vvEPA
United August 2007
Environmental Protection EPA/600/R-07/093
Annexes for the
Integrated Science Assessment
for Oxides of Nitrogen -
Health Criteria
(First External Review Draft)
-------
EPA/600/R-07/093
August 2007
Annexes for the
Integrated Science Assessment
for Oxides of Nitrogen - Health Criteria
National Center for Environmental Assessment-RTF Division
Office of Research and Development
U.S. Environmental Protection Agency
Research Triangle Park, NC
-------
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.
August 2007 ii DRAFT-DO NOT QUOTE OR CITE
-------
Annexes for the Integrated Science Assessment
for Oxides of Nitrogen - 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 - AMBIENT CONCENTRATIONS AND
EXPOSURES AX3-1
AX4. CHAPTER 4 ANNEX - TOXICOLOGICAL EFFECTS OF NITROGEN
DIOXIDE AND RELATED OXIDES OF NITROGEN AX4-1
AX5. CHAPTER 5 ANNEX - CONTROLLED HUMAN EXPOSURE STUDIES
OF NITROGEN OXIDES AX5-1
AX6. CHAPTER 6 ANNEX - EPIDEMIOLOGICAL STUDIES OF HUMAN
HEALTH EFFECTS ASSOCIATED WITH AMBIENT OXIDES OF
NITROGEN EXPO SURE AX6-1
August 2007 iii DRAFT-DO NOT QUOTE OR CITE
-------
Annex Table of Contents
AX1. CHAPTER 1 ANNEX - INTRODUCTION AX1-1
AX1.1 LEGISLATIVE REQUIREMENTS AX1-2
AX1.2 HISTORY OF REVIEWS OF THE PRIMARY NAAQS FORNO2 AX1-3
AX1.3 REFERENCES AX1-5
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
AX2.8.1 Availability and Accuracy of Ambient Measurements
forNOy AX2-86
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
August 2007 iv DRAFT-DO NOT QUOTE OR CITE
-------
Annex Table of Contents
(cont'd)
AX3. CHAPTER 3 ANNEX - AMBIENT CONCENTRATIONS AND
EXPOSURES AX3-1
AX3.1 INTRODUCTION AX3-1
AX3.2 AMBIENT CONCENTRATIONS OF NITROGEN OXIDES
AND RELATED SPECIES AX3-2
AX3.2.1 Spatial and Temporal Variability in Ambient
Concentrations of NO2 and Related Species in
Urban Areas AX3-4
AX3.2.2 Temporal Variability in Nitrogen Oxides AX3-8
AX3.2.4 Relationships Between NO2 and Other Pollutants AX3-19
AX3.2.5 Abundance of NOy Species AX3-24
AX3.3 METHODS FOR MEASURING PERSONAL AND INDOOR
NO2 CONCENTRATIONS AX3-30
AX3.3.1 Issues in Measuring Personal/Indoor NO2 AX3-30
AX3.4 NITROGEN OXIDES IN INDOOR AIR AX3-40
AX3.4.1 Indoor Sources and Concentrations of Nitrogen Oxides AX3-40
AX3.4.2 Reactions of NO2 in Indoor Air AX3-47
AX3.4.3 Contributions from Outdoor NO2 AX3-53
AX3.5 PERSONAL EXPOSURE AX3-55
AX3.5.1 Personal Exposures and Ambient (Outdoor)
Concentrations AX3-57
AX3.5.2 Personal Exposure in Microenvironments AX3-67
AX3.5.3 Exposure Indicators AX3-84
AX3.6 CONFOUNDING AND SURROGATE IS SUES AX3-86
AX3.7 A FRAMEWORK FOR MODELING HUMAN EXPOSURES TO
NO2 AND RELATED PHOTOCHEMICAL AIR POLLUTANTS AX3-94
AX3.7.1 Introduction: Concepts, Terminology, and
Overall Summary AX3-94
AX3.7.2 Population Exposure Models: Their Evolution and
Current Status AX3-100
AX3.7.3 Characterization of Ambient Concentrations of NO2
and Related Air Pollutants AX3-103
AX3.7.4 Characterization of Microenvironmental Concentrations.... AX3-105
AX3.7.5 Concluding Comments AX3-113
AX3.8 EXPO SURE ERROR AX3-114
AX3.9 REFERENCES AX3-169
August 2007 v DRAFT-DO NOT QUOTE OR CITE
-------
Annex Table of Contents
(cont'd)
AX4. CHAPTER 4 ANNEX - TOXICOLOGICAL EFFECTS OF NITROGEN
DIOXIDE AND RELATED OXIDES OF NITROGEN AX4-1
AX4.2 DOSIMETRY OF INHALED NITROGEN OXIDES AX4-7
AX4.2.1 Mechanisms of NO2 Absorption AX4-8
AX4.2.3 Regional and Total Respiratory Absorption of NO2 AX4-11
AX4.3 REFERENCES AX4-50
AX5. CHAPTER 5 ANNEX - CONTROLLED HUMAN EXPOSURE STUDIES
OF NITROGEN OXIDES AX5-1
AX5.1 INTRODUCTION AX5-1
AX5.1.1 Considerations in Controlled Human Exposure Studies AX5-2
AX5.2 EFFECTS OF NITROGEN DIOXIDE IN HEALTHY SUBJECTS AX5-4
AX5.3 THE EFFECTS OF NITROGEN OXIDE EXPOSURE IN
SENSITIVE SUBJECTS AX5-4
AX5.4 EFFECTS OF MIXTURES CONTAINING NITROGEN OXIDES AX5-6
AX5.5 REFERENCES AX5-16
AX6. CHAPTER 6 ANNEX - EPIDEMIOLOGICAL STUDIES OF HUMAN
HEALTH EFFECTS ASSOCIATED WITH AMBIENT OXIDES OF
NITROGEN EXPO SURE AX6-1
AX6.1 REFERENCES AX6-143
August 2007 vi DRAFT-DO NOT QUOTE OR CITE
-------
Annex List of Figures
Number
AX2-1. Schematic diagram of the cycle of reactive nitrogen species in
the atmosphere [[[ AX2-3
AX2-2. 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) ....... AX2-12
AX2-3 . Structures of nitro-poly cyclic aromatic hydrocarbons ................................... AX2- 1 6
AX2-4. Formation of 2-nitropyrene (2NP) from the reaction of OH with
gaseous pyrene (PY) [[[ AX2-17
AX2-5 . Transformations of sulfur compounds in the atmosphere .............................. AX2-26
AX2-6. Comparison of aqueous-phase oxidation paths .............................................. AX2-29
AX2-7. 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 .......................................... AX2-43
AX2-8. Simple NOX photochemical canopy model outputs ....................................... AX2-44
AX2-9. Hourly (dots) and median nightly (pluses) NO2 flux vs. concentration,
with results of least-squares fit on the hourly data (curve) ............................ AX2-45
AX2-1 1 . 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
AX2-12. 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
AX2-13. Scatter plot of total nitrate (HNO3 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
AX2-14. Same as Figure AX2-13 but for sulfate wet deposition
'1 [[[ AX2-71
AX2-15a,b. Impact of model uncertainty on control strategy predictions
for O3 for two days (August lOa and lib, 1992) in Atlanta, GA .................. AX2-76
-------
Annex List of Figures
(cont'd)
Number Page
AX2-17a. Time series for measured gas-phase species in comparison with results
from a photochemical model AX2-78
AX2-17b. Time series for measured gas-phase species in comparison
with results from a photochemical model AX2-79
AX2-18. 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
AX2-19a,b. Evaluation of model versus measured O3 versus NOy for
two model scenarios for Atlanta AX2-83
AX2-20a,b. Evaluation of model versus: (a) measured O3 versus NOZ
and (b) O3 versus the sum 2H2O2 + NOZ for Nashville, TN AX2-84
AX2-21. 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
AX2-22. Tropospheric NO2 columns (molecules NO2/ cm2) retrieved
from the SCIAMACHY satellite instrument for 2004-2005 AX2-97
AX2-23. 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
AX2-24. Same as Figure AX2-23 but for SO2 concentrations AX2-113
AX2-25. Same as for Figure AX2-23 but for wet and dry deposition of
HNO3, NH4NO3, NOX, HO2NO2, and organic nitrates (mg N m~VJ) AX2-114
AX2-26. Same as Figure AX2-23 but for SOX deposition
(SO2 + SO4)(mgSm"V1) AX2-115
AX2-27. 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. Location of ambient NO2 monitors in the United States AX3-3
AX3.2. NO2 concentrations measured at 4 m (Van) and at 15 m at NY
Department of Environmental Conservation sites (DEC709406 and
DEC709407) AX3-7
August 2007 viii DRAFT-DO NOT QUOTE OR CITE
-------
Annex List of Figures
(cont'd)
Number Page
AX3.3. Composite, diurnal variability in 1-h average NO2 in urban areas AX3-9
AX3.4a-e. Time series of 24-h average NO2 concentrations at individual sites
in New York City from 2003 through 2006 AX3-10
AX3.5a-e. Time series of 24-h average NO2 concentrations at individual sites in
Atlanta, GA from 2003 through 2005 AX3-11
AX3.6a-g. Time series of 24-h average NO2 concentrations at individual sites
in Chicago, IL from 2003 through 2005 AX3-12
AX3.7a-b. Time series of 24-h average NC>2 concentrations at individual sites
in Baton Rouge, LA from 2003 through 2005 AX3-13
AX3.8a-g. Time series of 24-h average NC>2 concentrations at individual sites
in Houston, TX from 2003 through 2005 AX3-14
AX3.9a-h. Time series of 24-h average NC>2 concentrations at individual sites
in Los Angeles, CA from 2003 through 2005 AX3-15
AX3.9i-n. Time series of 24-h average NC>2 concentrations at individual sites
in Los Angeles, CA from 2003 through 2006 AX3-16
AX3. lOa-d. Time series of 24-h average NC>2 concentrations at individual sites
in Riverside, CA from 2003 through 2006 AX3-17
AX3. lOe-i. Time series of 24-h average NC>2 concentrations at individual sites
in Riverside, CA from 2003 through 2006 AX3-18
AX3.11. Nationwide trends in annual meanNO2 concentrations AX3-20
AX3.12. Trends in annual meanNO2 concentrations by site type AX3-20
AX3.13a-d. Correlations of NC>2 to O3 vs. correlations of NC>2 to CO for Los
Angeles, CA (2001-2005) AX3-22
AX3.14. Relationship between O3, NO, and NO2 as a function of NOX
concentration AX3-23
AX3.15. Variation of odd oxygen (= O3 + NO2) with NOX AX3-23
AX3.16a-d. Measured O3 (ppbv) versus PAN (pptv) in Tennessee, including
(a) aircraft measurements, and (b, c, and d) suburban sites near
Nashville AX3-26
AX3.17. Relationship between benzene and NOy at a measurement site in
Boulder, CO AX3-27
AX3.18. Ratios of PAN to NO2 observed at Silwood Park, Ascot, Berkshire,
U.K. from July 24 to August 12 1999 AX3-27
August 2007 ix DRAFT-DO NOT QUOTE OR CITE
-------
Annex List of Figures
(cont'd)
Number Page
AX3.19. Ratios of HONO to NC>2 observed in a street canyon (Marylebone Road)
in London, U.K. from 11 a.m. to midnight during October 1999 AX3-28
AX3.20. Concentrations of particulate nitrate measures as part of the
Environmental Protection Agency PA's speciation network AX3-29
AX3.21. Percentage of time people spend in different environments AX3-41
AX3.22. Average residential outdoor concentration versus concentration during
commuting forNC>2 AX3-77
AX3.23. Schematic description of a general framework identifying the processes
(steps or components) involved in assessing inhalation exposures and
doses for individuals and populations AX3-98
AX3.24. Errors associated with components of the continuum from ambient
air pollution to adverse health outcome AX3-115
AX3.25. A systematic approach to evaluate whether NC>2 itself is causing the
observed adverse health outcome or NO2 is acting as a surrogate for
other pollutants AX3-120
AX5.1. Airway inflammation in response to NC>2 inhalation in healthy subjects AX5-5
AX5.2. Effects of NC>2 inhalation on allergen challenge in subjects with asthma AX5-6
August 2007 x DRAFT-DO NOT QUOTE OR CITE
-------
Annex List of Tables
Number
AX2-1. Atmospheric Lifetimes of Sulfur Dioxide and Reduced Sulfur
Species With Respect to Reaction With OH, NO3, and Cl Radicals AX2-119
AX2-2a. Relative Contributions of Various Reactions to the Total S(IV)
Oxidation Rate within a Sunlit Cloud, 10 Minutes after Cloud
Formation AX2-120
AX2-2b. Relative Contributions of Various Gas and Aqueous Phase Reactions
to Aqueous Nitrate Formation within a Sunlit Cloud, 10 Minutes
after Cloud Formation AX2-121
AX2-3. Emissions of Nitrogen Oxides, Ammonia, and Sulfur Dioxide in the
United States in 2002 AX2-122
AX2-3. Satellite Instruments Used to Retrieve Tropospheric NO2 Columns AX2-128
AX3.1. Summary of Percentiles of NO2 Data Pooled Across Monitoring
Sites (2003-2005) Concentrations are in ppm AX3-121
AX3.2. Spatial Variability of NO2 in Selected United states Urban Areas AX3-122
AX3.3. NOX and NOy Concentrations at Regional Background Sites in the
Eastern United States. Concentrations are GIVEN in ppb AX3-122
AX3.4. Range of Pearson Correlation Coefficients Between NO2 and O3, CO
andPM2.5AX3-123
AX3.5. Passive Samplers Used inNO2 Measurements AX3-124
AX3.6. The Performance of Sampler/Sampling Method for NO2
Measurements in the Air AX3-125
AX3.7. NO2 Concentrations (ppb) in Homes in Latrobe Valley, Victoria,
Australia AX3-126
AX3.8. NO2 Concentrations (ppb) in Homes in Connecticut AX3-126
AX3.9. NO2 Concentrations Near Indoor Sources - Short-Term Averages AX3-127
AX3.10. NO2 Concentrations Near Indoor Sources - Long-Term Averages AX3-128
AX3.11. Summary of Regression Models of Personal Exposure to
Ambient/Outdoor NO2 AX3-129
AX3.12. Average Ambient and Nonambient Contributions to Population
Exposure AX3-130
AX3.13. The Association Between Personal Exposures and Ambient
Concentrations AX3-131
August 2007 xi DRAFT-DO NOT QUOTE OR CITE
-------
Annex List of Tables
(cont'd)
Number Page
AX3.14. Indoor/Outdoor Ratio and the Indoor vs. Outdoor Regression Slope AX3-134
AX3.15. NO2 Concentrations (ppb) in Different Rooms AX3-141
AX3.16. Indoor and Outdoor Contributions to Indoor Concentrations AX3-143
AX3.17. The Association Between Indoor, Outdoor, and Personal NO2 AX3-145
AX3.19. Personal NO2 Levels Stratified by Demographic and Socioeconomic
Factors (Concentrations are in ppb and Slopes are Dimensionless) AX3-164
AX3.20. Correlations (Pearson Correlation Coefficient) Between Ambient
NO2 and Ambient Copollutants AX3-165
AX3.21. Correlations (Pearson Correlation Coefficient) Between Personal
NO2 and Personal Copollutants AX3-166
AX3.22. Correlations (Pearson Correlation Coefficient) Between Personal
NO2 and Ambient Copollutants AX3-166
AX3.23. Correlations (Pearson Correlation Coefficient) Between Ambient
NO2 and Personal Copollutants AX3-167
AX3.24. The Essential Attributes of the pNEM, HAPEM, APEX, SHEDS,
andMENTOR-lA AX3-168
AX4.1. Effects of Nitrogen Dioxide (NO2) on Oxidant and
Antioxidant Homeostasis AX4-22
AX4.2. Effect of Nitrogen dioxide (NO2) on Lung Amino Acids,
Proteins, and Enzymes AX4-25
AX4.3. Effects of Nitrogen oxide (NO2) on the Immune System
of Animals AX4-29
AX4.4. Effects of Nitrogen Dioxide on Alveolar Macrophages AX4-30
AX4.5. Effect of Nitrogen Dioxide (NO2) on Susceptibility to
Infectious Agents AX4-36
AX4.6. Effect of Nitrogen Dioxide (NO2) on Hematological Parameters21 AX4-44
AX4.7. Effects of Nitric Oxide with Iron and on Enzymes and Nucleic Acids AX4-46
AX4.8A. Genotoxicity of NO2 In Vitro and In Plants AX4-47
AX4.8B. Genoticity of NO2 In Vivo AX4-48
AX4.8C. Genotoxicity of NO In Vitro and In Vivo AX4-49
August 2007 xii DRAFT-DO NOT QUOTE OR CITE
-------
Annex List of Tables
(cont'd)
Number Page
AX5.1. Clinical Studies of NO2 Exposure in Healthy Subjects AX5-7
AX5.2. Effects of NO2 Exposure in Subjects with Respiratory Disease
(see Table AX5-3 for Studies with Allergen Challenge) AX5-11
AX5.3. Effects of NO2 Exposure on Response to Inhaled Allergen AX5-12
AX5.4. Effects of Exposure to NO2 with Other Pollutants AX5-14
AX6.1. Studies Examining Exposure to Indoor NO2 and Respiratory Symptoms AX6-1
AX6.2. Studies Examining Exposure to Ambient NO2 and Acute Respiratory
Symptoms Using Generalized Estimating Equations (GEE) in the
Analysis Method AX6-4
AX6.3-1. Respiratory Health Effects of Oxides of Nitrogen: Hospital Admissions AX6-7
AX6.3-2. Respiratory Health Effects of Oxides of Nitrogen: Emergency
Department Visits AX6-42
AX6.3-3. Respiratory Health Effects of Oxides of Nitrogen: General
Practitioner/Clinic Visits AX6-66
AX6.4-1. Human Health Effects of Oxides of Nitrogen: CVD Hospital
Admissions and Visits: United States and Canada AX6-70
AX6.4-2. Human Health Effects of Oxides of Nitrogen: CVD Hospital
Admissions and Visits: Australia and New Zealand AX6-85
AX6.4-3. Human Health Effects of Oxides of Nitrogen: CVD Hospital
Admissions and Visits: Europe AX6-90
AX6.4-4. Human Health Effects of Oxides of Nitrogen: CVD Hospital
Admissions and Visits: Asia AX6-99
AX6.5-1. Studies Examining Exposure to Ambient NO2 and Heart Rate
Variability as Measured by Standard Deviation of Normal-to-Normal
Intervals (SDNN) AX6-105
AX6.6-1. Birth Weight and Long-Term NO2 Exposure Studies AX6-107
AX6.6-2. Preterm Delivery and Long-Term NO2 Exposure Studies AX6-109
AX6.6-3. Fetal Growth and Long-Term NO2 Exposure Studies AX6-111
AX6.7-1. Lung Function and Long-Term NO2 Exposure AX6-112
AX6.7-2. Asthma and Long-Term NO2 Exposure AX6-114
AX6.7-3. Respiratory Symptoms and Long-Term NO2 Exposure AX6-118
August 2007 xiii DRAFT-DO NOT QUOTE OR CITE
-------
Annex List of Tables
(cont'd)
Number Page
AX6.8. Lung Cancer AX6-121
AX6.9. Effects of Acute NOX Exposure on Mortality. Risk Estimates are
Standardized for per 20 ppb 24-h Avg NO2 Increment AX6-122
August 2007 xiv DRAFT-DO NOT QUOTE OR CITE
-------
Authors, Contributors, and Reviewers
Authors
Dr. Dennis J. Kotchmar (NOX Team Leader)—National Center for Environmental Assessment
(B243-01), U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Mary Ross (Branch Chief)—National Center for Environmental Assessment (B243-01),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Kathleen Belanger, Yale University, Epidemiology and Public Health, 60 College Street,
New Haven, CT 06510-3210
Dr. James S. Brown—National Center for Environmental Assessment (B243-01),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Douglas Bryant—Cantox Environmental Inc., 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. Mark Frampton—Strong Memorial Hospital, 601 Elmwood Ave., Box 692, Rochester, NY
14642-8692
Dr. Janneane Gent—Yale University, CPPEE, One Church Street, 6th Floor, New Haven, CT
06510
Dr. Vic Hasselblad—Duke University, 29 Autumn Woods Drive, Durham, NC 27713
Dr. Kazuhiko Ito—New York University School of Medicine, 57 Old Forge Road, Tuxedo, NY
10987
Dr. Jee Young Kim—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. Thomas Luben—National Center for Environmental Assessment (B243-01),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Andrew Maier—Toxicology Excellence for Risk Assessment, 2300 Montana Avenue,
Suite 409, Cincinnati, OH 45211
August 2007 xv DRAFT-DO NOT QUOTE OR CITE
-------
Authors, Contributors, and Reviewers
(cont'd)
Authors
(cont'd)
Dr. Qingyu 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
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
Contributors
Dr. Dale Allen, University of Maryland, College Park, MD
Dr. Jeffrey Arnold—National Center for Environmental Assessment (B243-01),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
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. Arlene Fiore, NOAA/GFDL, Princeton, NJ
Dr. Panos Georgopoulos, EOHSI/UMDNJ, Piscataway, NJ
Dr. Larry Horowitz, NOAA/GFDL, Princeton, NJ
Dr. William Keene, University of Virginia, Charlottesville, VA
Dr. Randall Martin, Dalhousie University, Halifax, Nova Scotia
August 2007 xvi DRAFT-DO NOT QUOTE OR CITE
-------
Authors, Contributors, and Reviewers
(cont'd)
Contributors
(cont'd)
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
Ms. Debra Walsh—National Center for Environmental Assessment (B243-01),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
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
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 and Radiation, U.S. Environmental Protection Agency,
Research Triangle Park, NC 27711
August 2007 xvii DRAFT-DO NOT QUOTE OR CITE
-------
Authors, Contributors, and Reviewers
(cont'd)
Reviewers
(cont'd)
Ms. Beth Hassett-Sipple—U.S. Environmental Protection Agency (C504-06), Research Triangle
Park, NC 27711
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, U.S. Environmental Protection
Agency (C504-02), 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—U.S. Environmental Protection Agency (C504-06), Research Triangle Park,
NC27711
Dr. Morton Lippmann—NYU School of Medicine, 57 Old Forge Road, Tuxedo, NY 10987
Dr. Thomas Long—National Center for Environmental Assessment (B243-01),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Karen Martin—Office of Air and Radiation, U.S. Environmental Protection Agency
(C504-06), Research Triangle Park, NC 27711
Dr. William McDonnell—William F. McDonnell Consulting, 1207 Hillview Road, Chapel Hill,
NC27514
Dr. Dave McKee—Office of Air and Radiation/Office of Air Quality Planning and Standards,
U.S. Environmental Protection Agency (C504-06), 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
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
August 2007 xviii DRAFT-DO NOT QUOTE OR CITE
-------
Authors, Contributors, and Reviewers
(cont'd)
Reviewers
(cont'd)
Dr. Jennifer Peel—Colorado State University, 1681 Campus Delivery, Fort Collins, CO 80523-
1681
Mr. Harvey Richmond—Office of Air Quality Planning and Standards/Health and
Environmental Impacts Division, U.S. Environmental Protection Agency (C504-06), Research
Triangle Park, NC 27711
Mr. Joseph Somers—Office of Transportation and Air Quality, U.S. Environmental Protection
Agency, 2000 Traverwood Boulevard, Ann Arbor, MI 48105
Ms. Susan Stone—U.S. Environmental Protection Agency (C504-06), Research Triangle Park,
NC27711
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
Dr. William Wilson—Office of Research and Development, National Center for Environmental
Assessment (B243-01), U.S. Environmental Protection Agency, Research Triangle Park, NC
27711
August 2007 xix DRAFT-DO NOT QUOTE OR CITE
-------
U.S. Environmental Protection Agency Project Team
for Development of Integrated Scientific Assessment
for Oxides of Nitrogen
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. Dennis Kotchmar (NOX 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. Jee Young Kim—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. 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
Dr. Mary Ross—National Center for Environmental Assessment (B243-01),
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
August 2007 xx DRAFT-DO NOT QUOTE OR CITE
-------
U.S. Environmental Protection Agency Project Team
for Development of Integrated Scientific Assessment
for Oxides of Nitrogen
(cont'd)
Scientific Staff
(cont'd)
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
Mrs. Melissa Cesar—Publication/Graphics Specialist, Computer Sciences Corporation,
2803 Slater Road, Suite 220, Morrisville, NC 27560
August 2007 xxi DRAFT-DO NOT QUOTE OR CITE
-------
U.S. Environmental Protection Agency Project Team
for Development of Integrated Scientific Assessment
for Oxides of Nitrogen
(cont'd)
Document Production Staff
(cont'd)
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
August 2007 xxii DRAFT-DO NOT QUOTE OR CITE
-------
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
August 2007 xxiii DRAFT-DO NOT QUOTE OR CITE
-------
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
August 2007 xxiv DRAFT-DO NOT QUOTE OR CITE
-------
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
August 2007 xxv DRAFT-DO NOT QUOTE OR CITE
-------
Annex Abbreviations and Acronyms
a
AA
ACCENT
AgNOR
AIRPEX
AIRQUIS
AIRS
AM
AMF
AMI
APEX
APIMS
AQCD
AQEG
ATS
ATTILA
BAL
BALF
BERLIOZ
BHPN
BLF
BME
Br
Br
Br2
BrCl
BrdU
BrO
C
Cx T
CAA
CAPs
CARB
CASAC
CB4
alpha; probability value
arachidonic acid
European Union project Atmospheric Composition Change: the
European Network of Excellence
argyrophilic nucleolar organizer region
Air Pollution Exposure (model)
Air Quality Information System (model)
Aerometric Information Retrieval System
alveolar macrophage
air mass factor
average medial thickness
Air Pollution Exposure (model)
atmospheric pressure ionization mass spectrometer
Air Quality Criteria Document
Air Quality Expert Group
American Thoracic Society
type of Lagrangian model
bronchoalveolar lavage
bronchoalveolar lavage fluid
Berlin Ozone Experiment
7V-bis(2-hydroxyl-propyl)nitrosamine
bronchial lavage fluid
Bayesian Maxim Eutropy
bromine
bromine ion
molecular bromine
bromine chloride
bromodeoxyuridine
bromine oxide
concentration
concentration x time; concentration times duration of exposure
Clean Air Act
concentrated ambient particles
California Air Resources Board
Clean Air Scientific Advisory Committee
Carbon Bond 4 (chemical mechanism)
August 2007
XXVI
DRAFT-DO NOT QUOTE OR CITE
-------
CC10
CCN
CD
CD4+
CD8+
CDC
CEPEX
CFD
CG
cGMP
CH4
C5H8
CHAD
CH3-CHO
CH3CH(O)OONO2
CH3CN
CH3-C(O)
CH3-C(O)H
CH3C(O)O
CH3-C(0)02,
CH3-C(O)OO
CH3OOH
(CH3)2S
CH3-S-H
CH3S03H
CH3-S-S-CH3
Cl
cr
CLaMS
C1NO2
C1NO3
CMAQ
CMAQ
CMSA
CO
COD
Clara cell 10-kDa protein
cyanomethylidyne radical
criteria document
helper T lymphocyte
suppressor T lymphocyte
Centers for Disease Control and Prevention
Central Equatorial Pacific Experiment
Computational Fluid Dynamics
cloud-to-ground (flash)
cyclic guanosine-3',5'-monophosphate
methane
ethene
ethane
isoprene
Consolidated Human Activities Database
acetaldehyde
peroxyacetyl nitrate
acetonitrile
acetyl radical
acetaldehyde
peroxyacetyl radical
acetyl peroxy, peroxyacetyl
methyl hydroperoxide
dimethylsulfide
methyl mercaptan
methanesulfonic acid
dimethyl disulfide
chorine
chorine ion
type of Lagrangian model
nitryl chloride
chlorine nitrate
Community Model for Air Quality;
Community Multiscale Air Quality (model)
consolidated metropolitan statistical area
carbon monoxide
coefficient of divergence
August 2007
XXVll
DRAFT-DO NOT QUOTE OR CITE
-------
COPD
CPBM
CS2
CTM
DEP
DEPcCBP
DL
DMN
DMSO
DNA
DNS
DOAS
DU
EC
ECRHS
EDMAS
EDXRF
EE
eGPx
ELF
EMD
EPA
ER
ESR
ETS
EXPOLIS
FEVi
FL
FLEXPART
FPD
FT
FTIR
FVC
FW2
yGCS
yGT
yN205
chronic obstructive pulmonary disease
Canyon Plume-Box Model
carbon disulfide
chemistry transport model
diesel exhaust particulates
diesel exhaust particulates extract-coated carbon black particles
detection limit
dimethylnitrosamine
dimethylsulfoxide
deoxyribonucleic acid
Direct Numerical Simulation
differential optical absorption spectroscopy
Dob son units
molecular carbon
European Community Respiratory Health Survey
Exposure and Dose Modeling and Analysis System
energy dispersive X-ray fluorescence
energy expenditure
extracellular glutathione peroxidase
epithelial lining fluid
Ecole des Mines de Douai (laboratory)
U.S. Environmental Protection Agency
emergency room
electron spin resonance (spectroscopy)
environmental tobacco smoke
Air Pollution Exposure Distributions of Adult Urban Populations in
Europe
forced expiratory volume in 1 second
fluoranthene
type of Lagrangian model
flame photometric detection
free troposphere
Fourier Transform Infrared Spectroscopy
forced vital capacity
black carbon soot model
gamma-glutamylcysteine synthetase
glutamyltranspeptidase
uptake coefficient for N2O5
August 2007
XXVlll
DRAFT-DO NOT QUOTE OR CITE
-------
GC/ECD
GEE
GEOS-CHEM
GEOS-1 DAS
GIS
GMP
GOME
GS
GSH
GSSG
GST
H+
H2
HAPEM
HCHO
HC1
HCN
HCs
HEADS
5-HETE
HNO3
HNO4
H02
H202
HOBr
HOC1
HONO, HNO2
HO2NO2
HOX
H2S
HSO3
HSO3
HSO4
H2SO4
hv
I
I2
gas chromatography-electron capture detection
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
Geographic Information System
guanosine-3',5'-monophosphate
Global Ozone Monitoring Instrument
glutathione synthetase
glutathione; reduced glutathione
oxidized glutathione; glutathione disulfide
glutathione S-transferase (e.g., GST Ml, GST PI, GST Tl)
hydrogen ion
molecular hydrogen; hydrogen gas
Hazardous Air Pollutant Exposure Model
formaldehyde
hydrochloric acid
hydrogen cyanide
hydrocarbons
Harvard-EPA Annular Denuder System
5-hydroxyeicosatetraenoic acid
nitric acid
pernitric acid
hydroperoxyl; hydroperoxy radical
hydrogen peroxide
hypobromous acid
hypochlorous acid
nitrous acid
peroxynitric acid
hypohalous acid
hydrogen sulfide
hydrogen sulfite ion
hydrogen sulfite
bisulfate ion
sulfuric acid
solar ultraviolet photon
iodine
molecular iodine
August 2007
XXIX
DRAFT-DO NOT QUOTE OR CITE
-------
IBEM
1C
ICAM-1
ICARTT
Ig
IIASA
IL
IMPROVE
INDAIR
IN03
INTEX-NA
IQR
JPL
Ka
KH
KH
Kw
LDH
LES
LIF
LP
LPG
LT
LWC
M
MAQSIP
MAX
MBL
MCM
MEM
MENTOR-1A
MET
MgO
MIESR
MM5
MOBILE6
Individual Based Exposure Models
intracloud (flash); ion chromatography
intercellular adhesion molecule-1
International Consortium for Atmospheric Research on Transport and
Transformation
immunoglobulin (e.g., IgA, IgE, IgG)
International Institute for Applied Systems Analysis
interleukin (e.g., IL-1, IL-6, IL-8)
Interagency Monitoring of Protected Visual Environments
(model)
iodine nitrate
NASA Intercontinental Chemical Transport Experiment - North
America
interquartile range
Jet Propulsion Laboratory
acid dissociation constant in M
Henry's Law constant in M atm"1
potassium hydride
ion product of water
lactic acid dehydrogenase
Large Eddy Simulation
laser-induced fluorescence
long-path
liquified propane gas
leukotriene (e.g., LTB4, LTC4, LTD4, LTE4)
liquid water content
air molecule
Multiscale Air Quality Simulation Platform
multi axis
marine boundary layer
master chemical mechanism
model ensemble mean
Modeling Environment for Total Risk for One-Atmosphere studies
metabolic equivalent of work
magnesium oxide
matrix isolation electron spin resonance (spectroscopy)
National Center for Atmospheric Research/Penn State Mesoscale
Model
Highway Vehicle Emission Factor Model
August 2007
XXX
DRAFT-DO NOT QUOTE OR CITE
-------
MoOx
MOZART-2
MPAN
mRNA
MSA
15N
N
N, n
NA, N/A, N.A.
NAAQS
Na2CO3
NADP
NADPH
NaHCO3
NARSTO
NASA
NCAR
NDMA
NEM
NERL
2NF
NH2
NH3
NH4+
NH4C1
NHLBI
NH4NO3
(NH4)2SO4
NIST
NK
NMHCs
NMOCs
NMOR
NO
NO2
NO2
NO3
NO3
molybdenum oxide
(model)
peroxymethacryloyl nitrate; peroxy-methacrylic nitric anhydride
messenger ribonucleic acid
metropolitan statistical area
nitrogen-15 radionuclide
nitrogen
number of observations
not available
National Ambient Air Quality Standards
sodium carbonate
National Atmospheric Deposition Program
reduced nicotinamide adenine dinucleotide phosphate
sodium bicarbonate
North American Regional Strategy for Atmospheric Ozone
National Aeronautics and Space Administration
National Center for Atmospheric Research
7V-nitrosodimethylamine
National Ambient Air Quality Standards Exposure Model
National Exposure Research Laboratory
2-nitrofluoranthene
amino
ammonia
ammonium ion
ammonium chloride
U.S. National Heart, Lung and Blood Institute
ammonium nitrate?
ammonium sulfate
National Institute of Standards and Technology
natural killer (lymphocytes)
nonmethane hydrocarbons
nonmethane organic compounds
7V-nitrosomorpholine
nitric oxide
nitrogen dioxide
nitrite
nitrate (radical)
nitrate
August 2007
XXXI
DRAFT-DO NOT QUOTE OR CITE
-------
N205
NOX
NOy
NOZ
NP
1NP
NPAHs
NR
NR, N.R., N/R
NRC
NSA
nss
NTRMs
16Q
03
OAQPS
OC
OCS
OC'D)
OH
OMI
0(3P)
OPE
OSPM
Ox
P(HN03)
P,P
P9o
PAHs
PAMS, PAMs
PAN
Pa02
PAQSMs
PAR
PBEM
PIXE
PM
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
national park
1-nitropyrene
nitro polycyclic aromatic hydrocarbons
data not relevant
not reported
National Research Council
nitrosating agent
non-sea-salt
NIST Traceable Reference Materials
oxygen-16 radionuclide
ozone
Office of Air Quality Planning and Standards
organic carbon
carbonyl sulfide
electronically excited oxygen atom
hydroxyl radical
Ozone Monitoring Instrument
ground-state oxygen atom
ozone production efficiency
Danish Operational Street Pollution Model
odd oxygen species; total oxidants
particulate nitrate
probability value
values of the 90th percentile absolute difference in concentrations
polycyclic aromatic hydrocarbons
Photochemical Aerometric Monitoring System
peroxyacetyl nitrate; peroxyacyl nitrate
partial pressure of arterial oxygen
photochemical air quality simulation models
proximal alveolar region
Population Based Exposure Models
particle induced X-ray emission
particulate matter
combination of coarse and fine particulate matter
August 2007
xxxn
DRAFT-DO NOT QUOTE OR CITE
-------
PMiQ-2.5
PM2.5
PMA
PM-CAMx
PMN
PMT
pNEM
POM
ppb
ppbv
ppm x h
ppm
PPN
ppt
pptv
PRB
psi
PTEAM
PTEP
PTFE
PY
r
R2
RACM
RADM
RAMs
RANS
RAPS
RBC
RCS
RDBMS
REHEX
RH
RIOPA
RMR
RNO2
RO2
coarse particulate matter
fine particulate matter
phorbol myri state acetate
Particulate Matter Comprehensive Air Quality Model with
Extensions
polymorphonuclear leukocytes
photomultiplier tube
Probabilistic National Ambient Air Quality Standard Exposure Model
particulate organic matter
parts per billion
parts per billion by volume
parts per million x hours
parts per million
peroxypropionyl nitrate; peroxypropionic nitric anhydride
parts per trillion
parts per trillion by volume
policy relevant background
pounds per square inch
Particle Total Exposure Assessment Methodology (study)
PMio Technical Enhancement Program
polytetrafluoroethylene (Teflon)
pyrene
correlation coefficient
coefficient of determination
Regional Air Chemistry Mechanism
Regional Acid Deposition Model
Regional Atmospheric Modeling System
Reynolds Averaged Numerical Simulation
Regional Air Pollution Study
red blood cell
Random Component Superposition (model)
Relational Database Management Systems
Regional Human Exposure Model
relative humidity
Relationship of Indoor, Outdoor, and Personal Air (study)
resting metabolic rate
nitro compounds
organic peroxyl; organic peroxy
August 2007
XXXlll
DRAFT-DO NOT QUOTE OR CITE
-------
RONO2
ROONO2, RO2NO2
ROS
rP
rs
RSD
G
34§
S2*
S20
SAPALDIA
SCE
SCIAMACHY
SCOS97
SGV
SHEDS
SMOKE
SO
SO2
S03
SOA
SONEX
SOS
SP
SRM
STE
STEP
STN
STPD
STREET
STRF
T
T
TAR
TEA
TOLAS
TEA
organic nitrate
peroxy nitrate
reactive oxygen species
Pearson correlation coefficient
Spearman rank correlation coefficient
relative standard deviation
sigma; standard deviation
sulfur-34 radionuclide
electronically excited sulfur molecules
disulfur monoxide
Study of Air Pollution and Lung Diseases in Adults
sister chromatid exchange
Scanning Imaging Absorption Spectrometer for Atmospheric
Chartography
1997 Southern California Ozone Study
subgrid variability
Simulation of Human Exposure and Dose System
Spare-Matrix Operator Kernel Emissions (system)
sulfur monoxide
sulfur dioxide
sulfur trioxide
secondary organic aerosol
Subsonic Assessment Ozone and Nitrogen Oxides Experiment
Southern Oxidant Study
surfactant protein (e.g., SP-A, SP-D)
standard reference material
stratospheric-tropospheric exchange
Stratospheric-Tropospheric-Exchange Proj ect
Speciation Trends Network
standard temperature and pressure, dry
type of street canyon model
Spatio-Temporal Random Field (theory)
tau; atmospheric lifetime
time; duration of exposure
Third Assessment Report
thiobarbituric acid
tunable-diode laser absorption spectroscopy
triethanolamine
August 2007
XXXIV
DRAFT-DO NOT QUOTE OR CITE
-------
TexAQS
Tg
THEES
TNF
TOR
Torr
TRACE-P
TTFMS
TVOCs
TX
UAM
UMD-CTM
UV
VE
VESTA
VOC
VT
WHO
XRF
Texas Air Quality Study
teragram
Total Human Environmental Exposure Study
tumor necrosis factor (e.g., TNF-a)
thermal-optical reflectance
unit of pressure
Transport and Chemical Evolution over the Pacific
two-tone frequency-modulated spectroscopy
total volatile organic compounds
thromboxane (e.g., TXA2, TXB2)
Urban Airshed Model
University of Maryland Chemical Transport Model
ultraviolet
total ventilation rate
Five (V) Epidemiological Studies on Transport and Asthma
volatile organic compound
tidal volume
World Health Organization
X-ray fluorescence
August 2007
XXXV
DRAFT-DO NOT QUOTE OR CITE
-------
i AX1. CHAPTER 1 ANNEX - INTRODUCTION
2
O
4 The draft Annexes are prepared in support of the draft Integrated Science Assessment for
5 Oxides of Nitrogen - Health Criteria (EPA/600/R-07/093). The Integrated Science Assessment
6 (ISA) presents a concise synthesis of the most policy-relevant science to form the scientific
7 foundation for the review of the primary (health-based) national ambient air quality standards
8 (NAAQS) for nitrogen dioxide (NO2). This series of Annexes provide more extensive and
9 detailed summaries of the most pertinent scientific literature. The Annexes identify, evaluate,
10 and summarize scientific research in the areas of atmospheric sciences, air quality analyses,
11 exposure assessment, dosimetry, controlled human exposure studies, toxicology, and
12 epidemiology, focusing on 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 Annex 1, we provide legislative
16 background and history of previous reviews of the NAAQS for oxides of nitrogen. In Annex 2,
17 we present evidence related to the physical and chemical processes controlling the production,
18 destruction, and levels of reactive nitrogen compounds in the atmosphere, including both
19 oxidized and reduced species. Annex 3 presents information on environmental concentrations,
20 patterns, and human exposure to ambient oxides of nitrogen; however, most information relates
21 to NC>2. Annex 4 presents results from toxicological studies as well as information on dosimetry
22 of oxides of nitrogen. Annex 5 discusses results from controlled human exposure studies, and
23 Annex 6 discusses evidence from epidemiological studies. These Annexes include more detailed
24 information on health or exposure studies that is summarized in tabular form, as well as more
25 extensive discussion of atmospheric chemistry, source, exposure, and dosimetry information.
26 Annex tables for health studies are generally organized to include information about
27 (1) concentrations of oxides of nitrogen levels or doses and exposure times, (2) description of
28 study methods employed, (3) results and comments, and (4) quantitative outcomes for oxides of
29 nitrogen measures.
30
31
August 2007 AX1-1 DRAFT-DO NOT QUOTE OR CITE
-------
1 AX1.1 LEGISLATIVE REQUIREMENTS
2 Two sections of the Clean Air Act (CAA) govern the establishment and revision of the
3 national ambient air quality standards (NAAQS). Section 108 (U.S. Code, 2003a) directs the
4 Administrator to identify and list "air pollutants" that "in his judgment, may reasonably be
5 anticipated to endanger public health and welfare" and whose "presence in the ambient air results
6 from numerous or diverse mobile or stationary sources" and to issue air quality criteria for those
7 that are listed. Air quality criteria are intended to "accurately reflect the latest scientific
8 knowledge useful in indicating the kind and extent of identifiable effects on public health or
9 welfare which may be expected from the presence of [a] pollutant in ambient air."
10 Section 109 (U.S. Code, 2003b) directs the Administrator to propose and promulgate
11 "primary" and "secondary" NAAQS for pollutants listed under Section 108. Section 109(b) (1)
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,
24 455 U.S. 1034(1982). Both kinds of uncertainties are components of the risk associated with
25 pollution at levels below those at which human health effects can be said to occur with
1 The legislative history of Section 109 indicates that a primary standard is to be set at "the maximum permissible
ambient air level ... which will protect the health of any [sensitive] group of the population" and that, for this
purpose, "reference should be made to a representative sample of persons comprising the sensitive group rather
than to a single person in such a group" [U.S. Senate (1970)].
2 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."
August 2007 AX1-2 DRAFT-DO NOT QUOTE OR CITE
-------
1 reasonable scientific certainty. Thus, in selecting primary standards that include an adequate
2 margin of safety, the Administrator is seeking not only to prevent pollution levels that have been
3 demonstrated to be harmful but also to prevent lower pollutant levels that may pose an
4 unacceptable risk of harm, even if the risk is not precisely identified as to nature or degree.
5 In selecting a margin of safety, the U.S. Environmental Protection Agency (EPA)
6 considers such factors as the nature and severity of the health effects involved, the size of
7 sensitive population(s) at risk, and the kind and degree of the uncertainties that must be
8 addressed. The selection of any particular approach to providing an adequate margin of safety is
9 a policy choice left specifically to the Administrator's judgment. See Lead Industries
10 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,
15 465-472, and 475-76 (U.S. Supreme Court, 2001).
16 Section 109(d)(l) requires that "not later than December 31, 1980, and at 5-year intervals
17 thereafter, the Administrator shall complete a thorough review of the criteria published under
18 Section 108 and the national ambient air quality standards and shall make such revisions in such
19 criteria and standards and promulgate such new standards as may be appropriate ...." Section
20 109(d)(2) requires that an independent scientific review committee "shall complete a review of
21 the criteria ... and the national primary and secondary ambient air quality standards ... and shall
22 recommend to the Administrator any new standards and revisions of existing criteria and
23 standards as may be appropriate ...." Since the early 1980s, this independent review function
24 has been performed by the Clean Air Scientific Advisory Committee (CAS AC) of EPA's
25 Science Advisory Board.
26
27
28 AX1.2 HISTORY OF REVIEWS OF THE PRIMARY NAAQS FOR NO2
29 On April 30, 1971, EPA promulgated identical primary and secondary NAAQS for
30 nitrogen dioxide (NC^), under Section 109 of the Act, set at 0.053 parts per million (ppm),
31 annual average (Federal Register, 1971). In 1982, EPA published Air Quality Criteria for
32 Oxides of Nitrogen (1982 NOX AQCD) (U.S. Environmental Protection Agency, 1982), which
August 2007 AX1-3 DRAFT-DO NOT QUOTE OR CITE
-------
1 updated the scientific criteria upon which the initial NC>2 standards were based. On February 23,
2 1984, EPA proposed to retain these standards (Federal Register, 1984). After taking into account
3 public comments, EPA published the final decision to retain these standards on June 19, 1985
4 (Federal Register, 1985).
5 On July 22, 1987, EPA announced that it was undertaking plans to revise the 1982 NOX
6 air quality criteria (Federal Register, 1987). In November 1991, EPA released an updated draft
7 air quality criteria document (AQCD) for CASAC and public review and comment (Federal
8 Register, 1991). The draft document provided a comprehensive assessment of the available
9 scientific and technical information on heath and welfare effects associated with NO2 and other
10 oxides of nitrogen. The CASAC reviewed the document at a meeting held on July 1, 1993, and
11 concluded in a closure letter to the Administrator that the document "provides a scientifically
12 balanced and defensible summary of current knowledge of the effects of this pollutant and
13 provides an adequate basis for EPA to make a decision as to the appropriate NAAQS for NCV
14 (Wolff, 1993).
15 The EPA also prepared a draft Staff Paper that summarized and integrated the key studies
16 and scientific evidence contained in the revised AQCD and identified the critical elements to be
17 considered in the review of the NO2 NAAQS. The Staff Paper received external review at a
18 December 12, 1994 CASAC meeting. CASAC comments and recommendations were reviewed
19 by EPA staff and incorporated into the final draft of the Staff Paper as appropriate. CASAC
20 reviewed the final draft of the Staff Paper in June 1995 and responded by written closure letter
21 (Wolff, 1995). In September of 1995, EPA finalized the document entitled, "Review of the
22 National Ambient Air Quality Standards for Nitrogen Dioxide Assessment of Scientific and
23 Technical Information" (U.S. Environmental Protection Agency, 1995).
24 Based on that review, the Administrator announced her proposed decision not to revise
25 either the primary or the secondary NAAQS for NO2 (Federal Register, 1995). The decision not
26 to revise the NO2 NAAQS was finalized after careful evaluation of the comments received on the
27 proposal. The level for both the existing primary and secondary NAAQS for NO2 is 0.053 ppm
28 annual arithmetic average, calculated as the arithmetic mean of the 1-h NO2 concentrations.
August 2007 AX1-4 DRAFT-DO NOT QUOTE OR CITE
-------
1 AX1.3 REFERENCES
2
3 Federal Register. (1971) National primary and secondary ambient air quality standards. F. R.
4 (April 30) 36: 8186-8201.
5 Federal Register. (1984) Proposed reaffirmation of the National Ambient Air Quality Standards
6 for Nitrogen Dioxide. F. R. 49 (February 23): 6866-6879.
7 Federal Register. (1985) Retention of the national ambient air quality standards for nitrogen
8 dioxide: final rule. F. R. (June 19) 50: 25,532-25,545.
9 Federal Register. (1987) Air quality criteria for carbon monoxide; air quality criteria for oxides
10 of nitrogen. F. R. 52 (July 22): 27580.
11 Federal Register. (1991) Draft criteria document for oxides of nitrogen. F. R. 56 (November 25):
12 59285.
13 Federal Register. (1995) National ambient air quality standards for nitrogen dioxide: proposed
14 decision. F. R. 60 (October 11): 52874-52889.
15 U.S. Code. (2003a) Clean Air Act, §108, air quality criteria and control techniques. U. S. C. 42:
16 §7408.
17 U.S. Code. (2003b) Clean Air Act, §109, national ambient air quality standards. U. S. C. 42:
18 §7409.
19 U.S. Code. (2005) Clean Air Act, §302, definitions. U. S. C. 42: §7602(h).
20 U.S. Court of Appeals for the District of Columbia. (1980) Lead Industries v. U.S.
21 Environmental Protection Agency. 647 F2d 1130, 1154 (DC Cir. 1980).
22 U.S. Court of Appeals for the District of Columbia. (1981) American Petroleum Institute v.
23 Costle. 665 F2d 1176, 1186 (DC Cir. 1981).
24 U.S. Environmental Protection Agency. (1982) Air quality criteria for oxides of nitrogen.
25 Research Triangle Park, NC: Office of Health and Environmental Assessment,
26 Environmental Criteria and Assessment Office; EPA report no. EPA-600/8-82-026.
27 Available from: NTIS, Springfield, VA; PB83-131011.
28 U.S. Environmental Protection Agency. (1995) Review of the national ambient air quality
29 standards for nitrogen dioxide: assessment of scientific and technical information.
30 Research Triangle Park, NC: Office of Air Quality Planning and Standards; report no.
31 EPA/452/R-95-005.
32 U.S. Senate. (1970) National Air Quality Standards Act of 1970: report of the Committee on
33 Public Works, United States Senate together with individual views to accompany S.
34 4358. Washington, DC: Committee on Public Works; report no. CONG/91-1196.
35 U.S. Supreme Court. (2001) Whitman v. American Trucking Association. 531 U.S. 457 (nos. 99-
36 1257 and 99-1426).
37 Wolff, G. T. (1993) On a NOx-focused control strategy to reduce O3. J. Air Waste Manage.
38 Assoc. 43: 1593-1596.
August 2007 AX1-5 DRAFT-DO NOT QUOTE OR CITE
-------
1 Wolff, G. T. (1996) Closure by the Clean Air Scientific Advisory Committee (CASAC) on the
2 staff paper for parti culate matter [letter to Carol M. Browner, Administrator, U.S. EPA].
3 Washington, DC: U.S. Environmental Protection Agency, Clean Air Scientific Advisory
4 Committee; EPA-SAB-CASAC-LTR-96-008; June 13.
5
6
August 2007 AX1-6 DRAFT-DO NOT QUOTE OR CITE
-------
i AX2. CHAPTER 2 ANNEX-ATMOSPHERIC
2 CHEMISTRY OF NITROGEN AND SULFUR OXIDES
o
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 (O3) 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. EPA Criteria Air Pollutant; similarly, oxides of sulfur (SOX) are defined here to be sulfur
11 monoxide (SO), sulfur dioxide (802), the largest component of SOX and also a U.S. EPA Criteria
12 Air Pollutant, and sulfur trioxide (SO3). SO3 rapidly reacts with water vapor to form H2SO4, and
13 only SO2 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 poly cyclic aromatic
17 hydrocarbons (PAHs) to form nitro-PAHs, which may be even more toxic than the precursors.
18 Nitrogen dioxide together with sulfur dioxide (802), another U.S. EPA criteria air pollutant, can
19 be oxidized to the strong mineral acids, nitric acid (HNO3) and sulfuric acid (H2SO4), which
20 contribute to the acidity of cloud, fog, and rainwater, and can form ambient particles.
21 The role of NOX in O3 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 O3 precursors, the factors controlling the efficiency of O3
25 production from NOX, methods for calculating O3 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 HNO3 to form ammonium nitrate (NH4NO3), which is a major
August 2007 AX2-1 DRAFT-DO NOT QUOTE OR CITE
-------
1 constituent of ambient Parti culate 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 SC>2 in Section
5 AX2.3. Mechanisms for the formation of aqueous-phase sulfate (SC>42 ) and nitrate (NOs ) are
6 reviewed in Section AX2.4. Sources and emissions of NOX, NHa, and SC>2 are discussed in
7 Section AX2.5. Modeling methods used to calculate the atmospheric chemistry, transport, and
8 fate of NOX and 862 and their oxidation products are presented in Section AX2.6. Measurement
9 techniques for the nitrogen-containing compounds and for 862, nitrates, sulfates, and ammonium
10 ion are discussed in Section AX2.8. Estimates of policy-relevant background concentrations of
1 1 NOX and SOX are given in Section AX2.9. An overall review of key points in this chapter is
12 given in Section AX2. 1 1 .
13 The overall chemistry of reactive nitrogen compounds in the atmosphere is summarized
14 in Figure AX2-1 and is described in greater detail in the following sections. Nitrogen oxides are
15 emitted primarily as NO with smaller quantities of NC>2. 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 . 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
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 NC>2
27 by solar UV-A radiation to yield NO and a ground-state oxygen atom, O(3P):
2g N02 + hv -> NO + 0(-lP)5 (AX2_1}
29 This ground-state oxygen atom can then combine with molecular oxygen (©2) to form Os; and,
30 colliding with any molecule from the surrounding air (M = N2, C>2, etc), the newly formed Os
3 1 molecule, transfers excess energy and is stabilized:
August 2007 AX2-2 DRAFT-DO NOT QUOTE OR CITE
-------
Hit
Long range transport to remote
regions at low temperatures
nitro-PAHs
R-G=C-lt
nitrosamines,
nitro-phenols,
quinones, etc.
RO.
• 2_^,RONO
deposition
emissions
Figure AX2-1.
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
5
6
7
O(3P) + O2 + M-*O3 + M,
where M = N2, O2. Reaction AX2-2 is the only significant reaction forming Os in the
troposphere.
NO and Os react to reform NO2:
(AX2-2)
NO+O3 , .,^ . V£. (AX2-3)
Reaction AX2-3 is responsible for O3 decreases and NO2 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 NO2 without the
participation of Os (as in Reaction AX2-3):
August 2007
AX2-3
DRAFT-DO NOT QUOTE OR CITE
-------
(AX2-4)
2 Ozone, therefore, can accumulate as NO2 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 O3 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 Ox = + 0('D) + Q3 + N02)
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
25
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 = I (HNO3 + HNO4 + NO 3 + 2NO2OS + PAN(CH3CHO - OO - NO2) + other
23 organic mitmties + halogen nitrates + partieulate nitrate);
NOy = NOX + NOZ +
=
x
August 2007 AX2-4 DRAFT-DO NOT QUOTE OR CITE
-------
The reaction of NO2 with O3 leads to the formation of N(V radical,
2 . (AX2.5)
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 (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, NO3, rather than the hydroxyl radical (OH), is the primary oxidant in the system.
1 1 Nitrate radicals can combine with NO2 to form dinitrogen pentoxide (N2Os):
12 NO 3 + N02 *-^*N205 (AX2_?)
13 and N2Os both photolyzes and thermally decomposes back to NO2 and NOs during the day;
14 however, N2O5 concentrations ([N2O5]) can accumulate during the night to parts per billion (ppb)
15 levels in polluted urban atmospheres.
16 The tropospheric chemical removal processes for NOX include reaction of NO2 with the
17 OH radical and hydrolysis of N2Os in aqueous aerosol solutions if there is no organic coating.
18 Both of these reactions produce
19 (AX2.g)
20 '^"^ ,-,-j (AX2-9)
21 The gas-phase reaction of the OH radical with NO2 (Reaction AX2-8) initiates one of the
22 major and ultimate removal processes for NOX in the troposphere. This reaction removes OH
23 and NO2 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
August 2007 AX2-5 DRAFT-DO NOT QUOTE OR CITE
-------
1 conversion of NOX to HNOs 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, HNO3 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 NC>2 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.
11 In addition to the uptake of HNO3 on particles and in cloud drops, it photolyzes and
12 reacts with OH radicals via
13 *"'~j «• ~" "~/ (AX2-10)
14 and
15 UNO 3 + OH-* NO3 + H2O, (AX2 11
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 NO2 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 ^Os due to lower temperatures and less sunlight. Note
28 that Reaction AX2-9 proceeds as a heterogeneous reaction. Recent work in the northeastern U.S.
August 2007 AX2-6 DRAFT-DO NOT QUOTE OR CITE
-------
1 indicates that this reaction is proceeds at a faster rate in power plant plumes than in urban plumes
2 (Brown et al., 2006a,b; Frost et al., 2006).
3 OH radicals also can react with NO to produce nitrous acid (HONO or HNO2):
4 (AX2_12)
5 In the daytime, HNO2 is rapidly photolyzed back to the original reactants:
f. HNO-t + hv. ,AYO irv
6 *- (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 NO2 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)
12 (e.g.) suggested that photolysis of this HNO2 at sunrise could provide an important early-
13 morning source of OH radicals to drive Os formation
14 Hydroperoxy (HO2) radicals can react with NO2 to produce pernitric acid (HNO4):
(AX2-14)
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 HNO4 and N2Os 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, HNOs, HNO4, and N2Os
22 serve as NOX reservoirs that can liberate NO2 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 (RO2), as discussed in the latest
27 AQCD for Ozone and Other Photochemical Oxidants (U.S. Environmental Protection Agency,
August 2007 AX2-7 DRAFT-DO NOT QUOTE OR CITE
-------
1 2006). Reaction of RC>2 radicals with NO and NC>2 produces organic nitrates (RONO2) and
2 peroxynitrates (RC^
3 RO^NO-H-^RONO, (AX2.15)
4 R02 + N02^i-+R02N02 (AX2_16)
5 Reaction (AX2-15) is a minor branch for the reaction of RO2 with NO. The major branch
6 produces RO and NO2, 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 NO2:
12 CH3C(0)-00 + N02 -> CH3C(0)OON02 (AX2-17)
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 -> CHs-C(0) + H (AX2-18)
CH^C(O) + H2O
18 Acetyl radicals then react with O2 to yield acetyl peroxy radicals
19 CH3-C(0) + 02 + M ^ CH3C(0)-00 + M
20 However, acetyl peroxy radicals will react with NO in areas of high NO concentrations
21 CH3(CO)-00 + NO ^ CH3(CO)-Q + NO2 (AX2-21)
22 and the acetyl-oxy radicals will then decompose
23 CH3(CO)-0 -» CH, + C02
August 2007 AX2-8 DRAFT-DO NOT QUOTE OR CITE
-------
1 Thus, the formation of PAN is favored at conditions of high ratios of NC>2 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 NO2 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(Os)) 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 O?, 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 Os 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, NO2 scavenges OH radicals which would otherwise oxidize VOCs to
29 produce peroxy radicals, which in turn would oxidize NO to NO2. 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 'NOx-limited' are used to describe
32 these two regimes. However, there are difficulties with this usage because: (l)VOC
August 2007 AX2-9 DRAFT-DO NOT QUOTE OR CITE
-------
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 NOx-limited and NOx-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 O?, 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 Os concentrations to emissions of precursors. It should also be noted at the outset that
11 in a NOx-limited (or NOx-sensitive) regime, O3 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 NOx-limited.
15 Various analytical techniques have been proposed that use ambient NOX and VOC
16 measurements to derive information about Os production and O3-NOX-VOC sensitivity.
17 Previously (e.g., National Research Council, 1991), it was suggested that Os 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 NOx-sensitive or VOC sensitive. This technique is inadequate to characterize Os formation
22 because it omits many factors recognized as important for P(Os), 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 NOx-limited to NOx-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
August 2007 AX2-10 DRAFT-DO NOT QUOTE OR CITE
-------
1 species including HNOs, organic nitrates, and hydrogen peroxide (H2O2). The relative rates of
2 P(Os) and the production of other species varies depending on photochemical conditions, and can
3 be used to provide information about O3-precursor sensitivity.
4 There are no hard and fast rules governing the levels of NOX at which the transition from
5 NOx-limited to NOx-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 O3 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 63 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 Os production to higher NOX
13 mixing ratios, thereby reducing or eliminating areas in which Os production rates decreased due
14 to aircraft emissions.
15 Trainer et al. (1993) suggested that the slope of the regression line between Os and
16 summed NOX oxidation products (NOZ, equal to the difference between measured total reactive
17 nitrogen, NOy, and NOX) can be used to estimate the rate of P(Os) per NOX (also known as the 63
18 production efficiency, or OPE). Ryerson et al. (1998, 2001) used measured correlations between
19 Os and NOZ to identify different rates of Os 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 NOx-limited conditions and NOx-saturated conditions.
22 The most important correlations are for Os versus NOy, Os versus NOZ, Os versus HNOs, and
23 H2O2 versus HNOs. The correlations between Os and NOy, and Os and NOZ are especially
24 important because measurements of NOy and NOX are widely available. Measured Os versus
25 NOZ (Figure AX2-2) shows distinctly different patterns in different locations. In rural areas and
26 in urban areas such as Nashville, TN, Os shows a strong correlation with NOZ and a relatively
27 steep slope to the regression line. By contrast, in Los Angeles Os also increases with NOZ, but
28 the rate of increase of O3 with NOZ is lower and the O3 concentrations for a given NOZ value are
29 generally lower.
August 2007 AX2-11 DRAFT-DO NOT QUOTE OR CITE
-------
1 The difference between NOx-limited and NOx-saturated regimes is also reflected in
2 measurements of H2O2. Formation of H2O2 takes place by self-reaction of photochemically-
3 generated HO2 radicals, so that there is large seasonal variation of H2O2 concentrations, and
250-1
.0
Q.
Q.
Figure AX2-2.
x
X
X
X
x-
X
X
X
-x-
X X
X
X
X
X
X
10
20
NOZ (ppb)
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 NOx-limited conditions. When the photochemistry is NOX-saturated, much less
7 H2O2 is produced. In addition, increasing NOX tends to slow the formation of H2O2 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 H2O2 concentrations likely due to differences in NOX availability at
13 these locations.
14
August 2007
AX2-12
DRAFT-DO NOT QUOTE OR CITE
-------
1 AX2.2.3 Multiphase Chemistry Involving NOX
2 Recent laboratory studies on sulfate and organic aerosols indicate that the reaction
3 probability yN2os 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 U.S., Brown et al. (2006b) found that
7 the uptake coefficient for N2Os, yN2Os, on the surfaces of particles depends strongly on their
8 sulfate content. They found that yN2Os was highest (0.017) in regions where the aerosol sulfate
9 concentration was highest and lower elsewhere (<0.0016). This result contrasts with that of
10 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 N2O5 would be saturated for typical ambient aerosol surface areas. The importance of this
13 reaction to tropospheric chemistry depends on the value of yN2Os. If it is 0.01 or lower, there
14 may be difficulty in explaining the loss of NOy and the formation of aerosol nitrate, especially
15 during winter. A decrease in N2Os slows down the removal of NOX by leaving more NO2
16 available for reaction and thus increases Os production. Based on the consistency between
17 measurements of NOy 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 NOy in marine air (e.g., Huebert et al., 1996). Consequently, some caution is
26 warranted when interpreting constituent ratios and NOy 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 NO2
31 (Zafiriou and True, 1979) in acidic sea salt solutions (Anastasio et al., 1999). Further photolytic
August 2007 AX2-13 DRAFT-DO NOT QUOTE OR CITE
-------
1 reduction of NC>2 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 NO3 (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 NO2 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 NO2 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] (Xu et al., 2006).
18 Ammann et al. (1998) reported the efficient conversion of NO2 to HONO on fresh soot
19 particles in the presence of water. They suggest that interaction between NO2 and soot particles
20 may account for high mixing ratios of HONO observed in urban environments. Conversion of
21 NO2 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 NO2 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 HNO3 on model black carbon soot
29 (FW2), graphite, hexane, and kerosene soot. They found that HNOs decomposed to NO2 and
30 H2O at higher HNOs surface coverages, i.e., P(HNO3) > = 10~4 Torr. None of the soot models
31 used were reactive at low HNOs coverages, at P(HNO3) = 5 x 10~7 Torr or at temperatures below
August 2007 AX2-14 DRAFT-DO NOT QUOTE OR CITE
-------
1 220 K. They conclude that it is unlikely that aircraft soot in the upper troposphere/lower
2 stratosphere reduces HNO3.
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 NO2. 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 NO2
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 NO2, HNO3, NO3/N2O5,
16 HO2/HO2NO2 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 NO2, NO3, HO2, and HO2NO2 deposition to soot; HNO3
19 reduction to NO2; and ^Os 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 O3 maximum, for a soot loading of 20 jig nT3, i.e.,
22 roughly a factor of 10 times observed black carbon loadings seen in U.S. urban areas, even
23 during air pollution episodes.
24 Mufioz and Rossi (2002) conducted Knudsen cell studies of HNO3 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 HNO3 uptake on grey soot, and NO and traces of NO2 from
27 black soot. They conclude that these reactions would only have relevance in special situations in
28 urban settings where soot and HNO3 are present in high concentrations simultaneously.
29
30 Formation ofNitro PAHs
31 Nitro-polycyclic aromatic hydrocarbons (nitro-PAHs) (see Figure AX2-3 for some
32 example nitro-PAHs) are generated from incomplete combustion processes through electrophilic
August 2007 AX2-15 DRAFT-DO NOT QUOTE OR CITE
-------
2-nitronaphthalene 9-nitroanthracene 2-nitrofluoranthene 6-nitrobenzo(a)pyrene
Figure AX2-3. Structures of nitro-polycyclic aromatic hydrocarbons.
1 reactions of polycyclic aromatic hydrocarbons (PAHs) in the presence of NC>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 AX2-3.
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,
August 2007
AX2-16
DRAFT-DO NOT QUOTE OR CITE
-------
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 NO2 to the
4 OH-PAH adduct and elimination of water to form the nitroarenes (Figure AX2-4, 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 cm3molecule~ V1 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 AX2-4.
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 NOs 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 (Os) with
14 NO2 in the atmosphere by Reaction AX2-5:
August 2007
AX2-17
DRAFT-DO NOT QUOTE OR CITE
-------
(AX2-5)
2 Similar to the mechanism of OH reactions with PAHs, NOs initially adds to the PAH ring
3 to form an NOs-PAH adduct, followed by loss of HNOs 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 N2Os-NO3-NO2, the major products formed through the NOs 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 NOs radical, respectively (Atkinson et al., 1990; Atkinson and Arey, 1994).
9 The reaction with NOs is of minor importance in the daytime because NOs radical is not
10 stable in sunlight. In addition, given the rapid reactions of NO with NOs and with Os in the
11 atmosphere (Finlay son-Pitts and Pitts 2000), concentrations of NOs at ground level are low
12 during daytime. However, at night, concentrations of NO3 radicals formed in polluted ambient
13 air are expected to increase. According to Atkinson et al. (1991), the average NOs 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 NOs radical concentrations are found at elevated altitudes where O3 is
16 high but NO is low (Reissell and Arey, 2001; Stutz et al., 2004). When NO3 reaches high
17 concentrations, the formation of nitro-PAHs by the reaction of gaseous PAHs with NO3 may be
18 of environmental significance. At 10~17 - 10~18 cm3 molecule V1, the rate constants of NO3
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 NO3 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 NO3
23 radical reactions (Atkinson and Arey 1994). Therefore, formation of nitro-PAHs via reactions of
24 NO3 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 NO2/N2Os in the presence of trace amounts of HNO3 (HNO3) 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 ^Os, and the
30 distribution of product NF isomers was 3- > 8- > 7- > 1- NF (Pitts et al., 1985a, b). The
August 2007 AX2-18 DRAFT-DO NOT QUOTE OR CITE
-------
1 proposed mechanism for this reaction was an ionic electrophilic nitration by nitronium ion
2 (NC>2+). It was speculated that ^Os became ionized prior to the reaction with FL (Zielinska
3 et al., 1986). Only 1NP was observed for the reaction of PY with ^Os on filters (Pitts et al.,
4 1985b). Compared to the reactions of OH and NOs, nitration of PAHs by NO2/N2Os is less
5 important.
6 Measurements of nitro-PAHs in ambient air provide evidence for the proposed reaction
7 mechanism, i.e. the reactions of OH and NOs radicals with PAHs are the major sources of
8 nitro-PAHs (Bamford and Baker, 2003; Reisen and Arey, 2005; and references therein). 2NF is
9 a ubiquitous component of ambient POM, much higher than 1NP, itself a marker of combustion
10 sources. Nitro-PAH isomer ratios show strong seasonality. For instance, the mean ratios of
11 2NF/1NP were higher in summer than in winter (Bamford et al., 2003; Reisen and Arey, 2005),
12 indicating that reactions of OH and NOs with FL are the major sources of nitro-PAHs in ambient
13 air in summer. The ratio of 2NF/1NP was lower in winter than in summer because of lower OH
14 concentrations and, therefore, less production of 2NF via atmospheric reactions. A ratio of
15 1NP/2NF greater than 1 was observed in locations with major contributions from vehicle
16 emissions (Dimashki et al., 2000; Feilberg et al., 2001). In addition, the ratio of 2NF/2NP was
17 also used to evaluate the contribution of OH and NOs initiated reactions to the ambient nitro-
18 PAHs (Bamford et al., 2003; Reisen and Arey, 2005).
19 The concentrations for most nitro-PAHs found in ambient air are much lower than
20 1 pg/m3, except NNs, 1NP, and 2NF, which can be present at several pg/m3. These levels are
21 much lower (~2 to -1000 times lower) than their parent PAHs. However, nitro-PAHs are much
22 more toxic than PAHs (Durant et al., 1996; Grosovsky et al., 1999; Salmeen et al., 1982; Tokiwa
23 et al., 1998; Tokiwa and Ohnishi, 1986). Moreover, most nitro-PAHs are present in particles
24 with a mass median diameter <0.1 |im.
25 Esteve et al. (2006) examined the reaction of gas-phase NO2 and OH radicals with
26 various PAHs adsorbed onto model diesel paniculate matter (SRM 1650a, NIST). Using pseudo
27 second order rate coefficients, they derived lifetimes for conversion of the particle-bound PAHs
28 to nitro-PAHs of a few days (for typical urban NO2 levels of 20 ppb). They also found that the
29 rates of reaction of OH with the PAHs were about four orders of magnitude larger than for the
30 reactions involving NO2. However, since the concentrations of NO2 used above are more than
31 four orders of magnitude larger than those for OH (106-107/cm3), they concluded that the
August 2007 AX2-19 DRAFT-DO NOT QUOTE OR CITE
-------
1 pathway involving NC>2 is expected to be favored over that involving OH radicals. Consistent
2 with the importance of the gas-phase formation of NPAHS, both the mutagenic potency of PM
3 and the content of NPAHs in PM vary by particle size, and are higher in the submicron size
4 range (Xu and Lee, 2000; Kawanaka et al., 2004).
5 The major loss process of nitro-PAHs is photodecomposition (Fan et al., 1996; Feilberg
6 et al., 1999; Feilberg and Nielsen, 2001), with lifetimes on the order of hours. However, lacking
7 direct UV light sources indoors, nitro-PAHs are expected have a longer lifetimes (days) indoors
8 than outdoors; and may therefore pose increased health risks. Many nitro-PAHs are semi- or
9 nonvolatile organic compounds. As stated above, indoor environments have much greater
10 surface areas than outdoors. Thus, it is expected that gas/particle distribution of nitro-PAHs
11 indoors will be different from those in ambient air. A significant portion of nitro-PAHs will
12 probably be adsorbed by indoor surfaces, such as carpets, leading to different potential exposure
13 pathways to nitro-PAHs in indoor environments. The special characteristics of indoor
14 environments, which can affect the indoor chemistry and potential exposure pathways
15 significantly, should be taken into consideration when conducting exposure studies of nitro-
16 PAHs.
17 Reaction with OH and NOs radicals is a major mechanism for removing gas-phase PAHs,
18 with OH radical initiated reactions predominating depending on season (Vione et al., 2004;
19 Bamford et al., 2003). Particle-bound PAH reactions occur but tend to be slower.
20 Nitronaphthalenes tend to remain in the vapor phase, but because phase partitioning depends on
21 ambient temperature, in very cold regions these species can condense (Castells et al., 2003)
22 while the higher molecular weight PAHs such as the nitroanthracenes, nitrophenantrenes and
23 nitrofluoranthenes condense in and on PM (Ciganek et al., 2004; Cecinato, 2003).
24
25 Multiphase Chemical Processes Involving Nitrogen Oxides and Halogens
26 Four decades of observational data on Os in the troposphere have revealed numerous
27 anomalies not easily explained by gas-phase HOX-NOX photochemistry. The best-known
28 example is the dramatic depletion of ground-level Os during polar sunrise due to multiphase
29 catalytic cycles involving inorganic Br and Cl radicals (Barrie et al., 1988; Martinez et al., 1999;
30 Foster et al., 2001). Other examples of anomalies in tropospheric O3 at lower latitudes include
31 low levels of Os (<10 ppbv) in the marine boundary layer (MBL) and overlying free troposphere
32 (FT) at times over large portions of the tropical Pacific (Kley et al., 1996), as well as post-sunrise
August 2007 AX2-20 DRAFT-DO NOT QUOTE OR CITE
-------
1 Os depletions over the western subtropical Pacific Ocean (Nagao et al., 1999), the temperate
2 Southern Ocean (Galbally et al., 2000), and the tropical Indian Ocean (Dickerson et al., 1999).
3 The observed O3 depletions in near-surface marine air are generally consistent with the model-
4 predicted volatilization of Br2, BrCl, and C12 from sea salt aerosols through autocatalytic halogen
5 "activation" mechanisms (e.g., Vogt et al., 1996; von Glasow et al., 2002a) involving these
6 aqueous phase reactions.
(AX2-23)
g HOCL + Br- + H+^ BrCl + H2O (AX2-24)
9 HOC1 + Cl~ + /f+ H» C/2 + H2O (AX2-25)
10 In polluted marine regions at night, the heterogeneous reaction
12 may also be important (Finlayson-Pitts et al., 1989; Behnke et al., 1997; Erickson et al., 1999).
13 Diatomic bromine, BrCl, C12, and C1NO2 volatilize and photolyze in sunlight to produce atomic
14 Br and Cl. The acidification of sea salt aerosol via incorporation of HNOs (and other acids)
15 leads to the volatilization of HC1 (Erickson et al., 1999), e.g.
16 HN03+C1--*HCI+N03- (AX2_2?)
17 and the corresponding shift in phase partitioning can accelerate the deposition flux to the surface
18 of total NO3 (Russell et al., 2003; Fischer et al., 2006). However, Pryor and Sorensen (2000)
19 have shown that the dominant form of nitrate deposition is a complex function of wind speed. In
20 polluted coastal regions where HC1 from Reaction 35 often exceeds 1 ppbv, significant
21 additional atomic Cl~ is produced via:
22 HC1 + OH -> Cl + H2O (AX2-28)
23 (Singh and Kasting, 1988; Keene et al., 2007). Following production, Br and Cl atoms
24 catalytically destroy O3 via:
August 2007 AX2-21 DRAFT-DO NOT QUOTE OR CITE
-------
XO + 02 (AX2-29)
XD+H02-*HOX+02 (AX2_30)
(AXM1)
4 where (X = Br and Cl).
5 Formation of Br and Cl nitrates via
6 (AX2.32)
7 and the subsequent reaction of XNO3 with sea salt and sulfate aerosols via
a XNO3 + H2O -> MM" + ,ff+ + NO3
o "k
9 and:
10 XN03+Y ~~»IY + .,^ (AX2-34)
11 (where Y = Cl, Br, or I) accelerates the conversion of NOX to paniculate NO3 and thereby
12 contributes indirectly to net O3 destruction (Sander et al., 1999; Vogt et al., 1999, Pszenny et al.,
13 2004). Most XNO3 reacts via reaction 34 on sea salt whereas reaction 33 is more important on
14 sulfate aerosols. Partitioning of HC1 on sulfate aerosols following Henry's Law provides Cl for
15 reaction 34 to form BrCl. Product NO3 from both reactions AX2-33 and AX2-34 partitions
16 with the gas-phase HNO3 following Henry's Law. Because most aerosol size fractions in the
17 MBL are near equilibrium with respect to HNO3 (Erickson et al., 1999; Keene et al., 2004), both
18 sulfate and sea salt aerosol can sustain the catalytic removal of NOX and re-activation of Cl and
19 Br with no detectable change in composition. The photolytic reduction of NO3" in sea salt
20 aerosol solutions recycles NOX to the gas phase (Pszenny et al., 2004). Halogen chemistry also
21 impacts O3 indirectly by altering OH/HO2 ratios (XO + HO2 -> HOX + O2 -> OH + X) (e.g.,
22 Stutz et al., 1999; Bloss et al., 2005).
23 In addition to O3 destruction via reaction AX2-37, atomic Cl oxidizes hydrocarbons
24 (HCs) primarily via hydrogen abstraction to form HC1 vapor and organz products (Jobson et al.,
25 1994; Pszenny et al., 2006). The enhanced supply of odd-H radicals from HC oxidation leads to
August 2007 AX2-22 DRAFT-DO NOT QUOTE OR CITE
-------
1 net 63 production in the presence of sufficient NOX (Pszenny et al., 1993). Available evidence
2 suggests that Cl~ radical chemistry may be a significant net source for 63 in polluted
3 coastal/urban air (e.g., Tanaka et al., 2003; Finley and Saltzman, 2006).
4 An analogous autocatalyic Os destruction cycle involving multiphase iodine chemistry
5 also operates in the marine atmosphere (Alicke et al., 1999, Vogt et al., 1999; McFiggans et al.,
6 2000; Ashworth et al., 2002). In this case, the primary source of I is believed to be either
7 photolysis of CH2I2, other I-containing gases (Carpenter et al., 1999; Carpenter, 2003), and/or
8 perhaps I2 (McFiggans et al., 2004; Saiz-Lopez and Plane, 2004; McFiggans, 2005) emitted by
9 micro-and macro flora. Sea salt and sulfate aerosols provide substrates for multiphase reactions
10 that sustain the catalytic I-IO cycle. The IO radical has been measured by long-path (LP) and/or
11 multi axis (MAX) differential optical absorption spectroscopy (DOAS) at Mace Head, Ireland;
12 Tenerife, Canary Islands; Cape Grim, Tasmania; and coastal New England, USA; having
13 average daytime levels of about 1 ppt with maxima up to 7 ppt (e.g., Allan et al., 2000; Pikelnaya
14 et al., 2006). Modeling suggests that up to 13% per day of Os in marine air may be destroyed via
15 multiphase iodine chemistry (McFiggans et al., 2000). The reaction of IO with NO2 followed by
16 uptake of INOs into aerosols (analogous to Reactions AX2-9 through AX2-11) accelerates the
17 conversion of NOX to particulate NOs and thereby contributes to net 63 destruction. The
18 reaction IO + NO —> I + NO2 also influences NOX cycling.
19 Most of the above studies have focused on halogen-radical chemistry and related
20 influences on NOX cycling in coastal and urban air. However, available evidence suggests that
21 similar chemical transformations proceed in other halogen-rich tropospheric regimes. For
22 example, Cl, Br, and/or I oxides have been measured at significant concentrations in near-surface
23 air over the Dead Sea, Israel, the Great Salt Lake, Utah (e.g., Hebestreit et al., 1999; Stutz et al.,
24 1999, 2002; Zingler and Platt, 2005), and the Salar de Uyuni salt pan in the Andes mountains
25 (U. Platt, unpublished data, 2006); high column densities of halogenated compounds have also
26 been observed from satellites over the northern Caspian Sea (Wagner et al., 2001; Hollwedel
27 et al., 2004). The primary source of reactive halogens in these regions is thought to be from
28 activation along the lives of that in reactions in AX2-23 through AX2-25 involving concentrated
29 salt deposits on surface evaporite pans. High concentrations of BrO have also been measured in
30 volcanic plumes (Bobrowski et al., 2003, Gerlach, 2004). Although virtually unexplored, the
31 substantial emissions of inorganic halogens during biomass burning (Lobert et al., 1999; Keene
August 2007 AX2-23 DRAFT-DO NOT QUOTE OR CITE
-------
1 et al., 2006) and in association with crustal dust (Keene et al., 1999; Sander et al., 2003) may
2 also support active halogen-radical chemistry and related transformations involving NOX
3 downwind of sources. Finally, observations from satellites, balloons, and aircraft indicate that
4 BrO is present in the free troposphere at levels sufficient to significantly influence
5 photochemistry (e.g., von Glasow et al., 2004).
6
7
8 AX2.3 CHEMISTRY OF SULFUR OXIDES IN THE TROPOSPHERE
9 The four known monomeric sulfur oxides are sulfur monoxide (SO), sulfur dioxide
10 (SO2), sulfur tri oxide (SO3), and disulfur monoxide (S2O). SO can be formed by photolysis of
1 1 SO2 at wavelengths less than 220 nm, and so could only be found in the middle and upper
12 stratosphere (Pinto et al., 1989). SOs can be emitted from the stacks of power plants and
13 factories however, it reacts extremely rapidly with H2O in the stacks or immediately after release
14 into the atmosphere to form H2SO4. Of the four species, only SO2 is present at concentrations
15 significant for atmospheric chemistry and human exposures.
16 Sulfur dioxide can be oxidized either in the gas phase, or, because it is soluble, in the
17 aqueous phase in cloud drops. The gas-phase oxidation of SO2 proceeds through the reaction
(AX2-35)
19 followed by
(AX2.36)
(AX2-37)
22 Since H2SO4 is extremely soluble, it will be removed rapidly by transfer to the aqueous phase of
23 aerosol particles and cloud drops. Rate coefficients for reaction of SO2 with HO2 or NOs are too
24 low to be significant (JPL, 2003).
25 SO2 is chiefly but not exclusively primary in origin; it is also produced by the
26 photochemical oxidation of reduced sulfur compounds such as dimethyl sulfide (CHa-S-CHa),
27 hydrogen sulfide (H2S), carbon disulfide (CS2), carbonyl sulfide (OCS), methyl mercaptan
28 (CHa-S-H), and dimethyl disulfide (CHa-S-S-CHa) which are all mainly biogenic in origin.
August 2007 AX2-24 DRAFT-DO NOT QUOTE OR CITE
-------
1 Their sources are discussed in Section AX2.5. Table AX2-1 lists the atmospheric lifetimes of
2 reduced sulfur species with respect to reaction with various oxidants. Except for OCS, which is
3 lost mainly by photolysis (x~6 months), all of these species are lost mainly by reaction with OH
4 and NOs radicals. Because OCS is relatively long-lived in the troposphere, it can be transported
5 upwards into the stratosphere. Crutzen (1976) proposed that its oxidation serves as the major
6 source of sulfate in the stratospheric aerosol layer sometimes referred to the "Junge layer,"
7 (Junge et al., 1961) during periods when volcanic plumes do not reach the stratosphere.
8 However, the flux of OCS into the stratosphere is probably not sufficient to maintain this
9 stratospheric aerosol layer. Myhre et al. (2004) propose instead that SO2 transported upwards
10 from the troposphere is the most likely source, have become the upward flux of OCS is too small
11 to sustain observed sulfate loadings in the Junge layer. In addition, insitu measurements of the
12 isotopic composition of sulfur do not match those of OCS (Leung et al., 2002). Reaction with
13 NOs radicals at night most likely represents the major loss process for dimethyl sulfide and
14 methyl mercaptan. The mechanisms for the oxidation of DMS are still not completely
15 understood. Initial attack by NOs and OH radicals involves H atom abstraction, with a smaller
16 branch leading to OH addition to the S atom. The OH addition branch increases in importance as
17 temperatures decrease and becoming the major pathway below temperatures of 285 K
18 (Ravishankara, 1997). The adduct may either decompose to form methane sulfonic acid (MSA),
19 or undergo further reactions in the main pathway, to yield dimethyl sulfoxide (Barnes et al.,
20 1991). Following H atom abstraction from DMS, the main reaction products include MSA and
21 SO2. The ratio of MSA to SO2 is strongly temperature dependent, varying from about 0.1 in
22 tropical waters to about 0.4 in Antarctic waters (Seinfeld and Pandis, 1998). Excess sulfate (over
23 that expected from the sulfate in seawater) in marine aerosol is related mainly to the production
24 of SO2 from the oxidation of DMS. Transformations among atmospheric sulfur compounds are
25 summarized in Figure AX2-5.
August 2007 AX2-25 DRAFT-DO NOT QUOTE OR CITE
-------
hvt O
OH
Tropopause
Figure AX2-5. 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 dimethyl sulfoxide (CH3)2SO (Barnes et al., 1991;
5 Toumi, 1994), and oxidation by atomic chloride leads to formation of SO2 (Keene et al., 1996).
6 (CHs^SO and SO2 are precursors for methanesulfonic acid (CHaSOsH) and H2SO/t. In the MBL,
7 virtually all H2SO4 and CHaSOsH vapor condenses onto existing aerosols or cloud droplet, which
8 subsequently evaporate, thereby contributing to aerosol growth and acidification. Unlike
9 CHsSOsH, H2SO4 also has the potential to produce new particles (Korhonen et al., 1999; Kumala
August 2007
AX2-26
DRAFT-DO NOT QUOTE OR CITE
-------
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 H2SO4 and
4 other condensible S species in the MBL. Sulfur dioxide is also scavenged by deliquesced
5 aerosols and oxidized to H2SO4 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 O3 dominates in fresh, alkaline sea salt aerosols, whereas
8 oxidation by hypohalous acids (primarily HOC1) dominates in moderately acidic solutions.
9 Additional paniculate non-sea salt (nss) SC>42 is generated by SO2 oxidation in cloud droplets
10 (Clegg and Toumi, 1998). Ion-balance calculations indicate that most nss SC>42 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 SC>42 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 ultrafme particle bursts at Mace Head (O'Dowd
22 et al., 1999, 2002). Observed bursts coincide with the elevated concentrations of IO 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 H2SO4. 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
August 2007 AX2-27 DRAFT-DO NOT QUOTE OR CITE
-------
1 AX2.4 MECHANISMS FOR THE AQUEOUS PHASE FORMATION OF
2 SULFATE AND NITRATE
3 The major species containing sulfur in clouds are HSOs and SOs2 , which are derived
4 from the dissolution of SC>2 in water and are referred to as S(IV); and HSO/f and SC>42 , which
5 are referred to as S(VI). The major species capable of oxidizing S(IV) to S(VI) in cloud water
6 are 63, peroxides (either H2O2 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 62.
8 The basic mechanism of the aqueous phase oxidation of 862 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 SC>2
1 1 can be summarized as follows (Jacobson, 2002):
12 Dissolution of SO2
13 SO2(g) <=> SO2(aq) (AX2-38)
14 The formation and dissociation of H2SO3
SO2(aq) + H2O(aq) o H2SO3 <£> H + + HSOf o 2H+ + SO32~
1 J *""•*"'* ™ if\.J\^~J 7 }
16 In the pH range commonly found in rainwater (2 to 6), the most important reaction converting
17 S(IV) to S(VI) is
1 g HS03- + H202 + H+^> S042~ + H20 (AX2-40)
19 as SOi2 is much less abundant than HSOs".
20 Major pathways for the aqueous phase oxidation of S(IV) to S(VI) as a function of pH are
21 shown in Figure AX2-6. For pH up to about 5.3, H2O2 is seen to be the dominant oxidant; above
22 5.3, Os, followed by Fe(III) becomes dominant. Higher pHs are expected to be found mainly in
23 marine aerosols. However, in marine aerosols, the chloride-catalyzed oxidation of S(IV) may be
24 more important (Zhang and Millero, 1991; Hoppel and Caffrey, 2005). Because NH4+ is so
25 effective in controlling acidity, it affects the rate of oxidation of S(IV) to S(VI) and the rate of
26 dissolution of SC>2 in particles and cloud drops.
27
August 2007 AX2-28 DRAFT-DO NOT QUOTE OR CITE
-------
Figure AX2-6.
10-'
T 10'10
5
•"****
~Q
\
55 10'12
I
10-14
10
-16
10'
,-18
T T
H2°2
I i ! I I
0123456
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 NOs , although it
2 is much less soluble than SC>2 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 SC>2 and NC>2 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
August 2007
AX2-29
DRAFT-DO NOT QUOTE OR CITE
-------
1 S(VI) and NO2 to NO3 10 minutes after cloud formation are given in Tables AX2-2a and
2 AX2-2b. The two columns show the relative contributions with and without transition metal
3 ions. As can be seen from Table AX2-2a, SO2 within a cloud (gas + cloud drops) is oxidized
4 mainly by H2O2 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 H2O2. After H2O2, HNO4 is the major
8 contributor to S(IV) oxidation. The contribution from the gas phase oxidation of SO2 to be small
9 by comparison to the aqueous -phase reactions given above.
10 In contrast to the oxidation of SO2, Table AX2-2b shows that the oxidation of NO2 occurs
11 mainly in the gas phase within clouds, implying that the gas phase oxidation of NO2 by OH
12 radicals predominates. Clouds occupy about 15%, on average, of the volume of the troposphere.
13 The values shown in Tables AX2-2a and AX2-2b indicate that only about 20% of SO2 is
14 oxidized in the gas phase, but about 90% of NO2 is oxidized in the gas phase. Thus, SO2 is
15 oxidized mainly by aqueous-phase reactions, but NO2 is oxidized mainly by gas phase reactions.
16 Multiphase Chemical Processes Involving Sulfur Oxides and Ammonia
17 The phase partitioning of NH3 with deliquesced aerosol solutions is controlled primarily
18 by the thermodynamic properties of the system expressed as follows:
KH K~h
AIH j -v r \ftf i j- ^ r ww ~M • v /r w+i
19 A**3g «-> yvn3aqi *+ \riti 4 j -r Aw/[/Tf j (AX2-41)
20
21 where KH and Kb are the temperature-dependent Henry's Law and dissociation constants
22 (62 M atnT1) (1.8 x 1CT5 M), respectively, for NH3, and Kw is the ion product of water (1.0 x
23 10~14 M) (Chameides, 1984). It is evident that for a given amount of NHX (NH3 + particulate
24 NH4+) in the system, increasing aqueous concentrations of particulate H+ will shift the
25 partitioning of NH3 towards the condensed phase. Consequently, under the more polluted
26 conditions characterized by higher concentrations of acidic sulfate aerosol, ratios of gaseous NH3
27 to particulate NH4+ decrease (Smith et al., 2007). It also follows that in marine air, where
28 aerosol acidity varies substantially as a function of particle size, NH3 partitions preferentially to
29 the more acidic sub-|im size fractions (e.g., Keene et al., 2004; Smith et al., 2007).
August 2007 AX2-30 DRAFT-DO NOT QUOTE OR CITE
-------
1 Because the dry-deposition velocity of gaseous NH3 to the surface is substantially greater
2 than that for the sub-|im, sulfate aerosol size factions with which most particulate NH4+ is
3 associated, dry-deposition fluxes of total NH3 are dominated by the gas phase fraction (Russell
4 et al., 2003; Smith et al., 2007). Consequently, partitioning with highly acidic sulfate aerosols
5 effectively increases the atmospheric lifetime of total NH3 against dry deposition. This shift has
6 important consequences forNH3 cycling and potential ecological effects. In coastal New
7 England during summer, air transported from rural eastern Canada contains relatively low
8 concentrations of particulate non-sea salt (nss) SC>42 and total NH3 (Smith et al., 2007). Under
9 these conditions, the roughly equal partitioning of total NH3 between the gas and particulate
10 phases sustains substantial dry-deposition fluxes of total NH3 to the coastal ocean (median of
11 10.7 jimol nT2 day"1). In contrast, heavily polluted air transported from the industrialized
12 midwestern United States contains concentrations of nss SC>42 and total NH3 that are, about a
13 factory of 3 greater, based on median values. Under these conditions, most total NH3 (>85%)
14 partitions to the highly acidic sulfate aerosol size fractions and, consequently, the median dry-
15 deposition flux of total NH3 is 30% lower than that under the cleaner northerly flow regime. The
16 relatively longer atmospheric lifetime of total NH3 against dry deposition under more polluted
17 conditions implies that, on average, total NH3 would accumulate to higher atmospheric
18 concentrations under these conditions and also be subject to atmospheric transport over longer
19 distances. Consequently, the importance NHX of removal via wet deposition would also increase.
20 Because of the inherently sporadic character of precipitation, we might expect by greater
21 heterogeneity in NH3 deposition fields and any potential responses by sensitive ecosystems
22 downwind of major S-emission regions.
23
24
25 AX2.5 TRANSPORT OF NITROGEN AND SULFUR OXIDES IN
26 THE ATMOSPHERE
27 Major episodes of high O3 concentrations in the eastern United Sates and in Europe are
28 associated with slow moving high-pressure systems. High-pressure systems during the warmer
29 seasons are associated with subsidence, resulting in warm, generally cloudless conditions with
30 light winds. The subsidence results in stable conditions near the surface, which inhibit or reduce
31 the vertical mixing of O3 precursors (NOX, VOCs, and CO). Photochemical activity is enhanced
32 because of higher temperatures and the availability of sunlight. However, it is becoming
August 2007 AX2-31 DRAFT-DO NOT QUOTE OR CITE
-------
1 increasingly apparent that transport of 63 and NOX and VOC from distant sources can provide
2 significant contributions to local [63] even in areas where there is substantial photochemical
3 production. There are a number of transport phenomena occurring either in the upper boundary
4 layer or in the free troposphere which can contribute to high Os values at the surface. These
5 phenomena include stratospheric-tropospheric exchange (STE), deep and shallow convection,
6 low-level jets, and the so-called "conveyor belts" that serve to characterize flows around frontal
7 systems.
8
9 Convective Transport
10 Crutzen and Gidel (1983), Gidel (1983), and Chatfield and Crutzen (1984) hypothesized
11 that convective clouds played an important role in rapid atmospheric vertical transport of trace
12 species and first tested simple parameterizations of convective transport in atmospheric chemical
13 models. At nearly the same time, evidence was shown of venting the boundary layer by shallow,
14 fair weather cumulus clouds (e.g., Greenhut et al., 1984; Greenhut, 1986). Field experiments
15 were conducted in 1985 which resulted in verification of the hypothesis that deep convective
16 clouds are instrumental in atmospheric transport of trace constituents (Dickerson et al., 1987).
17 Once pollutants are lofted to the middle and upper troposphere, they typically have a much
18 longer chemical lifetime and with the generally stronger winds at these altitudes, they can be
19 transported large distances from their source regions. Transport of NOX from the boundary layer
20 to the upper troposphere by convection tends to dilute the higher in the boundary layer
21 concentrations and extend the NOX lifetime from less than 24 hours to several days.
22 Photochemical reactions occur during this long-range transport. Pickering et al. (1990)
23 demonstrated that venting of boundary layer NOX by convective clouds (both shallow and deep)
24 causes enhanced Os production in the free troposphere. The dilution of NOX at the surface can
25 often increase Os production efficiency. Therefore, convection aids in the transformation of
26 local pollution into a contribution to global atmospheric pollution. Downdrafts within
27 thunderstorms tend to bring air with less NOX from the middle troposphere into the boundary
28 layer. Lightning produces NO which is directly injected chiefly into the middle and upper
29 troposphere. The total global production of NO by lightning remains uncertain, but is on the
3 0 order of 10% of the total.
31
August 2007 AX2-32 DRAFT-DO NOT QUOTE OR CITE
-------
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, NOy, 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 NOy 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, H2O2,
20 CHaOOH, and acetone. The hydroperoxyl radical is critical for oxidizing NO to NO2 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 NOy, and O3 (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 NOy 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
August 2007 AX2-33 DRAFT-DO NOT QUOTE OR CITE
-------
1 Ocean Experiment) (deLaat et al., 1999) where a series of ozonesonde ascents showed very low
2 upper tropospheric 63 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/NO2 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, HNOs 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
August 2007 AX2-34 DRAFT-DO NOT QUOTE OR CITE
-------
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 NO2 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 ^Os. 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 Os and NOX enhancement in the U.S.
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 SO2 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
August 2007 AX2-3 5 DRAFT-DO NOT QUOTE OR CITE
-------
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 NC>2 exchange with vegetation suggests that there should be emission of NC>2 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 NO2 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 N2O, some of which can escape. While N2O 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 N2O which depend on oxygen levels: in flooded
22 soils where oxygen levels are low, N2O is the dominant soil nitrogen gas; as soil dries, allowing
23 more O2 to diffuse, NO emissions increase. In very dry soils microbial activity is inhibited and
24 emissions of both N2O 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, HNOs,
32 NO2, PAN, and organic nitrates.
August 2007 AX2-36 DRAFT-DO NOT QUOTE OR CITE
-------
1 HNO3
2 Deposition of HNO3 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 NC>2 induces nitrate reductase (Weber et al., 1995, 1998), a necessary
17 enzyme for assimilating oxidized nitrogen. Understanding of NC>2 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 NO2 and measure the fraction of NO2 removed from the chamber air. A
20 key finding is that the fit of NC>2 flux to NC>2 concentration, has a non-zero intercept, implying a
21 compensation point or internal concentration. In studies at very low NO2 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 NC>2 (Teklemariam and Sparks, 2006). Foliar NC>2 emissions show some dependence on
27 nitrogen content (Teklemariam and Sparks, 2006). Internal NC>2 appears to derive from plant
28 nitrogen metabolism.
29 Two approaches to modeling NO2 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, Rb, and RC ; positive fluxes
32 are from atmosphere to foliage)
August 2007 AX2-37 DRAFT-DO NOT QUOTE OR CITE
-------
1 F - CVd (AX2-42)
2 Vd~ (Ra + Rb + RcT] (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 NC>2, Vd is less than that for Os, due to the surface's generally higher resistance to NO2 uptake,
8 consistent with N(Vs lower reactivity.
9 Alternatively, NC>2 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
11 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
Ss(Ca Q) (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 NC>2 concentration are consistent with metabolic pathways that include oxides of
19 nitrogen. In this formulation, the uptake will be linear with NC>2 concentration, which is
20 typically observed with foliar chamber studies.
21 Several studies have shown the UV dependence of NC>2 emission, which implies some
22 photo-induced surface reactions that release NC>2. 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 NC>2 flux is
27 confounded by the rapid interconversion of NO, NC>2, and Os (Gao et al., 1991).
28
August 2007 AX2-3 8 DRAFT-DO NOT QUOTE OR CITE
-------
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 O^.
1 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 NO2 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 NOy not accounted for by
22 NO, NO2 and PAN, which is attributed to the organic nitrates (Horii et al., 2006, Munger et al.,
23 1998). Furthermore, the total NOy 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 et al. (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 NO2 when it
30 is present instead to form
31
August 2007 AX2-39 DRAFT-DO NOT QUOTE OR CITE
-------
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 NO2
4 enhanced by moisture were proposed to explain these results. Production was evident at sites
5 with high ambient NO2; 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/HO2 budgets as
1 1 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 NO2 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 NO2 and H2O (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 H2O-NO2 water complex reacting with gas phase NO2 to produce HONO, which is
23 inconsistent with the formation of an N2O4 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.
3 1 Photolysis of HNOs or NOs absorbed on ice or in surface water films has been proposed
August 2007 AX2-40 DRAFT-DO NOT QUOTE OR CITE
-------
1 (Honrath et al., 2002; Ramazan et al., 2004; Zhou et al., 2001, 2003). Alternative pathways
2 include NO2 interaction with organic surfaces such as humic substances (George et al., 2005;
3 Stemmler et al., 2006). Note that either NO3 photolysis or heterogeneous reaction of NO2 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 NO2 and water films or organic molecules would decrease the effectiveness of
7 observed NO2 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 Os + VOC react with NOX in the canopy to produce HNOs 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 NO2 conversion and foliar
14 uptake of NO2 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, NOy, and
21 other pollutant concentrations and fluxes of total NOy 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 NO2 and HNOs. TDLAS has an inherently fast response, and
24 for species such as NO2 and HNOs 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 NOy eddy covariance fluxes were determined with two separate Os
August 2007 AX2-41 DRAFT-DO NOT QUOTE OR CITE
-------
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, NO2, and O3 over several annual cycles to examine their vertical profiles in the
4 forest canopy.
5 Overall, the results show typical NO2 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 NO2 were evident in the daytime and negative fluxes
8 (deposition) were observed at night (Figure AX2-7). Nitric oxide fluxes were negative during
9 the daytime and near zero at night.
10 In part the opposite NO and NO2 fluxes are simply consequences of variable NO/NO2
11 distributions responding to vertical gradients in light intensity and O3 concentration, which
12 resulted in no net flux of NOX (Gao et al., 1993). In the Harvard Forest situation, the NO and
13 NO2 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 (Figure AX2-8).
15 At night, when NO concentrations are near 0 due to titration by ambient O3 there is not a
16 flux of NO to offset NO2 fluxes. Nighttime data consistently show NO2 deposition (Figure
17 AX2-9), which increases with increasing NO2 concentrations. Concentrations above 10 ppb
18 were rare at this site, but the few high NO2 observations suggest a nonlinear dependence on
19 concentration. The data fit a model with Vd of -0.08 plus an enhancement term that was second
20 order in NO2 concentration. The second order term implies that NO2 deposition rates to
21 vegetation in polluted urban sites would be considerably larger than what was observed at this
22 rural site.
23 After accounting for the NO-NO2 null cycle the net NOX flux could be derived. Overall,
24 there was a net deposition of NOX during the night and essentially zero flux in the day, with large
25 variability in the magnitude and sign of individual flux observations (Figure AX2-10). For the
26 periods with [NO2] > 2 ppb, deposition was always observed. These canopy-scale field
27 observations are consistent with a finite compensation point for NO2 in the canopy that offsets
28 foliar uptake or even reverses it when concentrations are especially low. At concentrations
29 above the compensation point, NOX is absorbed by the canopy. Examination of concentration
30 profiles corroborates the flux measurements (Figure AX2-11). During daytime for low-NOx
31 conditions, there is a local maximum in the concentration profile near the top of the canopy
August 2007 AX2-42 DRAFT-DO NOT QUOTE OR CITE
-------
NW
SW
o
_
E
c
O
O
E
3b
x
3
0--
[NO]
[NO]
FNO
FNO., - A
FO /10
_f.
11
6 12 18
Hours
6 12 18
Hours
Figure AX2-7.
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: Horn et al. (2004).
August 2007
AX2-43
DRAFT-DO NOT QUOTE OR CITE
-------
Simple Model
100
80
60
- NO
,5s
*5
I
40
20
0
! i
NO,
NO
Figure AX2-8.
0,0 0,2 0.4 0.6 0,8 1.0-2-1 0 1 2
Concentration (nmol mol"1) Flux {nnnoi 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: Horn (2002).
August 2007
AX2-44
DRAFT-DO NOT QUOTE OR CITE
-------
FNO2 (night) = F0 + V0 [NO2] + a [NO2J2
o
_
c
O
z
-10-
-15-
-20-
V(,= -008+ 0,03 (ems')
a = -0 013 ± 0,001 (nmol"1 mol cm s ')
10
1
15
j Hourly Data (fitted) •
I Nightly Medians •*•,
I
20
[NOJ (nmol mol'1)
25
I
30
Figure AX2-9.
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: Horn et al. (2004).
1 where Os has a local minimum, which is consistent with foliar emission or light-dependent
2 production of NOX in the upper canopy. Depletion is evident for both NOX and Os near the forest
3 floor. Air reaching the ground has passed through the canopy where uptake is efficient and the
4 vertical exchange rates near the ground are slow. At night, the profiles generally decrease with
5 decreasing height above the ground, showing only uptake. At higher concentrations, the daytime
6 NOX concentrations are nearly constant through the canopy; no emission is evident from the
7 sunlit leaves.
8 Figure AX2-12 compares observed fluxes of all the observed species. The measured NOX
9 and estimated PAN fluxes are small relative to the observed total NOy flux. In clean air,
August 2007
AX2-45
DRAFT-DO NOT QUOTE OR CITE
-------
NO* PROFILES
30
Canopv
Top" ''" Js-
•AJ
£
**
JC «'
en
'5
10
5 '
0
^ "
i
I
.
1 O
f
1
!
1
I
Y
NO3 /NO,
• (i
o
.'
Night Low NOx c-
1
i
,'
o
/
/
,\
*»'
^'
30
25
20
15-
'0
<•
0 '
s
J
i
i-
/
e
'
T,
f i
s
o
•
,-• o
High NOx ,>
I
*
/
o
/*
/
*
/
/
. ^
I11
U70 075 080 085 1t> 20 A 30 34 38 42 4 p 14 18 18 20 22 24
3:1
Canopy
Top " ' ?5 -
*-» -o
§
4«*
"HI 15"
"5
£
10
5
o -
C
\-
/
.'
N0? \ NO
'i
1
/'
/
^, C
1 I
/
&
Day Low NOx
i
i
j
1
\
\
0, I
"
f
.,°
> ^
*-
J5-
«G
15 •
1u
5-
o -
e '
i
\
•
i
i
i
I
i
1
e
V X 1 — C
Day High NOx o
/
*
1
h
i
f
/
o
/
/**
*-•'
0 60 0 85 0 TO D 75 0 SO if JO ?; V( 36 4 ! 44 4 h 48 S 0 J2 Z3 24 25 26 27 28
Concentration (nmol mot"1) Concentration (nmol mol'1)
Figure AX2-11. 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: Horn et al. (2004).
August 2007
AX2-46 DRAFT-DO NOT QUOTE OR CITE
-------
Summer 2000
NW
SW
^ 12"
O
E
"o
E
£.
6
c
o
o
10
6
4
2
0
NCM-HNO.J+PAN —'
NO*+HNOS - •
N0«=
NO
I> 1.2 -
c
_o
""5 0.4 -
co
0.0 --
O
E
x
3
-5
"15
-25
FPAN (est J X
. FNO (param )
FNO, fparam) A
_FHNO,(DDIM) (
FNO, (ex) n
L
10
15
20 0
Hoyr
10
!
15
20
Figure AX2-12.
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: Horn et al. (2006).
August 2007
AX2-47
DRAFT-DO NOT QUOTE OR CITE
-------
1 accounts for nearly all the NOy flux and the sum of all measured species is about equal to the
2 NOy concentration. However, in polluted conditions, unmeasured species are up to 25% of the
3 NOy, and HNO3 fluxes cannot account for all the total NOy flux observed. Likely these
4 unmeasured NOy species are hydroxyalkyl nitrates and similar compounds and are rapidly
5 deposited. Although NO2 uptake may be important to the plant, because it is an input directly to
6 the interior of foliage that can be used immediately in plant metabolism, it is evidently not a
7 significant part of overall nitrogen deposition to rural sites. The deposition of HNOs and
8 multifunctional organic nitrates are the largest elements of the nitrogen dry deposition budget.
9 Two key areas of remaining uncertainty are the production of HONO over vegetation and the
10 role of very reactive biogenic VOCs. HONO is important because its photolysis is a source of
11 OH radicals, and its formation may represent an unrecognized mechanism to regenerate
12 photochemically active NOX from nitrate that had been considered terminally removed from the
13 atmosphere.
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, NH3, and SO2 for 2002 (U.S. Environmental
31 Protection Agency, 2006) are shown in Table AX2-3. Methods for estimating emissions of
August 2007 AX2-48 DRAFT-DO NOT QUOTE OR CITE
-------
1 criteria pollutants, quality assurance procedures, and examples of emissions calculated by using
2 data are given in U.S. Environmental Protection Agency (1999). Discussions of uncertainties in
3 current emissions inventories and strategies for improving them can be found in NARSTO
4 (2005).
5 As can be seen from the table, combustion by stationary sources, such as electrical
6 utilities and various industries, accounts for roughly half of total anthropogenic emissions of
7 NOX. Mobile sources account for the other half, with highway vehicles representing the major
8 mobile source component. Approximately half the mobile source emissions are contributed by
9 diesel engines, the remainder are emitted by gasoline-fueled vehicles and other sources.
10 Emissions of NOX associated with combustion arise from contributions from both fuel
1 1 nitrogen and atmospheric nitrogen. Combustion zone temperatures greater than about 1300 K
12 are required to fix atmospheric N2:
13 (AX2.4fi)
14 Otherwise, NO can be formed from fuel N according to this reaction:
15 CaHbOcNd + 02 -> xC02 + yH20 + zNO (AX2-47)
In addition to NO formation by the schematic reactions given above, some NO2 and CO
16 are also formed depending on temperatures, concentrations of OH and HO2 radicals and O2
17 levels. Fuel nitrogen is highly variable in fossil fuels, ranging from 0.5 to 2.0 percent by weight
1 8 (wt %) in coal to 0.05% in light distillates (e.g., diesel fuel), to 1 .5 wt % in heavy fuel oils (UK
19 AQEG, 2004). The ratio of NO2 to NOX in primary emissions ranges from 3 to 5 % from
20 gasoline engines, 5 to 12% from heavy-duty diesel trucks, 5 to 10% from vehicles fueled by
21 compressed natural gas and from 5 to 10% from stationary sources. In addition to NOX, motor
22 vehicles also emit HONO, with ratios of HONO to NOX ranging from 0.3% in the Caldecott
23 Tunnel, San Francisco Bay (Kirchstetter and Harley, 1996) to 0.5 to 1.0% in studies in the
24 United Kingdom (UK AQEG, 2004). The NO2 to NOX ratios in emissions from turbine jet
25 engines are as high as 32 to 35 % during taxi and takeoff (CD93). Sawyer et al. (2000) have
26 reviewed the factors associated with NOX emissions by mobile sources. Marine transport
August 2007 AX2-49 DRAFT-DO NOT QUOTE OR CITE
-------
1 represents a minor source of NOX, but it constitutes a larger source in the EU where it is expected
2 to represent about two-thirds of land-based sources (UK AQEG, 2004).
3
4 NOX Emissions from Natural Sources (Soil, Wild Fires, and Lightning)
5
6 Soil
1 Emission rates of NO from cultivated soil depend mainly on fertilization levels and soil
8 temperature. About 60% of the total NOX emitted by soils occurs in the central corn belt of the
9 United States. The oxidation of NHa, emitted mainly by livestock and soils, leads to the
10 formation of NO, also NH4+ and NOs fertilizers lead to NO emissions from soils. Estimates of
11 emissions from natural sources are less certain than those from anthropogenic sources. On a
12 global scale, the contribution of soil emissions to the oxidized nitrogen budget is on the order of
13 10% (van Aardenne et al., 2001; Finlayson-Pitts and Pitts, 2000; Seinfeld and Pandis, 1998), but
14 NOX emissions from fertilized fields are highly variable. Soil NO emissions can be estimated
15 from the fraction of the applied fertilizer nitrogen emitted as NOX, but the flux varies strongly
16 with land use and temperature. Estimated globally averaged fractional applied nitrogen loss as
17 NO varies from 0.3% (Skiba et al., 1997) to 2.5% (Yienger and Levy, 1995). Variability within
18 biomes to which fertilizer is applied, such as shortgrass versus tallgrass prairie, accounts for a
19 factor of three in uncertainty (Williams et al., 1992; Yienger and Levy, 1995; Davidson and
20 Kingerlee, 1997).
21 The local contribution can be much greater than the global average, particularly in
22 summer and especially where corn is grown extensively. Williams et al. (1992) estimated that
23 contributions to NO budgets from soils in Illinois are about 26% of the emissions from industrial
24 and commercial processes in that State. In Iowa, Kansas, Minnesota, Nebraska, and South
25 Dakota, all states with smaller human populations, soil emissions may dominate the NO budget.
26 Conversion of NHa to NOs (nitrification) in aerobic soils appears to be the dominant pathway to
27 NO. The mass and chemical form of nitrogen (reduced or oxidized) applied to soils, the
28 vegetative cover, temperature, soil moisture, and agricultural practices such as tillage all
29 influence the amount of fertilizer nitrogen released as NO.
30 Emissions of NO from soils peak in summer when Os formation is also at a maximum.
31 An NRC panel report (NRC, 2002) outlined the role of agriculture in emissions of air pollutants
32 including NO and NH3. That report recommends immediate implementation of best
August 2007 AX2-50 DRAFT-DO NOT QUOTE OR CITE
-------
1 management practices to control these emissions, and further research to quantify the magnitude
2 of emissions and the impact of agriculture on air quality. Civerolo and Dickerson (1998) report
3 that use of the no-till cultivation technique on a fertilized cornfield in Maryland reduced NO
4 emissions by a factor of seven.
5
6 NOxfrom Biomass Burning
1 During biomass burning, nitrogen is derived mainly from fuel nitrogen and not from
8 atmospheric N2, since temperatures required to fix atmospheric N2 are likely to be found only in
9 the flaming crowns of the most intense boreal forest fires. Nitrogen is present mainly in plants as
10 amino (NH2) groups in amino acids. During combustion, nitrogen is released mainly in
11 unidentified forms, presumably as N2, with very little remaining in fuel ash. Apart from N2, the
12 most abundant species in biomass burning plumes is NO. Emissions of NO account for only
13 about 10 to 20% relative to fuel N (Lobert et al., 1991). Other species such as NO2, nitriles,
14 ammonia, and other nitrogen compounds account for a similar amount. Emissions of NOX are
15 about 0.2 to 0.3% relative to total biomass burned (e.g., Andreae, 1991; Radke et al., 1991).
16 Westerling et al. (2006) have noted that the frequency and intensity of wildfires in the western
17 U.S. have increased substantially since 1970.
18
19 Lightning Production of NO
20 Annual global production of NO by lightning is the most uncertain source of reactive
21 nitrogen. In the last decade, literature values of the global average production rate range from
22 2 to 20 Tg N per year. However, the most likely range is from 3 to 8 Tg N per year, because the
23 majority of the recent estimates fall in this range. The large uncertainty stems from several
24 factors: (1) a large range of NO production rates per meter of flash length (as much as two orders
25 of magnitude); (2) the open question of whether cloud-to-ground (CG) flashes and intracloud
26 flashes (1C) produce substantially different amounts of NO; (3) the global flash rate; and (4) the
27 ratio of the number of 1C flashes to the number of CG flashes. Estimates of the amount of NO
28 produced per flash have been made based on theoretical considerations (e.g., Price et al., 1997),
29 laboratory experiments (e.g., Wang et al., 1998); field experiments (e.g., Stith et al., 1999;
30 Huntrieser et al., 2002, 2007) and through a combination of cloud-resolving model simulations,
31 observed lightning flash rates, and anvil measurements of NO (e.g., DeCaria et al., 2000, 2005;
32 Ott et al., 2007). The latter method was also used by Pickering et al. (1998), who showed that
August 2007 AX2-51 DRAFT-DO NOT QUOTE OR CITE
-------
1 only ~5 to 20% of the total NO produced by lightning in a given storms exists in the boundary
2 layer at the end of a thunderstorm. Therefore, the direct contribution to boundary layer 63
3 production by lightning NO is thought to be small. However, lightning NO production can
4 contribute substantially to Os production in the middle and upper troposphere. DeCaria et al.
5 (2005) estimated that up to 10 ppbv of ozone was produced in the upper troposphere in the first
6 24 hours following a Colorado thunderstorm due to the injection of lightning NO. A series of
7 midlatitude and subtropical thunderstorm events have been simulated with the model of DeCaria
8 et al. (2005), and the derived NO production per CG flash averaged 500 moles/flash while
9 average production per 1C flash was 425 moles/flash (Ott et al., 2006).
10 A major uncertainty in mesoscale and global chemical transport models is the
11 parameterization of lightning flash rates. Model variables such as cloud top height, convective
12 precipitation rate, and upward cloud mass flux have been used to estimate flash rates. Allen and
13 Pickering (2002) have evaluated these methods against observed flash rates from satellite, and
14 examined the effects on ozone production using each method.
15
16 Uses of Satellite Data to Derive Emissions
17 Satellite data have been shown to be useful for optimizing estimates of emissions of NO2.
18 (Leue et al., 2001; Martin et al., 2003; Jaegle et al., 2005). Satellite-borne instruments such as
19 GOME (Global Ozone Monitoring Experiment; Martin et al., 2003; and references therein) and
20 SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Chartography;
21 Bovensmann et al., 1999) retrieve tropospheric columns of NO2, which can then be combined
22 with model-derived chemical lifetimes of NOX to yield emissions of NOX.
23 Top-down inference of NOX emission inventory from the satellite observations of NO2
24 columns by mass balance requires at minimum three pieces of information: the retrieved
25 tropospheric NO2 column, the ratio of tropospheric NOX to NO2 columns, and the NOX lifetime
26 against loss to stable reservoirs. A photochemical model has been used to provide information
27 on the latter two pieces of information. The method is generally applied exclusively to land
28 surface emissions, excluding lightning. Tropospheric NO2 columns are insensitive to lightning
29 NOX emissions since most of the lightning NOX in the upper troposphere is present as NO at the
30 local time of the satellite measurements (Ridley et al., 1996), owing to the slower reactions of
31 NO with O3 there.
August 2007 AX2-52 DRAFT-DO NOT QUOTE OR CITE
-------
1 Jaegle et al. (2005) applied additional information on the spatial distribution of emissions
2 and on fire activity to partition NOX emissions into sources from fossil fuel combustion, soils,
3 and biomass burning. Global a posteriori estimates of soil NOX emissions are 68% larger than
4 the a priori estimates. Large increases are found for the agricultural region of the western United
5 States during summer, increasing total U.S. soil NOX emissions by a factor of 2 to 0.9 Tg N yr"1.
6 Bertram et al. (2005) found clear signals in the SCIAMACHY observations of short intense NOX
7 pulses following springtime fertilizer application and subsequent precipitation over agricultural
8 regions of the western United States. For the agricultural region in North-Central Montana, they
9 calculate a yearly SCIAMACHY top-down estimate that is 60% higher than a commonly used
10 model of soil NOX emissions by Yienger and Levy (1995).
11 Martin et al. (2006) retrieved tropospheric nitrogen dioxide (NC^) columns for
12 May 2004 to April 2005 from the SCIAMACHY satellite instrument to derive top-down NOX
13 emissions estimates via inverse modeling with a global chemical transport model (GEOS-Chem).
14 The top-down emissions were combined with a priori information from a bottom-up emission
15 inventory with error weighting to achieve an improved a posteriori estimate of the global
16 distribution of surface NOX emissions. Their a posteriori inventory improves the GEOS-Chem
17 simulation of NOX, PAN, and HNO3 with respect to airborne in situ measurements over and
18 downwind of New York City. Their a posteriori inventory shows lower NOX emissions from the
19 Ohio River valley during summer than during winter, reflecting recent controls on NOX
20 emissions from electric utilities. Their a posteriori inventory is highly consistent (R2 = 0.82,
21 bias = 3%) with the NEI99 inventory for the United States. In contrast, their a posteriori
22 inventory is 68% larger than a recent inventory by Streets et al. (2003) for East Asia for the year
23 2000.
24
25 Emissions ofNHs
26 Emissions of NH3 show a strikingly different pattern from those of NOX. Three-way
27 catalysts used in motor vehicles emit small amounts of NH3 as a byproduct during the reduction
28 of NOX. Stationary combustion sources make only a small contribution to emissions of NH3
29 because efficient combustion favors formation of NOX and, NH3 from combustion is produced
30 mainly by inefficient, low temperature fuel combustion. For these reasons, most emissions of
31 NH3 arise from fertilized soils and from livestock.
August 2007 AX2-53 DRAFT-DO NOT QUOTE OR CITE
-------
1 The initial step in the oxidation of atmospheric NH? to NO is by reaction with OH
2 radicals. However, the lifetime of NH? from this pathway is sufficiently long (-1-2 months
3 using typical OH values 1-2 x 106/cm3) that it is a small sink compared to uptake of NH3 by
4 cloud drops, dry deposition, and aerosol particles. Thus, the gas-phase oxidation of NH? makes a
5 very small contribution as a source of NO. Holland et al. (2005) estimated wet and dry
6 deposition of NHX, based on measurements over the continental U.S., and found that emissions
7 of NH3 in the National Emissions Inventory are perhaps underestimated by about a factor of two
8 to three. Reasons for this imbalance include under-representation of deposition monitoring sites
9 in populated areas and the neglect of off-shore transport in their estimate. The use of fixed
10 deposition velocities that do not reflect local conditions at the time of measurement introduces
11 additional uncertainty into their estimates of dry deposition.
12
13 Emissions of SO2
14 As can be seen from Table AX2-3, emissions of SO2 are due mainly to the combustion of
15 fossil fuels by electrical utilities and industry. Transportation related sources make only a minor
16 contribution. As a result, most SO2 emissions originate from point sources. Since sulfur is a
17 volatile component of fuels, it is almost quantitatively released during combustion and emissions
18 can be calculated on the basis of the sulfur content of fuels to greater accuracy than for other
19 pollutants such as NOX or primary PM.
20 The major natural sources of SO2 are volcanoes and biomass burning and DMS oxidation
21 over the oceans. SO2 constitutes a relatively minor fraction (0.005% by volume) of volcanic
22 emissions (Holland, 1978). The ratio of H2S to SO2 is highly variable in volcanic gases. It is
23 typically much less than one, as in the Mt. Saint Helen's eruption (Turco et al., 1983). However,
24 in addition to being degassed from magma, H2S can be produced if ground waters, especially
25 those containing organic matter, come into contact with volcanic gases. In this case, the ratio of
26 H2S to SO2 can be greater than one. H2S produced this way would more likely be emitted
27 through side vents than through eruption columns (Pinto et al., 1989). Primary particulate sulfate
28 is a component of marine aerosol and is also produced by wind erosion of surface soils.
29 Volcanic sources of SO2 are limited to the Pacific Northwest, Alaska, and Hawaii. Since
30 1980, the Mount St. Helens volcano in the Washington Cascade Range (46.20 N, 122.18 W,
31 summit 2549 m asl) has been a variable source of SO2. Its major effects came in the explosive
32 eruptions of 1980, which primarily affected the northern part of the mountainous western half of
August 2007 AX2-54 DRAFT-DO NOT QUOTE OR CITE
-------
1 the US. The Augustine volcano near the mouth of the Cook Inlet in southwestern Alaska
2 (59.363 N, 153.43 W, summit 1252 m asl) has had variable SC>2 emission since its last major
3 eruptions in 1986. Volcanoes in the Kamchatka peninsula of eastern region of Siberian Russia
4 do not significantly effect surface SO2 concentrations in northwestern North America. The most
5 serious effects in the U.S. from volcanic SC>2 occurs on the island of Hawaii. Nearly continuous
6 venting of SC>2 from Mauna Loa and Kilauea produces SC>2 in such large amounts that >100 km
7 downwind of the island SC>2 concentrations can exceed 30 ppbv (Thornton and Bandy, 1993).
8 Depending on wind direction, the west coast of Hawaii (Kona region) has had significant
9 deleterious effects from 862 and acidic sulfate aerosols for the past decade.
10 Emissions of SC>2 from burning vegetation are generally in the range of 1 to 2% of the
11 biomass burned (see e.g., Levine et al., 1999). Sulfur is bound in amino acids in vegetation.
12 This organically bound sulfur is released during combustion. However, unlike nitrogen, about
13 half of the sulfur initially present in vegetation is found in the ash (Delmas, 1982). Gaseous
14 emissions are mainly in the form of SC>2 with much smaller amounts of H2S and OCS. The ratio
15 of gaseous nitrogen to sulfur emissions is about 14, very close to their ratio in plant tissue
16 (Andreae, 1991). The ratio of reduced nitrogen and sulfur species such as NH3 and H2S to their
17 more oxidized forms, such as NO and 862, increases from flaming to smoldering phases of
18 combustion, as emissions of reduced species are favored by lower temperatures and 62 reduced
19 availability.
20 Emissions of reduced sulfur species are associated typically with marine organisms living
21 either in pelagic or coastal zones and with anaerobic bacteria in marshes and estuaries.
22 Mechanisms for their oxidation were discussed in Section AX2.2. Emissions of dimethyl sulfide
23 (DMS) from marine plankton represent the largest single source of reduced sulfur species to the
24 atmosphere (e.g., Berresheim et al., 1995). Other sources such as wetlands and terrestrial plants
25 and soils probably account for less than 5% of the DMS global flux, with most of this coming
26 from wetlands.
27 The coastal and wetland sources of DMS have a dormant period in the fall/winter from
28 senescence of plant growth. Marshes die back in fall and winter, so dimethyl sulfide emissions
29 from them are lower, reduced light levels in winter at mid to high latitudes reduce cut
30 phytoplankton growth which also tends to reduce DMS emissions. Western coasts at mid to high
31 latitudes have reduced levels of the light that drive photochemical production and oxidation of
August 2007 AX2-55 DRAFT-DO NOT QUOTE OR CITE
-------
1 DMS. Freezing at mid and high latitudes affects the release of biogenic sulfur gases, particularly
2 in the nutrient-rich regions around Alaska. Transport of 862 from regions of biomass burning
3 seems to be limited by heterogeneous losses that accompany convective processes that ventilate
4 the surface layer and the lower boundary layer (Thornton et al., 1996, TRACE-P data archive).
5 However, it should be noted that reduced sulfur species are also produced by industry.
6 For example, DMS is used in petroleum refining and in petrochemical production processes to
7 control the formation of coke and carbon monoxide. In addition, it is used to control dusting in
8 steel mills. It is also used in a range of organic syntheses. It also has a use as a food flavoring
9 component. It can also be oxidized by natural or artificial means to dimethyl sulfoxide (DMSO),
10 which has several important solvent properties.
11
12 AX2.6.3 Field Studies Evaluating Emissions Inventories
13 Comparisons of emissions model predictions with observations have been performed in a
14 number of environments. A number of studies of ratios of concentrations of CO to NOX and
15 NMOC to NOX during the early 1990s in tunnels and ambient air (summarized in Air Quality
16 Criteria for Carbon Monoxide (U.S. Environmental Protection Agency, 2000)) indicated that
17 emissions of CO and NMOC were systematically underestimated in emissions inventories.
18 However, the results of more recent studies have been mixed in this regard, with many studies
19 showing agreement to within ±50% (U.S. Environmental Protection Agency, 2000).
20 Improvements in many areas have resulted from the process of emissions model development,
21 evaluation, and further refinement. It should be remembered that the conclusions from these
22 reconciliation studies depend on the assumption that NOX emissions are predicted correctly by
23 emissions factor models. Roadside remote sensing data indicate that over 50% of NMHC and
24 CO emissions are produced by less than about 10% of the vehicles (Stedman et al., 1991). These
25 "super-emitters" are typically poorly maintained vehicles. Vehicles of any age engaged in off-
26 cycle operations (e.g., rapid accelerations) emit much more than if operated in normal driving
27 modes. Bishop and Stedman (1996) found that the most important variables governing CO
28 emissions are fleet age and owner maintenance.
29 Emissions inventories for North America can be evaluated by comparison to measured
30 long-term trends and or ratios of pollutants in ambient air. A decadal field study of ambient CO
31 at a rural site in the Eastern U.S. (Hallock-Waters et al., 1999) indicates a downward trend
August 2007 AX2-56 DRAFT-DO NOT QUOTE OR CITE
-------
1 consistent with the downward trend in estimated emissions over the period 1988 to 1999 (U.S.
2 Environmental Protection Agency, 1997), even when a global downward trend is accounted for.
3 Measurements at two urban areas in the United States confirmed the decrease in CO emissions
4 (Parrish et al., 2002). That study also indicated that the ratio of CO to NOX emissions decreased
5 by almost a factor of three over 12 years (such a downward trend was noted in AQCD 96).
6 Emissions estimates (U.S. Environmental Protection Agency, 1997) indicate a much smaller
7 decrease in this ratio, suggesting that NOX emissions from mobile sources may be underestimated
8 and/or increasing. Parrish et al. (2002) conclude that Os photochemistry in U.S. urban areas may
9 have become more NOx-limited over the past decade.
10 Pokharel et al. (2002) employed remotely sensed emissions from on-road vehicles and
11 fuel use data to estimate emissions in Denver. Their calculations indicate a continual decrease in
12 CO, HC, and NO emissions from mobile sources over the 6-year study period. Inventories based
13 on the ambient data were 30 to 70% lower for CO, 40% higher for HC, and 40 to 80% lower for
14 NO than those predicted by the MOBILE6 model.
15 Stehr et al. (2000) reported simultaneous measurements of CO, SO2, and NOy at an East
16 Coast site. By taking advantage of the nature of mobile sources (they emit NOX and CO but little
17 802) and power plants (they emit NOX and SO2 but little CO), the authors evaluated emissions
18 estimates for the eastern United States. Results indicated that coal combustion contributes 25 to
19 35% of the total NOX emissions in rough agreement with emissions inventories (U.S.
20 Environmental Protection Agency, 1997).
21 Parrish et al. (1998) and Parrish and Fehsenfeld (2000) proposed methods to derive
22 emission rates by examining measured ambient ratios among individual VOC, NOX and NOy.
23 There is typically a strong correlation among measured values for these species because emission
24 sources are geographically collocated, even when individual sources are different. Correlations
25 can be used to derive emissions ratios between species, including adjustments for the impact of
26 photochemical aging. Investigations of this type include correlations between CO and NOy (e.g.,
27 Parrish et al., 1991), between individual VOC species and NOy (Goldan et al., 1995, 1997, 2000)
28 and between various individual VOC (Goldan et al., 1995, 1997; McKeen and Liu, 1993;
29 McKeen et al., 1996). Buhr et al. (1992) derived emission estimates from principal component
30 analysis (PCA) and other statistical methods. Many of these studies are summarized in Trainer
31 et al. (2000), Parrish et al. (1998), and Parrish and Fehsenfeld (2000). Goldstein and Schade
August 2007 AX2-57 DRAFT-DO NOT QUOTE OR CITE
-------
1 (2000) also used species correlations to identify the relative impacts of anthropogenic and
2 biogenic emissions. Chang et al. (1996, 1997) and Mendoza-Dominguez and Russell (2000,
3 2001) used the more quantative technique of inverse modeling to derive emission rates, in
4 conjunction with results from chemistry-transport models.
5
6
7 AX2.7 METHODS USED TO CALCULATE CONCENTRATIONS OF
8 NITROGEN OXIDES AND THEIR CHEMICAL
9 INTERACTIONS IN THE ATMOSPHERE
10 Atmospheric chemistry and transport models are the major tools used to calculate the
11 relations among O3, other oxidants, and their precursors, the transport and transformation of air
12 toxics, the production of secondary organic aerosol, the evolution of the particle size distribution,
13 and the production and deposition of pollutants affecting ecosystems. Chemical transport
14 models are driven by emissions inventories for primary species such as the precursors for Os and
15 PM and by meterological fields produced by other numerical models. Emissions of precursor
16 compounds can be divided into anthropogenic and natural source categories. Natural sources can
17 be further divided into biotic (vegetation, microbes, animals) and abiotic (biomass burning,
18 lightning) categories. However, the distinction between natural sources and anthropogenic
19 sources is often difficult to make as human activities affect directly, or indirectly, emissions from
20 what would have been considered natural sources during the preindustrial era. Emissions from
21 plants and animals used in agriculture have been referred to as anthropogenic or natural in
22 different applications. Wildfire emissions may be considered to be natural, except that forest
23 management practices may have led to the buildup of fuels on the forest floor, thereby altering
24 the frequency and severity of forest fires. Needed meteorological quantities such as winds and
25 temperatures are taken from operational analyses, reanalyses, or circulation models. In most
26 cases, these are off-line analyses, i.e., they are not modified by radiatively active species such as
27 63 and particles generated by the model.
28 A brief overview of atmospheric chemistry-transport models is given in Section AX2.7.1.
29 A discussion of emissions inventories of precursors used by these models is given in Section
30 AX2.7.2. Uncertainties in emissions estimates have also been discussed in Air Quality Criteria
31 for Particulate Matter (U.S. Environmental Protection Agency, 2004). Chemistry-transport
August 2007 AX2-58 DRAFT-DO NOT QUOTE OR CITE
-------
1 model evaluation and an evaluation of the reliability of emissions inventories are presented in
2 Section AX2.7.4.
3
4 AX2.7.1 Chemistry-Transport Models
5 Atmospheric CTMs have been developed for application over a wide range of spatial
6 scales ranging from neighborhood to global. Regional scale CTMs are used: 1) to obtain better
7 understanding of the processes controlling the formation, transport, and destruction of gas-and
8 particle-phase criteria and hazardous air pollutants; 2) to understand the relations between Os
9 concentrations and concentrations of its precursors such as NOX and VOCs, the factors leading to
10 acid deposition, and hence to possible damage to ecosystems; and 3) to understand relations
11 among the concentration patterns of various pollutants that may exert adverse health effects.
12 Chemistry Transport Models are also used for determining control strategies for 63 precursors.
13 However, this application has met with varying degrees of success because of the highly
14 nonlinear relations between 63 and emissions of its precursors, and uncertainties in emissions,
15 parameterizations of transport, and chemical production and loss terms. Uncertainties in
16 meteorological variables and emissions can be large enough to lead to significant errors in
17 developing control strategies (e.g., Russell and Dennis, 2000; Sillman et al., 1995).
18 Global scale CTMs are used to address issues associated with climate change,
19 stratospheric ozone depletion, and to provide boundary conditions for regional scale models.
20 CTMs include mathematical (and often simplified) descriptions of atmospheric transport, the
21 transfer of solar radiation through the atmosphere, chemical reactions, and removal to the surface
22 by turbulent motions and precipitation for pollutants emitted into the model domain. Their upper
23 boundaries extend anywhere from the top of the mixing layer to the mesopause (about 80 km in
24 height), to obtain more realistic boundary conditions for problems involving stratospheric
25 dynamics. There is a trade-off between the size of the modeling domain and the grid resolution
26 used in the CTM that is imposed by computational resources.
27 There are two major formulations of CTMs in current use. In the first approach, grid-
28 based, or Eulerian, air quality models, the region to be modeled (the modeling domain) is
29 subdivided into a three-dimensional array of grid cells. Spatial derivatives in the species
30 continuity equations are cast in finite-difference there are also some finite-element models, but
31 not many applications form over this grid, and a system of equations for the concentrations of all
August 2007 AX2-59 DRAFT-DO NOT QUOTE OR CITE
-------
1 the chemical species in the model are solved numerically at each grid point. Time dependent
2 continuity (mass conservation) equations are solved for each species including terms for
3 transport, chemical production and destruction, and emissions and deposition (if relevant), in
4 each cell. Chemical processes are simulated with ordinary differential equations, and transport
5 processes are simulated with partial differential equations. Because of a number of factors such
6 as the different time scales inherent in different processes, the coupled, nonlinear nature of the
7 chemical process terms, and computer storage limitations, all of the terms in the equations are
8 not solved simultaneously in three dimensions. Instead, operator splitting, in which terms in the
9 continuity equation involving individual processes are solved sequentially, is used. In the second
10 CTM formulation, trajectory or Lagrangian models, a large number of hypothetical air parcels
11 are specified as following wind trajectories. In these models, the original system of partial
12 differential equations is transformed into a system of ordinary differential equations.
13 A less common approach is to use a hybrid Lagrangian/Eulerian model, in which certain
14 aspects of atmospheric chemistry and transport are treated with a Lagrangian approach and
15 others are treaded in an Eulerian manner (e.g., Stein et al., 2000). Each approach has its their
16 advantages and disadvantages. The Eulerian approach is more general in that it includes
17 processes that mix air parcels and allows integrations to be carried out for long periods during
18 which individual air parcels lose their identity. There are, however, techniques for including the
19 effects of mixing in Lagrangian models such as FLEXPART (e.g., Zanis et al., 2003), ATTILA
20 (Reithmeier and Sausen, 2002), and CLaMS (McKenna et al., 2002).
21
22 Regional Scale Chemistry Transport Models
23 Major modeling efforts within the U.S. Environmental Protection Agency center on the
24 Community Multiscale Air Quality modeling system (CMAQ, Byun and Ching, 1999; Byun and
25 Schere, 2006). A number of other modeling platforms using Lagrangian and Eulerian
26 frameworks have been reviewed in the 96 AQCD for Os (U.S. EPA, 1997), and in Russell and
27 Dennis (2000). The capabilities of a number of CTMs designed to study local- and regional-
28 scale air pollution problems are summarized by Russell and Dennis (2000). Evaluations of the
29 performance of CMAQ are given in Arnold et al. (2003), Eder and Y (2005), Appel et al. (2005),
30 and Fuentes and Raftery (2005). The domain of CMAQ can extend from several hundred km to
31 the hemispherical scale. In addition, both of these classes of models allow the resolution of the
32 calculations over specified areas to vary. CMAQ is most often driven by the MM5 mesoscale
August 2007 AX2-60 DRAFT-DO NOT QUOTE OR CITE
-------
1 meteorological model (Seaman, 2000), though it may be driven by other meteorological models
2 (e.g., RAMS). Simulations of 63 episodes over regional domains have been performed with a
3 horizontal resolution as low as 1 km, and smaller calculations over limited domains have been
4 accomplished at even finer scales. However, simulations at such high resolutions require better
5 parameterizations of meteorological processes such as boundary layer fluxes, deep convection
6 and clouds (Seaman, 2000), and finer-scale emissions. Finer spatial resolution is necessary to
7 resolve features such as urban heat island circulations; sea, bay, and land breezes; mountain and
8 valley breezes, and the nocturnal low-level jet.
9 The most common approach to setting up the horizontal domain is to nest a finer grid
10 within a larger domain of coarser resolution. However, there are other strategies such as the
11 stretched grid (e.g., Fox-Rabinovitz et al., 2002) and the adaptive grid. In a stretched grid, the
12 grid's resolution continuously varies throughout the domain, thereby eliminating any potential
13 problems with the sudden change from one resolution to another at the boundary. Caution
14 should be exercised in using such a formulation, because certain parameterizations that are valid
15 on a relatively coarse grid scale (such as convection) may not be valid on finer scales. Adaptive
16 grids are not fixed at the start of the simulation, but instead adapt to the needs of the simulation
17 as it evolves (e.g., Hansen et al., 1994). They have the advantage that they can resolve processes
18 at relevant spatial scales. However, they can be very slow if the situation to be modeled is
19 complex. Additionally, if adaptive grids are used for separate meteorological, emissions, and
20 photochemical models, there is no reason a priori why the resolution of each grid should match,
21 and the gains realized from increased resolution in one model will be wasted in the transition to
22 another model. The use of finer horizontal resolution in CTMs will necessitate finer-scale
23 inventories of land use and better knowledge of the exact paths of roads, locations of factories,
24 and, in general, better methods for locating sources and estimating their emissions.
25 The vertical resolution of these CTMs is variable, and usually configured to have higher
26 resolution near the surface and decreasing aloft. Because the height of the boundary layer is of
27 critical importance in simulations of air quality, improved resolution of the boundary layer height
28 would likely improve air quality simulations. Additionally, current CTMs do not adequately
29 resolve fine scale features such as the nocturnal low-level jet in part because little is known about
30 the nighttime boundary layer.
August 2007 AX2-61 DRAFT-DO NOT QUOTE OR CITE
-------
1 CTMs require time-dependent, three-dimensional wind fields for the period of
2 simulation. The winds may be either generated by a model using initial fields alone or with four-
3 dimensional data assimilation to improve the model's performance, fields (i.e., model equations
4 can be updated periodically or "nudged", to bring results into agreement with observations.
5 Modeling efforts typically focus on simulations of several days' duration, the typical time scale
6 for individual Os episodes, but there have been several attempts at modeling longer periods. For
7 example, Kasibhatla and Chameides (2000) simulated a four-month period from May to
8 September of 1995 using MAQSIP. The current trend in modeling applications is towards
9 annual simulations. This trend is driven in part by the need to better understand observations of
10 periods of high wintertime PM (e.g., Blanchard et al., 2002) and the need to simulate 63 episodes
11 occurring outside of summer.
12 Chemical kinetics mechanisms (a set of chemical reactions) representing the important
13 reactions occurring in the atmosphere are used in CTMs to estimate the rates of chemical
14 formation and destruction of each pollutant simulated as a function of time. Unfortunately,
15 chemical mechanisms that explicitly treat the reactions of each individual reactive species are too
16 computationally demanding to be incorporated into CTMs. For example, a master chemical
17 mechanism includes approximately 10,500 reactions involving 3603 chemical species (Derwent
18 et al., 2001). Instead, "lumped" mechanisms, that group compounds of similar chemistry
19 together, are used. The chemical mechanisms used in existing photochemical 63 models contain
20 significant uncertainties that may limit the accuracy of their predictions; the accuracy of each of
21 these mechanisms is also limited by missing chemistry. Because of different approaches to the
22 lumping of organic compounds into surrogate groups, chemical mechanisms can produce
23 somewhat different results under similar conditions. The CB-IV chemical mechanism (Gery
24 et al., 1989), the RADMII mechanism (Stockwell et al., 1990), the SAPRC (e.g., Wang et al.,
25 2000a,b; Carter, 1990) and the RACM mechanisms can be used in CMAQ. Jimenez et al. (2003)
26 provide brief descriptions of the features of the main mechanisms in use and they compared
27 concentrations of several key species predicted by seven chemical mechanisms in a box model
28 simulation over 24 h. The average deviation from the average of all mechanism predictions for
29 63 and NO over the daylight period was less than 20%, and was 10% for NC>2 for all
30 mechanisms. However, much larger deviations were found for HNOs, PAN, HO2, H^C^, C^4,
31 and CsHg (isoprene). An analysis for OH radicals was not presented. The large deviations
August 2007 AX2-62 DRAFT-DO NOT QUOTE OR CITE
-------
1 shown for most species imply differences between the calculated lifetimes of atmospheric
2 species and the assignment of model simulations to either NOx-limited or radical quantity limited
3 regimes between mechanisms. Gross and Stockwell (2003) found small differences between
4 mechanisms for clean conditions, with differences becoming more significant for polluted
5 conditions, especially for NC>2 and organic peroxy radicals. They caution modelers to consider
6 carefully the mechanisms they are using. Faraji et al. (2006) found differences of 40% in peak
7 Ih Os in the Houston-Galveston-Brazoria area between simulations using SAPRAC and CB4.
8 They attributed differences in predicted 63 concentrations to differences in the mechanisms of
9 oxidation of aromatic hydrocarbons.
10 CMAQ and other CTMs (e.g., PM-CAMx) incorporate processes and interactions of
11 aerosol-phase chemistry (Mebust et al., 2003). There have also been several attempts to study
12 the feedbacks of chemistry on atmospheric dynamics using meteorological models, like MM5
13 (e.g., Grell et al., 2000; Liu et al., 2001a; Lu et al., 1997; Park et al., 2001). This coupling is
14 necessary to simulate accurately feedbacks such as may be caused by the heavy aerosol loading
15 found in forest fire plumes (Lu et al., 1997; Park et al., 2001), or in heavily polluted areas.
16 Photolysis rates in CMAQ can now be calculated interactively with model produced 63, NC>2,
17 and aerosol fields (Binkowski et al., 2007).
18 Spatial and temporal characterizations of anthropogenic and biogenic precursor emissions
19 must be specified as inputs to a CTM. Emissions inventories have been compiled on grids of
20 varying resolution for many hydrocarbons, aldehydes, ketones, CO, NH3, and NOX. Emissions
21 inventories for many species require the application of some algorithm for calculating the
22 dependence of emissions on physical variables such as temperature and to convert the
23 inventories into formatted emission files required by a CTM. For example, preprocessing of
24 emissions data for CMAQ is done by the SMOKE (Spare-Matrix Operator Kernel Emissions)
25 system. For many species, information concerning the temporal variability of emissions is
26 lacking, so long-term (e.g., annual or Os-season) averages are used in short-term, episodic
27 simulations. Annual emissions estimates are often modified by the emissions model to produce
28 emissions more characteristic of the time of day and season. Significant errors in emissions can
29 occur if an inappropriate time dependence or a default profile is used. Additional complexity
30 arises in model calculations because different chemical mechanisms are based on different
31 species, and inventories constructed for use with another mechanism must be adjusted to reflect
August 2007 AX2-63 DRAFT-DO NOT QUOTE OR CITE
-------
1 these differences. This problem also complicates comparisons of the outputs of these models
2 because one chemical mechanism may produce some species not present in another mechanism
3 yet neither may agree with the measurements.
4 In addition to wet deposition, dry deposition (the removal of chemical species from the
5 atmosphere by interaction with ground-level surfaces) is an important removal process for
6 pollutants on both urban and regional scales and must be included in CTMs. The general
7 approach used in most models is the resistance in series method, in which where dry deposition
8 is parameterized with a Vd, which is represented as Vd = (ra + rb + re)"1 where ra, ib, and rc
9 represent the resistance due to atmospheric turbulence, transport in the fluid sublayer very near
10 the elements of surface such as leaves or soil, and the resistance to uptake of the surface itself.
11 This approach works for a range of substances, although it is inappropriate for species with
12 substantial emissions from the surface or for species whose deposition to the surface depends on
13 its concentration at the surface itself. The approach is also modified somewhat for aerosols: the
14 terms rb and rc are replaced with a surface Vd to account for gravitational settling. In their
15 review, Wesley and Hicks (2000) point out several shortcomings of current knowledge of dry
16 deposition. Among those shortcomings are difficulties in representing dry deposition over
17 varying terrain where horizontal advection plays a significant role in determining the magnitude
18 of ra and difficulties in adequately determining a Vd for extremely stable conditions such as those
19 occurring at night (e.g., Mahrt, 1998). Under the best of conditions, when a model is exercised
20 over a relatively small area where dry deposition measurements have been made, models still
21 commonly show uncertainties at least as large as ±30% (e.g., Massman et al., 1994; Brook et al.,
22 1996; Padro, 1996). Wesely and Hicks (2000) state that an important result of these comparisons
23 is that the current level of sophistication of most dry deposition models is relatively low, and that
24 deposition estimates therefore must rely heavily on empirical data. Still larger uncertainties exist
25 when the surface features in the built environment are not well known or when the surface
26 comprises a patchwork of different surface types, as is common in the eastern United States.
27 The initial conditions, i.e., the concentration fields of all species computed by a model,
28 and the boundary conditions, i.e., the concentrations of species along the horizontal and upper
29 boundaries of the model domain throughout the simulation must be specified at the beginning of
30 the simulation. It would be best to specify initial and boundary conditions according to
31 observations. However, data for vertical profiles of most species of interest are sparse. The
August 2007 AX2-64 DRAFT-DO NOT QUOTE OR CITE
-------
1 results of model simulations over larger, preferably global, domains can also be used. As may be
2 expected, the influence of boundary conditions depends on the lifetime of the species under
3 consideration and the time scales for transport from the boundaries to the interior of the model
4 domain (Liu et al., 2001 b).
5 Each of the model components described above has an associated uncertainty, and the
6 relative importance of these uncertainties varies with the modeling application. The largest
7 errors in photochemical modeling are still thought to arise from the meteorological and
8 emissions inputs to the model (Russell and Dennis, 2000). Within the model itself, horizontal
9 advection algorithms are still thought to be significant source of uncertainty (e.g., Chock and
10 Winkler, 1994), though more recently, those errors are thought to have been reduced (e.g.,
11 Odman et al., 1996). There are also indications that problems with mass conservation continue
12 to be present in photochemical and meteorological models (e.g., Odman and Russell, 1999);
13 these can result in significant simulation errors. The effects of errors in initial conditions can be
14 minimized by including several days "spin-up" time in a simulation to allow the model to be
15 driven by emitted species before the simulation of the period of interest begins.
16 While the effects of poorly specified boundary conditions propagate through the model's
17 domain, the effects of these errors remain undetermined. Because many meteorological
18 processes occur on spatial scales which are smaller than the model grid spacing (either
19 horizontally or vertically) and thus are not calculated explicitly, parameterizations of these
20 processes must be used and these introduce additional uncertainty.
21 Uncertainty also arises in modeling the chemistry of Os formation because it is highly
22 nonlinear with respect to NOX concentrations. Thus, the volume of the grid cell into which
23 emissions are injected is important because the nature of Os chemistry (i.e., Os production or
24 titration) depends in a complicated way on the concentrations of the precursors and the OH
25 radical as noted earlier. The use of ever-finer grid spacing allows regions of Os titration to be
26 more clearly separated from regions of Os production. The use of grid spacing fine enough to
27 resolve the chemistry in individual power-plant plumes is too demanding of computer resources
28 for this to be attempted in most simulations. Instead, parameterizations of the effects of sub-
29 grid-scale processes such as these must be developed; otherwise serious errors can result if
30 emissions are allowed to mix through an excessively large grid volume before the chemistry step
31 in a model calculation is performed. In light of the significant differences between atmospheric
August 2007 AX2-65 DRAFT-DO NOT QUOTE OR CITE
-------
1 chemistry taking place inside and outside of a power plant plume (e.g., Ryerson et al., 1998 and
2 Sillman, 2000), inclusion of a separate, meteorological module for treating large, tight plumes is
3 necessary. Because the photochemistry of O3 and many other atmospheric species is nonlinear,
4 emissions correctly modeled in a tight plume may be incorrectly modeled in a more dilute plume.
5 Fortunately, it appears that the chemical mechanism used to follow a plume's development need
6 not be as detailed as that used to simulate the rest of the domain, as the inorganic reactions are
7 the most important in the plume see (e.g., Kumar and Russell, 1996). The need to include
8 explicitly plume-in-grid chemistry only down to the level of the smallest grid disappears if one
9 uses the adaptive grid approach mentioned previously, though such grids are more
10 computationally intensive. The differences in simulations are significant because they can lead
11 to significant differences in the calculated sensitivity of O3 to its precursors (e.g., Sillman et al.,
12 1995).
13 Because the chemical production and loss terms in the continuity equations for individual
14 species are coupled, the chemical calculations must be performed iteratively until calculated
15 concentrations converge to within some preset criterion. The number of iterations and the
16 convergence criteria chosen also can introduce error.
17
18 Global Scale CTMs
19 The importance of global transport of 63 and 63 precursors and their contribution to
20 regional 63 levels in the United States is slowly becoming apparent. There are presently on the
21 order of 20 three-dimensional global models that have been developed by various groups to
22 address problems in tropospheric chemistry. These models resolve synoptic meteorology,
23 O3-NOx-CO-hydrocarbon photochemistry, have parameterizations for wet and dry deposition,
24 and parameterize sub-grid scale vertical mixing processes such as convection. Global models
25 have proven useful for testing and advancing scientific understanding beyond what is possible
26 with observations alone. For example, they can calculate quantities of interest that cannot be
27 measured directly, such as the export of pollution from one continent to the global atmosphere or
28 the response of the atmosphere to future perturbations to anthropogenic emissions.
29 Global simulations are typically conducted at a horizontal resolution of about 200 km2.
30 Simulations of the effects of transport from long-range transport link multiple horizontal
31 resolutions from the global to the local scale. Finer resolution will only improve scientific
32 understanding to the extent that the governing processes are more accurately described at that
August 2007 AX2-66 DRAFT-DO NOT QUOTE OR CITE
-------
1 scale. Consequently, there is a critical need for observations at the appropriate scales to evaluate
2 the scientific understanding represented by the models.
3 During the recent IPCC-AR4 tropospheric chemistry study coordinated by the European
4 Union project Atmospheric Composition Change: the European Network of excellence
5 (ACCENT), 26 atmospheric CTMs were used to estimate the impacts of three emissions
6 scenarios on global atmospheric composition, climate, and air quality in 2030 (Dentener et al.,
7 2006a). All models were required to use anthropogenic emissions developed at IIASA (Dentener
8 et al., 2005) and GFED version 1 biomass burning emissions (van der Werf et al., 2003) as
9 described in Stevenson et al. (2006). The base simulations from these models were evaluated
10 against a suite of present-day observations. Most relevant to this assessment report are the
11 evaluations with ozone and NO2, and for nitrogen and sulfur deposition (Stevenson et al., 2006;
12 van Noije et al., 2006; Dentener et al., 2006a), which are summarized briefly below.
13 An analysis of the standard deviation of zonal mean and tropospheric column Os reveals
14 large inter-model variability in the tropopause region and throughout the polar troposphere,
15 likely reflecting differences in model tropopause levels and the associated stratospheric injection
16 of 63 to the troposphere (Stevenson et al., 2006). Ozone distributions in the tropics also exhibit
17 large standard deviations (-30%), particularly as compared to the mid-latitudes (-20%),
18 indicating larger uncertainties in the processes that influence ozone in the tropics: deep tropical
19 convection, lightning NOX, isoprene emissions and chemistry, and biomass burning emissions
20 (Stevenson et al., 2006).
21 Stevenson et al., (2006) found that the model ensemble mean (MEM) typically captures
22 the observed seasonal cycles to within one standard deviation. The largest discrepancies
23 between the MEM and observations include: (1) an underestimate of the amplitude of the
24 seasonal cycle at 30°-90°N with a 10 ppbv overestimate of winter ozone, possibly due to the lack
25 of a seasonal cycle in anthropogenic emissions or to shortcomings in the stratospheric influx of
26 63, and (2) an overestimate of 63 throughout the northern tropics. However, the MEM was
27 found to capture the observed seasonal cycles in the Southern Hemisphere, suggesting that the
28 models adequately represent biomass burning and natural emissions.
29 The mean present-day global ozone budget across the current generation of CTMs differs
30 substantially from that reported in the IPCC TAR, with a 50% increase in the mean chemical
31 production (to 5100 Tg Oj yr"1), a 30% increase in the chemical and deposition loss terms (to
August 2007 AX2-67 DRAFT-DO NOT QUOTE OR CITE
-------
1 4650 and 1000 Tg 63 yr *, respectively) and a 30% decrease in the mean stratospheric input flux
2 (to 550 Tg 63 yr"1) (Stevenson et al., 2006). The larger chemical terms as compared to the IPCC
3 TAR are attributed mainly to higher NOX (as well as an equatorward shift in distribution) and
4 isoprene emissions, although more detailed NMHC schemes and/or improved representations of
5 photolysis, convection, and stratospheric-tropospheric exchange may also contribute (Stevenson
6 et al., 2006).
7 A subset of 17 of the 26 models used in the Stevenson et al. (2006) study was used to
8 compare with three retrievals of NO2 columns from the GOME instrument (van Noije et al.,
9 2006) for the year 2000. The higher resolution models reproduce the observed patterns better,
10 and the correlation among simulated and retrieved columns improved for all models when
11 simulated values are smoothed to a 5° x 5° grid, implying that the models do not accurately
12 reproduce the small-scale features of NO2 (van Noije et al., 2006). Van Noije et al. (2006)
13 suggest that variability in simulated NC>2 columns may reflect a model differences in OH
14 distributions and the resulting NOX lifetimes, as well as differences in vertical mixing which
15 strongly affect partitioning between NO and NO2. Overall, the models tend to underestimate
16 concentrations in the retrievals in industrial regions (including the eastern United States) and
17 overestimate them in biomass burning regions (van Noije et al., 2006).
18 Over the eastern United States, and industrial regions more generally, the spread in
19 absolute column abundances is generally larger among the retrievals than among the models,
20 with the discrepancy among the retrievals particularly pronounced in winter (van Noije et al.,
21 2006), suggesting that the models are biased low, or that the European retrievals may be biased
22 high as the Dalhousie/SAO retrieval is closer to the model estimates. The lack of seasonal
23 variability in fossil fuel combustion emissions may contribute to a wintertime model
24 underestimate (van Noije et al., 2006) that is manifested most strongly over Asia. In biomass
25 burning regions, the models generally reproduce the timing of the seasonal cycle of the
26 retrievals, but tend to overestimate the seasonal cycle amplitude, partly due to lower values in the
27 wet season, which may reflect an underestimate in wet season soil NO emissions (van Noije
28 et al., 2006, Jaegle et al., 2004, 2005).
29
August 2007 AX2-68 DRAFT-DO NOT QUOTE OR CITE
-------
1 Deposition in Global CTMs
2 Both wet and dry deposition are highly parameterized in global CTMs. While all current
3 models implement resistance schemes for dry deposition, the generated Vd generated from
4 different models can vary highly across terrains (Stevenson et al., 2006). The accuracy of wet
5 deposition in global CTMs is tied to spatial and temporal distribution of model precipitation and
6 the treatment of chemical scavenging. Dentener et al. (2006b) compared wet deposition across
7 23 models with available measurements around the globe. Figures AX2-13 and AX2-14 below
8 extract the results of a comparison of the 23-model mean versus observations from Dentener
9 et al. (2006b) over the eastern United States for nitrate and sulfate deposition, respectively. The
10 mean model results are strongly correlated with the observations (r > 0.8), and usually capture
11 the magnitude of wet deposition to within a factor of 2 over the eastern United States (Dentener
12 et al., 2006b). Dentener et al. (2006b) conclude that 60-70% of the participating models capture
13 the measurements to within 50% in regions with quality controlled observations. This study then
14 identified world regions receiving >1000 mg (N) nT2 yr"1 (the "critical load") and found that
15 20% of the natural vegetation (non-agricultural) in the United States is exposed to nitrogen
16 deposition in excess of the critical load threshold (Dentener et al., 2006b).
17
18 Modeling the Effects of Convection
19 The effects of deep convection can be simulated using cloud-resolving models, or in
20 regional or global models in which the convection is parameterized. The Goddard Cumulus
21 Ensemble (GCE) model (Tao and Simpson, 1993) has been used by Pickering et al. (1991;
22 1992a,b; 1993; 1996), Scala et al. (1990) and Stenchikov et al. (1996) in the analysis of
23 convective transport of trace gases. The cloud model is nonhydrostatic and contains a detailed
24 representation of cloud microphysical processes. Two- and three-dimensional versions of the
25 model have been applied in transport analyses. The initial conditions for the model are usually
26 from a sounding of temperature, water vapor and winds representative of the region of storm
27 development. Model-generated wind fields can be used to perform air parcel trajectory analyses
28 and tracer advection calculations.
29
August 2007 AX2-69 DRAFT-DO NOT QUOTE OR CITE
-------
600
400
0)
•o
o
200
Ave. model; 227 Ave. Mea. 195 r: 0.82 n = 228
2 param, fit: y - 51,1 + 0.90x
1 param. fit; y ~ 1.08x
Percentage within ± 50%; 74,8
0
200 400
Measurement
600
Figure AX2-13.
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).
August 2007
AX2-70
DRAFT-DO NOT QUOTE OR CITE
-------
1000
800-
600-
400-
200
_
'• "• • . • ... l\ «
Jl "J * •
• fmm.-j • •
Ave. modsi; 383 Ave. B^eas; 322 n 0,8? n - 226
2 param fit; y = 114.0 + 0 J?x
1 param fit; y = 1,00x
0 200 400 600 800 1000
Measurement
2
Figure AX2-14. Same as Figure AX2-13 but for sulfate wet deposition (mg(S)m yr
Source: Dentener et al. (2006b).
1 Such methods were used by Pickering et al. (1992b) to examine transport of urban
2 plumes by deep convection. Transport of an Oklahoma City plume by the 10-11 June 1985
3 PRE-STORM squall line was simulated with the 2-D GCE model. This major squall line passed
4 over the Oklahoma City metropolitan area, as well as more rural areas to the north. Chemical
5 observations ahead of the squall line were conducted by the PRE-STORM aircraft. In this event,
6 forward trajectories from the boundary layer at the leading edge of the storm showed that almost
7 75% of the low-level inflow was transported to altitudes exceeding 8 km. Over 35% of the air
8 parcels reached altitudes over 12 km. Tracer transport calculations were performed for CO,
9 NOX, O3, and hydrocarbons. Rural boundary layer NOX was only 0.9 ppbv, whereas the urban
10 plume contained about 3 ppbv. In the rural case, mixing ratios of 0.6 ppbv were transported up
11 to 11 km. Cleaner air descended at the rear of the storm lowering NOX at the surface from 0.9 to
August 2007
AX2-71
DRAFT-DO NOT QUOTE OR CITE
-------
1 0.5 ppbv. In the urban plume, mixing ratios in the updraft core reached 1 ppbv between 14 and
2 15 km. At the surface, the main downdraft lowered the NOX mixing ratios from 3 to 0.7 ppbv.
3 Regional chemical transport models have been used for applications such as simulations
4 of photochemical Os production, acid deposition, and fine PM. Walcek et al. (1990) included a
5 parameterization of cloud-scale aqueous chemistry, scavenging, and vertical mixing in the
6 chemistry model of Chang et al. (1987). The vertical distribution of cloud microphysical
7 properties and the amount of sub-cloud-layer air lifted to each cloud layer are determined using a
8 simple entrainment hypothesis (Walcek and Taylor, 1986). Vertically integrated 63 formation
9 rates over the northeast U. S. were enhanced by -50% when the in-cloud vertical motions were
10 included in the model.
11 Wang et al. (1996) simulated the 10-11 June 1985 PRE-STORM squall line with the
12 NCAR/Penn State Mesoscale Model (MM5; Grell et al., 1994; Dudhia, 1993). Convection was
13 parameterized as a sub-grid-scale process in MM5 using the Kain Fritsch (1993) scheme. Mass
14 fluxes and detrainment profiles from the convective parameterization were used along with the
15 3-D wind fields in CO tracer transport calculations for this convective event.
16 Convective transport in global chemistry and transport models is treated as a sub-grid-
17 scale process that is parameterized typically using cloud mass flux information from a general
18 circulation model or global data assimilation system. While GCMs can provide data only for a
19 "typical" year, data assimilation systems can provide "real" day-by-day meteorological
20 conditions, such that CTM output can be compared directly with observations of trace gases.
21 The NASA Goddard Earth Observing System Data Assimilation System (GEOS-1 DAS and
22 successor systems; Schubert et al., 1993; Bloom et al., 1996; Bloom et al., 2005) provides
23 archived global data sets for the period 1980 to present, at 2° x 2.5° or better resolution with
24 20 layers or more in the vertical. Deep convection is parameterized with the Relaxed
25 Arakawa-Schubert scheme (Moorthi and Suarez, 1992) in GEOS-1 and GEOS-3 and with the
26 Zhang and McFarlane (1995) scheme in GEOS-4. Pickering et al. (1995) showed that the cloud
27 mass fluxes from GEOS-1 DAS are reasonable for the 10-11 June 1985 PRE-STORM squall line
28 based on comparisons with the GCE model (cloud-resolving model) simulations of the same
29 storm. In addition, the GEOS-1 DAS cloud mass fluxes compared favorably with the regional
30 estimates of convective transport for the central U. S. presented by Thompson et al. (1994).
31 However, Allen et al. (1997) have shown that the GEOS-1 DAS overestimates the amount and
August 2007 AX2-72 DRAFT-DO NOT QUOTE OR CITE
-------
1 frequency of convection in the tropics and underestimates the convective activity over
2 midlatitude marine storm tracks.
3 Global models with parameterized convection and lightning have been run to examine
4 the roles of these processes over North America. Lightning contributed 23% of upper
5 tropospheric NOy over the SONEX region according to the UMD-CTM modeling analysis of
6 Allen et al. (2000). During the summer of 2004 the NASA Intercontinental Chemical Transport
7 Experiment - North America (INTEX-NA) was conducted primarily over the eastern two-thirds
8 of the United States, as a part of the International Consortium for Atmospheric Research on
9 Transport and Transformation (ICARTT). Deep convection was prevalent over this region
10 during the experimental period. Cooper et al. (2006) used a particle dispersion model simulation
11 for NOX to show that 69-84% of the upper tropospheric Oj enhancement over the region in
12 Summer 2004 was due to lightning NOX. The remainder of the enhancement was due to
13 convective transport of Os from the boundary layer or other sources of NOX. Hudman et al.
14 (2007) used a GEOS-Chem model simulation to show that lightning was the dominant source of
15 upper tropospheric NOX over this region during this period. Approximately 15% of North
16 American boundary layer NOX emissions were shown to have been vented to the free troposphere
17 over this region based on both the observations and the model.
18
19 AX2.7.2 CTM Evaluation
20 The comparison of model predictions with ambient measurements represents a critical
21 task for establishing the accuracy of photochemical models and evaluating their ability to serve
22 as the basis for making effective control strategy decisions. The evaluation of a model's
23 performance, or its adequacy to perform the tasks for which it was designed can only be
24 conducted within the context of measurement errors and artifacts. Not only are there analytical
25 problems, but there are also problems in assessing the representativeness of monitors at ground
26 level for comparison with model values which represent typically an average over the volume of
27 a grid box.
28 Evaluations of CMAQ are given in Arnold et al. (2003) and Fuentes and Raftery (2005).
29 Discrepancies between model predictions and observations can be used to point out gaps in
30 current understanding of atmospheric chemistry and to spur improvements in parameterizations
31 of atmospheric chemical and physical processes. Model evaluation does not merely involve a
August 2007 AX2-73 DRAFT-DO NOT QUOTE OR CITE
-------
1 straightforward comparison between model predictions and the concentration field of the
2 pollutant of interest. Such comparisons may not be meaningful because it is difficult to
3 determine if agreement between model predictions and observations truly represents an accurate
4 treatment of physical and chemical processes in the CTM or the effects of compensating errors in
5 complex model routines. Ideally, each of the model components (emissions inventories,
6 chemical mechanism, meteorological driver) should be evaluated individually. However, this is
7 rarely done in practice.
8 Chemical transport models for 63 formation at the urban/regional scale have traditionally
9 been evaluated based on their ability to simulate correctly 63. A series of performance statistics
10 that measure the success of individual model simulations to represent the observed distribution
11 of ambient O3, as represented by a network of surface measurements at the urban scale were
12 recommended by the U.S. Environmental Protection Agency (U.S. EPA, 1991; see also Russell
13 and Dennis, 2000). These statistics consist of the following:
14 • Unpaired peak Os concentration within a metropolitan region (typically for a
15 single day).
16 • Normalized bias equal to the summed difference between model and measured
17 hourly concentrations divided by the sum of measured hourly concentrations.
18 • Normalized gross error, equal to the summed unsigned (absolute value) difference
19 between model and measured hourly concentrations divided by the sum of
20 measured hourly concentrations.
21
22 Unpaired peak prediction accuracy, Au;
-*100%,
23 ^o(x't)max (AX2-48)
24 Normalized bias, D;
D=L| {cp(Xi,t)-c0(Xi,t)} t = ]24
25 N i=l C0(Xj,t) (AX2-49)
26 Gross error, Ed (for hourly observed values of Os >60 ppb)
August 2007 AX2-74 DRAFT-DO NOT QUOTE OR CITE
-------
1 N u CX-Y/./) (AX2-50)
2 The following performance criteria for regulatory models were recommended in U.S.
3 Environmental Protection Agency (1991): unpaired peak Os to within ±15% or ±20%;
4 normalized bias within ± 5% to ± 15%; and normalized gross error less than 30% to 35%, but
5 only when 63 the concentration >60 ppb. This can lead to difficulties in evaluating model
6 performance since nighttime and diurnal cycles are ignored. A major problem with this method
7 of model evaluation is that it does not provide any information about the accuracy of O3-
8 precursor relations predicted by the model. The process of Os formation is sufficiently complex
9 that models can predict Os correctly without necessarily representing the Os formation process
10 properly. If the Os formation process is incorrect, then the modeled source-receptor relations
11 will also be incorrect.
12 Studies by Sillman et al. (1995, 2003), Reynolds et al. (1996) and Pierce et al. (1998)
13 have identified instances in which different model scenarios can be created with very different
14 (Vprecursor sensitivity, but without significant differences in the predicted 63 fields.
15 Figures AX2-15a,b provides an example. Referring to the O3-NOX-VOC isopleth plot (Figure
16 AX2-16), it can be seen that similar Os concentrations can be found for photochemical
17 conditions that have very different sensitivity to NOX and VOCs.
18 Global-scale CTMs have generally been evaluated by comparison with measurements for
19 a wide array of species, rather than just for Os (e.g., Wang et al., 1998; Emmons et al., 2000; Bey
20 et al., 2001; Hess, 2001; Fiore et al., 2002). These have included evaluation of major primary
21 species (NOX, CO, and selected VOCs) and an array of secondary species (HNOs, PAN, H2O2)
22 that are often formed concurrently with Os. Models for urban and regional Os have also been
23 evaluated against a broader ensemble of measurements in a few cases, often associated with
24 measurement intensives (e.g., Jacobson et al., 1996; Lu et al., 1997; Sillman et al., 1998). The
25 results of a comparison between observed and computed concentrations from Jacobson et al.
26 (1996) for the Los Angeles Basin are shown in Figures AX2-17a,b.
27 The highest concentrations of primary species usually occur in close proximity to
28 emission sources (typically in urban centers) and at times when dispersion rates are low. The
29 diurnal cycle includes high concentrations at night, with maxima during the morning rush hour,
30 and low concentrations during the afternoon (Figure AX2-17a). The afternoon minima are
August 2007 AX2-75 DRAFT-DO NOT QUOTE OR CITE
-------
25
> 20
JQ
0.
3 15
c
o
„
o
3
T3
-------
10,00
E
o
d
£
o
£
3,16
r. 1.0
c
o
8
I
UJ
x
O
z
0.316
0.1
JO
ex
Q.
1.0 3,16 10.0 31.6 100,0
VOC Emission Rate (1012 molec. cm-2 s-1)
Figure AX2-16. 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. 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.
1 driven by the much greater rate of vertical mixing at that time. Primary species also show a
2 seasonal maximum during winter, and are often high during fog episodes in winter when vertical
3 mixing, is suppressed. By contrast, secondary species such as Os are typically highest during the
4 afternoon (the time of greatest photochemical activity), on sunny days and during summer.
5 During these conditions, concentrations of primary species may be relatively low. Strong
6 correlations between primary and secondary species are generally observed only in downwind
7 rural areas where all anthropogenic species are simultaneously elevated. The difference in the
8 diurnal cycles of primary species (CO, NOX and ethane) and secondary species (Os, PAN, and
9 HCHO) is evident in Figure AX2-17b.
10 Models for urban and regional chemistry have been evaluated less extensively than
11 global-scale models in part because the urban/regional context presents a number of difficult
August 2007
AX2-77
DRAFT-DO NOT QUOTE OR CITE
-------
I
0.
a
OJ
._
I
i
a.
a.
Ui
0,30
0,25
0-20
0.15
0.10
0.05
0,00
0.30
0,25
0.20
0.15
0.10
0.05
0,00
a.
a
n
on
o»
,_
I
Figure AX2-17a.
Reseda
03<9)
Predicted
Observed
0 8 16 24 32 40 48 56 64 72
Hoyr After First Midnight
Reseda
NOX (g)
• Predicted
• • Observed
0 8 16 24 32 40 48 56 64 72
Hour After First Midnight
Riverside
C0(g)
Predicted
- - - - Observed
0 8 16 24 32 40 48 56 64 72
Hoyr 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: Jacobsonetal. (1996).
August 2007
AX2-78
DRAFT-DO NOT QUOTE OR CITE
-------
0,060
| 0.050
a
3 0,040
o
••g 0,030
DC
Ui 0.020
s °'010
0,000
0.030
| 0.025
a
3 0.020
o
"•§ 0.015
o£
0) 0.010
~ 0.005
0.000
IB
m
0,020
">*
E 0,016
a.
3;
°'012
0,008
0 004
0.000
Claremont
Ethane (g)
• Predicted
O Observed
8 16 24 32 40 48 56 64 72
Hour After First Midnight
Claremont
Formaldehyde (g)
Predicted
O Observed
o
j_
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 64 72
Hour After First Midnight
Figure AX2-17b.
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: Jacobsonetal. (1996).
August 2007
AX2-79
DRAFT-DO NOT QUOTE OR CITE
-------
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 63 formation, including
11 VOCs, NOX, PAN, HNO3, and H2O2 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
August 2007 AX2-80 DRAFT-DO NOT QUOTE OR CITE
-------
1 of evaluating the accuracy of transport parameterizations. Sillman and He (2002) examined
2 differences in correlation patterns between 63 and NOZ in Los Angeles, CA, Nashville, TN, and
3 various sites in the rural United States. Model calculations (Figure AX2-18) show differences in
4 correlation patterns associated with differences in the sensitivity of Os to NOX and VOCs.
5 Primarily NOx-sensitive (NOx-limited) areas in models show a strong correlation between Os and
6 NOZ with a relatively steep slope, while primarily VOC-sensitive (NOX-saturated) areas in
7 models show lower Os for a given NOZ and a lower O3-NOZ slope. They found that differences
8 found in measured data ensembles were matched by predictions from chemical transport models.
250
0
Figure AX2-18.
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).
August 2007
AX2-81
DRAFT-DO NOT QUOTE OR CITE
-------
1 Measurements in rural areas in the eastern U.S. show differences in the pattern of
2 correlations for 63 versus NOZ between summer and autumn (Jacob et al., 1995; Hirsch et al.,
3 1996), corresponding to the transition from NOx-limited to NOx-saturated patterns, a feature
4 which is also matched by CTMs.
5 The difference in correlations between secondary species in NOx-limited to NOX-
6 saturated environments can also be used to evaluate the accuracy of model predictions in
7 individual applications. Figures AX2-19a and AX2-19b 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 NOy and 63 formation apparently suppressed by high NOy. Measurements show much
10 lower NOy in the Atlanta plume. This error was especially significant because the model
11 locations sensitive to NOX. The second model scenario (with primarily NOx-sensitive
12 conditions) shows much better agreement with measured values. Figure AX2-20a,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 Os episodes in central Europe are often associated with SE winds.)
20 Concentrations of major compounds such as O3, 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 NOy were not sensitive to NOX, while locations with lower NOy were
23 primarily based method. Figure AX2-21 compares the concentrations of RO2, HO2, 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 Os 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 RO2, HO2, 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
August 2007 AX2-82 DRAFT-DO NOT QUOTE OR CITE
-------
10 20
NOy (ppb)
Figure AX2-19a,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: Sillmanetal. (1997).
August 2007
AX2-83
DRAFT-DO NOT QUOTE OR CITE
-------
Q.
•S
n
O
160
140
0
160
10
20
NOZ (ppb)
30
40
0
10 20 30
2H2O2 + NOZ (ppb)
40
Figure AX2-20a,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: Sillmanetal. (1998).
August 2007
AX2-84
DRAFT-DO NOT QUOTE OR CITE
-------
I"O
I
o
CO
O
«M
O
2
0
6
4
2
0
10
(0
o
O o
s* 10
3
a.
~ 5
6
«««»<
°- o
& 20
Q.
5 10
z
0
O L!F
• MIESR
8
W I
y.yv
J(O1Df 106 (s-
10 12 14
UT 20.7.98
16
Figure AX2-21.
Time series of concentrations of ROi, HOi, and OH radicals, local Os
photochemical production rate and concentrations of NOX from
measurements made during BERLIOZ. Also shown are comparisons
with results of photochemical box model calculations using the RACM
and MCM chemical mechanisms.
Source: Volz-Thomas et al. (2003).
August 2007
AX2-85
DRAFT-DO NOT QUOTE OR CITE
-------
1 moderately overestimate HO2 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 HO2 to OH and RO2 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 HO2 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 HO2 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 NOy 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.
August 2007 AX2-86 DRAFT-DO NOT QUOTE OR CITE
-------
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 (NOy) is included in the PAMS network only at a few sites. The
11 possible expansion of PAMS to include more widespread NOy measurements has been suggested
12 (McClenny, 2000). NOy measurements are also planned for inclusion in the NCore network
13 (U.S. EPA, 2005). A major issue to be considered when measuring NOX and NOy is the
14 possibility that HNOs, a major component of NOy, is sometimes lost in inlet tubes and not
15 measured (Luke et al., 1998; Parrish and Fehsenfeld, 2000). This problem is especially critical if
16 measured NOy is used to identify NOx-limited versus NOx-saturated conditions. The problem is
17 substantially alleviated although not necessarily completely solved by using much shorter inlets
18 on NOy monitors than on NOX monitors and by the use of surfaces less likely to take up HNOs.
19 The correlation between Os and NOy differs for NOx-limited versus NOx-saturated locations, but
20 this difference is driven primarily by differences in the ratio of Oj to HNOs. If HNOs were
21 omitted from the NOy measurements, then the measurements would represent a biased estimate
22 and their use would be problematic.
23
24 AX2.8.1.1 Calibration Standards
25 Calibration gas standards of NO, in N2 (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
August 2007 AX2-87 DRAFT-DO NOT QUOTE OR CITE
-------
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 Oj (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 NO2 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
August 2007 AX2-88 DRAFT-DO NOT QUOTE OR CITE
-------
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 NO2. 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 NO2 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 NO2. However, the substrate used in the reduction of NO2 to
26 NO is not specific to NO2; hence the chemiluminescence analyzers are subject to interference
27 nitrogen oxides other than NO2 produced by oxidized NOy compounds, or NOZ. Thus, this
28 technique will overestimate NO2 concentrations particularly in areas downwind of sources of NO
29 and NO2 as NOX is oxidized to NOZ in the form of PANs and other organic nitrates, and HNOs
30 and HNO4. Many of these compounds are reduced at the catalyst with nearly the same efficiency
31 as NO2. Interferences have also been found from a wide range of other compounds as described
32 in the latest AQCD for NO2.
