: 25.4
(17.8) ppb
95th: 62.0
Max: 137.0
CO; r= 0.31
O3;r=0.10
NO2; r = 0.41
TRS;r=0.08
PM2 5; r = 0.36
PM2.5;r = 0.31
If; r = 0.24
SO42~; r = 0.26
COH;r = 0.31
H2S;r= -0.01
.Assessed
multipollutant
models
Non-linear effect of
SO2 on summertime
respiratory visits
observed and log
transformation
strengthened the
association.
Increment: 23.8 ppb (mean)
1-hmax:
Respiratory visits: 3. 9% lag 5
May to Sept: 3. 9% lag 0-3
Multipollutant model (SO2, O3, NO2)
Allyr: 3.7% [1.5, 6.0] lag 5
Multipollutant model (In (NO2), O3, SO2
COH)
May to Sept: 3.9% [1.1, 6.7] lag 0-3
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
to
o
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CANADA (cont'd)
X
(Si
I
ON
O
Burnett etal. (1997b)
Toronto, Canada
Period of Study:
1992-1994
Hospital Admissions
Outcomes
(ICD 9 codes): Respiratory
tracheobronchitis (480-6),
COPD(491-4,496)
Study design: Time series
Statistical analyses: Poisson
regression, GEE, GAM
Covariates: Temperature, dew
point temperature, Long-term
trend, season, influenza, day of
wk
Seasons: Summers only
Lag: 0,1,2,3,4 days
MeanSO2: 7.9 ppb
CV: 64
Range: 0,26
5th: 1
25th: 4
50th: 7
75th: 11
95th: 18
Number of Stations: 6-
11
CO; r = 0.37
H+;r=0.45
SO4; r = 0.42
TP;r = 0.55
FP;r = 0.49
CP; r = 0.44
COH; r = 0.50
O3;r = 0.18
NO2; r = 0.46
Risks of hospitalization
for respiratory disease
were summed for O3,
NO2)andSO2atll%
increase in admissions.
The proportion
associated with the
single-pollutant SO2 was
3.6%. CoHwasthe
strongest predictor of
hospitalization indicating
particle associated
pollutants are
responsible for effects
and outcomes measured.
Increment: 4.00 ppb (IQR)
Respiratory-percent increase
4.0% (t = 4.14) lag 0
Copollutant and multipollutant
models RR
(t-statistic):
S02,COH: 1.012(1.10)
SO^Lf: 1.022(1.96)
SO2, SO4: 1.021(1.93)
S02,TP: 1.021(1.72)
S02,FP: 1.022(1.92)
SO2, CP: 1.023(2.03)
S02,03,N02: 1.019(1.64)
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
to
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X
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Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels & Copollutants
Monitoring Stations & Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
CANADA (cont'd)
Burnett etal. (1999)
Metro Toronto, Canada
Period of Study:
1980-1994
Hospital Admissions
Outcomes (ICD 9 codes):
Asthma (493); obstructive
lung disease (490-2, 496);
Respiratory infection
(464, 466, 480-7, 494)
Study design: Time series
Statistical analyses:
Poisson regression model
with stepwise analysis
Covariates: Long-term
trends, season, day of wk,
dciilv in3.xirrru.iTi
temperature, daily
minimum temperature,
daily avg dew point
temperature, daily avg
relative humidity
Statistical package: S-
Plus, SAS
Lag: 0,1, 2 days,
cumulative
24-hmean: 5.35ppb, PM2.5;r=0.46
CV=110; PM2.5_2.5; r = 0.28
5th: 0 PM2.5;r=0.44
25th: 1 CO; r= 0.37
50th: 4 NO2;r = 0.54
75th: 8 03;r = 0.02
95th: 17
inntiv «7
1 VJVJLll. J I
Number of stations: 4
The percent hospital
admissions associated
with SO2 increased for:
asthma, COPD, and
respiratory infection.
However, in
multipollutant models
significant increases
were only seen in
asthma and respiratory
infection
SO2 effects could be
largely explained by
other variables in the
pollution mix as
demonstrated by the
Multipollutant model.
The greatest
contribution of SO2 is
to respiratory infection.
However, overall SO2
is a small factor in total
hospitalization
response.
Increment: 5.35 ppb (Mean)
Single-pollutant model percent increase (t
statistic)
Asthma: 1.01% (1.76) lag 0-2
OLD 0.03% (0.05) lag 0-1
Respiratory infection: 2.40% (5.04) lag 0-
2
Multipollutant model percent
increase (SE)
A tV,
Astnma:
SO2 + CO + O3: 0.89%
/CT7 ,*• T\
(SE < 2)
S02 + CO + 03 + PM2.5: 0.69%
(SE < 2)
SO2+CO + O3 + PM25_25: 0.16%
(SE < 2)
S02 + CO + 03 + PM2 5: 0.76%
(SE<2)
Respiratory infection:
SO2 + NO2 + O3: 1.85%
SO2 + NO2 + O3 + PM2 5: 0.67
(SE < 2)
S02 + N02 + 03 + PM25_25: 1.71
(SE > 3)
S02 + N02 + 03 + PM25: 1.00
(SE>2)
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design, &
Methods
Mean Levels & Copollutants Method, Findings, Effects: Relative Risk or % Change &
Monitoring Stations & Correlations Interpretation Confidence Intervals (95%)
CANADA (cont'd)
Burnett* etal. (2001)
Toronto, Canada
Period of Study:
1980-1994
Hospital Admissions
Outcomes (ICD 9 codes):
Croup (464.4), pneumonia
(480-486), asthma (493),
acute
bronchitis/bronchiolitis
(466)
Age groups analyzed:
<2yrs
Study design: Time series
Statistical analyses:
Poisson regression with
GAM
Covariates: Temporal
trend, day of wk,
temperature, relative
humidity
Statistical package:
S-Plus
Lag: 0-5 days
l-hmaxSO2(ppb) O3;r=0.39
Mean: 11.8 SO2
CV: 93 CO
5th: 0 PM2.5
25th: 5 PM2.5_2.5
50th: 10
75th: 15
95th: 32
99th: 55
100th: 110
Number of stations: 4
SO2 had the smallest Increment: NR
effect on respiratory
admissions of all All respiratory admissions:
pollutants considered. single-pollutant:
Percent increase: 3. 1% (t = 1
lag 3
.900)
Multipollutant (adjusted for O3):
Percent increase: 1.21% (t =
1 "5
lag 3
0.67)
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design, Mean Levels &
& Methods Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
CANADA (cont'd)
Fung et al. (2006)
Vancouver, BC,
Canada
Period of Study:
6/1/95-3/31/99
Hospital Admissions SO2 24-h avg:
Outcomes (ICD 9 codes): All Mean: 3.46ppb
respiratory hospitalizations SD=1.82
(460-519) iQR: 2.50ppb
Age groups analyzed: 65+ Range: 0.00,12.50
Study design: (1) Time series
(2) Case-crossover, (3) DM-
models (Dewanji and
Moolgavkar, 2000, 2002)
N- 40 974
Statistical analyses: (1)
Poisson, (2) conditional
logistic regression, (3) DM
method-analyze recurrent
data in which the occurrence
of events at the individual
level over time is available
Covariates: Day of wk
Statistical package: S-Plus
andR
Lag: Current day, 3 and
5 day lag
CO; r= 0.61
COH; r = 0.65
NO2;r = 0.57
PM2.5;r=0.61
PM2.5; r = 0.42
PM2.5_2.5; r = 0.57
03;r=-30.35
No significant
association was found
between hospital
admissions and current
day SO2 levels (lag 0).
Significant
associations were
found with SO2 using a
3,5, and 7 day moving
avg, with the strongest
association observed
with a 7 day lag. The
DM method produced
slightly higher relative
risks compared to the
Time series and case
crossover results.
Increment: 2.5ppb(IQR)
NO2 Time series
RR 1.0 13 [0.997, 1.028] lag 0
RR 1.030 [1.010, 1.051] lag 0-3
RR 1.032 [1.008, 1.056] lag 0-5
RR 1.031 [1.003, 1.060] lag 0-7
NO2 Case-crossover
RR 1.0 10 [0.992, 1.027] lag 0
RR 1.028 [1.005, 1.050] lag 0-3
RR 1.030 [1.004, 1.057] lag 0-5
RR 1.028 [0.998, 1.058] lag 0-7
NO2 DM model
RR 1.0 13 [0.998, 1.027] lag 0
RR 1.034 [1.015, 1.053] lag 0-3
RR 1.039 [1.016, 1.061] lag 0-5
RR 1.044 [1.018, 1.070] lag 0-7
DM method produced slightly
higher RR estimates on O3, SO2 and
PM2 5 compared to time-series and
case-crossover, and slightly lower
RR estimates on COH, NO2, and PM2.5,
though the results were not significantly
different from one another.
-------
TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
to
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CANADA (cont'd)
Kestenetal. (1995)
Toronto, ON, Canada
Period of Study:
1991-1992
ED Visits
Outcome(s) (ICD 9):
Asthma (493)
Age groups analyzed:
Study design: Time
series
N: 854
# of Hospitals: 1
Statistical analyses:
Auto regression
Statistical package: SAS
Lag: 1 or 7
SO2 24-h avg
No data was provided
for concentration or for
correlation with other
pollutants.
N02
03
API (TRS, CO,
TSP)
X
Fit of an auto-regression
model with covariates
linked to same day gave
no evidence of association
between asthma and SO2.
Despite multiple attempts
to correlate individual or
combinations of pollutants
with air quality indices, no
association was found
between ER visits for
asthma and ambient daily,
weekly, or monthly levels
ofSO2,NO2, orO3.
No relative risks were provided.
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
to
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X
H
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o
H
O
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O
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H
W
Reference, Study Outcomes, Design, Mean Levels & Copollutants Method, Findings,
Location, & Period & Methods Monitoring Stations & Correlations Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
CANADA (cont'd)
Lin etal. (2003) Hospital Admissions SO2 24-h avg: 0.36 ppb CO; r= 0.37
Toronto, ON Outcomes (ICD 9 SD = 5.90 NO2;r=0.54
codes): Asthma Range: 0,57.00 PM25;r = 0.44
Period of Study: (493) 25th: 1.00 O3;r=-0.01
1981-1993 Age groups analyzed: 50th: 4.00 PM25T = 046
6-12 75th: 8.00 PM25'25;
Study design: Bi- r = 0 28 '
directional case- Number of Stations: 4
crossover
N: 7,319
Statistical analyses:
Conditional logistic
regression
Covariates: Daily
maximum and
minimum
temperatures and avg
relative humidity
Lag: Cumulative lag
f i *7 J,,, 7C,
oi I- / days.
SO2 is positively Increment: 7 ppb (IQR)
associated with
asthma Boys 6-12 yrs; Girls 6-12 yrs
hospitalizations, Lag Q. OR 1.00 [0.95, 1.05]; 1.
although the m
relationship varies T
in boys and girls. TQ
La
[0.
La
[0.
La
[1.
La
[1.
La
[1-
97,1.
gO-1
95,1.
gO-2
95,1.
gO-3
98,1.
gO-4
00,1.
gO-5
02,1.
gO-6
04,1.
,11]
: ORO.
,13]
: ORO.
,16]
: ORO.
,22]
: ORO.
,28]
: ORO.
,34]
: ORO.
,39]
,99
,98
,96
,95
,93
,93
[0.
[0.
[0.
[0.
[0.
[0.
Multipollutant model
Br
IVS 6-
1 9 vrs' '
k(
93,
90,
87,
86,
83,
83,
1.06];
1.06];
1.05];
1.05];
1.03];
1.04];
with PM2
S-1 >
' vrs
04
1.04
1.05
1.09
1.13
1.17
1.20
.5-2.5 and PM2 5
Lag 0: OR 0.98 [0.93, 1.04]; 1.06 [0.98, 1.14]
Lag 0-1: OR 0.99 [0.91, 1.06]; 1.03
[0.93,1.14]
Lag 0-2: OR 0.96 [0.88, 1.05]; 1.04
[0.92,1.17]
Lag 0-3: OR 0.95 [0.85, 1.05]; 1.08
[0.95, 1.23]
Lag 0-4: OR 0.94 [0.84, 1.06]; 1.12
[0.97, 1.29]
Lag 0-5: OR 0.91 [0.80,1.04]; 1.18 [1.00, 1.38]
Lag 0-6: OR 0.91 [0.80,1.04]; 1.28 [1.08, 1.51]
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
to
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Oi
Oi
H
6
o
o
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O
o
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W
O
O
HH
H
W
Reference, Study
Location, & Period
Outcomes, Design, &
Methods
Mean Levels &
Monitoring Stations
Copollutants Method, Findings,
& Correlations Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
CANADA (cont'd)
Lin* et al. (2004b)
Vancouver, BC
Canada
Period of Study:
1987-1998
Hospital Admissions
Outcomes (ICD 9
codes):
Asthma (493)
Age groups analyzed:
6-12
Study design: Time
series
N: 3,754(2,331 male,
1 423 female)
Statistical analyses:
Semi-parametric
Poisson regression
with GAM (with
default and more
stringent criteria)
Covariates: Trend, day
of wk,
Statistical package: S-
Plus
Lag: Cumulative
1-7 day
24-h avg SO2 (ppb)
Mean: 4.77
SD = 2.75
Min: 0
25th: 2.75
50th: 4.25
75th: 6.00
Max: 24.00
Number of stations: 30
CO; r = 0.67 Results presented are
NO2; r = 0.67 default GAM, but
Q • r = -0 10 authors state that use
pjyj . r_ of natural cubic
pAyr ' _ splines with a more
stringent
convergence rate
produced similar
results
Increment: 3. 3 ppb (I QR)
Boys 6-12 yrs by SES status: Low; High
Lag 0 RR 1.02[0.94, 1.10]; 1.03 [0.95, 1.12]
Lag 0-1 RR 1.03 [0.94, 1.13]; 1.06 [0.96, 1.17]
Lag 0-2 RR 1.03 [0.93, 1.15]; 1.06 [0.95, 1.18]
Lag 0-3 RR 1.01 [0.90, 1.13]; 1.04 [0.92, 1.17]
Lag 0-4 RR 0.98 [0.88, 1.10]; 1.02 [0.90, 1.14]
Lag 0-5 RR 0.97 [0.86, 1.10]; 1.02 [0.89, 1.16]
Lag 0-6 RR 0.98 [0.86, 1.12]; 1.05 [0.91, 1.21]
Girls 6-12 yrs by SES status: Low; High
Lag 0 RR 1.05 [0.95, 1.16]; 1.07 [0.96, 1.19]
Lag 0-1 RR 1.11 [0.99, 1.25]; 1.07 [0.94, 1.21]
Lag 0-2 RR 1.11 [0.97,1.26]; 1.07 [0.93, 1.23]
Lag 0-3 RR 1.18 [1.02, 1.36]; 1.02 [0.87, 1.19]
Lag 0-4 RR 1.18 [1.02, 1.35]; 0.99 [0.85, 1.15]
Lag 0-5 RR 1.19 [1.01, 1.40]; 0.95 [0.80, 1.13]
Lag 0-6 RR 1.15 [0.97, 1.36]; 0.98 [0.81, 1.17]
Multipollutant model (adjusted for NO2)
Girls, Low SES:
1.17 [1.00, 1.37] lag 0-3
1.19 [1.00, 1.42] lag 0-5
-------
TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
to
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X
H
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o
o
H
O
o
H
W
O
O
HH
H
W
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels & Copollutants
Monitoring Stations & Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
CANADA (cont'd)
Lin etal. (2005)
Toronto, ON, Canada
Period of Study:
1998-2001
Hospital Admissions
Outcomes (ICD 9
codes): Respiratory
infections (464,466, 480-
487)
Age groups analyzed: 0-
14
Study design: Case-
crossover
N: 6,782
# of Hospitals:
Statistical analyses:
Conditional logistic
regression
Covariates:
Statistical package: SAS
8.2
Lag: 0-6 days
24-h avg: PM2.5; r = 0.47
Mean: 4.73 ppb PM2 5_2 5; r = 0.29
SD = 2.58 ppb PM2.5;r = 0.48
Range: 1.00,19.67 CO;r=0.12
25th: 3.00 NO2;r=0.61
50th: 4.00
75th: 6.00
Number of monitors: 5
Asthma hospitalization
for boys was
associated with SO2
before the adjustment
for fine and coarse PM.
Asthma hospitalization
for girls was not
associated with SO2 for
any lag.
Increment: 3 ppb (IQR)
Unadjusted Boys only:
OR 1.06 [0.97, 1.1 6] lag 0-3
OR 1.02 [0.92, 1.1 3] lag 0-5
Girls only:
OR 1.05 [0.94, 1.1 6] lag 0-3
OR 1.07 [0.95, 1.21] lag 0-5
Boys and Girls
OR 1.06 [0.99, 1.1 3] lag 0-3
OR 1.04 [0.96, 1.1 3] lag 0-5
Adjusted Boys only:
OR 1.11 [1.01, 1.21] lag 0-3
OR 1.08 [0.97, 1.21] lag 0-5
Girls only:
OR 1.07 [0.96, 1.1 9] lag 0-3
OR 1.12 [0.98, 1.28] lag 0-5
Boys and Girls
OR 1.10 [1.02, 1.18] lag 0-3
OR 1.10 [1.01, 1.20] lag 0-5
-------
TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
to
o
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CANADA (cont'd)
Linetal. (2005) (cont'd)
Multipollutant model with PM2 5 and PM2;
Boys only:
OR 1.02 [0.90, 1.15] lag 0-3
OR 0.99 [0.85, 1.16] lag 0-5
Girls only:
OR 1.09 [0.0.94, 1.26] lag 0-3
OR 1.07 [0.90, 1.28] lag 0-5
Boys and Girls
OR 1.05 [0.95, 1.15] lag 4
OR 1.03 [0.91, 1.16] lag 6
X
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O
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
to
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H
6
o
o
H
O
o
H
W
O
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HH
H
W
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
CANADA (cont'd)
Luginaah et al. (2005)
Windsor, ON, Canada
Period of Study:
4/1/95-12/31/00
Hospital Admissions
Outcomes (ICD 9
codes): Respiratory
admissions
(460-519)
Age groups analyzed:
0-14,
15-64, 65+, all ages
Study design:
(1) Time series and (2)
case-crossover
SO2 mean 1 h
Max: 27.5 ppb,
SD= 16.5;
Range: 0, 129
IQR:
Number of stations: 4
NO2; r = 0.22
CO; r=0.16
PM2.5; r = 0.22
COH;r = 0.14
O3;r=-0.02
TRS;r=0.13
The effect of SO2 on
respiratory
hospitalization varies
considerably,
especially at low
levels of exposure.
Increment: 19.25 ppb (IQR)
Time-series, females; males
All ages, 1.041 [0.987, 1.098]; 0.953
[0.900, 1.009] lag 1
0-14 yrs, 1.111 [1.011, 1.221] ; 0.952
[0.874, 1.037] lag 1
15-65 yr, 1.031 [0.930, 1.144] ; 0.971
[0.845, 1.15] lag 1
65+ yr, 1.030 [0.951, 1.115] ; 0.9409
[0.860, 1.029] lag 1
N: 4,214
# of Hospitals: 4
Statistical analyses:
(1 )Poisson regression,
GAM with natural
splines (stricter
criteria), (2)
conditional logistic
regression with Cox
proportional hazards
model
Covariates:
Temperature,
humidity, change in
barometric pressure,
day of wk
Statistical package: S-
Plus
Lag: 1,2,3 days
Case-crossover, females; males
All ages, 1.047 [0.978, 1.122]; 0.939
[0.874, 1.009] lag 1
0-14 yrs, 1.119 [0.995, 1.259] ; 0.923
[0.831, 1.025] lag 1
15-65 yr, 1.002 [0.879, 1.141] ; 0.944
[0.798, 1.116] lag 1
65+yr, 1.020 [0.924, 1.126] ; 0.968
[0.867, 1.082] lag 1
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design, Mean Levels &
& Methods Monitoring Stations
Copollutants Method, Findings,
& Correlations Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
CANADA (cont'd)
Villeneuve et al., 2006
Toronto, ON, Canada
Period of Study:
1995-2000
Days: 2,190
GP Visits 24-havg: 4.7 ppb
Outcome(s) (ICD9): SD = 2.8
Allergic Rhinitis (177) IQR: 3.2 ppb
Age groups analyzed: Range: 0, 24.8
^65 Number of stations: 9
Study design: Time series
N: 52,691
Statistical analyses:
GLM, using natural
splines (more stringent
criteria than default)
Covariates: Day of wk,
holiday, temperature,
relative humidity,
aero-allergens
Season: All Yr;
Warm, May-Oct;
Cool, Nov-Apr
Statistical package:
S-Plus
Lag: 0-6
NO2 There were positive
O3 associations between
CO allergic rhinitis and
pjyj SO2 for exposures
r>A , occurring on the
rlvl2 5-25 ,
same day as
physician visits, but
only during the
winter time
Increment: 10.3 ppb (IQR)
All results estimated from Stick Graph:
All Yr:
Mean increase: 1.7% [-0.4, 2.8] lag 0
\Vcinrr
Mean increase: 0.3% [-1.9, 2.5] lag 0
Cool:
Mean increase: 1 . 9% [- 0.2, 4. 1 ] lag 0
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels & Copollutants
Monitoring Stations & Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
CANADA (cont'd)
Yang et al. (2003a)
Vancouver, Canada
Period of Study:
1986-1998
Days: 4748
Hospital admissions
outcomes (ICD 9 codes):
All respiratory
admissions (460-519)
Study design:
Case-crossover
Age groups analyzed:
<^"5 -^£\^
<-J, >OJ
Statistical analyses:
conditional logistic
regression
Statistical package: NR
Lag: 0-5 days
24-h avg SO2 (ppb): CO
Mean: 4.84 NO2
SD = 2.84 03;r=-0.37
5th: 1.50 COH
25th: 2.75
50th: 4.25
75th: 6.25
100th: 24.00
IQR: 3.50
Number of stations: 30
SO2 showed the
weakest effect among
children and the
second weakest effect
among older adults
when compared to all
other pollutants
considered in the
study.
Increment: 3. 50 ppb (IQR)
All respiratory admissions <3 yrs:
SO2 alone: OR 1.01 [0.98, 1.05] lag 2
S02 + 03: OR 1.01 [0.97, 1.04] lag 2
SO2 + O3 + CO + COH + NO2: OR 0.98
[0.94, 1.03] lag 2
All respiratory admissions >65 yrs:
SO2 alone: OR 1.02 [1.00, 1.04] lag 0
SO2 + O3: OR 1.02 [1.00, 1.04] lag 0
S02 + 03 + CO + COH + N02: OR 1.01
[0.98, 1.03] lag 0
X
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels & Copollutants
Monitoring Stations & Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
CANADA (cont'd)
Yang et al. (2005)
Vancouver, BC,
Canada
Period of Study:
1994-1998
Days: 1826
Hospital admissions
outcomes (ICD 9 codes):
COPD excluding asthma
(490-2, 494, 496)
Age groups analyzed: 65+
Study design: Time series
N: 6,027
Statistical analyses:
Poisson regression with
GAM (with more stringent
criteria)
Covariates: Temperature,
relative humidity, day of
wk, temporal trends,
season
Statistical package:
S-Plus
Lag: 0-6 days, moving
averages
24-havg: 3.79 ppb, SD = PM2.5;r = 0.62
2.12; N02;r=0.61
IQR: 2.75 ppb; CO; r= 0.67
Range: 0.75,22.67 O3;r=-0.34
Winter: 4.10(2.87)
Spring: 3.40(1.58)
Summer: 4.10(1.79)
Fall: 3.56(1.92)
Number of Stations: 3 1
This study produced a
marginally significant
association between
COPD hospitalization
and 6-day SO2
exposure. Most
previous studies have
not QGTGCTGQ 3.
significant effect of
SO2 on respiratory ED
visits or
hospitalizations.
Increment: 2.75 ppb (IQR)
COPD
> 65 yrs, yr round
RR 1.00 [0.97, 1.04] lag 0
RR 1.02 [0.98, 1.06] lag 0-1
RR 1.04 [0.99, 1.08] lag 0-2
RR 1.04 [0.99, 1.09] lag 0-3
RR 1.05 [0.99, 1.11] lag 0-4
RR 1.06 [1.00, 1.13] lag 0-5
RR 1.06 [0.99, 1.1 3] lag 0-6
2-pollutant model
NO2: RR 0.99 [0.91, 1.08] lag 0
CO: RR 0.97 [0.87, 1.07] lag 0-6
03: RR 1.07 [1.00, 1.14] lag 0-6
PM25: 0.97 [0.88, 1.06] lag 0-6
Multipollutant models
SO2, CO, NO2, O3, PM2.5: RR0.94
[0.85, 1.05]
S02, CO, N02, 03: RR 0.96 [0.86,
1.06]
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
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Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
AUSTRALIA/NEW ZEALAND
Barnett et al. (2005)
Multicity,
Australia/New
Zealand; (Auckland,
Brisbane, Canberra,
Christchurch,
Melbourne, Perth,
Sydney)
Period of Study:
1998-2001
Hospital admissions
outcomes (ICD 9/ICD 10):
All respiratory
(460-5 19/JOO-J99 excluding
J95.4-J95.9,RO9.1,RO9.8),
asthma (493/J45, J46, J44.8),
COPD (490-492, 494-
496/J40-J44, J47, J67),
pneumonia with bronchitis
(466, 480-486/J12-17, J18.0
J18.1 J18.8J18.9
J20J21)
Age groups analyzed: 0, 1-4,
5-14
Study design: Case-crossover
Statistical analyses:
Conditional logistic
regression, random effects
meta-analysis
Covariates: Temperature,
current-previous day
temperature, relative
humidity, pressure, extremes
of hot and cold, day of wk,
holiday, day after holiday
Season: Cool, May-Oct;
Warm, Nov-Apr
Statistical package: SAS
Lag: 0-1 days
24-h avg (ppb) (range):
Auckland: 4.3
(0, 24.3)
Brisbane: 1.8
(0, 8.2)
Canberra: NA
Christchurch: 2.8
(0,11.9)
Melbourne: NA
Perth: NA
Sydney: 0.9
fc\ "$ n\
(0, 3.9)
Daily 1-h max (range):
Auckland: NA
Brisbane: 7.6
(0,46.5)
Canberra: NA
Christchurch: 10.1 (0.1,
42.1)
Melbourne: NA
Perth: NA
Sydney: 3.7
(0.1,20.2)
IQR: 5.4 ppb
BS;r= 0.07, 0.29
PM25;r = 0.12,
0.35
PM25;r = 0.17,
0.33
CO; r = 0.25, 0.41
N02;r=0.15,
0.58
O3;r=-0.12,
0.16
Increased hospital
admissions were
significantly associated
with SO2 for acute
bronchitis, pneumonia,
and respiratory
diseases. In
multipollutant models
the impacts of
particulate matter and
NO2 were isolated.
There were seasonal
impacts on pneumonia
and acute bronchitis
admissions in the
1- to 4-yr-old age
group for SO2.
Increment: 5. 4 ppb
(1-h max IQR)
Pneumonia and acute bronchitis
Oyrs 3.5% [-0.3, 7.3] lag 0-1
1-4 yrs 6.9% [2.3, 11.7] lag 0-1
Respiratory
Oyrs 3.2% [0.3, 6. 3] lag 0-1
1-4 yrs 2.7% [0.6, 4.8] lag 0-1
5-14 yrs 2.0% [-5.5, 10.1] lag 0-1
Asthma
0 yrs No analysis (poor diagnosis)
1-4 yrs 3. 4% [-4. 3, 11.6]
lag 0-1
5-14 yrs 3. 3% [-5.6, 13.0]
lag 0-1
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
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Reference, Study
Location, & Period
AUSTRALIA/NEW
Petroeschevsky et al.
(2001)
Brisbane, Australia
Period of Study:
1987-1994
Days: 2922
Outcomes, Design,
& Methods
ZEALAND (cont'd)
Hospital admissions
outcomes (ICD 9): All
respiratory (460-519);
Asthma (493)
Age groups analyzed: 0-4, 5-
14,15-64, 65+, all ages
Study design: Time series
N. 11 71 n
. J J, / 1U
(13,246 = asthma)
Statistical analyses: APHEA
protocol, Poisson regression,
f^T7T7
U\c,ii
Covariates: Temperature,
humidity, season, infectious
disease, day of wk, holiday
Season: Summer, Autumn,
Winter, Spring, All yr
Dose-response investigated?:
Yes
Statistical package: SAS
Lag: Single: 1,2,3 day
Cumulative: 0-2,0-4
Mean Levels &
Monitoring Stations
Mean: 24-h avg:
Overall: 4.1 ppb
Summer: 3. 9 ppb
Autumn: 4.2 ppb
Winter: 4. 8 ppb
Spring' 3 7 ppb
Mean: 1-hmax
Overall: 9.2 ppb
Summer: 7.8 ppb
Autumn: 9.3 ppb
Winter: 11. 3 ppb
Spring: 8.4 ppb
# of stations: 3
Copollutants Method, Findings,
& Correlations Interpretation
Bsp SO2 was highly
O3 correlated with
NO2 maximum daily ER
admissions for
respiratory conditions.
The highest association
was observed in the
winter followed by
autumn, spring, and
surrmiGr For Q.sthrn.3.
the highest association
was observed in the
winter and autumn.
No statistically
significant
contributions for
respiratory admissions
were reported for the
age group 5-14 yr olds
for any pollutant.
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
Increment: 0 ppb
Respiratory:
0-4 yrs 24-h avg 1.224 [1.087, 1.377] lag 0-4
5-14 yrs 1-hmax 1.049 [0.986, 1.116] lag 0-
4
15-64 yrs 24-h avg 1.033 [0.895, 1.118] lag
1
65+ yrs 24-h avg 1.121 [1.019, 1.234] lag 0
All ages 24-h avg 1.080 [1.030, 1.131] lag 1
Asthma:
0-14 yrs 24-h avg 1.080 [0.971, 1.201] lag 0
15-64 yrs 1-h max 0.941 [0.900, 0.984] lag 0
All ages 24-h avg 0.941 [0.876, 1.011] lag 2
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
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Reference, Study
Location, & Period
Outcomes, Design, &
Methods
Mean Levels & Copollutants & Method, Findings,
Monitoring Stations Correlations Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
EUROPE
Anderson etal. (1997)
Multicity, Europe
(Amsterdam,
Barcelona, London,
Paris, Rotterdam)
Period of Study:
1977-1989 for
Amsterdam and
Rotterdam
1986-1992 for
Barcelona
1987- 1991 for London
1980-1989 for Milan
1987- 1992 for Paris
Hospital admissions
outcomes (ICD 9):
COPD-unspecified
bronchitis (490),
chronic bronchitis
(491), emphysema
(492), chronic airways
obstruction (496)
Study design: Time
series
Statistical analyses:
APHEA protocol,
Poisson regression,
meta-analysis
Covariates: Trend,
season, day of wk,
holiday, influenza,
temperature, humidity
Season:
Cool, Oct-Mar; Warm,
Apr-Sep
Statistical package:
NR
Lag: 0,1, 2 days and 0-
3 cumulative
24-h all yr avg (ug/m3): NO2
Amsterdam: 21 BS
Barcelona: 40 TSP
London: 31 O3
Milan: 53
Paris: 23
Rotterdam: 32
1-hmax
Amsterdam: 50
Barcelona: 60
London: NR
Milan: NR
Paris: 47
Rotterdam: 82
The effect of SO2 varied
considerably across the cities;
however, the summer estimate
was significantly associated
with COPD for the 1-h
measure and borderline
significant for the daily mean.
Both 24-h and 1-h SO2
concentrations were
significantly associated with
COPD ER admissions in the
warm season. Only
cumulative lags of SO2
showed borderline
significance.
Increment: 50 ug/m
COPDC-Warm season
24 hi. 05 [1.01,1.10]
1 hi. 02 [1.00, 1.04]
COPD-Cool season
24 h 1.02 [0.98, 1.05]
Ih 1.01 [0.99,1.03]
COPD-A11 yr
24-h avg 1.022 [0.981, 1.055] la:
24-h avg 1.021 [0.998, 1.054] la:
cumulative
1-hmax 1.01 [0.994, 1.029] lag
1-hmax 1.015 [1.003, 1.027] lag
cumulative
gl
gO-3,
1
5 0-3,
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design, &
Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
EUROPE (cont'd)
Atkinson etal. (2001)
Multicity, Europe
(Barcelona,
Birmingham, London,
Milan, Netherlands,
Paris, Rome,
Stockholm)
Period of Study:
1998-1997
Hospital admissions
outcomes (ICD 9):
Asthma (493), COPD
(490-496), All
respiratory (460-519)
Study design: Time
series
Statistical analyses:
APHEA protocol,
Poisson regression,
meta-analysis
Covariates: Season,
temperature, humidity,
holiday, influenza
Statistical package:
NR
Lag: NR
l-hmaxofSO2(ug/m3)
Barcelona: NR
Birmingham: 24.3
London: 23.6
Milan: 29.1
Netherlands: 8.5
Paris: 17.7
Rome: 9.8
Stockholm: 3.8
N02, 03, CO, BS
PM2.5;r =
Barcelona: 0.32
B'gham: 0.77
London: 0.72
Milan: 0.64
Netherlands: 0.67
Paris: 0.63
Rome: 0.15
Stockholm: 0.36
The inclusion of SO2 in
the models only
modified PM2 5
associations in the
0- to 14-yr age group.
Increment: 10 ug/m3 for PM2 s; change in
SO2 not described.
Asthma, 0 to 14 yrs:
ForPM2.5: 1.2 [0.2, 2. 3]
ForPM2.5 + S02: 0.8 [-3.7, 5.6]
Asthma, 1 5 to 64 yrs:
ForPM2.5: 1.1 [0.3,1.8]
ForPM2.5 + SO2: 1.6 [0.6, 2.6]
COPD + Asthma, >65 yrs
ForPM2.5: 1.0 [0.4, 1.5]
ForPM2.5 + SO2: 1.3 [0.7, 1.8]
All respiratory, >65 yrs of age
ForPM2.5: 0.9 [0.6, 1.3]
ForPM2.5 + S02: 1.1 [0.7,1.4]
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels & Copollutants Method, Findings,
Monitoring Stations & Correlations Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
EUROPE (cont'd)
Schouten et al. (1996)
Multicity, The
Netherlands
(Amsterdam,
Rotterdam)
Period of Study:
04/01/77-09/30/89
Hospital admissions
outcomes (ICD 9): All
respiratory (460-519),
COPD (490-2, 494, 496),
Asthma (493)
Age groups analyzed: 15-
64, 65+, all ages
Study design: Time series
Statistical analyses:
APHEA protocol, Poisson
regression
Covariates: Long-term
trend, season, influenza,
day of wk, holiday,
temperature, humidity
Sficisori'
Cool, Nov-Apr;
Warm: May-Oct
Statistical package: NR
Lag: 0,1,2 days; and
cumulative 0-1 and 0-3
day lags
24-h avg SO2 NO2
Amsterdam BS
Mean/Med: O3
28/21 ug/m3
Rotterdam
Mean: 40/32 ug/m3
Daily 1-hmax
Amsterdam
Mean/Med:
65/50 ug/m3
Rotterdam
Mean/Med:
99/82 ug/m3
# of stations: 1 per city
The relationship between
short-term air pollution
and hospital admissions
was not always
consistent at low levels
of expo sure. One
statistically significant
association between
hospital admissions and
asthma (all ages)
occurred in Amsterdam
after a cumulative lag of
1-3 days in the summer.
Higher SO2 levels were
reported for the winter;
therefore, this
association was not a
concentration response.
Increment: 100 ug/m3 increment.
All respiratory, Amsterdam
24-h avg
15-64 yrs
RR 0.944 [0.864, 1.032] lag 2
RR 0.915 [0.809, 1.035] lag 0-3
>65 yrs
RR 1.046 [0.965, 1.1 34] lag 2
RR 1.008 [0.899, 1.131] lag 0-3
1-hmax
15-64 yrs
RR0.989[0.952, 1028] lag 2
RR 0.977 [0.927, 1.030] lag 0-3
>65 yrs
RR 1.022 [0.985, 1060] lag 2
RR 1.010 [0.955, 1.068] lag 0-3
RR 0.941 [0.863, 1.026] lag 0-3
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
to
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EUROPE (cont'd)
X
oo
Schouten et al. (1996)
(cont'd)
In Rotterdam neither
1 day nor cumulative lags
in the summer or winter
increased asthma
admissions to statistical
significance. Rotterdam
had much higher mean
SO2 concentrations. There
were no significant
associations to hospital
admissions when higher
pollution levels were
prevalent.
COPD, Amsterdam
24-h avg-all ages
RR 0.907 [0.814,1.011] lag 0
RR 0.948 [0.838,1.072] lag 0-1
1-hmax-all ages
RR 0.978 [0.933,1.026] lag 0
RR 0.995 [0.940,1.053] lag 0-1
Asthma, Amsterdam
24-h avg-all ages
RR 0.802 [0.696, 0.924] lag 1
RR 0.792 [0.654, 0.958] lag 0-3
1-hmax-all ages
RR 0.995 [0.942,1.051] lag 0
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
EUROPE (cont'd)
Schouten et al. (1996)
(cont'd)
The analysis of all
respiratory hospital
admissions for all ages
in the entire country
(Netherlands) produced
a statistically significant
association for both
1-h and 24-h periods
(100 |ig/m3).
All respiratory, Rotterdam
24-h avg
15-64yrs
RR 0.941 [0.855,1.036] lag 1
RR 0.895 [0.787,1.019] lag 0-2
>65yrs 1977-1981
RR 1.027 [0.904,1.165] lag 2
RR1.011 [0.834,1.227] lag 0-3
>65yrs 1982-1984
RR 1.087 [0.890,1.328] lag 0
RR 1.258 [0.926,1.710] lag 0-3
>65yrs 1985-1989
RR 1.045 [0.908,1.204] lag 0
RR 0.968 [0.787,1.190] lag 0-3
1-h max
15-64yrs
RR0.989[0.953, 1025] lag 1
RR 0.965 [0.915,1.018] lag 0-2
>65yrs 1977-1981
RR 0.892 [0.842, 0.945] lag 0
RR 0.987 [0.907,1.074] lag 0-3
>65yrs 1982-1984
RR 1.005 [0.933,1.081] lag 0
RR 1.062 [0.938,1.202] lag 0-3
>65yrs 1985-1989
RR 1.010 [0.955,1.068] lag 0
RR 1.064 [0.992,1.141] lag 0-1
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
to
o
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EUROPE (cont'd)
Schouten et al.
(1996) (cont'd)
COPD, Rotterdam
24-h avg-all ages
RR 0.963 [0.874,1.059] lag 2
RR 1.019 [0.887,1.172] lag 0-3
1-hmax-allages
RR 0.991 [0.955,1.029] lag 2
RR 1.013 [0.953,1.076] lag 0-3
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Spixetal. (1998)
Multicity (London,
Amsterdam,
Rotterdam, Paris,
Milan), Europe
Period of Study:
1977 and 1991
Hospital Admissions
Outcomes (ICD 9 codes): All
respiratory (460-519); Asthma
(493)
Age groups analyzed:
15-64, 65+
Study design: Time series
# of Hospitals:
Statistical analyses: Poisson
regression following APHEA
protocol. Pooled meta-analysis
adjusted for heterogeneity
Covariates: trend, seasonality,
day of wk, holiday,
temperature, humidity, unusual
events (strikes, etc.)
Statistical package:
Lag: 1 to 3 days
SO2 daily mean (ug/m3) NO2, O3, BS, TSP
London: 29
Amsterdam: 21
Rotterdam: 25
Paris: 23
Milan: 66
Daily counts of adult
respiratory
admissions were not
consistently
associated with daily
mean levels of SO2.
Heterogeneity
between cities was
likely due to the
number of stations or
temperature. Only
hospital admissions
for >65 yr olds were
significantly
associated with SO2
in the warm season.
Increment: 50 ug/m
All cities, yr round
15-64 yrsRR 1.009 [0.992,1.025]
Warm RR 1.01 [0.98,1.04]
Cold RR 1.01 [0.97,1.07]
>65 yrsRR 1.02 [1.005, 1.046]
Warm RR 1.06 [1.01, 1.11]
Cold RR 1.02 [0.99, 1.04]
APHEA protocol pooled result from >65
yrs old from Europe
All respiratory
RR 1.02 [1.00, 1.05]
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
to
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Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels & Copollutants Method, Findings,
Monitoring Stations & Correlations Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
EUROPE (cont'd)
Sunyeretal. (1997)
Multicity, Europe
(Barcelona, Helsinki,
Paris, London)
Period of Study:
1986-1992
Hospital admissions/ED
visits
outcomes (ICD 9):
Asthma (493)
Age groups analyzed:
<15, 15-64
Study design: Time
series
Statistical analyses:
APHEA protocol,
Poisson regression,
GEE; meta-analysis
Covariates: Humidity,
temperature, influenza,
soybean, Long-term
trend, season, day of wk
Season:
Cool, Oct-Mar; Warm:
Apr-Sep
Statistical package: NR
Lag: 0,1, 2,3 and
cumulative 1-3
24-h median (range)
(ug/m3)
Barcelona: 41
(2, 160)
Helsinki: 16
(3, 95)
London: 31
(9, 100)
Paris: 23
(1,219)
# of stations:
Barcelona: 3
London: 4
Paris: 4
Helsinki: 8
NO2 SO2 alone or as part of a
black smoke mixture was a factor that
O3 exacerbated asthma
admissions.
In 2-pollutant models
with SO2 and BS, the
association of BS with
SO2 was attenuated for
<15 yr olds, compared to
single-pollutant model
associations. In
addition, the association
of NO2 was also
attenuated by the
inclusion of SO2.
Increment: 50 ug/m3 of 24-h avg for all
cities combined.
Asthma
15-64yrs
0.997 [0.961, 1.034] lag
1.003 [0.959, 1.050] lag
<15 yrs
1.075 [1.026, 1.126] lag
1.061 [0.996, 1.1 31]lag
2-pollutant models:
SO2/Black smoke
<15 yrs 1.092 [1.031,1.
SO2/NO2
<15yrs 1.075 [1.019, 1
,2
; 0-3, cum
i
i -i
2-3, cum
156] lag 0-1
.135]
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
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Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants Method, Findings,
& Correlations Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
EUROPE (cont'd)
Sunyer et al. (2003)
Multicity study
(Birmingham (B),
London (L), Milan
(M), Netherlands (N),
Paris (P), Rome (R)
and Stockholm (S),
Europe
Period of Study:
1992 and 1997
Hospital admissions/ED
visits
outcome(s) (ICD 9):
Asthma (493); COPD
and Asthma
(490-496);
All respiratory
(460-519)
Age groups analyzed:
All, 0-14 yrs; 16-64yrs;
>65 yrs
Study design:
Time series
Poisson regression with
GAM following APHEA
2 protocol
Covariates: temperature,
humidity, Long-term
trend, season
Statistical package: NR
Lag: 0, 1
SO2 24-h avg and SD
(ug/m3)
B 24. 3 (12.7)
L 23.6 (23.7)
M 32.5 (37.5)
N 8. 5 (7.7)
P 17.7 (12.5)
R9.8 (9.9)
S 6.8 (6.2)
PM2 5; r = 0.64 The magnitude of
CO; r = 0 . 5 3 association with asthma
across the seven cities was
comparable to earlier
studies of London,
Helsinki and Paris.
Exposure factors may be
important. Children may
spend greater time
outdoors compared with
adults. Pneumonia
requires chronic exposure
to produce inflammatory
response and infection,
whereas asthma is an
acute response.
Increment: 10 ug/m3
Asthma
0-14 yrs 1.3% [0.4, 2.2]
15-64 yrs 0.0% [-0.9, 1.00]
COPD and Asthma
>65 yrs 0.6% [0.0, 1.2]
All Respiratory
>65 yrs 0.5% [0.1, 0.9]
Asthma
0-14 yrs
SO2 + PM2.5: -3.7% (p> 0.1)
SO2 + CO: -0.7%(p>0.1)
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
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Anderson etal. (1998)
London, England
Period of Study:
Apr 1987-Feb 1992
Days: 1,782
Hospital admissions
outcomes (ICD 9):
Asthma (493)
Age groups analyzed:
<15, 15-64,65+
Study design: Time
series
Statistical analyses:
APHEA protocol,
Poisson regression
Covariates: Time trends,
seasonal cycles, day of
wk, public holidays,
influenza epidemics,
temperature, humidity
Season:
Cool (Oct- Mar); Warm
(Apr-Sep)
Statistical package: NR
Lag: 0, 1,2 days
24-h avg SO2 (ug/m3)
Mean: 32.0
SD=11.7
Range: 9, 100
5th: 16
10th: 18
25th: 24
50th: 31
75th: 38
90th: 46
95th: 52
# of monitors: 2
03
NO2
BS
The strongest association
between SO2 and asthma
admissions was for those
>65 yrs in the cool season.
A weaker association was
observed for children in the
warm season and all yr.
The adult population
showed no association.
In 2-pollutant models ozone
was overall the strongest
pollutant associated with
hospital admission with
weaker associations with
NO2andBS. The most
consistent yr-round
association for All ages was
found with BS. When
looking at all ages
combined, SO2 association
remained significant in all
2-pollutant models except
with NO2, both for all yr
and the summer (warm)
season.
Increment: 10 ppb in 24-h SO2
0-14 yrs
Whole yr 1.64% [0.29, 3.01] lag 1
2.04% [0.29, 3.83] lag 0-3
+ 03 1.77% [0.22, 3.36] lag 1
+ N02 1.23% [-0.22, 2.69] lag 1
+ BS 1.66% [0.23, 3.12] lag 1
Warm season 3.33% [1.09, 5.63] lag 1
3.40% [0.41, 6.48] lag 0-3
+ O3 3.35% [0.89, 5.87] lag 1
+ N02 2.92% [0.58, 5.32] lag 1
+ BS 3.66% [1.35, 6.02] lag 1
Cool season 0.56% [-1.16, 2.32] lag 1
1.24% [-0.95, 3.49] lag 0-2
15-64 yrs
Whole yr -0.69% [-2.28, 0.94] lag 2;
-0.71% [-2.69, 1.30] lag 0-2
Warm -1.39% [-3.97, 1.27] lag 0;
-2.2% [-5.46, 11.8] lag 0-2
Cool season -0.24% [-2.28, 1.84] lag 0
0.20% [-2.28, 2.74] lag 0-2
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
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Anderson etal. (1998)
(cont'd)
65+ yrs
Whole yr 2.82% [-0.82, 5.96] lag 2;
3.06% [-0.72, 6.98] lag 0-3
Warm -2.62% [-7.31,2.31] lag 2;
-4.27% [-9.89, 1.71] lag 0-3
Cool season 5.85% [1.81, 10.05] lag 2;
7.28% [2.19, 12.62] lag 0-3
+ O3 7.84% [2.48, 13.48] lag 1
+ NO2 4.19% [-0.53, 9.13] lag 1
+ BS 5.29% [0.42, 10.40] lag 1
All Ages
Whole yr 1.64% [0.54, 2.75] lag 1;
2.75% [1.22, 4.30] lag 0-3
+ O3 1.48% [0.24, 2.73] lag 1
+ SO2 1.14% [-0.04, 2.33] lag 1
+ BS 1.54%[0.36, 2.73] lag 1
Warm 2.02% [0.22, 3.85] lag 1;
2.60% [0.02, 5.25] lag 0-3
+ O3 1.91% [0.05, 3.81] lag 1
+ NO2 1.64% [-0.23, 3.56] lag 1
+ BS 2.18% [0.32, 4.07] lag 1
Cool season 1.41% [0.0, 2.83] lag 1;
2.83% [0.89, 4.81] lag 0-3
+ 03 -0.09% [-1.61, 1.82] lag 1
+ N02 0.83% [-0.67,2.34] lag 1
+ BS 1.11% [-0.41,2.66] lag 1
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
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Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels & Copollutants
Monitoring Stations & Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
EUROPE (cont'd)
Anderson etal. (2001)
West Midlands
conurbation, United
Kingdom
Period of Study:
10/1994-12/1996
Hospital admissions
outcomes (ICD 9
codes): All respiratory
(460-5 19), Asthma
(493), COPD
(490-496, excluding
493)
Age groups analyzed:
0-14,
15-64, 65+
Study design: Time
series
Statistical analyses:
followed APHEA 2
protocol, GAM
Covariates: Season,
temperature, humidity,
epidemics, day of wk,
holidays
Statistical package: S-
Plus4.5Pro
Lag: 0,1,2,3,0-1,
0-2, 0-3
24-havg: 7.2 ppb, 4.7 PM2.5;r = 0.55
(SD) PM2.5_10;r = 0.31
Min: 1.9 ppb BS;r=0.50
Max: 59.8 ppb SO4;r = 0.19
10th: 3. 3 ppb NO2;r=0.52
90th: 12. 3 ppb O3;r=0.22
#of monitors: 5
When admissions were
analyzed by subgroups,
respiratory and asthma
admissions were positively
correlated with SO2.
SO2 significantly associated
with asthma and respiratory
admissions for the
0 to 14-yr-age group;
however, little evidence of a
seasonal interaction was
observed.
Increment: 9 ppb (90th- 10th)
All respiratory
All ages 1.3% [-0.7, 3.4] lag 0-1
0-14 yrs 4.6% [1.40, 7.8] lag 0-1
15-64 yrs -0.9% [-4.8, 3.3] lag 0-1
>65 yrs -2.0% [-4.9, 1.1] lag 0-1
COPD with asthma
0-14 yrs 10.9% [4.50, 17.8] lag 0-1
15-64 yrs 2.4% [-5. 5, 10.9] lag 0-1
>65 yrs -4.2% [-8.9, 0.8] lag 0-1
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
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Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants Method, Findings,
& Correlations Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
EUROPE (cont'd)
Atkinson et al. (1999a)
London, England
Period of Study:
1992-1994
Hospital admissions
outcomes (ICD 9 codes):
All respiratory (460-5 19);
Asthma (493); Asthma
and COPD (490-496);
LRD (466,480-486)
Age groups analyzed: all
ages, 0-14 yr,
15-64 yr and >65 yr
Study design:
Time series
N: 165,032
Statistical analyses:
Poisson regression
following APHEA
protocol
Covariates: Long-term
seasonal patterns, day of
wk, temperature,
humidity, influenza.
Statistical package: SAS
Investigated
Dose/Response: Yes
Lag: 0,1, 2,3 days
SO2_ 24-h (ug/m3)
Mean: 21.2 (7.8) ug/m3
Min: 7.4
10th: 13
50th: 19.8
90th: 31
Max: 82.2
# of monitors: 5
O3,CO, PM2 5, BS, Asthma was closely linked
N02 with PM, CO, N02, and
traffic pollution.
Correlation
coefficients When SO2 and PM2 5 were
ranged between included in the same
r = 0.5 and 0.6 model, the magnitude of
the individual associations
was reduced, as were their
statistical significance.
This reduction occurred in
children, adults and the
elderly. The other
pollutants all had the
effect of reducing the
magnitude of the
individual SO2 and PM2 s
associations, although
their statistical
significance was
unaffected. This indicates
that both SO2 and PM2 5
were indicators of the
same pollutant mixture.
Increment: 18 ug/m
All respiratory
All ages 2.01% [0.29, 3.76] lag 1
0-14 yrs 5.14% [2.59, 7.76] lag 0
15-64 yrs 1.90% [-0.79, 4.660 lag 3
>65 yrs 2.25 [-0.09, 4.65] lag 3
Asthma
All ages 3.38 [0.42, 6.43] lag 1
0-14 yrs 6.74% [2.92, 10.69] lag 1
15-64 yrs 4. 58% [-0.18, 9.57] lag 3
>65 yrs 6.31% [-1.59, 14.83] lag 2
COPD and Asthma
>65 yrs 1.53% [-1.83, 5.00] lag 3
Lower Respiratory
>65 yrs 5.16% [1.19, 9.28] lag 3
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design, &
Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
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Atkinson et al. (1999b)
London, United
Kingdom
Period of Study:
1/92-1294
ED visits
outcome(s) (ICD 9):
Respiratory ailments (490-
496), including asthma,
wheezing, inhaler request,
chest infection, COPD,
difficulty in breathing,
cough, croup, pleurisy,
noisy breathing
Age groups analyzed: 0-
14; 15-64; >65;
All ages
Study design: Time series
N: 98,685
# of Hospitals: 12
Statistical analyses:
Poisson regression,
APHEA protocol
Covariates: Long-term
trend, season, day of wk,
influenza, temperature,
humidity
Statistical package: SAS
Lag: 0,1,0-2 and
0-3 days
24-havg: 21.2ug/m,
SD = 7.8
10th: 13.0
50th: 19.8
90th: 31.0
Range: 7.4,82.2
# of Stations: 5
SO2
03 (8 h)
CO(24h),PM25
(24 h) BS
SO2 was closely related
to PM2 5, but
2-pollutant models
showed that the effect of
SO2 was decreased by
NO2 and PM2 5
inclusion. Inclusion of
other pollutants did not
significantly decrease
the influence of SO2 on
ER admissions in
2-pollutant models.
Increment: 18 ug/m in 24-h
Single-pollutant model
Asthma only
0-14 yrs 9.92% [4.75, 15.34] lag 1
15-64 yrs 4.19% [-0.53, 9.13] lag 1
All ages 4.95% [1.53, 8.48] lag 1
All respiratory
0-14 yrs 6.01% [2.98, 9.12] lag 2
15-64 yrs 2.72% [-0.18, 5.70] lag 3
65+ yrs -1.82% [-5.72, 2.25] lag 3
All Ages 2.81% [0.72, 4.93] lag 1
Copollutant models for asthma among
children:
SO2 + NO2: 5.42% [0.18, 10.93]
S02 + 03: 8.39% [3.82, 13.17]
SO2 + CO: 8.05% [3.45, 12.86]
S02 + PM25: 5.63 [0.53, 10.98]
S02 + BS: 8.03 [3.32, 12.96]
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design, Mean Levels & Copollutants Method, Findings,
& Methods Monitoring Stations & Correlations Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
EUROPE (cont'd)
Boutin-Forzano et al.
(2004)
Marseille, France
Period of Study:
4/97-3/98
ED visits Mean: SO2:
outcome(s): Asthma 22.5 ug/m3
ICD9Code(s): NR Range: 0.0,94.0
Age groups analyzed: 3-
49
Study design: Case-
NO2; r = 0.56 No association was
O3;r=-0.25 observed between ER
visits for asthma and SO2
levels.
Only single-pollutant
Increment: 10 ug/m
Increased ER visits
OR 1.0023 [0.9946, 1.0101] lag 0
OR 0.9995 [0.9923, 1.0067] lag 1
crossover
N: 549
Statistical analyses:
Logistic regression
Covariates: Minimal daily
temperature, maximum
daily temperature,
minimum daily relative
humidity, maximum daily
relative humidity, day of
wk
Statistical package: NR
Lag: 0-4 days
models were utilized.
