Integrated Science Assessment for
Oxides of Nitrogen — Health Criteria
Annexes
EPA/600/R-08/072
July 2008
United States |.
Environmental Protection
Agency
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July 2008
EPA/600/R-08/072
Integrated Science Assessment
for Oxides of Nitrogen - Health Criteria
Annexes
National Center for Environmental Assessment-RTF Division
Office of Research and Development
U.S. Environmental Protection Agency
Research Triangle Park, NC
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Disclaimer
This document has been reviewed in accordance with U.S. Environmental Protection Agency
policy and approved for publication. Mention of trade names or commercial products does not constitute
endorsement or recommendation for use.
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Table of Contents
List of Tables
List of Figures iii
Abbreviations and Acronyms v
Annex 1. Framework for Review 1-1
AX1.1. Literature Selection and Retrieval 1-1
AX1.1.1. General Criteria for Study Selection 1-3
AX1.1.2. Criteria for Selecting Epidemiology Studies 1-3
AX1.1.3. Criteria for Selecting Toxicology Studies 1-4
AX1.2. Guidelines for the Determination of Causality 1-4
AX1.2.1. Surgeon General's Report: The Health Consequences of Smoking 1-4
AX1.2.2. The EPA Guidelines for Carcinogen Risk Assessment 1-6
AX1.2.3. Improving Presumptive Disability Decision-Making Process for Veterans 1-9
AX1.2.4. Formulation of Scientific Findings for Policy Purposes 1-13
AX1.2.5. IARC Cancer Guidelines for Scientific Review and Evaluation 1-14
AX1.2.6. National Toxicology Program Criteria 1-21
Annex 2. Atmospheric Chemistry of NOx 2-1
AX2.1. Introduction 2-1
AX2.2. Chemistry of NOx in the Troposphere 2-2
AX2.2.1. Basic Chemistry 2-2
AX2.2.2. NOx Concentrations and Oi Formation 2-6
AX2.2.3. Multiphase Chemistry Involving NOx 2-8
AX2.2.4. Formation of Nitro-PAHs 2-10
AX2.2.5. Multiphase Chemical Processes Involving NOx and Halogens 2-13
AX2.3. Transport of NOx in the Atmosphere 2-16
AX2.3.1. Convective Transport 2-16
AX2.3.2. Observations of the Effects of Convective Transport 2-16
AX2.3.3. Effects on Photolysis Rates and Wet Scavenging 2-17
AX2.4. Sources and Emissions of NOx 2-18
AX2.4.1. Interactions of NOx with the Biosphere 2-18
AX2.4.1.1. N02 and HN03 Flux Data from Harvard Forest 2-21
AX2.4.2. Emissions of NOx 2-27
AX2.4.2.1. Emissions of N02 from Motor Vehicles 2-27
AX2.4.2.2. NOx Emissions from Natural Sources 2-29
AX2.4.2.3. Uses of Satellite Data to Derive Emissions 2-30
AX2.4.2.4. Field Studies Evaluating Emissions Inventories 2-31
AX2.5. Methods for Calculating NOx Concentrations in the Atmosphere 2-32
AX2.5.1. Chemistry-Transport Models 2-32
AX2.5.1.1. Regional Scale Chemistry Transport Models 2-33
AX2.5.1.2. Intra-urban Scale Dispersion Modeling 2-37
AX2.5.1.3. Global Scale CTMs 2-37
AX2.5.1.4. Modeling the Effects of Convection 2-39
AX2.5.2. CTM Evaluation 2-40
AX2.6. Sampling and Analysis of NOx 2-49
AX2.6.1. Availability and Accuracy of Ambient NOv Measurements 2-49
AX2.6.1.1. Measurement of NO 2-49
AX2.6.1.2. Measurements of N02 2-50
AX2.6.1.3. Monitoring for N02 Compliance Versus Monitoring for Os Formation 2-52
AX2.6.2. Summary of Methods for Measuring N02 2-52
AX2.6.3. Measurements of HN03 2-52
AX2.6.4. Techniques for Measuring Other NOv Species 2-54
AX2.6.5. Remote Sensing of Tropospheric N02 Columns for Surface NOx Emissions and Surface N02 Concentrations 2-54
AX2.7. Policy-relevant Background NOx Concentrations 2-57
AX2.7.1. Analysis of PRB Contribution to U.S. NOx Concentrations and Deposition 2-57
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Annex 3. Ambient Concentrations and Exposures 3-66
AX3.1. Introduction 3-66
AX3.1.1. Characterizing Ambient N02 Concentrations 3-66
AX3.1.2. Relationship to the 1993 AQCD for NOx 3-66
AX3.2. Ambient Concentrations of NOx 3-66
AX3.2.1. Temporal Variability in Ambient NOx Concentrations in Urban Areas 3-67
AX3.2.1.1. Diurnal Variability in N02 Concentrations 3-67
AX3.2.1.2. Seasonal Variability in N02 Concentrations 3-67
AX3.2.1.3. Urban Sites 3-67
AX3.2.1.4. Regional Background Sites 3-68
AX3.2.2. Relationships between N02 and Other Pollutants 3-76
AX3.2.3. Abundance of NOv Species 3-78
AX3.2.3.1. PANs 3-78
AX3.2.3.2. MONO 3-81
AX3.2.3.3. HN03 and N03 3-81
AX3.2.3.4. Nitro-PAHs 3-83
AX3.3. Measuring Personal and Indoor N02 Concentrations 3-84
AX3.3.1. Issues in Measuring Personal/Indoor N02 3-84
AX3.3.1.1. Active (Pumped) Sampling 3-85
AX3.3.1.2. Passive (Diffusive) Sampling 3-85
AX3.3.1.3. Tube Type Samplers 3-87
AX3.3.1.4. Badge-Types Samplers 3-88
AX3.3.1.5. Radial Sampler Types 3-88
AX3.4. NOx in Indoor Air 3-89
AX3.4.1. Indoor Sources and Concentrations of NOx 3-89
AX3.4.1.1. Gas Cooking Appliances 3-89
AX3.4.1.2. Other Combustion Sources 3-90
AX3.4.1.3. Other Indoor Environments 3-92
AX3.4.2. Reactions of N02 in Indoor Air 3-92
AX3.5. Personal Exposure 3-96
AX3.5.1. Personal Exposure in the Residential Indoor Environment 3-96
AX3.5.1.1. School and Office 3-99
AX3.5.1.2. Exposure Reconstruction 3-100
AX3.5.2. Factors Affecting Exposure 3-100
AX3.5.3. Associations between MONO and N02 3-101
AX3.6. Modeling Human Exposures to N02 3-102
AX3.6.1. Exposure Models 3-102
AX3.6.1.1. Population Exposure Models 3-106
AX3.6.1.2. Ambient Concentrations of N02 and Related Air Pollutants 3-108
AX3.6.1.3. Characterization of Microenvironmental Concentrations 3-109
AX3.6.1.4. Characterization of Outdoor Microenvironments Concentrations 3-110
AX3.6.1.5. Characterization of Indoor Microenvironments 3-111
AX3.6.1.6. Characterization of Activity Events 3-113
AX3.6.1.7. Characterization of Inhalation Intake and Uptake 3-113
AX3.6.1.8. Issues to be Addressed in Future Exposure Modeling Efforts 3-114
Annex 4. Toxicological Effects of NOx 4-1
AX4.1. Pulmonary Effects of NOx 4-1
AX4.1.1. Effects of N02 on Oxidant and Antioxidant Metabolism 4-1
AX4.1.2. Lipid Metabolism and Content of the Lung 4-2
AX4.1.3. Lung Host Defense, Lung Permeability and Inflammation, Immune Responses, and Infectious Agents 4-3
AX4.1.4. Emphysema Following N02 Exposure 4-3
AX4.1.5. Lung Function 4-4
AX4.1.6. Nitrates (NOr) 4-4
AX4.2. Dosimetry of Inhaled NOx 4-4
AX4.2.1. Mechanisms of N02 Absorption 4-5
AX4.2.2. Regional and Total Respiratory Absorption of N02 4-6
AX4.2.2.1. Dosimetry Models 4-7
AX4.3. Experimental Studies of N02 Uptake 4-8
AX4.3.1. Upper Respiratory Tract Absorption 4-8
AX4.3.2. Lower Respiratory Tract Absorption 4-9
AX4.3.3. Total Respiratory Tract Absorption 4-9
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AX4.4. Metabolism, Distribution and Elimination of N02 4-9
AX4.5. Extra-Pulmonary Effects of N02 and NO 4-10
AX4.5.1. Body Weight, Hepatic, Renal, and Miscellaneous Effects 4-10
AX4.5.2. Brain Effects 4-11
AX4.5.3. NO 4-11
AX4.5.4. Effects of Mixtures Containing N02 4-11
AX4.5.5. Simple Mixtures Containing N02 4-12
AX4.5.6. Complex Mixtures Containing N02 4-12
Annex 5. Clinical Studies: Exposure to NOx 5-1
AX5.1. Introduction 5-1
AX5.1.1. Considerations in Controlled Human Clinical Studies 5-2
AX5.2. Effects of N02 in Healthy Subjects 5-2
AX5.3. Effects of NOx Exposure in Sensitive Subjects 5-3
AX5.4. Effects of Mixtures Containing NOx 5-3
Annex 6. Epidemiologic Studies Related to Ambient Exposure to NOx 6-2
AX6.1. Interpretation of Epidemiologic Studies 6-2
AX6.1.1. Exposure Assessment and Measurement Error 6-2
AX6.1.2. N02 Exposure Indices Used 6-4
AX6.1.3. Lag Time: Period between Exposure and Health Effect 6-4
AX6.1.4. Model Specification for Temporal Trends and Meteorological Effects 6-5
AX6.1.5. Confounding Effects of Copollutants 6-6
AX6.1.6. Generalized Estimating Equations 6-6
AX6.1.7. Hypothesis Testing and Model Selection 6-6
AX6.1.8. Generalized Additive Models 6-7
AX6.2. Cardiovascular Effects Related to Short-Term N02 Exposure 6-8
AX6.2.1. Hospital Admissions and ED Visits: All CVD 6-8
AX6.2.2. Hospital Admissions and ED Visits: Myocardial Infarction (Ml) 6-13
AX6.2.3. Hospital Admissions and ED Visits: Arrhythmia and Congestive Heart Failure (CHF) 6-13
AX6.2.4. Hospital Admissions and ED Visits: Cerebrovascular Disease 6-13
AX6.2.4.1. Vaso-Occlusion in Sickle Cell 6-13
AX6.2.5. Hospital Admissions and ED Visits: Multipollutant Modeling Results 6-14
AX6.2.6. Heart Rate Variability 6-16
AX6.2.7. Repolarization Changes 6-17
AX6.2.8. Arrhythmias Recorded on Implantable Cardioverter Defibrilators (ICDs) 6-17
AX6.2.9. Markers of Cardiovascular Disease 6-18
AX6.3. Epidemiologic Studies 6-19
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List of Tables
Table AX2.6-1.
Table AX2.4-1.
Table AX3.2-1.
Table AX3.3-1.
Table AX3.3-2.
Table AX3.4-1.
Table AX3.4-2.
Table AX3.4-3.
Table 3.5-1a.
Table 3.5-1b.
Table AX3.5-2.
Table AX3.5-3a.
Table AX3.5-3b.
Table AX3.5-4.
Table AX3.5-5.
Table AX3.5-6.
Table AX3.6-1.
Table AX4.1.
Table AX4.2.
Table AX4.3.
Table AX4.4.
Table AX4.5.
Table AX4.6.
Table AX4.7.
Table AX4.8.
Table AX4.9.
Table AX4. 10.
Table AX4.11.
Table AX4. 12.
Table AX4. 13.
Table AX5.2-1.
Table AX5.3-1.
Table AX5.3-2.
Table AX5.4-1.
Table AX6.3-1.
Satellite Instruments Used to Retrieve Tropospheric NO2 Columns.
Emissions of nitrogen oxides and ammonia in the United States in 2002
NOX and NOY concentrations at regional background sites in the Eastern United States.
Concentrations are given in ppb.
Passive samplers used in NC>2 measurements.
The performance of sampler/sampling method for NC>2 measurements in the air.
NO2 concentrations (ppb) in homes in Latrobe Valley, Victoria, Australia.
NO2 concentrations (ppb) in homes in Connecticut.
NO2 concentrations near indoor sources - short-term averages.
Indoor/outdoor ratio and the indoor vs. outdoor regression slope.
Summary of regression models of personal exposure to ambient/outdoor NO\2x
NO2 concentrations (ppb) in different rooms.
Average ambient and nonambient contributions to population exposure
Indoor and outdoor contributions to indoor concentrations.
The association between indoor, outdoor, and personal NO2
Indoor, outdoor, and personal NO2 levels stratified by exposure factors (concentrations
are in ppb and slopes are dimensionless)
Personal NO2 levels stratified by demographic and socioeconomic factors (concentrations
are in ppb and slopes are dimensionless).
The essential attributes of the pNEM, HAPEM, APEX, SHEDS, and MENTOR-1A.
Oxidant and antioxidant homeostasis.
Lung amino acids, proteins, lipids, and enzymes.
Alveolar macrophages and lung host defense.
Lung permeability and inflammation.
Immune responses.
Infectious agents.
Lung structure.
Pulmonary function.
Hematological parameters.
Iron, enzymes, and nucleic acids.
Genotoxicity in vitro and in plants.
Genoticity in vivo.
Genotoxicity.
Clinical studies - healthy subjects.
Subjects with respiratory disease.
Inhaled allergen.
NO2 and other pollutants.
Studies examining exposure to indoor NO2 and respiratory symptoms.
2-55
2-61
3-115
3-115
3-116
3-116
3-117
3-117
3-118
3-120
3-122
3-123
3-123
3-124
3-127
3-133
3-134
4-13
4-15
4-18
4-22
4-25
4-28
4-31
4-34
4-34
4-35
4-36
4-37
4-37
5-3
5-6
5-7
5-8
6-19
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Table AX6.3-2. Studies examining exposure to ambient NO2 and acute respiratory symptoms using
generalized estimating equations (GEE) in the analysis method 6-22
Table AX6.3-3. Respiratory health effects of oxides of nitrogen: hospital admissions. 6-24
Table AX6.3-4. Respiratory Health Effects of Oxides of Nitrogen: Emergency Department Visits 6-56
Table AX6.3-5. Respiratory Health Effects of Oxides of Nitrogen: General Practitioner/Clinic Visits 6-73
Table AX6.3-6. Human Health Effects of Oxides of Nitrogen: CVD Hospital Admissions and Visits: United
States and Canada 6-78
Table AX6.3-7. Human Health Effects of Oxides of Nitrogen: CVD Hospital Admissions and Visits:
Australia and New Zealand 6-88
Table AX6.3-8. Human health effects of oxides of nitrogen: cvd hospital admissions and visits: Europe 6-92
Table AX6.3-9. Human health effects of oxides of nitrogen: cvd hospital admissions and visits: Asia 6-100
Table AX6.3-10. Studies examining exposure to ambient NO2 and heart rate variability as measured by
standard deviation of normal-to-normal intervals (SDNN). 6-104
Table AX6.3-11. Studies examining exposure to ambient NO2 and heart rate variability as measured by
variables recorded on implantable cardioverter defibrillators (ICDs). 6-105
Table AX6.3-12. Birth weight and long-term NO2 exposure studies 6-106
Table AX6.3-13. Preterm delivery and long-term NO2 exposure studies 6-109
Table AX6.3-14. Fetal growth and long-term NO2 exposure studies 6-111
Table AX6.3-15. Lung function and long-term NO2 exposure. 6-112
Table AX6.3-16. Asthma and long-term NO2 exposure. 6-114
Table AX6.3-17. Respiratory symptoms and long-term NO2 exposure. 6-118
Table AX6.3-18. Lung cancer. 6-124
Table AX6.3-19. Effects of acute NOx exposure on mortality. Risk estimates are standardized for per
20 ppb 24-h avg NO2 increment. 6-125
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List of Figures
Figure AX1.1-1. Selection process for studies included in ISA. 1-2
Figure AX1.2-1. Focusing on unmeasured confounders/covariates, or other sources of spurious
association from bias. 1-11
Figure AX1.2-2. Example posterior distribution for the determination of Sufficient. 1-12
Figure AX1.2-3. Example posterior distribution for the determination of Equipoise and Above. 1-12
Figure AX1.2-4. Example posterior distribution for the determination of Against. 1-12
Figure AX2.2-1. Schematic diagram of the cycle of reactive nitrogen species in the atmosphere. 2-2
Figure AX2.2-2. Measured values of O3 and NOZ (NOY - NOX). 2-8
Figure AX2.2-3. Structures of same nitro-polycyclic aromatic hydrocarbons. 2-10
Figure AX2.2-4. Formation of 2-nitropyrene (2NP) from the reaction of OH with gaseous pyrene (PY). 2-11
Figure AX2.4-1. Diel cycles of median concentrations (upper panels) and fluxes (lower panels). 2-22
Figure AX2.4-2. Simple NOX photochemical canopy model outputs. 2-23
Figure AX2.4-3. Hourly (dots) and median nightly (pluses) NO2 flux vs. concentration. 2-24
Figure AX2.4-4. Averaged profiles at Harvard Forest. 2-25
Figure AX2.4-5. Summer (June-August) 2000 median concentrations (upper panels), fractions of NOy
(middle panels), and fluxes (lower panels) of NOY and component species. 2-26
Figure AX2.5-1. Time series for measured gas-phase species in comparison with results from
aphotochemical model. 2-41
Figure AX2.5-2. Time series for measured gas-phase species in comparison with results from a
photochemical model. 2-42
Figure AX2.5-3. 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. 2-44
Figure AX.2.5-4. Evaluation of model versus measured O3 versus NOy for two model scenarios for Atlanta. 2-45
Figure AX2.5-5. Evaluation of model versus: (a) measured O3 versus NOZ and (b) O3 versus the sum
2H2O2 + NOZ for Nashville, TN. 2-46
Figure AX2.5-6. Time series of concentrations of RO2, HO2, and OH radicals, local O3 photochemical
production rate and concentrations of NOX 2-48
Figure AX2.6-1. Tropospheric NO2 columns (molecules NO2/cm2) retrieved from the SCIAMACHY satellite
instrument for 2004-2005. 2-56
Figure AX2.7-1. Annual mean concentrations of NO2 (ppb) in surface air over the United States in the
present-day (upper panel) and policy relevant background (middle panel) MOZART-2
simulations. 2-58
The bottom panel shows the percentage contribution of the background to the present-day concentrations.
Please see text for details. 2-58
Figure AX2.7-2. Same as for Figure AX2.7-1 but for wet and dry deposition of HNO3, NH4NO3, NOX,
HO2NO2, and organic nitrates (mg N m"2/y). 2-59
Figure AX2.7-3. July mean soil NO emissions (upper panels; 1 H 10 9 molecules cm"2 s1) and surface PRB
NOX concentrations (lower panels; ppt). 2-60
Figure AX3.2-1. Time series of 24-h avg NO2 concentrations at individual sites in New York City from 2003
through 2006. 3-69
Figure AX3.2-2. Time series of 24-h average NO2 concentrations at individual sites in Chicago, IL from
2003 through 2005. 3-70
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Figure AX3.2-3. Time series of 24-h avg NO2 concentrations at individual sites in Baton Rouge, LA from
2003 through 2005. 3-71
Figure AX3.2-4. Time series of 24-h avg NC>2 concentrations at individual sites in Houston, TX from 2003
through 2005. 3-72
Figure AX3.2-5. Time series of 24-h avg NO2 concentrations at individual sites in Los Angeles, CA from
2003 through 2005. 3-73
Figure AX3.2-5. (Continued) Time series of 24-h avg NC>2 concentrations at individual sites in Los Angeles,
CA from 2003 through 2006. 3-74
Figure AX3.2-6. Time series of 24-h avg NC>2 concentrations at individual sites in Riverside, CA from 2003
through 2006. 3-75
Figure AX3.2-6. (Continued) Time series of 24-h avg NO2 concentrations at individual sites in Riverside,
CA from 2003 through 2006. 3-76
Figure AX3.2-7. Relationship between O^, NO, and NC>2 as a function of NOx concentration. 3-77
Figure AX3.2-8. Variation of odd oxygen (= O3 + NO2) with NOX. 3-78
Figure AX3.2-9. Measured Os (ppb) versus PAN (ppt) in Tennessee, including (a) aircraft measurements,
and (b, c, and d) suburban sites near Nashville. 3-79
Figure AX3.2-10. Relationship between benzene and NCv at a measurement site in Boulder, CO. 3-80
Figure AX3.2-11. Ratios of PAN to NO2 observed at Silwood Park, Ascot, Berkshire, U.K. from July 24 to
August 12 1999. 3-82
Figure AX3.2-12. Ratios of MONO to NO2 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 avg
concentrations of MONO and NO2. 3-82
Figure AX3.2-13. Concentrations of particulate nitrate measures as part of the EPA's speciation network. 3-84
Figure AX3.6-1. Schematic description of a general framework identifying the processes (steps or
components) involved in assessing inhalation exposures and doses for individuals and
populations. 3-104
Figure AX6.2-1. Relative risks (95% Cl) for associations of 24-h NO2 (per 20 ppb) and daily 1-h max NO2*
with hospitalizations or emergency department visits for all cardiovascular diseases
(CVD). 6-9
Figure AX6.2-2. Relative risks (95% Cl) for associations of 24-h NO2 (per 20 ppb) and daily 1-h max NO2*
(per 30 ppb) with hospitalizations for Ischemic Heart Disease (IHD). 6-10
Figure AX6.2-3. Relative risks (95% Cl) for associations between 24-h avg NO2 (per 20 ppb) and
hospitalizations for myocardial infarction (Ml). 6-11
Figure AX6.2-4. Relative risks (95% Cl) for associations of 24-h avg NO2 (per 20 ppb) and 1-h max NO2*
with hospitalizations for congestive heart failure (CHF). 6-12
Figure AX6.2-5. Relative risks (95% Cl) for associations of 24-h avg NO2 exposure (per 20 ppb) and daily
1-h max NO2* (per 30 ppb) with hospitalizations or emergency department visits for CVD. 6-15
IV
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Abbreviations and Acronyms
8-OHdG 8-hydroxy-2N-deoxyguanosine
5-HETE 5-hydroxyeicosatebrachoic
AA arachidonic acid
ACP accumulation mode particle
AHH aryl hydrocarbon hydroxylase
ALT alanine-amino-transferase
AM alveolar macrophages
AMMN N-nitroso-acetoxymethylmethylamine
AP alkaline phosphatase
ARIC Atherosclerosis Risk in Communities (Study)
AST aspartate-amino-transferase
B[a]P benzo[a]pyrene
BAL bronchoalveolar lavage
BALF bronchoalveolar lavage fluid
BC black carbon
BHPN N-bis(2-hydroxypropyl) nitrosamine
BLF bronchial lavage fluid
bpm beats per minute
bw body weight
C carbon or carbon black particles
CA chromosome aberrations
CAT catalase
CHD coronary heart disease
CHF congestive heart failure
Choi cholesterol
CMD count median diameter
CO carbon monoxide
COPD Chronic obstructive pulmonary disease
CRP C-reactive protein
CVD cardiovascular disease
CYP Cytochrome P450
Dae aerodynamic diameter
DEcCBP DEP extract coated carbon black particles
DEN diethylnitrosamine
DEP diesel exhaust particles
DEP+C diesel exhaust particle extract adsorbed to C
dG 2N-deoxyguanosine
DMBA 7, 12-dimethylbenzanthracene
DMSO dimethyl sulfoxide
EC elemental carbon
ED emergency department
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ELF epithelial lining fluid
FHLC fetal hamster lung cells
GAM generalized additive models
yGCS y-glutamylcysteine synthetase
GPx glutathione peroxidase
GPx Se-dependent glutathione peroxidase
GPx glutathione peroxidase
GRed glutathione reductase
GS glutathione synthetase
GSD geometric standard deviation
GSH glutathione
GSSG glutathione disulfide
GSSO3H glutathione S-sulfonate
GST glutathione-S-transferase
yGT y-glutamyl transpeptidase
HF heart failure
HNO2 nitrous acid
HNO3 nitric acid
HP hydrolyzed protein
HR heart rate
HRV heart rate variability
GT y-glutamyl transpeptidase
HVA-ICa high-voltage activated calcium currents
ICAM-1 intercellular adhesion molecule
ICD implantable cardioverter defibrillators
IgG immunoglobulin
IHD ischemic heart disease
IQR interquartile range
LDH lactate dehydrogenase
LOESS locally estimated smoothing splines
LTB4 leukotrine B4
MAD median aerodynamic diameter
MI myocardial infraction
MMAD mass median aerodynamic density
MMD mass median diameter
MN micronuclei
MNPCE micronucleated PCE
Mo molybdenum
NADPH nicotinamide adenie dinuclotide phosphate
NDMA nitrosodimethylamine
NHANES III Third National Health and Nutrition Examination Survey
NHLBI National Heart, Lung, and Blood Institute
NMBzA N-nitrosomethylbenzylamine
NO nitric oxide
VI
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NO2 nitrogen dioxide
NOX oxides of nitrogen; mono-nitrogent oxides (e.g. NO, NO2)
NOY total reactive nitrogen (all oxided forms of nitrogen)
[SumofNOxandNOz)
NOZ other oxides of nitrogen (e.g. HNO3 and PAN)
NR Not Reported
O3 ozone
OC organic carbon
PAF paroxysmal atrial fibrillation
PAH polycyclic aromatic hydrocarbons
PAR proximal alveolar region
PCE polychromatic erythrocytes
PEC pulmonary endocrine cells
PKA cyclic AMP-dependent protein kinase A
PKI synthetic peptide inhibitor of PKA
PL phospholipids
PM2 5 paniculate matter particles equal to or smaller than 2.5 um
PM10 paniculate matter particles between 2.5 um and 10 um
PNC particle number concentration
ppb parts per billion
ppm parts per million
RBC red blood cell or erythrocyte
RH relative humidity
r-MSSD root mean square successive difference (in heart period series) a time
domain measurement of heart period variability
SCE sister chromatid exchanges
SDNN standard deviation of all normal-to-normal R-R intervals
SEPs somatosensory-evoked potentials
SO2 sodium dioxide
SOD superoxide dismutase
SP-A surfactant protein A
SPF specific pathogen free
SPM suspended paniculate matter extract
SQCA squamous cell carcinoma
SSO seabuckthorn seed oil
SV40 simian virus 40
TEA thiobarbituric acid
TEARS thiobarbituric acid-reactive substance
TOC potassium channel transient outward currents
TTX tetrodotoxin
TTX-R tetrodotoxin-resistant
TTX-S tetrodotoxin-sensitive
TxB2 thromboxane B2
UFP ultafine particles
um micon, micrometer: 10"6 meter or (1/1000 of a millimeter)
VII
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VE ventilation rate
VEPs visual-evoked potentials
VT tidal volume
VWF von Willibrand factor
W tungsten
WBC white blood cell
VIM
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Annex 1. Framework for Review
This Integrated Science Assessment (ISA) presents a concise synthesis of the most policy-relevant
science to form the scientific foundation for the review of the primary (health-based) National Ambient
Air Quality Standards (NAAQS) for nitrogen dioxide (NO2). The Annexes: (1) provide more details of
the most pertinent scientific literature relative to the review of the NO2 NAAQS in the areas of
atmospheric sciences, air quality analyses, exposure assessment, dosimetry, controlled human exposure
studies, toxicology, and epidemiology; and (2) focus on the key policy relevant questions and studies
published since the last NAAQS review.
Annex AX1 details the methods used to identify and select studies; and frameworks for evaluating
scientific evidence relative to causality determination. The overarching framework for evaluation of
evidence for causality is outlined in Chapter 1 of this ISA, and this Annex provides supporting
information for that framework, including excerpts from decision frameworks or criteria developed by
other organizations.
Annex AX2 presents evidence related to the physical and chemical processes controlling the
production, destruction, and levels of reactive nitrogen compounds in the atmosphere, including both
oxidized and reduced species. Annex AX3 presents information on environmental concentrations,
patterns, and human exposure to ambient oxides of nitrogen; however, most information relates to NO2.
Annex AX4 presents results from toxicological studies as well as information on dosimetry of oxides of
nitrogen. Annex AX5 discusses results from controlled human exposure studies, and Annex AX6 presents
evidence from epidemiologic studies. These Annexes include more detailed information on health or
exposure studies that is summarized in tabular form, as well as more extensive discussion of atmospheric
chemistry, source, exposure, and dosimetry information. Annex tables for health studies are generally
organized to include information about (1) concentrations of oxides of nitrogen levels or doses and
exposure times, (2) description of study methods employed, (3) results and comments, and (4)
quantitative outcomes for oxides of nitrogen measures.
AX1.1. Literature Selection and Retrieval
Literature searches were conducted routinely to identify studies published since the last review. The
review included publications from 1-2 years prior to the publication of 1993 AQCD for Oxides of
Nitrogen (U.S. Environmental Protection Agency, 1993). Search strategies were iteratively modified in an
effort to optimize the identification of pertinent publications. Additional papers were identified for
inclusion in several ways: review of pre-publication tables of contents for journals in which relevant
papers may be published; independent identification of relevant literature by expert authors; and.
identification by public and CAS AC during the external review process. Generally, only information that
had undergone scientific peer review and had been published or accepted for publication was considered.
The following sections briefly summarize criteria for selection of studies for this ISA.
The selection process for studies included in this ISA is shown in Figure AX1.1-1. Studies were
evaluated by EPA staff and outside experts to determine if the studies merited inclusion. Criteria used for
study selection are summarized below.
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Identification of Studies for Inclusion in the ISA
Continuous,
comprehensive
literature review
of peer-reviewed
journal articles
Informative studies
are identified.
Studies added
to the docket
during public
comment period.
Studies identified
during EPA
sponsored kickoff
meeting (including
studies in
preparation).
^FORMATIVE studies are well-designed?^
properly implemented, thoroughly
described.
HIGHLY INFORMATIVE studies reduce
uncertainty on critical issues, may include
analyses of confounding or effect
modification by copollutants or other
variables, analyses of concentration-
response or dose-response relationships,
analyses related to time between
exposure and response, and offer
innovation in method or design.
POLICY-RELEVANT studies may include
those conducted at or near ambient
concentrations and studies conducted in
.U.S. and Canadian airsheds.
tudies are
evaluated for
inclusion in the ISA
and included
in the
Annexes
Studies that do
not address
exposure and/or
effects of air
pollutant(s) under
review are
excluded.
Selection of
studies
discussed and
additional studies
identified during
CASAC peer
review of draft
document.
Studies
summarized
in figures are
included
because they are
sufficiently
comparable
to be displayed
together. A study
highlighted in the
text may not
appear in a
summary figure.
A figure from a
highlighted study
maybe
reproduced
in its entirely.
Studies
REFERENCED
in the text include
those that provide
a basis for or
describe
the association
between the
criteria pollutant
and effects. In
addition, policy
relevant
and highly
informative
studies
are discussed
in the text.
Figure AX1.1-1. Selection process for studies included in ISA.
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AX1.1.1. General Criteria for Study Selection
In assessing the scientific quality and relevance of epidemiological and human or animal
toxicological studies, the following considerations have been taken into account.
• Are the study populations adequately selected and are they sufficiently well defined to allow for
meaningful comparisons between study groups?
• Are the statistical analyses appropriate, properly performed, and properly interpreted?
• Are likely covariates (i.e., potential confounders or effect modifiers) adequately controlled or
taken into account in the study design and statistical analysis?
• Are the reported findings internally consistent, biologically plausible, and coherent in terms of
consistency with other known facts?
• To what extent are the aerometric data, exposure, or dose metrics of adequate quality and
sufficiently representative to serve as indicators of exposure to ambient NOX?
Consideration of these issues informs the judgments on the relative quality of individual studies and
allows the most pertinent studies to be the focus of the assessment.
AX1.1.2. Criteria for Selecting Epidemiology Studies
In selecting epidemiological studies, EPA considered whether a given study contained information
on (1) associations with measured oxides of nitrogen concentrations using short- or long-term exposures
at or near ambient levels of oxides of nitrogen, (2) health effects of specific oxides of nitrogen species or
indicators related to oxides of nitrogen sources (e.g., motor vehicle emissions, combustion-related
particles), (3) health endpoints and populations not previously extensively researched, (4) multiple
pollutant analyses and other approaches to address issues related to potential confounding and
modification of effects, and/or (5) important methodology issues (e.g., lag of effects, model
specifications, thresholds, mortality displacement) related to interpretation of the health evidence. Among
the epidemiological studies, particular emphasis was placed on those studies most relevant to the review
of the NAAQS. Specifically, studies conducted in the United States or Canada were discussed in more
detail than those from other geographical regions. Particular emphasis was placed on: (1) recent multeity
studies that employ standardized analyses methods for evaluating effects of oxides of nitrogen and that
provide overall estimates for effects based on combined analyses of information pooled across multiple
cities, (2) new studies that provide quantitative effect estimates for populations of interest, and (3) studies
that consider oxides of nitrogen as a component of a complex mixture of air pollutants.
Not all studies are accorded equal weight in the overall interpretive assessment of evidence
regarding NO2-associated health effects. Among well-conducted studies with adequate control for
confounding, increasing scientific weight is accorded in proportion to the precision of their effect
estimates. Small-scale studies without a wide range of exposures generally produced less precise
estimates compared to larger studies with a broad exposure gradient. For time-series studies, the size of
the study, as indicated by the length of the study period and total number of events, and the variability of
NO2 exposures are important components to determine the precision of the health effect estimates. In
evaluating the epidemiologic evidence in this chapter, more weight is accorded to estimates from studies
with narrow confidence bands.
The goal was to perform a balanced and objective evaluation that summarized, interpreted, and
synthesized the most important studies and issues in the epidemiologic database pertaining to oxides of
nitrogen exposure. For each study presented, the quality of the exposure and outcome data, as well as the
quality of the statistical analysis methodology were discussed. The framework for evaluation of evidence
is further described below.
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AX1.1.3. Criteria for Selecting Toxicology Studies
Criteria for the selection of research evaluating animal toxicological or controlled human exposure
studies included a focus on studies conducted within an order of a magnitude of ambient NO2
concentrations and those studies that approximated expected human exposure conditions in terms of
concentration and duration. Studies that elucidated mechanisms of action and/or susceptibility,
particularly if the studies were conducted under atmospherically relevant conditions, were emphasized
whenever possible.
The selection of research evaluating controlled human exposures to oxides of nitrogen is mainly
limited to studies in which subjects are exposed to <5 ppm NO2. For these controlled human exposures,
emphasis is placed on studies that (1) investigate potentially susceptible populations such as asthmatics,
particularly studies that compare responses in susceptible individuals with those in age-matched healthy
controls; (2) address issues such as concentration-response or time-course of responses; (3) investigate
exposure to NO2 separately and in combination with other pollutants such as O3 and SO2; (4) include
control exposures to filtered air; and (5) have sufficient statistical power to assess findings.
AX1.2. Guidelines for the Determination of Causality
The following sections include excerpts from several reports that have documented approaches for
the determination of causality, or related decision-making processes. These sections provide
supplementary documentation of approaches that are similar in nature to EPA's framework for evaluation
of health evidence.
AX1.2.1. Surgeon General's Report: The Health Consequences of
Smoking
The Surgeon General's Report (CDC, 2004) evaluates the health effects of smoking; it builds upon
the first Surgeon General's report published in 1964 (USDEHW, 1964). It also updates the methodology
for evaluating evidence that was first presented in the 1964 report. The 2004 report acknowledges the
effectiveness of the previous methodology, but attempts to standardize the language surrounding causality
of associations.
The Surgeon General's Reports on Smoking played a central role in the translation of scientific
evidence into policy. As such, it was important that scientific evidence was presented in a manner that
conveyed most succinctly the link between smoking and a health effect. Specifically, the report stated:
The statement that an exposure "causes" a disease in humans represents a serious
claim, but one that carries with it the possibility of prevention. Causal
determinations may also carry substantial economic implications for society and
for those who might be held responsible for the exposure or for achieving its
prevention.
To address the issue of identifying causality, the 2004 report provided the following summary of
the earlier 1964 report:
When a relationship or an association between smoking... and some condition in
the host was noted, the significance of the association was assessed.
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The characterization of the assessment called for a specific term. ... The word
cause is the one in general usage in connection with matters considered in this
study, and it is capable of conveying the notion of a significant, effectual
relationship between an agent and an associated disorder or disease in the host.
No member was so naive as to insist upon mono-etiology in pathological
processes or in vital phenomena. All were thoroughly aware... that the end
results are the net effect of many actions and counteractions.
Granted that these complexities were recognized, it is to be noted clearly that the
Committee's considered decision to use the words "a cause," or "a major cause,"
or "a significant cause," or "a causal association" in certain conclusions about
smoking and health affirms their conviction (USDHEW, 1964, p. 21).
This 2004 report created uniformly labeled conclusions that were used throughout the document.
The following excerpts from the report included a description of the methodology and the judgments used
to reach a conclusion:
Terminology of Conclusions and Causal Claims
The first step in introducing this revised approach is to outline the language that
will be used for summary conclusions regarding causality, which follows
hierarchical language used by Institute of Medicine committees (Institute of
Medicine, 1999) to couch causal conclusions, and by IARC to classify
carcinogenic substances (IARC, 1986). These entities use a four-level hierarchy
for classifying the strength of causal inferences based on available evidence as
follows:
Evidence is sufficient to infer a causal relationship.
Evidence is suggestive but not sufficient to infer a causal relationship.
Evidence is inadequate to infer the presence or absence of a causal relationship
(which encompasses evidence that is sparse, of poor quality, or conflicting).
Evidence is suggestive of no causal relationship.
For this report, the summary conclusions regarding causality are expressed in this
four-level classification. Use of these classifications should not constrain the
process of causal inference, but rather bring consistency across chapters and
reports, and greater clarity as to what the final conclusions are actually saying.
As shown in Table 1.1 [see original document], without a uniform classification
the precise nature of the final judgment may not always be obvious, particularly
when the judgment is that the evidence falls below the "sufficient" category.
Experience has shown that the "suggestive" category is often an uncomfortable
one for scientists, since scientific culture is such that any evidence that falls short
of causal proof is typically deemed inadequate to make a causal determination.
However, it is very useful to distinguish between evidence that is truly
inadequate versus that which just falls short of sufficiency.
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There is no category beyond "suggestive of no causal relationship" as it is
extraordinarily difficult to prove the complete absence of a causal association. At
best, "negative" evidence is suggestive, either strongly or weakly. In instances
where this category is used, the strength of evidence for no relationship will be
indicated in the body of the text. In this new framework, conclusions regarding
causality will be followed by a section on implications. This section will separate
the issue of causal inference from recommendations for research, policies, or
other actions that might arise from the causal conclusions. This section will
assume a public health perspective, focusing on the population consequences of
using or not using tobacco and also a scientific perspective, proposing further
research directions. The proportion of cases in the population as a result of
exposure (the population attributable risk), along with the total prevalence and
seriousness of a disease, are more relevant for deciding on actions than the
relative risk estimates typically used for etiologic determinations. In past reports,
the failure to sharply separate issues of inference from policy issues resulted in
inferential statements that were sometimes qualified with terms for action. For
example, based on the evidence available in 1964, the first Surgeon General's
report on smoking and health contained the following statement about the
relationship between cardiovascular diseases and smoking:
It is established that male cigarette smokers have a higher death rate
from coronary artery disease than non-smoking males. Although the
causative role of cigarette smoking in deaths from coronary disease
is not proven, the Committee considers it more prudent from the
public health viewpoint to assume that the established association
has causative meaning, than to suspend judgment until no
uncertainty remains (USDHEW, 1964, p. 32).
Using this framework, this conclusion would now be expressed differently,
probably placing it in the "suggestive" category and making it clear that although
it falls short of proving causation, this evidence still makes causation more likely
than not. The original statement makes it clear that the 1964 committee judged
that the evidence fell short of proving causality but was sufficient to justify
public health action. In this report, the rationale and recommendations for action
will be placed in the implications section, separate from the causal conclusions.
This separation of inferential from action-related statements clarifies the degree
to which policy recommendations are driven by the strength of the evidence and
by the public health consequences acting to reduce exposure. In addition, this
separation appropriately reflects the differences between the processes and goals
of causal inference and decision making.
AX1.2.2. The EPA Guidelines for Carcinogen Risk Assessment
The EPA Guidelines for Carcinogen Risk Assessment, published in 2005 (U.S. Environmental
Protection Agency, 2005), was an update to the previous risk assessment document published in 1986.
This document served to guide EPA staff and public about the Agency's risk assessment development and
methodology. In the 1986 Guidelines, a step-wise approach was used to evaluate the scientific findings.
However, this newer document was similar to the Surgeon General's Report on Smoking in that it used a
single integrative step after assessing all of the individual lines of evidence. Five standard descriptors are
used to evaluate the weight of evidence:
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1. Carcinogenic to Humans
2. Likely to Be Carcinogenic to Humans
3. Suggestive Evidence of Carcinogenic Potential
4. Inadequate Information to Assess Carcinogenic Potential
5. Not Likely to Be Carcinogenic to Humans.
The 2005 Guidelines recommended that a separate narrative be prepared on the weight of evidence and
the descriptor. The Guidelines further recommended that the descriptors should only be used in the
context of a weight of evidence discussion.
The following excerpt describes how a weight of evidence narrative should be developed and a how
a descriptor should be selected (U.S. Environmental Protection Agency, 2005):
The weight of the evidence should be presented as a narrative laying out the
complexity of information that is essential to understanding the hazard and its
dependence on the quality, quantity, and type(s) of data available, as well as the
circumstances of exposure or the traits of an exposed population that may be
required for expression of cancer. For example, the narrative can clearly state to
what extent the determination was based on data from human exposure, from
animal experiments, from some combination of the two, or from other data.
Similarly, information on mode of action can specify to what extent the data are
from in vivo or in vitro exposures or based on similarities to other chemicals. The
extent to which an agent's mode of action occurs only on reaching a minimum
dose or a minimum duration should also be presented. A hazard might also be
expressed disproportionately in individuals possessing a specific gene; such
characterizations may follow from a better understanding of the human genome.
Furthermore, route of exposure should be used to qualify a hazard if, for
example, an agent is not absorbed by some routes. Similarly, a hazard can be
attributable to exposures during a susceptible lifestage on the basis of our
understanding of human development.
The weight of evidence narrative should highlight:
• the quality and quantity of the data;
• all key decisions and the basis for these major decisions; and
• any data, analyses, or assumptions that are unusual for or new to EPA.
To capture this complexity, a weight of evidence narrative generally includes
• conclusions about human carcinogenic potential (choice of descriptor(s), described
below)
• a summary of the key evidence supporting these conclusions (for each descriptor used),
including information on the type(s) of data (human and/or animal, in vivo and/or in
vitro) used to support the conclusion(s)
• available information on the epidemiologic or experimental conditions that characterize
expression of carcinogenicity (e.g., if carcinogenicity is possible only by one exposure
route or only above a certain human exposure level),
• a summary of potential modes of action and how they reinforce the conclusions,
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• indications of any susceptible populations or lifestages, when available, and
• a summary of the key default options invoked when the available information is
inconclusive.
To provide some measure of clarity and consistency in an otherwise free-form
narrative, the weight of evidence descriptors are included in the first sentence of
the narrative. Choosing a descriptor is a matter of judgment and cannot be
reduced to a formula. Each descriptor may be applicable to a wide variety of
potential data sets and weights of evidence. These descriptors and narratives are
intended to permit sufficient flexibility to accommodate new scientific
understanding and new testing methods as they are developed and accepted by
the scientific community and the public. Descriptors represent points along a
continuum of evidence; consequently, there are gradations and borderline cases
that are clarified by the full narrative. Descriptors, as well as an introductory
paragraph, are a short summary of the complete narrative that preserves the
complexity that is an essential part of the hazard characterization. Users of these
cancer guidelines and of the risk assessments that result from the use of
these cancer guidelines should consider the entire range of information
included in the narrative rather than focusing simply on the descriptor.
In borderline cases, the narrative explains the case for choosing one descriptor
and discusses the arguments for considering but not choosing another. For
example, between "suggestive" and "likely" or between "suggestive" and
"inadequate," the explanation clearly communicates the information needed to
consider appropriately the agent's carcinogenic potential in subsequent decisions.
Multiple descriptors can be used for a single agent, for example, when
carcinogenesis is dose- or route-dependent. For example, if an agent causes
point-of-contact tumors by one exposure route but adequate testing is negative by
another route, then the agent could be described as likely to be carcinogenic by
the first route but not likely to be carcinogenic by the second. Another example is
when the mode of action is sufficiently understood to conclude that a key event
in tumor development would not occur below a certain dose range. In this case,
the agent could be described as likely to be carcinogenic above a certain dose
range but not likely to be carcinogenic below that range.
Descriptors can be selected for an agent that has not been tested in a cancer
bioassay if sufficient other information, e.g., toxicokinetic and mode of action
information, is available to make a strong, convincing, and logical case through
scientific inference. For example, if an agent is one of a well-defined class of
agents that are understood to operate through a common mode of action and if
that agent has the same mode of action, then in the narrative the untested agent
would have the same descriptor as the class. Another example is when an
untested agent's effects are understood to be caused by a human metabolite, in
which case in the narrative the untested agent could have the same descriptor as
the metabolite. As new testing methods are developed and used, assessments may
increasingly be based on inferences from toxicokinetic and mode of action
information in the absence of tumor studies in animals or humans.
When a well-studied agent produces tumors only at a point of initial contact, the
descriptor generally applies only to the exposure route producing tumors unless
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the mode of action is re levant to other routes. The rationale for this conclusion
would be explained in the narrative.
When tumors occur at a site other than the point of initial contact, the descriptor
generally applies to all exposure routes that have not been adequately tested at
sufficient doses. An exception occurs when there is convincing information, e.g.,
toxicokinetic data that absorption does not occur by another route.
When the response differs qualitatively as well as quantitatively with dose, this
information should be part of the characterization of the hazard. In some cases
reaching a certain dose range can be a precondition for effects to occur, as when
cancer is secondary to another toxic effect that appears only above a certain dose.
In other cases exposure duration can be a precondition for hazard if effects occur
only after exposure is sustained for a certain duration. These considerations differ
from the issues of relative absorption or potency at different dose levels because
they may represent a discontinuity in a dose-response function.
When multiple bioassays are inconclusive, mode of action data are likely to hold
the key to resolution of the more appropriate descriptor. When bioassays are few,
further bioassays to replicate a study's results or to investigate the potential for
effects in another sex, strain, or species may be useful.
When there are few pertinent data, the descriptor makes a statement about the
database, for example, "Inadequate Information to Assess Carcinogenic
Potential," or a database that provides "Suggestive Evidence of Carcinogenic
Potential." With more information, the descriptor expresses a conclusion about
the agent's carcinogenic potential to humans. If the conclusion is positive, the
agent could be described as "Likely to Be Carcinogenic to Humans" or, with
strong evidence, "Carcinogenic to Humans." If the conclusion is negative, the
agent could be described as "Not Likely to Be Carcinogenic to Humans."
Although the term "likely" can have a probabilistic connotation in other contexts, its use as a
weight of evidence descriptor does not correspond to a quantifiable probability of whether the chemical is
carcinogenic. This is because the data that support cancer assessments generally are not suitable for
numerical calculations of the probability that an agent is a carcinogen. Other health agencies have
expressed a comparable weight of evidence using terms such as "Reasonably Anticipated to Be a Human
Carcinogen" (NTP) or "Probably Carcinogenic to Humans" (International Agency for Research on
Cancer).
AX1.2.3. Improving Presumptive Disability Decision-Making Process
for Veterans
A recent publication by the Institute of Medicine (IOM) also provided the foundation for the
causality framework adapted in this ISA (IOM, 2007). The Committee on Evaluation of the Presumptive
Disability Decision-Making Process for Veterans was charged by the Veterans Association to describe
how presumptive decisions are made for veterans with health conditions arising from military service
currently, as well as recommendations for how such decisions could made in the future. The committee
proposed a multiple-element approach that included a quantification of the extent of disease attributable
to an exposure. This process involved a review of all relevant data to decide the strength of evidence for
causation, using one of four categories:
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• Sufficient: the evidence is sufficient to conclude that a causal relationship exists.
• Equipoise and Above: the evidence is sufficient to conclude that a causal relationship is at least as
likely as not, but not sufficient to conclude that a causal relationship exists.
• Below Equipoise: the evidence is not sufficient to conclude that a causal relationship is at least as
likely as not, or is not sufficient to make a scientifically informed judgment.
• Against: the evidence suggests the lack of a causal relationship.
The following is an excerpt from this report and describes these four categories in detail:
In light of the categorizations used by other health organizations and agencies as
well as considering the particular challenges of the presumptive disability
decision-making process, we propose a four-level categorization of the strength
of the overall evidence for or against a causal relationship from exposure to
disease.
We use the term "equipoise" to refer to the point at which the evidence is in
balance between favoring and not favoring causation. The term "equipoise" is
widely used in the biomedical literature, is a concept familiar to those concerned
with evidence-based decision-making and is used in VA processes for rating
purposes as well as being a familiar term in the veterans' community.
Below we elaborate on the four-level categorization which the Committee
recommends.
Sufficient
If the overall evidence for a causal relationship is categorized as Sufficient, then
it should be scientifically compelling. It might include:
• replicated and consistent evidence of a causal association: that is, evidence of an
association from several high-quality epidemiologic studies that cannot be explained
by plausible noncausal alternatives (e.g., chance, bias, or confounding)
• evidence of causation from animal studies and mechanistic knowledge
• compelling evidence from animal studies and strong mechanistic evidence from
studies in exposed humans, consistent with (i.e., not contradicted by) the
epidemiologic evidence.
Using the Bayesian framework to illustrate the evidential support and the
resulting state of communal scientific opinion needed for reaching the Sufficient
category (and the lower categories that follow), consider again the causal
diagram in Figure AX 1.2-1. In this model, used to help clarify matters
conceptually, the observed association between exposure and health is the result
of: (1) measured confounding, parameterized by a; (2) the causal relation,
parameterized by |3; and (3) other, unmeasured sources such as bias or
unmeasured confounding, parameterized by y. The belief of interest, after all the
evidence has been weighed, is in the size of the causal parameter (3. Thus, for
decision making, what matters is how strongly the evidence supports the
proposition that (3 is above 0. As it is extremely unlikely that the types of
exposures considered for presumptions reduce the risk of developing disease, we
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exclude values of (3 below 0. If we consider the evidence as supporting degrees of
belief about the size of (3, and we have a posterior distribution over the possible
size of (3, then a posterior like Figure AX 1.2-2 illustrates a belief state that might
result when the evidence for causation is considered Sufficient.
Measured
Confounders/Covariates
Exposure >. Health
to Substance 6 Outcome
Unmeasured Confounders/Covariates, or
Other Sources of Spurious Association from Bias
Source: IOM (2007).
Figure AX1.2-1. Focusing on unmeasured confounders/covariates, or other sources of spurious association
from bias.
Equipoise and Above
To be categorized as Equipoise and Above, the scientific community should
categorize the overall evidence as making it more confident in the existence of a
causal relationship than in the non-existence of a causal relationship, but not
sufficient to conclude causation.
For example, if there are several high-quality epidemiologic studies, the
preponderance of which show evidence of an association that cannot readily be
explained by plausible noncausal alternatives (e.g., chance, bias, or
confounding), and the causal relationship is consistent with the animal evidence
and biological knowledge, then the overall evidence might be categorized as
Equipoise and Above. Alternatively, if there is strong evidence from animal
studies or mechanistic evidence, not contradicted by human or other evidence,
then the overall evidence might be categorized as Equipoise and Above.
Equipoise is a common term employed by VA and the courts in deciding
disability claims.
Again, using the Bayesian model to illustrate the idea of Equipoise and Above,
Figure AX 1.2-3 shows a posterior probability distribution that is an example of
belief compatible with the category Equipoise and Above.
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Posterior Mass
Over an Effect
Size of the Causal Effect p
Figure AX1.2-2. Example posterior distribution for the determination of Sufficient.
Posterior Mass
Over an Effect
Size of the Causal Effect p
Source: IOM (2007).
Figure AX1.2-3. Example posterior distribution for the determination of Equipoise and Above.
P(P)
Posterior Over p
Posterior Mass
Over an Effect
Size of the Causal Effect p •
Source: IOM (2007).
Figure AX1.2-4. Example posterior distribution for the determination of Against.
In this figure, unlike the one for evidence classified as Sufficient, there is
considerable mass over zero, which means that the scientific community has
considerable uncertainty as to whether exposure causes disease at all; that is,
whether (3 is greater than zero. At least half of the mass is to the right of the zero,
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however, so the community judges causation to be at least as likely as not, after
they have seen and combined all the evidence available.
Below Equipoise
To be categorized as Below Equipoise, the overall evidence for a causal
relationship should either be judged not to make causation at least as likely as
not, or not sufficient to make a scientifically informed judgment.
This might occur:
• when the human evidence is consistent in showing an association, but the evidence is
limited by the inability to rule out chance, bias, or confounding with confidence, and
animal or mechanistic evidence is weak
• when animal evidence suggests a causal relationship, but human and mechanistic
• evidence is weak or inconsistent
• when mechanistic evidence is suggestive but animal and human evidence is weak or
inconsistent
• when the evidence base is very thin.
Against
To be categorized as Against, the overall evidence should favor belief that there
is no causal relationship from exposure to disease. For example, if there is human
evidence from multiple studies covering the full range of exposures encountered
by humans that are consistent in showing no causal association, or there are is
animal or mechanistic evidence supporting the lack of a causal relationship, and
combining all of the evidence results in a posterior resembling Figure AX 1.2-4
then the scientific community should categorize the evidence as Against
causation.
AX1.2.4. Formulation of Scientific Findings for Policy Purposes
The following guidelines in the form of questions were developed and published in 1991 by the
National Acid Precipitation Assessment Program (NAPAP) Oversight Review Board for NAPAP to assist
scientists in formulating presentations of research results to be used in policy decision processes.
Is the statement sound? Have the central issues been clearly identified? Does each statement
contain the distilled essence of present scientific and technical understanding of the phenomenon
or process to which it applies? Is the statement consistent with all relevant evidence - evidence
developed either through NAPAP research or through analysis of research conducted outside of
NAPAP? Is the statement contradicted by any important evidence developed through research
inside or outside of NAPAP? Have apparent contradictions or interpretations of available
evidence been considered in formulating the statement of principal findings?
Is the statement directional and, where appropriate, quantitative? Does the statement correctly
quantify both the direction and magnitude of trends and relationships in the phenomenon or
process to which the statement is relevant? When possible, is a range of uncertainty given for
each quantitative result? Have various sources of uncertainty been identified and quantified, for
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example, does the statement include or acknowledge errors in actual measurements, standard
errors of estimate, possible biases in the availability of data, extrapolation of results beyond the
mathematical, geographical, or temporal relevancy of available information, etc. In short, are
there numbers in the statement? Are the numbers correct? Are the numbers relevant to the general
meaning of the statement?
Is the degree of certainty or uncertainty of the statement indicated clearly? Have appropriate
statistical tests been applied to the data used in drawing the conclusion set forth in the statement?
If the statement is based on a mathematical or novel conceptual model, has the model or concept
been validated? Does the statement describe the model or concept on which it is based and the
degree of validity of that model or concept?
Is the statement correct without qualification? Are there limitations of time, space, or other
special circumstances in which the statement is true? If the statement is true only in some
circumstances, are these limitations described adequately and briefly?
Is the statement clear and unambiguous? Are the words and phrases used in the statement
understandable by the decision makers of our society? Is the statement free of specialized jargon?
Will too many people misunderstand its meaning?
Is the statement as concise as it can be made without risk of misunderstanding? Are there any
excess words, phrases, or ideas in the statement which are not necessary to communicate the
meaning of the statement? Are there so many caveats in the statement that the statement itself is
trivial, confusing, or ambiguous?
Is the statement free of scientific or other biases or implications of societal value judgments? Is
the statement free of influence by specific schools of scientific thought? Is the statement also free
of words, phrases, or concepts that have political, economic, ideological, religious, moral, or
other personal-, agency-, or organization-specific values, overtones, or implications? Does the
choice of how the statement is expressed rather than its specific words suggest underlying biases
or value judgments? Is the tone impartial and free of special pleading? If societal value judgments
have been discussed, have these judgments been identified as such and described both clearly and
objectively?
Have societal implications been described objectively? Consideration of alternative courses of
action and their consequences inherently involves judgments of their feasibility and the
importance of effects. For this reason, it is important to ask if a reasonable range of alternative
policies or courses of action have been evaluated? Have societal implications of alternative
courses of action been stated in the following general form?
"If this [particular option] were adopted then that [particular outcome] would be
expected."
Have the professional biases of authors and reviewers been described openly? Acknowledgment
of potential sources of bias is important so that readers can judge for themselves the credibility of
reports and assessments.
AX1.2.5. IARC Cancer Guidelines for Scientific Review and Evaluation
The following is excerpted from the International Agency for Research on Cancer (IARC)
Monographs on the evaluation of carcinogenic risks to humans (IARC, 2006).
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The available studies are summarized by the Working Group, with particular
regard to the qualitative aspects discussed below. In general, numerical findings
are indicated as they appear in the original report; units are converted when
necessary for easier comparison. The Working Group may conduct additional
analyses of the published data and use them in their assessment of the evidence;
the results of such supplementary analyses are given in square brackets. When an
important aspect of a study that directly impinges on its interpretation should be
brought to the attention of the reader, a Working Group comment is given in
square brackets.
The scope of the IARC Monographs programme has expanded beyond chemicals
to include complex mixtures, occupational exposures, physical and biological
agents, lifestyle factors and other potentially carcinogenic exposures. Over time,
the structure of a Monograph has evolved to include the following sections:
1. Exposure data
2. Studies of cancer in humans
3. Studies of cancer in experimental animals
4. Mechanistic and other relevant data
5. Summary
6. Evaluation and rationale
In addition, a section of General Remarks at the front of the volume discusses the
reasons the agents were scheduled for evaluation and some key issues the
Working Group encountered during the meeting.
This part of the Preamble discusses the types of evidence considered and
summarized in each section of a Monograph, followed by the scientific criteria
that guide the evaluations.
Evaluation and rationale
Evaluations of the strength of the evidence for carcinogenicity arising from
human and experimental animal data are made, using standard terms. The
strength of the mechanistic evidence is also characterized.
It is recognized that the criteria for these evaluations, described below, cannot
encompass all of the factors that may be relevant to an evaluation of
carcinogenicity. In considering all of the relevant scientific data, the Working
Group may assign the agent to a higher or lower category than a strict
interpretation of these criteria would indicate.
These categories refer only to the strength of the evidence that an exposure is
carcinogenic and not to the extent of its carcinogenic activity (potency). A
classification may change as new information becomes available.
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An evaluation of the degree of evidence is limited to the materials tested, as
defined physically, chemically or biologically. When the agents evaluated are
considered by the Working Group to be sufficiently closely related, they may be
grouped together for the purpose of a single evaluation of the degree of evidence.
(a) Carcinogenicity in humans
The evidence relevant to carcinogenicity from studies in humans is classified into
one of the following categories:
Sufficient evidence of carcinogenicity. The Working Group considers that a
causal relationship has been established between exposure to the agent and
human cancer. That is, a positive relationship has been observed between the
exposure and cancer in studies in which chance, bias and confounding could be
ruled out with reasonable confidence. A statement that there is sufficient evidence
is followed by a separate sentence that identifies the target organ(s) or tissue(s)
where an increased risk of cancer was observed in humans. Identification of a
specific target organ or tissue does not preclude the possibility that the agent may
cause cancer at other sites.
Limited evidence of carcinogenicity. A positive association has been observed
between exposure to the agent and cancer for which a causal interpretation is
considered by the Working Group to be credible, but chance, bias or confounding
could not be ruled out with reasonable confidence.
Inadequate evidence of carcinogenicity. The available studies are of insufficient
quality, consistency or statistical power to permit a conclusion regarding the
presence or absence of a causal association between exposure and cancer, or no
data on cancer in humans are available.
Evidence suggesting lack of carcinogenicity. There are several adequate studies
covering the full range of levels of exposure that humans are known to encounter,
which are mutually consistent in not showing a positive association between
exposure to the agent and any studied cancer at any observed level of exposure.
The results from these studies alone or combined should have narrow confidence
intervals with an upper limit close to the null value (e.g. a relative risk of 1.0).
Bias and confounding should be ruled out with reasonable confidence, and the
studies should have an adequate length of follow-up. A conclusion of evidence
suggesting lack of carcinogenicity is inevitably limited to the cancer sites,
conditions and levels of exposure, and length of observation covered by the
available studies. In addition, the possibility of a very small risk at the levels of
exposure studied can never be excluded.
In some instances, the above categories may be used to classify the degree of
evidence related to carcinogenicity in specific organs or tissues.
When the available epidemiological studies pertain to a mixture, process,
occupation or industry, the Working Group seeks to identify the specific agent
considered most likely to be responsible for any excess risk. The evaluation is
focused as narrowly as the available data on exposure and other aspects permit.
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(b) Carcinogenicity in experimental animals
Carcinogenicity in experimental animals can be evaluated using conventional
bioassays, bioassays that employ genetically modified animals, and other in-vivo
bioassays that focus on one or more of the critical stages of carcinogenesis. In the
absence of data from conventional long-term bioassays or from assays with
neoplasia as the end-point, consistently positive results in several models that
address several stages in the multistage process of carcinogenesis should be
considered in evaluating the degree of evidence of Carcinogenicity in
experimental animals.
The evidence relevant to Carcinogenicity in experimental animals is classified
into one of the following categories:
Sufficient evidence of Carcinogenicity. The Working Group considers that a
causal relationship has been established between the agent and an increased
incidence of malignant neoplasms or of an appropriate combination of benign
and malignant neoplasms in (a) two or more species of animals or (b) two or
more independent studies in one species carried out at different times or in
different laboratories or under different protocols. An increased incidence of
tumours in both sexes of a single species in a well-conducted study, ideally
conducted under Good Laboratory Practices, can also provide sufficient evidence.
A single study in one species and sex might be considered to provide sufficient
evidence of Carcinogenicity when malignant neoplasms occur to an unusual
degree with regard to incidence, site, type of tumour or age at onset, or when
there are strong findings of tumours at multiple sites.
Limited evidence of Carcinogenicity. The data suggest a carcinogenic effect but
are limited for making a definitive evaluation because, e.g. (a) the evidence of
Carcinogenicity is restricted to a single experiment; (b) there are unresolved
questions regarding the adequacy of the design, conduct or interpretation of the
studies; (c) the agent increases the incidence only of benign neoplasms or lesions
of uncertain neoplastic potential; or (d) the evidence of Carcinogenicity is
restricted to studies that demonstrate only promoting activity in a narrow range of
tissues or organs.
Inadequate evidence of Carcinogenicity. The studies cannot be interpreted as
showing either the presence or absence of a carcinogenic effect because of major
qualitative or quantitative limitations, or no data on cancer in experimental
animals are available.
Evidence suggesting lack of Carcinogenicity: Adequate studies involving at least
two species are available which show that, within the limits of the tests used, the
agent is not carcinogenic. A conclusion of evidence suggesting lack of
Carcinogenicity is inevitably limited to the species, tumour sites, age at exposure,
and conditions and levels of exposure studied.
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(c) Mechanistic and other relevant data
Mechanistic and other evidence judged to be relevant to an evaluation of
carcinogenicity and of sufficient importance to affect the overall evaluation is
highlighted. This may include data on preneoplastic lesions, tumour pathology,
genetic and related effects, structure-activity relationships, metabolism and
toxicokinetics, physicochemical parameters and analogous biological agents.
The strength of the evidence that any carcinogenic effect observed is due to a
particular mechanism is evaluated, using terms such as 'weak', 'moderate' or
'strong'. The Working Group then assesses whether that particular mechanism is
likely to be operative in humans. The strongest indications that a particular
mechanism operates in humans derive from data on humans or biological
specimens obtained from exposed humans. The data may be considered to be
especially relevant if they show that the agent in question has caused changes in
exposed humans that are on the causal pathway to carcinogenesis. Such data
may, however, never become available, because it is at least conceivable that
certain compounds may be kept from human use solely on the basis of evidence
of their toxicity and/or carcinogenicity in experimental systems.
The conclusion that a mechanism operates in experimental animals is
strengthened by findings of consistent results in different experimental systems,
by the demonstration of biological plausibility and by coherence of the overall
database. Strong support can be obtained from studies that challenge the
hypothesized mechanism experimentally, by demonstrating that the suppression
of key mechanistic processes leads to the suppression of tumour development.
The Working Group considers whether multiple mechanisms might contribute to
tumour development, whether different mechanisms might operate in different
dose ranges, whether separate mechanisms might operate in humans and
experimental animals and whether a unique mechanism might operate in a
susceptible group. The possible contribution of alternative mechanisms must be
considered before concluding that tumours observed in experimental animals are
not relevant to humans. An uneven level of experimental support for different
mechanisms may reflect that disproportionate resources have been focused on
investigating a favoured mechanism.
For complex exposures, including occupational and industrial exposures, the
chemical composition and the potential contribution of carcinogens known to be
present are considered by the Working Group in its overall evaluation of human
carcinogenicity. The Working Group also determines the extent to which the
materials tested in experimental systems are related to those to which humans are
exposed.
(d) Overall evaluation
Finally, the body of evidence is considered as a whole, in order to reach an
overall evaluation of the carcinogenicity of the agent to humans.
An evaluation may be made for a group of agents that have been evaluated by the
Working Group. In addition, when supporting data indicate that other related
agents, for which there is no direct evidence of their capacity to induce cancer in
1-18
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humans or in animals, may also be carcinogenic, a statement describing the
rationale for this conclusion is added to the evaluation narrative; an additional
evaluation may be made for this broader group of agents if the strength of the
evidence warrants it.
The agent is described according to the wording of one of the following
categories, and the designated group is given. The categorization of an agent is a
matter of scientific judgement that reflects the strength of the evidence derived
from studies in humans and in experimental animals and from mechanistic and
other relevant data.
Group 1: The agent is carcinogenic to humans.
This category is used when there is sufficient evidence of carcinogenicity in
humans. Exceptionally, an agent may be placed in this category when evidence
of carcinogenicity in humans is less than sufficient but there is sufficient evidence
of carcinogenicity in experimental animals and strong evidence in exposed
humans that the agent acts through a relevant mechanism of carcinogenicity.
Group 2.
This category includes agents for which, at one extreme, the degree of evidence
of carcinogenicity in humans is almost sufficient, as well as those for which, at
the other extreme, there are no human data but for which there is evidence of
carcinogenicity in experimental animals. Agents are assigned to either Group 2A
(probably carcinogenic to humans} or Group 2B (possibly carcinogenic to
humans) on the basis of epidemiological and experimental evidence of
carcinogenicity and mechanistic and other relevant data. The terms probably
carcinogenic and possibly carcinogenic have no quantitative significance and are
used simply as descriptors of different levels of evidence of human
carcinogenicity, with probably carcinogenic signifying a higher level of evidence
than possibly carcinogenic.
Group 2A: The agent is probably carcinogenic to humans.
This category is used when there is limited evidence of carcinogenicity in humans
and sufficient evidence of carcinogenicity in experimental animals. In some
cases, an agent may be classified in this category when there is inadequate
evidence of carcinogenicity in humans and sufficient evidence of carcinogenicity
in experimental animals and strong evidence that the carcinogenesis is mediated
by a mechanism that also operates in humans. Exceptionally, an agent may be
classified in this category solely on the basis of limited evidence of
carcinogenicity in humans. An agent may be assigned to this category if it clearly
belongs, based on mechanistic considerations, to a class of agents for which one
or more members have been classified in Group 1 or Group 2A.
Group 2B: The agent is possibly carcinogenic to humans.
This category is used for agents for which there is limited evidence of
carcinogenicity in humans and less than sufficient evidence of carcinogenicity in
experimental animals. It may also be used when there is inadequate evidence of
1-19
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carcinogenicity in humans but there is sufficient evidence of carcinogenicity in
experimental animals. In some instances, an agent for which there is inadequate
evidence of carcinogenicity in humans and less than sufficient evidence of
carcinogenicity in experimental animals together with supporting evidence from
mechanistic and other relevant data may be placed in this group. An agent may
be classified in this category solely on the basis of strong evidence from
mechanistic and other relevant data.
Group 3: The agent is not classifiable as to its carcinogenicity to humans.
This category is used most commonly for agents for which the evidence of
carcinogenicity is inadequate in humans and inadequate or limited in
experimental animals.
Exceptionally, agents for which the evidence of carcinogenicity is inadequate in
humans but sufficient in experimental animals may be placed in this category
when there is strong evidence that the mechanism of carcinogenicity in
experimental animals does not operate in humans.
Agents that do not fall into any other group are also placed in this category.
An evaluation in Group 3 is not a determination of non-carcinogenicity or overall
safety. It often means that further research is needed, especially when exposures
are widespread or the cancer data are consistent with differing interpretations.
Group 4: The agent is probably not carcinogenic to humans.
This category is used for agents for which there is evidence suggesting lack of
carcinogenicity in humans and in experimental animals. In some instances,
agents for which there is inadequate evidence of carcinogenicity in humans but
evidence suggesting lack of carcinogenicity in experimental animals,
consistently and strongly supported by a broad range of mechanistic and other
relevant data, may be classified in this group.
(e) Rationale
The reasoning that the Working Group used to reach its evaluation is presented
and discussed. This section integrates the major findings from studies of cancer
in humans, studies of cancer in experimental animals, and mechanistic and other
relevant data. It includes concise statements of the principal line(s) of argument
that emerged, the conclusions of the Working Group on the strength of the
evidence for each group of studies, citations to indicate which studies were
pivotal to these conclusions, and an explanation of the reasoning of the Working
Group in weighing data and making evaluations. When there are significant
differences of scientific interpretation among Working Group Members, a brief
summary of the alternative interpretations is provided, together with their
scientific rationale and an indication of the relative degree of support for each
alternative.
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AX1.2.6. National Toxicology Program Criteria
The criteria for listing an agent, substance, mixture, or exposure circumstance in the National
Toxicology Program's Report on Carcinogens (NTP, 2005) are as follows:
Known To Be Human Carcinogen:
There is sufficient evidence of carcinogenicity from studies in humans*, which
indicates a causal relationship between exposure to the agent, substance, or
mixture, and human cancer.
Reasonably Anticipated To Be Human Carcinogen:
There is limited evidence of carcinogenicity from studies in humans*, which
indicates that causal interpretation is credible, but that alternative explanations,
such as chance, bias, or confounding factors, could not adequately be excluded,
or
there is sufficient evidence of carcinogenicity from studies in experimental
animals, which indicates there is an increased incidence of malignant and/or a
combination of malignant and benign tumors (1) in multiple species or at
multiple tissue sites, or (2) by multiple routes of exposure, or (3) to an unusual
degree with regard to incidence, site, or type of tumor, or age at onset,
or
there is less than sufficient evidence of carcinogenicity in humans or laboratory
animals; however, the agent, substance, or mixture belongs to a well-defined,
structurally related class of substances whose members are listed in a previous
Report on Carcinogens as either known to be a human carcinogen or reasonably
anticipated to be a human carcinogen, or there is convincing relevant information
that the agent acts through mechanisms indicating it would likely cause cancer in
humans.
Conclusions regarding carcinogenicity in humans or experimental animals are
based on scientific judgment, with consideration given to all relevant
information. Relevant information includes, but is not limited to, dose response,
route of exposure, chemical structure, metabolism, pharmacokinetics, sensitive
sub-populations, genetic effects, or other data relating to mechanism of action or
factors that may be unique to a given substance. For example, there may be
substances for which there is evidence of carcinogenicity in laboratory animals,
but there are compelling data indicating that the agent acts through mechanisms
which do not operate in humans and would therefore not reasonably be
anticipated to cause cancer in humans.
*This evidence can include traditional cancer epidemiology studies, data from clinical studies,
and/or data derived from the study of tissues or cells from humans exposed to the substance in question
that can be useful for evaluating whether a relevant cancer mechanism is operating in people.
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Annex 2. Atmospheric Chemistry of NOx
AX2.1. Introduction
Nitrogen oxides (NOX) along with volatile organic compounds (VOCs) including anthropogenic
and biogenic hydrocarbons, aldehydes, etc. and carbon monoxide (CO) serve as precursors in the
formation of ozone (O3) and other elements of photochemical smog. Nitrogen dioxide is an oxidant and
can further react to form other photochemical oxidants, in particular the organic nitrates, including peroxy
acetyl nitrates (PAN) and higher PAN analogues. It can also react with toxic compounds such as
polycyclic aromatic hydrocarbons (PAHs) to form nitro-PAHs, which may be even more toxic than the
precursors.
The role of NOX in O3 formation was reviewed in Chapter 2 (Section 2.2) of the 2006 AQCD for
Ozone and Other Photochemical Oxidants (U.S. Environmental Protection Agency, 2006), and in
numerous texts (e.g., Seinfeld and Pandis, 1998; Jacob, 2000; Jacobson, 2002). Mechanisms for
transporting O3 precursors, the factors controlling the efficiency of O3 production from NOX, methods for
calculating O3 from its precursors, and methods for measuring NOX were all reviewed in Section 2.6 of
the 2006 O3 AQCD. The main points from those discussions in the 2006 O3 AQCD and updates, based on
new materials will be presented here.
The overall chemistry of reactive nitrogen compounds in the atmosphere is summarized in Figure
AX2.2-1 and is described in greater detail in the following sections. Nitrogen oxides are emitted primarily
as NO with smaller quantities of NO2. Emissions of NOX are spatially distributed vertically with some
occurring at or near ground level (e.g., mobile sources) and others aloft (e.g., electric generating utility
(EGUs) stacks) as indicated in Figure AX2.2-1. Because of atmospheric chemical reactions, the relative
abundance of different compounds contributed by different sources varies with location. Both
anthropogenic and natural (biogenic) processes emit NOX. In addition to gas phase reactions, multiphase
processes are important for forming aerosol-phase pollutants, including aerosol NO3".
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Long range transport to remote
regions at low temperatures
NH
X HNO,, „ f
nit rosa mines,
nitre-phenols, etc. |
I \°O >
emissions
Figure AX2.2-1. Schematic diagram of the cycle of reactive nitrogen species in the atmosphere. MPP refers
to multi-phase process; h< to a photon of solar energy.
AX2.2. Chemistry of NOxin the Troposphere
AX2.2.1. Basic Chemistry
There is a rapid photochemical cycle in the troposphere that involves photolysis of NO2 by solar
UV-A radiation to yield NO and a ground-state oxygen atom, O(3P)
NO2+hv->NO+O(3P)
(AX2.2-1)
This ground-state oxygen atom can then combine with molecular oxygen (O2) to form O3; and,
colliding with any molecule from the surrounding air (M= N2, O2, etc.), the newly formed O3 molecule
transfers excess energy and is stabilized
(AX2.2-2)
where M = N2, O2. Reaction AX2.2-2 is the only significant reaction forming O3 in the troposphere.
NO and O3 react to reform NO2
NO + O3 -» NO 2 + O2
(AX2.2-3)
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Reaction AX2.2-3 is responsible for O3 decreases and NO2 increases found near sources of NO
(e.g., highways). The falloff of NO2 from a road depends on wind speed and direction and the local
structure of turbulent mixing, temperature (through the temperature dependence of Reaction AX2.2-3)
and the amount of sunlight (through Reaction AX2.2-1). Oxidation of reactive VOCs leads to formation
of reactive radical species that allow the conversion of NO to NO2 without participation of O3 as in
Reaction AX2.2-3
N0 N02 (AX2 M
O3, therefore, can accumulate as NO2 photolyzes as in Reaction AX2.2-1, followed by Reaction
AX2.2-2. Specific mechanisms for the oxidation of a number of VOCs were discussed in the O3 AQCD
(U.S. Environmental Protection Agency, 2006).
It is often convenient to speak about families of chemical species defined in terms of members that
interconvert rapidly among themselves on time scales that are shorter than those for formation or
destruction of the family as a whole. For example, an "odd oxygen" (Ox) family can be defined as
Ox = I,(0(3P) + 0((D) + 03 + N02)
In much the same way, NOX is sometimes referred to as "odd nitrogen". Hence, we see that
production of Ox occurs by the schematic Reaction AX2.2-5, and that the sequence of reactions given by
reactions AX2.2-1 through AX2.2-3 represents no net production ofOx. Definitions of species families
and methods for constructing families are discussed in Jacobson (1999) and references therein. Other
families that include nitrogen-containing species, and which will be referred to later in this chapter, are:
NOZ = I HNO3 + HNO4 + NO3 + 2NO2O5 + PAN(CH3CHO - OO - NO2) + other
organic nitrates + halogen nitrates + paniculate nitrate)
NOY = NOX + NOZ + MONO;
andNHx =
(AX2.2-6)
In this equation, NOZ refers to the sum of the oxidation products ofNOx.
The reaction of NO2 with O3 leads to the formation of NO3" radical
However, because the NO3 radical photolyzes rapidly (lifetime of ~5 s during the photochemically
most active period of the day around local solar noon (Atkinson et al., 1992),
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its concentration remains low during daylight hours, but can increase after sunset to nighttime
concentrations of <5 x 107 to 1 x 1010 molecules/cm"3 (<2 to 430 parts per trillion (ppt)) over continental
areas influenced by anthropogenic emissions of NOX (Atkinson et al., 1986). At night, NO3, rather than
the hydroxyl radical (OH), is the primary oxidant in the system.
Nitrate radicals can combine with NO2 to form dinitrogen pentoxide (N2O5)
2] (AX22.9)
and N2O5 both photolyzes and thermally decomposes back to NO2 and NO3 during the day; however,
N2O5 concentrations can accumulate during the night to parts per billion (ppb) levels in polluted urban
atmospheres.
The tropospheric chemical removal processes for NOX include reaction of NO2 with the OH radical
and hydrolysis of N2O5 in aqueous aerosol solutions if there is no organic coating. Both of these reactions
produce HNO3
(AX22.10)
The gas-phase reaction of OH radical with NO2 (Reaction AX2.2-1 1) initiates one of the major and
ultimate removal processes for NOX in the troposphere. This reaction removes OH and NO2 radicals and
competes with hydrocarbons for OH radicals in areas characterized by high NOX concentrations, such as
urban centers (see Section AX2.2.2). The timescale (T) for conversion of NOX to HNO3 in the planetary
boundary layer at 40 N latitude ranges from about 4 h in July to about 16 h in January. The corresponding
range in T at 25 N latitude is between 4 and 5 h, while at 50 N latitude, HNO3 T ranges from about 4 to 20
h (Martin et al., 2003). In addition to gas-phase HNO3, Golden and Smith (2000) have shown on the basis
of theoretical studies that pernitrous acid (HOONO) is also produced by the reaction of NO2 and OH
radicals. However, this channel of production most likely represents a minor yield approximately 15% at
the surface (Jet Propulsion Laboratory, 2003).
Pernitrous acid will thermally decompose and can photolyze. Gas-phase HNO3 formed from
Reactions AX2.2-10 and AX2.2-1 1 undergoes wet and dry deposition to the surface, and uptake by
ambient aerosol particles. Reaction AX2.2-10 limits NOX T to a range of hours to days. Geyer and Platt
(2002) concluded that Reaction AX2.2-10 constituted about 10% of the removal of NOX at a site near
Berlin, Germany during spring and summer. However, other studies found a larger contribution to HNO3
production from Reaction AX2.2-10. Dentener and Crutzen (1993) estimated 20% in summer and 80% of
HNO3 production in winter is from Reaction AX2.2-10. Tonnesen and Dennis (2000) found 16 to 3 1% of
summer HNO3 production was from Reaction AX2.2-10. The contribution of Reaction AX2.2-1 1 to
HNO3 formation is highly uncertain during both winter and summer. The importance of Reaction AX2.2-
1 1 could be much higher during winter than during summer because of the much lower concentration of
OH radicals and the enhanced stability of N2O5 due to lower temperatures and less sunlight. Note that
Reaction AX2.2-1 1 proceeds as a heterogeneous reaction. Recent work in the northeastern United States
indicates that this reaction proceeds at a faster rate in power plant plumes than in urban plumes (Brown
et al., 2006a,b; Frost et al., 2006).
In addition to the uptake of HNO3 on particles and in cloud drops, it photolyzes and reacts with OH
radicals via
2-4
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and
HNO3 + OH^> NO3 + H2O
(AX2.2-13)
The lifetime of HNO3 with respect to these two reactions is long enough for HNO3 to act as a
reservoir species for NOX during long-range transport.
OH radicals also can react with NO to produce nitrous acid (HONO or HNO2)
(AX2.2-14)
In the daytime, HNO2 is rapidly photolyzed back to the original reactants.
(AX2.2-15)
Reaction AX2.2-15 is, however, a negligible source of HONO, which is formed mainly by
multiphase processes (see Section AX2.2.3). At night, heterogeneous reactions of NO2 in aerosols or at
the earth's surface result in accumulation of HONO (Lammel and Cape, 1996; Jacob, 2000; Sakamaki
et al., 1983; Pitts et al., 1984; Svensson et al., 1987; Jenkin et al., 1988; Lammel and Perner, 1988;
Notholt et al., 1992a,b). Harris et al. (1982) suggested that photolysis of this HNO2 at sunrise could
provide an important early-morning source of OH radicals to drive O3 formation.
Hydroperoxy (HO2) radicals can react with NO2 to produce pernitric acid (HNO4)
HO 2 + N02 + M^ HN04 + M (AX2.2-16)
which then can thermally decompose and photolyze back to its original reactants. The acids formed in
these gas-phase reactions are all water soluble. Hence, they can be incorporated into cloud drops and in
the aqueous phase of particles.
Although the lifetimes of HNO4 and N2O5 are short (minutes to hours) during typical summer
conditions, they can be much longer at the lower temperatures and darkness found during the polar night.
Under these conditions, species such as PAN, HNO3, HNO4, and N2O5 serve as NOX reservoirs that can
liberate NO2 upon the return of sunlight during the polar spring.
A broad range of organic nitrogen compounds can be directly emitted by combustion sources or
formed in the atmosphere from NOX emissions. Organic nitrogen compounds include the PANs,
nitrosamines, nitro-PAHs, and the more recently identified nitrated organics in the quinone family.
Oxidation of VOCs produces organic peroxy radicals (RO2), as discussed in the 2006 AQCD for Ozone.
Reaction of RO2 radicals with NO and NO2 produces organic nitrates (RONO2) and peroxynitrates
(RO2NO2)
Reaction (AX2.2-17) is a minor branch for the reaction of RO2 with NO; the major branch involves
NO2 as in Reaction AX2.2-18. Note, however, that organic nitrate yields increase with carbon number
(Atkinson, 2000).
The most important of these organic nitrates is PAN, the dominant member of the broader family of
peroxyacylnitrates which includes peroxypropionyl nitrate (PPN) of anthropogenic origin and
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peroxymethacrylic nitrate (MPAN) produced from isoprene oxidation. The PANs are formed by the
combination reaction of acetyl peroxy radicals with NO2
CH3C(0)-00 + NO 2 -> CH3C(0)OON02 (AX2.2-19)
where the acetyl peroxy radicals are formed mainly during the oxidation of ethane (C2H6). Acetaldehyde
(CH3CHO) is formed as an intermediate species during the oxidation of ethane. Acetaldehyde can be
photolyzed or react with OH radicals to yield acetyl radicals.
CH3-C(0)H + hv^ CH3-C(0) + H (AX2.2-20)
CH3-C(0)H + OH^ CH3-C(0} + H2O (AX2.2-21)
Acetyl radicals then react with O2 to yield acetyl peroxy radicals.
CH3-C(0) + 02 + M-+ CH3C(0)-00 + M (AX2.2-22)
However, acetyl peroxy radicals will react with NO in areas of high NO concentrations
CH3(CO)-00 + N0^ CH3(CO)-0 + NO2 (AX2.2-23)
and the acetyl-oxy radicals will then decompose
CH3(CO)-0 -> CH, + C02
Thus, the formation of PAN is favored at conditions of high ratios of NO2 to NO which are most
typically found under low NOX conditions. The PANs both thermally decompose and photolyze back to
their reactants on timescales of a few hours during warm sunlit conditions, having lifetimes with respect
to thermal decomposition ranging from ~1 hour at 298 K to -2.5 days at 273 K, up to several weeks at
250 K. Thus, they can provide an effective sink of NOX at cold temperatures and high solar zenith angles,
allowing release of NO2 as air masses warm, in particular by subsidence. The PANs are also removed by
uptake to vegetation (Sparks et al., 2003; Teklemariam and Sparks, 2004).
The organic nitrates may react further, depending on the functionality of the R group, but they will
typically not return NOX and can therefore be viewed mainly as a permanent sink for NOX as alkyl
nitrates are sparingly soluble and will photolyze. This sink is usually small compared to HNO3 formation
although the formation of isoprene nitrates may be a significant sink for NOX in the United States in
summer (Liang et al., 1998).
The peroxynitrates produced by AX2.2-19 are thermally unstable and most have very short
lifetimes (less than a few minutes) owing to thermal decomposition back to the original reactants. They
are thus not effective sinks of NOX.
AX2.2.2. NOx Concentrations and Os Formation
Ozone is unlike some other species whose rates of formation vary directly with the emissions of
their precursors in that O3 production (P(O3)) changes nonlinearly with the concentrations of its
precursors. At the low NOX concentrations found in most environments ranging from remote continental
areas to rural and suburban areas downwind of urban centers, the net production of O3 increases with
2-6
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increasing NOX. At the high NOX concentrations found in downtown metropolitan areas especially near
busy streets and roadways and in power plant plumes, there is net destruction of O3 by (titration) reaction
with NO. Between these two regimes is a transition stage in which O3 shows only a weak dependence on
NOX concentrations. In the high NOX regime, NO2 scavenges OH radicals which would otherwise oxidize
VOCs to produce peroxy radicals, which in turn would oxidize NO to NO2. In the low NOX regime, VOC
oxidation generates, or at least does not consume, free radicals, and O3 production varies directly with
NOX. Sometimes the terms 'VOC-limited' and 'NOx-limited' are used to describe these two regimes;
also, the terms NOx-limited and NOx-saturated are used, e.g., by Jaegle et al., 2001. The chemistry of OH
radicals, which are responsible for initiating the oxidation of hydrocarbons, shows behavior similar to that
for O3 with respect to NOX concentrations (Hameed et al., 1979; Pinto et al., 1993; Poppe et al., 1993;
Zimmerman and Poppe, 1993). These considerations introduce a high degree of uncertainty into attempts
to relate changes in O3 concentrations to emissions of precursors. It should also be noted at the outset that
in a NOx-limited (or NOx-sensitive) regime, O3 formation is not insensitive to radical production or the
flux of solar UV photons, just that O3 formation is more sensitive to NOX. For example, global
tropospheric O3 is sensitive to the concentration of CH4 even though the troposphere is predominantly
NOx-limited.
Various analytical techniques have been proposed that use ambient NOX and VOC measurements to
derive information about O3 production and O3-NOX-VOC sensitivity. Previously (e.g., National Research
Council, 1991), it was suggested that O3 formation in individual urban areas could be understood in terms
of measurements of ambient NOX and VOC concentrations during the early morning. In this approach, the
ratio of summed VOC to NOX concentrations (unweighted by chemical reactivity) is used to determine
whether conditions are NOx-sensitive or VOC sensitive. This technique is inadequate to characterize O3
formation because it omits many factors recognized as important for P(O3), including: the effect of
biogenic VOCs (which are not present in urban centers during early morning); important individual
differences in the ability of VOCs to generate free radicals, rather than just from total VOC concentration
and other differences in O3-forming potential for individual VOCs (Carter, 1995); the effect of multiday
transport; and general changes in photochemistry as air moves downwind from urban areas (Milford
etal., 1994).
Jacob et al. (1995) used a combination of field measurements and a chemical transport model
(CTM) to show that the formation of O3 changed from NOx-limited to NOx-saturated as the season
changed from summer to fall at a monitoring site in Shenandoah National Park, VA. Photochemical
production of O3 generally occurs together with production of other species including HNO3, organic
nitrates, and hydrogen peroxide (H2O2). The relative rates of P(O3) and the production of other species
varies depending on photochemical conditions and can be used to provide information about O3-precursor
sensitivity.
There are no hard and fast rules governing the levels of NOX at which the transition from NOX-
limited to NOx-saturated conditions occurs. The transition between these two regimes is highly spatially
and temporally dependent. In the upper troposphere, responses to NOX additions from commercial aircraft
have been found which are very similar to these in the lower troposphere (Briihl et al., 2000). Briihl et al.
(2000) found that the NOX levels for O3 production versus loss are highly sensitive to the radical sources
included in model calculations. They found that inclusion of only CHt and CO oxidation leads to a
decrease in net O3 production in the North Atlantic flight corridor due to NO emissions from aircraft.
However, the additional inclusion of acetone photolysis was found to shift the maximum in O3 production
to higher NOX mixing ratios, thereby reducing or eliminating areas in which O3 production rates
decreased due to aircraft emissions.
Trainer et al. (1993) suggested that the slope of the regression line between O3 and NOZ can be used
to estimate the rate of P(O3) per NOX ,also known as the O3 production efficiency or OPE. Ryerson et al.
(1998, 2001) used measured correlations between O3 and NOZ to identify different rates of O3 production
in plumes from large point sources.
Sillman (1995) and Sillman and He (2002) identified several secondary reaction products that show
different correlation patterns for NOx-limited conditions and NOx-saturated conditions. The most
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important correlations are for O3 versus NOY, O3 versus NOZ, O3 versus HNO3, and H2O2 versus HNO3.
The correlations between O3 and NOY, and O3 and NOZ are especially important because measurements
of NOY and NOX are widely available. Measured O3 versus NOZ (Figure AX2.2-2) shows distinctly
different patterns in different locations. In rural areas and in urban areas such as Nashville, TN, O3 shows
a strong correlation with NOZ and a relatively steep slope to the regression line. By contrast, in Los
Angeles O3 also increases with NOZ, but the rate of increase of O3 with NOZ is lower and the O3
concentrations for a given NOZ value are generally lower.
X
X
X
X
X
X X
X
X X
X
10
20
NOZ (ppb)
30
40
Figure AX2.2-2. Measured values of 03 and NOZ (NOY - NOX). Measured during the afternoon at rural sites in
the eastern United States (gray circle) and in urban areas and urban plumes associated with
Nashville, TN (gray dash), Paris, FR (black diamond) and Los Angeles, CA (X)
The difference between NOx-limited and NOx-saturated regimes is also reflected in measurements
of H2O2. Formation of H2O2 takes place by self-reaction of photochemically generated HO2 radicals, so
that there is large seasonal variation of H2O2 concentrations, and values in excess of 1 ppb are mainly
limited to the summer months when photochemistry is more active (Kleinman, 1991). Hydrogen peroxide
is produced in abundance only when O3 is produced under NOx-limited conditions. When the
photochemistry is NOx-saturated, much less H2O2 is produced. In addition, increasing NOX tends to slow
the formation of H2O2 under NOx-limited conditions. Differences between these two regimes are also
related to the preferential formation of sulfate during summer and to the inhibition of sulfate and
hydrogen peroxide during winter (Stein and Lamb, 2003). Measurements in the rural eastern United
States (Jacob et al., 1995), at Nashville (Sillman et al., 1998), and at Los Angeles (Sakugawa and Kaplan,
1989) show large differences in H2O2 concentrations likely due to differences in NOX availability at these
locations.
AX2.2.3. Multiphase Chemistry Involving NOx
Recent laboratory studies on sulfate and organic aerosols indicate that the reaction probability
yN2O5 is in the range of 0.01 to 0.05 (Kane et al., 2001; Hallquist et al., 2003; Thornton et al., 2003). Tie
2-8
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et al. (2003) found that a value of 0.04 in their global model gave the best simulation of observed NOX
concentrations over the Arctic in winter.
Using aircraft measurements over the northeastern United States, Brown et al. (2006b) found that
the uptake coefficient for N2O5, yN2O5, on the surfaces of particles depends strongly on their sulfate
content. They found that yN2O5 was highest (0.017) in regions where the aerosol sulfate concentration
was highest and lower elsewhere (<0.0016). This result contrasts with that of Dentener and Crutzen
(1993) who concluded that yN2O5 would be independent of aerosol composition, based on a value for
yN2O5 of 0.1, implying that the heterogeneous hydrolysis of N2O5 would be saturated for typical ambient
aerosol surface areas. The importance of this reaction to tropospheric chemistry depends on the value of
yN2O5. If it is 0.01 or lower, there may be difficulty explaining the loss of NOY and the formation of
aerosol nitrate, especially during winter. A decrease in N2O5 slows down removal of NOX by leaving
more NO2 available for reaction and thus increases O3 production. Based on the consistency between
measurements of NOY partitioning and gas-phase models, Jacob (2000) considered it unlikely that HNO3
is recycled to NOX in the lower troposphere in significant concentrations. However, only one of the
reviewed studies (Schultz et al., 2000) was conducted in the marine troposphere and none was conducted
in the MBL. An investigation over the equatorial Pacific reported discrepancies between observations and
theory (Singh et al., 1996) which might be explained by HNO3 recycling. It is important to recognize that
both Schultz et al. (2000) and Singh et al. (1996) involved aircraft sampling at altitude which, in the
MBL, can significantly under-represent sea salt aerosols and thus most total NO3 (defined to be HNO3 +
NO3") and large fractions of NOY in marine air (see Huebert et al., 1996). Consequently, some caution is
warranted when interpreting constituent ratios and NOY budgets based on such data.
Recent work in the Arctic has quantified significant photochemical recycling of NO3" to NOX and
attendant perturbations of OH chemistry in snow (Honrath et al., 2000; Dibb et al., 2002; Domine and
Shepson, 2002) which suggest the possibility that similar multiphase pathways could occur in aerosols.
As mentioned above, NO3" is photolytically reduced to NO2" (Zafiriou and True, 1979) in acidic sea salt
solutions (Anastasio et al., 1999). Further photolytic reduction of NO2" to NO (Zafiriou and True, 1979)
could provide a possible mechanism for HNO3 recycling. Early experiments reported production of NOX
during the irradiation of artificial seawater concentrates containing NO3" (Petriconi and Papee, 1972).
Based on the above, HNO3 recycling in sea salt aerosols is potentially important and warrants further
investigation. Other possible recycling pathways involving highly acidic aerosol solutions and soot are
reviewed by Jacob (2000).
Stemmler et al. (2006) studied the photosensitized reduction of NO2 to HONO on humic acid films
using radiation in the UV-A through the visible spectral regions. They also found evidence for reduction
occurring in the dark, reactions which may occur involving surfaces containing partly oxidized aromatic
structures. For example, Simpson et al. (2006) found that aromatic compounds constituted -20% of
organic films coating windows in downtown Toronto. They calculated production rates of HONO that are
compatible with observations of high HONO levels in a variety of environments. The photolysis of
HONO formed this way could account for up to 60% of the integrated source of OH radicals in the inner
planetary boundary layer. A combination of high NO2 levels and surfaces of soil and buildings and other
man-made structures exposed to diesel exhaust would then be conducive to HONO formation and, hence,
to high OH concentrations.
Ammann et al. (1998) reported the efficient conversion of NO2 to HONO on fresh soot particles in
the presence of water. They suggested that interaction between NO2 and soot particles may account for
high concentrations of HONO observed in urban environments. Conversion of NO2 to HONO and the
subsequent photolysis of HONO to NO + OH would constitute a NOx-catalyzed O3 sink involving snow.
High concentrations of HONO can lead to the rapid growth in OH concentrations shortly after sunrise,
giving a "jump start" to photochemical smog formation. Prolonged exposure to ambient oxidizing agents
appears to deactivate this process. Broske et al. (2003) studied the interaction of NO2 on secondary
organic aerosols and concluded that the uptake coefficients were too low for this reaction to be an
important source of HONO in the troposphere.
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Choi and Leu (1998) evaluated the interactions of HNO3 on model black carbon soot (FW2),
graphite, hexane, and kerosene soot. They found that HNO3 decomposed to NO2 and H2O at higher HNO3
surface coverages, i.e., P(HNO3) >1CT4 Torr. None of the soot models used were reactive at low HNO3
coverages, at P(HNO3) = 5 x 1CT7 Torr or at temperatures below 220 K. They concluded that it is unlikely
that aircraft soot in the upper troposphere/lower stratosphere reduces HNO3.
Heterogeneous production on soot at night is believed to be the mechanism by which HONO
accumulates to provide an early morning source of HOX in high NOX environments (Harrison et al., 1996;
Jacob, 2000). HONO has been frequently observed to accumulate to levels of several ppb overnight, and
this has been attributed to soot chemistry (Harris et al., 1982; Calvert et al., 1994; Jacob, 2000).
Longfellow et al. (1999) observed the formation of HONO when methane, propane, hexane, and
kerosene soots were exposed to NO2. They suggested that this reaction may account for some part of the
unexplained high levels of HONO observed in urban areas. They commented that without details about
the surface area, porosity, and amount of soot available for this reaction, reactive uptake values cannot be
estimated reliably. They further commented that soot and NO2 are produced in close proximity during
combustion, and that large quantities of HONO have been observed in aircraft plumes.
Saathoff et al. (2001) studied the heterogeneous loss of NO2, HNO3, NO3/N2O5, HO2/HO2NO2 on
soot aerosol using a large aerosol chamber. Reaction periods of up to several days were monitored and
results used to fit a detailed model. Saathoff et al. derived reaction probabilities at 294 K and 50% relative
humidity (RH) for NO2, NO3, HO2, and HO2NO2 deposition to soot; HNO3 reduction to NO2; and N2O5
hydrolysis. When these probabilities were included in photochemical box model calculations of a 4-day
smog event, the only noteworthy influence of soot was a 10% reduction in the second day O3 maximum,
for a soot loading of 20 (ig/m3, i.e., roughly a factor of 10 times observed black carbon loadings seen in
U.S. urban areas, even during air pollution episodes.
Muiioz and Rossi (2002) conducted Knudsen cell studies of HNO3 uptake on black and grey decane
soot produced in lean and rich flames, respectively. They observed HONO as the main species released
following HNO3 uptake on grey soot, and NO and traces of NO2 from black soot. They concluded that
these reactions would only have relevance in special situations in urban settings where soot and HNO3 are
present in high concentrations simultaneously.
AX2.2.4. Formation of Nitro-PAHs
Nitro-polycyclic aromatic hydrocarbons (nitro-PAHs) (see Figure AX2.2-3 for some example nitro-
PAHs) are generated from incomplete combustion processes through electrophilic reactions of poly cyclic
aromatic hydrocarbons (PAHs) in the presence of NO2 (International Agency for Research on Cancer
[IARC], 1989; World Health Organization [WHO], 2003). Among combustion sources, diesel emissions
have been identified as the major source of nitro-PAHs in ambient air (Bezabeh et al., 2003; Gibson,
1983; Schuetzle, 1983; Tokiwa and Ohnishi, 1986). Direct emissions of nitro-PAHs in PM vary with type
of fuel, vehicle maintenance, and ambient conditions (Zielinska et al., 2004).
2-nitronaphthalene 9-nitroanthracene 2-nitrofluoranthene 6-nitrobenzo(a)pyrene
Figure AX2.2-3. Structures of same nitro-polycyclic aromatic hydrocarbons.
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In addition to being directly emitted, nitro-PAHs can also be formed from both gaseous and
heterogeneous reactions of PAHs with gaseous nitrogenous pollutants in the atmosphere (Arey et al.,
1986, 1989, Arey, 1998; Perrini, 2005; Pitts, 1987; Sasaki etal., 1997; Zielinskaet al., 1989). Different
isomers of nitro-PAHs are formed through different formation processes. For example, the most abundant
nitro-PAH in diesel particles is 1-nitropyene (1NP), followed by 3-nitrofluoranthene (3NF) and 8-
nitrofluoranthene (8NF) (Bezabeh et al., 2003; Gibson, 1983; Schuetzle, 1983; Tokiwa and Ohnishi,
1986). However, in ambient particulate organic matter (POM), 2-nitrofluoranthene (2NF) is the dominant
compound, followed by 1NP and 2-nitropyrene (2NP) (Arey et al., 1989; Bamford et al., 2003; Reisen
and Arey, 2005; Zielinska et al., 1989), although 2NF and 2NP are not directly emitted from primary
combustion emissions. The reaction mechanisms for the different nitro-PAH formation processes have
been well documented and are presented in Figure AX2.2-4.
The dominant process for the formation of nitro-PAHs in the atmosphere is gas-phase reaction of
PAHs with OH radicals in the presence of NOX (Arey et al., 1986, Arey, 1998; Atkinson and Arey, 1994;
Ramdahl et al., 1986; Sasaki et al., 1997). Hydroxyl radicals can be generated photochemically or at night
through ozone-alkene reactions, (Finlayson-Pitts and Pitts, 2000). The postulated reaction mechanism of
OH with PAHs involves the addition of OH at the site of highest electron density of the aromatic ring, for
example, the 1-position for pyrene (PY) and the 3-position for fluoranthene (FL). This reaction is
followed by the addition of NO2 to the OH-PAH adduct and elimination of water to form the nitroarenes
(Figure AX2.2-4) (Arey et al., 1986; Atkinson et al., 1990; Pitts, 1987). After formation, nitro-PAHs with
low vapor pressures (such as 2NF and 2NP) immediately migrate to particles under ambient conditions
(Fan et al., 1995; Feilberg et al., 1999). The second order rate-constants for the reactions of OH with most
PAHs range from 10"10 to 10"12 cm3/molecule/s at 298 K with the yields ranging from -0.06 to -5%
(Atkinson and Arey, 1994). 2NF and 2NP have been found as the most abundant nitro-PAHs formed via
reactions of OH with gaseous FL and PY, respectively, in ambient air.
OH
OH
OH
2NP
Figure AX2.2-4. Formation of 2-nitropyrene (2NP) from the reaction of OH with gaseous pyrene (PY).
The second important process for the formation of nitro-PAHs in the atmosphere is the nitration of
PAHs by NO3" in the presence of NOX at night (Atkinson et al., 1990; Atkinson and Arey, 1994; Sasaki
et al., 1997). Nitrate radicals can be generated by reaction of ozone (O3) with NO2 in the atmosphere by
Reaction AX2.2-25.
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0} + N02 -> NO, + 02
Similar to the mechanism of OH reactions with PAHs, NO3 initially adds to the PAH ring to form
an NO3-PAH adduct, followed by loss of HNO3 to form nitro-PAHs (Atkinson et al., 1990; Atkinson and
Arey, 1994; Sasaki et al., 1997). For example, in the mixture of naphthalene and N2O5-NO3-NO2, the
major products formed through the NO3 reaction are 1- and 2-nitro-naphthalene (INN and 2NN)
(Atkinson et al., 1990; Feilberg et al., 1999; Sasaki et al., 1997). 2NF and 4NP were reported as the
primary products of the gas-phase reactions of FL and PY with NO3 radical, respectively (Atkinson et al.,
1990; Atkinson and Arey, 1994).
The reaction with NO3 is of minor importance in the daytime because NO3 radical is not stable in
sunlight. In addition, given the rapid reactions of NO with NO3 and with O3 in the atmosphere (Finlayson-
Pitts and Pitts 2000), concentrations of NO3 at ground level are low during daytime. However, at night,
concentrations of NO3 radicals formed in polluted ambient air are expected to increase. According to
Atkinson (1991), the average NO3 concentration is about 20 ppt in the lower troposphere at night and can
be as high as 430 ppt. It is also worth noting that significant NO3 radical concentrations are found at
elevated altitudes where O3 is high but NO is low (Reissell and Arey, 2001; Stutz et al., 2004a). When
NO3 reaches high concentrations, the formation of nitro-PAHs by the reaction of gaseous PAHs with NO3
may be of environmental significance. At 10"17 to 10"18 cm3 /molecule/s, the rate constants of NO3 with
most PAHs are several orders of magnitude lower than those of OH with the same PAHs; however, the
yields of nitro-PAHs from NO3 reactions are generally much higher than those of OH reactions. For
example, the yields of 1-NN and 2NF are 0.3% and 3%, respectively from OH reactions, but the yields
are 17% and 24% for these two compounds generated from the NO3 radical reactions (Atkinson and Arey,
1994). Therefore, formation of nitro-PAHs via reactions of NO3 at nighttime under certain circumstances
can be significant.
The third process of nitro-PAH formation in the atmosphere is nitration of PAHs by NO2/N2O5 in
the presence of trace amounts of HNO3 in both gas and particle phases. This mechanism could be
operative throughout the day and night (Pitts, 1983, 1985a,b; Grosjean et al., 1983; Ramdahl et al., 1984;
Kamens et al., 1990). The formation of nitro-fluoranthenes was observed when adsorbed FL was exposed
to gaseous N2O5, and the distribution of product NF isomers was 3- > 8- > 7- > 1- NF (Pitts et al.,
1985a,b). The proposed mechanism for this reaction was an ionic electrophilic nitration by nitronium ion
(NO2+). It was speculated that N2O5 became ionized prior to the reaction with FL (Zielinska et al., 1986).
Only 1NP was observed for the reaction of PY with N2O5 on filters (Pitts et al., 1985b). Compared to the
reactions of OH and NO3, nitration of PAHs by NO2/N2O5 is less important.