August 2007 AX2-89 DRAFT-DO NOT QUOTE OR CITE
-------
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 NC>2
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 HO2NO2 for example, dissociate
12 to NC>2 at higher temperatures.
13 Laser induced fluorescence for NC>2 detection involves excitation of atmospheric NC>2
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 NC>2, 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 NC>2 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, HNO4 and HNO3 (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
August 2007 AX2-90 DRAFT-DO NOT QUOTE OR CITE
-------
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 NO2.
3 DOAS systems can also be configured to measure NO, HONO, and NO3 radicals. Typical
4 detection limits are 0.2 to 0.3 ppbv for NO, 0.05 to 0.1 ppbv for NO2, 0.05 to 0.1 ppbv for
5 HONO, and 0.001 to 0.002 ppbv for NO3, 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 NO2
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 NO2 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 NO2 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 DO AS method that
25 uses the sun as a light source and compares well with an in situ chemiluminescence detector in
26 an urban environment.
27 Chemiluminescence on the surface of liquid Luminol has also been used for measurement
28 of NO2 (Gaffney et al., 1998; Kelly et al., 1990; Marley et al., 2004; Nikitas et al., 1997; Wendel
29 et al., 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
August 2007 AX2-91 DRAFT-DO NOT QUOTE OR CITE
-------
1 measurement compares well with more specific techniques for moderate to high (> 1 ppb) mixing
2 ratios of NO2.
3 Several tunable diode laser spectroscopy techniques have been used successfully for NO2
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 NOy 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, RNO2,
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 NOy 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 NO2 have been focused on demonstrating compliance with
21 the NAAQS for NO2. Today, few locations violate that standard, but NO2 and related NOy
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 NO2 to NO, and generate a signal referred to as NOX. These
25 converters, generally constructed of molybdenum oxides (MoOx), reduce not only NO2 but also
26 most other NOy species. Unfortunately, with an internal converter, the instruments may not give
27 a faithful indication of NOy 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 NO/NOy 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 NOy, suitable for evaluation of photochemical models. (Crosley, 1996;
32 Fehsenfeld et al., 1987; Nunnermacker et al., 1998; Rodgers and Davis, 1989). Under conditions
August 2007 AX2-92 DRAFT-DO NOT QUOTE OR CITE
-------
1 of fresh emissions, such as in urban areas during the rush hour, NOy ~ 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 NOy 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 NC>2 and related
9 reactive nitrogen species. For demonstration of compliance with the NAAQS for NC>2,
10 commercial chemiluminescence instruments are adequate. For certain conditions, luminol
11 chemiluminescence is adequate. Precise measurements of NC>2 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 NOy species, but do not measure NOy quantitatatively. NOy 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,
23 HN03g 4~A> [HN03aq] <-2-Kff+] + \N03-] (AX2-51)
24 where KH is the Henry's Law constant in M ataf * and Ka is the acid dissociation constant in M.
25 Thus, the primary controls on HNOs 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-Jim predominantly sea
August 2007 AX2-93 DRAFT-DO NOT QUOTE OR CITE
-------
1 salt and sub-|im predominantly S aerosol), HNOs 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 HNO3 vapor
4 and participate NOs and the size distribution and corresponding atmospheric lifetimes of
5 particles against deposition. Sub-jim 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 HNOs 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 HNO3 (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 NO3 is associated. This change in pH may cause the bulk mix to be
16 supersaturated with respect to HNOs leading to volatilization and, thus, positive measurement
17 bias in HNOs sampled downstream. Alternatively, when undersaturated super-Jim size fractions
18 (e.g., sea salt) accumulate on a bulk filter and chemically interact over time with HNOs in the
19 sample air stream, scavenging may lead to negative bias in HNOs 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 HNOs measured downstream.
24 Widely used methods for measuring HNOs 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 HNOs measurements based on the principle of Chemical
31 lonization Mass Spectroscopy (CIMS) have been reported (e.g., Huey et al., 1998; Mauldin
August 2007 AX2-94 DRAFT-DO NOT QUOTE OR CITE
-------
1 et al., 1998; Furutani and Akimoto, 2002; Neuman et al., 2002). CIMS relies on selective
2 formation of ions such as SiFs -FINOs or HSC>4 -FINOs followed by detection via mass
3 spectroscopy. Two CIMS 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 CIMS
5 instruments and between the CIMS 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 CIMS 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 NOs corresponded to
10 relatively greater fractions of total NOs .
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 HNOs, HNO2, and SO2. 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 5 and the more alkaline PMi0-2.5 as in a
28 dichotomous sampler or multistage impactor.
29
August 2007 AX2-95 DRAFT-DO NOT QUOTE OR CITE
-------
1 AX2.8.4 Remote Sensing of Tropospheric NO2 Columns for Surface NOX
2 Emissions and Surface NO2 Concentrations
3 Table AX2-3 contains an overview of the three satellite instruments that are used retrieve
4 tropospheric NC>2 columns from measurements of solar backscatter. All three instruments are in
5 polar sun-synchronous orbits with global measurements in the late morning and early afternoon.
6 The spatial resolution of the measurement from SCIAMACHY is 7 times better than that from
7 GOME (Ozone Monitoring Instrument), and that from OMI (Ozone Monitoring Instrument) is
8 40 times better than that from GOME.
9 Figure AX2-22 shows tropospheric NO2 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 NO2 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 NO2/NO ratio from the surface to the upper troposphere (Bradshaw
16 et al., 1999) that is driven by the temperature dependence of the NO + Os reaction. Martin et al.
17 (2004a) integrated in situ airborne measurements of NO2 and found that during summer the
18 lower mixed layer contains 75% of the tropospheric NO2 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 NO2 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 NO2, and by (3) over regions in regions of
27 elevated tropospheric NO2 (Martin et al., 2002; Boersma et al., 2004).
28 The paucity of in situ NO2 measurements motivates the inference of surface NO2
29 concentrations from satellite measurements of tropospheric NO2 columns. This prospect would
30 take advantage of the greater sensitivity of tropospheric NO2 columns to NOX in the lower
August 2007 AX2-96 DRAFT-DO NOT QUOTE OR CITE
-------
*/*.'
l^ol\ \ -^
1-3 ^> \dr~*9rr
^V:^i r-p^
>, L. " _'^wf "\. .,* ^- /* %,
rv%fe^-
, - O.?i ?~v« .^xv , " r, '7
*——•—' "S^HJf" ,/"-<-" 7~;V
f I*1 IWMmj-^^^m^H^^ iClN^lf"1""•T'"'—•• f^-Tr-lt—I'.u l.i.Mi.vmm-jiijjrifeiir,-,-!
i-» 5 ^^^f^^T^ /
^~~^4__L3a
r^/^x ^'-*
-nSf^^x'
i,\-^,, ^r" ,'
-^4 , / ! /n>^4 ;^
34 n?.-' ^ ^ t
\ •?" ^jLtr-VT?!^.'""
patu-.s^sTf^fP^ v
\ * ci: i-. i _/ -
J "C • ,t AWtiZr^-T-S-11 '
--v r ^sff^J!^p^;Af
V *>, ^ ^JT^v4^-» L i -i\
•f—r^ < i
S > ' y
^ \
'^ 4
-t'li
o
Figure AX2-22.
8
10
Tropospheric NOi columns (molecules NOi/ cm ) retrieved from the
SCIAMACHY satellite instrument for 2004-2005.
Source: Martin et al. (2006).
1
2
3
4
5
6
1
8
9
10
11
12
troposphere than in the upper troposphere as discussed earlier. Tropospheric NC>2 columns show
a strong correlation with in situ NC>2 measurements in northern Italy (Ordonez et al., 2006).
Quantitative calculation of surface NO2 concentrations from a tropospheric NO2 column
would require information on the relative vertical profile. Comparison of vertical profiles of
NO2 in a chemical transport model (GEOS-Chem) versus in situ measurements over and
downwind of North America shows a high degree of consistency (Martin et al., 2004b; Martin
et al., 2006), suggesting that chemical transport models could be used to infer the relationship
between surface NO2 concentrations and satellite observations of the tropospheric NO2 column.
However, the satellites carrying the spectrometer (GOME/SCIAMACHY/OMI) are in
near polar, sun-synchronous orbits. As a result, these measurements are made only once per day,
typically between about 10:00 to 1 1 :00 a.m. or 1 p.m. local time, during a brief overflight. Thus
the utility of these measurements is limited as they would likely miss short-term features.
August 2007
AX2-97
DRAFT-DO NOT QUOTE OR CITE
-------
1 AX2.8.5 SAMPLING AND ANALYSIS FOR SO2
2 Currently, ambient 862 is measured using instruments based on pulsed fluorescence. The
3 UV fluorescence monitoring method for atmospheric 862 was developed to improve upon the
4 flame photometric detection (FPD) method for SO2, which in turn had displaced the
5 pararosaniline wet chemical method for SC>2 measurement. The pararosaniline method is still the
6 FRM for atmospheric SC>2, 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, 862 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 SC>2 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 S02+hv,^S02* (AX2_52)
19 S02*^S02+hv2 (AX2_53)
20 where SO2* represents the excited state of SC>2, h vj, and h V2 represent the energy of the
21 excitation and fluorescence photons, respectively, and hv22 molecules in the sample gas.
23 In commercial analyzers, light from a high intensity UV lamp passes through a
24 bandwidth filter, allowing only photons with wavelengths around the 862 absorption peak (near
25 214 nm) to enter the optical chamber. The light passing through the source bandwidth filter is
26 collimated using a UV lens and passes through the optical chamber, where it is detected on the
27 opposite side of the chamber by the reference detector. A photomultiplier tube (PMT) is offset
28 from and placed perpendicular to the light path to detect the SC>2 fluorescence. Since the SC>2
29 fluorescence (330 nm) is at a wavelength that is different from the excitation wavelength, an
August 2007 AX2-98 DRAFT-DO NOT QUOTE OR CITE
-------
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 SO2 analyzer is 10 parts per billion (ppb) (Code of Federal Regulations, Volume 40,
5 Part 53.23c). The SC>2 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 SC>2 when exposed to far UV radiation. The most significant of these are poly cyclic
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 862 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 SC>2 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 SO2 fluorescence. However, in high sensitivity SO2 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 SO2 that the NO rejection ratio of the SO2 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 SO2 monitoring is stray light reaching the optical
25 chamber. Since SO2 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 SO2 in the
27 sample and increase the fluorescence signal.
28 Furthermore, stray light at the wavelength of the SO2 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.
August 2007 AX2-99 DRAFT-DO NOT QUOTE OR CITE
-------
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 SO2 chemiluminescence detector, which
9 was based on a differential measurement where response from ambient SCh is determined by the
10 difference between air containing 862 and air scrubbed of 862 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 SC>2 molecules can occur from
15 collisions with common molecules in air, including nitrogen, oxygen, and water. During
16 collisional quenching, the excited SC>2 molecule transfers energy, kinetically allowing the SC>2
17 molecule to return to the original lower energy state without emitting a photon. Collisional
18 quenching results in a decrease in the 862 fluorescence and results in the underestimation of 862
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 SO2 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 862 concentrations, reactions
28 between electronically excited 862 and ground state 862 to form 863 and SO might occur
29 (Calvert et al., 1978). However, this possibility has not been examined.
30
August 2007 AX2-100 DRAFT-DO NOT QUOTE OR CITE
-------
1 Other Techniques for Measuring SO 2
2 A more sensitive SC>2 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 SO2 (34S16C>2) 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 862 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 SO2
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 DO AS instrument that can measure NC>2.
14 Photoacoutsic techniques have been employed for SC>2 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 34SC>2
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 |im 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 paniculate: (1) the flow of air through the
31 sampler must be at a flow rate that ensures that the size cut at 2.5 jim occurs; and (2) the flow
August 2007 AX2-101 DRAFT-DO NOT QUOTE OR CITE
-------
1 rate must be optimized to capture the desired amount of paniculate 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 U.S.: the
12 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 NO3 and SO4, (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 5 Teflon filter sample. The PM2 5 Teflon filter sample is also used to determine
20 concentrations of selected elements. The PM2 5 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
August 2007 AX2-102 DRAFT-DO NOT QUOTE OR CITE
-------
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, SC>2, HONO, and HNOs. The second denuder
5 is coated with 0.5% phosphoric acid in methanol for collecting NHa. 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 NaCCVglycerol and citric acid, respectively. This configuration was adopted to remove
10 HNOs quantitatively on the first NaCl denuder. The third and forth denuder remove 862 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 NHa) 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 IO-3.3, U.S. EPA, 1997;
20 see 2004 PM CD for details) and PIXE are the most commonly used methods. Since sample
21 filters often contain very small amounts of particle deposits, preference is given to methods that
22 can accommodate small sample sizes and require little or no sample preparation or operator time
23 after the samples are placed into the analyzer. X-ray fluorescence (XRF) meets these needs and
24 leaves the sample intact after analysis so it can be submitted for additional examinations by other
25 methods as needed. To obtain the greatest efficiency and sensitivity, XRF typically places the
26 filters in a vacuum which may cause volatile compounds (nitrates and organics) to evaporate.
27 As a result, species that can volatilize such as ammonium nitrate and certain organic compounds
28 can be lost during the analysis. The effects of this volatilization are important if the PTFE filter
29 is to be subjected to subsequent analyses of volatile species.
30 Polyatomic ions such as sulfate, nitrate, and ammonium are quantified by methods such
31 as ion chromatography (1C) (an alternative method commonly used for ammonium analysis is
August 2007 AX2-103 DRAFT-DO NOT QUOTE OR CITE
-------
1 automated colorimetry). All ion analysis methods require a fraction of the filter to be extracted
2 in deionized distilled water for sulfate and NaCOs/NaHCOs solution for nitrate and then filtered
3 to remove insoluble residues prior to analysis. The extraction volume should be as small as
4 possible to avoid over-diluting the solution and inhibiting the detection of the desired
5 constituents at levels typical of those found in ambient PM2.5 samples. During analysis, the
6 sample extract passes through an ion-exchange column which separates the ions in time for
7 individual quantification, usually by an electroconductivity detector. The ions are identified by
8 their elution/retention times and are quantified by the conductivity peak area or peak height.
9 In a side-by-side comparison of two of the major aerosol monitoring techniques (Hains
10 et al., 2007), PM2.5 mass and major contributing species were well correlated among the different
11 methods with r-values in excess of 0.8. Agreement for mass, sulfate, OC, TC, and ammonium
12 was good while that for nitrate and BC was weaker. Based on reported uncertainties, however,
13 even daily concentrations of PM2.5 mass and major contributing species were often significantly
14 different at the 95% confidence level. Greater values of PM2.5 mass and individual species were
15 generally reported from Speciation Trends Network methods than from the Desert Research
16 Institute Sequential Filter Samplers. These differences can only be partially accounted for by
17 known random errors. The authors concluded that the current uncertainty estimates used in the
18 STN network may underestimate the actual uncertainty.
19
20 Positive Sampling Artifacts
21 The reaction of 862 (and other acid gases) with basic sites on glass fiber filters or with
22 basic coarse particles on the filter leads to the formation of sulfate (or other nonvolatile salts,
23 e.g., nitrate, chloride). These positive artifacts lead to the overestimation of total mass, and
24 sulfate, and probably also nitrate concentrations. These problems were largely overcome by
25 changing to quartz fiber or Teflon filters and by the separate collection of PM2.5. However, the
26 possible reaction of acidic gases with basic coarse particles remains a possibility, especially with
27 PMio and PMio-2.5 measurements. These positive artifacts could be effectively eliminated by
28 removing acidic gases in the sampling line with denuders coated with NaCl or Na2CC>3.
29 Positive sampling artifacts also occur during measurement of particulate NH4. The
30 reaction of NHa with acidic particles (e.g. 2NH3 + H2SO4 -> (NH/t^SO/t), either during sampling
31 or during transportation, storage, and equilibration could lead to an overestimation of parti culate
August 2007 AX2-104 DRAFT-DO NOT QUOTE OR CITE
-------
1 NH4 concentrations. Techniques have been developed to overcome this problem: using a
2 denuder to remove NH3 during sampling and to protect the collected PM from NH3 (Suh et al.,
3 1992, 1994; Brauer et al., 1991; Koutrakis et al., 1988a,b; Keck and Wittmaack, 2006;
4 Possanzini et al., 1999; Winberry et al., 1999). Hydrogen fluoride, citric acid, and phosphorous
5 acids have been used as coating materials for the NH3 denuder. Positive artifacts for particulate
6 NH4 can also be observed during sample handling due to contamination. No chemical analysis
7 method, no matter how accurate or precise, can adequately represent atmospheric concentrations
8 if the filters to which these methods are applied are improperly handled. Ammonia is emitted
9 directly from human sweat, breath and smoking. It can then react with acidic aerosols on the
10 filter to form ammonium sulfate, ammonium bisulfate and ammonium nitrate if the filter was not
11 properly handled (Sutton el al., 2000). Therefore, it is important to keep filters away from
12 ammonia sources, such as human breath, to minimize neutralization of the acidic compounds.
13 Also, when filters are handled, preferably in a glove box, the analyst should wear gloves that are
14 antistatic and powder-free to act as an effective contamination barrier.
15
16 Negative Sampling Artifact
17 Although sulfate is relatively stable on a Teflon filter, it is now well known that
18 volatilization losses of particulate nitrates occur during sampling.
19 For nitrate, the effect on the accuracy of atmospheric particulate measurements from
20 these volatilization losses is more significant for PM2.5 than for PMio. The FRM for PM2.5 will
21 likely suffer a loss of nitrates similar to that experienced with other simple filter collection
22 systems. Sampling artifacts resulting from the loss of particulate nitrates represents a significant
23 problem in areas such as southern California that experience high loadings of nitrates. Hering
24 and Cass (1999) discussed errors in PM2.5 mass measurements due to the volatilization of
25 particulate nitrate. They examined data from two field measurement campaigns that were
26 conducted in southern California: (1) the Southern California Air Quality Study (SCAQS)
27 (Lawson, 1990) and (2) the 1986 CalTech study (Solomon et al., 1992). In both these studies,
28 side-by-side sampling of PM2.5 was conducted. One sampler collected particles directly onto a
29 Teflon filter. The second sampler consisted of a denuder to remove gaseous HNO3 followed by
30 a nylon filter that absorbed the HNO3 as it evaporated from NITXNO3. In both studies, the
31 denuder consisted of MgO-coated glass tubes (Appel et al., 1981). Fine particulate nitrate
32 collected on the Teflon filter was compared to fine particulate nitrate collected on the denuded
August 2007 AX2-105 DRAFT-DO NOT QUOTE OR CITE
-------
1 nylon filter. In both studies, the PM2.5 mass lost because of ammonium nitrate volatilization
2 represented a significant fraction of the total PM2.5 mass. The fraction of mass lost was higher
3 during summer than during fall (17% versus 9% during the SCAQS study, and 21% versus 13%
4 during the CalTech study). In regard to percentage loss of nitrate, as opposed to percentage loss
5 of mass discussed above, Hering and Cass (1999) found that the amount of nitrate remaining on
6 the Teflon filter samples was on average 28% lower than that on the denuded nylon filters.
7 Hering and Cass (1999) also analyzed these data by extending the evaporative model
8 developed by Zhang and McMurry (1987). The extended model used by Hering and Cass (1999)
9 takes into account the dissociation of collected particulate ammonium nitrate on Teflon filters
10 into HNOs and NHs via three mechanisms: (1) the scrubbing of HNOs and NH? in the sampler
11 inlet (John et al. (1988) showed that clean PMi0 inlet surfaces serve as an effective denuder for
12 HNOs); (2) the heating of the filter substrate above ambient temperature by sampling; and (3) the
13 pressure drop across the Teflon filter. For the sampling systems modeled, the flow-induced
14 pressure drop was measured to be less than 0.02 atm, and the corresponding change in vapor
15 pressure was 2%, so losses driven by pressure drop were not considered to be significant in this
16 work. Losses from Teflon filters were found to be higher during the summer than during the
17 winter, higher during the day compared to night, and reasonably consistent with modeled
18 predictions.
19 Finally, during the SCAQS (Lawson, 1990) study, particulate samples also were collected
20 using a Berner impactor and greased Tedlar substrates in size ranges from 0.05 to 10 jim in
21 aerodynamic diameter. The Berner impactor PM2.5 nitrate values were much closer to those
22 from the denuded nylon filter than those from the Teflon filter, the impactor nitrate values being
23 -2% lower than the nylon filter nitrate for the fall measurements and -7% lower for the summer
24 measurements. When the impactor collection was compared to the Teflon filter collection for a
25 nonvolatile species (sulfate), the results were in agreement. Chang et al. (2000) discuss reasons
26 for reduced loss of nitrate from impactors.
27 Brook and Dann (1999) observed much higher nitrate losses during a study in which they
28 measured particulate nitrate in Windsor and Hamilton, Ontario, Canada, by three techniques:
29 (1) a single Teflon filter in a dichotomous sampler, (2) the Teflon filter in an annular denuder
30 system (ADS), and (3) total nitrate including both the Teflon filter and the nylon back-up filter
31 from the ADS. The Teflon filter from the dichotomous sampler averaged only 13% of the total
August 2007 AX2-106 DRAFT-DO NOT QUOTE OR CITE
-------
1 nitrate, whereas the Teflon filter from the ADS averaged 46% of the total nitrate. The authors
2 concluded that considerable nitrate was lost from the dichotomous sampler filters during
3 handling, which included weighing and X-ray fluorescence (XRF) measurement in a vacuum.
4 Kim et al. (1999) also examined nitrate-sampling artifacts by comparing denuded and
5 non-denuded quartz and nylon filters during the PMio Technical Enhancement Program (PTEP)
6 in the South Coast Air Basin of California. They observed negative nitrate artifacts (losses) for
7 most measurements; however, for a significant number of measurements, they observed positive
8 nitrate artifacts. Kim et al. (1999) pointed out that random measurement errors make it difficult
9 to measure true amounts of nitrate loss.
10 Diffusion denuder samplers, developed primarily to measure particle strong acidity
11 (Koutrakis et al., 1988b, 1992), also can be used to study nitrate volatilization. Such techniques
12 were used to measure loss of particulate nitrate from Teflon filters in seven U.S. cities (Babich
13 et al., 2000). Measurements were made with two versions of the Harvard-EPA Annular Denuder
14 System (HEADS). HNOs vapor was removed by a Na2CC>3-coated denuder. Particulate nitrate
15 was the sum of nonvolatile nitrate collected on a Teflon filter and volatized nitrate collected on a
16 Na2CC>3-coated filter downstream of the Teflon filter (full HEADS) or on a Nylon filter
17 downstream of the Teflon filter (Nylon HEADS). It was found that the full HEADS (using a
18 Na2CO3 filter) consistently underestimated the total parti culate nitrate by approximately 20%
19 compared to the nylon HEADS. Babich et al. (2000) found significant nitrate losses in
20 Riverside, CA; Philadelphia, PA; and Boston, MA, but not in Bakersfield, CA; Chicago, IL;
21 Dallas, TX; or Phoenix, AZ, where measurements were made only during the winter. Tsai and
22 Huang (1995) used a diffusion denuder to study the positive and negative artifacts on glass and
23 quartz filters. They found positive artifacts attributed to SC>2 and HNOs reaction with basic sites
24 on glass fibers and basic particles and negative artifacts attributed to loss of HNOs and HC1 due
25 to volatilization of NH4NO3 and NH4C1 and reaction of these species with acid sulfates.
26 Volatile compounds can also leave the filter after sampling and prior to filter weighing or
27 chemical analysis. Losses of NOs, NH4, and Cl from glass and quartz-fiber filters that were
28 stored in unsealed containers at ambient air temperatures for 2 to 4 weeks prior to analysis
29 exceeded 50 percent (Witz et al., 1990). Storing filters in sealed containers and under
30 refrigeration will minimize these losses.
August 2007 AX2-107 DRAFT-DO NOT QUOTE OR CITE
-------
1 Negative sampling artifacts due to decomposition and volatilization are also significant
2 for particulate ammonium. Ammonium particulates, especially NH4 N? nitrate NH4 Cl are very
3 sensitive to some environmental factors, such as temperature, relative humidity, acidity of
4 aerosols, as well as to filter type (Spurny, 1999; Keck and Wittmaack, 2005). Any change in
5 these parameters during the sampling period influences the position of the equilibrium between
6 the particle phase and the gas phase. Keck and Wittmaack (2005) observed that at temperatures
7 below OC, acetate-nitrate, quartz fiber, and Teflon filters could properly collect particulate NH/t
8 NH3 and Cl. At temperature above 0°C, the salts were lost from quartz fiber and Teflon filters,
9 more so the higher the temperature and with no significant difference between quartz fiber and
10 Teflon filters. The salts were lost completely from denuded quartz fiber filters above about 20C,
11 and from non-undenuded quartz fiber and Teflon filters above about 25C. It is anticipated that
12 current sampling techniques underestimate NH4 concentrations due to the volatilization of NH4,
13 but fine particle mass contains many acidic compounds and consequently, a fraction of
14 volatilized NH4 (in the form of NHa) can be retained on a PTFE filter by reaction with the acid
15 compounds. Therefore, it is reasonable to assume that NH4 loss will be less than the nitrate loss.
16 Techniques have been applied to particulate ammonium sampling to correct particulate
17 ammonium concentrations due to evaporation: a backup filter coated with hydrofluoric acids,
18 citric acid, or phosphorous acids, is usually introduced to absorb the evaporated ammonium (as
19 ammonia); the total ammonium concentration is the sum of the particle phase ammonium
20 collected on the Teflon filter and the ammonia concentration collected on the backup filter.
21
22 Other Measurement Techniques
23
24 Nitrate
25 An integrated collection and vaporization cell was developed by Stolzenburg and Hering
26 (2000) that provides automated, 10-min resolution monitoring of fine-particulate nitrate. In this
27 system, particles are collected by a humidified impaction process and analyzed in place by flash
28 vaporization and chemiluminescent detection of the evolved NOX. In field tests in which the
29 system was collocated with two FRM samplers, the automated nitrate sampler results followed
30 the results from the FRM, but were offset lower. The system also was collocated with a HEADS
31 and a SASS speciation sampler (MetOne Instruments). In all these tests, the automated sampler
32 was well correlated to other samplers with slopes near 1 (ranging from 0.95 for the FRM to 1.06
August 2007 AX2-108 DRAFT-DO NOT QUOTE OR CITE
-------
1 for the HEADS) and correlation coefficients ranging from 0.94 to 0.996. During the Northern
2 Front Range Air Quality Study in Colorado (Watson et al., 1998), the automated nitrate monitor
3 captured the 12-min variability in fine-particle nitrate concentrations with a precision of
4 approximately ± 0.5 |ig/m3 (Chow et al., 1998). A comparison with denuded filter
5 measurements followed by ion chromatographic (1C) analysis (Chow and Watson, 1999) showed
6 agreement within ± 0.6 |ig/m3 for most of the measurements, but exhibited a discrepancy of a
7 factor of two for the elevated nitrate periods. More recent intercomparisons took place during
8 the 1997 Southern California Ozone Study (SCOS97) in Riverside, CA. Comparisons with
9 14 days of 24-h denuder-filter sampling gave a correlation coefficient of R2 = 0.87 and showed
10 no significant bias (i.e., the regression slope is not significantly different from 1). As currently
11 configured, the system has a detection limit of 0.7 |ig/m3 and a precision of 0.2 |ig/m3.
12
13 Sulfate
14 Continuous methods for the quantification of aerosol sulfur compounds first remove
15 gaseous sulfur (e.g., SC>2, H^S) from the sample stream by a diffusion tube denuder followed by
16 the analysis of particulate sulfur (Cobourn et al., 1978; Durham et al., 1978; Huntzicker et al.,
17 1978; Mueller and Collins, 1980; Tanner et al., 1980). Another approach is to measure total
18 sulfur and gaseous sulfur separately by alternately removing particles from the sample stream.
19 Particulate sulfur is obtained as the difference between the total and gaseous sulfur (Kittelson
20 et al., 1978). The total sulfur content is measured by a flame photometric detector (FPD) by
21 introducing the sampling stream into a fuel-rich, hydrogen-air flame (e.g., Stevens et al., 1969;
22 Farwell and Rasmussen, 1976) that reduces sulfur compounds and measures the intensity of the
23 chemiluminescence from electronically excited sulfur molecules (S2*). Because the formation
24 of S2* requires two sulfur atoms, the intensity of the chemiluminescence is theoretically
25 proportional to the square of the concentration of molecules that contain a single sulfur atom.
26 In practice, the exponent is between 1 and 2 and depends on the sulfur compound being analyzed
27 (Dagnall et al., 1967; Stevens et al., 1971). Calibrations are performed using both particles and
28 gases as standards. The FPD can also be replaced by a chemiluminescent reaction with ozone
29 that minimizes the potential for interference and provides a faster response time (Benner and
30 Stedman, 1989, 1990). Capabilities added to the basic system include in situ thermal analysis
31 and sulfuric acid speciation (Cobourn et al., 1978; Huntzicker et al., 1978; Tanner et al., 1980;
32 Cobourn and Husar, 1982). Sensitivities for particulate sulfur as low as 0.1 |ig/m3, with time
August 2007 AX2-109 DRAFT-DO NOT QUOTE OR CITE
-------
1 resolution ranging from 1 to 30 min, have been reported. Continuous measurements of
2 particulate sulfur content have also been obtained by on-line XRF analysis with resolution of
3 30 min or less (Jaklevic et al., 1981). During a field-intercomparison study of five different
4 sulfur instruments, Camp et al. (1982) reported four out of five FPD systems agreed to within
5 ± 5% during a 1-week sampling period.
6
7
8 AX2.9 POLICY RELEVANT BACKGROUND CONCENTRATIONS OF
9 NITROGEN AND SULFUR OXIDES
10 Background concentrations of nitrogen and sulfur oxides used for purposes of informing
11 decisions about NAAQS are referred to as Policy Relevant Background (PRB) concentrations.
12 Policy Relevant Background concentrations are those concentrations that would occur in the
13 United States in the absence of anthropogenic emissions in continental North America (defined
14 here as the United States, Canada, and Mexico). Policy Relevant Background concentrations
15 include contributions from natural sources everywhere in the world and from anthropogenic
16 sources outside these three countries. Background levels so defined facilitate separation of
17 pollution levels that can be controlled by U.S. regulations (or through international agreements
18 with neighboring countries) from levels that are generally uncontrollable by the United States.
19 EPA assesses risks to human health and environmental effects from NC>2 and SC>2 levels in
20 excess of PRB concentrations.
21 Contributions to PRB concentrations include natural emissions of NC>2, 862, and
22 photochemical reactions involving natural emissions of reduced nitrogen and sulfur compounds,
23 as well as their long-range transport from outside North America. Natural sources of NO2 and its
24 precursors include biogenic emissions, wildfires, lightning, and the stratosphere. Natural sources
25 of reduced nitrogen compounds, mainly NHa, include biogenic emissions and wildfires. Natural
26 sources of reduced sulfur species include anaerobic microbial activity in wetlands and volcanic
27 activity. Volcanos and biomass burning are the major natural source of SC>2. Biogenic
28 emissions from agricultural activities are not considered in the formation of PRB concentrations.
29 Discussions of the sources and estimates of emissions are given in Section AX2.6.2.
30
August 2007 AX2-110 DRAFT-DO NOT QUOTE OR CITE
-------
1 Analysis ofPRB Contribution to Nitrogen and Sulfur oxide Concentrations and Deposition
2 over the United States
3 The MOZART-2 global model of tropospheric chemistry (Horowitz et al., 2003) is used
4 to diagnose the PRB contribution to nitrogen and sulfur oxide concentrations, as well as to total
5 (wet plus dry) deposition. The model setup for the present-day simulation has been published in
6 a series of papers from a recent model intercomparison (Dentener et al., 2006a,b; Shindell et al.,
7 2006; Stevenson et al., 2006; van Noije et al., 2006). MOZART-2 is driven by National Center
8 for Environmental Prediction meteorological fields and IIASA 2000 emissions at a resolution of
9 1.9° x 1.9° with 28 sigma levels in the vertical, and it includes gas- and aerosol phase chemistry.
10 Results shown in Figures AX2-23 to AX2-27 are for the meteorological year 2001. Note that
11 color images are available on the web. An additional "policy relevant background" simulation
12 was conducted in which continental North American anthropogenic emissions were set to zero.
13 We first examine the role of PRB in contributing to NC>2 and SC>2 concentrations in
14 surface air. Figure AX2-23 shows the annual mean NC>2 concentrations in surface air in the base
15 case simulation (top panel) and the PRB simulation (middle panel), along with the percentage
16 contribution of the background to the total base case NO2 (bottom panel). Maximum
17 concentrations in the base case simulation occur along the Ohio River Valley and in the
18 Los Angeles basin. While present-day concentrations are often above 5 ppbv, PRB is less than
19 300 pptv over most of the continental United States, and less than 100 pptv in the eastern United
20 States. The distribution of PRB (middle panel of Figure AX2-23) largely reflects the distribution
21 of soil NO emissions, with some local enhancements due to biomass burning such as is seen in
22 western Montana. In the northeastern United States, where present-day NO2 concentrations are
23 highest, PRB contributes < 1 % to the total.
24 The spatial pattern of present-day SO2 concentrations over the United States is similar to
25 that of NO2, with highest concentrations (>5 ppbv) along the Ohio River valley (upper panel
26 Figure AX2-24). Background SO2 concentrations are orders of magnitude smaller, below
27 10 pptv over much of the United States (middle panel of Figure AX2-24). Maximum PRB
28 concentrations of SO2 are 30 ppt. In the Northwest where there are geothermal sources of SO2,
29 the contribution of PRB to total SO2 is 70 to 80%. However, with the exception of the West
30 Coast where volcanic SO2 emissions enhance PRB concentrations, the PRB contributes <1% to
31 present-day SO2 concentrations in surface air (bottom panel Figure AX2-24).
August 2007 AX2-111 DRAFT-DO NOT QUOTE OR CITE
-------
Total
ioo°W
sfw
Background
Percent BacKg round Contribution
100°'»V
eo°w
Figure AX2-23.
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.
August 2007
AX2-112
DRAFT-DO NOT QUOTE OR CITE
-------
Total
soDN ?
4Q°N
30°N
10Q°W
30°W
Background
::::; Ciffe^
Percent Background Contribution
Figure AX2-24. Same as Figure AX2-23 but for SOi concentrations.
August 2007
AX2-113 DRAFT-DO NOT QUOTE OR CITE
-------
Total
Background
Percent Background Contribution
13Q°W
ao°w
Figure AX2-25. Same as for Figure AX2-23 but for wet and dry deposition of HNO
-2 -K
'3?
NH4NO3, NOX, HO2NO2, and organic nitrates (mg N m y ).
August 2007
AX2-114 DRAFT-DO NOT QUOTE OR CITE
-------
Total
Background
Percent Background Contribution
Figure AX2-26. Same as Figure AX2-23 but for SOX deposition (SO2 + SO4)
(mg S m V1).
August 2007
AX2-115 DRAFT-DO NOT QUOTE OR CITE
-------
50°N
45°N
30°N
25°N
12Q°>A
MOZART-2 SOIL NO
GEOS-Chem SOIL NO,
100°»
SfN
45DN
40°N
35°N
3tfN
Z5°N
MOZART-2 Surf ace NO JUL
GEOS-Chem Surface NO
I3AV
IOO°'.V
scrtv
100°VV
aa°vv
Figure AX2-27.
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 The spatial pattern of NOy (defined here as HNO3, NH4NO3, NOX, HO2NO2, and organic
2 nitrates) wet and dry deposition is shown in Figure AX2-25. Figure AX2-25 (upper panel)
3 shows that highest values are found in the eastern U.S. in and downwind of the Ohio River
4 Valley. The pattern of nitrogen deposition in the PRB simulation (Figure AX2-25, middle
5 panel), however, shows maximum deposition centered over Texas and in the Gulf Coast region,
6 reflecting a combination of nitrogen emissions from lightning in the Gulf region, biomass
7 burning in the Southeast, and from microbial activity in soils (maximum in central Texas and
8 Oklahoma). The bottom panel of Figure AX2-25 shows that the PRB contribution to nitrogen
August 2007
AX2-116
DRAFT-DO NOT QUOTE OR CITE
-------
1 deposition is less than 20% over the eastern United States, and typically less than 50% in the
2 western United States where NOy deposition is low (25-50 mg N nT2 yr"1).
3 Present-day SOX (SO2 + SO4 ~) deposition is largest in the Ohio River Valley, likely due
4 to coal-burning power plants in that region, while background deposition is typically at least an
5 order of magnitude smaller (Figure AX2-26). Over the eastern United States, the background
6 contribution to SOX deposition is <10%, and it is even smaller (<1%) where present-day SOX
7 deposition is highest. The contribution of PRB to sulfate deposition is highest in the western
8 United States (>20%) because of geothermal sources of SO2 and oxidation of dimethyl sulfide in
9 the surface of the eastern Pacific.
10 Thus far, the discussion has focused on results from the MOZART-2 tropospheric
11 chemistry model. In Figure AX2-27, results from MOZART-2 are compared with those from
12 another tropospheric chemistry model, GEOS-Chem (Bey et al., 2001), which was previously
13 used to diagnose PRB O3 (Fiore et al., 2003; U.S. EPA, 2006). In both models, the surface PRB
14 NOX concentrations tend to mirror the distribution of soil NO emissions, which are highest in the
15 Midwest. The higher soil NO emissions in GEOS-Chem (by nearly a factor of 2) as compared to
16 MOZART-2 reflect different assumptions regarding the contribution to soil NO emissions
17 largely through fertilizer, since GEOS-Chem total soil NO emissions are actually higher than
18 MOZART-2 (0.07 versus 0.11 Tg N) over the United States in July. Even with the larger PRB
19 soil NO emissions, surface NOX concentrations in GEOS-Chem are typically below 500 pptv.
20 It is instructive to also consider measurements of SO2 at relatively remote monitoring
21 sites, i.e., site located in sparsely populate areas not subject to obvious local sources of pollution.
22 Berresheim et al. (1993) used a type of atmospheric pressure ionization mass spectrometer
23 (APIMS) at Cheeka Peak, WA (48.30N 124.62W, 480 m asl), in April 1991 during a field study
24 for DMS oxidation products. Sulfur Dioxide concentrations ranged between 20 and 40 pptv.
25 Thornton et al. (2002) have also used an APIMS with an isotopically labeled internal standard to
26 determine background SO2 levels. SO2 concentrations of 25 to 40 pptv were observed in
27 northwestern Nebraska in October 1999 at 150m above ground using the NCAR C-130
28 (Thornton, unpublished data). These data are comparable to remote central south Pacific
29 convective boundary layer SO2 (Thornton et al., 1999).
30 Volcanic sources of SO2 in the US are limited to the Pacific Northwest, Alaska, and
31 Hawaii. Since 1980 the Mt. St. Helens volcano in Washington Cascade Range (46.20 N,
August 2007 AX2-117 DRAFT-DO NOT QUOTE OR CITE
-------
1 122.18 W, summit 2549 m asl) has been a variable source of 862. Its major impact came in the
2 explosive eruptions of 1980, which primarily affected the northern part of the mountain west of
3 the US. The Augustine volcano near the mouth of the Cook Inlet in southwestern Alaska
4 (59.363 N, 153.43 W, summit 1252 m asl) has had SC>2 emissions of varying extents since its last
5 major eruptions in 1986. Volcanoes in the Kamchatka peninsula of eastern region of Siberian
6 Russia do not particularly impact the surface concentrations in the northwestern NA. The most
7 serious impact in the US from volcanic SC>2 occurs on the island of Hawaii. Nearly continuous
8 venting of 862 from Mauna Loa and Kilauea produce 862 in such large amounts so that
9 >100 km downwind of the island SC>2 concentrations can exceed 30 ppbv (Thornton and Bandy,
10 1993). Depending on the wind direction the west coast of Hawaii (Kona region) has had
11 significant impacts from SO2 and acidic sulfate aerosols for the past decade. Indeed, SO2 levels
12 in Volcanoes National Park, HI exceeded the 3-h and the 24-h NAAQS in 2004 -2005. The
13 area's design value is 0.6 ppm for the 3-h, and 0.19 ppm for the 24-h NAAQS (U.S. EPA, 2006).
14 Overall, the background contribution to nitrogen and sulfur oxides over the United States
15 is relatively small, except for SC>2 in areas where there is volcanic activity.
August 2007 AX2-118 DRAFT-DO NOT QUOTE OR CITE
-------
TABLE AX2-1. ATMOSPHERIC LIFETIMES OF SULFUR DIOXIDE AND
REDUCED SULFUR SPECIES WITH RESPECT TO REACTION WITH OH, NO3,
AND CL RADICALS
OH
Compound
SO2
CH3-S-CH3
H2S
CS2
ocs
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
N03
k x 1012
NA
1.0
NA
<0.0004
<0.0001
0.89
0.53
T
l.lh
>116d
>1.3y
1.2 h
2.1 h
Cl
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; NO3 = 2.5 x 108/cm3; Cl = 1 x 103/cm3.
1 Rate coefficients were taken from JPL Chemical Kinetics Evaluation No. 14 (JPL, 2003).
August 2007 AX2-119 DRAFT-DO NOT QUOTE OR CITE
-------
TABLE AX2-2A. 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+SO2 3.5 3.1
Aqueous Phase
O3 + HSO3 0.6 0.7
O3 + SO32 7.0 8.2
H2O2 + SO3 78.4 82.1
CH3OOH + HSO3 0.1 0.1
HNO4 + HSO3 9.0 4.4
HOONO + HSO3 <0.1 <0.1
HSO5 +HSO3 1.2 <0.1
SO5 +SO32 <0.1 <0.1
HSO5 + Fe2+ 0.6
a In the absence of transition metals.
b In the presence of iron and copper ions.
Source: Adapted from Warneck (1999).
August 2007 AX2-120 DRAFT-DO NOT QUOTE OR CITE
-------
TABLE AX2-2B. 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 + NO2 + M
Aqueous Phase
N2O5g + H2O
NO3 + cr
NO3 + HSO3
NO3 + HCOO
HNO4 + HSO3
HOONO + NO3
O3+NO2
57.7
8.1
<0.1
0.7
0.6
31.9
0.8
<0.1
67.4
11.2
0.1
1.0
0.8
20.5
<0.1
<0.1
a In the absence of transition metals.
b In the presence of iron and copper ions.
Source: Adapted from Warneck (1999).
August 2007 AX2-121 DRAFT-DO NOT QUOTE OR CITE
-------
TABLE AX2-3. 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^ark 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
O.01
0.01
O.01
O.01
O.01
O.01
O.01
0.01
O.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
August 2007
AX2-122
DRAFT-DO NOT QUOTE OR CITE
-------
TABLE AX2-3 (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
O.01
0.02
O.01
0.02
0.01
O.01
O.01
0.01
0.01
S02
0.63
0.16
0.28
0.02
0.01
0.01
0.16
0.15
O.01
O.01
1.54
0.36
0.01
0.18
0.17
0.02
O.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
August 2007 AX2-123 DRAFT-DO NOT QUOTE OR CITE
-------
TABLE AX2-3 (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
O.01
O.01
O.01
O.01
0.01
0.01
NH3
O.01
O.01
0.01
0.01
O.01
0.05
O.01
O.01
0.01
0.01
0.01
O.01
O.01
0.05
0.01
0.01
O.01
O.01
O.01
O.01
S02
0.38
0.11
0.11
0.01
0.26
0.16
0.07
0.01
0.46
0.01
O.01
0.10
0.01
0.33
0.19
0.09
O.01
O.01
0.01
0.02
0.01
0.01
O.01
O.01
O.01
O.01
August 2007 AX2-124 DRAFT-DO NOT QUOTE OR CITE
-------
TABLE AX2-3 (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
O.01
O.01
0.01
0.17
0.06
0.10
O.01
0.01
0.01
0.01
O.01
NH3
O.01
O.01
0.01
0.01
0.01
O.01
O.01
0.14
0.01
0.01
0.14
0.01
0.01
0.01
O.01
S02
0.01
O.01
0.01
0.01
0.01
0.01
O.01
O.01
O.01
O.01
0.03
0.02
0.01
0.01
O.01
O.01
O.01
0.01
0.01
0.01
0.01
O.01
O.01
August 2007
AX2-125
DRAFT-DO NOT QUOTE OR CITE
-------
TABLE AX2-3 (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
O.01
0.05
1.76 O.01
0.00
0.84
0.15
0.05
0.57
0.08
0.02
0.01
O.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
August 2007
AX2-126
DRAFT-DO NOT QUOTE OR CITE
-------
TABLE AX2-3 (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
3.10
O.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
0.03
1 Emissions are expressed in terms of NO2.
2 Estimate based on Guenther et al. (2000).
Source: U.S. Environmental Protection Agency (2006).
August 2007
AX2-127
DRAFT-DO NOT QUOTE OR CITE
-------
TABLE AX2-3. 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)
320 x 40
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.
August 2007
AX2-128
DRAFT-DO NOT QUOTE OR CITE
-------
1 AX2.10 REFERENCES
3 Aber, I; 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 (NOy) simulation during SONEX using a stretched-grid chemical
18 transport model. J. Geophys. Res. [Atmos.] 105: 3851-3876.
19 Ammann, M.; Kalberer, M.; lost, 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. O. (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, O., ed. The
38 Handbook of Environmental Chemistry series).
August 2007 AX2-129 DRAFT-DO NOT QUOTE OR CITE
-------
1 Arey, 1; 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 NO*. 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 OIO
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 NO3 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 N2O5 concentrations
21 from ambient NO2 and NO3 radical concentrations and the role of N2Os 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 NO3 radical-initiated reactions of naphthalene-t^, fluoranthene-t/io 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.
August 2007 AX2-130 DRAFT-DO NOT QUOTE OR CITE
-------
1 Bandy, A.; Maroulis, P. (1980) Impact of recent measurements of OCS, €82, and 862 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, L; Bastian, V.; Becker, K. H.; Overath, R. D. (1991) Kinetic studies of the reactions of
12 IO, 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 C1NO2 from the
17 reaction of gaseous ^Os 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 NOy 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
August 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 HO2
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 NO2 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.; Shelter, 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 NO2 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.
August 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 862,
2 HNO3, and SO42" estimated with two inferential models. Water Air Soil Pollut. 87: 205-
3 218.
4 Broske, R.; Kleffmann, J.; Wiesen, P. (2003) Heterogeneous conversion of NC>2 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, O. 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.
August 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 poly cyclic
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.
August 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 deary, P. A.; Wooldridge, P. J.; Cohen, R. C. (2002) Laser-induced fluorescence detection of
35 atmospheric NC>2 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.
August 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 NO2 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, O. 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) NOy 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, CHjCl 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 H2O2 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.
August 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, SO2, H2SO4(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 NO2, peroxy
8 nitrates, alkyl nitrates, and HNO3. 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, O3, and the "missing NOy". 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(D14303): 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 O3 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 N2Os on tropospheric aerosols: impact on the
28 global distributions of NOX, O3, 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.; VanNoije, 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.; Muller, 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, O.; 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.;
August 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, O. (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) 23 5: 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(D10310): 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.
August 2007 AX2-138 DRAFT-DO NOT QUOTE OR CITE
-------
1 Durant, J. L.; Busby, W. F., 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 SO2-denuder 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.; Shelter, 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.-F.; 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 CINO2 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 NC>2 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-poly cyclic
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 NC>2 fluxes using eddy covariance. Atmos. Chem. Phys. 6: 3471-3486.
36 Farwell, S. O.; 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(D16): 10.1029/2001JD000980.
41 Fehsenfeld and Parrish (2000)
August 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. O.; Murphy, P. C.; Norton, R. B.; Pickering,
4 K. E.; Ridley, B. A. (1987) A ground-based intercomparison of NO, NOX, and NOy
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 parti culate nitro-poly cyclic 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 ^Os and C1ONO2.
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 NO2 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(L11809): 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): 10.1029/2002GLO 15601.
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.
August 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(D24): 10.1029/2002 JD002177.
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.] 111(D12306):
10 10.1029/2005 JD0063 54.
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, NO2, and O3 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 NO*, 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 NO2 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 NO2 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.
August 2007 AX2-141 DRAFT-DO NOT QUOTE OR CITE
-------
1 Geyer, A.; Platt, U. (2002) Temperature dependence of the NOs loss frequency: A new indicator
2 for the contribution of NO3 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 + NO2 + 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 SO2 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. O.;
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.
August 2007 AX2-142 DRAFT-DO NOT QUOTE OR CITE
-------
1 Grell, G. A.; Dudhia, I; 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 5 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 HNOs and SO2 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 NO2, H2O, and Os measured by long-path and in situ techniques during the 1993
August 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 PM25 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.
August 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. O.; 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 NCV. 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 HNO3 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(D08305): 10.1029/2003 JD004326.
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, O. R.; Evans, M. J.; Heald, C. L.; Park, R. J.; Fehsenfeld,
41 F.; Flocke, F.; Holloway, J.; Hubler, G.; Kita, K.; Koike, M.; Kondo, Y.; Neuman, A.;
August 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) CIMS
20 measurements of HNOs and SC>2 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.; Flatey, 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.
August 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, O. 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. O.; 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 HO2 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. O. (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. L;
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 C2-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.
August 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, O. 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 N2Os 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 N2O5 by (NH4)2SO4,
17 NH4HSO4, and H2SO4 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):
32 i-iii.
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.
August 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(D04301): 10.1029/2005 JD006319.
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 NO2 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 SO2, NO2, and O3. 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 NO2 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 NO2 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 NOX regimes. J. Geophys. Res. [Atmos.] 96: 20,721-20,733.
38 Kleinman, L. L; 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.
August 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 H2SO4, NH3, and H2O 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. J. 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 NO2 flux conundrum. Science 289: 2291,
37 2293.
38 Leue, C.; Wenig, M.; Wagner, T.; Klimm, O.; 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.
August 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.pdffl7 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. O. 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. (200la) 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(D7): 8373-8389.
30 Longfellow, C. A.; Ravishankara, A. R.; Hanson, D. R. (1999) Reactive uptake on hydrocarbon
31 soot: focus onNO2. 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 862 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.
August 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.; NuB, H.; Van Aardenne, J. A. (2006) Comparison of model-
6 simulated tropospheric NC>2 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(D22): 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. L; Bey, L; 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): 10.1029/2001DOO1027.
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 NO2 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 NO2 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
August 2007 AX2-152 DRAFT-DO NOT QUOTE OR CITE
-------
1 and downwind of eastern North America. J. Geophys. Res. [Atmos.] 111(D15308):
2 10.1029/2005 JD006680.
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 NC>2. Anal. Chem. 73: 5485-5493.
12 Matsumi, Y.; Shigemori, H.; Takahashi, K. (2005) Laser-induced fluorescence instrument for
13 measuring atmospheric SC>2. 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, NC>2,
18 NOy, 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.; Groofi, J.-U.; Gunther, G.; Muller, 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.
August 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 NO2 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 Mufioz, 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, I; 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.; Shelter, R. E.; Lefer, B. L.; Avery, M.; Matsumoto, J. (2003) Measurement
36 of NC>2 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 10.1029/2003 JD003712.
39 NARSTO. (2005) Improving emission inventories for effective air quality management across
40 North America. A NARSTO assessment. Pasco, WA: The NARSTO Emission Inventory
August 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.; Hubler, 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 HNO3 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 SO2 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-NO2 detector combined
21 with a thermal converter. J. Atmos. Chem. 28: 339-359.
22 Notholt, J.; Hjorth, J.; Raes, F. (1992a) Formation of HNO2 on aerosol surfaces during foggy
23 periods in the presence of NO and NO2. Atmos. Environ. Part A 26: 211-217.
24 Notholt, J.; Hjorth, J.; Raes, F.; Schrems, O. (1992b) Simultaneous long path field measurements
25 of HNO2, CH2O and aerosol. Ber. Bunsen Ges. Phys. Chem. 96: 290-293.
26 Nunnermacker, L. J.; Imre, D.; Daum, P. H.; Kleinman, L.; Lee, Y.-N.; Lee, J. H.; 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: Grying, 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. H.; 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.
August 2007 AX2-155 DRAFT-DO NOT QUOTE OR CITE
-------
1 O'Dowd, C. D.; Jimenez, J. L.; Bahrein!, 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.; NuB, H.; Burrows, J. P.; Prevot, A. S.
5 H. (2006) Comparison of 7 years of satellite-borne and ground-based tropospheric NC>2
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 NC>2 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. O.
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.
August 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.; Muller, 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.
August 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, O.; 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, O. 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 NOs 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 ^Os 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.
August 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 NC>2. 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.; NuB, H.; Granier, C.; Niemeier, U. (2005) Increase in tropospheric
41 nitrogen dioxide over China observed from space. Nature (London, U.K.) 437: 129-132.
August 2007 AX2-159 DRAFT-DO NOT QUOTE OR CITE
-------
1 Ridley, B. A.; Dye, J. E.; Walega, J. G.; Zheng, I; 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.; Hubler, 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. O.; 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.; Hubler, 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 NO2 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.; Hubler, 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, O.; Schurath, U.; Vogel, H.;
29 Vogel, B. (2001) The loss of NO2, HNO3, NO3/N2O5, and HO2/HOONO2 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) H2O2 and O3 in the atmosphere of Los Angeles and its
39 vicinity: factors controlling their formation and their role as oxidants of SO2. J. Geophys.
40 Res. [Atmos.] 94: 12,957-12,973.
August 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 BrNO3 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.; Muller, K. P.; Rudolph, J.; Neubert, R.; SchuBler, 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 15NO2 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.
August 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. 1; Ellingsen, K.; Schultz, M. G.; Wild, O.; Amann, M.;
6 Atherton, C. S.; Bergmann, D. I; 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(D19306):
13 10.1029/2006JD007100.
14 Siegwolf, R. T. W.; Matyssek, R.; Sauer, M.; Maurer, S.; Gunthardt-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 NOy, H2O2 and HNO3 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 O3-NOX-VOC chemistry and
24 NOX-VOC indicators. J. Geophys. Res. [Atmos.] 107: 10.1029/2001JDOO1123.
25 Sillman, S.; Al-Wali, K. L; Marsik, F. J.; Nowacki, P.; Samson, P. J.; Rodgers, M. O.; 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 O3-NOX-
35 hydrocarbon chemistry. J. Geophys. Res. [Atmos.] 103: 22,629-22,644.
36 Sillman, S.; Vautard, R.; Menut, L.; Kley, D. (2003) O3-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: 10.1029/2002JDOO1561.
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.
August 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 NOy , CO, and SO2 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.
August 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, O.; 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, O.;
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(D08301): 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.; Hubler, 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(D21): 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.
August 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, N2Os, Os, and NOX in the nocturnal boundary layer: 1. Observations during the
8 Texas Air Quality Study 2000. J. Geophys. Res. [Atmos.] 109(D12306):
9 10.1029/2003 JD004209.
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, O. (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 HNO3: 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 NO2 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.
August 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) ^Os 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): 10.1029/2001JD001508.
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.; Kara, 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. O.; Wang, T.;
36 Berresheim, H.; Demerjian, K. L.; Roychowdhury, U. K. (1993) Correlation of ozone
37 with NOy 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.
August 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 PMi0 sampler. Environ. Int. 21: 283-291.
5 Turco, R. P.; Toon, O. 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 NOy to a coniferous forest. J. Geophys. Res. [Atmos.] 111(D09304):
14 10.1029/2005 JD006631.
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.defira.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 ://cfpub2. 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].
August 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. (2006) 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 Van Aardenne, J. A.; Dentener, F. J.; Olivier, J. G. J.; Klein Goldewijk, C. G. M.; Lelieveld, J.
13 (2001) A 1° x 1° resolution data set of historical anthropogenic trace gas emissions for
14 the period 1980-1990. Global Biogeochem. Cycles 15: 909-928.
15 Van der Werf, G. R.; Randerson, J. T.; Collatz, J.; Giglio, L. (2003) Carbon emissions from fires
16 in tropical and subtropical ecosystems. Glob. Change Biol. 9: 547-562.
17 Van Noije, T. P. C.; Eskes, H. J.; Dentener, F. J.; Stevenson, D. S.; Ellingsen, K.; Schultz, M. G.;
18 Wild, O.; Amann, M.; Atherton, C. S.; Bergmann, D. J.; Bey, I; Boersma, K. F.; Butler,
19 T.; Cofala, J.; Drevet, J.; Fiore, A. M.; Gauss, M.; Hauglustaine, D. A.; Horowitz, L. W.;
20 Isaksen, I. S. A.; Krol, M. C.; Lamarque, J.-F.; Lawrence, M. G.; Martin, R. V.;
21 Montanaro, V.; Muller, J.-F.; Pitari, G.; Prather, M. J.; Pyle, J. A.; Richter, A.;
22 Rodriguez, J. M.; Savage, N. H.; Strahan, S. E.; Sudo, K.; Szopa, S.; Van Roozendael, M.
23 (2006) Multi-model ensemble simulations of tropospheric NO2 compared with GOME
24 retrievals for the year 2000. Atmos. Chem. Phys. Discuss. 6: 2965-3047.
25 Vautard, R.; Martin, D.; Beekman, M.; Drobinski, P.; Friedrich, R.; Jaubertie, A.; Kley, D.;
26 Lattuati, M.; Moral, P.; Neininger, B.; Theloke, J. (2002) Paris emission inventory
27 diagnostics from the ESQUIF airborne measurements and a chemistry transport model. J.
28 Geophys. Res. [Atmos.] 108(D17): 10.1029/2002JD002797.
29 Vione, D.; Barra, S.; De Gennaro, G.; De Rienzo, M.; Gilardoni, S.; Perrone, M. G.; Pozzoli, L.
30 (2004) Polycyclic aromatic hydrocarbons in the atmosphere: monitoring, sources, sinks
31 and fate. II: sinks and fate. Ann. Chim. 94: 257-268.
32 Vogt, R.; Crutzen, P. J.; Sander, R. (1996) A mechanism for halogen release from sea-salt
33 aerosol in the remote marine boundary layer. Nature (London, U.K.) 383: 327-330.
34 Vogt, R.; Sander, R.; Von Glasow, R.; Crutzen, P. J. (1999) Iodine chemistry and its role in
35 halogen activation and ozone loss in the marine boundary layer: a model study. J. Atmos.
36 Chem. 32: 375-395.
37 Volz-Thomas, A.; Geiss, H.; Hofzumahaus, A.; Becker, K.-H. (2003) Introduction to special
38 section: photochemistry experiment in BERLIOZ. J. Geophys. Res. [Atmos.] 108(D4):
39 10.1029/JD002029.
40 Von Glasow, R.; Sander, R.; Bott, A.; Crutzen, P. J. (2002a) Modeling halogen chemistry in the
41 marine boundary layer. 1. Cloud-free MBL. J. Geophys. Res. [Atmos.] 107(D17):
42 10.1029/2001JD000942.
August 2007 AX2-168 DRAFT-DO NOT QUOTE OR CITE
-------
1 Von Glasow, Sander, R.; Bott, A.; Crutzen, P. J. (2002b) Modeling halogen chemistry in the
2 marine boundary layer. 2. Interactions with sulfur and cloud-covered MBL. J. Geophys.
3 Res. [Atmos.] 107(D17): 10.1029/2001JD000943.
4 Von Glasow, R.; Von Kuhlmann, R.; Lawrence, M. G.; Platt, U.; Crutzen, P. J. (2004) Impact of
5 reactive bromine chemistry in the troposphere. Atmos. Chem. Phys. 4: 2481-2497.
6 Wagner, T.; Leue, C.; Wenig, M.; Pfeilsticker, K.; Platt, U. (2001) Spatial and temporal
7 distribution of enhanced boundary layer BrO concentrations measured by the GOME
8 instrument aboard ERS-2. J. Geophys. Res. [Atmos.] 106: 24,225-24,235.
9 Walcek, C. J.; Taylor, G. R. (1986) A theoretical method for computing vertical distributions of
10 acidity and sulfate production within cumulus clouds. J. Atmos. Sci. 43: 339-355.
11 Walcek, C. J.; Stockwell, W. R.; Chang, J. S. (1990) Theoretical estimates of the dynamic,
12 radiative and chemical effects of clouds on tropospheric trace gases. Atmos. Res. 25: 53-
13 69.
14 Walega, J. G; Stedman, D. H.; Shelter, R. E.; Mackay, G. I; Iguchi, T.; Schiff, H. I. (1984)
15 Comparison of a chemiluminescent and a tunable diode laser absorption technique for the
16 measurement of nitrogen oxide, nitrogen dioxide, and nitric acid. Environ. Sci. Technol.
17 18: 823-826.
18 Wang, Y.; Tao, W.-K.; Pickering, K. E.; Thompson, A. M.; Kain, J. S.; Adler, R. F.; Simpson, J.;
19 Keehn, P. R.; Lai, G. S. (1996) Mesoscale model simulations of TRACE A and
20 preliminary regional experiment for storm-scale operational and research meteorology
21 convective systems and associated tracer transport. J. Geophys. Res. [Atmos.] 101:
22 24,013-24,027.
23 Wang, Y.; DeSilva, A. W.; Goldenbaum, G. C.; Dickerson, R. R. (1998) Nitric oxide production
24 by simulated lightning: dependence on current, energy, and pressure. J. Geophys. Res.
25 [Atmos.] 103: 19,149-19,159.
26 Wang, L. H.; Milford, J. B.; Carter, W. P. L. (2000a) Reactivity estimates for aromatic
27 compounds. Part I: uncertainty in chamber-derived parameters. Atmos. Environ. 34:
28 4337-4348.
29 Wang, L. H.; Milford, J. B.; Carter, W. P. L. (2000b) Reactivity estimates for aromatic
30 compounds. Part 2. uncertainty in incremental reactivities. Atmos. Environ. 4349-4360.
31 Warneck, P. (1999) The relative importance of various pathways for the oxidation of sulfur
32 dioxide and nitrogen dioxide in sunlit continental fair weather clouds. Phys. Chem.
33 Chem. Phys. 1: 5471-5483.
34 Watson, J. G; Fujita, E. M.; Chow, J. C.; Zielinska, B.; Richards, L. W.; Neff, W.; Dietrich, D.
35 (1998) Northern front range air quality study. Final report. Fort Collins, CO: Colorado
36 State University, Cooperative Institute for Research in the Atmosphere. Available:
37 http://www.nfraqs.colostate.edu/index2.html (16 Jan 2002).
38 Weber, P.; Nufibaum, S.; Fuhrer, J.; Gfeller, H.; Schlunegger, U. P.; Brunold, C.; Rennenberg,
39 H. (1995) Uptake of atmospheric 15NO2 and its incorporation into free amino-acids in
40 wheat (Triticum-aestivum). Physiol. Plant. 94: 11-11.
August 2007 AX2-169 DRAFT-DO NOT QUOTE OR CITE
-------
1 Weber, P.; Thoene, B.; Rennenberg, H. (1998) Absorption of atmospheric NO2 by spruce (Picea
2 abies) trees. III. Interaction with nitrate reductase activity in the needles and phloem
3 transport. Bot. Acta 111: 377-382.
4 Wedin, D. A.; Tilman, D. (1996) Influence of nitrogen loading and species composition on the
5 carbon balance of grasslands. Science (Washington, DC) 274: 1720-1723.
6 Wendel, G. J.; Stedman, D. H.; Cantrell, C. A.; Damrauer, L. (1983) Luminol-based nitrogen
7 dioxide detector. Anal. Chem. 55: 937-940.
8 Wesely, M. L. (1989) Parameterization of surface resistances to gaseous dry deposition in
9 regional-scale numerical models. Atmos. Environ. 23: 1293-1304.
10 Wesely, M. L.; Hicks, B. B. (1977) Some factors that affect the deposition rates of sulfur dioxide
11 and similar gases on vegetation. J. Air Pollut. Control Assoc. 27: 1110-1116.
12 Wesely, M. L.; Hicks, B. B. (2000) A review of the current status of knowledge on dry
13 deposition. Atmos. Environ. 34: 2261-2282.
14 Westerling, A. L.; Hildalgo, H. G.; Cayan, D. R.; Swetnam, T. W. (2006) Warming and earlier
15 spring increase western U.S. forest wildfire activity. Science (Washington, DC, U.S.)
16 313:940-943.
17 Williams, E. J.; Guenther, A.; Fehsenfeld, F. C. (1992) An inventory of nitric oxide emissions
18 from soils in the United States. J. Geophys. Res. [Atmos.] 97: 7511-7519.
19 Winberry, W. T., Jr.; Ellestad, T.; Stevens, B. (1999) Compendium method IO-4.2:
20 determination of reactive acidic and basic gases and strong acidity of atmospheric fine
21 particles (< 2.5 um). Compendium of methods for the determination of inorganic
22 compounds in ambient air. Cincinnati, OH: U.S. Environmental Protection Agency,
23 Center for Environmental Research Information; report no. EPA/625/R-96/010a.
24 Available: http://www.epa.gov/ttn/amtic/files/ambient/inorganic/mthd-4-2.pdff2 May,
25 2007].
26 Witz, S.; Eden, R. W.; Wadley, M. W.; Dunwoody, C.; Papa, R.; Torre, K. J. (1990) Rapid loss
27 of particulate nitrate, chloride and ammonium on quartz fiber filters during storage. J. Air
28 Waste Manage. Assoc. 40: 53-61.
29 World Health Organization (WHO). (2003) Nitrogenated polycyclic aromatic hydrocarbons.
30 Geneva, Switzerland: World Health Organization. (Environmental Health Criteria 229).
31 Xu, J. H.; Lee, F. S. C. (2000) Quantification of nitrated polynuclear aromatic hydrocarbons in
32 atmospheric particulate matter. Anal. Chim. Acta 416: 111-115.
33 Xu et al. (2006) [P. AX2-14]
34 Yienger, J. J.; Levy, H., II. (1995) Empirical model of global soil-biogenic NOX emissions. J.
35 Geophys. Res. [Atmos.] 100: 11,447-11,464.
36 Young, V. L.; Kieser, B. N.; Chen, S. P.; Niki, H. (1997) Seasonal trends and local influences on
37 nonmethane hydrocarbon concentrations in the Canadian boreal forest. J. Geophys. Res.
38 [Atmos.] 102: 5913-5918.
39 Zafiriou, O. C.; True, M. B. (1979) Nitrate photolysis in seawater by sunlight. Mar. Chem. 8: 33-
40 42.
August 2007 AX2-170 DRAFT-DO NOT QUOTE OR CITE
-------
1 Zanis, P.; Trickl, T.; Stohl, A.; Wernli, H.; Cooper, O.; Zerefos, C.; Gaeggeler, H.; Schnabel, C.;
2 Tobler, L.; Kubik, P. W.; Priller, A.; Scheel, H. E.; Kanter, H. J.; Cristofanelli, P.;
3 Forster, C.; James, P.; Gerasopoulos, E.; Delcloo, A.; Papayannis, A.; Claude, H. (2003)
4 Forecast, observation and modelling of a deep stratospheric intrusion event over Europe.
5 Atmos. Chem. Phys. 3: 763-777.
6 Zhang, G. J.; McFarlane, N. A. (1995) Sensitivity of climate simulations to the parameterization
7 of cumulus convection in the Canadian Climate Centre general circulation model. Atmos.
8 Ocean 33: 407-446.
9 Zhang, X. Q.; McMurry, P. H. (1987) Theoretical analysis of evaporative losses from impactor
10 and filter deposits. Atmos. Environ. 21: 1779-1789.
11 Zhang, J.-Z.; Millero, F. J. (1991) The rate of sulfite oxidation in seawater. Geochim.
12 Cosmochim. Acta 55: 677-685.
13 Zhang et al. (2006) [P. AX2-7]
14 Zhou, X.; Beine, H. J.; Honrath, R. E.; Fuentes, J. D.; Simpson, W.; Shepson, P. B.; Bottenheim,
15 J. W. (2001) Snowpack photochemical production of HONO: a major source of OH in
16 the Arctic boundary layer in springtime. Geophys. Res. Lett. 28: 4087-4090.
17 Zhou, X.; Civerolo, K.; Dai, H. (2002a) Summertime nitrous acid chemistry in the atmospheric
18 boundary layer at a rural site in New York state. J. Geophys. Res. [Atmos.] 107(D21):
19 10.1029/2001JD001539.
20 Zhou, X. L.; He, Y.; Huang, G.; Thornberry, T. D.; Carroll, M. A.; Bertman, S. B. (2002b)
21 Photochemical production of nitrous acid on glass sample manifold surface. Geophys.
22 Res. Lett. 29(1681): 10.1029/2002GLO 15080.
23 Zhou, X.; Gao, H.; He, Y.; Huang, G. (2003) Nitric acid photolysis on surfaces in low-NOx
24 environments: significant atmospheric implications. Geophys. Res. Lett. 30:
25 10.1029/2003GL018620.
26 Zielinska, B.; Arey, J.; Atkinson, R.; Ramdahl, T.; Winer, A. M.; Pitts, J. N., Jr. (1986) Reaction
27 of dinitrogen pentoxide with fluoranthene. J. Am. Chem. Soc. 108: 4126-4132.
28 Zielinska, B.; Arey, J.; Atkinson, R.; Winer, A. M. (1989) The nitroarenes of molecular weight
29 247 in ambient particulate samples collected in southern California. Atmos. Environ. 23:
30 223-229.
31 Zielinska, B.; Sagebiel, J.; McDonald, J. D.; Whitney, K.; Lawson, D. R. (2004) Emission rates
32 and comparative chemical composition from selected in-use diesel and gasoline-fueled
33 vehicles. J. Air Waste Manage. Assoc. 54: 1138-1150.
34 Zimmermann, J.; Poppe, D. (1993) Nonlinear chemical couplings in the tropospheric NOX—HO
35 gas phase chemistry. J. Atmos. Chem. 17: 141-155.
36 Zingler, J.; Platt, U. (2005) Iodine oxide in the Dead Sea Valley: evidence for inorganic sources
37 of boundary layer IO. J. Geophys. Res. [Atmos.] 110(D07307): 10.1029/2004JD004993.
38
X
August 2007 AX2-171 DRAFT-DO NOT QUOTE OR CITE
-------
i AX3. CHAPTER 3 ANNEX-AMBIENT
2 CONCENTRATIONS AND EXPOSURES
o
4
5 AX3.1 INTRODUCTION
6 Topics discussed in this chapter include the characterization of ambient air quality for
7 nitrogen dioxide (NO2), the uses of these data in assessing human exposures to NO2;
8 concentrations and sources of NC>2 in different microenvironments, and personal exposures to
9 NC>2. The NC>2 data contained in this chapter are taken mainly from the U.S. Environmental
10 Protection Agency's Air Quality System (AQS) database (formerly the AIRS database) (U.S.
11 Environmental Protection Agency, 2007).
12
13 Characterizing Ambient NO2 Concentrations
14 The "concentration" of a specific air pollutant is typically defined as the amount (mass)
15 of that material per unit volume of air. However, most of the data presented in this chapter are
16 expressed as "mixing ratios" in terms of a volume-to-volume ratio (e.g., parts per million [ppm]
17 or parts per billion [ppb]. Data expressed this way are often referred to as concentrations, both in
18 the literature and in the text, following common usage. Human exposures are expressed in units
19 of mixing ratio times time.
20
21 Relationship to the 1993 Air Quality Criteria Document for Nitrogen Oxides
22 The 1993 AQCD for Oxides of Nitrogen emphasized NC>2 indoor sources (gas stoves)
23 and the relationship between personal total exposure and indoor or outdoor NC>2 concentrations.
24 At that time, only few personal exposure studies had been conducted with an emphasis on
25 residential indoor NC>2 sources and concentrations. Although the concept of microenvironment
26 had been introduced in the document, NO2 concentrations were seldom reported for
27 microenvironments other than residences. Exposure measurements at that time relied on Palmes
28 tubes and Yanagisawa badges; and exposure-modeling techniques were limited mainly to simple
29 linear regression. In the 1993 AQCD, NC>2 was treated as an independent risk factor, and
30 confounding issues were not mentioned in the human environmental exposure chapters.
31 The current chapter summarizes and discusses the state-of-the-science and technology
32 regarding NC>2 human exposures since 1993. Since then, numerous human exposure studies
August 2007 AX3-1 DRAFT-DO NOT CITE OR QUOTE
-------
1 have been conducted with new measurement and modeling techniques. Microenvironmental
2 measurements were not limited to residential indoor environments; NC>2 concentrations were also
3 measured in vehicles, schools and offices, and microenvironments close to traffic. More indoor
4 sources have been identified and more NO2 formation and transformation mechanisms in the
5 indoor environment have been reported. Both indoor and outdoor NC>2 have been treated as
6 components of a pollutant mixture, and therefore the concepts of confounding and surrogacy
7 have been discussed in the current chapter.
8
9
10 AX3.2 AMBIENT CONCENTRATIONS OF NITROGEN OXIDES AND
11 RELATED SPECIES
12 As discussed in Chapter 2, most measurements of NOX are made by instruments that
13 convert NC>2 to NO, which is then measured by chemiluminescence. However, the surface
14 converters that reduce NC>2 to NO also reduce other reactive NOy species. As indicated in
15 Chapter 2, NOy compounds consist of NOX, gas phase inorganic nitrates, such as C1NO3; organic
16 nitrates, such as PANs; inorganic acids, given by the formulas HNOy (y = 2 to 4); and particulate
17 nitrate. In urban areas or in rural areas where there are large local sources, NO and NO2 are
18 expected to be the major forms of NOy. Thus, interference from PANs and other NOy species
19 near sources are expected to minor; in most rural and remote areas, interference may be
20 substantial as concentrations of other NOy species may be much larger than those for NO and
21 NO2 (National Research Council, 1991). Examples will be presented in Section AX3.3.5.
22 Data for NOX in addition to NO2 is reported into the U. S. Environmental Protection
23 Agency's Air Quality System (AQS), but data for NO is not reported, even though measurements
24 of NO are not affected by artifacts caused by products of NO2 oxidation and therefore should be
25 the most reliable. By definition, NOX is equal to the sum of NO and NO2, so the concentration of
26 NO can be found by subtraction. However, measurements are obtained for NO and NOX every 2
27 to 3 min, but hourly averages for NO2 and NOX are reported into AQS. The locations of NO2
28 monitoring sites are shown in Figure AX3.1. As can be seen from Figure AX3.1, there are large
29 areas of the United States for which data for ambient NO2 are not collected. The percentile
30 distribution of NO2 concentrations in urban and nonurban areas in the U.S. for different
31 averaging periods is shown in Table AX3.1.
August 2007 AX3-2 DRAFT-DO NOT CITE OR QUOTE
-------
Monitor Locator Map - Criteria Air Pollutants
United States
,• •>
*,,:-.*
Monitor Location: A NO2 frt=3?5) ' «-;'
Shaded states have monitors
Figure AX3.1. Location of ambient NOi monitors in the United States.
1 Because of their short lifetime with respect to oxidation to PANs and HNOs, NOX
2 concentrations are highly spatially and temporally variable. Average concentrations range from
3 tens of ppt in remote areas of the globe to tens of ppb in urban cores, i.e., by three orders of
4 magnitude. Median NO, NOX, and NOy concentrations at the surface are typically below 0.01,
5 0.05, and 0.3 ppb, respectively, in remote areas such as Alaska, northern Canada, and the eastern
6 Pacific; median NOy concentrations range from about 0.7 to about 4.3 ppb at regional
7 background sites in the eastern United States (Emmons et al., 1997). Note that the last two
8 values, especially, contain a substantial contribution from pollution. Maximum short-term
9 average (1-h) NOX concentrations near heavy traffic (e.g., in Los Angeles, CA) approach 1 ppm,
10 but these levels decrease rapidly away from sources. Even at sites where such high hourly
11 values are found, 24-h average concentrations are much lower. For example, the maximum 24-h
12 average NOX concentration at any site in Los Angeles in 2004 was 82 ppb.
August 2007 AX3-3 DRAFT-DO NOT CITE OR QUOTE
-------
1 NO2 concentrations are likewise highly spatially and temporally variable. The overall
2 annual mean concentration of NO2 at U.S. monitoring sites is about 15 ppb. Most sites
3 monitoring NO2 are located in populated areas and values outside of urban and suburban areas
4 can be much lower. Perhaps the most comprehensive characterization of ambient NO2 levels has
5 been performed by the California Air Resources Board (CARB) as part of the review of the air
6 quality standards for California (CARB, 2007). On a statewide basis, the average NO2
7 concentration was about 15 ppb from 2002 to 2004. Highest average values of about 27 ppb
8 were found in the South Coast Air Basin. The maximum 1-h average NO2 concentration during
9 the same period was 262 ppb, again in the South Coast Air Basin. However, maximum 1-h
10 concentrations of NO2 were about 150 ppb in Los Angeles, CA in 2004, implying that the high
11 NOX level (~lppm) cited above for Los Angeles consisted mainly of NO. It is highly unlikely
12 that NOX oxidation products constituted a significant fraction of the NOX reported.
13
14 AX3.2.1 Spatial and Temporal Variability in Ambient Concentrations of
15 NO2 and Related Species in Urban Areas
16 As noted earlier, the number of monitoring sites reporting data for NO2 is considerably
17 smaller than for other criteria pollutants. As a result, there are few urban areas where there exist
18 sufficient data to evaluate the spatial variability in NO2 even though most of the NO2 monitors
19 are found in urban or suburban areas. Analyses of spatial variability in NO and NO2 are thus
20 limited to Los Angeles, CA and Chicago, IL. Also, as noted in Chapter 2, current methods for
21 measuring NO2 are subject to interference from its oxidation products. Hence the reported
22 values represent upper limits for the true NO2 concentration. Near highways or other NOX
23 sources, the measurements should give more accurate values, but because of variability in the
24 time needed for conversion of NOX to NOZ, no firm rules can be applied to account for the
25 presence of NOZ species such as HNOs and PANs. These considerations introduce additional
26 uncertainty into the interpretation of any metrics (e.g., correlation coefficients, concentration
27 differences) that are used to characterize spatial variability in NO2 concentrations.
28 The spatial variability in 1 h average NO2 concentrations in New York, NY; Atlanta, GA;
29 Chicago, IL; Houston, TX; Los Angeles, CA; and Riverside, CA is characterized in this section.
30 These areas were chosen to provide analyses to help guide risk assessment and to provide a
31 general overview of the spatial variability of NO2 in cities where health outcome studies have
32 been conducted. Statistical analyses of the human health effects of airborne pollutants based on
August 2007 AX3-4 DRAFT-DO NOT CITE OR QUOTE
-------
1 aggregate population time-series data have often relied on ambient concentrations of pollutants
2 measured at one or more central sites in a given metropolitan area. In particular, cities with low
3 traffic densities that are located downwind of major sources of precursors are heavily influenced
4 by long range transport and tend to show smaller spatial variability (e.g., New Haven, CT) than
5 those source areas with high traffic densities located upwind (e.g., New York, NY).
6 Metrics for characterizing spatial variability include the use of Pearson correlation
7 coefficients, values of the 90th percentile (P90) of the absolute difference in concentrations, and
8 coefficients of divergence (COD) The COD is defined as follows:
COD-I - V- V ( XJj" xft V?
^uujk- VpL\x;i + xJ
9 *=/ IJ Ik (AX3-1)
10 where xy- and x;k represent observed concentrations averaged over some measurement averaging
11 period (hourly, daily, etc.), for measurement period /' at sitey and site k andp is the number of
12 observations. These methods of analysis follow those used for characterizing PM2.5 and PMi0-2.5
13 concentrations in Pinto et al. (2004) and in the latest edition of the PM AQCD (U.S.
14 Environmental Protection Agency, 2004a).
15 Summary statistics for the spatial variability in several urban areas across the United
16 States are shown in Table AX3.2. These areas were chosen because they are the major urban
17 areas with at least five monitors operating from 2003 to 2005. Values in parentheses below the
18 city name refer to the number of sites colleting data. The second column shows the mean 1 h
19 average concentration across all sites and the range in means at individual sites. The third
20 column gives the range of Pearson correlation coefficients between individual site pairs in the
21 urban area. The fourth column shows the 90th percentile absolute difference in concentrations
22 between site pairs. The fifth column gives the coefficient of divergence (COD).
23 As can be seen from the table, mean concentrations at individual sites vary by factors of
24 1.5 to 6 in the MS As examined. Correlations between individual site pairs range from slightly
25 negative to highly positive in a given urban area. The sites in New York City tend to be the most
26 highly correlated and show the highest mean levels, reflecting their proximity to traffic, as
27 evidenced by the highest mean concentration of all the entries. However, correlation coefficients
28 are not sufficient for describing spatial variability as concentrations at two sites may be highly
29 correlated but show differences in levels. Thus, the range in mean concentrations is given. Even
August 2007 AX3-5 DRAFT-DO NOT CITE OR QUOTE
-------
1 in New York City, the spread in mean concentrations is about 40% of the city-wide mean
2 (12/29). The relative spread in mean concentrations is larger in the other urban areas shown in
3 Table AX3.2. As might be expected, the 90th percentile concentration spreads are even larger
4 than the spreads in the means.
5 The same statistics shown in Table AX3.2 have been used to describe the spatial
6 variability of PM2.5 (U.S. Environmental Protection Agency, 2004; Pinto et al., 2005) and 63
7 (U.S. Environmental Protection Agency, 2006). However, because of relative sparseness in data
8 coverage for NO2, spatial variability in all cities that were considered for PM2.5 and 63 could not
9 be considered here. Thus, the number of cities included below is much smaller than for either Os
10 (24 urban areas) or PM2.5 (27 urban areas). Even in those cities where there are monitors for all
11 three pollutants, data may not have been collected at the same locations and even if they were,
12 there would be variable influence from local sources. For example, concentrations of NO2
13 collected near traffic will be highest in an urban area, but concentrations of Os will tend to be
14 lowest because of titration by NO forming NO2. However, some general observations can still
15 be made. Mean concentrations of NO2 at individual monitoring sites are not as highly variable
16 as for 63 but are more highly variable than PM2.5. Lower bounds on inter-site correlation
17 coefficients for PM2 5 and for Os tend to be much higher than NO2 in the same areas shown in
18 Table AX3.2. CODs for PM2.5 are much lower than for O3, whereas CODs for NO2 tend to be
19 the largest among the three pollutants. Therefore, it is apparent that there is the potential for
20 errors from the use of ambient monitors to characterize exposures either at the community or
21 personal level, and that this potential may be higher than for either Os or PM2 5.
22
23 Small Scale Vertical Variability
24 Inlets to instruments for monitoring gas phase criteria pollutants can be located from 3 to
25 15m above ground level (CFR 58, Appendix E, 2002). Depending on the pollutant, either there
26 can be positive, negative or no vertical gradient from the ground to the monitor inlet. Pollutants
27 that are formed over large areas by atmospheric photochemical reactions and are destroyed by
28 deposition to the surface or by reaction with pollutants emitted near the surface show positive
29 vertical gradients. Pollutants that are emitted by sources at or just above ground level show
30 negative vertical gradients. Pollutants with area sources and have minimal deposition velocities
August 2007 AX3-6 DRAFT-DO NOT CITE OR QUOTE
-------
1 show little or no vertical gradient. Restrepo et al. (2004) compared data for criteria pollutants
2 collected at fixed monitoring sites at 15 m above the surface on a school rooftop to those
3 measured by a van whose inlet was 4 m above the surface at monitoring sites in the South Bronx
4 during two sampling periods in November and December 2001. They found that CO, SO2, and
5 NC>2 showed positive vertical gradients, whereas Os showed a negative vertical gradient and
6 PM2.5 showed no significant vertical gradient. As shown in Figure AX3.2, NC>2 mixing ratios
7 obtained at 4 m (mean -74 ppb) were about a factor of 2.5 higher than at 15 m (mean -30 ppb).
8 Because tail pipe emissions occur at lower heights, NC>2 values could have been much higher
9 nearer to the surface, and the underestimation of NC>2 values by monitoring at 15 m even larger.
10 Restrepo et al. (2004) note that the use of the NC>2 data obtained by the stationary monitors
11 would result in an underestimate of human exposures to NC>2 in the South Bronx. However, this
12 issue is most likely not unique to the South Bronx and could arise in other large urban areas in
13 the U.S. with populations of similar demographic and socioeconomic characteristics.
0.12
0,1
0,08
I 0.06
a.
0,04
0.02
* *
* *
* *
* *
4 *
V-A-
* * ,*
AK f"f^
i A^ a / / \ f
V V<
.4... -Van — *--DEC709408---A--DEC709407
Figure AX3.2. NOi concentrations measured at 4 m (Van) and at 15 m at NY
Department of Environmental Conservation sites (DEC709406 and
DEC709407).
Source: Restrepo et al. (2004).
August 2007
AX3-7
DRAFT-DO NOT CITE OR QUOTE
-------
1 AX3.2.2 Temporal Variability in Nitrogen Oxides
2
3 AX3.2.2.1 Diurnal Variability in NOi Concentrations
4 As might be expected from a pollutant having a major traffic source, the diurnal cycle of
5 NO2 in typical urban areas is characterized by traffic emissions, with peaks in emissions
6 occurring during morning and evening rush hour traffic. Motor vehicle emissions consist mainly
7 of NO, with only about 10% of primary emissions in the form of NC>2. The diurnal pattern of
8 NO and NO2 concentrations is also strongly influenced by the diurnal variation in the mixing
9 layer height. Thus, during the morning rush hour when mixing layer heights are still low, traffic
10 produces a peak in NO and NO2 concentrations. As the mixing layer height increases during the
11 day, dilution of emissions occurs. During the afternoon rush hour, mixing layer heights are at or
12 are near their daily maximum values resulting in dilution of traffic emissions through a larger
13 volume than in the morning. Starting near sunset, the mixing layer height drops and conversion
14 of NO to NO2 occurs without photolysis of NO2 recycling NO.
15 The composite diurnal variability of NO2 in selected urban areas with multiple sites (New York,
16 NY; Atlanta, GA; Baton Rouge, LA; Chicago. IL; Houston, TX; Riverside, CA; and
17 Los Angeles, CA) is shown in Figure AX3.3. Figure AX3.3 shows that lowest hourly median
18 concentrations are typically found at around midday and that highest hourly median
19 concentrations are found either in the early morning or in mid-evening. Median values range by
20 about a factor of two from about 13 ppb to about 25 ppb. However, individual hourly
21 concentrations can be considerably higher than these typical median values, and hourly NO2
22 concentrations > 0.10 ppm can be found at any time of day.
23
24 AX3.2.2.2 Seasonal Variability in NO2 Concentrations
25
26 Urban Sites
27 As might be expected from an atmospheric species that behaves essentially like a primary
28 pollutant emitted from surface sources, there is strong seasonal variability in NOX and NO2
29 concentrations. Highest concentrations are found during winter, consistent with lowest mixing
30 layer heights found during the year. Mean and peak concentrations in winter can be up to a
31 factor of two larger than in the summer at several sites in Los Angeles County.
32
August 2007 AX3-8 DRAFT-DO NOT CITE OR QUOTE
-------
a
3
c
o
1
O
C
0
o
0.20-
0.19-
0.18-
0.17-
0.16-
0.15-
0.14-
0.13-
0.12-
0.11-
0.10-
0.09-
0.08-
0.07-
0,06-
0.05-
0,04-
0,03-
0.02-
0.01-
0.00-
I
!>.
0
1 ' I ' I'1 I ' f ' I ' I
1234567
i r i ' i ' i f i • i • i ' i ' i ' i > i ' i ' i ' i ' i ' i '
8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
Figure AX3.3.
Composite, diurnal variability in 1-h average NOi in urban areas. Values
shown are averages from 2003 through 2005. Boxes define the
interquartile range, and the whiskers the 5th and 95th percentile values.
Asterisks denote individual values above the 95th percentile.
1 The month-to-month variability in NC>2 at individual sites in selected urban areas is
2 illustrated in Figures AX3.4 to AX3.10. Seasonal patterns can be found at some sites but not in
3 others. There appears to be a somewhat regular pattern for the southern cities with winter
4 maxima and summer minima. Monthly maxima tend to be found from late winter to early spring
5 in Chicago and New York with minima occurring from summer through the fall. However, in
6 Los Angeles and Riverside, monthly maxima tend to occur from autumn through early winter
7 with minima occurring from spring through early summer.
8
9 Regional Background Sites
10 Surface NOX and NOy data obtained in Shenandoah National Park, VA from 1988 to
11 1989 show wintertime maxima and summertime minima (Doddridge et al., 1991, 1992; Poulida
12 et al., 1991). NOX and NOy data collected in Harvard Forest, MA from 1990 to 1993 show a
13 similar seasonal pattern (Munger et al., 1996). In addition the within-season variability was
14 found to be smaller in the summer than in the winter as shown in Table AX3.3.
August 2007
AX3-9
DRAFT-DO NOT CITE OR QUOTE
-------
a. New York, NY,
SUBURBAN
b. Hew York, NY. URBAN and CENTER CITY
-
S ,„
£
,2 CJb-
c
O i' II"
O
v r
I
O
O
Sample Date (mm/d<%yyy)
Sample Date (mm/dd/yyyy)
c. New York, NY URBAN and CENTER CITY
d. New York, NY. URBAN and CENTER CITY
JR.
C
S I|1J
c
o J iv
O
Jl'l
SampI* Date (mm/ddYyyyy)
Sample Date (mm/dd/yyyy)
Figure AX3.4a-e.
a. Mew York, NY, URBAN and CENTER CITY
I -
a
w (JL6
I Ji»
8 «"
C
O
-------
a. Atlanta, GA.
SUBURBAN
E
Q.
•S?
c
2
o
c
o
O
0.09:
0.06-
0.07 \
0,06:
0,05:
004-
0,03-
0.02^
0.01:
0.00-
site id=130890002 poc=1
= Natural Spline Fit w/ 9 df
01/01/2003 07/01/2003 01/01/2004 07/01/2004 01/01/2005 07/01/2005 01/01/2006
Sample Date (mm/dd/yyyy)
b. Atlanta, GA.
URBAN and CENTER CITY
Q.
3
c
,2
+3
O
c
o
o
0.09 4
0,08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0.00
site id=131210048poc=1
f,.^1 |,ii i,
i7/m/?nra m/m/?nn4 n?/m/?nn4 ni/ni/?ons n7/m/?nns m/m
01/01/2003 07/01/2003 01/01/2004 07/01/2004 01/01/2005 07/01/2005 01/01/2006
Sample Date fmm/dd/yyyy)
Figure AX3.5a-e.
Time series of 24-h average NOi concentrations at individual sites in
Atlanta, GA from 2003 through 2005. A natural spline function (with
9 degrees of freedom) was fit and overlaid to the data (dark solid line).
August 2007
AX3-11
DRAFT-DO NOT CITE OR QUOTE
-------
>, IL.
RURAL
SUBURBAN
Sample Date (mm/dd/yyyy)
Sample Dale {mmftldfyyyy)
c Chicago, IL.
SUBURBAN
f v-,(li. -jj t 'f •' ": u'^'i i'!v\[' • \,"'" V5'
j ,.- "-'ier. '. •>.-,- *^-f r;*r "i ••.;•*
Sampf© Date f
o, )L
SUBURBAN
Sampie Date C
e Chicago. IL
I *„
SUBURiAN
f. ChKwgo. it URBAN and CENTER CITY
Sample Date (mntfdd/yyyy)
Sampie Date ^
Figure AX3.6a-g.
a chi«go,iL URBAN and CENTER CITY
o "•
O
Sample Date {
Time series of 24-h average NOi concentrations at individual sites in
Chicago, IL from 2003 through 2005. A natural spline function (with
9 degrees of freedom) was fit and overlaid to the data (dark solid line).
August 2007
AX3-12 DRAFT-DO NOT CITE OR QUOTE
-------
a. Baton Rouge, LA,
SUBURBAN
0.
£:
c
O
s-
*•>
C,
0>
o
c
o
o
0.09:
0.08^
0.07^
*
0.06:
0.05:
0.04^
;
0.03-j
0.02-j
0,01-
0.00^
site 121 0001 poc=1
^\^> = Natural Spline Fit w/ 9 df ]
!
' »
a w> *" m ^
( •. . i II,
f» s,* | ' «f \.! I 81 ^ B &
3, -; « ^ . . V^%-^ i*-'i f'r *\i „ ^ V«« \ "^ «?« « ,J^n * ^
•SS^^^^S^^SIf^!^
** fa » ^ % v A, » i^ ^ *»****'**'* i,
s, «
01/01/2004 01/01/2006
b. Baton Rouge, LA,
Sample Date fmm/dd/yyyy)
and CITY
a.
c
o
*mmt
*J
s
*J
dJ
o
c
o
u
0,09
0,08
0.07
0,06
0.05
0.04
0.03
0.02
0.01
0.00
site id=22033000i poc=1
^
T I I I I I T
07/01/2004
Sample Date (mm/dd/yyyy)
Figure AX3.7a-b.
Time series of 24-h average NOi concentrations at individual sites in
Baton Rouge, LA from 2003 through 2005. A natural spline function
(with 9 degrees of freedom) was fit and overlaid to the data (dark
solid line).
August 2007
AX3-13
DRAFT-DO NOT CITE OR QUOTE
-------
a. H0ystona TX
SUBURBAN
13. Houston, TX,
SUBURBAN
5 J :
V «. .
*•«,«•' *' l]
^4^ua4fe^
E
I M'
I --':
£ :
U "
u
If*
, "» > M M t
r'v '•,..»'. '''"""'U', ' -?-, ';r' '{•''• •*'*, <-,.»' iv
"^ " O^,.*;/*' \ "^ 'r^^ (i V" '„'** t ' ^^'^ Wv *J L
,>!«*.
Sample Date (mmfdd/yyyy^
Simple Date |
c Houston, TX,
SUBURBAN
5 ,19 .
I ,H
o
t1 J'"
Sample Date (mmftW/yyyy)
d. Houston, TX
SUBURBAN
> t
'.'liLw^w^T
^ $V» [
Sample Date (mmldd/yyyyj
• Houston, TX URBAN and CENTER CITY
Q, s '
Sample Date (mm/dd/yyyy)
i Houston. TX, URBAN and CENTER CITY
Sample Date jmm/dd/yyyy)
Figure AX3.8a-g.
fl, Houston, TX. URBAN and CENTER CITY
E i,,.
5 l*
Sample Date (mm/dd/yyyy)
Time series of 24-h average NOi concentrations at individual sites in
Houston, TX from 2003 through 2005. A natural spline function (with
9 degrees of freedom) was fit and overlaid to the data (dark solid line).
August 2007
AX3-14 DRAFT-DO NOT CITE OR QUOTE
-------
- Los Anfpt^g, CA,
SUBURBAN
Sample Date {mmfdd/yyyy)
SUBURBAN
„ I1* ^tfr&vffirr "«t , *Tmt»Sf*ii***lf " ^ i ' i
Sample Dst© fmm/eld/yyyy)
c Los Angeles, CA
SUBURBAN
Samplo Date |m
t) Los Anides, CA SUBURBAN
Sample Date (mmMd
-------
i, LOS Angeles, CA, URBAN and CENTER CITY
j. LOS Angeles, CA, URBAN and CENTER CITY
I "
a
• •
is CM-
O
o
,
Sample Date (mm/dd/yyyy)
I •'•--
c
o
O
Sample Date {mmlMlyyyyj
k. Los Anae!es. CA. URBAN and CENTER CITY
504-
§ 3
§ 3E-
O
; t> i , i ! - i -s \ „ ^ T ^ ' f r
^l^ilfi^lW
Sample Date |mm/dd/yyyy)
i. Los Angetes, CA, URBAN and CENTER CITY
. -
I rr,
y c TKH ry;3M ^CUM
Sample Date (mm/dd/yyyy)
m. LOS Angeles, CA. URBAN and CENTER CITY
n. LOS Angeles, CA, URBAN and CENTER CITY
I-
c
O
SI
gc,
o
1 1 i i
1., vl'WU OKI AW ;(',-,VX L'1'.'i.At Ci I
Sample Oats (mm/dd/yyyy)
Sampit Date (mm/dd/yyyy)
Figure AX3.9i-n.
Time series of 24-h average NOi concentrations at individual sites in
Los Angeles, CA from 2003 through 2006. A natural spline function
(with 9 degrees of freedom) was fit and overlaid to the data (dark
solid line).
August 2007
AX3-16 DRAFT-DO NOT CITE OR QUOTE
-------
a Ri¥erside, CA,
RURAL
t>. Riverside, CA,
SUBURBAN
I CJ54
C
O
o
"<-f
Sample Date (mm/dd/yyyy)
C
4)
O
0
o
I'l •" « .V,l '"i* U'l, 1,'Jw'
Sampis Date (mm/ddlyyyy)
c. Riverside, CA,
SUBURBAN
d. Riverside, CA.
SUBURBAN
i
r"> K
tli'TW ("C'llJM ClIT'j* CI'"J'?TB ut"*.*'.- 3,"i'JBr ,<3 OT6
Sample Date (mm/dd/yyyy)
>-w if* 11 .j! :,'>s jv.itr.'-xi o
Sample Date (mm/2 concentrations from
3 1983 to 2002. As can be seen from the figure, NC>2 concentrations have decreased by about 10%
4 per decade. As can be seen from Figure AX3.12, most monitoring sites are located in either
5 urban (49) or suburban (58) areas and comparatively few monitoring sites are located in rural
6 areas (14). Figure AX3.12 also shows that decreases have been at least twice as large in urban
1 and suburban areas than in rural areas and that NO2 concentrations in urban and suburban areas
8 are roughly twice those in rural areas. Note that a land use characterization of rural does not
August 2007
AX3-17
DRAFT-DO NOT CITE OR QUOTE
-------
e. Riverside, CA.
SUBURBAN
JCS
I -
Q,
— * 3D?
g 'JIB
C
O 3tP
O
'JBU ,1i ulQifii ClrT'JiKK H'r'LliaS* ClO'Clhfi
Sample Date (mm/dd/yyyy)
f. Riverside, CA.
SUBURBAN
a
&
c
O
O
O
Sample Oate (mm/dd/yyyy)
g. Riverside, CA.
SUBURBAN
h. Riverside, CA. URBAN and CENTER CITY
0-
&
c
O
c
O
O
U
e
o
O
IW-i
fsa
VK
ow
HIS,
ON
003
OU2
0(M
OW-
DM '
Sample Date jmm/dd/yyyy}
Sample Date (mm/ddfyyyy)
i. Riverside, CA. URBAN and CENTER CITY
3!i 1CC«
Sample
j-cu* o"iH'»s
Figure AX3.10e-i.
Time series of 24-h average NOi concentrations at individual sites in
Riverside, CA from 2003 through 2006. A natural spline function
(with 9 degrees of freedom) was fit and overlaid to the data (dark
solid line).
August 2007
AX3-18
DRAFT-DO NOT CITE OR QUOTE
-------
1 imply that a site is free of local pollution influences, as evidenced by the still relatively high
2 values at rural sites compared to those found in remote areas of the globe. Rural sites can be
3 affected by nearby highways, power plants, and other sources.
4 In addition to the downward trend in annual mean concentrations of NC>2 shown in
5 Figures AX3.11 and AX3.12, hourly maximum concentrations have also declined, as evidenced
6 by a number of peak values above 250 ppb across the United States in 1988. In contrast only one
7 hourly maximum concentration above 250 ppb was found in 2004 (however, this may have been
8 a measurement artifact as it represented a one h spike that was many times the next highest
9 concentration at this site), and all other values were less than about 150 ppb.
10
11 AX3.2.4 Relationships Between NO2 and Other Pollutants
12 Determining the relationships between NC>2 and other pollutants is important for better
13 understanding the findings of time-series epidemological studies relating NO2 to mortality
14 (e.g., Burnett et al., 2004). Correlations between NO2 and CO, 63, and PM2.5 were calculated for
15 monitoring sites in Los Angeles and Riverside, CA; Chicago, IL; Washington, D.C.; and New
16 York City. Correlations were calculated using both hourly and 24-h average data with similar
17 results. The ranges of Pearson correlation coefficients between 24-h average NC>2 and Os, CO
18 and PM2.5 for 2000 through 2004 at monitoring sites in a few urban areas are shown in Table
19 AX3.4. As can be seen from the table, correlations of NO2 with O3 range from negative to
20 slightly positive; with CO they range from slightly negative to highly positive, and with PM2.5
21 they range from slightly to moderately positive. However, it should be noted that these
22 correlations are based on annual data from sites influenced by local sources. In general, there is
23 a strong seasonal variation in the correlations, r, with lowest values of r between NO2 and O3
24 found in winter.
25 In order to understand the relations between atmospheric species as shown in Table
26 AX3.4, an important distinction must be made between primary (directly emitted) species and
27 secondary (photochemically produced) species. In general, it is more likely that primary species
28 will be more highly correlated with each other, and that secondary species will be more highly
29 correlated with each other. By contrast, primary and secondary species are less likely to be
30 correlated with each other. Secondary reaction products tend to correlate with each other, but
August 2007 AX3-19 DRAFT-DO NOT CITE OR QUOTE
-------
e
r»
a.
o
«
E
»*rf
c
1
I
WM1J
0.05
004
fi (T?
0.02
0,01
**% A
128
-
of this line
' ' ^~*~^r^*-~- "Xv^
- f ; . . . • >- ^
1 -——*_,
1 0% of this line
83 90 SI §2 §3 94 §5 96 97 98 99 00 01 02
Figure AX3.11. Nationwide trends in annual mean NOi concentrations.
Source: U.S. Environmental Protection Agency (2003).
.030
.025
I, ,020
C
A
1 ,015
.1 .010
.COS
.MO
Rurstlsites
14
Suburban Sites 58
62 S3 64 85 B& S? 88 »„-• $0 91 92 8»3 94 95 y6 97 9© y§ 00 01
Year
Figure AX3.12. Trends in annual mean NOi concentrations by site type.
Source: U.S. Environmental Protection Agency (2003)
August 2007
AX3-20 DRAFT-DO NOT CITE OR QUOTE
-------
1 there is considerable variation. Some species (e.g., 63 and organic nitrates) are closely related
2 photochemically and correlate with each other strongly.
3 Although NC>2 is produced mainly by the reaction of directly emitted NO with Oj with
4 a small contribution from direct emissions, in practice, it behaves like a primary species. The
5 timescale for conversion of NO to NO2 is relatively rapid (~1 or 2 min for Os = 40 ppb and
6 ambient temperatures from 273 to 298 K), so NO and NO2 ambient concentrations rapidly
7 approach values determined by the photochemical steady state. The sum of NO and NO2 (NOX)
8 behaves like a typical primary species, while NO and NO2 reflect some additional complexity
9 based on photochemical interconversion. Chemical interactions among Os, NO and NO2 have
10 the effect of converting Os to NO2 and vice versa, which can result in a significant negative
1 1 correlation between O3 and NO2.
12 Most CO in urban air is emitted from motor vehicles and so is primary in origin. Os is a
13 secondary pollutant. Figures AX3.13a-d show seasonal plots of correlations between NO2 and
14 Os versus correlations between NO2 and CO. As can be seen from the figures, NO2 is positively
15 correlated with CO during all seasons at all sites. However, the sign of the correlation of NO2
16 with Os varies with season, ranging from negative during winter to slightly positive during
17 summer. There are at least two main factors contributing to the observed seasonal behavior.
18 Os and radicals correlated with it tend to be higher during the summer, thereby tending to
19 increase the NO2 to NO ratio according to the expression below (Equation AX3-2).
NO? _ kj(O3) + k?(HO7) + k,(RO2)
20 (AX3-2)
21 NOZ compounds formed from the oxidation of NOX are also expected to be correlated
22 with Os and increased photochemical activity. Because of interference of NOZ compounds with
23 the measurement of NO2 by conventional chemiluminescent monitors, they may also tend to
24 increase the correlation of NO2 with Os during the warmer months. However, there is not
25 enough information on the seasonal behavior in their concentrations to quantify the contribution
26 of NOZ compounds.
27 Relationships between Os, NO, and NO2 are shown in Figures AX3. 14 and AX3. 15.
28 Figure AX3. 14 shows daylight average concentrations based on data collected from November
29 1998 and 1999 at several sites in the United Kingdom representing a wide range of pollution
August 2007 AX3-21 DRAFT-DO NOT CITE OR QUOTE
-------
P>
O
*Csf
O-
w
O
CM
O ~
z
Winter
0.8-
0.6-
0,4-
0,2-
-0,8 -0.6 -0,4 -0.2 C
-0,2-
-0.4 -
-0,6-
-0,8 -
0.2 0,4 0.6 0.8
* /%*$
* **
NO2; CO
Spring
0.8-
0,6-
0.4-
0.2-
1 -0.8 -0.6 -0.4 -0.2 (
-0.2-
-0.4 •
•0.6-
-0,8-
^
'^ * A.***
i o^ 0.4 0.6 ie
cT
O •
«
O
«
0"
z
Summer
0.8-
0.6-
0.4-
0.2-
i i i i 0
1 -0.8 -0.6 -0.4 -0.2
-0.2 •
-0,4-
-0,6-
-0.8-
.« « »
«•* S
x i i » ^ •
) 0.2 0.4 0,6 0.8
NO2; CO
Fall
0.8-
0,8-
0,4-
0.2-
1 -0.8 -0,6 -0.4 -0,2 C
-0.2-
-0.4-
-0.6 -
-0.8-
^ ** \ *
} 0,2 0,4 "%X6 ^5.8
+ •
NO2: CO
NO2; CO
Figure AX3.13a-d. Correlations of NOi to Os vs. correlations of NOi to CO for Los
Angeles, CA (2001-2005).
4
5
6
conditions (open symbols). The solid lines represent calculations of photostat!onary state values
subject to the constraint that Ox = 31.1 + 0.104(NOX), where Ox = O3 + NO2. Note that Ox is
defined in the UK AQG report as oxidant, as used in this document, and in the latest AQCD for
Ozone and other Photochemical Oxidants (U.S. Environmental Protection Agency, 2006) it is
taken to refer to "odd oxygen" as defined in Section 2.2. The reason is that oxidants also include
PANs, peroxides, and reactive oxygen species in particles etc., in addition to Os and NO2. The
August 2007
AX3-22
DRAFT-DO NOT CITE OR QUOTE
-------
a)
100
100
200
300
400
500
60C
Figure AX3.14.
Relationship between Os, NO, and NOi as a function of NOX
concentration. Open circles represent data collected at a number of
sites in the United Kingdom. Lines represent calculated relationships
based on photostationary state.
Source: Clapp and Jenkin (2001).
b) 100
80
local contribution to oxidant
(NO,-dependent)
regional contribution to oxidant
(NO < -independent)
100 200 300 400
[N0,xl(ppb)
500 600
Figure AX3.15. Variation of odd oxygen (= Os + NOi) with NOX. The figure shows the
"regional" and the "local" contributions. Note that Ox refers to odd
oxygen in the document and the latest Os AQCD.
Source: Clapp and Jenkin (2001).
August 2007
AX3-23 DRAFT-DO NOT CITE OR QUOTE
-------
1 intercept of 63 with the y-axis at about 30 ppb is representative of background values of 63 in
2 the UK. The figure shows how Os decreases with increasing NO and NOX. NO constitutes more
3 than about 90% of NOX at high values of NOX as available O3 is titrated away.
4 Figure AX3.15 shows how the concentration of Ox (=03 + NO2) varies with that of NOX.
5 As in Figure AX3.14, Os intercepts the y-axis at about 30 ppb, corresponding to background Ox
6 which is composed almost exclusively of Os. Ox increases in a linear fashion, as given by the
7 regression relation above, as NOX increases. This relationship results from the emissions of NO2
8 (an oxidant and a component of odd oxygen) varying linearly with emissions of NOX, especially
9 after NO has reacted with Os to form NO2 as shown in Figure AX3.14. Thus the concentration
10 of Ox (and not Os, as is often stated) can be taken to be the sum of regional and local
11 contributions.
12 Figure AX3.15 shows that primary emissions from motor vehicles are major sources of
13 oxidant in the form of NO2, as evidenced by the high values of Ox at elevated NOX.
14
15 AX3.2.5 Abundance of NOy Species
16 Data for individual NOy species are much less abundant than for either oxides of nitrogen
17 or for total NOy. Data for NOy species are collected typically as part of research field studies,
18 e.g., the Southern Oxidant Study (SOS), Texas Air Quality Study (TexAQS I and TexAQS II) in
19 the United States. So this information is simply not available for a large number of areas in the
20 United States.
21
22 PANs
23 Organic nitrates consist of PAN, a number of higher-order species with photochemistry
24 similar to PAN (e.g., PPN), and species such as alkyl nitrates with somewhat different
25 photochemistry. These species are produced by a photochemical process very similar to that of
26 Os. Photochemical production is initiated by the reaction of primary and secondary VOCs with
27 OH radicals, the resulting organic radicals subsequently react with NO2 (producing PAN and
28 analogous species) or with NO (producing alkyl nitrates). The same sequence (with organic
29 radicals reacting with NO) leads to the formation of 03.
30 In addition, at warm temperatures, the concentration of PAN forms a photochemical
31 steady state with its radical precursors on a timescale of roughly 30 min. This steady state value
32 increases with the ambient concentration of Os (Sillman et al., 1990). Ozone and PAN may
August 2007 AX3-24 DRAFT-DO NOT CITE OR QUOTE
-------
1 show different seasonal cycles, because they are affected differently by temperature. Ambient
2 63 increases with temperature, driven in part by the photochemistry of PAN (see description in
3 Chapter 2). The atmospheric lifetime of PAN decreases rapidly with increasing temperature due
4 to thermal decomposition. Based on the above, the ratio of Os to PAN is expected to show
5 seasonal changes, with highest ratios in summer, although there is no evidence from
6 measurements. Measured ambient concentrations (Figures AX3.16a-d) show a strong nonlinear
7 association between Os and PAN, and between Os and other organic nitrates (Pippin et al., 2001;
8 Roberts et al., 1998). Moreover, the uncertainty in the relationship between 63 and PAN grows
9 as the level of PAN increases. Individual primary VOCs are generally highly correlated with
10 each other and with NOX (Figure AX3.17).
11 Measurements and models show that PAN in the United States includes major
12 contributions from both anthropogenic and biogenic VOC precursors (Horowitz et al., 1998;
13 Roberts et al., 1998). Measurements in Nashville during the 1999 summertime Southern
14 Oxidants Study (SOS) showed PPN and MPAN amounting to 14% and 25% of PANs,
15 respectively (Roberts et al., 2002). Measurements during the TexAQS 2000 study in Houston
16 indicated PAN concentrations of up to 6.5 ppbv (Roberts et al., 2003). PAN measurements in
17 southern California during the SCOS97-NARSTO study indicated peak concentrations of
18 5-10 ppbv, which can be contrasted to values of 60-70 ppbv measured back in 1960 (Grosjean,
19 2003). Vertical profiles measured from aircraft over the United States and off the Pacific coasts
20 typically show PAN concentrations above the boundary layer of only a few hundred pptv,
21 although there are significant enhancements associated with long-range transport of pollution
22 plumes from Asia (Kotchenruther et al., 2001a; Roberts et al., 2004).
23 Observed ratios of PAN to NO2 as a function of NOX at a site at Silwood Park, Ascot,
24 Berkshire, UK are shown in Figure AX3.18 United Kingdom Air Quality Expert Group (U.K.
25 AQEG, 2004). As can be seen there is a very strong inverse relation between the ratio and the
26 NOX concentration, indicating photochemical oxidation of NOX has occurred in aged air masses
27 and that PAN can make a significant contribution to measurements of NO2 especially at low
28 levels of NO2 (cf Section 2-8). It should be noted that these ratios will likely differ from those
29 found in the U.S. because of differences in the composition of precursor emissions, the higher
30 solar zenith angles found in the UK compared to the U.S., and different climactic conditions.
August 2007 AX3-25 DRAFT-DO NOT CITE OR QUOTE
-------
ti
12(7 » "-
v • > " f
100[ '- 4»r * -t> i ': t, •-
Jf?fr 1
| 20
c >C
S 120^ ,J I
o , .i*|!Y
100 , +, fe.^'1
so: jjfr
40 *
20;
JL! ' j i!
} J v *
' f
#r
F
IT4^' ^~*4--^
, , 1 \
PAM (pptv)
Figure AX3.16a-d. Measured O3 (ppbv) versus PAN (pptv) in Tennessee, including (a)
aircraft measurements, and (b, c, and d) suburban sites near
Nashville.
1
2
3
5
6
9
10
11
12
13
14
15
Source: Roberts etal. (1998).
Nevertheless, these results indicate the potential importance of interference from NOy
compounds in measurements
HONO
The ratio of HONO to NO2 as a function of NOX measured at a curbside site in a street
canyon in London, UK is shown in Figure AX3.19 (U.K. AQEG, 2004). The ratio is highly
variable, ranging from about 0.01 to 0.1, with a mean -0.05. As NO2 constitutes several percent
of motor vehicle emissions of NOX, the above implies that emissions of HONO represent a few
tenths of a percent of mobile NOX emissions. A similar range of ratios have been observed at
other urban sites in the United Kingdom (Lammel and Cape, 1996). The ratios of HONO to
NO2 shown in Figure AX3.19 indicate that HONO can make a measurable contribution to
measurements of NO2 (cf. Section 2-8). However, similar arguments about extrapolating the
use of UK data to the U.S. can be made for HONO as for PAN.
August 2007
AX3-26
DRAFT-DO NOT CITE OR QUOTE
-------
Figure AX3.17.
1.5-
i < 10 ppbv
o SO2> 10 ppbv
0.0
40 60 80 100 120
NOy {ppbv)
Relationship between benzene and NOy at a measurement site in
Boulder, CO. Instances with SOi >10 ppb are identified separately
(open circles), because these may reflect different emission sources.
Source: Goldanetal. (1995).
0,20
40 60
[NOX] (ppb}
80
100
Figure AX3.18. Ratios of PAN to NOi observed at Silwood Park, Ascot, Berkshire,
U.K. from July 24 to August 12 1999. Each data point represents a
measurement averaged over 30 minutes.
Source: UK AQEG (2004).
August 2007
AX3-27
DRAFT-DO NOT CITE OR QUOTE
-------
0,3 i - — —-
0,2
o
o
X 0.1 ^
200
400
600
{ppb)
800
1000
Figure AX3.19.
Ratios of HONO to NOi observed in a street canyon (Marylebone
Road) in London, U.K. from 11 a.m. to midnight during October
1999. Data points reflect 15-min average concentrations of HONO
and NO2.
Source: UK AQEG (2004).
1 HNO3 and NO 3
2 Elevated Os is generally accompanied by elevated HNOs, although the correlation is not
3 as strong as between Os and organic nitrates. Ozone is often associated with HNOs, because
4 they have the same precursor (NOX). However, HNOs can be produced in significant quantities
5 in winter, even when Os is low. The ratio between Os and HNOs also shows great variation in
6 air pollution events, with NOx-saturated environments having much lower ratios of Os to HNOs
7 (Ryerson et al., 2001). Aerosol nitrate is formed primarily by the combination of nitrate
8 (supplied by HNOs) with ammonia, and may be limited by the availability of either nitrate or
9 ammonia. Nitrate is expected to correlate loosely with O3 (see above), whereas ammonia is not
10 expected to correlate with Os.
11 Concentrations of particulate nitrate measured as part of the Environmental Protection
12 Agency's speciation network at several locations are shown in Figure AX3.20. Concentrations
13 shown are annual averages for 2003. Also shown are the estimated contributions from regional
14 and local sources. A concentration of 1 |ig/m3 corresponds to -0.40 ppb equivalent gas phase
15 concentration for MV. Thus, annual average parti culate nitrate can account for several ppb of
August 2007
AX3-28
DRAFT-DO NOT CITE OR QUOTE
-------
Nitrates
Figure AX3.20.
Fresno
Missoula
Salt Lake City
Tulsa
St. Louis
Birmingham
Indianapolis
Atlanta
Cleveland
Charlotte
Richmond
Baltimore
New York City
[3 Regional
Contribution
local
Contribution
10
12
Annual Average Concentration
of Nitrates, pg/m3
Concentrations of particulate nitrate measures as part of the
Environmental Protection Agency PA's speciation network. 1 ug/m3
-0.45 ppb equivalent gas phase concentration for NOs". (Note:
Regional concentrations are derived from the rural IMPROVE
monitoring network, http://vista.cira.colostate.edu/improve.
Source: U.S. Environmental Protection Agency (2004).
1 NOy, with the higher values in the West. There is a strong seasonal variation, which is
2 especially pronounced in western areas where there is extensive wood burning in the winter
3 resulting in a larger fractional contribution of local sources. Areas in the East where there are
4 topographic barriers might be expected to show higher fractional contributions from local
5 sources than other eastern areas that are influenced by regionally dispersed sources.
6 However, depending on the acidity of the particles, which in turn depends strongly on
7 their sulfate and ammonium contents, higher nitrate concentrations could be found in coarse
8 mode particles PMio-2.5 than in PM2.5 samples. The average nitrate content of PM2.5 and PMio is
9 typically about a percent in the eastern United States; and 15.7% and 4.5% in the western United
10 States (U.S. Environmental Protection Agency, 1996). These values suggest that most of the
11 nitrate was in the PM2.5 size fraction in the studies conducted in the western United States, but
12 nitrate in the studies in the eastern United States was mainly in the PMio-2.5 size fraction.
August 2007
AX3-29
DRAFT-DO NOT CITE OR QUOTE
-------
1 Nitro-PAHs
2 Nitro-PAHs (NPAHs) are widespread and found even in high altitude, relatively
3 unpolluted environments (Schauer et al., 2004) but there are differences in composition and
4 concentration profiles both within and between sites (rural vs. urban) as well as between and
5 within urban areas (Albinet et al., 2006; Soderstrom et al., 2005; Naumova et al., 2002, 2003),
6 with some differences in relative abundances of nitro- and oxo-PAHs also reported. Source
7 attribution has remained largely qualitative with respect to concentrations or mutagenicity (Eide
8 et al., 2002). The spatial and temporal concentration pattern for the NPAHs may differ from that
9 of the parent compounds (PAHs) because concentrations of the latter are dominated by direct
10 emission from local combustion sources. These emissions results in higher concentrations
11 during atmospheric conditions more typical of wintertime when mixing heights tend to be low.
12 The concentrations of secondary NPAHs are elevated under conditions that favor hydroxyl and
13 nitrate radical formation, i.e., during conditions more typical of summertime, and are enhanced
14 downwind of areas of high emission density of parent PAHs and show diurnal variation (Fraser
15 et al., 1998; Reisen and Arey, 2005; Kameda et al., 2004). Nitro-napthalene concentrations in
16 Los Angeles, CA varied between about 0.15 to almost 0.30 ng/m3 compared to 760 to
17 1500 ng/m3 for napthalene. Corresponding values for Riverside, CA were 0.012 to more than
18 0.30 ng/m3 for nitro-napthalene and 100 to 500 ng/m3 for napthalene. Nitro-pyrene
19 concentrations in LA varied between approximately 0.020 to 0.060 ng/m3 compared to 3.3 to
20 6.9 ng/m3 pyrene, whereas corresponding values for Riverside were 0.012 to 0.025 ng/m3 and 0.9
21 to 2.7 ng/m3.
22
23
24 AX3.3 METHODS FOR MEASURING PERSONAL AND INDOOR NO2
25 CONCENTRATIONS
26
27 AX3.3.1 Issues in Measuring Personal/Indoor NO2
28
29 Background
30 Nitrogen dioxide, a criteria air pollutant, has been sampled in ambient and indoor air
31 using active pumped systems both for continuous monitoring and collection onto adsorbents, and
32 by diffusive samplers of various designs, including badges and tubes. Nitrogen dioxide
33 concentrations in personal air have been typically measured using diffusive samplers because
August 2007 AX3-30 DRAFT-DO NOT CITE OR QUOTE
-------
1 they are: (1) small in size and light-weight, (2) unobtrusive and thus more readily used by study
2 participants, (3) comparatively easier to use and handle in field studies because they do not
3 require power (e.g., battery or extra electrical sources), (4) cost-effective, and (5) usable not only
4 for residential indoor and outdoor air sampling but also personal monitoring. However, diffusive
5 samplers usually have lower equivalent sampling rates than active methods and so require
6 relatively long sampling times (24 h or longer). Consequently, diffusive samplers including
7 those used for NC>2 monitoring provide integrated but not short-term concentration
8 measurements.
9 Both active and passive sampling methods can collect other gas-phase nitrogen oxide
10 species. However, semivolatile nitrogen oxide compounds require separation of the gas- and
11 particle-bound phases. This selective separation of gases from gas-particle matrices is
12 commonly done by means of diffusion denuders (Vogel, 2005), an approach also useful for
13 measuring other gas phase airborne contaminants such as SC>2 (Rosman et al., 2001).
14 Application of denuder sampling to personal exposure or indoor air monitoring has been
15 relatively limited.
16 Active air sampling with a pump can collect larger volumes of air and thus detect the
17 lower concentrations found in community environments within relatively short time periods.
18 Automated active sampling methods have been the preferred method used to monitor NO2
19 continuously at ambient sites for environmental regulation compliance purposes. However,
20 practical considerations impede the use of these continuous monitors in residential air and
21 exposure monitoring studies. Small, low flow active samplers using battery-operated pumps
22 have been used instead, however, there are only a few such studies.
23 The first passive sampling devices for NO2 were intended for occupational exposure
24 monitoring, but were later adapted for environmental monitoring purposes. Since this sampler,
25 the Palmes tubes (Palmes et al., 1976), was first developed, other tube, badge-type (Yanagisawa
26 and Nishimura, 1982) and radial (Cocheo et al., 1996) diffusive samplers have been employed as
27 monitors in exposure studies worldwide. The theories behind and applications of Palmes Tubes
28 and Yanagisawa badges have been described in the last AQCD for Oxides of Nitrogen (U.S.
29 Environmental Protection Agency, 1993). There are currently several commercially available
30 samplers (e.g., Ogawa, Radiello®, Analyst™) which are modifications of the original Palmes
31 tube design. Most modifications are directed at reducing effects related to meteorological
August 2007 AX3-31 DRAFT-DO NOT CITE OR QUOTE
-------
1 conditions (e.g., insufficient or too high a wind speed, humidity, temperature), increasing the
2 sampling uptake rate, and improving analytical sensitivity.
3
4 Active (Pumped) Sampling
5 Nitrogen dioxide measurement by active pumping systems as part of continuous monitors
6 has been widely employed for ambient air monitoring as these instruments require relatively
7 little maintenance; however they have been used less frequently for indoor sampling. Devices
8 needing a pump to draw air can measure average concentrations of pollutants over short time
9 periods, but are not generally suitable for measuring personal exposures because they are heavy
10 and large. Some exposure studies employed this approach for active sampling with stationary
11 chemiluminescent analyzers or portable monitors to measure nitrogen dioxide levels in
12 residential indoor air (Mourgeon et al., 1997; Levesque et al., 2000; Chau et al., 2002).
13 Recently, Staimer and his colleagues (2005) evaluated a miniaturized active sampler, suitable for
14 personal exposure monitoring, to estimate the daily exposure of pediatric asthmatics to nitrogen
15 dioxide, and reported that this small active sampling system is useful for this purpose in
16 environmental exposure epidemiology studies where daily measurements are desired.
17
18 Passive (Diffusive) Sampling
19 Passive samplers are based on the well known diffusion principle described by Pick's law
20 (Krupa and Legge, 2000). A convenient formulation of this law that can be easily related to
21 sampler design considerations is:
22
23 where:
24 J = flux (mg/s)
25 D = diffusion coefficient in air (cm2/s)
26 A = diffusion cross-sectional area of the sampler (cm2)
27 L = diffusion path length from the inlet to sorbent (cm),
28 Cair = concentration of analyte in air (mg/cm3)
29 Csor = concentration of analyte at the sorbent (mg/cm3)
30
J — U (A /L)( L, ajr- C ,5Or} (AX3 -3 )
August 2007 AX3-32 DRAFT-DO NOT CITE OR QUOTE
-------
1 The term D(A/L) can be related to the uptake or sampling rate (cmVs) which is
2 conceptually analogous to the sampling rate in an active monitor. Once the amount of analyte in
3 the passive sampler sorbent is determined, the concentration in air (Ca;r) can be calculated as:
4 Concentration(mg/cm3) = M'(mg)/D(A/L)(cm3/$)/t(sec) (AX3-4)
5 where:
6 M = mass of analyte collected in the sorbent
7 t = sampling time
8
9 Pick's law strictly applies only under ideal, steady state conditions assuming that the
10 sorbent is a perfect sink. However, there can be deviations between the theoretical sampling rate
11 for a given analyte and the actual rate depending on sampling conditions. It is also clear that
12 sampling rate can be optimized by modifying the geometry of the diffusive sampler, either by
13 reducing L, increasing A or a suitable combination. However, the impact of deviations from
14 ideality on actual sampling rate due to geometry also poses a limit to the extent of possible
15 modifications. Thus, passive samplers, either diffusive or permeation, are prepared as tubes or
16 badges. These two main designs are the basis for all further modifications which, as indicated
17 above, have been made in order to improve efficiency, reduce sensitivity to wind turbulence of
18 the samplers, and to simplify analyte desorption. Tube-type samplers are characterized by a
19 long, axial diffusion length, and a low cross-sectional area; this results in relatively low sampling
20 rates (Namiesnik et al., 2005). Badge-type samplers have a shorter diffusion path length and a
21 greater cross-sectional area which results in uptake rates that are typically higher than diffusion
22 tubes (Namiesnik et al., 2005) but the sampling rate may be more variable because it is more
23 affected by turbulence. Physical characteristics of these two fundamental passive sampler types,
24 tube-type and badge-type, are summarized and provided in Table AX3.5. Performance
25 characteristics are presented in Table AX3.6.
26 The sorbent can be either physically sorptive or chemisorptive; passive samplers for NC>2
27 are chemisorptive, that is, a reagent coated on a support (e.g., metal mesh, filter) reacts with the
28 NC>2. The sorbent is extracted and analyzed for one or more reactive derivatives; the mass of
29 NC>2 collected is derived from the concentration of the derivative(s) based on the stoichiometry
30 of the reaction. Thus, an additional approach to reducing detection limits associated with passive
August 2007 AX3-33 DRAFT-DO NOT CITE OR QUOTE
-------
1 samplers is to modify the chemisorptive reaction and the extraction and analysis methods to
2 increase analytical sensitivity. However, although chemisorption is less prone to the back
3 diffusion phenomenon of sorptive-only methods, analyte losses could occur due to interferences
4 from other pollutants that also react with the sorbent or the derivatives. The most commonly
5 used NC>2 passive samplers rely on the classical reaction with triethanolamine (TEA). TEA
6 requires hydration for quantitative NC>2 sampling (i.e., 1:1 conversion to nitrite) and the reaction
7 products have been subject to a number of investigations and several have been reported,
8 including TEA-nitrate and nitrite, triethanolammonium nitrate, nitrosodiethanolamine, and
9 triethanolamine N-oxide (Glasius et al., 1999). Known interferences include HONO, PAN, and
10 nitric acid (Gair et al., 1991.).
11 The tube-type passive samplers (Palmes tubes) require week-long sampling periods and
12 have been extensively used for residential indoor/outdoor measurements, mostly for exploring
13 the relationship between indoor and outdoor levels (Cyrys et al., 2000; Raw et al., 2004; Simoni
14 et al., 2004; Janssen et al., 2001). Passive diffusion tubes have also been widely used for
15 measurements of NC>2 in ambient air (Gonzales et al., 2005; Gauderman et al., 2005; Da Silva
16 et al., 2006; Lewne et al., 2004; Stevenson et al., 2001; Glasius et al., 1999). Personal exposure
17 studies have also been conducted using the Palmes tubes (Mukala et al., 1996; Kousa et al.,
18 2001). Some of these studies evaluated passive sampler performance by collocating them with
19 chemiluminescence analyzers during at least some portion of the field studies (Gair et al., 1991;
20 Gair and Penkett, 1995; Plaisance et al., 2004; Kirby et al., 2001). The majority of these studies
21 indicate that these samplers have very good precision (generally within 5%) but tend to
22 overestimate NO2 by 10 to 30%. However, there has not been a methodical evaluation of
23 variables contributing to variance for the range of samplers available when used in field
24 conditions. Thus, it is not clear if the bias is due to deviations from ideal sampling conditions
25 that can affect actual sampling rates, contributions from co-reacting contaminants or, most
26 probably, a combination of these variables.
27 A badge-type sampler was introduced by Yanagisawa and Nishimura (1982) to overcome
28 the long sampling time required by Palmes tubes. Since then, these sensitive NO2 short path
29 length samplers (Toyo Roshi Ltd) have been optimized and evaluated for indoor air and for
30 personal monitoring (Lee et al., 1993a,b). They have been used extensively for personal
31 exposure studies (Ramirez-Aguilar et al., 2002; Yanagisawa et al., 1986; Berglund et al., 1994,
August 2007 AX3-34 DRAFT-DO NOT CITE OR QUOTE
-------
1 Lee et al., 2004) and indoor air measurements (Kodoma et al., 2002; Bae et al., 2004; Algar
2 et al., 2004; Shima and Adachi, 2000; Smedje, et al., 1997) and to a more limited amount for
3 ambient monitoring (Tashiro and Taniyama, 2002; Levy et al., 2006; Norris and Larson, 1999).
4 Due to the greater uptake rate resulting from the larger cross sectional area of the badges and
5 shorter diffusion length compared to the tube-type samplers, sampling times can be decreased
6 from one-week to one-day for typical environmental air concentrations. This makes diffusive
7 filter-badges more suitable for shorter-term sampling while long-term ambient monitoring can
8 still be conducted using the Palmes-tubes.
9
10 Tube Type Samplers
11 Gradko Sampler (http://www.gradko.co.uk)
12 The Gradko sampler is based on the Palmes tube design (Gerboles et al., 2006b).
13 It collects O3 or NO2 by molecular diffusion along an inert tube by chemisorption. A stable
14 complex is formed with triethanolamine coated on a stainless steel screen in the tube. The
15 complex is spectroscopically analyzed by adding an azo die (Chao and Law, 2000). The sampler
16 has a detection limit of 0.5 ppb for NO/NO2 and the precision of ± 6% above 5 ppb levels when
17 used for two weeks (Table AX3.6). This sampler has been used to measure personal exposures,
18 concentrations of residential air indoors such as in the kitchen and bedroom, and concentrations
19 of outdoor air (Chao and Law, 2000; Gallelli et al., 2002; Lai et al., 2004). It has been used to
20 measure ambient NO2 levels in Southern California as a marker of traffic-related pollution in San
21 Diego County (Ross et al., 2006).
22
23 Passam Sampler (http://www.passam.ch)
24 This sampler is also based on the design of the Palmes tube (Palmes et al., 1976).
25 It collects NO2 by molecular diffusion along an inert polypropylene tube to an absorbent,
26 triethanolamine. The collected NO2 is determined spectrophotometrically by the well-
27 established Saltzmann method. When used outdoors the samplers are placed in a special shelter
28 to protect them from rain and minimize wind turbulence effects. The Passam sampler is sold in
29 two different models, one for long-term and one for short-term sampling.
August 2007 AX3-35 DRAFT-DO NOT CITE OR QUOTE
-------
1 Analyst™Sampler (http://www.monitoreurope.com)
2 The Analyst™ sampler is also a modification of the open-Palmes-tube design and was
3 developed by the Italian National Research Council (CNR - Institute Inquinamento
4 Atmosferico) in 2000 (Bertoni et al., 2001). The Analyst™ consists of a glass vessel, which
5 contains a reactant supported on a stainless steel grid. It is suitable for long-term monitoring
6 (typically one month) of oxides of nitrogen, sulfur dioxide, and volatile organic compounds in
7 ambient air. The target compound is analyzed by gas chromatography with minimum detection
8 limit of 0.1 mg/m3 (-52 ppb) for a twelve-week sample duration, and has relatively high
9 precision. The Analyst™ method development (De Santis et al., 1997, 2002) and actual field
10 application (De Santis et al., 2004) have been described. The primary use for Analyst™ is as a
11 reliable tool for long-term determination of concentration in indoor as well as outdoor
12 environments (Bertoni et al., 2001) and as a screening tool for ambient monitoring to identify
13 pollution "hot spots" (De Santis et al., 2004).
14
15 Badge-Types Samplers
16 Ogawa Passive Sampler (http://www.ogawausa.com)
17 This sampler is a double face badge that can monitor NO, NOX, and NC>2. The design can
18 be used also for the determination of 862, 63, and NH3 levels in air. The manufacturer-reported
19 detection limits for nitrogen oxides are 2.3 ppb and 0.32 ppb for 24-h and 168-h sampling,
20 respectively. Reported actual sampling rates for NO2 are two to three times higher than the
21 manufacturer's values. The normal operation ranges are 0 to 25 ppm for 24-h exposure and 0 to
22 3.6 ppm for 168-h exposure. The manufacturer recommends a sampling height of 2.5 meters and
23 storage time of up to 1 year when kept frozen. Ogawa passive samplers have been extensively
24 used for human exposure studies to measure personal air concentrations and (or) indoor/outdoor
25 levels for residents in a number of locations, including adults of Richmond, Virginia (Zipprich
26 et al., 2002), children of Santiago, Chile (Rojas-Bracho et al., 2002), office workers of Paris,
27 France (Mosqueron et al., 2002), and cardiac compromised individuals of Toronto, Canada (Kim
28 et al., 2006). The samplers have been used also in air monitoring networks to assess traffic-
29 related pollutant exposure (Singer et al., 2004), as well as to evaluate spatial variability of
30 nitrogen dioxide ambient concentrations in Montreal, Canada (Gilbert et al., 2005).
August 2007 AX3-36 DRAFT-DO NOT CITE OR QUOTE
-------
1 IVL Sampler (http//www.ivl.se/en/business/monitoring/diffusive_samplers.asp)
2 The IVL method development has been described in detail by Perm and Svanberg (1998).
3 It was developed by Swedish Environmental Research Institute in the mid of 1980s (Sjodin et al.,
4 1996), is designed to minimize turbulent wind effects outdoors as well as "starvation effects"
5 indoors (i.e., very low face velocities), interferences from within sampling tube chemistry,
6 temperature and humidity effects, and artifacts and losses during post-sampling storage.
7 Manufacturer-reported detection limits for this sampler with sampling times of ~1 month are
8 0.1 |ig/m3 (0.05 ppb) for NO2, and 0.5 |ig/m3 (0.42 ppb) for NO, respectively. Due to its long
9 sampling time, this sampler has been extensively used for NO2 background monitoring in
10 ambient air of rural or urban (Fagundez et al., 2001; Sjodin et al., 1996; Pleijel et al., 2004).
11
12 Willems Badge Sampler
13 The Willems badge, a short-term diffusion sampler, was developed at the University of
14 Wageningen, Netherlands, originally for airborne ammonia measurements and later for
15 measuring NO2 (Hagenbjork-Gustafsson et al., 1996). It consists of a cylinder of polystyrene
16 with a Whatman GF-A glass fiber filter impregnated with triethanolamine at its based held in
17 place by a 6 mm distance ring. A Teflon filter is placed on the 6 mm polystyrene ring, which is
18 secured with a polystyrene ring of 3 mm (Hagenbjork-Gustafsson et al., 1996). The badge is
19 closed by a polyethylene cap to limit influences by air turbulence. The diffusion length in the
20 badge is 6 mm. This sampler was evaluated for ambient air measurements in laboratory and
21 field tests (Hagenbjork-Gustafsson et al., 1999). It has a manufacturer's reported detection limit
22 of 2 |ig/m3 (~1 ppb) for 48 h sampling duration. When used for personal sampling in an
23 occupational setting with a minimum wind velocity of 0.3 m/s, detection limits of 18 (-9.4 ppb)
24 and 2 |ig/m3 (~1 ppb) for 1-h and 8-h sampling, respectively, have been reported (Hagenbjork-
25 Gustafsson et al., 2002, Glas et al., 2004).
26
27 Radial Sampler Types
28 Radiello® -the radial diffusive sampler (http://www.radiello.com)
29 Radiello® samplers use radial diffusion over a microporous cylinder into an absorbing
30 inner cylinder, instead of axial diffusion, which increases the uptake rate by a factor of about
31 100 (Hertel et al., 2001). Nitrogen dioxide is chemiadsorbed onto triethanolamine as nitrite,
32 which is quantified by visible spectrometry. Sample collection of up to 15 days is feasible but
August 2007 AX3-37 DRAFT-DO NOT CITE OR QUOTE
-------
1 relative humidity higher than 70% can cause interferences when used for extended periods of
2 more than 7 days. The manufacturer-reported typical sampling rate for nitrogen dioxide
3 sampling is 75 ± 3.72 ml/min at temperatures between -10 and 40 °C. The rate can vary with
4 humidity in the range of 15 to 90% and wind speed between 0.1 and 10 m/s (Radiello® Manual,
5 2006). A Danish study (S0rensen et al., 2005) recruited 30 subjects during each of four seasons
6 in Copenhagen, and measured the subjects' personal exposures, home indoor/front door air
7 concentrations during 2-day periods with this sampler.
8
9 EMD (Ecole des Mines de Douai) Sampler
10 A new high-uptake rate diffusive sampler has been recently developed by the Ecole
11 des Mines de Douai (EMD) laboratory (Piechocki-Minguy et al., 2003) and evaluated in the
12 laboratory and field for measurement of NC>2 levels in ambient air. It is composed of a porous
13 cartridge impregnated with triethanolamine and fitted in a cylindrical protective box equipped
14 with caps at its extremities (Piechocki-Minguy et al., 2006). The large sampling area (cartridge
15 surface) and the two circular openings provide a high uptake rate (exceeding 50 cmVmin). The
16 sampling rate was reported to be on average 0.89 cm3/s for indoor sampling and 1.00 cm3/s for
17 outdoor sampling. Detection limits were determined to be 11 |ig/m3 (-5.8 ppb) for 1-h
18 measurement. The sampling rate was not significantly influenced by wind at speeds higher than
19 0.3 m/s (Piechocki-Minguy et al., 2003). This sampler has been used in France to assess
20 personal exposures in a series of microenvironments (home, other indoor places, transport and
21 outdoor) for two 24-h time periods (weekday and weekend) (Piechocki-Minguy et al., 2006).
22
23 NO2 Measurements in Epidemological Studies
24 Since passive samplers are the most frequently used monitoring method in epidemiology
25 studies of NO2 effects, their performance compared to the long established chemiluminescence
26 monitoring method is critical for determining the contribution of measurement error to exposure
27 estimates. First, most passive samplers developed and used for personal and indoor exposure
28 studies need to be employed for at least 24 h to collect sufficient NO2 to be detected. Therefore,
29 the majority of measurements of personal exposure concentrations done to date represents daily
30 or longer integrated or average exposure and cannot be used to assess acute, peak exposure
31 concentrations. Some newer passive samplers for nitrogen dioxide have higher uptake rates and
32 active pump samplers with traditional battery operated sampling pumps and appropriate
August 2007 AX3-38 DRAFT-DO NOT CITE OR QUOTE
-------
1 adsorbents can collect sufficient NO2 in approximately one h and have been used in a few studies
2 providing information on exposure in microenvironments and shorter term exposure
3 concentration. Hourly fluctuations in nitrogen dioxide concentrations may be important to the
4 evaluation of exposure-health effects relationship, so continuous monitors, such as those used at
5 central site monitoring stations are still the only approach for estimating short-term exposures.
6 Second, interferences for other nitrogen oxide species can contribute to NC>2 exposure
7 monitoring errors. Both the chemiluminescence analyzer and passive samplers experience these
8 interferences but the kinetics and stoichiometry of interferent compound reactions have not been
9 well established, especially for the passive samplers. As indicated earlier, TEA-based diffusive
10 sampling methods tend to overestimate NO2 concentrations in field comparisons with
11 chemiluminescence analyzers. This could be in part the result of chemical reactions between
12 ozone and nitric oxide (NO) within the diffusion tube, leading to as much as an overestimate up
13 to 30%, or differential sensitivity to other nitrogen oxides between the passive and active
14 samplers. Due to spatially and temporally variability of NO and NO2 concentrations, especially
15 at roadsides where nitric oxide concentrations are relatively high and when sufficient ozone is
16 present for interconversion between the species, lack of agreement between the passive sampler
17 and central continuous monitor can represent differences in sampler response (Heal et al., 1999,
18 Cox, 2003). In the U.K., an alternative nitrogen dioxide monitoring plan using cost-effective and
19 simpler tube-type passive sampler has been proposed and implemented countrywide. However,
20 careful investigation of nitrogen dioxide levels revealed an overestimation, around 30% by the
21 passive sampler (Campbell et al., 1994). Another evaluation study (Bush et al., 2001) showed
22 that the overall average NO2 concentrations calculated from diffusion tube measurements were
23 likely to be within 10% of chemiluminescent measurement data.
24 Third, the effect of environmental conditions (e.g., temperature, wind speed, and
25 humidity) on the performance of passive samplers is still a concern when using it for residential
26 indoor, outdoor, and personal exposure studies, because of sampling rates that deviate from ideal
27 and can vary through the sampling period. Overall, field test results of passive sampler
28 performance are not consistent and they have not been extensively studied over a wide range of
29 concentrations, wind velocities, temperatures and relative humidities (Varshney and Singh,
30 2003). Therefore, studies directed at investigating the contributions from environmental
31 conditions to the performance of diffusive samplers in multiple locations need to be undertaken.
August 2007 AX3-39 DRAFT-DO NOT CITE OR QUOTE
-------
1 AX3.4 NITROGEN OXIDES IN INDOOR AIR
2
3 AX3.4.1 Indoor Sources and Concentrations of Nitrogen Oxides
4 Penetration of outdoor NO2 and combustion in various forms are the major sources of
5 NO2 to indoor environments. These environments include homes, schools, restaurants, theaters
6 etc. As might be expected, indoor concentrations of NO2 in the absence of combustion sources
7 are determined by the infiltration of outdoor NO2 (Spengler et al., 1994; Weschler et al., 1994;
8 Levy et al., 1998a), with a much smaller contribution from chemical reactions in indoor air.
9 Indoor sources of nitrogen oxides have been characterized in several reviews, namely the last
10 AQCD for Oxides of Nitrogen (U.S. Environmental Protection Agency, 1993); the Review of the
11 Health Risks Associated with Nitrogen Dioxide and Sulfur Dioxide in Indoor Air for Health
12 Canada (Brauer et al., 2002); and the Staff Recommendations for revision of the NO2 Standard in
13 California (CARB, 2006). Mechanisms by which nitrogen oxides are produced in the
14 combustion zones of indoor sources were reviewed in the last AQCD for Oxides of Nitrogen
15 (U.S. Environmental Protection Agency, 1993) and will not be repeated here. Sources of
16 ambient NO2 are reviewed in Chapter 2 of this document. It should also be noted that indoor
17 sources can affect ambient NO2 levels, particularly in areas in which atmospheric mixing is
18 limited.
19 Because most people spend most of their time indoors, personal exposure is primarily
20 determined by indoor air quality as shown in Figure AX3.21. Ideally, exposure to NO2 should
21 be cumulated over all indoor environments in which an individual spends time. These indoor
22 environments may include homes, schools, offices, restaurants, theaters, ice skating rinks, stores,
23 etc. However, in a study by Leaderer et al. that used two-week integrated measures,
24 concentrations of NO2 inside the home accounted for 80% of the variance in total personal
25 exposure, indicating that home concentrations are a reasonable proxy for personal exposure
26 (Leaderer etal., 1986).
27
28 Homes
29 Combustion of fossil and biomass fuels produce nitrogen oxides and the importance of
30 such sources for determining human exposures depends on how emissions are allowed to mix
31 into living areas and whether emissions are vented to the outdoors or not. Combustion of fossil
August 2007 AX3-40 DRAFT-DO NOT CITE OR QUOTE
-------
NHAPS - Nation, Percentage Time Spent
Total n = 9,W6
IN A RESIDENCE 16S.7%}
TOTAL TIME SPENT
INDOORS 2 emissions. Depending on geographical
location, season, other sources, length of monitoring period, and household characteristics,
homes with gas cooking appliances have approximately 50% to over 400% higher NC>2
concentrations than homes with electric cooking appliances (Gilbert et al., 2006; Lee et al., 2002;
Lee et al., 2000; Garcia-Algar et al., 2004; Raw et al., 2004; Leaderer et al., 1986; Garcia-Algar,
2003). Gas cooking appliances remain significantly associated with indoor NC>2 concentrations
August 2007
AX3-41
DRAFT-DO NOT CITE OR QUOTE
-------
1 after adjusting for several potential confounders including season, type of community,
2 socioeconomic status, use of extractor fans, household smoking, and type of heating
3 (Garcia-Algar et al., 2004; Garrett, 1999).
4 Gas appliances with pilot lights emit more NC>2 than gas appliances with electronic
5 ignition. Spengler et al. (1994) found that NC>2 concentrations in bedrooms of homes with a gas
6 range without a pilot light averaged 4 ppb higher than in homes with an electric range, but were
7 15 ppb higher in homes with gas ranges with pilot lights. Lee et al. (1998) found somewhat
8 larger differences in NC>2 concentrations in homes in the Boston area, with minor seasonal
9 variation. Homes with gas stoves without pilot lights averaged between 11 ppb (summer) and
10 18 ppb (fall) higher than homes with electric stoves, while those with pilot lights averaged
11 between 19 ppb (summer) and 27 ppb (fall) higher than electric stove homes.
12 Use of extractor fans reduces NO2 concentrations in homes with gas cooking appliances
13 (Gallelli et al., 2002; Garcia-Algar et al., 2003), although absolute NO2 levels tend to remain
14 higher than in homes with electric stoves. In a multivariate analysis, Garcia-Algar et al. (2004)
15 found that having a gas cooker remained significantly increased NC>2 concentrations even after
16 adjusting for extractor fan use. Raw et al. (2004) found only a small effect of extraction fan use
17 on NC>2 levels in the bedroom in gas cooker homes. Among homes with gas cooking, geometric
18 mean bedroom NC>2 levels were 1.7 ppb lower in homes with an extractor fan than in homes
19 without one. As expected, among homes with no fossil fuel cooking, there were no differences
20 in mean bedroom levels of NC>2 in homes with and without extractor fans.
21
22 Other Combustion Sources
23 Secondary heating appliances are additional sources of NC>2 in indoor environments,
24 particularly if they are unvented or inadequately vented. As heating costs increase, the use of
25 these secondary heating appliances tends to increase. From 1988 to 1994, an estimated
26 13.7 million homes used unvented heating appliances, with disproportionately higher usage rates
27 among southern, rural, low-income, and African-American homes (Slack and Heumann, 1997).
28 Of the 83.1 million households using gas stoves or ovens for cooking, 7.7 million (9.3%) also
29 used the stove for heating (Slack and Heumann, 1997).
30 Gas heaters, particularly when unvented or inadequately vented, produce high levels of
31 NC>2. Kodoma et al. (2002) examined the associations between secondary heating sources and
32 NC>2 concentrations measured over a 48-h exposure period in the living rooms of homes in
August 2007 AX3-42 DRAFT-DO NOT CITE OR QUOTE
-------
1 Tokyo, Japan. They found much higher NC>2 concentrations during February 1998 and January
2 1999 in homes with kerosene heaters in both southern (152.6 ppb and 139.7 ppb for 1998 and
3 1999, respectively) and northern (102.4 and 93.1 ppb for 1998 and 1999, respectively) areas of
4 Tokyo compared to homes with electric heaters (30.8 and 31.1 for the southern and 37.2 and
5 31.6 for northern areas, 1998 and 1999, respectively).
6 In a study by Garrett et al. (1999) of 78 homes in Latrobe Valley, Australia, the two
7 highest indoor NC>2 levels recorded in the study were 129 ppb for the only home with an
8 unvented gas heater and 69 ppb for a home with a vented gas heater. Levels of NO2 in the
9 kitchens and living rooms of homes with a vented gas heater (mean = 6.9 ppb in living room,
10 7.3 ppb in kitchen, n = 15) were comparable to homes with gas stoves (mean = 6.7 ppb in living
11 room, 8.0 ppb in kitchen, n = 15) (Table AX3.7). These concentrations include results from all
12 seasons combined, so the levels are somewhat lower than those found by Triche et al. (2005) for
13 winter monitoring periods only.
14 Triche et al. (2005) also found high levels of NO2 in homes with gas space heaters,
15 although information on whether the appliance was vented or unvented was not available. Data
16 from this study were analyzed in more detail and are shown in Table AX3.8. The median NC>2
17 concentration in the 6 homes with gas space heater use during monitoring periods with no gas
18 stove use was 15.3 ppb; a similar incremental increase in total NC>2 levels was noted for homes
19 with gas space heater use during periods when gas stoves were also used (Median = 36.6 ppb)
20 compared to homes where gas stoves were used but no secondary heating sources were present
21 (Median = 22.7 ppb) (Table AX3.8).
22 Shima and Adachi (1998) examined associations between household characteristics,
23 outdoor NC>2, and indoor NC>2 in 950 homes during the heating season (640 with unvented and
24 310 vented heaters) and 905 homes during the non-heating season in urban, suburban, and rural
25 areas of Japan. While no information is provided on gas stove use, the authors note that nearly
26 all homes in Japan have gas stoves, though relatively few have pilot lights. During the heating
27 season, geometric mean NC>2 levels in homes with unvented heaters (66.4 ppb) are about three
28 times higher than in homes with vented heaters (20.6 ppb). In the non-heating season, the mean
29 levels were lower at only 13.8 ppb, suggesting a contribution from vented heaters as well.
August 2007 AX3-43 DRAFT-DO NOT CITE OR QUOTE
-------
1 In multivariate analyses, Gilbert et al. (2006) found that gas and mixed/other heating
2 systems were significantly associated with NO2 levels, adjusting for presence of gas stoves and
3 air exchange rates in 96 homes in Quebec City, Canada during the winter/early spring period.
4 Many homes with gas space heaters also have gas stoves, and the contribution from multiple
5 sources is much higher than from any single source alone (Garrett et al., 1999). In the Garrett
6 et al. (1999) study, homes were classified into five categories: no indoor source (n = 15), gas
7 stove only (n = 15), gas heater only (n = 14), smoker in the household only (n = 7), and multiple
8 sources (n = 29). Homes with multiple sources had much higher NC>2 concentrations homes with
9 either a gas stove only or gas heater only (Table AX3.9).
10 Kerosene heaters are also important contributors to indoor NC>2 levels. Leaderer et al.
11 (1986) enrolled a cohort of kerosene heater users identified from local kerosene dealers and a
12 cohort of controls systematically chosen from the same neighborhoods with each matched pair
13 treated as a sampling unit (i.e., sampled at the same randomly assigned time period). A total of
14 302 homes were monitored for at least one two-week period. While outdoor concentrations
15 never exceeded 100 |ig/m3 (53 ppb), approximately 5% of homes with either no gas but
16 1 kerosene heater or gas but no kerosene heater had levels exceeding 53 ppb. Between
17 17%-33% of homes with both gas and kerosene heater(s) exceeded this limit, while nearly one
18 quarter of homes with no gas, but two or more kerosene heaters had these levels.
19 Data from Triche et al. (2005) (Table AX3.8) also indicated increased levels of NO2 for
20 kerosene heater homes during monitoring periods with no gas stove use (Median = 18.9 ppb)
21 compared to homes with no sources (Median = 6.3 ppb), which is similar to levels found in
22 homes using gas space heaters (Median =15.3 ppb). However, these NC>2 concentrations are of
23 the same magnitude as those in homes with gas stove use (Median = 17.2 ppb).
24 Data are available for unvented gas hot water heaters from a number of studies conducted
25 in the Netherlands. Results summarized by Brauer et al. (2002) indicate that concentrations of
26 NC>2 in homes with unvented gas hot water heaters were 10 to 21 ppb higher than in homes with
27 vented heaters, which in turn, had NC>2 concentrations 7.5 to 38 ppb higher than homes without
28 gas hot water heaters.
29 The contribution from combustion of biomass fuels has not been studied as extensively as
30 that from gas. A main conclusion from the previous AQCD was that properly vented wood
31 stoves and fireplaces would make only minor contributions to indoor NC>2 levels. Several studies
August 2007 AX3-44 DRAFT-DO NOT CITE OR QUOTE
-------
1 conclude that use of wood burning appliances does not increase indoor NO2 concentrations.
2 Levesque et al. (2001) examined the effects of wood-burning appliances on indoor NO2
3 concentrations in 49 homes in Quebec City, Canada. The homes, which had no other
4 combustion source, were sampled for 24 h while the wood-burning appliance was being used.
5 No significant differences in mean NC>2 levels were found in homes with (6.6 + 3.6 ppb) and
6 without (8.8 + 1.9 ppb) a wood-burning appliance. Data from Triche et al. (2005) confirm these
7 findings (Table AX3.8). Homes with wood burning sources had comparable NC>2 concentrations
8 to homes without other secondary heating sources, with (Median = 5.9 ppb) and without (Median
9 =16.7 ppb) gas stove use.
10 Table AX3.9 shows short-term average (minutes to a few hours) concentrations of NC>2 in
11 homes with combustion sources. The concentrations represent those found in different rooms in
12 houses sampled. However, concentrations are much higher in those persons directly exposed to
13 emissions. For example, Dennekamp et al. (2001) found NC>2 concentrations of about 1 ppm at
14 face level in front of a 4-burner gas range. Table AX3.10 shows long-term average (24-h to
15 2 week) concentrations of NC>2 in homes with combustion sources (mainly gas fired).
16 Data are available for unvented gas hot water heaters from a number of studies conducted
17 in the Netherlands. Results summarized by Brauer et al. (2002) indicate that concentrations of
18 NC>2 in homes with unvented gas hot water heaters were 10 to 21 ppb higher than in homes with
19 vented heaters, which in turn, had NC>2 concentrations 7.5 to 38 ppb higher than homes without
20 gas hot water heaters.
21 As can be seen from the tables, shorter-term average concentrations tend to be much
22 higher than longer term averages. However, as Triche et al. (2005) point out, the 90th percentile
23 concentrations can be substantially greater than the medians, even for two week long samples.
24 This finding illustrates the high variability found among homes. This variability reflects
25 differences in ventilation of emissions from sources, air exchange rates, the size of rooms etc.
26 The concentrations for short averaging periods that are listed in Table AX3.9 correspond to
27 about 10 to 30 ppb on a 24-h average basis. As can be seen from inspection of Table AX3.10,
28 these sources would contribute significantly to the longer term averages reported there if
29 operated on a similar schedule on a daily basis. This implies that measurements made with long
30 averaging periods may not capture the nature of the diurnal pattern of indoor concentrations in
August 2007 AX3-45 DRAFT-DO NOT CITE OR QUOTE
-------
1 homes with strong indoor sources. This problem becomes more evident as ambient NC>2 levels
2 decrease due to more efficient controls on outdoor sources.
3 In 10% of homes with fireplaces studied by Triche et al. (2005), NC>2 concentrations were
4 greater than or equal to 80 ppb, or about twice the level found in homes with no indoor
5 combustion source (see Figure AX3.30). In a study of students living in Copenhagen, S0rensen
6 et al. (2005) found that personal exposures to NO2 were significantly associated with time
7 exposed to burning candles in addition to other sources. However, they did not provide data for
8 concentrations in spaces in which candles were burned. Results of studies relating NO2
9 concentrations and exposures to environmental tobacco smoke (ETS) have been mixed. Several
10 studies found positive associations between NC>2 levels and ETS (e.g., Linaker et al., 1996);
11 Farrow et al., 1997; Aim et al., 1998; Levy et al., 1998a; Monn et al., 1998; Cyrys et al., 2000;
12 Lee et al., 2000; Garcia-Algar, 2004) whereas others have not (e.g., Hackney et al., 1992;
13 Kawamoto et al., 1993). In a study of 57 homes in Brisbane, Australia (Lee et al., 2000), levels
14 of NC>2 were higher in homes with smokers present (14.9 + 7.7 ppb) than without smokers (9.9 +
15 5.0 ppb). However, these concentrations did not account for presence of a gas range (n = 18 of
16 57 homes had a gas range). Garrett et al. (1999) found that smoking in the home increased levels
17 of NC>2 in the winter, but not in the summer when windows tended to be opened. In a study of
18 students living in Copenhagen, S0rensen et al. (2005) did not find a significant association
19 between ETS and personal exposures to NC>2. However, they found that burning candles was a
20 significant prediction of bedroom levels of NC>2.
21
22 Other Indoor Environments
23 Indoor ice skating rinks have been cited as environments containing high levels of NC>2
24 when fuel powered ice resurfacing machines are used especially without ventilation. As part of a
25 three year study, Levy et al. (1998b) measured NC>2 concentrations at 2 locations at the outside of
26 the ice surface in 19 skating rinks in the Boston area over 3 winters. Although different passive
27 samplers were used in the first year (Palmes tubes, 7 day sampling time) and in years 2 and
28 3 (Yanagisawa badges, 1 day working hours) of the study, consistently high mean NC>2
29 concentrations were associated with the use of propane fueled resurfacers (248 ppb in the first
30 year and 206 ppb in the following years) and gasoline fueled resurfacers (54 ppb in the first year
31 and 132 ppb in the following years) than with electric resurfacers (30 ppb in the first year and
32 37 ppb in the following years). During all three years of the study peak NC>2 concentrations were
August 2007 AX3-46 DRAFT-DO NOT CITE OR QUOTE
-------
1 several times higher in the rinks with propane and gasoline fueled resurfacers than the values
2 given above. A number of earlier studies have also indicated NO2 concentrations of this order
3 and even higher (Paulozzi et al., 1993; Berglund et al., 1994; Lee et al., 1994; Brauer et al.,
4 1997). In these studies peak averages were in the range of a few ppm.
5
6 AX3.4.2 Reactions of NOi in Indoor Air
7 Chemistry in indoor settings can be both a source and a sink for NO2 (Weschler and
8 Shields, 1997). NO2 is produced by reactions of NO with ozone or peroxy radicals, while NO2 is
9 removed by gas phase reactions with ozone and assorted free radicals and by surface promoted
10 hydrolysis and reduction reactions. The concentration of indoor NO2 also affects the
1 1 decomposition of peroxyacyl nitrates. Each of these processes is discussed in the following
12 paragraphs. They are important not only because they influence the indoor NO2 concentrations
13 to which humans are exposed, but also because certain products of indoor chemistry may
14 confound attempts to examine associations between NO2 and health.
15 Indoor NO can be oxidized to NO2 by reaction with ozone or peroxy radicals; the latter
16 are generated by indoor air chemistry involving Os and unsaturated hydrocarbons such as
17 terpenes found in air fresheners and other household products (Sawar et al., 2002a,b; Nazaroff
18 and Weschler, 2004; Carslaw, 2007). The rate coefficient for the reaction
19 (AX3.5)
20 at room temperature (298 K) is 1.9 x io~14 cmVmolec-sec or 4.67 x 10~4 ppb"1 s"1 (Jet
21 Propulsion Laboratory, 2006). At an indoor Os concentration of 10 ppb and an indoor NO
22 concentration that is significantly less than that of Os, the half-life of NO is 2.5 min. This
23 reaction is sufficiently fast to compete with even relatively fast air exchange rates. Hence, the
24 amount of NO2 produced from NO tends to be limited by the amount of O3 available. The
25 indoor concentrations of NO and Os are negatively correlated; significant concentrations of NO
26 can only accumulate when small amounts of Os are present and vice versa (Weschler et al.,
27 1994).
28 The rapid reaction between NO and Os also means that humans, themselves, can be
29 indirect sources of NO2 in the rooms they occupy. Exhaled human breath contains NO that is
30 generated endogenously (Gustafsson et al., 1991). For a typical adult male, the average nasal
August 2007 AX3-47 DRAFT-DO NOT CITE OR QUOTE
-------
1 NO output is 325 nL min l or 23.9 jig h l (Imada et al., 1996). If ozone is present in the indoor
2 air, some or all of these exhaled NO molecules will be oxidized to NO2. To put this source in
3 perspective, consider the example of an adult male in a 30 m3 room ventilated at 1 air change per
4 hour (h-1) with outdoor air. The steady-state concentration of NO in the room as a consequence
5 of NO in exhaled breath is 0.80 jig m3 or 0.65 ppb if none of the NO were to be oxidized.
6 However, assuming a meaningful concentration of ozone in the ventilation air (>5 ppb), most of
7 this NO is oxidized to NO2 before it is exhausted from the room. In this scenario, the single
8 human occupant is indirectly a source for 0.65 ppb of NO2 in the surrounding air. At higher
9 occupant densities, lower air exchange rates and elevated concentrations of O3 in the ventilation
10 air, human exhaled breath could contribute as much as 5 ppb to the total concentration of indoor
1 1 NO2.
12 The reaction of NO2 with ozone produces nitrate radicals (NO3):
(AX3.6)
14 The second order rate-constant for this reaction at room temperature (298 K) is
15 3.2 x 1Q~17 cmVmolec-sec or 7.9 x 10~7 ppb"1 s"1 (Jet Propulsion Labatory, 2006). For indoor
16 concentrations of 20 ppb and 30 ppb for O3 and NO2, respectively, the production rate of
17 NO3 radicals is 1.7 ppb h"1. This reaction is strongly temperature dependent, an important
1 8 consideration given the variability of indoor temperatures with time of day and season. The
19 nitrate radical is photolytically unstable (Finlayson-Pitts and Pitts, 2000). As a consequence,
20 it rapidly decomposes outdoors during daylight hours. Indoors, absent direct sunlight, nitrate
21 radical concentrations may approach those measured during nighttime hours outdoors. To date
22 there have been no indoor measurements of the concentration of nitrate radicals in indoor
23 settings. Modeling studies by Nazaroff and Cass (1986), Weschler et al. (1992), Sarwar et al.
24 (2002b), and Carslaw et al. (2007) estimate indoor nitrate radical concentrations in the range of
25 0.01 to 5 ppt, depending on the indoor levels of O3 and NO2.
26 The nitrate radical and NO2 are in equilibrium with dinitrogen pentoxide (N2Os):
27 (AX3.?)
28 Dinitrogen pentoxide reacts with water to form nitric acid. The gas phase reaction with water is
29 too slow (Sverdrup et al., 1987) to compete with air exchange rates in most indoor environments.
August 2007 AX3-48 DRAFT-DO NOT CITE OR QUOTE
-------
1 Due to mass transport limits on the rate at which N2Os is transported to indoor surfaces, reactions
2 of N2Os with water sorbed to indoor surfaces are much slower than gas phase reactions between
3 nitrate radicals and commonly occurring indoor alkenes.
4 Once formed, NOs radicals can oxidize organic compounds by either adding to an
5 unsaturated carbon bond or abstracting a hydrogen atom (Wayne et al., 1991). In certain indoor
6 settings, the nitrate radical may be a more important indoor oxidant than either ozone or the
7 hydroxyl radical. Table 8 in Nazaroff and Weschler (2004) illustrates this point. Assuming
8 indoor concentrations of 20 ppb, 5 x 10~6 ppb, and 0.001 ppb for 63, OH, and NOs, respectively,
9 the pseudo first-order rate constants for reactions of most terpenoids are larger for reactions with
10 NOs than for reactions with either 63 or OH. For example, for the stated conditions, the half-
11 lives of d-limonene and a-pinene are roughly three times shorter as a consequence of reaction
12 with NOs versus reaction with Os. The products of reactions between NOs and various organic
13 compounds include nitric acid, aldehydes, ketones, organic acids and organic nitrates; these have
14 been summarized by Wayne et al. (1991). Nitrate radicals and the products of nitrate radical
15 chemistry may be meaningful confounders in NO2 exposure studies.
16 Reactions between NO2 and various free radicals can be an indoor source of organo-
17 nitrates, analogous to the chain-terminating reactions observed in photochemical smog
18 (Weschler and Shields, 1997). Additionally, based on laboratory measurements and
19 measurements in outdoor air (Finlayson-Pitts and Pitts, 2000), one would anticipate that NO2,
20 in the presence of trace amounts of HNOs, can react with PAHs sorbed on indoor surfaces to
21 produce mono- and dinitro-PAHs.
22 As noted earlier in Chapter 2, HONO occurs in the atmosphere mainly via multiphase
23 processes involving NO2. HONO is observed to form on surfaces containing partially oxidized
24 aromatic structures (Stemmler et al., 2006) and on soot (Ammann et al., 1998). Indoors, surface-
25 to-volume ratios are much larger than outdoors, and the surface mediated hydrolysis of NO2 is a
26 major indoor source of HONO (Brauer et al., 1990; Febo and Perrino, 1991; Spicer et al., 1993;
27 Brauer et al., 1993; Spengler et al., 1993; Wainman et al., 2001; Lee et al., 2002). Spicer et al.
28 (1993) made measurements in a test house that demonstrated HONO formation as a consequence
29 of NO2 surface reactions and postulated the following mechanism to explain their observations:
30
August 2007 AX3-49 DRAFT-DO NOT CITE OR QUOTE
-------
2 NO? + H2O/surface -> HONO(aq) +
1 " '
2 HONO(aq) ^ HONO(g) (AX3.9)
3 In a series of chamber studies, Brauer et al. (1993) reported HONO formation as a consequence
4 of NC>2 surface reactions and further reported that HONO production increased with increasing
5 relative humidity. Wainman et al. (2001) confirmed Brauer's findings regarding the influence of
6 relative humidity. They also found that NO2 removal and concomitant HONO production was
7 greater on synthetic carpet surfaces compared to Teflon surfaces, and that the affinity of a
8 surface for water influences HONO's desorption from that surface. Lee et al. (2002) measured
9 HONO and NO2 concentrations in 119 Southern California homes. Average indoor HONO
10 levels were about 6 times larger than outdoors (4.6 ppb versus 0.8 ppb). Indoor HONO
11 concentrations averaged 17% of indoor NO2 concentrations, and the two were strongly
12 correlated. Indoor HONO levels were higher in homes with humidifiers compared to homes
13 without humidifiers (5.9 ppb versus 2.6 ppb). This last observation is consistent with the studies
14 of Brauer et al. (1993) and Wainman et al. (2001) indicating that the production rate of HONO
15 from NO2/surface reactions is larger at higher relative humidities. Based on detailed laboratory
16 studies, the hydrolysis mechanism, Equations AX3-8 and AX3-9, have been refined. Finlayson-
17 Pitts et al. (2003) hypothesize that the symmetric form of the NO2 dimer is sorbed on surfaces,
18 isomerizes to the asymmetric dimer which auto ionizes to NO^STOs ; the latter then reacts with
19 water to form HONO and surface adsorbed HNOs. FTIR-based analyses indicate that the surface
20 adsorbed HNOs exists as both undissociated nitric acid-water complexes, (HNO3)x(H2O)y, and
21 nitrate ion-water complexes, (MV)x(H2O)y (Dubowski et al., 2004, Ramazan et al., 2006).
22 Such adsorbed species may serve as oxidizing agents for organic compounds sorbed to these
23 same surfaces (Ramazan et al., 2006).
24 HONO and much smaller amounts of HNO3 are also emitted directly by combustion by
25 gas appliances and can infiltrate from outdoors. Spicer et al. (1993) compared the measured
26 increase in HONO in a test house resulting from direct emissions of HONO from a gas range and
27 from production by surface reactions of NO2. They found that emissions from the gas range
28 could account for about 84% of the measured increase in HONO and surface reactions for 11%
29 in an experiment that lasted several hours. An equilibrium between adsorption of HONO from
30 the gas range (or other indoor combustion sources) and HONO produced by surface reactions
August 2007 AX3-50 DRAFT-DO NOT CITE OR QUOTE
-------
1 (see Equation AX3-9) also determines the relative importance of these processes in producing
2 HONO in indoor air. In a study of Southern CA homes (Lee et al., 2002), indoor levels of NO2
3 and HONO were positively associated with the presence of gas ranges.
4 It is known that the photolysis of HONO (g) in the atmosphere (outdoors) is a major
5 source of the hydroxyl radical (OH). Given high indoor HONO concentrations and the presence
6 of lighting (sun light penetrating windows, incandescent lights, fluorescent lights), the photolysis
7 of indoor HONO may be a meaningful source of indoor hydroxyl radical, under favorable
8 reaction conditions. Given the large suite of man-made chemicals present indoors at elevated
9 concentrations, indoor free radicals (e.g., OH and NOs) can initiate and drive a complex series of
10 indoor chemical reactions.
1 1 NO2 can also be reduced on certain surfaces, forming NO. Spicer et al. (1989) found that
12 as much as 15% of the NO2 removed on the surfaces of masonite, ceiling tile, plywood,
13 plasterboard, bricks, polyester carpet, wool carpet, acrylic carpet and oak paneling was re-
14 emitted as NO. Weschler and Shields (1996) found that the amount of NO2 removed by charcoal
15 building filters were almost equally matched by the amount of NO subsequently emitted by these
16 same filters.
17 Spicer et al. (1993) determined the 1st order rate constants for removal of several NOy
1 8 components by reaction with indoor surfaces. They found lifetimes (e-folding times) of about
19 half an hour for HNOs, an hour for NO2, and hours for NO and HONO. Thus the latter two
20 components, if generated indoors are more likely to be lost to the indoor environment through
21 exchange with outside air than by removal on indoor surfaces. However, HONO is in
22 equilibrium with the nitrite ion (NO2 ) in aqueous surface films:
23 HONO(aq) <-> H+ + NOf (3_1Q)
24 Ozone oxidation of nitrite ions in such films is a potential sink for indoor HONO (Lee et al.,
25 2002).
26 Jakobi and Fabian (1997) measured indoor and outdoor concentrations of ozone and
27 peroxyacetyl nitrate (PAN) in several offices, private residences, a classroom, a gymnasium and
28 a car. They found that indoor levels of PAN were 70% to 90% outdoor levels, and that PAN's
29 indoor half-life ranged from 0.5 to 1 h. The primary indoor removal process is thermal
30 decomposition:
August 2007 AX3-5 1 DRAFT-DO NOT CITE OR QUOTE
-------
1 CH3C(0)OON02 O CH3C(0)00 + NO2
2 As is indicated by Equation AX3-11, PAN is in equilibrium with the peroxylacetyl radical and
3 NO2. Hence, the indoor concentration of NO2 affects the thermal decomposition of PAN and,
4 analogously, other peroxyacyl nitrates. Peroxylalkyl radicals rapidly oxidize NO to NO2, so the
5 indoor concentration of NO also influences the thermal decomposition of PAN type species
6 (Finlayson-Pitts and Pitts, 2000).
7 Reactions between hydroxyl radicals and aldehydes in the presence of NO2 can lead to
8 the formation of peroxyacyl nitrates. Weschler and Shields (1997) have speculated that such
9 chemistry may sometimes occur indoors. For example, the requisite conditions for the formation
10 of the highly irritating compound peroxybenzoyl nitrate may occur when ozone, certain terpenes,
11 styrene and NO2 are present simultaneously at low air exchange rates. This relatively common
12 indoor mixture of pollutants produces hydroxyl radicals and benzaldehyde, which can
13 subsequently react as noted above. In her detailed model of indoor chemistry, Carslaw (2007)
14 explores the indoor formation of PAN-type species (see Figure 2 in the cited reference).
15 Recent work indicates that indoor NO2 also can affect the formation of secondary organic
16 aerosols (SOA) resulting from the reaction of Os with terpenes such as d-limonene and oc-pinene
17 (N0jgaard et al., 2006). At concentrations of 50 ppb for Os and the terpenes, NO2 decreased the
18 formation of SOA compared to the levels formed in the absence of NO2. The effect was more
19 pronounced for SOA derived from oc-pinene than d-limonene, and at lower NO2 concentrations,
20 appears to be explained by the Os loss resulting from its reaction with NO2. The resultant nitrate
21 radicals apparently are not as efficient at producing SOA as the lost Os.
22 Nitro-PAHs have been found in indoor environments (Mumford et al., 1991; Wilson
23 et al., 1991). The major indoor sources of nitro-PAHs include cooking, wood burning, and the
24 use of kerosene heater (World Health Organization (WHO), 2003). It is also likely that nitro-
25 PAHs outdoors can infiltrate indoors. One of the potential sources of nitro-PAHs indoors, which
26 has not been characterized, is reactions via indoor chemistry. The reactions of PAHs with OH
27 and NOs may occur in indoor environments. Although no direct measurements of OH or NOs in
28 indoor environments, OH and NOs can be formed via indoor chemistry and may present at
29 significant levels indoors (Nazaroff and Cass 1986, Sarwar et al., 2002a; Carslaw, 2007).
30 Concentrations of ~10~6 ppb for OH and 0.01-5 ppt of NOs have been predicted through indoor
August 2007 AX3-52 DRAFT-DO NOT CITE OR QUOTE
-------
1 chemical reactions (Nazaroff and Cass 1986, Sarwar et al., 2002a, Carslaw, 2007), depending on
2 the indoor levels of 63, alkenes, and NC>2. Observation of secondary organic aerosols (SOA)
3 formation in a simulated indoor environment also suggested that ~10~5 ppb steady-state OH
4 radicals were generated from the reactions of Os with terpenes (Fan et al., 2003). PAHs are
5 common indoor air pollutants (Chuang et al., 1991; Naumova et al., 2002), and the
6 concentrations of some PAHs indoors are often higher than outdoors (Naumova et al., 2002).
7 Therefore, the reactions of OH and NOs with PAHs may occur at rates comparable to air
8 exchange rates to form nitro-PAHs indoors. In addition, the reactions of NOs with PAHs may be
9 more significant indoors than outdoors because indoor NOs is more stable due to the low uv in
10 indoor environments. Given the high surface areas available indoors, the formation of nitro-
11 PAHs via surface reactions of PAHs with nitrating species may be more important compared to
12 heterogeneous reactions outdoors.
13 In summary, indoor chemistry can meaningfully alter the indoor concentration of NO2.
14 Indoor exposure to NO2 may be accompanied by indoor exposures to nitrate radicals, organic
15 nitrates, and nitro-PAHs.
16
17 AX3.4.3 Contributions from Outdoor NOi
18 As might be expected, indoor concentrations of NO2 in the absence of combustion
19 sources are primarily determined by outdoor NO2 concentrations (Spengler et al., 1994;
20 Weschler et al., 1994; Levy et al., 1998a), with a much smaller contribution from chemical
21 reactions in indoor air.
22 The exchange between NO2 in ambient air and in the indoor environment is influenced by
23 infiltration (air leakage), natural ventilation (air flow through intentional openings such as
24 windows), and mechanical ventilation (rarely used in residences) (Yang et al., 2004).
25 In temperate climates, winter is associated with lower indoor/outdoor ratios of NO2 since
26 windows and doors are usually tightly closed and the only source of exchange is infiltration.
27 Newer homes tend to be built more tightly than older homes, so have even lower rates of
28 infiltration. During warmer weather, air conditioner use and opening of windows increase air
29 exchange between outdoors and indoors.
30 Yang et al. (2004) used multiple integrated (7-day) NO2 measurements indoors and
31 outdoors to calculate penetration and source strength factors in Seoul, Korea and Brisbane,
August 2007 AX3-53 DRAFT-DO NOT CITE OR QUOTE
-------
1 Australia using a mass balance model considering a residence as a single chamber (Yang et al.,
2 2004). They showed that, while penetration factors did not differ significantly between gas and
3 electric range homes, source strength factors were much higher in homes with gas ranges in both
4 Brisbane and Seoul (5.77 ± 3.55 and 9.12 ± 4.50, respectively) than in electric range homes in
5 Brisbane (1.49 ± 1.25). Similarly, calculated NO2 source strengths (|ig/m3/h) were
6 21.9 ± 21.8 and 44.7 ± 38.1 in gas homes in Brisbane and Seoul, respectively, and 6.6 ± 6.3 in
7 electric homes in Brisbane.
8
9 Household Characteristics
10 Yang et al. (2004) found that levels of indoor NO2 (in |ig/m3) were associated with house
11 characteristics in 28 homes in Brisbane (where there were both electric and gas range homes).
12 Homes with a gas water heater had higher levels than those without (34.5 ± 16.4 versus 22.8 ±
13 12.1, p = 0.048), but these were unadjusted associations, and it is likely that many of the homes
14 with gas water heaters also had gas ranges. Homes with an attached garage had higher levels of
15 NO2 (33.1 ± 18.3) compared to homes without one (21.8 ± 8.8) (p = 0.039). Attached garages
16 were not, however, associated with NO2 levels in a study in Quebec City, Canada (Gilbert et al.,
17 2006). The authors suggested that the lack of association might be attributed to small numbers
18 (n = 18 homes with attached garages) or to the airtightness of homes in Canada compared to
19 those in Australia.
