Integrated Science Assessment for
Oxides of Nitrogen — Health Criteria
EPA/600/R-08/071
July 2008
1
United States
Environmental Protection
Agency
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July 2008
EPA/600/R-08/071
Integrated Science Assessment
for Oxides of Nitrogen - Health Criteria
National Center for Environmental Assessment-RTF Division
Office of Research and Development
U.S. Environmental Protection Agency
Research Triangle Park, NC
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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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Contents
List of Tables vii
List of Figures ix
Acronyms and Abbreviations xiii
Authors, Contributors, Reviewers xix
NOx Project Team xxiii
Clean Air Scientific Advisory Committee Oxides of Nitrogen Primary NAAQS Review Panel xxv
Preface xxvii
Chapter 1. Introduction
1 .1 . Document Development
1 .2. Document Oraanization
1 .3. EPA Framework for Causal Determinations
1 .3.1 . Scientific Evidence Used in Establishina Causality
1 .3.2. Movina from Association to Causation
1 .3.3. Multifactorial Causation
1 .3.4. Uncertainty
1 .3.5. Application of Framework
1.3.6. First Step— Determination of Causality
1 .3.7. Second Step— Evaluation of Population Response
1-1
1-1
1-2
1-2
1-3
1-3
1-5
1-6
1-7
1-8
1-9
Chapter 2. Source to Exposure 2-1
2.1. Introduction 2-1
2.2. Sources and Atmospheric Chemistry 2-2
2.2.1. Sources of NOx 2-2
2.2.2. Chemical Transformations 2-4
2.2.2.1. Formation of Nitro-PAHs 2-5
2.2.3. Os Formation 2-6
2.3. Measurement Methods 2-6
2.4. Atmospheric Concentrations 2-8
2.4.1. Ambient Concentrations 2-8
2.4.2. N02 Concentrations 2-22
2.4.3. Seasonal Variability in N02 at Urban Sites 2-23
2.4.4. Diurnal Variability in N02 Concentrations 2-25
2.4.5. Concentrations of NOz Species 2-26
2.4.6. Policy-Relevant Background Concentrations of N02 2-26
2.4.6.1. Analysis of Policy-Relevant Background Contribution 2-26
2.4.7. Summary of Ambient and Policy-Relevant Background Concentrations of N02 2-28
2.5. Exposure Issues 2-28
2.5.1. Introduction 2-28
2.5.2. Personal Sampling of N02 2-31
2.5.3. Spatial Variability in N02 Concentrations 2-32
2.5.3.1. Variability of N02 Concentrations across Ambient Monitoring Sites 2-32
2.5.3.2. Small-Scale Horizontal Variability 2-33
2.5.3.3. Small-Scale Vertical Variability 2-34
2.5.4. N02 On or Near Roads 2-36
2.5.5. Indoor Sources and Sinks of N02 and Associated Pollutants 2-37
2.5.5.1. Indoor Air Chemistry 2-40
2.5.6. Relationship of Personal Exposure to Ambient Concentrations 2-41
2.5.6.1. Associations between Personal Exposure and Ambient and Outdoor Concentrations 2-41
2.5.6.2. Ambient Contribution to Personal Exposure 2-51
2.5.7. N02 as a Component and Indicator of Pollutant Mixtures 2-53
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2.5.7.1. Associations between Ambient N02 and Ambient Copollutants 2-53
2.5.7.2. Associations among N02 and Other Pollutants in Indoor Environments 2-54
2.5.7.3. Personal and Ambient Associations between N02 and Copollutants 2-56
2.5.7.4. N02 as an Indicator of the Mixture of Traffic Pollutants 2-57
2.5.8. Exposure Error in Epidemiologic Studies 2-59
2.5.8.1. Community Time-Series Studies 2-59
2.5.8.2. Long-Term Exposure Studies 2-60
2.5.9. Summary of Issues in Assessing Exposures to N02 2-61
2.6. Dosimetry of Inhaled NOx 2-62
Chapter 3. Integrated Health Effects 3-1
3.1. Respiratory Morbidity Related to Short-Term Exposure 3-2
3.1.1. Lung Host Defenses and Immunity 3-2
3.1.2. Airway Inflammation 3-5
3.1.3. Airway Hyperresponsiveness 3-9
3.1.3.1. Allergen Responsiveness 3-10
3.1.3.2. Nonspecific Responsiveness 3-14
3.1.3.3. Summary of Short-Term Exposure on Airway Responsiveness 3-17
3.1.4. Effects of Short-Term Exposure on Respiratory Symptoms 3-18
3.1.4.1. Indoor and Personal Exposure and Respiratory Outcomes 3-18
3.1.4.2. Ambient N02 Exposure and Respiratory Symptoms 3-23
3.1.4.3. Summary of Short-Term Exposure on Respiratory Symptoms 3-26
3.1.5. Effects of Short-Term Exposure on Lung Function 3-27
3.1.5.1. Epidemiologic Studies of Lung Function 3-27
3.1.5.2. Clinical Studies of Lung Function 3-29
3.1.5.3. Summary of Short-Term Exposure on Lung Function 3-30
3.1.6. Hospital Admissions and ED Visits 3-31
3.1.6.1. All Respiratory Outcomes 3-31
3.1.6.2. Asthma 3-39
3.1.6.3. COPD 3-40
3.1.6.4. Respiratory Diseases Other than Asthma or COPD 3-41
3.1.6.5. Summary of Short-Term Exposure on Respiratory ED Visits and Hospitalizations 3-41
3.1.7. Summary and Integration—Respiratory Health Effects with Short-Term Exposure 3-41
3.2. Cardiovascular Effects Related to Short-Term Exposure 3-43
3.2.1. Heart Rate Variability 3-43
3.2.2. Arrhythmias Recorded on Implanted Defibrillators 3-44
3.2.3. Repolarization Changes 3-44
3.2.4. Markers of Cardiovascular Disease Risk 3-44
3.2.5. Toxicology of Inhaled Nitric Oxide 3-45
3.2.6. Hospital Admissions and ED Visits for CVD 3-45
3.2.7. Cardiac Disease 3-46
3.2.8. Hospital Admissions for Stroke and Cerebrovascular Disease 3-47
Summary of Cardiovascular Effects Related to Short-Term Exposure 3-49
3.3. Mortality Related to Short-Term Exposure 3-49
3.3.1. Multicity Studies and Meta-Analyses 3-49
3.3.1.1. National Morbidity, Mortality, and Air Pollution Study (NMMAPS) 3-50
3.3.1.2. Canadian Multicity Studies 3-50
3.3.1.3. Air Pollution and Health: A European Approach (APHEA) Studies 3-51
3.3.1.4. The Netherlands Study 3-52
3.3.1.5. Other Multicity Studies 3-52
3.3.1.6. Meta-Analyses of N02 Mortality Studies 3-53
3.3.2. Summary of Mortality Related to Short-Term Exposure 3-53
3.4. Respiratory Effects Related to Long-Term Exposure 3-56
3.4.1. Lung Function Growth 3-56
3.4.2. Asthma Prevalence and Incidence 3-62
3.4.3. Respiratory Symptoms 3-63
3.4.4. Respiratory Morphology 3-65
3.4.5. Summary of Respiratory Effects Related to Long-Term Exposure 3-65
3.5. Other Morbidity Effects Related to Long-Term Exposure 3-67
3.5.1. Cancer Incidence 3-67
3.5.1.1. Animal and In Vitro Carcinogenicity and Genotoxicity Studies 3-68
IV
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3.5.1.2. Toxicological Studies of Coexposure with Known Carcinogens 3-68
3.5.1.3. Studies in Animals with Spontaneously High Tumor Rates 3-69
3.5.1.4. Facilitation of Metastases 3-69
3.5.1.5. Production of A/-Nitroso Compounds and other Nitro Derivatives 3-69
3.5.1.6. Summary of Cancer Incidence Related to Long-Term Exposure 3-70
3.5.2. Reproductive and Developmental Effects 3-71
3.5.2.1. Summary of Reproductive and Developmental Effects Related to Long-Term Exposure 3-73
3.5.3. Summary of Other Morbidity Effects Related to Long-Term Exposure 3-73
3.6. Mortality Related to Long-Term Exposure 3-74
3.6.1. U.S. Studies on Mortality Related to Long-Term Exposure 3-74
3.6.2. European Studies on Mortality Related to Long-Term Exposure 3-76
3.6.3. Summary of Mortality Related to Long-Term Exposure 3-78
Chapter 4. Public Health Impact 4-1
4.1. Defining Adverse Health Effects 4-1
4.2. Concentration-Response Functions and Potential Thresholds 4-2
4.3. Susceptible and Vulnerable Populations 4-4
4.3.1. Preexisting Disease as a Potential Risk Factor 4-4
4.3.1.1. Asthmatics 4-4
4.3.1.2. Cardiopulmonary Disease and Diabetes 4-5
4.3.2. Age as a Potential Risk Factor 4-6
4.3.3. Gender as a Potential Risk Factor 4-7
4.3.4. Genetic Factors for Oxidant and Inflammatory Damage 4-7
4.3.5. Other Potentially Susceptible Populations 4-8
4.3.6. Increased Vulnerability Associated with Increased Exposure 4-8
4.4. At-Risk Susceptible Population Estimates 4-9
4.5. Summary of Public Health Issues 4-12
Chapter 5. Summary and Conclusions 5-1
5.1. Introduction 5-1
5.2. Key Source to Exposure Findings 5-2
5.2.1. Atmospheric Science and Ambient Concentrations 5-2
5.2.2. Exposure Assessment 5-3
5.3. Key Health Effects Findings 5-4
5.3.1. Findings from the Previous Review 5-4
5.3.2. New Health Effects Findings 5-4
5.3.2.1. Respiratory Effects Related to Short-Term Exposure 5-6
5.3.2.2. Cardiovascular Effects Related to Short-Term Exposure 5-12
5.3.2.3. Mortality Related to Short-Term Exposure 5-12
5.3.2.4. Respiratory Morbidity Related to Long-Term Exposure 5-12
5.3.2.5. Other Morbidity Related to Long-Term Exposure 5-13
5.3.2.6. Mortality Related to Long-Term Exposure 5-13
5.3.2.7. Exposure Indices 5-14
5.3.2.8. Susceptible and Vulnerable Populations 5-14
5.3.2.9. Concentration-Response Relationships and Thresholds 5-14
5.4. Conclusions 5-15
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List of Tables
Table 1.3-1.
Table 1.3-2.
Table 2.2-1.
Table 2.5-1.
Table 2.5-2.
Table 2.5-3.
Table 2.5-4.
Table 2.5-5.
Table 2.5-6.
Table 2.5-7.
Table 2.5-8.
Table 2.5-9.
Table 2.5-10.
Table 2.5-11.
Table 3.1-1.
Table 3.1-2.
Table 3.1-3.
Table 3.1-4.
Table 4.1-1.
Table 4.4-1.
Table 5.3-1.
Table 5.3-2.
Table 5.3-3.
Table 5.4-1.
Aspects to aid judging causality.
Weight of evidence for causal determination (adapted from Institute of Medicine, 2007).
Annual 2002 average anthropogenic NOX emissions in the U.S. (million metric tons).
Spatial variability of NO2 in selected U.S. urban areas.
NO2 concentration near indoor sources: minute to hour averages.
NO2 concentration near indoor sources: 24-h to 2 week averages.
Association between personal exposure and ambient concentration (longitudinal
correlation coefficients).
Pearson correlation coefficient between ambient NO2 and ambient copollutants. _
Pearson correlation coefficient between ambient NO2 and personal copollutants. _
Pearson correlation coefficient between personal NO2 and ambient copollutants. _
Pearson correlation coefficient between personal NO2 and personal copollutants.
Pearson correlation coefficient between NOX and traffic-generated pollutants.
Proposed mechanisms whereby NO2 and respiratory virus infections may exacerbate
upper and lower airway symptoms.
Changes in airway responsiveness associated with NO2 exposure.
Fraction of NO2-exposed asthmatics with increased non-specific airway
hyperresponsiveness.
Mean rates (SD) per 100 days at risk and unadjusted rate ratio (RR) for
symptoms/activities over 12 weeks during the winter heating period.
Gradation of individual responses to short-term NO2 exposure in persons with impaired
respiratory systems.
Summary of evidence from epidemiologic, human clinical, and animal toxicological studies
on the health effects associated with short- and long-term exposure to NO2.
Key studies and effects of exposure to NO2 from clinical studies.
Summary of toxicological effects in rats from NO2 exposure.
1-7
1-9
2-2
Association between personal exposure and ambient concentration (pooled correlation
coefficients).
Association between personal exposure and outdoor concentration.
2-33
2-39
2-39
2-42
2-43
2-44
2-54
2-56
2-56
2-57
2-57
3-3
3-15
3-16
3-19
4-2
Prevalence of selected respiratory disorders by age group and by geographic region in the
U.S.(2004 [U.S. Adults] and 2005 [U.S. Children] National Health Interview Survey).
4-10
_ 5-5
5-9
Ambient NO2 concentrations and selected effect estimates from studies of respiratory
symptoms, ED visits and hospital admissions in the U.S. and Canada.
5-10
5-17
VII
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List of Figures
Figure 1.3-1.
Figure 1.3-2.
Figure 2.1-1.
Figure 2.2-1.
Figure 2.4-1.
Figure 2.4-2.
Figure 2.4-3.
Figure 2.4-4.
Figure 2.4-5.
Figure 2.4-6.
Figure 2.4-7.
Figure 2.4-8.
Figure 2.4-9.
Figure 2.4-10.
Figure 2.4-11.
Figure 2.4-12.
Figure 2.4-13.
Figure 2.4-14.
Figure 2.4-15.
Figure 2.4-16.
Exposure-disease-stress model for environmental health disparities.
Potential relationships of NOX with adverse health effects.
A generalized conceptual model for integrating research on NOX pollution and human
health effects.
Schematic diagram of the cycle of reactive oxidized N species in the atmosphere.
Location of ambient NO? monitors in the U.S. as of November 5, 2007.
NO2 monitor locations in the Atlanta, GA CMSA shown in relation to major roadways,
point-source electric generating units, and population densities for total population, and
fractions < 17 years and > 65 years.
NO2 monitor locations in the Boston, MA CMSA shown in relation to major roadways,
point-source electric generating units, and population densities for total population, and
fractions < 17 years and > 65 years.
NO2 monitor locations in the Chicago, IL CMSA shown in relation to major roadways,
point-source electric generating units, and population densities for total population, and
fractions < 17 years and > 65 years.
NO2 monitor locations in the Houston, TX CMSA shown in relation to major roadways,
point-source electric generating units, and population densities for total population, and
fractions < 17 years and > 65 years.
NO2 monitor locations in the Los Angeles, CA CMSA shown in relation to major roadways,
point-source electric generating units, and population densities for total population, and
fractions < 17 years and > 65 years.
Detail of NO2 monitor locations in the Los Angeles, CA CMSA shown in relation to major
roadways, point-source electric generating units, and total population density.
NO2 monitor locations in the New York City, NY and Philadelphia, PA CMSAs shown in
relation to major roadways, point-source electric generating units, and population densities
for total population, and fractions < 17 years and > 65 years.
Detail of NO2 monitor locations in the New York City, NY and Philadelphia, PA CMSAs
shown in relation to major roadways, point-source electric generating units, and total
population density.
NO2 monitor locations in the Steubenville, OH CMSA shown in relation to major roadways,
point-source electric generating units, and population densities for total population, and
fractions < 17 years and > 65 years.
NO2 monitor locations in the Washington, DC and Baltimore, MD CMSAs shown in relation
to major roadways, point-source electric generating units, and population densities for total
population, and fractions < 17 years and > 65 years.
Detail of NO2 monitor locations in the Washington, DC and Baltimore, MD CMSAs shown
in relation to major roadways, point-source electric generating units, and total population
density.
Ambient concentrations of NO2 measured at all monitoring sites located within MSAs in
the U.S. from 2003 through 2005.
Monthly average NO2 concentrations in ppb for January 2002 (left panel) and July 2002
(right panel) calculated by CMAQ
Nationwide trend in NO? concentrations.
Time series of 24-h avg NO2 concentrations at individual sites in Atlanta, GA from 2003
through 2005.
1-4
1-6
2-1
2-3
2-9
2-10
2-11
2-12
2-13
2-14
2-15
2-16
2-17
2-18
2-19
2-20
2-21
2-22
2-23
2-24
IX
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Figure 2.4-17.
Figure 2.4-18.
Figure 2.5-1.
Figure 2.5-2.
Figure 2.5-3.
Figure 2.5-4.
Figure 2.5-5.
Figure 2.5-6.
Figure 2.5-7.
Figure 3.1-1.
Figure 3.1-2.
Figure 3.1-3.
Figure 3.1-4.
Figure 3.1-5.
Figure 3.1-6.
Figure 3.1-7.
Figure 3.1-8.
Figure 3.1-9.
Figure 3.1-10.
Mean hourly NO2 concentrations on weekdays and weekends measured at two sites in
Atlanta, GA.
Upper panel: Annual mean NO2 concentrations (in ppb) in the U.S. Middle panel: Annual
mean PRB concentrations (in ppb) for NO? in the U.S.
Percentage of time people spend in different environments in the U.S.
NO2 and NOX concentrations normalized to ambient values, plotted as a function of
downwind distance from the freeway.
NO2 concentrations measured at 4 m (Van) and at 15 m at NY Department of
Environmental Conservation ambient monitorinq sites
Distribution of correlation coefficients (U.S. studies) between personal NO2 exposure and
ambient NO? concentrations based on Fisher's Z transform.
Distribution of correlation coefficients (European studies) between personal NO2 exposure
and ambient NO? concentrations based on Fisher's Z transform.
Correlations of NO2 to O3 versus correlations of NO2 to CO for Los Angeles, CA
(2001-2005).
Composite, diurnal variability in 1-h avq NO? in urban areas.
Studies of airway inflammatory responses in relation to the total exposure to NO2,
expressed as ppm-minutes.
Airway responsiveness to allergen challenge in asthmatic subjects following a single
exposure to NO?.
Geometric mean symptom rates (95% Cl) for cough with phlegm (panel A) and proportions
(95% Cl) of children absent from school
Adjusted association of increasing indoor NO2 concentrations with number of days with
persistent cough (panel a) or shortness of breath (panel b) for 762 infants during the first
year of life.
Odds ratios (95%CI) for daily asthma symptoms (panel A) and rate ratios (95% Cl) for
daily rescue inhaler use
Odds ratios (95% Cl) for associations between asthma symptoms in children and 24-h
average NO? concentrations (per 20 ppb).
Odds ratios and 95% Cl for associations between asthma symptoms and 24-h average
NO? concentrations (per 20 ppb) from multipollutant models.
Relative risks (95% Cl) for hospital admissions or ED visits for all respiratory disease
stratified by all aqes or children.
Relative Risks (95% Cl) for hospital admissions or ED visits for all respiratory disease
stratified by adults or older adults (> 65 years).
Relative risks (95% Cl) for hospital admissions or emergency department visits for all
2-25
2-27
2-29
2-34
2-35
2-47
2-48
2-55
2-58
3-7
3-11
3-20
3-22
3-24
3-26
3-27
3-32
3-33
respiratory causes, standardized from two-pollutant models adjusted for particle
concentration. 3-34
Figure 3.1-11. Relative risks (95% Cl) for hospital admissions or emergency department visits for all
respiratory causes, standardized from two-pollutant models adjusted for gaseous pollutant
concentration. 3-35
Figure 3.1-12. Relative Risks (95% Cl) for hospital admissions or emergency department visits for
asthma stratified by all ages or children. 3-36
Figure 3.1-13. Relative risks (95% Cl) for hospital admissions or emergency department visits for asthma
stratified by adults and older adults (> 65 years). 3-37
Figure 3.2-1. Relative risks (95% Cl) for associations of 24-h NO2 (per 20 ppb) and daily 1-h max* NO2
(per 30 ppb) with hospitalizations or emergency department visits for cardiac diseases. 3-47
Figure 3.2-2. 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 all cerebrovascular disease. 3-48
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Figure 3.3-1. Posterior means and 95% posterior intervals of national average estimates for NO2 effects
on total mortality from nonexternal causes at lags 0, 1, and 2 within sets of the 90 cities
with pollutant data available. 3-50
Figure 3.3-2. Combined NO2 mortality risk estimates from multicity and meta-analysis studies. 3-54
Figure 3.3-3. Combined NO2 mortality risk estimates for broad cause-specific categories from multicity
studies. 3-55
Figure 3.4-1. Decrements in forced expiratory volume in 1 s (FENA,) associated with a 20-ppb increase in
NO2 (A) and a 20-ug/m3 increase in PM10 (B) in children, standardized per year of
follow-up. 3-57
Figure 3.4-2. Decrements in FVC associated with a 20-ppb increase in NO2 (A) and a 20-ug/m3
increase in PM10 (B) in children, standardized per year of follow-up. 3-58
Figure 3.4-3. Proportion of 18-year olds with a FEV-i below 80% of the predicted value plotted against
the average levels of pollutants from 1994 through 2000 in the 12 southern California
communities of the Children's Health Study. 3-59
Figure 3.4-4. Estimated annual growth in FEV^ of O3, PM10, and NO2 in girls and boys. 3-60
Figure 3.4-5. Odds ratios for within-community bronchitis symptoms associations with NO2, adjusted for
other pollutants in two-pollutant models for the 12 communities of the Children's Health
Study. 3-64
Figure 3.4-6. Biological pathways of long-term NO2 exposure on morbidity. 3-66
Figure 3.6-1. Age-adjusted, nonparametric smoothed relationship between NO2 and mortality from all
causes in Oslo, Norway, 1992 through 1995. 3-78
Figure 3.6-2. Total mortality relative risk estimates from long-term studies. 3-79
Figure 4.4-1. Fraction of the study populations living within a specified distance from roadways. 4-11
Figure 5.3-1. Summary of epidemiologic studies examining short-term exposures to ambient NO2 and
respiratory outcomes. 5-7
XI
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Acronyms and Abbreviations
1NP 1-nitropyrene
2NF 2-nitrofluoranthene
2NP 2-nitropyrene
a Alpha
ACS American Cancer Society
a, air exchange rate (for microenvironment /')
AIRE (Italian Study) Asma Infantile Ricerca in Emilia-Romagna
AM alveolar macrophages
APEX Air Pollution Exposure (model)
APHEA Air Pollution on Health: a European Approach (study)
AQCD Air Quality Criteria Document
AQS EPA's Air Quality System
ATS American Thoracic Society
P beta; the calculated health effect parameter
BAL bronchoalveolar lavage
BALF bronchoalveolar lavage fluid
BHPN TV-bis (2-hydroxy-propyl) nitrosamine
BLF bronchial lavage fluid
Br bromide
BTEX benzene, toluene, ethylbenzene, and o-, m-, p-xylene
C carbon
CAA Clean Air Act
CALINE4 California line source dispersion (model)
CAMP Childhood Asthma Management Program
CAP(s) concentrated ambient particle(s)
CARB California Air Resources Board
CASAC Clean Air Scientific Advisory Committee
CASTNet Clean Air Status and Trends Network
CDC Centers for Disease Control and Prevention
CDPFs catalyzed diesel particle filters
CFD computational fluid dynamics
CH4 methane
CHAD Consolidated Human Activities Database
CHF congestive heart failure
CHS Children's Health Study
CI confidence interval
CMAQ EPA's Community Multiscale Air Quality (CMAQ) model
CMSA consolidated metropolitan statistical area
CO carbon monoxide
CO2 carbon dioxide
XIII
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COD coefficient of divergence
CoH coefficient of haze
COPD chronic obstructive pulmonary disease
CRP C-reactive protein
CTM chemical transport model
CVD cardiovascular disease
DEP diesel exhaust particle
DEPcCBP diesel exhaust particle extract-coated carbon black particles
DMA dimethylamine
DMN dimethylnitrosamine
DOAS differential optical absorption spectroscopy
EC elemental carbon
ECP eosinophil cationic protein
ED emergency department
ECG electrocardiography; electrocardiogram
EGU(s) electricity generating unit(s)
ELF epithelial lining fluid
EPA U.S. Environmental Protection Agency
EPO eosinophil peroxidase
ETS environmental tobacco smoke
FEF forced expiratory flow
FEF75 forced expiratory flow after exhaling 75% of FVC
FEF25-75 average forced expiratory flow over middle 50% of FVC
FeNO fractional exhaled NO
FEMs Federal Equivalent Methods
FEVo.5 forced expiratory volume in 0.5 seconds
FEVi forced expiratory volume in 1 second
Fmf. infiltration factor (for microenvironment /')
FRM Federal Reference Method
FVC forced vital capacity
GAM Generalized Additive Model(s)
GIS Geographic Information System
GLM Generalized Linear Model(s)
GSH glutathione; reduced glutathione
GST glutathione S-transferase (e.g., GSTM1, GSTP1, GSTT1)
H+ hydrogen ion
H2O water
H2SO4 sulfuric acid
HCHO formaldehyde
HDL high-density lipoprotein cholesterol
HNO3 nitric acid
HONO nitrous acid
HR heart rate
HRV heart rate variability
XIV
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HS hemorrhagic stroke
H2S hydrogen sulfide
HSO4 bisulfate ion
H2SO4 sulfuric acid
hv solar ultraviolet photon
/ microenvironment
IARC International Agency for Research on Cancer
1C AM-1 intercellular adhesion molecule-1
ICD9 International Classification of Diseases, Ninth Revision
ICDs implanted cardioverter defibrillators
Ig immunoglobulin (e.g., IgA, IgE, IgG)
IHD ischemic heart disease
IIASA International Institute for Applied Systems Analysis
IL interleukin (e.g., IL-4, IL-6, IL-8)
IOM Institute of Medicine
IQR interquartile range
IS ischemic stroke
ISA Integrated Science Assessment
ISAAC International Study of Asthma and Allergies in Children
K mass transfer coefficient
k, decay rate (for microenvironment i)
KI potassium iodide
LIF laser induced fluorescence
LOESS locally estimated smoothing splines
LRD lower respiratory disease
MEF25 maximal expiratory flow at 25%
MEF50 maximal expiratory flow at 50%
MENTOR Modeling Environment for Total Risk for One-Atmosphere
MI myocardial infarction
MMEF maximal midexpiratory flow
MoOx molybdenum oxide
MOZART-2 Model for Ozone and Related Chemical Tracers, version 2
MPP multiphase processes
MSA metropolitan statistical area
N, n number of observations
NAAQS National Ambient Air Quality Standards
NaAsO2 sodium arsenite
NAL inflammatory nasal lavage markers
NAMS National Air Monitoring Stations
NAPAP National Acid Precipitation Assessment Program
NAS National Academy of Sciences
NCEA National Center for Environmental Assessment
NCEP National Center for Environmental Prediction
NCICAS National Cooperative Inner-City Asthma Study
xv
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NDMA jV-nitrosodimethylamine
NEI National Emissions Inventory
NERL National Exposure Research Laboratory
NHAPS National Human Activity Pattern Survey
nitro-PAHs nitro-polycylic aromatic hydrocarbons
NK natural killer cells
NLCS Netherlands Cohort Study on Diet and Cancer
NMMAPS National Morbidity, Mortality, and Air Pollution Study
NMOR TV-nitrosomorpholine
NN nitronapthalene
NO nitric oxide
NO2- nitrite
NO2 nitrogen dioxide
NO3- nitrate ion
NO3 nitrate radical
NOAA National Oceanic and Atmospheric Administration
NOX oxides of nitrogen
NOY total oxidized nitrogen
NOZ oxidized N species
NR not reported
NRC National Research Council
NSA nitrosating agent
O3 ozone
OAQPS Office of Air Quality Planning and Standards
OC organic carbon
OH hydroxyl radical
OR odds ratio
ORD Office of Research and Development
OVA ovalbumin
P, p probability value
PAARC Air Pollution and Chronic Respiratory Diseases (study)
PAF paroxysmal atrial fibrillation
PAH(s) polycyclic aromatic hydrocarbon(s)
PAMS Photochemical Assessment Monitoring Stations
PAN(s) peroxyacyl nitrate(s)
Pb lead
PD20 provocative dose that produces a 20% decrease in FEVi
PD100 provocative dose that produces a 100% increase in sRAW
PEACE Pollution Effects on Asthmatic Children in Europe (study)
PEF peak expiratory flow
P, penetration coefficient (for microenvironment /')
PIH primary intracerebral hemorrhage
PM paniculate matter
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PM2 5 paniculate matter with 50% upper cut point aerodynamic diameter of
2.5 um for sample collection; surrogate for fine PM
PM10 paniculate matter with 50% upper cut point aerodynamic diameter of
10 umfor sample collection
PM10-2.5 paniculate matter with 10 um as upper cut point aerodynamic diameter
and 2.5 um as lower cut point for sample collection; surrogate for
thoracic coarse PM (does not include fine PM)
PMN(s) polymorphonuclear leukocyte(s)
PNO3" paniculate nitrate
POM paniculate organic matter
ppb parts per billion
ppm parts per million
ppt parts per trillion
PRB policy-relevant background
PT prothrombin time
PS passive sample
R, r correlation coefficient
RADS reactive airway dysfunction syndrome
RAPS (St. Louis) Regional Air Pollution Study
RCS random component superposition (model)
RONO2 organic nitrates
RR rate ratio; relative risk
RSV respiratory syncytial virus
RTF Research Triangle Park
S APALDIA Study of Air Pollution and Lung Diseases in Adults
SCC Source Classification Codes
SCE(s) sister chromatid exchange(s)
SD standard deviation
SES socioeconomic status
SHEDS Simulation of Human Exposure and Dose System
SIDS sudden infant death syndrome
SNP single nucleotide polymorphism3^ sulfur-35 radionuclide
SLAMS State and Local Air Monitoring Stations
SO sulfur monoxide
SO2 sulfur dioxide
SO42~ sulfate ion
SOX sulfur oxides
sRaw specific airway resistance
STN Speciation Trends Network
T tau; atmospheric lifetime
TEA triethanolamine
TNF tumor necrosis factor (e.g., TNF-a)
TSP total suspended particles
UFP Ultrafine particles (<100 nm)
URI upper respiratory infections
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UV ultraviolet
VIII-C factor Classic hemophilia A (factor VIII: C deficiency)
VOC(s) volatile organic compound(s)
VWF von Willibrand Factor
WBC white blood cell
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Authors, Contributors, Reviewers
Authors
Dr. Dennis J. Kotchmar (NOX Team Leader)—National Center for Environmental Assessment, U.S.
Environmental Protection Agency, Research Triangle Park, NC
Dr. Thomas J. Luben (NOX Team Leader)—National Center for Environmental Assessment,
U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Ila L. Cote (Acting Division Director)—National Center for Environmental Assessment, U.S.
Environmental Protection Agency, Research Triangle Park, NC
Ms. Debra B. Walsh (Deputy Division Director)—National Center for Environmental Assessment, U.S.
Environmental Protection Agency, Research Triangle Park, NC
Dr. Mary A. Ross (Branch Chief)—National Center for Environmental Assessment, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Dr. Jeffrey Arnold—National Center for Environmental Assessment, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Mr. Chad Bailey—Office of Air Quality and Transportation, U.S. Environmental Protection Agency, Ann
Arbor, MI
Dr. Kathleen Belanger, Yale University, Epidemiology and Public Health, New Haven, CT
Dr. James S. Brown—National Center for Environmental Assessment, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Dr. Douglas Bryant—Cantox Environmental Inc., Mississauga, Ontario Canada
Mr. Allen Davis— Oak Ridge Institute for Science and Education, Research Fellow to National Center for
Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Mark Frampton—Strong Memorial Hospital, Rochester, NY
Dr. Janneane Gent—Yale University, CPPEE, New Haven, CT
Dr. Vic Hasselblad—Duke University, Durham, NC
Dr. Kazuhiko Ito—New York University School of Medicine, Tuxedo, NY
Dr. Jee Young Kim—National Center for Environmental Assessment, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Dr. Ellen F. Kirrane—National Center for Environmental Assessment, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Dr. Thomas C. Long—National Center for Environmental Assessment, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Dr. Andrew Maier—Toxicology Excellence for Risk Assessment, Cincinnati, OH
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Dr. Qingyu Meng—Oak Ridge Institute for Science and Education, Postdoctoral Research Fellow to
National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research
Triangle Park, NC
Dr. Joseph P. Pinto—National Center for Environmental Assessment, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Dr. Paul G. Reinhart—National Center for Environmental Assessment, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Dr. Joseph Somers—Office of Air Quality and Transportation, U.S. Environmental Protection Agency,
Ann Arbor, MI
Dr. David J. Svendsgaard—National Center for Environmental Assessment, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Dr. Lori D. White—National Center for Environmental Assessment, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Contributors
Dr. Dale Allen—Department of Atmospheric and Oceanic Sciences, University of Maryland, College
Park, MD
Ms. Louise Camalier—Office of Air Quality Planning and Standards, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Ms. Rebecca Daniels—National Center for Environmental Assessment, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Dr. Russell Dickerson—Department of Atmospheric and Oceanic Science, University of Maryland,
College Park, MD
Dr. Tina Fan—Environmental and Occupational Health Sciences Institute, Piscataway, NJ
Mr. William Keene—Department of Environmental Sciences, University of Virginia, Charlottesville, VA
Dr. Randall Martin—Department of Physics and Atmospheric Science, Dalhousie University, Halifax,
Nova Scotia, Canada
Dr. Maria Morandi—Department of Environmental Sciences, School of Public Health, University of
Texas-Houston Health Science Center, Houston, TX
Dr. William Munger—Division of Engineering and Applied Sciences, Harvard University, Cambridge,
MA
Mr. Charles Piety—Department of Meteorology, University of Maryland, College Park, MD
Mr. Jason Sacks—National Center for Environmental Assessment, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Dr. Sandy Sillman—Department of Atmospheric, Ocean, and Space Sciences, University of Michigan,
Ann Arbor, MI
Dr. Helen Suh—Department of Environmental Health, Harvard School of Public Health, Boston, MA
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Dr. Charles Wechsler—Environmental and Occupational Health Sciences Institute, Piscataway, NJ
Dr. Clifford Weisel—Environmental and Occupational Health Sciences Institute, Piscataway, NJ
Dr. Jim Zhang—Environmental and Occupational Health Sciences Institute, Piscataway, NJ
Reviewers
Dr. Tina Bahadori—American Chemistry Council, Arlington, VA
Dr. Timothy Benner—Office of Science Policy, U.S. Environmental Protection Agency, Washington, DC
Dr. Daniel L. Costa—National Program Director for Air, U.S. Environmental Protection Agency,
Research Triangle Park, NC
Dr. Robert B. Devlin—National Health and Environmental Effects Research Laboratory, U.S.
Environmental Protection Agency, Chapel Hill, NC
Dr. Judy Graham—American Chemistry Council, Arlington, VA
Dr. Stephen Graham—Office of Air Quality Planning and Standards, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Ms. Beth Hassett-Sipple—Office of Air Quality Planning and Standards, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Dr. Gary E. Hatch—National Health and Environmental Effects Research Laboratory, U.S.
Environmental Protection Agency, Research Triangle Park, NC
Dr. Scott Jenkins—Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency,
Research Triangle Park, NC
Dr. David D. Kryak—National Exposure Research Laboratory, U.S. Environmental Protection Agency,
Research Triangle Park, NC
Mr. John Langstaff—Office of Air Quality Planning and Standards, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Dr. Morton Lippmann—Department of Environmental Medicine, New York University School of
Medicine, Tuxedo, NY
Dr. Karen Martin—Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency,
Research Triangle Park, NC
Dr. William McDonnell—William F. McDonnell Consulting, Chapel Hill, NC
Dr. Dave McKee—Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency,
Research Triangle Park, NC
Dr. Lucas M. Neas—National Health and Environmental Effects Research Laboratory, U.S.
Environmental Protection Agency, Chapel Hill, NC
Dr. Russell D. Owen—National Health and Environmental Effects Research Laboratory, U.S.
Environmental Protection Agency, Research Triangle Park, NC
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Dr. Haluk A. Ozkaynak—National Exposure Research Laboratory, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Dr. Jennifer Peel—Department of Environmental and Radiological Health Sciences, Colorado State
University, Fort Collins, CO
Mr. Harvey Richmond—Office of Air Quality Planning and Standards, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Mr. Steven Silverman—Office of General Counsel, U.S. Environmental Protection Agency, Washington,
DC
Dr. Michael Stewart—Office of Air Quality Planning and Standards, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Ms. Susan Stone—Office of Air Quality Planning and Standards, Office of Air and Radiation, U.S.
Environmental Protection Agency, Research Triangle Park, NC
Ms. Chris Trent—Office of Air Quality Planning and Standards, Office of Air and Radiation, U.S.
Environmental Protection Agency, Research Triangle Park, NC
Dr. John J. Vandenberg—National Center for Environmental Assessment, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Dr. Alan F. Vette—National Exposure Research Laboratory, U.S. Environmental Protection Agency,
Research Triangle Park, NC
Ms. Debra B. Walsh—National Center for Environmental Assessment, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Mr. Ronald W. Williams—National Exposure Research Laboratory, U.S. Environmental Protection
Agency, Research Triangle Park, NC
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NOx Project Team
Executive Direction
Dr. Ila L. Cote (Acting Director)—National Center for Environmental Assessment, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Ms. Debra B. Walsh (Deputy Director)—National Center for Environmental Assessment, U.S.
Environmental Protection Agency, Research Triangle Park, NC
Dr. Mary A. Ross—National Center for Environmental Assessment, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Scientific Staff
Dr. Dennis J. Kotchmar (NOxTeam Leader)—National Center for Environmental Assessment, U.S.
Environmental Protection Agency, Research Triangle Park, NC
Dr. Thomas J. Luben (NOxTeam Leader)—National Center for Environmental Assessment, U.S.
Environmental Protection Agency, Research Triangle Park, NC
Dr. Jeffrey Arnold—National Center for Environmental Assessment, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Dr. James S. Brown—National Center for Environmental Assessment, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Dr. Barbara Buckley—National Center for Environmental Assessment, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Dr. Douglas Johns—National Center for Environmental Assessment, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Dr. Jee-Young Kim—National Center for Environmental Assessment, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Dr. Ellen F. Kirrane—National Center for Environmental Assessment, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Dr. Thomas C. Long—National Center for Environmental Assessment, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Dr. Qingyu Meng—Oak Ridge Institute for Science and Education, Postdoctoral Research Fellow to
National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research
Triangle Park, NC
Dr. Joseph P. Pinto—National Center for Environmental Assessment, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Mr. Jason Sacks—National Center for Environmental Assessment, U.S. Environmental Protection
Agency, Research Triangle Park, NC
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Dr. David J. Svendsgaard—National Center for Environmental Assessment, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Dr. Lori D. White—National Center for Environmental Assessment, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Dr. William W. Wilson—National Center for Environmental Assessment, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Technical Support Staff
Ms. Ellen Lorang—Information Manager, National Center for Environmental Assessment, U.S.
Environmental Protection Agency, Research Triangle Park, NC
Ms. Connie A. Meacham—Biologist, National Center for Environmental Assessment, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Ms. Christine M. Searles—Management Analyst, National Center for Environmental Assessment, U.S.
Environmental Protection Agency, Research Triangle Park, NC
Ms. Deborah A. Wales—Information Services Specialist, National Center for Environmental Assessment,
U.S. Environmental Protection Agency, Research Triangle Park, NC
Mr. Richard N. Wilson—Clerk, National Center for Environmental Assessment, U.S. Environmental
Protection Agency, Research Triangle Park, NC
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Clean Air Scientific Advisory Committee
Oxides of Nitrogen
Primary NAAQS Review Panel
Chairperson
Dr. Rogene Henderson*, Scientist Emeritus, Lovelace Respiratory Research Institute, Albuquerque, NM
Members
Mr. Ed Avol, Professor, Preventive Medicine, Keck School of Medicine, University of Southern
California, Los Angeles, CA
Dr. John R. Balmes, Professor, Department of Medicine, Division of Occupational and Environmental
Medicine, University of California, San Francisco, CA
Dr. Ellis Cowling*, University Distinguished Professor At-Large, North Carolina State University,
Colleges of Natural Resources and Agriculture and Life Sciences, North Carolina State University,
Raleigh, NC
Dr. James D. Crapo*, Professor, Department of Medicine, National Jewish Medical and Research
Center, Denver, CO
Dr. Douglas Crawford-Brown*, Director, Carolina Environmental Program; Professor, Environmental
Sciences and Engineering; and Professor, Public Policy, Department of Environmental Sciences and
Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC
Dr. Terry Gordon, Professor, Environmental Medicine, NYU School of Medicine, Tuxedo, NY
Dr. Dale Hattis, Research Professor, Center for Technology, Environment, and Development, George
Perkins Marsh Institute, Clark University, Worcester, MA
Dr. Donna Kenski, Data Analyst, Lake Michigan Air Directors Consortium, Des Plaines, IL
Dr. Patrick Kinney, Associate Professor, Department of Environmental Health Sciences, Mailman
School of Public Health, Columbia University, New York, NY
Dr. Steven Kleeberger, Professor, Laboratory Chief, Laboratory of Respiratory Biology, NIH/NIEHS,
Research Triangle Park, NC
Dr. Timothy Larson, Professor, Department of Civil and Environmental Engineering, University of
Washington, Seattle, WA
Dr. Kent Pinkerton, Professor, Regents of the University of California, Center for Health and the
Environment, University of California, Davis, CA
Mr. Richard L. Poirot*, Environmental Analyst, Air Pollution Control Division, Department of
Environmental Conservation, Vermont Agency of Natural Resources, Waterbury, VT
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Dr. Edward Postlethwait, Professor and Chair, Department of Environmental Health Sciences, School of
Public Health, University of Alabama at Birmingham, Birmingham, AL
Dr. Armistead (Ted) Russell*, Georgia Power Distinguished Professor of Environmental Engineering,
Environmental Engineering Group, School of Civil and Environmental Engineering, Georgia Institute of
Technology, Atlanta, GA
Dr. Jonathan M. Samet, Professor and Chair of the Department of Epidemiology, Bloomberg School of
Public Health, Johns Hopkins University, Baltimore, MD
Dr. Richard Schlesinger, Associate Dean, Department of Biology, Dyson College, Pace University, New
York, NY
Dr. Christian Seigneur, Vice President, Atmospheric and Environmental Research, Inc., San Ramon, CA
Dr. Elizabeth A. (Lianne) Sheppard, Research Professor, Biostatistics and Environmental &
Occupational Health Sciences, Public Health and Community Medicine, University of Washington,
Seattle, WA
Dr. Frank Speizer*, Edward Kass Professor of Medicine, Channing Laboratory, Harvard Medical
School, Boston, MA
Dr. George Thurston, Associate Professor, Environmental Medicine, NYU School of Medicine, New
York University, Tuxedo, NY
Dr. James Ultman, Professor, Chemical Engineering, Bioengineering Program, Pennsylvania State
University, University Park, PA
Dr. Ronald Wyzga, Technical Executive, Air Quality Health and Risk, Electric Power Research Institute,
Palo Alto, CA
SCIENCE ADVISORY BOARD STAFF
Dr. Angela Nugent, CASAC Designated Federal Officer
1200 Pennsylvania Avenue, N.W.
Washington, DC, 20460
Phone: 202-343-9981
Fax: 202-233-0643
E-mail: nugent.angela@epa.gov
Physical/Courier/FedEx Address:
Angela Nugent, Ph.D,
EPA Science Advisory Board Staff Office
Mail Code 1400F
Woodies Building, Room 3614
1025 F Street, N.W.
Washington, DC 20004
Telephone: 202-343-9981)
* Members of the statutory Clean Air Scientific Advisory Committee (CASAC) appointed by the EPA Administrator
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Preface
National Ambient Air Quality Standards (NAAQS) are promulgated by the U.S. Environmental
Protection Agency (EPA) to meet requirements set forth in Sections 108 and 109 of the Clean Air Act
(CAA). These sections require the EPA Administrator (1) to list widespread air pollutants that reasonably
may be expected to endanger public health or welfare; (2) to issue air quality criteria that assess the latest
available scientific information on the nature and effects of ambient exposure to the criteria pollutants;
(3) to set "primary" NAAQS to protect human health with adequate margin of safety and to set
"secondary" NAAQS to protect against welfare effects (e.g., effects on vegetation, ecosystems, visibility,
climate, manmade materials, etc); and (4) to periodically review and revise, as appropriate, the criteria
and NAAQS for a given listed pollutant or class of pollutants.
The purpose of this Integrated Science Assessment (ISA) for Oxides of Nitrogen (NOX) - Health
Criteria is to critically evaluate and assess the latest scientific information published since the 1993 NOX
Air Quality Criteria Document (AQCD), with the main focus on pertinent new information useful in
evaluating health effects data associated with ambient air nitrogen oxides exposures. A First External
Review Draft of this ISA (dated August 2007) was released for public comment and was reviewed by the
Clean Air Scientific Advisory Committee (CASAC) in October 2007; a Second External Review Draft
was made available to the public in March 2008. Public comments and CASAC recommendations have
been taken into account in making revisions to the document for incorporation into this final ISA. This
document will provide inputs to the risk and exposure analyses prepared by EPA's Office of Air Quality
Planning and Standards (OAQPS), which will lead to the proposal and, ultimately, promulgation of
decisions on potential retention or revision, as appropriate, of the current Nitrogen Dioxide (NO2)
NAAQS by the EPA Administrator.