OR 0.9996 [0.9923, 1.0069] lag 2
OR 0.9970 [0.9896, 1.0045] lag 3
OR 0.9964 [0.9889, 1.0040] lag 4
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
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Buchdahletal. (1996)
London, United
Kingdom
Period of Study:
3/1/92-2/28/93
ED visits
outcomes: Daily acute
wheezy episodes
ICD9: NR
Age groups analyzed:
Study design: Case-
control
N: 1,025 cases, 4,285
controls
Number of hospitals: 1
Statistical analyses:
Poisson regression
Covariates: Season,
temperature, wind
speed
Season:
Spring (Apr-Jun),
Summer (Jul-Sep),
Autumn (Oct-Dec),
Winter (Jan-Mar)
Statistical package:
Stata
Lag: 0-7 days
SO2 24-h yr round Mean:
22 ug/m3,
SD= 14
IQR: ug/m3
Spring: 20(14)
Summer: 18(22)
Fall: 24(14)
Winter: 25(14)
NO2;r=0.62 Variations in SO2 could Increment: 14 ug/m3 (Std. Dev.)
C>3; r = -0.28 not explain the U-shaped
relationship between No adjustments to model
ozone and incidence of RR U6 [UOj 1.23] lag not specified
asthma.
Adjusted for temperature and season.
RR 1.12 [1.06, 1.19] lag not specified
Adjusted for temperature, season and wind
speed.
RR 1.08 [1.00, 1.16] lag not specified
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
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Reference, Study
Location, & Period
Outcomes, Design, &
Methods
Mean Levels &
Monitoring Stations
Copollutants Method, Findings, Effects: Relative Risk or % Change
& Correlations Interpretation & Confidence Intervals (95%)
EUROPE (cont'd)
Castellsague et al.
(1995)
Barcelona, Spain
Period of Study:
1986-1989
ED visits
outcome(s): Asthma
ICD9Code(s): NR
Age groups analyzed:
15-64
Study design: Time
series
# of Hospitals: 4
Statistical analyses:
Poisson regression
covariates: Long time
trend, day of wk,
temperature, relative
humidity, dew point
temperature
Seasons :
Winter : Jan-Mar;
Summer : Jul-Sep
Dose-response
investigated: Yes
Statistical package: NR
Lag: 0, 1-5 days and
cumulative.
Summer: lag 2 days
Winter: lag 1 day
Mean SO2 (ug/m )
Summer: 40.8
25th: 25
50th: 36
75th: 54
95th: 82
Winter: 52.0
25th: 36
50th: 49
75th: 67
95th: 94
# of Stations: 15
manual, 3 automatic
NO2 Interaction between Increment: 25 ug/m3
O3 pollutants and asthma
emergency room visits Seasonal differences
was influenced by soy- Summer'
bean dust in the air. RR l Q52 [Q 980> U29] kg 2
Winter:
The daily mean of asthma RR l mo [0.960, 1.084] lag 1
visits and level of SO2
were higher in the winter
than in the summer. A
positive but not
statistically significant
increase in relative risk
was found for SO2 in the
summer. SO2 levels were
higher in the winter, but
the RR was lower
compared to the RR in the
summer. SO2 was not
significantly associated
with asthma related ER
visits.
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
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Reference, Study Outcomes, Design, &
Location, & Period Methods
Mean Levels & Copollutants Method, Findings,
Monitoring Stations & Correlations Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
EUROPE (cont'd)
Dab+ et al. (1996) Hospital admissions
Paris, France outcomes (ICD 9): All
respiratory
Period of Study: (460-519), Asthma
1/1/87-9/30/92 (493), COPD
(490-496)
Age groups analyzed:
All ages
Study design: Time
series
Number of hospitals:
27
Statistical analyses:
Poisson regression,
followed APHEA
protocol
Covariates:
Temperature, relative
humidity, influenza,
Long-term trend,
season, holiday,
medical worker strike
Lag: 0,1, 2 days, 0-3
cumulative
All Yr: NO2
24-h avg: 29.7 ug/m3 O3
Median: 23.0 PM13
5th: 7.0 BS
99th: 125.0
1-hmax: 59.9
Median: 46.7
5th: 14.0
99th: 232.7
Warm season
24-h avg: 20.1
Median: 18.3
5th' 6 0
99th: 49.3
1-hmax: 42.7
Median: 37.0
5th: 13.0
99th: 133.7
Cold season
24-h avg: 40.1 ug/m3
ivieciian. 31.3
C4-1-.. Q H
jtn. 5. /
99th: 149.0
1-hmax: 78.3
Median: 60.7
5th: 17.0
99th: 268.3
1-h maximum SO2 levels
yielded lower relative risk
when compared to 24-h avg
levels. COPD effects were
only significantly associated
with SO2 with no lag.
The strongest association was
observed with PM13; 4.5%
increase in respiratory
admission per 100 ug/m3
increment. SO2 was a close
second.
Neither analysis by age or by
season showed a significant
sensitivity for hospital
admissions. The strongest
association for asthma
admission for all pollutants
was with SO2 24-h avg of 7%
[0.14, 14. 10], but one hour
maximum level was not
significant. The strongest
association for admission
with COPD diagnosis was
also for 24-h avg of SO2
(9.9% [2.3, 18]).
Increment: 100 ug/m
All respiratory (1987-1990)
24-h avg RR 1.042 [1.005,
lag 0-2
1-h max RR 1.018 [0.988, 1
lag 0-2
Asthma (1987-1992)
24-h avg RR 1.070 [1.004,1
lag 2
1-h max RR 1.047 [0.998, 1
lag 2
COPD
24-h avg RR 1.099 [1.023,
lagO
1-h max RR 1.051 [1.025,1
lagO
1.080]
.048]
.141]
.098]
1.180]
.077]
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants Method, Findings,
& Correlations Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
EUROPE (cont'd)
de Diego Damia et al.
(1999)
Valencia, Spain
3/1994-3/1995
ED visits
outcome(s) (ICD 9):
Asthma (493)
Age groups analyzed: >12
Study design:
N: 515
# of Hospitals: 1
Statistical analyses:
Stepwise regression and
ANOVA; Linear
regression
Covariates: Season and
temperature
Statistical package: SPSS
Lag:
24-h avg SO2
(Hg/m3)
Winter
Mean: 56
Range: 30,86
Spring
Mean: 47
Range: 34,75
Summer
Mean: 40
Range: 12,62
Autumn
Mean: 50
Range: 42, 59
Number of monitors: 1
BS; r = 0.54 The SO2 concentration
was averaged for each
season and quartiles of
concentration
determined. Asthma
visits that occurred in
each season were
examined. There were
no significant
associations with
asthma ER visits with
any season or with any
quartile of SO2
exposure.
Mean number of asthma-related ED
visits based on quartile of SO2
All yr:
<41 ng/m3: 8.6
41-50 ng/m3: 9.1
51-56 ng/m3: 11.6
>56|ig/m3: 11.9
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design, &
Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
EUROPE (cont'd)
Fuscoetal. (2001)
Rome, Italy
Period of Study:
1/1995-10/1997
Hospital admissions
outcomes
(ICD9codes): All
Respiratory
(460-519, excluding
24-havg: 9.1 (5.8)
ug/m3
25th: 5.1
50th: 7.9
75th: 12.0
03;r=-0.35
CO; r= 0.56
NO2;r=0.33
Particles;
r = 0.25
SO2 did not have an
effect on respiratory
hospitalizations.
Increment: 6.9 ug/m3 (IQR)
Respiratory conditions: All ages:
0.4% [-1.3, 2.2] lag 0
0.8% [-0.9, 2.41 lag 1
470-478); Acute
respiratory infections
including pneumonia
(460-466, 480-486),
COPD
(490-492, 494-496),
asthma (493)
Age groups analyzed:
All ages, 0-14
Study design: Time
series
# of Hospitals:
Statistical analyses:
Poisson regression with
GAM
# of monitors: 5
0.3% [-1.3, 1.8] lag 2
0-14 yrs:
-0.7% [-4.0, 2.7] lag 0
-2.0 [-5.2, 1.3] lag 1
-0.8 [-3.8, 2.3] lag 2
Acute respiratory infections: All ages:
0.4% [-2.1, 3.0] lag 0
1.4% [-1.0, 3.9] lag 1
1.2% [-1.0, 3.5] lag 2
0-14 yrs:
-0.1% [-3.9, 3.8] lag 0
-2.7% [-6.3, 1.0] lag 1
-1.2% [-4.5, 2.2] lag 2
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
EUROPE (cont'd)
Fuscoetal. (2001)
(cont'd)
Covariates:
Influenza epidemics,
Asthma:
All ages:
day of study,
temperature,
humidity, day of wk,
holidays
Statistical package:
S-Plus 4
Lag: 0,1,2,3,4
-1.5% [-6.6, 3.9] lag 0
-1.5% [-6.5, 3.7] lag 1
2.5% [-2.2, 7.4] lag 2
0-14yrs:
-2.6 [-10.4, 6.0] lag 0
4.3% [-3.5, 12.7] lag 1
5.5% [-1.8, 13.2] lag 2
COPD:
All ages:
1.0%[-1.9,4.0]lagO
-1.1% [-3.9,1.8] lag 1
-0.5% [-3.1,2.1] lag 2
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels & Copollutants
Monitoring Stations & Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
EUROPE (cont'd)
Galan et al. (2003)
Madrid, Spain
Period of Study:
1995-1998
ED visits
outcome(s) (ICD9):
Asthma (493)
Age groups analyzed: All
Study design:
Time series
N: 4,827
Statistical analyses:
Poisson regression, (1)
classic APHEA protocol
and (2) GAM with
stringent criteria
Covariates: Trend, yr,
season, day of wk,
holidays, temperature,
humidity, influenza, acute
respiratory infections,
pollen
Statistical package: NR
Lag: 0-4 days
24-h mean: 23.6 ug/m3 PM2.5; r = 0.581
SD=15.4 NO2;r= 0.717
10th: 9.2 O3;r= -0.188
25th: 12.3
50th: 18.7
75th: 31.3
90th: 43.9
Range: 5,121.2
# of Stations: 15
SO2 registered a
predominately winter
based pattern, and was
positively correlated with
PM2.5,NO2. The lag that
described the strongest
association was 3 days.
Multipollutant models
were fitted for cold season
pollutants. SO2 was the
most affected when PM2 5
was included in the model.
Parametric estimates using
APHEA protocol
produced similar results as
GAM.
The SO2 association may
Increment : 10 ug/m3
Asthma :
RR lag 01.0 18 [0.984,1
RRlagl 1.005 [0.972,1
RR lag 2 1.002 [0.970,1
RR lag 3 1.029 [0.997,1
RR lag 4 1.025 [0.994,1
Multipollutant model:
SO2/PM2.5 0.966 [0.925,
.054]
.039]
.036]
.062]
.058]
1.009]
be due to the concealing
effects of other pollutants.
PM2 5 accounted for most
of the observed effects.
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study Outcomes, Design, Mean Levels & Copollutants Method, Findings,
Location, & Period & Methods Monitoring Stations & Correlations Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
EUROPE (cont'd)
Garty et al. (1 998) ED visits 24-h mean of SO2 NOX
Tel Aviv, Israel outcome(s): Asthma (estimated from SO2
1993 ICD9Code(s): NR histogram): 27 ug/m3 O3
Age groups analyzed: 1- Range: 11,64
18
Study design: Descriptive
study with correlations
N: 1,076
Statistical analyses:
Pearson correlation and
partial correlation
coefficients
Covariates: Maximum
and minimum ambient
temperatures, relative
humidity and barometric
pressure
Statistical package:
Statistix
Asthma morbidity was
higher in the autumn and
winter than the rest of the
yr. The number of ER
visits is September was
exceptionally high.
The percent of total
variance showed positive
correlation between
asthma ER visits in
children and high levels of
NOX, SO2 and increased
barometric pressure. NOX
enhances the effects of
SO2, whereas O3 had a
reverse relation to SO2.
Air borne pollen was not a
significant contributor to
ER visits.
Correlation between SO2 and ER visits
for asthma:
All Yr:
Daily data r = 0.24
Running mean for 7 days r = 0.53
Excluding September:
Daily data r= 0.31
Running mean for 7 days r = 0.64
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
EUROPE (cont'd)
Hagen et al. (2000)
Drammen, Norway
Period of Study:
1994-1997
Hospital admissions
outcomes (ICD 9): All
respiratory admissions
(460-519)
Age groups analyzed:
All ages
Study design:
Time series
Number of hospitals: 1
Statistical analyses:
Poisson regression with
GAM (adhered to HEI
phase l.B report)
Covariates: Time trends,
day of wk, holiday,
influenza, temperature,
humidity
Lag: 0,1, 2,3 days
SO2 24-h avg (ug/m3):
3.64, SD = 2.41
25th: 2.16
50th: 2.92
75th: 4.38
# of Stations: 2
PM2.5; r = 0.42
NO2;r = 0.58
benzene; r = 0.29
NO; r = 0.47
O3;r=-0.24
Formaldehyde;
r=0.54
Toluene; r = 0.48
SO2 was significantly
associated with
respiratory hospital
admissions. This
relationship was robust
to the inclusion of PM2 5,
but attenuated when both
PM2 5 and benzene were
included in the model.
Increment: SO2: 2.22 ug/m3 (IQR)
Single-pollutant model
Respiratory disease only
1.056 [1.013, 1.101]
All disease
0.990 [0.974, 1.007]
2-pollutant model with PM2 5
1.051 [1.005, 1.099]
3-pollutant model with
PM2 5 ~t~ Benzene
1.040 [0.993, 1.089]
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
to
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oo
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Reference, Study
Location, & Period
Outcomes, Design, &
Methods
Mean Levels & Copollutants
Monitoring Stations & Correlations
Method, Findings, Effects: Relative Risk or % Change &
Interpretation Confidence Intervals (95%)
EUROPE (cont'd)
Hajatetal. (1999)
London, United
Kingdom
Period of Study:
1992-1994
GP visits
outcome (ICD9):
Asthma (493); Lower
respiratory disease
(464, 466, 476, 480-3,
490-2, 485-7, 4994-6,
500,503-5,510-5)
Age groups analyzed:
0-14;
15-64; 65+; all ages
Study design: Time-
series analysis
Statistical analysis:
Poisson regression,
APHEA protocol
Covariates: Long-term
trends, seasonality, day
of wk, temperature,
humidity
Seasons:
Warm, Apr-Sep; Cool,
Oct-Mar;
All-yr
Dose-response
investigated? Yes
Statistical package:
SAS
Lag: 0-3 days,
cumulative
Allyr NO2;r = 0.61
24-havg: 21.2 ug/m3, BS;r=0.57
SD = 7.8 CO; r= 0.51
10th: 13.0 PM2.5;r=0.63
90th: 31.0 O3;r=-0.11
Warm:
24-h avg: 20.5 ug/m3,
SD = 6.5
10th: 13.4
90th: 28.4
Cool:
24-h avg: 22.0 ug/m3,
SD = 9.0
10th: 12.8
90th: 33.3
This study showed weak, Increment: 1 8 ug/m3
but consistent (90th-10th percentile)
associations between Asthma
SO2 and consultations All ages 3.6% [0.3, 6.9] lag 2; 4.4% [0.9,
for asthma and other 79] jag Q-2
LRD, especially in 0-14 yrs 4.9% [0.1, 9.8] lag 1; 4.4%
children. Bubble plot [-0 7 9 7] lag 0-2
suggests a concentration- Wam. g Q% [22 162] { l
response relationship. „ . ~ „„, r, c om, ,
F F Cool: 2.0% [4.5, 8.9] lag 1
15-64 yrs 3.6% [-0.6, 8.0] lag 2; 3.5%
[-1.0,8.2] lag 0-3
Warm: 2.5% [-3.3, 8.7] lag 2
Cool: 4. 5% [-1.4, 10.7] lag 2
65 + yrs 4. 5% [-3.5, 13.1] lag 1;4.8%
[-2.9, 13.2] lag 0-1
Warm: 7.5% [-4.0, 20.3] lag 1
Cool: 2.0% [-8.6, 13.9] lag 1
Lower respiratory disease
All ages 1.8% [0.2, 3.4] lag 2; 2.2%
[0.4, 4.1] lag 0-2
0-14 yrs 4.5% [1.4, 7.8] lag 2; 5.7%
[1.7, 9.7] lag 0-3
Warm: 2.4% [-2.6, 7.7] lag 2
Cool: 5.8% [1.6, 10.2] lag 2
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design, &
Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
to
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EUROPE (cont'd)
X
VO
VO
Hajatetal. (1999)
(cont'd)
15-64 yrs 1.5% [-0.7, 3.7] lag 1; 1.6%
[-0.9, 4.1] lag 0-3
Warn: -0.5% [-3.8, 2.9] lag 1
Cool: 2.5% [-0.5, 5.5] lag 1
65 + -2.2% [-4.9, 0.6] lag 0; -1.4%
[-4.4, 1.7] lag 0-1
Warn: -3.1% [-6.9, 0.9] lag 0
Cool: -1.6% [-5.3, 2.3] lag 0
2-pollutant model - Asthma
S02 alone 4.9% [0.1, 9.8]
SO2/O3 5.9% [1.1,10.9]
S02/N022.7%[-2.7, 8.4]
S02/PM2.5 3.4% [-3.0, 10.2]
2-pollutant model-Lower respiratory disease
SO2 alone 4.5% [1.4, 7.8]
S02/034.8%[1.6,8.1]
SO2/NO23.1%[-0.6, 6.9]
S02/PM2.5 3.8% [0.4, 7.2]
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
to
o
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EUROPE (cont'd)
NO2;r = 0.61
BS;r=0.57
CO; r= 0.51
PM2.5; r = 0.63
O3;r=-0.11
>
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Hajat*etal. (2001)
London, United
Kingdom
Period of Study:
1992-1994
GP visits
outcome (ICD9): Allergic
Rhinitis (477)
Age groups analyzed: 0-
14; 15-64; 65+;
all ages
Study design:
Time series analysis
N: 4,214
Statistical analysis:
Poisson regression, GAM
Covariates: Long-term
trends, seasonality, day of
wk, temperature,
humidity, variation in
practice population,
counts for lagged allergic
pollen measures, daily
number of consultations
for influenza
Dose-response
investigated? Yes
Statistical package:
S-Plus
Lag: 0-6 days, cumulative
24-havg: 21.2ug/m,
SD = 7.8
10th: 13.0
90th: 31.0
The number of
allergic rhinitis
admissions peaked in
April and June. After
2-pollutant model
analysis, SO2 still
remained highly
significant in the
presences of other
pollutants. For both
children and adults
exposure-response
associations showed
that risk levels off at
higher SO2 levels.
Increment: 18 ug/m
(90th-10thpercentile)
Single-pollutant model
<1 to 14yrs
24.5% [14.6, 35.2] lag 4
24.9% [11.9, 39.4] lag 0-4
15 to 64 yrs
14.3% [6.2, 23.0] lag 3
15.5% [9.1, 22.3] lag 0-5
>64 yrs-too small for analysis
2-pollutant models
<1 to 14 yrs
S02&03: 22.1% [12.0, 33.1]
S02&N02: 28.5% [15.5, 42.9]
SO2&PM2.5: 27.2% [15.3, 40.2]
15 to 64 yrs
S02 & 03: 8.5% [3.4, 13.9]
SO2&NO2: 8.3% [1.7,15.3]
S02&PM2.5: 6.7% [0.7, 13.0]
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
to
o
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EUROPE (cont'd)
X
(Si
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Hajat*etal. (2002)
London, United
Kingdom
Period of Study:
1992-1994
GP visits
outcome (ICD9): Upper
respiratory disease,
excluding Rhinitis
(460-3, 465, 470-5, 478)
Age groups analyzed:
0-14; 15-64; 65+; all ages
Study design:
Time series analysis
Statistical analysis: Poisson
regression, GAM
Covariates: Long-term
trends, seasonality, day of
wk, holidays, temperature,
humidity, variation in
practice population, counts
for lagged allergic pollen
measures, daily number of
consultations for influenza
Seasons:
Warm, Apr-Sep;
Cool, Oct-Mar
Dose-response investigated?
Yes
Statistical package:
S-Plus
Lag: 0,1,2,3 days
Allyr
24-havg: 21.2 ug/m3,
SD = 7.8
10th: 13.0
90th: 31.0
Warm:
24-h avg: 20.5 ug/m3,
SD = 6.5
10th: 13.4
90th: 28.4
Cool:
24-h avg: 22.0 ug/m3,
SD = 9.0
10th: 12.8
90th: 33.3
# of Stations: 3
NO2;r = 0.61
BS;r=0.57
CO; r= 0.51
PM2.5; r = 0.63
O3;r=-0.11
Increased
consultations for
URD were most
strongly associated
with SO2 in children.
For adults and the
elderly the strongest
associations were for
PM2.5andN02. The
most consistent lag in
adults and the elderly
for development of
URD was 2 days (one
day after a pollution
event).
Increment: 18 ug/m
(90th-10thpercentile)
Single-pollutant model
All yr
0-14 yr 3.5% [1.4, 5.8] lag 0
15-64 yrs 3.5% [0.5, 6.5] lag 1
>65 yrs 4.6% [0.4, 9.0] lag 2
Warm
0-14 yrs 3.2% [-0.5, 7.0] lag 0
15-64 yrs 4.6% [1.5, 7.7] lag 1
>65 yrs 1.6% [-4.8, 8.5] lag 2
Cool
0-14 yrs 5.5% [2.4, 8.7] lag 0
15-64 yrs 2.7 [0.0, 5.4] lag 1
>65 yrs 5.7% [0.4, 11.4] lag 2
2-pollutant models
0-14 yrs
SO2&O3: 1.0% [-2.2, 4.2]
SO2 & NO2: 4.7% [2.2, 7.4]
S02&PM2.5: 4.6% [2.1,7.2]
For 15-64 yrs
S02 & 03: 3.7% [0.6, 7.0]
SO2 & NO2: 2.6% [-0.0, 5.2]
SO2&PM2.5: 2.4% [-0.1, 5.0]
For >65 yrs
S02&03: 9.0% [1.7, 16.9]
S02&N02: 4.3% [-1.2, 10.2]
SO2 & PM2.5: 3.2% [-1.9,8.7]
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design, Mean Levels & Copollutants Method, Findings,
& Methods Monitoring Stations & Correlations Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
EUROPE (cont'd)
Llorca et al. (2005)
Torrelavega, Spain
Period of Study:
1992-1995
Days: 1,461
Hospital admissions 24-h avg SO2:
outcomes (ICD 9): All 13.3 ug/m3,
respiratory admissions SD = 16.7
(460-519)
Age groups analyzed: All # of Stations: 3
ages
Study design:
Time series
Number of hospitals: 1
Statistical analyses:
Poisson regression
Covariates: Short and
Long-term trends
Statistical package: Stata
Lag: NR
NO2; r = 0.588 Associations between
NO; r = 0.544 SO2 and admissions
TSP' r = - 0 40 observed in the
SH2;' r = 0.957 Single-pollutant
model disappear in a
5-pollutant model.
Only NO2 was
significantly
associated with
ciclrrii s s 10 ns
No relation was
described for sulphur
compounds including
H2SorSO2. The
concentration of SO2
changes with
temperature changes,
which may be
responsible for
cardiac stress.
Increment: 100 ug/m3
Single-pollutant model
All cardio-respiratory admissions:
[0.89, 1.07]
Respiratory admissions: 1 .04
[0.90,1.19]
5-pollutant model
All cardio-respiratory admissions:
[0.80, 1.21]
RR0.98
RR0.98
Respiratory admissions: 0.89 [0.64, 1.24]
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SO2 was not
significantly
associated with
cardiac respiratory or
cardio-respiratory
admissions
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
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Oftedal et al. (2003)
Drammen, Norway
Period of Study:
1994-2000
Hospital admissions
outcomes (ICD 10): All
respiratory admissions
(JOO-J99)
Age groups analyzed: All
ages
Study design:
Time series
Statistical analyses: Semi-
parametric Poisson
regression, GAM with
more stringent criteria
Covariates: Temperature,
humidity, influenza
Lag: 2,3 days
Mean: 2.9 ug/m ,
SD = 2.1
IQR: 2.03 ug/m3
PM2.5
N02
03
Benzene
Formaldehyde
Toluene
The study found
positive associations
between daily
number of hospital
admissions for acute
respiratory diseases
and concentrations of
SO2; associations did
not change
substantially from the
first to the second 3-
yr period.
Increment: 2.03 ug/mj (IQR)
All respiratory disease
1.042 [1.011, 1.073]
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
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Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels & Copollutants
Monitoring Stations & Correlations
Method, Findings, Effects: Relative Risk or % Change &
Interpretation Confidence Intervals (95%)
EUROPE (cont'd)
Ponce de Leon et al.
(1996)
London, England
Period of Study:
04/1987-1988;
1991-02/1992
Hospital admissions
outcomes (ICD 9):
All respiratory (460-
519)
Age groups analyzed:
0-14, 15-64, 65+, all
ages
Study design: Time
series
N: 19,901
Statistical analyses:
APHEA protocol,
Poisson regression
GAM
Covariates: Long-
term trend, season,
influenza, day of wk,
holiday, temperature,
iiiumuiiy
Season:
Cool, Oct-Mar;
Warm: Apr-Sep
Dose-response
Investigated?: Yes
Statistical package:
SAS
Lag: 0,1, 2 days, 0-3
cumulative avg
SO2 24-h avg: NO2; r = 0.44
32.2 ug/m3, SD = 12.6 BS; r = 0.44
5th: 15 03;r= -0.067
10th: 18
25th: 24
50th: 31
75th: 39
90th: 47
95th: 54
# of stations: 2
Though significant effects Increment: 90th- 1 Oth percentile
were observed with SO2 in (24-h avg: 29 ug/m3).
some age groups, they were
not consistent or similar in All yr
magnitude to those of O3. A11 ages j Q092
[0.9926, 1.0261] lag 1
0- 14 yrs 1.0093
[0.9837, 1.0356] lagl
1 5-64 yr 1.0223
[0.9942, 1.05 11] lagl
>65yr 1.0221
[0.9970, 1.0478] lag 2
Warm season
All ages 1.01 11
[0.9864, 1.0364] lagl
0-14 yrs 1.0468
[1.0066, 1.0885] lagl
15-64 yr 0.9996
[0.9596, 1.0411] lagl
>65yr 1.0124
[0.9772, 1.0489] lag 2
Cool season
All ages 1 0079
[0.9857, 1.0306] lagl
0-14 yrs 0.9848
[0.9515, 1.0192] lagl
15-64 yr 1.0389
[1.0010, 1.0783] lagl
>65yr 1.0280
[0.9945, 1.0625] lag 2
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
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Ponka(1991)
Helsinki, Finland
Period of Study:
1987-1989
Hospital admissions
outcomes (ICD 9
codes): Asthma
(493)
Age groups analyzed:
0-14; 15-64; >65yrs
Study design: Time
series
N: 4,209
Statistical analyses:
Correlations and
partial correlations
Covariates:
Minimum
temperature
Statistical package:
Lag: 0-1
24-havg: 19.2
(12.6) ug/m3
Range: 0.2, 94.6
Number of monitors: 4
NO2;r = 0.4516
NO; r = 0.4773
03;r = 0.1778
TSP;r = 0.1919
CO
The frequency of all
admissions for asthma was
significantly correlated to
SO2.
Child asthma admissions
were not significantly
correlated with SO2, but
were correlated to O3 and
NO. SO2 was also
significantly correlated with
elderly admissions.
Increased hospitalization
correlated with SO2 was
also observed for adults.
Hospital admissions were
more strongly correlated
with SO2 than other
pollutants. ER visits were
more strongly correlated
with a mixture of pollutants
(TSP, SO2, O3> and
temperature).
Multipollutant model
co-linear results of SO2,
CO, NO2> and NO suggest a
mixture of pollutants is
responsible for asthma
admissions.
Correlations between hospital admissions
(HA) for asthma and pollutants and
temperature by ages.
0-14yrs
HA: -0.01391
Emergency HA: 0.0332
15-64yrs
HA: 0.1039 p = 0.0006
Emergency HA: 0.1199 p< 0.0001
>65 yrs
HA: 0.0796 p = 0.0085
Emergency HA: 0.1169 p< 0.0001
Partial correlations between admissions
for asthma and SO2 were standardized for
temperature.
HA: 0.0770 p = 0.0172
Emergency HA: 0.1050
p = 0.0011
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65
Study design: Time
series
Statistical analyses:
Poisson regression
Covariates: Season,
day ofwk, yr,
influenza, humidity,
temperature
Season: Summer
(Jun-Aug), Autumn
(Sep-Nov), Winter
(Dec-Feb), Spring
(Mar-May)
Lag: 0-7 days
In the steps leading to
regression analysis no
association was observed
between SO2 levels and the
>65 population.
Multipollutant models were
only used to examine NO2
and SO2.
SO2 had no significant
association with morbidity
caused by chronic bronchitis
and emphysema in the >65
yr old population.
RR 0.78 [0.59, 1.03] lag 2
RR 1.39 [1.05, 1.86] lag 3
RR 0.89 [0.68, 1.16] lag 4
RR 1.28 [0.97, 1.70] lag 5
RR0.91 [0.69, 1.20] lag 6
RR 1.09 [0.84, 1.40] lag 7
65+ yrs
NR
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design, Mean Levels &
& Methods Monitoring Stations
Copollutants Method, Findings,
& Correlations Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
EUROPE (cont'd)
Ponka and Virtanen,
(1996)
Helsinki, Finland
Period of Study:
1987-1989
Hospital admissions 24-h avg (ug/m3):
outcomes (ICD 9 Winter: 26
codes): Asthma Spring: 22
(493) Summer: 13
Age groups analyzed: fa\\- 15
0-14,
15-64, 65+
Study design: Time
series
Statistical analyses:
Covariates: Long-
term trend, season,
epidemics, day of
wk, holidays,
temperature, relative
humidity
Statistical package:
Lag: 0-2
NO2 Significant
O3 associations were
Xgp observed between daily
SO2 concentrations and
daily counts of
hospitalizations among
15-to64-yr-old
patients and among
those over 64 years
old, but not among
children. These effects
were observed when
mean daily SO2 values
were lower than the
maximum value
recommended by
WHO (125 ug/m3).
Parameter estimates (PE) and standard erro (SE)
for a 1-unit increase:
Asthma 15-64yrs :
PE 0.2176 (0.1081) p = 0.44 lag 2
PE 0.3086 (0.1545) p = 0.046 lag 0-3
Asthma 65+ yrs :
PE 0.2412 (0.0956) p = 0.012 lag 2
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
EUROPE (cont'd)
Prescottetal. (1998)
Edinburgh, United
Kingdom
Period of Study:
10/92-6/95
Hospital admissions
outcomes (ICD 9):
Pneumonia
(480-7), COPD + Asthma
(490-496)
Age groups analyzed: <65,
SO2: 14.5(9.0)ppb
Min: 0 ppb
Max: 153 ppb
# of Stations: 1
CO
PM2.5
NO2
03
BS
No effect of SO2 on
hospitalizations
observed in either
age category.
Increment: 10 ppb
Respiratory admissions
>65yrs -2.5 [-11. 0,6.9] lag 0-2
< 65 yrs 0.0 [-8.3, 9.1] lag 0-2
o
oo
65+
Study design:
Time series
Statistical analyses:
Poisson log linear
regression
Covariates: Trend, seasonal
and weekly variation,
temperature, wind speed,
day of wk
Lag: 0,1, or 3 day rolling
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels & Copollutants
Monitoring Stations & Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
EUROPE (cont'd)
Rossi etal. (1993)
Oulu, Finland
Period of Study:
10/1/1985-9/30/1986
ED visits
outcome(s) (ICD 9):
Asthma (493)
Age groups analyzed: 15-
85
Study design:
24-h mean: 10.0 ug/m3 NO2; r = 0.48
Range: 0,56 TSP; r=0.31
H2S
1-hmax:
31.0 ug/m3
Range: 1,24
Same day ER visits
were correlated to
daily SO2 levels, but
the significance was
lost with longer lag
periods.
Pearson correlation coefficients
ED asthma visits and same day
r = 0.13p<0.01
lagO
SO2:
Time series
N: 232
Statistical analyses:
Pearson's and partial
correlation coefficients and
multiple regression with
stepwise discriminate
analysis
Covariates: Temperature,
humidity
Statistical package: BMDP
software
Lag: 0,1,2,3
# of monitoring stations:
4
When asthma visits
were analyzed, SO2
was positively and
significantly
correlated with
asthma visits in the
same wk and the wk
after.
After regression
analyses, SO2
became insignificant.
Weekly ED asthma visits and same wk
SO2: r = 0.28 p< 0.05
Weekly ED asthma visits and previous wk
SO2: 0.30p<0.05
Multipollutant (NO2; TSP; H2S)
Regression coefficient:
Allyr: (3 = 0.037, p = 0.535
Winter: p = -0.024, p = 0.710
Summer: p =-0.003, p = 0.991
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
EUROPE (cont'd)
Sunyeretal. (1991)
Barcelona, Spain
Period of Study:
1985-1986
ED visits
outcome(s) COPD (ICD
9): 490-496
Age groups analyzed: >14
Study design:
Time series
# of Hospitals: 4
Statistical analyses:
multivariate linear
regression
Covariates: Meteorology,
season, day of wk
Statistical package:
Lag: 0 to 2 days
24-havg(SD): 56.5
(22.5) ug/m3
98th: 114.3
Range: 17, 160
l-hmax(SD): 141.9
(98.8) ug/m3
98th: 461.3
Range: 17, 160
Number of monitors:
14-720
BS, CO, NO2, O3
An incremental
change of 25 ug/m3
in SO2 was correlated
with an adjusted
increase of 0.5 daily
visits due to COPD.
SO2 and ER visits
were more strongly
correlated in warm
weather.
Even at 24-h avg
levels less than
100 ug/m3, effects of
SO2 were statistically
significant for COPD
admissions.
Change in 24-h SO2 daily ER
ug/m3 admissions P-value
1500.55 < 0.01
100 0.7 < 0.01
72 0.7 0.04
52 0.41 >0.05
39 -1.27 >0.05
0.5 excess daily admissions per 25 ug/m
increment of SO2.
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants Method, Findings,
& Correlations Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
EUROPE (cont'd)
Sunyeretal. (1993)
Barcelona, Spain
Period of Study:
1985-1989
ED visits
outcome(s) (ICD 9):
COPD (490-492;
494-496)
Study design:
Time series
Statistical analyses:
Autoregressive linear
regression
Statistical package:
T on- I?
l^ag . 1 ,^
SO2, 24-h
Winter Tertiles (ug/m3)
<40.4
40.4, 61
>61
Winter Tertiles (ug/m3)
<28.1
28.1,46.1
>46.1
BS SO2 concentrations were
associated with the
number of COPD ER
admissions in the winter
and summer. An increase
of 25 ug/m3 in SO2
produced an adjusted
change of ~6% and 9%,
respectively, in the
number of COPD
emergencies in the winter
and summer. Controlling
for particulate matter
resulted in a loss of
significance. Co linearity
ofBSwithSO2was
observed.
Effects were expressed as adjusted
changes in daily COPD ER admissions
based on an increment of 25 ug/m3.
Winter: 6%
Summer: 9%
Mean ER admissions for COPD (winter)
were 15.8 (range 3, 34) and 8.3
(range 1, 24) in the summer.
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
to
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EUROPE (cont'd)
>
X
Teniasetal. (1998)
Valencia, Spain
Period of Study:
1993-1995
Seasons:
Cold: Nov-Apr
Warm: May-Oct
ED visits
outcome(s): Asthma
ICD 9 Code(s): NR
Age groups analyzed: >14
Study design: Time series
N: 734
Statistical analyses:
Poisson regression,
APHEA protocol
Covariates: seasonality,
temperature, humidity,
long-term trend, day of
wk, holidays, influenza
Seasons:
Cold: Nov-Apr;
Warm: May-Oct
Dose-Response
Investigated: Yes
Statistical package: NR
Lag: 0-3 days
24 h: 26.6 ug/m3
25th: 17.9
50th: 26.2
75th: 34.3
95th: 42.6
Cold: 31.7
Warm: 21.7
1-hmax: 56.3 ug/m3
25th: 36.3
50th: 52.2
75th: 72.2
95th: 95.2
Cold: 64.6
Warm: 48.2
# of Stations: 2
24 h:
O3;r= -0.431
NO2 (24 h);
r = 0.265
N02 (1 h);
r = 0.199
Ih:
O3;r= -0.304
NO2 (24 h);
r = 0.261
NO2 (1 h);
r = 0.201
SO2 showed the strongest
correlation to asthma
admissions during the
warm months.
Multipollutant models
showed that O3 and black
smoke had a small effect
on the association between
SO2 and asthma ER visits
while NO2 greatly
depressed these effects. It
is likely that NO2 was the
dominant pollutant for
respiratory outcomes. SO2
was the "most vulnerable
pollutant" to the presence
of other pollutants.
Increment: 10 ug/m
SO2 24-h avg
All yr 1.050
[0.973, 1.133] lag 0
Cold 1.032
[0.937, 1.138] lag 0
Warm 1.070
[0.936, 1.224] lag 0
SO2 1-hmax
Allyr 1.027 [0.998, 1.057] lag 0
Cold 1.018 [0.980, 1.057] lag 0
Warm 1.038 [0.990, 1.090] lag 0
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
to
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>
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Tenias et al. (2002)
Valencia, Spain
Period of Study:
1994-1995
ED visits
outcome(s): COPD
ICD 9 Code(s): NR
Age groups analyzed: >14
Study design:
Time series
N: 1,298
# of Hospitals: 1
Statistical analyses:
Poisson regression,
APHEA protocol; basal
models and GAM
Covariates: Seasonality,
annual cycles,
temperature, humidity,
day of wk, feast days
Seasons:
Cold, Nov-Apr;
Warm, May-Oct
Dose-response
Investigated: Yes
Statistical package: NR
Lag: 0-3 days
24 h: 26.6 ug/m3
25th: 17.9
50th: 26.2
75th: 34.3
95th: 42.6
Cold: 31.7
Warm: 21.7
1-hmax: 56.3 ug/m3
25th: 36.3
50th: 52.2
75th: 72.2
95th: 95.2
Cold: 64.6
Warm: 48.2
BS;r= 0.687
NO2;r=0.194
CO; r= 0.734
O3;r= -0.431
SO2 did not show any
significant association
with COPD ER visits
for all seasons
analyzed.
SO2 did not affect O3
or CO association to
ER admission for
COPD when assessed
together in the
Multipollutant model.
Possibility of a linear
relationship between
pollution and risk of
emergency cases could
not be ruled out.
Increment: 10 ug/m .
24-h avg SO2
All yrRR 0.971
[0.914, 1.031] lag 0
Cold, 24-h avg: RR 0.970
[0.905, 1.038] lag 0
Warm, 24-h avg: RR 0.982
[0.885, 1.090] lag 0
1-hmax SO2
All yrRR 0.981 [0.958, 1.027] lag 3
Cold, 24-h avg: RR 0.972
[0.945,1.000] lag 3
Warm, 24-h avg: RR 1.003
[0.979, 1.056] lag 3
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design, &
Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
EUROPE (cont'd)
Thompson etal. (2001)
Belfast, Northern
Ireland
Period of Study:
1993-1995
Hospital admissions/ED
visits
Outcome(s): Asthma
ICD9Code(s): NR
Age groups analyzed:
Children
Study design: Time series
N: 1,044
Statistical analyses:
Followed APHEA
protocol, Poisson
regression analysis
Covariates: Season, long-
term trend, temperature,
day of wk, holiday
Season:
Warm (May-Oct); Cold
(Nov-Apr)
Statistical package: Stata
Lag: 0-3
Warm Season
SO2 (ppb):
Mean: 12.60;
SD = 10.60;
IQR: 6.0,16.0
Cold Season
S02 (ppb):
Mean: 20.40;
SD = 17.90;
IQR: 11.0,24.0
PM2.5; r = 0.66
NO2;r=0.82
N0x;r=0.83
NO; r = 0.76
O3;r=-0.58
CO; r= 0.64
Benzene;
r=0.80
This study found weak,
positive associations for
SO2 and adverse
respiratory outcomes in
asthmatic children.
SO2 Increment: Per doubling (ppb)
Lag ORR 1.07 [1.03, 1.11]
Lag 0-1 RR 1.09
[1.04,1.15]
Lag 0-2 RR 1.08
[1.02,1.15]
Lag 0-3 RR 1.08
[1.01,1.15]
Warm only Lag 0-1 RR 1.11
[1.04,1.19]
Cold only Lag 0-1 RR 1.07
[1.00,1.15]
Adjusted for Benzene Lag 0-1 RR 0.99
[0.90, 1.09]
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design, & Mean Levels & Copollutants Method, Findings, Effects: Relative Risk or % Change &
Methods Monitoring Stations & Correlations Interpretation Confidence Intervals (95%)
EUROPE (cont'd)
Tobias etal. (1999)
Barcelona, Spain
Period of Study:
1986-1989
ED visits 24-h avg SO2 ug/m3 BS
outcome(s): Asthma NO2
ICD9: NR Non-epidemic days: 85.8 O3
Age groups analyzed: (62.4)
>14 Epidemic days: 116.3
Study design: (79.3)
Time series
Statistical analyses:
Poisson regression,
followed APHEA
protocol
Covariates:
Temperature,
humidity, long-term
trend, season, day of
wk
Statistical package:
NR
Lag: NR
The study failed to find a (3 x 104 (SE x 104 ) using Std Poisson
significant association Without modeling asthma epidemics: 3 ,
between SO2 and asthma (4.14)
ED visits. Modeling epidemics with 1 dummy
variable: 1. 64 (2.76)
Modeling epidemics with 6 dummy
variables: 1.53(2.75)
Modeling each epidemic with dummy
variable: 2.20(2.65)
.99
P x 104 (SE x 104) using Autoregressive
Poisson
Without modeling asthma epidemics: 6,
(14.37)
Modeling epidemics with 1 dummy
variable: 1.68(2.77)
Modeling epidemics with 6 dummy
variables: 1.72(2.75)
Modeling each epidemic with dummy
variable: 2.85(2.89)
.99
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
to
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Reference, Study Outcomes, Design, &
Location, & Period Methods
Mean Levels & Copollutants Method, Findings,
Monitoring Stations & Correlations Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
EUROPE (cont'd)
Vigotti et al. (1996) Hospital admissions
Milan, Italy outcomes
(ICD 9 codes):
Period of Study : Respiratory disease
1980-1989 (460-519).
Age groups analyzed:
15-64yrsand
>64 vrs
Study design: Time
series
N: >73,000
# of Hospitals:
Statistical analyses:
APHEA protocol
Covanates'
Season: cold season
(Oct. to March) and
warm season
(Apr to Sept.)
Statistical package:
Lag: 0, cumulative 4
day
(0-3)
24-havg: 117.7 ug/m3 TSP;r=0.63 The effect of single day or
Range: 3.0,827.8
5th: 15.0
25th: 34.0
50th: 65.5
75th: 162.5
95th: 376.3
Winter: 248.6
Range: 30.6,827.8
5th: 78.8
25th: 138.5
50th: 216.0
75th: 327.8
95th: 527.0
Summer: 30.5
Range: 3.0,113.8
5th: 9.1
25th: 18.5
50th: 27.8
75th: 39.2
95th: 62.7
# of monitors: 4;
r = 0.89, 0.91
cumulative day exposure
to SO2 was more
pronounced during the
cool months. Interaction
between seasons was not
significant. SO2 did not
interact with TSP. No
differences were noted
between age groups.
There were increased, but
not significant
(borderline), risks for
increased hospital
admissions based on an
increment change in SO2
of 125 ug/m3 in the
winter.
Increment: 100 ug/m
All respiratory
15-64yrs
All yr round:
RR 1.05 [1.00, 1.10] lag
Warm:
RR 1.04 [0.98, 1.11] lag
Cool:
RR 1.06 [1.00, 1.13] lag
>64 yrs
All yr:
RR 1.04 [1.00, 1.09] lag
Warm:
RR 1.02 [0.96, 1.08] lag
Cool:
RR 1.05 [1.00, 1.11] lag
;0
;0
;0
;0
;0
;0
O
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH EMERGENCY
DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design, &
Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
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EUROPE (cont'd)
>
X
Walters etal. (1994)
Birmingham, United
Kingdom
Period of Study:
1988-1990
Hospital admissions
outcomes
(ICD 9 codes):
Asthma (493) and acute
respiratory conditions
(466, 480-486,
490-496)
Study design:
Time series
Statistical analyses:
Least squares regression
Covariates: Temperature,
pressure, humidity
Lag: 3 day moving avg.
SO2 24-h mean (ug/m )
All yr: 39.06
Max: 126.3
Spring: 42.9
Summer: 37.8
Autumn: 40.9
Winter: 34.2
BS
In 2-pollutant models BS
remained significant but
SO2 was no longer
associated significantly
with admission.
A 100 ug/m3 increment
in SO2 might result in
four (0-7) more asthma
admissions and 15.5
(6-25) move respiratory
admissions/day. Spring
and autumn did not show
associations with
admissions for asthma or
respiratory.
Increment of 100 ug/m
Asthma
Summer: 1.4% [-10, 39] lag 0
Winter: 2.7% [-0.8, 6.1] lag 0
All respiratory
Summer: 5.9% [1.1, 10.6] lag 0
(p < 0.02)
Winter: 18% [8.8,26.8] lag 0
(p < 0.0002)
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
to
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Reference, Study
Location, & Period
LATIN AMERICA
Braga*etal. (1999)
Sao Paulo, Brazil
Period of Study:
10/1992-10/1993
Outcomes, Design, & Mean Levels & Copollutants
Methods Monitoring Stations & Correlations
Hospital admissions 24-h avg 22.40 (9.90) PM2.5; r = 0.73
outcomes (ICD 9 codes): ug/m3 CO; r = 0.62
All respiratory (466,480- Min: 6.4 NO2; r = 0.53
486,491-492,496) Max: 69.6 O3;r =
Age groups analyzed: <13
Yrs # of monitors: 13
Study design:
Time series
N/~o c\ 1 O
Do y 1 o
# of Hospitals:
1 12 Statistical analyses:
Multiple linear regression
models (least squares).
Also used Poisson
regression techniques.
GLM and GAM using
LOESS for smoothing.
Covariates: Season,
temperature, humidity, day
of wk,
Statistical package: SPSS,
S-Plus
Lag: 1,2,3,4,5,6,7 moving
avgs
Method, Findings, Effects: Relative Risk or % Change
Interpretation & Confidence Intervals (95%)
SO2 did not show a Increment: 22.4 ug/m3
correlation with
respiratory hospital 0. 12 [- 0.04, 0.28] lag 0
admissions with any lag Q lg f_0 OQ; Q 37] lag Q.J
structure. Qlg ^ Q Q^ Q ^ lag Q_2
0.18 [-0.04, 0.40] lag 0-3
0.18 [-0.05, 0.42] lag 0-4
0.12 [-0.13, 0.36] lag 0-5
0.08 [-0.18, 0.35] lag 0-6
-------
TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
to
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LATIN AMERICA (cont'd)
>
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Braga*etal. (2001)
Sao Paulo, Brazil
Period of Study:
1/93-11/97
Hospital admissions
outcomes (ICD 9):
All respiratory
admissions
(460-519)
Age groups analyzed:
0-19, <2, 3-5,
6-13, 14-19
Study design: Time
series
Statistical analyses:
Poisson regression
with GAM
term trend, season,
temperature, relative
humidity, day of wk,
holiday
Statistical package:
S-Plus 4.5
Lag: 0-6 moving avg
SO2 mean:
21.4 ug/m3;
SD= 11.2
IQR: 14.4 ug/m3
Range: 1.6,76.1
# of stations: 5-6
PM2.5;r = 0.61
N02;r=0.54
CO; r= 0.47
O3;r=0.17
Children <2 yrs were most
susceptible to the effect of
each pollutant.
Pneumonia and
bronchopneumonia were
the main cause of hospital
admissions (71%) in the
<2-yr-old group.
Bronchitis/asthma were
more important for the
intermediate age groups.
However, in all age
groups the largest increase
in admissions was caused
by chronic disease in
tonsils and adenoids.
Multipollutant models
rendered all pollutants
except PM2 5 and SO2
from significance. The
effect of PM2 5 stayed
relatively unchanged
while SO2 was reduced;
however, it remained
significant.
Increment: ug/m (IQR)
All respiratory admissions
<2 yrs 5.9% [4.5, 7.4]
3-5 yrs 1.6% [-1.3, 4.4]
6-13 yrs 0.6% [-2.2, 3.5]
14-19 yrs 1.3% [-3.2, 5.8]
All ages 4.5% [3.3, 5.8]
-------
TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design, &
Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
to
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LATIN AMERICA (cont'd)
>
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Farhat* et al. (2005)
Sao Paulo, Brazil
Period of Study:
1996-1997
Hospital Admissions/ED
Visits
Outcome(s) (ICD9):
Lower Respiratory
Disease (466, 480-5)
Age groups analyzed: <13
Study design: Time series
N: 4,534
# of Hospitals: 1
Statistical analyses: 1)
Poisson regression and 2)
GAM - no mention of
more stringent criteria
Covariates: Long-term
trends, seasonality,
temperature, humidity
Statistical package: S-
Plus
Lag: 0-7 days, 2,3,4 day
moving avg
24-h avg:
Mean: 23.7 ug/m3
SD = 10.0
Range: 3.4,75.2
IQR: 12.5
# of Stations: 6
PM2.5; r = 0.69
N02; r = 0.66
CO; r= 0.49
O3; r = 0.28
This study reports a
significant effect of air
pollution on respiratory
morbidity, though several
pollutants were associated
with increased respiratory
events, making it difficult
to isolate a single agent as
the main atmospheric
contaminant.
Increment: 12.5 ug/m (IQR)
Single-pollutant models (estimated from
graphs):
Pneumonia-21% [4.8, 37]
Asthma-12% [-10, 38]
Pneumonia multipollutant models:
Adjusted for:
PM2.5 13.3 [-5.7, 32.3] 6-day avg
NO2 16.5 [-1.6, 34.6] 6-day avg
CO 18.4 [0.5, 36.2] 6-day avg
O3 18.4 [0.5, 36.2] 6-day avg
Multipollutant model
13.3 [-5.9, 32.6] 6-day avg
Asthma multipollutant models:
Adjusted for:
PM2.5 3.8 [-23.3, 31.0] 2-day avg
N02 -1.2 [-27.4, 25.0] 2-day avg
CO 6.2 [-18.8, 31.2] 2-day avg
O3 9.4 [-14.6, 33.5] 2-day avg
Multipollutant model
-0.5 [-27.7, 26.6] 2-day avg
-------
TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
to
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O
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W
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants Method, Findings,
& Correlations Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
LATIN AMERICA (cont'd)
Gouveia and Fletcher,
(2000)
Sao Paulo, Brazil
Period of Study:
11/92-9/94
Hospital admissions
outcomes (ICD 9): All
respiratory; Pneumonia
(480-486); asthma or
bronchitis (466, 490, 491,
493)
Age groups analyzed:
<1; <5 years
Study design:
Time series
Statistical analyses:
Poisson regression
Covariates: Long-term
trend, season, temperature,
relative humidity, day of
wk, holiday, strikes in
public transport or health
services
Season:
Cool (May-Oct),
Warm (Nov-Apr)
Statistical package: SAS
Lag: 0, 1, 2 days
24-h avg:
Mean: 18.3 ug/m3
SD = 9.0
Range: 3.2,61.1
5th: 7.6
25th: 11.9
50th: 16.6
75th: 22.2
95th: 35.8
$ of stations' 4
PM2 5; r = 0.72 Current ambient air
NO2;r=0.37 pollution
CO' r = 0 65 concentrations have
O • r = 0 08 short-term adverse
effects on children's
respiratory morbidity
assessed through
admissions to
hospitals.
Increment: 27.1 ug/m
(90th -10th)
All Respiratory
<5yrs RR 1.038
[0.983, 1.096] lag 1
<5yrsCool RR1.06
[0.99, 1.11] (estimated from graph)
<5 yrs Warm RR 0.98
[0.89, 1.07] (estimated from graph)
Pneumonia
<5yrs RR 1.024
[0.961, 1.091] lag 1
<1 yr RR 1.071
[0.998, 1.149] lag 0
Asthma
<5yrs RR 1.106
[0.981, 1.247] lag 2
-------
TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
to
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to
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W
O
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H
W
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels & Copollutants
Monitoring Stations & Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
LATIN AMERICA (cont'd)
Ilabacaetal. (1999)
Santiago, Chile
Period of Study:
2/1/95-8/31/96
Days: 578
ED visits
outcome(s) (ICD9):
Upper respiratory
illness (460-465,
487); Lower
respiratory illness
(466, 480-486, 490-
494,496,519.1,
033.9); Pneumonia
(480-486)
Age groups analyzed:
Studv desisn' Time
series
# of Hospitals: 1
Statistical analyses:
Poisson regression
Covariates: Long-
term trend, season,
day of wk,
temperature,
humidity, influenza
epidemic
Season:
Warm (Sep-Apr),
Cool (May-Aug)
Statistical package:
NR
Lag: 0-3 days
24-h avg SO2 (ug/m3) Warm:
Warm: NO2; r = 0.6556
Mean: 14.9 O3;r= 0.1835
Median: 13.2 PM2.5; r = 0.6687
SD = 8.8 PM2.5; r = 0.5764
Range: 1.9,60.2
5th: 5.6 Cool:
95th: 32.0 NO2;r= 0.7440
O3;r= 0.1252
Cool: PM2 5; r = 0.7337
Mean: 31.8 PM2.5; r = 0.6874
Median: 28.2
SD = 18.4
Range: 5.6,92.1
5th: 9.4
95th: 75.2
# of stations: 4
SO2 was related to the
number of respiratory
ED visits, but because of
the high correlation
between contaminants, it
is difficult to establish
independent health
effects. These results
support the fact that
exposure to air pollution
mixtures may decrease
immune functions and
increase the risk for
respiratory infections
among children.