20 Location in a city center was associated with higher NO2 levels in homes in Menorca
21 (one of the Balearic Islands off the coast of Spain with rural and small town residences), after
22 adjusting for gas cooker, extractor fan use, smoking in the home, type of central heating, season,
23 and social class (Garcia-Algar., 2004). In the same study, levels of indoor NO2 in Barcelona (a
24 large coastal city in Spain) and Ashford (a medium-sized town in the southeast UK) were
25 significantly higher than those in Menorca
26 In a study of a random sample of 845 homes in England (Raw et al., 2004), levels of NO2
27 were significantly associated with dwelling type and age of home, but the authors attributed
28 these effects to the geographical location of the home (e.g., inner city). Garrett et al. (1999) also
29 found that age of house was significantly associated with NO2 levels in winter and summer. In
30 the study by Shima and Adachi, (1998), differences in concentrations of NO2 between homes
31 with and without unvented heaters in the heating season were slightly lower among homes with
32 wood compared to aluminum window frames. Type of window frames, but not structure type,
August 2007 AX3-54 DRAFT-DO NOT CITE OR QUOTE
-------
1 was associated with NO2 concentrations in the heating period for homes with unvented heaters
2 (76.2 ±1.4 ppb versus 55.9 ±3.9 ppb in homes with aluminum and wood windows,
3 respectively), but not in homes with vented heaters. In the non-heating season, mean NO2 levels
4 in the home varied by type of structure (steel/concrete or wood) and type of window frames
5 (aluminum or wood), with wood structures and frames indicating a less airtight dwelling.
6
7
8 AX3.5 PERSONAL EXPOSURE
9
10 Components of Personal Exposure
11 Human exposure to NO2 consists of contact at the air boundary layer between the human
12 and the environment at a specific concentration for a specified period of time. People spend
13 various amount of time in different microenvironments with various NO2 concentrations. The
14 integrated NO2 exposure is the sum of the individual NO2 exposures over all possible time
15 intervals for all environments. Therefore, the assessment of human exposures to NO2 can be
16 represented by the following equation:
ET= I
17 '=' (AX3-12)
18 where ET is the time-weighted personal exposure concentration over a certain period of time, n is
19 the total number of environments that a person encounters,/ is the fraction of time spent in the
20 /'th environment, and Q is the average NO2 concentration in the rth environment during the time
21 fraction/. Depending upon the time fraction and environmental concentration we consider
22 during exposure assessment, the exposure a person experiences can be classified into
23 instantaneous exposure, peak exposure, averaged exposure, or integrated exposure. These
24 distinctions are important because health effects caused by long-term low-level exposures may
25 be different from those resulting from short-term peak exposures.
26 The equation above represents the average personal exposure concentration is a linear
27 combination of the average concentration in the ambient environment and each
28 microenvironment, weighted by an individual's fraction of time spent in that environment.
29 Hence, personal exposure to NO2 is influenced by the microenvironmental concentration and the
30 amount of time spent in each microenvironment. In theory, a microenvironment could be any
August 2007 AX3-55 DRAFT-DO NOT CITE OR QUOTE
-------
1 three-dimensional space having a volume in which people spend a certain amount of time.
2 In practice, microenvironments typically used to determine NC>2 exposures include residential
3 indoor environment, other indoor locations, near-traffic outdoor environment, other outdoor
4 locations, and in-vehicles. In other words, total personal exposure to NC>2 can be decomposed
5 into exposure to NC>2 in different environments. An individual's total exposure (Ex) can also be
6 represented by the following equation:
ET = Ea + Enona = {y0 + lM/W(«i + */)]}C0 + Enona = 0>o + I >'/i,,/:}Q + Enona
1 ' ' (AX3-13)
8 subject to the constraint
y0 + I#i = 1
9 ' (AX3-14)
10 where E& is the person's exposure to pollutants of ambient origin; Enona is the person's exposure
11 to pollutants that are not of ambient origin; y0 is the fraction of time people spend outdoors and y\
12 is the fraction of time they spend in microenvironment /'; F^R, P{, ah and k; are the infiltration
13 factor, penetration coefficient, air exchange rate, and decay rate for microenvironment /'.
14 In the case where microenvironmental exposures are dominated by one
15 microenvironment, Equation AX3-13 may be approximated by
16 ET = Ea + Enona + {>' + ( ' -y)[Pa/(a + k)] } Ca + ERma = aCa + Eaona (AX3_15)
17 where E\ is the total personal exposure, Ea is the exposure to ambient generated pollutants, Enonag
18 is the nonambient generated pollutants, and_y is the time fraction people spent outdoors. Other
19 symbols have the same definitions in Equation AX3-13. If microenvironmental concentrations
20 are considered, then Equation AX3-15 can be recast as
21 Cme = Ca + Cnona = [Pa/(a + k)]Ca + S/[V(a + k)] (AX3-16)
22 where Cme is the concentration in a microenvironment; Ca and Cnona the contributions to Cme from
23 ambient and nonambient sources; S is the microenvironmental source strength; Fis the volume
24 of the microenvironment, and the symbols in brackets have the same meaning as in Equation
25 AX3-15. In this equation, it is assumed that microenvironments do not exchange air with each
26 other, but only with ambient air.
August 2007 AX3-56 DRAFT-DO NOT CITE OR QUOTE
-------
1 The NO2 concentration in each microenvironment can show substantial spatial and
2 temporal variability, which is determined by many factors, such as season, day of the week,
3 personal age, occupation, house characteristics, personal activities, source emission rate, air
4 exchange rate, and transport and removal mechanisms of NC>2. Failure to disaggregate total
5 human exposure and assess human exposure in various microenvironments may result in
6 exposure misclassification, which may obscure the true relations between ambient air pollution
7 and health outcomes.
8 Studies reviewed in this section were generally conducted in North America (Canada, the
9 United States, and Mexico) and European countries. Studies conducted in other parts of the
10 world were not the primary focus of this science review because exposure patterns may not be
11 similar to those in the United States. However, studies which might support general conclusions
12 (not country or cultural specific conclusions) about NC>2 exposures will be included.
13 Either Palmes tubes or Yanagisawa badges or Ogawa samplers were used to measure
14 personal exposures in most of the reviewed studies, and sometimes residential indoor and
15 outdoor concentrations. Sampling time for each cartridge varied from 8 h to two weeks, and the
16 study design covered (1) longitudinal, in which each subject is measured for many days;
17 (2) pooled, in which each subject is measured for only one or two days, different days for
18 different subjects; and (3) daily-average, in which many subjects are measured on the same day.
19 Most studies focused primarily on children, and in some studies adults or people with respiratory
20 diseases were taken as study population.
21
22 AX3.5.1 Personal Exposures and Ambient (Outdoor) Concentrations
23 Numerous epidemiological studies have shown a positive association between ambient
24 (outdoor) NC>2 concentrations and adverse health effects. Since a causal association requires
25 exposure, it is very important to evaluate personal exposure to ambient (outdoor) generated NC>2.
26 In this section, topics related to the total personal exposure and ambient (outdoor) generated NC>2
27 will be evaluated, such as the levels of personal exposure and ambient (outdoor) NC>2, the
28 attenuation factor of personal exposure to NC>2, the correlation between personal and ambient
29 (outdoor) NC>2, and the factors determining the associations between personal exposure and
30 ambient (outdoor) level. Based on the science review, the following key questions will be
31 addressed: 1) When, where, how and how much are people exposed to ambient (outdoor)
August 2007 AX3-57 DRAFT-DO NOT CITE OR QUOTE
-------
1 generated NO2? and 2) Is ambient (outdoor) NO2 a good surrogate for personal total exposure or
2 personal exposure to ambient (outdoor) NCh?
3 Personal exposures in most of the studies considered here were less than the
4 corresponding outdoor or ambient concentrations. In the presence of local sources (indoor or
5 local traffic sources), personal exposure levels could be higher than outdoor or ambient levels
6 (Spengler et al., 1994; Nakai et al., 1995; Linn et al., 1996; Spengler et al., 1996; Raaschou-
7 Nielsen et al., 1997; Aim et al., 1998; Levy et al., 1998a; Monn et al., 1998; Liard et al., 1999;
8 Kramer et al., 2000; Linaker et al., 2000; Mukala et al., 2000; Gauvin et al., 2001; Monn, 2001;
9 Rotko et al., 2001; Sarnat et al., 2001; Kodama et al., 2002; Mosqueron et al., 2002; Ramirez-
10 Aguilar et al., 2002; Rojas-Bracho et al., 2002; Lai et al., 2004; Nerriere et al., 2005; Sarnat
11 et al., 2005; S0rensen et al., 2005; Kim et al., 2006; Sarnat et al., 2006).
12 In a probability based population exposure study in Los Angeles Basin, 48 h indoor,
13 outdoor and personal exposures (pooled exposures) were reported for 682 participants (Spengler
14 et al., 1994). Spengler et al. (1994) found that the median personal exposure was 35 ppb and the
15 median outdoor level was 36 ppb. Linn et al. (1996) reported the results of a personal exposure
16 study for 269 school children from three Southern California communities. During this
17 longitudinal study, 24 h averaged personal exposures, as well as inside school, outside school
18 and ambient central site NO2 levels, were measured by Yanagisawa badges for one week for
19 each season from 1992 to 1994. Results showed that mean personal exposure was 22 ppb and
20 the mean central site concentration was 37 ppb. Kim et al. (2006) conducted a longitudinal,
21 multi-pollutant exposure study in Toronto, Canada. During the study, personal exposures (24-h
22 integrated by Ogawa sampler) to PM2.5, NC>2 and CO were measured for 28 subjects with
23 coronary artery disease one day a week for a maximum of 10 weeks, and were compared with
24 ambient fixed site measurements. The mean NC>2 personal exposure was 14.4 ppb, which was
25 lower than the ambient site concentrations (20-26 ppb). Sarnat et al. (2001) and Sarnat et al.
26 (2005) reported multi-pollutant exposure studies in Baltimore and Boston. In the Baltimore
27 study, 24 h averaged personal exposure and ambient PM2.5, 63, NC>2, 862, and CO were
28 measured for 56 subjects (20 older adults, 21 children and 15 individuals with COPD) in the
29 summer of 1998 and the winter of 1999. All subjects were monitored for 12 or 8 consecutive
30 days in each of the one or two seasons. Median ambient NC>2 levels were higher than the median
31 personal levels in both seasons (about 10 ppb in difference). During the winter, both ambient
August 2007 AX3-58 DRAFT-DO NOT CITE OR QUOTE
-------
1 and personal exposure to NO2 were higher than the summer, the difference between ambient and
2 personal exposure in winter was 1 to 2 ppb smaller than the difference in the summer. In the
3 Boston study, 24-h averaged personal and ambient PM2 5, O3, NO2, and SO2 were measured for
4 20 healthy seniors and 23 schoolchildren. All subjects were measured for 12 consecutive 24-h
5 periods in each of the 1 or 2 seasons. Ambient NO2 levels were on average 6 to 20 ppb higher
6 than the personal exposure levels for seniors during all sampling sessions. For children's
7 exposure, ambient NO2 levels were 7 to 13 ppb higher than the personal exposures in 4 out of
8 6 sampling sessions, and in the other two sampling sessions (one in summer and one in winter)
9 ambient levels were 1.8 to 2.6 ppb lower than personal exposures. Sarnat et al. (2006) measured
10 24-h averaged ambient and personal PM2.5, sulfate, elemental carbon, 63, and SO2 for 10 non-
11 smoking seniors in Steubenville, Ohio during the summer and fall of 2000. For each subject,
12 two consecutive 24 h personal exposure measurements were collected during each week for
13 23 weeks. Data were stratified by the presence of gas stoves in homes. Personal exposure was
14 lower than the ambient level for homes without gas stoves (9.0 ppb for personal exposure versus
15 9.5 ppb for ambient level during the summer and 9.9 ppb versus 11.3 ppb during the fall), and
16 higher than ambient levels for homes with gas stoves (12.3 ppb for personal exposure versus 9.5
17 ppb for ambient level during the summer and 15.7 ppb versus 11.3 ppb during the fall).
18 Nerriere et al. (2005) investigated factors determining the discrepancies between personal
19 exposure and ambient levels in the Genotox ER study. During the study, forty-eight h averaged
20 PM2.5, PMio, and NO2 were collected in both summer and winter for each person in a cohort,
21 with 60 to 90 nonsmoking volunteers composed of two groups of equal size for adults and
22 children at four metropolitan areas in France (Grenoble, Paris, Rouen, and Strasbourg). In each
23 city, subjects were selected so as to live in three different urban sectors contrasted in terms of air
24 pollution: one highly exposed to traffic emissions, one influenced by local industrial sources,
25 and a background urban environment. In each urban sector, a fixed ambient air monitoring
26 station was used to simultaneously collect the same air pollutants as personal exposure samplers.
27 Factors affecting the concentration discrepancies between personal exposure and corresponding
28 ambient monitoring site were investigated by a multiple linear regression model. Results showed
29 that the discrepancies were season, city and land use dependent. During the winter, city and land
30 use can interpret 31% of the variation of the discrepancy, and during the summer 54% of the
31 variation in the discrepancy can be interpreted by those factors. In most cases, ambient
August 2007 AX3-59 DRAFT-DO NOT CITE OR QUOTE
-------
1 concentrations were higher than the corresponding personal exposures. When using the ambient
2 site to represent ambient levels, the largest difference between ambient and personal exposure
3 was found at the "proximity to traffic" site, while the smallest difference was found at the
4 "background" site. When using urban background site as ambient level, the largest difference
5 was observed at the "industry" site, and the smallest difference was observed at the background
6 site, which reflected the heterogeneous distribution of NC>2 in an urban area. During winter,
7 differences between ambient site and personal exposure were larger than those in the summer.
8 Age was not found to be a significant factor interpreting the discrepancies between ambient level
9 and personal exposure.
10 S0rensen et al. (2005) reported that during the cold season, median personal exposure
11 was higher than residential indoor and urban background concentrations, but lower than the
12 residential outdoor and street station concentrations (designed to capture the close to traffic
13 exposure). During the warm season, personal exposure was again lower than the street station
14 concentration but higher than the residential indoor, outdoor, and urban background
15 concentrations. The implication of these findings is that ambient concentrations are the primary
16 factor in determining exposures when there is no or little contribution from indoor sources and
17 that traffic is the most significant NC>2 source in this study.
18 The relative levels of ambient and personal exposure can also be expressed as ratios of
19 personal/ambient (Levy et al., 1998a; Rojas-Bracho et al., 2002; Sarnat et al., 2006). As shown
20 in Equation AX3-15, personal exposure is related to ambient concentration through the
21 infiltration factor, the fraction of time people spend outdoors, indoor sources and outdoor
22 concentration. In the absence of indoor sources, the ratio of personal exposure to ambient
23 concentration is sometimes also called the attenuation factor (a), which is always less than or
24 equal to one, and it is a function of infiltration factor (F[nf) and the fraction of time people spend
25 outdoors (y). The attenuation factor can be derived directly from measured personal and outdoor
26 concentrations or calculated from measured or estimated values of the parameters a, k, and P
27 (see Equation AX3-13 and Equation AX3-15) and the time spent in various microenvironments
28 from activity pattern diaries (Wilson et al., 2000). Because a depends on building and lifestyle
29 factors, air exchange rate, and NC>2 decay rate, it will vary to a certain extent from region-to-
30 region, season-to-season, and by the type of indoor microenvironment. Consequently, predicted
31 exposures based on these physical modeling concepts provide exposure distributions derived
August 2007 AX3-60 DRAFT-DO NOT CITE OR QUOTE
-------
1 conceptually as resulting from building, lifestyles, and meteorological considerations. For any
2 given population, the distribution of the coefficient a may represent substantial intra- and inter-
3 personal variability based on personal activity patterns, building and other microenvironmental
4 characteristics, and proximity to ambient and indoor sources. Distributions of a should be
5 determined using population studies in order to evaluate the uncertainty and variability
6 associated with model exposures. Unfortunately, only a few studies have reported the value and
7 distribution of the ratio of personal to ambient, and even fewer studies reported the value and
8 distribution of attenuation factors based on sophisticated study designs. Rojas-Bracho et al.
9 (2002) reported the median personal/outdoor ratio was 0.64 (with an interquartile range (IQR) of
10 0.45). Although it was less than one, the authors also reported the indoor/outdoor ratio (0.95
11 with an IQR of 0.48) of NO2 and based on the indoor/outdoor ratio, the authors pointed out that
12 the high median indoor/outdoor ratio was greater than the estimated effective penetration
13 efficiency, which supports the argument of the importance of indoor sources to indoor NO2
14 levels. Therefore, the attenuation factor in this study should be smaller than the ratio of
15 personal/ambient, which was 0.64. Sarnat et al. (2006) reported that the ratio of
16 personal/ambient for NO2 was 2.05 and 1.27 for subjects with and without gas stoves in their
17 homes. The large personal/ambient ratio for the latter might be attributed to the influence of
18 indoor or local sources that were not identified and/or partly to measurement error.
19 The attenuation factor is one of the keys to evaluate personal exposure to ambient
20 generated NO2, or ambient contribution to personal exposure. However, the ratio of personal
21 exposure/ambient concentration will not accurately reflect the attenuation factor in the presence
22 of indoor sources. As shown above, in many cases, the ratio of personal exposure and ambient
23 concentration was above one, which is physically impossible for the attenuation factor. The
24 random component superposition (RCS) model is an alternative way to calculate attenuation
25 factor using observed ambient and personal exposure concentrations (Ott et al., 2000). The
26 Random Component Superposition (RCS) statistical model (shown in Equation AX3-15) uses
27 the slope of the regression line of personal concentration on the ambient or outdoor NO2
28 concentration to estimate the population average attenuation factor and means and distributions
29 of ambient/outdoor and nonambient contributions to personal NO2 concentrations (the intercept
30 of the regression is the averaged nonambient contribution to personal exposure). This model
August 2007 AX3-61 DRAFT-DO NOT CITE OR QUOTE
-------
1 assumes a linear superposition of the ambient and nonambient components of exposure and lack
2 of correlation between these two components.
3 The RCS model derives a mean a across all homes (assuming the infiltration behavior
4 and time budget for all people are the same) from the linear regression of measured values of Et
5 on Ca.. The product of the constant a and Ca from each home provides an estimate of the mean
6 and distribution of Ea for the population of study homes. In practice, the mean and distribution
7 of nonambient contributions (Enona) are given by the difference, Et - Ea, on a home-by-home
8 basis. The RCS-predicted distribution of Ea across the population of study homes is given by the
9 product of the constant a and Ca from each home, and the mean of the ambient contribution is
10 the difference between the mean total personal exposure and the intercept of the regression line.
11 The RCS model has been widely applied to PM exposure studies PTEAM, THEES, Toronto, and
12 RIOPA studies (Ott et al., 2000; Meng et al., 2005), but researchers have not intentionally used
13 this model for NC>2 exposure assessments. Although many studies explored the relationship
14 between personal exposure and ambient NC>2 concentrations using regression models, most of
15 those studies are not useful for evaluating the attenuation factor or helping answer the question
16 of how much personal NCh exposure comes from ambient air, either because only R2 was
17 reported, or because log-transformed concentrations were used in the regression model, or
18 because physically meaningless multiple linear regression models (exploratory variables were
19 not independent of each other, e.g., both indoor, outdoor, indoor sources from questionnaire
20 responses and air exchange rate were used as exploratory variables) were used to interpret
21 personal exposure variations. Only those simple linear regression models (personal versus
22 ambient or personal versus outdoor) and physically meaningful multiple linear regression models
23 (personal versus ambient + indoor source measured or identified by questionnaire) are useful for
24 evaluating the attenuation factor, and those models are summarized in Table AX3.11. The
25 intercept of the regressions (i.e., the nonambient contribution to personal exposure) varies widely
26 from study to study (5 ppb to 18 ppb) and thus depends strongly on time and location. The slope
27 of these regression models (i.e., the population average attenuation factor) varies between 0.3 to
28 0.6 in most of the studies. The attenuation factor is determined by air exchange rate, penetration
29 and decay rate of NC>2 and also the fraction of time people spend outdoors. S0rensen et al.
30 (2005) found that the attenuation factor was larger in the summer than in the winter. However,
31 Sarnat et al. (2006) found opposing results and said the reason was unknown. Based on the
August 2007 AX3-62 DRAFT-DO NOT CITE OR QUOTE
-------
1 regression model and reported mean personal exposure values, the ambient and nonambient
2 contribution to personal exposure could be calculated using the method described above. Since
3 most researchers did not report the mean personal exposure and the regression model at the same
4 time, ambient and nonambient contributions can only be calculated in four studies as shown in
5 Table AX3.12. The ambient contribution to population exposures varies from 20% to 50% in
6 these four studies.
7 The RCS model calculates ambient contributions to indoor concentrations and personal
8 exposures based on the statistical inferences of regression analysis. However, personal-outdoor
9 regressions could be affected by extreme values (outliers either on the x or the y axis), such as a
10 high nonambient exposure on a day with low ambient concentration or vice versa. For this
11 reason outliers must be identified and their influence on the infiltration factor or attenuation
12 factor in the RCS model must be evaluated in order to obtain a robust result. Another limitation
13 of the RCS model is that this model is not designed to estimate ambient and nonambient
14 contributions for individuals, in part because the use of a single attenuation factor does not
15 account from the large home-to home variations in actual air exchange rates, and penetration and
16 decay rates of NO2. As suggested by Meng et al. (2005) the use of a fixed attenuation factor
17 might underestimate ambient contributions to indoor concentrations and personal exposures and
18 could also overlook some of the exposure errors and cause large uncertainties in risk estimates.
19 The estimation of the ambient and nonambient contribution to personal exposure could be
20 improved by allowing for variations in air exchange rate, penetration and decay rate of NO2, and
21 the variations in the fraction of time people spend outdoors. The mass balance model described
22 in Equation AX3-15 gives more flexibility than the RCS model if the distributions of P, k, a, and
23 y are known. A comprehensive assessment of the impact of ambient sources on personal
24 exposure would require detailed consideration of the mechanisms of NO2 formation,
25 transformation, transport and decay. In the research field of NO2 exposure assessment, no
26 published reports were found that use the mass balance model to explore the relationship of
27 personal exposures to ambient NO2 concentrations. As mentioned in Section 3.4.2, the only
28 reported k values were 0.99 h-1 by Yamanaka (1984), and people always assumes the
29 penetration coefficient (P) is one for NO2, which might overestimate the ambient contribution
30 due to the chemical reactivity of NO2 during penetration.
August 2007 AX3-63 DRAFT-DO NOT CITE OR QUOTE
-------
1 The association between personal exposure and ambient NO2 was quantified by Pearson
2 correlation coefficient (rp), Spearman correlation coefficient (rs), or coefficient of determination
3 (R2) in regression models (Spengler et al., 1994; Linn et al., 1996; Spengler et al., 1996;
4 Raaschou-Nielsen et al., 1997; Aim et al., 1998; Levy et al., 1998a; Monn et al., 1998; Liard
5 et al., 1999; Kramer et al., 2000; Linaker et al., 2000; Mukala et al., 2000; Gauvin et al., 2001;
6 Monn, 2001; Rotko et al., 2001; Sarnat et al., 2001; Kodama et al., 2002; Rojas-Bracho et al.,
7 2002; Lai et al., 2004; Sarnat et al., 2005; Kim et al., 2006; Sarnat et al., 2006). In Table
8 AX3.13, the associations between personal exposure and ambient concentration found in these
9 studies are summarized.
10 The association between personal NO2 exposure and ambient/outdoor NO2 concentration
11 varied from poor to good as shown in Table AX3.13. The strength of the correlation between
12 personal exposure and ambient/outdoor concentration for a population is determined by the
13 variations in indoor or other local sources, air exchange rate, penetration and decay rate of NO2
14 in different microenvironment, and time people spend in different microenvironments with
15 different NC>2 concentrations. The relationship is also a function of season and location
16 (rural/urban). Aim et al. (1998) indicated that the association between personal exposure and
17 outdoor concentration was stronger than the correlation between personal exposure and central
18 site concentration. However, Kim et al. (2006) pointed out that the association was not improved
19 using the ambient sampler closest to a home. Home ventilation is another important factor
20 modifying the personal-ambient relationships; we expect to observe the strongest associations for
21 subjects spending time indoors with open windows. Aim et al. (1998) and Kodama et al. (2002)
22 observed the association between personal exposure and ambient concentration became stronger
23 during the summer than the winter. However, Sarnat et al. (2006) reported that R2 decreased
24 from 0.34 for low ventilation population to 0.16 for high ventilation population in the summer,
25 and from 0.47 to 0.34 in the fall. This might be a caution that the association between personal
26 exposure and ambient concentration is complicated and is determined by many factors.
27 Exposure misclassification might happen if a single factor, such as season or ventilation status, is
28 used as an exposure indicator. Another factor affecting the personal to ambient association is the
29 subject's location, with higher correlation for subjects living in the rural areas and lower
30 correlation with subjects living in the urban areas (Rojas-Bracho et al., 2002; Aim et al., 1998).
31 Spengler et al. (1994) also observed that the relationship between personal exposure and outdoor
August 2007 AX3-64 DRAFT-DO NOT CITE OR QUOTE
-------
1 concentration was highest in areas with lower ambient NC>2 levels (R2 = 0.47) and lowest in areas
2 with higher ambient NC>2 levels (R2 = 0.33). This might reflect the highly heterogeneous
3 distribution, or the effect of local sources of NO2 in an urban area, and personal activities are
4 more diverse in an urban area. However, this factor (location: urban vs. rural) might also interact
5 with indoor sources because indoor sources could explain more personal exposure when ambient
6 concentrations become lower and more homogeneously distributed.
7 The association is also affected by indoor or local sources, and the association becomes
8 stronger after those sources are controlled in the model. Raaschou-Nielsen et al. (1997) observed
9 that R2 increased from 0.15 for general population to 0.49 for a population who spent less than
10 2% of their time close to gas appliances and passive smoking in Copenhagen urban area, and R2
11 increased from 0.35 to 0.45 in the rural area for the population with the same characteristics.
12 When those who reported exposure to either gas appliances or passive smoking were excluded,
13 R2 increased to 0.59 in urban and 0.46 in the rural districts. Spengler et al. (1994) observed that
14 less of the variation in personal exposure was explained by outdoor concentrations for those who
15 had gas ranges with pilot lights (R2 = 0.44) than it is for the other two groups (R2 = 0.52). When
16 there is little or no contribution from indoor sources, ambient concentrations are the primary
17 factor in determining exposure, but if there are continuous indoor sources, the influence of
18 outdoor levels decrease. In the VESTA study, Gauvin et al. (2001) reported low R2s in all three
19 cities. R2s increased for all three cities after controlling indoor air sources (e.g., gas cooking)
20 and ambient traffic densities: R2 increased from 0.01 to 0.43 for Grenoble, from 0.04 to 0.50 for
21 Toulouse, and from 0.02 to 0.37 for Paris. Other factors, such as cross-sectional vs. longitudinal
22 study design, and sampling duration might also affect the strength of the association. However,
23 the current science review cannot give a clear picture of the effects by those factors due the lack
24 of key studies and data.
25 The correlation coefficient between personal exposure and ambient/outdoor concentration
26 has different meanings for different study designs. There are three types of correlations
27 generated from different study designs: longitudinal, "pooled," and daily-average correlations.
28 Longitudinal correlations are calculated when data from a study includes measurements over
29 multiple days for each subject (longitudinal study design). Longitudinal correlations describe the
30 temporal relationship between daily personal NCh exposure or microenvironment concentration
31 and daily ambient NO2 concentration for each individual subject. The longitudinal correlation
August 2007 AX3-65 DRAFT-DO NOT CITE OR QUOTE
-------
1 coefficient may differ for each subject. The distribution of correlations across a population could
2 be obtained with this type of data. Pooled correlations are calculated when a study involves one
3 or only a few measurements per subject and when different subjects are studied on subsequent
4 days. Pooled correlations combine individual subject/individual day data for the calculation of
5 correlations. Pooled correlations describe the relationship between daily personal NO2 exposure
6 and daily ambient NO2 concentration across all subjects in the study. Daily-average correlations
7 are calculated by averaging exposure across subjects for each day. Daily-average correlations
8 then describe the relationship between the daily average exposure and daily ambient NO2
9 concentration.
10 The type of correlation analysis can have a substantial effect on the value of the resultant
11 correlation coefficient. Mage et al. (1999) mathematically demonstrated that very low
12 correlations between personal exposure and ambient concentrations could be obtained when
13 people with very different nonambient exposures are pooled, even though their individual
14 longitudinal correlations are high. Data shown in Table AX3.13 demonstrate that the
15 longitudinal correlations between personal exposure and ambient NC>2 concentrations were
16 higher than the correlations obtained from a pooled data set.
17 In conclusion, personal exposure to ambient/outdoor NC>2 is determined by many factors.
18 Physically, the determinant factors are ambient concentration, air exchange rate, NC>2 penetration
19 and decay rate, and also the fraction of time people spend outdoors. These factors are in turn
20 determined by factors, such as season, location of home, outdoor temperature and so on. These
21 factors all help determine the contribution of ambient/outdoor generated NC>2 to personal
22 exposures. Personal activities determine when, where and how people are exposed to NC>2. The
23 variations of these physical factors and indoor sources determine the strength of the association
24 between personal exposure and ambient concentrations both longitudinally and cross-sectionally.
25 In the absence of indoor and local sources, the personal exposure level is in between the ambient
26 level and the indoor level, but in the presence of indoor and local sources, personal exposures
27 could be much higher than both indoor and outdoor concentrations. Again, the discrepancies
28 between personal exposures and ambient levels are determined by the considerations given
29 above. Most researchers found that personal NC>2 was significantly associated with ambient NC>2
30 but the strength of the association ranged from poor to good. Based on that finding, some
31 researchers concluded that ambient NC>2 is a good surrogate for personal exposure, while others
August 2007 AX3-66 DRAFT-DO NOT CITE OR QUOTE
-------
1 reminded us that caution must be exercised if ambient NO2 is used as a surrogate for personal
2 exposure. The crude association between personal exposure and ambient monitors could be
3 improved when indoor or other local sources are well controlled during exposure assessment.
4 The ambient contribution to personal exposure could be evaluated by the attenuation factor,
5 which is the ratio of personal exposure to ambient level in the absence of indoor sources, or the
6 slope of the RCS regression model. The attenuation factor in the studies shown in Table AX3. 1 1
7 ranged from 0.3 to 0.6. The ambient and nonambients contributions could also be calculated
8 from the RCS model, although only a few studies provide enough information for us to calculate
9 them. The accuracy and precision of the estimation of ambient and nonambient contributions to
10 personal exposures could be improved if the variations for the physical factors given above were
1 1 known. The mass balance model could give a more accurate and precise estimation if we knew
12 the distributions of these key physical factors.
13 Because people are exposed to ambient NO2 in microenvironments, and the fact that NO2
14 is heterogeneously distributed in urban areas (as shown in Section AX3.3.2), the association of
15 personal exposure to ambient NC>2 could be modified by microenvironmental characteristics.
16 Personal total exposure will be decomposed and further evaluated in each microenvironment in
17 the following section.
18
19 AX3.5.2 Personal Exposure in Microenvironments
20
2 1 Personal Exposure in the Residential Indoor Environment
22 People spend most of their daily time in a residential indoor environment (Klepeis et al.,
23 2001). NC>2 found in an indoor environment originates both indoor and outdoors; and therefore,
24 people in an indoor environment are exposed to both indoor and outdoor generated NC>2. The
25 physical parameters, which determine personal exposure to ambient and nonambient generated
26 NC>2, have been shown in Equations AX3-13 to AX3-16. In a residential indoor environment,
27 personal exposure to NC>2 can be summarized below (notations are the same as those in
28 Equations AX3-13 to AX3-16):
Et = Ea + Enona = aCo + Enona = !>' + (/ ~ y}[PaAfl + k)]}Ca
29 tf ~*~ ' ~infa nona (AX3-17)
30 if people spend 100% of their time indoors, the equation above can be recast as
August 2007 AX3-67 DRAFT-DO NOT CITE OR QUOTE
-------
Enona = aCa + Enona = FinfCa + Enona = [Pa/(a + k)]Ca + S/[V(a + k)] =
(AX3-18)
2 In other words, in a residential indoor environment, personal exposure concentration equals the
3 residential indoor concentration (if there is no personal cloud) which can be broken down into
4 two parts: indoor generation and ambient contribution.
5 In a residential indoor environment, the relationship between personal NC>2 exposure and
6 ambient NC>2 can be modified by the indoor environment in the following ways: (1) during the
7 infiltration processes, ambient NC>2 can be lost through penetration and decay (chemical and
8 physical processes) in the indoor environment, and therefore, the concentration of indoor NC>2 of
9 ambient origin is not the ambient NC>2 concentration but the product of the ambient NC>2
10 concentration and the infiltration factor (FM, or a if people spend 100% of their time indoors);
11 (2) in an indoor environment, people are exposed to not only ambient generated NC>2 but also
12 indoor generated NC>2, and therefore, the relative contribution of ambient and nonambient NC>2 to
13 personal exposure depends not only on the ambient NC>2 concentration but also on the infiltration
14 factor (attenuation factor) and the indoor source contribution; (3) the strength of the association
15 between personal exposure to NO2 of ambient origin and ambient NO2 concentration is
16 determined by the temporal and spatial variation in the infiltration factor; and (4) the strength of
17 the association between personal total exposure and ambient NC>2 is determined by the variation
18 in the indoor source contribution and the variation in the infiltration factor. Below, factors
19 affecting infiltration factor and the indoor source contribution will be evaluated, and the key
20 issues, such as those mentioned above, related to ambient contribution to personal NC>2 exposure
21 will be addressed.
22 Infiltration factor (F;nf) of NC>2, the physical meaning of which is the fraction of ambient
23 NO2 found in the indoor environment, is determined by the NO2 penetration coefficient (P), air
24 exchange rate (a), and the NC>2 decay rate (&), through the equation F[nf= Pa/(a + k). Information
25 on P and k for NC>2 is sparse. In most mass balance modeling work, researchers assume P
26 equals 1 because NC>2 is a gas, and assume k equals 0.99 IT1, which is cited from Yamanaka
27 (1984). Yamanaka (1984) systematically studied the decay rates of NC>2 in a typical Japanese
28 living room. The author used a chemical luminescence method to monitor the decay process of
29 indoor-originated NC>2. The author observed that the decay process of NC>2 followed
30 approximately first-order kinetics. The author also pointed out that the NC>2 decay processes was
August 2007 AX3-68 DRAFT-DO NOT CITE OR QUOTE
-------
1 both surface type and relative humidity (RH) dependent: Under low RH (43.5-50%), the sink
2 rate of NO2 was 0.99 ± 0.19 IT1, independent of interior surface properties; however, the NO2
3 decay rate increased in proportion to RH above 50%, and in that RH range, the decay rate
4 depended on the interior surface properties. Yang et al. (2004) estimated a decay rate of 0.94 h"1
5 for Seoul and 1.05 h"1 for Brisbane. As it is well known, the decay rate is dependent on lots of
6 indoor parameters, such as indoor temperature, relative humidity, surface properties, surface-to-
7 volume ratio, the turbulence of air flow, and co-existing pollutants, et al. However, in the indoor
8 air modeling studies, a decay rate of 0.99 h"1 is a widely accepted parameter (Dimitroulopoulou
9 et al., 2001; Kulkarni et al., 2002). As a result, it will over- or underestimate the real NC>2 decay
10 rate. A penetration coefficient (P) of 1 is also widely accepted for NO2 (Kulkarni et al., 2002;
11 Yang et al., 2004). No systematic investigations have been found on NO2 penetration behaviors.
12 As a general principle, the upper limit of the penetration coefficient is 1, and it would be less
13 than 1 if NC>2 lost during penetration due to diffusion and chemical reactions. Therefore, using a
14 penetration coefficient of 1 gives an upper bound to the estimated infiltration coefficient.
15 Among P, k, and a, air exchange rate (a) is the most solidly based parameter and can be obtained
16 from a nationwide database (Pandian et al., 1998).
17 Although specific P, &, and a were not reported by most studies, a number of studies
18 investigated factors affecting P, &, and a (or indicators of P, k, and a), and their effects on indoor
19 and personal exposures (Lee et al., 1996; Cotterill et al., 1997; Monn et al., 1998; Garcia-Algar
20 et al., 2003; S0rensen et al., 2005; Zota et al., 2005). Garcia-Algar et al. (2003) observed that
21 double-glazed windows had significant effect on indoor NC>2 concentrations. Homes with
22 double-glazed windows had lower indoor concentrations (6 ppb lower) than homes with single
23 glazed windows. Cotterill et al. (1997) reported that single or double glazed window was a
24 significant factor affecting NC>2 concentrations in kitchen in the gas-cooker homes (31.4 ppb and
25 39.8 ppb for homes with single and double glazed windows, respectively). The reduction of
26 ventilation can block outdoor NC>2 from coming into the indoor environment, and at the same
27 time it can also increase the accumulation of indoor generated NC>2. The same effect was found
28 for homes using air conditioners. Lee et al. (2002) observed that NO2 was 9 ppb higher in homes
29 with an air conditioner than homes without. The authors also observed that the use of humidifier
30 would reduce indoor NC>2 by 6 ppb. House type was another factor reported affecting ventilation
31 (Lee et al., 1996; Garcia-Algar et al., 2003). Lee et al. (1996) reported that the building type was
August 2007 AX3-69 DRAFT-DO NOT CITE OR QUOTE
-------
1 significantly associated with air exchange rate: the air exchange rate ranged from 1.04 h l for
2 single dwelling unit to 2.26 h"1 for large multiple dwelling unit. Zota et al. (2005) reported that
3 the air exchange rates were significantly lower in the heating season than the non-heating season
4 (0.49 h"1 for the heating season and 0.85 h"1 for the non-heating season respectively). It should
5 be pointed out that both P and k are functions of complicated mass transfer mechanisms on the
6 indoor surfaces, and therefore they are associated with air exchange rate, which has an impact on
7 the turbulence of air flows indoors. However, the relationship between P, k, and a has not been
8 thoroughly investigated. Factors mentioned above can significantly affect P, k, and a, and thus
9 affect the relationship between indoor and outdoor NO2 concentration, and personal exposure
10 and outdoor NO2 concentration.
11 Due to the lack of specific P, k, and a for study homes or a study population, instead of using P,
12 k, and a, alternative approaches to obtain the infiltration factor are the ratio of indoor/outdoor
13 NC>2 and the regression based RCS model. The basic rationale of the RCS model has been
14 introduced in the previous section. Without indoor sources, the ratio between indoor NC>2 and
15 outdoor NC>2 should be always less than or equal to 1. If the indoor to outdoor ratio is larger than
16 1 (after adjusting for measurement error), we can surely say that indoor sources exist. However,
17 if an indoor/outdoor ratio is less than one, we cannot exclude the effect of indoor sources;
18 otherwise, the infiltration factor would be overestimated. In order to use an indoor/outdoor ratio
19 as the infiltration factor, study designs and questionnaires must be carefully read, and only the
20 ratio for homes without identified indoor sources can be used as an indicator of infiltration
21 factor. The population averaged infiltration factor is the slope of the regression line of indoor
22 concentration vs. outdoor concentration. The reliability of the regression slope is dependent
23 upon the sample size and how to deal with the outlier effects. Indoor/outdoor ratios and the
24 regression slopes are summarized in Table AX3.14. Those numbers, which can be considered as
25 an infiltration factor, are underlined and marked with bold font. Most of the infiltration factors
26 ranges from 0.4 to 0.7. Theoretically, infiltration factor is a function of air exchange rate, which
27 has been indicated by season in some studies. However, most studies do not report the
28 infiltration factor by season, and therefore, a seasonal trend of infiltration factor could not be
29 observed in Table AX3.14.
30
August 2007 AX3-70 DRAFT-DO NOT CITE OR QUOTE
-------
1 As mentioned before, personal NCh exposure is not only affected by air infiltrating from
2 outdoors but also by indoor sources. The NO2 residential indoor sources reported are gas
3 cooking, gas heating, kerosene heating, smoking and burning candles (Schwab et al., 1994;
4 Spengler et al., 1994; Nakai et al., 1995; Lee et al., 1996; Linaker et al., 1996; Cotterill et al.,
5 1997; Farrow et al., 1997; Kawamoto et al., 1997; Lee, 1997; Raaschou-Nielsen et al., 1997;
6 Aim et al., 1998; Levy et al., 1998a; Monn et al., 1998; Garrett et al., 1999; Chao, 2001;
7 Dennekamp et al., 2001; Dutton et al., 2001; Emenius et al., 2003; Kodama et al., 2002; Lee
8 et al., 2002; Mosqueron et al., 2002; Garcia-Algar et al., 2003; Garcia-Algar et al., 2004; Lai
9 et al., 2004; Lee et al., 2004; Yang et al., 2004; Zota et al., 2005; S0rensen et al., 2005; Lai et al.,
10 2006). Spengler et al. (1994) reported that personal exposures in homes with gas range with
11 pilot light were 15 ppb higher than those in homes with electric range, and it was 5 ppb higher in
12 homes with gas range without pilot light than homes with electric ranges. Schwab et al. (1994)
13 reported that homes with gas stove with pilot light had higher indoor NC>2 concentrations (peak
14 concentrations ranging from 30 to 35 ppb), followed by homes with gas stove without a pilot
15 light (peak concentrations ranging from 15 to 20 ppb) and then homes with electric stoves (peak
16 concentrations ranging from 5 to 10 ppb). In an international study, Levy et al., (1998a) reported
17 that the use of a gas stove in the home was the dominant activity influencing NC>2 concentrations
18 with a 67% increase in mean personal NC>2 exposure and an increase in indoor-outdoor ratios
19 from 0.7 to 1.2. Smoking was found to be another significant factor elevating personal and
20 indoor NO2 exposure. Monn et al. (1998) reported that during 1-week integrated measurement,
21 smoking contributed 1 ppb more NC>2 exposure. Aim et al. (1998) reported that one-week
22 integrated personal NC>2 exposure for smokers and nonsmokers were 12.9 ppb and 10.7 ppb,
23 respectively. Zota et al. (2005) observed that smoking was not a significant indoor source.
24 However, the authors pointed out that the effect of smoking might have been overwhelmed by
25 the presence of the gas stove. S0rensen et al. (2005) found that burning candles were
26 significantly associated with the elevation of indoor NC>2 (p = 0.02). NC>2 concentration in an
27 indoor environment affected by the indoor sources is not homogeneously distributed: NC>2
28 concentration is usually the highest in the kitchen, lowest in the bedroom and the concentration
29 in a livingroom is in between as shown in Table AX3.15. The concentration differences between
30 a bedroom and a kitchen ranged from 1 ppb to 28 ppb, and largest difference occurred in homes
31 with gas stoves.
August 2007 AX3-71 DRAFT-DO NOT CITE OR QUOTE
-------
1 The concentration differences in indoor microenvironments reflect the differences in
2 personal exposure in those microenvironments, which is related to personal activities and
3 behaviors. People who spend more time in a kitchen are expected to have higher NO2 exposures.
4 Also, in most exposure studies, integrated indoor and personal exposures were measured from
5 2 days to 2 weeks with passive samplers. Therefore, the peak exposure concentration could be
6 even higher.
7 Indoor source contributions to indoor and personal exposure are determined by indoor
8 source strength (S), house volume (F), air exchange rate (a) and the NO2 decay rate (K) in an
9 indoor environment, through the equation Cnona = SI[V(ci + &)]. Indoor source strength has been
10 summarized in a previous section (Indoor sources and concentrations of nitrogen oxides). With a
11 mass balance approach, Yang et al. (2004) reported that the source strength for electric range
12 was 3.5 ppb/h, 11.5 ppb/h for gas range in Brisbane, and 23.4 ppb/h for gas range in Seoul. The
13 age of house and the house type are associated with ventilation, indoor sources, and house
14 volume. As mentioned before, Lee et al. (1996) reported that the building type was significantly
15 associated with volume of dwelling unit, and air exchange rate. Garrett et al. (1999) reported
16 that older houses were associated with higher nitrogen dioxide levels, possibly as a result of
17 older and less efficient appliances in older homes or due to smaller rooms.
18 The relative contribution of indoor and outdoor NO2 to personal and indoor exposures
19 can be easily and precisely calculated if we know the physical determinants, such as P, k, a, and
20 indoor source strength. Probability based exposure models, such as SHEDS and APEX, could be
21 used to evaluate the personal exposure to indoor and outdoor generated NO2. Basically, those
22 exposure models incorporate the physical and chemical processes determining indoor pollutant
23 concentrations as a function of outdoor concentration, indoor emission rates and building
24 characteristics; the combination of a microenvironment model and personal activity model will
25 allow researchers to evaluate the personal exposure to indoor and outdoor generated NO2. Due
26 to the lack of those parameters in publications, we are going to use a regression based RCS
27 model to evaluate the contribution of indoor and outdoor generated NO2 to personal exposure.
28 The rationale to use the RCS model to estimate indoor and outdoor contribution to indoor and
29 personal NO2 have been introduced in the previous section. In summary, the regression intercept
30 of indoor NO2 concentration vs. outdoor NO2 concentration is the population mean indoor
31 contribution to indoor NO2; and the difference between the population mean NO2 and the
August 2007 AX3-72 DRAFT-DO NOT CITE OR QUOTE
-------
1 intercept in the population mean of outdoor contribution to indoor NC>2. The RCS model results
2 are summarized in Table AX3.16. As shown in Table AX3.16, the overall ambient contribution
3 to indoor NC>2 is around 70% with a wide range from 40 to 90%. Indoor generated NC>2
4 contribution is 10-20% less for homes with electric stoves if electric stove then indoor
5 contribution is usually zero. With the lack of indoor sources, the role of indoor environment is a
6 sink for outdoor generated NC>2 due to physical and chemical losses of NC>2 in the indoor
7 environment (Yamanaka et al., 1984; Ekberg 1996; Kraenzmer 1999; Chao et al., 2001). Chao
8 (2001) reported that the average sink strength of NC>2 in an indoor environment in Hong Kong
9 was 0.42 mg/h.
10 In theory, personal exposure of ambient origin should be at least as much as the indoor
11 NC>2 of ambient origin in that people spend time in either an indoor or an outdoor environment.
12 However, it was shown in the previous part (Table AX3.12) that the ambient contribution to
13 population exposure ranged from 20% to 50% based on four studies (Rojas-Bracho et al., 2002;
14 Monn et al., 1998; Levy et al., 1998a; Spengler et al., 1994); and results here show that the
15 ambient contribution to indoor NC>2 is around 70% with a wide range from 40 to 90% based on
16 another four studies (Mosqueron et al., 2002; Yang et al., 2004; Kulkarni et al., 2002; Monn
17 et al., 1998). It is not clear at present why the indoor NC>2 of ambient origin is larger than the
18 personal NC>2 exposure of ambient origin.
19 The strength of the indoor, outdoor and personal NC>2 associations (rp: Pearson
20 correlation coefficient; rs: Spearman correlation coefficient; and R2: coefficient of
21 determination) are summarized in Table AX3.17. The strength of the associations are
22 determined by the variation in F;nf (P, k, and a) and indoor source contributions from home to
23 home and from day to day. In general, the correlation between indoor and outdoor NC>2 ranges
24 from poor to good (rp: 0.06 to 0.86). When we break down the correlation coefficient by season
25 and indoor sources, it is obvious that the association between indoor and outdoor NC>2 is stronger
26 during spring and summer but weaker during wintertime, and the association is stronger for
27 homes without indoor sources but weaker for homes with strong indoor sources. Mukala et al.
28 (2000) reported an rp of 0.86 for the indoor and outdoor NO2 association during the spring and it
29 reduced to 0.54 during the winter. Spengler et al. (1994) reported that the associations were
30 0.66 and 0.75 (rp) for homes with and without air conditioning system, respectively. Emenius
31 et al. (2003) reported that the association between indoor and outdoor NC>2 was 0.69 (rp) for
August 2007 AX3-73 DRAFT-DO NOT CITE OR QUOTE
-------
1 homes without smoker and without gas stove using, but the association was not significant for
2 homes with gas stove or smokers. Yang et al. (2004) reported that the indoor and outdoor NC>2
3 association was 0.70 (R2) for homes with electric ranges, and was 0.57 (R2) for homes with gas
4 ranges. In other words, personal exposure to ambient NC>2 in a residential indoor environment
5 will be modified the least when the air exchange rate is high and the indoor source contribution
6 is not significant. Considering the large spatial variation in ambient NC>2 concentrations and the
7 relative sparseness of ambient NC>2 monitors, the associations between indoor and outdoor
8 concentrations are usually stronger than the associations between indoor and ambient
9 concentrations. As shown in Table AX3.17, a stronger personal vs. residential indoor
10 relationship than the personal vs. outdoor relationship has been reported by most studies (Lai
11 et al., 2004; Monn et al., 1998, Levy et al., 1998a; Spengler et al., 1994; Kousa et al., 2001;
12 Linaker et al., 1996), which is a reminder that personal exposure to ambient NC>2 mostly happens
13 in the residential indoor environment. It should be pointed out that the association between
14 indoor, outdoor and personal NC>2 and the relative contributions of indoor and outdoor NC>2 to
15 indoor and personal exposures were calculated based on time integrated indoor, outdoor and
16 personal NC>2 measurement with passive samplers and an average measurement time of a couple
17 of days to two weeks. In most studies, an equilibrium condition was assumed and the effects of
18 dynamics on the indoor, outdoor, and personal association were not evaluated, which could result
19 in missing the peak exposure and obscuring the real short-term outdoor contribution to indoor
20 and personal exposure. For example, the NO2 concentrations at locations close to busy streets in
21 urban environments may vary drastically with time. If the measurement is carried out during a
22 non-steady-state period, the indoor/outdoor concentration ratio may indicate either a too low
23 relative importance of indoor sources (if the outdoor concentration is in an increasing phase) or a
24 too high relative importance of indoor resources (if the outdoor concentration is in a decreasing
25 phase). The lower the air exchange rate, the greater the error due to the effects of transients
26 (Ekberg et al., 1996).
27
28 School and Office
29 Workplaces (schools and offices) are the places where people spend most of their time after
30 homes in an urban area. The location, indoor sources as well as the ventilation pattern of schools
31 and offices could be different from people's homes. Therefore, personal exposure patterns in
August 2007 AX3-74 DRAFT-DO NOT CITE OR QUOTE
-------
1 schools and offices could be different from exposure patterns at home. However, NO2
2 concentrations in schools and offices have only been measured in only a few exposure studies.
3 Most studies reported the personal exposure levels were lower than or equal to office
4 NC>2 levels. Lai et al. (2004) reported that a cohort in Oxford spent 17.5% of their daily time in
5 offices, and mean personal total NO2 exposure was 15 ppb and 16.8 ppb for mean office
6 concentrations. Mosqueron et al. (2002) reported Paris office worker exposure levels and no
7 significant difference was found between personal total exposure (22.8 ppb) and NC>2
8 concentrations in office (23.5 ppb). Personal exposures in schools were studied in Helsinki,
9 Southampton and Southern California. Aim et al. (1998) and Mukala et al. (2000) reported the
10 personal exposure levels in Helsinki for pre-school children. They reported that median personal
11 exposures were lower than the median NO2 concentrations measured inside the day care center
12 (13.1 ppb for personal exposure versus 18.8 ppb for inside day-care center for downtown winter;
13 14.7 ppb versus 24.1 ppb for downtown spring; 8.9 ppb versus 15.2 ppb for suburban winter; and
14 8.9 ppb versus 13.1 ppb for suburban spring). Linaker et al. (1996) found that the geometric
15 mean of school children exposures (18.8 ppb) was higher than geometric means of the NC>2
16 concentrations in classrooms (8.4 to 14.1 ppb) in a study of children's exposures to NC>2 in
17 Southampton, UK. A similar exposure pattern was found by Linn et al. (1996) during the
18 Southern California school children exposure study. During the study, personal exposure
19 (22 ppb) was higher than the NC>2 concentration inside school (16 ppb). NC>2 concentration in
20 school/office is determined by ambient NO2 level, local traffic sources, floor height and building
21 ventilation pattern. Partti-Pellinen et al. (2000) studied the effect of ventilation and air filtration
22 systems on indoor air quality in a children's day-car center in Finland. Without filtration, NOX
23 and PM generated by nearby motor traffic penetrated readily indoors. With chemical filtration,
24 50 to 70% of nitrogen oxides could be removed. The authors suggested that the possible adverse
25 health effects of nitrogen oxides and particles indoors could be countered by efficient filtration.
26 Mosqueron et al. (2002) reported 24% of variations in in-office NC>2 concentrations could be
27 explained by outdoor NC>2 levels (18%), and floor height (6%) and an inverse relation was
28 observed between in-office concentration and floor height. Aim et al. (1998) attributed the high
29 NC>2 concentration in the day-care center to its close to major roads. Obviously, the relative
30 scale of personal exposure and school concentration also depends on personal activities outside
31 schools and workplaces.
August 2007 AX3-75 DRAFT-DO NOT CITE OR QUOTE
-------
1 Significant associations between personal exposure and workplace concentrations were
2 reported by most studies. Mosqueron et al. (2002) reported office NO2 was a significant
3 predictor of personal exposure and 15% of the personal exposure was explained by time
4 weighted office NO2 concentrations. Aim et al. (1998) reported population NO2 exposures were
5 highly correlated with the NC>2 levels inside the day-care centers (R2 = 0.88). However, Lai et al.
6 (2004) reported a nonsignificant Pearson correlation coefficient (0.15) between personal
7 exposure and workplace indoor concentration and the authors suggested that the strong
8 residential indoor sources and long time indoors obscured the personal versus office relationship.
9 Personal total exposure is a function of NC>2 concentrations in different indoor and outdoor
10 microenvironments and how long a person stays in that microenvironment. The large variation
11 of NC>2 exposure in some microenvironments could obscure the association between personal
12 exposure and NC>2 concentrations in other microenvironments.
13
14 In Traffic
15 On-road NC>2 concentrations could be substantially higher than ambient or residential
16 outdoor NC>2 concentrations, especially in a street canyon, which are narrow with enclosing
17 architecture and slow-moving traffic. As shown in Figure AX3.22, NC>2 in heavy traffic
18 (-60 ppb) can be over twice the concentration in a residential outdoor level (-26 ppb) in North
19 America (Lee et al., 2000). The UK and Scandinavian data in the plot may have been obtained
20 outside homes close to traffic. Westerdahl et al. (2005) reported on-road NC>2 concentrations in
21 Los Angeles ranging from 40 to 70 ppb on freeways, and 20 to 40 ppb on residential or arterial
22 roads. People in traffic can potentially experience such high concentrations and NO2 exposures
23 due to the high air exchange rates for vehicles. Park et al. (1998) measured the air exchange
24 rates in three stationary automobiles under four conditions: windows closed and no mechanical
25 ventilation, windows closed with fan set on recirculation, windows open with no mechanical
26 ventilation, and windows closed with the fan set on fresh air. The reported air exchange rates
27 varied from 1.0 to 3.0 IT1 with windows closed and no mechanical ventilation to 36.2 to 47.5 IT1
28 with windows closed and the fan set on fresh air. It implies that the NC>2 concentration inside a
29 vehicle is at least the same as the surrounding NC>2 concentration, or in other words, "on-road"
30 NO2 can quickly and almost completely infiltrate into the "in-vehicle" environment contribute to
31 in-vehicle personal exposures. Although people only spend a small fraction of their time in
August 2007 AX3-76 DRAFT-DO NOT CITE OR QUOTE
-------
— «.
•3
$$
o
0
1
3
O
E
o
O»
2
0
<
v>U
45-
40-
35-
30-
25-
20-
15-
10-
5-
n-
0
t
f
/ Korea
t
,oUK
'
>
t
Western Europe * North America
' O
/ o Japan
/<> o Brisbane
Scandinavia
'
t
t
0 50 100 150
Estimated NO2 Level during Commuting (ppb)
Figure AX3.22. Average residential outdoor concentration versus concentration
during commuting for
Source: Lee et al. (2000).
1 traffic (5% to 7%), exposure while commuting could be a significant contributor to personal
2 exposure to NC>2 due to the high concentration of NC>2 in traffic. Liard et al. (1999) reported that
3 both NO and NC>2 exposure levels increased with the number of hours spent in a car. During the
4 study, NO and NO2 concentrations were separated into three levels according to the distribution
5 tertiles. Personal exposure levels increased from low to high when accordingly people spent
6 from 2.5 h in a car to 6.7 h in a car. The same relationship only held for one of the two sampling
7 periods, in which personal NO2 exposures increased from low to high when the time people
8 spent in a car increased from 3.5 h to 5.7 h.
9 Bell and Ashenden (1997) and Kirby et al. (1998) reported the NO2 concentration along
10 major roads and street canyons in UK, and they found that monthly mean NO2 concentrations on
1 1 major roads were consistently higher (up to 20 ppb) than those found 250 m away from the
12 major roads. It is important to distinguish between short-term peak exposure and chronic
August 2007
AX3-77
DRAFT-DO NOT CITE OR QUOTE
-------
1 exposures because health effects associated with short-term peak exposures might be different
2 from chronic exposures to ambient NC>2.
3 Other than infiltration of ambient air, the intrusion of the vehicle's own exhaust into the
4 passenger cabin is another NO2 source contributing to personal exposure while commuting. The
5 intrusion of a school bus's own exhaust into the bus cabin was found by Sabin et al. (2005), but
6 the fraction of air inside the bus cabin from the bus's own exhaust was small, ranging from
7 0.02% to 0.28%. Marshall and Behrentz (2005) also reported the intrusion of exhaust into the
8 bus cabin and indicated that average per capita inhalation of emissions from any single bus is
9 105-106 times greater for a passenger on that school bus than for a typical resident in the same
10 area. CARB (2007) reported that self-pollution increased with increasing age of the bus. Fuel
11 type could be another factor affecting personal exposure while commuting. Sonetal. (2004)
12 found that the two-day averaged NO2 exposures for taxi drivers using LPG fueled vehicles
13 (26.3 ppb) were significantly lower than those using diesel-fueled vehicle (38.1 ppb). However,
14 in another taxi driver exposure study, Lewne et al. (2006) did not find an effect on taxi driver
15 exposures to NC>2 due to fuel differences (diesel versus petrol). Sabin et al. (2005) reported that
16 NC>2 concentrations were significantly higher inside diesel buses than inside the compressed
17 natural gas buses. CARB (2006) showed that the NC>2 concentrations on a conventional diesel
18 bus was 2.8 times higher than the ambient concentration (76 ppb in cabin versus 27 ppb in
19 ambient) while windows were closed, and 3.85 times higher than the ambient concentration
20 (77 ppb in cabin versus 20 ppb in ambient) while windows were open. However, the ratio of
21 cabin NC>2 to ambient NC>2 was much lower for a compressed natural gas bus: 1.2 for windows
22 closed and 2.2 for windows opened.
23 While commuting, concentrations for personal exposure or in a vehicle cabin could be
24 substantially higher than corresponding residential indoor, outdoor, and ambient concentrations.
25 Sabin et al. (2005) measured concentrations of a number of pollutants (black carbon, particulate
26 PAHs and NC>2 in school buses on routes in Los Angeles. Mean cabin concentrations for
27 individual runs ranged from 24 to 120 ppb. Concentrations of NC>2 tended to be slightly higher
28 for open compared to closed windows on urban routes. These concentrations were typically
29 factors of 2.3 to 3.4 higher than at ambient monitors in the area. However, the highest ratios
30 found ranged from 3.9 to 5.3. They concluded that children commuting in areas such as Los
31 Angeles may be exposed to much higher levels of pollutants than are obtained at ambient, central
August 2007 AX3-78 DRAFT-DO NOT CITE OR QUOTE
-------
1 site monitors. Lewne et al. (2006) reported work hour exposures to NO2 for taxi drivers
2 (25.1 ppb), bus drivers (31.4 ppb) and lorry drivers (35.6 ppb). The ratios of in-vehicle
3 exposures to urban background were 1.8, 2.7, and 2.8 for taxi drivers, bus drivers and lorry
4 drivers, respectively. Due to the high peak exposures during commuting, total personal exposure
5 could be underestimated if exposure in traffic are not considered; and sometimes exposure in
6 traffic can dominate personal exposure to NO2. In a personal exposure study in Brisbane and
7 Queensland, Australia, two-day averaged indoor, outdoor, and personal NO2 were measured by
8 Yanagisawa badges (Lee et al., 2000). Lee et al. (2000) found that estimated personal exposures
9 (22.5 ppb) significantly underestimated the measured personal exposures (28.8 ppb) if personal
10 exposures in traffic were not considered. Son et al. (2004) reported two-day averaged indoor,
11 outdoor, in vehicle and personal NO2 concentrations measured by passive filter badges for
12 31 taxi drivers in Korea. Measured personal concentrations (30.3 ppb) were higher than both
13 residential indoor (24.7 ppb) and residential outdoor concentrations (23.8 ppb). A stronger
14 correlation was observed between personal NO2 exposures and interior vehicle NO2 levels, than
15 for residential indoor and residential outdoor levels (rp = 0.89 for Personal versus Vehicle, rp =
16 0.74 for Personal versus Indoor; and rp = 0.71 for Personal versus Outdoor).
17 Variations in traffic exposure could be attributed to time spent in traffic, type of vehicle,
18 traffic congestion levels, encounters with other diesel vehicles, type of fuel and driving location
19 (urban/rural) (Sabin et al., 2005; Son et al., 2004; Chan et al., 1999).
20
21 Microenvironments Close to NO2 Sources
22 As suggested previously in this chapter, both large and small-scale variations exist in
23 ambient NO2 concentrations. In this section, those microenvironments and associated personal
24 exposures, which are close to traffic sources and might make significant contributions to total
25 personal NO2 exposures are analyzed. These microenvironments could be residential outdoor
26 environments and some other outdoor environments, such as parking lots and playgrounds; they
27 could also be indoor environments as well, such as homes and classrooms. Concentrations in
28 these microenvironments and personal exposure characteristics in these microenvironments will
29 be summarized below.
30 Many studies show that outdoor NO2 levels are strongly associated with distance from
31 major roads (the closer to a major road, the higher the NO2 concentration) (Gilbert et al., 2005;
32 Roorda-Knape et al., 1998; Lai et al., 2001; Kodama et al., 2002; Gonzales et al., 2005; Cotterill
August 2007 AX3-79 DRAFT-DO NOT CITE OR QUOTE
-------
1 et al., 1997; Nakai et al., 1995). Meteorological factors (wind direction and wind speed), and
2 traffic density are also important for interpreting measured NO2 concentrations (Gilbert et al.,
3 2005; Roorda-Knape et al., 1998; Rotko et al., 2001; Aim et al., 1998; Singer et al., 2004; Nakai
4 et al., 1995). Gonzales et al. (2005) found an inverse correlation between NO2 concentration and
5 distance from a highway (rp = -0.81, p < 0.001) in the El Paso region. Nakai et al. (1995)
6 reported the results of a study designed to explore the differences of indoor, outdoor and personal
7 exposure levels among residence zones located varying distances from major roads with heavy
8 traffic in Tokyo. The authors found that outdoor NO2 concentrations in Zone A (0-20 m from
9 the road) was always the highest among the three zones (Zone B was 20-150 m from the road,
10 and Zone C was a reference zone in a suburban area). The differences of the mean levels
11 between Zone A and Zone C ranged from 11 ppb to 39 ppb. Kodama et al. (2002) reported NO2
12 levels for indoor, outdoor and personal exposure among 150 junior high school student homes in
13 two major traffic areas in Tokyo. Forty-eight h average NO2 concentrations were measured by
14 Yanagisawa badge. NO2 tended to decrease according to distance from the roadside; the
15 difference was about 10 ppb between the roadside (0-50 m) and the site far away from the road
16 (200 m). Singer et al. (2004) reported results of the East Bay Children's Respiratory Health
17 Study. The authors reported weekly integrated NO2 and NOX concentrations measured by
18 Ogawa passive samplers placed outside ten elementary schools and selected student residences
19 during 14 weeks in spring and 8 weeks in fall 2001. The authors found that NO2 concentrations
20 increased with decreasing downwind distance for school and neighborhood sites within 350 m
21 downwind of a freeway, and schools located upwind or far downwind of freeways were
22 generally indistinguishable from one another and regional pollution levels. An exponential
23 equation was used to fit the measured concentrations to distance from the freeway: C(x) = KixK2
24 where C is the measured concentration and x is the distance (m) from a freeway. A high R2 was
25 observed (R2 = 0.80, KI = 128, and K2 = -0.356 for NO2; R2 = 0.76, KI = 376, and K2 = -0.468).
26 According to this equation, NO2 concentrations 100 m away from the freeway are about 20% of
27 those at roadside.
28 Elevated NO2 concentrations were also observed and reported in parking lots and school
29 playgrounds. Lee et al. (1999) reported the concentration of NO2 at a parking lot in Hong Kong
30 was 60 ppb, and the level was about the same for NO. Colbeck (1998) reported that
31 concentrations in two parking lots in Colchester, UK were similar to those measured at the curb
August 2007 AX3-80 DRAFT-DO NOT CITE OR QUOTE
-------
1 side. Exposure of car parking lot users to NC>2 is comparable to that arising in the vicinity of
2 roads with moderate traffic density (-9000 vehicles per day). NO2 concentrations in one parking
3 lot ranged from 30.4 to 47.1 ppb, while those in the payment booth ranged from 22.5 to 31.4 ppb.
4 Rundell et al. (2006) reported PMi, NO2, SO2, CO, and Os concentrations at four elementary
5 school playgrounds and one university soccer field in Pennsylvania. NC>2 concentrations were
6 below 100 ppb. The number concentration in the PMi size fraction decreased with distance
7 away from the highway (from 140,000 number/cm3 within 10 m of the road to 40,000
8 number/cm3 at 80 m).
9 Indoor environments, which are close to traffic, include buildings and houses along
10 major, busy roads. Most studies show that indoor NC>2 is correlated with outdoor NC>2, and is
11 also a function of distance to traffic, traffic density and meteorological parameters. The level of
12 indoor NC>2 in those microenvironments is also affected by indoor sources. Bae et al. (2004)
13 reported indoor and outdoor concentrations of NC>2 in 32 shoe stalls in Seoul, which were located
14 on busy streets. Working-hour (10 ± 2.1 h) NC>2 was measured by Yanagisawa passive filter
15 badges. Mean indoor and outdoor NC>2 concentrations were 57.4 and 58.1 ppb with a mean
16 indoor vs. outdoor ratio of 0.93. Maximum indoor and outdoor NC>2 concentrations were 94.1
17 and 96.3 ppb. In this study, outdoor traffic generated NC>2 is likely the main source of indoor
18 exposures due to the lack of indoor NC>2 sources. Outdoor and in-classroom NO2 were measured
19 using Palmes tubes during three 2-week periods in six city districts near motor ways in the West
20 of the Netherlands (Roorda-Knape et al., 1998). NO2 concentrations in classrooms were
21 significantly correlated with car and total traffic density (rp = 0.68), percentage of time
22 downwind (rp = 0.88) and distance of the school from the motorway (rp = -0.83). Cotterill et al.
23 (1997) measured indoor and outdoor NO2 in 40 homes in Huddersfield, UK, over three
24 consecutive two-week periods in late 1994 using Palmes tubes. The authors found that
25 proximity to a main road had little effect on indoor levels of nitrogen dioxide (a mean of 1 ppb
26 indoor concentration difference was found for homes close to main roads and homes close to
27 side roads). A t-test suggested that there was no difference in indoor levels of nitrogen dioxide
28 due to proximity to the main road after indoor sources were controlled by the type of cookers.
29 In this study, meteorological parameters were measured, but meteorological parameters were not
30 controlled during data analysis.
August 2007 AX3-81 DRAFT-DO NOT CITE OR QUOTE
-------
1 Personal exposure is determined by both indoor and outdoor levels of NC>2. Most studies
2 show significant associations between personal exposure and the traffic density. The influence
3 of indoor sources on personal exposure was also observed in those studies. Aim et al. (1998)
4 reported the weekly personal NC>2 exposures of 246 children aged 3-6 years in Helsinki. Weekly
5 personal exposures were measured for 13 weeks in winter and spring in 1991 using Palmes
6 tubes. The 13 week geometric mean of the NO2 exposures was higher for the children living in
7 the downtown (13.9 ppb) than in the suburban area (9.2 ppb, p = 0.0001). Rotko et al. (2001)
8 reported the EXPOLIS-Helsinki study results and observed that the NO2 exposure was
9 significantly associated with traffic volume near homes. The average exposure level of
10 138 subjects having low or moderate traffic near their homes was 12.3 ppb, while the level was
11 15.8 ppb for the 38 subjects having high traffic volume near home. Gauvin et al. (2001) reported
12 the VESTA study results. An index of traffic density and proximity was constructed as the ratio
13 of traffic density to distance from a roadway. The index was one of the significant interpreters of
14 personal exposure in all three cities (p < 0.05 for Grenoble and Toulouse, and 0.05 < p < 0.15 for
15 Paris). Kodama et al. (2002) showed that personal exposure was similar to residential home NC>2
16 concentration for residences along busy roads. The authors also observed that personal exposure
17 levels were higher than outdoor levels during the winter, while during the summer, personal
18 exposure levels were lower than ambient levels, due to the influence of indoor sources and low
19 ventilations in the winter. Although the personal to outdoor relationship was dominated by
20 indoor sources, the effects of outdoor NO2 on personal exposure could still be observed after
21 controlling the indoor source effects. Nakai et al. (1995) observed that personal exposure levels
22 basically followed the ambient concentrations patterns given above; i.e., exposures in Zone A
23 (0-20 m from the road) were the highest and exposures in Zone C (the suburban background
24 area) were the lowest for residents not using an unvented heater (as defined before, Zone A was
25 0-20 m from the road; Zone B was 20-150 m from the road. The maximum difference of
26 personal exposure between Zone A and Zone C was approximately 20 ppb. The NC>2 exposure
27 for a special population, athletes, was addressed by Carlisle et al. (2001). The authors pointed
28 out that athletes could be a potential population at risk, if the ambient NO2 concentration is high
29 because (1) inhalation rate increases during exercise, (2) a large fraction of air is inhaled through
30 the mouth during exercise, effectively bypassing the normal nasal mechanisms for the filtration
31 of large particles and soluble vapors, and (3) the increased air flow velocity carries pollutants
August 2007 AX3-82 DRAFT-DO NOT CITE OR QUOTE
-------
1 deeper into the respiratory tract and pulmonary diffusion capacity increases during exercise.
2 This might also be true for outdoor workers but few data are available to perform the exposure
3 assessment.
4 Although traffic is a maj or source of ambient NC>2, industrial point sources are also
5 contributors to ambient NC>2. However, no published reports were found to address the effect of
6 those sources on population exposure within the United States. Nerriere et al. (2005) measured
7 personal exposures to PM2 5, PMi0, and NC>2 in traffic dominated, urban background and
8 industrial settings in Paris, Grenoble, Rouen, and Strasbourg, France. They always found highest
9 ambient concentrations and personal exposures close to traffic. In some cases, urban and
10 background, concentrations of NC>2 were higher than in the industrial zone. However, PM levels
11 and personal exposures tended to be higher in the industrial area than in the traffic dominated
12 area. It should be remembered that there can be high traffic emissions in industrial zones, such
13 as in the Ship Channel in Houston, TX. In rural areas where traffic is sparse, other sources could
14 dominate. For example, Martin et al. (2003) found pulses of NC>2 release from agricultural areas
15 following rainfall and there are contributions from wildfires and residential wood burning.
16
17 Exposure Reconstruction
18 Personal exposure has been evaluated in each major microenvironment, where either the
19 NC>2 concentration is high or people spend most of their daily time. As shown in Equation AX3-
20 13, personal exposure could be reasonably reconstructed if we know the NC>2 concentration in
21 each microenvironment and the duration of personal exposure in each microenvironment. Levy
22 et al., (1998a) reconstructed personal exposures measured in an international study with a time-
23 weighted average exposure model (Equation AX3-12). The personal exposure was reconstructed
24 based on the measured NC>2 concentrations in residential indoor, residential outdoor, and
25 workplace microenvironments, and the time people spent in those environments. The mean
26 measured personal NC>2 exposure was 28.8 ppb and a mean of estimated NC>2 exposure was 27.2
27 ppb. The Spearman correlation coefficient between personal measured exposure and
28 reconstructed exposure was 0.81. The same approach was applied by Kousa et al. (2001) to
29 reconstruct the personal exposures in the EXPOLIS study. A correlation coefficient of 0.86 was
30 observed for the association between measured NO2 exposure and reconstructed NO2 exposure
31 (data were log-transformed), and the slope and the intercept were 0.90 and 0.22 respectively for
32 the reconstructed exposure vs. measured exposure. In the two studies mentioned above, NC>2
August 2007 AX3-83 DRAFT-DO NOT CITE OR QUOTE
-------
1 exposure during commuting was not measured. Probably that is part of the reason why
2 reconstructed NC>2 exposure was lower than the measured NO2 exposure.
3
4 AX3.5.3 Exposure Indicators
5 Physically, personal exposure levels are determined by those physical parameters in
6 Equations AX3-12 to AX3-16, i.e., the time people spend in each microenvironment and the NC>2
7 concentrations in each microenvironment, which is determined by source emission strength, air
8 exchange rate, penetration coefficient, the NO2 decay rate and the volume of the
9 microenvironment. Any factors that can influence the above physical parameters can modify the
10 level of personal exposure. These factors are defined as exposure indicators in this section. The
11 indoor, outdoor and personal NC>2 levels on each stratum of those factors will be summarized.
12 Those factors can be classified in to the following categories: (1) factors associated with
13 environmental conditions, such as weather and season; (2) factors associated with dwelling
14 conditions, such as the location of the house and ventilation system; (3) factors associated with
15 indoor sources, such as the type of range and the fuel type; (4) factors associated with personal
16 activities, such as the time spent on cooking or commuting; (5) socioeconomic status, such as the
17 level of education and the income level; and (6) demographic factors, such as age and gender.
18 Most studies addressed the influences of dwelling condition and indoor sources on indoor
19 and personal exposures. A few studies explored the impacts of environmental factors and
20 personal activities on personal exposures. Indoor and personal exposures have rarely been
21 stratified by socioeconomic and demographic factors. Indoor, outdoor, and personal exposure
22 levels are presented in Table AX3.18, stratified by environmental factors, dwelling conditions,
23 indoor sources, and personal activities factors. The effects of socioeconomic and demographic
24 factors on the indoor, outdoor, and personal levels are summarized in Table AX3.19.
25 Season is an environmental factor affecting both indoor and outdoor levels, and thus
26 personal NC>2 levels. During the wintertime, the mixing height is usually lower than during the
27 summer, and therefore concentrations of many primary pollutants are higher than in the summer.
28 Wintertime is also a heating season, which usually leads to higher indoor source emissions and
29 lower air exchange rates. Therefore, a higher indoor NC>2 concentration can be expected during
30 the winter. For most cases, the differences of indoor or personal NC>2 exposure between the
31 heating and non-heating season are within several ppb, but sometimes the difference could be
August 2007 AX3-84 DRAFT-DO NOT CITE OR QUOTE
-------
1 close to 20 ppb (Zota et al., 2005). Other environmental factors include day of the week
2 (weekday versus weekend), and the wind direction, as shown in Table AX3.18.
3 The dwelling conditions are also associated with indoor, outdoor, and personal NO2
4 levels. Location of the dwelling unit is an indicator of ambient NO2 source strength. A house
5 located in an urban center or close to a major road is expected to have higher outdoor and indoor
6 NO2 levels, and the differences in NO2 exposures are often within 20 ppb based on passive
7 sampler monitoring. The age of the house, house type, and window type can affect the
8 ventilation of dwelling units, and sometimes the type of heating and cooking applicances in a
9 house. Range and fuel type are the indoor source factors discussed the most in the literature. It
10 is common to see differences larger than 10 ppb in indoor and personal NO2 exposures between a
11 gas range home (especially gas range with pilot light) and an electric range home. Sometimes
12 the differences could be as high as 40 ppb. For peak short-term exposures, the difference could
13 reach 100 ppb.
14 The level of personal exposure is dependent upon the time a person spends in each
15 microenvironment. Kawamoto et al. (1997), Levy et al. (1998a), and Chao and Law (2000)
16 clearly showed that personal NO2 exposure increases with time spent cooking or commuting.
17 The common findings are summarized above. However, there are inconsistencies in the
18 literature. For example, smoking is claimed to be a significant factor in some studies but not in
19 others, and the same can be said for proximity to a major road. For another example, a higher
20 indoor NO2 level could be found in a rural home rather than in an urban home (Table AX3.18),
21 although most studies found the opposite. Part of the reason is that exposure indicators function
22 together, as a multidimensional parameter space, on indoor and personal exposures. They are
23 not independent of each other. Unfortunately, studies have rarely been conducted to understand
24 the associations between these exposure indicators and to use the study findings to explain
25 indoor and personal NO2 exposures.
26 More effort put on exposure indicator studies should help in finding better surrogate
27 measurements for personal exposures. Although misclassifying exposures in epidemiological
28 studies is almost inevitable, and it is unlikely that the personal exposures of all subjects will be
29 measured, a better knowledge of the effects of exposure indicators on personal exposure will
30 help reduce exposure errors in exposure and epidemiological studies and help interpret those
31 study results.
August 2007 AX3-85 DRAFT-DO NOT CITE OR QUOTE
-------
1 AX3.6 CONFOUNDING AND SURROGATE ISSUES
2 Confounding is the technical term for finding an association for the wrong reason. It is
3 associated with both the exposure and the disease being studied, but is not a consequence of the
4 exposure. The confounder does not need to be an exposure for the disease under study. The
5 confounding variable can either inflate or deflate the true relative risk.
6 Since epidemological studies of NO2 often use ambient concentrations to reflect
7 exposures, whether confounding of NC>2 findings is possible can be determined by examining
8 associations among ambient concentrations and personal exposures to NC>2 and its relevant
9 copollutants. Importantly, by examining these associations, it is also possible to evaluate
10 whether a copollutant may act as a confounder or as a proxy of ambient NC>2 concentrations.
11 The potential for confounding of ambient NC>2 health effects is discussed in terms of four
12 relationships: (1) ambient NO2 and ambient copollutant concentrations, (2) personal NO2 and
13 personal copollutant exposures, (3) personal NC>2 exposures and ambient copollutant
14 concentrations, and (4) ambient NC>2 concentrations and personal copollutant exposures.
15
16 1) Associations between Ambient NO 2 and Ambient Copollutant Concentrations
17 Confounding of NC>2 health effects is often examined at the ambient level, since ambient
18 concentrations are generally used to reflect exposures in epidemiological studies. The majority
19 of studies examining pollutant associations in the ambient environment have focused on ambient
20 NC>2, PM2.s (and its components), and CO, with fewer studies reporting the relationship between
21 ambient NC>2 and ambient 63 or 862.
22 Correlations between concentrations of ambient NO2 and other ambient pollutants, PM2.5
23 (and its components where available), CO, Os and SC>2 are summarized in Table AX3.20. Data
24 were compiled from Environmental Protection Agency's Air Quality System and a number of
25 exposure studies. Mean values of site-wise correlations are shown. As can be seen from the
26 table, NO2 is moderately correlated with PM2.5 (range: 0.37 to 0.78) and with CO (0.41 to 0.76)
27 in suburban and urban areas. At rural locations, such as Riverside, CA, associations between
28 ambient NO2 and ambient CO concentrations (both largely traffic-related pollutants) are much
29 lower, likely as the result of other sources of both CO and NO2 increasing in importance in rural
30 areas. These sources include oxidation of CH4 and other biogenic compounds, wood burning
31 and wildfires (for CO); and soil emissions, lightning, and wood burning and wildfires for NO2.
August 2007 AX3-86 DRAFT-DO NOT CITE OR QUOTE
-------
1 In urban areas, the ambient NO2-CO correlations vary widely. The strongest correlations are
2 seen between NO2 and elemental carbon. Note that the results of Hochadel et al. (2006) for
3 PM2.s optical absorbance have been interpreted in terms of elemental carbon (EC). Correlations
4 between ambient NO2 and ambient Os are mainly negative, with again considerable variability in
5 the observed correlations. Only one study (Sarnat et al., 2001) examined associations between
6 ambient NO2 and ambient SO2 concentrations, showing a negative correlation during winter.
7 The robustness of this result needs to be examined in other cities.
8 Kim et al. (2006) reported the associations between 24 h averaged NO2 and other
9 pollutions for personal exposures and ambient concentrations in a study in Toronto, Canada from
10 August 1999 to November 2001. The median, mean, and standard deviation of the correlations
11 between ambient NO2 and ambient PM2 5 were 0.52, 0.44, and 0.35 respectively; and 0.81, 0.72,
12 and 0.22 respectively for the correlation between NO2 and CO.
13 In an exposure study in Steubenville, Ohio, Sarnat et al. (2006) reported the associations
14 between ambient concentrations and personal exposures for different pollutants. Ambient NO2
15 was significantly associated with ambient PM2 5, sulfate and EC during the fall (slope = 0.38,
16 0.96, and 7.01; and R2 = 0.61, 0.49, and 0.68 respectively) but not during the summer (slope =
17 -0.01, -0.17, and 3.76; andR2 = 0.0, 0.01, and 0.06 respectively).
18 In a related study, Connell et al. (2005) reported the correlation between ambient NOX
19 and PM2 5 during a comprehensive air monitoring program in Steubenville, Ohio. Across the two
20 year study (August 2000-April 2002), the Spearman correlation coefficient (rs) between hourly
21 ambient PM2 5 and NO concentrations was 0.33, and between hourly ambient PM2 5 and NO2
22 concentrations was 0.50. The authors suggested the importance of a common factor influencing
23 ambient concentrations of these species.
24 Kim et al. (2005) analyzed particle composition and gas phase data collected during the
25 RAPS/RAMS study on St. Louis, MO from 1975 to 1977 in terms of source contributions to
26 PM2.5. This study examined the spatial variability of source contributions to PM2.5 at the ten
27 monitoring sites in that study.
28 Sarnat et al. (2001) and reported associations between personal exposures and ambient
29 concentration across pollutants in a study conducted in the Baltimore area. At the ambient level,
30 NO2 was significantly correlated with PM2 5 (rs = 0.37) and CO (rs = 0.75) during the summer
31 and with CO (rs = 0.76), SO2 (rs = -0.17), PM2.5 (rs = 0.75) and O3 (rs = -0.71) during the winter.
August 2007 AX3-87 DRAFT-DO NOT CITE OR QUOTE
-------
1 Linn et al. (1996) reported short-term air pollution exposures in Los Angeles area school
2 children. Correlations between different pollutants were weaker: rp = 0.11 for ambient NO2 and
3 O3; rp = 0.25 for ambient NO2 and outdoor PM2 5.
4 Lee et al. (2002) found that ambient NO2 was significantly correlated with Os
5 (rp = -0.34).
6
7 Foreign Studies
8 Hochadel et al. (2006) reported the results of research which is part of a cohort study on
9 the impact of traffic-related air pollution on respiratory health, conducted at the western end of
10 the Ruhr-area in North-Rhine Westphalia, Germany. Strong correlations across the measurement
11 sites were observed between annual average PM2.5 absorbance and NO2 concentrations
12 (rp = 0.93), whereas PM2.5 mass concentration was less strongly correlated with NO2 (rp = 0.41).
13 The only major absorbing agent in PM2.5 is elemental carbon (EC) as other components (sulfate,
14 nitrate, organic carbon) either do not absorb or at best are only weakly absorbing. Therefore,
15 correlations between PM2 5 absorbance and NO2 may be inferred as correlations between EC and
16 NO2.
17 Hazenkamp-von Ark et al. (2004) reported the PM2 5 and NO2 associations across 21
18 European study centers during ECRHS II. The correlation between annual NO2 and PM2.5
19 concentrations is fair (Spearman correlation coefficient rs = 0.75), but when considered as
20 monthly means, the correlation is far less consistent and varies substantially between centers.
21 The authors pointed out that NO2 is attributed to traffic emissions, a relatively constant source of
22 pollution throughout the year. PM2 5 on the other hand, can be driven by other sources such as
23 wind-blown dust, although usually it consists predominantly of primary and secondary particles
24 from combustion processes. Sources, such as Saharan dust in Spain, probably cause some of the
25 observed patterns. The wide range of correlations between PM2 5 and NO2 evokes the hypothesis
26 that monthly PM2 5 mass concentrations in some centers may be driven by traffic emissions,
27 whereas in other centers, particles from other sources may be of further relevance.
28 Cyrys et al. (2003) reported the results of a source apportionment study in Erfurt,
29 Germany. Hourly NO2 was correlated with NO, CO, PM0.oi-2.5 number concentration, and
30 PM0.oi-2.5 mass concentration (rp = 0.73, 0.74, 0.55, and 0.50 respectively). Stronger correlations
31 were found daily correlation between NO2 and NO, CO, PMo.oi-2.5 number concentration, and
August 2007 AX3-88 DRAFT-DO NOT CITE OR QUOTE
-------
1 PMo.oi-2.5 mass concentration (rp = 0.87, 0.76, 0.71, and 0.66 respectively). The observed high
2 correlations between CO, NO, and NO2 indicate that direct emissions from mobile sources might
3 be the major contributors to the concentrations of these gaseous pollutants.
4 Rojas-Bracho et al. (2002) conducted a study of children's exposures in Santiago, Chile.
5 During the study, indoor, outdoor, and personal PM2.5, PMio, PMio-2.s, and NO2 were measured
6 24 h averaged for five consecutive days). Outdoor NO2 was significantly associated with all PM
7 fractions (slope = 1.82 and R2 = 0.59 for PM2.5; slope = 3.12 and R2 = 0.57 for PMi0; and slope =
8 1.11 and R2 = 0.32 for PM2.5-io).
9 Modig et al. (2004) investigated whether NO2 can be used to indicate ambient and
10 personal levels of benzene and 1, 3-butadiene in air. The stationary measurements showed
11 strong relations between 1,3-butadiene, benzene and NO2 (rp = 0.70 for NO2 and benzene; and
12 r = 0.77 for NO2 and 1,3-butadiene). This study supports NO2 as a potential indicator for
13 1,3 butadiene and benzene levels in streets or urban background air.
14 In summary, ambient NO2 was moderately correlated with corresponding ambient
15 concentrations of its co- pollutants. Based on associations in the ambient environment, results
16 suggest a possibility of confounding of ambient NO2 health effects by ambient PM2.5 (and its
17 components) and by ambient CO.
18
19 2) Associations between Personal (NO 2) and Personal Copollutant Exposures
20 For this section, measured personal NO2 exposures are regarded as the "true" personal
21 exposure. The correlation between personal NO2 exposure and personal exposure to other
22 pollutants are summarized below in Table AX3.21.
23 In Kim et al. (2006), the median, mean and standard deviation of the correlation between
24 NO2 and PM2 5 personal exposures for eleven subjects were 0.43, 0.41, and 0.28 respectively;
25 and 0.16, 0.12, and 0.42 respectively for the correlation between NO2 and CO (Kim et al., 2006).
26 Although Sarnat et al. (2001) found that personal exposures to PM2 5 were generally not
27 significantly associated with personal exposures to gases in Baltimore, personal NO2 was
28 significantly associated with personal PM2.5 (slope = 0.18, intercept = 18.65, p < 0.01, and
29 n = 213) and personal PM2.5 of ambient origin (slope = 0.17, intercept = 12.77, p < 0.05, and
30 n = 150) during the summer. There was some evidence to indicate that the strength of the
31 association was driven largely by the cohort of older adult subjects, and not by the children's or
32 COPD patients cohorts. They noted that gas stove usage did not significantly affect personal
August 2007 AX3-89 DRAFT-DO NOT CITE OR QUOTE
-------
1 NO2 to PM2.5 relations, but did affect relations between personal NO2 and personal PM2.5 of
2 ambient origin. They further pointed out that associations observed among pollutants in ambient
3 air may not be reflected in personal exposures and that they may not persist across seasons.
4 However, Lai et al. (2004) found that personal exposure to NO2 was slightly negatively
5 correlated with personal exposure to PM2 5 and total VOCs in a study conducted from 1998 to
6 2000 in Oxford, UK (- 0.1 for PM2.5, 0.3 for CO, and -0.11 for TVOCs).
7 Modig et al. (2004) investigated whether NO2 can be used to indicate ambient and
8 personal levels of benzene and 1, 3-butadiene in air. The results from the personal
9 measurements showed a negligible association of NO2 with 1,3-butadiene (rp = 0.06) as well as
10 with benzene (rp = 0.10), while the correlation coefficient between benzene and 1,3-butadiene
11 was high and significant (rp = 0.67). The weak relations found for the personal measurements do
12 not support the use of NO2 as an indicator for personal 1,3-butadiene and benzene exposure.
13 Although gas stove and kerosene heaters were almost absent in the study area, this study
14 included both smokers and non-smokers, but the data were not stratified. Smoking is a major
15 source of both benzene and 1,3-butadiene, in addition to motor vehicles. If smoking were the
16 major cause of the poor association between NO2 and the gases in the personal measurements,
17 then this would indicate that smoking was not a major source of personal NO2. Thus, this study
18 cannot determine whether personal NO2 is an indicator of traffic generated VOCs and so the
19 interpretation of results in this paper is problematic.
20 In the Paris office worker study, no relation was observed between personal NO2 and
21 PM2.5 exposures (rp = 0.12, n = 53, p = 0.38) (Mosqueron et al., 2002). In addition, NO2 and
22 PM2.s concentrations were correlated neither in-home (rp = 0.06, n = 54, p = 0.69) nor in-office
23 (rp = 0.05, n = 55, p = 0.74).
24
25 Associations with HONO
26 Spicer et al. (1993) and Wainman et al. (2000) suggested the presence of a strong indoor
27 source of HONO from heterogeneous reactions involving NO2 and water films on indoor
28 surfaces. Hence, combustion appliances are sources for exposures to both NO2 exposure and
29 HNO2. Epidemiological studies of NO2 health effects should consequently consider the potential
30 confounding effects of NO2 and vice versa.
August 2007 AX3-90 DRAFT-DO NOT CITE OR QUOTE
-------
1 Jarvis et al. (2005) reported the indoor nitrous acid and lung function in adults as part of
2 European Community Respiratory Health Survey (ECRHS). Indoor HONO and indoor and
3 outdoor NO2 were measured. Indoor NO2 were correlated with HONO (rp = 0.77) but no
4 significant association of indoor NO2 with symptoms or lung function was observed.
5 Lee et al. (2002) studied the nitrous acid, nitrogen dioxide, and ozone concentrations in
6 residential environments. The authors found that indoor NO2 was significantly correlated with
7 HONO (rp = 0.511).
8 As shown above, very few studies showed the relationship between personal NO2
9 exposure and other pollutant exposures. In general, personal NO2 was moderately correlated
10 with PM2.5 and CO. Due to the lack of personal HONO exposure data, indoor HONO was used
11 as an indicator for personal exposure, and current studies showed that indoor HONO was
12 correlated with indoor NO2 with high correlation coefficients, which suggested that the collect
13 ion of HONO exposure data would help interpret adverse health outcome in the NO2 health risk
14 assessment.
15
16 3) Personal (NO2) -Ambient Copollutants
17 The relationship between personal NO2 exposure and other ambient pollutants are
18 summarized in Table AX3.22.
19 In Steubenville, Ohio, Sarnat et al. (2006) found that personal NO2 was significantly
20 associated with ambient PM2.5 and ambient sulfate during the fall (slope = 0.17 and R2 = 0.21 for
21 PM2.5; and slope = 0.34 and R2 = 0.12 for sulfate); and was significantly associated with ambient
22 EC in both summer and fall (slope =1.81 and R2 = 0.03 for the summer; and slope = 3.71 and
23 R2 = 0.32 for the fall).
24 Kim et al. (2006) also reported correlations between personal exposure and ambient
25 measurements across pollutants. The median, mean, and standard deviation of the correlation
26 between personal NO2 and ambient PM2 5 were 0.36, 0.30, and 0.30 respectively; and 0.17, 0.20,
27 and 0.41 respectively for the correlation between personal NO2 and ambient CO. The authors
28 suggested that the existing correlation between PM2.5 and NO2 for both ambient measurements
29 and personal exposures suggests that there is potential for NO2 to be a confounder of PM2.5, and
30 vice versa. Therefore, it may be appropriate for time-series epidemiological studies to control
31 for confounding by NO2 in PM2 5 risk models, and vice versa.
August 2007 AX3-91 DRAFT-DO NOT CITE OR QUOTE
-------
1 In a study conducted in Santiago, Chile (Rojas-Bracho et al., 2002) personal NO2 was
2 moderately associated with PM2.5 (slope = 1.99 and r2 = 0.42) and PMio (slope = 2.13 and
3 r2 = 0.15) but not coarse particles. At the indoor level, the same observation held (slope = 0.86
4 and r2 = 0.22 for PM2 5; slope =1.0 and R2 = 0.2 for PMio). "However, in comparing the indoor
5 and outdoor associations, we find that the latter is more highly significant and that the intercept
6 is smaller. It is likely that in outdoor environments, there are more high-temperature combustion
7 processes, which are associated with nitrogen oxide emissions. Since nitrogen oxides are
8 precursors of secondary particles, which partly form PM2.5, our results showed a stronger
9 association between these two pollutions outdoors."
10 Lee et al. (2002) studied nitrous acid, nitrogen dioxide, and ozone concentrations in
11 residential environments. The authors found that indoor NO2 was significantly correlated with
12 outdoor O3 (rp = -0.220).
13 These studies above show moderate correlations between personal NO2 exposure and
14 ambient PM2 5, PMio, EC, sulfate, and CO. Based on our knowledge that, moderate to strong
15 personal-ambient correlations exist for all the other pollutants mentioned above all of those
16 species might serve as confounders for NO2 exposure (detailed evaluation of the personal vs.
17 ambient relationship for these pollutants are beyond the scope of this document).
18
19 4) Ambient NO2-Personal Copollutant
20 Correlation between ambient NO2 and personal exposure to copollutants are summarized
21 in Table AX3.23.
22 Sarnat et al. (2006) found that ambient NO2 was significantly associated with personal
23 PM2.5 and personal sulfate during the fall (slope = 0.93 and R2 = 0.25 for PM2.5; and
24 slope = 0.28 and R2 = 0.27 for sulfate); and was significantly associated with personal EC during
25 both summer and fall (slope = 0.02 and R2 = 0.07 during the summer; and slope = 0.08 and
26 R2 = 0.49 during the fall) in Steubenville, OH. Sarnat et al. (2006) suggested that for most cases,
27 ambient gas concentrations, although not suitable proxies of gas exposures are equally not
28 suitable for particle exposures in time-series health studies. Despite this, numerous
29 epidemological studies have linked 24-h ambient gas concentrations to adverse health impacts,
30 suggesting that the gases may indeed elicit biological responses alone or in combination with
31 other pollutants, or are acting as proxies for shorter-term exposures. The authors pointed out that
August 2007 AX3-92 DRAFT-DO NOT CITE OR QUOTE
-------
1 for Steubenville in the fall, a season with strong associations between ambient particle and NO2
2 concentrations, the separation of particle and NO2 health effects in daily time-series studies may
3 be difficult, and more precise exposure metrics may be needed. The authors suggested that
4 personal-ambient relationships are greatly dependent on ambient conditions (e.g., season and
5 meteorology) and behavior (e.g., use of windows). However, further factors such as building
6 design will also be extremely important, further exposure assessment work, particularly in
7 different geographic and climatic zones, is needed.
8 During both summer and winter in Baltimore (Sarnat et al., 2001), ambient NO2 was
9 significantly associated with personal PM2.5 (slope = 0.42, intercept = 12.38, and n = 225 during
10 the summer; and slope = 0.24, intercept = 13.16, and n = 487 during the winter). Also significant
11 relationships held for ambient NO2 and personal exposures to PM2 5 of ambient origin. Ambient
12 NO2 was also significantly associated with personal EC (slope = 0.05 and p = 0.0001), as an
13 indicator of mobile source pollution. In conclusion, the authors suggested that ambient gases
14 were acting as surrogates for personal PM2.s exposure instead of confounding effects of personal
15 PM25 exposure.
16 Vinzents et al. (2005) found that ambient temperature and NO2 concentrations at one of
17 the street stations were the only significant predictors of ultra fine particle exposure during
18 bicycling in traffic (R2 = 0.74). Kim et al. (2006) also reported correlations between personal
19 exposure and ambient measurements across pollutants. The median, mean, and standard
20 deviation of the correlation between ambient NO2 and personal PM2 5 were 0.24, 0.29, and 0.33
21 respectively; and 0.26, 0.22, and 0.32 respectively for the correlation between ambient NO2 and
22 personal CO.
23 Studies above shows that ambient NO2 is moderately correlated with personal EC and
24 ultrafme particle exposures, but only weakly to moderately correlated with personal PM2 5 mass
25 and sulfate exposures. Since ambient NO2 concentrations has been shown to be significant
26 proxy for corresponding personal NO2 exposures, these findings suggest that ambient NO2 may
27 be acting as a proxy not only for its own exposures but also to exposures to EC and ultrafme
28 particles. As a result, it may not be possible to separate the health effects of from those of other
29 pollutants, especially from the same source.
30 In the analysis of the confounding effect of exposure, we are limited by the lack of key
31 data: (1) multipollutant exposure studies were rarely conducted and even fewer studies reported
August 2007 AX3-93 DRAFT-DO NOT CITE OR QUOTE
-------
1 the cross-level (ambient and personal exposure) and cross-pollutant correlations; (2) most studies
2 focus on a several copollutants (PM and its components, CO, 63, and some VOCs) with little
3 data available for other possibly important copollutants; (3) the impact of indoor and personal
4 sources on the possibility of confounding has not yet been examined; and (4) the impact of
5 measurement uncertainties, which can be large as mentioned in Section AX3.4.1, on
6 confounding needs to be examined. Finally, the analysis shown above in the exposure
7 assessment should be integrated with other analysis in other parts of the risk assessment.
8
9
10 AX3.7 A FRAMEWORK FOR MODELING HUMAN EXPOSURES TO
11 NO2 AND RELATED PHOTOCHEMICAL AIR POLLUTANTS
12
13 AX3.7.1 Introduction: Concepts, Terminology, and Overall Summary
14 Predictive (or prognostic) exposure modeling studies1, specifically focusing on NO2,
15 could not be identified in the literature, though, often, statistical (diagnostic) analyses have been
16 reported using data obtained in various field exposure studies (see Section AX3.5.1). However,
17 existing prognostic modeling systems for the assessment of inhalation exposures can in principle
18 be directly applied to, or adapted for, NO2 studies; specifically, such systems include APEX,
19 SHEDS, and MENTOR-1 A, to be discussed in the following sections. Nevertheless, it should be
20 mentioned that such applications will be constrained by data limitations, such as the degree of
21 ambient concentration characterization (e.g., concentrations at the local level) and quantitative
22 information on indoor sources and sinks.
23 Predictive models of human exposure to ambient air pollutants such as NO2 can be
24 classified and differentiated based upon a variety of attributes. For example, exposure models
25 can be classified as:
26 • models of potential (typically maximum) outdoor exposure versus models of
27 actual exposures (the latter including locally modified microenvironmental
28 exposures, both outdoor and indoor),
29 • Population Based Exposure Models (PBEM) versus Individual Based Exposure
30 Models (IBEM),
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.
August 2007 AX3-94 DRAFT-DO NOT CITE OR QUOTE
-------
1 • deterministic versus probabilistic (or statistical) exposure models,
2 • observation-driven versus mechanistic air quality models (see Section AX3.7.3
3 for discussions about the construction, uses and limitations of this class of
4 mathematical models.
5 Some points should be made regarding terminology and essential concepts in exposure
6 modeling, before proceeding to the overview of specific developments reported in the current
7 research literature:
8 First, it must be understood that there is significant variation in the definitions of many of
9 the terms used in the exposure modeling literature; indeed, the science of exposure modeling is a
10 rapidly evolving field and the development of a standard and commonly accepted terminology is
11 an ongoing process (see, e.g., WHO, 2004).
12 Second, it should also be mentioned that, very often, procedures that are called exposure
13 modeling, exposure estimation, etc. in the scientific literature, may in fact refer to only a sub-set
14 of the complete set of steps or components required for a comprehensive exposure assessment.
15 For example, certain self-identified exposure modeling studies focus solely on refining the sub-
16 regional or local spatio-temporal dynamics of pollutant concentrations (starting from raw data
17 representing monitor observations or regional grid-based model estimates). Though not
18 exposure studies per se, such efforts have value and are included in the discussion of the next
19 sub-section, as they provide potentially useful tools that can be used in a complete exposure
20 assessment. On the other hand, formulations that are self-identified as exposure models but
21 actually focus only on ambient air quality predictions, such as chemistry-transport models, are
22 not included in the discussion that follows.
23 Third, the process of modeling human exposures to photochemical pollutants
24 (traditionally focused on ozone) is very often identified explicitly with population-based
25 modeling, while models describing the specific mechanisms affecting the exposure of an actual
26 individual (at specific locations) to an air contaminant (or to a group of co-occurring gas and/or
27 aerosol phase pollutants) are usually associated with studies focusing specifically on indoor air
28 chemistry modeling.
29 Finally, fourth, the concept of microenvironments, introduced in earlier sections of this
30 document, should be clarified further, as it is critical in developing procedures for exposure
31 modeling. In the past, microenvironments have typically been defined as individual or aggregate
August 2007 AX3-95 DRAFT-DO NOT CITE OR QUOTE
-------
1 locations (and sometimes even as activities taking place within a location) where a homogeneous
2 concentration of the pollutant is encountered. Thus a microenvironment has often been
3 identified with an ideal (i.e. perfectly mixed) compartment of classical compartmental modeling.
4 More recent and general definitions view the microenvironment as a control volume, either
5 indoors or outdoors, that can be fully characterized by a set of either mechanistic or
6 phenomenological governing equations, when appropriate parameters are available, given
7 necessary initial and boundary conditions. The boundary conditions typically would reflect
8 interactions with ambient air and with other microenvironments. The parameterizations of the
9 governing equations generally include the information on attributes of sources and sinks within
10 each microenvironment. This type of general definition allows for the concentration within a
11 microenvironment to be non-homogeneous (non-uniform), provided its spatial profile and
12 mixing properties can be fully predicted or characterized. By adopting this definition, the
13 number of microenvironments used in a study is kept manageable, but variability in
14 concentrations in each of the microenvironments can still be taken into account.
15 Microenvironments typically used to determine exposure include indoor residential
16 microenvironments, other indoor locations (typically occupational microenvironments), outdoors
17 near roadways, other outdoor locations, and in-vehicles. Outdoor locations near roadways are
18 segregated from other outdoor locations (and can be further classified into street canyons,
19 vicinities of intersections, etc.) because emissions from automobiles alter local concentrations
20 significantly compared to background outdoor levels. Indoor residential microenvironments
21 (kitchen, bedroom, living room, etc. or aggregate home microenvironment) are typically
22 separated from other indoor locations because of the time spent there and potential differences
23 between the residential environment and the work/public environment.
24 Once the actual individual and relevant activities and locations (for Individual Based
25 Modeling), or the sample population and associated spatial (geographical) domain (for
26 Population Based Modeling) have been defined along with the temporal framework of the
27 analysis (time period and resolution), the comprehensive modeling of individual/population
28 exposure to NO2 (and related pollutants) will in general require seven steps (or components, as
29 some of them do not have to be performed in sequence) that are listed below. This list represents
30 a composite based on approaches and frameworks described in the literature over the last twenty -
31 five years (Ott, 1982; Ott, 1985; Lioy, 1990; U.S. Environmental Protection Agency, 1992;
August 2007 AX3-96 DRAFT-DO NOT CITE OR QUOTE
-------
1 Georgopoulos and Lioy, 1994; U.S. Environmental Protection Agency, 1997; Buck et al., 2003;
2 Price et al., 2003; Georgopoulos et al., 2005; WHO, 2005; U.S. Environmental Protection
3 Agency, 2006a; Georgopoulos and Lioy, 2006) as well on the structure of various inhalation
4 exposure models (NEM/pNEM, HAPEM, SHEDS, REHEX, EDMAS, MENTOR, ORAMUS,
5 APEX, AIRPEX, AIRQUIS, etc., to be discussed in the following section) that have been used in
6 the past or in current studies to specifically assess inhalation exposures. Figure AX3.23, adapted
7 from Georgopoulos et al. (2005), schematically depicts the sequence of steps involved that are
8 summarized here (and further discussed in the following sub-sections).
9
10 1. Estimation of the background or ambient levels of both NO2 and related
11 photochemical pollutants. This is done through either (or a combination of):
12
13 a. multivariate spatio-temporal analysis of fixed monitor data, or
14 b. emissions-based, photochemical, air quality modeling (typically
15 with a regional, grid-based model such as Models-3/CMAQ or CAMx)
16 applied in a coarse resolution mode.
17
18 2. Estimation of local outdoor pollutant levels of both NO2 and related
19 photochemical pollutants. These levels could typically characterize the ambient
20 air of either an administrative unit (such as a census tract, a municipality, a
21 county, etc.) or a conveniently defined grid cell of an urban scale air quality
22 model. Again, this may involve either (or a combination of):
23
24 a. spatio-temporal statistical analysis of monitor data, or
25 b. application of an urban multi-scale, grid based model (such as
26 CMAQ or CAMx) at its highest resolution (typically around 2-4 km), or
27 c. correction of the estimates of the regional model using some
28 scheme that adjusts for observations and/or for subgrid chemistry and
29 mixing processes.
30
31 3. Characterization of relevant attributes of the individuals or populations under
32 study (residence and work locations, occupation, housing data, income, education,
33 age, gender, race, weight, and other physiological characteristics). For Population
34 Based Exposure Modeling (PBEM) one can either:
August 2007 AX3-97 DRAFT-DO NOT CITE OR QUOTE
-------
i,a. imissions: NEI (NET, NTI), State; Processing with SMOKE,
EMS-HAP, MOBILE/MOVES, NONROAO, FAAED, BEIS, etc.
i.b, Meteorole»: NWS, NCOC; Modeling MMS, RAMS, CAtMET
I.e. Land Use/land Cover, Topography: NLDC (USGS), etc.
il.a. Emissions: EMS-HAP
tLb, Local Meteorology - Local
Effects: RAMS, FLUENT
;, Estimate background
. 9 levels of air pollutants
through
a. multivartate spatio-
temporal analysis of
monitor data
1b emisstons-tasad air »
quality mothflng
(with regional,
grid-based models: '
Models-3/ CMAQ, CAMx ]
and R1MSAD)
'•v Develop datalsase of
t,r individual subjects
\ attributes {residence 8t
j work location, housing
i characteristics, age,
j gender, race, income, etc.) >_
""l a. osllect study-specific
information
E>. supplement with
available relevant local,
I regional, and national
demographic
] information
f:
/' Study-specific survey
} (also OS Census,
I US Housing Survey)
- , Estimate local outdoor
-~ pollutant levels that
characterize tfte amiitent air of
! an administrative unit {such as
i census tract) or a conveniently
1 defined grid through
ti a. sp.stiotempoi ,ii statistical
analysis of monitor data
b. application of urban scale
model at tuft* resolution
c. swb§rid (e.§. pl«me-in-§rfd)
m0«l@fir)g
d. data/m@del assimilation
; ;,'; estimate icveJs and
J temporal profiles of
!»QiMit3nts i^ various
microenvironnientss (streets,
residences, offices, restaurants,
vehicles, etc.) through
—*• ». regression of ottservational
data
b. simple linear mass balance
c lymped (nonlin^sr) p€
§as/aer&soi chemistry models
d. combit^od chemistry St CF0
(DNS, IES, RAMS) motfels
^r • $
1 -f Be¥elop acthrily event
1 '" " |or exposure ©vent)
sequences for eaclt individual
! of the study for the exposure
j period
.] a. collect stud^'Sp^'fc
I information
l b. supplement with other
" available dat«i
I c. organize time-actwity
( database in format
i compatible with CHAD
i
,;• Calculate appropriate
' o inhalation rales for th«
members of the sample
population, combining the
phy$k»t®giol attriliut^s of the
W study subjects and the
activities pursued dyHng the
individual exposure events
j
1
'. i^'iJi:'^., JLL: "i::
f ' Study-specific suwey S ICRI3 apd Other
(or default front f l>hystologiGil & METS
1 CHAD, NHAPS) i | Oatabases
^
' 1
Calculate ' ^ Hiolggieaily
exposures/ <" tased ,
intakes target tissue :
j dose modeling i
i
t »
_J
Figure AX3.23.
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).
2
3
4
5
6
7
8
9
10
11
12
a. select a fixed-size sample population of virtual individuals in a
way that statistically reproduces essential demographics (age, gender,
race, occupation, income, education) of the administrative population unit
used in the assessment (e.g., a sample of 500 people is typically used to
represent the demographics of a given census tract, whereas a sample of
about 10,000 may be needed to represent the demographics of a county),
or
b. divide the population-of interest into a set of cohorts representing
selected subpopulations where the cohort is defined by characteristics
known to influence exposure.
August 2007
AX3-98
DRAFT-DO NOT CITE OR QUOTE
-------
1 4. Development of activity event (or exposure event) sequences for each member of
2 the sample population (actual or virtual) or for each cohort for the exposure
3 period. This could utilize:
4
5 a. study-specific information, if available
6 b. existing databases based on composites of questionnaire
7 information from past studies
8 c. time-activity databases, typically in a format compatible with U.S.
9 Environmental Protection Agency's Consolidated Human Activity
10 Database (CHAD - McCurdy et al., 2000)
11
12 5. Estimation of levels and temporal profiles of both NO2 and related photochemical
13 pollutants in various outdoor and indoor microenvironments such as street
14 canyons, roadway intersections, parks, residences, offices, restaurants, vehicles,
15 etc. This is done through either:
16
17 a. linear regression of available observational data sets,
18 b. simple mass balance models (with linear transformation and sinks)
19 over the volume (or a portion of the volume) of the microenvironment,
20 c. lumped (nonlinear) gas or gas/aerosol chemistry models, or
21 d. detailed combined chemistry and Computational Fluid Dynamics
22 modeling.
23
24 6. Calculation of appropriate inhalation rates for the members of the sample
25 population, combining the physiological attributes of the (actual or virtual) study
26 subjects and the activities pursued during the individual exposure events.
27
28 7. Calculation of target tissue dose through biologically based modeling estimation
29 (specifically, respiratory dosimetry modeling in the case of NO2 and related
30 reactive photochemical pollutants) if sufficient information is available.
31
32 Implementation of the above framework for comprehensive exposure modeling has
33 benefited significantly from recent advances and expanded availability of computational
34 technologies such as Relational Database Management Systems (RDBMS) and Geographic
35 Information Systems (GIS) (Purushothaman and Georgopoulos, 1997, 1999a,b; Georgopoulos
36 et al., 2005).
August 2007 AX3-99 DRAFT-DO NOT CITE OR QUOTE
-------
1 In fact, only relatively recently comprehensive, predictive, inhalation exposure modeling
2 studies for ozone, PM, and various air toxics, have attempted to address/incorporate all the
3 components of the general framework described here. In practice, the majority of past exposure
4 modeling studies have either incorporated only subsets of these components or treated some of
5 them in a simplified manner, often focusing on the importance of specific factors affecting
6 exposure. Of course, depending on the objective of a particular modeling study, implementation
7 of only a limited number of steps may be necessary. For example, in a regulatory setting, when
8 comparing the relative effectiveness of emission control strategies, the focus can be on expected
9 changes in ambient levels (corresponding to those observed at NAAQS monitors) in relation to
10 the density of nearby populations. The outdoor levels of pollutants, in conjunction with basic
11 demographic information, can thus be used to calculate upper bounds of population exposures
12 associated with ambient air (as opposed to total exposures that would include contributions from
13 indoor sources) useful in comparing alternative control strategies. Though the metrics derived
14 would not be quantitative indicators of actual human exposures, they can serve as surrogates of
15 population exposures associated with outdoor air, and thus aid in regulatory decision making
16 concerning pollutant standards and in studying the efficacy of emission control strategies. This
17 approach has been used in studies performing comparative evaluations of regional and local
18 emissions reduction strategies in the Eastern U.S (e.g., Purushothaman and Georgopoulos, 1997;
19 Georgopoulos et al., 1997a; Foley et al., 2003).
20
21 AX3.7.2 Population Exposure Models: Their Evolution and Current Status
22 Existing comprehensive inhalation exposure models consider the trajectories of
23 individual human subjects (actual or virtual), or of appropriately defined cohorts, in space and
24 time as sequences of exposure events. In these sequences each event is defined by time, a
25 geographic location, a microenvironment, and the activity of the subject. US Environmental
26 Protection Agency offices (OAQPS and NERL) have supported the most comprehensive efforts
27 in developing models implementing this general concept (see, e.g., Johnson, 2002), and these
28 efforts have resulted in the NEM/pNEM (National Exposure Model and Probabilistic National
29 Exposure Model - Whitfield et al., 1997), HAPEM (Hazardous Air Pollutant Exposure Model -
30 Rosenbaum, 2005), SHEDS (Simulation of Human Exposure and Dose System - Burke et al.,
31 2001), APEX (Air Pollutants Exposure model - US Environmental Protection Agency, 2006b,c),
August 2007 AX3-100 DRAFT-DO NOT CITE OR QUOTE
-------
1 and MENTOR (Modeling Environment for Total Risk studies - Georgopoulos et al., 2005;
2 Georgopoulos and Lioy, 2006) families of models. European efforts have produced some
3 formulations with similar general attributes as the above U.S. models but, generally, involving
4 simplifications in some of their components. Examples of European models addressing
5 exposures to photochemical oxidants (specifically ozone) include the AirPEx (Air Pollution
6 Exposure) model (Freijer et al., 1998), which basically replicates the pNEM approach and has
7 been applied to the Netherlands, and the AirQUIS (Air Quality Information System) model
8 (Clench-Aas et al., 1999).
9 The NEM/pNEM, SHEDS, APEX, and MENTOR-1A (MENTOR for One-Atmosphere
10 studies) families of models provide exposure estimates defined by concentration and breathing
11 rate for each individual exposure event, and then average these estimates over periods typically
12 ranging from one h to one year. These models allow simulation of certain aspects of the
13 variability and uncertainty in the principal factors affecting exposure. An alternative approach is
14 taken by the HAPEM family of models that typically provide annual average exposure estimates
15 based on the quantity of time spent per year in each combination of geographic locations and
16 microenvironments. The NEM, SHEDS, APEX, and MENTOR-type models are therefore
17 expected to be more appropriate for pollutants with complex chemistry such as NO2, and could
18 provide useful information for enhancing related health assessments.
19
20 More specifically, regarding the consideration of population demographics and activity patterns:
21 1. pNEM divides the population of interest into representative cohorts based on the
22 combinations of demographic characteristics (age, gender, and employment),
23 home/work district, residential cooking fuel and replicate number, and then
24 assigns activity diary record from CHAD (Consolidated Human Activities
25 Database) to each cohort according to demographic characteristic, season, day-
26 type (weekday/weekend) and temperature.
27 2. HAPEM6 divides the population of interest into demographic groups based on
28 age, gender and race, and then for each demographic group/day-type
29 (weekday/weekend) combination, select multiple activity patterns randomly (with
30 replacement) from CHAD and combine them to find the averaged annual time
31 allocations for group members in each census tract for different day types.
32 3. SHEDS, APEX, and MENTOR-1 A generate population demographic files, which
33 contain a user-defined number of person records for each census tract of the
34 population based on proportions of characteristic variables (age, gender,
35 employment, and housing) obtained from the population of interest, and then
36 assign a matching activity diary record from CHAD to each individual record of
August 2007 AX3-101 DRAFT-DO NOT CITE OR QUOTE
-------
1 the population based on the characteristic variables. It should be mentioned that,
2 in the formulations of these models, workers may commute from one census tract
3 to another census tract for work. So, with the specification of commuting
4 patterns, the variation of exposure concentrations due to commuting between
5 different census tracts can be captured.
6
7 The essential attributes of the pNEM, HAPEM, APEX, SHEDS, and MENTOR-1A
8 models are summarized in Table AX3.24.
9 The conceptual approach originated by the SHEDS models was modified and expanded
10 for use in the development of MENTOR-1 A (Modeling Environment for Total Risk - One
11 Atmosphere). Flexibility was incorporated into this modeling system, such as the option of
12 including detailed indoor chemistry of the O3-NOX system and other relevant
13 microenvironmental processes, and providing interactive linking with CHAD for consistent
14 definition of population characteristics and activity events (Georgopoulos et al., 2005).
15 NEM/pNEM implementations have been extensively applied to ozone studies in the
16 1980s and 1990s. The historical evolution of the pNEM family of models of OAQPS started
17 with the introduction of the first NEM model in the 1980's (Biller et al., 1981). The first such
18 implementations of pNEM/Os in the 1980's used a regression-based relationship to estimate
19 indoor ozone concentrations from outdoor concentrations. The second generation of pNEM/Os
20 was developed in 1992 and included a simple mass balance model to estimate indoor ozone
21 concentrations. A report by Johnson et al. (2000) describes this version of pNEM/Os and
22 summarizes the results of an initial application of the model to 10 cities. Subsequent
23 enhancements to pNEM/Os and its input databases included revisions to the methods used to
24 estimate equivalent ventilation rates, to determine commuting patterns, and to adjust ambient
25 ozone levels to simulate attainment of proposed NAAQS. During the mid-1990's,
26 Environmental Protection Agency applied updated versions of pNEM/O3 to three different
27 population groups in selected cities: (1) the general population of urban residents, (2) outdoor
28 workers, and (3) children who tend to spend more time outdoors than the average child. This
29 version of pNEM/Os used a revised probabilistic mass balance model to determine ozone
30 concentrations over one-h periods in indoor and in-vehicle microenvironments (Johnson, 2001).
31 In recent years, pNEM has been replaced by (or "evolved to") the Air Pollution Exposure
32 Model (APEX). APEX differs from earlier pNEM models in that the probabilistic features of the
33 model are incorporated into a Monte Carlo framework (Langstaff, 2007; US Environmental
August 2007 AX3-102 DRAFT-DO NOT CITE OR QUOTE
-------
1 Protection Agency, 2006b,c). Like SHEDS and MENTOR-1A, instead of dividing the
2 population-of-interest into a set of cohorts, APEX generates individuals as if they were being
3 randomly sampled from the population. APEX provides each generated individual with a
4 demographic profile that specifies values for all parameters required by the model. The values
5 are selected from distributions and databases that are specific to the age, gender, and other
6 specifications stated in the demographic profile. Environmental Protection Agency has applied
7 APEX to the study of exposures to ozone and other criteria pollutants; APEX can be modified
8 and used for the estimation of NC>2 exposures, if required.
9 Reconfiguration of APEX for use with NC>2 or other pollutants would require significant
10 literature review, data analysis, and modeling efforts. Necessary steps include determining
11 spatial scope and resolution of the model; generating input files for activity data, air quality and
12 temperature data; and developing definitions for microenvironments and pollutant-
13 microenvironment modeling parameters (penetration and proximity factors, indoor source
14 emissions rates, decay rates, etc.) (ICF Consulting 2005, Decision Points for Configuring APEX
15 for Air Toxics Exposure Assessments). To take full advantage of the probablistic capabilities of
16 APEX, distributions of model input parameters should be used wherever possible.
17
18 AX3.7.3 Characterization of Ambient Concentrations of NO2 and Related
19 Air Pollutants
20 As mentioned earlier, background and regional outdoor concentrations of pollutants, over
21 a study domain, may be estimated either through emissions-based mechanistic modeling, through
22 ambient-data-based modeling, or through a combination of both. Emissions-based models
23 calculate the spatio-temporal fields of the pollutant concentrations using precursor emissions and
24 meteorological conditions as inputs. The ambient-data-based models typically calculate spatial
25 or spatio-temporal distributions of the pollutant through the use of interpolation schemes, based
26 on either deterministic or stochastic models for allocating monitor station observations to the
27 nodes of a virtual regular grid covering the region of interest. The geostatistical technique of
28 kriging provides various standard procedures for generating an interpolated spatial distribution
29 for a given time, from data at a set of discrete points. Kriging approaches were evaluated by
30 Georgopoulos et al. (Georgopoulos et al., 1997b) in relation to the calculation of local ambient
31 ozone concentrations for exposure assessment purposes, using either monitor observations or
32 regional/urban photochemical model outputs. It was found that kriging is severely limited by the
August 2007 AX3-103 DRAFT-DO NOT CITE OR QUOTE
-------
1 nonstationary character of the concentration patterns of reactive pollutants; so the advantages this
2 method has in other fields of geophysics do not apply here. The above study showed that the
3 appropriate semivariograms had to be hour-specific, complicating the automated reapplication of
4 any purely spatial interpolation over an extended time period.
5 Spatio-temporal distributions of pollutant concentrations, such as ozone, PM, and various
6 air toxics have alternatively been obtained using methods of the Spatio-Temporal Random Field
7 (STRF) theory (Christakos and Vyas, 1998a,b). The STRF approach interpolates monitor data in
8 both space and time simultaneously. This method can thus analyze information on temporal
9 trends, which cannot be incorporated directly in purely spatial interpolation methods such as
10 standard kriging. Furthermore, the STRF method can optimize the use of data that are not
11 uniformly sampled in either space or time. STRF was further extended within the Bayesian
12 Maximum Entropy (BME) framework and applied to ozone interpolation studies (Christakos and
13 Hristopulos, 1998; Christakos and Kolovos, 1999; Christakos, 2000). It should be noted that
14 these studies formulate an over-arching scheme for linking air quality with population dose and
15 health effects; however they are limited by the fact that they do not include any
16 microenvironmental effects. MENTOR has incorporated STRF/BME methods as one of the
17 steps for performing a comprehensive analysis of exposure to ozone and PM (Georgopoulos
18 etal.,2005).
19 Subgrid spatial variability is a major issue with respect to characterizing local
20 concentrations of NC>2. Indeed, the fast rates of the reactions involving the O3-NOX system result
21 in significant concentration gradients in the vicinity of sources of NOX. These gradients are not
22 resolved directly by currently operational grid photochemical air quality simulation models
23 (PAQSMs) such as CMAQ and CAMx. However, both these models include a plume-in-grid.
24 (PinG) option (AER, 2004; Emery and Yarwood, 2005; Gillani and Godowitch, 1999; US
25 Environmental Protection Agency, 2006d) that can be used for large point NOX sources (such as
26 smokestacks). Nevertheless, PinG formulations typically will resolve gradients in upper
27 atmospheric layers and thus are not necessarily relevant to human exposure calculations, which
28 are affected by gradients caused by a multiplicity of smaller ground level or near ground level
29 combustion sources such as motor vehicles.
30 Currently PAQSMs are typically applied with horizontal resolutions of 36 km, 12 km,
31 and 4 km and a surface layer thickness that is typically of the order of 30 m. Though
August 2007 AX3-104 DRAFT-DO NOT CITE OR QUOTE
-------
1 computationally it is possible to increase the resolution of these simulations, there are critical
2 limits that reflect assumptions inherent in the governing equations for both (a) the fluid
3 mechanical processes embodied in the meteorological models (e.g., typically MM5 and RAMS)
4 that provide the inputs for the PAQSMs, and (b) the dispersion processes which become more
5 complex at fine scales (see, e.g., Georgopoulos and Seinfeld, 1989) and thus cannot be described
6 by simple formulations (such as constant dispersion coefficients) when the horizontal resolutions
7 is 2 km or finer.
8 Application of PAQSMs to urban domains is further complicated by urban topography,
9 the urban heat island, etc. It is beyond the scope, however, of the present discussion, to overview
10 the various issues relevant to urban fluid dynamics and related transport/fate processes of
11 contaminants. However, the issue of modeling subgrid atmospheric dispersion phenomena
12 within complex urban areas in a consistent manner is a very active research area. Reviews of
13 relevant issues and of available approaches for modeling urban fluid mechanics and dispersion
14 can be found in, e.g., Fernando et al. (2001) and Britter and Hanna (2003).
15 The issue of subgrid variability (SGV) from the perspective of interpreting and evaluating
16 the outcomes of grid-based, multiscale, PAQSMs is discussed in Ching et al. (2006), who
17 suggest a framework that can provide for qualitative judgments on model performance based on
18 comparing observations to the grid predictions and its SGV distribution. From the perspective of
19 Population Exposure Modeling, the most feasible/practical approach for treating subgrid
20 variability of local concentrations is probably through 1) the identification and proper
21 characterization of an adequate number of outdoor microenvironments (potentially related to
22 different types of land use within the urban area as well as to proximity to different types of
23 roadways) and 2) then, concentrations in these microenvironments will have to be adjusted from
24 the corresponding local background ambient concentrations through either regression of
25 empirical data or various types of local atmospheric dispersion/transformation models. This is
26 discussed further in the next subsection.
27
28 AX3.7.4 Characterization of Microenvironmental Concentrations
29 Once the background and local ambient spatio-temporal concentration patterns have been
30 derived, microenvironments that can represent either outdoor or indoor settings when individuals
31 come in contact with the contaminant of concern (e.g., NO2) must be characterized. This process
August 2007 AX3-105 DRAFT-DO NOT CITE OR QUOTE
-------
1 can involve modeling of various local sources and sinks, and interrelationships between ambient
2 and microenvironmental concentration levels. Three general approaches have been used in the
3 past to model microenvironmental concentrations:
4 • Empirical (typically linear regression) fitting of data from studies relating ambient/local
5 and microenvironmental concentration levels to develop analytical relationships.
6 • Parameterized mass balance modeling over, or within, the volume of the
7 microenvironment. This type of modeling has ranged from very simple formulations, i.e.
8 from models assuming ideal (homogeneous) mixing within the microenvironment (or
9 specified portions of it) and only linear physicochemical transformations (including
10 sources and sinks), to models incorporating analytical solutions of idealized dispersion
11 formulations (such as Gaussian plumes), to models that take into account aspects of
12 complex multiphase chemical and physical interactions and nonidealities in mixing.
13 • Detailed Computational Fluid Dynamics (CFD) modeling of the outdoor or indoor
14 microenvironment, employing either a Direct Numerical Simulation (DNS) approach, a
15 Reynolds Averaged Numerical Simulation (RANS) approach, or a Large Eddy
16 Simulation (LES) approach, the latter typically for outdoor situations (see, e.g., Milner
17 et al., 2005; Chang and Meroney, 2003; Chang, 2006).
18 Parameterized mass balance modeling is the approach currently preferred for exposure
19 modeling for populations. As discussed earlier, the simplest microenvironmental setting
20 corresponds to a homogeneously mixed compartment, in contact with possibly both
21 outdoor/local environments as well as other microenvironments. The air quality of this idealized
22 microenvironment is affected mainly by the following processes:
23 a. Transport processes: These can include advection/convection and dispersion that
24 are affected by local processes and obstacles such as vehicle induced turbulence,
25 street canyons, building structures, etc.
26 b. Sources and sinks: These can include local outdoor emissions, indoor emissions,
27 surface deposition, etc.
28 c. Transformation processes: These can include local outdoor as well as indoor gas
29 and aerosol phase chemistry, such as formation of secondary organic and
30 inorganic aerosols.
August 2007 AX3-106 DRAFT-DO NOT CITE OR QUOTE
-------
1 Examples of the above are discussed next, specifically for outdoor and for indoor
2 microenvironments.
3
4 AX3.7.4.1 Characterization of Outdoor Microenvironments
5 Empirical regression analyses have been used in some studies to relate specific outdoor
6 locations - that can be interpreted as generalized types of exposure microenvironments - to
7 spatial variability of NO2 concentrations. For example, Gilbert et al. (2005) in May 2003
8 measured NO2 for 14 consecutive days at 67 sites across Montreal, Canada. Concentrations
9 ranged from 4.9 to 21.2 ppb (median 11.8 ppb), and they used linear regression analysis to assess
10 the association between logarithmic values of NO2 concentrations and land-use variables via a
11 geographic information system. In univariate analyses, NC>2 was negatively associated with the
12 area of open space and positively associated with traffic count on nearest highway, the length of
13 highways within any radius from 100 to 750 m, the length of major roads within 750 m, and
14 population density within 2000 m. Industrial land-use and the length of minor roads showed no
15 association with NC>2. In multiple regression analyses, distance from the nearest highway, traffic
16 count on the nearest highway, length of highways and major roads within 100 m, and population
17 density showed significant associations with NC>2. The authors of that study point out the value
18 of using land-use regression modeling to assign exposures in large-scale epidemological studies.
19 Similar analyses have been performed in a predictive setting by Sahsuvaroglu et al. (2006) for
20 Hamilton, Ontario, Canada.
21 The category of parameterized mass balance models for outdoor microenvironments
22 includes various local roadway, intersection, and street canyon models. For example, Fraigneau
23 et al. (1995) developed a simple model to account for fast nitrogen oxide - ozone
24 reaction/dispersion in the vicinity of a motorway. Venegas and Mazzeo (2004) applied a
25 combination of simple point and area source analytical plume models to characterize NO2
26 concentration patterns in Buenos Aires, Argentina, which they used for a simplified (potential)
27 population exposure study. ROADWAY-2 (Rao, 2002), is another near-highway pollutant
28 dispersion model that incorporates vehicle wake parameterizations derived from canopy flow
29 theory and wind tunnel measurements. The atmospheric velocity and turbulence fields are
30 adjusted to account for velocity-deficit and turbulence production in vehicle wakes and a
31 turbulent kinetic energy closure model of the atmospheric boundary layer is used to derive the
32 mean velocity, temperature, and turbulence profiles from input meteorological data.
August 2007 AX3-107 DRAFT-DO NOT CITE OR QUOTE
-------
1 In parameterized street canyon models, typically, concentrations of exhaust gases are
2 calculated using a combination of a plume model for the direct contribution and a box model for
3 the recirculating part of the pollutants in the street. Parameterization of flow and dispersion
4 conditions in these models is usually deduced from analysis of experimental data and model tests
5 that considered different street configurations and various meteorological conditions.
6 An example of a current model that belongs in the parameterized mass balance category is the
7 Danish Operational Street Pollution Model (OSPM) (Berkowicz, 2002), which updates earlier
8 formulations of street canyon models such as STREET of Johnson et al. (1973) and CPBM
9 (Canyon Plume-Box Model) of Yamartino and Weigand (1986). A variation of this simple
10 approach is the model of Proyou et al. (1998), which uses a three-layer photochemical box model
11 to represent a street canyon.
12 A variety of CFD based street canyon models have been developed in recent years (see,
13 e.g., the series of International Conferences on Harmonization - http://www.harmo.org),
14 employing various alternatives for closure of the turbulent transport equations. A review and
15 intercomparison of five of these models (CHENSI, CHENSI-2, MIMO, MISKAM, TASCflow)
16 vis-a-vis field data from a street canyon in Hannover, Germany can be found in the articles by
17 Sahm et al. (2002) and by Ketzel et al. (2002).
18 These complex localized models could be useful for improving population exposure
19 model estimates by calculating pollutant concentrations at the microenvironmental level. Lack
20 of input parameter data and parameter variation across the modeling domain (spatial and
21 temporal) contributes to uncertainty in microenvironmental concentrations calculated by exposre
22 models. In such cases, parameterized mass balance models could provide outdoor concentration
23 values for estimating exposure. If infiltration factors are known, these concentrations could also
24 be used to estimate indoor exposures.
25
26 AX3.7.4.2 Characterization of Indoor Microenvironments
27 Numerous indoor air quality modeling studies have been reported in the literature;
28 however, depending on the modeling scenario, only few of them address (and typically only a
29 limited subset of) physical and chemical processes that affect photochemical oxidants indoors
30 (Nazaroff and Cass, 1986; Hayes, 1989, 1991; Freijer and Bloemen, 2000).
31 It is beyond the scope of the present discussion to review in detail the current status of
32 indoor air modeling. Existing indoor air concentration models indeed are available as a wide
August 2007 AX3-108 DRAFT-DO NOT CITE OR QUOTE
-------
1 range of (a) empirical regression relationships, (b) parameterized mass balance models (that can
2 be either single-zone—that is, single well-mixed room—or multi-zone models), and (c) CFD
3 formulations. Recent overviews of this area can be found in Milner et al. (2005), who focus, in
4 particular, on the issue of entrainment from outdoor sources, and in Teshome and Haghighat,
5 (2004), who focus on different formulations of zonal models and on how they compare with
6 more complex CFD models.
7 Few indoor air models have considered detailed nonlinear chemistry, which, however,
8 can have a significant effect on the indoor air quality, especially in the presence of strong indoor
9 sources (e.g., gas stores and kerosene heaters, in the case of NCh). Indeed, the need for more
10 comprehensive models that can take into account the complex, multiphase processes that affect
11 indoor concentrations of interacting gas phase pollutants and particulate matter has been
12 recognized and a number of formulations have appeared in recent years. For example, the
13 Exposure and Dose Modeling and Analysis System (EDMAS) (Georgopoulos et al., 1997c)
14 included an indoor model with detailed gas-phase atmospheric chemistry to estimate indoor
15 concentrations resulting from penetration and reaction of ambient pollutants. This indoor model
16 was dynamically coupled with (a) the outdoor photochemical air quality models UAM-IV and
17 UAM-V, which provided the gas-phase composition of influent air; and (b) with a
18 physiologically based uptake and dosimetry model. Subsequent work (Isukapalli et al., 1999)
19 expanded the approach of the EDMAS model to incorporate alternative representations of gas-
20 phase chemistry as well as multiphase photochemistry and gas/aerosol interactions. The
21 microenvironmental model corresponding to this more general formulation is mathematically
22 represented by the following equation, when an assumption of uniform mixing is used for each
23 component (e.g., individual room) of the indoor environment. Sarwar et al. (2001) presented a
24 more comprehensive modeling study of the gas phase aspects of ozone indoor chemistry
25 focusing on the impact of different factors (such as outdoor ozone, indoor emissions, ventilation
26 rates, etc.) on the levels of indoor hydroxyl radicals (OH), which in turn are expected to control
27 the rate of formation of secondary toxicants indoors.
vt
28 "' /-/ j=l .1=1 (AX3-19)
29 where,
August 2007 AX3-109 DRAFT-DO NOT CITE OR QUOTE
-------
1 Pi = volume of compartment (m3)
2
3 Q = concentration of species in compartment (mol/m3)
4
5 Xij = mass transfer coefficient from compartment (m/h)
6
7 ay = interfacial air exchange area between compartments (m2)
8
9 QJ = concentration in compartment i in equilibrium with concentration in j (mol/m3)
10
11 <2ij = volumetric flow rate from compartment i to j (m3/h)
12
13 R[ = rate of formation of species in compartment i (gmol/h)
14
15 and,
c _ c _ c
e ( °i,emis °i,depos ^i.candens for gases
i 1C C 4-*? 4-S* 4- ^ 4- ?
jg ^i,emis ~ "i.depos ^i.resusp "r °i,condens ^i.nucl ~r **i,coagfor PM
17 More recent work (S0rensen and Weschler, 2002) has coupled CFD calculations with
1 8 gas-phase atmospheric chemistry mechanisms to account for the impact of nonideal flow mixing
19 (and associated concentration gradients) within a room on the indoor spatial distribution of ozone
20 and other secondary pollutants. This work has identified potential limitations associated with the
21 assumption of uniform mixing in indoor microenvironments when calculating personal
22 exposures.
23 A recent indoor air model that specifically focuses on NC>2 (along with CO, PMio, and
24 PM2.5 is INDAIR (Dimitroulopoulou et al., 2006). The INDAIR model considers three
25 interconnected residential microenvironments: kitchen, lounge, and bedroom. Removal
26 processes are lumped together and quantified via an apparent deposition velocity. Specifically, a
27 loss rate of 0.99 ± 0.19 IT1 (Yamanaka, 1984), is used in this model corresponding to a mean
28 deposition velocity of 1.2 x 10~4 m s ~l. The sources of NO2 considered in INDAIR are from gas
August 2007 AX3-110 DRAFT-DO NOT CITE OR QUOTE
-------
1 stove cooking and from cigarette smoking, but only the former contributes significantly to indoor
2 NO2 levels, based on available model parameterizations.
3 Estimation of NC>2 emission rates from gas cooking utilized the following empirical
4 information: (a) NOX emission rate equal to 0.125 g kWlT1 (Wooders, 1994); (b) an assumption
5 that NC>2 represents 25% of the total NOX emissions and (c) gas consumption per household in
6 cooking equal to 5-7 kWh day"1, assuming 1 h cooking per day. By multiplying the estimates in
7 (a), (b), and (c) together, NC>2 gas cooking emission rates were calculated to be in the range 0.16
8 to 0.22 g h'1, with a uniform distribution.
9 In a range of simulations performed with INDAIR for houses in the UK, it was found that
10 the predicted maximum 1-h mean concentrations in the kitchen were increased, compared to no-
11 source simulations, by a factor of 10 for NO2 (30 for PMi0 and 15 for PM2.5) and were higher in
12 winter than in summer. Cooking activity in the kitchen resulted in significantly elevated 24 h
13 mean concentrations of NC>2, PMio, and PM2.5 in the lounge, as well as the kitchen, while there
14 was a relatively small effect in the bedroom, which was not connected directly to the kitchen in
15 the model structure (i.e., the direct internal air exchange rate was zero).
16 A very wide range of predictions was derived from the INDAIR simulations. The 95th
17 percentile concentrations were typically 50% higher than mean concentrations during periods of
18 average concentration, and up to 100% higher than mean concentrations during concentration
19 peaks, which were associated with cooking emissions. There was approximately a factor of
20 2 variation in concentrations, and all modeled concentrations were below those outdoors. The
21 effect of cooking was to shift the distribution to the right, but the degree of variation was not
22 greatly increased. This may reflect the fact that for the fixed emission scenarios that were used,
23 the additional variation in emission rates was small compared to that of other factors such as
24 deposition rate and air exchange rate. In this scenario, modeled concentrations in the lounge all
25 remained below those outdoors, but a proportion of kitchens (16%) had modeled values above
26 the outdoor concentration. For the gas-cooking scenario, indoor/outdoor ratios for NC>2 ranged
27 from 0.5 to 0.8 for the bedroom, 0.7 to 1.6 for the lounge and 0.9 to 3.6 for the kitchen.
28 According to Dimitrolopoulou et al. (2006), these results were broadly consistent with
29 indoor/outdoor ratios reported for the UK. Modeled peak concentrations associated with gas
30 cooking, of about 300 ppb in the kitchen and 100 ppb in the lounge, were also consistent with
31 results from UK studies.
August 2007 AX3-111 DRAFT-DO NOT CITE OR QUOTE
-------
1 AX3.7.4.3 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 AX3.7.4.4 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
32 uptake rate is then converted into an estimate of total ventilation rate (VE), expressed in liters
August 2007 AX3-112 DRAFT-DO NOT CITE OR QUOTE
-------
1 min"1. Johnson (2001) reviewed briefly the physiological principles incorporated into the
2 algorithms used in pNEM to convert each activity classification code to an oxygen uptake rate
3 and describes the additional steps required to convert oxygen uptake to VE.
4 McCurdy (1997a,b, 2000) has recommended that the ventilation rate should be estimated
5 as a function of energy expenditure rate. The energy expended by an individual during a
6 particular activity can be expressed as EE = (MET)(RMR) in which EE is the average energy
7 expenditure rate (kcal min"1) during the activity and RMR is the resting metabolic rate of the
8 individual expressed in terms of number of energy units expended per unit of time (kcal min"1).
9 MET (the metabolic equivalent of tasks) is a ratio specific to the activity and is dimensionless. If
10 RMR is specified for an individual, then the above equation requires only an activity-specific
11 estimate of MET to produce an estimate of the energy expenditure rate for a given activity.
12 McCurdy et al. (2000) developed distributions of MET for the activity classifications appearing
13 in the CHAD database.
14 Finally, in order to relate intake to dose delivered to the lungs, it is important to take into
15 account the processes affecting uptake following inhalation intake of NC>2, in a biologically
16 based dosimetry modeling framework. As a reactive gas, NO2 participates in transformation
17 reactions in the lung epithelial lining fluid, and products of these reactions are thought to be
18 responsible for toxic effects (Postlethwait et., 1991), although kinetic modeling of these reactions
19 has not been performed. Dosimetry models indicate that deposition varies spatially within the
20 lung and that this spatial variation is dependent on ventilation rate (Miller et al., 1982; Overton
21 and Graham, 1995). Controlled exposure studies found that fractional uptake of NO2 increases
22 with exercises and ventilation rate (e.g., Bauer et al., 1986), making activities with high MET
23 values important for quantifying total NC>2 exposure. Further discussion of NC>2 dosimetry
24 modeling is provided in Section 4.2.
25
26 AX3.7.5 Concluding Comments
27 An issue that should be mentioned in closing is that of evaluating comprehensive
28 prognostic exposure modeling studies, for either individuals or populations, with field data.
29 Although databases that would be adequate for performing a comprehensive evaluation are not
30 expected to be available any time soon, there have been a number of studies, reviewed in earlier
31 sections of this Chapter, that can be used to start building the necessary information base. Some
August 2007 AX3-113 DRAFT-DO NOT CITE OR QUOTE
-------
1 of these studies report field observations of personal, indoor, and outdoor ozone levels and have
2 also developed simple semi-empirical personal exposure models that were parameterized using
3 the observational data and regression techniques.
4 In conclusion, though existing inhalation exposure modeling systems have evolved
5 considerably in recent years, limitations of available modeling methods and data, in relation to
6 potential NO2 studies that include the following, should be taken into account and be addressed
7 by future research efforts:
8 • Ambient photochemical modeling systems are not optimized for estimating NO2 at a
9 local scale.
10 • Subgrid scale modeling (LES, RANS, DNS) is needed to properly characterize effects of
11 nonhomogeneous mixing (i.e., of spatial subgrid variability) on fast nonlinear chemical
12 transformations; the outcomes of this characterization then should be incorporated in
13 simpler models, appropriate for use in conjunction with exposure modeling systems.
14 • Microenvironmental modeling efforts need to balance mechanistic detail and usability by
15 developing:
16 — A simplified but adequate indoor chemistry mechanism for NO2 and related
17 oxidants,
18 — Databases of realistic distributions of indoor NO2 source magnitudes and
19 activities,
20 — Flexible, multi-zonal models of indoor residential and occupational
21 microenvironments.
22 Existing prognostic modeling systems for inhalation exposure can in principle be directly
23 applied to, or adapted for, NO2 studies; APEX, SHEDS, and MENTOR-1A are candidates.
24 However, such applications would be constrained by data limitations such as ambient
25 characterization at the local scale and by lack of quantitative information for indoor sources and
26 sinks.
27
28
29 AX3.8 EXPOSURE ERROR
30 Discussions in this section focus on the errors associated with exposure assessments and,
31 in particular, with those that may be associated with using ambient NO2 as a surrogate of
32 personal NO2 exposure in epidemological time series studies. As shown in Figure AX3.24,
33 exposure error is one of the errors associated with epidemological studies linking pollutant
August 2007 AX3-114 DRAFT-DO NOT CITE OR QUOTE
-------
EPI
Health
_^ Outcome
Figure AX3.24.
Errors associated with components of the continuum from ambient
air pollution to adverse health outcome.
1 concentrations in ambient air and human health responses. How exposure errors influence the
2 epidemological findings depend upon the design of the epidemological study. In this section, the
3 exposure errors will be discussed in the context of two common environmental epidemological
4 study designs, time-series studies and chronic studies, in which central site NO2 concentrations
5 are used as surrogates of personal exposure.
6 In a broader sense, NO2 is an indicator of a chemical mixture, which might be the real
7 agent(s) leading to epidemological findings. Ambient, indoor or personal NO2 might indicate
8 different chemical mixtures because of differences in the infiltration efficiency or chemical
9 reactivity of other NOy species or in the composition of nearby sources. When using ambient
10 NO2 as a surrogate of personal exposure, issues of confounding and surrogate are raised.
August 2007
AX3-115
DRAFT-DO NOT CITE OR QUOTE
-------
1 Confounding issues have been discussed in Section AX3.6. A brief summary of the confounding
2 issues and a brief discussion of the surrogate issues will be provided in this section.
3 Usually when discussing errors in the context of exposure assessments, errors resulting
4 from limitations of analytical capabilities of monitoring instruments are lumped together with
5 those caused by environmental factors such as spatial heterogeneity in ambient concentrations,
6 the lack of identification of indoor and neighborhood sources etc. In certain instances these
7 different errors may be linked.
8 Measurements of NC>2 are subject to artifacts both at the ambient level and at the personal
9 level. A discussion of the errors associated with ambient monitors is given in Section 2.8, and
10 one for errors associated with personal monitors is given in Section AX3.4. As noted earlier,
11 measurements of ambient NO2 are subject to variable interference caused by other NOy
12 compounds, in particular PANs, organic nitrates, particulate nitrate and HNC>2 and HNOs. The
13 latter is taken up on inlet walls to varying degrees and likely causes variable (positive) artifacts
14 in NC>2 measurements.
15 Personal monitors are subject to interference by SC>2 and HONO and it is not clear to
16 what extent they are affected by interference by the NOy species interfering with the ambient
17 monitors. In addition, personal monitors generally require longer sampling times (typically from
18 about a day to two weeks) and so will not be able to identify peak exposures occurring on time
19 scales of a few hours or less. As noted by Pilotto et al. (1997) these exposures would have been
20 averaged out and associated health outcomes would not be properly attributed by monitors
21 requiring longer sampling times. Often personal concentrations may either be below or not very
22 much above detection limits for the most commonly used personal samplers (see Table AX3.6).
23 Thus, associations between ambient and personal concentrations could be weakened between
24 ambient and personal concentrations of a given pollutant. In studies of multiple pollutants,
25 personal concentrations of one pollutant may be more strongly associated with ambient
26 concentrations of another pollutant if the measurements of the latter at the personal level are
27 subject to larger analytical errors than are measurements of the former at the personal level.
28 Spatial heterogeneity in ambient concentrations helps determine how well concentrations
29 measured at ambient monitoring sites reflect exposures at the community and personal levels.
30 Correlations between different pairs of monitoring sites are not sufficient for characterizing
31 spatial variability, as there may be significant differences in concentrations among monitoring
August 2007 AX3-116 DRAFT-DO NOT CITE OR QUOTE
-------
1 sites. This point has been demonstrated in Chapter 3 the latest AQCD for PM (U.S.
2 Environmental Protection Agency, 2004) and Chapter 3 the latest AQCD for ozone and other
3 photochemical oxidants (U.S. Environmental Protection Agency, 2006a). As described earlier in
4 Section AX3.2, concentrations of NC>2 are highly variable across the urban areas examined and
5 will result in exposure characterization errors at least as significant as, if not larger, than those
6 for Os and PM2.5. The problem is exacerbated for NO2 because of the sparseness of NOX
7 monitors, compared to monitors for PM and O^. Thus, the use of central site monitors may be
8 more problematic for NC>2 than for PM2.5 (e.g.). As a result, little relation might be found
9 between ambient central site monitors and personal exposures and/or indoor concentrations and
10 stronger associations might be found between cross pollutants at the ambient and personal levels.
11 In this case, it may be necessary to supplement existing ambient measurements to derive ambient
12 concentrations that are consistent with those of other pollutants, e.g., by the use of supplemental
13 'outdoor' monitors. Additional complexity arises if horizontal spatial gradients are large enough,
14 as might happen in going from urban to rural environments, as the lowest values measured might
15 be beneath quantification limits or even beneath detection. Small scale horizontal variability
16 especially as found near roads could be large.
17 As noted earlier in Section AX3.2, variability in the vertical must be considered in
18 addition to horizontal variability. NC>2 emitted at or near ground level exhibits strong vertical
19 gradients. Restrepo et al. (2004) found that NC>2 measured at 15 m above the surface was a
20 factor of higher than measurements of NO2 at 4 m. Monitors placed at heights such as these will
21 be found in many inner urban areas.
22 In the framework developed by Zeger (2000) for analyzing errors in time-series
23 epidemological studies associated with exposure measurement errors, exposure errors could be
24 classified into three components: (1) the difference between true ambient concentration and the
25 measured ambient concentration, (2) the difference between the measured ambient concentration
26 and the community ambient exposure, and (3) the difference between the community ambient
27 exposure and the personal ambient exposure. These differences mentioned above are determined
28 by (1) the reliability of measurement techniques, (2) the spatial and temporal variation of
29 ambient NC>2 concentrations, and (3) personal activity and microenvironment characteristics.
30 In the context of chronic epidemological studies, the issue of misclassification also arises.
31 Personal exposure is composed of exposures to both ambient sources and nonambient sources. If
August 2007 AX3-117 DRAFT-DO NOT CITE OR QUOTE
-------
1 total personal NO2 exposure is assumed to be responsible for the observed health outcomes, the
2 use of ambient concentration as a surrogate for personal exposure could lead to misclassification
3 and bias the epidemological findings. The degree of the misclassification also depends on the
4 spatial and temporal variation of ambient NO2, personal activities and microenvironment
5 characteristics.
6 In the Danish children exposure study, front door NC>2 as well as personal NC>2
7 concentrations were measured (Raaschou-Nielsen et al., 1997). To evaluate the extent of
8 misclassification using outdoor NC>2 as an indicator of personal exposure, Raaschou-Nielsen et
9 al. (1997) reported that both the sensitivity (the proportion of correctly classified highly
10 exposure) and the specificity (the proportion of correctly classified low exposure) were 81% in
11 Copenhagen and 74% in the rural areas. Similar results were reported by Lee, et al., (2004).
12 Exposure measurement errors could also be evaluated by comparing the within subject
13 and between subject variations of individual exposures. The higher the ratio of within variance
14 and between variance, the more the true exposure-effect relationship is biased (Armstrong et al.,
15 1992). During the Los Angeles NC>2 exposure study, Spengler et al. (1994) reported that the
16 within personal variation was 61.2 |ig/m3 and the variation between personal exposure was 608.2
17 |ig/m3. Aim et al. (1998) reported that within personal variation explained 59% of the total
18 personal exposure variation and 41% of the total variation was accounted by between-subject
19 variation.
20 Simply speaking, two parameters could be used to evaluate the feasibility of using
21 ambient NC>2 concentrations as a surrogate for personal exposure: the correlation coefficient
22 between personal exposure and ambient concentrations (especially in the context of longitudinal
23 design and daily-averaged design), and the difference between personal exposure and ambient
24 concentration. Extensive discussions of this issue have been provided in Section AX3.5, such
25 discussions are not repeated here and only general conclusions will be provided. The correlation
26 between personal exposure and ambient concentrations range from moderate to good. Personal
27 exposure concentrations are generally lower than ambient concentrations for homes with no
28 indoor or local sources but higher than ambient concentration for homes with indoor or local
29 sources.
30 In a broader context, NC>2 serves as an indicator of a pollutant mixture whose components
31 have different physical and chemical properties that may be the real agent(s) causing the adverse
August 2007 AX3-118 DRAFT-DO NOT CITE OR QUOTE
-------
1 health effects. The components of the mixture are either primary or secondary, i.e., they either
2 come from direct emissions or form through atmospheric chemical reactions. When the ambient
3 mixture infiltrates into microenvironments, some components are lost due to absorption and
4 chemical reaction, while some new components are formed through chemical reactions in indoor
5 air. At the same time, indoor primary sources could add more NC>2 along with other pollutants in
6 the indoor environments. When evaluating the question of whether ambient NC>2 is the agent
7 causing the observed adverse health effects, the two issues of confounding and surrogacy are
8 raised.
9 The definition and discussion of the confounding issue from the perspective of exposure
10 analysis could be found in Section AX3.6. In Section AX3.6, the following five questions were
11 evaluated (the five arrows in Figure AX3.24): (1) Are ambient copollutant concentrations
12 significantly associated with ambient NC>2? (2) Are personal exposures to copollutants
13 significantly associated with personal exposures to NO2? (3) Are ambient pollutant
14 concentrations associated with their respective personal exposures? (4) Are ambient copollutants
15 surrogates for personal exposure to NC>2? (5) Is ambient NC>2 a surrogate for personal exposure
16 to copollutants? Based on the fact that NC>2 is correlated with other copollutants at both ambient
17 level and personal exposure levels and that cross-level correlations were also observed, we
18 concluded that caution should be exercised when dealing with the observed NC>2 health effect
19 and a more comprehensive analysis should be performed in conjunction with other components
20 of the risk assessment.
21 Another issue raised is the surrogate issue. There are different meanings associated with,
22 to use the word "surrogate". In summary, there are three scenarios involving the concept of a
23 surrogate and each one is associated with a question: (1) At ambient level, is ambient NC>2 a
24 good surrogate (tracer) for some ambient chemical or chemical mixture? (2) At personal
25 exposure levels, is personal NC>2 exposure a good surrogate (tracer) for some chemical or
26 chemical mixture of personal exposure? and (3) At health effect levels, is NC>2 a good surrogate
27 for some chemical or chemical mixture causing an adverse health outcome? The first two
28 questions could be sufficiently answered by various source apportionment approaches to
29 evaluate the co-variation of NC>2 with other pollutants. The third question is evaluated in Figure
30 AX3.25 with a systematic approach considering biological plausibility and exposure assessment.
August 2007 AX3-119 DRAFT-DO NOT CITE OR QUOTE
-------
ersonal NO2 Exposure
rrelated with ambie
o-pollutant
Personal NO2 Exposure
orrelated with ambie
NO2?
Causal Effects for NO2?
rsonal NO2 Exposure
rrelated with ambie
co-pollutants?
NO2 is a surrogate for
rsonal NO2 Exposure
orrelated with ambie
-pollutants
Figure AX3.25.
A systematic approach to evaluate whether NO2 itself is causing the
observed adverse health outcome or NOi is acting as a surrogate for
other pollutants.
August 2007
AX3-120
DRAFT-DO NOT CITE OR QUOTE
-------
TABLE AX3.1. SUMMARY OF PERCENTILES OF NO2 DATA POOLED ACROSS MONITORING SITES (2003-2005)
CJQ
r-K
to
o
o
X
OJ
to
o
H
6
o
0
H
O
HH
H
W
O
O
O
CONCENTRATIONS ARE IN PPM
Percentiles
Pooled Group/ Number of
Avg Time Values Mean 1 5 10 25 30 50 70 75
1-h Max Concentrations
Monitors in
CMSAs 288008 0.029 0.003 0.007 0.010 0.017 0.019 0.027 0.036 0.038
^™"torsnotm 460913 0.008 0.001 0.001 0.001 0.002 0.003 0.005 0.009 0.010
CMSAs
1-h Avg. Concentrations
Monitors in
CMSAs 6163408 0.015 0.001 0.003 0.003 0.006 0.007 0.012 0.019 0.022
ct/rs A*8 ^ m 460913 0.008 0.001 0.001 0.001 0.002 0.003 0.005 0.009 0.010
Daily 24-h Avg. Concentrations
Monitors in
CMSAs 282810 0.015 0.002 0.003 0.005 0.008 0.009 0.012 0.019 0.021
Monitors not in 2Q635
CMSAs
2-week Avg. Concentrations
Monitors in
CMSAs 21779 0.015 0.003 0.005 0.006 0.009 0.010 0.014 0.019 0.020
M°°1*°rsnotm 1588 0.008 0.001 0.001 0.001 0.003 0.003 0.007 0.009 0.012
Yearly Avg. Concentrations
Monitors in
CMSAs 758 0.015 0.004 0.006 0.007 0.011 0.012 0.015 0.018 0.019
Monitors not in M Q Qog OOQ1 OOQ1 Q OQ2 Q OQ3 Q OQ5 Q OQ9 OQ12 Qfin
CMSAs
3-yr Avg. Concentrations
Monitors in
CMSAs 247 0.015 0.004 0.006 0.007 0.011 0.012 0.015 0.018 0.019
Monitors not in
CMSAs 15 0.008 0.001 0.001 0.002 0.003 0.006 0.008 0.012 0.012
90 95 99 Max
0.048 0.055 0.072 0.201
0.019 0.026 0.040 0.189
0.033 0.040 0.053 0.201
0.019 0.026 0.040 0.189
0.028 0.034 0.045 0.129
0.017 0.021 0.030 0.081
0.026 0.031 0.038 0.076
0.016 0.020 0.030 0.039
0.025 0.028 0.033 0.037
0.015 0.016 0.017 0.017
0.025 0.028 0.032 0.033
0.014 0.016 0.016 0.016
-------
TABLE AX3.2. SPATIAL VARIABILITY OF NO2 IN SELECTED UNITED STATES
URBAN AREAS
Mean 1-h
Concentration(ppb) r P90 (ppb)
New York, NY
(5)
Atlanta, GA
(5)
Chicago, IL
(7)
Houston, TX
(7)
Los Angeles, CA
(14)
Riverside, CA
(9)
29
(25-37) 0.77-0.90 7-19
** 0.22-0.89 7-24
(5-16)
(6-230) -°05-°-83 10-39
(T-'lS) °31-°-8° 6-2°
25
(14-33) 0.01-0.90 8-32
(, 2* 0.03-0.84 10-40
COD
0.08-0.23
0.15-0.59
0.13-0.66
0.13-0.47
0.08-0.51
0.14-0.70
TABLE AX3.3. NOX AND NOY CONCENTRATIONS AT REGIONAL
BACKGROUND SITES IN THE EASTERN UNITED STATES. CONCENTRATIONS
ARE GIVEN IN PPB
NO
Winter
Summer
NOX
Winter
Summer
NOy
Winter
Summer
Shenandoah NP, VA
0.39-2.2 l
0.12-0.28
—
—
—
2.7-8.6
2.3-5.7
Harvard Forest, MA
—
—
—
1-15
0.4-1.2
4.4 2
2.7 2
1 Ranges represent lo limits.
2 Values represent medians.
August 2007 AX3-122 DRAFT-DO NOT CITE OR QUOTE
-------
TABLE AX3.4. RANGE OF PEARSON CORRELATION COEFFICIENTS
BETWEEN NO2 AND O3, CO AND PM2.5
Monitoring Sites in Copollutant
Selected Areas
Los Angeles, CA
Riverside, CA
03
-0.59 to 0.19
-0.26 to 0.28
CO
0.11 to 0.83
0.15 to 0.65
PM25
0.45 to 0.56
Chicago, IL -0.20 to-0.13 -0.10 to 0.53 0.21 to 0.49
Washington, DC — — —
New York City, NY — — —
August 2007 AX3-123 DRAFT-DO NOT CITE OR QUOTE
-------
TABLE AX3.5. PASSIVE SAMPLERS USED IN NO2 MEASUREMENTS
JW
r-K
to
o
o
X
OJ
to
o
H
6
o
0
H
O
HH
H
W
Passive Sampler
Palmes tube
Gradko sampler
Passam Short
sampler Long
Analyst™
Yanagisawa badge
Ogawa sampler
IVL sampler
Willems badge
Radiello®
EMD sampler
Dimension
(diffusion length x
cross-sectional area) Absorbent
7.1cm x 0.71cm2 Triethanolamine
7.1cm x 0.93cm2 Triethanolamine
0.74cm x 0.75cm2 Triethanolamine
2.54cm x 3.27cm2 Active charcoal
1.0cm x 20cm2 Triethanolamine
0.6cm x 0.79cm2 Triethanolamine
Potassium iodide
1.0cm x 3.14cm2 & sodium arsenite
Triethanolamine-
0 . 6cm x 5 . 3 1 cm2 acetone
1 . 8cm x 2 . Ocm2 Triethanolamine
N.A. Triethanolamine
Analytical
Method
Spectrophotometry
Spectrophotometry
Spectrophotometry
Gas
chromatography
Spectrophotometry
Spectrophotometry
Spectrophotometry
Spectrophotometry
Spectrophotometry
Ion
chromatography
Sampling Rate
Manufacturer Experiment Reference
N.A. 0.92 cnrYmin Palmes et al. (1976)
Plaisance et al.
(2004)
1.2 cnrYmin 1.212 cnrYmin Gradko (2007)
15.5 cnrYmin N.A.
0.854 cnrYmin 0.833 cnrYmin Passam (2007)
De Santis et al.
N.A. 12.3 cnrYmin (2002)
Yanagisawa et al.
N.A. N.R. (1982
Ogawa & Company
(1998a) Gerboles
N.A. 16.2 cnrYmin et al. (2006a)
N.A. 29 cnrYmin Perm etal. (1998)
Hagenbjork-
Gustafsson et al.
N.A. 46 cnrYmin (2002)
75 cnrYmin N.R. Radiello® (2006)
Piechocki-Minguy
N.A. 53.4 cnrYmin etal. (2006)
*N.A.: not available; N.R.: notreported.
O
-------
CJQ
TABLE AX3.6. THE PERFORMANCE OF SAMPLER/SAMPLING METHOD FOR NO2 MEASUREMENTS
IN THE AIR
r-K
to
o
o
X
OJ
to
o
H
6
o
0
H
O
HH
H
W
Type Sampler
Active Impinger method
sampling
Chemiluminescence
Personal monitor
Passive Palmes tube
sampling
Gradko sampler
Passam Short
sampler
Long
Analyst™
Yanagisawa badge
Ogawa sampler
IVL sampler
Willems badge
Radiello®
EMD sampler
N.R.: not reported.
Optimal Duration of
Sampling
2-24 h
Continuous
Real-time
1-4 wks
2-4 wks
8-48 h
1-4 wks
1-3 mos
1-14 days
24-168 h
1 mo +
2-8 h& 1-7 days
1-24 h& 1-7 days
1-24 h
Concentration
Range
10 - 400 ppb
0.5 - 1000 ppb
0.1 -50ppm
10 - 100 ppb
1.0 -10,000 ppb
5 - 240 ug/m3
1 - 200 ug/m3
24 - 1,237 ug/m3
N.R.
0 - 3,600 ppb
0.1-400 ug/m3
2.0 - 150 ug/m3
1.0 -496 ppb
N.R.
Detection Limit
0.2 ppb
0.05 ppb
0.1 ppm
10 ppb
0.5 ppb
2-5 ug/m3
0.64 ug/m3
100 ug/m3
3.0 ppb
2.3 ppb
0.1 ug/m3
2 ug/m3
1.0 ppb
11 ug/m3
RSD < 5%
Accuracy ±
Precision ±
Uncertainty
Uncertainty
Accuracy ±
3%
RSD - 4%
Uncertainty
Uncertainty
Uncertainty
Comment
5%
5% above 5 ppb
-27% at 80 ug/m3
- 25% at 20-40 ug/m3
5%; Precision within
- 24%; RSD 22%
- 12%
-28%
O
-------
TABLE AX3.7. NO2 CONCENTRATIONS (PPB) IN HOMES IN LATROBE VALLEY,
VICTORIA, AUSTRALIA
Living Room
No source
Gas stove only
Gas heater only
Smoking only
Multiple sources
Mean ppb
3.77
6.70
6.86
6.02
14.50
Min ppb
<0.37
1.57
2.20
0.94
2.25
Max ppb
9.27
18.32
18.06
14.61
114.66
Mean ppb
3.82
8.01
7.33
6.60
10.73
Kitchen
Min ppb
<0.37
2.62
2.88
1.83
2.62
Max ppb
8.17
24.14
26.23
16.44
128.80
Source: Garrettetal. (1999).
TABLE
AX3.8.
. NO2 CONCENTRATIONS (PPB) IN HOMES IN
No Gas Stove Used in Monitoring Period
Secondary
Heating
Source
None
Gas space
heater
Wood
burning
source
Kerosene
heater
GSH +
Wood
GSH + KH
Wood + KH
GSH +
Wood + KH
N
1018
6
200
159
3
0
73
0
10th
1.7
0.1
1.8
3.3
12.6
-
1.9
—
25th
3.5
9.2
3.6
7.1
12.6
-
8.2
—
Median
6.3
15.3
5.9
18.9
80.6
—
16.4
—
75th
12.3
68
12.2
42.7
81.9
—
35.2
—
90th
28.2
69.6
28.2
88.3
81.9
—
66.8
—
CONNECTICUT
Yes Gas Stove Used in Monitoring Period
N
564
6
78
14
5
1
5
1
10th
8.4
19.5
6
0
36.2
n/a
8.9
n/a
25th
14.5
34.6
9.5
9.6
44.8
n/a
12.7
n/a
Median
22.7
36.6
16.7
17.2
57.1
147.7
17.3
107.8
75th
33.8
54.8
31.4
33.6
114.
2
n/a
23.5
n/a
90th
48.1
147.2
58.6
46.1
156.6
n/a
72.9
n/a
Source: Triche et al. (2005).
August 2007
AX3-126
DRAFT-DO NOT CITE OR QUOTE
-------
TABLE AX3.9. NO2 CONCENTRATIONS NEAR INDOOR SOURCES -
SHORT-TERM AVERAGES
Average
Concentration
(PPb)
Peak Concentration
(PPb)
Comment
Reference
191 kitchen
195 living room
184 bedroom
400 kitchen,
living room,
bedroom
90 (low setting)
350 (med setting)
360 (high setting)
N/R
N/R
180 to 650
375 kitchen
401 living room
421 bedroom
673 bedroom
N/R1
1000
1500
N/R
Cooked full meal with
use of gas stove and
range for 2 h, 20 min;
avg cone, is time-
weighted over 7 h.
Automatic oven
cleaning of gas stove.
Avgs are over the entire
cycle.
Natural gas unvented
fireplace,2 2-h-time-
weighted avg in main
living area of house
(177 m3).
Room concentration
with kerosene heater
operating for 46 min.
Room concentration
with gas heater
operating for 10 min.
Calculated steady-state
concentration from
specific unvented gas
space heaters operating
in a 1400 ft2 house,
1.0 ach.
Fortmann et al.
(2001)
Fortmann et al.
(2001)
Button et al.
(2001)
Girman et al.
(1982)
Girman et al.
(1982)
Girman et al.
(1982)
1 N/R = Not Reported.
2 Unvented fireplaces are not permitted in many areas such as California.
Source: Adapted from CARB (2007).
August 2007
AX3-127
DRAFT-DO NOT CITE OR QUOTE
-------
TABLE AX3.10. NO2 CONCENTRATIONS NEAR INDOOR SOURCES -
LONG-TERM AVERAGES
Average Concentration
(ppb) Comment Reference
30 to 33
22
6 to 11
55 (Median)
41 (90th %-ile)
80 (90th %-ile)
84 (90th %-ile)
147 (90th %-ile)
52 (90th %-ile)
18 bedrooms
19 living rooms
15 outdoors
Gas stoves with pilot lights.
Gas stoves without pilot lights.
Electric ranges. Study conducted in 517
homes in Boston, values represent 2-wk
avgs.
Gas space heaters.
No indoor combustion source.
Fireplaces.
Kerosene heater.
Gas space heaters.
Wood stove.
All values represent 2-wk avgs in living
rooms.
Almost all homes had gas stoves. Values
represent 2-wk avgs.
Lee etal. (1998)
Triche et al.
(2005)
Zipprich et al.
(2002)
August 2007
AX3-128
DRAFT-DO NOT CITE OR QUOTE
-------
CJQ
to
o
o
TABLE AX3.11. SUMMARY OF REGRESSION MODELS OF PERSONAL EXPOSURE TO AMBIENT/OUTDOOR NO2
Study Location
Rojas-Bracho et al. Santiago, urban
(2002)
Aim et al. (1998) Helsinki, downtown +
Monn et al.
suburban
(1998) Four urban + two rural
+ two alpine
Levy et al. (1998a) 15 cities in 18
^
X
OJ
1
to
VO
o
l>
H
6
o
0
H
o
i — i
H
W
O
O
Spengler et
(1994)
S0rensen et
(2005)
Sarnat et al.
Sarnat et al.
Sarnat et al.
countries
al. Los Angeles Basin
al. Copenhagen, urban
(2001) Baltimore
(2005) Boston
(2006) Steubenville
Season
Winter
Winter +
Spring
All
Winter
All
All
(>8 °C)
(<8 °C)
All
Summer
Winter
Summer
Winter
Summer
Fall
Model type
Personal vs. outdoor
Personal vs. central
Personal vs. outdoor
Personal (all subjects) vs. outdoor
Personal (no smokers and gas
cooking) vs. outdoor
Personal vs. outdoor
Personal vs. outdoor
Personal vs. outdoor
Personal vs. outdoor
Personal vs. outdoor
Personal vs. central
Personal vs. central
Personal vs. central
Personal vs. central
Personal vs. central
Personal vs. central
Personal vs. central
Slope (SE) Intercept / ppb R2
0.33 (0.05)
0.3
0.4
0.45
0.38
0.49
0.56
0.60 (0.07)
0.68 (0.09)
0.32(0.13)
0.56 (0.09)
0.04*
-0.05*
0.19
-0.03*
0.25 (0.06)
0.49 (0.05)
7.2 0.27
5.0 0.37
4.7 0.86
7.2 0.33
7.2 0.27
14.5 —
15.8 0.51
— —
— —
— —
— —
9.5 —
18.2 —
— —
— —
— 0.14
— 0.43
mificant at 1
-------
TABLE AX3.12. AVERAGE AMBIENT AND NONAMBIENT CONTRIBUTIONS TO POPULATION EXPOSURE
CJQ
r-K
to
o
o
OJ
i
o
O
H
6
o
0
H
O
HH
H
W
O
O
O
Study
Rojas-B radio
et al. (2002)
Aim et al.
(1998)
Monn et al.
(1998)
Levy et al.
(1998a)
Spengler et al.
(1994)
** Not reported.
Slope
Model Type (SE)
Personal vs. outdoor 0.33
(0.05)
Personal vs. central 0.3
Personal vs. outdoor 0.4
Personal (all subjects) 0.45
vs. outdoor
Personal (no smokers 0.38
and gas cooking) vs.
outdoor
Personal vs. outdoor 0.49
Personal vs. outdoor 0.56
Mean of Percent Percent
Personal Total Mean Ambient Ambient Nonambient
Intercept / Exposure / Contribution / Contribution Contribution
ppb ppb Ppb °/° 0//0
7.2 36.4 7.2 19.8 80.2
5.0 — 5.0 — —
4.7 — 4.7 — —
7.2 14.1 7.2 51.1 48.9
7.2 — 7.2 — —
14.5 28.8 14.5 50.3 49.7
15.8 37.6 15.8 42.0 58.0
-------
CJQ
TABLE AX3.13. THE ASSOCIATION BETWEEN PERSONAL EXPOSURES AND
AMBIENT CONCENTRATIONS
tyj
to
o
o
X
OJ
^
O
H
6
o
2;
o
H
O
HH
H
W
O
O
O
Study
Linn et al.
(1996)
Kramer et al.
(2000)
Rojas-B radio
et al. (2002)
Raaschou-
Nielsen et al.
(1997)
Aim et al.
(1998)
Monn et al.
(1998)
Study Design
Children, Southern California, 24 h averaged, one wk
consecutive measurement for each season (fall, winter, and
spring 1992-1994) for each child.
Children, West Germany, two one-wk averaged
measurements for each child each in March and Sept 1996
Children, Santiago, 24 h averaged sample for five
consecutive days for each child, winters of 1998 and 1999
Children, Copenhagen and rural areas, one-wk averaged, 2
measurements for each child in each month (Oct 1994,
April, May, and June 1995)
Children, Helsinki, one-week averaged, 13 wks for each
child in each season (winter and spring 1991)
Adults, Switzerland, eight regions in Swiss (four
urban/suburban, two rural and two alpine regions), one-wk
averaged, one measurement each mo (the first wk of the
mo) for each subject, between Dec 1993 to Dec 1994
Association Variable
Personal vs. central
Personal vs. outdoor
Personal vs. outdoor
Personal vs. outdoor
Personal vs. outdoor
Personal vs. outdoor
Personal vs. outdoor
Personal vs. outdoor
Personal vs. outdoor
Personal vs. outdoor
Personal vs. central
Personal vs. central
Personal vs. outdoor
Personal vs. central
Personal vs. outdoor
Location
pooled
pooled
urban
urban
urban
rural
downtown
suburban
downtown
suburban
downtown
suburban
pooled
pooled
pooled
Season
pooled
pooled
pooled
winter
pooled
pooled
winter
winter
spring
spring
spring
spring
pooled
pooled
pooled
rp, rs, or R2
0.63 (rp)
0.37 (rp)
0.06 (rp)
0.27 (R2)
0.15 (R2)
0.35 (R2)
0.46 (rp)
0.49 (rp)
0.80 (rp)
0.82 (rp)
0.64 (rp)
0.78 (rp)
0.86 (R2)
0.37 (R2)
0.33 (R2)
-------
CJQ
TABLE AX3.13 (cont'd). THE ASSOCIATION BETWEEN PERSONAL EXPOSURES AND
AMBIENT CONCENTRATIONS
tyj
to
o
o
^
X
OJ
i
to
o
fj*
i-rj
H
6
o
o
^^
H
o
i — i
H
W
O
Study
Levy et al.
(1998a)
Kodama et al.
(2002)
Liard et al.
(1999)
Gauvin et al.
(2001)
Spengler et al.
(1994)
Kousa
etal. (2001)
Study Design
Adults, 18 cities across 15 countries, two-day averaged,
one measurement for each person, all people were
measured on the same winter day in February or March
1996
Junior high school students and their family members,
Tokyo, three-day averaged, samples were simultaneously
collected on Feb 24-26, Jun 2-4, July 13-15, and Oct 14-16
in 1998 and Jan 26-28 in 1999
Adults and Children, Paris, 4-day averaged, three
measurements for each person, during each measurement
session, all subjects were measured at the same time
during May/June 1996
Children, three French metropolitan areas, 48-h averaged,
one measurement for each child, all children in the same
city were measured on the same day. The study occurred
between April-June 1998 in Grenoble, May-June 1998 in
Toulouse, and June-Oct 1998 in Paris.
Probability based population, Los Angeles Basin, 48-h
averaged, one measurement per person in one of the eight
sampling cycles (microenvironmental component of the
study), from May 1987 to May 1988
Probability based population, Helsinki, Basel, and Prague,
48-h averaged, one measurement per person, during 1996
and 1997
Association Variable Location Season
Personal vs.
Personal vs.
Personal vs.
outdoor
outdoor
outdoor
Adults vs. central
Children vs.
Personal vs.
(Grenoble)
Personal vs.
(Toulouse)
Personal vs.
(Paris)
Personal vs.
Personal vs.
central
central
central
central
outdoor
outdoor
urban
urban
urban
urban
urban
urban
urban
urban
pooled
urban
winter
summer
winter
summer
summer
pooled
pooled
pooled
pooled
pooled
rp, rs, or R2
0.57 (rs)
0.24 (rp)
0.08 (rp)
0.41 (R2)
0.17(R2)
0.01 (R2)
0.04 (R2)
0.02 (R2)
0.48 (R2)
0.40 (R2)
O
-------
CJQ
TABLE AX3.13 (cont'd). THE ASSOCIATION BETWEEN PERSONAL EXPOSURES AND
AMBIENT CONCENTRATIONS
j~^
r-K
to
o
o
>
OJ
i
OJ
>
H
6
o
0
H
O
HH
H
W
Study
Linaker et al.
(2000)
Lai et al.
(2004)
Kim et al.
(2006)
Sarnat et al.
(2005)
Sarnat et al.
(2006)
Study Design
Asthmatic children, Southampton, one-wk averaged,
13 mos for each child, until Dec 1995
Adults, Oxford, 48-h averaged, once per person, between
Dec 1998 and Feb 2000
Coronary artery adults, Toronto, 24-h averaged, one day
a wk for 10 wks for each person, from Aug 1999 to Nov
2001
Seniors and schoolchildren, Boston, 24-h averaged, 12
consecutive days in each of the 1 or 2 seasons, summer of
1999 and winter of 2000
Seniors, Steubenville, 24-h averaged, the same two
consecutive days each wk for 23 wks, summer and fall of
2000
Association Variable
Personal vs. outdoor
(Overall
measurements across
children and time)
Personal vs. outdoor
(subject-wise)
Personal vs. outdoor
Personal vs. central
(ambient)
Personal vs. central
(subject wise)
Personal vs. central
Location Season
pooled, pooled
urban, no
major indoor
sources
By person pooled
urban pooled
urban pooled
urban summer
winter
urban summer
fall
rp, rs, or R2
Not
significant
-0.77 to 0.68
and median
-0.02(rp)
0.41 (rp)
0.57 (rs)
-0.25 to 0.5
(rs) with a
median of
0.3*
-0.5 to 0.9
(rs) with a
median of
0.4*
0.14(R2)
0.43 (R2)
* Values were estimated from figures in the original paper.
O
-------
TABLE AX3.14. INDOOR/OUTDOOR RATIO AND THE INDOOR VS. OUTDOOR REGRESSION SLOPE
OQ
to
o
o
Study
Description
Season
Regression Format or
Ratio
Indoor
Characteristics
Slope/Ratio/
Fjnf
Comments
>
X
Mosqueron et al.