Measurements of nitro-PAHs in ambient air provide evidence for the proposed reaction mechanism,
i.e. the reactions of OH and NO3 radicals with PAHs are the major sources of nitro-PAHs (Bamford and
Baker, 2003; Reisen and Arey, 2005; and references therein). 2NF is a ubiquitous component of ambient
POM, much higher than 1NP, itself a marker of combustion sources. Nitro-PAH isomer ratios show
strong seasonality. For instance, the mean ratios of 2NF/1NP were higher in summer than in winter
(Bamford et al., 2003; Reisen and Arey, 2005), indicating that reactions of OH and NO3 with FL are the
major sources of nitro-PAHs in ambient air in summer. The ratio of 2NF/1NP was lower in winter than in
summer because of lower OH concentrations and, therefore, less production of 2NF via atmospheric
reactions. A ratio of 1NP/2NF greater than 1 was observed in locations with major contributions from
vehicle emissions (Dimashki et al., 2000; Feilberg et al., 2001). In addition, the ratio of 2NF/2NP was
also used to evaluate the contribution of OH and NO3 initiated reactions to the ambient nitro-PAHs
(Bamford et al., 2003; Reisen and Arey, 2005).
The concentrations for most nitro-PAHs found in ambient air are much lower than 1 pg/m3, except
NNs, 1NP, and 2NF, which can be present at several pg/m3. These levels are much lower (~2 to -1000
times lower) than their parent PAHs. However, nitro-PAHs are much more toxic than PAHs (Durant
et al., 1996; Grosovsky et al., 1999; Salmeen et al., 1982; Tokiwa et al., 1998; Tokiwa and Ohnishi,
1986). Moreover, most nitro-PAHs are present in particles with a mass median diameter <0.1 (im.
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Esteve et al. (2006) examined the reaction of gas-phase NO2 and OH radicals with various PAHs
adsorbed onto model diesel particulate matter (SRM 1650a, NIST). Using pseudo second order rate
coefficients, they derived lifetimes for conversion of the particle -bound PAHs to nitro-PAHs of a few
days (for typical urban NO2 levels of 20 ppb) . They also found that the rates of reaction of OH with the
PAHs were about four orders of magnitude larger than for the reactions involving NO2. However, since
the concentrations of NO2 used above are more than four orders of magnitude larger than those for OH
(106-107/cm3), they concluded that the pathway involving NO2 is expected to be favored over that
involving OH radicals. Consistent with the importance of the gas-phase formation of NPAHS, both the
mutagenic potency of PM and the content of NPAHs in PM vary by particle size, and are higher in the
submicron size range (Xu and Lee, 2000; Kawanaka et al., 2004).
The major loss process of nitro-PAHs is photodecomposition (Fan et al., 1996; Feilberg et al.,
1999; Feilberg and Nielsen, 2001), with lifetimes on the order of hours. However, lacking direct UV light
sources indoors, nitro-PAHs are expected have a longer lifetimes (days) indoors than outdoors; and may
therefore pose increased health risks. Many nitro-PAHs are semi- or nonvolatile organic compounds. As
stated above, indoor environments have much greater surface areas than outdoors. Thus, it is expected
that gas/particle distribution of nitro-PAHs indoors will be different from those in ambient air. A
significant portion of nitro-PAHs will probably be adsorbed by indoor surfaces, such as carpets, leading
to different potential exposure pathways to nitro-PAHs in indoor environments. The special
characteristics of indoor environments, which can affect the indoor chemistry and potential exposure
pathways significantly, should be taken into consideration when conducting exposure studies of nitro-
PAHs.
Reaction with OH and NO3 radicals is a major mechanism for removing gas-phase PAHs, with OH
radical initiated reactions predominating depending on season (Vione et al., 2004; Bamford et al., 2003).
Particle-bound PAH reactions occur but tend to be slower. Nitronaphthalenes tend to remain in the vapor
phase, but because phase partitioning depends on ambient temperature, in very cold regions these species
can condense (Castells et al., 2003) while the higher molecular weight PAHs such as the nitroanthracenes,
nitrophenantrenes and nitrofluoranthenes condense in and on PM (Ciganek et al., 2004; Cecinato, 2003).
AX2.2.5. Multiphase Chemical Processes Involving NOx and Halogens
Four decades of observational data on O3 in the troposphere have revealed numerous anomalies not
easily explained by gas-phase HOX-NOX photochemistry. The best-known example is the dramatic
depletion of ground-level O3 during polar sunrise due to multiphase catalytic cycles involving inorganic
Br and Cl radicals (Barrie et al., 1988; Martinez et al., 1999; Foster et al., 2001). Other examples of
anomalies in tropospheric O3 at lower latitudes include low levels of O3 (<10 ppb) in the marine boundary
layer (MBL) and overlying free troposphere (FT) at times over large portions of the tropical Pacific (Kley
et al., 1996), as well as post-sunrise O3 depletions over the western subtropical Pacific Ocean (Nagao
et al., 1999), the temperate Southern Ocean (Galbally et al., 2000), and the tropical Indian Ocean
(Dickerson et al., 1999). The observed O3 depletions in near-surface marine air are generally consistent
with the model-predicted volatilization of Br2, BrCl, and C12 from sea salt aerosols through autocatalytic
halogen "activation" mechanisms (e.g., Vogt et al., 1996; Von Glasow et al., 2002a) involving these
aqueous phase reactions.
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^ z (AX2.2-28)
In polluted marine regions at night, the heterogeneous reaction
NjO<; + Cl~ —> ClNO-> + NO
2 -* ^ •* (AX2.2-29)
may also be important (Finlayson-Pitts et al., 1989; Behnke et al., 1997; Erickson et al., 1999). Diatomic
bromine, BrCl, C12, and C1NO2 volatilize and photolyze in sunlight to produce atomic Br and Cl. The
acidification of sea salt aerosol via incorporation of HNO3 (and other acids) leads to the volatilization of
HC1 (Erickson et al., 1999), e.g.
HNO3 + C1-^HC1 + NO3-
and the corresponding shift in phase partitioning can accelerate the deposition flux to the surface of total
NO3 (Russell et al., 2003; Fischer et al., 2006). However, Pryor and Sorensen (2000) have shown that the
dominant form of nitrate deposition is a complex function of wind speed. In polluted coastal regions
where HC1 from Reaction AX2.2-30 often exceeds 1 ppb, significant additional atomic Cl" is produced via
(Singh and Kasting, 1988; Keene et al., 2007). Following production, Br and Cl atoms catalytically
destroy O3 via
(AX2.2-32)
(AX2.2-33)
(AX2.2-34)
where (X=Br and Cl).
Formation of Br and Cl nitrates via
2 3 (AX2.2-35)
and the subsequent reaction of XNO3 with sea salt and sulfate aerosols via
XNO, + H20 -» HOX + If + N03- ^^
and
(AX2.2-37)
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(where Y= Cl, Br, or I) accelerates the conversion of NOX to participate NO3" and thereby contributes
indirectly to net O3 destruction (Sander et al., 1999; Vogt et al., 1999, Pszenny et al., 2004).
Most XNO3 reacts via Reaction AX2.2-36 on sea salt whereas reaction AX2.2-37 is more important
on sulfate aerosols. Partitioning of HC1 on sulfate aerosols following Henry's Law provides Cl" for
Reaction AX2.2-37 to form BrCl. Product NO3" from both Reactions AX2.2-36 and AX2.2-37 partitions
with the gas-phase HNO3 following Henry's Law. Because most aerosol size fractions in the MBL are
near equilibrium with respect to HNO3 (Erickson et al., 1999; Keene et al., 2004), both sulfate and sea salt
aerosol can sustain the catalytic removal of NOX and re-activation of Cl and Br with no detectable change
in composition. The photolytic reduction of NO3" in sea salt aerosol solutions recycles NOX to the gas
phase (Pszenny et al., 2004). Halogen chemistry also impacts O3 indirectly by altering OH/HO2 ratios
through the steps of Reactions AX2.2-33 and AX2.2-34 (e.g., Stutz et al., 1999; Bloss et al., 2005).
In addition to O3 destruction via Reaction AX2.2-32, atomic Cl oxidizes hydrocarbons (HCs)
primarily via hydrogen abstraction to form HC1 vapor and organic products (Jobson et al., 1994; Pszenny
et al., 2006). The enhanced supply of odd-H radicals from HC oxidation leads to net O3 production in the
presence of sufficient NOX (Pszenny et al., 1993). Available evidence suggests that Cl" radical chemistry
may be a significant net source for O3 in polluted coastal/urban air (e.g., Tanaka et al., 2003; Finley and
Saltzman, 2006).
An analogous autocatalyic O3 destruction cycle involving multiphase iodine (I) chemistry also
operates in the marine atmosphere (Alicke et al., 1999, Vogt et al., 1999; McFiggans et al., 2000;
Ashworth et al., 2002). In this case, the primary source of I is believed to be either photolysis of CH2I2,
other I-containing gases (Carpenter et al., 1999; Carpenter, 2003), or perhaps I2 (McFiggans et al., 2004;
Saiz-Lopez and Plane, 2004; McFiggans, 2005) emitted by micro-and macro flora. Sea salt and sulfate
aerosols provide substrates for multiphase reactions that sustain the catalytic I-IO cycle. The IO radical
has been measured by long-path (LP) and multi axis (MAX) differential optical absorption spectroscopy
(DOAS) at Mace Head, Ireland; Tenerife, Canary Islands; Cape Grim, Tasmania; and coastal New
England, USA. These studies have established that the IO radical has average daytime levels of about 1
ppt with maxima up to 7 ppt (e.g., Allan et al., 2000; Pikelnaya et al., 2006). Modeling suggests that up to
13% per day of O3 in marine air may be destroyed via multiphase iodine chemistry (McFiggans et al.,
2000). The reaction of IO with NO2 followed by uptake of INO3 into aerosols (analogous to Reactions
AX2.2-32 through AX2.2-34) accelerates the conversion of NOX to particulate NO3" and thereby
contributes to net O3 destruction. The reaction IO + NO —> I + NO2 also influences NOX cycling.
Most of the above studies focused on halogen-radical chemistry and related influences on NOX
cycling in coastal and urban air. However, available evidence suggests that similar chemical
transformations proceed in other halogen-rich tropospheric regimes. For example, Cl, Br, and/or I oxides
have been measured at significant concentrations in near-surface air over the Dead Sea, Israel, the Great
Salt Lake, Utah (e.g., Hebestreit et al., 1999; Stutz et al., 1999, 2002; Zingler and Platt, 2005), and the
Salar de Uyuni salt pan in the Andes mountains (U. Platt, unpublished data, 2006); high column densities
of halogenated compounds have also been observed from satellites over the northern Caspian Sea
(Wagner et al., 2001; Hollwedel et al., 2004). The primary source of reactive halogens in these regions is
thought to be from activation along the lives of that in Reactions AX2.2-26 through AX2.2-28 involving
concentrated salt deposits on surface evaporative pans. High concentrations of BrO have also been
measured in volcanic plumes (Bobrowski et al., 2003, Gerlach, 2004). Although virtually unexplored, the
substantial emissions of inorganic halogens during biomass burning (Lobert et al., 1999; Keene et al.,
2006) and in association with crustal dust (Keene et al., 1999; Sander et al., 2003) may also support active
halogen-radical chemistry and related transformations involving NOX downwind of sources. Finally,
observations from satellites, balloons, and aircraft indicate that BrO is present in the free troposphere at
levels sufficient to significantly influence photochemistry (e.g., Von Glasow et al., 2004).
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AX2.3. Transport of NOx in the Atmosphere
Major episodes of high O3 concentrations in the eastern United Sates and in Europe are associated
with slow moving high-pressure systems. High-pressure systems during the warmer seasons are
associated with subsidence, resulting in warm, generally cloudless conditions with light winds. The
subsidence results in stable conditions near the surface, which inhibit or reduce the vertical mixing of O3
precursors (NOx, VOCs, and CO). Photochemical activity is enhanced because of higher temperatures
and the availability of sunlight. However, it is becoming increasingly apparent that transport of O3 and
NOX and VOC from distant sources can provide significant contributions to local [O3] even in areas
where there is substantial photochemical production. There are a number of transport phenomena
occurring either in the upper boundary layer or in the free troposphere which can contribute to high O3
values at the surface. These phenomena include stratospheric-tropospheric exchange (STE), deep and
shallow convection, low-level jets, and the so-called "conveyor belts" that serve to characterize flows
around frontal systems.
AX2.3.1. Convective Transport
Crutzen and Gidel (1983), Gidel (1983), and Chatfield and Crutzen (1984) hypothesized that
convective clouds played an important role in rapid atmospheric vertical transport of trace species and
first tested simple parameterizations of convective transport in atmospheric chemical models. At nearly
the same time, evidence was shown of venting the boundary layer by shallow, fair weather cumulus
clouds (e.g., Greenhut et al., 1984; Greenhut, 1986). Field experiments were conducted in 1985 which
resulted in verification of the hypothesis that deep convective clouds are instrumental in atmospheric
transport of trace constituents (Dickerson et al., 1987). Once pollutants are lofted to the middle and upper
troposphere, they typically have a much longer chemical lifetime and with the generally stronger winds at
these altitudes, they can be transported large distances from their source regions. Transport of NOX from
the boundary layer to the upper troposphere by convection tends to dilute the higher in the boundary layer
concentrations and extend the NOX lifetime from less than 24 h to several days. Photochemical reactions
occur during this long-range transport. Pickering et al. (1990) demonstrated that venting of boundary
layer NOX by convective clouds (both shallow and deep) causes enhanced O3 production in the free
troposphere. Dilution of NOX at the surface can often increase O3 production efficiency. Therefore,
convection aids in the transformation of local pollution into a contribution to global atmospheric
pollution. Downdrafts within thunderstorms tend to bring air with less NOX from the middle troposphere
into the boundary layer. Lightning produces NO which is directly injected chiefly into the middle and
upper troposphere. The total global production of NO by lightning remains uncertain, but is on the order
of 10% of the total.
AX2.3.2. Observations of the Effects of Convective Transport
The first unequivocal observations of deep convective transport of boundary layer pollutants to the
upper troposphere were documented by Dickerson et al. (1987). Instrumentation aboard three research
aircraft measured CO, O3, NO, NOX, NOY, and hydrocarbons in the vicinity of an active mesoscale
convective system near the Oklahoma/Arkansas border during the 1985 PRE-STORM experiment. Anvil
penetrations about 2 h after maturity found greatly enhanced mixing ratios inside the cloud of all of the
aforementioned species compared with outside it. NO mixing ratios in the anvil averaged 3 to 4 ppb, with
individual 3-min observations reaching 6 ppb; boundary layer NOX was typically 1.5 ppb or less outside
the cloud. Therefore, the anvil observations represent a mixture of boundary layer NOX and NOX
contributed by lightning. Luke et al. (1992) summarized the air chemistry data from all 18 flights during
PRE-STORM by categorizing each case according to synoptic flow patterns. Storms in the maritime
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tropical flow regime transported large amounts of CO, O3, and NOY into the upper troposphere with the
midtroposphere remaining relatively clean. During frontal passages a combination of stratiform and
convective clouds mixed pollutants more uniformly into the middle and upper levels.
Prather and Jacob (1997) and Jaegle et al. (1997) noted that precursors of HOX are also transported
to the upper troposphere by deep convection, in addition to primary pollutants (e.g., NOX, CO, VOCs).
The HOX precursors of most importance are water vapor, HCHO, H2O2, CH3OOH, and acetone. The
hydroperoxyl radical is critical for oxidizing NO to NO2 in the O3 production process as described above.
Over remote marine areas, the effects of deep convection on trace gas distributions differ from
those over moderately polluted continental regions. Chemical measurements taken by the NASA ER-2
aircraft during the Stratosphere-Troposphere Exchange Project (STEP) off the northern coast of Australia
show the influence of very deep convective events. Between 14.5 and 16.5 km on the February 2-3, 1987
flight, chemical profiles that included pronounced maxima in CO, water vapor, and CCN, and minima of
NOY, and O3 (Pickering et al., 1993). Trajectory analysis showed that these air parcels likely were
transported from convective cells 800-900 km upstream. Very low marine boundary layer mixing ratios
of NOY and O3 in this remote region were apparently transported upward in the convection. A similar
result was noted in Central Equatorial Pacific Experiment (CEPEX) (Kley et al., 1996) and in Indian
Ocean Experiment (INDOEX) (DeLaat et al., 1999) where a series of ozonesonde ascents showed very
low upper tropospheric O3 following deep convection. It is likely that similar transport of low-ozone
tropical marine boundary layer air to the upper troposphere occurs in thunderstorms along the east coast
of Florida. Deep convection occurs frequently over the tropical Pacific. Low-ozone and low-NOx
convective outflow likely will descend in the subsidence region of the subtropical eastern Pacific, leading
to some of the cleanest air that arrives at the west coast of the United States.
The discussion above relates to the effects of specific convective events. Observations have also
been conducted by NASA aircraft in survey mode, in which the regional effects of many convective
events can be measured. The Subsonic Assessment Ozone and Nitrogen Oxides Experiment (SONEX)
field program in 1997 conducted primarily upper tropospheric measurements over the North Atlantic. The
regional effects of convection over North America and the Western Atlantic on upper tropospheric NOX
were pronounced (Crawford et al., 2000; Allen et al., 2000). A discussion of the results of model
calculations of convection and its effects can be found in Section AX2.7.
AX2.3.3. Effects on Photolysis Rates and Wet Scavenging
Thunderstorm clouds are optically very thick, and, therefore, have major effects on radiative fluxes
and photolysis rates. Madronich (1987) provided modeling estimates of the effects of clouds of various
optical depths on photolysis rates. In the upper portion of a thunderstorm anvil, photolysis is likely to be
enhanced by a factor of 2 or more due to multiple reflections off the ice crystals. In the lower portion and
beneath the cloud, photolysis is substantially decreased. With enhanced photolysis rates, the NO/NO2
ratio in the upper troposphere is driven to larger values than under clear-sky conditions.
Thunderstorm updraft regions, which contain copious amounts of water, are regions where efficient
scavenging of soluble species can occur (Balkanski et al., 1993). NO2 itself is not very soluble and
therefore wet scavenging is not a major removal process for it. However, a major NOX reservoir species,
HNO3 is extremely soluble. Very few direct field measurements of the amounts of specific trace gases
that are scavenged in storms are available. Pickering et al. (2001) used a combination of model estimates
of soluble species that did not include wet scavenging and observations of these species from the upper
tropospheric outflow region of a major line of convection observed near Fiji. Over 90% of the NOX in the
outflow air appeared to have been removed by the storm; about 50% of CH3OOH and about 80% of
HCHO had been lost.
Convective processes and small-scale turbulence transport pollutants both upward and downward
throughout the planetary boundary layer and the free troposphere. Ozone and its precursors (NOX, CO,
and VOCs) can be transported vertically by convection into upper part of the mixed layer on one day,
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then transported overnight as a layer of elevated mixing ratios, perhaps by a nocturnal low-level jet, and
then entrained into a growing convective boundary layer downwind and brought back to the surface.
Because NO and NO2 are only slightly soluble, they can be transported over longer distances in the
gas phase than can more soluble species which can be depleted by deposition to moist surfaces, or taken
up more readily on aqueous surfaces of particles. During transport, they can be transformed into reservoir
species such as HNO3, PANs, and N2O5. These species can then contribute to local NOX concentrations in
remote areas. For example, it is now well established that PAN decomposition provides a major source of
NOX in the remote troposphere (Staudt et al., 2003). PAN decomposition in subsiding air masses from
Asia over the eastern Pacific could make an important contribution to O3 and NOX enhancement in the
United States (Kotchenruther et al., 2001; Hudman et al., 2004). Further details about mechanisms for
transporting ozone and its precursors were described at length in the 2006 AQCD for Ozone.
AX2.4. Sources and Emissions of NOx
NOX has natural and anthropogenic sources. In Section AX2.4.1, interactions of NOX with the
terrestrial biosphere are discussed. Because of the tight coupling between processes linking emissions and
deposition, they are discussed together. Additional sources are described in Section AX2.4.2. Field
studies evaluating emissions inventories are discussed in Section AX2.4.2.4.
AX2.4.1. Interactions of NOx with the Biosphere
NOX affect vegetated ecosystems, and in turn the atmospheric chemistry of NOX is influenced by
vegetation. Extensive research on nitrogen inputs from the atmosphere to forests was conducted in the
1980s as part of the Integrated Forest Study, and is summarized by Johnson and Lindberg (1992). The
following sections discuss sources of NOX from soil, deposition of NOxto foliage, reactions with
biogenic hydrocarbons, and ecological effects of nitrogen deposition.
NO from soil metabolism is the dominant but not exclusive source of NOX from the biosphere to
the atmosphere. As noted below, our understanding of NO2 exchange with vegetation suggests that there
should be emission of NO2 from foliage when ambient concentrations are less than about 1 ppb. However,
Lerdau et al. (2000) have pointed out that present understanding of the global distribution of NOX is not
consistent with a large source that would be expected in remote forests if NO2 emission was important
when atmospheric concentrations were below the compensation point.
The pathways for nitrification and denitrification include two gas-phase intermediates, NO and
N2O, some of which can escape. While N2O is of interest for its greenhouse gas potential and role in
stratospheric chemistry it is not considered among the reactive NOX species important for urban and
regional air quality and will not be discussed further. Temperature and soil moisture are critical factors
that control the rates of reaction and importantly the partitioning between NO and N2O which depend on
oxygen levels: in flooded soils where oxygen levels are low, N2O is the dominant soil nitrogen gas; as soil
dries, allowing more O2 to diffuse, NO emissions increase. In very dry soils, microbial activity is
inhibited and emissions of both N2O and NO decrease. Nitrogen metabolism in soil is strongly dependent
on the substrate concentrations. Where nitrogen is limiting, nitrogen is efficiently retained and little
gaseous nitrogen is released. Where nitrogen is in excess of demand, gaseous nitrogen emissions increase;
consequently, soil NO emissions are highest in fertilized agriculture and tropical soils (Davidson and
Kingerlee, 1997; Williams etal., 1992).
Several reactive nitrogen are species are deposited to vegetation, among them, HNO3, NO2, PAN,
and organic nitrates. Deposition of HNO3 appears to be relatively simple. Field observations based on
concentration gradients and recently using eddy covariance demonstrate rapid deposition that approaches
the aerodynamic limit (as constrained by atmospheric turbulence) in the Wesely (1989) formulation based
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on analogy to resistance. Surface resistance for HNO3 uptake by vegetation is negligible. Deposition rates
are independent of leaf area or stomatal conductance, implying that deposition occurs to branches, soil,
and leaf cuticle as well as internal leaf surfaces.
Deposition velocities (Vd) typically exceed 1 cm/s and exhibit a daily pattern controlled by
turbulence characteristics such that midday maximum and lower values at night when there is stable
boundary layer.
Nitrogen dioxide interaction with vegetation is more complex. Application of 15N-labeled NO2
demonstrates that NO2 is absorbed and metabolized by foliage (Siegwolf et al., 2001; Mocker et al., 1998;
Segschneider et al., 1995; Weber, et al., 1995). Exposure to NO2 induces nitrate reductase (Weber et al.,
1995, 1998), a necessary enzyme for assimilating oxidized nitrogen. Understanding NO2 interactions with
foliage is largely based on leaf cuvette and growth chamber studies, which expose foliage or whole plants
to controlled levels of NO2 and measure the fraction of NO2 removed from the chamber air. A key finding
is that the fit of NO2 flux to NO2 concentration, has a non-zero intercept, implying a compensation point
or internal concentration. In studies at very low NO2 concentrations emission from foliage is observed
(Teklemariam and Sparks, 2006). Evidence for a compensation point is not solely based on the fitted
intercept. NO2 uptake rate to foliage is clearly related to stomatal conductance. Internal resistance is
variable, and may be associated with concentrations of reactive species such as ascorbate in the plant
tissue that react with NO2 (Teklemariam and Sparks, 2006). Foliar NO2 emissions show some dependence
on nitrogen content (Teklemariam and Sparks, 2006). Internal NO2 appears to derive from plant nitrogen
metabolism.
Two approaches to modeling NO2 uptake by vegetation are the resistance-in-series analogy which
considers flux (F) as the product of concentration (C) and Vd, where is related to the sum of aerodynamic,
boundary layer, and internal resistances (Ra, Rb, and Rc; positive fluxes are from atmosphere to foliage)
F=CVd (AX2.4-1)
Ra and Rb and controlled by turbulence in the mixed layer; Rc is dependent on characteristics of the
foliage and other elements of the soil, and may be viewed as 2 combination of resistance internal to the
foliage and external on the cuticle, soils, and bark. This approach is amenable to predicting deposition in
regional air quality models (Wesely, 1989). Typically, the NO2, Vd is less than that for O3, due to the
surface's generally higher resistance to NO2 uptake, consistent with NO2's lower reactivity.
Alternatively, NO2 exchange with foliage can be modeled from a physiological viewpoint where
the flux from the leaf is related to the stomatal conductance and a concentration gradient between the
ambient air and interstitial air in the leaf. This approach best describes results for exchange with
individual foliage elements, and is expressed per unit leaf (needle) area. While this approach provides
linkage to leaf physiology, it is not straightforward to scale up from the leaf to ecosystem scale
J=gs(Ca-Ci) (AX2.4-3)
This model implicitly associates the compensation point with a finite internal concentration.
Typically observed compensation points are around 1 ppb. Finite values of internal NO2 concentration are
consistent with metabolic pathways that include oxides of nitrogen. In this formulation, the uptake will be
linear with NO2 concentration, which is typically observed with foliar chamber studies.
Several studies have shown the UV dependence of NO2 emission, which implies some photo-
induced surface reactions that release NO2. Rather than model this as a UV-dependent internal
concentration, it would be more realistic to add an additional term to account for emission that is
dependent on light levels and other surface characteristics
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(AX24.4)
The mechanisms for surface emission are discussed below. Measurement of NO2 flux is
confounded by the rapid interconversion of NO, NO2, and O3 (Gao et al., 1991).
PAN is phytotoxic, absorbed largely at the leaf. Observations based on inference from
concentration gradients and rates of decline at night (Shepson et al., 1992; Schrimpf et al., 1996) and leaf
chamber studies (Teklemariam and Sparks, 2004) have indicated that PAN uptake is slower than that of
O3; however, recent work in coniferous canopy with direct eddy covariance PAN flux measurements
indicated a Vd more similar to that of O3. Uptake of PAN is under stomatal control, has a non-zero
deposition at night, and is influenced by leaf wetness (Turnipseed et al., 2006). On the other hand, flux
measurements determined by gradient methods over a grass surface showed a Vd closer to 0.1 cm/s, with
large uncertainty (Doskey et al., 2004). Whether the discrepancies are methodological or indicate intrinsic
differences between different vegetation is unknown. Uptake of PAN is smaller than its thermal
decomposition in all cases.
The biosphere also interacts with NOX through hydrocarbon emissions and their subsequent
reactions to form multi-functional organic nitrates. Isoprene nitrates are an important class of these.
Isoprene reacts with OH to form a radical that adds NO2 to form a hydroxyalkyl nitrate. The combination
of hydroxyl and nitrate functional group makes these compounds especially soluble with low vapor
pressures; they likely deposit rapidly (Shepson et al., 1996; Treves et al., 2000). Many other unsaturated
hydrocarbons react by analogous routes. Observations at Harvard Forest show a substantial fraction of
total NOY not accounted for by NO, NO2 and PAN, which is attributed to the organic nitrates (Horii et al.,
2006, Munger et al., 1998). Furthermore, the total NOY flux exceeds the sum of HNO3, NOX, and PAN,
which implies that the organic nitrates are a substantial fraction of nitrogen deposition. Other observations
that show evidence of hydoxyalkyl nitrates include those of Grossenbacher et al. (2001) and Day et al.
(2003).
Formation of the hydroxyalkyl nitrates occurs following OH attack on VOCs. In some sense, this
mechanism is just an alternate pathway for OH to react with NOX to form a rapidly depositing species,
because if VOC were not present, OH would be available to react with any NO2 present to form HNO3.
HNO2 formation on vegetative surfaces at night has long been observed based on measurements of
positive gradients (Harrison and Kitto, 1994). Surface reactions of NO2 enhanced by moisture were
proposed to explain these results. Production was evident at sites with high ambient NO2; at low
concentration, uptake of HONO exceeded the source. Daytime observations of HONO when rapid
photolysis is expected to deplete ambient concentrations to very low levels implies a substantial source of
photo-induced HONO formation at a variety of forested sites where measurements have been made.
Estimated source strengths are 200-1800/ppt-h in the surface layer (Zhou et al., 2002a, 2003), which is
about 20 times faster than all nighttime sources. HNO2 sources could be important to OH/HO2 budgets as
HONO is rapidly photolyzed by sunlight to OH and NO. Additional evidence of light-dependent reactions
to produce HONO comes from discovery of a HONO artifact in pyrex sample inlet lines exposed to
ambient light. Either covering the inlet or washing it eliminated the HONO formation (Zhou et al.,
2002b). Similar reactions might serve to explain observations of UV-dependent production of NOX in
empty foliar cuvettes that had been exposed to ambient air (Hari et al., 2003; Raivonen et al., 2003).
Production of HONO in the dark is currently believed to occur via a heterogeneous reaction
involving NO2 on wet surfaces (Jenkin et al., 1988; Pitts et al., 1984; He et al., 2006; Sakamaki et al.,
1983), and it is proposed that the mechanism has first-order dependence in both NO2 and H2O (Kleffmann
et al., 1998; Svensson et al., 1987) despite the stoichiometry. However, the molecular pathway of the
mechanism is still under debate. Jenkin et al. (1988) postulated a H2O-NO2 water complex reacting with
gas phase NO2 to produce HONO, which is inconsistent with the formation of an N2O4 intermediate
leading to HONO as proposed by Finlayson-Pitts et al. (2003). Another uncertainty is whether the
reaction forming HONO is dependent on water vapor (Svensson et al., 1987; Stutz et al., 2004b) or water
adsorbed on surfaces (Kleffmann et al., 1998). Furthermore, the composition of the surface and the
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available amount of surface or surface-to-volume ratio can significantly influence the HONO production
rates (Kaiser and Wu, 1977; Kleffmann et al., 1998; Svensson et al., 1987), which may explain the
difference in the rates observed between laboratory and atmospheric measurements.
There is no consensus on a chemical mechanism for photo-induced HONO production. Photolysis
of HNO3 or NO3" absorbed on ice or in surface water films has been proposed (Honrath et al., 2002;
Ramazan et al., 2004; Zhou et al., 2001, 2003); but alternative pathways include NO2 interaction with
organic surfaces such as humic substances (George et al., 2005; Stemmler et al., 2006). Note that either
NO3" photolysis or heterogeneous reaction of NO2 are routes for recycling deposited NOX back to the
atmosphere in an active form. NO3 photolysis would return nitrogen that heretofore was considered
irreversibly deposited, surface reactions between NO2 and water films or organic molecules would
decrease the effectiveness of observed NO2 deposition if the HONO were re-emitted.
AX2.4.1.1. N02 and HN03 Flux Data from Harvard Forest
Harvard Forest is a rural site in central Massachusetts, where ambient NOX, NOY, and other
pollutant concentrations and fluxes of total NOY have been measured since 1990 (Munger et al., 1996).
An intensive study in 2000 utilized a Tunable Diode Laser Absorption Spectrometer (TDLAS) to measure
NO2 and HNO3. TDLAS has an inherently fast response, and for species such as NO2 and HNO3 with
well-characterized spectra it provides an absolute and specific measurement. Absolute concentrations of
HNO3 were measured, and the flux inferred based on the dry deposition inferential method that uses
momentum flux measurements to compute a deposition velocity and derives an inferred flux (Wesely and
Hicks, 1977; Hicks et al., 1987). Direct eddy covariance calculations for HNO3 were not possible because
the atmospheric variations were attenuated by interaction with the inlet walls despite very short residence
time and use of fluorinated silane coatings to make the inlet walls more hydrophobic. Nitrogen oxide
response was adequate to allow both concentration and eddy covariance flux determination.
Simultaneously, NO and NOY eddy covariance fluxes were determined with two separate O3
chemiluminescence detectors, one equipped with a H2-gold catalyst at the inlet to convert all reactive
nitrogen compounds to NO. Additionally, the measurements include concentration gradients for NO,
NO2, and O3 over several annual cycles to examine their vertical profiles in the forest canopy.
Overall, the results show typical NO2 concentrations of 1 ppb under clean-air conditions and mean
concentrations up to 3 ppb at night and 1 ppb during daytime for polluted conditions. Net positive fluxes
(emission) of NO2 were evident in the daytime and negative fluxes (deposition) were observed at night
(Figure AX2.4-1). Nitric oxide fluxes were negative during the daytime and near zero at night.
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NW
SW
o
"o
E
O
N
£
"o
X
3
Source: Horiietal. (2004).
Figure AX2.4-1. Diel cycles of median concentrations (upper panels) and fluxes (lower panels). These are
for the Northwest (clean sector, left panels) and Southwest (polluted sector, right panels)
wind sectors at Harvard Forest, April-November, 2000, for NO, N02, and 03/10. NO and 03
were sampled at a height of 29 m, and N02 at 22 m. Vertical bars indicate 25th and 27th
quartiles for NO and N02 measurements. N02 concentration and nighttime deposition are
enhanced under southwesterly conditions, as are 03 and the morning NO maximum.
In part the opposite NO and NO2 fluxes are simply consequences of variable NO/NO2 distributions
responding to vertical gradients in light intensity and O3 concentration, which resulted in no net flux of
NOX (Gao et al., 1993). In the Harvard Forest situation, the NO and NO2 measurements were not at the
same height above the canopy, and the resulting differences derive at least in part from the gradient in
flux magnitude between the two inlets (Figure AX2.4-2).
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Simple Model
100
80
S 60
40
20
0
NO
NO,
NO
NO
0.0 0.2 0.4 0.6 0.8 1.0-2-1 0 1 2
Concentration (nmol mol'1) Flux (nmol mol'1 cm s'1)
Source: Horii (2002).
Figure AX2.4-2. Simple NOx photochemical canopy model outputs. Left panel, concentrations of NO
(dashed) and N02 (solid); right, fluxes of NO (dashed) and N02 (solid). Symbols indicate
measurement heights for NO (29m) and N02 (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 = [N0]/t1, LNO = [N0]/t2, and zero net deposition or emission of NOx is allowed.
NOx (NO + N02) is normalized to 1ppb. t1 = 70s in this example. Due to the measurement
height difference, observed upward N02flux due to photochemical cycling alone should be
substantially larger than observed downward NO flux attributable to the same process.
At night, when NO concentrations are near 0 due to titration by ambient O3 there is not a flux of
NO to offset NO2 fluxes. Nighttime data consistently show NO2 deposition (Figure AX2.4-3), which
increases with increasing NO2 concentrations. Concentrations above 10 ppb were rare at this site, but the
few high NO2 observations suggest a nonlinear dependence on concentration. The data fit a model with
Vd of-0.08 plus an enhancement term that was second order in NO2 concentration. The second order
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term implies that NO2 deposition rates to vegetation in polluted urban sites would be considerably larger
than what was observed at this rural site.
FN02 (night) = F0 + V0 [NO2] + a [NO2]2
5-
-15-
-20-
Hourly Data (fitted) •
Nightly Medians +
V0= -0.08 ±0.03 (ems'1)
a = -0.013 ± 0.001 (nmol~1 mol cm s"1)
R2=0.63
0
I
10
I
15
20
[NO2] (nmol moM)
25 30
Source: Horiietal. (2004).
Figure AX2.4-3. Hourly (dots) and median nightly (pluses) N02 flux vs. concentration. The results of least-
squares fit on the hourly data (curve). The flux is expressed in units of concentration times
velocity (nmol/mol-cm-s) 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.
After accounting for the NO-NO2 null cycle the net NOX flux could be derived. Overall, there was a
net deposition of NOX during the night and essentially zero flux in the day, with large variability in the
magnitude and sign of individual flux observations. For the periods with [NO2] > 2 ppb, deposition was
always observed. These canopy-scale field observations are consistent with a finite compensation point
for NO2 in the canopy that offsets foliar uptake or even reverses it when concentrations are especially
low. At concentrations above the compensation point, NOX is absorbed by the canopy. Examination of
concentration profiles corroborates the flux measurements (Figure AX2.4-4). During daytime for low-
NOX conditions, there is a local maximum in the concentration profile near the top of the canopy where
O3 has a local minimum, which is consistent with foliar emission or light-dependent production of NOX in
the upper canopy. Depletion is evident for both NOX and O3 near the forest floor. Air reaching the ground
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has passed through the canopy where uptake is efficient and the vertical exchange rates near the ground
are slow. At night, the profiles generally decrease with decreasing height above the ground, showing only
uptake. At higher concentrations, the daytime NOX concentrations are nearly constant through the canopy;
no emission is evident from the sunlit leaves.
NOX PROFILES
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Concentration (nmol mol-1) Concentration (nmol mol'1)
Source: Horiietal. (2004).
Figure AX2.4-4. Averaged profiles at Harvard Forest. These give some evidence of some N02 input near the
canopy top from light-mediated ambient reactions, or emission from open stomates.
2-25
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Summer 2000
NW
SW
_. 12-:
-25
15
20
Source: Horiietal. (2006).
Figure AX2.4-5. Summer (June-August) 2000 median concentrations (upper panels), fractions of NOy
(middle panels), and fluxes (lower panels) of NOy and component species. These are
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(HNOs); negative fluxes
represent deposition; F(NOx) is derived from eddy covariance F(NO) and F(N(>2)
measurements (corrected for photochemical cycling), F(HNOs) 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.
Figure AX2.4-5 compares observed fluxes of all the observed species. The measured NOX and
estimated PAN fluxes are small relative to the observed total NOY flux. In clean air, HNO3 accounts for
nearly all the NOY flux and the sum of all measured species is about equal to the NOY concentration.
However, in polluted conditions, unmeasured species are up to 25% of the NOY, and HNO3 fluxes cannot
2-26
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account for all the total NOY flux observed. Likely these unmeasured NOY species are hydroxyalkyl
nitrates and similar compounds and are rapidly deposited. Although NO2 uptake may be important to the
plant, because it is an input directly to the interior of foliage that can be used immediately in plant
metabolism, it is evidently not a significant part of overall nitrogen deposition to rural sites. The
deposition of HNO3 and multifunctional organic nitrates are the largest elements of the nitrogen dry
deposition budget. Two key areas of remaining uncertainty are the production of HONO over vegetation
and the role of very reactive biogenic VOCs. HONO is important because its photolysis is a source of OH
radicals, and its formation may represent an unrecognized mechanism to regenerate photochemically
active NOX from nitrate that had been considered terminally removed from the atmosphere.
AX2.4.2. Emissions of NOx
Estimated annual emissions of NOX for 2002 (U.S. Environmental Protection Agency, 2006) are
shown at end of Annex AX2, in Table AX2.4-1. Information relevant for estimating emissions of criteria
pollutants is given in U.S. Environmental Protection Agency (1999). Discussions of uncertainties in
current emissions inventories and strategies for improving them can be found in NARSTO (2005).
As can be seen from the table, combustion by stationary sources, such as electrical utilities and
various industries, accounts for roughly half of total anthropogenic emissions of NOX. Mobile sources
account for the other half, with highway vehicles representing the major mobile source component.
Approximately half the mobile source emissions are contributed by diesel engines, the remainder are
emitted by gasoline-fueled vehicles and other sources.
Emissions of NOX associated with combustion arise from contributions from both fuel nitrogen and
atmospheric nitrogen. Combustion zone temperatures greater than about 1300 K are required to fix
atmospheric N2
Otherwise, NO can be formed from fuel N according to this reaction
CaHbOcNd + O2 -> xCO2 + yH2O + zNO
(AX2.4-6)
In addition to NO formation by the schematic reactions given above, some NO2 and CO are also
formed depending on temperatures, concentrations of OH and HO2 radicals and O2 levels. Fuel nitrogen is
highly variable in fossil fuels, ranging from 0.5 to 2.0 percent by weight (wt %) in coal to 0.05% in light
distillates (e.g., diesel fuel), to 1.5 wt % in heavy fuel oils (UK AQEG, 2004).
AX2.4.2.1. Emissions of N02 from Motor Vehicles
NO2 in exhaust gasoline-powered engines generally comprises
-------
Emission control devices for diesel engines can increase the fraction of exhaust NOX emitted as
NO2. The two major types that can increase NO2/NOX ratios include diesel oxidation catalysts (DOCs)
and catalyzed diesel particle filters (CDPFs). Both are considered "after treatment" devices, in that they
affect exhaust after it leaves an engine. In both DOCs and CDPFs, the effect on NO2/NOX ratios depends
on the composition of the catalytic washcoats.
DOCs are flow-through devices consisting of porous ceramic with a catalytic washcoat. Catalysis
within the DOC reduces exhaust concentrations of organic material, including PM and hydrocarbons. In
doing so, DOCs convert NO to NO2, the excess of which may be emitted through the tailpipe.
Like DOCs, CDPFs also may be a part of the emission control system for diesel vehicles. CDPFs
can reduce PM emissions from a diesel vehicle by > 90%. They commonly consist of a DOC and a
particle trapping filter or a particle trap with a catalytic wash coat, generally containing noble metals. As
exhaust gases pass through a CDPF, soot and organic material in the exhaust becomes trapped in the
filter. The DOC or the filter's catalytic washcoat converts exhaust NO to NO2 to use in oxidizing the
particulate matter trapped by the filter. Excess NO2 is emitted through the tailpipe.
Widespread use of CDPFs in European cities has been identified as a cause for increasing
NO2/NOX ratios at urban air quality monitors. Carslaw et al. (2007a) report upward trends in roadside
concentrations of NO2 between 2002 and 2006 at Marylebone Road, site on the edge of central London,
UK. Using ambient data analysis methods developed in earlier publications (Carslaw et al., 2005a),
Carslaw et al. (2007b) estimated the NO2/NOX emission ratio from the London vehicle fleet overtime.
NO2/NOX emission ratios increased significantly along Marylebone Road between 2002 and 2006, from
approximately 10% by volume to over 20% by volume. Using multivariate regression, Carslaw et al.
(2005b) estimated that the largest contributor to the increasing NO2/NOX emission ratios was the retrofit
of London transit buses with catalyzed diesel particle filters (CDPF).
Kessler et al. (2006) employed the methods developed by Carslaw et al. (2005a) to estimate the
NO2/NOX ratio from traffic in Baden-Wurttemberg, Germany between 1995 and 2005. The estimates
increase from approximately 5% in 1995 to over 20% in 2005. The investigators attributed the increase to
an increase in the use of oxidation catalysts in diesel-fueled passenger cars.
Ayala et al. (2002) compared NO2/NOX ratios in a bus before and after installation of a CDPF.
NO2 emissions before retrofit were 0.92-2.14 g/mi across different test cycles, comprising 3-9% of total
NOX. After retrofit, NO2 emissions were 9.0-24.0 g/mi, comprising 39-49% of total NOX.
Two U.S. studies where investigators sampled the plumes of heavy-duty diesel vehicles found
that, vehicles retrofitted with CDPFs had lower PM emissions and higher NO2/NOX ratios compared to
diesel vehicles without CDPFs. Shorter et al. (2005) conducted a "chase" experiment of vehicles in New
York City, including metropolitan transit buses equipped with CDPFs. Overall, while following CDPF-
equipped buses, approximately one-third of exhaust NOX was NO2. In contrast, less than 10% of NOX was
NO2 behind city buses lacking a CDPF. Kittleson et al. (2006) used an on-road laboratory to sample the
exhaust plumes of a truck under highway cruise conditions, equipped with one of two different CDPFs.
NO2/NOX ratios for CDPF-treated exhaust under highway cruise conditions ranged from 59-70%. The
comparison of these two studies illustrates the role that temperature can play in increasing CDPF catalytic
activity.
The ratio of NO2 to NOX in primary emissions ranges from 3 to 5 % from gasoline engines, 5 to
12% from heavy-duty diesel trucks, 5 to 10% from vehicles fueled by compressed natural gas and from 5
to 10% from stationary sources. In addition to NOX, motor vehicles also emit HONO, with ratios of
HONO to NOX ranging from 0.3% in the Caldecott Tunnel, San Francisco Bay (Kirchstetter and Harley,
1996) to 0.5 to 1.0% in studies in the United Kingdom (UK AQEG, 2004).
The NO2 to NOX ratios in emissions from turbine jet engines are as high as 32 to 35 % during taxi
and takeoff (1993 AQCD for NOX). Sawyer et al. (2000) have reviewed the factors associated with NOX
emissions by mobile sources. Marine transport represents a minor source of NOX, but constitutes a larger
source in the EU where it is expected to represent about two-thirds of land-based sources (UK AQEG,
2004).
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AX2.4.2.2. NOx Emissions from Natural Sources
Soil
Emission rates of NO from cultivated soil depend mainly on fertilization levels and soil
temperature. About 60% of the total NOX emitted by soils occurs in the central corn belt of the United
States. The oxidation of NH3, emitted mainly by livestock and soils, leads to the formation of NO, as do
NH4+ and NO3" fertilizers on soils. Estimates of emissions from natural sources are less certain than those
from anthropogenic sources. On a global scale, the contribution of soil emissions to the oxidized nitrogen
budget is on the order of 10% (Van Aardenne et al., 2001; Finlayson-Pitts and Pitts, 2000; Seinfeld and
Pandis, 1998), butNOx emissions from fertilized fields are highly variable. Soil NO emissions can be
estimated from the fraction of the applied fertilizer nitrogen emitted as NOX, but the flux varies strongly
with land use and temperature. Estimated globally averaged fractional applied nitrogen loss as NO varies
from 0.3% (Skiba et al., 1997) to 2.5% (Yienger and Levy, 1995). Variability within biomes to which
fertilizer is applied, such as shortgrass versus tallgrass prairie, accounts for a factor of three in uncertainty
(Williams et al., 1992; Yienger and Levy, 1995; Davidson and Kingerlee, 1997).
The local contribution can be much greater than the global average, particularly in summer and
especially where corn is grown extensively. Williams et al. (1992) estimated that contributions to NO
budgets from soils in Illinois are about 26% of the emissions from industrial and commercial processes in
that State. In Iowa, Kansas, Minnesota, Nebraska, and South Dakota, all states with smaller human
populations, soil emissions may dominate the NO budget. Conversion of NH3 to NO3 (nitrification) in
aerobic soils appears to be the dominant pathway to NO. The mass and chemical form of nitrogen
(reduced or oxidized) applied to soils, the vegetative cover, temperature, soil moisture, and agricultural
practices such as tillage all influence the amount of fertilizer nitrogen released as NO.
Emissions of NO from soils peak in summer when O3 formation is also at a maximum. An NRC
panel report (NRC, 2002) outlined the role of agriculture in emissions of air pollutants including NO and
NH3. That report recommends immediate implementation of best management practices to control these
emissions, and further research to quantify the magnitude of emissions and the impact of agriculture on
air quality. Civerolo and Dickerson (1998) report that use of the no-till cultivation technique on a
fertilized cornfield in Maryland reduced NO emissions by a factor of seven.
NOx from Biomass Burning
During biomass burning, nitrogen is derived mainly from fuel N and not from atmospheric N2,
since temperatures required to fix atmospheric N2 are likely to be found only in the flaming crowns of the
most intense boreal forest fires. Nitrogen is present mainly in plants as amino (NH2) groups in amino
acids. During combustion, nitrogen is released mainly in unidentified forms, presumably as N2, with very
little remaining in fuel ash. Apart from N2, the most abundant species in biomass burning plumes is NO.
Emissions of NO account for only about 10 to 20% relative to fuel N (Lobert et al., 1991). Other species
such as NO2, nitriles, ammonia, and other nitrogen compounds account for a similar amount. Emissions
of NOX are about 0.2 to 0.3% relative to total biomass burned (e.g., Andreae, 1991; Radke et al., 1991).
Westerling et al. (2006) have noted that the frequency and intensity of wildfires in the western United
States have increased substantially since 1970.
Lightning Production of NO
Annual global production of NO by lightning is the most uncertain source of reactive nitrogen. In
the last decade, literature values of the global average production rate range from 2 to 20 Tg N per year.
However, the most likely range is from 3 to 8 Tg N per year, because the majority of the recent estimates
fall in this range. The large uncertainty stems from several factors: (1) a large range of NO production
rates per meter of flash length (as much as two orders of magnitude); (2) the open question of whether
2-29
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cloud-to-ground (CG) flashes and intracloud flashes (1C) produce substantially different amounts of NO;
(3) the global flash rate; and (4) the ratio of the number of 1C flashes to the number of CG flashes.
Estimates of the amount of NO produced per flash have been made based on theoretical considerations
(e.g., Price et al., 1997), laboratory experiments (e.g., Wang et al., 1998); field experiments (e.g., Stith
et al., 1999; Huntrieser et al., 2002, 2007); and through a combination of cloud-resolving model
simulations, observed lightning flash rates, and anvil measurements of NO (e.g., DeCaria et al., 2000,
2005; Ott et al., 2007). The latter method was also used by Pickering et al. (1998), who showed that only
~5 to 20% of the total NO produced by lightning in a given storm exists in the boundary layer at the end
of a thunderstorm. Therefore, the direct contribution to boundary layer O3 production by lightning NO is
thought to be small. However, lightning NO production can contribute substantially to O3 production in
the middle and upper troposphere. DeCaria et al. (2005) estimated that up to 10 ppb of O3 was produced
in the upper troposphere in the first 24 h following a Colorado thunderstorm due to the injection of
lightning NO. A series of midlatitude and subtropical thunderstorm events have been simulated with the
model of DeCaria et al. (2005), and the derived NO production per CG flash averaged 500 moles/flash
while average production per 1C flash was 425 moles/flash (Ott et al., 2006).
A major uncertainty in mesoscale and global chemical transport models is the parameterization of
lightning flash rates. Model variables such as cloud top height, convective precipitation rate, and upward
cloud mass flux have been used to estimate flash rates. Allen and Pickering (2002) have evaluated these
methods against observed flash rates from satellite, and examined the effects on O3 production using each
method.
AX2.4.2.3. Uses of Satellite Data to Derive Emissions
Satellite data have been shown to be useful for optimizing estimates of emissions of NO2 (Leue
et al., 2001; Martin et al., 2003; Jaegle et al., 2005). Satellite-borne instruments such as Global Ozone
Monitoring Experiment (GOME) (Martin et al., 2003; and references therein) and Scanning Imaging
Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) (Bovensmann et al., 1999)
retrieve tropospheric columns of NO2, which can then be combined with model-derived chemical
lifetimes of NOxto yield emissions of NOX.
Top-down inference of the NOX emission inventory from the satellite observations of NO2 columns
by mass balance requires at minimum three pieces of information: the retrieved tropospheric NO2 column,
the ratio of tropospheric NOX to NO2 columns, and the NOX lifetime against loss to stable reservoirs. A
photochemical model has been used to provide information on the latter two pieces of information. The
method is generally applied exclusively to land surface emissions, excluding lightning. Tropospheric NO2
columns are insensitive to lightning NOX emissions since most of the lightning NOX in the upper
troposphere is present as NO at the local time of the satellite measurements (Ridley et al., 1996), owing to
the slower reactions of NO with O3 there.
Jaegle et al. (2005) applied additional information on the spatial distribution of emissions and on
fire activity to partition NOX emissions into sources from fossil fuel combustion, soils, and biomass
burning. Global a posteriori estimates of soil NOX emissions are 68% larger than the a priori estimates.
Large increases are found for the agricultural region of the western United States during summer,
increasing total U.S. soil NOX emissions by a factor of 2 to 0.9 Tg N/yr. Bertram et al. (2005) found clear
signals in the SCIAMACHY observations of short intense NOX pulses following springtime fertilizer
application and subsequent precipitation over agricultural regions of the western United States. For the
agricultural region in North-Central Montana, they calculate a yearly SCIAMACHY top-down estimate
that is 60% higher than a commonly used model of soil NOX emissions by Yienger and Levy (1995).
Martin et al. (2006) retrieved tropospheric NO2 columns for May 2004 to April 2005 from the
SCIAMACHY satellite instrument to derive top-down NOX emissions estimates via inverse modeling
with a global chemical transport model (GEOS-Chem). The top-down emissions were combined with a
priori information from a bottom-up emission inventory with error weighting to achieve an improved a
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posteriori estimate of the global distribution of surface NOX emissions. Their a posteriori inventory
improves the GEOS-Chem simulation of NOX, PAN, and HNO3 with respect to airborne in situ
measurements over and downwind of New York City. Their a posteriori inventory shows lower NOX
emissions from the Ohio River valley during summer than during winter, reflecting recent controls on
NOX emissions from electric utilities. Their a posteriori inventory is highly consistent (R2 = 0.82, bias =
3%) with the NEI99 inventory for the United States. In contrast, their a posteriori inventory is 68% larger
than a recent inventory by Streets et al. (2003) for East Asia for the year 2000.
AX2.4.2.4. Field Studies Evaluating Emissions Inventories
Comparisons of emissions model predictions with observations have been performed in a number
of environments. A number of studies of ratios of concentrations of CO to NOX and NMOC to NOX
during the early 1990s in tunnels and ambient air (summarized in Air Quality Criteria for Carbon
Monoxide (U.S. Environmental Protection Agency, 2000)) indicated that emissions of CO and NMOC
were systematically underestimated in emissions inventories. However, the results of more recent studies
have been mixed in this regard, with many studies showing agreement to within ±50% (U.S.
Environmental Protection Agency, 2000). Improvements in many areas have resulted from the process of
emissions model development, evaluation, and further refinement. It should be remembered that the
conclusions from these reconciliation studies depend on the assumption that NOX emissions are predicted
correctly by emissions factor models. Roadside remote sensing data indicate that > 50% of NMHC and
CO emissions are produced by less than about 10% of the vehicles (Stedman et al., 1991). These "super-
emitters" are typically poorly maintained vehicles. Vehicles of any age engaged in off-cycle operations
(e.g., rapid accelerations) emit much more than if operated in normal driving modes. Bishop and Stedman
(1996) found that the most important variables governing CO emissions are fleet age and owner
maintenance.
Emissions inventories for North America can be evaluated by comparison to measured long-term
trends and or ratios of pollutants in ambient air. A decadal field study of ambient CO at a rural site in the
eastern United States (Hallock-Waters et al., 1999) indicates a downward trend consistent with the
downward trend in estimated emissions over the period 1988 to 1999 (U.S. Environmental Protection
Agency, 1997), even when a global downward trend is accounted for. Measurements at two urban areas in
the United States confirmed the decrease in CO emissions (Parrish et al., 2002). That study also indicated
that the ratio of CO to NOX emissions decreased by a factor of almost three over 12 years. (Such a
downward trend was noted in the 1996 O3 AQCD). Emissions estimates (U.S. Environmental Protection
Agency, 1997) indicate a much smaller decrease in this ratio, suggesting that NOX emissions from mobile
sources may be underestimated and/or increasing. Parrish et al. (2002) conclude that O3 photochemistry in
U.S. urban areas may have become more NOx-limited over the past decade.
Pokharel et al. (2002) employed remotely sensed emissions from on-road vehicles and fuel use data
to estimate emissions in Denver. Their calculations indicate a continual decrease in CO, HC, and NO
emissions from mobile sources over the 6-year study period. Inventories based on the ambient data were
30 to 70% lower for CO, 40% higher for HC, and 40 to 80% lower for NO than those predicted by the
MOBILE6 model.
Stehr et al. (2000) reported simultaneous measurements of CO, SO2, and NOY at an East Coast site.
By taking advantage of the nature of mobile sources (they emit NOX and CO but little SO2) and power
plants (they emit NOX and SO2 but little CO), the authors evaluated emissions estimates for the eastern
United States. Results indicated that coal combustion contributes 25 to 35% of the total NOX emissions in
rough agreement with emissions inventories (U.S. Environmental Protection Agency, 1997).
Parrish et al. (1998) and Parrish and Fehsenfeld (2000) proposed methods to derive emission rates
by examining measured ambient ratios among individual VOC, NOX and NOY. There is typically a strong
correlation among measured values for these species because emission sources are geographically
collocated, even when individual sources are different. Correlations can be used to derive emissions ratios
2-31
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between species, including adjustments for the impact of photochemical aging. Investigations of this type
include correlations between CO and NOY (e.g., Parrish et al., 1991), between individual VOC species
and NOY (Goldan et al., 1995, 1997, 2000) and between various individual VOC (Goldan et al., 1995,
1997; McKeen and Liu, 1993; McKeen et al., 1996). Buhr et al. (1992) derived emission estimates from
principal component analysis (PCA) and other statistical methods. Many of these studies are summarized
in Trainer et al. (2000), Parrish et al. (1998), and Parrish and Fehsenfeld (2000). Goldstein and Schade
(2000) also used species correlations to identify the relative impacts of anthropogenic and biogenic
emissions. Chang et al. (1996, 1997) and Mendoza-Dominguez and Russell (2000, 2001) used the more
quantitative technique of inverse modeling to derive emission rates, in conjunction with results from
chemistry-transport models.
AX2.5. Methods for Calculating NOx Concentrations in the
Atmosphere
Atmospheric chemistry and transport models are the major tools used to calculate the relations
among O3, other oxidants, and their precursors, the transport and transformation of air toxics, the
production of secondary organic aerosol, the evolution of the particle size distribution, and the production
and deposition of pollutants affecting ecosystems. Chemical transport models are driven by emissions
inventories for primary species such as the precursors for O3 and PM and by meteorological fields
produced by other numerical models. Emissions of precursor compounds can be divided into
anthropogenic and natural source categories. Natural sources can be further divided into biotic
(vegetation, microbes, animals) and abiotic (biomass burning, lightning) categories. However, the
distinction between natural sources and anthropogenic sources is often difficult to make because human
activities can affect directly or indirectly emissions from what would have been considered natural
sources during the preindustrial era. Moreover, emissions from plants and animals used in agriculture
have been referred to as anthropogenic or natural in different applications. Wildfire emissions may be
considered to be natural, except that forest management practices may have led to the buildup of fuels on
the forest floor, thereby altering the frequency and severity of forest fires. Needed meteorological
quantities such as winds and temperatures are taken from operational analyses, reanalyses, or circulation
models. In most cases, these are off-line analyses, i.e., they are not modified by radiatively active species
such as O3 and particles generated by the model.
A brief overview of atmospheric chemistry-transport models is given in Section AX2.5.1.
A discussion of emissions inventories of precursors used by these models is given in Section AX2.5.
Uncertainties in emissions estimates have also been discussed in the AQCD for PM (U.S. Environmental
Protection Agency, 2004). Chemistry-transport model evaluation and an evaluation of the reliability of
emissions inventories are presented in Section AX2.5.4.
AX2.5.1. Chemistry-Transport Models
Atmospheric CTMs have been developed for application over a wide range of spatial scales ranging
from neighborhood to global. Regional scale CTMs are used chiefly for four purposes: (1) to obtain better
understanding of the processes controlling the formation, transport, and destruction of gas-and particle-
phase criteria and hazardous air pollutants; (2) to understand the relations between O3 concentrations and
concentrations of its precursors such as NOX and VOCs, the factors leading to acid deposition, and hence
to possible damage to ecosystems; (3) to understand relations among the concentration patterns of various
pollutants that may exert adverse health effects; (4) for determining control strategies for O3 precursors.
However, this last application has met with varying degrees of success because of the highly nonlinear
2-32
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relations between O3 and emissions of its precursors, and uncertainties in emissions, parameterizations of
transport, and chemical production and loss terms. Uncertainties in meteorological variables and
emissions can be large enough to lead to significant errors in developing control strategies (e.g., Russell
and Dennis, 2000; Sillman et al, 1995).
Global scale CTMs are used to address issues associated with climate change, stratospheric ozone
depletion, and to provide boundary conditions for regional scale models. CTMs include mathematical
(and often simplified) descriptions of atmospheric transport, the transfer of solar radiation through the
atmosphere, chemical reactions, and removal to the surface by turbulent motions and precipitation for
pollutants emitted into the model domain. Their upper boundaries extend anywhere from the top of the
mixing layer to the mesopause (about 80 km in height), to obtain more realistic boundary conditions for
problems involving stratospheric dynamics. There is a trade-off between the size of the modeling domain
and the grid resolution used in the CTM that is imposed by computational resources.
There are two major formulations of CTMs in current use. The first approach, grid-based or
Eulerian air quality models subdivide the region to be modeled (the modeling domain) into a three-
dimensional array of grid cells. Spatial derivatives in the species continuity equations are cast in finite-
difference form over this grid and a system of equations for the concentrations of all the chemical species
in the model are solved numerically at each grid point. Finite element Eulerian models also exist and have
been applied, but less frequently. Time dependent continuity (mass conservation) equations are solved for
each species including terms for transport, chemical production and destruction, and emissions and
deposition (if relevant), in each cell. Chemical processes are simulated with ordinary differential
equations, and transport processes are simulated with partial differential equations. Because of a number
of factors such as the different time scales inherent in different processes, the coupled, nonlinear nature of
the chemical process terms, and computer storage limitations, all of the terms in the equations are not
solved simultaneously in three dimensions. Instead, operator splitting, in which terms in the continuity
equation involving individual processes are solved sequentially, is used. In the second CTM formulation,
trajectory or Lagrangian models, a number of hypothetical air parcels are specified as following wind
trajectories. In these models, the original system of partial differential equations is transformed into a
system of ordinary differential equations.
A less common approach is to use a hybrid Lagrangian/Eulerian model, in which certain aspects of
atmospheric chemistry and transport are treated with a Lagrangian approach and others are treated in an
Eulerian manner (e.g., Stein et al., 2000). Each approach has its advantages and disadvantages. The
Eulerian approach is more general in that it includes processes that mix air parcels and allows integrations
to be carried out for long periods during which individual air parcels lose their identity. There are,
however, techniques for including the effects of mixing in Lagrangian models such as FLEXPART (e.g.,
Zanis et al., 2003), ATTILA (Reithmeier and Sausen, 2002), and CLaMS (McKenna et al., 2002).
AX2.5.1.1. Regional Scale Chemistry Transport Models
Major modeling efforts within the U.S. Environmental Protection Agency center on the Community
Multiscale Air Quality modeling system (CMAQ) (Byun and Ching, 1999; Byun and Schere, 2006). A
number of other modeling platforms using Lagrangian and Eulerian frameworks have been reviewed in
the 96 AQCD for O3 (U.S. Environmental Protection Agency, 1997), and in Russell and Dennis (2000).
The capabilities of a number of CTMs designed to study local- and regional-scale air pollution problems
were summarized by Russell and Dennis (2000). Evaluations of the performance of CMAQ are given in
Arnold et al. (2003), Eder and Yu (2005), Appel et al. (2005), and Fuentes and Raftery (2005). The
domain of CMAQ and other Eulerian CTMs can extend from several hundred km to the entire
hemisphere. In addition, both of these classes of models allow resolution of the calculations over
specified areas to vary. CMAQ is most often driven by the MM5 mesoscale meteorological model
(Seaman, 2000), though it may be driven by other meteorological models including RAMS. Simulations
of pollution episodes over regional domains have been performed with a horizontal resolution as low as 1
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km, and smaller calculations over limited domains have been accomplished at even finer scales. However,
simulations at such high resolutions require better parameterizations of meteorological processes such as
boundary layer fluxes, deep convection and clouds (Seaman, 2000), as well as finer-scale emissions. Finer
spatial resolution is necessary to resolve features such as urban heat island circulation; sea, bay, and land
breezes; mountain and valley breezes, and the nocturnal low-level jet, all of which can affect pollutant
concentrations.
The most common approach to setting up the horizontal domain is to nest a finer grid within a
larger domain of coarser resolution. However, there are other strategies such as the stretched grid (e.g.,
Fox-Rabinovitz et al., 2002) and the adaptive grid. In a stretched grid, the grid's resolution continuously
varies throughout the domain, thereby eliminating any potential problems with the sudden change from
one resolution to another at the boundary. Caution should be exercised in using such a formulation,
because certain parameterizations (such as for convection) valid on a relatively coarse grid scale may not
be valid on finer scales. Adaptive grids are not fixed at the start of the simulation, but instead adapt to the
needs of the simulation as it evolves (e.g., Hansen et al., 1994). They have the advantage that they can
resolve processes at relevant spatial scales. However, they can be very slow if the situation to be modeled
is complex. Additionally, if adaptive grids are used for separate meteorological, emissions, and
photochemical models, there is no reason a priori why the resolution of each grid should match, and the
gains realized from increased resolution in one model will be wasted in the transition to another model.
The use of finer horizontal resolution in CTMs will necessitate finer-scale inventories of land use and
better knowledge of the exact paths of roads, locations of factories, and, in general, better methods for
locating sources and estimating their emissions.
The vertical resolution of these CTMs is variable, and usually configured to have higher resolution
near the surface and decreasing aloft. Because the height of the boundary layer is of critical importance in
simulations of air quality, improved resolution of the boundary layer height would likely improve air
quality simulations. Additionally, current CTMs do not adequately resolve fine scale features such as the
nocturnal low-level jet in part because little is known about the nighttime boundary layer.
CTMs require time-dependent, three-dimensional wind fields for the period of simulation. The
winds may be generated either by a model using initial fields alone or with four-dimensional data
assimilation to improve the model's performance; (i.e., model equations can be updated periodically
"nudged" to bring results into agreement with observations). Modeling efforts typically focus on
simulations of several days' duration, the typical time scale for individual O3 episodes; but longer term
modeling series of several months or multiple seasons of the year are now common. The current trend in
modeling applications is towards annual simulations. This trend is driven in part by the need to better
understand observations of periods of high wintertime PM (e.g., Blanchard et al., 2002) and the need to
simulate O3 episodes occurring outside of summer.
Chemical kinetics mechanisms (a set of chemical reactions) representing the important reactions
occurring in the atmosphere are used in CTMs to estimate the rates of chemical formation and destruction
of each pollutant simulated as a function of time. Unfortunately, chemical mechanisms that explicitly treat
the reactions of individual reactive species are too computationally demanding to be incorporated into
CTMs for regulatory use. So, for example, one very extensive "master mechanism" (Derwent et al., 2001)
includes approximately 10,500 reactions involving 3603 chemical species (Derwent et al., 2001), but
"lumped" mechanisms that group compounds of similar chemistry together may be used. The chemical
mechanisms used in existing photochemical O3 models contain significant uncertainties that may limit the
accuracy of their predictions; the accuracy of each of these mechanisms is also limited by missing
chemistry. Because of different approaches to the lumping of organic compounds into surrogate groups,
chemical mechanisms can produce somewhat different results under similar conditions. The CB-IV
chemical mechanism (Gery et al., 1989), the RADM II mechanism (Stockwell et al., 1990), the SAPRC
(e.g., Wang et al., 2000a,b; Carter, 1990) and the RACM mechanisms can be used in CMAQ. Jimenez
et al. (2003) provide brief descriptions of the features of the main mechanisms in use and they compared
concentrations of several key species predicted by seven chemical mechanisms in a box model simulation
over 24 h. The average deviation from the average of all mechanism predictions for O3 and NO over the
2-34
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daylight period was less than 20%, and was 10% for NO2 for all mechanisms. However, much larger
deviations were found for HNO3, PAN, HO2, H2O2, ethylene (C2H4), and isoprene (C5H8). An analysis for
OH radicals was not presented. The large deviations shown for most species imply differences between
the calculated lifetimes of atmospheric species and the assignment of model simulations to either NOX-
limited or radical quantity limited regimes between mechanisms. Gross and Stockwell (2003) found small
differences between mechanisms for clean conditions, with differences becoming more significant for
polluted conditions, especially for NO2 and organic peroxy radicals. Faraji et al. (2005) found differences
of 40% in peak 1-h O3 in the Houston-Galveston-Brazoria area between simulations using SAPRAC and
CB-IV. They attributed differences in predicted O3 concentrations to differences in the mechanisms of
oxidation of aromatic hydrocarbons.
CMAQ and other CTMs (e.g., PM-CAMx) incorporate processes and interactions of aerosol-phase
chemistry (Mebust et al., 2003). There have also been several attempts to study the feedbacks of
chemistry on atmospheric dynamics using meteorological models, like MM5 (e.g., Grell et al., 2000; Liu
et al., 2001a; Lu et al., 1997; Park et al., 2001). This coupling is necessary to simulate accurately
feedbacks such as may be caused by the heavy aerosol loading found in forest fire plumes (Lu et al.,
1997; Park et al., 2001), or in heavily polluted areas. Photolysis rates in CMAQ can now be calculated
interactively with model produced O3, NO2, and aerosol fields (Binkowski et al., 2007).
Spatial and temporal characterizations of anthropogenic and biogenic precursor emissions must be
specified as inputs to a CTM. Emissions inventories have been compiled on grids of varying resolution
for many hydrocarbons, aldehydes, ketones, CO, NH3, and NOX. Emissions inventories for many species
require the application of algorithms for calculating the dependence of emissions on physical variables
such as temperature and to convert the inventories into formatted emission files which can be used by a
CTM. For example, preprocessing of emissions data for CMAQ is done by the Spare-Matrix Operator
Kernel Emissions (SMOKE) system. For many species, information concerning the temporal variability
of emissions is lacking, so long-term (e.g., annual or O3-season) averages are used in short-term, episodic
simulations. Annual emissions estimates are often modified by the emissions model to produce emissions
more characteristic of the time of day and season. Significant errors in emissions can occur if an
inappropriate time dependence or a default profile is used. Additional complexity arises in model
calculations because different chemical mechanisms can include different species, and inventories
constructed for use with another mechanism must be adjusted to reflect these differences. This problem
also complicates comparisons of the outputs of these models because one chemical mechanism may
produce some species not present in another mechanism yet neither prediction may agree with the
measurements.
In addition to wet deposition, dry deposition (the removal of chemical species from the atmosphere
by interaction with ground-level surfaces) is an important removal process for pollutants on both urban
and regional scales and must be included in CTMs. The general approach used in most models is the
resistance in series method, in which where dry deposition is parameterized with a deposition velocity
(Vd), which is represented as
vd = (ra + rb + rc)'1 AX2.5-1
where ra, rb, and rc represent the resistance due to atmospheric turbulence, transport in the fluid sublayer
very near the elements of surface such as leaves or soil, and the resistance to uptake of the surface itself.
This approach works for a range of substances, although it is inappropriate for species with substantial
emissions from the surface or for species whose deposition to the surface depends on its concentration at
the surface itself. The approach is also modified somewhat for aerosols: the terms rb and rc are replaced
with a surface Vd to account for gravitational settling. In their review, Wesely and Hicks (2000) pointed
out several shortcomings of current knowledge of dry deposition. Among those shortcomings are
difficulties in representing dry deposition over varying terrain where horizontal advection plays a
2-35
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significant role in determining the magnitude of ra and difficulties in adequately determining a Vd for
extremely stable conditions such as those occurring at night (e.g., Mahrt, 1998). Under the best of
conditions, when a model is exercised over a relatively small area where dry deposition measurements
have been made, uncertainties as large as ±30% (e.g., Massman et al, 1994; Brook et al, 1996; Padro,
1996) persist. Wesely and Hicks (2000) stated that an important result of these comparisons is that the
current level of sophistication of most dry deposition models is relatively low, and that deposition
estimates therefore must rely heavily on empirical data. Still larger uncertainties exist when the surface
features in the built environment are not well known or when the surface comprises a patchwork of
different surface types, as is common in the eastern U.S.
The initial conditions, i.e., the starting concentration fields of all species computed by a model, and
the boundary conditions, i.e., the concentrations of species along the horizontal and upper boundaries of
the model domain throughout the simulation must be specified at the beginning of the simulation. Both
initial and boundary conditions can be estimated from models or data or, more generally, model-data
hybrids. Because data for vertical profiles of most species of interest are sparse, results of model
simulations over larger, usually global, domains are often used. As may be expected, the influence of
boundary conditions depends on the lifetime of the species under consideration and the time scales for
transport from the boundaries to the interior of the model domain (Liu et al., 200 Ib).
Each of the model components described above has associated uncertainties and the relative
importance of these uncertainties varies with the modeling application. The largest errors in
photochemical modeling are still thought to arise from the meteorological and emissions inputs to the
model (Russell and Dennis, 2000). Within the model itself, horizontal advection algorithms are still
thought to be significant source of uncertainty (e.g., Chock and Winkler, 1994), though more recently,
those errors are thought to have been reduced (e.g., Odman and Ingram, 1996). There are also indications
that problems with mass conservation continue to be present in photochemical and meteorological models
(e.g., Odman and Russell, 1999), and can result in significant simulation errors. The effects of errors in
initial conditions can be minimized by including several days "spin-up" time in a simulation to allow the
model to be driven by emitted species before the simulation of the period of interest begins.
While the effects of poorly specified boundary conditions propagate through the model's domain,
the effects of these errors remain undetermined. Because many meteorological processes occur on spatial
scales which are smaller than the model grid spacing (either horizontally or vertically) and thus are not
calculated explicitly, parameterizations of these processes must be used and these introduce additional
uncertainty.
Uncertainty also arises in modeling the chemistry of O3 formation because it is highly nonlinear
with respect to NOX concentrations. Thus, the volume of the grid cell into which emissions are injected is
important because the nature of O3 chemistry (i.e., O3 production or titration) depends in a complicated
way on the concentrations of the precursors and the OH radical as noted earlier. The use of ever-finer grid
spacing allows regions of O3 titration to be more clearly separated from regions of O3 production. The use
of grid spacing fine enough to resolve the chemistry in individual power-plant plumes is too demanding
of computer resources for this to be attempted in most simulations. Instead, parameterizations of the
effects of sub-grid-scale processes such as these must be developed, else serious errors can result if
emissions are allowed to mix through an excessively large grid volume before the chemistry step in a
model calculation is performed. In light of the significant differences between atmospheric chemistry
taking place within and without of a power plant plume (e.g., Ryerson et al., 1998 and Sillman, 2000),
inclusion of a separate, meteorological module for treating large, tight plumes can be useful. Because the
photochemistry of O3 and many other atmospheric species is nonlinear, emissions correctly modeled in a
tight plume may be incorrectly modeled in a more dilute plume. Fortunately, it appears that the chemical
mechanism used to follow a plume's development need not be as detailed as that used to simulate the rest
of the domain, as the inorganic reactions are the most important in the plume see (e.g., Kumar and
Russell, 1996). The need to include explicit plume-in-grid chemistry only down to the level of the
smallest grid disappears if one uses the adaptive grid approach mentioned previously, though such grids
2-36
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are more computationally intensive. These differences in simulations may be significant for calculated
sensitivity of O3 to its precursors (e.g., Sillman et al., 1995).
Because the chemical production and loss terms in the continuity equations for individual species
are coupled, the chemical calculations must be performed iteratively until calculated concentrations
converge to within some preset criterion. The number of iterations and the convergence criteria chosen
also can introduce error.
AX2.5.1.2. Intra-urban Scale Dispersion Modeling
The grid spacing in regional chemistry transport models between 1 and 12 km2, is usually too
coarse to resolve spatial variations on the neighborhood scale. The interface between regional scale
models and models of personal exposure described in Annex 3, Section AX3.5 is provided by smaller
scale dispersion models. Several models could be used to simulate concentration fields near roads, each
with its own set of strengths and weaknesses. For example, AERMOD
(http://www.epa.gov/scramOOl/dispersion_prefrec.htm) is a steady-state plume model that was
formulated as a replacement to the ISC3 dispersion model. In the stable boundary layer (SBL), it assumes
the concentration distribution to be Gaussian in both the vertical and horizontal. In the convective
boundary layer, the horizontal distribution is also assumed to be Gaussian, but the vertical distribution is
described with a bi-Gaussian probability density function (pdf). AERMOD has provisions to be applied to
flat and complex terrain, and multiple source types (including, point, area and volume sources) in both
urban and rural areas. It incorporates air dispersion based on planetary boundary layer turbulence
structure and scaling concepts, and is meant to treat both surface and elevated sources and simple and
complex terrain in rural and urban areas. The dispersion of emissions from line sources like highways is
treated as the sum of emissions from a number of point sources placed side by side. However, emissions
are usually not in steady state and there are different functional relationships between buoyant plume rise
in point and line sources. It should be remembered that NO2 is largely secondary in nature as it is
produced by Reaction AX 2.2-3. However, AERMOD does not have provision for including secondary
sources. The more appropriate use of AERMOD would be to simulate the total of NO and NO2, or NOX.
There are models that are non-steady state and can incorporate plume rise explicitly from different
types of sources. For example, CALPUFF (http://www.src.com/calpuff/calpuffl.htm) is anon-steady-
state puff dispersion model that simulates the effects of time- and space-varying meteorological
conditions on pollution transport, transformation, and removal and has provisions for calculating
dispersion from surface sources. However, it should be noted that neither model was designed to treat the
dispersion of emissions from roads or to include secondary sources. In using either model, the user would
have to specify dispersion parameters that are specific to traffic. The distinction between a steady-state
and time varying model might not be important for long time scales; however for short time scales, the
temporal variability in traffic emissions could result in underestimation of peak concentration and
exposures.
AX2.5.1.3. Global Scale CTMs
The importance of global transport of O3 and O3 precursors and their contribution to regional O3
levels in the United States is now apparent. There are at present on the order of 20 three-dimensional
global models developed by various groups to address problems in tropospheric chemistry. These models
resolve synoptic meteorology, O3-NOx-CO-hydrocarbon photochemistry, have parameterizations for wet
and dry deposition, and parameterize sub-grid scale vertical mixing processes such as convection. Global
models have proven useful for testing and advancing scientific understanding beyond what is possible
with observations alone. For example, they can calculate quantities of interest that cannot be measured
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directly, such as the export of pollution from one continent to the global atmosphere or the response of the
atmosphere to future perturbations to anthropogenic emissions.
Global simulations are typically conducted at a horizontal resolution of 200 km2 or more.
Simulations of the effects of transport from long-range transport link multiple horizontal resolutions from
the global to the local scale. Finer resolution will only improve scientific understanding to the extent that
the governing processes are more accurately described at that scale. Consequently, there is a critical need
for observations at the appropriate scales to evaluate the scientific understanding represented by the
models.
During the recent IPCC-AR4 tropospheric chemistry study coordinated by the European Union
Atmospheric Composition Change: the European Network of excellence (ACCENT), 26 atmospheric
CTMs were used to estimate the impacts of three emissions scenarios on global atmospheric composition,
climate, and air quality in 2030 (Dentener et al., 2006a). All models were required to use anthropogenic
emissions developed at IIASA (Dentener et al., 2005) and GFED version 1 biomass burning emissions
(Van der Werf et al., 2003) as described in Stevenson et al. (2006). The base simulations from these
models were evaluated against a suite of present-day observations. Most relevant to this assessment report
are the evaluations with ozone, NO2, and nitrogen deposition (Stevenson et al., 2006; Van Noije et al.,
2006; Dentener et al., 2006a), which are summarized briefly below.
An analysis of the standard deviation of zonal mean and tropospheric column O3 reveals large inter-
model variability in the tropopause region and throughout the polar troposphere, likely reflecting
differences in model tropopause levels and the associated stratospheric injection of O3 to the troposphere
(Stevenson et al., 2006). Ozone distributions in the tropics also exhibit large standard deviations (-30%),
particularly as compared to the mid-latitudes (-20%), indicating larger uncertainties in the processes that
influence ozone in the tropics: deep tropical convection, lightning NOX, isoprene emissions and
chemistry, and biomass burning emissions (Stevenson et al., 2006).
Stevenson et al., (2006) found that the model ensemble mean (MEM) typically captures the
observed seasonal cycles to within one standard deviation. The largest discrepancies between the MEM
and observations include: (1) an underestimate of the amplitude of the seasonal cycle at 30°-90°N with a
10 ppb overestimate of winter ozone, possibly due to the lack of a seasonal cycle in anthropogenic
emissions or to shortcomings in the stratospheric influx of O3, and (2) an overestimate of O3 throughout
the northern tropics. However, the MEM was found to capture the observed seasonal cycles in the
southern hemisphere, suggesting that the models adequately represent biomass burning and natural
emissions.
The mean present-day global ozone budget across the current generation of CTMs differs
substantially from that reported in the IPCC Third Assessment Report (TAR), with a 50% increase in the
mean chemical production (to 5100 Tg O3/yr), a 30% increase in the chemical and deposition loss terms
(to 4650 and 1000 Tg O3/yr, respectively) and a 30% decrease in the mean stratospheric input flux (to 550
Tg O3/yr) (Stevenson et al., 2006). The larger chemical terms as compared to the IPCC TAR are
attributed mainly to higher NOX (as well as an equatorward shift in distribution) and isoprene emissions,
although more detailed schemes and/or improved representations of photolysis, convection, and
stratospheric-tropospheric exchange may also contribute (Stevenson et al., 2006).
A subset of 17 of the 26 models used in the Stevenson et al. (2006) study was used to compare with
three retrievals of NO2 columns from the GOME instrument (van Noije et al., 2006) for the year 2000.
The higher resolution models reproduce the observed patterns better, and the correlation among simulated
and retrieved columns improved for all models when simulated values are smoothed to a 5° H 5° grid,
implying that the models do not accurately reproduce the small-scale features of NO2 (Van Noije et al.,
2006). Van Noije et al. (2006) suggest that variability in simulated NO2 columns may reflect model
differences in OH distributions and the resulting NOX lifetimes, as well as differences in vertical mixing
which strongly affect partitioning between NO and NO2. Overall, the models tend to underestimate
concentrations in the retrievals in industrial regions (including the eastern United States) and overestimate
them in biomass burning regions (Van Noije et al., 2006).
2-38
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Over the eastern United States and in industrial regions more generally, the spread in absolute
column abundances is generally larger among the retrievals than among the models, with the discrepancy
among the retrievals particularly pronounced in winter (Van Noije et al, 2006), suggesting that the
models are biased low, or that the European retrievals may be biased high as the Dalhousie/SAO retrieval
is closer to the model estimates. The lack of seasonal variability in fossil fuel combustion emissions may
contribute to a wintertime model underestimate (Van Noije et al., 2006) manifested most strongly over
Asia. In biomass burning regions, the models generally reproduce the timing of the seasonal cycle of the
retrievals, but tend to overestimate the seasonal cycle amplitude, partly due to lower values in the wet
season, which may reflect an underestimate in wet season soil NO emissions (Van Noije et al., 2006,
Jaegle et al., 2004, 2005).
AX2.5.1.4. Modeling the Effects of Convection
The effects of deep convection can be simulated using cloud-resolving models, or in regional or
global models in which the convection is parameterized. The Goddard Cumulus Ensemble (GCE) model
(Tao and Simpson, 1993) has been used by Pickering et al. (1991, 1992a,b, 1993, 1996), Scala et al.
(1990), and Stenchikov et al. (1996) in the analysis of convective transport of trace gases. The cloud
model is nonhydrostatic and contains a detailed representation of cloud microphysical processes. Two-
and three-dimensional versions of the model have been applied in transport analyses. The initial
conditions for the model are usually from a sounding of temperature, water vapor and winds
representative of the region of storm development. Model-generated wind fields can be used to perform
air parcel trajectory analyses and tracer advection calculations.
Such methods were used by Pickering et al. (1992b) to examine transport of urban plumes by deep
convection. Transport of an Oklahoma City plume by the 10-11 June 1985 PRE-STORM squall line was
simulated with the 2-D GCE model. This major squall line passed over the Oklahoma City metropolitan
area, as well as more rural areas to the north. Chemical observations ahead of the squall line were
conducted by the PRE-STORM aircraft. In this event, forward trajectories from the boundary layer at the
leading edge of the storm showed that almost 75% of the low level inflow was transported to altitudes
exceeding 8 km. Over 35% of the air parcels reached altitudes over 12 km. Tracer transport calculations
were performed for CO, NOX, O3, and hydrocarbons. Rural boundary layer NOX was only 0.9 ppb,
whereas the urban plume contained about 3 ppb. In the rural case, mixing ratios of 0.6 ppb were
transported up to 11 km. Cleaner air descended at the rear of the storm lowering NOX at the surface from
0.9 to 0.5 ppb. In the urban plume, mixing ratios in the updraft core reached 1 ppb between 14 and 15 km.
At the surface, the main downdraft lowered the NOX mixing ratios from 3 to 0.7 ppb.
Regional chemical transport models have been used for applications such as simulations of
photochemical O3 production, acid deposition, and fine PM. Walcek et al. (1990) included a
parameterization of cloud-scale aqueous chemistry, scavenging, and vertical mixing in the chemistry
model of Chang et al. (1987). The vertical distribution of cloud microphysical properties and the amount
of sub-cloud-layer air lifted to each cloud layer are determined using a simple entrainment hypothesis
(Walcek and Taylor, 1986). Vertically integrated O3 formation rates over the northeast U. S. were
enhanced by -50% when the in-cloud vertical motions were included in the model.
Wang et al. (1996) simulated the 10-11 June 1985 PRE-STORM squall line with the NCAR/Penn
State Mesoscale Model (MM5) (Grell et al., 1994; Dudhia, 1993). Convection was parameterized as a
sub-grid-scale process in MM5 using the Kain and Fritsch (1993) scheme. Mass fluxes and detrainment
profiles from the convective parameterization were used along with the 3-D wind fields in CO tracer
transport calculations for this convective event.
Convective transport in global chemistry and transport models is treated as a sub-grid-scale process
that is parameterized typically using cloud mass flux information from a general circulation model or
global data assimilation system. While GCMs can provide data only for a "typical" year, data assimilation
systems can provide "real" day-by-day meteorological conditions, such that CTM output can be compared
2-39
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directly with observations of trace gases. The NASA Goddard Earth Observing System Data Assimilation
System (GEOS-1 DAS and successor systems; Schubert et al., 1993; Bloom et al., 1996; Bloom et al.,
2005) provides archived global data sets for the period 1980 to present, at 2E H 2.5E or better resolution
with 20 layers or more in the vertical. Deep convection is parameterized with the Relaxed
Arakawa-Schubert scheme (Moorthi and Suarez, 1992) in GEOS-1 and GEOS-3 and with the Zhang and
McFarlane (1995) scheme in GEOS-4. Pickering et al. (1995) showed that the cloud mass fluxes from
GEOS-1 DAS are reasonable for the 10-11 June 1985 PRE-STORM squall line based on comparisons
with the GCE model (cloud-resolving model) simulations of the same storm. In addition, the GEOS-1
DAS cloud mass fluxes compared favorably with the regional estimates of convective transport for the
central United States presented by Thompson et al. (1994). However, Allen et al. (1997) have shown that
the GEOS-1 DAS overestimates the amount and frequency of convection in the tropics and
underestimates the convective activity over midlatitude marine storm tracks.
Global models with parameterized convection and lightning have been run to examine the roles of
these processes over North America. Lightning contributed 23% of upper tropospheric NOY over the
SONEX region according to the UMD-CTM modeling analysis of Allen et al. (2000). During the summer
of 2004 the NASA Intercontinental Chemical Transport Experiment - North America (INTEX-NA) was
conducted primarily over the eastern two-thirds of the United States, as a part of the International
Consortium for Atmospheric Research on Transport and Transformation (ICARTT). Deep convection
was prevalent over this region during the experimental period. Cooper et al. (2006) used a particle
dispersion model simulation for NOX to show that 69-84% of the upper tropospheric O3 enhancement
over the region in Summer 2004 was due to lightning NOX. The remainder of the enhancement was due to
convective transport of O3 from the boundary layer or other sources of NOX. Hudman et al. (2007) used a
GEOS-Chem model simulation to show that lightning was the dominant source of upper tropospheric
NOX over this region during this period. Approximately 15% of North American boundary layer NOX
emissions were shown to have been vented to the free troposphere over this region based on both the
observations and the model.
AX2.5.2. CTM Evaluation
The comparison of model predictions with ambient measurements represents a critical task for
establishing the accuracy of photochemical models and evaluating their ability to serve as the basis for
making effective control strategy decisions. The evaluation of a model's performance, or its adequacy to
perform the tasks for which it was designed can only be conducted within the context of measurement
errors and artifacts. Not only are there analytical problems, but there are also problems in assessing the
representativeness of monitors at ground level for comparison with model values which represent
typically an average over the volume of a grid box.
Global-scale CTMs have generally been evaluated by comparison with measurements for a wide
array of species, rather than just for O3 (e.g., Wang et al., 1998; Emmons et al., 2000; Bey et al., 2001;
Hess, 2001; Fiore et al., 2002). These have included evaluation of major primary species (NOX, CO, and
selected VOCs) and an array of secondary species (HNO3, PAN, H2O2) that are often formed concurrently
with O3. Models for urban and regional O3 have also been evaluated against a broader ensemble of
measurements in a few cases, often associated with measurement intensives (e.g., Jacobson et al., 1996;
Lu et al., 1997; Sillman et al., 1998). The results of a comparison between observed and computed
concentrations from Jacobson et al. (1996) for the Los Angeles Basin are shown in Figures AX2.5-1 and
AX2.5-2.
The highest concentrations of primary species usually occur in close proximity to emission sources
(typically in urban centers) and at times when dispersion rates are low. The diurnal cycle includes high
concentrations at night, with maxima during the morning rush hour, and low concentrations during the
afternoon (Figure AX2.5-1). The afternoon minima are driven by the much greater rate of vertical mixing
at that time. Primary species also show a seasonal maximum during winter, and are often high during fog
2-40
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episodes in winter when vertical mixing, is suppressed. By contrast, secondary species such as O3 are
typically highest during the afternoon (the time of greatest photochemical activity), on sunny days and
during summer.
During these conditions, concentrations of primary species may be relatively low. Strong
correlations between primary and secondary species are generally observed only in downwind rural areas
where all anthropogenic species are simultaneously elevated. The difference in the diurnal cycles of
primary species (CO, NOX and ethane) and secondary species (O3, PAN, and HCHO) is evident in Figure
AX2.5-2.
o
8 16 24 32 40 48 56 64 72
Hour After First Midnight
ra
QL
0.
&.
q
'•s
o>
c
'x
0.30
0.25
0.20
0.15
0.10
0.05
0.00
6
5
4
3
2
1
0
Reseda
NOX (g)
Predicted
Observed
0 8 16 24 32 40 48 56 64 72
Hour After First Midnight
Riverside
C0(g)
Predicted
Observed
16 24 32 40 48 56 64 72
Hour After First Midnight
Source: Jacobson et al. (1996)
Figure AX2.5-1 . Time series for measured gas-phase species in comparison with results from
aphotochemical 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 03 and NOX at Reseda, and CO at Riverside.
2-41
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0.060
| 0.050
o.
3 0.040
o
~ 0.030
£
O) 0.020
_e
~ 0.010
0.000
Claremont
Ethane (g)
Predicted
O Observed
_L
J_
_L
_L
J_
_L
0 8 16 24 32 40 48 56 64
Hour After First Midnight
72
0.030
| 0.025
Q.
3 0.020
o
~ 0.015
ft
D) 0.010
~ 0.005
0.000
0.020
£ 0.016
Q.
a.
'Q' 0.012
a 0.008
TO
* 0.004
E
0.000
Claremont
Formaldehyde (g)
Predicted
Observed
16 24 32 40 48 56
Hour After First Midnight
64
72
Los Angeles
PAN (g)
---- Observed
16 24 32 40 48 56 64
Hour After First Midnight
72
Source: Jacobson etal. (1996).
Figure AX2.5-2. 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.
Models for urban and regional chemistry have been evaluated less extensively than global-scale
models in part because the urban/regional context presents a number of difficult challenges. Global-scale
models typically represent continental-scale events and can be evaluated effectively against a sparse
network of measurements. By contrast, urban/regional models are critically dependent on the accuracy of
local emission inventories and event-specific meteorology, and must be evaluated separately for each
urban area that is represented.
The evaluation of urban/regional models is also limited by the availability of data. Measured NOX
and speciated VOC concentrations are widely available through the regulatory networks of the U.S. EPA.
Evaluation of urban/regional models versus measurements has generally relied on results from a limited
number of field studies in the United States. Short-term, research-grade measurements for species
relevant to O3 formation, including VOCs, NOX, PAN, HNO3, and H2O2 are also available at selected
2-42
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rural and remote sites (e.g., Daum et al., 1990, 1996; Martin et al., 1997; Young et al., 1997; Thompson
et al., 2000; Hoell et al., 1997, 1999; Fehsenfeld et al., 1996; Emmons et al., 2000; Hess, 2001; Carroll
et al., 2001). The equivalent measurements are available for some polluted rural sites in the eastern
United States, but only at a few urban locations (Meagher et al., 1998; Hiibler et al., 1998; Kleinman
et al., 2000, 2001; Fast et al., 2002; Lu et al., 1997). Extensive measurements have also been made in
Vancouver (Steyn et al., 1997) and in several European cities (Staffelbach et al., 1997; Prevot et al., 1997,
Dommen et al., 1999; Geyer et al., 2001; Thielman et al., 2001; Martilli et al., 2002; Vautard et al., 2002).
The results of straightforward comparisons between observed and predicted concentrations of O3
can be misleading because of compensating errors, although this possibility is diminished when a number
of species are compared. Ideally, each of the main modules of a CTM system (for example, the
meteorological model and the chemistry and radiative transfer routines) should be evaluated separately.
However, this is rarely done in practice. To better indicate how well physical and chemical processes are
being represented in the model, comparisons of relations between concentrations measured in the field
and concentrations predicted by the model can be made. These comparisons could involve ratios and
correlations between species. For example, correlation coefficients could be calculated between primary
species as a means of evaluating the accuracy of emission inventories or between secondary species as a
means of evaluating the treatment of photochemistry in the model. In addition, spatial relations involving
individual species (correlations, gradients) can also be used as a means of evaluating the accuracy of
transport parameterizations. Sillman and He (2002) examined differences in correlation patterns between
O3 and NOZ in Los Angeles, CA, Nashville, TN, and various sites in the rural United States. Model
calculations (Figure AX2.5-3) show differences in correlation patterns associated with differences in the
sensitivity of O3 to NOX and VOCs. Primarily NOx-sensitive (NOx-limited) areas in models show a
strong correlation between O3 and NOZ with a relatively steep slope, while primarily VOC-sensitive
(NOx-saturated) areas in models show lower O3 for a given NOZ and a lower O3-NOZ slope. They found
that differences found in measured data ensembles were matched by predictions from chemical transport
models. Measurements in rural areas in the eastern United States show differences in the pattern of
correlations for O3 versus NOZ between summer and autumn (Jacob et al., 1995; Hirsch et al., 1996),
corresponding to the transition from NOx-limited to NOx-saturated patterns, a feature which is also
matched by CTMs.
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250
voc-
x Sensitive
20
NOZ (ppb)
Source: Sillman and He (2002).
Figure AX2.5-3. Correlations for 03 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.
The difference in correlations between secondary species in NOx-limited to NOx-saturated environments
can also be used to evaluate the accuracy of model predictions in individual applications. Figures
AX2.5-4, show results for two different model scenarios for Atlanta. As shown in the figures, the first
model scenario predicts an urban plume with high NOY and O3 formation apparently suppressed by high
NOY. Measurements show much lower NOY in the Atlanta plume. This error was especially significant
because the model locations sensitive to NOX. The second model scenario (with primarily NOX sensitive
conditions) shows much better agreement with measured values. Figure AX2.5-5 shows model-
measurement comparison for secondary species in Nashville, showing better agreement with measured
conditions. Greater confidence in the predictions made by CTMs will be gained by the application of
techniques such as these on a more routine basis.
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200
10
20
NOy (ppb)
30
40
20
NOy (ppb)
Source: Sillmanetal. (1997).
Figure AX.2.5-4. 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.
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.Q
a.
a.
160
140
120
100
20
0
160
140
10
20
NOZ (ppb)
30
40
0
10 20 30
2H2O2 + NOZ (ppb)
Source: Sillmanetal. (1998).
Figure AX2.5-5. Evaluation of model versus: (a) measured Os versus NOz and (b) Os versus the sum 2H202 +
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.
The ability of chemical mechanisms to calculate the concentrations of free radicals under
atmospheric conditions was tested in the Berlin Ozone Experiment, BERLIOZ (Volz-Thomas et al., 2003)
during July and early August at a site located about 50 km NW of Berlin. (This location was chosen
because O3 episodes in central Europe are often associated with SE winds.)
Concentrations of major compounds such as O3, hydrocarbons, etc., were fixed at observed values.
Figure AX2.5-6 compares the concentrations of RO2, HO2, and OH radicals predicted by RACM and
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MCM with observations made by the laser-induced fluorescence (LIF) technique and by matrix isolation
ESR spectroscopy (MIESR). Also shown are the production rates of O3 calculated using radical
concentrations predicted by the mechanisms and those obtained by measurements, and measurements of
NOX concentrations. As can be seen, there is good agreement between measurements of RO2, HO2, OH,
radicals with values predicted by both mechanisms at high concentrations of NOX (>10 ppb). However, at
lower NOX concentrations, both mechanisms substantially overestimate OH concentrations and
moderately overestimate HO2 concentrations. Agreement between models and measurements is generally
better for organic peroxy radicals, although the MCM appears to overestimate their concentrations
somewhat. In general, the mechanisms reproduced the HO2 to OH and RO2 to OH ratios better than the
individual measurements. The production of O3 was found to increase linearly with NO (for NO <
0.3 ppb) and to decrease with NO (for NO > 0.5 ppb).
OH and HO2 concentrations measured during the PM2 5 Technology Assessment and
Characterization Study conducted at Queens College in New York City in the summer of 2001 were also
compared with those predicted by RACM (Ren et al., 2003). The ratio of observed to predicted HO2
concentrations over a diurnal cycle was 1.24 and the ratio of observed to predicted OH concentrations
was about 1.10 during the day, but the mechanism significantly underestimated OH concentrations during
the night.
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CO
I
o
CO
O
CM
O
CO
I
o
CO
O
CM
O
6
4
2
0
I 10
(O
I 5
o o
£• 10
B
a.
~ 5
CO
o
r o
1 20
Q.
5 10
z
0
O LIF
• MiESR
VVV
J(d1D)*106(s-1)
8
10 12 14
UT 20.7.98
16
Source: Volz-Thomas et al. (2003).
Figure AX2.5-6. Time series of concentrations of R02, H02, and OH radicals, local Os photochemical
production rate and concentrations of NOx. These are from measurements made during
BERLIOZ. Also shown are comparisons with results of photochemical box model
calculations using the RACM and MCM chemical mechanisms.
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AX2.6. Sampling and Analysis of NOx
AX2.6.1. Availability and Accuracy of Ambient NOv Measurements
Sections AX2.6.1-AX2.6.4 focus on current methods and on promising new technologies, but no
attempt is made here to cover the extensive development of these methods or of methods such as wet
chemical techniques no longer in widespread use. More detailed discussions of these methods may be
found elsewhere (U.S. Environmental Protection Agency, 1993, 1996). McClenny (2000), Parrish and
Fehsenfeld (2000), and Clemitshaw (2004) reviewed methods for measuring NOX and NOY compounds.
Discussions in Sections 2.6.1-2.6.4 center on chemiluminescence and optical Federal Reference and
Equivalent Methods (FRM and FEM, respectively).
The use of methods such as observationally based methods or source apportionment models, either
as stand-alone methods or as a basis for evaluating chemical transport models, is often limited by the
availability and accuracy of measurements. Measured NOX and speciated VOC concentrations are widely
available in the United States through the Photochemical Assessment Monitoring Stations (PAMS)
network. The PAMs network currently includes measured NO and NOX. However, Cardelino and
Chameides (2000) reported that measured NO during the afternoon was frequently at or below the
detection limit of the instruments (1 ppb), even in large metropolitan regions (Washington, DC; Houston,
TX; New York, NY). NO2 measurements are made with commercial chemiluminescent detectors with hot
molybdenum converters. However, these measurements typically include a wide variety of other reactive
N species, such as organic nitrates in addition to NOX, and cannot be interpreted as a "pure" NOX
measurement (see summary in Parrish and Fehsenfeld, 2000).
Total reactive nitrogen (NOY) is included in the PAMS network only at a few sites. The possible
expansion of PAMS to include more widespread NOY measurements has been suggested (McClenny,
2000). NOY measurements are also planned for inclusion in the NCore network (U.S. Environmental
Protection Agency, 2005). A major issue to be considered when measuring NOX and NOY is the
possibility that HNO3, a major component of NOY, is sometimes lost in inlet tubes and not measured
(Luke et al., 1998; Parrish and Fehsenfeld, 2000). This problem is especially critical if measured NOY is
used to identify NOx-limited versus NOx-saturated conditions. The problem is substantially alleviated
although not necessarily completely solved by using much shorter inlets on NOY monitors than on NOX
monitors and by the use of surfaces less likely to take up HNO3. The correlation between O3 and NOY
differs for NOx-limited versus NOx-saturated locations, but this difference is driven primarily by
differences in the ratio of O3 to HNO3. If HNO3 were omitted from the NOY measurements, then the
measurements would represent a biased estimate and their use would be problematic.
AX2.6.1.1. Measurement of NO
Gas-phase Chemiluminescence (CL) Methods
Nitric oxide can be measured reliably using the principle of gas-phase chemiluminescence induced
by the reaction of NO with O3 at low pressure. Modern commercial NOX analyzers have sufficient
sensitivity and specificity for adequate measurement in urban and many rural locations (U.S.
Environmental Protection Agency, 1993, 1996, 2006). Research grade CL instruments have been
compared under realistic field conditions to spectroscopic instruments, and the results indicate that both
methods are reliable (at concentrations relevant to smog studies) to better than 15 percent with 95 percent
confidence. Response times are on the order of 1 minute. For measurements meaningful for understanding
O3 formation, emissions modeling, and N deposition, special care must be taken to zero and calibrate the
instrument frequently. A chemical zero, obtained by reacting the NO up-stream and out of view of the
photomultiplier tube, is preferred because it accounts for interferences such as light emitting reactions
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with unsaturated hydrocarbons. Standard additions of NO at the inlet will account for NO loss or
conversion to NO2 in the lines. In summary, CL methods, when operated carefully in an appropriate
manner, can be suitable for measuring or monitoring NO (e.g., Crosley, 1996).
Spectroscopic Methods for NOx
NO has also been successfully measured in ambient air with direct spectroscopic methods; these
include two-photon laser-induced fluorescence (TPLIF), tunable diode laser absorption spectroscopy
(TDLAS), and two-tone frequency-modulated spectroscopy (TTFMS). These were reviewed thoroughly
in the previous AQCD and will be only briefly summarized here. The spectroscopic methods demonstrate
excellent sensitivity and selectivity for NO with detection limits on the order of 10 parts per trillion (ppt)
for integration times of 1 min. Spectroscopic methods compare well with the CL method for NO in
controlled laboratory air, ambient air, and heavily polluted air (e.g., Walega et al, 1984; Gregory et al.,
1990; Kireev et al., 1999). These spectroscopic methods remain in the research arena due to their
complexity, size, and cost, but are essential for demonstrating that CL methods are reliable for monitoring
NO concentrations involved in O3 formation, from around 20 ppt to several hundred of ppb.
Atmospheric pressure laser ionization followed by mass spectroscopy has also been deployed for
detection of NO and NO2. Garnica et al. (2000) describe a technique involving selective excitation at one
wavelength followed by ionization at a second wavelength. They report good selectivity and detection
limits well below 1 ppb. The practicality of the instrument for ambient monitoring, however, has yet to be
demonstrated.
AX2.6.1.2. Measurements of N02
Gas-Phase Chemiluminescence Methods
Reduction of NO2 to NO on the surface of a heated (to 300 to 400°C) molybdenum oxide
(MoOx)substrate followed by detection of the chemiluminescence produced during the reaction of NO
with O3 at low pressure as described earlier for measurement of NO serves as the basis of the FRM for
measurement of ambient NO2. However, the substrate used in the reduction of NO2 to NO is not specific
to NO2; hence the chemiluminescence analyzers are subject to interference from nitrogen oxides other
than NO2 produced by oxidized NOY compounds or NOZ. Thus, this technique will overestimate NO2
concentrations particularly in areas downwind of sources of NO and NO2 as NOX is oxidized to NOZ in
the form of PANs and other organic nitrates, and HNO3 and HNO4. Many of these compounds are
reduced at the catalyst with nearly the same efficiency as NO2. Interferences have also been found from a
wide range of other compounds as described in the latest AQCD for NO2.
Other Methods
NO2 can be selectively converted to NO by photolysis. For example, Ryerson et al. (2000)
developed a gas-phase chemiluminescence method using a photolytic converter based on a Hg lamp with
increased radiant intensity in the region of peak NO2 photolysis (350 to 400 nm) and producing
conversion efficiencies of 70% or more in less than 1 s. Metal halide lamps with conversion efficiency of
about 50% and accuracy on the order of 20% (Nakamura et al., 2003) have been used. Because the
converter produces little radiation at wavelengths less than 350 nm, interferences from HNO3 and PAN
are minimal. Alternative methods to photolytic reduction followed by CL are desirable to test the
reliability of this widely used technique. Any method based on a conversion to measured species presents
potential for interference a problem. Several atmospheric species, PAN and HO2NO2 for example,
dissociate to NO2 at higher temperatures.
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Laser induced fluorescence for NO2 detection involves excitation of atmospheric NO2 with laser
light emitted at wavelengths too long to induce photolysis. The resulting excited molecules relax in a
photoemissive mode and the fluorescing photons are counted. Because collisions would rapidly quench
the electronically excited NO2, the reactions are conducted at low pressure. Matsumi et al. (2001) describe
a comparison of LIF with a photofragmentation chemiluminescence instrument. The LIF system involves
excitation at 440 nm with a multiple laser system. They report sensitivity of 30 ppt in 10 s and good
agreement between the two methods under laboratory conditions at mixing ratios up to 1.0 ppb. This
high-sensitivity LIF system has yet to undergo long-term field tests. Cleary et al. (2002) describe field
tests of a system that uses continuous, supersonic expansion followed by excitation at 640 nm with a
commercial tunable diode laser. More recently, LIF has been successfully used to detect NO2 with
accuracy of about 15% and detections limits well below 1 ppb. When coupled with thermal dissociation,
the technique also measures peroxy nitrates such as PAN, alkyl nitrates, HNO4 and HNO3 (Cohen, 1999;
Day et al., 2002; Farmer et al., 2006; Perez et al., 2007; Thornton et al., 2003). This instrument can have
very fast sampling rates (>1 Hz) and shows good correlation with chemiluminescent techniques but
remains a research-grade device.