Increment: IQR
All respiratory
Cool
Lag 2 IQR: RR 1.0289
[1.0151,1.0428]
Lag 3 IQR: RR 1.0374
[1.0236,1.0513]
Lag avg 7 IQR: RR 1.0230
[1.0086,1.0377]
TTT
Warm
Lag 2 IQR: RR 1.0029
[0.9860,1.0200]
Lag 3 IQR: RR 1.0108
[0.9937, 1.0282]
Lag avg 7 IQR: RR 1.0108
[0.9756, 1.0473]
Upper respiratory
Cool
Lag 2 IQR: RR 1.05 84
[1.0394,1.0778]
Lag 3 IQR: RR 1.05 13
[1.0324,1.0706]
Lag avg 7 IQR: RR 1.03 16
[1.0120,1.0515]
Warm
Lag 2 IQR: RR 1.0061
[0.9850,1.0277]
Lag 3 IQR: RR 1.01 30
[0.9916,1.0349]
Lag avg 7 IQR: RR 0.981 5
[0.9390,1.0260]
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
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LATIN AMERICA (cont'd)
>
X
(Si
I
to
Ilabacaetal. (1999)
(cont'd)
Pneumonia
Cool
Lag2IQR: RR 1.0164
[0.9757,1.0587]
LagSIQR: RR 1.0342
[0.9938,1.0762]
Lagavg7IQR: RR 1.0291
[0.9850,1.0751]
Warm
Lag2IQR: RR 1.1010
[1.0404,1.1653]
LagSIQR: RR 1.0248
[0.9669,1.0862]
Lagavg7IQR: RR 1.2151
[1.0771,1.3709]
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
LATIN AMERICA (cont'd)
>
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Lin etal. (1999)
Sao Paulo, Brazil
Period of Study:
May 1991-Apr 1993
Days: 621
ED visits
outcome(s): Respiratory
disease, Upper respiratory
illness, Lower respiratory
illness, Wheezing
ICD9Code(s): NR
Age groups analyzed: <13
Study design: Time series
# of Hospitals: 1
Statistical analyses: Gaussian
and Poisson regression
Covariates: Long-term trend,
seasonality, day of wk,
temperature, humidity
Statistical package: NR
Lag: 5-day lagged moving
avgs
SO2 ug/m3:
Mean: 20
SD = 8
Range: 4,60
Number of stations: 3
N02;r=0.38
CO; r= 0.56
PM2.5; r = 0.73
O3;r=0.21
The results of this
study demonstrate a
significant association
between the increase in
emergency visits for all
respiratory illness,
especially URI, and
SO2 levels.
Increment: W ug/m
All respiratory illness
SO2 alone RR 1.079
[1.052, 1.107] 5-day moving avg
S02 + PM2 5 + 03 + N02 + CO RR 0.938
[0.900, 0.977]
Lower respiratory illness
SO2 alone RR 1.052
[0.984, 1.125] 5-day moving avg
S02 + PM2 5 + 03 + N02 + CO RR 0.872
[0.783,0.971]
Upper respiratory illness
SO2 alone RR 1.075
[1.044, 1.107] 5-day moving avg
SO2 + PM2 5 + O3 + NO2 + CO RR 0.951
[0.906, 0.999]
Wheezing
S02 alone RR 1.034 [0.975, 1.096]
5-day moving avg
SO2 + PM2 5 + O3 + NO2 + CO RR 0.908
[0.824,1.002]
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
to
o
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LATIN AMERICA (cont'd)
64
Study design: Time series
N: 712
# of Hospitals: 1
Catchment area: 13,163 total
ER visits
Statistical analyses: Poisson
regression and GAM-no
mention of more stringent
criteria
Covariates: Weekdays, time,
minimum temperature,
relative humidity, daily
number of non-respiratory
emergency room visits made
by elderly
Statistical package:
S-Plus
Lag: 2-7 days and 3 day
moving avgs
SO2 24-h avg (ug/m3):
18.7,
SD = 10.6
Range: 2.0,75.2
IQR: 15.1 ug/m3
# of Stations: 13
O3;r=0.28
N02; r = 0.67
PM2.5; r = 0.72
CO; r= 0.51
The results of the
study show a
significant
association between
SO2 and CLRD
among the elderly.
Increment: IQR of ug/m
Percent increase: 17.5
[5.0, 23.0] lag 3-day moving avg
(estimated from graph)
Single-pollutant model
P = 0.0140 (0.0056)
Multipollutant model (with ozone)
P = 0.0104 (0.0059)
-------
TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
to
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>
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Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants Method, Findings,
& Correlations Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
ASIA
Wong et al. (2002a)*
London England and
Hong Kong
Period of Study:
London:
1992-1994
Hong Kong:
1995-1997
Days: 1,096
Hospital admissions
outcomes (ICD 9): All
respiratory admissions
(460-519); asthma (493)
Age groups analyzed:
15-64, 65+, all ages
Study design:
Time series
Statistical analyses:
APHEA protocol,
Poisson regression with
GAM
Covanates' Lons-term
trend, season, influenza,
day of wk, holiday,
temperature, humidity,
thunderstorms
Season:
Cool, Oct-Mar; Warm:
Apr-Sep
Dose-Response
Investigated?: Yes
Statistical package:
S-Plus
Lag: 0, 1,2, 3, 4 days,
0-1 cum. avg.
24-h SO2 ug/m3
Hong Kong
Mean: 17.7
Warm: 18.3
Cool: 17.2
SD= 12.3
Range: 1.1,90.0
10th: 6.2
50th: 14.5
90th: 32.8
London
Mean: 23.7
Warm: 22.2
Cool: 25.3
SD=12.3
Range: 6.2,113.6
10th: 13.2
50th: 20.6
90th: 38.1
Hong Kong Similar non-statistically
PM2 5; r = 0.30 significant associations
NCy r = 0 37 between asthma hospital
O • r = -0 18 admissions and SO2 were
found in both cities. The
T , association between
London . ...
respiratory hospital
PM2.5; r - 0.64 admissions and SO2 showed
NO2, r-0.71 significance in the cold
O3; r = -0.25 season in Hong Kong and
on an all yr basis.
Respiratory hospital
admissions were not
significantly associated with
SO2 in Britain.
In the 2-pollutant model the
association between
respiratory hospital
admission and SO2 in
London was insignificant,
and remained insignificant
after adjusted for the second
pollutants.
Increment: 10 ug/m
Asthma, 15-64 years
Hong Kong ER - 0.1
[-2.4, 2.2] lag 0-1
ER- 1.5 [-3.4, 0.5] lag 2
Warm ER 1.5
[-1.5, 4. 6] lag 0-1
Cool ER -2.0
[-5.4, 1.4] lag 0-1
London ER 0.7
[-1.0, 2. 5] lag 0-1
ER2.1 [0.7, 3.6] lag 3
Warm ER-1.4
[-4.7, 1.9] lag 0-1
Cool ER 1.6
[-0.5, 3. 8] lag 0-1
Respiratory 65+ years
Hong Kong ER 1.8
[0.9, 2.6] lag 0-1
ER1.7[1.0,2.4]lagO
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
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Reference, Study
Location, & Period
Outcomes, Design, Mean Levels &
& Methods Monitoring Stations
Copollutants Method, Findings,
& Correlations Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
ASIA (cont'd)
Wong et al. (2002a)*
(cont'd)
# of stations:
Hong Kong: 7, r
London: 3, r =
In Hong Kong, the
positive association of
SO2 was most affected
by NO2, losing statistical
significance. The
positive association
remained robust when
adjusted for O3, and a
slight decrease in
association after adjusted
forPM2.5.
Warm ER 1.1 [0.0, 2.2] lag 0-1
Cool ER 2.7 [1.4, 4.0] lag 0-1
+O3 ER 1.9 [1.1, 2. 8] lag 0-1
+PM2.5 ER 1.2 [0.3, 2.2] lag 0-1
+NO2 ER 0.3 [-0.7, lag 1.4] lag 0-1
London ER 0.2 [-0.6, 1.1] lag 0-1
ER 1.2 [0.5, 2.0] lag 3
Warm ER 1.3 [-0.5, 3.1] lag 0-1
Cool ER -0.3 [-1.3, 0.8] lag 0-
+O3 ER 0.5 [-0.4, 1.5] lag 0-1
+PM2.5 ER 1.2 [0.3, 2.2] lag 0-1
+N02 ER 0.5 [-0.7, 1.7] lag 0-1
Chewetal. (1999)
Singapore
Period of Study:
1990-1994
Hospital
Admissions/ED Visits
Outcome(s) (ICD 9):
Asthma (493)
Age groups analyzed:
3-12,
13-21
Study design: Time
series
N: 23,000
# of Hospitals: 2
Statistical analyses:
Linear regression,
GLM
24-havg: 38.1 ug/m3, NO2;r =
SD = 21.8 Q3;r =
Range: 3.0,141.0 TSP;r =
# of Stations: 15
SO2 was positively
correlated to daily ER
visits and hospitalization
for asthma in children
(3-12 yrs), but not
adolescents. The
association of ER visits
with SO2 persisted after
standardization for
meteorological and
temporal variables. An
adjusted increase in 2.9
ER visits for every 20
ug/m3 increase in
ambient SO2 levels with
a lag of 1 was observed.
Categorical analysis (via ANOVA)
p-value and Pearson correlation coefficient
(r) using continuous data comparing daily
air pollutant levels and daily number of
ER visits
Age Group: 3-12 13-21
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design, Mean Levels &
& Methods Monitoring Stations
Copollutants Method, Findings,
& Correlations Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
ASIA (cont'd)
Chewetal. (1999)
(cont'd)
Covariates: variables that
were significantly
associated with ER visits
were retained in the model
Statistical package:
SAS/STAT, SAS/ETS
6.08
Lag: 1,2 days avgs
The increased number
of ER visits/day for
each quartile are listed
below:
Ql: <9
Q2: 10-12
Q3: 13-16
Q4: >16
LagO r = 0.04 r = 0.05
p< 0.001 p = 0.086
Lagl r = 0.10 r = 0.06
p< 0.001 p = 0.016
Lag 2 r = 0.08 r = 0.07
p < 0.001 p = 0.019
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Hwang and Chan (2002)
Taiwan
Period of Study:
1998
ED Visits
Outcome(s) (ICD 9):
Lower Respiratory
Disease (LRD) (466, 480-
6) including acute
bronchitis, acute
bronchiolits, pneumonia
Age groups analyzed: 0-
14,
15-64, >65, all ages
Study design: Time series
Catchment area: Clinic
records from 50
communities
Statistical analyses:
Linear regression, GLM
Covariates: temperature,
dew point temperature,
season, day of wk, holiday
Statistical package: NR
Lag: 0,1,2 days and avgs
24-h avg: 5.4 ppb,
SD = 3.0
Range: 1.5, 16.9
N02
PM25
03
CO
No correlations
for individual
pollutants.
Colinearity of
pollutants prevented
use of multipollutant
models
Increment: 10% change in SO2 (natural
avg) which is equivalent to 2.4 ppb.
NOTE: The percent change is for the
rate of clinic use NOT for relative risk for
adverse effect.
Increased clinic visits for lower
respiratory disease (LRD) by age group
0-14yrs
Lag 0 0.5% [0.3, 0.6]
15-64yrs
Lag 0 0.7% [0.5, 0.8]
>65 yrs
Lag 00.8% [0.6, 1.1]
All ages
Lag 0 0.5% [0.4, 0.7]
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants Method, Findings,
& Correlations Interpretation
Effects: Relative Risk or % Change &
Confidence Intervals (95%)
ASIA (cont'd)
Lee et al. (2006)
Hong Kong, China
Period of Study:
1997-2002
Days: 2,191
Hospital admissions
outcomes (ICD 9):
Asthma (493)
Age groups analyzed:
Study design: Time
series
N: 26,663
Statistical analyses:
Semi-parametric
Poisson regression
with GAM (similar to
APHEA 2)
Covariates: Long-
term trend,
temperature, relative
humidity, influenza,
day of wk, holiday
Statistical package:
SAS 8.02
Lag: 0-5 days
SO2 24-h mean:
17.7 ug/m3,
SD= 10.7
IQR: 11.1 ug/m3
25th: 10.6
50th: 15.2
75th: 21.7
# of stations:
9-10
PM2 5; r = 0.37 Absence of an association of
PM2 5; r = 0.47 SO2 with asthma admissions
NOy r = 0 49 was attributed to low ambient
O • r = -0 17 ^®2 levels during the study
period due to restrictions on
sulfur content in fuel.
Increment: 1 1 . 1 ug/m3 (IQR)
Asthma
Single-pollutant model
Lag 0-1. 57% [-2. 87, -0.26]
Lagl -1.77% [-3.06, -0.46]
Lag 2 -1.1 5% [-2.42, 0.14]
Lag 3 0.82% [-0.45, 2.11]
Lag 4 1.40% [0.1 3, 2.69]
Lag5 1.46% [0.1 9, 2.74]
Multipollutant model-including PM,
NO2s and O3
0.81% [-0.75, 2.4] lag 5
Other lags NR
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants
& Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
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ASIA (cont'd)
>
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Lee* et al. (2002)
Seoul, Korea
Period of Study:
12/1/97-12/31/99
Days: 822
Hospital Admissions
Outcomes (ICD 10): Asthma
(J45-J46)
Age groups analyzed: <15
Study design: Time series
N: 6,436
Statistical analyses: Poisson
regression, log link with GAM
Covariates: Time, day of wk,
temperature, humidity
Season: Spring (Mar-May),
Summer (Jun-Aug),
Fall (Sep-Nov),
Winter (Dec-Feb)
Statistical package: NR
Lag: 0-2 days cumulative
24-h SO2 (ppb)
Mean: 7.7
SD = 3.3
5th: 3.7
25th: 5.1
50th: 7.0
75th: 9.5
95th: 14.3
# of stations: 27
NO2; r = 0.723
O3;r= -0.301
CO; r= 0.812
PM2.5; r = 0.585
This study reinforces
the possible role of
SO2 on asthma attacks,
although it should be
interpreted with
caution because the
effect estimates are
close to the null and
because results in the
multipollutant models
are inconsistent.
Increment: 14.6 ppb (IQR)
Asthma
SO2RR1.11 [1.06, 1.17] lag 0-2
S02 + PM25RR1.08
[1.02, 1.14] lag 0-2
S02 + N02RR 0.95 [0.88, 1.03]
lag 0-2
SO2 + O3RR 1.12 [1.06, 1.17]
lag 0-2
SO2 + CO RR 0.99 [0.92, 1.07]
lag 0-2
S02 + 03 + CO + PM25 + N02 RR
0.949 [0.868, 1.033]
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Tanakaetal. (1998)
Kushiro, Japan
Period of Study:
1992-1993
ED Visits
Outcome(s): Asthma
ICD9Code(s): NR
Age groups analyzed:
15-79
Study design: Time series
N: 102
# of Hospitals: 1
Statistical analyses: Poisson
regression
Covariates: temperature, vapor
pressure, barometric pressure,
relative humidity, wind velocity,
wind direction at maximal
velocity
Statistical package: NR
SO2 24-h avg
3.2 (2.4) ppb in fog
3.7 (1.9) ppb in fog
free days
Max SO2 24-h avg
<11 ppb
N02; r = NR
SPM (TSP);
r = O3; r = NR
The results reveal that
ED visits by atopic
subjects increased on
low SO2 days. This
observation is
inconsistent with most
air pollution
epidemiology, as high
levels of air pollutants
have conventionally
been linked with
asthma exacerbation.
Increment: 5 ppb
Nonatopic
OR 1.18 [0.96, 1.46]
Atopic
OR 0.78 [0.66, 0.93]
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
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ASIA (cont'd)
X
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Tsai et al. (2006)
Kaohsiung, Taiwan
Period of Study:
1996-2003
Days: 2922
Hospital admissions
outcomes (ICD 9): Asthma
(493)
Study design:
Case-crossover
N: 17,682
Statistical analyses:
Conditional logistic
regression
Covariates: Temperature,
humidity
Season:
Warm(>25°C);
Cool (< 25 °C)
Statistical package: SAS
Lag: 0-2 days Cumulative
SO2 24-h mean:
9.49 ppb
Range: 0.92,31.33
25th: 6.37
50th: 8.94
75th: 12.16
# of stations: 6
PM2.5
N02
03
CO
Positive associations
were observed between
air pollutants and
hospital admissions for
stroke. In single-
pollutant models SO2
was not associated
with either PIH or IS.
The season did not
affect these
associations. SO2 was
also not significant in
2-pollutant models.
Increment: 5.79ppb(IQR)
Seasonality
Single-pollutant model
>25°C 1.018 [0.956, 1.083]
lag 0-2
<25°C 1.187 [1.073, 1.314]
lag 0-2
Dual pollutant model
Adjusted for PM2.5
>25°C 0.993 [0.932, 1.058]
lag 0-2
<25°C 1.027 [0.921, 1.146]
lag 0-2
Adjusted for CO
>25 °C] 0.910 [0.847, 0.978]
lag 0-2
<25°C 1.036 [1.027, 1.046]
lag 0-2
Adjusted for NO2
>25°C 0.967 [0.903, 1.035]
lag 0-2
<25 °C 0.735 [0.646, 0.835]
lag 0-2
Adjusted for O3
>25°C 1.055 [0.990, 1.123]
lag 0-2
<25°C 1.195 [1.080, 1.323]
lag 0-2
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
to
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Reference, Study
Location, & Period
Outcomes, Design, & Mean Levels & Copollutants &
Methods Monitoring Stations Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
ASIA (cont'd)
Wong etal. (1999)
Hong Kong, China
Period of Study:
1994-1995
Hospital admissions Median 24-h SO2: 17.05 O3
outcomes (ICD 9): All ug/m3 SO2
respiratory admissions Range: 2.74,68.49 PM25
(460-6,471-8,480-7, 25th: 12.45
490-6); Asthma (493), 75m: 25.01
COPD (490-496),
Pneumonia (480-7) #of stations:
Age groups analyzed: 0- 7 r =
4, 5-64, >65, all ages
# of hospitals: 12
Study design:
Time series
Statistical analyses:
Poisson regression
(followed APHEA
protocol)
Covariates: Trend,
season, day of wk,
holiday, temperature,
humidity
Statistical package: SAS
8.02
Lag: days 0-3 cumulative
Adverse respiratory
effects of SO2 were noted
at low concentrations.
Results for respiratory
outcomes were attributed
to the elderly population.
This was also true for the
other pollutants.
Therefore, it is difficult to
be certain that the effects
were due mainly to SO2.
Pair- wise comparisons in
multipollutant models
showed significant
interactions of PM25,
NO2, and O3.
Increment =10 ug/m
Overall increase in admissions:
1. 013 [1.004, 1.021] lag 0
Respiratory relative risks (RR)
0-4 yrs: 1.005 [0.991, 1.018] lag 0
5-64 yrs: 1.008 [0.996, 1.021] lag 0
>65yrs: 1.023 [1.012, 1.036] lag 0
Asthma: 1.017 [0.998, 1.036] lag 0
COPD: 1.023 [1.011, 1.035] lag 0
Pneumonia: 0.990
[0.977, 1.004] lag 4
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TABLE AX5.2 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR RESPIRATORY DISEASES
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Reference, Study
Location, & Period
ASIA (cont'd)
Wongetal. (200 la)
Hong Kong, China
Period of Study:
1993-1994
Outcomes, Design,
& Methods
Hospital admissions
outcomes (ICD 9):
Asthma (493)
Age groups analyzed:
<15
N: 1,217
# of hospitals: 1
Study design:
Time series
Statistical analyses:
Poisson regression
(followed APHEA
protocol)
Covariates: Season,
temperature, humidity
Season:
Summer (Jun-Aug),
Autumn (Sep-Nov),
Winter (Dec-Feb), Spring
(Mar-May)
Lag: 0,1,2,3,4,5 days; and
cumulative 0-2 and 0-3
days.
Mean Levels &
Monitoring Stations
24-h avg SO2
mean: 12.2 ug/m3
SD= 12.9
Range: 0, 98 ug/m3
Autumn: 10.6(9.6)
Winter: 10.0(7.5)
Spring: 9.6(8.8)
Summer: 18.5(19.5)
# of stations: 9
Copollutants Method, Findings, Effects: Relative Risk or % Change &
& Correlations Interpretation Confidence Intervals (95%)
PM25 SO2 levels were found Increment: 10 ug/m
NO2 to be the highest during
the summer. There Asthma
were consistent and AU yr RR i 06 p = 0 004
statistically significant Autumn: -^
associations between ,,,. , XTr)
. . . . . Winter: NR
asthma admission and .
increased daily levels Sprmg: NK
ofS02. No Summer: NR
associations were
noted in the spring or
winter. No significant
associations were
found between hospital
admissions and day of
the wk, humidity,
temperature or
atmospheric pressure.
Total admissions were
limited to one hospital.
*Default GAM
+Did not report correction for over-dispersion
NR: Not Reported
APHEA: Air Pollution and Health: a European Approach
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TABLE AX5.3. ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
CARDIOVASCULAR MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, and Period
Outcomes and Methods
Mean SO, Levels
Copollutants
Considered
Findings, Interpretation
Effects
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Liao et al. (2004)
Three locations in
United States:
Minneapolis, MN;
Jackson, MS; Forsyth
County, NC
1996-1998
Liao et al. (2005)
United States,
1996-1998
Cross-sectional study of 6,784 cohort
members of the Atherosclerosis Risk in
Communities Study. Participants were 45-
64 yrs of age; baseline clinical
examinations conducted from 1987-1989.
HRV data collected from 1996-1998. Air
pollutants obtained form EPA AIRS for
this same period. Resting, supine, 5-min
beat-to-beat RR interval data were
collected over a 4-h period. Multivariable
linear regression models used to assess
associations between pollutants measured
1-3 days prior to HRV measurements.
Models controlled for age, ethnicity-center,
sex, education, current smoking, BMI,
heart rate, use of cardiovascular
medication, hypertension, prevalent
coronary heart disease, and diabetes.
Cross-sectional survey 10,208 participants
(avg age 54 yrs) from Atherosclerosis Risk
in Communities (ARIC) study cohort to
assess the association between criteria air
pollutants and hemostatic and
inflammatory markers. 57% of
participants were female and 66% male.
Used hemostatis/inflammation variables
collected during the baseline examination
and air pollution data 1-3 days prior to the
event. Used multiple linear regression
models that controlled for age, sex,
ethnicity-center, education, smoking,
drinking status, BMI, history of chronic
respiratory disease, humidity, seasons,
cloud cover, and temperature. Also history
of CVD and diabetes if not effect modifier
in a particular model.
Mean (SD) SO2
measured 1 day
prior to HRV
measurement was
4(4)ppb
PM10
03
CO
N02
SO2 mean (SD)
0.0005 (0.004)
ppm
Ql-3: 0.005
(0.003) ppm
Q4: 0.006
(0.005) ppm
PM10
CO
NO2
03
Significant interaction
between SO2 and
prevalence of coronary
heart disease for low-
frequency power analyses
SO2 inversely associated
with SD of normal R-R
intervals and low-frequency
power and positively
associated with heart rate.
SO2 association with low-
frequency power stronger
among those with history of
coronary heart disease.
Effect size of PM10 larger
than for gaseous pollutants.
Significant curvilinear
association between SO2
with factor VHI-C, WBC,
and serum albumin.
Curvilinear association
indicated threshold effect
Log-transformed low-frequency
power effect estimate and SE per
1 SD increment (4 ppb) SO2 lag
1 day:
Log transformed high-frequency
power -0.024
(SE 0.016)
Standard deviation of normal R-
R intervals-0.532
(SE 0.270), p< 0.05
Heart rate: 0.295 (SE 0.130),
p<0.05
Prevalent CHD: -0.122
(SE 0.056), p< 0.01
No prevalent CHD -0.012
(SE 0.016)
Results shown in graph.
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TABLE AX5.3 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
CARDIOVASCULAR MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, and Period
Outcomes and Methods
Mean SO, Levels
Copollutants
Considered
Findings, Interpretation
Effects
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UNITED STATES (cont'd)
>
X
Dockery etal. (2005)
Boston, MA
Jull995-Jul2002
Cohort study of 203 cardiac
patients with implanted
cardioverter defibrillators.
Patients were followed for
an avg of 3.1 yrs from
1995-2002 to assess the role
of air pollution on the
incidence of ventricular
arrhythmias. The
association of arrhythmic
episode-days and air
pollutions analyzed with
logistic regression using
GEE with random effects.
Model adjusted for patient,
season, minimum
temperature, mean
humidity, day of the wk,
and previous arrhythmia
within 3 days. Only effects
of 2-day running mean of
air pollution concentration
reported.
48-h avg SO2;
Median: 4.9 ppb
25th%: 3.3 ppb
75%: 7.4 ppb
95%: 12.8 ppb
PM2.5
BC
S04
PN
N02
CO
03
No statistically significant
association between any of the air
pollutant and ventricular
arrhythmias when all events were
considered. However, ventricular
arrhythmias within 3 days of a prior
event were statistically significant
with SO2, PM2.5, BC, NO2, CO, and
marginally with SO4, but not with
O3orPN. CO,NO2,BC,andPM2.5
correlated, thus it was impossible to
differentiate the independent effects.
Since the increased risk of
ventricular tachyarrhythmia was
associated with air pollution
observed among patients with a
recent tachyarrhythmia, it was
suggested that air pollution acts in
combination with cardiac electrical
instability to increase risk of
arrhythmia.
For IQR (4.0 ppb) increase in
48-h mean SO2:
All events: OR = 1.04 (0.94,
1.14),p = 0.28
Prior arrhythmia event
<3days: 1.30
(95% CI: 1.06,1.61),
p = 0.013
Prior arrhythmia event
>3days: 0.98(0.87,1.11)
p = 0.78
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TABLE AX5.3 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
CARDIOVASCULAR MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, and Period
Outcomes and Methods
Mean SO, Levels
Copollutants
Considered
Findings, Interpretation
Effects
to
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UNITED STATES (cont'd)
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Gold et al. (2000)
Boston, MA
Jun-Sep 1997
Panel study on 21 active
Boston residents aged 53-87
yrs to investigate the
association between short-term
changes in ambient air
pollution and short-term
changes in cardiovascular
function. Participants observed
up to 12 times from June to Sep
1997 (163 observations made
in total). Protocol involved
25 mins per wk of continuous
ECG monitoring, that included
5 mins of rest, 5 mins of
standing, 5 mins of exercise
outdoors, 5 mins of recovery,
and 20 cycles of slow
breathing. Fixed effects
models adjusted for time-
varying covariates and
individuals traits.
24-h avg mean
3.2ppb
Range: 0, 12.6 ppb
IQR: 3.0 ppb
PM2.5
coarse matter
03
NO2
CO
In single-pollutant models,
24-h mean SO2 associated
with reduced heart rate in
the first rest period but not
overall. Associations
weaker for shorter
averaging periods.
Association between SO2
and heart rate not
significant with the
multipollutant model (SO2
andPM25). SO2 not
associated with r-MSSD.
Heart rate, first rest period, mean
66.3 bpm
single-pollutant model
estimated effect (SE) -1.0 (0.5)
%meanl.5,p = 0.03
Heart rate, first rest period, mean
66.3 bpm
Multipollutant model (PM2 5 and SO2):
SO2 estimated effect (SE) -0.8 (0.5)
% mean 1.2, p = 0.09
PM2 5 estimated effect (SE) -1.6 (0.7)
% mean 2.5, p = 0.03
Overall heart rate, mean 74.9 bpm
single-pollutant model
estimated effect (SE) -0.5 (0.5),
p = 0.30
Overall heart rate, mean 74.9 bpm
Multipollutant model
SO2 estimated effect (SE) -0.2 (0.5),
p = 0.6
PM2 5 estimated effect (SE) -1.9 (0.7)
p = 01% mean 2.6
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TABLE AX5.3 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
CARDIOVASCULAR MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, and Period
Outcomes and Methods
Mean SO2
Levels
Copollutants
Considered
Findings,
Interpretation
Effects
to
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UNITED STATES (cont'd)
>
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Park et al. (2005b)
Greater Boston area, MA
Nov2000-0ct2003
Cross-sectional study of effect of ambient air
pollutants on heart rate variability (HRV) in
497 men who were in the Normative Aging
Study and who were examined from Nov 2000
and Oct 2003. HRV measured between 0600
and 1300 h after resting for 5 mins.
4-h, 24-h, and 48-h moving avgs of air
pollution matched to time of ECG
measurement. Linear regression models
included: age, BMI, fasting blood glucose,
cigarette smoking, use of cardiac medications,
room temp, season, and the lagged moving
avg of apparent temp corresponding to the
moving avg period for the air pollutant. Mean
arterial blood pressure (MAP) and apparent
temperature also included. Assessed
modifying effects of hypertension, IHD,
diabetes or use of cardiac/antihypertensive
meds.
24-h avg SO2
4.9 ppb
SD = 3.4
Range: 0.95,
24.7 ppb
PM2.5
particle number
concentration
BC
N02
03
CO
4-h moving avg SO2
(per 1 SD, 3.4 ppb SO2)
No significant
association
between HRV and
SO2 for any of the Log 10 SDNN:
averaging periods, 2.3 (-1.7, 6.4)
but positive
relationship. LoglOHF:
5.6 (-4.9, 17.3)
LoglOLF:
2.2 (-5.9, 11.1)
LoglO (LF:HF)
-3.2 (-10.1, 4.2)
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TABLE AX5.3 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
CARDIOVASCULAR MORBIDITY IN FIELD/PANEL STUDIES
oo
Reference, Study
Location, and Period
Mean SO2 Copollutants
Outcomes and Methods Levels Considered
Findings,
Interpretation
Effects
UNITED STATES (cont'd)
Peters et al. (2000a)
Eastern Massachusetts,
U.S.
1995-1997
Pilot study to test hypothesis that patients with 24-h avg SO2: PM10
implanted cardio verier defibrillators would 7 ppb PM2 5
experience potentially life-threatening gQ
arrhythmias associated with air pollution Median: 5 ppb QQ
episodes. Records detected arrhythmias and Max: 87 ppb Q
therapeutic interventions downloaded from the „„
implanted defibrillator. Mean age of patients 2
62.2 yrs. 100 patients followed for over 3 yrs
for 63,628 person-days. 33 patients with any
discharges and 6 patients with 10 or more
events. Data analyzed by logistic regression
models using fixed effects models with
individual intercepts for each patient. Model
controlled for trend, season, meteorologic
conditions, and day of week. Evaluated air
pollutants on same day, lags 1, 2, and 3 days,
and 5-day mean.
No association
between increased
defibrillator
discharges and
SO2.
33 patients with at least 1
defibrillator discharge
Lag 00. 76 (0.48, 1.21)
Lag 10.91 (0.60, 1.37)
Lag 2 0.89 (0.59, 1.34)
Lag 3 1.09(0.78,1.52)
5-day mean 0.85 (0.50, 1.43)
6 patients with at least
10 discharges
Lag 00.72 (0.40, 1.31)
Lag 10. 77 (0.44, 1.37)
Lag 2 1.01 (0.63, 1.61)
Lag 3 1.08 (0.72, 1.62)
5-day mean 0.75 (0.38, 1.47)
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Peters etal. (2001)
Greater Boston area, MA
Janl995-May 1996
Case cross over Study design used to 24-h avg SO2:
investigate association between air pollution 7 ppb
and risk of acute myocardial infarctions in 772 SD = 7 ppb
patients (mean age 61.6 yrs) with MI as part of
the Determinants of Myocardial Infarction 1-h avg SO2:
Onset Study. For each subject, one case 7 ppb
period was matched to 3 control periods, 24 h SD = 10 ppb
apart. Used conditional logistic regression
models that controlled for season, day of wk,
temperature, and relative humidity.
PM2.5
PM10
Coarse mass
BC
O3
CO
NO2
SO2 not
statistically
associated with
risk of onset of
MI. Limitation of
study is only 1 air
pollution
monitoring site
available.
OR for 2-h avg SO2 and 24-h avg
SO2 estimated jointly:
2 h per 2-ppb increase SO2
Unadjusted: 1.00(0.87,1.14)
Adjusted: 0.96(0.83,1.12)
24 h per 2-ppb increase
Unadjusted: 0.92(0.71,1.20)
Adjusted: 0.91(0.67,1.23)
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TABLE AX5.3 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
CARDIOVASCULAR MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, and Period
Outcomes and Methods
Mean SO2 Levels
Copollutants
Considered
Findings,
Interpretation
Effects
to
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UNITED STATES (cont'd)
>
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VO
Rich et al. (2005) Case cross-over design used to evaluate
Boston, MA association between ventricular
Jul 1995-Jul 2002 arrhythmias detected by implantable
cardioverter defibrillators and air
pollution. Same study population as
Dockery et al. (2005): 203 patients with
ICD and residential zip codes within
40 km of central particle monitoring site.
Analyses conducted on 84 subjects with
confirmed ventricular arrhythmias during
the follow-up. Case periods defined by
time of each confirmed arrhythmic event.
Control periods (3-4 per case) selected by
matching on weekday and hour of the
day within the same calendar mo. Used
conditional logistic regression that
controlled for temperature, dew point,
barometric pressure, and a frailty term for
each subject. ORs presented for IQR
increase in mean concentration and
averaging time. Moving avg of
concentrations considered: lags 0-2, 0-6,
0-23, and 0-47 h.
l-havgSO2:
Median: 4.3 ppb
25th %: 2.6
75th %: 7.5
Max: 71.6
24-h avg SO2:
Median: 4.8
25th %: 3.2
75th %: 7.3
Max: 31.4
PM2.5
BC
N02
CO
03
An IQR increase in the
24-h moving avg SO2
(4.1 ppb) marginally
associated with a 9%
increased risk of
ventricular arrhythmia
and an increased risk
with 48-h moving avg.
There was no
risk associated with
24-h moving avg after
controlling for PM2 5
cases that had a prior
ventricular arrhythmia
within 72 h had greater
risk associated with
SO2 compared to those
without a recent event,
suggesting that risk is
greater among cases
with more irritable or
unstable myocardium.
Odds ratios- single-pollutant model
0-2-h lag (per 4.7 ppb) 1.07
(0.97,1.18)
0-6-h lag (per 4.5 ppb) 1.09
(0.98, 1.20)
0-23-hlag (per 4.1 ppb) 1.09 (0.97,
1.22)
0-47-hlag(per4.0ppb) 1.17(1.02,
1.34)
Odds ratios- 2-pollutant model
SO2 and PM2 5
Per 4.1 ppb SO2: 1.00(0.84,1.20)
SO2 and O3
Per 4.1 ppb SO2: 1.12(0.99,1.27)
Per 4.1-ppb increase SO2
Prior arrhythmia event <3 days:
1.20(1.01,1.44)
Prior arrhythmia event >3 days:
0.96(0.83,1.10)
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TABLE AX5.3 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
CARDIOVASCULAR MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, and Period
Outcomes and Methods
Mean SO2 Levels
Copollutants
Considered
Findings,
Interpretation
Effects
UNITED STATES (cont'd)
Rich et al. (2006)
St. Louis, Missouri,
May 2001 -Dec 2002
Case-crossover design study of
56 patients with implantable cardioverter
defibrillators. Subjects ranged from 20 to
88 years (mean 63). Case period defined
by time of confirmed ventricular
arrhythmia. Control periods matched on
weekday and hour of the day within the
same calendar mo. Used conditional
logistic regression model that included
mean of the previous 24-h temperature,
relative humidity, barometric pressure,
mean pollutant concentration in the 24 h
before the arrhythmia. Model also
included a frailty term for each subject.
599 days
25th percentile:
2ppb
50th percentile:
4 ppb
75th percentile:
7 ppb
Daily IQR: 5 ppb
Case/control IQR:
5 nnb
cc
PM2.5
EC
OC
NO2
CO
o,
v/3
Statistically significant
increase in risk of
ventricular arrhythmias
associated with each 5-
ppm increase in 24-h
moving avg SO2.
OR for ventricular arrhythmia
associated with IQR increase
6-h moving avg SO2 per 4 ppb: 1 .04
(95% CI: 0.96,1.12)
12-h moving avg SO2 per 5 ppb: 1.17
(95% CI: 1.04,1.30)
24-h moving avg SO2 per 5 ppb:
1.24
(95% CI: 1.07,1.44)
48-h moving avg SO2 per 4 ppb:
Schwartz et al. (2005)
Boston, MA
12 wks during the
summer of 1999
Panel study of 28 subjects (aged
61-89 yrs) to examine association
between summertime air pollution and
HRV. Subjects examined once a wk up
to 12 wks and HRV measured for
approximately 30 mins. Analyses used
hierarchical models that controlled for
baseline medical condition, smoking
history, day of wk and hour of day,
indicator variable for whether subjects
had taken their medication before they
came, temperature and time trend.
24-h avg SO2:
25th %:
0.017 ppm
50th %:
0.020 ppm
75th %: 0.54 ppm
03
NO2
CO
PM2.5
black carbon
No significant
association with SO2
1.15 (95% CI: 1.00,1.34)
Percentage change in HRV associated
with IQR (0.523 ppm) increase in SO2
SDNN(ms)0.4(-1.3to2.1)
RMSSD (ms) 1.4 (-2.6 to 5.5)
PNN50 (ms) 3.8 (-12.1 to 22.5) for
1-h avg SO2
SDNN (ms) 0.4 (-4.2 to 5.1) for 24-h
avg SO2
RMSSD (ms) -0.3 (-1.3 to 0.8)
PNN50 (%) -0.2 (20.9 to 17.6)
LFHFR2.9(-4.9toll.4)
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TABLE AX5.3 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
CARDIOVASCULAR MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, and Period
Outcomes and Methods
Mean SO2 Levels
Copollutants
Considered
Findings, Interpretation
Effects
to
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CANADA
>
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Rich et al. (2004)
Vancouver, British
Columbia, Canada
Feb-Dec 2000
Vedal et al. (2004)
Vancouver, British
Columbia, Canada
1997-2000
Case-crossover analysis used to
investigate association between air
pollution and cardiac arrhythmia in
34 patients (aged 15-85 yrs, mean
62) with implantable cardioverter
defibrillators. Study included only
patients who experienced at least 1
ICD discharge during the study
period. Control days were 7 days
before and 7 days after day of ICD
discharge. Conditional logistic
regression analyses were stratified
by individual.
Retrospective, longitudinal panel
study of 50 patients, aged 12-77 yrs
with implantable cardioverter
defibrillators. Total of
40,328 person-days over 4-yr
period. GEE used to assess
associations between short term
increases in air pollutants and
implantable cardioverter
defibrillator discharges. Models
controlled for temporal trends,
meteorology, and serial
autocorrelation.
24-havg: 2.6 ppb
SD= 1.3 ppb
IQR: 1.6 ppb
24-h mean (SD) SO2:
2.4 (1.2) ppb
Range: 0.3, 8.1 ppb
Median: 2.2 ppb
25th percentile: 1.5
75th percentile: 3.1
PM2.5
EC
OC
PM10
CO
N02
03
PM10
03
N02
CO
No statistically significant association
between SO2 and implantable cardioverter
defibrillator discharges. However, when
an analysis was stratified by season, OR
for SO2 were higher in the summer
compared to winter.
Concluded that in general no consistent
effect of air pollution on cardiac
arrhythmias in this population.
There were no statistically significant
associations between SO2 and cardiac
arrhythmias at any lag day, but positive
associations at lag 2. When analysis was
restricted to only patients who had at least
2 arrhythmias per yr over their period of
observation
(n = 16), a positive and significant
association was seen with SO2 at 2 days
lag. When analysis was restricted to
patients averaging 3 or more arrhythmias
per yr (n = 13), there was no significant
association, but a positive association was
seen at 2 day slag.
No quantitative
results provided.
Results shown in
graph.
No quantitative
results, but %
change in
arrhythmia event-
day rate for each
SD increase in
pollution
concentration on
log scale provided
in figures.
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TABLE AX5.3 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
CARDIOVASCULAR MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, and Period
Outcomes and Methods
Mean SO2 Levels
Copollutants
Considered
Findings, Interpretation
Effects
to
o
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CANADA (cont'd)
Vedal et al. (2004)
(cont'd)
When stratified by season, SO2 effects
were in the in the positive direction in
the winter, but in the negative
direction in the summer. Authors
noted results may be due to chance
because of multiple comparisons or
SO2 may be surrogate for some other
factor.
>
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Summer analysis: significant
negative association with SO2 at lag
days 2 and 3 (data not shown). When
stratified to patients with 2 or more
arrhythmia event-days per yr,
significant negative associations
observed with SO2 at lag of 3 days.
to
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Winter analysis: significant positive
effect of SO2 at 3 days lag (data not
shown). If restricted to patients with
at least 2 arrhythmias per yr, a
significant positive association was
seen at lags 2 and 3 days. When
restricted to patients with 3 or more
arrhythmia event days per yr, positive
associations observed for SO2 at lags
of 2 and 3 days.
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TABLE AX5.3 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
CARDIOVASCULAR MORBIDITY IN FIELD/PANEL STUDIES
to
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Reference, Study
Location, and Period Outcomes and Methods
Copollutants
Mean SO2 Levels Considered
Findings, Interpretation
Effects
EUROPE
Ibald-Mullietal. (2001)
Augsburg, Germany
1984-85, 1987-88
Retrospective analysis of 2607
subjects (25-64 yrs, subset of the
participants of first and second
MONICA survey who had valid
electrocardiograms recordings in
both surveys and blood pressure
measurements). Used regression
models for repeated measures
that controlled for age, current
smoking, and cardiovascular
medication, BMI, total and high
density lipoprotein cholesterol,
temp, RH, and barometric
pressure.
24-h avg SO2 (|ig/mj) TSP, CO
1984-1985:
Mean: 60.2
SD = 47.4
Range: 13.0,238.2
follow up 1987-1988
Mean: 23.8
SD=12.3
Range: 5.6,71.1
SO2 and TSP associated
with increases in systolic
blood pressure. In the
multipollutant model with
TSP, the effect of IPS
remained significant, but
the SO2 effect was
substantially reduced. No
clear association between
SO2 and CO and diastolic
blood pressure was
observed.
Same day concentrations:
mean change in systolic blood pressure
per 5th to 95th percentile increase in
SO2 (per 80 |ig/m3)
Same day concentrations
(per 80 ng/m ):
Men(n=1339): 0.96(0.07,1.85)
Women (n= 1268): 0.96
(-0.46, 1.49)
Men and women: 0.74 (0.08, 1.40)
5-day avgs:
Mean change in systolic blood
pressure per 5th to 95th percentile
increase in SO2 (per 75 |ig/m3)
Men: 0.97(0.09,1.85)
Women: 1.23(0.23,2.22)
Men and women: 1.07
(0.41,1.73)
2-pollutant model
Men and women: 0.23
(-0.50,0.96)
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TABLE AX5.3 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
CARDIOVASCULAR MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, and Period
Outcomes and Methods
Mean SO2 Levels
Copollutants
Considered
Findings,
Interpretation
Effects
to
o
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EUROPE (cont'd)
Peters etal. (1999)
Augsburg, Germany
winter 1984-1985
winter 1987-1988
Retrospective analysis on
subsample of 2,681 subjects (25-64
yrs) of the MONICA cohort who
had valid electrocardiogram
readings from both surveys and no
acute infections. GEE for clusters
used to assess association between
heart rate and air pollution.
Analyses adjusted for temperature,
relative humidity, and air pressure.
24-h avg SO2 (ng/m3)
Winter 1984-1985
Outside episode:
Mean: 48.1
SD = 23.1
Range: 13,103
Winter 1984-1985
During episode:
Mean: 200.3
SD = 26.6
Range: 160,238
Winter: 1987-1988
Mean: 23.6
SD= 12.2
Range: 6, 71
CO
TSP
Increases in SO2 Mean change in heart rate per 5th to 95th
concentrations percentile SO2
associated with Same day concentrations
increases in heart (per 80 |ig/m3 SO2)
rate Men: 1.02(0.41,1.63)
Women: 1.07(0.41,1.73)
Men and women: 1.04 (0.60, 1.49)
5-day avg (per 75 |ig/m3 SO2)
Men: 1.29(0.68,1.90)
Women: 1.26(0.57,1.95)
Men and women: 1.28 (0.82, 1.74)
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TABLE AX5.3 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
CARDIOVASCULAR MORBIDITY IN FIELD/PANEL STUDIES
Reference, Study
Location, and Period
Outcomes and Methods
Mean SO2 Levels
Copollutants
Considered
Findings,
Interpretation
Effects
to
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EUROPE (cont'd)
>
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Ruidavets et al. (2005)
Toulouse, France
1995-1997
Cross-sectional survey of 863 randomly
chosen adults (35-65 yrs) living in
Toulouse (MONICA center) to examine
the relationship between resting heart rate
and air pollution. Resting heart rate was
measured twice in a sitting position after
a five minute rest. Used polytomous
logistic regression models with quintiles
ofRHR. Final model controlled for sex,
physical activity, systolic blood pressure,
cardiovascular drug use, CRP, relative
humidity, and season mos.
MeanSO2: 13.3
(7.5) ug/m3
Range: 1.3,
47.7 ug/m3
NO2
03
Marginally significant
association between
SO2 and RHR in Q5
compared with Ql.
No association with
SO2at 1,2, or 3 days
OR based on daily levels of SO2
OR for resting heart rate =1.19
(95% CI: 1.02, 1.39) in 5th
quintile (>75 bpm) compared to
first quintile (<60 bpm) for
5 ug/m3 increase in SO2 same day
0 am-12 pm
OR for resting heart rate 1.14
(95% CI: 1.01 to 1.30) in 5th
quintile (>75 bpm) compared to
first quintile (<60 bpm) for
5 ug/m3 increase in SO2 same day
12 am-12 pm
Not-significant associations not
listed
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LATIN AMERICA
Holguinetal. (2003)
Mexico City, Mexico
Feb 8 to April 30, 2000
Panel study of 34 nursing home residents
(60-96 yrs) to assess association between
heart rate variability and air pollution.
Heart rate variability measured every
alternate day for 3 mos. Thirteen of the
subjects had hypertension. Used GEE
models that controlled for age and avg
heart rate during HRV measurement.
24-h mean SO2
(Ppb)
Mean: 24
SD= 12
Range: 6, 85
Indoor PM2 5
Outdoor PM2 5
03
NO2
CO
SO2 not related to
heart rate variability
on the same day or
lag 1 day
Change in HRV per
HRV-HF -0.003
(-0.035,0.035)
HRV-LF -0.004
(-0.004,0.003)
HRV-LF/HF 0.012
(-0.060,0.082)
lOppb
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TABLE AX5.4. ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH EMERGENCY
DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR CARDIOVASCULAR DISEASES
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels & Monitoring
Stations
Copollutants Effects: Relative Risk or % Change & Confidence
(Correlations) Intervals ([95% Lower, Upper])
to
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UNITED STATES
>
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Morris etal. (1995)
U.S. (Chicago, Detroit,
LA, Milwaukee, NYC,
Philadelphia)
Study period:
1986-1989, 4 yrs
Outcome(s) (ICD9): CHF 428.
Daily Medicare hospital admission
records.
Study design: Time series
Statistical analyses: GLM, negative
binomial distribution
Age groups analyzed: >65 yrs
Covariates: Temperature, indicator
variables for mo to adjust for weather
effects and seasonal trends, day of
wk, yr
Statistical software: S-PLUS
Lag(s): 0-7 days
SO2 1-h max (ppm)
Mean (SD)
LA: 0.010(0.005)
Chicago: 0.025(0.011)
Philadelphia: 0.029(0.015)
New York: 0.032(0.015)
Detroit: 0.025(0.013)
Houston: 0.018(0.009)
Milwaukee: 0.017(0.013)
NO2 1 -h max Results reported for RR of admission for CHF
O3 1-h max associated with an incremental increase in SO2 of
CO 1-h max 0.05 ppm.
Correlations of CHF:
S02 with other LA: 1.60(1.41,1.82)
pollutants strong. Chicago: 1.05(1.00,1.10)
Philadelphia: 1.01(0.96,1.06)
Multipollutant New York: 1.04(1.01,1.08)
models run. Detroit: 1.00 (0.95, 1.06)
Houston: 1.07(0.97,1.17)
Milwaukee: 1.07(0.99,1.15)
RR diminished in multipollutant (4 copollutants)
models for all cities.
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TABLE AX5.4 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH EMERGENCY
DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR CARDIOVASCULAR DISEASES
to
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Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants
(Correlations)
Effects: Relative Risk or % Change &
Confidence Intervals ([95% Lower, Upper])
UNITED STATES (cont'd)
Moolgavkar (2000)*
Cook County IL, Los
Angeles County, CA,
Maricopa County, AZ
1987-1995
Outcome(s) (ICD9): CVD
390-429; Cerebrovascular disease
430-448. Hospital admissions from
CA department of health database.
Age groups analyzed: 20-64, 65+
yrs
Study design: Time series
N: 118 CVD admissions/days
# Hospitals: NR
Statistical analysis: Poisson
regression, GAM
Covariates: Adjustment for day of
wk, long-term temporal trends,
relative humidity, temperature
Statistical package: SPLUS
Lag: 0-5 days
SO2 24-h avg (ppb)
Cook County:
Min: 0.5
Ql: 4
Median: 6
Q3: 8
Max: 36
LA County:
Min: 0
Ql: 1
Median: 2
Q3: 4
Max: 16
Maricopa County:
Min: 0
Ql: 0.5
Median: 2
Q3: 4
Max: 14
PMio (0.1 1,0.42)
PM2.5 (0.42) (LA only)
CO (0.35, 0.78)
NO2 (0.02, 0.74)
03 (-0.37, 0.01)
2-pollutant models
(see results)
Results reported for percent change in hospital
admissions per 10 ppb increase in SO2. T statistic
in parentheses.
CVD, 65+:
Cook County
4.0(6.1), lag 0
3. 1 (4.5), lag 0, 2-pollutant model (CO)
1.0(1 .4), lag 0, 2-pollutant model (NO2)
LA County
14.4 (15.2), lag 0
-2.5 (- 1 .6), lag 0, 2-pollutant model (CO)
7.7 (5.7), lag 0, 2-pollutant model (NO2)
Maricopa County
7.4 (4.5), lag 0
3.0 (1 .8), lag 0, 2-pollutant model (CO)
3.9(1 .5), lag 0, 2-pollutant model (SO2)
Cerebrovascular Disease, 65+:
Cook County
3.1(3.3)
LA County
6.5 (4.9)
Lags 1-5 also presented. Effect size generally
diminished with increasing lag time. Increase in
hospital admissions (10.3 for CVD and 9.0 for
Cerebrovascular) also observed for the
20-64 age group.
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TABLE AX5.4 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH EMERGENCY
DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR CARDIOVASCULAR DISEASES
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants
(Correlations)
Effects: Relative Risk or Percent Change &
Confidence Intervals ([95% Lower, Upper])
UNITED STATES (cont'd)
Moolgavkar (2003)
Cook County IL, Los
Angeles County, CA,
Maricopa County, AZ
Outcome(s) (ICD9): CVD
390-429; Cerebrovascular disease
430-448 was not considered in the
reanalysis. Hospital admissions from
See original analysis
(Moolgavkar, 2000)
above.
See original
analysis
(Moolgavkar,
2000) above.
Use of stringent criteria in GAM did not alter results
substantially. However, increased smoothing of
temporal trends attenuated results for all gases and effect
size diminished with increasing lag.
1987-1995
CA department of health database.
Age groups analyzed: 20-64,
65+ yrs
Study design: Time series
N: 118 CVD admissions/day
# Hospitals: NR
Statistical analysis: Poisson
regression, GAM with strict
convergence criteria (10-8), GLM
using natural splines
Covariates: Adjustment for day of
wk, long-term temporal trends,
relative humidity, temperature
Statistical package: SPLUS
Lag: 0-5 days
Results reported for incremental increase of 10 ppb SO2.
Estimated coefficient and T statistic in parentheses.
GLM with 100 df (LA County)
13.67 (11.82), lag 0
6.44 (5.23), lag 1
0.23(0.18), lag 2
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TABLE AX5.4 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH EMERGENCY
DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR CARDIOVASCULAR DISEASES
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants Effects: Relative Risk or % Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
to
o
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UNITED STATES (cont'd)
>
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Wellenius et al. (2005a)
Birmingham, Chicago,
Cleveland, Detroit,
Minneapolis, New
Haven, Pittsburgh,
Seattle
Study period:
Jan 1986-Nov 1999
(varies slightly
depending on city)
Outcome(s) IS, primary diagnosis of acute but
ill-defined cerebrovascular disease or
occlusion of the cerebral arteries; HS,
primary diagnosis of intracerebral
hemorrhage. ICD codes not provided.
Hospital admissions ascertained from the
Centers for Medicare and Medicaid Services.
Cases determined from discharge data were
admitted from the ER to the hospital.
NIS: 155,503
NHS: 19,314
Study design: Time-stratified Case-crossover.
Control days chosen such that they fell in
same mo and same day of wk. Design
controls for seasonality, time trends, chronic
and other slowly varying potential
confounders.
Statistical analysis: 2-stage hierarchical
model (random effects), conditional logistic
regression for city effects in the first stage
Software package: SAS
Covariates:
Lag(s): 0-2, unconstrained distributed lags
SO2 24 h (ppb)
10th: 2.17
25th: 3.57
Median: 6.22
75th: 10.26
90th: 16.17
SO2 data not available
for Birmingham, AL
PM10 (0.39)
CO,N02
Correlation only
provided for PM
because study
hypothesis
involves PM
Results reported for percent increase in stroke
admissions for an incremental increase in SO2
equivalent to one IQR (6.69).
Ischemic Stroke: 1.35 (0.43, 2.29), lag 0
Hemorrhagic Stroke: 0.68 (-1.77, 3.19)
Multipollutant models not run.
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TABLE AX5.4 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH EMERGENCY
DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR CARDIOVASCULAR DISEASES
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants Effects: Relative Risk or % Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
to
o
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UNITED STATES (cont'd)
>
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Kokenetal. (2003)
Denver, United States
Study period:
Jul and Aug,
1993-1997,
n= 310 days
Outcome(s) (ICD9):
Acute MI 410.00-410.92;
Atherosclerosis 14.00-414.05;
Pulmonary Heart Failure 416.0-416.9;
Dysrhythmia 427.0-427.9; CHF 428.0.
Discharge data from Agency for Healthcare
Research and Quality (AHRQ) database.
Age group analyzed: 65+ yrs
Study population: 60,000
Covariates : Seasonal adjustment not needed.
Adjustment for temperature, dew point
temperature made.
Study design: Time series
Statistical analysis: GLMs to analyze frequency
of admissions as a function of exposure. GEEs
to estimate parameters in Poisson regression
models, adjusting for overdispersion.
Lag(s): 0-4 day
SO2 24-h avg (ppb)
Mean(SD): 5.7(2.94)
Min: 0.4
25th: 3.8
50th: 5.3
75th: 7.2
Max: 18.9
O3 (- 0.10) Effects were reported as percent change in
CO (0.21) hospitalizations based on an increment of
PM10(0.36) 3.4 ppb.
NO2(0.46) Single-pollutant model
Dysrhythmia
8.9% (-0.34, 18.93) lag 0, adjusted for gender
but not temperature
SO2 was found to be associated with cardiac
dysrhythmia but not other outcomes. No
association was observed for PM or NO2 with the
outcomes.
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Low et al. (2006)
New York City, NY
Study period:
1995-2003, 3287 days
Outcome(s) (ICD): Ischemic stroke 433-434;
Undetermined stroke 436; monitored intake in
11 hospitals (ER or clinic visits). Excluded
stroke patients admitted for rehabilitation.
Study design: Time series
Statistical Analysis: Autoregressive integrated
moving avg (ARIMA) models
Software package: SAS
SO2 24-h avg (ppm)
Mean(SD): 0.009124
Min: 0
25th: 0.005
Median: 0.009
75th: 0.014
Max: 0.096
PM10 (0.042) At the highest concentration of SO2 (96 ppb) in
NO2 (0.33) New York city over the study period the expected
CO (0.303) increase in strokes would be 0.857 visits on the
Pollen (0.085) day of the event.
Each 1000 ppb (1 ppm) SO2 would produce an
additional 8.878 visits (SE 4.471)
(p = 0.0471) for stroke.