Preparation of this document was coordinated by staff of EPA's National Center for Environmental
Assessment in Research Triangle Park (NCEA-RTP). NCEA-RTP scientific staff, together with experts
from other EPA/Office of Research and Development (ORD) laboratories and academia, contributed to
writing of document chapters. Earlier drafts of document materials were reviewed by non-EPA experts in
peer consultation workshops held by EPA. This ISA describes the nature, sources, distribution,
measurement, and concentrations of nitrogen oxides in outdoor (ambient) and indoor environments. It
also evaluates the latest data on human exposures to ambient nitrogen oxides and consequent health
effects in exposed human populations, to support decision making regarding the primary (health-based)
NO2 NAAQS.
NCEA acknowledges the valuable contributions provided by authors, contributors, and reviewers
and the diligence of its staff in the preparation of this document.
Legislative Requirements
Two sections of the CAA govern the establishment and revision of the NAAQS. Section 108 (U.S.
Code, 2003a) directs the Administrator to identify and list "air pollutants" that "in his judgment, may
reasonably be anticipated to endanger public health and welfare" and whose "presence in the ambient air
results from numerous or diverse mobile or stationary sources" and to issue air quality criteria for those
that are listed. Air quality criteria are intended to "accurately reflect the latest scientific knowledge useful
in indicating the kind and extent of identifiable effects on public health or welfare which may be expected
from the presence of [a] pollutant in ambient air."
Section 109 (U.S. Code, 2003b) directs the Administrator to propose and promulgate "primary" and
"secondary" NAAQS for pollutants listed under Section 108. Section 109(b)(l) defines a primary
standard as one "the attainment and maintenance of which in the judgment of the Administrator, based on
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such criteria and allowing an adequate margin of safety, are requisite to protect the public health."1 A
secondary standard, as defined in Section 109(b)(2), must "specify a level of air quality the attainment
and maintenance of which, in the judgment of the Administrator, based on such criteria, is required to
protect the public welfare from any known or anticipated adverse effects associated with the presence of
[the] pollutant in the ambient air."2
The requirement that primary standards include an adequate margin of safety was intended to
address uncertainties associated with inconclusive scientific and technical information available at the
time of standard setting. It was also intended to provide a reasonable degree of protection against hazards
that research has not yet identified. See Lead Industries Association v. EPA, 647 F.2d 1130, 1154 (B.C.
Cir 1980), cert, denied, 449 U.S. 1042 (1980); American Petroleum Institute v. Costle, 665 F.2d 1176,
1186 (D.C. Cir. 1981), cert, denied, 455 U.S. 1034 (1982). Both kinds of uncertainties are components of
the risk associated with pollution at levels below those at which human health effects can be said to occur
with reasonable scientific certainty. Thus, in selecting primary standards that include an adequate margin
of safety, the Administrator seeks to limit pollution levels demonstrated to be harmful as well as lower
pollutant levels that may pose an unacceptable risk of harm, even if the nature or degree of risk is not
precisely identified.
In selecting a margin of safety, EPA considers such factors as the nature and severity of the health
effects involved, the size of sensitive population(s) at risk, and the kind and degree of the uncertainties
that must be addressed. The selection of any particular approach to providing an adequate margin of
safety is a policy choice left specifically to the Administrator's judgment. See Lead Industries Association
v. EPA, supra, 647 F.2d at 1161-62.
In setting standards that are "requisite" to protect public health and welfare, as provided in Section
109(b), EPA's task is to establish standards that are neither more nor less stringent than necessary for
these purposes. In so doing, EPA may not consider the costs of implementing the standards. See generally
Whitman v. American Trucking Associations, 531 U.S. 457, 465-472, and 475-76 (U.S. Supreme Court,
2001).
Section 109(d)(l) requires that "not later than December 31, 1980, and at 5-year intervals
thereafter, the Administrator shall complete a thorough review of the criteria published under Section 108
and the national ambient air quality standards and shall make such revisions in such criteria and standards
and promulgate such new standards as may be appropriate..." Section 109(d)(2) requires that an
independent scientific review committee "shall complete a review of the criteria... and the national
primary and secondary ambient air quality standards... and shall recommend to the Administrator any new
standards and revisions of existing criteria and standards as may be appropriate..." Since the early 1980s,
this independent review function has been performed by CASAC.
History of Reviews of the Primary NAAQS for N02
On April 30, 1971, EPA promulgated identical primary and secondary NAAQS for NO2, under
Section 109 of the Act, set at 0.053 parts per million (ppm), annual average (Federal Register, 1971). In
1982, EPA published Air Quality Criteria for Oxides of Nitrogen (1982 NOX AQCD) (U.S.
Environmental Protection Agency, 1982), which updated the scientific criteria upon which the initial NO2
standards were based. On February 23, 1984, EPA proposed to retain these standards (Federal Register,
1984). After taking into account public comments, EPA published the final decision to retain these
standards on June 19, 1985 (Federal Register, 1985).
1 The legislative history of Section 109 indicates that a primary standard is to be set at "the maximum permissible ambient air level...which will protect
the health of any [sensitive] group of the population" and that, for this purpose, "reference should be made to a representative sample of persons
comprising the sensitive group rather than to a single person in such a group" [U.S. Senate, 1970].
2 Welfare effects as defined in Section 302(h) [U.S. Code, 2005] include, but are not limited to, "effects on soils, water, crops, vegetation, man-made
materials, animals, wildlife, weather, visibility and climate, damage to and deterioration of property, and hazards to transportation, as well as effects
on economic values and on personal comfort and well-being."
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On July 22, 1987, EPA announced it was undertaking plans to revise the 1982 NOX air quality
criteria (Federal Register, 1987). In November 1991, EPA released an updated draft AQCD for CASAC
and public review and comment (Federal Register, 1991). The draft document provided a comprehensive
assessment of the available scientific and technical information on health and welfare effects associated
with NO2 and other oxides of nitrogen. CASAC reviewed the document at a meeting held on July 1, 1993,
and concluded in a closure letter to the Administrator that the document "provides a scientifically
balanced and defensible summary of current knowledge of the effects of this pollutant and provides an
adequate basis for EPA to make a decision as to the appropriate NAAQS for NO2" (Wolff, 1993).
EPA prepared a draft Staff Paper that summarized and integrated the key studies and scientific
evidence contained in the revised AQCD and identified the critical elements to be considered in the
review of the NO2 NAAQS. The Staff Paper received external review at a December 12, 1994 CASAC
meeting. CASAC comments and recommendations were reviewed by EPA staff and incorporated into the
final draft of the Staff Paper as appropriate. CASAC reviewed the final draft of the Staff Paper in June
1995 and responded by written closure letter (Wolff, 1996). In September of 1995, EPA finalized the
document entitled, "Review of the National Ambient Air Quality Standards for Nitrogen Dioxide
Assessment of Scientific and Technical Information" (U.S. Environmental Protection Agency, 1995).
Based on that review, the Administrator announced her proposed decision not to revise either the
primary or the secondary NAAQS for NO2 (Federal Register, 1995). The decision not to revise the NO2
NAAQS was finalized after careful evaluation of the comments received on the proposal. The level for
both the existing primary and secondary NAAQS for NO2 is 0.053 ppm annual arithmetic average,
calculated as the arithmetic mean of the 1-h NO2 concentrations.
The current review was initiated on December 9, 2005 (70 FR 73236) with a request for submission
of recent scientific information on specified topics. EPA's draft Integrated Review Plan for the Primary
National Ambient Air Quality Standard for Nitrogen Dioxide was made available in February, 2007 for
public comment and was discussed by the Clean Air Science Advisory Committee (CASAC) via a
publicly accessible teleconference consultation on May 11, 2007 (72 FR 20336). The first external review
draft of this ISA was released for public comment and review by CASAC on August 31, 2007 (72 FR
50107), and was reviewed by CASAC at a public meeting held on October 24-25, 2007. The second draft
of this ISA was released for public comment and review by CASAC in March 2008 (73 FR 11916), and
was reviewed by CASAC at a public meeting held on May 1-2, 2008.
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Chapter 1. Introduction
This concise evaluation and synthesis of the most policy-relevant science forms the scientific
foundation for the review of the primary (health-based) NAAQS for nitrogen dioxide (NO2) currently set
at 0.053 ppm, annual average. Its intent is to "accurately reflect the latest scientific knowledge useful in
indicating the kind and extent of all identifiable effects on public health or welfare which may be
expected from the presence of such pollutant in ambient air" (Clean Air Act, Section 108, 2003).: Key
information and judgments from the previous AQCDs for NOX are incorporated in this assessment.
Details of the pertinent scientific literature published since the last review and selected older studies of
particular interest are included in annexes.
The terms "oxides of nitrogen" or "nitrogen oxides" refer to all forms of oxidized nitrogen
compounds, including nitric oxide (NO), NO2, and all other oxidized nitrogen-containing compounds
transformed from NO and NO2 (defined further in Chapter 2, Section 2.1). Descriptions of the
atmospheric chemistry of NOX include both gaseous and particulate species; a meaningful analysis would
not be possible otherwise. Most studies on the health effects of gaseous NOX focus on NO2; effects of
other gaseous species are considered as information is available. The health effects of particulate NOX are
included in the review of the NAAQS for particulate matter (PM). In evaluating the health evidence,
possible influences of other co-occurring atmospheric pollutants such as PM, sulfur dioxide (SO2), carbon
monoxide (CO), and ozone (O3) are considered.
The Integrated Plan for the Review of the Primary National Ambient Air Quality Standard for
Nitrogen Dioxide (U.S. Environmental Protection Agency, 2007) identifies key policy-relevant questions
which provide a framework for review of the scientific evidence. These questions are:
• Has new information altered the scientific support for the occurrence of health effects following
short- and/or long-term exposure to levels of nitrogen oxides found in the ambient air?
• What do recent studies focused on the near-roadway environment tell us about health effects of
nitrogen oxides?
• At what levels of nitrogen oxides exposure do health effects of concern occur?
• Has new information altered conclusions from previous reviews regarding the plausibility of
adverse health effects caused by exposure to nitrogen oxides?
• To what extent have important uncertainties identified in the last review been reduced and/or have
new uncertainties emerged?
• What are the air quality relationships between short- and long-term exposures to nitrogen oxides?
1.1. Document Development
The EPA initiated the current review of the NO2 NAAQS in the Federal Register with a call for
information (U.S. Environmental Protection Agency, 2005). Publications were identified through an
extensive literature search process; additional publications were identified by EPA scientists in a variety
of disciplines. In addition to peer-reviewed literature, previous EPA reports and materials obtained from
scouring reference lists were examined. All relevant epidemiologic, human clinical, animal toxicological,
and in vitro studies, including those related to exposure-response relationships, mechanism(s) of action,
1The secondary NOX NAAQS, in conjunction with a review of the secondary NAAQS for SOX, is underway independently, as is a review of the primary
NAAQS for SOX and a review of the primary and secondary effects of PM.
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or susceptible subpopulations published since the last review were considered. In addition, EPA conducts
analyses of air quality and emissions data, and evaluates studies on atmospheric chemistry, transport, and
fate of these emissions, as well as issues related to exposure. Further information was acquired from
consultation with content and area experts, CASAC and the public. Annex AX1 has more discussion of
search strategies and considerations for study inclusion.
1.2. Document Organization
The ISA has five chapters. The introduction presents background information, discusses the ISA's
purpose, search and study evaluation process, and sets the framework for causal determination. Chapter 2
highlights key concepts for understanding the atmospheric chemistry, sources, exposure, and dosimetry of
NOX, following a "source-to-exposure" paradigm. Chapter 3 evaluates and integrates epidemiologic,
human clinical, and toxicological data as it pertains to review of the primary NAAQS. Chapter 4 deals
with the public health impact of ambient NOX exposure, with an emphasis on susceptible and vulnerable
population groups. Lastly, Chapter 5 summarizes key findings and conclusions from the atmospheric
sciences, ambient air data analyses, exposure assessment, dosimetry, and health effects.
The annexes supplement the ISA with additional details from the recent literature, as well as
selected older studies of particular interest. They contain information on:
• framework for causal determination (Annex 1);
• atmospheric chemistry of NOX, sampling, and analytic methods (Annex 2);
• environmental concentrations and human exposure (Annex 3);
• toxicological studies of health effects in laboratory animals (Annex 4);
• human clinical studies of health effects related to short-term exposure (Annex 5); and
• epidemiologic studies of health effects from short- and long-term exposure (Annex 6).
1.3. EPA Framework for Causal Determinations
A consistent and transparent basis for evaluating the causal nature of air pollution-induced health
effects is important. EPA's framework uses standardized language, drawing on other agencies and the
scientific community, especially from the National Academy of Sciences (NAS) Institute of Medicine
(IOM) document, Improving the Presumptive Disability Decision-Making Process for Veterans (Institute
of Medicine, 2007).
This section:
• describes the type of scientific evidence used in establishing a causal relationship between
exposure and health effects;
• defines cause, in contrast to statistical association;
• discusses the sources of evidence necessary to reach a conclusion about the existence of a causal
relationship;
• highlights the issue of multifactorial causation;
• identifies issues and approaches related to uncertainty; and
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• provides a framework for classifying and characterizing the weight of evidence in support of a
general causal relationship.
Approaches to assessing the separate and combined lines of evidence (e.g., epidemiologic, animal
toxicological, human clinical, and in vitro studies) have been formulated by a number of regulatory and
science agencies in addition to the IOM, including the International Agency for Research on Cancer
(IARC, 2006), EPA Guidelines for Carcinogen Risk Assessment (U.S. Environmental Protection Agency,
2005), Centers for Disease Control and Prevention (2004), and the National Acid Precipitation
Assessment Program (NAPAP, 1991). (See Annex AX1 for excerpts from these documents.) These
approaches are similar in nature, although adapted to different purposes, and have proven effective in
providing a uniform structure and language for causal determinations, and support decision-making under
conditions of uncertainty.
1.3.1. Scientific Evidence Used in Establishing Causality
The most compelling evidence of a causal relationship between pollutant exposure and health
effects comes from human clinical studies, which evaluate health effects of administered exposure under
controlled laboratory conditions.
In epidemiologic or observational studies of humans, the investigator does not control exposure or
intervene with the study population. Observational studies can describe associations between exposure
and effect; study designs include cross-sectional, case-control, cohort, time-series, and panel studies.
"Natural experiments" occur occasionally, and compare health effects before and after an exposure
change, such as closure or elimination of a pollution source. They can provide compelling evidence of
causality.
Experimental animal data complement clinical and observational data. These studies help
characterize effects of concern, exposure-response relationships, sensitive subpopulations, and modes of
action. In the absence of clinical or epidemiologic data in cases where humans are assumed or known to
respond similarly to the experimental species, animal data alone may be sufficient to support a likely
causal determination.
1.3.2. Moving from Association to Causation
"Cause" explains a significant, effectual relationship between an agent and an associated disorder
or disease. "Association" is the statistical dependence among events, characteristics, or other variables.
An association is prima facie evidence for causation; alone, however, it is insufficient proof of a causal
relationship between exposure and disease. Unlike an association, a causal claim supports the creation of
counterfactual claims; that is, a claim about what the world would have been like under different or
changed circumstances (Institute of Medicine, 2007). Much of the newly available health information
evaluated in this ISA comes from epidemiologic studies that report a statistical association between
exposure and health outcome.
Many of the health outcomes reported in these studies have complex etiologies. The diseases, such
as asthma, coronary heart disease or cancer are typically initiated by a web of multiple agents. Outcomes
depend on a variety of factors, such as age, genetic susceptibility, nutritional status, immune competence,
and social factors, as shown in Figure 1.3-1 (Gee and Payne-Sturges, 2004; Institute of Medicine, 2007).
Further, exposure to a combination of agents could cause synergistic or antagonistic effects. Thus, net
effects are the result of many actions and counteractions.
Moving from association to causation involves eliminating alternative explanations for the
association. Controlled human exposure studies, or human clinical studies, randomly allocate subjects
groups, usually called study and control groups, and exposed to a pollutant or a sham, respectively.
Results are assessed by rigorous comparison of rates of relevant outcomes between the groups. This type
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of study is generally regarded as the most scientifically rigorous method of hypothesis testing. By
assigning exposure randomly, the study design attempts to remove the effect of factors that influence
exposure. Only a causal relationship between exposure and health outcome should produce observed
associations in randomized clinical trials.
In another type of human clinical study, a subject is exposed to a pollutant and a sham at different
points in time, and the responses are compared. This design effectively controls for potential confounders,
since the subject serves as his or her own control.
Cumulative Risks
(The combined risks from aggregate exposures to multiple agents or stressors)
Community
Level
Vulnerability
Individual
Level
Vulnerability
Race/Ethnicity >•
^
, ^ \ —
T t *^ 1
Neighborhood ^ ^ Co
Resources St
""^
Residential Location
^ / \^
T S
1* »
mmunity ^ Structur
ressors Factor
\ y
Community *-
Stress
^ p
Stress/Coping, -
Life Stage/Style •
*
Individual Stressors
*
^^
— ^"^ i
^ Jt±5»J[
al . Environmental
i Haza
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^-^H
rds &
tants
Exposure
^^^^--^^ i
r
---"""""" Internal dose
— ~~r=^ 1
Biologically
effective dose
'
i
Health effects/
disparities
Figure 1.3-1. Exposure-disease-stress model for environmental health disparities.
A lack of observable effects from human clinical studies does not necessarily mean that a causal
relationship does not occur. One limitation is a small study population, which restricts the ability to
discern statistically significant findings. These studies are also confined to limited real-world conditions
that can be feasibly studied. In addition, the most susceptible individuals or groups may be explicitly
excluded, such as those with nutritional deficits, for practical and ethical reasons.
Inferring causation from epidemiologic studies requires consideration of uncertainties, particularly
potential confounders. One way to remove spurious association is through statistical control of these
potential confounders, a method termed "adjustment." Multivariable regression models are an example of
a tool for estimating the association between exposure and outcome that involves such adjustment. Study
designs that include matching of case and control exposure groups can also address confounding.
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Likewise, stratified analysis, i.e., examining the association within homogeneous groups of the
confounding variable, controls for confounders. Stratified analyses have an additional benefit: the
examination of effect modification through comparison of the effect estimates across different groups. If
investigators successfully measure characteristics that distort results, the method's adjustment helps
separate a spurious from a true causal association. Appropriate statistical adjustment for confounders
requires identification and measurement of all reasonably expected confounders.
Measurement error is another problem encountered when adjusting for spurious associations. In
multivariate analyses in the time series study design, the effects of a we 11-measured covariate may be
overestimated, in contrast to a more poorly measured covariate. Several components contribute to
exposure measurement error in these studies, including differences between true and measured ambient
concentrations, differences between average personal exposure to ambient pollutants and ambient
concentrations at central monitoring sites, and the use of average population exposure rather than
individual exposure estimates.
Confidence that unmeasured confounders are not biasing the results is increased when multiple
studies are conducted in various settings using different subjects or exposures. Thus, multicity studies
which use a consistent method to analyze data from across locations with different levels of covariates
can provide insight on potential confounding in associations. The number and degree of diversity of
covariates, as well as their relevance to the potential confounders, remain matters of scientific judgment.
Intervention studies, because of their experimental nature, can be particularly useful in characterizing
causation.
In addition to clinical and epidemiologic studies, the tools of experimental biology are valuable for
providing insight into human physiology and pathology. Laboratory tools have been extended to explore
the effects of putative toxicants on human health, especially through the study of model systems in other
species. Background knowledge of the biological mechanisms involved can prove crucial in establishing,
or negating, a causal claim. On the other hand, species can differ from each other in fundamental aspects
of physiology and anatomy (e.g., metabolism, airway branching, hormonal regulation) that may hamper
extrapolation. Testable hypotheses about the causal nature of proposed mechanisms or modes of action
are central to utilizing experimental data in causal determinations.
1.3.3. Multifactorial Causation
Scientific judgment regarding likely sources and magnitude of confounding, as well as the pros and
cons of various existing study designs, results, and analyses is crucial. One key consideration in this ISA
was the evaluation of the potential contribution of NOX to health effects, when it is a component of a
complex air pollutant mixture. There are multiple ways by which NOX might cause or be associated with
adverse health effects. First, the reported NOX effect estimates in epidemiologic studies may reflect
independent NOX effects on respiratory health. Second, ambient NOX may be serving as an indicator of
complex ambient air pollution mixtures that share the same source, such as motor vehicles or electricity
generation. Finally, copollutants may mediate the effects of NOX, or NOX may influence the toxicity of
copollutants. These relationships are illustrated in Figure 1.3-2.
Epidemiologists use the terms "interaction" and "effect modification" to denote the departure of the
observed joint risk from expectations based on the separate effects of the factors. These possibilities are
not necessarily exclusive. In addition, confounding can result in the fabrication of an association between
adverse health effects and NOX that is actually attributable to another factor closely associated with NOX.
Multivariate models are the most widely used strategy to address confounding in epidemiologic studies,
but such models are not readily interpreted when assessing effects of covarying pollutants such as PM,
CO, O3, and SO2.
1-5
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1.3.4. Uncertainty
The science of estimating the causal influence of an exposure on disease is an uncertain one. There
are two distinct levels of uncertainty to be considered here:
• Model uncertainty—uncertainty regarding gaps in scientific theory required to make predictions
on the basis of causal inferences.
• Parameter uncertainty—uncertainty as to the statistical estimates within each model.
Assessment of model uncertainty involves: (1) whether exposure causes the health outcome; (2) the
set of confounders associated with exposure and health outcome; (3) which parametric forms best
describe the relationships among exposure, confounders, and outcome; and (4) whether other forms of
bias could be affecting the association.
Direct Causal Effect
NOV
Risk for outcome
Mediated Effect
PM
o,
Risk for outcome
Source
Surrogate
NOV
Other
Pollutants
t
Risk for outcome
Confounding
NOV
T
Confounder
t
Risk for outcome
Figure 1.3-2. Potential relationships of NOx with adverse health effects.
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1.3.5. Application of Framework
EPA uses a two-step approach to evaluate the scientific evidence on health effects of exposure to
criteria pollutants. These two steps address two policy-relevant questions noted above - what are (if any)
the effects of NOX on susceptible populations, given the total body of evidence, and at what levels of NOX
exposure do health effects of concern occur. The first step determines the weight of evidence in support of
causation and characterizes the strength of any resulting causal classification. The second step includes
further evaluation of the quantitative evidence regarding the concentration-response relationships and the
levels, duration and pattern of exposures at which effects are observed.
To aid judgment, various "aspects"1 of causality have been discussed by many philosophers and
scientists; the most widely cited were articulated by Sir Austin Bradford Hill in 1965 (Centers for Disease
Control and Prevention, 2004; EPA, 2005; IARC, 2006; Institute of Medicine, 2007). These elements
(Hill, 1965) have been modified (below) for use in causal determinations specific to health and
environmental effects and pollutant exposures.2
Table 1.3-1. Aspects to aid judging causality.
1. Consistency of the observed association. An inference of causality is strengthened when a
pattern of elevated risks is observed across several independent studies. The reproducibility of
findings constitutes one of the strongest arguments for causality. If there are discordant results
among investigations, possible reasons such as differences in exposure, confounding factors, and
the power of the study are considered.
2. Strength of the observed association. The finding of large, precise risks increases confidence
that the association is not likely due to chance, bias, or other factors. A modest risk, however,
does not preclude a causal association and may reflect a lower level of exposure, an agent of
lower potency, or a common disease with a high background level.
3. Specificity of the observed association. As originally intended, this refers to increased
inference of causality if one cause is associated with a single effect or disease. Based on our
current understanding this is now considered one of the weaker guidelines for causality; for
example, many agents cause respiratory disease and respiratory disease has multiple causes. The
ability to demonstrate specificity under certain conditions remains, however, a powerful attribute
of experimental studies. Thus, although the presence of specificity may support causality, its
absence does not exclude it.
4. Temporal relationship of the observed association. A causal interpretation is strengthened
when exposure is known to precede development of the disease.
5. Biological gradient (exposure-response relationship). A clear exposure-response
relationship (e.g., increasing effects associated with greater exposure) strongly suggests cause and
effect, especially when such relationships are also observed for duration of exposure (e.g.,
increasing effects observed following longer exposure times). There are, however, many possible
reasons that a study may fail to detect an exposure-response relationship. Thus, although the
presence of a biological gradient may support causality, the absence of an exposure-response
relationship does not exclude a causal relationship.
1The "aspects" described by Hill (1965) have become, in the subsequent literature, more commonly described as "criteria." The original term "aspects"
is used here to avoid confusion with 'criteria' as it is used, with different meaning, in the Clean Air Act.
2 The Hill apects were developed for use with epidemiology data. They have been modified here for use with a broader array of data, i.e.,
epidemiologic, human clinical, and animal toxicologic studies, as well as in vitro data, and to be more consistent with EPA's Guidelines for
Carcinogen Risk Assessment.
1-7
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6. Biological plausibility. An inference of causality tends to be strengthened by consistency
with data from experimental studies or other sources demonstrating plausible biological
mechanisms. A lack of biological understanding, however, is not a reason to reject causality.
7. Coherence. An inference of causality may be strengthened by other lines of evidence that
support a cause-and-effect interpretation of the association. For instance, similar findings between
clinical and animal studies, or closely related health effects, which are expected to be associated
with exposure, are in fact observed together. The absence of other lines of evidence, however, is
not a reason to reject causality.
8. Experimental evidence (from human populations). Experimental evidence is generally
available from human populations for the criteria pollutants. The strongest evidence for causality
can be provided when a change in exposure brings about a change in adverse health effect or
disease frequency in either clinical or observational studies (e.g., natural experiments,
intervention studies).
9. Analogy. Structure activity relationships and information on the agent's structural analogs
can provide insight into whether an association is causal. Similarly, information on mode of
action for a chemical, as one of many structural analogs, can inform decisions regarding likely
causality.
While these aspects provide a framework for assessing the evidence, they do not lend themselves to
consideration in terms of simple formulas or fixed rules of evidence leading causality conclusions. A
tallying of studies reporting statistically significant or nonsignificant results does not point toward
credible conclusions about the relative weight of the evidence and the likelihood of causality. Rather,
these considerations are taken into account with the goal of producing an objective appraisal of the
evidence, informed by peer and public comment and advice. The principles in Table 1.3-1 cannot be used
as a strict checklist, but rather as a determination of the weight of the evidence for inferring causality. In
particular, the absence of one or more of the principles does not automatically exclude a study from
consideration (e.g., see discussion in Centers for Disease Control and Prevention, 2004).
1.3.6. First Step—Determination of Causality
To draw conclusions on the causal relationships between relevant pollutant exposures and health
outcomes, EPA assessed results from recent publications, in light of evidence available from the previous
NAAQS review. Using a five-level hierarchy to classify the overall weight of evidence for causation, not
just association (see Table 1.3-2), EPA drew on the work of previous evaluations, most prominently the
lOM's Improving the Presumptive Disability Decision-Making Process for Veterans (Institute of
Medicine, 2007), EPA's Guidelines for Carcinogen Risk Assessment (U.S. Environmental Protection
Agency, 1986), and the U.S. Surgeon General's report on the benefits of smoking cessation (Centers for
Disease Control and Prevention, 2004). These efforts are presented in more detail in Annex AX1. The
weight of evidence evaluation is based on various lines of evidence from human clinical, epidemiologic,
animal studies, and in vitro studies. The separate judgments are then integrated into a qualitative
statement about the overall weight of the evidence and causality.
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1.3.7. Second Step—Evaluation of Population Response
Beyond judgments regarding causality are questions relevant to characterizing exposure and risk to
populations; in other words, at what levels do health effects occur? Such questions include:
• What is the concentration-response relationship?
• Under what exposure conditions (dose or exposure, duration and pattern) are effects seen?
• What population groups appear to be affected or more susceptible to effects?
Table 1.3-2. Weight of evidence for causal determination (adapted from Institute of Medicine,
2007).
Sufficient to infer a
causal relationship
Evidence is sufficient to conclude that there is a causal relationship between
relevant pollutant exposure and the outcome. Causality is supported when an
association has been observed between the pollutant and the outcome in
studies in which chance, bias, and confounding could be ruled out with
reasonable confidence. That is, human clinical studies provide the strongest
evidence for causality. Causality is also supported by findings from
epidemiologic "natural experiments" or observational studies supported by
other lines of evidence. Generally, determination is based on multiple studies
from more than one research group.
Sufficient to infer a
likely causal
relationship (i.e.,
more likely than not).
Evidence is sufficient to conclude that there is a likely causal association
between relevant pollutant exposures and the outcome. That is, an association
has been observed between the pollutant and the outcome in studies in which
chance, bias and confounding are minimized, but uncertainties remain. For
example, observational studies show associations but confounding and other
issues are difficult to address and/or other lines of evidence (human clinical,
animal, or mechanism of action information) are limited or inconsistent.
Generally, determination is based on multiple studies from more than one
research group.
Suggestive, but not
sufficient to infer a
causal relationship
Evidence is suggestive of an association between relevant pollutant exposures
and the outcome, but is weakened because chance, bias and confounding
cannot be ruled out. For example, at least one high-quality study shows an
association, while the results of other studies are inconsistent.
Inadequate to infer the
presence or absence of
a causal relationship
The available studies are inadequate to infer the presence or absence of a
causal relationship. That is, studies are of insufficient quality, consistency or
statistical power to permit a conclusion regarding the presence or absence of
an association between relevant pollutant exposure and the outcome. For
example, studies which fail to control for confounding or which have
inadequate exposure assessment, fall into this category.
Suggestive of no causal
relationship
The available studies are suggestive of no causal relationship. That is, several
adequate studies, examining relationships between relevant population
exposures and outcomes, and considering sensitive subpopulations, are
mutually consistent in not showing an association between exposure and the
outcome at any level of exposures. In addition, the possibility of a small
elevation in risk at the levels of exposure studied can never be excluded.
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On the population level, causal and likely causal claims typically characterize how risk—the
probability of health effects—changes in response to exposure. Generally, the response is evaluated
within the range of observation, which is determined by the type of study and methods of exposure
measurement. Extensive human data to inform risk analyses exists for all criteria pollutants, unlike most
other environmental pollutants. Animal data also can inform concentration-response, particularly relative
to dosimetry, mechanisms of action, and characteristics of sensitive subpopulations.
An important consideration in characterizing the public health impacts associated with exposure to
a pollutant is whether the concentration-response relationship is linear across the full concentration range
encountered, or whether nonlinear relationships exist along any part of this range. Of particular interest is
the shape of the concentration-response curve at and below the level of the current standard. The complex
molecular and cellular events that underlie cancer and noncancer toxicity are likely to be both linear and
nonlinear, and vary depending on concentration. Additionally, many factors may act by perturbing
naturally occurring background processes related to disease.
At the human population level, various sources of variability and uncertainty tend to smooth and
"linearize" the concentration-response function, such as low data density in the lower concentration
range, possible influence of measurement error, and individual differences in susceptibility to air
pollution health effects. These attributes of population concentration-response may explain why the
available human data at ambient concentrations for some environmental pollutants (e.g., O3, lead [Pb],
PM, secondhand tobacco smoke, radiation) do not exhibit evident thresholds for health effects, even
though likely mechanisms of action include nonlinear processes. These attributes of human population
concentration-response relationships have been extensively discussed in the broader epidemiologic
literature (e.g., Rothman and Greenland, 1998).
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Chapter 2. Source to Exposure
2.1. Introduction
This chapter provides concepts and findings relating to emissions sources, atmospheric science,
human exposure assessment, and human dosimetry. The order of these topics essentially follows that
given in the National Research Council paradigm for integrating air pollutant research (NRC, 2004) as
shown in Figure 2.1-1. This chapter is meant to serve as a prologue for detailed discussions on the
evidence on health effects that follow in Chapters 3 and 4, and as a source of information to help interpret
those effects against data about atmospheric concentrations and exposures.
Sources
of Airborne —
Oxides „
of Nitrogen
Emissions
Mechanisms
~^. Indicator —
. in Ambient ,
(Outdoor) Air
determining Human tir
_,. Personal ___
Exposure
k Ji
tie-activity Depositor
Dose to
— > Target
k Tissues '
i, clearance, Mech
Human
~~*" Health
11 Response
anisms
emissions, chemical patterns, indoor retention, and of damage and repair
transformation, and (or microcenvironmental) disposition of oxides
transport in air sources, and sinks of of nitrogen presented
oxides of nitrogen to an individual
Source: Adapted from National Research Council (2004).
Figure 2.1-1. A generalized conceptual model for integrating research on NOX pollution and human
health effects.
The definition of "nitrogen oxides," as it appears in the NAAQS legislation, differs from the one
commonly used in air pollution research and control communities. In this document, the terms "oxides of
nitrogen" and "nitrogen oxides" (NOX) refer to all forms of oxidized nitrogen (TV) compounds, including
NO, NO2, and all other oxidized TV-containing compounds formed from NO and NO2.1
NO and NO2, along with volatile organic compounds (VOCs), anthropogenic and biogenic
hydrocarbons, aldehydes, etc., and CO, are precursors in the formation of O3 and photochemical smog.
NO2 is an oxidant and can react to form other photochemical oxidants, including organic nitrates
(RONO2) like the peroxyacyl nitrates (PANs). NO2 can also react with toxic compounds such as
poly cyclic aromatic hydrocarbons (PAHs) to form nitro-PAHs, some of which are more toxic than either
reactant alone. NO2 and SO2, another EPA criteria air pollutant, can also be oxidized to form the strong
mineral acids nitric acid (HNO3) and sulfuric acid (H2SO4), thereby contributing to the acidity of cloud,
fog, and rainwater, as well as ambient particles.
1 This follows usage in the Clean Air Act Section 108(c): "Such criteria [for oxides of nitrogen] shall include a discussion of nitric and nitrous acids,
nitrites, nitrates, nitrosamines, and other carcinogenic and potentially carcinogenic derivatives of oxides of nitrogen." By contrast, within the air
pollution research and control communities, the terms "oxides of nitrogen" and "nitrogen oxides" are restricted to refer only to the sum of NO and
NO2, and this sum is commonly abbreviated as NOX. The category label used by this community for the sum of all forms of oxidized nitrogen
compounds including those listed in Section 108(c) is NOY.
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2.2. Sources and Atmospheric Chemistry
The role of NOX in O3 formation was reviewed in the Air Quality Criteria for Ozone and Related
Photochemical Oxidants (U.S. Environmental Protection Agency, 2006a). Mechanisms for transporting
O3 precursors including NOX, the factors controlling the efficiency of O3 production from NOX, methods
for calculating O3 from its precursors, and methods for measuring total oxidized nitrogen (NOY) were all
reviewed the 2006 O3 AQCD. The chemistry of reactive, oxidized TV compounds in the atmosphere is
summarized in Figure 2.2-1.
2.2.1. Sources of NOx
Both anthropogenic and natural (biogenic) processes emit NOX. NOX is emitted by combustion
sources as mainly NO with smaller quantities of NO2. The major sources of NOX in the U.S. are listed in
Table 2.2-1 (see Annex Table AX2-3 for more detail). On-road mobile sources constitute the largest
source of NOX followed by electricity generating units (EGUs) and non-road mobile sources. Stationary
engines and industrial facilities also emit NOX, but because they are fewer in number or burn less fuel,
their mass contribution is relatively smaller. It should be remembered in viewing Table 2.2-1 that the
values shown are nationwide averages and may not reflect relative contributions of the different sources
to ambient NO2 at any given location and to an individual's exposures to NO2.
Table 2.2-1. Annual 2002 average anthropogenic NOX emissions in the U.S.
(million metric tons).
SOURCE CATEGORY
On-Road Mobile Sources
Electricity-Generating Units
Non-Road Mobile Sources
Industrial/Commercial/Residential Fuels
Industrial Processes
Prescribed Fires
Waste Disposal
Residential Wood
Fertilizers and Livestock
Miscellaneous
Road Dust
Solvent Use
TOTAL
EMISSIONS
7.4
4.3
4.0
2.3
1.1
0.19
0.10
0.04
0.019
0.011
<0.01
0.0082
19.4
Source: NEI 2002 Emissions Booklet (U.S. Environmental Protection Agency, 2006b)
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As defined above, NOX is a complex mixture of many oxides of nitrogen, so many factors can
contribute to ambient NOX concentrations at any given time or location. Relatedly, there are several
methods for determining NOX emissions from these many sources. One method is to consider the ratio of
components of NOX to each other or to the total concentration of NO+NO2. The ratio of NO2 to the sum
of NO and NO2 in the exhaust from gasoline fueled vehicles is variable, but generally of the order of a
few percent (Heeb et al., 2008; Milliard and Wheeler, 1979) as determined by dynamometer studies.
Catalyzed diesel particle filters (CDPFs) can increase the NO2 to NO+NO2 ratio in diesel exhaust from
<0.1 to around 0.3 to 0.7, depending on the engine, design of the CDPF and mode of operation. For
example, Shorter et al. (2005) in a study of public transit buses in New York City found that ratios in
emissions from retrofitted diesel engines range from 0.3 to 0.6.
Sources of NOX are distributed with height, some are at or near ground level (e.g., motor vehicles)
and others aloft (e.g., electric utilities stacks). Figure 2.2-1 shows schematically on-road motor vehicles
and electric utilities, the two largest NOX sources in the U.S., along with NOX species and major reaction
pathways. Because the prevailing winds aloft are generally stronger than those at the surface, emissions
from elevated sources can be distributed over a wider area than those emitted at the surface. The elevated
emissions besides being widely dispersed are diluted to much lower levels than near their source.
Long range transport to remote
regions at low temperatures
-NO — — — — —,
NH
nitrosamines,
nitro-phcnols, etc.
Po T
emissions
Figure 2.2-1. Schematic diagram of the cycle of reactive oxidized N species in the atmosphere. NOy refers
to all the species shown within the inner and outer box; NOx to NO and N02 (in the inner box);
and NOz to all the species outside of the inner box. IN refers to inorganic particulate species
(e.g., sodium [Na+], calcium [Ca"]), MPP to multiphase processes, hvto a solar photon and R
to an organic radical. Particle-phase RON02 are formed from the species shown on the right
side. For the purposes of this EPA document, "NOx" is defined as the group of all nitrogen-
containing compounds inside the large dashed-line box, the same group generally termed
"NOy" by atmospheric scientists.
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Biomass burning also produces NOX. Apart from these anthropogenic sources, there are also
smaller natural sources which include microbial activity in soils (particularly fertilized soils) and
lightning. Wildfires can be large but episodic and highly variable sources of NOX. NOX sources and
emissions are described in greater detail in Annex Section AX2.4.
2.2.2. Chemical Transformations
NO and NO2 are often grouped together and given the category label "NOX" because they are
emitted together and can rapidly interconvert as shown in the inner box in Figure 2.2-1. The NO emitted
from the sources shown in Figure 2.2-1 can be oxidized to form NO2, meaning that atmospheric NO2
concentrations nearly always have a large fraction created by secondary formation in the atmosphere, not
direct emission. The timescale for the conversion of NO to NO2 is on the order of a minute.1 NO2 reacts
with O3 and various free radicals in the gas phase and on surfaces in multiphase processes to form the
oxidation products shown in Figure 2.2-1. These products include inorganic species (shown on the left
side of the outer box in Figure 2.2-1) and organic species (shown on the right side of the outer box in
Figure 2.2-1). The oxidized TV species in the outer box are often collectively termed NOZ; thus,
NOX+NO-Z=NOY.
The concentrations and atmospheric lifetimes (T) of inorganic and organic products from reactions
of NOX vary widely in space and time. Inorganic reaction products include nitrous acid (HONO), HNO3,
pernitric acid, and particulate nitrate (PNO3~). While a broad range of organic TV compounds are emitted
by combustion sources (e.g., nitrosamines and nitro-PAHs), they are also formed in the atmosphere from
reactions of NO and NO2. These include PANs and isoprene nitrates, other nitro-PAHs, and the more
recently identified nitrated organic compounds in the quinone family. Most of the mass of products
shown in the outer box of Figure 2.2-1 is in the form of PAN and HNO3, although other organic nitrates,
e.g., isoprene nitrates and specific biogenic PANs can be important at locations closer to biogenic sources
(Horowitz et al., 2007; Singh et al., 2007).
In addition to gas-phase reactions, reactions occurring on surfaces or occurring in multiphase
processes (MPP) are important for the formation of HONO and PNO3". These reactions can occur on the
surfaces of suspended particles, soil, and buildings, and within aqueous media. Further details about these
processes can be found in Annex section AX2.2.3. PAN, and other peroxyacyl nitrates, both thermally
decompose and photolyze back to reactants. Atmospheric lifetimes with respect to photolysis are a few
hours during warm sunlit conditions. For thermal decomposition, they range from ~1 hour at 298 K to
~2.5 days at 273 K, up to several weeks at 250 K. Thus, PAN can provide an effective sink of NOX at
cold temperatures and high solar zenith angles; its lifetime is long enough at low temperatures so that
PAN can be transported tens or hundreds of kilometers (depending on meteorological conditions) before
decomposing to release NO2, which can then participate in O3 formation in these regions, remote from the
original NOX source. HNO3 can act similarly to some extent, but its high solubility and high deposition
rate imply that it is removed from the gas phase faster than PAN, and thus would not be as important as a
source of NOX in remote regions. Characteristic concentrations of many of the NOX species are given in
Annex section AX3.2.
The timescale for reactions of NOX to form NOZ products like PAN and HNO3 typically ranges
from a few hours during summer to about a day during winter. As a result, morning rush hour emissions
of NOX from motor vehicles can be converted almost completely to NOZ products by late afternoon
during warm, sunny conditions. Because the time required for mixing emissions down to the surface is
similar to or longer than the time for oxidation of NOX, emissions of NOX from elevated sources (like the
stacks of electric utilities) tend to be transformed to NOZ before they reach the surface. However, people
live closer to emissions from on-road and off-road motor vehicles, fixed-site combustion engines (e.g.,
generators), and indoor sources, and so are more likely to be exposed to NO and NO2 from these sources.
1 This estimate assumes a background concentration of O3 of 40 ppb and a rate coefficient at 298 K of 1.9 X 10~14 cm3/molec-sec (Jet Propulsion
Laboratory, 2003).
2-4
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Hence, because atmospheric dispersion and chemical reactions in this way determine the partitioning of a
person's exposure to NO2 and its reaction products from multiple different sources, a person's total
exposure to NOX cannot be judged solely by the NO and NO2 source strengths given in the national
emissions inventories (NEI).
Ultimately, oxidized TV compounds are lost from the atmosphere by deposition to the earth's
surface. Soluble species are taken up by aqueous aerosols and cloud droplets that can then be removed by
either wet or dry deposition. Insoluble species are lost by dry deposition and washout. Discussions of the
reactions in particles are beyond the scope of this review, but once in particles, a variety of organic and
inorganic nitrates can be formed, which are then removed either by dry deposition to the surface or by
rainout or washout.
2.2.2.1. Formation of Nitro-PAHs
Nitro-PAHs are produced either by direct emissions or by atmospheric reactions. 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 vary with fuel type, vehicle maintenance, and ambient conditions (Arey et al., 1986; 1989;
Arey, 1998; Perrini et al., 2005; Pitts, 1987; Sasaki et al., 1997; Zielinska et al., 2004). In addition to
direct emission, nitro-PAHs are formed from both gaseous and heterogeneous reactions of PAHs with
gaseous TV-containing pollutants in the atmosphere; reactions of hydroxyl (OH) and nitrate (NO3) radicals
with PAHs are the major sources of nitro-PAHs (Arey et al., 1986; 1989; Arey, 1998; Perrini et al., 2005;
Pitts, 1987; Sasaki et al., 1997; Zielinska et al., 1989; Bamford and Baker, 2003; Reisen and Arey, 2005).
Reactions involving OH radicals occur mainly during the day, while reactions with NO3 radicals occur
mainly during the night. 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, followed by
reactions with OH and NO3 radicals. The reaction mechanisms for forming and destroying nitro-PAHs in
the atmosphere are described in Annex section AX2.2.4 .
In ambient particulate organic matter (POM), 2-nitrofluoranthene (2NF) is the dominant compound,
followed by 1-nitropyrene (1NP) and 2-nitropyrene (2NP) (Arey et al., 1989; Bamford et al., 2003;
Reisen and Arey, 2005; Zielinska et al., 1989). 2NF and 2NP are not directly emitted from primary
combustion emissions, but are formed in the atmosphere. 1NP is generally regarded as a tracer of primary
combustion sources, in particular, diesel exhaust. After formation, nitro-PAHs with low vapor pressures
(such as 2NF and 2NP) immediately migrate to particles under ambient conditions (Arey et al., 1989;
Bamford et al., 2003; Reisen and Arey, 2005). More volatile nitro-PAHs, such as nitronapthalene (NN),
remain mainly in the gas phase.
The concentrations for most nitro-PAHs found in ambient air are typically lower than 1 pg/m3,
except NNs, 1NP, and 2NF, which can be present at levels up to several tens or hundreds of pg/m3. These
levels are from ~2 to -1000 times lower than those of 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 and
Ohnishi, 1986; Tokiwa et al., 1998). Moreover, most nitro-PAHs are present in particles with a mass
median diameter of <0.1 (im.
It should be noted that the first step involved in the multiphase processes noted above is adsorption
of NO2. NO2 adsorbed onto particles could then be inhaled along with the products of NO2 reactions such
as HONO and nitro-PAHs. However, quantitative details for the fraction of NO2 adsorbed onto particles
at ambient levels of NO2 are lacking.
2-5
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2.2.3. Os Formation
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
increasing NOX. At the high NOX concentrations found in downtown metropolitan areas, and especially
near busy streets and roadways and in power plant plumes, net destruction of O3 is initiated with the
excess NO found there. In the airshed regimes with high NOX concentrations, NO2 scavenges OH radicals
that would otherwise oxidize VOCs to produce peroxy radicals, which would in turn oxidize NO to NO2.