NO2 can be detected by differential optical absorption spectroscopy (DOAS) in an open, long-path
system by measuring narrow band absorption features over a background of broad band extinction (e.g.,
Stutz et al., 2000; Kim and Kim, 2001). A DOAS system manufactured by OPSIS is designated as a
Federal Equivalent Method for measuring NO2. DOAS systems can also be configured to measure NO,
HONO, andNO3 radicals. Typical detection limits are 0.2 to 0.3 ppb for NO, 0.05 to 0.1 ppb forNO2,
0.05 to 0.1 ppb for HONO, and 0.001 to 0.002 ppb forNO3, at path lengths of 0.2, 5, 5, and 10 km,
respectively. The obvious advantage compared to fixed point measurements is that concentrations
relevant to a much larger area are obtained, especially if multiple targets are used. At the same time, any
microenvironmental artifacts are minimized over the long path integration. However, comparisons to
other measurements made at point not a real location are difficult. A major limitation in this technique
had involved inadequate knowledge of absorption cross sections. Harder et al. (1997) conducted an
experiment in rural Colorado involving simultaneous measurements of NO2 by DOAS and by photolysis
followed by chemiluminescence. They found differences of as much as 110% in clean air from the west,
but for NO2 mixing ratios in excess of 300 ppt, the two methods agreed to better than 10%. Stutz et al.
(2000) cites two intercomparisons of note. NO was measured by DOAS, by photolysis of NO2 followed
by chemiluminescence, and by LIF during July 1999 as part of the SOS in Nashville, TN. On average, the
three methods agreed to within 2%, with some larger differences likely caused by spatial variability over
the DOAS path. In another study in Europe, and a multi-reflection set-up over a 15 km path, negated the
problem of spatial averaging here agreement with the chemiluminescence detector following photolytic
conversion was excellent (slope = 1.006 ± 0.005; intercept = 0.036 ± 0.019; r = 0.99) over a concentration
range from about 0.2 to 20 ppb.
NO2 can also be detected from space with DOAS-like UV spectroscopy techniques (Kim et al.,
2006; Ma et al., 2006). These measurements appear to track well with emissions estimates and can be a
useful indicator of column content as well as for identifying hot spots in sources. See also Richter et al.,
2005. Leigh (2006) report on a DOAS method that uses the sun as a light source and compares well with
an in situ chemiluminescence detector in an urban environment.
Chemiluminescence on the surface of liquid luminol has also been used for measurement of NO2
(Gaffney et al., 1998; Kelly et al., 1990; Marley et al., 2004; Nikitas et al., 1997; Wendel et al., 1983).
This technique is sensitive and linear and more specific than hot MoOx. Luminol does not emit light
when exposed to HNO3 or alkyl nitrates, but does react with PAN. This interference can be removed by
chromatographic separation prior to detection and the resulting measurement compares well with more
specific techniques for moderate to high (>1 ppb) mixing ratios of NO2.
Several tunable diode laser spectroscopy techniques have been used successfully for NO2 detection
(Eisele et al., 2003; Osthoff et al., 2006). These devices remain research grade instruments, not yet
practical for urban monitoring.
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Measurements of NOv
Gold catalyzed CO or H2 reduction for conversion on hot MoOx catalyst has been used to reduce
NOY to NO before then detection by chemiluminescence (Fehsenfeld et al., 1987; Crosley, 1996). Both
techniques offer generally reliable measurements, with response times on the order of 60 s and a linear
dynamic range demonstrated in field intercomparisons from about 10 ppt to 10s of ppb. Under certain
conditions, HCN, NH3, RNO2, and CH3CN can be converted to NO, but at normal concentrations and
humidity these are minor interferences. Thermal decomposition followed by LIF has also been used for
NOY detection, as described above. In field comparisons, instruments based on these two principles
generally showed good agreement (Day et al., 2002). The experimental uncertainty is estimated to be of
15-30%.
AX2.6.1.3. Monitoring for N02 Compliance Versus Monitoring for Os Formation
Regulatory measurements of NO2 have been focused on demonstrating compliance with the
NAAQS for NO2. Today, few locations violate that standard, but NO2 and related NOY compounds
remain among the most important atmospheric trace gases to measure and understand. Unfortunately,
with an internal MoOx converter for NOX to NO conversion, the instruments may not give a faithful
indication of total NOY either; reactive species such as HNO3 will adhere to the walls of the inlet system.
Most recently, commercial vendors such as Thermo Environmental (Franklin, MA) have offered NO/NOY
detectors with external MoOx converters. If such instruments are calibrated through the inlet with a
reactive nitrogen species such as propyl nitrate, they give accurate measurements of total NOY suitable for
evaluation of photochemical models. (Crosley, 1996; Fehsenfeld et al., 1987; Nunnermacker et al., 1998;
Rodgers and Davis, 1989). Under conditions of fresh emissions, such as in urban areas during the rush
hour, NOY ~ NOX and these monitors can be used for testing emissions inventories (Dickerson, et al.,
1995; Parrish, 2006).
AX2.6.2. Summary of Methods for Measuring N02
A variety of techniques exist for reliable monitoring of atmospheric NO2 and related reactive
nitrogen species. For demonstration of compliance with the NAAQS for NO2, commercial
chemiluminescence instruments are adequate. For certain conditions, luminol chemiluminescence is
adequate. Precise measurements of NO2 can be made with research grade instruments such as LIF and
TDLS. For path-integrated concentration determinations UV spectroscopic methods provide useful
information. Commercial NOX instruments are sensitive to other NOY species, but do not measure NOY
quantitatively. NOY instruments with external converters offer measurements more useful for comparison
to chemical transport model calculations.
AX2.6.3. Measurements of HN03
Accurate measurement of HNO3, has presented a long-standing analytical challenge to the
atmospheric chemistry community. In this context, it is useful to consider the major factors that control
HNO3 partitioning between the gas and deliquesced-particulate phases in ambient air. In equation form,
where KH is the Henry's Law constant in M atnf : and Ka is the acid dissociation constant in M.
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Thus, the primary controls on HNO3 phase partitioning are its thermodynamic properties (KH, Ka,
and associated temperature corrections), aerosol liquid water content (LWC), solution pH, and kinetics.
Aerosol LWC and pH are controlled by the relative mix of different acids and bases in the system,
hygroscopic properties of condensed compounds, and meteorological conditions such as RH, temperature,
and pressure. It is evident from relationship AX2.6-1 that, in the presence of chemically distinct aerosols
of varying acidities (e.g., supermicron predominantly sea salt and submicron predominantly S aerosol),
HNO3 will partition preferentially with the less-acidic particles; and this is consistent with observations
(e.g., Huebert et al., 1996; Keene and Savoie, 1998; Keene et al., 2002). Kinetics are controlled by
atmospheric concentrations of HNO3 vapor and particulate NO3" and the size distribution and
corresponding atmospheric lifetimes of particles against deposition. Submicron diameter aerosols
typically equilibrate with the gas phase in seconds to minutes while supermicron aerosols require hours to
a day or more (e.g., Meng and Seinfeld, 1996; Erickson et al., 1999). Consequently, smaller aerosol size
fractions are typically close to thermodynamic equilibrium with respect to HNO3 whereas larger size
fractions (for which atmospheric lifetimes against deposition range from hours to a few days) are often
undersaturated (e.g., Erickson et al., 1999; Keene and Savioe, 1998).
Many sampling techniques for HNO3 (e.g., annular denuder, standard filterpack and mist-chamber
samplers) employ upstream prefilters to remove particulate species from sample air. However, when
chemically distinct aerosols with different pHs (e.g., sea salt and S aerosols) mix together on a bulk filter,
the acidity of the bulk mixture will be greater than that of the less acidic aerosols with which most NO3" is
associated. This change in pH may cause the bulk mix to be supersaturated with respect to HNO3 leading
to volatilization and, thus, positive measurement bias in HNO3 sampled downstream. Alternatively, when
undersaturated supermicron size fractions (e.g., sea salt) accumulate on a bulk filter and chemically
interact over time with HNO3 in the sample air stream, scavenging may lead to negative bias in HNO3
sampled downstream. Because the magnitude of both effects will vary as functions of the overall
composition and thermodynamic state of the multiphase system, the combined influence can cause net
positive or net negative measurement bias in resulting data. Pressure drops across particle filters can also
lead to artifact volatilization and associated positive bias in HNO3 measured downstream.
Widely used methods for measuring HNO3 include standard filterpacks configured with nylon or
alkaline-impregnated filters (e.g., Goldan et al., 1983; Bardwell et al., 1990), annular denuders (EPA
Method IP-9), and standard mist chambers (Talbot et al., 1990). Samples from these instruments are
typically analyzed by ion chromatography. Intercomparisons of these measurement techniques (e.g.,
Hering et al., 1988; Tanner et al., 1989; Talbot et al., 1990) report differences on the order of a factor of
two or more.
More recently, sensitive HNO3 measurements based on the principle of Chemical lonization Mass
Spectroscopy (CIMS) have been reported (e.g., Huey et al., 1998; Mauldin et al., 1998; Furutani and
Akimoto, 2002; Neuman et al., 2002). CIMS relies on selective formation of ions such as SiF5"^HNO3 or
HSO4"^HNO3 followed by detection via mass spectroscopy. Two CIMS techniques and a filter pack
technique were intercompared in Boulder, CO (Fehsenfeld et al., 1998). Results indicated agreement to
within 15% between the two CIMS instruments and between the CIMS and filterpack methods under
relatively clean conditions with HNO3 mixing ratios between 50 and 400 ppt. In more polluted air, the
filterpack technique generally yielded higher values than the CIMS suggesting that interactions between
chemically distinct particles on bulk filters is a more important source of bias in polluted continental air.
Differences were also greater at lower temperature when particulate NO3 corresponded to relatively
greater fractions of total NO3".
Three semi-continuous methods for detecting nitric acid (HNO3) were tested against the annular
denuder filter pack (ADS) integrated collection technique at the Tampa Bay Regional Atmospheric
Chemistry Experiment (BRACE) Sydney research station ~20 km downwind of the Tampa, Florida,
urban core. The semi-continuous instruments included: two slightly differing implementations of the NOY
~ NOY* (total oxides of nitrogen minus that total denuded of HNO3) denuder difference technique, one
from the NOAA Air Resources Lab (ARL), and one from Atmospheric Research and Analysis, Inc.
(ARA); the parallel plate wet diffusion scrubber online ion chromatography technique from Texas Tech
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University (TTU); and the chemical ionization mass spectrometer from the Georgia Institute of
Technology (GIT). Twelve hour ADS samples were collected by the University of South Florida (USF).
Results for 10 min samples computed from the various higher sampling frequencies of each semi-
continuous instrument showed good agreement (R2>0.7)for afternoon periods of the highest production
and accumulation of HNO3. Further, agreement was within ± 30% for these instruments even at HNO3
concentrations 0.30 ppb. The USF ADS results were biased low, however, by 44%, on average, compared
to the corporate 12-h aggregated means from the semi-continuous methods, and by 460% for the
nighttime samples; ADS results were below the corporate mean maximum HNO3 concentration by 430%
as well. The four instruments using semi-continuous methods, by contrast, were all within 10% of each
other's 12-h mean mixing ratios. While only ARA employed a formal minimum detection limit at
0.050 ppb, error analysis with the other techniques established that at the same level of precision, TTU's
effective limit was approximately the same as ARA's and that ARL's limit was 0.030 ppb; analysis for
GIT showed no apparent effective limit at the levels of HNO3 encountered in this field study. The
importance of sample inlet height for HNO3 measurements was indirectly shown through comparison to
previous field work at this site when sample inlet heights ranged from 1.5-10 m and produced systematic
discrepancies in HNO3 concentrations correlated with height of more than a factor of 2 (Arnold et al.,
2007).
AX2.6.4. Techniques for Measuring Other NOy Species
Methods for sampling and analysis of alkyl nitrates in the atmosphere have been reviewed by
Parrish and Fehsenfeld (2000). Peroxyacetylnitrate, PPN, and MPAN are typically measured using a
chromatograph followed by electron capture detectors or GC/ECD (e.g., Gaffney et al., 1998), although
other techniques such as FTIR could also be used. Field measurements are made using GC/ECD with a
total uncertainty of ± 5 ppt +15% (Roberts et al., 1998).
In the IMPROVE network and in the EPA Chemical Speciation Network (CSN), particulate nitrate
in the PM2 5 size range is typically collected on nylon filters downstream of annular denuders coated with
a basic solution capable of removing acidic gases such as HNO3, HNO2, and SO2. Filter extracts are then
analyzed by ion chromatography (1C) for nitrate, sulfate, and chloride. Nitrite ions are also measured by
this technique but their concentrations are almost always beneath detection limits. However, both of these
networks measure nitrate only in the PM2 5 fraction. Because of interactions with more highly acidic
components on filter surfaces, there could be volatilization of nitrate in PM10 samples. These effects are
minimized if separate aerosol size fractions are collected, i.e., the more acidic PM25 and the more alkaline
PMi 0-2.5 as in a dichotomous sampler or multistage impactor.
AX2.6.5. Remote Sensing of Tropospheric N02 Columns for Surface
NOx Emissions and Surface N02 Concentrations
Table AX2.6-1 contains an overview of the three satellite instruments that are used retrieve
tropospheric NO2 columns from measurements of solar backscatter. All three instruments are in polar
sun-synchronous orbits with global measurements in the late morning and early afternoon. The spatial
resolution of the measurement from SCIAMACHY is 7 times better than that from Ozone Monitoring
Instrument (GOME), and that from Ozone Monitoring Instrument (OMI) is 40 times better than that from
GOME.
2-54
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Table AX2.6-1. Satellite Instruments Used to Retrieve Tropospheric NO2 Columns.
INSTRUMENT
GOME
SCIAMACHY
OMI
COVERAGE
1995-2002
2002-
2004-
TYPICAL U.S.
MEASUREMENT TIME
10:30-1 1:30 AM
10:00-1 1:00 AM
1 2:45-1 :45PM
TYPICAL
RESOLUTION (KM)
320 H 40
30H60
13H24
RETURN TIME
(DAYS)1
3
6
1
INSTRUMENT
OVERVIEW
Burrows etal. (1999)
Bovensmann et al.
(1999)
Level! 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.
Figure AX2.6-1 shows tropospheric NO2 columns retrieved from SCIAMACHY. Pronounced
enhancements are evident over major urban and industrial emissions. The high degree of spatial
heterogeneity over the southwestern United States provides empirical evidence that most of the
tropospheric NO2 column is concentrated in the lower troposphere. Tropospheric NO2 columns are more
sensitive to NOX in the lower troposphere than in the upper troposphere (Martin et al., 2002). This
sensitivity to NOX in the lower troposphere is due to the factor of 25 decrease in the NO2/NO ratio from
the surface to the upper troposphere (Bradshaw et al., 1999) that is driven by the temperature dependence
of the NO + O3 reaction. Martin et al. (2004a) integrated in situ airborne measurements of NO2 and found
that during summer the lower mixed layer contains 75% of the tropospheric NO2 column over Houston
and Nashville. However, it should be noted that these measurements are also sensitive to surface albedo
and aerosol loading.
2-55
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SCIAMACHY Tropospheric NC>2 (10] 5 molec cm 2)
u
Source: Martin etal. (2006).
Figure AX2.6-1. Tropospheric N02 columns (molecules N02/cm2) retrieved from the SCIAMACHY satellite
instrument for 2004-2005.
The retrieval involves three steps: (1) determining total NO2 line-of-sight (slant) columns by
spectral fitting of solar backscatter measurements, (2) removing the stratospheric columns by using data
from remote regions where the tropospheric contribution to the column is small, and (3) applying an air
mass factor (AMF) for the scattering atmosphere to convert tropospheric slant columns into vertical
columns. The retrieval uncertainty is determined by (1) and (2) over remote regions where there is little
tropospheric NO2, and by (3) over regions in regions of elevated tropospheric NO2 (Martin et al., 2002;
Boersma et al., 2004).
The paucity of in situ NO2 measurements motivates the inference of surface NO2 concentrations
from satellite measurements of tropospheric NO2 columns. This prospect would take advantage of the
greater sensitivity of tropospheric NO2 columns to NOX in the lower troposphere than in the upper
troposphere as discussed earlier. Tropospheric NO2 columns show a strong correlation with in situ NO2
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, 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
2-56
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about 10:00 to 11:00 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.
AX2.7. Policy-relevant Background NOx Concentrations
Background concentrations of nitrogen oxides used for purposes of informing decisions about
NAAQS are referred to as Policy-relevant Background (PRB) concentrations. Policy Relevant
Background concentrations are those concentrations that would occur in the United States in the absence
of anthropogenic emissions in continental North America (defined here as the United States, Canada, and
Mexico). Concentrations include contributions from natural sources everywhere in the world and from
anthropogenic sources outside these three countries. Background levels so defined facilitate separation of
pollution levels that can be controlled by U.S. regulations (or through international agreements with
neighboring countries) from levels that are generally uncontrollable by the United States. EPA assesses
risks to human health and environmental effects from NO2 levels in excess of PRB concentrations.
Contributions to PRB concentrations include natural emissions of NO2 and photochemical reactions
involving natural emissions of reduced nitrogen compounds, as well as their long-range transport from
outside North America. Natural sources of NO2 and its precursors include biogenic emissions, wildfires,
lightning, and the stratosphere. Natural sources of reduced nitrogen compounds, mainly NH3, include
biogenic emissions and wildfires. Biogenic emissions from agricultural activities are not considered in the
formation of PRB concentrations. Discussions of the sources and estimates of emissions are given in
Section AX2.4.2.
AX2.7.1. Analysis of PRB Contribution to U.S. NOx Concentrations and
Deposition
The MOZART-2 global model of tropospheric chemistry (Horowitz et al., 2003) was used to
diagnose the PRB contribution to nitrogen oxide concentrations, as well as to total (wet plus dry)
deposition. The model setup for the present-day simulation has been published in a series of papers from a
recent model intercomparison (Dentener et al., 2006a,b; Shindell et al., 2006; Stevenson et al., 2006; Van
Noije et al., 2006). MOZART-2 is driven by National Center for Environmental Prediction
meteorological fields and IIASA 2000 emissions at a resolution of 1.9° H 1.9° with 28 sigma levels in the
vertical, and it includes gas- and aerosol phase chemistry. Results shown in Figures AX2.7-1 to AX2.7-3
are for the meteorological year 2001. Note that color images are available on the web. An additional PRB
simulation was conducted in which continental North American anthropogenic emissions were set to zero.
We first examine the role of PRB in contributing to NO2 concentrations in surface air. Figure
AX2.7-1 shows the annual mean NO2 concentrations in surface air in the base case simulation (top panel)
and the PRB simulation (middle panel), along with the percentage contribution of the background to the
total base case NO2 (bottom panel). Maximum concentrations in the base case simulation occur along the
Ohio River Valley and in the Los Angeles basin. While present-day concentrations are often above 5 ppb,
PRB is less than 300 ppt over most of the continental United States, and less than 100 ppt in the eastern
United States. The distribution of PRB (middle panel of Figure AX2.7-1) largely reflects the distribution
of soil NO emissions, with some local enhancements due to biomass burning such as is seen in western
Montana. In the northeastern United States, where present-day NO2 concentrations are highest, PRB
contributes <1% to the total.
2-57
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120°W
Total
10Q°W
50
290
530
770
1010
1250
Background
12D°W
100°W
BO°W
25
45
65
85
105
125
Percent Background Contribution
1ZO°W
100°W
ao°w
14
23
32
41
50
Figure AX2.7-1. Annual mean concentrations of N02 (ppb) 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.
2-58
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Total
120°W
50
290
530
770
1010
1250
Background
120°W
ao°w
25
45
65
105
125
Percent Background Contribution
14
23
32
41
50
Figure AX2.7-2. Same as for Figure AX2.7-1 but for wet and dry deposition of HN03, NH4N03, NOX, H02N02,
and organic nitrates (mg N nr2/y).
2-59
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GEOS-QfuSfrt SOIL NO
>VOZART-2SOiLSO
< D 5 II IQ
Surface SO
MOZART-2 SLffaca MOX JUL
< 5D 150 250 3K> 450 5SO
50 150 2 X 3» 450 350
Figure AX2.7-3. July mean soil NO emissions (upper panels; 1 H 109 molecules cm-2 s1) and surface PRB
NOx concentrations (lower panels; ppt). These are 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.
The spatial pattern of NOY (defined here as HNO3, NF^NOs, NOX, HO2NO2, and organic nitrates)
wet and dry deposition is shown in Figure AX2.7-2. Figure AX2.7-2 (upper panel) shows that highest
values are found in the eastern United States in and downwind of the Ohio River Valley. The pattern of
nitrogen deposition in the PRB simulation (Figure AX2.7-2, middle panel), however, shows maximum
deposition centered over Texas and in the Gulf Coast region, reflecting a combination of nitrogen
emissions from lightning in the Gulf region, biomass burning in the Southeast, and from microbial
activity in soils (maximum in central Texas and Oklahoma). The bottom panel of Figure AX2.7-2 shows
that the PRB contribution to nitrogen deposition is less than 20% over the eastern United States, and
typically less than 50% in the western United States where NOY deposition is low (25-50 mg N/m2/yr).
Thus far, the discussion has focused on results from the MOZART-2 tropospheric chemistry model.
In Figure AX2.7-3, results from MOZART-2 are compared with those from another tropospheric
chemistry model, GEOS-Chem (Bey et al., 2001), which was previously used to diagnose PRB O3 (Fiore
et al., 2003; U.S. Environmental Protection Agency, 2006). In both models, the surface PRB NOX
concentrations tend to mirror the distribution of soil NO emissions, which are highest in the Midwest. The
higher soil NO emissions in GEOS-Chem (by nearly a factor of 2) as compared to MOZART-2 reflect
different assumptions regarding the contribution to soil NO emissions largely through fertilizer, since
GEOS-Chem total soil NO emissions are actually higher than MOZART-2 (0.07 versus 0.11 Tg N) over
2-60
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the United States in July. Even with the larger PRB soil NO emissions, surface NOX concentrations in
GEOS-Chem are typically below 500 ppt.
Table AX2.4-1. Emissions of nitrogen oxides and ammonia in the United States in 2002
2002 EMISSIONS (TG/YR)
Source Category
TOTAL ALL SOURCES
FUEL COMBUSTION TOTAL
FUEL COMB. ELEC. UTIL
Coal
Bituminous
Subituminous
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
NOX1
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
NH3
4.08
0.02
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
2-61
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2002 EMISSIONS (TG/YR)
Wood/bark waste
Liquid waste
Other
Internal Combustion
FUEL COMB. OTHER
Commercial/Institutional Coal
Commercial/Institutional Oil
Commercial/Institutional Gas
Misc. Fuel Comb. (Except Residential)
Residential Wood
Residential Other
Distillate oil
Bituminous/subituminous
Other
INDUSTRIAL PROCESS 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
PETROLEUM & RELATED INDUSTRIES
Oil & Gas Production
NOX1
0.11
0.01
0.04
1.15
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
0.16
0.07
NH3
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
0.21
0.02
<0.01
<0.01
<0.01
0.02
<0.01
0.02
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
2-62
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2002 EMISSIONS (TG/YR)
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
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
NOX1
0.05
0.04
0.54
0.01
<0.01
0.09
<0.01
0.42
0.24
0.01
0.10
<0.01
<0.01
<0.01
0.01
0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
0.01
<0.01
<0.01
0.01
NH3
<0.01
<0.01
<0.01
0.05
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
0.05
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
2-63
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2002 EMISSIONS (TG/YR)
WASTE DISPOSAL & RECYCLING
Incineration
Industrial
Other
Open Burning
Industrial
Land clearing debris
Other
Public Operating Treatment Works
Industrial Waste Water
Treatment, Storage, and Disposal Facility
Landfills
Industrial
Other
Other
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
NOX1
0.17
0.06
0.10
<0.01
<0.01
<0.01
<0.01
<0.01
12.58
8.09
2.38
2.36
0.02
1.54
1.07
0.47
0.44
3.73
3.71
0.01
0.01
4.49
0.23
0.01
0.01
0.01
0.10
0.01
0.04
<0.01
NH3
0.14
<0.01
<0.01
0.14
<0.01
<0.01
<0.01
<0.01
0.32
0.32
0.20
0.10
<0.01
<0.01
<0.01
<0.01
2-64
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2002 EMISSIONS (TG/YR)
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
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
NOX1
<0.01
<0.01
0.05
1.76
0.00
0.84
0.15
0.05
0.57
0.08
0.02
0.01
<0.01
0.03
0.09
1.11
1.11
0.98
0.32
0.29
0.04
0.39
<0.01
3.10
NH3
<0.01
<0.01
3.53
3.45
<0.01
2.66
0.08
0.03
2-65
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Annex 3. Ambient Concentrations
and Exposures
AX3.1. Introduction
Topics discussed in this chapter include the characterization of ambient air quality for nitrogen
dioxide (NO2), the uses of these data in assessing human exposures to NO2; concentrations and sources of
NO2 in different microenvironments, and personal exposures to NO2. The NO2 data contained in this
chapter are taken mainly from the U.S. Environmental Protection Agency's Air Quality System (AQS)
database (formerly the AIRS database) (U.S. Environmental Protection Agency, 2007).
AX3.1.1. Characterizing Ambient N02 Concentrations
The "concentration" of a specific air pollutant is typically defined as the amount (mass) of that
material per unit volume of air. However, most of the data presented in this chapter are expressed as
"mixing ratios" in terms of a volume-to-volume ratio (e.g., parts per million [ppm] or parts per billion
[ppb]. Data expressed this way are often referred to as concentrations, both in the literature and in the
text, following common usage. Human exposures are expressed in units of mixing ratio times time.
AX3.1.2. Relationship to the 1993AQCDforNOx
The 1993 AQCD for Oxides of Nitrogen emphasized NO2 indoor sources (gas stoves) and the
relationship between personal total exposure and indoor or outdoor NO2 concentrations
(U.S. Environmental Protection Agency, 1993). At that time, only few personal exposure studies had been
conducted with an emphasis on residential indoor NO2 sources and concentrations. Although the concept
of microenvironment had been introduced in the document, NO2 concentrations were seldom reported for
microenvironments other than residences. Exposure measurements at that time relied on Palmes tubes and
Yanagisawa badges; and exposure-modeling techniques were limited mainly to simple linear regression.
In the 1993 AQCD, NO2 was treated as an independent risk factor, and confounding issues were not
mentioned in the human environmental exposure chapters.
The current chapter summarizes and discusses the state-of-the-science and technology regarding
NO2 human exposures since 1993. Since then, numerous human exposure studies have been conducted
with new measurement and modeling techniques. Microenvironmental measurements were not limited to
residential indoor environments; NO2 concentrations were also measured in vehicles, schools and offices,
and microenvironments close to traffic. More indoor sources have been identified and more NO2
formation and transformation mechanisms in the indoor environment have been reported.
AX3.2. Ambient Concentrations of NOx
As discussed in Chapter 2 of the ISA, most measurements of NOX are made by instruments that
convert NO2 to NO, which is then measured by chemiluminescence. However, the surface converters that
reduce NO2 to NO also reduce other reactive NOY species. As indicated in Chapter 2, NOY compounds
consist of NOX, gas phase inorganic nitrates, such as chlorine nitrate (C1NO3); organic nitrates, such as
3-66
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PANs; inorganic acids, given by the formulas HNOY (Y = 2 to 4); and participate nitrate. In urban areas
or in rural areas where there are large local sources, NO and NO2 are expected to be the major forms of
NOY. Thus, interference from PANs and other NOY species near sources are expected to minor; in most
rural and remote areas, interference may be substantial as concentrations of other NOY species may be
much larger than those for NO and NO2 (National Research Council, 1991).
Because of their short lifetime with respect to oxidation to PANs and HNO3, NOX concentrations
are highly spatially and temporally variable. Average concentrations range from tens of ppt in remote
areas of the globe to tens of ppb in urban cores, i.e., by three orders of magnitude. Median NO, NOX, and
NOY concentrations at the surface are typically below 0.01, 0.05, and 0.3 ppb, respectively, in remote
areas such as Alaska, northern Canada, and the eastern Pacific; median NOY concentrations range from
about 0.7 to about 4.3 ppb at regional background sites in the eastern United States (Emmons et al.,
1997). Note that the last two values, especially, contain a substantial contribution from pollution.
Maximum short-term average (1-h) NOX concentrations near heavy traffic (e.g., in Los Angeles, CA)
approach 1 ppm, but these levels decrease rapidly away from sources. Even at sites where such high
hourly values are found, 24-h avg concentrations are much lower. For example, the maximum 24-h
average NOX concentration at any site in Los Angeles in 2004 was 82 ppb.
AX3.2.1. Temporal Variability in Ambient NOx Concentrations in Urban
Areas
AX3.2.1.1. Diurnal Variability in N02 Concentrations
As might be expected from a pollutant having a major traffic source, the diurnal cycle of NO2 in
typical urban areas is characterized by traffic emissions, with peaks in emissions occurring during
morning and evening rush hour traffic. Motor vehicle emissions consist mainly of NO, with only about
10% of primary emissions in the form of NO2. The diurnal pattern of NO and NO2 concentrations is also
strongly influenced by the diurnal variation in the mixing layer height. Thus, during the morning rush
hour when mixing layer heights are still low, traffic produces a peak in NO and NO2 concentrations. As
the mixing layer height increases during the day, dilution of emissions occurs. During the afternoon rush
hour, mixing layer heights are at or are near their daily maximum values resulting in dilution of traffic
emissions through a larger volume than in the morning. Starting near sunset, the mixing layer height
drops and conversion of NO to NO2 occurs without photolysis of NO2 recycling NO.
The composite diurnal variability of NO2 in selected urban areas with multiple sites (New York,
NY; Atlanta, GA; Baton Rouge, LA; Chicago. IL; Houston, TX; Riverside, CA; and Los Angeles, CA)
shows that lowest hourly median concentrations are typically found at around midday and that highest
hourly median concentrations are found either in the early morning or in mid-evening. Median values
range by about a factor of two from about 13 ppb to about 25 ppb (Figures AX3.2-1 to AX3.2-6).
However, individual hourly concentrations can be considerably higher than these typical median values,
and hourly NO2 concentrations > 0.10 ppm can be found at any time of day.
AX3.2.1.2. Seasonal Variability in N02 Concentrations
AX3.2.1.3. Urban Sites
As might be expected from an atmospheric species that behaves essentially like a primary pollutant
emitted from surface sources, there is strong seasonal variability in NOX and NO2 concentrations. Highest
concentrations are found during winter, consistent with lowest mixing layer heights found during the year.
3-67
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Mean and peak concentrations in winter can be up to a factor of two larger than in the summer at several
sites in Los Angeles County.
The month-to-month variability in NO2 at individual sites in selected urban areas is illustrated in
Figures AX3.2-1 to AX3.2-6. Seasonal patterns can be found at some sites but not in others. There
appears to be a somewhat regular pattern for the southern cities with winter maxima and summer minima.
Monthly maxima tend to be found from late winter to early spring in Chicago and New York with minima
occurring from summer through the fall. However, in Los Angeles and Riverside, monthly maxima tend
to occur from autumn through early winter with minima occurring from spring through early summer.
AX3.2.1.4. Regional Background Sites
Surface NOX and NOY data obtained in Shenandoah National Park, VA from 1988 to 1989 show
wintertime maxima and summertime minima (Doddridge et al., 1991, 1992; Poulida et al., 1991). NOX
and NOY data collected in Harvard Forest, MA from 1990 to 1993 show a similar seasonal pattern
(Munger et al., 1996). In addition the within-season variability was found to be smaller in the summer
than in the winter as shown in Table AX3.2-1.
3-68
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a. New York, NY.
SUBURBAN
b. New York, NY. URBAN and CENTER CITY
»ite«j -3OM10124 p
OX-
0-06:
007
005 ^
0.05.
0.04'
033-
0.02-
031
ooo-
01/01/3003 07/01/2003 01/01*2004 07/01/20W 01/01/2005 07/01/2005 01/01/2006
Sample Date (mm/dd/yyyy)
009
008
0-07 •
006
035
034
033
0.02
091
1X00
01/0112003 07/01/3003 01/01/2004 07/01/20W 01/01/2005 07(01/2005 01/01/2006
Sample Date (mm/dd/yyyy)
c. New York, NY. URBAN and CENTER CITY
d. New York, NY URBAN and CENTER CITY
•307
3E-
o.os:
OM
3C3
00?-
u
07/01(2003 OUOIQOM 07/01(2004 01*1/2006 07/01(2005 01(01/2006
Sample Date (mm/dd/yyyy)
01/01/2OH 07/01/3003 OW1/JOW 07/01/3004 01(01/2005 07/01(3005
Sample Date (mm/dd/yyyy)
01(01/2006
e. New York, NY. URBAN and CENTER CITY
I
01(01/2003 07/01/2003 01/01/2004 07/01/2004 01/01(2005 07/01/2005
Sample Date (mm/dd/yyyy)
Source: U.S. EPAAQS, 2007
Figure AX3.2-1. Time series of 24-h avg NOa concentrations at individual sites in New York City from 2003
through 2006. A natural spline function (with 9 degrees of freedom) was fit and overlaid to
the data (dark solid line).
3-69
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a. Chicago, IL.
RURAL
b. Chicago, IL.
SUBURBAN
0.07-
0.08-
005-
0.04-
0.03-
0,02-
0.01
aoo-
I -^f = Nalura! Spline Fil w/9 df I
07/01/2003 01/01/2004 07/01/2004 01/01/2005 07/01/2005
Sample Date (mm/dd/yyyy)
01/01/2003 07/01/2003 01/01/2004 07/01/2004 01/01/2005
Sample Date (mm/dd/yyyy)
07/01/2005 01/01/2006
c. Chicago, IL.
SUBURBAN
oca.
E 007-
o.
3 006.
O 005-
E aw-
I 003
O 002-
site id = 170313103 pec = 1
d. Chicago, IL.
SUBURBAN
01101/2003 07101/2003 01/01/2004 07/01/2004 01W1/2005 07/01/2005 01/01/2006 01/0112003 07/0112003 01/01/2004 07/01/2004 01/01/2005 07(01/2005 01/0112006
Sample Date (mm/dd/yyyy)
Sample Date (mm/dd/yyyy)
e. Chicago, IL.
SUBURBAN
f. Chicago, IL.
URBAN and CENTER CITY
07(01-2003 01/01/2004 07)01/2004 01/01(2005 07(01/2005 01/01/2006 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)
Sample Date (mm/dd/yyyy)
g. Chicago, IL. URBAN and CENTER CITY
2 004-
o 003
5C 002-j
„_.
erf = 170310072 poc =
001
0.00-
01/01/2003 07/01/2003 01/01/2004 07/01/2004 01/01/2005 07/01/2005 01(01/2005
Sample Date (mm/dd/yyyy)
Figure AX3.2-2. Time series of 24-h average N02 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).
3-70
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a. Baton Rouge, LA.
SUBURBAN
Q.
Q.
C
o
*J
2
o>
u
C
o
o
0.09-
0.08-
0.07-
0.06-
0.05-
0.04-
0.03-
0.02-1
0.01:
0.00-
siteid=221210001 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. Baton Rouge, LA.
URBAN and CENTER CITY
Q.
O
'+">
re
0)
o
o
O
0.09-
0.08-
0.07:
0.06:
0.05-
0.04:
0.03:
0.02-
0.01-
0.00-
site id=220330009 poc=1
i i I
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)
Figure AX3.2-3. Time series of 24-h avg N02 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).
3-71
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a. Houston, TX.
SUBURBAN
b. Houston, TX.
SUBURBAN
07/01/2003 01/01/2004 07/01/2004 01/01/2005 07/01/2005 01)01/2006
Sample Date (mm/dd/yyyy)
01/01/2003 07/01/2003 01/01/2004 07/01/2004 01/01/2005 07/01/2005 01/01.7006
Sample Date (mm/dd/yyyy)
,TX.
SUBURBAN
d. Houston, TX.
SUBURBAN
07/01/2003 01K)1/2004 07/01/2004 01/01/2005 07/01/2005
Sample Date (mm/dd/yyyy)
01/01/2003 07/01/2003 01/01/2004 07/01/2004 01/01/2005 07/W/2005
Sample Date (mm/dd/yyyy)
e. Houston, TX.
URBAN and CENTER CITY
0.001
01/01/2003
f. Houston, TX.
URBAN and CENTER CITY
site id *<*820i 1034 pot
01/01/20CM 07/01/2004 01/01(2006
Sample Date (mm/dd/yyyy)
07/01/2005 01/01/2006
009-
QOS-
0.07
0.06-
005.
0.04
0-03
002-
0-01
000.
01/01/2003 07/01/20CO 01«)1f2004 07/01/2004 01/01/2005
Sample Date (mm/dd/yyyy)
07/01/2005 Qiraif2006
g. Houston, TX. URBAN and CENTER CITY
E C07
3 006
si» 1(3 = 483390078 pi
004<
OCIS
o.oe
0.01
o.oo
01/01/2003 07/01/2003 01/01/2004 07/01/2004 01/01/2006 07/01/2005 01/01/2006
Sample Date (mm/dd/yyyy)
Figure AX3.2-4. Time series of 24-h avg N02 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).
3-72
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a. Los Angeles, CA.
SUBURBAN
b. Los Angeles, CA.
SUBURBAN
07/01/2003 01*01/2004 07/01/2004 01/01/2005
Sample Date (mm/dd/yyyy)
01/01/2003 07/01/2003 01/01/2004 07/01/2004 01M1/2005 07/0112005
Sample Date (mm/dd/yyyy)
c. Los Angeles, CA.
SUBURBAN
d. Los Angeles, CA.
SUBURBAN
01/01/2003 07/01/2003 01/01/2004 07/01/2004 01/01/2005 07/01/2005
Sample Date (mm/dd/yyyy)
01/01/2003 07/01/2003 01/01/2004 07/01/2004 01/01/2005 07/01/2005
Sample Date (mm/dd/yyyy)
e. Los Angeles, CA.
SUBURBAN
f. Los Angeles, CA.
SUBURBAN
Ci 1.11 •. ;.nns 07/01/2003 01/01/2004
Sample Date (mm/dd/yyyy)
Sample Date (mm/dd/yyyy)
g. Los Angeles, CA.
SUBURBAN
h. LOS Angeles, CA. URBAN and CENTER CITY
I603760l2poc=1
07/01/2003 01/01/2004 07^01/2004 01/01/2005 07/01/2005 01/01/2006
Sample Date (mm/dd/yyyy)
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)
Figure AX3.2-5. Time series of 24-h avg N02 concentrations at individual sites in Los Angeles, CAfrom 2003
through 2005. A natural spline function (with 9 degrees of freedom) was fit and overlaid to
the data (dark solid line).
3-73
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LOS Angeles, CA. URBAN and CENTER CITY
j LOS Angeles, CA. URBAN and CENTER CITY
Q.
Q.
c
II
o
c
o
u
01/01/2001 07/01/2003 01/01/2004 07/01/200* 01/01/2005 07/01(2005
Sample Date (mm/dd/yyyy)
owtaoos 07/01/2003 01/01/2004 07/0112004 owinooe 07)0112005
Sample Date (mm/dd/yyyy)
k. Los Angeles, CA. URBAN and CENTER CITY
I. Los Angeles, CA. URBAN and CENTER CITY
o ra-
ses-
007
006-
005
904-
003-
002-
OU1-
ooo
oi/otMJ03 07/01/2003 01/01/2004 07/01/2004 01*110005 owinoos 01/01/2005
Sample Date (mm/dd/yyyy)
a.
a.
o
c
o
U
0101/2003 07/01/2003
07m/20os a:••.::•:••
Sample Date (mm/dd/yyyy)
LOS Angeles, CA. URBAN and CENTER CITY
ite K! = 060375001 pxx:=1
n. Los Angeles, CA. URBAN and CENTER CITY
I
a
I
•s
I
§
c
o
u
01(01/2003 0701/2003 01/01(2004 07/01/2004 01/01(2005 07/010005
01«1/2003 07/01(2003 01/01/2004 07*1(2004 01101/2005 07/01/2005
Sample Date (mm/dd/yyyy)
Sample Date (mm/doVyyyy)
Figure AX3.2-5. (Continued) Time series of 24-h avg NO? 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).
3-74
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a. Riverside, CA.
RURAL
b. Riverside, CA.
SUBURBAN
O
o
0.08-
005^
one.
00!..
004-
003-
002'
001.
000-
sice a = MQ711234p« = 1
•^ i Nalural Spjine F« w) 9 df
tjl t m f l^b .lIlhL^&l > nudlft I ^IhTA tl i LlM
01/01(2003 07(01)3003 01/01(2004 07(01/20CM 01/01(2035 07101/2005 01)01(2005
Sample Date {mm/dd/yyyy)
a
_a
c
o
«
2
o
o
01iWJ2003 07I01M03 DVOtnOM 07/OU20M OWJIOXB 07fl)1/2006
Sample Date (mm/dd/yyyy)
c. Riverside, CA,
307
006
0.05'
OOa
003
0.02
101
000-_^
01(010003
SUBURBAN
d. Riverside, CA.
SUBURBAN
o
o
07/01(2003 01KJ1/2004
OMJ1/2005 07)01(2005
Sample Date (mm/dd/yyyy}
01(010003 07/01 «»3 01(01(2001 07/01(200* 01/01(2005 07)01 «»5
Sample Date (mm/dd/yyyy)
Figure AX3.2-6. Time series of 24-h avg NCh concentrations at individual sites in Riverside, CAfrom 2003
through 2006. A natural spline function (with 9 degrees of freedom) was fit and overlaid to
the data (dark solid line).
3-75
-------
. Riverside, CA,
SUBURBAN
f. Riverside, CA.
SUBURBAN
01*1/2003 07/01/2003 01*1/2004 07/01/2004 01(0112005 07/01(2005 01*1/2006
01101/2003 07/01/2003 OliOt/2004 07/01/2004 01J010005 07/01/2005 01/01(2006
Sample Date {mm/dd/yyyy)
Sample Date (mm/dd/yyyy)
g. Riverside, CA.
0.09-
SUBURBAN
h. Riverside, CA URBAN and CENTER CITY
01*1/2003 07/016)003 01*1/2004 07/01/2034 01(01/2005 07/01(2005 01*1/2006 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)
Sample Date (mm/dd/yyyy)
i. Riverside, CA. URBAN and CENTER CITY
01/01/2003 07*1/2003 01/01/2004 07*1/2004 01/01/2035 07*1/2005 01/01/2003
Sample Date (mm/dd/yyyy)
Figure AX3.2-6. (Continued) Time series of 24-h avg NC-2 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).
Source: U.S. Environmental Protection Agency (2003)
AX3.2.2. Relationships between N02 and Other Pollutants
Relationships between O3, NO, and NO2 are shown in Figures AX3.2-7 and AX3.2-8. Figure
AX3.2-7 shows daylight average concentrations based on data collected from November 1998 and 1999
at several sites in the United Kingdom representing a wide range of pollution conditions (open symbols).
The solid lines represent calculations of photostationary state values subject to the constraint that Ox =
3-76
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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, 2006a) 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 O3 and NO2. The concentrations of NO2 (an oxidant and a component of odd oxygen) varying
linearly with emissions of NOX, especially after NO has reacted with O3 to form NO2 as shown in Figure
AX3.2-7. Thus the concentration of Ox (and not O3, as is often stated) can be taken to be the sum of
regional and local contributions.
Figure AX3.2-8 shows that primary emissions from motor vehicles are major sources of oxidant in
the form of NO2, as evidenced by the high values of Ox at elevated NOX.
a)
100 ,
100 200 300 400
[N0x](ppb)
500
600
Source: Clapp and Jenkin (2001).
Figure AX3.2-7. Relationship between Os, NO, and N02 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.
3-77
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local contribution to oxidant
(NOx-dependent)
regional contribution to oxidant
(N0x-independent)
0 100 200 300 400 500 600
[NOxKppb)
Figure AX3.2-8. Variation of odd oxygen (= 03 + N02) with NOX. The figure shows the "regional" and the
"local" contributions. Note that Ox refers to odd oxygen in the document and the latest 03
AQCD.
AX3.2.3. Abundance of NOv Species
Data for individual NOY species are much less abundant than for either oxides of nitrogen or for
total NOY. Data for several NOY species are collected typically only as part of research field studies, e.g.,
the Southern Oxidant Study (SOS), Texas Air Quality Study (TexAQS I and TexAQS II) in the United
States. As a result, this information is simply not available for a large number of areas in the United
States.
AX3.2.3.1. PANs
Organic nitrates consist of PAN, a number of higher-order species with photochemistry similar to
PAN (e.g., PPN), and species such as alkyl nitrates with somewhat different photochemistry. These
species are produced by a photochemical process very similar to that of O3. Photochemical production is
initiated by the reaction of primary and secondary VOCs with OH radicals, the resulting organic radicals
subsequently react with NO2 (producing Source: Clapp and Jenkin (2001). PAN and analogous species)
or with NO (producing alkyl nitrates). The same sequence (with organic radicals reacting with NO) leads
to the formation of O3.
In addition, at warm temperatures, the concentration of PAN forms a photochemical steady state
with its radical precursors on atimescale of roughly 30 min. This steady state value increases with the
ambient concentration of O3 (Sillman et al., 1990). O3 and PAN may show different seasonal cycles,
because they are affected differently by temperature. Ambient O3 increases with temperature, driven in
part by the photochemistry of PAN (see description in Chapter 2). The atmospheric lifetime of PAN
decreases rapidly with increasing temperature due to thermal decomposition. Based on the above, the
3-78
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ratio of O3 to PAN is expected to show seasonal changes, with highest ratios in summer, although there is
no evidence from measurements. Measured ambient concentrations (Figure AX3.2-9) show a strong
nonlinear association between O3 and PAN, and between O3 and other organic nitrates (Pippin et al.,
2001; Roberts et al., 1998). Moreover, uncertainty in the relationship between O3 and PAN grows as the
level of PAN increases. Individual primary VOCs are generally highly correlated with each other and
with NOX (Figure AX3.2-10).
140
0 1000 2000 3000 4000 0 1000 2000 3000 4000 5000
PAN (pptv)
Source: Roberts etal. (1998).
Figure AX3.2-9. Measured Os (ppb) versus PAN (ppt) in Tennessee, including (a) aircraft measurements, and
(b, c, and d) suburban sites near Nashville.
3-79
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1.5-
2
Q.
& 1.0-1
0)
• Sp2< 16 ppbv
6 Sp2> 10 ppbv
0.5-
20
40 60 80
NOy (ppbv)
100 120
Source: Goldanetal. (1995).
Figure AX3.2-10. Relationship between benzene and NOy at a measurement site in Boulder, CO. Instances
with SC-2 >10 ppb are identified separately (open circles), because these may reflect
different emission sources.
Measurements and models show that PAN in the United States includes major contributions from
both anthropogenic and biogenic VOC precursors (Horowitz et al., 1998; Roberts et al., 1998).
Measurements in Nashville during the 1999 summertime Southern Oxidants Study (SOS) showed PPN
and MPAN amounting to 14% and 25% of PANs, respectively (Roberts et al., 2002). Measurements
during the TexAQS 2000 study in Houston indicated PAN concentrations of up to 6.5 ppb (Roberts et al.,
2003). PAN measurements in southern California during the SCOS97-NARSTO study indicated peak
concentrations of 5-10 ppb, which can be contrasted to values of 60-70 ppb measured back in 1960
(Grosjean, 2003). Vertical profiles measured from aircraft over the United States and off the Pacific
coasts typically show PAN concentrations above the boundary layer of only a few hundred ppt, although
there are significant enhancements associated with long-range transport of pollution plumes from Asia
(Kotchenruther et al., 2001; Roberts et al., 2004).
Observed ratios of PAN to NO2 as a function of NOX at a site at Silwood Park, Ascot, Berkshire,
UK are shown in Figure AX3.2-11 United Kingdom Air Quality Expert Group (U.K. AQEG, 2004). As
can be seen there is a very strong inverse relation between the ratio and the NOX concentration, indicating
photochemical oxidation of NOX has occurred in aged air masses and that PAN can make a significant
contribution to measurements of NO2 especially at low levels of NO2 (cf ISA, Section 2-3). It should be
noted that these ratios will likely differ from those found in the United States because of differences in the
composition of precursor emissions, the higher solar zenith angles found in the UK compared to the
United States., and different climactic conditions.
Nevertheless, these results indicate the potential importance of interference from NOY compounds
in measurements of NO2.
3-80
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AX3.2.3.2. MONO
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 AX3.2-12 (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 AX3.2-12 indicate that
HONO can make a measurable contribution to measurements of NO2 (cf. ISA, Section 2-3). However,
similar arguments about extrapolating the use of UK data to the United States can be made for HONO as
for PAN.
AX3.2.3.3. HN03andN03
Elevated O3 is generally accompanied by elevated HNO3, although the correlation is not as strong
as between O3 and organic nitrates. O3 is often associated with HNO3 because they have the same
precursor NOX. However, HNO3 can be produced in significant quantities in winter, even when O3 is low.
The ratio between O3 and HNO3 also shows great variation in air pollution events, with NOx-saturated
environments having much lower ratios of O3 to HNO3 (Ryerson et al., 2001). Aerosol nitrate is formed
primarily by the combination of nitrate (supplied by HNO3) with ammonia, and may be limited by the
availability of either nitrate or ammonia. Nitrate is expected to correlate loosely with O3 (see above),
whereas ammonia is not expected to correlate with O3. Concentrations of particulate nitrate measured as
part of the Environmental Protection Agency's speciation network at several locations are shown in
Figure AX3.2-13. Concentrations shown are annual averages for 2003. Also shown are the estimated
contributions from regional and local sources. A concentration of 1 (ig/m3 corresponds to -0.40 ppb
equivalent gas phase concentration for NO3".
3-81
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0.20
0.00
20
40 60
[NOX] (ppb)
80
Source: UK AQEG (2004).
Figure AX3.2-11. Ratios of PAN to N02 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.
0.3
o
o
I 0.1
200 400 600 800
[NOXJ (ppb)
1000
Source: UK AQEG (2004).
Figure AX3.2-12. Ratios of MONO to N02 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 avg concentrations of
MONO and N02.
Thus, annual average particulate nitrate can account for several ppb of NOY, with the higher values
in the West. There is a strong seasonal variation, which is especially pronounced in western areas where
3-82
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there is extensive wood burning in the winter resulting in a larger fractional contribution of local sources.
Areas in the East where there are topographic barriers might be expected to show higher fractional
contributions from local sources than other eastern areas that are influenced by regionally dispersed
sources.
However, depending on the acidity of the particles, which in turn depends strongly on their sulfate
and ammonium contents, higher nitrate concentrations could be found in coarse mode particles PMi 0-2.5
than in PM2 5 samples. The average nitrate content of PM2 5 and PM10 is typically about a percent in the
eastern United States; and 15.7% and 4.5% in the western United States (U.S. Environmental Protection
Agency, 1996). These values suggest that most of the nitrate was in the PM2 5 size fraction in the studies
conducted in the western United States, but nitrate in the studies in the eastern United States was mainly
in the PMi0_2 5 size fraction.
AX3.2.3.4. Nitro-PAHs
Nitro-PAHs are widespread and found even in high altitude, relatively unpolluted environments
(Schauer et al., 2004) but there are differences in composition and concentration profiles both within and
between sites (rural vs. urban) as well as between and within urban areas (Albinet et al., 2006; Soderstrom
et al., 2005; Naumova et al., 2002, 2003), with some differences in relative abundances of nitro- and oxo-
PAHs also reported. Source attribution has remained largely qualitative with respect to concentrations or
mutagenicity (Eide et al., 2002). The spatial and temporal concentration pattern for the NPAHs may differ
from that of the parent compounds (PAHs) because concentrations of the latter are dominated by direct
emission from local combustion sources. These emissions results in higher concentrations during
atmospheric conditions more typical of wintertime when mixing heights tend to be low. The
concentrations of secondary Nitro-PAHs are elevated under conditions that favor hydroxyl and nitrate
radical formation, i.e., during conditions more typical of summertime, and are enhanced downwind of
areas of high emission density of parent PAHs and show diurnal variation (Fraser et al., 1998; Reisen and
Arey, 2005; Kameda et al., 2004). Nitro-napthalene concentrations in Los Angeles, CA varied between
about 0.15 to almost 0.30 ng/m3 compared to 760 to 1500 ng/m3 for napthalene. Corresponding values for
Riverside, CA were 0.012 to more than 0.30 ng/m3 for nitro-napthalene and 100 to 500 ng/m3 for
napthalene. Nitro-pyrene concentrations in LA varied between approximately 0.020 to 0.060 ng/m3
compared to 3.3 to 6.9 ng/m3 pyrene, whereas corresponding values for Riverside were 0.012 to 0.025
ng/m3 and 0.9 to 2.7 ng/m3.
3-83
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Nitrates
Fresno
Missoula
Salt Lake City
Tulsa
St. Louis
Birmingham
Indianapolis
Atlanta
Cleveland
Charlotte
Richmond
Baltimore
New York City
WEST
EAST
n Regional
Contribution
• Local
Contribution
6
10
12
Annual Average Concentration
of Nitrates, pg/m3
Source: U.S. Environmental Protection Agency (2004).
Figure AX3.2-13. Concentrations of participate nitrate measures as part of the EPA's speciation network. 1
|jg/m3 -0.45 ppb equivalent gas phase concentration for NCV. (Note: Regional
concentrations are derived from the rural IMPROVE monitoring network,
http://vista.cira.colostate.edu/improve.
AX3.3. Measuring Personal and Indoor N02 Concentrations
AX3.3.1. Issues in Measuring Personal/Indoor N02
Nitrogen dioxide has been sampled in ambient and indoor air using active pumped systems both for
continuous monitoring and collection onto adsorbents, and by diffusive samplers of various designs,
including badges and tubes. Nitrogen dioxide concentrations in personal air have been typically measured
using diffusive samplers because they are: (1) small in size and light-weight, (2) unobtrusive and thus
more readily used by study participants, (3) comparatively easier to use and handle in field studies
because they do not require power (e.g., battery or extra electrical sources), (4) cost-effective, and (5)
usable not only for residential indoor and outdoor air sampling but also personal monitoring. However,
diffusive samplers usually have lower equivalent sampling rates than active methods and so require
relatively long sampling times (24-h or longer). Consequently, diffusive samplers including those used for
NO2 monitoring provide integrated but not short-term concentration measurements.
Both active and passive sampling methods can collect other gas-phase nitrogen oxide species.
However, semivolatile nitrogen oxide compounds require separation of the gas- and particle-bound
phases. This selective separation of gases from gas-particle matrices is commonly done by means of
diffusion denuders (Vogel, 2005), an approach also useful for measuring other gas phase airborne
contaminants such as SO2 (Rosman et al., 2001). Application of denuder sampling to personal exposure or
indoor air monitoring has been relatively limited.
Active air sampling with a pump can collect larger volumes of air and thus detect the lower
concentrations found in community environments within relatively short time periods. Automated active
3-84
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sampling methods have been the preferred method used to monitor NO2 continuously at ambient sites for
environmental regulation compliance purposes. However, practical considerations impede the use of these
continuous monitors in residential air and exposure monitoring studies. Small, low flow active samplers
using battery-operated pumps have been used instead, however, there are only a few such studies.
The first passive sampling devices for NO2 were intended for occupational exposure monitoring,
but were later adapted for environmental monitoring purposes. Since this sampler, the Palmes tubes
(Palmes et al., 1976), was first developed, other tube, badge-type (Yanagisawa and Nishimura, 1982) and
radial (Cocheo et al., 1996) diffusive samplers have been employed as monitors in exposure studies
worldwide. The theories behind and applications of Palmes tubes and Yanagisawa badges have been
described in the last AQCD for Oxides of Nitrogen (U.S. Environmental Protection Agency, 1993). There
are currently several commercially available samplers (e.g., Ogawa, 1998; Radiello, 2006) which are
modifications of the original Palmes tube design. Most modifications are directed at reducing effects
related to meteorological conditions (e.g., insufficient or too high a wind speed, humidity, temperature),
increasing the sampling uptake rate, and improving analytical sensitivity.
AX3.3.1.1. Active (Pumped) Sampling
Nitrogen dioxide measurement by active pumping systems as part of continuous monitors has been
widely employed for ambient air monitoring as these instruments require relatively little maintenance;
however they have been used less frequently for indoor sampling. Devices needing a pump to draw air
can measure average concentrations of pollutants over short time periods, but are not generally suitable
for measuring personal exposures because they are heavy and large. Some exposure studies employed this
approach for active sampling with stationary chemiluminescent analyzers or portable monitors to measure
nitrogen dioxide levels in residential indoor air (Mourgeon et al., 1997; Levesque et al., 2000; Chau et al.,
2002). Recently, Staimer and his colleagues (2005) evaluated a miniaturized active sampler, suitable for
personal exposure monitoring, to estimate the daily exposure of pediatric asthmatics to nitrogen dioxide,
and reported that this small active sampling system is useful for this purpose in exposure studies where
daily measurements are desired.
AX3.3.1.2. Passive (Diffusive) Sampling
Passive samplers are based on the well known diffusion principle described by Pick's law (Krupa
and Legge, 2000). A convenient formulation of this law that can be easily related to sampler design
considerations is:
J = D(A/L)(Cair-Csor)
where:
J = flux (mg/s)
D = diffusion coefficient in air (cm2/s)
A = diffusion cross-sectional area of the sampler (cm2)
L = diffusion path length from the inlet to sorbent (cm),
Catr = concentration of analyte in air (mg/cm3)
Csor = concentration of analyte at the sorbent (mg/cm3)
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The term D(A/L) can be related to the uptake or sampling rate (cm3/s) which is conceptually
analogous to the sampling rate in an active monitor. Once the amount of analyte in the passive sampler
sorbent is determined, the concentration in air (Cair) can be calculated as:
Concentration(mg/cm3) = M(mg)/D(A/L)(cm^/s)/t(sec) (AX3 3-2)
where:
M= mass of analyte collected in the sorbent
t = sampling time
Pick's law strictly applies only under ideal, steady state conditions assuming that the sorbent is a
perfect sink. However, there can be deviations between the theoretical sampling rate for a given analyte
and the actual rate depending on sampling conditions. It is also clear that sampling rate can be optimized
by modifying the geometry of the diffusive sampler, either by reducing L, increasing A or a suitable
combination. However, the impact of deviations from ideality on actual sampling rate due to geometry
also poses a limit to the extent of possible modifications. Thus, passive samplers, either diffusive or
permeation, are prepared as tubes or badges. These two main designs are the basis for all further
modifications which, as indicated above, have been made in order to improve efficiency, reduce
sensitivity to wind turbulence of the samplers, and to simplify analyte desorption. Tube-type samplers are
characterized by a long, axial diffusion length, and a low cross-sectional area; this results in relatively low
sampling rates (Namiesnik et al., 2005). Badge-type samplers have a shorter diffusion path length and a
greater cross-sectional area which results in uptake rates that are typically higher than diffusion tubes
(Namiesnik et al., 2005) but the sampling rate may be more variable because it is more affected by
turbulence. Physical characteristics of these two fundamental passive sampler types, tube-type and badge-
type, are summarized and provided in Table AX3.3-1. Performance characteristics are presented in Table
AX3.3-2.
The sorbent can be either physically sorptive or chemisorptive; passive samplers for NO2 are
chemisorptive, that is, a reagent coated on a support (e.g., metal mesh, filter) reacts with the NO2. The
sorbent is extracted and analyzed for one or more reactive derivatives; the mass of NO2 collected is
derived from the concentration of the derivative(s) based on the stoichiometry of the reaction. Thus, an
additional approach to reducing detection limits associated with passive samplers is to modify the
chemisorptive reaction and the extraction and analysis methods to increase analytical sensitivity.
However, although chemisorption is less prone to the back diffusion phenomenon of sorptive-only
methods, analyte losses could occur due to interferences from other pollutants that also react with the
sorbent or the derivatives. The most commonly used NO2 passive samplers rely on the classical reaction
with triethanolamine (TEA). TEA requires hydration for quantitative NO2 sampling (i.e., 1:1 conversion
to nitrite) and the reaction products have been subject to a number of investigations and several have been
reported, including TEA-nitrate and nitrite, triethanolammonium nitrate, nitrosodiethanolamine, and
triethanolamine N-oxide (Glasius et al., 1999). Known interferences include HONO, PAN, and nitric acid
(Gairetal., 1991.).
The tube-type passive samplers (Palmes tubes) require week-long sampling periods and have been
extensively used for residential indoor/outdoor measurements, mostly for exploring the relationship
between indoor and outdoor levels (Cyrys et al., 2000; Raw et al., 2004; Simoni et al., 2004; Janssen
et al., 2001). Passive diffusion tubes have also been widely used for measurements of NO2 in ambient air
(Gonzales et al., 2005; Gauderman et al., 2005; Da Silva et al., 2006; Lewne et al., 2004; Stevenson et al.,
2001; Glasius et al., 1999). Personal exposure studies have also been conducted using the Palmes tubes
(Mukala et al., 1996; Kousa et al., 2001). Some of these studies evaluated passive sampler performance
by collocating them with chemiluminescence analyzers during at least some portion of the field studies
(Gair et al., 1991; Gair and Penkett, 1995; Plaisance et al., 2004; Kirby et al., 2001). The majority of these
studies indicate that these samplers have very good precision (generally within 5%) but tend to
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overestimate NO2 by 10 to 30%. However, there has not been a methodical evaluation of variables
contributing to variance for the range of samplers available when used in field conditions. Thus, it is not
clear if the bias is due to deviations from ideal sampling conditions that can affect actual sampling rates,
contributions from co-reacting contaminants or, most probably, a combination of these variables.
A badge-type sampler was introduced by Yanagisawa and Nishimura (1982) to overcome the long
sampling time required by Palmes tubes. Since then, these sensitive NO2 short path length samplers (Toyo
Roshi Ltd) have been optimized and evaluated for indoor air and for personal monitoring (Lee et al.,
1993a,b). They have been used extensively for personal exposure studies (Ramirez-Aguilar et al., 2002;
Yanagisawa et al., 1986; Berglund et al., 1994, Lee et al., 2004) and indoor air measurements (Kodama
et al., 2002; Bae et al., 2004; Algar et al., 2004; Shima and Adachi, 2000; Smedje, et al., 1997) and to a
more limited amount for ambient monitoring (Tashiro and Taniyama, 2002; Levy et al., 2006; Norris and
Larson, 1999). Due to the greater uptake rate resulting from the larger cross sectional area of the badges
and shorter diffusion length compared to the tube-type samplers, sampling times can be decreased from
one-week to one-day for typical environmental air concentrations. This makes diffusive filter-badges
more suitable for shorter-term sampling while long-term ambient monitoring can still be conducted using
the Palmes-tubes.
AX3.3.1.3. Tube Type Samplers
Gradko Sampler (http://www.gradko.co.uk)
The Gradko sampler is based on the Palmes tube design (Gerboles et al., 2006a). It collects O3 or
NO2 by molecular diffusion along an inert tube by chemisorption. A stable complex is formed with
triethanolamine coated on a stainless steel screen in the tube. The complex is spectroscopically analyzed
by adding an azo dye (Chao and Law, 2000). The sampler has a detection limit of 0.5 ppb for NO/NO2
and the precision of ± 6% above 5 ppb levels when used for two weeks (Table AX3.3-2). This sampler
has been used to measure personal exposures, concentrations of residential air indoors such as in the
kitchen and bedroom, and concentrations of outdoor air (Chao and Law, 2000; Gallelli et al., 2002; Lai
et al., 2004). It has been used to measure ambient NO2 levels in Southern California as a marker of traffic-
related pollution in San Diego County (Ross et al., 2006).
Passam Sampler (http://www.passam.ch)
This sampler is also based on the design of the Palmes tube (Palmes et al., 1976). It collects NO2 by
molecular diffusion along an inert polypropylene tube to an absorbent, triethanolamine. The collected
NO2 is determined spectrophotometrically by the well-established Saltzmann method. When used
outdoors the samplers are placed in a special shelter to protect them from rain and minimize wind
turbulence effects. The Passam sampler is sold in two different models, one for long-term and one for
short-term sampling.
Analyst ™Sampler (http://www.monitoreurope.com)
The Analyst™ sampler is also a modification of the open-Palmes-tube design and was developed by
the Italian National Research Council (CNR - Institute Inquinamento Atmosferico) in 2000 (Bertoni
et al., 2001). The Analyst™ consists of a glass vessel, which contains a reactant supported on a stainless
steel grid. It is suitable for long-term monitoring (typically one month) of oxides of nitrogen, sulfur
dioxide, and volatile organic compounds in ambient air. The target compound is analyzed by gas
chromatography with minimum detection limit of 0.1 mg/m3 (~52 ppb) for a twelve-week sample
duration, and has relatively high precision. The Analyst™ method development (De Santis et al., 1997,
2002) and actual field application (De Santis et al., 2004) have been described. The primary use for
Analyst™ is as a reliable tool for long-term determination of concentration in indoor as well as outdoor
environments (Bertoni et al., 2001) and as a screening tool for ambient monitoring to identify pollution
"hot spots" (De Santis et al., 2004).
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AX3.3.1.4. Badge-Types Samplers
Ogawa Passive Sampler (http://www.ogawausa.com)
This sampler is a double face badge that can monitor NO, NOX, and NO2. The design can be used
also for the determination of SO2, O3, and NH3 levels in air. The manufacturer-reported detection limits
for nitrogen oxides are 2.3 ppb and 0.32 ppb for 24-h and 168-h sampling, respectively. Reported actual
sampling rates for NO2 are two to three times higher than the manufacturer's values. The normal
operation ranges are 0 to 25 ppm for 24-h exposure and 0 to 3.6 ppm for 168-h exposure. The
manufacturer recommends a sampling height of 2.5 meters and storage time of up to 1 year when kept
frozen. Ogawa passive samplers have been extensively used for human exposure studies to measure
personal air concentrations and (or) indoor/outdoor levels for residents in a number of locations, including
adults of Richmond, Virginia (Zipprich et al., 2002), children of Santiago, Chile (Rojas-Bracho et al.,
2002), office workers of Paris, France (Mosqueron et al., 2002), and cardiac compromised individuals of
Toronto, Canada (Kim et al., 2006). The samplers have been used also in air monitoring networks to
assess traffic-related pollutant exposure (Singer et al., 2004), as well as to evaluate spatial variability of
nitrogen dioxide ambient concentrations in Montreal, Canada (Gilbert et al., 2005).
WL Sampler (http//www.ivl.se/en/business/monitoring/diffusive_samplers.asp)
The IVL method development has been described in detail by Perm and Svanberg (1998). It was
developed by Swedish Environmental Research Institute in the mid of 1980s (Sjodin et al., 1996), is
designed to minimize turbulent wind effects outdoors as well as "starvation effects" indoors (i.e., very
low face velocities), interferences from within sampling tube chemistry, temperature and humidity
effects, and artifacts and losses during post-sampling storage. Manufacturer-reported detection limits for
this sampler with sampling times of ~1 month are 0.1 (ig/m3 (0.05 ppb) forNO2, and 0.5 (ig/m3 (0.42 ppb)
for NO, respectively. Due to its long sampling time, this sampler has been extensively used for NO2
background monitoring in ambient air in rural or urban areas (Fagundez et al., 2001; Sjodin et al., 1996;
Pleijel et al., 2004).
Willems Badge Sampler
The Willems badge, a short-term diffusion sampler, was developed at the University of
Wageningen, Netherlands, originally for airborne ammonia measurements and later for measuring NO2
(Hagenbjork-Gustafsson et al., 1996). It consists of a cylinder of polystyrene with a Whatman GF-A glass
fiber filter impregnated with triethanolamine at its based held in place by a 6 mm distance ring. A Teflon
filter is placed on the 6 mm polystyrene ring, which is secured with a polystyrene ring of 3 mm
(Hagenbjork-Gustafsson et al., 1996). The badge is closed by a polyethylene cap to limit influences by air
turbulence. The diffusion length in the badge is 6 mm. This sampler was evaluated for ambient air
measurements in laboratory and field tests (Hagenbjork-Gustafsson et al., 1999). It has a manufacturer's
reported detection limit of 2 (ig/m3 (~1 ppb) for 48-h sampling duration. When used for personal
sampling in an occupational setting with a minimum wind velocity of 0.3 m/s, detection limits of 18
(-9.4 ppb) and 2 (ig/m3 (~1 ppb) for 1-h and 8-h sampling, respectively, have been reported (Hagenbjork-
Gustafsson et al., 2002, Glas et al., 2004).
AX3.3.1.5. Radial Sampler Types
Radiello® -the radial diffusive sampler (http://www.radiello.com)
Radiello® samplers use radial diffusion over a microporous cylinder into an absorbing inner
cylinder, instead of axial diffusion, which increases the uptake rate by a factor of about 100 (Hertel et al.,
2001). Nitrogen dioxide is chemiadsorbed onto triethanolamine as nitrite, which is quantified by visible
spectrometry. Sample collection of up to 15 days is feasible but relative humidity higher than 70% can
cause interferences when used for extended periods of more than 7 days. The manufacturer-reported
typical sampling rate for nitrogen dioxide sampling is 75 ± 3.72 ml/min at temperatures between -10 and
40 °C. The rate can vary with humidity in the range of 15 to 90% and wind speed between 0.1 and 10 m/s
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(Radiello® Manual, 2006). A Danish study (S0rensen et al., 2005) recruited 30 subjects during each of
four seasons in Copenhagen, and measured the subjects' personal exposures, home indoor/front door air
concentrations during 2-day periods with this sampler.
EMD (Ecole des Mines de Douai) Sampler
A new high-uptake rate diffusive sampler has been recently developed by the Ecole des Mines de
Douai (EMD) laboratory (Piechocki-Minguy et al., 2003) and evaluated in the laboratory and field for
measurement of NO2 levels in ambient air. It is composed of a porous cartridge impregnated with
triethanolamine and fitted in a cylindrical protective box equipped with caps at its extremities (Piechocki-
Minguy et al., 2006). The large sampling area (cartridge surface) and the two circular openings provide a
high uptake rate (exceeding 50 cmVmin). The sampling rate was reported to be on average 0.89 cm3/s for
indoor sampling and 1.00 cm3/s for outdoor sampling. Detection limits were determined to be 11 (ig/m3
(~5.8 ppb) for 1-h measurement. The sampling rate was not significantly influenced by wind at speeds
higher than 0.3 m/s (Piechocki-Minguy et al., 2003). This sampler has been used in France to assess
personal exposures in a series of microenvironments (home, other indoor places, transport and outdoor)
for two 24-h time periods (weekday and weekend) (Piechocki-Minguy et al., 2006).
AX3.4. NOx in Indoor Air
AX3.4.1. Indoor Sources and Concentrations of NOx
Penetration of outdoor NO2 and combustion in various forms are the major sources of NO2 to
indoor environments. These environments include homes, schools, restaurants, theaters etc. As might be
expected, indoor concentrations of NO2 in the absence of combustion sources are determined by the
infiltration of outdoor NO2 (Spengler et al., 1994; Weschler et al., 1994; Levy et al., 1998a), with a much
smaller contribution from chemical reactions in indoor air. Indoor sources of nitrogen oxides have been
characterized in several reviews, namely the last AQCD for Oxides of Nitrogen (U.S. Environmental
Protection Agency, 1993); the Review of the Health Risks Associated with Nitrogen Dioxide and Sulfur
Dioxide in Indoor Air for Health Canada (Brauer et al., 2002); and the Staff Recommendations for
revision of the NO2 Standard in California (CARB, 2007). Mechanisms by which nitrogen oxides are
produced in the combustion zones of indoor sources were reviewed in the last AQCD for Oxides of
Nitrogen (U.S. Environmental Protection Agency, 1993) and will not be repeated here. Sources of
ambient NO2 are reviewed in Chapter 2 of this document. It should also be noted that indoor sources can
affect ambient NO2 levels, particularly in areas in which atmospheric mixing is limited.
Ideally, exposure to NO2 should be cumulated over all indoor environments in which an individual
spends time. These indoor environments may include homes, schools, offices, restaurants, theaters, ice
skating rinks, stores, etc. However, in a study by Leaderer et al. that used two-week integrated measures,
concentrations of NO2 inside the home accounted for 80% of the variance in total personal exposure,
indicating that home concentrations are a reasonable proxy for personal exposure (Leaderer et al., 1986).
AX3.4.1.1. Gas Cooking Appliances
A large number of studies, as described in the reviews cited above, have all noted the importance of
gas cooking appliances as sources of NO2 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 NO2 concentrations than homes with electric cooking
appliances (Gilbert et al., 2006; Lee et al., 2000, 2002; Garcia-Algar et al., 2004; Raw et al., 2004;
Leaderer et al., 1986; Garcia-Algar, 2003). Gas cooking appliances remain significantly associated with
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indoor NO2 concentrations after adjusting for several potential confounders including season, type of
community, socioeconomic status, use of extractor fans, household smoking, and type of heating
(Garcia-Algar et al, 2004; Garrett et al, 1999).
Gas appliances with pilot lights emit more NO2 than gas appliances with electronic ignition.
Spengler et al. (1994) found that NO2 concentrations in bedrooms of homes with a gas range without a
pilot light averaged 4 ppb higher than in homes with an electric range, but were 15 ppb higher in homes
with gas ranges with pilot lights. Lee et al. (1998) found somewhat larger differences in NO2
concentrations in homes in the Boston area, with minor seasonal variation. Homes with gas stoves
without pilot lights averaged between 11 ppb (summer) and 18 ppb (fall) higher than homes with electric
stoves, while those with pilot lights averaged between 19 ppb (summer) and 27 ppb (fall) higher than
electric stove homes.
Use of extractor fans reduces NO2 concentrations in homes with gas cooking appliances (Gallelli et
al., 2002; Garcia-Algar et al., 2003), although absolute NO2 levels tend to remain higher than in homes
with electric stoves. In a multivariate analysis, Garcia-Algar et al. (2004) found that having a gas cooker
remained significantly increased NO2 concentrations even after adjusting for extractor fan use. Raw et al.
(2004) found only a small effect of extraction fan use on NO2 levels in the bedroom in gas cooker homes.
Among homes with gas cooking, geometric mean bedroom NO2 levels were 1.7 ppb lower in homes with
an extractor fan than in homes without one. As expected, among homes with no fossil fuel cooking, there
were no differences in mean bedroom levels of NO2 in homes with and without extractor fans.
AX3.4.1.2. Other Combustion Sources
Secondary heating appliances are additional sources of NO2 in indoor environments, particularly if
they are unvented or inadequately vented. As heating costs increase, the use of these secondary heating
appliances tends to increase. From 1988 to 1994, an estimated 13.7 million homes used unvented heating
appliances, with disproportionately higher usage rates among southern, rural, low-income, and African-
American homes (Slack and Heumann, 1997). Of the 83.1 million households using gas stoves or ovens
for cooking, 7.7 million (9.3%) also used the stove for heating (Slack and Heumann, 1997).
Gas heaters, particularly when unvented or inadequately vented, produce high levels of NO2.
Kodama et al. (2002) examined the associations between secondary heating sources and NO2
concentrations measured over a 48-h exposure period in the living rooms of homes in Tokyo, Japan. They
found much higher NO2 concentrations during February 1998 and January 1999 in homes with kerosene
heaters in both southern (152.6 ppb and 139.7 ppb for 1998 and 1999, respectively) and northern (102.4
and 93.1 ppb for 1998 and 1999, respectively) areas of Tokyo compared to homes with electric heaters
(30.8 and 31.1 for the southern and 37.2 and 31.6 for northern areas, 1998 and 1999, respectively).
In a study by Garrett et al. (1999) of 78 homes in Latrobe Valley, Australia, the two highest indoor
NO2 levels recorded in the study were 129 ppb for the only home with an unvented gas heater and 69 ppb
for a home with a vented gas heater. Levels of NO2 in the kitchens and living rooms of homes with a
vented gas heater (mean = 6.9 ppb in living room, 7.3 ppb in kitchen, n = 15) were comparable to homes
with gas stoves (mean = 6.7 ppb in living room, 8.0 ppb in kitchen, n = 15) (Table AX3.4-1). These
concentrations include results from all seasons combined, so the levels are somewhat lower than those
found by Triche et al. (2005) for winter monitoring periods only.
Triche et al. (2005) also found high levels of NO2 in homes with gas space heaters, although
information on whether the appliance was vented or unvented was not available. Data from this study
were analyzed in more detail and are shown in Table AX3.4-2. The median NO2 concentration in the 6
homes with gas space heater use during monitoring periods with no gas stove use was 15.3 ppb; a similar
incremental increase in total NO2 levels was noted for homes with gas space heater use during periods
when gas stoves were also used (Median = 36.6 ppb) compared to homes where gas stoves were used but
no secondary heating sources were present (Median = 22.7 ppb) (Table AX3.4-2).
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Shima and Adachi (1998) examined associations between household characteristics, outdoor NO2,
and indoor NO2 in 950 homes during the heating season (640 with unvented and 310 vented heaters) and
905 homes during the non-heating season in urban, suburban, and rural areas of Japan. While no
information is provided on gas stove use, the authors note that nearly all homes in Japan have gas stoves,
though relatively few have pilot lights. During the heating season, geometric mean NO2 levels in homes
with unvented heaters (66.4 ppb) are about three times higher than in homes with vented heaters
(20.6 ppb). In the non-heating season, the mean levels were lower at only 13.8 ppb, suggesting a
contribution from vented heaters as well.
In multivariate analyses, Gilbert et al. (2006) found that gas and mixed/other heating systems were
significantly associated with NO2 levels, adjusting for presence of gas stoves and air exchange rates in 96
homes in Quebec City, Canada during the winter/early spring period. Many homes with gas space heaters
also have gas stoves, and the contribution from multiple sources is much higher than from any single
source alone (Garrett et al., 1999). In the Garrett et al. (1999) study, homes were classified into five
categories: no indoor source (n = 15), gas stove only (n = 15), gas heater only (n = 14), smoker in the
household only (n = 7), and multiple sources (n = 29). Homes with multiple sources had much higher
NO2 concentrations homes with either a gas stove only or gas heater only Table 3.4-1.
Kerosene heaters are also important contributors to indoor NO2 levels. Leaderer et al. (1986)
enrolled a cohort of kerosene heater users identified from local kerosene dealers and a cohort of controls
systematically chosen from the same neighborhoods with each matched pair treated as a sampling unit
(i.e., sampled at the same randomly assigned time period). A total of 302 homes were monitored for at
least one two-week period. While outdoor concentrations never exceeded 100 (ig/m3 (53 ppb),
approximately 5% of homes with either no gas but 1 kerosene heater or gas but no kerosene heater had
levels exceeding 53 ppb. Between 17%-33% of homes with both gas and kerosene heater(s) exceeded this
limit, while nearly one quarter of homes with no gas, but two or more kerosene heaters had these levels.
Data from Triche et al. (2005) (Table AX3.4-2) also indicated increased levels of NO2 for kerosene
heater homes during monitoring periods with no gas stove use (Median =18.9 ppb) compared to homes
with no sources (Median = 6.3 ppb), which is similar to levels found in homes using gas space heaters
(Median = 15.3 ppb). However, these NO2 concentrations are of the same magnitude as those in homes
with gas stove use (Median =17.2 ppb).
Data are available for unvented gas hot water heaters from a number of studies conducted in the
Netherlands. Results summarized by Brauer et al. (2002) indicate that concentrations of NO2 in homes
with unvented gas hot water heaters were 10 to 21 ppb higher than in homes with vented heaters, which in
turn, had NO2 concentrations 7.5 to 38 ppb higher than homes without gas hot water heaters.
The contribution from combustion of biomass fuels has not been studied as extensively as that from
gas. A main conclusion from the previous AQCD was that properly vented wood stoves and fireplaces
would make only minor contributions to indoor NO2 levels. Several studies conclude that use of wood
burning appliances does not increase indoor NO2 concentrations. Levesque et al. (2001) examined the
effects of wood-burning appliances on indoor NO2 concentrations in 49 homes in Quebec City, Canada.
The homes, which had no other combustion source, were sampled for 24 h while the wood-burning
appliance was being used. No significant differences in mean NO2 levels were found in homes with (6.6 +
3.6 ppb) and without (8.8 +1.9 ppb) a wood-burning appliance. Data from Triche et al. (2005) confirm
these findings (Table AX3.4-2). Homes with wood burning sources had comparable NO2 concentrations
to homes without other secondary heating sources, with (Median = 5.9 ppb) and without (Median =
16.7 ppb) gas stove use.
Data are available for unvented gas hot water heaters from a number of studies conducted in the
Netherlands. Results summarized by Brauer et al. (2002) indicate that concentrations of NO2 in homes
with unvented gas hot water heaters were 10 to 21 ppb higher than in homes with vented heaters, which in
turn, had NO2 concentrations 7.5 to 38 ppb higher than homes without gas hot water heaters.
As can be seen from the tables, shorter-term average concentrations tend to be much higher than
longer term averages. However, as Triche et al. (2005) point out, the 90th percentile concentrations can be
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substantially greater than the medians, even for two week long samples. This finding illustrates the high
variability found among homes.
In 10% of homes with fireplaces studied by Triche et al. (2005), NO2 concentrations were greater
than or equal to 80 ppb, or about twice the level found in homes with no indoor combustion source (see
Table AX3.4-2). In a study of students living in Copenhagen, S0rensen et al. (2005) found that personal
exposures to NO2 were significantly associated with time exposed to burning candles in addition to other
sources. However, they did not provide data for concentrations in spaces in which candles were burned.
Results of studies relating NO2 concentrations and exposures to environmental tobacco smoke (ETS) have
been mixed. Several studies found positive associations between NO2 levels and ETS (e.g., Linaker et al.,
1996); Farrow et al., 1997; Aim et al., 1998; Levy et al., 1998a; Monn et al., 1998; Cyrys et al., 2000; Lee
et al., 2000; Garcia-Algar et al., 2004) whereas others have not (e.g., Hackney et al., 1992; Kawamoto
et al., 1993). In a study of 57 homes in Brisbane, Australia (Lee et al., 2000), levels of NO2 were higher in
homes with smokers present (14.9 ± 7.7 ppb) than without smokers (9.9 ±5.0 ppb). However, these
concentrations did not account for presence of a gas range (n = 18 of 57 homes had a gas range). Garrett
et al. (1999) found that smoking in the home increased levels of NO2 in the winter, but not in the summer
when windows tended to be opened. In a study of students living in Copenhagen, S0rensen et al. (2005)
did not find a significant association between ETS and personal exposures to NO2. However, they found
that burning candles was a significant prediction of bedroom levels of NO2.
AX3.4.1.3. Other Indoor Environments
Indoor ice skating rinks have been cited as environments containing high levels of NO2 when fuel
powered ice resurfacing machines are used especially without ventilation. As part of a three year study,
Levy et al. (1998b) measured NO2 concentrations at 2 locations at the outside of the ice surface in 19
skating rinks in the Boston area over 3 winters. Although different passive samplers were used in the first
year (Palmes tubes, 7 day sampling time) and in years 2 and 3 (Yanagisawa badges, 1 day working hours)
of the study, consistently high mean NO2 concentrations were associated with the use of propane fueled
resurfacers (248 ppb in the first year and 206 ppb in the following years) and gasoline fueled resurfacers
(54 ppb in the first year and 132 ppb in the following years) than with electric resurfacers (30 ppb in the
first year and 37 ppb in the following years). During all three years of the study peak NO2 concentrations
were several times higher in the rinks with propane and gasoline fueled resurfacers than the values given
above. A number of earlier studies have also indicated NO2 concentrations of this order and even higher
(Paulozzi et al., 1993; Berglund et al., 1994; Lee et al., 1994; Brauer et al., 1997). In these studies peak
averages were in the range of a few ppm.
AX3.4.2. Reactions of N02 in Indoor Air
Chemistry in indoor settings can be both a source and a sink for NO2 (Weschler and Shields, 1997).
NO2 is produced by reactions of NO with ozone or peroxy radicals, while NO2 is removed by gas phase
reactions with ozone and assorted free radicals and by surface promoted hydrolysis and reduction
reactions. The concentration of indoor NO2 also affects the decomposition of peroxyacyl nitrates. Each of
these processes is discussed in the following paragraphs. They are important not only because they
influence the indoor NO2 concentrations to which humans are exposed, but also because certain products
of indoor chemistry may confound attempts to examine associations between NO2 and health.
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Indoor NO can be oxidized to NO2 by reaction with ozone or peroxy radicals; the latter are
generated by indoor air chemistry involving O3 and unsaturated hydrocarbons such as terpenes found in
air fresheners and other household products (Sarwar et al., 2002a,b; Nazaroff and Weschler, 2004;
Carslaw, 2007). The rate coefficient for the reaction
-> O2 + O2 (AX3.4-1)
at room temperature (298 K) is 1.9 x 10~14 cm3/molec-sec or 4.67 H 10~4 ppb"1 s"1 (Jet Propulsion
Laboratory, 2006). At an indoor O3 concentration of 10 ppb and an indoor NO concentration that is
significantly less than that of O3, the half-life of NO is 2.5 min. This reaction is sufficiently fast to
compete with even relatively fast air exchange rates. Hence, the amount of NO2 produced from NO tends
to be limited by the amount of O3 available. The indoor concentrations of NO and O3 are negatively
correlated; significant concentrations of NO can only accumulate when small amounts of O3 are present
and vice versa (Weschler et al., 1994).
The rapid reaction between NO and O3 also means that humans, themselves, can be indirect sources
of NO2 in the rooms they occupy. Exhaled human breath contains NO that is generated endogenously
(Gustafsson et al., 1991). For a typical adult male, the average nasal NO output is 325 nL min"1 or 23.9
(ig h"1 (Imada et al., 1996). If ozone is present in the indoor air, some or all of these exhaled NO
molecules will be oxidized to NO2. To put this source in perspective, consider the example of an adult
male in a 30 m3 room ventilated at 1 air change per hour (h"1) with outdoor air. The steady-state
concentration of NO in the room as a consequence of NO in exhaled breath is 0.80 (ig/m3 or 0.65 ppb if
none of the NO were to be oxidized. However, assuming a meaningful concentration of ozone in the
ventilation air (>5 ppb), most of this NO is oxidized to NO2 before it is exhausted from the room. In this
scenario, the single human occupant is indirectly a source for 0.65 ppb of NO2 in the surrounding air. At
higher occupant densities, lower air exchange rates and elevated concentrations of O3 in the ventilation
air, human exhaled breath could contribute as much as 5 ppb to the total concentration of indoor NO2.
The reaction of NO2 with ozone produces nitrate radicals (NO3):
The second order rate-constant for this reaction at room temperature (298 K) is 3.2 x 10~17 cm3/molec-sec
or 7.9 x 10~7 ppb"1 s"1 (Jet Propulsion Labatory, 2006). For indoor concentrations of 20 ppb and 30 ppb
for O3 and NO2, respectively, the production rate of NO3 radicals is 1.7 ppb h"1. This reaction is strongly
temperature dependent, an important consideration given the variability of indoor temperatures with time
of day and season. The nitrate radical is photolytically unstable (Finlayson-Pitts and Pitts, 2000). As a
consequence, it rapidly decomposes outdoors during daylight hours. Indoors, absent direct sunlight,
nitrate radical concentrations may approach those measured during nighttime hours outdoors. To date
there have been no indoor measurements of the concentration of nitrate radicals in indoor settings.
Modeling studies by Nazaroff and Cass (1986), Weschler et al. (1992), Sarwar et al. (2002b), and Carslaw
(2007) estimate indoor nitrate radical concentrations in the range of 0.01 to 5 ppt, depending on the
indoor levels of O3 and NO2.
The nitrate radical and NO2 are in equilibrium with dinitrogen pentoxide (N2O5):
Dinitrogen pentoxide reacts with water to form nitric acid. The gas phase reaction with water is too
slow (Sverdrup et al., 1987) to compete with air exchange rates in most indoor environments. Due to
mass transport limits on the rate at which N2O5 is transported to indoor surfaces, reactions of N2O5 with
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water sorbed to indoor surfaces are much slower than gas phase reactions between nitrate radicals and
commonly occurring indoor alkenes.
Once formed, NO3 radicals can oxidize organic compounds by either adding to an unsaturated
carbon bond or abstracting a hydrogen atom (Wayne et al., 1991). In certain indoor settings, the nitrate
radical may be a more important indoor oxidant than either ozone or the hydroxyl radical. Table 8 in
Nazaroff and Weschler (2004) illustrates this point. Assuming indoor concentrations of 20 ppb,
5 x 10"6 ppb, and 0.001 ppb for O3, OH, and NO3, respectively, the pseudo first-order rate constants for
reactions of most terpenoids are larger for reactions with NO3 than for reactions with either O3 or OH. For
example, for the stated conditions, the half-lives of d-limonene and a-pinene are roughly three times
shorter as a consequence of reaction with NO3 versus reaction with O3. The products of reactions between
NO3 and various organic compounds include nitric acid, aldehydes, ketones, organic acids and organic
nitrates; these have been summarized by Wayne et al. (1991). Nitrate radicals and the products of nitrate
radical chemistry may be meaningful confounders in NO2 exposure studies.
Reactions between NO2 and various free radicals can be an indoor source of organo-nitrates,
analogous to the chain-terminating reactions observed in photochemical smog (Weschler and Shields,
1997). Additionally, based on laboratory measurements and measurements in outdoor air (Finlayson-Pitts
and Pitts, 2000), one would anticipate that NO2, in the presence of trace amounts of HNO3, can react with
PAHs sorbed on indoor surfaces to produce mono- and dinitro-PAHs.
As noted in the ISA, Section 2.2, HONO occurs in the atmosphere mainly via multiphase processes
involving NO2. HONO is observed to form on surfaces containing partially oxidized aromatic structures
(Stemmler et al., 2006) and on soot (Ammann et al., 1998). Indoors, surface-to-volume ratios are much
larger than outdoors, and the surface mediated hydrolysis of NO2 is a major indoor source of HONO
(Brauer et al., 1990; Febo andPerrino, 1991; Spiceretal., 1993; Braueretal, 1993; Spengler et al., 1993;
Wainman et al., 2001; Lee et al., 2002). Spicer et al. (1993) made measurements in atest house that
demonstrated HONO formation as a consequence of NO2 surface reactions and postulated the following
mechanism to explain their observations.
2NO2 + H2O/surface -» HONO(aq} + H++NO3~
HONO(aq} <-> HONO(g) (AX3 4.5)
In a series of chamber studies, Brauer et al. (1993) reported HONO formation as a consequence of
NO2 surface reactions and further reported that HONO production increased with increasing relative
humidity. Wainman et al. (2001) confirmed Brauer's findings regarding the influence of relative
humidity. They also found that NO2 removal and concomitant HONO production was greater on synthetic
carpet surfaces compared to Teflon surfaces, and that the affinity of a surface for water influences
HONO's desorption from that surface. Lee et al. (2002) measured HONO and NO2 concentrations in 119
Southern California homes. Average indoor HONO levels were about 6 times larger than outdoors
(4.6 ppb versus 0.8 ppb). Indoor HONO concentrations averaged 17% of indoor NO2 concentrations, and
the two were strongly correlated. Indoor HONO levels were higher in homes with humidifiers compared
to homes without humidifiers (5.9 ppb versus 2.6 ppb). This last observation is consistent with the studies
of Brauer et al. (1993) and Wainman et al. (2001) indicating that the production rate of HONO from
NO2/surface reactions is larger at higher relative humidities. Based on detailed laboratory studies, the
hydrolysis mechanism, Equations AX3.4-4 and AX3.4-5, have been refined. Finlayson-Pitts et al. (2003)
hypothesize that the symmetric form of the NO2 dimer is sorbed on surfaces, isomerizes to the
asymmetric dimer which auto ionizes to NO+NO3"; the latter then reacts with water to form HONO and
surface adsorbed HNO3. FTIR-based analyses indicate that the surface adsorbed HNO3 exists as both
undissociated nitric acid-water complexes, (HNO3)X(H2O)Y, and nitrate ion-water complexes,
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(NO3")x(H2O)Y (Dubowski et al., 2004, Ramazan et al., 2006). Such adsorbed species may serve as
oxidizing agents for organic compounds sorbed to these same surfaces (Ramazan et al., 2006).
HONO and much smaller amounts of HNO3 are also emitted directly by combustion by gas
appliances and can infiltrate from outdoors. Spicer et al. (1993) compared the measured increase in
HONO in a test house resulting from direct emissions of HONO from a gas range and from production by
surface reactions of NO2. They found that emissions from the gas range could account for about 84% of
the measured increase in HONO and surface reactions for 1 1% in an experiment that lasted several hours.
An equilibrium between adsorption of HONO from the gas range (or other indoor combustion sources)
and HONO produced by surface reactions (see Equation AX3.4-5) also determines the relative
importance of these processes in producing HONO in indoor air. In a study of Southern CA homes (Lee
et al., 2002), indoor levels of NO2 and HONO were positively associated with the presence of gas ranges.
It is known that the photolysis of HONO (g) in the atmosphere (outdoors) is a major source of the
hydroxyl radical (OH). Given high indoor HONO concentrations and the presence of lighting (sun light
penetrating windows, incandescent lights, fluorescent lights), the photolysis of indoor HONO may be a
meaningful source of indoor hydroxyl radical, under favorable reaction conditions. Given the large suite
of man-made chemicals present indoors at elevated concentrations, indoor free radicals (e.g., OH and
NO3) can initiate and drive a complex series of indoor chemical reactions.
NO2 can also be reduced on certain surfaces, forming NO. Spicer et al. (1989) found that as much
as 15% of the NO2 removed on the surfaces of masonite, ceiling tile, plywood, plasterboard, bricks,
polyester carpet, wool carpet, acrylic carpet and oak paneling was re-emitted as NO. Weschler and
Shields (1996) found that the amount of NO2 removed by charcoal building filters were almost equally
matched by the amount of NO subsequently emitted by these same filters.
Spicer et al. (1993) determined the 1st order rate constants for removal of several NOY components
by reaction with indoor surfaces. They found lifetimes (e-folding times) of about half an hour for HNO3,
an hour for NO2, and hours for NO and HONO. Thus the latter two components, if generated indoors are
more likely to be lost to the indoor environment through exchange with outside air than by removal on
indoor surfaces. However, HONO is in equilibrium with the nitrite ion (NO2 ) in aqueous surface films
HONO(aq} <-> H* + NO2~ (AX3.4-6)
Ozone oxidation of nitrite ions in such films is a potential sink for indoor HONO (Lee et al., 2002).
Jakobi and Fabian (1997) measured indoor and outdoor concentrations of ozone and peroxyacetyl nitrate
(PAN) in several offices, private residences, a classroom, a gymnasium and a car. They found that indoor
levels of PAN were 70% to 90% outdoor levels, and that PAN's indoor half-life ranged from 0.5 to 1 h.
The primary indoor removal process is thermal decomposition.
CH3C(0)OON02 <-> CH3C(0}00 + NO2
As is indicated by Equation AX3.4-7, PAN is in equilibrium with the peroxylacetyl radical and
NO2. Hence, the indoor concentration of NO2 affects the thermal decomposition of PAN and,
analogously, other peroxyacyl nitrates. Peroxylalkyl radicals rapidly oxidize NO to NO2, so the indoor
concentration of NO also influences the thermal decomposition of PAN type species (Finlayson-Pitts and
Pitts, 2000).
Reactions between hydroxyl radicals and aldehydes in the presence of NO2 can lead to the
formation of peroxyacyl nitrates. Weschler and Shields (1997) have speculated that such chemistry may
sometimes occur indoors. For example, the requisite conditions for the formation of the highly irritating
compound peroxybenzoyl nitrate may occur when ozone, certain terpenes, styrene and NO2 are present
simultaneously at low air exchange rates. This relatively common indoor mixture of pollutants produces
hydroxyl radicals and benzaldehyde, which can subsequently react as noted above. In her detailed model
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of indoor chemistry, Carslaw (2007) explores the indoor formation of PAN-type species (see Figure 2 in
the cited reference).
Recent work indicates that indoor NO2 also can affect the formation of secondary organic aerosols
(SOA) resulting from the reaction of O3 with terpenes such as d-limonene and ot-pinene (N0jgaard et al.,
2006). At concentrations of 50 ppb for O3 and the terpenes, NO2 decreased the formation of SOA
compared to the levels formed in the absence of NO2. The effect was more pronounced for SOA derived
from ot-pinene than d-limonene, and at lower NO2 concentrations, appears to be explained by the O3 loss
resulting from its reaction with NO2. The resultant nitrate radicals apparently are not as efficient at
producing SOA as the lost O3.
Nitro-PAHs have been found in indoor environments (Mumford et al., 1991; Wilson et al., 1991).
The major indoor sources of nitro-PAHs include cooking, wood burning, and the use of kerosene heater
(World Health Organization (WHO), 2003). It is also likely that nitro-PAHs outdoors can infiltrate
indoors. One of the potential sources of nitro-PAHs indoors, which has not been characterized, is
reactions via indoor chemistry. The reactions of PAHs with OH and NO3 may occur in indoor
environments. Although no direct measurements of OH or NO3 in indoor environments, OH and NO3 can
be formed via indoor chemistry and may present at significant levels indoors (Nazaroff and Cass 1986,
Sarwar et al., 2002a; Carslaw, 2007). Concentrations of ~10~6 ppb for OH and 0.01-5 ppt of NO3 have
been predicted through indoor chemical reactions (Nazaroff and Cass 1986, Sarwar et al., 2002a, Carslaw,
2007), depending on the indoor levels of O3, alkenes, and NO2. Observation of secondary organic aerosols
(SOA) formation in a simulated indoor environment also suggested that ~10~5 ppb steady-state OH
radicals were generated from the reactions of O3 with terpenes (Fan et al., 2003). PAHs are common
indoor air pollutants (Chuang et al., 1991; Naumova et al., 2002), and the concentrations of some PAHs
indoors are often higher than outdoors (Naumova et al., 2002). Therefore, the reactions of OH and NO3
with PAHs may occur at rates comparable to air exchange rates to form nitro-PAHs indoors. In addition,
the reactions of NO3 with PAHs may be more significant indoors than outdoors because indoor NO3 is
more stable due to the low uv in indoor environments. Given the high surface areas available indoors, the
formation of nitro-PAHs via surface reactions of PAHs with nitrating species may be more important
compared to heterogeneous reactions outdoors.
In summary, indoor chemistry can meaningfully alter the indoor concentration of NO2. Indoor
exposure to NO2 may be accompanied by indoor exposures to nitrate radicals, organic nitrates, and nitro-
PAHs.
AX3.5. Personal Exposure
AX3.5.1. Personal Exposure in the Residential Indoor Environment
People spend most of their daily time in a residential indoor environment (Klepeis et al., 2001).
NO2 found in an indoor environment originates both indoor and outdoors; and therefore, people in an
indoor environment are exposed to both indoor and outdoor generated NO2. In a residential indoor
environment, personal exposure concentration equals the residential indoor concentration (if there is no
personal cloud) which can be broken down into two parts: indoor generation and ambient contribution.
The relationship between personal NO2 exposure and ambient NO2 can be modified by the indoor
environment in the following ways: (1) during the infiltration processes, ambient NO2 can be lost through
penetration and decay (chemical and physical processes) in the indoor environment, and therefore, the
concentration of indoor NO2 of ambient origin is not the ambient NO2 concentration but the product of the
ambient NO2 concentration and the infiltration factor (^mf, or a if people spend 100% of their time
indoors); (2) in an indoor environment, people are exposed to not only ambient generated NO2 but also
indoor generated NO2, and therefore, the relative contribution of ambient and nonambient NO2 to
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personal exposure depends not only on the ambient NO2 concentration but also on the infiltration factor
(attenuation factor) and the indoor source contribution; (3) the strength of the association between
personal exposure to NO2 of ambient origin and ambient NO2 concentration is determined by the temporal
and spatial variation in the infiltration factor; and (4) the strength of the association between personal total
exposure and ambient NO2 is determined by the variation in the indoor source contribution and the
variation in the infiltration factor. Below, factors affecting infiltration factor and the indoor source
contribution will be evaluated, and the key issues, such as those mentioned above, related to ambient
contribution to personal NO2 exposure will be addressed.
Due to the lack of specific P, k, and a for study homes or a study population, instead of using P, k,
and a, alternative approaches to obtain the infiltration factor are the ratio of indoor/outdoor NO2 and the
regression based RCS model. The basic rationale of the RCS model has been introduced in the previous
section. Without indoor sources, the ratio between indoor NO2 and outdoor NO2 should be always less
than or equal to 1. If the indoor to outdoor ratio is larger than 1 (after adjusting for measurement error),
we can surely say that indoor sources exist. However, if an indoor/outdoor ratio is less than one, we
cannot exclude the effect of indoor sources; otherwise, the infiltration factor would be overestimated. In
order to use an indoor/outdoor ratio as the infiltration factor, study designs and questionnaires must be
carefully read, and only the ratio for homes without identified indoor sources can be used as an indicator
of infiltration factor. The population averaged infiltration factor is the slope of the regression line of
indoor concentration vs. outdoor concentration. The reliability of the regression slope is dependent upon
the sample size and how to deal with the outlier effects. Indoor/outdoor ratios and the regression slopes
are summarized in Table AX3.5-la. Most of the infiltration factors range from 0.4 to 0.7. Similarly, a
ranges from 0.3 to 0.61 (Table AX3.5-lb). Theoretically, infiltration factor is a function of air exchange
rate, which has been indicated by season in some studies. However, most studies do not report the
infiltration factor by season, and therefore, a seasonal trend of infiltration factor could not be observed in
TableAX3.5-la.
As mentioned before, personal NO2 exposure is not only affected by air infiltrating from outdoors
but also by indoor sources. The NO2 residential indoor sources reported are gas cooking, gas heating,
kerosene heating, smoking and burning candles (Schwab et al., 1994; Spengler et al., 1994; Nakai et al.,
1995; Lee et al., 1996; Linaker et al., 1996; Cotterill and Kingham, 1997; Farrow et al., 1997; Kawamoto
et al., 1997; Lee, 1997; Raaschou-Nielsen et al., 1997; Aim et al., 1998; Levy et al., 1998a; Monn et al.,
1998; Garrett et al., 1999; Chao, 2001; Dennekamp et al., 2001; Dutton et al., 2001; Emenius et al., 2003;
Kodama et al., 2002; Lee et al., 2002; Mosqueron et al., 2002; Garcia-Algar et al., 2003; Garcia-Algar
et al., 2004; Lai et al., 2004; Lee et al., 2004; Yang et al., 2004; Zota et al., 2005; S0rensen et al., 2005;
Lai et al., 2006). Spengler et al. (1994) reported that personal exposures in homes with gas range with
pilot light were 15 ppb higher than those in homes with electric range, and it was 5 ppb higher in homes
with gas range without pilot light than homes with electric ranges. Schwab et al. (1994) reported that
homes with gas stove with pilot light had higher indoor NO2 concentrations (peak concentrations ranging
from 30 to 35 ppb), followed by homes with gas stove without a pilot light (peak concentrations ranging
from 15 to 20 ppb) and then homes with electric stoves (peak concentrations ranging from 5 to 10 ppb). In
an international study, Levy et al., (1998a) reported that the use of a gas stove in the home was the
dominant activity influencing NO2 concentrations with a 67% increase in mean personal NO2 exposure
and an increase in indoor-outdoor ratios from 0.7 to 1.2. Smoking was found to be another significant
factor elevating personal and indoor NO2 exposure. Monn et al. (1998) reported that during 1-week
integrated measurement, smoking contributed 1 ppb more NO2 exposure. Aim et al. (1998) reported that
one-week integrated personal NO2 exposure for smokers and nonsmokers were 12.9 ppb and 10.7 ppb,
respectively. Zota et al. (2005) observed that smoking was not a significant indoor source. However, the
authors pointed out that the effect of smoking might have been overwhelmed by the presence of the gas
stove. S0rensen et al. (2005) found that burning candles were significantly associated with the elevation
of indoor NO2 (p = 0.02). NO2 concentration in an indoor environment affected by the indoor sources is
not homogeneously distributed: NO2 concentration is usually the highest in the kitchen, lowest in the
bedroom and the concentration in a living room is in between as shown in Table AX3.5-2. The
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concentration differences between a bedroom and a kitchen ranged from 1 ppb to 28 ppb, and largest
difference occurred in homes with gas stoves.
The concentration differences in indoor microenvironments reflect the differences in personal
exposure in those microenvironments, which is related to personal activities and behaviors. People who
spend more time in a kitchen are expected to have higher NO2 exposures. Also, in most exposure studies,
integrated indoor and personal exposures were measured from 2 days to 2 weeks with passive samplers.
Therefore, the peak exposure concentration could be even higher.
Indoor source contributions to indoor and personal exposure are determined by indoor source
strength (S), house volume (F), air exchange rate (a) and the NO2 decay rate (k) in an indoor environment,
through the equation Cnona = SI[V(a + k)]. Indoor source strength has been summarized in a previous
section (Indoor sources and concentrations of nitrogen oxides). With a mass balance approach, Yang et al.
(2004) reported that the source strength for electric range 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 age of house and the house type are associated with
ventilation, indoor sources, and house volume. As mentioned before, Lee et al. (1996) reported that the
building type was significantly associated with volume of dwelling unit, and air exchange rate. Garrett
et al. (1999) reported that older houses were associated with higher nitrogen dioxide levels, possibly as a
result of older and less efficient appliances in older homes or due to smaller rooms.