(2002)
Lee etal. (1999)
48 h residential indoor,
workplace, outdoor and
personal exposure were
measured for 62 Paris office
workers using Ogawa badges
from Dec 1999 to Sept 2000
The indoor and outdoor air
quality of 14 public places
with mechanical ventilation
systems in Hong Kong; from
Oct 1996 to March 1997;
Teflon bags were used to
collect indoor and outdoor
NO and NO2 during peak h
Overall study Residential indoor vs.
ambient and using gas
cooking
Cooking
seasons
Office indoor vs. ambient None
and floor height
0.26
0.56
Overall study Indoor vs. outdoor
seasons
Indoor/outdoor ratio
0.59
0.53-1.03
(mean: 0.75)
The overall R is 0.14,
and ambient NO2 and
indoor cooking
account for 0.07 each.
The overall R2 is 0.24,
partial R2 for ambient
and floor height were
0.18 and 0.06,
respectively.
R2 was 0.59. The
slopes for NO and
NOX were 1.11 and
1.04 respectively.
0.83-2.68 for NO
(mean: 0.99)
0.78-1.68 for NOX
(mean: 0.94)
H
6
o
2
o
H
O
HH
H
W
O
-------
OQ
to
o
o
TABLE AX3.14 (cont'd). INDOOR/OUTDOOR RATIO AND THE INDOOR VS. OUTDOOR REGRESSION SLOPE
Study
Description
Season
Regression Format or Indoor
Ratio Characteristics
Slope/Ratio/
Finf Comments
>
X
H
6
o
2
o
H
O
HH
H
W
Monn et al.
(1997)
Monn et al.
(1997)
Garcia-Algar
et al. (2003)
During the SAPALDIA
(Spain) study, 48-72 h
indoor, outdoor, and
personal NO2 were
measured by Palmes tubes
between the winter of
1994 to the summer of
1995, and between May
and July of 1996
During the SAPALDIA
(Spain) study, 48-72 h
indoor, outdoor, and
personal NO2 were
measured by Palmes tubes
between the winter of
1994 to the summer of
1995, and between May
and July of 1996
Yanagisawa passive filter
badges were used to
measure indoor NO2
concentrations for 7~15
days for 340 homes in
Barcelona, Spain during
1996-1999. Outdoor
NO2 concentrations were
obtained from the fixed
monitoring stations by the
method of CL.
Overall study
seasons
Overall study
seasons
Overall study
seasons
Indoor/outdoor ratio With gas-cooking
Without gas
cooking
Indoor/outdoor ratio With gas-cooking
Without gas
cooking
Indoor/outdoor ratio
> 1.2
0.4-0.7
> 1.2
0.4-0.7
0.8-1.0 Including
both homes
with and
without
indoor
sources.
O
-------
OQ
to
o
o
TABLE AX3.14 (cont'd). INDOOR/OUTDOOR RATIO AND THE INDOOR VS. OUTDOOR REGRESSION SLOPE
>
X
H
6
o
2
o
H
O
HH
H
W
Study
Lee et al.
(1995)
Lee et al.
(2002)
Lee (1997)
Description Season
Two-wk averaged indoor Summer
(kitchen, living room, and
bedroom) and outdoor NO2
were measured by Palmes
tube for 517 homes from
November 1984 to Oct 1986
in Boston area.
Six-day integrated indoor and Overall
outdoor concentrations of study
NO2 in two communities in seasons
Southern California were
measured using Yanagisawa
badges for 119 homes in
April and May 1996.
Indoor and outdoor air Overall
quality at two staff quarters study
in Hong Kong were measured seasons
from January to Feb of 1996
by Chemical Luminescent
method in two staff quarters
in Hong Kong (TSTE, in a
downtown area; and ST in a
suburban area).
Regression Format Indoor
or Ratio Characteristics
Indoor/outdoor Electric stove
ratio homes
Indoor/outdoor With gas range
ratio ± SD
Without gas range
With air
conditioner
Without air
conditioner
Indoor/outdoor Downtown area
ratio (Range)
Slope/Ratio/Finf
0.81 (kitchen)
0.81 (living room)
0.77 (bedroom)
2.27 ±1.88
1.22 ±0.52
1.07 ±0.26
3.03 ±2.01
0.78 (0.70-0.87)
forNO2
0.92(0.77-1.10)
for NO
0.86(0.78-0.95)
forNOx
Comments
Homes with gas
stove and gas
stove with pilot
light have an I/O
ratio >1, but the
values were not
reported.
—
—
—
—
—
Suburban area
0.97(0.89-1.03)
forNO2
0.92(0.77-3.14)
for NO
O
0.86(0.89-1.03)
forMX
-------
CJQ
TABLE AX3.14 (cont'd). INDOOR/OUTDOOR RATIO AND THE INDOOR VS. OUTDOOR REGRESSION SLOPE
r-K
to
o
X
UJ
1
OJ
o
rrt
H
6
o
0
o
1 — I
H
W
O
O
^^
Study Description
Garrett et al. Four-day averaged indoor
(1999) (bedroom, living room, and
kitchen) and outdoor NO2
was monitored using
Yanagisawa passive samplers
for 80 homes in the Latrobe
Valley, Victoria, Australia, in
March-April 1994, and Jan-
Feb, 1995.
Zotaetal. Two-wk integrated NO2 was
(2005) measured in 77 homes within
three Boston public housing
developments (low-income,
urban neighborhoods, where
asthma prevalence is high),
using Palmes tubes. Homes
were sampled between June
2002 and May 2003 for 2-wk
periods with up to three
sampling sessions in each
home.
Yang et al. Daily indoor and outdoor
(2004) NO2 concentrations were
measured for 30 consecutive
days in 28 house in Brisbane
(between April and May in
1999), and for 21 consecutive
days in 37 houses in Seoul
(between June and Aug in
2000) using Yanagisawa
badges.
Season
Overall
study
seasons
Overall
study
seasons
Overall
study
seasons
Regression Format or Indoor
Ratio Characteristics
Indoor/outdoor ratio No major indoor
sources (major
sources were gas
stove, vented gas
heater, and smoking)
Residential indoor vs. —
residential outdoor
Residential indoor vs. Brisbane with
residential outdoor electric range
Brisbane with gas
range
Seoul with gas range
Indoor/outdoor ratio Brisbane
Seoul
Slope/Ratio/
Finf Comments
0.8 The ratio
increased to
1.3, to 1.8 and
to 2.2 for
homes with
one, two, and
three major
indoor sources.
1.21 —
0.65 ±0.18 R2 was 0.70.
0.56 ±0.12 R2 was 0.57.
0.58 ±0.12 R2 was 0.52.
0.82 ±0.41 —
0.88 ±0.32 —
-------
OQ
to
o
o
TABLE AX3.14 (cont'd). INDOOR/OUTDOOR RATIO AND THE INDOOR VS. OUTDOOR REGRESSION SLOPE
>
X
oo
H
6
O
2
o
H
O
HH
H
W
Regression Format or
Indoor
Study
Chao (2001)
Kulkarni etal.
(2002)
Monn et al.
(1998)
Description
48-h averaged indoor
and outdoor NO, and
NO2 were measured in
ten non-smoking
residential buildings
using Ogawa passive
samplers in the summer
of 1997 in Hong Kong.
48-h averaged indoor
and outdoor NO2 were
measured using passive
filter badge sampler in
the winter (Feb 1996)
and summer of 1996
(April) for 43 residence
in Mumbai.
One-wk averaged
indoor, outdoor, and
personal NO2 were
measured for more than
500 subjects between
Dec 1993 to Dec 1994
for a SAPALDIA study
subpopulation, once per
home.
Season
Overall
study
seasons
Overall
study
seasons
Overall
study
seasons
Ratio
Indoor/outdoor ratio
Residential indoor vs.
residential outdoor
Residential indoor vs.
residential outdoor
Residential indoor vs.
residential outdoor + gas
cooking + smoking +
ventilation
Indoor/outdoor ratio
Characteristics
—
Homes using LPG
Homes using
Kerosene
All homes
Homes without
smokers and gas-
cooking
All homes
All homes
Slope/Ratio/Finf
0.79 ±0.30
(range: 0.75 -
1.36)forNO2
0.98 ±0.19
(range: 0.29 -
1.25) for NO
0.92
0.73
0.47
0.40
0.55
0.7-0.8
Comments
—
R2 was 0.80.
R2 was 0.40.
R2 was 0.37.
R2 was 0.33.
Overall R2 was
0.58, but partial
R2 cannot be
derived.
—
O
-------
OQ
to
o
o
TABLE AX3.14 (cont'd). INDOOR/OUTDOOR RATIO AND THE INDOOR VS. OUTDOOR REGRESSION SLOPE
Study Description Season
Regression Format or
Ratio
Slope/Ratio
Indoor Characteristics / Finf Comments
>
X
VO
H
6
o
2
o
H
O
HH
H
W
O
Levy et al.
(1998a);
Spengler et al.
(1996)
48-h averaged indoor,
outdoor, and personal
exposures to NO2 were
measured in 18 cities in
15 countries around the
world during a 2-day
period in Feb or March
1996.
Overall
study
seasons
Indoor/outdoor ratio
Boston, US 0.6 ± 0.4
Ottawa, Canada 0.5 ± 0.2
Mexico City, Mexico 1.9 ± 1.0
London, UK 0.6 ± 0.4
Watford, UK 0.8 ± 0.4
Geneva, Switzerland 0.8 ± 0.6
Kjeller, Norway 0.7 ± 0.4
Kuopio, Finland 0.5 ± 0.5
Berlin, Germany 0.3 ± 0.2
Erfurt, Germany 0.8 ± 0.7
Homes without gas stove 0.7
Homes with gas stove 1.2
Homes without kerosene 0.85
heater
Homes with kerosene 2.27
heater
Homes without gas space 0.96
heater
Homes with gas space 1.93
heater
Homes without gas water 0.94
heater
Homes with gas water 1.07
heater
Homes without smokers 0.92
present
Homes with smokers 1.16
present
-------
OQ
to
o
o
TABLE AX3.14 (cont'd). INDOOR/OUTDOOR RATIO AND THE INDOOR VS. OUTDOOR REGRESSION SLOPE
Regression Format or Indoor Slope/Ratio/
Study Description Season Ratio Characteristics Finf
Comments
>
X
Spengler et al.
(1994)
Lai et al. (2004)
A Yanagisawa type of
passive sample was used
to measure the 48-h
integrated indoor, outdoor
and personal NO2 levels
from the May of 1987 to
the May of 1988.
48-h averaged personal,
indoor, outdoor and
workplace NO2 levels
were measured by passive
filter badges for 50 adults
in Oxford between 1998
and 2000, once per person.
Overall study Residential indoor vs.
seasons residential outdoor
Overall study Indoor/outdoor ratio
seasons
Gas range with pilot
light
Gas range without
pilot light
Electric stove
All homes
0.49 R2 was 0.44.
0.4 R2 was 0.39.
0.4 R2 was 0.41.
0.9 —
Smoking homes
Non-smoking homes
1.5
1
H
6
o
2
o
H
O
HH
H
W
Note: *Only data that are marked by underline and bold font can be considered as an infiltration factor.
O
-------
TABLE AX3.15. NO2 CONCENTRATIONS (PPB) IN DIFFERENT ROOMS
CJQ
to
O
o
X
OJ
H
6
o
o
H
O
HH
H
W
Study
Topp et al.
(2004)
Garrett et al.
(1999)
Cotterill et al.
(1997)
Zota et al.
(2005)
Conditions
First visit
Second visit
No identified
indoor sources
Gas stove homes
Gas heater
homes
Smoking homes
Homes with
multiple sources
Gas Stove homes
Electric cooker
homes
Gas cooker home
with single
glazing window
Gas cooker home
with double
glazing window
Overall
Heating season
Non-heating
season
Outdoor
12.4
12.5
4.7
4.7
4.7
4.7
4.7
20.9
20.9
20.9
20.9
19
21
17
Kitchen
—
—
3.8
8.0
7.3
6.6
10.7
35.6
9.9
31.4
39.8
43
50
33
Living Room
7.8
8.0
3.8
6.7
6.9
6.0
14.5
17.3
8.9
16.8
18.3
36
43
26
Bedroom Comments
7.2 Indoor and outdoor NO2 concentrations for 777 residential
homes in five study areas were measured: Erfurt,
Hamburg, Zerbst, Bitterfeld and Hettstedt during two
visits (from June 1995 to May 1997, and from April 1996
7.6 to Sept 1998). In the study, one-week averaged NO2 were
measured by Palmes tube.
3.0 Garrett (1999) investigated the levels and sources of NO2
in Australian homes. During the study, four-day averaged
6.3 NO2 was monitored using Yanagisawa passive samplers in
5 Q 80 homes in the Latrobe Valley, Victoria in March-April
1994, and Jan-Feb 1995.
5.7
11.2
11.5 Three consecutive two-week averaged outdoor, kitchen,
7.3 living room, and bedroom NO2 were measured using
Palmes tubes in 40 houses in Huddersfield, UK in late
H 0 1994. Half the houses were located close to a busy main
road and half on residential roads set back and parallel to
the main road. The sample was split so that half had gas
cookers and half had electric cookers. These subsets were
12-0 Spiit again so that half had double glazing and half had
single glazed windows.
— The indoor and outdoor NO2 concentrations for low-
income, urban neighborhoods were measured, where
asthma prevalence is high. NO2 was measured in 77
homes within three Boston public housing developments,
using Palmes tubes (two-wk integrated sample) placed in
the kitchen, living room, and outdoors. Air exchange rate
for each home was also measured.
O
-------
TABLE AX3.15 (cont'd). NO2 CONCENTRATIONS (PPB) IN DIFFERENT ROOMS
CJQ
r- 1-
to
o
o
>
!x!
rS
OJ
I
to
Study
Gallelli et al.
(2002)
Linaker
etal. (1996)
Kodama
et al. (2002)
Conditions
Overall study
With vent
Without vent
Overall study
Feb 1998
June 1998
July 1998
Oct 1998
Jan 1999
Outdoor Kitchen
— 24.6
— 18.1
— 30.9
— 27.2
40,31.3 81.8
38,28 33.2
29, 26.7 24.8
40,35 23.5
49, 50 70.9
Living Room
—
—
20.9
73.5
28.8
21.9
24.7
65.8
Bedroom
13.0
—
—
55.2
24
17.4
18.2
50.7
Comments
During the study, one-wk integrated indoor (kitchen and
bedroom) and personal NO2 were measured in Genoa,
Italy, for 89 subjects with Palmes samplers. Study
volunteers included students, workers, and housewives
living in three areas of Genoa differing by street traffic and
industrial plant location.
During the study, one-wk integrated personal, indoor
(kitchen, living room), classroom, and playground NO2
were measured using Palmes tubes for school children in
Southampton.
The first number in outdoor column was the ambient
concentration in the South Area; and the second number is
the ambient concentration in the North Area. During the
study, personal, indoor (kitchen, living room, bedroom and
study room), and outdoor NO2 were measured for 150
junior high school students with Yanagisawa badges in
Tokyo. The investigation was conducted five times
seasonally, 3 days each, from February 1998 to January
1999.
H
6
o
2
o
H
O
HH
H
W
Chao and
Law (2000)
Overall study
37.6
51.9
28.2
26.4 Personal and indoor exposures were monitored with
passive sampler in Hong Kong for 60 subjects. Twelve of
the subjects were selected to conduct more detailed study
to examine the behavioral and microenvironmental effects
on personal exposure to NO2.
O
-------
TABLE AX3.16. INDOOR AND OUTDOOR CONTRIBUTIONS TO INDOOR CONCENTRATIONS
CJQ
r-K
to
o
o
X
OJ
1^
OJ
o
H
6
o
0
H
O
HH
H
W
O
O
O
Study
Mosquero
net al.
(2002)
Yang
etal.
(2004)
Monn
etal.
(1998)
Percent
Mean Indoor Mean Outdoor Outdoor
Condition Slope Intercept Concentration Concentration Contribution
Overall 0.258 — 18.4 31.5 44.2
study
Brisbane, 0.65 0.8 10.3 — 92.4
electric
range
Brisbane, 0.56 3.0 18.3 — 83.5
gas range
Seoul, gas 0.58 4.8 33.4 40.4 85.7
range
Overall 0.47 3.2 11.0 16.2 70.5
study
Homes 0.40 3.2 6.8 16.2 53.1
without
smokers
and gas
cooking
Percent Indoor
Indoor Source
Contribution Strength Comments
55.8 — —
7.6 3.5ppb/h —
16.5 11.5ppb/ —
h
14.3 23.4 ppb/ —
h
29.5 — —
46.9 — Mean indoor
was
estimated
based on the
text
description.
-------
TABLE AX3.16 (cont'd). INDOOR AND OUTDOOR CONTRIBUTIONS TO INDOOR CONCENTRATIONS
JQ
r-K
to
o
o
>
X
OJ
1
£
"^
o
H
6
o
0
H
O
HH
H
W
Percent Percent
Mean Indoor Mean Outdoor Outdoor Indoor
Study Condition Slope Intercept Concentration Concentration Contribution Contribution
Spengler Gas range 0.49 — 30 37 60.4 39.6
etal. (1994) with pilot
light
Gas range 0.4 — 22 33 60.0 40.0
without
pilot light
Electric 0.4 — 17 33 77.6 22.4
stove
Overall 0.49 8.64 27.2 38.3 68.2 31.8
Indoor
Source
Strength Comments
— Mean indoor
and mean
outdoor are
estimated from
Figure 2 in
Spengler et al.
(1994).
— Mean indoor
and mean
outdoor are
estimated from
Figure 2 in
Spengler et al.
(1994).
— Mean indoor
and mean
outdoor are
estimated from
Figure 2 in
Spengler et al.
(1994).
— —
O
-------
OQ
to
o
o
TABLE AX3.17. THE ASSOCIATION BETWEEN INDOOR, OUTDOOR, AND PERSONAL NO2
Study
Summary
Condition
Indoor vs.
Outdoor
Personal vs.
Indoor
Personal vs.
Outdoor
Comments
Mosqueron
et al. (2002)
Simultaneous personal,
indoor, and in-office
48-h averaged NO2
concentrations were
measured with Ogawa
badges for 62 people,
and ambient
concentrations were
provided by local air
monitoring network.
Overall study
0.07
(partial R2)
Gas cooking
interpreted another
7% of indoor NO2
variation
>
X
H
6
o
2
o
H
O
HH
H
W
Emenius
et al. (2003)
Palmes tubes were
used to measure indoor
(in the main living
room) and outdoor
(outside the window of
this room) NO2
concentrations during
a four-wk period
(mean 28 days, range
26-31) in the first
winter season
following recruitment
in the case-control
study.
Without smoker and 0.69 (rp)
gas stove was not
used
With gas stove and 0.13 (rp)
with smoker
With gas stove but 0.06 (rp)
without smoker
p = 0.43
p = 0.75
O
-------
OQ
to
o
o
TABLE AX3.17 (cont'd). THE ASSOCIATION BETWEEN INDOOR, OUTDOOR, AND PERSONAL NO2
Study
Summary
Condition
Indoor vs.
Outdoor
Personal vs.
Indoor
Personal vs.
Outdoor
Comments
Lee et al. Indoor and outdoor air
(1999) quality of 14 public places
with mechanical
ventilation systems in
Hong Kong were measured
fromOct 1996 to March
1997. Traffic peak h NO,
NO2 was sampled using
Teflon bags and then
shipped back to the
laboratory for further
analysis.
Overall study
0.59 (R2)
0.92 for NO and
0.92 for NOX.
>
X
H
6
o
2
o
H
O
HH
H
W
Garcia-Algar
et al. (2003)
Yanagisawa passive filter
badges were used to
measure indoor NO2
concentrations for 7~15
days for 340 homes in
Barcelona, Spain during
1996-1999. Outdoor NO2
concentrations were
obtained from the fixed
monitoring stations by the
method of CL.
Overall study
0.15(rp)
p = 0.007
O
-------
OQ
to
o
o
TABLE AX3.17 (cont'd). THE ASSOCIATION BETWEEN INDOOR, OUTDOOR, AND PERSONAL NO2
Study
Summary
Condition
Indoor vs.
Outdoor
Personal vs.
Indoor
Personal vs.
Outdoor
Comments
>
X
H
6
o
2
o
H
O
HH
H
W
Lai et al. The study was conducted
(2006) between 1996 and 2000 in six
EU cities: Athens, Basel,
Helsinki, Milan, Oxford, and
Prague. 48 h averaged indoor
and outdoor NO2 were
collected each home using
diffusion tubes for 302 homes.
Lee et al. Six-day integrated indoor and
(2002) outdoor concentrations of NO2
were measured in two
communities in Southern
California using Yanagisawa
badges for 119 homes in April
and May 1996.
Mukala The one-week averaged
et al. indoor (day-care center),
(2000) outdoor (outside day care
center) and personal NO2 for
162 children aged 3-6 years
old nitrogen dioxide exposure
were measured by Palmes
tube in Helsinki, in 1991.
Overall study
0.13 (partial R")
The overall R for the
multiple linear
regression was 0.67
Overall study
0.60 (rp)
Spring
Winter
Spring (ambient vs.
indoor)
Winter (ambient vs.
indoor)
0.86 (rp)
0.54 (rp)
0.45 (rp)
0.36 (rp)
O
-------
OQ
to
o
o
TABLE AX3.17 (cont'd). THE ASSOCIATION BETWEEN INDOOR, OUTDOOR, AND PERSONAL NO2
Indoor vs. Personal vs. Personal vs.
Study Summary Condition Outdoor Indoor Outdoor Comments
Garrett et al. Four-day averaged NO2 was
(1999) monitored using Yanagisawa passive
samplers in 80 homes in the Latrobe
Valley, Victoria, Australia in March-
April 1994, and Jan-Feb 1995.
Overall study
0.28 (R2)
Log 10
transformed data
>
X
oo
H
6
o
2
o
H
O
HH
H
W
Cotterill et al. Three consecutive two-week averaged
(1997) outdoor, kitchen, living room, and
bedroom NO2 were measured using
Palme's tubes in 40 houses in
Huddersfield, UK in late 1994. Half
the houses were located close to a
busy main road and half on residential
roads set back and parallel to the main
road. The sample was split so that
half had gas cookers and half had
electric cookers. These subsets were
split again so that half had double
glazing and half had single glazed
windows.
Overall study
0.59 (rp)
O
-------
OQ
to
o
o
VO
TABLE AX3.17 (cont'd). THE ASSOCIATION BETWEEN INDOOR, OUTDOOR, AND PERSONAL NO2
Indoor vs. Personal vs. Personal vs.
Study Summary Condition Outdoor Indoor Outdoor Comments
Yangetal. Daily indoor and outdoor
(2004) NO2 concentrations were
measured for 30 consecutive
days in 28 house in Brisbane
(between April and May in
1999), and for 21
consecutive days in 37
houses in Seoul (between
June and Aug in 2000) using
Yanagisawa badges.
Lai et al. During the study, 48-
(2004) averaged personal,
residential indoor,
residential outdoor, and
workplace indoor pollutants
were measured for 50 adults
between 1998 and 2000 in
Oxford, once per person.
NO2 were measured using
passive sampling badges.
Brisbane, electric
range house
Brisbane, gas range
house
Seoul, gas range
house
Overall study
0.70 (R2)
0.57 (R2)
0.52 (R2)
0.29 (rp) 0.47 (rp)
(not (p<0.0i)
significant)
-0.41 (rp)
(p < 0.05)
Data were log
transformed
H
6
o
2
o
H
O
HH
H
W
O
Monn et al. During the study, one-wk
(1998) integrated indoor, outdoor
and personal samples were
collected for a
subpopulation (n = 140) of
SAPALDIA study using
Pamles tube between Dec
1993 and Dec 1994 at eight
study centers in Switzerland.
Overall study
Homes without
smoker and without
gas-cooking
0.37 (R2)
0.34 (R2)
0.51(R2)
0.47 (R2)
0.33 (R2)
0.27 (R2)
-------
>
X
H
6
o
2
o
H
O
HH
H
W
Study
Levy et
al.,
(1998a)
Spengler
etal.
(1994)
Summary
48-h averaged indoor,
outdoor and personal
NO2 were measured in
18 cities in 15
countries around the
world with passive
filter badges in Feb or
March, 1996.
Probability based
population, Los
Angeles Basin, 48-h
averaged indoor,
outdoor and personal
NO2 were measured
(microenvironmental
component of the
study), from May 1987
to May 1988
Condition
Overall study
Overall study
Electric range
Gas range without
pilot light
Gas range with pilot
light
With air conditioning
Without air
conditioning
High ambient
concentration
Low ambient
concentration
Indoor vs. Personal vs.
Outdoor Indoor
— 0.75 (rs)
0.4 (R2) 0.6 (R2)
0.41 (R2) —
0.39 (R2) —
0.44 (R2) —
0.66 (rp) —
0.75 (rp) -
— —
— —
Personal vs.
Outdoor Comments
0.57 (rs) —
0.51 (R2) —
0.52 (R2) —
—
0.44 (R2) —
— —
— —
0.47 (R2) —
0.33 (R2) —
O
-------
CJQ
to
o
o
TABLE AX3.17 (cont'd). THE ASSOCIATION BETWEEN INDOOR, OUTDOOR, AND PERSONAL NO2
X
Study
Kousa
etal.
(2001)
Linaker
etal.
(1996)
Summary Condition
The indoor, outdoor, and Overall study
personal NO2 relationship in
three EXPOLIS centers
(Basel, Helsinki, and Prague)
were reported. During the
study, 48-averaged indoor, Helsinki
outdoor, and personal NO2
were measured with Palmes
tubes during 1996-1997.
During the study, one-wk Overall study
integrated personal, indoor
(kitchen, living room),
classroom and playground
NO2 were measured using
Palmes tubes for 46 school
children aged 9-11 in
Southampton, UK.
Indoor vs. Personal vs. Personal vs.
Outdoor Indoor Outdoor Comments
0.44 (R2) 0.53 (R2) 0.37 (R2) Data were log-
transformed
— 0.45 (R2) 0.40 (R2) Data were log-
transformed
— 0.53-0.76 (rp) 0.61-0.65 (rp) Data were log-
transformed
H
6
o
2
o
H
O
HH
H
W
O
-------
OQ
to
o
o
TABLE AX3.17 (cont'd). THE ASSOCIATION BETWEEN INDOOR, OUTDOOR, AND PERSONAL NO2
>
X
to
H
6
o
2
o
H
O
HH
H
W
O
Study
Aim et al.
(1998)
Summary
During the study,
weekly personal,
indoor (day care
center), outdoor (day
care center), and
ambient site NO2
exposures of 246
children aged 3-6 yrs
O J
were measured with
Palmes tubes during
13 wks in winter and
spring in 1991 in
Helsinki.
Indoor vs.
Condition Outdoor
Overall study —
Winter —
Spring —
Winter downtown 0.44 (rp)
Spring downtown 0.84 (rp)
Winter suburban 0.22 (rp)
Spring suburban 0.46 (rp)
Downtown electric —
stove
Downtown gas stove —
Downtown non- —
smoking
Downtown smoking —
Suburban electric —
stove
Suburban gas stove —
Suburban non- —
smoking
Suburban smoking —
Personal vs.
Indoor
0.88 (R2)
—
—
0.32 (rp)
0.75 (rp)
0.04 (rp)
0.75 (rp)
0.67 (rp)
0.50 (rp)
0.67 (rp)
0.47 (rp)
0.55 (rp)
0.50 (rp)
0.48 (rp)
Personal vs.
Outdoor
0.86 (R2)
0.04 (partial R2)
0.50 (partial R2)
0.46 (rp)
0.80 (rp)
0.49 (rp)
0.82 (rp)
0.55 (rp)
0.59 (rp)
0.73 (rp)
0.51(rp)
0.63 (rp)
0.59 (rp)
0.46 (rp)
Comments
0.37 (R2) for personal
vs. ambient
p = 0.01; log
transformed data
p = 0.0001; log
transformed data
Personal vs. indoor
was not significant
(day-care center, not
residential indoor).
Personal vs. indoor,
and indoor vs. outdoor
were not significant
—
—
—
—
—
—
—
—
—
-------
OQ
to
o
o
TABLE AX3.17 (cont'd). THE ASSOCIATION BETWEEN INDOOR, OUTDOOR, AND PERSONAL NO2
Indoor vs. Personal vs. Personal vs.
Study Summary Condition Outdoor Indoor Outdoor Comments
Kodama During the study,
et al. personal, indoor
(2002) (kitchen, living room,
bedroom, and study
room), and outdoor
NO2 were measured
for 150 junior high
school students with
Yanagisawa badges in
Tokyo. The
investigation was
conducted five times
seasonally, 3 days
each, from Feb 1998 to
Jan 1999.
Summer
Winter
0.31(rp)
0.57 (rp)
0.24 (rp)
0.08 (rp)
H
6
o
2
o
H
O
HH
H
W
O
-------
CJQ
TABLE AX3.18. INDOOR, OUTDOOR, AND PERSONAL NO2 LEVELS STRATIFIED BY
EXPOSURE INDICATORS
(CONCENTRATIONS ARE IN PPB AND SLOPES ARE DIMENSIONLESS)
to
o
o
>
X
OJ
L/l
[\
O
>
H
1
O
o
1— J
z;
0
H
O
H
W
O
o
o
Ambient
NO2
References Factor Name Factor levels Level
Environmental conditions
Singer etal. Wind Direction Upwind of freeway 20.5
(2004) Downwind and close 26.5
to freeway
Downward and far 21
from freeway
Zotaetal. Season Heating 21
(2005) Non-Heating 17
Serensen et al. Season < 8C 14.6
(2005) > 8C 7.8
Aim et al. Season Winter downtown —
(1998) smoker
Spring downtown —
smoker
Winter downtown —
nonsmoker
Spring downtown —
nonsmoker
Winter suburban —
smoker
Spring suburban —
smoker
Winter suburban —
nonsmoker
Spring suburban —
nonsmoker
Indoor Personal
Ambient NO2 Indoor NO2 Personal
Slope Level Slope Level Slope Comments
— — — — — —
— — — — — —
— — — — — —
— 43 — — — —
— 26 — — — —
— 8.9 — 11.4 — —
— 6.6 — 9.2 — —
— — — 13.5 — —
— — — 15.4 — —
— — — 13.0 — —
— — — 14.1 — —
— — — 11.2 — —
— — — 10.7 — —
— — — 9.2 — —
— — — 8.7 — —
-------
CJQ
to
O
o
>
X
H
6
O
O
H
O
HH
H
W
O
TABLE AX3.18 (cont'd). INDOOR, OUTDOOR, AND PERSONAL NO2 LEVELS STRATIFIED
EXPOSURE INDICATORS
(CONCENTRATIONS ARE IN PPB AND SLOPES ARE DIMENSIONLESS)
BY
References Factor Name
Zota et al. Heating season
(2005)
Vukovich et al. Day
(2000)
Lee (1997) Day
Dwelling conditions
Levy et al. Window open
(1998a)
Cotterill et al. Window
(1997)
Partti-Pellinen Type of Filtration
et al. (2000)
Ambient
NO2
Factor Levels Level
— —
Weekday
Weekday —
Weekend —
With —
Without —
Single Glazing —
Double Glazing —
Single Glazing —
Double Glazing —
Mechanical filter 12.3
Mechanical intake 11.5
and mechanical filter
Mechanical intake 12.4
and mechanical and
chemical filter
Indoor Personal
Ambient NO2 Indoor NO2 Personal
Slope Level Slope Level Slope Comments
3.87 — 17.3 — — —
— — — — — 39% more than
weekend
— — — — — The effect of
weekday/week-
end is clear but
the paper didn't
give a value to
cite
— — — — — —
— — — 30 — —
— — — 26.7 — —
— 9.4 — — — —
— 9.4 — — — —
— 11.0 — — — Gas cooker
homes
— 12.0 — — — Gas cooker
homes
— 9.6 — — — —
— 12.5 — — — —
— 6.5 — — — —
-------
CJQ
TABLE AX3.18 (cont'd). INDOOR, OUTDOOR, AND PERSONAL NO2 LEVELS STRATIFIED BY
EXPOSURE INDICATORS
to
o
o
.
X
OJ
^
ON
O
^
i-rj
H
6
o
0
H
O
HH
H
W
O
O
O
(CONCENTRATIONS ARE IN PPB AND SLOPES ARE DIMENSIONLESS)
References Factor Name
Yanmanaka Surface type
etal. (1984)
Zota et al. Occupancy
(2005)
Levy et al. Occupancy
(1998a)
Emenius et al. Location
(2003)
Cotterill et al. Location
(1997)
Zota et al. Location
(2005)
Lee et al. (2004) Location
Liard et al. Location
(1999)
Factor Levels
—
—
1
2
Urban
Semi-urban
Suburban
On Main Road
50-85m from Main
Road
—
Industrial
Residential
Main Road
Side Road
Ambient Indoor Personal
NO2 Ambient NO2 Indoor NO2 Personal
Level Slope Level Slope Level Slope Comments
— — — — — Affect decay
rate
— — — 3.2 — — —
— — — — 25.9 — —
— — — — 30.8 — —
16.5 — 9.6 — — — —
11.3 — 6.4 — — — —
7.2 — 4.2 — — — —
— — 7.9 — — — Electric cooker
homes
— — 6.8 — — — Electric cooker
homes
— -0.0093 — — — — —
— — — — 34.9 — —
— — — — 27.8 — —
— — — — 28.1 — —
— — — — 24.3 — —
-------
CJQ
TABLE AX3.18 (cont'd). INDOOR, OUTDOOR, AND PERSONAL NO2 LEVELS STRATIFIED BY
EXPOSURE INDICATORS
(CONCENTRATIONS ARE IN PPB AND SLOPES ARE DIMENSIONLESS)
to
o
o
X
OJ
01
^
References Factor Name Factor Levels
Nakai et al. Location < 20 m
(1995)
20-150 m
>150m
Aim et al. Location Downtown smoker
(1998) Suburban smoker
Downtown
nonsmoker
Suburban
nonsmoker
Ambient Indoor Personal
NO2 Ambient NO2 Indoor NO2
Level Slope Level Slope Level
42.4 — 43.8 — 43.1
34.9 — 38.4 — 35.9
20.3 — 36.4 — 30.1
— — — — 14.6
— — — — 10.9
— — — — 13.6
— — — — 9.0
Personal
Slope Comments
— Recalculated
based published
data
— Recalculated
based published
data
— Recalculated
based published
data
— —
— —
— —
fe
-LJ
H
1
O
o
0
H
O
H H
H
W
O
&
Lee etal. (1996) House structure Single DU
Small multi-DU
Large multi-DU
Single DU
Small multi-DU
Large multi-DU
Single DU
Small multi-DU
Large multi-DU
17
23
23.6
18.4
25.1
25.1
15.9
23.7
24.5
— 17 — —
— 28.9 — —
— 26.8 — —
— 17.8 — —
— 30.2 — —
— 25.4 — —
— 17.3 — —
— 27.8 — —
— 29.1 — —
— Winter
— Winter
— Winter
— Fall
— Fall
— Fall
— Summer
— Summer
— Summer
O
-------
CJQ
TABLE AX3.18 (cont'd). INDOOR, OUTDOOR, AND PERSONAL NO2 LEVELS STRATIFIED
EXPOSURE INDICATORS
(CONCENTRATIONS ARE IN PPB AND SLOPES ARE DIMENSIONLESS)
BY
to
o
o
;>
X
OJ
01
oo
o
[>
H
1
O
o
2;
0
H
o
1 — I
H
W
O
O
O
References
Gallelli et al.
(2002)
Zota et al.
(2005)
Mosqueron
et al. (2002)
Liard et al.
(1999)
Gallelli et al.
(2002)
Yang et al.
(2004)
Garrett et al.
(1999)
Indoor sources
Zota et al.
(2005)
Lai et al. (2004)
Levy et al.
(1998a)
Belanger et al.
(2006)
Factor Name
Heating system
Frames
Floor level
Floor level
Extractor fan over
cooker
Chimney
Attached garage
Age of house
Supplemental
Heating with stove
Smoking
Smokers present
Ranges
Factor Levels
Individual
Central
Metal
Wood
—
—
Without
With
With vent
Without vent
With
Without
—
—
Smoking
Nonsmoking
With
Without
Electric
Gas
Ambient Indoor Personal
NO2 Ambient NO2 Indoor NO2
Level Slope Level Slope Level
— — 13.7 — —
— — 12.5 — —
— — 12.6 — —
— — 15.0 — —
— 2 — — —
— — — -1.78 —
— — — — 27.5
— — — — 24.8
— — 18.1 — —
— — 30.9 — —
— — 17.3 — —
— — 11.4 — —
— — — 0.5 —
— — — 7.84 —
— — 10.9 — 10.8
— — 11.5 — 14.1
— — — — 34.8
— — — — 26.8
— — 8.6 — —
— — 25.9 — —
Personal
Slope Comments
— Bedroom data
— Bedroom data
— Bedroom data
— Bedroom data
— —
— —
— —
— —
— Kitchen data
— Kitchen data
— —
— —
— —
— —
— —
— —
— —
— —
— —
-------
CJQ
TABLE AX3.18 (cont'd). INDOOR, OUTDOOR, AND PERSONAL NO2 LEVELS STRATIFIED BY
EXPOSURE INDICATORS
(CONCENTRATIONS ARE IN PPB AND SLOPES ARE DIMENSIONLESS)
to
o
o
X
r N
OJ
L/l
VO
o
^
Prj
H
6
o
o
H
O
H
W
O
References
Cotterill et al.
(1997)
Yang et al.
(2004)
Schwab et al.
(1994)
Monn et al.
(1998)
Spengler et al.
(1994)
Aim et al.
(1998)
Raaschou-
Nielsen et al.
(1997)
Factor Name
Ranges
Ranges
Ranges
Ranges
Ranges
Ranges
Near fire
Factor Levels
Gas
Electric
Gas
Electric
Gas
Not Gas
Gas with pilot light
Gas without pilot
light
Electric
Gas Geneva
Electric Geneva
Gas Basle
Electric Basle
Gas Lugano
Electric Lugano
Electric smoker
Ambient Indoor
NO2 Ambient NO2 Indoor
Level Slope Level Slope
— — 35.6 —
— — 9.9 —
— — 11.5 —
— — 7.3 —
— — 18.3 —
— — 10.3 —
— — 20.3 —
— — 11.7 —
— — 8 —
— — 20.9 —
— — 16.8 —
— — 15.2 —
— — 12.6 —
— — 18.8 —
— — 15.7 —
— — — —
— — — —
Personal
NO2
Level
—
—
—
—
—
—
—
—
—
23.6
19.9
18.3
16.2
20.9
18.3
—
13.0
Personal
Slope Comments
— Kitchen
— Kitchen
— Bedroom
— Bedroom
— —
— —
— Summer 1998 data
— Summer 1998 data
— Summer 1998 data
— —
— —
— —
— —
— —
— —
— Gas with pilot was
15 ppb higher than
electric; gas without
pilot was 4 ppb
higher than electric
— —
0.052 —
O
-------
CJQ
to
o
o
X
OJ
H
6
o
o
H
O
HH
H
W
O
TABLE AX3.18 (cont'd). INDOOR, OUTDOOR, AND PERSONAL NO2 LEVELS STRATIFIED
EXPOSURE INDICATORS
(CONCENTRATIONS ARE IN PPB AND SLOPES ARE DIMENSIONLESS)
BY
References
Kawamoto et al.
(1997)
Lee et al. (2004)
Liardetal. (1999)
Kodama et al.
(2002)
Yang et al. (2004)
Levy et al.
(1998a)
Monn et al.
(1997)
Mosqueron et al.
(2002)
Raaschou-Nielsen
etal. (1997)
Factor Name
Heating time
Heating fuel
Heating appliance
Heater
Gas water heater
Gas water heater
Gas range
Gas cooking
Gas cooking
Gas appliances at
home
Factor Levels
Oil fan heater
Kerosene heater
Clean heater
Coal briquette
Petroleum
Gas
Other
Kerosene heater
Gas stove
Electric heater
With
Without
With
Without
With
Without
With
Without
With
Without
Ambient Indoor Personal
NO2 Ambient NO2 Indoor NO2 Personal
Level Slope Level Slope Level Slope Comments
— — — — — 2.59 —
— — — — — 1.17 —
— — — — — — —
— — — — 22.2 — —
— — — — 33.1 — —
— — — — 27.9 — —
— — — — 25.2 — —
— — 152.6 — — — Sourth area, Feb
1998
— — 77.5 — — — Sourth area, Feb
1998
— — 30.8 — — — Sourth area, Feb
1998
— — 18.1 — — — —
— — 11.9 — — — —
— — — — 30.5 — —
— — — — 28.2 — —
— — — — 36.4 — —
— — — — 28.5 — —
— — — — 34.8 — —
— — — — 20.5 — —
— — — — — — I/O > 1.2
— — — — — — I/O -0.4 -0.7
— — — 0.068 — — —
— — — — — 0.202 —
-------
OQ
to
o
o
H
6
o
2
o
H
O
HH
H
W
TABLE AX3.18 (cont'd). INDOOR, OUTDOOR, AND PERSONAL NO2 LEVELS STRATIFIED BY
EXPOSURE INDICATORS
(CONCENTRATIONS ARE IN PPB AND SLOPES ARE DIMENSIONLESS)
References
Garrett et al.
(1999)
Button et al.
(2001)
Serensen et al.
(2005)
Liard et al.
(1999)
Raaschou-
Nielsen et al.
(1997)
Lee et al. (2004)
Liard et al.
(1999)
Factor Name
Gas and smoking
Fireplace setting
Exposure to burning
candle
Exposure to ETS
Exposure to ETS
Cooking fuel
Cooking appliance
Factor Levels
None
Gas stove
Gas heater
Smoking
Multiple
Low
Middle
High
—
With
Without
Petroleum
Gas
Coal briquette
Gas
Electric
Ambient Indoor
NO2 Ambient NO2 Indoor
Level Slope Level Slope
— — 3.0 —
— — 6.3 —
— — 5.0 —
— — 5.7 —
— — 11.2 —
— — 90 —
— — 350 —
— — 360 —
— — — —
— — — —
— — — —
— — — —
— — — —
— — — —
— — — —
— — — —
— — — —
Personal
NO2 Personal
Level Slope Comments
— — I/O ratio increase
from 0.8 to 1.3 to
1.8 to 2.2 in houses
with no, one, two,
or three major
indoors sources
— — —
— — —
— — —
— — —
— — —
— — —
— — —
— 0.031 —
25.1 — —
26.3 — —
— 0.056 —
26.1 — —
33.1 — —
20.6 — —
25.8 — —
25.5 — —
O
-------
CJQ
TABLE AX3.18 (cont'd). INDOOR, OUTDOOR, AND PERSONAL NO2 LEVELS STRATIFIED BY
EXPOSURE INDICATORS
(CONCENTRATIONS ARE IN PPB AND SLOPES ARE DIMENSIONLESS)
to
o
o
J>
X
OJ
Oi
to
o
s>
H
6
o
0
H
O
HH
H
W
References Factor Name Factor Levels
Dennekamp Cooking 1 ring
etal. (2001)
2 rings
3 rings
4 rings
Boil water
Stir fry
Fry bacon
Bake cake
Roast meat
Bake potatoes
Ambient Indoor Personal
NO2 Ambient NO2 Indoor NO2
Level Slope Level Slope Level
— — 437 — —
— — 310 — —
— — 584 — —
— — 996 — —
— — 184 — —
— — 92 — —
— — 104 — —
— — 230 — —
— — 296 — —
— — 373 — —
Personal
Slope Comments
— The max 5 min
concentrations
— The max 5 min
concentrations
— The max 5 min
concentrations
— The max 5 min
concentrations
— The max 5 min
concentrations
— The max 5 min
concentrations
— The max 5 min
concentrations
— The max 5 min
concentrations
— The max 5 min
concentrations
— The max 5 min
concentrations
O
-------
OQ
to
o
o
H
6
o
2
o
H
O
HH
H
W
TABLE AX3.18 (cont'd). INDOOR, OUTDOOR, AND PERSONAL NO2 LEVELS STRATIFIED BY
EXPOSURE INDICATORS
(CONCENTRATIONS ARE IN PPB AND SLOPES ARE DIMENSIONLESS)
References
Factor
Name
Factor Levels
Ambient
NO2
Level
Ambient
Slope
Indoor
NO2
Level
Indoor
Slope
Personal
NO2
Level
Personal
Slope
Comments
Personal activities
Levy et al.
(1998a)
Commute
Commuting less than
Ih
29.9
Chao and Law
(2000)
Kawamoto et al.
(1997)
Commute
Cooking to
stay home h
ratio
Cooking time
Without commuting — — —
< 1 h — — —
1-2 h — — —
2-3 h — — —
3-4 h — — —
4-6 h — — —
— — — —
— — — —
— 27.9 — —
— 21.7 — —
— 24.7 — —
— 24.6 — —
— 20.1 — —
— 27.9 — —
— — 55.4 —
— — 1.61 —
O
-------
CJQ
TABLE AX3.19. PERSONAL NO2 LEVELS STRATIFIED BY DEMOGRAPHIC AND SOCIOECONOMIC FACTORS
(CONCENTRATIONS ARE IN PPB AND SLOPES ARE DIMENSIONLESS)
j~^
r-K
to
o
o
^
^
X
OJ
ON
DRAFT-DO I
^
0
H
O
HH
H
W
References
Rotkoetal. (2001)
Rotkoetal. (2001)
Raaschou-Nielsen (1997)
Lee etal., (2004)
Lee etal., (2004)
Rotkoetal. (2001)
Rotkoetal. (2001)
Raaschou-Nielsen (1997)
Rotkoetal. (2001)
Rotkoetal. (2001)
Rotkoetal. (2001)
Rotkoetal. (2001)
Rotkoetal. (2001)
Rotkoetal. (2001)
Algar et al. (2004)
Algar et al. (2004)
Algar et al. (2004)
Factor Type
Demography
Demography
Demography
Demography
Demography
Demography
Demography
Demography
Socioeconomic
Socioeconomic
Socioeconomic
Socioeconomic
Socioeconomic
Socioeconomic
Socioeconomic
Socioeconomic
Socioeconomic
Factor Name
Age
Age
Age
Gender
Gender
Gender
Gender
Gender
Education years
Education years
Employment
Employment
Occupational status
Occupational status
Employment
Employment
Employment
Factor levels Personal NO2 Level Personal Slope
25-34
35-55
Female
Male
Female
Male
<14 years
> 14 years
Employed
Not employed
Non white collar
White collar
Managerial, technical and
professional (Barcelona)
Skilled (manual and non-
manual) (Barcelona)
Unskilled and partly skilled
(Barcelona)
13.1
13.1
0.056
33
29
12.9
13.4
0.267
13.8
12.8
13.3
11.5
13.4
13.0
12.2
12.3
12.1
O
-------
TABLE AX3.20. CORRELATIONS (PEARSON CORRELATION COEFFICIENT)
BETWEEN AMBIENT NO2 AND AMBIENT COPOLLUTANTS
Study (ambient)
This CD
This CD
This CD
This CD
Kim et al. (2006)
Sarnat et al.
(2006)
Sarnat et al.
(2006)
Connell et al.
(2005)
Kim et al. (2005)
Sarnat et al.
(200 1)4
Sarnat et al.
(2001)
Hochadel et al.
(2006)
Hazenkamp-von
Arx et al. (2004)
Cyrys et al.
(2003)
Mosqueron et al.
(2002)
Rojas-Bracho
et al. (2002)
Location
Los Angeles
Riverside, CA
Chicago
New York City
Toronto
Steubenville, OH
(autumn)
Steubenville, OH
(summer)
Steubenville, OH
St. Louis (RAPS)
Baltimore, MD
(summer)
Baltimore, MD
(winter)
Ruhr area,
Germany
2 1 European cities
Erfurt, Germany
Paris
Santiago, Chile
PM25
0.49 (u3), 0.56 (s)
0.49 (s)
0.58 (u)
0.44
0.78 (0.70 for sulfate,
0.82 for EC)
0.00
(0.1 for sulfate, 0.24
for EC)
0.50
0.37
0.75
0.41, (0.93 for EC2)
0.75
0.50
0.69
0.77
CO O3 SO2
0.59 (u), -0.29 (u),
0.64 (s) -O.ll(s)
0.43 (u), 0.045 (u),
0.41 (s), 0.10(s),
0.15(r) -0.31(r)
0.53 (u), -0.20(u)
0.46 (s)
0.46 (u) -0.06 (u)
0.72
0.641
0.75 0.02
not significant
0.76 -0.71 -0.17
0.74
'Value with respect to NOX.
2Inferred based on EC as dominant contributor to PM2 5 absorbance.
u: urban; s: suburban; and r: rural
4Spearman correlation coefficient was reported
August 2007
AX3-165
DRAFT-DO NOT QUOTE OR CITE
-------
TABLE AX3.21. CORRELATIONS (PEARSON CORRELATION COEFFICIENT)
BETWEEN PERSONAL NO2 AND PERSONAL COPOLLUTANTS
Study
Kim et al. (2006)
Modig et al.
(2004)
Mosqueron et al.
(2002)
Jarvis et al.
(2005)
Location
Toronto
Umea
Paris
21
European
cities
PM25
0.41
0.12 but not
significant
CO VOCs HONO
0.12
0.06 for 1,3-butadiene;
and 0.10 for benzene
0.77 for indoor
NO2 and indoor
HONO
Lee et al. (2002)
Lai et al. (2004) Oxford
-0.1
0.3 - 0.11 for TVOCs
0.51 for indoor
NO2 and indoor
HONO
TABLE AX3.22. CORRELATIONS (PEARSON CORRELATION COEFFICIENT)
BETWEEN PERSONAL NO2 AND AMBIENT COPOLLUTANTS
Study
Location
PM25
Sulfate
EC PMio CO
Sarnat et al.
(2006)
Sarnat et al.
Steubenville /
Fall
Steubenville /
0.46
0.00
0.35
0.1
0.57
0.17
(2006)
Kim et al. (2006)
Rojas-Bracho
et al. (2002)
Summer
Toronto
Santiago
not significant
0.30
0.65
0.20
0.39
August 2007
AX3-166
DRAFT-DO NOT QUOTE OR CITE
-------
TABLE AX3.23. CORRELATIONS (PEARSON CORRELATION COEFFICIENT)
BETWEEN AMBIENT NO2 AND PERSONAL COPOLLUTANTS
Study Location PM2.5 Sulfate EC Ultrafine-particle
Sarnatetal. Steubenville / 0.71 0.52 0.70
(2006) Fall
Sarnatetal. Steubenville/ 0.00 0.1 0.26
(2006) Summer not significant
Vinzents Copenhagen 0.49 (R2) explained by
et al. (2005) ambient NO2 and ambient
temperature
August 2007 AX3-167 DRAFT-DO NOT QUOTE OR CITE
-------
TABLE AX3.24. 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
Microenviron-
mental (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")
August 2007
AX3-168
DRAFT-DO NOT QUOTE OR CITE
-------
1 AX3.9 REFERENCES
2
3 Ainsworth, B. E.; Haskell, W. L.; Leon, A. S.; Jacobs, D. R., Jr.; Montoye, H. J.; Sallis, J. F.;
4 Paffenbarger, R. S., Jr. (1993) Compendium of physical activities: classification of
5 energy costs of human physical activities. Med. Sci. Sports Exer. 25: 71-80.
6 Albinet, A.; Leoz-Garziandia, E.; Budzinski, H.; Villenave, E. (2006) Simultaneous analysis of
7 oxygenated and nitrated polycyclic aromatic hydrocarbons on standard reference material
8 1646a (urban dust) and on natural ambient air samples by gas chromatography-mass
9 spectrometry with negative ion chemical ionization. J. Chromatogr. A 1121: 106-113.
10 Algar, O. G.; Pichini, S.; Basagana, X.; Puig, C.; Vail, O.; Torrent, M.; Harris, J.; Sunyer, J.;
11 Cullinan, P., the AMICS group. (2004) Concentrations and determinations of NO2 in
12 homes of Ashford, UK and Barcelona and Menorca, Spain. Indoor Air 14: 298-304.
13 Aim, S.; Mukala, K.; Pasanen, P.; Tiittanen, P.; Ruuskanen, J.; Tuomisto, J.; Jantunen, M. J.
14 (1998) Personal NO2 exposures of preschool children in Helsinki. J. Exposure Anal.
15 Environ. Epidemiol. 8: 79-100.
16 Ammann, M.; Kalberer, M.; Jost, D. T.; Tobler, L.; Rossler, E.; Piguet, D.; Gaggeler, H. W.;
17 Baltensperger, U. (1998) Heterogeneous production of nitrous acid on soot in polluted air
18 masses. Nature (London) 395: 157-160.
19 Armstrong, B. K.; White, E.; Saracci, R. (1992) Principles of exposure measurement in
20 epidemiology. Oxford, United Kingdom: Oxford University Press. (Monographs in
21 epidemiology and biostatistics: v. 21).
22 Atmospheric and Environmental Research, Inc. (AER). (2004) Subgrid scale modeling of plumes
23 in three-dimensional air quality models for ozone and particulate matter (plume-in-grid).
24 Available: http://www.aer.com/scienceResearch/aq/web_update_3.htm (5 March, 2007).
25 Bae, H.; Yang, W.; Chung, M. (2004) Indoor and outdoor concentrations of RSP, NO2 and
26 selected volatile organic compounds at 32 shoe stalls located near busy roadways in
27 Seoul, Korea. Sci. Total Environ. 323: 99-105.
28 Bauer, M. A.; Utell, M. J.; Morrow, P. E.; Speers, D. M.; Gibb, F. R. (1986) Inhalation of 0.30
29 ppm nitrogen dioxide potentiates exercise-induced bronchospasm in asthmatics. Am.
30 Rev. Respir. Dis. 134: 1203-1208.
31 Belanger, K.; Gent, J. F.; Triche, E. W.; Bracken, M. B.; Leaderer, B. P. (2006) Association of
32 indoor nitrogen dioxide exposure with respiratory symptoms in children with asthma.
33 Am. J. Respir. Crit. Care Med. 173: 297-303.
34 Bell, S.; Ashenden, T. W. (1997) Spatial and temporal variation in nitrogen dioxide pollution
35 adjacent to rural roads. Biomed. Life Sci. Earth Environ. Sci. 95: 87-98.
36 Berglund, M.; Braback, L.; Bylin, G.; Jonson, J.-O.; Vahter, M. (1994) Personal NO2 exposure
37 monitoring shows high exposure among ice-skating schoolchildren. Arch. Environ.
38 Health 49: 17-24.
39 Berkowicz, R.; Ketzel, M.; Vachon, G.; Louka, P.; Rosant, J.-M.; Mestayer, P. G.; Sini, J.-F.
40 (2002) Examination of traffic pollution distribution in a street canyon using the Nantes'99
August 2007 AX3-169 DRAFT-DO NOT QUOTE OR CITE
-------
1 experimental data and comparison with model results. Water Air Soil Pollut. Focus 2:
2 311-324.
3 Bertoni, G.; Toppa, R.; Allegerni, I. (2001) The internal consistency of the "analyst" diffusive
4 sampler—a long-term field test. Chromatographia 54: 653-657.
5 Biller, W. F.; Feagans, T. B.; Johnson, T. R.; Duggan, G. M.; Paul, R. A.; McCurdy, T.; Thomas,
6 H. C. (1981) A general model for estimating exposure associated with alternative
7 NAAQS. Presented at: 74th annual meeting of the Air Pollution Control Association;
8 June; Philadelphia, PA. Pittsburgh, PA: Air Pollution Control Association; paper no. 81-
9 18.4.
10 Brauer, M.; Rasmussen, T. R.; Kjaergaard, S. K.; Spengler, J. D. (1993) Nitrous acid formation in
11 an experimental exposure chamber. Indoor Air 3: 94-105.
12 Brauer, M.; Ryan, P. B.; Suh, H. H.; Koutrakis, P.; Spengler, J. D.; Leslie, N. P.; Billick, I. H.
13 (1990) Measurements of nitrous acid inside two research houses. Environ. Sci. Technol.
14 24:1521-1527.
15 Brauer, M.; Lee, K.; Spengler, J. D.; Salonen, R. O.; Pennanen, A.; Braathen, O. A.; Mihalikova,
16 E.; Miskovic, P.; Nozaki, A.; Tsuzuki, T.; Rui-Jin, S.; Xu, Y.; Qing-Xiang, Z.;
17 Drahonovska, H.; Kjaergaard, S. (1997) Nitrogen dioxide in indoor ice skating facilities:
18 an international survey. J. Air Waste Manage. Assoc. 47: 1095-1102.
19 Brauer, M.; Henderson, S.; Kirkham, T.; Lee, K. S.; Rich, K.; Teschke, K. (2002) Review of the
20 health risks associated with nitrogen dioxide and sulfur dioxide in indoor air. Vancouver,
21 British Columbia, Canada: University of British Columbia, School of Occupational and
22 Environmental Hygiene. Report to Health Canada.
23 Breysse, P. N.; Buckley, T. J.; Williams, D'A.; Beck, C. M.; Jo, S.-J.; Merriman, B.;
24 Kanchanaraksa, S.; Swartz, L. J.; Callahan, K. A.; Butz, A. M.; Rand, C. S.; Diette, G.
25 B.; Krishnan, J. A.; Moseley, A. M.; Curtin-Brosnan, J.; Durkin, N. B.; Eggleston, P. A.
26 (2005) Indoor exposures to air pollutants and allergens in the homes of asthmatic children
27 in inner-city Baltimore. Environ. Res. 98: 167-176.
28 Britter, R. E.; Hanna, S. R. (2003) Flow and dispersion in urban areas. Ann. Rev. Fluid Mech.
29 35:469-496.
30 Brugge, D.; Vallarino, J.; Ascolillo, L.; Osgood, N.-D.; Steinbach, S.; Spengler, J. (2003)
31 Comparison of multiple environmental factors for asthmatic children in public housing.
32 Indoor Air 13: 18-27.
33 Buck, J. W.; Tolle, D. A.; Whelan, G; Mast, T. J.; Peffers, M. S.; Evers, D. P.; Corley, R. A.;
34 Eslinger, M. A.; Kirk, J. L.; Pelton, M. A.; Townsend, C. C.; Nishioka, M. G.; Kogan, V.;
35 Mahasenan, S.; Dorow, K. E.; Stenner, R. D.; Strenge, D. L. (2003) Design of the
36 comprehensive chemical exposure framework and identification of research needs for
37 American Chemistry Council. Prepared for: American Chemistry Council Long Range
38 Research Initiative Team; Arlington, VA. Richland, WA: Battelle Pacific Northwest
39 Division; PNWD-3184.
40 Burke, J. M.; Zufall, M. J.; Ozkaynak, H. (2001) A population exposure model for particulate
41 matter: case study results for PM2.5 in Philadelphia, PA. J. Exposure Anal. Environ.
42 Epidemiol. 11: 470-489.
August 2007 AX3-170 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 Bush, T.; Smith, S.; Stevenson, K.; Moorcroft, S. (2001) Validation of nitrogen dioxide diffusion
5 tube methodology in the UK. Atmos. Environ. 35: 289-296.
6 California Air Resources Board. (2007) Review of the California ambient air quality standard for
7 nitrogen dioxide. Staff report: initial statement of reasons for proposed rulemaking.
8 Sacramento, CA: California Environmental Protection Agency, Air Resources Board.
9 Available: http://www.arb.ca.gov/research/aaqs/no2-rs/no2tech.pdffl4 August, 2007].
10 Campbell, G. W.; Stedman, J. R.; Stevenson, K. (1994) A survey of nitrogen dioxide
11 concentrations in the United Kingdom using diffusion tubes, July-December 1991.
12 Atmos. Environ. 28: 477-486.
13 Carlisle, A. J.; Sharp, N. C. C. (2001) Exercise and outdoor ambient air pollution. Br. J. Sports
14 Med. 35: 214-222.
15 Carslaw, N. (2007) A new detailed chemical model for indoor air pollution. Atmos. Environ. 41:
16 1164-1179.
17 Chan, L. Y.; Chan, C. Y.; Qin, Y. (1999) The effect of commuting microenvironment on
18 commuter exposures to vehicular emission in Hong Kong. Atmos. Environ. 33: 1777-
19 1787.
20 Chang, C.-H. (2006) Computational fluid dynamics simulation of concentration distributions
21 from a point source in the urban street canyons. J. Aerospace Eng. 19: 80-86.
22 Chang, C.-H.; Meroney, R. N. (2003) Concentration and flow distributions in urban street
23 canyons: wind tunnel and computational data. J. Wind Eng. Ind. Aerodyn. 91: 1141-
24 1154.
25 Chao, C. Y. (2001) Comparison between indoor and outdoor air contaminant levels in residential
26 buildings from passive sampler study. Build. Environ. 36: 999-1007.
27 Chao, C. Y. H.; Law, A. (2000) A study of personal exposure to nitrogen dioxide using passive
28 samplers. Build. Environ. 35: 545-553.
29 Chau, C. K.; Tu, E. Y.; Chan, D. W. T.; Burnett, J. (2002) Estimating the total exposure to air
30 pollutants for different population age groups in Hong Kong. Environ. Int. 27: 617-630.
31 Ching, J.; Herwehe, J.; Swall, J. (2006) On joint deterministic grid modeling and sub-grid
32 variability conceptual framework for model evaluation. Atmos. Environ. 40: 4935-4945.
33 Christakos, G. (2000) Modern spatiotemporal geostatistics. New York, NY: Oxford University
34 Press. (International Association for Mathematical Geology; studies in mathematical
35 geology: 6).
36 Christakos, G.; Hristopulos, D. T. (1998) Spatiotemporal environmental health modelling. A
37 tractatus stochasticus. Boston, MA: Kluwer Academic Publishers.
38 Christakos, G.; Kolovos, A. (1999) A study of the spatiotemporal health impacts of ozone
39 exposure. J. Exposure Anal. Environ. Epidemiol. 9: 322-335.
August 2007 AX3-171 DRAFT-DO NOT QUOTE OR CITE
-------
1 Christakos, G.; Vyas, V. M. (1998a) A composite space/time approach to studying ozone
2 distribution over eastern United States. Atmos. Environ. 32: 2845-2857.
3 Christakos, G.; Vyas, V. M. (1998b) A novel method for studying population health impacts of
4 spatiotemporal ozone distribution. Soc. Sci. Med. 47: 1051-1066.
5 Chuang, J. C.; Mack, G. A.; Kuhlman, M. R.; Wilson, N. K. (1991) Polycyclic aromatic
6 hydrocarbons and their derivatives in indoor and outdoor air in an eight-home study.
7 Atmos. Environ. Part B 25: 369-380.
8 Clapp, L.; Jenkin, M. (2001) Analysis of the relationship between ambient levels of Os, NC>2 and
9 NO as a function of NOX in the UK. Atmos. Environ. 35: 6391-6405.
10 Clench-Aas, J.; Bartonova, A.; B0hler, T.; Granskei, K. E.; Sivertsen, B.; Larssen, S. (1999) Air
11 pollution exposure monitoring and estimating. Part I. Integrated air quality monitoring
12 system. J. Environ. Monit. 1: 313-319.
13 Cocheo, V.; Boaretto, C.; Sacco, P. (1996) High uptake rate radial diffusive sampler suitable for
14 both solvent and thermal desorption. Am. Ind. Hyg. Assoc. J. 57: 897-904.
15 Code of Federal Regulations. (2002) Ambient air quality surveillance; appendix E - probe and
16 monitoring path citing criteria for ambient air quality monitoring. C. F. R. 40: §58.
17 Colbeck, I. (1998) Nitrogen dioxide in the workplace environment. Environ. Monit. Assess. 52:
18 123-130.
19 Connell, D. P.; Withum, J. A.; Winter, S. E.; Statnick, R. M. (2005) The Steubenville
20 Comprehensive Air Monitoring Program (SCAMP): analysis of short-term and episodic
21 variations in PM2.5 concentrations using hourly air monitoring data. J. Air Waste
22 Manage. Assoc. 55: 559-573.
23 Cotterill, A.; Kingham, S. (1997) Nitrogen dioxide in the home: cooking, double glazing, or
24 outdoor air? Indoor Built Environ. 6: 344-349.
25 Cox, R. M. (2003) The use of passive sampling to monitor forest exposure to 63, NC>2, and SC^:
26 a review and some case studies. Environ. Pollut. 126: 301-311.
27 Cyrys, J.; Heinrich, J.; Richter, K.; Wolke, G.; Wichmann, H. E. (2000) Sources and
28 concentrations of indoor nitrogen dioxide in Hamburg (west Germany) and Erfurt (east
29 Germany). Sci. Total Environ. 250: 51-62.
30 Cyrys, J.; Stolzel, M.; Heinrich, J.; Kreyling, W. G.; Menzel, N.; Wittmaack, K.; Tuch, T.;
31 Wichmann, H.-E. (2003) Elemental composition and sources of fine and ultrafme
32 ambient particles in Erfurt, Germany. Sci. Total Environ. 305: 143-156.
33 Da Silva, A. S.; Cardoso, M. R.; Meliefste, K.; Brunekreef, B. (2006) Use of passive diffusion
34 sampling method for defining NC>2 concentrations gradient in Sao Paulo, Brazil. Environ.
35 Health: Global Access Sci. Source 5: 19.
36 De Santis, F.; Allegrini, L; Fazio, M. C.; Pasella, D.; Piredda, R. (1997) Development of a
37 passive sampling technique for the determination of nitrogen dioxide and sulphur dioxide
38 in ambient air. Anal. Chim. Acta 346: 127-134.
August 2007 AX3-172 DRAFT-DO NOT QUOTE OR CITE
-------
1 De Santis, F.; Dogeroglu, T.; Fino, A.; Menichelli, S.; Vazzana, C.; Allergrini, I. (2002)
2 Laboratory development and field evaluation of a new diffusive sampler to collect
3 nitrogen oxides in the ambient air. Anal. Bioanal. Chem. 373: 901-907.
4 De Santis, F.; Fino, A.; Menichelli, S.; Vazzana, C.; Allegrini, I. (2004) Monitoring the air
5 quality around an oil refinery through the use of diffusive sampling. Anal. Bioanal.
6 Chem. 378: 782-788.
7 Dennekamp, M.; Howarth, S.; Dick, C. A. I; Cherrie, J. W.; Donaldson, K.; Seaton, A. (2001)
8 Ultrafine particles and nitrogen oxides generated by gas and electric cooking. Occup.
9 Environ. Med. 58: 511-516.
10 Dimitroulopoulou, C.; Ashmore, M. R.; Byrne, M. A.; Kinnersley, R. P. (2001) Modelling of
11 indoor exposure to nitrogen dioxide in the UK. Atmos. Environ. 35: 269-279.
12 Dimitroulopoulou, C.; Ashmore, M. R.; Hill, M. T. R.; Byrne, M. A.; Kinnersley, R. (2006)
13 INDAIR: a probabilistic model of indoor air pollution in UK homes. Atmos. Environ. 40:
14 6362-6379.
15 Doddridge, B. G.; Dickerson, R. R.; Holland, J. Z.; Cooper, J. N.; Wardell, R. G.; Poulida, O.
16 (1991) Observations of tropospheric trace gases and meteorology in rural Virginia using
17 an unattended monitoring system: Hurricane Hugo (1989), a case study. J. Geophys. Res.
18 96: 9341-9360.
19 Doddridge, B. G.; Dickerson, R. R.; Wardell, R. G.; Civerolo, K. L.; Nunnermacker, L. J. (1992)
20 Trace gas concentrations and meteorology in rural Virginia. 2. Reactive nitrogen
21 compounds. J. Geophys. Res. 97: 20631-20646.
22 Drakou, G.; Zerefos, C.; Ziomas, I. (2000) A sensitivity study of parameters in the Nazaroff-Cass
23 IAQ model with respect to indoor concentratons of Os, NO, NO2. Environ. Technol. 21:
24 483-503.
25 Dubowski, Y.; Sumner, A. L.; Menke, E. J.; Gaspar, D. J.; Newberg, J. T.; Hoffman, R. C.;
26 Penner, R. M.; Hemminger, J. C.; Finlayson-Pitts, B. J. (2004) Interactions of gaseous
27 nitric acid with surfaces of environmental interest. Phys. Chem. Chem. Phys. 6: 3879-
28 3888.
29 Dutton, S. J.; Hannigan, M. P.; Miller, S. L. (2001) Indoor pollutant levels from the use of
30 unvented natural gas fireplaces in Boulder, Colorado. J. Air Waste Manage. Assoc. 51:
31 1654-1661.
32 Eide, L; Neverdal, G.; Thorvaldsen, B.; Grung, B.; Kvalheim, O. M. (2002) Toxicological
33 evaluation of complex mixtures by pattern recognition: correlating chemical fingerprints
34 to mutagenicity. Environ. Health Perspect. Suppl. 110(6): 985-988.
35 Ekberg, L. E. (1996) Relationships between indoor and outdoor contaminants in mechanically
36 ventilated buildings. Indoor Air 6: 41-47.
37 Emenius, G.; Pershagen, G.; Berglind, N.; Kwon, H.-J.; Lewne, M.; Nordvall, S. L.; Wickman,
38 M. (2003) NO2, as a marker of air pollution, and recurrent wheezing in children: a nested
39 case-control study within the BAMSE birth cohort. Occup. Environ. Med. 60: 876-881.
40 Emery, C.; Yarwood, G. (2005) Implementing PM chemistry in the CAMx "IRON PiG" plume-
41 in-grid module. Prepared for: Lake Michigan Air Directors Consortium; Des Plaines, IL.
August 2007 AX3-173 DRAFT-DO NOT QUOTE OR CITE
-------
1 Novato, CA: ENVIRON International Corporation. Available:
2 http://www.ladco.org/reports/rpo/MWRPOprojects/Modeling/CAMx_PiG.pdf [5 March,
3 2007].
4 Emmons, L. K.; Carroll, M. A.; Hauglustaine, D. A.; Brasseur, G. P.; Atherton, C.; Penner, J.;
5 Sillman, S.; Levy, H., II; Rohrer, F.; Wauben, W. M. F.; van Velthoven, P. F. J.; Wang,
6 Y.; Jacob, D.; Bakwin, P.; Dickerson, R.; Doddridge, B.; Gerbig, C.; Honrath, R.; Hubler,
7 G; Jaffe, D.; Kondo, Y.; Munger, J. W.; Torres, A.; Volz-Thomas, A. (1997)
8 Climatologies of NOX and NOy: a comparison of data and models. Atmos. Environ. 31:
9 1837-1850.
10 Fagundez, L. A.; Fernandez, V. L.; Marino, T. H.; Martin, I; Persano, D. A.; Benitez, R. M. Y.;
11 Sadafiiowski, I. V.; Codnia, J.; Zalts, A. (2001) Preliminary air pollution monitoring in
12 San Miguel, Buenos Aires. Environ. Monitor. Assess. 71: 61-70.
13 Fan, Z.; Lioy, P.; Weschler, C.; Fiedler, N.; Kipen, H.; Zhang, J. (2003) Ozone-initiated
14 reactions with mixtures of volatile organic compounds under simulated indoor
15 conditions. Environ. Sci. Technol. 37: 1811-1821.
16 Farrow, A.; Greenwood, R.; Preece, S.; Golding, J. (1997) Nitrogen dioxide, the oxides of
17 nitrogen, and infants' health symptoms. Arch. Environ. Health 52: 189-194.
18 Febo, A.; Perrino, C. (1991) Prediction and experimental evidence for high air concentration of
19 nitrous acid in indoor environments. Atmos. Environ. Part A 25: 1055-1061.
20 Feng, Y.; Penner, J. E. (2007) Global modeling of nitrate and ammonium: interaction of aerosols
21 and tropospheric chemistry. J. Geophys. Res. [Atmos.] 112(D01304):
22 10.1029/2005 JD006404.
23 Ferm, M.; Svanberg, P.-A. (1998) Cost-efficient techniques for urban and background
24 measurements of SO2 and NO2. Atmos. Environ. 32: 1377-1381.
25 Fernando, H. J. S.; Lee, S. M.; Anderson, J.; Princevac, M.; Pardyjak, E.; Grossman-Clarke, S.
26 (2001) Urban fluid mechanics: air circulation and contaminant dispersion in cities.
27 Environ. Fluid Mech. 1: 107-164.
28 Finlayson-Pitts, B. J.; Pitts, J. N., Jr. (2000) Chemistry of the upper and lower atmosphere:
29 theory, experiments and applications. San Diego, CA: Academic Press.
30 Finlayson-Pitts, B. J.; Wingen, L. M.; Sumner, A. L.; Syomin, D.; Ramazan, K. A. (2003) The
31 heterogeneous hydrolysis of NO2 in laboratory system and in outdoor and indoor
32 atmospheres: an integrated mechanism. Phys. Chem. Chem. Phys. 5: 223-242.
33 Foley, G. J.; Georgopoulos, P. G.; Lioy, P. J. (2003) Accountability within new ozone standards.
34 Environ. Sci. Technol. 37: 392A-399A.
35 Fortmann, R.; Kariher, P.; Clayton, R. (2001) Indoor air quality: residential cooking exposures.
36 Final report. Sacramento, CA: State of California Air Resources Board; ARE Contract
37 No. 97-330. Available: http://arb.ca.gov/research/abstracts/97-330.htm [22 May, 2007].
38 Fraigneau, Y. C.; Gonzalez, M.; Coppalle, A. (1995) Dispersion and chemical reaction of a
39 pollutant near a motorway. Sci. Total Environ. 169: 83-91.
August 2007 AX3-174 DRAFT-DO NOT QUOTE OR CITE
-------
1 Fraser, M. P.; Cass, G. R.; Simoneit, B. R. T.; Rasmussen, R. A. (1998) Air quality model
2 evaluation data for organics. 5. C6-C22 nonpolar and semipolar aromatic compounds.
3 Environ. Sci. Technol. 32: 1760-1770.
4 Freijer, J. I; Bloemen, H. J. T. (2000) Modeling relationships between indoor and outdoor air
5 quality. J. Air Waste Manage. Assoc. 50: 292-300.
6 Freijer, J. I.; Bloemen, H. J. T.; de Loos, S.; Marra, M.; Rombout, P. J. A.; Steentjes, G. M.; Van
7 Veen, M. P. (1998) Modelling exposure of the Dutch population to air pollution. J.
8 Hazard. Mater. 61: 107-114.
9 Gair, A. J.; Penkett, S. A. (1995) The effects of wind speed and turbulence on the performance of
10 diffusion tube samplers. Atmos. Environ. 29: 2529-2533.
11 Gair, A. J.; Penkett, S. A.; Oyola, P. (1991) Development of a simple passive technique for the
12 determination of nitrogen-dioxide in remote continental locations. Atmos. Environ. Part
13 A 25: 1927-1939.
14 Gallelli, G.; Orlando, P.; Perdelli, F.; Panatoo, D. (2002) Factors affecting individual exposure to
15 NO2 in Genoa (northern Italy). Sci. Total Environ. 287: 31-36.
16 Garcia Algar, 6.; Pichini, S.; Basagafia, X.; Puig, C.; Vail, O.; Torrent, M.; Harris, J.; Sunyer, J.;
17 Cullinan, P. (2004) Concentrations and determinants of NC>2 in homes of Ashford, UK
18 and Barcelona and Menorca, Spain. Indoor Air 14: 298-304.
19 Garcia-Algar, 6.; Zapater, M.; Figueroa, C.; Vail, O.; Basagafia, X.; Sunyer, J.; Freixa, A.;
20 Guardino, X.; Pichini, S. (2003) Sources and concentrations of indoor nitrogen dioxide in
21 Barcelona, Spain. J. Air Waste Manage. Assoc. 53: 1312-1317.
22 Garrett, M. H.; Hooper, M. A.; Hooper, B. M. (1999) Nitrogen dioxide in Australian homes:
23 levels and sources. J. Air Waste Manage. Assoc. 49: 76-81.
24 Gauderman, W. J.; Avol, E.; Lurmann, F.; Kuenzli, N.; Gilliland, F.; Peters, J.; McConnell, R.
25 (2005) Childhood asthma and exposure to traffic and nitrogen dioxide. Epidemiology 16:
26 737-743.
27 Gauvin, S.; Le Moullec, Y.; Bremont, F.; Momas, I.; Balducci, F.; Ciognard, F.; Poilve, M.-P.;
28 Zmirou, D.; VESTA Investigators. (2001) Relationships between nitrogen dioxide
29 personal exposure and ambient air monitoring measurements among children in three
30 French metropolitan areas: VESTA study. Arch. Environ. Health 56: 336-341.
31 Georgopoulos, P. G.; Lioy, P. J. (1994) Conceptual and theoretical aspects of human exposure
32 and dose assessment. J. Exposure Anal. Environ. Epidemiol. 4: 253-285.
33 Georgopoulos, P. G.; Lioy, P. J. (2006) From theoretical aspects of human exposure and dose
34 assessment to computational model implementation: the Modeling Environment for
35 TOtal Risk studies (MENTOR). J. Toxicol. Environ. Health Part B 9: 457-483.
36 Georgopoulos, P. G.; Seinfeld, J. H. (1989) Nonlocal description of turbulent dispersion. Chem.
37 Eng. Sci. 44: 1995-2016.
38 Georgopoulos, P. G.; Arunachalam, S.; Wang, S. (1997a) Alternative metrics for assessing the
39 relative effectiveness of NOX and VOC emission reductions in controlling ground-level
40 ozone. J. Air Waste Manage. Assoc. 47: 838-850.
August 2007 AX3-175 DRAFT-DO NOT QUOTE OR CITE
-------
1 Georgopoulos, P. G.; Purushothaman, V.; Chiou, R. (1997b) Comparative evaluation of methods
2 for estimating potential human exposure to ozone: photochemical modeling and ambient
3 monitoring. J. Exposure Anal. Environ. Epidemiol. 7: 191-215.
4 Georgopoulos, P. G.; Walia, A.; Roy, A.; Lioy, P. J. (1997c) Integrated exposure and dose
5 modeling and analysis system. 1. Formulation and testing of microenvironmental and
6 pharmacokinetic components. Environ. Sci. Technol. 31: 11-21.
1 Georgopoulos, P. G.; Wang, S.-W.; Vyas, V. M.; Sun, Q.; Burke, J.; Vedantham, R.; McCurdy,
8 T.; Ozkaynak, H. (2005) A source-to-dose assessment of population exposures to fine
9 PM and ozone in Philadelphia, PA, during a summer 1999 episode. J. Exposure Anal.
10 Environ. Epidemiol. 15: 439-457.
11 Gerboles, M.; Buzica, D.; Amantini, L.; Lagler, F.; Hafkenscheid, T. (2006a) Feasibility study of
12 preparation and certification of reference materials for nitrogen dioxide and sulfur
13 dioxide in diffusive samplers. J. Environ. Monit. 8: 174-182.
14 Gerboles, M.; Buzica, D.; Amantini, L.; Lagler, F. (2006b) Laboratory and field comparison of
15 measurement obtained using the available diffusive samplers for ozone and nitrogen
16 dioxide in ambient air. J. Environ. Monit. 8: 112-119.
17 Gilbert, N. L.; Goldberg, M. S.; Beckerman, B.; Brook, J. R.; Jerrett, M. (2005) Assessing spatial
18 variability of ambient nitrogen dioxide in Montreal, Canada, with a land-use regression
19 model. J. Air Waste Manage. Assoc. 55: 1059-1063.
20 Gilbert, N. L.; Gauvin, D.; Guay, M.; Heroux, M.-E.; Dupuis, G.; Legris, M.; Chan, C. C.; Dietz,
21 R. N.; Levesque, B. (2006) Housing characteristics and indoor concentrations of nitrogen
22 dioxide and formaldehyde in Quebec City, Canada. Environ. Res. 102: 1-8.
23 Gillani, N. V.; Godowitch, J. M. (1999) Plume-in-grid treatment of major point source
24 emissions. In: Byun, D. W.; Ching, J. K. S., eds. Science algorithms of the EPA models-3
25 community multiscale air quality modeling system; EPA/600/R-99/030. Research
26 Triangle Park, NC: U.S. Environmental Protection Agency, National Exposure Research
27 Laboratory; chapter 9 . Available: http://www.epa.gov/asmdnerl/CMAQ/ch09.pdf [6
28 March, 2007].
29 Girman, J. R.; Apte, M. G.; Traynor, G. W.; Allen, J. R.; Hollowell, C. D. (1982) Pollutant
30 emission rates from indoor combustion appliances and sidestream cigarette smoke.
31 Environ. Int. 8:213-221.
32 Glas, B.; Levin, J.-O.; Stenberg, B.; Stenlund, H.; Sunesson, A.-L. (2004) Variability of personal
33 chemical exposure in eight office buildings in Sweden. J. Exposure Anal. Environ.
34 Epidemiol. 14(suppl.): S49-S57.
35 Glasius, M.; Carlsen, M. F.; Hansen, T. S.; Lohse, C. (1999) Measurements of nitrogen dioxide
36 on Funen using diffusion tubes. Atmos. Environ. 33: 1177-1185.
37 Goldan, P. D.; Trainer, M.; Kuster, W. C.; Parrish, D. D.; Carpenter, J.; Roberts, J. M.; Yee, J.
38 E.; Fehsenfeld, F. C. (1995) Measurements of hydrocarbons, oxygenated hydrocarbons,
39 carbon monoxide, and nitrogen oxides in an urban basin in Colorado: implications for
40 emission inventories. J. Geophys. Res. [Atmos.] 100: 22,771-22,783.
August 2007 AX3-176 DRAFT-DO NOT QUOTE OR CITE
-------
1 Gonzales, M.; Quails, C.; Hudgens, E.; Neas, L. (2005) Characterization of a spatial gradient of
2 nitrogen dioxide across a United States-Mexico border city during winter. Sci. Total
3 Environ. 337: 163-173.
4 Gradko International Limited. (2007) Technical data sheet: TDS 1. DIP 100 RTU - nitrogen
5 dioxide (NO2). Winchester, England: Gradko Environmental. Available:
6 http://www.gradko.co.uk/pdf/Nitrogen_Dioxide.pdf [15 August, 2007].
7 Grosjean, D. (2003) Ambient PAN and PPN in southern California from 1960 to the SCOS97-
8 NARSTO. Atmos. Environ. 37(suppl. 2): S221-S238.
9 Gustafsson, L. E.; Leone, A. M.; Persson, M. G.; Wiklund, N. P.; Moncada, S. (1991)
10 Endogenous nitric oxide is present in the exhaled air of rabbits, guinea pigs and humans.
11 Biochem. Biophys. Res. Commun. 181: 852-857.
12 Hackney, J. D.; Linn, W. S.; Avol, E. L.; Shamoo, D. A.; Anderson, K. R.; Solomon, J. C.;
13 Little, D. E.; Peng, R.-C. (1992) Exposures of older adults with chronic respiratory illness
14 to nitrogen dioxide: a combined laboratory and field study. Am. Rev. Respir. Dis. 146:
15 1480-1486.
16 Hagenbjork-Gustafsson, A.; Forsberg, B.; Hestvik, G.; Karlsson, D.; Wahlberg, S.; Sandstrom,
17 T. (1996) Measurements of indoor and outdoor nitrogen dioxide concentrations using a
18 diffusive sampler. Analyst (Cambridge, U.K.) 121: 1261-1264.
19 Hagenbjork-Gustafsson, A.; Lindahl, R.; Levin, J. O.; Karlsson, D. (1999) Validation of a
20 diffusive sampler for NO2. J. Environ. Monit. 1: 349-352.
21 Hagenbjork-Gustafsson, A.; Lindahl, R.; Levin, J.-O.; Karlsson, D. (2002) Validation of the
22 Willems badge diffusive sampler for nitrogen dioxide determinations in occupational
23 environments. Analyst (Cambridge, U.K.) 127: 163-168.
24 Hanisco, T. F.; Moyer, E. J.; Weinstock, E. M.; St. Clair, J. M.; Sayres, D. S.; Smith, J. B.;
25 Lockwood, R.; Anderson, J. G.; Dessler, A. E.; Keutsch, F. N. Spackman, J. R.; Read, W.
26 G.; Bui, T. P. (2007) Observations of deep convective influence on stratospheric water
27 vapor and its isotopic composition. Geophys. Res. Lett. 34(L04814):
28 10.1029/2006GL027899.
29 Harrison, R. M.; Thornton, C. A.; Lawrence, R. G.; Mark, D.; Kinnersley, R. P.; Ayres, J. G.
30 (2002) Personal exposure monitoring of particulate matter, nitrogen dioxide, and carbon
31 monoxide, including susceptible groups. Occup. Environ. Med. 59: 671-679.
32 Hayes, S. R. (1989) Estimating the effect of being indoors on total personal exposure to outdoor
33 air pollution. JAPCA 39: 1453-1461.
34 Hayes, S. R. (1991) Use of an indoor air quality model (IAQM) to estimate indoor ozone levels.
35 J. Air Waste Manage. Assoc. 41: 161-170.
36 Hazenkamp-von Arx, M. E.; Gotschi, T.; Ackermann-Liebrich, U.; Bono, R.; Burney, P.; Cyrys,
37 J.; Jarvis, D.; Lillienberg, L.; Luczynska, C.; Maldonado, J. A.; Jaen, A.; de Marco, R.;
38 Mi, Y.; Modig, L.; Bayer-Oglesby, L.; Payo, F.; Soon, A.; Sunyer, J.; Villani, S.; Weyler,
39 J.; Kiinzli, N. (2004) PM2.5 and NO2 assessment in 21 European study centres of ECRHS
40 II: annual means and seasonal differences. Atmos. Environ. 38: 1943-1953.
August 2007 AX3-177 DRAFT-DO NOT QUOTE OR CITE
-------
1 Heal, M. R.; O'Donoghue, M. A.; Cape, J. N. (1999) Overestimation of urban nitrogen dioxide
2 by passive diffusion tubes: a comparative exposure and model study. Atmos. Environ. 33:
3 513-524.
4 Hertel, O.; Jensen, S. S.; Andersen, H. V.; Palmgren, F.; Wahlin, P.; Skov, H.; Nielsen, I. V.;
5 S0rensen, M.; Loft, S.; Raaschou-Nielsen, O. (2001) Human exposure to traffic pollution
6 experience from Danish studies. Pure Appl. Chem. 73: 137-145.
7 Hochadel, M.; Heinrich, J.; Gehring, U.; Morgenstern, V.; Kuhlbusch, T.; Link, E.; Wichmann,
8 H.-E.; Kramer, U. (2006) Predicting long-term average concentrations of traffic-related
9 air pollutants using GIS-based information. Atmos. Environ. 40: 542-553.
10 Hong, Y.-C.; Leem, J.-H.; Lee, K.-H.; Park, D.-H.; Jang, J.-Y.; Kim, S.-T.; Ha, E.-H. (2005)
11 Exposure to air pollution and pulmonary function in university students. Int. Arch.
12 Occup. Environ. Health 78: 132-138.
13 Horowitz, L. W.; Liang, J.; Gardner, G. M.; Jacob, D. J . (1998) Export of reactive nitrogen from
14 North America during summertime: sensitivity to hydrocarbon chemistry. J. Geophys.
15 Res. [Atmos.] 103(D11): 13451-13476.
16 Imada, M.; Iwamoto, J.; Nonaka, S.; Kobayashi, Y.; Unno, T. (1996) Measurement of nitric
17 oxide in human nasal airways. Eur. Respir. J. 9: 556-559.
18 Indrehus, O.; Vassbotn, P. (2001) CO and NC>2 pollution in a long two-way traffic road tunnel:
19 investigation of NC^/NOx ratio and modelling of NC>2 concentration. J. Environ. Monit.
20 3:220-225.
21 Isukapalli, S. S.; Purushothaman, V.; Georgopoulos, P. G. (1999) Mechanistic modeling of the
22 interrelationships between indoor/outdoor air quality and human exposure in a GIS
23 framework. Presented at: 92nd Air & Waste Management Association Annual Meeting;
24 June; St. Louis, MO. Pittsburgh, PA: Air & Waste Management Association.
25 Jakobi, G.; Fabian, P. (1997) Indoor/outdoor concentrations of ozone and peroxyacetyl nitrate
26 (PAN). Int. J. Biometeorol. 40: 162-165.
27 Janssen, N. A. H.; van Vliet, P. H. N.; Aarts, F.; Harssema, H.; Brunekreef, B. (2001)
28 Assessment of exposure to traffic related air pollution of children attending schools near
29 motorways. Atmos. Environ. 35: 3875-3884.
30 Jarvis, D. L.; Leaderer, B. P.; Chinn, S.; Burney, P. G. (2005) Indoor nitrous acid and respiratory
31 symptoms and lung function in adults. Thorax 60: 474-479.
32 Jet Propulsion Laboratory. (2006) Chemical kinetics and photochemical data for use in
33 atmospheric studies. Evaluation number 15. Pasadena, CA: California Institute of
34 Technology. JPL publication 06-2.
35 Johnson, T. (2001) A guide to selected algorithms, distributions, and databases used in exposure
36 models developed by the Office of Air Quality Planning and Standards (DRAFT).
37 Research Triangle Park, NC: U.S. Environmental Protection Agency, CERM report
38 TR01.
39 Johnson, T. (2002) A guide to selected algorithms, distributions, and databases used in exposure
40 models developed by the Office of Air Quality Planning and Standards [revised draft].
41 Research Triangle Park, NC: U.S. Environmental Protection Agency, Office of Research
August 2007 AX3-178 DRAFT-DO NOT QUOTE OR CITE
-------
1 and Development; EPA grant no. CR827033. Available:
2 http://www.epa.gov/ttn/fera/data/human/report052202.pdf [6 March, 2007].
3 Johnson, T.; Long, T.; Ollison, W. (2000) Prediction of hourly microenvironmental
4 concentrations of fine particles based on measurements obtained from the Baltimore
5 scripted activity study. J. Exposure Anal. Environ. Epidemiol. 10: 403-411.
6 Johnson, W. B.; Ludwig, F. L.; Dabbert, W. F.; Allen, R. J. (1973) An urban diffusion simulation
7 model for carbon monoxide. J. Air Pollut. Control Assoc. 23: 490-498.
8 Kameda, T.; Takenaka, N.; Bandow, H.; Inazu, K.; Hisamatsu, Y. (2004) Determination of
9 atmospheric nitro-polycyclic aromatic hydrocarbons and their precursors at a heavy
10 traffic roadside and at a residential area in Osaka, Japan. Poly cyclic Aromat. Compd. 24:
11 657-666.
12 Kanaroglou, P. S.; Jerrett, M.; Morrison, J.; Beckerman, B.; Arain, M. A.; Gilbert, N. L.; Brook,
13 J. R. (2004) Establishing an air pollution monitoring network for intra-urban population
14 exposure assessment: a location-allocation approach. Atmos. Environ. 39: 2399-2409.
15 Kawamoto, T.; Yoshikawa, M.; Matsuno, K.; Kayama, F.; Oyama, T.; Arashidani, K.; Kodama,
16 Y. (1993) Effect of side-stream cigarette smoke on the hepatic cytochrome P450. Arch.
17 Environ. Contain. Toxicol. 25: 255-259.
18 Kawamoto, T.; Matsuno, K.; Arashidani, K.; Kodama, Y. (1997) Personal exposure to indoor
19 nitrogen dioxide. In: Subramanian, K. S.; lyengar, G. V., eds. Environmental
20 biomonitoring: exposure assessment and specimen banking. Washington, DC: American
21 Chemical Society; pp. 178-182. (ACS symposium series: v. 654).
22 Ketzel, M.; Louka, P.; Sahm, P.; Guilloteau, E.; Sini, J.-F.; Moussiopoulos, N. (2002)
23 Intercomparison of numerical urban dispersion models - Part II: street canyon in
24 Hannover, Germany. Water Air Soil Pollut. Focus 2: 603-613.
25 Kim, E.; Hopke, P. K.; Pinto, J. P.; Wilson, W. E. (2005) Spatial variability of fine particle mass,
26 components, and source contributions during the regional air pollution study in St. Louis.
27 Environ. Sci. Technol. 39: 4172-4179.
28 Kim, D.; Sass-Kortsak, A.; Purdham, J. T.; Dales, R. E.; Brook, J. R. (2006) Associations
29 between personal exposures and fixed-site ambient measurements of fine particulate
30 matter, nitrogen dioxide, and carbon monoxide in Toronto, Canada. J. Exposure Sci.
31 Environ. Epidemiol. 16: 172-183.
32 Kirby, C.; Greig, A.; Drye, T. (1998) Temporal and spatial variations in nitrogen dioxide
33 concentrations across an urban landscape: Cambridge, UK. Environ. Monit. Assess. 52:
34 65-82.
35 Kirby, C.; Fox, M.; Waterhouse, J.; Drye, T. (2001) Influences of environmental parameters on
36 the accuracy of nitrogen dioxide passive diffusion tubes for ambient measurement. J.
37 Environ. Monit. 3: 150-158.
38 Klepeis, N. E.; Nelson, W. C.; Ott. W. R.; Robinson, J. P. Tsang, A. M.; Switzer, P.; Behar, J.
39 V.; Hern, S. C.; Engelmann, W. H. (2001) The National Human Activity Pattern Survey
40 (NHAPS): a resource for assessing exposure to environmental pollutants. J. Exposure
41 Anal. Environ. Epidemiol. 11: 231-252.
August 2007 AX3-179 DRAFT-DO NOT QUOTE OR CITE
-------
1 Kodama, Y.; Arashidani, K.; Tokui, N.; Kawamoto, T.; Matsuno, K.; Kunugita, N.; Minakawa,
2 N. (2002) Environmental NO2 concentration and exposure in daily life along main roads
3 in Tokyo. Environ. Res. A 89: 236-244.
4 Kotchenruther, R. A.; Jaffe, D. A.; Beine, H. J.; Anderson, T. L.; Bottenheim, J. W.; Harris, J.
5 M.; Blake, D. R.; Schmitt, R. (2001) Observations of ozone and related species in the
6 northeast Pacific during the PHOBEA campaigns: 2. Airborne observations. J. Geophys.
7 Res. (Atmos.) 106: 7463-7483.
8 Kousa, A.; Monn, C.; Rotko, T.; Aim, S.; Oblesby, L.; Jantunen, M. J. (2001) Personal exposures
9 to NO2 in the EXPOLIS-study: relation to residential indoor, outdoor and workplace
10 concentrations in Basel, Helsinki and Prague. Atmos. Environ. 35: 3405-3412.
11 Kraenzmer, M. (1999) Modeling and continuous monitoring of indoor air pollutants for
12 identification of sources and sinks. Environ. Int. 25: 541-551.
13 Kramer, U.; Koch, T.; Ranft, U.; Ring, J.; Behrendt, H. (2000) Traffic-related air pollution is
14 associated with atopy in children living in urban areas. Epidemiology 11: 64-70.
15 Krupa, S. V.; Legge, A. H. (2000) Passive sampling of ambient, gaseous air pollutants: an
16 assessment from an ecological perspective. Environ. Pollut. 107: 31-45.
17 Kukkonen, J.; Valkonen, E.; Walden, J.; Koskentalo, T.; Aarnio, P.; Karppinen, A.; Berkowicz,
18 R.; Kartastenpaa, R. (2001) A measurement campaign in a street canyon in Helsinki and
19 comparison of results with predictions of the OSPM model. Atmos. Environ. 35: 231-
20 243.
21 Kukkonen, J.; Valkonen, E.; Walden, J.; Koskentalo, T.; Karppinen, A.; Berkowicz, R.;
22 Kartastenpaa, R. (2000) Measurements and modelling of air pollution in a street canyon
23 in Helsinki. Environ. Monit. Assess. 65: 371-379.
24 Kulkarni, M. M.; Patil, R. S. (2002) An empirical model to predict indoor NO2 concentrations.
25 Atmos. Environ. 36: 4777-4785.
26 Kurten, T.; Torpo, L.; Ding, C.-G.; Vehkamaki, H.; Sundberg, M. R.; Laasonen, K.; Kulmala, M.
27 (2007) A density functional study on water-sulfuric acid-ammonia clusters and
28 implications for atmospheric cluster formation. J. Geophys. Res. 112(D04210):
29 10.1029/2006JD007391.
30 Lai, H. K.; Bayer-Oglesby, L.; Colvile, R.; Gotschi, T.; Jantunen, M. J.; Kiinzli, N.; Kulinskaya,
31 E.; Schweizer, C.; Nieuwenhuijsen, M. J. (2006) Determinants of indoor air
32 concentrations of PM2.5, black smoke and NO2 in six European cities (EXPOLIS study).
33 Atmos. Environ. 40: 1299-1313.
34 Lai, H. K.; Kendall, M.; Ferrier, H.; Lindup, L; Aim, S.; Hanninen, O.; Jantunen, M.; Mathys, P.;
35 Colvile, R.; Ashmore, M. R.; Cullinan, P.; Nieuwenhuijsen, M. J. (2004) Personal
36 exposures and microenvironment concentrations of PM2.5, VOC, NO2 and CO in Oxford,
37 UK. Atmos. Environ. 38: 6399-6410.
38 Lai, S.; Patil, R. S. (2001) Monitoring of atmospheric behaviour of NOx from vehicular traffic.
39 Environ. Monit. Assess. 68: 37-50.
40 Lammel, G.; Cape, J. N. (1996) Nitrous acid and nitrite in the atmosphere. Chem. Soc. Rev. 25:
41 361-369.
August 2007 AX3-180 DRAFT-DO NOT QUOTE OR CITE
-------
1 Langstaff, J. E. (2007) Analysis of uncertainty in ozone population exposure modeling [technical
2 memorandum]. Research Triangle Park, NC: U.S. Environmental Protection Agency,
3 Office of Air Quality Planning and Standards.
4 Leaderer, B. P.; Zagraniski, R. T.; Berwick, M.; Stolwijk, J. A. J. (1986) Assessment of exposure
5 to indoor air contaminants from combustion sources: methodology and application. Am.
6 J. Epidemiol. 124: 275-289.
7 Lee, S.-C. (1997) Comparison of indoor and outdoor air quality at two staff quarters in Hong
8 Kong. Environ. Int. 23: 791-797.
9 Lee, S.-C.; Chan, L.-Y. (1998) Indoor/outdoor air quality correlation and questionnaire survey at
10 two staff quarters in Hong Kong. Environ. Int. 24: 729-737.
11 Lee, K.; Yanagisawa, Y.; Spengler, J. D.; Ozkaynak, H.; Billick, I. H. (1993a) Sampling rate
12 evaluation of NC>2 badge: (I) in indoor environments. Indoor Air 3: 124-130.
13 Lee, K.; Yanagisawa, Y.; Spengler, J. D.; Ozkaynak, H.; Billick, I. H. (1993b) Sampling rate
14 evaluation for NO2 badge: (II) in personal monitoring. Environ. Int. 19: 3-7.
15 Lee, K.; Yanagisawa, Y.; Spengler, J. D.; Nakai, S. (1994) Carbon monoxide and nitrogen
16 dioxide exposures in indoor ice skating rinks. J. Sports Sci. 12: 279-283.
17 Lee, K.; Yanagisawa, Y.; Spengler, J. D.; Billick, I. H. (1995) Classification of house
18 characteristics based on indoor nitrogen dioxide concentrations. Environ. Int. 21: 277-
19 282.
20 Lee, K.; Yanagisawa, Y.; Spengler, J. D.; Fukumura, Y.; Billick, I. H. (1996) Classification of
21 house characteristics in a Boston residential nitrogen dioxide characterization study.
22 Indoor Air 6: 211-216.
23 Lee, K.; Levy, J. I; Yanagisawa, Y.; Spengler, J. D.; Billick, I. H. (1998) The Boston Residential
24 Nitrogen Dioxide Characterization Study: classification and prediction of indoor NO2
25 exposure. J. Air Waste Manage. Assoc. 48: 736-742.
26 Lee, S. C.; Chan, L. Y.; Chiu, M. Y. (1999) Indoor and outdoor air quality investigation at 14
27 public places in Hong Kong. Environ. Int. 25: 443-450.
28 Lee, K.; Yang, W.; Bofinger, N. D. (2000) Impact of microenvironmental nitrogen dioxide
29 concentrations on personal exposures in Australia. J. Air Waste Manage. Assoc. 50:
30 1739-1744.
31 Lee, K.; Xue, J.; Geyh, A. S.; Ozkaynak, H.; Leaderer, B. P.; Weschler, C. J.; Spengler, J. D.
32 (2002) Nitrous acid, nitrogen dioxide, and ozone concentrations in residential
33 environments. Environ. Health Perspect. 110: 145-150.
34 Lee, K.; Bartell, S. M.; Paek, D. (2004) Interpersonal and daily variability of personal exposures
35 to nitrogen dioxide and sulfur dioxide. J. Exposure Anal. Environ. Epidemiol. 14: 137-
36 143.
37 Levesque, B.; Allaire, S.; Prud'homme, H.; Dupuis, K.; Bellemare, D. (2000) Air quality
38 monitoring during indoor monster truck and car demolition shows. J. Exposure Anal.
39 Environ. Epidemiol. 10: 58-65.
August 2007 AX3-181 DRAFT-DO NOT QUOTE OR CITE
-------
1 Levesque, B.; Allaire, S.; Gauvin, D.; Koutrakis, P.; Gingras, S.; Rhainds, M.; Prud'Homme, H.;
2 Duchesne, J.-F. (2001) Wood-burning appliances and indoor air quality. Sci. Total
3 Environ. 281:47-62.
4 Levy, J. I.; Lee, K.; Spengler, J. D.; Yanagisawa, Y. (1998a) Impact of residential nitrogen
5 dioxide exposure on personal exposure: an international study. J. Air Waste Manage.
6 Assoc. 48: 553-560.
7 Levy, J. I.; Lee, K.; Yanagisawa, Y.; Hutchinson, P.; Spengler, J. D. (1998b) Determinants of
8 nitrogen dioxide concentrations in indoor ice skating rinks. Am. J. Public Health 88:
9 1781-1786.
10 Levy, J. L.; Baxter, L. K.; Clougherty, J. E. (2006) The air quality impacts of road closures
11 associated with the 2004 Democratic National Convention in Boston. Environ. Health:
12 Global Access Sci. Source 5: 16.
13 Lewne, M.; Cyrys, J.; Meliefste, K.; Hoek, G.; Brauer, M.; Fischer, P.; Gehring, U.; Heinrich, J.;
14 Brunekreef, B.; Bellander, T. (2004) Spatial variation in nitrogen dioxide in three
15 European areas. Sci. Total Environ. 332: 217-230.
16 Lewne, M.; Nise, G.; Lind, M. L.; Gustavsson, P. (2006) Exposure to particles and nitrogen
17 dioxide among taxi, bus and lorry drivers. Int. Arch. Occup. Environ. Health 79: 220-226.
18 Liard, R.; Zureik, M.; Le Moullec, Y.; Soussan, D.; Glorian, M.; Grimfeld, A.; Neukirch, F.
19 (1999) Use of personal passive samplers for measurement of NC>2, NO, and Os levels in
20 panel studies. Environ. Res. 81: 339-348.
21 Linaker, C. H.; Chauhan, A. J.; Inskip, H. M.; Holgate, S. T.; Coggon, D. (2000) Personal
22 exposures of children to nitrogen dioxide relative to concentrations in outdoor air. Occup.
23 Environ. Med. 57: 472-476.
24 Linaker, C. H.; Chauhan, A. J.; Inskip, H.; Frew, A. J.; Sillence, A.; Coggon, D.; Holgate, S. T.
25 (1996) Distribution and determinants of personal exposure to nitrogen dioxide in school
26 children. Occup. Environ. Med. 53: 200-203.
27 Linn, W. S.; Shamoo, D. A.; Anderson, K. R.; Peng, R.-C.; Avol, E. L.; Hackney, J. D.; Gong,
28 H., Jr. (1996) Short-term air pollution exposures and responses in Los Angeles area
29 schoolchildren. J. Exposure Anal. Environ. Epidemiol. 6: 449-472.
30 Lioy, P. J. (1990) Assessing total human exposure to contaminants. Environ. Sci. Technol. 24:
31 938-945.
32 Mage, D.; Wilson, W.; Hasselblad, V.; Grant, L. (1999) Assessment of human exposure to
33 ambient particulate matter. J. Air Waste Manage. Assoc. 49: 1280-1291.
34 Marshall, J. D.; Behrentz, E. (2005) Vehicle self-pollution intake fraction: children's exposure to
35 school bus emissions. Environ. Sci. Technol. 39: 2559-2563.
36 Martin, R. V.; Jacob, D. J.; Chance, K. V.; Kurosu, T. P.; Palmer, P. L; Evans, M. J. (2003)
37 Global inventory of nitrogen oxide emissions constrained by space-based observations of
38 NO2 columns. J. Geophys. Res. [Atmos.] 108(D17): 10.1029/2003JD003453.
39 McCurdy, T. (1997a) Human activities that may lead to high inhaled intake doses in children
40 aged 6-13. Environ. Toxicol. Pharmacol. 4: 251-260.
August 2007 AX3-182 DRAFT-DO NOT QUOTE OR CITE
-------
1 McCurdy, T. (1997b) Modeling the dose profile in human exposure assessments: ozone as an
2 example. Rev. Toxicol. 1: 3-23.
3 McCurdy, T. (2000) Conceptual basis for multi-route intake dose modeling using an energy
4 expenditure approach. J. Exposure Anal. Environ. Epidemiol. 10: 86-97.
5 McCurdy, T.; Glen, G.; Smith, L.; Lakkadi, Y. (2000) The National Exposure Research
6 Laboratory's Consolidated Human Activity Database. J. Exposure Anal. Environ.
7 Epidemiol. 10: 566-578.
8 Meng, Q. Y.; Turpin, B. J.; Polidori, A.; Lee, J. H.; Weisel, C.; Morandi, M.; Colome, S.; Stock,
9 T.; Winer, A.; Zhang, J. (2005) PM2.5 of ambient origin: estimates and exposure errors
10 relevant to PM epidemiology. Environ. Sci. Technol. 39: 5105-5112.
11 Miller, F. J.; Overton, J. H.; Myers, E. T.; Graham, J. A. (1982) Pulmonary dosimetry of nitrogen
12 dioxide in animals and man. In: Schneider, T.; Grant, L., eds. Air pollution by nitrogen
13 oxides: proceedings of the US-Dutch international symposium; May; Maastricht, The
14 Netherlands. Amsterdam, The Netherlands: Elsevier Scientific Publishing Company; pp.
15 377-386. (Studies in environmental science 21).
16 Milner, J. T.; Dimitroulopoulou, C.; ApSimon, H. (2005) Indoor concentrations in buildings
17 from sources outdoors. In: Atmospheric Dispersion Modelling Liaison Committee
18 Annual Report 2004-2005, Annex B. Available: http:www.admlc.org.uk/ar04-05.htm [6
19 March, 2007].
20 Modig, L.; Sunesson, A.-L.; Levin, J.-O.; Sundgren, M.; Hagenbjork-Gustafsson, A.; Forsberg,
21 B. (2004) Can NO2 be used to indicate ambient and personal levels of benzene and 1,3-
22 butadiene in air? J. Environ. Monit. 6: 957-962.
23 Monn, C. (2001) Exposure assessment of air pollutants: a review on spatial heterogeneity and
24 indoor/outdoor/personal exposure to suspended particulate matter, nitrogen dioxide and
25 ozone. Atmos. Environ. 35: 1-32.
26 Monn, C.; Brandli, O.; Schindler, C.; Ackermann-Liebrich, U.; Leuenberger, P.; SAPALDIA
27 team. (1998) Personal exposure to nitrogen dioxide in Switzerland. Sci. Total Environ.
28 215:243-251.
29 Monn, C.; Fuchs, A.; Hogger, D.; Junker, M.; Kogelschatz, D.; Roth, N.; Wanner, H.-U. (1997)
30 Particulate matter less than 10|i (PMio) and fine particles less than 2.5|i (PM2.5):
31 relationships between indoor, outdoor and personal concentrations. Sci. Total Environ.
32 208: 15-21.
33 Mosqueron, L.; Momas, L; Le Moullec, Y. (2002) Personal exposure of Paris office workers to
34 nitrogen dioxide and fine particles. Occup. Environ. Med. 59: 550-555.
35 Mourgeon, E.; Levesque, E.; Duveau, C.; Law-Koune, J.-D.; Charbit, B.; Ternissien, E.; Coriat,
36 P.; Rouby, J-J. (1997) Factors influencing indoor concentrations of nitric oxide in a
37 Parisian intensive care unit. Am. J. Respir. Crit. Care Med. 156: 1692-1695.
38 Mukala, K.; Aim, S.; Tiittanen, P.; Salonen, R. O.; Jantunen, M.; Pekkanen, J. (2000) Nitrogen
39 dioxide exposure assessment and cough among preschool children. Arch. Environ.
40 Health. 55:431-438.
August 2007 AX3-183 DRAFT-DO NOT QUOTE OR CITE
-------
1 Mukala, K.; Pekkanen, J.; Tiittanen, P.; Aim, S.; Salonen, R. O.; Jantunen, M.; Tuomisto, J.
2 (1996) Seasonal exposure to NO2 and respiratory symptoms in preschool children. J.
3 Exposure Anal. Environ. Epidemiol. 6: 197-210.
4 Mukerjee, S.; Smith, L. A.; Norris, G. A.; Morandi, M. T.; Gonzales, M.; Noble, C. A.; Neas, L.
5 M.; Ozkaynak, A. H. (2004) Field method comparison between passive air sampling and
6 continuous monitors for VOCs and NO2 in El Paso, Texas. J. Air Waste Manage. Assoc.
7 54:307-319.
8 Mumford, J. L.; Williams, R. W.; Walsh, D. B.; Burton, R. M.; Svendsgaard, D. J.; Chuang, J.
9 C.; Houk, V. S.; Lewtas, J. (1991) Indoor air pollutants from unvented kerosene heater
10 emissions in mobile homes: studies on particles, semivolatile organics, carbon monoxide,
11 and mutagenicity. Environ. Sci. Technol. 25: 1732-1738.
12 Munger, J. W.; Wofsy, S. C.; Bakwin, P. S.; Fan, S.-M.; Goulden, M. L.; Daube, B. C.;
13 Goldstein, A. H. (1996) Atmospheric deposition of reactive nitrogen oxides and ozone in
14 a temperate deciduous forest and a subarctic woodland. 1. Measurements and
15 mechanisms. J. Geophys. Res. 101: 12639-12657.
16 Nakai, S.; Nitta, H.; Maeda, K. (1995) Respiratory health associated with exposure to automobile
17 exhaust II. Personal NO2 exposure levels according to distance from the roadside. J.
18 Exposure Anal. Environ. Epidemiol. 5: 125-136.
19 Namiesnik, J.; Zabiegala, B.; Kot-Wasik, A.; Partyka, M.; Wasik, A. (2005) Passive sampling
20 and/or extraction techniques in environmental analysis: a review. Anal. Bioanal. Chem.
21 381:279-301.
22 National Research Council. (1991) Rethinking the ozone problem in urban and regional air
23 pollution. Washington, DC: National Academy Press. Available:
24 http://www.nap.edu/books/0309046319/html/ [26 March, 2004].
25 Naumova, Y. Y.; Eisenreich, S. J.; Turpin, B. J.; Weisel, C. P.; Morandi, M. T.; Colome, S. D.;
26 Totten, L. A.; Stock, T. H.; Winer, A. M.; Alimokhtari, S.; Kwon, J.; Shendell, D.; Jones,
27 J.; Maberti, S.; Wall, S. J. (2002) Poly cyclic aromatic hydrocarbons in the indoor and
28 outdoor air of three cities in the U.S. Environ. Sci. Technol. 36: 2552-2559.
29 Naumova, Y. Y.; Offenberg, J. H.; Eisenreich, S. J.; Meng, Q. Y.; Polidori, A.; Turpin, B. J.;
30 Weisel, C. P.; Morandi, M. T.; Colome, S. D.; Stock, T. H.; Winer, A. M.; Alimokhtari,
31 S.; Kwon, J.; Maberti, S.; Shendell, D.; Jones, J.; Farrar, C. (2003) Gas/particle
32 distribution of poly cyclic aromatic hydrocarbons in coupled outdoor/indoor atmospheres.
33 Atmos. Environ. 37: 703-719.
34 Nazaroff, W. W.; Cass, G. R. (1986) Mathematical modeling of chemically reactive pollutants in
35 indoor air. Environ. Sci. Technol. 20: 924-934.
36 Nazaroff, W. W.; Weschler, C. J. (2004) Cleaning products and air fresheners: exposure to
37 primary and secondary air pollutants. Atmos. Environ. 38: 2841-2865.
38 Nerriere, E.; Zmirou-Navier, D.; Blanchard, O.; Momas, L; Ladner, J.; Le Moullec, Y.;
39 Personnaz, M.-B.; Lameloise, P.; Delmas, V.; Target, A.; Desqueyroux, H. (2005) Can
40 we use fixed ambient air monitors to estimate population long-term exposure to air
41 pollutants? The case of spatial variability in the Genotox ER study. Environ. Res. 97: 32-
42 42.
August 2007 AX3-184 DRAFT-DO NOT QUOTE OR CITE
-------
1 Nicolai, T.; Carr, D.; Weiland, S. K.; Duhme, H.; Von Ehrenstein, O.; Wagner, C.; Von Mutius,
2 E. (2003) Urban traffic and pollutant exposure related to respiratory outcomes and atopy
3 in a large sample of children. Eur. Respir. J. 21: 956-963.
4 N0jgaard, J. K.; Bilde, M.; Stenby, C.; Nielsen, O. J.; Wolkoff, P. (2006) The effect of nitrogen
5 dioxide on particle formation during ozonolysis of two abundant monoterpenes indoors.
6 Atmos. Environ. 40: 1030-1042.
7 Norris, G.; Larson, T. (1999) Spatial and temporal measurements of NO2 in an urban area using
8 continuous mobile monitoring and passive samplers. J. Exposure Anal. Environ.
9 Epidemiol. 9: 586-593.
10 Ogawa & Company. (1998) NO, NO2, NOX and SO2 sampling protocol using the Ogawa
11 sampler. Version 3. Pompano Beach, FL: Ogawa & Co., USA, Inc. Available:
12 http://www.ogawausa.com/protocol.html [30 March, 2007].
13 Ott, W. R. (1982) Concepts of human exposure to air pollution. Environ. Int. 7: 179-196.
14 Ott, W. R. (1985) Total human exposure: an emerging science focuses on humans as receptors of
15 environmental pollution. Environ. Sci. Technol. 19: 880-886.
16 Ott, W.; Wallace, L.; Mage, D. (2000) Predicting particulate (PMi0) personal exposure
17 distributions using a random component superposition statistical model. J. Air Waste
18 Manage. Assoc. 50: 1390-1406.
19 Overton, J. H.; Graham, R. C. (1995) Simulation of the uptake of a reactive gas in a rat
20 respiratory tract model with an asymmetric tracheobronchial region patterned on
21 complete conducting airways cast data. Comput. Biomed. Res. 28: 171-190.
22 Palmes, E. D.; Gunnison, A. F.; DiMattio, J.; Tomczyk, C. (1976) Personal sampler for nitrogen
23 dioxide. Am. Ind. Hyg. Assoc. J. 37: 570-577.
24 Pandian, M. D.; Behar, J. V.; Ott, W. R.; Wallace, L. A.; Wilson, A. L.; Colome, S, D. (1998)
25 Correcting errors in the nationwide data base of residential air exchange rates. J.
26 Exposure Anal. Environ. Epidemiol. 8: 577-586.
27 Park, J.-H.; Spengler, J. D.; Yoon, D.-W.; Dumyahn, T.; Lee, K.; Ozkaynak, H. (1998)
28 Measurement of air exchange rate of stationary vehicles and estimation of in-vehicle
29 exposure. J. Exposure Anal. Environ. Epidemiol. 8: 65-78.
30 Partti-Pellinen, K.; Marttila, O.; Ahonen, A.; Suominen, O.; Haahtela, T. (2000) Penetration of
31 nitrogen oxides and particles from outdoor into indoor air and removal of the pollutants
32 through filtration of incoming air. Indoor Air. 10: 126-132.
33 Passam ag. (2007) Nitrogen oxides NOX [sampler]. Mannedorf, Switzerland: Passam ag,
34 Laboratory for Environmental Analysis. Available: http://www.passam.ch/nox.htm [15
35 August, 2007].
36 Paulozzi, L. J.; Spengler, R. F.; Vogt, R. L.; Carney, J. K. (1993) A survey of carbon monoxide
37 and nitrogen dioxide in indoor ice arenas in Vermont. J. Environ. Health 56(5): 23-25.
38 Penard-Morand, C.; Schillinger, C.; Armengaud, A.; Debotte, G.; Chretien, E.; Pellier, S.;
39 Annesi-Maesano, I. (2006) Assessment of schoolchildren's exposure to traffic-related air
August 2007 AX3-185 DRAFT-DO NOT QUOTE OR CITE
-------
1 pollution in the French Six Cities Study using a dispersion model. Atmos. Environ. 40:
2 2274-2287.
3 Piechocki-Minguy, A.; Plaisance, H.; Garcia-Fouque, S.; Galloo, J. C.; Guillermo, R. (2003)
4 Validation tests of a new high uptake rate passive sampler for nitrogen dioxide
5 measurements. Environ. Technol. 24: 1527-1535.
6 Piechocki-Minguy, A.; Plaisance, H.; Schadkowski, C.; Sagnier, I; Saison, J. Y.; Galloo, J. C.;
7 Guillermo, R. (2006) A case study of personal exposure to nitrogen dioxide using a new
8 high sensitive diffusive sampler. Sci. Total Environ. 366: 55-64.
9 Pilotto, L. S.; Douglas, R. M.; Attewell, R. G.; Wilson, S. R. (1997) Respiratory effects
10 associated with indoor nitrogen dioxide exposure in children. Int. J. Epidemiol. 26: 788-
11 796.
12 Pinto, J. P.; Lefohn, A. S.; Shadwick, D. S. (2004) Spatial variability of PM2.5 in urban areas in
13 the United States. J. Air Waste Manage. Assoc. 54: 440-449.
14 Pippin, M. R.; Bertman, S.; Thornberry, T.; Town, M.; Carroll, M. A.; Sillman, S. (2001)
15 Seasonal variation of PAN, PPN, and O3 at the upper Midwest PROPHET site. J.
16 Geophys. Res. (Atmos.) 106: 24,451-24,463.
17 Plaisance, H.; Piechocki-Minguy, A.; Garcia-Fouque, S.; Galloo, J. C. (2004) Influences of
18 meteorological factors on the NO2 measurements by passive diffusion tube. Atmos.
19 Environ. 38: 573-580.
20 Pleijel, H.; Karlsson, G. P.; Gerdin, E. B. (2004) On the logarithmic relationship between NO2
21 concentration and the distance from a highroad. Sci. Total Environ. 332: 261-264.
22 Postlethwait, E. M.; Langford, S. D.; Bidani, A. (1991) Transfer of NO2 through pulmonary
23 epithelial lining fluid. Toxicol. Appl. Pharmacol. 109: 464-471.
24 Poulida, O.; Dickerson, R. R.; Doddridge, B. G.; Holland, J. Z.; Wardell, R. G.; Watkins, J. G.
25 (1991) Trace gas concentrations and meteorology in rural Virginia. 1. Ozone and carbon
26 monoxide. J. Geophys. Res. [Atmos.] 96: 22,461-22,475.
27 Price, P. S.; Chaisson, C. F.; Koontz, M.; Wilkes, C.; Ryan, B.; Macintosh, D.; Georgopoulos, P.
28 G. (2003) Construction of a comprehensive chemical exposure framework using person
29 oriented modeling. Prepared for: The Exposure Technical Implementation Panel,
30 American Chemistry Council; contract #1338. Annandale, VA: The LifeLine Group.
31 Available: http://www.thelifelinegroup.org/lifeline/docs.htm [6 March, 2007].
32 Proyou, A. G.; Ziomas, I. C.; Stathopoulos, A. (1998) Application of a three-layer photochemical
33 box model in an Athens street canyon. J. Air Waste Manage. Assoc. 48: 427-433.
34 Purushothaman, V.; Georgopoulos, P. G. (1997) Computational tools to aid the estimation and
35 visualization of potential human exposure to ozone. In: Proceedings of the Air & Waste
36 Management Association Specialty Conference on Computing in Environmental
37 Resource Management; December, 1996; Research Triangle Park, NC. Pittsburgh, PA:
38 A&WMA; VIP-68; pp. 17-28.
39 Purushothaman, V.; Georgopoulos, P. G. (1999a) Evaluation of regional emissions control
40 strategies for ozone utilizing population exposure metrics: a new GIS-based approach
41 applied to the July 1995 episode over the OTAG domain. Piscataway, NJ: Environmental
August 2007 AX3-186 DRAFT-DO NOT QUOTE OR CITE
-------
1 and Occupational Health Sciences Institute, Ozone Research Center; ORC technical
2 report, ORC-TR99-02.
3 Purushothaman, V.; Georgopoulos, P. G. (1999b) Integrating photochemical modeling,
4 geostatistical techniques and geographical information systems for ozone exposure
5 assessment. Piscataway, NJ: Environmental and Occupational Health Sciences Institute,
6 Ozone Research Center; ORC technical report, ORC-TR99-01.
7 Raaschou-Nielsen, O.; Skov, H.; Lohse, C.; Thomsen, B. L.; Olsen, J. H. (1997) Front-door
8 concentrations and personal exposures of Danish children to nitrogen dioxide. Environ.
9 Health Perspect. 105: 964-970.
10 Radiello®. (2006) Radiello® manual. Padova, Italy: Fondazione Salvatore Maugeri Clinica del
11 Lavoro e Delia Riabilitazione I.R.C.C.S., Centre di Ricerche Ambientali. Available:
12 http://www.radiello.com/english/Radiello%27s%20manual%2001-06.pdf [15 August,
13 2007].
14 Ramazan, K. A.; Wingen, L. M.; Miller, Y.; Chaban, G. M.; Gerber, R. B.; Xantheas, S. S.;
15 Finlayson-Pitts, B. J. (2006) New experimental and theoretical approach to the
16 heterogeneous hydrolysis of NO2: key role of molecular nitric acid and its complex. J.
17 Phys. Chem. A 110: 6886-6897.
18 Ramirez-Aguilar, M.; Cicero-Fernandez, P.; Winer, A. M.; Romieu, I; Meneses-Gonzales, F.;
19 Hernandez-Avila, M. (2002) Measurements of personal exposure to nitrogen dioxide in
20 four Mexican cities in 1996. J. Air Waste Manage. Assoc. 52: 50-57.
21 Rao, K. S. (2002) ROADWAY-2: a model for pollutant dispersion near highways. Water Air
22 Soil Pollut. Focus 2: 261-277.
23 Raw, G. J.; Coward, S. K. D.; Brown, V. M.; Crump, D. R. (2004) Exposure to air pollutants in
24 English homes. J. Exposure Anal. Environ. Epidemiol. 14(suppl. 1): S85-S94.
25 Reeuwijk, H. V.; Fischer, P. H.; Harssema, H.; Briggs, D. J.; Smallbone, K.; Lebret, E. (1998)
26 Field comparison of two NO2 passive samplers to assess spatial variation. Environ.
27 Monit. Assess. 50: 37-51.
28 Reisen, F.; Arey, J. (2005) Atmospheric reactions influence seasonal PAH and nitro-PAH
29 concentrations in the Los Angeles basin. Environ. Sci. Technol. 39: 64-73.
30 Restrepo, C.; Zimmerman, R.; Thurston, G.; Clemente, J.; Gorczynski, J.; Zhong, M.; Blaustein,
31 M.; Chen, L. C. (2004) A comparison of ground-level air quality data with New York
32 State Department of Environmental Conservation monitoring stations data in South
33 Bronx, New York. Atmos. Environ. 38: 5295-5304.
34 Roberts, J. M.; Williams, J.; Baumann, K.; Buhr, M. P.; Goldan, P. D.; Holloway, J.; Hubler, G.;
35 Kuster, W. C.; McKeen, S. A.; Ryerson, T. B.; Trainer, M.; Williams, E. J.; Fehsenfeld,
36 F. C.; Bertman, S. B.; Nouaime, G.; Seaver, C.; Grodzinsky, G.; Rodgers, M.; Young, V.
37 L. (1998) Measurements of PAN, PPN, and MPAN made during the 1994 and 1995
38 Nashville Intensives of the Southern Oxidant Study: implications for regional ozone
39 production from biogenic hydrocarbons. J. Geophys. Res. [Atmos.] 103: 22,473-22,490.
40 Roberts, J. M.; Flocke, F.; Stroud, C. A.; Hereid, D.; Williams, E.; Fehsenfeld, F.; Brune, W.;
41 Martinez, M.; Harder, H. (2002) Ground-based measurements of peroxycarboxylic nitric
August 2007 AX3-187 DRAFT-DO NOT QUOTE OR CITE
-------
1 anhydrides (PANs) during the 1999 Southern Oxidants Study Nashville Intensive. J.
2 Geophys. Res. [Atmos.] 107(D21): 10.1029/2001JD000947.
3 Roberts, J. M.; Jobson, B. T.; Kuster, W.; Goldan, P.; Murphy, P.; Williams, E.; Frost, G.;
4 Riemer, D.; Apel, E.; Stroud, C.; Wiedinmyer, C.; Fehsenfeld, F. (2003) An examination
5 of the chemistry of peroxycarboxylic nitric anhydrides and related volatile organic
6 compounds during Texas Air Quality Study 2000 using ground-based measurements. J.
7 Geophys. Res. [Atmos.] 108(D16): 10.1029/2003JD003383.
8 Roberts, J. M.; Flocke, F.; Chen, G.; De Gouw, J.; Holloway, J. S.; Hiibler, G.; Neuman, J. A.;
9 Nicks, D. K., Jr.; Nowak, J. B.; Parrish, D. D.; Ryerson, T. B.; Sueper, D. T.; Warneke,
10 C.; Fehsenfeld, F. C. (2004) Measurement of peroxycarboxylic nitric anhydrides (PANs)
11 during the ITCT 2K2 aircraft intensive experiment. J. Geophys. Res. [Atmos.]
12 109(D23S21): 10.1029/2004JD004960.
13 Rojas-Bracho, L.; Suh, H. H.; Oyola, P.; Koutrakis, P. (2002) Measurements of children's
14 exposures to particles and nitrogen dioxide in Santiago, Chile. Sci. Total Environ. 287:
15 249-264.
16 Roorda-Knape, M. C.; Janssen, N. A. H.; De Hartog, J. J.; Van Vliet, P. H. N.; Harssema, H.;
17 Brunekreef, B. (1998) Air pollution from traffic in city districts near major motorways.
18 Atmos. Environ. 32: 1921-1930.
19 Rosenbaum, A. (2005) The HAPEMS user's guide - hazardous air pollutant exposure model,
20 version 5. Prepared for: U.S. Environmental Protection Agency. Research Triangle Park,
21 NC: ICF Consulting.
22 Rosman, K.; Shimmo, M.; Karlsson, A.; Hansson, H.-C.; Keronen, P.; Allen, A.; Hoenninger, G.
23 (2001) Laboratory and field investigations of a new and simple design for the parallel
24 plate denuder. Atmos. Environ. 35: 5301-5310.
25 Ross, Z.; English, P. B.; Scalf, R.; Gunier, R.; Smorodinsky, S.; Wall, S.; Jerrett, M. (2006)
26 Nitrogen dioxide prediction in southern California using land use regression modeling:
27 potential for environmental health analyses. J. Exposure Sci. Environ. Epidemiol. 16:
28 106-114.
29 Rotko, T.; Kousa, A.; Aim, S.; Jantunen, M. (2001) Exposures to nitrogen dioxide in EXPOLIS-
30 Helsinki: microenvironment, behavioral and sociodemographic factors. J. Exposure Anal.
31 Environ. Epidemiol. 11: 216-223.
32 Rundell, K. W.; Caviston, R.; Hollenbach, A. M.; Murphy, K. (2006) Vehicular air pollution,
33 playgrounds, and youth athletic fields. Inhalation Toxicol. 18: 541-547.
34 Ryerson, T. B.; Trainer, M.; Holloway, J. S.; Parrish, D. D.; Huey, L. G.; Sueper, D. T.; Frost, G.
35 J.; Donnelly, S. G.; Schauffler, S.; Atlas, E. L.; Kuster, W. C.; Goldan, P. D.; Hubler, G.;
36 Meagher, J. F.; Fehsenfeld, F. C. (2001) Observations of ozone formation in power plant
37 plumes and implications for ozone control strategies. Science (Washington, DC) 292:
38 719-723.
39 Sabin, L. D.; Kozawa, K.; Behrentz, E.; Winer, A. M.; Fitz, D. R.; Pankratz, D. V.; Colome, S.
40 D.; Fruin, S. A. (2005) Analysis of real-time variables affecting children's exposure to
41 diesel-related pollutants during school bus commutes in Los Angeles. Atmos. Environ.
42 39: 5243-5254.
August 2007 AX3-188 DRAFT-DO NOT QUOTE OR CITE
-------
1 Sahm, P.; Louka, P.; Ketzel, M.; Guilloteau, E.; Sini, J.-F. (2002) Intercomparison of numerical
2 urban dispersion models - Part I: street canyon and single building configurations. Water
3 Air Soil Pollut. Focus 2: 587-601.
4 Sahsuvaroglu, T.; Arain, A.; Kanaroglou, P.; Finkelstein, N.; Newbold, B.; Jerrett, M.;
5 Beckerman, B.; Brook, J.; Finkelstein, M.; Gilbert, N. L. (2006) A land use regression
6 model for predicting ambient concentrations of nitrogen dioxide in Hamilton, Ontario,
7 Canada. J. Air Waste Manage. Assoc. 56: 1059-1069.
8 Sarnat, J. A.; Brown, K. W.; Schwartz, J.; Coull, B. A.; Koutrakis, P. (2005) Ambient gas
9 concentrations and personal particulate matter exposures: implications for studying the
10 health effects of particles. Epidemiology 16: 385-395.
11 Sarnat, J. A.; Schwartz, J.; Catalano, P. J.; Suh, H. H. (2001) Gaseous pollutants in particulate
12 matter epidemiology: confounders or surrogates? Environ. Health Perspect. 109: 1053-
13 1061.
14 Sarnat, S. E.; Coull, B. A.; Schwartz, J.; Gold, D. R.; Suh, H. H. (2006) Factors affecting the
15 association between ambient concentrations and personal exposures to particles and
16 gases. Environ. Health Perspect. 114: 649-654.
17 Sarwar, G.; Corsi, R.; Allen, D.; Weschler, C. J. (2002a) Production and levels of selected indoor
18 radicals: a modeling assessment. In: Proceedings of 9th International Conference on
19 Indoor Air Quality and Climate, Indoor Air 2002; June-July; Monterey, CA.
20 Sarwar, G.; Corsi, R.; Kumura, Y.; Allen, D.; Weschler, C. J. (2002b) Hydroxyl radicals in
21 indoor environments. Atmos. Environ. 36: 3973-3988.
22 Sarwar, M.; Corsi, R.; Kimura, Y.; Allen, D.; Weschler, C. (2001) Hydroxyl radicals in indoor
23 environments. In: Proceedings of the Air & Waste Management Association's 94th
24 Annual Conference & Exhibition; June; Orlando, FL. Pittsburgh, PA: Air & Waste
25 Management Association.
26 Schauer, C.; Niessner, R.; Poschl, U. (2004) Analysis of nitrated polycyclic aromatic
27 hydrocarbons by liquid chromatography with fluorescence and mass spectrometry
28 detection: air particulate matter, soot, and reaction product studies. Anal. Bioanal. Chem.
29 378: 725-736.
30 Schwab, M.; McDermott, A.; Spengler, J. D.; Samet, J. M.; Lambert, W. E. (1994) Seasonal and
31 yearly patterns of indoor nitrogen dioxide levels: data from Albuquerque, New Mexico.
32 Indoor Air 4: 8-22.
33 Shima, M.; Adachi, M. (1998) Indoor nitrogen dioxide in homes along trunk roads with heavy
34 traffic. Occup. Environ. Med. 55: 428-433.
35 Shima, M.; Adachi, M. (2000) Effect of outdoor and indoor nitrogen dioxide on respiratory
36 symptoms in schoolchildren. Int. J. Epidemiol. 29: 862-870.
37 Sillman, S.; Logan, J. A.; Wofsy, S. C. (1990) The sensitivity of ozone to nitrogen oxides and
38 hydrocarbons in regional ozone episodes. J. Geophys. Res. [Atmos.] 95: 1837-1851.
39 Simoni, M.; Scognamiglio, A.; Carrozzi, L.; Baldacci, S.; Angino, A.; Pistelli, F.; Di Pede, F.;
40 Viegi, G. (2004) Indoor exposures and acute respiratory effects in two general population
August 2007 AX3-189 DRAFT-DO NOT QUOTE OR CITE
-------
1 samples from a rural and an urban area in Italy. J. Exposure Anal. Environ. Epidemiol.
2 14(suppl 1): S144-S152.
3 Singer, B. C.; Hodgson, A. T.; Hotchi, T.; Kim, J. J. (2004) Passive measurement of nitrogen
4 oxides to assess traffic-related pollutant exposure for the East Bay Children's Respiratory
5 Health Study. Atmos. Environ. 38: 393-403.
6 Sjodin, A.; Sjoberg, K.; Svanberg, P. A.; Backstrom, H. (1996) Verification of expected trends in
7 urban traffic NO* emissions from long-term measurements of ambient NO2
8 concentrations in urban air. Sci. Total Environ. 189/190: 213-220.
9 Slack, H. H.; Heumann, M. A. (1997) Use of unvented residential heating appliances—United
10 States, 1988-1994. Morb. Mortal. Wkly. Rep. 46: 1221-1224.
11 Smedje, G.; Norback, D.; Edling, C. (1997) Subjective indoor air quality in schools in relation to
12 exposure. Indoor Air 7: 143-150.
13 Soderstrom, H.; Hajslova, J.; Kocourek, V.; Siegmund, B.; Kocan, A.; Obiedzinski, M. W.;
14 Tysklind, M.; Bergqvist, P.-A. (2005) PAHs and nitrated PAHs in air of five European
15 countries determined using SPMDs as passive samplers. Atmos. Environ. 39: 1627-1640.
16 Son, B.; Yang, W.; Breysse, P.; Chung, T.; Lee, Y. (2004) Estimation of occupational and
17 nonoccupational nitrogen dioxide exposure for Korean taxi drivers using a
18 microenvironmental model. Environ. Res. 94: 291-296.
19 S0rensen, D. N.; Weschler, C. J. (2002) Modeling-gas phase reactions in indoor environments
20 using computational fluid dynamics. Atmos. Environ. 36: 9-18.
21 S0rensen, M.; Loft, S.; Andersen, H. V.; Raaschou-Nielsen, O.; Skovgaard, L. T.; Knudsen, L.
22 E.; Nielsen, I. V.; Hertel, O. (2005) Personal exposure to PM2 5, black smoke and NO2 in
23 Copenhagen: relationship to bedroom and outdoor concentrations covering seasonal
24 variation. J. Exposure Anal. Environ. Epidemiol. 15: 413-422.
25 Spengler, J. D.; Brauer, M.; Samet, J. M.; Lambert, W. E. (1993) Nitrous acid in Albuquerque,
26 New Mexico, homes. Environ. Sci. Technol. 27: 841-845.
27 Spengler, J.; Schwab, M.; Ryan, P. B.; Colome, S.; Wilson, A. L.; Billick, L; Becker, E. (1994)
28 Personal exposure to nitrogen dioxide in the Los Angeles Basin. J. Air Waste Manage.
29 Assoc. 44: 39-47.
30 Spengler, J. D.; Lee, K.; Yanagisawa, Y.; Bischof, W.; Braathan, O.; Chung, S.; Coward, K.;
31 Gutschmidt, V.; Isidorov, V.; Jahng, D.; Jin, K.; Korenaga, T.; Maroni, M.; Ohkoda, Y.;
32 Pastuszka, J.; Patil, R. S.; Qing, X.; Raizenne, M.; Romieu, L; Salonen, R.; Sega, K.;
33 Seifert, B.; Shah, S.; Torres, E.; Yoon, D.; Zhang, X. (1996) Impact of residential
34 nitrogen exposure on personal exposure: an international study. In: Indoor air '96: the 7th
35 international conference on indoor air quality, volume I; July; Nagoya, Japan. Tokyo,
36 Japan: Institute of Public Health; pp. 931-936.
37 Spicer, C. W.; Coutant, R. W.; Ward, G. F.; Joseph, D. W.; Gaynor, A. J.; Billick, I. H. (1989)
38 Rates and mechanisms of NO2 removal from indoor air by residential materials. Environ.
39 Int. 15: 643-654.
40 Spicer, C. W.; Kenny, D. V.; Ward, G. F.; Billick, I. H. (1993) Transformations, lifetimes, and
41 sources of NO2, HONO, and HNOs in indoor environments. Air Waste 43: 1479-1485.
August 2007 AX3-190 DRAFT-DO NOT QUOTE OR CITE
-------
1 Staimer, N.; Delfmo, R. 1; Bufalino, C.; Fine, P. M.; Sioutas, C.; Kleinman, M. T. (2005) A
2 miniaturized active sampler for the assessment of personal exposure to nitrogen dioxide.
3 Anal. Bioanal. Chem. 383: 955-962.
4 Stemmler, K.; Ammann, M.; Bonders, C.; Kleffmann, J.; George, C. (2006) Photosensitized
5 reduction of nitrogen dioxide on humic acid as a source of nitrous acid. Nature (London,
6 U.K.) 440: 195-198.
7 Stevenson, K.; Bush, T.; Mooney, D. (2001) Five years of nitrogen dioxide measurement with
8 diffusion tube samplers at over 1000 sites in the UK. Atmos. Environ. 35: 281-287.
9 Sverdrup, G. M.; Spicer, C. W.; Ward, G. F. (1987) Investigation of the gas-phase reaction of
10 dinitrogen pentoxide with water-vapor. Int. J. Chem. Kinet. 19: 191-205.
11 Tashiro, Y.; Taniyama, T. (2002) Atmospheric NO2 and CO concentration in Lima, Peru.
12 Environ. Int. 28: 227-233.
13 Teshome, E. J.; Haghighat, F. (2004) Zonal models for indoor air flow - a critical review. Int. J.
14 Ventil. 3: 119-129.
15 Topp, R.; Cyrys, J.; Gebefiigi, I; Schnelle-Kreis, J.; Richter, K.; Wichmann, H.-E.; Heinrich, J.
16 (2004) Indoor and outdoor air concentrations of BTEX and NO2: correlation of repeated
17 measurements. J. Environ. Monit. 6: 807-812.
18 Triche, E. W.; Belanger, K.; Bracken, M. B.; Beckett, W. S.; Holford, T. R.; Gent, J. F.;
19 McSharry, J.-E.; Leaderer, B. P. (2005) Indoor heating sources and respiratory symptoms
20 in nonsmoking women. Epidemiology 16: 377-384.
21 United Kingdom Air Quality Expert Group (U.K. AQEG). (2004) Nitrogen dioxide in the United
22 Kingdom. London, United Kingdom: Department for Environment, Food and Rural
23 Affairs. Available:
24 http://www.defira.gov.uk/environment/airquality/panels/aqeg/index.htm [12 April, 2007].
25 U.S. Environmental Protection Agency. (1992) Final guidelines for exposure assessment.
26 Research Triangle Park, NC: National Center for Environmental Assessment; EPA/600Z-
27 92/001.
28 U.S. Environmental Protection Agency. (1993) Air quality criteria for oxides of nitrogen.
29 Research Triangle Park, NC: Office of Health and Environmental Assessment,
30 Environmental Criteria and Assessment Office; report nos. EPA/600/8-91/049aF-cF. 3v.
31 Available from: NTIS, Springfield, VA; PB95-124533, PB95-124525, and PB95-124517.
32 U.S. Environmental Protection Agency. (1996) Air quality criteria for particulate matter.
33 Research Triangle Park, NC: National Center for Environmental Assessment-RTF Office;
34 report nos. EPA/600/P-95/001aF-cF. 3v.
35 U.S. Environmental Protection Agency. (1997) Exposure factors handbook. Washington, DC:
36 Office of Research and Development, National Center for Environmental Assessment;
37 report nos. EPA/600/P-95/002Fa-c.
38 U.S. Environmental Protection Agency. (2003) National air quality and emissions trends report.
39 2003 special studies edition. Research Triangle Park, NC: Office of Air Quality
40 Standards; Emissions Monitoring and Analysis Division; report no. EPA 454/R-03-005.
41 Available: http://www.epa.gov/air/airtrends/aqtrnd03/toc.html (27 August, 2004).
August 2007 AX3-191 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. (2006a) Air quality criteria for ozone and related
5 photochemical oxidants. Research Triangle Park, NC: National Center for Environmental
6 Assessment; report no. EPA/600/R-05/004aF-cF. 3v. Available:
7 http://cfpub.epa.gov/ncea/ [24 March, 2006].
8 U.S. Environmental Protection Agency. (2006b) Total risk integrated methodology (TRIM) air
9 pollutants exposure model documentation (TRIM.Expo/APEX, version 4). Volume I:
10 User's guide. Research Triangle Park, NC: U.S. Environmental Protection Agency.
11 Available: http://www.epa.gov/ttn/fera/data/apex/APEX4UsersGuideJuly2006.pdf [7
12 March, 2007].
13 U.S. Environmental Protection Agency. (2006c) Total risk integrated methodology (TRIM) air
14 pollutants exposure model documentation (TREVI.Expo/APEX, version 4). Volume II:
15 technical support document. Research Triangle Park, NC: U.S. Environmental Protection
16 Agency. Available: http://www.epa.gov/ttn/fera/data/apex/APEX4TSDJuly2006.pdff7
17 March, 2007].
18 U.S. Environmental Protection Agency. (2006d) Plume-in-grid model. Available:
19 http://www.epa.gov/asmdnerl/ModelDevelopment/plumeInGrid.html [7 March, 2007].
20 U.S. Environmental Protection Agency. (2007) Air Quality System (AQS). Washington, DC:
21 Office of Air and Radiation. Available: http://www.epa.gov/ttn/airs/airsaqs/ [12 April,
22 2007].
23 Varshney, C. K.; Singh, A. P. (2003) Passive samplers for NOX monitoring: a critical review.
24 Environmentalist 23: 127-136.
25 Venegas, L. E.; Mazzeo, N. A. (2004) Aplication of atmospheric dispersion models to evaluate
26 population exposure to NO2 concentration in Buenos Aires. In: Proceedings of the 9th
27 International Conference on Harmonisation Within Atmospheric Dispersion Modelling
28 for Regulatory Purposes; p. 182-186.
29 Vinzents, P. S.; M011er, P.; S0rensen, M.; Knudsen, L. E.; Herte, L. Q.; Jensen, F. P.; Schibye,
30 B.; Loft, S. (2005) Personal exposure to ultrafme particles and oxidative DNA damage.
31 Environ. Health Perspect. 113: 1485-1490.
32 Vogel, M. (2005) Sampling of airborne pollutants: strategies and developments. Anal. Bioanal.
33 Chem. 381: 84-86.
34
35 Vukovich, F. (2000) The spatial variation of the weekday/weekend differences in the Baltimore
36 area. J. Waste Manage. Assoc. 50: 2067-2072.
37 Wainman, T.; Zhang, J.; Weschler, C. J.; Lioy, P. J. (2000) Ozone and limonene in indoor air: a
38 source of submicron particle exposure. Environ. Health Perspect. 108: 1139-1145.
39 Wainman, T.; Weschler, C.; Lioy, P.; Zhang, J. (2001) Effects of surface type and relative
40 humidity on the production and concentration of nitrous acid in a model indoor
41 environment. Environ. Sci. Technol. 35: 2200-2206.
August 2007 AX3-192 DRAFT-DO NOT QUOTE OR CITE
-------
1 Wayne, R. P.; Barnes, I; Biggs, P.; Burrows, J. P.; Canosa-Mas, C. E.; Hjorth, J.; Le Bras, G.;
2 Moortgat, G. K.; Perner, D.; Poulet, G.; Restelli, G.; Sidebottom, H. (1991) The nitrate
3 radical: physics, chemistry, and the atmosphere. Atmos. Environ. Part A 25: 1-203.
4 Weschler, C. J.; Brauer, M.; Koutrakis, P. (1992) Indoor ozone and nitrogen dioxide: a potential
5 pathway to the generation of nitrate radicals, dinitrogen pentaoxide, and nitric acid
6 indoors. Environ. Sci. Technol. 26: 179-184.
7 Weschler, C. J.; Shields, H. C. (1996) The conversion (reduction) of nitrogen dioxide to nitric
8 oxide as a consequence of charcoal filtration. In: Yoshizawa, S.; Kimura, K.; Ikeda, K.;
9 Tanabe, S.; Iwata, T., eds. Indoor Air '96: proceedings of the 7th international conference
10 on indoor air quality and climate, v. 3, July; Nagoya, Japan. Toykyo, Japan: Indoor Air
11 '96; pp. 453-458.
12 Weschler, C. J.; Shields, H. C. (1997) Potential reactions among indoor pollutants. Atmos.
13 Environ. 31: 3487-3495.
14 Weschler, C. J.; Shields, H. C.; Naik, D. V. (1994) Indoor chemistry involving O3, NO, and NO2
15 as evidenced by 14 months of measurements at a site in southern California. Environ. Sci.
16 Technol. 28: 2120-2132.
17 Westerdahl, D.; Fruin, S.; Sax, T.; Fine, P. M.; Sioutas, C. (2005) Mobile platform
18 measurements of ultrafine particles and associated pollutant concentrations on freeways
19 and residential streets in Los Angeles. Atmos. Environ. 39: 3597-3610.
20 Whitfield, R. G.; Richmond, H. M.; Johnson, T. R. (1997) Overview of ozone human exposure
21 and health risk analyses used in the U.S. EPA's review of the ozone air quality standard.
22 In: Proceedings of the U.S.-Dutch International Symposium on Air Pollution in the 21st
23 Century; April; Noordwijk, The Netherlands. Argonne, IL: Argonne National Laboratory;
24 ANL/DIS/CP-92660. Available: http://www.osti.gov/dublincore/gpo/servlets/purl/9748-
25 Nh3avx/webviewable/9748.pdf (22 July 2003).
26 Wilson, N. K.; Chuang, J. C.; Kuhlman, M. R. (1991) Sampling polycyclic aromatic
27 hydrocarbons and other semivolatile organic compounds in indoor air. Indoor Air 4: 513-
28 521.
29 Wilson, W. E.; Mage, D. T.; Grant, L. D. (2000) Estimating separately personal exposure to
30 ambient and nonambient particulate matter for epidemiology and risk assessment: why
31 and how. J. Air Waste Manage. Assoc. 50: 1167-1183.
32 Wooders, P. J. (1994) The external costs of UK domestic gas use (thesis). London, United
33 Kingdom: London University, Imperial College.
34 World Health Organization (WHO). (2003) Nitrogenated polycyclic aromatic hydrocarbons.
35 Geneva, Switzerland: World Health Organization. (Environmental Health Criteria 229).
36 World Health Organization (WHO). (2004) IPCS risk assessment terminology. Part 2: IPCS
37 glossary of key exposure assessment terminology. Geneva, Switzerland: IPCS
38 Harmonization Project document no. 1. Available:
39 http://www.who.int/ipcs/methods/harmonization/areas/ipcsterminologypartsland2.pdf
40 [19 April, 2007].
August 2007 AX3-193 DRAFT-DO NOT QUOTE OR CITE
-------
1 World Health Organization (WHO). (2005) Principles of characterizing and applying human
2 exposure models. Geneva, Switzerland: World Health Organization. (IPCS
3 harmonization project document; no. 3). Available:
4 http://whqlibdoc.who.int/publications/2005/9241563117_eng.pdf [7 March, 2007].
5 Yamanaka, S. (1984) Decay rates of nitrogen oxides in a typical Japanese living room. Environ.
6 Sci. Technol. 18: 566-570.
7 Yamartino, R. J.; Wiegand, G. (1986) Development and evaluation of simple models for flow,
8 turbulence and pollutant concentration fields within an urban street canyon. Atmos.
9 Environ. 20: 2137-2156.
10 Yanagisawa, Y.; Nishimura, H. (1982) A badge-type personal sampler for measurement of
11 personal exposure to NO2 and NO in ambient air. Environ. Int. 8: 235-242.
12 Yanagisawa, Y.; Nishimura, H.; Matsuki, H.; Osaka, F.; Kasuga, H. (1986) Personal exposure
13 and health effect relationship for NO2 with urinary hydroxyproline to creatinine ratio as
14 indicator. Arch. Environ. Health 41: 41-48.
15 Yang, W.; Lee, K.; Chung, M. (2004) Characterization of indoor air quality using multiple
16 measurements of nitrogen dioxide. Indoor Air 14: 105-111.
17 Zeger, S. L.; Thomas, D.; Dominici, F.; Samet, J. M.; Schwartz, J.; Dockery, D.; Cohen, A.
18 (2000) Exposure measurement error in time-series studies of air pollution: concepts and
19 consequences. Environ. Health Perspect. 108: 419-426.
20 Zipprich, J. L.; Harris, S. A.; Fox, J. C.; Borzelleca, J. F. (2002) An analysis of factors that
21 influence personal exposure to nitrogen oxides in residents of Richmond, Virginia. J.
22 Exposure Anal. Environ. Epidemiol. 12: 273-285.
23 Zota, A.; Adamkiewicz, G.; Levy, J. L; Spengler, J. D. (2005) Ventilation in public housing:
24 implications for indoor nitrogen dioxide concentrations. Indoor Air 15: 393-401.
25
26
27
August 2007 AX3-194 DRAFT-DO NOT QUOTE OR CITE
-------
i AX4. CHAPTER 4 ANNEX - TOXICOLOGICAL
2 EFFECTS OF NITROGEN DIOXIDE AND RELATED
3 OXIDES OF NITROGEN
4
5
6 Effects of Nitrogen Dioxide on Antioxidant and Antioxidant Metabolism
7 Nitrogen dioxide is an oxidant and lipid peroxidation is believed to be a major molecular
8 event responsible for its toxicity. As a result, there has been considerable attention paid to the
9 effect of NO2 on the antioxidant defense system in the epithelial lining fluid and in pulmonary
10 cells. Repeated exposures to NO2 at concentrations ranging from 75 to 62,040 |ig/m3 (0.04 to
11 33 ppm) have revealed effects on low molecular weight antioxidants such as glutathione, vitamin
12 E, and vitamin C, as well as some enzymes involved in cell oxidant homeostasis.
13 A number of studies have investigated the hypothesis, originally proposed by Menzel
14 (1970), that antioxidants might protect the lung from NC>2 damage by inhibiting lipid
15 peroxidation (see Table AX4.1). Changes in the activity of enzymes in the lungs of NO2-
16 exposed animals that regulate levels of glutathione (GSH) have been reported at relatively low
17 exposure concentrations. Sagai et al. (1984) studied the effects of prolonged (9 and 18 months)
18 exposure to 75, 752, and 7520 |ig/m3 (0.04, 0.4, and 4.0 ppm) NC>2 on rats. After either exposure
19 duration, non-protein sulfhydryl levels were increased at 752 |ig/m3 or greater, and exposure to
20 7520 |ig/m3 (4.0 ppm) decreased the activity of GSH peroxidase but increased
21 glucose-6-phosphate dehydrogenase activity. Glutathione peroxidase activity was also decreased
22 in rats exposed to 752 |ig/m3 NC>2 for 18 months. Three GSH S-transferases were also studied,
23 two of which (aryl ^-transferase and aralkyl ^-transferase) exhibited decreased activities after
24 18 months of exposure to 752 |ig/m3 or greater NC>2. No effects were observed on the activities
25 of 6-phosphogluconate dehydrogenase, superoxide dismutase, or disulfide reductase. Effects
26 followed a concentration- and exposure-duration response function. The decreases in
27 glutathione-related enzyme activities were inversely related to the apparent formation of lipid
28 peroxides (see lipid peroxidation subsection). Shorter exposures (4 months) to NC>2 between
29 752 and 7520 |ig/m3 (0.4 and 4.0 ppm) also caused concentration- and duration-dependent
30 effects on antioxidant enzyme activities (Ichinose and Sagai, 1982). For example,
31 glucose-6-phosphate dehydrogenase increased, reaching a peak at 1 month, and then decreased
August 2007 AX4-1 DRAFT-DO NOT QUOTE OR CITE
-------
1 towards the control value. Briefer (2-week) exposures to 752 |ig/m3 (0.4 ppm) NC>2 caused no
2 such effects in rats or guinea pigs (Ichinose and Sagai, 1989).
3 The activities of GSH reductase and glucose-6-phosphate dehydrogenase were
4 significantly increased during exposure tol 1,700 |ig/m3 (6.2 ppm) NC>2 for 4 days; GSH
5 peroxidase activity was not affected (Chow et. al., 1974). The possible role of edema and
6 cellular inflammation in these findings was not examined. Since NC>2 had no significant effect
7 on lung GSH peroxidase activity in this study, but did significantly increase the activities of GSH
8 reductase and glucose-6-phosphate dehydrogenase, the authors concluded that NO2 attacks
9 mainly GSH and NADPH.
10 Newer studies also identified effects on glutathione. Changes in glutathione status in the
11 blood and lung (bronchoalveolar lavage (BAL) fluid) occurred in rats exposed to 9400 |ig/m3
12 (5 ppm) and 18,800 |ig/m3 (10 ppm) NC>2 continuously for 24 h, but not for 7 days (Pagani et al.,
13 1994). Total glutathione - total of reduced (GSH) and oxidized (GSSG) form - was significantly
14 increased in blood but not in BAL fluid; however, GSSG was elevated in BAL fluid only. A
15 decreased GSH/GSSG ratio was observed in the blood and BAL fluid, but not in lung type II
16 cells, in rats continuously exposed to 18,800 |ig/m3 (10 ppm) NC>2 for 3 or 20 days (Hochscheid
17 et al., 2005). Interestingly, lipid peroxidation was decreased in type II cells at 3 days, but was
18 similar to controls at 20 days. Gene expression, as measured by mRNA levels of the enzymes
19 involved in the biosynthesis of glutathione - gamma-glutamylcysteine synthetase (yGCS) and
20 glutathione synthetase (GS), was decreased at both time points, but gamma-
21 glutamyltranspeptidase (yGT) mRNA expression was increased. No GSH peroxidase activity
22 (important for hydroperoxide reduction of complex lipids) was detected at 3 days, and was
23 barely detected at 20 days.
24 Malnutrition of animals can drastically affect their response to toxicants, including NC>2.
25 Experimental interest in this area has mainly focused on dietary lipids, vitamin E and other lipid-
26 soluble antioxidants, and vitamin C and other water-soluble antioxidants. Ayaz and Csallany
27 (1978) exposed vitamin E-deficient and vitamin E-supplemented (30 or 300 mg/kg opf diet)
28 weanling mice continuously for 17 months to 940 or 1880 |ig/m3 (0.5 or 1.0 ppm) NC>2 and
29 assayed blood, lung, and liver tissues for GSH peroxidase activity. Exposure to 1880 |ig/m3
30 (1.0 ppm) NC>2 alone or combined with vitamin E deficiency decreased the enzyme activity in
31 the blood and lungs. Neither vitamin E deficiency nor NC>2 exposure affected liver GSH
August 2007 AX4-2 DRAFT-DO NOT QUOTE OR CITE
-------
1 peroxidase activity. However, in vitamin E-supplemented mice, GSH peroxidase activity
2 increased at 940 |ig/m3 (0.5 ppm) and 1880 |ig/m3 (1.0 ppm) NO2.
3
4 Lipid Metabolism and Content of the Lung
5 Lipid peroxidation is an important mechanism of cell damage arising from changes in
6 cell membrane structure and function. The ability of NO2 exposure to induce lipid peroxidation
7 in the respiratory tract has been well demonstrated in available studies as measured by increased
8 ethane exhalation in the breath, as thiobarbituric acid (TEA) reactive substances in tissues, and
9 as the content of conjugated dienes in tissue homogenates.
10 A number of studies have investigated the effects of NO2 exposure on lipid metabolism
11 and content of the lung. Lipid peroxidation induced by NO2 exposure has been detected at
12 exposure concentrations as low as 75 |ig/m3 (0.04 ppm). Increased ethane exhalation was
13 observed in rats exposed to 75 or 225 |ig/m3 (0.04 or 0.12 ppm) after 9 and 18 months of
14 exposure (Sagai et al., 1984). Exposure to 752 |ig/m3 (0.4 ppm) NO2 for 9 months or longer and
15 to 7520 |ig/m3 (4.0 ppm) for 6 months resulted in increased TEA reactants (Ichinose et al.,
16 1983). NO2 exposures for shorter durations also increased lipid peroxidation in rats. For
17 example, NO2 concentrations of 2256 |ig/m3 (1.2 ppm) or greater for 1 week (Ichinose and
18 Sagai, 1982; Ichinose et al., 1983) increased ethane exhalation in rats, while exposure of
19 pregnant rats to 1000 |ig/m3 or 10,000 |ig/m3 (0.53 or 5.3 ppm) NO2 for 5 h/day for 21 days rats
20 resulted in increases in lung lipid peroxidation products (Balabaeva and Tabakova, 1985). These
21 results indicate at least some degree of duration-dependence in the formation of lipid
22 peroxidation, with lower effect thresholds identified with longer durations of exposure.
23 Lipid peroxidation results in altered phospholipid composition, which in turn may affect
24 membrane fluidity and thus, membrane function. Significant depression of lipid content and
25 total content of saturated fatty acids such as phosphatidyl-ethanolamine, lecithin
26 (phosphatidylcholine), phosphatidylinositol, and phosphatidylserine were found in rats exposed
27 to 5450 |ig/m3 (2.9 ppm) NO2 for 24 h/day, 5 days/week for 9 months (Arner and Rhoades,
28 1973). Exposure of rabbits to 1880 |ig/m3 (1.0 ppm) NO2 for 2 weeks also caused depression of
29 lecithin synthesis after one week of exposure (Seto et al., 1975), while exposure of rats to
30 10,300 |ig/m3 (5.5 ppm) NO2 for 3 h/day for 7 or 14 days elicited only few changes in lipid
31 metabolism (Yokoyama et al., 1980). In beagle dogs, the amount of unsaturated fatty acids in
32 the phospholipids from the lungs was increased after exposure to concentrations ranging from
August 2007 AX4-3 DRAFT-DO NOT QUOTE OR CITE
-------
1 9400 to 30,080 |ig/m3 (5 to 16 ppm), but not to 5640 |ig/m3 (3 ppm) (Dowell et al., 1971).
2 Exposure of either mice or guinea pigs to an NC>2 level of 750 |ig/m3 (0.4 ppm) for a week
3 resulted in a decreased concentration of phosphatidylethanolamine and a relative increase in the
4 phosphatidylcholine concentration (Sagai et al., 1987). Concentration-and exposure duration-
5 dependent increases were reported in phospholipid components in BAL fluid, when rats were
6 exposed to 10 ppm NO2 continuously for 1 day or 3 days (Miiller et. al., 1994).
7 Functional studies conducted on surfactant phospholipid extracts indicated that NC>2
8 exposures of 5 ppm or greater, but not to 0.8 ppm, directly impaired surface tension, although the
9 structure of the surfactant protein A (SP-A) was not altered by NC>2 exposure. Changes in the
10 phospholipid composition of membranes may result in disruption of the cell membrane barrier.
11 Miiller et al. (2003) found that uptake of liposomes by type II lung cells occurred more easily
12 from animals exposed to 10 ppm NC>2 for 3 to 28 days, possibly as a result of increased demand
13 of phosphatidylcholine during lung injury.
14 Lipid peroxidation can also activate phospholipases. Activation of phospholipase Al in
15 cultured endothelial cells occurred atNC>2 concentration of 9400 |ig/m3 (5 ppm) after 40 h of
16 exposure and was speculated to depend on a specific MVinduced increase in phosphatidyl
17 serine in the plasma membranes (Sekharam et al., 1991).
18 One function of phospholipases is the release of arachidonic acid (AA), which serves as a
19 mediator of inflammatory response. NC>2 exposure affects the release and metabolism of
20 arachidonic acid both in vivo and in vitro. The products of arachidonic acid metabolism, such as
21 prostaglandins, prostacyclin, thromboxanes, and leukotrienes play an important role (such as
22 recruitment of neutrophils to sights of local irritation) in modulating inflammatory response.
23 Schlesinger et al. (1990) reported elevated concentrations of thromboxane B2 (1x82) following
24 NC>2 exposures of 1880 |ig/m3 (1.0 ppm) for 2 h, depressed concentrations at 5640 |ig/m3
25 (3.0 ppm), and significant depression 24 h postexposure at 18,880 |ig/m3 (10 ppm) NC>2. The
26 same investigators also reported depressed level of 6-keto-prostaglandin Fla at 1880 |ig/m3
27 (1.0 ppm) NC>2, but exposure to NC>2 did not affect prostaglandins E2 and F2 and leukotriene B4
28 (LTB4) levels.
29 Changes in activation of arachidonate metabolism were also reported in rat alveolar
30 macrophages (AMs) when these animals were exposed to 940 |ig/m3 (0.5 ppm) NC>2 for 0.5, 1, 5,
31 and 10 days (Robison et al., 1993). Unstimulated AM synthesis of LTB4 was depressed after
August 2007 AX4-4 DRAFT-DO NOT QUOTE OR CITE
-------
1 0.5 days and again after 5 days of exposure to NO2. Alveolar macrophage production of TxB2,
2 LTB4, and 5-hydroxyeicosatetraenoic acid (5-HETE) in response to stimulation with the calcium
3 ionophore, A23187, was depressed after 0.5 days of exposure and recovered to air-control values
4 with longer exposure periods. 5-HETE levels were increased after 10 days of exposure.
5 However, AM production of LTB4 in response to zymosan-activated rat serum was depressed
6 only after 5 days of exposure.
7 The effects of NO2 on structural proteins of the lungs have been of concern because
8 elastic recoil is lost after exposure. Collagen synthesis rates are increased in rats exposed to NO2
9 concentrations as low as 9400 |ig/m3 (5.0 ppm) NO2. It has been assumed that increased
10 collagen synthesis reflect increases in total lung collagen which, if sufficient, could result in
11 pulmonary fibrosis after longer periods of exposure. Such correlation has yet to be confirmed by
12 in vivo studies involving NO2 exposure.
13 Alterations in xenobiotic metabolism pathways following NO2 exposure are also
14 summarized in Table AX4.2, in addition to changes in phase I enzymes (such as cytochrome
15 P450s) and phase II enzymes (GST as described earlier). While these changes are not
16 necessarily toxic manifestations of NO2 per se, such changes may impact the metabolism and
17 toxicity of other chemicals. Glycolytic pathways are also apparently affected. For example,
18 glycolytic metabolism was increased by NO2 exposure, apparently due to a concurrent increase
19 in type II cells (Mochitate et al., 1985).
20
21 Emphysema Follow ing Nitrogen Dioxide Exposure
22 Emphysema as a result of chronic exposure to NO2 has been reported in animal studies.
23 The definition of emphysema has changed since the time that some of the studies have been
24 published; thus, it is important to compare the findings of the studies with the current definition
25 of emphysema. U.S. Environmental Protection Agency (1993) evaluated the animal studies
26 reporting emphysema from chronic exposure to NO2 based upon the most recent definition of
27 emphysema from the report of the National Heart, Lung and Blood Institute (NHLBI), Division
28 of Lung Diseases Workshop (Snider et al., 1985); see U.S. Environmental Protection Agency
29 (1993) for the definitions of emphysema. Because the focus of this document is extrapolation of
30 NO2 exposures to potential hazards for humans, only those studies showing emphysema of the
31 type seen in human lungs will be discussed.
32
August 2007 AX4-5 DRAFT-DO NOT QUOTE OR CITE
-------
1 Emphysema was reported by Hay don et al. (1967) in rabbits exposed continuously
2 (presumably 24 h/day) for 3 to 4 months to 15,000 or 22,600 |ig/m3 (8.0 or 12.0 ppm) NO2. The
3 investigators reported enlarged lungs that failed to collapse when the thorax was opened. When
4 the lungs were fixed in an expanded state via the trachea using formaldehyde, there was evidence
5 of enlarged airspaces with destructive changes in alveolar walls. Although no stereology was
6 performed, the changes observed appear to be emphysema of the type seen in human lungs.
7 WHO (1997) has also reported a study by Freeman et al. (1972) in which rats were
8 exposed to 37,600 |ig/m3 (20.0 ppm) NO2, which was reduced during the exposure to
9 28,200 |ig/m3 (15.0 ppm) or to 18,800 |ig/m3 (10.0 ppm), for varying periods up to 33 months.
10 The lungs were fixed via the trachea, and morphometric analysis of the lung and alveolar size
11 indicated an enlargement of alveolar, reduction in alveolar surface, and alveolar destruction.
12 Although the investigators concluded that their study demonstrated emphysema in their NO2-
13 exposed rats, WHO (1997) noted that it was not entirely clear whether the experimental groups
14 or only the group exposed to 28,200 |ig/m3 (15.0 ppm) had emphysema.
15 Although many of the papers reviewed (U.S. Environmental Protection Agency, 1993)
16 reported finding emphysema, some of these studies were reported according to previous,
17 different criteria; some reports did not fully describe the methods used; and/or the results
18 obtained were not in sufficient detail to allow independent confirmation of the presence of
19 emphysema. For example, Hyde et al. (1978) reported no emphysema in beagle dogs exposed
20 16 h daily for 68 months to 1200 |ig/m3 (0.64 ppm) NO2 with 310 |ig/m3 (0.25 ppm) NO or to
21 263 |ig/m3 (0.14 ppm) NO2 with 2050 |ig/m3 (1.67 ppm) NO. The dogs then breathed clean air
22 during a 32- to 36-month post-exposure period. The right lungs were fixed via the trachea at
23 30-cm fixative pressure in a distended state. Semiautomated image analysis was used for
24 morphometry of air spaces. The dogs exposed to 1200 |ig/m3 NO2 with 310 |ig/m3 NO had
25 significantly larger lungs with enlarged air spaces and evidence of destruction of alveolar walls.
26 These effects were not observed in dogs exposed to 270 |ig/m3 NO2 with 2050 |ig/m3 NO,
27 implying a significant role of the NO2 in the production of the lesions. The lesions in the dogs
28 exposed to the higher NO2 concentration meet the criteria of the 1985 NHLBI workshop for
29 emphysema of the type seen in human lungs.
30
August 2007 AX4-6 DRAFT-DO NOT QUOTE OR CITE
-------
1 Nitrates (NO3 )
2 Busch et al. (1986) exposed rats and guinea pigs with either normal lungs or elastase-
3 induced emphysema to ammonium nitrate aerosols at 1 mg/m3 for 6 h/day, 5 days/week for
4 4 weeks. Using light and electron microscopy, the investigators concluded that there were no
5 significant effects of exposure on lung structure.
6
7
8 AX4.2 DOSIMETRY OF INHALED NITROGEN OXIDES
9 This section provides an overview of NC>2 dosimetry and updates information provided in
10 the 1993 AQCD for Oxides of Nitrogen. Dosimetry of NC>2 refers to the measurement or
11 estimation of the amount of NC>2 or its reaction products reaching and persisting at specific sites
12 in the respiratory tract following an exposure. Nitrogen dioxide, classified as a reactive gas,
13 interacts with surfactants, antioxidants, and other compounds in the epithelial lining fluid (ELF).
14 The compounds thought responsible for adverse pulmonary effects of inhaled NC>2 are the
15 reaction products themselves or the metabolites of these products in the ELF. At the time of the
16 1993 AQCD for Oxides of Nitrogen, it was thought that inhaled NO2 probably reacted with the
17 water molecules in the ELF to form nitrous acid (HNO2) and nitric acid (HNOs). However,
18 some limited data suggested that the absorption of NO2 was linked to reactive substrates in the
19 ELF and subsequent nitrite production. Since then, the reactive absorption of NO2 has been
20 examined in a number of studies (see Section 4.2.2). These studies have characterized the
21 absorption kinetics and reactive substrates for NO2 delivered to various sites in the respiratory
22 tract. Researchers have attempted to obtain a greater understanding of how these complex
23 interactions affect NO2 absorption and NO2-induced injury.
24 With respect to quantifying absolute NO2 absorption, the following were reported in the
25 1993 AQCD for Oxides of Nitrogen. The principles of Os uptake were generally assumed
26 applicable for NO2 modeling studies. The results indicated that NO2 is absorbed throughout the
27 lower respiratory tract, but the major delivery site is the centriacinar region, i.e., the junction
28 between the conducting and respiratory airways in humans and animals. Experimental studies
29 have found that the total respiratory tract uptake in humans ranges from 72 to 92% depending on
30 the study and the breathing conditions. The percent total uptake increases with increasing
31 exercise level. In laboratory animals, upper respiratory tract uptakes ranged from as low as 25%
32 to as high as 94% depending on the study, species, air flow rate, and mode of breathing (nasal or
August 2007 AX4-7 DRAFT-DO NOT QUOTE OR CITE
-------
1 oral). Upper respiratory tract uptake of NO2 was found to decrease with increasing ventilation.
2 Uptake during nasal breathing was determined to be significantly greater than during oral
3 breathing.
4
5 AX4.2.1 Mechanisms of NOi Absorption
6 The ELF is the initial barrier against NO2 delivery to the underlying epithelial cells.
7 Postlethwait and Bidani (1990) suggested that acute NO2 uptake in the lower respiratory tract
8 was rate limited by chemical reactions of NO2 with ELF constituents rather than by gas solubility
9 in the ELF. Subsequently, Postlethwait et al. (1991) reported that inhaled NO2 (10 ppm) does
10 not penetrate the ELF to reach underlying sites and suggested that cytotoxicity may be due to
11 NO2 reactants formed in the ELF. Since then, the reactive absorption of NO2 has been examined
12 in a number studies that have sought to identify reactive substrates for NO2 and quantify the
13 absorption kinetics of NO2 in the respiratory tract.
14 Postlethwait and Bidani (1994) concluded that the reaction between NO2 and water does not
15 significantly contribute to the absorption of inhaled NO2. Uptake is a first-order process for NO2
16 concentrations less than 10 ppm, is aqueous substrate-dependent, and is saturable. The
17 absorption of inhaled NO2 is thought to be coupled with free radical-mediated hydrogen
18 abstraction to form FDSTO2 and an organic radical (Postlethwait and Bidani, 1989, 1994). At
19 physiologic pH, the HNO2 subsequently dissociates to H+ and nitrite (NO2 ). The concentration
20 of the resulting nitrite is thought insufficient to be toxic, so effects are thought to be due to the
21 organic radical and/or the proton load. Nitrite may enter the underlying epithelial cells and
22 blood. In the presence of red blood cells, nitrite is oxidized to nitrate (NOs ) (Postlethwait and
23 Mustafa, 1981). Beyond cell susceptibility and the concentration of NO2 in the lumen, site-
24 specific injury was proposed to depend on rate of 'toxic' reaction product formation and the
25 quenching of these products within the ELF. Related to the balance between reaction product
26 formation and removal, it was further suggested that cellular responses may be nonlinear with
27 greater responses being possible at low levels of NO2 uptake versus higher levels of uptake.
28 Since the ELF may vary throughout the respiratory tract, the uptake of inhaled NO2 and reaction
29 with constituents of the pulmonary ELF may be related to the heterogeneous distribution of
30 epithelial injury observed from NO2 exposure.
31
August 2007 AX4-8 DRAFT-DO NOT QUOTE OR CITE
-------
1 Postlethwait et al. (1995) sought to determine the absorption substrates for NO2 in the
2 ELF lavaged from male Sprague-Dawley rats. Since the bronchoalveolar lavage fluid (BALF)
3 collected from the rats may be diluted up to 100-fold relative to the native ELF, the effect of
4 concentrating the BAL fluid on NC>2 absorption was investigated. A linear association was
5 found between the first-order rate constant for NO2 absorption and the concentration of the
6 BALF. This suggests that concentration of the reactive substrates in the ELF determines the rate
7 of NC>2 absorption. The absorption due to specific ELF constituents was also examined in
8 chemically pure solutions. Albumin, cysteine, reduced glutathione (GSH), ascorbic acid, and
9 uric acid were hydrophilic moieties found to be active substrates for NC>2 absorption.
10 Unsaturated fatty acids (such as oleic, linoleic, and linolenic) were also identified as active
11 absorption substrates and thought to account for up to 20% of NO2 absorption. Vitamins A and
12 E exhibited the greatest reactivity of the substrates that were examined. However, the low
13 concentrations of uric acid and vitamins A and E were thought to preclude them from being
14 appreciable substrates in vivo. The authors concluded that ascorbate and GSH were the primary
15 NC>2 absorption substrates in rat ELF. Postlethwait et al. (1995) also found that the pulmonary
16 surfactant, dipalmitoyl phosphatidylcholine, was not an effective substrate for NC>2 absorption.
17 Later, Connor et al. (2001) suggested that dipalmitoyl phosphatidylcholine may actually inhibit
18 NO2 absorption.
19 In a subsequent study, Velsor and Postlethwait (1997) investigated the mechanisms of
20 acute epithelial injury from NO2 exposure. The impetus for this work was to evaluate the
21 supposition that NC>2 reaction products rather than NC>2 itself cause epithelial injury. Red blood
22 cell membranes were immobilized to the bottom of Petri dishes, covered with a variety of well
23 characterized aqueous layers, and exposed to gaseous NC>2 (10 ppm for 20 min). The study
24 focused on the potential roles of GSH and ascorbic acid reaction products in mediating cellular
25 injury. Based on negligible membrane oxidation when covered with only an aqueous phosphate
26 buffer, the diffusive/reactive resistance of a thin aqueous layer clearly prevented direct
27 interaction between NC>2 and the underlying membrane. The presence of unsaturated fatty acids
28 was not observed to affect NO2 absorption, but a sufficiently thin liquid layer was required for
29 membrane oxidation to occur. Interestingly, membrane oxidation was not a simple monotonic
30 function of GSH and ascorbic acid levels. The maximal levels of membrane oxidation were
31 observed at low antioxidant levels versus null or high antioxidant levels. Glutathione and
August 2007 AX4-9 DRAFT-DO NOT QUOTE OR CITE
-------
1 ascorbic acid related membrane oxidation were superoxide and hydrogen peroxide dependent,
2 respectively. The authors suggested that at the higher antioxidant concentrations, there was
3 increased absorption of NO2, but little secondary oxidation of the membrane because the reactive
4 species (e.g., superoxide and hydrogen peroxide) generated during absorption were quenched.
5 At the low antioxidant concentrations, there was a lower rate of NO2 absorption, but oxidants
6 were not quenched and so were available to interact with the cell membrane.
7 Kelly et al. (1996a) examined the effect of a 4-h NO2 (2 ppm) exposure on antioxidant
8 levels in bronchial lavage fluid (BLF) and BALF of 44 healthy nonsmoking adults (19-45 year,
9 median 24 years). Subjects were randomly assigned to three groups and lavaged at either 1.5 h
10 (n = 15), 6 h (n = 15), or 24 h (n = 14) after the NO2 exposure. The baseline concentrations of
11 uric acid and ascorbic acid were strongly correlated between the BLF and BALF within
12 individuals (r = 0.88, p < 0.001; r = 0.78, p = 0.001; respectively), whereas the concentrations of
13 GSH in the BLF and BALF were not correlated. Uric acid levels in both lavage fractions were
14 significantly reduced at 1.5 h (p < 0.04), significantly increased at 6 h (p < 0.05), and back to
15 baseline at 24 h postexposure. A statistically significant loss of ascorbic acid was also found in
16 both lavage fractions at 1.5 h (p < 0.05). At 6 and 24 h postexposure, the ascorbic acid levels
17 had returned to baseline. In contrast, GSH levels were significantly increased at both 1.5 h
18 (p < 0.01) and 6 h (p < 0.03) in BLF. At 24 h postexposure, the GSH levels in BLF returned to
19 baseline. Although GSH in BLF increased at 1.5 and 6 h postexposure, oxidized GSH levels
20 remained similar to baseline in both BLF and BALF. No changes in BALF levels of GSH were
21 observed at any time point.
22 The depletion of uric acid and ascorbic acid, but not GSH has also been observed with
23 ex vivo exposure of human BALF to NO2. Kelly et al. (1996b) collected BALF from male lung
24 cancer patients (n = 16) and exposed the BALF ex vivo at 37°C to NO2 (0.05 to 2.0 ppm; 4 h) or
25 O3 (0.05 to 1.0 ppm; 4 h). Kelly and Tetley (1997) also collected BALF from lung cancer
26 patients (n = 12, 54 + 16 years) and exposed the BALF ex vivo to NO2 (0.05 to 1.0 ppm; 4 h).