In the airshed regimes with low NOX concentrations, oxidation of VOCs generates excess free radicals;
hence O3 production is more nearly linear with NOX. Between these two regimes, there is a transition
zone in which O3 shows only a weak dependence on NOX concentration.
2.3. Measurement Methods
In the EPA regulatory networks as in nearly all large-scale NOX monitoring networks world-wide,
NO2 is the component of most interest. However, in most of these networks, NO2 is not measured
directly, but by subtraction following a measurement of the total of NO+NO2 and NO alone. Both these
measurements and hence the determination of NO2 by subtraction depend on the same technique for
measuring NO. In that technique, NO is measured using the principle of gas-phase chemiluminescence
induced by the reaction of NO with O3 at low pressure. The Federal Reference Method (FRM) for NO2
makes use of this technique of NO detection. A prerequisite step is the catalytic reduction of NO2 to NO
most often on the surface of a molybdenum oxide (MoOx) substrate, heated to between 300 and 400°C.
Because the FRM monitor cannot detect NO2 specifically, the concentration of NO2 is determined as the
difference between the air sample passed over the heated MoOx substrate (the nitrogen oxides total) and
the air sample that has not passed over the substrate (the NO).
Reduction of NO2 to NO on the MoOx substrate is not specific to NO2; hence, the
chemiluminescence analyzers are subject to varying interferences produced by the presence in the sample
of the other oxidized TV compounds (the NOZ species shown in the outer box of Figure 2.2-1). This
interference is often termed a "positive artifact" in the NO2 concentration estimate since the presence of
NOZ always results in an over-estimate of the NO2 concentration in the reported measurement. This
interference by NOZ compounds has long been known (Fehsenfeld et al., 1987; Rodgers and Davis, 1989;
U.S. Environmental Protection Agency, 1993, 2006a; Crosley, 1996; Nunnermacker et al., 1998; Parrish
and Fehsenfeld, 2000; McClenny et al., 2002; Dunlea et al., 2007; Steinbacher et al., 2007). These studies
have relied on intercomparisons of measurements using the FRM and other techniques for measuring
NO2. The sensitivity of the FRM to potential interference by individual NOZ compounds is variable and
also depends in part on characteristics of individual monitors, such as the design of the instrument inlet,
the temperature and composition of the reducing substrate, and on the interactions of atmospheric species
with the reducing substrate.
Only recently have attempts been made to systematically quantify the magnitude and variability of
the interference by NOZ species in ambient measurements of NO2. Dunlea et al. (2007) found an average
of-22% of ambient NO2 (~9 to 50 parts per billion [ppb]) measured in Mexico City was due to
interference from NOZ compounds; that is to say, the actual NO2 concentration was -22% lower than
what was reported at monitors using the difference technique. Comparable levels of NO2 are found in
many locations in the U.S., but the same comparison for distinct places in the U.S. is difficult to make
because significant uncertainty remains in determining the concentrations of the higher oxidation NOZ
products since they are not routinely measured. Dunlea et al. (2007) compared NO2 measured using the
conventional chemiluminescent instrument with other (optical) techniques. The main sources of
interference were HNO3 and various organic nitrates (RONO2) which can be converted to NO on the
2-6
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catalyst with varying rates of efficiency. In this study, the efficiency of conversion on the catalyst — that
is, how much of the compound introduced to the catalyst was converted to NO — was estimated to be
-38% for HNO3; for PAN, -95% and - 95% for other RONO2. Peak interference (over-estimation) in the
reported estimate of NO2 concentrations from the presence of NOZ compounds of up to 50% was found
during afternoon hours and was associated with O3 and NOZ compounds such as HNO3 and the alkyl and
multifunctional alkyl nitrates.
In a study in rural Switzerland, Steinbacher et al. (2007) compared measurements of NO2
continuously measured using a conventional NOX monitor and measurements in which NO2 was
photolyzed to NO. They found the conventional technique using catalytic reduction (as in the FRM)
overestimated the reported NO2 concentration using the photolytic technique on average by 10% during
winter and 50% during summer.
Another approach to estimating the measurement interference is to use model calculations in
conjunction with known data on the reduction efficiencies of NOZ species on the MoOx converters as
described above. Lamsal et al. (2008) used the conversion efficiencies noted above along with output for
NOY species from the GEOS-CHEM global chemical transport model (CTM) to derive seasonal
correction factors for the ambient monitoring data across the U.S. These factors range from <10% in
winter in the East to >80% in the West, with the highest values found during summer in relatively
unpopulated areas. Lamsal et al. (2008) also used these corrected data to determine the feasibility of using
satellite data to supplement ground based data. However, the current generation of satellite monitors are
in low earth orbiting mode and so the NO2 values are restricted to time of satellite overpass in early
afternoon. Future generations of geostationary satellites are planned that will obtain more continuous data
across the U.S. throughout the day.
Calculations using EPA's Community Multiscale Air Quality (CMAQ) modeling system for the
Mid-Atlantic region in a domain extending from Virginia to southern New Jersey were made at much
higher spatial resolution than the GEOS-CHEM simulations (see
http://www.mde.state.md.us/Programs/AirPrograms/air_planning/index.asp). The daily average
interference for an episode during the summer of 2002 estimated using model-derived concentration
fields for NOZ species and using the conversion efficiencies for NOZ species given above, ranged from
-20% in Baltimore to -80% in Madison, VA. Highest values were found during the afternoon, when
photochemical activity is highest and production and accumulation of the higher oxidized NOZ
compounds is greatest, and lowest values during the middle of the night when photochemistry stops. The
model calculations showed episode averages of the NOZ/NO2 ratio ranging from 0.26 to 3.6 in rural
Virginia; the highest ratios were in rural areas, and lowest were in urban centers closer to sources of fresh
NOX emissions. (The capabilities of three-dimensional CTMs such as GEOS-CHEM and CMAQ and
issues associated with their use are presented in Annex section AX2.5.)
In summary, it appears that interference is likely to be on the order of 10% or less during most or
all of the day during winter, but much larger interference is likely to be found during summer in the
afternoon. In general, the interference in the measurement of NO2 is greater downwind of urban source
areas and in relatively remote areas away from concentrated sources as compared to the level of
interference at measurements in urban cores with fresh NOX emissions.
There are approaches to measuring NO2 not affected by the artifacts mentioned above. For
example, NO2 can be photolytically reduced to NO with an efficiency of-70% as used in the Steinbacher
et al. (2007) study. This method requires additional development to ensure its cost effectiveness and
reliability for extensive field deployment. The relatively low and variable conversion efficiency of this
technique would necessitate more frequent calibration. Optical methods such as those using differential
optical absorption spectroscopy (DOAS) or laser induced fluorescence (LIF) are also available, as
described in Annex section AX2.6. However, these particular methods are more expensive than either the
FRM monitors or photolytic reduction technique and require specialized expertise to operate. Moreover,
the DOAS obtains an integrated measurement over the instrument path length rather than a point
measurement. Cavity attenuated phase shift monitors are an alternative optical approach that is potentially
2-7
-------
less costly than DOAS or LIF (Kebabian et al., 2007). However, this technique is not highly specific to
NO2 and is subject to interference by other species absorbing at 440 nm, such as the 1,2-dicarbonyl
compounds. The extent of this interference and the potential of the cavity attenuated phase shift technique
for extensive field deployment have not been evaluated.
Commercially available NOX monitors have been converted to NOY monitors by moving the MoOx
converter to interface directly with the sample inlet. Because of losses on inlet surfaces and differences in
the efficiency of reduction of NOZ compounds on the heated MoOx substrate, NOX cannot be considered
as a universal surrogate for NOY. However, in settings close to relatively high-concentration fresh
emissions like those during urban rush hour, most of the NOY is present as NOX. To the extent that all the
major oxidized TV species can be reduced quantitatively to NO, measurements of NOY should be more
reliable than those of NOX, particularly at typical ambient levels of NO2. It is worth reiterating that with
the current FRM monitors, the direct measurements of NO are the most specific. Measurements of total
NOY characterize the entire suite of oxidized TV compounds to which humans are exposed. Reliable
measurements of NOY and NO2, especially at the low concentrations observed in many areas remote from
sources are also crucial for evaluating the performance of three-dimensional, chemical transport models
of oxidant and acid production in the atmosphere (described in Annex section AX2.5).
To summarize this discussion of NO2 measurement techniques and interferences: the current
method of determining ambient NOX and then reporting NO2 concentrations by subtraction of NO is
subject to a consistently positive interference by NOX oxidation products, chiefly HNO3 and peroxyacetyl
nitrate (PAN) as well as other oxidized TV-containing compounds, though the magnitude of this positive
bias is largely unknown and can be rapidly changing. Measurements of these oxidation products in urban
areas are sparse. Concentrations of these oxidation products are expected to peak in the afternoon because
of the continued oxidation of NO2 emitted during the morning rush hours and during conditions
conducive to photochemistry in areas well downwind of sources, particularly during summer.
Within the urban core of metropolitan areas, where many of the ambient monitors are sited close to
strong NOX sources such as motor vehicles on busy streets and highways (i.e., where NO2 concentrations
are highest), the positive artifacts due to the NO2 oxidation products are much smaller on a relative basis,
typically <~10%. Conversely, the positive artifacts are larger in locations more distant from NOX sources
(i.e, where NO2 concentrations are lowest) and could exceed 50%. Therefore, variable, positive artifacts
associated with measuring NO2 using the Federal Reference Method (FRM) severely hamper its ability to
serve as an accurate and precise indicator of NO2 concentrations at the typical ambient levels generally
encountered outside of urban cores.
2.4. Atmospheric Concentrations
This section provides a brief overview of ambient concentrations of NO2 and associated oxidized
TV compounds in the U.S.; it also provides estimates of Policy-Relevant Background (PRB) concentrat-
ions, i.e., background concentrations used to inform risk and policy assessments for the review of the
NAAQS.
2.4.1. Ambient Concentrations
Figure 2.4-1 shows the distribution of monitoring sites for NO2 across the U.S. Data for ambient
NO2 are not collected or collected at very few sites over large areas of the U.S. Few cities have more than
two monitors and several large cities, including Seattle, WA, have none. Note that the number of NO2
monitors has been decreasing in the U.S. as ambient average concentrations have fallen to a few tenths of
the level of the NAAQS. There were, for example, 375 NO2 monitors identified in mid-2006, but only
280 in November 2007.
2-8
-------
Criteria for siting ambient monitors for NAAQS pollutants are given in the SLAMS / NAMS /
PAMS Network Review Guidance (U.S. Environmental Protection Agency, 1998). As might be expected,
criteria for siting monitors differ by pollutant. NO2 monitors are meant to be representative of several
scales: middle (several city blocks, 300 to 500 m), neighborhood (0.5 to 4 km), and urban (4 to 50 km).
Middle- and neighborhood-scale monitors are used to determine highest concentrations and source
impacts, while neighborhood- and urban-scale monitors are used for monitoring population exposures. As
can be seen, there is considerable overlap between monitoring objectives and scales of representativeness.
The distance of neighborhood- and urban-scale monitor inlets from roadways increases with traffic
volume and can vary from 10 to 250 m away from roadways as traffic volume increases. Where the
distance of an inlet to a road is shorter than the value in this range for the indicated traffic volume on that
road, that monitor is classified as middle scale. Vertically, the inlets to NO2 monitors can be set at a
height from 2 to 15 m.
Figure 2.4-1. Location of ambient N02 monitors in the U.S. as of November 5, 2007. Shaded states have
N02 monitors; unshaded states have none.
Figures 2.4-2 through 2.4-12 depict the spatial and population coverage of NO2 monitors in these
Consolidated Metropolitan Statistical Areas (CMSAs): Atlanta, GA; Boston, MA; Chicago, IL; Houston,
TX; Los Angeles, CA; New York City, NY; Philadelphia, PA; Steubenville, OH; and Baltimore,
MD/Washington, DC. (These CMSAs were selected for this depiction to maintain consistency with
CMSAs used elsewhere in this assessment for health effects studies or ambient concentration
representations.)
2-9
-------
Atlanta Metropolitan Statistical Area
Di sb nc e to N 0 2 Mon itors
I I 1 km
5km
I I 10 km
I I 15 km
I External Combustion Boilers
I Internal Combustion Engines
Major Highways
I Atlanta
Distance to NO,
Monitors (Ion)
1
5
10
15
Total Population
Population.; Mi
AD
(H total)
1*66
«OJ29)
332236
(6.67)
1128 64B
(2266)
1SOS306
(36.31)
49804+7
(100.00)
Under 17
(WtotJ)
1392
(0.12)
66339
C5-S7)
247200
(21.86)
399158
(35.29)
1131056
(100.00)
Orerfii
65 years.
2-10
-------
Boston Metropolitan Statistical Area
ncetoN02 Monitors
1 km
5 km
HI 10 km
HI 15km
I External Combustion Boilers
I Internal Combustion Engines
Major Highways
Boston
DiaancetoNO,
MonkoiB (km)
1
5
10
IS
Total Population
Population. MM
An
65 years.
2-11
-------
Chicago Metropolitan Statistical Area
Distance to HO2 Monitors
1 km
5km
10 km
15 km
External Combustion Boilers
Internal Combustion Engines
Major Highways
Chicago
Distance to .NO,
Monkois (km)
1
5
10
15
Total Population
Population, 2005
An
(wtotm
5S892
«>.62)
1261964
(13.26)
356604S
(39.37)
5068 806
(53.15)
9536173
(100.00)
Under IT
(WtMal)
1429S
(OJS)
341S14
(13.97)
934752
(3S.20)
1305513
(53.35)
2447166
(100.00)
Orer65
(W total)
4427
(0.44)
133355
(13.47)
414529
(41S6)
603605
(60.96)
990200
(100.00)
2005 Population Density
CZ1N02 Monitors
Population per Sq Mile
• 0-571
• 572-1142
1143-5710
5711 -11420
•111421 -28550
• 23551 -114150
0 15 30
60
90
I Kilometers
120
Figure 2.4-4. N02 monitor locations in the Chicago, IL CMSA shown in relation to major roadways, point-
source electric generating units, and population densities for total population, and fractions <
17 years and > 65 years.
2-12
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Houston Metropolitan Statistical Area
Di star ce to N 02 Mo n itors
1 km
5 km
10 km
15 km
External Combustion Boilers
Internal Combustion Engines
Major Highways
Houston
Dtaaue to NO,
Monkois (km)
1
S
10
15
Total Population
PopnlatxHi, 2005
AD
(W total)
34129
0.00)
1097773
(20.32)
2814311
(52.09)
3877034
(71.76)
54Q2S23
(100.00)
Under 17
(Waal)
15092
(1.10)
2S3725
(20.74)
716626
(5237)
9S6429
(72.09)
1368254
(100.00)
OrerSS
(ti total)
3362
(0.91)
7S280
(21.29)
20S994
(56.S5)
271202
(73.77)
367640
(100.00)
200 5 Population Density
I I N02 Monitors
Population per Sq Mile
• 0 - 472
• 473 - 944
945 - 4720
4721 - 9440
• 9441 - 23600
• 23601 - 94367
0 15 30
60
90
• Kilometers
120
Figure 2.4-5. N02 monitor locations in the Houston, TX CMSA shown in relation to major roadways, point-
source electric generating units, and population densities for total population, and fractions <
17 years and > 65 years.
2-13
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Los Angeles/Riverside Metropolitan Statistical Areas
Di sbnce to H O2 Mortlore
1 km
5km
10km
15km
External Combustion Boilers
Internal Combustion Engines
Major Highways
Los Angeles/Riverside
Distance to NO,
Monitors 65 years.
2-14
-------
Los Angeles/Riverside Metropolitan Statistical Areas
Detail of Metro Area
0 12.5 25 50 75 100
I Kilometers
Figure 2.4-7. Detail of NOa monitor locations in the Los Angeles, CA CMSA shown in relation to major
roadways, point-source electric generating units, and total population density.
2-15
-------
New York City/Philadelphia Metropolitan
Statistical Areas
Di stance to N 0 2 Monitors
1 kin
5km
10 km
15 km
External Combustion Boilers
Internal Combustion Engines
Major Highways
New York City/Philadelphia
Distance to NO,
Monkois (Ion)
1
5
10
15
Total --"cyi lation
Population. :tl CIS
An
(W total)
396076
0.58)
70152S3
(37.95)
13851648
(55 JO)
18424872
(73.42)
25094739
(100.00)
Under IT
(WtotaO
83623
(1.41)
1630229
(27.31)
32S5210
(54.37)
4425362
(73-24)
6042057
(100.00)
Over 65
(ft total)
48601
(1.56)
S06750
(25.91)
1691600
(54 31)
2274972
(73.06)
3113768
(100.00)
2005 Population Density
IZZ1N02 Monitors
Population pe Sq Mile
• 0-2154
• 2155-4309
4310-21545
21546-43090
43091 - 107725
• 107726-43Q90Q
0 20 40
80
120
• Kilometers
160
Figure 2.4-8. NQa monitor locations in the New York City, NY and Philadelphia, PA CMSAs shown in relation
to major roadways, point-source electric generating units, and population densities for total
population, and fractions < 17 years and > 65 years.
2-16
-------
New York City/Philadelphia Metropolitan Statistical Areas
New York City Metro Area
Philadelphia Metro Area
I Kilometers
04.59 18 27 36
Figure 2.4-9. Detail of N02 monitor locations in the New York City, NY and Philadelphia, PA CMSAs shown
in relation to major roadways, point-source electric generating units, and total population
density.
2-17
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Steubenville Metropolitan Statistical Area
Distance to NO2 Monitors
] 1 km
5km
10km
15km
External Combustion Boileis
Major High ways
Steubenville
Diaanceco.NO,
Monkois (km)
1
5
10
15
Total Popuhtijn
Population, M 05
An
(H total)
0
SUE)
0
(0.00)
2603
(204)
19209
(15.06)
127533
(100.00)
Undsr 17
W cotall
0
(0.00)
0
(0.00)
«2
(1-77)
3909
(14.05)
27S23
(100.00)
OverK
(Htatajl
0
(0.00)
0
(0.00)
555
(227)
3914
(16.02)
24431
(100.00)
2005 Population Density
| | N02 rrorcitjrs
Population per Sq Mile
^H 31-60
j^B 6^--20
121-600
601-1200
I 1201-3000
I Kilometers
0 4.5 9
18
27
36
Figure 2.4-10. NOa monitor locations in the Steubenville, OH CMSA shown in relation to major roadways,
point-source electric generating units, and population densities for total population, and
fractions < 17 years and > 65 years.
2-18
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Washington DC/Baltimore Metropolitan
Statistical Areas
Distance to NO2 Monitor?
I I 1 km
5km
i i 10 km
I I 15km
I External Combustion Boilers
I Internal Combustion E ngines
Major Highways
I I Washington DC/Baltimore
Distance to NOj
Moulds (km)
1
5
1C
ii
Ibbl Itopuhtion
Population, 20 1*
All
(»* t«a|
103169
(130)
1724245
(21.70)
362SS2-
(45.67)
4542107
(62.19)
7946411
(100.00)
Umfcrl?
65 years.
2-19
-------
V\fashington DC/Baltimore Metropolitan Statistical Areas
Detail of Metro Areas
0 5 10 20 30 40
• Kilometers
Figure 2.4-12. Detail of NOa monitor locations in the Washington, DC and Baltimore, MD CMSAs shown in
relation to major roadways, point-source electric generating units, and total population
density.
2-20
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The study areas included 8 regions comprising 11 metropolitan statistical areas (MSAs), as defined
by the U.S. Census Bureau (http://www.census.gov/): Atlanta, Boston, Chicago, Houston, Steubenville,
Los Angeles/Riverside, Washington DC/Baltimore, and New York City/Philadelphia. All pertinent census
data (state, Metropolitical Statistical Area [MSA], county, and census block maps) were obtained from the
ArcGis 9.2 Media Kit provided by ESRI (2006). Census blocks served as the unit of analysis for
population calculations. All geospatial analyses were performed in ArcMap (Build 1324). NO2 monitor
location data (i.e., latitude/longitude) for 2004 were obtained from EPA's AirData website
(http://www.epa.gov/air/data/). Monitors were mapped for a particular region if they were within 15 kms
of its boundary. Information on point sources (i.e., fossil fueled electrical power generators) for 2002 was
obtained from the National Emissions Inventory (U.S. Environmental Protection Agency, 2006b). Point
sources were classified by their Source Classification Codes (SCC): lOlOxxxx for electric generation:
external combustion boilers, and 2010xxxx for electric generation: internal combustion engines. Point
sources were mapped for a particular region if they were within 30 kms from its boundary.
All census maps were projected into ArcMap using the North American Datum of 1983 and the
USA Contiguous Lambert Conformal Conic projection coordinate system to allow for calculation of
linear distances. NO2 monitor and point source locations for a particular region were then imported into
their respective maps and buffer zones of 1, 5, 10, and 15 kms were constructed around the monitor
locations. The total population and populations of 2 sensitive subgroups (those < 17 and those > 65) were
then calculated for those areas using the population data contained within the census block maps.
100
90
80
S1 70
Q.
Q.
~ 60
O
'« 50
IM
^
0) 40
O
e
o ^n
0
on
£v
10
0
* 1201 * — ^201 * Il29
-
_
*
0
-
0
0
~~ o nfl A v
o ^( MAX
TO 99
I ~T 95
I — — I 75
• • • • 50 • Mean
25
I ^T~^ I
_l_ j 1 —1— ~~^ D
1- h max
1-h
24-h
2 week
1 -year
Figure 2.4-13. Ambient concentrations of N02 measured at all monitoring sites located within MSAs in the
U.S. from 2003 through 2005.
* max; • mean
Figure 2.4-13 shows box plots of ambient concentrations of NO2 measured at all monitoring sites
located within MSAs or urbanized areas in the U.S. from 2003 through 2005. As can be seen from Figure
2-21
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2.4-13 mean NO2 concentrations are ~15 ppb for averaging periods ranging from a day to a year, with an
interquartile range (IQR) of 10 to 25 ppb. However, the average of the daily 1-h maxNO2 concentration
over this 3-year period is ~30 ppb. These values are about twice as high as the 24-h avg. The highest
maximum hourly concentration (-200 ppb) found during the period of 2003 to 2005 was more than a
factor often greater than the overall mean 24-h concentrations. The ratio of the 99th percentile
concentration to the mean ranges from 2.1 for the 1-year avgs to 3.5 for the 1-havgs.
Because ambient NO2 monitoring data are so sparse across the U.S. (see Figure 2.4-1) and
particularly so in rural areas, it would not be appropriate to use these data in constructing a map of NO2
concentrations across the continental U.S. The short T of NO2 with respect to conversion to NOZ species
and the concentrated nature of NO2 emissions result in steep gradients and low concentrations away from
major sources that are not adequately captured by the existing monitoring networks. Model predictions
might be more useful for showing large-scale features in the distribution of NO2 and could be used in
conjunction with the values shown in Figure 2.4-13 to provide a more complete picture of the variability
of NO2 across the U.S. Monthly avg NO2 concentrations for January and July 2002 calculated using
EPA's CMAQ model are shown in Figures 2.4-14. (A description of the capabilities of CMAQ and other
three-dimensional CTMs is given in Annex section AX2.5.) The high variation in NO2 concentrations of
at least a factor of 10 is apparent in these model estimates. As expected, the highest NO2 concentrations
are seen in large urban regions, such as the Northeast Corridor, and lowest values are found in sparsely
populated regions located mainly in the West. NO2 concentrations tend to be higher in January than in
July.
January 2002
Min = 0.019 at (1,1), Max = 45.966 at (23,46)
July 2002
Min = 0.012 at (6,4), Max = 40.802 at (23,46)
Figure 2.4-14. Monthly average NOa concentrations in ppb for January 2002 (left panel) and July 2002 (right
panel) calculated by CMAQ (36 X 36 km horizontal resolution).
2.4.2. N02 Concentrations
Trends in NO2 concentrations across the U.S. from 1980 to 2006 are shown in Figure 2.4-15. The
white line shows the mean values and the upper and lower borders of the blue (shaded) areas represent the
10th and 90th percentile values. Information on trends at individual, local air monitoring sites can be
found at www.epa.gov/airtrends/nitrogen.html.
Concentrations were substantially higher during earlier years in selected locations and contributed
in those years to the "brown clouds" observed in many cities. Residents in Chattanooga, TN, for example,
were exposed more than 30 years ago to high levels of NO2 from a munitions plant (Shy and Love, 1980).
Annual mean NO2 concentrations there declined from -102 ppb in 1968 to -51 ppb in 1972. There was a
2-22
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strike at the munitions plant in 1973 and levels declined to -32 ppb. With the implementation of control
strategies, values dropped further. In 1988, the annual mean NO2 concentration varied from -20 ppb in
Dallas, TX and Minneapolis, MN to 61 ppb in Los Angeles, CA. However, New York City, with the
second-highest annual mean concentration in the U.S. in 1988, the mean NO2 concentration was 41 ppb.
NO2 Air Quality, 1980 - 2006
(Based on Annual Arithmetic Average)
National Trend based on 87 Sites
0.00
111111111111111111112222222
999999999999999999990000000
888888888899999999990000000
012345678901234567890123456
1980 to 2006: 41% decrease in National Average
Figure 2.4-15. Nationwide trend in NOa concentrations.The white line shows the mean values, and the upper
and lower borders of the blue (shaded) areas represent the 10th and 90th percentile values.
Information on trends at local air monitoring sites: www.epa.gov/airtrends/nitrogen.html.
In contrast to most urban areas in the U.S., in other countries, NO2 concentrations have increased.
For example, annual mean NO2 concentrations in central London increased during the 1980s from
-25 ppb in 1978 to -40 ppb in 1989 at the background measurement site and from -35 to -45 ppb at the
roadside site. Corresponding NO concentrations increased from -20 ppb to -40 ppb at the background
site and from -125 to -185 ppb at the roadside site (Elsom, 2002). Increased use of motor vehicles may
have contributed to much of these increases in NO2 levels.
2.4.3. Seasonal Variability in NOa at Urban Sites
The month-to-month variability in 24-h avg NO2 concentrations at two sites in Atlanta, GA is
shown in Figure 2.4-16. Variability at other individual sites in selected urban areas is shown in Annex
Figures AX3.2-1 to AX3.2-6. 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 NO2
concentrations in the data shown in Figures 2.4-16a-b. Higher concentrations are found during winter,
consistent with the lowest mixing layer heights found during the year. Lower concentrations are found
during summer, consistent with higher mixing layer heights and increased rates of photochemical
oxidation of NO2 to NOZ. Note also the day-to-day variability in NO2 concentration, which also tends to
be larger during the winter. There appears to be a somewhat regular pattern for the other southern cities
examined with their winter maxima and summer minima.
2-23
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a. Atlanta, GA.
SUBURBAN
Q.
a.
c
o
4-1
2
+•«
o
c
o
o
0.09:
0.08^
0.07:
0.06 •:
0.05^
0.04^
0.03:
0.02-
0.01:
0.00-
site id=130890002 poc=1
= Natural Spline Fit w/ 9 df
01/01/2003 07/01/2003 01/01/2004 07/01/2004 01/01/2005 07/01/2005 01/01/2006
Sample Date (mm/dd/yyyy)
b. Atlanta, GA.
URBAN and CENTER CITY
Q.
Q.
C.
O
O
o
o
siteid=131210048poc=1
0.09:
0.08-i
0.07 •!
0.06-;
0.05-;
0.04 •;
0.03-:
0.02-i
0.01^
o.oo 1
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 2.4-16. Time series of 24-h avg NCh concentrations at individual sites in Atlanta, GAfrom 2003
through 2005. A natural spline function (with 9 degrees of freedom) was fit and overlaid to the
data (dark solid line).
2-24
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Monthly maxima tend to be found from late winter to early spring in Chicago, IL, and New York,
NY, with minima occurring from summer through the fall. However, in Los Angeles and Riverside, CA,
monthly maxima tend to occur from autumn through early winter, with minima occurring from spring
through early summer. Mean and peak NO2 concentrations during winter can be up to a factor of two
greater than those during the summer at sites in Los Angeles.
2.4.4. Diurnal Variability in N02 Concentrations
The diurnal variability in NO2 concentrations at the same two sites in the Atlanta metropolitan area
shown in Figure 2.4-16 is illustrated in Figure 2.4-17. As can be seen from these figures, NO2 typically
exhibits daily maxima during the morning rush hours, although they can occur at other times of day. In
addition, there are differences between weekdays and weekends. At both sites, NO2 concentrations are
generally lower and the diurnal cycles more compressed on weekends than on weekdays. The diurnal
variability of NO2 at these sites is typical of that observed at other urban sites. Monitor siting plays a role
in determining diurnal variability in the sense that monitors located farther from traffic will measure
lower concentrations and show a flatter overall distribution of data compared to monitors located closer to
traffic.
A. Atlanta, GA Suburban Weekday B. Atlanta, GA Suburban
.15i
Weekend
a.
.o ,10
(D
*J
•
» .05
o
o
CM
.10
05
0 2 4 6 8 10 12 14 16 18 20 22 24 0 2 4 6 8 10 12 14 16 18 20 22 24
Hour Hour
C, Atlanta, GA Urban & City Center Weekday D. Atlanta, GA Urban & City Center Weekend
-^ .15, 1 .15,
.1 .10
.05
.10
.05
x x
0 2 4 6 8 10 12 14 16 18 20 22 24 0 2 4 6 8 10 12 14 16 18 20 22 24
Hour Hour
Figure 2.4-17. Mean hourly HOi concentrations on weekdays and weekends measured at two sites in
Atlanta, GA. A and B refer to a suburban site, and C and D refer to a site classified as urban
and city center.
2-25
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2.4.5. Concentrations of NOz Species
Data for concentrations of NOZ species in urban areas in the U.S. are sparse. The most
comprehensive set of data for any NOZ species was obtained for HNO3 as part of the Children's Health
Study for which gas-phase HNO3 was measured at 12 sites in southern California from 1994 through
2001 (Alcorn et al., 2004). Two week avg concentrations ranged from <1 ppb to >10 ppb, with the
highest HNO3 concentrations and the highest ratio of HNO3/NO2 (~0.2) found downwind from central
Los Angeles in the San Bernadino during summer, as one would expect for this more oxidized TV product.
Measurements of HONO in urban areas are very limited; however, data from Stutz et al. (2004) and
Wang and Lu (2006) indicate that levels of HONO are <1 ppb even under heavily polluted conditions,
with the highest levels found during the night and just after dawn and the lowest values found in the
afternoon. However, data collected in the U.K. (AQEG, 2004; Lammel and Cape, 1996) and in the U.S.
(Kirchstetter and Harley, 1996) indicate that HONO to NOX ratios could be of the order of ~5 % in motor
vehicle emissions. These results indicate that HONO levels in traffic could be comparable to those of
NO2. Several field studies conducted at ground level (Hayden et al., 2003, near Boulder CO; Williams et
al., 1987) and aircraft flights (Singh et al., 2007, over eastern North America), have found much higher
NOZ concentrations than NOX concentrations in relatively unpolluted rural air. Additional information for
the concentrations of NOZ species can be found in Annex section AX3.2.5.
2.4.6. Policy-Relevant Background Concentrations of N02
Background NO2 concentrations used for purposes of informing decisions about NAAQS are
referred to as PRB concentrations. PRB concentrations are those that would occur in the U.S. in the
absence of anthropogenic emissions in continental North America (defined here as the U.S., Canada, and
Mexico). PRB concentrations include contributions from natural sources everywhere in the world and
from anthropogenic sources outside these three countries. Background levels defined in this way 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 U.S. These
levels may also be used in quantitative risk assessments of human health and environmental effects.
Contributors to PRB concentrations include natural emissions of NO, NO2, and 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. Biogenic
emissions from agricultural activities, such as emissions of NO from fertilized soils, are not considered to
be contributing to the formation of PRB concentrations. Discussions of the sources and estimates of
emissions are given in Annex section AX2.4.2.
2.4.6.1. Analysis of Policy-Relevant Background Contribution
The MOZART-2 global model of tropospheric chemistry (Horowitz et al., 2003) is used to estimate
the PRB contribution to NO2 concentration. 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; 2006b;
Shindell et al., 2006; Stevenson et al., 2006). MOZART-2 is driven by the U.S. National Oceanic and
Atmospheric Administration's National Center for Environmental Prediction (NOAA NCEP)
meteorological fields using 2001 data and using 2000 emissions from the International Institute for
Applied Systems Analysis (IIASA). The model was run at a resolution of 1.9° X 1.9° with 28 sigma
levels in the vertical dimension with both gas-phase and aerosol chemistry.
2-26
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Total
5.90 510 7.40 9.70
Background
70! 0.06 0.11 O.lft 0.20
F«cefU feKKgrwjna Contntution
ppb
40
Figure 2.4-18. Upper panel: Annual mean N02 concentrations (in ppb) in the U.S. Middle panel: Annual mean
PRB concentrations (in ppb) for N(>2 in the U.S. These simulations were made using the
MOZART-2 global, chemical transport model. The lower panel shows PRB concentrations
expressed as a percentage of total N02 concentrations shown in the upper panel. See text in
Annex section AX2.7 for details.
2-27
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Figure 2.4-18 shows the annual mean NO2 concentration 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 total surface NO2
concentrations are often >5 ppb, PRB is <300 parts per trillion (ppt) over most of the continental U.S. and
<100 ppt in the eastern U.S. The distribution of PRB (middle panel of Figure 2.4-18 largely reflects the
distribution of soil NO emissions, with some local increases like those in western Montana due to
biomass burning. In the northeastern U.S., where present-day NO2 concentrations are highest, PRB
contributes <1% to the total. Thus, it appears that PRB levels of NO2 are much smaller than observed
levels.
2.4.7. Summary of Ambient and Policy-Relevant Background
Concentrations of N02
The annual avg NO2 concentrations of ~15 ppb reported by the regulatory monitoring networks are
well below the level of the current NAAQS (0.053 ppm). However, daily maximum 1-h avg
concentrations can be greater than 100 ppb in some locations, e.g., areas with heavy traffic. Policy-
Relevant Background concentrations of NO2 are much lower than average ambient concentrations and are
typically less than 0.1 ppb over most of the U.S., with the highest values found in agricultural areas.
2.5. Exposure Issues
2.5.1. Introduction
Human exposure to an airborne pollutant consists of contact between the human and the pollutant at
a specific concentration for a specified period of time. People spend various amounts of time in different
microenvironments characterized by different pollutant concentrations. The integrated exposure of a
person to a given pollutant is the sum of the exposures over all time intervals for all microenvironments in
which the individual spends time. Figure 2.5-1 represents a composite average of activity patterns across
all age groups in the U.S. based on data collected in the National Human Activity Pattern Survey
(NHAPS) (Klepeis et al., 2001). The demographic distribution of the respondents was designed to be
similar to that of overall U.S. Census data. Different cohorts, e.g., the elderly, young and middle-aged
working adults, and children exhibit different activity patterns.1
An individual's exposure to a pollutant, such as NO2, can be represented by:
(2.5-1)
where ET is an individual's total personal exposure for a specific time period, n is the total number of
microenvironments encountered, Ct is the average concentration, and tt is the time spent in the rth
microenvironment. The exposure a person experiences can be characterized as an instantaneous exposure,
a peak exposure such as might occur during cooking, an average exposure, or an integrated exposure over
1 For example, the cohort of working adults between the ages of 18 and 65 represents ~50% of the population. Of this total, about 60% work outside
the home, spending ~24% (40 h/168 h) of their time in factory/office environments. Thus, this cohort is likely to spend considerably more time in
offices and factories than shown in the figure (5.4%), which reflects the entire population, and is also likely to spend less time in a residence
compared to small children or the elderly.
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all environments a person encounters. These distinctions are important because health effects caused by
long-term, low-level exposures may differ from those caused by short-term, peak exposures.
NHAPS - Nation, Percentage Time Spent
Total n = y,lW>
IN A RESIDENCE iMi.7%)
OFF1C E-FACTORY < 5.4% I
TOTAL TIME SPENT
IN DOORS (8<>.s>%)
OUTDOORS (7.6%)
IN A VEHICLE (5.5%)
\
OTHER INDOOR LOCATION ( 1 1% i
BAR-RESTAURANT 1 1.8%)
Source: Klepeisetal. (2001)
Figure 2.5-1. Percentage of time people spend in different environments in the U.S.
An individual's total exposure (ET) can also be represented by the following equation:
ET=Ea + Ena = {y0
subject to the constraint,
= {y0
•'na
(2.5-2)
(2.5-3)
where Ea is the person's exposure to pollutants of ambient origin; Ena is the person's exposure to
pollutants that are not of ambient origin; y0 is the fraction of time people spend outdoors and j, is the
fraction of time they spend in microenvironment /'; Finf., Pt, at, and kt are the infiltration factor, penetration
coefficient, air exchange rate, and decay rate for microenvironment /'. In this equation, it is assumed that
microenvironments do not exchange air with each other, but only with the ambient environment. In the
case where microenvironmental exposures occur mainly in a single microenvironment, Equation 2.5-2
may be approximated by Equation 2.5-4:
na (2.5-4)
where y is the fraction of time persons spend outdoors, and a is the ratio of a person's exposure to a
pollutant of ambient origin to the pollutant's ambient concentration (or the ambient exposure factor with a
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value between 0 and 1). Other symbols have the same definitions in Equation 2.5-2 and 2.5-3. If
microenvironmental concentrations are considered, then Equation 2.5-4 can be recast as:
Cme-Ca+CIHI- [Pa/(a+k)]Ca + S/[V(a+k )] (2 5_5)
where Cme is the concentration in a microenvironment; Ca and Cna are the contributions to Cme from
ambient and nonambient sources; S is the microenvironmental source strength; and Vis the volume of the
microenvironment. The symbols in brackets have the same meaning as in Equation 2.5-4.
Microenvironments in which people are exposed to air pollutants such as NO2 typically include
residential indoor environments, other indoor locations, near-traffic outdoor environments, other outdoor
locations, and in vehicles, as shown in Figure 2.5-1. Indoor combustion sources such as gas stoves and
space heaters need to be considered when evaluating exposures to NO2. Exposure misclassification may
result when total human exposure is not disaggregated between various microenvironments, and this may
obscure the true relationship between ambient air pollutant exposure and health outcome.
In a given microenvironment, the ambient component of a person's microenvironmental exposure
to a pollutant is determined by the following physical factors:
• The ambient concentration, Ca
• The air exchange rate, at
* The pollutant specific penetration coefficient, Pt
* The pollutant specific decay rate, kt
* The fraction of time an individual spends in the microenvironment, yt
These factors are in turn affected by the following exposure factors (see Annex section AX3.5):
• Environmental conditions, such as weather and season
• Dwelling conditions, such as house location, which determines proximity to sources and
geographical features that can modify transport from sources; the amount of natural ventilation
(e.g., open windows and doors, and the "draftiness" of the dwelling) and ventilation system (e.g.,
filtration efficiency and operation cycle)
• Personal activities (e.g., the time spent cooking or commuting)
• Indoor sinks of a pollutant
• Microenvironmental line and point sources (e.g., lawn equipment)
Microenvironmental exposures can also be influenced by the individual-specific factors such as age,
gender, health, or socioeconomic status.
Time-activity diaries, completed by study participants, are often used in exposure models and
assessments. The EPA's National Exposure Research Laboratory (NERL) has consolidated the majority
of the most useful human activity databases into one comprehensive database: the Consolidated Human
Activity Database (CHAD). Eleven different human activity pattern studies were evaluated to obtain over
22,000 person-days of 24-h human activities in CHAD (McCurdy et al., 2000). These data can be useful
in assembling population cohorts to be used in exposure modeling and analysis.
In general, the relationship between personal exposures to pollutants of ambient origin and ambient
concentrations can be modified by microenvironments. During infiltration, ambient pollutants can be lost
through chemical and physical loss processes; therefore, the ambient component of a pollutant's
concentration in a microenvironment is not the same as its ambient concentration but the product of the
ambient concentration and the infiltration factor (FirfOr a if people spend 100% of their time indoors). In
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addition, exposure to nonambient, microenvironmental sources modifies the relationship between
personal exposures and ambient concentrations.
In practice, it is extremely difficult to characterize community exposure by individual personal
exposure. Instead, the distribution of personal exposure in a community, or the population exposure, is
characterized by extrapolating measurements of personal exposure using various techniques or by
stochastic, deterministic, or hybrid exposure modeling approaches such as Air Pollution Exposure
(APEX), Simulation of Human Exposure and Dose System (SHEDS), and Modeling Environment for
Total Risk for One-Atmosphere (MENTOR) (see Annex section AX3.6 for a description of exposure
modeling methods). Variations in community-level personal exposures are determined by cross-
community variations in ambient pollutant concentrations and the physical and exposure factors
mentioned above. These factors also determine the strength of the association between population
exposure to NO2 of ambient origin and ambient NO2 concentrations.
Of major concern is the ability of NO2 (as measured by ambient monitors) to serve as a reliable
indicator of personal exposure to NO2 of ambient origin. The key question is what errors are associated
with using NO2 measured by ambient monitors as a surrogate for personal exposure to ambient NO2
and/or its oxidation products in epidemiologic studies. There are three aspects of this issue: (1) ambient
and personal sampling issues; (2) the spatial variability of ambient NO2 concentrations; (3) the
associations between ambient concentrations and personal exposures as influenced by exposure factors,
e.g., proximity to traffic, indoor sources and sinks, and the time people spend indoors and outdoors. These
issues are treated individually in the following subsections.
2.5.2. Personal Sampling of N02
Personal exposures in human exposure and panel studies of NO2 health effects are monitored by
passive samplers. Their performance is evaluated by comparison to the chemiluminescence monitoring
method. Some form of evaluation is crucial for determining measurement errors associated with exposure
estimates. However, measurements of NO2 are subject to artifacts both at the ambient level and at the
personal level. As discussed in Section 2.3, measurements of ambient NO2 are subject to interference
caused by other NOY compounds, in particular HNO3, PANs, HONO, and RONO2.
The most widely used passive samplers are Palmes tubes (Palmes et al., 1976), Yanagisawa badges
(Yanagisawa and Nishimura, 1982), Ogawa samplers (Ogawa and Company, http://www.ogawausa.com),
and radial diffusive samplers (Cocheo et al., 1996). The methodology and application of Palmes tubes and
Yanagisawa badges were described in the last NOX AQCD (U.S. Environmental Protection Agency,
1993). Descriptions of other commercialized samplers are given in Annex section AX3.3. These samplers
do not use a pump to bring air into contact with the sampling substrate; rather, they rely on diffusion or
small scale turbulence to transport NO2 to a sorbent (Krupa and Legge, 2000). The sorbent can be either
physically sorptive (e.g., active carbon) or chemisorptive (e.g., triethanolamine [TEA], KI, sodium
arsenite [NaAsO2]); passive samplers for NO2 are chemisorptive, i.e., a reagent coated on a support (e.g.,
metal mesh, filter) chemically reacts with and captures 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. A number of studies indicate that passive
samplers have very good precision, generally within 5% (Gair et al., 1991; Gair and Penkett, 1995; Kirby
et al., 2001; Plaisance et al., 2004).
NO2 concentrations measured outdoors by Palmes tube passive samplers were a factor of 1.26
higher than those measured by the chemiluminescence method in a study in the UK (Campbell et al.,
1994). Campbell et al. proposed that differences could be attributed mainly to wind-driven turbulent
mixing in the mouth of the Palmes tube. Deposition driven by turbulence would raise the uptake rate on
the tube surface compared to the theoretical value, which is based on molecular diffusion along the length
of the tube. Other field evaluation studies showed that the overall avg NO2 concentrations calculated from
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diffusion tube measurements were likely to be within ± 10% of chemiluminescent measurement data
(Bush et al., 2001) and for Ogawa passive samplers (Mukerjee et al., 2004).
A number of factors could affect the performance of passive samplers. Passive sampler
performance has not been extensively studied over a wide range of concentrations, wind velocities,
temperatures, and relative humidities (Varshney and Singh, 2003). Variability in environmental
conditions (e.g., temperature, wind speed, humidity) can affect the performance of passive samplers
because they can cause variations in sampling rates throughout the sampling period. Chemical reactions
between O3 and NO occur in the diffusion tube especially at roadsides where NO concentrations are
relatively high and when sufficient O3 is present for interconversion between the species resulting in an
overestimate of NO2 (Heal et al., 1999). There could also be differential sensitivity to other forms of NOY,
such as HONO, PAN, and HNO3, between the passive samplers and the chemiluminescence analyzers
(Gair et al., 1991). However, the kinetics and stoichiometry of interferent compound reactions have not
been well established. The lack of specificity of the substrate towards uptake of NO2 could also be an
issue, as SO2 can interfere with the uptake of NO2 (Cox, 2003). Another aspect of passive sampler
performance is that, compared with ambient chemiluminescence monitors, passive samplers give
relatively longer time-averaged concentrations (from days to weeks), with higher detection limits over
short sampling periods. Consequently, diffusive samplers including those used for NO2 monitoring
provide integrated but not high time-resolution concentration measurements. Hourly fluctuations in NO2
concentrations may be important to the evaluation of exposure-health effects relationships, and
continuous monitors, such as the chemilumine scent monitors, remain the only approach for estimating
short-term, peak exposures.
2.5.3. Spatial Variability in N02 Concentrations
2.5.3.1. Variability of N02 Concentrations across Ambient Monitoring Sites
Summary statistics for the spatial variability in several urban areas across the U.S. are shown in
Table 2.5-1. Data were obtained from EPA's Air Quality System (AQS). These areas were chosen
because they are major urban areas with at least five monitors operating from 2003 to 2005. Values in
parentheses indicate the number of monitoring sites in that particular city. The second column shows the
3-year mean concentration across all monitors and the range in these means at individual monitor sites.