In theory, personal exposure of ambient origin should be at least as much as the indoor NO2 of
ambient origin in that people spend time in either an indoor or an outdoor environment. However, it was
shown in the previous part (Table AX3.5-3a and Table AX3.5-3b) that the ambient contribution to
population exposure ranged from 20% to 50% based on four studies (Rojas-Bracho et al., 2002; Monn
et al., 1998; Levy et al., 1998a; Spengler et al., 1994); and results here show that the ambient contribution
to indoor NO2 is around 70% with a wide range from 40 to 90% based on another four studies
(Mosqueron et al., 2002; Yang et al., 2004; Kulkarni et al., 2002; Monn et al., 1998). It is not clear at
present why the indoor NO2 of ambient origin is larger than the personal NO2 exposure of ambient origin.
The strength of the indoor, outdoor and personal NO2 associations (rp: Pearson correlation
coefficient; rs: Spearman correlation coefficient; and R2: coefficient of determination) are summarized in
Table AX3.5-4. The strength of the associations are determined by the variation in Fmf (P, k, and a) and
indoor source contributions from home to home and from day to day. In general, the correlation between
indoor and outdoor NO2 ranges from poor to good (rp: 0.06 to 0.86). When we break down the correlation
coefficient by season and indoor sources, it is obvious that the association between indoor and outdoor
NO2 is stronger during spring and summer but weaker during wintertime, and the association is stronger
for homes without indoor sources but weaker for homes with strong indoor sources. Mukala et al. (2000)
reported an rp of 0.86 for the indoor and outdoor NO2 association during the spring and it reduced to 0.54
during the winter. Spengler et al. (1994) reported that the associations were 0.66 and 0.75 (rp) for homes
with and without air conditioning system, respectively. Emenius et al. (2003) reported that the association
between indoor and outdoor NO2 was 0.69 (rp) for homes without smoker and without gas stove using,
but the association was not significant for homes with gas stove or smokers. Yang et al. (2004) reported
that the indoor and outdoor NO2 association was 0.70 (R2) for homes with electric ranges, and was 0.57
(R2) for homes with gas ranges. In other words, personal exposure to ambient NO2 in a residential indoor
environment will be modified the least when the air exchange rate is high and the indoor source
contribution is not significant. Considering the large spatial variation in ambient NO2 concentrations and
the relative sparseness of ambient NO2 monitors, the associations between indoor and outdoor
concentrations are usually stronger than the associations between indoor and ambient concentrations. As
shown in Table AX3.5-4, a stronger personal vs. residential indoor relationship than the personal vs.
outdoor relationship has been reported by most studies (Lai et al., 2004; Monn et al., 1998, Levy et al.,
1998a; Spengler et al., 1994; Kousa et al., 2001; Linaker et al., 1996), which is a reminder that personal
exposure to ambient NO2 mostly happens in the residential indoor environment. It should be pointed out
that the association between indoor, outdoor and personal NO2 and the relative contributions of indoor
and outdoor NO2 to indoor and personal exposures were calculated based on time integrated indoor,
outdoor and personal NO2 measurement with passive samplers and an average measurement time of a
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couple of days to two weeks. In most studies, an equilibrium condition was assumed and the effects of
dynamics on the indoor, outdoor, and personal association were not evaluated, which could result in
missing the peak exposure and obscuring the real short-term outdoor contribution to indoor and personal
exposure. For example, the NO2 concentrations at locations close to busy streets in urban environments
may vary drastically with time. If the measurement is carried out during a non-steady-state period, the
indoor/outdoor concentration ratio may indicate either a too low relative importance of indoor sources (if
the outdoor concentration is in an increasing phase) or a too high relative importance of indoor resources
(if the outdoor concentration is in a decreasing phase). The lower the air exchange rate, the greater the
error due to the effects of transients (Ekberg, 1996).
AX3.5.1.1. School and Office
Workplaces (schools and offices) are the places where people spend most of their time after homes
in an urban area. The location, indoor sources as well as the ventilation pattern of schools and offices
could be different from people's homes. Therefore, personal exposure patterns in schools and offices
could be different from exposure patterns at home. However, NO2 concentrations in schools and offices
have only been measured in only a few exposure studies.
Most studies reported the personal exposure levels were lower than or equal to office NO2 levels.
Lai et al. (2004) reported that a cohort in Oxford spent 17.5% of their daily time in offices, and mean
personal total NO2 exposure was 15 ppb and 16.8 ppb for mean office concentrations. Mosqueron et al.
(2002) reported Paris office worker exposure levels and no significant difference was found between
personal total exposure (22.8 ppb) and NO2 concentrations in office (23.5 ppb). Personal exposures in
schools were studied in Helsinki, Southampton and Southern California. Aim et al. (1998) and Mukala
et al. (2000) reported the personal exposure levels in Helsinki for pre-school children. They reported that
median personal exposures were lower than the median NO2 concentrations measured inside the day care
center (13.1 ppb for personal exposure versus 18.8 ppb for inside day-care center for downtown winter;
14.7 ppb versus 24.1 ppb for downtown spring; 8.9 ppb versus 15.2 ppb for suburban winter; and 8.9 ppb
versus 13.1 ppb for suburban spring). Linaker et al. (1996) found that the geometric mean of school
children exposures (18.8 ppb) was higher than geometric means of the NO2 concentrations in classrooms
(8.4 to 14.1 ppb) in a study of children's exposures to NO2 in Southampton, UK. A similar exposure
pattern was found by Linn et al. (1996) during the Southern California school children exposure study.
During the study, personal exposure (22 ppb) was higher than the NO2 concentration inside school
(16 ppb). NO2 concentration in school/office is determined by ambient NO2 level, local traffic sources,
floor height and building ventilation pattern. Partti-Pellinen et al. (2000) studied the effect of ventilation
and air filtration systems on indoor air quality in a children's day-car center in Finland. Without filtration,
NOX and PM generated by nearby motor traffic penetrated readily indoors. With chemical filtration, 50 to
70% of nitrogen oxides could be removed. The authors suggested that the possible adverse health effects
of nitrogen oxides and particles indoors could be countered by efficient filtration. Mosqueron et al. (2002)
reported 24% of variations in in-office NO2 concentrations could be explained by outdoor NO2 levels
(18%), and floor height (6%) and an inverse relation was observed between in-office concentration and
floor height. Aim et al. (1998) attributed the high NO2 concentration in the day-care center to its close to
major roads. Obviously, the relative scale of personal exposure and school concentration also depends on
personal activities outside schools and workplaces.
Significant associations between personal exposure and workplace concentrations were reported by
most studies. Mosqueron et al. (2002) reported office NO2 was a significant predictor of personal
exposure and 15% of the personal exposure was explained by time weighted office NO2 concentrations.
Aim et al. (1998) reported population NO2 exposures were highly correlated with the NO2 levels inside
the day-care centers (R2 = 0.88). However, Lai et al. (2004) reported a nonsignificant Pearson correlation
coefficient (0.15) between personal exposure and workplace indoor concentration and the authors
suggested that the strong residential indoor sources and long time indoors obscured the personal versus
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office relationship. Personal total exposure is a function of NO2 concentrations in different indoor and
outdoor microenvironments and how long a person stays in that microenvironment. The large variation of
NO2 exposure in some microenvironments could obscure the association between personal exposure and
NO2 concentrations in other microenvironments.
AX3.5.1.2. Exposure Reconstruction
Personal exposure has been evaluated in each major microenvironment, where either the NO2
concentration is high or people spend most of their daily time. Personal exposure could be reasonably
reconstructed if we know the NO2 concentration in each microenvironment and the duration of personal
exposure in each microenvironment. Levy et al., (1998a) reconstructed personal exposures measured in an
international study with a time-weighted average exposure model. The personal exposure was
reconstructed based on the measured NO2 concentrations in residential indoor, residential outdoor, and
workplace microenvironments, and the time people spent in those environments. The mean measured
personal NO2 exposure was 28.8 ppb and a mean of estimated NO2 exposure was 27.2 ppb. The Spearman
correlation coefficient between personal measured exposure and reconstructed exposure was 0.81. The
same approach was applied by Kousa et al. (2001) to reconstruct the personal exposures in the EXPOLIS
study. A correlation coefficient of 0.86 was observed for the association between measured NO2 exposure
and reconstructed NO2 exposure (data were log-transformed), and the slope and the intercept were 0.90
and 0.22 respectively for the reconstructed exposure vs. measured exposure. In the two studies mentioned
above, NO2 exposure during commuting was not measured. Probably that is part of the reason why
reconstructed NO2 exposure was lower than the measured NO2 exposure.
AX3.5.2. Factors Affecting Exposure
Physically, personal exposure levels are determined by the time people spend in each
microenvironment and the NO2 concentrations in each microenvironment, which is determined by source
emission strength, air exchange rate, penetration coefficient, the NO2 decay rate and the volume of the
microenvironment. Any factors that can influence the above physical parameters can modify the level of
personal exposure. The indoor, outdoor and personal NO2 levels on each stratum of those factors will be
summarized.
Those factors can be classified in to the following categories: (1) factors associated with
environmental conditions, such as weather and season; (2) factors associated with dwelling conditions,
such as the location of the house and ventilation system; (3) factors associated with indoor sources, such
as the type of range and the fuel type; (4) factors associated with personal activities, such as the time
spent on cooking or commuting; (5) socioeconomic status, such as the level of education and the income
level; and (6) demographic factors, such as age and gender.
Most studies addressed the influences of dwelling condition and indoor sources on indoor and
personal exposures. A few studies explored the impacts of environmental factors and personal activities
on personal exposures. Indoor and personal exposures have rarely been stratified by socioeconomic and
demographic factors. Indoor, outdoor, and personal exposure levels are presented in Table AX3.5-5,
stratified by environmental factors, dwelling conditions, indoor sources, and personal activities factors.
The effects of socioeconomic and demographic factors on the indoor, outdoor, and personal levels are
summarized in Table AX3.5-6.
Season is an environmental factor affecting both indoor and outdoor levels, and thus personal NO2
levels. During the wintertime, the mixing height is usually lower than during the summer, and therefore
concentrations of many primary pollutants are higher than in the summer. Wintertime is also a heating
season, which usually leads to higher indoor source emissions and lower air exchange rates. Therefore, a
higher indoor NO2 concentration can be expected during the winter. For most cases, the differences of
indoor or personal NO2 exposure between the heating and non-heating season are within several ppb, but
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sometimes the difference could be close to 20 ppb (Zota et al., 2005). Other environmental factors include
day of the week (weekday versus weekend), and the wind direction.
The dwelling conditions are also associated with indoor, outdoor, and personal NO2 levels. A house
located in an urban center or close to a major road is expected to have higher outdoor and indoor NO2
levels, and the differences in NO2 exposures are often within 20 ppb based on passive sampler
monitoring. The age of the house, house type, and window type can affect the ventilation of dwelling
units, and sometimes the type of heating and cooking appliances in a house. Range and fuel type are the
indoor source factors discussed the most in the literature. It is common to see differences larger than
10 ppb in indoor and personal NO2 exposures between a gas range home (especially gas range with pilot
light) and an electric range home. Sometimes the differences could be as high as 40 ppb. For peak short-
term exposures, the difference could reach 100 ppb.
The level of personal exposure is dependent upon the time a person spends in each
microenvironment. Kawamoto et al. (1997), Levy et al. (1998a), and Chao and Law (2000) clearly
showed that personal NO2 exposure increases with time spent cooking or commuting.
There are inconsistencies in the literature. For example, smoking is found to be a significant factor
in some studies but not in others, and the same can be said for proximity to a major road. For another
example, a higher indoor NO2 level could be found in a rural home rather than in an urban home,
although most studies found the opposite. Part of the reason is that exposure indicators function together,
as a multidimensional parameter space, on indoor and personal exposures. They are not independent of
each other. Unfortunately, studies have rarely been conducted to understand the associations between
these exposure indicators and to use the study findings to explain indoor and personal NO2 exposures.
More effort put on exposure indicator studies should help in finding better surrogate measurements
for personal exposures. Although misclassifying exposures in epidemiological studies is almost
inevitable, and it is unlikely that the personal exposures of all subjects will be measured, a better
knowledge of the effects of exposure indicators on personal exposure will help reduce exposure errors in
exposure and epidemiological studies and help interpret those study results.
AX3.5.3. Associations between MONO and N02
Spicer et al. (1993) and Wainman et al. (2000) suggested the presence of a strong indoor source of
HONO from heterogeneous reactions involving NO2 and water films on indoor surfaces. Hence,
combustion appliances are sources for exposures to both NO2 exposure and FINO2. Epidemiological
studies of NO2 health effects should consequently consider the potential confounding effects of NO2 and
vice versa.
Jarvis et al. (2005) reported the indoor nitrous acid and lung function in adults as part of European
Community Respiratory Health Survey (ECRHS). Indoor HONO and indoor and outdoor NO2 were
measured. Indoor NO2 were correlated with HONO (rp = 0.77) but no significant association of indoor
NO2 with symptoms or lung function was observed.
Lee et al. (2002) studied the nitrous acid, nitrogen dioxide, and ozone concentrations in residential
environments. The authors found that indoor NO2 was significantly correlated with HONO (rp = 0.511).
As shown above, very few studies showed the relationship between personal NO2 exposure and
other pollutant exposures. In general, personal NO2 was moderately correlated with PM2 5 and CO. Due to
the lack of personal HONO exposure data, indoor HONO was used as an indicator for personal exposure,
and current studies showed that indoor HONO was correlated with indoor NO2 with high correlation
coefficients, which suggested that the collect ion of HONO exposure data would help interpret adverse
health outcome in the NO2 health risk assessment.
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AX3.6. Modeling Human Exposures to N02
AX3.6.1. Exposure Models
Predictive (or prognostic) exposure modeling studies1, specifically focusing on NO2, could not be
identified in the literature, though, often, statistical (diagnostic) analyses have been reported using data
obtained in various field exposure studies. However, existing prognostic modeling systems for the
assessment of inhalation exposures can in principle be directly applied to, or adapted for, NO2 studies;
specifically, such systems include APEX, SHEDS, and MENTOR-1 A, to be discussed in the following
sections. Nevertheless, it should be mentioned that such applications will be constrained by data
limitations, such as the degree of ambient concentration characterization (e.g., concentrations at the local
level) and quantitative information on indoor sources and sinks.
Predictive models of human exposure to ambient air pollutants such as NO2 can be classified and
differentiated based upon a variety of attributes. For example, exposure models can be classified as:
• models of potential (typically maximum) outdoor exposure versus models of actual exposures
(the latter including locally modified microenvironmental exposures, both outdoor and indoor),
• Population Based Exposure Models (PBEM) versus Individual Based Exposure Models (IBEM),
• deterministic versus probabilistic (or statistical) exposure models,
• observation-driven versus mechanistic air quality models.
Some points should be made regarding terminology and essential concepts in exposure modeling,
before proceeding to the overview of specific developments reported in the current research literature:
First, it must be understood that there is significant variation in the definitions of many of the terms
used in the exposure modeling literature; indeed, the science of exposure modeling is a rapidly evolving
field and the development of a standard and commonly accepted terminology is an ongoing process (see,
e.g., WHO, 2004).
Second, it should also be mentioned that, very often, procedures that are called exposure modeling,
exposure estimation, etc. in the scientific literature, may in fact refer to only a sub-set of the complete set
of steps or components required for a comprehensive exposure assessment. For example, certain self-
identified exposure modeling studies focus solely on refining the sub-regional or local spatio-temporal
dynamics of pollutant concentrations (starting from raw data representing monitor observations or
regional grid-based model estimates). Though not exposure studies per se, such efforts have value and are
included in the discussion of the next sub-section, as they provide potentially useful tools that can be used
in a complete exposure assessment. On the other hand, formulations that are self-identified as exposure
models but actually focus only on ambient air quality predictions, such as chemistry-transport models, are
not included in the discussion that follows.
Third, the process of modeling human exposures to photochemical pollutants (traditionally focused
on ozone) is very often identified explicitly with population-based modeling, while models describing the
specific mechanisms affecting the exposure of an actual individual (at specific locations) to an air
contaminant (or to a group of co-occurring gas and/or aerosol phase pollutants) are usually associated
with studies focusing specifically on indoor air chemistry modeling.
Finally, fourth, the concept of microenvironments, introduced in earlier sections of this document,
should be clarified further, as it is critical in developing procedures for exposure modeling. In the past,
microenvironments have typically been defined as individual or aggregate locations (and sometimes even
as activities taking place within a location) where a homogeneous concentration of the pollutant is
1 i.e. Assessments that start from emissions and demographic information and explicitly consider the physical and chemical processes of
environmental and microenvironmental transport and fate, in conjunction with human activities, to estimate inhalation intake and uptake.
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encountered. Thus a microenvironment has often been identified with an ideal (i.e. perfectly mixed)
compartment of classical compartmental modeling. More recent and general definitions view the
microenvironment as a control volume, either indoors or outdoors, that can be fully characterized by a set
of either mechanistic or phenomenological governing equations, when appropriate parameters are
available, given necessary initial and boundary conditions. The boundary conditions typically would
reflect interactions with ambient air and with other microenvironments. The parameterizations of the
governing equations generally include the information on attributes of sources and sinks within each
microenvironment. This type of general definition allows for the concentration within a
microenvironment to be non-homogeneous (non-uniform), provided its spatial profile and mixing
properties can be fully predicted or characterized. By adopting this definition, the number of
microenvironments used in a study is kept manageable, but variability in concentrations in each of the
microenvironments can still be taken into account. Microenvironments typically used to determine
exposure include indoor residential microenvironments, other indoor locations (typically occupational
microenvironments), outdoors near roadways, other outdoor locations, and in-vehicles. Outdoor locations
near roadways are segregated from other outdoor locations (and can be further classified into street
canyons, vicinities of intersections, etc.) because emissions from automobiles alter local concentrations
significantly compared to background outdoor levels. Indoor residential microenvironments (kitchen,
bedroom, living room, etc. or aggregate home microenvironment) are typically separated from other
indoor locations because of the time spent there and potential differences between the residential
environment and the work/public environment.
Once the actual individual and relevant activities and locations (for Individual Based Modeling), or
the sample population and associated spatial (geographical) domain (for Population Based Modeling)
have been defined along with the temporal framework of the analysis (time period and resolution), the
comprehensive modeling of individual/population exposure to NO2 (and related pollutants) will in general
require seven steps (or components, as some of them do not have to be performed in sequence) that are
listed below. This list represents a composite based on approaches and frameworks described in the
literature over the last twenty-five years (Ott, 1982; Ott, 1985; Lioy, 1990; U.S. Environmental Protection
Agency, 1992; Georgopoulos and Lioy, 1994; U.S. Environmental Protection Agency, 1997; Buck et al.,
2003; Price et al., 2003; Georgopoulos et al., 2005; WHO, 2005; U.S. Environmental Protection Agency,
2006a; Georgopoulos and Lioy, 2006) as well on the structure of various inhalation exposure models
(NEM/pNEM, HAPEM, SHEDS, REHEX, EDMAS, MENTOR, ORAMUS, APEX, AIRPEX,
AIRQUIS, etc., to be discussed in the following section) that have been used in the past or in current
studies to specifically assess inhalation exposures. Figure AX3.6-1, adapted from Georgopoulos et al.
(2005), schematically depicts the sequence of steps involved that are summarized here (and further
discussed in the following sub-sections).
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i.a. Emissions: NEI (NET, NTI), State; Processing with SMOKE,
EMS-HAP, MOBILE/MOVES, NONROAD, FAAED, BEIS, etc.
i.b. Meteorology: NWS, NCDC; Modeling MM5, RAMS, CALMET
i.e. Land Use/Land Cover, Topography: NLDC (USGS), etc.
^
1 Estimate background
levels of air pollutants
through
a. multivariate spatio-
temporal analysis of
monitor data
b. emissions" based air
quality modeling
(with regional,
grid- based models:
Models-3/ CMAQ, CAMx
and REMSAD)
Develop database of
, ' individual subjects
attributes (residence &
work location, housing
characteristics, age,
gender, race, income, etc.)
a. collect study-specific
information
b, supplement with
available relevant local,
regional, and national
demographic
information
4
r Study-specific survey
(also US Census,
US Housing Survey)
*
^ A
2 Estimate local outdoor
pollutant levels that
characterize the ambient air of
an administrative unit (such as
census tract) or a conveniently
defined grid through
a. spatiotemporaE statistical
analysis of monitor data
b. application of urban scale
model at high resolution
c. subgrid (e.g. plume-in-grid)
modeling
d. data/model assimilation
4 Develop activity event
(or exposure event)
sequences for each individual
of the study for the exposure
period
a. collect study-specific
information
b. supplement with other
available data
c. organize time-activity
database in format
compatible with CHAD
^
r Study-specific survey
(or default from
CHAD, NHAPS)
ii.a. Emissions: EMS-HAP
ii.b. Local Meteorology - Local
Effects: RAMS, FLUENT
r \
*
/*
-*
1" Estimate levels and
temporal profiles of
pollutants in various
microenvironments (streets,
residences, offices, restaurants,
vehicles, etc.) through
a. regression of observational
data
b. simple linear mass balance
c. lumped (nonlinear) uE
gas/aerosol chemistry models
d. combined chemistry & CFD
(DNS, LES, RANS) models
^
!'* Calculate appropriate
'" , inhalation rates for the
members of the sample
population, combining the
physiological attributes of the
study subjects and the
activities pursued during the
individual exposure events
t
/ ICRP and Other
Physiological & METS
Databases
^m
— \
i
1
Calculate
exposures/
intakes
A
>
7 Biologically
based
target tissue
dose modeling
t
Source: Figure adapted with modifications from
Georgopoulosetal. (2005).
Figure AX3.6-1. Schematic description of a general framework identifying the processes (steps or
components) involved in assessing inhalation exposures and doses for individuals and
populations. In general terms, existing comprehensive exposure modeling systems such
as SHEDS, APEX, and MENTOR-1A follow this framework.
6. Estimation of the background d or ambient levels of both NO2 and related photochemical
pollutants. This is done through either (or a combination of):
a. multivariate spatio-temporal analysis of fixed monitor data, or
b. emissions-based, photochemical, air quality modeling (typically with a regional,
grid-based model such as Models-3/CMAQ or CAMx) applied in a coarse
re solution mode.
7. Estimation of local outdoor pollutant levels of both NO2 and related photochemical
pollutants. These levels could typically characterize the ambient air of either an administrative
unit (such as a census tract, a municipality, a county, etc.) or a conveniently defined grid cell of
an urban scale air quality model. Again, this may involve either (or a combination of):
a. spatio-temporal statistical analysis of monitor data, or
b. application of an urban multi-scale, grid based model (such as CMAQ or CAMx)
at its highest resolution (typically around 2-4 km), or
c. correction of the estimates of the regional model using some scheme that adjusts
for observations and/or for subgrid chemistry and mixing processes.
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8. Characterization of relevant attributes of the individuals or populations under study
(residence and work locations, occupation, housing data, income, education, age, gender, race,
weight, and other physiological characteristics). For Population Based Exposure Modeling
(PBEM) one can either:
a. select a fixed-size sample population of virtual individuals in a way that
statistically reproduces essential demographics (age, gender, race, occupation,
income, education) of the administrative population unit used in the assessment
(e.g., a sample of 500 people is typically used to represent the demographics of a
given census tract, whereas a sample of about 10,000 may be needed to represent
the demographics of a county), or
b. divide the population-of interest into a set of cohorts representing selected
subpopulations where the cohort is defined by characteristics known to influence
exposure.
9. Development of activity event (or exposure event) sequences for each member of the sample
population (actual or virtual) or for each cohort for the exposure period. This could utilize:
a. study-specific information, if available
b. existing databases based on composites of questionnaire information from past
studies
c. time-activity databases, typically in a format compatible with U.S.
Environmental Protection Agency's Consolidated Human Activity Database
(CHAD - McCurdy et al, 2000)
10. Estimation of levels and temporal profiles of both NO2 and related photochemical pollutants
in various outdoor and indoor microenvironments such as street canyons, roadway intersections,
parks, residences, offices, restaurants, vehicles, etc. This is done through either:
a. linear regression of available observational data sets,
b. simple mass balance models (with linear transformation and sinks) over the
volume (or a portion of the volume) of the microenvironment,
c. lumped (nonlinear) gas or gas/aerosol chemistry models, or
d. detailed combined chemistry and Computational Fluid Dynamics modeling.
11. Calculation of appropriate inhalation rates for the members of the sample population,
combining the physiological attributes of the (actual or virtual) study subjects and the activities
pursued during the individual exposure events.
12. Calculation of target tissue dose through biologically based modeling estimation (specifically,
respiratory dosimetry modeling in the case of NO2 and related reactive photochemical pollutants)
if sufficient information is available.
Implementation of the above framework for comprehensive exposure modeling has benefited
significantly from recent advances and expanded availability of computational technologies such as
Relational Database Management Systems (RDBMS) and Geographic Information Systems (GIS)
(Purushothaman and Georgopoulos, 1997, 1999a,b; Georgopoulos et al., 2005).
In fact, only relatively recently comprehensive, predictive, inhalation exposure modeling studies for
ozone, PM, and various air toxics, have attempted to address/incorporate all the components of the
general framework described here. In practice, the majority of past exposure modeling studies have either
incorporated only subsets of these components or treated some of them in a simplified manner, often
focusing on the importance of specific factors affecting exposure. Of course, depending on the objective
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of a particular modeling study, implementation of only a limited number of steps may be necessary. For
example, in a regulatory setting, when comparing the relative effectiveness of emission control strategies,
the focus can be on expected changes in ambient levels (corresponding to those observed at NAAQS
monitors) in relation to the density of nearby populations. The outdoor levels of pollutants, in conjunction
with basic demographic information, can thus be used to calculate upper bounds of population exposures
associated with ambient air (as opposed to total exposures that would include contributions from indoor
sources) useful in comparing alternative control strategies. Though the metrics derived would not be
quantitative indicators of actual human exposures, they can serve as surrogates of population exposures
associated with outdoor air, and thus aid in regulatory decision making concerning pollutant standards
and in studying the efficacy of emission control strategies. This approach has been used in studies
performing comparative evaluations of regional and local emissions reduction strategies in the eastern
United States (e.g., Purushothaman and Georgopoulos, 1997; Georgopoulos et al., 1997a; Foley et al.,
2003).
AX3.6.1.1. Population Exposure Models
Existing comprehensive inhalation exposure models consider the trajectories of individual human
subjects (actual or virtual), or of appropriately defined cohorts, in space and time as sequences of
exposure events. In these sequences each event is defined by time, a geographic location, a
microenvironment, and the activity of the subject. U.S. Environmental Protection Agency offices
(OAQPS and NERL) have supported the most comprehensive efforts in developing models implementing
this general concept (see, e.g., Johnson, 2002), and these efforts have resulted in the NEM/pNEM
(National Exposure Model and Probabilistic National Exposure Model - Whitfield et al., 1997), HAPEM
(Hazardous Air Pollutant Exposure Model - Rosenbaum, 2005), SHEDS (Simulation of Human Exposure
and Dose System - Burke et al., 2001), APEX (Air Pollutants Exposure model - U.S. Environmental
Protection Agency, 2006b,c), and MENTOR (Modeling Environment for Total Risk studies -
Georgopoulos et al., 2005; Georgopoulos and Lioy, 2006) families of models. European efforts have
produced some formulations with similar general attributes as the above U.S. models but, generally,
involving simplifications in some of their components. Examples of European models addressing
exposures to photochemical oxidants (specifically ozone) include the AirPEx (Air Pollution Exposure)
model (Freijer et al., 1998), which basically replicates the pNEM approach and has been applied to the
Netherlands, and the AirQUIS (Air Quality Information System) model (Clench-Aas et al., 1999).
The NEM/pNEM, SHEDS, APEX, and MENTOR-1A (MENTOR for One-Atmosphere studies)
families of models provide exposure estimates defined by concentration and breathing rate for each
individual exposure event, and then average these estimates over periods typically ranging from one hour
to one year. These models allow simulation of certain aspects of the variability and uncertainty in the
principal factors affecting exposure. An alternative approach is taken by the HAPEM family of models
that typically provide annual average exposure estimates based on the quantity of time spent per year in
each combination of geographic locations and microenvironments. The NEM, SHEDS, APEX, and
MENTOR-type models are therefore expected to be more appropriate for pollutants with complex
chemistry such as NO2, and could provide useful information for enhancing related health assessments.
More specifically, regarding the consideration of population demographics and activity patterns:
1. pNEM divides the population of interest into representative cohorts based on the
combinations of demographic characteristics (age, gender, and employment), home/work
district, residential cooking fuel and replicate number, and then assigns activity diary record
from CHAD (Consolidated Human Activities Database) to each cohort according to
demographic characteristic, season, day-type (weekday/weekend) and temperature.
2. HAPEM6 divides the population of interest into demographic groups based on age, gender
and race, and then for each demographic group/day-type (weekday/weekend) combination,
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select multiple activity patterns randomly (with replacement) from CHAD and combine
them to find the averaged annual time allocations for group members in each census tract
for different day types.
3. SHEDS, APEX, and MENTOR-1A generate population demographic files, which contain a
user-defined number of person records for each census tract of the population based on
proportions of characteristic variables (age, gender, employment, and housing) obtained
from the population of interest, and then assign a matching activity diary record from
CHAD to each individual record of the population based on the characteristic variables.
It should be mentioned that, in the formulations of these models, workers may commute
from one census tract to another census tract for work. So, with the specification of
commuting patterns, the variation of exposure concentrations due to commuting between
different census tracts can be captured.
The essential attributes of the pNEM, HAPEM, APEX, SHEDS, and MENTOR-1A models are
summarized in Table AX3.6-1.
The conceptual approach originated by the SHEDS models was modified and expanded for use in
the development of MENTOR-1A (Modeling Environment for Total Risk - One Atmosphere). Flexibility
was incorporated into this modeling system, such as the option of including detailed indoor chemistry of
the O3-NOX system and other relevant microenvironmental processes, and providing interactive linking
with CHAD for consistent definition of population characteristics and activity events (Georgopoulos et
al., 2005).
NEM/pNEM implementations have been extensively applied to ozone studies in the 1980s and
1990s. The historical evolution of the pNEM family of models of OAQPS started with the introduction of
the first NEM model in the 1980's (Biller et al., 1981). The first such implementations of pNEM/O3 in the
1980's used a regression-based relationship to estimate indoor ozone concentrations from outdoor
concentrations. The second generation of pNEM/O3 was developed in 1992 and included a simple mass
balance model to estimate indoor ozone concentrations. A report by Johnson et al. (2000) describes this
version of pNEM/O3 and summarizes the results of an initial application of the model to 10 cities.
Subsequent enhancements to pNEM/O3 and its input databases included revisions to the methods used to
estimate equivalent ventilation rates, to determine commuting patterns, and to adjust ambient ozone levels
to simulate attainment of proposed NAAQS. During the mid-1990's, Environmental Protection Agency
applied updated versions of pNEM/O3 to three different population groups in selected cities: (1) the
general population of urban residents, (2) outdoor workers, and (3) children who tend to spend more time
outdoors than the average child. This version of pNEM/O3 used a revised probabilistic mass balance
model to determine ozone concentrations over one-h periods in indoor and in-vehicle microenvironments
(Johnson, 2001).
In recent years, pNEM has been replaced by (or "evolved to") the Air Pollution Exposure Model
(APEX). APEX differs from earlier pNEM models in that the probabilistic features of the model are
incorporated into a Monte Carlo framework (Langstaff, 2007; U.S. Environmental Protection Agency,
2006b,c). Like SHEDS and MENTOR-1A, instead of dividing the population-of-interest into a set of
cohorts, APEX generates individuals as if they were being randomly sampled from the population. APEX
provides each generated individual with a demographic profile that specifies values for all parameters
required by the model. The values are selected from distributions and databases that are specific to the
age, gender, and other specifications stated in the demographic profile. Environmental Protection Agency
has applied APEX to the study of exposures to ozone and other criteria pollutants; APEX can be modified
and used for the estimation of NO2 exposures, if required.
Reconfiguration of APEX for use with NO2 or other pollutants would require significant literature
review, data analysis, and modeling efforts. Necessary steps include determining spatial scope and
resolution of the model; generating input files for activity data, air quality and temperature data; and
developing definitions for microenvironments and pollutant-microenvironment modeling parameters
(penetration and proximity factors, indoor source emissions rates, decay rates, etc.) (ICF Consulting
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2005). To take full advantage of the probablistic capabilities of APEX, distributions of model input
parameters should be used wherever possible.
AX3.6.1.2. Ambient Concentrations of N02 and Related Air Pollutants
As mentioned earlier, background and regional outdoor concentrations of pollutants, over a study
domain, may be estimated either through emissions-based mechanistic modeling, through ambient-data-
based modeling, or through a combination of both. Emissions-based models calculate the spatio-temporal
fields of the pollutant concentrations using precursor emissions and meteorological conditions as inputs.
The ambient-data-based models typically calculate spatial or spatio-temporal distributions of the pollutant
through the use of interpolation schemes, based on either deterministic or stochastic models for allocating
monitor station observations to the nodes of a virtual regular grid covering the region of interest. The
geostatistical technique of kriging provides various standard procedures for generating an interpolated
spatial distribution for a given time, from data at a set of discrete points. Kriging approaches were
evaluated by Georgopoulos et al. (Georgopoulos et al., 1997b) in relation to the calculation of local
ambient ozone concentrations for exposure assessment purposes, using either monitor observations or
regional/urban photochemical model outputs. It was found that kriging is severely limited by the
nonstationary character of the concentration patterns of reactive pollutants; so the advantages this method
has in other fields of geophysics do not apply here. The above study showed that the appropriate
semivariograms had to be hour-specific, complicating the automated reapplication of any purely spatial
interpolation over an extended time period.
Spatio-temporal distributions of pollutant concentrations, such as ozone, PM, and various air toxics
have alternatively been obtained using methods of the Spatio-Temporal Random Field (STRF) theory
(Christakos and Vyas, 1998a,b). The STRF approach interpolates monitor data in both space and time
simultaneously. This method can thus analyze information on temporal trends, which cannot be
incorporated directly in purely spatial interpolation methods such as standard kriging. Furthermore, the
STRF method can optimize the use of data that are not uniformly sampled in either space or time. STRF
was further extended within the Bayesian Maximum Entropy (BME) framework and applied to ozone
interpolation studies (Christakos and Hristopulos, 1998; Christakos and Kolovos, 1999; Christakos,
2000). It should be noted that these studies formulate an over-arching scheme for linking air quality with
population dose and health effects; however they are limited by the fact that they do not include any
microenvironmental effects. MENTOR has incorporated STRF/BME methods as one of the steps for
performing a comprehensive analysis of exposure to ozone and PM (Georgopoulos et al., 2005).
Subgrid spatial variability is a major issue with respect to characterizing local concentrations of
NO2. Indeed, the fast rates of the reactions involving the O3-NOX system result in significant
concentration gradients in the vicinity of sources of NOX. These gradients are not resolved directly by
currently operational grid photochemical air quality simulation models (PAQSMs) such as CMAQ and
CAMx. However, both these models include a plume-in-grid. (PinG) option (AER, 2004; Emery and
Yarwood, 2005; Gillani and Godowitch, 1999; U.S. Environmental Protection Agency, 2006d) that can
be used for large point NOX sources (such as smokestacks). Nevertheless, PinG formulations typically
will resolve gradients in upper atmospheric layers and thus are not necessarily relevant to human
exposure calculations, which are affected by gradients caused by a multiplicity of smaller ground level or
near ground level combustion sources such as motor vehicles.
Currently PAQSMs are typically applied with horizontal resolutions of 36 km, 12 km, and 4 km
and a surface layer thickness that is typically of the order of 30 m. Though computationally it is possible
to increase the resolution of these simulations, there are critical limits that reflect assumptions inherent in
the governing equations for both (a) the fluid mechanical processes embodied in the meteorological
models (e.g., typically MM5 and RAMS) that provide the inputs for the PAQSMs, and (b) the dispersion
processes which become more complex at fine scales (see, e.g., Georgopoulos and Seinfeld, 1989) and
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thus cannot be described by simple formulations (such as constant dispersion coefficients) when the
horizontal resolutions is 2 km or finer.
Application of PAQSMs to urban domains is further complicated by urban topography, the urban
heat island, etc. It is beyond the scope, however, of the present discussion, to overview the various issues
relevant to urban fluid dynamics and related transport/fate processes of contaminants. However, the issue
of modeling subgrid atmospheric dispersion phenomena within complex urban areas in a consistent
manner is a very active research area. Reviews of relevant issues and of available approaches for
modeling urban fluid mechanics and dispersion can be found in, e.g., Fernando et al. (2001) and Britter
and Hanna (2003).
The issue of subgrid variability (SGV) from the perspective of interpreting and evaluating the
outcomes of grid-based, multiscale, PAQSMs is discussed in Ching et al. (2006), who suggest a
framework that can provide for qualitative judgments on model performance based on comparing
observations to the grid predictions and its SGV distribution. From the perspective of Population
Exposure Modeling, the most feasible/practical approach for treating subgrid variability of local
concentrations is probably through (1) the identification and proper characterization of an adequate
number of outdoor microenvironments (potentially related to different types of land use within the urban
area as well as to proximity to different types of roadways) and (2) then, concentrations in these
microenvironments will have to be adjusted from the corresponding local background ambient
concentrations through either regression of empirical data or various types of local atmospheric
dispersion/transformation models. This is discussed further in the next subsection.
AX3.6.1.3. Characterization of Microenvironmental Concentrations
Concentrations in microenvironments that can represent either outdoor or indoor settings when
individuals come in contact with the contaminant of concern (e.g., NO2) can be characterized. This
process can involve modeling of various local sources and sinks, and interrelationships between ambient
and microenvironmental concentration levels. Three general approaches have been used in the past to
model microenvironmental concentrations:
• Empirical (typically linear regression) fitting of data from studies relating ambient/local and
microenvironmental concentration levels to develop analytical relationships.
• Parameterized mass balance modeling over, or within, the volume of the microenvironment. This
type of modeling has ranged from very simple formulations, i.e. from models assuming ideal
(homogeneous) mixing within the microenvironment (or specified portions of it) and only linear
physicochemical transformations (including sources and sinks), to models incorporating
analytical solutions of idealized dispersion formulations (such as Gaussian plumes), to models
that take into account aspects of complex multiphase chemical and physical interactions and
nonidealities in mixing.
• Detailed Computational Fluid Dynamics (CFD) modeling of the outdoor or indoor
microenvironment, employing either a Direct Numerical Simulation (DNS) approach, a Reynolds
Averaged Numerical Simulation (RANS) approach, or a Large Eddy Simulation (LES) approach,
the latter typically for outdoor situations (see, e.g., Milner et al., 2005; Chang and Meroney, 2003;
Chang, 2006).
Parameterized mass balance modeling is the approach currently preferred for exposure modeling
for populations. As discussed earlier, the simplest microenvironmental setting corresponds to a
homogeneously mixed compartment, in contact with possibly both outdoor/local environments as well as
other microenvironments. The air quality of this idealized microenvironment is affected mainly by the
following processes:
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a. Transport processes: These can include advection/convection and dispersion that are
affected by local processes and obstacles such as vehicle induced turbulence, street canyons,
building structures, etc.
b. Sources and sinks: These can include local outdoor emissions, indoor emissions, surface
deposition, etc.
c. Transformation processes: These can include local outdoor as well as indoor gas and aerosol
phase chemistry, such as formation of secondary organic and inorganic aerosols.
Examples of the above are discussed next, specifically for outdoor and for indoor
microenvironments.
AX3.6.1.4. Characterization of Outdoor Microenvironments Concentrations
Empirical regression analyses have been used in some studies to relate specific outdoor locations -
that can be interpreted as generalized types of exposure microenvironments - to spatial variability of NO2
concentrations. For example, Gilbert et al. (2005) in May 2003 measured NO2 for 14 consecutive days at
67 sites across Montreal, Canada. Concentrations ranged from 4.9 to 21.2 ppb (median 11.8 ppb), and
they used linear regression analysis to assess the association between logarithmic values of NO2
concentrations and land-use variables via a geographic information system. In univariate analyses, NO2
was negatively associated with the area of open space and positively associated with traffic count on
nearest highway, the length of highways within any radius from 100 to 750 m, the length of major roads
within 750 m, and population density within 2000 m. Industrial land-use and the length of minor roads
showed no association with NO2. In multiple regression analyses, distance from the nearest highway,
traffic count on the nearest highway, length of highways and major roads within 100 m, and population
density showed significant associations with NO2. The authors of that study point out the value of using
land-use regression modeling to assign exposures in large-scale epidemiologic studies. Similar analyses
have been performed in a predictive setting by Sahsuvaroglu et al. (2006) for Hamilton, Ontario, Canada.
The category of parameterized mass balance models for outdoor microenvironments includes
various local roadway, intersection, and street canyon models. For example, Fraigneau et al. (1995)
developed a simple model to account for fast nitrogen oxide - ozone reaction/dispersion in the vicinity of
a motorway. Venegas and Mazzeo (2004) applied a combination of simple point and area source
analytical plume models to characterize NO2 concentration patterns in Buenos Aires, Argentina, which
they used for a simplified (potential) population exposure study. ROADWAY-2 (Rao, 2002), is another
near-highway pollutant dispersion model that incorporates vehicle wake parameterizations derived from
canopy flow theory and wind tunnel measurements. The atmospheric velocity and turbulence fields are
adjusted to account for velocity-deficit and turbulence production in vehicle wakes and a turbulent kinetic
energy closure model of the atmospheric boundary layer is used to derive the mean velocity, temperature,
and turbulence profiles from input meteorological data.
In parameterized street canyon models, typically, concentrations of exhaust gases are calculated
using a combination of a plume model for the direct contribution and a box model for the recirculating
part of the pollutants in the street. Parameterization of flow and dispersion conditions in these models is
usually deduced from analysis of experimental data and model tests that considered different street
configurations and various meteorological conditions. An example of a current model that belongs in the
parameterized mass balance category is the Danish Operational Street Pollution Model (OSPM)
(Berkowicz, 2002), which updates earlier formulations of street canyon models such as STREET of
Johnson et al. (1973) and CPBM (Canyon Plume-Box Model) of Yamartino and Weigand (1986). A
variation of this simple approach is the model of Proyou et al. (1998), which uses a three-layer
photochemical box model to represent a street canyon.
A variety of CFD based street canyon models have been developed in recent years (see, e.g., the
series of International Conferences on Harmonization - http://www.harmo.org), employing various
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alternatives for closure of the turbulent transport equations. A review and intercomparison of five of these
models (CHENSI, CHENSI-2, MIMO, MISKAM, TASCflow) vis-a-vis field data from a street canyon in
Hannover, Germany can be found in the articles by Sahm et al. (2002) and by Ketzel et al. (2002).
These complex localized models could be useful for improving population exposure model
estimates by calculating pollutant concentrations at the microenvironmental level. Lack of input
parameter data and parameter variation across the modeling domain (spatial and temporal) contributes to
uncertainty in microenvironmental concentrations calculated by exposure models. In such cases,
parameterized mass balance models could provide outdoor concentration values for estimating exposure.
If infiltration factors are known, these concentrations could also be used to estimate indoor exposures.
AX3.6.1.5. Characterization of Indoor Microenvironments
Numerous indoor air quality modeling studies have been reported in the literature; however,
depending on the modeling scenario, only few of them address (and typically only a limited subset of)
physical and chemical processes that affect photochemical oxidants indoors (Nazaroff and Cass, 1986;
Hayes, 1989, 1991; Freijer and Bloemen, 2000).
It is beyond the scope of the present discussion to review in detail the current status of indoor air
modeling. Existing indoor air concentration models indeed are available as a wide range of (a) empirical
regression relationships, (b) parameterized mass balance models (that can be either single-zone—that is,
single we 11-mixed room—or multi-zone models), and (c) CFD formulations. Recent overviews of this
area can be found in Milner et al. (2005), who focus, in particular, on the issue of entrainment from
outdoor sources, and in Teshome and Haghighat, (2004), who focus on different formulations of zonal
models and on how they compare with more complex CFD models.
Few indoor air models have considered detailed nonlinear chemistry, which, however, can have a
significant effect on the indoor air quality, especially in the presence of strong indoor sources (e.g., gas
stores and kerosene heaters, in the case of NO2). Indeed, the need for more comprehensive models that
can take into account the complex, multiphase processes that affect indoor concentrations of interacting
gas phase pollutants and particulate matter has been recognized and a number of formulations have
appeared in recent years. For example, the Exposure and Dose Modeling and Analysis System (EDMAS)
(Georgopoulos et al., 1997c) included an indoor model with detailed gas-phase atmospheric chemistry to
estimate indoor concentrations resulting from penetration and reaction of ambient pollutants. This indoor
model was dynamically coupled with (a) the outdoor photochemical air quality models UAM-IV and
UAM-V, which provided the gas-phase composition of influent air; and (b) with a physiologically based
uptake and dosimetry model. Subsequent work (Isukapalli et al., 1999) expanded the approach of the
EDMAS model to incorporate alternative representations of gas-phase chemistry as well as multiphase
photochemistry and gas/aerosol interactions. The microenvironmental model corresponding to this more
general formulation is mathematically represented by the following equation, when an assumption of
uniform mixing is used for each component (e.g., individual room) of the indoor environment. Sarwar et
al. (2001) presented a more comprehensive modeling study of the gas phase aspects of ozone indoor
chemistry focusing on the impact of different factors (such as outdoor ozone, indoor emissions,
ventilation rates, etc.) on the levels of indoor hydroxyl radicals (OH), which in turn are expected to
control the rate of formation of secondary toxicants indoors.
N
at ' • j-' J=l (AX3.6-1)
where,
Vi = volume of compartment (m3)
Ci = concentration of species in compartment (mol/m3)
3-111
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K[j = mass transfer coefficient from compartment (m/h)
oij = interfacial air exchange area between compartments (m2)
Cy = concentration in compartment /' in equilibrium with concentration in j (mol/m3)
Qij = volumetric flow rate from compartment /' to j (m3/h)
Ri = rate of formation of species in compartment /' (gmol/h)
and,
C _ C _ C1
c / o/, em is °/, depos °i,condens ; for gases
Ac _ o 4.0 4- c1 4. c1 4- c
°/, depos °i,resusp ^i,condens °i,nucl °i,coag; for PM (AX3 6 -2)
More recent work (S0rensen and Weschler, 2002) has coupled CFD calculations with gas-phase
atmospheric chemistry mechanisms to account for the impact of nonideal flow mixing (and associated
concentration gradients) within a room on the indoor spatial distribution of ozone and other secondary
pollutants. This work has identified potential limitations associated with the assumption of uniform
mixing in indoor microenvironments when calculating personal exposures.
A recent indoor air model that specifically focuses on NO2 (along with CO, PMi0, and PM2 5 is
INDAIR (Dimitroulopoulou et al., 2006). The IND AIR model considers three interconnected residential
microenvironments: kitchen, lounge, and bedroom. Removal processes are lumped together and
quantified via an apparent deposition velocity. Specifically, a loss rate of 0.99 ± 0.19/h (Yamanaka,
1984), is used in this model corresponding to a mean deposition velocity of 1.2 H 10"4 m/s. The sources of
NO2 considered in INDAIR are from gas stove cooking and from cigarette smoking, but only the former
contributes significantly to indoor NO2 levels, based on available model parameterizations.
Estimation of NO2 emission rates from gas cooking utilized the following empirical information:
(a) NOX emission rate equal to 0.125 g kWh"1 (Wooders, 1994); (b) an assumption thatNO2 represents
25% of the total NOX emissions and (c) gas consumption per household in cooking equal to 5-7
kWh/day, assuming 1 h cooking per day. By multiplying the estimates in (a), (b), and (c) together, NO2
gas cooking emission rates were calculated to be in the range 0. 16 to 0.22 g/h, with a uniform
distribution.
In a range of simulations performed with INDAIR for houses in the UK, it was found that the
predicted maximum 1-h mean concentrations in the kitchen were increased, compared to no-source
simulations, by a factor of 10 for NO2 (30 for PMi0 and 15 for PM2 5) and were higher in winter than in
summer. Cooking activity in the kitchen resulted in significantly elevated 24-h mean concentrations of
NO2, PMio, and PM2 5 in the lounge, as well as the kitchen, while there was a relatively small effect in the
bedroom, which was not connected directly to the kitchen in the model structure (i.e., the direct internal
air exchange rate was zero).
A very wide range of predictions was derived from the INDAIR simulations. The 95th percentile
concentrations were typically 50% higher than mean concentrations during periods of average
concentration, and up to 100% higher than mean concentrations during concentration peaks, which were
associated with cooking emissions. There was approximately a factor of 2 variation in concentrations, and
all modeled concentrations were below those outdoors. The effect of cooking was to shift the distribution
to the right, but the degree of variation was not greatly increased. This may reflect the fact that for the
fixed emission scenarios that were used, the additional variation in emission rates was small compared to
that of other factors such as deposition rate and air exchange rate. In this scenario, modeled
concentrations in the lounge all remained below those outdoors, but a proportion of kitchens (16%) had
modeled values above the outdoor concentration. For the gas-cooking scenario, indoor/outdoor ratios for
NO2 ranged 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.
According to Dimitrolopoulou et al. (2006), these results were broadly consistent with indoor/outdoor
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ratios reported for the UK. Modeled peak concentrations associated with gas cooking, of about 300 ppb in
the kitchen and 100 ppb in the lounge, were also consistent with results from UK studies.
AX3.6.1.6. Characterization of Activity Events
An important development in inhalation exposure modeling has been the consolidation of existing
information on activity event sequences in the Consolidated Human Activity Database (CHAD)
(McCurdy, 2000; McCurdy et al., 2000). Indeed, most recent exposure models are designed (or have been
re-designed) to obtain such information from CHAD which incorporates 24-h time/activity data
developed from numerous surveys. The surveys include probability-based recall studies conducted by
Environmental Protection Agency and the California Air Resources Board, as well as real-time diary
studies conducted in individual U.S. metropolitan areas using both probability-based and volunteer
subject panels. All ages of both genders are represented in CHAD. The data for each subject consist of
one or more days of sequential activities, in which each activity is defined by start time, duration, activity
type (140 categories), and microenvironment classification (110 categories). Activities vary from one min
to one h in duration, with longer activities being subdivided into clock-hour durations to facilitate
exposure modeling. A distribution of values for the ratio of oxygen uptake rate to body mass (referred to
as metabolic equivalents or METs) is provided for each activity type listed in CHAD. The forms and
parameters of these distributions were determined through an extensive review of the exercise and
nutrition literature. The primary source of distributional data was Ainsworth et al. (1993), a compendium
developed specifically to facilitate the coding of physical activities and to promote comparability across
studies.
AX3.6.1.7. Characterization of Inhalation Intake and Uptake
Use of the information in CHAD provides a rational way for incorporating realistic intakes into
exposure models by linking inhalation rates to activity information. As mentioned earlier, each cohort of
the pNEM-type models, or each (virtual or actual) individual of the SHEDS, MENTOR, APEX, and
HAPEM4 models, is assigned an exposure event sequence derived from activity diary data. Each
exposure event is typically defined by a start time, a duration, assignments to a geographic location and
microenvironment, and an indication of activity level. The most recent versions of the above models have
defined activity levels using the activity classification coding scheme incorporated into CHAD. A
probabilistic module within these models converts the activity classification code of each exposure event
to an energy expenditure rate, which in turn is converted into an estimate of oxygen uptake rate. The
oxygen uptake rate is then converted into an estimate of total ventilation rate (VE), expressed in liters/min.
Johnson (2001) reviewed briefly the physiological principles incorporated into the algorithms used in
pNEM to convert each activity classification code to an oxygen uptake rate and describes the additional
steps required to convert oxygen uptake to VE.
McCurdy (1997a,b, 2000) has recommended that the ventilation rate should be estimated as a
function of energy expenditure rate. The energy expended by an individual during a particular activity can
be expressed as EE = (MET)(RMR) in which EE is the average energy expenditure rate (kcal/min) during
the activity and RMR is the resting metabolic rate of the individual expressed in terms of number of
energy units expended per unit of time (kcal/min). MET (the metabolic equivalent of tasks) is a ratio
specific to the activity and is dimensionless. If RMR is specified for an individual, then the above
equation requires only an activity-specific estimate of MET to produce an estimate of the energy
expenditure rate for a given activity. McCurdy et al. (2000) developed distributions of MET for the
activity classifications appearing in the CHAD database.
Finally, in order to relate intake to dose delivered to the lungs, it is important to take into account
the processes affecting uptake following inhalation intake of NO2, in a biologically based dosimetry
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modeling framework. As a reactive gas, NO2 participates in transformation reactions in the lung epithelial
lining fluid, and products of these reactions are thought to be responsible for toxic effects (Postlethwait
et, 1991), although kinetic modeling of these reactions has not been performed. Dosimetry models
indicate that deposition varies spatially within the lung and that this spatial variation is dependent on
ventilation rate (Miller et al., 1982; Overton and Graham, 1995). Controlled exposure studies found that
fractional uptake of NO2 increases with exercises and ventilation rate (e.g., Bauer et al., 1986), making
activities with high MET values important for quantifying total NO2 exposure. Further discussion of NO2
dosimetry modeling is provided in Section 4.2.
AX3.6.1.8. Issues to be Addressed in Future Exposure Modeling Efforts
An issue that should be mentioned in closing is that of evaluating comprehensive prognostic
exposure modeling studies, for either individuals or populations, with field data. Although databases that
would be adequate for performing a comprehensive evaluation are not expected to be available any time
soon, there have been a number of studies, reviewed in earlier sections of this Chapter, that can be used to
start building the necessary information base. Some of these studies report field observations of personal,
indoor, and outdoor ozone levels and have also developed simple semi-empirical personal exposure
models that were parameterized using the observational data and regression techniques.
In conclusion, though existing inhalation exposure modeling systems have evolved considerably in
recent years, limitations of available modeling methods and data, in relation to potential NO2 studies that
include the following, should be taken into account and be addressed by future research efforts:
• Ambient photochemical modeling systems are not optimized for estimating NO2 at a local scale.
• Subgrid scale modeling (LES, RANS, DNS) is needed to properly characterize effects of
nonhomogeneous mixing (i.e., of spatial subgrid variability) on fast nonlinear chemical
transformations; the outcomes of this characterization then should be incorporated in simpler
models, appropriate for use in conjunction with exposure modeling systems.
• Microenvironmental modeling efforts need to balance mechanistic detail and usability by
developing:
• — A simplified but adequate indoor chemistry mechanism for NO2 and related
oxidants,
• — Databases of realistic distributions of indoor NO2 source magnitudes and
activities,
• — Flexible, multi-zonal models of indoor residential and occupational
microenvironments.
Existing prognostic modeling systems for inhalation exposure can in principle be directly applied
to, or adapted for, NO2 studies; APEX, SHEDS, and MENTOR-1A are candidates. However, such
applications would be constrained by data limitations such as ambient characterization at the local scale
and by lack of quantitative information for indoor sources and sinks.
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Table AX3.2-1. 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 1
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 1 a limits.
2 Values represent medians.
Table AX3.3-1. Passive samplers used in NO2 measurements.
PASSIVE
SAMPLER
Palmes tube
Gradko sampler
Passam Short
Long
Analyst™
Yanagisawa
badge
Ogawa sampler
IVL sampler
Willems badge
Radiello®
EMD sampler
DIMENSION
(DIFFUSION LENGTH x
CROSS-SECTIONAL
AREA)
7.1cm x 0.71cm2
7.1cm x 0.93cm2
0.74cm x 0.75cm2
2.54cm x 3.27cm2
1 .Ocm x 20cm2
0.6cm x 0.79cm2
1.0cm x 3.14cm2
0.6cm x 5.31cm2
1.8cm x 2.0cm2
N.A.
ABSORBENT
Triethanolamine
Triethanolamine
Triethanolamine
Active charcoal
Triethanolamine
Triethanolamine
Potassium iodide &
sodium arsenite
Triethanolamine-
acetone
Triethanolamine
Triethanolamine
ANALYTICAL
METHOD
Spectrophotometry
Spectrophotometry
Spectrophotometry
Gas
chromatography
Spectrophotometry
Spectrophotometry
Spectrophotometry
Spectrophotometry
Spectrophotometry
Ion chromatography
SAMPLING RATE
MANUFACTURER
N.A.
1 .2 cm3/min
15.5 cm3/min
0.854 cm3/min
N.A.
N.A.
N.A.
N.A.
N.A.
75 cm3/min
N.A.
EXPERIMENT
0.92 cm3/min
1.212cm3/min
N.A.
0.833 cm3/min
12.3cm3/min
N.R.
16.2 cm3/min
29 cm3/min
46 cm3/min
N.R.
53.4 cm3/min
REFERENCE
Palmes etal. (1976)
Plaisance et al.(2004)
Gradko (2007)
Passam (2007)
De Santis et al.
(2002)
Yanagisawa and
Nishimura (1982)
Ogawa & Company
(1998a)Gerboles
et al. (2006a)
Perm and Svanberg
(1998)
Hagenbjb'rk-
Gustafsson et al.
(2002)
Radiello® (2006)
Piechocki-Minguy
et al. (2006)
*N.A.: not available;
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Table AX3.3-2. The performance of sampler/sampling method for NO2 measurements in the air.
TYPE
Active
sampling
Passive
sampling
SAMPLER
Impinger method
Chemiluminescence
Personal monitor
Palmes tube
Gradko sampler
Passam Short
sampler
Long
Analyst™
Yanagisawa badge
Ogawa sampler
IVL sampler
Willems badge
Radiello®
EMD sampler
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
1-1 4 days
24-168 h
1 mo +
2-8 h& 1-7 days
1-24 h& 1-7 days
1-24h
CONCENTRATION
RANGE
10-400ppb
0.5- 1000 ppb
0.1 -50 ppm
10 -100 ppb
1.0 -10,000 ppb
5 - 240 pg/m3
1 - 200 pg/m3
24 - 1 ,237 pg/m3
NR
0-3,600 ppb
0.1 - 400 pg/m3
2.0- 150pg/m3
1 .0 - 496 ppb
NR
DETECTION
LIMIT
0.2 ppb
0.05 ppb
0.1 ppm
10 ppb
0.5 ppb
2-5 pg/m3
0.64 pg/m3
100 pg/m3
3.0 ppb
2.3 ppb
0.1 pg/m3
2 pg/m3
1 .0 ppb
11 pg/m3
COMMENT
SD < 5%
Accuracy ± 5%
Precision ± 5% above
5 ppb
Uncertainty ~ 27% at 80
pg/m3
Uncertainty ~ 25% at 20-
40 pg/m3
Accuracy ± 5%; Precision
within 3%
SD ~ 4%
Uncertainty ~ 24%; RSD
22%
Uncertainty- 12%
Uncertainty ~ 28%
Table AX3.4-1. NO2 concentrations (ppb) in homes in Latrobe Valley, Victoria, Australia.
No source
Gas stove only
Gas heater only
Smoking only
Multiple sources
LIVING ROOM
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
KITCHEN
Mean ppb
3.82
8.01
7.33
6.60
10.73
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: Garrett et al.
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Table AX3.4-2. NO2 concentrations (ppb) in homes in Connecticut.
Secondary Heating Source
None
Gas space heater
Wood burning source
Kerosene heater
GSH + Wood
GSH + KH
Wood + KH
GSH + Wood + KH
NO GAS STOVE USED IN MONITORING PERIOD
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
-
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).
Table AX3.4-3. NO2 concentrations near indoor sources - short-term averages.
AVERAGE
CONCENTRATION
(PPb)
191 kitchen
195 living room
184 bedroom
400 kitchen,
living room,
bedroom
90 (low setting)
350 (med setting)
360 (high setting)
NR
NR
180 to 650
PEAK
CONCENTRATION
(PPb)
375 kitchen
401 living room
421 bedroom
673 bedroom
NR
1000
1500
NR
COMMENT
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,1 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.
REFERENCE
Fortmann et al.
(2001)
Fortmann et al.
(2001)
Dutton et al.
(2001)
Girman et al.
(1982)
Girman et al.
(1982)
Girman et al.
(1982)
1 Unvented fireplaces are not permitted in many areas such as California.
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Table 3.5-1 a. Indoor/outdoor ratio and the indoor vs. outdoor regression slope.
STUDY
Baxter
etal.
(2007a)
Baxter
ot al
ei ai.
(2007b)
Mosqueron
etal.
(2002)
Lee
etal.
(1999)
Monn
etal.
(1997)
DESCRIPTION
Location: Boston, MA
Subjects: 43 homes (a
lower social-economic
status population)
Time period: May-October
(non-heating season), and
Dec-Mar (heating season),
2003-2005
Method: indoor and outdoor
3- to 4-day samples of NO2
were collected
simultaneously at each
home in both seasons;
when possible, 2
consecutive measurements
were collected.
Location: Boston, MA
Subjects: 43 homes (a
lower social-economic
status population)
Time period: May-Oct (non-
heating season), and Dec-
Mar (heating season), 2003-
2005
Method: indoor and outdoor
3- to 4-day samples of NO2
were collected
simultaneously at each
home in both seasons;
when possible, 2
consecutive measurements
were collected.
Location: Paris, France
Subjects: 62 office workers
Time period: Dec 1999 to
Sept 2000
Method: 48-h residential
indoor, workplace, outdoor,
and personal exposure
were measured.
Location: Hong Kong, China
Subjects: 14 public places
with mechanical ventilation
systems,
Time period: Oct 1996 to
Mar 1997
Method: Teflon bags were
used to collect indoor and
outdoor NO and NOR2R
ring peak hours.
Location: Switzerland
Subjects: 17 homes across
Switzerland
Time period: winter 1994 to
summer 1995
Method: 48- to 72-h indoor,
outdoor, and personal
NOR2R were measured.
SEASON
Overall
study
seasons
Overall
study
seasons
Overall
study
seasons
Overall
study
seasons
Overall
study
seasons
REGRESSION
FORMAT OR
RATIO
Residential indoor
vs. ambient and
indoor source and
proximity to traffic
Residential indoor
vs. residential
outdoor
Residential indoor
vs. residential
outdoor and indoor
sources
Residential indoor
vs. ambient and
using gas cooking
Office indoor vs.
ambient and floor
height
Indoor vs. outdoor
Indoor/outdoor ratio
INDOOR
CHARACTERISTICS
Gas stove usage
Overall homes
Homes with high ventilation
rate
Homes with low ventilation
rate
Overall homes
Cooking
None
Without gas cooking
FINF
0.66-0.79
0.48
0.56
0.47
0.53
0.26 (n =
62)
0.56 (n =
62)
0.59 (n =
14)
0.4, -0.7 (n
= 26)
COMMENTS
The overall R2 was 0.20-
0.25.
Home with an
indoor/outdoor sulfur
ratio larger than 0.76 (the
median) was defined as
a hidh ventilation home'
Home with an
indoor/outdoor sulfur
ratio less than 0.76 (the
median) was defined as
a low ventilation home.
The overall R2 was 0.16.
The overall R2 was 0.14,
and ambient NOR2R and
indoor cooking account
accounted for 0.07 each.
The overall R2 was 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.
3-118
-------
STUDY
Lee
etal.
(1995)
Garrett
etal.
(1999)
Monn
etal.
(1998)
Spengler
etal.
(1994)
DESCRIPTION
Location: Boston area, MA
Subjects: 517 residential
homes
Time period: Nov 1984 to
Oct 1 986
Method: 2-wk averaged
indoor (kitchen, living room,
and bedroom) and outdoor
NO2 were measured.
Location: Latrobe Valley,
Victoria, Australia
Subjects: 80 homes
Time period: Mar-Apr 1994,
and Jan-Feb 1995
Method: 4-day averaged
indoor (bedroom, living
room, and kitchen) and
outdoor NO2 was monitored.
Location: Geneva, Basle,
Lugano, Aarau, Wald,
Payerne, Montana, and
Davos (SAPALDIA study,
Switzerland)
Subjects: 140 subjects
Time period: Dec 1993 to
Dec 1994
Method: each home was
monitored for 3 periods of 1
mo; in the 1 st wk of each
period, personal, indoor and
outdoor levels were
measured; for the next 3
wks, only outdoor levels
were measured (1-wk
averaged measurement).
Location: Los Angeles
Basin, CA
Subjects: probability-based
sample, 70 subjects
Time period: May 1987 to
May 1988
Method: 48-h averaged, in
the micro-environmental
component, each participant
was monitored during each
of 8 sampling cycles
throughout theyr.
SEASON
Summer
Overall
study
seasons
Overall
study
seasons
Overall
study
seasons
REGRESSION
FORMAT OR
RATIO
Indoor/outdoor ratio
Indoor/outdoor ratio
Residential indoor
vs. residential
outdoor
Residential indoor
vs. residential
outdoor
INDOOR
CHARACTERISTICS
Electric stove homes
No major indoor sources
(major sources were gas
stove, vented gas heater,
and smoking)
All homes
Homes without smokers
and gas-cooking
Gas range with pilot light
Gas range without pilot light
Electric stove
FINF
0.77
(bedroom)
(Sample
size was
reported)
0.8 (n =
15)
0.47 (n =
1544)
0.40 (n =
968)
0.49 (n =
314)
0.4 (n =
148)
0.4 (n =
170)
COMMENTS
Homes with gas stove
and gas stove with pilot
light have an I/O ratio
> 1 , but the values were
not reported.
The ratio increased to
1.3, to 1.8, and to 2.2 for
homes with one, two and
three major indoor
sources.
R2 was 0.37.
R2 was 0.33.
R2 was 0.44.
R2 was 0.39.
R2 was 0.41.
3-119
-------
Table 3.5-1 b. Summary of regression models of personal exposure to ambient/outdoor NO2
STUDY
Rojas-
Bracho et al.
(2002)
Aim et al.
(1998)
Monn et al.
(1998)
Levy et al.
(1998b)
Spengler
etal. (1994)
S0rensen
et al. (2005)
Piechocki-
LOCATION
Location: Santiago, Chile
Subjects: 20 children
Time period: winters of 1998 and 1999
Method: five, 24-h avg samples on consecutive
days for each child.
Location: Helsinki, Finland
Subjects: 246 children aged 3-6 yrs
Time period: winter and spring of 1991
Method: 1-wk averaged sample for each person,
6 consecutive wks in the winter and 7 consecutive wks
in the spring.
Location: Geneva, Basle, Lugano, Aarau, Wald,
Payerne, Montana, and Davos (SAPALDIA study,
Switzerland)
Subjects: 140 subjects
Time period: Dec 1993 to Dec 1994
Method: each home was monitored for 3 periods of
1 mo; in the 1st wk of each period, personal, indoor rand
outdoor levels were measured, and in the next
3 consecutive wks, only outdoor levels were measured
(1-wk averaged measurement).
Location: 18 cities across 15 countries
Subjects: 568 adults
Time period: Feb or Mar 1996
Method: One, 48-h avg measurement for each person,
all people were measured on the same day.
Location: Los Angeles Basin
Subjects: probability-based sample, 70 subjects
Time period: May 1987 to May 1988
Method: in the microenvironmental component of the
study, each participant was monitored for 48 h during
each of 8 sampling cycles throughout the yr.
Location: Copenhagen, Denmark
Subjects: 30 subjects (20-33 yrs old) in each
measurement campaign
Time period: fall 1999, and winter, spring and summer of
2000
Method: four measurement campaigns in 1 yr; each
campaign lasted 5 wks with 6 subjects each wk; one 48-
h avg NOR2R measurement for each subject.
Location: Pooled, Lille (northern France)
SEASON
Winter
Winter + Spring
All
Winter
All
All
(>8 °C)
(<8 °C)
All
MODEL TYPE
Personal vs.
outdoor
(n = 87)
Population vs.
outdoor
(n = 23)
Personal (all
subjects) vs.
outdoor
(n = 1 ,494)
Personal (no
smokers and gas
cooking) vs.
outdoor (n = 943)
Personal vs.
outdoor
(n = 546)
Personal vs.
outdoor
Personal vs.
outdoor
(n = 73)
Personal vs.
outdoor
(n = 35)
Personal vs.
outdoor
(n = 38)
Personal vs.
SLOPE
(SE)
0.33
(0.05)
0.4
0.45
0.38
0.49
0.56
0.60
(0.07)
0.68
(0.09)
0.32
(0.13)
0.13
INTERCEPT
/ppb
7.2
4.7
7.2
7.2
14.5
15.8
"
—
6.0
R2
0.27
0.86
0.33
0.27
0.51
"
—
0.09
3-120
-------
STUDY
Minguy et al.
(2006)
S0rensen
etal.
(2005)
Aim et al.
(1998)
Sarnat et al.
(2001)
Sarnat et al.
(2005)
Sarnat et al.
(2006)
LOCATION
Subjects: 13 participants in the first campaign, and 31
participants in the second campaign
Time period: winter 2001 (first campaign), and summer
2002 (second campaign)
Method: two 24-h sampling periods (one during the
workdays and the other during the weekends) for each
subject in each campaign; during each sampling period,
each subject received four samplers to measure
personal exposure in four different microenvironments
(home, other indoor environment, transport, and
outdoors).
Location: Copenhagen
Subjects: 30 subjects (20-33 yrs old) in each
measurement campaign
Time period: fall 1999, and winter, spring and summer of
2000
Method: four measurement campaigns in 1 yr; each
campaign lasted 5 wks with 6 subjects each wk; one 48-
h avg NOR2R measurement for each subject.
Location: Helsinki, Finland
Subjects: 246 children aged 3-6 yrs
Time period: winter and spring of 1991
Method: 1-wk averaged sample for each person,
6 consecutive wks in the winter and 7 consecutive wks
in the spring.
Location: Baltimore, MD
Subjects: 56 seniors, Schoolchildren, and people with
COPD
Time period: summer of 1998 and winter of 1999
Method: 14 of 56 subjects participated in both sampling
seasons; all subjects were monitored for 12 consecutive
days (24-h avg sample) in each of the one or two
seasons, with the exception of children who were
measured for 8 consecutive days during the summer.
Location: Boston, MA
Subjects: 43 seniors and schoolchildren
Time period: summer of 1999 and winter of 2000
Method: Similar study design as Sarnat et al., 2001 .
Location: Steubenville
Subjects: 15 senior subjects
Time period: summer and fall of 2000
Method: two consecutive 24-h samples were collected
for each subject for each wk, 23 wks total.
SEASON
Summer
(homes with no
major indoor
NOR2R
sources)
All
Winter + Spring
Summer
Winter
Summer
Winter
Summer
MODEL TYPE
central
(Assuming
people stayed
indoors all the
time)
Personal vs.
central
(n = 66)
Population vs.
central (n = 24)
Personal vs.
central
(n = 225 for 24
subjects)
Personal vs.
(n = 487 for 45
subjects)
Personal vs.
central
(n = 341)
Personal vs.
central
(n = 298)
Personal vs.
central
(n = 122)
SLOPE
(SE)
0.86
0.56
(0.09)
0.3
0.04*
I0.05*
0.19
I0.03*
0.25
(0.06)
INTERCEPT
/ppb
!9.7
5.0
9.5
18.2
_
—
—
R2
0.61
0.37
—
_
—
0.14
3-121
-------
Table AX3.5-2. NO2 concentrations (ppb) in different rooms.
STUDY
Topp et al.
(2004)
Garrett
etal.
(1999)
Cotterill
and
Kingham
(1997)
Zota et al.
(2005)
Gallelli
etal.
(2002)
Linaker
etal.
(1996)
Kodama
etal.
(2002)
Chao and
Law
(2000)
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
Overall study
With vent
Without vent
Overall study
Feb 1998
June 1998
July 1998
Oct 1998
Jan 1999
Overall study
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
—
—
—
40,31.3
38,28
29,26.7
40,35
49,50
37.6
KITCHEN
—
3.8
8.0
7.3
6.6
10.7
35.6
9.9
31.4
39.8
43
50
33
24.6
18.1
30.9
27.2
81.8
33.2
24.8
23.5
70.9
31.9
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
—
—
—
20.9
73.5
28.8
21.9
24.7
65.8
28.2
BEDROOM
7.2
7.6
3.0
6.3
5.0
5.7
11.2
11.5
7.3
11.0
12.0
—
—
—
13.0
—
—
55.2
24
17.4
18.2
50.7
26.4
COMMENTS
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 to Sept 1998). In
the study, one-week averaged NO2 were measured by
Palmes tube.
Garrett (1999) investigated the levels and sources of NO2 in
Australian homes. During the study, four-day averaged NO2
was monitored using Yanagisawa passive samplers in 80
homes in the Latrobe Valley, Victoria in March-April 1994,
and Jan-Feb 1995.
Three consecutive two-week averaged outdoor, kitchen,
living room, and bedroom NO2were measured using
Palmes 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.
The indoor and outdoor NO2 concentrations for low-income,
urban neighborhoods were measured, where asthma
prevalence is high. NO2was 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.
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 NO2were
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.
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.
3-122
-------
Table AX3.5-3a. Average ambient and nonambient contributions to population exposure
STUDY
Rojas-
Bracho
etal.
(2002)
Aim et al.
(1998)
Monn
etal.
(1998)
Levy et al.
(1998a)
Spengler
etal.
(1994)
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
SLOPE
(SE)
0.33
(0.05)
0.3
0.4
0.45
0.38
0.49
0.56
INTERCEPT
/ppb
7.2
5.0
4.7
7.2
7.2
14.5
15.8
MEAN OF
PERSONAL
TOTAL
EXPOSURE
/ppb
36.4
_
_
14.1
—
28.8
37.6
MEAN AMBIENT
CONTRIBUTION
/ppb
7.2
5.0
4.7
7.2
7.2
14.5
15.8
PERCENT
AMBIENT
CONTRIBUTION
%
19.8
_
_
51.1
—
50.3
42.0
PERCENT
NONAMBIENT
CONTRIBUTION %
80.2
_
_
48.9
—
49.7
58.0
Table AX3.5-3b. Indoor and outdoor contributions to indoor concentrations.
STUDY
Mosqueron
etal.
(2002)
Yang et al.
(2004)
Monn et al.
(1998)
CONDITION
Overall study
Brisbane,
electric range
Brisbane, gas
range
Seoul, gas
range
Overall study
Homes
without
smokers and
gas cooking
SLOPE
0.258
0.65
0.56
0.58
0.47
0.40
INTERCEPT
—
0.8
3.0
4.8
3.2
3.2
MEAN
INDOOR
CONCEN-
TRATION
18.4
10.3
18.3
33.4
11.0
6.8
MEAN
OUTDOOR
CONCEN-
TRATION
31.5
—
—
40.4
16.2
16.2
PERCENT
OUTDOOR
CONTRIBU-
TION
44.2
92.4
83.5
85.7
70.5
53.1
PERCENT
INDOOR
CONTRIBU-
TION
55.8
7.6
16.5
14.3
29.5
46.9
INDOOR
SOURCE
STRENGTH
—
3.5 ppb/h
1 1.5 ppb/ h
23.4 ppb/ h
—
COMMENTS
—
—
—
—
—
Mean indoor
was estimated
based on the
text
description.
3-123
-------
STUDY
Spengler
etal.
(1994)
CONDITION
Gas range
with pilot light
Gas range
without pilot
light
Electric stove
Overall
SLOPE
0.49
0.4
0.4
0.49
INTERCEPT
—
"
_
8.64
MEAN
INDOOR
CONCEN-
TRATION
30
22
17
27.2
MEAN
OUTDOOR
CONCEN-
TRATION
37
33
33
38.3
PERCENT
OUTDOOR
CONTRIBU-
TION
60.4
60.0
77.6
68.2
PERCENT
INDOOR
CONTRIBU-
TION
39.6
40.0
22.4
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).
—
Table AX3.5-4. The association between indoor, outdoor, and personal NO2
STUDY
Mosqueron
et al. (2002)
Emenius
et al. (2003)
Lee et al.
(1999)
SUMMARY
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.
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.
Indoor and outdoor air quality of 14 public
places with mechanical ventilation systems
in Hong Kong were measured from Oct 1996
to March 1997. Traffic peak h NO, NO2 was
sampled using Teflon bags and then shipped
back to the laboratory for further analysis.
CONDITION
Overall study
Without smoker
and gas stove
was not used
With gas stove
and with
smoker
With gas stove
but without
smoker
Overall study
INDOOR
VS.
OUTDOOR
0.07
(partial R2)
0.69 eg
0.1 3 (rp)
0.06 (rp)
0.59 (R2)
PERSONAL
VS.
INDOOR
—
—
—
—
PERSONAL
VS.
OUTDOOR
—
—
—
—
COMMENTS
Gas cooking
interpreted
another 7% of
indoor NO2
variation
p < 0.001
p = 0.43
p = 0.75
0.92 for NO and
0.92 for NOX.
3-124
-------
STUDY
Garcia-
Algaret al.
(2003)
Lai et al.
(2006)
Lee et al.
(2002)
Mukala
et al. (2000)
Garrett
etal. (1999)
Cotterill and
Kingham
(1997)
Yang et al.
(2004)
Lai et al.
(2004)
Monn et al.
(1998)
SUMMARY
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.
The study was conducted 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.
Six-day integrated indoor and outdoor
concentrations of NO2 were measured in two
communities in Southern California using
Yanagisawa badges for 119 homes in April
and May 1996.
The one-week averaged indoor (day-care
center), 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.
Four-day averaged NO2was monitored using
Yanagisawa passive samplers in 80 homes
in the Latrobe Valley, Victoria, Australia in
March-April 1994, and Jan-Feb 1995.
Three consecutive two-week averaged
outdoor, kitchen, living room, and bedroom
NO2were 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.
Daily indoor and outdoor 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.
During the study, 48-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.
During the study, one-wk integrated indoor,
outdoor and personal samples were
collected fora subpopulation (n = 140) of
SAPALDIAstudy using Pamlestube between
Dec 1993 and Dec 1994 at eight study
centers in Switzerland.
CONDITION
Overall study
Overall study
Overall study
Spring
Winter
Spring (ambient
vs. indoor)
Winter (ambient
vs. indoor)
Overall study
Overall study
Brisbane,
electric range
house
Brisbane, gas
range house
Seoul, gas
range house
Overall study
Overall study
Homes without
smoker and
without gas-
cooking
INDOOR
VS.
OUTDOOR
0.1 5 (rp)
0.13 (partial
R5)
0.60 (rp)
0.86 (rp)
0.54 (rp)
0.45 (rp)
0.36 (rp)
0.28 (R2)
0.59 (rp)
0.70 (R2)
0.57 (R2)
0.52 (R2)
0.29 (rp) (not
significant)
0.37 (R2)
0.34 (R2)
PERSONAL
VS.
INDOOR
—
—
—
—
—
—
—
0.47 (rp) (p <
0.01)
0.51 (R2)
0.47 (R2)
PERSONAL
VS.
OUTDOOR
—
—
—
—
—
—
—
-0.41 (rp)
(p < 0.05)
0.33 (R2)
0.27 (R2)
COMMENTS
p = 0.007
The overall R2for
the multiple linear
regression was
0.67
—
—
—
—
Log 10
transformed data
—
—
—
Data were log-
transformed
—
3-125
-------
STUDY
Levy et al.,
(1998a)
Spengler
etal. (1994)
Kousa et al.
(2001)
Linaker
etal. (1996)
Aim et al.
(1998)
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
The indoor, outdoor, and personal NO2
relationship in three EXPOLIS centers
(Basel, Helsinki, and Prague) were reported.
During the study, 48-averaged indoor,
outdoor, and personal NO2 were measured
with Palmes tubes during 1996-1997.
During the study, one-wk 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.
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 were measured with
Palmes tubes during 13 wks in winter and
spring in 1991 in Helsinki.
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
Overall study
Helsinki
Overall study
Overall study
Winter
Spring
Winter
downtown
Spring
downtown
Winter
suburban
Spring
suburban
Downtown
electric stove
INDOOR
VS.
OUTDOOR
0.4 (R2)
0.41 (R2)
0.39 (R2)
0.44 (R2)
0.66 (rp)
0.75 (rp)
—
—
0.44 (R2)
—
—
—
—
0.44 (rp)
0.84 (rp)
0.22 (rp)
0.46 (rp)
—
PERSONAL
VS.
INDOOR
0.75 (rs)
0.6 (R2)
—
—
—
—
—
—
—
0.53 (R2)
0.45 (R2)
0.53-0.76 (rp)
0.88 (R2)
—
—
0.32 (rp)
0.75 (rp)
0.04 (rp)
0.75 (rp)
0.67 (rp)
PERSONAL
VS.
OUTDOOR
0.57 (rs)
0.51 (R2)
0.52 (R2)
0.44 (R2)
—
—
0.47 (R2)
0.33 (R2)
0.37 (R2)
0.40 (R2)
0.61-0.65 (rp)
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)
COMMENTS
—
—
—
—
—
—
—
—
Data were log-
transformed
Data were log-
transformed
Data were log-
transformed
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
—
—
3-126
-------
STUDY
Kodama
et al. (2002)
SUMMARY
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 Feb 1998 to Jan 1999.
CONDITION
Downtown gas
stove
Downtown non-
smoking
Downtown
smoking
Suburban
electric stove
Suburban gas
stove
Suburban non-
smoking
Suburban
smoking
Summer
Winter
INDOOR
VS.
OUTDOOR
—
—
—
—
—
—
—
—
"
PERSONAL
VS.
INDOOR
0.50 (rp)
0.67 (rp)
0.47 (rp)
0.55 (rp)
0.50 (rp)
0.48 (rp)
0.31 (rp)
0.57 (rp)
PERSONAL
VS.
OUTDOOR
0.59 (rp)
0.73 (rp)
0.51 (rp)
0.63 (rp)
0.59 (rp)
0.46 (rp)
0.24 (rp)
0.08 (rp)
COMMENTS
—
—
—
—
—
—
—
—
"
Table AX3.5-5. Indoor, outdoor, and personal NO2 levels stratified by exposure factors
(concentrations are in ppb and slopes are dimensionless)
REFERENCE
FACTOR
NAME
FACTOR
LEVEL
AMBIENT
NO2
LEVEL
AMBIENT
SLOPE
INDOOR
NO2
LEVEL
INDOOR
SLOPE
PERSONAL
NO2 LEVEL
PERSONAL
SLOPE
COMMENTS
Environmental conditions
Singer et al.
(2004)
Zota et al.
(2005)
S0rensen et al.
(2005)
Aim et al.
(1998)
Wind
Direction
Season
Season
Season
Upwind of
freeway
Downwind
and close to
freeway
Downward
and far from
freeway
Heating
Non-Heating
<8C
>8C
Winter
downtown
smoker
20.5
26.5
21
21
17
14.6
7.8
—
—
—
—
—
—
—
—
—
—
43
26
8.9
6.6
—
—
—
—
—
—
—
—
—
—
—
—
11.4
9.2
13.5
—
—
—
—
—
—
—
—
—
—
—
—
—
—
3-127
-------
REFERENCE
Zota et al.
(2005)
Vukovich (2000)
Lee (1997)
FACTOR
NAME
Heating
season
Day
Day
FACTOR
LEVEL
Spring
downtown
smoker
Winter
downtown
nonsmoker
Spring
downtown
nonsmoker
Winter
suburban
smoker
Spring
suburban
smoker
Winter
suburban
nonsmoker
Spring
suburban
nonsmoker
—
Weekday
Weekday
Weekend
AMBIENT
N02
LEVEL
—
—
—
—
—
—
—
—
—
AMBIENT
SLOPE
—
—
—
—
—
—
—
3.87
—
—
INDOOR
NO2
LEVEL
—
—
—
—
—
—
—
—
—
—
INDOOR
SLOPE
—
—
—
—
—
—
—
17.3
—
—
PERSONAL
N02 LEVEL
15.4
13.0
14.1
11.2
10.7
9.2
8.7
—
—
—
PERSONAL
SLOPE
—
—
—
—
—
—
—
—
—
—
COMMENTS
—
—
—
—
—
—
—
—
39% more than
weekend
The effect of
weekday/week-
end is clear but
the paper didn't
give a value to
cite
—
Dwelling conditions
Levy et al.
(1998a)
Cotterill and
Kingham (1997)
Partti-Pellinen
et al. (2000)
Window open
Window
Type of
Filtration
With
Without
Single
Glazing
Double
Glazing
Single
Glazing
Double
Glazing
Mechanical
filter
Mechanical
intake and
mechanical
filter
—
—
—
—
—
—
12.3
11.5
—
—
—
—
—
—
—
—
—
9.4
9.4
11.0
12.0
9.6
12.5
—
—
—
—
—
—
—
30
26.7
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
Gas cooker
homes
Gas cooker
homes
—
3-128
-------
REFERENCE
Yamanaka
(1984)
Zota et al.
(2005)
Levy et al.
(1998a)
Emenius et al.
(2003)
Cotterill and
Kingham (1997)
Zota et al.
(2005)
Lee et al.
(2004)
Liard et al.
(1999)
Nakai et al.
(1995)
Aim et al.
(1998)
Lee et al.
(1996)
FACTOR
NAME
Surface type
Occupancy
Occupancy
Location
Location
Location
Location
Location
Location
Location
House
structure
FACTOR
LEVEL
Mechanical
intake and
mechanical
and chemical
filter
—
—
1
2
Urban
Semi-urban
Suburban
On Main
Road
50-85m from
Main Road
—
Industrial
Residential
Main Road
Side Road
< 20 m
20-1 50m
> 150m
Downtown
smoker
Suburban
smoker
Downtown
nonsmoker
Suburban
nonsmoker
Single DU
Small multi-
DU
Large multi-
DU
AMBIENT
N02
LEVEL
12.4
—
—
—
—
16.5
11.3
7.2
—
—
—
—
—
—
—
42.4
34.9
20.3
—
—
—
—
17
23
23.6
AMBIENT
SLOPE
—
—
—
—
—
—
—
—
—
-0.0093
—
—
—
—
—
—
—
—
—
—
—
—
—
—
INDOOR
NO2
LEVEL
6.5
—
—
—
—
9.6
6.4
4.2
7.9
6.8
—
—
—
—
—
43.8
38.4
36.4
—
—
—
—
17
28.9
26.8
INDOOR
SLOPE
—
3.2
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
PERSONAL
N02 LEVEL
—
—
25.9
30.8
—
—
—
—
—
—
34.9
27.8
28.1
24.3
43.1
35.9
30.1
14.6
10.9
13.6
9.0
—
—
—
PERSONAL
SLOPE
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
COMMENTS
Affect decay rate
—
—
—
—
—
—
Electric cooker
homes
Electric cooker
homes
—
—
—
—
—
Recalculated
based published
data
Recalculated
based published
data
Recalculated
based published
data
—
—
—
—
Winter
Winter
Winter
3-129
-------
REFERENCE
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)
FACTOR
NAME
Heating
system
Frames
Floor level
Floor level
Extractor fan
over cooker
Chimney
Attached
garage
Age of house
FACTOR
LEVEL
Single DU
Small multi-
DU
Large multi-
DU
Single DU
Small multi-
DU
Large multi-
DU
Individual
Central
Metal
Wood
—
—
Without
With
With vent
Without vent
With
Without
—
AMBIENT
NO2
LEVEL
18.4
25.1
25.1
15.9
23.7
24.5
—
—
—
—
—
—
—
—
—
—
—
—
—
AMBIENT
SLOPE
—
—
—
—
—
—
—
—
—
—
2
—
—
—
—
—
—
—
—
INDOOR
NO2
LEVEL
17.8
30.2
25.4
17.3
27.8
29.1
13.7
12.5
12.6
15.0
—
—
—
—
18.1
30.9
17.3
11.4
—
INDOOR
SLOPE
—
—
—
—
—
—
—
—
—
—
—
-1.78
—
—
—
—
—
—
0.5
PERSONAL
N02 LEVEL
—
—
—
—
—
—
—
—
—
—
—
—
27.5
24.8
—
—
—
—
—
PERSONAL
SLOPE
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
COMMENTS
Fall
Fall
Fall
Summer
Summer
Summer
Bedroom data
Bedroom data
Bedroom data
Bedroom data
—
—
—
—
Kitchen data
Kitchen data
—
—
—
Indoor sources
Zota et al.
(2005)
Lai et al. (2004)
Levy et al.
(1998a)
Belanger et al.
(2006)
Cotterill and
Kingham (1997)
Yang et al.
Supplemental
Heating with
stove
Smoking
Smokers
present
Ranges
Ranges
Ranges
—
Smoking
Nonsmoking
With
Without
Electric
Gas
Gas
Electric
Gas
Electric
Gas
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
10.9
11.5
—
—
8.6
25.9
35.6
9.9
11.5
7.3
18.3
7.84
—
—
—
—
—
—
—
—
—
—
—
—
10.8
14.1
34.8
26.8
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
Kitchen
Kitchen
Bedroom
Bedroom
—
3-130
-------
REFERENCE
(2004)
Schwab et al.
(1994)
Monn et al.
(1998)
Spengler et al.
(1994)
Aim et al.
(1998)
Raaschou-
Nielsen et al.
(1997)
Kawamoto et al.
(1997)
Lee et al.
(2004)
Liard et al.
(1999)
Kodama et al.
(2002)
Yang et al.
(2004)
Levy et al.
(1998a)
FACTOR
NAME
Ranges
Ranges
Ranges
Ranges
Near fire
Heating time
Heating fuel
Heating
appliance
Heater
Gas water
heater
Gas water
heater
FACTOR
LEVEL
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
Oil fan heater
Kerosene
heater
Clean heater
Coal
briquette
Petroleum
Gas
Other
Kerosene
heater
Gas stove
Electric
heater
With
Without
With
Without
With
AMBIENT
NO2
LEVEL
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
AMBIENT
SLOPE
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
INDOOR
NO2
LEVEL
10.3
20.3
11.7
8
20.9
16.8
15.2
12.6
18.8
15.7
—
—
—
—
—
—
—
—
—
152.6
77.5
30.8
18.1
11.9
—
—
—
INDOOR
SLOPE
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
PERSONAL
N02 LEVEL
—
—
—
—
23.6
19.9
18.3
16.2
20.9
18.3
13.0
—
—
—
—
22.2
33.1
27.9
25.2
—
—
—
—
—
30.5
28.2
36.4
PERSONAL
SLOPE
—
—
—
—
—
—
—
—
—
—
—
0.052
2.59
1.17
—
—
—
—
—
—
—
—
—
—
—
—
—
COMMENTS
—
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
—
—
—
—
—
—
—
—
—
Sourth area, Feb
1998
Sourth area, Feb
1998
Sourth area, Feb
1998
—
—
—
—
—
3-131
-------
REFERENCE
Monn et al.
(1997)
Mosqueron
et al. (2002)
Raaschou-
Nielsen et al.
(1997)
Garrett et al.
(1999)
Dutton et al.
(2001)
S0rensen et al.
(2005)
Liard et al.
(1999)
Raaschou-
Nielsen et al.
(1997)
Lee et al.
(2004)
Liard et al.
(1999)
Dennekamp
et al. (2001)
FACTOR
NAME
Gas range
Gas cooking
Gas cooking
Gas
appliances at
home
Gas and
smoking
Fireplace
setting
Exposure to
burning
candle
Exposure to
ETS
Exposure to
ETS
Cooking fuel
Cooking
appliance
Cooking
FACTOR
LEVEL
Without
With
Without
With
Without
None
Gas stove
Gas heater
Smoking
Multiple
Low
Middle
High
—
With
Without
Petroleum
Gas
Coal
briquette
Gas
Electric
1 ring
2 rings
3 rings
AMBIENT
NO2
LEVEL
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
AMBIENT
SLOPE
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
INDOOR
NO2
LEVEL
—
—
—
—
—
—
—
3.0
6.3
5.0
5.7
11.2
90
350
360
—
—
—
—
—
—
—
—
—
437
310
584
INDOOR
SLOPE
—
—
—
—
—
0.068
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
PERSONAL
N02 LEVEL
28.5
34.8
20.5
—
—
—
—
—
—
—
—
—
—
—
—
25.1
26.3
—
26.1
33.1
20.6
25.8
25.5
—
—
—
PERSONAL
SLOPE
—
—
—
—
—
—
0.202
—
—
—
—
—
—
—
0.031
—
—
0.056
—
—
—
—
—
—
—
—
COMMENTS
—
—
—
I/O > 1 .2
I/O -0.4 -0.7
—
—
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
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
The max 5 min
concentrations
The max 5 min
concentrations
The max 5 min
concentrations
3-132
-------
REFERENCE
FACTOR
NAME
FACTOR
LEVEL
4 rings
Boil water
Stir fry
Fry bacon
Bake cake
Roast meat
Bake
potatoes
AMBIENT
NO2
LEVEL
—
—
—
—
—
—
—
AMBIENT
SLOPE
—
—
—
—
—
—
—
INDOOR
NO2
LEVEL
996
184
92
104
230
296
373
INDOOR
SLOPE
—
—
—
—
—
—
—
PERSONAL
N02 LEVEL
—
—
—
—
—
—
—
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
Personal activities
Levy et al.
(1998a)
Chao and Law
(2000)
Kawamoto et al.
(1997)
Commute
Commute
Cooking to
stay home h
ratio
Cooking time
Commuting
less than 1 h
Without
commuting
< 1 h
1-2 h
2-3 h
3-4 h
4-6 h
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
29.9
27.9
21.7
24.7
24.6
20.1
27.9
—
—
—
—
—
—
—
—
—
55.4
1.61
—
—
—
—
—
—
—
—
—
Table AX3.5-6. Personal NO2 levels stratified by demographic and socioeconomic factors
(concentrations are in ppb and slopes are dimensionless).
REFERENCES
Rotko et al. (2001)
Rotko et al. (2001)
Raaschou-Nielsen (1997)
Lee et al., (2004)
Lee et al., (2004)
Rotko et al. (2001)
FACTOR TYPE
Demography
Demography
Demography
Demography
Demography
Demography
FACTOR NAME
Age
Age
Age
Gender
Gender
Gender
FACTOR LEVELS
25-34
35-55
Female
Male
Female
PERSONAL N02 LEVEL
13.1
13.1
33
29
12.9
PERSONAL SLOPE
0.056
3-133
-------
REFERENCES
Rotko et al. (2001)
Raaschou-Nielsen (1997)
Rotko etal. (2001)
Rotko etal. (2001)
Rotko et al. (2001)
Rotko et al. (2001)
Rotko et al. (2001)
Rotko et al. (2001)
Algar etal. (2004)
Algar et al. (2004)
Algar et al. (2004)
FACTOR TYPE
Demography
Demography
Socioeconomic
Socioeconomic
Socioeconomic
Socioeconomic
Socioeconomic
Socioeconomic
Socioeconomic
Socioeconomic
Socioeconomic
FACTOR NAME
Gender
Gender
Education years
Education years
Employment
Employment
Occupational status
Occupational status
Employment
Employment
Employment
FACTOR LEVELS
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)
PERSONAL N02 LEVEL
13.4
13.8
12.8
13.3
11.5
13.4
13.0
12.2
12.3
12.1
PERSONAL SLOPE
0.267
Table AX3.6-1. The essential attributes of the pNEM, HAPEM, APEX, SHEDS, and MENTOR-1 A.
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
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
HAPEM
Annual averaged
No
Ranging from
urban to
national/ Census
tract level
Ayr/one h
Top-down
approach
Linear
relationship
method (hard-
coded)
Random
samples from
probability
distributions
Available; set to
zero in HAPEM6
Yes
APEX
Hourly averaged
Yes
Urban area/Census
tract level
Ayr/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
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
MENTOR-1A
Activity event based
Yes
Multiscale/ Census tract
level
Ayr/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
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Exposure Routes
Potential Dose
Calculation
Physiologically
Based Dose
Variability/
Uncertainty
PNEM
Inhalation
Yes
No
Yes
HAPEM
Inhalation
No
No
No
APEX
Inhalation
Yes
No
Yes
SHEDS
Inhalation
Yes
Yes
Yes
MENTOR-1A
Multiple (optional)
Yes
Yes
Yes (Various "Tools")
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Annex 4. Toxicological Effects of NOx
AX4.1. Pulmonary Effects of NOx
AX4.1.1. Effects of NC^on Oxidant and Antioxidant Metabolism
NO2 is an oxidant; lipid peroxidation is believed to be a major molecular event responsible for its
toxicity. As a result, there has been considerable attention paid to the effect of NO2 on the antioxidant
defense system in the epithelial lining fluid and in pulmonary cells. Repeated exposure to NO2 at
concentrations ranging from 0.04 to 33 ppm has been shown to alter low molecular weight antioxidants
such as glutathione, vitamin E, and vitamin C, as well as some enzymes involved in cell oxidant
homeostasis.
A number of studies have investigated the hypothesis, originally proposed by Menzel (1970), that
antioxidants might protect the lung from NO2 damage by inhibiting lipid peroxidation (see Table AX4.1).
Changes in the activity of enzymes in the lungs of NO2-exposed animals that regulate levels of
glutathione (GSH) have been reported in response to relatively low exposure concentrations. Sagai et al.
(1984) studied the effects of prolonged (9 and 18 months) exposure to 0.04, 0.4, and 4.0 ppm NO2 on rats.
After exposure duration, non-protein sulfhydryl levels were increased at 0.4 ppm or greater, and exposure
to 4.0 ppm decreased the activity of GSH peroxidase but increased glucose-6-phosphate dehydrogenase
activity. Glutathione peroxidase activity was also decreased in rats exposed to 0.4 ppm NO2 for 18
months. Three GSH S-transferases were also studied, two of which (aryl S-transferase and aralkyl S-
transferase) exhibited decreased activities after 18 months of exposure to 0.4 ppm or greater NO2. No
effects were observed on the activities of 6-phosphogluconate dehydrogenase, superoxide dismutase, or
disulfide reductase. Effects followed a concentration- and exposure-duration response function. The
decreases in glutathione-related enzyme activities were inversely related to the apparent formation of lipid
peroxides (see lipid peroxidation subsection). Shorter exposures (4 months) to NO2 between 0.4 and
4.0 ppm also caused concentration- and duration-dependent effects on antioxidant enzyme activities
(Ichinose and Sagai, 1982). For example, glucose-6-phosphate dehydrogenase increased, reaching a peak
at 1 month, and then decreased towards the control value. Shorter (2-week) exposure to 0.4 ppm NO2
caused no such effects in rats or guinea pigs (Ichinose and Sagai, 1989).
The activities of GSH reductase and glucose-6-phosphate dehydrogenase were significantly
increased during exposure to 6.2 ppm NO2 for 4 days; GSH peroxidase activity was not affected (Chow et
al., 1974). The possible role of edema and cellular inflammation in these findings was not examined.
Since NO2 had no significant effect on lung GSH peroxidase activity in this study, but did significantly
increase the activities of GSH reductase and glucose-6-phosphate dehydrogenase, the authors concluded
that NO2 attacks mainly GSH and NADPH.
Newer studies also identified effects on GSH. Changes in GSH status in the blood and lung
(bronchoalveolar lavage (BAL) fluid) occurred in rats exposed to 5 ppm and 10 ppm NO2 continuously
for 24 h, but not for 7 days (Pagani et al., 1994). Total glutathione - total of reduced (GSH) and oxidized
(GSSG) form - was significantly increased in blood but not in BAL fluid; however, GSSG was elevated
in BAL fluid only. A decreased GSH/GSSG ratio was observed in the blood and BAL fluid, but not in
lung type II cells, of rats continuously exposed to 10 ppm NO2 for 3 or 20 days (Hochscheid et al., 2005).
Interestingly, lipid peroxidation was decreased in type II cells at 3 days, but was similar to controls at 20
days. Gene expression, as measured by mRNA levels of the enzymes involved in the biosynthesis of
glutathione - gamma-glutamylcysteine synthetase (yGCS) and glutathione synthetase (GS), decreased at
both time points, but gamma-glutamyltranspeptidase (yGT) mRNA expression increased. No GSH
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peroxidase activity (important for hydroperoxide reduction of complex lipids) was detected at 3 days, and
was barely detected at 20 days.
Malnutrition of animals can drastically affect their response to toxicants, including NO2.
Experimental interest in this area has mainly focused on dietary lipids, vitamin E and other lipid-soluble
antioxidants, and vitamin C and other water-soluble antioxidants. Ayaz and Csallany (1978) exposed
vitamin E-deficient and vitamin E-supplemented (30 or 300 mg/kg of diet) weanling mice continuously
for 17 months to 0.5 or 1.0 ppm NO2 and assayed blood, lung, and liver tissues for GSH peroxidase
activity. Exposure to 1.0 ppm NO2 alone or combined with vitamin E deficiency decreased the enzyme
activity in the blood and lungs. Neither vitamin E deficiency nor NO2 exposure affected liver GSH
peroxidase activity. However, in vitamin E-supplemented mice, GSH peroxidase activity increased at
0.5 ppm and 1.0 ppm NO2.
AX4.1.2. Lipid Metabolism and Content of the Lung
Lipid peroxidation is an important mechanism of cell damage arising from changes in cell
membrane structure and function. The ability of NO2 exposure to induce lipid peroxidation in the
respiratory tract has been demonstrated; as measured by increased ethane exhalation in the breath, as
thiobarbituric acid (TEA) reactive substances in tissues, and as the content of conjugated dienes in tissue
homogenates.
A number of studies investigated the effects of NO2 exposure on lipid metabolism and lipid content
of the lung. Lipid peroxidation induced by NO2 exposure has been detected at exposure concentrations as
low as 0.04 ppm. Increased ethane exhalation was observed in rats exposed to 0.04 or 0.12 ppm after 9
and 18 months of exposure (Sagai et al., 1984). Exposure to 0.4 ppm NO2 for 9 months or longer and to
4.0 ppm for 6 months resulted in increased TEA reactants (Ichinose et al., 1983). NO2 exposure for
shorter durations also increased lipid peroxidation in rats. For example, NO2 exposure of 1.2 ppm or
greater for 1 week (Ichinose and Sagai, 1982; Ichinose et al., 1983) increased ethane exhalation in rats,
while exposure of pregnant rats to 0.53 or 5.3 ppm NO2 for 5 h/day for 21 days resulted in increased lung
lipid peroxidation products (Balabaeva and Tabakova, 1985). These results indicate at least some degree
of duration-dependence in the formation of lipid peroxidation, with lower effect thresholds identified with
longer durations of exposure.
Lipid peroxidation results in altered phospholipid composition, which in turn may adversely affect
membrane fluidity and thus, membrane function. Significant depression of lipid content and total content
of saturated fatty acids such as phosphatidyl-ethanolamine, lecithin (phosphatidylcholine),
phosphatidylinositol, and phosphatidylserine were measured in rats exposed to 2.9 ppm NO2 for 24 h/day,
5 days/week for 9 months (Arner and Rhoades, 1973). Exposure of rabbits to 1.0 ppm NO2 for 2 weeks
also caused depression of lecithin synthesis after one week of exposure (Seto et al., 1975), while exposure
of rats to 5.5 ppm NO2 for 3 h/day for 7 or 14 days elicited only few changes in lipid metabolism
(Yokoyama et al., 1980). In beagle dogs, the amount of unsaturated fatty acids in the phospholipids from
the lungs was increased after exposure to concentrations ranging from 5 to 16 ppm, but not to 3 ppm
(Dowell et al., 1971). Exposure of either mice or guinea pigs to 0.4 ppm for a week resulted in a
decreased concentration of phosphatidylethanolamine and a relative increase in the phosphatidylcholine
concentration (Sagai et al., 1987). Concentration- and exposure duration-dependent increases were
reported in phospholipid components in BAL fluid, when rats were exposed to 10 ppm NO2 continuously
for 1 day or 3 days (Miiller et. al., 1994).
Functional studies conducted on surfactant phospholipid extracts indicated that NO2 exposures of
5 ppm or greater, directly impaired surface tension, although the structure of the surfactant protein A (SP-
A) was not altered by NO2 exposure. Changes in the phospholipid composition of membranes may result
in disruption of the cell membrane barrier. Miiller et al. (2003) found that uptake of liposomes by type II
lung cells occurred more easily from animals exposed to 10 ppm NO2 for 3 to 28 days, possibly as a result
of increased demand of phosphatidylcholine during lung injury.
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Lipid peroxidation can also activate phospholipases. Activation of phospholipase Al in cultured
endothelial cells occurred at 5 ppm after 40 h of exposure and was speculated to depend on a specific
NO2-induced increase in phosphatidyl serine in the plasma membranes (Sekharam et al., 1991).
One function of phospholipases is the release of arachidonic acid (AA), which serves as a mediator
of inflammatory response. NO2 exposure affects the release and metabolism of arachidonic acid both in
vivo and in vitro. The products of arachidonic acid metabolism, such as prostaglandins, prostacyclin,
thromboxanes, and leukotrienes play an important role (such as recruitment of neutrophils to sights of
local irritation) in modulating inflammatory response. Schlesinger et al. (1990) reported elevated
concentrations of thromboxane B2 (TxB2) following NO2 exposures of 1.0 ppm for 2 h, depressed
concentrations at 3.0 ppm, and significant depression 24-h postexposure at 10 ppm NO2. The same
investigators also reported depressed level of 6-keto-prostaglandin Fla at 1.0 ppm NO2, but exposure to
NO2 did not affect prostaglandins E2 and F2 and leukotriene B4 (LTB4) levels.
Changes in activation of arachidonate metabolism were also reported in rat alveolar macrophages
(AMs) when these animals were exposed to 0.5 ppm NO2 for 0.5, 1, 5, and 10 days (Robison et al., 1993).
Unstimulated AM synthesis of LTB4 was depressed after 0.5 days and again after 5 days of exposure to
NO2. Alveolar macrophage production of TxB2, LTB4, and 5-hydroxyeicosatetraenoic acid (5-HETE) in
response to stimulation with the calcium ionophore, A23187, was depressed after 0.5 days of exposure
and recovered to air-control values with longer exposure periods. 5-HETE levels were increased after 10
days of exposure. However, AM production of LTB4 in response to zymosan-activated rat serum was
depressed only after 5 days of exposure.