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TABLE AX5.4 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH EMERGENCY
DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR CARDIOVASCULAR DISEASES
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants Effects: Relative Risk or % Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
to
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UNITED STATES (cont'd)
X
Metzger et al. (2004)
Atlanta, GA
Period of Study:
Janl993-Aug312000,
4yrs
Outcome(s): IHD 410-414; AMI 410;
Dysrhythmias 427; cardiac arrest 427.5;
congestive heart failure 428; peripheral and
cerebrovascular disease 433-437, 440, 443-
444, 451-453; atherosclerosis 440; stroke
436. ED visits from billing records.
N: 4,407,535 visits, 37 CVD visits/days
# Hospitals: 31
Age groups analyzed: adults > 19, elderly
56+
Statistical Analysis: Poisson regression,
GLM. Sensitivity analyses using GEE and
GAM (strict convergence criteria)
Covariates: long-term trends, mean and
dew point temp, relative humidity (cubic
splines)
Statistical Software: SAS
Season: Warm, Apr 15-Oct 14,
Cool, Oct 15-Apr 14.
Lag(s): 0-3 days
SO2l-hmax(ppb)
Median: 11.0
10th-90th range: 2.0 to 39
ppb
PM10 (0.20)
03(0.19)
NO2 (0.34)
CO (0.26)
PM2.5(0.17)
Course PM (0.21)
Ultrafme (0.24)
Multipollutant
models used. All
models specified a
priori.
Results presented for RR of an incremental
increase in SO2 of 20 ppb (a priori lag 3 day
moving avg).
All CVD: 1.007(0.993,1.022)
Dysrhythmia: 1.001 (0.975, 1.028)
CHF: 0.992(0.961,1.025)
IHD: 1.007(0.981,1.033)
PERI: 1.028(0.999,1.059)
Finger wounds 1.007 (0.998, 1.026)
Single day lag models presented graphically.
No multipollutant models run for SO2 since
association was not observed in single-
pollutant models.
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Michaud et al. (2004)
Hilo, Hawaii
Study period:
1997-2001,
N= 1385 days
Outcome(s) (ICD9):
Cardiac 410-414, 425-429,
Emergency visits, primary diagnosis.
Study design: Time series
Statistical Analysis: Exponential
regression, autocorrelation assessed by
regressing square root of number of ED
visits on covariates (Durbin-Watson
statistic). Newey-West procedure also
conducted for assessment of
autocorrelation.
Covariates: Temperature, humidity,
interaction between SO2 and PM
Lag(s): 1-3 days
SO2 (all hourly
measurements) (ppb)
Mean(SD): 1.92(12.2)
Min: 0
Max: 447
Daily SO2
(12am-6am) (ppb)
Mean(SD): 1.97(7.12)
Min: 0
Max: 108.5
PM
Effects were presented as relative risk based
on an increment of 10 ppb and the 24-h avg
SO2 concentration.
Cardiac
0.92(0.85, 1.00) lag 3
No associations of cardiac ER visits with
VOG (SO2-acidic aerosols) observed.
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TABLE AX5.4 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH EMERGENCY
DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR CARDIOVASCULAR DISEASES
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels & Monitoring
Stations
Copollutants Effects: Relative Risk or % Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
to
o
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UNITED STATES (cont'd)
>
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to
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Peel et al. (2007)
Atlanta, GA
Study period:
Jan 1993-Aug 2000
Outcome(s) (ICD9): IHD 410-414;
dysrhythmia 427; CHF 428; peripheral
vascular and cerebrovascular disease 433-
437= 440, 443, 444, 451-453.
Computerized billing records for ED
visits.
Comorbid conditions: Hypertension 401-
405; diabetes 250; dysrhythmia 427, CHF
428; atherosclerosis 440; COPD 491, 492,
496; pneumonia 480-486; upper
respiratory infection 460-465, 466.0;
asthma 493, 786.09.
# Hospitals: 31
N: 4,407,535 visits
Study design: Case-crossover. CVD
outcomes among susceptible groups with
comorbid conditions.
Statistical analyses: Conditional logistic
regression.
Covariates: Cubic splines for temperature
and humidity included in models. Time
independent variables controlled through
design.
Statistical Software: SAS
Lag(s): 3 day avg, lagged 0-2 day
SO2 1-h max (ppb)
Mean(SD): 16.5 (17.1)
10th: 2
90th: 39
PM10 24-h avg
O3 8 h-max
NO2l-hmax
CO 1-h max
Correlations not
reported
Results expressed as OR for association of CVD
admissions with a 20 ppb incremental increase in
S02.
All CVD
1.009 (0.995,1.024), 3 day moving avg
IHD
1.013 (0.988, 1.039), 3 day moving avg
Dysrhythmia
1.003 (0.975, 1.031), 3 day moving avg
Peripheral and Cerebrovascular
1.024 (0.993, 1.055), 3 day moving avg
CHF
0.993 (0.961, 1.026), 3 day moving avg
Effect modification by comorbid conditions was
not observed.
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TABLE AX5.4 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH EMERGENCY
DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR CARDIOVASCULAR DISEASES
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants Effects: Relative Risk or % Change & Confidence
(Correlations) Intervals ([95% Lower, Upper])
to
o
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UNITED STATES (cont'd)
Schwartz and Morris
(1995)*
Detroit, MI
Study period:
1986-1989
Outcome(s) (ICD9): IHD 410-414; CHF
428; Dysrhythmia 427. Medicare data,
diagnosis at discharge.
Study design: Time series
Statistical analysis: Poisson regression,
GAM
Age groups analyzed: 65+ yrs
Covariates: Adjustments for long-term
patterns, temperature, humidity, days of
the wk, holidays, viral infections, etc.
Lag(s): 0-3, cumulative up to 3 days
SO2 24-h avg (ppb):
Mean: 25.4
IQR: 18 ppb
Q2: 15
Q3: 33
# Stations: 6
PM10 (0.42)
CO (0.23)
03(0.15)
Effects were expressed as relative risk based on an
increment of 18 ppb.
IHD
1.014 (1.003, 1.026) lag 0, single pollutant
1.009 (0.994, 1.023), 2-pollutant model with PM10
CHF
1.002 (0.978, 1.017), single-pollutant model
Risks for dysrhythmia were not reported for SO2.
>
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Schwartz (1997)*
Tuscon, AZ
Study period:
Jan 1988-Dec 1990
Outcome(s) (ICD9): CVD 390-429.
Ascertained from hospital discharge
records.
Study design: Time series
Statistical analysis: Poisson regression,
GAM
Age groups analyzed: 65+
Covariates: Long-term and seasonal
trends, day of the wk, temperature, dew
point,
Statistical software: S-PLUS
SO2 24-h avg (ppb)
Mean: 4.6 ppb
IQR: 3.9 ppb
10th: 0.7
Q2: 2.0
Median: 3.4
Q3: 5.9
90th: 10.1
PM10 (0.095) Results were expressed as percent change based on an
NO2 (0.482) increment of 3.9 ppb.
CO(0.395) 0.14% (-1.3%, 1.6)
O3 (- 0.271) No other statistically significant associations for
cardiovascular outcomes were observed.
-------
TABLE AX5.4 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH EMERGENCY
DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR CARDIOVASCULAR DISEASES
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels & Monitoring
Stations
Copollutants Effects: Relative Risk or % Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
to
o
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UNITED STATES (cont'd)
>
X
Wellenius et al. (2005b)
Allegheny County, PA
(near Pittsburgh)
Study period:
Jan 1987-Nov 1999
Outcome(s): CHF 428. Cases are
Medicare patients admitted from ER
with discharge of CHF
Study design: Case-crossover, control
exposures same mo and day of wk,
controlling for season by design.
Statistical analysis: Conditional
logistic regression
N: 55,019 admissions, including
repeat admissions, 86% admitted < 5
times
Age groups analyzed: 65+ yrs
(Medicare recipients)
Covariates: Temperature and pressure.
Effect modification by age, gender,
secondary diagnosis arrhythmias, atrial
fibrillation, COPD, hypertension, type
2 diabetes, AMI within 30 days, angina
pectoris, IHD, acute respiratory
infection.
Statistical software: SAS
Lag(s): 0-3
SO2 24-h avg (ppb):
Mean(SD): 14.78(9.8
5th: 3.98
25th: 7.70
Median: 12.24
75th: 18.98
95th: 33.93
# Stations: 10
PM10 (0.51)
CO (0.54)
NO2 (0.52)
Os (-0.19)
Effects were reported as percent change based on
an increment of 11 ppb.
CHF, single-pollutant models:
2.36(1.05, 3.69) lag 0, or
2.14 (0.95, 3.35) lag 0 after adjusted to an
increment of 10 ppb.
CHF, 2-pollutant models:
1.35 (-0.27, 2.99), S02/PMio
0.10 (-1.35, 1.57), SO2/CO
0.68 (-0.82, 2.21), SO2/NO2
2.02 (0.68, 3.37), SO2/O3
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TABLE AX5.4 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH EMERGENCY
DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR CARDIOVASCULAR DISEASES
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels & Monitoring
Stations
Copollutants Effects: Relative Risk or % Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
to
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CANADA
>
X
Burnett etal. (1997)*
Metropolitan Toronto
(Toronto, North York,
East York, Etobicoke,
Scarborough, York),
Canada
Study period:
1992-1994, 388 days,
summers only
Outcome(s) (ICD9):
IHD 410-414;
Cardiac Dysrhythmias 427;
Heart failure 428. All Cardiac 410-
414, 427, 428. Obtained from
hospital discharge data.
Population: 2.6 Million residents
Study design: Time series
Age groups analyzed: All
# Hospitals: NR
Statistical analysis: Relative risk
regression models, GAMs.
Covariates: Adjusted for long- term
trends, seasonal and subseasonal
variation, day of the wk,
temperature, dew point
Seasons: Summer only
Dose response: Figures presented
Statistical package: NR
Lag: 1-4 days
SO2 daily 1-h max (ppb): H+ (0.45)
Mean: 7.9 SO4(0.42)
CV: 64 TP(0.55)
Min: 0 FP (0.49)
25th percentile: 4 CP(0.44)
50th percentile: 7 COH(O.SO)
75th percentile: 11 O3(0.18)
Max: 26 NO2 (0.46)
CO (0.37)
# of Stations: 4-6
(Results are reported for
additional metrics including 24-h
avg and daytime avg (day)
Effects were expressed as relative risk based on an
increment of 7.00 ppb (IQR). T ratio in parentheses.
All cardiac disease
Single-pollutant model
1.041 (2.66), daily max over 4 days, lag 0
Multipollutant model w/ SO2, O3, NO2
Of 7.72 excess hospital admissions, 2.8% attributed
to SO2.
Objective of study was to evaluate the role of particle
size and chemistry on cardiac and respiratory
diseases.
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TABLE AX5.4 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH EMERGENCY
DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR CARDIOVASCULAR DISEASES
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels & Monitoring
Stations
Copollutants Effects: Relative Risk or Percent Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
to
o
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CANADA (cont'd)
>
X
Burnett etal. (1999)*
Metropolitan Toronto
(Toronto, North York,
East York, Etobicoke,
Scarborough, York),
Canada
Study period:
1980-1995, 15 yrs
Outcome(s) (ICD9): IHD 410-414;
Cardiac Dysrhythmias 427; Heart
failure 428; All cardiac 410-414,
427, 428; Cerebrovascular Disease
obtained from hospital discharge
data 430-438; Peripheral Circulation
Disease 440-459.
Population: 2.13-2.42 million
residents
Study design: Time series
Statistical analysis: GAMs to
estimate log RR per unit changes,
stepwise regression used to select
minimum number of air pollutants in
multipollutant models.
Covariates: Long-term trends,
seasonal variation, day of wk,
temperature, and humidity.
Statistical package: S-PLUS
Lag(s): 0-2 days
SO2 daily avg (ppb)
Mean: 5.35
5th percentile: 0
25th percentile: 1
50th percentile: 4
75th percentile: 8
95th percentile: 17
Max: 57
Multiple day avgs used in
models
PM2.5 (0.50)
PM10-2.5 (0.38)
PM10 (0.52)
CO (0.55)
SO2 (0.55)
O3 (-0.04)
Effects were reported as % change based on an
increment of 5.35 ppb.
Single-pollutant model
Dysrhythmias 0.8% (-0.3, 1.9)
Cerebrovascular 0.04% (-0.7, 0.8)
CHF 1.93% (0.9, 2.9)
IHD 2.32% (1.6, 3.1)
Attributed percent increase in admissions for SO2
were determined from multipollutant models.
IHD
Attributed percent increase: 0.95%
Authors note SO2 effects could be largely
explained by other variables in the pollution mix as
demonstrated by the multipollutant model.
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TABLE AX5.4 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR CARDIOVASCULAR DISEASES
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants Effects: Relative Risk or % Change & Confidence
(Correlations) Intervals ([95% Lower, Upper])
to
o
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CANADA (cont'd)
>
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H
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Fung etal. (2005)
Windsor, Ontario,
Canada
Study period:
Apr 1995-Jan 2000
Stieb et al. (2000) *
Saint John, New
Brunswick Canada
Study period:
Jull992-Marl996
Outcome(s) (ICD9): CHF 428; IHD
410-414; dysrhythmias 427 and all
cardiac. Hospital admissions from
Ontario Health Insurance Plan
records.
Study design: Time series
Statistical analysis: GLM
N: 11,632 cardiac admission,
4.4/day for 65+ age group
Age groups analyzed: 65+, <65 yrs
Statistical Software: SPLUS
Lag(s): lag 0, 2, 3 day avg
Outcome(s): Angina pectoris; MI;
dysrhythmia/conduction disturbance;
CHF; All Cardiac. ED Visits
collected prospectively.
Study design: Time series
Statistical analyses: Poisson
regression, GAM
N: 19,821ER visits
# Hospitals: 2
Lag(s): 1-8 days
SO2 1-hmax (ppb)
Mean(SD): 27.5(16.5)
Min:
Max:
0
129
IQR: 19.3 ppb
SO2 24-h avg (ppb)
Mean(SD): 6.7(5.6)
95th: 18
Max: 60
SO2 max (ppb)
Mean(SD): 23.8(21.0)
95th: 62
Max: 161
CO (0.16)
O3(-0.02)
PM10 (0.22)
N02 (0.22)
CO, (0.31)
H2S(-0.01)
O3(-0.02)
NO2(0.41)
PM10 (0.36)
PM2.5(0.31)
H+(-0.24)
SO4 (0.26)
COH(0.31)
Effects were expressed as percent change of cardiac
disease hospital admissions based on an increment of
19.3 ppb.
Single-pollutant model:
<65 yrs
2.3% (-1.8, 6.6) lag 0
3.9% (-1.5, 9.6) lag 0-1
3.4% (-3.0, 10.1) lag 0-2
>65 yrs
2.6% (0.0, 5.3) lag 0
4.0% (0.6, 1.6) lag 0-1
5.6% (1.5, 9.9) lag 0-2
Inclusion of particulate matter and adjustment for
meteorological variables did not change the association
between SO2 and cardiac hospitalization.
Results reported for percent change in admissions based
on a single-pollutant model for incremental increase in
NO2 equivalent to one IQR (8.9 ppb)
Cardiac visits (p-value in parentheses):
4.9 (0.002), 1 day avg, lag 8, all yr
2.8 (0.067), 5 day avg, lag 6, May-Sept
Multi-pollutant models:
4.9, (1.7, 8.2), 1 day avg, lag 8, all yr (O3)
Lags 0-10 presented graphically. All but lag 8 in single-
pollutant model approximately null.
-------
TABLE AX5.4 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR CARDIOVASCULAR DISEASES
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants Effects: Relative Risk or % Change & Confidence
(Correlations) Intervals ([95% Lower, Upper])
to
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CANADA (cont'd)
>
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Villeneuve et al.
(2006a)
Edmonton, Canada
Study period: Apr
1992-Mar2002
Outcome(s) (ICD9): Acute
ischemic stroke 434, 436;
hemorrhagic stroke 430, 432;
transient ischemic attach (TIA)
435; Other 433, 437, 438. ED
visits supplied by Capital Health.
N: 12,422
Stroke Visits Catchment area: 1.5
million people
Study design: Case-crossover,
exposure index time compared to
referent time. Time independent
variables controlled in the design.
Index and referent day matched by
day of wk.
Statistical Analysis: Conditional
logistic regression, stratified by
season and gender.
Covariates: Temperature and
humidity
Statistical software: SAS
Season:
Warm: Apr-Sept;
Cool: Oct-Mar.
Lag(s): 0, 1, 3 day avg
SO2 24 h ppb:
Allyr
Mean(SD): 2.6(1.9)
Median: 2.0
25th: 1.0
75th: 4.0
IQR: 3.0
Summer
Mean(SD): 2.1(1.6)
Median: 2.0
25th: 1.0
75th: 3.0
IQR: 2
Winter
Mean(SD): 3.1(2.0)
Median: 3.0
25th: 2.0
75th: 4.0
IQR: 2.0
Correlation
between SO2 and
other pollutants
(all yr):
NO2 (0.42)
CO (0.41)
03(-0.25)
PM25(0.22)
PM10(0.19)
Effects were reported as odds ratios based on an
increment of 3 ppb.
Acute Ischemic stroke, >65 yrs
Allyr OR 1.05 (0.99,1.11) lag 0
Warm OR 1.11 (1.01, 1.22) lag 0
Cold OR 1.00 (0.93, 1.09) lag 0
Effect stronger among males
Hemorrhagic stroke, >65 yrs
Allyr: 0.98 (0.90, 1.06), lag 0
Cold: 0.94(0.84, 1.05), lag 0
Warm: 1.03(0.90,1.17)
Effect stronger among males
Transient Cerebral Ischemic Attack, >65 yrs
Allyr: 1.06 (1.00, 1.12), lag 0
Cold: 1.03(0.95, 1.11), lag 0
Warm OR 1.11 (1.02, 1.22) lag 0
2-pollutant models presented graphically. Association
of SO2 with Acute Ischemic stroke diminished with
inclusion of CO and NO?.
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TABLE AX5.4 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH EMERGENCY
DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR CARDIOVASCULAR DISEASES
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants Effects: Relative Risk or % Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
to
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Ballester et al. (2006)
Multi-city, Spain:
Barcelona, Bilbao,
Castellon, Gijon,
Huelva, Madrid,
Granada, Oviedo,
Seville, Valencia,
Zaragoza
Period of Study:
1995/1996-1999,
N= 1,096 days
Outcome(s) (ICD9):
All CVD 390-459;
Heart diseases 410-414,427,428.
Emergency admission from hospital
records. Discharge data used.
Study design: Time series, meta-
analysis to pool cities
N: Daily mean admissions reported by
city
Statistical analyses: Poisson regression
and GAM, with stringent convergence
criteria, meta-analysis with random
effect model. Tested linearity by
modeling pollutant in linear and non-
linear way (spline smoothing). Linear
model provided best results 55% of time
but used in all cases to facilitate
comparability.
Covariates: Temperature, humidity and
influenza, day of wk unusual events,
seasonal variation and trend of the series
Seasons:
Hot: MaytoOct;
Cold: NovtoApr
Statistical package: S-PLUS
Lag: 0-3
SO224-havg (ug/m2)
Mean, 10th, 90th
Barcelona: 15.5,6.6,27.9
Bilbao: 18.6,10.2,29.3
Cartagena: 27.1,14.6,40.8
Castellon: 7.7, 3.8, 12.7
Gijon: 29.4, 10.3, 52.4
Granada: 19.1,8.8,31.5
Huelva: 11.9,4.5,22.6
Madrid: 21.8,8.7,41.8
Oviedo: 40.9,16.3,75.5
Pamplona: 7.6, 1.8, 17.0
Seville: 9.6,5.6,14.6
Valencia: 16.6,9.4,24.4
Vigo: 9.3,2.6,18.2
Zaragoza: 9.3,2.0, 19.9
# of Stations: Depends on the
city
Correlation among stations:
Correlations between SO2
stations within cities poor.
CO 8-h max (0.58)
O38-h max (-0.03)
NO2 24 h (0.46)
BS 24 h (0.24)
TSP24h(0.31)
PM10 24 h (0.46)
Correlations reported
are the median for all
cities.
2-pollutant models
used to adjust for
copollutants
Results reported for % change in admissions,
increment 10 (ug/m3).
All cardiovascular
1.33% (0.21,2.46) lag 0-1
Heart diseases
1.72% (0.50,2.95) lag 0-1
Single day lags presented graphically. Effect
size decreased with increasing lag.
Multi-pollutant results presented graphically.
Control for CO and particulates diminished SO2
effects.
-------
TABLE AX5.4 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH EMERGENCY
DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR CARDIOVASCULAR DISEASES
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants Effects: Relative Risk or % Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
to
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EUROPE (cont'd)
>
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Anderson et al. (2001)*
West Midlands
conurbation, UK
Study period:
1994-1996,
n = 832 days
Outcome(s) (ICD9): All CVD 390-459;
cardiac disease 390-429; IHD 410-414;
stroke 430-438. Emergency admissions
counted.
Catchment area: 2.3 million
Age groups analyzed: 0-14, 15-64, >65.
Study design: Time series, APHEA 2
methods
Statistical analyses: GAMs for modeling
non-liner dependence of some variables.
Covariates: Adjusted for effects of seasonal
patterns, temperature and humidity,
influenza episodes, day of wk and holidays.
Software package: S-PLUS
Seasons: Interaction by warm and cool
season investigated.
Lag(s): 0-3 days
SO2 24-h avg (ppb)
Mean(SD): 7.2(4.7)
Min: 1.9
10th: 3.3
Median: 5.8
90th: 12.3
Max: 59.8
# of Stations: 5 sites
PM10 (0.55)
PM2.5 (0.52)
PM2.5.10(0.31)
BS(0.50)
SO4(0.19)
NO2 (0.52)
03 (-0.22)
Results reported for % change in admissions,
increment = 9 ppb (10th-90th).
All CVD all ages
-0.4 (-2.2, 1.5), mean lags 0 + 1
Cardiac all ages:
0.7 (-1.3, 2.8), mean lags 0 + 1
IHD >65 yrs
1.5 (-2.5, 5.6), mean lags 0 + 1
Stroke >65 yrs
-5.1 (-9.6, -0.4), mean lags 0+ 1
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TABLE AX5.4 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH EMERGENCY
DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR CARDIOVASCULAR DISEASES
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants Effects: Relative Risk or % Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
to
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Atkinson etal. (1999a)
London, England
Period of Study:
1992-1994,
N= 1,096 days
Outcome(s) (ICD9):
All CVD 390-459; IHD 410-414.
Emergency admissions obtained from
the Hospital Episode Statistics (HES)
database (complaints).
Ages groups analyzed: 0-14 yrs, 15-64
yrs, 0-64 yrs, 65+ yrs, 65-74 yrs, 75+
yrs
Study design: Time series, hospital
admission counts
N: 189,109 CVD admissions
Catchment area: 7 million residing in
1,600 Km area of Thames basin.
Statistical analyses: APHEA protocol,
Poisson regression
Covariates: adjusted long-term seasonal
patterns, day of wk, influenza,
temperature, humidity (compared
alternative methods for modeling
meteorological including linear,
quadradic, piece-wise, spline)
Seasons: Warm season Apr-Sept, cool
season remaining mos, interactions
between season investigated
Dose response investigated: Yes,
bubble charts presented
Statistical package: SAS
Lag: 0-3
Dose response: Bubble plots presented
SO2 24 h avg (ppb):
Mean: 21.2
SD: 7.8
Min: 7.4
10th: 13
Median: 19.8
90th: 31
Max: 82.2
10th-90thpercentile:
11.2
# of Stations: 3, results
averaged across stations
PM10 24 h
C024h
S02 24 h
O38h
BS24h
Correlations of SO2 with
CO, NO2, O3, BS ranged
from 0.5-0.6
Correlation of SO2 with
O3 negative
2-pollutant models to
used adjust for
copollutants
Results reported for % change in admissions,
increment 10th-90th percentile (11.2 ppb).
All CVD, all ages
1.57(0.22,2.93), lag 0
All CVD, 0-64 yrs
2.44(0.3,4.63), lag 0
All CVD, 65+
1.72(0.15, 3.32), lag 0
IHD, 0-64 yrs
-2.03 (-5.35, 0.91), lag 2
IHD, 65+
3.10(0.61,5.65), lag 0
Effect size and significance diminished in models
containing SO2 and BS.
-------
TABLE AX5.4 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH EMERGENCY
DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR CARDIOVASCULAR DISEASES
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants Effects: Relative Risk or Percent Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
to
o
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EUROPE (cont'd)
>
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Ballesteretal. (2001)*
Valencia, Spain
Period of Study:
1992-1996
Outcome(s) (ICD9):
All CVD 390-459;
heart diseases 410-414, 427,428;
cerebrovascular diseases 430-438.
Admissions from city registry - discharge
codes used.
Study design: Time series
N: 1080 CVD admissions
# of Hospitals: 2
Catchment area: 376,681 inhabitants of
Urban Valencia
Statistical analyses: Poisson regression,
GAM, APHEA/ Spanish EMECAM
protocol. Both Linear and nonparametric
model, including a loess term was fitted,
departure from linearity assess by
comparing deviance of both models.
Covariates: Long-term trend and
seasonality, temperature and humidity,
weekdays, flu, special events, air pollution.
Seasons:
Hot season May to Oct;
Cold season Nov to Apr
Statistical package: SAS
Lag: 0-4
24 h (ug/mj):
Mean: 25.6
SD: NR
Min: 4.4
Max: 68.4
median: 25
# of Stations: 14
manual, 5 automatic
Correlation among
stations: 0.3-0.62 for
BS, 0.46-0.78 for
gaseous pollutants
CO 24 h, (0.74)
N02 24 h, (0.22)
O38h, (-0.35)
BS, (0.63)
2-pollutant models
used to adjust for
copollutants
Results expressed as relative risk, increment of
10 ug/m3.
All CVD
1.0302(1.0042, 1.0568), lag 2
Heart disease
1.0357(1.0012, 1.0714), lag 2
Cerebrovascular disease
1.0378 (0.9844 to 1.0940), lag 5
Digestive diseases
1.0234(0.9958, 1.0518), lag 1
All CVD, hottest semester
1.050(1.010,1.092), lag 2
Effect size for all CVD and cerebrovascular
disease diminished in 2-pollutant models.
-------
TABLE AX5.4 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH EMERGENCY
DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR CARDIOVASCULAR DISEASES
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants Effects: Relative Risk or % Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
to
o
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EUROPE (cont'd)
>
X
D'lppolitietal. (2003)
Rome, Italy
Study period:
Jan 1995-June 1997
Outcome(s) (ICD): AMI 410 (first episode).
Computerized hospital admission data.
Study design: Case-crossover, time stratified,
control days within same mo falling on the
same day.
Statistical analyses: Conditional logistic
regression, examined homogeneity across co-
morbidity categories
N: 6531 cases
Age groups analyzed: 18-64yrs,
65-74 yrd, >75
Season: Cool: Oct-Mar;
Warm: Apr-Sept.
Lag(s): 0-4 day, 0-2 day cum avg
Dose Response: OR for increasing quartiles
presented and p-value for trend.
SO2 24 h (ug/mj)
All yr:
Mean(SD): 9.5(6.0)
25th: 5.4
50th: 8.2
75th: 12.6
IQR: 7.2
Cold season:
Mean(SD): 12.7(6.5)
Warm season:
Mean(SD): 88.3(15.4)
# Stations: 5
TSP 24 h (0.29)
N02 24 h (0.37)
CO 24 h (0.56)
No multipollutant
models
Results reported as odds ratios for increment
equal to one IQR (7.2 ug/m3).
AMI
Quartile I (referent)
Quartile II
0.987(0.894, 1.089), lag 0-2
Quartile III
1.008(0.892, 1.140), lag 0-2
Quartile IV
1.144 (0.991, 1.321), lag 0-2
Results at various lags not reported for SO2.
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Llorca et al. (2005)
Torrelavega, Spain
Study period:
1992-1995
Outcome(s) (ICD): CVD (called cardiac in
paper) 390-459. Emergency admissions,
excluding nonresidents. Obtained admissions
records from hospital admin office.
Study design: Time series
Statistical analyses: Poisson regression,
APHEA protocol
Covariates: Rainfall, temperature, wind speed
direction
N: 1 8, 1 37 admissions
Statistical software: STATA
Lag(s): not reported
SO2 24 h ug/m3:
Mean(SD): 13.3(16.7)
TSP (-0.40)
NO2 (0.588)
SH2 (0.957)
NO (0.544)
Multipollutant
models
Results expressed as rate ratios. Increment =
100 ug/m3.
Cardiac admissions, single-pollutant model
0.94(0.84,1.05)
Five-pollutant model
1.09(0.83,1.42)
All cardiorespiratory admissions, single-
pollutant model
RR 0.98 (0.89, 1.07)
Five-pollutant model
0.98(0.80,1.21)
-------
TABLE AX5.4 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH EMERGENCY
DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR CARDIOVASCULAR DISEASES
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants Effects: Relative Risk or % Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
to
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>
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Poloniecki et al. (1997)*
London, UK
Study period: Apr, 1987-
Mar 1994, 7yrs
Outcome(s): All CVD 390-459; MI 410;
Angina pectoris 413; other IHD 414;
ARR 427; congestive heart failure 428;
cerebrovascular disease 430-438.
Hospital Episode Statistics (HES) data
on emergency hospital admissions.
Study design: Time series
N: 373, 556 CVD admissions
Statistical analyses: Poisson regression
with GAM, APHEA protocol
Covariates: Long-term trends, seasonal
variation, day of wk, influenza,
temperature and humidity.
Season: Warm, Apr-Sept;
Cool, Oct-Mar.
Lag: 0-1
SO2 24 h ppb:
Min: 0
10%: 2
Median: 6
90%: 21
Max: 114
Black Smoke
CO24h
N02 24 h
O38h
Correlations
between
pollutants high
but not specified.
Effects were expressed as relative risk based on
an increment of 19 ppb
(10th-90thpercentile).
Single-pollutant models (lag 0-1)
MI: 1.0326(1.0133,1.0511)
Angina: 1.0133(0.9907,1.0383)
IHD: 0.9944(0.9651,1.0239)
ARR: 1.0181(1.0000,1.0448)
CHF: 1.0057(0.9846,1.0258)
Cerebrovascular: 1.0019 (0.9837,1.0189)
All circulatory: 1.0248 (1.0062, 1.0444)
MI, 2-pollutant models, cool season
1.0399 (1.0171, 1.0628), SO2 only
1.0285 (1.0019, 1.0571), SO2/NO2
1.0380 (1.0057, 1.0704), SO2/CO
1.0285 (1.0019, 1.0552), SO2/BS
1.0476 (1.0209, 1.0742), SO2/O3
In the warm season no significant associations
were observed in 2-pollutant models.
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TABLE AX5.4 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH EMERGENCY
DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR CARDIOVASCULAR DISEASES
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants Effects: Relative Risk or % Change & Confidence
(Correlations) Intervals ([95% Lower, Upper])
to
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EUROPE (cont'd)
Prescott et al. (1998)*
Edinburgh, UK
Study period:
Oct 1992-Jun 1995
Outcome(s) (ICD9): Cardiac and
cerebral ischemia 410-414,426-429, 434-
440. Extracted from Scottish record
linkage system.
Study design: Time series
Statistical analysis: Poisson, log linear
regression models
Age groups analyzed: <65, 65+ yrs
Covariates: Seasonal and weekday
variation, temperature, and wind speed.
Lag(s): 0, 1, 3 day moving avg
NO2 24 h ppb
Mean(SD): 8.3(5.6)
Range: 1-50
90th-10th
Percentile = 12 ppb
O3,24 h
PM, 24 h
N02,24 h
CO, 24 h
Correlations not
reported.
Results reported as % increase in admissions,
increment 10 ppb.
A11CVD, < 65 yrs
4.9 (-1.0, 11.1), 3 day moving avg
A11CVD, > 65 yrs
-3.7 (-12.4, 5.9), 3 day moving avg
>
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Yallop et al. (2007)
London, England
Study period:
Jan. 1998-Oct. 2001,
>1400 days
Outcome(s): Acute pain in Sickle Cell
Disease (HbSS, HbSC, HbS/pO,
thalassaemia, HbS/(3+). Admitted to
hospital for at least one night.
Study design: Time series
Statistical analyses: Cross-correlation
function
N: 1047 admissions
Covariates: No adjustment made in
analysis, discussion includes statement
that the effects of weather variables and
copollutants are inter-related.
Statistical package: SPSS
Lag(s): 0-2 days
Dose response: quartile analysis, graphs
presented, ANOVA comparing means
across quartiles.
NR
03,CO,NO,N02,
PM10:
daily avg used for
all copollutants
No association for SO2
-------
TABLE AX5.4 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH EMERGENCY
DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR CARDIOVASCULAR DISEASES
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels & Monitoring
Stations
Copollutants Effects: Relative Risk or % Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
to
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AUSTRALIA
>
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Jalaludin et al. (2006)
Sydney, Australia
Period of Study:
Jan. 1997-Dec. 2001
Outcome(s) (ICD9): All CVD 390-
459; cardiac disease
390-429; IHD 410-413; and
cerebrovascular disease or stroke
430-438; Emergency room
attendances obtained from health
department data.
Age groups included: 65+
Study design: Time series, multi-city
APHEA2 Protocol.
Statistical analysis: GAM (with
appropriate convergence criteria) and
GLM Models. Only GLM presented.
Lag: 0-3
Covariates: Daily avg temperature
and daily relative, humidity, long-
term trends, seasonality, weather, day
of wk, public school holidays,
outliers and influenza epidemics.
Dose response: quartile analysis
Season: Separate analyses for warm
(Nov-Apr) and cool periods (May-
Oct).
SO2 24 h avg (ppb)
Mean(SD): 1.07(0.58)
Min: 0.09
25th: 0.64
Median: 1.01
75th: 1.39
Max: 3.94
IQR: 0.75
# of Stations: 14
BS(0.21)
PM10 (0.37)
O3 (0.454)
NO2(lh)(0.52)
CO (8 h) (0.46)
2-pollutant models
to adjust for
copollutants
Effects were presented as percent change based on
an increment of 0.75 ppb.
Single-pollutant model:
All CVD, all yr
1.33% (0.24,2.43) lag 0
Cardiac: 1.62% (0.33, 2.93) lag 0
IHD: 1.12% (-0.84, 3.12) lag 0
Stroke: -1.41% (-3.67, 0.90) lag 0
Cool Season
All cardiovascular: 2.15% (0.84, 3.46) lag 0
Cardiac: 2.48% (0.94, 4.04) lag 0
IHD: 2.49% (0.13,4.91) lag 0
Stroke: -0.19% (-2.90,2.60) lag 0
Warm Season
All cardiovascular: 0.06% (-1.48, 1.62) lag 0
Cardiac: 0.38% (-1.37, 2.16) lag 0
IHD: -0.47% (-3.08, 2.22) lag 0
Stroke: -2.74% (-5.92, 0.55) lag 0
Results for lags 0-3 presented. In general, effect
size diminished with increasing lag.
Effects of SO2 on all CVD were diminished with
inclusion of PM and CO (graphically presented.)
-------
TABLE AX5.4 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH EMERGENCY
DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR CARDIOVASCULAR DISEASES
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels & Monitoring
Stations
Copollutants Effects: Relative Risk or % Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
to
o
o
AUSTRALIA (cont'd)
>
X
H
6
o
o
H
O
o
H
W
O
O
HH
H
W
Petroeschevsky et al.
(2001)
Brisbane, Australia
Study period:
Jan 1987-Dec 1994,
2,922 days
Outcome(s) (ICD9): CVD 390-459.
Hospital admissions, non-residents
excluded.
Study design: Time series
Statistical analyses: Poisson
regression, APHEA protocol, linear
regression and GEEs
Age groups analyzed: 15-64,65+
Covariates: Temperature, humidity,
rainfall. Long-term trends, season,
flu, day of wk, holidays.
Dose response: Quintile analysis.
Statistical software: SAS
Lag(s): lag 0-4, 3 day avg,
5 day avg
SO2 24-h avg (pphm)
Summer: Mean, min, max
0.39,0.0,1.63
Fall: Mean, min, max
0.42,0.01,3.55
Winter: Mean, min, max
0.48,0.0,2.08
Spring: Mean, min, max
0.37,0.0,6.02
Overall: Mean, min, max
0.41,0.0,3.55
SO2 1-h max (pphm)
Summer: Mean, min, max
0.78,0.0,5.5
Fall: Mean, min, max
0.93,0.05,5.95
Winter: Mean, min, max
1.13,0.0,6.68
Spring: Mean, min, max
0.84,0.0,6.01
Overall: Mean, min, max
0.92,0.0,6.68
BSP
03
NO2
Correlation
between
pollutants not
reported.
Effects were expressed as relative risk based on an
increment of 10 ppb and the 24-h avg SO2
concentrations.
All CVD
15 to >65 yrs
1.028(0.987, 1.070) lag 0
15 to 64 yrs
1.081(1.010, 1.158) lag 0
>65 yrs
1.038(0.988, 1.091) lag 1
Non-significant increasing risk for CVD in those
15-64 by quintile of SO2 concentration observed.
-------
TABLE AX5.4 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH EMERGENCY
DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR CARDIOVASCULAR DISEASES
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants Effects: Relative Risk or % Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
to
o
o
ASIA
>
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oo
H
6
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W
O
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Chan et al. (2006) *
Taipai, Taiwan
Period of Study:
Apr 1997-Dec 2002,
2090 days
Outcome(s) (ICD9): Cerebrovascular
disease 430-437; stroke 430-434;
hemorrhagic stroke 430-432; ischemic
stroke 433-434. Emergency admission
data collected from National Taiwan
University Hospital.
Ages groups analyzed: age >50
included in study
Study design: Time series
N: 7341 Cerebrovascular admissions
among those >50 yrs old
# of Hospitals:
Catchment area:
Statistical analyses: Poisson
regression, GAMs used to adjust for
non-linear relation between
confounders and ER admissions.
Covariates: Time trend variables: yr,
mo, and day of wk, daily temperature
difference, and dew point temperature.
Linearity: Investigated graphically by
using the LOESS smoother.
Statistical package: NR
Lag: 0-3, cumulative lag up to 3 days
SO224-havg (ppb):
Mean: 4.3
SD: 2.4
Min: 0.4
Max: 17.1
IQR: 3.1 ppb
# of Stations: 16
Correlation among
stations: NR
PM10 24 h (0.59)
PM2.524h(0.51)
CO 8-h avg (0.63)
N02 24 h (0.64)
O3l-h max (0.51)
2-pollutant models to
adjust for copollutants
but not for SO2, which
was not associated
with health outcomes.
Results reported for OR for association of emergency
department admissions with an IQR increase in SO2
(3.1 ppb)
Cerebrovascular:
1.008(0.969,1.047), lag 0
Stroke:
0.991(0.916,1.066), lag 0
Ischemic stroke:
1.044(0.966,1.125), lag 0
Hemorrhagic stroke:
0.918(0.815,1.021), lag 0
No significant associations for SO2 reported. Lag 0
shown but similar null results were obtained for lags
0-3.
-------
TABLE AX5.4 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH EMERGENCY
DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR CARDIOVASCULAR DISEASES
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants Effects: Relative Risk or % Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
to
o
o
ASIA (cont'd)
>
X
Chang et al. (2005)
Taipei, Taiwan
Study period:
1997-2001, 5 yrs
Outcome(s) (ICD9): CVD 410-429.
Daily clinic visits or hospital admission from
computerized records of National Health
Insurance. Discharge data.
Source population: 2.64 Million
N: 40.8 admissions/day, 74,509/5 yrs
# Hospitals: 41
Study design: Case-crossover, referent day 1
wk before or after index day
Statistical analyses: Conditional logistic
regression.
Covariates: Same day temperature and
humidity.
Season: warm/cool (stratified by temperature
cutpointof20°C)
Lag(s): 0-2 days
SO2 24-h avg (ppb)
Mean: 4.32
Min: 0.15
25th: 2.74
Median: 3.95
75th: 5.49
Max: 14.57
IQR: 2.75
# of Stations: 6
CO 24-h avg
O3 24-h avg
NO2 24-h avg
PM10 24-h avg
Correlations not
reported.
2-pollutant models
to adjust for
copollutants
Effects were expressed as odds ratios based on an
increment of 2.75 ppb.
Warm (>20 °C) 0.967 (0.940, 0.995)
Cool(<20 °C) 1.015 (0.965, 1.069)
In 2-pollutant models with (PM10, NO2, CO, or O3)
the effect of SO2 was attenuated for both
temperature ranges such that it was negatively
associated with CVD.
>20°C: 0.874 (0.77, 0.880), w/PM10
<20 °C: 0.986 (0.928, 1.048), w/ PM10
>20°C: 0.826 (0.798, 0.854), w/N02
<20°C: 0.922(0.865, 0.984), w/NO2
>20°C: 0.903(0.876, 0.931), w/CO
<20°C: 0.960(0.901, 1.022), w/ CO
>20°C: 0.953(0.926, 0.981), w/O3
<20°C: 1.014(0.963, 1.067), w/O3
H
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Lee et al. (2003)
Seoul, Korea
Study period:
Dec 1997-Dec 1999,
822-day s, 184 day sin
summer
Outcome(s)(ICD10): IHD: Angina pectoris
120; Acute or subsequent MI 121-123; other
acute IHD 124. Electronic medical
insurance data used.
Study design: Time series
Statistical methods: Poisson regression,
GAM with strict convergence criteria.
Age groups analyzed: all ages, 64+
Covariates: Long-term trends LOESS
smooth, temperature, humidity, day of wk.
Season: Presented results for summer (Jun,
Jul, Aug) and entire period.
Lag(s): 0-6
SO2 24 h (ppb):
5th: 3.7
10th: 5.1
Median: 7.0
75th: 9.5
95th: 14.3
Mean(SD): 7.7(3.3)
IQR: 4.4
Allyr
NO2 (0.72)
03(-0.30)
CO (0.81)
PM10 (0.59)
Warm season
N02 (0.79)
O3(-0.56)
CO (0.41)
PM10 (0.61)
2-pollutant
models
Results reported for RR of IHD hospital admission
for an incremental increase in SO2 equivalent to
one IQR (4.4 ppb).
Single-pollutant model:
Entire season- IHD
All ages 0.96 (0.92, 0.99) lag 3
>64 yrs 0.95 (0.90, 1.01) lag 3
Summer- IHD
All ages 1.09 (0.96, 1.24) lag 3
>64 yrs 1.32 (1.08, 1.62) lag 3
2-pollutant model:
Entire season; SO2 and PM10
>64 yrs 0.98 (0.94, 1.03) lag 3
-------
TABLE AX5.4 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH
EMERGENCY DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR CARDIOVASCULAR DISEASES
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants Effects: Relative Risk or % Change & Confidence
(Correlations) Intervals ([95% Lower, Upper])
to
o
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ASIA (cont'd)
>
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W
O
O
HH
H
W
Tsai et al. (2003a)
Kaohsiung, Taiwan
Study period:
1997-2000
Wong etal. (1999)
Hong Kong, China
Study period:
1994-1995
Outcome(s) (ICD9): All cerebrovascular 430-
438; SHS 430;
PIH 431-432; IS 433-435; Other
436-438. Ascertained from National Health
Insurance Program computerized admissions
records.
Study design: Case-crossover
Statistical analysis: Conditional logistic
regression.
N: 23,179 stroke admissions
# Hospitals: 63
Statistical software: SAS
Seasons: Warm(>20°C); Cool (<20 °C).
Lag(s): 0-2, cumulative lag up to 2 previous
days
Outcome(s) (ICD9): CVD:
410-417, 420-438, 440-444; CHF 428; IHD
410-414; Cerebrovascular Disease 430-438.
Hospital admissions through ER departments
via Hospital Authority (discharge data).
Study design: Time series
Statistical analyses: Poisson regression,
APHEA protocol
# Hospitals: 12
Covariates: Daily temperature, relative
humidity day of wk, holidays, influenza, long-
term trends (yr and seasonality variables).
Interaction of pollutants with cold season
examined.
Season: Cold (Dec-March)
Lag(s): 0-3 days
S02 (ppb)
Min: 1.25
25th: 6.83
Median: 9.76
75th: 13.00
Max: 26.80
Mean: 10.08
# Station: 6
PM10
S02
CO
03
SO2 24-h avg (ug/m3) PM10
Mean: 20.2 SO2
IQR: 10 03
Results reported as OR for the association of admissions
with an incremental increase of SO2 equivalent to the
IQRof6.2ppb
PIH admissions
Warm: 1.06 (0.95, 1.18), lag 0-2
Cool: 0.85(0.58, 1.26), lag 0-2
IS admissions:
Warm: 1.06 (1.00, 1.13), lag 0-2
Cool: 1.11(0.83, 1.48), lag 0-2
2-pollutant models:
PIH 0.91 (0.80, 1.03) w/N02
180.93(0.87, 1.00) w/NO2
PIH 0.94 (0.83, 1.06), w/CO
180.94(0.88, 1.02), w/ CO
PIH 1.08 (0.96, 1.20) w/O3
IS 1.08 (1.01,1.15) w/03
PIH0.99 (0.88, 1.11) w/PM
IS 1.01 (0.95-1.08) w/PM
Results reported for RR associated with incremental
increase inNO2 equal to 10 ug/m .
All CVD, All ages
1.016(1.006, 1.026) lag 0-1
All CVD, 5-65 yrs
1.004(0.989, 1.020) lag 0-1
All CVD, >65 yrs
1.021(1.010, 1.032) lag 0-1
CHF
1.036(1.013, 1.059) lag 0
IHD
1.010(0.995, 1.025) lag 0-1
Cerebrovascular
0.990(0.978, 1.002) lag 3
2-pollutant model results not presented for SO2
-------
TABLE AX5.4 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH EMERGENCY
DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR CARDIOVASCULAR DISEASES
to
o
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Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels & Monitoring
Stations
Copollutants Effects: Relative Risk or % Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
ASIA (cont'd)
X
H
6
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o
H
O
o
H
W
O
O
HH
H
W
Wong et al. 2002a*
Hong Kong, London
Study period: 1995-1997
(Hong Kong),
1992-1994 (London)
Outcome(s) (ICD9): Cardiac disease
390-429; IHD 410-414. Patients
admitted to hospitals from emergency
departments, out patient departments
or directly to inpatient wards.
Statistical analysis: Poisson
regression, GAMs
Covariates: Smooth functions of time,
temperature, humidity (up to 3 days
before admission) day of wk, holidays
and unusual events.
Statistical software:
S-PLUS
Seasons: Warm/cold
Lag(s): 0-3, cumulative 0-1
SO224-havg (ug/mj)
Hong Kong
Mean, all yr: 17.7(12.3)
Mean, warm: 18.3
Mean, cold: 17.2
Min: 1.1
10th: 6.2
50th: 14.5
90th: 32.8
Max: 90
London
Mean, all yr: 23.7(12.3)
Mean, warm: 22.2
Mean, cold: 25.3
Min 6.2
10th: 13.2
50th: 20.6
90th: 38.1
Max: 113.6
Hong Kong Effects expressed as % change, increment was
NO2 (0.37) 10 ug/m3 Cardiac (all ages)
PM10 (0.30) Hong Kong
O3(-0.18) Allyr: 2.1% (1.3, 2.8) lag 0-1
Warm: 1.0% (0.0, 2.0) lag 0-1
London Cold: 1.9% (1.2, 2.7) lag 0-1
NO2 (0.71) London
PM10(0.64) Allyr: 1.6% (1.0, 2.2) lag 0-1
O3 (-0.25) Warm: 0.6% (-0.6, 1.7) lag 0-1
Cold: 1.9% (1.2, 2.7) lag 0-1
IHD (all ages)
Hong Kong
Allyr: 0.1% (-1.1, 1.2) lag 0-1
Warm: -0.6% (-2.0, 0.8) lag 0-1
Cold: 1.0% (-0.8, 2.8) lag 0-1
London
Allyr: 1.7% (0.8, 2.6) lag 0-1
Warm: 1.0% (-0.6, 2.6) lag 0-1
Cold: 2.0% (0.9, 3.1) lag 0-1
Multipollutant model
Cardiac (all ages)
Hong Kong
SO2 alone 2.1% (1.3, 2.8)
SO2/NO21.4%(0.4,2.3)
S02/032.1%(1.4,2.9)
SO2/PMi02.0%(l.l,2.8)
London
SO2 1.6% (1.0, 2.2)
SO2/NO21.4%(0.6,2.3)
SO2/O31.6%(0.9,2.2)
S02/PM102.2%(1.2, 3.2)
-------
TABLE AX5.4 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH EMERGENCY
DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR CARDIOVASCULAR DISEASES
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants Effects: Relative Risk or % Change & Confidence
(Correlations) Intervals ([95% Lower, Upper])
to
o
o
ASIA (cont'd)
>
X
to
Yang et al. (2004a)
Kaohsiung, Taiwan
Period of Study:
1997-2000
Outcome(s) (ICD9): All CVD: 410-
429 * (All CVD typically defined to
include ICD9 codes 390-459)
N: 29,661
Study design: Case-crossover
Statistical analysis: Poisson Time
series regression models, APHEA
protocol
# of Hospitals: 63
Seasons: Authors indicate not
considered because the Taiwanese
climate is tropical with no apparent
seasonal cycle
Covariates: Stratified by warm (>25°)
and cold (<25°) days, temperature, and
humidity measurements included in
the model
Statistical package: SAS
Lag: 0-2 days
SO2 24-h avg (ppb)
Min: 1.25
25%: 6.83
50%: 9.76
75%: 13.00
Max: 26.80
Mean: 10.08
# of Stations: 6
Correlation among
stations: NR
PM10
CO
SO2
038
2-pollutant models
used to adjust for
copollutants
Correlations NR
OR's for the association of one IQR (17.08 ppb)
increase in SO2 with daily counts of CVD hospital
admissions are reported
All CVD (ICD9: 410-429), one-pollutant model
>25°: 0.999(0.954,1.047)
<25°: 1.187(1.092,1.291)
All CVD (ICD9: 410-429), 2-pollutant models
Adjusted for PM10:
>25°: 0.961(0.917,1.008)
<25°: 1.048(0.960,1.145)
Adjusted for NO2:
>25°: 0.921(0.875,0.969)
<25°: 0.711(0.641,0.789)
Adjusted for CO:
>25°: 0.831(0.785,0.879)
<25°: 0.996(0.910,1.089)
Adjusted for: O3
>25°: 1.034(0.987,1.084)
<25°: 1.194(1.098,1.299)
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TABLE AX5.4 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE WITH EMERGENCY
DEPARTMENT VISITS AND HOSPITAL ADMISSIONS FOR CARDIOVASCULAR DISEASES
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants Effects: Relative Risk or % Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
to
o
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MIDDLE EAST
Hosseinpoor et al.
(2005)
Tehran, Iran
Study period:
Mar 1996-Mar 2001,
5yrs
Outcome(s) (ICD9): Angina pectoris
413. Primary discharge diagnosis from
registry databases or records.
Study design: Time series
Statistical methods: Poisson regression
# Hospitals: 25
Covariates: Long-term trends,
seasonality, temperature, humidity,
holiday, post-holiday, day of wk.
Lag(s): 0-3
SO2 24-h avg (ug/m3)
Mean(SD): 73.74(33.30)
Min: 0.30
25th: 48.23
Median: 74.05
75th: 98.64
Max: 499.26
NO2 CO O3 PM1(
Correlations not
reported
Results reported for relative risk in hospital
admissions per increment of 10 ug/m3 SO2.
Angina
0.99995 (0.99397, 1.00507), lag 1
In a multipollutant model only CO (lag 1) was
significantly associated with angina pectoris related
hospital admissions.
>
X
* Default GAM
AMI Acute Myocardial
Infarction
ARR Arrhythmia
BC Black Carbon
COH coefficient of haze
CP Course Particulate
CVD Cardiovascular Disease
EC Elemental Carbon
FP Fine Particulate
HS Hemorrhagic Stroke
ICD9 International Classification of Disease,
9th Revision
IHD Ischemic Heart Disease
IS ischemic stroke
MI Myocardial Infarction
OC Organic Carbon
OHC Oxygenated
Hydrocarbons
PERI Peripheral Vascular and
Cerebrovascular Disease
PM Particulate Matter
PIH primary intracerebral hemorrhage
PNC Particle Number Concentration
SHS Subarachnoid hemorrhagic stroke
TP Total Particulate
UBRE Unbiased Risk Estimator
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HH
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TABLE AX5.5. ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE ON MORTALITY
to
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>
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O
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W
Reference, Study Outcome
Location, and Period Measure
META ANALYSIS
Stieb et al. All cause
(2002; reanalysis 2003)
meta-analysis of estimates
from various countries.
UNITED STATES
Dockery et al. (1992) All cause
St. Louis, MO and Eastern
Tennessee
1985-1986
Moolgavkar (2000; Cardiovascular;
reanalysis 2003a). cerebrovascular;
Cook County, IL; Los COPD
Angeles County, CA; and
Maricopa County, AZ
1987-1995
Copollutants Lag Structure
Mean SO2 Levels Considered Reported
24-h avg ranged from PM10, O3, NO2, The lags and
0.7 ppb CO multiday
(San Bernardino) to averaging used
75 ppb (Shenyang) varied
"Representative"
concentration:
9.4 ppb
24-h avg: PM10, PM2.5, 1
St. Louis: 9 ppb SO42", 1^,03,
Eastern Tennessee: NO2,
5 ppb
24-h avg median: PM2 5, PM10, O3, 0, 1, 2, 3, 4, 5
Cook County: 6 ppb NO2, CO; 2- and
Los Angeles: 2 ppb 3-pollutant
Maricopa County: models
2 ppb
Method/Design
Meta-analysis of time-
series study results.
Poisson with GEE.
Time-series study.
Poisson GAM with
default convergence
criteria in the original
Moolgavkar (2000);
GAM with stringent
convergence criteria
and GLM with natural
splines in the 2003 re-
analysis. The 2000
analysis presented total
death risk estimates
only in figures.
Effect Estimates* % Increase
in Risk (95% CI)
Single-pollutant model
(29 estimates):
1.0% (0.6, 1.3)
Multipollutant model estimates
(10 estimates):
0.9% (0.3, 1.4)
All cause:
St. Louis, MO:
0.8% (-1.7, 3.2)
Eastern Tennessee:
0.4% (-0.4, 1.1)
GLM (re-analysis):
Cook County:
All-cause:
Lag 1: 2. 6% (1.4, 3.8)
Cardiovascular:
Lag 1: 2. 9% (1.0, 4. 8)
Los Angeles: Cardiovascular:
Lag 1: 5. 9% (3. 0,9.0)
-------
TABLE AX5.5 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE ON MORTALITY
to
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W
Reference, Study Outcome
Location, and Period Measure
Mean SO2 Levels
Copollutants Lag Structure
Considered Reported
Method/Design
Effect Estimates* % increase
in risk (95% CI)
UNITED STATES (cont'd)
Moolgavkar (2003b) All cause;
Cook County, IL and cardiovascular
Los Angeles County, CA
1987-1995
Samet et al. (2000a,b; All cause;
reanalysis Dominici cardiopulmonary
et al., 2003)
90 U.S. cities
(58U.S.citieswithSO2
data) 1987-1994
Schwartz (2004) All cause
14 U.S. cities that had
daily PM10 data
24-h avg median:
Cook County: 6 ppb
Los Angeles: 2 ppb
24-h avg ranged from
0.4 ppb (Riverside) to
14.2 ppb (Pittsburgh)
24-h avg median ranged
from 2.2 ppb (Spokane,
WA) to 39.4 ppb
(Pittsburgh, PA)
PM25,PM10, 03, 0,1,2,3,4,5
N02, CO; 2-
pollutant models
PM10,03,N02, 0,1,2
CO;
multipollutant
models
PM10 risk 1
estimates
computed,
matched by the
levels of SO2,
CO, NO2, and
03
Poisson GAM with
default convergence
criteria. Time-series
study.