27 Both studies found that NO2 depletes uric acid and ascorbic acid, but not GSH from BALF.
28 Kelly et al. (1996b) noted a differential consumption of the antioxidants with uric acid loss being
29 greater than that of ascorbic acid which was lost at a much greater rate than GSH. Kelly and
30 Tetley (1997) found that the rates of uric acid and ascorbic acid consumption were correlated
31 with their initial concentrations in the BAL fluid, such that higher initial antioxidant
August 2007 AX4-10 DRAFT-DO NOT QUOTE OR CITE
-------
1 concentrations were associated with a greater rate of antioxidant depletion. Illustrating the
2 complex interaction of antioxidants, these studies also suggest that GSH oxidized by NO2 may
3 be again reduced by uric acid and/or ascorbic acid.
4
5 AX4.2.3 Regional and Total Respiratory Absorption of NOi
6 There has been very limited work related to the quantification of NC>2 uptake since the
7 1993 AQCD for Oxides of Nitrogen. As a result, there is an abbreviated discussion here of some
8 papers that were reviewed in the 1993 AQCD for Oxides of Nitrogen.
9
10 AX4.2.3.1 Dosimetry Models
11 There is a paucity of theoretical studies investigating NO2 dosimetry. Like Os, NO2 is
12 highly reactive in ELF and is not very soluble. An Os model has been utilized to predict the
13 uptake of NO2 in the lower respiratory tract of humans, rats, guinea pigs, and rabbits (Miller
14 et al., 1982; Overton, 1984). In this model, there was a strong distinction between uptake and
15 dose. Uptake referred to the amount of NO2 being removed from gas phase per lung surface area
16 (jig/cm2), whereas, dose referred to the amount of NO2 per lung surface area (jig/cm2) that
17 diffused through the ELF and reached the underlying tissues.
18 Miller et al. (1982) and subsequently Overton (1984) did not attempt to predict the
19 amount of reactants in the ELF or the transport of reactants to the tissues. Rather, they focused
20 mainly on the sensitivity of NO2 tissue dose on NO2 reaction rates in the ELF and the Henry's
21 law constant. Reaction rates of NO2 in the ELF were varied from zero, 50%, and 100% of the
22 reaction rate for Os in ELF. The Henry's law constant was varied from half to double the
23 Henry's law constant for NO2 in water at 37 °C. Effects of species, lung morphology, and tidal
24 volume (Vx) were also examined. In general, the model predicted that NO2 is taken up
25 throughout the lower respiratory tract. In humans, NO2 uptake was fairly constant from the
26 trachea to the first generation of respiratory bronchioles, beyond which uptake decreased with
27 distal progression. The NO2 tissue dose was highly dependent on the Henry's law constant and
28 reaction rate in the ELF. In the conducting airways, the NO2 tissue dose decreased as the
29 Henry's law constant increased (i.e., decreased gas solubility), whereas the NO2 tissue dose in
30 the alveolar region increased. The site of maximal NO2 tissue dose was fairly similar between
31 species, ranging from the first generation of respiratory bronchioles in humans to the alveolar
32 ducts in rats. In guinea pigs and rabbits, the maximal NO2 tissue dose was predicted to occur in
August 2007 AX4-11 DRAFT-DO NOT QUOTE OR CITE
-------
1 the last generation of respiratory bronchioles. The simulations showed that exercise increases
2 the NO2 tissue dose in the pulmonary region relative to rest. Miller et al. (1982) also reported
3 that increasing the NO2 reaction rate decreased NO2 tissue dose in the conducting airways, but
4 had no effect on the dose delivered to the pulmonary region.
5 Simultaneously occurring diffusion and chemical reactions in the ELF have been
6 suggested as the limiting factors in Os (Santiago et al., 2001) and NO2 uptake (Postlethwait and
7 Bidani, 1990). Hence, Miller et al. (1982) should have found an increase in the uptake of NO2 in
8 the conducting airways with increasing the rate of chemical reactions in the ELF. This increase
9 in NO2 uptake in the conducting airways would then lead to a reduction in the amount of NO2
10 reaching and taken up in the pulmonary region. The Miller et al. (1982) model considered
11 reactions of NO2 with constituents in the ELF as protective in that these reactions reduced the
12 flux of NO2 to the tissues. Others have postulated that NO2-reactants formed in the ELF, rather
13 than NO2 itself, could actually cause adverse responses (Overton, 1984; Postlethwait and Bidani,
14 1994; Velsor and Postlethwait, 1997).
15 More recently, Overton and Graham (1995) examined NO2 uptake in an asymmetric
16 anatomic model of the rat lung. The multiple path model of Overton and Graham (1995)
17 allowed for variable path lengths from the trachea to the terminal bronchioles, whereas Miller
18 et al. (1982) used a single or typical path model of the conducting airways. The terms dose and
19 uptake were used synonymously to describe the amount of NO2 gas lost from the gas phase in a
20 particular lung region or generation by Overton and Graham (1995). Reactions of NO2 in the
21 ELF were not explicitly considered. Their simulations were conducted for rats breathing at
22 2 mL VT at a frequency of 150 breaths per minute. The mass transfer coefficients of 0.173,
23 0.026, and 0.137 cm/sec were assumed for the upper respiratory tract, the tracheobronchial
24 airways, and the pulmonary region, respectively. Uptake was predicted to decrease with distal
25 progression into the lung. In general, the modeled NO2 dose varied among anatomically
26 equivalent ventilatory units as a function of path length from the trachea with shorter paths
27 showing greater dose. A sudden increase in NO2 uptake was predicted in the proximal alveolar
28 region (PAR) which was due to the increase in the assumed mass transfer coefficient relative to
29 the adjacent terminal bronchiole. Overton et al. (1996) showed that increasing the mass transfer
30 coefficient of the tracheobronchial airways would decrease the dose to the PAR and vice versa.
31 Additionally, the PAR dose would also be reduced by the more realistic modeling of
August 2007 AX4-12 DRAFT-DO NOT QUOTE OR CITE
-------
1 tracheobronchial airways expansion during inspiration versus the static condition employed by
2 Overton and Graham (1995).
3 In summary, these modeling studies predict that the net NO2 dose (NO2 flux to air-liquid
4 interface) gradually decreases distally from the trachea to the terminal bronchioles and then
5 rapidly decreases in the pulmonary region. However, the tissue dose of NO2 (NO2 flux to liquid-
6 tissue interface) is low in the trachea, increases to a maximum in the terminal bronchioles and
7 the first generation of the pulmonary region, and then decreases rapidly with distal progression.
8 The production of toxic NO2-reactants in the ELF and the movement of the reactants to the
9 tissues as not been modeled.
10
11 Experimental Studies of NOi Uptake
12
13 Upper Respiratory Tract A bsorption
14 The nasal uptake of NO2 has been experimentally measured in dogs, rabbits, and rats
15 under conditions of unidirectional flow. Yokoyama (1968) reported 42.1 + 14.9%
16 (Mean + StDev) uptake of NO2 (4 to 41 ppm) in the isolated nasal passages of two dogs
17 (3.5 L/min) and three rabbits (0.75 L/min) exposed to 7520 to 77,100 |ig/m3 (4 and 41 ppm)
18 NO2. Uptake did not appear to depend on the exposure concentration and was relatively constant
19 over a 10 to 15 min period. Cavanagh and Morris (1987) measured uptakes of 28% and 25%
20 uptake of NO2 (76,000 |ig/m3; 40.4 ppm) in the noses of four naive and four previously exposed
21 rats (0.10 L/min), respectively. Uptake was not affected by a 4-h prior exposure (naive versus
22 previously exposed rats) to 76,000 |ig/m3 (40.4) ppm NO2 and was constant over the 24-min
23 period during which uptake was determined.
24 Kleinman and Mautz (1991) measured the penetration of NO2 through the upper airways
25 during inhalation in six tracheotomized dogs exposed to 1880 or 9400 |ig/m3 (1.0 or 5.0 ppm)
26 NO2. Uptake in the nasal passages was significantly greater at 1880 |ig/m3 (1.0 ppm) than at
27 9400 |ig/m3 (5.0 ppm), although the magnitude of this difference was not reported. The mean
28 uptake of NO2 (1880 |ig/m3; 1.0 ppm) in the nasal passages decreased from 55% to 40% as the
29 ventilation rate increased from about 2 to 8 L/min. During oral breathing, uptake was not
30 dependent on concentration. The mean oral uptake of NO2 (1880 and 9400 |ig/m3; 1.0 and
31 5.0 ppm) decreased from 65% to 30% as the ventilation rate increased from 2 to 8 L/min.
32
August 2007 AX4-13 DRAFT-DO NOT QUOTE OR CITE
-------
1 Lower Respiratory Tract Absorption
2 Postlethwait and Mustafa (1989) investigated the effect of exposure concentration and
3 breathing frequency on the uptake of NC>2 in isolated perfused rat lungs. To evaluate the effect
4 of exposure concentration, the lungs were exposed to NO2 (7520 to 37,600 |ig/m3; 4 to 20 ppm)
5 while ventilated at 50 breaths/min with a VT of 2.0 mL. To examine the effect of breathing
6 frequency, the lungs were exposed to NC>2 (94,000 |ig/m3; 5 ppm) while ventilated at 30-90
7 breaths/min with a VT of 1.5 mL. All exposures were for 90 min. The uptake of NC>2 ranged
8 from 59 to 72% with an average of 65% and was not affected by exposure concentration or
9 breathing frequency. A combined regression showed a linear relationship between NC>2 uptake
10 and total inspired dose (25 to 330 jig NO2). Illustrating variability in NO2 uptake measurements,
11 Postlethwait and Mustafa (1989) observed 59% NC>2 uptake in lungs ventilated at 30 breaths/min
12 with a VT of 1.5 mL, whereas, Postlethwait and Mustafa (1981) measured 35% NC>2 uptake for
13 the same breathing condition. In another study, 73% uptake of NC>2 was reported for rat lungs
14 ventilated 50 breaths/min with a VT of 2.3 mL (Postlethwait et al., 1992). It should be noted that
15 typical breathing frequencies are around 80, 100, and 160 breaths/min for rats during sleep, rest,
16 and light exercise, respectively (Winter-Sorkina and Cassee, 2002). Hence, the breathing
17 frequencies at which NC>2 uptake has been measured are lower than for rats breathing normally.
18 In addition to measuring upper respiratory tract uptakes, Kleinman and Mautz (1991) also
19 measured NO2 uptake in the dog lung. In general, there was about 90% NO2 uptake in the lungs
20 which was independent of ventilation rates from 3 to 16 L/min.
21
22 Total Respiratory Tract Absorption
23 Bauer et al. (1986) measured the uptake of NO2 (560 |ig/m3; 0.3 ppm) in 15 adult
24 asthmatics exposed for 30 min (20 min at rest, then 10 min exercising on a bicycle ergometer)
25 via a mouthpiece during rest and exercise. There was a statistically significant increase in uptake
26 from 72% during rest to 87% during exercise. The minute ventilation also increased from
27 8.1 L/min during rest to 30.4 L/min during exercise. Hence, exercise increased the dose rate of
28 NC>2 by 5-fold in these subjects. In an earlier study of seven healthy adults in which subjects
29 were exposed to a nitric oxide (NO)/NO2 mixture containing 550 to 13500 |ig/m3 (0.29 to
30 7.2 ppm) NC>2 for brief (but unspecified) periods, Wagner (1970) reported that NC>2 uptake
31 increased from 80% during normal respiration (VT, 0.4 L) to 90% during maximal respiration
32 (VT, 2 to 4 L).
August 2007 AX4-14 DRAFT-DO NOT QUOTE OR CITE
-------
1 Kleinman and Mautz (1991) also measured the total respiratory tract uptake of NO2
2 (9400 |ig/m3; 5 ppm) in female beagle dogs while standing at rest or exercising on a treadmill.
3 The dogs breathed through a small face mask. Total respiratory tract uptake of NO2 was 78%
4 during rest and increased to 94% during exercise. In large part, this increase in uptake may be
5 due to the increase in VT from 0.18 L during rest to 0.27 L during exercise. Coupled with an
6 increase in minute ventilation from 3.8 L/min during rest to 10.5 L/min during exercise, the dose
7 rate of NO2 was 3-fold greater for the dogs during exercise than rest.
8
9 Distribution and Elimination of NO 2 Products
10 As stated earlier, NO2 absorption is coupled with nitrous acid (HNO2) formation, which
11 subsequently dissociates to H+ and nitrite (NO2 ). Nitrite enters the underlying epithelial cells
12 and subsequently the blood. In the presence of red blood cells and possibly involving
13 oxyhemoglobin, nitrite is oxidized to nitrate (NOs) (Postlethwait and Mustafa, 1981). Nitrate
14 may subsequently be excreted in the urine. There has been concern that inhaled NO2 may lead to
15 N-nitrosamine production, many of which are carcinogenic, since NO2 can produce nitrite and
16 nitrate (in blood). Nitrate can be converted to nitrite by bacterial reduction in saliva, the
17 gastrointestinal tract, and the urinary bladder. Nitrite has been found to react with secondary
18 amines to form N-nitrosamines. This remains speculative since nitrosamines are not detected in
19 tissues of animals exposed by inhalation to NO2 unless precursors to nitrosamines and/or
20 inhibitors of nitrosamine metabolism are co-administered. Rubenchik et al. (1995) could not
21 detect N-nitrosodimethylamine (NDMA) in tissues of mice exposed to 7.5 to 8.5 mg/m3 NO2 for
22 1 h. NDMA was found in tissues, however, if mice were simultaneously given oral doses of
23 amidopyrine and 4-methylpyrazole, an inhibitor of NDMA metabolism. Nevertheless, the main
24 source of NO2 in the body is formed endogenously, and food is also a contributing source of
25 nitrite from the conversion of nitrates. Thus, the relative importance of inhaled NO2 to N-
26 nitrosamine formation has yet to be demonstrated.
27 Metabolism of inhaled NO2 may also transform other chemicals that may be present in
28 the body, in some cases into mutagens and carcinogens. Van Stee et al. (1995) exposed mice to
29 approximately 37,600 |ig/m3 (20 ppm) 15NO2 and to 1 g/kg morpholine simultaneously.
30 N-nitrosomorpholine (NMOR), a nitrosamine that is a potent animal carcinogen, was found in
31 the body of the exposed mice. Ninety-eight point four percent was labeled with 15N that was
August 2007 AX4-15 DRAFT-DO NOT QUOTE OR CITE
-------
1 derived from the inhaled 15NC>2 and 1.6% was derived presumably from endogenous sources.
2 Miyanishi et al. (1996) co-exposed rats, mice, guinea pigs and hamsters to NC>2 and various
3 polycyclic aromatic hydrocarbons (PAHs) such as pyrene, fluorene, or anthracene. Nitro
4 derivatives of these PAHs were excreted in the urine of co-exposed animals, which were found
5 to be highly mutagenic in the Ames/S. typhimurium assay. Specifically, the nitrated metabolite
6 of pyrene (l-nitro-6/8-hydroxypyrene and l-nitro-3hydroxypyrene) was detected in the urine.
7 Further studies indicated that these metabolites are nitrated by an ionic reaction in vivo after the
8 hydroxylation of pyrene in the liver.
9
10 Extra-Pulmonary Effects of NO2 and NO
11 Exposure to NC>2 produces a wide array of health effects beyond the confines of the lung.
12 Thus, NO2 and/or some of its reactive products penetrate the lung or nasal epithelial and
13 endothelial layers to enter the blood and produce alteration in blood and various other organs.
14 Effects on the systemic immune system were discussed above and the summary of other
15 systemic effects is quite brief because the database suggests that effects on the respiratory tract
16 and immune response are of greatest concern. A more detailed discussion of extrapulmonary
17 responses can be found in U.S. Environmental Protection Agency (1993).
18
19 Body Weight, Hepatic, and Renal Effects
20 Conflicting results have been reported on whether NC>2 affects body weight gain in
21 experimental animals as a general indicator of toxicity (U.S. Environmental Protection Agency,
22 1993). Newer subchronic studies show no significant effects on body weight in rats, guinea
23 pigs, and rabbits exposed up to 7526 |ig/m3 (4 ppm) NC>2 (Tepper et al., 1993; Douglas et al.,
24 1994; Fujimaki andNohara, 1994).
25 Effects on the liver, such as changes in serum chemistry and xenobiotic metabolism, have
26 been reported by various investigators to result from exposure to NC>2 (U.S. Environmental
27 Protection Agency 1993). Drozdz et al. (1976) found decreased total liver protein and sialic
28 acid, but increased protein-bound hexoses in guinea pigs exposed to 2000 |ig/m3 (1.05 ppm)
29 NC>2, 8 h/day for 180 days. Liver alanine and aspartate aminotransferase activity was increased
30 in the mitochondrial fraction but decreased in the cytoplasmic fraction of the liver. Electron
31 micrographs of the liver showed intracellular edema and inflammatory and parenchymal
32 degenerative changes.
August 2007 AX4-16 DRAFT-DO NOT QUOTE OR CITE
-------
1 No new studies on liver effects were located in the literature since the 1993 AQCD for
2 Oxides of Nitrogen. Several older studies have shown changes in kidney function and
3 xenobiotic metabolism in animals following NO2, although no histopathological changes were
4 reported.
5 Increases in urinary protein and specific gravity of the urine were reported by Sherwin
6 and Layfield (1974) in guinea pigs exposed continuously to 940 |ig/m3 (0.5 ppm) NO2 for
7 14 days. Proteinuria (albumin and alpha-, beta-, and gamma-globulins) was found in another
8 group of animals when the exposure was reduced to 752 |ig/m3 (0.4 ppm) NO2 for 4 h/day.
9 However, differences in water consumption or in the histology of the kidney were not found.
10 No new studies were located in the literature since the 1993 AQCD for Oxides of Nitrogen.
11
12 Brain Effects
13 There are several studies suggesting that NO2 affects the brain. Decreased activity of
14 protein metabolizing enzymes, increased glycolytic enzymes, changes in neurotransmitter levels
15 (5-HT and noradrenaline), and increased lipid peroxidation, accompanied by lipid profile and
16 antioxidant changes, have been reported (Farahani and Hasan, 1990, 1991, 1992; Sherwin et al.,
17 1986; Drozdz et al., 1975). The U.S. Environmental Protection Agency (1993) concluded that
18 "none of these effects have been replicated and all reports lack sufficient methodological rigor;
19 thus, the implications of these findings, albeit important, are not clear and require further
20 investigation".
21 A developmental neurotoxicity study by Tabacova et al.(1985) suggest that in utero
22 exposure to NO2 may result in postnatal neurobehavioral development changes as described in
23 the section on reproductive and developmental toxicology.
24 Van Stee et al. (1983) reported NMOR production in mice gavaged with 1 g of
25 morpholine/kg body weight per day and then exposed (5-6 h daily for 5 days) to 31,000 to
26 38,500 |ig/m3 (16.5 to 20.5 ppm) NO2. The single site containing the greatest amount of NMOR
27 was the gastrointestinal tract. In a later experiment, 98.4% of the NMOR found in the body of
28 mice exposed to -20 ppm (i.e., -37 600 mg/m3) 15NO2 and to 1 g/kg morpholine was labeled
29 with 15N that was derived from the 15NO2 to which the animals had been exposed by inhalation,
30 and 1.6% was derived from 14NO2 from presumably endogenous sources (Van Stee et. al., 1995).
31 Inhaled NO2 may also be involved in the production of mutagenic (and carcinogenic)
32 nitro derivatives of other co-exposed compounds, such as polycyclic aromatic hydrocarbons
August 2007 AX4-17 DRAFT-DO NOT QUOTE OR CITE
-------
1 (PAHs), via nitration reactions. Miyanishi et al. (1996) co-exposed rats, mice, guinea pigs and
2 hamsters to 37,600 |ig/m3 (20 ppm) NO2 and various PAHs (pyrene, fluoranthene, fluorene,
3 anthracene, or chrysene). Nitro derivatives of these PAHs were excreted in the urine of these
4 animals, which were found to be highly mutagenic in the AmesA?. typhimurium assay.
5 Specifically, the nitrated metabolite of pyrene (l-nitro-6/8-hydroxypyrene and 1-nitro-
6 3hydroxypyrene) was detected in the urine. Further studies indicated that these metabolites are
7 nitrated by an ionic reaction in vivo after the hydroxylation of pyrene in the liver.
8
9 NO
10 The genotoxicity of NO has been studied both in vitro and in vivo (Arroyo et al., 1992;
11 Nguyen et al., 1992) (see Table AX4.8). Overall, the synthesis of these older studies suggests
12 that NO has some genotoxic potential; however, the effect is slight and to a lesser extent when
13 compared to NO2.
14
15 Effects of Mixtures Containing NO 2
16 Humans are generally exposed to NO2 in a mixture with other air pollutants. A limitation
17 of animal toxicity studies is the extrapolation of dose-response data from controlled exposures to
18 NO2 alone to air pollutant mixtures that are typically found in the environment. It is difficult to
19 predict the effects of NO2 in a mixture based on the effects of NO2 alone. In order to understand
20 how NO2 is affected by mixtures of other air pollutants, studies are typically conducted with
21 mixtures containing NO2 and one or two other air pollutants, such as Os and/or H2SO/t. The
22 result of exposure to two or more pollutants may be simply the sum of the responses to
23 individual pollutants (additivity), may be greater than the sum of the individual responses,
24 suggesting some type of interaction or augmentation of the response (synergism) or may be less
25 than additive (antagonism).
26 Animal toxicity studies have shown an array of interactions, including no interaction,
27 additivity or synergism. Because no clear understanding of NO2 interactions has yet emerged
28 from this database, only a brief overview is provided here. A more substantive review can be
29 found in U.S. Environmental Protection Agency (1993). There are animal studies, which have
30 studied the effects of ambient air mixtures containing NO2 or gasoline or diesel combustion
31 exhausts containing NOX. Generally these studies provide useful information on the mixtures,
32 but lack NO2-only groups, making it impossible to discern the influence of NO2. Therefore, this
August 2007 AX4-18 DRAFT-DO NOT QUOTE OR CITE
-------
1 class of research is not described here, but is reviewed elsewhere (U.S. Environmental Protection
2 Agency, 1993).
3
4 Simple Mixtures Containing NO2
5 Most of the interaction studies have involved NO2 and O3. After subchronic exposure,
6 lung morphology studies did not show any interaction of NO2 with Os (Freeman et al., 1974) or
7 with SO2 (Azouley et al., 1980). Some biochemical responses to NO2 plus Os display no
8 positive interaction or synergism. For example, Mustafa et al. (1984) found synergism for some
9 endpoints (e.g., increased activities of O2 consumption and antioxidant enzymes), but no
10 interaction for others (e.g., DNA or protein content) in rats exposed for 7 days. Ichinose and
11 Sagai (1989) observed a species dependence in regard to the interaction of 63 (752 |ig/m3,
12 0.4 ppm) and NO2 (752 |ig/m3, 0.4 ppm) after 2 weeks of exposure. Guinea pigs, but not rats,
13 had a synergistic increase in lung lipid peroxides. Rats, but not guinea pigs, had synergistic
14 increases in antioxidant factors (e.g., non-protein thiols, vitamin C, glucose-6-phosphate
15 dehydrogenase, GSH peroxidase). Duration of exposure can have an impact. Schlesinger et al.
16 (1990) observed a synergistic increase in prostaglandin E2 and F2a in the lung lavage of rabbits
17 exposed acutely for 2 h to 5640 |ig/m3 (3.0 ppm) NO2 plus 588 |ig/m3 (0.3 ppm) Os; the response
18 appeared to have been driven by 63. However, with 7 or 14 days of repeated 2-h exposures, only
19 prostaglandin E2 was decreased and appeared to have been driven by NO2; there was no
20 synergism (Schlesinger et al., 1991).
21 Using the infectivity model (see Section AX4.3.2.5 for protocol details), Ehrlich et al.
22 (1977) found additivity after acute exposure to mixtures of NO2 and O3 and synergism after
23 subchronic exposures. Exposure scenarios involving NO2 and Os have also been performed
24 using a continuous baseline exposure to one concentration or mixture, with superimposed short-
25 term peaks to a higher level (Ehrlich et al., 1979; Gardner, 1980, 1982; Graham et al., 1987).
26 Differences in the pattern and concentrations of the exposure are responsible for the increased
27 susceptibility to pulmonary infection, without indicating clearly the mechanism controlling the
28 interaction.
29 Some aerosols may potentiate response to NO2 by producing local changes in the lungs
30 that enhance the toxic action of co-inhaled NO2. The impacts of NO2 and H2SO4 on lung host
31 defenses have been examined by Schlesinger and Gearhart (1987) and Schlesinger (1987a). In
32 the former study, rabbits were exposed for 2 h/day for 14 days to either 564 |ig/m3 (0.3 ppm) or
August 2007 AX4-19 DRAFT-DO NOT QUOTE OR CITE
-------
1 1880 |ig/m3 (1.0 ppm) NO2, or 500 |ig/m3 H2SO4 alone, or to mixtures of the low and high NO2
2 concentrations with H2SO4. Exposure to either concentration of NO2 accelerated alveolar
3 clearance, whereas H2SO4 alone retarded clearance. Exposure to either concentration of NO2
4 with H2SC>4 resulted in retardation of clearance in a similar manner to that seen with H2SC>4
5 alone. Using a similar exposure design but different endpoints, exposure of rabbits to
6 1800 |ig/m3 (1.0 ppm) NO2 increased the numbers of PMNs in lavage fluid at all time points (not
7 seen with either pollutant alone), and increased phagocytic capacity of AMs after two or six
8 exposures (Schlesinger et al., 1987). Exposure to 564 |ig/m3 (0.3 ppm) NO2 with acid, however,
9 resulted in depressed phagocytic capacity and mobility. The NO2/H2SO4 mixture was generally
10 either additive or synergistic, depending on the specific cellular endpoint being examined.
11 Exposure to high levels of NO2 (<9400 |ig/m3, 5.0 ppm) with very high concentrations of
12 H2SC>4 (1 mg/m3) caused a synergistic increase in collagen synthesis rate and protein content of
13 the lavage fluid of rats (Last and Warren, 1987; Last, 1989).
14
15 Complex Mixtures Containing NO2
16 Although many studies have examined the response to NO2 with only one additional
17 pollutant, the atmosphere in most environments is a complex mixture of more than two materials.
18 A number of studies have attempted to examine the effects of multi-component atmospheres
19 containing NO2, but as mentioned before, in many cases the exact role of NO2 in the observed
20 responses is not always clear. One study by Stara et al. (1980) deserves mention because
21 pulmonary function changes appeared to progress after exposure ceased.
22 In the study by Stara et al. (1980), dogs were exposed for 68 months (16 h/day) to raw or
23 photochemically reactive vehicle exhaust which included mixtures of NOX: one with a high NO2
24 level and a low NO level (1200 |ig/m3, 0.64 ppm, NO2; 310 |ig/m3, 0.25 ppm, NO), and one with
25 a low NO2 level and a high NO level (270 |ig/m3, 0.14 ppm, NO2; 2050 |ig/m3, 1.67 ppm, NO).
26 At the end of exposure, the animals were maintained for about 3 years in normal indoor air.
27 Numerous pulmonary functions, hematological and histological endpoints were examined at
28 various times during and after exposure. The lack of an NO2-only or NO-only group precludes
29 determination of the nature of the interaction. Nevertheless, the main findings are of interest.
30 Pulmonary function changes appeared to progress after exposure ceased. Dogs in the high NO2
31 group had morphological changes considered to be analogous to human centrilobular
August 2007 AX4-20 DRAFT-DO NOT QUOTE OR CITE
-------
1 emphysema. Because these morphological measurements were made after a 2.5- to 3-year
2 holding period in clean air, it cannot be determined with certainty whether these disease
3 processes abated or progressed during this time. This study suggests progression of damage after
4 exposure ends.
August 2007 AX4-21 DRAFT-DO NOT QUOTE OR CITE
-------
TABLE AX4.1. EFFECTS OF NITROGEN DIOXIDE (NO2) ON OXIDANT AND ANTIOXIDANT HOMEOSTASIS"
OQ
to
o
o
to
to
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
NO2 Concentration
jig/m3
75
752
7,520
752
752
2,260
7,520
752-940
940
1,880
1,880
1,880
4,330
11,600
2,260
3,380
3,760
18,800
ppm
0.04
0.4
4.0
0.4
0.4
1.2
4.0
0.4-0.5
0.5
1.0
1.0
1.0
2.3
6.2
1.2
1.8
2.0
10.0
Exposure
Continuous,
9 and
18 mos
2 wks
Continuous,
4 mos
Continuous,
1.5yrs
Continuous,
17 mos
4 h/day,
6 days
Continuous,
4 days
Continuous,
3 days
3 days
Gender Age
M 8 wks
NS NS
M 13
wks
F NS
F 4 wks
NS NS
M 8 wks
M 12
wks
M/F 5->60
days
Species (Strain)
Rat (Wistar)
Rat
Guinea Pig
Rat (Wistar)
Mouse
(NS)
Mouse (C57B 1/6 J)
Rat (Sprague-Dawley)
Rat
(Sprague-Dawley)
Rat (Sprague-Dawley)
Rat (Wistar) Guinea pig
(Dunkin Hartley)
Effects
NPSHs increased at >0.4 ppm after 9 or
18 mos; GSH peroxidase activity increased
after a 9-mo exposure to 4.0 ppm; G-6-P
dehydrogenase was increased after a 9- and
18-mo exposure to 4.0 ppm; no effects on
6-P-G dehydrogenase , SOD disulfide
reductase; some GSH S-transferase had
decreased activities after 18-mo exposure to
> 0.4 ppm.
No effect on TEA reactants, antioxidants, or
antioxidant enzyme activities.
Duration dependent pattern for increase in
activities of antioxidant enzymes; increase,
peaking at wk 4 and then decreasing.
Concentration-dependent effects.
Growth reduced; Vitamin E (30 or 300
mg/kg diet) improved growth.
At 1 ppm, GSH-peroxidase activity
decreased in vitamin E-deficient mice and
increased in Vitamin E- supplemented mice.
Vitamin E-supplement reduced lipid
peroxidation.
Activities of GSH reductase and G-6-P
dehydrogenase increased at 6.2 ppm
proportional to duration of exposure; plasma
lysozyme and GSH peroxidase not affected
at 6.2 ppm; no effects at 1.0 or 2.3 ppm.
Increases in G-6-P dehydrogenase, isocitrate
dehydrogenase, disulfide reductase, and
NADPH cytochrome c reductase activities at
1.8 ppm only.
Decreased SOD activity in 21 -day -old
animals.
References
Sagai et al.
(1984)
Ichinose et al.
(1983)
Ichinose and
Sagai (1989)
Ichinose and
Sagai
(1982)
Csallany (1975)
Ayaz and
Csallany (1978)
Thomas et al.
(1967)
Chow et al.
(1974)
Lee etal. (1989,
1990)
Azoulay-Dupuis
etal. (1983)
-------
OQ
to
o
o
TABLE AX4.1 (cont'd). EFFECTS OF NITROGEN DIOXIDE (NO2) ON OXIDANT AND ANTIOXIDANT
HOMEOSTASIS"
NO2 Concentration
Hg/m3
17,900
5,600
13,200
ppm
Exposure
Gender
Age
Species (Strain)
Effects
References
3,760 2.0 14 days
7,500 4.0 10 days
18,800 10.0 7 days
5,600 3.0 7 days
M 12-24 wks
M/F 1 day to
Rat (Wistar)
Rat (Sprague-Dawley)
G-6-P dehydrogenase increased at >2 ppm;
at 2 ppm, 14 days of exposure needed
Increased lipid peroxidation (TBA-reactive
Mochitate
etal. (1985)
Sevanian
9.5
7.0
7 h/day,
5 days/wks,
6 mos
4 days
4 days
M
M
>8wks
In utero and
6 mos
NS
Rat (Fischer 344)
Rat (Sprague-Dawley)
substances) with vitamin E deficiency.
Increase in GSH reductase activity in
younger rats and SDH peroxidase activity
in older rats.
No effects on parameters tested.
Increase in lung weight, G-6-P
dehydrogenase, GSH reductase, and GSH
peroxidase activities.
etal. (1982)
Mauderly
etal. (1987)
Mustafa
etal. (1979)
to
Increased lung weight, G-6-P
dehydrogenase; and GSH reductase
activities.
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
18,800 10 4 days
28,200 15 1-7 days
7,520 4.0 3 h
M/F 21-33yrs Human
9,400 5.0 Continuous, 24 h M NS
18,800 10.0 7 days
Rats (CD Cobs)
Increase in lung weight, DNA content,
G-6-P dehydrogenase, 6-P-G
dehydrogenase, GSH reductase, disulfide
reductase, GSH peroxidase, disulfide
reductase, succinate oxidase, and
cytochrome oxidase activities; no effect on
lung protein
Decreased elastase inhibitory capacity and
increased lipid peroxidation products in
BAL of subjects not administered
supplement of vitamin C and E prior to
NO2 exposure.
Changes in the GSH levels in blood and
lung occurred in rats exposed for 24 h, but
returned to normal after 7 days.
Mohsenin
(1991)
Pagani et al.
(1994)
-------
OQ
to
o
o
TABLE AX4.1 (cont'd). EFFECTS OF NITROGEN DIOXIDE (NO2) ON OXIDANT AND ANTIOXIDANT
HOMEOSTASIS"
to
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
NO2 Concentration
fig/m3
11,000
28,000
53,000
17,900
18,800
26,320
ppm
6.0
15
28
9.5
10.0
14.0
Exposure
4 h/day,
30 days
7 days
7 h/day, 5 days/wk,
24 mos
Continuous
3 days,
20 days
NS
Gender Age Species (Strain) Effects
F NS Mouse Increase in GSH reductase and G-6-P
(NS) dehydrogenase activities.
Increase in GSH levels, G-6-P dehydrogenase,
and GSH peroxidase activities.
M 18 wks Rat (Fischer 344) Increase in GSH reductase activity in BAL.
NS NS Rat (Fisher 344) Decreased GSH/GSSG ratio in blood and
BALF, but not in lung type II cells. Lipid
peroxidation was decreased in type II cells at
3 days, but was similar to controls at 20 days.
mRNA expression of the enzymes involved in
the biosynthesis (yGCS and GS) was
decreased at both time points. jGT (redox of
GSH) mRNA expression was increased.
NS NS Human Rapid depletion of vitamin C, glutathione and
vitamin E
References
Csallany
(1975)
Mauderly
etal.,
(1990)
Hochscheid
et al. (2005)
Halliwell
etal. (1992)
"Modified from US Environmental Protection Agency (1993).
M= Male
NPSHs= Nonprotein sulfhydryls.
GSH= Glutathione.
G-6-P dehydrogenase= Glucose-6-phosphate dehydrogenase.
6-P-G dehydrogenase= 6-phosphosgluconate dehydrogenase.
SOD= Superoxide dismutase.
F-Female.
NS= Not Stated.
NADP= Nicotinamide-adenine dinucleotide phosphate (reduced form).
TBA= Thiobarbituric acid
-------
OQ
to
o
o
TABLE AX4.2. EFFECT OF NITROGEN DIOXIDE (NO2) ON LUNG AMINO ACIDS, PROTEINS, AND ENZYMES3
NO2 Concentration
ppm
Exposure Gender
Age
Species
Effects
References
X
I
to
H
6
o
o
H
/O
o
H
W
O
n
HH
H
w
752
1,880
5,640
9,400
752
2,260
7,520
940
1,880
1,880
14,100
28,200
47,000
56,400
0.4
1.0
3.0
5.0
0.4
1.2
4.0
0.5
1.0
1.0
7.5
15
25
30
72 h
9,400
752
752
5.0
0.4
0.4
3h
Continuous,
1 wk
Continuous,
Iwk
1-14 wks
M
M
NS Guinea Pig
22-24 wks Rat (Wistar)
Sherwin and
Carlson
(1973)
Takahashi
etal. (1986)
6 h/day,
5 days/wk,
4 wks
6 h/day,
2 days
M NS Guinea Pig No effect at 0.4 ppm; increase in BAL protein in Selgrade
(Hartley) vitamin C-depleted, but not normal, animals at 1.0 ppm etal. (1981)
and above.
Increased BAL protein in vitamin C-depleted guinea
pigs 15 h postexposure.
No effect on BAL protein.
Increased protein content of BAL from
vitamin-C-deficient guinea-pigs.
Complex concentration and duration dependence of
effects. Example: at 0.4 ppm, cytochrome P-450
levels decreased at 2 wks, returned to control level by
5 wks. At 1.2 ppm, cytochrome P-450 levels decreased
initially, increased at 5 wks, and decreased at 10 wks.
Effects on succinate-cytochrome c reductase also.
0.5 ppm; increase in urinary hydroxylysine output
starting during wk 1; BAL hydroxylysine level,
angiotensin-converting enzyme level, and BAL protein
content unchanged.
1.0 ppm: gradual increase in urinary hydoxylysine
output, becoming significant the week after exposure
ended; BAL hydroxylysine level lower following
exposure and 4 wks postexposure; andiotensin-
converting enzyme level increased.
Concentration dependent increase in urinary
hydroxylysine output and BAL hydroxyxlysine content,
but only significant at >7.5 ppm and 15 ppm,
respectively; angiotensin-converting enzyme levels and
BAL protein increased in highest-exposed groups.
M
NS Rat
(Fischer 344)
Evans et al.
(1989)
-------
OQ
TABLE AX4.2 (cont'd). EFFECT OF NITROGEN DIOXIDE (NO2) ON LUNG AMINO ACIDS, PROTEINS, AND
ENZYMES3
" NOi Concentration
K")
l^
O
o
^
.
X
^
to
o
i-rj
H
6
o
0
H
/O
r*^S
O
H
W
O
O
HH
H
W
Hg/m3
1,880
9,400
752
2,260
7,520
3,760
1,504
9,400
18,800
3,760
7,520
18,800
5,640
6,770
13,500
20,300
27,100
7,520
18,800
7,520
18,800
47,000
ppm
1.05.0
1.2
1.2
4.0
2.0
0.8
5
10
2.0
4.0
10
3.0
3.6
7.2
10.8
14.4
4.0
10
4.0
10
25
Exposure
7h/day,
5 days/wk,
up to 15 wks
7 days
1,2, or 3 wks
1 or 3 days
14 days
10 days
7 days
7 days
24 h
12 h
8h
6h
10 days
7 days
6h/day
5 days/wk,
7, 14, and
21 days
Gender Age Species
M/F 14-16 wks Rat (Fischer 344)
M 10 wks Rat (Wistar)
M NS Guinea pig
? [check] ? [check] Rat ([check])
M 12-24 wks Rats (Wistar)
M/F 8 wks Rat
(Sprague-Dawley)
M 10-12 wks Rat
(Sprague-Dawley)
M 2 1-24 wks Rat (Wistar)
M NS Rat (Wistar)
Effects
Change in BAL and tissue levels of enzymes early
in exposure, resolved by 15 wks.
Decrease in levels of cytochrome P-450 at
1.2 ppm.
Increased lactate dehydrogenase (LDH) content of
the lower lobes of the lung
BAL protein content significantly increased in a
concentration- and exposure duration-dependent
manner, with the change becoming significant at
5 ppm for 3 days and at 10 ppm for > 1 day of
exposure.
Increase activity of lung glycolytic enzymes.
Various changes in lung homogenate protein and
DNA content and enzyme activities, changes more
severe in vitamin E-deficient rats.
Increased BAL protein >7.2 ppm.
Initial decrease in lung protein content followed by
an increase; changes on microsomal enzyme
activities.
Increased gamma-glutamyl transferase on days
14 and 21; no consistent effect on alkaline
phosphatase, LDH, or total protein.
References
Gregory
etal. (1983)
Mochitate
etal. (1984)
Sherwin and
Carlson
(1973)
Muller et al.
(1994)
Mochitate
etal. (1985)
Elsayed and
Mustafa
(1982)
Gelzleichter
etal. (1992)
Mochitate
etal. (1984)
Hooftman
etal. (1988)
-------
OQ
to
o
o
TABLE AX4.2 (cont'd). EFFECT OF NITROGEN DIOXIDE (NO2) ON LUNG AMINO ACIDS, PROTEINS, AND
ENZYMES"
to
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
NO2 Concentration
Hg/m3
8,100
9,020
9,020
9,400
9,400
9,400
9,400-
47,000
9,400
37,600
94,000
9,400
15,000
17,900
17,900
ppm
4.5
4.8
4.8
5.0
5.0
5.0
5.0-25.0
5.0
20.0
50.0
5.0
8.0
9.5
9.5
Exposure
16hrs
3hrs
8 h/day,
7 days
14-72 h
2wks
6 h/day,
6 days
Continuous,
7 days
3h
Continuous,
1, 3, or 7 days
Continuous, 14
days
7 h/day, 5
days/wk, 6 mos
7 h/day, 5
days/wk, 24 mos
Gender
M/F
M
M
F
M
NS
M
NS
M
F
M
M
Age
NS
8wks
NS
5 wks
NS
10-11 wks
NS
NS
NS
In utero
and 6 mos
18 wks
Species
Guinea pig
(Hartley)
Mouse (Swiss
Webster)
Mouse (NS)
Rat
(Fischer 344)
Mice
Rat (Sprague-
Dawley)
Rabbit
(New Zealand)
Rat
(Sprague-
Dawley)
Mouse (NS)
Rat
(Fischer 344)
Rat
(Fischer 344)
Effects
Increased lung wet weight, alterations in lung
antioxidant levels in Vitamin C- deficient animals.
Increased lung lavage fluid protein content in
vitamin C-deficient animals.
No significant changes in lung homogenate
parameters.
Increase in lung protein (14 to 58 h) by radioactive
label incorporation.
Increased amounts of the tryptophan metabolites
and xanthurenic and kynurenic acids excreted in
urine during wk 2 of exposure, but had returned to
normal levels by wk 4.
Modest increase in albumin in BAL; no effect on
LDH or lysosomal enzyme peroxidase.
Concentration-related increase in collagen synthesis
rate; 125% increase in rats exposed to 5.0 ppm.
Benzo [a] pyrene hydroxylase activity of trachea!
mucosa not affected.
Increased BAL protein at 3 days (day 7 not
measured); increased (120% collagen synthesis at
7 days (not measured other days).
Increase in lung protein.
Increase in BAL alkaline phosphatase, acid
phosphatase, and LDH in older rats only.
Increase in BAL levels of LDH and alkaline
phosphatase activities and in collagenous peptides.
References
Hatch et al.
(1986)
Mustafa et al.
(1984)
Csallany
(1975)
Suzuki et al.
(1988)
Rose et al.
(1989)
Last et al.
(1983)
Palmer et al.
(1972)
Last&
Warren
(1987)
Csallany
(1975)
Mauderly
etal. (1987)
Mauderly
etal. (1990)
-------
OQ
to
o
o
to
oo
TABLE AX4.2 (cont'd). EFFECT OF NITROGEN DIOXIDE (NO2) ON LUNG AMINO ACIDS, PROTEINS, AND
ENZYMES"
NO2 Concentration
Hg/m3
18,800
18,800
18,800
37,600
56,400
75,200
ppm Exposure Gender Age
10 24 h or 7 days M NS
10 Continuous, 14 M 8 wks
days
10 4 h M NS
20
30
40
Species
Rat (CD cobs)
Rat (Wistar)
Rat
(Long Evans)
Effects
Protein content of B ALF increased significantly in
rats after only 24 h. BALF elastase activity was not
affected, concentration-dependent increase in a-
1 proteinase inhibitor content after 24 h of exposure,
but not with longer exposures.
Changes in several enzymes in whole lung
homogenates.
Increased activities of various enzymes, sialic acid,
and B AL protein; attenuation by high dietary levels of
vitamin E.
References
Pagani et al.
(1994)
Sagai et al.
(1982)
Guthand
Mavis (1985,
1986)
"Modified from US Environmental Protection Agency (1993).
NS = Not Stated
LTB4 = Leukotriene B4
LDH = Lactate Dehydrogenase
M=Male
F= Female
BAL= Bronchoalveolar lavage
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
-------
TABLE AX4.3. EFFECTS OF NITROGEN OXIDE (NO2) ON THE IMMUNE SYSTEM OF ANIMALS"
OQ
to
o
o
to
VO
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
NO2 Concentration
fig/m3
940
188 base +
470, 940, or
1,880 peak
470
470
658
752
3,010
940 base +
2,820 peak
940 base +
3,760 peak
3,760
ppm
0.5
0.1 base +
0.25,0.5,
or 1.0
peak
0.25
0.25
0.35
0.4
1.6
0.5 base +
1.5 peak
0.5 base +
2.0 peak
Exposure
Continuous
Continuous base +
3 h/day, 5 days/wk
peak for 1, 3, 6, 9,
12 mos
7 h/day,
5 days/wk, 7wks
7 h/day,
5 days/ wk, 36 wks
7 h/day,
5 days/ wk, 12 wks
24 h/day
4 wks
22 h/day, 7 days/wk
base + 6 h/day,
5 days/wk peak for
1,3, 13, 52, 78 wks
24 h/day, 5 days/wk
base + 1 h/day,
5 days/wk peak for
3 mos
Gender Age Species
(Strain)
NS NS Mouse
F 6 wks Mouse
(AKR/cum)
F 5 wks Mouse
(AKR/cum)
M 6 wks Mouse
(C57BL/6J)
M 7 wks Mouse
(BALB/c)
M 10 wks Rat
(Fischer
344)
M 6 wks Mouse
(CD-I)
Effects
Suppression of splenic T and B cell
responsiveness to mitogens variable and not
related to concentration or duration, except for
the 940 ug/m3 continuous group, which had a
linear decrease in PHA-induced mitogenesis
with NO2 duration.
Reduced percentage of total T-cell population
and trend towards reduced percentage of
certain T-cell subpopulations; no reduction of
mature T cells or natural killer cells.
Reduced percentage of total T-cell population
and percentages of T helper/inducer cells on
days 37 and 181.
Trend towards suppression in total percentage
of T-cells. No effects on percentages of other
T-cell subpopulations.
Decrease in primary PFC response at
>752 ug/m3. Increase in secondary PFC
response at 3010 ug/m3.
No effect on splenic or circulatory B or T cell
response to mitogens. After 3 weeks of
exposure only, decrease in splenic natural
killer cell activity. No histological changes in
lymphoid tissues.
Vaccination with influenza A2/Taiwan virus
after exposure. Decrease in serum
neutralizing antibody; hemagglutination
inhibition antibody liters unchanged. Before
virus challenge, NO2 exposure decreased
serum IgA and increased IgGl, IgM, and
IgG2; after virus, serum IgA unchanged and
IgM increased.
References
Maigetter et al.
(1978)
Richters and Damji
(1988)
Richters and Damji
(1990)
Richters and Damji
(1988)
Fujimaki et al.
(1982)
Selgrade et al.
(1991)
Ehrlich et al.
(1975)
-------
TABLE AX4.4. EFFECTS OF NITROGEN DIOXIDE ON ALVEOLAR MACROPHAGES
OQ
to
o
o
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
NO2 Concentration
Hg/m3
94 base + 3,760
1,130
188
1,880
9,400
37,600
376
940
3760
940
188 base +
1,880 peak
3,760
940 base+ 3,760
peak
ppm
0.05 base +
2.0 peaks
0.6
0.1
1.0
5.0
20
0.2
0.5
2.0
0.5
0.1 base +
1.0 peak
2.0
0.5 base +
2.0 peak
Exposure Gender Age
3 h base + three NS NS
15-minpeaks
3h
1 h NS NS
Gestation
12wks
Continuous, NS NS
24wks
Continuous
base + 3 -h peak,
5 days/wk, 24 wks
Continuous,
33 wks
Continuous base +
1-hpeak,
5 days/wk, 33 wks
Species (Strain) Effects
Human No effects at 0.05 ppm NO2 with
peaks; trend (p < 0.07) towards
AMs losing ability to inactivate
influenza virus at 0.6 ppm.
Rat At 5.0 ppm: increase in LTB4;
(Sprague-Dawley) concentration-related decrease in
(in vitro) SOD production in AMs at
> 1.0 ppm; increase in LDH in
AMS at 5.0 and 20 ppm
Rat (Brown- Reactive oxygen species
Norway) generation from alveolar
macrophages was significantly
suppressed in NO2 exposed
weanling animals; no changes in
reactive oxygen generating
capability in the embryonic
exposed animals.
Mouse No effects on AM morphology
at 0.5 ppm continuous or
0. 1 ppm base + peak.
After 21 weeks of exposure to
2.0 ppm continuous or 0.5 ppm
base + peak, morphological
changes were identified, such a
loss of surface processes,
appearance of fenestrae, bleb
formation, and denuded surface
areas.
Reference
Frampton
etal. (1989)
Robison
etal. (1990)
Kumae and
Arakawa
(2006)
Aranyi
etal. (1976)
-------
TABLE AX4.4 (cont'd). EFFECTS OF NITROGEN DIOXIDE ON ALVEOLAR MACROPHAGES
OQ
to
o
o
>
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
NO2 Concentration
fig/m3
560
1,880
560
1,880
560
1,880
5,640
18,800
560
1,880
940
940 base +
2,820 peak
3, 760 base +
11,300
ppm
0.3
1.0
0.3
1.0
0.3
1.0
3.0
10
1.0
10
0.5
0.5 base +
1.5 peak
2.0 base
+6.0 peak
Exposure Gender Age
2 h/day M NS
2, 6, 13 days
2 h/day up to M NS
14 days
2h M NS
2 h/day, 14 days
0.5, 1, 5 and NS NS
10 days exposure
Base 22 h/day, 7 M 1 day and
days/wk + two 1-h 6 wks
peaks, 5 days/wk,
6 wks
Species (Strain) Effects
Rabbit (New Zealand) Decreased phagocytic ability of
AMs at 0.3 ppm after 2 days of
exposure; increased at 1.0 ppm
after 2 days of exposure; no
effect on cell number or
viability; random mobility
reduced at 0.3 ppm only; no
effects after 6 days of exposure.
Rabbit (New Zealand) Increase in alveolar clearance.
Rabbit (New Zealand) Concentration-related
acceleration in clearance of
particles from lung with the
greatest increase at two lowest
concentrations, effects from
repeated exposures similar to
those seen after acute exposures
to same concentrations.
Rat Superoxide production in
(NS) alveolar macrophages from
B ALF, stimulated by phorbol
myrisate acetate (PMA), was
decreased after 0.5 days of
exposure, and continued to be
depressed after 1, 5, and 10 days.
Rat (Fischer 344) Trend towards increase in
number of AMs and cell volume
in younger animals; increase in
number of AMs and cell volume
in older rats.
Reference
Schlesing
er(1987)
Schlesing
erand
Gearhart
(1987)
Vollmuth
etal.
(1986)
Robinson
etal.
(1993)
Crapo
etal.
(1984)
Chang
etal.
(1986)
-------
TABLE AX4.4 (cont'd). EFFECTS OF NITROGEN DIOXIDE ON ALVEOLAR MACROPHAGES
OQ
to
o
o
to
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
NO2 Concentration
Hg/m3
1,000
2,500
5,000
1,880
3,760
7,520
1,880
9,400
28,200
1,880
3,760
7,520
1,880
28,200
45,120
1,880
9,400
1,880 base +
9,400 peaks
2,440-32,000
ppm
0.5
1.3
2.7
1.0
2.0
4.0
1.0
5.0
15
1.0
2.0
4.0
1.0 +
0.9 ppm No
15
24
1.0
5.0
base +
5.0 peaks
1.3-17
Exposure Gender
Continuous, M
28 days
24 h/day, 12 wks
6 h/day, 2 days NS
24 h/day, 12 wks
7 h/day, NS
5 days/wks for
1 1 or 22 exposures
7 h/day, M/F
5 days/wks
Base 7 h/day,
5 days/wks; two
1. 5 -h peaks/day;
15 wks
NS ("acute") F
Age Species (Strain)
6 wks Rat (Wistar)
Guinea pig
(NS)
Rat
(NS)
4-6 wks Mouse (CD 1)
Guinea pig
(NS)
Rat
(NS)
NS Rat (Long Evans)
14-16 wks Rat (Fischer 344)
NS Rat
(Sprague-Dawley)
Effects
Increase in AMs in highest exposed
group; no effects noted in 2 lowest
exposure groups.
IgE-mediated histamine release
from lung mast cells was enhanced
in guinea pigs, but not rats exposed
to 4.0 ppm. No effect observed at
lower concentrations.
Exposure-related decrease in AM
phagocytosis from 1.0-5.0 ppm,
decrease was not further affected by
15 ppm.
IgE-mediated histamine release
from lung mast cells was enhanced
in guinea pigs, but not rats exposed
to 4.0 ppm. No effect observed at
lower concentrations.
Stimulated clearance of particles
from lung at lowest concentration,
but decreased clearance rate at two
highest concentrations.
Accumulation of AMs.
Superimposed peak exposures
produced changes that may persist
with continued exposures.
Decreased production of superoxide
anion radical.
Reference
Rombout
etal.
(1986)
Fujimaki
and
Nohara,
1994
Rose et al.
(1989)
Fujimaki
and
Nohara,
(1994)
Ferin and
Leach
(1977)
Gregory
etal.
(1983)
Amoruso
etal.
(1981)
-------
TABLE AX4.4 (cont'd). EFFECTS OF NITROGEN DIOXIDE ON ALVEOLAR MACROPHAGES
CJQ
r-K
to
o
o
^
X
-^
w
O
£
H
6
o
2;
0
_J
^^
/o
r*^S
O
H
W
O
O
HH
H
W
NO2 Concentration
jig/m3 PPm
3,760 2.0
19,000 10
3,760 2.0
3,760 2.0
5,000 2.7
5,640-30,100 3-6
6,770 3.6
22,700 12.1
7,520 4
19,000 10
47,000 25
7,520 4.0
Exposure Gender Age Species (Strain)
3 days M/F 5,10,21, Guinea pig (Dunkin
45, 55, 60, Hartley)
and >60 Rat (Wistar)
days
8 h/day, 5 days/wk, M/F 3-4 yrs Baboon
6 mo
4h NS NS Human
24 h M 6 wks Rat (Wistar)
3h NS NS Dog (Beagle)
Ih F NS Rat (Sprague-Dawley)
2h (in vitro)
6 h/day, 7, 14, or M NS Rat (Wistar)
21 days
10 days 19-23 wks
Effects
Newborns were less affected
than adults when AMs were
tested for SOD levels.
Impaired AM responsiveness
to migration inhibitory factor.
Decreased phagocytosis and
superoxide anion release.
Increase in number of AMs.
Enhanced swelling of AMs.
Enhanced macrophage
agglutination with
concanavalin A at both
concentrations tested.
Changes in morphology at all
concentrations; increase in
number of AMs at > 10 ppm;
phagocytic capacity reduced
after 14 and 21 days of
exposure to 25 ppm.
Increase in number of AMs; no
increase in PMNs; increased
metabolic activity, protein, and
DNA synthesis; all responses
peaked on day 4 and returned
to normal on day 10.
Reference
Azoulay-
Dupuis et al.
(1983)
Green and
Schneider
(1978)
Devlin et al.
(1992)
Rombout
etal. (1986)
Dowell et al.
(1971)
Goldstein
etal. (1977)
Hooftman
etal. (1988)
Mochitate
etal. (1986)
-------
OQ
to
o
o
TABLE AX4.4 (cont'd). EFFECTS OF NITROGEN DIOXIDE ON ALVEOLAR MACROPHAGES
NO2 Concentration
ppm
Exposure
Gender
Age
Species (Strain)
Effects
Reference
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
7,520
15,000
4.0
8.0
Up to 10 days
NS
NS
Rat (Fischer 344)
Increase in number of AMs at both
concentrations, reaching a peak on
day 3 and 5; no increase in number
of PMNs; decrease in AM viability
throughout exposure period.
Suppression of phagocytic activity
after 7 days of exposure to 4 ppm
and after 5 days of exposure to
8 ppm; returned to normal value at
10 days. Decrease in superoxide
radical production, but at 4 ppm,
the effect became significant on
days 3, 5, and 10; at 8 ppm, the
effect was significant at all time
periods tested.
Suzuki et al.
(1986)
9,400
9,400
28,200
9,400
18,800
28,200
9,400-113,000
13,200
17,900
5.0
5
15
5
10
15
5-60
7.0
9.5
7 days
3 h after infection
with parainfluenza
3 virus
3h
3h
24 h
7 h/day; 5 days/wk;
18-22 mo
F
NS
M
Fb
NS
NS
M
NS
NS
NS
NS
NS
18wks
Mouse (CD-I)
Rabbit (New Zealand)
Humans (in vitro
exposure)
Rabbit (New Zealand)
Rabbit
Rat (Fischer 344)
No effect on phagocytic activity.
AMs lost resistance to challenge
with rabbit pox virus after exposure
to 15 ppm.
No change in cell viability, release
of neutrophil chemotactic factor, or
interleukin-1.
Inhibition of phagocytic activity.
Increased rosette formation in AMs
treated with lipase.
No effect on long-term clearance of
radiolabeled tracer particles.
Lefkowitz
etal. (1986)
Acton and
Myrvik
(1972)
Pinkston
etal. (1988)
Gardner et al.
(1969)
Acton and
Myrvik
(1972)
Hadley et al.
(1977)
Mauderly
etal. (1990)
-------
TABLE AX4.4 (cont'd). EFFECTS OF NITROGEN DIOXIDE ON ALVEOLAR MACROPHAGES
CJQ
r-K
to
o
o
NO2 Concentration
fig/m3
18,800
19,000
19,000
19,000
47,000
ppm
10
10
10
10
25
Exposure Gender Age
Continuous 7 days NS NS
35 days NS NS
4h F NS
24 h M 12-13
wks
Species (Strain)
Rat
(NS)
Guinea pig
Mouse (Swiss)
Rat (Sprague-Dawley)
Effects
High influx of PMNs in the lung
(BALF) after 24 h of exposure,
reversed for macrophages; no
change in the lymphocyte
population.
63% increase in epithelial cells
positive for macrophage
congregation.
Increase in total pulmonary cells in
animals infected with some species
of bacteria.
Decreased phagocytosis at 25 ppm
only.
Reference
Pagani et al.
(1994)
Sherwin
etal. (1968)
Jakab (1988)
Katz and
Laskin
(1976)
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
aNS = Not stated.
AMs = Alveolar macrophages.
LTB4 = Leukotriene B4.
LDH = Lactate dehydrogenase.
M = Male
F = Female
SOD = Superoxide dismutase.
PMNs = Polymorphonuclear leukocytes.
bOnly one female used in study.
-------
OQ
to
o
o
TABLE AX4.5. EFFECT OF NITROGEN DIOXIDE (NO2) ON SUSCEPTIBILITY TO
INFECTIOUS AGENTS"
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
NO2 Concentration
fig/m3 ppm
100 base + 0.05 base +
188 peak 0.1 peak
940 base + 0.5
1,880 peak +
peak
2,256 base
+ 4,700
peak 1.2 base +
2.5 peak
376 base 0.2 base +
+ 1,504 0.8 peak
peak
564-940 0.3-0.5
Species
Exposure Gender Age (Strain) Infective Agent Effects
Continuous, base + F NS Mouse Streptococcus sp. No effect.
twice/day 1 h peaks, (CD-I)
5 days/wk for 15 days
Increased mortality.
Increased mortality.
23 h/day, 7 days/wk F 6-8 Mouse Streptococcus sp. Peak plus baseline
base+ twice daily 1 h wks (CD-I) caused significantly
peaks, 5 days/wk for greater mortality than
1 yr baseline.
Continuous, 3 mos F 4 wks Mouse A/PR/8 virus High incidence of
(ICR:JCL) adenomatous
proliferation peripheral
and bronchial epithelial
cells; NO2 alone and
virus alone caused less
„ ^. , severe alterations.
References
Gardner (1980,
1982)
Graham et al.
(1987)
Miller et al.
(1987)
Motomiya
etal. (1973)
No enhancement of
effect of NO2 and virus.
-------
OQ
to
o
o
TABLE AX4.5 (cont'd). EFFECT OF NITROGEN DIOXIDE (NO2) ON SUSCEPTIBILITY TO
INFECTIOUS AGENTS"
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
NO2 Concentration
jig/m3 ppm Exposure
940 0.5 Intermittent, 6 or
18 h/day, up to
12 mos
Continuous, 90
days
940-1,880 0.5-1.0 Continuous, 39
days
18,800
10 2 h/day, 1, 3, and
5 days
940-52,700 0.5-28 Varied
940 0.5 3 h/day, 3 mos
940 0.5 24 h/day,
1,880 1.0 7days/wk,
2,820 1.5 3 mos
9,400 5.0 3 days
Species
Gender Age (Strain) Infective Agent Effects
F NS Mouse (Swiss) K. pneumoniae Increased mortality
after 6 mos
intermittent exposure
or after 3, 6, 9, or 12
mos continuous
exposure, increased
mortality was
significant only in
continuously exposed
mice.
F NS Mouse (ICR, A/PR/8 Increased
dd) virus susceptibility to
infection.
F NS Mouse (CD-I) Streptococcus sp. Increase mortality
with increased time
and concentration;
concentrations is
more important than
time.
F 6-8 Mouse (CD2 Streptococcus sp. Increase in mortality
wks F!, CD-I) with reduction in
mean survival time.
F NS Mouse (CF-1) K. pneumoniae Significant increase in
mortality after 3 -day
exposure to 5.0 ppm;
no effect at other
concentrations, but
control mortality very
high.
References
Ehrlich and
Henry (1968)
Ito (1971)
Gardner et al.
(1977 a,b)
Coffin etal.
(1977)
Ehrlich et al.
(1979)
McGrath and
Oyervides
(1985)
-------
OQ
to
o
o
TABLE AX4.5 (cont'd). EFFECT OF NITROGEN DIOXIDE (NO2) ON SUSCEPTIBILITY TO
INFECTIOUS AGENTS"
NO2 Concentration
jig/m3 ppm
Exposure
Gender Age Species (Strain) Infective Agent
Effects
References
oo
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
940
1,880
3,760
9,400
1,880
4,324
12,408
1,880
4,700
9,400
18,800
1.0
2.0
5.0
1.0
2.3
6.6
4h
17 h
M/F
M
8-10 wks
NS
Mouse
(C57BL/6N)
Mouse (Swiss)
Mycoplasma
pulmonis
S. aureus after
exposure
1.0
2.5
5.0
10.0
4h
NS Mouse (Swiss) S. aureus
Decrease in
intrapulmonary killing
only at 5.0 ppm.
No difference in
number of bacteria
deposited, but at the
two highest
concentrations, there
was a decrease in
pulmonary
bactericidal activity of
6 and 35%,
respectively; no effect
at 1.0 ppm
Injection with
corticosteroids
increased NO2-
induced impairment of
bactericidal activity at
>2.5 ppm.
Davis et al.
(1991, 1992)
Goldstein et al.
(1974)
Jakab (1988)
1,880 1.0 48 h M NS Mouse (Swiss
Webster)
1,800 1.0 3 h F 5-6 wks Mouse (CD-I)
5,640 3.0
Streptococcus
sp. S. aureus
Streptococcus
sp.
Increased proliferation
of Streptococcus in
lung of exposed mice
but no effect with
Streptococcus
Exercise on
continuously moving
wheels during
exposure increased
mortality at 3.0 ppm.
Sherwood et al.
(1981)
Illing et al.
(1980)
-------
OQ
to
o
o
OJ
VO
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
TABLE AX4.5 (cont'd). EFFECT OF NITROGEN DIOXIDE (NO2) ON SUSCEPTIBILITY TO
INFECTIOUS AGENTS"
NO2 Concentration
jig/m3
1,880
4,700
9,400
2,820-
94,000
2,820
ppm
1.0
2.5
5.0
1.5-
50
1.5
Exposure Gender Age Species (Strain)
6 h/day, 6 days NS
2h NS
Continuous or F
intermittent, 7 h/day,
7 days/wk, up to
15 days
4-6 wks Mouse (CD-I)
NS Mouse
(NS)
Hamster
(NS)
Monkey
(Squirrel)
NS Mouse (CD-I)
Infective Agent Effects
Cytomegalovirus Increase in virus
susceptibility at 5.0 ppm
only.
K. pneumoniae Increased mortality in
mice, hamsters, and
monkeys at >3.5, >35,
and 50 ppm NO2,
respectively
Streptococcus sp. After 1 wk, mortality with
continuous exposure was
greater than that for
intermittent after 2 wks,
References
Rose et al.
(1988, 1989)
Ehrlich
(1980)
Gardner et al.
(1979)
Coffin et al.
(1977)
6,580
3.5
no significant difference
between continuous and
intermittent exposure.
Increased mortality with
increased duration of
exposure; no significant
difference between
continuous and
intermittent exposure;
with data adjusted for total
difference in C x T,
mortality essentially the
same.
-------
OQ
to
o
o
-k
o
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
TABLE AX4.5 (cont'd). EFFECT OF NITROGEN DIOXIDE (NO2) ON SUSCEPTIBILITY TO
INFECTIOUS AGENTS"
NO2 Concentration
jig/m3 PPm
2,820 base + 1.5 base +
8, 100 peak 4.5 peak
Exposure
Continuous 64 h,
then peak for 1,
3.5, or 7 h, then
continuous 18 h
base
Species
Gender Age (Strain) Infective Agent
F NS Mouse (CD-I) Streptococcus sp.
Effects
Mortality increased with 3.5-
and 7 h single peak when
bacterial challenge was after
an 18 h baseline exposure.
References
Gardner
(1980)
Gardner
(1982)
Graham et al.
(1987)
8,100
4.5
1, 3.5, or 7 h
2,820
3,570
7,140
13,200
17,200
27,800
2,820-
9,400
1.5
1.9
3.8
7.0
9.2
14.8
1.5-
5.0
Mortality proportional to
duration when bacterial
challenge was immediate,
but not 18 h postexposure.
7 h/day, 4, 5, and NS NS Mouse Streptococcus sp. Elevated temperature (32°C)
7 days increased mortality after
7 days.
4 h M NS Mouse (NS) S. aureus Physical removal of bacteria
unchanged by exposure.
Bactericidal activity
decreased by 7, 14, and 50%,
respectively, in three highest
NO2-exposed groups.
3h F 6-10 Mouse (CF-1, Streptococcus sp. Increased mortality in mice
wks CD2FO exposed to > 2.0 ppm
Gardner
(1982)
Goldstein et al.
(1973)
Ehrlich et al.
(1977)
Ehrlich (1980)
-------
OQ
to
o
o
TABLE AX4.5 (cont'd). EFFECT OF NITROGEN DIOXIDE (NO2) ON SUSCEPTIBILITY TO
INFECTIOUS AGENTS"
>
^
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
NO2 Concentration
jig/m3 ppm Exposure Gender Age Species (Strain) Infective Agent Effects
2,820
4,700
6,580
9,400
18,800
28,200
3,760
4,700
7,500
9,400
18,800
28,200
1.5 2h NS
2.5
3.5
5.0
10
15
2.0 1.5 h/day, 5 NS
days/wkfor 1,
2, and 3 wks
2.5 4 h F
4.0
5.0
10
15
6-8 wks Mouse (Swiss K. pneumoniae No effect at 1.5 or 2. 5 ppm;
Webster) increased mortality at 3.5 ppm
and above. Increase in
mortality when K. pneumoniae
challenge 1 and 6 h after 5 or
10 pm NO2 exposure; when K.
pneumoniae challenge 27 h
following NO2 exposure, effect
only at 15 ppm.
2 wks Hamster A/PR/8/34 Peak virus production in
(Golden Syrian) influenza virus tracheal explants occurred
(in vitro) earlier.
NS Mouse (Swiss) S. aureus, Concentration-related decrease
Proteus mirabilis, in bactericidal activity at > 4.0
Pasteurella ppm with S. aureus when NO2
pneumotropica exposure after bacterial
challenge; when NO2 exposure
was before challenge, effect at
10 ppm; NO2 concentrations
>5.0 ppm required to affect
bactericidal activity for other
tested microorganisms.
References
Purvis and
Ehrlich(1963)
Ehrlich(1979)
Schiff(1977)
Jakab (1987,
1988)
-------
OQ
to
o
o
to
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
TABLE AX4.5 (cont'd). EFFECT OF NITROGEN DIOXIDE (NO2) ON SUSCEPTIBILITY TO
INFECTIOUS AGENTS"
NO2 Concentration
jig/m3 ppm Exposure Gender Age
9,400 5.0 Continuous, M NS
2 mos
18,800 10 Continuous,
1 mo
Species (Strain)
Monkey
(Squirrel)
Infective Agent
K. pneumoniae
orA/PR/8
influenza virus
Effects
Increased viral-induced mortality
(1/3). Increase mKlebsiella-
induced mortality (2/7); no control
deaths.
Increased virus-induced mortality
(6/6) within 2-3 days after
References
Henry et al.
(1970)
9,400
18,880
5.0
10
4h
M/F 6-10 wks Mouse
(C57B16N,
C3H/HeN)
infection; no control deaths.
Increase in Klebsiella-mduced
mortality (1/4), no control deaths.
Mycoplasma NO2 increased incidence and
pulmonis severity of pneumonia lesions and
decreased the number of organisms
needed to induce pneumonia; no
effect on physical clearance;
decreased mycoplasmal killing and
increased growth; no effect on
specific IgM in serum; C57B1/6N
mice generally more sensitive than
C3H/HeN mice. At 10 ppm, one
strain (C57B1/6N) of mice had
increased mortality.
Parker et al.
(1989)
-------
> TABLE AX4.5 (cont'd). EFFECT OF NITROGEN DIOXIDE (NO2) ON SUSCEPTIBILITY TO
INFECTIOUS AGENTS3
to
o
o
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
NO2 Concentration
jig/m3 ppm Exposure Gender Age Species (Strain) Infective Agent
18,800
28,200
65,800
94,000
9,400
10 2h
15
35
50
5 ?
M/F NS Monkey K. pneumoniae
(Squirrel)
? ? Mice Parainfluenza
(murine sendei
virus)
Effects
Clearance of bacteria from
lungs of 10-, 15-, and 35-ppm
groups delayed or prevented.
All three animals in highest
exposed group died.
Altered the severity but not
the course of the infection
References
Henry et al.
(1969)
Jakab (1988)
"Modified from US Environmental Protection Agency (1993)
F = Female.
M = Male.
NS = Not stated
K. pneumoniae = Klebsiella pneumoniae
S. aureus = Staphylococcus aureus.
C x T = The product of concentration and time
-------
TABLE AX4.6. EFFECT OF NITROGEN DIOXIDE (NO2) ON HEMATOLOGICAL PARAMETERS"
CJQ
to
O
o
^
X
^
£
O
^
^
H
O1
O
0
H
O
H
W
O
O
HH
H
W
NO2 Concentration
Hg/m3
94
677
940-1,500 +
1500
1,880
1,800
9,400
1,880-56,400
2,400-5,640
3,760
3,760
7,520
ppm Exposure
0.05 Continuous 90
days
0.36 1 wk
0.5-0.8 + Continuous
1 to 1.5 mos
0.8 Continuous,
5 days
1.0 Continuous,
16 mos
1.0 Continuous,
5.0 18 mos
1-30 18 h
1.3-3.0 2h/day,
15 and 17 wks
2.0 Continuous,
14 mos
2.0 Continuous, up
to 6 wks
4.0 1-10 days
Gender
NS
NS
M/F
M
M
M
NS
NS
M/F
M
M
NS
Age
NS
NS
4 wks
7 wks
NS
NS
NS
NS
NS
8 wks
NS
Species (Strain)
Rat
Guinea Pig
Mouse (ICR:JCL)
Mouse (ICR)
Monkey (Squirrel)
Dog (Mongrel)
Mouse (NS)
Rabbit
(NS)
Monkey (Macaca
speciosa)
Rat
(Sprague-Dawley)
Rat
(Wistar)
Rat
(NS)
Effects
No effect on blood hemoglobin or RBCs.
Increase of red blood cell D-2,3-
diphosphogly cerate
Addition of 50 ppm CO to NO2 failed to affect
carboxy hemoglobin.
No effect on methemoglobin.
No effect on hematocrit or hemoglobin with
NO2 and influenza exposure.
No changes in hemoglobin or hematocrit. .
Concentration-related increase in
methemoglobin and nitrosylhemoglobin
Decreased RBCs.
With or without NaCl (330 ug/m3):
polycythemia with reduced mean corpuscular
volume and normal mean corpuscular
hemoglobin.
No effect on hemoglobin, hematocrit or RBC
count; no methemoglobin was observed.
Azoulayetal. (1978)
Increase in RBC sialic acid.
References
Shalamberidze
(1960)
Mersch et al.
(1973)
Nakajima and
Kusumoto
(1970)
Nakajima and
Kusumoto
(1968)
Fenters et al.
(1973)
Wagner et al.
(1965)
Case et al.
(1979)
Mitina (1962)
Furiosi et al.
(1973)
Azoulay et al.
(1978)
Kunimoto et al.
(1984)
-------
TABLE AX4.6 (cont'd). EFFECT OF NITROGEN DIOXIDE (NO2) ON HEMATOLOGICAL PARAMETERS"
CJQ
«j
to
O
o
^
NO2 Concentration
fig/m3
7,520
9,400-
75,200
18,800
ppm
4.0
5-40
10
Exposure
NS
Ih
2 h/day,
5 days/wk, up to
30wks
Gender
NS
F
F
Age
NS
4 mos
6-8 wks
Species (Strain)
NS
Mouse (JCL:ICR)
Mouse (BALB/c)
Effects
Decrease in RBCs.
No increase in methemoglobin. Increased
nitrite and especially nitrate.
Small decrease in hemoglobin and mean
corpuscular hemoglobin concentration.
References
Mochitate and
Miura (1984)
Oda et al.
(1981)
Holt et al.
(1979)
"Modified from US Environmental Protection Agency (1993).
NS = Not stated.
RBCs = Red blood cells.
M = Male.
F = Female.
CO = Carbon monoxide.
NaCl = Sodium chloride.
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
-------
TABLE AX4.7. EFFECTS OF NITRIC OXIDE WITH IRON AND ON ENZYMES
AND NUCLEIC ACIDS
Effect
Reference
Sodium nitroprusside (NO donor) mobilizes iron
from ferritin
Reif and Simmons (1990)
Modulation of arachidonic acid metabolism via
interference with iron
Kanneretal. (1991, 1992)
Inhibition of aconitase (an enzyme in the Krebs
cycle, and also complex 1 and 2 of the respiratory
chain)
Hibbsetal. (1988)
Perssonetal. (1990)
Stadleretal. (1991)
Permanent modification of hemoglobin, possibly via Moriguchi et al. (1992)
deamination
Deamination of DNA
Wink etal. (1991)
DNA strand breaks
Nguyen etal. (1992)
Inhibition of DNA polymerase and ribonucleotide Lepoivre et al. (1991)
reductase Kwon et al. (1991)
Antimitogenic; inhibition of T cell proliferation in Fu & Blankenhorn (1992)
rat spleen cells
Inhibition of DNA synthesis, cell proliferation, and Nakaki et al. (1990)
mitogenesis in vascular tissue
Inhibition of mitogenesis and cell proliferation
(vascular smooth muscle cells)
Garg and Hassid (1989)
Adenosine diphosphate ribosylation is stimulated by Nakaki et al. (1990)
NO-generating agents
August 2007
AX4-46
DRAFT-DO NOT QUOTE OR CITE
-------
TABLE AX4.8A. GENOTOXICITY OF NO2 IN VITRO AND IN PLANTS
OQ
to
o
o
Test Organism
End Point
Exposure
Comments
Results
Reference
Salmonella TA100
Salmonella TA100
Salmonella TA 100
and TA 102
Mutations
Mutations
Mutations
SalmonellaTAlOO SOS repair
E. co//, WP2
E. coll
Bacillus sub tills
spores
V79 hamster cells
Mutations
SOS repair
Mutations
Chromatid-type
aberrations, SCE
6-10 ppm, 40 mins
10-15 ppm, 6h
Bubbling of 10-90 ppm
through bact. susp.,
30 mins as above
Bubbling of 10-90 ppm
through bact. susp.,
30 mins
Bubbling of 10-90 ppm
through bact. susp.,
30 mins
Bubbling of 10-90 ppm
through bact. susp.,
30 mins
500 ppm, 2-3 h
10-100 ppm, 10 mins
Concentrations >10 ppm were
bacteriotoxic
Effect not considered solely
attributed to nitrite in
suspension. No effect seen
with NO gas.
Effect shown not to be solely
due to nitric acid or nitrite.
No effect if cells not washed
with Hank's salt solution
+ Isomuraetal. (1984)
+ Victorin and
Stahlberg (1988)
Kosakaetal. (1985)
Kosakaetal. (1985)
Kosakaetal. (1986,
1987)
Kosakaetal. (1986,
1987)
Sasaki etal. (1980)
Tsudaetal. (1981)
H
6
o
0
H
O
O
H
W
O
O
HH
H
W
V79 hamster cells
Don hamster cells
V79 hamster cells
Tradescantia
Tradescantia
Source: Victorin et al.
SCE
Mutations (8-G resistance)
DNA single -strand breaks
Micronuclei in pollen
Mutations in stamen hair
(1994).
2-3 ppm, 10 mins
2-3 ppm, 10 mins
10 ppm, 20 mins
5 ppm, 24 h
50 ppm, 6 h
VVAU-A A .1X4J.AAV J kJCt-Al. kJV/±L*l.±V/±±
prior to exposure
Slight response
Effect not due to formation of
nitrite
+ Shiraishi and
Bandow (1985)
Isomuraetal. (1984)
+ Gorsdorf et al.
(1990)
+ Ma etal. (1982)
+ Schairer etal. (1979)
-------
TABLE AX4.8B. GENOTICITY OF NO2 IN VIVO
CJQ
to
o
o
oo
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Test Organism
Drosophila
Drosophila
Rats
Rats
Mice
Mice
End Point
Recessive lethals
Somatic mutations (wing spot test)
Mutations in lung cells (oubain res.)
Chromosome aberrations in lung cells
Chromosome aberrations in lymphocytes and
spermatocytes
Micronuclei in bone marrow
Exposure
500-7000 ppm, 1 h
50-280 ppm, 2 days
50-560 ppm, >12 days
27 ppm, 3 h
0. 1-10 ppm, 6 h
20 ppm, 23 h
Result Reference
Inoueetal. (1981)
Victorinetal. (1990)
+ Isomuraetal. (1984)
+ Isomuraetal. (1984)
Goochetal. (1977)
Victorinetal. (1990)
Source: Victorin (1994).
-------
TABLE AX4.8C. GENOTOXICITY OF NO IN VITRO AND IN VIVO
^ Test Organism End Point Exposure Result Reference
^ Salmonella TA100 Mutations 25-30 ppm, 40 min + Isomuraetal. (1984)
o Salmonella SOS repair Bubbling of 10-90 ppm - Kosakaetal. (1985)
VO
H
6
o
o
H
O
o
H
W
O
O
HH
H
W
Don hamster cells Mutations (8-AG resistance) 2-3 ppm, 10 min + Isomuraetal. (1984)
V79 hamster cells DNA single-strand breaks 500 ppm, 30 min - Gorsdorf etal. (1990)
TK 6 human cells Mutations, DNA single-strand Injection of 0.12-0.38 ml NO gas/ml + Nguyen etal. (1992)
breaks of culture medium, 1 h
Salmonella TA1535 Mutations 30 min to 5-90 ppm + Arroyo etal. (1992)
Rats Mutations in lung cells (oubain 27 ppm, 3 h - Isomuraetal. (1984)
res.)
Source: Victorin (1994); Arroyo et al. (1992) added.
-------
1 AX4.3 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 Abdul-Kareem, H. S.; Sharma, R. P.; Brown, D. B. (1991) Effects of repeated intermittent
7 exposures to nitrous oxide on central neurotransmitters and hepatic methionine synthetase
8 activity in CD-I mice. Toxicol. Ind. Health 7: 97-108.
9 Abraham, W. M.; Kim, C. S.; King, M. M.; Oliver, W., Jr.; Yerger, L. (1982) Effects of nitric
10 acid on carbachol reactivity of the airways in normal and allergic sheep. Arch. Environ.
11 Health 37: 36-40.
12 Ackermann-Liebrich, U.; Leuenberger, P.; Schwartz, J.; Schindler, C.; Monn, C.; Bolognini, B.;
13 Bongard, J. P.; Brandli, O.; Domenighetti, G.; Elsasser, S.; Grize, L.; Karrer, W.; Keller,
14 R.; Keller-Wossidlo, H.; Kunzli, N.; Martin, B. W.; Medici, T. C.; Perruchoud, A. P.;
15 Schoni, M. H.; Tschopp, J. M.; Villiger, B.; Wuthrich, B.; Zellweger, J. P.; Zemp, E.
16 (1997) Lung function and long term exposure to air pollutants in Switzerland. Am. J.
17 Respir. Crit. Care Med. 155: 122-129.
18 Acton, J. D.; Myrvik, Q. N. (1972) Nitrogen dioxide effects on alveolar macrophages. Arch.
19 Environ. Health 24: 48-52.
20 Adamkiewicz, G.; Ebelt, S.; Syring, M.; Slater, J.; Speizer, F. E.; Schwartz, J.; Suh, H.; Gold, D.
21 R. (2004) Association between air pollution exposure and exhaled nitric oxide in an
22 elderly population. Thorax 59: 204-209.
23 Adams, W. C.; Brookes, K. A.; Schelegle, E. S. (1987) Effects of NO2 alone and in combination
24 with Os on young men and women. J. Appl. Physiol. 62: 1698-1704.
25 Adkins, B., Jr.; Van Stee, E. W.; Simmons, J. E.; Eustis, S. L. (1986) Oncogenic response of
26 strain A/J mice to inhaled chemicals. J. Toxicol. Environ. Health 17:311-322.
27 Aga, E.; Samoli, E.; Touloumi, G.; Anderson, H. R.; Cadum, E.; Forsberg, B.; Goodman, P.;
28 Goren, A.; Kotesovec, F.; Kriz, B.; Macarol-Hiti, M.; Medina, S.; Paldy, A.; Schindler,
29 C.; Sunyer, J.; Tittanen, P.; Wojtyniak, B.; Zmirou, D.; Schwartz, J.; Katsouyanni, K.
30 (2003) Short-term effects of ambient particles on mortality in the elderly: results from 28
31 cities in the APHEA2 project. Eur. Respir. J. 21(suppl. 40): 28s-33s.
32 Albritton, D. L.; Greenbaum, D. S., eds. (1998) Atmospheric observations: helping build the
33 scientific basis for decisions related to airborne particulate matter: report of the PM
34 Measurements Research Workshop; July; Chapel Hill, NC. Cambridge, MA: Health
35 Effects Institute.
36 Aim, S.; Mukala, K.; Pasanen, P.; Tiittanen, P.; Ruuskanen, J.; Tuomisto, J.; Jantunen, M. J.
37 (1998) Personal NO2 exposures of preschool children in Helsinki. J. Exposure Anal.
38 Environ. Epidemiol. 8: 79-100.
39 Alving, K.; Fornhem, C.; Lundberg, J. M. (1993) Pulmonary effects of endogenous and
40 exogenous nitric oxide in the pig: relation to cigarette smoke inhalation. Br. J. Pharmacol.
41 110:739-746.
August 2007 AX4-50 DRAFT-DO NOT QUOTE OR CITE
-------
1 American Academy of Pediatrics, Committee on Environmental Health. (2004) Ambient air
2 pollution: health hazards to children. Pediatrics 114: 1699-1707.
3 American Thoracic Society. (2000a) Guidelines for methacholine and exercise challenge testing-
4 1999. Am. J. Respir. Crit. Care Med. 161: 309-329.
5 American Thoracic Society. (2000b) What constitutes an adverse health effect of air pollution?
6 Am. J. Respir. Crit. Care Med. 161: 665-673.
7 Ammann, M.; Kalberer, M.; lost, D. T.; Tobler, L.; Rossler, E.; Piguet, D.; Gaggeler, H. W.;
8 Baltensperger, U. (1998) Heterogeneous production of nitrous acid on soot in polluted air
9 masses. Nature (London) 395: 157-160.
10 Amoruso, M. A.; Witz, G.; Goldstein, B. D. (1981) Decreased superoxide anion radical
11 production by rat alveolar macrophages following inhalation of ozone or nitrogen
12 dioxide. Life Sci. 28:2215-2221.
13 Anderson, H. R.; Spix, C.; Medina, S.; Schouten, J. P.; Castellsague, J.; Rossi, G.; Zmirou, D.;
14 Touloumi, G.; Wojtyniak, B.; Ponka, A.; Bacharova, L.; Schwartz, J.; Katsouyanni, K.
15 (1997) Air pollution and daily admissions for chronic obstructive pulmonary disease in 6
16 European cities: results from the APHEA project. Eur. Respir. J. 10: 1064-1071.
17 Anderson, H. R.; Ponce de Leon, A.; Bland, J. M.; Bower, J. S.; Emberlin, J.; Strachen, D. P.
18 (1998) Air pollution, pollens, and daily admissions for asthma in London 1987-92.
19 Thorax 53: 842-848.
20 Aranyi, C.; Fenters, J.; Erhlich, R.; Gardner, D. (1976) Scanning electron microscopy of alveolar
21 macrophages after exposure to oxygen, nitrogen dioxide, and ozone. Environ. Health
22 Perspect. 16: 180.
23 Arner, E. C.; Rhoades, R. A. (1973) Long-term nitrogen dioxide exposure: effects on lung lipids
24 and mechanical properties. Arch. Environ. Health 26: 156-160.
25 Arroyo, P. L.; Hatch-Pigott, V.; Mower, H. F.; Cooney, R. V. (1992) Mutagenicity of nitric
26 oxide and its inhibition by antioxidants. Mutat. Res. Lett. 281: 193-202.
27 Atkinson, R. W.; Anderson, H. R.; Strachan, D. P.; Bland, J. M.; Bremner, S. A.; Ponce de Leon,
28 A. (1999a) Short-term associations between outdoor air pollution and visits to accident
29 and emergency departments in London for respiratory complaints. Eur. Respir. J. 13:
30 257-265.
31 Atkinson, R. W.; Bremner, S. A.; Anderson, H. R.; Strachan, D. P.; Bland, J. M.; Ponce de Leon,
32 A. (1999b) Short-term associations between emergency hospital admissions for
33 respiratory and cardiovascular disease and outdoor air pollution in London. Arch.
34 Environ. Health 54: 398-411.
35 Avissar, N. E.; Reed, C. K.; Cox, C.; Frampton, M. W.; Finkelstein, J. N. (2000) Ozone, but not
36 nitrogen dioxide, exposure decreases glutathione peroxidases in epithelial lining fluid of
37 human lung. Am. J. Respir. Crit. Care Med. 162: 1342-1347.
38 Avol, E. L.; Gauderman, W. J.; Tan, S. M.; London, S. J.; Peters, J. M. (2001) Respiratory
39 effects of relocating to areas of differing air pollution levels. Am. J. Respir. Crit. Care
40 Med. 164: 2067-2072.
August 2007 AX4-51 DRAFT-DO NOT QUOTE OR CITE
-------
1 Ayaz, K. L.; Csallany, A. S. (1978) Long-term NC>2 exposure of mice in the presence and
2 absence of vitamin E. II. Effect of glutathione peroxidase. Arch. Environ. Health 33: 292-
3 296.
4 Azadniv, M.; Utell, M. J.; Morrow, P. E.; Gibb, F. R.; Nichols, J.; Roberts, N. 1, Jr.; Speers, D.
5 M.; Torres, A.; Tsai, Y.; Abraham, M. K.; Voter, K. Z.; Frampton, M. W. (1998) Effects
6 of nitrogen dioxide exposure on human host defense. Inhalation Toxicol. 10: 585-602.
7 Azoulay, E.; Soler, P.; Blayo, M. C.; Basset, F. (1977) Nitric oxide effects on lung structure and
8 blood oxygen affinity in rats. Bull. Eur. Physiopathol. Respir. 13: 629-644.
9 Azoulay, E.; Bouley, G.; Blayo, M. C. (1981) Effects of nitric oxide on resistance to bacterial
10 infection in mice. J. Toxicol. Environ. Health 7: 873-882.
11 Azoulay-Dupuis, E.; Torres, M.; Soler, P.; Moreau, J. (1983) Pulmonary NC>2 toxicity in neonate
12 and adult guinea pigs and rats. Environ. Res. 30: 322-339.
13 Balabaeva, L.; Tabakova, S. (1985) Lipidnata peroksidatsiya v dvye pokoleniya zhenski beli
14 plukhove, inkhalirani s azoten dvuokis [Lipid peroxidation in two progenies of female
15 albino rats inhaling nitrogen dioxide]. Khig. Zdraveopaz. 28: 41-46.
16 Ballester, F.; Tenias, J. M.; Perez-Hoyos, S. (2001) Air pollution and emergency hospital
17 admissions for cardiovascular diseases in Valencia, Spain. J. Epidemiol. Community
18 Health 55: 57-65.
19 Ballester, F.; Saez, M.; Perez-Hoyos, S.; Ifiiguez, C.; Gandarillas, A.; Tobias, A.; Bellido, J.;
20 Taracido, M.; Arribas, F.; Daponte, A.; Alonso, E.; Canada, A.; Guillen-Grima, F.;
21 Cirera, L.; Perez-Boillos, M. J.; Saurina, C.; Gomez, F.; Tenias, J. M. (2002) The
22 EMECAM project: a multicentre study on air pollution and mortality in Spain: combined
23 results for particulates and for sulfur dioxide. Occup. Environ. Med. 59: 300-308.
24 Ballester, F.; Rodriguez, P.; Ifiiguez, C.; Saez, M.; Daponte, A.; Galan, L; Taracido, M.; Arribas,
25 F.; Bellido, J.; Cirarda, F. B.; Canada, A.; Guillen, J. J.; Guillen-Grima, F.; Lopez, E.;
26 Perez-Hoyos, S.; Lertxundi, A.; Toro, S. (2006) Air pollution and cardiovascular
27 admisisons association in Spain: results within the EMECAS project. J. Epidemiol.
28 Community Health 60: 328-336.
29 Barck, C.; Sandstrom, T.; Lundahl, J.; Hallden, G; Svartengren, M.; Strand, V.; Rak, S.; Bylin,
30 G. (2002) Ambient level of NC>2 augments the inflammatory response to inhaled allergen
31 in asthmatics. Respir. Med. 96: 907-917.
32 Barck, C.; Lundahl, J.; Hallden, G.; Bylin, G. (2005a) Brief exposures to NCh augment the
33 allergic inflammation in asthmatics. Environ. Res. 97: 58-66.
34 Barck, C.; Lundahl, J.; Holmstrom, M.; Bylin, G. (2005b) Does nitrogen dioxide affect
35 inflammatory markers after nasal allergen challenge? Am. J. Rhinol. 19: 560-566.
36 Barinaga, M. (1991) Is nitric oxide the "retrograde messenger"? Science 254: 1296-1297.
37 Barnett, A. G.; Williams, G. M.; Schwartz, J.; Neller, A. H.; Best, T. L.; Petroeschevsky, A. L.;
38 Simpson, R. W. (2005) Air pollution and child respiratory health: a case-crossover study
39 in Australia and New Zealand. Am. J. Respir. Crit. Care Med. 171: 1272-1278.
August 2007 AX4-52 DRAFT-DO NOT QUOTE OR CITE
-------
1 Barnett, A. G.; Williams, G. M.; Schwartz, J.; Best, T. L.; Neller, A. H.; Petroeschevsky, A. L.;
2 Simpson, R. W. (2006) The effects of air pollution on hospitalization for cardiovascular
3 disease in elderly people in Australian and New Zealand cities. Environ. Health Perspect.
4 114:1018-1023.
5 Barth, P. J.; Miiller, B. (1999) Effects of nitrogen dioxide exposure on Clara cell proliferation
6 and morphology. Pathol. Res. Pract. 195: 487-493.
7 Barth, P. J.; Miiller, B.; Wagner, U.; Bittinger, A. (1994a) Assessment of proliferative activity in
8 type II pneumocytes after inhalation of NO2 by agnor-analysis. Exp. Toxicol. Pathol. 46:
9 335-342.
10 Barth, P. J.; Uhlarik, S.; Bittinger, A.; Wagner, U.; Riischoff, J. (1994b) Diffuse alveolar damage
11 in the rat lung after short and long term exposure to nitrogen dioxide. Pathol. Res. Pract.
12 190:33-41.
13 Barth, P. J.; Miiller, B.; Wagner, U.; Bittinger, A. (1995) Quantitative analysis of parenchymal
14 and vascular alterations in NO2-induced lung injury in rats. Eur. Respir. J. 8: 1115-1121.
15 Bateson, T. F.; Schwartz, J. (2004) Who is sensitive to the effects of particulate air pollution on
16 mortality? A case-crossover analysis of effect modifiers. Epidemiology 15: 143-149.
17 Bauer, M. A.; Utell, M. J.; Morrow, P. E.; Speers, D. M.; Gibb, F. R. (1986) Inhalation of 0.30
18 ppm nitrogen dioxide potentiates exercise-induced bronchospasm in asthmatics. Am.
19 Rev. Respir. Dis. 134: 1203-1208.
20 Beckett, W. S.; Russi, M. B.; Haber, A. D.; Rivkin, R. M.; Sullivan, J. R.; Tameroglu, Z.;
21 Mohsenin, V.; Leaderer, B. P. (1995) Effect of nitrous acid on lung function in
22 asthmatics: a chamber study. Environ. Health Perspect. 103: 372-375.
23 Beil, M.; Ulmer, W. T. (1976) Wirkung von NO2 im MAK-Bereich auf Atemmechanik und
24 bronchiale Acetylcholinempfmdlichkeit bei Normalpersonen [Effect of NO2 in workroom
25 concentrations on respiratory mechanics and bronchial susceptibility to acetylcholine in
26 normal persons]. Int. Arch. Occup. Environ. Health 38: 31-44.
27 Belanger, K.; Beckett, W.; Triche, E.; Bracken, M.; Holford, T.; Ren, P.; McSharry, J.-E.; Gold,
28 D.; Platts-Mills, T.; Leaderer, B. (2003) Symptoms of wheeze and persistent cough in the
29 first year of life: associations with indoor allergens, air contaminants and maternal history
30 of asthma. Am. J. Epidemiol. 158: 195-202.
31 Belanger, K.; Gent, J. F.; Triche, E. W.; Bracken, M. B.; Leaderer, B. P. (2006) Association of
32 indoor nitrogen dioxide exposure with respiratory symptoms in children with asthma.
33 Am. J. Respir. Crit. Care Med. 173: 297-303.
34 Bell, M. L.; Ebisu, K.; Belanger, K. (2007) Ambient air pollution and low birth weight in
35 Connecticut and Massachusetts. Environ. Health Perspect. 115:1118-1125.Bermiidez, E.
36 (2001) Detection of poly(ADP-ribose) synthetase activity in alveolar macrophages of rats
37 exposed to nitrogen dioxide and ozone. Inhalation Toxicol. 13: 69-84.
38 Bernard, N.; Saintot, M.; Astre, C.; Gerber, M. (1998) Personal exposure to nitrogen dioxide
39 pollution and effect on plasma antioxidants. Arch. Environ. Health 53: 122-128.
August 2007 AX4-53 DRAFT-DO NOT QUOTE OR CITE
-------
1 Biggeri, A.; Baccini, M.; Bellini, P.; Terracini, B. (2005) Meta-analysis of the Italian studies of
2 short-term effects of air pollution (MISA), 1990-1999. Int. J. Occup. Environ. Health 11:
3 107-122.
4 Bils, R. F. (1976) The connective tissues and alveolar walls in the lungs of normal and oxidant-
5 exposed squirrel monkeys. J. Cell Biol. 70: 318.
6 Blaise, G. A.; Gauvin, D.; Gangal, M.; Authier, S. (2005) Nitric oxide, cell signaling and cell
7 death. Toxicology 208: 177-192.
8 Blomberg, A.; Krishna, M. T.; Bocchino, V.; Biscione, G. L.; Shute, J. K.; Kelly, F. J.; Frew, A.
9 J.; Holgate, S. T.; Sandstrom, T. (1997) The inflammatory effects of 2 ppm NC>2 on the
10 airways of healthy subjects. Am. J. Respir. Crit. Care Med. 156: 418-424.
11 Blomberg, A.; Krishna, M. T.; Helleday, R.; Soderberg, M.; Ledin, M.-C.; Kelly, F. J.; Frew, A.
12 J.; Holgate, S. T.; Sandstrom, T. (1999) Persistent airways inflammation but
13 accomodated antioxidant and lung function responses after repeated daily exposure to
14 nitrogen dioxide. Am. J. Respir. Crit. Care Med. 159: 536-543.
15 Boezen, M.; Schouten, J.; Rijcken, B.; Vonk, J.; Gerritsen, J.; Van Der Zee, S.; Hoek, G.;
16 Brunekreef, B.; Postma, D. (1998) Peak expiratory flow variability, bronchial
17 responsiveness, and susceptibility to ambient air pollution in adults. Am. J. Respir. Crit.
18 Care Med. 158: 1848-1854.
19 Borja-Aburto, V. H.; Castillejos, M.; Gold, D. R.; Bierzwinski, S.; Loomis, D. (1998) Mortality
20 and ambient fine particles in southwest Mexico City, 1993-1995. Environ. Health
21 Perspect. 106: 849-855.
22 Boutin-Forzano, S.; Adel, N.; Gratecos, L.; Jullian, H.; Gamier, J. M.; Ramadour, M.;
23 Lanteaume, A.; Hamon, M.; Lafay, V.; Charpin, D. (2004) Visits to the emergency room
24 for asthma attacks and short-term variations in air pollution. A case-crossover study.
25 Respiration 71: 134-137.
26 Brady, T. C.; Crapo, J. D.; Mercer, R. R. (1998) Nitric oxide inhalation transiently elevates
27 pulmonary levels of DGMP, inos mrna, and tnf-alpha. Am. J. Physiol. 275: L509-L515.
28 Braga, A. L. F.; Saldiva, P. H. N.; Pereira, L. A. A.; Menezes, J. J. C.; Concei9ao, G. M. C.; Lin,
29 C. A.; Zanobetti, A.; Schwartz, J.; Dockery, D. W. (2001) Health effects of air pollution
30 exposure on children and adolescents in Sao Paulo, Brazil. Pediatr. Pulmonol. 31: 106-
31 113.
32 Braner, M.; Henderson, S.; Kirkham, T.; Lee, K. S.; Rich, K.; Teschke, K. (2002) Review of the
33 health risks associated with nitrogen dioxide and sulfur dioxide in indoor air. Vancouver,
34 BC, Canada: University of British Columbia, School of Occupational and Environmental
35 Hygiene. Available: http://www.cher.ubc.ca/PDFs/IAQNO2SO2full.pdf [18 July, 2007].
36 Brauer, M.; Ryan, P. B.; Suh, H. H.; Koutrakis, P.; Spengler, J. D.; Leslie, N. P.; Billick, I. H.
37 (1990) Measurements of nitrous acid inside two research houses. Environ. Sci. Technol.
38 24:1521-1527.
39 Brauer, M.; Rasmussen, T. R.; Kjaergaard, S. K.; Spengler, J. D. (1993) Nitrous acid formation in
40 an experimental exposure chamber. Indoor Air 3: 94-105.
August 2007 AX4-54 DRAFT-DO NOT QUOTE OR CITE
-------
1 Brauer, M.; Hoek, G.; Van Vliet, P.; Meliefste, K.; Fischer, P. H.; Wijga, A.; Koopman, L. P.;
2 Neijens, H. J.; Gerritsen, J.; Kerkhof, M.; Heinrich, J.; Bellander, T.; Brunekreef, B.
3 (2002) Air pollution from traffic and the development of respiratory infections and
4 asthmatic and allergic symptoms in children. Am. J. Respir. Crit. Care Med. 166: 1092-
5 1098.
6 Brauer, M.; Hoek, G.; Smit, H. A.; De Jongste, J. C.; Gerritsen, J.; Postma, D. S.; Kerkhof, M.;
7 Brunekreef, B. (2007) Air pollution and development of asthma, allergy and infections in
8 a birth cohort. Eur. Respir. J. 29: 879-888.
9 Braun-Fahrlander, C.; Ackermann-Liebrich, U.; Schwartz, J.; Gnehm, H. P.; Rutishauser, M.;
10 Wanner, H. U. (1992) Air pollution and respiratory symptoms in preschool children. Am.
11 Rev. Respir. Dis. 145: 42-47.
12 Bremner, S. A.; Anderson, H. R.; Atkinson, R. W.; McMichael, A. J.; Strachan, D. P.; Bland, J.
13 M.; Bower, J. S. (1999) Short term associations between outdoor air pollution and
14 mortality in London 1992-4. Occup. Environ. Med. 56: 237-244.
15 Brook, R. D.; Franklin, B.; Cascio, W.; Hong, Y.; Howard, G.; Lipsett, M.; Luepker, R.;
16 Mittleman, M.; Samet, J.; Smith, S. C., Jr.; Tager, I. (2004) Air pollution and
17 cardiovascular disease. A statement for healthcare professionals from the Expert Panel on
18 Population and Prevention Science of the American Heart Association. Circulation 109:
19 2655-2671.
20 Brown, S. K.; Mahoney, K. J.; Cheng, M. (2004) Room chamber assessment of the pollutant
21 emission properties of (nominally) low-emission unflued gas heaters. Indoor Air
22 14(suppl. 8): 84-91.
23 Burnett, R. T.; Cakmak, S.; Brook, J. R.; Krewski, D. (1997a) The role of particulate size and
24 chemistry in the association between summertime ambient air pollution and
25 hospitalization for cardiorespiratory diseases. Environ. Health Perspect. 105: 614-620.
26 Burnett, R. T.; Brook, J. R.; Yung, W. T.; Dales, R. E.; Krewski, D. (1997b) Association
27 between ozone and hospitalization for respiratory diseases in 16 Canadian cities. Environ.
28 Res. 72:24-31.
29 Burnett, R. T.; Cakmak, S.; Brook, J. R. (1998) The effect of the urban ambient air pollution mix
30 on daily mortality rates in 11 Canadian cities. Can. J. Public Health 89: 152-156.
31 Burnett, R. T.; Smith-Doiron, M.; Stieb, D.; Cakmak, S.; Brook, J. R. (1999) Effects of
32 particulate and gaseous air pollution on cardiorespiratory hospitalizations. Arch. Environ.
33 Health 54: 130-139.
34 Burnett, R. T.; Brook, J.; Dann, T.; Delocla, C.; Philips, O.; Cakmak, S.; Vincent, R.; Goldberg,
35 M. S.; Krewski, D. (2000) Association between particulate- and gas-phase components of
36 urban air pollution and daily mortality in eight Canadian cities. In: Grant, L. D., ed.
37 PM2000: particulate matter and health. Inhalation Toxicol. 12(suppl. 4): 15-39.
38 Burnett, R. T.; Goldberg, M. S. (2003) Size-fractionated particulate mass and daily mortality in
39 eight Canadian cities. In: Revised analyses of time-series studies of air pollution and
40 health. Special report. Boston, MA: Health Effects Institute; pp. 85-89. Available:
41 http://www.healtheffects.org/news.htm [16 May, 2003].
August 2007 AX4-55 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 Busch, R. H.; Buschbom, R. L.; Cannon, W. C.; Lauhala, K. E.; Miller, F. J.; Graham, J. A.;
5 Smith, L. G. (1986) Effects of ammonium nitrate aerosol exposure on lung structure of
6 normal and elastase-impaired rats and guinea pigs. Environ. Res. 39: 237-252.
7 Bush, T.; Smith, S.; Stevenson, K.; Moorcroft, S. (2001) Validation of nitrogen dioxide diffusion
8 tube methodology in the UK. Atmos. Environ. 35: 289-296.
9 Bylin, G.; Lindvall, T.; Rehn, T.; Sundin, B. (1985) Effects of short-term exposure to ambient
10 nitrogen dioxide concentrations on human bronchial reactivity and lung function. Eur. J.
11 Respir. Dis. 66: 205-217.
12 Cabral-Anderson, L. J.; Evans, M. J.; Freeman, G. (1977) Effects of NO2 on the lungs of aging
13 rats: I. morphology. Exp. Mol. Pathol. 27: 353-365.
14 California Air Resources Board. (2007a) Review of the California ambient air quality standard
15 for nitrogen dioxide. Staff report: initial statement of reasons for proposed rulemaking.
16 Sacramento, CA: California Environmental Protection Agency, Air Resources Board.
17 Available: http://www.arb.ca.gov/research/aaqs/no2-rs/no2-doc.htm [23 July, 2007].
18 California Air Resources Board. (2007b) Review of the California ambient air quality standard
19 for nitrogen dioxide. Technical support document. Sacramento, CA: California
20 Environmental Protection Agency, Air Resources Board. Available:
21 http://www.arb.ca.gov/research/aaqs/no2-rs/no2-doc.htm [23 July, 2007].
22 Campbell, G. W.; Stedman, J. R.; Stevenson, K. (1994) A survey of nitrogen dioxide
23 concentrations in the United Kingdom using diffusion tubes, July-December 1991.
24 Atmos. Environ. 28: 477-486.
25 Carslaw, N. (2007) A new detailed chemical model for indoor air pollution. Atmos. Environ. 41:
26 1164-1179.
27 Case, G. D.; Dixon, J. S.; Schooley, J. C. (1979) Interactions of blood metalloproteins with
28 nitrogen oxides and oxidant air pollutants. Environ. Res. 20: 43-65.
29 Castellsague, J.; Sunyer, J.; Saez, M.; Anto, J. M. (1995) Short-term association between air
30 pollution and emergency room visits for asthma in Barcelona. Thorax 50: 1051-1056.
31 Cavanagh, D. G.; Morris, J. B. (1987) Mucus protection and airways peroxidation following
32 nitrogen dioxide exposure in the rat. J. Toxicol. Environ. Health 22: 313-328.
33 Centers for Disease Control and Prevention. (2005) Asthma prevalence, health care use and
34 mortality: United States, 2003-05. Atlanta, GA: National Center for Health Statistics.
35 Available: http://www.cdc.gov/nchs/products/pubs/pubd/hestats/ashtma03-05/asthma03-
36 05.htm [7 August, 2007].
37 Centers for Disease Control and Prevention. (2007a) Prevalence of heart disease — United States,
38 2005. Morb. Mortal. Wkly. Rep. MMWR 56: 113-118.
39 Centers for Disease Control and Prevention. (2007b) Prevalence of stroke — United States, 2005.
40 Morb. Mortal. Wkly. Rep. MMWR 56: 469-474.
August 2007 AX4-56 DRAFT-DO NOT QUOTE OR CITE
-------
1 Chan, C.-C.; Chuang, K.-J.; Su, T.-C.; Lin, L.-Y. (2005) Association between nitrogen dioxide
2 and heart rate variability in a susceptible population. Eur. J. Cardiovasc. Prev. Rehabil.
3 12: 580-586.
4 Chan, C.-C.; Chuang, K.-J.; Chien, L.-C.; Chen, W.-J.; Chang, W.-T. (2006) Urban air pollution
5 and emergency admissions for cerebrovascular diseases in Taipei, Taiwan. Eur. Heart J.
6 27: 1238-1244.
7 Chang, L.-Y.; Graham, J. A.; Miller, F. J.; Ospital, J. J.; Crapo, J. D. (1986) Effects of
8 subchronic inhalation of low concentrations of nitrogen dioxide. I. The proximal alveolar
9 region of juvenile and adult rats. Toxicol. Appl. Pharmacol. 83: 46-61.
10 Chang, L.-Y.; Mercer, R. R.; Stockstill, B. L.; Miller, F. J.; Graham, J. A.; Ospital, J. J.; Crapo, J.
11 D. (1988) Effects of low levels of NC>2 on terminal bronchiolar cells and its relative
12 toxicity compared to O^. Toxicol. Appl. Pharmacol. 96: 451-464.
13 Chang, C.-C.; Tsai, S.-S.; Ho, S.-C.; Yang, C.-Y. (2005) Air pollution and hospital admissions
14 for cardiovascular disease in Taipei, Taiwan. Environ. Res. 98: 114-119.
15 Chauhan, A. J.; Inskip, H. M.; Linaker, C. H.; Smith, S.; Schreiber, J. ; Johnston, S. L.; Holgate,
16 S. T. (2003) Personal exposure to nitrogen dioxide (NC^) and the severity of virus-
17 induced asthma in children. Lancet 361: 1939-1944.
18 Chiodi, H.; Mohler, J. G. (1985) Effects of exposure of blood hemoglobin to nitric oxide.
19 Environ. Res. 37: 355-363.
20 Chow, C. K.; Tappel, A. L. (1972) An enzymatic protective mechanism against lipid
21 peroxidation damage to lungs of ozone-exposed rats. Lipids 7: 518-524.
22 Chow, C. K.; Dillard, C. J.; Tappel, A. L. (1974) Glutathione peroxidase system and lysozyme in
23 rats exposed to ozone or nitrogen dioxide. Environ. Res. 7: 311-319.
24 Cocheo, V.; Boaretto, C.; Sacco, P. (1996) High uptake rate radial diffusive sampler suitable for
25 both solvent and thermal desorption. Am. Ind. Hyg. Assoc. J. 57: 897-904.
26 Cockcroft, D. W.; Davis, B. E.; Todd, D. C.; Smycniuk, A. J. (2005) Methacholine challenge:
27 comparison of two methods. Chest 127: 839-844.
28 Cockcroft, D. W.; Davis, B. E. (2006) Airways hyperresponsiveness as a determinant of the early
29 asthmatic response to inhaled allergen. J. Asthma 43: 175-178.
30 Code of Federal Regulations. (2002) Ambient air quality surveillance; appendix E - probe and
31 monitoring path citing criteria for ambient air quality monitoring. C. F. R. 40: §58.
32 Coffin, D. L.; Gardner, D. E. (1972) Interaction of biological agents and chemical air pollutants.
33 Ann. Occup. Hyg. 15: 219-234.
34 Coffin, D. L.; Gardner, D. E.; Sidorenko, G. L; Pinigin, M. A. (1977) Role of time as a factor in
35 the toxicity of chemical compounds in intermittent and continuous exposures. Part II.
36 Effects of intermittent exposure. J. Toxicol. Environ. Health 3: 821-828.
37 Connell, D. P.; Withum, J. A.; Winter, S. E.; Statnick, R. M.; Bilonick,, R. A. (2005) The
38 Steubenville Comprehensive Air Monitoring Program (SCAMP): associations among
39 fine particulate matter, co-pollutants, and meteorological conditions. J. Air Waste
40 Manage. Assoc. 55: 481-496.
August 2007 AX4-57 DRAFT-DO NOT QUOTE OR CITE
-------
1 Connor, L. M.; Bidani, A.; Goerke, 1; Clements, J. A.; Postlethwait, E. M. (2001) NO2
2 interfacial transfer is reduced by phospholipid monolayers. J. Appl. Physiol. 91: 2024-
3 2034.
4 Cotterill, A.; Kingham, S. (1997) Nitrogen dioxide in the home: cooking, double glazing, or
5 outdoor air? Indoor Built Environ. 6: 344-349.
6 Cox, R. M. (2003) The use of passive sampling to monitor forest exposure to Os, NC>2, and SC^:
7 a review and some case studies. Environ. Pollut. 126: 301-311.
8 Crapo, J. D.; Barry, B. E.; Chang, L.-Y.; Mercer, R. R. (1984) Alterations in lung structure
9 caused by inhalation of oxidants. J. Toxicol. Environ. Health 13: 301-321.
10 Crosley, D. R. (1996) NO^ blue ribbon panel. J. Geophys. Res. [Atmos.] 101: 2049-2052.
11 Cyrys, J.; Heinrich, J.; Richter, K.; Wolke, G.; Wichmann, H. E. (2000) Sources and
12 concentrations of indoor nitrogen dioxide in Hamburg (west Germany) and Erfurt (east
13 Germany). Sci. Total Environ. 250: 51-62.
14 Cyrys, J.; Stolzel, M.; Heinrich, J.; Kreyling, W. G.; Menzel, N.; Wittmaack, K.; Tuch, T.;
15 Wichmann, H.-E. (2003) Elemental composition and sources of fine and ultrafme
16 ambient particles in Erfurt, Germany. Sci. Total Environ. 305: 143-156.
17 Dab, W.; Medina, S.; Quenel, P.; Le Moullec, Y.; Le Tertre, A.; Thelot, B.; Monteil, C.;
18 Lameloise, P.; Pirard, P.; Momas, I; Ferry, R.; Festy, B. (1996) Short term respiratory
19 health effects of ambient air pollution: results of the APHEA project in Paris. In: St
20 Leger, S., ed. The APHEA project. Short term effects of air pollution on health: a
21 European approach using epidemiological time series data. J. Epidemiol. Commun.
22 Health 50(suppl. 1): S42-S46.
23 DeMarco, V.; Skimming, J. W.; Ellis, T. M.; Cassin, S. (1996) Nitric oxide inhalation: effects on
24 the ovine neonatal pulmonary and systemic circulation. Reprod. Fertil. Dev. 8: 431-438.
25 Delfino, R. J. (2002) Evaluation of health effects of toxic air pollutants in a southern California
26 community: a pilot study. Sacramento, CA: California State Air Resources Board;
27 contract no. ARB-99-302. Available: ftp://ftp.arb.ca.gov/carbis/research/apr/past/99-
28 302.pdf [18 December, 2003]. Available: NTIS, Springfield, VA; PM2003-107639.
29 Delfino, R. J.; Gone, H.; Linn, W. S.; Pellizzari, E. D.; Hu, Y. (2003a) Asthma symptoms in
30 Hispanic children and daily ambient exposures to toxic and criteria air pollutants.
31 Environ. Health Perspect. Ill: 647-656.
32 Delfino, R. J.; Gong, H.; Linn, W. S.; Hu, Y.; Pellizzari, E. D. (2003b) Respiratory symptoms
33 and peak expiratory flow in children with asthma in relation to volatile organic
34 compounds in exhaled breath and ambient air. J. Exposure Anal. Environ. Epidemiol. 13:
35 348-363.
36 Dennekamp, M.; Howarth, S.; Dick, C. A. J.; Cherrie, J. W.; Donaldson, K.; Seaton, A. (2001)
37 Ultrafme particles and nitrogen oxides generated by gas and electric cooking. Occup.
38 Environ. Med. 58: 511-516.
39 Desqueyroux, H.; Pujet, J.-C.; Prosper, M.; Le Moullec, Y.; Momas, I. (2002) Effects of air
40 pollution on adults with chronic obstructive pulmonary disease. Arch. Environ. Health
41 57: 554-560.
August 2007 AX4-58 DRAFT-DO NOT QUOTE OR CITE
-------
1 Devalia, J. L.; Rusznak, C.; Herdman, M. J.; Trigg, C. I; Tarraf, H.; Davies, R. J. (1994) Effect
2 of nitrogen dioxide and sulphur dioxide on airways response of mild asthmatic patients to
3 allergen inhalation. Lancet 344: 1668-1671.
4 Devlin, R. B.; Horstman, D. P.; Gerrity, T. R.; Becker, S.; Madden, M. C. (1999) Inflammatory
5 response in humans exposed to 2.0 PPM nitrogen dioxide. Inhalation Toxicol. 11: 89-
6 109.
7 Dewanji, A.; Moolgavkar, S. H. (2000) A Poisson process approach for recurrent event data with
8 environmental covariates. Environmetrics 11: 665-673.
9 Dietert, R. R.; Etzel, R. A.; Chen, D.; Halonen, M.; Holladay, S. D.; Jarabek, A. M.; Landreth,
10 K.; Peden, D. B.; Pinkerton, K.; Smialowicz, R. J.; Zoetis, T. (2000) Workshop to
11 identify critical window of exposure for children's health: immune and respiratory
12 systems work group summary. Environ. Health Perspect. Suppl. 108(3): 483-490.
13 Di Giovanni, V.; Cagiano, R.; Carratu, M. R.; De Salvia, M. A.; Giustino, A.; Cuomo, V. (1994)
14 Alterations in the ontogeny of rat pup ultrasonic vocalization produced by prenatal
15 exposure to nitrogen dioxide. Psychopharmacology 116: 423-427.
16 Dijkstra, L.; Houthuijs, D.; Brunekreef, B.; Akkerman, I; Boleij, J. S. M. (1990) Respiratory
17 health effects of the indoor environment in a population of Dutch children. Am. Rev.
18 Respir. Dis. 142: 1172-1178.
19 Dimitroulopoulou, C.; Ashmore, M. R.; Byrne, M. A.; Kinnersley, R. P. (2001) Modelling of
20 indoor exposure to nitrogen dioxide in the UK. Atmos. Environ. 35: 269-279.
21 D'Ippoliti, D.; Forastiere, F.; Ancona, C.; Agabiti, N.; Fusco, D.; Michelozzi, P.; Perucci, C. A.
22 (2003) Air pollution and myocardial infarction in Rome: a case-crossover analysis.
23 Epidemiology 14: 528-535.
24 Dockery, D. W.; Speizer, F. E.; Stram, D. O.; Ware, J. H.; Spengler, J. D.; Ferris, B. G., Jr.
25 (1989) Effects of inhalable particles on respiratory health of children. Am. Rev. Respir.
26 Dis. 139: 587-594.
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.; Brunekreef, B. (1996) Longitudinal studies of air pollution effects on lung
31 function. Am. J. Respir. Crit. Care Med. 154(suppl.): S250-S256.
32 Dockery, D. W.; Luttmann-Gibson, H.; Rich, D. Q.; Link, M. S.; Schwartz, J. D.; Gold, D. R.;
33 Koutrakis, P.; Verrier, R. L.; Mittleman, M. A. (2005) Particulate air pollution and
34 nonfatal cardiac events. Part II. Association of air pollution with confirmed arrhythmias
35 recorded by implanted defibrillators. Boston, MA: Health Effects Institute; research
36 report no. 124; pp. 83-126; discussion; pp. 127-148. Available:
37 http://pubs.healtheffects.org/ [7 June, 2007].
38 Dominici, F.; McDermott, A.; Zeger, S. L.; Samet, J. M. (2002) On the use of generalized
39 additive models in time-series studies of air pollution and health. Am. J. Epidemiol. 156:
40 193-203.
August 2007 AX4-59 DRAFT-DO NOT QUOTE OR CITE
-------
1 Dominici, F.; McDermott, A.; Daniels, M.; Zeger, S. L.; Samet, J. M. (2003) Mortality among
2 residents of 90 cities. In: Revised analyses of time-series studies of air pollution and
3 health. Special report. Boston, MA: Health Effects Institute; pp. 9-24. Available:
4 http://www.healtheffects.org/Pubs/TimeSeries.pdf [12 May, 2004].
5 Douglas, G. J.; Price, J. F.; Page, C. P. (1994) A method for the long-term exposure of rabbits to
6 environmental pollutant gases. Eur. Respir. J. 7: 1516-1526.
7 Dowell, A. R.; Kilburn, K. H.; Pratt, P. C. (1971) Short-term exposure to nitrogen dioxide:
8 effects on pulmonary ultrastructure, compliance, and the surfactant system. Arch. Intern.
9 Med. 128: 74-80.
10 Drechsler-Parks, D. M. (1995) Cardiac output effects of O3 and NO2 exposure in healthy older
11 adults. Toxicol. Ind. Health 11: 99-109.
12 Drozdz, M.; Kucharz, E.; Ludyga, K.; Molska-Drozdz, T. (1976) Studies on the effect of long-
13 term exposure to nitrogen dioxide on serum and liver proteins level and enzyme activity
14 in guinea pigs. Eur. J. Toxicol. 9: 287-293.
15 Dunlea, E. J.; Herndon, S. C.; Nelson, D. D.; Volkamer, R. M.; San Martini, F.; Sheehy, P. M.;
16 Zahniser, M. S.; Shorter, J. H.; Wormhoudt, J. C.; Lamb, B. K.; Allwine, E. J.; Gaffney,
17 J. S.; Marley, N. A.; Grutter, M.; Marquez, C.; Blanco, S.; Cardenas, B.; Retama, A.;
18 Ramon Villegas, C. R.; Kolb, C. E.; Molina, L. T.; Molina, M. J. (2007) Evaluation of
19 nitrogen dioxide chemiluminescence monitors in a polluted urban environment. Atmos.
20 Chem. Phys. 7: 2691-2704.
21 Dupuy, P. M.; Shore, S. A.; Drazen, J. M.; Frostell, C.; Hill, W. A.; Zapol, W. M. (1992)
22 Bronchodilator action of inhaled nitric oxide in guinea pigs. J. Clin. Invest. 90: 421-428.
23 Dutton, S. J.; Hannigan, M. P.; Miller, S. L. (2001) Indoor pollutant levels from the use of
24 unvented natural gas fireplaces in Boulder, Colorado. J. Air Waste Manage. Assoc. 51:
25 1654-1661.
26 Ehrlich, R. (1966) Effect of nitrogen dioxide on resistance to respiratory infection. Bacteriol.
27 Rev. 30: 604-614.
28 Ehrlich, R. (1980) Interaction between environmental pollutants and respiratory infections.
29 Environ. Health Perspect. 35: 89-100.
30 Ehrlich, R.; Henry, M. C. (1968) Chronic toxicity of nitrogen dioxide: I. effect on resistance to
31 bacterial pneumonia. Arch. Environ. Health 17: 860-865.
32 Ehrlich, R.; Silverstein, E.; Maigetter, R.; Fenters, J. D.; Gardner, D. (1975) Immunologic
33 response in vaccinated mice during long-term exposure to nitrogen dioxide. Environ. Res.
34 10:217-223.
35 Ehrlich, R.; Findlay, J. C.; Fenters, J. D.; Gardner, D. E. (1977) Health effects of short-term
36 inhalation of nitrogen dioxide and ozone mixtures. Environ. Res. 14: 223-231.
37 Ehrlich, R.; Findlay, J. C.; Gardner, D. E. (1979) Effects of repeated exposures to peak
38 concentrations of nitrogen dioxide and ozone on resistance to streptococcal pneumonia. J.
39 Toxicol. Environ. Health 5: 631-642.
August 2007 AX4-60 DRAFT-DO NOT QUOTE OR CITE
-------
1 Ekwo, E. E.; Weinberger, M. M.; Lachenbruch, P. A.; Huntley, W. H. (1983) Relationship of
2 parental smoking and gas cooking to respiratory disease in children. Chest 84: 662-668.
3 Erbas, B.; Hyndman, R. J. (2005) Sensitivity of the estimated air pollution-respiratory
4 admissions relationship to statistical model choice. Int. J. Environ. Health Res. 15: 437-
5 448.
6 Erbas, B.; Kelly, A.-M.; Physick, B.; Code, C.; Edwards, M. (2005) Air pollution and childhood
7 asthma emergency hospital admissions: estimating intra-city regional variations. Int. J.
8 Environ. Health Res. 15: 11-20.
9 Evans, M. J.; Stephens, R. J.; Cabral, L. J.; Freeman, G. (1972) Cell renewal in the lungs of rats
10 exposed to low levels of NO2. Arch. Environ. Health 24: 180-188.
11 Evans, M. J.; Cabral, L. J.; Stephens, R. J.; Freeman, G. (1973a) Cell division of alveolar
12 macrophages in rat lung following exposure to NC>2. Am. J. Pathol. 70: 199-208.
13 Evans, M. J.; Cabral, L. J.; Stephens, R. J.; Freeman, G. (1973b) Renewal of alveolar epithelium
14 in the rat following exposure to NC>2. Am. J. Pathol. 70: 175-190.
15 Evans, M. J.; Cabral, L. C.; Stephens, R. J.; Freeman, G. (1974) Acute kinetic response and
16 renewal of the alveolar epithelium following injury by nitrogen dioxide. Chest 65(suppl.):
17 62S-65S.
18 Evans, H. L.; Laties, V. G.; Weiss, B. (1975) Behavioral effects of mercury and methylmercury.
19 Fed. Proc. 34: 1858-1867.
20 Evans, M. J.; Johnson, L. V.; Stephens, R. J.; Freeman, G. (1976) Renewal of the terminal
21 bronchiolar epithelium in the rat following exposure to NC>2 or Os. Lab. Invest. 35: 246-
22 257.
23 Evans, M. J.; Cabral-Anderson, L. J.; Freeman, G. (1977) Effects of NO2 on the lungs of aging
24 rats: II. cell proliferation. Exp. Mol. Pathol. 27: 366-376.
25 Evans, J. N.; Hemenway, D. R.; Kelley, J. (1989) Early markers of lung injury. Cambridge, MA:
26 Health Effects Institute; research report no. 29. Available from: NTIS, Springfield, VA;
27 PB91-171983.
28 Ewetz, L. (1993) Absorption and metabolic fate of nitrogen oxides. Scand. J. Work Environ.
29 Health 19 [suppl. 2]: 21-27.
30 Fairley, D. (1999) Daily mortality and air pollution in Santa Clara County, California: 1989-
31 1996. Environ. Health Perspect. 107: 637-641.
32 Farahani, H.; Hasan, M. (1992) Nitrogen dioxide induced changes in level of free fatty acids,
33 triglyceride, esterified fatty acid, ganglioside and lipase activity in the guinea pig brain. J.
34 Environ. Sci. Health B 27: 53-71.
35 Farhat, S. C. L.; Paulo, R. L. P.; Shimoda, T. M.; Conceicao, G. M. S.; Lin, C. A.; Braga, A. L.
36 F.; Warm, M. P. N.; Saldiva, P. H. N. (2005) Effect of air pollution on pediatric
37 respiratory emergency room visits and hospital admissions. Braz. J. Med. Biol. Res. 38:
38 227-235.
39 Farrow, A.; Greenwood, R.; Preece, S.; Golding, J. (1997) Nitrogen dioxide, the oxides of
40 nitrogen, and infants' health symptoms. Arch. Environ. Health 52: 189-194.
August 2007 AX4-61 DRAFT-DO NOT QUOTE OR CITE
-------
1 Febo, A.; Perrino, C. (1991) Prediction and experimental evidence for high air concentration of
2 nitrous acid in indoor environments. Atmos. Environ. Part A 25: 1055-1061.
3 Federal Register. (1971) National primary and secondary ambient air quality standards. F. R.
4 (April 30) 36: 8186-8201.
5 Federal Register. (1984) Proposed reaffirmation of the National Ambient Air Quality Standards
6 for Nitrogen Dioxide. F. R. 49 (February 23): 6866-6879.
7 Federal Register. (1987) Air quality criteria for carbon monoxide; air quality criteria for oxides
8 of nitrogen. F. R. 52 (July 22): 27580.
9 Federal Register. (1991) Draft criteria document for oxides of nitrogen. F. R. 56 (November 25):
10 59285.
11 Federal Register. (1995) National ambient air quality standards for nitrogen dioxide: proposed
12 decision. F. R. 60 (October 11): 52874-52889.
13 Federal Register. (2005) Air quality criteria for oxides of nitrogen. F. R. 70 (December 9):
14 73236-73237.
15 Federal Register. (2007) Workshop on assessment of health science for the review of the
16 NAAQS for nitrogen (NOX) and sulfur oxides (SOX). F. R. 72 (February 9): 6238.
17 Fehls, A. O. S.; Cohn, Z. A. (1986) The alveolar macrophage. J. Appl. Physiol. 60: 353-369.
18 Fehsenfeld, F. C.; Dickerson, R. R.; Hiibler, G.; Luke, W. T.; Nunnermacker, L. J.; Williams, E.
19 J.; Roberts, J. M.; Calvert, J. G.; Curran, C. M.; Delany, A. C.; Eubank, C. S.; Fahey, D.
20 W.; Fried, A.; Gandrud, B. W.; Langford, A. O.; Murphy, P. C.; Norton, R. B.; Pickering,
21 K. E.; Ridley, B. A. (1987) A ground-based intercomparison of NO, NOX, and NOy
22 measurement techniques. J. Geophys. Res. [Atmos.] 92: 14,710-14,722.
23 Fels, A. O. S.; Cohn, Z. A. (1986) The alveolar macrophage. J. Appl. Physiol. 60: 353-369.
24 Fenters, J. D.; Findlay, J. C.; Port, C. D.; Ehrlich, R.; Coffin, D. L. (1973) Chronic exposure to
25 nitrogen dioxide: immunologic, physiologic, and pathologic effects in virus-challenged
26 squirrel monkeys. Arch. Environ. Health 27: 85-89.
27 Filleul, L.; Rondeau, V.; Vandentorren, S.; Le Moual, N.; Cantagrel, A.; Annesi-Maesano, L;
28 Charpin, D.; Declercq, C.; Neukirch, F.; Paris, C.; Vervloet, D.; Brochard, P.; Tessier, J.
29 F.; Kauffmann, F.; Baldi, I. (2005) Twenty five year mortality and air pollution: results
30 from the French PAARC survey. Occup. Environ. Med. 62: 453-460.
31 Finlayson-Pitts, B. J.; Pitts, J. N., Jr. (2000) Chemistry of the upper and lower atmosphere:
32 theory, experiments and applications. San Diego, CA: Academic Press.
33 Folinsbee, L. J. (1992) Does nitrogen dioxide exposure increase airways responsiveness?
34 Toxicol. Ind. Health 8: 273-283.
35 Folinsbee, L. J.; Horvath, S. M.; Bedi, J. F.; Delehunt, J. C. (1978) Effect of 0.62 ppm NO2 on
36 cardiopulmonary function in young male nonsmokers. Environ. Res. 15: 199-205.
37 Forsberg, B.; Stjernberg, N.; Linne, R.; Segerstedt, B.; Wall, S. (1998) Daily air pollution levels
38 and acute asthma in southern Sweden. Eur. Respir. J. 12: 900-905.
August 2007 AX4-62 DRAFT-DO NOT QUOTE OR CITE
-------
1 Fortmann, R.; Kariher, P.; Clayton, R. (2001) Indoor air quality: residential cooking exposures.
2 Final report. Sacramento, CA: State of California Air Resources Board; ARB Contract
3 No. 97-330. Available: http://arb.ca.gov/research/abstracts/97-330.htm [22 May, 2007].
4 Frampton, M. W.; Smeglin, A. M.; Roberts, N. J., Jr.; Finkelstein, J. N.; Morrow, P. E.; Utell, M.
5 J. (1989) Nitrogen dioxide exposure in vivo and human alveolar macrophage inactivation
6 of influenza virus in vitro. Environ. Res. 48: 179-192.
7 Frampton, M. W.; Morrow, P. E.; Cox, C.; Gibb, F. R.; Speers, D. M.; Utell, M. J. (1991) Effects
8 of nitrogen dioxide exposure on pulmonary function and airways reactivity in normal
9 humans. Am. Rev. Respir. Dis. 143: 522-527.
10 Frampton, M. W.; Boscia, J.; Roberts, N. J., Jr.; Azadniv, M.; Torres, A.; Cox, C.; Morrow, P.
11 E.; Nichols, J.; Chalupa, D.; Frasier, L. M.; Gibb, F. R.; Speers, D. M.; Tsai, Y.; Utell, M.
12 J. (2002) Nitrogen dioxide exposure: effects on airways and blood cells. Am. J. Physiol.
13 282:L155-L165.
14 Frampton, M. W.; Pietropaoli, A. P.; Morrow, P. E.; Utell, M. J. (2006) Human clinical studies
15 of airborne pollutants. In: Gardner, D. E. Toxicology of the lung. Boca Raton, FL: CRC
16 Press; pp. 29-82. (Target organ toxicology series).
17 Fratacci, M. D.; Frostell, C. G.; Chen, T. Y.; Wain, J. C.; Robinson, D. R.; Zapol, W. M. (1991)
18 Inhaled nitric oxide. A selective pulmonary vasodilator of heparin-protamine
19 vasoconstriction in sheep. Anesthesiology 75: 990-999.
20 Freeman, B. A.; Mudd, J. B. (1981) Reaction of ozone with sulfhydryls of human erythrocytes.
21 Arch. Biochem. Biophys. 208: 212-220.
22 Freeman, G.; Furiosi, N. J.; Haydon, G. B. (1966) Effects of continuous exposure of 0.8 ppm
23 NC>2 on respiration of rats. Arch. Environ. Health 13: 454-456.
24 Freeman, G.; Stephens, R. J.; Crane, S. C.; Furiosi, N. J. (1968) Lesion of the lung in rats
25 continuously exposed to two parts per million of nitrogen dioxide. Arch. Environ. Health
26 17:181-192.
27 Freeman, G.; Crane, S. C.; Furiosi, N. J.; Stephens, R. J.; Evans, M. J.; Moore, W. D. (1972)
28 Covert reduction in ventilatory surface in rats during prolonged exposure to subacute
29 nitrogen dioxide. Am. Rev. Respir. Dis. 106: 563-579.
30 Fujimaki, H.; Nohara, O. (1994) Changes in the response of lung mast cells isolated from rats
31 and guinea pigs exposed to nitrogen dioxide. Inhalation Toxicol. 6: 515-520.
32 Fujimaki, H.; Shimizu, F.; Kubota, K. (1982) Effect of subacute exposure to NC>2 on
33 lymphocytes required for antibody responses. Environ. Res. 29: 280-286.
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.
August 2007 AX4-63 DRAFT-DO NOT QUOTE OR CITE
-------
1 Furiosi, N. J.; Crane, S. C.; Freeman, G. (1973) Mixed sodium chloride aerosol and nitrogen
2 dioxide in air: biological effects on monkeys and rats. Arch. Environ. Health 27: 405-
3 408.
4 Fusco, D.; Forastiere, F.; Michelozzi, P.; Spadea, T.; Ostro, B.; Area, M.; Perucci, C. A. (2001)
5 Air pollution and hospital admissions for respiratory conditions in Rome, Italy. Eur.
6 Respir. J. 17: 1143-1150.
7 Gair, A. J.; Penkett, S. A. (1995) The effects of wind speed and turbulence on the performance of
8 diffusion tube samplers. Atmos. Environ. 29: 2529-2533.
9 Gair, A. J.; Penkett, S. A.; Oyola, P. (1991) Development of a simple passive technique for the
10 determination of nitrogen-dioxide in remote continental locations. Atmos. Environ. Part
11 A 25: 1927-1939.
12 Galan, I; Tobias, A.; Banegas, J. R.; Aranguez, E. (2003) Short-term effects of air pollution on
13 daily asthma emergency room admissions. Eur. Respir. J. 22: 802-808.
14 Garcia-Algar, 6.; Zapater, M.; Figueroa, C.; Vail, O.; Basagafia, X.; Sunyer, J.; Freixa, A.;
15 Guardino, X.; Pichini, S. (2003) Sources and concentrations of indoor nitrogen dioxide in
16 Barcelona, Spain. J. Air Waste Manage. Assoc. 53: 1312-1317.
17 Garcia Algar, 6.; Pichini, S.; Basagafia, X.; Puig, C.; Vail, O.; Torrent, M.; Harris, J.; Sunyer, J.;
18 Cullinan, P. (2004) Concentrations and determinants of NC>2 in homes of Ashford, UK
19 and Barcelona and Menorca, Spain. Indoor Air 14: 298-304.
20 Garcia-Aymerich, J.; Tobias, A.; Anto, J. M.; Sunyer, J. (2000) Air pollution and mortality in a
21 cohort of patients with chronic obstructive pulmonary disease: a time series analysis. J.
22 Epidemiol. Community Health 54: 73-74.
23 Gardiner, T. H.; Schanker, L. S. (1976) Effect of oxygen toxicity and nitric acid-induced lung
24 damage on drug absorption from the rat lung. Res. Commun. Chem. Pathol. Pharmacol.
25 15: 107-120.
26 Gardner, D. E. (1980) Influence of exposure patterns of nitrogen dioxide on susceptibility to
27 infectious respiratory disease. In: Lee, S. D., ed. Nitrogen oxides and their effects on
28 health. Ann Arbor, MI: Ann Arbor Science Publishers, Inc.; pp. 267-288.
29 Gardner, D. E. (1982) Use of experimental airborne infections for monitoring altered host
30 defenses. Environ. Health Perspect. 43: 99-107.
31 Gardner, D. E.; Coffin, D. L.; Pinigin, M. A.; Sidorenko, G. I. (1977a) Role of time as a factor in
32 the toxicity of chemical compounds in intermittent and continuous exposures. Part I.
33 Effects of continuous exposure. J. Toxicol. Environ. Health 3: 811-820.
34 Gardner, D. E.; Miller, F. J.; Blommer, E. J.; Coffin, D. L. (1977b) Relationships between
35 nitrogen dioxide concentration, time, and level of effect using an animal infectivity
36 model. In: Dimitriades, B., ed. International conference on photochemical oxidant
37 pollution and its control: proceedings, v. I; September 1976; Raleigh, NC. Research
38 Triangle Park, NC: U.S. Environmental Protection Agency, Environmental Sciences
39 Research Laboratory; pp. 513-525; EPA report no. EPA-600/3-77-00la. Available from:
40 NTIS, Springfield, VA; PB-264232. (Ecological research series).
August 2007 AX4-64 DRAFT-DO NOT QUOTE OR CITE
-------
1 Gardner, D. E.; Miller, F. J.; Blommer, E. J.; Coffin, D. L. (1979) Influence of exposure mode on
2 the toxicity of NO2. Environ. Health Perspect. 30: 23-29.
3 Gardner, D. E.; Miller, F. J.; Tiling, J. W.; Graham, J. A. (1982) Non-respiratory function of the
4 lungs: host defenses against infection. In: Schneider, T.; Grant, L., eds. Air pollution by
5 nitrogen oxides: proceedings of the US-Dutch international symposium; May; Maastricht,
6 The Netherlands. Amsterdam, The Netherlands: Elsevier Scientific Publishing Company;
7 pp. 401-415. (Studies in environmental science 21).
8 Garrett, M. H.; Hooper, M. A.; Hooper, B. M.; Abramson, M. J. (1998) Respiratory symptoms in
9 children and indoor exposure to nitrogen dioxide and gas stoves. Am. J. Respir. Crit.
10 Care Med. 158: 891-895.
11 Garrett, M. H.; Hooper, M. A.; Hooper, B. M. (1999) Nitrogen dioxide in Australian homes:
12 levels and sources. J. Air Waste Manage. Assoc. 49: 76-81.
13 Garthwaite, J. (1991) Glutamate, nitric oxide and cell-cell signalling in the nervous system.
14 Trends Neurosci. 14: 60-67.
15 Gauderman, W. J.; Avol, E.; Gilliland, F.; Vora, H.; Thomas, D.; Berhane, K.; McConnell, R.;
16 Kuenzli, N.; Lurmann, F.; Rappaport, E.; Margolis, H.; Bates, D.; Peters, J. (2004) The
17 effect of air pollution on lung development from 10 to 18 years of age. N. Engl. J. Med.
18 351: 1057-1067.
19 Gauderman, W. J.; Avol, E.; Lurmann, F.; Kuenzli, N.; Gilliland, F.; Peters, J.; McConnell, R.
20 (2005) Childhood asthma and exposure to traffic and nitrogen dioxide. Epidemiology 16:
21 737-743.
22 Gauderman, W. J.; Vora, H.; McConnell, R.; Berhane, K.; Gilliland, F.; Thomas, D.; Lurmann,
23 F.; Avol, E.; Kunzli, N. (2007) Effect of exposure to traffic on lung development from 10
24 to 18 years of age: a cohort study. Lancet 369: 571-577.
25 Gauvin, S.; Le Moullec, Y.; Bremont, F.; Momas, L; Balducci, F.; Ciognard, F.; Poilve, M.-P.;
26 Zmirou, D.; VESTA Investigators. (2001) Relationships between nitrogen dioxide
27 personal exposure and ambient air monitoring measurements among children in three
28 French metropolitan areas: VESTA study. Arch. Environ. Health 56: 336-341.
29 Gavras, J. B.; Frampton, M. W.; Ryan, D. H.; Levy, P. C.; Looney, R. J.; Cox, C.; Morrow, P.
30 E.; Utell, M. J. (1994) Expression of membrane antigens on human alveolar macrophages
31 after exposure to nitrogen dioxide. Inhalation Toxicol. 6: 633-646.
32 Gehring, U.; Cyrys, J.; Sedlmeir, G.; Brunekreef, B.; Bellander, T.; Fischer, P.; Bauer, C. P.;
33 Reinhardt, D.; Wichmann, H. E.; Heinrich, J. (2002) Traffic-related air pollution and
34 respiratory health during the first 2 yrs. of life. Eur. Respir. J. 19: 690-698.
35 Gehring, U.; Heinrich, J.; Kramer, U.; Grote, V.; Hochadel, M.; Sugiri, D.; Kraft, M.; Rauchfuss,
36 K.; Eberwein, H. G.; Wichmann, H.-E. (2006) Long-term exposure to ambient air
37 pollution and cardiopulmonary mortality in women. Epidemiology 17: 545-551.
38 Gilbert, N. L.; Gauvin, D.; Guay, M.; Heroux, M.-E.; Dupuis, G.; Legris, M.; Chan, C. C.; Dietz,
39 R. N.; Levesque, B. (2006) Housing characteristics and indoor concentrations of nitrogen
40 dioxide and formaldehyde in Quebec City, Canada. Environ. Res. 102: 1-8.
August 2007 AX4-65 DRAFT-DO NOT QUOTE OR CITE
-------
1 Gilbert, N. L.; Goldberg, M. S.; Beckerman, B.; Brook, J. R.; Jerrett, M. (2005) Assessing spatial
2 variability of ambient nitrogen dioxide in Montreal, Canada, with a land-use regression
3 model. J. Air Waste Manage. Assoc. 55: 1059-1063.
4 Gilliland, F. D.; McConnell, R.; Peters, J.; Gong, H, Jr. (1999) A theoretical basis for
5 investigation ambient air pollution and children's respiratory health. Environ. Health
6 Perspect. 107(suppl. 3): 403-407.
7 Gilliland, F. D.; Berhane, K.; Rappaport, E. B.; Thomas, D. C.; Avol, E.; Gauderman, W. J.;
8 London, S. J.; Margolis, H. G.; McConnell, R.; Islam, K. T.; Peters, J. M. (2001) The
9 effects of ambient air pollution on school absenteeism due to respiratory illnesses.
10 Epidemiology 12: 43-54.
11 Gilliland, F. D.; Rappaport, E. B.; Berhane, K.; Islam, T.; Dubeau, L.; Gauderman, W. J.;
12 McConnell, R. (2002) Effects of glutathione S-Transferase PI, Ml, and Tl on acute
13 respiratory illness in school children. Am. J. Respir. Crit. Care Med. 166: 346-351.
14 Gilmour, M. L; Park, P.; Belgrade, M. K. (1996) Increased immune and inflammatory responses
15 to dust mite antigen in rats exposed to 5 ppm NC>2. Fundam. Appl. Toxicol. 31: 65-70.
16 Giordano, A. M.; Morrow, P. E. (1972) Chronic low-level nitrogen dioxide exposure and
17 mucociliary clearance. Arch. Environ. Health 25: 443-449.
18 Girman, J. R.; Apte, M. G.; Traynor, G. W.; Allen, J. R.; Hollowell, C. D. (1982) Pollutant
19 emission rates from indoor combustion appliances and sidestream cigarette smoke.
20 Environ. Int. 8: 213-221.
21 Goings, S. A. J.; Kulle, T. J.; Bascom, R.; Sauder, L. R.; Green, D. J.; Hebel, J. R.; Clements, M.
22 L. (1989) Effect of nitrogen dioxide exposure on susceptibility to influenza A virus
23 infection in healthy adults. Am. Rev. Respir. Dis. 139: 1075-1081.
24 Goldberg, M. S.; Burnett, R. T. (2003) Revised analysis of the Montreal time-series study. In:
25 Revised analyses of time-series studies of air pollution and health. Special report. Boston,
26 MA: Health Effects Institute; pp. 113-132. Available:
27 http://www.healtheffects.org/Pubs/TimeSeries.pdf [18 October, 2004].
28 Goldstein, E.; Eagle, M. C.; Hoeprich, P. D. (1973) Effect of nitrogen dioxide on pulmonary
29 bacterial defense mechanisms. Arch. Environ. Health 26: 202-204.
30 Goldstein, E.; Warshauer, D.; Lippert, W.; Tarkington, B. (1974) Ozone and nitrogen dioxide
31 exposure: murine pulmonary defense mechanisms. Arch. Environ. Health 28: 85-90.
32 Goldstein, B. D.; Hamburger, S. J.; Falk, G. W.; Amoruso, M. A. (1977) Effect of ozone and
33 nitrogen dioxide on the agglutination of rat alveolar macrophages by concanavalin A.
34 LifeSci. 21: 1637-1644.
35 Gong, H., Jr., Linn, W. S.; Clark, K. W.; Anderson, K. R.; Geller, M. D.; Sioutas, C. (2005)
36 Respiratory responses to exposures with fine particulates and nitrogen dioxide in the
37 elderly with and without COPD. Inhalation Toxicol. 17: 123-132.
38 Gonzales, M.; Quails, C.; Hudgens, E.; Neas, L. (2005) Characterization of a spatial gradient of
39 nitrogen dioxide across a United States-Mexico border city during winter. Sci. Total
40 Environ. 337: 163-173.
August 2007 AX4-66 DRAFT-DO NOT QUOTE OR CITE
-------
1 Gooch, P. C.; Luippold, H. E.; Creasia, D. A.; Brewen, J. G. (1977) Observations on mouse
2 chromosomes following nitrogen dioxide inhalation. Mutat. Res. 48: 117-119.
3 Gorsdorf, S.; Appel, K. E.; Engeholm, C.; Obe, G. (1990) Nitrogen oxide induces DNA single-
4 strand breaks in cultured Chinese hamster cells. Carcinogenesis 11: 37-41.
5 Goss, C. H.; Newsom, S. A.; Schildcrout, J. S.; Sheppard, L.; Kaufman, J. D. (2004) Effect of
6 ambient air pollution on pulmonary exacerbations and lung function in cystic fibrosis.
7 Am. J. Respir. Crit. Care Med. 169: 816-821.
8 Graham, J. A.; Gardner, D. E.; Blommer, E. J.; House, D. E.; Menache, M. G.; Miller, F. J.
9 (1987) Influence of exposure patterns of nitrogen dioxide and modifications by ozone on
10 susceptibility to bacterial infectious disease in mice. J. Toxicol. Environ. Health 21: 113-
11 125.
12 Green, N. D.; Schneider, S. L. (1978) Effects of NO2 on the response of baboon alveolar
13 macrophages to migration inhibitory factor. J. Toxicol. Environ. Health 22: 655-662.
14 Greenberg, S. D.; Gyorkey, F.; Jenkins, D. E.; Gyorkey, P. (1971) Alveolar epithelial cells
15 following exposure to nitric acid: electron microscopic study in rats. Arch. Environ.
16 Health 22: 655-662.
17 Gregory, R. E.; Pickrell, J. A.; Hahn, F. F.; Hobbs, C. H. (1983) Pulmonary effects of
18 intermittent subacute exposure to low-level nitrogen dioxide. J. Toxicol. Environ. Health
19 11:405-414.
20 Hackney, J. D.; Thiede, F. C.; Linn, W. S.; Pedersen, E. E.; Spier, C. E.; Law, D. C.; Fischer, D.
21 A. (1978) Experimental studies on human health effects of air pollutants. IV. Short-term
22 physiological and clinical effects of nitrogen dioxide exposure. Arch. Environ. Health 33:
23 176-181.
24 Hackney, J. D.; Linn, W. S.; Avol, E. L.; Shamoo, D. A.; Anderson, K. R.; Solomon, J. C.;
25 Little, D. E.; Peng, R.-C. (1992) Exposures of older adults with chronic respiratory illness
26 to nitrogen dioxide: a combined laboratory and field study. Am. Rev. Respir. Dis. 146:
27 1480-1486.
28 Hajat, S.; Haines, A.; Goubet, S. A.; Atkinson, R. W.; Anderson, H. R. (1999) Association of air
29 pollution with daily GP consultations for asthma and other lower respiratory conditions in
30 London. Thorax 54: 597-605.
31 Harre, E. S. M.; Price, P. D.; Ayrey, R. B.; Toop, L. J.; Martin, I. R.; Town, G. I. (1997)
32 Respiratory effects of air pollution in chronic obstructive pulmonary disease: a three
33 month prospective study. Thorax 52: 1040-1044.
34 Hasselblad, V.; Eddy, D. M.; Kotchmar, D. J. (1992) Synthesis of environmental evidence:
35 nitrogen dioxide epidemiology studies. J. Air Waste Manage. Assoc. 42: 662-671.
36 Hatch, G. E.; Slade, R.; Selgrade, M. K.; Stead, A. G. (1986) Nitrogen dioxide exposure and
37 lung antioxidants in ascorbic acid-deficient guinea pigs. Toxicol. Appl. Pharmacol. 82:
38 351-359.
39 Hayden, K. L.; Anlauf, K. G.; Hastie, D. R.; Bottenheim, J. W. (2003) Partitioning of reactive
40 atmospheric nitrogen oxides at an elevated site in southern Quebec, Canada. J. Geophys.
41 Res. [Atmos.] 108(D19): 10.1029/2002JD003188.
August 2007 AX4-67 DRAFT-DO NOT QUOTE OR CITE
-------
1 Hazenkamp-von Arx, M. E.; Gotschi, T.; Ackermann-Liebrich, U.; Bono, R.; Burney, P.; Cyrys,
2 J.; Jarvis, D.; Lillienberg, L.; Luczynska, C.; Maldonado, J. A.; Jaen, A.; de Marco, R.;
3 Mi, Y.; Modig, L.; Bayer-Oglesby, L.; Payo, F.; Soon, A.; Sunyer, J.; Villani, S.; Weyler,
4 J.; Kunzli, N. (2004) PM2.5 and NO2 assessment in 21 European study centres of ECRHS
5 II: annual means and seasonal differences. Atmos. Environ. 38: 1943-1953.
6 Hazucha, M. J.; Ginsberg, J. F.; McDonnell, W. F.; Haak, E. D., Jr.; Pimmel, R. L.; Salaam, S.
7 A.; House, D. E.; Bromberg, P. A. (1983) Effects of 0.1 ppm nitrogen dioxide on airways
8 of normal and asthmatic subjects. J. Appl. Physiol.: Respir. Environ. Exercise Physiol.
9 54: 730-739.
10 Hazucha, M. J.; Folinsbee, L. J.; Seal, E.; Bromberg, P. A. (1994) Lung function response of
11 healthy women after sequential exposures to NO2 and Os. Am. J. Respir. Crit. Care Med.
12 150: 642-647.
13 Heal, M. R.; O'Donoghue, M. A.; Cape, J. N. (1999) Overestimation of urban nitrogen dioxide
14 by passive diffusion tubes: a comparative exposure and model study. Atmos. Environ. 33:
15 513-524.
16 Helleday, R.; Sandstrom, T.; Stjernberg, N. (1994) Differences in bronchoalveolar cell response
17 to nitrogen dioxide exposure between smokers and nonsmokers. Eur. Respir. J. 7: 1213-
18 1220.
19 Helleday, R.; Huberman, D.; Blomberg, A.; Stjernberg, N.; Sandstrom, T. (1995) Nitrogen
20 dioxide exposure impairs the frequency of the mucociliary activity in healthy subjects.
21 Eur. Respir. J. 8: 1664-1668.
22 Henneberger, A.; Zareba, W.; Ibald-Mulli, A.; Ruckerl, R.; Cyrys, J.; Couderc, J.-P.; Mykins, B.;
23 Woelke, G.; Wichmann, H.-E.; Peters, A. (2005) Repolarization changes induced by air
24 pollution in ischemic heart disease patients. Environ. Health Perspect. 113: 440-446.
25 Henry, M. C.; Findlay, J.; Spangler, J.; Ehrlich, R. (1970) Chronic toxicity of NO2 in squirrel
26 monkeys: III. effect on resistance to bacterial and viral infection. Arch. Environ. Health
27 20: 566-570.
28 Hibbs, J. B.; Taintor, R. R.; Vavrin, Z.; Rachlin, E. M. (1988) Nitric oxide: a cytotoxic activated
29 macrophage effector molecule. Biochem. Biophys. Res. Commun. 157: 87-94.
30 Higgins, B. G.; Francis, H. C.; Yates, C. J.; Warburton, C. J.; Fletcher, A. M.; Reid, J. A.;
31 Pickering, C. A. C.; Woodcock, A. A. (1995) Effects of air pollution on symptoms and
32 peak expiratory flow measurements in subjects with obstructive airways disease. Thorax
33 50: 149-155.
34 Higgins, B. G.; Francis, H. C.; Yates, C.; Warburton, C. J.; Fletcher, A. M.; Pickering, C. A. C.;
35 Woodcock, A. A. (2000) Environmental exposure to air pollution and allergens and peak
36 flow changes. Eur. Respir. J. 16: 61- 66.
37 Hiltermann, T. J. N.; Stolk, J.; Van der Zee, S. C.; Brunekreef, B.; De Bruijne, C. R.; Fischer, P.
38 H.; Ameling, C. B.; Sterk, P. J.; Hiemstra, P. S.; Van Bree, L. (1998) Asthma severity
39 and susceptibility to air pollution. Eur. Respir. J. 11: 686-693.
40 Hinwood, A. L.; De Klerk, N.; Rodriguez, C.; Jacoby, P.; Runnion, T.; Rye, P.; Landau, L.;
41 Murray, F.; Feldwick, M.; Spickett, J. (2006) The relationship between changes in daily
August 2007 AX4-68 DRAFT-DO NOT QUOTE OR CITE
-------
1 air pollution and hospitalizations in Perth, Australia 1992-1998: a case-crossover study.
2 Int. J. Environ. Health Res. 16: 27-46.
3 Hirsch, T.; Weiland, S. K.; Von Mutius, E.; Safeca, A. F.; Grafe, H.; Csaplovics, E.; Duhme, H.;
4 Keil, U.; Leupold, W. (1999) Inner city air pollution and respiratory health and atopy in
5 children. Eur. Respir. J. 14: 669-677.
6 Hochadel, M.; Heinrich, J.; Gehring, U.; Morgenstern, V.; Kuhlbusch, T.; Link, E.; Wichmann,
7 H.-E.; Kramer, U. (2006) Predicting long-term average concentrations of traffic-related
8 air pollutants using GIS-based information. Atmos. Environ. 40: 542-553.
9 Hochscheid, R.; Schuchmann, U.; Kotte, E.; Kranz, S.; Heinrichs, S.; Muller, B. (2005) NO2-
10 induced acute and chronic lung injury cause imbalance of glutathione metabolism in type
11 II pneumocytes. Med. Sci. Monit. 11: BR273-279.
12 Hoek, G. (2003) Daily mortality and air pollution in The Netherlands. In: Revised analyses of
13 time-series studies of air pollution and health. Special report. Boston, MA: Health Effects
14 Institute; pp. 133-141. Available: http://www.healtheffects.org/Pubs/TimeSeries.pdffl2
15 May, 2004].
16 Hoek, G.; Brunekreef, B. (1994) Effects of low-level winter air pollution concentrations on
17 respiratory health of Dutch children. Environ. Res. 64: 136-150.
18 Hoek, G.; Brunekreef, B.; Verhoeff, A.; Van Wijnen, J.; Fischer, P. (2000) Daily mortality and
19 air pollution in the Netherlands. J. Air Waste Manage. Assoc. 50: 1380-1389.
20 Hoek, G.; Brunekreef, B.; Fischer, P.; Van Wijnen, J. (2001) The association between air
21 pollution and heart failure, arrhythmia, embolism, thrombosis, and other cardiovascular
22 causes of death in a time series study. Epidemiology 12: 355-357.
23 Hoek, G.; Brunekreef, B.; Goldbohm, S.; Fischer, P.; Van den Brandt, P. A. (2002) Association
24 between mortality and indicators of traffic-related air pollution in the Netherlands: a
25 cohort study. Lancet 360: 1203-1209.
26 Hogman, M.; Frostell, C.; Arnberg, H.; Hedenstierna, G. (1993) Inhalation of nitric oxide
27 modulates methacholine-induced bronchoconstriction in the rabbit. Eur. Respir. J. 6: 170-
28 180.
29 Holguin, F.; Tellez-Rojo, M. M.; Hernandez, M.; Cortez, M.; Chow, J. C.; Watson, J. G.;
30 Mannino, D.; Romieu, I. (2003) Air pollution and heart rate variability among the elderly
31 in Mexico City. Epidemiology 14: 521-527.
32 Holopainen, R.; Aho, H.; Laine, J.; Halkola, L.; Kaapa, P. (1999) Nitric oxide inhalation inhibits
33 pulmonary apoptosis but not inflammatory injury in porcine meconium aspiration. Acta
34 Paediatr. 88: 1147-1155.
35 Holroyd, K. J.; Eleff, S. M.; Zhang, L.-Y.; Jakab, G. J.; Kleeberger, S. R. (1997) Genetic
36 modeling of susceptibility to nitrogen dioxide-induced lung injury in mice. Am. J.
37 Physiol. 273: L595-L602.
38 Holt, P. G.; Finlay-Jones, L. M.; Keast, D.; Papadimitrou, J. M. (1979) Immunological function
39 in mice chronically exposed to nitrogen oxides (NOX). Environ. Res. 19: 154-162.
August 2007 AX4-69 DRAFT-DO NOT QUOTE OR CITE
-------
1 Hooftman, R. N.; Kuper, C. F.; Appelman, L. M. (1988) Comparative sensitivity of histo-
2 pathology and specific lung parameters in the detection of lung injury. J. Appl. Toxicol.
3 8: 59-65.
4 Horowitz, L. W.; Fiore, A. M.; Milly, G. P.; Cohen, R. C.; Perring, A.; Wooldridge, P. J.; Hess,
5 P. G.; Emmons, L. K.; Lamarque, J. F. (2007) Observational constraints on the chemistry
6 of isoprene nitrates over the eastern United States. J. Geophys. Res. (Atmos.)
7 112(D12S08): 10.1029/2006JD007747.
8 Hurford, W. E. (1997) The biological basis for inhaled nitric oxide. Respir. Care Clin. N. Am. 3:
9 357-369.
10 Ichinose, T.; Sagai, M. (1982) Studies on biochemical effects of nitrogen dioxide: III. changes of
11 the antioxidative protective systems in rat lungs and of lipid peroxidation by chronic
12 exposure. Toxicol. Appl. Pharmacol. 66: 1-8.
13 Ichinose, T.; Sagai, M. (1989) Biochemical effects of combined gases of nitrogen dioxide and
14 ozone. III. Synergistic effects on lipid peroxidation and antioxidative protective systems
15 in the lungs of rats and guinea pigs. Toxicology 59: 259-270.
16 Ichinose, T.; Sagai, M. (1992) Combined exposure to NO2, O?, and H2SO4-aerosol and lung
17 tumor formation in rats. Toxicology 74: 173-184.
18 Ichinose, T.; Sagai, M.; Kubota, K. (1983) [Changes of lipid peroxidation and antioxidative
19 protective systems in lungs of rats exposed acutely, subacutely and chronically to
20 nitrogen dioxide]. Taiki Osen Gakkaishi 18: 132-146.
21 Ichinose, T.; Fujii, K.; Sagai, M. (1991) Experimental studies on tumor promotion by nitrogen
22 dioxide. Toxicology 67: 211-225.
23 Ichinose, F.; Adrie, C.; Hurford, W. E.; Zapol, W. M. (1995) Prolonged pulmonary vasodilator
24 action of inhaled nitric oxide by Zaprinast in awake lambs. J. Appl. Physiol. 78: 1288-
25 1295.
26 Ignarro, L. J. (1989) Biological actions and properties of endothelium-derived nitric oxide
27 formed and released from artery and vein. Circ. Res. 65: 1-21.
28 Illing, J. W.; Miller, F. J.; Gardner, D. E. (1980) Decreased resistance to infection in exercised
29 mice exposed to NO2 and Oi. J. Toxicol. Environ. Health 6: 843-851.
30 Inoue, H.; Fukunaga, A.; Okubo, S. (1981) Mutagenic effects of nitrogen dioxide combined with
31 methylurea and ethylurea in Drosophila melanogaster. Mutat. Res. 88: 281-290.
32 Iqbal, Z. M. (1984) In-vivo nitrosation of amines in mice by inhaled nitrogen dioxide and
33 inhibition of biosynthesis of 7V-nitrosamines. In: O'Neill, I. K.; Von Borstel, R. C.; Miller,
34 C. T.; Long, J.; Bartsch, H., eds. N-nitroso compounds: occurrence, biological effects and
35 relevance to human cancer: proceedings of the VHIth international symposium on N-
36 nitroso compounds; September 1983; Banff, Canada. Lyon, France: International Agency
37 for Research on Cancer; pp. 291-300. (IARC scientific publications no. 57).
38 Iqbal, Z. M.; Dahl, K.; Epstein, S. S. (1980) Role of nitrogen dioxide in the biosynthesis of
39 nitrosamines in mice. Science (Washington, DC) 207: 1475-1477.
August 2007 AX4-70 DRAFT-DO NOT QUOTE OR CITE
-------
1 Iqbal, Z. M.; Dahl, K.; Epstein, S. S. (1981) Biosynthesis of dimethylnitrosamine in
2 dimethylamine-treated mice after exposure to nitrogen dioxide. JNCI J. Natl. Cancer Inst.
3 67: 137-141.
4 Islam, T.; Gauderman, W. J.; Berhane, K.; McConnell, R.; Avol, E.; Peters, J. M.; Gilliland, F.
5 D. (2007) The relationship between air pollution, lung function and asthma in
6 adolescents. Thorax: 10.1136/thx.2007.078964.
7 Isomura, K.; Chikahira, M.; Teranishi, K.; Hamada, K. (1984) Induction of mutations and
8 chromosome aberrations in lung cells following in vivo exposure of rats to nitrogen
9 oxides. Mutat. Res. 136: 119-125.
10 Ito, K. (1971) [Effect of nitrogen dioxide inhalation on influenza virus infection in mice].
11 Nippon Eiseigaku Zasshi 26: 304-314.
12 Ito, K. (2003) Associations of particulate matter components with daily mortality and morbidity
13 in Detroit, Michigan. In: Revised analyses of time-series studies of air pollution and
14 health. Special report. Boston, MA: Health Effects Institute; pp. 143-156. Available:
15 http://www.healtheffects.org/Pubs/TimeSeries.pdf [12 May, 2004].
16 Ito, K. (2004) Revised ozone risk estimates for daily mortality and hospitalizations in Detroit,
17 Michigan [personal communication with attachments to Jee Young Kim]. New York,
18 NY: New York University School of Medicine, Nelson Institute of Environmental
19 Medicine; October 31.
20 Jacob, D. J. (2000) Heterogeneous chemistry and tropospheric ozone. Atmos. Environ. 34: 2131-
21 2159.
22 Jacobson, M. Z. (2002) Atmospheric pollution: history, science, and regulation. New York, NY:
23 Cambridge University Press.
24 Jaffe, D. H.; Singer, M. E.; Rimm, A. A. (2003) Air pollution and emergency department visits
25 for asthma among Ohio Medicaid recipients, 1991-1996. Environ. Res. 91: 21-28.
26 Jakab, G. J. (1987) Modulation of pulmonary defense mechanisms by acute exposures to
27 nitrogen dioxide. Environ. Res. 42: 215-228.
28 Jalaludin, B. B.; O'Toole, B. I; Leeder, S. R. (2004) Acute effects of urban ambient air pollution
29 on respiratory symptoms, asthma medication use, and doctor visits for asthma in a cohort
30 of Australian children. Environ Res. 95: 32-42.
31 Jalaludin, B.; Morgan, G; Lincoln, D.; Sheppeard, V.; Simpson, R.; Corbett, S. (2006)
32 Associations between ambient air pollution and daily emergency department attendances
33 for cardiovascular disease in the elderly (65+ years), Sydney, Australia. J. Exposure Sci.
34 Environ. Epidemiol. 16: 225-237.
35 Jarvis, D. L.; Leaderer, B. P.; Chinn, S.; Burney, P. G. (2005) Indoor nitrous acid and respiratory
36 symptoms and lung function in adults. Thorax 60: 474-479.
37 Jenkins, H. S.; Devalia, J. L.; Mister, R. L.; Bevan, A. M.; Rusznak, C.; Davies, R. J. (1999) The
38 effect of exposure to ozone and nitrogen dioxide on the airways response of atopic
39 asthmatics to inhaled allergen: dose- and time-dependent effects. Am. J. Respir. Crit.
40 Care Med. 160:33-39.
August 2007 AX4-71 DRAFT-DO NOT QUOTE OR CITE
-------
1 Jerrett, M. (2007) Does traffic-related air pollution contribute to respiratory disease formation in
2 children? Eur. Respir. J. 29: 825-826.
3 Jet Propulsion Laboratory. (2006) Chemical kinetics and photochemical data for use in
4 atmospheric studies. Evaluation number 15. Pasadena, CA: California Institute of
5 Technology. JPL publication 06-2.
6 Jiang, B. H.; Maruyama, J.; Yokochi, A.; Amano, H.; Mitani, Y.; Maruyama, K. (2002)
7 Correlation of inhaled nitric-oxide induced reduction of pulmonary artery pressure and
8 vascular changes. Eur. Respir. J. 20: 52-58.
9 Jorres, R.; Magnussen, H. (1990) Airways response of asthmatics after a 30 min exposure, at
10 resting ventilation, to 0.25 ppm NC>2 or 0.5 ppm SC>2. Eur. Respir. J. 3: 132-137.
11 Jorres, R.; Magnussen, H. (1991) Effect of 0.25 ppm nitrogen dioxide on the airways response to
12 methacholine in asymptomatic asthmatic patients. Lung 169: 77-85.
13 Jorres, R.; Nowak, D.; Grimminger, F.; Seeger, W.; Oldigs, M.; Magnussen, H. (1995) The
14 effect of 1 ppm nitrogen dioxide on bronchoalveolar lavage cells and inflammatory
15 mediators in normal and asthmatic subjects. Eur. Respir. J. 8: 416-424.
16 Just, J.; Segala, C.; Sahraoui, F.; Priol, G.; Grimfeld, A.; Neukirch, F. (2002) Short-term health
17 effects of particulate and photochemical air pollution in asthmatic children. Eur. Respir.
18 J. 20: 899-906.
19 Karr, C.; Lumley, T.; Shepherd, K.; Davis, R.; Larson, T.; Ritz, B.; Kaufman, J. (2006) A case-
20 crossover study of wintertime ambient air pollution and infant bronchiolitis. Environ.
21 Health Perspect. 114:277-281.
22 Katsouyanni, K.; Schwartz, J.; Spix, C.; Touloumi, G.; Zmirou, D.; Zanobetti, A.; Wojtyniak, B.;
23 Vonk, J. M.; Tobias, A.; Ponka, A.; Medina, S.; Bacharova, L.; Andersen, H. R. (1996)
24 Short term effects of air pollution on health: a European approach using epidemiology
25 time series data: the APHEA protocol. In: St Leger, S., ed. The APHEA project. Short
26 term effects of air pollution on health: a European approach using epidemiological time
27 series data. J. Epidemiol. Community Health 50(suppl. 1): S12-S18.
28 Katsouyanni, K.; Touloumi, G.; Samoli, E.; Gryparis, A.; Le Tertre, A.; Monopolis, Y.; Rossi,
29 G.; Zmirou, D.; Ballester, F.; Boumghar, A.; Anderson, H. R.; Wojtyniak, B.; Paldy, A.;
30 Braunstein, R.; Pekkanen, J.; Schindler, C.; Schwartz, J. (2001) Confounding and effect
31 modification in the short-term effects of ambient particles on total mortality: results from
32 29 European cities within the APHEA2 project. Epidemiology 12: 521-531.
33 Katsouyanni, K.; Touloumi, G.; Samoli, E.; Petasakis, Y.; Analitis, A.; Le Tertre, A.; Rossi, G.;
34 Zmirou, D.; Ballester, F.; Boumghar, A.; Anderson, H. R.; Wojtyniak, B.; Paldy, A.;
35 Braunstein, R.; Pekkanen, J.; Schindler, C.; Schwartz, J. (2003) Sensitivity analysis of
36 various models of short-term effects of ambient particles on total mortality in 29 cities in
37 APHEA2. In: Revised analyses of time-series studies of air pollution and health. Special
38 report. Boston, MA: Health Effects Institute; pp. 157-164. Available:
39 http://www.healtheffects.org/Pubs/TimeSeries.pdf [18 October, 2004].
40 Katsuki, S.; Arnold, W.; Mittal, C.; Murad, F. (1977) Stimulation of guanylate cyclase by
41 sodium nitroprusside, nitroglycerin and nitric oxide in various tissue preparations and
August 2007 AX4-72 DRAFT-DO NOT QUOTE OR CITE
-------
1 comparison to the effects of sodium azide and hydroxylamine. J. Cyclic Nucleotide Res.
2 3:23-35.
3 Kauffmann, F.; Post Genome Respiratory Epidemiology Group. (2004) Post-genome respiratory
4 epidemiology: a multidisciplinary challenge. Eur. Respir. J. 24: 471-480.
5 Kawamoto, T.; Matsuno, K.; Arashidani, K.; Yoshikawa, M.; Kayama, F.; Kodama, Y. (1993)
6 Personal exposure to nitrogen dioxide from indoor heaters and cooking stoves. Arch.
7 Environ. Contam. Toxicol. 25: 534-538.
8 Keller, M. D.; Lanese, R. R.; Mitchell, R. I; Cote, R. W. (1979a) Respiratory illness in
9 households using gas and electricity for cooking: I. survey of incidence. Environ. Res.
10 19:495-503.
11 Keller, M. D.; Lanese, R. R.; Mitchell, R. I; Cote, R. W. (1979b) Respiratory illness in
12 households using gas and electricity for cooking: II. symptoms and objective findings.
13 Environ. Res. 19: 504-515.
14 Kelly, F. J.; Tetley, T. D. (1997) Nitrogen dioxide depletes uric acid and ascorbic acid but not
15 glutathione from lung lining fluid. Biochem. J. 325: 95-99.
16 Kelly, F. J.; Blomberg, A.; Frew, A.; Holgate, S. T.; Sandstrom, T. (1996) Antioxidant kinetics
17 in lung lavage fluid following exposure of humans to nitrogen dioxide. Am. J. Respir.
18 Crit. CareMed. 154: 1700-1705.
19 Kelly, F. J.; Dunster, C.; Mudway, I. (2003) Air pollution and the elderly: oxidant/antioxidant
20 issues worth consideration. Eur. Respir. J. Suppl. 40: 70S-75S.
21 Kelsall, J. E.; Samet, J. M.; Zeger, S. L.; Xu, J. (1997) Air pollution and mortality in
22 Philadelphia, 1974-1988. Am. J. Epidemiol. 146: 750-762.
23 Kerr, H. D.; Kulle, T. J.; Mcllhany, M. L.; Swidersky, P. (1979) Effects of nitrogen dioxide on
24 pulmonary function in human subjects: an environmental chamber study. Environ. Res.
25 19: 392-404.
26 Khoury, M. J.; Davis, R.; Gwinn, M.; Lindegren, M. L.; Yoon, P. (2005) Do we need genomic
27 research for the prevention of common diseases with environmental causes? Am. J.
28 Epidemiol. 161: 799-805.
29 Kim, S. U.; Koenig, J. Q.; Pierson, W. E.; Hanley, Q. S. (1991) Acute pulmonary effects of
30 nitrogen dioxide exposure during exercise in competitive athletes. Chest 99: 815-819.
31 Kim, J. J.; Smorodinsky, S.; Lipsett, M.; Singer, B. C.; Hodgson, A. T.; Ostro, B. (2004a)
32 Traffic-related air pollution near busy roads: the East Bay children's Respiratory Health
33 Study. Am. J. Respir. Crit. Care Med. 170: 520-526.
34 Kim, S.-Y.; Lee, J.-T.; Hong, Y.-C.; Ahn, K.-J.; Kim, H. (2004b) Determining the threshold
35 effect of ozone on daily mortality: an analysis of ozone and mortality in Seoul, Korea,
36 1995-1999. Environ. Res. 94: 113-119.
37 Kim, E.; Hopke, P. K.; Pinto, J. P.; Wilson, W. E. (2005) Spatial variability of fine particle mass,
38 components, and source contributions during the regional air pollution study in St. Louis.
39 Environ. Sci. Technol. 39: 4172-4179.
August 2007 AX4-73 DRAFT-DO NOT QUOTE OR CITE
-------
1 Kim, D.; Sass-Kortsak, A.; Purdham, J. T.; Dales, R. E.; Brook, J. R. (2006) Associations
2 between personal exposures and fixed-site ambient measurements of fine particulate
3 matter, nitrogen dioxide, and carbon monoxide in Toronto, Canada. J. Exposure Sci.
4 Environ. Epidemiol. 16: 172-183.
5 Kinney, P. L.; Ozkaynak, H. (1991) Associations of daily mortality and air pollution in Los
6 Angeles County. Environ. Res. 54: 99-120.
7 Kirby, C.; Fox, M.; Waterhouse, J.; Drye, T. (2001) Influences of environmental parameters on
8 the accuracy of nitrogen dioxide passive diffusion tubes for ambient measurement. J.
9 Environ. Monit. 3: 150-158.
10 Kita, H.; Omichi, S. (1974) [Effects of air pollutants on cilial movement in airway]. Nippon
11 Eiseigaku Zasshi 29: 100.
12 Kitabatake, M.; Yamamoto, H.; Yuan, P. F.; Manjurul, H.; Murase, S.; Yamauchi, T. (1995)
13 Effects of exposure to NO2 or 862 on bronchopulmonary reaction induced by Candida
14 albicam in guinea pigs. J. Toxicol. Environ. Health 45: 75-82.
15 Kleeberger, S. R.; Zhang, L. Y.; Jakab, G. J. (1997) Differential susceptibility to oxidant
16 exposure in inbred strains of mice: nitrogen dioxide versus ozone. Inhalation Toxicol. 9:
17 601-621.
18 Kleinman, M. T.; Mautz, W. J. (1991) The effects of exercise on dose and dose distribution of
19 inhaled automotive pollutants. Cambridge, MA: Health Effects Institute; research report
20 no. 45.
21 Kleinman, M. T.; Bailey, R. M.; Linn, W. S.; Anderson, K. R.; Whynot, J. D.; Shamoo, D. A.;
22 Hackney, J. D. (1983) Effects of 0.2 ppm nitrogen dioxide on pulmonary function and
23 response to bronchoprovocation in asthmatics. J. Toxicol. Environ. Health 12: 815-826.
24 Klepeis, N. E.; Nelson, W. C.; Ott. W. R.; Robinson, J. P. Tsang, A. M.; Switzer, P.; Behar, J.
25 V.; Hern, S. C.; Engelmann, W. H. (2001) The National Human Activity Pattern Survey
26 (NHAPS): a resource for assessing exposure to environmental pollutants. J. Exposure
27 Anal. Environ. Epidemiol. 11: 231-252.
28 Kobayashi, T.; Miura, T. (1995) Concentration- and time-dependent increase in specific airways
29 resistance after induction of airways hyperresponsiveness by subchronic exposure of
30 guinea pigs to nitrogen dioxide. Fundam. Appl. Toxicol. 25: 154-158.
31 Kodama, Y.; Arashidani, K.; Tokui, N.; Kawamoto, T.; Matsuno, K.; Kunugita, N.; Minakawa,
32 N. (2002) Environmental NC>2 concentration and exposure in daily life along main roads
33 in Tokyo. Environ. Res. A 89: 236-244.
34 Koenig, J. Q.; Covert, D. S.; Morgan, M. S.; Horike, M.; Horike, N.; Marshall, S. G.; Pierson,
35 W.E. (1985) Acute effects of 0.12 ppm ozone or 0.12 ppm nitrogen dioxide on
36 pulmonary function in healthy and asthmatic adolescents. Am. Rev. Respir. Dis. 132:
37 648-651.
38 Koenig, J. Q.; Pierson, W. E.; Marshall, S. G.; Covert, D. S.; Morgan, M. S.; Van Belle, G.
39 (1988) The effects of ozone and nitrogen dioxide on lung function in healthy and
40 asthmatic adolescents. Cambridge, MA: Health Effects Institute; research report no. 14.
41 Available from: NTIS, Springfield, VA; PB88-234455.
August 2007 AX4-74 DRAFT-DO NOT QUOTE OR CITE
-------
1 Koenig, J. Q.; Covert, D. S.; Pierson, W. E.; Hanley, Q. S.; Rebolledo, V.; Dumler, K.;
2 McKinney, S. E. (1994) Oxidant and acid aerosol exposure in healthy subjects and
3 subjects with asthma. Part I: effects of oxidants, combined with sulfuric or nitric acid, on
4 the pulmonary function of adolescents with asthma. Cambridge, MA: Health Effects
5 Institute; pp. 1-36; research report no. 70.
6 Kon, K.; Maeda, N.; Shiga, T. (1977) Effect of nitric oxide on the oxygen transport of human
7 erythrocytes. J. Toxicol. Environ. Health 2: 1109-1113.