The effects of NO2 on structural proteins of the lungs have been of concern because elastic recoil is
lost after exposure. Collagen synthesis rates were increased in rats exposed to NO2 concentrations as low
as 5.0 ppm NO2. It has been assumed that increased collagen synthesis reflect increases in total lung
collagen which, if sufficient, could result in pulmonary fibrosis after longer periods of exposure. Such
correlation has yet to be confirmed by in vivo studies involving NO2 exposure.
Alterations in xenobiotic metabolism pathways following NO2 exposure are also summarized in
Table AX4.2, in addition to changes in phase I enzymes (such as cytochrome P450s) and phase II
enzymes (GST as described earlier). While these changes are not necessarily toxic manifestations of NO2
per se, such changes may impact the metabolism and toxicity of other chemicals. Glycolytic pathways
may also be affected. For example, glycolytic metabolism was increased by NO2 exposure, possibly due
to a concurrent increase in type II cells (Mochitate et al., 1985).
AX4.1.3. Lung Host Defense, Lung Permeability and Inflammation,
Immune Responses, and Infectious Agents
Impaired lung host-defenses, increased risk of susceptibility to both viral and bacterial agents, as
well as increased lung permeability and inflammation, provide some important evidence for mechanisms
of action potentially underlying the health effects observed in epidemiology studies. These effects are
discussed in Chapter 3 but study details are provided in Tables AX 4.3, 4.4, 4.5, and 4.6.
AX4.1.4. Emphysema Following N02 Exposure
Emphysema as a result of chronic exposure to NO2 has been reported in animal studies. The
definition of emphysema has changed overtime; thus, it is important to compare the findings of studies
using the current definition of emphysema. U.S. Environmental Protection Agency (1993) evaluated
animal studies reporting emphysema caused by or in response to chronic exposure to NO2 based upon the
most recent definition of emphysema from the report of the National Heart, Lung and Blood Institute
(NHLBI), Division of Lung Diseases Workshop (Snider et al., 1985), and U.S. Environmental Protection
Agency (1993). Because the focus of this document is an extrapolation from NO2 exposure to potential
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hazard for humans, only those studies reporting emphysema of the type seen in human lungs were
included AQCD (1993).
Humanlike emphysema linked to > 5 ppm NO2 exposure was reported by Haydon et al. (1967) in
rabbits and rats (Freeman et al., 1972; Earth et al., 1995). See Annex Table AX4.7 for more recent studies
linking NO2 exposure and lung structure changes.
AX4.1.5. Lung Function
Lung function is discussed extensively in Section 3.1.5 of the ISA. The limited animal toxicology
data is presented in Table AX4.8.
AX4.1.6. Nitrates (N03-)
Busch et al. (1986) exposed rats and guinea pigs with either normal lungs or elastase-induced
emphysema to ammonium nitrate aerosols at 1 mg/m3 for 6 h/day, 5 days/week for 4 weeks. Using light
and electron microscopy, the investigators concluded that there were no significant effects of exposure on
lung structure.
AX4.2. Dosimetry of Inhaled NOx
This section provides an overview of NO2 dosimetry and updates information provided in the 1993
AQCD for NOX. Dosimetry of NO2 refers to the measurement or estimation of the amount of NO2 or its
reaction products reaching and persisting at specific sites in the respiratory tract following an exposure.
NO2, classified as a reactive gas, interacts with surfactants, antioxidants, and other compounds in the
epithelial lining fluid (ELF). The compounds thought responsible for adverse pulmonary effects of
inhaled NO2 are the reaction products themselves or the metabolites of these products in the ELF. At the
time of the 1993 NOX AQCD, it was thought that inhaled NO2 probably reacted with the water molecules
in the ELF to form nitrous acid (FINO2) and nitric acid (HNO3). However, some limited data suggested
that the absorption of NO2 was linked to reactive substrates in the ELF and subsequent nitrite production.
Since then, the reactive absorption of NO2 has been examined in a number of studies (see Section 4.2.2).
These studies have characterized the absorption kinetics and reactive substrates for NO2 delivered to
various sites in the respiratory tract. Researchers have attempted to obtain a greater understanding of how
these complex interactions affect NO2 absorption and NO2-induced injury.
With respect to quantifying absolute NO2 absorption, the following were reported in the 1993 NOX
AQCD. The principles of O3 uptake were generally assumed applicable forNO2 modeling studies. The
results indicated that NO2 is absorbed throughout the lower respiratory tract, but the major delivery site is
the centriacinar region, i.e., the junction between the conducting and respiratory airways in humans and
animals. Experimental studies have found that the total respiratory tract uptake in humans ranges from 72
to 92% depending on the study and the breathing conditions. The percent total uptake increases with
increasing exercise level. In laboratory animals, upper respiratory tract uptakes ranged from as low as
25% to as high as 94% depending on the study, species, air flow rate, and mode of breathing (nasal or
oral). Upper respiratory tract uptake of NO2 was found to decrease with increasing ventilation. Uptake
during nasal breathing was determined to be significantly greater than during oral breathing.
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AX4.2.1. Mechanisms of N02 Absorption
The ELF is the initial barrier against NO2 delivery to the underlying epithelial cells. Postlethwait
and Bidani (1990) suggested that acute NO2 uptake in the lower respiratory tract was rate limited by
chemical reactions of NO2 with ELF constituents rather than by gas solubility in the ELF. Subsequently,
Postlethwait et al. (1991) reported that inhaled NO2 (10 ppm) did not penetrate the ELF to reach
underlying sites and suggested that cytotoxicity may be due to NO2 reactants formed in the ELF. Since
then, the reactive absorption of NO2 has been examined in a number studies that have sought to identify
reactive substrates for NO2 and quantify the absorption kinetics of NO2 in the respiratory tract.
Postlethwait and Bidani (1994) concluded that the reaction between NO2 and water does not
significantly contribute to the absorption of inhaled NO2. Uptake is a first-order process for NO2
concentrations less than 10 ppm, is aqueous substrate-dependent, and is saturable. The absorption of
inhaled NO2 is thought to be coupled with free radical-mediated hydrogen abstraction to form FiNO2 and
an organic radical (Postlethwait and Bidani, 1989, 1994). At physiologic pH, the FiNO2 subsequently
dissociates to FT and nitrite (NO2~). The concentration of the resulting nitrite is thought insufficient to be
toxic, so effects are thought to be due to the organic radical and/or the proton load. Nitrite may enter the
underlying epithelial cells and blood. In the presence of red blood cells, nitrite is oxidized to nitrate
(NO3~) (Postlethwait and Mustafa, 1981). Beyond cell susceptibility and the concentration of NO2 in the
lumen, site-specific injury was proposed to depend on rate of 'toxic' reaction product formation and the
quenching of these products within the ELF. Related to the balance between reaction product formation
and removal, it was further suggested that cellular responses may be nonlinear with greater responses
being possible at low levels of NO2 uptake versus higher levels of uptake. Since the ELF may vary
throughout the respiratory tract, the uptake of inhaled NO2 and reaction with constituents of the
pulmonary ELF may be related to the heterogeneous distribution of epithelial injury observed from NO2
exposure.
Postlethwait et al. (1995) sought to determine the absorption substrates for NO2 in the ELF lavaged
from male Sprague-Dawley rats. Since the bronchoalveolar lavage fluid (BALF) collected from the rats
may be diluted up to 100-fold relative to the native ELF, the effect of concentrating the BAL fluid on NO2
absorption was investigated. A linear association was found between the first-order rate constant for NO2
absorption and the concentration of the BALF. This suggested that concentration of the reactive substrates
in the ELF determines the rate of NO2 absorption. The absorption due to specific ELF constituents was
also examined in chemically pure solutions. Albumin, cysteine, reduced GSH, ascorbic acid, and uric acid
were hydrophilic moieties found to be active substrates for NO2 absorption. Unsaturated fatty acids (such
as oleic, linoleic, and linolenic) were also identified as active absorption substrates and thought to account
for up to 20% of NO2 absorption. Vitamins A and E exhibited the greatest reactivity of the substrates that
were examined. However, the low concentrations of uric acid and vitamins A and E were thought to
preclude them from being appreciable substrates in vivo. The authors concluded that ascorbate and GSH
were the primary NO2 absorption substrates in rat ELF. Postlethwait et al. (1995) also found that the
pulmonary surfactant, dipalmitoyl phosphatidylcholine, was not an effective substrate for NO2 absorption.
Later, Connor et al. (2001) suggested that dipalmitoyl phosphatidylcholine may actually inhibit NO2
absorption.
In Vitro
In a subsequent study, Velsor and Postlethwait (1997) investigated the mechanisms of acute
epithelial injury from NO2 exposure. The impetus for this work was to evaluate the supposition that NO2
reaction products rather than NO2 itself caused epithelial injury. Red blood cell membranes were
immobilized to the bottom of Petri dishes, covered with a variety of well characterized aqueous layers,
and exposed to gaseous NO2 (10 ppm for 20 min). The study focused on the potential roles of GSH and
ascorbic acid reaction products in mediating cellular injury. Based on negligible membrane oxidation
when covered with only an aqueous phosphate buffer, the diffusive/reactive resistance of a thin aqueous
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layer clearly prevented direct interaction between NO2 and the underlying membrane. The presence of
unsaturated fatty acids was not observed to affect NO2 absorption, but a sufficiently thin liquid layer was
required for membrane oxidation to occur. Interestingly, membrane oxidation was not a simple monotonic
function of GSH and ascorbic acid levels. The maximal levels of membrane oxidation were observed at
low antioxidant levels versus null or high antioxidant levels. Glutathione and ascorbic acid related
membrane oxidation were superoxide and hydrogen peroxide dependent, respectively. The authors
suggested that at the higher antioxidant concentrations, there was increased absorption of NO2, but little
secondary oxidation of the membrane because the reactive species (e.g., superoxide and hydrogen
peroxide) generated during absorption were quenched. At the low antioxidant concentrations, there was a
lower rate of NO2 absorption, but oxidants were not quenched and so were available to interact with the
cell membrane.
Humans (In Vivo)
Kelly et al. (1996a) examined the effect of a 4-h NO2 (2 ppm) exposure on antioxidant levels in
bronchial lavage fluid (BLF) and BALF of 44 healthy nonsmoking adults (19-45 year, median 24 years).
Subjects were randomly assigned to three groups and lavaged at either 1.5-h (n = 15), 6-h (n = 15), or
24-h (n = 14) after the NO2 exposure. The baseline concentrations of uric acid and ascorbic acid were
strongly correlated between the BLF and BALF within individuals (r = 0.88, p < 0.001; r = 0.78, p =
0.001; respectively), whereas the concentrations of GSH in the BLF and BALF were not correlated. Uric
acid levels in both lavage fractions were significantly reduced at 1.5-h (p < 0.04), significantly increased
at 6-h (p < 0.05), and back to baseline at 24-h postexposure. A statistically significant loss of ascorbic
acid was also found in both lavage fractions at 1.5-h (p < 0.05). At 6 and 24-h postexposure, the ascorbic
acid levels had returned to baseline. In contrast, GSH levels were significantly increased at both 1.5-h
(p < 0.01) and 6-h (p < 0.03) in BLF. At 24 h postexposure, the GSH levels in BLF returned to baseline.
Although GSH in BLF increased at 1.5 and 6 h postexposure, oxidized GSH levels remained similar to
baseline in both BLF and BALF. No changes in BALF levels of GSH were observed at any time point.
Humans (Ex Vivo)
The depletion of uric acid and ascorbic acid, but not GSH has also been observed with ex vivo
exposure of human BALF to NO2. Kelly et al. (1996b) collected BALF from male lung cancer patients (n
= 16) and exposed the BALF ex vivo at 37°C to NO2 (0.05 to 2.0 ppm; 4 h) or O3 (0.05 to 1.0 ppm; 4 h).
Kelly and Tetley (1997) also collected BALF from lung cancer patients (n = 12, 54 ± 16 years) and
exposed the BALF ex vivo to NO2 (0.05 to 1.0 ppm; 4 h). Both studies found that NO2 depletes uric acid
and ascorbic acid, but not GSH from BALF. Kelly et al. (1996b) noted a differential consumption of the
antioxidants with uric acid loss being greater than that of ascorbic acid which was lost at a much greater
rate than GSH. Kelly and Tetley (1997) found that the rates of uric acid and ascorbic acid consumption
were correlated with their initial concentrations in the BAL fluid, such that higher initial antioxidant
concentrations were associated with a greater rate of antioxidant depletion. Illustrating the complex
interaction of antioxidants, these studies also suggest that GSH oxidized by NO2 may be again reduced by
uric acid and/or ascorbic acid.
AX4.2.2. Regional and Total Respiratory Absorption of N02
There has been very limited work related to the quantification of NO2 uptake since the 1993 NOX
AQCD. As a result, there is an abbreviated discussion here of some papers that were previously reviewed.
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AX4.2.2.1. Dosimetry Models
There is a paucity of theoretical studies investigating NO2 dosimetry. Like O3, NO2 is highly
reactive in ELF and is not very soluble. An O3 model has been utilized to predict the uptake of NO2 in the
lower respiratory tract of humans, rats, guinea pigs, and rabbits (Miller et al., 1982; Overton, 1984). In
this model, there was a strong distinction between uptake and dose. Uptake referred to the amount of NO2
being removed from gas phase per lung surface area ((ig/cm2), whereas, dose referred to the amount of
NO2 per lung surface area ((ig/cm2) that diffused through the ELF and reached the underlying tissues.
These investigators assessed NO2 uptake and dose on a breath by breath basis. Miller et al. (1988)
provided uptake and dose rates ((ig/cm2-min) for O3 in the same species.
Miller et al. (1982) and subsequently Overton (1984) did not attempt to predict the amount of
reactants in the ELF or the transport of reactants to the tissues. Rather, they focused mainly on the
sensitivity of NO2 tissue dose on NO2 reaction rates in the ELF and the Henry's law constant. Reaction
rates of NO2 in the ELF were varied from zero, 50%, and 100% of the reaction rate for O3 in ELF. The
Henry's law constant was varied from half to double the Henry's law constant for NO2 in water at 37 °C.
Effects of species, lung morphology, and tidal volume (VT) were also examined. In general, the model
predicted that NO2 is taken up throughout the lower respiratory tract. In humans, NO2 uptake was fairly
constant from the trachea to the terminal bronchioles, beyond which uptake decreased with distal
progression. This pattern of NO2 uptake predicted for humans is very similar to the pattern of O3 uptake
per unit time predicted for humans, rats, rabbits, and guinea pigs by Miller et al. (1988). Thus, it is
reasonable to expect that the pattern NO2 uptake per unit time will also be similar between these species.
The NO2 tissue dose was highly dependent on the Henry's law constant and reaction rate in the ELF. In
the conducting airways, the NO2 tissue dose decreased as the Henry's law constant increased (i.e.,
decreased gas solubility), whereas the NO2 tissue dose in the alveolar region increased. The site of
maximal NO2 tissue dose was fairly similar between species, ranging from the first generation of
respiratory bronchioles in humans to the alveolar ducts in rats. In guinea pigs and rabbits, the maximal
NO2 tissue dose was predicted to occur in the last generation of respiratory bronchioles. Based on Miller
et al. (1988), the dose rate of NO2 is also expected to be similar between species. The simulations also
showed that exercise increases the NO2 tissue dose in the pulmonary region relative to rest. Miller et al.
(1982) also reported that increasing the NO2 reaction rate decreased NO2 tissue dose in the conducting
airways, but had no effect on the dose delivered to the pulmonary region.
Simultaneously occurring diffusion and chemical reactions in the ELF have been suggested as the
limiting factors in O3 (Santiago et al., 2001) and NO2 uptake (Postlethwait and Bidani, 1990). Hence,
Miller et al. (1982) should have found an increase in the uptake of NO2 in the conducting airways with
increasing the rate of chemical reactions in the ELF. This increase in NO2 uptake in the conducting
airways would then lead to a reduction in the amount of NO2 reaching and taken up in the pulmonary
region. The Miller et al. (1982) model considered reactions of NO2 with constituents in the ELF as
protective in that these reactions reduced the flux of NO2 to the tissues. Others have postulated that NO2"
reactants formed in the ELF, rather than NO2 itself, could actually cause adverse responses (Overton,
1984; Postlethwait and Bidani, 1994; Velsor and Postlethwait, 1997).
Overton and Graham (1995) examined NO2 uptake in an asymmetric anatomic model of the rat
lung. The multiple path model of Overton and Graham (1995) allowed for variable path lengths from the
trachea to the terminal bronchioles, whereas Miller et al. (1982) used a single or typical path model of the
conducting airways. The terms "dose" and "uptake" were used synonymously to describe the amount of
NO2 gas lost from the gas phase in a particular lung region or generation by Overton and Graham (1995).
Reactions of NO2 in the ELF were not explicitly considered. Their simulations were conducted for rats
breathing at 2 mL VT at a frequency of 150 breaths per minute. The mass transfer coefficients of 0.173,
0.026, and 0.137 cm/sec were assumed for the upper respiratory tract, the tracheobronchial airways, and
the pulmonary region, respectively. Uptake was predicted to decrease with distal progression into the
lung. In general, the modeled NO2 dose varied among anatomically equivalent ventilatory units as a
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function of path length from the trachea with shorter paths showing greater dose. A sudden increase in
NO2 uptake was predicted in the proximal alveolar region (PAR) which was due to the increase in the
assumed mass transfer coefficient relative to the adjacent terminal bronchiole. Overton et al. (1996)
showed that increasing the mass transfer coefficient of the tracheobronchial airways decreased the dose to
the PAR and vice versa. Additionally, the PAR dose would also be reduced by the more realistic
modeling of tracheobronchial airways expansion during inspiration versus the static condition employed
by Overton and Graham (1995).
More recently, two studies examined the influence of age on reactive gas dosimetry in humans
(Ginsberg et al., 2005; Sarangapani et al., 2003). Both studies specifically considered the dosimetry O3
during light activity (on average) in their analysis. It is assumed here that their general findings should
also be applicable to NO2. Sarangapani et al. (2003) used a physiologically based pharmacokinetic model
and found that regional uptake of O3 is relatively insensitive to age (range: infants to elderly). Ozone
uptake per unit surface area was 2- to 8-fold higher in infants compared to adults. However, this finding
(i.e., uptake per unit surface area) is a less informative expression of dose than the rate of uptake per unit
surface area. The rate of uptake, obtained by multiplying by the ventilation rate, adjusts for the greater
rate of gas intake by adults relative to children. Ginsberg et al. (2005) utilized the U.S. EPA (1994)
reference concentration methodology and found no effect of age (infants vs. adults) on the uptake rate of
O3 per unit surface area.
In summary, these modeling studies predict that the net NO2 dose (NO2 flux to air-liquid interface)
is relatively constant from the trachea to the terminal bronchioles and then rapidly decreases in the
pulmonary region. The pattern of net NO2 dose rate or uptake rate is expected to be similar between
species and unaffected by age in humans. The predicted tissue dose and dose rate of NO2 (NO2 flux to
liquid-tissue interface) is low in the trachea, increases to a maximum in the terminal bronchioles and the
first generation of the pulmonary region, and then decreases rapidly with distal progression. The site of
maximal NO2 tissue dose is predicted to be fairly similar between species, ranging from the first
generation of respiratory bronchioles in humans to the alveolar ducts in rats. The production of toxic
NO2-reactants in the ELF and the movement of the reactants to the tissues have not been modeled.
AX4.3. Experimental Studies of N02 Uptake
AX4.3.1. Upper Respiratory Tract Absorption
The nasal uptake of NO2 has been experimentally measured in dogs, rabbits, and rats under
conditions of unidirectional flow. Yokoyama (1968) reported 42.1 ± 14.9% (Mean ± SD) uptake of NO2
in the isolated nasal passages of two dogs (3.5 L/min) and three rabbits (0.75 L/min) exposed to 4 and
41 ppm NO2. Uptake did not appear to depend on the exposure concentration and was relatively constant
over a 10 to 15 min period. Cavanagh and Morris (1987) measured uptakes of 28% and 25% uptake of
NO2 (40.4 ppm) in the noses of four naive and four previously exposed rats (0.10 L/min), respectively.
Uptake was not affected by a 4-h prior exposure (naive versus previously exposed rats) to 40.4 ppm NO2
and was constant over the 24-min period during which uptake was determined.
Kleinman and Mautz (1991) measured the penetration of NO2 through the upper airways during
inhalation in six tracheotomized dogs exposed to 1.0 or 5.0 ppm NO2. Uptake in the nasal passages was
significantly greater at 1.0 ppm than at 5.0 ppm, although the magnitude of this difference was not
reported. The mean uptake of NO2 (1.0 ppm) in the nasal passages decreased from 55% to 40% as the
ventilation rate increased from about 2 to 8 L/min. During oral breathing, uptake was not dependent on
concentration. The mean oral uptake of NO2 (1.0 and 5.0 ppm) decreased from 65% to 30% as the
ventilation rate increased from 2 to 8 L/min.
4-8
-------
AX4.3.2. Lower Respiratory Tract Absorption
Postlethwait and Mustafa (1989) investigated the effect of exposure concentration and breathing
frequency on the uptake of NO2 in isolated perfused rat lungs. To evaluate the effect of exposure
concentration, the lungs were exposed to NO2 (4 to 20 ppm) while ventilated at 50 breaths/min with a VT
of 2.0 mL. To examine the effect of breathing frequency, the lungs were exposed to NO2 (5 ppm) while
ventilated at 30-90 breaths/min with a VT of 1.5 mL. All exposures were for 90 min. The uptake of NO2
ranged from 59 to 72% with an average of 65% and was not affected by exposure concentration or
breathing frequency. A combined regression showed a linear relationship between NO2 uptake and total
inspired dose (25 to 330 (ig NO2). Illustrating variability in NO2 uptake measurements, Postlethwait and
Mustafa (1989) observed 59% NO2 uptake in lungs ventilated at 30 breaths/min with a VT of 1.5 mL,
whereas, Postlethwait and Mustafa (1981) measured 35% NO2 uptake for the same breathing condition. In
another study, 73% uptake of NO2 was reported for rat lungs ventilated 50 breaths/min with a VT of 2.3
mL (Postlethwait et al., 1992). It should be noted that typical breathing frequencies are around 80, 100,
and 160 breaths/min for rats during sleep, rest, and light exercise, respectively (Winter-Sorkina and
Cassee, 2002). Hence, the breathing frequencies at which NO2 uptake has been measured are lower than
for rats breathing normally.
In addition to measuring upper respiratory tract uptakes, Kleinman and Mautz (1991) also measured
NO2 uptake in the dog lung. In general, there was about 90% NO2 uptake in the lungs which was
independent of ventilation rates from 3 to 16 L/min.
AX4.3.3. Total Respiratory Tract Absorption
Bauer et al. (1986) measured the uptake of NO2 (0.3 ppm) in 15 adult asthmatics exposed for 30
min (20 min at rest, then 10 min exercising on a bicycle ergometer) via a mouthpiece during rest and
exercise. There was a statistically significant increase in uptake from 72% during rest to 87% during
exercise. The minute ventilation also increased from 8.1 L/min during rest to 30.4 L/min during exercise.
Hence, exercise increased the uptake exposure rate of NO2 by 5-fold in these subjects. In an earlier study
of seven healthy adults in which subjects were exposed to a nitric oxide (NO)/NO2 mixture containing
0.29 to 7.2 ppm NO2 for brief (but unspecified) periods, Wagner (1970) reported that NO2 uptake
increased from 80% during normal respiration (VT, 0.4 L) to 90% during maximal respiration (VT, 2 to
4L).
Kleinman and Mautz (1991) also measured the total respiratory tract uptake of NO2 (5 ppm) in
female beagle dogs while standing at rest or exercising on a treadmill. The dogs breathed through a small
face mask. Total respiratory tract uptake of NO2 was 78% during rest and increased to 94% during
exercise. In large part, this increase in uptake may be due to the increase in VT from 0.18 L during rest to
0.27 L during exercise. Coupled with an increase in minute ventilation from 3.8 L/min during rest to 10.5
L/min during exercise, the uptake rate of NO2 was 3-fold greater for the dogs during exercise than rest.
AX4.4. Metabolism, Distribution and Elimination of N02
As stated earlier, NO2 absorption is coupled with nitrous acid (HNO2) formation, which
subsequently dissociates to fT and nitrite (NO2~). Nitrite enters the underlying epithelial cells and
subsequently the blood. In the presence of red blood cells and possibly involving oxyhemoglobin, nitrite
is oxidized to nitrate (NO3~) (Postlethwait and Mustafa, 1981). Nitrate may subsequently be excreted in
the urine. There has been concern that inhaled NO2 may lead to N-nitrosamine production, many of which
are carcinogenic, since NO2 can produce nitrite and nitrate (in blood). Nitrate can be converted to nitrite
by bacterial reduction in saliva, the gastrointestinal tract, and the urinary bladder. Nitrite has been found
4-9
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to react with secondary amines to form N-nitrosamines. This remains speculative since nitrosamines are
not detected in tissues of animals exposed by inhalation to NO2 unless precursors to nitrosamines and/or
inhibitors of nitrosamine metabolism are co-administered. Rubenchik et al. (1995) could not detect N-
nitrosodimethylamine (NDMA) in tissues of mice exposed to 4 to 4.5 ppm NO2 for 1 h. However, NDMA
was found in tissues if mice were simultaneously given oral doses of amidopyrine and 4-methylpyrazole,
an inhibitor of NDMA metabolism. Nevertheless, the main source of NO2 in the body is formed
endogenously, and food is also a contributing source of nitrite from the conversion of nitrates. Thus, the
relative importance of inhaled NO2" to N-nitrosamine formation has yet to be demonstrated.
Metabolism of inhaled NO2 may also transform other chemicals that may be present in the body, in
some cases into mutagens and carcinogens. Van Stee et al. (1983) reported N-nitrosomorpholine
(NMOR), production in mice gavaged with 1 g of morpholine/kg body weight per day and then exposed
(5-6 h daily for 5 days) to 16.5 to 20.5 ppm NO2. N-nitrosomorpholine is a nitrosamine that is a potent
animal carcinogen. The single site containing the greatest amount of NMOR was the gastrointestinal tract.
Later, Van Stee et al. (1995) exposed mice to approximately 20 ppm 15NO2 and to 1 g/kg morpholine
simultaneously. N-nitrosomorpholine was found in the body of the exposed mice. Ninety-eight point four
percent was labeled with 15N that was derived from the inhaled 15NO2 and 1.6% was derived presumably
from endogenous sources.
Inhaled NO2 may also be involved in the production of mutagenic (and carcinogenic) nitro
derivatives of other co-exposed compounds, such as PAHs, via nitration reactions. Miyanishi et al. (1996)
co-exposed rats, mice, guinea pigs and hamsters to 20 ppm NO2 with various PAHs (pyrene,
fluoranthene, fluorene, anthracene, or chrysene). Nitro derivatives of these PAHs were excreted in the
urine of these animals, which were found to be highly mutagenic in the Ames/X typhimurium assay.
Specifically, the nitrated metabolite of pyrene (l-nitro-6/8-hydroxypyrene and l-nitro-3hydroxypyrene)
was detected in the urine. Further studies indicated that these metabolites are nitrated by an ionic reaction
in vivo after the hydroxylation of pyrene in the liver.
AX4.5. Extra-Pulmonary Effects of N02 and NO
Exposure to NO2 produces a wide array of health effects beyond the confines of the lung. Thus,
NO2 and/or some of its reactive products penetrate the lung or nasal epithelial and endothelial layers to
enter the blood and produce alteration in blood and various other organs. Effects on the systemic immune
system were discussed above and the summary of other systemic effects is quite brief because the
literature suggests that effects on the respiratory tract and immune response are of greatest concern. A
more detailed discussion of extrapulmonary responses can be found in U.S. Environmental Protection
Agency (1993).
AX4.5.1. Body Weight, Hepatic, Renal, and Miscellaneous Effects
Conflicting results have been reported on whether NO2 affects body weight gain in experimental
animals as a general indicator of toxicity (U.S. Environmental Protection Agency, 1993). More recent
subchronic studies show no significant effects on body weight in rats, guinea pigs, and rabbits exposed up
to 4 ppm NO2 (Tepper et al., 1993; Douglas et al., 1994; Fujimaki and Nohara, 1994).
Effects on the liver, such as changes in serum chemistry and xenobiotic metabolism, have been
reported by various investigators to result from exposure to NO2 (U.S. Environmental Protection Agency
1993). Drozdz et al. (1976) found decreased total liver protein and sialic acid, but increased protein-bound
hexoses in guinea pigs exposed to 1 ppm NO2, 8 h/day for 180 days. Liver alanine and aspartate
aminotransferase activity was increased in the mitochondrial fraction but decreased in the cytoplasmic
4-10
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fraction of the liver. Electron micrographs of the liver showed intracellular edema and inflammatory and
parenchymal degenerative changes.
No new studies on liver effects were located in the literature since the 1993 NOX AQCD. Several
older studies have shown changes in kidney function and xenobiotic metabolism in animals following
NO2, although no histopathological changes were reported.
Increases in urinary protein and specific gravity of the urine were reported by Sherwin and Layfield
(1974) in guinea pigs exposed continuously to 0.5 ppm NO2 for 14 days. Proteinuria (albumin and alpha-,
beta-, and gamma-globulins) was found in another group of animals exposed to 0.4 ppm NO2 for 4 h/day.
However, differences in water consumption or in the histology of the kidney were not found. No new
studies were located in the literature since the 1993 NOX AQCD.
Four studies (Table AX4.9) consider hematologic parameters. Several additional studies report on
iron, enzymes and nucleic acid (Table AX4.10).
AX4.5.2. Brain Effects
There are several studies suggesting that NO2 affects the brain. Decreased activity of protein
metabolizing enzymes, increased glycolytic enzymes, changes in neurotransmitter levels (5-HT and
noradrenaline), and increased lipid peroxidation, accompanied by lipid profile and antioxidant changes,
have been reported (Farahani and Hasan, 1990, 1991, 1992; Sherwin et al., 1986; Drozdz et al, 1975).
The U.S. Environmental Protection Agency (1993) concluded that "none of these effects have been
replicated and all reports lack sufficient methodological rigor; thus, the implications of these findings,
albeit important, are not clear and require further investigation".
A developmental neurotoxicity study by Tabacova et al.(1985) suggest that in utero exposure to
NO2 may result in postnatal neurobehavioral development changes as described in the section on
reproductive and developmental toxicology.
AX4.5.3. NO
The genotoxicity of NO has been studied both in vitro and in vivo (Arroyo et al., 1992; Nguyen
et al., 1992) (see Tables AX4.11-4.13). Overall, the synthesis of these older studies suggests that NO has
some genotoxic potential; however, the effect is slight and to a lesser extent when compared to NO2.
AX4.5.4. Effects of Mixtures Containing N02
Humans are generally exposed to NO2 in a mixture with other air pollutants. A limitation of animal
toxicity studies is the extrapolation of concentration-response data from controlled exposures to NO2
alone, to air pollutant mixtures that are typically found in the environment. It is difficult to predict the
effects of NO2 in a mixture based on the effects of NO2 alone. In order to understand how NO2 is affected
by mixtures of other air pollutants, studies are typically conducted with mixtures containing NO2 and one
or two other air pollutants, such as O3 and/or H2SO4. The result of exposure to two or more pollutants
may be simply the sum of the responses to individual pollutants (additivity), may be greater than the sum
of the individual responses, suggesting some type of interaction or augmentation of the response
(synergism) or may be less than additive (antagonism).
Animal toxicity studies have shown an array of interactions, including no interaction, additivity or
synergism. Because no clear understanding of NO2 interactions has yet emerged from this database, only
a brief overview is provided here. A more substantive review can be found in U.S. Environmental
Protection Agency (1993). There were animal studies, which studied the effects of ambient air mixtures
containing NO2 or gasoline or diesel combustion exhausts containing NOX. Generally these studies
provided useful information on the mixtures, but lacked NO2-only groups, making it impossible to discern
4-11
-------
the influence of NO2. Therefore, this class of research is not described here, but is reviewed elsewhere
(U.S. Environmental Protection Agency, 1993).
AX4.5.5. Simple Mixtures Containing N02
Most of the interaction studies involved NO2 and O3. After subchronic exposure, lung morphology
studies did not show any interaction of NO2 with O3 (Freeman et al., 1974) or with SO2 (Azoulay et al.,
1980). Some biochemical responses to NO2 plus O3 display no positive interaction or synergism. For
example, Mustafa et al. (1984) found synergism for some endpoints (e.g., increased activities of O2
consumption and antioxidant enzymes), but no interaction for others (e.g., DNA or protein content) in rats
exposed for 7 days. Ichinose and Sagai (1989) observed a species dependence in regard to the interaction
of O3 (0.4 ppm) and NO2 (0.4 ppm) after 2 weeks of exposure. Guinea pigs, but not rats, had a synergistic
increase in lung lipid peroxides. Rats, but not guinea pigs, had synergistic increases in antioxidant factors
(e.g., non-protein thiols, vitamin C, glucose-6-phosphate dehydrogenase, GSH peroxidase). Duration of
exposure can have an impact. Schlesinger et al. (1990) observed a synergistic increase in prostaglandin E2
and F2a in the lung lavage of rabbits exposed acutely for 2 h to 3.0 ppm NO2 plus 0.3 ppm O3; the
response appeared to have been driven by O3. However, with 7 or 14 days of repeated 2-h exposures, only
prostaglandin E2 was decreased and appeared to have been driven by NO2; there was no synergism
(Schlesinger et al., 1991).
Using an infectivity model, Ehrlich et al. (1977) found additivity after acute exposure to mixtures
of NO2 and O3 and synergism after subchronic exposures. Exposure scenarios involving NO2 and O3 have
also been performed using a continuous baseline exposure to one concentration or mixture, with
superimposed short-term peaks to a higher level (Ehrlich et al., 1979; Gardner, 1980, 1982; Graham et al.,
1987). Differences in the pattern and concentrations of the exposure are responsible for the increased
susceptibility to pulmonary infection, without indicating clearly the mechanism controlling the
interaction.
Some aerosols may potentiate response to NO2 by producing local changes in the lungs that
enhance the toxic action of co-inhaled NO2. The impacts of NO2 and H2SO4 on lung host defenses have
been examined by Schlesinger and Gearhart (1987) and Schlesinger (1987). In the former study, rabbits
were exposed for 2 h/day for 14 days to either 0.3 ppm or 1.0 ppm NO2, or 500 (ig/m3 H2SO4 alone, or to
mixtures of the low and high NO2 concentrations with H2SO4. Exposure to either concentration of NO2
accelerated alveolar clearance, whereas H2SO4 alone retarded clearance. Exposure to either concentration
of NO2 with H2SO4 resulted in retardation of clearance in a similar manner to that seen with H2SO4 alone.
Using a similar exposure design but different endpoints, exposure of rabbits to 1.0 ppm NO2 increased the
numbers of PMNs in lavage fluid at all time points (not seen with either pollutant alone), and increased
phagocytic capacity of AMs after two or six exposures (Schlesinger et al., 1987). Exposure to 0.3 ppm
NO2 with acid, however, resulted in depressed phagocytic capacity and mobility. The NO2/H2SO4 mixture
was generally either additive or synergistic, depending on the specific cellular endpoint being examined.
Exposure to high levels of NO2 (#5.0 ppm) with very high concentrations of H2SO4 (1 mg/m3)
caused a synergistic increase in collagen synthesis rate and protein content of the lavage fluid of rats (Last
and Warren, 1987; Last, 1989).
AX4.5.6. Complex Mixtures Containing N02
Although many studies have examined the response to NO2 with only one additional pollutant, the
atmosphere in most environments is a complex mixture of more than two materials. A number of studies
have attempted to examine the effects of multi-component atmospheres containing NO2, but as mentioned
before, in many cases the exact role of NO2 in the observed responses is not always clear. One study by
Stara et al. (1980) deserves mention because pulmonary function changes appeared to progress after
exposure ceased.
4-12
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In the study by Stara et al. (1980), dogs were exposed for 68 months (16 h/day) to raw or
photochemically reactive vehicle exhaust which included mixtures of NOX: one with a high NO2 level and
a low NO level (0.64 ppm, NO2; 0.25 ppm, NO), and one with a low NO2 level and a high NO level
(0.14 ppm, NO2; 1.67 ppm, NO). At the end of exposure, the animals were maintained for about 3 years
in normal indoor air. Numerous pulmonary functions, hematological and histological endpoints were
examined at various times during and after exposure. The lack of an NO2-only or NO-only group
precludes determination of the nature of the interaction. Nevertheless, the main findings are of interest.
Pulmonary function changes appeared to progress after exposure ceased. Dogs in the high NO2 group had
morphological changes considered to be analogous to human centrilobular emphysema. Because these
morphological measurements were made after a 2.5- to 3-year holding period in clean air, it cannot be
determined with certainty whether these disease processes abated or progressed during this time. This
study suggests progression of damage after exposure ends.
Table AX4.1.
Oxidant and antioxidant homeostasis.
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
2.0
4.0
10.0
EXPOSURE
Continuous,
9 and 18 mos
2 wks
Continuous,
4 mos
Continuous,
1 .5 yrs
Continuous,
17 mos
4 h/day,
6 days
Continuous,
4 days
Continuous,
3 days
3 days
1 4 days
10 days
7 days
GENDER
M
NR
M
F
F
NR
M
M
M/F
M
AGE
8 wks
NR
13 wks
NR
4 wks
NR
8 wks
12 wks
5->60 days
12-24 wks
SPECIES
(STRAIN)
Rat (Wistar)
Rat
Guinea Pig
Rat (Wistar)
Mouse
(NR)
Mouse (C57B1/6J)
Rat
(Sprague-Dawley)
Rat
(Sprague-Dawley)
Rat
(Sprague-Dawley)
Rat (Wistar) Guinea
pig (Dunkin Hartley)
Rat (Wistar)
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 TBA 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.
G-6-P dehydrogenase increased at >2 ppm; at
2 ppm, 14 days of exposure needed
REFERENCES
Sagaietal. (1984)
Ichinose et al.
(1983)
Ichinose and
Sagai (1989)
Ichinose and
Sagai
(1982)
Csallany (1975)
Ayaz and Csallany
(1978)
Thomas et al.
(1967)
Chowetal. (1974)
Lee etal. (1989,
1990)
Azoulay-Dupuis
etal. (1983)
Mochitate et al.
(1985)
4-13
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ppm
3.0
9.5
3.0
7.0
10
15
4.0
5.0
10.0
6.0
15
28
9.5
10.0
14.0
M = Ma
EXPOSURE
7 days
7 h/day,
5 days/wks,
6 mos
4 days
4 days
4 days
1-7 days
3h
Continuous,
24 h 7 days
4 h/day,
30 days
7 days
7 h/day,
5 days/wk,
24 mos
Continuous
3 days,
20 days
NR
GENDER
M/F
M
M
M/F
M
F
M
NR
NR
AGE
1 day to
>8 wks
In utero and
6 mos
NR
21-33yrs
NR
NR
18 wks
NR
NR
SPECIES
(STRAIN)
Rat
(Sprague-Dawley)
Rat (Fischer 344)
Rat (Sprague-
Dawley)
Human
Rats (CD Cobs)
Mouse
(NR)
Rat (Fischer 344)
Rat (Fischer 344)
Human
EFFECTS
Increased lipid peroxidation (TBA-reactive
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.
Increased lung weight, G-6-P dehydrogenase; and
GSH reductase activities.
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.
Increase in GSH reductase and G-6-P
dehydrogenase activities.
Increase in GSH levels, G-6-P dehydrogenase, and
GSH peroxidase activities.
Increase in GSH reductase activity in BAL.
Decreased GSH/GSSG ratio in blood and BAL fluid,
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 ((GCS and
GS) was decreased at both time points. (GT (redox
of GSH) mRNA expression was increased.
Rapid depletion of vitamin C, glutathione and
vitamin E
REFERENCES
Sevanian et al.
(1982)
Mauderly et al.
(1987)
Mustafa et al.
(1979)
Mohsenin (1991)
Pagani et al.
(1 994)
Csallany (1975)
Mauderly et al.,
(1 990)
Hochscheid et al.
(2005)
Halliwell et al.
(1992)
e
NPSHs = Nonprotein sulfhydryls
G-6-P dehydrogenase = Glucose-6-phosphate dehydrogenase
G6-P-G dehydrogenase = 6-phosphosgluconate dehydrogenase
SOD = superoxide dismutase
F = Female
NR = Not Reported
NADP = Nicotinamide-adenine dinucleotide phosphate (reduced form)
TEA = Thiobarbituric acid
BAL = Bronchoalveolar lavage
GR = Glutathione reductase
GS = Glutathione synthetase
GSH = Reduced glutathione
GSSG = Oxidized glutathione
(-GCS - (-Glutamyl-cystein synthetase
(-GT - (-Glutamyltranspeptidase
4-14
-------
Table AX4.2. Lung amino acids, proteins, lipids, and enzymes.
ppm
0.4
1.0
3.0
5.0
5.0
0.4
0.4
0.4
1 2
4.0
0.5
1.0
1.0
7.5
15
25
30
1.0
5.0
1.2
1.2
4.0
2.0
0.8
5
10
2.0
4.0
10
3.0
EXPOSURE
72 h
3h
Continuous, 1 wk
Continuous, 1 wk
1-14 wks
6 h/day,
5 days/wk,
4 wks
6 h/day, 2 days
7 h/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
GENDER
M
M
M
M
M
M/F
M
M
M
M
M/F
AGE
NR
NR
22-24 wks
NR
NR
14-1 6 wks
10 wks
NR
NR
12-24 wks
8 wks
SPECIES
(STRAIN)
Guinea Pig
(Hartley)
Guinea Pig
Rat (Wistar)
Rat (Fischer 344)
Rat (Fischer 344)
Rat (Fischer 344)
Rat (Wistar)
Guinea pig
Rat
(Sprague-Dawley)
Rats (Wistar)
Rat
(Sprague-Dawley)
EFFECTS
No effect at 0.4 ppm; increase in BAL protein
in vitamin C-depleted, but not normal, animals
at 1 .0 ppm 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.
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.
REFERENCES
Selgrade et al.
(1981)
Sherwin and
Carlson (1973)
Takahashi et al.
(1986)
Evans et al. (1989)
Evans et al. (1989)
Gregory et al.
(1983)
Mochitate et al.
(1984)
Sherwin and
Carlson (1 973)
Mulleretal. (1994)
Mochitate et al.
(1985)
Elsayed and
Mustafa (1982)
4-15
-------
ppm
3.6
7.2
10.8
14.4
4.0
10
4.0
10
25
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
10
10
EXPOSURE
24 h
12 h
8h
6h
10 days
7 days
6 h/day
5 days/wk,
7, 14, and 21 days
16hrs
3h
8 h/day,
7 days
14-72 h
2 wks
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
24 h or 7 days
Continuous, 14 days
GENDER
M
M
M
M/F
M
M
F
M
NR
M
NR
M
F
M
M
M
M
AGE
10-1 2 wks
2 1-24 wks
NR
NR
NR
8 wks
NR
5 wks
NR
10-11 wks
NR
NR
NR
In utero
and 6 mos
18 wks
NR
8 wks
SPECIES
(STRAIN)
Rat
(Sprague-Dawley)
Rat (Wistar)
Rat (Wistar)
Guinea pig
(Hartley)
Guinea pig
(Hartley)
Mouse (Swiss
Webster)
Mouse (NR)
Rat
(Fischer 344)
Mice
Rat (Sprague-
Dawley)
Rabbit
(New Zealand)
Rat
(Sprague-Dawley)
Mouse (NR)
Rat
(Fischer 344)
Rat
(Fischer 344)
Rat (CD cobs)
Rat (Wistar)
EFFECTS
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.
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 peroxidaseA
Concentration-related increase in collagen
synthesis rate; 125% increase in rats exposed
to 5.0 ppm.
Benzo [a] pyrene hydroxylase activity of
tracheal 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.
Protein content of BALF 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.
REFERENCES
Gelzleichter et al.
(1992)
Mochitate et al.
(1984)
Hooftman et al.
(1988)
Hatch etal. (1986)
Hatch etal. (1986)
Mustafa et al.
(1984)
Csallany(1975)
Suzuki et al.
(1988)
Rose etal. (1989)
Last etal. (1983)
Palmer et al.
(1972)
Last & Warren
(1987)
Csallany(1975)
Mauderly et al.
(1987)
Mauderly et al.
(1990)
Pagani et al.
(1994)
Sagaietal. (1982)
4-16
-------
ppm
10
20
30
40
10
10
0.8
5 0
10
EXPOSURE
4h
24 h/day, 0 (control),
3 days or 20 days
24 h/day, for 0,3,
20, or 28 days
Presumably
continuous, 1 day or
3 days
GENDER
M
NR
M
M
AGE
NR
NR
NR
NR
SPECIES
(STRAIN)
Rat (Long Evans)
Rat (Fischer 344)
Rat (Sprague-
Dawley)
Rat (Sprague-
Dawley)
EFFECTS
Increased activities of various enzymes, sialic
acid, and BAL protein; attenuation by high
dietary levels of vitamin E.
LPO decreased in type II pneumocytes after 3
days compared to controls but remained
comparable to controls after 20 days. Authors
stated that LPO is a very early reaction during
oxidative stress and that the decrease after 3-
day exposure suggests an adaptation
mechanism.
Exposure duration-dependent, statistically
significant increase in GPx and GR enzyme
activities after 3 and 20 days over control
values. Changes in mRNA expression of GSH
synthesizing enzymes in type II pneumocytes
were also observed.
Uptake of surfactant-like liposomes by type II
pneumocytes in the presence or absence of
SP-A was faster and significantly higher in
cells from all NO2 exposed groups than in
control cells. No difference in the uptake
kinetics between cells from exposed groups of
different duration. Increase in liposome
uptake suggests NO2 exposure likely
disrupted cell membranes to allow liposomes
to enter the cells easily. Lipid uptake
associated with duration-dependent increase
in internalization of label found in PC fraction.
Suggests increased demand of PC in lung
injury.
Phospholipid component in BAL increased in
a concentration- and exposure duration-
dependent manner, with significance only at
10 ppm, but not at 5 ppm or below, for >1 day
of exposure. PC content in BAL of exposed
animal did not change significantly compared
to controls. At 10 ppm, but not at 5 ppm or
below, percentage of saturated PC decreased
and that of unsaturated PC increased
statistically significant, while significant
decreases in palmitic acid (16:0) and
increases in arachidonic acid (20:4) contained
in PC were observed. Sphingomyelin - as a
marker for an influx of serum into the alveolar
airspace - was also not changed by NO2
exposure.
Functional studies on surfactant phospholipid
extracts indicated increased values for the
surface tension at equilibrium, and for the
maximal and minimal surface tension of
animals exposed to >5 ppm, but not to
0.8 ppm. Suggests NO2 directly impaired
surface tension at >5 ppm. Structure of the
SP-A not altered by NO2 exposure.
Authors suggested exposure to NO2 impaired
surfactant components may be used as
markers of altered surfactant metabolism.
REFERENCES
Guth and Mavis
(1985, 1986)
Hochscheid et al.
(2005)
Mulleretal. (2003)
Mulleretal. (1994)
4-17
-------
ppm
5
10
EXPOSURE
24 h or 24 h/day for
7 days
GENDER
M
AGE
NR
SPECIES
(STRAIN)
Rat (CD Cobs)
EFFECTS
Concentration-dependent increase in a-1 PI
content. Exposure to 5 and 10 ppm NO2
significantly increased a-1 PI content only
after 24 h and returned to control values after
7 days at each exposure concentration. Since
blood GSH content was also increased
together with a-1 PI content at 24 h, authors
suggested the increase in these parameters
can be considered a prompt protective
response resulting in no further increase of a-
1 PI.
REFERENCES
Pagani et al.
(1994)
LPO = Lipid peroxidation
PC = Phosphatidylcholine
SP-A = Surfactant protein-AGPx = Glutathione peroxidase
GPx = Glutathione peroxidase
GR = Glutathione reductase
GSH = Glutathione
a-1 PI = a-1 proteinase inhibitor
Table AX4.3. Alveolar macrophages and lung host defense.
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
0.3
1.0
0.3
1.0
EXPOSURE
3 h base + three
15-min peaks
3h
1 h
Continuous,
24 wks
Continuous
base + 3-h peak,
5 days/wk,
24 wks
Continuous,
33 wks
Continuous
base + 1-h peak,
5 days/wk,
33 wks
2 h/day
2, 6, 13 days
2 h/day up to
1 4 days
GENDER
NR
NR
NR
NR
M
M
AGE
NR
NR
NR
Gestation
12 wks
NR
NR
NR
SPECIES
(STRAIN)
Human
Human
Rat
(Sprague-Dawley)
(in vitro)
Rat (Brown-
Norway)
Mouse
Rabbit
(New Zealand)
Rabbit
(New Zealand)
EFFECTS
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.
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.
At 5.0 ppm: increase in LTB4; concentration-related
decrease in SOD production in AMs at >1 .0 ppm;
increase in LDH in AMS at 5.0 and 20 ppm
Reactive oxygen species generation from alveolar
macrophages was significantly suppressed in NO2
exposed weanling animals; no changes in reactive
oxygen generating capability in the embryonic
exposed animals.
No effects on AM morphology at 0.5 ppm
continuous or 0.1 ppm base + peak.
After 21 wks of exposure to 2.0 ppm continuous or
0.5 ppm base + peak, morphological changes were
identified, such a loss of surface processes,
appearance offenestrae, bleb formation, and
denuded surface areas.
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.
Increase in alveolar clearance.
REFERENCE
Frampton et al.
(1989)
Frampton et al.
(1989)
Robinson et al.
(1990)
Kumae and
Arakawa (2006)
Aranyi et al.
(1976)
Schlesinger
(1987)
Schlesinger and
Gearhart (1987)
4-18
-------
ppm
0.3
1.0
3.0
10
1.0
10
0.5
0.5 base
+ 1.5
peak
2.0 base
+6.0
peak
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
2.0
10
2.0
EXPOSURE
2 h
2 h/day, 14 days
0.5, 1, 5 and
1 0 days
exposure
Base 22 h/day,
7 days/wk + two
1-h peaks, 5
days/wk, 6 wks
Continuous,
28 days
24 h/day, 12 wks
6 h/day, 2 days
24 h/day, 12 wks
7 h/day,
5 days/wks for
11 or 22
exposures
7 h/day,
5 days/wks
Base 7 h/day,
5 days/wks;
two 1 .5-h
peaks/day;
15 wks
NR ("acute")
3 days
8 h/day, 5
days/wk, 6 mo
GENDER
M
NR
M
M
NR
NR
M/F
F
M/F
M/F
AGE
NR
NR
1 day and
6 wks
6 wks
4-6 wks
NR
14-1 6 wks
NR
5, 10,21,
45, 55, 60,
and
>60 days
3-4 yrs
SPECIES
(STRAIN)
Rabbit
(New Zealand)
Rat (NR)
Rat (Fischer 344)
Rat (Wistar)
Guinea pig (NR)
Rat (NR)
Mouse (CD1)
Guinea pig (NR)
Rat (NR)
Rat (Long Evans)
Rat (Fischer 344)
Rat
(Sprague-Dawley)
Guinea pig
(Dunkin Hartley)
Rat (Wistar)
Baboon
EFFECTS
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.
Superoxide production in alveolar macrophages
from BALF, 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.
Trend towards increase in number of AMs and cell
volume in younger animals; increase in number of
AMs and cell volume in older rats.
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.
Newborns were less affected than adults when
AMs were tested for SOD levels.
Impaired AM responsiveness to migration inhibitory
factor.
REFERENCE
Vollmuth et al.
(1986)
Robinson et al.
(1993)
Crapo et al.
(1984)
Chang et al.
(1986)
Rombout et al.
(1986)
Fujimaki and
Nohara(1994)
Rose et al.
(1989)
Fujimaki and
Nohara, (1994)
Ferin and Leach
(1977)
Gregory et al.
(1983)
Amoruso et al.
(1981)
Azoulay-Dupuis
etal. (1983)
Green and
Schneider
(1978)
4-19
-------
ppm
2.0
2.7
3-6
3.6
12.1
4
10
25
4.0
4.0
8.0
5.0
5
15
5
10
15
5-60
7.0
9.5
10
10
10
10
25
EXPOSURE
4h
24 h
3h
1 h
2h
6h/day, 7, 14, or
21 days
10 days
Up to 10 days
7 days
3 h after infection
with
parainfluenza 3
virus
3h
3h
24 h
7 h/day; 5
days/wk;
18-22 mo
Continuous 7
days
35 days
4h
24 h
GENDER
NR
M
NR
F
M
NR
F
NR
M
Fb
NR
NR
M
NR
NR
F
M
AGE
NR
6wks
NR
NR
NR
19-23wks
NR
NR
NR
NR
NR
NR
18wks
NR
NR
NR
12-1 3 wks
SPECIES
(STRAIN)
Human
Rat (Wistar)
Dog (Beagle)
Rat (Sprague-
Dawley) (in vitro)
Rat (Wistar)
Rat (Fischer 344)
Mouse (CD-1)
Rabbit (New
Zealand)
Humans (in vitro
exposure)
Rabbit (New
Zealand)
Rabbit
Rat (Fischer 344)
Rat (NR)
Guinea pig
Mouse (Swiss)
Rat (Sprague-
Dawley)
EFFECTS
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.
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.
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.
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
Devlin et al.
(1992)
Rombout et al.
(1986)
Dowell et al.
(1971)
Goldstein et al.
(1977)
Hooftman et al.
(1988)
Mochitate et al.
(1986)
Suzuki et al.
(1986)
Lefkowitz et al.
(1986)
Acton and
Myrvik(1972)
Pinkston et al.
(1988)
Gardner et al.
(1969)
Acton and
Myrvik(1972)
Hadley et al.
(1977)
Mauderly et al.
(1990)
Pagani et al.
(1994)
Sherwin et al.
(1968)
Jakab (1988)
Katz and Laskin
(1976)
4-20
-------
ppm
EXPOSURE
GENDER
AGE
SPECIES
(STRAIN)
EFFECTS
REFERENCE
Clearance
3
g
10
20
6 h/day,
6 days/wk, for 2
wks
14 h/day, for 15
days, 20 or 25
days
F
M
NR
NR
Guinea Pig
Mouse (C57BL/5)
Significant, dose-dependent decrease in ciliary
activity, significant at 3 ppm (12%) and 9 ppm
(30%), and increase in eosinophil accumulation on
epithelium and submucosal layer.
20 ppm NO2 induced an increased mucus
production due to goblet cell hyperplasia in the
central airways.
Ohashi et al.
(1994)
Wegman and
Herz (2002)
Alveolar Macrophage Endpoints
0.5
0.2
0 5
2.0
8 h/day,
5 days/wk, for
0.5, 1,5, or 10
days
Continuous,
presumably
7 days/wk, up to
12 wks
M
F
NR
Neonates
or 5 wks
old
Rat (Sprague-
Dawley)
Rat (Brown-
Norway)
Acute depression of pulmonary arachidonate
metabolism observed. Unstimulated AM synthesis
of LTB4 depressed within 1 day and also on day 5
of exposure. Acute depression of AM synthesis of
TxB2, LBT4, and 5-HETE on stimulation by the
calcium ionophore, A23187, within 1 day of
exposure but not with longer exposure, while 5-
HETE increased significantly at 10 days of
exposure only. Suggests rapid depression of
cyclooxygenase and 5-lipoxygenase activities.
ZAS-stimulated LTB4 production delayed until
5 days and remained lower at 10 days. AM
superoxide production stimulated by PMAwas
rapidly and continuously depressed throughout the
study. BAL fluid levels of LTB4 and TxB2 paralleled
ex vivo depression of AM production.
Animals were exposed during embryonic or
weanling (5-wks old) period. ROS generation was
significantly suppressed at 0.5 and 2.0 ppm NO2 in
animals exposed during the weanling period.
Cytokine level measurement in AM culture
mediums indicated that inflammatory reactions
(significant increases in TFNa and IFNy) were
initiated at 8 wks and terminated at 12 wks in
animals exposed during the embryonic period while
inflammatory reactions (significantly increased
TFNa level) were not initiated at 8 wks but take
place at 12 wks in animals exposed as weanlings.
Results suggest that NO2 exposure from the
weanling period has stronger effects on AM activity.
Robinson et al.
(1993)
Kumae and
Arakawa (2006)
4-21
-------
Table AX4.4. Lung permeability and inflammation.
ppm
0.8
5
10
5
10
5
25
5
20
5
10
20
1.2
EXPOSURE
Presumably
continuous, 1 day
or 3 days
24 h or 24 h/day
for 7 days
6 h/day for 1 , 3, or
5 days
3h
24 h/day, for 3 or
25 days
24 h/day, for
3 days
GENDER
M
M
NR
M
M
M
AGE
NR
NR
NR
NR
NR
NR
SPECIES
(STRAIN)
Rat
(Sprague-
Dawley)
Rat (CD
Cobs)
Mouse
(C57BL/6)
Mouse
(BALB/c)
Rat
(Sprague-
Dawley)
Rat
(Sprague-
Dawley)
EFFECTS
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 10 ppm for >1 day of exposure.
Exposure induced inflammatory response in the lungs. At
10 ppm, influx of PMN, maximal at 24 h, but no influx
observed after 7 days of exposure, a trend that was also
observed with protein content in BAL fluid. In contrast, no
influx of macrophages was observed, but the influx was
maximal after 7 days of exposure. No significant changes in
lymphocyte counts at any exposure concentration and
protein content in BAL fluid not significantly affected at
5 ppm.
Protein content of BAL fluid increased significantly only after
24 h of exposure to 1 0 ppm NO2.
Exposure to 5 ppm NO2 did not cause any lung inflammation
or injury. Exposure to 25 ppm NO2 induced acute lung injury
(characterized by increases in protein, LDH, macrophages,
and neutrophils recovered by BAL) that peaked after 3 days,
lesions within terminal bronchioles, and AHR. OVA-
sensitized animals exposed to 25 ppm, but not 5 ppm, NO2
showed augmentation of eosinophilic inflammation and
terminal bronchiolar lesions, which extended significantly
into the alveoli. No increased expression of mucus cell-
associated gene products in non-sensitized or OVA-
sensitized animals exposed at any concentration.
Exposure of OVA-challenged animals to 20 ppm produced
BHR and caused significant increase in neutrophils and
fibronectin concentration, significant reduction in eosinophil
count, and exudation and release of IL-5 in BAL fluid 24 h
and/or 72 h after exposure. Exposure to 5 ppm did not
modify BHR, but significantly reduced pulmonary eosiniphilic
inflammation (reduced esinophilic count and eosinophil
peroxidase activity) and the production of IL-5 in the BAL
fluid. Exposure to NO2 did not cause any significant changes
in IgE anti-OVA antibody in exposed animals, while lgG1
liters were significantly increased in animals exposed only to
5 ppm NO2, compared to controls. There was no
development of mucosal metaplasia in any NO2 exposed
group compared to controls.
Exposure to NO2 exhibited concentration- and exposure
duration-dependent, and tissue localization-specific
differences in Clara cell proliferation. Increased proliferation
(measured as BrdU-LI) in both bronchial and bronchiolar
epithelium of all exposed groups, with significance starting at
exposure to 5 ppm for 3 or 25 days. Exposure to 5 and
10 ppm NO2 for 3 days showed significantly higher
proliferative activity in bronchiolar epithelium than in the
bronchial epithelium for corresponding exposure groups,
while the difference in proliferation between proximal and
distal airways diminished after 25 days of exposure. Clara
cell proliferation was not accompanied by a change in Clara
cell numbers in any of exposure groups, a finding the
authors explained by the assumption that the process of
proliferation takes place at the same rate as Clara cell
differentiates into other cells, a transition that leads to the
loss of phenotypic Clara properties in differentiating cells.
No significant differences in cell viability and percentages of
pulmonary AMs or PMNs between animals exposed to
1 .2 ppm NO2 and controls.
REFERENCE
Muller et al.
(1994)
Pagani et al.
(1994)
Poynter et al.
(2006)
Proust et al.
(2002)
Barth and Muller
(1999)
Bermudez
(2001)
4-22
-------
ppm
10
20
3.6
7.2
10.8
14.4
0.5
5
10
20
EXPOSURE
14 h/day, for
15 days, 20 or 25
days
24 h, 12h, 8h, and
6 h, respectively,
for 3 days, giving a
CHTof86.4ppm-
h
8 h/day, 5 days/wk,
for 0.5, 1,5, or 10
days
24 h/day, for 3 or
25 days
GENDER
M
M
M
M
AGE
NR
NR
NR
NR
SPECIES
(STRAIN)
Mouse
(C57BL/5)
Rat
(Sprague-
Dawley)
Rat
(Sprague-
Dawley)
Rat
(Sprague-
Dawley)
EFFECTS
Exposure to 10 ppm for 15 days -the only exposure
duration used for this concentration - did not significantly
affect influx of inflammatory cells (leukocyte subpopulations
and macrophages). 20 ppm NO2 induced airway and
parenchymal inflammation - dominated by a significant influx
of macrophages and neutrophils -, peaking at 1 5 days of
exposure and declining at day 25.
Significant cell proliferation (increased labeling index) in
peripheral airways compared to controls, regardless of
concentration or exposure duration. Suggests Haber's law (c
H t = k) was not followed over the concentration ranges
studied. No proliferative response was observed in alveolar
epithelium.
No effect on weight gain. No effects on neutrophil,
lymphocyte macrophage/monocyte levels or cell population
percentages in BAL. Suggests no significant influx of
inflammatory cells into lung airways and alveolar spaces.
Compared to controls, proliferative activity significantly
increased, but with no concentration-dependence in
respiratory bronchiolar epithelium at 5 ppm and above after
3-day exposure, but increase was concentration-dependent,
with significance at >10 ppm following 25-day exposure.
Proliferative activity increased in a concentration-dependent
manner in bronchial epithelium, with significance only at
20 ppm after 3 days of exposure and at >10 ppm following
25 days of exposure.
Concentration-dependent thickening of alveolar septa and
bronchiolar walls, significant only at >10 ppm after 3- and 25-
day exposure, but 5 ppm caused significant thickening only
in bronchiolar walls after 25 days, but not after 3 days, of
exposure compared to controls.
Statistically significant reduction in alveoli surface density at
>1 0 ppm after 25 days of exposure and at 20 ppm only after
3 days of exposure, while alveolar circumference increased
statistically significantly at >10 ppm after 25 days of
exposure and at 20 ppm following 3 days of exposure.
5 ppm did not significantly affect these endpoints after 3 or
25 days of exposure compared to controls. Data suggest
concentration- and exposure duration-dependent
development of emphysema, significant only at >10 ppm, but
not at 5 ppm, after 3 or 25 days of exposure. Alveolar duct
length increased significantly at >10 ppm after 25 days and
at 20 ppm after 3 days of exposure; no effect at 5 ppm.
Radial alveolar count values decreased significantly at
20 ppm after 3 and 25 days and at 10 ppm after 25 days
exposure; no effect at 5 ppm. Increase in avg medial
thickness at 10 ppm with significance only after 25 days of
exposure and at 20 ppm with significance after 3 and 25
days of exposure. In contrast, 5 ppm caused significant
increase at both 3 and 25 days of exposure. Authors
suggested effect at 5 ppm was due a reduction of pulmonary
arterial mass and not as a result of vasodilation induced by
nitrogen oxide.
REFERENCE
Wegman and
Herz (2002)
Rajini et al.
(1993)
Robinson et al.
(1993)
Barth et al.
(1994b)
4-23
-------
ppm
0,
1.0,
2.0,
4.0
0.2
0 5
2
EXPOSURE
24h/day, for 12
wks
Continuous,
presumably
7 days/wk, up to
12 wks
GENDER
M
F
AGE
10 wks
Neonates
or 5 wks
old
SPECIES
(STRAIN)
Rat (Wistar)
Guinea Pid
(Hartley)
Rat (Brown -
Norway)
EFFECTS
No change in body weight or absolute lung weight in any
exposed group compared to controls, but relative lung
weight was significantly increased in animals exposed to
4.0 ppm NO2. Number of lung cells from animals exposed to
>2.0 ppm significantly reduced (but extent of reduction at
4.0 ppm < 2.0 ppm), whereas number of mast cells not
significantly different in exposed animals compared to
controls. IgE-mediated histamine release from lung mast
cells significantly reduced at 2.0 ppm NO2 but not at 4.0 ppm
or 1 .0 ppm, but no difference observed in A231 87-stimulated
histamine release in lung mast cells in any exposed group
compared to controls. Results from histamine release
suggest that NO2 exposure to rat lung mast cells did not
induce histamine-releasing activity.
No change in body weight or absolute or relative lung weight
in any exposed group compared to controls. Number of lung
cells or mast cells was not significantly different in exposed
animals compared to controls. Increasing trend observed in
IgE-mediated histamine release from lung mast cells, the
change becoming significant only at 4.0 ppm. No significant
change in ionophore A2318-stimulated histamine release in
exposed groups compared to controls. Data suggest NO2
exposure in guinea pigs enhanced histamine-releasing
activity in lung mast cells.
Rats were exposed from embryonic or weanlings (5 wks old)
period up to 12 wks of age. Significantly decreased levels of
AM + Mo in weanling animals exposed to 0.5 and 2.0 ppm,
and significantly increased levels of neutrophils in animals
exposed to 2.0 ppm at 12 wks. Levels of AM + Mo
significantly increased in animals exposed to 0.5 ppm during
embryonic period while levels of neutrophil population
significantly decreased, compared to controls, at 12 wks.
[Changes in cell populations in BAL fluid not investigated in
animals exposed to 2.0 ppm during the embryonic period
due to loss of sample.] Mean level of lymphocytes
significantly increased in the embryonic group exposed to
0.2 ppm and in the weanling group exposed to 0.5 and
2.0 ppm, but increase not concentration-dependent in the
weanling group. Except for exposure to 0.2 ppm, NO2
exposure appeared to improve allergic conditions in the BAL
fluid of the embryonic group, but caused inflammatory
changes in the BAL fluid of the weanling group.
Data suggest NO2 exposure from the weanling period has
stronger effects on AM activity.
REFERENCE
Fujimaki and
Nohara(1994)
Kumae and
Arakawa (2006)
AHR = Airway hype (responsiveness
AM = Alveolar macrophage
BAL = Bronchoalveolar lavage
BHR = Bronchopulmonary hyperreactivity
BrdU-LI = Bromodeoxyuridine-laebling index
IgE = Immunoglobulin E
IgG = Immunoglobulin G
IL-5= lnterleukin-5
LDH = Lactate dehydrogenase
Mo = Monocytes
OVA = Ovalbumin
PMN = Polynorphonuclear neutrophil
4-24
-------
Table AX4.5. Immune responses.
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
0.5
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
8 h/day, 5 days/wk,
for 0.5, 1, 5, or
10 days
GENDER
NR
F
F
M
M
M
M
M
AGE
NR
6 wks
5 wks
6 wks
7 wks
10 wks
6 wks
NR
SPECIES
(STRAIN)
Mouse
Mouse
(AKR/cum)
Mouse
(AKR/cum)
Mouse
(C57BL/6J)
Mouse
(BALB/c)
Rat
(Fischer
344)
Mouse
/prj-1^
VUL-* ' )
Rat
(Sprague-
Dawley)
EFFECTS
Suppression of splenic T and B cell responsiveness to
mitogens variable and not related to concentration or
duration, except for the 940 pg/m3 continuous group, which
had a linear decrease in PHA-induced mitogenesis with
NO? 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 RFC response at > 752 pg/m3.
Increase in secondary RFC response at 3010 pg/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 lgG1 ,
IgM, and lgG2; after virus, serum IgA unchanged and IgM
increased.
Levels of TxB2, LTB4, and PGE2 in BAL fluid were
depressed within 4 h of exposure. Suggests acute
depression of pulmonary arachidonate metabolism
observed in BAL fluid. Unstimulated AM synthesis of LTB4
was depressed within 1 day and also on day 5 of exposure.
The AM synthesis of TxB2, LTB4, and 5-HETE on
stimulation by the calcium ionophore, A23187, was acutely
depressed within 1 day of exposure but not with longer
exposure, while 5-HETE was significantly increased at 10
days. Suggests rapid depression of cyclooxygenase and 5-
lipoxygenase activities. ZAS-stimulated LTB4 production
was delayed until 5 days and remained lower at 10 days.
BAL fluid levels of LTB4 and TxB2 paralleled ex vivo
depression of AM production. AM superoxide production
stimulated by PMA was rapidly and continuously depressed
throughout the study.
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)
Robinson et al.
(1993)
4-25
-------
ppm
5
5
20
4
4.76
EXPOSURE
3h
3h
2 h/day
4 h/day, 5 days/wk,
for 6 wks
(30 exposures,
total)
GENDER
F
M
MandF
M
AGE
7 wks
NR
From
birth
until 3
mos of
age
NR
SPECIES
(STRAIN)
Rat (Brown -
Norway)
Mouse
(BALB/c)
Rabbit
(NZW)
Guinea Pig
(Hartley)
EFFECTS
Rats were immunized intraperitoneally and challenged
intratracheally with house mite dust. Animals were exposed
to NO2 after sensitization or challenge or after sensitization
and challenge (double exposure). A single exposure after
sensitization or challenge caused a significant decrease in
antigen-specific IgG in BAL fluid, single exposure after
sensitization caused significant increase in serum IgE,
while exposure only after challenge caused significant
decrease in antigen-specific IgA levels in BAL fluid. Double
NO2 exposure caused significantly higher levels of antigen-
specific serum IgE and local IgA, IgG, and IgE antibody,
and significant increase in lymphocyte responsiveness to
antigen in the spleen and mediastinal lymph nodes. Double
exposure also significantly increased the ratio of
inflammatory cells to alveolar macrophages without
affecting the total number of lavageable cells.
Data suggest a 3-h exposure to 5 ppm NO2 after
intraperitoneal sensitization and pulmonary challenge with
house dust mite allergen was necessary to enhance
specific immune responses to the allergen and increased
the number of inflammatory cells in the lungs. Additionally,
data suggest an upregulation of specific immune responses
and subsequent immune-mediated pulmonary
inflammation.
Animals were sensitized and challenged with the antigen
OVA to generate airway inflammation before being exposed
to NO2.
Exposure of OVA-challenged animals to 20 ppm produced
BHR and caused significant increase in neutrophils and
fibronectin concentration, significant reduction in eosinophil
count, and exudation and release of IL-5 in broncholaveolar
fluid 24 h and/or 72 h after exposure. Exposure to 5 ppm
did not modify BHR, but significantly reduced pulmonary
eosiniphilic inflammation (reduced esinophilic count and
eosinophil peroxidase activity) and the production of IL-5 in
the BAL fluid. Authors suggested that potentiation of BHR
by 20 ppm NO2 in allergic mice may be accounted for by an
increased vascular/epithelial permeability, facilitating the
allergen availability and accelerating the inflammatory
process.
Exposure to NO2 did not cause any significant changes in
IgE anti-OVA antibody in exposed animals, while lgG1 liters
were significantly increased in animals exposed only to
5 ppm NO2, compared to controls. There was no
development of mucosal metaplasia in any NO2 exposed
group compared to controls.
No effect on mortality, health, behavior, body weight, or
basal pulmonary function (lung resistance, dynamic
compliance, respiration rates, tidal volume, and minute
volumes) in animals immunized against house mite dust
compared to littermates exposed to air. Immune parameters
were not evaluated.
Animals were intraperitoneally sensitized twice and then
challenged pulmonarily with C. albicans. Animals were
exposed, from the first day of sensitization, and throughout
the study period. Exposure to NO2 resulted in significantly
increased respiratory rate (tachypnea) (2.3-2.7times/s) 15
h after antigen challenge compared to controls, but did not
significantly affect the expiration/inspiration ratio. Authors
indicated that delayed-type dyspneic symptoms in this
study were increased by exposure to NO2.
REFERENCES
Gilmour et al.
(1996)
Proust et al.
(2002)
Douglas et al.
(1994)
Kitabatake et al.
(1995)
4-26
-------
ppm
0.06
0.5
1
2
4
1
2
4
5
25
EXPOSURE
24 h/day, for 6 or
12wks
24 h/day, for 12
wks
6 h/day for 1 , 3, or
5 days
GENDER
M
M
M
NR
AGE
NR
10 wks
10 wks
NR
SPECIES
(STRAIN)
Guinea Pig
(Hartley)
Rat (Wistar)
Guinea Pig
(Hartley)
Mouse
(C57BL/6)
EFFECTS
Concentration- and exposure duration-dependent increases
in airway responsiveness to inhaled histamine aerosol,
significant at 1.0 ppm and above in animals exposed for
12 wks and at 2 ppm and above in those exposed for 6
wks. No significant increase in specific airway resistance
(sRaw) values at any NO2 concentration at 6 wks of
exposure, but there was a concentration-dependent
increase in this parameter at 12 wks of exposure, with
significance at 2.0 ppm and above. Authors concluded NO2
could be a potent risk factor for alteration of pulmonary
function and airway responsiveness.
No change in body weight or absolute lung weight in any
exposed group compared to controls, but relative lung
weight significantly increased in animals exposed to
4.0 ppm NO2. Number of lung cells from animals exposed
to > 2.0 ppm significantly reduced (but extent of reduction
at 4.0 ppm < 2.0 ppm), but number of mast cells not
significantly different in exposed animals compared to
controls. IgE-mediated histamine release from lung mast
cells significantly reduced at 2.0 ppm NO2 but not at
4.0 ppm or 1.0 ppm, but no difference observed in A23187-
stimulated histamine release in lung mast cells in any
exposed group compared to controls. Results from
histamine release suggest that NO2 exposure to rat lung
mast cells did not induce histamine-releasing activity in
rats.
No change in body weight or absolute or relative lung
weight in any exposed group compared to controls. Number
of lung cells or mast cells not significantly different in
exposed animals compared to controls. Increasing trend
observed in IgE-mediated histamine release from lung mast
cells, the change becoming significant only at 4.0 ppm. No
significant change in ionophore A2318-stimulated histamine
release in exposed groups compared to controls. Data
suggest NO2 exposure in guinea pigs enhanced histamine-
releasing activity in lung mast cells. Thus, species
differences exist between the rat and guinea pig in
response to induction of histamine-releasing activity
following NO2 exposure.
Exposure to 25 ppm, but not 5 ppm, NO2 induced acute
lung injury (characterized by increases in protein, LDH,
macrophages, and neutrophils recovered by
bronchoalveolar lavage) that peaked after 3 days, lesions
within terminal bronchioles, and AHR. OVA-sensitized
animals exposed to 25 ppm, but not 5 ppm, NO2 showed
augmentation of eosinophilic inflammation and terminal
bronchiolar lesions, which extended significantly into the
alveoli. There was no increased expression of mucus cell-
associated gene products in non-sensitized or OVA-
sensitized animals exposed at any concentration.
REFERENCES
Kobayashi and
Miura(1995)
Fujimaki and
Nohara(1994)
Poynter et al.
(2006)
5-HETE = 5-Hydroxyeicosatrtraenoate
AM = Alveolar macrophage
AHR = AHR = Airway hyperresponsiveness
BAL = Bronchoalveolar lavage
BHR = Bronchopulmonary hyperreactivity
IFNy = Interferon y
IgA = Immunoglobulin A
IgE = Immunoglobulin E
IgG = Immunoglobulin G
IL-5 = lnterleukin-5
LDH = Lactate dehydrogenase
LTB4 = Leukotriene B4
OVA = Ovalbumin
PGE2 = Prostaglandin E2
PMA = Phorbol myristate acetate
ROS = Reactive oxygen species
SRawo = Specific airway resistance
TNFa = Tumor necrosis factor a
TxB2 = Thromboxane B2
ZAS = Zymosan-activated rat serum
4-27
-------
Table AX4.6. Infectious agents.
ppm
0.05
base
+ 0.1
peak
0.5
peak
1.2
base
+
2.5
peak
0.2
base
+ 0.8
peak
0.3-
0.5
0.5
0.5-
1.0
10
0.5-28
0.5
0.5
1.0
1.5
5.0
0.5
1.0
2.0
5.0
EXPOSURE
Continuous, base
+ twice/day 1 -h
peaks, 5 days/wk
for 1 5 days
23 h/day, 7
days/wk base+
twice daily 1-h
peaks, 5 days/wk
for 1 yr
Continuous, 3
mos
Continuous, 6
mos
Intermittent, 6 or
18 h/ day, up to
12 mos
Continuous,
90 days
Continuous,
39 days
2 h/day, 1, 3, and
5 days
Varied
3 h/day, 3 mos
24 h/day,
7 days/wk,
3 mos
3 days
4h
GENDER
F
F
F
F
F
F
F
F
M/F
AGE
NR
6-8
wks
4 wks
NR
NR
NR
6-8
wks
NR
8-10
wks
SPECIES
(STRAIN)
Mouse
(CD-1)
Mouse
(CD-1)
Mouse
(ICRJCL)
Mouse
(Swiss)
Mouse (ICR,
dd)
Mouse (CD-
1)
Mouse (CD2
F1.CD-1)
Mouse (CF-
1)
Mouse
(C57BL/6N)
INFECTIVE
AGENT
Streptococcus sp.
Streptococcus sp.
A/PR/8 virus
K. pneumoniae
A/PR/8
virus
Streptococcus sp.
Streptococcus sp.
K. pneumoniae
Mycoplasma
pulmonis
EFFECTS
No effect.
Increased mortality
Increased mortality
Peak plus baseline caused significantly
greater mortality than baseline.
High incidence of adenomatous
proliferation peripheral and bronchial
epithelial cells; NO2 alone and virus alone
caused less severe alterations.
No enhancement of effect of NO2 and
virus.
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.
Increased susceptibility to infection.
Increase mortality with increased time
and concentration; concentrations is
more important than time.
Increase in mortality with reduction in
mean survival time.
Significant increase in mortality after 3-
day exposure to 5.0 ppm; no effect at
other concentrations, but control mortality
very high.
Decrease in intrapulmonary killing only at
5.0 ppm.
REFERENCES
Gardner (1980,
1982)
Graham et al.
(1987)
Miller etal. (1987)
Motomiya et al.
(1973)
Ehrlich and Henry
(1968)
lto(1971)
Gardner et al.
(1977 a,b)
Coffin etal. (1977)
Ehrlich et al.
(1979)
McGrath and
Oyervides (1985)
Davis etal. (1991,
1992)
4-28
-------
ppm
1.0
2.3
6.6
1.0
2 5
5.0
10.0
1.0
1.0
3.0
1.0
2.5
5.0
1.5-
50
1.5
3.5
1.5
base
+ 4.5
peak
4.5
1.5
1.9
3 8
7.0
9.2
14.8
1.5-
5.0
EXPOSURE
17h
4h
48 h
3h
6 h/day, 6 days
2h
Continuous or
intermittent, 7
h/day, 7 days/wk,
up to 15 days
Continuous 64 h,
then peak for 1 ,
3.5, or 7 h.then
continuous 18 h
base
1, 3.5, or 7 h
7 h/day, 4, 5, and
7 days
4h
3h
GENDER
M
F
M
F
NR
NR
F
F
NR
M
F
AGE
NR
NR
NR
5-6
wks
4-6
wks
NR
NR
NR
NR
NR
6-10
wks
SPECIES
(STRAIN)
Mouse
(Swiss)
Mouse
(Swiss)
Mouse
(Swiss
Webster)
Mouse (CD-
1)
Mouse
(CD-1)
Mouse (NR)
Hsmstsr
(NR)
Monkey
(Squirrel)
Mouse
(CD-11
\*-ILJ \)
Mouse (CD-
1)
Mouse (NR)
Mouse (NR)
Mouse (CF-
1.CD2F1)
INFECTIVE
AGENT
S. aureus after
exposure
S. aureus
Streptococcus sp.
S. aureus
Streptococcus sp.
Cytomegalovirus
K. pneumoniae
Streptococcus sp.
Streptococcus sp.
Streptococcus sp.
S. aureus
Streptococcus sp.
EFFECTS
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.
Increased proliferation of Streptococcus
in lung of exposed mice but no effect with
S. aureus.
Exercise on continuously moving wheels
during exposure increased mortality at
3.0 ppm.
Increase in virus susceptibility at 5.0 ppm
only.
Increased mortality in mice, hamsters,
and monkeys at >3.5, >35, and 50 ppm
NO2, respectively
After 1 wk, mortality with continuous
exposure was greater than that for
intermittent after 2 wks, 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 H T, mortality
essentially the same.
Mortality increased with 3.5- and 7-h
single peak when bacterial challenge was
after an 18 h baseline exposure.
Mortality proportional to duration when
bacterial challenge was immediate, but
not 18 h postexposure.
Elevated temperature (32°C) increased
mortality after 7 days.
Physical removal of bacteria unchanged
by exposure. Bactericidal activity
decreased by 7, 14, and 50%,
respectively, in three highest NO2-
exposed groups.
Increased mortality in mice exposed to
>2.0 ppm
REFERENCES
Goldstein et al.
(1974)
Jakab(1988)
Sherwood et al.
(1981)
Illingetal. (1980)
Rose etal. (1988,
1989)
Ehrlich (1980)
Gardner et al.
(1979)
Coffin etal. (1977)
Gardner (1980)
Gardner (1982)
Graham et al.
(1987)
Gardner (1982)
Goldstein et al.
(1973)
Ehrlich et al.
(1977)
Ehrlich (1980)
4-29
-------
ppm
1.5
2.5
3.5
5.0
10
15
2.0
2.5
4.0
5.0
10
15
5.0
10
5.0
10
10
15
35
50
5
EXPOSURE
2h
1 .5 h/day,
5 days/wk for 1 ,
2, and 3 wks
4h
Continuous,
2 mos
Continuous, 1 mo
4h
2h
NR
GENDER
NR
NR
F
M
M/F
M/F
NR
AGE
6-8
wks
2 wks
NR
NR
6-10
wks
NR
NR
SPECIES
(STRAIN)
Mouse
(Swiss
Webster)
Hamster
(Golden
Syrian)
(in vitro)
Mouse
(Swiss)
Monkey
(Squirrel)
Mouse
(C57BL6N,
C3H/HeN)
Monkey
(Squirrel)
Mice (NR)
INFECTIVE
AGENT
K. pneumoniae
A/PR/8/34
influenza virus
S. aureus, Proteus
mirabilis,
Pasteurella
pneumotropica
K. pneumoniae or
A/PR/8 influenza
virus
Mycoplasma
pulmonis
K. pneumoniae
Parainfluenza
(murine sendei
virus)
EFFECTS
No effect at 1 .5 or 2.5 ppm; 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.
Peak virus production in tracheal
explants occurred earlier.
Concentration-related decrease in
bactericidal activity at > 4.0 ppm with S.
aureus when NO2 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.
Increased viral-induced mortality (1/3).
Increase in Klebsiella-induced mortality
(2/7); no control deaths.
Increased virus-induced mortality (6/6)
within 2-3 days after infection; no control
deaths. Increase in Klebsiella-induced
mortality (1/4), no control deaths.
NO2 increased incidence and 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.
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
Purvis and Ehrlich
(1963)
Ehrlich (1979)
Sch iff (1977)
Jakab(1987,
1988)
Henry et al.
(1970)
Parker et al.
(1989)
Henry et al.
(1969)
Jakab(1988)
Source: Modified from U.S. Environmental Protection Agency (1993).
4-30
-------
Table AX4.7. Lung structure.
ppm
3
g
5
25
5
10
20
5
10
20
5
10
20
5
10
20
EXPOSURE
6 h/day, 6 days/wk,
for 2 wks
6 h/day for 1 , 3, or 5
days
24 h/day, for 3 days
24 h/day, for 25
days
24 h/day, for 25
days
24 h/day, for 3 days
GENDER
F
NR
M
M
M
M
AGE
NR
NR
NR
NR
NR
NR
SPECIES
(STRAIN)
Guinea Pig
Mouse
(C57BL/6)
Rat
(Sprague-
Dawley)
Rat
(Sprague-
Dawley)
Rat
(Sprague-
Dawley)
Rat
(Sprague-
Dawley)
EFFECTS
Morphological changes observed at both concentrations.
Ciliated cells displayed pathological changes such as
cytoplasmic vacuolation and protrusion at 3 ppm, but
eosinophils displayed normal morphology. Minor to major
changes in epithelial cells (decreased number of specific
granules, cytoplasmic vacuolization, and morpholigcal changes
in specific granules) and ciliated cells showed compound cilia,
cytoplasmic vacuolization, and sloughing) at 9 ppm.
No lung inflammation or injury observed at 5 ppm at any time,
compared to controls. 25 ppm NO2 induced acute lung injury
(characterized by increases in protein, LDH, macrophages, and
neutrophils recovered by BAL) at 25 ppm that peaked after
3 days, lesions within terminal bronchioles, and AHR. Another
group of mice exposed for 5 days to 25 ppm and allowed to
recover in room air for 20 days demonstrated resolution of the
pattern of acute lung injury in these animals.
Significant alteration in morphology of Clara cells (loss of apical
intra-luminal projects and damaged epithelium covered by a
layer of CC10-reactive material) at >5 ppm.
No significant alteration of morphology of Clara cells compared
to controls.
Exposure to 5 ppm showed no significant qualitative changes of
the lung tissue, but animals exhibited slight fibrosis of the
centroacinar alveolar septa, respiratory bronchioli, and
interstitium, and irregularly shaped alveolar spaces at >10 ppm.
Morphometric analysis showed significantly diminished alveolar
surface density at >10 ppm. Suggests development of
emphysema at >10 ppm. The avg medial thickness of the
pulmonary artery was significantly increased at >10 ppm, but at
5 ppm, this parameter was significantly decreased, compared
to controls. Authors reported negative correlation between avg
medial thickness and alveolar surface density.
Histopathology revealed structural alterations extending from
slight interstitial edema after exposure to 5 ppm, to epithelial
necrosis and interstitial inflammatory infiltration after exposure
to 10 ppm, and an additional intra-alveolar edema after 20 ppm.
Light microscopic examination did not confirm the qualitative
histological changes, particularly muscularization of intra-acinar
vessels. Exposure to >10 ppm for 25 days caused emphysema
and slight centrilobular interstitial fibrosis.
Morphometric analysis showed significantly diminished alveolar
surface density at 10 ppm after 25 days of exposure and at
20 ppm after 3 and 25 days of exposure. Avg medial thickness
of the pulmonary artery significantly increased at >10 ppm, but
at 5 ppm, this parameter was significantly decreased both
during after 3-day and 25-day exposures, compared to controls.
Authors regarded the decrease in the medial thickness at
5 ppm as reflecting a reduction of pulmonary arterial mass and
not as a result of vasodilation. Avg medial thickness and
alveolar surface density were negatively correlated. Study
indicates exposures to as low as 5 ppm is not likely to induce
structural changes in the lung of rats. Effect on the
morphometry of the alveolar region appeared to be time-
dependent, since significant changes were seen at 10 ppm
after 25 days, but only at 20 ppm in the 3-day exposure study.
REFERENCE
Ohashi et al.
(1994)
Poynter et al.
(2006)
Barth and Muller
(1999)
Barth and Muller
(1999)
Barth et al.
(1995)
Barth et al.
(1995)
4-31
-------
ppm
10
20
0.8
5
10
5
10
20
EXPOSURE
14h/day, for 15
days, 20 or 25 days
24 h/day, for 1 or 3
days
24 h/day, for 3 or 25
days
GENDER
M
M
M
AGE
NR
NR
NR
SPECIES
(STRAIN)
Mouse
(C57/BL/5)
Rat
(Sprague-
Dawley)
Rat
(Sprague-
Dawley)
EFFECTS
Initial dose response experiment identified 20 ppm NO2 as
concentration causing lung injury and air inflammation (marked
influx of inflammatory cells in the airways, predominated by
macrophages and neutrophils and to a lesser extent by
lymphocytes) for exposure that lasted 15 days, whereas
10 ppm did not induce significant increase in leukocyte amount
in BAL fluid. Actual study using 20 ppm NO2 observed induction
of air space enlargement (evidenced by a significant increase in
mass-specific lung volume and volume-weighted alveolar
volume). No significant changes in total alveolar surface area.
Significant increase in Type II cell proliferation (evidenced by
increases in AgNOR-number and BrdU-LI) after exposure to
5 ppm NO2for3 days and 10 ppm for 1 and 3 days. Significant
increase in bronchiolar epithelial proliferation (increases in
AgNOR-number and BrdU-LI) at >0.8 ppm for 1 and 3 days. In
the bronchial epithelium, statistically significant increase in
proliferation as increase in AgNOR-number at 10 ppm only after
3 days of exposure and as increase in BrdU-LI after exposure
to 5 and 10 ppm for >1 day. Results showed highest rate of
epithelial proliferation in the bronchiolar epithelium compared to
bronchial epithelium and Type II cells. Study indicates cell
proliferation changes beginning at concentrations as low
0.8 ppm NO2 following a single day of exposure.
Compared to controls, proliferative activity (evidenced by
increase in AgNOR-number) significantly increased, but with no
concentration-dependence in respiratory bronchiolar epithelium
at 5 ppm and above after 3-day exposure, but the increase was
concentration-dependent, with significance at >10 ppm
following 25-day exposure. Activity was increased in a
concentration-dependent manner in bronchial epithelium with
significance only at 20 ppm after 3 days of exposure and at
>10 ppm following 25 days of exposure.
Concentration-dependent thickening of alveolar septa and
bronchiolar walls, significant only at >10 ppm after 3- and 25-
day exposure, but 5 ppm caused significant thickening only in
bronchiolar walls after 25 days, but not 3 days, of exposure
compared to controls.
Statistically significant reduction in alveoli surface density at
>10 ppm after 25 days of exposure and at 20 ppm only after 3
days of exposure, while alveolar circumference increased
statistically significantly at >10 ppm after 25 days of exposure
and at 20 ppm following 3 days of exposure. 5 ppm did not
significantly affect these endpoints after 3 or 25 days of
exposure compared to controls. Data suggest concentration-
and exposure duration-dependent development of emphysema,
significant only at >10 ppm, but not at 5 ppm, after 3 or 25 days
of exposure.
Alveolar duct length increased significantly at >10 ppm after 25
days and at 20 ppm after 3 days of exposure; no effect at
5 ppm.
Radial alveolar count values decreased significantly at 20 ppm
after 3 and 25 days and at 10 ppm after 25 days exposure; no
effect at 5 ppm.
Increase in avg medial thickness at 10 ppm with significance
only after 25 days of exposure and at 20 ppm with significance
after 3 and 25 days of exposure. However, 5 ppm caused
significant decrease at both 3 and 25 days of exposure. Authors
suggested effect at 5 ppm was due to vasodilation induced by
nitrogen oxide.
REFERENCE
Wegman and
Herz (2002)
Barth et al.
(1994a)
Barth et al.
(1994b)
4-32
-------
ppm
25
50
75
100
150
200
250
3.6
7.2
10.8
14.4
0.5
base +
1.5
n0ak
EXPOSURE
5, 15, or 30 min
2, 5, 15, or 30 min
2 5 or 1 5 min
24 h, 12 h,8h,and
6 h, respectively,
for 3 days, giving a
C H T of 86.4 ppm h
Base presumably
continuous, two 1-h
peaks/day, for 9 wks
GENDER
M
M
M
AGE
NR
NR
7
wks
SPECIES
(STRAIN)
Rat
(Fischer-
344)
Rat
(Sprague-
Dawley)
Rat
(Fischer-
344)
EFFECTS
Animals exposed to >200 ppm NO2 for 30 min died within 24 h
after exposure.
NO2 exposure induced proportional increases in LWW,
indicative of pulmonary edematous responses, over the same
exposure period after the 15-min exposures. Animals exposed
to 25 ppm NO2 did not produce observable lung injury (i.e.,
occurrence of alveolar fibrin and Type II cell hyperplasia) on
exposure for 5 min. Animals exposed to all exposure
concentrations for 15 min showed alveolar fibrin, while Type II
hyperplasia occurred at 50 ppm and its level of expression
correlated with exposure concentration. After 30 min of
exposure, the occurrence of fibrin increased as a function of
exposure concentration, while over a concentration range of
25-150 ppm NO2, the Type II cell hyperplastic response
increased with increasing exposure concentration. Data
suggest exposure concentration was evidently more important
than exposure time in terms of causing lung injury when high
concentrations of NO2 are inhaled.
Short-term exposure was not sufficient to produce significant
type I alveolar cell necrosis or a significant migration of
inflammatory cells across the interstitium and alveolar
epithelium.
No significant differences in thickness of the alveolar septal
components, between controls and exposed group.
Analysis of parenchymal cell populations showed no significant
differences in the avg volumes of different cell types or in their
surface areas. Total number of fenestrae in the lungs of NO2-
exposed animals occurred at a greater frequency than in
controls, but no significant alterations were found in the
connective tissue matrix or interstitial cell population,
suggesting that connective tissue matrix and interstitial cells of
the lung parenchyma did not undergo significant degeneration
on exposure to the low level of NO2 used in this study.
REFERENCE
Lehnert et al.
(1994)
Rajini et al.
(1993)
Mercer et al.
(1995)
AgNOR = Silver-stainable nucleolar organizer regions
AHR = Airway hyperresponsiveness
BAL = Bronchoalveolar lavage
BrdU-LI = Bromodeoxyuridine-laebling index
LDH = Lactate dehydrogenase
LWW = Lung wet weight
4-33
-------
Table AX4.8. Pulmonary function.
ppm
0.5
1.5
0.5
base
+ 1.5
peak
10
20
EXPOSURE
0.5 ppm background
level for 16 h, a 6-h
exposure spike, and a 2-
h downtime; profile was
run each day for 1 , 3,
13, 52 or 78 wks
Base presumably
continuous, two 1-h
peaks/day, for 9 wks
1 4 h/day, for 1 5 days, 20
or 25 days
GENDER
M
M
M
AGE
67
days
7 wks
NR
SPECIES
(STRAIN)
Rat
(Fischer-
344)
Rat
(Fischer-
344)
Mouse
(C57/BL/6)
EFFECTS
No NO2 related-effects on body weight or pulmonary
function (total lung capacity, vital capacity, residual volume
(difference between total lung capacity and vital capacity),
respiratory system compliance, single-breath diffusing
capacity of carbon monoxide, slope of nitrogen wash-out
curve, and end-expiratory volume) observed following 13-,
52-, or 78-wk exposure. NO2-related effects on small
airway function [FVC, peak flow, flow at 50% (FEF 50%),
25% (FEF 25%) and 10% (FEF 10%) of FVC] evaluated at
52- or 78-wk exposure or at 26- and 17-wks postexposure
were not significantly different from controls. However,
breathing patterns and mechanics (tidal volume, expiratory
resistance, inspiratory and expiratory time) were generally
greater and FOB was significantly slower in NO2-exposed
animals compared to controls at all of the time points
evaluated.
No significant differences in lung volume, total air volume
of the lungs, total lung tissue volume, surface area, body
weight, or thickness of the alveolar septal components,
between controls and exposed group.
Analysis of parenchymal cell populations showed no
significant differences in the avg volumes of different cell
types or in their surface areas. Total number of fenestrae in
the lungs of NO2-exposed animals occurred at a greater
frequency than in baseline controls, but no significant
alterations were found in the connective tissue matrix or
interstitial cell population, suggesting that connective tissue
matrix and interstitial cells of the lung parenchyma did not
undergo significant degeneration on exposure to the low
level of NO2 used in this study.
20 ppm NO2 induced development of progressive airflow
obstruction (evidenced by decreases in midexpiratory
airflow, breathing frequency and tidal volume, with
statistical significance only at day 25 of exposure).
REFERENCE
Tepper et al.
(1993)
Mercer et al.
(1995)
Wegman and
Herz (2002)
FVC = Forced vital capacity
FOB = Frequency of breathing
Table AX4.9. Hematological parameters.
ppm
0.05
0.36
0.5-
0.8 +
0.8
1.0
1.0
5.0
EXPOSURE
Continuous 90 days
1 wk
Continuous 1 to
1 .5 mos
Continuous, 5 days
Continuous,
16 mos
Continuous,
18 mos
GENDER
NR
NR
M/F
M
M
M
AGE
NR
NR
4 wks
7 wks
NR
NR
SPECIES
(STRAIN)
Rat
Guinea Pig
Mouse
(ICRiJCL)
Mouse (ICR)
Monkey
(Squirrel)
Dog (Mongrel)
EFFECTS
No effect on blood hemoglobin or RBCs.
Increase of red blood cell D-2,3-diphosphoglycerate
Addition of 50 ppm CO to NO2 failed to affect
carboxyhemoglobin.
No effect on methemoglobin.
No effect on hematocrit or hemoglobin with NO2 and
influenza exposure.
No changes in hemoglobin or hematocrit.
REFERENCES
Shalamberidze
(1969)
Merschetal. (1973)
Nakajima and
Kusumoto (1970)
Nakajima and
Kusumoto (1968)
Fentersetal. (1973)
Wagner etal. (1965)
4-34
-------
ppm
1-30
1.3-
3.0
2.0
2.0
4.0
4.0
5-40
10
EXPOSURE
18h
2 h/day, 15 and
17wks
Continuous,
14 mos
Continuous, up to
6wks
1-10 days
NR
1 h
2 h/day, 5 days/wk,
up to 30 wks
GENDER
NR
NR
M/F
M
M
NR
NR
F
F
AGE
NR
NR
NR
8 wks
NR
NR
4 mos
6-8
wks
SPECIES
(STRAIN)
Mouse (NR)
Rabbit (NR)
Monkey
(Macaca
speciosa)
Rat (Sprague-
Dawley)
Rat
(Wistar)
Rat
(NR)
NR
Mouse
(JCLICR)
Mouse
(BALB/c)
EFFECTS
Concentration-related increase in methemoglobin and
nitrosylhemoglobin
Decreased RBCs.
With or without NaCI (330 pg/m3): polycythemia with
reduced mean corpuscular volume and normal mean
corpuscular hemoglobin.
No effect on hemoglobin, hematocrit or RBC count; no
methemoglobin was observed.
Increase in RBC sialic acid.
Decrease in RBCs.
No increase in methemoglobin. Increased nitrite and
especially nitrate.
Small decrease in hemoglobin and mean corpuscular
hemoglobin concentration.
REFERENCES
Caseetal. (1979)
Mitina(1962)
Furiosietal. (1973)
Azoulayetal. (1978)
Kunimoto et al.
(1984)
Mochitate and Miura
(1984)
Odaetal. (1981)
Holtetal. (1979)
Source: Modified from U.S. Environmental Protection Agency (1993).
Table AX4.10. Iron, enzymes, and nucleic acids.
EFFECT
Sodium nitroprusside (NO donor) mobilizes iron from ferritin
Modulation of arachidonic acid metabolism via interference with iron
Inhibition of aconitase (an enzyme in the Krebs cycle, and also complex 1 and 2 of the respiratory chain)
Permanent modification of hemoglobin, possibly via deamination
Deamination of DNA
DNA strand breaks
Inhibition of DNA polymerase and ribonucleotide reductase
Antimitogenic; inhibition of T cell proliferation in rat spleen cells
Inhibition of DNA synthesis, cell proliferation, and mitogenesis in vascular tissue
Inhibition of mitogenesis and cell proliferation (vascular smooth muscle cells)
Adenosine diphosphate ribosylation is stimulated by NO-generating agents
REFERENCE
Reif and Simmons (1990)
Kanneretal. (1991, 1992)
Hibbsetal. (1988)
Persson et al. (1990)
Stadleretal. (1991)
Moriguchi etal. (1992)
Wink etal. (1991)
Nguyen etal. (1992)
Lepoivre et al. (1991)
Kwon etal. (1991)
Fu & Blankenhorn (1992)
Nakakietal. (1990)
Garg and Hassid (1989)
Nakakietal. (1990)
4-35
-------
Table AX4.11. Genotoxicity in vitro and in plants.
TEST
ORGANISM
Salmonella TA1 00
Salmonella TA1 00
Salmonella TA1 00
andTA102
Salmonella TA1 00
E.coli, WP2
E.coli
Bacillus subtilis
spores
V79 hamster cells
V79 hamster cells
Don hamster cells
V79 hamster cells
Tradescantia
Tradescantia
ENDPOINT
Mutations
Mutations
Mutations
SOS repair
Mutations
SOS repair
Mutations
Chromatid-type
aberrations, SCE
SCE
Mutations (8-
G resistance)
DMA single-strand
breaks
Micronuclei in
pollen
Mutations in
stamen hair
EXPOSURE
6-10 ppm, 40 mins
10-15 ppm, 6 h
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
2-3 ppm, 10 mins
2-3 ppm, 10 mins
10 ppm, 20 mins
5 ppm, 24 h
50 ppm, 6 h
COMMENTS
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 prior to exposure
Slight response
Effect not due to formation of nitrite
RESULTS
+
+
-
+
+
+
+
+
+
-
+
+
+
REFERENCE
Isomura et al.
(1984)
Victorin and
Stahlberg (1988)
Kosaka et al.
(1985)
Kosaka et al.
(1985)
Kosaka et al.
(1986, 1987)
Kosaka et al.
(1986, 1987)
Sasaki et al.
(1980)
Tsudaetal. (1981)
Shiraishi and
Bandow(1985)
Isomura et al.
(1984)
Gb'rsdorf et al.
(1990)
Maetal. (1982)
Schairer et al.
(1979)
Source: Victorin (1994).
4-36
-------
Table AX4.12. Genoticity in vivo.
TEST ORGANISM
Drosophila
Drosophila
Rats
Rats
Mice
Mice
ENDPOINT
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)
Victorin et al. (1990)
Isomura etal. (1984)
Isomuraet al. (1984)
Goochetal. (1977)
Victorin et al. (1990)
Source: Victorin (1994).
Table AX4.13. Genotoxicity.
TEST
ORGANISM
Salmonella TA1 00
Salmonella
Don hamster cells
V79 hamster cells
TK 6 human cells
Salmonella TA1 535
Rats
ENDPOINT
Mutations
SOS repair
Mutations (8-AG resistance)
DMA single-strand breaks
Mutations, DMA single-strand
breaks
Mutations
Mutations in lung cells (oubain
res.)
EXPOSURE
25-30 ppm, 40 min
Bubbling of 10-90 ppm
2-3 ppm, 10 min
500 ppm, 30 min
Injection of 0.12-0.38 ml NO gas/ml of culture medium,
1 h
30 min to 5-90 ppm
27 ppm, 3 h
RESULT
+
-
+
-
+
+
-
REFERENCE
Isomura etal. (1984)
Kosakaetal. (1985)
Isomura etal. (1984)
Gb'rsdorf et al.
(1990)
Nguyen etal. (1992)
Arroyo etal. (1992)
Isomura etal. (1984)
Source: Victorin (1994); Arroyo et al. (1992) added.
4-37
-------
Annex 5. Clinical Studies:
Exposure to NOx
AX5.1. Introduction
This annex summarizes the effects of NOX on human volunteers exposed under controlled
conditions. The goal is to review the scientific literature on human clinical studies of NOX exposure
published since the 1993 NOX Air Quality Criteria Document (AQCD) (EPA 1993). The primary focus
was on NO2 because it is the most abundant NOX species in the atmosphere and there are few human
studies of exposure to other NOX species.
Clinical summary conclusions from the 1993 AQCD are provided below:
• NO2 causes decrements in lung function, particularly increased airway resistance in healthy
subjects at concentrations exceeding 2.0 ppm for 2 h.
• NO2 exposure results in increased airway responsiveness in healthy, nonsmoking subjects
exposed to concentrations exceeding 1.0 ppm for 1 hour or longer.
• NO2 exposure at levels above 1.5 ppm may alter numbers and types of inflammatory cells in the
distal airways or alveoli, but these responses depend upon exposure concentration, duration, and
frequency. NO2 may alter function of cells within the lung and production of mediators that may
be important in lung host defenses.
• NO2 exposure of asthmatics causes, in some subjects, increased airway responsiveness to a
variety of provocative mediators, including cholinergic and histaminergic chemicals, SO2 and
cold air. However, the presence of these responses appears to be influenced by the exposure
protocol, particularly whether or not the exposure includes exercise.
• Modest decrements in spirometric measures of lung function (3 to 8%) may occur in some
asthmatics and COPD patients under certain NO2 exposure conditions.
• Nitric acid levels in the range of 50 to 200 ppb may cause some pulmonary function responses in
adolescent asthmatics, but not in healthy adults. Other commonly occurring NOX species do not
appear to cause any pulmonary function responses at concentrations expected in the ambient
environment, even at higher levels than in worst-case scenarios. However, not all NOxacid
species have been studied sufficiently.
• No association between lung function responses and respiratory symptom responses were
observed. Furthermore, there is little evidence of a concentration-response relationship for
changes in lung function, airway responsiveness, or symptoms at the NO2 levels that are reviewed
here.
In the summary and integration chapter of the 1993 NOX criteria document, one of the key health
effects at near ambient concentrations of NO2 was increased airway responsiveness or
hyperresponsiveness in asthmatic individuals after short-term exposures. The 1993 AQCD notes the
absence of a concentration-response relationship for NO2 exposure and airway responsiveness in
asthmatics. For example, most responses to NO2 that had been observed in asthmatics occurred at
concentrations between 0.2 and 0.5 ppm. However, other studies showed an absence of effects on airway
responsiveness or hyperresponsiveness at much higher concentrations, up to 4 ppm. Since 1993,
additional studies have suggested that exposure to low concentrations of NO2, either alone or in
5-1
-------
combination with other pollutants such as SO2, may enhance allergen responsiveness in asthmatic
subjects.