Poisson GAM,
reanalyzed with
stringent convergence
criteria; Poisson GLM.
Time-series study.
Case-crossover design,
estimating PM10 risks
by matching by the
levels of gaseous
pollutants.
All cause:
Cook County:
Single pollutant:
Lagl: 2.6% (1.5, 3.7)
WithPM10:
Lagl: 1.9% (0.6, 3.2)
Los Angeles:
Single pollutant:
Lagl: 6. 9% (5 .4, 8.4)
With PM2.5:
Lagl: 7.6% (3 .4, 12.0)
Posterior means:
All cause:
Single pollutant:
Lagl: 0.6% (0.3, 1.0)
With PM10 and N02:
Lagl: 0.4% (-0.6, 1.4)
SO2 risk estimates not
computed. PM10 risk estimates
showed the largest risk estimate
when matched for SO2.
-------
TABLE AX5.5 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE ON MORTALITY
t^) '
CD
^
to
O
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Reference, Study
Location, and Period
Outcome Measure Mean SO2 Levels
Copollutants Lag Structure
Considered Reported Method/Design
Effect Estimates* %
increase in risk (95% CI)
UNITED STATES (cont'd)
Chock et al. (2000)
Pittsburgh, PA
1989-1991
De Leon etal. (2003)
New York City
1985-1994
Gamble (1998)
Dallas, TX
1990-1994
Gwynn et al. (2000)
Buffalo, NY
All cause; age <74 Not reported.
yrs; age 75+ yrs
Circulatory and 24-h avg: 15 ppb
cancer with and
without
contributing
respiratory causes
All cause; 24-h avg: 3 ppb
respiratory;
cardiovascular
All cause; 24-h avg: 12 ppb
respiratory;
circulatory
PMio, O3,NO2, 0,1,2,3 Poisson GLM. Time-
CO; 2-, 5-, and 6- series study. Numerous
pollutant models results
PMio, O3, NO2, 0 or 1 Poisson GAM with
CO; 2-pollutant stringent convergence
models criteria;
Poisson GLM.
Time-series study.
PM10, 03,NO2, 0 Poisson GLM.
CO; 2-pollutant Time-series study.
models
PMio, CoH, 0, 1, 2, 3 Poisson GAM with
SO4 , O3, NO2, Default convergence
CO, ET" criteria. Time-series
study.
All cause:
Age 0-74 yrs:
Lagl: 0.7% (-0.7, 2.2)
Age 75+ yrs:
Lag 1: -0.2% (-1.6, 1.3)
Gaseous pollutants results
were given only in figures.
Circulatory:
Age <75 yrs: -2%
Age 75+ yrs: -2%
All cause:
-0.8% (-3.8, 2.4)
Respiratory:
-1.0% (-5. 8, 4.1)
Cardiovascular:
-0.5% (-11.4, 11.8)
All cause:
LagO: -0.1% (-1.8, 1.7)
Circulatory:
Lag 3: 1.3% (-2.9, 5.6)
Respiratory:
Lag 0: 6.4% (-2.5, 16.2)
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TABLE AX5.5 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE ON MORTALITY
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Reference, Study
Location, and Period
Outcome Copollutants
Measure Mean SO2 Levels Considered
Lag Structure
Reported Method/Design
Effect Estimates* %
increase in risk (95% CI)
UNITED STATES (cont'd)
Kelsalletal. (1997)
Philadelphia, PA
1974-1988
All cause; 24-havg: 17ppb TSP, CO, NO2, O3
respiratory;
cardiovascular
0 Poisson GAM.
(AIC presented
for 0 through 5)
All cause:
Single-pollutant:
0.8% (0.3, 1.4)
With all other pollutants:
0.8% (0.1, 1.6)
Kinney and Ozkaynak All cause;
(1991) respiratory;
Los Angeles County, CA circulatory
1970-1979
24-havg: 15ppb
KM (particle optical
reflectance), Ox, NO2,
CO; multipollutant
models
OLS (ordinary least
squares) on high-pass
filtered variables.
Time-series study.
All cause:
Exhaustive multipollutant
model:
0.0% (-1.1, 1.2)
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Klemm and Mason
(2000); Klemm et al.
(2004)
Atlanta, GA
Aug 1998-M2000
Lipfert et al. (2000a)
Seven counties in
Philadelphia, PA area
1991-1995
All cause; 1-hmax: 19ppb
respiratory;
cardiovascular;
cancer; other;
age <65 yrs; age
65+ yrs
All cause; 24-h avg: 8 ppb
respiratory; 1-hmax: 18 ppb
cardiovascular;
all ages; age 65+
yrs; age
<65 yrs; various
subregional
boundaries
PM25,PM10.25,EC,OC, 0-1
S042\ N03", 03, N02,
CO
PM10, PM2.5, PM10.2.5, 0-1
SO42 , other PM indices,
O3, NO2, CO; 2-
pollutant models
Poisson GLM using
quarterly, monthly, or
biweekly knots for
temporal smoothing.
Time-series study.
Linear with 1 9-day
weighted avg
Shumway filters.
Time-series study.
Numerous results.
All cause
Age 65+ yrs:
Quarterly knots:
4.7% (-2.6, 12.5)
Monthly knots:
3.4% (-4.1, 11. 5)
Bi-weekly knots:
1.0% (-6.7, 9.3)
All-cause:
Philadelphia:
0.7% (p > 0.05)
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TABLE AX5.5 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE ON MORTALITY
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Reference, Study Outcome
Location, and Period Measure Mean SO2 Levels
Copollutants Lag Structure
Considered Reported
Method/Design
Effect Estimates* % increase
in risk (95% CI)
UNITED STATES (cont'd)
Lippmann et al. (2000; All cause; 24-h avg:
reanaly sis Ito, 2003, respiratory; 1985-1990: lOppb
2004) circulatory; 1992-1994: 7ppb
Detroit, MI cause-specific
1985-1990
1992-1994
Mar et al. (2000; All cause, 24-h avg: 3.1 ppb
re-analysis in 2003) cardiovascular
Phoenix, AZ.
1995-1997.
Moolgavkar et al. (1995) All cause 24-h avg:
Philadelphia, PA Spring: 17 ppb
1973-1988. Summer: 16 ppb
Fall: 18 ppb
Winter: 25 ppb
PM10,PM25, 0,1,2,3,0-1,
PM10.25,S042-, 0-2,0-3
FI+,03,N02,CO;
2-pollutant
models
PM2 5, PMi o, 0 for all cause;
PM10.2.5,CO, 0,1,2, 3, 4 for
NO2, O3, and cardiovascular
selected trace
elements, ions,
EC, OC, TOC,
and factor
analysis
components
TSP, O3; 1
2-pollutant
models
Poisson GAM,
reanalyzed with
stringent convergence
criteria; Poisson GLM.
Numerical SO2 risk
estimates were not
presented in the re-
analysis. Time-series
study.
Poisson GAM with
default convergence
criteria (only
cardiovascular deaths
were reanalyzed in
2003). Time-series
study.
Poisson GLM. Time-
series study.
Poisson GAM:
All cause:
1985-1990:
Lagl: 0.5% (-1.5, 2.4)
1992-1994:
Lagl: 1.1% (-1.4, 3.6)
Poisson GAM:
All cause:
LagO: 11. 2% (-1.5, 25.6)
Poisson GLM:
Cardiovascular:
Lagl: 7.4% (-13. 1,32.6)
Allyr: 1.3% (0.8, 1.8)
Spring: 1.7% (0.6, 2.9)
Summer: 0.9% (-0.7, 2.5)
Fall: 1.3% (0.0, 2.6)
Winter: 2.0% (0.9, 3.0)
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TABLE AX5.5 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE ON MORTALITY
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Reference, Study Outcome
Location, and Period Measure
Mean SO2 Levels
Copollutants Lag Structure
Considered Reported
Method/Design
Effect Estimates* % increase in
risk (95% CI)
UNITED STATES (cont'd)
Schwartz (1991) All cause
Detroit, MI
1973-1982
Schwartz (2000) All cause
Philadelphia, PA
1974-1988
24-h avg: 12 ppb
24-h avg summer
mean declined from
20 ppb in 1974 to 9
ppb in 1988; winter
mean declined from
35 ppb in 1974 to 17
in 1988
TSP (predicted 0,1,0-1
from
extinction
coefficient); 2-
pollutant
models
TSP, 0
extinction
coefficient
Poisson GEE. Time-
series study.
Poisson GAM model
in 1 5 winter and 1 5
summer periods. The
second stage
regressed the TSP
and SO2 risk
estimates on
SO2/TSP
relationships.
Poisson regression coefficient
Single pollutant:
Lagl: 0.863 (SE = 0.323)
With TSP:
Lagl: 0.230 (SE = 0.489)
(Though SO2 levels were reported in
ppb, these coefficients must have been
for SO2 inppm.)
Single pollutant:
2. 3% (1.6, 3.0)
With TSP:
0.4% (-2.2, 3.1)
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TABLE AX5.5 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE ON MORTALITY
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Reference, Study
Location, and Period
Outcome Measure
Mean SO2 Levels
Copollutants
Considered
Lag Structure
Reported Method/Design
Effect Estimates* %
increase in risk (95% CI)
CANADA
Burnett et al. (2004)
12 Canadian cities
1981-1999
Burnett etal. (1998a)
1 1 Canadian cities
1980-1991
Burnett etal. (1998b)
Toronto
1980-1994
Goldberg et al. (2003)
Montreal, Quebec
1984-1993
All cause
All cause
All cause
Congestive heart
failure (CHF) as
underlying cause of
death versus those
classified as having
CHF 1 yr prior to death
24-h avg ranged from
1 ppb (Winnipeg) to
10 ppb (Halifax)
24-h avg ranged from
1 ppb (Winnipeg) to
1 1 ppb (Hamilton)
24-h avg: 5 ppb
24-h avg: 6 ppb
PM25,PM10.25,
03, N02, CO
O3, NO2, CO
03, N02, CO,
TSP, COH,
estimated PM10,
estimated PM2 5
PM2.5,
coefficient of
haze, SO42~, O3,
NO2, CO
1 Poisson GLM. Time-
series study.
0,1,2,0-1,0-2 Poisson GAM with
examined but the default convergence
best lag/averaging criteria. Time-series
for each city study.
chosen
0,1,0-1 Poisson GAM with
default convergence
criteria. Time-series
study.
0,1,0-2 Poisson GLM with
natural splines. Time-
series study.
Single pollutant:
0.7% (0.3, 1.2)
WithNO2:
0.4% (0.0, 0.8)
Single pollutant:
3.4% (2.0, 4.7)
With all gaseous pollutants:
2.6% (1.3, 3.9)
Single pollutant:
LagO: 1.0% (0.3, 1.8)
With CO:
LagO: 0.6% (-0.4, 1.5)
CHF as underlying cause of
death:
Lag 1: -0.1% (-8. 9, 9.6)
Having CHF 1 yr prior to
death:
Lag 1: 5.4% (1.3, 9.5)
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TABLE AX5.5 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE ON MORTALITY
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Reference, Study
Location, and Period
CANADA (cont'd)
Vedal et al. (2003)
Vancouver, British
Columbia
1994-1996
Villeneuve et al. (2003)
Vancouver, British
Columbia
1986-1999
EUROPE
Ballester et al. (2002)
13 Spanish cities
1990-1996
Biggeri et al. (2005)
8 Italian cities
Period variable between
1990-1999
Outcome
Measure
All cause;
respiratory;
cardiovascular
All cause;
respiratory;
cardiovascular;
cancer;
socioeconomic
status
All cause,
cardiovascular,
respiratory
All cause;
respiratory;
cardiovascular
Copollutants Lag Structure
Mean SO2 Levels Considered Reported Method/Design
24-havg: 3 ppb PM10, 03,NO2, 0,1,2 Poisson GAM with
CO stringent convergence
criteria. Time-series
study. By season.
24-havg: 5 ppb PM25, PM10, 0,1,0-2 Poisson GLM with
PM10.2.5, TSP, natural splines. Time-
coefficient of series study.
haze, SO42~, O3,
N02, CO
24-h avg SO2 ranged TSP, BS, PM10 0- 1 for 24-h avg Poisson GAM with
from 2.8 ppb (Sevilla) SO2;Oforl-h default convergence
to 15.6ppb(Oviedo) max SO2 criteria. Time-series
study.
24-h avg ranged from O3,NO2,CO, 0-1 Poisson GLM. Time-
2 ppb (Verona) to 14 PM10 series study.
ppb (Milan)
Effect Estimates* %
increase in risk (95% CI)
Results presented in figures
only.
All cause:
Summer:
LagO: -3%
Winter:
Lagl: -1%
All yr:
All cause:
Lagl: 1.7% (-1.1, 4. 5)
Cardiovascular:
Lagl: 1.1% (-3. 1,5.4)
Respiratory:
Lagl: 8. 3% (0.6, 16.6)
All cause:
Lag 0-1: 1.4% (0.2, 2.7)
Cardiovascular:
Lag 0-1: 1.4% (-0.4, 3.3)
Respiratory:
Lag 0-1: 3. 5% (1.0, 6.0)
All cause:
4.1% (1.1, 7.3)
Respiratory:
7.4% (-3.6, 19.6)
Cardiovascular:
4.9% (0.4, 9.7)
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TABLE AX5.5 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE ON MORTALITY
Reference, Study Location, Outcome Copollutants Lag Structure Effect Estimates* % increase
and Period
Measure
Mean SO2 Levels
Considered
Reported
Method/Design
in risk (95% CI)
EUROPE (cont'd)
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Hoek et al.
(2000; reanalysis Hoek, 2003)
The Netherlands: Entire
country, four urban areas
1986-1994
All cause;
COPD;
pneumonia;
cardiovascular
24-h avg median:
3.5 ppb in the
Netherlands;
5.6 ppb in the four
major cities
PMio, BS, SO/
NO3~, O3,NO2,
CO;
2-pollutant
models
1,0-6
Poisson GAM,
reanalyzed with
stringent
convergence criteria;
Poisson GLM. Time-
series study.
Poisson GLM:
All cause:
Lagl: 1.3% (0.7, 1.9)
Lag 0-6: 1.8% (0.9, 2.7) With
BS: 1.1% (-0.3, 2.4)
Cardiovascular:
Lag 0-6: 2.7% (1.3, 4.1)
COPD:
Lag 0-6: 3.6% (-0.3, 7.7)
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Katsouyanni et al. (1997)
12 European cities Study
periods vary by city, ranging
from 1977 to 1992
All cause
24-h avg median of
the median across
the cities was
14 ppb, ranging
from 5 ppb
(Bratislava) to
26 ppb (Cracow)
BS,PM1(
"Best" lag
variable across
cities from
Oto3
Poisson
autoregressive.
Time-series study.
Pneumonia:
Lag 0-6: 6.6% (1.2, 12.2)
All cities:
1.1% (0.9, 1.4)
Western cities:
2.0% (1.2, 2.8)
Central eastern cities:
0.5% (-0.4, 1.4)
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Le Tertre et al. (2002)
Bordeaux , Le Havre, Lille,
Lyon, Marseille, Paris, Rouen,
Strasbourg, France
Study periods vary by city,
ranging from 1990-1995
All cause;
respiratory;
cardiovascular
24-h avg ranged
from 3 ppb
(Bordeaux) to 9 ppb
(Rouen)
BS, 03, N02
0-1
Poisson GAM with
default convergence
criteria. Time-series
study.
8-city pooled estimates:
All cause:
2.0% (1.2, 2.9)
Respiratory:
3.2% (0.1, 6.3)
Cardiovascular:
3.0% (1.5, 4.5)
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TABLE AX5.5 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE ON MORTALITY
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Reference, Study
Location, and Period
Outcome
Measure
Mean SO2 Levels
Copollutants
Considered
Lag Structure
Reported
Method/Design
Effect Estimates* % increase in
risk (95% CI)
EUROPE (cont'd)
Peters et al. (2000b)
NE Bavaria, Germany
1982-1994
Coal basin in Czech
Republic
1993-1994
Saez et al. (2002)
Seven Spanish cities
Variable study periods
between 199 land 1996
Zmirouetal. (1998)
10 European cities
Study periods vary by city,
ranging from 1985-1 992
All cause;
respiratory;
cardiovascular;
cancer
All cause;
respiratory;
cardiovascular
Respiratory;
cardiovascular
24-h avg:
Czech Republic:
35ppb
Bavaria, Germany:
14 ppb
Values for SO2 not
reported.
24-h avg:
Cold season:
Ranged from 12 ppb
(London) to 87 ppb
(Milan) ppb
TSP, PMio, 03,
NO2, CO
03, PM, N02,
CO
BS, TSP, NO2,
03
0,1,2,3
0-3
0,1,2,3,0-1,0-
2, 0-3 (best lag
selected for each
city)
Poisson GLM.
Time-series study.
Poisson GAM with
default convergence
criteria. Time-series
study.
Poisson GLM.
Time-series study.
Czech Republic:
All cause:
Lagl: 0.8% (-0.2, 1.8)
Bavaria, Germany:
All cause:
Lagl: 0.3% (-0.3, 0.9)
Risk estimates for SO2 was not
reported. Including SO2 in
regression model did not appear to
reduce NO2 risk estimates.
Western cities:
Respiratory:
2.8% (1.7, 4.0)
Cardiovascular:
2. 3% (0.9, 3.7)
Warm season:
Ranged from 5 ppb
(Bratislava) to 21 ppb
(Cracow) in warm
season
Central eastern cities:
Respiratory:
0.6%(-1.1,2.3)
Cardiovascular:
0.6% (0.0, 1.1)
Zeghnoun et al. (2001)
Rouen and Le Havre,
France 1990-1995
All cause;
respiratory;
cardiovascular
24-h avg: NO2, BS, PM13, 0, 1, 2, 3, 0-3,
Rouen: 10 ppb O3
Le Havre: 12 ppb
Poisson GAM with
default convergence
criteria. Time-series
study.
All cause:
Rouen:
Lagl: 2.3% (-1.
Le Havre:
Lagl: 1.1% (-0.
.1,5.9)
.3,2.5)
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TABLE AX5.5 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE ON MORTALITY
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Reference, Study
Location, and Period
Outcome Measure Mean SO2 Levels
Copollutants
Considered
Lag Structure
Reported Method/Design
Effect Estimates* % increase
in risk (95% CI)
EUROPE (cont'd)
Anderson et al. (1996)
London, England
1987-1992
Anderson etal. (2001)
West Midlands region,
England
1994-1996
Bremner etal. (1999)
London, England
1992-1994
Clancy et al. (2002)
Dublin, Ireland
1984-1996
All cause; 24-h avg: 11 ppb
respiratory;
cardiovascular
All cause; 24-h avg: 7 ppb
respiratory;
cardiovascular
All cause; 24-h avg: 7 ppb
respiratory;
cardiovascular; all
cancer; all others; all
ages; age specific (0-
64, 65+, 65-74,
75+ yrs)
All cause, 24-h avg:
cardiovascular, and
respiratory 1984-1990:
11. 7 ppb
1990-1996:
7.7 ppb
BS, 03, N02;
2-pollutant
models
PM10, PM2.5,
PM10.2 5, BS,
S042\ 03, N02,
CO
BS, PMio, O3,
NO2, CO;
2-pollutant
models
BS
1 Poisson GLM.
Time-series
study.
0-1 Poisson GAM
with default
convergence
criteria. Time-
series study.
Selected best from Poisson GLM.
0, 1, 2, 3, (all Time-series
cause); study.
0,1,2,3,0-1,0-2,
0-3 (respiratory,
cardiovascular)
NA Comparing
standardized
mortality rates
for 72 mos
before and after
the ban on coal
sales in Sept
1990.
All cause:
1.0% (0.0, 2.0)
Respiratory:
Cardiovascular:
0.2% (-1.4, 1.8)
All cause:
-0.2% (-2.5, 2.1)
Respiratory:
-2.2% (-7.4, 3.2)
Cardiovascular:
-0.2% (-3. 5, 3.1)
All cause:
Lagl: 1.6% (-0.5, 3.7)
Respiratory:
Lag 2: 4.8% (-0.2, 10.0)
Cardiovascular
Lagl: 1.3% (-1.7, 4.3)
BS mean declined by a larger
percentage (70%) than SO2
(34%) between the two
periods.
All cause death rates reduced
by 5.7% (4, 7); respiratory
deaths by 15.5% (12, 19);
cardiovascular deaths by
10. 3% (8, 13).
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TABLE AX5.5 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE ON MORTALITY
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TABLE AX5.5 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE ON MORTALITY
Reference, Study
Location, and Period
Outcome Measure Mean SO2 Levels
Copollutants
Considered
Lag Structure
Reported
Method/Design
Effect Estimates* %
increase in risk (95% CI)
EUROPE (cont'd)
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Garcia-Ay merich et al.
(2000)
Barcelona, Spain
1985-1989
All cause;
respiratory;
cardiovascular;
general population;
patients with COPD
Levels not reported. BS, O3, NO2
Selected best
averaged lag
Poisson GLM.
Time-series study.
All cause:
General population:
Lag 0-3: 4.4% (2.3, 6.5)
COPD patients:
Lag 0-2: 2.6% (-5.0, 10.7)
Respiratory:
General population:
Lag 0-1: 3.5% (-0.6, 7.8)
COPD patients:
Lag 0-2: 2.3% (-8.9, 15.0)
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Hoek et al. (2002) All cause 24-h avg median: TSP, BS, Fe, O3, 1
Rotterdam, the 7.7 ppb CO
2 Netherlands
^ 1983-1991
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Cardiovascular:
General population:
Lag 0-3: 5.1% (2.3, 8.0)
COPD patients:
Lag 0-2: 2.0%
(-11.5,17.5)
Poisson GAM Single pollutant:
with default \1 .5% (0.0, 3.0)
convergence
criteria. Time- With TSP and O3:
senes study. 0.5% (-1.2, 2.3)
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TABLE AX5.5 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE ON MORTALITY
Reference, Study Copollutants Lag Structure
Location, and Period Outcome Measure Mean SO2 Levels Considered Reported
EUROPE (cont'd)
Hoeketal. (2001; Total 24-havg median: 3.5 PM10, 03,NO2, 0-6
reanalysis Hoek, 2003) cardiovascular; ppb in the CO
The Netherlands myocardial Netherlands; 5.6 ppb
1986-1994 infarction; in the four major
arrhythmia; heart cities
failure;
cerebrovascular;
thrombosis-related
Method/Design
Poisson GAM,
reanalyzed with
stringent
convergence
criteria; Poisson
GLM. Time-
series study.
Effect Estimates* %
increase in risk (95% CI)
Poisson GLM:
Total cardiovascular:
2. 7% (1.3, 4.1)
Myocardial infarction:
0.8% (-1.2, 2. 8)
Arrhythmia:
2. 3% (-3. 9, 8.8)
Heart failure:
7.1% (2.6, 11.7)
Cerebrovascular:
4.4% (1.4, 7.5)
oo
Thrombo sis-related:
9.6% (3.1,16.6)
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W
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Kotesovec et al. (2000) All cause, 24-havg: 34. 9 ppb TSP
Northern Bohemia, cardiovascular
Czech Republic (only age = <65
1982-1994 presented), cancer
Michelozzi et al. (1998) All-cause 24-havg: 5.7 ppb PM13,NO2, 03,
Rome, Italy 1992-1995 CO
0,1,2,3,4,5,6,0-6 Poisson GLM,
time-series study
0,1,2,3,4 Poisson GAM
with default
convergence
criteria. Time-
series study.
All cause:
Lag 1:0.1% (-0.1, 0.4)
Lagl: -2.0% (-4.4, 0.5);
(negative estimates at all
examined)
lags
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TABLE AX5.5 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE ON MORTALITY
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TABLE AX5.5 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE ON MORTALITY
Copollutants Lag Structure Effect Estimates* % increase
Reference, Study
Location, and Period
Outcome Measure Mean SO2 Levels
Considered
Reported
Method/Design
in risk (95% CI)
EUROPE (cont'd)
to
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Spix and Wichman (1996)
Koln, Germany
1977-1985
All-cause
24-h avg: 15 ppb
1-hmax: 32 ppb
TSP,PM7,NO2 0,1,0-3 PoissonGLM. Lag 1: 0.8% (0.2, 1.4)
Time-series study.
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Sunyer et al. (2002)
Barcelona, Spain
1986-1995
All cause,
respiratory, and
cardiovascular
mortality in a
cohort of patients
with severe asthma
24-h avg median:
6.6 ppb
PM10, BS, NO2, O3, 0-2
CO, pollen
Conditional
logistic (case-
crossover)
Odds ratio:
Patients with 1 asthma
admission:
All cause:
14.8% (-19.8, 64.4)
Patients with more than 1
asthma adm:
All cause:
50.4% (-48.6, 340.4)
Patients with more than 1
asthma or COPDadm:
All cause:
20.2% (-17.5, 75.0)
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NO2 and O3 were more
strongly associated with
outcomes than SO7.
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TABLE AX5.5 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE ON MORTALITY
T3
3
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^
Reference, Study
Location, and Period
EUROPE (cont'd)
Sunyeretal. (1996)
Barcelona, Spain
1985-1991
Outcome Measure
All cause;
respiratory;
cardiovascular; all
ages; age
70+ yrs
Copollutants
Mean SO2 Levels Considered
24-h avg median: BS, NO2, O3
Summer: 1 3 ppb
Winter: 16 ppb
Lag Structure
Reported
Selected best
single-day lag
Effect Estimates* % increase
Method/Design
Autoregressive
Poisson. Time-
series study.
in risk (95% CI)
All
All
Lag
yr, all ages:
cause:
;1: 3.5% (1
.9,5.1)
Respiratory:
LagO: 3.5% (-0.2, 5.0)
Cardiovascular:
Lagl: 2.2% (0.5, 3.9)
Verhoeffetal. (1996) All cause; all ages; 24-h avg: 4.5 ppb BS, PM10, 03, CO; 0,1,2 Poisson GLM. Single pollutant:
Amsterdam, the age 65+ yrs multipollutant models Time-series study. Lagl: 1.4% (-1.4, 4.2)
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0
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W
Netherlands
1986-1992 WithBS:
-3.7% (-8.1, 0.9)
Zmirouetal. (1996) All cause; 24-h avg: 16 ppb PMi3,NO2, 03 Selected best Poisson GLM. All cause:
Lyon, France respiratory; from 0, 1,2, 3 Time-series study. LagO: 3.4% (1.4,
1985-1990 cardiovascular; Respiratory:
digestive Lag 3: 2.8% (0.9,
Cardiovascular:
Lag 0-3: 4. 5% (2.
5.4)
4.8)
0, 7.0)
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TABLE AX5.5 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE ON MORTALITY
Lag Structure Effect Estimates* %
Reference, Study
Location, and Period
Outcome Measure Mean SO2 Levels
Copollutants
Considered
Reported
Method/Design
increase in risk (95% CI)
LATIN AMERICA
to
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Borja-Aburto et al.
(1998)
SW Mexico City
1993-1995
All cause;
respiratory;
cardiovascular; other;
all ages; age >65 yrs
24-havg: 5.6ppb
PM2.5, O3, NO2; 2- 0,1,2,3,4,5,
pollutant models and multiday
Poisson GAM with
default convergence
criteria (only one
smoother). Time-
series study.
SO2 risk estimates not
reported. PM2 5 and O3 were
associated with mortality.
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Borja-Aburto et al.
(1997)
Mexico City
1990-1992
All cause;
respiratory;
cardiovascular; all
ages; age <5 yrs; age
>65 yrs
24-h avg median:
5.3 ppb
TSP, O3 CO;
2-pollutant models
0,1,2
Poisson iteratively
weighted and filtered
least-squares method.
Time-series study.
All-cause:
LagO: 0.2% (-1.1, 1.5)
Cardiovascular:
LagO: 0.7% (-1.6, 3.0)
Respiratory:
LagO: -1.0%(-5.0, 3.2)
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TABLE AX5.5 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE ON MORTALITY
Lag Structure Effect Estimates* %
Reference, Study
Location, and Period
Outcome Measure
Mean SO2 Levels
Copollutants
Considered
Reported
Method/Design
increase in risk (95% CI)
LATIN AMERICA (cont'd)
to
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>
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Cakmak et al. (2007)
7 Chilean urban centers
1997-2003
All cause;
respiratory;
cardiovascular; all
ages; age
<65 yrs; age
65-74 yrs; age 75-84
yrs; age 85+ yrs
24-h avg ranged from
9.12ppb(LasCondes)
to 64.06 ppb
(Independencia)
Population-weighted
avg concentration:
14.08 ppb
PM10,03,CO
0,1,2,3,4,5,0-5
Poisson GLM with
random effects
between cities.
Time-series study.
All cause:
All ages:
Single pollutant:
Lagl: 4.0% (2.4, 5.6)
Lag 0-5: 6.5% (4.5, 8.5)
Multipollutant:
Lagl: 3.2% (1.3, 5.1)
<65 yrs:
Lag 0-5: 3.0% (0.6, 5.5)
65-74 yrs:
Lag 0-5: 5.1% (1.2, 9.1)
75-84 yrs:
Lag 0-5: 7.8% (4.1, 11.6)
85+ yrs:
7.8% (4.2, 11.5)
vo
to
Warm season:
Lag 0-5: 7.2% (4.1,10.3)
Cool season:
Lag 0-5: 3.0% (-0.4, 6.5)
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Cifuentes et al. (2000)
Santiago, Chile
1988-1966
All cause
24-h avg: 18.1 ppb
PM25,PM10.25,
CO, NO2, O3
1-2
Poisson GAM with
default convergence
criteria;
Poisson GLM. Time-
series study.
Poisson GLM:
Single pollutant:
Lag ^ 0.2% (-0.9, 1.3)
With other pollutants:
Lag 1-2: -0.6%
(-1.7,0.5)
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TABLE AX5.5 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE ON MORTALITY
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O
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W
Reference, Study
Location, and Period
Outcome Measure Mean SO2 Levels
Copollutants Lag Structure
Considered Reported
Method/Design
Effect Estimates* %
increase in risk (95% CI)
LATIN AMERICA (cont'd)
Concei9aoetal. (2001)
Sao Paulo, Brazil
1994-1997
Loomisetal. (1999)
Mexico City
1993-1995
Ostroetal. (1996)
Santiago, Chile
1989-1991
Pereiraetal. (1998)
Sao Paulo, Brazil
1991-1992
Saldivaetal. (1994)
Sao Paulo, Brazil
1990-1991
Saldivaetal. (1995)
Sao Paulo, Brazil
1990-1991
Child mortality (age 24-h avg: 7.4 ppb
under 5 yrs)
Infant mortality 24-h avg: 5. 6 ppb
All cause 1-hmax: 60 ppb
Intrauterine mortality 24-h avg: 6.6 ppb
Respiratory; age 24-h avg: 6.0 ppb
<5yrs
All cause; 24-h avg: 6. 5 ppb
age 65+ yrs
PMio, CO, O3 2
PM2.5,03 0,1,2,3,4,5,3-5
PMio, O3, NO2; 2- 0
pollutant models
PMio, 03, N02, CO 0
PMio, 03, N02, 0-2
CO; multipollutant
models
PMm, O3, NO2, 0-1
CO; 2-pollutant
models
Poisson GAM with
default convergence
criteria. Time-series
study.
Poisson GAM with
default convergence
criteria. Time-series
study.
OLS, Poisson. Time-
series study.
Poisson GLM. Time-
series study.
OLS of raw or
transformed data.
Time-series study.
OLS; Poisson with
GEE. Time-series
study.
Single pollutant:
Lag 2: 17.0% (7.0, 28.0);
With all other pollutants:
Lag2: 13.7%(-1.1, 30.8)
SO2 risk estimates not
reported. PM2 5 and O3 were
associated with mortality.
LagO: 0.7% (-0.3, 1.7)
Single-pollutant model:
11. 5% (-0.3, 24.7)
With other pollutants:
8.6% (-8.7, 29.3)
-1.0% (-47.1, 45.1)
Single pollutant: 8. 5% (1.3,
15.6)
With other pollutants:
-3.1% (-13.0, 6.9)
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TABLE AX5.5 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE ON MORTALITY
to
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O
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Reference, Study
Location, and Period
Outcome Measure Mean SO2 Levels
Copollutants Lag Structure
Considered Reported Method/Design
Effect Estimates* %
increase in risk (95% CI)
ASIA
Lee et al. (2000)
7 Korean cities
1991-1997
Leeetal. (1999)
Seoul and Ulsan, Korea
1991-1995
Ha et al. (2003)
Seoul, Korea
1995-1999
Hong et al. (2002)
Seoul, Korea
1995-1998
Kwonetal. (2001)
Seoul, Korea
1994-1998
All cause 24-h avg SO2 ranged
from
12.1 ppb (Kwangju) to
3 1 .4 ppb (Taegu)
All cause 1-h max:
Seoul: 26 ppb
Ulsan: 31 ppb
All cause; 24-h avg: 11.1 ppb
respiratory;
postneonatal(l mo
to 1 yr); age 2-
64 yrs; age 65+
Acute stroke 24-h avg: 12.1 ppb
mortality
Mortality in a 24-h avg: 13. 4 ppb
cohort of patients
with congestive
heart failure
TSP, NO2, O3, CO 0- 1 Poisson GAM with
default convergence
criteria. Time-series
study.
TSP, O3 0-2 Poisson with GEE.
Time-series study.
PM10, O3, NO2, CO 0 Poisson GAM with
default convergence
criteria. Time-series
study.
PMio, O3, NO2, CO 2 Poisson GAM with
default convergence
criteria. Time-series
study.
PMm, O3, NO2, CO 0 Poisson GAM with
default convergence
criteria; case-crossover
analysis using
conditional logistic
regression.
Single pollutant:
Lag 0-1: 0.6% (0.3, 0.8)
Multipollutant:
Lag 0-1: 0.6% (0.2, 0.9)
Seoul:
1.5% (1.1, 1.9)
Ulsan:
1.0% (-0.2, 2.2)
All cause:
Postneonates:
11.3% (4.0, 19.1)
Age 65+ yrs:
3.2% (3. 1,3. 3)
5.2% (1.4, 9.0)
Odds ratio in general
population:
1.0% (-0.1, 2.1)
Congestive heart failure
cohort:
6.9% (-3.4, 18.3)
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TABLE AX5.5 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE ON MORTALITY
to
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O
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HH
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W
Reference, Study
Location, and Period
Copollutants
Outcome Measure Mean SO2 Levels Considered
Lag Structure
Reported Method/Design
Effect Estimates* %
increase in risk (95% CI)
ASIA (cont'd)
Lee and Schwartz (1999)
Seoul, Korea
1991-1995
Tsai et al. (2003b)
Kaohsiung, Taiwan
1994-2000
Verniers etal. (2003)
Chonqing, China
1995
Wong et al. (2002b)
Hong Kong
1995-1998
All cause 1-hmax: 26 ppb TSP, O3
All cause; 24-havg: 11. 2 ppb PM10, NO2, O3, CO
respiratory;
cardiovascular;
tropical area
All cause, 24-havg: 74.5 ppb PM2.5
cardiovascular,
respiratory, cancer,
and other
Respiratory; 24-havg: 29 ppb PM10, 03, NO2; 2-
cardiovascular; pollutant models
COPD; pneumonia
and influenza;
ischemic heart dis.;
cerebrovascular
0-2 Conditional logistic
regression. Case-
crossover with
bidirectional control
sampling.
0-2 Conditional logistic
regression. Case-
crossover analysis.
0, 1 , 2, 3, 4, 5 Poisson GLM, time-
series study
0, 1 , 2, 0- 1 , Poisson GLM. Time-
0-2 series study.
Two controls, ± 1 wk:
0.3% (-0.5, 1.0)
Four controls, ± 2 wks:
1.0% (0.3, 1.6)
Odds ratios:
All cause:
1.1% (-4.4, 6.8)
Respiratory:
3. 5% (-17.6, 29. 9)
Cardiovascular:
2.4% (-9. 1,1 5.4)
All cause:
Lag 2: 1.1% (-0.1, 2.4)
Cardiovascular:
Lag 2: 2.8% (0.4, 5.2)
Respiratory:
Lag 2: 3.0% (0.4, 5.7)
Respiratory:
Lag 0-1: 2.6% (0.2, 5.1)
Cardiovascular:
Lag 0-1: 1.2% (-1.0, 3.5)
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TABLE AX5.5 (cont'd). ASSOCIATIONS OF SHORT-TERM EXPOSURE TO SULFUR DIOXIDE ON MORTALITY
<*J
i
a-
^
to
o
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.
X
VO
ON
O
>
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0
H
O
o
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W
O
O
H
W
Reference, Study Copollutants Lag Structure
Location, and Period Outcome Measure Mean SO2 Levels Considered Reported Method/Design
ASIA (cont'd)
Wong et al. (2001 b) All cause; 24-h avg: PM10, 03,NO2; 0,1,2 Poisson GAM with
Hong Kong respiratory; Warm season: 2-pollutant models default convergence
1995-1997 cardiovascular 6. 4 ppb criteria. Time-series
Cool season: 6.0 ppb smdy.
Yang et al. (2004b) All cause; 24-h avg: 5.5 ppb PM10, NO2, 03, CO 0-2 Conditional logistic
Taipei, Taiwan respiratory; regression. Case-
1994-1998 cardiovascular; crossover analysis.
subtropical area
AUSTRALIA
Simpson etal. (1997) All cause; 24-h avg: 4.2 ppb PM10, bsp, O3, NO2, 0 Autoregressive
Brisbane, Australia respiratory; CO Poisson with GEE.
1987-1993 cardiovascular 1-hmax: 9.6 ppb Time-series study.
* Effect estimates standardized to 10 ppb incremental change in 24-h avg SO2 or 40 ppb incremental change in 1-h max SO2.
Effect Estimates* %
increase in risk (95% CI)
All cause:
Lagl: 3.2% (1.1, 5. 3)
Respiratory:
LagO: 5. 3% (2.2, 8.6)
Cardiovascular:
Lagl: 4. 3% (1.1, 7.5)
Odds ratios:
All cause:
-0.5% (-7.0, 6.6);
Respiratory:
-1.8% (-23.1, 25. 3);
Cardiovascular:
-3.4% (-15.2, 10.0)
All cause:
All yr:
LagO: -2.8% (-2.7, 8.6)
Summer:
LagO: 2. 8% (-8. 3, 15.2)
Winter:
LagO: 2. 8% (-3. 9, 9.8)
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TABLE AX5.6. ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY
Reference, Study
Location, and Period
Mean SO2 Levels
Study Description
Results and Comments
to
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UNITED STATES and CANADA
>
X
Dockeryetal. (1996)
18 sites in U.S.
6 sites in Canada
Sulfur Dioxide mean
4.8 ppm
SD3.5
Range 0.2, 12.9
Study of the respiratory health effects of acid
aerosols in 13,369 white children aged 8 to 12 yrs
old from 24 communities in the United States and
Canada between 1988 and 1991. Information was
gathered by questionnaire and a pulmonary
function.
With the exception of the gaseous acids (nitrous and nitric
acid), none of the particulate or gaseous pollutants,
including SO2, were associated with increased asthma or
any asthmatic symptoms. Stronger associations with
particulate pollutants were observed for bronchitis and
bronchitic symptoms.
Odds Ratio (95% CI) for 12.7 ppb range of SO2 pollution
Asthma 1.05 (057, 1.93)
Attacks of Wheeze 1.07 (0.75, 1.55)
Persistent Wheeze 1.19 (0.80, 1.79)
Any asthmatic symptoms 1.16 (0.80,1.68)
Bronchitis 1.56 (0.95,2.56)
Chronic cough 1.02 (0.66, 1.58)
Chronic phlegm 1.55 (1.01, 2.37)
Any Bronchitic symptoms 1.29 (0.98, 1.71)
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TABLE AX5.6 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY
Reference, Study
Location, and Period
Mean SO2 Levels
Study Description
Results and Comments
UNITED STATES and CANADA (cont'd)
>
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Dockeryetal. (1989)
Watertown, MA;
St. Louis, MO;
Portage, WI;
Kingston-Harriman, TN;
Steubenville, OH; Topeka,
KS
1980-1981 school yr
Daily mean concentrations,
averaging hourly
concentrations for each day
with at least 18 hourly
values
Portage: 4.2 ppb
Topeka: 3.5
Watertown: 10.5
Kingston: 6.5
St. Louis: 13.5
Steubenville: 27.8
Cross-sectional assessment of the association
between air pollution and chronic respiratory
health of 5,422 (10-12 yrs) white children
examined in the 1980-1981 school yr. Children
were part of the cohort of children in the Six Cities
Study of Air pollution and Health. Symptoms
were analyzed using logistic regression that
included sex, age, indicators of parental education,
maternal smoking, indicator for gas stove, and an
indicator for city. Respiratory symptoms
investigated were bronchitis, chronic cough, chest
illness, persistent wheeze, asthma. The logarithm
of pulmonary function was fitted to a multiple
linear regression model that included sex, sex-
specific log of height, age, indicators of parental
education, maternal smoking, a gas stove indicator,
and city indicator. Annual means of the 24 h avg
air pollutant concentration for the 12 mos
preceding the examination of each child was
calculated for each city.
No significant associations between SO2 and any
pulmonary function measurements. No significant
association between SO2 and symptoms.
Relative odds and 95% CI between most/least polluted
cities:
Bronchitis: 1.5(0.4,5.8)
Chronic cough: 1.8 (0.3, 12.5)
Chest illness: 1.5(0.4,5.9)
Persistent wheeze: 0.9(0.4,1.9)
Asthma: 0.6(0.3,1.2)
Reference symptoms:
Hay fever: 0.6(0.2,1.7)
Earache: 1.2(0.3,5.3)
Nonrespiratory illness: 1.0 (0.6, 1.5)
Analysis stratified by asthma or persistent wheeze
bronchitis
No wheeze or asthma 1.5 (0.5, 4.3)
Yes wheeze or asthma 2.0 (0.3, 14.3)
Chronic cough
No wheeze or asthma 2.4 (0.5, 11.7)
Yes wheeze or asthma 1.9 (0.1, 44.1)
Chest illness
No wheeze or asthma 1.5 (0.4, 5.6)
Yes wheeze or asthma 1.9 (0.3, 13.0)
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TABLE AX5.6 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY
Reference, Study
Location, and Period
Mean SO2 Levels
Study Description
Results and Comments
to
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UNITED STATES and CANADA (cont'd)
>
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Euleretal. (1987)
California, USA
None provided
Cross-sectional study of 7,445 (25 yrs or older)
Seventh-Day Adventists who lived in their 1977
residential areas (Los Angeles and it border
counties, San Francisco, and San Diego) for at
least 10 yrs to determine the effect of long-term
cumulative exposure to ambient levels of TSP and
SO2 on COPD symptoms. Study population is
subgroup of NCI-funded ASHMOG study that
enrolled 36,805 Seventh-Day Adventists in 1974.
Each participant's cumulative exposure to the
pollutant exceeding 4 different threshold levels
were estimated using moly residence ZIP code
histories and interpolated dosages from state
monitoring stations. Participants completed a
questionnaire on respiratory symptoms, smoking
history, occupational history, and residence
history.
Study reported that SO2 exposure was not associated with
symptoms of COPD until concentrations exceeded 4 ppm.
The correlation coefficient of SO2 (above 4 ppm) with TSP
(above 200 ug/m3) the highest exposure levels for these two
pollutants was 0.30; thus, the authors believed that it was
possible to separate the effects of SO2 from TSP. Multiple
regressions used in the analysis. No significant effect at
exposures levels below 4 ppm or above 8 ppm.
Relative risk estimate (based on 1,003 cases)
SO2 exposure above 2 ppm during 11 yrs of study
2000 h/yr: 1.09
lOOOh/yr: 1.04
500 h/yr: 1.03
SO2 exposure above 4 ppm
500 h/yr: 1.18
250 h/yr: 1.09
100 h/yr: 1.03
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SO2 above 8 ppm
60 h/yr: 1.07
30 h/yr: 1.03
15 h/yr: 1.02
SO2 above 14 ppm
10 h/yr: 1.03
5 h/yr: 1.01
Ih/yr: 1.00
-------
TABLE AX5.6 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY
Reference, Study
Location, and Period
Mean SO, Levels
Study Description
Results and Comments
to
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UNITED STATES and CANADA (cont'd)
X
to
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Goss et al. (2004)
U.S. nationwide
1999-2000
Mean(SD): 4.91
(2.6) ppb
Median: 4.3 ppb
IQR: 2.7-5.9 ppb
McDonnell etal. (1999)
California, U.S.
1973-1992
Mean: SO2 6.8 ug/m3
Range: 0.0-10.2 ug/m3
Correlation coefficient
r = 0.25 with O3
Cohort study of 18,491 cystic fibrosis patients over
6 yrs of age who were enrolled in the Cystic
Fibrosis Foundation National Patient Registry in
1999 and 2000. Mean age of patients was 18.4 yrs;
92% had pancreatic insufficiency. Air pollution
from the Aerometric Information Retrieval System
linked with patient's home ZIP code. Air
pollutants studied included O3, NO2, SO2, CO,
PM10, and PM2 5. Health endpoints of interest were
pulmonary exacerbations, lung function, and
mortality. However, study did not have enough
power to assess the outcome of mortality. Logistic
regression and polytomous regression models that
adjusted for sex, age, weight, race, airway
colonization, pancreatic function, and insurance
status were used.
Prospective study (over 15 yrs) of 3,091
nonsmokers aged 27-87 yrs that evaluated the
association between long-term ambient O3
exposure and the development of adult-onset
asthma. Cohort consisted of nonsmoking, non-
Hispanic white, California Seventh Day Adventists
who were enrolled in 1977 in the AHSMOG study.
Logistic regression used to assess the association
between the 1973-1992 mean 8-h avg ambient O3
concentration and the 1977-1992 incidence of
doctor-told asthma. Levels of PM10, NO2, and SO4
were measured but no effect estimates were given.
With the single-pollutant model, no significant association
between SO2 and pulmonary exacerbations.
Odds ratio per 10 ppb increase in SO2:
0.83 (95% CI: 0.71, 1.01), p = 0.068
No clear association between pulmonary function and SO2.
No effect estimates provided.
No significant positive association between SO2 and asthma
for males or females. Addition of a second pollutant to the
O3 model for the male subjects, did not result in a decrease
of more than 10% in the magnitude of the regression
coefficient for O3, and for the females addition did not
cause the coefficient for O3 to become significantly positive
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TABLE AX5.6 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY
Reference, Study
Location, and Period
Mean SO2 Levels
Study Description
Results and Comments
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Ackermann-Liebrich et al.
(1997)
8 communities in
Switzerland
Aarau, Basel, Davos,
Geneva, Lugano,
Montana, Payerne, and
Wald
1991-1993
Mean SO2 in 1991 (ug/m3)
Mean: 11.7
SD = 7.1
Range: 2.5,25.5
Cross-sectional population based study of 9,651
adults (18-60 yrs) in 8 areas in Switzerland
(SAPALDIA), to evaluate the effect of long-term
exposure of air pollutants on lung function.
Examined the effects of SO2, NO2, O3, TSP, and
PM10. Participants were given a medical exam that
included questionnaire data, lung function tests,
skin prick testing, and end-expiratory CO
concentration. Subjects had to reside in the area
for at least 3 yrs to be in the study.
Mean values of SO2, PM10, andNO2 were significantly
associated with reduction in pulmonary function. SO2 was
correlated with PM30 (r = 0.78), PM10 (r = 0.93) and NO2 (r
= 0.86). Authors stated that the association with SO2
disappeared after controlling for PM10 but no data was
shown.
Regression coefficients and 95% CI in healthy never
smokers (perlO ug/m3 increase in annual avg SO2)
FVC: -0.0325 (-0.0390,-0.0260)
FEVj: -0.0125 (-0.0192,-0.0058)
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Braun-Fahrlander et al.
(1997)
10 communities in
Switzerland
Anieres, Bern, Biel,
Geneva, Langnau, Lugano,
Montana, Pay erne,
Rheintal, Zurich
1992-1993
Annual mean SO2 (ug/m3)
Lugano: 23
Geneva: 13
Zurich: 16
Bern: 11
Anieres: 4
Biel: 15
Rheintal: 8
Langnau: NA
Payerne: 3
Montan: 2
Cross-sectional study of 4,470 children
(6-15 yrs) living in 10 different communities in
Switzerland to determine the effects of long term
exposure to PM10, NO2, SO2, and O3 on respiratory
and allergic symptoms and illnesses. Part of the
Swiss Study on Childhood Allergy and Respiratory
Symptoms with Respect to Air Pollution
(SCARPOL).
This study reported that the annual mean SO2, PM10, and
NO2 were positively and significantly associated with
prevalence rates of chronic cough, nocturnal dry cough, and
bronchitis and conjunctivitis symptoms. Strongest
association found with PM10. However, there was no
significant association between SO2 and asthma or allergic
rhinitis.
Adjusted relative odds between the most/least polluted
community 2-23 ug/m2 (0.8, 8.8 ppb)
Chronic cough: 1.57(1.02,2.42)
Nocturnal dry cough: 1.66 (1.16,2.38)
Bronchitis: 1.48(0.98,2.24)
Wheeze: 0.88(0.54,1.44)
Asthma (ever): 0.74(0.45,1.21)
Sneezing during pollen season: 1.07 (0.67, 1.70)
Hay fever: 0.84(0.55,1.29)
Conjunctivitis symptoms: 1.74 (1.22, 2.46)
Diarrhea: 1.02(0.75,1.39)
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TABLE AX5.6 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY
Reference, Study
Location, and Period
Mean SO2 Levels
Study Description
Results and Comments
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Charpinetal. (1999)
Etang de Berre area of
France: Aries, Istres, Port
de Bouc, Rognac-Velaux,
Salon de Provence,
Sausset, Vitrolles
Jan-Feb 1993
Frischer et al. (2001)
Nine communities in
Austria
Sep-0ctl997
24-h mean (SD) SO2
(ug/m3)
Aries: 29.7(15.5)
Istres: 23.8(12.7)
Port de Bouc: 32.3(24.5)
Rognanc-Velaux:
39.5(21.8)
Salon de Provence:
17.3(11.6)
Sausset: 29.0(28.7)
Vitrolles: 57.4(32.0)
'/2-hour avg SO2:
30-day mean 2.70 ppb
IQR2.1ppb
Cross-sectional cohort study of 2,073 children (10-
11 yrs) from 7 communities in France (some with
the highest photochemical exposures in France) to
test the hypothesis that atopy is greater in towns
with higher photochemical pollution levels. Mean
levels of SO2, NO2, and O3 were measured for 2
mos in 1993. Children tested for atopy based on
skin prick test (house dust mite, cat dander, grass
pollen, cypress pollen, and Altemaria). To be
eligible for the study, subjects must have resided in
current town for at least 3 yrs. Questionnaire filled
out by parents that included questions on
socioeconomic status and passive smoking at
home. Two-mo mean level of air pollutants used
in logistic regression analysis.
Cross-sectional cohort study of 877 children (mean
age 11.2 yrs) living in 9 sites with different O3
exposures. Urinary eosinophil protein U-EPX)
measured as a marker of eosinophil activation. U-
EPX determined from a single spot urine sample
analyzed with linear regression models.
Study did not demonstrate any association between air
pollution and atopic status of the children living in the
seven communities, some with high photochemical
exposures. A limitation of study is that authors did not
consider short-term variation in air pollution and did not
have any indoor air pollution measurements.
No significant association between SO2 and U-EPX
Regression coefficient and SE -10.57 (0.25) per ppb SO2
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TABLE AX5.6 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY
Reference, Study
Location, and Period
Mean SO2 Levels
Study Description
Results and Comments
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Frischeretal. (1999)
Nine communities in
Austria
1994-1996
Frye et al. (2003)
Zerbst, Hettstedt,
Bitterfeld,East Germany,
1992-93, 1995-1996,
1998-1999
Annual mean SO2 (ppb) in
1994
Amstetten: 3.75
St. Valentin: 3.00
Krems: 3.75
Heidenreichstein: 4.13
Gansemdorf: 5.63
Mistelbach: 5.25
Wiesmath: 6.00
Bruck: 4.88
Pollau: 2.25
Used avg of annual means
of pollutants 2 yrs
preceding health
measurement
High of 113 ug/m3 (in
Bitterfeld) to a low of
6 ug/m . (Pollution values
only described in figure)
Longitudinal cohort study of 1150 children (mean age
7.8 yrs) to investigate the long-term effects of O3 on
lung growth. Children were followed for 3 yrs and lung
function was recorded biannually, before and after
summertime. The dependant variables were change in
FVC, FEVi, and MEF50. The 9 sites were selected to
represent a broad range of O3 exposures. GEE models
adjusted for baseline function, atopy, gender, site,
environmental tobacco smoke exposure, season, and
change in height. Other pollutants studied included
PM10, SO2, andNO2.
Three consecutive cross-sectional surveys of children
(11-14 yrs) from three communities in East Germany.
Parents of 3,155 children completed a questionnaire on
symptoms. Lung function tests performed on
2,493 children. Study excluded children if they lived
for less than 2 yrs in current home and if their previous
home was more than 2 km away. The log-transformed
lung function parameters were used as the response
variables in a linear regression analysis that controlled
for sex, height, season of examination, lung function
equipment, parental education, parental atopy, and
environmental tobacco smoke. Used avg of annual
means of pollutants 2 yrs preceding each survey.
No consistent association observed between lung
function and SO2, NO2 and PM10. A negative effect
estimate was observed during the summer and a positive
estimate during the winter.
Change in lung function (per ppb SO2):
FEVj (mL/day):
Summer: -0.018 (0.004), p< 0.001
Winter : 0.003 (0.001), p < 0.001
FVC (mL/day):
Summer: -0.009 (0.004), p = 0.02
Winter: 0.002 (0.001), p = 0.03
MEF50 (mL/s/day):
Summer: -0.059 (0.010), p< 0.001
Winter: 0.003 (0.003), p = 0.26
The annual mean TSP declined from 79 to 25 ug/m3 and
SO2 from 113 to 6 ug/m3 and the mean FVC and FEVj
increased from 1992-1993 to 1998-1999. Study
concluded that reduction of air pollution in a short time
period may improve children's lung function.