8 Kosaka, H.; Oda, Y.; Uozumi, M. (1985) Induction ofumuC gene expression by nitrogen dioxide
9 in Salmonella typhimurium. Mutat. Res. 142: 99-102.
10 Kosaka, H.; Yamamoto, K.; Oda, Y.; Uozumi, M. (1986) Induction of SOS functions by nitrogen
11 dioxide in Escherichia coli with different DNA-repair capacities. Mutat. Res. 162: 1-5.
12 Kosaka, H.; Uozumi, M.; Nakajima, T. (1987) Induction of SOS functions in Escherichia coli
13 and biosynthesis of nitrosamine in rabbits by nitrogen dioxide. Environ. Health Perspect.
14 73: 153-156.
15 Kosmider, S.; Luciak, M.; Zajusz, K.; Misiewicz, A.; Szygula, J. (1973) Badania nad
16 rozedmotworczym dzialaniem tlenkow azotu [Studies on emphysogenic action of
17 nitrogen oxides]. Patol. Pol. 24: 107-125.
18 Kousa, A.; Monn, C.; Rotko, T.; Aim, S.; Oblesby, L.; Jantunen, M. J. (2001) Personal exposures
19 to NO2 in the EXPOLIS-study: relation to residential indoor, outdoor and workplace
20 concentrations in Basel, Helsinki and Prague. Atmos. Environ. 35: 3405-3412.
21 Kramer, U.; Koch, T.; Ranft, U.; Ring, J.; Behrendt, H. (2000) Traffic-related air pollution is
22 associated with atopy in children living in urban areas. Epidemiology 11: 64-70.
23 Krewski, D.; Burnett, R. T.; Goldberg, M. S.; Hoover, K.; Siemiatycki, J.; Jerrett, M.;
24 Abrahamowicz, M.; White, W. H. (2000) Reanalysis of the Harvard Six Cities study and
25 the American Cancer Society study of particulate air pollution and mortality: a special
26 report of the Institute's Particle Epidemiology Reanalysis Project. Cambridge, MA:
27 Health Effects Institute. Available: http://pubs.healtheffects.org/view.php?id=6 [6 March,
28 2007].
29 Kripke, B. J.; Sherwin, R. P. (1984) Nitrogen dioxide exposure - influence on rat testes. Anesth.
30 Analg. (NY) 63: 526-528.
31 Krishna, M. T.; Chauhan, A. J.; Frew, A. J.; Holgate, S. T. (1998) Exposure to nitrogen dioxide
32 and respiratory disease risk. Rev. Environ. Health 13: 73- 90.
33 Krupa, S. V.; Legge, A. H. (2000) Passive sampling of ambient, gaseous air pollutants: an
34 assessment from an ecological perspective. Environ. Pollut. 107: 31-45.
35 Kubota, K.; Murakami, M.; Takenaka, S.; Kawai, K.; Kyono, H. (1987) Effects of long-term
36 nitrogen dioxide exposure on rat lung: morphological observations. Environ. Health
37 Perspect. 73: 157-169.
38 Kulkarni, M. M.; Patil, R. S. (2002) An empirical model to predict indoor NO2 concentrations.
39 Atmos. Environ. 36: 4777-4785.
August 2007 AX4-75 DRAFT-DO NOT QUOTE OR CITE
-------
1 Kumae, T.; Arakawa, H. (2006) Comparison of effects of in vivo nitrogen dioxide exposure
2 starting from different periods on alveolar macrophage activity, assessed by a
3 chemiluminescence technique in Brown-Norway rats. Luminescence 21: 226-232.
4 Kunimoto, M.; Mochitate, K.; Kaya, K.; Miura, T.; Kubota, K. (1984) Effects of nitrogen
5 dioxide on red blood cells of rats: alterations of cell membrane components and
6 populational changes of red blood cells during in vivo exposure to NO2. Environ. Res. 33:
7 361-369.
8 Kiinzli, N.; Tager, I. B. (1997) The semi-individual study in air pollution epidemiology: a valid
9 design as compared to ecologic studies. Environ. Health Perspect. 105: 1078-1083.
10 Kwon, H.-J.; Cho, S.-H.; Nyberg, F.; Pershagen, G. (2001) Effects of ambient air pollution on
11 daily mortality in a cohort of patients with congestive heart failure. Epidemiology 12:
12 413-419.
13 Kyono, H.; Kawai, K. (1982) Morphometric study on age-dependent pulmonary lesions in rats
14 exposed to nitrogen dioxide. Ind. Health 20: 73-99.
15 Lafuma, C.; Harf, A.; Lange, F.; Bozzi, L.; Poncy, J. L.; Bignon, J. (1987) Effect of low-level
16 NO2 chronic exposure on elastase-induced emphysema. Environ. Res. 43: 75-84.
17 Lagorio, S.; Forastiere, F.; Pistelli, R.; lavarone, L; Michelozzi, P.; Fano, V.; Marconi, A.;
18 Ziemacki, G.; Ostro, B. D. (2006) Air pollution and lung function among susceptible
19 adult subjects: a panel study. Environ. Health 5: 11. Available:
20 http://www.ehjournal.net/content/5/l/ll [16 January, 2006].
21 Lai, H. K.; Kendall, M.; Ferrier, H.; Lindup, L; Aim, S.; Hanninen, O.; Jantunen, M.; Mathys, P.;
22 Colvile, R.; Ashmore, M. R.; Cullinan, P.; Nieuwenhuijsen, M. J. (2004) Personal
23 exposures and microenvironment concentrations of PM2.5, VOC, NO2 and CO in Oxford,
24 UK. Atmos. Environ. 38: 6399-6410.
25 Lai, S.; Patil, R. S. (2001) Monitoring of atmospheric behaviour of NOx from vehicular traffic.
26 Environ. Monit. Assess. 68: 37-50.
27 Lanki, T.; Pekkanen, J.; Aalto, P.; Elosua, R.; Berglind, N.; D'Ippoliti, D.; Kulmala, M.; Nyberg,
28 F.; Peters, A.; Picciotto, S.; Salomaa, V.; Sunyer, J.; Tiittanen, P.; Von Klot, S.;
29 Forastiere, F.; for the HEAPSS Study Group. (2006) Associations of traffic-related air
30 pollutants with hospitalisation for first acute myocardial infarction: the HEAPSS study.
31 Occup. Environ. Med. 63: 844-851.
32 La Rovere, M. T.; Pinna, G. D.; Maestri, R.; Mortara, A.; Capomolla, S.; Febo, O.; Ferrari, R.;
33 Franchini, M.; Gnemmi, M.; Opasich, C.; Riccardi, P. G.; Traversi, E.; Cobelli, F. (2003)
34 Short-term heart rate variability strongly predicts sudden cardiac death in chronic heart
35 failure patients. Circulation 107: 565-570.
36 Last, J. A.; Gerriets, J. E.; Hyde, D. M. (1983) Synergistic effects on rat lungs of mixtures of
37 oxidant air pollutants (ozone or nitrogen dioxide) and respirable aerosols. Am. Rev.
38 Respir. Dis. 128: 539-544.
39 Leaderer, B. P.; Zagraniski, R. T.; Berwick, M.; Stolwijk, J. A. J. (1986) Assessment of exposure
40 to indoor air contaminants from combustion sources: methodology and application. Am.
41 J. Epidemiol. 124: 275-289.
August 2007 AX4-76 DRAFT-DO NOT QUOTE OR CITE
-------
1 Lee, K.; Yanagisawa, Y.; Spengler, J. D.; Billick, I. H. (1995) Classification of house
2 characteristics based on indoor nitrogen dioxide concentrations. Environ. Int. 21: 277-
3 282.
4 Lee, K.; Yanagisawa, Y.; Spengler, J. D.; Fukumura, Y.; Billick, I. H. (1996) Classification of
5 house characteristics in a Boston residential nitrogen dioxide characterization study.
6 Indoor Air 6: 211-216.
7 Lee, K.; Levy, J. I; Yanagisawa, Y.; Spengler, J. D.; Billick, I. H. (1998) The Boston Residential
8 Nitrogen Dioxide Characterization Study: classification and prediction of indoor NO2
9 exposure. J. Air Waste Manage. Assoc. 48: 736-742.
10 Lee, K.; Yang, W.; Bofmger, N. D. (2000) Impact of microenvironmental nitrogen dioxide
11 concentrations on personal exposures in Australia. J. Air Waste Manage. Assoc. 50:
12 1739-1744.
13 Lee, K.; Xue, J.; Geyh, A. S.; Ozkaynak, H.; Leaderer, B. P.; Weschler, C. J.; Spengler, J. D.
14 (2002) Nitrous acid, nitrogen dioxide, and ozone concentrations in residential
15 environments. Environ. Health Perspect. 110: 145-150.
16 Lee, J.-T.; Kim, H.; Cho, Y.-S.; Hong, Y.-C.; Ha, E.-H.; Park, H. (2003a) Air pollution and
17 hospital admissions for ischemic heart diseases among individuals 64+ years of age
18 residing in Seoul, Korea. Arch. Environ. Health 58: 617-623.
19 Lee, B. E.; Ha, E. H.; Park, H. S.; Kim, Y. J.; Hong, Y. C.; Kim, H.; Lee, J. T. (2003b) Exposure
20 to air pollution during different gestational phases contributes to risks of low birth
21 weight. Hum. Reprod. 18: 638-643.
22 Lee, Y.-L.; Lin, Y.-C.; Lee, Y.-C.; Wang, J.-Y.; Hsiue, T.-R.; Guo, Y. L. (2004) Glutathione S-
23 transferase PI gene polymorphism and air pollution as interactive risk factors for
24 childhood asthma. Clin. Exp. Allergy 34: 1707-1713.
25 Lee, S. L.; Wong, W. H. S.; Lau, Y. L. (2006) Association between air pollution and asthma
26 admission among children in Hong Kong. Clin. Exp. Allergy 36: 1138-1146.
27 Lefkowitz, S. S.; McGrath, J. J.; Lefkowitz, D. L. (1986) Effects of NO2 on immune responses.
28 J. Toxicol. Environ. Health 17: 241-248.
29 Lehnert, B. E.; Archuleta, D. C.; Ellis, T.; Session, W. S.; Lehnert, N. M.; Gurley, L. R.; Stavert,
30 D. M. (1994) Lung injury following exposure of rats to relatively high mass
31 concentrations of nitrogen dioxide. Toxicology 89: 239-277.
32 Le Tertre, A.; Quenel, P.; Eilstein, D.; Medina, S.; Prouvost, H.; Pascal, L.; Boumghar, A.;
33 Saviuc, P.; Zeghnoun, A.; Filleul, L.; Declercq, C.; Cassadou, S.; Le Goaster, C. (2002)
34 Short-term effects of air pollution on mortality in nine French cities: a quantitative
35 summary. Arch. Environ. Health 57: 311-319.
36 Levesque, B.; Allaire, S.; Gauvin, D.; Koutrakis, P.; Gingras, S.; Rhainds, M.; Prud'Homme, H.;
37 Duchesne, J.-F. (2001) Wood-burning appliances and indoor air quality. Sci. Total
38 Environ. 281:47-62.
39 Levy, J. L; Lee, K.; Yanagisawa, Y.; Hutchinson, P.; Spengler, J. D. (1998a) Determinants of
40 nitrogen dioxide concentrations in indoor ice skating rinks. Am. J. Public Health 88:
41 1781-1786.
August 2007 AX4-77 DRAFT-DO NOT QUOTE OR CITE
-------
1 Levy, J. I; Lee, K.; Spengler, J. D.; Yanagisawa, Y. (1998b) Impact of residential nitrogen
2 dioxide exposure on personal exposure: an international study. J. Air Waste Manage.
3 Assoc. 48: 553-560.
4 Lewne, M.; Nise, G.; Lind, M. L.; Gustavsson, P. (2006) Exposure to particles and nitrogen
5 dioxide among taxi, bus and lorry drivers. Int. Arch. Occup. Environ. Health 79: 220-226.
6 Li, Y.-F.; Gauderman, W. J.; Avol, E.; Dubeau, L.; Gilliland, F. D. (2006) Associations of tumor
7 necrosis factor G-308A with childhood asthma and wheezing. Am. J. Respir. Crit. Care
8 Med. 173:970-976.
9 Liang, K.-Y.; Zeger, S. L. (1986) Longitudinal data analysis using generalized linear models.
10 Biometrika73: 13-22.
11 Liao, D.; Duan, Y.; Whitsel, E. A.; Zheng, Z.-J.; Heiss, G; Chinchilli, V. M.; Lin, H.-M. (2004)
12 Association of higher levels of ambient criteria pollutants with impaired cardiac
13 autonomic control: a population-based study. Am. J. Epidemiol. 159: 768-777.
14 Liard, R.; Zureik, M.; Le Moullec, Y.; Soussan, D.; Glorian, M.; Grimfeld, A.; Neukirch, F.
15 (1999) Use of personal passive samplers for measurement of NC>2, NO, and O3 levels in
16 panel studies. Environ. Res. 81: 339-348.
17 Lin, M.; Chen, Y.; Burnett, R. T.; Villeneuve, P. J.; Krewski, D. (2003) Effect of short-term
18 exposure to gaseous pollution on asthma hospitalisation in children: a bi-directional case-
19 crossover analysis. J. Epidemiol. Community Health 57: 50-55.
20 Lin, M.; Chen, Y.; Villeneuve, P. J.; Burnett, R. T.; Lemyre, L.; Hertzman, C.; McGrail, K. M.;
21 Krewski, D. (2004) Gaseous air pollutants and asthma hospitalization of children with
22 low household income in Vancouver, British Columbia, Canada. Am. J. Epidemiol. 159:
23 294-303.
24 Linaker, C. H.; Chauhan, A. J.; Inskip, H.; Frew, A. J.; Sillence, A.; Coggon, D.; Holgate, S. T.
25 (1996) Distribution and determinants of personal exposure to nitrogen dioxide in school
26 children. Occup. Environ. Med. 53: 200-203.
27 Linaker, C. H.; Chauhan, A. J.; Inskip, H. M.; Holgate, S. T.; Coggon, D. (2000) Personal
28 exposures of children to nitrogen dioxide relative to concentrations in outdoor air. Occup.
29 Environ. Med. 57: 472-476.
30 Linn, W. S.; Solomon, J. C.; Trim, S. C.; Spier, C. E.; Shamoo, D. A.; Venet, T. G.; Avol, E. L.;
31 Hackney, J. D. (1985a) Effects of exposure to 4 ppm nitrogen dioxide in healthy and
32 asthmatic volunteers. Arch. Environ. Health 40: 234-239.
33 Linn, W. S.; Shamoo, D. A.; Spier, C. E.; Valencia, L. M.; Anzar, U. T.; Venet, T. G.; Avol, E.
34 L.; Hackney, J. D. (1985b) Controlled exposure of volunteers with chronic obstructive
35 pulmonary disease to nitrogen dioxide. Arch. Environ. Health 40: 313-317.
36 Linn, W. S.; Shamoo, D. A.; Avol, E. L.; Whynot, J. D.; Anderson, K. R.; Venet, T. G.;
37 Hackney, J. D. (1986) Dose-response study of asthmatic volunteers exposed to nitrogen
38 dioxide during intermittent exercise. Arch. Environ. Health 41: 292-296.
39 Linn, W. S.; Shamoo, D. A.; Anderson, K. R.; Peng, R.-C.; Avol, E. L.; Hackney, J. D.; Gong,
40 H., Jr. (1996) Short-term air pollution exposures and responses in Los Angeles area
41 schoolchildren. J. Exposure Anal. Environ. Epidemiol. 6: 449-472.
August 2007 AX4-78 DRAFT-DO NOT QUOTE OR CITE
-------
1 Linn, W. S.; Szlachcic, Y.; Gong, H., Jr.; Kinney, P. L.; Berhane, K. T. (2000) Air pollution and
2 daily hospital admissions in metropolitan Los Angeles. Environ. Health Perspect. 108:
3 427-434.
4 Lipfert, F. W.; Perry, H. M., Jr.; Miller, J. P.; Baty, J. D.; Wyzga, R. E.; Carmody, S. E. (2000a)
5 The Washington University-EPRI veterans' cohort mortality study: preliminary results.
6 In: Grant, L. D., ed. PM2000: particulate matter and health. Inhalation Toxicol. 12(suppl.
7 4): 41-73.
8 Lipfert, F. W.; Morris, S. C.; Wyzga, R. E. (2000b) Daily mortality in the Philadelphia
9 metropolitan area and size-classified particulate matter. J. Air Waste Manage. Assoc. 50:
10 1501-1513.
11 Lipfert, F. W.; Perry, H. M., Jr.; Miller, J. P.; Baty, J. D.; Wyzga, R. E.; Carmody, S. E. (2003)
12 Air pollution, blood pressure, and their long-term associations with mortality. Inhalation
13 Toxicol. 15:493-512.
14 Lipfert, F. W.; Wyzga, R. E.; Baty, J. D.; Miller, J. P. (2006a) Traffic density as a surrogate
15 measure of environmental exposures in studies of air pollution health effects: long-term
16 mortality in a cohort of US veterans. Atmos. Environ. 40: 154-169.
17 Lipfert, F. W.; Baty, J. D.; Miller, J. P.; Wyzga, R. E. (2006b) PM2.5 constituents and related air
18 quality variables as predictors of survival in a cohort of U.S. military veterans. Inhalation
19 Toxicol. 18: 645-657.
20 Lippmann, M.; Ito, K.; Nadas, A.; Burnett, R. T. (2000) Association of particulate matter
21 components with daily mortality and morbidity in urban populations. Cambridge, MA:
22 Health Effects Institute; research report no. 95.
23 Liu, S.; Krewski, D.; Shi, Y.; Chen, Y.; Burnett, R. T. (2003) Association between gaseous
24 ambient air pollutants and adverse pregnancy outcomes in Vancouver, Canada. Environ.
25 Health Perspect. Ill: 1773-1778.
26 Llorca, J.; Salas, A.; Prieto-Salceda, D.; Chinchon-Bengoechea, V.; Delgado-Rodriguez, M.
27 (2005) Nitrogen dioxide increases cardiorespiratory admissions in Torrelavega (Spain). J.
28 Environ. Health 68: 30-35.
29 Loomis, D.; Castillejos, M.; Gold, D. R.; McDonnell, W.; Borja-Aburto, V. H. (1999) Air
30 pollution and infant mortality in Mexico City. Epidemiology 10: 118-123.
31 Luginaah, I. N.; Fung, K. Y.; Gorey, K. M.; Webster, G.; Wills, C. (2005) Association of
32 ambient air pollution with respiratory hospitalization in a government designated "area of
33 concern": the case of Windsor, Ontario. Environ. Health Perspect. 113: 290-296.
34 Luttmann-Gibson, H.; Suh, H. H.; Coull, B. A.; Dockery, D. W.; Sarnet, S. E.; Schwartz, J.;
35 Stone, P. H.; Gold, D. R. (2006) Short-term effects of air pollution on heart rate
36 variability in senior adults in Steubenville, Ohio. J. Occup. Environ. Med. 48: 780-788.
37 Ma, T.-H.; Anderson, V. A.; Shmed, I. (1982) Environmental clastogens detected by meiotic
38 pollen mother cells of Tradescantia. Environ. Sci. Res. 25: 141-157.
39 Mage, D.; Wilson, W.; Hasselblad, V.; Grant, L. (1999) Assessment of human exposure to
40 ambient particulate matter. J. Air Waste Manage. Assoc. 49: 1280-1291.
August 2007 AX4-79 DRAFT-DO NOT QUOTE OR CITE
-------
1 Maigetter, R. Z.; Fenters, J. D.; Findlay, J. C.; Ehrlich, R.; Gardner, D. E. (1978) Effect of
2 exposure to nitrogen dioxide on T and B cells in mouse spleens. Toxicol. Lett. 2: 157-
3 161.
4 Mann, J. K.; Tager, I. B.; Lurmann, F.; Segal, M.; Quesenberry, C. P., Jr.; Lugg, M. M.; Shan, J.;
5 Van den Eeden, S. K. (2002) Air pollution and hospital admissions for ischemic heart
6 disease in persons with congestive heart failure or arrhythmia. Environ. Health Perspect.
7 110:1247-1252.
8 Mannes, T.; Jalaludin, B.; Morgan, G.; Lincoln, D.; Sheppeard, V.; Corbett, S. (2005) Impact of
9 ambient air pollution on birth weight in Sydney, Australia. Occup. Environ. Med. 62:
10 524-530.
11 Maples, K. R.; Sandstrom, T.; Su, Y.-F.; Henderson, R. F. (1991) The nitric oxide/heme protein
12 complex as a biological marker of exposure to nitrogen dioxide in humans, rats, and in
13 vitro models. Am. J. Respir. Cell Mol. Biol. 4: 538-543.
14 Margolis, P. A.; Greenberg, R. A.; Keyes, L. L.; Lavange, L. M.; Chapman, R. S.; Denny, F. W.;
15 Bauman, K. E.; Boat, B. W. (1992) Lower respiratory illness in infants and low
16 socioeconomic status. Am. J. Public Health 82: 1119-1126.
17 Martin, R. V.; Jacob, D. J.; Chance, K. V.; Kurosu, T. P.; Palmer, P. L; Evans, M. J. (2003)
18 Global inventory of nitrogen oxide emissions constrained by space-based observations of
19 NO2 columns. J. Geophys. Res. [Atmos.] 108(D17): 10.1029/2003JD003453.
20 McClenny, W. A.; Williams, E. J.; Cohen, R. C.; Stutz, J. (2002) Preparing to measure the
21 effects of the NOX SIP Call—methods for ambient air monitoring of NO, NC>2, NOy, and
22 individual NOZ species. J. Air Waste Manage. Assoc. 52: 542-562.
23 McConnell, R.; Berhane, K.; Gilliland, F.; Molitor, J.; Thomas, D.; Lurmann, F.; Avol, E.;
24 Gauderman, W. J.; Peters, J. M. (2003) Prospective study of air pollution and bronchitic
25 symptoms in children with asthma. Am. J. Respir. Crit. Care Med. 168: 790-797.
26 McConnell, R.; Berhane, K.; Yao, L.; Jerrett, M.; Lurmann, F.; Gilliland, F.; Kunzli, N.;
27 Gauderman, J.; Avol, E.; Thomas, D.; Peters, J. (2006) Traffic, susceptibility, and
28 childhood asthma. Environ. Health Perspect. 114: 766-772.
29 Melia, R. J. W.; Du Ve Florey, C.; Altman, D. G.; Swan, A. V. (1977) Association between gas
30 cooking and respiratory disease in children. Br. Med. J. 2: 149-152.
31 Melia, R. J. W.; Du Ve Florey, C.; Chinn, S. (1979) The relation between respiratory illness in
32 primary schoolchildren and the use of gas for cooking: I - results from a national survey.
33 Int. J. Epidemiol. 8: 333-338.
34 Melia, R. J. W.; Du Ve Florey, C.; Morris, R. W.; Goldstein, B. D.; Clark, D.; John, H. H.
35 (1982a) Childhood respiratory illness and the home environment. I. Relations between
36 nitrogen dioxide, temperature and relative humidity. Int. J. Epidemiol. 11: 155-163.
37 Melia, R. J. W.; Du Ve Florey, C.; Morris, R. W.; Goldstein, B. D.; John, H. H.; Clark, D.;
38 Craighead, I. B.; Mackinlay, J. C. (1982b) Childhood respiratory illness and the home
39 environment: II. association between respiratory illness and nitrogen dioxide, temperature
40 and relative humidity. Int. J. Epidemiol. 11: 164-169.
August 2007 AX4-80 DRAFT-DO NOT QUOTE OR CITE
-------
1 Melia, R. J. W.; Chinn, S.; Rona, R. J. (1990) Indoor levels of NO2 associated with gas cookers
2 and kerosene heaters in inner city areas of England. Atmos. Environ. Part B 24: 177-180.
3 Menzel, D. B. (1976) The role of free radicals in the toxicity of air pollutants (nitrogen oxides
4 and ozone). In: Pryor, W. A., ed. Free radicals in biology: v. II. New York, NY:
5 Academic Press, Inc.; pp. 181-202.
6 Mercer, R. R. (1999) Morphometric analysis of alveolar responses of F344 rats to subchronic
7 inhalation of nitric oxide. Cambridge, MA: Health Effects Institute; research report no.
8 88.
9 Mercer, R. R.; Costa, D. L.; Crapo, J. D. (1995) Effects of prolonged exposure to low doses of
10 nitric oxide or nitrogen dioxide on the alveolar septa of the adult rat lung. Lab. Invest. 73:
11 20-28.
12 Mersch, J.; Dyce, B. J.; Haverback, B. J.; Sherwin, R. P. (1973) Diphosphoglycerate content of
13 red blood cells: measurements in guinea pigs exposed to 0.4 ppm nitrogen dioxide. Arch.
14 Environ. Health 27: 94-95.
15 Metzger, K. B.; Tolbert, P. E.; Klein, M.; Peel, J. L.; Flanders, W. D.; Todd, K. H.; Mulholland,
16 J. A.; Ryan, P. B.; Frumkin , H. (2004) Ambient air pollution and cardiovascular
17 emergency department visits. Epidemiology 15: 46-56.
18 Migliaretti, G.; Cavallo, F. (2004) Urban air pollution and asthma in children. Pediatr. Pulmonol.
19 38: 198-203.
20 Migliaretti, G.; Cadum, E.; Migliore, E.; Cavallo, F. (2005) Traffic air pollution and hospital
21 admission for asthma: a case-control approach in a Turin (Italy) population. Int. Arch.
22 Occup. Environ. Health. 78: 164-169.
23 Miller, F. J.; Overton, J. H.; Myers, E. T.; Graham, J. A. (1982) Pulmonary dosimetry of nitrogen
24 dioxide in animals and man. In: Schneider, T.; Grant, L., eds. Air pollution by nitrogen
25 oxides: proceedings of the US-Dutch international symposium; May; Maastricht, The
26 Netherlands. Amsterdam, The Netherlands: Elsevier Scientific Publishing Company; pp.
27 377-386. (Studies in environmental science 21).
28 Miller, F. J.; Graham, J. A.; Raub, J. A.; Illing, J. W.; Menache, M. G.; House, D. E.; Gardner,
29 D. E. (1987) Evaluating the toxicity of urban patterns of oxidant gases. II. Effects in mice
30 from chronic exposure to nitrogen dioxide. J. Toxicol. Environ. Health 21: 99-112.
31 Miller, K. A.; Siscovick, D. S.; Sheppard, L.; Shepherd, K.; Sullivan J. H.; Anderson, G. L.;
32 Kaufman, J. D. (2007) Long-term exposure to air pollution and incidence of
33 cardiovascular events in women. N. Engl. J. Med. 356: 447-458.
34 Millstein, J.; Gilliland, F.; Berhane, K.; Gauderman, W. J.; McConnell, R.; Avol, E.; Rappaport,
35 E. B.; Peters, J. M. (2004) Effects of ambient air pollutants on asthma medication use and
36 wheezing among fourth-grade school children from 12 Southern California communities
37 enrolled in The Children's Health Study. Arch. Environ. Health 59: 505-514.
38 Mink, S. N.; Coalson, J. J.; Whitley, L.; Greville, H.; Jadue, C. (1984) Pulmonary function tests
39 in the detection of small airways obstruction in a canine model of bronchiolitis obliterans.
40 Am. Rev. Respir. Dis. 130: 1125-1133.
August 2007 AX4-81 DRAFT-DO NOT QUOTE OR CITE
-------
1 Mirvish, S. S.; Issenberg, P.; Sams, J. P. (1981)7V-nitrosomorpholine synthesis in rodents
2 exposed to nitrogen dioxide and morpholine. In: Scanlan, R. A.; Tannenbaum, S. R., eds.
3 7V-nitroso compounds: based on a symposium cosponsored by the Divisions of
4 Agricultural and Food Chemistry and Pesticide Chemistry at the 181st meeting of the
5 American Chemical Society; March-April; Atlanta, GA. Washington, DC: American
6 Chemical Society; pp. 181-191. (ACS symposium series 174).
7 Mirvish, S. S.; Sams, J. P.; Issenberg, P. (1983) The nitrosating agent in mice exposed to
8 nitrogen dioxide: improved extraction method and localization in the skin. Cancer Res.
9 43:2550-2554.
10 Mirvish, S. S.; Babcook, D. M.; Deshpande, A. D.; Nagel, D. L. (1986) Identification of
11 cholesterol as a mouse skin lipid that reacts with nitrogen dioxide to yield a nitrosating
12 agent, and of cholesteryl nitrite as the nitrosating agent produced in a chemical system
13 from cholesterol. Cancer Lett. (Shannon, Irel.) 31: 97-104.
14 Mirvish, S. S.; Ramm, M. D.; Sams, J. P.; Babcook, D. M. (1988) Nitrosamine formation from
15 amines applied to the skin of mice after and before exposure to nitrogen dioxide. Cancer
16 Res. 48: 1095-1099.
17 Miyanishi, K.; Kinouchi, T.; Kataoka, K.; Kanoh, T.; Ohnishi, Y. (1996) In vivo formation of
18 mutagens by intraperitoneal administration of poly cyclic aromatic hydrocarbons in
19 animals during exposure to nitrogen dioxide. Carcinogenesis 17: 1483-1490.
20 Mochitate, K.; Miura, T. (1984) In vivo effect of nitrogen dioxide on the activities of glycolytic
21 enzymes in red blood cells of rats. Toxicol. Lett. 22: 315-321.
22 Mochitate, K.; Kaya, K.; Miura, T.; Kubota, K. (1984) In vivo effects of nitrogen dioxide on
23 membrane constituents in lung and liver of rats. Environ. Res. 33: 17-28.
24 Mochitate, K.; Miura, T.; Kubota, K. (1985) An increase in the activities of glycolytic enzymes
25 in rat lungs produced by nitrogen dioxide. J. Toxicol. Environ. Health 15: 323-331.
26 Mochitate, K.; Takahashi, Y.; Ohsumi, T.; Miura, T. (1986) Activation and increment of alveolar
27 macrophages induced by nitrogen dioxide. J. Toxicol. Environ. Health 17: 229-239.
28 Modig, L.; Sunesson, A.-L.; Levin, J.-O.; Sundgren, M.; Hagenbjork-Gustafsson, A.; Forsberg,
29 B. (2004) Can NC>2 be used to indicate ambient and personal levels of benzene and 1,3-
30 butadiene in air? J. Environ. Monit. 6: 957-962.
31 Mohsenin, V. (1987a) Effect of vitamin C on NO2-induced airways hyperresponsiveness in
32 normal subjects: a randomized double-blind experiment. Am. Rev. Respir. Dis. 136:
33 1408-1411.
34 Mohsenin, V. (1987b) Airways responses to nitrogen dioxide in asthmatic subjects. J. Toxicol.
35 Environ. Health 22: 371-380.
36 Mohsenin, V. (1988) Airways responses to 2.0 ppm nitrogen dioxide in normal subjects. Arch.
37 Environ. Health 43: 242-246.
38 Mohsenin, V.; Gee, J. B. L. (1987) Acute effect of nitrogen dioxide exposure on the functional
39 activity of alpha-1-protease inhibitor in bronchoalveolar lavage fluid of normal subjects.
40 Am. Rev. Respir. Dis. 136: 646-650.
August 2007 AX4-82 DRAFT-DO NOT QUOTE OR CITE
-------
1 Molitor, 1; Jerrett, M.; Chang, C.-C.; Molitor, N.-T.; Gauderman, 1; Berhane, K.; McConnell,
2 R.; Lurmann, F.; Wu, J.; Winer, A.; Thomas, D. (2007) Assessing uncertainty in spatial
3 exposure models for air pollution health effects assessment. Environ. Health Perspect.
4 115:1147-1153
5 Moncada, S. (1992) Nitric oxide gas: mediator, modulator, and pathophysiologic entity. J. Lab.
6 Clin.Med. 120: 187-191.
7 Moncada, S.; Palmer, R. M. J.; Higgs, E. A. (1991) Nitric oxide: physiology, pathophysiology,
8 and pharmacology. Pharmacol. Rev. 43: 109-142.
9 Monn, C.; Fuchs, A.; Hogger, D.; Junker, M.; Kogelschatz, D.; Roth, N.; Wanner, H.-U. (1997)
10 Particulate matter less than 10 jim (PMi0) and fine particles less than 2.5 jim (PM2.5):
11 relationships between indoor, outdoor and personal concentrations. Sci. Total Environ.
12 208: 15-21.
13 Monn, C.; Brandli, O.; Schindler, C.; Ackermann-Liebrich, U.; Leuenberger, P.; SAPALDIA
14 team. (1998) Personal exposure to nitrogen dioxide in Switzerland. Sci. Total Environ.
15 215:243-251.
16 Moolgavkar, S. H. (2000) Air pollution and hospital admissions for diseases of the circulatory
17 system in three U.S. metropolitan areas. J. Air Waste Manage Assoc. 50: 1199-1206.
18 Moolgavkar, S. H. (2003) Air pollution and daily deaths and hospital admissions in Los Angeles
19 and Cook counties. In: Revised analyses of time-series studies of air pollution and health.
20 Special report. Boston, MA: Health Effects Institute; pp. 183-198. Available:
21 http://www.healtheffects.org/news.htm [16 May, 2003].
22 Moolgavkar, S. H.; Luebeck, E. G. (1996) A critical review of the evidence on particulate air
23 pollution and mortality. Epidemiology 7: 420-428.
24 Morgan, G.; Corbett, S.; Wlodarczyk, J. (1998) Air pollution and hospital admissions in Sydney,
25 Australia, 1990 to 1994. Am. J. Public Health 88: 1761-1766.
26 Morgan, G.; Corbett, S.; Wlodarczyk, J.; Lewis, P. (1998) Air pollution and daily mortality in
27 Sydney, Australia, 1989 through 1993. Am. J. Public Health 88: 759-764.
28 Morris, R. D.; Naumova, E. N.; Munasinghe, R. L. (1995) Ambient air pollution and
29 hospitalization for congestive heart failure among elderly people in seven large US cities.
30 Am. J. Public Health 85: 1361-1365.
31 Morrow, P. E.; Utell, M. J. (1989) Responses of susceptible subpopulations to nitrogen dioxide.
32 Cambridge, MA: Health Effects Institute; research report no. 23.
33 Morrow, P. E.; Utell, M. J.; Bauer, M. A.; Smeglin, A. M.; Frampton, M. W.; Cox, C.; Speers,
34 D. M.; Gibb, F. R. (1992) Pulmonary performance of elderly normal subjects and
35 subjects with chronic obstructive pulmonary disease exposed to 0.3 ppm nitrogen
36 dioxide. Am. Rev. Respir. Dis. 145: 291-300.
37 Mortimer, K. M.; Neas, L. M.; Dockery, D. W.; Redline, S.; Tager, I. B. (2002) The effect of air
38 pollution on inner-city children with asthma. Eur. Respir. J. 19: 699-705.
August 2007 AX4-83 DRAFT-DO NOT QUOTE OR CITE
-------
1 Moseler, M.; Hendel-Kramer, A.; Karmaus, W.; Forster, J.; Weiss, K.; Urbanek, R.; Kuehr, J.
2 (1994) Effect of moderate NC>2 air pollution on the lung function of children with
3 asthmatic symptoms. Environ. Res. 67: 109-124.
4 Mosqueron, L.; Momas, I.; Le Moullec, Y. (2002) Personal exposure of Paris office workers to
5 nitrogen dioxide and fine particles. Occup. Environ. Med. 59: 550-555.
6 Mukala, K.; Pekkanen, J.; Tiittanen, P.; Aim, S.; Salonen, R. O.; Tuomisto, J. (1999) Personally
7 measured weekly exposure to NO2 and respiratory health among preschool children. Eur.
8 Respir. J. 13: 1411-1417.
9 Mukala, K.; Aim, S.; Tiittanen, P.; Salonen, R. O.; Jantunen, M.; Pekkanen, J. (2000) Nitrogen
10 dioxide exposure assessment and cough among preschool children. Arch. Environ.
11 Health. 55:431-438.
12 Muller, B.; Schafer, H.; Earth, P.; Von Wichert, P. (1994) Lung surfactant components in
13 bronchoalveolar lavage after inhalation of NO2 as markers of altered surfactant
14 metabolism. Lung 172: 61-72.
15 Muller, B.; Garn, H.; Hochscheid, R. (2003) Impaired recycling of surfactant-like liposomes in
16 type II pneumocytes from injured lungs. Thorax 58: 127-134.
17 Murphy, S. D.; Ulrich, C. E.; Frankowitz, S. H.; Xintaras, C. (1964) Altered function in animals
18 inhaling low concentrations of ozone and nitrogen dioxide. Am. Ind. Hyg. Assoc. J. 25:
19 246-253.
20 Nadziejko, C. E.; Nansen, L.; Mannix, R. C.; Kleinman, M. T.; Phalen, R. F. (1992) Effect of
21 nitric acid vapor on the response to inhaled ozone. Inhalation Toxicol. 4: 343-358.
22 Naess, 0.; Nafstad, P.; Aamodt, G.; Claussen, B.; Rosland, P. (2007) Relation between
23 concentration of air pollution and cause-specific mortality: four-year exposures to
24 nitrogen dioxide and particulate matter pollutants in 470 neighborhoods in Oslo, Norway.
25 Am. J. Epidemiol. 165: 435-443.
26 Nafstad, P.; Haheim, L. L.; Oftedal, B.; Gram, F.; Holme, L; Hjermann, L; Leren, P. (2003)
27 Lung cancer and air pollution: a 27 year follow up of 16,209 Norwegian men. Thorax 58:
28 1071-1076.
29 Nafstad, P.; Haheim, L. L.; Wisloff, T.; Gram, F.; Oftedal, B.; Holme, L; Hjermann, L; Leren, P.
30 (2004) Urban air pollution and mortality in a cohort of Norwegian men. Environ. Health
31 Perspect. 112:610-605.
32 Nakai, S.; Nitta, H.; Maeda, K. (1995) Respiratory health associated with exposure to automobile
33 exhaust II. Personal NO2 exposure levels according to distance from the roadside. J.
34 Exposure Anal. Environ. Epidemiol. 5: 125-136.
35 Nakajima, T.; Kusumoto, S. (1968) [Effect of nitrogen dioxide exposure on the contents of
36 reduced glutathione in mouse lung]. Osaka-Furitsu Koshu Eisei Kenkyusho Kenkyu
3 7 Hokoku Rodo Ei sei Hen 6: 17-21.
38 Nakajima, T.; Oda, H.; Kusumoto, S.; Nogami, H. (1980) Biological effects of nitrogen dioxide
39 and nitric oxide. In: Lee, S. D., ed. Nitrogen oxides and their effects on health. Ann
40 Arbor, MI: Ann Arbor Science Publishers, Inc.; pp. 121-141.
August 2007 AX4-84 DRAFT-DO NOT QUOTE OR CITE
-------
1 National Research Council. (1986) Environmental tobacco smoke: measuring exposures and
2 assessing health effects. Washington, DC: National Academy Press.
3 National Research Council. (2004) Research priorities for airborne particulate matter. IV.
4 Continuing research progress. Washington, DC: National Academies Press. Available:
5 http://www.nap.edu/catalog.php?record_id= 10957 [1 August, 2007].
6 Nazaroff, W. W.; Cass, G. R. (1986) Mathematical modeling of chemically reactive pollutants in
7 indoor air. Environ. Sci. Technol. 20: 924-934.
8 Nazaroff, W. W.; Weschler, C. J. (2004) Cleaning products and air fresheners: exposure to
9 primary and secondary air pollutants. Atmos. Environ. 38: 2841-2865.
10 Neas, L. M.; Dockery, D. W.; Ware, J. H.; Spengler, J. D.; Speizer, F. E.; Ferris, B. G., Jr. (1991)
11 Association of indoor nitrogen dioxide with respiratory symptoms and pulmonary
12 function in children. Am. J. Epidemiol. 134: 204-219.
13 Nerriere, E.; Zmirou-Navier, D.; Blanchard, O.; Momas, I.; Ladner, J.; Le Moullec, Y.;
14 Personnaz, M.-B.; Lameloise, P.; Delmas, V.; Target, A.; Desqueyroux, H. (2005) Can
15 we use fixed ambient air monitors to estimate population long-term exposure to air
16 pollutants? The case of spatial variability in the Genotox ER study. Environ. Res. 97: 32-
17 42.
18 Ng, T. P.; Seet, C. S. R.; Tan, W. C.; Foo, S. C. (2001) Nitrogen dioxide exposure from domestic
19 gas cooking and airways response in asthmatic women. Thorax 56: 596-601.
20 Nguyen, T.; Brunson, D.; Crespi, C. L.; Penman, B. W.; Wishnok, J. S.; Tannenbaum, S. R.
21 (1992) DNA damage and mutation in human cells exposed to nitric oxide in vitro. Proc.
22 Natl. Acad. Sci. 89: 3030-3034.
23 Nicolai, T.; Carr, D.; Weiland, S. K.; Duhme, H.; Von Ehrenstein, O.; Wagner, C.; Von Mutius,
24 E. (2003) Urban traffic and pollutant exposure related to respiratory outcomes and atopy
25 in a large sample of children. Eur. Respir. J. 21: 956-963.
26 Nieding, G. von; Wagner, H. M. (1977) Experimental studies on the short-term effect of air
27 pollutants on pulmonary function in man: two-hour exposure to NC>2, Os and 862 alone
28 and in combination. In: Kasuga, S.; Suzuki, N.; Yamada, T.; Kimura, G.; Inagaki, K.;
29 Onoe, K., eds. Proceedings of the fourth international clean air congress; May; Tokyo,
30 Japan. Tokyo, Japan: Japanese Union of Air Pollution Prevention Associations; pp. 5-8.
31 Nieding, G. von; Wagner, H. M.; Krekeler, H.; Loellgen, H.; Fries, W.; Beuthan, A. (1979)
32 Controlled studies of human exposure to single and combined action of NO2, 63, and
33 SO2. Int. Arch. Occup. Environ. Health 43: 195-210.
34 Nieding, G. von; Wagner, H. M.; Casper, H.; Beuthan, A.; Smidt, U. (1980) Effect of
35 experimental and occupational exposure to NC>2 in sensitive and normal subjects. In: Lee,
36 S. D., ed. Nitrogen oxides and their effects on health. Ann Arbor, MI: Ann Arbor Science
37 Publishers, Inc.; pp. 315-331.
38 Nitschke, M.; Pilotto, L. S.; Attewell, R. G.; Smith, B. J.; Pisaniello, D.; Martin, J.; Ruffm, R. E.;
39 Hiller, J. E. (2006) A cohort study of indoor nitrogen dioxide and house dust mite
40 exposure in asthmatic children. J. Occup. Environ. Med. 48: 462-469.
August 2007 AX4-85 DRAFT-DO NOT QUOTE OR CITE
-------
1 Nunnermacker, L. J.; Imre, D.; Daum, P. H.; Kleinman, L.; Lee, Y.-N.; Lee, J. H.; Springston,
2 S. R.; Newman, L.; Weinstein-Lloyd, J.; Luke, W. T.; Banta, R.; Alvarez, R.; Senff, C.;
3 Sillman, S.; Holdren, M.; Keigley, G. W.; Zhou, X. (1998) Characterization of the
4 Nashville urban plume on July 3 and July 18, 1995. J. Geophys. Res. [Atmos.] 103:
5 28,129-28,148.
6 Nyberg, F.; Gustavsson, P.; Jarup, L.; Bellander, T.; Berglind, N.; Jakobsson, R.; Pershagen, G.
7 (2000) Urban air pollution and lung cancer in Stockholm. Epidemiology 11: 487-495.
8 Oda, H.; Kusumoto, S.; Nakajima, T. (1975) Nitrosyl-hemoglobin formation in the blood of
9 animals exposed to nitric oxide. Arch. Environ. Health 30: 453-456.
10 Oda, H.; Nogami, H.; Kusumoto, S.; Nakajima, T.; Kurata, A.; Imai, K. (1976) [Long-term
11 exposure to nitric oxide in mice]. Taiki Osen Kenkyu 11: 150-160.
12 Oda, H.; Nogami, H.; Nakajima, T. (1979) Alteration of hemoglobin reacted with nitrogen
13 oxides in vitro. J. Toxicol. Sci. 4: 299-300.
14 Oda, H.; Nogami, H.; Kusumoto, S.; Nakajima, T.; Kurata, A. (1980a) Lifetime exposure to 2.4
15 ppm nitric oxide in mice. Environ. Res. 22: 254-263.
16 Oda, H.; Nogami, H.; Nakajima, T. (1980b) Reaction of hemoglobin with nitric oxide and
17 nitrogen dioxide in mice. J. Toxicol. Environ. Health 6: 673-678.
18 Oftedal, B.; Nafstad, P.; Magnus, P.; Bj0rkly, S.; Skrondal, A. (2003) Traffic related air pollution
19 and acute hospital admission for respiratory diseases in Drammen, Norway 1995-2000.
20 Eur. J. Epidemiol. 18: 671-675.
21 Ogawa & Company. (2007) Ambient air passive sampler for NO-NO2, NOx, SO2, Os, NH3.
22 Pompano Beach, FL. Available: http://www.ogawausa.com/passive.html [18 July, 2007].
23 Ogston, S. A.; Du Ve Florey, C.; Walker, C. H. M. (1985) The Tayside infant morbidity and
24 mortality study: effect on health of using gas for cooking. Br. Med. J. 290: 957-960.
25 Ohashi, Y.; Nakai, Y.; Sugiura, Y.; Ohno, Y.; Okamoto, H.; Tanaka, A.; Kakinoki, Y.; Hayashi,
26 M. (1994) Nitrogen dioxide-induced eosinophilia and mucosal injury in the nose of the
27 guinea pig. Acta Oto Laryngol. 114: 547-551.
28 Ohyama, K.; Ito, T.; Kanisawa, M. (1999) The roles of diesel exhaust particle extracts and the
29 promotive effects of NO2 and/or SO2 exposure on rat lung tumorigenesis. Cancer Lett.
30 139: 189-197.
31 Ostro, B.; Lipsett, M.; Mann, J.; Braxton-Owens, H.; White, M. (2001) Air pollution and
32 exacerbation of asthma in African-American children in Los Angeles. Epidemiology 12:
33 200-208.
34 Ott, W.; Wallace, L.; Mage, D. (2000) Predicting particulate (PMio) personal exposure
35 distributions using a random component superposition statistical model. J. Air Waste
36 Manage. Assoc. 50: 1390-1406.
37 Overton, J. H., Jr. (1984) Physicochemical processes and the formulation of dosimetry models.
38 In: Miller, F. J.; Menzel, D. B., eds. Fundamentals of extrapolation modeling of inhaled
39 toxicants: ozone and nitrogen dioxide. Washington, DC: Hemisphere Publishing
40 Corporation; pp. 93-114.
August 2007 AX4-86 DRAFT-DO NOT QUOTE OR CITE
-------
1 Overton, J. H.; Graham, R. C. (1995) Simulation of the uptake of a reactive gas in a rat
2 respiratory tract model with an asymmetric tracheobronchial region patterned on
3 complete conducting airways cast data. Comput. Biomed. Res. 28: 171-190.
4 Overton, J. H.; Graham, R. C.; Menache, M. G.; Mercer, R. R.; Miller, F. J. (1996) Influence of
5 tracheobronchial region expansion and volume on reactive gas uptake and interspecies
6 dose extrapolations. Inhalation Toxicol. 8: 723-745.
7 Pagani, P.; Romano, M.; Erroi, A.; Ferro, M.; Salmona, M. (1994) Biochemical effects of acute
8 and subacute nitrogen dioxide exposure in rat lung and bronchoalveolar lavage fluid.
9 Arch. Environ. Contam. Toxicol. 27: 426-430.
10 Palmes, E. D.; Gunnison, A. F.; DiMattio, J.; Tomczyk, C. (1976) Personal sampler for nitrogen
11 dioxide. Am. Ind. Hyg. Assoc. J. 37: 570-577.
12 Pantazopoulou, A.; Katsouyanni, K.; Kourea-Kremastinou, J.; Trichopoulos, D. (1995) Short-
13 term effects of air pollution on hospital emergency outpatient visits and admissions in the
14 greater Athens, Greece area. Environ. Res. 69: 31-36.
15 Park, J.-H.; Spengler, J. D.; Yoon, D.-W.; Dumyahn, T.; Lee, K.; Ozkaynak, H. (1998)
16 Measurement of air exchange rate of stationary vehicles and estimation of in-vehicle
17 exposure. J. Exposure Anal. Environ. Epidemiol. 8: 65-78.
18 Park, H.; Lee, B.; Ha, E.-H.; Lee, J.-T.; Kim, H.; Hong, Y.-C. (2002) Association of air pollution
19 with school absenteeism due to illness. Arch. Pediatr. Adolesc. Med. 156: 1235-1239.
20 Park, J. W.; Lim, Y. H.; Kyung, S. Y.; An, C. H.; Lee, S. P.; Jeong, S. H.; Ju, S.-Y. (2005a)
21 Effects of ambient particulate matter on peak expiratory flow rates and respiratory
22 symptoms of asthmatics during Asian dust periods in Korea. Respirology 10: 470-476.
23 Park, S. K.; O'Neill, M. S.; Vokonas, P. S.; Sparrow, D.; Schwartz, J. (2005b) Effects of air
24 pollution on heart rate variability: the VA normative aging study. Environ. Health
25 Perspect. 113: 304-309.
26 Parrish, D. D.; Fehsenfeld, F. C. (2000) Methods for gas-phase measurements of ozone, ozone
27 precursors and aerosol precursors. Atmos. Environ. 34: 1921-1957.
28 Pathmanathan, S.; Krishna, M. T.; Blomberg, A.; Helleday, R.; Kelly, F. J.; Sandstrom, T.;
29 Holgate, S. T.; Wilson, S. J.; Frew, A. J. (2003) Repeated daily exposure to 2 ppm
30 nitrogen dioxide upregulates the expression of IL-5, IL-10, IL-13, and ICAM-1 in the
31 bronchial epithelium of healthy human airways. Occup. Environ. Med. 60: 892-896.
32 Pattemore, P. K.; Asher, M. I.; Harrison, A. C.; Mitchell, E. A.; Rea, H. H.; Stewart, A. W.
33 (1990) The interrelationship among bronchial hyperresponsiveness, the diagnosis of
34 asthma, and asthma symptoms. Am. Rev. Respir. Dis. 142: 549-554.
35 Peacock, J. L.; Symonds, P.; Jackson, P.; Bremner, S. A.; Scarlett, J. F.; Strachan, D. P.;
36 Anderson, H. R. (2003) Acute effects of winter air pollution on respiratory function in
37 schoolchildren in southern England. Occup. Environ. Med. 60: 82-89.
38 Peel, J. L.; Tolbert, P. E.; Klein, M.; Metzger, K. B.; Flanders, W. D.; Knox, T.; Mulholland, J.
39 A.; Ryan, P. B.; Frumkin, H. (2005) Ambient air pollution and respiratory emergency
40 department visits. Epidemiology 16: 164-174.
August 2007 AX4-87 DRAFT-DO NOT QUOTE OR CITE
-------
1 Peel, J. L.; Metzger, K. B.; Klein, M.; Flanders, W. D.; Mulholland, J. A.; Tolbert, P. E. (2006)
2 Ambient air pollution and cardiovascular emergency department visits in potentially
3 sensitive groups. Am. J. Epidemiol. 165: 625-633.
4 Pekkanen, J.; Brunner, E. J.; Anderson, H. R.; Tiittanen, P.; Atkinson, R. W. (2000) Daily
5 concentrations of air pollution and plasma fibrinogen in London. Occup. Environ. Med.
6 57: 818-822.
7 Pekkanen, J.; Peters, A.; Hoek, G.; Tiittanen, P.; Brunekreef, B.; de Hartog, J.; Heinrich, J.;
8 Ibald-Mulli, A.; Kreyling, W. G.; Lanki, T.; Timonen, K. L.; Vanninen, E. (2002)
9 Particulate air pollution and risk of ST-segment depression during repeated submaximal
10 exercise tests among subjects with coronary heart disease: the exposure and risk
11 assessment for fine and ultrafme particles in ambient air (ULTRA) study. Circulation
12 106: 933-938.
13 Peng. R. D.; Dominici, F.; Louis, T. A. (2006) Model choice in time series studies of air
14 pollution and mortality. J. R. Stat. Soc. Ser. A 169: 179-203.
15 Pereira, L. A. A.; Loomis, D.; Concei9ao, G. M. S.; Braga, A. L. F.; Areas, R. M.; Kishi, H. S.;
16 Singer, J. M.; Bohm, G. M.; Saldiva, P. H. N. (1998) Association between air pollution
17 and intrauterine mortality in Sao Paulo, Brazil. Environ. Health Perspect. 106: 325-329.
18 Peters, J. M.; Avol, E.; Navidi, W.; London, S. J.; Gauderman, W. J.; Lurmann, F.; Linn, W. S.;
19 Margolis, H.; Rappaport, E.; Gong, H., Jr.; Thomas, D. C. (1999) A study of twelve
20 southern California communities with differing levels and types of air pollution. I.
21 Prevalence of respiratory morbidity. Am. J. Respir. Crit. Care Med. 159: 760-767.
22 Peters, A.; Liu, E.; Verrier, R. L.; Schwartz, J.; Gold, D. R.; Mittleman, M.; Baliff, J.; Oh, J. A.;
23 Allen, G.; Monahan, K.; Dockery, D. W. (2000) Air pollution and incidence of cardiac
24 arrhythmia. Epidemiology 11: 11-17.
25 Peters, A.; Von Klot, S.; Heier, M.; Trentinaglia, L; Hermann, A.; Wichmann, H. E.; Lowel, H.
26 (2004) Exposure to traffic and the onset of myocardial infarction. N. Engl. J. Med. 351:
27 1721-1730.
28 Pilotto, L. S.; Douglas, R. M.; Attewell, R. G.; Wilson, S. R. (1997a) Respiratory effects
29 associated with indoor nitrogen dioxide exposure in children. Int. J. Epidemiol. 26: 788-
30 796.
31 Pilotto, L. S.; Douglas, R. M.; Samet, J. M. (1997b) Nitrogen dioxide, gas heating and
32 respiratory illness. Med. J. Aust. 167: 295-296.
33 Pilotto, L. S.; Nitschke, M.; Smith, B. J.; Pisaniello, D.; Ruffm, R. E.; McElroy, H. J.; Martin, J.;
34 Hiller, J. E. (2004) Randomized controlled trial of unflued gas heater replacement on
35 respiratory health of asthmatic schoolchildren. Int. J. Epidemiol. 33: 208-214.
36 Pino, P.; Walter, T.; Oyarzun, M.; Villegas, R.; Romieu, I. (2004) Fine particulate matter and
37 wheezing illnesses in the first year of life. Epidemiology 15: 702-708.
38 Pinto, J. P.; Lefohn, A. S.; Shadwick, D. S. (2004) Spatial variability of PM2.5 in urban areas in
39 the United States. J. Air Waste Manage. Assoc. 54: 440-449.
August 2007 AX4-88 DRAFT-DO NOT QUOTE OR CITE
-------
1 Plaisance, H.; Piechocki-Minguy, A.; Garcia-Fouque, S.; Galloo, J. C. (2004) Influences of
2 meteorological factors on the NC>2 measurements by passive diffusion tube. Atmos.
3 Environ. 38: 573-580.
4 Plunkett, L. M.; Turnbull, D.; Rodricks, J. V. (1992) Differences between adults and children
5 affecting exposure assessment. In: Guzelian, P. S.; Henry, D. J.; Olin, S. S., eds.
6 Similarities and differences between children and adults: implications for risk
7 assessment. Washington, DC: ILSI Press, pp. 79-96.
8 Poloniecki, J. D.; Atkinson, R. W.; Ponce de Leon, A.; Anderson, H. R. (1997) Daily time series
9 for cardiovascular hospital admissions and previous day's air pollution in London, UK.
10 Occup. Environ. Med. 54: 535-540.
11 Ponce de Leon, A.; Anderson, H. R.; Bland, J. M.; Strachan, D. P.; Bower, J. (1996) Effects of
12 air pollution on daily hospital admissions for respiratory disease in London between
13 1987-88 and 1991-92. In: St Leger, S., ed. The APHEA project. Short term effects of air
14 pollution on health: a European approach using epidemiological time series data. J.
15 Epidemiol. Community Health 50(suppl. 1): S63-S70.
16 Ponka, A.; Virtanen, M. (1994) Chronic bronchitis, emphysema, and low-level air pollution in
17 Helsinki, 1987-1989. Environ. Res. 65: 207-217.
18 Ponka, A.; Virtanen, M. (1996) Asthma and ambient air pollution in Helsinki. In: St Leger, S.,
19 ed. The APHEA project. Short term effects of air pollution on health: a European
20 approach using epidemiological time series data. J. Epidemiol. Community Health
21 50(suppl. 1): S59-S62.
22 Ponsonby, A.-L.; Glasgow, N.; Gatenby, P.; Mullins, R.; McDonald, T.; Hurwitz, M.; Pradith,
23 B.; Attewell, R. (2001) The relationship between low level nitrogen dioxide exposure and
24 child lung function after cold air challenge. Clin. Exp. Allergy 31: 1205-1212.
25 Pope, C. A., Ill; Thun, M. J.; Namboodiri, M. M.; Dockery, D. W.; Evans, J. S.; Speizer, F. E.;
26 Heath, C. W., Jr. (1995) Particulate air pollution as a predictor of mortality in a
27 prospective study of U.S. adults. Am. J. Respir. Crit. Care Med. 151: 669-674.
28 Pope, C. A., Ill; Burnett, R. T.; Thun, M. J.; Calle, E. E.; Krewski, D.; Ito, K.; Thurston, G. D.
29 (2002) Lung cancer, cardiopulmonary mortality, and long-term exposure to fine
30 particulate air pollution. JAMA J. Am. Med. Assoc. 287: 1132-1141.
31 Port, C. D.; Ketels, K. V.; Coffin, D. L.; Kane, P. (1977) A comparative study of experimental
32 and spontaneous emphysema. J. Toxicol. Environ. Health 2: 589-604.
33 Posin, C.; Clark, K.; Jones, M. P.; Patterson, J. V.; Buckley, R. D.; Hackney, J. D. (1978)
34 Nitrogen dioxide inhalation and human blood biochemistry. Arch. Environ. Health 33:
35 318-324.
36 Postlethwait, E. M.; Bidani, A. (1989) Pulmonary disposition of inhaled NO2-nitrogen in isolated
37 rat lungs. Toxicol. Appl. Pharmacol. 98: 303-312.
38 Postlethwait, E. M.; Bidani, A. (1990) Reactive uptake governs the pulmonary air space removal
39 of inhaled nitrogen dioxide. J. Appl. Physiol. 68: 594-603.
40 Postlethwait, E. M.; Bidani, A. (1994) Mechanisms of pulmonary NC>2 absorption. Toxicology
41 89:217-237.
August 2007 AX4-89 DRAFT-DO NOT QUOTE OR CITE
-------
1 Postlethwait, E. M.; Mustafa, M. G. (1981) Fate of inhaled nitrogen dioxide in isolated perfused
2 rat lung. J. Toxicol. Environ. Health 7: 861-872.
3 Postlethwait, E. M.; Mustafa, M. G. (1989) Effect of altered dose rate on NO2 uptake and
4 transformation in isolated lungs. J. Toxicol. Environ. Health 26: 497-507.
5 Postlethwait, E. M.; Langford, S. D.; Bidani, A. (1991) Transfer of NO2 through pulmonary
6 epithelial lining fluid. Toxicol. Appl. Pharmacol. 109: 464-471.
7 Postlethwait, E. M.; Langford, S. D.; Bidani, A. (1992) Kinetics of NO2 air space absorption in
8 isolated rat lungs. J. Appl. Physiol. 73: 1939-1945.
9 Postlethwait, E. M.; Langford, S. D.; Jacobson, L. M.; Bidani, A. (1995) NO2 reactive absorption
10 substrates in rat pulmonary surface lining fluids. Free Radical Biol. Med. 19: 553-563.
11 Poynter, M. E.; Persinger, R. L.; Irvin, C. G.; Butnor, K. J.; Van Hirtum, H.; Blay, W.; Heintz,
12 N. H.; Robbins, J.; Hemenway, D.; Taatjes, D. J.; Janssen-Heininger, Y. (2006) Nitrogen
13 dioxide enhances allergic airways inflammation and hyperresponsiveness in the mouse.
14 Am. J. Physiol. 290: L144-L152.
15 Prescott, G. J.; Cohen, G. R.; Elton, R. A.; Fowkes, F. G. R.; Agius, R. M. (1998) Urban air
16 pollution and cardiopulmonary ill health: a 14.5 year time series study. Occup. Environ.
17 Med. 55: 697-704.
18 Proust, B.; Lacroix, G.; Robidel, F.; Marliere, M.; Lecomte, A.; Vargaftig, B. B. (2002)
19 Interference of a short-term exposure to nitrogen dioxide with allergic airways responses
20 to allergenic challenges in BALB/c mice. Mediators Inflammation 11: 251-260.
21 Raaschou-Nielsen, O.; Skov, H.; Lohse, C.; Thomsen, B. L.; Olsen, J. H. (1997) Front-door
22 concentrations and personal exposures of Danish children to nitrogen dioxide. Environ.
23 Health Perspect. 105: 964-970.
24 Rajini, P.; Gelzleichter, T. R.; Last, J. A.; Witschi, H. (1993) Alveolar and airways cell kinetics
25 in the lungs of rats exposed to nitrogen dioxide, ozone, and a combination of the two
26 gases. Toxicol. Appl. Pharmacol. 121: 186-192.
27 Ramirez-Aguilar, M.; Cicero-Fernandez, P.; Winer, A. M.; Romieu, L; Meneses-Gonzales, F.;
28 Hernandez-Avila, M. (2002) Measurements of personal exposure to nitrogen dioxide in
29 four Mexican cities in 1996. J. Air Waste Manage. Assoc. 52: 50-57.
30 Ramsay, T. O.; Burnett, R. T.; Krewski, D. (2003) The effect of concurvity in generalized
31 additive models linking mortality to ambient particulate matter. Epidemiology 14: 18-23.
32 Ranzi, A.; Gambini, M.; Spattini, A.; Galassi, C.; Sesti, D.; Bedeschi, M.; Messori, A.; Baroni,
33 A.; Cavagni, G.; Lauriola, P. (2004) Air pollution and respiratory status in asthmatic
34 children: hints for a locally based preventive strategy. AIRE study. Eur. J. Epidemiol. 19:
35 567-576.
36 Rasmussen, T. R.; Kjaergaard, S. K.; Tarp, U.; Pedersen, O. F. (1992) Delayed effects of NO2
37 exposure on alveolar permeability and glutathione peroxidase in healthy humans. Am.
38 Rev. Respir. Dis. 146: 654-659.
39 Rasmussen, T. R.; Brauer, M.; Kjaergaard, S. (1995) Effects of nitrous acid exposure on human
40 mucous membranes. Am. J. Respir. Crit. Care Med. 151: 1504-1511.
August 2007 AX4-90 DRAFT-DO NOT QUOTE OR CITE
-------
1 Raw, G. 1; Coward, S. K. D.; Brown, V. M.; Crump, D. R. (2004) Exposure to air pollutants in
2 English homes. J. Exposure Anal. Environ. Epidemiol. 14(suppl. 1): S85-S94.
3 Rehn, T.; Svartengren, M.; Philipson, K.; Camner, P. (1982) Mukociliaer transport i lunga och
4 naesa samt luftvaegsmotstand efter exponering foer kvaevedioxid [Mucociliary transport
5 in the lung and nose after exposure to nitrogen dioxide]. Vallingby, Sweden: Swedish
6 State Power Board; project KHM technical report no. 40.
7 Restrepo, C.; Zimmerman, R.; Thurston, G.; Clemente, J.; Gorczynski, J.; Zhong, M.; Blaustein,
8 M.; Chen, L. C. (2004) A comparison of ground-level air quality data with New York
9 State Department of Environmental Conservation monitoring stations data in South
10 Bronx, New York. Atmos. Environ. 38: 5295-5304.
11 Rich, D. Q.; Schwartz, J.; Mittleman, M. A.; Link, M.; Luttmann-Gibson, H.; Catalano, P. J.;
12 Speizer, F. E.; Dockery, D. W. (2005) Association of short-term ambient air pollution
13 concentrations and ventricular arrhythmias. Am. J. Epidemiol. 161: 1123-1132.
14 Rich, D. Q.; Kim, M. H.; Turner, J. R.; Mittleman, M. A.; Schwartz, J.; Catalano, P. J.; Dockery,
15 D. W. (2006a) Association of ventricular arrhythmias detected by implantable
16 cardioverter defibrillator and ambient air pollutants in the St Louis, Missouri
17 metropolitan area. Occup. Environ. Med. 63: 591-596.
18 Rich, D. Q.; Mittleman, M. A.; Link, M. S.; Schwartz, J.; Luttmann-Gibson, H.; Catalano, P. J.;
19 Speizer, F. E.; Gold, D. R.; Dockery, D. W. (2006b) Increased risk of paroxysmal atrial
20 fibrillation episodes associated with acute increases in ambient air pollution. Environ.
21 Health Perspect. 114: 120-123.
22 Richters, A.; Damji, K. S. (1988) Changes in T-lymphocyte subpopulations and natural killer
23 cells following exposure to ambient levels of nitrogen dioxide. J. Toxicol. Environ.
24 Health 25: 247-256.
25 Richters, A.; Damji, K. S. (1990) The relationship between inhalation of nitrogen dioxide, the
26 immune system, and progression of a spontaneously occurring lymphoma in AKR mice.
27 J. Environ. Pathol. Toxicol. Oncol. 10: 225-230.
28 Richters, A.; Kuraitis, K. (1981) Inhalation of NC>2 and blood borne cancer cell spread to the
29 lungs. Arch. Environ. Health 36: 36-39.
30 Richters, A.; Kuraitis, K. (1983) Air pollutants and the facilitation of cancer metastasis. Environ.
31 Health Perspect. 52: 165-168.
32 Richters, A.; Richters, V. (1983) A new relationship between air pollutant inhalation and cancer.
33 Arch. Environ. Health 38: 69-75.
34 Richters, A.; Richters, V.; Alley, W. P. (1985) The mortality rate from lung metastases in
35 animals inhaling nitrogen dioxide (NC^). J. Surg. Oncol. 28: 63-66.
36 Riediker, M.; Williams, R.; Devlin, R.; Griggs, T.; Bromberg, P. (2003) Exposure to particulate
37 matter, volatile organic compounds, and other air pollutants inside patrol cars. Environ.
38 Sci. Technol.37: 2084-2093.
39 Riesenfeld, E.; Chalupa, D.; Gibb, F. R.; Oberdorster, G.; Gelein, R.; Morrow, P. E.; Utell, M. J.;
40 Frampton, M. W. (2000) Ultrafine particle concentrations in a hospital. In: Phalen, R. F.,
41 ed. Inhalation toxicology: proceedings of the third colloquium on particulate air pollution
August 2007 AX4-91 DRAFT-DO NOT QUOTE OR CITE
-------
1 and human health (second special issue); June, 1999; Durham, NC. Inhalation Toxicol.
2 12(suppl. 2): 83-94.
3 Rigas, M. L.; Ben-Jebria, A.; Ultman, J. S. (1997) Longitudinal distribution of ozone absorption
4 in the lung: effects of nitrogen dioxide, sulfur dioxide, and ozone exposures. Arch.
5 Environ. Health 52: 173-178.
6 Ristovski, Z. D.; Tass, L; Morawska, L.; Saxby, W. (2000) Investigation into the emission of fine
7 particles, formaldehyde, oxides of nitrogen and carbon monoxide from natural gas
8 heaters. J. Aerosol. Sci. 31(suppl. 1): S490-S491.
9 Ritz, B.; Yu, F.; Chapa, G.; Fruin, S. (2000) Effect of air pollution on preterm birth among
10 children born in Southern California between 1989 and 1993. Epidemiology 11: 502-511.
11 Roberts, S. (2004) Biologically plausible particulate air pollution mortality concentration-
12 response functions. Environ. Health Perspect. 112: 309-313.
13 Roberts, S. (2006) A new model for investigating the mortality effects of multiple air pollutants
14 in air pollution mortality time-series studies. J. Toxicol. Environ. Health Part A 69: 417-
15 435.
16 Robison, T. W.; Murphy, J. K.; Beyer, L. L.; Richters, A.; Forman, H. J. (1993) Depression of
17 stimulated arachidonate metabolism and superoxide production in rat alveolar
18 macrophages following in vivo exposure to 0.5 ppm NO2. J. Toxicol. Environ. Health 38:
19 273-292.
20 Rodgers, M. O.; Davis, D. D. (1989) A UV-photofragmentation/laser-induced fluorescence
21 sensor for the atmospheric detection of HONO. Environ. Sci. Technol. 23: 1106-1112.
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.; Clench-Aas, J.; Englert, N.; Hoek, G.; Katsouyanni, K.; Pekkanen, J.; Brunekreef,
26 B. (1999) Inhomogeneity in response to air pollution in European children (PEACE
27 project). Occup. Environ. Med. 56: 86-92.
28 Roger, L. J.; Horstman, D. H.; McDonnell, W.; Kehrl, H.; Ives, P. J.; Seal, E.; Chapman, R.;
29 Massaro, E. (1990) Pulmonary function, airways responsiveness, and respiratory
30 symptoms in asthmatics following exercise in NC>2. Toxicol. Ind. Health 6: 155-171.
31 Rogge, W. F.; Hildemann, L. M.; Mazurek, M. A.; Cass, G. R.; Simoneit, B. R. T. (1993)
32 Sources of fine organic aerosol. 5. Natural gas home appliances. Environ. Sci. Technol.
33 27:2736-2744.
34 Rojas-Bracho, L.; Suh, H. H.; Oyola, P.; Koutrakis, P. (2002) Measurements of children's
35 exposures to particles and nitrogen dioxide in Santiago, Chile. Sci. Total Environ. 287:
36 249-264.
37 Rombout, P. J. A.; Dormans, J. A. M. A.; Marra, M.; Van Esch, G. J. (1986) Influence of
38 exposure regimen on nitrogen dioxide-induced morphological changes in the rat lung.
39 Environ. Res. 41: 466-480.
August 2007 AX4-92 DRAFT-DO NOT QUOTE OR CITE
-------
1 Romieu, I; Sienra-Monge, J. J.; Ramirez-Aguilar, M.; Moreno-Macias, H.; Reyes-Ruiz, N. I;
2 Estela del Rio-Navarro, B.; Hernandez-Avila, M.; London, S. J. (2004) Genetic
3 polymorphism ofGSTMl and antioxidant supplementation influence lung function in
4 relation to ozone exposure in asthmatic children in Mexico City. Thorax 59: 8-10.
5 Romieu, I.; Ramirez-Aguilar, M.; Sienra-Monge, J. J.; Moreno-Macias, H.; Del Rio-Navarro, B.
6 E.; David, G.; Marzec, J.; Hernandez-Avila, M.; London, S. (2006) GSTM1 and GSTP1
1 and respiratory health in asthmatic children exposed to ozone. Eur. Respir. J. 28: 953-
8 959.
9 Rondeau, V.; Berhane, K.; Thomas, D. C. (2005) A three-level model for binary time-series data:
10 the effects of air pollution on school absences in the southern California Children's
11 Health Study. Stat. Med. 24: 1103-1115.
12 Roorda-Knape, M. C.; Janssen, N. A. H.; De Hartog, J. J.; Van Vliet, P. H. N.; Harssema, H.;
13 Brunekreef, B. (1998) Air pollution from traffic in city districts near major motorways.
14 Atmos. Environ. 32: 1921-1930.
15 Rose, R. M.; Pinkston, P.; Skornik, W. A. (1989) Altered susceptibility to viral respiratory
16 infection during short-term exposure to nitrogen dioxide. Cambridge, MA: Health Effects
17 Institute; research report no. 24. Available from: NTIS, Springfield, VA; PB90-111139.
18 Rotko, T.; Kousa, A.; Aim, S.; Jantunen, M. (2001) Exposures to nitrogen dioxide in EXPOLIS-
19 Helsinki: microenvironment, behavioral and sociodemographic factors. J. Exposure Anal.
20 Environ. Epidemiol. 11: 216-223.
21 Roy-Burman, P.; Pattengale, P. K.; Sherwin, R. P. (1982) Effect of low levels of nitrogen
22 dioxide inhalation on endogenous retrovirus gene expression. Exp. Mol. Pathol. 36: 144-
23 155.
24 Rubenchik, B. L.; Glavin, A. A.; Galenko, P. M.; Kilkichko, A. A.; Olenick, I. O.; Artemov, K.
25 V. (1995) Gaseous nitrogen dioxide increases the endogenous synthesis of carcinogenic
26 7V-nitrosodimethylamine in animals. J. Environ. Pathol. Toxicol. Oncol. 14: 111-115.
27 Rubenstein, I; Bigby, B. G.; Reiss, T. F.; Boushey, H. A., Jr. (1990) Short-term exposure to 0.3
28 ppm nitrogen dioxide does not potentiate airways responsiveness to sulfur dioxide in
29 asthmatic subjects. Am. Rev. Respir. Dis. 141: 381-385.
30 Rubenstein, L; Reiss, T. F.; Bigby, B. G.; Stites, D. P.; Boushey, H. A., Jr. (1991) Effects of 0.60
31 PPM nitrogen dioxide on circulating and bronchoalveolar lavage lymphocyte phenotypes
32 in healthy subjects. Environ. Res. 55: 18-30.
33 Rudell, B.; Blomberg, A.; Helleday, R.; Ledin, M.-C.; Lundback, B.; Stjernberg, N.; Horstedt,
34 P.; Sandstrom, T. (1999) Bronchoalveolar inflammation after exposure to diesel exhaust:
35 comparison between unfiltered and particle trap filtered exhaust. Occup. Environ. Med.
36 56: 527-534.
37 Ruidavets, J.-B.; Cassadou, S.; Cournot, M.; Bataille, V.; Meybeck, M.; Ferrieres, J. (2005)
38 Increased resting heart rate with pollutants in a population based study. J. Epidemiol.
39 Community Health 59: 685-693.
40 Rusznak, C.; Devalia, J. L.; Davies, R. J. (1996) Airways response of asthmatic subjects to
41 inhaled allergen after exposure to pollutants. Thorax 51: 1105-1108.
August 2007 AX4-93 DRAFT-DO NOT QUOTE OR CITE
-------
1 Sabin, L. D.; Kozawa, K.; Behrentz, E.; Winer, A. M.; Fitz, D. R.; Pankratz, D. V.; Colome, S.
2 D.; Fruin, S. A. (2005) Analysis of real-time variables affecting children's exposure to
3 diesel-related pollutants during school bus commutes in Los Angeles. Atmos. Environ.
4 39: 5243-5254.
5 Saez, M.; Tobias, A.; Mufioz, P.; Campbell, M. J. (1999) A GEE moving average analysis of the
6 relationship between air pollution and mortality for asthma in Barcelona, Spain. Stat.
7 Med. 18: 2077-2086.
8 Saez, M.; Ballester, F.; Barcelo, M. A.; Perez-Hoyos, S.; Bellido, J.; Tenias, J. M.; Ocafia, R.;
9 Figueiras, A.; Arribas, F.; Aragones, N.; Tobias, A.; Cirera, L.; Canada, A.; on behalf of
10 the EMECAM Group. (2002) A combined analysis of the short-term effects of
11 photochemical air pollutants on mortality within the EMECAM project. Environ. Health
12 Perspect. 110:221-228.
13 Sagai, M.; Ichinose, T.; Kubota, K. (1984) Studies on the biochemical effects of nitrogen
14 dioxide. IV. Relation between the change of lipid peroxidation and the antioxidative
15 protective system in rat lungs upon life span exposure to low levels of NC>2. Toxicol.
16 Appl. Pharmacol. 73:444-456.
17 Sagai, M.; Arakawa, K.; Ichinose, T.; Shimojo, N. (1987) Biochemical effects on combined
18 gases of nitrogen dioxide and ozone. I. Species differences of lipid peroxides and
19 phospholipids in lungs. Toxicology 46: 251-265.
20 Salam, M. T.; Millstein, J.; Li, Y.-F.; Lurmann, F. W.; Margolis, H. G; Gilliland, F. D. (2005)
21 Birth outcomes and prenatal exposure to ozone, carbon monoxide, and particulate matter:
22 results from the Children's Health Study. Environ. Health Perspect. 113: 1638-1644.
23 Samet, J. M.; Bell, M. L. (2004) Commentary: nitrogen dioxide and asthma redux. Int. J.
24 Epidemiol. 33:215-216.
25 Samet, J. M.; Utell, M. J. (1990) The risk of nitrogen dioxide: what have we learned from
26 epidemiological and clinical studies? Toxicol. Ind. Health 6: 247-262.
27 Samet, J. M.; Lambert, W. E.; Skipper, B. J.; Cushing, A. H.; McLaren, L. C.; Schwab, M.;
28 Spengler, J. D. (1992) A study of respiratory illnesses in infants and nitrogen dioxide
29 exposure. Arch. Environ. Health 47: 57-63.
30 Samet, J. M.; Lambert, W. E.; Skipper, B. J.; Cushing, A. H.; Hunt, W. C.; Young, S. A.;
31 McLaren, L. C.; Schwab, M.; Spengler, J. D. (1993) Health outcomes. In: Nitrogen
32 dioxide and respiratory illness in children, part I. Cambridge, MA: Health Effects
33 Institute; pp. 1-32; Research Report no. 58.
34 Samet, J. M.; Lambert, W. E.; Skipper, B. J.; Cushing, A. H.; Hunt, W. C.; Young, S. A.;
35 McLaren, L. C.; Schwab, M.; Spengler, J. D. (1993) Nitrogen dioxide and respiratory
36 illnesses in infants. Am. Rev. Respir. Dis. 148: 1258-1265.
37 Samet, J. M.; Zeger, S. L.; Dominici, F.; Curriero, F.; Coursac, L; Dockery, D. W.; Schwartz, J.;
38 Zanobetti, A. (2000) The national morbidity, mortality, and air pollution study. Part II:
39 morbidity, mortality, and air pollution in the United States. Cambridge, MA: Health
40 Effects Institute; research report no. 94, part II.
August 2007 AX4-94 DRAFT-DO NOT QUOTE OR CITE
-------
1 Samoli, E.; Analitis, A.; Touloumi, G.; Schwartz, J.; Anderson, H. R.; Sunyer, J.; Bisanti, L.;
2 Zmirou, D.; Vonk, J. M.; Pekkanen, J.; Goodman, P.; Paldy, A.; Schindler, C.;
3 Katsouyanni, K. (2005) Estimating the exposure-response relationships between
4 particulate matter and mortality within the APHEA multicity project. Environ. Health
5 Perspect. 113: 88-95.
6 Samoli, E.; Aga, E.; Touloumi, G.; Nisiotis, K.; Forsberg, B.; Lefranc, A.; Pekkanen, J.;
7 Wojtyniak, B.; Schindler, C.; Niciu, E.; Brunstein, R.; Dodic Fikfak, M.; Schwartz, J.;
8 Katsouyanni, K. (2006) Short-term effects of nitrogen dioxide on mortality: an analysis
9 within the APHEA project. Eur. Respir. J. 27: 1129-1137.
10 Sandstrom, T.; Andersson, M. C.; Kolmodin-Hedman, B.; Stjernberg, N.; Angstrom, T. (1990)
11 Bronchoalveolar mastocytosis and lymphocytosis after nitrogen dioxide exposure in man:
12 a time-kinetic study. Eur. Respir. J. 3: 138-143.
13 Sandstrom, T.; Stjernberg, N.; Eklund, A.; Ledin, M.-C.; Bjermer, L.; Kolmodin-Hedman, B.;
14 Lindstrom, K.; Rosenhall, L.; Angstrom, T. (1991) Inflammatory cell response in
15 bronchoalveolar lavage fluid after nitrogen dioxide exposure of healthy subjects: a dose-
16 response study. Eur. Respir. J. 4: 332-339.
17 Sandstrom, T.; Helleday, R.; Bjermer, L.; Stjernberg, N. (1992a) Effects of repeated exposure to
18 4 ppm nitrogen dioxide on bronchoalveolar lymphocyte subsets and macrophages in
19 healthy men. Eur. Respir. J. 5: 1092-1096.
20 Sandstrom, T.; Ledin, M.-C.; Thomasson, L.; Helleday, R.; Stjernberg, N. (1992b) Reductions in
21 lymphocyte subpopulations after repeated exposure to 1.5 ppm nitrogen dioxide. Br. J.
22 Ind. Med. 49: 850-854.
23 Santiago, L. Y.; Hann, M. C.; Ben-Jebria, A.; Ultman, J. S. (2001) Ozone adsorption in the
24 human nose during unidirectional airflow. J. Appl. Physiol. 91: 725-732.
25 Sarnat, J. A.; Schwartz, J.; Catalano, P. J.; Suh, H. H. (2001) Gaseous pollutants in particulate
26 matter epidemiology: confounders or surrogates? Environ. Health Perspect. 109: 1053-
27 1061.
28 Sarnat, J. A.; Brown, K. W.; Schwartz, J.; Coull, B. A.; Koutrakis, P. (2005) Ambient gas
29 concentrations and personal particulate matter exposures: implications for studying the
30 health effects of particles. Epidemiology 16: 385-395.
31 Sarnat, S. E.; Suh, H. H.; Coull, B. A.; Schwartz, J.; Stone, P. H.; Gold, D. R. (2006) Ambient
32 particulate air pollution and cardiac arrhythmia in a panel of older adults in Steubenville,
33 Ohio. Occup. Environ. Med. 63: 700-706.
34 Sarwar, G.; Corsi, R.; Allen, D.; Weschler, C. (2002a) Production and levels of selected indoor
35 radicals: a modeling assessment. In: Proceedings of 9th International Conference on
36 Indoor Air Quality and Climate, Indoor Air 2002; June-July; Monterey, CA.
37 Sarwar, G.; Corsi, R.; Kumura, Y.; Allen, D.; Weschler, C. J. (2002b) Hydroxyl radicals in
38 indoor environments. Atmos. Environ. 36: 3973-3988.
39 Sasaki, Y.; Endo, R.; Koido, Y. (1980) Direct mutagens in the gaseous component of automobile
40 exhaust detected with Bacillus subtilis spores. Mutat. Res. 79: 181-184.
August 2007 AX4-95 DRAFT-DO NOT QUOTE OR CITE
-------
1 Scarlett, J. F.; Abbott, K. I; Peacock, J. L.; Strachan, D. P.; Anderson, H. R. (1996) Acute
2 effects of summer air pollution on respiratory function in primary school children in
3 southern England. Thorax 51: 1109-1114.
4 Schairer, L. A.; Van't Hof, J.; Hayes, C. G.; Burton, R. M.; de Serres, F. J. (1979) Measurement
5 of biological activity of ambient air mixtures using a mobile laboratory for in situ
6 exposures: preliminary results from the Tradescantia plant test system. Environ. Sci. Res.
7 419-440.
8 Schildcrout, J. S.; Sheppard, L.; Lumley, T.; Slaughter, J. C.; Koenig, J. Q.; Shapiro, G. G.
9 (2006) Ambient air pollution and asthma exacerbations in children: an eight-city analysis.
10 Am. J. Epidemiol. 164: 505-517.
11 Schindler, C.; Ackermann-Liebrich, U.; Leuenberger, P.; Monn, C.; Rapp, R.; Bolognini, G.;
12 Bongard, J.-P.; Brandli, O.; Domenighetti, G.; Karrer, W.; Keller, R.; Medici, T. G.;
13 Perruchoud, A. P.; Schoni, M. H.; Tschopp, J.-M.; Villiger, B.; Zellweger, J.-P.;
14 SAPALDIA Team. (1998) Associations between lung function and estimated average
15 exposure to NC>2 in eight areas of Switzerland. Epidemiology 9: 405-411.
16 Schindler, C.; Kiinzli, N.; Bongard, J.-P.; Leuenberger, P.; Karrer, W.; Rapp, R.; Monn, C.;
17 Ackermann-Liebrich, U.; Swiss Study on Air Pollution and Lung Diseases in Adults
18 Investigators. (2001) Short-term variation in air pollution and in average lung function
19 among never-smokers. Am. J. Respir. Crit. Care Med. 163: 356-361.
20 Schlesinger, R. B. (1987a) Effects of intermittent inhalation exposures to mixed atmospheres of
21 NC>2 and H2SO4 on rabbit alveolar macrophages. J. Toxicol. Environ. Health 22: 301-312.
22 Schlesinger, R. B. (1987b) Intermittent inhalation of nitrogen dioxide: effects on rabbit alveolar
23 macrophages. J. Toxicol. Environ. Health 21: 127-139.
24 Schlesinger, R. B. (2000) Properties of ambient PM responsible for human health effects:
25 coherence between epidemiology and toxicology. In: Phalen, R. F., ed. Inhalation
26 toxicology: proceedings of the third colloquium on parti culate air pollution and human
27 health (first special issue); June, 1999; Durham, NC. Inhalation Toxicol. 12(suppl. 1): 23-
28 25.
29 Schlesinger, R. B.; Gearhart, J. M. (1987) Intermittent exposures to mixed atmospheres of
30 nitrogen dioxide and sulfuric acid: effect on particle clearance from the respiratory region
31 of rabbit lungs. Toxicology 44: 309-319.
32 Schlesinger, R. B.; Driscoll, K. E.; Vollmuth, T. A. (1987) Effect of repeated exposures to
33 nitrogen dioxide and sulfuric acid mist alone or in combination on mucociliary clearance
34 from the lungs of rabbits. Environ. Res. 44: 294-301.
35 Schlesinger, R. B.; Driscoll, K. E.; Gunnison, A. F.; Zelikoff, J. T. (1990) Pulmonary
36 arachidonic acid metabolism following acute exposures to ozone and nitrogen dioxide. J.
37 Toxicol. Environ. Health 31: 275-290.
38 Schlesinger, R. B.; El-Fawal, H. A. N.; Zelikoff, J. T.; Gorczynski, J. E.; McGovern, T.;
39 Nadziejko, C. E.; Chen, L. C. (1994) Pulmonary effects of repeated episodic exposures to
40 nitric acid vapor alone and in combination with ozone. Inhalation Toxicol. 6: 21-41.
August 2007 AX4-96 DRAFT-DO NOT QUOTE OR CITE
-------
1 Schouten, J. P.; Vonk, J. M.; de Graaf, A. (1996) Short term effects of air pollution on
2 emergency hospital admissions for respiratory disease: results of the APHEA project in
3 two major cities in The Netherlands, 1977-89. In: St Leger, S., ed. The APHEA project.
4 Short term effects of air pollution on health: a European approach using epidemiological
5 time series data. J. Epidemiol. Community Health 50(suppl. 1): S22-S29.
6 Schwartz, J. (1989) Lung function and chronic exposure to air pollution: a cross-sectional
7 analysis of NHANES II. Environ. Res. 50: 309-321.
8 Schwartz, J. (1997) Air pollution and hospital admissions for cardiovascular disease in Tucson.
9 Epidemiology 8: 371-377.
10 Schwartz, J. (2006) Comments on the paper by Peng, Dominici and Louis [(2006) Model choice
11 in time series studies of air pollution and mortality. J. R. Stat. Soc. Ser. A 169: 179-203].
12 J. R. Stat. Soc. Ser. A 169: 198-200.
13 Schwartz, J.; Zeger, S. (1990) Passive smoking, air pollution, and acute respiratory symptoms in
14 a diary study of student nurses. Am. Rev. Respir. Dis. 141: 62-67.
15 Schwartz, J.; Spix, C.; Wichmann, H. E.; Malin, E. (1991) Air pollution and acute respiratory
16 illness in five German communities. Environ. Res. 56: 1-14.
17 Schwartz, J.; Dockery, D. W.; Neas, L. M.; Wypij, D.; Ware, J. H.; Spengler, J. D.; Koutrakis,
18 P.; Speizer, F. E.; Ferris, B. G., Jr. (1994) Acute effects of summer air pollution on
19 respiratory symptom reporting in children. Am. J. Respir. Crit. Care Med. 150: 1234-
20 1242.
21 Schwartz, J.; Litonjua, A.; Suh, H.; Verrier, M.; Zanobetti, A.; Syring, M.; Nearing, B.; Verrier,
22 R.; Stone, P.; MacCallum, G.; Speizer, F. E.; Gold, D. R. (2005) Traffic related pollution
23 and heart rate variability in a panel of elderly subjects. Thorax 60: 455-461.
24 Seaton, A.; Dennekamp, M. (2003) Hypothesis: ill health associated with low concentrations of
25 nitrogen dioxide—an effect of ultrafine particles? Thorax 58: 1012-1015.
26 Segala, C.; Fauroux, B.; Just, J.; Pascual, L.; Grimfeld, A.; Neukirch, F. (1998) Short-term effect
27 of winter air pollution on respiratory health of asthmatic children in Paris. Eur. Respir. J.
28 11:677-685.
29 Segala, C.; Poizeau, D.; Neukirch, F.; Aubier, M.; Samson, J.; Gehanno, P. (2004) Air pollution,
30 passive smoking, and respiratory symptoms in adults. Arch. Environ. Health 59: 669-676.
31 Seinfeld, J. H.; Pandis, S. N. (1998) Atmospheric chemistry and physics: from air pollution to
32 climate change. New York, NY: John Wiley & Sons, Inc.
33 Sekharam, K. M.; Patel, J. M.; Block, E. R. (1991) Plasma membrane-specific phospholipase-Al
34 activation by nitrogen dioxide in pulmonary artery endothelial cells. Toxicol. Appl.
35 Pharmacol. 107: 545-554.
36 Selgrade, M. K.; Mole, M. L.; Miller, F. J.; Hatch, G. E.; Gardner, D. E.; Hu, P. C. (1981) Effect
37 of NC>2 inhalation and vitamin C deficiency on protein and lipid accumulation in the lung.
38 Environ. Res. 26: 422-437.
August 2007 AX4-97 DRAFT-DO NOT QUOTE OR CITE
-------
1 Seto, K.; Kon, M.; Kawakami, M.; Yagishita, S.; Sugita, K.; Shishido, M. (1975) [Influence of
2 nitrogen dioxide inhalation on the formation of protein in the lung]. Igaku to
3 Seibutsugaku 90: 103-106.
4 Shalamberidze, O. P.; Tsereteli, N. T. (1971) Effect of low concentrations of sulfur and nitrogen
5 dioxides on the estrual cycle and reproductive functions of experimental animals. Hyg.
6 Sanit. (USSR) 36: 178-182.
7 Sheppard, L. (2005) Acute air pollution effects: consequences of exposure distribution and
8 measurements. J. Toxicol. Environ. Health Part A 68: 1127-1135.
9 Sheppard, L.; Slaughter, J. C.; Schildcrout, J.; Liu, L.-J. S.; Lumley, T. (2005) Exposure and
10 measurement contributions to estimates of acute air pollution effects. J. Exposure Anal.
11 Environ. Epidemiol. 15: 366-376.
12 Sherwin, R. P.; Carlson, D. A. (1973) Protein content of lung lavage fluid of guinea pigs exposed
13 to 0.4 ppm nitrogen dioxide: disc-gel electrophoresis for amount and types. Arch.
14 Environ. Health 27: 90-93.
15 Sherwin, R. P.; Dibble, J.; Weiner, J. (1972) Alveolar wall cells of the guinea pig: increase in
16 response to 2 ppm nitrogen dioxide. Arch. Environ. Health 24: 43-47.
17 Shima, M.; Adachi, M. (2000) Effect of outdoor and indoor nitrogen dioxide on respiratory
18 symptoms in schoolchildren. Int. J. Epidemiol. 29: 862-870.
19 Shiraishi, F.; Bandow, H. (1985) The genetic effects of the photochemical reaction products of
20 propylene plus NC>2 on cultured Chinese hamster cells exposed in vitro. J. Toxicol.
21 Environ. Health 15: 531-538.
22 Silkoff, P. E.; Zhang, L.; Button, S.; Langmack, E. L.; Vedal, S.; Murphy, J.; Make, B. (2005)
23 Winter air pollution and disease parameters in advanced chronic obstructive pulmonary
24 disease panels residing in Denver, Colorado. J. Allergy Clin. Immunol. 115: 337-344.
25 Simoni, M.; Carrozzi, L.; Baldacci, S.; Scognamiglio, A.; Di Pede, F.; Sapigni, T.; Viegi, G.
26 (2002) The Po River delta (north Italy) indoor epidemiological study: effects of pollutant
27 exposure on acute respiratory symptoms and respiratory function in adults. Arch.
28 Environ. Health 57: 130-136.
29 Simoni, M.; Scognamiglio, A.; Carrozzi, L.; Baldacci, S.; Angino, A.; Pistelli, F.; Di Pede, F.;
30 Viegi, G. (2004) Indoor exposures and acute respiratory effects in two general population
31 samples from a rural and an urban area in Italy. J. Exposure Anal. Environ. Epidemiol.
32 14(suppl. 1): S144-S152.
33 Simpson, R.; Williams, G.; Petroeschevsky, A.; Best, T.; Morgan, G.; Denison, L.; Hinwood, A.;
34 Neville, G. (2005a) The short-term effects of air pollution on hospital admissions in four
35 Australian cities. Aust. N. Z. J. Public Health 29: 213-221.
36 Simpson, R.; Williams, G.; Petroeschevsky, A.; Best, T.; Morgan, G.; Denison, L.; Hinwood, A.;
37 Neville, G.; Neller, A. (2005b) The short-term effects of air pollution on daily mortality
38 in four Australian cities. Aust. N. Z. J. Public Health 29: 205-212.
39 Sindhu, R. K.; Mautz, W. J.; Kikkawa, Y. (1998) Chronic exposure to ozone and nitric acid
40 vapor results in increased levels of rat pulmonary putrescine. Arch. Toxicol. 72: 445-449.
August 2007 AX4-98 DRAFT-DO NOT QUOTE OR CITE
-------
1 Singer, B. C.; Hodgson, A. T.; Hotchi, T.; Kim, J. J. (2004) Passive measurement of nitrogen
2 oxides to assess traffic-related pollutant exposure for the East Bay Children's Respiratory
3 Health Study. Atmos. Environ. 38: 393-403.
4 Singh, H. B.; Salas, L.; Herlth, D.; Kolyer, R.; Czech, E.; Avery, M.; Crawford, J. H.; Pierce, R.
5 B.; Sachse, G. W.; Blake, D. R.; Cohen, R. C.; Bertram, T. H.; Perring, A.; Wooldridge,
6 P. J.; Dibb, J.; Huey, G.; Hudman, R. C.; Turquety, S.; Emmons, L. K.; Flocke, F.; Tang,
7 Y.; Carmichael, G. R.; Horowitz, L. W. (2007) Reactive nitrogen distribution and
8 partitioning in the North American troposphere and lowermost stratosphere. J. Geophys.
9 Res. [Atmos.] 112(D12S04): 10.1029/2006JD007664.
10 Slack, H. H.; Heumann, M. A. (1997) Use of unvented residential heating appliances—United
11 States, 1988-1994. Morb. Mortal. Wkly. Rep. 46: 1221-1224.
12 Slade, R.; Highfill, J. W.; Hatch, G. E. (1989) Effects of depletion of ascorbic acid or nonprotein
13 sulfhydryls on the acute inhalation toxicity of nitrogen dioxide, ozone, and phosgene.
14 Inhalation Toxicol. 1: 261-271.
15 Smith, B. J.; Nitschke, M.; Pilotto, L. S.; Ruffrn, R. E.; Pisaniello, D. L.; Wilson, K. J. (2000)
16 Health effects of daily indoor nitrogen dioxide exposure in people with asthma. Eur.
17 Respir. J. 16: 879-885.
18 Snyder, S. H.; Bredt, D. S. (1992) Biological roles of nitric oxide. Sci. Am. 266(5): 68-71, 74-77.
19 Solomon, C.; Christian, D. L.; Chen, L. L.; Welch, B. S.; Kleinman, M. T.; Dunham, E.; Erie, D.
20 J.; Balmes, J. R. (2000) Effect of serial-day exposure to nitrogen dioxide on airways and
21 blood leukocytes and lymphocyte subsets. Eur. Respir. J. 15: 922-928.
22 Son, B.; Yang, W.; Breysse, P.; Chung, T.; Lee, Y. (2004) Estimation of occupational and
23 nonoccupational nitrogen dioxide exposure for Korean taxi drivers using a
24 microenvironmental model. Environ. Res. 94: 291-296.
25 S0rensen, M.; Loft, S.; Andersen, H. V.; Raaschou-Nielsen, O.; Skovgaard, L. T.; Knudsen, L.
26 E.; Nielsen, I. V.; Hertel, O. (2005) Personal exposure to PM2.5, black smoke and NO2 in
27 Copenhagen: relationship to bedroom and outdoor concentrations covering seasonal
28 variation. J. Exposure Anal. Environ. Epidemiol. 15: 413-422.
29 Spengler, J. D.; Brauer, M.; Koutrakis, P. (1990) Acid air and health. Environ. Sci. Technol. 24:
30 946-956.
31 Spengler, J.; Schwab, M.; Ryan, P. B.; Colome, S.; Wilson, A. L.; Billick, L; Becker, E. (1994)
32 Personal exposure to nitrogen dioxide in the Los Angeles Basin. J. Air Waste Manage.
33 Assoc. 44: 39-47.
34 Spengler, J. D.; Lee, K.; Yanagisawa, Y.; Bischof, W.; Braathan, O.; Chung, S.; Coward, K.;
35 Gutschmidt, V.; Isidorov, V.; Jahng, D.; Jin, K.; Korenaga, T.; Maroni, M.; Ohkoda, Y.;
36 Pastuszka, J.; Patil, R. S.; Qing, X.; Raizenne, M.; Romieu, L; Salonen, R.; Sega, K.;
37 Seifert, B.; Shah, S.; Torres, E.; Yoon, D.; Zhang, X. (1996) Impact of residential
38 nitrogen exposure on personal exposure: an international study. In: Indoor air '96: the 7th
39 international conference on indoor air quality, volume I; July; Nagoya, Japan. Tokyo,
40 Japan: Institute of Public Health; pp. 931-936.
August 2007 AX4-99 DRAFT-DO NOT QUOTE OR CITE
-------
1 Spicer, C. W.; Coutant, R. W.; Ward, G. F.; Joseph, D. W.; Gaynor, A. J.; Billick, I. H. (1989)
2 Rates and mechanisms of NC>2 removal from indoor air by residential materials. Environ.
3 Int. 15: 643-654.
4 Spicer, C. W.; Kenny, D. V.; Ward, G. F.; Billick, I. H. (1993) Transformations, lifetimes, and
5 sources of NC>2, HONO, and HNOs in indoor environments. Air Waste 43: 1479-1485.
6 Steerenberg, P. A.; Nierkens, S.; Fischer, P. H.; Van Loveren, H.; Opperhuizen, A.; Vos, J. G.;
7 Van Amsterdam, J. G. (2001) Traffic-related air pollution affects peak expiratory flow,
8 exhaled nitric oxide, and inflammatory nasal markers. Arch. Environ. Health 56: 167-
9 174.
10 Steerenberg, P. A.; Bischoff, E. W. M. A.; de Klerk, A.; Verlaan, A. P. J.; Jongbloets, L. M. N.;
11 Van Loveren, H.; Opperhuizen, A.; Zomer, G.; Heisterkamp, S. H.; Hady, M.; Spieksma,
12 F. T. M.; Fischer, P. H.; Dormans, J. A. M. A.; van Amsterdam, J. G. C. (2003) Acute
13 effect of air pollution on respiratory complaints, exhaled NO and biomarkers in nasal
14 lavages of allergic children during the pollen season. Int. Arch. Allergy Immunol. 131:
15 127-137.
16 Stemmler, K.; Ammann, M.; Bonders, C.; Kleffmann, J.; George, C. (2006) Photosensitized
17 reduction of nitrogen dioxide on humic acid as a source of nitrous acid. Nature (London,
18 U.K.) 440: 195-198.
19 Stephens, R. J.; Freeman, G.; Crane, S. C.; Furiosi, N. J. (1971) Ultrastructural changes in the
20 terminal bronchiole of the rat during continuous, low-level exposure to nitrogen dioxide.
21 Exp. Mol. Pathol. 14: 1-19.
22 Stephens, R. J.; Freeman, G.; Evans, M. J. (1972) Early response of lungs to low levels of
23 nitrogen dioxide: light and electron microscopy. Arch. Environ. Health 24: 160-179.
24 Stevens, R. K.; Dzubay, T. G.; Russwurm, G.; Rickel, D. (1978) Sampling and analysis of
25 atmospheric sulfates and related species. Atmos. Environ. 12: 55-68.
26 Stevens, M. A.; Menache, M. G.; Crapo, J. D.; Miller, F. J.; Graham, J. A. (1988) Pulmonary
27 function in juvenile and young adult rats exposed to low-level NO2 with diurnal spikes. J.
28 Toxicol. Environ. Health 23: 229-240.
29 Stieb, D. M.; Burnett, R. T.; Beveridge, R. C.; Brook, J. R. (1996) Association between ozone
30 and asthma emergency department visits in Saint John, New Brunswick, Canada.
31 Environ. Health Perspect. 104: 1354-1360.
32 Stieb, D. M.; Judek, S.; Burnett, R. T. (2002) Meta-analysis of time-series studies of air pollution
33 and mortality: effects of gases and particles and the influence of cause of death, age, and
34 season. J. Air Waste Manage. Assoc. 52: 470-484.
35 Stieb, D. M.; Judek, S.; Burnett, R. T. (2003) Meta-analysis of time-series studies of air pollution
36 and mortality: update in relation to the use of generalized additive models. J. Air Waste
37 Manage. 53:258-261.
38 Stolzel, M.; Peters, A.; Wichmann, H.-E. (2003) Daily mortality and fine and ultrafine particles
39 in Erfurt, Germany. In: Revised analyses of time-series studies of air pollution and health.
40 Special report. Boston, MA: Health Effects Institute; pp. 231-240. Available:
41 http://www.healtheffects.org/Pubs/TimeSeries.pdf [18 October, 2004].
August 2007 AX4-100 DRAFT-DO NOT QUOTE OR CITE
-------
1 Strand, V.; Salomonsson, P.; Lundahl, J.; Bylin, G. (1996) Immediate and delayed effects of
2 nitrogen dioxide exposure at an ambient level on bronchial responsiveness to histamine in
3 subjects with asthma. Eur. Respir. J. 9: 733-740.
4 Strand, V.; Rak, S.; Svartengren, M.; Bylin, G. (1997) Nitrogen dioxide exposure enhances
5 asthmatic reaction to inhaled allergen in subjects with asthma. Am. J. Respir. Crit. Care
6 Med. 155: 881-887.
7 Strand, V.; Svartengren, M.; Rak, S.; Barck, C.; Bylin, G. (1998) Repeated exposure to an
8 ambient level of NO2 enhances asthmatic response to nonsymptomatic allergen dose. Eur.
9 Respir. J. 12: 6-12.
10 Studnicka, M.; Hackl, E.; Pischinger, J.; Fangmeyer, C.; Haschke, N.; Kuhr, J.; Urbanek, R.;
11 Neumann, M.; Frischer, T. (1997) Traffic-related NO2 and the prevalence of asthma and
12 respiratory symptoms in seven year olds. Eur. Respir. J. 10: 2275-2278.
13 Stutz, J.; Ackermann, R.; Fast, J. D.; Barrie, L. (2002) Atmospheric reactive chlorine and
14 bromine at the Great Salt Lake, Utah. Geophys. Res. Lett. 29: 10.1029/2002GL014812.
15 Sunyer, J.; Basagafia, X. (2001) Particles, and not gases, are associated with the risk of death in
16 patients with chronic obstructive pulmonary disease. Int. J. Epidemiol. 30: 1138-1140.
17 Sunyer, J.; Spix, C.; Quenel, P.; Ponce-de-Leon, A.; Ponka, A.; Barumandzadeh, T.; Touloumi,
18 G.; Bacharova, L.; Wojtyniak, B.; Vonk, J.; Bisanti, L.; Schwartz, J.; Katsouyanni, K.
19 (1997) Urban air pollution and emergency admissions for asthma in four European cities:
20 the APHEA project. Thorax 52: 760-765.
21 Sunyer, J.; Basagafia, X.; Belmonte, J.; Anto, J. M. (2002) Effect of nitrogen dioxide and ozone
22 on the risk of dying in patients with severe asthma. Thorax 57: 687-693.
23 Suzuki, A. K.; Tsubone, H.; Ichinose, T.; Oda, H.; Kubota, K. (1981) [Effects of subchronic
24 nitrogen dioxide exposure on arterial blood pHa, PaCo2 and Pa02 in rats]. Nippon
25 Eiseigaku Zasshi 36: 816-823.
26 Suzuki, T.; Terada, N.; Ikeda, S.; Ohsawa, M.; Endo, K.; Mizoguchi, I. (1984) [Effect of NO2
27 exposure on the activity of angiotensin converting enzyme in lung]. Kenkyu Nenpo
28 Tokyo-toritsu Eisei Kenkyusho 35: 279-285.
29 Suzuki, T.; Ikeda, S.; Kanoh, T.; Mizoguchi, I. (1986) Decreased phagocytosis and superoxide
30 anion production in alveolar macrophages of rats exposed to nitrogen dioxide. Arch.
31 Environ. Contam. Toxicol. 15: 733-739.
32 Svartengren, M.; Strand, V.; Bylin, G.; Jarup, L.; Pershagen, G. (2000) Short-term exposure to
33 air pollution in a road tunnel enhances the asthmatic response to allergen. Eur. Resp. J.
34 15: 716-724.
35 Tabacova, S. (1984) Behavioral effects of prenatal exposure to nitrogen dioxide. In: European
36 Teratology Society llth conference; August 1983; Paris, France. Teratology 29: 33A-
37 34A.
38 Tabacova, S.; Balabaeva, L. (1988) Nitrogen dioxide embryotoxicity and lipid peroxidation.
39 Teratology 38: 29A.
August 2007 AX4-101 DRAFT-DO NOT QUOTE OR CITE
-------
1 Tabacova, S.; Balabaeva, L.; Vardev, F. (1984) Nitrogen dioxide: maternal and fetal effects. In:
2 Abstracts of the 25th congress of the European Society of Toxicology, p. 40; June;
3 Budapest, Hungary.
4 Tabacova, S.; Nikiforov, B.; Balabaeva, L. (1985) Postnatal effects of maternal exposure to
5 nitrogen dioxide. Neurobehav. Toxicol. Teratol. 7: 785-789.
6 Tager, I. B.; Balmes, J.; Lurmann, F.; Ngo, L.; Alcorn, S.; Kiinzli, N. (2005) Chronic exposure to
7 ambient ozone and lung function in young adults. Epidemiology 16: 751-759.
8 Takahashi, Y.; Mochitate, K.; Miura, T. (1986) Subacute effects of nitrogen dioxide on
9 membrane constituents of lung, liver, and kidney of rats. Environ. Res. 41: 184-194.
10 Takano, T.; Miyazaki, Y. (1984) Combined effect of nitrogen dioxide and cold stress on the
11 activity of the hepatic cytochrome P-450 system in rats. Toxicology 33: 239-244.
12 Takano, H.; Yanagisawa, R.; Inoue, K.-L; Shimada, A.; Ichinose, T.; Sadakane, K.; Yoshino, S.;
13 Yamaki, K.; Morita, M.; Yoshikawa, T. (2004) Nitrogen dioxide air pollution near
14 ambient levels is an atherogenic risk primarily in obese subjects: a brief communication.
15 Exp. Biol. Med. 229: 361-364.
16 Tenias, J. M.; Ballester, F.; Rivera, M. L. (1998) Association between hospital emergency visits
17 for asthma and air pollution in Valencia, Spain. Occup. Environ. Med. 55: 541-547.
18 Tepper, J. S.; Costa, D. L.; Winsett, D. W.; Stevens, M. A.; Doerfler, D. L.; Watkinson, W. P.
19 (1993) Near-lifetime exposure of the rat to a simulated urban profile of nitrogen dioxide:
20 pulmonary function evaluation. Fund. Appl. Toxicol. 20: 88-96.
21 Thompson, A. J.; Shields, M. D.; Patterson, C. C. (2001) Acute asthma exacerbations and air
22 pollutants in children living in Belfast, Northern Ireland. Arch. Environ. Health 56: 234-
23 241.
24 Timonen, K. L.; Pekkanen, J. (1997) Air pollution and respiratory health among children with
25 asthmatic or cough symptoms. Am. J. Respir. Crit. Care Med. 156: 546-552.
26 Timonen, K. L.; Pekkanen, J.; Tiittanen, P.; Salonen, R. O. (2002) Effects of air pollution on
27 changes in lung function induced by exercise in children with chronic respiratory
28 symptoms. Occup. Environ. Med. 59: 129-134.
29 Timonen, K. L.; Hoek, G.; Heinrich, J.; Bernard, A.; Brunekreef, B.; De Hartog, J.; Hameri, K.;
30 Ibald-Mulli, A.; Mirme, A.; Peters, A.; Tiittanen, P.; Kreyling, W. G.; Pekkanen, J.
31 (2004) Daily variation in fine and ultrafme particulate air pollution and urinary
32 concentrations of lung Clara cell protein CC16. Occup. Environ. Med. 61: 908-914.
33 Tolbert, P. E.; Mulholland, J. A.; Macintosh, D. L.; Xu, F.; Daniels, D.; Devine, O. J.; Carlin, B.
34 P.; Klein, M.; Dorley, J.; Butler, A. J.; Nordenberg, D. F.; Frumkin, H.; Ryan, P. B.;
35 White, M. C. (2000) Air quality and pediatric emergency room visits for asthma in
36 Atlanta, Georgia. Am. J. Epidemiol. 151: 798-810.
37 Totten, R. S.; Moran, T. J. (1961) Cortisone and atypical pulmonary "epithelial" hyperplasia:
38 effects of pretreatment with cortisone on repair of chemically damaged rabbit lungs. Am.
39 J. Pathol. 38: 575-586.
August 2007 AX4-102 DRAFT-DO NOT QUOTE OR CITE
-------
1 Touloumi, G.; Katsouyanni, K.; Zmirou, D.; Schwartz, J.; Spix, C.; Ponce de Leon, A.; Tobias,
2 A.; Quennel, P.; Rabczenko, D.; Bacharova, L.; Bisanti, L.; Vonk, J. M.; Ponka, A.
3 (1997) Short-term effects of ambient oxidant exposure on mortality: a combined analysis
4 within the APHEA project. Am. J. Epidemiol. 146: 177-185.
5 Touloumi, G.; Samoli, E.; Pipikou, M.; Le Tertre, A.; Atkinson, R.; Katsouyanni, K. (2006)
6 Seasonal confounding in air pollution and health time-series studies: effect on air
7 pollution effect estimates. Stat. Med. 25: 4164-4178.
8 Tozuka, Y.; Watanabe, N.; Ohsawa, M.; Toriba, A.; Kizu, R.; Hayakawa, K. (2004) Transfer of
9 poly cyclic aromatic hydrocarbons to fetuses and breast milk of rats exposed to diesel
10 exhaust. J. Health Sci. 250: 497-502.
11 Triche, E. W.; Belanger, K.; Bracken, M. B.; Beckett, W. S.; Holford, T. R.; Gent, J. F.;
12 McSharry, J.-E.; Leaderer, B. P. (2005) Indoor heating sources and respiratory symptoms
13 in nonsmoking women. Epidemiology 16: 377-384.
14 Tsai, S.-S.; Goggins, W. B.; Chiu, H.-F.; Yang, C.-Y. (2003) Evidence for an association
15 between air pollution and daily stroke admissions in Kaohsiung, Taiwan. Stroke 34:
16 2612-2616.
17 Tsai, S.-S.; Cheng, M.-H.; Chiu, H.-F.; Wu, T.-N.; Yang, C.-Y. (2006) Air pollution and hospital
18 admissions for asthma in a tropical city: Kaohsiung, Taiwan. Inhalation Toxicol. 18: 549-
19 554.
20 Tsubone, H.; Suzuki, A. K. (1984) Reflex cardiopulmonary responses by stimulation to type J
21 receptors in rats exposed to NC>2. J. Toxicol. Environ. Health 13: 905-917.
22 Tsuda, H.; Kushi, A.; Yoshida, D.; Goto, F. (1981) Chromosomal aberrations and sister-
23 chromatic exchanges induced by gaseous nitrogen dioxide in cultured Chinese hamster
24 cells. Mutat. Res. 89: 303-309.
25 Tunnicliffe, W. S.; Burge, P. S.; Ayres, J. G. (1994) Effect of domestic concentrations of
26 nitrogen dioxide on airways responses to inhaled allergen in asthmatic patients. Lancet
27 344:1733-1736.
28 U.S. Census Bureau. (2001) Statistical abstract of the United States: 2001. The national data
29 book. 121st ed. Washington, DC: U.S. Census Bureau.
30 U.S. Code. (2003a) Clean Air Act, §108, air quality criteria and control techniques. U. S. C. 42:
31 §7408.
32 U.S. Code. (2003b) Clean Air Act, §109, national ambient air quality standards. U. S. C. 42:
33 §7409.
34 U.S. Code. (2005) Clean Air Act, §302, definitions. U. S. C. 42: §7602(h).
35 U.S. Court of Appeals for the District of Columbia. (1980) Lead Industries v. U.S.
36 Environmental Protection Agency. 647 F2d 1130, 1154 (DC Cir. 1980).
37 U.S. Court of Appeals for the District of Columbia. (1981) American Petroleum Institute v.
38 Costle. 665 F2d 1176, 1186 (DC Cir. 1981).
39 U.S. Environmental Protection Agency. (1982) Air quality criteria for oxides of nitrogen.
40 Research Triangle Park, NC: Office of Health and Environmental Assessment,
August 2007 AX4-103 DRAFT-DO NOT QUOTE OR CITE
-------
1 Environmental Criteria and Assessment Office; EPA report no. EPA-600/8-82-026.
2 Available from: NTIS, Springfield, VA; PB83-131011.
3 U.S. Environmental Protection Agency. (1993) Air quality criteria for oxides of nitrogen.
4 Research Triangle Park, NC: Office of Health and Environmental Assessment,
5 Environmental Criteria and Assessment Office; report nos. EPA/600/8-91/049aF-cF. 3v.
6 Available from: NTIS, Springfield, VA; PB95-124533, PB95-124525, and PB95-124517.
7 U.S. Environmental Protection Agency. (1994) Methods for derivation of inhalation reference
8 concentrations and application of inhalation dosimetry. Research Triangle Park, NC:
9 Office of Health and Environmental Assessment, Environmental Criteria and Assessment
10 Office; report no. EPA/600/8-88/066F. Available:
11 http://cfpubl.epa.gov/ncea/cfm/recordisplay.cfm?deid=71993 (11 April, 2005).
12 U.S. Environmental Protection Agency. (1995) Review of the national ambient air quality
13 standards for nitrogen dioxide: assessment of scientific and technical information.
14 Research Triangle Park, NC: Office of Air Quality Planning and Standards; report no.
15 EPA/452/R-95-005.
16 U.S. Environmental Protection Agency. (2004) Air quality criteria for particulate matter.
17 Research Triangle Park, NC: National Center for Environmental Assessment; report no.
18 EPA/600/P-99/002aF-bF. 2v. Available: http://cfpub.epa.gov/ncea/ [9 November, 2004].
19 U.S. Environmental Protection Agency. (2005) Approaches for the application of physiologically
20 based pharmacokinetic (PBPK) models and supporting data in risk assessment (external
21 review draft 2005). Washington, DC: U.S. Environmental Protection Agency; report no.
22 EPA/600/R-05/043A. Available:
23 http://oaspub.epa.gov/eims/eimsapi.dispdetail?deid=135427 [3 July, 2007].
24 U.S. Environmental Protection Agency. (2006) Air quality criteria for ozone and related
25 photochemical oxidants. Research Triangle Park, NC: National Center for Environmental
26 Assessment; report no. EPA/600/R-05/004aF-cF. 3v. Available:
27 http://cfpub.epa.gov/ncea/ [24 March, 2006].
28 U.S. Environmental Protection Agency. (2007) Integrated plan for the primary National Ambient
29 Air Quality Standard for Nitrogen Dioxide. Research Triangle Park, NC: National Center
30 for Environmental Assessment.
31 U.S. Senate. (1970) National Air Quality Standards Act of 1970: report of the Committee on
32 Public Works, United States Senate together with individual views to accompany S.
33 4358. Washington, DC: Committee on Public Works; report no. CONG/91-1196.
34 U.S. Supreme Court. (2001) Whitman v. American Trucking Association. 531 U.S. 457 (nos. 99-
35 1257 and 99-1426).
36 Vagaggini, B.; Paggiaro, P. L.; Giannini, D.; Franco, A. D.; Cianchetti, S.; Carnevali, S.;
37 Taccola, M.; Bacci, E.; Bancalari, L.; Dente, F. L.; Giuntini, C. (1996) Effect of short-
38 term NO2 exposure on induced sputum in normal, asthmatic and COPD subjects. Eur.
39 Respir. J. 9: 1852-1857.
40 Van Der Zee, S. C.; Hoek, G.; Boezen, H. M.; Schouten, J. P.; Van Wijnen, J. H.; Brunekreef, B.
41 (1999) Acute effects of urban air pollution on respiratory health of children with and
42 without chronic respiratory symptoms. Occup. Environ. Med. 56: 802-813.
August 2007 AX4-104 DRAFT-DO NOT QUOTE OR CITE
-------
1 Van Der Zee, S. C.; Hoek, G.; Boezen, M. H.; Schouten, J. P.; Van Wijnen, J. H.; Brunekreef, B.
2 (2000) Acute effects of air pollution on respiratory health of 50-70 yr old adults. Eur.
3 Respir. J. 15: 700-709.
4 Van Stee, E. W.; Sloane, R. A.; Simmons, J. E.; Moorman, M. P.; Brunnemann, K. D. (1995)
5 Endogenous formation of 7V-nitrosomorpholine in mice from 15NC>2 by inhalation and
6 morpholine by gavage. Carcinogenesis 16: 89-92.
7 Van Strien, R. T.; Gent, J. F.; Belanger, K.; Triche, E.; Bracken, M. B.; Leaderer, B. P. (2004)
8 Exposure to NO2 and nitrous acid and respiratory symptoms in the first year of life.
9 Epidemiology 15: 471-478.
10 Varshney, C. K.; Singh, A. P. (2003) Passive samplers for NOX monitoring: a critical review.
11 Environmentalist 23: 127-136.
12 Vedal, S.; Schenker, M. B.; Munoz, A.; Samet, J. M.; Batterman, S.; Speizer, F. E. (1987) Daily
13 air pollution effects on children's respiratory symptoms and peak expiratory flow. Am. J.
14 Public Health 77: 694-698.
15 Vedal, S.; Brauer, M.; White, R.; Petkau, J. (2003) Air pollution and daily mortality in a city
16 with low levels of pollution. Environ. Health Perspect. Ill: 45-51.
17 Velsor, L. W.; Postlethwait, E. M. (1997) MVinduced generation of extracellular reactive
18 oxygen is mediated by epithelial lining layer antioxidants. Am. J. Physiol. 17: L1265-
19 L1275.
20 Victorin, K. (1994) Review of the genotoxicity of nitrogen oxides. Mutat. Res. 317: 43-55.
21 Victorin, K.; Stahlberg, M. (1988) A method for studying the mutagenicity of some gaseous
22 compounds in Salmonella typhimurium. Environ. Mol. Mutagen. 11: 65-77'.
23 Victorin, K.; Busk, L.; Cederberg, H.; Magnusson, J. (1990) Genotoxic activity of 1,3-butadiene
24 and nitrogen dioxide and their photochemical reaction products in Drosophila and in the
25 mouse bone marrow micronucleus assay. Mutat. Res. 228: 203-209.
26 Villeneuve, P. J.; Chen, L.; Stieb, D.; Rowe, B. H. (2006) Associations between outdoor air
27 pollution and emergency department visits for stroke in Edmonton, Canada. Eur. J.
28 Epidemiol. 21: 689-700.
29 Vinzents, P. S.; M011er, P.; S0rensen, M.; Knudsen, L. E.; Herte, L. Q.; Jensen, F. P.; Schibye,
30 B.; Loft, S. (2005) Personal exposure to ultrafme particles and oxidative DNA damage.
31 Environ. Health Perspect. 113: 1485-1490.
32 Vollmuth, T. A.; Driscoll, K. E.; Schlesinger, R. B. (1986) Changes in early alveolar particle
33 clearance due to single and repeated nitrogen dioxide exposures in the rabbit. J. Toxicol.
34 Environ. Health 19: 255-266.
35 Von Klot, S.; Wolke, G; Tuch, T.; Heinrich, J.; Dockery, D. W.; Schwartz, J.; Kreyling, W. G;
36 Wichmann, H. E.; Peters, A. (2002) Increased asthma medication use in association with
37 ambient fine and ultrafme particles. Eur. Respir. J. 20: 691-702.
38 Von Klot, S.; Peters, A.; Aalto, P.; Bellander, T.; Berglind, N.; DTppoliti, D.; Elosua, R.;
39 Hermann, A.; Kulmala, M.; Lanki, T.; Lowel, H.; Pekkanen, J.; Picciotto, S.; Sunyer, J.;
40 Forastiere, F.; Health Effects of Particles on Susceptible Subpopulations (HEAPSS)
August 2007 AX4-105 DRAFT-DO NOT QUOTE OR CITE
-------
1 Study Group. (2005) Ambient air pollution is associated with increased risk of hospital
2 cardiac readmissions of myocardial infarction survivors in five European cities.
3 Circulation 112: 3073-3079.
4 Wade, K. S.; Mulholland, J. A.; Marmur, A.; Russell, A. G.; Hartsell, B.; Edgerton, E.; Klein,
5 M.; Waller, L.; Peel, J. L.; Tolbert, P. E. (2006) Effects of instrument precision and
6 spatial variability on the assessment of the temporal variation of ambient air pollution in
7 Atlanta, Georgia. J. Air Waste Manage. Assoc. 56: 876-888.
8 Wagner, H.-M. (1970) Absorption von NO und NO2 in MIK- und MAK-Konzentrationen bei der
9 Inhalation [Absorption of NO and NO2 in mik- and mak-concentrations during
10 inhalation]. Staub Reinhalt. Luft 30: 380-381.
11 Wagner, W. D.; Duncan, B. R.; Wright, P. G.; Stokinger, H. E. (1965) Experimental study of
12 threshold limit of NO2. Arch. Environ. Health 10: 455-466.
13 Wainman, T.; Weschler, C.; Lioy, P.; Zhang, J. (2001) Effects of surface type and relative
14 humidity on the production and concentration of nitrous acid in a model indoor
15 environment. Environ. Sci. Technol. 35: 2200-2206.
16 Wallace, L. A.; Emmerich, S. J.; Howard-Reed, C. (2004) Source strengths of ultrafine and fine
17 particles due to cooking with a gas stove. Environ. Sci. Technol. 38: 2304-2311.
18 Walles, S. A.; Victorin, K.; Lundborg, M. (1995) DNA damage in lung cells in vivo and in vitro
19 by 1,3-butadiene and nitrogen dioxide and their photochemical reaction products. Mutat.
20 Res. 328: 11-19.
21 Wang, J. H.; Devalia, J. L.; Duddle, J. M.; Hamilton, S. A.; Davies, R. J. (1995a) Effect of six-
22 hour exposure to nitrogen dioxide on early-phase nasal response to allergen challenge in
23 patients with a history of seasonal allergic rhinitis. J. Allergy Clin. Immunol. 96: 669-
24 676.
25 Wang, J. H.; Duddle, J.; Devalia, J. L.; Davies, R. J. (1995b) Nitrogen dioxide increases
26 eosinophil activation in the early-phase response to nasal allergen provocation. Int. Arch.
27 Allergy Immunol. 107: 103-105.
28 Wang, J. H.; Devalia, J. L.; Rusznak, C.; Bagnall, A.; Sapsford, R. J.; Davies, R. J. (1999) Effect
29 of fluticasone propionate aqueous nasal spray on allergen-induced inflammatory changes
30 in the nasal airways of allergic rhinitics following exposure to nitrogen dioxide. Clin.
31 Exp. Allergy 29: 234-240.
32 Wang, X.-K.; Lu, W.-Z. (2006) Seasonal variation of air pollution index: Hong Kong case study.
33 Chemosphere 63: 1261-1272.
34 Ward, D. J.; Miller, M. R.; Walters, S.; Harrison, R. M.; Ayres, J. G. (2000) Impact of correcting
35 peak flow for nonlinear errors on air pollutant effect estimates from a panel study. Eur.
36 Respir. J. 15: 137-140.
37 Ward, D. J.; Roberts, K. T.; Jones, N.; Harrison, R. M.; Ayres, J. G.; Hussain, S.; Walters, S.
38 (2002) Effects of daily variation in outdoor particulates and ambient acid species in
39 normal and asthmatic children. Thorax 57: 489-502.
August 2007 AX4-106 DRAFT-DO NOT QUOTE OR CITE
-------
1 Ware, J. H.; Dockery, D. W.; Spiro, A., Ill; Speizer, F. E.; Ferris, B. G., Jr. (1984) Passive
2 smoking, gas cooking, and respiratory health of children living in six cities. Am. Rev.
3 Respir. Dis. 129: 366-374.
4 Watanabe, N. (2005) Decreased number of sperms and Sertoli cells in mature rats exposed to
5 diesel exhaust as fetuses. Toxicol. Lett. 155: 51-58.
6 Watanabe, N.; Kurita, M. (2001) The masculinization of the fetus during pregnancy due to
7 inhalation of diesel exhaust. Environ. Health Perspect. 109: 111-119.
8 Watanabe, N.; Nakamura, T. (1996) Inhalation of diesel engine exhaust increases bone mineral
9 concentrations in growing rats. Arch. Environ. Contam. Toxicol. 30: 407-411.
10 Watanabe, N.; Oonuki, Y. (1999) Inhalation of diesel engine engine exhaust affects
11 spermatogenesis in growing male rats. Environ. Health Perspect. 107: 539-544.
12 Watanabe, H.; Fukase, O.; Isomura, K. (1980) Combined effects of nitrogen oxides and ozone on
13 mice. In: Lee, S. D., ed. Nitrogen oxides and their effects on health. Ann Arbor, MI: Ann
14 Arbor Science Publishers, Inc.; pp. 181-189.
15 Waxman, M. B.; Cameron, D.; Wald, R. W. (1994) Vagal activity and ventricular
16 tachyarrhythmias. In: Levy, M.; Schwartz, P. Vagal control of the heart: experimental
17 basis and clinical implications. Armonk, NY: Futura Publishing Co.; pp. 579-612.
18 Wayne, R. P.; Barnes, I; Biggs, P.; Burrows, J. P.; Canosa-Mas, C. E.; Hjorth, J.; Le Bras, G.;
19 Moortgat, G. K.; Perner, D.; Poulet, G.; Restelli, G.; Sidebottom, H. (1991) The nitrate
20 radical: physics, chemistry, and the atmosphere. Atmos. Environ. Part A 25: 1-203.
21 Wegmann, M.; Renz; Herz, U. (2002) Long-term NC>2 exposure induces pulmonary
22 inflammation and progressive development of airflow obstruction in C57BL/6 mice: a
23 mouse model for chronic obstructive pulmonary disease. Pathobiology 70: 284-286.
24 Weinberg, E. D. (1992) Iron depletion: a defense against intracellular infection and neoplasia.
25 Life Sci. 50: 1289-1297.
26 Weinberger, B.; Laskin, D. L.; Heck, D. E.; Laskin, J. D. (2001) The toxicology of inhaled nitric
27 oxide. Toxicol. Sci. 59: 5-16.
28 Wellenius, G. A.; Bateson, T. F.; Mittleman, M. A.; Schwartz, J. (2005) Particulate air pollution
29 and the rate of hospitalization for congestive heart failure among medicare beneficiaries
30 in Pittsburgh, Pennsylvania. Am. J. Epidemiol. 161: 1030-1036.
31 Wellenius, G. A.; Schwartz, J.; Mittleman, M. A. (2006) Particulate air pollution and hospital
32 admissions for congestive heart failure in seven United States cities. Am. J. Cardiol. 97:
33 404-408.
34 Wensley, D. C.; Silverman, M. (2001) The quality of home spirometry in school children with
35 asthma. Thorax 56: 183-185.
36 Weschler, C. J.; Shields, H. C. (1997) Potential reactions among indoor pollutants. Atmos.
37 Environ. 31: 3487-3495.
38 Weschler, C. J.; Hodgson, A. T.; Wooley, J. D. (1992) Indoor chemistry: ozone, volatile organic
39 compounds, and carpets. Environ. Sci. Technol. 26: 2371-2377.
August 2007 AX4-107 DRAFT-DO NOT QUOTE OR CITE
-------
1 Weschler, C. 1; Shields, H. C.; Naik, D. V. (1994) Indoor chemistry involving O3, NO, and NO2
2 as evidenced by 14 months of measurements at a site in southern California. Environ. Sci.
3 Technol. 28: 2120-2132.
4 Weschler, C. J.; Shields, H. C. (1996) The conversion (reduction) of nitrogen dioxide to nitric
5 oxide as a consequence of charcoal filtration. In: Yoshizawa, S.; Kimura, K.; Ikeda, K.;
6 Tanabe, S.; Iwata, T., eds. Indoor Air '96: proceedings of the 7th international conference
7 on indoor air quality and climate, v. 3, July; Nagoya, Japan. Toykyo, Japan: Indoor Air
8 '96; pp. 453-458.
9 Westerdahl, D.; Fruin, S.; Sax, T.; Fine, P. M.; Sioutas, C. (2005) Mobile platform
10 measurements of ultrafine particles and associated pollutant concentrations on freeways
11 and residential streets in Los Angeles. Atmos. Environ. 39: 3597-3610.
12 Wheeler, A.; Zanobetti, A.; Gold, D. R.; Schwartz, J.; Stone, P.; Suh, H. H. (2006) The
13 relationship between ambient air pollution and heart rate variability differs for individuals
14 with heart and pulmonary disease. Environ. Health Perspect. 114: 560-566.
15 Wichmann, H.-E.; Spix, C.; Tuch, T.; Wolke, G.; Peters, A.; Heinrich, J.; Kreyling, W. G.;
16 Heyder, J. (2000) Daily mortality and fine and ultrafine particles in Erfurt, Germany. Part
17 I: role of particle number and particle mass. Cambridge, MA: Health Effects Institute;
18 research report no. 98.
19 Wiley, J. A.; Robinson, J. P.; Piazza, T.; Garrett, K.; Cirksena, K.; Cheng, Y.-T.; Martin, G.
20 (1991a) Activity patterns of California residents. Final report. Sacramento, CA:
21 California Air Resources Board; report no. ARB/R93/487. Available from: NTIS,
22 Springfield, VA.; PB94-108719.
23 Wiley, J. A.; Robinson, J. P.; Cheng, Y.-T.; Piazza, T.; Stork, L.; Pladsen, K. (1991b) Study of
24 children's activity patterns: final report. Sacramento, CA: California Air Resources
25 Board; report no. ARB-R-93/489.
26 Williams, E. J.; Parrish, D. D.; Fehsenfeld, F. C. (1987) Determination of nitrogen oxide
27 emissions from soils: results from a grassland site in Colorado, United States. J. Geophys.
28 Res. [Atmos.] 92: 2173-2179.
29 Winter-Sorkina, R. de; Cassee, F. R. (2002) From concentration to dose: factors influencing
30 airborne particulate matter deposition in humans and rats. Bilthoven, The Netherlands:
31 National Institute of Public Health and the Environment (RIVM); report no.
32 650010031/2002. Available: http://www.rivm.nl/bibliotheek/rapporten/650010031.html
33 (13 June 2003).
34 Witschi, H. (1988) Ozone, nitrogen dioxide and lung cancer: a review of some recent issues and
35 problems. Toxicology 48: 1-20.
36 Wolff, G. T. (1993) On a NOx-focused control strategy to reduce Os. J. Air Waste Manage.
37 Assoc. 43: 1593-1596.
38 Wolff, G. T. (1996) Closure by the Clean Air Scientific Advisory Committee (CASAC) on the
39 draft Air Quality Criteria for Particulate Matter [letter to Carol M. Browner,
40 Administrator, U.S. EPA]. Washington, DC: U.S. Environmental Protection Agency,
41 Clean Air Scientific Advisory Committee.; EPA-SAB-CASAC-LTR-96-005; March 15.
August 2007 AX4-108 DRAFT-DO NOT QUOTE OR CITE
-------
1 Wong, T. W.; Lau, T. S.; Yu, T. S.; Neller, A.; Wong, S. L.; Tarn, W.; Pang, S. W. (1999) Air
2 pollution and hospital admissions for respiratory and cardiovascular diseases in Hong
3 Kong. Occup. Environ. Med. 56: 679-683.
4 Wong, G. W.; Ko, F. W.; Lau, T. S.; Li, S. T.; Hui, D.; Pang, S. W.; Leung, R.; Fok, T. F.; Lai,
5 C. K. (2001) Temporal relationship between air pollution and hospital admissions for
6 asthmatic children in Hong Kong. Clin. Exp. Allergy 31: 565-569.
7 World Health Organization. (1997) Nitrogen oxides. 2nd ed. Geneva, Switzerland: World Health
8 Organization. (Environmental health criteria 188).
9 Yallop, D.; Duncan, E. R.; Norris, E.; Fuller, G. W.; Thomas, N.; Walters, J.; 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 Yamanaka, S. (1984) Decay rates of nitrogen oxides in a typical Japanese living room. Environ.
14 Sci. Technol. 18: 566-570.
15 Yanagisawa, Y.; Nishimura, H. (1982) A badge-type personal sampler for measurement of
16 personal exposure to NO2 and NO in ambient air. Environ. Int. 8: 235-242.
17 Yang, W.; Lee, K.; Chung, M. (2004a) Characterization of indoor air quality using multiple
18 measurements of nitrogen dioxide. Indoor Air 14: 105-111.
19 Yang, C.-Y.; Chen, Y.-S.; Yang, C.-H.; Ho, S.-C. (2004b) 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, Q.; Chen, Y.; Krewski, D.; Burnett, R. T.; Shi, Y.; McGrail, K. M. (2005) Effect of short-
23 term exposure to low levels of gaseous pollutants on chronic obstructive pulmonary
24 disease hospitalizations. Environ. Res. 99: 99-105.
25 Yang, C.-Y.; Hsieh, H.-J.; Tsai, S.-S.; Wu, T.-N.; Chiu, H.-F. (2006) Correlation between air
26 pollution and postneonatal mortality in a subtropical city: Taipei, Taiwan. J. Toxicol.
27 Environ. Health Part A 69: 2033-2040.
28 Yokoyama, E. (1968) Uptake of SO2 and NO2 by the isolated upper airways. Bull. Inst. Public
29 Health (Tokyo) 17: 302-306.
30 Yokoyama, E.; Ichikawa, L; Kawai, K. (1980) Does nitrogen dioxide modify the respiratory
31 effects of ozone? In: Lee, S. D., ed. Nitrogen oxides and their effects on health. Ann
32 Arbor, MI: Ann Arbor Science Publishers, Inc.; pp. 217-229.
33 Zanobetti, A.; Schwartz, J. (2006) Air pollution and emergency admissions in Boston, MA. J.
34 Epidemiol. Community Health 60: 890-895.
35 Zeger, S. L.; Thomas, D.; Dominici, F.; Samet, J. M.; Schwartz, J.; Dockery, D.; Cohen, A.
36 (2000) Exposure measurement error in time-series studies of air pollution: concepts and
37 consequences. Environ. Health Perspect. 108: 419-426.
38 Zeka, A.; Schwartz, J. (2004) Estimating the independent effects of multiple pollutants in the
39 presence of measurement error: an application of a measurement-error-resistant
40 technique. Environ. Health Perspect. 112: 1686-1690.
August 2007 AX4-109 DRAFT-DO NOT QUOTE OR CITE
-------
1 Zhang, H.; Lindwall, R.; Zhu, L.; Frostell, C.; Sun, B. (2003) Lung physiology and
2 histopathology during cumulated exposure to nitric oxide in combination with assisted
3 ventilation in healthy piglets. Pulm. Pharmacol. Ther. 16: 163-169.
4 Zidek, J. V. (1997) Interpolating air pollution for health impact assessment. In: Barnett, E. V.;
5 Turkman, K. F., eds. Pollution Assessment and Control. New York, NY: John Wiley &
6 Sons. (Statistics for the Environment, no. 3).
7 Zipprich, J. L.; Harris, S. A.; Fox, J. C.; Borzelleca, J. F. (2002) An analysis of factors that
8 influence personal exposure to nitrogen oxides in residents of Richmond, Virginia. J.
9 Exposure Anal. Environ. Epidemiol. 12: 273-285.
10 Zmirou, D.; Schwartz, J.; Saez, M.; Zanobetti, A.; Wojtyniak, B.; Touloumi, G.; Spix, C.; Ponce
11 de Leon, A.; Le Moullec, Y.; Bacharova, L.; Schouten, J.; Ponka, A.; Katsouyanni, K.
12 (1998) Time-series analysis of air pollution and cause-specific mortality. Epidemiology
13 9:495-503.
14 Zota, A.; Adamkiewicz, G.; Levy, J. L; Spengler, J. D. (2005) Ventilation in public housing:
15 implications for indoor nitrogen dioxide concentrations. Indoor Air 15: 393-401.
16
17
August 2007 AX4-110 DRAFT-DO NOT QUOTE OR CITE
-------
i AX5. CHAPTER 5 ANNEX - CONTROLLED HUMAN
2 EXPOSURE STUDIES OF NITROGEN OXIDES
o
4
5 AX5.1 INTRODUCTION
6 This annex summarizes the effects of nitrogen oxides (NOX) on human volunteers
7 exposed under controlled conditions. The goal is to review the scientific literature on human
8 clinical studies of NOX exposure published since the 1993 Air Quality Criteria Document
9 (AQCD) for Oxides of Nitrogen (U.S. Environmental Protection Agency, 1993). Summary
10 findings from the 1993 AQCD are provided below. The primary focus will be on nitrogen
11 dioxide because it is the most abundant NOX species in the atmosphere and there are few human
12 studies of exposure to other NOX species.
13 The following are the conclusions drawn from the review of clinical studies of nitrogen
14 oxide exposure in the 1993 criteria document.
15 1. Nitrogen dioxide causes decrements in lung function, particularly increased
16 airway resistance in healthy subjects at concentrations exceeding 2.0 ppm for 2 h.
17 2. Nitrogen dioxide exposure results in increased airway responsiveness in healthy,
18 nonsmoking subjects exposed to concentrations exceeding 1.0 ppm for exposure
19 durations of 1 hour or longer.
20 3. Nitrogen dioxide exposure at levels above 1.5 ppm may alter numbers and types
21 of inflammatory cells in the distal airways or alveoli, but these responses depend
22 upon exposure concentration, duration, and frequency. Nitrogen dioxide may
23 alter function of cells within the lung and production of mediators that may be
24 important in lung host defenses.
25 4. Nitrogen dioxide exposure of asthmatics causes, in some subjects, increased
26 airway responsiveness to a variety of provocative mediators, including cholinergic
27 and histaminergic chemicals, 862 and cold air. However, the presence of these
28 responses appears to be influenced by the exposure protocol, particularly whether
29 or not the exposure includes exercise.
30 5. Modest decrements in spirometric measures of lung function (3 to 8%) may occur
31 in some asthmatics and COPD patients under certain NC>2 exposure conditions.
32 6. Nitric acid levels in the range of 50 to 200 ppb may cause some pulmonary
33 function responses in adolescent asthmatics, but not in healthy adults. Other
34 commonly occurring NOX species do not appear to cause any pulmonary function
35 responses at concentrations expected in the ambient environment, even at higher
36 levels than in worst-case scenarios. However, not all nitrogen oxides acid species
37 have been studied sufficiently.
August 2007 AX5-1 DRAFT-DO NOT QUOTE OR CITE
-------
1 7. No association between lung function responses and respiratory symptom
2 responses were observed. Furthermore, there is little evidence of a concentration-
3 response relationship for changes in lung function, airway responsiveness, or
4 symptoms at the NO2 levels that are reviewed here.
5
6 In the summary and integration chapter of the 1993 NOX criteria document, one of the
7 key health effects of most concern at near ambient concentrations of NO2 was increases in
8 airway responsiveness of asthmatic individuals after short-term exposures. The 1993 AQCD
9 notes the absence of a concentration-response relationship for NO2 exposure and airways
10 responsiveness in asthmatics. For example, most responses to NO2 that had been observed in
11 asthmatics occurred at concentrations between 0.2 and 0.5 ppm. However, other studies showed
12 an absence of effects on airways responsiveness at much higher concentrations, up to 4 ppm.
13 Since 1993, additional studies suggest that exposure to low concentrations of NO2, either alone
14 or in combination with other pollutants such as SO2, may enhance allergen responsiveness in
15 asthmatic subjects.
16 In the years since the preparation of the 1993 AQCD, many studies from a variety of
17 disciplines have convincingly demonstrated that exposure to particulate air pollution increases
18 the risk for cardiovascular events. In addition, a number of epidemiological studies have shown
19 associations between ambient NO2 levels and adverse cardiovascular outcomes, at concentrations
20 well below those shown to cause respiratory effects. However, to date there remain very few
21 clinical studies of NO2 that include endpoints relevant to cardiovascular disease.
22
23 AX5.1.1 Considerations in Controlled Human Exposure Studies
24
25 Strengths and Limitations of Controlled Human Studies
26 The database for air pollution risk assessment arises from four investigative approaches:
27 epidemiology, animal toxicology, in vitro studies, and human inhalation studies. Each possesses
28 advantages but also carries significant limitations. For example, the epidemiological
29 investigation examines exposures in the "real world" but struggles with the realities of
30 conducting research in the community, where cigarette smoking, socioeconomic status,
31 occupational exposures, meteorological variability, and exposure characterization are important
32 confounders. Outcomes are often evaluated from available health or mortality records or from
33 administered questionnaires, all of which have inherent limitations. Sophisticated measures of
August 2007 AX5-2 DRAFT-DO NOT QUOTE OR CITE
-------
1 physiological responses are often not practical in studies involving large populations, although
2 they may be used in panel studies. In contrast, inhalation studies in animals allow precision in
3 quantifying exposure duration and concentration, measurement of a wide variety of physiologic,
4 biochemical, and histological endpoints, and examination of extremes of the exposure-response
5 relationship. Often, however, interpretation of these studies is constrained by difficulty in
6 extrapolating findings from animals to humans, especially when exposure concentrations are
7 unrealistically high.
8 Controlled, quantitative studies of exposed humans offer a third approach (Frampton
9 et al., 2006). Human clinical studies attempt to engineer laboratory atmospheric conditions
10 relevant to ambient pollutant atmospheres, with careful control of concentrations, duration,
11 timing, and other conditions which may impact responses. These studies provide the opportunity
12 to measure symptoms and physiological markers of health effects that result from breathing the
13 atmospheres. The carefully controlled environment allows investigators to identify responses to
14 individual pollutants, to characterize exposure-response relationships, to examine interactions
15 among pollutants, and to study the effects of other variables such as exercise, humidity, or
16 temperature. Susceptible populations can participate, including individuals with acute and
17 chronic respiratory and cardiovascular diseases, with appropriate limitations based on subject
18 comfort and protection from risk. Endpoint assessment traditionally has included symptoms and
19 pulmonary function, but more recently a variety of markers of pulmonary, systemic, and
20 cardiovascular function have been used to assess pollutant effects.
21 Human clinical studies have limitations. For practical and ethical reasons, studies must
22 be limited to relatively small groups, to short durations of exposure, and to pollutant
23 concentrations that are expected to produce only mild and transient responses. Findings from the
24 short-term exposures in clinical studies may provide limited insight into the health effects of
25 chronic or repeated exposures.
26 Specific issues of protocol design in human clinical studies have been reviewed
27 (Frampton et al., 2006), and will not be considered further here, except in the context of specific
28 studies of NC>2 exposure described in the following pages.
29
30 Assessing the Findings from Controlled Human Studies
31 In clinical studies, humans are the species of interest, so findings have particular
32 relevance in risk assessment. However, the utility of clinical studies in risk assessment is
August 2007 AX5-3 DRAFT-DO NOT QUOTE OR CITE
-------
1 tempered by the obvious need to avoid adverse health effects of the study itself. This usually
2 means selecting subjects that are not the most susceptible to the pollutant being studied.
3 Furthermore, clinical studies depend on outcome markers with variable relevance or validation
4 as markers of true health effects. The statement from the American Thoracic Society, "What
5 constitutes an adverse health effect?" (American Thoracic Society, 2000) addresses issues
6 relevant to selection and interpretation of outcome markers in clinical studies.
7 The 1993 NC>2 AQCD included a description of key outcome measures that had been in
8 use to that date. These included primarily respiratory outcomes, including pulmonary function
9 tests such as spirometry, lung volumes, and airways resistance, and tests of pulmonary clearance
10 of inhaled aerosols. A brief description of bronchoalveolar lavage was also included, which had
11 come into use prior to 1993 to assess airway inflammation and changes in the epithelial lining
12 fluid in response to NC>2 exposure.
13
14
15 AX5.2 EFFECTS OF NITROGEN DIOXIDE IN HEALTHY SUBJECTS
16 Table AX5.1 summarizes the key clinical studies of NC>2 exposure in healthy subjects
17 since 1993, with a few key studies included prior to that date. Figure AX5.1 summarizes the
18 findings of these studies of airway inflammatory responses in relation to the total exposure to
19 NC>2, expressed as ppm-minutes. Studies that did not include a proper control air exposure were
20 not included, and studies using multiple daily exposures were not included. All of the studies
21 portrayed in Figure AX5.1 involved intermittent exercise, and no attempt was made to adjust the
22 exposure metric for varying intensity and duration of exercise.
23
24
25 AX5.3 THE EFFECTS OF NITROGEN OXIDE EXPOSURE IN
26 SENSITIVE SUBJECTS
27 Table AX5.2 summarizes studies of potentially sensitive subjects. The potential for NC>2
28 exposure to enhance responsiveness to allergen challenge in asthmatics deserves special mention.
29 Several recent studies, summarized in Table AX5.3, have reported that low-level exposures to
30 NO2, both at rest and with exercise, enhance the response to specific allergen challenge in mild
31 asthmatics.
32 These recent studies involving allergen challenge suggest that NC>2 may enhance the
33 sensitivity to allergen-induced decrements in lung function, and increase the allergen-induced
August 2007 AX5-4 DRAFT-DO NOT QUOTE OR CITE
-------
1 airway inflammatory response. Figure AX5.2 categorizes the allergen challenge studies as
2 "positive", i.e. showing evidence for increased responses to allergen in association with NC>2
3 exposure, or "negative", with the exposure metric expressed as ppm-min. In comparing Figure
4 AX5.2 with Figure AX5.1, it can be seen that enhancement of allergic responses in asthmatics
5 occurs at exposure levels more than an order of magnitude lower than those associated with
6 airway inflammation in healthy subjects. The dosimetry difference is even greater when
7 considering that the allergen challenge studies were generally performed at rest, while the airway
8 inflammation studies in healthy subjects were performed with intermittent exercise.
Cellylar
Response
Mediator
Response
No
Response
(2,3)
( 1 ')
Study ppm-min
1. Azadnivetal. 1998 720
2. Blomberg et al, 199? 430
3. Devlin et al. 1999 480
4. Frampton et at. 2002 270
5. Frampton et al. 2002 108
6. J6rresetal. 1995 180
7. Vagaggirmietal. 1996 18
0
200 400 600
IMCL ppm-minutes
800
Figure AX5.1. Airway inflammation in response to NOi inhalation in healthy subjects.
August 2007
AX5-5
DRAFT-DO NOT QUOTE OR CITE
-------
2,8
5)(3h)
9)3
4)(6h)
Study ppm-min
1. Barck et a!. 2002 7.8
2. Barck et a!. 2005a 23.4
3. Barck et a!. 2005b 7.8
4. Jenkins et al. 1999 72 (6h)
5. Jenkins et a!. 1999 72 (3h)
6. Strand etal. 1997 7.8
7. Strand etal. 1998 31.2
S.Tunnicliffeetal. 1994 24
9. Tunnicliffe et al. 1994 6
10. Wang etal. 1995a,b 144
0
50 100
IMCL ppm-minutes
150
Figure AX5.2.
Effects of NOi inhalation on allergen challenge in subjects with asthma.
+: Significant effect of NOi. -: No significant effect
*: Exposures included intermittent exercise.
1 AX5.4 EFFECTS OF MIXTURES CONTAINING NITROGEN OXIDES
2 Table AX5.4 summarizes human clinical studies of NO2-containing mixtures or
3 sequential exposures that are most relevant to ambient exposure scenarios.
August 2007
AX5-6
DRAFT-DO NOT QUOTE OR CITE
-------
CJQ
TABLE AX5.1. CLINICAL STUDIES OF NO2 EXPOSURE IN HEALTHY SUBJECTS
t-*
r+
O
O
X
^
>
H
6
o
!2
O
H
O
0
H
W
O
^
O
1 — I
w
Reference
Avissar et al.
(2001)
Azadniv et al.
(1998)
Blomberg et al.
(1997)
Blomberg et al.
(1999)
Devlin et al.
(1999)
Drechsler-Parks
(1995)
Location
Rochester,
NY, USA
Rochester,
NY, USA
Sweden
Sweden
Chapel Hill,
North
Carolina,
USA
Santa
Barbara, CA,
USA
Participants
21 healthy
nonsmokers
2 studies,
12 healthy
nonsmokers in
each
30 healthy
nonsmokers
12 healthy
nonsmokers
8 healthy
nonsmokers
8 older healthy
nonsmokers
Approach & Methods
Measurements of extracellular
glutathione peroxidase (eGPx)
activity and protein levels in
epithelial lining fluid from NO2
exposure study described in
Frampton et al. (2002) (see
below).
Air vs. 2 ppm NO2 for 6 h with
intermittent exercise. Phase 1 :
BAL 18 h after exposure;
Phase 2: BAL immediately
after exposure.
Air vs. 2 ppm NO2 for 4 h,
with intermittent exercise
Air vs. 2 ppm NO2 for 4 h on
4 days, with intermittent
exercise.
Air and 2.0 ppm NO2 for 4 h
with intermittent exercise.
4 2-h exposures with
intermittent exercise: air, 0.60
ppm NO2, 0.45 ppm O3, and
0.60 ppm NO2 + 0.45 ppm O3.
Findings
No effects of NO2 exposure on eGPx
activity and protein concentrations.
(Ozone exposure decreased eGPx
activity and protein concentrations.)
Increased BAL neutrophils, decreased
blood CD8+ and null T lymphocytes 18
h after exposure. No effects on
symptoms or lung function.
Increased neutrophils and interleukin-8
in bronchial wash. Increases in specific
lymphocyte subsets in BAL fluid.
Symptoms/lung function not reported.
After 4 days of NO2, increased
neutrophils in bronchial wash but
decreased neutrophils in bronchial
biopsy. 2% decrease in FEVi after first
exposure to NO2, attenuated with
repeated exposure. Symptoms not
reported.
Increased bronchial lavage neutrophils,
IL-6, IL-8, alphai-antitrypsin, and tissue
plasminogen activator. Decreased
alveolar macrophage phagocytosis and
superoxide production. No effects on
pulmonary function. Symptoms not
reported.
Significant reduction in cardiac output
during exercise, estimated using
noninvasive impedance cardiography,
with NO2 + O3. Symptoms and
pulmonary function not reported.
Comments
NO2 up to 1.5 ppm for
3 hours did not deplete
this mode of
antioxidant defense in
the epithelial lining
fluid.
2 ppm NO2 for 6 h
caused mild
inflammation.
2 ppm NO2 for 4 h
caused airway
inflammation.
Decreased lung
function, not confirmed
in other studies at t his
concentration.
Conflicting information
on airway
inflammation.
2 ppm NO2 for 4 h
caused airway
inflammation.
Suggests cardiac effects
ofNO2 + O3. Small
number of subjects
limits statistical power,
has not been replicated.
-------
CJQ
O
O
TABLE AX5.1 (cont'd). CLINICAL STUDIES OF NO2 EXPOSURE IN HEALTHY SUBJECTS
X
-------
OQ
to
o
o
TABLE AX5.1 (cont'd). CLINICAL STUDIES OF NO2 EXPOSURE IN HEALTHY SUBJECTS
Reference
Location
Participants
Approach & Methods
Findings
Comments
X
H
6
o
o
H
O
o
H
W
O
O
HH
H
W
Torres et al.
(1995)
Germany
Kim et al.
(1991)
Morrow et al.
(1992)
Pathmanathan
et al. (2003)
Posin et al.
(1978)
Rasmussen
etal. (1992)
Rigas et al.
(1997)
Seattle,
Washington,
USA
Rochester,
NY, USA
United
Kingdom,
Sweden
Downey,
CA, USA
Denmark
8 healthy
nonsmokers &
12 mild asthmatics
9 healthy athletes
20 COPD subjects
(14 current
smokers) and
20 elderly healthy
(13 never-smokers,
4 former smokers,
3 current smokers)
12 healthy
nonsmokers
10 healthy
nonsmokers
14 healthy
nonsmokers
12 healthy
nonsmokers
Air or 1 ppm NO2 exposure for
3 h with intermittent exercise.
Air, 0.18, and 0.30 ppm NO2 for
30 min with exercise
Air vs. 0.3 ppm NO2 for 4 h with
intermittent exercise.
Air vs. 2 ppm NO2 for 4 h on
4 days, with intermittent
exercise. Bronchoscopy and
biopsy 1 h after exposure.
3 daily exposures for 2.5 h.
1st day: air; 2nd and 3rd days:
1 or 2 ppm NO2. Intermittent
exercise. Subsequent control
series of 3 daily air exposures.
Air vs. 2.3 ppm NO2 for 5 h
2 h of 0.36 ppm NO2, 0.75 ppm
NO2, 0.36 ppm SO2, or 0.36 ppm
O3. Boluses of O3 every 30 min
to measure O3 absorption.
In asthmatics, 2.5% decrease FEVi
after NO2 vs. 1.3% decrease after air,
p = 0.01. FEVj decreased 20% in
1 subject after NO2. No significant
lung function effect in healthy
subjects. Changes in eicosanoids
(more pronounced in asthmatics), but
not inflammatory cells, in B AL fluid.
No effects on pulmonary function.
Symptoms not reported.
COPD: small declines in FVC and
FEVjwithNOz. Healthy: No
symptoms or pulmonary function
effects for group as a whole. Healthy
smokers showed a 2.3% decline in
FEVj with NO2, and differed from
nonsmokers.
Epithelial expression of IL-5, IL-10,
IL-13, and ICAM-1 increased
following NO2 exposure. No data on
inflammatory cells in BAL fluid.
Reduced hemoglobin and hematocrit,
and red blood cell acetyl
cholinesterase.
Small increases in FVC and
Reduced lung permeability and blood
glutathione peroxidase after
exposure.
NO2 and SO2 increased O3 absorption
by increasing biochemical substrates.
Lung function effects
consistent with other
studies, suggesting some
asthmatics susceptible.
Evidence for mild airway
inflammation.
Small number of subjects
limits conclusions.
Mild lung function effects
of 0.3 ppm for 4 h in
exercising patients with
COPD. Small number of
healthy smoking subjects
limits conclusions
regarding this group.
Supportive evidence for
pro-allergic airway
inflammation favoring
following NO2 exposure.
Suggests red blood cell
effects of NO2(see
Frampton et al., 2002).
Exposures not randomized.
Only 1 week between
exposures may have
confounded results.
Suggests breathing
mixtures of NO2 and O3
would increase O3 dose to
airways.
-------
OQ
to
o
o
TABLE AX5.1 (cont'd). CLINICAL STUDIES OF NO2 EXPOSURE IN HEALTHY SUBJECTS
Reference Location
Participants
Approach & Methods
Findings
Comments
X
(Si
I
o
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Sandstrom
etal. (1990)
Sandstrom
etal. (1991)
Sandstrom
etal.
(1992a)
Sandstrom
etal.
(1992b)
Solomon
et al. (2000)
Vagaggini
etal. (1996)
Sweden
Sweden
Sweden
Sweden
San
Francisco,
California,
USA
Italy
32 healthy
nonsmokers,
4 groups of
8 subjects
18 healthy
nonsmokers
10 healthy
nonsmoking men
8 healthy
nonsmokers
15 healthy
nonsmokers
7 healthy
nonsmokers
4 ppm NO2 for 20 min with 15 min
exercise. BAL 4, 8, 24, 72 h after
exposure, compared with non-
exposure control BAL
2.25, 4.0, 5.5 ppm NO2 for 20 min
with light exercise. BAL 24 h after
exposure, compared with non-
exposure control BAL
4 daily exposures to 4 ppm NO2 for
20 min with 15 min exercise. BAL
24 h after exposure, compared with
non-exposure control BAL.
1.5 ppm NO2 for 20 min with
15 min exercise, every 2nd day x 6.
BAL 24 h after exposure compared
with non-exposure control BAL.
Air or 2.0 ppm NO2 with
intermittent exercise, for 4 h
daily x 4. BAL 18 hours after
exposure.
Air vs. 0.3 ppm NO2 for 1 h with
intermittent exercise.
Increase in BAL mast cells and
lymphocytes 4-24 h after exposure.
Increase in BAL mast cells (all
concentrations) and lymphocytes
(4.0 and 5.5 ppm).
Reduction in alveolar macrophages,
NK cells, and CDS lymphocytes in
BAL; reduction in total lymphocytes
in blood.
Reduced CD8+ T lymphocytes and
NK cells in BAL fluid.
Increased neutrophils in bronchial
lavage decreased CD4+ T
lymphocytes in BAL. No changes in
blood.
Mild increase in symptoms. No
effects on lung function, nasal lavage,
or induced sputum.
Study weakened by lack of
control air exposure.
Study weakened by lack of
control air exposure.
Study weakened by lack of
control air exposure.
Study weakened by lack of
control air exposure.
Airway inflammation with
2 ppm NO2 for 4 daily 4 h
exposures.
Small number of subjects
limits statistical power.
-------
OQ
to
o
o
TABLE AX5.2. EFFECTS OF NO2 EXPOSURE IN SUBJECTS WITH RESPIRATORY DISEASE (SEE TABLE AX5-3
FOR STUDIES WITH ALLERGEN CHALLENGE)
Reference Location
Participants
Approach & Methods
Findings
Comments
>
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Gong et al.
(2005)
Hackney
etal.
(1992)
Torres and
Magnussen
(1991)
Torres et al.
(1995)
Morrow
etal.
(1992)
Strand et al.
(1996)
Vagaggini
etal.
(1996)
Downey, 6 healthy
CA, USA nonsmokers and
18 ex-smokers with
COPD
Downey, 26 smokers with
CA, USA symptoms and
reduced FEVi
Germany 11 mild asthmatics
Germany 8 healthy
nonsmokers &
12 mild asthmatics
Rochester, 20 COPD,
NY, USA 20 healthy elderly
Sweden
19 mild asthmatics
Italy
8 mild asthmatics,
7 COPD
2 h exposures with
intermittent exercise to:
1) air, 2)0.4 ppmNO2>
3) 200 ug/m3 concentrated
ambient paniculate matter
(CAPs), 4) NO2 + CAPs
Personal monitoring and
chamber exposure to air and
0.3 ppm NO2 for 4 h with
intermittent exercise
Air vs. 0.25 ppm NO2 for
30 min with 10 min exercise
Air or 1 ppm NO2 exposure
for 3 h with intermittent
exercise.
Air vs. 0.3 ppm NO2 for 4 h
with intermittent exercise
Air vs. 0.26 ppm NO2 for
30 min with intermittent
exercise
Air vs. 0.3 ppm NO2 for 1 h
with intermittent exercise.
Reduced maximum mid-expiratory
flow rate and oxygen saturation with
CAPs exposures; no effects of NO2
alone or additive effect with CAPs.
No significant effects on lung
function.
No effects on lung function or
airways responsiveness to
methacholine.
In asthmatics, 2.5% decrease FEVi
after NO2 vs. 1.3% decrease after air,
p = 0.01. FEVj decreased 20% in
1 subject after NO2. No significant
lung function effect in healthy
subjects. Changes in eicosanoids
(more pronounced in asthmatics), but
not inflammatory cells, in B AL fluid.
Equivocal reduction in FVC with
COPD patients, but not healthy
subjects.
Increased airway responsiveness to
histamine 5 h after exposure. No
effects on lung function.
Mild decrease in FEY: in COPD
subjects in comparison with air
exposure, but not with baseline. No
effects on nasal lavage or induced
sputum.
Exposures not fully randomized.
Small number of subjects limits
interpretation for healthy group.
Lung function effects consistent
with other studies, suggesting some
asthmatics susceptible. Evidence
for mild airway inflammation.
Small number of healthy subjects
limits statistical power.
Suggests increased nonspecific
airways responsiveness at much
lower concentration than healthy
subjects. Differs from findings in
Torres and Magnussen 1991
No convincing effect of NO2 in this
study. Small number of subjects
limits statistical power.
-------
TABLE AX5.3. EFFECTS OF NO2 EXPOSURE ON RESPONSE TO INHALED ALLERGEN
OQ
to
o
o
Reference Location Participants
Approach & Methods
Findings
Comments
Key study suggesting that NO2
enhances inflammatory
response to allergen in mild
asthmatics.
Provides supporting evidence
that NO2 enhances the airway
inflammatory response to
allergen.
0.26 ppm NO2 did not enhance
nasal inflammatory response to
allergen challenge.
Small number of subjects
limits statistical power.
Suggests 0.4 ppm for 3 h with
intermittent exercise increases
allergen responsiveness.
X
to
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Barcketal. Sweden 13 mild 30 min exposures to air and 0.26 ppm
(2002) asthmatics, NO2 (at rest?), allergen challenge 4 h
4 ex-smokers and BAL 19 h after exposure.
Randomized, crossover, double blind.
Barcketal. Sweden 18 mild Day 1: 15 min exposures, Day 2:
(2005a) asthmatics, 2 15-min exposures to air and 0.26 ppm
4 ex-smokers NO2 separated by 1 h, at rest. Allergen
challenge 4 h after exposure on day
1 and 3 h after exposure on day 2.
Sputum induction before exposure on
days 1 & 2, and morning of day 3.
Randomized, crossover, single blind.
Barck et al. Sweden 16 mild 30 min exposures to air and 0.26 ppm
(2005b) asthmatics NO2 at rest, nasal allergen challenge 4 h
with rhinitis after exposure. Nasal lavage before and
at intervals after exposure and
challenge.
Devalia et al. United 8 mild 6 h exposures to combination of
(1994) Kingdom asthmatics 0.4 ppm NO2 and 0.2 ppm SO2.
Jenkins etal. United 11 mild 1) 6-h exposures to air, 0.1 ppm ozone,
(1999) Kingdom asthmatics 0.2 ppm NO2, and combination followed
by allergen challenge;
2) 3-h exposures to air, 0.2 ppm ozone,
0.4 ppm NO2, and combination;
All exposures with intermittent exercise.
Increased PMN in bronchial wash
and BAL fluid, increased
eosinophil cationic protein in
bronchial wash, and reduced cell
viability and BAL volume with
NO2 + allergen. No effects on
lung function response to allergen.
Increased eosinophilic cationic
protein in sputum and blood, and
increased myeloperoxidase in
blood with NO2 + allergen. No
differences in lung function or
sputum cells.
No significant differences
between air and NO2 exposure.
Increased allergen responsiveness
10 min after exposure to
combination of NO2 and SO2, but
not to individual gases.
All of the second exposure
scenarios (ozone, NO2, and
combination), but none of the first
exposure scenarios, resulted in
reduced concentration of allergen
causing a 20% decline in FEVi.
Authors conclude that
concentration more important than
total inhaled pollutant.
-------
OQ
to
o
o
TABLE AX5.3 (cont'd). EFFECTS OF NO2 EXPOSURE ON RESPONSE TO INHALED ALLERGEN
Reference Location Participants
Approach & Methods
Findings
Comments
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Rusznak
etal. (1996)
Strand et al.
(1997)
Strand et al.
(1998)
Tunnicliffe
etal. (1994)
Wang et al.
(1995a);
Wang et al.
(1995b)
Wang et al.
(1999)
United
Kingdom
Sweden
Sweden
United
Kingdom
United
Kingdom
United
Kingdom
13 mild
asthmatics
18 patients
with mild
asthma, age
18-50yrs
16 patients
with mild to
moderate
asthma, age
21-52yrs
10
nonsmoking
mild
asthmatics age
16-60 yrs.
8 subjects
completed.
2 groups of
8 subjects with
allergic rhinitis
16 subjects
with allergic
rhinitis
6 h exposures to combination of
0.4 ppm NO2 and 0.2 ppm SO2
Exposure to 0.26 ppm NO2 for 30 min
at rest, allergen challenge 4 h after
exposure
4 daily repeated exposures to 0.26 ppm
NO2 for 30 min at rest
Exposure to air, 0.1 ppm, and 0.4 ppm
NO2 for 1 h at rest, separated by at
least 1 week, followed by allergen
challenge
Exposure to 0.4 ppm NO2 (at rest?) for
6 h followed by nasal allergen
challenge and nasal lavage
Treatment with nasal fluticasone or
placebo for 4 weeks followed by
exposure to 0.4 ppm NO2 for 6 h,
allergen challenge, and nasal lavage
Increased allergen responsiveness
to combination of NO2 and SO2,
10 min, 24, and 48 h after
exposure.
Late phase, but not early phase,
response to allergen enhanced by
N02.
Significant increases in both early
and late phase response to allergen
after 4th day of exposure.
Post-challenge reduction in FEVi
after 0.4 ppm NO2 was greater
than after air, for both the early
(p < 0.009) and late (p < 0.02)
responses. No difference in
nonspecific airway
responsiveness.
Increase in myeloperoxidase and
eosinophil cationic protein in
nasal lavage fluid following
allergen challenge.
Fluticasone suppressed the NO2
and allergen-induced increase in
eosinophil cationic protein in
nasal lavage fluid.
Confirms findings of Devalia
etal. (1994), thatNO2 + SO2
for 6 h increases allergen
responsiveness.
Suggests 0.26 ppm NO2 for 30
min at rest increases late
response.
Suggests repeated 0.26 ppm
NO2 at rest increases allergen
response.
Suggests threshold for allergen
responsiveness effect is
between 0.1 and 0.4 ppm for 1
h resting exposure.
Suggests enhanced nasal
inflammatory response to
allergen with 0.4 ppm.
Confirms earlier findings of
this group that 0.4 ppm NO2
enhances nasal allergen
response.
-------
TABLE AX5.4. EFFECTS OF EXPOSURE TO NO2 WITH OTHER POLLUTANTS
CJQ
r-K
to
o
o
^
^
X
l
£
O
l-rj
H
6
o
1— J
-Z
o
H
O
H
W
O
O
HH
H
W
Reference
Devalia et al.
(1994)
Drechsler-
Parks (1995)
Gong et al.
(2005)
Hazucha et al.
(1994)
Torres and
Magnussen
(1990)
Koenig et al.
(1994)
Rubenstein
etal. (1990)
Location
United
Kingdom
Santa Barbara,
CA, USA
Downey, CA,
USA
Chapel Hill,
North
Carolina, USA
Germany
Seattle,
Washington,
USA
San Francisco,
California,
USA
Participants
8 mild asthmatics
8 older healthy
nonsmokers
6 healthy nonsmokers
and 18 ex-smokers
with COPD
21 healthy female
nonsmokers
14 nonsmoking mild
asthmatics
28 asthmatic
adolescents; 6 subjects
did not complete.
9 stable asthmatics
Approach & Methods
6 h exposures to combination of
0.4 ppm NO2 and 0.2 ppm SO2.
4 2-h exposures with intermittent
exercise: air, 0.60 ppm NO2,
0.45 ppm O3, and 0.60 ppm NO2 +
0.45 ppm O3.
2 h exposures with intermittent
exercise to: 1) air, 2) 0.4 ppm NO2,
3) 200 ug/m3 concentrated ambient
particulate matter (CAPs), 4) NO2 +
CAPs
2 h exposure to air or 0.6 ppm NO2
followed 3 h later by exposure to
0.3 ppm O3, with intermittent
exercise.
30 min exposures to air, 0.25 ppm
NO2, or 0.5 ppm SO2 at rest followed
15 min later by 0.75 ppm SO2
hyperventilation challenge.
Exposure for 90 min with
intermittent exercise to: 1) 0.12 ppm
ozone + 0.3 ppmNO2, 2) 0.12 ppm
ozone + 0.3 ppm NO2 + 68 ug/m3
H2SO4, or 3) 0.12 ppm ozone +
0.3 ppm NO2 + 0.05 ppm nitric acid.
30 min exposures to air or 0.3 ppm
NO2 with 20 min exercise, followed
1 h later by SO2 inhalation challenge.
Findings
Increased allergen
responsiveness 10 min after
exposure to combination of
NO2 and SO2, but not to
individual gases.
Significant reduction in cardiac
output during exercise,
estimated using noninvasive
impedance cardiography, with
NO2 + O3. Symptoms and
pulmonary function not
reported.
Reduced maximum mid-
expiratory flow rate and
oxygen saturation with CAPs
exposures; no effects of NO2
alone or additive effect with
CAPs.
NO2 enhanced spirometric
responses and airways
responsiveness following
subsequent O3 exposure.
NO2 but not SO2 increased
airways responsiveness to SO2
challenge.
No effects on pulmonary
function
No effects on pulmonary
function or SO2
responsiveness.
Comments
Small number of
subjects limits
statistical power.
Suggests cardiac
effects of NO2 + O3.
Small number of
subjects limits
statistical power, has
not been replicated.
Exposures not fully
randomized. Small
number of healthy
subjects limits
interpretation for
healthy group.
0.6ppmNO2
enhanced ozone
responses.
Findings contrast with
Rubenstein, et al.
(1990).
Absence of lung
function effects of 0.3
ppm NO2 consistent
with other studies; no
effects of mixtures.
Findings contrast with
Torres & Magnussen
etal. (1990).
-------
CJQ
to
o
o
TABLE AX5.4 (cont'd). EFFECTS OF EXPOSURE TO NO2 WITH OTHER POLLUTANTS
Reference
Location
Participants
Approach & Methods
Findings
Comments
Rudell et al. Sweden
(1999)
10 healthy
nonsmokers
Air and diesel exhaust for 1 h, with
and without particle trap. NO2
concentration 1.2-1.3 ppm. BAL
24 h after exposures.
Increased neutrophils in BAL
fluid, no significant reduction
in effect with particle trap.
Filter only partially
trapped particles.
Unable to draw
conclusions about role
of NO2 in causing
effects.
Rusznak et al.
(1996)
United
Kingdom
13 mild asthmatics
6 h exposures to combination of
0.4 ppm NO2 and 0.2 ppm SO2.
Increased allergen
responsiveness to combination
of NO2 and SO2, 10 min, 24,
and 48 h after exposure.
Confirms findings of
Devaliaetal. (1994),
that NO2 + SO2 for 6 h
increases allergen
responsiveness.
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
-------
1 AX5.5 REFERENCES
2
3 American Thoracic Society. (2000) What constitutes an adverse health effect of air pollution?
4 Am. J. Respir. Crit. Care Med. 161: 665-673.
5 Avissar, N. E.; Reed, C. K.; Cox, C.; Frampton, M. W.; Finkelstein, J. N. (2000) Ozone, but not
6 nitrogen dioxide, exposure decreases glutathione peroxidases in epithelial lining fluid of
7 human lung. Am. J. Respir. Crit. Care Med. 162: 1342-1347.
8 Azadniv, M.; Utell, M. J.; Morrow, P. E.; Gibb, F. R.; Nichols, J.; Roberts, N. J., Jr.; Speers, D.
9 M.; Torres, A.; Tsai, Y.; Abraham, M. K.; Voter, K. Z.; Frampton, M. W. (1998) Effects
10 of nitrogen dioxide exposure on human host defense. Inhalation Toxicol. 10: 585-602.
11 Barck, C.; Sandstrom, T.; Lundahl, J.; Hallden, G.; Svartengren, M.; Strand, V.; Rak, S.; Bylin,
12 G. (2002) Ambient level of NO2 augments the inflammatory response to inhaled allergen
13 in asthmatics. Respir. Med. 96: 907-917.
14 Barck, C.; Lundahl, J.; Hallden, G.; Bylin, G. (2005a) Brief exposures to NO2 augment the
15 allergic inflammation in asthmatics. Environ. Res. 97: 58-66.
16 Barck, C.; Lundahl, J.; Holmstrom, M.; Bylin, G. (2005b) Does nitrogen dioxide affect
17 inflammatory markers after nasal allergen challenge? Am. J. Rhinol. 19: 560-566.
18 Blomberg, A.; Krishna, M. T.; Bocchino, V.; Biscione, G. L.; Shute, J. K.; Kelly, F. J.; Frew, A.
19 J.; Holgate, S. T.; Sandstrom, T. (1997) The inflammatory effects of 2 ppm NO2 on the
20 airways of healthy subjects. Am. J. Respir. Crit. Care Med. 156: 418-424.
21 Blomberg, A.; Krishna, M. T.; Helleday, R.; Soderberg, M.; Ledin, M.-C.; Kelly, F. J.; Frew, A.
22 J.; Holgate, S. T.; Sandstrom, T. (1999) Persistent airway inflammation but accomodated
23 antioxidant and lung function responses after repeated daily exposure to nitrogen dioxide.
24 Am. J. Respir. Crit. Care Med. 159: 536-543.
25 Devalia, J. L.; Rusznak, C.; Herdman, M. J.; Trigg, C. J.; Tarraf, H.; Davies, R. J. (1994) Effect
26 of nitrogen dioxide and sulphur dioxide on airway response of mild asthmatic patients to
27 allergen inhalation. Lancet 344: 1668-1671.
28 Devlin, R. B.; Horstman, D. P.; Gerrity, T. R.; Becker, S.; Madden, M. C. (1999) Inflammatory
29 response in humans exposed to 2.0 PPM nitrogen dioxide. Inhalation Toxicol. 11: 89-
30 109.
31 Drechsler-Parks, D. M. (1995) Cardiac output effects of O3 and NO2 exposure in healthy older
32 adults. Toxicol. Ind. Health 11: 99-109.
33 Frampton, M. W.; Morrow, P. E.; Cox, C.; Gibb, F. R.; Speers, D. M.; Utell, M. J. (1991) Effects
34 of nitrogen dioxide exposure on pulmonary function and airway reactivity in normal
35 humans. Am. Rev. Respir. Dis. 143: 522-527.
36 Frampton, M. W.; Boscia, J.; Roberts, N. J., Jr.; Azadniv, M.; Torres, A.; Cox, C.; Morrow, P.
37 E.; Nichols, J.; Chalupa, D.; Frasier, L. M.; Gibb, F. R.; Speers, D. M.; Tsai, Y.; Utell, M.
38 J. (2002) Nitrogen dioxide exposure: effects on airway and blood cells. Am. J. Physiol.
39 282:L155-L165.
August 2007 AX5-16 DRAFT-DO NOT QUOTE OR CITE
-------
1 Frampton, M. W.; Pietropaoli, A. P.; Morrow, P. E.; Utell, M. J. (2006) Human clinical studies
2 of airborne pollutants. In: Gardner, D. E. Toxicology of the lung. Boca Raton, FL: CRC
3 Press; pp. 29-82. (Target organ toxicology series).
4 Gong, H., Jr., Linn, W. S.; Clark, K. W.; Anderson, K. R.; Geller, M. D.; Sioutas, C. (2005)
5 Respiratory responses to exposures with fine particulates and nitrogen dioxide in the
6 elderly with and without COPD. Inhalation Toxicol. 17: 123-132.
7 Hackney, J. D.; Thiede, F. C.; Linn, W. S.; Pedersen, E. E.; Spier, C. E.; Law, D. C.; Fischer, D.
8 A. (1978) Experimental studies on human health effects of air pollutants. IV. Short-term
9 physiological and clinical effects of nitrogen dioxide exposure. Arch. Environ. Health 33:
10 176-181.