In the years since the preparation of the 1993 AQCD, many studies from a variety of disciplines
have convincingly demonstrated that exposure to particulate air pollution increases the risk for
cardiovascular events. In addition, a number of epidemiological studies have shown associations between
ambient NO2 levels and adverse cardiovascular outcomes, at concentrations well below those shown to
cause respiratory effects. However, to date there remain very few clinical studies of NO2 that include
endpoints relevant to cardiovascular disease.
AX5.1.1. Considerations in Controlled Human Clinical Studies
Human clinical studies attempt to engineer laboratory atmospheric conditions relevant to ambient
pollutant atmospheres, with careful control of concentrations, duration, timing, and other conditions
which may impact responses. These studies provide the opportunity to measure symptoms and
physiological markers of health effects that result from breathing the atmospheres. The carefully
controlled environment allows investigators to identify responses to individual pollutants, to characterize
exposure-response relationships, to examine interactions among pollutants, and to study the effects of
other variables such as exercise, humidity, or temperature. Susceptible populations can participate,
including individuals with acute and chronic respiratory and cardiovascular diseases, with appropriate
limitations based on subject comfort and protection from risk. Endpoint assessment traditionally has
included symptoms and pulmonary function, but more recently a variety of markers of pulmonary,
systemic, and cardiovascular function have been used to assess pollutant effects.
Human clinical studies have limitations. For practical and ethical reasons, studies must be limited to
relatively small groups, to short durations of exposure, and to pollutant concentrations that are expected to
produce only mild and transient responses. Findings from the short-term exposures in clinical studies may
provide limited insight into the health effects of chronic or repeated exposures.
Specific issues of protocol design in human clinical studies have been reviewed (Frampton,
Pietropaoli et al. 2006), and will not be considered further here, except in the context of specific studies of
NO2 exposure described in the following pages.
In clinical studies, humans are the species of interest, so findings have particular relevance to risk
assessment. However, the utility of clinical studies in risk assessment is tempered by the obvious need to
avoid adverse health effects of the study itself. This usually means selecting subjects that are not the most
susceptible to the pollutant being studied. Furthermore, clinical studies depend on outcome markers with
variable relevance or validation as markers of true health effects. The statement from the American
Thoracic Society, "What constitutes an adverse health effect?" (American Thoracic Society 2000)
addresses issues relevant to selection and interpretation of outcome markers in clinical studies.
The 1993 NOX AQCD included a description of key outcome measures that had been in use up to
that date. These included primarily respiratory outcomes, including pulmonary function tests such as
spirometry, lung volumes, and airway resistance, and tests of pulmonary clearance of inhaled aerosols. A
brief description of bronchoalveolar lavage was also included, which had come into use prior to 1993 to
assess airway inflammation and changes in the epithelial lining fluid in response to NO2 exposure.
AX5.2. Effects of N02 in Healthy Subjects
Table AX5.2-1 summarizes the key clinical studies of NO2 exposure in healthy subjects since 1993,
with a few key studies included prior to that date.
5-2
-------
AX5.3. Effects of NOx Exposure in Sensitive Subjects
Table AX5.3-1 summarizes studies of potentially sensitive subjects. The potential for NO2 exposure
to enhance responsiveness to allergen challenge in asthmatics deserves special mention. Several recent
studies, summarized in Table AX5.3-2, have reported that low-level exposures to NO2, both at rest and
with exercise, enhance the response to specific allergen challenge in mild asthmatics.
These recent studies involving allergen challenge suggest that NO2 may enhance the sensitivity to
allergen-induced decrements in lung function, and increase the allergen-induced airway inflammatory
response.
AX5.4. Effects of Mixtures Containing NOx
Table AX5.4-1 summarizes human clinical studies of NO2-containing mixtures or sequential
exposures that are most relevant to ambient exposure scenarios.
Table AX5.2-1. Clinical studies - healthy subjects.
STUDY
Avissar et al.
(2000)
Azadniv et al.
(1998)
Blomberg et al.
(1997)
Blomberg et al.
(1999)
LOCATION
Rochester,
NY, USA
Rochester,
NY, USA
Sweden
Sweden
PARTICIPANTS
21 healthy nonsmokers
2 studies, 12 healthy
nonsmokers in each
30 healthy nonsmokers
12 healthy nonsmokers
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.
FINDINGS
No effects of NO2 exposure on
eGPx activity and protein
concentrations. (O3 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
FEV1 after first exposure to NO2,
attenuated with repeated
exposure. Symptoms not
reported.
COMMENTS
NO2 up to 1 .5 ppm for
3 h 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 this
concentration.
Conflicting information
on airway inflammation.
5-3
-------
STUDY
Devlin et al.
(1999;
Frampton,
Boscia et al.
2002)
Drechsler-Parks
(1995)
Frampton et al.
(1991)
Frampton
et al.(2002)
Gong et al.
(2005)
Helleday et al.
(1994)
Helleday et al.
(1995)
Jb'rres et al.
(1995)
LOCATION
Chapel Hill,
NC, USA
Santa
Barbara, CA,
USA
Rochester,
NY, USA
Rochester,
NY, USA
Downey, CA,
USA
Sweden
Sweden
Germany
PARTICIPANTS
8 healthy nonsmokers
8 older healthy
nonsmokers
39 healthy nonsmokers
21 healthy nonsmokers
6 healthy nonsmokers
and 18 ex-smokers with
COPD
8 healthy smokers, 8
healthy nonsmokers
24 healthy nonsmokers,
8 in each of 3 groups
8 healthy nonsmokers &
12 mild asthmatics
METHODS
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.
3 protocols, all for 3 h
with control air exposure:
(1) continuous 0.06 ppm
NO2, (2) baseline
0.05 ppm NO2with
peaks of 2.0 ppm, and
(3) continuous 1.5 ppm
NO2.
Exposure to air, 0.6,
1 .5 ppm NO2 for 3 h with
intermittent exercise.
2 h exposures with
intermittent exercise to:
(1) air,
(2) 0.4 ppm NO2,
(3) 200 pg/m3
concentrated ambient
particulate matter
(CAPs), (4) NO2 + CAPs.
3.5 ppm NO2for20 min
with 15 min exercise.
BAL 24 h after exposure
compared with non-
exposure control BAL.
Branch oscopic
assessment of
mucociliary activity:
(1) 45 min after 1 .5 ppm
NO2for20 min,
(2) 45 min after 3.5 ppm
NO2for20 min, and
(3) 24 h after 3.5 ppm
NO2for4h.
Air or 1 ppm NO2
exposure for 3 h with
intermittent exercise.
FINDINGS
Increased bronchial lavage
neutrophils, IL-6, IL-8, alphal-
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.
No symptoms or direct effects on
pulmonary function. Increased
airways responsiveness to
carbachol after 1 .5 ppm NO2.
Dose-related decrease in
hematocrit, hemoglobin, blood
lymphocytes, and T lymphocytes.
Mild increase in neutrophils
recovered in bronchial portion of
BAL fluid. In vitro viral challenge
of bronchial epithelial cells
showed increased cytotoxicity
after NO2. No effects on
symptoms or pulmonary function.
Reduced maximum mid-
expiratory flow rate and oxygen
saturation with CAPs exposures;
no effects of NO2 alone or
additive effect with CAPs.
Different inflammatory cell
increases in smokers and
nonsmokers. No effects on
symptoms. Pulmonary function
not reported.
Complete abolition of mucociliary
activity 20 min after NO2;
increased activity 24 h after NO2.
Symptoms/ pulmonary function
not reported.
In asthmatics, 2.5% decrease
FEV1 after NO2 vs. 1 .3%
decrease after air, p = 0.01 .
FEV1 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 BAL fluid.
COMMENTS
2 ppm NO2 for 4 h
caused airway
inflammation.
Suggests cardiac effects
of NO2 + O3. Small
number of subjects
limits statistical power,
has not been replicated.
Evidence for increased
nonspecific airways
responsiveness with
NO2 as low as 1 .5 ppm
forSh.
Indicates NO2 causes
airway inflammation.
Suggest subtle effects
on red blood cells,
possibly RBC
destruction (hemolysis).
Exposures not fully
randomized. Small
number of healthy
subjects limits
interpretation for healthy
group.
Lack of control air
exposure with exercise
is problematic.
No true air control
exposure, order of
procedures not
randomized, subjects
not blinded.
Lung function effects
consistent with other
studies, suggesting
some asthmatics
susceptible. Evidence
for mild airway
inflammation.
5-4
-------
STUDY
Kimetal. (1991)
Morrow et al.
(1992)
Pathmanathan
et al. (2003)
Posin et al.
(1978)
Rasmussen
etal. (1992)
Rigas et al.
(1997)
Sandstrb'm et al.
(1990)
Sandstrb'm et al.
(1991)
Sandstrb'm et al.
(1992a)
Sandstrbm et al.
(1992b)
LOCATION
Seattle, WA,
USA
Rochester,
NY, USA
United
Kingdom
Downey, CA,
USA
Denmark
State
College, PA,
USA
Sweden
Sweden
Sweden
Sweden
PARTICIPANTS
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
32 healthy nonsmokers,
4 groups of 8 subjects
18 healthy nonsmokers
10 healthy nonsmoking
men
8 healthy nonsmokers
METHODS
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 NO2for
5h.
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.
4 ppm NO2 for 20 min
with 15 min exercise.
BAL4.8, 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 H 6. BAL
24 h after exposure
compared with non-
exposure control BAL.
FINDINGS
No effects on pulmonary function.
Symptoms not reported.
COPD: small declines in FVC
and FEV1 with NO2. Healthy: No
symptoms or pulmonary function
effects for group as a whole.
Healthy smokers showed a 2.3%
decline in FEV1 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
FEV1. Reduced lung
permeability and blood
glutathione peroxidase after
exposure.
NO2 and SO2 increased O3
absorption by increasing
biochemical substrates.
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.
COMMENTS
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 wk between
exposures may have
confounded results.
Suggests breathing
mixtures of NO2 and O3
would increase O3 dose
to airways.
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.
5-5
-------
STUDY
Solomon
et al.(2000)
Vagaggini et al.
(1996)
LOCATION
San
Francisco,
CA, USA
Italy
PARTICIPANTS
15 healthy nonsmokers
7 healthy nonsmokers
METHODS
Air or 2.0 ppm NO2 with
intermittent exercise, for
4 h daily H 4. BAL18h
after exposure.
Air vs. 0.3 ppm NO2 for 1
h with intermittent
exercise.
FINDINGS
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.
COMMENTS
Airway inflammation
with 2 ppm NO2 for 4
daily 4 h exposures.
Small number of
subjects limits statistical
power.
Table AX5.3-1. Subjects with respiratory disease.
REFERENCE
Gong et al.
(2005)
Hackney et al.
(1992)
Jb'rres and
Magnussen
(1991)
Jb'rres et al.
(1995)
Morrow et al.
(1992)
Strand et al.
(1997)
Vagaggini et al.
(1996)
LOCATION
Downey, CA,
USA
Downey, CA,
USA
Germany
Germany
Rochester,
NY, USA
Sweden
Italy
PARTICIPANTS
6 healthy
nonsmokers and
18 ex-smokers with
COPD
26 smokers with
symptoms and
reduced FEVi
11 mild asthmatics
8 healthy
nonsmokers &
12 mild asthmatics
20 COPD, 20 healthy
elderly
19 mild asthmatics
8 mild asthmatics,
7 COPD
APPROACH &
METHODS
2 h exposures with
intermittent exercise to:
(1)air,
(2) 0.4 ppm NO2,
(3) 200 pg/m3 concentrated
ambient particulate 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.
FINDINGS
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 FEV, after NO2
vs. 1 .3% decrease after air,
p = 0.01 . FEV, 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 BAL
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 FEVi in
COPD subjects in
comparison with air
exposure, but not with
baseline. No effects on
nasal lavage or induced
sputum.
COMMENTS
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 Jb'rres and
Magnussen (1991).
No convincing effect of NO2
in this study. Small number
of subjects limits statistical
power.
5-6
-------
Table AX5.3-2. Inhaled allergen.
REFERENCE
Barck et al.
(2002)
Barck et al.
(2005)
Barck et al.
(2005)
Devalia et al.
(1994)
Jenkins et al.
(1999)
Rusznak et al.
(1996)
Strand et al.
(Strand, Rak et
al. 1997)
Strand et al.
(1998)
Tunnicliffe et al.
(1994)
LOCATION
Sweden
Sweden
Sweden
United
Kingdom
United
Kingdom
United
Kingdom
Sweden
Sweden
United
Kingdom
PARTICIPANTS
13 mild asthmatics,
4 ex-smokers
18 mild asthmatics,
4 ex-smokers
16 mild asthmatics
with rhinitis
8 mild asthmatics
11 mild asthmatics
13 mild asthmatics
18 patients with mild
asthma, age 18-50
yrs
16 patients with mild
to moderate asthma,
age 21-52 yrs
10 nonsmoking mild
asthmatics age
16-60 yrs.
8 subjects
completed.
APPROACH & METHODS
30 min exposures to air and
0.26 ppm NO2 (at rest?),
allergen challenge 4 h and BAL
19 h after exposure.
Randomized, crossover,
double blind.
Day 1:15 min exposures,
Day 2: 2 15-min exposures to
air and 0.26 ppm 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.
30 min exposures to air and
0.26 ppm NO2 at rest, nasal
allergen challenge 4 h after
exposure. Nasal lavage before
and at intervals after exposure
and challenge.
6 h exposures to combination
of 0.4 ppm NO2 and 0.2 ppm
SO2.
(1) 6-h exposures to air,
0.1 ppm O3, 0.2 ppm NO2, and
combination followed by
allergen challenge;
(2) 3-h exposures to air,
0.2 ppm O3, 0.4 ppm NO2, and
combination;
All exposures with intermittent
exercise.
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 wk,
followed by allergen challenge.
FINDINGS
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 (O3, NO2, and
combination), but none of the
first exposure scenarios, resulted
in reduced concentration of
allergen causing a 20% decline
in FEV1 . Authors conclude that
concentration more important
than total inhaled pollutant.
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 NO2.
Significant increases in both
early and late phase response to
allergen after 4th day of
exposure.
Post-challenge reduction in
FEV1 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.
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.
Confirms findings of
Devalia et al. (1994),
that NO2 + 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.
5-7
-------
REFERENCE
Wang et al.
(1995)
Wang et al.
(1999)
LOCATION
United
Kingdom
United
Kingdom
PARTICIPANTS
2 groups of
8 subjects with
allergic rhinitis
16 subjects with
allergic rhinitis
APPROACH & METHODS
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
wks followed by exposure to
0.4 ppm NO2for6 h, allergen
challenge, and nasal lavage.
FINDINGS
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.
COMMENTS
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 AX5.4-1. NO2 and other pollutants.
STUDY
Devalia
etal.(1994)
Drechsler-
Parks(1995)
Gong et al.
(2005)
Hazucha
etal. (1994)
Jb'rres and
Magnussen
(1990)
Koenig et al.
(1994)
Rubenstein
etal. (1990)
LOCATION
United
Kingdom
Santa
Barbara, CA,
USA
Downey, CA,
USA
Chapel Hill,
NC, USA
Germany
Seattle, WA,
USA
San
Francisco,
CA, 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
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 pg/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 ppm NO2, (2) 0.12 ppm
ozone + 0.3 ppm NO2 +
68 pg/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.
0.6 ppm NO2 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
Jb'rres and Magnussen
etal. (1990).
5-8
-------
STUDY
Rudell et al.
(1999)
Rusznak et
al. (1996)
LOCATION
Sweden
United
Kingdom
PARTICIPANTS
10 healthy
nonsmokers
13 mild asthmatics
METHODS
Air and diesel exhaust for 1
h, with and without particle
trap. NO2 concentration 1 .2-
1 .3 ppm. BAL 24 h after
exposures.
6 h exposures to
combination of 0.4 ppm NO2
and 0.2 ppm SO2.
FINDINGS
Increased neutrophils in BAL
fluid, no significant reduction in
effect with particle trap.
Increased allergen
responsiveness to combination
of NO2 and SO2, 10 min, 24, and
48 h after exposure.
COMMENTS
Filter only partially
trapped particles. Unable
to draw conclusions about
role of NO2 in causing
effects.
Confirms findings of
Devaliaetal. (1994), that
NO2 + SO2 for 6 h
increases allergen
responsiveness.
5-9
-------
Annex 6. Epidemiologic Studies Related to
Ambient Exposure to NOx
This annex provides supplemental information on various epidemiologic methods and studies that
are referenced in the 2008 NOX ISA. The first section describes considerations in the interpretation of
epidemiologic studies. This is followed by a section on cardiovascular effects that are related to short-
term exposure to NO2. This topic is discussed in the ISA, but more detail is provided in this annex due to
inconsistency with supporting studies. The second section of this annex presents tables detailing the
epidemiologic studies presented in the ISA. In general, these tables are divided into sections based on the
endpoint of concern. Tables AX6.3-1 through AX6.3-5 cover respiratory endpoints, while tables AX6.3-6
to AX6.3-9 address cardiovascular disease. The two tables, AX6.3-10 and AX6.3-11 summarize heart rate
variability. Tables AX6.3-12 through AX6.3-14 address birth outcomes: birthweight, pre-term births, and
lung growth, respectively, and tables AX6.3-15 through AX6.3-19 look at the long-term effects
associated with NO2 exposure (i.e., lung function, asthma, respiratory symptoms, lung cancer, and
mortality). Lastly, table AX6.3-20 summarizes the effects of NO2 on asthmatics.
AX6.1. Interpretation of Epidemiologic Studies
This section mainly focuses on the topics of exposure assessment and model specification in air
pollution epidemiologic studies. The initial discussion addresses potential biases that may result from
NO2 exposure measurement error and from the choice of exposure index and lag period. The remaining
discussion highlights model specification issues and potential confounding by temporal factors,
meteorological effects, seasonal trends, and copollutants.
AX6.1.1. Exposure Assessment and Measurement Error
In many air pollution epidemiologic studies, especially time-series studies that use administrative
data on mortality and hospitalization outcomes, air pollution data from central ambient monitoring sites
are used as the estimate of exposure. Personal exposures of individual study subjects, usually, are not
directly measured in epidemiologic studies. The relationship between NO2 concentrations from ambient
monitors and personal NO2 exposures was discussed previously (Annex AX3). Routinely collected
ambient monitor data, though readily available and convenient, may not represent true personal exposure,
which includes both ambient and nonambient (i.e., indoor) source exposures. Also, personal exposure
measurements and ambient measurements are subject to different types of artifacts and measurement
error. Therefore, they may not be measuring the same quantities.
As discussed thoroughly in the 2004 PM AQCD (Section 8.4.5), the resulting exposure
measurement error and its effect on the estimates of relative risk is an important consideration for
interpreting epidemiologic study results. In theory, there are three components to exposure measurement
error in time-series studies as described by Zeger et al. (2000): (1) the use of average population rather
than individual exposure data; (2) the difference between average personal ambient exposure and ambient
concentrations at central monitoring sites; and (3) the difference between true and measured ambient
concentrations. The first error component, having aggregate rather than individual exposure data, is a
Berksonian measurement error, which in a simple linear model increases the standard error, but does not
bias the risk estimate. The second error component, which results from the difference between community
average personal ambient exposure and outdoor ambient concentration level, has the greatest potential to
6-2
-------
introduce bias. If the error is of a fixed amount (i.e., absolute differences do not change with increasing
concentrations), there is no bias. However, if the error is not a fixed difference, this error will likely
attenuate the NO2 risk estimate as personal NO2 exposures are generally lower than ambient NO2
concentrations in homes without sources, while they are higher in homes with sources. The third error
component, the instrument measurement error in the ambient levels, is referred to as nondifferential
measurement error and is unlikely to cause substantial bias, although it can lead to a bias toward the null.
The impact of exposure measurement error on NO2 effect estimates was demonstrated in a study by
Kim et al. (2006), which is a longitudinal study that investigated personal exposures to NO2, PM2 5, and
CO for cardiac compromised individuals in Toronto, Canada. The mean (SD) personal exposure for NO2
was 14 ppb (6). NO2 personal exposures were less than central-fixed-site ambient measurements.
Ambient NO2 was correlated with the personal exposure to NO2 (median Spearman's correlation
coefficient of 0.57). Personal exposures to PM25 were correlated with the personal exposure to NO2
(median Spearman's correlation coefficient of 0.43). This study suggests that central-fixed-site
measurements of PM2 5 and NO2 may be treated as surrogates for both exposure to PM2 5 and NO2 in time-
series epidemiology studies, and that NO2 is a potential confounder of PM25 and vice versa. As described
in Chapter 2, Nerriere et al. (2005) provided additional data from European cities, noting that season, city,
and land use dependence were important factors affecting the relationship between personal exposure to
ambient NO2 and corresponding ambient monitoring site concentrations, and recommended a site-specific
analysis for a specific study. Zidek (1997) noted that a statistical analysis must balance bias and
imprecision (error variance). Ignoring measurement error in air pollution epidemiologic studies often
results in underestimated risk estimates and standard errors.
In addition, the use of ambient NO2 concentrations may obscure the presence of thresholds in
epidemiologic studies at the population level due to the overestimation of exposure and the resulting
underestimation of effects. Using PM2 5 as an example, Brauer et al. (2002) examined the relationship
between ambient concentrations and mortality risk in a simulated population with specified common
individual threshold levels. They found that no population threshold was detectable when a low threshold
level was specified. Even at high-specified individual threshold levels, the apparent threshold at the
population level was much lower than the specified threshold. Brauer et al. (2002) concluded that
surrogate measures of exposure (i.e., those from centrally-located ambient monitors) that were not highly
correlated with personal exposures obscured the presence of thresholds in epidemiologic studies at the
population level, even if a common threshold exists for individuals within the population.
As discussed in Chapter 2, NO2 concentrations measured at central ambient monitors may explain,
at least partially, the variance of individual personal exposures; however, this relationship is influenced by
factors such as air exchange rates in housing and time spent outdoors, which may vary by city. Other
studies conducted in various cities observed that the daily averaged personal NO2 exposures from the
population were well correlated with monitored ambient NO2 concentrations, although substantial
variability existed among the personal measurements. Thus, there is supportive evidence that ambient
NO2 concentrations from central monitors may serve as valid surrogate measures for mean personal NO2
exposures experienced by the population, which is of most relevance to time-series studies (See
Chapter 2). Respiratory hospital visit and admission studies are influenced by the visits and admission of
asthmatics. In children, for whom asthma is more prevalent, ambient monitors may correlate, to some
extent, with personal exposure to NO2 of ambient origin because children spend more time outdoors in
the warm season and have an increased potential for exposure due to traffic. However, of some concern
for mortality and hospitalization time-series studies is the extent to which ambient NO2 concentrations are
representative of personal NO2 exposures in another particularly susceptible group of individuals, the
debilitated elderly. To date, the correlation between the two measurements has not been examined in this
population. A better understanding of the relationship between ambient concentrations and personal
exposures, as well as the factors that affect the relationship will improve the interpretation of the ambient
concentration-population health response associations observed.
Existing epidemiologic models may not fully take into consideration all of the biologically relevant
exposure history or reflect the complexities of all the underlying biological processes. Using ambient
6-3
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concentrations to determine exposure may overestimate true personal NO2 exposures (depending on
indoor sources), resulting in biased descriptions of underlying concentration-response relationships (i.e.,
in attenuated risk estimates). The implication is that the effects being estimated occur at exposures that
are uncertain and the potency of NO2 is different than these effect estimates indicate. As very few studies
evaluating NO2 health effects with personal NO2 exposure measurements exist in the literature, effect
estimates determined from ambient NO2 concentrations must be evaluated and used with caution to assess
the health risks of NO2.
The ultimate goal of the NO2 NAAQS is to set a standard for the ambient level, not personal
exposure level, of NO2. Confidence in the use of ambient concentrations in epidemiologic studies is
greatly strengthened if they are shown to be associated with personal exposures. However, until more
information on personal NO2 exposure becomes available, the use of routinely monitored ambient NO2
concentrations as a surrogate for personal exposures is not generally expected to change the principal
conclusions from NO2 epidemiologic studies.
AX6.1.2. N02 Exposure Indices Used
The NO2-related effect estimates for mortality and morbidity health outcomes are usually presented
in this document as relative risk, i.e., the risk rate relative to a baseline mortality or morbidity rate.
Relative risks are based on an incremental change in exposure. To enhance comparability between
studies, presenting these relative risks by a uniform exposure increment is needed. However, determining
a standard increment is complicated by the use of different NO2 exposure indices in the existing health
studies. The daily NO2 exposure indices that most often appear in the literature are the maximum 1-h
average within a 24-h period (1-h max) and 24-h avg (24-h avg) concentrations. As levels are lower and
less variable for the longer averaging times, relative risk of adverse health outcomes for a specific
numeric concentration range are not directly comparable across metrics. Using the nationwide
distributional data for NO2 monitors in U.S. Metropolitan Statistical Areas, increments representative of a
low-to-high change in NO2 concentrations were approximated on the basis of annual mean to 95th
percentile differences (Langstaff, 2006):
Daily Exposure Index
Exposure Increment ppb
l-havgNO2 30
24-h avg NO2 20
2-wk avg NO2 20
Efforts were made to standardize the NO2 risk estimates using these increments throughout the ISA,
except as noted. The specified incremental change for each daily NO2 exposure index ensures that risk
estimates are comparable across the different metrics. The different increments for each NO2 exposure
index do not represent inconsistencies; rather, they are appropriately scaled to facilitate comparisons
between the various studies that used different indices. Note that in the Chapter 6 Annex Tables (see
Annex Section AX6.3), effect estimates are not standardized; there, the results are presented in the tables
as reported in the published papers.
AX6.1.3. Lag Time: Period between Exposure and Health Effect
Exposure lags may reflect the distribution of effects across time in a population and the potential
mechanisms of effects. The choice of lag days for the relationship between exposure and health effects
depends on the hypothesis being tested and the mechanism involved in the expression of the outcome.
6-4
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Effects can occur acutely with exposure on the same or previous day, cumulatively over several days, or
after a delayed period of a few days. With knowledge of the mechanism of effect, the choice of lag days
can be determined prior to analysis. As one example, one would expect cough to occur acutely after
exposure with a lag of 0 or 1 day, given that NO2 can act as a short-term irritant. However, an NO2-related
inflammatory response may not lead to asthma exacerbation until several days later. An asthmatic may be
impacted by NO2 on the first day of exposure, have further effects triggered on the second day, and then
report to the emergency room for an asthmatic attack three days after exposure. Further, within a
population of asthmatics, exacerbation of asthma symptoms may be observed over a period of several
days, since each asthmatic may have varying individual aspects of the disease and may be affected by the
exposure differently depending on his/her sensitivity and disease severity. The results from controlled
human studies may be useful in assessing the adequacy of lags for some respiratory health outcomes.
The concepts of lags are well discussed in the O3 AQCD (2006) and are only briefly reviewed here,
as the concept for O3 pertains to NO2 as well. Selection of lag periods should depend on the hypothesis of
the study and the potential mechanism of the effect. When the mechanism of the health effect is unknown,
investigating the association between outcome and exposure using cumulative distributed lag models may
be informative. Analyzing a large number of lags and simply choosing the largest and most significant
results may bias the air pollution risk estimates towards or away from the null. Most studies have shown
that NO2 has a fairly consistent, immediate effect on health outcomes, including respiratory
hospitalizations and mortality. Several studies also observed significant NO2 effects over longer
cumulative lag periods, suggesting that in addition to single-day lags, multiday lags should be
investigated to fully capture a delayed NO2 effect on health outcomes. In this document, discussion
largely focuses on effect estimates from 0- and 1-day lags, with some consideration of cumulative,
multiday lag effects. It is not straightforward to compare and contrast results from single-day versus
multiday lag models, because the parameters estimated from these models are not the same. These
complications need to be taken into consideration when interpreting results from various lag models.
AX6.1.4. Model Specification for Temporal Trends and Meteorological
Effects
Several challenges are encountered with respect to designing and interpreting time-series studies.
The principal challenge facing the analyst in the daily time-series context is avoiding bias due to
confounding by short-term temporal factors operating over time scales from days to seasons, thus
adjusting for long-term trends in the evaluation of acute or short-term associations. In the current
regression models used to estimate short-term effects of air pollution, two major potential confounders
generally need to be considered: (1) seasonal trend and other "long-wave" temporal trends; and (2)
weather effects. Both of these variables tend to predict a significant fraction of fluctuations in time-series
analyses.
Current weather models used in time-series analyses can be classified by their use of: (1) quantile
(e.g., quartile, quintile) indicators; (2) parametric functional forms such as V- or U-shape functions; and
(3) parametric (e.g., natural splines) or nonparametric (e.g., locally estimated smoothing splines
[LOESS]) smoothing functions. More recent studies tend to use smoothing functions. While these
methods provide flexible ways to fit health outcomes as a function of temperature and other weather
variables, there are two major issues that need further examination to enable more meaningful
interpretation of NO2 morbidity and mortality effects.
The first issue is the interpretation of weather or temperature effects. Most researchers agree that
extreme temperatures (i.e., heat waves or cold spells) contribute to morbidity and mortality effects.
However, as extreme hot or cold temperatures, by definition, happen rarely, much of the health effects
occur in the mild or moderate temperature range. Given the significant correlation between NO2 and
temperature, ascribing the association between temperature and health outcomes solely to temperature
effects may underestimate the effect of NO2. The second issue deals with the fact that weather model
6-5
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specifications are fitted for year-round data in most studies. Such models will ignore the correlation
structure that can change across seasons, resulting in inefficiency and model mis-specification. This is
particularly important for NO2, which appears to change its relationship with temperature as well as with
other pollutants across seasons.
This changing relationship between NO2 and temperature, as well as between NO2 and other
pollutants across seasons, and its potential implications for health effects modeling have not been
examined thoroughly in the time-series literature. Even the flexible smoother-based adjustments for
seasonal and other time-varying variables cannot fully take into account these complex relationships. One
way to alleviate or avoid this complication is to analyze data by season. While this practice reduces
sample size, the reduction would not be as serious as for PM (which is collected only every sixth day in
most locations) because NO2 is collected daily. An alternative approach is to include separate NO2
concentration variables for each season (e.g., by multiplying NO2 concentrations by a season indicator
variable). However, this approach assumes that all effects in the model that are not indicated to be season-
specific do not vary seasonally.
In locations where seasonal variability may be a factor, NO2 effect estimates calculated using year-
round data can be misleading, as the changing relationship between NO2, temperature, and other
pollutants across seasons may have a significant influence on the estimates. Analyses have indicated that
confounding from seasonal variability may be controlled effectively by stratifying the data by season.
AX6.1.5. Confounding Effects of Copollutants
Extensive discussion of issues related to confounding effects among air pollutants in time-series
studies are provided in Section 8.4.3 of the 2004 PM AQCD (U.S. Environmental Protection Agency,
2004). Since the general issues discussed there are applicable to all pollutants, such discussions are not
repeated here.
AX6.1.6. Generalized Estimating Equations
Since the publication of the 1993 NO2 AQCD (U.S. Environmental Protection Agency, 1993),
methods to analyze panel and longitudinal studies have improved. The general mixed model method of
Stiratelli et al. (1984) was an improvement over the method of Korn and Whittemore (1979) in that all the
data could be used, including that from subjects with insufficient data to permit fitting of a separate
logistical regression model. Generalized Estimating Equations, Liang and Zeger (1986) is an extension of
generalized linear models. The joint distribution of the subject's observations does not have to be
specified to derive the estimating equations. This is avoided by assuming a marginal distribution at each
time. However, a covariate that is constant for a subject cannot be included in this model. Besides
Gaussian outcome variables, the method can also be used for binomial or Poisson variables.
AX6.1.7. Hypothesis Testing and Model Selection
Epidemiologic studies investigated the association between various measures of NO2 (e.g., multiple
lags, different metrics, etc.) and various health outcomes using different model specifications. Statistically
testing a null hypothesis (i.e., there is no effect of NO2) requires one to calculate the value of a test
statistic (i.e., the t-value). If the observed test statistic exceeds a critical value (oftentimes the 95th
percentile) or is outside a range of values, the null hypothesis is rejected. However, when multiple testing
is done using a critical value determined for a single test, the chance that at least one of the hypotheses is
significant may be greater than the specified error rate. Procedures are available to ensure that the
rejection error rate does not exceed the expected error rate (usually 5%) when conducting multiple
6-6
-------
hypothesis testing. However, often the multiple hypotheses being tested are not statistically independent,
thus some corrections, such as the Bonferroni adjustment, may be overly conservative.
Multiple hypothesis testing and model selection also contribute to publication bias. Publication bias
is the tendency of investigators to submit manuscripts or editors to accept manuscripts for publication
based on the strength of the study findings. Although publication bias commonly exists for many topics of
research, it may be present to a lesser degree in the air pollution literature. Several air pollutants often are
examined in a single study, leading to the publication of significant, as well as nonsignificant, individual
pollutant results. For example, many air pollution papers with a focus on PM health effects also published
NO2 results. NO2 was largely considered a potentially confounding copollutant of PM; thus, NO2 effect
estimates were often presented regardless of the statistical significance of the results. Another aspect of
publication bias is only selecting the largest or statistically strongest effect estimate to report and not the
array of models evaluated. See a full discussion in the O3 AQCD (U.S. Environmental Protection Agency,
2006).
The summary of health effects in this ISA was vulnerable to the errors of publication bias and
multiple testing. Efforts have been made to reduce the impact of multiple testing errors on the conclusions
in this document. To address multiple hypothesis testing, emphasis was placed in this document on a
priori hypotheses. As identifying a priori hypotheses is difficult in the majority of the studies, the most
common hypotheses were considered. For example, although many studies examined multiple single-day
lag models, priority was given to effects observed at 0- or 1-day lags rather than at longer lags. Both
single- and multiple-pollutant models that include NO2 were considered and examined for robustness of
results. Analyses of multiple model specifications for adjustment of temporal or meteorological trends
were considered sensitivity analyses. Sensitivity analyses were not granted the same inferential weight as
the original hypothesis-driven analysis; however, these analyses are discussed in this document as
appropriate given their valuable insights that may lead scientific knowledge in new directions.
AX6.1.8. Generalized Additive Models
Generalized Additive Models (GAM) have been widely utilized for epidemiologic analysis of the
health effects attributable to air pollution. The impact of the GAM convergence issue was thoroughly
discussed in Section 8.4.2 of the 2004 PM AQCD. Reports have indicated that using the default
convergence criteria in the Splus software package for the GAM function can lead to biased regression
estimates for PM and an underestimation of the standard error of the effect estimate (Dominici et al.,
2002; Ramsay et al., 2003). The GAM default convergence criterion in the Splus software package is 10
with a maximum number of 10 iterations. The user can specify convergence criteria, that is orders of
magnitude smaller than the default value and can also allow for many more iterations before terminating
the program. The use of the default convergence criterion was found to be a problem when the estimated
relative risks were small and two or more nonparametric smoothing curves were included in the GAM
(Dominici et al., 2002). The magnitude and direction of the bias depend in part on the concurvity of the
independent variables in the GAM and the magnitude of the risk estimate. Recent focus has been on the
influence of the GAM function on effect estimates for PM.
The GAM convergence problem appears to vary depending on data sets, and likely depends upon
the intercorrelation among covariates and the magnitude of the risk estimate; thus, its impact on the
results of individual studies cannot be known without a reanalysis. Consistent with the approach used in
the 2004 PM AQCD, the results from studies that analyzed data using GAM with the default convergence
criterion and at least two nonparametric smoothing terms are generally not considered in this chapter,
with some exceptions as noted.
6-7
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AX6.2. Cardiovascular Effects Related to Short-Term N02
Exposure
AX6.2.1. Hospital Admissions and ED Visits: All CVD
Results of studies of short-term NO2 exposure and hospitalization or Emergency Department visits
for cardiovascular disease are summarized in Figure AX6.2-1. Studies of both 1-h max NO2 level and
24-h avg NO2 level are included. With the exception of lag 1 results reported by Jalaludin et al. (2006),
most point estimates are positive with confidence limits excluding the null value. Jalaludin et al. report a
lag 0 cool season relative risk (not pictured) of 1.09 (95%CI: 1.05, 1.13) per 30 ppb increase in 1-h max
NO2 level (Jalaludin et al., 2006). (Note that the IQR reported by Jalaludin et al. is 9.3 ppb so the 30-ppp
increase into which the results were standardized may be unlikely in Sydney, where the study was
conducted.) Although results for cardiovascular diseases were not tabulated for a reanalysis of GAM
impacted study of Los Angeles and Cook County hospital admissions, authors note that they observed an
association of NO2 with CVD hospital admissions in the reanalyses (Moolgavkar, 2003). The association
was diminished with the use of increasingly stringent convergence criteria, however (Moolgavkar, 2003).
Ischemic Heart Disease
Studies that further narrowed the cardiac disease grouping to evaluate Ischemic Heart Diseases (IHD) are
summarized in Figure AX6.2-2. Several US studies examined the association of ambient NO2 level with
IHD (Ito, 2004; Mann et al. 2002; Metzger et al. 2004; Peel et al. 2007). Ito (2004) reported a null
association of 24-h avg NO2 level with IHD admissions in Ontario where the mean ambient level was
21.3 ppb. Mann et al. (2002) examined the association of 24-h avg NO2 level with IHD and secondary
diagnoses of arrhythmia, IHD and secondary diagnosis of CHF, IHD and no secondary diagnosis, and all
IHD regardless of secondary diagnosis in single-pollutant models. The authors noted that the strongest
effect observed (IHD with secondary diagnosis of CHF) may have been driven by the MI primary
diagnoses. The 24-h avg NO2 level in the South Coast Air Basin of California, where this study was
conducted was approximately 37 ppb. This study was novel in that exposure level was assigned based on
the zip code of the health insurance participant and proximity to the monitoring station (Mann et al.,
2002). A non-significant increased risk of ED visit for IHD was observed in single-pollutant models,
among those with hypertension but not diabetes in a study conducted in Atlanta where the daily 1-h max
NO2 level was approximately 46 ppb (Peel et al., 2007).
Two studies of IHD and hospital admissions conducted in Europe have produced conflicting results
(Atkinson et al., 1999a; Poloniecki et al., 1997). Atkinson et al. (1999a) reported a significant increase in
IHD admission in a study in London. A study conducted in Helsinki reported an association of NO with
both hospitalization and ED visits for IHD while no association with NO2 was observed (Ponka and
Virtanen, 1996). In addition, Several Australian studies, including two multicity studies, supported an
association of hospital admissions and emergency visits for IHD and ambient NO2 level among older
adults in single-pollutant models (Jalaludin et al., 2006; Barnett et al., 2006; Simpson et al., 2005a,b).
One study conducted in Hong Kong reports slightly elevated non-significant association of IHD with 24-h
avg NO2 level (Wong et al., 1999). In addition, Lee et al. (2003a) reported an increase in IHD admissions
associated with 24-h avg NO2 level at lag 5.
6-8
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erence
Linn et al. (2000)
Metzger et al. (2004)*
Tolbert et al. (2007)*
Ballester et al. (2006)
Andersen et al. (2007a)
Andersen et al. (2007b)
Atkinson etal. (1999a)*
Atkinson etal. (1999a)*
Ballester etal. (2001)*
Poloniecki etal. (1997)
Barnett et al. (2006)
Barnett et al. (2006)
Hinwood etal. (2006)
Hinwood et al. (2006)
Jalaludin et al. (2006)*
Jalaludin etal. (2006)*
Jalaludin et al (2006)*
Chang et al. (2005)
Chang et al. (2005)
Wong etal. (1999)
Wong etal. (1999)
Yang et al. (2004a)
Yang et al. (2004a)
Location Season
Metro LA
Atlanta
Atlanta
Spain
Copenhagen
Copenhagen
London
London
Valencia
London
Australia Multicity
Australia Multicity
Perth
Perth
Sydney
Sydney
Sydney
Taipei Warm
Taipei Cold
Hong Kong
Hong Kong
Kaohsiung Warm
Kaohsiung Cool
Age
All ages
All ages
All ages
All ages
65 +
65 +
0-64
65+
All ages
All ages
65+
15-64
65+
All ages
65+
65+
65+
All ages
All ages
5-64
65+
All ages
All ages
Lag
0
3dav .
0-2 .
0-1 .
0-3
0-3 .
0
0
0
1
0-1
0-1 .
1
1
0
1
0-1
0-2 .
0-2 .
0-1 .
0-1 .
0-2 .
0-2 -
f
f
t-
4-
t
*
-i—
B-
u
-1-
_l_
_,_
— 1 —
H-
i —
-1-
-t-
+
_j_
I I I I I I I I I I I
.9 1.1 1.3 1.5 1.7 1.9 2.1 2.32.52.72.9
Relative risk
Figure AX6.2-1. Relative risks (95% Cl) for associations of 24-h N02 (per 20 ppb) and daily 1-h max N02* with
hospitalizations or emergency department visits for all cardiovascular diseases (CVD).
Primary author and year of publication, city, stratification variable(s), and lag are listed.
Results for lags 0 or 1 are presented, as available. *N021 h max; all others 24 h avg.
6-9
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Reference
Lippmannetal. (2000)
Ito (2004)
Mann etal. (2002)
Mann etal. (2002)
Mann etal. (2002)
Mann etal. (2002)
Metzger et al. (2004)*
Peel etal. (2007)*
Peel etal. (2007)*
Atkinson etal. (1999a)*
Atkinson etal. (1999a)*
Poloniecki etal. (1997)
Barnett et al. (2006)
Barnett et al. (2006)
Simpson et al. (2005a,b)*
Simpson et al. (2005a,b)*
Jalaludin et al. (2006)*
Jalaludin et al. (2006)*
Jalaludin et al. (2006)*
Wong etal. (1999)
Relative risk
Figure AX6.2-2. Relative risks (95% Cl) for associations of 24-h N02 (per 20 ppb) and daily 1-h max N02* (per
30 ppb) with hospitalizations for Ischemic Heart Disease (IHD). Primary author and year of
publication, city, stratification variable(s), and lag are listed. Results for lags 0 or 1 are
presented, as available. *N021 h max; all others 24 h avg.
Location
Ontario
Ontario
South Coast Air Basin
South Coast Air Basin
South Coast Air Basin
South Coast Air Basin
Atlanta
Atlanta
Atlanta
London
London
London
Australia, NZ
Australia, NZ
Australia, Multicity
Australia, Multicity
Sydney
Sydney
Sydney
Hong Kong
Disease Age
All Ages
All Ages
All Ages
All Ages
Arrhythmia All Ages
CHF All Ages
No Secondary All Ages
All IHD All Ages
All Ages
Hypertension 0-64
Diabetes 65+
All Ages
65+
15-64
All Ages
65+
65+
65+
65+
All Ages
Lag
1.01
0.98 .
1.02
1.02
1.03 .
1.02
1.03 .
1.04
1.00 .
1.02 .
1.03 .
1.00 .
1.03 .
1.01 .
1.00
1.00 .
1.02 .
1.01
1.02 .
1.01 .
H
+
1
1.
.
1 1
9 1.1
6-10
-------
Linn etal. (2000)
Mann etal. (2002)
Location
Metropolitan LA All ages
So Coast Air Basin All Ages
Zanobetti and Schwartz (2006) Boston
Zanobetti and Schwartz (2006) Boston
65+
65+
Lanki etal. (2006)
Von Klotetal. (2005)
D'lppoliti et al. (2003)
D'lppoliti et al. (2003)
Barnett et al. (2006)
Barnett et al. (2006)
Poloniecki etal. (1997)
Europe
Europe
Italy
Italy
Australia, NZ
Australia, NZ
London
65+
je Lag
ages 0 .
Ages 0
+ 0
+ 0-1.
ages 0 .
Survivors 35+ 0 .
ages 0 .
ages 0-2 .
+ 0-1.
-64 0-1 -
ages 1 .
-H
_e_
1.
i
I I I
.9 1.1 1.3
Relative risk
Figure AX6.2-3. Relative risks (95% Cl) for associations between 24-h avg N02 (per 20 ppb) and
hospitalizations for myocardial infarction (Ml). Primary author and year of publication, city,
stratification variable(s), and lag are listed. Results for lags 0 or 1 are presented as
available. *N021 h max; all others 24 h avg.
6-11
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Refere
Morris etal. (1995)
Location Other Age
US, multicity LA 65+
Morris etal. (1995)
Morris etal. (1995)
Morris etal. (1995)
Morris etal. (1995)
Morris etal. (1995)
Morris etal. (1995)
Linn et al. (2000)
Itoetal. (2004)
Ito et al. (2004)
Metzger et al. (2004) Atlanta
Peel etal. (2007) Atlanta
Peel etal. (2007) Atlanta
Wellenius et al. (2005a) Pittsburg
Poloniecki et al. (1997) London
Barnett et al. (2006) Australia, NZ
Barnett et al. (2006) Australia, NZ
Wong et al. (1999) Hong Kong
US, multicity Chicago 65+
US, multicity Philadelphia 65+
US, multicity New York 65+
US, multicity Detroit 65+
US, multicity Houston 65+
US, multicity Milwaukee 65+
Metro LA All Ages 0
Ontario All Ages 0
Ontario All Ages 1
Lag
0
0
0
0
0
0
0
All Ages 3 d moving
Hypertension All Ages 0-2
Diabetes All Ages 0-2
65+ 0
All Ages 1
65+ 0-1
15-64 0-1
All Ages 0-3
I I [ I I
.7 .9 1.1 1.3 1.5 1.7 1.9
Relative risk
Figure AX6.2-4. Relative risks (95% Cl) for associations of 24-h avg NO? (per 20 ppb) and 1-h max NCV with
hospitalizations for congestive heart failure (CHF). Primary author and year of publication,
city, stratification variable(s), and lag are listed. Results for lags 0 or 1 are presented as
available.
6-12
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AX6.2.2. Hospital Admissions and ED Visits: Myocardial Infarction (Ml)
Studies of hospital admissions for MI are summarized in Figure AX6.2-3. In the United States,
positive single-pollutant associations for emergency admissions for MI and increases in ambient NO2
level were reported in Boston (Zanobetti and Schwartz, 2006) and California (Linn et al., 2000; Mann
et al., 2002).
Pooled results from two European multicity studies are inconsistent. Von Klot et al. (2005) reported an
increase in MI readmissions at lag 0 while Lanki et al. (2006) reported a null effect at lag 1. The NO2
levels were similar in the cities studied with Lanki et al. (2006) reporting the 24-h avg level of 23 ppb and
Von Klot et al. (2005) reporting a range in 24-h avg across the cities studied of 12-37 ppb. A single-city
study in Italy (D'Ippoliti et al., 2003) found positive significant associations between 24-h avg NO2 level
and admission for the first episode of MI. The 24-h avg NO2 level reported by D'Ippoliti was
approximately 45 ppb. A study conducted in London reported a positive association of ED visit for MI
with 24-h avg NO2 where the 24-h avg reported was approximately 35 ppb. Finally, positive associations
were reported for MI in a multicity study conducted in Australia and New Zealand among older adults
and adults from 15 to 64 years old (Barnett et al., 2006). The 24-h avg NO2 level ranged from
approximately 7-12 ppb in the Australian cities studied (Barnett et al., 2006).
AX6.2.3. Hospital Admissions and ED Visits: Arrhythmia and
Congestive Heart Failure (CHF)
Hospital or ED admissions for arrhythmia were inconsistently associated with increases in ambient
NO2 level. Some studies reported positive associations (Rich et al., 2006a; Mann et al., 2002; Barnett
et al., 2006) while others reported null associations (Metzger et al., 2004; Lippmann et al., 2000;
reanalysis Ito, 2004). Single-pollutant models of hospital admissions and ED visits for CHF have also
produced mixed results (Figure AX6.2-4). A seven city study conducted in the U.S. among the elderly
found positive associations in Los Angeles (RR: = 1.52 [1.35, 1.71]), Chicago (RR: = 1.60 [1.24, 2.07])
and New York (RR: = 1.23 [1.05, 1.43]) per 30-ppb increase in NO2 (Morris et al., 1995). Estimates were
close to the null value in Philadelphia, Detroit, Houston, and Milwaukee and only the estimate for New
York remained significant in multi-pollutant models (Morris et al., 1995). The 1-h max NO2 level in the
cities studied ranged from 40 ppb in Milwaukee to 77 ppb in Los Angeles (Morris et al., 1995). Ito, 2004
reported null associations for CHF and NO2 in Ontario where the 24-h avg NO2 level is approximately
21 ppb (Ito, 2004). Elevated but non-significant associations were reported in Atlanta (Metzger et al.,
2004; Peel et al., 2007) and elevated significant associations were reported in Pittsburgh (Wellenius et al.,
2005a). Null associations were reported in London (Poloniecki et al., 1997) while positive significant
associations were reported in a multicity study in Australia and New Zealand (Barnett et al., 2006) and in
Hong Kong (Wong et al., 1999).
AX6.2.4. Hospital Admissions and ED Visits: Cerebrovascular Disease
AX6.2.4.1. Vaso-Occlusion in Sickle Cell
A recent study evaluated the association of pain in Sickle Cell patients, which is thought to be
caused by vaso-occlusion, with air pollution (Yallop et al., 2007). A time series analysis was performed to
link daily hospital admissions for acute pain among sickle cell patients with daily air pollution levels in
London using the cross correlation function. No association was reported for NO2. However, Yallop et al.
(2007) observed an association (CCF = -0.063, lag 0) for NO, CO, and O3.
6-13
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AX6.2.5. Hospital Admissions and ED Visits: Multipollutant Modeling
Results
Multipollutant models may have limited utility to distinguish the independent effects of specific
pollutants if model assumptions are not met. However, these models are widely used in air pollution
research and results for CVD hospital admissions and ED visits are summarized in Figure AX6.2-5. This
figure includes only those studies that present two-pollutant results in tabular form. Studies with
qualitative descriptions or figures summarizing two-pollutant results are discussed in the text that follows
(Linn et al., 2000; Mann et al., 2002; Metzger et al., 2004; Tolbert et al., 2007; Zanobetti and Schwartz,
2006; Jalaludin et al., 2006; Von Klot et al., 2005; Ballester et al., 2006; Wong et al., 1999). In addition,
we included text discussion of studies that simultaneous adjust for several pollutants (Morris et al., 1995;
Llorca et al., 2005) and several cerebrovascular disease studies that report multipollutant results (Ballester
et al., 2001; Villeneuve et al., 2006; Tsai et al., 2003a; Chan et al., 2006).
A U.S. study showed a diminishment of the relative risk for CHF in two-pollutant models
(Wellenius et al., 2005a). Morris et al. (1995) also observed a similar diminishment of the CHF
association in multipollutant models containing SO2, CO, and Ozone (Morris et al., 1995). The
association of cardiac disease admissions with NO2 in several non-U.S. studies was not robust in two-
pollutant models (Simpson et al., 2005a,b; Poloniecki et al., 1997; Barnett et al., 2006; Ballester et al.,
2006). Llorca et al. (2005) reported a similar lack of robustness in models containing NO2, TSP, H2S, NO,
and SO2. Estimates from studies conducted in Taiwan reporting relatively high associations of NO2 with
CVD in single-pollutant models remained robust in two-pollutant models during the cool (Yang et al.,
2004a) or warm (Chang et al., 2005) seasons only. In an Australian study of older adults (65+ years), the
effect estimate for NO2 was robust to simultaneous adjustment for O3 and particles (Morgan et al., 1998a).
6-14
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Reference Location Season Age Lag
Yang et al.
(2004a)
Kaohsiung, Warm All 0-2
Taiwan
Cool
Andersen et al.
(2007a)
Andersen et al.
(2007a)
Simpson et al.
(2005a,b)
Barnettetal.
(2006)*
Chang et al.
(2005)
Copenhagen 65+ 0-3
Copenhagen 65+ 0-3
4 Cities, All All
Australia year
7 Cities,
Australia
andNZ
Taipei, Warm
Taiwan
Wellenius et al.
(2005a)
Poloniecki et al. London
(1997)
Polluants
N02
N02PM,0
N02S010
N02CO
N0203
N02
N02PM10
N02 S02
N02CO
N0203
N02
N02PM
N02
N02NCto[
N02
N02 BSP
N0203
0.5
65+ 0-1 NO,
N02CO
0-2 NO,
Cool
Allegheny All 65+ 0
Co PA year
N02PM10
N02 S02
N02CO
N0203
N02
N02PM10
N02 S02
N02CO
N02°3
NO
N02PM10
N02CO
N0203
N02 S02
Cool All 0-1 NO,
N02 S02
N02CO
N02BS
N0203
Relative risk
1.0 1.5 2.0 2.5 3.0 3.5
Cardiovascular Disease
Congestive heart failure
Figure AX6.2-5. Relative risks (95% Cl) for associations of 24-h avg N(>2 exposure (per 20 ppb) and daily 1-h
max NCV (per 30 ppb) with hospitalizations or emergency department visits for CVD.
Studies with 2 pollutant model results. Primary author and year of publication, city,
stratification variable(s), and lag are listed. Results for lags 0 or 1 are presented as
available.
6-15
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In two additional U.S. studies, investigators provided text descriptions of multipollutant model
results and indicated that their analyses were unable to distinguish the effects of NO2 from PM, CO, and
other traffic pollutants (Linn et al., 2000; Mann et al., 2002.) In both studies, CO was more highly
correlated with NO2 than PM. In a Canadian study in which default GAM procedures were used, the
significant association of NO2 with ED visits for cardiac disease was reduced and non-significant in
multipollutant models (Stieb et al., 2000). Further, in a study of emergency department visits to Atlanta
hospitals, Metzger et al., 2004 observed a diminishment of the effect of NO2 on visits for cardiovascular
disease when CO was modeled with NO2, while the effect of CO remained robust. This finding was
repeated in an analysis that included several additional years of data (Tolbert et al., 2007). In this paper,
Tolbert et al. (2007) discussed the limitations of multipollutant models in detail and conclude that these
models may help researchers identify the strongest predictor of disease but may not isolate the
independent effect of each pollutant. In an Australia study (Jalaludin et al., 2006) and a Spanish multicity
study (Ballester et al., 2006) presenting multipollutant results, the association of NO2 with cardiac disease
was not robust to adjustment for other pollutants (CO, SO2, particles). However, in a European multicity
study investigators reported that the effect of NO2 on cardiac readmissions among MI survivors was not
diminished in multipollutant models (Von Klot et al., 2005).
Burnett et al. (1997a) reported robust estimates for cardiac disease hospital admissions and NO2,
whereas the observed association for cardiac hospitalizations and PM were explained by gaseous
pollutants. In another multicity study conducted in the same area, associations of NO2 with cardiac
disease were not attenuated when CO, SO2, and PM variables were included in the models (Burnett et al.,
1999).
The association of NO2 with stroke was not robust to adjustment for CO in a Canadian study
(Villeneuve et al., 2006). The association of NO2 with all cerebrovascular disease was not robust to
adjustment for BS and SO2 in a Spanish single city study (Ballester et al., 2001). Although results from a
Taiwanese study indicated that the effect of NO2 on stroke admissions is not diminished in two-pollutant
models, the authors noted that the association of NO2 with stroke may not be causal if NO2 is a surrogate
other components of the air pollution mixture (Tsai et al., 2003a).
Studies using alternative methods to investigate the influence of copollutants on observed
associations of NO2 with cardiovascular disease are few in number. In an study of emergency admissions
for MI and ambient pollution in Boston investigators attempt to distinguish traffic from non-traffic related
pollutants through their definition of an exposure metric for non-traffic PM (residuals in model of PM2 5
regressed against BC) but found NO2, PM2 5 and non-traffic PM each may trigger MI during the warm
season (Zanobetti and Schwartz, 2006). In a study conducted in Hong Kong, investigators looked at the
association of NO2 with CVD during high PMi0 and high ozone days (Wong et al., 1999). An interaction
between NO2 and ozone was observed (in the single-pollutant model NO2 associated with heart failure,
RR: 1.18 95% CI: 1.10, 1.26 per 20 ppb, lag 0-3).
AX6.2.6. Heart Rate Variability
Liao et al. (2004) investigated short-term associations between ambient pollutants and cardiac
autonomic control from the fourth cohort examination (1996 to 1998) of the population-based
Atherosclerosis Risk in Communities (ARIC) Study. PMi0, NO2, and other gaseous air pollutants were
examined in this study. PMi0 (24-h avg) and NO2 (24-h avg) 1 day prior to the randomly allocated
examination date were used. The mean ± SD NO2 level was 21 ± 8 ppb. PM10 concentrations measured 1
day prior to the HRV measurements were inversely associated with both frequency- and time-domain
HRV indices. Ambient NO2 concentrations were inversely associated with high-frequency power and
SDNN. In single-pollutant models, a 20-ppb increase in ambient NO2 was associated with a 5% reduction
(95% CI: 0.7, 9.2), in mean SDNN. Consistently more pronounced associations were suggested between
and HRV among persons with a history of hypertension.
6-16
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Various measures of HRV have been examined in relation to daily levels of ambient air pollution in
other studies (Chan et al., 2005; Wheeler et al., 2006; Holguin et al., 2003; Luttmann-Gibson et al., 2006;
Schwartz et al., 2005). Chan et al. (2005) recruited 83 patients from the cardiology section of a hospital in
Taiwan. Patients included 39 with coronary heart disease (CHD) and 44 with more than one risk factor for
CHD. The authors reported finding significant associations between increases in NO2 and decline in
SDNN (NO2 lagged 4 to 8 h) and LF (NO2 lagged 5 or 7 h) (see Annex Table AX6.3-10 for quantitative
results). There were no significant associations for r-MSSD or HF and NO2. None of the other pollutants
tested (PMio, CO, SO2, O3) were significantly associated with any of the HRV measured. Wheeler et al.
(2006) examined HRV and ambient air pollution in Atlanta in 12 patients who had an MI from 3 to 12
months prior to enrollment and 18 COPD patients. The results in the two patient groups were quite
different: increasing concentration of NO2 in the previous 4-h significantly reduced SDNN in MI patients
and significantly increased SDNN in COPD patients (see Annex Table AX6.10). Similar significant
associations were seen with increases in 4-h ambient PM2 5. The PM2 5 concentrations were moderately
correlated with NO2 levels (r =0.4).
In contrast, Holguin et al. (2003) found PM2 5 concentrations were moderately correlated with NO2
levels (v = 0.04) in 34 elderly adults in Mexico City and found no significant associations with increases
in NO2, but did find significant effects of PM2 5 on HF, particularly among hypertensive subjects.
Similarly, Luttmann-Gibson et al. (2006) also found significant effects of PM25 and SO4 on HRV
measured in a panel of 32 senior adults in Steubenville, OH, but observed no effect of increasing NO2.
Likewise, Schwartz et al. (2005) found significant effects of increases in PM25 on measures of HRV,
while no associations with NO2 were observed. A population-based study of air pollutants and HRV was
conducted in Boston, MA on 497 men from the VA Normative Aging Study (NAS) (Park et al., 2005).
The mean ± SD 24-h avg NO2 concentration was 22.7 ± 6.2 ppb. Associations with HRV outcomes were
observed with a 4-h moving avg of O3 and PM2 5 concentrations, but not with NO2.
AX6.2.7. Repolarization Changes
A prospective panel study, conducted in East Germany, analyzed 12 repeat ECG recordings for
56 males with IHD (Henneberger et al., 2005). Ambient air pollutants measured at fixed monitoring sites
were used to assign individual exposures for 0 to 5, 5 to 11, 12 to 17, 18 to 23, 0 to 23 h and for 2 to 5
days prior to the EEG. Pollutants considered were ultrafine particles (UFP), accumulation mode particle
(ACP), PM2 5, elemental carbon (EC), organic carbon (OC), SO2, NO2, NO, and CO. Associations were
observed between (1) QT duration and EC and OC; (2) T-wave amplitude and UFP, ACP and PM2 5; and
(3) T-wave complexity and PM10, EC, and OC. NO (r = 0.83) and NO2 (0.76) were highly correlated with
UFP but were not associated with repolarization abnormalities.
AX6.2.8. Arrhythmias Recorded on Implantable Cardioverter
Defibrilators (ICDs)
In a pilot study, Peters et al. (2000a) abstracted device records for 3 years for each of 100 patients
with ICDs. Defibrillator discharge events were positively associated with the previous day and 5-day
mean NO2 concentrations: each 20-ppb increase in the previous day's NO2 level was associated with an
increased risk of a discharge event (OR = 1.55 [95%CI: 1.05, 2.29]) (see Annex Table AX6.3-1 for the
increase associated with a 20-ppb increase in NO2).
Three papers by the same team of investigators examined the association between air pollution and
the incidence of ventricular arrhythmias (Dockery et al., 2005; Rich et al., 2005) and PAF episode (Rich
et al., 2006b) in Boston. A total of 203 patients with ICDs who lived within 25 miles of the ambient
monitoring site were monitored. Data included a total of 635 person-years of follow-up or an average of
3.1 years per subject. The median (IQR) 48-h avg NO2 concentration was 22.7 (7.7) ppb. In the study by
6-17
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Dockery et al. (2005), significant positive associations were observed between ventricular arrhythmias
within 3 days of a prior event, and a 2-day mean exposure to several air pollutants including PM2 5, BC,
NO2, CO, and SO2. Rich et al. (2005) examined associations between ambient air pollution levels less
than 24 h before the occurrence of a ventricular arrhythmia to make use of the precise time definition
available from the implantable cardioverter defibrillator (Rich et al., 2005). In single-pollutant models,
each 20-ppb increase in the mean NO2 level over the previous 2 days was associated with an increased
likelihood of ventricular arrhythmia, OR = 1.54 (95% CI: 1.11, 2.18). The association with NO2 was not
significant in two-pollutant models with PM2 5, but remained marginally significant in models with O3
(2.0-ppb increase in 24-h moving avg NO2 was associated with an OR = 1.36 [95% CI: 1.00, 1.80]).
There was a strong association between an increase of NO2 (by 20 ppb) and ventricular arrhythmia in the
presence of ventricular arrhythmia within the previous 72 h (OR = 2.09 [95% CI: 1.26, 3.51]). Increased
but non-significant associations were observed in this population between NO2 levels and PAF, as well as
fine particles and black carbon (Rich et al., 2006b).
A study conducted in St. Louis, which also examined the association of air pollutant level within 24
h of a ventricular arrhythmia, reports non-significant increases for NO2 and elemental carbon, while SO2
was significantly associated with increased occurrence of arrhythmia (Rich et al., 2006a). Metzger et al.
(2007) examined the association of ventricular tachyarrhythmias with air pollutants in the largest study to
date (N = 518), which was conducted in Atlanta. These investigators reported "suggestive" findings for
course particulate but generally no evidence of an association of NO2 and other pollutants with
tachyarrhythmias (Metzger et al., 2007).
AX6.2.9. Markers of Cardiovascular Disease
In a large cross-sectional study of 7,205 office workers in London, Pekkanen et al. (2000) collected
blood samples and analyzed the association between plasma fibrinogen, a risk factor for CVD, and
ambient levels of air pollution. In models adjusting for weather, demographic, and socioeconomic factors,
there was an increased likelihood of blood levels of fibrinogen >3.19 g/1 (90th percentile) for each 20-ppb
increase in NO2 lagged by 3 days (OR =1.14 [95% CI: 1.03, 1.25]). The correlation between daily NO2
and other traffic-related pollutants were high: daily levels of black smoke (r = 0.75), PMi0 (r = 0.76), SO2
(r = 0.62), CO (r = 0.81). The authors proposed that the increased concentrations of fibrinogen, a mediator
of cardiovascular morbidity and mortality, may be an indicator of inflammatory reactions caused by air
pollution.
Schwartz (2001) examined the association between fibrinogen, platelet count, and white blood cell
(WBC) count in the Third National Health and Nutrition Examination Survey (NHANES III). In single-
pollutant models NO2 was associated with platelet counts and fibrinogen. However, in a two-pollutant
model with PMi0 these associations became negative.
Pekkanen et al. (2002) enrolled a panel of 45 adults with coronary heart disease in order to examine
associations between heart function as measured by risk of ST-segment depression and particulate
pollution. Level of particulate and gaseous pollutants, including NO2, lagged by 2 days was found to have
the strongest effect on risk of ST-segment depression during mild exercise tests (OR = 14.1 [95% CI: 3.0,
65.4] for ST-segment depression of >0.1mV with a 20-ppb increase in NO2 lagged by 2 days). A large
(n = 863) cross-sectional study of resting heart rate (HR) of adults in France found significant
associations between elevated levels of NO2 within 8-h of measurement and resting HR of >75 beats per
minute (bpm) (OR = 2.7 [95% CI: 1.2, 5.4] for resting HR >75 bpm for each 20-ppb increase in NO2)
(Ruidavets et al., 2005).
In a population based study of participants in the Atherosclerosis Risk in Communities (ARIC)
study, Liao et al., 2005 did not observe differences in White Blood Cell (WBC) count, Factor VIII C,
fibrinogen, Von Willibrand Factor (VWF) or albumin depending on 24-h avg NO2 level lagged 1 to 3
days prior to the examination date. However, PMi0 was associated with factor VIII-C in this cohort. An
6-18
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association between PMi0 and serum albumin was observed only among persons with a history of CVD
(Liao et al., 2005).
Ruckerl et al. (2006) examined several markers of inflammation, cell adhesion, and coagulation
among a panel of 57 male patients with CHD. These authors primary hypothesis was that C-reactive
protein (CRP) would be increased with increases in air pollution. They also investigated the effect of air
pollution on other markers including serum amyloid A (SAA), E-selectin, von Wildebrand factor antigen,
intercellular adhesion molecule 1 (ICAM-1), fibrinogen, factor VII, prothrombin fragment 1+2, and D-
dimer. A significant association was observed for NO2 with CRP greater than the 90th percentile but the
strongest effect on CRP was observed for ultrafine particles.
Steinvil et al. (2007) investigated the association of air pollutants with several markers of
inflammation (fibrinogen, CRP, and WBC). Significant decreases in fibrinogen associated with increases
of 13 ppb in ambient NO2 were reported among men (all lags 0-7 and 7 day avg) and women (lag 0, 7 day
avg). The absolute change in fibrinogen ranged from 7.9 to 16.7 mg/dL (Steinvil et al., 2007). The mean
NO2 level was 19.5 ppb (Steinvil et al., 2007). PM10 was significantly associated with increased
fibrinogen only at day 7. No correlations with CRP and WBC were observed (Steinvil et al., 2007).
Baccarelli et al. (2007) investigated the effect of ambient NO2 with prothrombin time (PT) and
activated partial thromboplastin time (APTT) in 1218 normal subjects in Italy. Both NO2 (coefficient = -
0.08 95%CI: -0.15, 0.00) and PM10 (coefficient = -0.08 95% CI: -0.14, -0.01) on the same day and the
average for 30 days prior to the examination were negatively correlated with PT (e.g., PT became shorter
indicating hypercoagulability), while no effect on APTT was reported (Baccarelli et al., 2007).
AX6.3. Epidemiologic Studies
Table AX6.3-1. Studies examining exposure to indoor NO2 and respiratory symptoms.
AUTHOR,
YEAR,
LOCATION
Pilotto et al. (2004)
Australia
Pilotto etal. (1997)
Australia
STUDY DESIGN
Subjects: 118 asthmatic
children
Analysis: negative
binomial
Monitoring Device:
passive diffusion badges
Subjects: 388 children
Analysis: generalized
linear mixed models
Monitoring Device:
passive diffusion badges
EXPOSURE
TIME
6h
6h
MEAN (SD)
intervention: 16
(7)
control: 47 (27)
RANGE (PPB)
intervention: 7, 38
control: 12, 116
overall: 4, 132
OUTCOME & ESTIMATE
(95% CI)
difficulty breathinci. dav
RR 2.44 (1.02, 14.29)*
chest tiqhtness, dav
RR 2.22 (1.23, 4.00)*
asthma attacks, dav
RR 2.56 (1.08, 5.88)*
difficulty breathinq, niqht
RR 3.12 (1.45, 7.14)*
wheeze (>40 ppb vs. < 40 ppb)
OR 1.41 (0.63,3.15)
Drv couah (>40 oob vs. < 40 oob)
OR 1.08(0.62, 1.90)
6-19
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AUTHOR,
YEAR,
LOCATION
Nitschke et al.
(2006)
Australia
Garrett et al.
(1998)
Australia
STUDY DESIGN
Subjects: 174 asthmatic
children
Analysis: negative
binomial
Monitoring Device:
passive diffusion badges
Subjects: 148 children
(age 7-1 4)
Analysis: multiple logistic
regression
Monitoring Device:
passive monitors
EXPOSURE
TIME
6h
4 days
MEAN (SD)
home: 20 (22)
school: 34 (28)
med 6
RANGE (PPB)
OUTCOME & ESTIMATE
(95% Cl)
Wheeze
Day: school RR 1.01 (0.97,1.06)
home RR 0.98 (0.92, 1.04)
Night: school RR 0.99 (0.93, 1.06)
home RR 1.00 (0.90, 1.11)
difficulty breathinq
Day: school RR 1 .09 (1 .03, 1 . 1 5)
home RR 1.00 (0.98, 1.03)
Night: school RR 1.11 (1.05, 1.18)
home RR 1.03 (1.01, 1.05)
chest tiqhtness
Day: school RR 1.08 (0.99, 1.19)
home RR 0.97 (0.89, 1.06)
Night: school RR 1.12 (1.07, 1.17)
homeRR 1.02 (0.95, 1.09)
Cough
Day: school RR 1.01 (0.99, 1.03)
home:RR 1.01 (0.97, 1.05)
Night: school RR 1.01 (0.98, 1.04)
home RR 0.99 (0.96, 1.02)
asthma attacks
Day: school RR 1 .03 (0.98, 1 .08)
home: RR 1.00(0.95, 1.05)
Night: school RR 1.00 (0.93, 1.08)
homeRR 1.04(1.00, 1.07)
results given for 10-ppb increase in NO2
Cough
Gas stove OR 2.25 (1.13, 4.49)
Bedroom OR 1.47 (0.99, 2.18)
Shortness of Breath
Gas stove OR 1 .49 (0.72, 3.08)
Bedroom OR 1.23 (0.92, 1.64)
wheeze
Gas stove OR 1 .79 (0.80, 3.99)
Bedroom OR 1.15 (0.85, 1.54)
asthma attack
Gas stove OR 1 .73 (0.77, 3.90)
Bedroom OR 1.06 (0.77, 1.46)
chest tiahtness
Gas stove OR 3.11 (1 .07, 9.05)
Bedroom OR 1.12 (0.81, 1.56)
6-20
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AUTHOR,
YEAR,
LOCATION
Smith et al. (2000)
Australia
Belanger et al.
(2006)
Northeast U.S.
Chauhan et al.
(2003)
Southampton, U.K.
Van Strien et al.
(2004)
Northeast U.S.
STUDY DESIGN
Subjects: 125 asthmatic
adults/children
Analysis: GEE
Monitoring Device:
passive diffusion badges
Subjects: 728 asthmatic
children
Analysis: logistic, Poisson
regression
Monitoring Device:
Palmes tubes
Subjects: 114 asthmatic
children
Monitoring Device:
Palmes diffusion tubes
Subjects: 762 infants
Analysis: Poisson
regression
EXPOSURE
TIME
4.5 h
2 wks
7d
MEAN (SD)
gas home: 26
(18)
elect home: 9
(9)
Exposure
tertiles:
<7.5; 7.5-14;
>14
med: 10
RANGE (PPB)
overall: 4, 147
OUTCOME & ESTIMATE
(95% Cl)
children (n = 48, age 0-14)
wheeze
OR 1.04(0.89, 1.12)
breathlessness
OR 0.95 (0.70, 1.31)
chest tiqhtness
OR 1.29(1.16, 1.43)
Cough
OR 1.07(0.89, 1.29)
Asthma attack, dav
OR 1.13(1.02, 1.26)
Asthma stack, niqht
OR 1.16(1.03, 1.30)
results given for 1 SD (43.8-ppb)
increase in NO2
multifamilv housinq
wheeze RR 1 .33 (1 .05, 1 .68)
persistent cough RR 1.07 (0.84, 1.35)
breathlessness RR 1 .23 (0.95, 1 .59)
chest tightness RR 1.51 (1.18, 1.91)
sinqle-familv housinq
wheeze RR 0.98 (0.78, 1.22)
persistent cough RR 0.91 (0.69, 1.20)
breathlessness RR 0.86 (0.63, 1.18)
chest tightness RR 0.92 (0.68, 1 .25)
Increased symptom score for all virus
types
3rd vs. 1st fertile: 0.6 (0.01, 1.18)
Increased symptom score for RSV only
3rd vs. 1st fertile: 2.1 (0.52,3.81)
wheeze
<5.1 ppb: RR 1.0
5.1,9.9ppb:RR 1.15(0.79, 1.67)
9.9, 17.4 ppb: RR 1.03(0.69, 1.53)
>17.4 ppb: RR 1.45 (0.92, 2.27)
persistent couqh
<5.1 ppb:RR 1.0
5.1, 9.9 ppb: RR 0.96 (0.69, 1.36)
9.9, 17.4 ppb: RR 1.33(0.94, 1.88)
>1 7.4 ppb: RR1.52 (1.00,2.31)
shortness of breath
<5.1 ppb: RR 1.0
5.1, 9.9 ppb: RR 1.59 (0.96, 2.62)
9.9, 17.4 ppb: RR 1.95(1.17,3.27)
>1 7.4 ppb: RR 2.38 (1.31, 4.34)
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 et al. (2004) as protective effects for intervention vs. control.
6-21
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Table AX6.3-2. Studies examining exposure to ambient NO2 and acute respiratory
symptoms using generalized estimating equations (GEE) in the analysis method
AUTHOR,
YEAR,
LOCATION
SUBJECTS
AVG
TIME
MID-
RANGE
(PPb)
RANGE (ppb)
COPOLLUTANTS &
CORRELATIONS
OUTCOME
OR (95% Cl)
Children: Multi-City Studies
Schwartz
etal. (1994)
6 U.S. Cities
Mortimer
et al. (2002)
U.S.,
NCICAS
Schildcrout
et al. (2006)
North
America
CAMP
Ward et al.
(2002)
Birmingham
and
Sandwell,
UK
1844 children
864 asthmatic
children
990 asthmatic
children
162 children
24 h
4h
24 h
24 h
med 13
med 25
med 23
Winter
med
18.0
Summer
med
13.3
p10-p90,5, 24
7,90
min p10to max
p90, 10, 37
Winter: 4-35
Summer 2-33
PM2.5: r=0.35
PM10:r = 0.36
O3: r = -0.28
SO2: r= 0.51
O3:r=0.27
PM10:r = 0.26, 0.64
O3:r = 0.04, 0.47
SO2:r = 0.23, 0.68
CO: r = 0.63, 0.92
O3, PM10, PM2.5, SO2,
SO42"
cough, incidence:
lag 1-4 mean
asthma symptoms:
lag 1 -6 mean
asthma symptoms:
lag 0
lag 1
lag 2
3-day moving sum
APEF, morning , lag 0
APEF, afternoon , lag
1
1.61 (1.08, 2.43)
1.48(1.02,2.16)
1.06(1.00, 1.13)
1.04(0.97, 1.10)
1.09(1.03, 1.15)
1 .04 (1 .01 , 1 .07)
-0.81 (-3.66, 2.01)(
-1.76 (-4.61, 0.96)
Children: Single-City Studies
Pino et al.
(2004)
Chile
Ostro et al.
(2001)
Southern
California
Delfino et al.
(2002)
Southern
California
504 infants
138 asthmatic
children,
African
American
22 asthmatic
children
24 h
1 h
8h
mean
(sd) 41
(19)
mean
(sd) 80
(4)
mean
(sd) 15
(7)
p5-p95, 20, 81
20, 220
6,34
PM2.5:r = 0.34
PM10:r = 0.63
O3: r = 0.48
PM10:r = 0.55
O3:r = 0.26
wheezy bronchitis:
6 day lag
cough, incidence:
Iag3
wheeze, incidence:
Iag3
asthma symptoms:
lagO
1.14(1.04, 1.30)
1.07(1.00, 1.14)
1 .05 (1 .01 , 1 .09)
1.91 (1.07,3.39)
6-22
-------
AUTHOR,
YEAR,
LOCATION
Segala et al.
(1998)
Paris
Delfino et al.
(2003a)
Linn et al.
(1996)
Just et al.
(2002)
Paris
Jalaludin
et al. (2004)
Austrailia
SUBJECTS
84 asthmatic
children
24 asthmatic
children
269 children
82 asthmatic
children
148 children
with wheeze
history
AVG
TIME
24 h
1 h
24 h
1 h
24 h
15h
MID-
RANGE
(PPb)
mean
(sd) 30
(8)
mean
(sd) 7 (2)
32-42
175-195
mean
(sd) 29
(9)
mean
(sd) 15
(6)
RANGE (ppb)
13,65
3, 13
12,59
3, 79
COPOLLUTANTS &
CORRELATIONS
PM2.5:r=(0.61)*
PM10:r = 0.55
SO2: r=0.54
03
CO
SO2
PM10:r = 0.48
O3:r = 0.61
PM2.5: r = 0.92*
PM10:r = 0.54
O3: r = 0.09
SO2: r=0.69
PM10:r = 0.26
O3: r = -0.31
OUTCOME
asthma symptoms:
incidence: lag 0
lag 1
lag 4
nocturnal cough:
incidence: lag 3
lag 4
lagO
asthma symptoms
FVC. a.m.
FEV1,a.m.
FVC, p.m.
FEV1,p.m.
AFVC, p.m. -a.m.
AFEV1, p.m. -a.m.
Total score, a.m.
Total score, p.m.
nocturnal cough:
incidence: lag 0
lag 0-2
lag 0-4
Wet cough: lag 0
OR (95% Cl)
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)
NO2:12.0(1.98, 72.7)
NO2+Benzene: 3.58
(0.49, 26.2)
NO2+Toluene: 5.97
(0.83,43.1)
NO2+xylene: 6.33
(0.72, 55.4)
-0.40(0.16)
-0.11 (0.18)
-0.18(0.21)
-0.26(0.19)
-0.11 (0.18)
-0.39(0.16)
-0.01 (0.022)
-0.028 (0.025)
2.11 (1.20,3.74)
1.80(0.89,3.84)
1.58(0.73,3.54)
1.13(1.00, 1.26)
Adults
Segala et al.
(2004) Paris
46 nonsmoking
adults
24 h
mean
(sd) 30
(9)
12,71
PM2.5: r = 0.82*
PM10:r = 0.83
sore throat, cough:
lag 0-4
4.05(1.20, 13.60)
6-23
-------
AUTHOR,
YEAR,
LOCATION
Von Klot
et al. (2002)
Germany
SUBJECTS
53 asthmatic
adults
AVG
TIME
24 h
MID-
RANGE
(PPb)
med24
RANGE (ppb)
4,63
COPOLLUTANTS &
CORRELATIONS
PM10:r = 0.74
SO2: r=0.36
CO: r = 0.82
OUTCOME
wheeze, prev:
5-day mean
phlegm, prev:
5-day mean
cough, prev:
5-day mean
breathing prob in
a.m.: 5-day mean
OR (95% Cl)
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
Table AX6.3-3. 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
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: 19 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
SO2' r = 0 74
O3;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% [Cl 0.36, 3.05] lag
0 - GAM default
Chicago 2.04% [t = 2.99] lag 0 -
GAM-100
Los Angeles 2.5% [Cl 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
O-NR-100
Phoenix 4.4% [Cl 1.07, 7.84] lag
5
Chronic Respiratory Disease
Los Angeles
0-19 yrs 4.9% [Cl 4.1, 5.7] lag 2
20-64 yrs 1.7% [Cl 0.9, 2.1] lag 2
Multi-pollutant model
NO2and PM10: 1.72% [t = 3.18]
lag 0- GAM-100
NO2andPM2.5: 1.51% [t = 2.07]
lag 0- GAM-100
6-24
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Moolgavkar*et al.
(1997)
United States:
Minneapolis-St.
Paul
Period of Study:
1986-1991
Neidell (2004)
California
Period of Study:
1992-1998
OUTCOMES, DESIGN, &
METHODS
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
Outcomes (ICD 9 codes): Asthma
Age groups analyzed: <18;0-1; 1-3;
3-6;
6-12; 12-18
Study Design: Time-series
Statistical Analyses: NR
Covariates: Temperature,
precipitation, influenza epidemic
Seasons: Nov-Mar only
Lag: 0-4 days
MEAN LEVELS &
MONITORING
STATIONS
NO2 24-h avg (ppb)
16.3 ppb
IQR: 9.5 ppb
N02 (ppb)
Mean: 45. 947
SD = 17.171
COPOLLUTANTS
&
CORRELATIONS
PM10; r = 0.31
SO2; r = 0.09
CO; r = 0.58
03
CO
PM10
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
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 1,20df
1.7% [-0.8, 4.2] lag 1, 130df
Increment: NR
Age 0-1
Fixed effects: 0.009 (0.014)
Controlled for avoidance
behavior: 0.009 (0.01 4)
Single-pollutant: 0.001 (0.011)
Adjusted for SES: 0.021 (0.017)
Interaction with Low SES: -0.017
(0.029)
Age: 1-3
Fixed effects: 0.002 (0.016)
Controlled for avoidance
behavior: 0.002 (0.01 6)
Single-pollutant: 0.009 (0.013)
Adjusted for SES: -0.001 (0.020)
Interaction with Low SES: -0.004
(0.032)
Age 3-6
Fixed effects: 0.006 (0.016)
Controlled for avoidance
behavior: 0.006 (0.01 6)
Single-pollutant: 0.028 (0.013)
Adjusted for SES: 0.020 (0.020)
Interaction with Low SES: -0.037
(0.033)
Age 6-1 2
Fixed effects: 0.041 (0.015)
Controlled for avoidance
behavior: 0.042 (0.01 5)
Single-pollutant: 0.047 (0.012)
Adjusted for SES: 0.040 (0.018)
Interaction with Low SES: -0.016
(0.031)
Age: 12-18
Fixed effects: 0.005 (0.013)
Controlled for avoidance
behavior: 0.005 (0.01 3)
Single-pollutant: 0.015 (0.010)
Adjusted for SES: 0.013 (0.017)
Interaction with Low SES: -0.020
(0.026)
6-25
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Karr et al. (2006)
Southern LA
County, CA, United
States
Period of Study:
1995-2000
Linn et al. (2000)
Los Angeles,
United States
Period of Study:
1992-1995
Magas et al.
(2007)
Oklahoma City, OK
Period of Study:
2001-2003
OUTCOMES, DESIGN, &
METHODS
Outcomes (I CD 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-Mar only
Lag: 0-4 days
Outcomes (I CD 9 codes): Asthma
(493), COPD (APR-DRG 88),
Pulmonary diagnoses
(APR-DRG 75-1 01)
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
Outcomes (I CD 9 codes): Asthma
(493)
Age groups analyzed: 0-14
Study Design: Time-series
N: 1,270
Statistical Analyses: negative
binomial regression
Covariates: gender, day of wk,
holiday,
Lag:
MEAN LEVELS &
MONITORING
STATIONS
1-h max NO2 (ppb)
Mean: 59 ppb
IQR: 26 ppb
Number of Stations: 34
All concentrations are in
pphm.
Winter: 3.4 ±1.3
Spring: 2.8 ±0.9
Summer: 3.4 ± 1.0
Autumn' 41 + 14
Overall: 3.4 ±1.3
24-h avg: 11.7 ppb
Number of monitors: 10
COPOLLUTANTS
&
CORRELATIONS
CO
PM2.5
Winter
CO; r = 0.89
PM10; r = 0.88
O3; r = -0.23
Spring
CO; r = 0.92
PM10; r = 0.67
O3; r = 0.35
Summer
CO; r = 0.94
PM10; r = 0.80
O3; r = 0.11
Winter
CO; r = 0.84
PM10; r = 0.80
O3; r = -0.00
03
PM2.5
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
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, 1.1 3] 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
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%
Qualitative results: ambient
concentrations of NO2 increased
pediatric asthma hospitalizations
6-26
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Gwynn*et al.
(2000)
Buffalo, NY
United States
Period of Study:
1988-1990
Days: 1,090
Zanobetti and
Schwartz (2006)
Boston, MA,
United States
Period of Study:
1995-1999
OUTCOMES, DESIGN, &
METHODS
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
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
MEAN LEVELS &
MONITORING
STATIONS
24-h avg NO2 (ppb):
Min:4.0
25th: 15.5
Mean: 20.5
75th: 24.5
Max: 47.5
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
COPOLLUTANTS
&
CORRELATIONS
H+; r=0.22
SO42-; r = 0.36
PM10; r = 0.44
O3;r = 0.06
SO2; r = 0.36
CO; r = 0.65
COM; r = 0.72
PM2.5; r=0.55
BC; r=0.70
CO; r = 0.67
O3; r = -0.14
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
Increment: 27 9 ppb (Max-Mean;
IQR)
NO2 alone:
Max-Mean RR 1 .033 (t = 1 .32)
lag 1
IQR RR 1.01 (t= 1.32) lag 1
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
CANADA
Burnett et al.
(1997a)
16 cities
Canada
Period of Study:
4/1981-12/1991
Days: 3,927
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
1-h max NO2 (ppb)
Mean: 35.5
SD= 16.5
25th: 25
50th: 33
75th: 43
95th: 62
99th: 87
O3;r = 0.20
CO
SO2
COM
Increment: 10 ppb
Single-pollutant
NO2 and respiratory admissions,
p = 0.772
Multipollutant model (adjusted for
CO, O3, SO2, COM, dew point):
RR 0.999 [0.9922, 1.0059] lag 0
6-27
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Yang et al. (2003)
Vancouver,
Canada
Period of Study:
1986-1998
Days: 4748
Fung et al. (2006)
Vancouver, BC,
Canada
Period of Study:
6/1/95-3/31/99
OUTCOMES, DESIGN, &
METHODS
Outcomes (I CD 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
Outcomes (I CD 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 overtime is
available
Covariates: Day of wk
Statistical Package: S-Plus and R
Lag: Current day, 3 and 5 day lag
MEAN LEVELS &
MONITORING
STATIONS
24-h avg NO2 (ppb):
Mean: 18.74
SD = 5.66
5th: 11.35
25th' 14 88
50th: 17.80
75th: 2 1.45
100th: 49.00
IQR: 5.57
Number of stations: 30
NO224-h avg:
Mean: 16.83 ppb,
SD = 4.34;
IQR: 5.43 ppb;
Range: 7.22, 33.89
COPOLLUTANTS
&
CORRELATIONS
CO
SO2
O3; r = -0.32
COM
CO; r = 0.74
COM; r = 0.72
SO2;r = 0.57
PM10; r = 0.54
PM2.5; r=0.35
PM10-2.5;r = 0.52
O3; r = -0.32
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
Increment: 5.57 ppb (IQR)
All Respiratory Admissions <3
yrs:
NO2 alone: OR 1 .05 [1 .02, 1 .09]
lag 1
NO2 + O3: OR 1.05 [1.02, 1.09]
lag 1
NO2 + O3 + CO + COM + 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
NO2 + O3: OR 1.04 [1.02, 1.07]
lag 1
NO2 + O3 + CO + COM + SO2:
OR 1 .05
[1.01, 1.08] lag 1
Increment: 5.43 ppb. (IQR)
NO2 Time-series
RR 1.01 8 [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 1.01 2 [0.997, 1 .027] lag 0
RR 1.01 8 [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
COM, NO2, and PM10, though the
results were not significantly
different from one another.
6-28
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Yang (2005)
Vancouver, BC,
Canada
Period of Study:
1994-1998
Days: 1826
OUTCOMES, DESIGN, &
METHODS
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
MEAN LEVELS &
MONITORING
STATIONS
24-h avg: 17.03 ppb, SD =
4.48;
IQR: 5.47 ppb;
Range: 4.28, 33.89
Winter: 19.20(4.86)
Spring: 15.36(3.72)
Summer: 16.33 (4.57)
Fall: 17.27(3.77)
Number of Stations: 31
COPOLLUTANTS
&
CORRELATIONS
PM10; r = 0.61
SO2;r = 0.61
CO; r = 0.73
O3;r = -0.10
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
Increment: 5.5 ppb (IQR)
COPD
>65 yrs, yr 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, 1.1 3] 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
O3:RR 1.12 [1.04, 1.20] lag 0-6
Multipollutant models
NO2, CO, SO2, O3, PM10: RR
1.01 [0.88, 1.16]
NO2, CO, SO2, O3: RR 1 .06 [0.95,
1.19]
NO2 was strongest predictor of
hospital admission for COPD
among all gaseous pollutants in
single-pollutant models.
6-29
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Lin* et al. (2004)
Vancouver, BC
Canada
Period of Study:
1987-1991
Chen et al. (2005)
Vancouver, BC
Period of Study:
6/1995-3/1999
OUTCOMES, DESIGN, &
METHODS
Outcomes (I CD 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
Outcomes (I CD 9 codes): All
Respiratory (460-519)
Age groups analyzed: 65+
Study Design: Time-series
N: 12,869 overall admissions
Statistical Analyses: Poisson
regression with GLM
Covariates: Trend, day of wk, weather
Statistical package: S-Plus
Lag: 1-7
MEAN LEVELS &
MONITORING
STATIONS
24-h avg NO2 (ppb)
Mean: 18.65
SD = 5.59
Min:4.28
25th: 14.82
50th: 17.75
75th: 2 1.36
Max: 45.36
Number of stations: 30
24-h avg: 16.8(4.3) ppb
Range: 7.2-33.9
IQR: 5.4
COPOLLUTANTS
&
CORRELATIONS
CO; r = 0.73
SO2;r = 0.67
O3; r = -0.03
PM2.5; r=0.37
PM10; r = 0.55
PM10; r = 0.54
PM10-2.5;r = 0.54
PM2.5; r=0.36
CO; r = 0.74
SO2;r = 0.57
O3; r = -0.32
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
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]
Lag2RR 1.13 [1.02, 1.24]; 1.06
[0.95, 1.18]
LagSRR 1.14 [1.02, 1.27]; 1.06
[0.94, 1.19]
Lag4RR 1.14 [1.02, 1.28]; 1.05
[0.92, 1.19]
LagSRR 1.12 [0.99, 1.27]; 1.10
[0.96, 1.26]
Lag6RR 1.12 [0.98, 1.28]; 1.07
[0.93, 1.23]
Lag 7RR 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]
Lag2RR 1.03 [0.91, 1.17]; 0.98
[0.85, 1.12]
LagSRR 1.04 [0.91, 1.20]; 0.98
[0.84, 1.13]
Lag4RR 1.11 [0.95, 1.29]; 1.01
[0.86, 1.19]
LagSRR 1.11 [0.94, 1.30]; 0.99
[0.83, 1.17]
Lag6RR 1.08 [0.91, 1.28]; 1.03
[0.86, 1.24]
Lag 7RR 1.07 [0.90, 1.28]; 1.09
[0.90, 1.32]
Multipollutant model (adjusted for
S02)
Boys, Low SES:
1.1 6 [1.06, 1.28] lag 1
1.1 8 [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.
No analyses for NO2
6-30
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Lin et al. (2003)
Toronto, ON
Period of Study:
1981-1993
Burnett et al.
(1997b)
Toronto, Canada
Period of Study:
1992-1994
OUTCOMES, DESIGN, &
METHODS
Outcomes (I CD 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
MEAN LEVELS &
MONITORING
STATIONS
NO2 24-h avg: 25.24 ppb, SD
= 9.04;
IQR: 11 ppb;
Range: 3.00, 82.00
Number of Stations: 4
Mean NO2: 38.5 ppb
IQR NO2: 5.75 ppb
Range: 12, 81
Number of Stations:
6-11
COPOLLUTANTS
&
CORRELATIONS
CO; r = 0.55
SO2; r = 0.54
PM10; r = 0.52
O3; r = 0.03
PM2.5; r= 0.50
PM10-2.5; r = 0.38
PM10; r = 0.61
CO; r = 0.25
H+; r=0.25
SO4; r = 0.34
TP;r = 0.61
FP;r = 0.45
Qp. r = 0 57
COM' r = 0 61
Q • r = 0 07
SO2; r = 0.46
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
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];
1.1 4 [1.02, 1.28]
Lag 0-5: OR 1.1 2 [1.01, 1.23];
1.16 [1.02, 1.31]
Lag 0-6: OR 1.11 [1.00, 1.24];
1.1 6 [1.02, 1.32]
Increment: 5.75 ppb (IQR)
Respiratory - Percent increase
4.4% [Cl 2.4, 6.4], lag 0
Copollutant and multipollutant
models RR
(t-statistic):
NO2, COM: 1.018(1.36)
NO2, H+: 1.037(3.61)
NO2, SO4: 1.033(3.05)
NO2, PM10: 1.039(2.85)
NO2, PM2.5: 1.037(3.13)
NO2, PM10-2.5: 1.037(2.96)
NO2, O3, SO2: 1.028(2.45)
NO2, O3, SO2, COM: 1.010 (0.71)
NO2, O3, SO2, H+: 1.027 (2.39)
NO2, O3, SO2, SO4: 1.027 (2.36)
NO2, O3, SO2, PM10: 1.028 (1.77)
NO2, O3, SO2, PM2.5: 1.028
(2.26)
NO2, O3, SO2, PM10-2.5: 1.022
(1.71)
6-31
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Burnett et al.
(1999)
Metro Toronto,
Canada
Period of Study:
1980-1994
Lin et al. (2005)
Toronto, Canada
Period of Study:
1998-2001
Burnett* et al.
(2001)
Toronto, Canada
Period of Study:
1980-1994
OUTCOMES, DESIGN, &
METHODS
Outcomes (I CD 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 days, cumulative
Outcomes (ICD 9 codes): Respiratory
infections (464, 466, 480-487)
Age groups analyzed: 0-14
N: 6,782
Study Design: Bidirectional case-
crossover
Statistical Analyses: Conditional
logistic regression
Covariates: Temperature, dew point
temperature
Statistical Package: SAS v 8.2
Lag: 1-7 day exposure averages
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
MEAN LEVELS &
MONITORING
STATIONS
24-h mean: 25.2 ppb, SD =
9.1,
CV: 36;
IQR: 23
Number of stations: 4
24-h avg: 24.54 (7.56) ppb
Range: 9.2-53.75
25th: 18.75
50th: 24.00
75th: 29.33
Number of monitors: 7
1-h max NO2 (ppb)
Mean: 44.1
CV:33
5th: 25
25th: 35
50th: 42
75th' 52
95th: 70
99th: 86
100th: 146
Number of stations: 4
COPOLLUTANTS
&
CORRELATIONS
COH;r=NR
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
CO; r = 0.20
SO2;r = 0.61
03; r = 0
PM10; r = 0.54
PM2.5; r=0.48
PM10-2.5;r = 0.40
O3;r = 0.52
SO2
CO
PM2.5
PM10-2.5
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
Increment: 25.2 ppb (Mean)
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: 10.6 ppb (IQR)
All children:
NO2 alone: 1.20 [1.08, 1.34] lag
0-5
NO2 + PM2.5 + PM10-2.5: 1 .13
[0.97, 1.31] lag 0-5
Boys:
NO2 alone: 1.13 [0.98, 1.29] lag
0-5
NO2 + PM2.5 + PM10-2.5: 1 .00
[0.83, 1.21] lag 0-5
Girls:
NO2 alone: 1 .28 [1 .09-1 .50] lag 0-
5
NO2 + PM2.5 + PM10-2.5: 1 .31
[1.05, 1.63] lag 0-5
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
6-32
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Fung et al. (2007)
Ontario, Canada
Period of Study:
1996-2000
Luginaah et al.
(2005)
Windsor, ON,
Canada
Period of Study:
4/1/95-12/31/00
OUTCOMES, DESIGN, &
METHODS
Outcomes (I CD 9 codes): All
respiratory (460-519)
Age groups analyzed: 0-4, 5-14, 15-
19, 20-24, 25-54, 55-64, 65-74, 75+
Study Design:
Statistical Analyses:
Covariates:
Statistical Package:
Lag:
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
MEAN LEVELS &
MONITORING
STATIONS
London
Mean: 18.10 ppb (7.86)
Range: 0-53
Windsor
Mean: 23.50 ppb (7.59)
Range: 6-50
Sarnia
Mean: 16.85 ppb (8. 13)
Range: 0-52
NO2 mean 1-h max:
38.9 ppb,
qr\ — 1 9 "V
OLJ — I ^.O,
IQR: 16
Number of stations: 4
COPOLLUTANTS
&
CORRELATIONS
SO2
03
CO
SO2;r = 0.22
CO; r = 0.38
PM10; r = 0.33
COM; r = 0.49
O3;r = 0.26
TRS; r=0.06
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
Not reported
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, lag 1 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]
6-33
-------
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
Simpson et al.
(2005a)
Multi-city study,
Australia (Sydney,
Melbourne,
Brisbane, Perth)
Period of Study:
1996-1999
Outcomes (ICD 9/ICD 10): All
respiratory (460-51 9/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 burn
Statistical Package: S-Plus, R
Lag: 0,1,2 days
1 h max NO2 ppb (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:
O3;r = 0.15
BSP; r = 0.50
Melbourne:
O3; r = 0.30
BSP; r = 0.29
Sidney:
O3;r = 0.24
BSP; r = 0.54
Perth:
O3;r = 0.28
BSP; r = 0.62
Increment: 1 ppb
Respiratory
>65 yrs 1.0027 [1.001 5, 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
NO2 Alone: 1.0027
[1.0015,1.0039] Iag0-1
NO2+BSP: 1.0023(1.0009,
1 .0038] lag 0-1
NO2 + O3: 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.
6-34
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Barnett et al.
(2005)
Multicity, Australia/
New Zealand;
(Auckland,
Brisbane,
Canberra,
Christchurch,
Melbourne, Perth,
Sydney)
Period of Study:
1998-2001
Erbas et al. (2005)
Melbourne,
Australia
Period of Study :
2000-2001
OUTCOMES, DESIGN, &
METHODS
Outcomes (ICD 9/ICD 10): All
respiratory (460-51 9/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
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
MEAN LEVELS &
MONITORING
STATIONS
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 1-h 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
1-h mean NO2: 16.80 ppb,
SD = 8.61;
Range: 2.43, 63.00
COPOLLUTANTS
&
CORRELATIONS
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 = -0.1 5, 0.28
PM10
03
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
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
1-4yrs4.8%[-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 max (9.0 ppb change)
Pneumonia and acute bronchitis
Oyrs 2. .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
Oyrs 2.2% [-1.6, 6.1] lag 0-1
1-4 yrs 2.8% [0.7, 4.9] lag 0-1
5-1 4 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
Increment: 90th-10th percentile
Inner Melbourne; increment =
25.54 ppb
RR 0.83 [0.68, 0.98] lag 0
Western Melbourne; increment =
28.86 ppb
RR 1.1 5 [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, 1.1 8] lag 1
6-35
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Hinwood et al.
(2006)
Perth, Australia
Period of Study:
1992-1998
Morgan et al.
(1998a)
Sydney, Australia
Period of Study:
1990-1994
OUTCOMES, DESIGN, &
METHODS
Outcomes (ICD 9): COPD (490-496,
excluding 493); Pneumonia (480-
489.99); Asthma (493)
Age groups analyzed: <15, 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
Outcomes (ICD 9): COPD (490-492,
494, 496); Asthma (493)
Age groups analyzed: 1-14, 15-64,
65+, all ages
Study Design: Time-series
# 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
MEAN LEVELS &
MONITORING
STATIONS
24-h mean [Std. Dev]
(10th and 90th percentile)
All yr 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-h max
Mean [Std. Dev]
All yr 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
24-h daily mean: 15 ppb,
SD = 6, Range: 0, 52,
IQR' 11 90-1 Oth
percentile: 17
Mean daily 1-h max: 29 ppb,
SD = 3,
Range: 0, 139,
IQR: 15, 90-1 Oth
percentile: 29
# of stations: 3-1 4, r= 0.52
COPOLLUTANTS
&
CORRELATIONS
O3; r = -0.06
CO' r = 0 57
BS' r = 0 39
PM10
PM2.5
24-h avg NO2:
PM(24h);r = 0.53
PM (1 h);r = 0.51
O3; r = -0.9
1-h max NO?'
PM(24h);r = 0.45
PM (1 h);r = 0.44
O3;r = 0.13
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
Increment: 1 ppb (all values were
estimated from the graphs)
All respiratory NO2 (24 h)
>65yrsOR: 1.005 [1.001, 1.011]
Ian 1
icay I
All ages OR: 1 .002 [0.998, 1 .004]
lag 1
Pneumonia NO2 (24 h)
>65yrsOR: 1.006[0.999, 1.014]
lag 1
All ages OR: 1.002[0.998, 1.010]
lag 1
COPD NO2 (24 h)
>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 h)
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
Increment: 90-10th 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 max (29 ppb)
Asthma: 1-1 4 yrs 5.29% [1.07,
9.68] lag 0
15-64 yrs. 3.18% [-
1.53,8.11] lagO
COPD: 65+ yrs. 4.60% [-0.17,
9.61] lag 1
Multipollutant model (29 ppb)
Asthma: 1-14yrs. 5.95% [1.11,
1 1.02] lag 0
COPD: 65+ yrs. 3.70% [-1 .03,
8.66] lag 1
6-36
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Petroeschevsky
et al. (2001)
Brisbane, Australia
Period of Study:
1987-1994
Days: 2922
OUTCOMES, DESIGN, &
METHODS
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 yr
Dose-response investigated?: Yes
Statistical Package: SAS
Lag: Single: 1,2, 3 day
MEAN LEVELS &
MONITORING
STATIONS
Mean (range) 24-h avg:
Cumulative: 0-2, 0-4
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
COPOLLUTANTS
&
CORRELATIONS
BSP
03
SO2
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
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
o
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, 1.01 8] lag
1
All ages 0.962 [0.936, 0.989] lag
0-2
EUROPE
Anderson et al.
(1997)
Multicity, Europe
(Amsterdam,
Barcelona,
London, Paris,
Rotterdam)
Period of study:
1977-1 989 for
Amsterdam and
Rotterdam
1986-1 992 for
Barcelona
1987-1991 for
London
1980-1989 for
Milan
1987-1 992 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: Time-series
Statistical Analyses: APHEA protocol,
Poisson regression, meta-analysis
Covariates: Season, temperature,
humidity, holiday, influenza
Statistical Package: NR
Lag: NR
24-h all yr avg: (pg/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 (pg/m3)
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 pg/m3
Meta-analytic results - Weighted
mean values from 6 cities
COPD-Warm season
24 h 1 .03 [1 .00, 1 .06] lag 1
1 h 1 .02 [1 .00, 1 .05] lag 1
COPD-Cool season
24 h 1.01 [0.99, 1.03]
1 h 1.02 [0.99, 1.05]
COPD-AII Year
24 hr 1.01 9 [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 pg/m3for PM10;
change in NO2 not described.
Asthma, Oto 14 yrs:
ForPMIO: 1.2% [0.2, 2.3]
ForPM10 + NO2:0.1 [-0.8, 1.0]
Asthma, 1 5 to 64 yrs:
ForPMIO: 1.1% [0.3, 1.8]
ForPM10 + NO2:0.4[-0.5, 1.3]
COPD + Asthma, >65 yrs
ForPMIO: 1.0% [0.4, 1.5]
ForPM10 + NO2:0.8[-0.6, 2.1]
All Respiratory, >65 yrs of age
ForPMIO: 0.9% [0.6, 1.3]
ForPM10 + NO2:0.7[-0.3, 1.7]
6-37
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Sunyer et al.
(1997)
Multicity, Europe
(Barcelona,
Helsinki, Paris,
London)
Period of Study:
1986-1992
Schouten et al.
(1996)
Multicity, The
Netherlands
(Amsterdam,
Rotterdam)
Period of Study:
04/01/77-09/30/89
OUTCOMES, DESIGN, &
METHODS
Outcomes (I CD 9): Asthma (493)
Age groups analyzed: <15, 15-64
Study Design: Time-series
Statistical Analyses: APHEA protocol,
Poisson regression, GEE; meta-
analvsis
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
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
MEAN LEVELS &
MONITORING
STATIONS
24-h median (range) (pg/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
24-h avg NO2
Amsterdam
Mean/Med: 50/50 pg/m3
Rotterdam
Mean: 54/52 pg/m3
Daily max 1 h
Amsterdam
Mean/Med: 75/75 pg/m3
Rotterdam
Mean/Med: 82/78 pg/m3
# of stations: 1 per city
COPOLLUTANTS
&
CORRELATIONS
SO2
black smoke
03
SO2
BS
03
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
Increment: 50 pg/m3 of 24-h avg
for all cities combined
Asthma
15-64yrs
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] -Winter only
Two-pollutant models:
NO2/Black smoke
15-64 yrs
1.055 [1.005, 1.1 09] lag 0-1
15-64 yrs 1.088 [1.025, 1.155]
cumulative
0-3
<15yrs
1.036[0.956, 1.122]
NO2/SO2
<15 yrs
1 .034 [0.988, 1 .082]
Increment: 100 pg/m3 increment
All respiratory, Amsterdam 24 h
mean; 1-h max
15-64 yrs RR 0.890 [0.783,
1.012]; 0.894 [0.821, 0.973] lag 1
>65 yrs RR 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] lag 1
>65 yrs RR 1.1 72 [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]
Ion 1
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
6-38
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Ponce de Leon
etal. (1996)
London, England
Period of Study:
04/1987-1988;
1991-02/1992
Atkinson et al.
(1999a)
London, England
Period of Study:
1992to 1994
Days: 1096
OUTCOMES, DESIGN, &
METHODS
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
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.