Metrics for characterizing spatial variability include the use of Pearson correlation coefficients (r; column
3), the 90th percentile of the absolute difference in concentrations (column 4), and coefficient of
divergence (COD; column 5). The ranges represent results of pairwise monitor comparisons.
These three metrics are calculated based on measurements of daily avg concentrations at individual
site pairs. The COD provides an indication of the variability across the monitoring sites in each city and is
defined in Equation 2.5-6, as follows
i=l '' (2.5-6)
where Xy and Xik represent observed concentrations averaged over some measurement averaging period
(hourly, daily, etc.), for measurement period / at site/ and site k, and/? is the number of observations. A
COD of 0 indicates there are no differences between concentrations at paired sites (spatial homogeneity),
while a COD approaching 1 indicates extreme spatial heterogeneity.
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Table 2.5-1. Spatial variability of NO2 in selected U.S. urban areas.
URBAN AREA
(# OF MONITORS)
New York, NY (5)
Atlanta, GA (5)
Chicago, IL (7)
Houston, TX (7)
Los Angeles, CA(14)
Riverside, CA(9)
3-YEAR MEAN
CONCENTRATION
(RANGE)
29 ppb (25-37)
11 ppb (5-1 6)
22 ppb (6-30)
13 ppb (7-1 8)
25 ppb (14-33)
21 ppb (5-32)
PEARSON
CORRELATION
COEFFICIENT
0.77-0.90
0.22-0.89
-0.05-0.83
0.31-0.80
0.01-0.90
0.03-0.84
90™ PERCENTILE
DIFFERENCE
BETWEEN
MONITORS
7-19
7-24
10-39
6-20
8-32
10-40
COEFFICIENT OF
DIVERGENCE
0.08-0.23
0.15-0.59
0.13-0.66
0.13-0.47
0.08-0.51
0.14-0.70
The same statistics shown in Table 2.5-1 have been used to describe the spatial variability of PM25
(U.S. Environmental Protection Agency, 2004; Pinto et al., 2004) and O3 (U.S. Environmental Protection
Agency, 2006a).
Because of relative sparseness in data coverage for NO2, spatial variability in all cities considered
for PM2 5 and O3 could not be considered here. Thus, the number of cities included here is much smaller
than for either O3 (24 urban areas) or PM2 5 (27 urban areas). For urban areas with monitors for all three
pollutants, data may have been collected at different locations, with different responses to local sources.
For example, concentrations of NO2 collected near traffic will be highest in an urban area, but
concentrations of O3 will tend to be lowest there because of titration by NO forming NO2. However, some
general observations can still be made. Mean concentrations of NO2 at individual monitoring sites are not
as highly variable as for O3 but are more highly variable than PM2 5. Lower bounds on intersite correlation
coefficients for PM2 5 and for O3 tend to be much higher than for NO2 in the same areas shown in Table
2.5-1. CODs for PM2 5 are much lower than for O3, whereas CODs for NO2 tend to be the largest among
these three pollutants. The greater spatial variability for NO2 compared to O3 and PM2 5 could lead to
larger exposure error in epidemiologic studies.
2.5.3.2. Small-Scale Horizontal Variability
Large gradients in NO2 concentrations near roadways have been observed in several studies, and
NO2 concentrations have been found to be correlated (or inversely correlated) with distance from
roadway, traffic volume, season, road length, open space, and population density (Bignal et al., 2007;
Cape et al., 2004; Gauderman et al., 2005; Gilbert et al., 2007; Maruo et al., 2003; Monn et al., 1997;
Pleijel et al., 2004; Roorda-Knape et al., 1998; 1999; Singer et al., 2004). A sample gradient is shown in
Figure 2.5-2.
Singer et al. (2004) found a strong gradient for concentrations downwind of freeways within the
first 230 m. An exponential decay model (e.g., Cape et al., 2004) has been fit to near-road concentration
data to estimate NO2 concentration as a function of distance from the roadway. Gilbert et al. (2007) found
that associations remained robust when sites within 200 m of roadways were removed from the analysis,
indicating that traffic influences concentrations as far as 2000 to 3000 m from roadways. Small-scale
spatial variations in NO2 concentrations are more pronounced during spring and summer seasons due to
meteorology and increased photochemical activity (Monn, 2001).
2-33
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CM
O 1.5
Z
.§ 1-°
_
0 0.5
Z
0.0
2.5
X
Z 2'°
fl) 4 £•
y 1.5
"ro
E 1.0
o
Z 0.5
0.0
0
Df
i i i
I
n
a
i i i
* 1-880
I D 1 - 580
„ — CA92
« I
I I I
T
1|
s 5
i i i
-2000 -1500 -1000 -500 0 500 1000 1500 2000
Downwind distance to nearest freeway (m)
Source: Singer etal. (2004).
Figure 2.5-2. N02 and NOx concentrations normalized to ambient values, plotted as a function of downwind
distance from the freeway. Symbols indicate freeway closest to each monitor.
Localized effects of roadway sources lead to variability in NO2 concentrations that is not captured
by the regulatory monitoring network. This variation affects population-level exposure estimates and adds
exposure error to time-series epidemiologic studies relying on ambient concentrations as indicators of
exposure. Elevated concentrations near roadways also increase exposure of anyone residing, working, or
attending school in the vicinity. As discussed in Chapter 4, these elevated concentrations found near
roadways may lead to increased vulnerability among those exposed to high near-roadway concentrations
ofNO2.
2.5.3.3. Small-Scale Vertical Variability
Inlets to instruments for monitoring gas-phase criteria pollutants can be located from 3 to 15m
above ground level (CFR, 2002). Depending on the pollutant, there can be a positive, negative, or no
vertical gradient from the surface to the monitor inlet. Positive gradients (i.e., concentrations increase
with height) result when pollutants are formed over large areas by atmospheric photochemical reactions
(i.e., secondary pollutants such as O3) and destroyed by deposition to the surface or by reaction with
2-34
-------
pollutants emitted near the surface. Pollutants that are emitted by sources at or just above ground level
show negative vertical gradients. Pollutants with area sources (widely dispersed surface sources) and that
have minimal deposition velocities show little or no vertical gradient. Restrepo et al. (2004) compared
data for criteria pollutants collected at fixed monitoring sites at 15 m above the surface on a school
rooftop to those measured by a van whose inlet was 4 m above the surface at monitoring sites in the South
Bronx during two sampling periods in November and December 2001. They found that CO, SO2, and
NO2 showed negative vertical gradients, whereas O3 showed a positive vertical gradient and PM2 5
showed no significant vertical gradient. As shown in Figure 2.5-3, NO2 mixing ratios obtained at 4 m
(mean ~74 ppb) were about a factor of 2.5 higher than at 15 m (mean ~30 ppb). Because tail pipe
emissions occur at lower heights, NO2 values could have been much higher nearer to the surface and the
underestimation of NO2 values by monitoring at 15 m even larger. Restrepo et al. (2004) noted that the
use of the NO2 data obtained by the stationary monitors underestimates human exposures to NO2 in the
South Bronx. This situation is not unique to the South Bronx and could arise in other large urban areas in
the U.S. with similar settings. This adds another dimension to the exposure assessment, namely, the
exposure of pedestrians who spend time walking in these street canyons, and urban residents, who have
windows opening onto these canyons. These groups may experience high exposures to near-road
concentrations of the same magnitude as exposures that occur on or adjacent to arterial and interstate
roadways.
0.12
0.1
0.08
0.06
0.04
0.02
* *
* *
* *
*
W*
^V
Van — *- - DEC709406 ---*-- DEC709407
Source: Restrepo etal. (2004).
Figure 2.5-3. N02 concentrations measured at 4 m (Van) and at 15 m at NY Department of Environmental
Conservation ambient monitoring sites(DEC709406 and DEC709407).
2-35
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The magnitude of the vertical gradient of NO2 in street canyons depends strongly on the
configuration of the buildings forming the canyons and the meteorological conditions; in particular, static
stability in the lower planetary boundary layer, local wind direction and speed, and differential solar
heating all affect turbulence in street canyons. These meteorological factors also help determine the
relative importance of turbulence induced by traffic, in addition to traffic volume and speed. Detailed
descriptions of the effects for many of these factors are available only from complex numerical models
such as large eddy simulations and very fine grid resolution computational fluid dynamics (CFD) models.
A semi-empirical integral model with simplifying assumptions has shown reasonable correlation to
measured NO2 concentrations over moderate time scales (1 month) (Berkowicz et al., 2008), while other
studies have applied such models to urban neighborhoods to estimate traffic emissions and evaluate the
representativeness of air quality monitoring data (Ghenu et al., 2008; Mensink and Cosemans, 2008;
Vardoulakis et al., 2005). By constructing simplified geometries, investigators can obtain good agreement
between the performance of integral and CFD models; however, generalization and quantitative
application of these results to complex urban situations, even at the same location at different times, is
difficult due to multi-scale variability in meteorological conditions, traffic composition and flow, building
geometry, street dimensions, street canyon aspect ratios, and building packing density (Di Sabatino et al.,
2007).
Weak associations might be found between concentrations at ambient monitors and other outdoor
locations and between concentrations in indoor microenvironments and personal exposures in part
because of the spatial (horizontal and vertical) variability in NO2. This variability is itself location- and
time-dependent, and can lead to either over- or underestimates of exposure, depending on the siting of
monitors and location of the exposed population. NO2 ambient monitors may be less representative of
community or personal exposures than are ambient monitors for O3 or PM25 for their respective
exposures. This conclusion is based on a comparison of metrics of spatial variability for O3 or PM25 used
in the last PM AQCD (U.S. Environmental Protection Agency, 2004) and O3 AQCD (U.S. Environmental
Protection Agency, 2006a), indicating generally lower correlations and larger relative spreads in
concentrations than for O3 or PM2 5. As mentioned earlier, there are far fewer monitors for NO2 than for
O3 or PM2 5, making estimation of the spatial variability in NO2 levels more difficult.
2.5.4. N02 On or Near Roads
Lee et al. (2000) reported that NO2 concentration in heavy traffic (-60 ppb) can be more than dou-
ble that of the residential outdoor level (-26 ppb) in North America. Westerdahl et al. (2005) reported
on-road NO2 concentrations in Los Angeles ranging from 40 to 70 ppb on freeways, compared to 20 to
40 ppb on residential or arterial roads. NOX concentrations measured at the Caldecott Tunnel in San Fran-
cisco in 1999 (Kean et al., 2001) were approximately 7-fold higher at the tunnel exit than at the entrance
(1500 ppb versus 200 ppb). People in traffic can potentially experience high concentrations of NO2 as a
result of the high air exchange rates in vehicles. Park et al. (1998) observed that the air exchange in cars
varied from 1 to 3 times per hour, with windows closed and no mechanical ventilation, to 36 to 47 times
per h, with windows closed and the fan set on fresh air. These results imply that the NO2 concentration
inside a vehicle could rapidly approach the level outside the vehicle during commuting.
While driving, concentrations for personal exposure in a vehicle cabin could be substantially higher
than ambient concentrations measured nearby. Sabin et al. (2005) reported that NO2 concentrations in the
cabins of school buses in Los Angeles ranged from 24 to 120 ppb, which were typically factors of 2 to 3
(max, 5) higher than at ambient monitors in the area. Lewne et al. (2006) reported work hour exposures to
NO2 for taxi drivers (25.1 ppb), bus drivers (31.4 ppb), and truck drivers (35.6 ppb). These levels were
1.8, 2.7, and 2.8 times the ambient concentrations. Riediker et al. (2003) studied the exposure to NO2
inside patrol cars. The authors found that the mean and maximum NO2 concentrations in a patrol car were
41.7 ppb and 548.5 ppb compared to 30.4 ppb and 69.5 ppb for the ambient sites. These studies indicate
that people in traffic can be exposed to much higher levels of NO2 than are measured at ambient
2-36
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monitoring sites. Due to high peak exposures while driving, total personal exposure could be
underestimated if exposures while commuting are not considered, and sometimes exposure in traffic can
dominate personal exposure to NO2 (Lee et al., 2000; Son et al., 2004). Variations in traffic-related expo-
sure could be attributed to time spent in traffic, type of vehicle, ventilation in the vehicle, and distance
from major roads (Chan et al., 1999; Sabin et al., 2005; Son et al., 2004). Sabin et al. (2005) reported that
the intrusion of the vehicle's own exhaust into the passenger cabin is another NO2 source contributing to
personal exposure while commuting, but that the fraction of air inside the cabin from a vehicle's own
exhaust was small, ranging from 0.02 to 0.28% and increasing with the age of the vehicle (CARB,
2007a,b).
Distance to major roadways could be another factor affecting indoor and outdoor NO2 concentra-
tion and personal NO2 exposure. Many studies show that outdoor NO2 levels are strongly associated with
distance from major roads (i.e., the closer to a major road, the higher the NO2 concentration) (Cotterill
and Kingham, 1997; Gilbert et al., 2005; Gonzales et al., 2005; Kodama et al., 2002; Lai and Patil, 2001;
Nakai et al., 1995; Roorda-Knape et al., 1998). Meteorological factors (wind direction and wind speed)
and traffic density are also important in interpreting measured NO2 concentrations (Aim et al., 1998;
Gilbert et al., 2005; Nakai et al., 1995; Roorda-Knape et al., 1998; Rotko et al., 2001; Singer et al., 2004).
For example, Roorda-Knape et al. (1998) reported that NO2 concentrations in classrooms were
significantly correlated with car and total traffic density (r=0.68), percentage of time downwind (r=0.88),
and distance of the school from the roadway (r=-0.83). Singer et al. (2004) reported results of the East
Bay Children's Respiratory Health Study. The authors found that NO2 concentrations increased with de-
creasing downwind distance for school and neighborhood sites within 350 m downwind of a freeway, and
schools located upwind or far downwind of freeways were generally indistinguishable from one another
or by regional pollution levels.
Personal exposure is associated with traffic density and proximity to traffic, although personal ex-
posure is also influenced by indoor sources. Aim et al. (1998) reported that weekly avg NO2 exposures
(geometric mean) were higher (p=0.0001) for children living in the downtown area of Helsinki (13.8 ppb)
than in the suburban area (9.1 ppb). Within the urban area of Helsinki, Rotko et al. (2001) observed that
the NO2 exposure was significantly associated with traffic volume near homes. The average exposure
level of 138 subjects having low or moderate traffic near their homes was 12.3 ppb, while the level was
15.8 ppb for the 38 subjects having high traffic volume near home. Gauvin et al. (2001) reported that the
ratio of traffic density to distance from a roadway was one of the significant predictors of personal
exposure in Grenoble, Toulouse, and Paris. After controlling for indoor source impacts on personal
exposure, Kodama et al. (2002) and Nakai et al. (1995) observed that personal exposure decreased with
increasing distance from residence to major road.
Although traffic is a major source of ambient NO2, industrial point sources are also contributors to
ambient NO2. Nerriere et al. (2005) measured personal exposures to PM2 5, PM with an aerodynamic
diameter of < 10 (im (PMi0), and NO2 in traffic-dominated, urban background, and industrial settings in
four French cities (Paris, Grenoble, Rouen, and Strasbourg). Ambient concentrations and personal
exposures for NO2 were generally highest in the traffic-dominated sector. It should be remembered that
there can be high traffic emissions (including shipping traffic) in industrial zones, such as in the Ship
Channel in Houston, TX, and in the Port of Los Angeles, CA. In rural areas where traffic is sparse, other
sources could dominate. Martin et al. (2003) found that pulses of NO2 released from agricultural areas
occur after rainfall. Other rural contributors to NO2 include wildfires and residential wood burning.
2.5.5. Indoor Sources and Sinks of N02 and Associated Pollutants
Indoor sources and indoor air chemistry of NO2 are important, because they influence the indoor
NO2 concentrations to which humans are exposed and contribute to total personal exposures. These
indoor source and sink terms must be characterized in an exposure assessment if the fraction of a person's
exposure to NO2 of ambient origin is to be determined.
2-37
-------
Penetration of outdoor NO2 and indoor combustion in various forms are the major sources of NO2
to indoor environments, e.g., homes, schools, restaurants, theaters. As might be expected, indoor
concentrations of NO2 in the absence of combustion sources are determined by the infiltration of outdoor
NO2 (Levy et al, 1998b; Spengler et al., 1994; Weschler et al., 1994). Contributions to indoor NO2 from
the reaction of NO in exhaled breath with O3 could potentially be important in certain circumstances (see
Annex section AX3.4.2 for sample calculations). Indoor sources of nitrogen oxides have been
characterized in several reviews, namely the last NOX AQCD (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, 2007a). Mechanisms by which NOX is produced in the combustion zones
of indoor sources were reviewed in the lastNOx AQCD (U.S. Environmental Protection Agency, 1993).
It should be noted that indoor sources can affect ambient NO2 levels, particularly in areas in which
atmospheric mixing is unlikely, such as in valleys.
Combustion of fossil and biomass fuels is the major indoor source of nitrogen oxides. Combustion
of fossil fuels occurs in appliances used for cooking, heating, and drying clothes, e.g., coal stoves, oil
furnaces, kerosene space heaters. Motor vehicles and various types of generators in structures attached to
living areas also contribute NO2 to indoor environments. Indoor sources of NO2 from combustion of
biomass include wood-burning fireplaces and wood stoves and tobacco.
Many studies have noted the importance of gas cooking appliances as sources of NO2 emissions.
Depending on geographical location, season, other sources of NO2, and household characteristics, homes
with gas cooking appliances have approximately 50% to over 400% higher NO2 concentrations than
homes with electric cooking appliances (Garcia-Algar et al., 2003; Gilbert et al., 2006; Leaderer et al.,
1986; Lee et al., 2000; Raw et al., 2004). Gas cooking appliances remain significantly associated with
indoor NO2 concentrations after adjusting for several factors that influence exposures, 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). Homes with gas appliances with pilot lights emit more
NO2, resulting in NO2 concentrations ~10 ppb higher than in homes with gas appliances with electronic
ignition (Lee et al., 1998; Spengler et al., 1994).
Secondary heating appliances are additional sources of NO2 in indoor environments, particularly if
the appliances are unvented or inadequately vented. As heating costs increase, the use of these secondary
heating appliances tends to increase. Gas heaters, particularly when unvented or inadequately vented,
produce high levels of indoor NO2 (Kodama et al., 2002). Results summarized by Brauer et al. (2007)
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. On the other hand, mean concentrations of NO2 were all
<10 ppb in a study of Canadian homes with vented gas and oil furnaces and electric baseboard heaters
(Weichenthal et al., 2007), indicating that these are not likely to be major sources of NO2 to indoor
environments.
Table 2.5-2 shows avg concentrations of NO2 in homes while combustion sources (mainly gas
fired) were in operation. Averaging periods ranged from minutes to hours in the studies shown.
Table 2.5-3 shows 24-h to 2-week-long avg concentrations of NO2 in homes with primarily gas
combustion sources.
As can be seen from Tables 2.5-2 and 2.5-3, average concentrations while appliances are in
operation tend to be much higher than longer-term averages. As Triche et al. (2005) indicated, the 90th
percentile concentrations can be substantially greater than the medians, even for 2-week samples. This
finding illustrates the high variability of indoor NO2 found among homes, reflecting differences in
ventilation of emissions from sources, air exchange rates, the size of rooms, etc. The concentrations for
short averaging periods listed in Table 2.5-2 correspond to -10 to 30 ppb on a 24-h avg basis. As can be
seen from inspection of Table 2.5-3, these sources would contribute significantly to the longer-term
averages reported if operated daily on a similar schedule. This implies measurements made with long
2-38
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averaging periods may not capture the nature of the diurnal pattern of indoor concentrations of NO2 in
homes with strong indoor sources, a problem that becomes more evident as ambient NO2 levels decrease
with more efficient controls on outdoor sources.
Table 2.5-2. NO2 concentration near indoor sources: minute to hour averages.
STUDY
Fortmann
et al. (2001)
Fortmann
et al. (2001)
Dutton et al.
(2001)
Girman et al.
(1982)
Girman et al.
(1982)
Girman et al.
(1982)
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 gas range for 2 h, 20 min; 7 h TWA.
Self-cleaning gas range. Avgs are over the entire cycle.
Natural gas unvented fireplace, 0.5 h TWA 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 heaters1 operating in a 1400 ft2 house, 1 .0/h for air
exchange rate.
NR=not reported; TWA=time-weighted avg; Unvented appliances are not permitted in many areas, including California.
Table 2.5-3. NO2 concentration near indoor sources: 24-h to 2 week averages.
STUDY
Lee etal. (1998)
Triche et al. (2005)
Zipprich et al. (2002)
AVERAGE CONCENTRATION
30 to 33 ppb
22 ppb
6 to 11 ppb
55 (Median)
41 (90th percentile)
80 (90th percentile)
84 (90th percentile)
1 47 (90th percentile)
52 (90th percentile)
18 ppb
19 ppb
15 ppb
COMMENT
Gas stoves with pilot lights
Gas stoves without pilot lights
Electric ranges
Study conducted in 517 homes in Boston. Values represent 2-wk avgs
Gas space heaters
No indoor combustion sources
Fireplaces
Kerosene heaters
Gas space heaters
Wood stoves
All values represent 2-wk avgs in living rooms
Bedrooms
Living rooms
Outdoors
Almost all homes had gas stoves. Values represent 2-wk avgs
2-39
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The emissions of NO2 from burning biomass fuels indoors have not been characterized as
extensively as those from burning gas. A main conclusion from the 1993 NOX AQCD was that properly
vented wood stoves and fireplaces would make only minor contributions to indoor NO2 levels, and
several studies have concluded that using wood-burning appliances does not increase indoor NO2
concentrations (Levesque et al., 2001; Triche et al., 2005).
Other indoor combustion sources of NO2 are candle burning and smoking. 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 (data not reported). 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., Aim et al., 1998;
Cyrys et al., 2000; Farrow et al., 1997; Garcia Algar et al., 2004; Lee et al., 2000; Levy et al., 1998a;
Linaker et al., 1996; Monn et al., 1998), whereas others have not (e.g., Hackney et al., 1992; Kawamoto
etal., 1993).
2.5.5.1. Indoor Air Chemistry
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 O3 or peroxyl radicals, while NO2 is removed by gas-phase
reactions with O3 and assorted free radicals and by surface-promoted hydrolysis and reduction reactions.
The concentration of indoor NO2 also affects the decomposition of PAN.
Indoors, NO can be oxidized to NO2 by reacting with O3 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; Sarwar et al., 2002b; Carslaw et al.,
2007; Nazaroff and Weschler, 2004).
At an indoor O3 concentration of 10 ppb and an indoor NO concentration that is significantly
smaller than that of O3, the half-life of NO is 2.5 min (using kinetic data contained in Jet Propulsion
Laboratory, 2003). 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
(Weschler et al., 1994).
NO2 reacts with O3 to produce nitrate radicals (NO3). To date, there have been no indoor
measurements of the concentration of NO3 radicals in indoor settings. Modeling studies by Nazaroff and
Cass (1986), Weschler et al. (1992), Sarwar et al. (2002b), and Carslaw (2007) estimate indoor NO3
radical concentrations in the range of 0.01 to 5 ppt, depending on the indoor levels of O3 and NO2. Once
formed, NO3 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 NO3 radical may be a
more important indoor oxidant than either O3 or the OH radical (Nazaroff and Weschler, 2004; Wayne et
al., 1991). Thus, NO3 radicals and the products of NO3 radical chemistry could contribute to uncertainty
in NO2 exposure-health outcome 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 onto indoor surfaces to produce mono- and dinitro-PAHs. 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
various indoor surfaces was reemitted as NO. (Weschler and Shields, 1996) found that the amount of NO2
removed by charcoal filters used in buildings were almost equally matched by the amount of NO
subsequently emitted by the same filters.
2-40
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NO2 can also be converted to HONO by reactions in indoor air. As noted above, HONO occurs in
the atmosphere mainly through multiphase processes involving NO2. HONO has been observed to form
on surfaces containing partially oxidized aromatic structures (Stemmler et al., 2006) and on soot particles
(Ammann et al., 1998). Indoors, surface-to-volume ratios are much larger than they are outdoors, and the
surface-mediated hydrolysis of NO2 is a major indoor source of HONO (Brauer et al., 1990; 1993; Febo
and Perrino, 1991; Lee et al., 2002; Spengler et al., 1993; Spicer et al., 1993; Wainman et al., 2001). Lee
et al. (2002) reported average indoor HONO levels were ~6 times higher than outdoor levels (4.6 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 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 increases with relative humidity. Spicer et al. (1993) reported that an equilibrium between
adsorption of HONO from the gas range (or other indoor combustion sources) and HONO produced by
surface reactions determines the relative importance of these processes in producing HONO in indoor air.
A person's total exposure to NO2 cannot be estimated based on consideration of the estimates of
emissions given in emissions inventories. Indoor and other microenvironmental sources and a person's
activity pattern must be considered in determining the sources that exert the largest influence on a
person's total exposure to NO2. As examples, exposures in vehicle cabins while commuting to/from
school or work, or exposures associated with operation of off-road engines (e.g., lawn and garden or
construction equipment), could be larger than integrated 24-h exposures due to infiltration of outdoor air
into a home.
2.5.6. Relationship of Personal Exposure to Ambient Concentrations
2.5.6.1. Associations between Personal Exposure and Ambient and Outdoor
Concentrations
Results of studies reporting associations between ambient concentrations and personal exposures
are shown in Table 2.5-4 for longitudinal correlation coefficients, Table 2.5-5 for pooled correlation
coefficients. Results of studies reporting associations between outdoor concentrations and personal
exposures are shown in Table 2.5-6. Study designs (longitudinal, daily-averaged, and pooled) used in
these studies are summarized in Tables 2.5-4 and 2.5-5.
2-41
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Table 2.5-4. Association between personal exposure and ambient concentration (longitudinal
correlation coefficients).
STUDY
Linaker et al.
(2000)
Location:
Southampton,
Hampshire, UK
Time period:
Oct 1994 to Dec
1995
Kim et al.
(2006)
Location:
'
ana a
Time period:
Aug 1999 to
Nov 2001
Sarnat et al.
(2001)
Koutrakis et al.
(2005)
Location:
Baltimore, MD
Time period:
summer of 1998
and winter of
1999
Sarnat et al.
(2005)
Koutrakis et al.
(2005)
Location:
Boston MA
Time period:
summer of
1999; winter of
2000
METHODS
Type: Longitudinal
Subjects: 114 asthmatic
children, aged 7-12
Method: at least 16 consecutive
samples (1-wk avgs) for each
child (mean duration of follow-
up: 32 wks).
Type: Longitudinal
Subjects: 28 adults with
coronary artery disease
Method: 1 day/wk, 24-h avg, for
a max of 10 wks for each
person.
Type: Longitudinal
Subjects: 56 seniors,
schoolchildren, and people with
COPD
Method: 14 of 56 subjects
participated in both sampling
seasons; all subjects were
monitored for 12 consecutive
days (24-h avg samples) in
each of the one or two seasons,
except children, who were
measured for 8 consecutive
days during the summer.
Type: Longitudinal
Subjects: 43 seniors and
schoolchildren
Method: Similar study design as
Sarnat et al. (2001).
MEAN
CONCENTRATION
Ambient: 6.5 ppb
Personal: 8.9 ppb
Ambient: 24 ppb
Personal: 14 ppb
Ambient: 20-25 ppb
Personal: 10-15 ppb
Ambient: 21.1-
32.6 ppb
Personal: 10.6-
29.6 ppb
ASSOCIATION
VARIABLE
Personal vs. central
(overall measure-
ments across
children and time)
Personal vs. central
(subject-wise)
Personal vs. central
(subject-wise)
Personal vs. central
(subject-wise)
Personal vs. central
(subject wise)
LOCATION
Pooled, urban,
no major
indoor sources
By person
Urban
Urban
Urban
SEASON
Pooled
Pooled
Summer
Winter
Summer
Winter
rp, rs, or R2
Not significant
(n=NR)
-0.77 to 0.68
and median
-0.02 (rp)
(n=NR)
-0.36 to 0.94 (rs)
with a median of
0.57 (15
subjects)
-0.45 to 0.85 (rs)
with a median of
0 05* (24
subjects)
-0.6 to 0.75 (rs)
with a median of
0 05* (45
subjects)
-0.25 to 0.5 (rs)
with a median of
0.3* (n=NR)
Slope=0.19
(95% Cl, 0.08-
0.30)
-0.5 to 0.9 (rs)
with a median of
0.4* (n=NR)
Slope=-0.03
(95% Cl, -0.21-
0.15)
* Values were estimated from figures in the original paper.
* NR: Not Reported.
2-42
-------
Table 2.5-5. Association between personal exposure and ambient concentration (pooled
correlation coefficients).
STUDY
Linn etal. (1996)
Location:
Southern
California
Time period: fall,
winter, spring,
1992-1994
Aim etal. (1998)
Location: Helsinki,
Finland
Time period:
winter and spring,
1991
Liard etal. (1999)
Location: Paris,
France
Time period: May-
June 1996
STUDY DESIGN
Type: Longitudinal
Subjects: 269 school children
Method: 24-h avg, 1-wk consecutive
measurement for each season for
each child.
Type: Longitudinal
Subjects: 246 children aged 3-6 yrs
old
Method: 1-wk averaged sample for
each person, 6 consecutive wks in the
winter and 7 consecutive wks in the
spring.
Type: Daily avg/cross-sectional
Subjects: 55 adults and 39 children
Method: Three 4-day avg measure-
ments for each person, during each
measurement session, all subjects
measured at same time.
MEAN
CONCENTRATION
Ambient: 37 ppb
Personal: 22 ppb
Ambient: 16.8-
26.3 ppb
Personal: 9-16.6 ppb
Ambient: 26.3-
36.8 ppb
Personal: 15.8-
26.3 ppb
ASSOCIATION
VARIABLE
Personal vs.
central
Personal vs.
central
Personal vs.
central
Personal vs.
central
Personal vs.
central
Personal vs.
central
Personal vs.
central
Personal vs.
central
Personal vs.
central
Personal vs.
central
Personal vs.
central
Personal vs.
central
Personal vs.
central
Adults vs. central
Children vs. central
LOCATION
Pooled
Downtown
Suburban
Downtown
Suburban
Downtown
(electric stove
home)
Downtown
(gas stove
home)
Suburban
(electric stove
home)
Downtown
(non-smoking
home)
Downtown
(smoking
home)
Suburban
(non-smoking
home)
Suburban
(smoking
home)
Pooled
Urban
Urban
SEASON
Pooled
Spring
Spring
Winter
Winter
Pooled
Pooled
Pooled
Pooled
Pooled
Pooled
Pooled
Pooled
Summer
Summer
rp, rs, or
R2
0.63 (rp)
(n=107)
0.64 (rp)
p<0.001
(n=NR**)
0.78 (rp)
p<0.001
(n=NR)
-0.06 (rp)
p >0.05
(n=NR)
0.32 (rp) p
>0.05
(n=NR)
0.42 (rp)
p<0.01
(n=NR)
0.16(rp)p
>0.01
(n=NR)
0.55 (rp)
p<0.001
(n=NR)
0.47 (rp)
p<0.001
(n=NR)
0.23 (rp) p
>0.01
(n=NR)
0.53 (rp)
p<0.001
(n=NR)
0.52 (rp)
p<0.001
(n=NR)
0.37 (R2)
(n=24)
0.41 (R2)
p<0.0001
(n=NR)
0.17(R2)
p=0.0004
(n=NR)
2-43
-------
STUDY
Gauvin et al.
(2001)
Location: 3 French
metropolitan areas
Time period: Apr-
June 1998 in Gre-
noble; May-June
1998 in Toulouse;
June-Oct 1998 in
Paris
Piechocki-Minguy
et al. (2006)
Location: Lille
(northern France)
Time period:
winter 2001 (first
campaign);
summer 2002
(second
campaign)
Sarnat et al.
(2006)
Location:
Steubenville, OH
Time period:
summer and fall of
2000
STUDY DESIGN
Type: Daily avg/cross-sectional
Subjects: 73 children
Method: one 48-h avg measurement
for each child; all children in the
same city were measured on the
same day.
Type: Pooled
Subjects: 13 in 1st campaign, 31 in 2nd
Method: two 24-h sampling periods
(1on workdays; 1on weekends) for
each subject in each campaign;
during each sampling period, each
subject received 4 samplers to
measure personal exposure in 4
different microenvironments (home,
other indoor, transport, and outdoors).
Type: Longitudinal
Subjects: 15 senior subjects
Method: two consecutive 24-h
samples were collected for each
subject for each wk, 23 wks total
MEAN
CONCENTRATION
Ambient: 10.2-
25.7 ppb
Personal: 13.2-17 ppb
Ambient: 15.8-
57.9 ppb
Personal: 8.9-
20.0 ppb
Ambient: 9.5-11.3 ppb
Personal: 9.9-
12.1 ppb
ASSOCIATION
VARIABLE
Personal vs.
central (Grenoble)
Personal vs.
central (Toulouse)
Personal vs.
central (Paris)
Personal
(exposure at
home) vs. central
Personal
(exposure at
home) vs. central
Personal vs.
central
LOCATION
Urban
Urban
Urban
Urban
Urban
(electric stove
and electric
heater home)
Urban
SEASON
Pooled
Pooled
Pooled
Pooled
Summer
Summer
Fall
rp, rs, or
R2
0.01 (R2)
(n=NR)
0.04 (R2)
(n=NR)
0.02 (R2)
(n=NR)
0.09 (R2)
p=0.0101
(n=NR)
0.61 (R2)
p=0.0001
(n=NR)
0.14 (R2)
(n=122)
p<0.05
0.43 (R2)
p<0.05
(n=138)
* Values were estimated from figures in the original paper.
Table 2.5-6. Association between personal exposure and outdoor concentration.
STUDY
Kramer et al.
(2000)
Location:
Germany
Time period:
Mar and Sep
1996
Rojas-Bracho
et al. (2002)
Location:
Santiago, Chile
Time period:
winters of 1 998
and 1999
METHODS
Subjects: 191 children
Method: two 1-wk averaged measurements for
each
child in each mo.
Subjects: 20 children
Method: five 24-h avg samples for 5 consecutive
days for each child.
ASSOCIATION
VARIABLE
Personal vs.
outdoor
Personal vs.
outdoor
Personal vs.
outdoor
LOCATION
Pooled
Urban
Urban
SEASON
Pooled
Pooled
Winter
rp, rs, or R2
0.37 (rp) (n=281)
0.06 (rp) (n=182)
0.27 (R2) (n=87)
2-44
-------
STUDY
Raaschou-
Nielsen et al.
(1997)
Location:
Copenhagen,
Denmark and
rural areas
Time period:
Oct 1994, Apr,
May, and June
1995
Aim et al.
(1998)
Location:
Helsinki,
Finland
Time period:
winter and
spring of 1991
Monn et al.
(1998)
Location: Ge-
neva, Basel,
Lugano, Aarau,
Wald, Payerne,
Montana, and
Davos (SA-
PALDIA study,
Switzerland)
Time period:
Dec 1 993 to
Dec 1994
METHODS
Subjects: 204 children
Method: two 1-wk avg measurements for each child
in each mo.
Subjects: 246 children aged 3-6 yrs old
Method: 1-wk averaged sample for each person for
6 consecutive wks in the winter and 7 consecutive
wks in the spring.
Subjects: 140 subjects
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).
ASSOCIATION
VARIABLE
Personal vs.
outdoor
Personal vs.
outdoor
Personal vs.
outdoor
Personal vs.
outdoor
Personal vs.
outdoor
Personal vs.
outdoor
Personal vs.
outdoor
Personal vs.
outdoor
Personal vs.
outdoor
Personal vs.
outdoor
Personal vs.
outdoor
Personal vs.
outdoor
Personal vs.
outdoor
Personal vs.
outdoor
Personal vs.
outdoor
LOCATION
Urban
Rural
Downtown
Suburban
Downtown
Suburban
Downtown
(electric stove
home)
Downtown (gas
stove home)
Suburban
(electric stove
home)
Downtown (non-
smoking home)
Downtown
(smoking home)
Suburban (non-
smoking home)
Suburban
(smoking home)
Pooled
Pooled
SEASON
Pooled
Pooled
Winter
Winter
Spring
Spring
Pooled
Pooled
Pooled
Pooled
Pooled
Pooled
Pooled
Pooled
Pooled
rp, rs, or R2
0.15 (R2) (n=97)
0.35 (R2) (n=99)
0.46 (rp) (n=NR)
0.49 (rp) (n=NR)
0.80 (rp) (n=NR)
0.82 (rp) (n=NR)
0.55 (rp) (n=NR)
0.59 (rp) (n=NR)
0.63 (rp) (n=NR)
0.73 (rp) (n=NR)
0.51 (rp) (n=NR)
0.59 (rp) (n=NR)
0.46 (rp) (n=NR)
0.86 (R2) (n=23)
0.33 (R2)(n=1, 494)
2-45
-------
STUDY
Levy et al.
(1998a)
Location: 18
cities across
15 countries
Time period:
Feb or Mar
1996
Kodama et al.
(2002)
Location:
Tokyo, Japan
Time period:
Feb 24-26,
June 2-4, July
13-15, and Oct
14-1 6 in 1998
and Jan 26-28
in 1999
Spengler et al.
(1994)
Location: Los
Angeles Basin,
CA
Time period:
May 1987to
May 1988
Lai et al.
(2004b)
Location:
Oxford,
England
Time period:
Dec 1 998 to
Feb 2000
METHODS
Subjects: 568 adults
Method: one 2-day avg measurement for each
person, all people were measured on the same
winter day.
Subjects: 150 junior-high school students and their
family members
Method: 3-day avg, personal exposures were
monitored on the same day.
Subjects: probability-based sample, 70 subjects
Method: each participant was monitored during
each of 8 cycles (48-h avg sampling period)
throughout the yr in the microenvironmental
component of the study.
Subjects: 50 adults
Method: one 48-h avg measurement per person.
ASSOCIATION
VARIABLE
Personal vs.
outdoor
Personal vs.
outdoor
Personal vs.
outdoor
Personal vs.
outdoor
Personal vs.
outdoor
LOCATION
Urban
Urban
Urban
Pooled
Urban
SEASON
Winter
Summer
Winter
Pooled
Pooled
rp, rs, or R2
0.57 (rs) (n=546)
0.24 (rp) (n=NR)
0.08 (rp) (n=NR)
0.48 (R2) (n=NR)
0.41 (rp) (n=NR)
* Values were estimated from figures in the original paper
** NR: Not Reported.
Figures 2.5-4 and 2.5-5 explicitly summarize the correlation coefficients between personal
exposures and ambient concentrations for different populations with a forest plot for U.S. studies and
European studies, respectively. Correlation coefficients and their 95% confidence intervals (CIs) shown
in Figures 2.5-4 and 2.5-5 were transformed from the coefficients in Tables 2.5-4 and 2.5-5 with the
consideration of the type of exposure studies. Fisher's Z transform was used, (Z=0.51n [(l+r)/(l - r)]),
where r is the originally reported and Z is the transformed correlation coefficient (Fisher, 1925). The
variance of Z is expressed as l/(n-3), where n is the number of observations defined by one of the
following three presentations; (1) when the correlation coefficient was based on the average across
subjects of personal exposures, n was the number of sampling days, (2) when the partial correlation
coefficient was used in the original study, n was the total number of sampling by individual observations
minus the sum of three and the number of covariates, and (3) when the mean of individual correlations
was used, the standard error was the standard deviation of the correlations divided by the square root of
the number of subjects minus one.
2-46
-------
Study Location Season
Linn el al. (1996) Southern California All
107 Day's avg of
children
Samatetal. (2001) Baltimore
Sarnat et al. (2001) Baltimore
Sarnat et al, (2605) Boston
Sarnat et al. (2005) Boston
Sarnat et al, (2006) Steubenvtlle
Sarnat et al. (2006) Steubenville
Kim etal. (2006) Toronto
" Note. NR = Not reported
" Percent of data below detection lirrat
N = Number of observations
Summer 1 day
Winter 1 day
Summer 1 day
Winter 1 day
Summer 1 day
Fall
All
1day
217
484
298
341
183
228
15
Individual
Individual
Individual
Individual
Individual
Individual
Avg of individual
correlations
%
-------
Study
Aim etal. (1998)
Aim etal (1998)
Aim etal (1998|
Aim etal. (1998)
Aim etal. (1998)
Aim etal, (1998)
Aim etal. (1998)
Almetal,(199B)
Aim etal. (1998)
Aim etal. (1998)
Aim etal (1998)
Aim etal. (1998)
Gamin etal (2001)
Gauvin etal. (2001)
Gauvin etal (2001)
Liard etal. (1999)
Lmaker et al (2000)
Uard etal. (1999)
PiectiockhMinguyelal.
Piectiocki-Mmguyetal
Location
Helsinki
Helsinki
Helsinki
Helsinki
Helsinki
Helsinki
Helsinki
Helsinki
Helsinki
Helsinki
Helsinki
Helsinki
Grenoble
Toulouse
Pans
Pans
Southampton
Pans
Lile
Lille
Season
Spnng
Spring
Wmler
Wmler
All
All
All
All
All
All
All
All
All
All
All
Summer
All
Summer
All
Summer
Other
Downtown
Suburban
Downtown
Suburban
Downtown, Electric stove
Suburban. Gas stove
Downtown. Eteclric stove
Suburban. Nonsmoking
Downtown, Smoking
Subuitjan, Nonsmoking
Downtown, Smoking
No major indoor sources
Samclim
Time
Iweek
1 week
Iweek
Iweek
Iweek
Iweek
Iweek
Iweek
Iweek
Iweek
Iweek
Iweek
22 exposure
and ambient N02 concentrations based on Fisher's Z transform.
Longitudinal correlations1 are calculated when data from a study includes consecutive multiple
measurements for each subject (longitudinal study design). Longitudinal correlations describe the
temporal relationship between personal NO2 exposure or microenvironment concentration and ambient
NO2 concentration for the same subject. The longitudinal correlation coefficient can differ between
subjects (i.e. each person may have a different correlation coefficient). The distribution of correlations for
each subject across a population could be obtained with this type of data (e.g., Kim et al., 2006; Linaker
et al., 2000; Sarnat et al., 2000; 2001; 2005). A longitudinal correlation coefficient between the ambient
component of personal exposures and ambient concentrations is relevant to the panel epidemiologic study
design. In Table 2.5-4, most longitudinal studies reported the association between personal total
exposures and ambient concentrations for each subject; for some subjects the associations were strong and
for some subjects the associations were weak. The weak personal and ambient associations do not
necessarily mean that ambient concentrations are not a good surrogate for personal exposures, because the
weak associations could have resulted from the day-to-day variation in the nonambient component of
total personal exposure. The type of correlation analysis can have a substantial effect on the value of the
resultant correlation coefficient. Mage et al. (1999) showed that very low correlations between personal
exposure and ambient concentrations could be obtained when people with very different nonambient
exposures are pooled, even though their individual longitudinal correlations are high.
r =•
(n-l>
where "r" is the longitudinal correlation coefficient between personal exposure and ambient concentration, "a" represents the ambient concentration,
"x" represents exposure,"/" represents the ith subject,"/' represents the jth measurement (with the averaging time ranging from two days to two weeks
for NO2 measurement), "s" represents the standard deviation, and "n" in the longitudinal studies is the number of measurements for each subject.
The ambient concentration ay could be measured by one ambient monitor or the average of several ambient monitors.
2-48
-------
Pooled correlations1 are calculated when a study involves one or only a few measurements per
subject and when different subjects are studied on subsequent days. Pooled correlations combine
individual-subject/individual-day data for the calculation of correlations. Pooled correlations describe the
relationship between daily personal NO2 exposure and daily ambient NO2 concentration across all
subjects in the study (e.g., Aim et al, 1998; Gauvin et al., 2001; Liard et al., 1999; Linn et al., 1996;
Piechocki-Minguy et al., 2006; Sarnat et al., 2006).
Daily-average correlations2 are calculated by averaging exposure across subjects for each day.
Daily-average correlations then describe the relationship between the daily average exposure and daily
ambient NO2 concentration (e.g., EPA, 2004; Gauvin et al., 2001; Liard et al., 1999; Monn, 2001). This
type of correlation (i.e. the association between community average exposures (ambient component) and
ambient concentrations) is more directly relevant to community time-series and long-term cohort
epidemiologic studies, in which ambient concentrations are used as a surrogate for community average
exposure to NO2 of ambient origin. However, exposure of the population to NO2 of ambient origin has
not been reported in all the studies examined. The following two European studies reported the
associations between population total exposures and ambient or outdoor concentrations of NO2. Liard
et al. (1999) conducted an exposure study of 55 office workers and 39 children in Paris. Measurements
were made during three 4-day-long measurement periods for each group. Apart from occasional lapses,
data from the same participants were collected during each period. Liard et al. (1999) correlated the five-
panel average personal exposures with ambient monitoring data and derived a longitudinal Spearman
correlation coefficient of 1 (p<0.001). R2 between ambient monitors and individual personal exposures
for adults was 0.41, and for children, R2 was 0.17. Four-day averaging periods were chosen in this study
to overcome limitations imposed by the levels of detection of the personal samplers. The results show that
passive samplers could be used to measure personal exposures in panel studies over multiday periods and
lend some credence to the use of stationary monitors as proxies for personal exposures to ambient NO2.