11 Hazucha, M. J.; Folinsbee, L. J.; Seal, E.; Bromberg, P. A. (1994) Lung function response of
12 healthy women after sequential exposures to NO2 and O^. Am. J. Respir. Crit. Care Med.
13 150: 642-647.
14 Helleday, R.; Sandstrom, T.; Stjernberg, N. (1994) Differences in bronchoalveolar cell response
15 to nitrogen dioxide exposure between smokers and nonsmokers. Eur. Respir. J. 7: 1213-
16 1220.
17 Helleday, R.; Huberman, D.; Blomberg, A.; Stjernberg, N.; Sandstrom, T. (1995) Nitrogen
18 dioxide exposure impairs the frequency of the mucociliary activity in healthy subjects.
19 Eur. Respir. J. 8: 1664-1668.
20 Jarvis, D. L.; Leaderer, B. P.; Chinn, S.; Burney, P. G. (2005) Indoor nitrous acid and respiratory
21 symptoms and lung function in adults. Thorax 60: 474-479.
22 Jenkins, H. S.; Devalia, J. L.; Mister, R. L.; Bevan, A. M.; Rusznak, C.; Davies, R. J. (1999) The
23 effect of exposure to ozone and nitrogen dioxide on the airway response of atopic
24 asthmatics to inhaled allergen: dose- and time-dependent effects. Am. J. Respir. Crit.
25 Care Med. 160:33-39.
26 Jorres, R.; Magnussen, H. (1990) Airways response of asthmatics after a 30 min exposure, at
27 resting ventilation, to 0.25 ppm NO2 or 0.5 ppm SO2. Eur. Respir. J. 3: 132-137.
28 Jorres, R.; Magnussen, H. (1991) Effect of 0.25 ppm nitrogen dioxide on the airway response to
29 methacholine in asymptomatic asthmatic patients. Lung 169: 77-85.
30 Jorres, R.; Nowak, D.; Grimminger, F.; Seeger, W.; Oldigs, M.; Magnussen, H. (1995) The
31 effect of 1 ppm nitrogen dioxide on bronchoalveolar lavage cells and inflammatory
32 mediators in normal and asthmatic subjects. Eur. Respir. J. 8: 416-424.
33 Kim, S. U.; Koenig, J. Q.; Pierson, W. E.; Hanley, Q. S. (1991) Acute pulmonary effects of
34 nitrogen dioxide exposure during exercise in competitive athletes. Chest 99: 815-819.
35 Koenig, J. Q.; Covert, D. S.; Pierson, W. E.; Hanley, Q. S.; Rebolledo, V.; Dumler, K.;
36 McKinney, S. E. (1994) Oxidant and acid aerosol exposure in healthy subjects and
37 subjects with asthma. Part I: effects of oxidants, combined with sulfuric or nitric acid, on
38 the pulmonary function of adolescents with asthma. Cambridge, MA: Health Effects
39 Institute; pp. 1-36; research report no. 70.
40 Morrow, P. E.; Utell, M. J.; Bauer, M. A.; Smeglin, A. M.; Frampton, M. W.; Cox, C.; Speers,
41 D. M.; Gibb, F. R. (1992) Pulmonary performance of elderly normal subjects and
August 2007 AX5-17 DRAFT-DO NOT QUOTE OR CITE
-------
1 subjects with chronic obstructive pulmonary disease exposed to 0.3 ppm nitrogen
2 dioxide. Am. Rev. Respir. Dis. 145: 291-300.
3 Pathmanathan, S.; Krishna, M. T.; Blomberg, A.; Helleday, R.; Kelly, F. J.; Sandstrom, T.;
4 Holgate, S. T.; Wilson, S. J.; Frew, A. J. (2003) Repeated daily exposure to 2 ppm
5 nitrogen dioxide upregulates the expression of IL-5, IL-10, IL-13, and ICAM-1 in the
6 bronchial epithelium of healthy human airways. Occup. Environ. Med. 60: 892-896.
7 Posin, C.; Clark, K.; Jones, M. P.; Patterson, J. V.; Buckley, R. D.; Hackney, J. D. (1978)
8 Nitrogen dioxide inhalation and human blood biochemistry. Arch. Environ. Health 33:
9 318-324.
10 Rasmussen, T. R.; Kjaergaard, S. K.; Tarp, U.; Pedersen, O. F. (1992) Delayed effects of NO2
11 exposure on alveolar permeability and glutathione peroxidase in healthy humans. Am.
12 Rev. Respir. Dis. 146: 654-659.
13 Rigas, M. L.; Ben-Jebria, A.; Ultman, J. S. (1997) Longitudinal distribution of ozone absorption
14 in the lung: effects of nitrogen dioxide, sulfur dioxide, and ozone exposures. Arch.
15 Environ. Health 52: 173-178.
16 Rubenstein, I; Bigby, B. G.; Reiss, T. F.; Boushey, H. A., Jr. (1990) Short-term exposure to 0.3
17 ppm nitrogen dioxide does not potentiate airway responsiveness to sulfur dioxide in
18 asthmatic subjects. Am. Rev. Respir. Dis. 141: 381-385.
19 Rudell, B.; Blomberg, A.; Helleday, R.; Ledin, M.-C.; Lundback, B.; Stjernberg, N.; Horstedt,
20 P.; Sandstrom, T. (1999) Bronchoalveolar inflammation after exposure to diesel exhaust:
21 comparison between unfiltered and particle trap filtered exhaust. Occup. Environ. Med.
22 56: 527-534.
23 Rusznak, C.; Devalia, J. L.; Davies, R. J. (1996) Airway response of asthmatic subjects to
24 inhaled allergen after exposure to pollutants. Thorax 51: 1105-1108.
25 Sandstrom, T.; Andersson, M. C.; Kolmodin-Hedman, B.; Stjernberg, N.; Angstrom, T. (1990)
26 Bronchoalveolar mastocytosis and lymphocytosis after nitrogen dioxide exposure in man:
27 a time-kinetic study. Eur. Respir. J. 3: 138-143.
28 Sandstrom, T.; Stjernberg, N.; Eklund, A.; Ledin, M.-C.; Bjermer, L.; Kolmodin-Hedman, B.;
29 Lindstrom, K.; Rosenhall, L.; Angstrom, T. (1991) Inflammatory cell response in
30 bronchoalveolar lavage fluid after nitrogen dioxide exposure of healthy subjects: a dose-
31 response study. Eur. Respir. J. 4: 332-339.
32 Sandstrom, T.; Helleday, R.; Bjermer, L.; Stjernberg, N. (1992a) Effects of repeated exposure to
33 4 ppm nitrogen dioxide on bronchoalveolar lymphocyte subsets and macrophages in
34 healthy men. Eur. Respir. J. 5: 1092-1096.
35 Sandstrom, T.; Ledin, M.-C.; Thomasson, L.; Helleday, R.; Stjernberg, N. (1992b) Reductions in
36 lymphocyte subpopulations after repeated exposure to 1.5 ppm nitrogen dioxide. Br. J.
37 Ind. Med. 49: 850-854.
38 Solomon, C.; Christian, D. L.; Chen, L. L.; Welch, B. S.; Kleinman, M. T.; Dunham, E.; Erie, D.
39 J.; Balmes, J. R. (2000) Effect of serial-day exposure to nitrogen dioxide on airway and
40 blood leukocytes and lymphocyte subsets. Eur. Respir. J. 15: 922-928.
August 2007 AX5-18 DRAFT-DO NOT QUOTE OR CITE
-------
1 Spengler, J. D.; Brauer, M.; Koutrakis, P. (1990) Acid air and health. Environ. Sci. Technol. 24:
2 946-956.
3 Strand, V.; Salomonsson, P.; Lundahl, J.; Bylin, G. (1996) Immediate and delayed effects of
4 nitrogen dioxide exposure at an ambient level on bronchial responsiveness to histamine in
5 subjects with asthma. Eur. Respir. J. 9: 733-740.
6 Strand, V.; Rak, S.; Svartengren, M.; Bylin, G. (1997) Nitrogen dioxide exposure enhances
7 asthmatic reaction to inhaled allergen in subjects with asthma. Am. J. Respir. Crit. Care
8 Med. 155: 881-887.
9 Strand, V.; Svartengren, M.; Rak, S.; Barck, C.; Bylin, G. (1998) Repeated exposure to an
10 ambient level of NO2 enhances asthmatic response to nonsymptomatic allergen dose. Eur.
11 Respir. J. 12: 6-12.
12 Tunnicliffe, W. S.; Burge, P. S.; Ayres, J. G. (1994) Effect of domestic concentrations of
13 nitrogen dioxide on airway responses to inhaled allergen in asthmatic patients. Lancet
14 344: 1733-1736.
15 U.S. Environmental Protection Agency. (1993) Air quality criteria for oxides of nitrogen.
16 Research Triangle Park, NC: Office of Health and Environmental Assessment,
17 Environmental Criteria and Assessment Office; report nos. EPA/600/8-91/049aF-cF. 3v.
18 Available from: NTIS, Springfield, VA; PB95-124533, PB95-124525, and PB95-124517.
19 Vagaggini, B.; Paggiaro, P. L.; Giannini, D.; Franco, A. D.; Cianchetti, S.; Carnevali, S.;
20 Taccola, M.; Bacci, E.; Bancalari, L.; Dente, F. L.; Giuntini, C. (1996) Effect of short-
21 term NO2 exposure on induced sputum in normal, asthmatic and COPD subjects. Eur.
22 Respir. J. 9: 1852-1857.
23 Wang, J. H.; Devalia, J. L.; Duddle, J. M.; Hamilton, S. A.; Davies, R. J. (1995a) Effect of six-
24 hour exposure to nitrogen dioxide on early-phase nasal response to allergen challenge in
25 patients with a history of seasonal allergic rhinitis. J. Allergy Clin. Immunol. 96: 669-
26 676.
27 Wang, J. H.; Duddle, J.; Devalia, J. L.; Davies, R. J. (1995b) Nitrogen dioxide increases
28 eosinophil activation in the early-phase response to nasal allergen provocation. Int. Arch.
29 Allergy Immunol. 107: 103-105.
30 Wang, J. H.; Devalia, J. L.; Rusznak, C.; Bagnall, A.; Sapsford, R. J.; Davies, R. J. (1999) Effect
31 of fluticasone propionate aqueous nasal spray on allergen-induced inflammatory changes
32 in the nasal airways of allergic rhinitics following exposure to nitrogen dioxide. Clin.
33 Exp. Allergy 29: 234-240.
34
August 2007 AX5-19 DRAFT-DO NOT QUOTE OR CITE
-------
AX6. CHAPTER 6 ANNEX - EPIDEMIOLOGICAL
STUDIES OF HUMAN HEALTH EFFECTS ASSOCIATED
WITH AMBIENT OXIDES OF NITROGEN EXPOSURE
August 2007 AX6-1 DRAFT-DO NOT QUOTE OR CITE
-------
TABLE AX6.1. STUDIES EXAMINING EXPOSURE TO INDOOR NO2 AND RESPIRATORY SYMPTOMS
NO2 Measurement
OQ
to
o
o
Author,
Year/Outcome
OR or RR (95% CI)
Exposure Range
Subjects/Location Analysis/Monitoring Device Time Mid-Range (ppb) (ppb)
Pilotto et al. (2004)
daytime symptoms
difficulty breathing
chest tightness
asthma attacks
difficulty breathing,
night
RR 2.44 (1.02, 14.29)=*
RR 2.22 (1.23, 4.00)*
RR 2.56 (1.08, 5.88)*
RR 3.12 (1.45, 7.14)*
118 asthmatic
children/Australia
negative binomial/passive
diffusion badges
mean (sd)
6 h intervention 16 (7) 7, 38
mean (sd) control 47
(27) 12, 116
X
Pilotto et al. (1997) OR 1.41 (0.63, 3.15)
wheeze (>40 ppb)
388 children/Australia
generalized linear mixed
models/passive diffusion badges
6 h
4, 132
H
6
o
o
H
O
o
H
W
O
O
HH
H
W
Nitschke et al. (2006)
night symptoms
difficulty breathing
school max
home max
chest tightness
school max
RR 1.23 (1.10, 1.39)
RR 1.06 (1.02, 1.10)
RR 1.25 (1.14, 1.37)
174 asthmatic
children/Australia
negative binomial/passive
diffusion badges
6 h mean home 20 (22)
mean school 34 (28)
-------
TABLE AX6.1 (cont'd). STUDIES EXAMINING EXPOSURE TO INDOOR NO2 AND RESPIRATORY SYMPTOMS
NO2 Measurement
OQ
to
o
o
Author, Year OR or RR (95% CI) Subjects/Location
Exposure Range
Analysis/Monitoring Device Time Mid-range (ppb) (ppb)
Garrett et al. (1998) 148 children/Australia
chest tightness OR 1.53 (0.45, 5.32)
multiple logistic regression/passive
monitors
4 days med 6
plO-p90,
3,15
Smith et al. (2000)
children (n= 49, 0-14)
chest tightness OR 1.12 (1.07, 1.18)
125 asthmatic
adults/children/Australia
GEE/passive diffusion badges
4.5 h
4, 147
X
ON
K>
Belanger et al. (2006)
multifamily housing
wheeze RR 1.33 (1.05, 1.68)
chest tightness RR1.51 (1.18, 1.91)
728 asthmatic
children/Northeast US
logistic, Poisson regression/Palmes
tubes
2 wks
mean (sd) gas
home 26 (18)
mean (sd) elect
home 9 (9)
H
6
o
o
H
O
o
H
W
O
O
HH
H
W
Chauhan et al. (2003)
Increased symptom
score, comparing first
and second tertiles of
NO2 exposure
Increased symptom
score, comparing first
and third tertiles of
NO2 exposure
0.6 (0.01, 1.18)
2.1(0.52,3.81)
114 asthmatic children/
Southampton U.K.
Palmes diffusion tubes
7d
Exposure tertiles: Chauhan et
<^4-7^>7 al. (2003)
-------
CJQ
to
o
o
X
TABLE AX6.1 (cont'd). STUDIES EXAMINING EXPOSURE TO INDOOR NO2 AND
RESPIRATORY SYMPTOMS
Author, Year
OR or RR (95% CI)
Subjects/Location
Analysis/Monitoring Device
Exposure
Time
NO2 Measurement
Mid-range (ppb)
Range
(ppb)
van Strien et al.
(2004)
persistent cough
<5.1ppb RR1.0
5.1, 9.9 ppb RR 0.96 (0.69, 1.36)
9.9, 17.4 ppb RR 1.33 (0.94, 1.88)
>17.4ppb RR 1.52 (1.00, 2.31)
shortness of breath
<5.1ppb RR1.0
5.1, 9.9 ppb RR 1.59 (0.96, 2.62)
9.9, 17.4 ppb RR 1.95 (1.17, 3.27)
>17.4ppb RR 2.38 (1.31, 4.34)
762 infants/Northeast US Poisson regression
med 10
H
6
o
o
H
O
o
H
W
O
O
HH
H
W
Notes:
Unless otherwise noted, results given for 20 ppb increase in NO2.
*For purpose of comparison, RRs from Pilotto et al. (2004) are shown here as risk of symptoms given greater exposure to NO2,
i.e., control (unflued gas heater) vs. intervention (flued or electric replacement heater).
RRs reported by Pilotto el al. (2004) as protective effects for intervention vs. control.
-------
> TABLE AX6.2. STUDIES EXAMINING EXPOSURE TO AMBIENT NO2 AND ACUTE RESPIRATORY SYMPTOMS
| USING GENERALIZED ESTIMATING EQUATIONS (GEE) IN THE ANALYSIS METHOD
" NO2 Measurement Correlation with Other Pollutants
to
§ Avg Mid-range
^ Author, Year OR(95%CI) Location Subjects Time (ppb) Range (ppb) PM2.S PM10 O3 SO2 CO
Children: Multicity
Studies
US, 1844
Schwartz etal. (1994) 6-Cities children 24 h med 13 plO-p90,5,24 0.35 0.36 -0.28 0.51
cough, incidence:
lag 1-4 mean 1.61(1.08,2.43)
864
US, asthmatic
Mortimer et al. (2002) NCICAS children 4 h med 25 7,90 0.27
asthma symptoms:
lag 1-6 mean 1.48(1.02,2.16)
North 990
t**J America, asthmatic minplOtomax
& Schildcrout etal. (2006) CAMP children 24 h med23 p90, 10,37 0.26,0.640.04,0.47 0.23,0.680.63,0.92
•**• asthma symptoms:
lagO 1.06(1.00,1.13)
lagl 1.04(0.97,1.10)
O lag 2 1.09(1.03,1.15)
P> 3-day moving sum 1.04(1.01,1.07)
H
6
o
2
o
H
O
c
o
H
W
O
V
O
HH
H
W
-------
OQ
to
o
o
TABLE AX6.2 (cont'd). STUDIES EXAMINING EXPOSURE TO AMBIENT NO2 AND ACUTE RESPIRATORY
SYMPTOMS USING GENERALIZED ESTIMATING EQUATIONS (GEE) IN THE ANALYSIS METHOD
NO-. Measurement
Correlation with Other Pollutants
Author, Year
Avg Mid-range
OR(95%CI) Location Subjects time (ppb) Range (ppb) PM2.5 PM10
03
SO,
CO
X
Children: Single City
Studies
Pino et al. (2004)
wheezy bronchitis:
6-day lag
Chile
504 infants 24 h
mean(sd) 41
(19) p5-p95, 20, 81
1.14(1.04, 1.30)
Ostroetal. (2001)
cough, incidence: lag 3 1.07 (1.00, 1.14)
wheeze, incidence:
138
asthmatic
children,
Southern African
CA American 1 h
Iag3
Delfino et al. (2002)
asthma symptoms:
lagO
1.05 (1.01, 1.09)
Southern 22 asthmatic
CA children 8 h
1.91 (1.07,3.39)
84 asthmatic
mean (sd) 80
(4) 20,220
mean(sd) 15
(7) 6,34
mean (sd) 30
0.34 0.63
0.48
0.55
0.26
Tl
H
6
o
0
H
O
0
H
W
O
O
HH
H
W
Segalaetal. (1998)
asthma symptoms,
incidence: lag 0
lagl
lag 4
nocturnal cough,
incidence: lag 3
lag 4
Paris children 24 h (8) 13,65 (0.61)* 0.55 0.54
1.89(1.13,3.17)
1.36(0.70,2.64)
1.80(1.07,3.01)
1.44 (0.99, 2.08)
1.74 (1.20, 2.52)
-------
CJQ
TABLE AX6.2 (cont'd). STUDIES EXAMINING EXPOSURE TO AMBIENT NO2 AND ACUTE RESPIRATORY
SYMPTOMS USING GENERALIZED ESTIMATING EQUATIONS (GEE) IN THE ANALYSIS METHOD
j~^
to
o
o
X
ON
i
ON
O
H
6
o
0
H
O
0
H
W
O
O
HH
H
W
Author, Year
Just et al. (2002)
nocturnal cough,
incidence: lagO
lag 0-2
lag 0-4
Jalaludin et al. (2004)
wet cough:
lagO
Pino et al. (2004)
Adults
Segala et al. (2004)
sore throat, cough:
lag 0-4
von Klotetal. (2002)
wheeze, prev: 5-day
mean
phlegm, prev: 5 -day
mean
cough, prev: 5 -day
mean
breathing prob in a.m.:
5 -day mean
NO2 Measurement Correlation with Other Pollutants
Avg Mid-range
OR(95%CI) Location Subjects time (ppb) Range (ppb) PM2.S PM10 O3 SO2 CO
82 asthmatic mean (sd) 29
Paris children 24 h (9) 12,59 0.92* 0.54 0.09 0.69
2.11 (1.20,3.74)
1.80 (0.89, 3.84)
1.58 (0.73, 3.54)
148 children
with wheeze mean (sd) 15
Australia history 15 h (6) 3,79 0.26 -0.31
1.13 (1.00, 1.26)
46
nonsmoking mean (sd) 30
Paris adults 24 h (9) 12,71 0.82* 0.83
4.05 (1.20,
13.60)
53 asthmatic
Germany adults 24 h med 24 4, 63 0.74 0.36 0.82
1.15(1.02, 1.31)
1.22(1.10, 1.39)
1.15(1.00, 1.31)
1.25(1.10, 1.39)
Odds ratios (OR) given for 20 ppb increase in NO2 with 24-h averaging time, or 30 ppb for 1-h averaging time. (20 ppb increases also used for times between 1 and 24 h.) *BS
-------
OQ
to
o
o
TABLE AX6.3-1. RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
X
H
6
o
o
H
O
o
H
W
O
O
HH
H
W
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Effects: Relative Risk or Percent Change
& Confidence Intervals (95%)
UNITED STATES
Moolgavkar (2000a,b,c)
Moolgavkar (2003)
Multi-city, United
States: Chicago, Los
Angeles, Maricopa
County, (Phoenix).
Period of Study:
1987-1995
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: 25 ppb
IQR: 10 ppb
Los Angeles
Median: 38 ppb
IQR: 18 ppb
Maricopa
Median: 1 9 ppb
IQR: 12 ppb
Chicago:
PM10; r = 0.49
CO; r= 0.63
SO2; r = 0.44
O3;r = 0.02
LA:
PM2.5; r = 0.73
PM10; r = 0.70
CO; r= 0.80
S02; r = 0.74
03;r=-0.10
Maricopa:
PM10; r = 0.22
CO; r= 0.66
SO2; r = 0.02
O3;r=-0.23
Increment: 10 ppb
COPD, >65 yrs
Chicago 1.7% [CI 0.36, 3.05] lag 0 - GAM default
Chicago 2.04% [t = 2.99] lag 0 - GAM-100
Los Angeles 2. 5% [CI 1.85, 3.15] lag 0 - GAM
default
Los Angeles 2.84% [t = 13.32] lag 0 - GAM - 30
Los Angeles 1 .80% [t = 9.60] lag 0 - GAM - 100
Los Angeles 1 .78% [t = 7.72] lag 0 - NS-100
Phoenix 4.4% [CI 1.07, 7.84] lag 5
Chronic Respiratory Disease
Los Angeles
0-19 yrs 4.9% [CI 4.1, 5.7] lag 2
20-64 yrs 1.7% [CI 0.9, 2.1] lag 2
Multi-pollutant model
Moolgavkar* et al.
(1997)
United States:
Minneapolis-St. Paul
Period of Study:
1986-1991
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: S Plus
Lag: 0-3 days
NO2 24-h avg (ppb)
16.3 ppb
IQR: 9.5 ppb
PM10;r=0.31
S02;r=0.09
CO; r= 0.58
NO2 andPM10: 1.72% [t = 3.18] lag 0 - GAM-100
NO2 and PM2 5: 1.51% [t = 2.07] lag 0 - GAM-100
Increment: 10 ppb
Sum of Pneumonia and COPD
2.2% [0.2, 4.2] lag 1
Pneumonia Only
3.1% [0.6, 5.6] lag 1,20 df
1.7% [-0.8, 4.2] lag 1, 130 df
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels & Copollutants & Effects: Relative Risk or Percent Change &
Monitoring Stations Correlations Confidence Intervals (95%)
UNITED STATES (cont'd)
X
ON
i
oo
H
6
o
o
H
O
o
H
W
O
O
HH
H
W
Neidell (2004)
California
Period of Study:
1992-1998
Outcomes (ICD 9 codes): Asthma
Age groups analyzed:
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels & Copollutants & Effects: Relative Risk or Percent Change &
Monitoring Stations Correlations Confidence Intervals (95%)
UNITED STATES (cont'd)
Karr et al. (2006)
Southern LA County,
CA, United States
Period of Study:
1995-2000
Outcomes (ICD 9 codes): Acute
bronchiolitis (466.1)
Age groups analyzed: 0-1 yr
Study Design: Case-crossover
N: 19,109
Statistical Analyses: Conditional logistic
regression
Covariates: day of wk, temperature,
humidity
Seasons: Nov-Maronly
Lag: 0-4 days
1-h max NO2 (ppb)
Mean: 59 ppb
IQR: 26 ppb
Number of Stations: 34
CO
PM2.5
X
Increment: 26 ppb (IQR)
Acute bronchiolitis
OR 0.96 [0.94, 0.99] lag 4
OR 0.97 [0.95, 0.99] lag 1
Stratified by Gestational Age at Birth:
37-44 wks
0.98 [0.95, 1.00] lag 1; 0.97 [0.94, 0.99] lag 4
34-36 wks
0.90 [0.84, 0.97] lag 1; 0.94 [0.88, 1.02] lag 4
29-33 wks
1.01 [0.91, 1.13] lag 1; 0.90 [0.80, 1.01] lag 4
25-28 wks
0.94 [0.78, 1.13] lag 1; 0.90 [0.73, 1.11] lag 4
H
6
o
o
H
O
o
H
W
O
O
HH
H
W
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
X
I
o
H
6
o
o
H
O
o
H
W
O
O
HH
H
W
Reference, Study
Location, & Period Outcomes, Design, & Methods
UNITED STATES
Linn et al. (2000)
Los Angeles, United
States
Period of Study:
1992-1995
(cont'd)
Outcomes (ICD 9 codes): Asthma (493),
COPD (APR-DRG 88), Pulmonary
diagnoses
(APR-DRG 75-101)
Age groups analyzed: >30
Study Design: Time series
N: 302,600
Statistical Analyses: Poisson regression,
GAM, OLS regression
Covariates: day of wk, holiday, max
temperature, min temperature, rain days,
mean temperature, barometric pressure,
season
Seasons: Winter (Jan-Mar), Spring (Apr-
Jun), Summer (Jul-Sep), Fall
(Oct-Dec)
Statistical Package: SPSS and SAS
Lag: 0, 1 days
Mean Levels &
Monitoring Stations
All concentrations are in
ppb.
Winter: 3. 4 ±1.3
Spring: 2.8 ±0.9
Summer: 3.4 ± 1.0
Autumn: 4.1 ± 1.4
Overall: 3.4 ±1.3
Copollutants &
Correlations
Winter:
CO; r= 0.89
PM10;r=0.88
O3;r=-0.23
Spring:
CO; r= 0.92
PM10; r = 0.67
03;r = 0.35
Summer
CO; r= 0.94
PM10;r=0.80
03;r = 0.11
Winter
CO; r= 0.84
PM10;r=0.80
03;r=-0.00
Effects: Relative Risk or Percent Change
& Confidence Intervals (95%)
Increment: 10 ppb
All pulmonary
All seasons: 0.7% ± 0.3%
Winter: 1.1% ±0.5%
Spring: 0.7% ±0.1%
Summer: 0.4% ±0.8%
Autumn: 1.2% ±0.4%
Asthma
All season: 1.4% ±0.5%
Winter: 2.8% ±0.1%
Spring: NR
Summer: NR
Autumn: 1.9% ±0.8%
COPD
All season: 0.8% ±0.4%
Winter: NR
Spring: NR
Summer: NR
Autumn: 1.6% ±0.6%
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Effects: Relative Risk or Percent
Change & Confidence Intervals (95%)
UNITED STATES (cont'd)
Gwynn* et al. (2000)
Buffalo, NY
United States
Period of Study:
1988-1990
Days: 1,090
Outcomes (ICD 9 codes): Respiratory
admissions: Acute bronchitis/bronchiolitis
(466); Pneumonia (480-4860); COPD and
Asthma (490-493, 496)
Age groups analyzed: 6
Study Design: Time series
N: 24,
Statistical Analyses: Poisson regression
with GLM and GAM
Covariates: season, day of wk, holiday,
temperature, relative humidity
Lag: 0-3 days
24-h avg NO2 (ppb):
Min: 4.0
25th: 15.5
Mean: 20.5
75th: 24.5
Max: 47.5
IT" r = 0.22
SO42T = 0.36
PM10 r = 0.44
O3r=0.06
SO2r=0.36
CO r = 0.65
COH r = 0.72
Increment: 27 9 ppb (Max-Mean; IQR)
NO2 alone:
Max-Mean RR 1.033 (t = 1.32) lag 1
IQRRR1.01(t= 1.32) lag 1
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Zanobetti and
Schwartz (2006)
Boston, MA, United
States
Period of Study:
1995-1999
Outcomes (ICD 9 codes): Pneumonia (480-
7)
Age groups analyzed: 65+
Special Population: Medicare patients only
Study Design: Case-crossover
N: 24,857
Statistical Analyses: Conditional logisitic
regression
Covariates: apparent temperature, day of
wk
Seasons: Warm (Apr-Sep), Cool (Oct-Mar)
Statistical Package: SAS
Lag: 0, 1 days, 0-1 avg
NO2 median 23.20 ppb;
90-10%: 20.41 ppb; For
lag 0-1 2 day avg 90-10%
= 16.8 ppb; IQR = 10.83
Number of Stations: 5
PM2.5;r=0.55
BC;r=0.70
CO; r= 0.67
O3;r=-0.14
Increment: 20.41 ppb (90-10%)
Pneumonia
-0.16% [-4.73, 4.42] lag 0
Increment: 16.78 ppb (90-10%)
Pneumonia
2.26% [-2.55, 7.01] lag 0-1
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
X
ON
to
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants & Effects: Relative Risk or Percent Change
Correlations & Confidence Intervals (95%)
CANADA
Burnett etal. (1997a)
16 cities
Canada
Period of Study:
4/1981-12/1991
Days: 3,927
Yang et al. (2003)
Vancouver, Canada
Period of Study: 1986-
1998
Days: 4748
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
Outcomes (ICD 9 codes): All respiratory
admissions (460-519)
Study Design: Case-crossover
Age groups analyzed: <3, >65
Statistical Analyses: conditional logistic
regression
Statistical Package: NR
Lag: 0-5 days
l-hmaxNO2(ppb)
Mean: 35.5
SD: 16.5
25th: 25
50th: 33
75th: 43
95th: 62
99th: 87
24-h avg NO2 (ppb):
Mean: 18.74
SD: 5.66
5th: 11.35
25th: 14.88
50th: 17.80
75th: 21.45
100th: 49.00
IQR: 5.57
Number of stations: 30
O3r=0.20 Increment: 10 ppb
CO
SO2 Single pollutant
COH NO2 and respiratory admissions, p = 0.772
Multipollutant model (adjusted for CO, O3, SO2,
COH, dew point):
RR 0.999 [0.9922, 1.0059] lag 0
CO Increment: 5. 57 ppb (IQR)
S02
O3 r = -0.32 All Respiratory Admissions <3 yrs:
COH NO2 alone: OR 1.05 [1.02, 1.09] lag 1
N02 + 03: OR 1.05 [1.02, 1.09] lag 1
NO2 + O3 + CO + COH + SO2: OR 1 .05
[0.99, 1.11] lag 1
All Respiratory Admissions >65 yrs:
NO2 alone: OR 1.05 [1.03, 1.07] lag 1
N02 + 03: OR 1.04 [1.02, 1.07] lag 1
N02 + 03 + CO + COH + S02: OR 1 .05
[1.01, 1.08] lag 1
-------
OQ
to
o
o
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels & Copollutants & Effects: Relative Risk or Percent
Monitoring Stations Correlations Change & Confidence Intervals (95%)
CANADA (cont'd)
Fung et al. (2006)
Vancouver, BC, Canada
Period of Study:
6/1/95-3/31/99
Outcomes (ICD 9 codes): All respiratory
hospitalizations (460-519)
Age groups analyzed: 65+
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 and R
Lag: Current day, 3 and 5 day lag
NO2 24-h avg: Mean: CO; r = 0.74
16.83 ppb,SD = 4.34; COH;r=0.72
IQR: 5.43 ppb; range: SO2;r=0.57
7.22,33.89 PM10;r=0.54
PM2.5;r=0.35
PM10.2.5;
r=0.52
O3;r=-0.32
Increment: 5.43 ppb. (IQR)
NO2 Time series
RR 1.018 [1.003, 1.034] lag 0
RR 1.024 [1.004, 1.044] lag 0-3
RR 1.025 [1.000, 1.050] lag 0-5
RR 1.027 [0.998, 1.058] lag 0-7
NO2 Case-crossover
RR 1.028 [1.010, 1.047] lag 0
RR 1.035 [1.012, 1.059] lag 0-3
RR 1.032 [1.006, 1.060] lag 0-5
RR 1.028 [0.997, 1.060] lag 0-7
NO2 DM model
RR 1.012 [0.997, 1.027] lag 0
RR 1.018 [1.000, 1.037] lag 0-3
RR 1.007 [0.988, 1.026] lag 0-5
RR 1.002 [0.981, 1.023] lag 0-7
DM method produced slightly higher RR
estimates on O3, SO2, and PM2 5 compared to
time series and case-crossover, and slightly
lower RR estimates on COH, NO2, and PM10,
though the results were not significantly
different from one another.
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
X
I
-1^
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels & Copollutants & Effects: Relative Risk or Percent Change
Monitoring Stations Correlations & Confidence Intervals (95%)
CANADA (cont'd)
Yang (2005)
Vancouver, BC, Canada
Period of Study:
1994-1998
Days: 1826
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 avgs
24-havg: 17.03 ppb, SD PM10;r=0.61
= 4.48;IQR: 5.47 ppb; SO2;r=0.61
Range: 4.28,33.89 CO; r= 0.73
03;r=-0.10
Winter: 19.20(4.86)
Spring: 15.36(3.72)
Summer: 16.33(4.57)
Fall: 17.27(3.77)
Number of Stations: 3 1
Increment: 5. 5 ppb (I QR)
COPD
>65 yrs, year round
RR 1.05 [1.01, 1.09] lag 0
RR 1.04 [1.00, 1.10] lag 0-1
RR 1.07 [1.01, 1.13] lag 0-2
RR 1.08 [1.02, 1.15] lag 0-3
RR 1.10 [1.03, 1.17] lag 0-4
RR1.11 [1.04, 1.19] lag 0-5
RR1.11 [1.04, 1.20] lag 0-6
Two-pollutant model
PM10: RR 1.03 [0.90, 1.17] lag 0
CO: RR 1.07 [0.96, 1.20] lag 0-6
03: RR 1.12 [1.04, 1.20] lag 0-6
Multipollutant models
N02, CO, S02, 03, PM10: RR 1.01 [0.88, 1.16]
N02, CO, S02, 03: RR 1.06 [0.95,1.19]
NO2 was strongest predictor of hospital admission
for COPD among all gaseous pollutants in single-
pollutant models
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Effects: Relative Risk or Percent Change
& Confidence Intervals (95%)
CANADA (cont'd)
Lin* et al. (2004)
Vancouver, BC
Canada
Period of Study:
1987-1991
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 NO2 (ppb)
Mean: 18.65
SD: 5.59
Min: 4.28
25th: 14.82
50th: 17.75
75th: 21.36
Max: 45.36
Number of stations: 30
CO r = 0.73
S02r=0.67
03r=-0.03
PM2.5r=0.37
PM10r = 0.55
Increment: 6.54 ppb (IQR)
Boys 6-12 yrs by SES status: Low; High
Lag 1 RR 1.13 [1.04, 1.23]; 1.04 [0.95, 1.14]
Lag 2 RR 1.13 [1.02, 1.24]; 1.06 [0.95, 1.18]
Lag 3 RR 1.14 [1.02, 1.27]; 1.06 [0.94, 1.19]
Lag 4 RR 1.14 [1.02, 1.28]; 1.05 [0.92, 1.19]
Lag 5 RR 1.12 [0.99, 1.27]; 1.10 [0.96, 1.26]
Lag 6 RR 1.12 [0.98, 1.28]; 1.07 [0.93, 1.23]
Lag 7 RR 1.11 [0.97, 1.28]; 1.09 [0.94, 1.27]
Girls 6-12 yrs by SES status: Low; High
Lag 1 RR 1.07 [0.96, 1.19]; 1.01 [0.90, 1.13]
Lag 2 RR 1.03 [0.91, 1.17]; 0.98 [0.85, 1.12]
Lag 3 RR 1.04 [0.91, 1.20]; 0.98 [0.84, 1.13]
Lag 4 RR 1.11 [0.95, 1.29]; 1.01 [0.86,1.19]
Lag 5 RR 1.11 [0.94, 1.30]; 0.99 [0.83, 1.17]
Lag 6 RR 1.08 [0.91, 1.28]; 1.03 [0.86, 1.24]
Lag 7 RR 1.07 [0.90, 1.28]; 1.09 [0.90, 1.32]
Multipollutant model (adjusted for SO2)
Boys, Low SES:
1.16 [1.06, 1.28] lag 1
1.18 [1.03, 1.34] lag 4
Results presented are default GAM, but authors
state that use of natural cubic splines with a more
stringent convergence rate produced similar results
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Effects: Relative Risk or Percent Change &
Confidence Intervals (95%)
CANADA (cont'd)
X
Oi
Oi
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Lin etal. (2003)
Toronto, ON
Period of Study:
1981-1993
Burnett etal. (1997b)
Toronto, Canada
Period of Study:
1992-1994
Outcomes (ICD 9 codes): Asthma (493)
Age groups analyzed: 6-12
Study Design: Bi-directional case-crossover
N: 7,319
Statistical Analyses: Conditional logistic
regression
Covariates: Daily maximum and minimum
temperatures and avg relative humidity
Lag: Cumulative lag of 1-7 days.
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
NO224-havg: 25.24
ppb, SD = 9.04; IQR: 11
ppb; Range: 3.00, 82.00
Number of Stations: 4
MeanNO2: 38.5 ppb
IQRNO2: 5.75 ppb
Range: 12,81
Number of Stations:
6-11
CO; r= 0.55
S02;r=0.54
PM10;r=0.52
O3;r = 0.03
PM25;r=0.50
PM10-2.5;
r=0.38
PM10;r=0.61
CO; r= 0.25
Lf;r=0.25
S04;r=0.34
TP;r = 0.61
FP;r = 0.45
CP;r=0.57
COH;r=0.61
03;r = 0.07
SO2; r = 0.46
Increment: 11 ppb. (IQR)
Boys 6-12 yrs; Girls 6-12 yrs
Lag 0: OR 1.04 [0.99, 1.10]; 0.99 [0.92, 1.06]
Lag 0-1: OR 1.07 [1.00, 1.14]; 1.03 [0.94, 1.12]
Lag 0-2: OR 1.09 [1.01, 1.17]; 1.07 [0.96, 1.18]
Lag 0-3: OR 1.10 [1.01, 1.20]; 1.09 [0.97, 1.21]
Lag 0-4: OR 1.10 [1.00, 1.20]; 1.14 [1.02, 1.28]
Lag 0-5: OR 1.12 [1.01, 1.23]; 1.16 [1.02, 1.31]
Lag 0-6: OR 1.11 [1.00, 1.24]; 1.16 [1.02, 1.32]
Increment: 5.75 ppb (IQR)
Respiratory - Percent increase
4.4% [CI 2.4, 6.4], lag 0
Copollutant and multipollutant models RR
(t-statistic):
NO2, COH: 1.018(1.36)
N02,Lf: 1.037(3.61)
NO2, SO4: 1.033(3.05)
NO2,PM10: 1.039(2.85)
N02,PM2.5: 1.037(3.13)
NO2, PM10.2.5: 1.037(2.96)
NO2, O3, SO2: 1.028(2.45)
N02, 03, S02, COH: 1.010(0.71)
NO2, O3, SO2, Lf: 1.027 (2.39)
N02, 03, S02, S04: 1.027(2.36)
N02, 03, S02, PM10: 1.028 (1.77)
NO2, O3, SO2, PM2.5: 1.028 (2.26)
N02, 03, S02, PM10.2.5: 1.022(1.71)
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Effects: Relative Risk or Percent Change
& Confidence Intervals (95%)
CANADA (cont'd)
COH;r = NR Increment: 25.2 ppb (Mean)
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Burnett etal. (1999)
Metro Toronto, Canada
Period of Study:
1980-1994
Burnett* etal. (2001)
Toronto, Canada
Period of Study:
1980-1994
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, daily maximum temperature, daily
minimum temperature, daily avg dew point
temperature, daily avg relative humidity
Statistical Package: S-Plus, SAS
Lag: 0,1,2 day s, cumulative
Outcomes (ICD 9 codes): Croup (464.4),
pneumonia (480-486), asthma (493), acute
bronchitis/bronchiolitis (466)
Age groups analyzed: <2 yrs
Study Design: Time series
Statistical Analyses: Poisson regression
with GAM
Covariates: temporal trend, day of wk,
temperature, relative humidity
Statistical Package: S-Plus
Lag: 0-5 days
24 h mean: 25.2 ppb,
SD9.1,CV=36;
IQR = 23
Number of stations: 4
l-hmaxNO2 (ppb)
Mean: 44.1
CV: 33
5th: 25
25th: 35
50th: 42
75th: 52
95th: 70
99th: 86
100th: 146
Number of stations: 4
PM2.5; r = 0.50
PM10.2.5;
r=0.38
PM10;r=0.52
CO; r= 0.55
SO2;r=0.54
O3;r=-0.03
O3r=0.52
S02
CO
PM2.5
PM10.2.5
7.72 excess daily admissions due to pollution of all
sorts. 40.4% increase; or 3 excess daily admissions
traced to NO2.
Single-pollutant model percent increase (t statistic)
Asthma: 3.33% (2.37) lag 0
OLD 2.21% (1.07) lag 1
Respiratory infection: 6.89% (5.53), lag 2
Multipollutant model percent increase (SE)
Respiratory infection:
NO2 alone: 4.64 (SE>3)
NO2 + SO2+O3+PM2.5: 4.04 (SE>2)
NO2 + SO2+O3+PM10.2.5: 4.56 (SE>3)
NO2 + SO2+O3+PM10: 4.16(SE>3)
NO2 + O3 + PM2.5: 4.44 (SE >2)
Increment: NR
All respiratory admissions:
Single-pollutant:
Percent increase: 20.2 (t = 3.43) lag 0-1
Multipollutant (adjusted for O3):
Percent increase: 7.1 (t = 1.09) lag 0-1
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels & Copollutants & Effects: Relative Risk or Percent Change &
Monitoring Stations Correlations Confidence Intervals (95%)
CANADA (cont'd)
X
oo
Luginaah et al. (2005)
Windsor, ON, Canada
Period of Study:
4/1/95-12/31/00
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
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
NO2 mean 1-h max:
38.9 ppb, SD= 12.3;
IQR: 16
Number of stations: 4
SO2; r = 0.22
CO; r= 0.38
PM10;r=0.33
COH; r=0.49
O3;r = 0.26
TRS; r = 0.06
Increment: 16 ppb (IQR)
Time series, females; males
All ages, lag 1 1.035 [0.971, 1.104]; 0.944
[0.886,1.006]
0-14 yrs, lag 2 1.114 [0.994, 1.248]; 0.955
[0.866,1.004]
15-65 yr, lag 3 1.121 [0.978,1.285], 1.012
[0.841,1.216]
65+yr,lagl 1.020 [0.930,1.119]; 0.9196
[0.832,1.016]
Case-crossover, females; males
All ages, lag 1 1.078 [0.995, 1.168]; 0.957
[0.883,1.036]
0-14 yrs, lag 2 1.189 [1.002, 1.411]; 0.933
[0.810,1.074]
15-65 yr, lag 3 1.114 [0.915,1.356]; 0.972
[0.744,1.268]
65+yr, lag 1 1.081 [0.964,1.212]; 0.915
[0.810,1.034]
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Effects: Relative Risk or Percent Change
& Confidence Intervals (95%)
AUSTRALIA/NEW ZEALAND
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Simpson et al. (2005a)
Multi-city study,
Australia (Sydney,
Melbourne, Brisbane,
Perth)
Period of Study:
1996-1999
Outcomes (ICD 9/ICD 10): All respiratory
(460-519/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 J18.8 J18.9 J20
J21)
Age groups analyzed: 15-64 (asthma), 65+
(all respiratory, COPD, asthma, pneumonia
with bronchitis)
Study Design: Time series
Statistical Analyses: Followed APHEA2
protocol: (1) Single city: (a) GAM with
default and more stringent criteria, (b) GLM
with default and more stringent criteria, (c)
penalized spline models. (2) Multicity meta
analysis: random effects meta-analysis
Covariates: Temperature, relative humidity,
day of wk, holiday, influenza epidemic,
brushfire/controlled bum
Statistical Package: S-Plus, R
Lag: 0,1,2 days
Maxi 1 hNO2ppb
(range)
Brisbane: 24.1
(2.1,63.3)
Sydney: 23.7
(6.5, 59.4)
Melbourne: 23.7
(4.4, 66.7)
Perth: 16.3(1.9,41.0)
Brisbane:
03;r = 0.15
BSP;r = 0.50
Melbourne:
O3;r = 0.30
BSP;r = 0.29
Sidney:
03; r = 0.24
BSP;r = 0.54
Perth:
O3;r = 0.28
BSP; r = 0.62
Increment: Maxi 1 hNO2 IQR
Meta-analysis:
Respiratory
>65 yrs 1.0027 [1.0015, 1.0039] lag 0-1
COPD and Asthma
>65 yrs 1.0020 [1.0003, 1.0037] lag 0-1
Pneumonia and Acute Bronchitis
>65 yrs 1.0030 [1.0011, 1.0048] lag 0-1
Multipollutant Model
Respiratory >65 yrs
N02 Alone: 1.0027 [1.0015,1.0039] lagO-1
NO2+BSP: 1.0023 [1.0009, 1.0038] lag 0-1
N02+03: 1.0028 [1.0016, 1.0040] lag 0-1
GAM results from S-Plus and R similar to one
another, but different than results from GLM.
GAM results from S-Plus presented.
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Effects: Relative Risk or Percent Change
& Confidence Intervals (95%)
AUSTRALIA/NEW ZEALAND (cont'd)
X
to
o
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Barnett et al. (2005)
Multicity,
Australia/New
Zealand; (Auckland,
Brisbane, Canberra,
Christchurch,
Melbourne, Perth,
Sydney)
Period of Study:
1998-2001
Outcomes (ICD 9/ICD 10): All respiratory
(460-519/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 J18.8 J18.9 J20
J21)
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 10.2
(1.7,28.9)
Brisbane 7.6 (1.4, 19.1)
Canberra 7.0 (0,22.5)
Christchurch 7.1
(0.2,24.5)
Melbourne 11.7 (2,29.5)
Perth 9.0 (2, 23.3)
Sydney 11.5(2.5,24.5)
IQR: 5.1 ppb
Daily Ih max (range):
Auckland 19.1
(4.2, 86.3)
Brisbane 17.3 (4, 44.1)
Canberra 17.9 (0, 53.7)
Christchurch 15.7
(1.2,54.6)
Melbourne 23.2
(4.4, 62.5)
Perth 21.3 (4.4, 48)
Sydney 22.6 (5.2, 51.4)
IQR: 9.0 ppb
BS;r= 0.39, 0.63
PM2.5;r= 0.34, 0.68
PM10;r= 0.21, 0.57
CO; r= 0.53, 0.73
SO2;r= 0.15, 0.58
O3;r =-0.15, 0.28
Increment: 5.1 ppb (24 h) or per 9 ppb (1-h max).
(IQR)
24-h avg NO2 (5.1 ppb change)
Pneumonia and acute bronchitis
Oyrs 3.2% [-1.8, 8.4] lag 0-1
1-4 yrs 4.8% [-1.0, 11.0] lag 0-1
5-14 yrs (sample size too small)
Respiratory
Oyrs 3.1% [-1.0, 7.3] lag 0-1
1-4 yrs 2.4% [-0.8, 5.7] lag 0-1
5-14 yrs 5.8% [1.7, 10.1] lag 0-1
Asthma
0 yrs No analysis (poor diagnosis)
1-4 yrs 2.6% [-1.3, 6.6] lag 0-1
5-14 yrs 6.0% [0.2, 12.1] lag 0-1
1 h NO2 maximum (9.0 ppb change)
Pneumonia and acute bronchitis
Oyrs2..8% [-1.8, 7.7] lag 0-1
1-4 yrs 4.1% [-2.4, 11.0] lag 0-1
5-14 yrs (sample size too small)
Respiratory
Oyrs2.2% [-1.6, 6.1] lag 0-1
1-4 yrs 2.8% [0.7, 4.9] lag 0-1
5-14 yrs 4.7% [1.6, 7.9] lag 0-1
Asthma
0 yrs No analysis (poor diagnosis)
1-4 yrs 2.5% [-0.2, 5.2] lag 0-1
5-14 yrs 2.6% [-2.2, 7.6] lag 0-1
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Effects: Relative Risk or Percent Change
& Confidence Intervals (95%)
AUSTRALIA/NEW ZEALAND (cont'd)
X
ON
to
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Erbas et al. (2005)
Melbourne, Australia
Period of Study :
2000-2001
Hinwood et al. (2006)
Perth, Australia
Period of Study:
1992-1998
Outcomes (ICD 10): Asthma (J45, J46)
Age groups analyzed: 1-15
Study Design: Time series
N: 8,955
# of Hospitals: 6
Statistical Analyses: Poisson regression,
GAM and GEE
Covariates: Day of wk
Dose-response investigated?: Yes
Statistical Package: NR
Lag: 0,1,2 days
Outcomes (ICD 9): COPD (490-496,
excluding 493); Pneumonia (480-489.99);
Asthma (493)
Age groups analyzed: <\5, 65+, all ages
Study Design: Case-crossover, time-
stratified
Statistical Analyses: Conditional logistic
regression
Covariates: Temperature, change in
temperature, maximum humidity, holiday,
day of wk
Statistical Package: NR
Lag: 0,1,2,3 days or cumulative 0-2 and 0-3
days
1 hour mean NO2: 16.80
ppb, SD = 8.61; range:
2.43,63.00
24 h Mean [Std. Dev]
(10th and 90th centile)
All year 10.3 [5.0]
(4.4, 17.1)
Summer 9.6 [4.8]
(4.3, 15.7)
Winter 11.1 [5.1]
(4.8, 18.0)
Daily 1-hmax
Mean [Std. Dev]
All year 24.8 [10.1]
(13.3,37.5)
Summer 24.9 [8.9]
(12.4, 39.2)
Winter 24.7 [11.1]
(14.4,35.7)
Number of stations: 3
PM10
03
O3,r=-0.06
CO, r= 0.57
BS,r=0.39
PM10
PM2.5
Increment: 90th-1 Oth percentile
Inner Melbourne; increment = 25.54 ppb
RR 0.83 [0.68, 0.98] lag 0
Western Melbourne; increment = 28.86 ppb
RR 1.15 [1.03, 1.27] lag 2
Eastern Melbourne; increment = 17.67 ppb
RR 1.07 [0.93, 1.22] lag 0
South/Southeastern; increment = 17.74 ppb
RR 0.98 [0.79, 1.18] lag 1
Increment: 1 ppb (all values were estimated from
the graphs)
All respiratory NO2 (24 hr)
>65 yrs OR 1.005 [1.001, 1.011] lag 1
All ages OR: 1.002 [0.998, 1.004] lag 1
Pneumonia NO2 (24 hr)
>65 yrs OR 1.006 [0.999, 1.014] lag 1
All ages OR: 1.002 [0.998, 1.010] lag 1
COPD NO2 (24 hr)
>65 yrs OR 1.004 [0.990, 1.012] lag 2
All ages OR: 1.001 [0.995, 1.010] lag 2
Asthma NO2 (24 hr)
0-14 yrs OR: 1.002 [0.998, 1.004] lag 0
>65 yrs OR 0.996 [0.988, 1.002] lag 0
All ages OR: 1.001 [0.999, 1.003] lag 0
-------
OQ
to
o
o
TABLE AX6.3-1. (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Effects: Relative Risk or Percent
Change & Confidence Intervals (95%)
AUSTRALIA/NEW ZEALAND (cont'd)
X
to
to
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Morgan etal. (1998a)
Sydney, Australia
Period of Study:
1990-1994
Petroeschevsky et al.
(2001)
Brisbane, Australia
Period of Study:
1987-1994
Days: 2922
Outcomes (ICD 9): COPD (490-492, 494,
496); Asthma (493)
Age groups analyzed: 1-14,15-64, 65+, all
ages
Study Design: Timeseries
# of hospitals: 27
Statistical Analyses: APHEA protocol,
Poisson regression, GEE
Covariates: long-term trend, temperature,
dew point, day of wk, holiday
Statistical Package: SAS
Lag: 0,1,2 days and cumulative
Outcomes (ICD 9): All respiratory (460-
519); Asthma (493)
Age groups analyzed: 0-4, 5-14, 15-64,
65+, all ages
Study Design: Timeseries
N: 33,710 (13,246 = asthma)
Statistical Analyses: APHEA protocol,
Poisson regression, GEE
Covariates: Temperature, humidity, season,
infectious disease, day of wk, holiday
Season: Summer, Autumn, Winter, Spring,
All year
Dose-response investigated?: Yes
Statistical Package: SAS
Lag: Single: 1,2,3 day
Cumulative: 0-2,0-4
24 h daily mean: 15 ppb,
SD = 6,range: 0, 52, IQR:
11,90-lOthpercentile: 17
Mean daily 1-h max: 29 ppb,
SD = 3,range: 0,139, IQR:
15, 90-1 Oth percentile: 29
# of stations: 3-14, r = 0.52
Mean (range) 24-h avg:
Overall: 139(12,497)
Summer: 97(20,331)
Autumn: 129(33,319)
Winter: 179(12,454)
Spring: 153(35,497)
Mean (range) 1-h max
Overall: 282 (35, 1558)
Summer: 206(35,580)
Autumn: 256 (70, 585)
Winter: 354(35,805)
Spring: 321 (35, 1558)
# of stations: 3,
r= 0.43, 0.53
PM(24 h), r = 0.53
PM(lh),r=0.51
O3,r=-0.9
l-hmaxNO2:
PM(24 h),r = 0.45
PM (1 h),r = 0.44
03,r=0.13
Bsp
03
SO,
Increment: 90-1 Oth percentile
24-h avg (17 ppb)
Asthma: 1-14 yrs 3.28% [-1.72, 8.54] lag 0
15-64 yrs 2.29% [-2.97, 7.83] lag 0
COPD: >65 yrs 4.30% [-0.75, 9.61] lag 1
Daily 1-h maximum (29 ppb)
Asthma: 1-14 yrs 5.29% [1.07, 9.68] lag 0
15-64 yrs. 3.18% [-1.53, 8.11] lag 0
COPD: 65+ yrs. 4.60% [-0.17, 9.61] lag 1
Multipollutant model (29 ppb)
Asthma: 1-14 yrs. 5.95% [1.11, 11.02] lag 0
COPD: 65+ yrs. 3.70% [-1.03, 8.66] lag 1
Increment: 10 ppb
Respiratory (1-h max):
0-4 yrs 1.015 [0.996, 1.035] lag 3
5-14 yrs 0.985 [0.950,1.021] lag 0
All ages 0.989 [0.977, 1.002] lag 1
Respiratory (24-h avg):
15-64 yrs 1.027 [0.984, 1.071] lag 0
>65 yrs 0.903 [0.851, 0.959] lag 5
Asthma (1-h max):
0-4 yrs 0.975 [0.947, 1.004] lagO
5-64 yrs 0.983 [0.949,1.018] lag 1
All ages 0.962 [0.936, 0.989] lag 0-2
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
X
ON
to
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Effects: Relative Risk or Percent 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
Atkinson et al. (2001)
Multicity, Europe
(Barcelona,
Birmingham, London,
Milan, Netherlands,
Paris, Rome,
Stockholm)
Period of study:
1998-1997
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
Outcomes (ICD 9): Asthma (493), COPD
(490-496), All respiratory (460-519)
Study Design: Timeseries
Statistical Analyses: APHEA protocol,
Poisson regression, meta-analysis
Covariates: season, temperature, humidity,
holiday, influenza
Statistical Package: NR
Lag: NR
24 h all year avg:
(ug/m3)
Amsterdam: 50
Barcelona: 53
London: 67
Paris: 42
Rotterdam: 52
1 -h max
Amsterdam: 75
Barcelona: 93
London' 67
Paris: 64
Rotterdam: 78
1-h max of NO2 (ug/m )
Barcelona: 94.4
Birmingham: 75.8
London: 95.9
Milan: 147.0
Netherlands: 50.1
Paris: 87.2
Rome: 139.7
Stockholm: 35.6
SO2
BS
TSP
03
SO2, O3, CO, BS
PM10; r =
Barcelona: 0.48
B'gham: 0.68
London: 0.70
Milan: 0.72
Netherlands: 0.64
Paris: 0.44
Rome: 0.32
Stockholm: 0.30
Increment: 50 ug/m3
Meta-analytic results - Weighted mean values from
6 cities
COPD- Warm season
24 hi. 03 [1.00, 1.06] lag 1
Ih 1.02 [1.00, 1.05] lag 1
COPD-Cool season
24 hi. 01 [0.99,1.03]
Ih 1.02 [0.99, 1.05]
COPD-A11 Year
24 hr 1.019 [1.002, 1.047] lag 1
24 hr 1.026 [1.004, 1.036] lag 0-3, cumulative
1 hr 1.013 [1.003, 1.022] lag 1
1 hr 1.014 [0.976, 1.054] lag 0-3, cumulative
Increment: 10 ug/m3 for PM10; change in NO2 not
described.
Asthma, 0 to 14 yrs:
ForPM10: 1.2% [0.2, 2.3]
ForPM10 + NO2: 0.1 [-0.8, 1.0]
Asthma, 1 5 to 64 yrs:
ForPM10: 1.1% [0.3, 1.8]
ForPM10 + N02: 0.4 [-0.5, 1.3]
COPD + Asthma, > 65 yrs
ForPM10: 1.0% [0.4, 1.5]
ForPM10 + N02: 0.8 [-0.6, 2.1]
All Respiratory, >65 yrs of age
ForPM10: 0.9% [0.6, 1.3]
ForPM10 + N02: 0.7 [-0.3, 1.7]
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Effects: Relative Risk or Percent Change &
Confidence Intervals (95%)
EUROPE (cont'd)
Increment: 50 ug/m of 24-h avg for all cities
combined.
Asthma
15-64 yrs
1.029 [1.003, 1.055] lag 0-1
1.038 [1.008-1.068] lag 0-3, cumulative
<15 yrs
1.026 [1.006, 1.049] lag 2
1.037 [1.004, 1.067] lag 0-3, cumulative
1.080 [1.025, 1.140] -Winteronly
Two-pollutant models:
NO2/Black smoke
15-64 yrs
1.055 [1.005, 1.109] lag 0-1
15-64 yrs 1.088 [1.025, 1.155] cumulative 0-3
<15 yrs
1.036 [0.956, 1.122]
NO2/SO2
<15 yrs
1.034 [0.988, 1.082]
X
ON
to
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Sunyeretal. (1997)
Multicity, Europe
(Barcelona, Helsinki,
Paris, London)
Period of Study: 1986-
1992
Outcomes (ICD 9): Asthma (493)
Age groups analyzed: <15, 15-64
Study Design: Timeseries
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: 53 (5, 142)
Helsinki: 35(9,78)
London: 69(27,347)
Paris: 42 (12, 157)
# of stations:
Barcelona: 3
London: 2
Paris: 4
Helsinki: 8
SO2
black smoke
03
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Effects: Relative Risk or Percent Change
& Confidence Intervals (95%)
EUROPE (cont'd)
X
ON
to
Schouten et al. (1996)
Multicity, The
Netherlands
(Amsterdam,
Rotterdam)
Period of Study:
04/01/77-09/30/89
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
Season: 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 NO2
Amsterdam
Mean/Med: 50/50 ug/m3
Rotterdam
Mean: 54/52 ug/m3
Daily max 1 h
Amsterdam
Mean/Med: 75/75 ug/m3
Rotterdam
Mean/Med: 82/78 ug/m3
# of stations: 1 per city
S02
BS
03
Increment: 100 ug/m3 increment.
All respiratory, Amsterdam 24 h mean; 1-h max
15-64 yrsRR 0.890 [0.783,1.012]; 0.894 [0.821,
0.973] lag 1
>65 yrsRR 1.023 [0.907, 1.154]; 0.996 [0.918,
1.080] lag 2
All respiratory, Rotterdam 24 h mean; 1-h max
(1985-89)
15-64 yrs RR 0.965 [0.833,1.118]; 1.036 [0.951,
1.129] lagl
>65 yrs RR 1.172 [0.990, 1.387]; 1.073 [0.970,
1.186] lag 0
COPD, Amsterdam, 24 h mean,
All ages RR 0.937 [0.818, 1.079] lag 1
Asthma Amsterdam, 24 h mean ,
All ages RR 1.062 [0.887, 1.271] lag 2
COPD, Rotterdam 24 h mean
All ages RR 1.051 [0.903, 1.223] lag 2
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
-------
OQ
to
o
o
X
ON
to
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants & Effects: Relative Risk or Percent Change
Correlations & Confidence Intervals (95%)
EUROPE (cont'd)
Ponce de Leon et al.
(1996)
London, England
Period of Study:
04/1987-1988;
1991-02/1992
Outcomes (ICD 9): All respiratory (460-
519)
Age groups analyzed: 0-14, 15-64, 65+, all
ages
Study Design: Timeseries
N: 19,901
Statistical Analyses: APHEA protocol,
Poisson regression GAM
Covariates: Long-term trend, season,
influenza, day of wk, holiday, temperature,
humidity
Season: Cool, Oct-Mar; Warm:
Apr-Sep
Dose-Response Investigated?: Yes
Statistical Package: SAS
Lag: 0,1,2 days, 0-3 cumulative avg.
NO224-havg: 37.3 ppb,
Med: 35
SD=13.8
IQR: 14 ppb
1-hmax: 57.4 ppb,
Med: 51
SD = 26.4
IQR: 21 ppb
# of stations: 2
SO2 r = 0.45 Increment: 90th-10th percentile (24-h avg:
BS r = 0.44 27 ppb)
03
All year
All ages 1.0114 [1.006, 1.0222] lag 2
0-14 yrs 1.0104 [0.9943, 1.0267] Iag2
15-64 yr 1.0113 [0.9920, 1.0309] lag 1
>65 yr 1.0216 [1.0049, 1.0386] lag 2
Warm season
All ages 1.0276 [1.0042, 1.0515] lag 2
0-14 yrs 1.038 [1.0009, 1.0765] lag 2
15-64 yr 1.0040 [0.9651, 1.0445] lag 1
>65 yr 1.0326 [0.9965, 1.0699] lag 2
Cool season
All ages 1.0060 [0.9943, 10177] Iag2
0-14 yrs 1.0027 [0.9855, 1.0202] Iag2
15-64 yr 1.0136 [0.9920, 1.0357] lag 1
>65 yr 1.0174 [0.9994, 1.0358] lag 2
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels & Monitoring
Stations
Copollutants &
Correlations
Effects: Relative Risk or Percent
Change & Confidence Intervals (95%)
EUROPE (cont'd)
X
ON
to
Atkinson etal. (1999a)
London, England
Period of Study:
1992 to 1994
Days: 1096
Outcomes (ICD 9): All respiratory (460-
519), Asthma (493), Asthma + COPD (490-
6), Lower respiratory disease (466, 480-6)
Age groups analyzed: 0-14,15-64, 65+, all
ages
Study Design: Time series
N: 165,032
Statistical Analyses: APHEA protocol,
Poisson regression
Covariates: Long-term trend, season,
influenza, day of wk, holiday, temperature,
humidity
Season: Cool, Oct-Mar; Warm:
Apr-Sep
Dose-Response Investigated?: Yes
Statistical Package: SAS
Lag: 0,1,2 days, 0-1,0-2, 0-3 cum. avg.
NO2 1 h mean: 50.3 ppb, SD
17.0, Range: 22.0, 224.3 ppb,
10th centile: 34.3, 90th centile:
70.3
# of stations: 3,
r= 0.7, 0.96
03,
CO,
PMio
BS,
S02
Increment: 36 ppb (90th-10th centile)
All ages
Respiratory 1.64% [0.14, 3.15] lag 1
Asthma 1.80% [-0.77, 4.44 lag 0
0-14yrs
Respiratory 1.94% [-0.39, 4.32] lag 2
Asthma . 1% [-1.42, 5.77] lag 3
15-64yrs
Respiratory 1.61% [-0.82, 4.09] lag 1
Asthma 5.08% [0.81, 9.53] lag 1
65+ yrs
Respiratory 2.53% [0.58, 4.52] lag 3
Asthma 4.53% [-2.36, 11.91] lag 3
COPD3.53% [0.64, 6.50] lag 3
Lower Resp. 3.47% [0.08, 6.97] lag 3
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Effects: Relative Risk or Percent Change
& Confidence Intervals (95%)
X
to
oo
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
EUROPE (cont'd)
Wong* et al. (2002)
London England and
Hong Kong
Period of Study:
London: 1992-1994
Hong Kong:
1995-1997
Days: 1,096
Outcomes (ICD 9): All respiratory
admissions (460-519); asthma (493)
Age groups analyzed: 15-64, 65+, all ages
Study Design: Timeseries
Statistical Analyses: APHEA protocol,
Poisson regression with GAM
Covariates: Long-term trend, season,
influenza, day of wk, holiday, temperature,
humidity, thunderstorms
Season: Cool, Oct-Mar; Warm: Apr-Sep
Dose-Response Investigated?: Yes
Statistical Package: S-Plus
Lag: 0,1,2,3,4 days, 0-1 cum. avg.
24 h NO2 ug/m3
Hong Kong
Mean: 55.9
Warm: 48.1
Cool 63. 8
SD 19.4
Range: 15.3,151.5
10th: 31.8
50th: 53.5
90th: 81.8
London
Mean: 64.3
Hong Kong
PM10r=0.82
S02r=0.37
O3r=0.43
London
PM10 r = 0.68
S02r=0.71
03r=-0.29
Increment: 10 ug/m
Asthma, 15-64 years
Hong Kong
ER -0.6 [-2. 1,1.0] lag 0-1
ER -1.3 [-2.6, 0.1] lag 1
Warm: ER -0.5 [-2.7, 1.6] lag 0-1
Cool: ER -0.6 [-2.8, 1.6] lag 0-1
London
ER 1.0 [0.0,2.1] lag 0-1
ER 1.1 [0.2,2.0] lag 2
Warm: ER 0.6 [-0.8, 2.0] lag 0-1
Cool- ER 1 3 F-0 1 7.81 lap 0-1
Warm: 62.6
Cool 66.1
SD 20.4
Range: 23.7,255.8
10th: 42.3
50th: 61.2
90th: 88.8
# of stations:
Hong Kong: 7,
r = 0.65, 0.90
London: 3,
r = 0.80
Respiratory 65+ years
Hong Kong
ER 1.8 [1.2, 2.4] lag 0-1
ER 1.3 [0.8,1.8] lag 0
Warm: ER 0.8 [0.1, 1.6] lag 0-1
Cool: ER 3.0 [2.1,3.9] lag 0-1
+03: ER 1.6 [1.0, 2.3] lag 0-1
+PM10: ER 1.7 [0.8, 2.7] lag 0-1
+SO2: ER 1.6 [0.8, 2.4] lag 0-1
London
ER-0.1 [-0.6, 0.5] lag 0-1
ER 0.9 [0.5, 1.3] lag 3
Warm: ER0.6 [-0.2, 1.4] lag 0-1
Cool: ER -0.7 [-1.4, 0.0] lag 0-1
+O3: ER -0.1 [-0.5, 0.6] lag 0-1
+PM10: ER-0.4 [-1.2, 0.4] lag 0-1
+S02: ER -0.2 [-0.9, 0.5] lag 0-1
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Effects: Relative Risk or Percent Change &
Confidence Intervals (95%)
EUROPE (cont'd)
X
to
VO
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Anderson etal. (1998)
London, England
Period of Study: April
1987-February 1992
Days: 1,782
Outcomes (ICD 9): Asthma (493)
Age groups analyzed: <15, 15-64, 65+
Study Design: Ti
N: 16
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)
Dose-Response Investigated?: Yes
Statistical Package: S
Lag: 0,1,2 days
24-h avg NO2 (ppb)
Mean: 37.2
SD: 12.3
Range: 14, 182
5th: 22
10th: 25
25th: 30
50th: 36
75th: 42
90th: 50
95th: 58
1-h max NO2 (ppb)
Mean: 57.2
SD: 23.0
Range: 21,370
5th: 35
10th: 38
25th: 44
50th: 52
75th: 64
90th: 81
95th: 98
Number of stations: 2
03
SO2
BS
Increment: 10 ppb in 24 h NO2
0-14yrs
Whole year RR 1.25 [0.3,2.2] lag 2; RR 1.77 [0.39,
3.18] lag 0-3
+ 03 RR 1.13 [-0.10, 2.36] lag 2
+ SO2 RR 0.97 [-0.05, 1.99] lag 2
+ BS RR 2.26 [0.83, 3.71] lag 2
Warm season RR 1.42 [-0.3, 3.17] lag 2; RR 3.01
[3.8, 5.72] lag 0-3
Cool season RR 1.18 [0.02, 2.35] lag 2; RR 1.22
[-0.48, 2.96] lag 0-3
15-64yrs
Whole year RR 0.95 [-0.26, 2.17] lag 0; RR 0.99
[-0.36, 3.36] lag 0-1
Warm RR 0.46 [-1.70, 2.67] lag 0; RR 0.05
[-2.45, 2.61] lag 0-1
Cool season RR 1.21 [-0.22,2.5] lag 0; RR 1.43
[-0.18, 3.06] lag 0-1
65+ yrs
Whole year RR 2.96 [0.67, 5.31] lag 2; RR 3.14
[-0.04, 6.42] lag 0-3
+ 03 RR 4.51 [1.43, 7.69] lag 2
+ S02 RR 2.49 [-0.25, 5.31] lag 2
+ BS RR 1.88 [-1.49, 5.36] lag 2
Warm RR 1.89 [-2.41, 6.38] lag 2;
RR-1.76 [-7.27, 4.07] lag 0-3
Cool season RR 3.52 [0.81, 6.30] lag 2; RR 5.57
[1.85, 9.43] lag 0-3
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
Reference, Study
Location, & Period
Outcomes, Design, &
Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Effects: Relative Risk or Percent Change &
Confidence Intervals (95%)
EUROPE (cont'd)
X
ON
OJ
O
Anderson etal. (1998)
(cont'd)
+ O3 RR 5.14 [0.69, 9.79] lag 2
+ SO2 RR2.10 [-1.08, 5.39] lag 2
+ BS RR 4.47 [-0.04, 9.19] lag 2
All ages
Whole year RR 1.25 [0.49, 2.02] lag 2; RR2.05 [0.96,
3.15] lag 0-3
+ O3 RR 1.08 [0.12, 2.05] lag 2
+ S02 RR 0.99 [0.18, 1.81] lag 2
+ BS RR 1.23 [0.47, 2.00] lag 2
Warm RR 1.15 [-0.25, 2.57] lag 2; RR 1.54
[-0.54, 3.67] lag 0-3
Cool season RR 1.30 [0.38, 2.23] lag 2; RR 2.26 [0.94,
3.59] lag 0-3
+ 03 RR 0.50 [-0.79, 1.81] lag 2
+ S02 RR 1.10 [0.12, 2.08] lag 2
+ BS RR 1.29 [0.37, 2.22] lag 2
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Prescottetal. (1998)
Edinburgh, United
Kingdom
Period of Study: 10/92-
6/95
Outcomes (ICD 9): Pneumonia
(480-7), COPD + Asthma (490-
496)
Age groups analyzed: <65, 65+
Study Design: Time series
Statistical Analyses: Poisson log
linear regression
Covariates: Trend, seasonal and
wkly variation, temperature, wind
speed, day of wk
Lag: 0,1 or 3 day rolling avg
N02: 26.4 ± 7.0 ppb
Min: 9 ppb
Max: 58 ppb
IQR: 10 ppb
# of Stations: 1
CO
PM10
SO2
03
BS
Increment: 10 ppb
Respiratory admissions
>65 yrs
3.1 [-4.6, 11.5] rolling 3 day avg
<65 yrs
-0.2% [-7.5, 7.7] rolling 3 day avg
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Effects: Relative Risk or Percent
Change & Confidence Intervals (95%)
EUROPE (cont'd)
X
Thompson etal. (2001)
Belfast, Northern Ireland
Period of Study:
1993-1995
Outcomes: Asthma
ICD9: NR
Age groups analyzed: 0-14
Study Design: Time series
N: 1,095
Number of hospitals: 1
Statistical Analyses: Poisson regression
Covariates: Season, long-term trend,
temperature, day of wk, holidays
Season: Warm (May-Oct), Cold
(Nov-Apr)
Statistical Package: Stata
Lag: 0,1,2,3 days
24 h mean:
Warm: 19.2 (7.9) ppb;
range: 13-23
Cold: 23.3 (9.0) ppb;
range: 18-28
SO2r=0.82
PM10 r = 0.77
CO r = 0.69
03r=-0.62
N0xr=0.93
log (NO) r = 0.84
log (CO) r = 0.69
Increment: 10 ppb
All seasons
RR 1.08 [1.03, 1.13] lag 0
RR1.11 [1.05, 1.17] lag 0-1
RR 1.10 [1.04, 1.17] lag 0-2
RR 1.12 [1.03, 1.02] lag 0-3
Warm season
RR 1.14 [1.04, 1.26] lag 0-1
Cold season
RR 1.10 [1.03, 1.17] lag 0-1
NO2 + Benzene
RR 0.99 [0.87, 1.13] lag 0-1
*Model made no allowance for possible
autocorrelation in the data or for extra-Poisson
variation
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Effects: Relative Risk or Percent Change
& Confidence Intervals (95%)
EUROPE (cont'd)
X
ON
OJ
to
Hagen et al. (2000)
Drammen, Norway
Period of Study:
1994-1997
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 1 .B
report)
Covariates: Time trends, day of wk,
holiday, influenza, temperature, humidity
Lag: 0,1,2,3 days
NO2 24-h avg (ug/m3):
36.15, SD= 16
IQR: 16.92 ug/m3
# of Stations: 2
PM10r = 0.61 Increment: NO2: 16.92 ug/m3 (IQR); NO:
S02r=0.58 29ug/m3(IQR)
benzene r= 0.31
NO r = 0.70 Single-pollutant model
O3 r = -0.47 Respiratory disease only
Formaldehyde NO2: RR 1.058 [0.994, 1.127]
r=0.68 NO: 1.048 [1.013, 1.084]
Toluene r = 0.65 All disease
NO2: RR 1.011 [0.988,1.035]
Two-pollutant model with PM10
NO2 1.044 [0.966, 1.127]
NO: 1.045 [1.007,1.084]
Three-pollutant model with PM10 + Benzene
N02 1.015 [0.939, 1.097]
NO: 1.031 [0.986,1.077]
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels & Copollutants & Effects: Relative Risk or Percent Change
Monitoring Stations Correlations & Confidence Intervals (95%)
EUROPE (cont'd)
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Oftedal et al. (2003)
Drammen, Norway
Period of Study:
1994-2000
Ponka and Virtanen
(1994)
Helsinki, Finland
Period of Study:
1987-1989
Days: 1096
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
Outcomes (ICD 9): Chronic bronchitis and
emphysema (493)
Age groups analyzed: <65, >65
Study Design: Time series
Statistical Analyses: Poisson regression
Covariates: Season, day of wk, year,
influenza, humidity, temperature
Season: Summer (Jun-Aug), Autumn (Sep-
Nov), Winter (Dec-Feb), Spring (Mar-May)
Lag: 0-7 days
Mean: 33.8 ug/m ,
SD = 16.2
IQR: 20.8 ug/m3
24 h mean: 39 ug/m3,
SD = 16.2;
range: 4, 170
# of stations: 2
PM10
S02
03
Benzene
Formaldehyde
Toluene
SO2
03
TSP
Increment: 20.8 ug/m3 (IQR)
All respiratory disease
Single-pollutant model
RR 1.060 [1.017,1.105] lag 3
Two-pollutant model
Adjusted for PM10 RR 1.063 [1.008, 1.120]
Adjusted for benzene RR 1.046 [1.002, 1.091]
Increment: NR
Chronic bronchitis and emphysema
>65 yrs
RR 0.87 [0.71, 1.07] lag 0
RR 1.07 [0.86, 1.33] lag 1
RR 1.16 [0.93, 1.46] lag 2
RR 1.08 [0.86, 1.35] lag 3
RR 0.94 [0.76, 1.18] lag 4
RR 0.90 [0.72, 1.12] lag 5
RR1.31 [1.03, 1.66] lag 6
RR 0.82 [0.67, 1.01] lag 7
<65 yrs
NR
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Effects: Relative Risk or Percent Change &
Confidence Intervals (95%)
EUROPE (cont'd)
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Dab+etal. (1996)
Paris, France
Period of Study :
1/1/87-9/30/92
Llorca et al. (2005)
Torrelavega, Spain
Period of Study:
1992-1995
Days: 1,461
Outcomes (ICD 9): All respiratory (460-
519), Asthma (493), COPD (490-496)
Age groups analyzed: All ages
Study Design: Time series
Number of hospitals: 27
Statistical Analyses: Poisson regression,
followed APHEA protocol
Covariates: Temperature, relative
humidity, influenza, long-term trend,
season, holiday, medical worker strike
Lag: 0,1,2 days, 0-3 cumulative
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
Covariates: Short and long-term trends
Statistical Package: Stata
Lag: NR
NO2 24-h avg: 45 ug/m3
5th: 22,99th: 108.3
Daily maximum 1 h
concentration: 73.8 ug/m3
5th: 37.5,99th: 202.7
SO2
03
PM13
BS
24-h avg NO2: SO2r= 0.588
21.3 ug/m3, SD= 16.5 NO r= 0.855
TSPr=-0.12
24-h avg NO: 12.2 ug/m3, SH2r= 0.545
SD= 15.2
# of Stations: 3
Increment: 100 ug/m
All respiratory (1987-1990)
24-h avg NO2 RR 1.043 [0.997,1.090] lag 0
l-hmaxNO2 RR 1.015 [0.993, 1.037] lag 0
Asthma (1987-1992)
24-h avg RR 1.175 [1.059, 1.304] lag 0-1
1-hmax RR 1.081 [1.019, 1.148] lag 0-1
COPD
24-h avg RR 0.974 [0.898, 1.058] lag 2
1-hmax RR0.961 [0.919,1.014] lag 2
Increment: 100 ug/m3
Single-pollutant model
All cardio-respiratory admissions
NO2: RR 1.37 [1.26, 1.49]
NO: RR 1.33 [1.22, 1.46]
Respiratory admissions
NO2: RR 1.54 [1.34, 1.76]
NO: RR 1.35 [1.17, 1.56]
5-pollutant model
All cardio-respiratory admissions
NO2: RR 1.20 [1.05, 1.39]
NO: RR0.93 [0.79,1.09]
Respiratory admissions
NO2: RR 1.69 [1.34, 2.13]
NO: RR 0.87 [0.67, 1.13]
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Effects: Relative Risk or Percent Change
& Confidence Intervals (95%)
EUROPE (cont'd)
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Migliaretti and Cavallo
(2004)
Turin, Italy
Period of Study:
1997-1999
Fusco*etal. (2001)
Rome, Italy
Period of Study :
1/1/95-10/31/97
Outcome(s) (ICD 9): Asthma (493)
Age groups analyzed: <4,4-\5
Study Design: Case-Control
Controls: age matched with other
respiratory disease (ICD9: 460-7, 490-2,
494-6,500-19)
N: cases = 734, controls = 25,523
Statistical Analyses: logistic regression
Covariates: seasonality, temperature,
humidity, solar radiation
Seasons: Cold: Oct-Mar; Warm: Apr-Sep
Statistical Package: SPSS
Lag: 0-3 days and cumulative
Outcomes (ICD 9): All respiratory (460-
519 excluding 470-478), Asthma (493),
COPD (490-492, 494-496), Respiratory
infections (460-466, 480-486)
Age groups analyzed: 0-14, all ages
Study Design: Time series
Statistical Analyses: Semi-parametric
Poisson regression with GAM
Covariates: Influenza, day, temperature,
humidity, day of wk, holiday
Season: Warm (Apr-Sep), Cold (Oct - Mar)
Statistical Package: S-Plus 4
Lag: 0-4 days
TSP
Controls:
Mean: 113.3 ug/m3,
SD = 30.5
Cases:
Mean: 117.4 ug/m3,
SD = 29.7
NO2 24-h avg (ug/m3):
86.7,
SD = 16.2
IQR: 22.3 ug/m3
# of stations: 5;
r= 0.66-0.79
PM10:
All year r= 0.35
Cold r = 0.50
Warm r= 0.25
S02:
All year r= 0.33
Cold r = 0.40
Warm r= 0.68
CO:
All year r= 0.31
Cold r = 0.41
Warm r= 0.59
03:
All year r= 0.19
Cold r = 0.19
Warmr = 0.13
Increment: 10 ug/m
<4 yrs 2.8% [0.03, 5.03] lag 1-3 cumulative
4-15 yrs 2.7% [-0.01, 6.06] lag 1-3 cumulative
All ages 2.8% [0.07, 4.09] lag 1-3 cumulative
Two-pollutant model adjusted for TSP
N02 2.1% [-0.1, 5.6]
Increment: 22.3 ug/m (IQR)
All respiratory
All ages 2.5% [0.9, 4.2] lag 0
0-14 yrs 4.0% [0.6, 7.5] lag 0
Respiratory infections
All ages 4.0% [1.6, 6.5] lag 0
0-14 yrs 4.0% [0.2, 8.0] lag 0
Asthma
All ages 4.6% [-0.5, 10.0] lag 0
0-14 yrs 10.7% [3.0, 19.0] lag 1
COPD
>65 yrs 2.2% [-0.7, 5.2] lag 0
Multipollutant models
All respiratory (NO2 + CO)
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels & Copollutants & Effects: Relative Risk or Percent Change &
Monitoring Stations Correlations Confidence Intervals (95%)
EUROPE (cont'd)
X
Oi
OJ
Oi
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Fusco*etal. (2001)
(cont'd)
Pantazopoulou et al.
(1995)
Athens, Greece
Outcomes: All respiratory admissions
ICD9: NR
Age groups analyzed: All ages
Study Design: Time series
Period of Study : 1988 N: 15,236
Number of hospitals: 14
Statistical Analyses: Multiple linear
regression
Covariates: Season, day of wk, holiday,
temperature, relative humidity
Season: Warm (3/22-9/21), Cold (1/1-3/21
and 9/22-12/31)
Lag: NR
NO2 24-h avg
Winter: 94 |ig/m3,
SD = 25
5th: 59,50th: 93,95th:
135
Summer: 111 |ig/m3,
SD = 32
5th: 65,50th: 108,
95th: 173
# of stations: 2
CO
BS
All ages: 0.9% [-0.8,2.8] lag 0
0-14 yrs: 3.3% [-0.2, 6.9] lag 0
Acute infections (NO2 + CO)
All ages: 3.9% [1.3, 6.7] lag 0
0-14 yrs: 2.9% [-1.0, 7.0] lag 0
Asthma (NO2 + CO)
All ages: 1.4% [-3.9, 7.1] lag 0
0-14 yrs: 8.3% [-0.1, 17.4] lag 1
COPD (N02 + CO)
>65yrs: -1.0%[-4.1,2.2] lag 0
Increment: 76 ng/m3 in winter and 108 ng/m3 in
summer (95th-5th)
Respiratory disease admissions
Winter: Percent increase: 24% [6.4, 43.5]
Summer: Percent increase: 9.3% [-14.1, 24.4]
-------
OQ
to
o
o
TABLE AX6.3.1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Effects: Relative Risk or Percent Change &
Confidence Intervals (95%)
LATIN AMERICA
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Gouveia and Fletcher,
(2000a)
Sao Paulo, Brazil
Period of Study:
11/92-9/94
Braga*etal. (2001)
Sao Paulo, Brazil
Period of Study:
1/93-11/97
Outcomes (ICD 9): All respiratory;
Pneumonia (480-486); asthma or bronchitis
(466,490,491,493)
Age groups analyzed:
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels & Copollutants & Effects: Relative Risk or Percent Change &
Monitoring Stations Correlations Confidence Intervals (95%)
LATIN AMERICA (cont'd)
X
oo
Farhat* et al. (2005)
Sao Paulo, Brazil
Period of Study: 8/96-
8/97
Days: 396
Outcomes (ICD 9):
Pneumonia/bronchiopheumonia (480-6),
asthma (493), bronchiolitis (466),
Obstructive disease 493, 466)
Age groups analyzed: <\3
Study Design: Time series
N: 1,021
Number of hospitals: 1
Statistical Analyses: Poisson regression
with GAM
Covariates: Time, temperature, humidity,
day of wk, season
Statistical package: S-Plus
Lag: 0-7 days, 2,3,4 day moving avg
Mean: 125.3 ug/m
SD = 51.7
IQR: 65.04 ug/m3
Range: 42.5,369.5
PM10r = 0.83
SO2r=0.66
CO r = 0.59
O3 r = 0.47
Increment: 65.04 ug/mj (IQR)
Single pollutant models (estimated from graphs)
Asthma: = 32% [8,56] lag 0-2
Pneumonia: = 17.5% [2.5, 32.5] lag 0-3
Asthma or Bronchiolitis
N02 + PM10 47.7% [1.15, 94.2] lag 0-2
NO2 + SO2 33.1% [5.7, 60.5] lag 0-2
N02 + CO 28.8% [-0.2, 57.9] lag 0-2
N02 + 03 28.0% [-1.0, 57.0] lag 0-2
Multipollutant model (PM10, SO2, CO, O3)
39.3% [-14.9, 93.5] 2 day avg.
Pneumonia or bronchopneumonia
N02 + PM10 8.11% [-11.4, 27.6] lag 0-2
N02 + S02 13.1% [-3.4, 29.7] lag 0-2
NO2 + CO 12.4% [-5.6, 30.4] lag 0-2
NO2 + O3 14.6% [-4.9, 34.1] lag 0-2
Multipollutant model (PM10, SO2, CO, O3)
1.8% [-23.9,27.6] lag 0-2
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
-------
OQ
to
o
o
X
ON
OJ
vo
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels & Copollutants & Effects: Relative Risk or Percent Change
Monitoring Stations Correlations & Confidence Intervals (95%)
ASIA
Lee et al. (2006)
Hong Kong, China
Period of Study: 1997-
2002
Days: 2,191
Outcomes (ICD 9): Asthma (493)
Age groups analyzed:
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Effects: Relative Risk or Percent Change
& Confidence Intervals (95%)
ASIA (cont'd)
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Tsai et al. (2006)
Kaohsiung, Taiwan
Period of Study:
1996-2003
Days: 2922
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
NO224hmean: 27.20 PM10
ppb SO2
IQR: 17 ppb 03
Range: 4.83,63.40 CO
# of stations: 6
Lee* et al. (2002) Outcomes (ICD 10): Asthma (J45 - J46) 24hNO2(ppb)
Seoul, Korea Age groups analyzed: <15 Mean: 31.5
Study Design: Time series SD: 10.3
Period of Study: N: 6,436 5th: 16.0
12/1/97-12/31/99 Statistical Analyses: Poisson regression, log 25th: 23.7
link with GAM 50th: 30.7
Days: 822 Covariates: Time, day of wk, temperature, 75th: 38.3
humidity 95th: 48.6
Season: Spring (Mar-May), Summer (Jun-
Aug), Fall (Sep-Nov), Winter (Dec-Feb) # of stations: 27
Statistical package: NR
Lag: 0-2 days cumulative
SO2 r = 0.72
O3r=-0.07
CO r = 0.79
PM10 r = 0.74
Increment: 17 ppb (IQR)
Seasonality
Single-pollutant model
>25°C 1.259 [1.111,1.427] lag 0-2
<25°C 2.119 [1.875,2.394] lag 0-2
Dual-pollutant model
Adjusted for PM10
>25°C 1.082 [0.913,1.283] lag 0-2
<25°C 2.105 [1.791,2.474] lag 0-2
Adjusted for CO
>25°C 0.949 [0.792,1.137] lag 0-2
<25°C 2.30 [1.915, 2.762] lag 0-2
Adjusted for SO2
>25°C 1.294 [1.128,1.485] lag 0-2
<25°C 2.627 [2.256, 3.058] lag 0-2
Adjusted for O3
>25°C 1.081 [0.945,1.238] lag 0-2
<25°C 2.096 [1.851,2.373] lag 0-2
Increment: 14.6 ppb (IQR)
Asthma
NO2RR 1.15 [1.10, 1.20] lag 0-2
NO2 + PM10RR1.13 [1.07, 1.19] lag 0-2
N02 + S02 RR 1.20 [1.11,1.29] lag 0-2
NO2 + O3 RR 1.14 [1.09, 1.20] lag 0-2
N02 + CO RR 1.12 [1.03, 1.22] lag 0-2
NO2 + O3 + CO + PM10 + SO2 RR 1.098 [1.002,
1.202]
-------
OQ
to
o
o
TABLE AX6.3-1 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
HOSPITAL ADMISSIONS
X
-k
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants &
Correlations
Effects: Relative Risk or Percent Change &
Confidence Intervals (95%)
ASIA (cont'd)
Wong etal. (1999)
Hong Kong, China
Outcomes (ICD 9): All respiratory
admissions (460-6, 471-8, 480-7, 490-6);
Asthma (493), COPD (490-496), Pneumonia
Median 24 hNO2:
5 1.39 ug/m3
03
SO2
PM10 r = 0.79
Increment =10 ug/m
Overall increase in admissions:
Period of Study: (480-7)
1994-1995 Age groups analyzed: 0-4, 5-64, >65, all
ages
# of hospitals: 12
Study Design: Time series
Statistical Analyses: Poisson regression
(followed APHEA protocol)
Covariates: Trend, season, day of wk,
holiday, temperature, humidity
Statistical package: SAS 8.02
Lag: days 0-3 cumulative
Range: 16.41, 122.44
25th: 39.93,75th: 66.50
# of stations: 7,
r= 0.68, 0.89
Wong etal. (200la)
Hong Kong, China
Period of Study: 1993-
1994
Outcomes (ICD 9): Asthma (493)
Age groups analyzed: <\5
N: 1,217
# of hospitals: 1
Study Design: Time series
Statistical Analyses: Poisson regression
(followed APHEA protocol)
Covariates: Season, temperature, humidity
Season: Summer (Jun-Aug), Autumn (Sep-
Nov), Winter (Dec-Feb), Spring (Mar-May)
Lag: 0,1,2,3,4,5 days; and cumulative 0-2
and 0-3 days.
24-h avg
NO2 mean: 43.3 ug/m3,
SD=16.6
Range: 9, 106 ug/m3
Autumn: 51.7(17.6)
Winter: 46.6(15.5)
Spring: 40.7(11.8)
Summer: 32.6(13.7)
# of stations: 9
PM10
SO,
1.020 [1.013, 1.028] lag 0-3
Respiratory Relative Risks (RR)
0-4 yrs: 1.020 [1.010, 1.030] lag 0-3
5-64yrs: 1.023 [1.011, 1.034] lag 0-3
>65 yrs: 1.024 [1.014, 1.035] lag 0-3
Cold Season: 1.004 [0.988, 1.020]
N02 + highPM10: 1.009 [0.993, 1.025]
NO2 + highO3: 1.013 [0.999, 1.026]
Asthma: 1.026 [1.01, 1.042] lag 0-3
COPD: 1.029 [1.019,1.040] lag 0-3
Pneumonia: 1.028 [1.015, 1.041] lag 0-3
Increment: 10 ug/m3
Asthma
All year: 1.08 p= 0.001
Autumn: 1.08 p = 0.017
Winter: NR
Spring: NR
Summer: NR
Default GAM
+Did not report correction for over-dispersion
NR: Not Reported
APHEA: Air Pollution and Health: A European Approach
-------
OQ
to
o
o
TABLE AX6.3-2. RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
EMERGENCY DEPARTMENT VISITS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels of NO2 &
Monitoring Stations
Copollutant Effects and Interpretation:
Correlations Relative Risk & Confidence Intervals (95%)
UNITED STATES
X
to
Jaffe et al. (2003)
3 cities, Ohio,
United States
(Cleveland,
Columbus,
Cincinnati)
Period of Study:
7/91-6/96
Outcome (ICD-9): Asthma (493)
Age Groups Analyzed: 5-34
Study Design: Time series
N: 4,416
Statistical Analyses: Poisson
regression using a standard GAM
approach
Covariates: city, day of wk, wk,
yr, minimum temperature, overall
trend, dispersion parameter
Season: June to August only
Dose-response investigated: Yes
Statistical Package: NR
Lag: 0-3 days
Cincinnati
24-havg: 50ppb,
SD=15
Cleveland
24-havg: 48ppb,
SD=16
NO2 was not monitored in
Columbus due to
relatively low levels
Cincinnati:
PM10;r=0.36
SO2;r=0.07
O3;r = 0.60
Cleveland:
PM10; 0.34
SO2;r=0.28
O3; r = 0.42
No
multipollutant
models were
utilized.
Increment: 10 ppb
Cincinnati: 6% [-1.0, 13] lag 1
Cleveland: 4% [-1,8] lag 1
All cities: 3% [-1.0, 7]
Attributable risk from NO2 increment:
Cincinnati 0.72 (RR 1.06)
Cleveland 0.44 (RR 1.04)
Regression diagnostics for Cincinnati showed
significant linear trend during entire study period
and for each wk (6/1-8/31). No trends observed
for Cleveland.
Regression Models assessing exposure thresholds
showed a possible dose-response for NO2 (percent
increase after 40 ppb). No increased risk until
minimum concentration of 40 ppb was reached.
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Norris*etal. (1999)
Seattle, WA,
United States
Period of Study:
1995-1996
Outcome (ICD-9): Asthma (493)
Age groups analyzed: <18 yrs
Study Design: Time series
N: 900 ER visits
Statistical Analyses:
Semi-parametric Poisson
regression using GAM.
Covariates: day of wk, time
trends, temperature, dew point
temperature
Dose-response investigated: Yes
Statistical Package: NR
Lag: 0,2 days
24 h: 20.2 ppb, SD = 7.1
IQR: 9 ppb
1-hmax: 34.0 ppb,
SD= 11.3
IQR: 12 ppb
CO; r = 0.66
PM; r = 0.66
SO2;r=0.25
Increment: IQR
24-h avg (9 ppb increment)
RR 0.99 [0.90, 1.08] lag 2
1-h max (12 ppb increment)
RR1.05 [0.99, 1.12] lag 0
Age and hospital utilization (high and low)
segregation (<5, 5-11, and 12-17 yrs) did not
figure significantly in the association between
emergency room visits and asthma.
-------
OQ
to
o
o
TABLE AX6.3-2 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
EMERGENCY DEPARTMENT VISITS
Reference, Study
Location, & Period Outcomes, Design, & Methods
Mean Levels of NO2 &
Monitoring Stations
Copollutant Effects and Interpretation:
Correlations Relative Risk & Confidence Intervals (95%)
UNITED STATES (cont'd)
X
Lipsettetal. (1997) Outcome(s): Asthma
Santa Clara County, ICD-9 Code(s): NR
California,
United States
Period of Study:
1988-1992
Age groups analyzed: All
Study Design: Time series
Statistical Analyses: Poisson
Regression; GEE repeated with
GAM
Covariates: minimum
temperature, day of study,
precipitation, hospital, day of
wk, yr, overdispersion parameter
Season: Winters only
Statistical Package: SAS,
S-Plus, Stata
Lag: 0-5 days
NO21-h mean: 69 ppb,
SD = 28
Range: 29, 150 ppb
PM10;r=0.82
COH; r = 0.8
No multipollutant
model due to high
correlation between
pollutants
Same day NO2 was associated with ER visits
for asthma (P = 0.013, p = 0.024)
Absence of association between lagged or
multiday specifications of NO2 and asthma ER
visits (data not shown) suggest that same day
association may be artifact of covariation with
PM10
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
-------
OQ
to
o
o
TABLE AX6.3-2 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
EMERGENCY DEPARTMENT VISITS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels of NO2 &
Monitoring Stations
Copollutant Effects and Interpretation:
Correlations Relative Risk & Confidence Intervals (95%)
UNITED STATES (cont'd)
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Peel et al. (2005)
Atlanta, GA, United
States
Period of Study:
1/93-8/2000
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, 2-18
Study Design: Time series
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.
1-hmax: 45.9ppb,
SD = 17.3
O3;r=0.42
SO2;r=0.34
CO; r = 0.68
PM10;
r = 0.46
Evaluated
multipollutant
models (data
not shown)
Increment: 20 ppb
All respiratory
RR 1.016 [1.006, 1.027] lag 0-2, 3 day moving av
Upper Respiratory Infection (URI)
RR 1.019 [1.006, 1.031] lag 0-2, 3 day moving av
Asthma
All: 1.014 [0.997, 1.030] lag 0-2, 3 day moving
avg
2-18: 1.027 [1.005, 1.050] lag 0-2, 3 day moving
avg
Pneumonia
RR 1.000 [0.983, 1.019] lag 0-2, 3 day moving av
COPD
RR 1.035 [1.006, 1.065] lag 0-2, 3 day moving av
-------
OQ
to
o
o
TABLE AX6.3-2 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
EMERGENCY DEPARTMENT VISITS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels of NO2 &
Monitoring Stations
Copollutant Effects and Interpretation:
Correlations Relative Risk & Confidence Intervals (95%)
UNITED STATES (cont'd)
X
Tolbert et al. (2000)
Atlanta, GA, United
States
Period of Study:
1993-1995
Outcome(s) (ICD-9): Asthma
(493), wheezing (786.09), Reactive
airways disease (RADS) (519.1)
Age groups analyzed: 0-16; 2-5,
6-10, 11-16
Study Design: Case-Control
N: 5,934
Statistical Analyses: Ecological
GEE analysis (Poisson model with
logit link) and logistic regression
Covariates: day of wk, day of
summer, yr, interaction of day of
summer and yr
Season: Summers only
Statistical Package: SAS
Lag: 1 day (a priori)
NOX 1-h max
continuous
Mean: 81.7ppb,
SD = 53.8
Range = 5.35, 306
Number of stations: 2
PM10;r=0.44
O3;r = 0.51
Increment: 50 ppb
Age 0-16:
RR 1.012 [0.987, 1.039] lag 1
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
-------
OQ
to
o
o
TABLE AX6.3-2 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
EMERGENCY DEPARTMENT VISITS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels of NO2 &
Monitoring Stations
Copollutant Effects and Interpretation:
Correlations Relative Risk & Confidence Intervals (95%)
UNITED STATES (cont'd)
X
Oi
-k
Oi
Cassino* et al. (1999)
New York City, NY
United States
Period of Study:
1/1989-12/1993
Outcome(s) (ICD-9): Asthma
(493); COPD (496), bronchitis
(490), emphysema (492),
bronchiectasis (494)
Study Design: Time series
N: 1,115
# of Hospitals: 11
Statistical Analyses: Time series
regression, Poisson regression with
GLM and GAM; Linear
regression, Logistic regression
with GEE
Covariates: Season, trend, day of
wk, temperature, humidity
Statistical Package: S Plus and
SAS
Lag: 0-3 days
24-h avg NO2:
Mean: 45.0 ppb
Median: 43 ppb
10% 31 ppb
25% 37 ppb
75% 53 ppb
90% 63 ppb
03
CO
SO2
Increment: 15 ppb (IQR)
RR 0.97 [0.85, 1.09] lag 0
RR 1.04 [0.92, 1.18] lag 1
RR 1.06 [0.94, 1.2] lag 2
RR 0.97 [0.86, 1.09] lag 3
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
-------
OQ
to
o
o
TABLE AX6.3-2 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
EMERGENCY DEPARTMENT VISITS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels of NO2 &
Monitoring Stations
Copollutant Effects and Interpretation:
Correlations Relative Risk & Confidence Intervals (95%)
CANADA
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Stiebetal. (1996)
St. John, New
Brunswick, Canada
Period of Study:
1984-1992
(May-Sept only)
Stieb* et al. (2000)
Saint John, New
Brunswick, Canada
Period of Study:
Retrospective:
7/92-6/94
Prospective:
7/94-3/96
Outcome(s): Asthma
ICD-9 Codes: NR
Age groups analyzed: 0-15, >15
Study Design: Time series
N: 1,163
# 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
Outcome(s): Asthma; COPD;
Respiratory infection (bronchitis,
bronchiolitis, croup, pneumonia);
All respiratory
ICD-9 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: Yes
Statistical Package: S-Plus
Lag: all yr = 0; summer
only = 8
l-hmaxNO2 (ppb)
Mean: 25.2
Range: 0, 120
95th 60
Annual mean: 8.9 ppb
spring/fall mean:
10.0 ppb Max: 82
O3r=0.16 Increment: NR
SO2r=-0.03
SO42~-r=0.16 NO2 + O3: P = -0.0037 (0.0023) lag 2
TSPr=0.15
O3;r=-0.02
SO2;r=0.41
TRS;r=0.16
PM10;r=0.35
PM25;r = 0.35
H+;r=0.25
SO42~;
r = 0.33
COH; r = 0.49
Assessed
multipollutant
models
Increment: 8.9 ppb (IQR)
Respiratory visits: -3.8%, p = 0.070 lag 0
May to Sept: 11.5%, p = 0.17 lag 8
Multipollutant model (NO2, O3, SO2)
-3.6% [-7.5, 0.5] lag0
Multipollutant model (ln(NO2), O3, SO2 COH)
May to Sept: 4.7% [0.8 to 8.6] lag 8
Non-linear effect of NO2 on summertime
respiratory visits observed and log
transformation strengthened the association.
-------
OQ
to
o
o
TABLE AX6.3-2 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
EMERGENCY DEPARTMENT VISITS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels of NO2 &
Monitoring Stations
Copollutant Effects and Interpretation:
Correlations Relative Risk & Confidence Intervals (95%)
EUROPE and MIDDLE EAST
X
oo
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Sunyeretal. (1997)
Multi-city, Europe
(Barcelona, Helsinki,
Paris, London)
Period of Study:
1986-1992
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: 53 (5, 142)
Helsinki: 35(9,78)
London: 69(27,347)
Paris: 42 (12, 157)
# of stations:
Barcelona: 3
London: 2
Paris: 4
Helsinki: 8
S02
black smoke
03
Increment: 50 ug/m3 of 24-h avg for all cities
combined
Asthma
15-64 yrs
1.029 [1.003, 1.055] lag 0-1
1.038 [1.008, 1.068] lag 0-3, cumulative
<15 yrs
1.026 [1.006, 1.049] lag 2
1.037 [1.004, 1.067] lag 0-3, cumulative
1.080 [1.025, 1.140] -Winteronly
Two-pollutant models:
NO2/Black smoke
15-64 yrs 1.055 [1.005, 1.109] lag 0-1
15-64 yrs 1.088 [1.025, 1.155] cumulative 0-3
<15yrs 1.036 [0.956, 1.122]
NO2/SO2
<15yrs 1.034 [0.988, 1.082]
-------
OQ
to
o
o
TABLE AX6.3-2 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
EMERGENCY DEPARTMENT VISITS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels of NO2 &
Monitoring Stations
Effects and Interpretation:
Copollutant Relative Risk & Confidence Intervals
Correlations (95%)
EUROPE and MIDDLE EAST (cont'd)
X
VO
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Atkinson et al. Outcome(s) (ICD-9): Respiratory
(1999b) ailments (490-496), including
London, United asthma, wheezing, inhaler request,
Kingdom chest infection, COPD, difficulty
in breathing, cough, croup,
Period of Study: pleurisy, noisy breathing
1/92-1294 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
1-hmax: 50.3 ppb, SD = 17.0
# of Stations: 3; r= 0.70, 0.96
NO2, O3
(8 h), SO2
(24 h), CO
(24 h), PM10
(24 h), BS
Increment: 36ppb in 1-hmax
Single-pollutant model
Asthma Only
0-14 yrs 8.97% [4.39, 13.74] lag 1
15-64 yrs 4.44% [0.14, 8.92] lag 1
All ages 4.37% [1.32, 7.52] lag 0
All Respiratory
0-14 yrs 2.17% [-0.49, 4.91] lag 1
15-64 yrs 1.87% [-0.69, 4.49] lag 2
>65 yrs 3.97% [0.51, 7.55] lag 0
All Ages 1.20% [-0.57, 3.00]
Two-pollutant model Asthma Only 0-14 yrs:
SO2: 5.75% [0.39, 11.40] lag 1
CO: 8.34% [3.61, 13.29] lag 0
PM10: 6.95% [1.96, 12.19] lag 2
BS: 8.32% [3.56, 13.30] lag 2
O3: 9.68% [5.02, 14.54] lag 0
-------
OQ
to
o
o
TABLE AX6.3-2 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
EMERGENCY DEPARTMENT VISITS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels of NO2 &
Monitoring Stations
Copollutant Effects and Interpretation:
Correlations Relative Risk & Confidence Intervals (95%)
EUROPE and MIDDLE EAST (cont'd)
X
ON
t^ft
O
Buchdahletal. (1996)
London, United
Kingdom
Period of Study:
3/1/92-2/28/93
Outcomes: Daily acute wheezy
episodes
ICD-9: NR
Age groups analyzed: < 16
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
NO2 24-h yr round mean:
60 ug/m3, SD = 17
IQR: 17 ug/m3
Spring: 59(19)
Summer: 55 (18)
Fall: 66(13)
Winter: 61 (17)
SO2r=0.62
O3r=-0.18
Increment: 17 ug/m3 (IQR)
No adjustments to model
RR 1.07 [1.01, 1.14] lag not specified
Adjusted for temperature and season.
RR 1.02 [0.96, 1.09] lag not specified
H
6
o
o
H
O
o
H
W
O
O
HH
H
W
-------
OQ
to
o
o
TABLE AX6.3-2 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
EMERGENCY DEPARTMENT VISITS
Reference, Study
Location, & Period
Outcomes, Design, &
Methods
Mean Levels of NO2 &
Monitoring Stations
Copollutant Effects and Interpretation:
Correlations Relative Risk & Confidence Intervals (95%)
EUROPE and MIDDLE EAST (cont'd)
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Thompson etal. (2001)
Belfast, Northern Ireland
Period of Study:
1993-1995
Outcome(s): Asthma
ICD-9 Code(s): NR
Age groups analyzed: Children
Study Design: Time series
N: 1,044
Statistical Analyses: Followed
APHEA protocol, Poisson
regression analysis
Covariates: Season, long-term
trend, temperature, day of wk,
holiday
Season: Warm (May-Oct); Cold
(Nov-Apr)
Statistical Package: Stata
Lag: 0-3
Warm Season
NO2(ppb): Mean: 19.20;
SD: 7.90; IQR: 13.0,23.0
NOx(ppb): Mean: 35.50;
SD: 25.50; IQR: 21.0,40.0
NO(ppb): Mean: 16.4;
SD: 19.70; IQR: 7.0,17.0
Cold Season
NO2(ppb): Mean: 23.30;
SD: 9.00; IQR: 18.0,28.0
NOx(ppb): Mean: 50.50;
SD: 50.50; IQR: 26.0, 56.0
NO(ppb): Mean: 27.30;
SD: 43.10; IQR: 9.0,28.0
NO2:
PM10 r = 0.77
S02r=0.82
NOxr = 0.93
NO r= 0.84
O3r=-0.62
CO r= 0.69
Benzene
r =0.83
NOX:
PM10 r = 0.73
S02r=0.83
NO2 r = 0.92
NO r= 0.97
O3r=-0.73
CO r = 0.74
Benzene
r=0.86
NO:
PM10r=0.65
S02 r = 0.76
NOxr = 0.97
NO2 r = 0.84
O3r=-0.76
CO r= 0.71
Benzene
r=0.82
NO2 Increment: lOppb
NOX Increment: per doubling
NO Increment: per doubling
NO2
Lag ORR 1.08 [1.03, 1.13]
Lag 0-1 RR 1.11 [1.05,1.17]
Lag 0-2 RR 1.10 [1.04, 1.17]
Lag 0-3 RR 1.12 [1.03, 1.20]
Warm only Lag 0-1 RR 1.14 [1.04, 1.26]
Cold only Lag 0-1 RR1.10 [1.03, 1.17]
Adjusted for Benzene Lag 0-1 RR 0.99 [0.87, 1.13]
NOX
Lag ORR 1.07 [1.02, 1.12]
Lag 0-1 RR 1.10 [1.05, 1.16]
Lag 0-2 RR 1.10 [1.03, 1.17]
Lag 0-3 RR 1.11 [1.04,1.20]
Warm only Lag 0-1 RR 1.13 [1.03, 1.24]
Cold only Lag 0-1 RR 1.09 [1.02, 1.16]
Adjusted for Benzene Lag 0-1 RR 0.89 [0.77, 1.03]
NO
Lag ORR 1.04 [1.01, 1.07]
Lag 0-1 RR 1.07 [1.03, 1.11]
Lag 0-2 RR 1.06 [1.02, 1.11]
Lag 0-3 RR 1.08 [1.02, 1.14]
Warm only Lag 0-1 RR 1.08 [1.01, 1.16]
Cold only Lag 0-1 RR 1.06 [1.01, 1.11]
Adjusted for Benzene Lag 0-1 RR 0.93 [0.85, 1.01]
-------
OQ
to
o
o
TABLE AX6.3-2 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
EMERGENCY DEPARTMENT VISITS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels of NO2 &
Monitoring Stations
Copollutant Effects and Interpretation:
Correlations Relative Risk & Confidence Intervals (95%)
EUROPE and MIDDLE EAST (cont'd)
X
ON
t^ft
to
Boutin-Forzano et al.
(2004)
Marseille, France
Period of Study:
4/97-3/98
Outcome(s): Asthma
ICD-9 Code(s): NR
Age groups analyzed: 3-49
Study Design: Case-Crossover
N: 549
Statistical Analyses: Logistic
regression
Covariates: minimal daily
temperature, maximum daily
temperature, minimum daily
relative humidity, maximum daily
relative humidity, day of wk
Statistical Package: NR
Lag: 0-4 days
MeanNO2: 34.9 ug/mj
Range: 3.0,85
SO2; r = 0.56 Increment: 10 ug/mj
O3;r=0.58
Increased ER visits
OR 1.0067 [0.9960, 1.0176] lagO
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
-------
OQ
to
o
o
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
TABLE AX6.3-2 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
EMERGENCY DEPARTMENT VISITS
Reference, Study
Location, & Period
EUROPE and MIDDLE
Castellsague etal. (1995)
Barcelona, Spain
Outcomes, Design, &
Methods
EAST (cont'd)
Outcome(s): Asthma
ICD-9 Code(s): NR
Age groups analyzed: 15-64
Mean Levels of NO2 &
Monitoring Stations
Mean NO2 (ug/m3)
Summer: 104.0
Winter: 100.8
Copollutant
Correlations
SO2;r = NR
O3; r = NR
Effects and Interpretation:
Relative Risk & Confidence Intervals
Increment: 25 ug/m3
Seasonal differences
(95%)
Period of Study:
1986-1989
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
IQR (ug/m3):
Summer: 48
Winter: 37
# of Stations:
15 manual, 3 automatic
Summer:
1.071 [1.101, 1.130] lag 0-5 cumulative
1.045 [1.009, 1.081] lag 0
Winter:
1.072 [1.010, 1.137] lag 0-2 cumulative
1.056 [1.011, 1.104] lag 0
Asthma visits increased across quartiles of NO2
in summer; a positive but less consistent
increase across quartiles was observed in
winter.
-------
OQ
to
o
o
TABLE AX6.3-2 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
EMERGENCY DEPARTMENT VISITS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels of NO2 & Copollutant Effects and Interpretation:
Monitoring Stations Correlations Relative Risk & Confidence Intervals (95%)
EUROPE and MIDDLE EAST (cont'd)
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Tobias etal. (1999)
Barcelona, Spain
Period of Study:
1986-1989
Outcome(s): Asthma
ICD-9: NR
Age groups analyzed: >14
Study Design: Time series
Statistical Analyses: Poisson
regression, followed APHEA
protocol
Covariates: temperature, humidity,
long-term trend, season, day of wk
Statistical Package: NR
Lag: NR
24-h-avg NO2 ug/mj
Non-epidemic days:
54.7 (20.8)
Epidemic days:
58.9 (26.7)
BS
S02
03
P x 104 (SE x 104) using Std Poisson
Without modeling asthma epidemics:
11.25 (11.79) p> 0.1
Modeling epidemics with 1 dummy variable:
1.18 (7.59) p> 0.1
Modeling epidemics with 6 dummy variables:
13.60 (1.19) p< O.I
Modeling each epidemic with dummy variable:
U.40(1.44)p<0.\
P x 104 (SE x 104) using Autoregressive
Poisson
Without modeling asthma epidemics:
13.65 (11.81)p>0.1
Modeling epidemics with 1 dummy variable:
3.28 (7.77) p> 0.1
Modeling epidemics with 6 dummy variables:
16.49 (^.01) p< 0.05
Modeling each epidemic with dummy variable:
-------
OQ
to
o
o
TABLE AX6.3-2 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
EMERGENCY DEPARTMENT VISITS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels of NO2 &
Monitoring Stations
Copollutant Effects and Interpretation:
Correlations Relative Risk & Confidence Intervals (95%)
EUROPE and MIDDLE EAST (cont'd)
X
Galan et al. (2003)
Madrid, Spain
Period of Study:
1995-1998
Outcome(s) (ICD-9): 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: 67.1 ug/m3
SD=18.0
IQR: 20.5
Max: 147.5
# of Stations: 15
PM10; r = 0.717 Increment: 10 ug/mj
SO2;r=0.610 Asthma:
O3;r=-0.209 RR 1.013 [0.991, 1.035] lag 0
RR 1.011 [0.989, 1.032] lag 1
RR 1.013 [0.992, 1.034] lag 2
RR 1.033 [1.013, 1.054] lag 3
RR 1.026 [1.006, 1.047] lag 4
Multipollutant model:
NO2/SO21.031 [1.004, 1.059] lag 3
NO2/PM101.001 [0.971, 1.031] lag 3
NO2/Pollen 1.024 [1.004, 1.044] lag 3
NO2/Pollen/O3 1.024 [1.005, 1.045] Poisson
NO2/Pollen/O3 1.022 [1.005, 1.040] GAM
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
-------
OQ
to
o
o
TABLE AX6.3-2 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
EMERGENCY DEPARTMENT VISITS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels of NO2 &
Monitoring Stations
Effects and Interpretation:
Copollutant Relative Risk & Confidence
Correlations Intervals (95%)
EUROPE and MIDDLE EAST (cont'd)
X
Oi
t^ft
Oi
Teniasetal. (1998)
Valencia, Spain
Period of Study:
1993-1995
Seasons:
Cold: Nov-Apr
Warm: May-Oct
Outcome(s): Asthma 24 h:
ICD-9 Code(s): NR 57.7 ug/m3
Age groups analyzed: >14 Cold: 55.9
Study Design: Time series Warm: 59.4
N: 734 1-hmax:
Statistical Analyses: Poisson 101.1 ug/m3
regression, APHEA protocol Cold: 97.3
Covariates: seasonality, temperature, Warm: 102.8
humidity, long-term trend, day of wk,
holidays, influenza # of Stations: 2
Seasons: Cold: Nov-Apr; Warm:
May-Oct
Dose-Response Investigated: Yes
Statistical Package: NR
Lag: 0-3 days
24 h:
O3;r=-0.304
S02 (24 h);
r = 0.265
SO2(lh);r= 0.261
Ih:
O3;r=-0.192
S02 (24 h);
r = 0.199
SO2(lh);r= 0.201
Increment: 10 ug/m
NO2 24-h avg
All yr 1.076 [1.020, 1.134] lag 0
Cold 1.083 [1.022, 1.148] lag 0
Warm 1.066 [0.989, 1.149] lagO
NO21-h max
Allyr 1.037 [1.008, 1.066] lagO
Cold 1.034 [1.004, 1.066] lagO
Warm 1.044 [1.002, 1.088] lagO
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
-------
OQ
to
o
o
TABLE AX6.3-2 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
EMERGENCY DEPARTMENT VISITS
Reference, Study
Location, &
Period
Outcomes, Design, & Methods
Mean Levels of NO2 &
Monitoring Stations
Copollutant
Correlations
Effects and Interpretation:
Relative Risk & Confidence Intervals (95%)
EUROPE and MIDDLE EAST (cont'd)
X
Tenias et al. (2002) Outcome(s): COPD
Valencia, Spain ICD-9 Code(s): NR
Age groups analyzed: >14
Study Design: Time series
Period of Study: N: 1,298
1994-1995 # 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
NO224-havg: 57.7 ug/m3;
Range: 12, 135
1-hmax: 100.1 ug/m3;
Range: 31,305
# of Stations: 6 manual and
5 automatic; r = 0.87
BS;r= 0.246
SO2;r= 0.194
CO; r= 0.180
O3;r=-0.192
Increment: 10 ug/m
24-havgNO2
All Year RR 0.979 [0.943, 1.042] lagO
Cold, 24-havg: RR0.991 [0.953, 1.030] lagO
Warm, 24-havg: RR0.961 [0.900, 1.023] lagO
l-hmaxNO2
All Year RR 0.986 [0.966, 1.007] lagO
Cold, 24-havg: RR0.996 [0.975, 1.018] lagO
Warm, 24-havg: RR0.968 [0.935, 1.003] lagO
Possibility of a linear relationship between
pollution and risk of emergency cases could not
be ruled out.
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
-------
OQ
to
o
o
TABLE AX6.3-2 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
EMERGENCY DEPARTMENT VISITS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels of NO2 &
Monitoring Stations
Effects and Interpretation:
Copollutant Relative Risk & Confidence Intervals
Correlations (95%)
EUROPE and MIDDLE EAST (cont'd)
X
oo
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Migliaretti et al. Outcome (ICD-9): Asthma (493)
(2005) Age groups analyzed: <15, 15-64,
Turin, Italy >64
Study Design: Case-Control
Period of Study: Controls: age matched with other
1997-1999 respiratory disease (ICD-9:
460-487, 490-2, 494-6, 500-19) or
heart disease (ICD-9: 390-405,
410-429)
N: cases = 1,401
controls = 201,071
Statistical Analyses: logistic
regression
Covariates: seasonality,
temperature, humidity, solar
radiation, wind velocity, day of
wk, holiday, gender, age,
education level
Seasons: Cold: Oct-Mar; Warm:
Apr-Sep
Statistical Package: NR
Lag: 0-3 days and cumulative
All Participants:
24-hmean: 112.7 ug/m3,
SD = 30.2, Median =107.7
Cases:
24-hmean: 117.1 ug/m3,
SD = 30.0, Median =113.0
Controls:
24-hmean: 112.7 ug/m3,
SD = 30.2, Median =107.7
# of Stations: 10, r= 0.79
TSP; r = 0.8 Increment: 10 ug/mj
Two-pollutant
model
adjusted for
TSP
Single Pollutant (NO2):
<15 yrs 2.3% [0.3, 4.40]
15-64 yrs 3.10% [-0.01, 7.70]
>64 yrs 7.70% [0.20, 15.20]
All ages 2.40% [0.5, 4.30]
Copollutant (NO2 and TSP)
<15 yrs 1.71% [-0.02, 5.00]
15-64 yrs 1.20% [-0.06, 6.50]
>64 yrs 0.91% [-0.08, 5.91]
All ages 1.10% [-0.02, 3.82]
-------
OQ
to
o
o
TABLE AX6.3-2 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
EMERGENCY DEPARTMENT VISITS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels of NO2 & Copollutant Effects and Interpretation:
Monitoring Stations Correlations Relative Risk & Confidence Intervals (95%)
EUROPE and MIDDLE EAST (cont'd)
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Pantazopoulou et al.
(1995)
Athens, Greece
Outcomes: All respiratory visits
ICD-9: NR
Age groups analyzed: All ages
Study Design: Time series
Period of Study : 1988 N: 213,316
Number of hospitals: 14
Statistical Analyses: Multiple
linear regression
Covariates: Season, day of wk,
holiday, temperature, relative
humidity
Season: Warm (3/22-9/21),
Cold (1/1-3/21 and 9/22-12/31)
Lag: NR
Gartyetal. (1998)
Tel Aviv, Israel
1993
Outcome(s): Asthma
ICD-9 Code(s): NR
Age groups analyzed: 1-18
Study Design: Descriptive study
with correlations
N: 1,076
Statistical Analyses: Pearson
correlation and partial
correlation coefficients
Covariates: maximum and
minimum ambient temperatures,
relative humidity and barometric
pressure
Statistical Package: Statistix
NO2 24-h avg
Winter: 94 ug/m3, SD = 25
5th: 59,50th: 93,
95th: 135
Summer: 111 ug/m3,
SD = 32
5th: 65,50th: 108,
95th: 173
# of stations: 2
24-h mean of NOX
(estimated from histogram):
60 ug/m3; Range 50, 250
CO
BS
Increment: 76 ug/m3 in winter and 108 ug/m3 in
summer (95th-5th)
Respiratory disease admissions
Winter: Percent increase: p = 66.8 [19.6, 113.9]
Summer: Percent increase: P = 21.2 [-35.1, 77.5]
Correlation between NOX and ER visits for
asthma:
All Year:
Daily data r = 0.30
Running mean for 7 days r = 0.62
Excluding September:
Daily data r = 0.37
Running mean for 7 days r = 0.74
38% of variance in number of ER visits explained
by fluctuations in NOX. Increases to 55% when
Sept. is omitted from analyses.
-------
OQ
to
o
o
X
ON
ON
O
TABLE AX6.3-2 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
EMERGENCY DEPARTMENT VISITS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels of NO2 &
Monitoring Stations
Copollutant
Correlations
Effects and Interpretation:
Relative Risk & Confidence Intervals (95%)
LATIN AMERICA
Farhat* et al. (2005)
Sao Paulo, Brazil
Outcome(s) (ICD-9): Lower
Respiratory Disease (466, 480-5)
Mean: 125.3 ug/m3
PM10;r=0.83
SO2;r=0.66
Increment: IQRof 65.04 ug/m3
Period of Study:
1996-1997
Age groups analyzed:
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
SD = 51.7
IQR: 65.04 ug/m3
# of Stations: 6
CO; r = 0.59 Single-pollutant models (estimated from
graphs):
LRD -17.5% [12.5, 24]
Multipollutant models:
Adjusted for:
PM1016.1% [5.4, 26.8] 4 day avg
SO2 24.7% [18.2, 31.3] 4 day avg
CO 19.2% [11.8, 26.6] 4 day avg
Multipollutant model
18.4% [3.4, 33.5] 4 day avg
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
-------
OQ
to
o
o
TABLE AX6.3-2 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
EMERGENCY DEPARTMENT VISITS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels of NO2 &
Monitoring Stations
Copollutant Effects and Interpretation:
Correlations Relative Risk & Confidence Intervals (95%)
LATIN AMERICA (cont'd)
X
Oi
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Martins* et al. (2002)
Sao Paulo, Brazil
Period of Study:
5/96-9/98
Outcome(s) (ICD-10): Chronic
Lower Respiratory Disease
(CLRD) (J40-J47); includes
chronic bronchitis, emphysema,
other COPDs, asthma,
bronchiectasia
Age groups analyzed: >64
Study Design: Time series
N: 712
# of Hospitals: 1
Catchment area: 13,163 total ER
visits
Statistical Analyses: Poisson
regression and GAM - no mention
of more stringent criteria
Covariates: weekdays, time,
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
NO2 max 1-h avg (ug/m3):
117.6, SD = 53.0,
Range 32.1, 421.6
IQR: 62.2 ug/m3
# of Stations: 4
O3; r = 0.44 Increment: IQR of 62.2 ug/mj
SO2;r=0.67
PM10; r = 0.83 Percent increase: 4.5% [-6.5, 15] lag 3 day
CO; r = 0.62 moving avg (estimated from graph)
-------
OQ
to
o
o
TABLE AX6.3-2 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
EMERGENCY DEPARTMENT VISITS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels of NO2 &
Monitoring Stations
Copollutant Effects and Interpretation:
Correlations Relative Risk & Confidence Intervals (95%)
LATIN AMERICA (cont'd)
X
Oi
ON
to
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Ilabacaetal. (1999)
Santiago, Chile
Period of Study:
2/1/95-8/31/96
Days: 578
Outcome(s) (ICD-9): Upper
respiratory illness
(460-465, 487);
Lower respiratory illness
(466, 480-486, 490-494, 496,
519.1,033.9);
Pneumonia (480-486)
Age groups analyzed: <15
Study Design: Time series
# of Hospitals: 1
Statistical Analyses: Poisson
regression
Covariates: Long-term trend,
season, day of wk, temperature,
humidity, influenza epidemic
Season: Warm (Sep-Apr),
Cool (May-Aug)
Statistical Package: NR
Lag: 0-3 days
24-havgNO2:
Warm:
Mean: 97.0
Median: 91.5
SD: 34.6
Range: 37.2,246
5th: 54.3
95th: 163.0
Cool:
Mean: 160.2
Median: 154.4
SD: 59.5
Range: 60.1,397.5
5th: 74.4
95th: 266.0
# of stations: 4,
r = 0.70, 0.88
Warm: Increment: IQR
SO2r=0.66
O3 r = 0.15 All respiratory
PM10r = 0.71 Cool
PM25r=0.70 Lag2IQR: 56.4 RR 1.0378 [1.0211, 1.0549]
Lag 3 IQR: 56.4 RR 1.0294 [1.0131, 1.0460]
Cool: Lag avg 7 IQR: 33.84 RR 1.0161 [1.0000, 1.0325]
SO2 r = 0.74 Warm
O3 r = 0.22 Lag 2 IQR: 30.08 RR 1.0208 [0.9992, 1.0428]
PM10r = 0.82 Lag 3 IQR: 30.08 RR 1.0395 [1.0181, 1.0612]
PM25 r = 0.80 Lag avg 7 IQR: 22.56 RR 1.0251 [0.9964, 1.0548]
Upper respiratory
Cool
Lag 2 IQR: 56.4 RR 1.0569 [1.0339, 1.0803]
Lag 3 IQR: 56.4 RR 1.0318 [1.0095, 1.0545]
Lag avg 7 IQR: 33.84 RR 1.0177 [0.9960, 1.0399]
Warm
Lag 2 IQR: 30.08 RR 1.0150 [0.9881, 1.0426]
Lag 3 IQR: 30.08 RR 1.0425 [1.0157, 1.0699]
Lag avg 7 IQR: 22.56 RR 0.9944 [0.9591, 1.0311]
Pneumonia
Cool
Lag 2 IQR: 56.4 RR 1.0824 [1.0300, 1.1374]
Lag 3 IQR: 56.4 RR 1.0768 [1.0273, 1.1287]
Lag avg 7 IQR: 33.84 RR 1.0564 [1.0062, 1.1092]
Warm
Lag 2 IQR: 30.08 RR 1.1232 [1.0450, 1.2072]
Lag 3 IQR: 30.08 RR 1.0029 [0.9332, 1.0779]
Lag avg 7 IQR: 22.56 RR 1.1084 [1.0071, 1.2200]
-------
OQ
to
o
o
TABLE AX6.3-2 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
EMERGENCY DEPARTMENT VISITS
Reference, Study
Location, &
Period
Outcomes, Design, & Methods
Mean Levels of NO2 &
Monitoring Stations
Copollutant Effects and Interpretation:
Correlations Relative Risk & Confidence Intervals (95%)
LATIN AMERICA (cont'd)
X
Oi
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Lin etal. (1999)
Sao Paulo, Brazil
Period of Study:
May 1991-April
1993
Days: 621
Outcome(s): Respiratory disease,
Upper respiratory illness, Lower
respiratory illness, Wheezing
ICD-9 Code(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
NO2 ug/mj:
Mean: 163
SD: 85
Range: 2,688
Number of stations: 3
SO2r=0.38 Increment: NR
CO r= 0.35
PMio r = 0.40 All respiratory illness
O3r=0.15 NO2 alone RR 1.003 [1.001, 1.005] 5-day
moving avg
NO2 + PM10+ O3+ SO2+ CO RR 0.996
[0.994, 0.998]
Lower respiratory illness
NO2 alone RR 0.999 [0.991, 1.007] 5-day
moving avg
NO2 + PM10+ O3+ SO2+ CO RR 0.990
[0.982, 0.998]
Upper respiratory illness
NO2 alone RR 1.003 [0.999, 1.007] 5-day
moving avg
NO2 + PM10+ O3+ SO2+ CO RR 0.996
[0.992, 1.000]
Wheezing
NO2 alone RR 0.996 [0.990, 1.002] 5-day
moving avg
NO2 + PM10+ O3+ SO2+ CO RR 0.991
[0.983, 0.999]
-------
OQ
to
o
o
TABLE AX6.3-2 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
EMERGENCY DEPARTMENT VISITS
X
Oi
Reference, Study
Location, &
Period
Mean Levels of NO2 &
Outcomes, Design, & Methods Monitoring Stations
Effects and Interpretation:
Copollutant Relative Risk & Confidence Intervals
Correlations (95%)
ASIA
Chew etal. (1999)
Singapore
Period of Study:
1990-1994
Outcome(s) (ICD-9): Asthma (493) 24-havg: 18.9 ug/m3,
Age groups analyzed: 3-12,13-21 SD = 1 5 .0, Max < 40
Study Design: Time series
N: 23,000 # of Stations: 15
# of Hospitals: 2
Statistical Analyses: Linear
regression, GLM
Covariates: variables that were
significantly associated with ER
visits were retained in the model
Statistical Package: SAS/STAT,
SAS/ETS 6.08
Lag: 1,2 days avgs
SO2; r = - 0.22 Categorical analysis (via ANOVA) p-value
O3; r = 0. 17 and Pearson correlation coefficient (r) using
TSP; r = 0.23 continuous data comparing daily air
pollutant levels and daily number of ER
visits
Age Group: 3-12 13-21
LagO r = 0.10 r = 0.09
p = 0.0019 p<0.00l
Lagl r = 0.12 r = 0.04
p<0.00l p = 0.0014
Lag 2 r=0.14 r=0.03
p<0.00l p = 0.0066
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Hwang and Chan
(2002)
Taiwan
Period of Study:
1998
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-hr avg: 23.6ppb,
SD = 5.4, Range: 13.0,34.1
S02
PM10
03
CO
No correlations
for individual
pollutants.
Colinearity of
pollutants
prevented use of
multipollutant
models
Increment: 10% change in NO2 (natural
avg) which is equivalent to 2.4 ppb. NOTE:
The percent change is for the rate of clinic
use NOT for relative risk for adverse effect.
Increased clinic visits for lower respiratory
disease (LRD) by age group
0-14 yrs
1.3% [1.0, 1.6] lag 0
15-64 yrs
1.5% [1.3, 1.8] lag 0
>65 yrs
1.8% [1.4, 2.2] lag 0
All ages
1.4% [1.2, 1.6]lagO
-------
OQ
to
o
o
TABLE AX6.3-2 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN:
EMERGENCY DEPARTMENT VISITS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels of NO2 &
Monitoring Stations
Copollutant Effects and Interpretation:
Correlations Relative Risk & Confidence Intervals (95%)
ASIA (cont'd)
X
Oi
Tanakaetal. (1998)
Kushiro, Japan
Period of Study:
1992-1993
Outcome(s): Asthma
ICD-9 Code(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
NO2 24-h avg
9.2 ±4.6ppb in fog
11.5 ± 5.7 in fog free days
Max NO2 24-h avg <30 ppb
NO2; r = NR
SO2;r = NR
SPM (TSP);
r = 0.70
O3; r = NR
Increment: 15 ppb
Nonatopic
OR 0.62 [0.45, 0.84]
Atopic
OR 0.81 [0.67,0.97]
*Default GAM
+Did not report correction for over-dispersion
NR: Not Reported
APHEA: Air Pollution and Health: a European Approach
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
-------
OQ
to
o
o
TABLE AX6.3-3. RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN: GENERAL
PRACTITIONER/CLINIC VISITS
X
ON
ON
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Reference, Study
Location, & Period
Mean Levels & Monitoring
Outcomes, Design, & Methods Stations
Copollutants Effects:
Correlations Relative Risk & Confidence Intervals (95%)
NORTH AMERICA
Hernandez-Garduno
etal. (1997)
Mexico City, Mexico
Period of Study:
May 15,1992-
January 31, 1993
Outcome(s): Respiratory illness
ICD9: NR
Age groups analyzed: <\5,\5+,a\\ Number of Stations: 5
ages (0-92)
Study Design: Time series
N: 24,113
Number of Clinics: 5
Statistical Analyses: Cross-correlation,
linear regression and autoregressive
error model analyses
Covariates: Long-term trend, day of
wk, temperature, humidity
Statistical Package: SAS
Lag: 0-6
O3 Increment: Maximum NO2 concentration of all
SO2 days-Mean NO2 concentration of all days
CO
NOX <\4yrs
NO2lagO: RR 1.29 ±0.09 (p< 0.01)
NO2lag6: RR1.18 ± 0.09 (p> 0.05)
15+yrs
NO2lagO: RR 1.14 ± 0.07 (p< 0.05)
NO2lag6: RR 1.10 ± 0.06 (p> 0.05)
All ages
NO2lagO: RR 1.43 ± 0.15 (p< 0.01)
NO2lag6: RR1.29 ± 0.15 (p> 0.05)
CANADA
Villeneuve et al.
(2006)
Toronto, ON, Canada
Period of Study:
1995-2000
Days: 2,190
Outcome(s) (ICD9): Allergic Rhinitis 24-havg: 25.4 ppb, SD = 7.7
(177) IQR: 10.3 ppb, range 9.2, 71.7
Age groups analyzed: >65
Study Design: Time series Number of stations: 9
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
SO2 Increment: 10. 3 ppb (IQR)
03
CO All results estimated from Stick Graph:
PM2.5
PM10_2.5 All Yr:
PM10 Mean Increase: 1.9% [-0.2, 3.8] lag 0
Warm:
Mean Increase: 0.1% [-3.2, 3.8] lag 0
Cool:
Mean Increase: 1.4% [0.0, 5.9] lag 0
-------
OQ
to
o
o
TABLE AX6.3-3 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN: GENERAL
PRACTITIONER/CLINIC VISITS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants
Correlations
Effects:
Relative Risk & Confidence Intervals (95%)
EUROPE
X
Oi
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Hajatetal. (1999)
London, United
Kingdom
Period of Study: 1992-
1994
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
Allyr24-havg:
33.6 ppb, SD = 10.5
Warm: 32.8(19.8)
Cool: 34.5(10.1)
10th-90th all yr
percentile: 24 ppb
SO2;r = 0.61 Increment: 24 ppb (90th-10th percentile)
BS; r = 0.70 Asthma
CO; r = 0.72 All ages 2.1% [-0.7, 4.9] lag 0; 3.1% [-0.4, 6.7] lag 0-1
PM10;r= 0.73 0-14 yrs 6.1% [1.2, 11.3] lag 1; 6.9% [1.7, 12.4] lag 0-1
03;r=-0.10 Warm: 13.2% [5.6, 21.3] lag 1
Cool: -0.1% [-6.3, 6.5] lag 1
15-64 yrs 3.0% [-0.7, 6.7] lag 0; 3.1% [-1.6, 7.9] lag 0-3
Warm: 3.3% [-2.0, 8.9] lag 0
Cool: 2.6% [-2.3, 7.7] lag 0
65+ yrs 9.9% [1.6, 18.7] lag 2; 5.3% [-3, 14.3] lag 0-3
Warm: 18.6% [6.3, 32.4] lag 2
Cool: -0.5%-9.6, 11.8] lag 2
Lower Respiratory disease
All ages 1.3% [-0.4, 3.0] lag 1; 1.2% [-0.7, 3.1] lag 0-2
0-14 yrs 4.8% [1.3, 8.3] lag 2; 4.5% [0.4, 8.7] lag 0-3
Warm: 1.4% [-3.8, 6.9] lag 2
Cool: 7.2% [2.8, 11.6] lag 2
15-64 yrs 1.1% [-1.1, 3.4] lag 2; 0.8% [-1.8, 3.5] lag 0-2
Warm: 2.3% [-1.2, 5.9] lag 2
Cool: 0.2% [-2.6, 3.1] lag 2
65+-1.7% [-4.3, 1.1] lag 0
Warm: -1.7% [-5.9, 2.6] lag 0
Cool: -1.6% [-4.8,1.8]lagO
Two-pollutant model-Asthma
NO2 alone 5.2% [0.8, 9.8]
N02/03 6.7% [2.2, 11.4]
N02/S023.9%[-1.2, 9.2]
N02/PM10 5.3% [-0.6, 11.6]
Two-pollutant model - Lower Respiratory disease
NO2 alone 4.2% [1.1, 7.3]
NO2/O34.9%[1.8, 8.2]
NO2/SO22.5%[-1.1,6.2]
NO2/PM103.5%[0.1,6.9]
-------
OQ
to
o
o
TABLE AX6.3-3 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN: GENERAL
PRACTITIONER/CLINIC VISITS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants
Correlations
Effects:
Relative Risk & Confidence Intervals (95%)
EUROPE (cont'd)
Increment: 24 ppb (90th-10th percentile)
Single-pollutant model
<1 to 14 yrs
11.0% [3.8, 18.8] lag 4
12.6% [4.6, 21.3] lag 0-4
15 to 64 yrs
5.5% [2.0, 9.1] lag 6
11.1% [6.8, 15.6] lag 0-6
>64 yrs - too small for analysis
Two-pollutant models
<1 to 14 yrs
N02&03: 7.9% [0.6, 15.8]
N02&S02: -3.8% [11.8, 5.0]
N02&PM10: 10.8% [0.1, 22.7]
15 to 64 yrs
N02&03: 4.8% [1.0, 8.8]
NO2&SO2: 1.0% [-3.7, 5.8]
NO2 & PM10: 0.5% [-4.9, 6.3]
X
Oi
ON
OO
Hajat*etal. (2001)
London, United
Kingdom
Period of Study:
1992-1994
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
NO224-havg: 33.6 ppb,
SD=10.5
# of Stations: 3;
r = 0.7-0.96
SO2;r=0.61
BS;r=0.70
CO; r = 0.72
PM10; r = 0.73
O3;r=-0.10
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
-------
OQ
to
o
o
TABLE AX6.3-2 (cont'd). RESPIRATORY HEALTH EFFECTS OF OXIDES OF NITROGEN: GENERAL
PRACTITIONER/CLINIC VISITS
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants
Correlations
Effects:
Relative Risk & Confidence Intervals (95%)
EUROPE (cont'd)
X
Oi
ON
VO
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Hajat*etal. (2002)
London, United
Kingdom
Period of Study: 1992-
1994
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
NO224havg: 33.6 ppb,
SD=10.5
Warm (April-Sept)
Mean: 32.8 ppb,
SD= 10.1
Cool (Oct-March)
Mean: 34.5 ppb,
SD=10.1
# of Stations: 3
SO2;r=0.61
BS;r=0.70
CO; r = 0.72
PM10; r = 0.73
O3;r=-0.10
Increment (90th-10th percentile): All yr: 24 ppb; Warm
season: 25.8 ppb; Cool season: 22.1 ppb
Single-pollutant model
Allyr
0-14 yr 2.0% [-0.3, 4.3] lag 3
15-64 yrs 5.1% [2.0, 8.3] lag 2
>65 yrs 8.7% [3.8, 13.8] lag 2
Warm
0-14 yrs 2.5% [-0.9, 6.1] lag 3
15-64 yrs 6.7% [3.7, 9.8] lag 2
>65 yrs 6.6% [-1.1, 14.9] lag 2
Cool
0-14 yrs 1.7% [-1.1, 4.6] lag 3
15-64 yrs 1.2% [-1.3, 3.9] lag 2
>65 yrs 9.4% [2.8, 16.4] lag 2
Two-pollutant models
0-14 yrs
N02&03: 1.7% [-0.6, 3.9]
N02&S02: 2.2% [-0.4, 5.0]
NO2&PM10: 1.5% [-1.7, 4.8]
For 15-64 yrs
N02 & 03: 4.4% [2.2, 6.8]
NO2&SO2: 4.4% [1.6, 7.2]
NO2 & PM10: 2.7% [-0.5, 5.9]
For >65 yrs
N02&03: 8.1% [3.0, 13.6]
NO2&SO2: 8.6% [2.1,15.4]
N02&PM10: 4.3% [-2.8, 11.8]
* Default GAM
+ Did not report correction for over-dispersion
NR: Not Reported
APHEA: Air Pollution and Health: a European Approach
-------
OQ
to
o
o
TABLE AX6.4-1. HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS AND
VISITS: UNITED STATES AND CANADA
Effects: Relative Risk or Percent Change
Copollutants & Confidence Intervals ([95% Lower,
(Correlations) Upper])
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels & Monitoring
Stations
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
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
NO2 daily 1-h max (ppb): H+ (0.25)
Mean: 38.5 SO4(0.34)
CV: 29 TP(0.61)
Min: 12 FP (0.45)
25thpercentile: 31 CP(0.61)
SOthpercentile: 38 COH (0.61)
75thpercentile: 45 O3 (0.07)
Max: 81 SO2(0.46)
CO (0.25)
# of Stations: 6-11
(Results are reported for
additional metrics including
24 h avg and daytime avg
(day))
Results reported for RR for an IQR increment
increase in NO2. T ratio in parentheses.
All Cardiac Disease
Single-pollutant model
1.049 (3.13), daily avg over 4 days, lag 0
Multipollutant model
1.30 (1.68), w/NO2,O3,SO2,
Objective of study was to evaluate the role of
particle size and chemistry on cardio and
respiratory diseases. NO2 attenuated the
effect of paniculate in this study.
-------
OQ
to
o
o
TABLE AX6.4-1 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: UNITED STATES AND CANADA
Effects: Relative Risk or Percent Change
Copollutants & Confidence Intervals ([95% Lower,
(Correlations) Upper])
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels & Monitoring
Stations
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Burnett etal. (1999)*
Metropolitan Toronto
(Toronto, North York,
East York, Etobicoke,
Scarborough, York),
Canada
Study Period:
1980-1995, 15 yr
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: SPLUS
Lag(s): 0-2 day
NO2 daily avg (ppb)
Mean: 25.2
Sthpercentile: 13
25thpercentile: 19
SOthpercentile: 24
75thpercentile: 30
95thpercentile: 42
Max: 82
Multiple day avgs used in
models
PM2.5 (0.50)
PMj 0-2.5 (0.38)
PM10 (0.52)
CO (0.55)
SO2 (0.55)
O3(-0.04)
Results reported for % increase in hospital
admissions for an increment increase in
NO2 equal to the mean value.
Single Pollutant Models:
Dysrhythmias: 5.33 (1.73) 3-day avg, lag 0
Heart Failure: 9.48 (6.33), 1 day, lagO
IHD: 9.73 (8.4) 2-day avg, lag 0
Cerebrovascular disease: 1.98 (1.34),
1 day, lag 0
Peripheral circulation: 3.57(1.78), 1-day,
lagO
Multipollutant Models:
Heart failure
6.89 (w/ CO)
6.68 (w/ CO, PM2 5)
6.33 (w/ CO, PM2.5, PMj 0-2.5)
6.45 (w/ CO, PM2.5, PMj 0-2.5, PM10)
IHD
8.34 (w/ CO, SO2)
7.76 (w/ CO, SO2, PM2 5)
8.41 (w/ CO, SO2, PM2.5, PMj0-2.5)
8.52 (w/ CO, SO2, PM2.5, PMj0-2.5, PM10)
In multipollutant models, gaseous
pollutants were selected by stepwise
regression. PM variables were then added
to the model.
-------
OQ
to
o
o
TABLE AX6.4-1 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: UNITED STATES AND CANADA
Effects: Relative Risk or Percent Change
Copollutants & Confidence Intervals ([95% Lower,
(Correlations) Upper])
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels & Monitoring
Stations
X
to
Morris etal. (1995)
US (Chicago, Detroit,
LA, Milwaukee,
NYC, Philadelphia)
Study Period:
1986-1989, 4 yr
Outcome(s) (ICD9): CHF 428.
Daily Medicare hospital
admission records.
Study Design: Time series
Statistical Analyses: GLM,
negative binomial distribution
Age groups analyzed: >65yrs
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 day
NO21 h-max (ppb)
Mean (SD)
LA: 0.077(0.028)
Chicago: 0.045 (0.013)
Philadelphia: 0.054 (0.017)
New York: 0.064(0.022)
Detroit: 0.041 (0.015)
Houston: 0.041 (0.017)
Milwaukee: 0.040(0.014)
SO21-hmax
Os 1-h max
CO 1-hmax
Correlations of
NOa with other
pollutants strong.
Multipollutant
models run.
Results reported for RR of admission for
CHF associated with an incremental increase
in NO2 of 10 ppb.
CHF:
LA: 1.15(1.10, 1.19)
Chicago: 1.17(1.07, 1.27)
Philadelphia: 1.03(0.95,1.12)
New York: 1.07(1.02, 1.13)
Detroit: 1.04(0.92,1.18)
Houston: 0.99(0.88, 1.10)
Milwaukee: 1.05 (0.89, 1.23)
RR diminished in multipollutant models
(4 copollutants).
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
-------
OQ
to
o
o
TABLE AX6.4-1 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: UNITED STATES AND CANADA
Effects: Relative Risk or Percent Change
& Confidence Intervals ([95% Lower,
Upper])
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants
(Correlations)
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
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
N02 24 h (ppb)
10th: 13.71
25th: 18.05
Median: 23.54
75th: 29.98
90th: 36.54
NO2 data not
available for
Birmingham, Salt
Lake, and Seattle
PM10 (0.53)
CO, S02
Correlation only
provided for PM
because study
hypothesis
involves PM
Results reported for percent increase in
stroke admissions for an incremental
increase in NO2 equivalent to one IQR
(11.93).
Ischemic Stroke: 2.94 (1.78, 4.12), lag 0
Hemorrhagic Stroke: 0.38 (-2.66, 3.51),
lagO
Multipollutant models not run.
-------
OQ
to
o
o
TABLE AX6.4-1 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: UNITED STATES AND CANADA
Effects: Relative Risk or Percent Change
Copollutants & Confidence Intervals ([95% Lower,
(Correlations) Upper])
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Fung et al. (2005)
Windsor, Ontario,
Canada
Study Period:
April 1995-Jan 2000
Outcome(s) (ICD9): CHF 428; IHD
410-414; dysrhythmias 427. 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 yr
Statistical Software: SPLUS
Lag(s): lag 0, 2, 3 day avg
NO21-hmax (ppb):
Mean(SD): 38.9(12.3)
Min: 0
Max: 117
S02 (0.22)
CO (0.38)
O3 (0.26)
COH (0.39)
PM10 (0.33)
X
Results expressed as percent change
associated with an incremental increase in
NO2 equivalent to the IQR (16 ppb)
Cardiac:
65+ age group:
0.8(2.2, 3.9), lag 0
0.9 (-2.7, 4.6), 2-day avg (lag 0-1)
0.8 (-3.3, 5.0), 3-day avg (lag 0-2)
Effect for NO2 not observed in these data.
Association of SO2 with cardiac admissions
observed.
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
-------
OQ
to
o
o
TABLE AX6.4-1 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: UNITED STATES AND CANADA
Effects: Relative Risk or Percent Change
Copollutants & Confidence Intervals ([95% Lower,
(Correlations) Upper])
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Linn et al. (2000) *
Metropolitan Los
Angeles, USA
Study Period:
1992-1995
Outcome(s) (ICD9): CVD
390-459; Cerebrovascular 430-
438; CHF 428; MI 410; cardiac
ARR 427; Occlusive Stroke 430-
435. Hospital admission records
used to ascertain cases.
Study Design: Time series
Statistical Analyses: Poisson
regression, GAM
Covariates: day of wk, holidays,
long-term trend, seasonal variation,
temperature, humidity
Lag(s): 0-1
Seasons: Winter, Spring, Summer,
Autumn
Statistical Software: SPSS, SAS
NO2 24 h (pphm)
Winter
Mean: (SD) 3.4 (1.3)
Range: 1.1,9.1
Spring
Mean(SD): 2.8(0.9)
Range: 1.1,6.1
Summer
Mean(SD): 3.4(1.0)
Range: 0.7,6.7
Autumn
Mean(SD): 4.1(1.4)
Range: 1.6,8.4
CO (0.84, 0.92)
O3 (-0.23, 0.11)
PM10(- 0.67, 0.8)
Range in
correlations depends
on the season,
independent effects
of pollutants could
not be distinguished.
# Stations: 6+
Results reported as increase % increase in
admission for a 10 ppb increase in NO2.
SD in parentheses. Season-specific
increases reported when statistically
significant.
CVD
All Seasons: 1.4(0.2)
Winter: 1.6(0.4)
Spring: 0.1 (0.6)
Summer: 1.1 (0.5)
Autumn: 1.4(0.3)
Cerebrovascular
All Seasons: 0.4(0.4)
Winter: -1.3(0.7)
Spring: 4.2(1.2)
Summer: 0.9 (1.2)
Autumn: 0.7(0.6)
MI
1.1(0.5)
CHF
1.0(0.5), winter 1.9 (0.9)
Cardiac Arrhythmia
0.6 (0.5)
Occlusive stroke
2.0 (0.5), winter 2.7 (1.0), autumn 0.1
(0.05)
-------
OQ
to
o
o
TABLE AX6.4-1 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: UNITED STATES AND CANADA
Effects: Relative Risk or Percent Change
Copollutants & Confidence Intervals ([95% Lower,
(Correlations) Upper])
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Lippmann et al.
(2000*; reanalysis Ito,
2003, 2004)
Windsor Ontario (near
Detroit MI)
Study period:
1992-1994 (hospital
admissions - mortality
study spanned longer
period)
Outcome(s): IHD 410-414;
dysrhythmias 427; heart failure 428;
stroke 431-437.
Study Design: Time series
Statistical Analysis: Poisson regression
GAM. Results of reanalysis by Ito
2003, 2004 with GLM are presented.
Lag(s): 0-3 day
NO2 24-h avg (ppb)
5th %: 11
25th %: 16
50th %: 21
75th %: 26
95th %: 36
Mean: 21.3
PM10 (0.49)
PM25 (0.48)
PM10_2.5 (0.32)
H+(0.14)
SO4 (0.35)
03(0.14)
SO2 (0.53)
CO (0.68)
Results reported for RR for incremental
increase in NO2 of 5th to 95th percentile.
IHD
1.01(0.94, 1.10), lag 0
Dysrhythmias
0.98(0.86, 1.12)
Heart Failure
1 (0.91, 1.09)
Stroke
0.99 (0.90, 1.09)
X
Oi
-!j
Oi
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Mann et al. (2002)*
South coast air basin
ofCA,US
Study Period:
1988-1995, Syr
Outcome(s) IHD 410-414; or IHD with
accompanying diagnosis of CHF 428;
or Arrhythmia 426, 427; Ascertained
through health insurance records.
Study Design: Time series
N: 54,863 IHD admissions
Age groups analyzed: <40; 40-59;
>60.
Statistical Analysis: Poisson regression
with GAM, results pooled across air
basins using inverse variance weighting
as no evidence of heterogeneity was
observed.
Covariates: study day, temperature,
relative humidity, day of wk.
Lag(s): 0-2, 2-4 day moving avg
Software: SPLUS
Seasons: Some analyses restricted to
April-October
NO2 24-h avg (ppb):
Exposure assigned for
each air basin based
on health insurance
participant's zip code.
Mean(SD): 37.2
(15.7)
Range: 3.69, 138
Median: 34.8
# Stations: 25-35
O3 8 h-max
(-0.16,0.54)
CO 8-h max
(0.64, 0.86)
PM10 24-h avg
(0.36, 0.60)
Range depends
on air basin
No multipollutant
models run.
Traffic pollution
generally
implicated in
findings.
Results reported for percent increase in
admissions for a 10 ppb incremental increase
inNO2.
All IHD
1.68(1.08,2.28)
IHD w/ secondary diagnosis of Arrhythmia:
1.81(0.78,2.85)
IHD w/ secondary diagnosis of CHF:
2.32 (0.69, 3.98)
IHD w/ no secondary diagnosis:
0.46 (-0.81, 1.74)
Effect of secondary diagnosis strongest in
the 40-59 age group.
Group with secondary CHF may be sensitive
subpopulation or their vulnerability may be
due to greater prevalence of MI as the
primary diagnosis.
-------
OQ
to
o
o
TABLE AX6.4-1 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: UNITED STATES AND CANADA
Effects: Relative Risk or Percent Change
Copollutants & Confidence Intervals ([95% Lower,
(Correlations) Upper])
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Metzger et al. (2004)
Atlanta, GA
Period of Study: Jan
1993-Aug312000,
4yr
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/day
# 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, April 15-October
14; Cool, October 15-April 14.
Lag(s): 0-3 day
NO21-hmax (ppb):
Median: 26.3
10th-90thpercentile
range 25, 68
PM10 24 h (0.49)
O3 8-h max (0.42)
SO2 (0.34)
CO 1 h (0.68)
1998-2000 Only
PM25 (0.46)
Course PM (.46)
Ultrafine PM (.26)
Water-soluble
metals (.32)
Sulfates(.17)
OC (0.63)
EC (.37)
OHC (0.3)
Multipollutant
models used. All
models specified a
priori.
Results presented for RR of an incremental
increase inNO2 equivalent to 1 SD (3-day
moving avg).
All CVD: 1.025 (1.012, 1.039)
Dysrhythmia: 1.019 (0.994, 1.044)
CHF: 1.010 (0.981, 1.040)
IHD: 1.029 (1.005, 1.053)
PERI: 1.041 (1.013, 1.069)
Finger wounds 1.010 (0.993, 1.027)
NO2 effect was generally attenuated in
two-pollutant models. The attenuation was
strongest in the period after 1998.
-------
OQ
to
o
o
TABLE AX6.4-1 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: UNITED STATES AND CANADA
Effects: Relative Risk or Percent Change
Copollutants & Confidence Intervals ([95% Lower,
(Correlations) Upper])
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
X
oo
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Moolgavkar (2000b)*
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/day
# 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
NO2 24-h avg (ppb)
Cook County:
Min: 7
Ql: 20
Median: 25
Q3: 30
Max: 58
NO2 24-h avg (ppb) LA
County:
Min: 10
Ql: 30
Median: 38
Q3: 48
Max: 102
NO2 24-h avg (ppb)
Maricopa County:
Min: 2
Ql: 14
Median: 19
Q3: 26
Max: 56
PM10 (0.22, 0.70)
PM25 (0.73) (LA
only)
CO (0.63, 0.80)
SO2 (0.02, 0.74)
O3(-0.23, 0.02)
Two-pollutant
models (see results)
Results reported for percent change in
hospital admissions per 10 ppb increase in
NO2. T statistic in parentheses.
CVD, 65+:
Cook County
2.9(10.2), lag 0
2.3 (6.7), lag 0, two-pollutant model (PM10)
2.9 (8.1), lag 0, two-pollutant model (CO)
2.8 (8.8), lag 0, two-pollutant model (SO2)
LA County
2.3(16.7), lag 0
-0.1 (-0.5), lag 0, two-pollutant model (CO)
1.7 (8.0), lag 0, two-pollutant model (SO2)
Maricopa County
2.9 (4.1), lag 0
-0.3 (-0.3), lag 0, two-pollutant model (CO)
2.6 (3.6), lag 0, two-pollutant model (SO2)
Cerebrovascular Disease, 65+:
Cook County
1.6(3.6)
LA County
(5.7)
Effect size generally diminished with
increasing lag time. Increase in hospital
admissions (1.3 for CVD and 1.9 for
Cerebrovascular) also observed for the
20-64 age group.
-------
CJQ
TABLE AX6.4-1 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: UNITED STATES AND CANADA
t-*
r- 1-
to
o
o
^
X
ON
i
VO
^>
H
6
o
0
H
O
o
H
W
O
O
HH
H
W
Reference, Study
Location, & Period Outcomes, Design, & Methods
Moolgavkar (2003) Outcome(s) (ICD9): CVD
Cook County IL, Los 390-429; Cerebrovascular disease
Angeles County, CA, 430-448 was not considered in the
Maricopa County, reanalysis. Hospital admissions
AZ from CA department of health
database.
1987-1995 Age groups analyzed: 20-64,
£_ C_l_ ~Trc,
to+ 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
Mean Levels & Copollutants
Monitoring Stations (Correlations)
NO2 24-h avg (ppb) PM10 (0.22, 0.70)
Cook County: PM25 (0.73) (LA only)
Min: 7 CO (0.63, 0.80)
Ql: 20 SO2 (0.02, 0.74)
Median: 25 O3 (-0.23, 0.02)
Q3: 30
Max: 58 Two-pollutant models
NO2 24-h avg (ppb) (see results)
LA County:
Min: 10
Ql: 30
Median: 38
Q3: 48
Max- 10?
IVAdA.. J.VJ £*
NO2 24-h avg (ppb)
Maricopa County:
Min: 2
Ql: 14
Median: 19
Q3: 26
Max: 56
Effects: Relative Risk or Percent Change
& Confidence Intervals ([95% Lower,
Upper])
Results for CVD not shown but use of
stringent criteria in GAM did not alter
results substantially. However, increased
smoothing of temporal trends attenuated
results for all gases.
-------
OQ
to
o
o
TABLE AX6.4-1 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: UNITED STATES AND CANADA
Effects: Relative Risk or Percent Change
Copollutants & Confidence Intervals ([95% Lower,
(Correlations) Upper])
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
X
oo
o
H
6
o
o
H
O
o
H
W
O
O
HH
H
W
Peel et al. (2006)
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
NO2l-hmax(ppb):
Mean(SD): 45.9(17.3)
10th: 25.0
90th: 68.0
PM10 24-h avg
Os 8-h max
SO2l-hmax
CO 1-hmax
Correlations not
reported
Results expressed as OR for association of
CVD admissions with a 20 ppb incremental
increase in NO2.
Comorbid Hypertension
IHD: 1.036 (0.997, 1.076)
Dysrhythmia: 1.095(1.030,1.165)
PERI: 1.031(0.987, 1.076)
CHF: 1.037 (0.985, 1.090)
Comorbid Diabetes:
IHD: 1.003 (0.95, 1.059)
Dysrhythmia: 1.158(1.046,1.282)
PERI: 1.012 (0.947, 1.082)
CHF: 1.017 (0.959, 1.078)
-------
OQ
to
o
o
TABLE AX6.4-1 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: UNITED STATES AND CANADA
Effects: Relative Risk or Percent Change
Copollutants & Confidence Intervals ([95% Lower,
(Correlations) Upper])
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
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: SPLUS
NO224-havg(ppb):
Mean: 19.3
10th: 9.9
25th: 13.2
50th: 19
75th: 24.6
90th: 29.8
PM10 (0.326)
O3(- 0.456)
S02 (0.482)
CO (0.673)
Results reported as a percent increase in
admission for an increment in NO2
equivalent to the IQR (11.4 ppb).
CVD 0.69% (-2.3, 3.8)
Tuscon selected to minimize correlations
between pollutants. Since there was no
association between NO2 and admissions,
author suggests results for CO not
confounded by NO2.
X
ON
oo
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Stieb et al. (2000) *
Saint John, New
Brunswick Canada
Study Period: July
1992-March 1996
Outcome(s): Angina pectoris; MI;
dysrhythmia/conduction
disturbance; CHF; All Cardiac.
ED Visits collected prospectively.
Study Design: Time series
Statistical Analyses: Poisson
regression, GAM
N: 19,821 ER visits
# Hospitals: 2
Lag(s): 1-8 days
NO2 24-h avg (ppb)
Mean(SD): 8.9(5.5)
95th: 19
Max: 35
NO2 max (ppb)
Mean(SD): 20.2
95th: 39
Max: 82
CO (0.68)
H2S(-0.07)
O3(-0.02)
SO2(0.41)
PM10 (0.35)
PM25 (0.35)
H+(-0.25)
SO4 (0.33)
COH (0.49)
Results reported for percent change in
admissions based on a single pollutant model
for incremental increase in NO2 equivalent
to 1 IQR (8.9 ppb)
Cardiac visits:
-3.9, p-value = 0.136, lag 2, allyr
10.1, p-value = 0.051, lag 5, May-September
For specific CVD diagnoses, ARR and CHF
approached significance. NO2 was not a
focus of this paper.
-------
CJQ
to
O
o
TABLE AX6.4-1 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: UNITED STATES AND CANADA
Effects: Relative Risk or Percent Change
Copollutants & Confidence Intervals ([95% Lower,
(Correlations) Upper])
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
X
Oi
oo
to
H
6
O
o
H
O
O
H
W
O
O
HH
H
W
Villeneuve et al.
(2006)
Edmonton, Canada
Study Period: April
1992-March 2002
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: April-September;
Cool: October-March.
Lag(s): 0, 1, 3 day avg
NO224hppb:
Allyr
Mean(SD): 24(9.8)
Median: 22.0
25th: 16.5
75th: 30.0
IQR: 13.5
Summer
Mean(SD): 18.6(6.4)
Median: 17.5
25th: 14.0
75th: 22.0
IQR: 8
Winter
Mean(SD): 29.4(9.6)
Median: 28.5
25th: 22.5
75th: 35.5
IQR: 13.0
O3 24-h max (-0.33)
O3 24-h avg (-0.51)
SO2 25-h avg (0.42)
CO 24-h avg (0.74)
PM10 24-h avg (0.34)
PM2 5 24-h avg (0.41)
All yr correlations
summarized.
Results reported for an incremental
increase in NO2 equivalent to one IQR
NO2.
Ischemic Stroke, Summer
.17(1.05,
.18(1.05,
.26(1.09,
Hemorrhagi
.16(0.99,
.14(0.97,
.18(0.95,
.31),lagO
.31),lagl
.46), 3 day avg
c stroke, Summer
.37)
.35)
.46)
TIA not associated with increase in NO2.
Above results are strongest effects, which
were observed during summer.
Authors attribute NO2 effect to vehicular
traffic since NO2 and CO are highly
correlated.
-------
OQ
to
o
o
TABLE AX6.4-1 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: UNITED STATES AND CANADA
Effects: Relative Risk or Percent Change
Copollutants & Confidence Intervals ([95% Lower,
(Correlations) Upper])
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
X
ON
oo
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Wellenius et al.
(2005b)
Allegheny County,
PA (near Pittsburgh)
Study Period: Jan
1987-Nov 1999
Outcome(s): CHF428. 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
NO224-havg(ppb):
Mean (SD) 26.48 (8.02)
5th: 15.10
25th: 20.61
Median: 25.70
75th: 31.30
95th: 4102
# Stations: 2
PM10 (0.64)
CO (0.70)
O3(-0.04)
S02 (0.52)
Results reported for the percent increase in
admissions for an increment of NO2
equivalent to one IQR (11 ppb)
CHF, single-pollutant model
4.22(2.61,5.85), lag 0
CHF, two-pollutant model
4.05 (1.83, 6.31), adjusted for PM10
-0.37 (-2.59, 1.89), adjusted for CO
3.73 (2.10, 5.39), adjusted for O3
3.79 (1.93, 5.67), adjusted for SO2
CHF admission was 3 x higher among those
with history of MI.
-------
OQ
to
o
o
TABLE AX6.4-1 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: UNITED STATES AND CANADA
Effects: Relative Risk or Percent Change
Copollutants & Confidence Intervals ([95% Lower,
(Correlations) Upper])
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
X
ON
oo
Zanobetti and
Schwartz (2006)
Boston, MA
1995-1999
Outcome(s) (ICD9): MI 410.
Admissions through the emergency
room from Medicare claims.
Age group analyzed: 65+ yrs
Study Design: Case crossover,
control days matched yr, mo and
temperature
Statistical Analysis: Conditional
logistic regression
N: 15,578
Covariates: temperature
(regression spline), day of wk
Seasons: Hot (April-September)
and cold
Software: SAS
Lags: 0, 0-1 previous day avg
NO2 24-h avg ppb
5th: 12.59
25th: 18.30
Median: 23.20
75th: 29.13
95th:
90th-10th: 20.41
# Stations: 4
Os (-0.14)
BC (0.70)
CO (0.67)
PM25 (0.55)
PM non-traffic (0.14)
(residuals from model
of PM2 5 regressed on
BC)
Results reported for percent increase in
admissions for incremental increase in NO2
equivalent to the 90th-1 Oth percentiles
(20.41 or 16.80 for 0-1, previous day avg).
MI
10.21(3.82, 15.61), lag 0
12.67 (5.82, 18.04), lag 0-1, previous
day avg
Results suggest traffic exposure is
responsible for the observed effect. Effects
more pronounced in the summer season.
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
* 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
-------
OQ
to
o
o
TABLE AX6.4-2. HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS AND
VISITS: AUSTRALIA AND NEW ZEALAND
X
ON
oo
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Reference, Study
Location, & Period
Barnett et al. (2006)
Australia and New
Zealand: Brisbane,
Canberra, Melbourne,
Perth, Sydney
Outcomes, Design, & Methods
Outcome(s) (ICD9): All CVD
390-459; ARR 427; Cardiac
disease 390-429; Cardiac failure
428; IHD 410-413; MI 410;
Stroke 430-438.
Mean Levels &
Monitoring Stations
N02 (ppb)
1-havg: 15.7,23-2
24-havg: 7.1, 11.5
IQR: 5.1
Copollutants
(Correlations)
PM10 24 h
CO24h
S02 24 h
038h
BS24h
Effects: Relative Risk or Percent Change &
Confidence Intervals ([95% Lower, Upper])
Results reported for % change in hospital
admissions associated with one IQR increase in
NO2
Arrhythmia
Period of Study:
1998-2001
Ages groups analyzed: 15-64 yrs,
>65yrs
Study Design: Time stratified,
case-crossover, multicity study
# of Hospitals: A11ER
admissions from state government
health departments
Statistical Analyses: Random
effects meta analysis,
heterogeneity assessed using I2
statistic.
Covariates: Matched analysis
controlling for long-term trend,
seasonal variation and respiratory
epidemics. Temperature (current-
previous day) and relative
humidity, pressure, extremes of
hot and cold, days of wk,
holidays, day after holiday,
rainfall in some models. Matched
on copollutants.
Statistical Package: SAS
Lag: 0-3
# of Stations: 1-13
depending on the city
Matched analysis
conducted to
control for
copollutants
>65: 0.4 (-1.8, 2.6)
15-64: 5.1(2.2,8.1)
Cardiac
>65: 3.4(1.9,4.9)
15-64: 2.2(0.9,3.4)
Cardiac failure
>65: 6.9(2.2, 11.8)
15-64: 4.6(0.1,6.1)
IHD
>65: 2.5(1.0,4.1)
15-64: 0.7 (-1.0, 2.4)
MI
>65: 4.4(1.0,8.0)
15-64: 1.7 (-1.1, 2.4)
All CVD
>65: 3.0(2.1,3.9)
15-64: 1.7(0.6,2.8)
NO2 association became smaller when matched
with CO. Authors hypothesize that NO2 is a
good surrogate for PM which may explain these
associations.
-------
OQ
to
o
o
TABLE AX6.4-2 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS AND
VISITS: AUSTRALIA AND NEW ZEALAND
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels & Monitoring
Stations
Copollutants Effects: Relative Risk or Percent Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
X
Oi
oo
Oi
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Simpson et al. (2005b)
Australia (Brisbane,
Melbourne, Perth,
Sydney).
Study Period:
Jan 1996-Dec 1999
Hinwood et al. (2006)
Perth, Australia
Study Period:
1992-1998
Outcome(s) (ICD9): Cardiac
disease 390-429; IHD 410-413;
stroke 430-438.
Study Design: Time series.
Statistical analysis: APHEA2
protocol, GAM (did not indicate
use of stringent convergence
criteria), GLM with natural
splines, penalized splines.
Random effects meta-analysis
with tests for homogeneity.
Age groups analyzed: All, 15-64,
65+
Covariates: long-term trend,
temperature, humidity, day of
wk, holidays, influenza
epidemics
Software package: SPLUS, R
Lag(s): 1-3 days
Outcome(s): All CVD
unscheduled admissions.
Obtained from discharge records
using ICD9 Codes.
Age groups analyzed: all ages,
65+
Study design: Case crossover,
time stratified with 3-4 controls
within same mo
Statistical Analysis: conditional
logistic regression
N: 26.5 daily CVD admissions
Seasons: Nov-April, May-Oct
NO l-hmax(ppb):
Mean (range):
Brisbane: 21.4(2.1,63.3)
Sydney: 23.7(6.5,59.4)
Melbourne: 23.7 (4.4, 66.7)
Perth: 16.3 (1.9, 41.0)
NO2 24 h (ppb)
Mean: 10.3
SD: 5
lOthpercentile: 4.4
90thpercentile: 17.1
NO21-h max (ppb)
Mean: 24.8
SD: 10.1
lOthpercentile: 13.3
90thpercentile: 37.5
# of Stations: 3
PM10 24 h
PM25
BS 24 h (0.29, 0.62)
03lh
C08h
Not all correlations
reported. NO2 affect
attenuated slightly
when modeled with
BS but not with O3
May be confounding
of NO2 effect by
paniculate.
O3lh, 8h(-.06)
CO 8 h (.57)
BSP24h(.39)
Single-city results reported for percent increase
for an increment in 1-h max NO2 equivalent to
one IQR. Pooled results reported for an
increment of 1 ppb NO2.
Cardiac
All ages: 1.0023 (1.0016, 1.0030), lag 0-1
15-64: 1.0015 (1.0006, 1.0025), lagO
>65: 1.0018(1.0011, 1.0025), lag 0-1
IHD
All ages: 1.0019(1.0010,1.0027)
>65: 1.0017(1.0007, 1.0027)
No effect for stroke.
Heterogeneity in CVD results among cities,
probably due to different pollutant mixtures,
may have affected the results.
Results reported for OR per incremental
increase of 1 ppb NO2.
All CVD (estimated from graph)
NO2 24 h >65: 1.005 (1.001, 1.006), lag 1
NO2 8 h All ages: 1.0045 (1.0012, 1.0075),
lagl
NO2 8 h >65: 1.0036 (1.001, 1.0065), lag 1
-------
OQ
to
o
o
TABLE AX6.4-2 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: AUSTRALIA AND NEW ZEALAND
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants Effects: Relative Risk or Percent Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
X
ON
oo
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
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 (November-April) and cool
periods (May-October).
NO2l-havg
Mean: 32.2
SD: 7.4
Min: 5.2
Ql: 18.2
Median: 23
Q3: 27.5
Max: 59.4
# of Stations: 14
BS24-havg
(0.35)
PM1024-havg
(0.44)
PM2 5 24-h avg
(0.45)
CO8-havg
(0.55)
O3 1-havg
(0.45)
SO2 24-h avg
(0.56)
Two-pollutant
models to adjust
for copollutants
Results reported for % change in hospital
admissions associated with one IQR increase in
24hNO2.
All CVD
2.32(1.45, 3.19), lag 0
Cardiac Disease
2.00(0.81, 3.20), lag 0
IHD
2.11(0.34, 3.91), lag 0
Stroke
-1.66 (-3.80, 0.51) lag 0
Effect of NO2 attenuated when CO was
included in the model. NO2 effect most
prominent during the cool season.
-------
OQ
to
o
o
TABLE AX6.4-2 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: AUSTRALIA AND NEW ZEALAND
Reference, Study
Location, & Period
Outcomes, Design, &
Methods
Mean Levels &
Monitoring Stations
Copollutants Effects: Relative Risk or Percent Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
X
ON
oo
oo
Morgan etal. (1998a)
Sydney, Australia
Study Period:
Jan 1990-Dec 1994
Outcome(s) (ICD9): Heart
Disease 410, 413, 427, 428.
Inpatient statistics database
for New South Wales Health
Department.
Study Design: Time series
Statistical Analysis: Poisson
regression, GEE
# Hospitals: 27
Covariates: daily mean
temperature, dew point
temperature
Lag(s): 0-2 days, cumulative
Statistical Software: SAS
NO224-havg(ppb):
Mean(SD): 15(6)
IQR: llppb
10th-90th: 17
NO21-hmax (ppb):
Mean(SD): 29(3)
10-90th: 29 ppb
NO224-hmax: 52
NO21-hmax: 139
# Stations: 3-14
(1990-1994)
O3 1-hmax (-0.086)
PM (0.533, 0.506)
Correlations for 24-h
avg NO2
concentrations
Multipollutant
models
Results reported as percent increase in
admissions associated with an incremental
increase in 1-h max NO2 equivalent to the
10th-90thpercentile.
Heart Disease:
6.71 (4.25, 9.23), single pollutant, lag 0, 1-h max
6.68 (3.61, 9.84), single pollutant, lag 0, 1-h max
Results lost precision but did not change
substantially when stratified by age or when 24-h
averaging time was used.
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
-------
TABLE AX6.4-2 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
CJQ
S
O
^"
X
ON
VO
o
^
H
6
21
n
s.**
H
O
^
O
H
W
O
O
HH
H
W
AND VISITS: AUSTRALIA AND NEW ZEALAND
Reference, Study
Location, & Period
Petroeschevsky et al.
(2001)
Brisbane, Australia
Study Period:
Jan 1987-Dec 1994,
2,922 days
* Default GAM
AMI Acute Myocardial
Infarction
ARR Arrhythmia
BC Black Carbon
COH coefficient of haze
CP Course Particulate
Outcomes, Design, &
Methods
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.
Statistical Software: SAS
Lag(s): lag 0-4, 3-day avg,
5-day avg
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
Mean Levels &
Monitoring Stations
NO2 1-h max (pphm)
Summer
Mean: 206
Min: 0.35
Max: 5.8
Fall
Mean: 2.56
Min: 0.70
Max: 5.85
Winter
Mean: 3.54
Min: 0.35
Max: 8.05
Spring
Mean: 3.12
Min: 0.55
Max: 15.58
Overall
Mean: 2.82
Min: 0.35
Max: 15.58
MI Myocardial Infarction
OC Organic Carbon
OHC Oxygenated Hydrocarbons
PERI Peripheral Vascular and
Cerebrovascular Disease
PM Particulate Matter
Copollutants Effects: Relative Risk or Percent Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
BSP Results reported for RR for CVD emergency
O3 admissions associated with a one-unit increase
SO2 m NO2 1-h max.
Correlation between CVD 15-64 yrs
pollutants not 0.986 (0.968, 1.005), lag 3
reported.
CVD 65+ yrs
0.990 (0.977, 1.003)
CVD all ages
0.987 (0.976, 0.998)
PIH primary intracerebral hemorrhage
PNC Particle Number Concentration
SHS Subarachnoid hemorrhagic stroke
TP Total Particulate
UBRE Unbiased Risk Estimator
-------
OQ
to
o
o
TABLE AX6.4-3. HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS AND
VISITS: EUROPE
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels & Monitoring
Stations
Copollutants Effects: Relative Risk or Percent Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
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 day
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 fixed 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: May to October;
Cold: November to April
Statistical Package: SPLUS
Lag: 0-3
NO224-havg (ug/m2):
Mean: 51.5
lOthpercentile: 29.5
90th percentile: 74.4
# of Stations: Depends on the
city
Correlation among stations: NR
CO 8-h max (0.58)
O38-h max (-0.03)
SO2 24 h (0.46)
BS 24 h (0.48)
TSP 24 h (0.48)
PM10 24 h (0.40)
Two-pollutant models
used to adjust for
copollutants
Results reported for % change in hospital
admissions associated with 10 ug/m2 increase in
N02
All CVD
0.38% (0.07%, 0.69%), lag 0-1
Heart Diseases:
0.86% (0.44%, 1.28%)
Effect of NO2 was diminished in two-pollutant
models.
-------
TABLE AX6.4-3 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
CJQ
r-K
to
o
o
;>
X
ON
S
O
l>
H
6
o
2;
0
H
O
O
H
W
O
O
HH
H
W
Reference, Study
Location, & Period Outcomes, Design, & Methods
Lanki et al. (2006) Outcome(s) (ICD9): AMI 410.
Europe (Augsburg, Ascertained from discharge
Helsinki, Rome, records or AMI registry data
Stockholm) depending on the city.
Study Design: Time series
Study period: Statistical Analysis: Poisson
1992-2000 regression, for non- linear
confounders - penalized splines
in GAM chosen to minimize
UBRE score. Random-effects
model for pooled estimates.
N: 26,854 hospitalizations
Statistical Software: R package
Covariates: barometric pressure,
temperature, humidity.
Lag(s): 0-3 day
AND VISITS: EUROPE
Mean Levels & Monitoring Copollutants Effects: Relative Risk or Percent Change &
Stations (Correlations) Confidence Intervals ([95% Lower, Upper])
NO2(ug/m3) PM10 (0.29, 0.64) Results reported as RR associated with an
CO (0.43, 0.75) incremental increase in NO2 equivalent to the
Augsburg: O3 (0.17, 0.38) IQR (8 ug/m2)
25th: 40.2
50th: 49.2 Range in correlations Pooled results for 5 Cities:
75th: 58.9 depends on the city 0.996 (0.998, 1.015), lag 0
98th: 88.7
Barcelona Two-pollutant models No significant results observed for analyses
25th: 34.8 for PNC with O3 and stratified by age or season for lag 0/1 .
50th: 45.0 PM10only.