MEAN LEVELS &
MONITORING
STATIONS
NO2 24-h avg: 37.3 ppb,
Med:35
SD= 13.8
IQR: 14 ppb
1-h max: 57.4 ppb,
Med:51
SD = 26 4
IQR: 21 ppb
# of stations: 2
NO2 1-h mean: 50.3 ppb,
SD= 17.0,
Range: 22.0, 224.3 ppb,
10th percentile: 34.3,
90th percentile: 70.3
# of stations: 3; r = 0.7, 0.96
COPOLLUTANTS
&
CORRELATIONS
SO2; r = 0.45
BS; r = 0.44
o
^3
03,
CO
PM10,
BS,
SO2
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
Increment: 90th-10th percentile
(24-h avg: 27 ppb)
Allyr
All ages 1.0114 [1.006, 1.0222]
lag 2
0-1 4 yrs 1 .01 04 [0.9943, 1 .0267]
lag 2
15-64 yr 1.0113 [0.9920, 1.0309]
lag 1
>65yr 1.02 16 [1.0049, 1.0386]
lag 2
Warm season
All ages 1 .0276 [1 .0042, 1 .051 5]
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 .0 1 74 [0 .9994 , 1 .0358]
lag 2
Increment: 36 ppb (90th-10th
centile)
All ages
Respiratory 1.64% [0.14, 3.15]
Ian 1
icay I
Asthma
1. 80% [-0.77, 4.44] lag 0
0-1 4 yrs
Respiratory 1.94% [-0.39, 4.32]
lag 2
Asthma
1% [-1.42, 5.77] lag 3
15-64 yrs
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
COPD 3.53% [0.64, 6.50] lag 3
Lower Resp. 3.47% [0.08, 6.97]
lag 3
6-39
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Spixetal. (1998)
Multi-city (London,
Amsterdam,
Rotterdam, Paris),
Europe
Period of Study:
1977 and 1991
Wong* et al.
(2002)
London England
and Hong Kong
Period of Study:
London: 1992-
1994
Hong Kong:
1995-1997
Days: 1 ,096
OUTCOMES, DESIGN, &
METHODS
Hospital Admissions
Outcomes (I CD 9 codes): All
respiratory
(460-519); Asthma (493)
Age groups analyzed: 15-64, 65+
Study Design: Time-series
# of Hospitals:
Statistical Analyses: Poisson
regression following APHEA protocol.
Pooled meta-analysis adjusted for
heterogeneity
Covariates: Trend, seasonality, day of
wk, holiday, temperature, humidity,
unusual events (strikes, etc.)
Statistical Package:
Lag: 1 to 3 days
Outcomes (ICD 9): All respiratory
admissions (460-519); asthma (493)
Age groups analyzed: 15-64, 65+, all
ages
Study Design: Time-series
Statistical Analyses: APHEA protocol,
Poisson regression with GAM
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
MEAN LEVELS &
MONITORING
STATIONS
NO2 daily mean (pg/m3)
London 35
Amsterdam 50
Rotterdam 53
Paris 42
24 h NO2 pg/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
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
COPOLLUTANTS
&
CORRELATIONS
SO2, O3, BS, TSP
Hong Kong
PM10; r = 0.82
SO2;r = 0.37
O3; r = 0.43
London
PM10; r = 0.68
SO2;r = 0.71
O3; r = -0.29
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
Increment: 50 pg/m3.
All cities, yr round
1 5-64 yrsRR 1.010 [0.985,
1 .036]
Warm RR 1.00 [0.96,
1.04]
Cold RR1.01 [0.98,
1.04]
>65yrsRR 1.01 9 [0.982, 1.060]
Warm RR 1.02 [0.99,
1.06]
Cold RR 1.00 [0.98,
1.03]
Increment: 10 pg/m3
Asthma, 15-64yrs
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 [-0.1, 2.8] lag 0-1
Respiratory 65+ yrs
Hong Kong
ER 1.8 [1.2, 2.4] lag 0-1
ER 1.3 [0.8, 1.8]lagO
Warm: ER 0.8 [0.1, 1.6] lag 0-1
Cool: ER 3.0 [2. 1,3.9] lag 0-1
+O3:ER1.6[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: ER 0.6 [-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
+PM 1 0: ER -0.4 [-1 .2 , 0.4] lag 0-1
+SO2: ER -0.2 [-0.9, 0.5] lag 0-1
6-40
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Anderson et al.
(1998)
London, England
Period of Study:
Apr1987-Feb
1992
Days: 1 ,782
OUTCOMES, DESIGN, &
METHODS
Outcomes (I CD 9): Asthma (493)
Age groups analyzed: <15, 15-64,
65+
Study Design: Time-series
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
MEAN LEVELS &
MONITORING
STATIONS
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
COPOLLUTANTS
&
CORRELATIONS
03
SO2
BS
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
Increment: 10 ppb in 24-h NO2
0-14yrs
Whole yr RR 1 .25 [0.3, 2.2] lag 2;
RR 1.77 [0.39, 3. 18] lag 0-3
+ O3 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,
3.1 7] 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 yr 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 yr RR 2.96 [0.67, 5.31] lag
2;RR3.14
[-0.04, 6.42] lag 0-3
+ O3 RR 4.51 [1.43, 7.69]
lag 2
+ SO2 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
6-41
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Anderson et al.
(1998) (cont'd)
Anderson et al.
(2001)
West Midlands
conurbation,
United Kingdom
Period of Study:
10/1994-12/1996
Prescott et al.
(1998)
Edinburgh, United
Kingdom
Period of Study:
10/92-6/95
OUTCOMES, DESIGN, &
METHODS
Hospital Admissions:
Outcomes (I CD 9 codes): All
respiratory (460-519), Asthma (493),
COPD (490-496, excluding 493)
Age groups analyzed: 0-14, 15-64,
65+
Study Design: Time-series
Statistical Analyses: Followed APHEA
2 protocol, GAM
Covariates: Season, temperature,
humidity, epidemics, day of wk,
holidays
Statistical Package: S-Plus 4.5 Pro
Lag: 0,1, 2,3, 0-1,0-2,0-3
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
MEAN LEVELS &
MONITORING
STATIONS
1-h max avg: 37.2 ppb,
15.1 (SD)
Min: 10.7 ppb
Max: 176.1 ppb
10th: 22.9 ppb
90th: 51 .7 ppb
$ of monitors' 5
NO2: 26.4 ± 7.0 ppb
Min: 9 ppb
Max: 58 ppb
IQR: 10 ppb
# of Stations: 1
COPOLLUTANTS
&
CORRELATIONS
PM10; r = 0.62
PM2.5; r=0.61
PM2.5-10;r = 0.25
BS; r = 0.65
SO4; r = 0.30
SO2; r = 0.52
O3; r = 0.08
CO; r = 0.73
CO
PM10
SO2
03
BS
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
+ O3 RR 5.14 [0.69, 9.79]
lag 2
+ SO2 RR 2. 10 [-1.08, 5.39]
lag 2
+ BS RR 4.47 [-0.04, 9.19]
lag 2
All ages
Whole yr RR 1 .25 [0.49, 2.02] lag
2; RR 2.05 [0.96, 3.15] lag 0-3
+ O3 RR 1.08 [0.1 2, 2.05]
lag 2
+ SO2 RR 0.99 [0.1 8, 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
+ O3 RR 0.50 [-0.79, 1.81]
lag 2
+ SO2 RR 1.10 [0.12, 2.08]
lag 2
+ BS RR 1.29 [0.37, 2.22]
lag 2
Increment: 25.5 ppb (90th - 10th)
All respiratory
All ages 1.7% [-0.2, 3.7] lag 0-1
0-14 yrs 2.3% [-0.6, 5.3] lag 0-1
15-64 yrs 0.0% [-3.7, 3.8] lag 0-1
>65 yrs 1.0% [-1.8, 3.9] lag 0-1
COPD with asthma
0-14 yrs 4.0% [-2.0, 10.2] lag 0-1
15-64 yrs -3.3% [-10.4, 4.4] lag 0-
1
>65 yrs 2.5% [-2.1 , 7.3] lag 0-1
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
6-42
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Thompson et al.
(2001)
Belfast, Northern
Ireland
Period of Study:
1993-1995
Hagen et al.
(2000)
Drammen, Norway
Period of Study:
1994-1997
Oftedal et al.
(2003)
Drammen, Norway
Period of Study:
1994-2000
OUTCOMES, DESIGN, &
METHODS
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
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
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,
Lag: 2,3 days
MEAN LEVELS &
MONITORING
STATIONS
24-h mean:
Warm: 19.2 (7.9) ppb;
Range: 13-23
Cold: 23.3 (9.0) ppb;
Range: 18-28
NO2 24-h avg (pg/m3): 36.15,
SD= 16
IQR: 16.92 pg/m3
# of Stations: 2
Mean: 33.8 pg/m3
SD= 16.2
IQR: 20.8 pg/m3
COPOLLUTANTS
&
CORRELATIONS
SO2; r = 0.82
PM10; r = 0.77
CO; r = 0.69
O3; r = -0.62
NOX; r = 0.93
log (NO); r = 0.84
log (CO); r = 0.69
PM10; r = 0.61
SO2' r = 0 58
Benzene; r = 0.31
NO' r = 0 70
O3- r = -0 47
Formaldehyde;
r = 0.68
Toluene; r = 0.65
PM10
SO2
03
Benzene
Formaldehyde
Toluene
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
Increment: 10 ppb
All seasons
RR 1.08 [1.03, 1.13]lagO
RR1.11 [1.05, 1.1 7] 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 S6cison
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 orforextra-Poisson
variation.
Increment: NO2: 16.92 pg/m3
(IQR);
NO: 29pg/m3 (IQR)
Single-pollutant model
Respiratory disease only
NO2:RR 1.058 [0.994, 1.127]
NO: 1.048 [1.01 3, 1.084]
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
NO2: 1.015[0.939, 1.097]
NO: 1.031 [0.986, 1.077]
Increment: 20.8 pg/m3 (IQR)
All respiratory disease
Single-pollutant model
RR 1.060 [1.01 7, 1.105] lag 3
Two-pollutant model
Adjusted for PM 10
RR 1.063 [1.008, 1.120]
Adjusted for benzene
RR 1.046 [1.002, 1.091]
6-43
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Ponka (1991)
Helsinki, Finland
Period of Study:
1987-1989
Ponka and
Virtanen (1994)
Helsinki, Finland
Period of Study:
1987-1989
Days: 1096
Ponka and
Virtanen, (1996)
Helsinki, Finland
Period of study:
1987-1989
OUTCOMES, DESIGN, &
METHODS
Outcomes (I CD 9 codes): Asthma
(493)
Age groups analyzed: 0-14; 15-64;
>65yrs
Study Design: Time-series
N: 4,209
Statistical Analyses: Correlations and
partial correlations
Covariates: Minimum temperature
Statistical Package:
Lag: 0-1
Outcomes (ICD 9): Chronic bronchitis
and emphysema (491-492)
Age groups analyzed: <65, >65
Study Design: Time-series
Statistical Analyses: Poisson
regression
Covariates: Season, day of wk, yr,
influenza, humidity, temperature
Season: Summer (Jun-Aug), Autumn
(Sep-Nov), Winter (Dec-Feb), Spring
(Mar-May)
Lag: 0-7 days
Hospital Admissions
Outcomes (ICD 9 codes): Asthma
(493)
Age groups analyzed: 0-14, 15-64,
65+
Study Design: Time-series
Statistical Analyses:
Covariates: Long-term trend, season,
epidemics, day of wk, holidays,
temperature, relative humidity
Statistical Package:
Lag: 0-2
MEAN LEVELS &
MONITORING
STATIONS
24-havg: 38.6 (16.3) pg/m3
Range: 4.0-169.6
Number of Monitors: 4
24-h mean: 39 pg/m3
SD= 16.2;
Range: 4, 170
# of stations: 2
24-h avg (pg/m3):
Winter: 38
Spring: 44
Summer: 39
Fall: 34
COPOLLUTANTS
&
CORRELATIONS
SO2; r= 0.451 6
NO; r = 0.6664
O3; r = -0.2582
TSP; r = 0.1962
CO
SO2
03
TSP
SO2
03
TSP
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
Correlations between hospital
admissions (HA) for asthma and
pollutants and temperature by
ages.
0-14yrs
HA: -0.0166
Emergency HA: 0.0061
15-64yrs
HA: 0.1648 p< 0.0001
Emergency HA: 0.1189 p <
0.0001
>65 yrs
HA: 0.1501 p< 0.0001
Emergency HA: 0.1392 p <
0.0001
Partial correlations between
admissions for asthma and SO2
were standardized for
temperature.
HA: 0.1830 p< 0.0001
Emergency HA: 0.1137 p =
0.0004
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 1.1 6 [0.93, 1.46] lag 2
RR 1.08 [0.86, 1.35] lag 3
RR 0.94 [0.76, 1.1 8] lag 4
RR 0.90 [0.72, 1.1 2] lag 5
RR1.31 [1.03, 1.66] lag 6
RR 0.82 [0.67, 1.01] lag 7
<65 yrs
NR
No results presented for NO2
because they were not
statistically significant
6-44
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Rossi etal. (1993)
Oulu, Finland
Period of Study:
10/1/1985-
9/30/1986
Andersen et al.
(2007a)
Copenhagen,
Denmark
Period of Study:
1999-2004
Andersen et al.
(2007b)
Copenhagen,
Period of Study:
5/15/2001-
12/31/2004
OUTCOMES, DESIGN, &
METHODS
ED Visits
Outcome(s) (ICD 9): Asthma (493)
Age groups analyzed: 15-85
Study Design: Time-series
N:232
Statistical Analyses: Pearson's and
partial correlation coefficients and
multiple regression with stepwise
discriminate analysis
Covariates: Temperature, humidity
Statistical Package: BMDP software
Lag: 0,1, 2,3
Outcomes (ICD 10): chronic
bronchitis (J41-42), emphysema
(J43), COPD (J44), asthma (J45),
status asthmaticus (J46)
Age groups analyzed: 5-18, 65+
Number of hospitals: 9
Study Design: Time-series
Statistical Analyses: Poisson
regression with GAM
Covariates: Temperature, long-term
trend, seasonality, influenza, day of
wk, public holidays, school holidays
Lag: 0, 1 , 2, 3, 4, 5, 0-4, 0-5 days
Outcomes (ICD 10): chronic
bronchitis
(J41-42), emphysema (J43), COPD
(J44), asthma (J45), status
asthmaticus (J46)
Age groups analyzed: 5-18, 65+
Number of hospitals: 9
Study Design: Time-series
Statistical Analyses: Poisson
regression with GAM
Covariates: Temperature, long-term
trend, seasonality, influenza, day of
wk, public holidays, school holidays
Lag:0, 1, 2, 3, 4, 5, 0-4, 0-5 days
MEAN LEVELS &
MONITORING
STATIONS
24-h mean: 13.4 pg/m3
Range: 0-69
1-hr max:
38.5 pg/m3
Range: 0-154
# of Monitoring Stations: 4
24-havg: 12(5)ppb
Statistical package: R
IQR: 7
25th: 8
75th: 15
24-havg: 11 (5) ppb
Statistical package: R
IQR: 6
25th: 8
50th: 11
75th: 14
99th' 28
COPOLLUTANTS
&
CORRELATIONS
NO2;r = 0.48TSP;
H2S
PM10; r = 0.42
PM10-biomass; r =
0.41
PM10-Secondary; r
= 0.43
PM10-OM; r=0.42
PM10-Crustal;r =
0.24
PM10-Seasalt;r = -
0.19
PM10-Vehicle;r =
0.65
CO; r = 0.74
PM10
PM2.5
CO
O,
^3
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
Pearson correlation coefficients
ED asthma visits and same day
SO2: r = 0.20 p < 0.001 lag 0
Weekly ED asthma visits and
same wk SO2: r = 0.42 p < 0.001
Weekly ED asthma visits and
previous wk SO2: r = 0.58 p <
0.001
Multi-pollutant (NO2; TSP; H2S)
Regression coefficient:
All yr: > = 0.209, p = 0.034
Winter: > = 0.201, p = 0.014
Summer: > = 0.041, p = 0.714
Increment: 7 ppb (IQR):
All respiratory disease (65+):
NO2: 1 .040 [1 .009, 1 .072] lag 5
day moving avg
NO2+PM10: 1.014[0.978,
1.051] lag 5 day ma
Asthma (5-18 yrs):
NO2: 1.1 28 [1.029, 1.235] lag 6
day ma
NO2+PM10: 1.032 [0.917-1.162]
lag 6 day ma
Increment: 6 ppb (IQR):
All respiratory disease (65+):
NO2: 1.06 [1.01, 1.12] lag 0-4
moving avg
NO2+ NCtot: 1.06 [0.99, 1.13] lag
0-4 ma
Asthma (5-18 yrs):
NO2: 1.04 [0.92, 1.18] lag 0-5 ma
NO2+NCtot: 0.97 [0.83-1.14] lag
0-5 ma
6-45
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Dab'etal. (1996)
Paris, France
Period of Study:
1/1/87-9/30/92
Linares et al.
(2006)
Madrid, Spain
Period of Study:
1995-2000
Llorca et al. (2005)
Torrelavega, Spain
Period of Study:
1992-1995
Days: 1,461
OUTCOMES, DESIGN, &
METHODS
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
(460-519), bronchitis (460-496),
pneumonia (480-487)
Age groups analyzed: <10
Study Design: Time-series
Number of hospitals: 1
Statistical Analyses: Poisson
regression
Covariates: Temperature, pressure,
relative humidity
Statistical Package: S Plus 2000
Lag:
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
MEAN LEVELS &
MONITORING
STATIONS
NO224-h avg:45 pg/m3
5th: 22, 99th: 108.3
Daily maximum 1-h
Concentration: 73.8 pg/m3
5th: 37.5, 99th: 202.7
24-havg: 64.8(17.1) ug/m3
Range: 23-144
Number of monitors: 24
24-havg NO2: 21.3 pg/m3,
SD= 16.5
24-havg NO: 12.2 pg/m3, SD
= 15.2
# of Stations: 3
COPOLLUTANTS
&
CORRELATIONS
SO2
03
PM13
BS
PM10; r = 0.71
O3; r = -0.41
SO2; r = 0.63
SO2; r= 0.588
NO; r = 0.855
TSP;r = -0.12
SH2; r = 0.545
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
Increment: 100 pg/m3
All respiratory (1987-1 990)
24-h avg NO2: RR 1 .043 [0.997,
1 .090] lag 0
1-h max NO2: RR 1.015 [0.993,
1 .037] lag 0
Asthma (1987-1 992)
24-havg: RR 1.175 [1.059,
1 .304] lag 0-1
1-h max: RR 1.081 [1.019, 1.148]
lag 0-1
COPD
24-h avg: RR 0.974 [0.898,
1 .058] lag 2
1-h max: RR 0.961 [0.919, 1.014]
lag 2
Qualitative results suggest linear
relationship without threshold for
NO2 concentration and
respiratory hospital admissions.
Increment: 100 pg/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 [1.1 7, 1.56]
5-pollutant model
All cardio-respiratory admissions
NO2: RR 1 .20 [1 .05, 1.39]
NO: RR 0.93 [0.79, 1.09]
Respiratory admissions
NO2:RR 1.69 [1.34, 2.13]
NO: RR 0.87 [0.67, 1.13]
6-46
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Migliaretti and
Cavallo (2004)
Turin, Italy
Period of Study:
1997-1999
Farchi et al. (2006)
Rome, Italy
Period of Study:
11/94-2/95
OUTCOMES, DESIGN, &
METHODS
Outcome(s) (ICD 9): Asthma (493)
Age groups analyzed: <4, 4-15
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
Outcome(s) (ICD 9): All respiratory
conditions (381-382, 460-466, 480-
493); acute upper respiratory tract
infections (380-382, 460-465); lower
respiratory tract conditions including
asthma (466, 480-493)
Age groups analyzed: 6-7
Study Design: Cohort (SIDRIA)
N: 2,947
Statistical Analyses: Cox regression
models, GAM
Covariates: Gender, paternal
education, paternal smoking
Statistical Package: STATA8.0
MEAN LEVELS &
MONITORING
STATIONS
Controls:
Mean: 113.3 pg/m3,
SD = 30.5
Cases:
Mean: 117.4 pg/m3,
SD = 29.7
Mean: 46.9 pg/m3 (10.2)
IQR: 17
Range: 24-66
COPOLLUTANTS
&
CORRELATIONS
TSP
Traffic
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
Increment: 10 pg/m3
<4 yrs 2.8% [0.03, 5.03] lag 1-3
cumulative
4-15 yrs 2.7% [-0.01 , 6.06] lag 1-
3 cumulstivs
All ages 2.8% [0.07, 4.09] lag 1-3
Two-pollutant model adjusted for
TSP
NO2 2.1% [-0.1, 5.6]
Increment: 10 pg/m3
All respiratory conditions:
HR: 1.28(0.98-1.68]
1st Quartile (24-35 pg/m3): 1.00
2nd Quartile (35-47 pg/m3): 1.06
[0.45-2.53]
3rd Quartile (47-52 pg/m3): 1 .57
[0.59-4.13]
4th quartile (52-66 pg/m3): 1.95
[0.81-4.71]
Acute URT infections:
HR: 1.56 [0.96-2.56]
1st Quartile (24-35 pg/m3): 1.00
2nd Quartile (35-47 pg/m3): 0.55
[0.08-3.61]
3rd Quartile (47-52 pg/m3): 1 .25
[0.25-6.24]
4th quartile (52-66 pg/m3): 3.04
[0.67-13.79]
Acute LRT infections and asthma:
HR: 1.10(0.80-1.51]
1st Quartile (24-35 pg/m3): 1.00
2nd Quartile (35-47 pg/m3): 1.34
[0.51-3.21]
3rd Quartile (47-52 pg/m3): 1 .58
[0.35-4.10]
4th quartile (52-66 pg/m3): 1.24
[0.64-3.08]
6-47
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Fusco* et al.
(2001)
Roms Itcilv
Period of Study:
1/1/95-10/31/97
Pantazopoulou
etal. (1995)
Athens, Greece
Period of Study:
1988
OUTCOMES, DESIGN, &
METHODS
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
Outcomes: All respiratory admissions
ICD9: NR
Age groups analyzed: All ages
Study Design: Time-series
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 9/22-1 2/31)
Lag: NR
MEAN LEVELS &
MONITORING
STATIONS
NO2 24-h avg (pg/m3): 86.7,
SD= 16.2
IQR: 22.3 pg/m3
# of stations: 5; r = 0.66-0.79
NO2 24-h avg
Winter: 94 pg/m3,
SD = 25
5th: 59, 50th: 93, 95th: 135
Summer: 111 pg/m3, SD = 32
5th: 65, 50th: 108,95th: 173
# of stations: 2
COPOLLUTANTS
&
CORRELATIONS
PM10:
Allyr; r=0.35
Cold; r=0.50
Warm; r = 0.25
SO2:
Allyr; r=0.33
Cold; r=0.40
Warm; r = 0.68
CO:
Allyr; r=0.31
Cold; r=0.41
Warm; r = 0.59
03:
Allyr; r=0.19
Cold; r=0.19
Warm; r = 0.13
CO
BS
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
Increment: 22.3 pg/m3 (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
0-1 4 yrs: 4.0% [0.2, 8.0] lag 0
Asthma
All ages: 4.6% [-0.5, 10.0] lag 0
0-1 4 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)
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
0-1 4 yrs: 8.3% [-0.1, 17.4] lag 1
COPD (NO2 + CO)
>65 yrs: -1.0%[-4. 1,2.2] lag 0
Increment: 76 pg/m3 in winter and
108 pg/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]
6-48
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
OUTCOMES, DESIGN, &
METHODS
MEAN LEVELS &
MONITORING
STATIONS
COPOLLUTANTS
&
CORRELATIONS
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
LATIN AMERICA
Gouveia and
Fletcher, (2000a)
Sao Paulo, Brazil
Period of Study:
11/92-9/94
Braga* et al.
(1999)
Sao Paulo, Brazil
Period of Study:
10/1992-10/1993
Braga* et al.
(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: <1 ; <5 yrs
Study Design: Time-series
Statistical Analyses: Poisson
regression Covariates: Long-term
trend, season, temperature, relative
humidity, day of wk, holiday, strikes in
public transport or health services
Season: Cool (May-Oct), Warm (Nov-
nnr\
Mpr;
Statistical Package: SAS
Lag: 0, 1, 2 days
Hospital Admissions
Outcomes (ICD 9 codes): All
respiratory (466,480-486,491-
492,496)
Age groups analyzed: <13 yrs
Study Design: Time-series
N: 68,918
# of Hospitals: 112
Statistical Analyses: Multiple linear
regression models (least squares).
Also used Poisson regression
techniques. GLM and GAM using
LOESS for smoothing.
Covariates: Season, temperature,
humidity, day of wk
Statistical Package: SPSS, S-Plus
Lag: 1,2,3,4,5,6,7 moving avgs
Outcomes (ICD 9): All respiratory
admissions (460-519)
Age groups analyzed: 0-19, #2, 3-5,
6-13, 14-19
Study Design: Time-series
Statistical Analyses: Poisson
regression with GAM
Covariates: Long-term trend, season,
temperature, relative humidity, day of
wk, holiday
Statistical Package: S-Plus 4.5
Lag: 0-6 moving avg
1-h max NO2(pg/m3)
Mean: 174.3
SD= 101.3
Range: 26.0, 692.9
5th: 62.0
25th: 108.8
50th: 151.7
75th: 210.0
95th: 388.0
$ of ststions' 4
24-havg 174.84
(101.38)pg/m3
Min:26.0
Max: 668.3
# of monitors: 13
NO2 mean: 141.4 pg/m3,
SD = 71.2
IQR: 80.5 pg/m3
Range: 25, 652.1
# of stations: 5-6
SO2;r = 0.37
PM10; r = 0.40
CO; r = 0.35
O3;r = 0.25
PM10; r = 0.53
CO; r = 0.42
SO2; r = 0.53
Oo- r =
W3, 1
PM10; r = 0.62
SO2; r = 0.54
CO; r = 0.58
O3; r = 0.34
Increment: 319.4 pg/m3 (90th-
10th)
All Respiratory
<5 yrs: RR 1.063 [0.999, 1.132]
lag 0
<5 yrs + O3:RR 1.050 [0.985,
1.120]
<5yrs+ PM10:RR 1.043 [0.972,
"1 "1 "1 Ql
i . 1 1 yj
<5yrs + O3+PM10:RR 1.035
[0.963, 1.113]
<5 yrs Cool: RR 1.04 [0.96, 1.11]
(estimated from graph)
<5 yrs Warm: RR 1.09 [1.01,
1 .16] (estimated from graph)
Pneumonia
<5 yrs: RR 1.093 [1.01 6, 1.177]
lagO
<1 yr: RR 1.091 [0.996, 1.193] lag
0
Asthma
<5 yrs: RR 1.1 07 [0.940, 1.300]
lag 2
Due to problems with NO2
monitors, this pollutant could not
be included in the analysis.
Increment: 80.5 pg/m3 (IQR)
All Respiratory admissions
<2 yrs 9.4% [6.2, 12.6] lag 5
3-5 yrs 1.6% [-6.4, 9.6]
6-1 3 yrs 2.3% [-5.9, 10.4]
14-19 yrs -3.0% [-15.7, 9.7]
All ages 6.5% [3.3, 9.7]
6-49
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Braga* et al.
(2001)
Sao Paulo, Brazil
Period of Study:
1/93-11/97
Farhat* et al.
(2005)
Sao Paulo, Brazil
Period of Study:
8/96-8/97
Days: 396
OUTCOMES, DESIGN, &
METHODS
Outcomes (ICD 9): All respiratory
admissions (460-519)
Age groups analyzed: 0-19, #2, 3-5,
6-13, 14-19
Study Design: Time-series
Statistical Analyses: Poisson
regression with GAM
Covariates: Long-term trend, season,
temperature, relative humidity, day of
wk, holiday
Statistical Package: S-Plus 4.5
Lag: 0-6 moving avg
Outcomes (ICD 9):
Pneumonia/bronchiopheumonia (480-
6), asthma (493), bronchiolitis (466),
Obstructive disease 493, 466)
Age groups analyzed: <13
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 LEVELS &
MONITORING
STATIONS
NO2 mean: 141.4 pg/m3,
SD = 71.2
IQR: 80.5 pg/m3
Range: 25, 652.1
# of stations: 5-6
Mean: 125.3 pg/m3
SD = 51.7
IQR: 65.04 pg/m3
Range: 42.5, 369.5
COPOLLUTANTS
&
CORRELATIONS
PM10; r = 0.62
SO2; r = 0.54
CO; r = 0.58
O3; r = 0.34
PM10; r = 0.83
SO2; r = 0.66
CO; r = 0.59
O3;r = 0.47
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
Increment: 80.5 pg/m3 (IQR)
All Respiratory admissions
<2 yrs 9.4% [6.2, 12.6] lag 5
3-5 yrs 1.6% [-6.4, 9.6]
6-1 3 yrs 2.3% [-5.9, 10.4]
14-19 yrs -3.0% [-15.7, 9.7]
All ages 6.5% [3.3, 9.7]
Increment: 65.04 pg/m3 (IQR)
Single-pollutant models
(estimated from graphs)
Lower respiratory tract disease:
NO2 alone: -18% [13, 24] lag 0-3
NO2+ PM10 16.1% [5.4, 26.8]
lag 0-2
NO2 + SO2 24.7% [18.2, 31 .3] lag
0-2
NO2+ CO 19.2% [11.8, 26.6] lag
0-2
NO2 + O3 16.1% [9.5, 22.7] lag 0-
2
Multipollutant model (PM10, SO2,
CO, O3) 18.4% [3.4, 33.5] 2 day
avg
Pneumonia:
NO2 alone: -17.5% [3, 32.5] lag
0-2
NO2 + PM10 8.1% [-11.4, 27.6]
lag 0-2
NO2 + SO2 13.1% [-3.4, 29.7] lag
0-2
NO2 + CO 14.6% [-4.9, 34.1] lag
0-2
NO2 + O3 12.4% [-5.6, 30.4] lag
0-2
Multipollutant model (PM10, SO2,
CO, O3)
1.8% [-23.9, 27.6] 2 day avg
Asthma or Bronchiolitis
NO2 alone: 30.5% [9, 56] lag 0-1
NO2 + PM10 47.7% [1.15, 94.2]
lag 0-2
NO2 + SO2 33.1% [5.7, 60.5] lag
0-2
NO2 + CO 28.8% [-0.2, 57.9] lag
0-2
NO2 + O3 28.0% [-1 .0, 57.0] lag
0-2
Multipollutant model (PM10, SO2,
CO, 03)
39.3% [-14.9, 93.5] 2 day avg
6-50
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
OUTCOMES, DESIGN, &
METHODS
MEAN LEVELS &
MONITORING
STATIONS
COPOLLUTANTS
&
CORRELATIONS
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
Arbex et al., 2007
Araraquara, Brazil
Period of Study:
3/2003-7/2004
Outcomes (ICD 10): asthma (J15)
Age groups analyzed: <13
Study Design: Ecological Time-series
N: 1,021
Number of hospitals: 1
Statistical Analyses: Poisson
regression with GLM
Covariates: Long-term trend, weather
Statistical package: S-Plus
Lag: 0-7 days, 2,3,4 day moving avg
TSP (Mg/m3)
Increment: 10 pg/m
Asthma hospital admissions:
11.6% [5.4, 17.7] lag 1-5
ASIA
Lee et al. (2006)
Hong Kong, China
Period of Study:
1997-2002
Days: 2,191
Outcomes (ICD 9): Asthma (493)
Age groups analyzed: #18
Study Design: Time-series
N: 26,663
Statistical Analyses: Semi-parametric
Poisson regression with GAM (similar
toAPHEA2)
Covariates: Long-term trend,
temperature, relative humidity,
influenza, day of wk, holiday
Statistical package: SAS 8.02
Lag: 0-5 days
NO2 24-h mean: 64.7 pg/m ,
SD = 20.9
IQR: 27.1 pg/m3
25th: 49.7, 75th: 76.8
# of stations: 9-10, r = 0.53,
0.94,
Mean: 0.78
PM10; r = 0.78
PM2.5; r=0.75
SO2; r = 0.49
O3; r = 0.35
Increment: 27.1 pg/m (IQR)
Asthma
Single-pollutant model
4.37% [2.51, 6.27] lag 0
5.88% [4.00, 7.70] lag 1
7.19% [5.37, 9.04] lag 2
9.08% [7.26, 10.93] lag 3
7.64% [5.84, 9.48] lag 4
6.40% [4.60, 8.22] lag 5
Multipollutant model - including
PM, SO2, and O3
5.64% [3.21, 8.14] lag 3
Other lags NR
Chew etal. (1999)
Singapore
Period of Study:
1990-1994
Outcome(s) (ICD 9): Asthma (493)
Age groups analyzed: 3-12, 13-21
Study Design: Time-series
N: 23,000
# of Hospitals: 2
Statistical Analyses: Linear
regression, GLM
Covariates: Variables that were
significantly associated with ER visits
were retained in the model
Statistical Package: SAS/STAT,
SAS/ETS 6.08
Lag: 1,2 days avgs
24-h avg: 18.9 pg/m
SD= 15.0,
Max < 40
# of Stations: 15
SO2; r = -0.22
O3;r = 0.17
TSP; r = 0.23
Categorical analysis (via ANOVA)
p-value and Pearson correlation
coefficient (r) using continuous
data comparing daily air pollutant
levels and daily number of
hospital admissions.
Age Group:3-12
Lag 0
Lag 1
Lag 2
r = 0.13
13-21
r = 0.05
p = 0.013 p<0.18
r = 0.13 r = 0.02
P = 0.02
r = 0.13
p = 0.35
p = 0.75
r = 0.07
p = 0.012
6-51
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Tsai et al. (2006)
Kaohsiung, Taiwan
Period of Study:
1996-2003
Days: 2922
Chen et al. (2006)
Taiwan
Period of Study:
1/1998-12/2001
Lee* etal. (2002)
Seoul, Korea
Period of Study:
12/1/97-12/31/99
Days: 822
OUTCOMES, DESIGN, &
METHODS
Outcomes (I CD 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
Outcomes (I CD 9): Asthma (493)
Age Groups: 0-4, 5-14, 15-44, 45-64,
65+
Study Design: Time-series
N: 126,671
Statistical Analyses: Spearman Rank
Correlations
Covariates:
Season:
Statistical package: SPSS
Lag:
Outcomes (ICD 10): Asthma (J45 -
J46)
Age groups analyzed: <15
Study Design: Time-series
N: 6,436
Statistical Analyses: Poisson
regression, log link with GAM
Covariates: Time, day of wk,
temperature, humidity
Season: Spring (Mar-May), Summer
(Jun-Aug), Fall (Sep-Nov), Winter
(Dec-Feb)
Statistical package: NR
Lag: 0-2 days cumulative
MEAN LEVELS &
MONITORING
STATIONS
NO2 24-h mean: 27.20 ppb
IQR: 17 ppb
Range: 4.83, 63.40
# of stations: 6
Mean monthly NO2 averaged
across 55 monitors: 37.64
(4.89) ppb
Min: 29.52
25th: 33.72
50th: 37.07
75th: 40.63
Max: 47.65
24-h NO2 (ppb)
Mean: 31.5
SD= 10.3
5th: 16.0
25th: 23.7
50th' 30 7
75th: 38.3
95th: 48.6
# of stations: 27
COPOLLUTANTS
&
CORRELATIONS
PM10
SO2
03
CO
PM10;r =
SO2; r =
CO; r =
rv- r =
l-'3i '
SO2;r = 0.72
O3; r = -0.07
CO; r = 0.79
PM10; r = 0.74
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
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 PM 10
>25°C 1.082 [0.913, 1.283] lag 0-
2
<25°C2.105 [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 .91 5, 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°C2.096 [1.851, 2.373] lag 0-
2
Spearman rank correlations show
that seasonal variations in adult
asthma admissions are
significantly correlated with levels
of NO2(r= 0.423, p = 0.003).
Increment: 14.6 ppb (IQR)
Asthma
NO2:RR 1.15 [1.10, 1.20] lag 0-2
NO2+PM10:RR 1.13 [1.07,
1.19] lag 0-2
NO2 + SO2:RR 1.20 [1.11, 1.29]
lag 0-2
NO2 + O3:RR1.14[1.09, 1.20]
lag 0-2
NO2+CO:RR 1.12 [1.03, 1.22]
lag 0-2
NO2 + O3 + CO + PM1 0 + SO2:
RR 1.098 [1.002, 1.202]
6-52
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Yang et al. (2007)
Taipei, Taiwan
Period of Study:
1996-2003
Yang and Chen
(2007)
Taipei, Taiwan
Period of Study:
1996-2003
OUTCOMES, DESIGN, &
METHODS
Outcomes (I CD 9): Asthma (493)
Age groups analyzed: All
Study Design: Case-crossover
N: 25,602
.
Number of hospitals: 47
Statistical Analyses: Conditional
logistic regression
Covariates: Temperature, humidity
Statistical package: SAS
Lag: 0-2
Outcomes (I CD 9): COPD (493)
Age groups analyzed:
Study Design: Case-crossover
N: 25,602
Number of hospitals: 47
Statistical Analyses: Conditional
logistic regression
Covariates: Temperature, humidity
Statistical package: SAS
Lag: 0-2
MEAN LEVELS &
MONITORING
STATIONS
24-h avg: 30.77 ppb
Range: 3.84-77.97
25th: 25.55
50th: 30.31
75th: 35.60
Number of monitors: 6
24-h avg: 30.77 ppb
Range: 3.84-77.97
25th: 25.55
50th: 30.31
75th: 35.60
Number of monitors: 6
COPOLLUTANTS
&
CORRELATIONS
SO2
PM10
CO
03
SO2
PM10
CO
03
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
Increment: 10.05 ppb (IQR)
NO2 alone:
225 EC: 1.178 [1.113, 1.247] lag
0-2
<25EC: 1.128, 1.076, 1.182] lag
0-2
NO2+PM10:
225 EC: 1 .328 [1 .224, 1 .441] lag
0-2
<25EC: 1.144 [1.077, 1.215] lag
0-2
NO2 + SO2:
225 EC: 1.224 [1.140, 1.314] lag
0-2
<25EC: 1.219 [1.150, 1.291] lag
0-2
NO2 + CO:
225 EC: 1 .084 [0.999, 1 .176] lag
0-2
<25EC: 1.198 [1.111, 1.291] lag
0-2
NO2 + O3:
225 EC: 1.219 [1.142, 1.301] lag
0-2
<25EC: 1.156 [1.102, 1.212] lag
0-2
Increment: 10.05 ppb (IQR)
NO2 alone:
220EC: 1.193 [1.158, 1.230] lag
0-2
<20 EC: 0.972 [0.922, 1.024] lag
0-2
NO2+PM10:
220 EC: 1.183 [1.137, 1.231] lag
0-2
<20 EC: 0.920 [0.862, 0.982] lag
0-2
NO2 + SO2:
220 EC: 1 .302 [1 .254, 1 .351] lag
0-2
<20 EC: 0.895 [0.837, 0.956] lag
0-2
NO2 + CO:
220EC: 1.154 [1.102, 1.208] lag
0-2
<20 EC: 0.972 [0.892, 1.059] lag
0-2
NO2 + O3:
220 EC: 1.163 [1.126, 1.200] lag
0-2
<20 EC: 0.952 [0.901 , 1 .006] lag
0-2
6-53
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Ko et al. (2007a)
Hong Kong
Period of Study
Lee et al. (2006)
Hong Kong, China
Period of Study:
1997-2002
Lee et al. (2007)
Kaohsiung, Taiwan
Period of Study:
1996-2003
OUTCOMES, DESIGN, &
METHODS
Outcomes (ICD 9):
Age groups analyzed:
# of hospitals:
Study Design:
Statistical Analyses:
Covariates:
Statistical package:
Lag:
Outcomes (ICD 9): asthma (493)
Age groups analyzed: <18
N: 26,663
Study Design: Time-series
Statistical Analyses: Poisson
regression with GAM
Covariates: Temperature, humidity,
influenza, day of wk, holidays
Statistical package: SAS v 8.02
Lag:0, 1,2,3,4,5
Outcomes (ICD 9): COPD (490-492,
494, 496)
Age groups analyzed: All
# of hospitals: 63
N: 25,108
Study Design: Case-crossover
Statistical Analyses: Conditional
logistic regression
Covariates: Temperature, humidity
Season: Warm: >25 EC, cool: <25 EC
Statistical package: SAS v 8.2
Lag: 0-2 cumulative avg
MEAN LEVELS &
MONITORING
STATIONS
24-h avg: 64.7 (20.9) pg/m3
25th: 49.7
50th: 63.5
75th: 76.8
IQR:27.1
Number of monitors: 10
24-h avg: 27.2 ppb
Range: 4.83-63.40
25th: 18.4
50th: 27.1 7
75th: 35.40
# of monitors: 6
COPOLLUTANTS
&
CORRELATIONS
SO2; r = 0.49
PM10; r = 0.78
PM2.5; r=0.75
O3; r = 0.35
SO2
PM10
CO
03
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
Increment: 27.1 pg/m3 (IQR)
Lag 0:4.37% [2.51, 6.27]
Lag 1 : 5.88% [4.00, 7.70]
Lag 2: 7.19% [5.37, 9.04]
Lag 3: 9.08% [7.26, 10.93]
Lag 4: 7.64% [5.84, 9.48]
Lag 5: 6.40% [4.60, 8.22]
NO2 alone: 9.08% [7.26, 10.93]
Iag3
NO2 + SO2 + PM10 + PM2.5 +
O3: 5.64% [3.21, 8.14] lag 3
Increment : 17 ppb (IQR)
NO2 alone:
>25C: 1.241 [1.117, 1.379] lag 0-
2
<25 C: 1.975 [1.785, 2.186] lag 0-
2
NO2 + PM10:
>25 C: 1 .083 [0.939, 1 .249] lag 0-
2
<25 C: 1.957 [1.709, 2.241] lag 0-
2
NO2 + SO2:
>25C: 1.264 [1.127, 1.418] lag 0-
2
<25 C: 2.378 [2.095, 2.700] lag 0-
2
NO2 + CO:
>25 C: 0.984 [0.848, 1.141] lag 0-
2
<25 C: 2.035 [1.746, 2.373] lag 0-
2
NO2 + O3:
>25 C: 1 .076 [0.961 , 1 .205] lag 0-
2
<25 C: 1.946 [1.755, 2.157] lag 0-
2
6-54
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Wong etal. (1999)
Hong Kong, China
Period of Study:
1994-1995
Wong et al.
(2001 a)
Hong Kong, China
Period of Study:
1993-1994
OUTCOMES, DESIGN, &
METHODS
Outcomes (ICD 9): All respiratory
admissions (460-6, 471-8, 480-7, 490-
6); Asthma (493), COPD (490-496),
Pneumonia (480-7)
Age groups analyzed: 0-4, 5-64, >65,
all ages
# of hospitals: 12
Study Design: Time-series
Statistical Analyses: Poisson
regression (followed APHEA protocol)
Covariates: Trend, season, day of wk,
holiday, temperature, humidity
Statistical package: SAS 8.02
Lag: days 0-3 cumulative
Outcomes (ICD 9): Asthma (493)
Age groups analyzed: #15
N: 1,217
# of hospitals: 1
Study Design: Time-series
Statistical Analyses: Poisson
regression (followed APHEA protocol)
Covariates: Season, temperature,
humidity
Season: Summer (Jun-Aug),
Autumn (Sep-Nov), Winter (Dec-Feb),
Spring (Mar-May)
Lag: 0,1,2,3,4,5 days; and
cumulative 0-2 and 0-3 days
MEAN LEVELS &
MONITORING
STATIONS
Median 24-h NO2: 51 .39
pg/m3
Range: 16.41, 122.44
25th: 39.93, 75th: 66.50
# of stations: 7,
r= 0.68, 0.89
24-h avg
NO2 mean: 43.3 pg/m3,
SD= 16.6
Range: 9, 106 pg/m3
Autumn: 51. 7 (17.6)
Winter: 46.6 (15.5)
Spring: 40. 7 (11. 8)
Summer: 32.6(13.7)
# of stations: 9
COPOLLUTANTS
&
CORRELATIONS
03
SO2
PM10; r = 0.79
PM10
SO2
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE
& CONFIDENCE
INTERVALS (95%)
Increment = 10 pg/m3
Overall increase in admissions:
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
>65yrs: 1.024 [1.014, 1.035] lag
0-3
Cold Season: 1.004[0.988,
1 .020]
NO2 + high PM1 0: 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 pg/m3
Asthma
Allyr: 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
APHEA: Air Pollution and Health: A European Approach
6-55
-------
Table AX6.3-4. Respiratory Health Effects of Oxides of Nitrogen: Emergency Department Visits
STUDY
METHODS
MEAN LEVELS OF
N02&
MONITORING
STATIONS
COPOLLUTANT
CORRELATIONS
EFFECTS AND INTERPRETATION:
RELATIVE RISK & CONFIDENCE
INTERVALS (95%)
UNITED STATES
Jaffe et al.
(2003)
3 cities, Ohio,
(Cleveland,
Columbus,
Cincinnati)
Period of Study:
7/91-6/96
Ito et al. (2007)
New York, NY
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: Jun to Aug only
Dose-response investigated:
Yes
Statistical Package: NR
Lag: 0-3 days
Outcome (ICD-9):
Asthma (493)
Age Groups Analyzed: All ages
Study Design: Time-series
N: 1,460
Statistical Analyses:
Poisson's Generalized Linear
Model
Covariates: temporal trends,
day of wk, weather, over-
dispersion
Season: all year; warm (April to
September), cold (October to
March)
Statistical Package: NR
Lag: 0-1 day
Cincinnati:
24-h avg: 50 ppb, SD
= 15
Cleveland:
24-h avg: 48 ppb, SD
= 16
NO2 was not
monitored in
Columbus due to
relatively low levels
All year
24-h avg: 31.1
SD = 8.7
Warm season
24-h avg: 30.4
SD = 8.8
Cold season
24-h avg: 31 .8
SD = 31.8
Cincinnati:
PM10;r = 0.36SO2;r =
0.07
O3; r = 0.60
Cleveland:
PM10; 0.34
SO2' r = 0 28
O3; r = 0.42
No multipollutant
models were utilized.
PM2.5:r = 0.91
O3: r = 0.89
SO2:r = 0.74
CO: r = 0.60
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.
All Year: Increment : 24 ppb
24-h avg, lag 0
RR 1.14 (1.09, 1.19)
Warm Months: Increment : 25 ppb
24-h avg, lag 0
RR 1.32 (1.23, 1.42)
6-56
-------
STUDY
NYDOH (2006)
Bronx and
Manhattan NY
Period of Study:
1/1999-11/2000
Morris* et al.
(1999)
Seattle, WA,
United States
Period of Study:
1995-1996
Lipsett et al.
(1997)
Santa Clara
County,
California,
United States
Period of Study:
1988-1992
METHODS
Outcome (ICD-9):
Asthma (493)
Age Groups Analyzed:
All ages
Study Design: Time-series
Statistical Analyses:
Poisson regression
Covariates: temporal cycles,
temperature, day of wk
Season: All year
Dose-response investigated:
Yes
Statistical Package: NR
Lag: 0-4 days
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
Outcome(s): Asthma
ICD-9 Code(s): NR
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
MEAN LEVELS OF
NO2&
MONITORING
STATIONS
24-h avg: 34 ppb
24h:20.2ppb, SD =
7.1
IQR: 9 ppb
1-h max: 34.0 ppb,
SD= 11.3
IQR: 12 ppb
NO2 1-h mean:
69 ppb,
SD = 28
Range: 29, 150 ppb
COPOLLUTANT
CORRELATIONS
PM2.5
SO2
03
CO; r = 0.66
PM;r = 0.66
SO2;r = 0.25
PM10;r = 0.82
COM; r = 0.8
No multipollutant model
due to high correlation
between pollutants
EFFECTS AND INTERPRETATION:
RELATIVE RISK & CONFIDENCE
INTERVALS (95%)
Increment : 20 ppb
lag 0-4
24-h avg
Bronx
NO2: 1.06(1.01, 1.10)
NO2 + O3: 1.05(1.00, 1.10)
NO2 + FRM PM2.5 : 1 .03 (0.98, 1 .08)
NO2 + PM2.5 max : 1 .02 (0.98, 1 .08)
NO2 + SO2: 1.01 (0.96, 1.07)
Manhattan
NO2: 0.97 (0.82, 1.14)
NO2 + O3 : 0.95 (0.80, 1.12)
NO2 + FRM PM2.5: 0.90 (0.41, 1.10)
NO2 + PM2.5 max: 0.90 (0.41, 1.11)
NO2 + SO2 : 0.97 (0.80, 1.17)
Increment: IQR
24-h avg (9-ppb increment)
RR 0.99 [0.90, 1.08] lag 2
1-h max (12-ppb increment)
RR 1.05 [0.99, 1.12]lagO
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.
Same day NO2 was associated with ER visits
for asthma (> = 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.
6-57
-------
STUDY
Peel et al. (2005)
Atlanta, GA,
United States
Period of Study:
1/93-8/2000
Tolbert et al.
(2000)
Atlanta, GA,
United States
Period of Study:
1993-1995
Tolbert et al.
(2007)
Atlanta f^A
MII3IH3, OM
Period of Study:
1993-2004
METHODS
Outcome(s)(ICD-9):AII
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
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)
Outcome(s) (ICD-9): Combined
respiratory diseases (493,
786.07, 786.09, 491, 492, 496,
460-465, 477, 480-486, 466.1,
466.11,466.19)
Age groups analyzed: All
Study Design: Time-series
N: 1,072,429
Number of hospitals: 41
Statistical Analyses: Poisson
regression with GLM
Covariates: Day of wk, season,
hospital, holiday, temperature,
dew point
Statistical Package: SAS vs.
9.1
Lag: 0-2 (a priori)
MEAN LEVELS OF
NO2&
MONITORING
STATIONS
1-h max: 45.9 ppb,
SD= 17.3
NOX 1-h max
continuous
Mean: 81.7 ppb,
SD = 53.8
Range = 5.35, 306
Number of stations: 2
1-h max: 43.2 ppb
Range: 1.0-181.0
10th: 22.0
25th: 31.0
50th: 41.0
75th: 54.0
90th: 66.0
COPOLLUTANT
CORRELATIONS
O3; r = 0.42
SO2;r = 0.34
CO; r = 0.68
PM10;r = 0.46
Evaluated multipollutant
models (data not
shown)
PM10;r = 0.44
O3; r = 0.51
PM10;r = 0.53
O3; r = 0.44
SO2;r = 0.36
CO; r = 0.70
PM2.5;r = 0.47
PM1 0-2.5; r = 0.48
PM2.5sulfate; r = 0.14
PM2.5EC; r = 0.64
PM25OC; r = 0.62
OHC; r = 0.24
EFFECTS AND INTERPRETATION:
RELATIVE RISK & CONFIDENCE
INTERVALS (95%)
Increment: 20 ppb
All respiratory
RR 1.016 [1.006, 1.027] lag 0-2, 3-day
moving avg
Upper Respiratory Infection (URI)
RR 1.019 [1.006, 1.031] lag 0-2, 3-day
moving avg
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 avg
COPD
RR 1.035 [1.006, 1.065] lag 0-2, 3-day
moving avg
Increment: 50 ppb
Age 0-1 6:
RR 1.012 [0.987, 1.039] lag 1
Increment: 23 ppb (IQR)
RR 1.015 [1.004, 1.025] lag 0-2
6-58
-------
STUDY
Cassino* et al.
(1999)
New York City,
NY
United States
Period of Study:
1/1989-12/1993
METHODS
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
MEAN LEVELS OF
NO2&
MONITORING
STATIONS
24-h avg NO2:
Mean: 45.0 ppb
Median: 43 ppb
10% 31 ppb
25% 37 ppb
75% 53 ppb
90% 63 ppb
COPOLLUTANT
CORRELATIONS
03
CO
SO2
EFFECTS AND INTERPRETATION:
RELATIVE RISK & CONFIDENCE
INTERVALS (95%)
Increment: 15 ppb (IQR)
RR 0.97 [0.85, 1.09] lag 0
RR 1.04 [0.92, 1.1 8] lag 1
RR 1.06 [0.94, 1.2] lag 2
RR 0.97 [0.86, 1.09] lag 3
6-59
-------
STUDY
METHODS
MEAN LEVELS OF
NO2&
MONITORING
STATIONS
COPOLLUTANT
CORRELATIONS
EFFECTS AND INTERPRETATION:
RELATIVE RISK & CONFIDENCE
INTERVALS (95%)
CANADA
Bates et al.
(1990)
Vancouver
Region,
BC, Canada
Period of Study:
7/1/1984-
10/31/1986
Outcome(s) (ICD 9): Asthma
(493); Pneumonia (480-486);
Chronic bronchitis (491,492,49
6);
Other respiratory (466)
Age groups analyzed: All; 15-
60
Study Design:
# of Hospitals: 9
Statistical Analyses: Pearson
correlation coefficients were
calculated between asthma
visits and environmental
variables
Season: Warm (May-Oct);
Cool (Nov-Apr)
Covariates: NR
Statistical Package: NR
Lag:0, 1,2
May-Oct
SO2 1-h max:
Range: 0.0337-
0.0458 ppm
Nov-Apr
Range: 0.0364-
0.0455 ppm
Number of stations: 11
May-Oct.
O3; r = 0.35
SO2;r = 0.67
CoH;r = 0.53
SO4;r = 0.50
Nov-Apr
O3; r = 0.31
SO2;r = 0.61
CoH;r = 0.69
SO4;r = 0.49
Correlation Coefficients:
Warm Season (May-Oct)
Asthma (1-14yrs)
NR lag 0
NR lag 1
NR lag 2
Respiratory (1-14)
NR lag 0
NR lag 1
NR lag 2
Total (1-14)
NR lag 0
NR lag 1
NR lag 2
Asthma (15-60yrs)
NR lag 0
NR lag 1
NR lag 2
Respiratory (15-60yrs)
r= 0.120 lag Op<0.01
NR lag 1
NR lag 2
Total (15-60yrs)
NR lag 0
NR lag 1
NR lag 2
Asthma (61+yrs)
NR lag 0
NR lag 1
NR lag 2
Respiratory (61+ yrs)
NR lag 0
NR lag 1
NR lag 2
6-60
-------
STUDY
Bates et al.
(1990) (cont'd)
Bates et al.
(1990)
(cont'd)
METHODS
MEAN LEVELS OF
NO2&
MONITORING
STATIONS
COPOLLUTANT
CORRELATIONS
EFFECTS AND INTERPRETATION:
RELATIVE RISK & CONFIDENCE
INTERVALS (95%)
Total (61+yrs)
NR lag 0
NR lag 1
NR lag 2
Cool Season (Nov -Apr)
Asthma (1-14yrs)
NR lag 0
NR lag 1
NR lag 2
Respiratory (1-14)
NR lag 0
NR lag 1
NR lag 2
Total (1-14)
NR lag 0
NR lag 1
NR lag 2
Asthma (15-60yrs)
NR lag 0
NR lag 1
NR lag 2
Respiratory (15-60yrs)
r= 0.120 lag Op<0.01
NR lag 1
NR lag 2
Total (15-60yrs)
NR lag 0
NR lag 1
NR lag 2
Asthma (61+yrs)
NR lag 0
NR lag 1
NR lag 2
Respiratory (61+ yrs)
r= 0.132 lag Op<0.01
r= 0.1 76 lag 1 p< 0.001
r=0.178lag2p<0.001
Total (61+yrs)
NR lag 0
NR lag 1
NR lag 2
6-61
-------
STUDY
Kesten et al.
(1995)
Toronto, ON
Period of Study:
7/1/1991-
6/30/1992
Stieb et al.
(1996)
St. John, New
Brunswick,
Canada
Period of Study:
1984-1992
Stieb* et al.
(2000)
Saint John, New
Brunswick,
Canada
Period of Study:
Retrospective:
7/92-6/94
Prospective:
7/94-3/96
METHODS
Outcome(s): Asthma
Age groups analyzed: All
Study Design: Time-series
N:854
# of Hospitals: 1
Statistical analysis:
Autoregressive technique
Statistical Package: SAS v 6.04
Lag: 0,1
Outcome(s): Asthma
(May-Sept only)
ICD-9 Codes: NR
Age groups analyzed: 0-15,
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
MEAN LEVELS OF
NO2&
MONITORING
STATIONS
24-h avg NO2:
Range: 2.20-3.75 H
0.01 ppm
1-h max NO2 (ppb)
Mean: 25.2
Range: 0, 120
95th: 60
Annual mean: 8.9 ppb
Spring/fall mean:
10.0 ppb Max: 82
COPOLLUTANT
CORRELATIONS
SO2
03
O3; r = 0.16
SO2; r=-0.03
SO42-;r = 0.16
TSP; r=0.15
O3; r = -0.02
SO2; r = 0.41
TRS;r = 0.16
PM10;r = 0.35
PM2.5; r = 0.35
H+;r = 0.25
SO42-; r = 0.33
COH;r = 0.49
Assessed multipollutant
models
EFFECTS AND INTERPRETATION:
RELATIVE RISK & CONFIDENCE
INTERVALS (95%)
Lag 0: "No statistically discernible regression
coefficients"
Lag 0-6: "No statistically discernible
regression coefficients"
Mean weekly indices lagged 1 wk behind
weekly mean number of visits: p = 0.005
Increment: NR
NO2 + O3: > = -0.0037 (0.0023) lag 2
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] lag 0
Multipollutant model (InfNOJ, O3, SO2 COH)
May to Sep: 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.
6-62
-------
STUDY
METHODS
MEAN LEVELS OF
NO2&
MONITORING
STATIONS
COPOLLUTANT
CORRELATIONS
EFFECTS AND INTERPRETATION:
RELATIVE RISK & CONFIDENCE
INTERVALS (95%)
EUROPE and MIDDLE-EAST
Sunyer et al.
(1997)
Multi-city,
Europe
(Barcelona,
Helsinki, Paris,
London)
Period of Study:
1986-1992
Atkinson et al.
(1999b)
London, United
Kingdom
Period of Study:
1/92-1294
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
Outcome(s) (ICD-9):
Respiratory ailments (490-
496), including asthma,
wheezing, inhaler request,
chest infection, COPD,
difficulty in breathing, cough,
croup, pleurisy, noisy breathing
Age groups analyzed: 0-14; 15-
64; >65; All ages
Study Design: Time-series
N: 98,685
# of Hospitals: 12
Statistical Analyses: Poisson
regression, APHEA protocol
Covariates: Long-term trend,
season, day of wk, influenza,
temperature, humidity
Statistical Package: SAS
Lag: 0,1,0-2, and 0-3 days
24-h median (range)
(Mg/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
1-h max: 50.3 ppb,
SD= 17.0
# of Stations: 3;
r = 0.70, 0.96
SO2
black smoke
O,
^3
NO2, O3
(8 h), SO2
(24 h), CO (24 h),
PM10(24h), BS
Increment: 50 pg/m3 of 24-h avg for all cities
combined
Asthma
15-64yrs
1.029 [1.003, 1.055] lag 0-1
1.038 [1.008, 1.068] lag 0-3, cumulative
<15yrs
1.026 [1.006, 1.049] lag 2
1.037 [1.004, 1.067] lag 0-3, cumulative
1.080 [1.025, 1.1 40] -Winter only
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]
Increment: 36 ppb in 1-h max
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
0-1 4 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 0-1 4 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
6-63
-------
STUDY
Buchdahl et al.
(1996)
London, United
Kingdom
Period of Study:
3/1/92-2/28/93
Thompson et al.
(2001)
Belfast, Northern
Ireland
Period of Study:
1993-1995
METHODS
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
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
MEAN LEVELS OF
NO2&
MONITORING
STATIONS
NO2 24-h yr round
mean: 60 pg/m3,
SD =17
IQR: 17pg/m3
Spring: 59 (19)
Summer: 55 (18)
Fall: 66 (13)
Winter: 61 (17)
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
COPOLLUTANT
CORRELATIONS
SO2r = 0.62
O3r = -0.18
NO2:
PM10;r = 0.77
SO2;r = 0.82
NOX; r = 0.93
NO; r = 0.84
O3; r = -0.62
CO; r = 0.69
Benzene; r = 0.83
NOX:
PM10;r = 0.73
SO2;r = 0.83
NO2' r = 0 92
NO; r = 0.97
O3; r = -0.73
CO; r = 0.74
Benzene; r = 0.86
NO:
PM10;r = 0.65
SO2;r = 0.76
NOX; r = 0.97
NO2' r = 0 84
O3- r = -0 76
CO' r = 0 71
Benzene; r = 0.82
EFFECTS AND INTERPRETATION:
RELATIVE RISK & CONFIDENCE
INTERVALS (95%)
Increment: 17 pg/m3 (IQR)
No adjustments to model
RR 1 .07 [1 .01 , 1 .1 4] lag not specified
Adjusted for temperature and season.
RR 1 .02 [0.96, 1 .09] lag not specified
NO2 Increment: 10 ppb
NOX Increment: per doubling
NO Increment: per doubling
NO2
Lag ORR 1.08 [1.03, 1.13]
Lag 0-1 RR1.11 [1.05, 1.17]
Lag 0-2 RR 1.10 [1.04, 1.17]
Lag 0-3 RR 1.1 2 [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]
NOv
iiwx
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 1.1 3 [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]
6-64
-------
STUDY
Boutin-Forzano
et al. (2004)
Marseille
France
Period of Study:
4/97-3/98
Castellsague
etal. (1995)
Barcelona, Spain
Period of Study:
1986-1989
Tobias et al.
(1999)
Barcelona, Spain
Period of Study:
1986-1989
METHODS
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
Outcome(s): Asthma
ICD-9 Code(s): NR
Age groups analyzed: 15-64
Study Design: Time-series
# of Hospitals: 4
Statistical Analyses: Poisson
regression
Covariates: long-time trend,
day of wk, temperature, relative
humidity, dew point
temperature
Seasons: Winter: Jan-Mar;
Summer: Jul-Sep
Dose-Response investigated:
Yes
Statistical Package: NR
Lag: 0, 1-5 days and
cumulative
Summer: lag 2 days
Winter: lag 1 day
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
MEAN LEVELS OF
NO2&
MONITORING
STATIONS
Mean NO2: 34.9 pg/m3
Range: 3.0, 85
Mean NO2 (pg/m3)
Summer: 104.0
Winter: 100.8
IQR (Mg/m3):
Summer: 48
Winter: 37
# of Stations: 15
manual, 3 automatic
24-h avg NO2 pg/m3
Non-epidemic days:
54.7 (20.8)
Epidemic days:
58.9 (26.7)
COPOLLUTANT
CORRELATIONS
SO2;r = 0.56
O3; r = 0.58
SO2;r=NR
O3; r = NR
BS
SO2
03
EFFECTS AND INTERPRETATION:
RELATIVE RISK & CONFIDENCE
INTERVALS (95%)
Increment: 10 pg/m3
Increased ER visits
OR 1.0067 [0.9960, 1.0176] lag 0
Increment: 25 pg/m3
Seasonal differences
Summer:
1.071 [1.101, 1.1 30] 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.
> H 104 (SE H 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 (7.79) p< 0.1
Modeling each epidemic with dummy
variable: 14.40 (7.44) p < 0.1
> H 104 (SE H 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 (8.01) p<0.05
Modeling each epidemic with dummy
variable: 18.18(8.01) p < 0.1
6-65
-------
STUDY
Galan et al.
(2003)
Madrid, Spain
Period of Study:
1995-1998
Tenias et al.
(1998)
Valencia, Spain
Period of Study:
1993-1995
'
Cold: Nov-Apr
Warm: May-Oct
Tenias et al.
(2002)
Valencia, Spain
Period of Study:
1994-1995
METHODS
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
Outcome(s): Asthma
ICD-9 Code(s): NR
Age groups analyzed: >14
Study Design: Time-series
N:734
Statistical Analyses: Poisson
regression, APHEA protocol
Covariates: Seasonality,
temperature, humidity, long-
term trend, day of wk, holidays,
influenza
Seasons: Cold: Nov-Apr;
Warm: May-Oct
Dose-Response Investigated:
Yes
Statistical Package: NR
Lag: 0-3 days
Outcome(s): COPD
ICD-9 Code(s): NR
Age groups analyzed: >14
Study Design: Time-series
N: 1,298
# of Hospitals: 1
Statistical Analyses: Poisson
regression, APHEA protocol;
basal models and GAM
Covariates: Seasonality,
annual cycles, temperature,
humidity, day of wk, feast days
Seasons: Cold: Nov-Apr;
Warm: May-Oct
Dose-Response Investigated:
Yes
Statistical Package: NR
Lag: 0-3 days
MEAN LEVELS OF
NO2&
MONITORING
STATIONS
24-h mean: 67.1
pg/m3
SD= 18.0
IQR: 20.5
Max: 147.5
# of Stations: 15
24 h:
57.7 pg/m3
Cold: 55.9
Warm: 59.4
1-h max:
101.1 pg/m3
Cold: 97.3
Warm: 102.8
# of Stations: 2
NO2 24-h avg:
7.7 pg/m3; Range: 12,
135
1-h max: 100.1 pg/m3;
Range: 31, 305
# of Stations: 6
manual and 5
automatic; r = 0.87
COPOLLUTANT
CORRELATIONS
PM10;r = 0.717
SO2;r = 0.610
O3; r = -0.209
24 h:
O3; r = -0.304
SO2(24h); r = 0.265
SO2(1 h);r = 0.261
1 h:
O3; r = -0.192
SO2(24h); r = 0.199
SO2(1 h);r = 0.201
BS;r = 0.246
SO2; r = 0.194
CO; r= 0.180
O3; r = -0.192
EFFECTS AND INTERPRETATION:
RELATIVE RISK & CONFIDENCE
INTERVALS (95%)
Increment: 10 pg/m3
Asthma:
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/SO2 1.031 [1.004, 1.059] lag 3
NO2/PM10 1.001 [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
Increment: 10 pg/m3
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] lag 0
NO2 1-h max
All yr 1.037 [1.008, 1.066] lag 0
Cold: 1.034 [1.004, 1.066] lag 0
Warm: 1 .044 [1 .002, 1 .088] lag 0
Increment: 10 pg/m3
24-h avg NO2
All yr RR 0.979 [0.943, 1 .042] lag 0
Cold: 24-h avg: RR 0.991 [0.953, 1.030] lag 0
Warm: 24-h avg: RR 0.961 [0.900, 1.023] lag
0
1-h max NO2
All yr RR 0.986 [0.966, 1 .007] lag 0
Cold: 24-h avg: RR 0.996 [0.975, 1.018] lag 0
Warm: 24-h avg: RR 0.968 [0.935, 1.003] lag
o
Possibility of a linear relationship between
pollution and risk of emergency cases could
not be ruled out.
6-66
-------
STUDY
Migliaretti et al.
(2005)
Turin, Italy
Period of Study:
1997-1999
Bedeschi et al.
(2007)
Reggio Emilia,
Italy
Period of Study:
03/2001-
03/2002
Vigotti et al.
(2007)
Pisa, Italy
Period of Study:
2000
METHODS
Outcome (ICD-9): Asthma
(493)
Age groups analyzed:
<15, 15-64, >64
Study Design: Case-Control
Controls: age matched with
other 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
Outcome (ICD-9): Respiratory
disorders, asthma or asthma-
like disorders, other respiratory
disorders
Age groups analyzed: <15
Study Design: Time-series
N: 854 children, 1051 visits
Statistical Analyses: Poisson
regression with GAM
Covariates: Weekday, festivity
day, humidity, precipitation,
temperature, flu epidemic,
pollen concentrations
Statistical Package: R software
Lag: 0-5 days and cumulative
Outcome (ICD-9): Respiratory
complaints (493, 468, 466)
Age groups analyzed: <10, >65
Study Design: Ecologic
N:966
Number of hospitals: 1
Statistical Analyses: Robust
poisson regression in GAM
model
Covariates: Day of study,
temperature, humidity, rain,
influenza epidemics, day of wk,
holidays
Statistical Package: NR
Lag: up to 5 d
MEAN LEVELS OF
NO2&
MONITORING
STATIONS
All Participants:
24-hmean: 112.7
M9/m3,
SD = 30.2,
Median: 107.7
Cases:
24-hmean: 117.1
Mg/m3,
SD = 30.0,
Median: 113.0
'
24-h mean: 112.7
pg/m3,
SD = 30.2,
Median: 107.7
# of Stations: 10; r =
0.79
24-h avg: NO2 (pg/m3)
Mean: 49 (13.8)
Range: 21. 6-1 07.5
Median: 47.5
24-h avg: 45.6 (11.0)
pg/m3
Range: 21. 3-74.0
50th: 44.8
Number of monitors: 3
COPOLLUTANT
CORRELATIONS
TSP; r=0.8
Two-pollutant model
adjusted for TSP
SO2;r = 0.56
CO;r = 0.77
TSP; r=0.58
PM10;r = 0.57
O3; r = -0.50
PM10;r = 0.58
CO; r = 0.62
EFFECTS AND INTERPRETATION:
RELATIVE RISK & CONFIDENCE
INTERVALS (95%)
Increment: 10 pg/m3
Single-pollutant (NO2):
<15yrs 2.3% [0.3, 4.40]
15-64 yrs 3.10% [-0.01, 7.70]
>64yrs 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]
Increment: 10 pg/m3
All Respiratory Disorders:
Italians: 9% [1.0, 17.6] lag 4
Foreigners: 17.6% [3.9, 33.0] lag 4
All: 11.0% [3.6, 18.8] lag 4
Increment: 10 pg/m3
Children: 1.118 [1.014, 1.233] lag 0-2
65+: 1.06 [0.967, 1.162] lag 0-2
6-67
-------
STUDY
Pantazopoulou
etal. (1995)
Athens, Greece
Period of Study :
1988
Garty et al.
(1998)
Tel Aviv, Israel
1993
METHODS
Outcomes: All respiratory visits
ICD-9: NR
Age groups analyzed: All ages
Study Design: Time-series
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
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
MEAN LEVELS OF
NO2&
MONITORING
STATIONS
NO2 24-h avg
Winter: 94 pg/m3,
SD = 25
5th: 59, 50th: 93,
95th' 135
Summer: 111 pg/m3,
SD = 32
5th: 65, 50th: 108,
95th: 173
# of stations: 2
24-h mean of NOX
(estimated from
histogram): 60 pg/m3;
Range: 50, 250
COPOLLUTANT
CORRELATIONS
CO
BS
EFFECTS AND INTERPRETATION:
RELATIVE RISK & CONFIDENCE
INTERVALS (95%)
Increment: 76 pg/m3 in winter and 108 pg/m3
in summer (95th-5th)
Respiratory disease admissions
Winter: Percent increase: > = 66.8 [19.6,
113.9]
Summer: Percent increase: a = 21.2 [-35.1,
77.5]
Correlation between NOxand ER visits for
asthma:
All Yr:
Daily data r = 0.30
Running mean for 7 days r = 0.62
Excluding Sept:
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.
LATIN AMERICA
Farhat* et al.
(2005)
Sao Paulo,
Brazil
Period of Study:
1996-1997
Outcome(s) (ICD-9): Lower
Respiratory Disease (466, 480-
^
"J
Age groups analyzed: <13
Study Design: Time-series
N: 4,534
# of Hospitals: 1
Statistical Analyses: (1) Poisson
regression and (2) GAM-no
mention of more stringent
criteria
Covariates: Long-term trends,
seasonality, temperature,
humidity
Statistical Package: S-Plus
Lag: 0-7 days, 2,3,4 day moving
avg
Mean: 125.3 pg/m3
SD = 51.7
IQR: 65.04 pg/m3
# of Stations: 6
PM10;r = 0.83
SO2;r = 0.66
CO; r = 0.59
Increment: IQR of 65.04 pg/m3
Single-pollutant models (estimated from
graphs):
LRD -17.5% [12.5, 24]
Multipollutant models:
Adjusted for:
PM10 16.1% [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
6-68
-------
STUDY
Martins* et al.
(2002)
Sao Paulo,
Brazil
Period of Study:
5/96-9/98
METHODS
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
MEAN LEVELS OF
NO2&
MONITORING
STATIONS
NO2 max 1 -h avg
(pg/m3):
117.6,
SD = 53.0,
Range: 32. 1,421. 6
IQR: 62.2 pg/m3
# of Stations: 4
COPOLLUTANT
CORRELATIONS
O3; r = 0.44
SO2;r = 0.67
PM10;r = 0.83
CO; r = 0.62
EFFECTS AND INTERPRETATION:
RELATIVE RISK & CONFIDENCE
INTERVALS (95%)
Increment: IQR of 62.2 pg/m3
Percent increase: 4.5% [-6.5, 15] lag 3 day
moving avg (estimated from graph)
6-69
-------
STUDY
llabaca et al.
(1999)
Santiago, Chile
Period of Study:
2/1/95-8/31/96
METHODS
Outcome(s) (ICD-9): Upper
respiratory illness (460 465,
487)'
Days: 578
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
MEAN LEVELS OF
N02&
MONITORING
STATIONS
24-h avg NO2:
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
COPOLLUTANT
CORRELATIONS
Warm:
SO2;r = 0.66
O3; r = 0.15
PM10;r = 0.71
PM2.5; r = 0.70
Cool:
SO2;r = 0.74
O3; r = 0.22
PM10;r = 0.82
PM2.5; r = 0.80
EFFECTS AND INTERPRETATION:
RELATIVE RISK & CONFIDENCE
INTERVALS (95%)
Increment: IQR
All respiratory
Cool
Lag 2 IQR: 56.4 RR 1.0378 [1.0211, 1.0549]
Lag 3 IQR: 56.4 RR 1.0294(1.0131, 1.0460]
Lag avg 7 IQR: 33.84 RR 1.0161 [1.0000,
1 0325]
Warm
Lag 2 IQR: 30.08 RR 1.0208 [0.9992, 1.0428]
Lag 3 IQR: 30.08 RR 1 .0395 [1 .01 81 , 1 .061 2]
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]
6-70
-------
STUDY
Linetal. (1999)
Sao Paulo,
Brazil
Period of Study:
May 1991 -Apr
1993
Days: 621
METHODS
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
MEAN LEVELS OF
NO2&
MONITORING
STATIONS
NO2 pg/m3:
Mean: 163
SD = 85
Range: 2, 688
Number of stations: 3
COPOLLUTANT
CORRELATIONS
SO2;r = 0.38
CO; r=0.35
PM10;r = 0.40
O3; r = 0.15
EFFECTS AND INTERPRETATION:
RELATIVE RISK & CONFIDENCE
INTERVALS (95%)
Increment: NR
All respiratory illness
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]
ASIA
Kim et al. (2007)
Seoul, Korea
Period of Study:
2002
Kim et al. (2007)
(cont'd)
Outcome(s) (ICD-10): Asthma
(J45, J46)
Age groups analyzed: All
Study Design: Case-crossover
N: 92,535
Statistical Analyses: Conditional
logistic regression
Covariates: Time trend,
weather conditions, seasonality
Statistical Package: NR
Lag:0, 1,2, 3, 4, 2-4 ma
24-h avg: 36.0
(14.7) ppb
Range: 2.3-108.0
50th: 34.3
IQR:20.1
PM10
CO
SO2
03
Increment: 20.1 ppb
Stratified by individual SES:
Highest SES quintile: 1.06 [1.02, 1.10] lag 2-4
2nd Quintile: 1.06 [1.02, 1.09] lag 2-4
3rd Quintile: 1.03 [0.99, 1.06] lag 2-4
4th Quintile: 1.06 [1.02, 1.10] lag 2-4
Lowest SES quintile: 1.05 [1.00, 1.10] lag 2-4
Stratified by Regional SES:
Highest SES quintile: 0.96 [0.90, 1.02] lag 2-4
2nd Quintile: 1.08 [1.04, 1.13] lag 2-4
3rd Quintile: 1.03 [1.00, 1.07] lag 2-4
4th Quintile: 1.06 [1.02, 1.10] lag 2-4
Lowest SES quintile: 1.06 [1.02, 1.09] lag 2-4
Overall: 1.05 [1.03, 1.06]
Relative Effect Modification for Interaction
Terms:
Stratified by individual SES H air pollution:
Highest SES quintile: 1.00 [referent]
2nd Quintile: 0.99 [0.95, 1.04]
3rd Quintile: 0.96 [0.92, 1.01]
4th Quintile: 1.00 [0.95, 1.05]
Lowest SES quintile: 0.99 [0.93, 1.05]
Stratified by Regional SES H air pollution:
Highest SES quintile: 1.00 [referent]
2nd Quintile: 1.11 [1.03, 1.20]
3rd Quintile: 1.07 [1.00, 1.15]
4th Quintile: 1.09 [1.02, 1.17]
Lowest SES quintile: 1.09 [1.02, 1.16]
6-71
-------
STUDY
Sun et al. (2006)
Central Taiwan
Period of Study:
2004
Chew et al.
(1999)
Singapore
Period of Study:
1990-1994
Hwang and
Chan (2002)
Taiwan
Period of Study:
1998
METHODS
Outcome(s) (ICD-9): asthma
(493)
Age groups analyzed: <16, 16-
55
Study Design: Cross-sectional
Number of hospitals: 4
Statistical Analyses: Multiple
correlation coefficients/multiple
regression analysis
Covariates:
Statistical Package: SPSS v
12.0
Lag:
Outcome(s) (ICD-9): Asthma
(493)
Age groups analyzed: 3-12, 13-
21
Study Design: Time-series
N: 23,000
# of Hospitals: 2
Statistical Analyses: Linear
regression, GLM
Covariates: Variables that were
significantly associated with ER
visits were retained in the
model
Statistical Package: SAS/STAT,
SAS/ETS 6.08
Lag: 1,2 days avgs
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
MEAN LEVELS OF
NO2&
MONITORING
STATIONS
Number of monitors:
11
24-h avg: 18.9 pg/m3,
SD = 15.0, Max < 40
# of Stations: 15
24-hr avg: 23.6 ppb,
SD = 5.4, Range:
13.0, 34.1
COPOLLUTANT
CORRELATIONS
SO2
03
CO
PM10
SO2; r=-0.22
O3; r = 0.17
TSP; r=0.23
SO2
PM10
03
CO
No correlations for
individual pollutants
Colinearity of pollutants
prevented use of
multipollutant models
EFFECTS AND INTERPRETATION:
RELATIVE RISK & CONFIDENCE
INTERVALS (95%)
Children:
r=0.72, p = 0.004
Adults:
r = 0.428, p = 0.083
Emergency visits for asthma increased with
increased levels of NO2 for children but not
for adults.
Categorical analysis (via ANOVA) p-value
and Pearson correlation coefficient (r) using
continuous data comparing daily air pollutant
levels and daily number of ER visits
Age Group:3-12 13-21
LagO r=0.10 r=0.09
p = 0.0019 p< 0.001
Lag 1 r=0.12 r=0.04
p< 0.001 p = 0.0014
Lag 2 r=0.14 r=0.03
p < 0.001 p = 0.0066
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
>65yrs
1.8% [1.4, 2.2] lagO
All ages
1.4% [1.2, 1.6] lag 0
6-72
-------
STUDY
Tanaka et al.
(1998)
Kushiro, Japan
Period of Study:
1992-1993
METHODS
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
MEAN LEVELS OF
NO2&
MONITORING
STATIONS
NO2 24-h avg
9.2 ± 4.6 ppb in fog
11.5 ± 5.7 in fog free
days
Max NO2 24-h avg
<30 ppb
COPOLLUTANT
CORRELATIONS
NO2; r=NR
SO2;r=NR
SPM (TSP);
r = 0.70
O3; r = NR
EFFECTS AND INTERPRETATION:
RELATIVE RISK & CONFIDENCE
INTERVALS (95%)
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
Table AX6.3-5.
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%)
NORTH AMERICA
Sinclair and
Tolsma (2004)
Atlanta, GA
Period of Study:
8/1998-8/2000
Outcome(s) (ICD9): Asthma
(493), URI (460-466, 477), LRI
(466.1,480-486)
Age groups analyzed: <18, >18
Study Design: Time-series
N: 232,350
Statistical Analyses: Poisson
regression with GLM
Covariates: Season, day of wk,
federal holiday, study month,
long-term trend
Statistical Package: SAS v 8.02
Lag: 0-2, 3-5, 6-8
1-h max: 51. 22
(18.54) ppb
Monitors: 1 ARIES
monitor in
downtown Atlanta
PM2.5,
PM10-2 5
PM10
PM25 components
PMuf
Polar VOCs
03
SO2
No NO2 results presented because they were not
statistically significant for any lag periods
examined.
6-73
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Hernandez-
Garduno et al.
(1997)
Mexico City,
Mexico
Period of Study:
May 15, 1992-
January 31,
1993
Villeneuve et al.
(2006)
Toronto, ON,
Canada
Period of Study:
lone onnn
1 993-ZUUU
Days: 2, 190
OUTCOMES, DESIGN, &
METHODS
Outcome(s): Respiratory illness
ICD9: NR
Age groups analyzed: <15, 15+,
all 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
Outcome(s) (ICD9): Allergic
Rhinitis (177)
Age groups analyzed: >65
Study Design: Time-series
N' 52 691
Statistical Analyses: GLM, using
natural splines (more stringent
criteria than default)
Covariates: Day of wk, holiday,
temperature, relative humidity,
aero-allergens
Season: All yr; Warm, May-Oct;
Cool, Nov-Apr
Statistical Package: S-Plus
Lag: 0-6
MEAN LEVELS
& MONITORING
STATIONS
Number of Stations:
5
24-h avg: 25.4 ppb,
SD= 7.7
IQR: 10.3 ppb,
Range 9.2, 71.7
Number of stations:
9
COPOLLUTANTS
CORRELATIONS
SO2
O3
CO
NOX
SO2
03
CO
PM2.5
PM10-2.5
PM10
EFFECTS:
RELATIVE RISK & CONFIDENCE
INTERVALS (95%)
Increment: Max NO2 concentration of all days-
Mean NO2 concentration of all days
<14yrs:
NO2 lag 0: RR 1 .29 ± 0.09 (p < 0.01)
NO2 lag 6: RR 1 .1 8 ± 0.09 (p > 0.05)
15+ yrs:
NO2lagO: RR 1.14 ± 0.07 (p < 0.05)
NO2 lag 6: RR 1 .1 0 ± 0.06 (p > 0.05)
All 8Q6S'
NO2lagO: RR 1.43 ± 0.15 (p < 0.01)
NO2lag6: RR 1.29 ±0.15 (p > 0.05)
Increment: 10.3 ppb (IQR)
All results estimated from Stick Graph:
All Yr
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
6-74
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
OUTCOMES, DESIGN, &
METHODS
MEAN LEVELS
& MONITORING
STATIONS
COPOLLUTANTS
CORRELATIONS
EFFECTS:
RELATIVE RISK & CONFIDENCE
INTERVALS (95%)
EUROPE
Hajat et al.
(1999)
London, United
Kingdom
Period of Study:
1992-1994
Hajat et al.
(1999)
(cont'd)
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-h avg:
33.6 ppb,
SD= 10.5
Warm: 32.8 (19.8)
Cool: 34.5 (10.1)
10th-90thallyr
percentile: 24 ppb
SO2; r= 0.61
BS; r = 0.70
CO; r=0.72
PM10;r = 0.73
O3;r = -0.10
Increment: 24 ppb (90th-10th percentile)
Asthma
All ages 2.1% [-0.7, 4.9] lag 0; 3.1%
[-0.4, 6.7] lag 0-1
0-14 yrs 6.1% [1.2, 11.3] lag 1;6.9%
[1.7, 12.4] lag 0-1
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]lagO
Warm: -1.7% [-5.9, 2.6] lag 0
Cool: -1.6% [-4.8, 1.8] lag 0
Two-pollutant model-Asthma
NO2 alone: 5.2% [0.8, 9.8]
NO2/O3:6.7%[2.2, 11.4]
NO2/SO2:3.9%[-1.2,9.2]
NO2/PM10: 5.3% [-0.6, 11.6]
Two-pollutant model - Lower Respiratory disease
NO2 alone 4.2% [1.1, 7.3]
NO2/O3 4.9% [1 .8, 8.2]
NO2/SO22.5%[-1.1,6.2]
NO2/PM103.5%[0.1,6.9]
6-75
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Hajat* et al.
(2001)
London, United
Kingdom
Period of Study:
1992-1994
Hajat* et al.
(2002)
London, United
Kingdom
Period of Study:
1992-1994
OUTCOMES, DESIGN, &
METHODS
Outcome (I CD9): 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
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
MEAN LEVELS
& MONITORING
STATIONS
NO2 24-h avg:
33.6 ppb,
SD =105
# of Stations: 3,
r= 0.7-0.96
NO2 24-h avg:
33.6 ppb,
SD= 10.5
Warm: (Apr-Sep)
Mean: 32.8 ppb,
SD= 10.1
Cool: (Oct-Mar)
Mean: 34.5 ppb,
SD = 10.1
# of Stations: 3
COPOLLUTANTS
CORRELATIONS
SO2; r=0.61
BS; r = 0.70
CO; r=0.72
PM10;r = 0.73
O3;r = -0.10
SO2; r=0.61
BS' r = 0 70
CO; r=0.72
PM10;r = 0.73
O3; r = -0.10
EFFECTS:
RELATIVE RISK & CONFIDENCE
INTERVALS (95%)
Increment: 24 ppb (90th-10th percentile)
Single-pollutant model
<1 to 14yrs
11.0% [3.8, 18.8] lag 4
12.6% [4.6, 21. 3] lag 0-4
15 to 64yrs
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
NO2 & O3: 7.9% [0.6, 15.8]
NO2&SO2:-3.8%[11.8, 5.0]
NO2& PM10: 10.8% [0.1, 22.7]
15 to 64 yrs
NO2 & O3: 4.8% [1 .0, 8.8]
NO2&SO2: 1.0% [-3.7, 5.8]
NO2&PM10:0.5%[-4.9, 6.3]
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
0-1 4 yrs
NO2&O3: 1.7% [-0.6, 3.9]
NO2&SO2:2.2%[-0.4, 5.0]
NO2&PM10: 1.5% [-1.7, 4.8]
For 15-64 yrs
NO2&O3:4.4%[2.2, 6.8]
NO2&SO2:4.4%[1.6, 7.2]
NO2&PM10:2.7%[-0.5, 5.9]
For >65 yrs
NO2&O3:8.1%[3.0, 13.6]
NO2&SO2:8.6%[2.1, 15.4]
NO2& PM10: 4.3% [-2.8, 11.8]
6-76
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Chardon et al.
(2007)
Greater Paris,
France
Period of Study:
2000-2003
OUTCOMES, DESIGN, &
METHODS
Outcome (ICD9): Asthma, URD,
LRD
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, 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: R software
Lag:0, 1,2,3,0-1,0-2,0-3
days
MEAN LEVELS
& MONITORING
STATIONS
24-h avg: 44.4
(14.92) pg/m3
Median: 43.6
IQR: 33.7-53.2
Range: 12.3-132.8
Number of
monitors: 12-15
COPOLLUTANTS
CORRELATIONS
PM10;r = 0.68
PM2.5; r = 0.68
EFFECTS:
RELATIVE RISK & CONFIDENCE
INTERVALS (95%)
Increment: 10 pg/m3
URD: 0.7% [-0.9, 2.3] lag 0-3
LRD: 1.1% [-0.7, 2.9], lag 0-3
Asthma: -0.3% [-3.3, 2.7], lag 0-3
' Default GAM
+ Did not report correction for over-dispersion
APHEA: Air Pollution and Health: a European Approach
6-77
-------
Table AX6.3-6.
Human Health Effects of Oxides of Nitrogen: CVD Hospital Admissions
and Visits: United States and Canada
REFERENCE,
STUDY
LOCATION, &
PERIOD
Burnett et al.
(1997a)
Metropolitan Toronto
(Toronto, North
York, East York,
Etobicoke,
Scarborough, York),
Canada
Study period:
1992-1994,388
days, summers only
OUTCOMES, DESIGN, &
METHODS
Outcome(s) (ICD9): IHD
410-414; Cardiac Dysrhythmias
427; Heart failure 428. All
Cardiac 41 0-41 4, 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, GEEs.
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
MEAN LEVELS
& MONITORING
STATIONS
NO2 daily 1-h max
(ppb):
Mean: 38.5
CV' 29
Min: 12
25th percentile: 31
50th percentile: 38
75th percentile: 45
Max: 81
# of Stations: 6-11
(Results are reported
for additional metrics
including 24-h avg and
daytime avg (day))
COPOLLUTANTS
(CORRELATIONS)
H+ (0.25)
SO4 (0.34)
TP (061)
FP (0.45)
r*p rt~i fi'n
Or ^U.D \ )
COH (0.61)
03 (0.07)
SO2 (0.46)
CO (0.25)
EFFECTS: RELATIVE RISK OR
PERCENT CHANGE*
CONFIDENCE INTERVALS ([95%
LOWER, UPPER])
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 particulate in this
s u y.
6-78
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Burnett etal. (1999)
Metropolitan Toronto
(Toronto, North
York, East York,
Etobicoke,
Scarborough, York),
Canada
Study Period:
1980-1995, 15yr
Morris etal. (1995)
US (Chicago,
Detroit, LA,
Milwaukee, NYC,
Philadelphia)
Study Period:
1986-1989, 4yr
OUTCOMES, DESIGN, &
METHODS
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 with
LOESS smoothers to remove
temporal and weather related
trends, 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, SAS
Lag(s): 0-2 day
Outcome(s) (ICD9): CHF 428.
Daily Medicare hospital
admission records.
Study Design: Time-series
Statistical Analyses: GLM,
negative binomial distribution
Age groups analyzed: >65 yrs
Covariates: Temperature,
indicator variables for mo to
adjust for weather effects and
seasonal trends, day of wk, yr
Statistical Software: S-PLUS
Lag(s): 0-7 day
MEAN LEVELS
& MONITORING
STATIONS
NO2 daily avg (ppb)
Mean: 25.2
5th percentile: 13
25th percentile: 19
50th percentile: 24
75th percentile: 30
95th percentile: 42
Max: 82
Multiple day avgs used
in models
NO2 1 h-max (ppm)
Mean: (SD)
LA: 0.077 (0.028)
Chicago: 0.045 (0.01 3)
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)
COPOLLUTANTS
(CORRELATIONS)
PM2.5 (0.50)
PM10-2.5(0.38)
PM10 (0.52)
CO (0.55)
SO2 (0.55)
03 (-0.04)
SO2 1-h max
O3 1-h max
CO 1-h max
Correlations of NO2
with other pollutants
strong.
Multipollutant models
run.
EFFECTS: RELATIVE RISK OR
PERCENT CHANGE*
CONFIDENCE INTERVALS ([95%
LOWER, UPPER])
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, lag 0
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, PM10-2.5)
6.45 (w/CO, PM2.5, PM10-2.5, PM10)
IHD
8.34 (w/ CO, SO2)
7.76 (w/ CO, SO2, PM2.5)
8.41 (w/CO, SO2, PM2.5, PM10-2.5)
8.52 (w/CO, SO2, PM25, PM10-2.5,
PM10)
In multipollutant models, gaseous
pollutants were selected by stepwise
regression. PM variables were then
added to the model.
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) for all cities with the
exception of New York.
6-79
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Dales et al (2006)
Canada (11 largest
cities)
Study period:
January 1, 1986-
December31, 2000.
Wellenius et al.
(2005b)
Birmingham,
Chicago, Cleveland,
Detroit, Minneapolis,
Pittsburgh, Seattle
Study Period:
Jan 1986-Nov 1999
(varies slightly
depending on city)
OUTCOMES, DESIGN, &
METHODS
Outcomes (ICD-9): Asphyxia
(799.0), respiratory failure
(799.1), dyspnea and respiratory
abnormalities (786.0), respiratory
distress syndrome (769),
unspecificed birth asphyxia in
live-born infant (768.9), other
respiratory problems after birth
(770.8) and pneumonia (486)
Data from: Canaidian Institutue
for Health Information:
9542 records for patients aged
birth to 27 days
Study Design: Time series
Statistical analysis: Random-
effects regression model
Statitsitical Software: S-PLUS
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.
N IS: 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
MEAN LEVELS
& MONITORING
STATIONS
NO2 24-hour mean
levels (ppb):
Calgary: 25.6
Edmonton: 24.6
Halifax: 15.1
Hamilton: 20.8
London' 20 0
Ottawa: 21. 2
Saint John: 9.2
Toronto: 25.1
Vancouver: 19.0
Windsor: 24.9
Winnipeg: 15.2
Population weighted
avg: 21.8
NO2 24-h (ppb)
10th: 13.71
25th: 18.05
Median: 23.54
75th: 29.98
QfuU. riC CA
9Uin. oD.O'f
NO2 data not available
for Birmingham, Salt
Lake, and Seattle.
COPOLLUTANTS
(CORRELATIONS)
Range of Pearson
pain/vise correlations
PM10: -0.26 to 0.69
O3: -0.55 to 0.05
SO2: 0.20 to 0.67
CO: 0.13 to 0.76
PM10 (0.53)
CO, SO2
Correlation only
provided for PM
because study
hypothesis involves
PM
EFFECTS: RELATIVE RISK OR
PERCENT CHANGE*
CONFIDENCE INTERVALS ([95%
LOWER, UPPER])
Pooled estimate of % increase in
neonatal resipartory hospital admissions
(95% Cl):
Interquartile range: 10.0
Single-pollutant model: 2.94 (1.93 to
3.95)
Multi pollutant model: 2.85 (1 .68 to 4.02)
Multipollutant model restricted to days
with PM10 measures: 2. 48 (1.18 to 3.80)
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),
lag 0
Multipollutant models not run.
6-80
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Fung et al. (2005)
Windsor, Ontario,
Canada
Study Period:
Apr 1995-Jan 2000
Linn et al. (2000)
Metropolitan Los
Angeles, USA
Study Period:
1992-1995
OUTCOMES, DESIGN, &
METHODS
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
Outcome(s): All Patient Refined
Diagnosis Related Groups
(based on medicare diagnosis
related groups). CVD APR-DRG
103-144; Cerebrovascular APR-
DRG 1 4-1 7 and 22 ; CHF APR-
DRG 127; Ml APR-DRG 111,
115, 121; cardiac ARR APR-DRG
138; Occlusive Stroke APR-DRG
14. Hospital admission records
used to ascertain cases.
Study Design: Time-series
Statistical Analyses: Poisson
regression, cubic spline smooth
on time, indicator variables to
adjust for temperature and rain.
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
MEAN LEVELS
& MONITORING
STATIONS
NO2 1-h max (ppb):
Mean (SD): 38.9 (12.3)
Min: 0
Max: 117
NO2 24-h (ppm)
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' 1684
COPOLLUTANTS
(CORRELATIONS)
SO2 (0.22)
CO (0.38)
O3 (0.26)
COH (0.39)
PM10 (0.33)
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
distinguished.
# Stations: 6+
EFFECTS: RELATIVE RISK OR
PERCENT CHANGE*
CONFIDENCE INTERVALS ([95%
LOWER, UPPER])
Results expressed as percent change
associated with an incremental increase
in NO2 equivalent to the IQR (16 ppb)
Cardiac*
<65 age group:
0.7 (-5.5, 6.6)
2.1 (-3.7, 8.2)
3.7 (-2.9, 10.7)
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.
Results reported as increase % increase
in admission for a 10 ppb increase in
NO2. SD in parentheses.
CVD lag 0
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, lag 0
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)
Ml, lag 0 (yr round)
1.1 (0.5)
CHF, lag 0 (yr round)
1.0(0.5), winter 1.9(0.9)
Cardiac Arrhythmia, lag 0
(yr round)
0.6 (0.5)
Occlusive stroke, lag 0
(yr round)
2.0 (0.5), winter 2.7 (1 .0), autumn 0.1
(0.05)
Significant effects observed in fall for
occlusive stroke and winter for CHF.
6-81
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Lippmann et al.
(2000*; reanalysis
Ito, 2003, 2004)
Windsor Ontario
(near Detroit Ml)
Study period:
1992-1 994 (hospital
admissions -
mortality study
spanned longer
period)
Mann et al. (2002)
South coast air
basin of CA, United
States
Study Period:
1988-1995, Syr
OUTCOMES, DESIGN, &
METHODS
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
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, GAM with
cubic splines, 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 Apr-Oct
MEAN LEVELS
& MONITORING
STATIONS
NO2 24-h avg (ppb)
5th %: 11
25th %: 16
50th %: 21
75th %: 26
95th %: 36
Mean: 21.3
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
COPOLLUTANTS
(CORRELATIONS)
PM10 (0.49)
PM2.5 (0.48)
PM10-2.5(0.32)
H+ (0.14)
SO4 (0.35)
03(0.14)
SO2 (0.53)
CO (0.68)
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.
EFFECTS: RELATIVE RISK OR
PERCENT CHANGE*
CONFIDENCE INTERVALS ([95%
LOWER, UPPER])
Results reported for RR for incremental
increase in NO2 of 5th to 95th percentile
(28 ppb).
IHD
(0.94, 1.10), lag 0
0.98(0.90, 1.06), lag 1
Dysrhythmias
0.98(0.86, 1.12), lag 0
1.03(0.90, 1.06), lag 1
Heart Failure
1.00(0.91, 1.09), lag 0
1.07(0.98, 1.17), lag 1
Stroke
0.99(0.90, 1.09), lagO
0.98(0.89, 1.08), lag 1
Results reported for percent increase in
admissions for a 10-ppb incremental
increase in NO2.
All IHD: 1.68(1.08, 2.28), lag 0
Ml: 1.04(1.05,3.02), lag 0
Other acute IHD: 1.75 (0.72, 2.78), lag 0
IHD w/ secondary diagnosis of
Arrhythmia:
1.81 (0.78, 2.85), lag 0
IHD w/ secondary diagnosis of CHF:
2.32 (0.69, 3.98), lag 0
IHD w/ no secondary diagnosis:
0.46 (-0.81, 1.74), lagO
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 Ml as the primary
diagnosis.
6-82
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Metzger et al.
(2004)
Atlanta, GA
Period of Study: Jan
1993-Aug 31 2000,
4 yr
Moolgavkar
(2000a,b,c)*
Cook County, IL,
Los Angeles County,
CA
Maricopa County,
AZ
1987-1995
OUTCOMES, DESIGN, &
METHODS
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: Apr 15-Oct 14;
CoohOct 15-Apr 14
Lag(s): 0-3 day
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
MEAN LEVELS
& MONITORING
STATIONS
NO2 1-h max (ppb):
Median: 26.3
10th-90th percentile
Range: 25, 68
NO2 24-h avg (ppb)
Cook County:
Min: 7
Q1:20
Median: 25
Q3:30
Max: 58
NO2 24-h avg (ppb) LA
County:
Min: 10
Q1: 30
Median' 38
Q3:48
Max: 102
NO2 24-h avg (ppb)
Maricopa County:
Min: 2
Q1: 14
Median' 1 9
Q3:26
Max: 56
COPOLLUTANTS
(CORRELATIONS)
PM1024h (0.49)
O3 8-h max (0.42)
SO2 (0.34)
CO 1 h (0.68)
1998-2000 Only
piwi r ir\ AP\
nvi25 ^u.tuy
Course PM (.46)
Ultrafine PM (.26)
Water-soluble metals
(321
\-°^-/
Sulfates(.17)
OC (0.63)
EC ( 371
l_vj V-°'/
OHC (0.3)
Multipollutant models
used. All models
specified a priori.
PM10 (0.22-0.70)
PM2.5 (0.73) (LA only)
CO (0.63-0.80)
SO2 (0.02- 0.74)
O3 (-0.23-0.02)
Two-pollutant models
(see results)
EFFECTS: RELATIVE RISK OR
PERCENT CHANGE*
CONFIDENCE INTERVALS ([95%
LOWER, UPPER])
Results presented for RR of an
incremental increase in 1-h max NO2
equivalent to 20 ppb (1 SD).
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)
Lag is 3-day moving avg for results
above.
NO2 effect was generally attenuated in
two-pollutant models. The attenuation
was strongest in the period after 1998.
Results reported for percent change in
hospital admissions per 10-ppb increase
in 24-h avg NO2. T statistic in
parentheses.
CVD, 65+:
Cook County
2.9(10.2), lagO
2.3 (6.7), lag 0, two-pollutant model
(PM101
\r^ IVI 1 u^
2.9 (8.1), lag 0, two-pollutant model (CO)
2.8 (8.8), lag 0, two-pollutant model
(S02)
LA County
2.3(16.7), lagO
-0.1 (-0.5), lag 0, two-pollutant model
(CO)
1.7 (8.0), lag 0, two-pollutant model
(S02)
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
(S02)
Cerebrovascular Disease, 65+:
Cook County
1 .6 (3.6)
LA County
1.1(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.
6-83
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Moolgavkar (2003)*
Cook County, IL,
Los Angeles County,
CA,
Maricopa County,
AZ
1987-1995
Peel et al. (2007)
Atlanta, GA
Study Period:
Jan 1993-Aug 2000
OUTCOMES, DESIGN, &
METHODS
Outcome(s) (ICD9): CVD
390-429; Cerebrovascular
disease
430-448 was not considered in
the reanalysis. Hospital
admissions from CA department
of health database.
Age groups analyzed: 20-64, 65+
yrs
Study Design: Time-series
N: 118 CVD admissions/day
# Hospitals: NR
Statistical Analysis: Poisson
regression, GAM with strict
convergence criteria (10-8), GLM
using natural splines
Covariates: Adjustment for day of
wk, long-term temporal trends,
relative humidity, temperature
Statistical Package: SPLUS
Lag: 0-5 days
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
MEAN LEVELS
& MONITORING
STATIONS
NO2 24-h avg (ppb)
Cook County:
Min: 7
Q1:20
Median: 25
Q3: 30
Max: 58
NO2 24-h avg (ppb)
LA County:
Min: 10
Q1:30
Median: 38
Q3:48
Max: 102
NO2 24-h avg (ppb)
Maricopa County:
Min: 2
Q1: 14
Median: 19
Q3:26
Max: 56
NO2 1-h max (ppb):
Mean (SD): 45.9 (17.3)
10th: 25.0
90th: 68.0
COPOLLUTANTS
(CORRELATIONS)
PM10 (0.22-0.70)
PM2.5 (0.73) (LA only)
CO (0.63-0.80)
SO2 (0.02-0.74)
O3 (-0.23-0.02)
Two-pollutant models
(see results)
PM 10 24-h avg
O3 8-h max
SO2 1-h max
CO 1-h max
Correlations not
reported
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.
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)
6-84
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Schwartz, (1997)*
Tucson , AZ
Study Period:
Jan 1988-Dec 1990
Stieb et al. (2000) *
Saint John, New
Brunswick Canada
Study Period:
July 1992-Mar 1996
Tolbert et al. (2007)
Atlanta, GA
Study Period:
1993-2004
OUTCOMES, DESIGN, &
METHODS
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
Outcome(s): Angina pectoris; Ml;
dysrhythmia/conduction
disturbance; CHF; All Cardiac.
ED Visits collected prospectively.
Study Design: Time-series
Statistical Analyses: Poisson
regression, GAM, LOESS
smooth for temporal and weather
related variables
N: 19,821 ER visits
# Hospitals: 2
Lag(s): 1-8 days
Outcome(s) (ICD9): All CVD
including IHD 410-414; cardiac
dysrhythmias 427; CHF 428;
peripheral vascular and
cerebrovascular disease 433-
437,440443-445,451-453.
Emergency visits primary and
secondary diagnostic codes.
Study Design: Time-series
Statistical Analysis: GLM, cubic
splines with monthly knots,
indicators for season, day of wk,
holiday, excluded days with
missing exposure data
Statistical Software: SAS
Lag(s): 3 day moving avg (0-2 d)
MEAN LEVELS
& MONITORING
STATIONS
NO2 24-h avg (ppb):
Mean: 19.3
10th: 9.9
25th: 13.2
50th: 19
75th: 24.6
90th: 29.8
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
NO2 1-h max (ppb):
Mean: 43.2
Min: 1.0
10th: 22
25th: 31
Median: 41
75th: 54
90th: 66
Max: 181
COPOLLUTANTS
(CORRELATIONS)
PM10 (0.326)
O3 (-0.456)
SO2 (0.482)
CO (0.673)
CO (0.68)
H2S (-0.07)
03 (-0.02)
SO2(0.41)
PM10 (0.35)
PM2.5 (0.35)
H+ (-0.25)
SO4 (0.33)
COH (0.49)
PM10 (0.53)
03 (0.44)
CO (0.70)
SO2 (0.36)
Course PM (0.70)
PM25 (0.47)
PM2.5SO4(0.14)
PM2.5 EC (0.64)
PM2.5 OC (0.62)
PM2.5 TC (0.65)
PM2 5 water sol
metals (0.32)
OHC (0.24)
EFFECTS: RELATIVE RISK OR
PERCENT CHANGE*
CONFIDENCE INTERVALS ([95%
LOWER, UPPER])
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)
Tucson selected to minimize correlations
between pollutants. Since there was no
association between NO2 and
admissions, author suggests results for
CO not confounded by NO2.
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, all yr
10.1, p-value = 0.051, lag 5, May-Sept
For specific CVD diagnoses, ARR and
CHF approached significance. NO2 was
not a focus of this paper.
Results reported for RR based on
incremental increase of NO2 equivalent
to 1 IQR (23 ppb):
Single-pollutant model results:
CVD 1 .01 5 (1 .004, 1 .025), lag 0-2
NO2 effect diminished in multipollutant
models containing CO and PM25TC
(shown in figure).
6-85
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Villeneuve et al.
(2006)
Edmonton Canada
Study Period:
Apr 1992-Mar 2002
Wellenius et al.