Monn et al. (1998) and Monn (2001) reported personal NO2 exposures obtained in the Study of Air
Pollution and Lung Diseases in Adults (SAPALDIA) study (eight study centers in Switzerland). In each
study location, personal exposures for NO2 were measured simultaneously for all participants; in addition,
residential outdoor concentrations were measured for 1 year (Table 2.5-6). Monn (2001) observed a
strong association between the average personal exposures in each study location and corresponding
average outdoor concentrations with an R2 of 0.965. As pointed out by the author, in an analysis of
individual single exposure and outdoor concentration data, personal versus outdoor R2 was less than 0.3
(Monn et al., 1998). Because spatial heterogeneity in NO2 concentrations likely produces stronger
associations between average personal exposures and residential monitors than with central site ambient
monitors in urban areas, caution should be exercised in using these data to infer that long-term averaged
ambient concentrations are a good surrogate for population exposures in long-term cohort epidemiologic
studies.
Not only does the exposure study design determine the meaning of the correlation coefficients in
the context of exposure assessment in epidemiologic studies, but it also affects the strength of the
association between personal exposures and ambient concentrations. The strength of the association
where "n" is the number of paired measurements of exposure and ambient concentration, and all other symbols are defined the same way as those
in the longitudinal correlation coefficient.
r - =
(n - l)s—s
\ / x . a
'•
where n is the number of measurement period, during each of which the exposure for all subjects are measured, and all other symbols are defined
the same way as those in the longitudinal correlation coefficient.
2-49
-------
between personal exposures and ambient and/or outdoor concentrations for a population is determined by
variations in several physical factors: indoor or other local sources, air exchange rate, penetration, and
decay rate of NO2 in different microenvironments and the time people spend in different
microenvironments with different NO2 concentrations. For different types of correlation coefficients, the
components of the variance of these physical factors are different, and therefore the strength of different
types of correlation coefficients is different. Longitudinal correlation coefficients reflect the intra-personal
variations of these physical factors; pooled correlation coefficient reflect both inter- and intra- personal
variations of these physical factors; and for the association between community average exposures and
ambient concentrations, inter-personal variations of these physical factors are reduced by averaging
personal exposures across a community. Therefore, the strength of the associations between personal
exposures and ambient concentrations may not be comparable directly, although these associations are
determined by the same set of physical factors (but affected in different ways).
Since correlations are standardized quantities that depend on multiple features of the data, in a
correlation, not only is the linear "relatedness" (covariance) of the two quantities important, but so is the
variability of each, which can be affected by exposure factors in various ways. In the following
assessments, the effects of these physical factors on the strength of correlation coefficients are primarily
examined within a study, and the purpose of the inter-study comparison is to examine the consistency of
the effects across different (types of) studies.
Home ventilation is an important factor modifying the personal-ambient relationships; one would
expect to observe the strongest associations for subjects spending time indoors with open windows. Aim
et al. (1998) and Kodama et al. (2002) observed the association between personal exposure and ambient
concentration became stronger during the summer than the winter. However, Sarnat et al. (2006) reported
that R2 values decreased from 0.34 for a low-ventilation population to 0.16 for a high-ventilation
population in the summer, and from 0.47 for a low-ventilation population to 0.34 for a high-ventilation
population in the fall. The mixed results serve as a reminder that the association between personal
exposures and ambient concentrations is complex and determined by many factors.
Local and indoor sources also affect the strength of the association between personal exposures and
ambient concentrations. Aim et al. (1998) found that the association between personal exposure and
outdoor concentration was stronger than the correlation between personal exposure and central site
concentration. However, Kim et al. (2006) found that the association was not improved using the ambient
sampler closest to a home. The lack of improvement in the strength of the association by choosing the
closest ambient monitor could be in part due to the differences in the small-scale spatial heterogeneity of
NO2 in different urban areas, as shown in Table 2.5-1. Higher personal to ambient correlations have been
found for subjects living in rural areas and lower correlations for subjects living in urban areas (Aim et
al., 1998; Rojas-Bracho et al., 2002). Spengler et al. (1994) also observed that the relationship between
personal exposure and outdoor concentration was highest in areas with lower ambient NO2 levels
(R2=0.47) and lowest in areas with higher ambient NO2 levels (R2=0.33). This might reflect the highly
heterogeneous distribution or the effect of local sources of NO2 in an urban area.
Associations between ambient concentrations and personal exposures for the studies examined for
NO2 were not stratified by the presence of indoor sources except in Aim et al. (1998), Sarnat et al. (2006),
Linaker et al. (2000) and Piechocki-Minguy et al. (2006). When there is little or no contribution from
indoor sources, ambient concentrations primarily determine exposure; however, if there are indoor
sources, the importance of outdoor levels in determining personal exposures decreases. The association
between ambient or outdoor concentrations and personal exposures strengthens after controlling for
indoor sources. Raaschou-Nielsen et al. (1997), Spengler et al. (1994), and Gauvin et al. (2001) reported
that R2 values increased by 10 to 40% after controlling for indoor sources, such as gas appliances and
ETS (see Tables 2.5-4 through 2.5-6).
The strength of the associations between personal exposures and ambient and outdoor
concentrations could also be affected by the quality of the data collected during the exposure studies.
There are at least six aspects associated with the quality of the data: method precision, method accuracy
2-50
-------
(compared with FRM), percent of data above method detection limits (based on field blanks),
completeness of the data collection, sample size, and soundness of the quality assurance/quality control
procedures. Unfortunately, not all studies reported the aspects of the data quality issue. Although data
imprecisions and inaccuracies are less than 10% in most studies (Section 2.5.2), the fraction of data below
the detection limit might be a concern for some studies (see e.g., Sarnat et al., 2000; 2001; 2006).
Correlation coefficients would be biased low if data used in their calculation are below detection limits.
Sampling interferences (caused by some NOY compounds and other gas species) associated with both
ambient (see Section 2.3) and personal sampling (see Section 2.5.2) could also affect data quality.
Therefore, caution must be exercised when interpreting the results in Tables 2.5-4 and 2.5-5.
In summary, the evidence relating ambient levels to personal exposures is inconsistent. Some of the
longitudinal studies examined found that ambient levels of NO2 were reliable proxies of personal
exposures to NO2. However, a number of studies did not find significant associations between ambient
and personal levels of NO2. The differences in results are related in large measure to differences in study
design and in exposure determinants. Measurement artifacts and differences in analytical measurement
capabilities could also have contributed to the inconsistent results. Indeed, in a number of the studies
examined, the majority of measurements of personal NO2 concentrations were beneath detection limits,
and in all studies some personal measurements were beneath detection limits.
2.5.6.2. Ambient Contribution to Personal Exposure
Another aspect of the relationship of personal NO2 exposure and ambient NO2 is the contribution of
ambient NO2 to personal exposures. The infiltration factor (Firj) and alpha (a) are the keys to evaluate
personal NO2 exposure of ambient origin. As defined in Equations 2.5-2 through 2.5-5, the FinfOf NO2,
the physical meaning of which is the fraction of ambient NO2 found in the indoor environment, is
determined by the NO2 penetration coefficient (P), air exchange rate (a), and the NO2 decay rate (k).
Alpha (a) is a function of Finf and the fraction of time people spend outdoors (y), and the physical
meaning of a is the ratio of personal ambient exposure concentration to ambient concentration, (i.e., in the
absence of exposures to nonambient sources (i.e., when Em=0).
The values for a and FinfCan be calculated physically using Equations 2.5-2 through 2.5-5, ifP, k,
a, and y are known. However, the values ofP and k for NO2 are rarely reported, and in most mass balance
modeling work, P is assumed to equal 1 and k is assumed to equal 0.99/h (Dimitroulopoulou et al., 2001;
Kulkarni and Patil, 2002; Yamanaka, 1984; Yang et al., 2004b). Loupa et al. (2006) reported that k was
0.08 to 0.12/h for NO and 0.04 to 0.11/h for NO2 based on real-time measurements in two medieval
churches in Cyprus. It is well known that P and k are dependent on a large number of indoor parameters,
such as temperature, relative humidity, surface properties, surface-to-volume ratio, the turbulence of
airflow, building type, and coexisting pollutants (Cotterill and Kingham, 1997; Garcia-Algar et al., 2003;
Lee et al., 1996; Monn et al., 1998; S0rensen et al., 2005; Zota et al., 2005). As a result, using a fixed
value, as mentioned above, would either over- or underestimate the true a or Finf.
Although specific P, k, and a were not reported by most studies, a number of studies investigated
factors affecting P, k, and a (or indicators ofP, k, and a), and their effects on indoor and personal
exposures (Cotterill and Kingham, 1997; Garcia-Algar et al., 2003; Lee et al., 1996; Monn et al., 1998;
S0rensen et al., 2005; Zota et al., 2005). Garcia-Algar et al. (2003) observed that double-glazed windows
had a significant effect on indoor NO2 concentrations. Homes with double-glazed windows had lower
indoor concentrations (6 ppb lower) than homes with single-glazed windows. Cotterill and Kingham
(1997) reported that having single- or double-glazed windows was a significant factor affecting NO2
concentrations in kitchens in homes with gas-cookers (31.4 ppb and 39.8 ppb for homes with single- and
double-glazed windows, respectively). The reduction of ventilation resulting from the presence of double-
glazed windows can block outdoor NO2 from coming into the indoor environment, and at the same time
can also increase the accumulation of indoor generated NO2.
2-51
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A similar effect was found for homes using air conditioners. Lee et al. (2002) observed that NO2
was 9 ppb higher in homes with an air conditioner than in homes without. The authors also observed that
the use of a humidifier would reduce indoor NO2 by 6 ppb.
House type was another factor reported affecting ventilation (Garcia-Algar et al., 2003; Lee et al.,
1996). Lee et al. (1996) reported that the building type was significantly associated with air exchange
rate: the air exchange rate ranged from 1.04/h for single dwelling unit to 2.26/h for large multiple
dwelling unit. Zota et al. (2005) reported that the air exchange rates were significantly lower in the
heating season than the nonheating season (0.49/h for the heating season and 0.85/h for the nonheating
season).
Steady state models based on equations 2.5-2 through 2.5-5 are typically used to simulate indoor
and outdoor concentrations and personal exposures. However, the assumption of steady state could result
in missing peak exposures and obscuring the real short-term outdoor contribution to indoor and personal
exposure. For example, the NO2 concentrations at locations close to busy streets vary with traffic density,
wind direction and speed etc. If steady state is assumed, the real-time indoor/outdoor concentration ratio
may indicate either a too low relative importance of indoor sources (if concentrations outdoors are
increasing) or a too high relative importance of indoor sources (if concentrations outdoors are decreasing)
(Ekberg, 1996). The time dependence of indoor and outdoor sources and meteorological conditions can
affect P, k, and a, and thus affect the relationships between indoor and outdoor NO2 concentration and
between personal exposure and outdoor NO2 concentrations on short time scales. Thus, relationships
among P, k, and a derived using a steady state model might not be representative of short term values. It
should also be pointed out that both P and k are functions of complex physical and chemical processes
that occur on indoor surfaces and therefore are associated with indoor-outdoor air exchange, which in turn
affects indoor air flows.
Alternatively, the ratio of personal exposure to ambient concentration can be regarded as a in the
absence of indoor or nonambient sources. Only a few studies have reported the value and distribution of
the ratio of personal NO2 exposure to ambient NO2 concentration, and even fewer studies have reported
the value and distribution of a based on sophisticated study designs. Rojas-Bracho et al. (2002) reported
the median personal-outdoor ratio was 0.64 (with an IQR of 0.45), but the authors reported that a was
overestimated by this ratio because of indoor sources.
The random component superposition (RCS) model is an alternative way to calculate Finfor a using
observed ambient and personal exposure concentrations (Ott et al., 2000). The RCS statistical model
(shown in Equations 2.5-2 through 2.5-5) uses the slope of the regression line of personal concentration
on the ambient NO2 concentration to estimate the population averaged attenuation factor and means and
distributions of ambient and nonambient contributions to personal NO2 concentrations (the intercept of
the regression is the averaged nonambient contribution to personal exposure) (U.S. Environmental
Protection Agency, 2004). As shown in Annex Table AX3.5-la, Finf ranges from 0.4 to 0.7. Similarly, as
shown in Annex Table AX3.5-lb, a, calculated by the RCS model, ranges from 0.3 to 0.6.
The RCS model calculates ambient contributions to indoor concentrations and personal exposures
based on the statistical inferences of regression analysis. However, personal-outdoor regressions could be
affected by extreme values (outliers on either the x or the y axis). Another limitation of the RCS model is
that this model is not designed to estimate ambient and nonambient contributions for individuals, in part
because the use of a single value for a does not account for the large home-to-home variations in actual
air exchange rates and penetration and decay rates of NO2. In the RCS model, a is also determined by the
selection of the predictor. Using residential outdoor NO2 concentrations as the model predictor might give
a different estimate of a than using ambient NO2 because of the spatial variability of NO2 mentioned early
in this section. As mentioned earlier, personal NO2 exposure is affected not only by air infiltrating from
outdoors but also by indoor sources (see Section 2.5.5).
Nerriere et al. (2005) used data from the Genotox ER study in France (Grenoble, Paris, Rouen, and
Strasbourg) and reported that factors affecting the differences between personal exposure to ambient NO2
and corresponding ambient monitoring site concentrations were season, city, and land use dependence.
2-52
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During the winter, city and land use categorization account for 31% of the variation, and during the
summer, 54% of the variation can be explained by these factors. When data from the ambient monitoring
site were used to represent personal exposures, the largest difference between ambient and personal
exposure was found at the "proximity to traffic" site, while the smallest difference was found at the
"background" site. When using data from the urban background site, the largest difference was observed
at the "industry" site, and the smallest difference was observed at the background site, which reflected the
heterogeneous distribution of NO2 in an urban area. During winter, differences between ambient site and
personal exposure concentrations were larger than those in the summer.
2.5.7. N02 as a Component and Indicator of Pollutant Mixtures
2.5.7.1. Associations between Ambient N02 and Ambient Copollutants
Relationships between ambient concentrations of NO2 and other pollutants that are emitted by the
same sources, such as motor vehicles, should be evaluated in designing and interpreting air pollution-
health outcome studies, as ambient concentrations are generally used to reflect exposures in
epidemiologic studies. Thus, the majority of studies examining pollutant associations in the ambient
environment have focused on ambient NO2, PM2 5 (and its components), and CO, with fewer studies
reporting the relationship between ambient NO2 and ambient O3 or SO2.
Data were compiled from EPA's AQS and a number of exposure studies. Correlations between
ambient concentrations of NO2 and other pollutants, PM2 5 (and its components, where available), CO, O3,
and SO2 are summarized in Table 2.5-7.
As can be seen from Table 2.5-7, NO2 was moderately correlated with PM2 5 (range: 0.37 to 0.78)
and with CO (0.41 to 0.76) in suburban and urban areas. At some sites (e.g., Riverside, CA) associations
between ambient NO2 and ambient CO concentrations (both largely traffic-related pollutants) are much
lower, likely as the result of other sources of both CO and NO2 increasing in importance in going from
urban environments to more rural and sparsely populated areas. These sources include oxidation of
methane (CFQ and other biogenic compounds; residential wood burning and prescribed and wild land
fires for CO; and soil emissions, lightning, and residential wood burning and wild land fires for NO2. In
urban areas, the ambient NO2-CO correlations vary widely. The strongest correlations are seen between
NO2 and elemental carbon (EC). Note that the results of Hochadel et al. (2006) for PM25 optical
absorbance have been interpreted in terms of EC. Brook et al. (2007) also found relatively high
correlations between ambient NO2 and combustion-related organics, including the BTEX compounds
(benzene, toluene, ethylbenzene, and o-, m-, p-xylene) (r=0.45-0.6) and hopanes (r=0.67-0.8).
Correlations between ambient NO2 and ambient O3 are mainly negative, owing to the chemical interaction
between the two, with again considerable variability in the observed correlations. Only one study (Sarnat
et al., 2001) examined associations between ambient NO2 and ambient SO2 concentrations, and it showed
a negative correlation during winter.
Figure 2.5-6 shows seasonal plots of correlations between NO2 and O3 versus correlations between
NO2 and CO. As can be seen from the figure, NO2 is positively correlated with CO during all seasons at
all sites. However, the sign of the correlation of NO2 with O3 varies with season, ranging from negative
during winter to slightly positive during summer. There are at least two main factors contributing to the
observed seasonal behavior. O3 and radicals correlated with it tend to be higher during the summer,
thereby tending to increase the ratio of NO2 to NO. Nitrogen oxide compounds formed by further
oxidation of NOX are also expected to be correlated with O3 and increased summertime photochemical
activity. Because some of these additionally oxidized TV compounds create a positive artifact in the FRM
for NO2, they may also tend to increase the correlation of NO2 with O3 during the warmer months.
2-53
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Table 2.5-7. Pearson correlation coefficient between ambient NO2 and ambient copollutants.
STUDY
AQS (2007)*
AQS (2007)*
AQS (2007)*
AQS (2007)*
Kim et al. (2006)
Sarnat et al. (2006)
Sarnat et al. (2006)
Connell et al. (2005)
Kim et al. (2005)
Sarnat et al. (2001)1
Sarnat et al. (2001)
Hochadel et al. (2006)
Hazenkamp-von Arx et al. (2004)
Cyrys et al. (2003)
Brook et al. (2007)
Mosqueron et al. (2002)
Rojas-Bracho et al. (2002)
LOCATION
Los Angeles, CA
Riverside, CA
Chicago, IL
New York, NY
Toronto, Canada
Steubenville, OH (autumn)
Steubenville, OH (summer)
Steubenville, OH
St. Louis, MO (RAPS)
Baltimore, MD (summer)
Baltimore, MD (winter)
Ruhr area, Germany
21 European cities
Erfurt, Germany
10 Canadian cities
Paris, France
Santiago, Chile
PM2.5
0.49 (u2)
0.56 (s)
NR
0.49 (s)
0.58 (u)
0.44
0.78 (0.70 for sulfate; 0.82 for EC)
0.00 (0.1 for sulfate; 0.24 for EC)
0.50
NR
0.37
0.75
0.41 (0.93 for EC3)
0.75
0.50
0.54
0.69
0.77
CO
0.59 (u)
0.64 (s)
0.43 (u)
0.41 (s)
0.15(r)
0.53 (u)
0.46 (s)
0.46 (u)
0.72
NR
NR
NR
0.644
0.75
0.76
NR
NR
0.74
NR
NR
NR
03
-0.29 (u)
-0.11 (s)
0.045 (u)
0.10 (s)
-0.31 (r)
-0.20 (u)
-0.06 (u)
NR
NR
NR
NR
NR
0.02 (not significant)
-0.71
NR
NR
NR
NR
NR
NR
S02
NR
NR
NR
NR
NR
NR
NR
NR
NR
NR
-0.17
NR
NR
NR
NR
NR
NR
Spearman correlation coefficient was reported.
2 u: urban; s\ suburban; and r: rural
3 Inferred based on EC as dominant contributor to PM2 5 absorbance.
4 Value with respect to NOX.
*Data obtained from EPA's AQS Database, available at http://www.epa.gov/ltn/airs/airsaqs/
2.5.7.2. Associations among N02 and Other Pollutants in Indoor Environments
In addition to NO2, indoor combustion sources such as gas ranges and unvented gas heaters emit
other pollutants that are already present in the fuel or are formed during combustion. The major products
from the combustion of natural gas are carbon dioxide (CO2), CO, followed by formaldehyde (HCHO),
with smaller amounts of other oxidized organic compounds in the gas phase. PM, especially in the
ultrafme-size range and HONO are also emitted. The production of pollutants by reactions of NO2 in
indoor air was covered in Section 2.5.5.
Dennekamp et al. (2001) measured levels of NO, NO2, and ultrafine particles (UFP) generated by
gas and electric cooking ranges in a test laboratory room. They found average levels of NO ranging from
-500 to -3,000 ppb, with peak (15-min avg) levels ranging from -1,000 to -6,000 ppb depending on how
many burners (1 to 4) were turned on and for how long (15 min to 2 h). Corresponding levels of NO2
tracked those of NO but were typically factors of 2 to 5 lower. 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
2-54
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account for -84% of the measured increase in HONO. In a study of homes in southern California, Lee
et al. (2002) found that indoor levels of NO2 and HONO were positively associated with the presence of
gas ranges.
Winter
Summer
1*1
o
*«
O
0,8-
0.6-
0.4-
02-
1 -0.8 -0.6 -0.4 -0.2
-0.2-
-0.4 -
-0,6-
-OB.
I 0.2 0.4 0.6 0.8
» ***•*$
**
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o
£.
0.8'
0.6-
04-
0?-
1 -0,8 -0.6 -0.4 -0,2
-0.2-
-0,4 •
•06-
-0.8-
.* . •
..' J
0.2 0.4 0.6 O.S
Spring
Fall
cT
d*
0.8-
OB-
0.4-
02-
1 -08 -06 -04 -0! I
-0.2-
-0.4 •
0.6-
-0.8-
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I Qf 0? 06 *,'s
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0.6-
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-0.4-
-o.s-
-0.8'
\ ** ^ *
i 0.2 0.4 *0.(T "^.8
+ *
N02: CO N02: CO
Figure 2.5-6. Correlations of N02 to 03 versus correlations of N02 to CO for Los Angeles, CA (2001-2005).
In a study of pollutants emitted by unvented gas heaters, Brown et al. (2004) found that CO in a
room test chamber ranged from 1 to 18 ppm and NO2, from 100 to 300 ppb. Corresponding levels of
HCHO were highly variable, ranging from <10 ppb to a few hundred ppb (with an outlier at >2 ppm).
PM in the sub-micrometer size range is produced during natural gas combustion. Dennekamp et al.
(2001) in the study mentioned above found enhancements in UFP concentrations when gas burners were
turned on. Peak (15-min avg) concentrations for different experiments ranged from -140,000 to
~400,000/cm3 corresponding to average levels of-80,000 to 160,000/cm3. Concentrations before the
experiments were begun were in the range of a few thousand per cm3. However, Ristovski et al. (2000)
measured emission rates for individual particles, which are expected to be present mainly in the UFP size
range but concluded that these rates are low, and they could not detect an increase in particle number
from one of the two heater models tested.
Rogge et al. (1993) found that at least 22% of the fine particle mass emitted by natural gas heaters
consists of PAHs, oxy-PAHs, and aza-and thia-arenes. They also identified emissions of speciated
alkanes, n-alkanoic acids, polycyclic aromatic ketones, and quinones. However, these accounted for only
another -4% of the emitted fine PM. Although the PM emissions rates were low and not likely to affect
2-55
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PM levels, the PAH content of natural gas combustion emissions in this study indicates that natural gas
combustion could be a substantial source of PAHs in indoor environments
2.5.7.3. Personal and Ambient Associations between N02 and Copollutants
Correlations between ambient concentrations of NO2 and PM2 5 (and PM components where
available), are summarized in Table 2.5-8. Correlations between personal concentrations of NO2 and
ambient copollutants, PM2 5, PMi0, EC, CO, and sulfate are summarized in Table 2.5-9, and correlations
between personal NO2 concentrations and personal copollutant concentrations are shown in Table 2.5-10.
Most studies examined showed that personal NO2 concentrations were significantly correlated with either
ambient or personal level PM25 or other combustion-generated pollutants, e.g., CO, EC.
Table 2.5-8. Pearson correlation coefficient between ambient NO2 and personal copollutants.
STUDY
Sarnat et al. (2006)
Sarnat et al. (2006)
Vinzents et al. (2005)
LOCATION
Steubenville, OH
Fall
Steubenville, OH Summer
Copenhagen, Denmark
PM2.5
0.71
0.00
—
SULFATE
0.52
0.1 not significant
—
EC
0.70
0.26
—
UFP
—
—
0.49 (R2) explained by ambient NO2 and ambient temperature
Table 2.5-9. Pearson correlation coefficient between personal NO2 and ambient copollutants.
STUDY
Sarnat et al. (2006)
Sarnat et al. (2006)
Kim et al. (2006)
Rojas-Bracho et al. (2002)
LOCATION
Steubenville, OH
Fall
Steubenville, OH
Summer
Toronto, Canada
Santiago, Chile
PM2.5
0.46
0.00
0.30
0.65
SULFATE
0.35
0.1 (not significant)
—
—
EC
0.57
0.17
—
—
PM10
—
—
—
0.39
CO
—
—
0.20
—
A number of case studies show correlations between ambient NO2 and other pollutants that are
associated with traffic. Particulate and gaseous copollutant data were analyzed at 10 sites in the St. Louis
Regional Air Pollution Study (RAPS) dataset (1975, 1977) by Kim et al. (2005). This study examined the
spatial variability in source contributions to PM2 5. Table 2.5-11 shows correlations between NOX and
traffic pollutants measured in ambient air.
Leaded gasoline was in use at the time of RAPS, making Pb and bromine (Br) good markers for
motor vehicle exhaust. Motor vehicle emissions are the main anthropogenic source of CO in urban areas.
However, outside of urban areas and away from sources burning fossil fuels, biomass burning and the
oxidation of biogenic hydrocarbons, in particular isoprene and methane, can represent the major source of
CO. In general, biogenic emissions of precursors to CO formation or CO from biomass burning can cause
the relationship between CO and motor vehicles to break down.
2-56
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Table 2.5-10. Pearson correlation coefficient between personal NO2 and personal copollutants.
STUDY
Kim et al. (2006)
Modig et al. (2004)
Mosqueron et al.
(2002)
Jarvis et al. (2005)
Lee et al. (2002)
Lai et al. (2004a)
LOCATION
Toronto, Canada
Umea, Sweden
Paris, France
21 European cities
—
Oxford, England
PM2.5
0.41
—
0.12 but not
significant
—
—
-0.1
CO
0.12
—
—
—
—
0.3
VOCs
0.06 for 1,3-butadiene;
0.10 for benzene
—
—
—
-0.11 for total VOCs
MONO
—
—
—
0.77 for indoor NO2 and
indoor MONO
0.51 for indoor NO2 and
indoor MONO
—
Table 2.5-11. Pearson correlation coefficient between NOX and traffic-generated pollutants.
SPECIES
NOX: PM2.s (motor vehicle component)
NOX: CO
NOX: Pb
NOX: Br
NO2: EC
NO2: EC
ALL SITES
0.48
-------
or those between PMi0 and number concentration indices. For example, the correlation between NC0.01-
0.10 (particle number concentration for particle diameter between 10 and 100 nm) and NO2, PM25, and
PMio were 0.66, 0.61, and 0.61, respectively.
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 -10% of
primary emissions in the form of NO2. The diurnal pattern of NO and NO2 concentrations are 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, and NO and NO2 are
converted to NOZ. During the afternoon rush hour, mixing layer heights are often still at or 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
subsequent photolysis of NO2 recreating 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) is
shown in Figure 2.5-7. Figure 2.5-7 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 ~13 ppb to ~25 ppb. However,
individual hourly concentrations can be considerably higher than these typical median values, and hourly
NO2 concentrations of >0.10 ppm can be found at any time of day. The diurnal pattern in median
concentrations shown in Figure 2.5-7 is consistent with that shown in Figures 2.4-5 and 2.4-6 for Atlanta,
indicating some commonality in sources across these cities. The pattern in the median concentrations is
consistent with traffic as the major source of variability. However, the patterns in the upper end of the
concentration distribution differ between cities and the composite, indicating that other sources and
meteorological processes affect NO2 levels, causing them to differ from city to city.
0.20 J
0.19-
0.18-
0.17-
0.16-
0.15-
0.14-
0.13-
0.12-
0.11-
0.10-
0.09-
0.08-
0.07-
0.06-
0.05-
0.04-
0.03-
0.02-
0.01-
0.00-
X
X
x
X
X
Figure 2.5-7.
0123456789 101112131415161718192021222324
Hour
Composite, diurnal variability in 1-h avg NC-2 in urban areas. Values shown are averages from
2003 through 2005. Boxes define the interquartile range, and the whiskers the 5th and 95th
percentile values. "X" denotes individual values above the 95th percentile.
2-58
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Information concerning the seasonal variability of ambient NO2 concentrations is given in Annex
section AX3.2. NO2 levels are highest during the cooler months of the year and still show positive
correlations with CO. Mean NO2 levels are lowest during the summer months, though of course, there can
be large positive excursions associated with the development of high-pressure systems. In this regard,
NO2 behaves as a primary pollutant, although there is no good reason to suspect strong seasonal
variations in its emissions.
Although traffic is a major source of ambient NO2, industrial point sources are also contributors to
ambient NO2. Nerriere et al. (2005) measured personal exposures to PM2 5, PMi0, and NO2 in traffic-
dominated, urban background, and industrial settings in four French cities (Paris, Grenoble, Rouen, and
Strasbourg). Ambient concentrations and personal exposures for NO2 were generally highest in the
traffic-dominated sector. It should be remembered that there can be high traffic emissions (including
shipping traffic) in industrial zones, such as in the Ship Channel in Houston, TX, and in the Port of Los
Angeles, CA. In rural areas where traffic is sparse, other sources could dominate. Martin et al. (2003)
found that pulses of NO2 released from agricultural areas occur after rainfall. Other rural contributors to
NO2 include wildfires and residential wood burning.
2.5.8. Exposure Error in Epidemiologic Studies
For the purposes of this ISA, the effects of exposure error on epidemiologic study results refers to
changes in the point estimate and in the standard error of the calculated health effect estimate, (3, that
result from using the concentration of an air pollutant as an exposure indicator rather than using the actual
personal exposure to the causal factor in the epidemiologic statistical analysis. There are many
assumptions made in going from the available experimental measurement of a pollution indicator, to an
estimate of the personal exposure, to the causal factor. The importance of these assumptions and their
effect on (3 depend on the type of epidemiologic study. A more detailed discussion of these issues is
provided in Annex section AX6.1.
2.5.8.1. Community Time-Series Studies
This section applies primarily to studies of the association between short-term NO2 concentrations
and short-term measures of mortality or morbidity. With NO2 time-series epidemiologic analysis, the
following three exposure issues are of primary concern: (1) the relationship of the experimental
measurement of NO2 to the true concentration of NO2; (2) the relationship of day-to-day variations of the
concentration of the indicator, as measured at a central monitoring site, with the corresponding variations
in the avg concentration of the indicator over the geographic area from which the health measurements
are drawn; and (3) the relationship of the community avg concentration of NO2 to the avg personal
exposure to ambient NO2. These three issues are described below.
Since there is always some instrumental error in the experimental measurement of NO2
concentration, the correlation of the measured NO2 with the true NO2, on either a 24-h or 1-h basis, will
be less than 1. Averaging across multiple unbiased ambient monitors in a region should reduce the
instrument measurement error (Sheppard, 2005; Wilson and Brauer, 2006; Zeger et al., 2000). This error
component is not expected to have a major effect on personal exposure estimation. It may tend to
attenuate the estimate of a (Sheppard, 2005) and is unlikely to greatly affect (3, particularly if the
instrument error is of the Berkson type. Zeger et al. (2000) showed that instrument error has both Berkson
and non-Berkson error components.
The concentration of NO2, measured at any given monitoring site, may not be highly correlated
with the avg community concentration. Large spatial variations (expressed as coefficient of divergence
(COD) have been observed in some urban areas, as shown in Table 2.5-1. Site-to-site correlations of NO2
concentrations, as shown for several cities in Table 2.5-1 included some very low values, possibly due to
2-59
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local sources, monitor siting, meteorology, and topography. Low correlations between the ambient
concentration and the community avg concentration quantitatively reduce (3 if the single pollutant model
is the true model. Similarly, |3 will be reduced if there are subareas of the community where the
correlation of the subarea avg concentrations with the concentrations measured at the ambient monitoring
site is <1. Therefore, if a local source affected a sizable portion of the population, that community might
not be suitable for time-series epidemiologic analyses.
Zeger et al. (2000) made a major contribution to the understanding of exposure error by pointing
out that for community time-series epidemiology, which analyzes the association between health effects
and potential causal factors at the community scale rather than the individual scale, it is the correlation of
the daily community avg personal exposure to the ambient concentration, XtA, with daily community avg
concentration, Ch that is important, not the correlation of each individual's exposure XitA with Ct. Thus,
the low correlation ofXitA with Ct, as frequently found in pooled panel exposure studies, is not relevant to
error in community time-series epidemiologic analysis. Unfortunately, few experimental studies provide
adequate information to calculate the community avg exposure. Most exposure panel studies measure one
or a few subjects on 1 day, and another one or a few subjects on the next day, etc. (i.e., a pooled study
design). A few studies have measured one subject for several days and another subject for a different set
of several days (i.e., a longitudinal study design). This requires measurement of the personal exposure of
every subject on every day along with sufficient information to separate the ambient component of
exposure from the measured total personal exposure. Such information was available from one study of
combined PMi0 and showed that the correlation of XtA with Ct was much greater than the correlation of
XtA with Ct (U.S. Environmental Protection Agency, 2004). The Research Triangle Park PM Panel Study
found similar effects in the relationship of outdoor and personal PM2 5 concentrations (Williams et al.,
2003). Ott et al. (2000) provided a statistical argument that such an increase in the correlation of the daily
avg over the individual values should be expected.
Inter-individual daily variation in alt around the daily community avg at tends to produce Berkson
error, which will not change the point estimate of (3, although it may increase the standard error (Zeger et
al., 2000). Overestimation of exposure by substitution of the ambient concentration for the ambient
exposure leads to underestimation of the effect estimate proportional to a, or bias toward the null
(Sheppard, 2005).
Panel epidemiology refers to time-series studies that follow a relatively small number of subjects
for a relatively short time, usually tens of subjects for 5 to 20 days a subject. Thus, neither the averaging
of exposure over millions of people, as in community time-series studies, or the averaging of exposure
overtime periods of years and hundreds or thousands of subjects, as in chronic cohort studies was
available. Therefore, exposure errors may be more important than in other types of epidemiology. Panel
studies typically examine the association between symptoms or health outcomes and either ambient
concentrations or personal exposures. Most panel epidemiology studies of NO2 used ambient
concentrations rather than personal exposures. Similar types of exposure error, as discussed for
community time series studies apply to panel studies, with some differences depending on whether
ambient concentrations or personal exposures are used.
2.5.8.2. Long-Term Exposure Studies
For long-term exposure epidemiologic studies, concentrations are integrated overtime periods of a
year or more, and usually for spatial areas the size of a city, county, or MSA, although integration over
smaller areas may be feasible. Health effects are then regressed, in a statistical model, against the avg
concentrations in the series of cities (or other areas). In time-series studies, a constant difference between
the measured and the true concentration (instrument offset) will not affect (3, nor will variations in the
daily average a or the daily average nonambient exposure, unless the variations are correlated with the
daily variations in concentrations. However, in long-term exposure epidemiologic studies, if instrument
measurement errors, long-term average values of a, or long-term averages of nonambient exposure differ
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for different cities (or other areas used in the analysis), the city-to-city long-term ambient NO2
concentrations will not be perfectly correlated with the long-term average exposure to either ambient or
total NO2. This lack of correlation would be expected to lead to a lowering of the point estimate of (3.
2.5.9. Summary of Issues in Assessing Exposures to N02
In summary, NO2 is monitored at far fewer sites than either O3 or PM. Large spatial variations in
ambient NO2 concentrations were observed in urban areas. Measurements of NO2 are subject to artifacts
both at the ambient level and at the personal level. Personal exposure to ambient and outdoor NO2 is
determined by many factors as listed in Sections 2.5.1 and 2.5.2. These factors all influence the
contribution of ambient NO2 to personal exposures. Personal activities determine when, where, and how
people are exposed to NO2. The variations of these physical and exposure factors determine the strength
of the association between personal exposure and ambient concentrations in both longitudinal and pooled
studies. In Section 2.5.6.1, three types of correlation coefficients were presented. The observed strength
of the association between personal exposures and ambient concentrations are not only affected by the
variation in physical parameters (e.g., P, k, a and indoor sources) but also affected by data quality and
study design. The association between the ambient component of personal exposures and ambient
concentrations is more re levant to the interpretation of epidemiologic evidence but this type of correlation
coefficient is not generally reported. The weak association between personal total exposures and ambient
concentrations in some longitudinal studies might not reflect the true association between the ambient
component of personal exposures and ambient concentrations. In the absence of indoor and local sources,
personal exposures to NO2 are between the ambient level and the indoor level. However, personal
exposures could be much higher than either indoor or outdoor concentrations in the presence of these
sources. A number of studies found that (community average) personal NO2 was associated with ambient
NO2, but the strength of the association ranged from poor to good.
The evidence relating ambient levels to personal exposures is inconsistent. Some of the longitudinal
studies examined found that ambient levels of NO2 were reliable proxies of personal exposures to NO2.
However, a number of studies did not find significant associations between ambient and personal levels of
NO2. The differences in results were related in large measure to differences in study design and in
exposure determinants. Measurement artifacts and differences in analytical measurement capabilities
could also have contributed to the inconsistent results. Indeed, in a number of the studies examined, the
majority of measurements of personal NO2 concentrations were beneath detection limits, and in all studies
some personal measurements were beneath detection limits.
Some researchers concluded that ambient NO2 may be a reasonable proxy for personal exposures,
while others noted that caution must be exercised if ambient NO2 is used as a surrogate for personal
exposure. Reasons for the differences in study results are not clear, but are related in large measure to
differences in study design, to the spatial heterogeneity of NO2 in study areas, to control of indoor
sources, to the seasonal and geographic variability in the infiltration of ambient NO2, and to differences in
the time spent in different microenvironments. Measurement artifacts at the ambient and personal levels
and differences in analytical measurement capabilities among different groups could also have
contributed to the mixed results. The collective variability in all of the above parameters, in general,
contributes to exposure errors in air pollution-health outcome studies. The errors and uncertainties
associated with the use of ambient NO2 concentrations as a surrogate for personal exposure to ambient
NO2 generally tend to reduce rather than increase (3, and therefore are not expected to change the principal
conclusions from NO2 epidemiologic studies.
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2.6. Dosimetry of Inhaled NOx
This section provides a brief overview of NO2 dosimetry and updates information provided in the
1993 NOX AQCD. A more extensive discussion of NO2 dosimetry appears in Annex AX4. NO2, classified
as a reactive gas, interacts with surfactants, antioxidants, and other compounds in the epithelial lining
fluid (ELF). The compounds thought to be responsible for adverse pulmonary effects of inhaled NO2 are
the reaction products themselves or the metabolites of these products in the ELF.
Acute NO2 uptake in the lower respiratory tract is thought to be rate-limited by chemical reactions
of NO2 with ELF constituents rather than by gas solubility in the ELF (Postlethwait and Bidani, 1990).
Postlethwait and Bidani (1994) concluded that the reaction between NO2 and water does not significantly
contribute to the absorption of inhaled NO2. Rather, uptake is a first-order process for NO2 concentrations
of <10 ppm, is aqueous substrate-dependent, and is saturable. Postlethwait et al. (1991) reported that
inhaled NO2 (<10 ppm) does not penetrate the ELF to reach underlying sites and proposed that
cytotoxicity may be due to NO2 reactants formed in the ELF. Related to the balance between reaction
product formation and removal, it was further hypothesized that cellular responses may be nonlinear with
greater responses being possible at low levels of NO2 uptake versus higher levels of uptake.
Glutathione (GSH) and ascorbate are the primary NO2 absorption substrates in rat ELF
(Postlethwait et al., 1995). Velsor and Postlethwait (1997) investigated the mechanisms of acute epithelial
injury from NO2 exposure. 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. GSH- and ascorbic acid-related membrane oxidation were
superoxide- and hydrogen peroxide-dependent, respectively. The authors proposed that increased
absorption of NO2 occurred at the higher antioxidant concentrations, but little secondary oxidation of the
membrane occurred because the reactive species (e.g., superoxide and hydrogen peroxide) generated
during absorption were quenched. A lower rate of NO2 absorption occurred at the low antioxidant
concentrations, but oxidants were not quenched and so were available to interact with the cell membrane.
In vitro studies have clearly illustrated the role of antioxidants in mediating NO2 uptake and
membrane oxidation; however, the temporal dynamics of biological responses to NO2 that occur in vivo
are far more complex. Antioxidant levels vary spatially between lung regions and temporally with NO2
exposure. Kelly et al. (1996a) examined the effect of a four-hour NO2 (2 ppm) exposure on antioxidant
levels in bronchial lavage fluid (BLF) and bronchoalveolar lavage fluid (BALF) of 44 healthy
nonsmoking adults (19-45 yr, median 24 yrs). 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. At
1.5 h after the NO2 exposure, uric acid and ascorbic acid levels were significantly reduced in both lavage
fractions while GSH levels were significantly increased but only in BLF. By 6 h postexposure, ascorbic
acid levels had returned to baseline in both lavage fractions, but uric acid had become significantly
increased in both lavage fractions and GSH levels remained elevated in BLF. By 24 hours postexposure,
all antioxidant levels had returned to baseline. The levels of GSH in BALF did not change from baseline
at any time point in response to NO2 exposure. 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., 1996a; 1996b).
Very little work related to the quantification of NO2 uptake has been reported since the 1993 NOX
AQCD. In both humans and animals, the uptake of NO2 by the upper respiratory tract decreases with
increasing ventilation rates. This causes a greater proportion of inhaled NO2 to be delivered to the lower
respiratory tract. In humans, the breathing pattern shifts from nasal to oronasal during exercise relative to
rest. Since the nasal passages absorb more inhaled NO2 than the mouth, exercise (with respect to the
resting state) delivers a disproportionately greater quantity of the inhaled mass to the pulmonary region of
the lung, where the NO2 is readily absorbed. Bauer et al. (1986) reported a statistically significant
increase in uptake from 72% during rest to 87% during exercise in a group of 15 asthmatic adults. The
minute ventilation also increased from 8.1 L/min during rest to 30.4 L/min during exercise. Hence,
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exercise increased the dose rate of NO2 by 5-fold in these subjects. Similar results have been reported for
beagle dogs where the dose rate of NO2 was 3-fold greater for the dogs during exercise than rest
(Kleinman and Mautz, 1991).
Modeling studies also 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 these reactants to the tissues have not been modeled. Contrary to what recent in vitro
studies have shown (Kelly et al., 1996a), modeling studies have generally considered NO2 reactions in the
ELF to be protective. The complex interactions between antioxidants, spatial differences in antioxidants
between lung regions, temporal changes in antioxidant levels in response to NO2 exposure, and species
differences in antioxidant defenses are poorly understood. Thus, the current dosimetry models are
inadequate to put response data collected from animals and humans on a comparative footing with each
other and with the exposure conditions in the epidemiologic studies.
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Chapter 3. Integrated Health Effects
In this chapter, we assess the health effects associated with human exposure to ambient NO2. The
main goal of this chapter is to (1) integrate newly available epidemiologic, human clinical, and animal
toxicological evidence with consideration of key findings from the 1993 NOX AQCD (U.S.
Environmental Protection Agency, 1993) and (2) draw conclusions about the causal nature of NO2
relative to a variety of health effects. These causal determinations utilize the framework outlined in
Chapter 1.
This chapter is organized to present morbidity and mortality associated with short-term exposures
to NO2, followed by morbidity and mortality associated with long-term exposures. Within these divisions,
the chapter is organized by health outcome, such as respiratory symptoms in asthmatics, emergency
department (ED) visits and hospital admissions for respiratory and cardiovascular diseases (CVDs), and
premature mortality. The sections describe the findings of epidemiologic studies that have characterized
the association between NO2 exposure and heath outcomes and includes relevant human clinical and
animal toxicological data, when available. This integrated discussion underlies judgments in causal
inference.
The epidemiologic studies contain important information on potential associations between health
effects and exposures of human populations to ambient levels of NO2, and they help to identify
susceptible subgroups and associated risk factors. However, the associations derived for specific air
pollutants and health outcomes in epidemiologic studies may be confounded by copollutants and/or
meteorological conditions and can be influenced by model specifications in the analytical methods.
Extensive discussion of issues related to confounding effects among air pollutants in epidemiologic
studies is provided in the 2004 PM AQCD and so is not repeated in detail here. Briefly, though, the use of
multipollutant regression models has been the approach most commonly used to control for potential
copollutant confounders.
One specific concern has been that a given pollutant may act as a surrogate for other unmeasured or
poorly measured pollutants or pollutant mixtures. Specifically, traffic is a nearly ubiquitous source of
combustion pollutant mixtures that include NO2 and can be an important contributor to NO2 levels in
near-road locations. This complicates efforts to disentangle specific NO2-related health effects as distinct
from those effects of the whole traffic-generated combustion mix. These multipollutant models use terms
for measured variables as important tools for estimating an effect in multisource epidemiologic studies.
Both single- and multipollutant models that include NO2 were considered and examined for robustness of
results.
Model specification and model selection also are factors to consider in the interpretation of the
epidemiologic evidence. Epidemiologic studies investigated the association between various measures of
NO2 (e.g., multiple lags, different exposure metrics) and various health outcomes using different model
specifications (for further discussion, see the 2006 O3 AQCD [U.S. Environmental Protection Agency,
2006a]).
Human clinical studies conducted in controlled exposure chambers use fixed concentrations of air
pollutants under carefully regulated environmental conditions and subject activity levels to minimize
possible confounding of the health associations by other factors. Additionally, sensitive experimental
techniques can be used to measure health effects (and markers of injury) that are not evaluated in
epidemiologic studies, e.g. airway hyperresponsiveness. These studies provide important information on
effects, concentration-response relationships, biological plausibility of associations observed between
NO2 exposure and health outcomes in epidemiologic studies, and insights into sensitive subpopulations.