Percent change of lung function for a 100-ug/m3
decrease in SO2 2 yrs before the investigation
(n= 1,911)
FVC: 4.9(0.7,9.3)
FEVi: 3.0 (-1.1, 7.2)
FEVj/FVC: -1.5 (-3.0, 0.1)
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TABLE AX5.6 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY
Reference, Study
Location, and Period
Mean SO2 Levels
Study Description
Results and Comments
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Heinrich et al. (2002)
Reunified Germany
Bitterfeld, Hettstedt,
Zerbst
1992-1993, 1995-1996,
1998-1999
SO2 concentration in ug/m
Yr Zerbst Bitterf. Hettst.
1991 78 113 84
1992 58 75 46
42
1993
1994
1995
1996
1997
1998
29
21
25
13
60
35
30
24
13
9
49
38
26
25
13
6
Three cross-sectional surveys of children (5-
14 yrs) from 3 areas that were formerly part of
East Germany to investigate the impact of
declines in TSP and SO2 on prevalence of
nonallergic respiratory disorders in children.
Study excluded children if they lived for less
than 2 yrs in current home and if their
previous home was more than 2 km away.
GEE used for analysis.
Study found that SO2 exposure was significantly
associated with prevalence of bronchitis, frequent colds,
and febrile infections. While results are reported as risk
for an increase in air pollutant, the respiratory health of
children improved with declines in TSP and SO2.
Authors concluded that exposure to combustion-derived
air pollution is causally related to nonallergic respiratory
health in children.
Odds ratio and 95% CI: (per 100 ug/m3 in 2 yr mean
S02)
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All children:
Bronchitis: 2.72(1.74,4.23)
Otitis media: 1.42(0.94,2.15)
Sinusitis: 2.26 (0.85, 6.04)
Frequent colds: 1.81(1.23,2.68)
Febrile infections: 1.76 (1.02, 3.03)
Cough in morning: 1.10(0.73,1.64)
Shortness of breath: 1.31(0.84,2.03)
Children without indoor exposures (living in damp
houses with visible molds, ETS in the home, gas
cooking emissions, and contact with cats)
Bronchitis: 4.26(2.15,8.46)
Otitis media: 1.43 (0.73, 2.81)
Sinusitis: 2.95 (0.52,16.6)
Frequent colds: 2.29(1.15,4.54)
Febrile infections: 1.75 (0.78, 3.91)
Cough in morning: 1.00(0.38,2.64)
Shortness of breath: 2.07(0.90,4.75)
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TABLE AX5.6 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY
Reference, Study
Location, and Period
Mean SO2 Levels
Study Description
Results and Comments
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Herbarthetal. (2001)
East Germany
1993-1997
Avg lifetime exposure
burden of SO2(ug/m3)
KIGA: 142
LISS: 48
LESS: R47
KIGA-IND: 59
Meta-analysis of three cross-sectional studies: (1)
Study on Airway Diseases and Allergies among
Kindergarten Children (KIGA), (2) the Leipzig
Infection, Airway Disease and Allergy Study on
School starters (LISS), and (3) KIGA-IND, which
was based on the KIGA design but conducted in 3
differentially polluted industrial areas. A total of
3,816 children participated in the three studies.
Analysis of data from parent-completed
questionnaires to determine the effect of life time
exposure to SO2 and TSP on the occurrence of
acute bronchitis. Total lifetime exposure burden
corresponds to the exposure duration from birth to
time of the study. The LISS study was divided in
to LISS-U for the urban area and LISS-R for the
rural area. Logistic regression analysis used that
adjusted for predisposition in the family (mother or
father with bronchitis), ETS, smoking during
pregnancy or in the presence of the pregnant
This study found the highest bronchitis prevalence in the
KIGA cohort and the lowest in the LISS cohort, which is
consistent with the SO2 concentrations in these cohorts.
Study found a correlative link between SO2 and bronchitis
(R = 0.96, p < 0.001) but not TSP (R = 0.59). Results of
study suggest that SO2 may be a more important factor than
TSP in the occurrence of bronchitis in these study areas.
Odds ratio for bronchitis adjusted for parental
predisposition, smoking, and lifetime exposure to SO2 and
TSP (2-pollutant model).
SO2: 3.51(2.56,4.82)
TSP: 0.72(0.49,1.04)
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TABLE AX5.6 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY
Reference, Study
Location, and Period
Mean SO2 Levels
Study Description
Results and Comments
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Horak et al. (2002)
Eight communities in
Austria
1994-1997
Seasonal mean SO2 ug/m
Winter:
Mean: 16.8
Range: 7.5,37.4
Summer:
Mean: 6.9 ug/m3
Range: 3.1,11.7
Longitudinal cohort study that continued the work
of Frischer et al. (1999) by adding one more yr of
data and analyzing the effects of PM10 in addition
to SO2, NO2, and O3. At the beginning of the study
975 children (mean age 8.11 yrs) were recruited for
the study, but only 80.6% of the children
performed all 6 lung function tests (twice a yr).
The difference for each lung function parameter
between two subsequent measures was divided by
the days between measurements and presents as
difference per day (dpd) for that parameter.
860 children were included in the GEE analysis
that controlled for sex, atopy, passive smoking,
initial height, height difference, site, and initial
lung function.
Moderate correlation between PM10 and SO2 in the winter
(r = 0.52). In a one-pollutant model for SO2, long term
seasonal mean concentration of SO2 was had a positive
association with FVC dpd and FEV! dpd in the winter, but
no effect on MEF25_75 dpd. In a two-pollutant model with
PMio, wintertime SO2 had a positive association with
FEVj dpd.
Single-pollutant model
FVC dpd:
Summer: 0.009, p = .336
Winter: 0.006, p = .009
FEVj dpd:
Summer: 0.005, p = 0.576
Winter: 0.005, p = 0.013
MEF25.75:
Summer: 0.015, p = 0.483
Winter: 0.003, p = 0.637
Two-pollutant model:
SO2+ PM10
FVC dpd:
Summer: 0.008, p = 0.395
Winter: 0.004, p = 0.225
FEVj dpd:
Summer: 0.010(0.271)
Winter: 0.007(0.025)
MEF25.75 dpd:
Summer: 0.037, p = 0.086
Winter: 0.007, p = 0.429
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TABLE AX5.6 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY
Reference, Study
Location, and Period
Mean SO2 Levels
Study Description
Results and Comments
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Kopp et al. (2000)
Ten communities in
Austria and
SW Germany
Mean SO2 (95% CI) ppb
Apr-Sep 1994
Amstetten: 3.7(0.7,3.9)
St Valentin: 2.6(1.5,5.2)
Krems: 3.7(0.7,7.5)
Villingen: 0.7(0,3.0)
Heindenreichstein: 3.7, (0.7, 7.5)
Gansemdorf: 3.7(0.7,11.2)
Mistelbach: 3.7(0.7,7.5)
Wiesmath: 6.3(3.4,9.4)
Bruck: 1.5(0.7,4.1)
Freudenstadt: 0.7(0,3.0)
Octl994-Marl995
Amstetten: 3.7(0.7,7.5)
St Valentin: 3.0(1.1,9.4)
Krems: 3.7(0.7,11.0)
Villingen: 1.9(0,3.0)
Heindenreichstein: 3.7(0.7,15.0)
Gansemdorf: 3.7(0.7,22.5)
Mistelbach: 3.7(0.7,22.5)
Wiesmath: 2.23(0.7,10.1)
Bruck: 15(1.1,7.9)
Freudenstadt: 1.57 (0.4, 5.3)
Apr-Sep 1995
Amstetten: 3.7(0.7,3.8)
St Valentin: 2.6(1.1,6.8)
Krems: 3.7(0.5,3.8)
Villingen: 0.7(0,2.6)
Heindenreichstein: 0.7 (0.5, 0.9)
Gansemdorf: 3.7(0.7,7.5)
Mistelbach: 3.7(0.7,7.5)
Wiesmath: 7.5 (0.7, 14.9)
Bruck: 3.7(0.4,4.9)
Freudenstadt: 0.7(0,3.4)
Longitudinal cohort study of 797 children (mean age
8.2 yrs) from 2nd and 3rd grades of 10 schools in
Austria and SW Germany to assess the effects of
ambient O3 on lung function in children over a
2-summer period. Study also examined the association
between avg daily lung growth and SO2, NO2, and
PM10. Each child performed 4 lung function tests
during spring 1994 and summer 1995. ISAAC
questionnaire used for respiratory history. Linear
regression models used to assess effect of air pollutants
on FVC and FEVi, which were surrogates of lung
growth.
Lower FVC and FEV! increases observed
in children exposed to high ambient O3
levels vs. those exposed to lower levels in
the summer. This study found no effect of
SO2 and PM10 on FVC increase during the
summer of 1995 and winter 1994/1995,
however, SO2 was negatively associated
with FVC during the summer of 1994.
Change in FVC (per ppb SO2)
Summer 1994: -0.044, p = 0.006
Winter 1994/95: 0.007, p = 0.243
Summer 1995: 0.045, p = 0.028
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TABLE AX5.6 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY
Reference, Study
Location, and Period
Mean SO2 Levels
Study Description
Results and Comments
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Kramer etal. (1999)
East and West Germany,
1991 to 1995
East Germany 2-yr avg
concentration ranged from
45 to 240 ng/m3
West Germany 2-yr avg
concentration ranged from
18-33
Repeated cross-sectional studies between 1991 and
1995 on 7-yr-old children in East Germany and
between 1991 and 1994 in West Germany.
Comparison of prevalence of airway diseases and
allergies in East and West Germany during the first
five yrs after reunification. A total of 19,090
children participated in the study. Logistic
regression used to assess the effect of SO2 and TSP
on airway diseases and allergies. Analysis
performed on 14,144 children with information on
all covariates of interest.
All infectious airway diseases and irritation of the airways
was associated with either SO2 or TSP in East Germany in
1991. The decrease of pollution between 1991 and 1995
had a favorable effect on the prevalence of these illnesses.
SO2 was significantly associated with more than 5 colds in
the last 12 mos, tonsillitis, dry cough in the last 12 mos, and
frequent cough in 1991-1995.
Odds ratio and 95% CI: (per 200 |ig/m3 SO2) in East
Germany areas, 1991-1995 for children living at least 2 yrs
in the areas, adjusted for time trend:
Infectious airway diseases
Pneumonia ever diagnosed: 1.17 (0.85, 1.62)
Bronchitis ever diagnosed: 0.85(0.68, 1.05)
>5 colds in last 12 mos: 1.55 (1.18, 2.04)
Tonsillitis in the last 12 mos: 1.89 (1.49, 2.39)
Dry cough in the last 12 mos: 1.46(1.12, 1.91)
Frequent cough ever: 2.51 (1.79. 3.53)
Allergic diseases and symptoms:
Irritated eyes in the last 12 mos: 1.06 (0.66, 1.70)
Irritated nose in the last 12 mos: 1.26 (0.96, 1.66)
Wheezing ever diagnosed: 0.68 (0.46,1.01)
Bronchial asthma ever diagnosed: 2.73 (1.24, 6.04)
Hay fever ever diagnosed: 0.60 (0.24, 1.52)
Eczema ever diagnosed: 0.87 (0.65,1.18)
Allergy ever diagnosed: 0.93 (0.67, 1.29)
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TABLE AX5.6 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY
Reference, Study
Location, and Period
Mean SO2 Levels
Study Description
Results and Comments
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Penard-Morand et al.
(2005)
Six communities in
France: Bordeaux,
Clermont-Ferrand,
Creteil, Marseille,
Strasbourg and Reims
Mar 1999-Oct 2000
Estimated 3-yr avg
concentrations at 108 schools
Low cone: 4.6 ug/m3
(range: 1.3,7.4),
High cone: 9.6 ug/m3
(range 7.7, 13.7)
Cross-sectional study of 4,901 children
(9-11 yrs) form 108 randomly selected schools in
6 cities to assess the association between long-
term exposure to background air pollution (NO2,
SO2, PMio, O3) and atopy and respiratory
outcomes. Analysis restricted to children who
had lived at least the last 3 yrs in their house at
the time of the examination. Analysis used three
yr avgd air pollutant concentrations at the
children's schools. Parents completed
questionnaire on respiratory and allergic
disorders (asthma, allergic rhinitis [AR], and
atopic dermatitis) and children underwent
examination that included a skin prick test to
assess allergic sensitization, evidence of visible
flexural dermatitis and measure of exercise-
induced bronchial reactivity (EIB).
Increased concentrations of SO2 were significantly
associated with an increased risk of EIB, lifetime asthma
and lifetime AR. Past yr wheeze and asthma were also
associated with SO2. In a two-pollutant model with PM10,
significant associations were observed between SO2 and
EIB and past yr wheeze.
Odds ratio and 95% CI (per 5 ug/m3 SO2)
EIB: 1.39(1.15, 1.66), p< 0.001
Flexural dermatitis: 0.86 (0.73, 1.02), p < 0.10
Past yr wheeze: 1.23(1.0, 1.51), p< 0.05
Past yr asthma: 1.28(1-00, 1.65), p< 0.01
Past yr rhinoconjunctivitis: 1.05 (0.89, 1.24)
Past yr atopic dermatitis: 1.01 (0.86, 1.18)
Lifetime asthma: 1.19 (1.00, 1.41), p< 0.10
Lifetime allergic rhinitis: 1.16 (1.01, 1.32), p < 0.05
Lifetime atopic dermatitis: 0.93 (0.82,1.05)
Two-pollutant model with PM10
EIB: 1.46(1.12,1.90)
Past yr wheeze: 1.45 (1.09, 1.93)
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Ramadour et al. (2000)
Seven towns in SE
France
Jan-Feb 1993
Mean (SD) ug/m3 of SO2 during
2-mo period
PortdeBouc: 32.3(24.5)
Istres: 23.8(12.7)
Sausset: 29.0(28.7)
Rognanc-Velaux: 39.5 (21.8)
Vitrolles: 57.4(32.0)
Aries: 29.7(15.5)
Salon: 17.3(11.6)
Cross-sectional cohort study of 2,445 children
(age 13-14 yrs) who had lived for at least 3 yrs in
their current residence to compare the levels of
O3, SO2, and NO2 to the prevalence rates of
rhinitis, asthma, and asthmatic symptoms. Some
of the communities had the heaviest
photochemical exposure in France. Subjects
completed ISAAC survey of asthma and
respiratory symptoms. Analysis conducted with
logistic regression models that controlled for
family history of asthma, personal history of early
-life respiratory diseases, and SES. Also
performed simple univariate linear regressions.
Study found no relationship between mean levels of SO2,
NO3, or O3 and rhinitis ever, 12-mo rhinitis,
rhinoconjunctivitis, and hay fever or asthmatic symptoms.
Simple regression analyses of respiratory outcomes vs.
mean SO2 levels in the 7 towns indicated that nocturnal
dry cough was associated with mean SO2 levels (r =
0.891). Potential confounding across towns.
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TABLE AX5.6 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY
Reference, Study
Location, and Period
Mean SO2 Levels
Study Description
Results and Comments
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Soysethetal. (1995)Ardal
and Laerdal, Norway
winter seasons 1989-92
Median SO2
37.1 ug/m3 at ages
0-12 mos
37.9 ug/m3 at
ages 13-36 mos
Garcia-Marcos et al.
(1999)
Cartagena, Spain
winter 1992
Annual mean SO2 (ug/m3)
Polluted areas 75 ug/m3
Nonpolluted areas:
20 ug/m
Cross-sectional study of 529 children
(aged 7-13 yrs) to determine whether exposure to
SO2 during infancy is related to the prevalence of
bronchial hyperresponsiveness (BHR). A sulfur
dioxide emitting aluminum smelter is present in
Ardal, but there is no air polluting industry in
Laerdal. Parents filled out questionnaire regarding
family history of asthma, type of housing,
respiratory symptoms and parent's smoking habits.
Spirometry was performed on each child and
bronchial hyperactivity was determined by
methacholine challenge or reversibility test. Skin
prick test done to assess atopy. Also examined, the
effects of fluoride.
A total of 340 children (10-11 yrs) living in and
attending schools within a polluted and a relatively
nonpolluted area were included in this study which
aimed to establish the relative contribution
socioeconomic status, parental smoking, and air
pollution on asthma symptoms, spirometry, and
bronchodilator response. Parents completed
questionnaire on respiratory symptoms and risk
factors including, living in polluted area, maternal
smoking, paternal smoking, number of people
living in the house, proximity to heavy traffic
roads. Spirometry was performed before and after
an inhaled 0.2 mg fenoterol was delivered to
determine bronchodilator response.
Bronchodilator response was considered positive if
the FVC after fenoterol was increased by at least
10% or PEF by 12%. Logistic regression included
as independent variables all the risk factors.
This study found that the risk of BHR was associated with
SO2 exposure at 0-12 mos
Odds ratio for BHR (per 10 ug/m3 SO2) for various ages at
exposure
0-12 mos: 1.62(1.11,2.35)
13-36 mos: 1.40(0.90,2.21)
37-72 mos: 1.19(0.77,1.82)
73-108 mos: 1.19(0.63,2.22)
This study found that living in the polluted areas reduced
the risk of a positive bronchodilator response (RR = 0.61,
p = 004).
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TABLE AX5.6 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY
Reference, Study
Location, and Period
Mean SO2 Levels
Study Description
Results and Comments
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Gokirmak et al. (2003)
Malatya, Turkey
SO2 cone ranged from
106.6 to 639.2 ppm in
9 apricot farms.
Mean cone around
sulfurization chamber:
324.1(35.1) ppm
Study on occupational exposure to SO2 in apricot
sulfurization workers that investigated the role of
oxidative stress resulting exposure to high
concentrations of SO2 on bronchoconstriction.
Forty workers (mean age: 28 yrs, range
16-60 yrs) who have been working in apricot
sulfurization for 20-25 days each yr and
20 controls (mean age: 29 yrs, range 17-42) who
had no SO2 exposure participated in the study.
Activities of antioxidant enzymes (glutathione
peroxidase [GSH-Px], superoxide dismutase
[SOD] and catalase) malondialdehyde (MDA)
concentrations (marker of lipid peroxidation), and
pulmonary function test measured in subjects.
SOD, GSH-Px, and catalase activities were lower and
malondialdehyde concentrations were higher in the apricot
sulfurization workers compared to controls. Pulmonary function
decreased after SO2 exposure among the apricot sulfurization
workers. Authors concluded that occupational exposure to high
concentrations of SO2 enhances oxidative stress and that lipid
peroxidation may be a mechanism of SO2 induced
bronchoconstriction.
Apricot sulfurization workers vs. controls
Mean (SD)
SOD (U/mL): 2.2 (0.6) vs. 3.2 (0.7) U/m , p < 0.0001
Glutathione peroxidase (U/mL): 0.6 (0.3) vs. 1.1 (0.3),
p< 0.0001
Catalase (L/L): 107.6 (27.4) vs. 152.6 (14.3), p < 0.0001
MDA(nmol/L): 4.1(0.9) vs. 1.9 (5.3), p< 0.0001
Before vs. after SO2 exposure among apricot sulfurization
workers
Mean (SD)
FVC (% predicted) 88 (17) vs. 84 (16), p < 0.001
FEVj (% predicted) 98 (14) vs. 87 (14), p < 0.001
FEVV1/FVC: 92 (7) vs. 86 (9), p < 0.001
FEF25-75% (% predicted) 108 (19) vs. 87 (23), p < 0.001
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TABLE AX5.6 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY
Reference, Study
Location, and Period
Mean SO, Levels
Study Description
Results and Comments
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Hirschetal. (1999)
Dresden, Germany
Mean(ug/m3): 48.3
Range: 29.0-69.3
25-75 percentile 42.7-54.3
Cross sectional study to relate the prevalence of
respiratory and allergic diseases in childhood to
measurements of outdoor air pollutants.
5,421 children ages 5-7 yrs and 9-11 yrs were
evaluated by questionnaires, skin-prick testing,
venipuncture for (Ig)E, lung function, and
bronchial challenge test.
Sox was positively associated with current morning
cough but not with bronchitis.
Prevalence odds ratio (95% CI) for symptoms within
past 12 mos, +10 ug/m3:
Wheeze:
Atopic 103 (0.79, 1.35) ug/m3
Nonatopic 1.36 (1.01, 1.84)
Morning Cough:
Atopic 1.22 (0.92, 1.61)
Nonatopic 1.32 (1.07, 1.63)
Prevalence odds ratio (95% CI) for doctor's diagnosis,
+10 ug/m3:
Asthma
Atopic 1.07 (0.79, 1.45)
Nonatopic 1.35 (1.00, 1.82)
Bronchitis
Atopic 1.04 (0.87, 1.25)
Nonatopic 0.99 (0.88, 1.12)
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Koksal et al. (2003)
Malatya, Turkey
SO2 cone ranged from 106.6 to
721.0ppm
Study on occupational exposure to high
concentrations of SO2 on respiratory symptoms and
pulmonary function on apricot sulfurization
workers. Apricot sulfurization workers (n = 69)
from 15 apricot farms who have been working in
sulfurization of apricots for 20-25 days a yr during
each summer were recruited for the study.
Subjects rated symptoms (itchy eyes, runny nose,
stuffy nose, itchy or scratchy throat, cough,
shortness of breath, phlegm, chest pain, and fever)
before during and 1 h after each exposure.
SO2 exposure at high concentrations increased
symptoms of itchy eyes, shortness of breath, cough,
running and/or stuffy nose, and itchy or scratchy throat
during exposure
(p < 0.05). Inhalation of high concentrations of SO2 for
1 h caused significant decreases in pulmonary function.
Difference in pulmonary function measured before and
after exposure:
FVC (L) 0.16 (0.42), p< 0.05
FEVi(L) 0.39 (0.36), p< 0.001
FEVj/FVC: 5.22 (6.75), p < 0.001
PEF (L/s) 1.39 (1.06), p < 0.001
FEF25.75% (L/s) 0.82 (0.70), p < 0.001
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TABLE AX5.6 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH
RESPIRATORY MORBIDITY
Reference, Study
Location, and Period
Mean SO2 Levels
Study Description
Results and Comments
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Pikhart et al. (200 1 )
Czech Republic, Poland,
1993-1994
Mean SO2 (ug/m3)
Prague: 83.9,
Range: 65.8-96.6
Poznan: 79.7,
Range: 44.2-140.2
Part of the small-area variation in air pollution and
health (SAVIAH) study to assess long-term effects of
air pollution on respiratory outcomes. Analysis on
data from two centers of the multicenter study:
Prague, Czech Republic, and Poznan, Poland. Both
cities had wide variation in air pollution levels.
Parents/guardians of 6,959 children
(7-10 yrs) completed a questionnaire about the
socioeconomic situation of the family, type of
housing, family history of atopy, parental smoking,
family composition, and health of the child. SO2 was
measured at 80 sites in Poznan and 50 sites in Prague
during 2-wk campaigns. From these data GIS was
used to estimate pollutant concentrations at a small
area level. Logistic regression used to assess effect of
air pollution on the prevalence of respiratory
outcomes.
SO2 levels (mean of home and school) were associated with
the prevalence of wheezing/whistling in the past 12 mos.
There was a marginal association between SO2 and lifetime
prevalence of wheezing and physician diagnosed asthma.
Fully adjusted model controlled for age, gender, maternal
education, number of siblings, dampness at home, heating and
cooking on gas, maternal smoking, and family history of atopy
and center. Authors noted SO2 is strongly spatially correlated
with particles in the Czech Republic and probably Poland, so
SO2 may be proxy for exposure to other pollutants. Not other
pollutants measured in study.
Odds ratio (per 50 ug/m3) SO2
Wheezing/whistling in past 12 mos: 1.32 (1.10, 1.57)
Wheezing/whistling ever: 1.13 (0.99, 1.30)
Asthma ever diagnosed by doctor: 1.39 (1.01, 1.92)
Dry cough at night: 1.06 (0.89, 1.27)
von Mutius et al. (1995)
Leipzig, East Germany,
Octl991-Jull992
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During winter mos,
SO2 daily max
concentrations ranged
from 40-1283 ug/m3.
During high pollution
period, mean
concentration of SO2
was 188 ug/m3 and
during low pollution
mean was 57 ug/m3.
The effects of high to moderate levels of air pollution
(SO2, NOX, and PM) on the incidence of upper
respiratory were investigated in 1,500 schoolchildren
(9-11 yrs) in Leipzig, East Germany.
Logistic regression models controlled for paternal
education, passive smoke exposure, number of
siblings, temperature, and humidity.
The daily mean values of SO2 and NOX were significantly
associated with increased risk of developing upper respiratory
illnesses during the high concentration period. In the low
concentration period, only NOX daily mean values were
associated with increased risks. In a two-pollutant model with
PM, similar estimates to the single-pollutant model were
obtained, thus collinearity of data may not account for the
effects of high mean concentrations of SO2.
Odds ratio and 95% CI: (did not indicate per what level of
SO2 increase)
Daily mean SO2
High period: 1.72(1.19,2.49)
Low period: 1 .40 (0.95, 2.07)
Daily maximum SO2
High period: 1.26 (0.80, 1.96)
Low period: 0.99(0.66,1.47)
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TABLE AX5.7. ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH
INCIDENCE OF CANCER
Reference, Study
Location, & Period
Design & Methods
Mean SO2 Levels
Copollutants
Considered
Conclusions
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Nafstad et al. (2003)
Oslo, Norway
1972-1998
Retrospective study associating
cardiovascular risk factors to a national
cancer register among 16,209 men ages
10-49yrs. Survival analyses and Cox
proportional hazards regression were
used to estimate associations.
Estimated for each person each
year from 1974 to 1998
Five-year median average
levels SO2 participants home
address, 1974-1978: 9.4 ug/m3
(range 0.2 to 55.8)
Median levels within the
quartiles:
2.5 ug/m3
6.2 ug/m
14.7 ug/m3
31.3 ug/m3
NOX
Adjusted risk ratios (95% CI) of developing
lung cancer:
Model 1:
0-9.99 ug/m3: Ref
10-19.99 ug/m3: 1.05(0.81,1.35)
20-29.99 ug/m3: 0.95(0.72,1.27)
30+ ug/m3: 1.06(0.79,1.43)
Model 2:
Per 10 ug/m3: 1.01(0.94,1.08)
Adjusted risk ratios (95% CI) of developing
non-lung cancer
Model 1:
0-9.99 ug/m3: Ref.
10-19.99 ug/m3: 1.07(0.96,1.19)
20-29.99 ug/m3: 0.90 (0.80, 1.02)
30+ ug/m3: 0.98(0.86,1.10)
Model 2:
Per 10 ug/m3: 0.99(0.96,1.02)
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TABLE AX5.7 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH
INCIDENCE OF CANCER
Reference, Study
Location, & Period
Design & Methods
Mean SO2 Levels
Copollutants
Considered
Conclusions
EUROPE (cont'd)
Nyberg et al. (2000)
Stockholm County,
Sweden
Janl,1985-Dec31,
1990
Case-control study of men 40-70 yrs,
with 1,042 cases of lung cancer and
1,274 controls, to evaluate the suitability
of an indicator of air pollution from
heating.
Annual levels computed for
each year between 1950 and
1990, but not provided herein
NOX/N02
Little effect of SOX in any time window, but
highest correlations in early years.
SOX RR (CI 95%) from heating (per 10 |ig/m3)
for 30-yr avg
<41.30ng/m3: 1
>41.30to<52.75: 1.06(0.83,1.35)
>52.75 to <67.14: 0.98 (0.77, 1.24)
>67.14to<78.20: 0.90(0.68,1.19)
>78.20: 1.00(0.73,1.37)
SOX RR (CI 95%) from heating (per 10 |ig/m3)
for 10-yravg
<66.20 ng/m3: 1
>66.20 to <87.60: 1.16(0.91,1.47)
>87.60to<110.30: 1.00(0.79,1.27)
>110.30to<129.10: 0.92(0.70,1.21)
>129.10: 1.21(0.89,1.66)
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TABLE AX5.8. ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH PRENATAL
AND NEONATAL OUTCOMES
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Reference, Study Outcomes, Design, Mean Levels & Copollutants & Method, Findings,
Location, & Period & Methods Monitoring Stations Correlations Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
UNITED STATES
Bell et al. (2007)
Connecticut and
Massachusetts
Period of Study:
1999-2002
Outcome: LEW Gestational exposure NO2
Study design: Case-control (ppb) CO
N: 358,504 live singleton PM10
births Mean: 4.7 PM2.5
Statistical analysis: Linear SD-1.2
models and logistic IQR. 1.6
regression
Covariates: Gestational
length, prenatal care, type
of delivery, child's sex,
birth order, weather, yr,
and mother's race,
education, marital status,
age, and tobacco use.
No relationship between
gestational exposure to
SO2 and birth weight.
First trimester exposure
to SO2 was associated
with low birth weight.
No statistical difference
n the effect estimates of
SO2 for infants of black
and white mothers.
Increment: 1.6 ppb (IQR)
Change in birth weight:
Entire pregnancy: -0.9 g (-4.4, 2.6)
Black mother: 1.2 (-6.5, 8.8)
White mother: -1.4 (-5.1, 2.3)
1 st trimester: - 3 .7 to -3.3 grams
LEW: OR 1.003 (0.961, 1.046)
Gilboa et al. (2005)
Seven Texas Counties
Period of Study:
1997-2000
Outcome: Selected birth
defects
Study design: Case-control
N: 4,570 cases and
3,667 controls
Statistical analysis:
Logistic regression
Covariates: Maternal
education, maternal
race/ethnicity, season of
conception, plurality,
maternal age, maternal
illness
Statistical package:
SAS vs. 8.2
NR
PM10
03
N02
CO
When the fourth quartile
of exposure was
compared with the first,
SO2 was associated with
increased risk of isolated
ventricular septal
defects. Inverse
associations were noted
for SO2 and risk of
isolated atrial septal
defects and multiple
endocardial cushion
defects.
Aortic artery and valve defects
<1.3ppb: 1.00
1.3to<1.9: NA
1.9to<2.7: 1.06 [0.34, 3.29]
>2.7: 0.83 [0.26, 2.68]
Atrial septal defects
<1.3ppb: 1.00
1.3to<1.9: 1.22 [0.79, 1.88]
1.9to<2.7: 0.76 [0.47, 1.23]
>2.7: 0.42 [0.22, 0.78]
Pulmonary artery and valve defects
<1.3ppb: 1.00
1.3to<1.9: 0.63 [0.23, 1.74]
1.9to<2.7: 0.93 [0.36, 2.38]
>2.7: 1.07 [0.43, 2.69]
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TABLE AX5.8 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH PRENATAL
AND NEONATAL OUTCOMES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
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Gilboa et al. (2005)
(cont'd)
Ventricular septal defects
<1.3ppb: 1.00
1.3to<1.9: 1.02 [0.68, 1.53]
1.9to<2.7: 1.13 [0.76,1.68]
>2.7: 2.16 [1.51, 3.09]
Conotruncal defects
<1.3ppb: 1.00
1.3to<1.9: 0.71 [0.46,1.09]
1.9to<2.7: 0.71 [0.46,1.09]
>2.7: 0.58 [0.37, 0.91]
Endocardial cushion and mitral valve defects
<1.3ppb: 1.00
1.3to<1.9: 0.89 [0.50, 1.61]
1.9to<2.7: 0.89 [0.49, 1.62]
>2.7: 1.18 [0.68, 2.06]
Cleft lip with or without cleft palate
<1.3ppb: 1.00
1.3to<1.9: 0.79 [0.52, 1.20]
1.9to<2.7: 0.95 [0.64, 1.43]
>2.7: 0.75 [0.49, 1.15]
Cleft palate
<1.3ppb: 1.00
1.3to<1.9: 0.89 [0.40, 1.97]
1.9to<2.7: 1.49 [0.72, 3.06]
>2.7: 1.22 [0.56, 2.66]
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TABLE AX5.8 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH PRENATAL
AND NEONATAL OUTCOMES
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Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels & Copollutants &
Monitoring Stations Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
UNITED STATES (cont'd)
Maisonet et al. (2001)
6 Northeastern cities of
U.S.
Period of Study:
1994-1996
Outcome: Term LEW
Study design: Case-
control
N: 89,557 live singleton
births
Statistical analysis:
Logistic regression
models linear regression
models
Covariates: Maternal
age, race, season of the
yr, smoking and alcohol
use during pregnancy,
firstborn, gender, marital
status, and previous
terminations, prenatal
care (ordinal variable),
weight gain, and
gestational age
Stratified by
race/ethnicity
Statistical package:
STATA
Exposure distribution CO
(<25th, 25th to <50th, PM10
50th to <75th, 75th to
<95th, >95th)
First trimester: <7.09,
7.090 to 8. 906, 8.907 to
11.969, 11. 970 to
18.447, > 18.448
Second trimester:
<6.596, 6.596 to 8.896,
8. 897 to 11. 959, 11.960
to 18.275, >18.276
Third trimester: <5.810,
5. 810 to 8.453,
8.454 to 11. 777, 11.778
to 18.134, >18.135
This study provides
evidence of an
increased risk for term
LEW in relation to
increased ambient air
levels of SO2 at
concentrations well
below the established
standards.
Higher risk estimates
among whites when
stratified by
race/ethnicity
First trimester:
<25th: Referent
25th-50th: 1.04 [0.88, 1.23]
50th-75th: 1.04 [0.94, 1.15]
75th-95th: 0.98 [0.81, 1.17]
>95th: 0.88 [0.73, 1.07]
Increment (10 ppm) : 0.98
[0.93, 1.03]
Second trimester:
25th-50th: 1.18 [1.12, 1.25]
50th-75th: 1.12 [1.07, 1.17]
75th-95th: 1.13 [1.05, 1.22]
>95th: 0.87 [0.80, 0.95]
Increment (10 ppm) : 1.01
[0.93,1.10]
Third trimester:
25th-50th: 1.04 [0.92, 1.18]
50th-75th: 1.02 [0.87, 1.18]
75th-95th: 1.04 [0.84, 1.28]
>95th: 1.06 [0.76, 1.47]
Increment (10 ppm) : 1.01
[0.86, 1.20]
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TABLE AX5.8 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH PRENATAL
AND NEONATAL OUTCOMES
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Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels & Copollutants &
Monitoring Stations Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
UNITED STATES (cont'd)
Sagivetal. (2005)
4 Pennsylvania counties
Period of Study:
1997-2001
Outcome: Pre-term birth
Study design:
Time-series
N: 187,997 births
Study design: Poisson-
regression models
Covariates: Long-term
trends, copollutants,
temperature, dew point
temperature, and day of
wk.
Lag: Daily lags ranging
from 1-7 days
6-wk mean: PM10; r = 0.46
7.9 ± 3.5 ppb CO
(Range: 0.8,17), NO2
Median: 8.1
Daily mean: 7.9 ± 6.2
(Range: 0,54.1),
Median: 6.4
This study found an
increased risk for
preterm delivery
during the last 6 wks
of pregnancy with
exposure to SO2.
Increment: 1 5 ppb
Mean: 6-wk SO2:
RR= 1.15 [1.00, 1.32]
<4.9ppb: Referent
4. 9 to 8.1 ppb: 1.02 [0.97, 1.
8.1 to 10. 6 ppb: 1.04 [0.98, :
10.6 to 17.0 ppb: 1.06 [0.99,
Mean: Daily SO2:
RR= 1.07 [0.99, 1.15] lag 3
06]
1. 10]
1.14]
CANADA
Dales et al. (2004)
12 Canadian cities
Period of Study:
1984-1999
Outcome: SIDS 24-havg:
Study design: Time
series 5.51 ppb
N: 1556 SIDS deaths IQR: 4.92
Statistical analysis:
Random effects
regression model
Covariates:
Temperature, humidity,
barometric pressure,
season
Lag: 0-5 days
CO
NO2
03
PM10
PM25
PM,n-2 5
J. J-VJ-IQ Z..-J
SIDS was associated Increment: 4.92ppb(IQR)
with air pollution,
with the effects of Increase in SIDS incidence: 8.49%;
SO2 seeming to be p = Q.0079 lag 1
independent of
sociodemographic
factors, temporal
trends, and weather.
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TABLE AX5.8 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH PRENATAL
AND NEONATAL OUTCOMES
to
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o
X
to
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Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
CANADA (cont'd)
Dales et al. (2006)
1 1 Canadian cities
Period of Study:
1986-2000
Liu et al. (2006)
Calgary, Edmonton and
Montreal, Canada
Period of Study:
1986-2000
Outcome: Hospitalization for
respiratory disease in the
neonatal period
Study design: Time series
N: 9,542
Statistical analysis: Random
effects regression model;
Poisson using fixed- or
random-effects model
Covariates: Fay of wk,
temperature, humidity, pressure
Lag: 0-5 days
Statistical package:
S-PLUS vs. 6.2
Outcome: IUGR
Study design: Case-control
N: 386,202 singleton live
births
Statistical analysis: Multiple
logistic regression
Covariates: Maternal age,
parity, infant sex, season of
birth, city of residence
24-h avg:
4.3 ppb
IQR: 3.8
24-h avg: 3. 9 ppb,
25% 2.0 ppb
50% 3.0 ppb
75% 5.0 ppb
95% 10.0 ppb
1-hmax: 10. 8 ppb,
25% 5.0 ppb
50% 8.6 ppb
75% 14.0 ppb
95% 28.0 ppb
NO2; r = 0.20,
0.67
CO; r= 0.19, 0.66
03;r= -0.41, 0.13
PM10;r=-0.09,
0.61
SO4
N02; r = 0.34
CO; r= 0.21
O3;r=-0.30
PM2.5; r = 0.44
This study detected a
significant
association for
respiratory disease
among neonates and
gaseous air
pollutants.
IUGR did not
increase with
maternal exposure to
SO2. Risk decreased
during first 3 mos.
Increment: 3. 8 ppb (IQR)
Increase in neonatal respiratory
hospital admissions:
S02 alone: 2.06% [1.04, 3.08]
Multipollutant model: 1 . 66%
[0.63,2.69]
Multipollutant model restricted to days
with PM10 measures: 1.41%
[0.35,2.47]
Increment: 3.0 ppb
ORs estimated from graph:
Istmo: OR~0. 966 (0.94, 0.99)
2nd mo: OR-0.97 (0.95, 0.995)
3rd mo: OR~0. 97 (0.95, 0.995)
1st trimester: OR~0 .96 (0.93, 0.99)
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TABLE AX5.8 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH PRENATAL
AND NEONATAL OUTCOMES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
to
o
o
CANADA (cont'd)
X
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to
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6
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W
Dugandzic et al. (2006)
Nova Scotia, Canada
Period of Study:
1988-2000
Outcome: Term LEW
Study design: Retrospective
cohort study
N: 74,284 term, singleton
births
Statistical analysis: Logistic
regression models
Covariates: Maternal age,
parity, prior fetal death, prior
neonatal death, and prior low
birth weight infant, smoking
during pregnancy,
neighborhood family income,
infant gender, gestational age,
weight change, and yr of birth.
Statistical package: SAS vs.
8.0
Mean: SO2 10 ppb
Median: 10
25th%: 7
75th%: 14
Max: 38
03
PM10
In the analyses
unadjusted for birth yr,
first trimester exposures
in the highest quartile
for SO2 associated with
increased risk of LEW.
After adjusting for birth
yr, RR attenuated and
not statistically
significant. There was a
linear concentration-
response effect with
increasing levels of SO2
during the first trimester.
First Trimester
25th-50th: 0.96 [0.73, 1.28]
51st-75th: 1.18 [0.88, 1.58]
>75th: 1.36 [1.04, 1.78]
Increment (7 ppb): 1.20 [1.05, 1.38]
Second Trimester
25th-50th: 1.12 [0.86, 1.46]
51st-75th: 1.13 [0.85, 1.50]
>75th: 1.04 [0.79, 1.37]
Increment (7 ppb): 0.99 [0.87, 1.13]
Third Trimester
25th-50th: 1.04 [0.80, 1.34]
51st-75th: 0.85 [0.63, 1.15]
>75th: 0.88 [0.67, 1.15]
Increment (7 ppb): 0.93 [0.81, 1.06]
Liu et al. (2003) Outcomes: Preterm birth,
Vancouver, Canada LEW, IUGR
Study design: Case-control
Period of Study: N: 229,085 singleton live
1986-1998 births
Statistical analysis: Multiple
logistic regressions Covariates:
Maternal age, parity, infant
sex, gestational age or birth
weight and season of birth
24-havg: 4.9ppb, NO2; r
5th: 1.5 CO;r =
25th: 2.8 Q3;r =
50th: 4.3
75th: 6.3
95th: 10.5
100th: 30.5
1-hmax: 13.4ppb,
5th: 4.3
25th: 7.8
50th: 11.7
75th: 16.8
95th: 28.3
100th: 128.5
= 0.61 LEW and IUGR were
: 0.64 associated with maternal
_0 35 exposure to SO2 during
the first mo of
pregnancy and preterm
birth was associated
with SO2 during the last
mo. These results were
robust to adjustment for
copollutants.
Increment: 5 ppb
Low birth weight
Firstmo: OR 1.11 [1.01,1.22]
Last mo: OR 0.98 [0.89, 1.08]
Preterm birth
Firstmo: OR 0.95 [0.88, 1.03]
Lastmo: OR 1.09 [1.01, 1.19]
IUGR
Firstmo: OR 1.07 [1.01, 1.13]
Lastmo: OR 1.00 [0.94, 1.06]
First trimester: OR 1.07 (1.00, 1.14)
Second trimester: 0.98 [0.91, 1.04]
Third trimester: 1.03 [0.96, 1.10]
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TABLE AX5.8 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH PRENATAL
AND NEONATAL OUTCOMES
to
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to
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to
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6
o
o
H
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O
O
HH
H
W
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels & Copollutants & Method, Findings,
Monitoring Stations Correlations Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
EUROPE
Mohorovic (2004)
Labin, Istra, Croatia
Period of Study:
1987-1989
Outcomes: LEW and
preterm delivery
Study design: Cross-
sectional
N: 704 births
Statistical analysis:
Multiple correlation
analyses, factor analyses,
chi-square
Statistical package:
DBASE IV, SPSS
Monthly ground The results show an
levels of SO2: association between
Range: 34.1, SO2 exposure at the
252.9ug/m3 end of the first and
second mo of
pregnancy and a
negative correlation
between length of
gestations and lower
birth weight of
newborns.
Correlation coefficients:
1st mo:
Gestation length: -0.09, p = 0.008
Birthweight: -0.08, p = 0.016
^nH inn'
Z.11U. 111U .
Gestation length: -0.08, p = 0.016
Birthweight: -0.07, p = 0.026
3rd mo:
Gestation length: - 0.04, p = 0. 147
Birthweight: -0.04, p = 0.135
6th mo:
Gestation length: -0.02, p = 0.266
Birthweight: -0.04, p = 0.151
Whole pregnancy:
Gestation length: -0.09, p = 0.007
Birthweight: -0.04, p = 0.153
Weekly avg during whole pregnancy:
Gestation length: -0.05, p = 0.086
Birthweight: -0.06, p = 0.069
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TABLE AX5.8 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH PRENATAL
AND NEONATAL OUTCOMES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
to
o
o
EUROPE (cont'd)
X
to
to
Bobak et al. (2000)
Czech Republic
Period of Study:
1990-1991
Outcomes: LEW, preterm
birth
Study design: Case-control
N: 108,173 live singleton
births
Statistical analysis:
Logistic regression
Covariates: Temperature,
humidity, day of wk,
season, residential area,
maternal age, gender
Statistical package:
STATA
Mean trimester
exposures
25th: 17.5 ug/m3
50th: 32.0 ug/m3
75th: 55.5 ug/m3
TSP;r = 0.68,
0.73
NOx;r=0.53,
0.63
LEW and preterm birth
were associated with
maternal exposure to SO2,
though the association
between SO2 and LEW
was explained to a large
extent by low gestational
age.
Increment: 50 ug/m
LEW (adjusted for sex, parity, maternal
age group, education, marital status, and
nationality, and mo of birth)
Isttrimester: 1.20(1.11,1.30)
2nd trimester: 1.14 (1.06, 1.22)
3rd trimester: 1.14(1.06,1.23)
LEW (also adjusted for gestational age)
Isttrimester: 1.01(0.88,1.17)
2nd trimester: 0.95(0.82,1.10)
3rd trimester: 0.97(0.85,1.10)
Preterm birth (AOR)
Isttrimester: 1.27(1.16,1.39)
2nd trimester: 1.25(1.14,1.38)
3rd trimester: 1.24(1.13,1.36)
Reduction in mean birth weight:
Isttrimester: 1.4 g (5.9, 16.9)
H
6
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TABLE AX5.8 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH PRENATAL
AND NEONATAL OUTCOMES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels & Copollutants & Method, Findings,
Monitoring Stations Correlations Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
LATIN AMERICA
Gouveia et al. (2004)
Sao Paulo, Brazil
Period of Study:
1997
Outcome: LEW
Study design: Case-control
N: 179,460 live singleton
births
Statistical analysis: Logistic
regression with GAM
Covariates: Gender,
gestational age, maternal age,
maternal education, antenatal
care, parity, delivery method
Statistical package: S-Plus
2000
Annual mean: SO2 PM10
(ug/m3) CO
Mean: 19.6 NO2
SD=10.3 o
Range: 3.4,56.9 3
Jan-Mar: 22.3(7.7)
Apr- June: 28.1
(10.1)
Jul-Aug: 17.9(8.7)
Oct-Dec: 10.3(3.9)
First and second
trimester exposures to
SO2 had a significant
association with birth
weight, though in
different directions.
When air pollutants
were divided into
quartiles and the lowest
quartile was used as the
referent exposure
category, SO2 during the
second trimester was
marginally associated
with low birth weight.
Increment: 10 ug/m3
Reduction in birth weight
First trimester: -24.2g(-55.5,7
Second trimester: 33.7 g (1 .6, 65.
Third trimester: 9.7 g (-25.6, 44.
First trimester:
2nd: 0.902 (0.843, 0.966)
3rd: 0.911(0.819,1.013)
4th: 0.906(0.793,1.036)
Second trimester:
2nd: 0.986(0.922,1.053)
3rd: 1.005(0.904,1.117)
4th: 1.017(0.883,1.173)
Third trimester:
2nd: 1.203(0.861,1.68)
3rd: 1.225(0.872,1.722)
4th: 1.145(0.749,1.752)
•1)
.8)
9)
Pereiraetal. (1998)
Sao Paulo, Brazil
Period of Study:
1991-1992
Outcome: Intrauterine
mortality
Study design: Time series
N:
Statistical analysis: Poisson
regression models
Covariates: Mo, day of wk,
minimum daily temperature,
relative humidity
Lag: 2 to 14 days
24-h avg SO2:
18.90(8.53)mg/m3
Range: 3.80,59.70
PM10; r = 0.45
NO2;r=0.41
03;r=0.17
CO; r = 0.24
SO2 exhibited a
marginal association
with intrauterine
mortality, but only when
Poisson regression was
employed. A
concentration-response
relationship was found.
Estimated regression coefficients and
standard errors:
SO2 alone: 0.0038 (0.0020)
SO2 + NO2 + CO + PM10 + O3: 0.0029
(0.0031)
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TABLE AX5.8 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH PRENATAL
AND NEONATAL OUTCOMES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
to
o
o
ASIA
X
to
to
H
6
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O
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O
O
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H
W
Lin et al. (2004c)
Kaohsiung and Taipei,
Taiwan
Period of Study:
1995-1997
Kaohsiung and Taipei,
Taiwan
Period of Study:
1995-1997
Lin et al. (2004d)
Kaohsiung and Taipei,
Taiwan
Period of Study:
1995-1997
Outcome: LEW
Study design: Case-control
N: 92,288 live births
Statistical analysis: Multiple
logisistic regression
Covariates: Gestational
period, gender, birth order,
maternal age, maternal
education, season of birth
Outcome: Term LEW
Study design: Cohort
N: 92,288 live births
Statistical analysis: Multiple
logisistic regression
Covariates: Gestational
period, gender, birth order,
maternal age, maternal
education, season of birth
24-h avg:
Kaohsiung
Range: 10.07,
25.36ppb
Taipei:
Range: 5.65,9.33
ppb
CO
NO2
03
PM10
24-h avg:
Kaohsiung
Range: 10.07,
25.36 ppb
Taipei:
Range: 5.65,9.33
ppb
CO
NO2
03
PM10
Few women living in
Taipei were exposed to
high levels of SO2. In
Kaohsiung, almost all
women were exposed to
high levels of SO2.
Women living in
Kaohsiung had
significantly higher risk of
term LEW compared with
women living in Taipei.
This study found a 26%
higher risk of term LEW
delivery for mothers
exposed to mean SO2
concentrations exceeding
11.4 ppb during the entire
pregnancy, as compared
with mothers exposed to
mean concentrations less
than 7.1 ppb. Trimester
specific analysis showed a
significant association
only for the third
trimester.
OR for Kaoshiung births (compared to
Taipei births)
All births:
OR: 1.13 [1.03, 1.24]
Female births only:
OR: 1.14 [1.01, 1.28]
Lowest quartile of exposure = referent
Entire pregnancy:
25th-75th: 1.16 [1.02, 1.33]
>75th: 1.26 [1.04, 1.53]
1 st trimester:
25th-75th: 1.02 [0.90, 1.16]
>75th: 1.11 [0.94,1.33]
2nd trimester:
25th-75th: 1.09 [0.96, 1.24]
>75th: 1.17 [0.99, 1.37]
3rd trimester:
25th-75th: 1.13 [0.99, 1.28]
>75th: 1.20 [1.01, 1.41]
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TABLE AX5.8 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH PRENATAL
AND NEONATAL OUTCOMES
Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Method, Findings,
Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
to
o
o
ASIA (cont'd)
Wang etal. (1997)
Four residential areas:
Dongcheng, Xicheng,
Congwen, Xuanwu
Beijing, China
Period of Study:
1988-1991
Outcome: Term LEW
Study design: Cohort study
N: 74,671 first parity live
births
Statistical analysis: Multiple
linear regression and logistic
regression with GAM
Covariates: Gestational age,
residence, yr of birth,
maternal age, and infant
gender.
Mean pollution
concentrations
provided in graph
TSP; r = 0.92 Exposure-response
relationship between
SO2 during the third
trimester of pregnancy
and low birth weight.
3rd trimester:
9 to 18 ug/m3 (reference)
18 to 55: 1.09(0.94,1.26)
55 to 146: 1.12(0.97,1.29)
146 to 239: 1.16(1.01,1.34)
239 to 308: 1.39(1.22,1.60)
SO2 as continuous variable:
Odds ratio per 100 ug/m3
1.11(1.06,1.16)
X
to
to
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Xuetal. (1995)
Four residential areas:
Dongchen, Xichen,
Congwen, Xuanwu
Beijing, China
Period of Study: 1988
Outcome: Preterm delivery
Study design: Prospective
cohort study
N: 25,370 singleton first live
births
Statistical analysis: Multiple
linear and logistic regression
Covariates: Temperature,
humidity, day of wk, season,
residential area, maternal age,
and gender of child.
2 monitors for SO2:
Dongcheng and
Xicheng
Dongcheng Annual
mean:
108 ug/m3
SD = 141 ug/m3)
Xicheng annual
mean:
93 ug/m
(SD = 122 ug/m3)
TSP
Exposure response
relationship between
quartiles of SO2 and
crude incidence rates of
preterm birth. Dose
dependent relationship
between SO2 and
gestational age. The
estimated reduced length
of gestation was
0.075 wks or 12.6 h per
100/m increase in SO2.
When TSP and SO2
included in a
multipollutant model,
the effect of SO2 was
reduced by 32%.
Effect on gestational age (wk) per
100 ug/m3
regression coef and SE for lagged
moving avg of SO2.
lagO: -0.016(0.021)
lagl: -0.022(0.021)
lag 6: -0.067 (0.024), p< 0.01
lag 7: -0.075 (0.024), p< 0.01
lag 8: -0.075 (0.025), p< 0.01
OR for each quartile of SO2
1st: 1.00
2nd: 1.70(1.15,2.52)
3rd: 1.74(1.03,2.92)
4th: 1.58(0.87,2.86)
Adjusted OR for preterm delivery:
1.21 (1.01, 1.46) per Ln ug/m3 increase
in SO,
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TABLE AX5.8 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH PRENATAL
AND NEONATAL OUTCOMES
to
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to
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6
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o
H
O
o
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O
O
HH
H
W
Reference, Study Outcomes, Design,
Location, & Period & Methods
Mean Levels &
Monitoring Stations
Copollutants & Method, Findings,
Correlations Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
ASIA (cont'd)
Haetal. (2001)
Seoul, Korea
Period of Study:
1996-1997
Lee et al. (2003)
Seoul, Korea
Period of Study:
1996-1998
Outcome: LEW
Study design: Case-control
N: 276,763
Statistical analysis: Logistic
regression, GAM
Covariates: Gestational age,
maternal age, parental
education level, infant's birth
order gender
Outcome: Term LEW
Study design:
N: 388,105 full-term
singleton births
Statistical analysis: GAM
Covariates: Infant sex, birth
order, maternal age, parental
education level, time trend,
and gestational age.
24-h avg:
1 st trimester:
25th: 10.0 ppb
50th: 13.2 ppb
75th: 16.2 ppb
3rd trimester:
25th: 8.4 ppb
50th: 12.2 ppb
75th: 16.3 ppb
Avg concentration
(Ppb)
Mean: 12.1
SD = 7.4
Range: 3, 46
25th: 6.8
50th: 9.8
75th: 15.6
CO; r = 0. 83 Ambient SO2
NO2; r = 0.70 concentrations during
TSP' r = 0 67 me fi1"8^ trimester of
O3; r = - 0.29 pregnancy were
associated with LEW
PM10; r = 0.78, Second trimester
0.85 exposures to SO2 as
CO; r= 0.79, 0.86 well as during the
NO2' r = 0 75 entire pregnancy were
0.76 associated with LEW.
Reduction in birth
weight was 14.6 g for
IQR increase in SO2 in
the second trimester.
When the exposure for
each mo of pregnancy
was evaluated
separately, SO2
exposure during 3 to 5
mos of pregnancy
associated with LEW.