(2005a)
Allegheny County,
PA (near Pittsburgh)
Study Period:
Jan 1987-Nov 1999
OUTCOMES, DESIGN, &
METHODS
Outcome(s) (ICD9): Acute
ischemic stroke 434, 436;
hemorrhagic stroke 430, 432;
transient ischemic attack (TIA)
435; Other 433, 437, 438. ED
visits supplied by Capital Health.
N: 12,422 Stroke Visits
Catchment area: 1.5 million
people
Study Design: Case-crossover,
exposure index time compared to
referent time. Time-independent
variables controlled in the
design. Index and referent day
matched by day of wk.
Statistical Analysis: Conditional
logistic regression, stratified by
season and gender.
Covariates: Temperature and
humidity
Statistical Software: SAS
Season: Warm: Apr-Sep;
Cool: Oct-Mar
Lag(s): 0, 1, 3-day avg
Outcome(s): CHF 428. Cases
are Medicare patients admitted
from ER with discharge of CHF.
Study Design: Case-crossover,
control exposures same mo and
day of wk, controlling for season
by design.
Statistical Analysis: Conditional
logistic regression
N: 55,019 admissions, including
repeat admissions, 86%
admitted #5 times
Age groups analyzed: 65+ yrs
(Medicare recipients)
Covariates: Temperature and
pressure. Effect modification by
age, gender, secondary
diagnosis arrhythmias, atrial
fibrillation, COPD, hypertension,
type 2 diabetes, AMI within 30
days, angina pectoris, IHD, acute
respiratory infection.
Statistical Software: SAS
Lag(s): 0-3
MEAN LEVELS
& MONITORING
STATIONS
NO2 24 h ppb:
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' 140
75th: 22.0
IQR: 8
Mean (SD): 29.4 (9.6)
Median' 28 5
25th: 22.5
75th: 35.5
IQR: 13.0
NO2 24-h avg (ppb):
Mean (SD): 26.48
(8.02)
5th: 15.10
25th: 20.61
Median: 25.70
75th' 31 30
95th' 4102
# Stations: 2
COPOLLUTANTS
(CORRELATIONS)
O3 24-h max (-0.33)
O3 24-h avg (-0.51)
SO225-havg (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
PM1 0(0.64)
CO (0.70)
O3 (-0.04)
SO2 (0.52)
EFFECTS: RELATIVE RISK OR
PERCENT CHANGE*
CONFIDENCE INTERVALS ([95%
LOWER, UPPER])
Results reported for an incremental
increase in NO2 equivalent to one IQR
NO2.
Ischemic Stroke, Summer
1.17(1.05, 1.31), lag 0
1.18(1.05, 1.31), lag 1
1.26(1.09, 1.46), 3-day avg
Hemorrhagic stroke, Summer
1.16(0.99, 1.37)
1.14(0.97, 1.35)
1.18 (0.95, 1.46)
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.
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 3x higher among
those with history of Ml .
6-86
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Zanobetti and
Schwartz (2006)
Boston MA
1995-1999
OUTCOMES, DESIGN, &
METHODS
Outcome(s) (ICD9): Ml 410.
Admissions through the
emergency room from Medicare
claims.
Age group analyzed: 65+ yrs
Study Design: Case-crossover,
control days matched yr, mos
and temperature
Statistical Analysis: Conditional
logistic regression
N: 15,578
Covariates: Temperature
(regression spline), day of wk
Seasons: Hot (Apr-Sept) and
cold
Software: SAS
Lags: 0, 0-1 previous day avg
MEAN LEVELS
& MONITORING
STATIONS
NO2 24-h avg ppb
5th: 12.59
25th: 18.30
Median: 23.20
75th: 29.13
95th:
90th-10th: 20.41
# Stations: 4
COPOLLUTANTS
(CORRELATIONS)
03(-0.14)
BC (0.70)
CO (0.67)
PM2.5 (0.55)
PM non-traffic (0.14)
(residuals from model
of PM2 5 regressed on
BC)
EFFECTS: RELATIVE RISK OR
PERCENT CHANGE*
CONFIDENCE INTERVALS ([95%
LOWER, UPPER])
Results reported for percent increase in
admissions for incremental increase in
NO2 equivalent to the 90th-10th
percentiles (20.41 or 16.80 for 0-1,
previous day avg).
Ml
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
•Default GAM
AMI Acute Myocardial Infarction
ARR Arrhythmia
BC Black Carbon
COM 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
Ml 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
6-87
-------
Table AX6.3-7.
Human Health Effects of Oxides of Nitrogen: CVD Hospital Admissions
and Visits: Australia and New Zealand
REFERENCE,
STUDY
LOCATION, &
PERIOD
Barnett et al.
(2006)
Australia and New
Zealand:
Brisbane,
Canberra,
Melbourne, Perth,
Sydney
Period of Study:
1998-2001
OUTCOMES, DESIGN, &
METHODS
Outcome(s) (ICD9): All CVD
390-459; ARR 427; Cardiac
disease 390-429; Cardiac
failure 428; IHD 410-413; Ml
410; Stroke 430-438.
Ages groups analyzed: 15-64
yrs, >65yrs
Study Design: Time stratified,
case-crossover, multicity study
# of Hospitals: All ER
admissions from state
government health
departments
Statistical Analyses: Random
effects meta analysis,
heterogeneity assessed using
12 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
MEAN LEVELS &
MONITORING
STATIONS
NO2 (ppb), Mean
(range)
Auckland
1-h max: 19.1 (4.2-
86.3)
24-h avg: 10.2 (1.7-
28.9)
Brisbane
1-h max: 17.3 (4-44.1)
24-h avg: 7.6 (1.4-
19.1)
Canberra
1-h max: 17.9 (0-53.7)
24-h avg: 7.0 (0-22.5)
1-h max: 15.7(1.2-
54.6)
24-h avg: 7.1 (0.2-
24 51
^.t.oy
Melbourne
1-h max: 23.2 (4.4-48)
24-h avg: 11.7(2-29.5)
Perth
1-h max: 21. 3 (4.4-48)
24-h avg: 9.0 (2 -23.3)
Sydney
1-h max: 22.6 (5.2-
51.4)
24-h avg: 11.5(2.5-
24.5)
24 h avg IQR: 5.1
# of Stations: 1-13
depending on the city
COPOLLUTANTS
(CORRELATIONS)
PM1024h
CO 24 h
SO2 24 h
O38h
BS 24 h
Matched analysis
conducted to control
for copollutants
EFFECTS: RELATIVE RISK OR
PERCENT CHANGE & CONFIDENCE
INTERVALS ([95% LOWER, UPPER])
Results reported for % change in hospital
admissions associated with one IQR
increase in 24-h avg NO2, lag 0-1 .
Arrhythmia
265: 0.4 (-1.8, 2.6)
15-64:5.1 (2.2,8.1)
Cardiac
265:3.4(1.9,4.9)
15-64:2.2(0.9,3.4)
Cardiac failure
265:6.9(2.2, 11.8)
15-64:4.6(0.1,6.1)
IHD
265:2.5(1.0,4.1)
15-64: 0.7 (-1.0, 2.4)
Ml
265:4.4 (1.0,8.0)
15-64: 1.7 (-1.1, 2.4)
All CVD
265:3.0(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.
6-88
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Simpson et al.
(2005a)
Australia
(Brisbane,
Melbourne, Perth,
Sydney).
Study Period:
Jan 1996-Dec
1999
Hinwood et al.
(2006)
Perth, Australia
Study Period:
1992-1998
OUTCOMES, DESIGN, &
METHODS
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-Apr, May-Oct
MEAN LEVELS &
MONITORING
STATIONS
NO 1-h max (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
10th percentile: 4.4
90th percentile: 17.1
NO2 1-h max (ppb)
Mean: 24.8
SD= 10.1
10th percentile: 13.3
90th percentile: 37.5
# of Stations: 3
COPOLLUTANTS
(CORRELATIONS)
PM1024h
PM2.5
BS 24 h (0.29, 0.62)
O31 h
CO8h
Not all correlations
reported. NO2 affect
attenuated slightly
when modeled with BS
but not with O3.
May be confounding of
NO2 effect by
particulate.
O3 1 h, 8 h (-.06)
CO 8 h (.57)
BSP 24 h (.39)
EFFECTS: RELATIVE RISK OR
PERCENT CHANGE & CONFIDENCE
INTERVALS ([95% LOWER, UPPER])
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), lag 0
>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 observed/reported for stroke.
Multipollutant results (65+ age group):
Cardiac:
1 .0032 (1 .0006, 1 .0022), w/ BS, lag 0-1
1 .0032 (1 .0024, 1 .0039), w/ O3, lag 0-1
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 24 h all ages: 1 .003 (1 .001 , 1 .007), lag
1
6-89
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
alaludin et al.
(2006)
Sydney, Australia
Period of Study:
Jan 1997-Dec
2001
Morgan et al.
(1998a)
Sydney, Australia
Study Period:
Jan 1990-Dec
1994
OUTCOMES, DESIGN, &
METHODS
Outcome(s) (ICD9): All CVD
390-459; cardiac disease 390-
429; IHD 410-413; and
cerebrovascular disease or
stroke 430-438; Emergency
room attendances obtained
from health department data.
Age groups included: 65+
Study Design: Time-series,
multicity APHEA2 Protocol.
Statistical Analysis: GAM (with
appropriate convergence
criteria) and GLM Models. Only
GLM presented.
Lag: 0-3
Covariates: Daily avg
temperature and daily relative,
humidity, long-term trends,
seasonality, weather, day of
wk, public school holidays,
outliers and influenza
epidemics.
Dose response: Quartile
analysis
Season: Separate analyses for
warm (Nov-Apr) and cool
periods (May-Oct).
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
MEAN LEVELS &
MONITORING
STATIONS
NO2 daily 1-h avg
Mean: 32.2
SD = 7.4
Min: 5.2
Q1: 18.2
Median: 23
Q3: 27.5
Max: 59.4
# of Stations: 14
NO2 24-h avg (ppb):
Mean(SD): 15(6)
IQR: 11 ppb
10th-90th: 17
NO2 1-h max (ppb):
Mean (SD): 29 (3)
10-90th:29ppb
NO2 24-h max: 52
NO2 1-h max: 139
# Stations: 3-1 4
(1990-1994)
COPOLLUTANTS
(CORRELATIONS)
BS 24-h avg (0.35)
PM 10 24-h avg (0.44)
PM2.5 24-h avg (0.45)
CO 8-h avg (0.55)
O3 1-h avg (0.45)
SO2 24-h avg (0.56)
Two-pollutant models
to adjust for
copollutants
O3 1-h max (-0.086)
PM (0.533, 0.506)
Correlations for
24-h avg NO2
concentrations
Multipollutant models
EFFECTS: RELATIVE RISK OR
PERCENT CHANGE & CONFIDENCE
INTERVALS ([95% LOWER, UPPER])
Results reported for % change in hospital
admissions associated with one IQR
increase in 1-h avg NO2.
All CVD
2.32(1.45,3.19), lag 0
0.45 (-0.52, 1.42), lag 1
1.31 (0.28, 2.35), lag 0-1
C d' D'
2.00(0.81,3.20), lag 0
0.91 (-0.26, 2.09), lag 1
1.78(0.54,3.04), lag 0-1
IHD
2.11 (0.34, 3.91), lag 0
0.76 (-0.97, 2.52), lag 1
1.73 (-0.10, 3.59), lag 0-1
Stroke
-1 .66 (-3.80, 0.51) lag 0
-1.11 (-3.19, 1.02), lag 1
-1.68 (-3.90, 0.60), lag 0-1
Effect of NO2 attenuated when CO was
included in the model. NO2 effect most
prominent during the cool season.
Results reported as percent increase in
admissions associated with an incremental
increase in 1-h max NO2 and 24-h avg
equivalent to the 10th-90th percentile.
Heart Disease:
24-h avg, lag 0
All ages: 7.52(5.21,9.88)
65+: 8.39 (5.41, 11.46)
0-64:5.81 (1.63, 10.17)
1-h max, lag 0
All ages: 6.08 (3.63, 8.59)
65+: 6.71 (4.25, 9.23)
0-64:4.79(1.18,8.53)
65+: 6.68 (3.61 , 9.84) Particulate, O3
Results lost precision but did not change
substantially when stratified by age or when
24-h averaging time was used.
6-90
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Petroeschevsky
et al. (2001)
Brisbane,
Australia
Study Period:
Jan 1987-Dec
1QQd
i yy*t,
2,922 days
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
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
COPOLLUTANTS
(CORRELATIONS)
BSP
03
SO2
Correlation between
pollutants not reported.
EFFECTS: RELATIVE RISK OR
PERCENT CHANGE & CONFIDENCE
INTERVALS ([95% LOWER, UPPER])
Results reported for RR for CVD emergency
admissions associated with a one-unit
increase in NO2 1-h max.
CVD 1 5-64 yrs
0.986(0.968, 1.005), lag 3
CVD 65+ yrs
0.990(0.977, 1.003)
CVD all ages
0.987 (0.976, 0.998)
•Default GAM
AMI Acute Myocardial Infarction
ARR Arrhythmia
BC Black Carbon
COM 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
Ml 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
6-91
-------
Table AX6.3-8.
Human health effects of oxides of nitrogen: cvd hospital admissions and visits:
Europe
REFERENCE,
STUDY
LOCATION, &
PERIOD
Ballester et al.
(2006)
Multicity, Spain:
Barcelona, Bilbao,
Castellon, Gijon,
Huelva, Madrid,
Granada, Oviedo,
Seville, Valencia,
Zaragoza
Period of Study:
1995/1996-1999,
N = 1 ,096 day
OUTCOMES, DESIGN, &
METHODS
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 Oct;
Cold: NovtoApr
Statistical Package: SPLUS
Lag: 0-3
MEAN
LEVELS &
MONITORING
STATIONS
NO2 24-h avg
ftjg/m2):
Mean: 51 .5
10th percentile:
29.5
90th percentile:
74.4
# of Stations:
Depends on the
city
Correlation
among stations:
NR
COPOLLUTANTS
(CORRELATIONS)
CO 8-h max (0.58)
O3 8-h max (-0.03)
SO2 24 h (0.46)
BS 24 h (0.48)
TSP 24 h (0.48)
PM1024h(0.40)
Two-pollutant models
used to adjust for
copollutants.
EFFECTS: RELATIVE RISK OR
PERCENT CHANGE*
CONFIDENCE INTERVALS ([95%
LOWER, UPPER])
Results reported for % change in hospital
admissions associated with 10 pg/m2
increase in NO2.
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.
6-92
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Lanki et al. (2006)
Europe (Augsburg,
Helsinki Rome
Stockholm)
Study period:
1992-2000
OUTCOMES, DESIGN, &
METHODS
Outcome(s) (ICD9): AMI 410.
Ascertained from discharge records
or AMI registry data depending on
the city.
Study Design: Time-series
Statistical Analysis: Poisson
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
MEAN
LEVELS &
MONITORING
STATIONS
NO2 (pg/m3)
Augsburg
25th: 40.2
50th: 49.2
75th: 58.9
98th' 88 7
Barcelona
OCAL. riA o
zoin. o'f.o
50th: 45.0
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
COPOLLUTANTS
(CORRELATIONS)
PM1 0(0.29, 0.64)
CO (0.43, 0.75)
O3 (0.1 7, 0.38)
Range in correlations
depends on the city.
Two-pollutant models
for PNC with O3 and
PM 10 only.
EFFECTS: RELATIVE RISK OR
PERCENT CHANGE*
CONFIDENCE INTERVALS ([95%
LOWER, UPPER])
Results reported as RR associated with an
incremental increase in NO2 equivalent to
the IQR (8 pg/m2).
Pooled results for 5 Cities:
First Ml:
0.996(0.988, 1.015), lag 0
0.998(0.986, 1.010), lag 1
1.003(0.994, 1.011), lag 2
1.001 (0.989, 1.014), Iag3
No significant results observed for analyses
stratified by age or season for lag 0/1 .
6-93
-------
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 pectoris411 and
413; Cardiac diseases including AMI
angina pectoris, dysrhythmia (427),
heart failure (428). Hospital
admissions database used to identify
cases.
Population: Incident cases of Ml
during 1992-2000 among those >35
yrs old.
N Augsburg: 1560
N Barcelona: 1134
N Helsinki: 4026
N Rome: 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
ftjg/m2):
Augsburg
Mean: 49.6
5th: 30
25th: 39.7
75th: 57.2
95th: 75.3
Barc0lona
IV/loan* A7 7
ivisan. *f / . /
5th: 18
25th: 34.0
75th: 60
95th: 83
Helsinki
Mean: 30.1
5th: 13
25th: 21. 2
75th: 36.7
95th: 52.9
Roms
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
COPOLLUTANTS
(CORRELATIONS)
CO24h
(0.44, 0.75)
O38h
(-0.2, -0.13)
PM10(.29, .66)
PNC (.44, .83)
Two-pollutant models
but NO2, CO, and PNC
not modeled together
because they were too
highly correlated.
EFFECTS: RELATIVE RISK OR
PERCENT CHANGE*
CONFIDENCE INTERVALS ([95%
LOWER, UPPER])
Results reported for RR for incremental
increases in same day NO2 equivalent to
the mean of the city specific IQR's multiplied
by 0.05 (8 pg/m3). Pooled results are below:
Ml
1.028(0.997, 1.060), lag 0
Angina Pectoris
1.032(1.006, 1.058), lag 0
Cardiac Dis0as0s
1.032(1.014, 1.051), lag 0
Two-pollutant models show that the effect of
NO2 independent of PM10 and O3. Traffic
exhaust may be associated with cardiac
6-94
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Andersen et al.
(2007a)
Copenhagen,
Denmark
Study Period:
1999-2004
Andersen et al.
(2007b)
Copenhagen,
Denmark
Study Period:
2001-2004
OUTCOMES, DESIGN, &
METHODS
Outcome(s) (ICD10): angina pectoris
I20; acute and subsequent Ml 121-
22; other acute IHD I24; chronic IHD
I25; pulmonary embolism I26;
cardiac arrest I46; cardiac
arrhythmias 148-49; hear failure ISO.
Hospital admissions from Danish
Hospital Register.
# Hospitals: 9 (within 15 km of
monitoring station)
Study Population: 65 +
Catchment area: 1.5 million
Study Design: Time-series
Statistical Analysis: Principal
components analysis for source
apportionment. Poisson GAM,
smoothing splines for weather, long-
term trends/seasonality, indicator
variables for influenza, holidays
Software: mgcv package, R
Lag(s): 0-5 d, 0-3 d avg
Outcome(s) (ICD10): Angina pectoris
I20; acute and subsequent Ml 121-
22; other acute IHD I24; chronic IHD
I25; pulmonary embolism I26;
cardiac arrest I46; cardiac
arrhythmias 148-49; heart failure ISO.
Hospital admissions from Danish
Hospital Register.
# Hospitals: 9 (within 15 km of
monitoring station)
Study Population: 65 +
Catchment area: 1.5 million
Study Design: Time-series
Statistical Analysis: Poisson GAM,
smoothing splines for weather, long-
term trends/seasonality, indicator
variables for influenza, holidays
Software: mgcv package, R
Lag(s): 0-5 d, 0-3 d avg
MEAN
LEVELS &
MONITORING
STATIONS
24-h avg NO2
(ppb)
Mean (SD): 12
(5)
25th: 8
75th: 15
IQR:7
24-h avg NO2
(Ppb)
Mean (SD): 11
(5)
25th: 8
50th: 11
75th: 14
99th: 28
IQR:6
24-h avg NOX
(Ppb)
Mean(SD): 15
/O\
(8)
25th: 9
50th: 12
75th: 18
99th: 46
IQR:9
24-h avg NOX
curbside (ppb)
Mean (SD): 83
(36)
25th: 58
50th: 76
75th: 103
99th: 207
IQR: 45
COPOLLUTANTS
(CORRELATIONS)
PM1 0(0.45)
CO (0.74)
Source Specific PM10
Biomass (0.41)
Secondary (0.43)
Oil (0.42)
Crustal (0.24)
Sea Salt (-0.19)
Vehicle (0.65)
NCtot w/ NO2 (0.68)
NCtot w/ NOX (0.66)
NCa57w/NO2(0.57)
EFFECTS: RELATIVE RISK OR
PERCENT CHANGE*
CONFIDENCE INTERVALS ([95%
LOWER, UPPER])
Results reported for RR associated with an
incremental increase in NO2 equivalent to
one IQR (7 ppb).
Single-pollutant
1.013 (0.993, 1.033), lag 0-3 avg
2-pollutant, NO2with PM10
1 .000 (0.975, 1 .026)
Results reported for associations of a 6-ppb
increase equivalent to one IQR of NO2 with
all CVD.
One-pollutant model:
1 .0 (0.98, 1 .03), lag 0-3
Two-pollutant model with NCtot
1 .0 (0.96, 1 .03)
6-95
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Atkinson et al.
(1999b)
London Enolcind
Period of Study:
1992-1994,
N = 1 096 day
Ballester et al.
(2001)
Valencia, Spain
Period of Study:
1992-1996
OUTCOMES, DESIGN, &
METHODS
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, quadratic, piece-wise, spline)
Seasons: Warm season Apr-Sep,
cool season remaining mos,
interactions between season
investigated
Dose response investigated: Yes,
bubble charts presented
Statistical Package: SAS
Lag: 0-3
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 with parametric
smoothers, 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 Apr
Statistical Package: SAS
Lag: 0-4
MEAN
LEVELS &
MONITORING
STATIONS
1-h max (ppb)
Mean' 50 3
SD = 17.0
Min' 22 0
Max' 224 3
10th-90th
percentile: 36
# of Stations: 3,
results averaged
across stations
Correlation
among stations:
0.7-0.96
1-h max (pg/m2)
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
COPOLLUTANTS
(CORRELATIONS)
PM1024h
CO 24 h
SO2 24 h
O3 8 h
BS 24 h
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
CO 24 h (0.03)
SO2 24 h (0.33)
O3 8 h (-0.26)
BS (0.33)
Two-pollutant models
used to adjust for
copollutants.
EFFECTS: RELATIVE RISK OR
PERCENT CHANGE*
CONFIDENCE INTERVALS ([95%
LOWER, UPPER])
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.
Results reported for RR corresponding to a
10 pg/m2 increase in NO2
All CVD
1.0302 (1.0042, 1.0568), lag 0
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
6-96
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
D'lppoliti et al.
(2003)
Rome, Italy
Study Period: Jan
1995- Jun 1997
Llorca et al. (2005)
Torrelavega, Spain
Study period:
1992-1995
Pantazopoulou
etal. (1995)
Athens, Greece
Study Period:
1988 (Winter and
Summer)
OUTCOMES, DESIGN, &
METHODS
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: Oct-Mar;
Warm: Apr-Sep
Lag(s): 0-4 day, 0-2 day cum avg
Dose Response: OR for increasing
quartiles presented and p-value for
trend.
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
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 Jan as referent.
Dummy variables for summer mos
with Apr as referent. Day of the wk,
holidays, temperature, relative
humidity,
N: 25,027 cardiac admissions.
Lag(s): NR
MEAN
LEVELS &
MONITORING
STATIONS
NO2 24 h
ftjg/m3)
Mean (SD): 86.4
(15.8)
25th: 74.9
50th: 86.0
75th: 96.9
IQR' 22
# Stations: 5
NO2 24 h pg/m3
Mean (SD): 2 1.3
(16.5)
NO2 1 -h max
(pg/m3): Winter
Mean (SD): 94
f)C\
(25)
5th: 59
50th: 93
95th: 135
Mean (SD): 111
n?t
\j£)
5th: 65
50th: 108
95th: 173
# Stations: 2
COPOLLUTANTS
(CORRELATIONS)
TSP 24 h (0.37)
SO224h(0.31)
CO 24 h (0.03)
No multipollutant
models
TSP (-0.12)
SO2 (0.588)
SH2 (0.545)
NO (0.855)
Multipollutant models
CO, BS
No correlations
provided
EFFECTS: RELATIVE RISK OR
PERCENT CHANGE*
CONFIDENCE INTERVALS ([95%
LOWER, UPPER])
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.
Results reported for RR of hospital
admissions for 100 pg/m3 increase in NO2.
CVD 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.
Results reported for regression coefficients
based on an incremental increase in NO2 of
76 pg/m3 in winter and 108 pg/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)
6-97
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Poloniecki et al.
(1997)
London UK
Study Period:
Apr1987-Mar
1994, 7yrs
Pb'nka and
Virtanen (1996)
Helsinki, Finland
Study Period:
1987-1989, 3 yrs
Prescott et al.
(1998)*
Edinburgh, UK
Study period:
Oct 1992 -June
1995
OUTCOMES, DESIGN, &
METHODS
Outcome(s): All CVD 390-459; Ml
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, linear and quadratic
terms to adjust for long-term trends.
APHEA protocol
Covariates: long-term trends,
seasonal variation, day of wk,
influenza, temperature and humidity.
Season: Warm: Apr-Sep;
Cool: Oct-Mar
Lag: 0-1 day
Outcome(s) (ICD9): IHD 410-414; Ml
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 435. 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
Outcome(s) (ICD9): Cardiac and
cerebral ischemia 410-414, 426-429,
434-440. Extracted from Scottish
record linkage system.
Study Design: Time-series
Statistical Analysis: Poisson, log
linear regression models
Age groups analyzed: <65, 65+yrs
Covariates: Seasonal and wkday
variation, temperature, and wind
speed.
Lag(s): 0,1,3 day moving avg
MEAN
LEVELS &
MONITORING
STATIONS
NO224hppb:
Min* 8
10%: 23
Median: 35
90%: 53
Max: 198
NO2 8 h (pg/m3)
Mean (SD): 39
(16.2)
Range: 4, 170
NO 8 h pg/m3
Mean (SD): 91
(61)
Range: 7, 467
# Stations: 2
NO2 24 h (ppb)
Mean (SD): 26.4
(7.0)
Range: 9, 58
IQR: 10 ppb
COPOLLUTANTS
(CORRELATIONS)
Black Smoke
CO 24 h
SO2 24 h
O38h
Correlations between
pollutants high but not
specified.
SO28h
NO8h
TSPSh
O3 8 h
NO2 highly correlated
with SO2 and TSP.
O3, 24 h
PM,24h
SO2, 24 h
CO, 24 h
Correlations not
reported.
EFFECTS: RELATIVE RISK OR
PERCENT CHANGE*
CONFIDENCE INTERVALS ([95%
LOWER, UPPER])
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)
Heart Failure: 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 Ml in
two-pollutant model (cool season).
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< 0.001, lag 1
NO with IHD, ED admissions
0.111 (0.030), p< 0.001, lag 1
Significant increase in admissions for
transient short-term ischemic attack and
cerebrovascular diseases for lag 6
associated with NO2 exposure.
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.
6-98
-------
REFERENCE,
STUDY
LOCATION, &
PERIOD
Yallop et al. (2007)
London, England
Study Period:
Jan. 1988-Oct.
2001, > 1400 days
OUTCOMES, DESIGN, &
METHODS
Outcome(s): Acute pain in Sickle Cell
Disease (HbSS, HbSC, HbS/>0,
thalassaemia, HbS/>+). Admitted to
hospital for at least one night.
Study Design: Time-series
Statistical Analyses: Cross-
correlation function
N: 1047 admissions
Covariates: No adjustment made in
analysis, discussion includes
statement that the effects of weather
variables and copollutants are inter-
related.
Statistical Package: SPSS
Lag(s): 0-2 days
Dose response: Quartile analysis,
graphs presented, ANOVA
comparing means across quartiles.
MEAN
LEVELS &
MONITORING
STATIONS
NR
COPOLLUTANTS
(CORRELATIONS)
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.
EFFECTS: RELATIVE RISK OR
PERCENT CHANGE*
CONFIDENCE INTERVALS ([95%
LOWER, UPPER])
Results reported are cross-correlation
coefficients. NO inversely correlated with
admission for acute pain in SCO.
CFF: -0.063, lag 0
•Default GAM
AMI Acute Myocardial Infarction
ARR Arrhythmia
BC Black Carbon
COM 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
Ml 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
6-99
-------
Table AX6.3-9.
Human health effects of oxides of nitrogen: cvd hospital admissions and visits:
Asia
REFERENCE,
STUDY LOCATION,
& PERIOD
Chan et al. (2006)
Taipai, Taiwan
Period of Study:
Apr 1997-Dec 2002, 2090
days
Chang et al. (2005)
Taipei, Taiwan
Study Period:
1997-2001, 5 yrs
OUTCOMES, DESIGN, &
METHODS
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
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
MEAN LEVELS &
MONITORING
STATIONS
NO2 24-h avg (ppb):
Mean: 29.9
SD = 8.4
Min: 8.3
Max: 77.1
IQR: 9.6 ppb
# of Stations: 16
Correlation among
stations: NR
NO2 24-h avg (ppb):
Mean: 31 .54
Min: 8.13
25th: 26.27
50th: 3 1.03
75th: 36.22
Max: 77.97
# of Stations: 6
COPOLLUTANTS
(CORRELATIONS)
PM1024h;r = 0.50
PM2.524h;r = 0.64
CO8-h avg; r = 0.77
SO224h;r = 0.64
O3 1-h max; r = 0.43
Two-pollutant models
to adjust for
copollutants.
CO 24-h avg
O3 24-h avg
SO2 24-h avg
PM 10 24-h avg
Correlations not
reported.
Two-pollutant models
to adjust for
copollutants
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE &
CONFIDENCE INTERVALS
([95% LOWER, UPPER])
Results reported for OR for
association of emergency
department admissions with an
IQR increase in NO2 (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
OR for the association of CVD
admissions with an incremental
increase in 24-h avg NO2
equivalent to one IQR, 9.95 ppb.
Warm (>20 °C)
1.177(1.150, 1.205), lag 0-2
Cool (<20 °C)
1.112 (1.058, 1.168), lag 0-2
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 PM1 0
6-100
-------
REFERENCE,
STUDY LOCATION,
& PERIOD
Hosseinpoor et al. (2005)
Tehran, Iran
Study period:
Mar 1996-Mar 2001,
5 yrs
Lee et al. (2003a)*
Seoul, Korea
Study period:
Dec 1997-Dec 1999,
822 days, 184 days in
Tsai et al. (2003a)
Kaohsiung, Taiwan
Study period:
1997-2000
OUTCOMES, DESIGN, &
METHODS
Outcome(s) (ICD9): Angina
pectoris 413. Primary discharge
diagnosis from registry databases
or records.
Study Design: Time-series
Statistical Methods: Poisson
regression
# Hospitals: 25
Covariates: Long-term trends,
seasonality, temperature, humidity,
holiday, post-holiday, day of wk.
Lag(s): 0-3
Outcome(s) (ICD10): IHD: Angina
pectoris 120; Acute or subsequent
Ml 121-123; other acute IHD 124.
Electronic medical insurance data
used.
Study Design: Time-series
Statistical Methods: Poisson
regression, GAM with default
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, Aug) 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
MEAN LEVELS &
MONITORING
STATIONS
NO2 24-h avg (pg/m3)
Mean (SD): 60.01
(39.69)
Min: 0.30
25th: 29.39
Median: 47.42
75th: 84.55
Max: 324.78
NO2 24 h (ppb):
5th: 16
10th: 23.7
Median: 30.7
75th: 38.3
95th: 48.6
Mean (SD): 31 .5 (10.3)
IQR: 14.6
24-h avg NO2 (ppb)
Min: 6.25
25th: 19.25
Median: 28.67
75th: 36.33
Max: 63.40
Mean: 28.17
COPOLLUTANTS
(CORRELATIONS)
NO2COO3PM10
Correlations not
reported
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
SO2
CO
03
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE &
CONFIDENCE INTERVALS
([95% LOWER, UPPER])
Results reported for relative risk in
hospital admissions per increment
of 10 pg/m3SO2.
Angina
1.00618(1.00261, 1.00976), lag 1
In a multipollutant model only CO
(lag 1) was significantly associated
with angina pectoris related
hospital admissions.
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), Iag5
64+, summer only:
1.25(1.11, 1.41), Iag5
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.
PIH:
1.31 (1.03, 1.66)NO2w/PM10
1.66(1.38,2.00), NO2w/SO2
1.60(1.25,2.05)NO2w/CO
1.51 (1.26, 1.80)NO2w/O3
IS:
1.39(1.20, 1.60)NO2w/PM10
1.62(1.45, 1.81), NO2w/SO2
1 .54 (1 .33, 1 .79), NO2 w/ CO
1.53(1.37, 1.71), NO2w/O3
6-101
-------
REFERENCE,
STUDY LOCATION,
& PERIOD
Wong etal. (1999)
Hong Kong, China
Study Period:
1994-1995
Wong et al. (2002)*
Hong Kong
London
Study Period:
1995-1997
OUTCOMES, DESIGN, &
METHODS
Outcome(s) (ICD9): CVD: 410-
417, 420-438, 440-444; CHF 428;
IHD 410-414; Cerebrovascular
Disease 430-438. Hospital
admissions through ER
departments via Hospital Authority
(discharge data).
Study Design: Time-series
Statistical Analyses: Poisson
regression, linear and quadradic
terms for long-term trends, APHEA
protocol
# Hospitals: 12
Covariates: Daily temperature,
relative humidity day of wk,
holidays, influenza, long-term
trends (yr and seasonality
variables). Interaction of pollutants
with cold season examined.
Season: Cold (Dec-Mar)
Lag(s): 0-3 days
Outcome(s) (ICD9): Cardiac
disease 396-429; IHD 410-414.
Admissions through the
emergency department, general
outpatient, or direct to inpatient
wards.
Study design:
Statistical analysis: Poisson
regression, GAMs, nonparametric
smooth functions (LOESS)
Covariates:
Statistical Software: SPIus
MEAN LEVELS &
MONITORING
STATIONS
NO2 24-h avg (pg/m3)
Min: 16.41
25th: 39.93
Median: 51. 39
75th: 5 1.39
Max: 122.44
24-h avg NO2
Hong Kong
Mean (warm/cool): 55.9
(48.1/63.8)
Min: 15.3
10th: 31.8
50th' 53 5
90th: 81 .8
Max: 151.5
Hong Kong
Mean (warm/cool): 64.3
(62.6.1/66.1)
Min: 23.7
10th: 42. 3
50th: 61 .2
90th: 88.8
Max: 255.8
COPOLLUTANTS
(CORRELATIONS)
PM10;r = 0.79
SO2
03
Range for other
pollutants: r= 0.68,
0.89.
Hong Kong
SO2;r = 0.37
PM10;r = 0.82
O3; r = 0.43
London
SO2;r = 0.71
PM10;r = 0.68
O3; r = -0.29
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE &
CONFIDENCE INTERVALS
([95% LOWER, UPPER])
Results reported for RR
associated with incremental
increase in NO2 equal to 10 pg/m3.
CVD
5-64 yrs: 1.008 (0.998, 1.018), lag
0-1
65+ yrs: 1.016(1.009, 1.023), lag
0-1
All ages: 1.013 (1.007, 1.020), lag
0-1
CHF
1.044(1.25, 1.063), lag 0-3
IHD
1.010(0.999, 1.020), lag 0-1
Cerebrovascular Disease
1.008(0.998, 1.018), lag 0-1
Interaction of NO2 with O3
observed
Results reported for excess risk
associated with a 10 pg/m3 change
in mean concentration
Single-pollutant model.
Hong Kong: 1 .8 (1 .2, 2.4), lag 0-1
London: -0.1 (-0.6, 0.5), lag 0-1
Multipollutant results
Hong Kong:
1 .6 (1 .0, 1 .3), lag 0-1 , adjusted for
Ozone
1 .7 (0.8, 2.7), lag 0-1 , adjusted for
PM10
1 .6 (0.8, 2.4), lag 0-1 , adjusted for
SO2
London:
0.1 (-0.5, 0.6), lag 0-1 , adjusted for
Ozone
-0.4 (-1.2, 0.4), lag 0-1, adjusted
forPMIO
-0.2 (-0.9, 0.5), lag 0-1, adjusted
for SO2
6-102
-------
REFERENCE,
STUDY LOCATION,
& PERIOD
Yang et al. (2004b)
Kaohsiung, Taiwan
Period of Study:
1997-2000
Ye et al. (2001)
Tokyo, Japan
Study Period:
Jul-Aug,
1980-1995
OUTCOMES, DESIGN, &
METHODS
Outcome(s) (ICD9): All CVD: 410-
429 *(AII 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
Outcome(s): Angina 413; Cardiac
insufficiency 428; Hypertension
401-405; Ml 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
MEAN LEVELS &
MONITORING
STATIONS
24-h avg (ppb)
Min:6.25
25%: 19.25
50%: 28.67
75%: 36.33
Max: 63.40
Mean: 28.17
# of Stations: 6
Correlation among
stations' NR
NO2 24-h avg (ppb)
Min:5.3
Max: 72.2
Mean (SD): 25.4 (11. 4)
COPOLLUTANTS
(CORRELATIONS)
PM10
CO
SO2
O38
Two-pollutant models
used to adjust for
copollutants
Correlations NR
O3;r = 0.183
PM10;r = 0.643
SO2; r = 0.333
CO; r = 0.759
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE &
CONFIDENCE INTERVALS
([95% LOWER, UPPER])
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.
Results reported for model
coefficient and 95% Cl.
Anoins'
0.007 (0.004, 0.009)
Cardiac insufficiency:
0.006 (0.003, 0.01)
Ml:
0.006 (0.003, 0.01)
6-103
-------
REFERENCE,
STUDY LOCATION,
& PERIOD
OUTCOMES, DESIGN, &
METHODS
MEAN LEVELS &
MONITORING
STATIONS
COPOLLUTANTS
(CORRELATIONS)
EFFECTS: RELATIVE RISK
OR PERCENT CHANGE &
CONFIDENCE INTERVALS
([95% LOWER, UPPER])
' Default GAM
AMI Acute Myocardial Infarction
ARR Arrhythmia
BC Black Carbon
COM 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
Ml 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
Table AX6.3-10. Studies examining exposure to ambient NO2 and heart rate variability as
measured by standard deviation of normal-to-normal intervals (SDNN).
AUTHOR,
YEAR,
LOCATION
Liao et al. (2004)
US, ARIC study
STUDY DESIGN
Chan et al. (2005)
Taiwan
Wheeler etal. (2006)
Atlanta
Subjects: 4,390 adults
Analysis Method:
multivariable linear
regression
Subjects: 83 adults
recruited from
cardiology
Analysis Method: linear
mixed effects
regression
Subjects: 30 adults (12
Ml + 22 COPD)
Analysis Method: linear
mixed models
AVG
TIME
24 h
1 h
4h
N02 CONC (PPB)
MEAN
(SD)
21(8)
33(15)
18 (nosd
given)
RANGE
1, 110
p10-p20,
7,30
COPOLLUTANT
CORRELATION
none
PM10: 0.4
03: -0.4
SO2: 0.5
CO: 0.7
PM2.5: 0.4
CO: 0.5
OUTCOME
Iag1
4-h lag
8-h lag
Ml patients
[N = 12]
4-h lag
COPD
patients
[N = 22] 4-h
lag
% CHANGE
(95% Cl)
-5.0% (-9.2, -
7)
-4.5% (-8.1,-
30)
-6.9% (-12.0, -
1.8)
-26.0% (-42.1,
-8.6)
16.6% (0.2,
34.3)
6-104
-------
AUTHOR,
YEAR,
LOCATION
STUDY DESIGN
Luttmann-Gibson et al.
(2006)
Steubenville
Schwartz et al. (2005)
Boston
Subjects: 32 adults
(>50 yrs)
Analysis Method:
mixed models
Subjects:
28 elderly adults
Analysis Method:
hierarchical models
AVG
TIME
24 h
24 h
N02 CONC (PPB)
MEAN
(SD)
10 (nosd
given)
med 18
RANGE
p25-p75,
6, 13
p25-p75,
14,23
COPOLLUTANT
CORRELATION
PM2.5: 0.4
03: -0.3
SO2: 0.3
PM2.5: :0.3
O3: 0.02
CO: 0.6
OUTCOME
Iag1
lag 1
% CHANGE
(95% Cl)
0.3% (-6.0,
6.6)
-1 .6% (-7.8,
5.1)
Table AX6.3-11. Studies examining exposure to ambient NO2 and heart rate variability as
measured by variables recorded on implantable cardioverter defibrillators (ICDs).
AUTHOR, YEAR,
LOCATION
Peters etal. (2000a)
Eastern MA
Rich et al. (2005)
Boston
Dockery et al.
(2005)
Boston
Pekkanen et al.
(2002)
Finland
Ruidavets et al.
(2005)
France
SUBJECTS
100 cardiac
outpatients
203 cardiac
outpatients
307 cardiac
outpatients
45 cardiac
patients
863 adults
ANALYSIS
METHOD
logistic regression,
fixed effects
case-crossover
logistic regression,
GEE
linear regression,
GAM
polytomous logistic
regression
NO2 CONC (PPB)
MEAN
(SD)
23 (no sd
given)
med 22
med 23
med 16
16(6)
RANGE
11,65
p25-max,
18,62
p25-p95,
19,34
p25-max,
12,36
2,48
COPOLLUTANT
CORRELATION
PM2.5: 0.6
03: -0.3
SO2: 0.3
CO: 0.7
PM2.5 > 0.4
O3 < -0.4
SO2>0.4
CO: 0.6
PM2.5: 0.4
CO: 0.3
O3: -0.3
SO2: 0.7
OUTCOME
Risk of ICD-
recorded
ventricular
arrhythmias
Iag1
lag 0-4
All patients lag
0-1
Patients with
recent
arrhythmia
(<3 days) lag
0-1
Patients with
recent
arrhythmia
(<3 days) lag
0-1
lag 2
Iag8h
OR (95% Cl)
1.55(1.05,
2.29)
1.88(1.01,
3.49)
1.54(1.11,
2.18)
2.09(1.26,
3.51)
2.14(1.14,
4.03)
14.1 (3.0, 65.4)
2.7(1.2,5.4)
I results given for 20-ppb increase in NO2 with 24-h averaging time.
6-105
-------
Table AX6.3-12. Birth weight and long-term NO2 exposure studies
AUTHOR,
YEAR,
LOCATION
Lin et al.
(2004)
Taiwan
Lee et al.
(2003b)
Seoul, Korea
STUDY DETAILS
Subjects: 92,288 birth cert
Years: 1995-1997
Group: Term LBW
Analysis method: Logistic
regression
Distance 3 km
Subjects: 388,105 birth cert
Years: 1996-1998
Group: Term LBW model (GAM) ,
Interquartile
Averaging time: 24h
Analysis method: Generalized
additive
CONC RANGE (PPB)
LOW
<26.1
<24.3
<24.0
<23.8
25
MID-
RANGE
26.1,32.9
24.3, 34.7
24.0, 34.4
23.8, 34.2
31.4
HIGH
>32.9
>34.7
>34.4
>34.2
39.7
CORRELATION WITH
OTHER POLLUTANTS
PM10: 0.66
SO2: 0.75
CO: 0.77
PM10:0.81
SO2: 0.77
CO: 0.78
PM10: 0.8
SO2: 0.76
CO: 0.82
OUTCOMES
Pregnancy
Medium NO2
High NO2
Trimester 1
Medium NO2
High NO2
Trimester 2
Medium NO2
High NO2
Trimester 3
Medium NO2
High NO2
Pregnancy
Trimester 1
Trimester 2
Trimester 3
ODDS
RATIO (95%
Cl)
1 .06 (0.93,
1.22)
1 .06 (0.89,
1.26)
1.10(0.96,
1.27)
1 .09 (0.89,
1.32)
0.87 (0.76,
1.00)
0.93 (0.77,
1.12)
1.01 (0.88,
1.16)
0.86(0.71,
1.03)
1.04(1.00,
1.08)
1 .02 (0.99,
1.04)
1.03(1.01,
1.06)
0.98 (0.96,
1.00)
6-106
-------
AUTHOR,
YEAR,
LOCATION
Bobak M.
(2000)
Czech
Gouveia et al.
(2004)
Sao Paulo city,
Brazil
Maroziene
and
Grazuleviciene
(2002)
Kaunas,
Lithuania
STUDY DETAILS
Subjects: 69,935 birth cert
Year: 1991
Group: LBW adjusted for GA
Averaging time: 24 h
Analysis method: Logistic
regression, 50 pg increase
Subjects: 179,460 live births
Group: Ministry of Health, Brazil
Year: 1997
Analysis method: GAM models
Subjects: 3,988 birth cert
Group: LBW adjusted for GA
Year: 1998
Analysis method: Logistic
regression, 10 pg increase
CONC RANGE (PPB)
LOW
12.2
43.5
MID-
RANGE
20
117.9
6.2 (5.7)
HIGH
31.1
399.6
CORRELATION WITH
OTHER POLLUTANTS
SO2: 0.53
SO2: 0.62
SO2: 0.63
OUTCOMES
Trimester 1
Trimester 2
Trimester 3
First Trimester
1Q
2Q
3Q
4Q
Second
Trimester
1Q
2Q
3Q
4Q
Third trimester
1Q
2Q
3Q
4Q
Pregnancy
Medium NO2
High NO2
ODDS
RATIO (95%
Cl)
0.98(0.81,
1.18)
0.99 (0.80,
1.23)
0.97 (0.80,
1.18)
1
1.060(0.971-
1.157)
1.197(0.885-
1.619)
1.126(0.812-
1 .560)
1
0.986 (0.902-
1 .076)
1.008(0.871-
1.167
1.034(0.861-
1 .243)
1
0.992 (0.913-
1 .078)
1 .041 (0.927-
1.169
1 .046 (0.889-
1.231)
1.28(0.97,
1.68)
0.96 (0.47,
1.96)
1 .54 (0.80,
2.96)
6-107
-------
AUTHOR,
YEAR,
LOCATION
Liu et al.
(2003)
Vancouver
Salam et al.
(2005)
Southern CA
Bell et al.
(2007)
CT and MA
Slama et al.
(2007)
Munich
STUDY DETAILS
Subjects: 229,085 birth cert
Years: 1986-1998
Group: LBW adjusted for GA
Averaging time: 24 h
Analysis method: Logistic
regression, 10 ppb increase
Subjects: 3,901 birth cert
Group Term LBW, CHS:
Years: 1975-1987
Analysis method: Logistic
regression
Distance: 5 km or 3 within 50 km,
within county
Subjects: 358,504 birth cert
Group: LBW adjusted for GA
Years: 1999-2002
Analysis method: logistic
regression, interquartile linear
regression, difference in gms per
IQR
Subjects: 1016 non-premature
births
Group: LISA
Analysis method: Poisson
Regression
CONC RANGE (PPB)
LOW
15.1
MID-
RANGE
18.1
36.1 (15.4)
IQR 25
0.52
17.4
(5.0)
IQR 4.8
0.75
HIGH
22.3
0.90
CORRELATION WITH
OTHER POLLUTANTS
O3: -0.25
SO2: 0.61
CO: 0.72
PM10: 0.55
03: -0.1
CO: 0.41
PM2.5: 0.64
PM10: 0.55
OUTCOMES
Trimester 1
Trimester 2
Trimester 3
First mo
Last mo
Pregnancy
Trimester 1
Trimester 2
Trimester 3
Pregnancy
Black mothers
White mothers
Adjusted 1Q
Adjusted 2Q
Adjusted 3Q
Adjusted 4Q
Continuous
coding
ODDS
RATIO (95%
Cl)
0.91 (0.53,
1.56)
0.93(0.61,
1.41)
1 .34 (0.94,
1.92)
0.98 (0.90,
1.07)
0.94 (0.85,
1.04)
0.8(0.4, 1.4)
0.9(0.5, 1.5)
1.0(0.6, 1.6)
0.6(0.4, 1.1)
1.027(1.002,
1.051)
-12.7 (-18.0, -
7.5)
-8.3 (-10.4, -
6.3)
1
0.80 (0.52-
1.28)
1.32(0.86-
2.09)
1.16(0.71-
1.71)
1.21 (0.86-
1.68)
6-108
-------
AUTHOR,
YEAR,
LOCATION
STUDY DETAILS
CONC RANGE (PPB)
LOW
MID-
RANGE
HIGH
CORRELATION WITH
OTHER POLLUTANTS
OUTCOMES
ODDS
RATIO (95%
Cl)
Table AX6.3-13. Preterm delivery and long-term NO2 exposure studies
AUTHOR,
YEAR,
LOCATION
Bobak (2000)
Czech
Liu S. et al. (2003)
Vancouver
Maroziene and
Grazuleviciene R.
(2002)
Kaunas, Lithuania
STUDY DETAILS
Subjects: 69,935 birth cert
Group: Preterm
Years: 1991
Avg time: 24 h
Analysis Method: Logistic regression, 50
pg increase
Subjects: 229,085 birth cert
Group: Preterm
Years: 1986-1998
Avg time: 24 h
Distance: 13 monitors
Analysis Method: 10 ppb increase
Subjects: 3,988 birth cert
Group: Preterm
Analysis Method: Logistic regression
CONC RANGE (PPB)
LOW
12.2
15.1
MID-
RANGE
20
18.1
6.2 (5.7)
HIGH
31.1
22.3
CORRELATION
WITH OTHER
POLLUTANTS
SO2: 0.62
O3: -0.25
SO2: 0.61
CO: 0.72
OUTCOME
Trimester 1
Trimester 2
Trimester 3
First mo
Last mo
Pregnancy
Medium NO2
High NO2
Trimester 1
Trimester 2
Trimester 3
ODDS
RATIO
(95% Cl)
1.10(1.00,
1.21)
1.08(0.98,
1.19)
1.11 (1.00,
1.23)
1.01 (0.94,
1.07)
1.08(0.99,
1.17)
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)
6-109
-------
AUTHOR,
YEAR,
LOCATION
Ritz et al. (2000)
Southern CA
Leem et al. (2006)
Inchon, Korea
Hansen et al.
(2006)
Brisbane
STUDY DETAILS
Subjects: 97,158 birth cert
Group: Preterm
Years: 1989-1993
Avg time: 24 h
Analysis Method: Logistic regression
Distance: Zip code within 2 miles
Subjects: 52, 113 birth cert
Group: Preterm
Years: 2001-2002
Analysis Method: Log binomial regression
Subjects: 28,200 birth cert
Group: Preterm
Years: 2000-2003
Avg time: 24 h
Analysis Method: Logistic regression
CONC RANGE (PPB)
LOW
32
15.78
MID-
RANGE
40.9
22.93
8.8(4.1)
HIGH
50.4
29.9
CORRELATION
WITH OTHER
POLLUTANTS
PM10: 0.74
O3:-0.12
CO: 0.64
PM10: 0.37
SO2: 0.54
CO: 0.63
PM10: 0.32
O3:0.13
OUTCOME
Frst mo
6 wks before
birth
Trimester 1
02
Trimester 1
Q3
Trimester 1
04
Trimester 3
02
Trimester 3
Q3
Trimester 3
Q4
Trimester 1
90 days
before birth
ODDS
RATIO
(95% Cl)
No effects
for any
preg
period
No effects
for any
preg
period
1.13(0.99,
1.27)
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
0.93 (0.78,
1.12)
1.03(0.86,
1.23)
6-110
-------
Table AX6.3-14. Fetal growth and long-term NO2 exposure studies
AUTHOR, YEAR,
LOCATION
Salam et al. (2005)
Southern CA, CHS
Mannes et al. (2005)
Sydney
Liu et al. (2003)
Vancouver
STUDY DETAILS
Subjects: 3,901 birth cert
Group: Term SGA, <15%
of data
Years: 1975-1987
Avg time: 24 h
Analysis Methods: Linear
mixed model, IQR = 25
Distance: 5 km or 3
monitors within 50 km
Subjects: 51,460 birth cert
Group: SGA, >2sd below
national data
Years: 1998-2000
Avg time: 1-h max
Analysis Methods:
Logistic regression, 1 ppb
Distance: 5 km
Subjects: 229,085 birth
cert
Group: Term SGA, <10%
national
Years: 1986-1998
Avg time: 24 h
Analysis Methods:
Logistic regression,
10 ppb
Distance: 13 monitors Avg
CONC RANGE (PPB)
LOW
18
15.1
MID-
RANGE
36.1 (15.4)
23
23.2 (7.4)
18.1
HIGH
27.5
22.3
CORRELATION WITH
OTHER POLLUTANTS
PM10: 0.55
03: -0.1
CO: 0.69
PM2.5: 0.66
PM10:0.47
O3: 0.29
CO: 0.57
SO2: 0.61
O3: -0.25
CO: 0.72
OUTCOME
Pregnancy
Trimester 1
Trimester 2
Trimester 3
Trimester 1
Trimester 2
Trimester 3
1 mo before
birth
Trimester 1
Trimester 2
Trimester 3
First mo
Last mo
ODDS
RATIO (95%
Cl)
1.1 (0.9, 1.3)
1.2 (1.0, 1.4)
1.0(0.8, 1.2)
1.0(0.8, 1.2)
1.06(0.99,
1.14)
1.14(1.07,
1.22)
1.13(1.05,
1.21)
1.07(1.00,
1.14)
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)
6-111
-------
Table AX6.3-15. Lung function and long-term NO2 exposure.
AUTHOR,
YEAR,
LOCATION
Gauderman
(2004)
Southern CA
Moseler et al.
(1994)
Frieberg,
Germany
Ackermann-
Liebrich et al.
(1997)
Switzerland
STUDY DETAILS
Subjects: 1757 children age 10-
18, CHS
Group: Lung function,
Longitudinal
Avg Time: 24-h annual
Anlaysis Method: 2-stage linear
Regression, 34.6 ppb
Distance: Study monitors in 12
towns
Subjects: 467 children age 9-16
Group: Lung function
Avg Time: Median wkly
Anlaysis Method: Linear
regression, Parameter estimates
Subjects: 3,115 adults, 3-yr
residents, nonsmokers,
SAPALDIA
Group: Lung function
Avg Time: 24-h annual
Anlaysis Method: 2-stage linear
Regression
Distance: Monitors in 8 Study
areas
CONC RANGE (ppb)
LOW
MID-
RANGE
21.28
threshold
18.9(8.5)
HIGH
CORRELATION WITH
OTHER POLLUTANTS
PM2.5: 0.79
PM10: 0.67
O3:-0.11
PM10: 0.91
O3 : -0.78
SO2: 0.86
OUTCOME
Difference in
lung growth
FVC
FEV,
MMEF
with asthma
symp
FEV,
lnMEF75%
lnMEF50%
lnMEF25%
no asthma
symp
FEV,
lnMEF75%
lnMEF50%
lnMEF25%
FVC
FEV,
ODDS
RATIO
(95% Cl)
-95 (-183.4,
-0.6)
-101.4(-
164.5,-
38.4)
-211 (-
377.6, -
44.4)
0.437
-0.011
-0.022
-0.029
-0.049
0.003
0.004
0.003
-0.01 23 (-
0.0152,-
0.0094)
-0.0070 (-
0.0099, -
0.0041)
6-112
-------
AUTHOR,
YEAR,
LOCATION
Schindler et al.
(1998)
Switzerland
Peters et al.
(1999a)
Southern CA
Tageretal. (2005)
Southern &
Northern CA
CONC RANGE (ppb)
STUDY DETAILS
LOW
Subjects: 560
adults, 3-yr
residents,
SAPALDIA
Group: Lung
function
Avg Time: Wkly avg
Anlaysis Method:
Linear regression
Distance: Personal
and Home monitors
Subjects: 3,293
children, CHS
Group: Lung
function
Avg Time: 24 h
Anlaysis Method:
Linear regression
Distance: Study
monitors in 12
towns
Subjects: 255
students UC
Berkeley
Group: Lung
function
Avg Time:
Anlaysis Method:
Linear regression
MID-
RANGE
HIGH °THERP°
(MEN)
22
30
40
(WOMEN)
21
27
40
O3: 0.57
LLUTZs °UTCOME ™™
LLUTANTS (g5% c|)
FVC home
FVC personal
FEV home
FEV personal
FVC all: 1986-1990
FVC girls: 1986-1990
FEV, all: 1986-1990
FEV, girls: 1986-
1990
FVC all: 1994
FVC girls: 1994
FEV, all: 1994
FEV, girls: 1994
InFEFTS men
InFEFTS women
% change -
0.59
(-1)
-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)
-0.029 (0.003)
-0.032 (0.002)
6-113
-------
Table AX6.3-16. Asthma and long-term NO2 exposure.
AUTHOR,
YEAR,
LOCATION
Garrett et al.
(1999)
Latrobe Valley,
Australia
Hirsch et al.
(1999)
Dresden,
Germany
Peters et al.
(1999b)
Southern CA,
CHS
STUDY
DESIGN
Subjects: 148
children ages 7-
14
Years: 1994-1995
Distance: In
home
Study Group:
Asthma, Monash
Q
Subjects: 5,421
children ages 5-7,
9-11
Years: 1995-
1996, 12 mo
residence
Distance: 4
monitors within 1
km
Study Group:
Asthma, ISAAC
Subjects: 3,676
children Age 9-1 6
Years: 1994
Avg time: 24 h
Distance: Study
monitors in 12
towns
Study Groups:
Asthma,
Questionnaire
ANALYSIS
METHOD
Logistic
regression
10 pg
Logistic
regression
10 pg
Logistic
regression
IQR = 25 ppb
CORRELATION
WITH OTHER
POLLUTANTS
CONC RANGE (PPB)
LOW
29.3
MID- ......
RANGE HIGH
6
33.8
21.5
mean
37.8
STUDY
FACTOR
Bedroom NO2
Indoor mean
winter
summer
Home address
Home & school
all children
boys
girls
ODDS RATIO
(95% Cl)
1.01
(0.75, 1.37)
1.00
(.075, 1.31)
0.99
(0.84, 1.16)
2.52
(0.99, 6.42)
1.16
(0.94, 1.42)
1.14
(0.86, 1.51)
1.21
(0.850, 1.71)
1.25
(0.90, 1.75)
1.07
(0.57, 2.02)
6-114
-------
AUTHOR,
YEAR,
LOCATION
Millstein et al.
(2004)
Southern CA,
CHS
Penard-Morand
et al. (2005)
France 6 towns
Studnicka et al.
(1997)
8 communities,
Lower Austria
Wang et al.
(1999)
Taiwan
STUDY
DESIGN
Subjects: 2,034
children age 9-11
Years: 1995
Distance: Study
monitors in 12
towns
Study Groups:
Asthma,
Medication use
Subjects: 4,901
children Age 9-11
Years: 1999-
2000, 3 yr
residence
Avg time: 3 yrs
Distance:
monitoring sites,
school address
Study Groups:
Asthma, ISAAC
Subjects: 843
children
Distance: monitor
in each
community
Avg time: 3 yrs
Study Group:
Asthma, ISAAC
Subjects:
11 7,080 students
age 11-16
Distance: 24
district monitors
Study Group:
Asthma
ANALYSIS
METHOD
Mixed effects
model
IQR =
5.74 ppb
Logistic
regression
10 pg
Logistic
regression
<.05
Logistic
regression
Above/below
median
CORRELATION
WITH OTHER
POLLUTANTS
PM2.5: 0.28
PM10: 0.39
PM10: 0.46
O3: 0.76
SO2: 0.35
CONC RANGE (PPB)
LOW
8.7,
16.0
8.0,8.7
MID-
RANGE
11.7, 13.3
28 median
HIGH
16.1,
25.7
14.7,
17.0
STUDY
FACTOR
Annual
Mar-Aug
Sep-Feb
Lifetime asthma
Current asthma
Ever asthma low
Ever asthma
medium
Ever asthma
high
Current asthma
low
Currrent asthma
medium
Current asthma
high
Current asthma
ODDS RATIO
(95% Cl)
0.94
(0.71, 1.22)
0.96
(0.68, 1.37)
0.90
(0.66, 1.24)
0.94
(0.83, 1.07)
0.92
(0.77, 1.10)
1.28
2.14
5.81
1.7
1.47
8.78
1.08
(1.04, 1.13)
6-115
-------
AUTHOR,
YEAR,
LOCATION
Ramadour et al.
(2000)
7 communities,
France
Shima and
Adachi et al.
(2000)
7 communities,
Japan
STUDY
DESIGN
Subjects: 2,445
children
Years: 3-yr
residence
Age 13-14
Distance:
Monitors in each
community
Study Group:
Asthma, ISAAC
Subjects: 905
children age 9-10
Distance: In
home
measurements
Monitors near
schools
Study Groups:
Asthma,
Prevalence,
Incidence
ANALYSIS
METHOD
Logisitic
regression
Logistic
regression
10-ppb
increase
CORRELATION
WITH OTHER
POLLUTANTS
CONC RANGE (PPB)
LOW
20-29
MID-
RANGE
11-27
mean
30-39
7-25 mean
Outdoors
HIGH
>40
STUDY
FACTOR
Outdoor 4th
grade girls
Outdoor 5th
grade girls
Outdoor 6th
grade girls
Indoor 4th grade
girls
Indoor 5th grade
girls
Indoor 6th grade
girls
Outdoor
Indoor
ODDS RATIO
(95% Cl)
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)
6-116
-------
AUTHOR,
YEAR,
LOCATION
Kim et al.
(2004a)
San Francisco
Bay area
Gauderman
et al. (2005)
Southern CA
CHS
Hwang et al.
(2005)
Taiwan ,
National study
STUDY
DESIGN
Subjects: 1,109
children Age 9-11
Distance: 10
school sites
Study Group:
Asthma
Subjects: 208
children
Avg time: 4 wk
Distance:
Outside home
Study Group:
Asthma
Subjects: 32,672
children
Distance:
Schools within
1 km of monitors
Study Group:
Asthma, ISAAC
ANALYSIS
METHOD
2-stage
Hierarchical
model
IQR = 3.6 NO2
IQR= 14.9
NOX
Logistic
regression
IQR = 5.7
2-stage
Hierarchical
model
10 ppb NOX
CORRELATION
WITH OTHER
POLLUTANTS
PM2.5: "low"
O3: "low"
PM10: 0.34 O3: -
0.39
SO2: 0.5
CONC RANGE (PPB)
LOW
21.5
MID-
RANGE
24 mean
13-51
29.6
HIGH
33.1
STUDY
FACTOR
All children
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
Lifetime asthma
Asthma med use
Parental atopy
No parental
atopy
ODDS RATIO
(95% Cl)
1.02
(0.97, 1.07)
1.04
(0.98, 1.10)
1.09
(1.03, 1.15)
1.00
(0.94, 1.07)
1.04
(0.97, 1.11)
1.07
(1.00, 1.14)
1.17
(1.06, 1.29)
1.02
(0.93, 1.11)
1.83
(1.04,3.21)
2.19
(1.20,4.01)
0.99
(0.92, 1.07)
1.02
(0.95, 1.10)
6-117
-------
Table AX6.3-17. Respiratory symptoms and long-term NO2 exposure.
STUDY
Karr et al.
(2006)
Karr et al.
(2006)
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)
LOCATION
Southern
California
Southern
California
Latrobe
Valley
Australia
Dresden
Germany
Southern CA
STUDY
GROUP
Infant
Bronchiolitis
Infant
Bronchiolitis
Symptoms
Monash Q
Symptoms
ISAAC
Symptoms
SUBJECTS
18,595
cases;
169,472
controls ages
3 wks to 1 yr
18,595
cases;
169,472
controls ages
3 wks to 1 yr
148 children
Age 7-1 4
1994-1995
5,421
children
Age 5-7, 911
1995-1996
12 mo
residence
3,676
children
ODDS
RATIO
(95% Cl)
1.03 [0.99,
1 .07] per
16 ppb
1.04 [1.00,
1 .08] per
15 ppb
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)
ANALYSIS
METHOD
Conditional
logistic
regression
Conditional
logistic
regression
Logistic
regression
10 pg
10 pg mean
10 pg winter
10 pg
summer
Logistic
regression
10 pg
Logistic
regression
AVG TIME
Chronic
(lifetime avg of
1-h daily max)
(Ppb)
Subchronic
(avg of 1 -h
daily max 1 mo
prior to
hospitalization)
(Ppb)
24 h
CONC RANGE (ppb)
LOW
12
12
29.3
MID-
RANGE
58
57
6
33.8
21.5
mean
HIGH
204
152
37.8
CORRE-
LATION
DISTANCE
34 monitors
34 monitors
in home
4 monitors
Within 1 km
Study
monitors
in 12 towns
6-118
-------
STUDY
wheeze
cough
wheeze boys
wheeze girls
Millstein et al.
(2004)
wheeze
wheeze
Mar-Aug
wheeze
Sep-Feb
Penard-Morand
et al. (2005)
wheeze past
12 mos.
Roemer et al.
(1993)
Mukala et al.
(1999)
cough
cough
nasal symp
winter
nasal symp
winter
LOCATION
CHS
Southern CA
CHS
France 6
towns
Wageningen
and
Bennekom,
Netherlands
Helsinki
Finland
STUDY
GROUP
Questionnaire
Symptoms
Symptoms
ISSAC
Symptoms
Questionnaire
Symptoms
SUBJECTS
Age 9-1 6
1994
2,034
children
Age 9-11
1995
4,901
children
Age 9-11
1999-2000
3-yr
residence
73 children
grades 3-8
Dec 1990-
Mar1991
163 children
Age 3-6
1991
ODDS
RATIO
(95% Cl)
1.12 (0.86,
1.45)
1.14 (0.94,
1.39)
1.54(1.04,-
2.29)
0.86 (0.57,
1.29)
0.93 (0.77,
1.12)
0.79 (0.40,
1.53)
0.85 (0.64,
1.14)
0.87 (0.75,
1.01)
No
associations
1.23(0.89,
1.70)
1 .52 (1 .00,
2.31)
0.99 (0.58,
1.68)
0.89 (0.44.
1.82)
ANALYSIS
METHOD
IQR =
25ppb
Mixed
effects
model
IQR =
5.74 ppb
Logistic
regression
10 pg
Time series
using Yule-
Walker
estimation
method
GEE
2nd fertile
3rd fertile
2nd fertile
3rd fertile
AVG TIME
Moly
3yrs
24 hr avg
Wkly Avg
CONC RANGE (ppb)
LOW
8.7, 16.0
<8.6
MID-
RANGE
8.6, 14.5
HIGH
16.1,
25.7
127
>14.5
CORRE-
LATION
PM2.5:
0.28
PM10:
0.39
O3: 0.76
SO2: 0.35
PM10:
0.46
PM10:
0.57
SO2: 0.26
BS:0.65
DISTANCE
Study
monitors
in 12 towns
29
monitoring
sites,
school
address
National Air
Quality
Monitoring
Network
Palmes
tubes
On outer
garment
6-119
-------
STUDY
nasal symp
spring
nasal symp
spring
Pikhart et al.
(2000)
wheeze
wheeze
wheeze
Setiani(1996)
Lacrimacy
Eye itch
Runny nose
Sore throat
Cough
Plegm
SOB
Sum of cough
with phlegm
and SOB
Van Strien
(2004)
wheeze
wheeze
wheeze
cough
cough
LOCATION
Prague
Czech
6 cities in
Japan
CTandMA
STUDY
GROUP
Symptoms
SAVIAH
Hiroshima
Community
Health Study
Symptoms
SUBJECTS
3,045
children
Age 7-10
1993-1994
13,836 adult
non-smoking
women aged
40-59
849 children
Age 12 mos
ODDS
RATIO
(95% Cl)
0.76 (0.56,
1.02)
0.68 (0.46,
1.01)
1.16(0.95,
1.42)
1.07(0.86,
1.33)
1.08(0.86,
1.36)
Logistic
regression
coefficient
(standard
error)
0.047
(0.046)
0.036
(0.046
-0.018
(0.076)
0.059
(0.042)
-0.046
(0.044)
-0.088
(0.049)
-0.056
(0.058)
-0.035
(0.030)
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)
ANALYSIS
METHOD
2nd fertile
3rd fertile
Multi-level
model
Individual
covariates
Ecological
covariates
Both
covariates
Individual
multiple
linear
regression
analysis
Poisson
regression
Q2
Q3
Q4
Q2
Q3
AVG TIME
24 h
10-1 4 day
Avg
CONC RANGE (ppb)
LOW
14.8
graph
5.1
MID-
RANGE
19
graph
9.9
HIGH
24.1
graph
17.4
CORRE-
LATION
SPM:
0.606
OX:-
0.337
DISTANCE
In home
6-120
-------
STUDY
cough
short of breath
short of breath
short of breath
Nitschke et al.
(2006)
Wheeze school
Wheeze home
Cough school
Cough home
Difficult breath
school
Difficult breath
home
Chest tight
school
Chest tight
home
Hoek and
Brukekreef
(1993)
Delfino et al.
(2006)
Personal NO2
Not taking anti-
inflammatory
meds
LOCATION
Adelaide
Australia
Wageningen,
Netherlands
Southern
California
STUDY
GROUP
Symptoms
Primary
school
Asthmatic
children
SUBJECTS
174
asthmatic
Children, age
5-13
2000
11 2 children
grades 4-7
45 children
ages 9-1 8
ODDS
RATIO
(95% Cl)
1 .52 (1 .00,
2.31)
1.59(0.96,
2.62)
1.95(1.17.
3.27)
2.38(1.31,
4.34)
0.99 (0.93,
1.06)
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)
No
association
0.80 (-3.01
to 4.61)
ANALYSIS
METHOD
Q4
Q2
Q3
Q4
Zero-inflated
negative
binomial
regression
10-ppb
increase
Individual
linear
regression
analysis and
distribution
of individual
regression
slopes
Linear mixed
effects
models
(Verbeke
and
Molenberghs
2001)
AVG TIME
24-h
24-h
CONC RANGE (ppb)
LOW
School
34 (28)
Home
20 (22)
MID-
RANGE
HIGH
117 max
147 max
127
CORRE-
LATION
PM10:
0.55
SO2: 0.28
BS:0.65
Personal
NO2,
personal
PM2.5:
0.33
Central
NO2,
personal
PM2.5:
0.22
Central
NO2,
central
PM2.5:
0.25
DISTANCE
9 days in
class
3 days at
home
Backpack
monitor,
active
sampling
system,
central site
exposure
6-121
-------
STUDY
Taking anti-
inflammatory
meds
Inhaled
corticosteroids
Antileukotrienes
± inhaled
corticosteroes
Central site
NO2
Not taking anti-
inflammatory
meds
Taking anti-
inflammatory
meds
Inhaled
corticosteroids
Antileukotrienes
± inhaled
corticosteroes
Salome et al.
(1996)
Day of
exposure- room
air
Change in
symptom score:
adult
Change in
symptom score:
Child
Wk following
exposure- room
air
Severity score:
adult
Severity score:
child
Pattenden et al.
(2006)
Wheeze
Asthma
Bronchitis
LOCATION
Australia
Russia,
Austria, Italy,
Switzerland,
Netherlands
STUDY
GROUP
asthmatic
PATY
SUBJECTS
20 (9 adults
and 11
children)
23,955
children ages
6-12
1993-1999
ODDS
RATIO
(95% Cl)
1.67(0.55
to 2.79)
1.22 (0.04
to 2.40
1.73 (-0.70
to 4.16)
0.96 (-1.34
to 3.26)
1.48(0.47
to 2.50)
1.32 (0.33
to 2.32)
-7.5 (-2.83
to 1 .32)
0.01 (0.38)
-0.02 (0.26)
4.38(1.5)
4.20(1.3)
1.01 (0.93-
1.10)
1.02(0.94-
1.09)
0.99 (0.88-
1.12)
ANALYSIS
METHOD
ANOVA
Logistic
regression,
Cochran %2
AVG TIME
CONC RANGE (ppb)
LOW
0.02 ppm
12.45
MID-
RANGE
HIGH
1.12 ppm
50.00
CORRE-
LATION
DISTANCE
variable
6-122
-------
STUDY
Phlegm
Nocturnal
cough
Morning cough
Sensitivity to
inhaled
allergens
Hay fever
Itchy rash
Woken by
wheeze
Allergy to pets
Chen et al.
(1999)
Day avg
1 day lag- FVC
1 day lag-
FEV10
2 day lag- FVC
2 day lag
FEV10
7 day lag- FVC
7 day lag-
FEV10
Daytime Peak
1 day lag- FVC
1 day lag-
FEV10
2 day lag- FVC
2 day lag
FEV10
7 day lag- FVC
7 day lag-
FEV10
LOCATION
Taiwan
STUDY
GROUP
Study on Air
Pollution and
Health
SUBJECTS
941 children
ages 8-1 3
ODDS
RATIO
(95% Cl)
1.05(0.95-
1.17)
1.13(0.94-
1.35)
1.15(1.01-
1.30)
1.13(1.01-
1.26)
1.04(0.98-
1.11)
1.05(0.98-
1.12)
1.06(0.89-
1.26)
1.14(0.99-
1.31)
Coefficient
(standard
error)
-2.66(1.23)
-0.46(1.16)
-3.32 (1 .53)
-0.93 (1 .45)
1.39(1.71)
2.52(1.61)
-0.59 (0.40)
-0.16 (0.37)
-1.33(0.72)
-0.36 (0.68)
-0.13(0.87)
0.43 (0.82
ANALYSIS
METHOD
models
AVG TIME
CONC RANGE (ppb)
LOW
9.2
MID-
RANGE
HIGH
141.6
CORRE-
LATION
DISTANCE
6-123
-------
Table AX6.3-18. Lung cancer.
AUTHOR, YEAR,
LOCATION
Nyberg et al. (2000)
Stockholm, Sweeden
Nafstad (2004)
Norway
EXPOSURE
From addresses
and traffic
Home address
1972-1974
STUDY SUBJECTS
1 ,042 cases, 2,364 controls
men age 40-75
16,209 men age 40-49 at entry
followed 1972-1998
CONC RANGE (PPB)
LOW
8.1
5.32
MID-
RANGE
10.6
10.6
HIGH
13.3
16
ANALYSIS
logistic regression
30-yr estimated
exposure
10 [ig
Q2
Q3
Q4
10-yr estimated
exposure
10 pg
Q2
Q3
Q4
90th percentile
Cox proportional
lung cancer
incidence
10 pg
Q2
Q3
Q4
non-lung cancer
10 pg
Q2
Q3
Q4
ODDS RATIO
(95% Cl)
1.05(0.93, 1.18)
1.18(0.93, 1.49)
0.90(0.71, 1.14)
1.05(0.79, 1.40)
1.10(0.97, 1.23)
1.15(0.91, 1.46)
1.01 (0.79, 1.29)
1.07(0.81, 1.42)
1.44(1.05, 1.99)
1.08(1.02, 1.15)
0.90(0.70, 1.15)
1.06(0.81, 1.38)
1.36(1.01, 1.83)
1.02(0.99, 1.06
0.98(0.88, 1.08)
1.05(0.94, 1.18)
1.04(0.91, 1.18)
6-124
-------
Table AX6.3-19. 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
META ANALYSIS
Stieb et al.
(2002),
re-analysis
(2003) meta-
analysis of
estimates from
multiple
countries.
All cause
24-h avg ranged
from 13 ppb
(Brisbane,
Australia) to
38 ppb (Santiago,
Chile).
"Representative"
concentration:
24 ppb
PM10, O3, SO2, CO
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% Cl: -
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
Kelsall et al.
(1997)
Philadelphia, PA,
1974-1988
Ostro et al.
(2000)
Coachella Valley,
CA
1989-1998
Fairley (1999;
reanalysis
Fairley, 2003)
Santa Clara
County, CA
1989-1996
All cause;
cardiopulmonary
All cause;
respiratory;
circulatory
All cause;
respiratory;
cardiovascular,
All cause;
respiratory;
cardiovascular;
cancer; other
All cause;
respiratory;
circulatory
Ranged from
9 ppb (Kansas
City) to 39 ppb
(Los Angeles),
24-h avg
69 ppb, 24-h avg
39.6 ppb, 24-h
avg
20 ppb, 24-h avg
28 ppb, 24-h avg
PM10, O3, SO2,
CO; two-pollutant
models
KM (particle optical
reflectance), NO2,
SO2, CO;
multipollutant models
TSP, CO, SO2, O3
PM10, PM2.5,
PM10-2.5, O3, CO
PM10, PM2.5,
PM10-2.5, SO42-,
coefficient of
haze, NO3-, O3,
SO2;
0, 1,2
1
0(AIC
presented for 0
through 5)
0-4
0, 1
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
Poisson GAM with
default convergence
criteria. Time-series
study.
Poisson GAM,
reanalyzed with
stringent convergence
criteria; Poisson GLM.
Time-series study.
24-h avg NO2 (per
20 ppb):
Posterior means:
All cause:
Lag 1 : 0.50%
(0.09, 0.90)
Lag 1 with PMio
and SO2: 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)
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)
Lag 1:
All cause: 1 .9%
(0.2, 3.7);
Cardiovascular:
1.4% (-1.7, 4.5);
Respiratory: 4.8%
(-0.3, 10.2)
6-125
-------
REFERENCE,
STUDY
LOCATION,
AND PERIOD
Gamble (1998)
Dallas, TX
1990-1994
Dockery et al.
(1992)
St. Louis, MO
and Eastern
Tennessee
1985-1986
Moolgavkar
(2003)
Cook County, IL
and Los Angeles
County, CA,
1987 1995
Moolgavkar
(2000a,b,c);
re-analysis
' ''
Cook County, IL;
Los Angeles
County, CA, and
Maricopa County,
AZ,
1987-1995
Lippmann et al.
(2000);
reanalysis Ito,
(2003, 2004)
Detroit, Ml
1985-1990
1992-1994
Lipfert et al.
(2000)
Seven counties
in Philadelphia,
PA area
1991-1995
OUTCOME
MEASURE
All cause;
cardiopulmonary
All cause
All cause;
cardiovascular
Cardiovascular;
cerebrovascular;
COPD
All cause;
respiratory;
circulatory;
cause-specific
All cause;
respiratory;
cardiovascular;
all ages; age
65+ yrs; age
<65 yrs; various
subregional
boundaries
MEAN NO2
LEVELS
15 ppb, 24-h avg
St. Louis: 20 ppb;
Eastern
Tennessee:
12.6 ppb, 24-h
avg
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
20.4 ppb, 24-h
avg
COPOLLUTANTS
CONSIDERED
PM10, O3, SO2,
CO; two-pollutant
models
PM10, PM25, SO4,
H+, 03, S02
PM2.5, PM10, 03,
SO2, CO; two-
pollutant models
PM2.5, PM10, O3,
SO2, CO; two- and
three-pollutant
models
PM,0, PM2.5,
PM10-2.5, SO42-, H+,
O3, SO2, CO;
two-pollutant
models
PM10, PM2.5,
PM10-2.5, SO4,
O3, other PM
indices, NO2, SO2,
CO; two-pollutant
models
LAG
STRUCTURE
REPORTED
Avg 4-5
Lag1
0, 1,2,3,4,5
0, 1,2,3,4,5
0, 1,2,3,0-1,
0-2, 0-3
0-1
METHOD/DESIGN
Poisson GLM. Time-
series study.
Poisson with GEE.
Time-series study.
Poisson GAM with
default convergence
criteria. Time-series
study.
Poisson GAM with
default convergence
criteria in the original
Moolgavkar (2000);
GAM with stringent
convergence criteria
and GLM with natural
splines in the 2003
re-analysis. The 2000
analysis presented total
death risk estimates
only in figures.
Poisson GAM,
reanalyzed with
stringent convergence
criteria; Poisson GLM.
Numerical NO2 risk
estimates were not
presented in the re-
analysis. Time-series
study.
Linear with 19-day
weighted avg
Shumway filters.
Time-series study.
Numerous results.
EFFECT
ESTIMATES
All cause: 4.4%
(0.0, 9.0)
1 .9% (-4.6, 9.0)
Respiratory:
13.7% (-2.0, 32.0)
All cause:
St. Louis, MO:
0.7% (-3.5, 5.1)
Tennessee: 3.9%
(-8.7, 18.2)
All cause:
Lag 1:
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);
withPM25: 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:
Lag 1:
1985-1990:
0.9% (-1.2, 3.0)
1992-1994:
1 .3% (-1 .5, 4.2)
All-cause, avg of
0- and 1-day lags,
Philadelphia:
2.2% (p > 0.05)
6-126
-------
REFERENCE,
STUDY
LOCATION,
AND PERIOD
Chock et al.
(2000)
Pittsburgh, PA
1989-1991
De Leon et al.
(2003)
New York City,
NY
1985-1994
Klemm and
Mason (2000);
Klemm et al.
(2004)
Atlanta, GA
Aug 1998-July
2000
Gwynn et al.
(2000)
Buffalo, NY
Time-series
study.
OUTCOME
MEASURE
All cause; age
<74yrs;
age 75+ yrs
Circulatory and
cancer with and
without
contributing
respiratory causes
All cause;
respiratory;
cardiovascular;
cancer; other; age
<65yrs; age
65+ yrs
All cause;
respiratory;
circulatory
MEAN NO2
LEVELS
Not reported.
40.6 ppb, 24-h
avg
51.3 ppb, max 1-
h.
24-h avg 21 ppb
COPOLLUTANTS
CONSIDERED
PM10, NO2,SO2,
CO; two-, five-,
and six-pollutant
models
PM10, O3, SO2,
CO; two-pollutant
models
PM2.5, PM10-2.5,
EC, OC, O3,
SO42-,
NO3, SO2, CO
PM10, CoH, O3, SO2,
CO, H+, SO42-
LAG
STRUCTURE
REPORTED
0, plus minus 3
days
Oor1
0-1
METHOD/DESIGN
Poisson GLM. Time-
series study. Numerous
results
Poisson GAM with
stringent convergence
criteria; Poisson GLM.
Time-series study.
Poisson GLM using
quarterly, moly, or
biweekly knots for
temporal smoothing.
Time-series study.
Poisson GAM with
Default convergence
criteria.
EFFECT
ESTIMATES
All cause, lag 0,
age
0-74: 0.5% (-2.4,
3.5); age 75+:
1 .0% (-1 .9, 4.0)
Gaseous
pollutants results
were given only in
figures.
Circulatory:
Age<75:~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)
CANADA
Burnett et al.
(2004)
12 Canadian
cities 198 1-1 999
Burnett et al.
(2000); re-
analysis (2003)
8 Canadian cities
1986-1996
Burnett et al.
(1998a), 11
Canadian cities
1980-1991
Burnett et al.
(1998b), Toronto,
1980-1994
All cause
All cause
All cause
All cause
24-h avg ranged
from 10 (Saint
John) to 26
(Calgary) ppb.
24-h avg ranged
from 15
(Winnipeg) to 26
(Calgary) ppb.
24-h avg ranged
from 14
(Winnipeg) to 28
(Calgary) ppb.
24-h avg 25 ppb
PM25, PM10-2.5, 03,
SO2, CO
PM2.5, PM10,
PM2.5-10, SO2,
O3, CO
SO2, O3, CO
SO2, O3, CO, TSP,
COH, estimated
PMio, estimated PM25
1,0-2
0, 1,0-2
0, 1,2,0-1,0-2
examined but
the best
lag/averaging for
each city chosen
0, 1,0-1
Poisson GLM. Time-
series study.
Poisson GAM with
default convergence
criteria. Time-series
study. The 2003 re-
analysis did not
consider gaseous
pollutants.
Poisson GAM with
default convergence
criteria. Time-series
study.
Poisson GAM with
default convergence
criteria. Time-series
study.
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
PM2.5: 2.8% (0.5,
c o\
o.z;
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)
6-127
-------
REFERENCE,
STUDY
LOCATION,
AND PERIOD
Vedal et al.
(2003)
Vancouver
British
Columbia,
Canada
1994-1996
Villeneuve et al.
(2003)
Vancouver,
Rritich
Dniisn
Columbia,
Canada
1986-1999
Goldberg et al.
(2003)
Montreal,
Quebec, Canada
1984-1993
OUTCOME
MEASURE
All cause;
respiratory;
cardiovascular
All cause;
respiratory;
cardiovascular;
cancer;
socioeconomic
status
Congestive Heart
Failure (CHF) as
underlying cause
of death vs. those
classified as
having congestive
heart failure 1 yr
prior to death
MEAN NO2
LEVELS
17 ppb, 24-h avg
19 ppb, 24-h avg
22 ppb, 24-h avg
COPOLLUTANTS
CONSIDERED
PM10, O3, SO2,
CO
PM2.5, PM10,
PM10-2.5, TSP,
coefficient of
haze, SO42-, SO2,
03, CO
PM2.s, coefficient
of haze, SO42-,
SO2, O3, CO
LAG
STRUCTURE
REPORTED
0, 1,2
0, 1,0-2
0, 1,0-2
METHOD/DESIGN
Poisson GAM with
stringent convergence
criteria. Time-series
study. By season.
Poisson GLM with
natural splines.
Time-series study.
Poisson GLM with
natural splines.
Time-series study.
EFFECT
ESTIMATES
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:
Lag 0:2.1%
(-3.0, 7.4)
Cardiovascular:
Lag 0: 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)
EUROPE
Samoli et al.
(2006)
30 APHEA2
cities. Study
periods vary
by city, ranging
from 1990 to
1997
Samoli et al.
(2005)
9 APHEA2 cities.
reported.
All cause,
respiratory;
cardiovascular
All-cause
1-h max ranged
from 24
(Wroclaw) to 81
(Milan) ppb
The selected
cities had 1-h
max medians
above 58 ppb and
the third quartiles
above 68.
BS,PM10, SO2, O3
None
01
01
Poisson model with
penalized splines.
Poisson model with
either non-parametric or
cubic spline smooth
function in each city,
and combined across
cities.
All-cause: single:
1.8% (1.3, 2.2);
with SO2: 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.
6-128
-------
REFERENCE,
STUDY
LOCATION,
AND PERIOD
Touloumi et al.
(1997)
Six European
cities:
London, Paris,
Lyon,
Barcelona,
Athens, Koln.
Study periods
vary by city,
ranging from
1977to 1992
Zmirou et al.
(1998)
Four European
cities: London,
Paris, Lyon,
Study periods
vary by city,
ranging from
1985-1992
Biggeri et al.
(2005)
8 Italian cities,
Period variable
between
1990-1999
Anderson et al.
(1996) London,
England
1987-1992
Bremner et al.
(1999) London,
England
1992-1994
Anderson et al.
(2001)
West Midlands
region, England
1994-1996
Prescott et al.
(1998)
Edinburgh,
Scotland
1992-1995
OUTCOME
MEASURE
All cause
Respiratory;
cardiovascular
All cause;
respiratory;
cardiovascular
All cause;
respiratory;
cardiovascular
All cause;
respiratory;
cardiovascular; all
cancer; all others;
all ages; age
specific (0-64,
65+, 65-74, 75+
yrs)
All cause;
respiratory;
cardiovascular.
All cause;
respiratory;
cardiovascular; all
ages; age <65
yrs; age > 65 yrs
MEAN NO2
LEVELS
Ranged from 37
(Paris) to 70
(Athens) ppb, 1-h
max
Ranged from 24
(Paris) to 37
(Athens) ppb in
cold season and
23 (Paris) to 37
(Athens) ppb in
warm season, 24-
h avg
24-h avg ranged
from 30 (Verona)
to 51 (Rome) ppb
37 ppb, 24-h avg
34 ppb, 24-h avg
37 ppb, 1-h max
26 ppb, 24-h avg
COPOLLUTANTS
CONSIDERED
BS, O3; two-pollutant
models
BS.TSP, SO2, O3
Only single-pollutant
models; O3, SO2, CO,
PM10
BS,O3, SO2;
two-pollutant models
BS,PM10, O3, SO2,
CO; two-pollutant
models
PM,0, PM2.5,
PM25-10, BS.SO42-,
O3, SO2, CO
BS,PM10, O3, SO2,
CO; two-pollutant
models
LAG
STRUCTURE
REPORTED
0, 1,2,3,0-1,0-
2, 0-3 (best lag
selected for
each city)
0, 1,2,3,0-1,0-
2, 0-3 (best lag
selected for
each city)
0-1
0, 1
Selected best
from
0, 1,2,3, (all
cause);
0, 1,2,3,0-1,0-
2
0-3 (respiratory,
cardiovascular)
0-1
0
METHOD/DESIGN
Poisson autoregressive.
Time-series study.
Poisson GLM. Time-
series study.
Poisson GLM. Time-
series study.
Poisson GLM. Time-
series study.
Poisson GLM. Time-
series study.
Poisson GAM with
default convergence
criteria. Time-series
study.
Poisson GLM.
Time-series study.
EFFECT
ESTIMATES
All-cause: Single-
pollutant model:
1.0% (0.6, 1.3);
With BS:
0.5% (0.0, 0.9).
Western Europe:
Respiratory:
0.0% (-1.1, 1.1)
Cardiovascular:
0.8% (0.0, 1 .5)
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.
6-129
-------
REFERENCE,
STUDY
LOCATION,
AND PERIOD
Le Tertre et al.
(2002)
Le Havre, Lyon,
Paris, Rouen,
Strasbourg, and
Toulouse, France
Study periods
vary by city,
ranging from
1990-1995
Zeghnoun et al.
(2001) Rouen
and Le Havre,
France 1990-
1995
Dab etal. (1996)
Paris, France
1987-1992
Zmirou et al.
(1996)
Lyon, France
1985-1990
Sartor et al.
(1995)
Belgium
Summer 1994
Hoek et al.
(2000);
reanalysis Hoek,
(2003)
The Netherlands:
entire country,
four urban areas
1986-1994
OUTCOME
MEASURE
All cause;
respiratory;
cardiovascular
All cause;
respiratory;
cardiovascular
Respiratory
All cause;
respiratory;
cardiovascular;
digestive
All cause; age
<65 yrs; age 65+
yrs
All cause; COPD;
pneumonia;
cardiovascular
MEAN NO2
LEVELS
Ranged from
15 (Toulouse) to
28 (Paris) ppb,
24-h avg
24-h avg 18 ppb
in Rouen; 20 ppb
in Le Havre
24 ppb, 24-h avg
37 ppb, 24-h avg
24-h avg NO2:
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
24-h avg median:
17 ppb in the
Netherlands;
24 ppb in the four
major cities
COPOLLUTANTS
CONSIDERED
BS,O3, SO2
SO2, BS.PM13, O3
BS.PM13, O3, SO2,
CO
PM13, SO2, O3
TSP, NO, O3, SO2
PM10, BS.SO42-,
NO3-, O3, SO2, CO;
two-pollutant models
LAG
STRUCTURE
REPORTED
0-1
0, 1,2,3,0-3,
0
Selected best
from
0, 1,2,3
0, 1,2
1,0-6
METHOD/DESIGN
Poisson GAM with
default convergence
criteria. Time-series
study.
Poisson GAM with
default convergence
criteria. Time-series
study.
Poisson autoregressive.
Time-series study.
Poisson GLM.
Time-series study.
Log-linear regression for
O3 and temperature.
Time-series study.
Poisson GAM,
reanalyzed with
stringent convergence
criteria; Poisson GLM.
Time-series study.
EFFECT
ESTIMATES
Six-city pooled
estimates:
All cause:
2.9% (1.6, 4.2)
Respiratory:
3.1% (-1.7, 8.0)
Cardiovascular:
3.5% (1.1, 5.9)
All cause in Rouen
(lag 1): 5.5%
(0.2, 11.1) ; in
Le Havre (lag 1):
2.4% (-3.4, 8.5)
Lag1:
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.
Poisson GLM:
All C3US6'
Lag 1: 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).
6-130
-------
REFERENCE,
STUDY
LOCATION,
AND PERIOD
Hoek et al.
(2001);
reanalysis Hoek,
(2003)
The Netherlands
1986-1994
Roemer and van
Wijinen (2001)
Amsterdam, The
Netherlands
1987-1998
Verhoeff et al.
(1996)
Amsterdam, The
Netherlands
1986-1992
Fischer et al.
(2003) The
Netherlands,
1986-1994
Spix and
Wichman (1996)
Koln, Germany
1977-1985
OUTCOME
MEASURE
Total
cardiovascular;
myocardial
infarction;
arrhythmia; heart
failure;
cerebrovascular;
thrombosis-
related
All cause
All cause; all
ages; age 65+ yrs
All-cause,
cardiovascular,
COPD, and
pneumonia in age
groups <45, 45-
64, 65-74, 75+
All-cause
MEAN NO2
LEVELS
24-h avg median:
17 ppb in the
Netherlands;
24 ppb in the four
major cities
24-h avg:
Background sites:
24 ppb
Traffic sites:
34 ppb
1-h max O3:
43 pg/m3
Max 301
24-h avg median
17 ppb
24-h avg 24 ppb;
1-h max 38 ppb
COPOLLUTANTS
CONSIDERED
PM10, O3, SO2, CO
BS,PM10, O3, SO2,
CO
PM10, O3, CO;
multipollutant models
NONO2!!!
PM10, BS,O3, SO2,
CO
TSP, PM7, SO2
LAG
STRUCTURE
REPORTED
1
1,2,0-6
0, 1,2
0-6
0, 1,0-1
METHOD/DESIGN
Poisson GAM,
reanalyzed with
stringent convergence
criteria; Poisson GLM.
Time-series study.
Poisson GAM with
default convergence
criteria (only one
smoother). Time-series
study.
Poisson. Time-series
study.
Poisson GAM with
default convergence
criteria. Time-series
study.
Poisson GLM.
Time-series study.
EFFECT
ESTIMATES
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)
Heart failure:
7.6% (1.4, 14.2)
Cerebrovascular:
5.1% (0.9, 9.6)
Thrombosis-
related:
-1.2% (-9.6, 8.1)
Total population
using background
sites: Lag 1 :
3.8% (1.7, 5.9);
Traffic pop. using
background sites:
Lag 1:5.7% (0.6,
11.0);
Total pop. using
traffic sites: Lag 1 :
1 .7% (0.4, 3.0)
1-h max O3 (per
100 pg/m3)
All ages:
Lag 0: 1 .8% (-3.8,
7.8)
Lag 1:0.1% (-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)
Lag 1 : 0.4%
(-0.4, 1.2)
6-131
-------
REFERENCE,
STUDY
LOCATION,
AND PERIOD
Peters et al.
(2000b)
NE Bavaria
Germany
1982-1994
Coal basin in
Czech Republic
1993-1994
Michelozzi et al.
(1998) Rome,
Italy 1992-1995
Ponka et al.
(1998)
Helsinki, Finland
1987-1993
Saez et al.
(2002)
Seven Spanish
cities, variable
study periods
between 1991
and 1996.
Garcia-Aymerich
et al. (2000)
Barcelona, Spain
1985-1989
OUTCOME
MEASURE
All cause;
respiratory;
cardiovascular;
cancer
All-cause
All cause;
cardiovascular;
age <65 yrs, age
65+ yrs
All cause;
respiratory;
cardiovascular
All cause;
respiratory;
cardiovascular;
general
population;
patients with
COPD
MEAN NO2
LEVELS
24-h avg:
Czech Republic:
17.6ppb
Germany:
13.2 ppb
24-h avg 52 ppb
24-h avg:
Median 20 ppb
24-h avg mean
ranged from
17 ppb in Huelva
to 35 ppb in
Valencia.
Levels not
reported.
COPOLLUTANTS
CONSIDERED
TSP, PM10, O3, SO2,
CO
PM13, SO2, O3, CO
TSP, PM10, O3, SO2
O3, PM, SO2, CO
BS,O3, SO2
LAG
STRUCTURE
REPORTED
0, 1,2,3
0, 1,2,3,4
0, 1,2,3,4,5,
6,7
0-3
Selected best
avg lag
METHOD/DESIGN
Poisson GLM.
Time-series study.
Poisson GAM with
default convergence
criteria. Time-series
study.
Poisson GLM.
Time-series study.
Poisson GAM with
default convergence
criteria. Time-series
study.
Poisson GLM.
Time-series study.
EFFECT
ESTIMATES
Czech Republic:
All cause:
Lag 1:2.1%
(-1.7,6.1)
Bavaria, Germany:
All cause:
Lag 1:-0.1%
(-3.6, 3.6)
Lag2: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.
PMio and O3 were
reported to have
stronger
associations.
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)
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)
Cardiovascular:
General
population:
Lag 0-3: 2.4% (-
0.9, 5.8)
COPD patients:
Lag 0-2: 4.3% (-
13.6, 25.8)
6-132
-------
REFERENCE,
STUDY
LOCATION,
AND PERIOD
Saez et al.
(1999)
Barcelona, Spain
1986-1989
Sunyer et al.
(1996)
Barcelona, Spain
1985-1991
Sunyer and
Basagana (2001)
Barcelona, Spain
1990-1995
Sunyer et al.
(2002)
Barcelona, Spain
1986-1995
Diazetal. (1999)
Madrid, Spain
1990-1992
OUTCOME
MEASURE
Asthma mortality;
age 2-45 yrs
All cause;
respiratory;
cardiovascular; all
ages; age 70+ yrs
Mortality in a
cohort of patients
with COPD
All cause,
respiratory, and
cardiovascular
mortality in a
cohort of patients
with severe
asthma
All cause;
respiratory;
cardiovascular
MEAN NO2
LEVELS
Levels not
reported.
1-h max:
Median:
Summer: 51 ppb
Winter: 46 ppb
Mean not
reported
IQR 8.9 ppb 24-h
avg
1-h max: median
47 ppb;
24-h avg median
27 ppb
24-h avg
Levels not
reported.
COPOLLUTANTS
CONSIDERED
BS,O3, SO2
BS,SO2, O3
PM10, O3, CO
PM10, BS, SO2, O3,
CO, pollen
TSP, O3, SO2, CO
LAG
STRUCTURE
REPORTED
0-2
Selected best
single-day lag
0-2
0-2
1,4, 10
METHOD/DESIGN
Poisson with GEE.
Time-series study.
Autoregressive Poisson.
Time-series study.
Conditional logistic
(case-crossover)
Conditional logistic
(case-crossover)
Autoregressive linear.
Time-series study.
EFFECT
ESTIMATES
RR = 4.1 (0.5,
35.0)
All yr, all ages:
All cause:
Lag 1: 1.9% (0.8,
3.1)
Respiratory:
Lag 0: 1 .5% (-1 .9,
5.0)
Cardiovascular:
Lag 1:2.2% (0.5,
3.9)
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
admission:
All cause:
2.14(1.10,4.14)
Cardiovascular:
1.53(0.46,5.07)
Only significant
risk estimates
were shown. For
NO2, only
respiratory
mortality was
significantly
(p < 0.05)
associated with an
excess percent
risk 8.5%.
6-133
-------
REFERENCE,
STUDY
LOCATION,
AND PERIOD
OUTCOME
MEASURE
MEAN NO2
LEVELS
COPOLLUTANTS
CONSIDERED
LAG
STRUCTURE
REPORTED
METHOD/DESIGN
EFFECT
ESTIMATES
LATIN AMERICA
Borja-Aburto
etal. (1997)
Mexico City
1990-1992
Borja-Aburto
etal. (1998)
SW Mexico City
1993-1995
Loomis et al.
(1999)
Mexico City
1993-1995
Gouveia and
Fletcher (2000b)
Sao Paulo, Brazil
1991-1993
Pereira et al.
(1998)
Sao Paulo, Brazil
1991-1992
Saldiva et al.
(1994)
Sao Paulo, Brazil
1990-1991
All cause;
respiratory;
cardiovascular; all
ages; age <5 yrs;
age >65 yrs
All cause;
respiratory;
cardiovascular;
other; all ages;
age >65 yrs
Infant mortality
All ages (all
cause); age <5
yrs (all cause,
respiratory,
pneumonia); age
65+ yrs (all cause,
respiratory,
cardiovascular)
Intrauterine
mortality
Respiratory; age
<5yrs
1-h max O3:
Median 155 ppb
8-h max O3:
Median 94 ppb
10-h avg O3
(8 a.m. -6 p.m.):
Median 87 ppb
24-h avg O3:
Median 54 ppb
37.7 ppb, 24-h
avg
24-h avg 38 ppb
1-h max: 84 ppb
24-h avg 82 ppb
24-h avg NOX
127 ppb
TSP, SO2, CO; two-
pollutant models
PM2.s, O3, SO2; two-
pollutant models
PM2.s, O3
PM10, O3, SO2, CO
PM10, O3, SO2, CO
PM10, O3, SO2, CO;
multipollutant models
0, 1,2
0, 1,2,3,4,5,
and multiday
avg
0, 1,2,3,4,5,
3-5
0, 1,2
0-4
0-2
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.
Poisson GLM.
Time-series study.
Poisson GLM.
Time-series study.
OLS of raw or
transformed data. Time-
series study.
1-h max O3 (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, 21.4);
with PM25:
2.9% (-10.2, 17.8)
All ages:
All cause:
Lag 0: -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
6-134
-------
REFERENCE,
STUDY
LOCATION,
AND PERIOD
Saldiva et al.
(1995)
Sao Paulo, Brazil
1990-1991
Cifuentes et al.
(2000) Santiago,
Chile
1988-1966
Ostro et al.
(1996) Santiago,
Chile 1989-1991
OUTCOME
MEASURE
All cause; age
65+yrs
All cause
All cause
MEAN NO2
LEVELS
24-h avg NOX
127 ppb
8-h avg 41 ppb
1-h max 56 ppb
COPOLLUTANTS
CONSIDERED
PM10, O3, SO2, CO;
two-pollutant models
PM25, PM10-2.5, CO,
S02, 03
PM10, O3, SO2; two-
pollutant models
LAG
STRUCTURE
REPORTED
0-1
0, 1,2,3,4,5,
1-2, 1 3, 1-4, 1-5
1
METHOD/DESIGN
OLS; Poisson with GEE.
Time-series study.
Poisson GAM with
default convergence
criteria; Poisson GLM.
Time-series study.
OLS, Poisson.
Time-series study.
EFFECT
ESTIMATES
NOX slope
estimate:
0.0341
deaths/day/ppb
(SE 0.0105)
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 et al.
(1998b)
Sydney, Australia
1989-1993
Simpson et al.
(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
From 1 6 To
24 ppb
24-h avg: whole
yr: 12 ppb; cool
season: 13 ppb;
warm season
9 ppb
24-h avg 13 ppb;
1-h max 26 ppb
24-h avg 14 ppb;
1-h max 28 ppb
PM10, PM25, Bsp
(Nephelometer), O3,
Co
PM10, PM25, BSP, O3,
CO
BSP, O3
PM10, TSP, O3, SO2,
CO
0, 1,2,3,0-1
0, 1,2,3,0-1
0-1
0
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)
ASIA
Kim et al.
(2004b)
Seoul, Korea
1995-1999
All cause
24-h avg 33 ppb
PM10, O3, SO2, CO;
two-pollutant models
1
Poisson GAM with
stringent convergence
criteria (linear model);
GLM with cubic natural
spline; GLM with B
mode spline (threshold
model). Time-series
study.
Risk estimates for
NO2 not reported.
6-135
-------
REFERENCE,
STUDY
LOCATION,
AND PERIOD
Lee etal. (1999)
Seoul and Ulsan,
Korea
1991-1995
Lee and
Schwartz (1999)
Seoul, Korea
1991-1995
Kwon et al.
(2001)
Seoul, Korea
1994-1998
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
OUTCOME
MEASURE
All cause
All cause
Mortality in a
cohort of patients
with congestive
heart failure
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
MEAN NO2
LEVELS
1-h max O3:
Seoul:
32.4ppb
10th %-90th %
14-55
Ulsan:
26.0 ppb
10th %-90th %
16-39
1-h max O3:
Seoul:
32 .4 ppb
10th %-90th %
14-55
24-h avg 32 ppb
24-h avg 33 ppb
24-h avg 33 ppb
24-h avg 29 ppb
24-h avg 31 ppb
COPOLLUTANTS
CONSIDERED
TSP, SO2
TSP, SO2
PM10, O3, SO2, CO
PM10, O3, SO2, CO
PM10, O3, SO2, CO
PM10, SO2, O3, CO
PM10, SO2, O3, CO
LAG
STRUCTURE
REPORTED
0
0
0
0
2
0-2
0-2
METHOD/DESIGN
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.
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.
EFFECT
ESTIMATES
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 - 1
wk:
1.5% (-1.2,4.2)
Four controls — 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)
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)
6-136
-------
REFERENCE,
STUDY
LOCATION,
AND PERIOD
Wong et al.
(2001 b)
Hong Kong
1995-1997
Wong et al.
(2002)
Hong Kong
1995-1998
Hedley et al.
(2002)
Hong Kong
1985-1995
Intervention July
1990 (switch to
low sulfur-
content fuel)
Yang et al.
(2004b)
Taipei, Taiwan
1994-1998
OUTCOME
MEASURE
All cause;
respiratory;
cardiovascular
Respiratory;
cardiovascular;
COPD'
pneumonia and
influenza;
ischemic heart
dis.;
cerebrovascular
All cause;
cardiovascular;
respiratory;
neoplasms and
other causes; all
ages; age 15-64
yrs; age 65+ yrs
All cause;
respiratory;
cardiovascular;
subtropical area
MEAN NO2
LEVELS
24-h avg 25 ppb
in warm season;
33 ppb in cold
season
24-h avg 29 ppb
Avg moly NO2:
Baseline: 29 ppb
1 yr after
intervention:
25 ppb
2-5 yrs after
intervention:
28 ppb
24-h avg 31 ppb
COPOLLUTANTS
CONSIDERED
PMm, O3, SO2; two-
pollutant models
PMm, O3, SO2; two-
pollutant models
SO2 (main pollutant of
interest, 45%
reduction observed
5 yrs after
intervention), PMio,
SO42-, NO2
PM10, SO2, O3, CO
LAG
STRUCTURE
REPORTED
0, 1,2
0, 1,2,0-1,0-2
Moly avgs
considered
without lags
0-2
METHOD/DESIGN
Poisson GAM with
default convergence
criteria. Time-series
study.
Poisson GLM. Time-
series study.
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.
EFFECT
ESTIMATES
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)
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.
AsNO2 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:
-1.1% (-9.5, 8.0)
6-137
-------
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