While human clinical studies provide a direct quantitative assessment of the NO2 exposure-health
response relationship, such studies have a number of limitations. First, it is requisite that subjects be either
healthy individuals or individuals whose level of illness does not preclude them from participating in the
study. Therefore, the results of human clinical studies may underestimate the health effects of exposure to
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certain sensitive subpopulations. Second, studies of controlled exposure to NO2 typically have used
concentrations that are higher than those normally present in ambient air. Third, human clinical studies
normally are conducted on a relatively small number of subjects, which reduces the power of the study to
detect significant differences in the health outcomes of interest and exposures to varying concentrations
of NO2 and clean air.
Similar to human clinical studies, animal toxicological studies have the advantage of being
conducted under controlled conditions, using fixed concentrations of air pollutants in carefully regulated
environmental conditions. These studies allow for evaluation of biological responses with exposures to
substances in doses that could be hazardous to human health and/or for extended durations that are not
possible in human clinical studies. However, restrictions on study population size require the use of
higher doses to allow the identification of rare events. An important caveat in interpretation of the
toxicological data is that the high doses used in many of the studies may produce different effects on the
lung than inhalation exposures at lower ambient concentrations. That is, "realistic" doses associated with
ambient nitrogen oxides exposures may activate cells and pathways entirely disparate from those
activated at high experimental doses. In addition, various differences in biology can exist, depending on
species and strain selected, that can affect the response and add uncertainty to extrapolating results to
humans.
This chapter focuses on important recent scientific studies, with emphasis on those conducted at or
near current ambient concentrations. The attached annexes include a broad survey of the relevant
epidemiology, human clinical, and toxicology literature to supplement the information presented here.
3.1. Respiratory Morbidity Related to Short-Term Exposure
3.1.1. Lung Host Defenses and Immunity
Lung host defenses are sensitive to NO2 exposure, with numerous measures of such effects
observed at concentrations of <1 ppm. The following discussion focuses on studies published since the
1993 AQCD and conducted at near-ambient exposure concentrations; as needed, it refers to studies
described in the 1993 AQCD. A major concern has been the potential for NO2 exposure to enhance
susceptibility to, or the severity of illness resulting from, respiratory infections and asthma, especially in
children. Potential mechanisms of lung host defense impairment (Chauhan and Johnston, 2003), include
"direct effects on the upper and lower airway by ciliary dyskinesis (Carson et al., 1993), epithelial
damage (Devalia et al., 1993a), increases in pro-inflammatory mediators and cytokines (Devalia et al.,
1993b), rises in IgE concentration (Siegel et al., 1997), and interaction with allergens (Tunnicliffe et al.,
1994), or indirectly through impairment of bronchial immunity (Sandstrom et al., 1992a)." Table 3.1-1
provides more details and summarizes a range of proposed mechanisms by which exposure to NO2 in
conjunction with viral infections may exacerbate upper and lower airway symptoms (Chauhan et al.,
1998).
Several epidemiologic studies investigated the relationship between NO2 exposure and effects
related to viral infection. Personal exposure to NO2 and the severity of virus-induced asthma (Chauhan et
al., 2003), including risk of airflow obstruction (Linaker et al., 2000) was studied in a group of 114
asthmatic children in England. Children were supplied with Palmes diffusion tubes, which they attached
to their clothing during the day and placed in their bedroom at night. Tubes were changed every week for
the duration of the 13-month study period. Nasal aspirates were obtained and analyzed for a variety of
respiratory illness-causing viruses. The authors observed that exposure to NO2 levels of greater than 14
(ig/m3 (7.4 ppb) in the week preceding any viral infection was associated with increases in the four-point
symptom severity score (score increase of 0.6 [95% CI: 0.01, 1.18]) in the week immediately after the
infection. Associations also were observed for the respiratory syncytial virus (RSV) alone (score increase
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of 2.1 [95% CI: 0.52, 3.81]). A significant reduction in peak expiratory flow (PEF) was associated with
exposure greater than 14 (ig/m3 (7.3 ppb) (by 12 L/min [95% CI: -23.6, -0.80]) (Chauhan et al., 2003).
Exploration of the relationship between PEF and NO2 showed that the risk of a PEF episode (as
diagnosed by a clinician's review of each child's PEF data) beginning within a week of an upper
respiratory infection was significantly associated with exposure to NO2 greater than 28 (ig/m3 (14.9 ppb)
(relative risk [RR]=1.9 [95% CI: 1.1, 3.4]) (Linaker et al., 2000). Thus, high personal NO2 exposure in the
week before an upper respiratory infection was associated with either increased severity of lower
respiratory tract symptoms or reduction of PEF for all virus types together and for two of the common
respiratory viruses, C-picornavirus and RSV, individually.
Table 3.1 -1. Proposed mechanisms whereby NO2 and respiratory virus infections may
exacerbate upper and lower airway symptoms.
PROPOSED MECHANISMS
Upper Airway
Epithelium
J. Ciliary beat frequency
t Epithelial permeability
Lower Airway
Epithelium
Cytokines
Inflammatory cells
Inflammatory mediators
Allergens
(as in upper airway)
I Epithelial-derived IL-8, GM-CSF, TNF-a
t Macrophage-derived IL-1b, IL-6, IL-8, TNF-a
t Mast cell tryptase
t Neutrophils
t Total lymphocytes
t NK lymphocytes
J. T-helper/T-cytotoxic cell ratio
t Free radicals, proteases, TXA2, TXB2, LTB4
t Penetrance due to ciliostasis
I PD20-FEV,
t Antigen-specific IgE
t Epithelial permeability
Peripheral Blood
J. B and NK lymphocytes
J. Total lymphocytes
Source: Adapted from Chauhan et al.
Several clinical studies have attempted to address the question of whether NO2 exposures impair
host defenses and/or increase susceptibility to infection (Devlin, 1992; Devlin et al., 1999; Frampton et
al., 2002; Goings et al., 1989; Rehn et al., 1982; Rubinstein et al., 1991; Sandstrom et al., 1990; 1991;
1992a; 1992b) (see the 1993 NOX AQCD details of older studies and Annex Table AX5.2-1 for additional
details on more recent studies). These studies have reported inconsistent results. One approach has been
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to examine the effects of in vivo NO2 exposure on the function of alveolar macrophages (AMs) obtained
by bronchoalveolar lavage (BAL), including the susceptibility of these cells to viral infection in vitro.
Two studies since 1993 involved 2.0-ppm NO2 exposures for 4 or 6 h with intermittent exercise and
found no effect on AM inactivation of influenza virus either immediately or 18 h after exposure (Azadniv
et al., 1998; Devlin et al., 1999). However, Devlin et al. (1999) found ex vivo AM phagocytic capacity
reduced following a 4-h exposure of healthy volunteers to 2 ppm NO2, indicating a reduced ability to
clear inhaled bacteria or other infectious agents. Frampton et al. (2002) examined NO2 effects on viral
infectivity of airway epithelial cells. Subjects were exposed to air, or 0.6- or 1.5-ppm NO2, for 3 h, and
bronchoscopy was performed 3.5 h after exposure. Epithelial cells were harvested from the airway by
brushing and then challenged in vitro with influenza virus and RSV. NO2 exposure did not alter viral
infectivity, but appeared to enhance epithelial cell injury in response to infection with RSV (p=0.024).
Similar results were reported with influenza virus. These findings indicate that prior exposure to NO2 may
increase the susceptibility of the respiratory epithelium to injury by subsequent viral challenge.
There is evidence from both animal and human studies indicating that exposure to NO2 may alter
lymphocyte subsets in the lung and possibly in the blood. Lymphocytes, particularly T lymphocytes and
natural killer (NK) cells, play a key role in the innate immune system and host defense against respiratory
viruses. Rubenstein et al. (1991) found that a series of four daily, 2-h exposures to 0.60-ppm NO2 resulted
in a small increase in NK cells recovered by BAL. Sandstrom et al. (1990; 1991) observed a significant,
dose-related increase in lymphocytes and mast cells recovered by BAL 24-h after a 20-min exposure to
NO2 at 2.25 to 5.50 ppm. In contrast, repeated exposures to 1.5- or 4-ppm NO2 for 20 min every second
day on six occasions resulted in decreased CD16+56+ (NK cells) and CD19+ cells (B lymphocytes) in
BAL fluid 24-h after the final exposure (Sandstrom et al., 1992a; 1992b). No effects were reported on
polymorphonuclear leukocytes (PMNs) or total lymphocyte numbers. Solomon et al. (2000) found a
decrease in CD4+T lymphocytes in BAL fluid 18-h after three daily, 4-h exposures to 2.0-ppm NO2.
Azadniv et al. (1998) observed a small but significant reduction in CD8+T lymphocytes in peripheral
blood, but not BAL fluid, 18 h following single 6-h exposures to 2.0-ppm NO2. Frampton et al. (2002)
found small increases in BAL lymphocytes and decreases in blood lymphocytes with exposures to 0.6 and
1.5ppmNO2for3h.
The observed effects on lymphocyte responses, as described above, have not been consistent among
studies. Differing exposure protocols and small numbers of subjects among these studies may explain the
varying and conflicting findings. Furthermore, the clinical importance of transient, small changes in
lymphocyte subsets is unclear. It is possible that the inflammatory response to NO2 exposure involves
both lymphocytes and PMNs, with lymphocyte responses occurring transiently and at lower
concentrations, and PMN responses predominating at higher concentrations or more prolonged exposures.
The airway lymphocyte responses do not provide convincing evidence of impairment in host defense.
One clinical study used fiber-optic bronchoscopy and found that 20-min exposures to NO2 at 1.5 to
3.5 ppm transiently reduced airway mucociliary activity (Helleday et al., 1995). Reduced mucus clearance
is expected to increase susceptibility to infection by reducing the removal rate of microorganisms from
airways. However, the study was weakened by the lack of a true air control exposure as well as by the
absence of randomization and blinding. As a clarification, Helleday et al. (1995) did not measure mucus
clearance rates directly using radiolabeled particles; rather they utilized an optical technique to
characterize ciliary activity. Rehn et al. (1982) examined the effect of NO2 exposure on mucociliary
clearance of a radiolabeled Teflon aerosol. After a 1-h exposure to either 0.27 or 1.06 ppm (500 or
2000 (ig/m3) NO2, there were no changes in airway clearance rates.
Animal studies provide clearer evidence that host defense system components such as mucociliary
transport and AMs (see Annex Table AX4.3) are targets for inhaled NO2. Animal studies further show
that NO2 can impair the respiratory host defense system sufficiently to render the host more susceptible to
respiratory infections (See Annex Table AX4.6).
Exposure of guinea pigs to 3- or 9-ppm NO2 6 h/day, 6 days/week for 2 weeks resulted in
concentration-dependent decreases in ciliary activity of 12 and 30% of control values, respectively
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(Ohashi et al., 1994). These concentration-dependent decreases were accompanied by a concentration-
dependent increase in eosinophil accumulation on the epithelium and submucosal connective tissue layer
of the nasal mucosa. For foreign agents such as some bacteria and viruses that deposit below the
mucociliary region in the gas-exchange region of the lung, AMs primarily provide host defenses by acting
to remove or kill viable particles, remove nonviable particles, and process and present antigens to
lymphocytes for antibody production. AMs are one of the sensitive targets for NO2, as evidenced by in
vivo animal exposures and in vitro studies (see Annex Table AX4.3 for details of studies related to each
of these morphological or functional parameters in exposed animals).
Suppression of host defense mechanisms by NO2 as described in the studies above is expected to
result in an increased incidence and severity of pulmonary infections (Coffin and Gardner, 1972; Gardner
et al., 1979; Miller et al., 1987). Various experimental approaches have been employed using animals in
an effort to determine the overall functional efficiency of the host's pulmonary defenses following NO2
exposure. In the most commonly used infectivity model, animals are exposed to either NO2 or filtered air
and the treatment groups are combined and exposed briefly to an aerosol of a viable agent, such as
Streptococcus spp., Klebsiella pneumoniae, Diplococcus pneumoniae, or influenza virus and mortality
rates are determined (Coffin and Gardner, 1972; Ehrlich, 1966; 1979; Gardner, 1982; Henry et al., 1970).
Although the endpoint is mortality, this experimental test is considered a sensitive indicator of the
depression of the defense mechanisms and is a commonly used assay for assessing immunotoxicity. The
susceptibility to bacterial and viral pulmonary infections in animals also increases with NO2 exposures of
as low as 0.5 ppm. No recent studies published since 1993 were identified that evaluated this endpoint.
Annex Table AX4.6 summarizes the effects of NO2 exposure and infectious agents in animal studies as
compiled in the 1993 NOX AQCD, and provides evidence that the host's response to inhaled NO2 can be
influenced by the duration and temporal patterns of exposure. This is important in considering continuous
versus intermittent exposures and attempting to understand observed differences in reported results.
Summary of Short-Term Exposure on Lung Host Defenses and Immunity
Impaired host-defense systems and increased risk of susceptibility to both viral and bacterial
infections have been observed in epidemiologic, human clinical, and animal toxicological studies. A
study by Chauhan et al. (2003) produced evidence that increased personal exposures to NO2 worsened
virus-associated lower respiratory tract symptoms in children with asthma. The limited evidence from
human clinical studies indicates that NO2 may increase susceptibility to injury by subsequent viral
challenge at exposures as low as 0.6 ppm for 3 h (Frampton et al., 2002). Toxicological studies have
shown that lung host defenses are sensitive to NO2 exposure, with several measures of such effects
observed at concentrations of less than 1 ppm. Together, the epidemiologic and experimental evidence
show coherence for effects of NO2 exposure on host defense or immune system effects. This group of
outcomes also provides biological plausibility for other respiratory effects described subsequently, such
as respiratory symptoms or ED visits for respiratory diseases.
3.1.2. Airway Inflammation
Epidemiologic studies have examined biological markers for inflammation (exhaled NO and
inflammatory nasal lavage [NAL] markers) and lung damage (urinary Clara cell protein CC16). Several
studies have been conducted in cohorts of children. Steerenberg et al. (2001) studied 126 schoolchildren
from urban and suburban communities in the Netherlands. Sampling of exhaled air and NAL fluid was
performed seven times, once per week over the course of 2 months. On average, the ambient NO2
concentrations were 1.5 times higher, and ambient NO concentrations were 7.8 times higher, in the urban
compared to the suburban community. Compared to children in the suburban community, urban children
had significantly greater levels of inflammatory NAL markers (interleukin [ILJ-8, urea, uric acid,
albumin) but not greater levels of exhaled NO. However, within the urban group, a statistically significant
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concentration-response relationship for exhaled NO was observed. Exhaled NO increased by 6.4 to
8.8 ppb per 20-ppb increase in NO2 lagged by 1 or 3 days. Another study by Steerenberg et al. (2003) of
119 schoolchildren in the Netherlands found associations between ambient NO2 and level of exhaled NO,
but quantitative regression results were not given. The authors concluded from their data that an
established, ongoing inflammatory response to pollen was not exacerbated by subsequent exposure to
high levels of air pollution or pollen.
In one recent U.S. study, Delfino et al. (2006) evaluated the relationship between personal and
ambient levels of fine PM (PM2 5), elemental carbon (EC), organic carbon (OC), and NO2 and fractional
exhaled NO (FeNO), a biomarker of airway inflammation, in a panel of 45 schoolchildren with persistent
asthma living in two southern California communities (Riverside and Whittier). FeNO was higher in
subjects with poorly controlled asthma. Positive associations were found for FeNO with several air
pollutants, including NO2, with evidence from multipollutant approaches indicating that traffic-related
sources of air pollutants underlie the findings. The authors concluded that the "association of FeNO with
personal and ambient NO2 was largely independent of personal and ambient EC and OC fractions of
PM2 5 in two-pollutant models", indicating that both ambient and personal NO2 represent other causal
pollutant components not sufficiently captured by ambient EC or OC in the study regions." While the
effect was small (< 2.5 ppb FeNO), making it difficult to determine if it is clinically relevant, the findings
support that air pollutant exposure increases inflammation in children with asthma.
Several studies have evaluated effects in adult cohorts. Adamkiewicz et al. (2004) studied 29
elderly adults in Steubenville, OH and found significant associations between increased exhaled NO and
increased daily levels of PM2 5, but no association was found with ambient NO2. Timonen et al. (2004)
collected biweekly urine samples for 6 months from 131 adults with coronary heart disease living in
Amsterdam, Helsinki, and Erfurt, Germany. Estimates using data from all three communities showed
significant associations between urinary levels of Clara cell protein CC16 (a marker for lung damage)
with elevations in daily PM2 5 concentration, but not ambient NO2. In Helsinki, however, a statistically
significant positive association was observed between NO2 lagged by 3 days and CC16 levels.
Interestingly, the correlation between NO2 and PM2 5 was lower in Helsinki (r=0.35) compared to this
correlation in Amsterdam (r=0.49) or Erfurt (r=0.82). Bernard et al. (1998) examined personal exposure
to NO2 and its effect on plasma antioxidants in a group of 107 healthy adults in Montpellier, France.
Subjects wore passive monitors for 14 days. When subjects were divided into two exposure groups
(above and below 21.3 ppb [40 jig/m3]), those in the high-exposure group had significantly lower plasma
(3-carotene levels. This difference was even greater when the analysis was stratified by dietary (3-carotene
intake: exposure to >21.3 ppb (40-(ig/m3) NO2 had the largest effect on plasma (3-carotene level among
subjects whose diet contained <4 mg/day (3-carotene (p<0.005). No other pollutants were included in this
study.
The 1993 NOX AQCD cited preliminary findings from two clinical studies showing modest airway
inflammation, as indicated by increased PMN numbers in BAL fluid after exposure to 2.0-ppm NO2 for 4
to 6 h with intermittent exercise. Both of those studies now have been published in complete form
(Azadniv et al., 1998; Devlin et al., 1999), and additional studies summarized below provide a clearer
picture of the airway inflammatory response to NO2 exposure.
Annex Table AX5.2-1 summarizes the key clinical studies of NO2 exposure in healthy subjects
published since 1993, with a few key studies included prior to that date. Figure 3.1-1 illustrates the
concentration-response relationship between NO2 exposure and inflammatory responses in
healthy subjects.
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Cellular
Response
©
0
Mediator
Response
No Response
©
© ©
Study
1. Azadnivetal. (1998)
2. Blombergetal. (1997)
3. Devlin etal. (1999)
4. Frampton et al. (2002)
5. Frampton et al. (2002)
6. JOrres et al. (1995)
7. Vagaggini et al. (1996)
ppm min
2.0 360
2.0 240
2.0 240
1.5 180
0.6 180
1.0 180
0.3 60
200 400 600
NO2 ppm-minutes
800
Figure 3.1-1.
Studies of airway inflammatory responses in relation to the total exposure to N02, expressed
as ppm-minutes. All of the studies involved intermittent exercise, and no attempt was made to
adjust the exposure metric for varying intensity and duration of exercise. Studies that did not
include a proper control air exposure and those that used multiple daily exposures were not
included in this figure.
Healthy volunteers exposed to 2.0-ppm NO2 for 6 h with intermittent exercise showed a slight
increase in the percentage of PMNs obtained in BAL fluid 18 h after exposure (air, 2.2 ± 0.3%; NO2, 3.1
± 0.4%) (Azadniv et al., 1998). Gavras et al. (1994) studied a separate group of subjects exposed using
the same protocol but assessed immediately after exposure. In this case, no effects were found in AM
phenotype or expression of the cell adhesion molecule CD1 Ib or receptors for IgG. Blomberg et al.
(1997) reported that 4-h exposures to 2.0-ppm NO2 resulted in an increase in IL-8 and PMNs in the
proximal airway of healthy subjects, although no changes were seen in bronchial biopsies. This group
also studied the effects of repeated 4-h exposures to 2-ppm NO2 on 4 consecutive days, with BAL,
bronchial biopsies, and BAL fluid antioxidant levels assessed 1.5-h after the last exposure (Blomberg
et al., 1999). The bronchial wash fraction of BAL fluid showed a 2-fold increase in PMNs and a 1.5-fold
increase in myeloperoxidase, indicating persistent mild airway inflammation with repeated NO2 exposure.
Devlin et al. (1999) exposed 8 healthy nonsmokers to 2.0-ppm NO2 for 4-h with intermittent exercise.
BAL performed the following morning showed a 3.1-fold increase in PMNs recovered in the bronchial
fraction, indicating small airway inflammation. These investigators also observed a reduction in AM
phagocytosis and superoxide production, indicating possible adverse effects on host defense.
Pathmanathan et al. (2003) conducted four repeated daily exposures of healthy subjects to 4-ppm
NO2 or air for 4 h, with intermittent exercise. Exposures were randomized and separated by 3 weeks.
Bronchoscopy and bronchial biopsies were performed 1-h after the last exposure. Immunohistochemistry
of the respiratory epithelium showed increased expression of IL-5, IL-10, and IL-13, as well as
intercellular adhesion molecule-1 (ICAM-1). These interleukins are upregulated in Th2 inflammatory
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responses, which are characteristic of allergic inflammation. The findings show that repeated NO2
exposures may drive the airway inflammatory response toward a Th2 or allergic-type response.
Unfortunately, the report provided no data on inflammatory cell responses in the epithelium or on the
cells or cytokines in BAL fluid. Thus, the findings cannot be considered conclusive regarding allergic
inflammation. Furthermore, the exposure concentrations of 4 ppm are considerably higher than ambient
outdoor concentrations.
Recent studies provide evidence for airway inflammatory effects at concentrations of <2.0 ppm.
Frampton et al. (2002) examined NO2 concentration responses in 21 healthy nonsmokers. Subjects were
exposed to air or 0.6- or 1.5-ppm NO2 for 3 h, with intermittent exercise, with exposures separated by at
least 3 weeks. BAL was performed 3.5-h after exposure. PMN numbers in the bronchial lavage fraction
increased slightly (<3-fold) but significantly (p=0.0003) after exposure to 1.5-ppm NO2; no increase was
evident at 0.6-ppm NO2. Lymphocyte numbers increased in the bronchial lavage fraction after 0.6-ppm
NO2, but not 1.5 ppm. CD4+T lymphocyte numbers increased in the alveolar lavage fraction, and
lymphocytes decreased in blood. These findings indicate a lymphocytic airway inflammatory response to
0.6-ppm NO2, which changes to a mild neutrophilic response at 1.5-ppm NO2. Solomon et al. (2000) also
showed increased PMNs in the bronchial fraction of BAL 18 h after the third consecutive day of exposure
to 2.0 ppm NO2 for 4 h with intermittent exercise. Torres et al. (1995) found that 3-h exposures to 1-ppm
NO2 with intermittent exercise altered levels of eicosanoids, but not inflammatory cells, in BAL fluid
collected 1-h after exposure. Eicosanoids are chemical mediators of the inflammatory response; their
increase in BAL fluid reported in this study indicates inflammation. The absence of an increase in PMN
numbers may reflect the timing of bronchoscopy (1 h after exposure). The peak influx of PMNs may
occur several hours after exposure, as it does following NO2 exposure.
The clinical studies summarized above provide evidence for airway inflammation at NO2
concentrations of < 2.0 ppm in healthy adults. Analyzing the bronchial fraction of BAL separately
appears to increase the sensitivity for detecting airway inflammatory effects of NO2 exposure. The onset
of inflammatory responses in healthy subjects appears to be between 100 and 200 ppm-min, i.e., 1 ppm
for 2 to 3 h (see Figure 3.1-1).
Animal toxicological studies demonstrating changes in protein and enzyme levels in the lung
following inhalation of NO2 are presented in Annex Table AX4.2. These include recent studies as well as
studies that were reported in the 1993 AQCD. Changes in protein and enzyme levels reflect the ability of
NO2 to cause lung inflammation associated with concomitant infiltration of serum protein, enzymes, and
inflammatory cells. However, interpretation of the array of changes observed may also reflect other
factors. For example, NO2 exposure may induce differentiation of some cell populations in response to
damage-induced tissue remodeling. Thus, some changes in lung enzyme activity and protein content may
reflect changes in cell types, rather than the direct effects of NO2 on protein infiltration. Furthermore,
some direct effects of NO2 on enzymes are possible because NO2 can oxidize certain reducible amino
acids or side chains of proteins in aqueous solution (Freeman and Mudd, 1981).
It has been reported that protein content changes in BAL fluid can be dependent on dietary
antioxidant status. NO2 exposure increases the protein content of BAL fluid in vitamin C-deficient guinea
pigs atNO2 levels of as low as 1880 (ig/m3 (1.0 ppm) after a 72-h exposure, but a 1-week exposure to 752
(ig/m3 (0.4 ppm) did not increase protein levels (Selgrade et al., 1981). However, Sherwin and Carlson
(1973) found increased protein content of BAL fluid from vitamin C-deficient guinea pigs exposed to
752-(ig/m3 (0.4 ppm) NO2 for 1 week. Differences in exposure techniques, protein measurement methods,
and/or degree of vitamin C deficiencies may explain the difference between the two studies. Hatch et al.
(1986) found that the NO2-induced increase in BAL protein in vitamin C-deficient guinea pigs was
accompanied by an increase in lung content of nonprotein sulfhydryls and ascorbic acid and a decrease in
vitamin E content. The increased susceptibility to NO2 was observed when lung vitamin C was reduced
(by diet) to levels <50% normal.
Studies in rats and mice published since the 1993 NOX AQCD have investigated the ability of NO2
to induce protein level changes consistent with inflammation. Overall, these more recent studies (included
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in Annex Table AX4.4), such as Miiller et al. (1994) and Pagani et al. (1994), propose that markers of
inflammation measured in BAL fluid such as total protein content and content of markers of cell
membrane permeability (e.g., lactate dehydrogenase increase only at or above 5-ppm exposure.
Summary of Short-Term Exposure on Airway Inflammation
Overall, short-term exposure to NO2 has been found to increase airway inflammation in human
clinical and animal toxicological studies with exposure concentrations that are higher than ambient levels.
Human clinical studies provide evidence for increased airway inflammation at NO2 concentrations of
<2.0 ppm; the onset of inflammatory responses in healthy subjects appears to be between 100 and
200 ppm-min, i.e., 1 ppm for 2 to 3 h. Increases in biological markers of inflammation were not observed
consistently in healthy animals at levels of less than 5 ppm; however, increased susceptibility to NO
concentrations of as low as 0.4 ppm was observed when lung vitamin C was reduced (by diet) to levels
<50% of normal. The few available epidemiologic studies point to an association between ambient NO2
concentrations and inflammatory response in the airways of children, though the associations were
inconsistent in the adult populations examined.
3.1.3. Airway Hyperresponsiveness
Inhaled pollutants such as NO2 may have direct effects on lung function or they may enhance the
inherent responsiveness of the airway to challenge by bronchoconstricting agents. Asthmatics are
generally more sensitive to nonspecific bronchoconstricting agents than nonasthmatics, and airway
challenge testing is used diagnostically in asthmatics. There is a wide range of airway responsiveness in
healthy people, and responsiveness is influenced by many factors, including medications, cigarette
smoke, air pollutants, respiratory infections, occupational exposures, and respiratory irritants. Several
drugs and other stimuli that cause bronchoconstriction have been used in challenge testing, including the
cholinergic drugs methacholine and carbachol, as well as histamine, hypertonic saline, cold air, and SO2.
Challenge with "specific" allergens is also considered in asthmatics. In asthmatics, there is strong
relationship between the degree of nonspecific airway hyperresponsiveness and the intensity of the early
airway response to allergens (Cockcroft and Davis 2006). Standards for airway challenge testing have
been developed for the clinical laboratory (American Thoracic Society, 2000a). Variations in methods for
administering the bronchoconstricting agents may substantially affect the results (Cockcroft et al., 2005).
Airway hyperresponsiveness appears to have two components: fixed and variable (Cockcroft and
Davis 2006). Presumably, exposure to air pollutants such as an NO2 or O3 could affect the variable
component, although long-term, repeated exposure to air pollutants may also contribute to the fixed
component. The mechanisms for these two components appear to differ. There is convincing evidence
that the fixed component reflects airway remodeling, due to the chronic, long-term effects of airway
inflammation (Cockcroft and Davis 2006). The variable component is thought to reflect airway
inflammation. It is linked to the late response to allergen challenge in asthmatics, during which an influx
of eosinophils and other inflammatory cells, in response to the allergen, reduces lung function and airway
caliber. The variable component is episodic and generally responds to bronchodilators and anti-
inflammatory agents, while the fixed component is less reversible and does not respond to anti-
inflammatory agents.
The degree of airway hyperresponsiveness is related to the severity of asthma (Juniper et al. 1981;
Murray et al. 1981). Airway responsiveness improves when asthma is treated with bronchodilators and
anti-inflammatory drugs (Newhouse and Dolovich 1986), and the severity of the airway
hyperresponsiveness predicts the lung function response to inhaled steroids (Kerstjens et al. 1993).
Increased airway responsiveness is linked with airway inflammation and airway remodeling (Chetta et al.
1996), increased risk for exacerbations (Van Schayck et al. 1991), reduced lung function (Xuan et al.
2000), and increased adverse respiratory symptoms (Murray et al. 1981). In adults with asthma, more
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severe airway responsiveness is predictive of a more rapid loss of lung function during follow-up (Van
Schayck et al. 1991). Increases in airway responsiveness in children may have important implications. For
instance, airway hyperresponsiveness is a risk factor for asthma development (Postma and Boezen 2004).
Airway hyperresponsiveness in children is also predictive of a reduced rate of growth in lung function
and associated with a subsequent decline in the forced expiratory volume in 1 s/forced vital capacity
(FEVi/FVC) ratio, a measure of airway obstruction (Xuan et al. 2000).
What is the clinical impact of transient increases in the variable component of airway
responsiveness following exposure to NO2 or other pollutants? In healthy adults without asthma or airway
hyperresponsiveness, there is likely little or no clinical impact of transient small increases in airway
responsiveness following low-level inhalation exposures. Acute inhalation exposures to very high
concentrations of respiratory irritants, however, can cause persistent asthma-like airway disease and
hyperresponsiveness, a condition known as the reactive airway dysfunction syndrome, or RADS (Bardana
Jr. 1999). In asthmatics, transient changes in nonspecific airway responsiveness in response to inhaled
pollutants may have clinical consequences. A variety of environmental challenges can transiently increase
airway responsiveness and worsen asthma control, such as allergen exposures (Brusasco et al. 1990), viral
infections (Cheung et al. 1995; Fraenkel et al. 1995), cigarette smoke (Tashkin et al. 1993), O3 (Kehrl et
al. 1999), and other respiratory irritants (Kinsella et al. 1991). An exposure that worsens airway
responsiveness to one agent in asthmatic subjects may enhance airway responsiveness to other challenge
agents. Transient increases in airway responsiveness following NO2 or other pollutant exposures have the
potential to increase symptoms and worsen asthma control, even if the pollutant exposure does not cause
acute decrements in lung function.
3.1.3.1. Allergen Responsiveness
Asthmatic Individuals
In asthmatics, inhalation of an allergen to which a person is sensitized can cause
bronchoconstriction and increased airway inflammation, and this is an important cause of asthma
exacerbations. Aerosolized allergens can be used in controlled airway challenge testing in the laboratory,
either clinically to identify specific allergens to which the individual is responsive or in research to
investigate the pathogenesis of the airway allergic response or the effectiveness of treatments. The degree
of responsiveness is a function of the concentration of inhaled allergen, the degree of sensitization as
measured by the level of allergen-specific IgE, and the degree of nonspecific airway responsiveness
(Cockcroft and Davis, 2006).
It is difficult to predict the level of responsiveness to an allergen, and although rare, severe
bronchoconstriction can occur in response to inhalation of very low concentrations of allergen. Allergen
challenge testing, therefore, involves greater risk than nonspecific airway challenge with drugs such as
methacholine. Asthmatics may experience both an "early" response, with declines in lung function within
minutes after the challenge, and a "late" response, with a decline in lung function hours after the
exposure. The early response primarily reflects release of histamine and other mediators by airway mast
cells; the late response reflects enhanced airway inflammation and mucous production. Responses to
allergen challenge are typically measured as changes in pulmonary function, such as declines in the
forced expiratory volume in 1 s (FEVi). However, the airway inflammatory response can also be assessed
using BAL, induced sputum, or exhaled breath condensate.
The potential for NO2 exposure to enhance responsiveness to allergen challenge in asthmatics
deserves special mention. Several recent studies, summarized in Annex Table AX5.3-2, have addressed
the question of whether 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 show
that NO2 may enhance the sensitivity to allergen-induced decrements in lung function and increase the
allergen-induced airway inflammatory response. Figure 3.1-2 categorizes the allergen challenge studies as
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"positive," i.e., showing evidence for increased responses to allergen in association with NO2 exposure
that reach statistical significance, or "negative" for responses that are null or not statistically significant,
with the exposure metric expressed as ppm-min. In comparing Figure 3.1-2 with Figure 3.1-1, the
enhancement of allergic responses in asthmatics occurs at exposure levels more than an order of
magnitude lower than those associated with airway inflammation in healthy subjects. The dosimetry
difference is even greater when considering that the allergen challenge studies generally were performed
at rest, while the airway inflammation studies in healthy subjects were performed with intermittent
exercise.
Tunnicliffe et al. (1994) exposed 8 subjects with mild asthma to 0.1 or 0.4 ppm NO2 for 1 h at rest
and reported that 0.4 ppm NO2 exposure slightly increased responsiveness to a fixed dose of allergen
during both the early and late phases of the response. In two U.K. studies (Devalia et al., 1994; Rusznak
et al., 1996), exposure to the combination of 0.4 ppm NO2 and 0.2-ppm SO2 increased responsiveness to
subsequent allergen challenge in mild atopic asthmatics, whereas neither pollutant alone altered allergen
responsiveness.
1
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Study
1. Barck et al. (2002)
2. Barck et al. (2005a)
3. Barck et al. (2005b)
4. Jenkins etal. (1999)
5. Jenkins etal. (1999)
6. Strand etal. (1997)
7. Strand etal. (1998)
ppm min
0.26 30
0.26 15
0.26 30
0.20 360
0.40 180
0.26 30
0.26 30
8. Tunnicliffe etal. (1994) 0.40 60
9. Tunnicliffe eta. (1994) 0.10 60
10. Wang etal. (1995a,b) 0.40 360
11. Wittenetal. (2005)
I
0.40 180
I
50
100
150
NO2 ppm-minutes
Figure 3.1-2. Airway responsiveness to allergen challenge in asthmatic subjects following a single
exposure to N02. Responsiveness was assessed using spirometric (circles) and
inflammatory (squares) endpoints. On the vertical axis, positive and negative indicate studies
finding statistically significant and non-significant effects of N02 on group mean
responsiveness to allergen, respectively.
A series of studies from the Karolinska Institute in Sweden have explored airway responses to
allergen challenge in asthmatics. Strand et al. (1997) demonstrated that single 30-min exposures to
0.26 ppm NO2 increased the late phase response to allergen challenge 4 h after exposure. In a separate
study (Strand et al., 1998), four daily repeated exposures to 0.26 ppm NO2 for 30 min increased both the
early and late phase responses to allergen. Barck et al. (2002) used the same exposure and challenge
protocol as used in the earlier Strand et al. (1997) studies (0.26 ppm for 30 min, with allergen challenge
4-h after exposure) and performed BAL 19-h after the allergen challenge to determine NO2 effects on the
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allergen-induced inflammatory response. NO2 followed by allergen caused increases in the BAL recovery
of PMN and eosinophil cationic protein (ECP), with reduced volume of BAL fluid and reduced cell
viability, compared with air followed by allergen. ECP is released by degranulating eosinophils, is toxic
to respiratory epithelial cells, and is thought to play a role in the pathogenesis of airway injury in asthma.
These findings indicate that NO2 exposure enhanced the airway inflammatory response to allergen.
Subsequently, Barck et al. (2005a) exposed 18 mild asthmatics to air or NO2 for 15 min on day 1,
followed by two 15-min exposures separated by 1-h on day 2, with allergen challenge after exposures on
both days 1 and 2. Sputum was induced before exposure on day 1 and after exposures (morning of day 3).
NO2+allergen, compared to air+allergen, treatment resulted in increased levels of ECP in both sputum and
blood and increased myeloperoxidase levels in blood. A separate study examined NO2 effects on nasal
responses to nasal allergen challenge (Barck et al., 2005b). Single 30-min exposures to 0.26 ppm NO2 did
not enhance nasal allergen responses. All exposures in the Karolinska Institute studies (Barck et al., 2002,
2005a; (Strand et al., 1997; 1998) used subjects at rest. These studies utilized an adequate number of
subjects, included air control exposures, randomized exposure order, and separated exposures by at least 2
weeks. Together, they indicate that quite brief exposures to 0.26-ppm NO2 can cause effects in allergen
responsiveness in asthmatics.
The findings in these studies of allergen responsiveness may shed some light on the variable results
in earlier studies of NO2 effects on nonspecific airway responsiveness. It is possible that some prior
studies may have been variably confounded by environmental allergen exposure, increasing the
variability in subject responses to NO2 and perhaps explaining some of the inconsistent findings.
Several studies have been conducted using longer NO2 exposures. Wang et al. (1995a,b, 1999)
found that more intense (0.4 ppm) and prolonged (6 h) NO2 exposures enhanced allergen responsiveness
in the nasal mucosa in subjects with allergic rhinitis. Jenkins et al. (1999) examined FEVi decrements and
airway responsiveness to allergen in a group of mild, atopic asthmatics. The subjects were exposed for 3 h
to 0.4 ppm NO2, 0.2 ppm O3, and 0.4 ppm NO2+0.2 ppm O3. The subjects were also exposed for 6 h to
produce exposure concentrations that would provide identical doses to the 3-h protocols (i.e., equivalent
in concentration times duration of exposure [C X T]). Significant increases in airway responsiveness to
allergen occurred following all the 3-h exposures, but not following the 6-h exposures. However, Witten
et al. (2005) did not find enhanced airway inflammation or a reduction in allergen provocative dose that
produces a 20% decrease in FEVi (PD20-FEVO with allergen challenge in 15 asthmatic subjects allergic
to house dust mite allergen who were exposed to air and 0.4 ppm NO2 for 3-h with intermittent exercise.
Allergen challenge was performed immediately after exposure, and sputum induction was performed 6
and 26 h after the allergen challenge. There was no overall effect of NO2 on allergen responsiveness,
although 3 subjects required a much smaller concentration of allergen after NO2 than after air exposure
and were deemed to be NO2 "responders." NO2 exposure was surprisingly associated with a reduction in
sputum eosinophils, with no increase in allergen-induced neutrophilic inflammation.
The differing findings in these studies may relate in part to differences in timing of the allergen
challenge, the use of multiple- versus single-dose allergen challenge, the use of BAL versus sputum
induction, exercise versus rest during exposure, and differences in subject susceptibility. Overall, these
studies indicate thatNO2 short-term exposures of less than 1 ppm enhance allergen responsiveness in
some allergic asthmatics.
Lastly, one study examined the effects on allergen responsiveness of exposure to traffic exhaust in a
tunnel (Svartengren et al., 2000). Twenty mild asthmatics sat in a stationary vehicle within a busy tunnel
for 30 min. Allergen challenge was performed 4 h later. The control exposure was in a hotel room in a
suburban area with low air pollution levels. Exposures were separated by 4 weeks and the order was
randomized. Median NO2 levels in the vehicle were 313 (ig/m3 (range, 203 to 462), or 0.166 ppm, (range,
0.106 to 0.242). PM10 levels were 170 (ig/m3 (range, 103 to 613), and PM25 levels were 95 (ig/m3 (range,
61 to 128). Median NO2 levels outside the hotel were 11 (ig/m3 or 0.006 ppm. Subjects in the tunnel
experienced increased cough, and also reported awareness of noise and odors. More importantly, there
was a greater allergen-induced increase in specific airway resistance after the tunnel exposure than after
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the control exposure (44% versus 31% respectively). Thoracic gas volume also was increased to a greater
degree after the tunnel exposure, demonstrating increased gas trapping within the lung. These findings
were most pronounced in the subjects exposed to the highest levels of NO2. This study proposes that
exposure to traffic exhaust, and particularly the NO2 component, increases allergen responsiveness in
asthmatics, and the results fit well with the findings in studies of clinical exposures of NO2 (Barck et al.,
2002, 2005a). However, it was not possible to blind the exposures, and the control exposure (hotel room,
presumably quiet and relaxed) was not well matched to the experimental exposure (vehicle, noisy,
odorous). It remains possible that factors other than NO2 contributed to, or were responsible for, the
observed differences in allergen responsiveness. These recent studies involving allergen challenge show
that NO2 may enhance the sensitivity to allergen-induced decrements in lung function and increase the
allergen-induced airway inflammatory response. Enhancement of allergic responses in asthmatics occurs
at exposure levels of more than an order of magnitude lower than those associated with airway
inflammation in healthy subjects. The dosimetry difference is even greater when considering that the
allergen challenge studies generally were performed at rest, while the airway inflammation studies in
healthy subjects were performed with intermittent exercise. Enhancement of allergen responses has been
found at exposures as low as 8 ppm-min, i.e., 0.26 ppm for 30 min. The exposure-response characteristics
of NO2 effects on allergen responses, as well as the effects of exercise, relationship to the severity of
asthma, the role of asthma medications, and other clinical factors are not fully understood.
lexicological Studies
Acute exposures of Brown Norway rats to NO2 at a concentration of 5 ppm for 3 h resulted in in-
creased specific immune response to house dust mite allergen and increased immune-mediated pulmonary
inflammation (Gilmour et al., 1996). Higher levels of antigen-specific serum IgE, local IgA, IgG, and IgE
were observed when rats were exposed to NO2 after both the immunization and challenge phase but not
after either the immunization or challenge phase alone. Increases in the number of inflammatory cells in
the lungs and lymphocyte responsiveness to house dust mite allergen in the spleen and mediastinal lymph
node were observed. The authors concluded that this increased immune responsiveness to house dust mite
allergen may be the result of the increased lung permeability caused by NO2 exposure, enhancing
translocation of the antigen to local lymph nodes and circulation to other sites in the body.
A delayed bronchial response, seen as increased respiration rate, occurred in NO2-exposed, Candida
albicans-sensitized guinea pigs 15 to 42 h after a challenge dose of C. albicans (Kitabatake et al., 1995).
Guinea pigs were given an intraperitoneal injection of C. albicans, followed by a second injection 4
weeks later. Two weeks after the second injection, the animals were given an inhalation exposure of
killed C. albicans. Animals were also exposed 4 h/day to 4.76 ppm NO2 from the same day as the first
injection of C. albicans, for a total of 30 exposures (5 days/week).
In a study with NO2-exposed rabbits, pulmonary function (lung resistance, dynamic compliance)
was not affected when immunized intraperitoneally within 24 h of birth until 3 months of age to either
Alternaria tenuis or house dust mite antigen. The rabbits were given intraperitoneal injections once
weekly for 1 month, and then every 2 weeks thereafter, and exposed to 4 ppm NO2 for 2 h daily (Douglas
etal., 1994).
To determine the effect of NO2 on allergenic airway responses in sensitized animals, Hubbard et al.
(2002) exposed ovalbumin (OVA)-sensitized mice to NO2 (0.7 or 5 ppm, 2 h/day for 3 days) or air. While
the air-exposed mice developed lower airway inflammation (increased total BAL cellularity and increased
eosinophil levels), the NO2-exposed mice had significantly lower levels of eosinophils for both NO2
concentrations, with the greatest effect seen at the lower NO2 concentration. These results were confirmed
in a subsequent experiment (0.7 ppm NO2 for 3 or 10 days) showing significant reductions in BAL
cellularity and eosinophil levels for both time points. In a similar study (Proust et al., 2002), mice were
sensitized and challenged with OVA and then exposed to NO2 (5 or 20 ppm, 3 h). The 20 ppm NO2
exposure resulted in a significant increase in bronchopulmonary hyperreactivity 24 h after exposure, as
3-13
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compared to the OVA-air and 5 ppm NO2 group. However, exposure to 5 ppm NO2 resulted in a marked
reduction in bronchopulmonary hyperreactivity as compared to both the 20 ppm NO2 and OVA-air
groups. By 72 h, bronchopulmonary hyperreactivity in all groups was comparable. The measurement of
fibronectin in the BAL fluid was used as a marker of epithelial permeability. At 24 h after exposure,
fibronectin levels were significantly higher in the 20 ppm NO2 group as compared to both the 5 ppm NO2
and air groups. However, fibronectin levels in the 5 ppm NO2 group were significantly lower than the
OVA-air group. After 72 h, there was no difference in fibronectin levels between the OVA-air and 5-ppm
NO2 groups, while fibronectin levels of the 20 ppm NO2 group remained significantly higher than the
5 ppm NO2 group. The recruitment of PMNs as measured in the BAL fluid at 24 h postexposure, revealed
a dose-dependent increase reaching significance only with the 20 ppm NO2 exposure. By 72 h, all groups
were comparable. In contrast, the recruitment of eosinophils, as measured in the BAL fluid, showed no
significant differences between groups at the 24 h time point, yet at the 72-h point, eosinophils were
significantly decreased in the 5 ppm NO2 group as compared to OVA-air group. Eosinophil peroxidase
(EPO) in the lung tissue showed a similar trend with NO2 exposure reducing the EPO levels as compared
to OVA-air controls. At 24 h, EPO was significantly lower in the 5 and 20 ppm NO2 groups as compared
to the OVA-air group, while at 72 h, only the 5 ppm NO2 group was significantly lower. IL-5 was
measured in the BAL fluid, and the 5 ppm NO2 group was significantly lower in IL-5 than all other
groups, and the 20 ppm NO2 group was significantly higher.