Increment: 1st trimester: 6. 2 ppb;
3rd trimester: 7.9 ppb
1st trimester: RR 1.06 [1.02, 1.10]
3rd trimester: RR 0.93 [0.88, 0.98]
Reduction in birth weight: 8.06 g
[5.59, 10.53]
Increment: 8. 8 ppb (IQR)
First trimester: 1 .02 (0.99, 1 .06)
Second trimester: 1.06 (1.02, 1.11)
Third trimester: 0.96 (0.91, 1.00)
All trimesters: 1.14 (1.04, 1.24)
-------
TABLE AX5.8 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH PRENATAL
AND NEONATAL OUTCOMES
to
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to
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oo
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O
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HH
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Reference, Study
Location, & Period
Outcomes, Design,
& Methods
Mean Levels &
Monitoring Stations
Copollutants & Method, Findings,
Correlations Interpretation
Effects: Relative Risk or % Change
& Confidence Intervals (95%)
ASIA (cont'd)
Leem et al. (2006)
Incheon, Korea
Period of Study:
2001-2002
Yang et al. (2003b)
Kaohsiung, Taiwan
Period of Study:
1995-1997
Outcome: Preterm
delivery
Study design:
N: 52,113 singleton
births
Statistical analysis:
Log-binomial regression
Covariates: Maternal
age, parity, sex, season,
maternal education,
paternal education
Outcome: Term LEW
Study design: Case-
control
N: 13, 3 96 first parity
singleton live births
Statistical Analysis:
Multiple linear
regression
Covariates: Maternal
age, season, marital
status, maternal
education, gender
Statistical package: SAS
Mean: SO2
Concentrations by
trimester:
1 st trimester:
Min: 7.86 ug/m3
25th: 17.61
50th: 22.74
75th: 45.85
Max: 103.96
3rd trimester:
Min: 6.55 ug/m3
25th: 17.03
50th: 25.62
75th: 46.53
Max: 103.15
Mean: trimester exposure
(ug/m3)
1 st trimester
33rd: 26.02
67th: 36.07
2nd trimester
33rd: 25.76
67th: 35.63
3rd trimester
"5 "1 A O £ "5 n
33rd: 25.39
67th: 36.96
NO2; r = 0.54 This study found the
CO; r= 0.31 highest SO2
PM10' r = 0 13 concentrations during
the first trimester to
be significantly
associated with
elevated risks of
preterm delivery.
PM10; r = 0.45, 0.46 A significant
expo sure-response
relationship between
maternal exposures
to SO2 and birth
weight was found
during the first
trimester of
pregnancy.
1 st trimester:
7.86 to 17.61 ug/m3: referent
17.62 to 22.74: 1.13 [0.99, 1.28]
22.75 to 45. 85: 1.13 [0.98, 1.30]
45.86 to 103. 96: 1.21 [1.04,1.42]
3rd trimester:
6.55 to 17.03 ug/m3: referent
17.04 to 25. 62: 0.87 [0.76, 1.01]
25.63 to 46.53: 0.97 [0.83, 1.13]
46. 54 to 103. 15: 1.11 [0.94,1.31]
Reduction in birth weight:
1 st trimester:
33rd-67th: 3.68 g [-12.45, 19.21]
>67th: 18.1 Ig [1.88, 34.34]
Continuous: 0.52 g [0.09, 2.63]
2nd trimester:
33rd-67th: 1.78 g [-17.91, 14.35]
>67th: 13. 53 g [-2.62, 29. 68]
Continuous: 0.19 g [-0.78, 1.8]
J1U. UllllCSlCl.
33rd-67th: 0.43 g [-16.56, 15.70]
>67th: 1. 97 g [- 18.24, 14.30]
Continuous: 0.03 g [-1.21, 1.37]
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TABLE AX5.9. ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH MORTALITY
Reference, Location,
Study Period
Mean SO,, Levels
Study Description
Results and Comments
UNITED STATES
to
o
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Abbey etal. (1999)
Three California air
basins: San Francisco,
South Coast (Los Angeles
and eastward), San Diego
1977-1992
24-h avg SO2: 5.6 ppb
Prospective cohort study of 6,338 nonsmoking non-
Hispanic white adult members of the Adventist Health
Study followed for all cause, cardiopulmonary,
nonmalignant respiratory, and lung cancer mortality.
Participants were aged
27-95 yrs at enrollment in 1977. 1,628
(989 females, 639 males) mortality events followed
through 1992. All results were stratified by gender.
Used Cox proportional hazards analysis, adjusting for
age at enrollment, past smoking, environmental tobacco
smoke exposure, alcohol use, education, occupation,
and body mass index. Analyzed mortality from all
natural causes, cardiopulmonary, nonmalignant
respiratory, and lung cancer.
SO2 was not associated with total (RR = 1.07
[95% CI: 0.92, 1.24] for male and 1.00
[95% CI: 0.88, 1.14] for female per 5-ppb increase in
multiyear average SO2), cardiopulmonary, or respiratory
mortality for either sex. Lung cancer mortality showed
large risk estimates for most of the pollutants in either
or both sexes, but the number of lung cancer deaths in
this cohort was very small (12 for female and 18 for
male) Generally wide confidence intervals (relative to
other U.S. cohort studies).
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Beesonetal. (1998)
Three California air
basins: San Francisco,
South Coast (Los Angeles
and eastward), San Diego
1977-1992
24-h avg SO2: 5.6 ppb
Prospective cohort study of 6,338 nonsmoking non-
Hispanic white adult members of the Adventist Health
Study aged 27-95 yrs at time of enrollment.
36 (20 females, 16 males) histologically confirmed lung
cancers were diagnosed through 1992. Extensive
exposure assessment, with assignment of individual
long-term exposures to O3, PM10, SO42~, and SO2, was a
unique strength of this study. All results were stratified
by gender. Used Cox proportional hazards analysis,
adjusting for age at enrollment, past smoking,
education, and alcohol use.
Lung cancer incidence relative risk:
Male: RR = 3.72 (95%CI: 1.91,7.28);
Female: RR = 2.78 (95%CI: 1.51,5.12)
per 5-ppb increase in SO2.
Case number very small (16 for male, 20 for female).
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TABLE AX5.9 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH MORTALITY
Study Description
Reference, Location,
Study Period
Mean SO,, Levels
Results and Comments
UNITED STATES (cont'd)
to
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Dockeiy etal. (1993)
Portage, WI; Topeka, KS;
Watertown, MA;
Harriman, TN;
St. Louis, MO;
Steubenville, OH
1974-1991.
24-h avg NO2 ranged
from 1.6 (Topeka) to 24.0
(Steubenville) ppb.
A prospective cohort study to study the effects of air
pollution with main focus on PM components in six
U.S. cities, which were chosen based on the levels of air
pollution (Portage, WI, the least polluted to
Steubenville, OH, the most polluted). Cox proportional
hazards regression was conducted with data from a
14-to-16-yr follow-up of 8,111 adults in the six cities,
adjusting for smoking, sex, BMI, occupational
exposures, etc. PM2 5 and sulfate were associated with
these causes of deaths.
SO2 result presented only graphically. Fine particles
and sulfate showed better fit than SO7.
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Krewski et al. (2000)
Re-analysis and
sensitivity analysis of
Dockery etal. (1993)
study.
Krewski et al. (2000);
Jerrett et al. (2003)
Re-analy sis/sensitivity
analysis of Pope et al.
(1995) study.
24-h avg NO2 ranged
from 1.6 (Topeka) to 24.0
(Steubenville) ppb
Gaseous pollutants risk estimates were presented.
Multiyear avg of 24-h
avg 9.3 ppb.
Re-analysis of Pope et al. (1995) study. Extensive
sensitivity analysis with ecological covariates and
spatial models to account of spatial pattern in the ACS
data.
SO2 showed positive associations with total
(RR = 1.05 [95% CI: 1.02, 1.09] per 5-ppb increase in
the average SO2 over the study period),
cardiopulmonary (1.05 [95% CI: 1.00, 1.10]), and lung
cancer deaths (1.03 [95% CI: 0.91, 1.16]), but in this
dataset, SO2 was highly correlated with PM2 5 (r = 0.85),
sulfate (r = 0.85), and NO2 (r = 0.84)
The relative risk estimates for total mortality was 1.06
(95% CI: 1.05, 1.07) per 5-ppb increase in the annual
average SO2. In the spatial filtering model (this was the
model that resulted in the largest reduction of SO2 risk
estimate when sulfate was included), the SO2 total
mortality risk estimate was 1.07 (95% CI: 1.03, 1.11) in
the single-pollutant model and 1.04 (95% CI: 1.02,
1.06) with sulfate in the model. The risk estimates for
PM2 5 and sulfate were diminished when SO2 was
included in the models.
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TABLE AX5.9 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH MORTALITY
Reference, Location,
Study Period
Mean SOV Levels
Study Description
Results and Comments
UNITED STATES (cont'd)
to
o
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Lipfert et al.
(2000b; 2003)
32 Veterans
Administration hospitals
nationwide in the U.S.
1976-1996
SO2 mean levels not
reported.
Cohort study of approximately 50,000 U.S. veterans
(all males) diagnosed with hypertension. Mean age at
recruitment was 51 yrs. Exposure to O3 during four
periods (1960-1974, 1975-1981, 1982-1988, 1989-1996)
associated with mortality over three periods (1976-1981,
1982-1988, 1989-1996). Long-term exposures to TSP,
PMis, PMio, PM2.5, PMi5.2.5, S042~, N02, and CO also
analyzed. Used Cox proportional hazards regression,
adjusting for race, smoking, age, systolic and diastolic
blood pressure, body mass index, and socioeconomic
factors.
"SO2 and Pb were considered less thoroughly". The
authors presented only qualitative results for SO2 from the
"Screening regressions" which indicated negatively
significant risk estimate in the univariate model and non-
significant positive estimate in the multivariate model.
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Lipfert et al. (2006a)
32 Veterans
Administration hospitals
nationwide in the U.S.
1976-2001
Mean of the 95th Update of the Lipfert et al. (2000) study, with follow-up
percentile of the period extended to 2001. Study focused on the traffic
24-h avg SO2 for density data. The county-level traffic density was derived
1997-2001 period: by dividing vehicle-km traveled by the county land area.
15.8 ppb. Because of the wide range of the traffic density variable,
log-transformed traffic density was used in their analysis.
They reported that traffic density was a better predictor of
mortality than ambient air pollution variables, with the
possible exception of O3. The log-transformed traffic
density variable was weakly correlated with SO2
(r = 0.32) in this data set.
P%T< using the 1997-2001 air quality data period:
0.99 (95% CI: 0.97, 1.01) per 5-ppb increase; in a single-
pollutant model.
The 2-pollutant model with the traffic density variable:
0.99 [95% CI: 0.96, 1.01] per 5 ppb.
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Lipfert et al. (2006b)
32 Veterans
Administration hospitals
nationwide in the U.S.
1997-2001
Mean of the 95th
percentile of the
24-h avg SO2 for
1999-2001 period:
16.3 ppb.
Update of the Lipfert et al. (2000) study, examined PM2.5
chemical constituents data. The analysis used county-
level air pollution data for the period 1999-2001 and
cohort mortality data for 1997-2001.
Traffic density was the most important predictor of
mortality, but associations were also seen for elemental
carbon, V, nitrate, and Ni. NO2, ozone, and PM10 also
showed positive but weaker associations. The risk estimate
for SO2 was essentially the same as that reported in the
2006a Lipfert et al. analysis (0.99 [95% CI: 0.96,1.01] per
5 ppb) in a single-pollutant model. Multipollutant model
results were not presented for SO2.
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TABLE AX5.9 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH MORTALITY
Reference, Location,
Study Period
Mean SO,, Levels
Study Description
Results and Comments
UNITED STATES (cont'd)
to
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Miller et al. (2007).
36 U.S. metropolitan
areas from 1994 to 1998
Not reported.
Pope etal. (1995)
U.S. nationwide
1982-1989
Not analyzed/ reported.
Cohort study of 65,893 postmenopausal women between
the ages of 50 and 79 yrs without previous cardiovascular
disease in 36 U.S. metropolitan areas from 1994 to 1998.
They examined the association between one or more fatal
or nonfatal cardiovascular events and the women's
exposure to air pollutants. Subject's exposures to air
pollution were estimated by assigning the annual mean
levels of air pollutants measured at the nearest monitor to
the location of residence on the basis of its five-digit ZIP
Code centroid. A total of 1,816 women had one or more
fatal or nonfatal cardiovascular events, including 261
deaths from cardiovascular causes. Hazard ratios were
estimated for the first cardiovascular event using Cox
proportional hazards model, adjusting for age, race or
ethnic group, smoking status, educational level,
household income, BMI, and presence or absence of
diabetes, hypertension, or hypercholesterolemia
Investigated associations between long-term exposure to
PM and the mortality outcomes in the American Cancer
Society cohort. Ambient air pollution data from 151 U.S.
metropolitan areas in 1981 were linked with individual
risk factors in 552,138 adults who resided in these areas
when enrolled in the prospective study in 1982. Death
outcomes were ascertained through 1989. Cox
proportional hazards model adjusted for smoking,
education, BMI, and occupational exposures. PM2 5 and
sulfate were associated with total, cardiopulmonary, and
lung cancer mortality, but not with mortality for all other
causes.
In the single-pollutant model results, PM2 5 showed the
strongest associations with the CVD events by far
among the pollutants, followed by SO2 (HR of 1.07
[95% CI: 0.95, 1.20] per 5 ppb increase in the annual
avg). In the multipollutant model (apparently, all the
pollutants were included in the model), the PM2 5's
association with the overall CVD events was even
stronger and the estimate larger, and the association
with SO2 also became stronger and the estimate larger
(1.13 [95% CI: 0.98,1.30]). Correlations among these
pollutants were not described, and therefore it is not
possible to estimate the extent of confounding among
these pollutants in these associations, but it is clear that
PM2 5 was the best predictor of the CVD events.
Gaseous pollutants not analyzed.
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TABLE AX5.9 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH MORTALITY
Reference, Location,
Study Period
Mean SO,, Levels
Study Description
Results and Comments
UNITED STATES (cont'd)
to
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Pope et al. (2002)
U.S. nationwide
1982-1998
24-h avg mean of
118 MSA's in!980:
9.7 ppb; mean of
126 MSA's during
1982-1998: 6.7 ppb.
Prospective cohort study of approximately 500,000
members of American Cancer Society cohort enrolled in
1982 and followed through 1998 for all cause,
cardiopulmonary, lung cancer, and all other cause
mortality. Age at enrollment was 30+yrs. Air
pollution concentrations in urban area of residence at
time of enrollment assessed from 1982 through 1998.
Other pollutants considered include TSP, PM15, PM10,
PM2.5, PMi5_2.5, SO42~, SO2, NO2, and CO.
PM2 5 was associated with total, cardiopulmonary, lung
cancer mortality, but not with deaths for all other
causes. SO2 was associated with all the mortality
outcomes, including all other causes of deaths. SO2's
risk estimate for total mortality was 1.03 (95% CI:
1.02, 1.05) per 5 ppb increase (1982-1998 average).
Residential location was known only at enrollment to
study in 1982. Thus, exposure misclassification
possible.
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Willis etal. (2003)
Re-analy sis/sensitivity
analysis of Pope et al.
(1995) study.
Multiyear average of 24-
h avg using MSA scales:
9.3 ppb; using county
scales: 10.7 ppb.
Investigation of the effects of geographic scale over
which the air pollution exposures are averaged.
Exposure estimates were averaged over the county
scale, and compared the original ACS results in which
MSA scale average exposures were used. Less than half
of the cohort used in the MSA-based study were used in
the county scale based analysis because of the limited
availability of sulfate monitors and because of the loss
of subjects from the use of five-digit zip codes
In the analysis comparing the 2-pollutant model with
sulfate and SO2, they found that, in the MSA-scale
model, the inclusion of SO2 reduced sulfate risk
estimates substantially (>25%), but not substantially
(<25%) in the county-scale model. In the MSA-level
anlaysis (with 113 MSA's), SO2 relative risk estimate
was 1.04 (95% CI: 1.02, 1.06) per 5 ppb increase, with
sulfate in the model. In the county-level anaysis
(91 counties) with sulfate in the model, the
corresponding estimate was smaller (RR = 1.02
[95% CI: 1.00,1.05]). The correlation between
covariates are different between the MSA-level data and
county-level data.
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TABLE AX5.9 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH MORTALITY
Reference, Location,
Study Period
Mean SO,, Levels
Study Description
Results and Comments
EUROPE
to
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Elliott et al. (2007)
Great Britain; 1966-1994
air pollution; 1982-1998
mortality in four periods.
24-h avg SO2 levels
declined from 41.4 ppb in
1966-1970 to 12.2 ppb in
1990-1994
A small area analysis of mortality rates in
electoral ward, with the mean area of 7.4 km2 and
the mean population of 5,301 per electoral ward.
Deaths rates were computed for four successive
4-yr periods from 1982 to 1994. The number of
wards in these four periods ranged from 118 in
the 1994-1998 period to 393 in the 1982-1986
period. Poisson model was fit to model observed
deaths for each ward with a linear function for
pollutant and random intercept, with and without
adjustment for social deprivation.
They observed associations for both BS and SO2 and mortality
outcomes. The estimated effects were stronger for respiratory
illness than other causes of mortality for the most recent
exposure periods and most recent mortality period (pollution
levels were lower). The adjustment for social deprivation
reduced the risk estimates for both pollutants. The adjusted
risk estimates for SO2 for the pooled mortality periods using
the most recent exposure windows were: 1.021 (95% CI:
1.018, 1.024) for all-cause; 1.015 (95% CI: 1.011, 1.019) for
cardiovascular; and 1.064% (95% CI: 1.056,1.072) for
respiratory causes per 5 ppb increase in SO2. The risk
estimates for the most recent mortality period using the most
recent exposure windows were larger.
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Filleul et al. (2005)
Seven French cities
1975-2001
24-h avg SO2 ranged from
5.9 ppb ("Area 3" in Lille)
to 29.7 ppb ("Area 3" in
Marseille) in the 24 areas
in seven cities during
1974-1976. Median
levels during 1990-1997
ranged from 3.0 ppb
(Bordeaux) to 8.2 ppb
(Rouen) in the five cities
where data were available.
Cohort study of 14,284 adults who resided in
24 areas from seven French cities when enrolled
in the PAARC survey (air pollution and chronic
respiratory diseases) in 1974. Daily
measurements of SO2, TSP, black smoke, NO2,
and NO were made in 24 areas for three yrs
(1974-76). Cox proportional hazards models
adjusted for smoking, educational level, BMI,
and occupational exposure. Models were run
before and after exclusion of six area monitors
influenced by local traffic as determined by the
NO/NO2 ratio >3.
Before exclusion of the six areas, none of the air pollutants
were associated with mortality outcomes. After exclusion of
these areas, analyses showed associations between total
mortality and TSP, BS, NO2, and NO, but not SO2 (1.01 [95%
CI: 0.97,1.06] per 5 ppb multi-yr average). From these
results, the authors noted that inclusion of air monitoring data
from stations directly influenced by local traffic could
overestimate the mean population exposure and bias the
results. It should be noted that the table describing air
pollution levels in Filleul et al.'s report indicates that the
SO2 levels in these French cities declined markedly between
1974-76 and 1990-1997 period, by a factor of 2 to 3,
depending on the city, whereas NO2 levels between the two
periods were variable, increased in some cities, and decreased
in others. These changes in air pollution levels over the study
period complicates interpretation of reported risk estimates.
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TABLE AX5.9 (cont'd). ASSOCIATIONS OF LONG-TERM EXPOSURE TO SULFUR DIOXIDE WITH MORTALITY
Reference, Location,
Study Period
Mean SO,, Levels
Study Description
Results and Comments
EUROPE (cont'd)
to
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Nafstad et al. (2004)
Oslo, Norway
1972-1998.
Nafstad et al. (2003)
Oslo, Norway
1972-1998
The yearly averages of
24-h avg SO2 were
reduced with a factor of
7 during the study period
from 5.6 ppb in 1974 to
O.Sppbin 1995.
The yrly averages of 24-h
avg. SO2 were reduced
with a factor of 7 during
the study period from
5.6 ppb in 1974 to 0.8 ppb
in 1995.
Cohort study of 16,209 Norwegian men 40-49 yrs of
age living in Oslo, Norway, in 1972-1973. Data from
the Norwegian Death Register were linked with
estimates of average yrly air pollution levels at the
participants' home addresses from 1974 to 1998. NOX,
rather than NO2 was used. Exposure estimates for NOX
and SO2 were constructed using models based on the
subject's address, emission data for industry, heating,
and traffic, and measured concentrations. Addresses
linked to 50 of the busiest streets were given an
additional exposure based on estimates of annual
average daily traffic. Cox proportional-hazards
regression was used to estimate associations between
exposure and total and cause-specific mortality,
adjusting for age strata, education, occupation, smoking,
physical activity level, and risk groups for
cardiovascular diseases.
Lang cancer incidence was examined in the above
cohort. During the follow-up period, 418 men
developed lung cancer.
NOX was associated with total, respiratory, lung cancer,
and ischemic heart disease deaths. SO2 did not show
any associations with mortality (e.g., 0.97 [95% CI:
0.94, 1.01] per 5 ppb multi-yr average). The risk
estimates presented for categorical levels of these
pollutants showed mostly monotonic exposure-response
relationships for NOX, but not for SO2. Note the very
low levels of SO2.
NOX was associated with lung cancer incidence. SO2
showed no association (1.01; [95% CI: 0.92, 1.12] per
5 ppb multi-yr average).
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1 AX5.1 REFERENCES
3 Abbey, D. E.; Nishino, N.; McDonnell, W. F.; Burchette, R. J.; Knutsen, S. F.; Beeson, W. L.;
4 Yang, J. X. (1999) Long-term inhalable particles and other air pollutants related to
5 mortality in nonsmokers. Am. J. Respir. Crit. Care Med. 159: 373-382.
6 Ackermann-Liebrich, U.; Leuenberger, P.; Schwartz, J.; Schindler, C.; Monn, C.; Bolognini, B.;
7 Bongard, J. P.; Brandli, O.; Domenighetti, G.; Elsasser, S.; Grize, L.; Karrer, W.; Keller,
8 R.; Keller-Wossidlo, H.; Kunzli, N.; Martin, B. W.; Medici, T. C.; Perruchoud, A. P.;
9 Schoni, M. H.; Tschopp, J. M.; Villiger, B.; Wuthrich, B.; Zellweger, J. P.; Zemp, E.
10 (1997) Lung function and long term exposure to air pollutants in Switzerland. Am. J.
11 Respir. Crit. Care Med. 155: 122-129.
12 Anderson, H. R.; Ponce de Leon, A.; Bland, J. M.; Bower, J. S.; Strachan, D. P. (1996) Air
13 pollution and daily mortality in London: 1987-92. Br. Med. J. 312: 665-669.
14 Anderson, H. R.; Spix, C.; Medina, S.; Schouten, J. P.; Castellsague, J.; Rossi, G.; Zmirou, D.;
15 Touloumi, G.; Wojtyniak, B.; Ponka, A.; Bacharova, L.; Schwartz, J.; Katsouyanni, K.
16 (1997) Air pollution and daily admissions for chronic obstructive pulmonary disease in 6
17 European cities: results from the APHEA project. Eur. Respir. J. 10: 1064-1071.
18 Anderson, H. R.; Ponce de Leon, A.; Bland, J. M.; Bower, J. S.; Emberlin, J.; Strachen, D. P.
19 (1998) Air pollution, pollens, and daily admissions for asthma in London 1987-92.
20 Thorax 53: 842-848.
21 Anderson, H. R.; Bremner, S. A.; Atkinson, R. W.; Harrison, R. M.; Walters, S. (2001)
22 Particulate matter and daily mortality and hospital admissions in the west midlands
23 conurbation of the United Kingdom: associations with fine and coarse particles, black
24 smoke and sulphate. Occup. Environ. Med. 58: 504-510.
25 Atkinson, R. W.; Bremner, S. A.; Anderson, H. R.; Strachan, D. P.; Bland, J. M.; Ponce de Leon,
26 A. (1999a) Short-term associations between emergency hospital admissions for
27 respiratory and cardiovascular disease and outdoor air pollution in London. Arch.
28 Environ. Health 54: 398-411.
29 Atkinson, R. W.; Anderson, H. R.; Strachan, D. P.; Bland, J. M.; Bremner, S. A.; Ponce de Leon,
30 A. (1999b) Short-term associations between outdoor air pollution and visits to accident
31 and emergency departments in London for respiratory complaints. Eur. Respir. J. 13:
32 257-265.
33 Atkinson, R. W.; Anderson, H. R.; Sunyer, J.; Ayres, J.; Baccini, M.; Vonk, J. M.; Boumghar,
34 A.; Forastiere, F.; Forsberg, B.; Touloumi, G.; Schwartz, J.; Katsouyanni, K. (2001)
35 Acute effects of particulate air pollution on respiratory admissions: results from APHEA
36 2 project. Am. J. Respir. Crit. Care Med. 164: 1860-1866.
37 Ballester, F.; Tenias, J. M.; Perez-Hoyos, S. (2001) Air pollution and emergency hospital
38 admissions for cardiovascular diseases in Valencia, Spain. J. Epidemiol. Community
39 Health 55: 57-65.
40 Ballester, F.; Saez, M.; Perez-Hoyos, S.; Iniguez, C.; Gandarillas, A.; Tobias, A.; Bellido, J.;
41 Taracido, M.; Arribas, F.; Daponte, A.; Alonso, E.; Canada, A.; Guillen-Grima, F.;
September 2007 AX5-236 DRAFT-DO NOT QUOTE OR CITE
-------
1 Cirera, L.; Perez-Boillos, M. 1; Saurina, C.; Gomez, F.; Tenias, J. M. (2002) The
2 EMECAM project: a multicentre study on air pollution and mortality in Spain: combined
3 results for particulates and for sulfur dioxide. Occup. Environ. Med. 59: 300-308.
4 Ballester, F.; Rodriguez, P.; Ifiiguez, C.; Saez, M.; Daponte, A.; Galan, I.; Taracido, M.; Arribas,
5 F.; Bellido, J.; Cirarda, F. B.; Canada, A.; Guillen, J. J.; Guillen-Grima, F.; Lopez, E.;
6 Perez-Hoyos, S.; Lertxundi, A.; Toro, S. (2006) Air pollution and cardiovascular
7 admisisons association in Spain: results within the EMECAS project. J. Epidemiol.
8 Community Health 60: 328-336.
9 Barnett, A. G.; Williams, G. M.; Schwartz, J.; Neller, A. H.; Best, T. L.; Petroeschevsky, A. L.;
10 Simpson, R. W. (2005) Air pollution and child respiratory health: a case-crossover study
11 in Australia and New Zealand. Am. J. Respir. Crit. Care Med. 171: 1272-1278.
12 Bates, D. V.; Baker-Anderson, M.; Sizto, R. (1990) Asthma attack periodicity: a study of
13 hospital emergency visits in Vancouver. Environ. Res. 51: 51-70.
14 Beeson, W. L.; Abbey, D. E.; Knutsen, S. F. (1998) Long-term concentrations of ambient air
15 pollutants and incident lung cancer in California adults: results from the AHSMOG
16 study. Environ. Health Perspect. 106: 813-823.
17 Bell, M. L.; Ebisu, K.; Belanger, K. (2007) Ambient air pollution and low birth weight in
18 Connecticut and Massachusetts. Environ. Health Perspect. 115: 1118-1125.
19 Biggeri, A.; Baccini, M.; Bellini, P.; Terracini, B. (2005) Meta-analysis of the Italian studies of
20 short-term effects of air pollution (MISA), 1990-1999. Int. J. Occup. Environ. Health 11:
21 107-122.
22 Bobak, M. (2000) Outdoor air pollution, low birth weight, and prematurity. Environ. Health
23 Perspect. 108: 173-176.
24 Boezen, M.; Schouten, J.; Rijcken, B.; Vonk, J.; Gerritsen, J.; Van Der Zee, S.; Hoek, G.;
25 Brunekreef, B.; Postma, D. (1998) Peak expiratory flow variability, bronchial
26 responsiveness, and susceptibility to ambient air pollution in adults. Am. J. Respir. Crit.
27 Care Med. 158: 1848-1854.
28 Boezen, H. M.; Van Der Zee, S. C.; Postma, D. S.; Vonk, J. M.; Gerritsen, J.; Hoek, G.;
29 Brunekreef, B.; Rijcken, B.; Schouten, J. P. (1999) Effects of ambient air pollution on
30 upper and lower respiratory symptoms and peak expiratory flow in children. Lancet 353:
31 874-878.
32 Boezen, H. M.; Vonk, J. M.; Van Der Zee, S. C.; Gerritsen, J.; Hoek, G.; Brunekreef, B.;
33 Schouten, J. P.; Postma, D. S. (2005) Susceptibility to air pollution in elderly males and
34 females. Eur. Respir. J. 25: 1018-1024.
35 Borja-Aburto, V. H.; Loomis, D. P.; Bangdiwala, S. L; Shy, C. M.; Rascon-Pacheco, R. A.
36 (1997) Ozone, suspended particulates, and daily mortality in Mexico City. Am. J.
37 Epidemiol. 145: 258-268.
38 Borja-Aburto, V. H.; Castillejos, M.; Gold, D. R.; Bierzwinski, S.; Loomis, D. (1998) Mortality
39 and ambient fine particles in southwest Mexico City, 1993-1995. Environ. Health
40 Perspect. 106: 849-855.
September 2007 AX5-237 DRAFT-DO NOT QUOTE OR CITE
-------
1 Boutin-Forzano, S.; Adel, N.; Gratecos, L.; Milan, H.; Gamier, J. M.; Ramadour, M.;
2 Lanteaume, A.; Hamon, M.; Lafay, V.; Charpin, D. (2004) Visits to the emergency room
3 for asthma attacks and short-term variations in air pollution. A case-crossover study.
4 Respiration 71: 134-137.
5 Braga, A. L. F.; Concei9ao, G. M. S.; Pereira, L. A. A.; Kishi, H. S.; Pereira, J. C. R.; Andrade,
6 M. F.; Gon9alves, F. L. T.; Saldiva, P. H. N.; Latorre, M. R. D. O. (1999) Air pollution
7 and pediatric respiratory hospital admissions in Sao Paulo, Brazil. J. Environ. Med. 1: 95-
8 102.
9 Braga, A. L. F.; Saldiva, P. H. N.; Pereira, L. A. A.; Menezes, J. J. C.; Concei9ao, G. M. C.; Lin,
10 C. A.; Zanobetti, A.; Schwartz, J.; Dockery, D. W. (2001) Health effects of air pollution
11 exposure on children and adolescents in Sao Paulo, Brazil. Pediatr. Pulmonol. 31: 106-
12 113.
13 Braun-Fahrlander, C.; Vuille, J. C.; Sennhauser, F. H.; Neu, U.; Kiinzle, T.; Grize, L.; Gassner,
14 M.; Minder, C.; Schindler, C.; Varonier, H. S.; Wuthrich, B.; SCARPOL team. (1997)
15 Respiratory health and long-term exposure to air pollutants in Swiss schoolchildren. Am.
16 J. Respir. Crit. Care Med. 155: 1042-1049.
17 Bremner, S. A.; Anderson, H. R.; Atkinson, R. W.; McMichael, A. J.; Strachan, D. P.; Bland, J.
18 M.; Bower, J. S. (1999) Short term associations between outdoor air pollution and
19 mortality in London 1992-4. Occup. Environ. Med. 56: 237-244.
20 Buchdahl, R.; Parker, A.; Stebbings, T.; Babiker, A. (1996) Association between air pollution
21 and acute childhood wheezy episodes: prospective observational study. Br. Med. J. 312:
22 661-664.
23 Burnett, R. T.; Brook, J. R.; Yung, W. T.; Dales, R. E.; Krewski, D. (1997a) Association
24 between ozone and hospitalization for respiratory diseases in 16 Canadian cities. Environ.
25 Res. 72:24-31.
26 Burnett, R. T.; Cakmak, S.; Brook, J. R.; Krewski, D. (1997b) The role of particulate size and
27 chemistry in the association between summertime ambient air pollution and
28 hospitalization for cardiorespiratory diseases. Environ. Health Perspect. 105: 614-620.
29 Burnett, R. T.; Cakmak, S.; Brook, J. R. (1998a) The effect of the urban ambient air pollution
30 mix on daily mortality rates in 11 Canadian cities. Can. J. Public Health 89: 152-156.
31 Burnett, R. T.; Cakmak, S.; Raizenne, M. E.; Stieb, D.; Vincent, R.; Krewski, D.; Brook, J. R.;
32 Philips, O.; Ozkaynak, H. (1998b) The association between ambient carbon monoxide
33 levels and daily mortality in Toronto, Canada. J. Air Waste Manage. Assoc. 48: 689-700.
34 Burnett, R. T.; Smith-Doiron, M.; Stieb, D.; Cakmak, S.; Brook, J. R. (1999) Effects of
35 particulate and gaseous air pollution on cardiorespiratory hospitalizations. Arch. Environ.
36 Health 54: 130-139.
37 Burnett, R. T.; Smith-Doiron, M.; Stieb, D.; Raizenne, M. E.; Brook, J. R.; Dales, R. E.; Leech,
38 J. A.; Cakmak, S.; Krewski, D. (2001) Association between ozone and hospitalization for
39 acute respiratory diseases in children less than 2 years of age. Am. J. Epidemiol. 153:
40 444-452.
September 2007 AX5-238 DRAFT-DO NOT QUOTE OR CITE
-------
1 Burnett, R. T.; Stieb, D.; Brook, J. R.; Cakmak, S.; Dales, R.; Raizenne, M.; Vincent, R.; Dann,
2 T. (2004) Associations between short-term changes in nitrogen dioxide and mortality in
3 Canadian cities. Arch. Environ. Health 59: 228-236.
4 Cakmak, S.; Dales, R. E.; Vidal, C. B. (2007) Air pollution and mortality in Chile: susceptibility
5 among the elderly. Environ. Health Perspect. 115: 524-527.
6 Castellsague, J.; Sunyer, J.; Saez, M.; Anto, J. M. (1995) Short-term association between air
7 pollution and emergency room visits for asthma in Barcelona. Thorax 50: 1051-1056.
8 Chan, C.-C.; Chuang, K.-J.; Chien, L.-C.; Chen, W.-J.; Chang, W.-T. (2006) Urban air pollution
9 and emergency admissions for cerebrovascular diseases in Taipei, Taiwan. Eur. Heart J.
10 27: 1238-1244.
11 Chang, C.-C.; Tsai, S.-S.; Ho, S.-C.; Yang, C.-Y. (2005) Air pollution and hospital admissions
12 for cardiovascular disease in Taipei, Taiwan. Environ. Res. 98: 114-119.
13 Charpin, D.; Pascal, L.; Birnbaum, J.; Armengaud, A.; Sambuc, R.; Lanteaume, A.; Vervloet, D.
14 (1999) Gaseous air pollution and atopy. Clin. Exp. Allergy 29: 1474-1480.
15 Chen, P.-C.; Lai, Y.-M.; Chan, C.-C.; Hwang, J.-S.; Yang, C.-Y.; Wang, J.-D. (1999) Short-term
16 effect of ozone on the pulmonary function of children in primary school. Environ. Health
17 Perspect. 107: 921-925.
18 Chew, F. T.; Goh, D. Y. T.; Ooi, B. C.; Saharom, R.; Hui, J. K. S.; Lee, B. W. (1999)
19 Association of ambient air-pollution levels with acute asthma exacerbation among
20 children in Singapore. Allergy (Copenhagen) 54: 320-329.
21 Chock, D. P.; Winkler, S. L.; Chen, C. (2000) A study of the association between daily mortality
22 and ambient air pollutant concentrations in Pittsburgh, Pennsylvania. J. Air Waste
23 Manage. Assoc. 50: 1481-1500.
24 Cifuentes, L. A.; Vega, J.; Kopfer, K.; Lave, L. B. (2000) Effect of the fine fraction of parti culate
25 matter versus the coarse mass and other pollutants on daily mortality in Santiago, Chile.
26 J. Air Waste Manage. Assoc. 50: 1287-1298.
27 Clancy, L.; Goodman, P.; Sinclair, H; Dockery, D. W. (2002) Effect of air pollution control on
28 death rates in Dublin, Ireland: an intervention study. Lancet 360: 1210-1214.
29 Concei9ao, G. M. S.; Miraglia, S. G. E. K.; Kishi, H. S.; Saldiva, P. H. N.; Singer, J. M. (2001)
30 Air pollution and child mortality: a time-series study in Sao Paulo, Brazil. Environ.
31 Health Perspect. Suppl. 109(3): 347-350.
32 Cuijpers, C. E. J.; Swaen, G. M. H.; Wesseling, G.; Wouters, E. F. M. (1994) Acute respiratory
33 effects of summer smog in primary school children. Toxicol. Lett. 72: 227-235.
34 Dab, W.; Medina, S.; Quenel, P.; Le Moullec, Y.; Le Tertre, A.; Thelot, B.; Monteil, C.;
35 Lameloise, P.; Pirard, P.; Momas, I; Ferry, R.; Festy, B. (1996) Short term respiratory
36 health effects of ambient air pollution: results of the APHEA project in Paris. In: St
37 Leger, S., ed. The APHEA project. Short term effects of air pollution on health: a
38 European approach using epidemiological time series data. J. Epidemiol. Commun.
39 Health 50(suppl. 1): S42-S46.
September 2007 AX5-239 DRAFT-DO NOT QUOTE OR CITE
-------
1 Dales, R.; Burnett, R. T.; Smith-Doiron, M.; Stieb, D. M.; Brook, J. R. (2004) Air pollution and
2 sudden infant death syndrome. Pediatrics 113: 628-631.
3 Dales, R. E.; Cakmak, S.; Doiron, M. S. (2006) Gaseous air pollutants and hospitalization for
4 respiratory disease in the neonatal period. Environ. Health Perspect. 114: 1751-1754.
5 De Diego Damia, A.; Fabregas, M. L.; Tordera, M. P.; Torrero, L. C. (1999) Effects of air
6 pollution and weather conditions on asthma exacerbation. Respiration 66: 52-58.
7 De Leon, S. F.; Thurston, G. D.; Ito, K. (2003) Contribution of respiratory disease to
8 nonrespiratory mortality associations with air pollution. Am. J. Respir. Crit. Care Med.
9 167:1117-1123.
10 Delfmo, R. J.; Gone, H.; Linn, W. S.; Pellizzari, E. D.; Hu, Y. (2003) Asthma symptoms in
11 Hispanic children and daily ambient exposures to toxic and criteria air pollutants.
12 Environ. Health Perspect. Ill: 647-656.
13 Desqueyroux, H.; Pujet, J.-C.; Prosper, M.; Squinazi, F.; Momas, I. (2002) Short-term effects of
14 low-level air pollution on respiratory health of adults suffering from moderate to severe
15 asthma. Environ. Res. A 89: 29-37.
16 Diaz, J.; Garcia, R.; Ribera, P.; Alberdi, J. C.; Hernandez, E.; Pajares, M. S.; Otero, A. (1999)
17 Modeling of air pollution and its relationship with mortality and morbidity in Madrid,
18 Spain. Int. Arch. Occup. Environ. Health 72: 366-376.
19 D'Ippoliti, D.; Forastiere, F.; Ancona, C.; Agabiti, N.; Fusco, D.; Michelozzi, P.; Perucci, C. A.
20 (2003) Air pollution and myocardial infarction in Rome: a case-crossover analysis.
21 Epidemiology 14: 528-535.
22 Dockery, D. W.; Speizer, F. E.; Stram, D. O.; Ware, J. H.; Spengler, J. D.; Ferris, B. G., Jr.
23 (1989) Effects of inhalable particles on respiratory health of children. Am. Rev. Respir.
24 Dis. 139: 587-594.
25 Dockery, D. W.; Schwartz, J.; Spengler, J. D. (1992) Air pollution and daily mortality:
26 associations with particulates and acid aerosols. Environ. Res. 59: 362-373.
27 Dockery, D. W.; Pope, C. A., Ill; Xu, X.; Spengler, J. D.; Ware, J. H.; Fay, M. E.; Ferris, B. G,
28 Jr.; Speizer, F. E. (1993) An association between air pollution and mortality in six U.S.
29 cities. N. Engl. J. Med. 329: 1753-1759.
30 Dockery, D. W.; Cunningham, J.; Damokosh, A. L; Neas, L. M.; Spengler, J. D.; Koutrakis, P.;
31 Ware, J. H.; Raizenne, M.; Speizer, F. E. (1996) Health effects of acid aerosols on North
32 American children: respiratory symptoms. Environ. Health Perspect. 104: 500-505.
33 Dockery, D. W.; Luttmann-Gibson, H.; Rich, D. Q.; Link, M. S.; Mittleman, M. A.; Gold, D. R.;
34 Koutrakis, P.; Schwartz, J. D.; Verrier, R. L. (2005) Association of air pollution with
35 increased incidence of ventricular tachyarrhythmias recorded by implanted cardioverter
36 defibrillators. Environ. Health Perspect. 113: 670-674.
37 Dominici, F.; McDermott, A.; Daniels, M.; Zeger, S. L.; Samet, J. M. (2003) Mortality among
38 residents of 90 cities. In: Revised analyses of time-series studies of air pollution and
39 health. Special report. Boston, MA: Health Effects Institute; pp. 9-24. Available:
40 http://www.healtheffects.org/Pubs/TimeSeries.pdf [12 May, 2004].
September 2007 AX5-240 DRAFT-DO NOT QUOTE OR CITE
-------
1 Dugandzic, R. Dodds, L.; Stieb, D.; Smith-Doiron, M. (2006) The association between low level
2 exposures to ambient air pollution and term low birth weight: a retrospective cohort
3 study. Environ. Health 5: 3. Available: http://www.ehjournal.net/content/5/l/3 [19
4 September, 2007].
5 Elliott, P.; Shaddick, G.; Wakefield, J. C.; Hoogh, C. de; Briggs, D. J. (2007) Long-term
6 associations of outdoor air pollution with mortality in Great Britain. Thorax
7 10.1136/thx.2006.076851.
8 Euler, G. L.; Abbey, D. E.; Magie, A. R.; Hodgkin, J. E. (1987) Chronic obstructive pulmonary
9 disease symptom effects of long-term cumulative exposure to ambient levels of total
10 suspended particulates and sulfur dioxide in California Seventh-Day Adventist residents.
11 Arch. Environ. Health 42: 213-222.
12 Farhat, S. C. L.; Paulo, R. L. P.; Shimoda, T. M.; Conceicao, G. M. S.; Lin, C. A.; Braga, A. L.
13 F.; Warm, M. P. N.; Saldiva, P. H. N. (2005) Effect of air pollution on pediatric
14 respiratory emergency room visits and hospital admissions. Braz. J. Med. Biol. Res. 38:
15 227-235.
16 Filleul, L.; Rondeau, V.; Vandentorren, S.; Le Moual, N.; Cantagrel, A.; Annesi-Maesano, L;
17 Charpin, D.; Declercq, C.; Neukirch, F.; Paris, C.; Vervloet, D.; Brochard, P.; Tessier, J.
18 F.; Kauffmann, F.; Baldi, I. (2005) Twenty five year mortality and air pollution: results
19 from the French PAARC survey. Occup. Environ. Med. 62: 453-460.
20 Fischer, P.; Hoek, G.; Brunekreef, B.; Verhoeff, A.; van Wijnen, J. (2003) Air pollution and
21 mortality in the Netherlands: are the elderly more at risk? Eur. Respir. J. 21(suppl. 40):
22 34S-38S.
23 Forsberg, B.; Stjernberg, N.; Falk, M.; Lundback, B.; Wall, S. (1993) Air pollution levels,
24 meteorological conditions and asthma symptoms. Eur. Respir. J. 6: 1109-1115.
25 Frischer, T.; Studnicka, M.; Gartner, C.; Tauber, E.; Horak, F.; Veiter, A.; Spengler, J.; Kiihr, J.;
26 Urbanek, R. (1999) Lung function growth and ambient ozone: a three-year population
27 study in school children. Am. J. Respir. Crit. Care Med. 160: 390-396.
28 Frischer, T.; Studnicka, M.; Halmerbauer, G.; Horak, F.; Gartner, C.; Tauber, E.; Roller, D. Y.
29 (2001) Ambient ozone exposure is associated with eosinophil activation in healthy
30 children. Clin. Exp. Allergy 31: 1213-1219.
31 Frye, C.; Hoelscher, B.; Cyrys, J.; Wjst, M.; Wichmann, H. E.; Heinrich, J. (2003) Association of
32 lung function with declining ambient air pollution. Environ. Health Perspect. Ill: 383-
33 387.
34 Fung, K. Y.; Luginaah, L; Gorey, K. M.; Webster, G. (2005) Air pollution and daily hospital
35 admissions for cardiovascular diseases in Windsor, Ontario. Can. J. Public Health 96: 29-
36 33.
37 Fung, K. Y.; Khan, S.; Krewski, D.; Chen, Y. (2006) Association between air pollution and
38 multiple respiratory hospitalizations among the elderly in Vancouver, Canada. Inhalation
39 Toxicol. 18: 1005-1011.
September 2007 AX5-241 DRAFT-DO NOT QUOTE OR CITE
-------
1 Fusco, D.; Forastiere, F.; Michelozzi, P.; Spadea, T.; Ostro, B.; Area, M.; Perucci, C. A. (2001)
2 Air pollution and hospital admissions for respiratory conditions in Rome, Italy. Eur.
3 Respir. J. 17: 1143-1150.
4 Galan, I.; Tobias, A.; Banegas, J. R.; Aranguez, E. (2003) Short-term effects of air pollution on
5 daily asthma emergency room admissions. Eur. Respir. J. 22: 802-808.
6 Gamble, J. L. (1998) Effects of ambient air pollution on daily mortality: a time series analysis of
7 Dallas, Texas, 1990-1994. Presented at: 91st annual meeting and exhibition of the Air &
8 Waste Management Association; June; San Diego, CA. Pittsburgh, PA: Air & Waste
9 Management Association; paper no. 98-MP26.03.
10 Garcia-Aymerich, J.; Tobias, A.; Anto, J. M.; Sunyer, J. (2000) Air pollution and mortality in a
11 cohort of patients with chronic obstructive pulmonary disease: a time series analysis. J.
12 Epidemiol. Community Health 54: 73-74.
13 Garcia-Marcos, L.; Guillen, J. J.; Dinwiddie, R.; Guillen, A.; Barbero, P. (1999) The relative
14 importance of socio-economic status, parental smoking and air pollution (802) on asthma
15 symptoms, spirometry and bronchodilator response in 11-year-old children. Pediatr.
16 Allergy Immunol. 10: 96-100.
17 Garty, B. Z.; Kosman, E.; Ganor, E.; Berger, V.; Garty, L.; Wietzen, T.; Waisman, Y.; Mimouni,
18 M.; Waisel, Y. (1998) Emergency room visits of asthmatic children, relation to air
19 pollution, weather, and airborne allergens. Ann. Allergy Asthma Immunol. 81: 563-570.
20 Gilboa, S. M.; Mendola, P.; Olshan, A. F.; Langlois, P. H.; Savitz, D. A.; Loomis, D.; Herring,
21 A. H.; Fixler, D. E. (2005) Relation between ambient air quality and selected birth
22 defects, seven county study, Texas, 1997-2000. Am. J. Epidemiol. 162: 238-252.
23 Gokirmak, M.; Yildirim, Z.; Hasanoglu, H. C.; Koksal, N.; Mehmet, N. (2003) The role of
24 oxidative stress in bronchoconstriction due to occupational sulfur dioxide exposure. Clin.
25 Chim. Acta 331: 119-126.
26 Gold, D. R.; Litonjua, A.; Schwartz, J.; Lovett, E.; Larson, A.; Nearing, B.; Allen, G.; Verrier,
27 M.; Cherry, R.; Verrier, R. (2000) Ambient pollution and heart rate variability.
28 Circulation 101: 1267-1273.
29 Goldberg, M. S.; Burnett, R. T. (2003) Revised analysis of the Montreal time-series study. In:
30 Revised analyses of time-series studies of air pollution and health. Special report. Boston,
31 MA: Health Effects Institute; pp. 113-132. Available:
32 http://www.healtheffects.org/Pubs/TimeSeries.pdf [18 October, 2004].
33 Goss, C. H.; Newsom, S. A.; Schildcrout, J. S.; Sheppard, L.; Kaufman, J. D. (2004) Effect of
34 ambient air pollution on pulmonary exacerbations and lung function in cystic fibrosis.
35 Am. J. Respir. Crit. Care Med. 169: 816-821.
36 Gouveia, N.; Fletcher, T. (2000) Respiratory diseases in children and outdoor air pollution in Sao
37 Paulo, Brazil: a time series analysis. Occup. Environ. Med. 57: 477-483.
38 Gouveia, N.; Bremner, S. A.; Novaes, H. M. D. (2004) Association between ambient air
39 pollution and birth weight in Sao Paulo, Brazil. J. Epidemiol. Community Health 58: 11-
40 17.
September 2007 AX5-242 DRAFT-DO NOT QUOTE OR CITE
-------
1 Gwynn, R. C.; Burnett, R. T.; Thurston, G. D. (2000) A time-series analysis of acidic particulate
2 matter and daily mortality and morbidity in the Buffalo, New York, region. Environ.
3 Health Perspect. 108: 125-133.
4 Ha, E.-H.; Hong, Y.-C.; Lee, B.-E.; Woo, B.-H.; Schwartz, I; Christian!, D. C. (2001) Is air
5 pollution a risk factor for low birth weight in Seoul? Epidemiology 12: 643-648.
6 Ha, E.-H.; Lee, J.-T.; Kim, H.; Hong, Y.-C.; Lee, B.-E.; Park, H.-S.; Christiani, D. C. (2003)
7 Infant susceptibility of mortality to air pollution in Seoul, South Korea. Pediatrics 111:
8 284-290.
9 Hagen, J. A.; Nafstad, P.; Skrondal, A.; Bj0rkly, S.; Magnus, P. (2000) Associations between
10 outdoor air pollutants and hospitalization for respiratory diseases. Epidemiology 11: 136-
11 140.
12 Hajat, S.; Haines, A.; Goubet, S. A.; Atkinson, R. W.; Anderson, H. R. (1999) Association of air
13 pollution with daily GP consultations for asthma and other lower respiratory conditions in
14 London. Thorax 54: 597-605.
15 Hajat, S.; Haines, A.; Atkinson, R. W.; Bremner, S. A.; Anderson, H. R.; Emberlin, J. (2001)
16 Association between air pollution and daily consultations with general practitioners for
17 allergic rhinitis in London, United Kingdom. Am. J. Epidemiol. 153: 704-714.
18 Hajat, S.; Anderson, H. R.; Atkinson, R. W.; Haines, A. (2002) Effects of air pollution on
19 general practitioner consultations for upper respiratory diseases in London. Occup.
20 Environ. Med. 59: 294-299.
21 Heinrich, J.; Hoelscher, B.; Frye, C.; Meyer, I; Pitz, M.; Cyrys, J.; Wjst, M.; Neas, L.;
22 Wichmann, H.-E. (2002) Improved air quality in reunified Germany and decreases in
23 respiratory symptoms. Epidemiology 13: 394-401.
24 Herbarth, O.; Fritz, G.; Krumbiegel, P.; Diez, U.; Franck, U.; Richter, M. (2001) Effect of sulfur
25 dioxide and particulate pollutants on bronchitis in children-a risk analysis. Environ.
26 Toxicol. 16: 269-276.
27 Higgins, B. G.; Francis, H. C.; Yates, C. J.; Warburton, C. J.; Fletcher, A. M.; Reid, J. A.;
28 Pickering, C. A. C.; Woodcock, A. A. (1995) Effects of air pollution on symptoms and
29 peak expiratory flow measurements in subjects with obstructive airways disease. Thorax
30 50: 149-155.
31 Hiltermann, T. J. N.; Stolk, J.; Van Der Zee, S. C.; Brunekreef, B.; De Bruijne, C. R.; Fischer, P.
32 H.; Ameling, C. B.; Sterk, P. J.; Hiemstra, P. S.; Van Bree, L. (1998) Asthma severity
33 and susceptibility to air pollution. Eur. Respir. J. 11: 686-693.
34 Hirsch, T.; Weiland, S. K.; Von Mutius, E.; Safeca, A. F.; Grafe, H.; Csaplovics, E.; Duhme, H.;
35 Keil, U.; Leupold, W. (1999) Inner city air pollution and respiratory health and atopy in
36 children. Eur. Respir. J. 14: 669-677.
37 Hoek, G. (2003) Daily mortality and air pollution in The Netherlands. In: Revised analyses of
38 time-series studies of air pollution and health. Special report. Boston, MA: Health Effects
39 Institute; pp. 133-141. Available: http://www.healtheffects.org/Pubs/TimeSeries.pdffl2
40 May, 2004].
September 2007 AX5-243 DRAFT-DO NOT QUOTE OR CITE
-------
1 Hoek, G.; Brunekreef, B. (1993) Acute effects of a winter air pollution episode on pulmonary
2 function and respiratory symptoms of children. Arch. Environ. Health 48: 328-335.
3 Hoek, G.; Brunekreef, B. (1995) Effect of photochemical air pollution on acute respiratory
4 symptoms in children. Am. J. Respir. Crit. Care Med. 151: 27-32.
5 Hoek, G.; Brunekreef, B.; Verhoeff, A.; Van Wijnen, J.; Fischer, P. (2000) Daily mortality and
6 air pollution in the Netherlands. J. Air Waste Manage. Assoc. 50: 1380-1389.
7 Hoek, G.; Brunekreef, B.; Fischer, P.; Van Wijnen, J. (2001) The association between air
8 pollution and heart failure, arrhythmia, embolism, thrombosis, and other cardiovascular
9 causes of death in a time series study. Epidemiology 12: 355-357.
10 Hoek, G.; Brunekreef, B.; Goldbohm, S.; Fischer, P.; Van den Brandt, P. A. (2002) Association
11 between mortality and indicators of traffic-related air pollution in the Netherlands: a
12 cohort study. Lancet 360: 1203-1209.
13 Holguin, F.; Tellez-Rojo, M. M.; Hernandez, M.; Cortez, M.; Chow, J. C.; Watson, J. G.;
14 Mannino, D.; Romieu, I. (2003) Air pollution and heart rate variability among the elderly
15 in Mexico City. Epidemiology 14: 521-527.
16 Hong, Y.-C.; Lee, J.-T.; Kim, H.; Kwon, H.-J. (2002) Air pollution: a new risk factor in ischemic
17 stroke mortality. Stroke 33: 2165-2169.
18 Horak, F., Jr.; Studnicka, M.; Gartner, C.; Spengler, J. D.; Tauber, E.; Urbanek, R.; Veiter, A.;
19 Frischer, T. (2002) Particulate matter and lung function growth in children: a 3-yr follow-
20 up study in Austrian schoolchildren. Eur. Respir. J. 19: 838-845.
21 Hosseinpoor, A. R.; Forouzanfar, M. H.; Yunesian, M.; Asghari, F.; Naieni, K. H.; Farhood, D.
22 (2005) Air pollution and hospitalization due to angina pectoris in Tehran, Iran: a time-
23 series study. Environ. Res. 99: 126-131.
24 Hwang, J.-S.; Chan, C.-C. (2002) Effects of air pollution on daily clinic visits for lower
25 respiratory tract illness. Am. J. Epidemiol. 155: 1-10.
26 Ibald-Mulli, A.; Stieber, J.; Wichmann, H.-E.; Koenig, W.; Peters, A. (2001) Effects of air
27 pollution on blood pressure: a population-based approach. Am. J. Public Health 91: 571-
28 577.
29 Ilabaca, M.; Olaeta, I; Campos, E.; Villaire, J.; Tellez-Rojo, M. M.; Romieu, I. (1999)
30 Association between levels of fine paniculate and emergency visits for pneumonia and
31 other respiratory illnesses among children in Santiago, Chile. J. Air Waste Manage.