75th: 60.0
98th: 86.0
Helsinki
25th: 21.8
50th: 28.7
75th: 37.6
98th: 64.7
Rome
25th: 61.9
50th: 70.6
75th: 80.4
98th: 102.5
Stockholm
25th: 16.3
50th: 22.2
75th: 28.6
98th: 45.9
-------
OQ
to
o
o
TABLE AX6.4-3 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: EUROPE
X
to
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Reference, Study
Location, & Period
Von Klot et al. (2005)
Europe (Augsburg,
Barcelona, Helsinki,
Rome, Stockholm)
Study Period:
1992-2000
Outcomes, Design, & Methods
Outcome(s) (ICD9): Re-admission for AMI
410; angina pectoris 411 and 413; Cardiac
diseases including AMI angina pectoris,
dysrhythmia (427), heart failure (428).
Hospital admissions database used to identify
cases.
Population: Incident cases of MI during
1992-2000 among those >35 yrs old.
N Augsburg: 1560
N Barcelona: 1134
N Helsinki: 4026
NRome: 7384
N Stockholm: 7902
Study Design: Prospective Cohort
Statistical Analyses: Poisson regression, at
risk period from the 29th day after the index
event until the event of interest, death,
migration or loss to follow-up. GLM models,
penalized spline functions for continuous
confounders. City results pooled using
random-effects model. Heterogeneity
assessed. Sensitivity analyses conducted
varying the smooth functions, convergence
criteria, and how confounders were specified.
Statistical Software: R package
Covariates: daily mean temperature, dew
point temperature, barometric pressure,
relative humidity, vacations or holidays.
Lag: 0-3 days
Mean Levels &
Monitoring Stations
NO2 24-h avg (ug/m2):
Augsburg
Mean: 49.6
5th: 30
25th: 39.7
75th: 57.2
95th: 75.3
Barcelona
Mean: 47.7
5th: 18
25th: 34.0
75th: 60
95th: 83
Hel sinki
A/T^on- "30 1
IVlColl. JU.l
5th: 13
25th: 21.2
75th: 36.7
95th: 52.9
Rome
Mean: 15.8
5th: 5.4
25th: 10.1
75th: 21.7
95th: 25.9
Stockholm
Mean: 22.8
5th: 10.3
25th: 16
75th: 28
95th: 39.4
# Stations: 1-5
Effects: Relative Risk or Percent Change
Copollutants & Confidence Intervals ([95% Lower,
(Correlations) Upper])
CO 24 h (0.44, 0.75) Results reported for RR for incremental
O38h(-0.2,-0.13) increases in same day NO2 equivalent to the
PMin ( 29 66) mean of the city specific IQR's multiplied by
PNC ( 44 83) 0-05 (8 ug/m2). Pooled results are below:
Two-pollutant models "^
but N02, CO, and PNC 1 -028 (0.997, 1 .060)
not modeled together Angina Pectoris
because they were too 1.032 (1.006, 1.058)
highly correlated. Cardiac Diseases
1.032(1.014,1.051)
Two-pollutant models show that the effect of
NO2 independent of PM10 and O3. Traffic
exhaust may be associated with cardiac
readmission.
-------
OQ
to
o
o
TABLE AX6.4-3 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: EUROPE
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants Effects: Relative Risk or Percent Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Atkinson etal. (1999a)
London, England
Period of Study: 1992-
1994,
N= 1,096 day
Outcome(s) (ICD9): All CVD 390-
459; IHD 410-414. Emergency
admissions obtained from the
Hospital Episode Statistics (HES)
database.
Ages groups analyzed: 0-14 yr, 15-64
yr, 0-64 yr, 65+ yr, 65-74 yr, 75+ yr
Study Design: Time series, hospital
admission counts
N: 189,109 CVD admissions
Catchment area: 7 million residing in
1600 Km2 area of Thames basin.
Statistical Analyses: APHEA
protocol, Poisson regression
Covariates: adjusted long-term
seasonal patterns, day of wk,
influenza, temperature, humidity
(compared alternative methods for
modeling meteorological including
linear, quadradic, piece-wise, spline)
Seasons: warm season April-
September, cool season remaining
mos, interactions between season
investigated
Dose response investigated: yes,
bubble charts presented
Statistical Package: SAS
Lag: 0-3
l-hmax(ppb)
Mean: 50.3
SD: 17.0
Min: 22.0
Max: 224.3
10th-90thpercentile: 36
# of Stations: 3, results
averaged across stations
Correlation among
stations: 0.7-0.96
PM10 24 h
C024h
S02 24 h
O38h
BS24h
Correlations of NO2 with
CO, SO2, O3, BS ranged
from 0.6-0.7
Correlation of NO2 with
O3 negative
Two-pollutant models
used adjust for
copollutants
Results reported for % change in hospital
admissions associated with 10th-90th percentile
increase in NO2 (36 ppb)
All CVD
Ages 0-64: 1.20% (-0.62%, 3.05%), lag 0
Ages 65+: 1.68% (0.32%, 3.06%), lag 0
IHD
Ages 0-64: 1.53% (-1.22%, 4.37%), lag 0
Ages 65+: 3.03% (0.87%, 5.24), lag 0
NO2 was associated with increased CVD
admissions for all ages but this association was
stronger among those 65+ yrs old. Similar
increase associated with IHD among those 65+ yrs
old.
Monitors close to roadways were not used in the
study. Correlations for NO2 between urban
monitoring sites were high. Authors suggest that
the pollution levels are uniform across the study
area. Authors did not investigate the interaction
between meteorological variables and air
pollution. In two pollutant models, O3 had little
impact on NO2. BS moderated the association of
NO2 with CVD among the 65+ age group.
Suggestion that NO2 associations were non-linear.
-------
OQ
to
o
o
TABLE AX6.4-3 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: EUROPE
Effects: Relative Risk or Percent Change
Copollutants & Confidence Intervals ([95% Lower,
(Correlations) Upper])
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Ballesteretal. (2001)*
Valencia, Spain
Period of Study:
1992-1996
Outcome(s) (ICD9): All CVD
390-459; heart diseases 390-459;
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 non parametric
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, wk days, flu, special
events, air pollution.
Seasons: Hot season May to Oct.;
Cold season Nov to April
Statistical Package: SAS
Lag: 0-4
1-hmax (ug/m)
Mean: 116.1
SD: NR
Min: 21.1
Max: 469.0
median: 113.2
# 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 (0.03)
SO224h(0.33)
O38h(-0.26)
BS(0.33)
Two-pollutant models
used to adjust for
copollutants
Results reported for RR corresponding to a
10 ug/m2 increase inNO2
All CVD
1.0302 (1.0042, 1.0568), lagO
Heart Disease
1.0085 (0.9984, 1.0188), lag 2
Cerebrovascular Disease
1.0362(1.0066, 1.0667), lag 4
Clear association of NO2 with
cerebrovascular disease observed.
Association persisted after Inclusion of BS
and SO2 in two-pollutant models with NO2.
Cases of digestive disorders served as a
control group - null association with NO2
observed.
-------
OQ
to
o
o
TABLE AX6.4-3 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: EUROPE
Effects: Relative Risk or Percent Change
Copollutants & Confidence Intervals ([95% Lower,
(Correlations) Upper])
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
X
D'Ippolitietal.
(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: 18-64 yrs,
65-74 yrs, >75
Season: Cool: October-March;
Warm: April-September.
Lag(s): 0-4 day, 0-2 day cum avg
Dose Response: OR for increasing
quartiles presented and p-value for
trend.
NO2 24 h (ug/m3)
Mean(SD): 86.4(15.8)
25th: 74.9
50th: 86.0
75th: 96.9
IQR: 22
# Stations: 5
TSP 24 h (0.37)
SO224h(0.31)
CO 24 h (0.03)
No multipollutant
models
Results presented for OR associated with
incremental increase in NO2 equivalent to
one IQR.
AMI
1.026(1.002, 1.052), lag 0
1.026(0.997, 1.057), lag 0-2
Association observed for NO2 but TSP
association more consistent. Authors think
that TSP, CO, and NO2 cannot be
distinguished from traffic-related pollution
in general.
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Llorca et al. (2005)
Torrelavega, Spain
Study period:
1992-1995
Outcome(s) (ICD): CVD (called
cardiac in paper) 390-459.
Emergency admissions, excluding
non residents. Obtained admissions
records from hospital admin office.
Study design: Time series
Statistical analyses: Poisson
regression, APHEA protocol
Covariates: rainfall, temperature,
wind speed direction
N: 18,137 admissions
Statistical software: STATA
Lag(s): not reported
NO2 24 h ug/m3:
Mean(SD): 21.3(16.5)
TSP (-0.12)
SO2 (0.588)
SH2 (0.545)
NO (0.855)
Multipollutant
models
Results reported for RR of hospital
admissions for 100 ug/m3 increase inNO2.
Cardiac admissions:
1.27 (1.14, 1.42), 1-pollutant model
1.10 (0.92, 1.32), 5-pollutant model
Effect of NO2 diminished in multipollutant
model.
-------
OQ
to
o
o
TABLE AX6.4-3 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: EUROPE
Effects: Relative Risk or Percent Change &
Confidence Intervals ([95% Lower,
Upper])
Reference, Study Location,
& Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants
(Correlations)
X
Oi
OD
Oi
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Pantazopoulou et al.
(1995)
Athens, Greece
Study Period: 1988
(Winter and Summer)
Poloniecki et al. (1997)
London, UK
Study Period:
April 1987-March 1994,
7yrs
Outcome(s): Cardiac Disease ICD
codes not provided. Cases ascertained
from National Center for Emergency
Service database. Cases diagnosed at
time of admission so they are ED visits
and were not necessarily admitted to the
hospital.
Study design: Time series
Statistical Analyses: Linear regression
(not well described)
Covariates: Dummy variables for
winter mos with January as referent.
Dummy variables for summer mos with
April as referent. Day of the wk,
holidays, temperature, relative humidity,
N: 25,027 cardiac admissions.
Lag(s): NR
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, April-September; Cool,
October-March.
Lag: 0-1 day
NO21-h max (ug/m3):
Winter
Mean(SD): 94(25)
5th: 59
50th: 93
95th: 135
Summer
Mean(SD): 111(32)
5th: 65
50th: 108
95th: 173
# Stations: 2
NO224hppb:
Min: 8
10%: 23
Median: 35
90%: 53
Max: 198
CO,BS
No correlations
provided
Black Smoke
CO24h
SO2 24 h
O38h
Correlations
between
pollutants high
but not
specified.
Results reported for regression coefficients
based on an incremental increase in NO2 of
76 ug/m3 in winter and 108 ug/m3 in
summer (5th to 95th percentile).
Winter (regression coefficient)
11.2(3.3, 19.2)
Summer (regression coefficient)
-0.06 (-6.6, 6.5)
Results expressed as a relative rate (RR)
for an incremental increase of NO2
equivalent to 30 ppb (10th-90th percentile)
AMI: 1.0274 (1.0084, 1.0479)
Angina Pectoris: 1.0212(0.9950, 1.0457)
Other IHD: 0.99(0.0067,1.0289)
Cardiac ARR: 1.0274(1.0006,1.0984)
HeartFailure: 0.9970(0.9769, 1.0194)
Cerebrovascular Disease: 0.9851 (0.9684,
1.0045)
Other Circulatory: 1.0182 (1.0000,
1.0398)
All CVD: 1.0243(1.0054,1.0448)
No attenuation of NO2 association with MI
in two-pollutant model (cool season).
-------
OQ
to
o
o
TABLE AX6.4-3 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: EUROPE
Effects: Relative Risk or Percent Change
& Confidence Intervals ([95% Lower,
Upper])
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants
(Correlations)
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Ponka and Virtanen
(1996)
Helsinki, Finland
Study Period:
1987-1989, 3 yrs
Outcome(s) (ICD9): IHD 410-414; MI
410; TIA411; Cerebrovascular diseases
430-438; Cerebral ischemia due to
occlusion of extracerebral vessels 433;
Cerebral ischemia due to occlusion of
cerebral vessels 434; Transient ischemic
cerebral attack 43 5. Case ascertainment
was for both emergency admission and
hospital admissions - done via registry
system.
Study Design: Time series
Statistical Analyses: Poisson
regression, pollutant concentrations log
transformed
N: 12,664 all IHD admissions; 7005
IHD ED admissions; 7232
cerebrovascular hospital admissions;
3737 cerebrovascular ED admissions.
Covariates: weather, day of wk, long-
term trends, influenza
Lag(s): 1-7 days
NO2 8 h (ug/m3)
Mean(SD): 39(16.2)
Range: 4, 170
NO 8 h ug/m3
Mean(SD): 91(61)
Range: 7,467
# Stations: 2
SO28h
NO8h
TSPSh
O38h
NO2 highly
correlated with
SO, and TSP
Results reported are regression coefficients
and standard errors (SE).
NO2 with ED admissions for transient short
term ischemic attack
-0.056(0.105), p = 0.59, lag 1
NO2 with ED admissions for
cerebrovascular disease
-0.025 (0.057), p = 0.657, lag 1
NO with IHD, all admissions
0.097 0.023,p<0.00l,lagl
NO with IHD, ED admissions
O.lll(0.030),p<0.00l,lagl
Significant increase in admissions for
transient short-term ischemic attack and
cerebrovascular diseases for lag 6
associated with NO2 exposure.
Prescott et al. (1998) * Outcome(s) (ICD9): Cardiac and
Edinburgh, UK
Study period: Oct
1992-June 1995
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 wkday
variation, temperature, and wind speed.
Lag(s): 0, 1, 3 day moving avg
N02 24 h (ppb)
Mean(SD): 26.4(7.0)
Range: 9,58
IQR: 10 ppb
03, 24 h
PM, 24 h
S02, 24 h
CO, 24 h
Correlations not
reported.
Results reported for percent change in
admissions based on an incremental
increase in NO2 equivalent to the IQR of
10 ppb.
<65 yrs, CVD admissions
-0.05 (-5.2, 4.5), 3 day moving avg
65+ yrs, CVD admissions
-0.9 (-8.2, 7.0), 3 day moving avg
Data for lag 1 not presented
-------
OQ
to
o
o
TABLE AX6.4-3 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: EUROPE
Effects: Relative Risk or Percent Change
Copollutants & Confidence Intervals ([95% Lower,
(Correlations) Upper])
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
X
oo
Yallop et al. (2007)
London, England
Study Period:
Jan. 1988-Oct. 2001,
>1400 days
Outcome(s): Acute pain in Sickle
Cell Disease (HbSS, HbSC,
HbS/pO, thalassaemia, HbS/p+).
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
O3, CO, NO, NO2,
PM10:
daily avg used for all
copollutants
High O3 levels
correlate with low
NO, low CO,
increased wind
speeds and low
humidity and each
was associated with
admission for pain.
Not possible to
distinguish
associations in
analysis.
Results reported are cross-correlation
coefficients. NO inversely correlated with
admission for acute pain in SCO.
CFF: -0.063, lag 0
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
* 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
-------
OQ
to
o
o
TABLE AX6.4-4. HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: ASIA
Reference, Study
Location, & Period
Outcomes, Design, &
Methods
Mean Levels &
Monitoring Stations
Copollutants Effects: Relative Risk or Percent Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Chan et al. (2006) *
Taipai, Taiwan
Period of Study:
April1997-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
NO224-havg (ppb):
Mean: 29.9
SD: 8.4
Min: 8.3
Max: 77.1
IQR: 9.6 ppb
# of Stations: 16
Correlation among
stations: NR
PM10 24 h, r = 0.50
PM2 5 24 h, r = 0.64
CO8-havg, r=0.77
SO2 24 h, r = 0.64
O3 1-h max, r = 0.43
Two-pollutant models
to adjust for
copollutants
Results reported for OR for association of
emergency department admissions with an IQR
increase inNO2 (9.3 ppb)
Cerebrovascular:
1.032(0.991, 1.074), lag 0
Stroke:
0.994(0.914, 1.074), lag 0
Ischemic stroke:
1.025(0.956, 1.094), lag 0
Hemorrhagic stroke:
0.963(0.884, 1.042), lag 0
No significant associations for NO2 reported. Lag
0 shown but similar null results were obtained for
lags 1-3. NO2 highly correlated with PM and CO.
-------
OQ
to
o
o
TABLE AX6.4-4 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: ASIA
Reference, Study
Location, & Period
Outcomes, Design, &
Methods
Mean Levels &
Monitoring Stations
Copollutants Effects: Relative Risk or Percent Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
X
O
O
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
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 cutpoint of
20 °C)
Lag(s): 0-2 days
NO224-havg(ppb):
Mean: 31.54
Min: 8.13
25th: 26.27
50th: 31.03
75th: 36.22
Max: 77.97
# of Stations: 6
CO 24-h avg
O3 24-h avg
SO2 24-h avg
PM10 24-h avg
Correlations not
reported.
Two-pollutant models
to adjust for
copollutants
OR for the association of CVD admissions with
an incremental increase in NO2 equivalent to
one IQR.
Warm(>20°C)
1.177(1.150, 1.205)
Cool (<20 °C)
1.112(1.058, 1.168)
NO2 effect remained in all warm season two-
pollutant models. Effect remained in cool
season two-pollutant models with the exception
of the model that included PM10.
-------
OQ
to
o
o
TABLE AX6.4-4 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: ASIA
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants Effects: Relative Risk or Percent Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
X
I
o
H
6
o
o
H
O
o
H
W
O
O
HH
H
W
Lee et al. (2003a)
Seoul, Korea
Study period:
Dec 1997-Dec 1999,
822 days, 184 days in
summer
Tsai et al. (2003a)
Kaohsiung, Taiwan
Study period:
1997-2000
Outcome(s)(ICD10): fflD:
Angina pectoris 120; Acute or
subsequent MI 121-123; other
acute fflD 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 (June, July, August)
and entire period.
Lag(s): 0-6
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.
Statistical Software: SAS
Seasons: >20°C;<20°C.
N: 23,179 stroke admissions
# Hospitals: 63
Lag(s): 0-2, cumulative lag up
to 2 previous days
NO224h(ppb):
5th: 16
10th: 23.7
Median: 30.7
75th: 38.3
95th: 48.6
Mean(SD): 31.5(10.3)
IQR: 14.6
N02 (ppb)
Min: 6.25
25th: 19.25
Median: 28.67
75th: 36.33
Max: 63.40
Mean: 28.17
PM10,r = 0.73, 0.74
SO2,r= 0.72, 0.79
O3,r=-0.07, 0.63
CO, r= 0.67, 0.79
Range depends on
summer vs. entire
period.
Two-pollutant
models
PM10
S02
CO
03
Results reported for RR of IHD hospital
admission for an incremental increase in NO2
equivalent to one IQR.
64+, entire study period:
1.08(1.03, 1.14), lag 5
64+, summer only:
1.25(1.11, 1.41), lag 5
Results for lag 5 presented above. Lag 0 or 1
results largely null - presented graphically.
Confounding by PM10 was not observed in
these data using two-pollutant models.
Results reported as OR for the association of
admissions with an incremental increase of
NO2 equivalent to the IQR of 17.1 ppb
PIH admissions
Warm: 1.56 (1.32, 1.84), lag 0-2
Cool: 0.81(0.0, 1.31), lag 0-2
IS admissions:
Warm: 1.55 (1.40, 1.71), lag 0-2
Cool: 1.16(0.81, 1.68), lag 0-2
Effects persisted after adjustment for PM10,
SO2, CO, and O3.
-------
OQ
to
o
o
TABLE AX6.4-4 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: ASIA
X
O
to
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Reference, Study
Location, & Period
Wong etal. (1999)
Hong Kong, China
Outcomes, Design, &
Methods
Outcome(s) (ICD9): CVD:
410-417, 420-438, 440-444;
CHF428;IHD410-414;
Mean Levels &
Monitoring Stations
NO2 24-h avg (ug/m3)
Mean: 51.39
PM
SO;
03
Copollutants
(Correlations)
10, r = 0.79
•)
Effects: Relative Risk or Percent Change
& Confidence Intervals ([95% Lower,
Upper])
Results reported for RR associated with
incremental increase in NO2 equal to
10 ug/m3.
Study Period:
1994-1995
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
Range for other CVD
pollutants: r= 0.68, 0.89. 65+yrs: 1.016(1.009,1.023)
All ages: 1.013(1.007, 1.020)
Two-pollutant models
CHF
1.044 (1.25, 1.063)
IHD
1.010 (0.999, 1.020)
Cerebrovascular Disease
1.008 (0.998, 1.018)
Interaction of NO2 with O3 observed
-------
OQ
to
o
o
TABLE AX6.4-4 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: ASIA
Reference, Study
Location, & Period
Outcomes, Design, &
Methods
Mean Levels &
Monitoring Stations
Copollutants Effects: Relative Risk or Percent Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
X
I
o
H
6
o
o
H
O
o
H
W
O
O
HH
H
W
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
Min: 6.25 ppb
25%: 19.25 ppb
50%: 28.67 ppb
75%: 36.33 ppb
Max: 63.40 ppb
Mean: 28.17 ppb
# of Stations: 6
Correlation among
stations: NR
PM10
CO
S02
O38
Two-pollutant models
used to adjust for
copollutants
Correlations NR
OR's for the association of one IQR
(17.08 ppb) increase in NO2 with daily counts
of CVD hospital admissions are reported
All CVD (ICD9: 410-429), one-pollutant
model
>25°: 1.380(1.246, 1.508)
<25°: 2.215 (2.014, 2.437)
All CVD (ICD9: 410-429), two-pollutant
models
Adjusted for PM10:
>25°: 1.380(1.246, 1.508)
<25°: 2.215 (2.014, 2.437)
Adjusted for SO2:
>25°: 1.149(1.017, 1.299)
<25°: 2.362 (2.081, 2.682)
Adjusted for CO
>25°: 1.039(0.919,1.176)
<25°: 2.472(2.138,2.858)
Adjusted for O3
>25°: 1.159(1.051,1.277)
<25°: 2.243 (2.037, 2.471)
Association of CVD admissions with NO2
attenuated on warm days after adjustment for
copollutants. Association persisted on cool
days. Kaohsiung is the center of Taiwan's
heavy industry.
-------
OQ
to
o
o
TABLE AX6.4-4 (cont'd). HUMAN HEALTH EFFECTS OF OXIDES OF NITROGEN: CVD HOSPITAL ADMISSIONS
AND VISITS: ASIA
Reference, Study
Location, & Period
Outcomes, Design, & Methods
Mean Levels &
Monitoring Stations
Copollutants Effects: Relative Risk or Percent Change &
(Correlations) Confidence Intervals ([95% Lower, Upper])
X
I
o
Yeetal. (2001)
Tokyo, Japan
Study Period:
July-August,
1980-1995
Outcome(s): Angina 413;
Cardiac insufficiency 428;
Hypertension 401-405; MI 410.
Diagnosis made by attending
physician for hospital
emergency transports.
Age groups analyzed: 65+ yrs
male and female
Statistical analysis: GLM
Covariates: maximum
temperature, confounding by
season minimal since only 2
summer mos included in
analysis
Statistical Software: SAS
Lag(s): 1-4 days
NO2 24-h avg (ppb)
Minimum: 5.3
Maximum: 72.2
Mean(SD): 25.4(11.4)
O3,r= 0.183
PM10,r = 0.643
SO2,r= 0.333
CO, r= 0.759
Results reported for model coefficient and
95% CI.
Angina:
0.007 (0.004, 0.009)
Cardiac insufficiency:
0.006 (0.003, 0.01)
MI:
0.006 (0.003, 0.01)
H
6
o
o
H
O
o
H
W
O
O
HH
H
W
* 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
-------
CJQ
c
r-K
to
o
X
ON
i
O
o
l-rj
H
6
o
0
H
O
o
H
W
O
O
HH
H
W
1 AKL,tL AAtO-1. MULUJL;
MEASURED BY
% Change
Author, Year (95% CI)
Lino et al. (2004)
lagl -5.0% (-9.2, -.7)
Chan et al. (2005)
4-hlag -4.5% (-8.1, -.30)
8-hlag -6.9% (-12.0, -1.8)
Wheeler et al. (2006)
MI patients [N = 12]
4hlag -26.0% (-42.1, -8.6)
COPD patients
[N = 22]
4 h lag 16.6% (0.2, 34.3)
Luttmann-Gibson
et al. (2006)
lagl 0.3% (-6.0, 6.6)
Schwartz et al. (2005)
lagl -1.6% (-7.8, 5.1)
5 JLAAlVlimrNLr JLAJ'UMJKt 1U AlVimJU> 1 l>Ui A1>L) UJLAKl KA1 tL VAK1AB1L11 Y AJ
STANDARD DEVIATION OF NORMAL-TO-NORMAL INTERVALS (SDNN)
NQj Cone (ppb) Copollutant Correlation
Analysis AVg
Location Subjects Method Time Mean (sd) Range PM O3 SO2
US, ARIC multivariable
study 4,390 adults linear regression 24 h 21(8)
83 adults
recruited from linear mixed
Taiwan cardiology effects regression Ih 33(15) 1,110 PMi00.4 -0.4 0.5
30 adults (12
MI + 22 linear mixed 18(nosd plO-p20,
Atlanta COPD) models 4h given) 7,30 PM25 0.4
32 adults 10 (no sd p25-p75,
Steubenville (>50yrs) mixed models 24 h given) 6,13 PM25 0.4 -0.3 0.3
28 elderly hierarchical
Boston adults models 24 h med 18 p25-p75, 14, 23 PM25 0.3 0.02
•>
CO
0.7
0.5
0.6
All results given for 20 ppb increase in NO2 with 24-h averaging time, or 30 ppb for 1-h averaging time. (20 ppb increases also used for averaging times between 1 and 24 h)
-------
TABLE AX6.5-2. STUDIES EXAMINING EXPOSURE TO AMBIENT NO2 AND HEART RATE VARIABILITY AS
CJQ
r-K
to
o
o
MEASURED BY
Author, Year
Risk of ICD discharge
Peters et al. (2000a)
lagl
lag 0-4
VARIABLES RECORDED
OR (95% CI) Location
Eastern MA
1.55(1.05,2.29)
1.88(1.01,3.49)
ON IMPLANTABLE CARDIOVERTER DEFIBRILLATORS (ICDS)
NO2 Cone (ppb) Copollutant Correlation
Subjects Analysis Method Mean(sd) Range PM2.5 O3 SO2
100 cardiac logistic regression,
outpatients fixed effects 23 (no sd given) 11,65 0.6 -0.3 0.3
CO
0.7
Risk of ICD-recorded ventricular arrhythmias
X
ON
O
ON
O
H
6
o
0
H
O
O
H
W
O
O
HH
H
W
Rich et al. (2005)
all patients
lag 0-1
patients with recent arrhythmia
(< 3 days)
lag 0-1
Dockery et al. (2005)
patients with recent arrhythmia
(<3 days)
lag 0-1
Risk of ST-segment depression
>0.1mV
Pekkanen et al. (2002)
lag 2
Risk of resting heart rate
>75 bpm
Ruidavets et al. (2005)
Iag8h
Boston
1.54(1.11,2.18)
2.09(1.26,3.51)
Boston
2.14(1.14,4.03)
Finland
14.1 (3.0,65.4)
France
2.7(1.2,5.4)
203 cardiac p25-max,
outpatients case-crossover med 22 1 8, 62
307 cardiac logistic regression, p25-p95,
outpatients GEE med 23 19,34 >0.4 <-0.4 >0.4
45 cardiac linear regression, p25-max,
patients GAM med 16 12, 36 0.4
polytomous logistic
863 adults regression 16(6) 2,48 -0.3 0.7
0.6
0.3
All results given for 20 ppb increase in NO2 with 24-h averaging time.
-------
TABLE AX6.6-1. BIRTH WEIGHT AND LONG-TERM NO2 EXPOSURE STUDIES
CJQ
r-K
to
o
o
^
X
ON
O
O
J>
H
6
o
2
0
H
O
O
H
W
O
O
HH
H
W
Study Study
Author, Year Location Group
Lin et al. (2004) Taiwan Term LEW
Pregnancy
Medium NO2
High N02
Trimester 1
Medium NO2
High NO2
Trimester 2
Medium NO2
High N02
Timester 3
Medium NO2
High NO2
Seoul,
Lee et al. (2003b) Korea Term LEW
Pregnancy
Trimester 1
Trimester 2
Trimester 3
LEW
Bobak M. (2000) Czech adjusted
Trimester 1 for GA
Trimester 2
Trimester 3
Study Odds Ratio
Subjects (95% CI)
92,288 birth
cert
1995-1997
1.06(0.93,
1.06(0.89,
1.10(0.96,
1.09(0.89,
0.87 (0.76,
0.93 (0.77,
1.01 (0.88,
0.86(0.71,
388, 105 birth
cert
1996-1998 1.04(1.00,
1.02(0.99,
1.03(1.01,
0.98 (0.96,
69,935 birth
cert
1991 only 0.98(0.81,
0.99 (0.80,
0.97(0.80,
1.22)
1.26)
1.27)
1.32)
1.00)
1.12)
1.16)
1.03)
1.08)
1.04)
1.06)
1.00)
1.18)
1.23)
1.18)
Unit of Cone Range (ppb) Correlation with Other Pollutants
Analysis Averaging
Method Time Low Mid-range High PM2.5 PM10 O3 SO2 CO BS Distance
3km
Logistic
regression <26.1 26.1,32.9 >32.9
<24.3 24.3, 34.7 >34.7
<24.0 24.0, 34.4 >34.4
<23.8 23.8, 34.2 >34.2
Generalized
additive 24 h 25 31.4 39.7
model
(GAM)
Interquartile 0.66 0.75 0.77
0.81 0.77 0.78
0.8 0.76 0.82
Logistic
regression 24 h 12.2 20 31.1
50 ng
increase 0.53
0.62
0.63
-------
TABLE AX6.6-1 (cont'd). BIRTH WEIGHT AND LONG-TERM NO2 EXPOSURE STUDIES
CJQ
O
O
X
Oi
O
OO
O
>
H
6
o
0
H
O
O
H
W
O
O
HH
H
W
Study Study
Author, Year Location Group
LEW
Maroziene and Kaunas adjusted
Grazuleviciene
(2002) Lithuania for GA
Pregnancy
Medium NO2
High N02
Trimester 1
Trimester 2
Trimseter 3
LEW
Liu et al. (2003) Vancouver adjusted
First mo for GA
Last mo
Salam et al. Southern
(2005) CA Term LEW
Pregnancy CHS
Trimester 1
Trimester 2
Trimester 3
Bell M et al. LEW
(2007) CT and MA adjusted
pregnancy for GA
black mothers
white mothers
Study Odds Ratio
subjects (95% CI)
3,988 birth cert
1998 only
1.28(0.97, 1.68)
0.96 (0.47, 1.96)
1.54(0.80,2.96)
0.91 (0.53, 1.56)
0.93(0.61, 1.41)
1.34(0.94, 1.92)
229,085 birth
cert
1986-1998 0.98(0.90,1.07)
0.94(0.85, 1.04)
3,901 birth cert
1975-1987 0.8(0.4,1.4)
0.9(0.5, 1.5)
1.0(0.6, 1.6)
0.6(0.4, 1.1)
3 5 8, 504 birth
cert
1999-2002 1.027(1.002,1.051)
-12.7 (-18.0, -7.5)
-8.3 (-10.4, -6.3)
Unit of Cone Range (ppb) Correlation with Other Pollutants
Analysis Averaging
Method Time Low Mid-range High PM2.5 PM10 O3 SO2 CO BS Distance
Logistic
regression
6.2 (5.7)
10 ug increase
10 ug increase
Logistic
regression 24h 15.1 18.1 22.3 -0.25 0.61 0.72
10 ppb increase
Logistic 5 km or 3
regression 36.1 (15. .4) 0.55 -0.1 0.41 within 50 km
within
IQR 25 county
logistic
regression 17.4(5.0) 0.64 0.55
interquartile IQR 4.8
linear regression
difference in
gms
per IQR
-------
TABLE AX6.6-2. PRETERM DELIVERY AND LONG-TERM NO2 EXPOSURE STUDIES
OQ
to
o
o
X
O
VO
H
6
o
o
H
O
O
H
W
O
Study Study
Author, Year Location Group
Bobak 2000 Czech Preterm
trimester 1
trimester 2
trimester 3
Liu S et al. (2003) Vancouver Preterm
first mo
last mo
Maroziene and Kaunas Preterm
Grazuleviciene R
(2002) Lituania
pregnancy
medium NO2
high NO2
trimester 1
trimester 2
trimester 3
Ritz etal. (2000) southern C A Preterm
first mo
6 wks before birth
Hansen C et al.
(2006) Brisbane Preterm
trimester 1
90 days before
birth
Odds Ratio (95%
Study subjects CI)
69,935 birth cert
1991 only 1.10(1.00,1.21)
1.08(0.98, 1.19)
1.11(1.00, 1.23)
229,085 birth cert
1986-1998 1.01 (0.94, 1.07)
1.08(0.99, 1.17)
3,988 birth cert
1.25 (1.07, 1.46)
1.14(0.77, 1.68)
1.68(1.15,2.46)
1.67(1.28,2.18)
1.13(0.90, 1.40)
1.19(0.96, 1.47)
97, 158 birth cert
1989-1993 No effects for
any preg period
28,200 birth cert
2000-2003 0.93(0.78,1.12)
1.03 (0.86, 1.23)
Unit of Cone Range (ppb) Correlation with Other Pollutants
Analysis Averaging M,d_
Method Time Low range High PM2.5 PM10 O3 SO2 CO BS
Logistic
regression 24 h 12.2 20 31.1 0.62
50 ug increase
24 h 15.1 18.1 22.3 -0.25 0.61 0.72
10 ppb increase
Logistic
regression
10 ug increase 6.2 (5.7)
10 ug increase
Logistic
regression 24 h 32 40.9 50.4 0.74 -0.12 0.64
Logistic
regression 24 h 8.8(4.1) 0.32 0.13
IQR 5.2 ppb
IQR4.5ppb
Distance
13 monitors
avg
Zipcode
within
2 miles
O
HH
H
W
-------
OQ
to
o
o
TABLE AX6.6-2 (cont'd). PRETERM DELIVERY AND LONG-TERM NO2 EXPOSURE STUDIES
Unit of Cone Range (ppb)
Correlation with Other Pollutants
Study Study
Author, Year Location Group Study Subjects
Odds Ratio (95% Analysis Averaging Mid
Method
CI)
Time Low range High PM2.5 PM10 O3 SO2 CO BS Distance
Leem et al.
(2006)
Trimester 1 Q2
Trimester 1 Q3
Trimester 1 Q4
Trimester 3 Q2
Trimester 3 Q3
Trimester 3 Q4
Inchon,
Korea Preterm 52,113 birth cert
2001-2002
Log binomial
1.13(0.99,1.27) regression
1.07(0.94, 1.21)
1.24(1.09,1.41) Trend .02
1.06(0.93, 1.20)
1.14(1.01, 1.29)
1.21(1.07,1.37) Trend <.001
15.78 22.93 29.9
0.37
0.54 0.63
Kriging
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
-------
TABLE AX6.6-3. FETAL GROWTH AND LONG-TERM NO2 EXPOSURE STUDIES
CJQ
r-K
to
o
o
X
ON
|zl
o
H
6
o
0
H
O
O
H
W
O
O
HH
H
W
Author, Year
Salam et al.
(2005)
Pregnancy
Trimester 1
Trimester 2
Trimester 3
Mamies et al.
(2005)
Trimester 1
Trimester2
Trimester 3
1 mo before
birth
Liu et al. (2003)
Trimester 1
Trimester 2
Trimester 3
First mo
Last mo
Study Study Study Odds Ratio
Location Group Subjects (95% CI)
Southern Term 3,901 birth
CA SGA cert
<15% of
CHS data 1975-1987 1.1(0.9,1.3)
1.2(1.0, 1.4)
1.0(0.8, 1.2)
1.0(0.8, 1.2)
5 1,460 birth
Sydney SGA cert
>2sd
below 1998-2000 1.06(0.99,1.14)
national
data 1.14(1.07,1.22)
1.13(1.05, 1.21)
1.07(1.00, 1.14)
229,085 birth
Vancouver term SGA cert
national 1986-1998 1.03(0.98,1.10)
0.94(0.88, 1.00)
0.98(0.92, 1.06)
1.05(1.01, 1.10)
0.98(0.92, 1.03)
Correlation with Other
Unit of Cone Range (ppb) Pollutants
Analysis Averaging
Method Time Low Mid-range High PM2.5 PM10 O3 SO2 CO
Linear
mixedmodel 24h 36.1(15.4) 0.55 -0.1 0.69
IQR = 25
Logistic
regression 1-hmax 18 23 27.5 0.66 0.47 0.29 0.57
23.2 (7.4)
Ippb
Logistic
regression 24 h 15.1 18.1 22.3 -0.25 0.61 0.72
10 ppb
US Distance
5 km or
3 monitors
within 50 km
5km
13 monitors
Avg
-------
TABLE AX6.7-1. LUNG FUNCTION AND LONG-TERM NO2 EXPOSURE
CJQ
«j
t— t-
to
o
o
^
J>
X
a\
i
p^
to
O
>
H
6
o
o
H
O
o
H
W
O
73
O
H H
H
W
Author, Year
Gauderman
(2004)
Difference in
lung growth
FVC
FEVj
MMEF
Moseler et al.
(1994)
with asthma
symp
FEVi
lnMEF75%
In MEF50%
lnMEF25%
no asthma symp
FEVj
lnMEF75%
lnMEF50%
lnMEF25%
Ackermann-
Liebrich et al.
(1997)
FVC
FEVi
Study Study Study
Location Group Subjects Odds Ratio (95% CI)
Lung 1757
southern CA function children
Longitudinal age 10-18
CHS -95 (-183.4, -0.6)
Correlation with Other
Unit of Cone Range (ppb) Pollutants
Analysis Averaging Mid-
Method Time Low Range High PM2.5 PM10 O3 SO2 CO B
2-stage
linear 24 h annual 0.79 0.67 -0.11
Regression Avg
IS Distance
Study monitors
in 12 towns
- 101.4 (- 164.5, -38.4) 34.6 ppb
-211 (-377.6, -44.4)
Frieberg lung function 467 children
Germany age 9- 16
0.437
-0.011
-0.022
-0.029
-0.049
0.003
0.004
0.003
Lung
Switzerland function 3, 115 adults
3 yr residents
nonsmokers -0.0123
SAPALDIA (-0.0152, -0.0094)
-0.0070
(-0.0099, -0.0041)
Linear Median
regression wkly 21.28
threshold
Parameter
estimates
j!g
2-stage 24-h
linear annual 18.9(8.5) 0.91 -0.78 0.86
Regression Avg
Parameter
estimate
10 ug
Sonitors in
8 Study area
-------
TABLE AX6.7-1 (cont'd). LUNG FUNCTION AND LONG-TERM NO2 EFFECTS
CJQ
r-K
to
o
o
.
X
ON
i—1
OJ
O
^
H
1
O
o
0
H
O
Cj
o
H
W
O
O
HH
H
W
Study Study
Author, Year Location Group
Schindler et al. Lung
(1998) Switzerland function
FVC home
FVC personal
FEV home
FEV personal
Peters et al. Southern Lung
(1999a) CA function
FVC all
1986-1990
FVC girls
1986-1990
FEVi all
1986-1990
FEVi girls
1986-1990
FVC all 1994
FVC girls 1994
FEVi all. 1994
FEVi girls 1994
Tager et al. Lung
(2005) Southern & function
Northern
lnFEF75 men CA
lnFEF75 women
Study Odds Ratio
Subjects (95% CI)
560 adults %change
3 yr residents -0.59 (-1)
SAPALDIA
3,293
children
CHS
-42.6(13.5)
-58.5(15.4)
-23.2(12.5)
-39.9(13.9)
-46.2(16.0)
-56.7(19.8)
-22.3(14.8)
-44.1 (16.1)
255 students
UC
Berkeley -0.029(0.003)
-0.032(0.002)
Correlation with Other
Unit of Cone Range (ppb) Pollutants
Analysis Averaging Mid-
Method Time Low range High PM2.5 PM10 O3 SO2 CO
Linear
regression Wkly avg
Linear
regression 24 h
Parameter
estimates
IQR = 25 ppb
IQR = 25 ppb
Linear
regression 22 30 40 Men 0.57
Parameter
estimates 21 27 40 Women
Results
substantially
US Distance
Personal and
Home monitors
Study monitors
in 12 tons
Lifetime history
-------
CJQ
to
O
o
-------
TABLE AX6.7-2 (cont'd). ASTHMA AND LONG-TERM NO2 EXPOSURE
CJQ
r-K
to
o
o
X
Oi
—
-------
TABLE AX6.7-2 (cont'd). ASTHMA AND LONG-TERM NO2 EXPOSURE
OQ
to
o
o
X
Oi
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Study
Author, Year Location Study Group
Wang et al.
(1999) Taiwan Asthma
Current asthma
Ramadour M
et al. (2000) 7 communities Asthma
France ISAAC
Shima and
Adachi et al.
(2000) 7 communities Asthma
Outdoor 4th
grade girls Japan Prevalence
Outdoor 5th
grade girls
Outdoor 6th
grade girls
Indoor 4th grade
girls
Indoor 5th grade
girls
Indoor 6th grade
girls
Outdoor Asthma
Indoor Incidence
Study
Subjects
117,080
students
age 11-16
2,445
children
age 13-14
3yr
residence
905
children
age 9- 10
Odds Ratio
(95% I)
1.08
(1.04, 1.13)
Nonsignificant
Results
1.14(0.65,
2.09)
1.14
(0.63,2.13)
0.95
(0.45, 2.05)
1.63
(1.06, 2.54)
1.67
(1.06,2.66)
1.18
(0.62,2.18)
2.10
(1.10,4.75)
0.87
(0.51, 1.43)
Analysis
Method
Logistic
regression
Above/below
median
Logisitic
regression
Logistic
regression
lOppb
increase
lOppb
increase
Correlation with Other
Unit of Cone Range (ppb) Pollutants Distance
Averaging Mid-
Time Low range High PM2.5 PM10 O3 SO2 CO BS
28 24 district
median monitors
Monitors in each
11 -27 mean community
In home
20-29 30-39 >40 measurements
7-25 Monitors near
mean schools
Outdoors
-------
£
CJQ
S-
to
o
o
J>
X
ON
i
^
O
i-rj
H
1
o
o
0
H
O
a,
o
H
W
O
O
HH
H
W
Study
Author, Year Location
Kim J et al.
(2004a) San Francisco
All children Bay area
All 1 yr
residents
1 yr resident
girls
1 yr resident
boys
All children
All 1 yr
residents
1 yr resident
girls
1 yr resident
boys
Gauderman W
et al. (2005) Southern CA
Lifetime asthma CHS
Asthma med use
Hwang et al.
(2005) Taiwan
Parental atopy National study
No parental
atopy
TABLE AX6.7-2 (cont'd). ASTHMA AND LONG-TERM NO2 EXPOSURE
Correlation with Other
Unit of Cone Range (ppb) Pollutants
Study Study Odds Ratio Analysis Averaging Mid-
Group Subjects (95% I) Method Time Low range High PM2.5 PM10 O3 SO2 CO
1,109 24
Asthma children 2-stage mean "low" "low"
Hierarchical
Age 9-11 1.02(0.97,1.07) model
1.04(0.98, 1.10)
1.09(1.03,1.15) IQR = 3.6NO2
1.00(0.94, 1.07)
1.04(0.97, 1.11)
1.07 (1.00, 1.14) IQR = 14.9 NOX
1.17(1.06, 1.29)
1.02(0.93, 1.11)
Logistic
Asthma 208 children regression 4wkavg 13-51
1.83(1.04,3.21) IQR= 5.7
2.19(1.20,4.01)
32,672
Asthma children 2-stage 21.5 29.6 33.1 0.34 -0.39 0.5
Hierarchical
ISAAC 0.99(0.92,1.07) model
1.02(0.95,1.10) lOppbNOx
US Distance
10 school
sites
Outside
home
Schools
within
1 km of
monitors
-------
TABLE AX6.7-3. RESPIRATORY SYMPTOMS AND LONG-TERM NO2 EXPOSURE
CJQ
c
r- 1-
to
o
o
^
>
X
ON
p^
oo
o
c
H
1
O
o
2|
0
H
O
O
H
W
O
O
HH
H
W
Author, Year
Garrett et al.
(1999)
wheeze
cough
short of breath
chest tightness
any symptoms
any symptoms
any symptoms
Hirsch et al.
(1999)
wheeze home
wheeze school
cough home
cough school
cough non-
atopic child
Peters et al.
(1999b)
wheeze
cough
wheeze boys
wheeze girls
Study Study
Location Study Group Subjects
148
Latrobe Valley Symptoms children
Australia Monash Q Age 7- 14
1994-1995
5,421
Dresden Symptoms children
Age 5-7,
Germany ISAAC 9-11
1995-1996
12 mo
residence
3,676
Southern CA Symptoms children
CHS Questionnaire Age 9- 16
1994
Odds Ratio
(95% CI)
1.15(0.85, 1.54)
1.47(0.99,2.18)
1.23 (0.92, 1.64)
1.12(0.81, 1.56)
1.24(0.91, 1.68)
1.12(0.93, 1.35)
2.71 (1.11,6.59)
1.13(0.93, 1.37)
0.95 (0.72, 1.26)
1.22(1.94, 1.44)
1.21 (0.96, 1.52)
1.42(1.10, 1.84)
1.12(0.86, 1.45)
1.14(0.94, 1.39)
1.54(1.04, -2.29)
0.86(0.57, 1.29)
Unit of Cone Range (ppb)
Analysis Averaging
Method Time Low Mid-range High PM2.5
Logistic
regression 6
10 ug
10 ugmean
10 ug winter
10 ug
summer
Logistic
regression 29.3 33.8 37.8
10 ug
Logistic
regression 24 h 21. 5 mean
IQR = 25 ppb
Correlation with Other
Pollutants
PM10 03 S02 CO BS Distance
In home
4 monitors
Within 1 km
Study
monitors
In 12 towns
-------
TABLE AX6.7-3 (cont'd). RESPIRATORY SYMPTOMS AND LONG-TERM NO2 EXPOSURE
CJQ
r-K
to
o
o
X
ON
VO
O
>
H
6
o
0
H
O
O
H
W
O
O
HH
H
W
Author, Year
Millstein et al.
(2004)
wheeze
wheeze Mar-Aug
wheeze Sept-Feb
Penard-Morand
et al. (2005)
wheeze past
12 mos.
Mukala et al.
(1999)
cough
cough
nasal symp winter
nasal symp winter
nasal symp spring
nasal symp spring
Pikhart et al.
(2000)
wheeze
wheeze
wheeze
Study Study Study
Location Group Subjects
Southern CA Symptoms 2,034 children
CHS Age 9- 11
1995
France 6
towns Symptoms 4,901 children
IS SAC Age 9- 11
1999-2000
3 yr residence
Helsinki Symptoms 163 children
Finland Age 3-6
1991
Prague Symptoms 3,045 children
Czech SAVIAH Age 7- 10
1993-1994
Odds Ratio
(95% CI)
0.93 (0.77,
0.79 (0.40,
0.85 (0.64,
0.87(0.75,
1.23 (0.89,
1.52(1.00,
0.99(0.58,
0.89 (0.44.
0.76 (0.56,
0.68 (0.46,
1.16(0.95,
1.07(0.86,
1.08(0.86,
1.12)
1.53)
1.14)
1.01)
1.70)
2.31)
1.68)
1.82)
1.02)
1.01)
1.42)
1.33)
1.36)
Correlation with Other
Unit of Cone Range (ppb) Pollutants
Analysis Averaging
Method Time Low Mid-range High PM2.5 PM10 O3 SO2 CO
Mixed effects
model Moly 0.28 0.39
IQR = 5.74 ppb
Logistic
regression 3 yrs
8.7, 16.1,
10 ng 16.0 25.7 0.46 0.76 0.35
GEE Wkly <8.6 8.6, 14.5 >14.5
2nd tertile Avg
3rd tertile
2nd tertile
3rd tertile
2nd tertile
3rd tertile
Multi-level
model 14.8 19 24.1
Individual
covariates
Ecological
covariates
Both covariates
US Distance
Study
monitors
In 12
towns
29
monitoring
sites,
school
address
Palms
tubes
On outer
garment
-------
TABLE AX6.7-3 (cont'd). RESPIRATORY SYMPTOMS AND LONG-TERM NO2 EXPOSURE
CJQ
r-K
to
o
o
X
Oi
to
o
o
H
6
o
0
H
O
o
H
W
O
O
HH
H
W
Study
Author, Year Study Location Group
van Strien, 2004 CT and MA Symptoms
wheeze
wheeze
wheeze
cough
cough
cough
short of breath
short of breath
short of breath
Nitschke et al.
(2006) Adelaide Symptoms
Wheeze school Australia
Wheeze home
Cough school
Cough home
Difficult breath
school
Difficult breath
home
Chest tight school
Chest tight home
Study Odds Ratio
Subjects (95% CI)
849 children
Agel2mos 1.15(0.79,1.67)
1.03 (0.69, 1.53)
1.45 (0.92, 2.27)
0.96 (0.69, 1.36)
1.33 (0.94, 1.88)
1.52(1.00,2.31)
1.59(0.96,2.62)
1.95(1.17. 3.27)
2.38(1.31,4.34)
174 asthmatic
Children, age
5-13 0.99(0.93,1.06)
2000 1.00(0.90,1.11)
1.01 (0.98, 1.04)
0.99 (0.96, 1.02)
1.11 (1.05, 1.18)
1.03(1.01, 1.05)
1.12(1.07, 1.17)
1.02(0.95, 1.09)
Unit of Cone Range (ppb)
Analysis Averaging
Method Time Low Mid-range High PM2.5
Poisson
regression 10-14 day 5.1 9.9 17.4
Q2 Avg
Q3
Q4
Q2
Q3
Q4
Q2
Q3
Q4
Zero-inflated 117
negative School 34(28) max
binomial 147
regression Home 20 (22) max
lOppb
increase
Correlation with Other
Pollutants
PM10 03 S02 CO BS Distance
In home
9 days in
class
3 days at
home
-------
TABLE AX6.8. LUNG CANCER
OQ
to
o
o
X
ON
to
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Author, Year
Nyberg et al.
(2000)
30-yr estimated
exposure
10-yr estimated
exposure
Nafstad (2004)
lung cancer
incidence
non-lung cancer
Study Study Study Odds Ratio
Location Group Subjects (95% CI)
lung
Stockholm cancer 1,042 cases
2,364
Sweden controls 1.05 (0.93,
men age
40-75 1.18(0.93,
0.90(0.71,
1.05 (0.79,
1.10(0.97,
1.15(0.91,
1.01 (0.79,
1.07(0.81,
1.44(1.05,
Norway lung caner 16,209 men
age 40-49
at entry 1.08(1.02,
followed
1972-1998 0.90(0.70,
1.06(0.81,
1.36(1.01,
1.02(0.99,
0.98 (0.88,
1.05 (0.94,
1.04(0.91,
1.18)
1.49)
1.14)
1.40)
1.23)
1.46)
1.29)
1.42)
1.99)
1.15)
1.15)
1.38)
1.83)
1.06
1.08)
1.18)
1.18)
Correlation with Other
Unit of Cone Range (ppb) Pollutants
Analysis Averaging Mid-
Method Time Low range High PM2.5 PM10 O3 SO2 CO BS Exposure
logistic From
regression 8.1 10.6 13.3 addresses
10 ug and traffic
Q2
Q3
Q4
10 ug
Q2
Q3
Q4
90th percentile
Cox Home
proportional 5.32 10.6 16 address
10 ug 1972-1974
Q2
Q3
Q4
10 ug
Q2
Q3
Q4
-------
OQ
to
o
o
TABLE AX6.9. EFFECTS OF ACUTE NOX EXPOSURE ON MORTALITY. RISK ESTIMATES ARE STANDARDIZED
FOR PER 20 PPB 24-H AVG NO2 INCREMENT
X
to
to
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Reference, Study Outcome
Location, and Period Measure
Copollutants
Mean NO2 Levels Considered
Lag Structure
Reported
Method/Design
Effect Estimates
META ANALYSIS
Stieb et al. (2002), re- All cause
analysis (2003) meta-
analysis of estimates
from multiple
countries.
24-h avg ranged from PM10, O3, SO2,
13 ppb (Brisbane, CO
Australia) to 38 ppb
(Santiago, Chile).
"Representative"
concentration: 24 ppb
The lags and multiday
averaging used in these
estimates varied
Meta-analysis of
time-series study
results
Single-pollutant
model
(11 estimates):
0.8%(95%CI: 0.2,
1.5);
Multipollutant
model estimates
(3 estimates): 0.4%
(95% CI: -0.2,1.1)
UNITED STATES
Samet et al. (2000a,b
reanalysis Dominici et
al., 2003)
90 U.S. cities (58 U.S.
cities with NO2 data)
1987-1994
Kinney and Ozkaynak
(1991)
Los Angeles County,
CA
1970-1979
Kelsalletal. (1997)
Philadelphia, PA,
1974-1988
All cause;
cardiopulmonary
All cause;
respiratory;
circulatory
All cause;
respiratory;
cardiovascular,
Ranged from 9 ppb PM10, O3, SO2, 0, 1 , 2
(Kansas City) to 39 ppb CO; two-pollutant
(Los Angeles), 24-h avg models
69 ppb, 24-h avg KM (particle optical 1
reflectance), NO2,
S02, CO;
multipollutant
models
39.6 ppb, 24-h avg TSP, CO, SO2, O3 0 (AIC presented for
0 through 5)
Poisson GAM,
reanalyzed with
stringent convergence
criteria; Poisson GLM.
Time-series study.
OLS (ordinary least
squares) on high-pass
filtered variables.
Time-series study.
Poisson GAM
24-h avg NO2
(per 20 ppb):
Posterior means:
All cause:
Lagl: 0.50%
(0.09, 0.90)
Lag 1 with PM10
andS02: 0.48%
(-0.54,1.51)
All cause:
Exhaustive
multipollutant
model:
0.5% (-0.1, 1.2);
Two-pollutant with
Ox: 0.7% (0.5, 1.0)
All cause:
Single pollutant:
0.3% (-0.6, 1.1);
With TSP:
-1.2% (-2.2, -0.2)
-------
OQ
to
o
o
TABLE AX6.9 (cont'd). EFFECTS OF ACUTE NOX EXPOSURE ON MORTALITY. RISK ESTIMATES ARE
STANDARDIZED FOR PER 20 PPB 24-H AVG NO2 INCREMENT
X
I
to
H
6
o
o
H
/O
o
H
W
O
n
HH
H
w
Reference, Study
Location, and Period
Outcome Measure Mean NO2 Levels
Copollutants Lag Structure
Considered Reported
Method/Design
Effect Estimates
UNITED STATES (cont'd)
Ostro et al. (2000)
Coachella Valley, CA
1989-1998
Fairley
(1999; reanalysis
Fairley, 2003)
Santa Clara County,
CA
1989-1996
Gamble (1998)
Dallas, TX
1990-1994
Dockery etal. (1992)
St. Louis, MO and
Eastern Tennessee
1985-1986
All cause; 20 ppb, 24-h avg
respiratory;
cardiovascular;
cancer; other
All cause; 28 ppb, 24-h avg
respiratory;
circulatory
All cause; 1 5 ppb, 24-h avg
cardiopulmonary
All cause St. Louis: 20 ppb;
Eastern Tennessee:
12.6 ppb, 24-h avg
PMio, PM2.5, 0-4
PM10-2.5, 03, CO
PMio, PM2.5, 0, 1
PM10-2.5, S042\
coefficient of
haze, NO3~, O3,
S02;
PMio, O3, SO2, Avg 4-5
CO; two-pollutant
models
PM10,PM2 5,804,!^, Lagl
03, S02
Poisson GAM with
default convergence
criteria. Time-series
study.
Poisson GAM,
reanalyzed with
stringent convergence
criteria; Poisson GLM.
Time-series study.
Poisson GLM. Time-
series study.
Poisson with GEE.
Time-series study.
Lag 0 day:
All cause:
5. 5% (1.0, 10.3)
Respiratory:
1.8%(-10.3, 15.5)
Cardiovascular:
3. 7% (-1.7, 9.3)
Lagl:
All cause:
1.9% (0.2, 3.7);
Cardiovascular:
1.4% (-1.7, 4. 5);
Respiratory:
4.8% (-0.3, 10.2)
All cause:
4.4% (0.0, 9.0)
Cardiovascular:
1.9% (-4.6, 9.0)
Respiratory:
13.7% (-2.0, 32.0)
All cause:
St. Louis, MO:
0.7% (-3. 5, 5.1)
Eastern Tennessee:
3. 9% (-8.7, 18.2)
-------
OQ
to
o
o
TABLE AX6.9 (cont'd). EFFECTS OF ACUTE NOX EXPOSURE ON MORTALITY. RISK ESTIMATES ARE
STANDARDIZED FOR PER 20 PPB 24-H AVG NO2 INCREMENT
X
I
to
H
6
o
o
H
/O
o
H
W
O
n
HH
H
w
Reference, Study
Location, and Period
UNITED STATES (cont'd)
Moolgavkar (2003)
Cook County, IL and Los
Angeles County, CA,
1987-1995
Moolgavkar (2000a,b,c);
Re-analysis (2003).
Cook County, IL;
Los Angeles County, CA;
and Maricopa County, AZ,
1987-1995
Lippmann et al. (2000;
reanalysis Ito, 2003, 2004)
Detroit, MI
1985-1990
1992-1994
Outcome
Measure
All cause;
cardiovascular
Cardiovascular;
cerebro vascular;
COPD
All cause;
respiratory;
circulatory;
cause-specific
Mean NO2 Levels
Cook County: 25
ppb; Los Angeles:
38 ppb, 24-h avg
Cook County: 25
ppb; Los Angeles: 38
ppb; Maricopa
County: 19 ppb, 24-h
avg
1985-1990: 23. 3 ppb,
24-h avg
1992-1994: 21. 3 ppb,
24-h avg
Copollutants
Considered
PM2.5, PMio, O3,
S02, CO;
two-pollutant models
PM2.5, PMio, O3,
SO2, CO; two- and
three-pollutant
models
PMio, PM2.5,
PMio-2.5, S042\ Lf,
O3, SO2, CO;
two-pollutant
models
Lag Structure
Reported Method/Design
0, 1, 2, 3, 4, 5 Poisson GAM with
default convergence
criteria. Time-series
study.
0, 1, 2, 3, 4, 5 Poisson GAM with
default convergence
criteria in the original
Moolgavkar (2000); GAM
with stringent convergence
criteria and GLM with
natural splines in the 2003
re-analysis. The 2000
analysis presented total
death risk estimates only in
figures.
0, 1, 2, 3, 0-1, Poisson GAM,
0-2, 0-3 reanalyzed with
stringent convergence
criteria; Poisson GLM.
Numerical NO2 risk
estimates were not
presented in the re-analysis.
Time-series study.
Effect Estimates
All cause:
Lagl:
Cook County:
Single pollutant:
2.2% (1.3, 3.1); with
PM10: 1.8% (0.7, 3.0);
Los Angeles:
Single pollutant: 2.0%
(1.6, 2.5); with PM25:
1.8% (0.1, 3. 6).
GAM, Lag 1:
Cardiovascular:
Cook County: 1.1%
(-0.5, 2. 8); Los Angeles:
2. 8% (2.0, 3.6);
Maricopa Co.: 4.6%
(0.5, 9.0);
Re-analysis, GLM:
Total deaths: 2.5% (1.5,
3.6)
Poisson GAM:
All cause:
Lagl:
1985-1990:
0.9% (-1.2, 3.0)
1992-1994:
1.3%(-1.5,4.2)
-------
OQ
to
o
o
TABLE AX6.9 (cont'd). EFFECTS OF ACUTE NOX EXPOSURE ON MORTALITY. RISK ESTIMATES ARE
STANDARDIZED FOR PER 20 PPB 24-H AVG NO2 INCREMENT
Reference, Study Outcome
Location, and Period Measure
Mean NO2 Levels
Copollutants
Considered
Lag Structure
Reported
Method/Design
Effect Estimates
UNITED STATES (cont'd)
Lipfert et al. (2000a)
Seven counties in
Philadelphia, PA area
1991-1995
All cause;
respiratory;
cardiovascular;
all ages; age
65+ yrs; age
<65 yrs; various
subregional
boundaries
20.4 ppb, 24-h avg
PMio, PM2.5,
PMio-2.5, SO4
O3, other PM
indices, NO2, SO2,
CO; two-pollutant
models
0-1
Linear with 19-day
weighted avg
Shumway filters.
Time-series study.
Numerous results.
All-cause, avg of
0- and 1-day lags,
Philadelphia:
2.2% (p > 0.05)
X
I
to
Chock et al. (2000)
Pittsburgh, PA
1989-1991
All cause; age
<74 yrs;
age 75+ yrs
Not reported.
PM10, N02, S02,
CO; two-, five-,
and six-pollutant
models
0, plus minus 3
days.
Poisson GLM. Time-series
study. Numerous results
All cause, lag 0, age 0-
74: 0.5% (-2.4, 3.5);
age 75+: 1.0% (-1.9,
4.0).
H
6
o
o
H
/O
o
H
W
O
n
HH
H
w
-------
OQ
to
o
o
X
I
to
TABLE AX6.9 (cont'd). EFFECTS OF ACUTE NOX EXPOSURE ON MORTALITY. RISK ESTIMATES ARE
STANDARDIZED FOR PER 20 PPB 24-H AVG NO2 INCREMENT
Reference, Study
Location, and Period
De Leon etal. (2003)
New York City
1985-1994
Klemm and Mason
(2000);
Klemm et al. (2004)
Atlanta, GA
Aug 1998- July 2000
Gwynn et al. (2000)
Buffalo, NY
Outcome Measure Mean NO2 Levels
Circulatory and 40.6 ppb, 24-h avg
cancer with and
without contributing
respiratory causes
All cause; 51.3 ppb, max 1-h.
respiratory;
cardiovascular;
cancer; other; age
<65 yrs; age
65+ yrs
All cause; 24-h avg 21 ppb
respiratory;
circulatory
Copollutants
Considered
PMio, O3, SO2,
CO; two-pollutant
models
PM2.5, PMio-2.5,
EC, OC, 03,
SO42~,
NO3~, SO2, CO
PMio, CoH, 03, S02,
CO, IT, S042~
Lag Structure
Reported Method/Design
0 or 1 Poisson GAM with
stringent convergence
criteria; Poisson GLM.
Time-series study.
0-1 Poisson GLM using
quarterly, moly, or
biweekly knots for
temporal smoothing.
Time-series study.
Poisson GAM with
Default convergence
criteria.
Time-series study.
Effect Estimates
Gaseous pollutants results
were given only in figures.
Circulatory:
Age < 7 5: -1%
Age 75+: -2%
All cause, age 65+ yrs: avg
0-1 days
Quarterly knots:
1.0% (-4.2,6.6);
Moly knots:
3.1% (-3.0, 9.7);
Bi-wkly knots:
0.9% (-5.9, 8.2).
All cause (lag 3): 2.1%
(-0.3,4.6);
Circulatory (lag 2): 1.3%
(-2.9, 5.6); Respiratory
(lag 1): 6.4% (-2. 5, 16.2)
H
6
o
o
H
/O
o
H
W
O
n
HH
H
w
-------
OQ
to
o
o
X
I
to
TABLE AX6.9 (cont'd). EFFECTS OF ACUTE NOX EXPOSURE ON MORTALITY. RISK ESTIMATES ARE
STANDARDIZED FOR PER 20 PPB 24-H AVG NO2 INCREMENT
Reference, Study
Location, and Period Outcome Measure
Copollutants Lag Structure
Mean NO2 Levels Considered Reported
Method/Design
Effect Estimates
CANADA
Burnett et al. (2004) All cause
12 Canadian cities
1981-1999
Burnett et al. (2000), All cause
re-analysis (2003)
8 Canadian cities
1986-1996
24-h avg ranged from PM2.5, PM10-2.5, O3, 1 , 0-2
10 (Saint John) to 26 SO2, CO
(Calgary) ppb.
24-h avg ranged from PM2.5, PM10, 0,1,0-2
15 (Winnipeg) to 26 PM2.5_10, SO2,
(Calgary) ppb. Q3, CO
Poisson GLM.
Time-series study.
Poisson GAM with
default convergence
criteria. Time-series
study. The 2003 re-
analysis did not
consider gaseous
pollutants.
Lag 0-2, single
pollutant: 2.0%
(1.1, 2.9); with O3:
1.8% (0.9, 2.7).
Days when PM
indices available,
lag 1 , single
pollutant: 2.4%
(0.7, 4.1); with
PM25: 3.1%
(1.2,5.1).
Days when PM
indices available,
lag 1 , single
pollutant: 3.6%
(1.6, 5.7); with
PM25: 2.8%
(0.5, 5.2).
H
6
o
o
H
/O
o
H
W
O
n
HH
H
w
Burnett et al. (1998a), All cause
11 Canadian cities
1980-1991
Burnett et al. (1998b), All cause
Toronto, 1980-1994
24-h avg ranged from
14 (Winnipeg) to 28
(Calgary) ppb.
24-h avg 25 ppb.
S02, 03, CO
SO2, O3, CO, TSP,
COH, estimated PM10,
estimated PM2 5
0,1,2,0-1,0-2
examined but the best
lag/averaging for each
city chosen
0,1,0-1
Poisson GAM with
default convergence
criteria. Time-series
study.
Poisson GAM with
default convergence
criteria. Time-series
study.
Single pollutant:
4.5% (3.0, 6.0); with
all gaseous
pollutants: 3.5%
(1.7,5.3).
Single pollutant
(lagO): 1.7%
(0.7, 2.7); with CO:
0.4% (-0.6, 1.5).
-------
OQ
to
o
o
TABLE AX6.9 (cont'd). EFFECTS OF ACUTE NOX EXPOSURE ON MORTALITY. RISK ESTIMATES ARE
STANDARDIZED FOR PER 20 PPB 24-H AVG NO2 INCREMENT
X
to
oo
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Reference, Study
Location, and Period
Copollutants
Outcome Measure Mean NO2 Levels Considered
Lag Structure
Reported Method/Design
Effect Estimates
CANADA (cont'd)
Vedal et al. (2003)
Vancouver, British
Columbia, Canada
1994-1996
Villeneuve et al.
(2003)
Vancouver, British
Columbia, Canada
1986-1999
Goldberg et al. (2003)
Montreal, Quebec,
Canada
1984-1993
All cause; 17 ppb, 24-h avg PM10, 03, SO2,
respiratory; CO
cardiovascular
All cause; 19 ppb, 24-h avg PM25, PM10,
respiratory; PM25_10, TSP,
cardiovascular; coefficient of
canceri haze, S042\ S02,
socioeconomic status .-. ^.^
U3, L.U
Congestive heart 22 ppb, 24-h avg PM2 5, coefficient
Failure (CHF) as of haze, SO42\
underlying cause of SO2, O3, CO
death vs. those
classified as having
congestive heart
failure 1 yr prior to
death
0,1,2 Poisson GAM with
stringent convergence
criteria. Time-series
study. By season.
0,1,0-2 Poisson GLM with
natural splines.
Time-series study.
0,1,0-2 Poisson GLM with
natural splines.
Time-series study.
Results presented in
figures only. NO2
showed associations
in winter but not in
summer.
All yr:
All cause
Lag 1: 4.0%
(0.9, 7.2)
Respiratory:
LagO: 2.1%
(-3.0,7.4)
Cardiovascular:
LagO: 4.3%
(-4.2, 13.4)
CHF as underlying
cause of death:
Lag 1: 1.0%
(-5.1,7.5)
Having CHF 1 yr
prior to death:
Lag 1: 3.4%
(0.9,6.0)
-------
OQ
to
o
o
TABLE AX6.9 (cont'd). EFFECTS OF ACUTE NOX EXPOSURE ON MORTALITY. RISK ESTIMATES ARE
STANDARDIZED FOR PER 20 PPB 24-H AVG NO2 INCREMENT
X
to
VO
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Reference, Study
Location, and Period Outcome Measure
Copollutants
Mean NO2 Levels Considered
Lag Structure
Reported Method/Design
Effect Estimates
EUROPE
Samoli et al. (2006) All cause,
30 APHEA2 cities. respiratory;
Study periods vary cardiovascular
by city, ranging from
1990 to 1997
Samoli et al. (2005) 9 All-cause
APHEA2 cities.
Period not reported.
Touloumi et al. (1997) All cause
Six European cities:
London, Paris, Lyon,
Barcelona, Athens,
Koln.
Study periods vary by
city, ranging from
1977 to 1992
Zmirouet al. (1998) Respiratory;
Four European cities: cardiovascular
London, Paris, Lyon,
Barcelona
Study periods vary by
city, ranging from
1985-1992
1-h max ranged from BS, PM10, SO2, O3
24 (Wroclaw) to 81
(Milan) ppb
The selected cities had None
1 -h max medians
above 58 ppb and the
third quartiles above
68.
Ranged from 37 BS, O3; two-pollutant
(Paris) to 70 (Athens) models
ppb, 1-h max
Ranged from 24 BS, TSP, SO2, O3
(Paris) to 37 (Athens)
ppb in cold season and
23 (Paris) to 37
(Athens) ppb in warm
season, 24-h avg
0 1 Poisson model with
penalized splines.
0 1 Poisson model with
either non-parametric
or cubic spline smooth
function in each city,
and combined across
cities.
0,1,2,3,0-1,0-2,0-3 Poisson
(best lag selected for autoregressive.
each city) Time-series study.
0, 1, 2, 3, 0-1, 0-2, 0-3 Poisson GLM.
(best lag selected for Time-series study.
each city)
All-cause: single:
1.8% (1.3, 2.2); with
S02: 1.5% (1.0, 2.0)
Cardiovascular:
single: 2.3%
(1.7, 3.0); with SO2:
1.9% (1.1, 2.7)
Respiratory: single:
2.2% (1.0, 3.4); with
SO2: 1.1%
(-0.4,2.6)
No numeric estimate
presented. The
concentration-
response was
approximately linear.
All-cause:
Single-pollutant
model:
1.0% (0.6, 1.3);
WithBS:
0.5% (0.0, 0.9).
Western Europe:
Respiratory:
0.0% (-1.1, 1.1)
Cardiovascular:
0.8% (0.0, 1.5)
-------
OQ
to
o
o
TABLE AX6.9 (cont'd). EFFECTS OF ACUTE NOX EXPOSURE ON MORTALITY. RISK ESTIMATES ARE
STANDARDIZED FOR PER 20 PPB 24-H AVG NO2 INCREMENT
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Reference, Study
Location, and Period
Outcome Measure Mean NO2 Levels
Copollutants
Considered
Lag Structure
Reported Method/Design
Effect Estimates
EUROPE (cont'd)
Biggeri et al. (2005)
8 Italian cities, Period
variable between
1990-1999
Anderson et al. (1996)
London, England
1987-1992
Bremneretal. (1999)
London, England
1992-1994
Anderson etal. (2001)
West Midlands region,
England
1994-1996
Prescott etal. (1998)
Edinburgh, Scotland
1992-1995
All cause; 24-h avg ranged from
respiratory; 30 (Verona) to 51
cardiovascular (Rome) ppb
All cause; 37 ppb, 24-h avg
respiratory;
cardiovascular
All cause; 34 ppb, 24-h avg
respiratory;
cardiovascular; all
cancer; all others; all
ages; age specific
(0-64, 65+, 65-74,
75+ yrs)
All cause; 37 ppb, 1-hmax
respiratory;
cardiovascular.
All cause; 26 ppb, 24-h avg
respiratory;
cardiovascular; all
ages; age <65 yrs;
age >65 yrs
Only single-pollutant
models; O3, SO2, CO,
PM10
BS, O3, SO2;
two-pollutant models
BS, PMio, O3, SO2, CO;
two-pollutant models
PMio, PM2.5,
PM25.10,BS, SO42~, O3,
S02, CO
BS, PMio, 03, S02, CO;
two-pollutant models
0-1 PoissonGLM.
Time-series study.
0, 1 Poisson GLM.
Time-series study.
Selected best from 0, Poisson GLM.
1, 2, 3, (all cause); Time-series study.
0,1,2,3,0-1,0-2,
0-3 (respiratory,
cardiovascular)
0-1 Poisson GAM with
default convergence
criteria. Time-series
study.
0 Poisson GLM.
Time-series study.
All cause:
3.6% (2. 3, 5.0);
Respiratory:
5. 6% (0.2, 11.2)
Cardiovascular:
5.1% (3.0, 7.3)
All cause (Lag 1 ):
0.6% (-0.1, 1.2);
Respiratory (lag 1 ):
-0.7% (-2. 3, 1.0)
Cardiovascular:
0.5% (-0.4, 1.4)
All cause (lag 1):
0.9% (0.0, 1.9)
Respiratory (lag 3):
1.9% (-0.3, 4.2)
Cardiovascular (lag
1): 1.9% (0.6, 3.2)
All cause:
1.7% (-0.5, 3.8)
Respiratory:
3. 3% (-1.9, 8.8)
Cardiovascular:
3.1% (-0.2, 6.4)
Results presented as
figures only.
Essentially no
associations in all
categories. Very
wide confidence
intervals.
-------
TABLE AX6.9 (cont'd). EFFECTS OF ACUTE NOX EXPOSURE ON MORTALITY. RISK ESTIMATES ARE
CJQ
r-K
to
o
o
1
UJ
O
£5
H
6
o
0
H
O
O
H
W
O
O
HH
H
W
STANDARDIZED FOR PER 20 PPB 24-H AVG NO2 INCREMENT
Reference, Study
Location, and Period
EUROPE (cont'd)
Le Tertre et al. (2002a)
Le Havre, Lyon, Paris,
Rouen, Strasbourg, and
Toulouse, France
Study periods vary by
city, ranging from
1 QQH 1 QCK
1 yy\j-i yyj
Zeghnoun etal. (2001)
Rouen and Le Havre,
France 1990-1995
Dab etal. (1996)
Paris, France
1987-1992
Zmirou etal. (1996)
Lyon, France
1985-1990
Sartor etal. (1995)
Belgium
Summer 1994
Outcome Measure
All cause;
respiratory;
cardiovascular
All cause;
respiratory;
cardiovascular
Respiratory
All cause;
respiratory;
cardiovascular;
digestive
All cause; age
<65 yrs; age 65+ yrs
Copollutants
Mean NO2 Levels Considered
Ranged from 15 BS, O3, SO2
(Toulouse) to 28 (Paris)
ppb, 24-h avg
24-h avg 18 ppb in SO2, BS, PM13, O3
Rouen; 20 ppb in
Le Havre
24 ppb, 24-h avg BS, PM13, O3, SO2,
CO
37 ppb, 24-h avg PM13, SO2, O3
24-h avg NO2: TSP, NO, O3, SO2
Geometric mean:
During heat wave
(42-day period): 17 ppb
Before heat wave
(43-day period): 15 ppb
After heat wave
(39-day period): 13 ppb
Lag Structure
Reported Method/Design
0-1 Poisson GAM with
default convergence
criteria. Time-series
study.
0, 1 , 2, 3, 0-3, Poisson GAM with
default convergence
criteria. Time-series
study.
0 Poisson
autoregressive.
Time-series study.
Selected best Poisson GLM.
from 0, 1, 2, 3 Time-series study.
0, 1 , 2 Log-linear regression
for O3 and
temperature.
Time-series study.
Effect Estimates
Six-city pooled estimates:
All cause:
2.9% (1.6, 4.2)
Respiratory:
3. !%(-!. 7, 8.0)
Cardiovascular:
3. 5% (1.1, 5. 9)
All cause in Rouen
(lagl): 5.5% (0.2, 11.1);
in Le Havre (lagl): 2.4%
(-3.4,8.5)
Lagl:
2.1% (3. 1,7.7)
All cause (lag 1):
1.5% (-1.5, 4.6)
Respiratory (lag 2):
-2. 3% (-15.6, 13.0)
Cardiovascular (lag 1):
0.8% (-2.7, 4.3)
Only correlation
coefficients presented for
NO2. Unlike O3, NO2 was
not particularly elevated
during the heat wave.
-------
OQ
to
o
o
X
to
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
TABLE AX6.9 (cont'd). EFFECTS OF ACUTE NOX EXPOSURE ON
STANDARDIZED FOR PER 20 PPB 24-H AVG
MORTALITY. RISK ESTIMATES ARE
NO2 INCREMENT
Reference, Study
Location, and Period
Outcome
Measure
Mean NO2 Levels
Lag
Copollutants Structure
Considered Reported Method/Design
Effect Estimates
EUROPE (cont'd)
Hoek et al. (2000;
reanalysis Hoek, 2003)
The Netherlands:
entire country, four
urban areas
1986-1994
Hoek etal. (2001;
reanalysis Hoek, 2003)
The Netherlands
1986-1994
All cause; COPD;
pneumonia;
cardiovascular
Total
cardiovascular;
myocardial
infarction;
arrhythmia; heart
failure;
cerebro vascular;
thrombosis-
related
24-h avg median:
17 ppb in the
Netherlands; 24 ppb
in the four major
cities
24-h avg median:
17 ppb in the
Netherlands; 24 ppb
in the four major
cities
PMio, BS, SO42 , 1,0-6 PoissonGAM,
N(V, O3, SO2, CO; reanalyzed with
two-pollutant models stringent
convergence
criteria;
Poisson GLM.
Time-series study.
PMio, O3, SO2, CO 1 Poisson GAM,
reanalyzed with
stringent
convergence
criteria; Poisson
GLM. Time-series
study.
Poisson GLM:
All cause:
Lagl: 1.9% (1.2, 2.7)
Lag 0-6: 2.6% (1.2, 4.0); with BS: 1.3%
(-0.9,3.5);
Cardiovascular (lag 0-6): 2.7% (0.7,
4.7).
COPD (lag 0-6): 10.4% (4.5, 16.7).
Pneumonia (lag 0-6): 19.9% (11.5, 29.0).
Poisson GLM:
Total cardiovascular:
2.7% (0.7, 4.7)
Myocardial infarction:
0.3% (-2.6, 3.2)
Arrhythmia:
1.7% (-6.6, 10.6)
Roemer and van
Wijinen(2001)
Amsterdam, the
Netherlands
1987-1998
All cause
BS, PM10, 03, S02, 1,2,0-6
CO
Background sites:
24 ppb
Traffic sites:
34 ppb
Poisson GAM with
default convergence
criteria (only one
smoother).
Time-series study.
Heart failure:
7.6% (1.4, 14.2)
Cerebro vascular:
5.1% (0.9, 9.6)
Thrombo sis-related:
-1.2% (-9.6, 8.1)
Total population using background sites:
Lagl: 3.8% (1.7, 5.9);
Traffic pop. using background sites:
lagl: 5.7% (0.6, 11.0);
Total pop. using traffic sites:
Lagl: 1.7% (0.4, 3.0)
-------
OQ
to
o
o
TABLE AX6.9 (cont'd). EFFECTS OF ACUTE NOX EXPOSURE ON MORTALITY. RISK ESTIMATES ARE
STANDARDIZED FOR PER 20 PPB 24-H AVG NO2 INCREMENT
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Reference, Study
Location, and Period
Outcome Measure
Mean NO2 Levels
Copollutants Lag Structure
Considered Reported Method/Design
Effect Estimates
EUROPE (cont'd)
Verhoeffetal. (1996)
Amsterdam, the
Netherlands
1986-1992
Fischer et al. (2003) The
Netherlands, 1986-1994
Spix and Wichman
(1 996) Koln, Germany
1977-1985
Peters et al. (2000b)
NE Bavaria, Germany
1982-1994
Coal basin in Czech
Republic
1993-1994
All cause; all ages;
age 65+ yrs
All-cause,
cardiovascular,
COPD, and
pneumonia in age
groups <45, 45-64,
65-74, 75+
All-cause
All cause;
respiratory;
cardiovascular;
cancer
l-hmaxO3:
43 ug/m3
Maximum 301
24-h avg median
17ppb
24-h avg 24 ppb; 1-h
max 38 ppb
24-h avg:
Czech Republic:
17.6 ppb
Bavaria, Germany:
13. 2 ppb
PMio, O3, CO; 0,1,2 Poisson. Time-series
multipollutant models study.
NON02!!!
PMio, BS, O3, SO2, 0-6 Poisson GAM with
CO default convergence
criteria. Time-series
study.
TSP,PM7, SO2 0,1,0-1 Poisson GLM.
Time-series study.
TSP, PMio, 03, S02, 0, 1, 2, 3 Poisson GLM.
CO Time-series study.
1-h max O3 (per
100 ug/m3)
All ages:
LagO: 1.8% (-3. 8, 7.8)
Lagl: 0.1% (-4.7, 5.1)
Lag 2: 4. 9% (0.1, 10.0)
Cardiovascular:
Age<45: -1.3% (-13.0,
12.1): age 45-64: -0.4%
(-4. 8, 4. 3); age 65-74:
4.4% (0.8, 8.0); age 75 and
up: 3. 5% (1.4, 5.6)
Lagl: 0.4% (-0.4, 1.2)
Czech Republic:
All cause:
Lagl: 2.1%(-1.7, 6.1)
Bavaria, Germany:
All cause:
Lagl: -0.1% (-3.6, 3.6)
-------
OQ
to
o
o
TABLE AX6.9 (cont'd). EFFECTS OF ACUTE NOX EXPOSURE ON MORTALITY. RISK ESTIMATES ARE
STANDARDIZED FOR PER 20 PPB 24-H AVG NO2 INCREMENT
Reference, Study
Location, and Period
Outcome Measure
Mean NO2 Levels
Copollutants
Considered
Lag Structure
Reported
Method/Design
Effect Estimates
EUROPE (cont'd)
Michelozzietal. (1998)
Rome, Italy 1992-1995
Ponkaetal. (1998)
Helsinki, Finland
1987-1993
All-cause
All cause;
cardiovascular; age
<65 yrs, age 65+ yrs
24-h avg 52 ppb
24-h avg:
Median 20 ppb
PM13, S02, 03, CO
TSP, PM10, 03, S02
0,1,2,3,4
0,1,2,3,4,5,6,7
Poisson GAM with
default convergence
criteria. Time-series
study.
Poisson GLM.
Time-series study.
Lag 2: all-yr: 1.6% (0.4,
2. 9); cold season 0.3%
(-1.2, 1.8); warm season:
4.2% (1.8,- 6.6)
No risk estimate
presented for NO2.
PM10 and O3 were
reported to have stronger
associations.
X
Saez et al. (2002)
Seven Spanish cities,
variable study periods
between 1991 and 1996.
All cause;
respiratory;
cardiovascular
03, PM, S02, CO 0-3
24-h avg mean ranged
from 17 ppb in Huelva
to 35 ppb in Valencia.
Poisson GAM with
default convergence
criteria. Time-series
study.
All cause:
2.6% (1.6, 3.6);
with all other poll.:
1.7% (0.0, 3.3);
Respiratory:
7.1% (-14.0, 33.5)
Cardiovascular:
4.4% (-0.2, 9.2)
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
-------
OQ
to
o
o
TABLE AX6.9 (cont'd). EFFECTS OF ACUTE NOX EXPOSURE ON MORTALITY. RISK ESTIMATES ARE
STANDARDIZED FOR PER 20 PPB 24-H AVG NO2 INCREMENT
Reference, Study
Location, and Period
Outcome Measure Mean NO2 Levels
Copollutants
Considered
Lag Structure
Reported
Method/Design
Effect Estimates
EUROPE (cont'd)
Garcia-Ay merich et al.
(2000)
Barcelona, Spain
1985-1989
All cause;
respiratory;
cardiovascular;
general population;
patients with COPD
Levels not reported. BS, O3, SO2
Selected best avg lag
Poisson GLM.
Time-series study.
All cause:
General population:
Lag 0-3: 3.3% (0.8, 5.8)
COPD patients:
Lag 0-2: 10.9% (0.4, 22.6)
Respiratory:
General population:
Lag 0-1: 3.3% (-2.3, 9.2)
COPD patients:
Lag 0-2: 12.1% (-4. 3,
31.4)
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Saezetal. (1999)
Barcelona, Spain
1986-1989
Asthma mortality;
age 2-45 yrs
Levels not reported. BS, O3, SO2
0-2
Poisson with GEE.
Time-series study.
Cardiovascular:
General population:
Lag 0-3: 2.4% (-0.9, 5.8)
COPD patients:
Lag 0-2: 4. 3% (-13.6,
25.8)
RR = 4.1 (0.5,35.0)
-------
OQ
to
o
o
TABLE AX6.9 (cont'd). EFFECTS OF ACUTE NOX EXPOSURE ON MORTALITY. RISK ESTIMATES ARE
STANDARDIZED FOR PER 20 PPB 24-H AVG NO2 INCREMENT
Reference, Study
Location, and Period
Outcome Measure
Copollutants
Mean NO2 Levels Considered
Lag Structure
Reported
Method/Design
Effect
Estimates
EUROPE (cont'd)
Sunyeretal. (1996)
Barcelona, Spain
1985-1991
All cause;
respiratory;
cardiovascular;
all ages; age 70+ yrs
1-hmax: BS, SO2, O3
Median:
Summer:
51 ppb
Winter:
46 ppb
Selected best
single-day lag
Autoregressive
Poisson. Time-
series study.
All yr, all ages:
All cause:
Lagl: 1.9%(0.l
Respiratory:
3,3.1)
LagO: 1.5% (-1.9, 5.0)
Cardiovascular:
Lagl: 2.2% (0.5, 3.9)
X
Oi
Oi
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Sunyer and Basagana
(2001)
Barcelona, Spain
1990-1995
Sunyer et al. (2002)
Barcelona, Spain
1986-1995
Mortality in a cohort
of patients with
COPD
All cause,
respiratory, and
cardiovascular
mortality in a cohort
of patients with
severe asthma
Mean not reported
IQR8.9ppb24-h
1-hmax: median
47 ppb;
24-h avg median
27 ppb
o, 03, CO
PMio, BS, S02,
O3, CO, pollen
0-2
0-2
Conditional logistic
(case-crossover)
Conditional logistic
(case-crossover)
Summer risk estimates larger than
winter risk estimates.
7.8% (-2.0, 18.6)
with PM10:
3.9% (-12.0,22.5)
Odds Ratio:
Patients with 1 asthma admission:
All cause:
1.10(0.80,1.51)
Cardiovascular:
1.70(0.96,2.99)
Patients with more than 1 asthma
adm:
All cause:
2.14(1.10,4.14)
Cardiovascular:
1.53(0.46,5.07)
-------
OQ
to
o
o
TABLE AX6.9 (cont'd). EFFECTS OF ACUTE NOX EXPOSURE ON MORTALITY. RISK ESTIMATES ARE
STANDARDIZED FOR PER 20 PPB 24-H AVG NO2 INCREMENT
X
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Reference, Study
Location, and Period
EUROPE (cont'd)
Diaz etal. (1999)
Madrid, Spain
1990-1992
Latin America
Borja-Aburto et al.
(1997)
Mexico City
1990-1992
Borja-Aburto et al.
(1998)
SW Mexico City
1993-1995
Loomisetal. (1999)
Mexico City
1993-1995
Outcome Measure
All cause;
respiratory;
cardiovascular
All cause;
respiratory;
cardiovascular; all
ages; age <5 yrs; age
>65 yrs
All cause;
respiratory;
cardiovascular;
other; all ages;
age >65 yrs
Infant mortality
Copollutants Lag Structure
Mean NO2 Levels Considered Reported
24-h avg TSP, O3, SO2, CO 1 , 4, 1 0
Levels not reported.
l-hmaxO3: TSP, SO2, CO; 0,1,2
Median 155 ppb two-pollutant models
8-h max O3:
Median 94 ppb
10-havgO3
(8 a.m.-6 p.m.):
Median 87 ppb
24-h avg O3:
Median 54 ppb
37.7 ppb, 24-h avg PM25,O3, SO2; 0, 1, 2, 3, 4, 5, and
two-pollutant models multiday avg
24-h avg 38 ppb PM2.5, 03 0,1,2,3,4,5,3-5
Method/Design
Autoregressive linear.
Time-series study.
Poisson iteratively
weighted and filtered
least-squares method.
Time-series study.
Poisson GAM with
default convergence
criteria (only one
smoother).
Time-series study.
Poisson GAM with
default convergence
criteria. Time-series
study.
Effect Estimates
Only significant risk
estimates were
shown. ForNO2,
only respiratory
mortality was
significantly
(p < 0.05) associated
with an excess
percent risk 8.5%.
l-hmaxO3
(per 100 ppb):
All ages:
Lag 1-5:
All cause: 2.3%
(-1.0,5.6);
Cardiovascular:
2.8% (-3.2, 9.2);
Respiratory: 4.7%
(-5.1,15.5).
Lag 3-5:
11. 4% (2.2, 2 1.4);
with PM2.5:
2.9% (-10.2, 17.8)
-------
OQ
to
o
o
TABLE AX6.9 (cont'd). EFFECTS OF ACUTE NOX EXPOSURE ON MORTALITY. RISK ESTIMATES ARE
STANDARDIZED FOR PER 20 PPB 24-H AVG NO2 INCREMENT
X
oo
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Reference, Study Location,
and Period
Outcome Copollutants Lag Structure
Measure Mean NO2 Levels Considered Reported
Method/Design
Effect Estimates
LATIN AMERICA (cont'd)
Gouveia and Fletcher (2000b)
Sao Paulo, Brazil
1991-1993
Pereiraetal. (1998)
Sao Paulo, Brazil
1991-1992
Saldivaetal. (1994)
Sao Paulo, Brazil
1990-1991
Saldivaetal. (1995)
Sao Paulo, Brazil
1990-1991
All ages (all 1-h max: PM10, O3, SO2, CO 0, 1, 2
cause); age <5 yrs 84 ppb
(all cause,
respiratory,
pneumonia);
age 65+ yrs
(all cause,
respiratory,
cardiovascular)
Intrauterine 24-h avg 82 ppb PM10, O3, SO2, CO 0-4
mortality
Respiratory; age 24-h avg NOX PM10, O3, SO2, CO; 0-2
<5 yrs 127 ppb multipollutant models
All cause; 24-h avg NOX PM10, 03, SO2, CO; 0-1
age 65+ yrs 127 ppb two-pollutant models
Poisson GLM.
Time-series study.
Poisson GLM.
Time-series study.
OLS of raw or
transformed data.
Time-series study.
OLS; Poisson with
GEE. Time-series
study.
All ages:
All cause:
LagO: -0.1%
(-0.7,0.4)
Age 65+:
All cause:
Lag 1: 0.4%
(-0.2, 1.1)
Respiratory:
Lag 2: 1.0%
(-0.6,2.5)
Cardiovascular:
Lag 1: 0.5%
(-0.4,1.3)
Single-pollutant
model:
5.1% (2.8, 7.5);
With other
pollutants:
4.7% (1.6, 7.9)
NOX slope estimate:
0.007197
deaths/day/ppb
(SE 0.003214),
p = 0.025
NOX slope estimate:
0.0341
deaths/day/ppb
(SE 0.0105)
-------
OQ
to
o
o
TABLE AX6.9 (cont'd). EFFECTS OF ACUTE NOX EXPOSURE ON MORTALITY. RISK ESTIMATES ARE
STANDARDIZED FOR PER 20 PPB 24-H AVG NO2 INCREMENT
X
vo
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Reference, Study Location,
and Period
Outcome
Measure
Mean NO2 Copollutants Lag Structure
Levels Considered Reported
Method/Design
Effect Estimates
LATIN AMERICA (cont'd)
Cifuentes et al. (2000)
Santiago, Chile
1988-1966
Ostro et al. (1996) Santiago,
Chile 1989-1991
All cause
All cause
8-h avg 41 ppb PM25, PM10-2.5, CO, 0, 1, 2, 3, 4, 5, 1-2,
SO2, O3 1-3, 1-4, 1-5
1-h max 56 ppb PM10, O3, SO2; 1
two-pollutant models
Poisson GAM with default
convergence criteria;
Poisson GLM. Time-
series study.
OLS, Poisson.
Time-series study.
GLM model, lag 1-2:
Single pollutant: 1.7%
(0.7, 2.7); with other
pollutants: 1.5% (0.3,
2.7)
(per 25ppb 8-h avg)
Poisson, lag 1: -0.5%
(-1.1,0)
AUSTRALIA
Simpson et al. (2005a,b)
Brisbane, Sydney,
Melbourne, and Perth,
Australia
1996-1999
Simpson et al. (2000)
Brisbane, Australia
1991-1996
Morgan etal. (1998b)
Sydney, Australia
1989-1993
Simpson etal. (1997)
Brisbane, Australia
1987-1993
All cause,
respiratory, and
cardiovascular
in all ages;
cardiovascular
in age 65+ yrs
All cause,
respiratory, and
cardiovascular
in all ages;
cardiovascular
in age 65+ yrs
All cause;
respiratory;
cardiovascular
All cause;
respiratory;
cardiovascular
1-h max ranged PM10, PM25, bsp 0, 1, 2, 3, 0-1
from 16 to 24 (nephelometer), O3,
ppb CO
24-h avg: whole PM10, PM2.5, bsp, O3, 0, 1, 2, 3, 0-1
yr: 12 ppb; cool CO
season: 1 3 ppb;
warm season
9 ppb
24-h avg 13 bsp, O3 0-1
ppb; 1-h max 26
ppb
24-h avg 14 PM10, bsp, O3, SO2, 0
ppb; 1-h max 28 CO
ppb
Poisson GLM, GAM with
stringent convergence
criteria. Time-series
study.
Poisson, GAM with
default convergence
criteria. Time-series
study.
Poisson with GEE. Time-
series study.
Autoregressive Poisson
with GEE. Time-series
study.
Lag 0-1, GAM, all-
cause, single pollutant:
3. 4% (1.1, 5. 7); with
bsp: 3.1% (0.3, 5.9);
cardiovascular: 4.3%
(0.9, 7.8); respiratory:
11.4% (3. 5, 19.9)
All-cause (lag 1): 9.7%
(4.7, 14.8); respiratory:
18.8% (1.2, 39.6)
Lag 0-1, single pollutant,
all-cause: 3.0% (0.1,
6.0); cardiovascular:
2.2% (-1.7, 6.4);
respiratory: 8.6% (-0.4,
18.4)
Lag 0-1, single pollutant,
all-cause, all-yr: -1.0%
(-5.2, 3.4); summer:
-3.6% (-11. 2, 4.7);
winter: 1.2% (-4.0, 6.9)
-------
TABLE AX6.9 (cont'd). EFFECTS OF ACUTE NOX EXPOSURE ON MORTALITY. RISK ESTIMATES ARE
CJQ
r-K
to
o
o
X
i
_^.
o
O
£
H
i
O
o
0
H
O
H
W
O
O
HH
H
W
Reference, Study
Location, and Period
ASIA
Kim et al. (2004b)
Seoul, Korea
1995-1999
Lee etal. (1999)
Seoul and Ulsan,
Korea
1991-1995
Lee and Schwartz
(1999)
Seoul, Korea
1991-1995
Kwon etal. (2001)
Seoul, Korea
1994-1998
STANDARDIZED FOR PER 20 PPB 24-H AVG
Lag
Outcome Copollutants Structure
Measure Mean NO2 Levels Considered Reported
All cause 24-h avg 33 ppb. PM10, O3, SO2, CO; 1
two-pollutant models
All cause l-hmaxO3: TSP, SO2 0
Seoul:
32.4 ppb
10th %-90th %
14-55
Ulsan:
26.0 ppb
10th %-90th %
16-39
All cause l-hmaxO3: TSP, SO2 0
Seoul:
32.4 ppb
10th %-90th %
14-55
Mortality in a 24-h avg 32 ppb PM10, O3, SO2, CO 0
cohort of
patients with
congestive heart
failure
NO2 INCREMENT
Method/Design
Poisson GAM with stringent
convergence criteria (linear
model); GLM with cubic natural
spline; GLM with B-mode spline
(threshold model). Time-series
study.
Poisson with GEE. Time-series
study.
Conditional logistic regression.
Case crossover with bidirectional
control sampling.
Poisson GAM with default
convergence criteria; case-
crossover analysis using
conditional logistic regression.
Effect Estimates
Risk estimates for NO2 not
reported.
1-h max O3 (per 50 ppb):
Seoul:
1.5% (0.5, 2. 5)
Ulsan:
2.0%(-11. 1,17.0)
1-h max O3 (per 50 ppb):
Two controls, plus and
minus 1 wk:
1.5% (-1.2, 4.2)
Four controls, plus and
minus 2 wks:
2.3% (-0.1, 4. 8)
Odds ratio in general
population:
1.1% (-0.3, 2. 5)
Congestive heart failure
cohort:
15. 8% (1.8, 31.7)
-------
OQ
to
o
o
TABLE AX6.9 (cont'd). EFFECTS OF ACUTE NOX EXPOSURE ON MORTALITY. RISK ESTIMATES ARE
STANDARDIZED FOR PER 20 PPB 24-H AVG NO2 INCREMENT
X
I
£
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
Reference, Study
Location, and Period
Outcome Measure
Mean NO2 Copollutants Lag Structure
Levels Considered Reported
Method/Design
Effect Estimates
ASIA (cont'd)
Ha et al. (2003)
Seoul, Korea
1995-1999
Hong et al. (2002)
Seoul, Korea
1995-1998
Tsai et al. (2003b)
Kaohsiung, Taiwan
1994-2000
Yang et al. (2004b)
Taipei, Taiwan
1994-1998
Wong et al. (200 Ib)
Hong Kong
1995-1997
Wong et al. (2002)
Hong Kong
1995-1998
All cause; respiratory;
postneonatal (1 mo to 1
yr); age 2-64 yrs; age 65+
Acute stroke mortality
All cause; respiratory;
cardiovascular; tropical
area
All cause; respiratory;
cardiovascular;
subtropical area
All cause; respiratory;
cardiovascular
Respiratory;
cardiovascular; COPD;
pneumonia and influenza;
ischemic heart dis.;
cerebrovascular
24-h avg 33 ppb PM10, O3, SO2, 0
CO
24-h avg 33 ppb PM10, O3, SO2, 2
CO
24-h avg 29 ppb PM10, SO2, O3, 0-2
CO
24-h avg 31 ppb PM10, SO2, O3, 0-2
CO
24-h avg 25 ppb PM10, O3, SO2; 0,1,2
in warm season; two-pollutant
33 ppb in cold models
season
24-h avg 29 ppb PM10, O3, SO2; 0, 1, 2, 0-1, 0-2
two-pollutant
models
Poisson GAM with default
convergence criteria.
Time-series study.
Poisson GAM with default
convergence criteria.
Time-series study.
Conditional logistic
regression. Case-crossover
analysis.
Conditional logistic
regression. Case-crossover
analysis.
Poisson GAM with default
convergence criteria.
Time-series study.
Poisson GLM. Time-series
study.
All cause for postneonates:
0.8% (-5.7, 7.7); age 65+:
3. 8% (3.7, 3.9)
4.3% (1.6, 7.0)
Odds ratios:
All cause:
0.1% (-5. 9, 6.6);
Respiratory:
-1.0% (-22.2, 25. 9);
Cardiovascular:
-1.8% (-14.0, 12.1)
Odds ratios:
All cause:
0.6% (-3. 9, 5.2);
Respiratory:
2.5%(-13.1,20.8);
Cardiovascular:
-1.1% (-9.5, 8.0)
All cause (lag 1 ):
2. 6% (0.9, 4.4);
Respiratory (lag 0):
6.1% (-1.8, 10.5);
Cardiovascular (lag 2):
5.2% (1.8, 8.7)
Respiratory (0-1):
5.1% (1.6, 8.7);
Cardiovascular (lag 0-2):
3.1% (-0.2, 6.5)
-------
OQ
to
o
o
TABLE AX6.9 (cont'd). EFFECTS OF ACUTE NOX EXPOSURE ON MORTALITY. RISK ESTIMATES ARE
STANDARDIZED FOR PER 20 PPB 24-H AVG NO2 INCREMENT
Reference, Study
Location, and Period
Outcome Measure Mean NO2 Levels
Copollutants
Considered
Lag Structure
Reported
Method/Design
Effect Estimates
ASIA (cont'd)
X
to
Hedley et al. (2002)
Hong Kong
1985-1995
Intervention Jul 1990
(switch to low sulfur-
content fuel)
Yang et al. (2004b)
Taipei, Taiwan
1994-1998
All cause;
cardiovascular;
respiratory;
neoplasms and other
causes; all ages;
age 15-64yrs;
age 65+ yrs
Avg moly NO2:
Baseline: 29 ppb
1 yr after intervention:
25 ppb
2-5 yrs after
intervention: 28 ppb
SO2 (main pollutant of
interest, 45%
reduction observed 5
yrs after intervention),
FMio,
SO42~, NO2
Moly avgs
considered
without lags
All cause;
respiratory;
cardiovascular;
subtropical area
24-havg31 ppb
o, S02, 03, CO
0-2
Poisson regression of
moly avgs to estimate
changes in the increase
in deaths from warm
to cool season.
Annual proportional
change in death rate
before and after the
intervention was also
examined.
Conditional logistic
regression. Case-
crossover analysis.
Declines observed in all
cause (2.1%, p = 0.001),
respiratory
(3.9%, p = 0.001), and
cardiovascular
(2.0%, p = 0.020)
mortality after the
intervention.
As NO2 levels did not
change before and after
the intervention, NO2
likely did not play a role
in the decline in observed
mortality.
Odds ratios:
All cause:
0.6% (-3.9, 5.2);
Respiratory:
2.5% (-13.1,20.8);
Cardiovascular:
-!.!%(- 9.5,8.0)
H
6
o
o
H
O
O
H
W
O
O
HH
H
W
-------
1 AX6.1 REFERENCES
2
3 Ackermann-Liebrich, U.; Leuenberger, P.; Schwartz, J.; Schindler, C.; Monn, C.; Bolognini, B.;
4 Bongard, J. P.; Brandli, O.; Domenighetti, G.; Elsasser, S.; Grize, L.; Karrer, W.; Keller,
5 R.; Keller-Wossidlo, H.; Kunzli, N.; Martin, B. W.; Medici, T. C.; Perruchoud, A. P.;
6 Schoni, M. H.; Tschopp, J. M.; Villiger, B.; Wuthrich, B.; Zellweger, J. P.; Zemp, E.
7 (1997) Lung function and long term exposure to air pollutants in Switzerland. Am. J.
8 Respir. Crit. Care Med. 155: 122-129.
9 Anderson, H. R.; Ponce de Leon, A.; Bland, J. M.; Bower, J. S.; Strachan, D. P. (1996) Air
10 pollution and daily mortality in London: 1987-92. Br. Med. J. 312: 665-669.
11 Anderson, H. R.; Spix, C.; Medina, S.; Schouten, J. P.; Castellsague, J.; Rossi, G.; Zmirou, D.;
12 Touloumi, G.; Wojtyniak, B.; Ponka, A.; Bacharova, L.; Schwartz, J.; Katsouyanni, K.
13 (1997) Air pollution and daily admissions for chronic obstructive pulmonary disease in 6
14 European cities: results from the APHEA project. Eur. Respir. J. 10: 1064-1071.
15 Anderson, H. R.; Ponce de Leon, A.; Bland, J. M.; Bower, J. S.; Emberlin, J.; Strachen, D. P.
16 (1998) Air pollution, pollens, and daily admissions for asthma in London 1987-92.
17 Thorax 53: 842-848.
18 Anderson, H. R.; Bremner, S. A.; Atkinson, R. W.; Harrison, R. M.; Walters, S. (2001)
19 Particulate matter and daily mortality and hospital admissions in the west midlands
20 conurbation of the United Kingdom: associations with fine and coarse particles, black
21 smoke and sulphate. Occup. Environ. Med. 58: 504-510.
22 Atkinson, R. W.; Bremner, S. A.; Anderson, H. R.; Strachan, D. P.; Bland, J. M.; Ponce de Leon,
23 A. (1999a) Short-term associations between emergency hospital admissions for
24 respiratory and cardiovascular disease and outdoor air pollution in London. Arch.
25 Environ. Health 54: 398-411.
26 Atkinson, R. W.; Anderson, H. R.; Strachan, D. P.; Bland, J. M.; Bremner, S. A.; Ponce de Leon,
27 A. (1999b) Short-term associations between outdoor air pollution and visits to accident
28 and emergency departments in London for respiratory complaints. Eur. Respir. J. 13:
29 257-265.
30 Atkinson, R. W.; Anderson, H. R.; Sunyer, J.; Ayres, J.; Baccini, M.; Vonk, J. M.; Boumghar,
31 A.; Forastiere, F.; Forsberg, B.; Touloumi, G.; Schwartz, J.; Katsouyanni, K. (2001)
32 Acute effects of particulate air pollution on respiratory admissions: results from APHEA
33 2 project. Am. J. Respir. Crit. Care Med. 164: 1860-1866.
34 Ballester, F.; Tenias, J. M.; Perez-Hoyos, S. (2001) Air pollution and emergency hospital
35 admissions for cardiovascular diseases in Valencia, Spain. J. Epidemiol. Community
36 Health 55: 57-65.
37 Ballester, F.; Rodriguez, P.; Iniguez, C.; Saez, M.; Daponte, A.; Galan, L; Taracido, M.; Arribas,
38 F.; Bellido, J.; Cirarda, F. B.; Canada, A.; Guillen, J. J.; Guillen-Grima, F.; Lopez, E.;
39 Perez-Hoyos, S.; Lertxundi, A.; Toro, S. (2006) Air pollution and cardiovascular
40 admisisons association in Spain: results within the EMECAS project. J. Epidemiol.
41 Community Health 60: 328-336.
August, 2007 AX6-143 DRAFT-DO NOT QUOTE OR CITE
-------
1 Barnett, A. G.; Williams, G. M.; Schwartz, J.; Neller, A. H.; Best, T. L.; Petroeschevsky, A. L.;
2 Simpson, R. W. (2005) Air pollution and child respiratory health: a case-crossover study
3 in Australia and New Zealand. Am. J. Respir. Crit. Care Med. 171: 1272-1278.
4 Barnett, A. G.; Williams, G. M.; Schwartz, J.; Best, T. L.; Neller, A. H.; Petroeschevsky, A. L.;
5 Simpson, R. W. (2006) The effects of air pollution on hospitalization for cardiovascular
6 disease in elderly people in Australian and New Zealand cities. Environ. Health Perspect.
7 114:1018-1023.
8 Belanger, K.; Gent, J. F.; Triche, E. W.; Bracken, M. B.; Leaderer, B. P. (2006) Association of
9 indoor nitrogen dioxide exposure with respiratory symptoms in children with asthma.
10 Am. J. Respir. Crit. Care Med. 173: 297-303.
11 Bell, M. L.; Ebisu, K.; Belanger, K. (2007) Ambient air pollution and low birth weight in
12 Connecticut and Massachusetts. Environ. Health Perspect. 115: 1118-1125.
13 Biggeri, A.; Baccini, M.; Bellini, P.; Terracini, B. (2005) Meta-analysis of the Italian studies of
14 short-term effects of air pollution (MISA), 1990-1999. Int. J. Occup. Environ. Health 11:
15 107-122.
16 Bobak, M. (2000) Outdoor air pollution, low birth weight, and prematurity. Environ. Health
17 Perspect. 108: 173-176.
18 Borja-Aburto, V. H.; Loomis, D. P.; Bangdiwala, S. L; Shy, C. M.; Rascon-Pacheco, R. A.
19 (1997) Ozone, suspended particulates, and daily mortality in Mexico City. Am. J.
20 Epidemiol. 145: 258-268.
21 Borja-Aburto, V. H.; Castillejos, M.; Gold, D. R.; Bierzwinski, S.; Loomis, D. (1998) Mortality
22 and ambient fine particles in southwest Mexico City, 1993-1995. Environ. Health
23 Perspect. 106: 849-855.
24 Boutin-Forzano, S.; Adel, N.; Gratecos, L.; Jullian, H.; Gamier, J. M.; Ramadour, M.;
25 Lanteaume, A.; Hamon, M.; Lafay, V.; Charpin, D. (2004) Visits to the emergency room
26 for asthma attacks and short-term variations in air pollution. A case-crossover study.
27 Respiration 71: 134-137.
28 Braga, A. L. F.; Saldiva, P. H. N.; Pereira, L. A. A.; Menezes, J. J. C.; Concei9ao, G. M. C.; Lin,
29 C. A.; Zanobetti, A.; Schwartz, J.; Dockery, D. W. (2001) Health effects of air pollution
30 exposure on children and adolescents in Sao Paulo, Brazil. Pediatr. Pulmonol. 31: 106-
31 113.
32 Bremner, S. A.; Anderson, H. R.; Atkinson, R. W.; McMichael, A. J.; Strachan, D. P.; Bland, J.
33 M.; Bower, J. S. (1999) Short term associations between outdoor air pollution and
34 mortality in London 1992-4. Occup. Environ. Med. 56: 237-244.
35 Buchdahl, R.; Parker, A.; Stebbings, T.; Babiker, A. (1996) Association between air pollution
36 and acute childhood wheezy episodes: prospective observational study. Br. Med. J. 312:
37 661-664.
38 Burnett, R. T.; Brook, J. R.; Yung, W. T.; Dales, R. E.; Krewski, D. (1997a) Association
39 between ozone and hospitalization for respiratory diseases in 16 Canadian cities. Environ.
40 Res. 72:24-31.
August, 2007 AX6-144 DRAFT-DO NOT QUOTE OR CITE
-------
1 Burnett, R. T.; Cakmak, S.; Brook, J. R.; Krewski, D. (1997b) The role of particulate size and
2 chemistry in the association between summertime ambient air pollution and
3 hospitalization for cardiorespiratory diseases. Environ. Health Perspect. 105: 614-620.
4 Burnett, R. T.; Cakmak, S.; Brook, J. R. (1998a) The effect of the urban ambient air pollution
5 mix on daily mortality rates in 11 Canadian cities. Can. J. Public Health 89: 152-156.
6 Burnett, R. T.; Cakmak, S.; Raizenne, M. E.; Stieb, D.; Vincent, R.; Krewski, D.; Brook, J. R.;
7 Philips, O.; Ozkaynak, H. (1998b) The association between ambient carbon monoxide
8 levels and daily mortality in Toronto, Canada. J. Air Waste Manage. Assoc. 48: 689-700.
9 Burnett, R. T.; Smith-Doiron, M.; Stieb, D.; Cakmak, S.; Brook, J. R. (1999) Effects of
10 particulate and gaseous air pollution on cardiorespiratory hospitalizations. Arch. Environ.
11 Health 54: 130-139.
12 Burnett, R. T.; Brook, J.; Dann, T.; Delocla, C.; Philips, O.; Cakmak, S.; Vincent, R.; Goldberg,
13 M. S.; Krewski, D. (2000) Association between particulate- and gas-phase components of
14 urban air pollution and daily mortality in eight Canadian cities. In: Grant, L. D., ed.
15 PM2000: particulate matter and health. Inhalation Toxicol. 12(suppl. 4): 15-39.
16 Burnett, R. T.; Smith-Doiron, M.; Stieb, D.; Raizenne, M. E.; Brook, J. R.; Dales, R. E.; Leech,
17 J. A.; Cakmak, S.; Krewski, D. (2001) Association between ozone and hospitalization for
18 acute respiratory diseases in children less than 2 years of age. Am. J. Epidemiol. 153:
19 444-452.
20 Burnett, R. T.; Goldberg, M. S. (2003) Size-fractionated particulate mass and daily mortality in
21 eight Canadian cities. In: Revised analyses of time-series studies of air pollution and
22 health. Special report. Boston, MA: Health Effects Institute; pp. 85-89. Available:
23 http://www.healtheffects.org/news.htm [16 May, 2003].
24 Burnett, R. T.; Stieb, D.; Brook, J. R.; Cakmak, S.; Dales, R.; Raizenne, M.; Vincent, R.; Dann,
25 T. (2004) Associations between short-term changes in nitrogen dioxide and mortality in
26 Canadian cities. Arch. Environ. Health 59: 228-236.
27 Cassino, C.; Ito, K.; Bader, I.; Ciotoli, C.; Thurston, G.; Reibman, J. (1999) Cigarette smoking
28 and ozone-associated emergency department use for asthma by adults in New York City.
29 Am. J. Respir. Crit. Care Med. 159: 1773-1779.
30 Castellsague, J.; Sunyer, J.; Saez, M.; Anto, J. M. (1995) Short-term association between air
31 pollution and emergency room visits for asthma in Barcelona. Thorax 50: 1051-1056.
32 Chan, C.-C.; Chuang, K.-J.; Su, T.-C.; Lin, L.-Y. (2005) Association between nitrogen dioxide
33 and heart rate variability in a susceptible population. Eur. J. Cardiovasc. Prev. Rehabil.
34 12: 580-586.
35 Chan, C.-C.; Chuang, K.-J.; Chien, L.-C.; Chen, W.-J.; Chang, W.-T. (2006) Urban air pollution
36 and emergency admissions for cerebrovascular diseases in Taipei, Taiwan. Eur. Heart J.
37 27: 1238-1244.
38 Chang, C.-C.; Tsai, S.-S.; Ho, S.-C.; Yang, C.-Y. (2005) Air pollution and hospital admissions
39 for cardiovascular disease in Taipei, Taiwan. Environ. Res. 98: 114-119.
August, 2007 AX6-145 DRAFT-DO NOT QUOTE OR CITE
-------
1 Chew, F. T.; Goh, D. Y. T.; Ooi, B. C.; Saharom, R.; Hui, J. K. S.; Lee, B. W. (1999)
2 Association of ambient air-pollution levels with acute asthma exacerbation among
3 children in Singapore. Allergy (Copenhagen) 54: 320-329.
4 Chock, D. P.; Winkler, S. L.; Chen, C. (2000) A study of the association between daily mortality
5 and ambient air pollutant concentrations in Pittsburgh, Pennsylvania. J. Air Waste
6 Manage. Assoc. 50: 1481-1500.
7 Cifuentes, L. A.; Vega, J.; Kopfer, K.; Lave, L. B. (2000) Effect of the fine fraction of parti culate
8 matter versus the coarse mass and other pollutants on daily mortality in Santiago, Chile.
9 J. Air Waste Manage. Assoc. 50: 1287-1298.
10 D'Ippoliti, D.; Forastiere, F.; Ancona, C.; Agabiti, N.; Fusco, D.; Michelozzi, P.; Perucci, C. A.
11 (2003) Air pollution and myocardial infarction in Rome: a case-crossover analysis.
12 Epidemiology 14: 528-535.
13 Dab, W.; Medina, S.; Quenel, P.; Le Moullec, Y.; Le Tertre, A.; Thelot, B.; Monteil, C.;
14 Lameloise, P.; Pirard, P.; Momas, L; Ferry, R.; Festy, B. (1996) Short term respiratory
15 health effects of ambient air pollution: results of the APFIEA project in Paris. In: St
16 Leger, S., ed. The APHEA project. Short term effects of air pollution on health: a
17 European approach using epidemiological time series data. J. Epidemiol. Commun.
18 Health 50(suppl. 1): S42-S46.
19 De Leon, S. F.; Thurston, G. D.; Ito, K. (2003) Contribution of respiratory disease to
20 nonrespiratory mortality associations with air pollution. Am. J. Respir. Crit. Care Med.
21 167:1117-1123.
22 Delfmo, R. J.; Zeiger, R. S.; Seltzer, J. M.; Street, D. H.; McLaren, C. E. (2002) Association of
23 asthma symptoms with peak particulate air pollution and effect modification by anti-
24 inflammatory medication use. Environ. Health Perspect. 110: A607-A617.
25 Dewanji, A.; Moolgavkar, S. H. (2000) A Poisson process approach for recurrent event data with
26 environmental covariates. Environmetrics 11: 665-673.
27 Dewanji, A.; Moolgavkar, S. H. (2002) Choice of stratification in Poisson process analysis of
28 recurrent event data with environmental covariates. Stat. Med. 21: 3383-3393.
29 Diaz, J.; Garcia, R.; Ribera, P.; Alberdi, J. C.; Hernandez, E.; Pajares, M. S.; Otero, A. (1999)
30 Modeling of air pollution and its relationship with mortality and morbidity in Madrid,
31 Spain. Int. Arch. Occup. Environ. Health 72: 366-376.
32 Dockery, D. W.; Schwartz, J.; Spengler, J. D. (1992) Air pollution and daily mortality:
33 associations with particulates and acid aerosols. Environ. Res. 59: 362-373.
34 Dockery, D. W.; Luttmann-Gibson, H.; Rich, D. Q.; Link, M. S.; Mittleman, M. A.; Gold, D. R.;
35 Koutrakis, P.; Schwartz, J. D.; Verrier, R. L. (2005) Association of air pollution with
36 increased incidence of ventricular tachyarrhythmias recorded by implanted cardioverter
37 defibrillators. Environ. Health Perspect. 113: 670-674.
38 Dominici, F.; McDermott, A.; Daniels, M.; Zeger, S. L.; Samet, J. M. (2003) Mortality among
39 residents of 90 cities. In: Revised analyses of time-series studies of air pollution and
40 health. Special report. Boston, MA: Health Effects Institute; pp. 9-24. Available:
41 http://www.healtheffects.org/Pubs/TimeSeries.pdf [12 May, 2004].
August, 2007 AX6-146 DRAFT-DO NOT QUOTE OR CITE
-------
1 Erbas, B.; Kelly, A.-M.; Physick, B.; Code, C.; Edwards, M. (2005) Air pollution and childhood
2 asthma emergency hospital admissions: estimating intra-city regional variations. Int. J.
3 Environ. Health Res. 15: 11-20.
4 Fairley, D. (1999) Daily mortality and air pollution in Santa Clara County, California: 1989-
5 1996. Environ. Health Perspect. 107: 637-641.
6 Fairley, D. (2003) Mortality and air pollution for Santa Clara County, California, 1989-1996. In:
7 Revised analyses of time-series studies of air pollution and health. Special report. Boston,
8 MA: Health Effects Institute; pp. 97-106. Available:
9 http://www.healtheffects.org/Pubs/TimeSeries.pdf [18 October, 2004].
10 Farhat, S. C. L.; Paulo, R. L. P.; Shimoda, T. M.; Conceicao, G. M. S.; Lin, C. A.; Braga, A. L.
11 F.; Warm, M. P. N.; Saldiva, P. H. N. (2005) Effect of air pollution on pediatric
12 respiratory emergency room visits and hospital admissions. Braz. J. Med. Biol. Res. 38:
13 227-235.
14 Fischer, P.; Hoek, G.; Brunekreef, B.; Verhoeff, A.; van Wijnen, J. (2003) Air pollution and
15 mortality in the Netherlands: are the elderly more at risk? Eur. Respir. J. 21(suppl. 40):
16 34S-38S.
17 Fung, K. Y.; Luginaah, L; Gorey, K. M.; Webster, G. (2005) Air pollution and daily hospital
18 admissions for cardiovascular diseases in Windsor, Ontario. Can. J. Public Health 96: 29-
19 33.
20 Fung, K. Y.; Khan, S.; Krewski, D.; Chen, Y. (2006) Association between air pollution and
21 multiple respiratory hospitalizations among the elderly in Vancouver, Canada. Inhalation
22 Toxicol. 18: 1005-1011.
23 Fusco, D.; Forastiere, F.; Michelozzi, P.; Spadea, T.; Ostro, B.; Area, M.; Perucci, C. A. (2001)
24 Air pollution and hospital admissions for respiratory conditions in Rome, Italy. Eur.
25 Respir. J. 17: 1143-1150.
26 Galan, L; Tobias, A.; Banegas, J. R.; Aranguez, E. (2003) Short-term effects of air pollution on
27 daily asthma emergency room admissions. Eur. Respir. J. 22: 802-808.
28 Gamble, J. L. (1998) Effects of ambient air pollution on daily mortality: a time series analysis of
29 Dallas, Texas, 1990-1994. Presented at: 91st annual meeting and exhibition of the Air &
30 Waste Management Association; June; San Diego, CA. Pittsburgh, PA: Air & Waste
31 Management Association; paper no. 98-MP26.03.
32 Garcia-Aymerich, J.; Tobias, A.; Anto, J. M.; Sunyer, J. (2000) Air pollution and mortality in a
33 cohort of patients with chronic obstructive pulmonary disease: a time series analysis. J.
34 Epidemiol. Community Health 54: 73-74.
35 Garrett, M. H.; Hooper, M. A.; Hooper, B. M.; Abramson, M. J. (1998) Respiratory symptoms in
36 children and indoor exposure to nitrogen dioxide and gas stoves. Am. J. Respir. Crit.
37 Care Med. 158: 891-895.
38 Garrett, M. H.; Hooper, M. A.; Hooper, B. M. (1999) Nitrogen dioxide in Australian homes:
39 levels and sources. J. Air Waste Manage. Assoc. 49: 76-81.
August, 2007 AX6-147 DRAFT-DO NOT QUOTE OR CITE
-------
1 Garty, B. Z.; Kosman, E.; Ganor, E.; Berger, V.; Garty, L.; Wietzen, T.; Waisman, Y.; Mimouni,
2 M.; Waisel, Y. (1998) Emergency room visits of asthmatic children, relation to air
3 pollution, weather, and airborne allergens. Ann. Allergy Asthma Immunol. 81: 563-570.
4 Gauderman, W. J.; Avol, E.; Gilliland, F.; Vora, H.; Thomas, D.; Berhane, K.; McConnell, R.;
5 Kuenzli, N.; Lurmann, F.; Rappaport, E.; Margolis, H.; Bates, D.; Peters, J. (2004) The
6 effect of air pollution on lung development from 10 to 18 years of age. N. Engl. J. Med.
7 351: 1057-1067.
8 Gauderman, W. J.; Avol, E.; Lurmann, F.; Kuenzli, N.; Gilliland, F.; Peters, J.; McConnell, R.
9 (2005) Childhood asthma and exposure to traffic and nitrogen dioxide. Epidemiology 16:
10 737-743.
11 Goldberg, M. S.; Burnett, R. T.; Valois, M.-F.; Flegel, K.; Bailar, J. C., Ill; Brook, J.; Vincent,
12 R.; Radon, K. (2003) Associations between ambient air pollution and daily mortality
13 among persons with congestive heart failure. Environ. Res. 91: 8-20.
14 Gouveia, N.; Fletcher, T. (2000a) Respiratory diseases in children and outdoor air pollution in
15 Sao Paulo, Brazil: a time series analysis. Occup. Environ. Med. 57: 477-483.
16 Gouveia, N.; Fletcher, T. (2000b) Time series analysis of air pollution and mortality: effects by
17 cause, age and socioeconomic status. J. Epidemiol. Community Health 54: 750-755.
18 Gwynn, R. C.; Burnett, R. T.; Thurston, G. D. (2000) A time-series analysis of acidic particulate
19 matter and daily mortality and morbidity in the Buffalo, New York, region. Environ.
20 Health Perspect. 108: 125-133.
21 Ha, E.-H.; Lee, J.-T.; Kim, H.; Hong, Y.-C.; Lee, B.-E.; Park, H.-S.; Christiani, D. C. (2003)
22 Infant susceptibility of mortality to air pollution in Seoul, South Korea. Pediatrics 111:
23 284-290.
24 Hagen, J. A.; Nafstad, P.; Skrondal, A.; Bj0rkly, S.; Magnus, P. (2000) Associations between
25 outdoor air pollutants and hospitalization for respiratory diseases. Epidemiology 11: 136-
26 140.
27 Hajat, S.; Haines, A.; Goubet, S. A.; Atkinson, R. W.; Anderson, H. R. (1999) Association of air
28 pollution with daily GP consultations for asthma and other lower respiratory conditions in
29 London. Thorax 54: 597-605.
30 Hajat, S.; Haines, A.; Atkinson, R. W.; Bremner, S. A.; Anderson, H. R.; Emberlin, J. (2001)
31 Association between air pollution and daily consultations with general practitioners for
32 allergic rhinitis in London, United Kingdom. Am. J. Epidemiol. 153: 704-714.
33 Hajat, S.; Anderson, H. R.; Atkinson, R. W.; Haines, A. (2002) Effects of air pollution on
34 general practitioner consultations for upper respiratory diseases in London. Occup.
35 Environ. Med. 59: 294-299.
36 Hansen, C.; Neller, A.; Williams, G.; Simpson, R. (2006) Maternal exposure to low levels of
37 ambient air pollution and preterm birth in Brisbane, Australia. BJOG 113: 935-941.
38 Hedley, A. J.; Wong, C.-M.; Thach, T. Q.; Ma, S.; Lam, T.-H.; Anderson, H. R. (2002)
39 Cardiorespiratory and all-cause mortality after restrictions on sulphur content of fuel in
40 Hong Kong: an intervention study. Lancet 360: 1646-1652.
August, 2007 AX6-148 DRAFT-DO NOT QUOTE OR CITE
-------
1 Hernandez-Gardufio, E.; Perez-Neria, J.; Paccagnella, A. M.; Pifia-Garcia, M.; Munguia-Castro,
2 M.; Catalan-Vazquez, M.; Rojas-Ramos, M. (1997) Air pollution and respiratory health
3 in Mexico City. J. Occup. Environ. Med. 39: 299-307.
4 Hinwood, A. L.; De Klerk, N.; Rodriguez, C.; Jacoby, P.; Runnion, T.; Rye, P.; Landau, L.;
5 Murray, F.; Feldwick, M.; Spickett, J. (2006) The relationship between changes in daily
6 air pollution and hospitalizations in Perth, Australia 1992-1998: a case-crossover study.
7 Int. J. Environ. Health Res. 16: 27-46.
8 Hirsch, T.; Weiland, S. K.; Von Mutius, E.; Safeca, A. F.; Grafe, H.; Csaplovics, E.; Duhme, H.;
9 Keil, U.; Leupold, W. (1999) Inner city air pollution and respiratory health and atopy in
10 children. Eur. Respir. J. 14: 669-677.
11 Hoek, G.; Brunekreef, B.; Verhoeff, A.; Van Wijnen, J.; Fischer, P. (2000) Daily mortality and
12 air pollution in the Netherlands. J. Air Waste Manage. Assoc. 50: 1380-1389.
13 Hoek, G.; Fischer, P.; Van Den Brandt, P.; Goldbohm, S.; Brunekreef, B. (2001) Estimation of
14 long-term average exposure to outdoor air pollution for a cohort study on mortality. J.
15 Exposure Anal. Environ. Epidemiol. 11: 459-469.
16 Hoek, G. (2003) Daily mortality and air pollution in The Netherlands. In: Revised analyses of
17 time-series studies of air pollution and health. Special report. Boston, MA: Health Effects
18 Institute; pp. 133-141. Available: http://www.healtheffects.org/Pubs/TimeSeries.pdffl2
19 May, 2004].
20 Hong, Y.-C.; Lee, J.-T.; Kim, H.; Kwon, H.-J. (2002) Air pollution: a new risk factor in ischemic
21 stroke mortality. Stroke 33: 2165-2169.
22 Hwang, J.-S.; Chan, C.-C. (2002) Effects of air pollution on daily clinic visits for lower
23 respiratory tract illness. Am. J. Epidemiol. 155: 1-10.
24 Hwang, B.-F.; Lee, Y.-L.; Lin, Y.-C.; Jaakkola, J. J. K.; Guo, Y. L. (2005) Traffic related air
25 pollution as a determinant of asthma among Taiwanese school children. Thorax 60: 467-
26 473.
27 Ilabaca, M.; Olaeta, I; Campos, E.; Villaire, J.; Tellez-Rojo, M. M.; Romieu, I. (1999)
28 Association between levels of fine particulate and emergency visits for pneumonia and
29 other respiratory illnesses among children in Santiago, Chile. J. Air Waste Manage.
30 Assoc. 49: 154-163.
31 Ito, K. (2003) Associations of parti culate matter components with daily mortality and morbidity
32 in Detroit, Michigan. In: Revised analyses of time-series studies of air pollution and
33 health. Special report. Boston, MA: Health Effects Institute; pp. 143-156. Available:
34 http://www.healtheffects.org/Pubs/TimeSeries.pdf [12 May, 2004].
35 Ito, K. (2004) Revised ozone risk estimates for daily mortality and hospitalizations in Detroit,
36 Michigan [personal communication with attachments to Jee Young Kim]. New York,
37 NY: New York University School of Medicine, Nelson Institute of Environmental
3 8 Medicine; October 31.
39 Jaffe, D. H.; Singer, M. E.; Rimm, A. A. (2003) Air pollution and emergency department visits
40 for asthma among Ohio Medicaid recipients, 1991-1996. Environ. Res. 91: 21-28.
August, 2007 AX6-149 DRAFT-DO NOT QUOTE OR CITE
-------
1 Jalaludin, B. B.; O'Toole, B. I; Leeder, S. R. (2004) Acute effects of urban ambient air pollution
2 on respiratory symptoms, asthma medication use, and doctor visits for asthma in a cohort
3 of Australian children. Environ Res. 95: 32-42.
4 Jalaludin, B.; Morgan, G.; Lincoln, D.; Sheppeard, V.; Simpson, R.; Corbett, S. (2006)
5 Associations between ambient air pollution and daily emergency department attendances
6 for cardiovascular disease in the elderly (65+ years), Sydney, Australia. J. Exposure Sci.
7 Environ. Epidemiol. 16: 225-237.
8 Just, J.; Segala, C.; Sahraoui, F.; Priol, G.; Grimfeld, A.; Neukirch, F. (2002) Short-term health
9 effects of particulate and photochemical air pollution in asthmatic children. Eur. Respir.
10 J. 20: 899-906.
11 Karr, C.; Lumley, T.; Shepherd, K.; Davis, R.; Larson, T.; Ritz, B.; Kaufman, J. (2006) A case-
12 crossover study of wintertime ambient air pollution and infant bronchiolitis. Environ.
13 Health Perspect. 114:277-281.
14 Kelsall, J. E.; Samet, J. M.; Zeger, S. L.; Xu, J. (1997) Air pollution and mortality in
15 Philadelphia, 1974-1988. Am. J. Epidemiol. 146: 750-762.
16 Kim, J. J.; Smorodinsky, S.; Lipsett, M.; Singer, B. C.; Hodgson, A. T.; Ostro, B. (2004) Traffic-
17 related air pollution near busy roads: the East Bay children's Respiratory Health Study.
18 Am. J. Respir. Crit. Care Med. 170: 520-526.
19 Kim, S.-Y.; Lee, J.-T.; Hong, Y.-C.; Ahn, K.-J.; Kim, H. (2004) Determining the threshold effect
20 of ozone on daily mortality: an analysis of ozone and mortality in Seoul, Korea, 1995-
21 1999. Environ. Res. 94: 113-119.
22 Kinney, P. L.; Ozkaynak, H. (1991) Associations of daily mortality and air pollution in Los
23 Angeles County. Environ. Res. 54: 99-120.
24 Klemm, R. J.; Mason, R. M., Jr. (2000) Aerosol Research and Inhalation Epidemiological Study
25 (ARIES): air quality and daily mortality statistical modeling—interim results. J. Air.
26 Waste Manage. Assoc. 50: 1433-1439.
27 Klemm, R. J.; Lipfert, F. W.; Wyzga, R. E.; Gust, C. (2004) Daily mortality and air pollution in
28 Atlanta: two years of data from ARIES. Inhalation Toxicol. 16(suppl. 1): 131-141.
29 Kwon, H.-J.; Cho, S.-H.; Nyberg, F.; Pershagen, G. (2001) Effects of ambient air pollution on
30 daily mortality in a cohort of patients with congestive heart failure. Epidemiology 12:
31 413-419.
32 Lanki, T.; Pekkanen, J.; Aalto, P.; Elosua, R.; Berglind, N.; D'Ippoliti, D.; Kulmala, M.; Nyberg,
33 F.; Peters, A.; Picciotto, S.; Salomaa, V.; Sunyer, J.; Tiittanen, P.; Von Klot, S.;
34 Forastiere, F.; for the HEAPSS Study Group. (2006) Associations of traffic-related air
35 pollutants with hospitalisation for first acute myocardial infarction: the HEAPSS study.
36 Occup. Environ. Med. 63: 844-851.
37 Le Tertre, A.; Quenel, P.; Eilstein, D.; Medina, S.; Prouvost, H.; Pascal, L.; Boumghar, A.;
38 Saviuc, P.; Zeghnoun, A.; Filleul, L.; Declercq, C.; Cassadou, S.; Le Goaster, C. (2002)
39 Short-term effects of air pollution on mortality in nine French cities: a quantitative
40 summary. Arch. Environ. Health 57: 311-319.
August, 2007 AX6-150 DRAFT-DO NOT QUOTE OR CITE
-------
1 Lee, J.-T.; Schwartz, J. (1999) Reanalysis of the effects of air pollution on daily mortality in
2 Seoul, Korea: a case-crossover design. Environ. Health Perspect. 107: 633-636.
3 Lee, J.-T.; Shin, D.; Chung, Y. (1999) Air pollution and daily mortality in Seoul and Ulsan,
4 Korea. Environ. Health Perspect. 107: 149-154.
5 Lee, J.-T.; Kim, H.; Song, H.; Hong, Y.-C.; Cho, Y.-S.; Shin, S.-Y.; Hyun, Y.-J.; Kim, Y.-S.
6 (2002) Air pollution and asthma among children in Seoul, Korea. Epidemiology 13: 481-
7 484.
8 Lee, J.-T.; Kim, H.; Cho, Y.-S.; Hong, Y.-C.; Ha, E.-H.; Park, H. (2003a) Air pollution and
9 hospital admissions for ischemic heart diseases among individuals 64+ years of age
10 residing in Seoul, Korea. Arch. Environ. Health 58: 617-623.
11 Lee, B. E.; Ha, E. H.; Park, H. S.; Kim, Y. J.; Hong, Y. C.; Kim, H.; Lee, J. T. (2003b) Exposure
12 to air pollution during different gestational phases contributes to risks of low birth
13 weight. Hum. Reprod. 18: 638-643.
14 Lee, S. L.; Wong, W. H. S.; Lau, Y. L. (2006) Association between air pollution and asthma
15 admission among children in Hong Kong. Clin. Exp. Allergy 36: 1138-1146.
16 Leem, J.-H.; Kaplan, B. M.; Shim, Y. K.; Pohl, H. R.; Gotway, C. A.; Bullard, S. M.; Rogers, J.
17 F.; Smith, M. M.; Tylenda, C. A. (2006) Exposures to air pollutants during pregnancy
18 and preterm delivery. Environ. Health Perspect. 114: 905-910.
19 Liao, D.; Duan, Y.; Whitsel, E. A.; Zheng, Z.-J.; Heiss, G.; Chinchilli, V. M.; Lin, H.-M. (2004)
20 Association of higher levels of ambient criteria pollutants with impaired cardiac
21 autonomic control: a population-based study. Am. J. Epidemiol. 159: 768-777.
22 Lin, C. A.; Martins, M. A.; Farhat, S. C. L.; Pope, C. A., Ill; Concei9ao, G. M. S.; Anastacio, V.
23 M.; Hatanaka, M.; Andrade, W. C.; Hamaue, W. R.; Bohm, G. M.; Saldiva, P. H. N.
24 (1999) Air pollution and respiratory illness of children in Sao Paulo, Brazil. Paediatr.
25 Perinat. Epidemiol. 13: 475-488.
26 Lin, M.; Chen, Y.; Burnett, R. T.; Villeneuve, P. J.; Krewski, D. (2003) Effect of short-term
27 exposure to gaseous pollution on asthma hospitalisation in children: a bi-directional case-
28 crossover analysis. J. Epidemiol. Community Health 57: 50-55.
29 Lin, C.-M.; Li, C.-Y.; Yang, G.-Y.; Mao, I-F. (2004) Association between maternal exposure to
30 elevated ambient sulfur dioxide during pregnancy and term low birth weight. Environ.
31 Res. 96: 41-50.
32 Linn, W. S.; Szlachcic, Y.; Gong, H., Jr.; Kinney, P. L.; Berhane, K. T. (2000) Air pollution and
33 daily hospital admissions in metropolitan Los Angeles. Environ. Health Perspect. 108:
34 427-434.
35 Lipfert, F. W.; Perry, H. M., Jr.; Miller, J. P.; Baty, J. D.; Wyzga, R. E.; Carmody, S. E. (2000)
36 The Washington University-EPRI veterans' cohort mortality study: preliminary results.
37 In: Grant, L. D., ed. PM2000: particulate matter and health. Inhalation Toxicol. 12(suppl.
38 4): 41-73.
39 Lippmann, M.; Ito, K.; Nadas, A.; Burnett, R. T. (2000) Association of particulate matter
40 components with daily mortality and morbidity in urban populations. Cambridge, MA:
41 Health Effects Institute; research report no. 95.
August, 2007 AX6-151 DRAFT-DO NOT QUOTE OR CITE
-------
1 Lipsett, M.; Hurley, S.; Ostro, B. (1997) Air pollution and emergency room visits for asthma in
2 Santa Clara County, California. Environ. Health Perspect. 105: 216-222.
3 Liu, S.; Krewski, D.; Shi, Y.; Chen, Y.; Burnett, R. T. (2003) Association between gaseous
4 ambient air pollutants and adverse pregnancy outcomes in Vancouver, Canada. Environ.
5 Health Perspect. Ill: 1773-1778.
6 Llorca, J.; Salas, A.; Prieto-Salceda, D.; Chinchon-Bengoechea, V.; Delgado-Rodriguez, M.
7 (2005) Nitrogen dioxide increases cardiorespiratory admissions in Torrelavega (Spain). J.
8 Environ. Health 68: 30-35.
9 Loomis, D.; Castillejos, M.; Gold, D. R.; McDonnell, W.; Borja-Aburto, V. H. (1999) Air
10 pollution and infant mortality in Mexico City. Epidemiology 10: 118-123.
11 Luginaah, I. N.; Fung, K. Y.; Gorey, K. M.; Webster, G.; Wills, C. (2005) Association of
12 ambient air pollution with respiratory hospitalization in a government designated "area of
13 concern": the case of Windsor, Ontario. Environ. Health Perspect. 113: 290-296.
14 Luttmann-Gibson, H.; Suh, H. H.; Coull, B. A.; Dockery, D. W.; Sarnet, S. E.; Schwartz, J.;
15 Stone, P. H.; Gold, D. R. (2006) Short-term effects of air pollution on heart rate
16 variability in senior adults in Steubenville, Ohio. J. Occup. Environ. Med. 48: 780-788.
17 Mann, J. K.; Tager, I. B.; Lurmann, F.; Segal, M.; Quesenberry, C. P., Jr.; Lugg, M. M.; Shan, J.;
18 Van den Eeden, S. K. (2002) Air pollution and hospital admissions for ischemic heart
19 disease in persons with congestive heart failure or arrhythmia. Environ. Health Perspect.
20 110:1247-1252.
21 Mannes, T.; Jalaludin, B.; Morgan, G; Lincoln, D.; Sheppeard, V.; Corbett, S. (2005) Impact of
22 ambient air pollution on birth weight in Sydney, Australia. Occup. Environ. Med. 62:
23 524-530.
24 Maroziene, L.; Grazuleviciene, R. (2002) Maternal exposure to low-level air pollution and
25 pregnancy outcomes: a population-based study. Environ. Health Glob. Access Sci. 1: 6.
26 Available: http://www.ehjournal.net/content/l/l/6 [10 October, 2006].
27 Martins, L. C.; Latorre, M. R. D. O.; Saldiva, P. H. N.; Braga, A. L. F. (2002) Air pollution and
28 emergency room visits due to chronic lower respiratory diseases in the elderly: an
29 ecological time-series study in Sao Paulo, Brazil. J. Occup. Environ. Med. 44: 622-627'.
30 Metzger, K. B.; Tolbert, P. E.; Klein, M.; Peel, J. L.; Flanders, W. D.; Todd, K. H.; Mulholland,
31 J. A.; Ryan, P. B.; Frumkin , H. (2004) Ambient air pollution and cardiovascular
32 emergency department visits. Epidemiology 15: 46-56.
33 Michelozzi, P.; Forastiere, F.; Fusco, D.; Perucci, C. A.; Ostro, B.; Ancona, C.; Pallotti, G.
34 (1998) Air pollution and daily mortality in Rome, Italy. Occup. Environ. Med. 55: 605-
35 610.
36 Migliaretti, G.; Cavallo, F. (2004) Urban air pollution and asthma in children. Pediatr. Pulmonol.
37 38: 198-203.
38 Migliaretti, G.; Cadum, E.; Migliore, E.; Cavallo, F. (2005) Traffic air pollution and hospital
39 admission for asthma: a case-control approach in a Turin (Italy) population. Int. Arch.
40 Occup. Environ. Health. 78: 164-169.
August, 2007 AX6-152 DRAFT-DO NOT QUOTE OR CITE
-------
1 Millstein, J.; Gilliland, F.; Berhane, K.; Gauderman, W. J.; McConnell, R.; Avol, E.; Rappaport,
2 E. B.; Peters, J. M. (2004) Effects of ambient air pollutants on asthma medication use and
3 wheezing among fourth-grade school children from 12 Southern California communities
4 enrolled in The Children's Health Study. Arch. Environ. Health 59: 505-514.
5 Moolgavkar, S. H. (2000a) Air pollution and hospital admissions for chronic obstructive
6 pulmonary disease in three metropolitan areas in the United States. In: Grant, L. D., ed.
7 PM2000: particulate matter and health. Inhalation Toxicol. 12(suppl. 4): 75-90.
8 Moolgavkar, S. H. (2000b) Air pollution and hospital admissions for diseases of the circulatory
9 system in three U.S. metropolitan areas. J. Air Waste Manage Assoc. 50: 1199-1206.
10 Moolgavkar, S. H. (2000c) Air pollution and daily mortality in three U.S. counties. Environ.
11 Health Perspect. 108: 777-784.
12 Moolgavkar, S. H. (2003) Air pollution and daily deaths and hospital admissions in Los Angeles
13 and Cook counties. In: Revised analyses of time-series studies of air pollution and health.
14 Special report. Boston, MA: Health Effects Institute; pp. 183-198. Available:
15 http://www.healtheffects.org/news.htm [16 May, 2003].
16 Moolgavkar, S. H.; Luebeck, E. G.; Anderson, E. L. (1997) Air pollution and hospital
17 admissions for respiratory causes in Minneapolis-St. Paul and Birmingham.
18 Epidemiology 8: 364-370.
19 Morgan, G.; Corbett, S.; Wlodarczyk, J. (1998a) Air pollution and hospital admissions in
20 Sydney, Australia, 1990 to 1994. Am. J. Public Health 88: 1761-1766.
21 Morgan, G.; Corbett, S.; Wlodarczyk, J.; Lewis, P. (1998b) Air pollution and daily mortality in
22 Sydney, Australia, 1989 through 1993. Am. J. Public Health 88: 759-764.
23 Morris, R. D.; Naumova, E. N.; Munasinghe, R. L. (1995) Ambient air pollution and
24 hospitalization for congestive heart failure among elderly people in seven large US cities.
25 Am. J. Public Health 85: 1361-1365.
26 Mortimer, K. M.; Neas, L. M.; Dockery, D. W.; Redline, S.; Tager, I. B. (2002) The effect of air
27 pollution on inner-city children with asthma. Eur. Respir. J. 19: 699-705.
28 Moseler, M.; Hendel-Kramer, A.; Karmaus, W.; Forster, J.; Weiss, K.; Urbanek, R.; Kuehr, J.
29 (1994) Effect of moderate NC>2 air pollution on the lung function of children with
30 asthmatic symptoms. Environ. Res. 67: 109-124.
31 Mukala, K.; Pekkanen, J.; Tiittanen, P.; Aim, S.; Salonen, R. O.; Tuomisto, J. (1999) Personally
32 measured weekly exposure to NC>2 and respiratory health among preschool children. Eur.
33 Respir. J. 13: 1411-1417.
34 Nafstad, P.; Haheim, L. L.; Wisloff, T.; Gram, F.; Oftedal, B.; Holme, L; Hjermann, L; Leren, P.
35 (2004) Urban air pollution and mortality in a cohort of Norwegian men. Environ. Health
36 Perspect. 112: 610-605.
37 Neidell, M. J. (2004) Air pollution, health, and socio-economic status: the effect of outdoor air
38 quality on childhood asthma. J. Health Econ. 23: 1209-1236.
August, 2007 AX6-153 DRAFT-DO NOT QUOTE OR CITE
-------
1 Nitschke, M.; Pilotto, L. S.; Attewell, R. G.; Smith, B. 1; Pisaniello, D.; Martin, 1; Ruffm, R. E.;
2 Hiller, J. E. (2006) A cohort study of indoor nitrogen dioxide and house dust mite
3 exposure in asthmatic children. J. Occup. Environ. Med. 48: 462-469.
4 Norris, G.; Young-Pong, S. N.; Koenig, J. Q.; Larson, T. V.; Sheppard, L.; Stout, J. W. (1999)
5 An association between fine particles and asthma emergency department visits for
6 children in Seattle. Environ. Health Perspect. 107: 489-493.
7 Nyberg, F.; Gustavsson, P.; Jarup, L.; Bellander, T.; Berglind, N.; Jakobsson, R.; Pershagen, G.
8 (2000) Urban air pollution and lung cancer in Stockholm. Epidemiology 11: 487-495.
9 Oftedal, B.; Nafstad, P.; Magnus, P.; Bj0rkly, S.; Skrondal, A. (2003) Traffic related air pollution
10 and acute hospital admission for respiratory diseases in Drammen, Norway 1995-2000.
11 Eur. J. Epidemiol. 18: 671-675.
12 Ostro, B.; Sanchez, J. M.; Aranda, C.; Eskeland, G. S. (1996) Air pollution and mortality: results
13 from a study of Santiago, Chile. In: Lippmann, M., ed. Papers from the ISEA-ISEE
14 annual meeting; September 1994; Research Triangle Park, NC. J. Exposure Anal.
15 Environ. Epidemiol. 6: 97-114.
16 Ostro, B. D.; Broadwin, R.; Lipsett, M. J. (2000) Coarse and fine particles and daily mortality in
17 the Coachella Valley, California: a follow-up study. J. Exposure Anal. Environ.
18 Epidemiol. 10: 412-419.
19 Ostro, B.; Lipsett, M.; Mann, J.; Braxton-Owens, H.; White, M. (2001) Air pollution and
20 exacerbation of asthma in African-American children in Los Angeles. Epidemiology 12:
21 200-208.
22 Pantazopoulou, A.; Katsouyanni, K.; Kourea-Kremastinou, J.; Trichopoulos, D. (1995) Short-
23 term effects of air pollution on hospital emergency outpatient visits and admissions in the
24 greater Athens, Greece area. Environ. Res. 69: 31-36.
25 Peel, J. L.; Tolbert, P. E.; Klein, M.; Metzger, K. B.; Flanders, W. D.; Knox, T.; Mulholland, J.
26 A.; Ryan, P. B.; Frumkin, H. (2005) Ambient air pollution and respiratory emergency
27 department visits. Epidemiology 16: 164-174.
28 Peel, J. L.; Metzger, K. B.; Klein, M.; Flanders, W. D.; Mulholland, J. A.; Tolbert, P. E. (2006)
29 Ambient air pollution and cardiovascular emergency department visits in potentially
30 sensitive groups. Am. J. Epidemiol. 165: 625-633.
31 Pekkanen, J.; Peters, A.; Hoek, G.; Tiittanen, P.; Brunekreef, B.; de Hartog, J.; Heinrich, J.;
32 Ibald-Mulli, A.; Kreyling, W. G.; Lanki, T.; Timonen, K. L.; Vanninen, E. (2002)
33 Particulate air pollution and risk of ST-segment depression during repeated submaximal
34 exercise tests among subjects with coronary heart disease: the exposure and risk
35 assessment for fine and ultrafine particles in ambient air (ULTRA) study. Circulation
36 106: 933-938.
37 Penard-Morand, C.; Charpin, D.; Raherison, C.; Kopferschmitt, C.; Caillaud, D.; Lavaud, F.;
38 Annesi-Maesano, I. (2005) Long-term exposure to background air pollution related to
39 respiratory and allergic health in schoolchildren. Clin. Exp. Allergy 35: 1279-1287.
August, 2007 AX6-154 DRAFT-DO NOT QUOTE OR CITE
-------
1 Pereira, L. A. A.; Loomis, D.; Concei9ao, G. M. S.; Braga, A. L. F.; Areas, R. M.; Kishi, H. S.;
2 Singer, J. M.; Bohm, G. M.; Saldiva, P. H. N. (1998) Association between air pollution
3 and intrauterine mortality in Sao Paulo, Brazil. Environ. Health Perspect. 106: 325-329.
4 Peters, J. M.; Avol, E.; Gauderman, W. J.; Linn, W. S.; Navidi, W.; London, S. J.; Margolis, H.;
5 Rappaport, E.; Vora, H.; Gong, H., Jr.; Thomas, D. C. (1999a) A study of twelve
6 southern California communities with differing levels and types of air pollution. II.
7 Effects on pulmonary function. Am. J. Respir. Crit. Care Med. 159: 768-775.
8 Peters, J. M.; Avol, E.; Navidi, W.; London, S. J.; Gauderman, W. J.; Lurmann, F.; Linn, W. S.;
9 Margolis, H.; Rappaport, E.; Gong, H., Jr.; Thomas, D. C. (1999b) A study of twelve
10 southern California communities with differing levels and types of air pollution. I.
11 Prevalence of respiratory morbidity. Am. J. Respir. Crit. Care Med. 159: 760-767.
12 Peters, A.; Liu, E.; Verrier, R. L.; Schwartz, J.; Gold, D. R.; Mittleman, M.; Baliff, J.; Oh, J. A.;
13 Allen, G.; Monahan, K.; Dockery, D. W. (2000a) Air pollution and incidence of cardiac
14 arrhythmia. Epidemiology 11: 11-17.
15 Peters, A.; Skorkovsky, J.; Kotesovec, F.; Brynda, J.; Spix, C.; Wichmann, H. E.; Heinrich, J.
16 (2000b) Associations between mortality and air pollution in central Europe. Environ.
17 Health Perspect. 108:283-287.
18 Petroeschevsky, A.; Simpson, R. W.; Thalib, L.; Rutherford, S. (2001) Associations between
19 outdoor air pollution and hospital admissions in Brisbane, Australia. Arch. Environ.
20 Health 56: 37-52.
21 Pikhart, H.; Bobak, M.; Kriz, B.; Danova, J.; Celko, M. A.; Prikazsky, V.; Pryl, K.; Briggs, D.;
22 Elliott, P. (2000) Outdoor air concentrations of nitrogen dioxide and sulfur dioxide and
23 prevalence of wheezing in school children. Epidemiology 11: 153-160.
24 Pilotto, L. S.; Douglas, R. M.; Attewell, R. G.; Wilson, S. R. (1997) Respiratory effects
25 associated with indoor nitrogen dioxide exposure in children. Int. J. Epidemiol. 26: 788-
26 796.
27 Pilotto, L. S.; Nitschke, M.; Smith, B. J.; Pisaniello, D.; Ruffm, R. E.; McElroy, H. J.; Martin, J.;
28 Killer, J. E. (2004) Randomized controlled trial of unflued gas heater replacement on
29 respiratory health of asthmatic schoolchildren. Int. J. Epidemiol. 33: 208-214.
30 Pino, P.; Walter, T.; Oyarzun, M.; Villegas, R.; Romieu, I. (2004) Fine particulate matter and
31 wheezing illnesses in the first year of life. Epidemiology 15: 702-708.
32 Poloniecki, J. D.; Atkinson, R. W.; Ponce de Leon, A.; Anderson, H. R. (1997) Daily time series
33 for cardiovascular hospital admissions and previous day's air pollution in London, UK.
34 Occup. Environ. Med. 54: 535-540.
35 Ponce de Leon, A.; Anderson, H. R.; Bland, J. M.; Strachan, D. P.; Bower, J. (1996) Effects of
36 air pollution on daily hospital admissions for respiratory disease in London between
37 1987-88 and 1991-92. In: St Leger, S., ed. The APHEA project. Short term effects of air
38 pollution on health: a European approach using epidemiological time series data. J.
39 Epidemiol. Community Health 50(suppl. 1): S63-S70.
40 Ponka, A.; Virtanen, M. (1994) Chronic bronchitis, emphysema, and low-level air pollution in
41 Helsinki, 1987-1989. Environ. Res. 65: 207-217.
August, 2007 AX6-155 DRAFT-DO NOT QUOTE OR CITE
-------
1 Ponka, A.; Virtanen, M. (1996) Low-level air pollution and hospital admissions for cardiac and
2 cerebrovascular diseases in Helsinki. Am. J. Public Health 86: 1273-1280.
3 Ponka, A.; Savela, M.; Virtanen, M. (1998) Mortality and air pollution in Helsinki. Arch.
4 Environ. Health 53:281 -286.
5 Prescott, G. J.; Cohen, G. R.; Elton, R. A.; Fowkes, F. G. R.; Agius, R. M. (1998) Urban air
6 pollution and cardiopulmonary ill health: a 14.5 year time series study. Occup. Environ.
7 Med. 55: 697-704.
8 Ramadour, M.; Burel, C.; Lanteaume, A.; Vervloet, D.; Charpin, D.; Brisse, F.; Dutau, H.;
9 Charpin, D. (2000) Prevalence of asthma and rhinitis in relation to long-term exposure to
10 gaseous air pollutants. Allergy (Copenhagen) 55: 1163-1169.
11 Rich, D. Q.; Schwartz, J.; Mittleman, M. A.; Link, M.; Luttmann-Gibson, H.; Catalano, P. J.;
12 Speizer, F. E.; Dockery, D. W. (2005) Association of short-term ambient air pollution
13 concentrations and ventricular arrhythmias. Am. J. Epidemiol. 161: 1123-1132.
14 Ritz, B.; Yu, F.; Chapa, G.; Fruin, S. (2000) Effect of air pollution on preterm birth among
15 children born in Southern California between 1989 and 1993. Epidemiology 11: 502-511.
16 Roemer, W. H.; Van Wijnen, J. H . (2001) Daily mortality and air pollution along busy streets in
17 Amsterdam, 1987-1998. Epidemiology 12: 649-653.
18 Ruidavets, J.-B.; Cassadou, S.; Cournot, M.; Bataille, V.; Meybeck, M.; Ferrieres, J. (2005)
19 Increased resting heart rate with pollutants in a population based study. J. Epidemiol.
20 Community Health 59: 685-693.
21 Saez, M.; Tobias, A.; Munoz, P.; Campbell, M. J. (1999) A GEE moving average analysis of the
22 relationship between air pollution and mortality for asthma in Barcelona, Spain. Stat.
23 Med. 18: 2077-2086.
24 Saez, M.; Ballester, F.; Barcelo, M. A.; Perez-Hoyos, S.; Bellido, J.; Tenias, J. M.; Ocafia, R.;
25 Figueiras, A.; Arribas, F.; Aragones, N.; Tobias, A.; Cirera, L.; Canada, A.; on behalf of
26 the EMECAM Group. (2002) A combined analysis of the short-term effects of
27 photochemical air pollutants on mortality within the EMECAM project. Environ. Health
28 Perspect. 110:221-228.
29 Salam, M. T.; Millstein, J.; Li, Y.-F.; Lurmann, F. W.; Margolis, H. G; Gilliland, F. D. (2005)
30 Birth outcomes and prenatal exposure to ozone, carbon monoxide, and particulate matter:
31 results from the Children's Health Study. Environ. Health Perspect. 113: 1638-1644.
32 Saldiva, P. H. N.; Lichtenfels, A. J. F. C.; Paiva, P. S. O.; Barone, I. A.; Martins, M. A.; Massad,
33 E.; Pereira, J. C. R.; Xavier, V. P.; Singer, J. M.; Bohm, G. M. (1994) Association
34 between air pollution and mortality due to respiratory diseases in children in Sao Paulo,
35 Brazil: a preliminary report. Environ. Res. 65: 218-225.
36 Saldiva, P. H. N.; Pope, C. A., Ill; Schwartz, J.; Dockery, D. W.; Lichtenfels, A. J.; Salge, J. M.;
37 Barone, I; Bohm, G. M. (1995) Air pollution and mortality in elderly people: a time-
38 series study in Sao Paulo, Brazil. Arch. Environ. Health 50: 159-163.
39 Samet, J. M.; Dominici, F.; Zeger, S. L.; Schwartz, J.; Dockery, D. W. (2000a) National
40 morbidity, mortality, and air pollution study. Part I: methods and methodologic issues.
41 Cambridge, MA: Health Effects Institute; research report no. 94.
August, 2007 AX6-156 DRAFT-DO NOT QUOTE OR CITE
-------
1 Samet, J. M.; Zeger, S. L.; Dominici, F.; Curriero, F.; Coursac, I; Dockery, D. W.; Schwartz, J.;
2 Zanobetti, A. (2000b) The national morbidity, mortality, and air pollution study. Part II:
3 morbidity, mortality, and air pollution in the United States. Cambridge, MA: Health
4 Effects Institute; research report no. 94, part II.
5 Samoli, E.; Analitis, A.; Touloumi, G.; Schwartz, J.; Anderson, H. R.; Sunyer, J.; Bisanti, L.;
6 Zmirou, D.; Vonk, J. M.; Pekkanen, J.; Goodman, P.; Paldy, A.; Schindler, C.;
7 Katsouyanni, K. (2005) Estimating the exposure-response relationships between
8 particulate matter and mortality within the APHEA multicity project. Environ. Health
9 Perspect. 113: 88-95.
10 Samoli, E.; Aga, E.; Touloumi, G.; Nisiotis, K.; Forsberg, B.; Lefranc, A.; Pekkanen, J.;
11 Wojtyniak, B.; Schindler, C.; Niciu, E.; Brunstein, R.; Dodic Fikfak, M.; Schwartz, J.;
12 Katsouyanni, K. (2006) Short-term effects of nitrogen dioxide on mortality: an analysis
13 within the APHEA project. Eur. Respir. J. 27: 1129-1137.
14 Sartor, F.; Snacken, R.; Demuth, C.; Walckiers, D. (1995) Temperature, ambient ozone levels,
15 and mortality during summer, 1994, in Belgium. Environ. Res. 70: 105-113.
16 Schildcrout, J. S.; Sheppard, L.; Lumley, T.; Slaughter, J. C.; Koenig, J. Q.; Shapiro, G. G.
17 (2006) Ambient air pollution and asthma exacerbations in children: an eight-city analysis.
18 Am. J. Epidemiol. 164: 505-517.
19 Schindler, C.; Ackermann-Liebrich, U.; Leuenberger, P.; Monn, C.; Rapp, R.; Bolognini, G.;
20 Bongard, J.-P.; Brandli, O.; Domenighetti, G.; Karrer, W.; Keller, R.; Medici, T. G.;
21 Perruchoud, A. P.; Schoni, M. H.; Tschopp, J.-M.; Villiger, B.; Zellweger, J.-P.;
22 SAPALDIA Team. (1998) Associations between lung function and estimated average
23 exposure to NC>2 in eight areas of Switzerland. Epidemiology 9: 405-411.
24 Schouten, J. P.; Vonk, J. M.; de Graaf, A. (1996) Short term effects of air pollution on
25 emergency hospital admissions for respiratory disease: results of the APHEA project in
26 two major cities in The Netherlands, 1977-89. In: St Leger, S., ed. The APHEA project.
27 Short term effects of air pollution on health: a European approach using epidemiological
28 time series data. J. Epidemiol. Community Health 50(suppl. 1): S22-S29.
29 Schwartz, J. (1997) Air pollution and hospital admissions for cardiovascular disease in Tucson.
30 Epidemiology 8: 371-377.
31 Schwartz, J.; Dockery, D. W.; Neas, L. M.; Wypij, D.; Ware, J. H.; Spengler, J. D.; Koutrakis,
32 P.; Speizer, F. E.; Ferris, B. G., Jr. (1994) Acute effects of summer air pollution on
33 respiratory symptom reporting in children. Am. J. Respir. Crit. Care Med. 150: 1234-
34 1242.
35 Segala, C.; Fauroux, B.; Just, J.; Pascual, L.; Grimfeld, A.; Neukirch, F. (1998) Short-term effect
36 of winter air pollution on respiratory health of asthmatic children in Paris. Eur. Respir. J.
37 11:677-685.
38 Segala, C.; Poizeau, D.; Neukirch, F.; Aubier, M.; Samson, J.; Gehanno, P. (2004) Air pollution,
39 passive smoking, and respiratory symptoms in adults. Arch. Environ. Health 59: 669-676.
40 Shima, M.; Adachi, M. (2000) Effect of outdoor and indoor nitrogen dioxide on respiratory
41 symptoms in schoolchildren. Int. J. Epidemiol. 29: 862-870.
August, 2007 AX6-157 DRAFT-DO NOT QUOTE OR CITE
-------
1 Simpson, R. W.; Williams, G.; Petroeschevsky, A.; Morgan, G.; Rutherford, S. (1997)
2 Associations between outdoor air pollution and daily mortality in Brisbane, Australia.
3 Arch. Environ. Health 52: 442-454.
4 Simpson, R.; Denison, L.; Petroeschevsky, A.; Thalib, L.; Williams, G. (2000) Effects of
5 ambient particle pollution on daily mortality in Melbourne 1991-1996. J. Exposure Anal.
6 Environ. Epidemiol. 10: 488-496.
7 Simpson, R.; Williams, G.; Petroeschevsky, A.; Best, T.; Morgan, G.; Denison, L.; Hinwood, A.;
8 Neville, G.; Neller, A. (2005a) The short-term effects of air pollution on daily mortality
9 in four Australian cities. Aust. N. Z. J. Public Health 29: 205-212.
10 Simpson, R.; Williams, G.; Petroeschevsky, A.; Best, T.; Morgan, G.; Denison, L.; Hinwood, A.;
11 Neville, G. (2005b) The short-term effects of air pollution on hospital admissions in four
12 Australian cities. Aust. N. Z. J. Public Health 29: 213-221.
13 Smith, B. J.; Nitschke, M.; Pilotto, L. S.; Ruffrn, R. E.; Pisaniello, D. L.; Wilson, K. J. (2000)
14 Health effects of daily indoor nitrogen dioxide exposure in people with asthma. Eur.
15 Respir. J. 16: 879-885.
16 Spix, C.; Wichmann, H. E. (1996) Daily mortality and air pollutants: findings from Koln,
17 Germany. In: St Leger, S., ed. The APHEA project. Short term effects of air pollution on
18 health: a European approach using epidemiological time series data. J. Epidemiol.
19 Commun. Health 50(suppl. 1): S52-S58.
20 Stieb, D. M.; Burnett, R. T.; Beveridge, R. C.; Brook, J. R. (1996) Association between ozone
21 and asthma emergency department visits in Saint John, New Brunswick, Canada.
22 Environ. Health Perspect. 104: 1354-1360.
23 Stieb, D. M.; Beveridge, R. C.; Brook, J. R.; Smith-Doiron, M.; Burnett, R. T.; Dales, R. E.;
24 Beaulieu, S.; Judek, S.; Mamedov, A. (2000) Air pollution, aeroallergens and
25 cardiorespiratory emergency department visits in Saint John, Canada. J. Exposure Anal.
26 Environ. Epidemiol. 10: 461-477.
27 Stieb, D. M.; Judek, S.; Burnett, R. T. (2002) Meta-analysis of time-series studies of air pollution
28 and mortality: effects of gases and particles and the influence of cause of death, age, and
29 season. J. Air Waste Manage. Assoc. 52: 470-484.
30 Stieb, D. M.; Judek, S.; Burnett, R. T. (2003) Meta-analysis of time-series studies of air pollution
31 and mortality: update in relation to the use of generalized additive models. J. Air Waste
32 Manage. 53:258-261.
33 Studnicka, M.; Hackl, E.; Pischinger, J.; Fangmeyer, C.; Haschke, N.; Kuhr, J.; Urbanek, R.;
34 Neumann, M.; Frischer, T. (1997) Traffic-related NC>2 and the prevalence of asthma and
35 respiratory symptoms in seven year olds. Eur. Respir. J. 10: 2275-2278.
36 Sunyer, J.; Basagafia, X. (2001) Particles, and not gases, are associated with the risk of death in
37 patients with chronic obstructive pulmonary disease. Int. J. Epidemiol. 30: 1138-1140.
38 Sunyer, J.; Castellsague, J.; Saez, M.; Tobias, A.; Anto, J. M. (1996) Air pollution and mortality
39 in Barcelona. In: St Leger, S., ed. The APHEA project. Short term effects of air pollution
40 on health: a European approach using epidemiological time series data. J. Epidemiol.
41 Community Health 50(suppl. 1): S76-S80.
August, 2007 AX6-158 DRAFT-DO NOT QUOTE OR CITE
-------
1 Sunyer, J.; Spix, C.; Quenel, P.; Ponce-de-Leon, A.; Ponka, A.; Barumandzadeh, T.; Touloumi,
2 G.; Bacharova, L.; Wojtyniak, B.; Vonk, J.; Bisanti, L.; Schwartz, J.; Katsouyanni, K.
3 (1997) Urban air pollution and emergency admissions for asthma in four European cities:
4 the APHEA project. Thorax 52: 760-765.
5 Sunyer, J.; Basagafia, X.; Belmonte, J.; Anto, J. M. (2002) Effect of nitrogen dioxide and ozone
6 on the risk of dying in patients with severe asthma. Thorax 57: 687-693.
7 Tager, I. B.; Balmes, J.; Lurmann, F.; Ngo, L.; Alcorn, S.; Kiinzli, N. (2005) Chronic exposure to
8 ambient ozone and lung function in young adults. Epidemiology 16: 751-759.
9 Tanaka, H.; Honma, S.; Nishi, M.; Igarashi, T.; Teramoto, S.; Nishio, F.; Abe, S. (1998) Acid
10 fog and hospital visits for asthma: an epidemiological study. Eur. Respir. J. 11: 1301-
11 1306.
12 Tenias, J. M.; Ballester, F.; Rivera, M. L. (1998) Association between hospital emergency visits
13 for asthma and air pollution in Valencia, Spain. Occup. Environ. Med. 55: 541-547.
14 Tenias, J. M.; Ballester, F.; Perez-Hoyos, S.; Rivera, M. L. (2002) Air pollution and hospital
15 emergency room admissions for chronic obstructive pulmonary disease in Valencia,
16 Spain. Arch. Environ. Health 57: 41-47.
17 Thompson, A. J.; Shields, M. D.; Patterson, C. C. (2001) Acute asthma exacerbations and air
18 pollutants in children living in Belfast, Northern Ireland. Arch. Environ. Health 56: 234-
19 241.
20 Tobias, A.; Campbell, M. J.; Saez, M. (1999) Modelling asthma epidemics on the relationship
21 between air pollution and asthma emergency visits in Barcelona, Spain. Eur. J.
22 Epidemiol. 15: 799-803.
23 Tolbert, P. E.; Klein, M.; Metzger, K. B.; Peel, J.; Flanders, W. D.; Todd, K.; Mulholland, J. A.;
24 Ryan, P. B.; Frumkin, H. (2000) Interim results of the study of parti culates and health in
25 Atlanta (SOPHIA). J. Exposure Anal. Environ. Epidemiol. 10: 446-460.
26 Touloumi, G.; Katsouyanni, K.; Zmirou, D.; Schwartz, J.; Spix, C.; Ponce de Leon, A.; Tobias,
27 A.; Quennel, P.; Rabczenko, D.; Bacharova, L.; Bisanti, L.; Vonk, J. M.; Ponka, A.
28 (1997) Short-term effects of ambient oxidant exposure on mortality: a combined analysis
29 within the APHEA project. Am. J. Epidemiol. 146: 177-185.
30 Tsai, S.-S.; Goggins, W. B.; Chiu, H.-F.; Yang, C.-Y. (2003) Evidence for an association
31 between air pollution and daily stroke admissions in Kaohsiung, Taiwan. Stroke 34:
32 2612-2616.
33 Tsai, S.-S.; Huang, C.-H.; Goggins, W. B.; Wu, T.-N.; Yang, C.-Y. (2003) Relationship between
34 air pollution and daily mortality in a tropical city: Kaohsiung, Taiwan. J. Toxicol.
35 Environ. Health Part A 66: 1341-1349.
36 Tsai, S.-S.; Cheng, M.-H.; Chiu, H.-F.; Wu, T.-N.; Yang, C.-Y. (2006) Air pollution and hospital
37 admissions for asthma in a tropical city: Kaohsiung, Taiwan. Inhalation Toxicol. 18: 549-
38 554.
39 Van Strien, R. T.; Gent, J. F.; Belanger, K.; Triche, E.; Bracken, M. B.; Leaderer, B. P. (2004)
40 Exposure to NC>2 and nitrous acid and respiratory symptoms in the first year of life.
41 Epidemiology 15: 471-478.
August, 2007 AX6-159 DRAFT-DO NOT QUOTE OR CITE
-------
1 Vedal, S.; Brauer, M.; White, R.; Petkau, J. (2003) Air pollution and daily mortality in a city
2 with low levels of pollution. Environ. Health Perspect. Ill: 45-51.
3 Verhoeff, A. P.; Hoek, G.; Schwartz, J.; Van Wijnen, J. H. (1996) Air pollution and daily
4 mortality in Amsterdam. Epidemiology 7: 225-230.
5 Villeneuve, P. J.; Burnett, R. T.; Shi, Y.; Krewski, D.; Goldberg, M. S.; Hertzman, C.; Chen, Y.;
6 Brook, J. (2003) A time-series study of air pollution, socioeconomic status, and mortality
7 in Vancouver, Canada. J. Exposure Anal. Environ. Epidemiol. 13: 427-435.
8 Villeneuve, P. J.; Chen, L.; Stieb, D.; Rowe, B. H. (2006) Associations between outdoor air
9 pollution and emergency department visits for stroke in Edmonton, Canada. Eur. J.
10 Epidemiol. 21: 689-700.
11 Von Klot, S.; Peters, A.; Aalto, P.; Bellander, T.; Berglind, N.; DTppoliti, D.; Elosua, R.;
12 Hermann, A.; Kulmala, M.; Lanki, T.; Lowel, H.; Pekkanen, J.; Picciotto, S.; Sunyer, J.;
13 Forastiere, F.; Health Effects of Particles on Susceptible Subpopulations (HEAPSS)
14 Study Group. (2005) Ambient air pollution is associated with increased risk of hospital
15 cardiac readmissions of myocardial infarction survivors in five European cities.
16 Circulation 112: 3073-3079.
17 Von Klot, S.; Wolke, G.; Tuch, T.; Heinrich, J.; Dockery, D. W.; Schwartz, J.; Kreyling, W. G.;
18 Wichmann, H. E.; Peters, A. (2002) Increased asthma medication use in association with
19 ambient fine and ultrafine particles. Eur. Respir. J. 20: 691-702.
20 Wang, T.-N.; Ko, Y.-C.; Chao, Y.-Y.; Huang, C.-C.; Lin, R.-S. (1999) Association between
21 indoor and outdoor air pollution and adolescent asthma from 1995 to 1996 in Taiwan.
22 Environ. Res. 81: 239-247.
23 Wellenius, G. A.; Schwartz, J.; Mittleman, M. A. (2005a) Air pollution and hospital admissions
24 for ischemic and hemorrhagic stroke among medicare beneficiaries. Stroke 36: 2549-
25 2553.
26 Wellenius, G. A.; Bateson, T. F.; Mittleman, M. A.; Schwartz, J. (2005b) Particulate air pollution
27 and the rate of hospitalization for congestive heart failure among medicare beneficiaries
28 in Pittsburgh, Pennsylvania. Am. J. Epidemiol. 161: 1030-1036.
29 Wheeler, A.; Zanobetti, A.; Gold, D. R.; Schwartz, J.; Stone, P.; Suh, H. H. (2006) The
30 relationship between ambient air pollution and heart rate variability differs for individuals
31 with heart and pulmonary disease. Environ. Health Perspect. 114: 560-566.
32 Wong, T. W.; Lau, T. S.; Yu, T. S.; Neller, A.; Wong, S. L.; Tarn, W.; Pang, S. W. (1999) Air
33 pollution and hospital admissions for respiratory and cardiovascular diseases in Hong
34 Kong. Occup. Environ. Med. 56: 679-683.
35 Wong, G. W.; Ko, F. W.; Lau, T. S.; Li, S. T.; Hui, D.; Pang, S. W.; Leung, R.; Fok, T. F.; Lai,
36 C. K. (2001a) Temporal relationship between air pollution and hospital admissions for
37 asthmatic children in Hong Kong. Clin. Exp. Allergy 31: 565-569.
38 Wong, C.-M.; Ma, S.; Hedley, A. J.; Lam, T.-H. (2001b) Effect of air pollution on daily
39 mortality in Hong Kong. Environ. Health Perspect. 109: 335-340.
August, 2007 AX6-160 DRAFT-DO NOT QUOTE OR CITE
-------
1 Wong, T. W.; Tarn, W. S.; Yu, T. S.; Wong, A. H. S. (2002) 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 Yallop, D.; Duncan, E. R.; Norris, E.; Fuller, G. W.; Thomas, N.; Walters, 1; Dick, M. C.;
5 Height, S. E.; Thein, S. L.; Rees, D. C. (2007) The associations between air quality and
6 the number of hospital admissions for acute pain and sickle-cell disease in an urban
7 environment. Br. J. Haematol. 136: 844-848.
8 Yang, Q.; Chen, Y.; Shi, Y.; Burnett, R. T.; McGrail, K. M.; Krewski, D. (2003) Association
9 between ozone and respiratory admissions among children and the elderly in Vancouver,
10 Canada. Inhalation Toxicol. 15: 1297-1308.
11 Yang, C.-Y.; Chang, C.-C.; Chuang, H.-Y.; Tsai, S.-S.; Wu, T.-N.; Ho, C.-K. (2004)
12 Relationship between air pollution and daily mortality in a subtropical city: Taipei,
13 Taiwan. Environ. Int. 30: 519-523.
14 Yang, C.-Y.; Chen, Y.-S.; Yang, C.-H.; Ho, S.-C. (2004) Relationship between ambient air
15 pollution and hospital admissions for cardiovascular diseases in Kaohsiung, Taiwan. J.
16 Toxicol. Environ. Health Part A 67: 483-493.
17 Yang, Q.; Chen, Y.; Krewski, D.; Burnett, R. T.; Shi, Y.; McGrail, K. M. (2005) Effect of short-
18 term exposure to low levels of gaseous pollutants on chronic obstructive pulmonary
19 disease hospitalizations. Environ. Res. 99: 99-105.
20 Ye, F.; Piver, W. T.; Ando, M.; Portier, C. J. (2001) Effects of temperature and air pollutants on
21 cardiovascular and respiratory diseases for males and females older than 65 years of age
22 in Tokyo, July and August 1980-1995. Environ. Health Perspect. 109: 355-359.
23 Zanobetti, A.; Schwartz, J. (2006) Air pollution and emergency admissions in Boston, MA. J.
24 Epidemiol. Community Health 60: 890-895.
25 Zeghnoun, A.; Czernichow, P.; Beaudeau, P.; Hautemaniere, A.; Froment, L.; Le Tertre, A.;
26 Quenel, P. (2001) Short-term effects of air pollution on mortality in the cities of Rouen
27 and Le Havre, France, 1990-1995. Arch. Environ. Health 56: 327-335.
28 Zmirou, D.; Barumandzadeh, T.; Balducci, F.; Ritter, P.; Laham, G.; Ghilardi, J.-P. (1996) Short
29 term effects of air pollution on mortality in the city of Lyon, France, 1985-90. In: St
30 Leger, S., ed. The APHEA project. Short term effects of air pollution on health: a
31 European approach using epidemiological time series data. J. Epidemiol. Community
32 Health 50(suppl. 1): S30-S35.
33 Zmirou, D.; Schwartz, J.; Saez, M.; Zanobetti, A.; Wojtyniak, B.; Touloumi, G.; Spix, C.; Ponce
34 de Leon, A.; Le Moullec, Y.; Bacharova, L.; Schouten, J.; Ponka, A.; Katsouyanni, K.
35 (1998) Time-series analysis of air pollution and cause-specific mortality. Epidemiology
36 9:495-503.
37
August, 2007 AX6-161 DRAFT-DO NOT QUOTE OR CITE
-------
Please make all necessary changes in the below label, PRESORTED STANDARD
detach copy or copy, and return to the address in the upper POSTAGE & FEES PAID
left-hand corner. EPA
United States
Environmental Protection ._.
Agency If you do not wish to receive these reports CHECK HERE LJ;
detach copy or copy, and return to the address in the upper
left-hand corner.
National Center for
Environmental Assessment
Research Triangle Park, NC 27711
Official Business
Penalty for Private Use
$300
EPA/600/R-07/093
August 2007
------- |