3.1.3.2. Nonspecific Responsiveness
Healthy Individuals
Several observations indicate that NO2 exposures in the range of 1.5 to 2.0 ppm cause small but
significant increases in airway responsiveness in healthy subjects. Mohsenin (1988) found that a 1-h
exposure to 2 ppm NO2 increased responsiveness to methacholine, as measured by changes in specific
airway conductance, without directly affecting lung function. Furthermore, pretreatment with ascorbic
acid prevented the NO2-induced increase in airway responsiveness (Mohsenin, 1987a). A mild increase in
responsiveness to carbachol was observed following a 3-h exposure to 1.5 ppm NO2, but not to
intermittent peaks of 2.0 ppm (Frampton et al., 1991). Thus, the lower threshold concentration of NO2 for
causing increases in nonspecific airway responsiveness in healthy subjects appears to be in the 1- to
2-ppm range.
Asthmatic Individuals
The 1993 NOX AQCD reported results that showed NO2 might enhance subsequent responsiveness
to challenge at relatively low NO2 concentrations within the range of 0.2 to 0.3 ppm. From the studies for
which individual data were readily available, the number of subjects whose airway responsiveness
increased and whose airway responsiveness decreased is listed in Table 3.1-2. Table 3.1-2 reproduces
airway responsiveness data for asthmatics that was previously provided in Table 15-9 of the 1993 NOX
AQCD. In the 19 studies included in this table, asthmatics were exposed to NO2 concentrations ranging
from 0.1 to 1.0 ppm. The vast majority (17 of 19) of these studies utilized nonspecific challenge agents.
Tabulation of data from this table provides information regarding the direction of the change (i.e.,
increase or decrease) in airway responsiveness following NO2 exposure. Some of the disparate findings of
these studies are discussed below.
3-14
-------
Table 3.1-2. Changes in airway responsiveness associated with NO2 exposure.
STUDY
Ahmed et al. (1983b)
Ahmed et al. (1983a)
Hazuchaetal. (1983)
Oreheketal. (1976)
Rasmussen et al. (1990)
Oreheketal. (1981)
Bylinetal. (1988)
Roger et al. (1990)
Kleinman et al. (1983)
Rasmussen et al. (1990)
Jorres and Magnussen
(1990)
Jorres and Magnussen
(1991)
Bylinetal. (1988)
Avoletal. (1988)
Avoletal. (1989)
Bauer etal. (1986a)
Linn etal. (1986)
Morrow and Utell (1989)
Roger etal. (1990)
Rubinstein etal. (1990)
Bylinetal. (1985)
Mohsenin (1987b)
Bylinetal. (1988)
Avoletal. (1988)
Roger etal. (1990)
Rasmussen et al. (1990)
Linn etal. (1986)
N
20
19
15
20
20
7
20
19
31
20
14
11
20
37
34
12
21
20
19
9
8
10
20
37
19
20
21
N02
(ppm)
0.1
0.1
0.1
0.1
0.1
0.11
0.14
0.15
0.20
0.20
0.25
0.25
0.27
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.48
0.50
0.53
0.60
0.60
0.80
1.00
EXP
(min)
60
60
60
60
120
60
30
80
120
120
30
30
30
120
30
30
120
225
80
30
20
60
30
120
80
120
120
CHAL-
LENGE
TYPE
CARB
RAG
METH
CARB
METH
GRASS
HIST
METH
METH
METH
SO2
METH
HIST
COLD
COLD
COLD
COLD
CARB
METH
SO2
HIST
METH
HIST
COLD
METH
METH
COLD
END
POINT
sGAW
sGAW
sRaw
sRaw
FEV,
sRaw
sRaw
sRaw
FEV,
FEV,
sRaw
sRaw
sRaw
FEV,
FEV,
FEV,
FEV,
sGaw
sRaw
sRaw
sRaw
sGaw
sRaw
FEV,
sRaw
FEV,
FEV,
TIME
POST
EXP
(min)
—
—
20
0
0
0
25
60
0
0
27
60
25
60
60
60
0
—
60
60
20
0
25
60
60
0
0
EXER
N
N
N
N
Y
N
N
Y
Y
Y
N
Y
N
Y
Y
Y
Y
Y
Y
Y
N
N
N
Y
Y
Y
Y
CHANGE IN AHR
INC
13
10
6
14
—
—
14
10
20
—
11
7
14
11
12
9
—
8
4
5
7
12
13
11
—
—
DEC
7
8
7
3
—
—
6
7
7
—
2
4
6
16
21
3
—
9
5
—
2
7
16
8
—
—
AVERAGE
PD±SD
AIR
6.0
9.0 ±25
1.9 ±0.4
0.6
NO2
2.7
3.4 ±4.6
2.0 ±1.0
0.4
(AIR-NO2=0.00)
1.2 ±0.3
—
3.3 ±0.7
8.6 ±16
1.3 ±0.3
—
3.1 ±0.7
3.0 ±6.2
(AIR-NO2=0.02)
47 ±5.1
0.4 ±1.6
—
-8.4 ±11
-5.3 ±12
0.8 ±0.4
-11.4
—
3.3 ±0.7
1.3 ±0.7
—
9.2 ±1.5
—
-8.4 ±11
3.3 ±0.7
37.7 ±3.5
0.4 ±1.6
—
-10.7 ±12
-4.7 ±13
0.5 ±0.3
-12.1
—
3.3 ±0.8
1.3 ±0.8
—
4.6 ±8.0
—
-10.4 ± 14
3.7 ±1.1
(AIR-NO2=-0.06)
-11.4
-11.2
NOTES
4 allergic
3 asth-
matic
Delta
FEV,
Delta
FEV,
PD10RHE
Delta
FEV,
Delta
FEV,
Delta
FEV,
AHR: Airway Hyperresponsiveness
CARB: Carbachol
COLD: Cold-dry air
GRASS: Grass pollen
HIST: Histamine
METH: Methacholine
RAG: Ragweed
PD: Provocative dose
PD10RHE=Respiratory heat exchange
(loss) for 10% drop in FEV,
3-15
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Roger et al. (1990), in a comprehensive, concentration-response experiment, were unable to
confirm the results of a pilot study indicating airway responses occur in asthmatic subjects. Twenty-one
male asthmatics exposed to NO2 at 0.15, 0.30, or 0.60 ppm for 75 min did not experience significant
effects on lung function or airway responsiveness compared with air exposure. Bylin et al. (1985) found
significantly increased bronchial responsiveness to histamine challenge compared with sham exposure in
8 atopic asthmatics exposed to 0.30-ppm NO2 for 20 min. Five of 8 asthmatics demonstrated increased
reactivity, while 3 subjects showed no change, as assessed by specific airway resistance. Mohsenin
(1987b) reported enhanced responsiveness to methacholine in 8 asthmatic subjects exposed to 0.50-ppm
NO2 at rest for 1 h; airway responsiveness was measured by partial expiratory flow rates at 40% vital
capacity, which may have increased the sensitivity for detecting small changes in airway responsiveness.
Torres and Magnussen (1991) found no effects on lung function or methacholine responsiveness in 11
patients with mild asthma after exposure to 0.25-ppm NO2 for 30 min with 10 min of exercise. Most
recently, Strand et al. (1996) performed a series of studies in mild asthmatics exposed to 0.26 ppm for
30 min and found increased responsiveness to histamine.
The effects of NO2 exposure on SO2-induced bronchoconstriction also have been examined, but
with inconsistent results. Torres and Magnussen (1990) found an increase in airway responsiveness to SO2
in asthmatic subjects following exposure to 0.25-ppm NO2 for 30 min at rest; yet Rubenstein et al. (1990)
found no change in responsiveness to SO2 inhalation following exposure of asthmatics to 0.30-ppm NO2
for 30 min with 20 min of exercise.
The varied results of these studies have not been satisfactorily explained. It is evident that a wide
range of responses occurs among asthmatics exposed to NO2. This variation may in part reflect
differences in individual subjects and exposure protocols: mouthpiece versus chamber, obstructed versus
non-obstructed asthmatics, rest versus exercise, and varying use of medication(s) among subjects. Indeed,
via meta-analysis, Folinsbee (1992) found that airway responsiveness was greater in asthmatics exposed
to NO2 at rest than during exercise. Following NO2 exposures between 0.2 and 0.3 ppm NO2, only 52% of
subjects exposed with exercise had increased responsiveness, whereas 76% of subjects had increased
responsiveness in protocols using resting exposures. The factors that predispose some asthmatics to NO2
responsiveness is still not understood. Studies have typically involved volunteers with mild asthma; data
are lacking from more severely affected asthmatics, who may be more susceptible.
Table 3.1-3 provides a meta-analysis of the non-specific airway responsiveness data in Table 3.1-2.
In terms of the fraction of asthmatics affected and statistical significance for the indicated exposure
concentration ranges, the data in Table 3.1-3 are similar to those provided in Table 15-10 of the 1993
NOX AQCD and by Folinsbee (1992). Table 3.1-3 differs from the prior analysis in that a study using
specific responses to ragweed was excluded (Ahmed et al., 1983a), a recent study using nonspecific
responses to histamine was included (Strand et al., 1996), and an additional concentration range of
0.1 ppm was considered. Overall, analysis of these data for 355 asthmatics indicates that short-term
exposures to NO2 at outdoor ambient concentrations (<0.3 ppm) are linked to nonspecific airway
hyperresponsiveness in people with mild asthma.
Table 3.1-3. Fraction of NO2-exposed asthmatics with increased non-specific airway hyperresponsiveness.
NO2 ppm
0.1
0.1 -0.15
0.2-0.3
>0.3
0.1 -0.6
ALL EXPOSURES
0.66 (50)B
0.66 (87)c
0.58(187)B
0.59 (81)
0.60 (355)c
EXPOSURE WITH EXERCISE
—
0.59 (1 7)
0.52 (136)
0.49 (48)
0.52 (201)
EXPOSURE AT REST
0.66 (50)B
0.67 (70)c
0.75 (51)c
0.73 (33)B
0.71 (154)c
Note: Values represent the fraction of asthmatics (out of the total number of individuals in parentheses) having an increase in airway responsiveness following NO2 exposure
versus air. Analysis is for 355 asthmatics in Table 3.1-2 with the exclusion of the Ahmed et al. (1983a) data for the specific airway responsiveness of 18 asthmatics to ragweed
and the addition of responses of 18 asthmatics (13 with increased responsiveness to histamine) 30 minutes after exposure to 0.26 ppm NO2 for 30 minutes during rest from
Strand et al. (1996). B p < 0.05 two-tailed sign test. c p < 0.01 two-tailed sign test.
3-16
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Toxicological Studies
In the previous review, toxicological evidence supported a conclusion that airway responsiveness
was one of the key health responses to NO2 exposure. A number of recent animal studies have also
reported airway responsiveness with NO2 exposure. Overall, many studies have demonstrated the ability
of NO2 exposure to increase bronchial sensitivity to various challenge agents, although the mechanisms
for this response are not fully known.
Kobayashi and Miura (1995) studied the concentration- and time-dependency of airway
hyperresponsiveness to inhaled histamine aerosol in guinea pigs exposed subchronically to NO2. In one
experiment, guinea pigs were exposed by inhalation to 0, 0.06, 0.5, or 4.0 ppm NO2, 24 h/day for 6 or 12
weeks. Immediately following the last exposure, airway responsiveness was assessed by measurement of
specific airway resistance as a function of increasing concentrations of histamine aerosol. Animals
exposed to 4 ppm NO2 for 6 weeks exhibited increased airway response to inhaled histamine aerosol;
airway response at 12 weeks was not determined. No effects were observed at the lower exposure levels.
In another experiment conducted in this study (Kobayashi and Miura, 1995), guinea pigs were exposed by
inhalation to 0, 1.0, 2.0, or 4.0 ppm NO2, 24 h/day for 6 or 12 weeks, and the airway hyperresponsiveness
was determined. Increased hyperresponsiveness to inhaled histamine was observed in animals exposed to
4 ppm for 6 weeks, 2 ppm for 6 and 12 weeks, and 1 ppm for 12 weeks only. The results also showed that
at 1 or 2 ppm NO2, airway hyperresponsiveness developed to a higher degree with the passage of time.
Higher concentrations of NO2 were found to induce airway hyperresponsiveness faster compared to lower
concentrations. When the specific airway resistance was compared to values determined 1 week prior to
initiation of the NO2 exposure, values were increased in the 2.0 and 4.0 ppm animals at 12 weeks only.
Specific airway resistance was also increased to a higher degree with the passage of time.
3.1.3.3. Summary of Short-Term Exposure on Airway Responsiveness
The evidence from human and animal experimental studies provides evidence for increased airway
responsiveness to specific allergen challenges following NO2 exposure. Recent human clinical studies
show that NO2 exposure may enhance sensitivity to allergen-induced decrements in lung function and
increase allergen-induced airway inflammatory response at exposures as low as 0.26 ppm NO2 for 30 min
(Figure 3.1-2). Inflammatory responses to the allergen challenge were not accompanied by changes in
pulmonary function or subjective symptoms. Increased immune-mediated pulmonary inflammation was
also observed in rats exposed to house dust mite allergen following exposure to 5 ppm NO2 for 3 h.
Exposure to NO2 also has been found to enhance the inherent responsiveness of airway to
subsequent nonspecific challenges in human clinical studies. In general, small but significant increases in
nonspecific airway responsiveness were observed in the range of 1.5 to 2.0 ppm for 3 h in healthy adults
and between 0.2 and 0.3 ppm NO2 for 30 min for asthmatics, but a wide range of responses were
observed, particularly among asthmatics. Subchronic NO2 exposure (6 to 12 weeks) of animals also
increases responsiveness to nonspecific challenges at 1 to 4 ppm NO2.
Results from human studies were inconsistent; some, reported increased responsiveness following
NO2 exposure. A variety of factors could contribute to this apparent inconsistency. For instance,
responsiveness has been observed to be greater following resting than exercising exposures to NO2,
despite the greater NO2 exposure to the respiratory tract during exercise. In addition, the methods for
administering the bronchoconstricting challenge agents and degree of sensitization to specific allergen
also are recognized to affect responsiveness (Cockcroft et al., 2005; Cockcroft and Davis, 2006).
3-17
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3.1.4. Effects of Short-Term Exposure on Respiratory Symptoms
Since the 1993 AQCD, additional studies have reported health effects associated with NO2 from
indoor exposure, personal exposure, and ambient concentration studies. The following section
characterizes the results of these studies.
3.1.4.1. Indoor and Personal Exposure and Respiratory Outcomes
Indoor NO2 exposures may differ from ambient exposures with respect to temporal pattern and
levels of NO2, and also with the copollutants associated with indoor NO2 sources (see Annex Table
AX6.3-1 for details). Samet and Bell (2004) state that while "evidence from studies of outdoor air
pollution cannot readily isolate an effect of NO2 because of its contribution to the formation of secondary
particles and O3, observational studies of exposure indoors can test hypotheses related to NO2 specifically
although confounding by combustion sources in the home is a concern."
Most of the studies conducted since 1993 have taken place in Australia and attempted to monitor
indoor exposures (with passive diffusion badges) from both cooking and heating sources in homes and
schools (Pilotto et al., 1997; 2004; Rodriguez et al., 2007; Garrett et al, 1998; Smith et al, 2000). Several
indoor exposure studies have also been conducted in the U.S. (Belanger et al., 2006; Kattan et al., 2007;
van Strien et al., 2004), Europe (Farrow et al., 1997; Simoni et al., 2002, 2004), and Singapore (Ng et al.,
2001). The results from these studies are summarized in Annex Table AX6.3-1.
One intervention study provided strong evidence of a detrimental effect of exposure to indoor
levels of NO2. Pilotto et al. (2004) conducted a randomized intervention study of respiratory symptoms of
asthmatic children in Australia before and after selective replacement of unflued gas heaters in schools. In
the study, 18 schools using unflued gas heaters were randomly allocated to have an electric heater (n=4)
or a flued gas heater (n=4) installed or to retain their original heaters (n=10). Changes to the heating
systems were disguised as routine maintenance to prevent bias in reporting of symptoms. Children were
eligible for the study if they had physician-diagnosed asthma and no unflued heater in their home. For the
114 children enrolled, symptoms were recorded daily and reported in biweekly telephone interviews
during 12 weeks in the winter. Passive diffusion badges were used to measure NO2 exposure in
classrooms (6 h/day) and in the children's homes. Schools in the intervention group (with new heaters)
averaged overall means (standard devision [SD]) of 15.5 (6.6) ppb NO2, while control schools (with
unflued heaters) averaged 47.0 (26.8) ppb. Exposure to NO2 in the children's homes was quite variable
but with similar mean levels. Indoor levels at homes for the intervention group were 13.7 (19.3) ppb and
14.6 (21.5) ppb for the control group. Children attending intervention schools had significant reductions
in several symptoms (see Table 3.1-4): difficulty breathing during the day (RR=0.41 [95% CI: 0.07,
0.98]) and at night (RR=0.32 [95% CI: 0.14, 0.69]); chest tightness during the day (RR=0.45 [95% CI:
0.25, 0.81]) and at night (RR=0.59 [95% CI: 0.28, 1.29]); and asthma attacks during the day (RR=0.39
[95% CI: 0.17,0.93]).
Samet and Bell (2004) stated that Pilotto et al. (2004) provided persuasive evidence of an
association between exposure to NO2 from classroom heaters and the respiratory health of children with
asthma; further, the intervention study design alleviated some potential limitations of observational
studies. The two groups of children studied had similar baseline characteristics. In addition,
concentrations in the home environment were similar for the two groups, implying that exposure at school
was likely to be the primary determinant of a difference in indoor NO2 exposure between the two groups.
However, it is possible that confounding by particle emissions, particularly UFP, may be present.
3-18
-------
Table 3.1-4. Mean rates (SD) per 100 days at risk and unadjusted rate ratio (RR) for
symptoms/activities over 12 weeks during the winter heating period.
SYMPTOM / ACTIVITY
Wheeze during the day
Wheeze during the night
Difficulty breathing during the day
Difficulty breathing during the night
Chest tightness during the day
Chest tightness during the night
Cough during the day
Cough during the night
Difficulty breathing after exercise
Asthma attacks during the day
Asthma attacks during the night
Missed school due to asthma
Visit to health care facilities due to asthma
Taking any asthma medication
Taking any reliever
Taking any preventer
MEAN RATE INTERVENTION
(N=45)
4.9(15.2)
2.2 (5.6)
2.2 (3.7)
0.8(2.2)
2.3 (4.3)
1.5(3.3)
17.5(21.5)
10.7(16.6)
3.8 (7.4)
1.1 (2.3)
0.7 (2.1)
1.6(2.0)
0.5 (0.8)
26.9 (36.7)
13.8(23.2)
26.2 (40.1)
MEAN RATE CONTROL
(N=69)
5.1 (10.5)
2.3 (5.5)
5.4(12.1)
2.6 (6.9)
5.1 (9.9)
2.5 (6.2)
13.7(13.7)
11.6(12.4)
6.4(13.9)
2.7 (5.3)
1 .8 (3.8)
1.2 (2.8)
0.8(1.2)
34.6 (37.1)
22.4 (28.8)
29.9 (42.2)
RR
0.95
0.94
0.41
0.32
0.45
0.59
1.27
0.92
0.59
0.39
0.38
1.34
0.60
0.77
0.62
0.87
(95% Cl)
(0.45, 2.01)
(0.36,2.50)
(0.07, 0.98)
(0.14,0.69)
(0.25, 0.81)
(0.28, 1 .29)
(0.81,2.00)
(0.49, 1.73)
(0.31, 1.13)
(0.17,0.93)
(0.13, 1.07)
(0.68,2.60)
(0.35, 1.03)
(0.49, 1.21)
(0.31, 1.25)
(0.53, 1.44)
Note: Following adjustment for hay fever and parental education at baseline, results remained substantially unchanged except that difficulty breathing during the day assumed
borderline significance (RR=0.46: 95% Cl: 0.19, 1.08), while the reduction in asthma attacks during the night reached statistical significance (RR=0.33; 95% Cl: 0.13, 0.84).
Source: Adapted from Pilotto et al. (2004).
In an earlier study of the health effects of unflued gas heaters on wintertime respiratory symptoms
of 388 Australian schoolchildren, Pilotto et al. (1997a) measured NO2 in 41 classrooms in 8 schools. Half
used unflued gas heaters; half used electric heat. Although similar methods were used to measure NO2
levels (passive diffusion badge monitors exposed for 6 h at a time), there were three major differences
between this study and the Pilotto et al. (2004) study: (1) the 1997 study was not a randomized trial, (2)
enrollment in the 1997 study was not restricted to asthmatic children, and (3) enrollment in the 1997
study was not restricted to children from homes without unflued gas heaters. In Pilotto et al. (1997a), only
children from nonsmoking homes were enrolled, and a subset of children (n=121) living in homes with
unflued gas heaters were given badges to be used at home. Parents recorded symptoms daily. Children
were classified into low- and high-exposure groups based on measured exposure at school, measured
exposure at home (if their homes had unflued gas heaters), or their reported use of electric heat.
Maximum hourly concentrations in these classrooms recorded during 2 weeks were highly correlated with
their corresponding 6 h concentrations (r=0.85). Hourly peaks of NO2 on the order of > 80 ppb were
associated with 6 h average levels of approximately > 40 ppb. The authors inferred that children in
classrooms with unflued gas heaters that had 6-h average levels of > 40 ppb were experiencing
approximately 4 fold or higher 1-h peaks of exposure than the NO2 levels experienced by children who
had no gas exposure (6-h average levels of 20 ppb). The importance of this study was that it examined the
effect of repeated peaks over time as have been used in the toxicological infectivity studies (e.g., Miller et
al., 1987) that were noted earlier in Section 3.1.1.
3-19
-------
« 0.12
"S.
1,0.08
(A
D>0.06
O>
^—
J-0.04
^ 0.02
0)
o 0.00
-
-
<
-
H
i
»
i
<
» t
i~~ "~
<40* 'interned*' 40-60 ' ' 60-80 ' ' 80-100 ' ' >100 '
n=105 n=39 n=46 n=12 n=94 n=92
Nitrogen dioxide, ppb
o
o
.c
o
I
J2
TO
O
•-E
o
Q.
2
Q.
0.10
0.08
0.06
0.04
0.02
0.00
Figure 3.1-3.
I I- (
<40* 'interned*' 40-60 ' ' 60-80 ' ' 80-100 '
n=105 n=39
n=46 n=12 n=94
Nitrogen dioxide, ppb
>ioo
n=92
Source: Adapted from Pilotto et al. (1997a)
Geometric mean symptom rates (95% Cl) for cough with phlegm (panel A) and proportions
(95% Cl) of children absent from school for at least 1 day (panel B) during the winter heating
period grouped by estimated NC-2 exposure at home and at school (n=number of children at
that NC-2 level). Group means estimated using mixed models * "<40 ppb" group (n=105)
includes children from electrically heated schools while the "Intermed" group (n=39) includes
children from unflued gas heaters where the exposures were consistently below 40 ppb. Both
groups of children did not have exposure to gas combustion at home.
Pilotto et al. (1997a) reported that during the winter heating season, children in the high-exposure
category (NO2>40 ppb) had higher rates of sore throat, colds, and absenteeism than all other children. In
models adjusted for personal risk factors including asthma, allergies, and geographic area, classroom NO2
level and school absence were significantly associated (odds ratio [OR]=1.92 [95% Cl: 1.13, 3.25]).
Increased likelihood of individual respiratory symptoms was not significantly associated with classroom
NO2 level (e.g., cough with phlegm adjusted OR=1.28 [95% Cl: 0.76, 2.15]). Exposure-response relation-
3-20
-------
ships are illustrated in Figure 3.1-3 for symptom rates for cough with phlegm and proportion of children
absent from school. Statistically significant positive exposure-response trends were found for mean rates
for cough with phlegm (p=0.04, adjusted for confounders) and proportion of children absent from school
(p=0.002) using mixed models allowing for correlation between children within classrooms. Pilotto et al.
(1997b) noted that this study "provides evidence that short-term exposure to the peak levels of NO2
produced by unflued gas appliances affects respiratory health and that the significant dose-response
relationship seen with increasing NO2 exposure strengthens the evidence for a cause-effect relationship."
In a cross-sectional survey of 344 children in Australia, Ponsonby et al. (1997b) used passive gas
samplers to measure personal exposure to NO2. Personal badges were pinned to a child's clothing at the
end of each school day and removed when the child arrived at school the next day. School exposures were
measured with passive samplers placed in each child's classroom. Sampling took place over two
consecutive days. Mean (SD) personal exposure was 10.4 (11.1) ppb and mean total NO2 exposure
(personal plus schoolroom) was 10.1 (8.6) ppb. Of the health outcomes measured (recent wheeze, asthma
ever, lung function measured when NO2 sampling stopped), only the FEVi/FVC ratio following cold air
challenge was significantly associated with NO2 levels measured with the personal badges (-0.12 [95%
CI: -0.23, -0.01]) per 1 ppb increase in personal exposure). In Finland, Mukala et al. (1999; 2000) studied
162 preschool-age children. Mukala et al. (2000) used passive monitors exposed for 1-week periods over
the course of 13 weeks both indoors and outdoors and on the clothing of preschool children attending
eight day care centers in Helsinki. The only significant association between personal NO2 measurements
and symptoms was for cough during the winter (RR=1.86 [95% CI: 1.15, 3.02] for NO2 at level above
27.5 (ig/m3 [14.5 ppb]). Similar results were obtained when data were analyzed unstratified by season, but
including a factor for season (RR=1.52 [95% CI: 1.00, 2.31] forNO2 at levels above 27.5 (ig/m3
[14.5 ppb], Mukala et al., 1999).
One recent birth cohort study in the U.S. measured indoor exposure to NO2 (Belanger et al., 2006;
van Strien et al., 2004). Families were eligible for this study if they had a child with physician-diagnosed
asthma (asthmatic sibling) and a newborn infant (birth cohort subject). NO2 levels were measured using
Palmes tubes left in the homes for 2 weeks. Higher levels of NO2 were measured in homes with gas stoves
(mean [SD], 26 [18] ppb) than in homes with electric ranges (9 [9] ppb). Children living in multifamily
homes were exposed to higher NO2 (23 [17] ppb) than children in single-family homes (10 [12] ppb). The
authors examined associations between NO2 concentrations and respiratory symptoms experienced by the
asthmatic sibling in the month prior to sampling (Belanger et al., 2006). For children living in multifamily
homes, each 20 ppb increase in NO2 concentration increased the likelihood of any wheeze or chest
tightness (OR for wheeze=l.52 [95% CI: 1.04, 2.21]; OR for chest tightness=1.61 [95% CI: 1.04,2.49])
as well as increasing the risk of suffering additional days of symptoms. No significant associations were
found between level of NO2 and symptoms for children living in single-family homes. The authors
proposed that the low levels of exposure may have been responsible for the lack of association observed
in single-family homes. In these same families, van Strien et al. (2004) compared the measured NO2
concentrations with respiratory symptoms experienced by the birth cohort infants during the first year of
life. Although wheeze was not associated with NO2 concentration, persistent cough was associated with
increasing NO2 concentration in an exposure-response relationship (Figure 3.1-4) (van Strien et al., 2004).
Results from a recent analysis of a subset of 469 asthmatic children enrolled in the National
Cooperative Inner City Asthma Study (NCICAS) (Kattan et al., 2007) where household measurements of
NO2 levels were also available, are consistent with those described above for Belanger et al. (2006). The
median level of indoor NO2, measured with Palmes tubes left for 7 days, was 29.8 ppb, with median level
in homes with gas stoves (31.4 ppb) significantly higher than levels in homes with electric stoves
(15.9 ppb). Associations between exposure to high levels of NO2 and symptoms in the previous 2 weeks
or peak flow of <80% predicted were examined with models that adjusted for study site, gender, medica-
tion use, household smoking, and socioeconomic status (SES) variables and were stratified by season or
by atopic status. Among the subset of 76 children without positive skin tests, the adjusted risk ratio (95%
CI) for asthma symptoms was 1.75 (95% CI: 1.10, 2.78) for those with higher NO2 exposure. Among the
3-21
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317 children with NO2 measured in the cold season, the risk ratio for a peak flow measurement of <80%
predicted was 1.46 (95% CI: 1.07, 1.97). One limitation of the study is that the "high" NO2 level was
defined vaguely as approaching the NAAQS level of 0.052 ppm (annual average) (52 ppb).
4.0
3.5
I 3.0
.1 2.5
|2.0
1.5
1.0
n K.
. a. persistent cough
-
:
: I ••
4.0
3.5
3.0
2.5
2.0
1.5
1.0
n *
b. shortness of breath
-
-
-
-
<
i
i
<5 5-10 10-17 >17
NO2 concentration quartile (ppb)
<5 5-10 10-17 >17
NO2 concentration quartile (ppb)
Source: Adapted from van Strien et al. (2004).
Figure 3.1-4. Adjusted association of increasing indoor N02 concentrations with number of days with
persistent cough (panel a) or shortness of breath (panel b) for 762 infants during the first year
of life. Relative risks from Poisson regression analyses adjusted for confounders.
Other recent studies have also collected personal exposure data for NO2. Nitschke et al. (2006) used
passive diffusion badges for measuring NO2 exposures in 6 h increments at home and school for 174
asthmatic children in Australia. School and home measurements were based on three consecutive days of
sampling. The maximum of 9 days of sampling (for 6 h each day) NO2 value was selected as the
representative daily exposure for exposure-response analyses. Children kept a daily record of respiratory
symptoms for the 12-week study period. Significant associations were found between the maximum NO2
level at school or home and respiratory symptom rates, though the exposure-response curve indicated that
the major difference in respiratory symptoms rates were between NO2 exposures of >80 ppb (see Annex
Table AX 6.3-1).
An important consideration in the evaluation of the indoor exposure studies is that NOX is part of a
complex mixture of chemicals emitted from unvented gas heaters. In addition to NO and NO2, indoor
combustion sources such as unvented gas heaters emit other pollutants that are present in the fuel or are
formed during combustion. These pollutants include CO2, CO, HCHO and other VOCs, PAHs, and PM,
particularly UFP, as described in Section 2.5.8.3. The studies of unvented heaters or gas stoves did not
measure indoor concentrations of other combustion-related emissions. Unvented combustion is a potential
source of UFP. High numbers of UFP, along with NO2, are generated during the operation of gas heaters,
gas stoves, and during cooking (Dennekamp et al., 2001; Wallace et al., 2004). It is possible that the
improved respiratory symptoms observed in the Pilotto et al. (2004) intervention study were related to
reductions in ultrafine particle exposure, other gaseous emissions, or the pollutant mix. The findings of
these recent indoor and personal exposure studies, combined with studies available in the previous
AQCD, provide evidence thatNO2 exposure is associated with respiratory effects. These studies provide a
potential bridge between epidemiologic studies using ambient concentrations from centrally located
monitors and human clinical studies, as discussed in the previous sections, and provide some evidence of
coherence for respiratory effects.
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3.1.4.2. Ambient N02 Exposure and Respiratory Symptoms
Since the 1993 AQCD, results have been published from several single- and multicity studies
investigating ambient NO2 levels, including three large longitudinal studies in urban areas covering the
continental U.S. and southern Ontario: the Harvard Six Cities study (Six Cities; Schwartz et al., 1994), the
National Cooperative Inner-City Asthma Study (NCICAS; Mortimer et al., 2002), and the Childhood
Asthma Management Program (CAMP; Schildcrout et al., 2006). Because of similar analytic techniques
(i.e., multistaged modeling and generalized estimating equations), one strength of all three of these studies
is that, as Schildcrout et al. (2006) stated, they could each be considered as a meta-analysis of "large,
within-city panel studies" without some of the limitations associated with meta-analyses, e.g., "between-
study heterogeneity and obvious publication bias."
The report from the Six Cities study includes 1,844 schoolchildren, followed for 1 year (Schwartz
et al., 1994). Symptoms (in 13 categories, analyzed as cough, lower or upper respiratory symptoms), were
recorded daily. Cities included Watertown, MA, Baltimore, MD, Kingston-Harriman, TN, Steubenville,
OH, Topeka, KS, and Portage, WI. In Mortimer et al. (2002), 864 asthmatic children from the eight
NCICAS cities (New York City, NY [two sites: Bronx and East Harlem], Baltimore, MD, Washington,
DC, Cleveland, OH, Detroit, MI, St Louis, MO, and Chicago, IL) were followed daily for four 2-week
periods over the course of 9 months. Morning and evening asthma symptoms (analyzed as none versus
any) and peak flow were recorded. Schildcrout et al. (2006) reported on 990 asthmatic children living
within 50 miles of one of 31 NO2 monitors located in eight North American cities, seven of which
included data for NO2 (Boston, MA, Baltimore, MD, Toronto, ON, St. Louis, MO, Denver, CO,
Albuquerque, NM, and San Diego, CA). Symptoms (analyzed as none versus any per day) and rescue
medication use (analyzed as number of uses per day) were recorded daily such that each subject had an
approximate average of 2 months of data. All three studies found significant associations between
ambient NO2 concentrations and risk of respiratory symptoms in children (Schwartz et al., 1994), and in
particular, asthmatic children (Mortimer et al., 2002; Schildcrout et al., 2006).
In Schwartz et al. (1994), a significant association was found between a 4-day mean of NO2
exposure and incidence of cough among all children in single-pollutant models: the odds ratio (OR)
standardized to a 20-ppb increase in NO2 was OR=1.61 (95% CI: 1.08, 2.43). Cough incidence was not
significantly associated with NO2 on the previous day. The local nonparametric smooth of the 4-day mean
concentration showed increased cough incidence up to approximately the mean concentration (~13 ppb)
(p=0.01), after which no further increase was observed. The significant association between cough and 4-
day mean NO2 remained unchanged in models that included O3, but was attenuated in two-pollutant
models including PM10 (OR for 20-ppb increase in NO2=1.37 [95% CI: 0.88, 2.13]) or SO2 (OR=1.42
[95% CI: 0.90,2.28]).
In Mortimer et al. (2002), the greatest effect of the pollutants studied for morning symptoms was
for a 6-day moving average. For increased NO2, the risk of any asthma symptoms (cough, wheeze,
shortness of breath) among the asthmatic children in the NCICAS was somewhat higher than for the
healthy children in the Six Cities study: OR=1.48 (95% CI: 1.02, 2.16). Effects were generally robust in
multipollutant models that included O3 (OR for 20-ppb increase in NO2=1.40 [95% CI: 0.93, 2.09]), O3
and SO2 (OR for NO2=1.31 [95% CI: 0.87, 2.09]), or O3, SO2, and PM with an aerodynamic diameter of <
10(im(PM10)(ORforNO2=1.45 [95% CI: 0.63,3.34]).
3-23
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Asthma Symptoms
0.75 0.85 0.95 1.05
Odds Ratio
Rescue Inhaler Uses
1.15
0.75
0.85
0.95 1.05
Rate Ratio
1.15
Nitrogen dioxide
LagO 1.00
i -in 1 n 07
Lag 2 1.
3-day moving sum 1.01
Nitrogen dioxide and PM10
i on n 0 QP
1 in -\ 0 Q7
Lag 2 1
3-day moving sum 1 .00
A
1.06
A in
1.04
* -i -in
1.09
T1 * -i i1^
1.04
* 1.07
1f 1 13
1.04
* 1 11
1.08
n2 * 1 1R
1.04
»A r\-j
1.25
Nitrogen dioxide
LagO 1.00
I an 1 n QQ
Lag 2 1.0'
3-day moving sum 1.0'
Nitrogen dioxide and PM10
i o^ n fl Q7
1 in ^ fl 07
Lag 2 LOO
3-day moving sum 1 .00 -
B
1.04
1.04
A 1 nr
1.05
* 1 no
1.03
-•—1.05
T 1 08
1.03
A 1 ns
1.04
• 1 09
1.02
— » — 1.05
1.25
Source: Schildcrout et al. (2006).
Figure 3.1-5. Odds ratios (95% Cl) for daily asthma symptoms (panel A) and rate ratios (95% Cl) for daily
rescue inhaler use (panel B) associated with shifts in within-subject concentrations of N02 for
single- and joint (with PMio)-pollutant models from the CAMP (November 1993-September
1995). The city-specific estimates from Boston, Baltimore, Toronto, St. Louis, Denver,
Albuquerque, and San Diego were included in the calculations of study-wide effects.
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In the CAMP study (Schildcrout et al., 2006), the strongest association between NO2 and increased
risk of cough was found for a 2-day lag: each 20-ppb increase in NO2 occurring 2 days before
measurement increased risk of cough (OR=1.09 [95% CI: 1.03, 1.15]). Joint-pollutant models including
CO, PMio, or SO2 produced similar results (see Figure 3.1-5, panel A). Further, increased NO2 exposure
was associated with increased use of rescue medication in the CAMP study, with the strongest association
for a 2-day lag, both for single- and joint-pollutant models (e.g., for an increase of 20 ppb NO2 in the
single-pollutant model, the RR for increased inhaler usage was 1.05 (95% CI: 1.01, 1.09) (See Figure 3.1-
5, panel B).
Single-city studies also provided updated information to the 1993 AQCD, particularly with regard
to children. Two 3-month-long panel studies recruited asthmatic children from one outpatient clinic in
Paris: one study followed 84 children in the fall of 1992 (Segala et al., 1998), and the other followed 82
children during the winter of 1996 (Just et al., 2002). Significant associations were observed between
respiratory symptoms and level of NO2 (See Annex Table AX6.3-2). No multipollutant analyses were
conducted. In metropolitan Sydney, 148 children with a history of wheeze were followed for 11 months
(Jalaludin et al., 2004). Daily symptoms, medication use, and doctor visits were studied. Associations
were found between increased likelihood of wet cough and each 20-ppb increase in NO2 (OR=1.13 [95%
CI: 1.00, 1.26]). The authors reported that estimates did not change in multipollutant models including O3
or PMio. Ward et al. (2002) examined respiratory symptoms in a panel of 162 children in the United
Kingdom. No significant associations were reported for the winter period, but a significant association
was reported for the summer period for cough and NO2 (lag 0; OR= 1.09 [95% CI: 1.17, 1.01]).
Another Australian study includes a large number of children (n=263) at risk for developing allergy
who were followed for 5 years (Rodriguez et al., 2007). Daily air pollutant concentrations, including
those for NO2, were averaged over 10 monitoring sites in the Perth metropolitan region. Mean level of 24-
h NO2 for the 8-year study period was 7 ppb (range 0-24 ppb). Significant associations were found
between same-day level of NO2 (both 1- and 24-h avg) and cough (OR 1.0005 [95% CI: 1.0000, 1.0011])
per 20-ppb increase in 24-h avg NO2). No multipollutant models were presented. Boezen et al. (1999)
reported associations between ambient NO2 exposure and lower respiratory symptoms among children
(n=121) with bronchial hyperreactivity and elevated total IgE in urban and rural areas of the Netherlands.
These effects were seen for all lags examined (lag 0-, 1-, 2-, and 5-day mean), with the strongest
association for the 5-day mean (OR=1.75 [95% CI: 1.37, 2.22]) for each 20 ppb increase). Significant
associations between lower respiratory symptoms and ambient exposures were seen in single-pollutant
models with PMio, black smoke, and SO2. No multipollutant models were reported.
For adults, most studies examining associations between ambient NO2 pollution and respiratory
symptoms have been conducted in Europe. Various studies have enrolled older adults, (van der Zee et al.,
2000); Harre et al., 1997; Silkoff et al., 2005), nonsmoking adults (Segala et al., 2004), patients with
chronic obstructive pulmonary disease (COPD) (Higgins et al., 1995; Desqueyroux et al., 2002), and
individuals with bronchial hyperresponsiveness (Boezen et al., 1998) or asthma (Hiltermann et al., 1998;
Forsberg et al., 1998; Von Klot et al., 2002). Associations were found between NO2 and either respiratory
symptoms or inhaler use in a number of studies (van der Zee et al., 2000); Harre et al., 1997; Silkoff et al.,
2005; Segala et al., 2004; Hiltermann et al., 1998), but not in all studies (Desqueyroux et al., 2002; Von
Klot et al., 2002).
Among the studies discussed above, odds ratios and 95% CI for associations with asthma symptoms
in children are presented in Figure 3.1-6. The figure shows the several lag periods presented in each
study. In the figure, the area of the square denoting the odds ratio represents the relative weight of that
estimate based on the width of the 95% CI. When combined in a random effect meta-analysis1, the
1 The effects used in the meta-analysis were selected using the following methodology. One lag period per study was selected, with studies having 0
lag preferred to 1-day lags and moving averages; longer single-day lags were not included in the meta-analysis. If a study had both incidence and
prevalence, then the incidence effect was to be used.
3-25
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combined OR for asthma symptoms was 1.14 (95% CI: 1.05, 1.24) and the test for heterogeneity had a
p value of 0.055. The results of multipollutant analyses for the three U.S. multicity studies are presented
in Figure 3.1-7. Associations with NO2 were generally robust to adjustment for copollutants, as stated
previously. Odds ratios were often unchanged with the addition of copollutants, though reductions in
magnitude are apparent in certain models, such as with adjustment for SO2 in the Six Cities study results
(Schwartz et al., 1994).
Study
Mortimer et al. (2002)
Schildcroutetal.(2006)
Schildcroutetal.(2006)
Schildcroutetal.(2006)
Delfinoetal. (2002)
Just et al. (2002)
Just et al. (2002)
Just et al. (2002)
Just et al. (2002)
Just et al. (2002)
Just et al. (2002)
Segalaetal. (1998)
Segalaetal. (1998)
.5 1 1.5 2 2.5 3 3.5 4 4.5 5
Odds ratio for asthma symptoms in std units
Figure 3.1-6. Odds ratios (95% CI) for associations between asthma symptoms in children and 24-h
average N02 concentrations (per 20 ppb). The size of the box of the central estimate
represents the relative weight of that estimate based on the width of the 95% CI.
3.1.4.3. Summary of Short-Term Exposure on Respiratory Symptoms
Consistent evidence has been observed for an association of respiratory effects with indoor and
personal NO2 exposures in children at levels similar to ambient concentrations. In particular, the Pilotto et
al. (2004) intervention study provided evidence of improvement in respiratory symptoms with reduced
NO2 exposure in asthmatic children.
The epidemiologic studies using community ambient monitors also find associations between
ambient NO2 concentration and respiratory symptoms. The results of recent U.S. multicity studies
(Schildcrout et al., 2006; Mortimer et al., 2002) provide further support for associations with respiratory
symptoms and medication use in asthmatic children. Associations were observed in cities where the
median range was 18 to 26 ppb for a 24-h avg (Schildcrout et al., 2006) and the mean NO2 level was
32 ppb for a 4-h avg (Mortimer et al., 2002). Multipollutant models in these multicity studies were
generally robust to adjustment for copollutants including O3, CO, and PMi0. Most human clinical studies
did not report or observe respiratory symptoms with NO2 exposure, and animal toxicological studies do
not measure effects that would be considered symptoms. The experimental evidence on airway
inflammation and immune system effects discussed previously, however, provides some plausibility and
coherence for the observed respiratory symptoms in epidemiologic studies.
Asthma
Prevalence
Prevalence
Prevalence
Incidence
Incidence
Incidence
Incidence
Lag
0
1
0-2
A
f\_A
0
0-4
0
1
I
1
3
-B-
— B—
-a
1 1 1 1 1 1 1 1
3-26
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Study Locations Avg Time
Schwartz et al. (1994) 6 cities, US 24-h
Schildcrout et al. (2006) 8 North American cities 24-h
24-h
0.6
Odds Ratio
1.4 1.8 2.2 2.6
3.0 3.4
Pollutants
N0
N02 + 03
N02 + S02
N02
N02 + CO
N02 + S02
N02
N02 + CO
Mortimer et al. (2002) 8 cities, US
4-h
N02 + S02
N02
N02 + 03
N02 + 03 + S02
N02+ 03 + S02 + PM10
Cough Incidence
0 • (5-11 years
0 o 5-12 years
0 • 4-9 years
0 n 9-1 7 years
Asthma Symptoms
Rescue Inhaler Use
*
•
Morning Asthma Symptoms
Figure 3.1-7. Odds ratios and 95% Cl for associations between asthma symptoms and 24-h average N02
concentrations (per 20 ppb) from multipollutant models.
3.1.5. Effects of Short-Term Exposure on Lung Function
3.1.5.1. Epidemiologic Studies of Lung Function
Spirometry in Children
Reliable measurement of lung function in children presents special challenges. The method that
produces the most accurate results is spirometry, which requires special equipment and trained examiners.
Of the short-term exposure studies reviewed here that did use spirometry (Hoek and Brunekreef, 1994;
Linn et al., 1996; Timonen et al., 2002; Moshammer et al., 2006), all conducted repeated lung function
measurements in schoolchildren. All found significant associations between small decrements in lung
function and increases in ambient NO2 levels. Hoek and Brunekreef (1994) enrolled 1,079 children in the
Netherlands to examine the effects of low-level winter air pollution on FVC, FEVi, maximal
midexpiratory flow (MMEF), and PEF. A significant effect was found only for the PEF measure: the
mean (over all subjects) slope (SE) was a reduction of 52 mL/s (95% CI: 21, 83) for a 20-ppb increase in
the previous day's NO2. The authors do not present mean values for lung function measurements, so it is
not possible to calculate what percentage of PEF this decrement represents. Linn et al. (1996) examined
269 Los Angeles-area schoolchildren and short-term air pollution exposures. The authors found
statistically significant associations between previous-day 24-h avg NO2 concentrations and FVC the next
morning (mean decline of 8 mL [95% CI: 2, 14] per 20-ppb increase in NO2) and current-day 24-h avg
NO2 concentrations and morning to evening changes in FEVi (mean decline of 8 mL [95% CI: 2, 14] per
20-ppb increase in NO2). Timonen et al. (2002) enrolled 33 Finnish children with chronic respiratory
3-27
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symptoms to study the effects of exercise-induced lung function changes and ambient air pollution. No
significant effects were observed for lung function changes due to exercise, but significant associations
were observed for level of NO2 lagged by 2 days and baseline FVC (mean decline of 21 mL [95% CI: -
29, -12] for 20 ppb NO2) and FEVj (mean decline of 20 mL [95% CI: -26, -13] for 20 ppb NO2). An
Austrian study enrolled 163 healthy children for repeated lung function testing (11 to 12 tests during the
school year) (Moshammer et al., 2006). A central site monitor adjacent to the school was used to calculate
8-h avg (midnight, to 8 a.m.) PM and NO2 concentrations. The median 8-h avg NO2 concentration was
17.5 (ig/m3 (9.2 ppb). In both single pollutant and multipollutant models including PM2 5, the authors
found each 20-ppb increase in NO2 level produced reductions in lung function of around 4% for FEVi,
FVC, forced expiratory volume in 0.5 s (FEV0 5), maximal expiratory flow at 50% (MEF50), and maximal
expiratory flow at 25% (MEF25). PM2 5 was not significantly associated with lung function decrements in
the multipollutant model.