32 Assoc. 49: 154-163.
33 Ito, K. (2003) Associations of particulate matter components with daily mortality and morbidity
34 in Detroit, Michigan. In: Revised analyses of time-series studies of air pollution and
35 health. Special report. Boston, MA: Health Effects Institute; pp. 143-156. Available:
36 http://www.healtheffects.org/Pubs/TimeSeries.pdf [12 May, 2004].
37 Jaffe, D. H.; Singer, M. E.; Rimm, A. A. (2003) Air pollution and emergency department visits
38 for asthma among Ohio Medicaid recipients, 1991-1996. Environ. Res. 91: 21-28.
39 Jalaludin, B.; Morgan, G; Lincoln, D.; Sheppeard, V.; Simpson, R.; Corbett, S. (2006)
40 Associations between ambient air pollution and daily emergency department attendances
September 2007 AX5-244 DRAFT-DO NOT QUOTE OR CITE
-------
1 for cardiovascular disease in the elderly (65+ years), Sydney, Australia. J. Exposure Sci.
2 Environ. Epidemiol. 16: 225-237.
3 Jerrett, M.; Burnett, R. T.; Willis, A.; Krewski, D.; Goldberg, M. S.; DeLuca, P.; Finkelstein, N.
4 (2003) Spatial analysis of the air pollution-mortality relationship in the context of
5 ecologic confounders. J. Toxicol. Environ. Health A 66: 1735-1777.
6 Katsouyanni, K.; Touloumi, G.; Spix, C.; Schwartz, J.; Balducci, F.; Medina, S.; Rossi, G.;
7 Wojtyniak, B.; Sunyer, J.; Bacharova, L.; Schouten, J. P.; Ponka, A.; Anderson, H. R.
8 (1997) Short term effects of ambient sulphur dioxide and particulate matter on mortality
9 in 12 European cities: results from time series data from the APFIEA project. Br. Med. J.
10 314: 1658-1663.
11 Kelsall, J. E.; Samet, J. M.; Zeger, S. L.; Xu, J. (1997) Air pollution and mortality in
12 Philadelphia, 1974-1988. Am. J. Epidemiol. 146: 750-762.
13 Kesten, S.; Szalai, J.; Dzyngel, B. (1995) Air quality and the frequency of emergency room visits
14 for asthma. Ann. Allergy Asthma Immunol. 74: 269-273.
15 Kinney, P. L.; Ozkaynak, H. (1991) Associations of daily mortality and air pollution in Los
16 Angeles County. Environ. Res. 54: 99-120.
17 Koken, P. J. M.; Piver, W. T.; Ye, F.; Elixhauser, A.; Olsen, L. M.; Portier, C. J. (2003)
18 Temperature, air pollution, and hospitalization for cardiovascular diseases among elderly
19 people in Denver. Environ. Health Perspect. Ill: 1312-1317.
20 Koksal, N.; Yildirim, Z.; Gokirmak, M.; Hasanoglu, H. C.; Mehmet, N.; Avci, H. (2003) The
21 role of nitric oxide and cytokines in asthma-like syndrome induced by sulfur dioxide
22 exposure in agricultural environment. Clin. Chim. Acta 336: 115-122.
23 Kopp, M. V.; Ulmer, C.; Ihorst, G.; Seydewitz, H. H.; Frischer, T.; Forster, J.; Kuehr, J. (1999)
24 Upper airway inflammation in children exposed to ambient ozone and potential signs of
25 adaptation. Eur. Respir. J. 14: 854-861.
26 Kopp, M. V.; Bohnet, W.; Frischer, T.; Ulmer, C.; Studnicka, M.; Ihorst, G.; Gardner, C.;
27 Forster, J.; Urbanek, R.; Kuehr, J. (2000) Effects of ambient ozone on lung function in
28 children over a two-summer period. Eur. Respir. J. 16: 893-900.
29 Kotesovec, F.; Skorkovsky, J.; Brynda, J.; Peters, A.; Heinrich, J. (2000) Daily mortality and air
30 pollution in northern Bohemia; different effects for men and women. Cent. Eur. J. Public
31 Health 8: 120-127.
32 Kramer, U.; Behrendt, H.; Dolgner, R.; Ranft, U.; Ring, J.; Wilier, H.; Schlipkoter, H.-W. (1999)
33 Airway diseases and allergies in East and West German children during the first 5 years
34 after reunification: time trends and the impact of sulphur dioxide and total suspended
35 particles. Int. J. Epidemiol. 28: 865-873.
36 Krewski, D.; Burnett, R. T.; Goldberg, M. S.; Hoover, K.; Siemiatycki, J.; Jerrett, M.;
37 Abrahamowicz, M.; White, W. H. (2000) Reanalysis of the Harvard Six Cities study and
38 the American Cancer Society study of parti culate air pollution and mortality: a special
39 report of the Institute's Particle Epidemiology Reanalysis Project. Cambridge, MA:
40 Health Effects Institute. Available: http://pubs.healtheffects.org/view.php?id=6 [6 March,
41 2007].
September 2007 AX5-245 DRAFT-DO NOT QUOTE OR CITE
-------
1 Kwon, H.-J.; Cho, S.-H.; Nyberg, F.; Pershagen, G. (2001) Effects of ambient air pollution on
2 daily mortality in a cohort of patients with congestive heart failure. Epidemiology 12:
3 413-419.
4 Lagorio, S.; Forastiere, F.; Pistelli, R.; lavarone, I.; Michelozzi, P.; Fano, V.; Marconi, A.;
5 Ziemacki, G.; Ostro, B. D. (2006) Air pollution and lung function among susceptible
6 adult subjects: a panel study. Environ. Health 5:11. Available:
7 http://www.ehjournal.net/content/5/l/ll [16 January, 2006].
8 Lee, J.-T.; Schwartz, J. (1999) Reanalysis of the effects of air pollution on daily mortality in
9 Seoul, Korea: a case-crossover design. Environ. Health Perspect. 107: 633-636.
10 Lee, J.-T.; Shin, D.; Chung, Y. (1999) Air pollution and daily mortality in Seoul and Ulsan,
11 Korea. Environ. Health Perspect. 107: 149-154.
12 Lee, J.-T.; Kim, H.; Hong, Y.-C.; Kwon, H.-J.; Schwartz, J.; Christiani, D. C. (2000) Air
13 pollution and daily mortality in seven major cities of Korea, 1991-1997. Environ. Res.
14 84:247-254.
15 Lee, J.-T.; Kim, H.; Song, H.; Hong, Y.-C.; Cho, Y.-S.; Shin, S.-Y.; Hyun, Y.-J.; Kim, Y.-S.
16 (2002) Air pollution and asthma among children in Seoul, Korea. Epidemiology 13: 481-
17 484.
18 Lee, J.-T.; Kim, H.; Cho, Y.-S.; Hong, Y.-C.; Ha, E.-H.; Park, H. (2003) Air pollution and
19 hospital admissions for ischemic heart diseases among individuals 64+ years of age
20 residing in Seoul, Korea. Arch. Environ. Health 58: 617-623.
21 Lee, S. L.; Wong, W. H. S.; Lau, Y. L. (2006) Association between air pollution and asthma
22 admission among children in Hong Kong. Clin. Exp. Allergy 36: 1138-1146.
23 Leem, J.-H.; Kaplan, B. M.; Shim, Y. K.; Pohl, H. R.; Gotway, C. A.; Bullard, S. M.; Rogers, J.
24 F.; Smith, M. M.; Tylenda, C. A. (2006) Exposures to air pollutants during pregnancy
25 and preterm delivery. Environ. Health Perspect. 114: 905-910.
26 Le Tertre, A.; Quenel, P.; Eilstein, D.; Medina, S.; Prouvost, H.; Pascal, L.; Boumghar, A.;
27 Saviuc, P.; Zeghnoun, A.; Filleul, L.; Declercq, C.; Cassadou, S.; Le Goaster, C. (2002)
28 Short-term effects of air pollution on mortality in nine French cities: a quantitative
29 summary. Arch. Environ. Health 57: 311-319.
30 Liao, D.; Duan, Y.; Whitsel, E. A.; Zheng, Z.-J.; Heiss, G; Chinchilli, V. M.; Lin, H.-M. (2004)
31 Association of higher levels of ambient criteria pollutants with impaired cardiac
32 autonomic control: a population-based study. Am. J. Epidemiol. 159: 768-777.
33 Liao, D.; Heiss, G.; Chinchilli, V. M.; Duan, Y.; Folsom, A. R.; Lin, H. M.; Salomaa, V. (2005)
34 Association of criteria pollutants with plasma hemostatic/inflammatory markers: a
35 population-based study. J. Exposure Anal. Environ. Epidemiol. 15: 319-328.
36 Lin, C. A.; Martins, M. A.; Farhat, S. C. L.; Pope, C. A., Ill; Concei9ao, G. M. S.; Anastacio, V.
37 M.; Hatanaka, M.; Andrade, W. C.; Hamaue, W. R.; Bohm, G. M.; Saldiva, P. H. N.
38 (1999) Air pollution and respiratory illness of children in Sao Paulo, Brazil. Paediatr.
39 Perinat. Epidemiol. 13: 475-488.
September 2007 AX5-246 DRAFT-DO NOT QUOTE OR CITE
-------
1 Lin, M.; Chen, Y.; Burnett, R. T.; Villeneuve, P. J.; Krewski, D. (2003) Effect of short-term
2 exposure to gaseous pollution on asthma hospitalisation in children: a bi-directional case-
3 crossover analysis. J. Epidemiol. Community Health 57: 50-55.
4 Lin, S.; Hwang, S.-A.; Pantea, C.; Kielb, C.; Fitzgerald, E. (2004a) Childhood asthma
5 hospitalizations and ambient air sulfur dioxide concentrations in Bronx County, New
6 York. Arch. Environ. Health 59: 266-275.
7 Lin, M.; Chen, Y.; Villeneuve, P. J.; Burnett, R. T.; Lemyre, L.; Hertzman, C.; McGrail, K. M.;
8 Krewski, D. (2004b) Gaseous air pollutants and asthma hospitalization of children with
9 low household income in Vancouver, British Columbia, Canada. Am. J. Epidemiol. 159:
10 294-303.
11 Lin, C.-M.; Li, C.-Y.; Mao, I.-F. (2004c) Increased risks of term low-birth-weight infants in a
12 petrochemical industrial city with high air pollution levels. Arch. Environ. Health 59:
13 663-668.
14 Lin, C.-M.; Li, C.-Y.; Yang, G.-Y.; Mao, I-F. (2004d) Association between maternal exposure to
15 elevated ambient sulfur dioxide during pregnancy and term low birth weight. Environ.
16 Res. 96: 41-50.
17 Lin, M.; Stieb, D. M.; Chen, Y. (2005) Coarse particulate matter and hospitalization for
18 respiratory infections in children younger than 15 years in Toronto: a case-crossover
19 analysis. Pediatrics 116: 235-240.
20 Lipfert, F. W.; Morris, S. C.; Wyzga, R. E. (2000a) Daily mortality in the Philadelphia
21 metropolitan area and size-classified particulate matter. J. Air Waste Manage. Assoc. 50:
22 1501-1513.
23 Lipfert, F. W.; Perry, H. M., Jr.; Miller, J. P.; Baty, J. D.; Wyzga, R. E.; Carmody, S. E. (2000b)
24 The Washington University-EPRI veterans' cohort mortality study: preliminary results.
25 In: Grant, L. D., ed. PM2000: particulate matter and health. Inhalation Toxicol. 12(suppl.
26 4): 41-73.
27 Lipfert, F. W.; Perry, H. M., Jr.; Miller, J. P.; Baty, J. D.; Wyzga, R. E.; Carmody, S. E. (2003)
28 Air pollution, blood pressure, and their long-term associations with mortality. Inhalation
29 Toxicol. 15:493-512.
30 Lipfert, F. W.; Wyzga, R. E.; Baty, J. D.; Miller, J. P. (2006a) Traffic density as a surrogate
31 measure of environmental exposures in studies of air pollution health effects: long-term
32 mortality in a cohort of US veterans. Atmos. Environ. 40: 154-169.
33 Lipfert, F. W.; Baty, J. D.; Miller, J. P.; Wyzga, R. E. (2006b) PM2.5 constituents and related air
34 quality variables as predictors of survival in a cohort of U.S. military veterans. Inhalation
35 Toxicol. 18: 645-657.
36 Liu, S.; Krewski, D.; Shi, Y.; Chen, Y.; Burnett, R. T. (2003) Association between gaseous
37 ambient air pollutants and adverse pregnancy outcomes in Vancouver, Canada. Environ.
38 HealthPerspect. Ill: 1773-1778.
39 Liu, S.; Krewski, D.; Shi, Y.; Chen, Y.; Burnett, R. (2006) Association between maternal
40 exposure to ambient air pollutants during pregnancy and fetal growth restriction. J.
41 Exposure Sci. Environ. Epidemiol.: 10.1038/sj.jes.7500503.
September 2007 AX5-247 DRAFT-DO NOT QUOTE OR CITE
-------
1 Llorca, J.; Salas, A.; Prieto-Salceda, D.; Chinchon-Bengoechea, V.; Delgado-Rodriguez, M.
2 (2005) Nitrogen dioxide increases cardiorespiratory admissions in Torrelavega (Spain). J.
3 Environ. Health 68: 30-35.
4 Loomis, D.; Castillejos, M.; Gold, D. R.; McDonnell, W.; Borja-Aburto, V. H. (1999) Air
5 pollution and infant mortality in Mexico City. Epidemiology 10: 118-123.
6 Low, R. B.; Bielory, L.; Qureshi, A. I; Dunn, V.; Stuhlmiller, D. F.; Dickey, D. A. (2006) The
7 relation of stroke admissions to recent weather, airborne allergens, air pollution, seasons,
8 upper respiratory infections, and asthma incidence, September 11, 2001, and day of the
9 week. Stroke 37: 951-957.
10 Luginaah, I. N.; Fung, K. Y.; Gorey, K. M.; Webster, G.; Wills, C. (2005) Association of
11 ambient air pollution with respiratory hospitalization in a government designated "area of
12 concern": the case of Windsor, Ontario. Environ. Health Perspect. 113: 290-296.
13 Maisonet, M.; Bush, T. J.; Correa, A.; Jaakkola, J. J. K. (2001) Relation between ambient air
14 pollution and low birth weight in the northeastern United States. Environ. Health
15 Perspect. Suppl. 109(3): 351-356.
16 Mar, T. F.; Norris, G. A.; Koenig, J. Q.; Larson, T. V. (2000) Associations between air pollution
17 and mortality in Phoenix, 1995-1997. Environ. Health Perspect. 108: 347-353.
18 Mar, T. F.; Norris, G. A.; Larson, T. V.; Wilson, W. E.; Koenig, J. Q. (2003) Air pollution and
19 cardiovascular mortality in Phoenix, 1995-1997. In: Revised analyses of time-series
20 studies of air pollution and health. Special report. Boston, MA: Health Effects Institute;
21 pp. 177-182. Available: http://www.healtheffects.org/Pubs/TimeSeries.pdffl8 October,
22 2004].
23 Martins, L. C.; Latorre, M. R. D. O.; Saldiva, P. H. N.; Braga, A. L. F. (2002) Air pollution and
24 emergency room visits due to chronic lower respiratory diseases in the elderly: an
25 ecological time-series study in Sao Paulo, Brazil. J. Occup. Environ. Med. 44: 622-627'.
26 McDonnell, W. F.; Abbey, D. E.; Nishino, N.; Lebowitz, M. D. (1999) Long-term ambient ozone
27 concentration and the incidence of asthma in nonsmoking adults: the Ahsmog study.
28 Environ. Res. 80: 110-121.
29 Metzger, K. B.; Tolbert, P. E.; Klein, M.; Peel, J. L.; Flanders, W. D.; Todd, K. H.; Mulholland,
30 J. A.; Ryan, P. B.; Frumkin , H. (2004) Ambient air pollution and cardiovascular
31 emergency department visits. Epidemiology 15: 46-56.
32 Michaud, J.-P.; Grove, J. S.; Krupitsky, D. (2004) Emergency department visits and "vog"-
33 related air quality in Hilo, Hawai'i. Environ. Res. 95: 11-19.
34 Michelozzi, P.; Forastiere, F.; Fusco, D.; Perucci, C. A.; Ostro, B.; Ancona, C.; Pallotti, G.
35 (1998) Air pollution and daily mortality in Rome, Italy. Occup. Environ. Med. 55: 605-
36 610.
37 Miller, K. A.; Siscovick, D. S.; Sheppard, L.; Shepherd, K.; Sullivan J. H.; Anderson, G. L.;
38 Kaufman, J. D. (2007) Long-term exposure to air pollution and incidence of
39 cardiovascular events in women. N. Engl. J. Med. 356: 447-458.
September 2007 AX5-248 DRAFT-DO NOT QUOTE OR CITE
-------
1 Mohorovic, L. (2004) First two months of pregnancy—critical time for preterm delivery and low
2 birthweight caused by adverse effects of coal combustion toxics. Early Human Dev. 80:
3 115-123.
4 Moolgavkar, S. H. (2000) Air pollution and hospital admissions for chronic obstructive
5 pulmonary disease in three metropolitan areas in the United States. In: Grant, L. D., ed.
6 PM2000: particulate matter and health. Inhalation Toxicol. 12(suppl. 4): 75-90.
7 Moolgavkar, S. H. (2003a) Air pollution and daily deaths and hospital admissions in Los
8 Angeles and Cook counties. In: Revised analyses of time-series studies of air pollution
9 and health. Special report. Boston, MA: Health Effects Institute; pp. 183-198. Available:
10 http://www.healtheffects.org/news.htm [16 May, 2003].
11 Moolgavkar, S. H. (2003b) Air pollution and daily mortality in two U.S. counties: season-
12 specific analyses and exposure-response relationships. Inhalation Toxicol. 15: 877-907.
13 Moolgavkar, S. H.; Luebeck, E. G.; Hall, T. A.; Anderson, E. L. (1995) Air pollution and daily
14 mortality in Philadelphia. Epidemiology 6: 476-484.
15 Moolgavkar, S. H.; Luebeck, E. G.; Anderson, E. L. (1997) Air pollution and hospital
16 admissions for respiratory causes in Minneapolis-St. Paul and Birmingham.
17 Epidemiology 8: 364-370.
18 Morris, R. D.; Naumova, E. N.; Munasinghe, R. L. (1995) Ambient air pollution and
19 hospitalization for congestive heart failure among elderly people in seven large US cities.
20 Am. J. Public Health 85: 1361-1365.
21 Mortimer, K. M.; Neas, L. M.; Dockery, D. W.; Redline, S.; Tager, I. B. (2002) The effect of air
22 pollution on inner-city children with asthma. Eur. Respir. J. 19: 699-705.
23 Nafstad, P.; Haheim, L. L.; Oftedal, B.; Gram, F.; Holme, L; Hjermann, L; Leren, P. (2003)
24 Lung cancer and air pollution: a 27 year follow up of 16,209 Norwegian men. Thorax 58:
25 1071-1076.
26 Nafstad, P.; Haheim, L. L.; Wisloff, T.; Gram, F.; Oftedal, B.; Holme, L; Hjermann, L; Leren, P.
27 (2004) Urban air pollution and mortality in a cohort of Norwegian men. Environ. Health
28 Perspect. 112: 610-605.
29 Neas, L. M.; Dockery, D. W.; Koutrakis, P.; Tollerud, D. J.; Speizer, F. E. (1995) The
30 association of ambient air pollution with twice daily peak expiratory flow rate
31 measurements in children. Am. J. Epidemiol. 141: 111-122.
32 Neukirch, F.; Segala, C.; Le Moullec, Y.; Korobaeff, M.; Aubier, M. (1998) Short-term effects of
33 low-level winter pollution on respiratory health of asthmatic adults. Arch. Environ.
34 Health 53: 320-328.
35 Newhouse, C. P.; Levetin, B. S.; Levetin, E. (2004) Correlation of environmental factors with
36 asthma and rhinitis symptoms in Tulsa, OK. Ann. Allergy Asthma Immunol. 92: 356-
37 366.
38 Nyberg, F.; Gustavsson, P.; Jarup, L.; Bellander, T.; Berglind, N.; Jakobsson, R.; Pershagen, G.
39 (2000) Urban air pollution and lung cancer in Stockholm. Epidemiology 11: 487-495.
September 2007 AX5-249 DRAFT-DO NOT QUOTE OR CITE
-------
1 Oftedal, B.; Nafstad, P.; Magnus, P.; Bj0rkly, S.; Skrondal, A. (2003) Traffic related air pollution
2 and acute hospital admission for respiratory diseases in Drammen, Norway 1995-2000.
3 Eur. J. Epidemiol. 18: 671-675.
4 Ostro, B.; Sanchez, J. M.; Aranda, C.; Eskeland, G. S. (1996) Air pollution and mortality: results
5 from a study of Santiago, Chile. In: Lippmann, M., ed. Papers from the ISEA-ISEE
6 annual meeting; September 1994; Research Triangle Park, NC. J. Exposure Anal.
7 Environ. Epidemiol. 6: 97-114.
8 Park, H.; Lee, B.; Ha, E.-H.; Lee, J.-T.; Kim, H.; Hong, Y.-C. (2002) Association of air pollution
9 with school absenteeism due to illness. Arch. Pediatr. Adolesc. Med. 156: 1235-1239.
10 Park, J. W.; Lim, Y. H.; Kyung, S. Y.; An, C. H.; Lee, S. P.; Jeong, S. H.; Ju, S.-Y. (2005a)
11 Effects of ambient paniculate matter on peak expiratory flow rates and respiratory
12 symptoms of asthmatics during Asian dust periods in Korea. Respirology 10: 470-476.
13 Park, S. K.; O'Neill, M. S.; Vokonas, P. S.; Sparrow, D.; Schwartz, J. (2005b) Effects of air
14 pollution on heart rate variability: the VA normative aging study. Environ. Health
15 Perspect. 113:304-309.
16 Peacock, J. L.; Symonds, P.; Jackson, P.; Bremner, S. A.; Scarlett, J. F.; Strachan, D. P.;
17 Anderson, H. R. (2003) Acute effects of winter air pollution on respiratory function in
18 schoolchildren in southern England. Occup. Environ. Med. 60: 82-89.
19 Peel, J. L.; Tolbert, P. E.; Klein, M.; Metzger, K. B.; Flanders, W. D.; Knox, T.; Mulholland, J.
20 A.; Ryan, P. B.; Frumkin, H. (2005) Ambient air pollution and respiratory emergency
21 department visits. Epidemiology 16: 164-174.
22 Peel, J. L.; Metzger, K. B.; Klein, M.; Flanders, W. D.; Mulholland, J. A.; Tolbert, P. E. (2007)
23 Ambient air pollution and cardiovascular emergency department visits in potentially
24 sensitive groups. Am. J. Epidemiol. 165: 625-633.
25 Penard-Morand, C.; Charpin, D.; Raherison, C.; Kopferschmitt, C.; Caillaud, D.; Lavaud, F.;
26 Annesi-Maesano, I. (2005) Long-term exposure to background air pollution related to
27 respiratory and allergic health in schoolchildren. Clin. Exp. Allergy 35: 1279-1287.
28 Pereira, L. A. A.; Loomis, D.; Concei9ao, G. M. S.; Braga, A. L. F.; Areas, R. M.; Kishi, H. S.;
29 Singer, J. M.; Bohm, G. M.; Saldiva, P. H. N. (1998) Association between air pollution
30 and intrauterine mortality in Sao Paulo, Brazil. Environ. Health Perspect. 106: 325-329.
31 Peters, A.; Goldstein, I. F.; Beyer, U.; Franke, K.; Heinrich, J.; Dockery, D. W.; Spengler, J. D.;
32 Wichmann, H.-E. (1996) Acute health effects of exposure to high levels of air pollution
33 in eastern Europe. Am. J. Epidemiol. 144: 570-581.
34 Peters, A.; Perz, S.; Doring, A.; Stieber, J.; Koenig, W.; Wichmann, H.-E. (1999) Increases in
35 heart rate during an air pollution episode. Am. J. Epidemiol. 150: 1094-1098.
36 Peters, A.; Liu, E.; Verrier, R. L.; Schwartz, J.; Gold, D. R.; Mittleman, M.; Baliff, J.; Oh, J. A.;
37 Allen, G.; Monahan, K.; Dockery, D. W. (2000a) Air pollution and incidence of cardiac
38 arrhythmia. Epidemiology 11: 11-17.
39 Peters, A.; Skorkovsky, J.; Kotesovec, F.; Brynda, J.; Spix, C.; Wichmann, H. E.; Heinrich, J.
40 (2000b) Associations between mortality and air pollution in central Europe. Environ.
41 Health Perspect. 108:283-287.
September 2007 AX5-250 DRAFT-DO NOT QUOTE OR CITE
-------
1 Peters, A.; Dockery, D. W.; Muller, J. E.; Mittleman, M. A. (2001) Increased particulate air
2 pollution and the triggering of myocardial infarction. Circulation 103: 2810-2815.
3 Petroeschevsky, A.; Simpson, R. W.; Thalib, L.; Rutherford, S. (2001) Associations between
4 outdoor air pollution and hospital admissions in Brisbane, Australia. Arch. Environ.
5 Health 56: 37-52.
6 Pikhart, H.; Bobak, M.; Gorynski, P.; Wojtyniak, B.; Danova, J.; Celko, M. A.; Kriz, B.; Briggs,
7 D.; Elliot, P. (2001) Outdoor sulphur dioxide and respiratory symptoms in Czech and
8 Polish school children: a small-area study (SAVIAH). Int. Arch. Occup. Environ. Health
9 74; 574-578.
10 Pino, P.; Walter, T.; Oyarzun, M.; Villegas, R.; Romieu, I. (2004) Fine particulate matter and
11 wheezing illnesses in the first year of life. Epidemiology 15: 702-708.
12 Pinter, A.; Rudnai, P.; Sarkany, E.; Goczan, M.; Paldy, A. (1996) Air pollution and childrens'
13 respiratory morbidity in the Tata area, Hungary. Cent. Eur. J. Public Health 4(suppl.): 17-
14 20.
15 Poloniecki, J. D.; Atkinson, R. W.; Ponce de Leon, A.; Anderson, H. R. (1997) Daily time series
16 for cardiovascular hospital admissions and previous day's air pollution in London, UK.
17 Occup. Environ. Med. 54: 535-540.
18 Ponce de Leon, A.; Anderson, H. R.; Bland, J. M.; Strachan, D. P.; Bower, J. (1996) Effects of
19 air pollution on daily hospital admissions for respiratory disease in London between
20 1987-88 and 1991-92. In: St Leger, S., ed. The APHEA project. Short term effects of air
21 pollution on health: a European approach using epidemiological time series data. J.
22 Epidemiol. Community Health 50(suppl. 1): S63-S70.
23 Ponka, A. (1990) Absenteeism and respiratory disease among children and adults in Helsinki in
24 relation to low-level air pollution and temperature. Environ. Res. 52: 34-46.
25 Ponka, A. (1991) Asthma and low level air pollution in Helsinki. Arch. Environ. Health 46: 262-
26 270.
27 Ponka, A.; Virtanen, M. (1994) Chronic bronchitis, emphysema, and low-level air pollution in
28 Helsinki, 1987-1989. Environ. Res. 65: 207-217.
29 Ponka, A.; Virtanen, M. (1996) Low-level air pollution and hospital admissions for cardiac and
30 cerebrovascular diseases in Helsinki. Am. J. Public Health 86: 1273-1280.
31 Ponka, A.; Savela, M.; Virtanen, M. (1998) Mortality and air pollution in Helsinki. Arch.
32 Environ. Health 53:281 -286.
33 Pope, C. A., Ill; Thun, M. J.; Namboodiri, M. M.; Dockery, D. W.; Evans, J. S.; Speizer, F. E.;
34 Heath, C. W., Jr. (1995) Particulate air pollution as a predictor of mortality in a
35 prospective study of U.S. adults. Am. J. Respir. Crit. Care Med. 151: 669-674.
36 Pope, C. A., Ill; Burnett, R. T.; Thun, M. J.; Calle, E. E.; Krewski, D.; Ito, K.; Thurston, G. D.
37 (2002) Lung cancer, cardiopulmonary mortality, and long-term exposure to fine
38 particulate air pollution. JAMA J. Am. Med. Assoc. 287: 1132-1141.
September 2007 AX5-251 DRAFT-DO NOT QUOTE OR CITE
-------
1 Prescott, G. I; Cohen, G. R.; Elton, R. A.; Fowkes, F. G. R.; Agius, R. M. (1998) Urban air
2 pollution and cardiopulmonary ill health: a 14.5 year time series study. Occup. Environ.
3 Med. 55: 697-704.
4 Rahlenbeck, S. I; Kahl, H. (1996) Air pollution and mortality in East Berlin during the winters
5 of 1981-1989. Int. J. Epidemiol. 25: 1220-1226.
6 Ramadour, M.; Burel, C.; Lanteaume, A.; Vervloet, D.; Charpin, D.; Brisse, F.; Dutau, H.;
7 Charpin, D. (2000) Prevalence of asthma and rhinitis in relation to long-term exposure to
8 gaseous air pollutants. Allergy (Copenhagen) 55: 1163-1169.
9 Rich, K. E.; Petkau, J.; Vedal, S.; Brauer, M. (2004) A case-crossover analysis of particulate air
10 pollution and cardiac arrhythmia in patients with implantable cardioverter defibrillators.
11 Inhalation Toxicol. 16: 363-372.
12 Rich, D. Q.; Schwartz, J.; Mittleman, M. A.; Link, M.; Luttmann-Gibson, H.; Catalano, P. J.;
13 Speizer, F. E.; Dockery, D. W. (2005) Association of short-term ambient air pollution
14 concentrations and ventricular arrhythmias. Am. J. Epidemiol. 161: 1123-1132.
15 Rich, D. Q.; Kim, M. H.; Turner, J. R.; Mittleman, M. A.; Schwartz, J.; Catalano, P. J.; Dockery,
16 D. W. (2006) Association of ventricular arrhythmias detected by implantable cardioverter
17 defibrillator and ambient air pollutants in the St Louis, Missouri metropolitan area.
18 Occup. Environ. Med. 63: 591-596.
19 Roemer, W.; Hoek, G.; Brunekreef, B. (1993) Effect of ambient winter air pollution on
20 respiratory health of children with chronic respiratory symptoms. Am. Rev. Respir. Dis.
21 147:118-124.
22 Roemer, W.; Hoek, G.; Brunekreef, B.; Haluszka, J.; Kalandidi, A.; Pekkanen, J. (1998) Daily
23 variations in air pollution and respiratory health in a multicentre study: the PEACE
24 project. Eur. Respir. J. 12: 1354-1361.
25 Roemer, W. H.; Van Wijnen, J. H . (2001) Daily mortality and air pollution along busy streets in
26 Amsterdam, 1987-1998. Epidemiology 12: 649-653.
27 Romieu, I; Meneses, F.; Ruiz, S.; Sienra, J. J.; Huerta, J.; White, M. C.; Etzel, R. A. (1996)
28 Effects of air pollution on the respiratory health of asthmatic children living in Mexico
29 City. Am. J. Respir. Crit. Care Med. 154: 300-307.
30 Ross, M. A.; Persky, V. W.; Scheff, P. A.; Chung, J.; Curtis, L.; Ramakrishnan, V.; Wadden, R.
31 A.; Hryhorczuk, D. O. (2002) Effect of ozone and aeroallergens on the respiratory health
32 of asthmatics. Arch. Environ. Health 57: 568-578.
33 Rossi, O. V. J.; Kinnula, V. L.; Tienari, J.; Huhti, E. (1993) Association of severe asthma attacks
34 with weather, pollen, and air pollutants. Thorax 48: 244-248.
35 Ruidavets, J.-B.; Cassadou, S.; Cournot, M.; Bataille, V.; Meybeck, M.; Ferrieres, J. (2005)
36 Increased resting heart rate with pollutants in a population based study. J. Epidemiol.
37 Community Health 59: 685-693.
38 Saez, M.; Tobias, A.; Mufioz, P.; Campbell, M. J. (1999) A GEE moving average analysis of the
39 relationship between air pollution and mortality for asthma in Barcelona, Spain. Stat.
40 Med. 18: 2077-2086.
September 2007 AX5-252 DRAFT-DO NOT QUOTE OR CITE
-------
1 Saez, M.; Ballester, F.; Barcelo, M. A.; Perez-Hoyos, S.; Bellido, J.; Tenias, J. M.; Ocafia, R.;
2 Figueiras, A.; Arribas, F.; Aragones, N.; Tobias, A.; Cirera, L.; Canada, A.; on behalf of
3 the EMECAM Group. (2002) A combined analysis of the short-term effects of
4 photochemical air pollutants on mortality within the EMECAM project. Environ. Health
5 Perspect. 110:221-228.
6 Sagiv, S. K.; Mendola, P.; Loomis, D.; Herring, A. H.; Neas, L. M.; Savitz, D. A.; Poole, C.
7 (2005) A time-series analysis of air pollution and preterm birth in Pennsylvania, 1997-
8 2001. Environ. Health Perspect. 113: 602-606.
9 Saldiva, P. H. N.; Lichtenfels, A. J. F. C.; Paiva, P. S. O.; Barone, I. A.; Martins, M. A.; Massad,
10 E.; Pereira, J. C. R.; Xavier, V. P.; Singer, J. M.; Bohm, G. M. (1994) Association
11 between air pollution and mortality due to respiratory diseases in children in Sao Paulo,
12 Brazil: a preliminary report. Environ. Res. 65: 218-225.
13 Saldiva, P. H. N.; Pope, C. A., Ill; Schwartz, J.; Dockery, D. W.; Lichtenfels, A. J.; Salge, J. M.;
14 Barone, L; Bohm, G. M. (1995) Air pollution and mortality in elderly people: a time-
15 series study in Sao Paulo, Brazil. Arch. Environ. Health 50: 159-163.
16 Samet, J. M.; Dominici, F.; Zeger, S. L.; Schwartz, J.; Dockery, D. W. (2000a) National
17 morbidity, mortality, and air pollution study. Part I: methods and methodologic issues.
18 Cambridge, MA: Health Effects Institute; research report no. 94.
19 Samet, J. M.; Zeger, S. L.; Dominici, F.; Curriero, F.; Coursac, L; Dockery, D. W.; Schwartz, J.;
20 Zanobetti, A. (2000b) The national morbidity, mortality, and air pollution study. Part II:
21 morbidity, mortality, and air pollution in the United States. Cambridge, MA: Health
22 Effects Institute; research report no. 94, part II.
23 Schildcrout, J. S.; Sheppard, L.; Lumley, T.; Slaughter, J. C.; Koenig, J. Q.; Shapiro, G. G.
24 (2006) Ambient air pollution and asthma exacerbations in children: an eight-city analysis.
25 Am. J. Epidemiol. 164: 505-517.
26 Schouten, J. P.; Vonk, J. M.; de Graaf, A. (1996) Short term effects of air pollution on
27 emergency hospital admissions for respiratory disease: results of the APHEA project in
28 two major cities in The Netherlands, 1977-89. In: St Leger, S., ed. The APHEA project.
29 Short term effects of air pollution on health: a European approach using epidemiological
30 time series data. J. Epidemiol. Community Health 50(suppl. 1): S22-S29.
31 Schwartz, J. (1991) Particulate air pollution and daily mortality in Detroit. Environ. Res. 56:
32 204-213.
33 Schwartz, J. (1995) Short term fluctuations in air pollution and hospital admissions of the elderly
34 for respiratory disease. Thorax 50: 531-538.
35 Schwartz, J. (1997) Air pollution and hospital admissions for cardiovascular disease in Tucson.
36 Epidemiology 8: 371-377.
37 Schwartz, J. (2000) Daily deaths are associated with combustion particles rather than SC>2 in
38 Philadelphia. Occup. Environ. Med. 57: 692-697.
39 Schwartz, J. (2004) Is the association of airborne particles with daily deaths confounded by
40 gaseous air pollutants? An approach to control by matching. Environ. Health Perspect.
41 112:557-561.
September 2007 AX5-253 DRAFT-DO NOT QUOTE OR CITE
-------
1 Schwartz, J.; Dockery, D. W.; Neas, L. M.; Wypij, D.; Ware, J. H.; Spengler, J. D.; Koutrakis,
2 P.; Speizer, F. E.; Ferris, B. G., Jr. (1994) Acute effects of summer air pollution on
3 respiratory symptom reporting in children. Am. J. Respir. Crit. Care Med. 150: 1234-
4 1242.
5 Schwartz, J.; Morris, R. (1995) Air pollution and hospital admissions for cardiovascular disease
6 in Detroit, Michigan. Am. J. Epidemiol. 142: 23-35.
7 Schwartz, J.; Spix, C.; Touloumi, G.; Bacharova, L.; Barumamdzadeh, T.; le Tertre, A.;
8 Piekarksi, T.; Ponce de Leon, A.; Ponka, A.; Rossi, G.; Saez, M.; Schouten, J. P. (1996)
9 Methodological issues in studies of air pollution and daily counts of deaths or hospital
10 admissions. In: St Leger, S., ed. The APFIEA project. Short term effects of air pollution
11 on health: a European approach using epidemiological time series data. J. Epidemiol.
12 Commun. Health 50(suppl. 1): S3-S11.
13 Schwartz, J.; Litonjua, A.; Suh, H.; Verrier, M.; Zanobetti, A.; Syring, M.; Nearing, B.; Verrier,
14 R.; Stone, P.; MacCallum, G.; Speizer, F. E.; Gold, D. R. (2005) Traffic related pollution
15 and heart rate variability in a panel of elderly subjects. Thorax 60: 455-461.
16 Segala, C.; Fauroux, B.; Just, J.; Pascual, L.; Grimfeld, A.; Neukirch, F. (1998) Short-term effect
17 of winter air pollution on respiratory health of asthmatic children in Paris. Eur. Respir. J.
18 11:677-685.
19 Sheppard, L. (2003) Ambient air pollution and nonelderly asthma hospital admissions in Seattle,
20 Washington, 1987-1994. In: Revised analyses of time-series studies of air pollution and
21 health. Special report. Boston, MA: Health Effects Institute; pp. 227-230. Available:
22 http://www.healtheffects.org/Pubs/TimeSeries.pdf [18 October, 2004].
23 Sheppard, L.; Levy, D.; Norris, G.; Larson, T. V.; Koenig, J. Q. (1999) Effects of ambient air
24 pollution on nonelderly asthma hospital admissions in Seattle, Washington, 1987-1994.
25 Epidemiology 10: 23-30.
26 Simpson, R. W.; Williams, G.; Petroeschevsky, A.; Morgan, G.; Rutherford, S. (1997)
27 Associations between outdoor air pollution and daily mortality in Brisbane, Australia.
28 Arch. Environ. Health 52: 442-454.
29 S0yseth, V.; Kongerud, J.; Haarr, D.; Strand, O.; Bolle, R.; Boe, J. (1995) Relation of exposure
30 to airway irritants in infancy to prevalence of bronchial hyper-responsiveness in
31 schoolchildren. Lancet 345: 217-220.
32 Spix, C.; Wichmann, H. E. (1996) Daily mortality and air pollutants: findings from Koln,
33 Germany. In: St Leger, S., ed. The APHEA project. Short term effects of air pollution on
34 health: a European approach using epidemiological time series data. J. Epidemiol.
35 Commun. Health 50(suppl. 1): S52-S58.
36 Spix, C.; Anderson, H. R.; Schwartz, J.; Vigotti, M. A.; LeTertre, A.; Vonk, J. M.; Touloumi, G.;
37 Balducci, F.; Piekarski, T.; Bacharova, L.; Tobias, A.; Ponka, A.; Katsouyanni, K. (1998)
38 Short-term effects of air pollution on hospital admissions of respiratory diseases in
39 Europe: a quantitative summary of APHEA study results. Arch. Environ. Health 53: 54-
40 64.
September 2007 AX5-254 DRAFT-DO NOT QUOTE OR CITE
-------
1 Stieb, D. M.; Burnett, R. T.; Beveridge, R. C.; Brook, J. R. (1996) Association between ozone
2 and asthma emergency department visits in Saint John, New Brunswick, Canada.
3 Environ. Health Perspect. 104: 1354-1360.
4 Stieb, D. M.; Beveridge, R. C.; Brook, J. R.; Smith-Doiron, M.; Burnett, R. T.; Dales, R. E.;
5 Beaulieu, S.; Judek, S.; Mamedov, A. (2000) Air pollution, aeroallergens and
6 cardiorespiratory emergency department visits in Saint John, Canada. J. Exposure Anal.
7 Environ. Epidemiol. 10: 461-477.
8 Stieb, D. M.; Judek, S.; Burnett, R. T. (2002) Meta-analysis of time-series studies of air pollution
9 and mortality: effects of gases and particles and the influence of cause of death, age, and
10 season. J. Air Waste Manage. Assoc. 52: 470-484.
11 Stieb, D. M.; Judek, S.; Burnett, R. T. (2003) Meta-analysis of time-series studies of air pollution
12 and mortality: update in relation to the use of generalized additive models. J. Air Waste
13 Manage. 53:258-261.
14 Sunyer, J.; Anto, J. M.; Murillo, C.; Saez, M. (1991) Effects of urban air pollution on emergency
15 room admissions for chronic obstructive pulmonary disease. Am. J. Epidemiol. 134: 277-
16 286.
17 Sunyer, J.; Saez, M.; Murillo, C.; Castellsague, J.; Martinez, F.; Anto, J. M. (1993) Air pollution
18 and emergency room admissions for chronic obstructive pulmonary disease: a 5-year
19 study. Am. J. Epidemiol. 137: 701-705.
20 Sunyer, J.; Castellsague, J.; Saez, M.; Tobias, A.; Anto, J. M. (1996) Air pollution and mortality
21 in Barcelona. In: St Leger, S., ed. The APHEA project. Short term effects of air pollution
22 on health: a European approach using epidemiological time series data. J. Epidemiol.
23 Community Health 50(suppl. 1): S76-S80.
24 Sunyer, J.; Spix, C.; Quenel, P.; Ponce-de-Leon, A.; Ponka, A.; Barumandzadeh, T.; Touloumi,
25 G.; Bacharova, L.; Wojtyniak, B.; Vonk, J.; Bisanti, L.; Schwartz, J.; Katsouyanni, K.
26 (1997) Urban air pollution and emergency admissions for asthma in four European cities:
27 the APHEA project. Thorax 52: 760-765.
28 Sunyer, J.; Basagafia, X.; Belmonte, J.; Anto, J. M. (2002) Effect of nitrogen dioxide and ozone
29 on the risk of dying in patients with severe asthma. Thorax 57: 687-693.
30 Taggart, S. C. O.; Custovic, A.; Francis, H. C.; Faragher, E. B.; Yates, C. J.; Higgins, B. G.;
31 Woodcock, A. (1996) Asthmatic bronchial hyperresponsiveness varies with ambient
32 levels of summertime air pollution. Eur. Respir. J. 9: 1146-1154.
33 Tanaka, H.; Honma, S.; Nishi, M.; Igarashi, T.; Teramoto, S.; Nishio, F.; Abe, S. (1998) Acid
34 fog and hospital visits for asthma: an epidemiological study. Eur. Respir. J. 11: 1301-
35 1306.
36 Tenias, J. M.; Ballester, F.; Rivera, M. L. (1998) Association between hospital emergency visits
37 for asthma and air pollution in Valencia, Spain. Occup. Environ. Med. 55: 541-547.
38 Tenias, J. M.; Ballester, F.; Perez-Hoyos, S.; Rivera, M. L. (2002) Air pollution and hospital
39 emergency room admissions for chronic obstructive pulmonary disease in Valencia,
40 Spain. Arch. Environ. Health 57: 41-47.
September 2007 AX5-255 DRAFT-DO NOT QUOTE OR CITE
-------
1 Thompson, A. J.; Shields, M. D.; Patterson, C. C. (2001) Acute asthma exacerbations and air
2 pollutants in children living in Belfast, Northern Ireland. Arch. Environ. Health 56: 234-
3 241.
4 Timonen, K. L.; Pekkanen, J. (1997) Air pollution and respiratory health among children with
5 asthmatic or cough symptoms. Am. J. Respir. Crit. Care Med. 156: 546-552.
6 Tobias, A.; Campbell, M. J.; Saez, M. (1999) Modelling asthma epidemics on the relationship
7 between air pollution and asthma emergency visits in Barcelona, Spain. Eur. J.
8 Epidemiol. 15: 799-803.
9 Tsai, S.-S.; Goggins, W. B.; Chiu, H.-F.; Yang, C.-Y. (2003a) Evidence for an association
10 between air pollution and daily stroke admissions in Kaohsiung, Taiwan. Stroke 34:
11 2612-2616.
12 Tsai, S.-S.; Huang, C.-H.; Goggins, W. B.; Wu, T.-N.; Yang, C.-Y. (2003b) Relationship
13 between air pollution and daily mortality in a tropical city: Kaohsiung, Taiwan. J.
14 Toxicol. Environ. Health Part A 66: 1341-1349.
15 Tsai, S.-S.; Cheng, M.-H.; Chiu, H.-F.; Wu, T.-N.; Yang, C.-Y. (2006) Air pollution and hospital
16 admissions for asthma in a tropical city: Kaohsiung, Taiwan. Inhalation Toxicol. 18: 549-
17 554.
18 Van Der Zee, S. C.; Hoek, G.; Boezen, H. M.; Schouten, J. P.; Van Wijnen, J. H.; Brunekreef, B.
19 (1999) Acute effects of urban air pollution on respiratory health of children with and
20 without chronic respiratory symptoms. Occup. Environ. Med. 56: 802-813.
21 Van Der Zee, S. C.; Hoek, G.; Boezen, M. H.; Schouten, J. P.; Van Wijnen, J. H.; Brunekreef, B.
22 (2000) Acute effects of air pollution on respiratory health of 50-70 yr old adults. Eur.
23 Respir. J. 15: 700-709.
24 Vedal, S.; Brauer, M.; White, R.; Petkau, J. (2003) Air pollution and daily mortality in a city
25 with low levels of pollution. Environ. Health Perspect. Ill: 45-51.
26 Vedal, S.; Rich, K.; Brauer, M.; White, R.; Petkau, J. (2004) Air pollution and cardiac
27 arrhythmias in patients with implantable cardiovascular defibrillators. Inhalation Toxicol.
28 16: 353-362.
29 Venners, S. A.; Wang, B.; Xu, Z.; Schlatter, Y.; Wang, L.; Xu, X. (2003) Particulate matter,
30 sulfur dioxide, and daily mortality in Chongqing, China. Environ. Health Perspect. Ill:
31 562-567.
32 Verhoeff, A. P.; Hoek, G.; Schwartz, J.; Van Wijnen, J. H. (1996) Air pollution and daily
33 mortality in Amsterdam. Epidemiology 7: 225-230.
34 Vigotti, M. A.; Rossi, G.; Bisanti, L.; Zanobetti, A.; Schwartz, J. (1996) Short term effects of
35 urban air pollution on respiratory health in Milan, Italy, 1980-89. In: Leger, S., ed. The
36 APHEA project. Short term effects of air pollution on health: a European approach using
37 epidemiological time series data. J. Epidemiol. Community Health 50(suppl. 1): S71-S75.
38 Villeneuve, P. J.; Burnett, R. T.; Shi, Y.; Krewski, D.; Goldberg, M. S.; Hertzman, C.; Chen, Y.;
39 Brook, J. (2003) A time-series study of air pollution, socioeconomic status, and mortality
40 in Vancouver, Canada. J. Exposure Anal. Environ. Epidemiol. 13: 427-435.
September 2007 AX5-256 DRAFT-DO NOT QUOTE OR CITE
-------
1 Villeneuve, P. J.; Chen, L.; Stieb, D.; Rowe, B. H. (2006a) Associations between outdoor air
2 pollution and emergency department visits for stroke in Edmonton, Canada. Eur. J.
3 Epidemiol. 21: 689-700.
4 Villeneuve, P. J.; Doiron, M. S.; Stieb, D.; Dales, R.; Burnett, R. T.; Dugandzic, R. (2006b) Is
5 outdoor air pollution associated with physician visits for allergic rhinitis among the
6 elderly in Toronto, Canada? Allergy (Oxford, U.K.) 61: 750-758.
7 Von Mutius, E.; Sherrill, D. L.; Fritzsch, C.; Martinez, F. D.; Lebowitz, M. D. (1995) Air
8 pollution and upper respiratory symptoms in children from East Germany. Eur. Respir. J.
9 8: 723-728.
10 Walters, S.; Griffiths, R. K.; Ayres, J. G. (1994) Temporal association between hospital
11 admissions for asthma in Birmingham and ambient levels of sulphur dioxide and smoke.
12 Thorax 49: 133-140.
13 Wang, X.; Ding, H.; Ryan, L.; Xu, X. (1997) Association between air pollution and low birth
14 weight: a community-based study. Environ. Health Perspect. 105: 514-520.
15 Ward, D. J.; Roberts, K. T.; Jones, N.; Harrison, R. M.; Ayres, J. G.; Hussain, S.; Walters, S.
16 (2002) Effects of daily variation in outdoor particulates and ambient acid species in
17 normal and asthmatic children. Thorax 57: 489-502.
18 Wellenius, G. A.; Schwartz, J.; Mittleman, M. A. (2005a) Air pollution and hospital admissions
19 for ischemic and hemorrhagic stroke among medicare beneficiaries. Stroke 36: 2549-
20 2553.
21 Wellenius, G. A.; Bateson, T. F.; Mittleman, M. A.; Schwartz, J. (2005b) Particulate air pollution
22 and the rate of hospitalization for congestive heart failure among medicare beneficiaries
23 in Pittsburgh, Pennsylvania. Am. J. Epidemiol. 161: 1030-1036.
24 Willis, A.; Jerrett, M.; Burnett, R. T.; Krewski, D. (2003) The association between sulfate air
25 pollution and mortality at the county scale: an exploration of the impact of scale on a
26 long-term exposure study. J. Toxicol. Environ. Health Part A 66: 1605-1624.
27 Wilson, A. M.; Wake, C. P.; Kelly, T.; Salloway, J. C. (2005) Air pollution, weather, and
28 respiratory emergency room visits in two northern New England cities: an ecological
29 time-series study. Environ. Res. 97: 312-321.
30 Wong, T. W.; Lau, T. S.; Yu, T. S.; Neller, A.; Wong, S. L.; Tarn, W.; Pang, S. W. (1999) Air
31 pollution and hospital admissions for respiratory and cardiovascular diseases in Hong
32 Kong. Occup. Environ. Med. 56: 679-683.
33 Wong, G. W.; Ko, F. W.; Lau, T. S.; Li, S. T.; Hui, D.; Pang, S. W.; Leung, R.; Fok, T. F.; Lai,
34 C. K. (200la) Temporal relationship between air pollution and hospital admissions for
35 asthmatic children in Hong Kong. Clin. Exp. Allergy 31: 565-569.
36 Wong, C.-M.; Ma, S.; Hedley, A. J.; Lam, T.-H. (2001b) Effect of air pollution on daily
37 mortality in Hong Kong. Environ. Health Perspect. 109: 335-340.
38 Wong, C.-M.; Atkinson, R. W.; Anderson, H. R.; Hedley, A. J.; Ma, S.; Chau, P. Y.-K.; Lam, T.-
39 H. (2002a) A tale of two cities: effects of air pollution on hospital admissions in Hong
40 Kong and London compared. Environ. Health Perspect. 110: 61-11.
September 2007 AX5-257 DRAFT-DO NOT QUOTE OR CITE
-------
1 Wong, T. W.; Tarn, W. S.; Yu, T. S.; Wong, A. H. S. (2002b) Associations between daily
2 mortalities from respiratory and cardiovascular diseases and air pollution in Hong Kong,
3 China. Occup. Environ. Med. 59: 30-35.
4 Xu, X.; Dockery, D. W.; Wang, L. (1991) Effects of air pollution on adult pulmonary function.
5 Arch. Environ. Health 46: 198-206.
6 Xu, X.; Ding, H.; Wang, X. (1995) Acute effects of total suspended particles and sulfur dioxides
7 on preterm delivery: a community-based cohort study. Arch. Environ. Health 50: 407-
8 415.
9 Yallop, D.; Duncan, E. R.; Norris, E.; Fuller, G. W.; Thomas, N.; Walters, 1; Dick, M. C.;
10 Height, S. E.; Thein, S. L.; Rees, D. C. (2007) The associations between air quality and
11 the number of hospital admissions for acute pain and sickle-cell disease in an urban
12 environment. Br. J. Haematol. 136: 844-848.
13 Yang, Q.; Chen, Y.; Shi, Y.; Burnett, R. T.; McGrail, K. M.; Krewski, D. (2003a) Association
14 between ozone and respiratory admissions among children and the elderly in Vancouver,
15 Canada. Inhalation Toxicol. 15: 1297-1308.
16 Yang, C.-Y.; Tseng, Y.-T.; Chang, C.-C. (2003b) Effects of air pollution on birthweight among
17 children born between 1995 and 1997 in Kaohsiung, Taiwan. J. Toxicol. Environ. Health
18 Part A 66: 807-816.
19 Yang, C.-Y.; Chen, Y.-S.; Yang, C.-H.; Ho, S.-C. (2004a) Relationship between ambient air
20 pollution and hospital admissions for cardiovascular diseases in Kaohsiung, Taiwan. J.
21 Toxicol. Environ. Health Part A 67: 483-493.
22 Yang, C.-Y.; Chang, C.-C.; Chuang, H.-Y.; Tsai, S.-S.; Wu, T.-N.; Ho, C.-K. (2004b)
23 Relationship between air pollution and daily mortality in a subtropical city: Taipei,
24 Taiwan. Environ. Int. 30: 519-523.
25 Yang, Q.; Chen, Y.; Krewski, D.; Burnett, R. T.; Shi, Y.; McGrail, K. M. (2005) Effect of short-
26 term exposure to low levels of gaseous pollutants on chronic obstructive pulmonary
27 disease hospitalizations. Environ. Res. 99: 99-105.
28 Zeghnoun, A.; Czernichow, P.; Beaudeau, P.; Hautemaniere, A.; Froment, L.; Le Tertre, A.;
29 Quenel, P. (2001) Short-term effects of air pollution on mortality in the cities of Rouen
30 and Le Havre, France, 1990-1995. Arch. Environ. Health 56: 327-335.
31 Zmirou, D.; Barumandzadeh, T.; Balducci, F.; Ritter, P.; Laham, G.; Ghilardi, J.-P. (1996) Short
32 term effects of air pollution on mortality in the city of Lyon, France, 1985-90. In: St
33 Leger, S., ed. The APHEA project. Short term effects of air pollution on health: a
34 European approach using epidemiological time series data. J. Epidemiol. Community
35 Health 50(suppl. 1): S30-S35.
36 Zmirou, D.; Schwartz, J.; Saez, M.; Zanobetti, A.; Wojtyniak, B.; Touloumi, G.; Spix, C.; Ponce
37 de Leon, A.; Le Moullec, Y.; Bacharova, L.; Schouten, J.; Ponka, A.; Katsouyanni, K.
38 (1998) Time-series analysis of air pollution and cause-specific mortality. Epidemiology
39 9:495-503.
40
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