Peak Flow Meter Measurements in Children
Studies involving supervised lung function measurements in schoolchildren using peak flow
devices do not show a consistent relationship between NO2 exposure and measurements of peak flow
(Scarlett et al., 1996; Peacock et al., 2003; (Steerenberg et al., 2001). Other studies using home-use peak
flow meters with children did not report any significant associations with ambient NO2 (Roemer et al.,
1998 [2,010 children in the Pollution Effects on Asthmatic Children in Europe (PEACE) study]; Roemer
et al., 1999 [a subset of 1,621 children from the PEACE study with chronic respiratory symptoms];
Mortimer et al., 2002 [846 asthmatic children from the NCICAS]; Van der Zee et al., 1999 [633 children
in the Netherlands]; Timonen and Pekkanen, 1997 [169 children including asthmatics in Finland]; Ranzi
et al., 2004 [118 children, some with asthma, in the Italian Asma Infantile Ricerca (AIRE) study]; Segala
et al., 1998 and Just et al., 2002 [over 80 asthmatic children in Paris]; Delfino et al., 2003 [22 asthmatic
children in southern California]).
Ward et al. (2000) examined the effect of correcting peak flow for nonlinear errors on NO2 effect
estimates in a panel study of 147 children (9-year olds, 47% female). The correction resulted in a small
increase in the group mean PEF (1.1 L-min-1). For the entire panel, NO2 effect estimates were all
corrected in the positive direction with a narrowing of the 95% CI, and all but the result for 0-day lag
were decreased in absolute size by up to 73% (e.g., effect estimate for NO2 lagged 3 days corrected from
-0.56 to -0.15% per 10 ppb). When only the symptomatic/atopic children (i.e., reported wheezing and
positive skin test) were considered, the estimates for associations with 5-d avg NO2 decreased in size from
-5.0 to -1.8% per 20 ppb. In the case of lag 0, the effect estimate became significant with an increase in
magnitude from -1.1 to -2.3% per 20 ppb. The authors concluded that correction for PEF meter
measurements resulted in small but important shifts in the direction and size of effect estimates and
probable interpretation of results. The effects of correction were, however, not consistent across pollutants
or lags and could not be easily predicted.
Lung Function in Adults
Spirometry was used in a large cross-sectional study in Switzerland (Schindler et al., 2001). A
subset of 3,912 lifetime nonsmoking adults participated in the spirometric lung function measurements in
the SAPALDIA study (Study of Air Pollution and Lung Diseases in Adults). Significant inverse
relationships were found between increases in NO2 and decreases in FVC (by 2.74% [95% CI: 0.83,
4.62]) and FEVj (by 2.52% [95% CI: 0.49, 4.55]) for a 20-ppb increase in NO2 on the same day as the
examination. Forced expiratory flow at 25 to 75% of FVC (FEF25_75) was found to decrease by 6.73%
(95% CI: 0.038, 13.31) for each 20-ppb increase in average NO2 concentration over the previous 4 days.
One study (Lagorio et al., 2006) of COPD patients found significant inverse relationships for FEVi in
both COPD and asthmatic patients. Another study of COPD subjects (Silkoff et al., 2005) observed no
adverse effects of ambient air pollution on lung function for the first winter; however, in the second
3-28
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winter, a significant decrease in morning PEF associated with same day and previous day NO2 level was
seen (quantitative results not provided). In a study of 60 asthmatic adults in London, decreases in two
lung function measures, FEVi and FEF25_75, and increased FeNO were reported with increased NO2
exposure while walking along a roadway with heavy traffic; associations were also reported with PM2 5,
UFP, and EC (McCreanor et al., 2007).
Of the adult studies reviewed that employed portable peak flow meters for subject-measured lung
function, none reported significant associations with NO2 levels (van der Zee et al., 2000) [489 adults in
the Netherlands]; Higgins et al., 1995 [153 adults in the United Kingdom, including COPD and asthma
patients]; Park et al., 2005a [64 asthmatic adults in Korea]; Hiltermann et al., 1998 [60 asthmatic adults in
the Netherlands]; Harre et al., 1997 [40 adults with COPD in New Zealand]; Forsberg et al., 1998 [38
adult asthmatics in Sweden]; Higgins et al., 2000 [35 adults with COPD or asthma in the United
Kingdom]).
3.1.5.2. Clinical Studies of Lung Function
Healthy Adults
Studies examining responses of healthy volunteers to acute exposure to NO2 have generally failed
to show alterations in lung mechanics such as airway resistance (Hackney et al., 1978); Kerr et al., 1979;
Linn et al., 1985a; Mohsenin, 1987a, 1988; Frampton et al., 1991; Kim et al., 1991; (Morrow et al., 1992)
Rasmussen et al., 1992; Vagaggini et al., 1996; (Azadniv et al., 1998; Devlin et al., 1999). Exposures
ranging from 75 min to 5 h at concentrations of up to 4.0 ppm NO2 did not alter pulmonary function.
Bylin et al. (1985) found increased airway resistance after a 20-min exposure to 0.25 ppm NO2 and
decreased airway resistance after a 20-min exposure to 0.5 ppm NO2, but no change in airway
responsiveness to aerosolized histamine challenge in the same subjects. These effects have not been
confirmed in other laboratories.
Few human clinical studies of NO2 have included elderly subjects. Morrow et al. (1992) studied the
responses of 20 healthy volunteers (13 smokers, 7 nonsmokers) of mean age 61 years, following exposure
to 0.3 ppm NO2 for 4 h with light exercise. There was no significant change in lung function related to
NO2 exposure for the group as a whole. However, the 13 smokers experienced a slight decrease in FEVi
during exposure, and their responses were significantly different from the 7 nonsmokers (percent change
in FEVi at end of exposure: -2.25 versus+1.25%, p=0.01). The post-hoc analysis and small numbers of
subjects, especially in the nonsmoking group, restricts the interpretation of these findings.
The human clinical studies reviewed in the O3 AQCD (U.S. Environmental Protection Agency,
2006a) generally reported only small pulmonary function changes after combined exposures of NO2 or
HNO3 with O3, regardless of whether the interactive effects were potentiating or additive. Hazucha et al.
(1994) found that preexposure of healthy women to 0.6 ppm NO2 for 2 h enhanced spirometric responses,
and methacholine airway responsiveness induced by a subsequent 2-h exposure to 0.3 ppm O3, with
intermittent exercise. Following a 1-h exposure with heavy exercise, Adams et al. (1987) found no
differences between spirometric responses to 0.3 ppm O3 and the combination of 0.6 ppm NO2+0.3 ppm
O3. However, the increase in airway resistance was significantly less for adults exposed to 0.6 ppm
NO2+0.3 ppm O3 compared to 0.3 ppm O3 alone.
Gong et al. (2005) studied 6 healthy elderly subjects (mean age 68 years) and 18 patients with
COPD (mean age 71 years), all exposed to: (a) air, (b) 0.4 ppm NO2, (c) -200 (ig/m3 concentrated am-
bient fine particles (CAPs), and (d) CAPs+NO2. Exposures were for 2 h with exercise for 15 min of each
half hour. CAPs exposure was associated with small reductions in midexpiratory flow rates on spiromet-
ry, and reductions in oxygen saturation, but there were no effects of NO2 on lung function, oxygen
saturation, or sputum inflammatory cells. However, the exposures were not fully randomized or blinded,
and most of the NO2 exposures took place months after completion of the CAPs and air exposures. In
addition, the small number of healthy subjects severely limits the statistical power for this group.
3-29
-------
Patients with COPD
Few studies have examined responses to NO2 in subjects with COPD. Hackney et al. (1978) found
no lung function effects of exposure to 0.3 ppm NO2 for 4 h with intermittent exercise in smokers with
symptoms and reduced FEVi. In a group of 22 subjects with moderate COPD, Linn et al. (1985b) found
no pulmonary effects of 1-h exposures to 0.5, 1.0, or 2.0 ppm NO2 with 30 min of exercise.
In a study by Morrow et al. (1992), 20 subjects with COPD were exposed for 4 h to 0.3 ppm NO2 in
an environmental chamber, with intermittent exercise. Progressive decrements in FVC occurred during
the exposure, becoming statistically significant only at the end of the exposure. The decrements in FVC
occurred without changes in flow rates. These changes in lung function were typical of the "restrictive"
pattern seen with NO2 rather than the obstructive changes described by some studies of NO2 exposure in
asthmatics.
As noted in the previous section, Gong et al. (2005) exposed 6 elderly healthy adults and 18 COPD
patients to four separate atmospheres: (a) air, (b) 0.4 ppm NO2, (c) -200 (ig/m3 CAPs, or (d) CAPs+NO2.
Nitrogen dioxide can become absorbed to particles (Kalberer et al., 1999). This could act as a mechanism
to increase NOX delivery to the peripheral lung. However, NO2+CAPs did not produce greater respiratory
effects than for CAPs alone in either healthy or the COPD patients. As noted above, there were also no
significant effects of NO2 alone in group of subjects.
Patients with Asthma
Kleinman et al. (1983) evaluated the response of lightly exercising asthmatic subjects to inhalation
of 0.2 ppm NO2 for 2 h, during which resting minute ventilation doubled. Forced expiratory flows and
airway resistance were not altered by the NO2 exposure. Bauer et al. (1986) studied the effects of
mouthpiece exposure to 0.3 ppm NO2 for 30 min (20 min at rest followed by 10 min of exercise at ~40
L/min) in 15 asthmatics. At this level, NO2 inhalation produced significant decrements in forced
expiratory flow rates after exercise, but not at rest. Torres and Magnussen (1991) found no effects on lung
function in 11 patients with mild asthma exposed to 0.25 ppm NO2 for 30 min, including 10 min of
exercise. However, small reductions in FEVi were observed following 1 ppm NO2 exposure for 3 h with
intermittent exercise in 12 mild asthmatics. Koenig et al. (1994) found no pulmonary function effects of
exposure to 0.3 ppm NO2 in combination with 0.12-ppm O3, with or without sulfuric acid (H2SO4)
(70 (ig/m3) or HNO3 (0.05 ppm), in 22 adolescents with mild asthma. However, 6 additional subjects
dropped out of the study citing uncomfortable respiratory symptoms.
Jenkins et al. (1999) examined FEVi decrements and airway responsiveness to allergen in a group
of mild, atopic asthmatics. The subjects were exposed during rest for 6 h to filtered air, 200 ppb NO2,
100 ppb O3, or 200 ppb NO2+100 ppb O3. The subjects were also exposed for 3 h to 400 ppb NO2,
200 ppb O3, or 400 ppb NO2+200 ppb O3 to provide doses identical to those in the 6-h protocols (i.e.,
equal C * T). Immediately following the 3-h exposure, but not after the 6-h exposure, there were
significant decrements in FEVi following O3 and NO2+O3 exposures.
3.1.5.3. Summary of Short-Term Exposure on Lung Function
In summary, epidemiologic studies using data from supervised lung function measurements
(spirometry or peak flow meters) report small decrements in lung function (Hoek and Brunekreef, 1994;
Linn et al., 1996; Moshammer et al., 2006; Schindler et al., 2001; Peacock et al., 2003). No significant
associations were reported in any studies using unsupervised, self-administered peak flow measurements
with portable devices. Correcting peak flow measurements for nonlinear errors resulted in small but
important shifts in the direction and size of effect estimates; however, these effects were not consistent
across pollutants or lags.
3-30
-------
Clinical studies have not provided compelling evidence of NO2 effects on pulmonary function.
Acute exposures of young, healthy volunteers to NO2 at levels of as high as 4.0 ppm did not alter lung
function as measured by spirometry or airway resistance. The small number of studies of COPD patients
prevented any conclusions about effects on pulmonary function. The Morrow et al. (1992) study,
performed in Rochester, NY, indicated restrictive type effects of 0.3 ppm NO2 exposure for 4 h. However,
three other studies, performed in southern California at similar exposure concentrations, found no effects.
The contrasting findings in these studies may, in part, reflect the difference in duration of exposure or the
differing levels of background ambient air pollution to which the subjects were exposed chronically; as
there were much lower background levels in Rochester, NY than in southern California. For asthmatics,
the effects of NO2 on pulmonary function have also been inconsistent at exposure concentrations of less
than 1 ppm NO2. Overall, clinical studies have failed to show effects of NO2 on pulmonary function at
exposure concentrations relevant to ambient exposures. However, the range of findings in COPD and
asthmatic patients may reflect that some individuals within such groups may be particularly more
susceptible to NO2 effects than others.
3.1.6. Hospital Admissions and ED Visits
Total respiratory causes for ED visits and hospitalizations typically include asthma, bronchitis and
emphysema (collectively referred to as COPD), upper and lower respiratory infections, and other minor
categories. Temporal associations between ED visits or hospital admissions for respiratory diseases and
the ambient concentrations of NO2 have been the subject of more than 50 well-conducted research
publications since 1993. These studies form a new body of literature that was unavailable in 1993, when
the previous criteria document was published. In addition to considerable statistical and analytical
refinements, the more recent studies have examined responses of morbidity in different age groups and
multipollutant models to evaluate potential confounding effects of copollutants.
3.1.6.1. All Respiratory Outcomes
Overall, the majority of studies that have examined all respiratory outcomes as a single group have
focused on hospital admission data. The results from the hospitalization and ED visit studies, for all ages
and stratified by age group are presented in Figures 3.1-8 and 3.1-9. More details are provided in Annex
Tables AX6.3-3, AX6.3-4, and AX6.3-5. These studies report generally positive associations between
ambient NO2 levels and ED visits and hospitalizations for all respiratory causes when participants of all
ages are considered in the analyses. Stronger and more consistent associations were observed among
children and older adults (65+ years) compared to adults (<65 years), with an interquartile range (IQR) of
1 to 13% excess risk estimated per 20 ppb incremental change in 24-h avg NO2 or 30 ppb incremental
change in 1-h max NO2.
Peel et al. (2005) examined ED visits for all respiratory causes among all ages in relation to
ambient NO2 concentrations in Atlanta, GA during the period of 1993 to 2000. They found a 2.4%
(95% CI: 0.9, 4.1) increase in respiratory ED visits associated with a 30-ppb increase in 1-h maxNO2
concentrations. Tolbert et al. (2007) recently reanalyzed these data with 4 additional years of data and
found similar results (2.0% increase, 95% CI: 0.5, 3.3). Results of a copollutant model with CO and NO2
were presented in a figure and indicated that NO2 was a stronger predictor of respiratory disease than CO,
though no quantitative results for copollutant models were presented.
3-31
-------
Reference
Tolbertetal. (2007)*A
Peel et al. (2005)*"
Luginaahetal. (2005)A
Luginaahetal. (2005)A
Anderson etal. (2001 )*A
Atkinson etal. (1999a)A
Atkinson etal. (1999b)*A
Ponce de Leon etal. (1996)
Llorcaetal. (2005)
Oftedal et al. (2003)
Hagen etal. (2000)
Bedeschi etal. (2007)*
Hinwoodetal. (2006)
Petroeschevsky et al. (2001)"
Yang etal. (2003)
Luginaah et al. (2005)*
Luginaahetal. (2005)A
Anderson etal. (2001 )A
Atkinson etal. (1999a)A
Atkinson etal. (1999b)*A
Ponce de Leon etal. (1996)
Vigotti et al. (2007)*
Petroeschevsky et al. (2001)A
Petroeschevsky et al. (2001 )A
Barnett et al. (2005)
Barnettetal.(2005)
Barnett etal.(2005)
Wong etal. (1999)
Lin etal. (1999)*
Gouveia and Fletcher (2000)"
Figure 3.1-8. Relative risks (95% Cl) for hospital admissions or ED visits for all respiratory disease
stratified by all ages or children. Results from studies using 24-h average standardized to a
20-ppb increase, results from studies using 1-h max standardized to a 30-ppb increase
(* indicates ED visits, all others are hospital admissions; A indicates 1-h max averaging times,
all others are 24-h mean averaging times).
Location
Atlanta, GA
Atlanta, GA
Windsor, ON
Windsor, ON
West Midlands, UK
London, UK
London, UK
London, UK
Torrelavega, Spain
Drammen, Norway
Drammen, Norway
Reggio Emilia, Italy
Perth, Australia
Brisbane, Australia
Vancouver, BC
Windsor, ON
Windsor, ON
West Midlands, UK
London, UK
London, UK
London, UK
Pisa, Italy
Brisbane, Australia
Brisbane, Australia
Multicity-Australia
Multicity-Australia
Multicity-Australia
Hong Kong, China
Sao Paulo, Brazil
Sao Paulo, Brazil
Lag Other
0-2
0-2
0-3 Female
0-3 Male
0-1
1
NR
2
NR
3
0-3
4
1
1
1
0-3 Girls
0-3 Boys
0-1
2
1
2
0-2
3 0-4 yrs
0 5-1 4 yrs
0-1 0 yrs
0-1 1-4 yrs
0-1 5-14 yrs
0-3
0-4
0
| All ages
—i-
-
—
•
— i —
Children
+-
>
f
3
— i —
_,_
L
__
I 1 I I I I I
75 1 1.25 1.5 1.75 2 2.25
Relative risk
3-32
-------
Luginaahetal., (2005)A
Luginaahetal., (2005)A
Spixetal. (1998)
Anderson etal. (2001 )A
Atkinson etal. (1999a)A
Atkinson etal.(1999b)*A
Ponce de Leon etal. (1996)
Schouten etal. (1996)
Schouten etal. (1996)
Petroeschevsky et al. (2001)
Wong etal. (1999)
Luginaah et al. (2005)A
Luginaah et al. (2005)A
Fung et al. (2006)
Yang etal. (2003)
Spix et al. (1998)
Anderson etal. (2001 )A
Atkinson etal. (1999a)A
Atkinson etal.(1999b)*A
Ponce de Leon etal. (1996)
Andersen et al. (2007b)
Andersen et al. (2007a)
Schouten etal. (1996)
Schouten etal. (1996)
Simpson et al. (2005a)A
Hinwood etal. (2006)
Petroeschevsky et al. (2001)
Wong etal. (1999)
Location Lag
Windsor, ON 0-3
Windsor, ON 0-3
Multicity, Europe 1-3
West Midlands, UK 0-1
London, UK 1
London, UK 2
London, UK 0
Amsterdam, Netherlands 1
Rotterdam, Netherlands 1
Brisbane, Australia 0
Hong Kong, China 0-3
Windsor, ON 0-3
Windsor, ON 0-3
Vancouver, BC 0-3
Vancouver, BC 1
Multicity, Europe 1-3
West Midlands, UK 0-1
London, UK 3
London, UK 0
London, UK 2
Copenhagen, Denmark 0-4
Copenhagen, Denmark 04
Amsterdam, Netherlands 2
Rotterdam, Netherlands 0
Multicity-Australia 0-1
Perth, Australia 1
Brisbane, Australia 5
Hong Kong, China 0-3
Other
Female
Male
Female
Male
.75 1 1.25
Relative risk
1.5
Figure 3.1-9. Relative Risks (95% Cl) for hospital admissions or ED visits for all respiratory disease
stratified by adults or older adults (> 65 years). Results from studies using 24-h average
standardized to a 20-ppb increase, results from studies using 1-h max standardized to a
30-ppb increase (* indicates ED visits, all others are hospital admissions; A indicates 1-h max
averaging times, all others are 24-h mean averaging times).
3-33
-------
Reference
Location
Age Lag Other Pollutants
Wong etal. (1999) Hong Kong All 0-3
Hagen et al. (2000) Drammen, Norway All 0-3
Oftedal et al. (2003) Drammen, Norway
NO,
NO,
N02+PM10
NO,
N02+PM10
Yang et al. (2007) Taipei, Taiwan All 0-2 >25C N02
N02+PM10
Yang et al. (2007) Taipei, Taiwan All 0-2 <25C N02
Burnett etal.(1997b)* Toronto, ON
All
Gouveia and Fletcher Sao Paulo, Brazil <5
(2000)*
Andersen et al. (2007b) Copenhagen, 65+ 0-4
Denmark
Andersen et al. (2007a) Copenhagen, 65+ 0-4
Denmark
Simpson et al. (2005a)* Multicity - Australia 65+ 0-1
N02+PM10
NO,
N02+PM10
N02+PM25
N02+PM,,M ,
NO,
N02+PM,0
N02
N02+NClot
NO,
N02+PM10
NO,
N02+BSP
0 Single pollutant model
• Copollutant model
I i I I
1.1 1.3 1.5 1.7 1.9 2.1
Relative risk
Figure 3.1-10. Relative risks (95% Cl) for hospital admissions or emergency department visits for all
respiratory causes, standardized from two-pollutant models adjusted for particle
concentration. (* indicates 1-h peak avg times, all others are 24-h avg; effect estimates from
studies using 1-h peak measurements are standardized to a 30-ppb increase; effect estimates
from studies using 24-h average measurements are standardized to a 20-ppb increase).
3-34
-------
Reference Location Age Lag Other Pollutants
Wong etal. (1999) Hong Kong All 0-3 NO,
N02+03
Yang et al. (2007) Taipei, Taiwan All 0-2 >25C N02
Yang etal. (2007) Taipei, Taiwan All 0-2 <25C N02
Yang et al. (2003) Vancouver, BC <3 1 ,,w2
N02+03
NO,
N02+03
Gouveia and Fletcher Sao Paulo, Brazil <5 0 NO,
(2000)
Simpson et al. (2005a)* Multicity - Australia 65+ 0-1 ,,^2
N02+03
NO,
65+
N02+03
Yang etal. (2003) Taipei, Taiwan 65+ 1 >25C N02
3
Yang etal. (2007) Taipei, Taiwan All 0-2 <25C N02
N02+03
N0
Yang et al. (2007) Taipei, Taiwan All 0-2
Burnett etal.(1997b)* Toronto, ON All 0
Yang et al. (2007) Taipei. Taiwan All 0-2 >25 C N02
y
Yang etal. (2007) Taipei. Taiwan All 0-2 <25 C N02
N(
Oftedal etal. (2003) Drammen, Norway All 3
N02+S02
N02
N02+S02
N02+C0
N02+C0
NO,
N02 + Benzene
° Single pollutant model
• Copollutant model
I \ \ I
.98 1.18 1.38 1.58
Relative risk
Figure 3.1-11. Relative risks (95% Cl) for hospital admissions or emergency department visits for all
respiratory causes, standardized from two-pollutant models adjusted for gaseous pollutant
concentration. (* indicates 1-h peak averaging times, all others are 24-h average; effect
estimates from studies using 1-h peak measurements are standardized to a 30-ppb increase;
effect estimates from studies using 24-h average measurements are standardized to a 20-ppb
increase).
3-35
-------
Reference Location
Peel et al. (2005)*A Atlanta, GA
Ito (2007)' New York, NY
Burnett et al. (1999) Toronto, ON
Anderson et al. (1 998) London , UK
Atkinson et al. (1 999a)A London , UK
Atkinson et al. (1 999b)*A London, UK
Galan et al. (2003)* Madrid, Spain
Chardon et al. (2007)* Paris, France
Schouten et al. (1996) Amsterdam, Netherlands
Migliaretti et al. (2005)* Turin, Italy
Migliaretti and Cavallo (2004) Turin, Italy
Hinwood et al. (2006) Perth, Australia
Petroeschevsky et al. (2001)" Brisbane, Australia
Wong et al. (1 999) Hong Kong, China
Tsai et al. (2006) Kaohsiung, Taiwan
Tsai et al. (2006) Kaohsiung, Taiwan
Yang et al. (2007) Taipei, Taiwan
Yang et al. (2007) Taipei, Taiwan
Peeletal.(2005)*A Atlanta, GA
Tolbertetal. (2000)*A Atlanta, GA
Lin et al. (2003) Toronto, ON
Lin et al. (2003) Toronto, ON
Sunyer et al. (1997)* Multicity-Europe
Anderson et al. (1998) London, UK
Atkinson et al. (1 999a)A London , UK
Atkinson et al. (1 999b)*A London , UK
Thompson et al. (2001)* Belfast, Ireland
Andersen et al. (2007b) Copenhagen, Denmark
Andersen et al. (2007a) Copenhagen, Denmark
Migliaretti et al. (2005)* Turin, Italy
Migliaretti and Cavallo (2004) Turin, Italy
Migliaretti and Cavallo (2004) Turin, Italy
Bamett et al. (2005) Multicity-Australia
Barnett et al. (2005) Multicity-Australia
Hinwood et al. (2006) Perth, Australia
Petroeschevsky et al. (2001 )A Brisbane, Australia
Petroeschevsky et al. (2001 )A Brisbane, Australia
Morgan et al. (1998a) Sydney, Australia
Ko et al. ( 2007a) Hong Kong, China
Lee et al. (2006) Hong Kong, China
Gouveia and Fletcher (2000)A Sao Paulo, Brazil
Ljfl Other
0-2
0-1
0
0-3
0
0
3
0-3
2
0-3
1-3
0
0-2
0-3
0-2 Warm
0-2 Cool
0-2 >25C
0-2 <25 C
2-18
1
0-5 Boys
0-5 Girls
0-3
0-3
1
1
0-3
0-5
0-5
0-3
1-3 4-15 yrs
1-3 <4yrs
0-1 1-4 yrs
0-1 4-15 yrs
0
0-2 0-4 yrs
1 5-14 yrs
0
0-4
3
2
•(§- | All ages
I
_^
L*~ | Children
f
I
^^
— — -
+
-1-
1 1 1 1 1 1 1 1
.75 1 1.25 1.5 1.75 2 2.25 2.5 2.75
Relative risk
Figure 3.1-12. Relative Risks (95% Cl) for hospital admissions or emergency department visits for asthma
stratified by all ages or children. Results from studies using 24-h average standardized to a
20-ppb increase, results from studies using 1-h max standardized to a 30-ppb increase
(* indicates ED visits, all others are hospital admissions; A indicates 1-h max averaging times,
all others are 24-h mean averaging times).
3-36
-------
Reference
Jaffeetal. (2003)*
Jaffeetal. (2003)*
Linn etal. (2000)
Sunyeretal.(1997)*
Anderson etal. (1998)
Atkinson etal.(1999a)A
Atkinson etal.(1999b)*A
Boutin-Forzanoetal. (2004)*
Teniasetal. (1998)*
Castellsague etal. (1995)*
Migliaretti et al. (2005)*
Morgan etal. (1998a)
Koetal.(2007a)
Anderson etal. (1998)
Atkinson etal. (1999a)A
Migliaretti et al. (2005)*
Hinwood et al. (2006)
Koetal.(2007a)
Location
Cleveland, OH
Cleveland, OH
Los Angeles, LA
Multicity-Europe
London, UK
London, UK
London, UK
Marseille, France
Valencia, Spain
Barcelona, Spain
Turin, Italy
Sydney, Australia
Hong Kong, China
London, UK
London, UK
Turin, Italy
Perth, Australia
Hong Kong, China
Lag
1
1
0-1
0-3
0-1
1
1
0
0
0-2
0-3
0
0-4
0-3
3
0-3
0
0-4
Other
-h
-I-
T
,75
I
1.25
Relative risk
1.5
1.75
Figure 3.1-13. Relative risks (95% Cl) for hospital admissions or emergency department visits for asthma
stratified by adults and older adults (> 65 years). Results from studies using 24-h average
standardized to a 20-ppb increase, results from studies using 1-h max standardized to a 30-
ppb increase (* indicates ED visits, all others are hospital admissions; A indicates 1-h max
averaging times, all others are 24-h mean averaging times).
Two multicity studies combined the effects of ambient air pollution (including NO2) in several
cities and describe similar response rates and respiratory health outcomes as measured by increased
hospital admissions (Barnett et al., 2005; Simpson et al., 2005a). Barnett et al. (2005) used a case-
crossover method to study ambient air pollution effects on respiratory hospital admissions of children
(age groups 0, 1 to 4, and 5 to 14 years) in multiple cities in both Australia and New Zealand during the
study period 1998 to 2001. No significant associations were observed between NO2 and increased
hospital admissions for infants. For all respiratory admissions among children 1 to 4 years, a 9.6% (95%
CI: 2.3, 17.3) increase was found for a 30-ppb increase in the daily 1-h max concentration of NO2, and for
children aged 5 to!4 years the same increase in NO2 resulted in a 16.5% increase in admission for all
respiratory disease (95% CI: 5.4, 28.8) both lagged 0 to 1 day (Barnett et al., 2005).
3-37
-------
In a multicity study of all hospitalizations for respiratory disease for adults ages > 65 years, Simp-
son et al. (2005a) examined the response to a change in the daily 1-h max level of NO2. The standardized
percent increase was 8.4% (95% CI: 4.6%, 12.4%; lag 0 to 1 day per 30-ppb increase). The authors pre-
sented results from three statistical models that produced similar results overall for the four cities.
Two Canadian studies compared multiple statistical methods for data analysis in studies of
hospitalizations for all respiratory outcomes. In Vancouver, Fung et al. (2006) used time-series analysis,
the method of Dewanji and Moolgavkar (2000), and case-crossover analyses to examine the association
of ambient NO2 concentrations with all respiratory hospitalizations for adults aged 65 years and older. All
three methods showed similar results, with positive associations between incremental changes in NO2 of
5.43 ppb (IQR) from a mean concentration of 16.83 ppb. Using a time-series analysis, Fung et al. (2006)
reported a percent increase (standardized to 20 ppb) of 6.8% ([95% CI: 1.1%, 13.1%] lag 0), while the
case-crossover analysis showed a significant change in the percent increase of 10.7% ([95% CI: 3.7%,
15.5%] lag 0). The Dewanji and Moolgavkar (2000) model did not produce a statistically significant
association between NO2 and hospitalization for an increase of 20 ppb, though the central estimate
remained positive (percent increase=4.5% [95% CI: -1.1%, 10.3%] lag 0)]. In the second of these two
studies, Luginaah et al. (2005) used two approaches that included both time-series and case-crossover
analyses segregated by sex. They noted a positive trend between an incremental change in 24-h avg NO2
of 20 ppb and respiratory admissions. Though associations for females in each of the age groups
examined were positive, the authors found only one statistically significant association in females aged 0
to 14 years that identified an increased percent of hospitalization of 24.1% using the case-crossover
analysis (24.1% [95% CI: 0.3%, 53.8%] lag 2). The results of the time-series analyses from the Luginaah
et al. (2005) and Fung et al. (2006) studies are presented in Figures 3.1-8 and 3.1-9, respectively.
European studies on associations with respiratory hospitalizations were conducted in London, Paris,
and in Drammen, Norway (Ponce de Leon et al., 1996; Dab et al., 1996; Oftedal et al., 2003). Ponce de
Leon et al. (1996) found significant positive relative risks for all ages and for children (0 to 14 year olds),
but not for adults (15 to 64 years). Dab et al. (1996) determined that there was no statistically significant
association between admissions for all respiratory causes combined based on an incremental change of
52.35 ppb, though the estimates were positive. Oftedal et al. (2003) reported that the relative rate of
hospitalizations for all respiratory disease increased based on an increment of 20 ppb NO2 (RR= 1.111
[95% CI: 1.031, 1.19.9] lag 3 days). Other studies also found positive outcomes (Andersen et al., 2007a,b;
Atkinson et al., 1999a,b; Bedeschi et al., 2007; Burnett et al., 2001; Farchi et al., 2006; Hinwood et al.,
2006; Lin et al., 1999; Llorca et al., 2005; Pantazopoulou et al., 1995; Vigotti et al., 2007; Wong et al.,
1999; Yang et al., 2003). Several studies presented null results (Anderson et al., 2001; Gouveia and
Fletcher, 2000a; Hagen et al., 2001; Schouten et al., 1996). Finally, a number of studies were considered
that did not quantitatively estimate the association of NO2 concentration on all respiratory disease hospital
admissions, ED visits or clinic visits (Atkinson et al., 2001; Buchdahl et al., 1996; Burnett et al., 1997a;
Chen et al., 2005; Fung et al., 2007; Linares et al., 2006; Pantazopoulou et al., 1995; Prescott et al., 1998;
Villeneuve et al., 2006). These studies are included in Annex Tables AX6.3-3, AX6.3-4, and AX6.3-5.
To assess potential confounding by copollutants, results from multipollutant models were
evaluated. Multipollutant models may have reduced utility to distinguish the independent effects of
specific pollutants if model assumptions are not met. Despite this limitation, these models are widely used
in air pollution research. Figures 3.1-10 and 3.1-11 present NO2 risk estimates for all respiratory causes
with and without adjustment for various particulate and gaseous copollutants, respectively, in two-
pollutant models. Collectively, copollutant regression analyses indicated that NO2 risk estimates for
respiratory ED visits and hospitalizations, in general, were robust to the inclusion of additional gaseous or
particulate pollutants.
3-38
-------
3.1.6.2. Asthma
Studies of ED visits and hospitalizations provide suggestive evidence of an association between
ambient NO2 levels and ED visits and hospitalizations for asthma among children and adults. Figures
3.1-12 and 3.1-13 show the relative risks (and 95% confidence limits) of hospitalizations and visits to the
ED for asthma associated with daily NO2 concentrations, for all ages and stratified by age. Larger effect
estimates were generally observed for children compared to adults and older adults (65+ years), with an
IQR of 1 to 25% excess risk estimated per 20 ppb incremental change in 24-h avg NO2 or 30 ppb
incremental change in 1-h max NO2. The few studies that examined the association of asthma and NO2
levels among older adults (65+ years) generally reported positive central estimates, though none of these
was statistically significant. When subjects of all ages were examined, the results of ED visits and
hospitalizations were overwhelmingly positive, especially when the 24-h averaging time was used. The
epidemiologic studies of ED and clinic visits and hospital admissions for asthma are summarized in
Annex Tables AX6.3-3, AX6.3-4, and AX6.3-5.
In Atlanta, GA, Peel et al. (2005) examined various respiratory ED visits in relation to pollutant
levels from 1993 to 2000. Results for the a priori single-pollutant models examining a 3-day moving
average (lag 0, 1, and 2) of NO2 showed a small positive, but not statistically significant, association with
asthma visits (percent increase=2.1% [95% CI: -0.4%, 4.5%) for all age groups. In a secondary analysis
of patients ages 2 to 18 years, a 30-ppb increase in the day 5 lag of the NO2 concentration yielded a
percent increase of 4.1% (95% CI: 0.8%, 7.6%).
In New York City, NY, Ito et al. (2007) examined numbers of ED visits for asthma in relation to
pollution levels from 1999 to 2002. NO2 was generally the most significant (and largest in effect size per
the same distributional increment) predictor of asthma ED visits among PM2 5, O3, SO2, and CO (percent
increase=12% [95% CI: 7%, 15%] per 20 ppb increase). Further, NO2's risk estimates were most robust to
the addition of other pollutants in the model, and the addition of NO2 reduced other pollutant's risk
estimates most consistently. A study conducted in (NY Dept of Health, 2006) found a 6% (95% CI: 1, 10)
excess risk in asthma hospital admissions per 20-ppb increase in 24-h avg NO2 for Bronx residents, but a
null association for the residents of Manhattan.
Jaffe et al. (2003) examined the effects of ambient pollutants during the summer months (June
through August) on the daily number of ED visits for asthma among Medicaid recipients aged 5 to 34
years from 1991 to 1996 in Cincinnati and Cleveland. The percent change in ED visits for asthma as the
primary diagnosis per 20-ppb increase in 24-h avg NO2 concentration was 12% (95% CI: -2, 28) in
Cincinnati and 8% (95% CI: -2, 16.6) in Cleveland, with an overall percent increase in ED visits of 6%
(95% CI: -2, 14).
Barnett et al. (2005) examined specific respiratory disease outcomes and did not find associations
between incremental changes in NO2 concentration and respiratory admissions for asthma among children
1 to 4 years old. The largest association found in this study was a 25.7% increase in asthma admissions in
the 5- to 14-year age group related to a 20-ppb increase in 24-h NO2, with evidence of a seasonal impact
that resulted in larger increases in admissions during the warm season. When the same groups were
examined for the effect of a 30-ppb change in the 1-h max concentration of NO2, there were no significant
associations between NO2 and hospitalizations for asthma.
Lin et al. (2004) studied gaseous air pollutants and 3,822 asthma hospitalizations (2,368 boys, and
1,454 girls) among children 6 to 12 years of age with low household income in Vancouver, Canada,
between 1987 and 1998. NO2 levels were derived from 30 monitoring stations, and daily levels were
found to be significantly and positively associated with asthma hospitalizations for males in the low
socioeconomic group but not in the high socioeconomic group. This effect did not persist among females.
Lin et al. (2003) conducted a case-crossover analysis of the effect of short-term exposure to gaseous
pollution on 7,319 asthma hospitalizations (4,629 boys, 2,690 girls), in children in Toronto between 1980
and 1994. NO2 concentrations measured from four monitoring stations were positively associated with
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asthma admissions in both sexes. Differences in the results of these two studies might be attributed to
differences in the study designs or differences in subject population sizes.
A time-series analysis in Sydney examined respiratory outcomes in children and adults, but
reported no association between changes in NO2 (24-h avg) for asthma admissions (Morgan et al., 1998a).
For children aged 1 to 14, a 10.9% increase in hospital admissions for asthma ([95% CI: 2.2, 20.3] lag 0)
was associated with the daily 1-h max value based on 30-ppb incremental change. The association with
adults was positive, but not statistically significant.
Studies of ED visits and hospitalizations for asthma have been reported in London, U.K. (Atkinson
et al., 1999a,b; Hajat et al., 1999); Belfast, Ireland (Thompson et al., 2001); Valencia, Barcelona, and
Madrid, Spain (Tenias et al., 1998; Galan et al., 2003; Castellsague et al., 1995); Turin, Italy (Migliaretti
and Cavallo, 2004; Migliaretti et al., 2005); Marseille and Paris, France (Boutin-Forzano et al., 2004; Dab
et al., 1996); Amsterdam and Rotterdam, the Netherlands (Schouten et al., 1996), and Melbourne,
Brisbane, and Perth, Australia (Erbas et al., 2005; Hinwood et al., 2006). Sunyer et al. (1997) have
described a meta-analysis of several cities under the umbrella of the Air Pollution on Health: a European
Approach (APHEA) protocol (Katsouyanni et al., 1996). Additional studies report a positive association
between NO2 concentration and hospital admissions or ED visits (Andersen et al., 2007a; Anderson et al.,
1998; Arbex et al., 2007; Burnett et al., 1999; Kim et al., 2007; Ko et al., 2007; Lee et al., 2006; Linn
et al., 2000; Tsai et al. 2006; Wong et al., 2001; Yang et al., 2007). Several studies have reported null or
negative associations (Andersen et al., 2007b; Anderson et al., 1998; Chardon et al., 2007; Gouveia and
Fletcher 2000a; Petroeschevsky et al., 2001; Spix et al., 1998; Tanaka et al., 1998; Tolbert et al., 2000).
Copollutant and multipollutant regression analyses were performed in several of these studies.
Results generally indicated that NO2 risk estimates for respiratory ED visits and hospitalizations were not
sensitive to the inclusion of additional gaseous or particulate pollutants.
Finally, there were a number of studies that were considered but did quantitatively estimate the
association of NO2 concentration on asthma hospital admissions or ED or clinic visits (Atkinson et al.,
2001; Bates et al., 1990; Chew et al., 1999; Garty et al., 1998; Kesten et al., 1995; Lipsett et al., 1997;
Magas et al., 2007; Neidell, 2004; Ponka, 1991; Ponka and Vitanen 1996; Rossi et al., 1993; Stieb et al.,
1996; Sun et al., 2006; Tobias et al., 1999). These studies are included in Annex Tables AX6.3-3,
AX6.3-4, andAX6.3-5.
3.1.6.3. COPD
Relatively few studies have examined the association of ED visits and hospitalizations for COPD
and ambient NO2 levels. The epidemiologic studies of ED and clinic visits and hospital admissions for
COPD are summarized in Annex Tables AX6.3-3, AX6.3-4, and AX6.3-5. Studies examining COPD
outcomes have focused on hospital admission data, including multicity studies in the U.S. (Moolgavkar,
2000, 2003), Europe (Anderson et al., 1997) and Australia (Simpson et al., 2005a), and single-city studies
in the U.S. (Peel et al., 2005), Canada (Yang et al., 2005), Europe (Anderson et al., 2001; Atkinson et al.,
1999a; Dab et al., 1996; Tenias et al., 2002), Australia (Morgan et al., 1998a; Hinwood et al., 2006), and
Asia (Lee et al., 2007; Yang and Chen, 2007).In a time-series study in Vancouver, an area with low
pollution concentrations (24-h mean NO2 of 17.03 ppb), Yang et al. (2005) reported associations between
NO2 and hospital admissions for COPD in patients > 65 years for both the lag 1 day (RR=1.19; 95% CI:
1.04, 1.37) and 7-day extended lag period (RR=1.46 [95% CI: 1.15, 1.94]). Additional studies found
weaker, though statistically significant positive associations with ambient levels of NO2 and COPD
(Moolgavkar, 2003; Anderson et al., 1997; Simpson et al., 2005a). A time-series analysis in Sydney,
Australia, examined respiratory outcomes in children and adults but did not show an association between
changes in NO2 (24-h average) for increased hospital admissions among COPD patients > 65 years
(Morgan et al., 1998a). Similarly, a study in Paris, France, of COPD and related obstructive respiratory
disease found that NO2 was not statistically significantly associated with increased hospital admissions
(Dabetal., 1996).
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3.1.6.4. Respiratory Diseases Other than Asthma or COPD
ED visits or hospital admissions for respiratory diseases include upper respiratory infections
(URIs), pneumonia, bronchitis, allergic rhinitis, and lower respiratory disease (LRD). The reviewed
epidemiologic studies of clinic and ED visits and hospital admissions for these respiratory diseases are
summarized in Annex Tables AX6.3-3, AX6.3-4, and AX6.3-5. Though some of these studies reported
positive and statistically significant results (Atkinson et al., 1999a; Burnett et al., 1997b, 1999; Farchi
et al., 2006; Gouveia and Fletcher, 2000a; Hwang and Chan, 2002; Ilabaca et al., 1999; Lin et al., 2005;
Peel et al., 2005; Simpson et al., 2005a), others reported null or negative associations (Barnett et al.,
2005; Chardon et al., 2007; Hinwood et al., 2006; Karr et al., 2006; Lin et al., 1999; Ponka and Virtanen,
1994; Zanobetti and Schwartz, 2006). Finally, there are two studies that were considered but that did not
quantitatively estimate the association of NO2 concentration on all respiratory disease hospital admissions
or ED and clinical visits (Bates et al., 1990; Linares et al., 2006). These studies are included in Annex
Tables AX6.3-3, AX6.3-4, and AX6.3-5.
3.1.6.5. Summary of Short-Term Exposure on Respiratory ED Visits and
Hospitalizations
In summary, many studies have observed positive associations between ambient NO2
concentrations and ED visits and hospitalizations for all respiratory diseases and asthma. These
associations are particularly consistent among children and older adults (65+ years) for hospital
admissions for all respiratory diseases. For asthma hospitalization, the effect estimates were largest when
children and subjects of all ages were included in the analysis. Results from copollutant models indicated
that the effect of NO2 on ED visits and hospitalizations for all respiratory causes and asthma were
generally robust and independent of the effects of ambient particles or gaseous copollutants. In preceding
sections, exposure to NO2 has been found to result in host defense and immune system changes, airway
inflammation, and airway responsiveness. While not providing specific mechanistic data linking exposure
to ambient NO2 and respiratory hospitalization or ED visits for asthma, these findings provide plausibility
and coherence for such a relationship.
However, the limited evidence does not support a relationship between ED visits and
hospitalizations for COPD and ambient NO2 levels, and there were limited studies providing inconsistent
results for many of the health outcomes other than asthma, making it difficult to draw conclusions about
the effects of NO2 on these other diseases.
3.1.7. Summary and Integration—Respiratory Health Effects with
Short-Term Exposure
The main body of evidence for an association between respiratory morbidity and NOX exposure
comes from epidemiologic studies. In addition, clinical and animal toxicological studies provide some
supporting data. Taken together, the findings of epidemiologic, human clinical, and animal toxicological
studies provided evidence that is sufficient to infer a likely causal relationship for respiratory effects with
short-term NO2 exposure. The body of evidence from epidemiologic studies has grown substantially since
the 1993 AQCD and provided scientific evidence that short-term exposure to NO2 is associated with a
broad range of respiratory morbidity effects, including altered lung host defense, inflammation, airway
hyperresponsiveness, respiratory symptoms, lung function decrements, and ED visits and hospital
admissions for respiratory diseases. New evidence came from large longitudinal studies, panel studies,
and time-series studies. NO2 exposure was associated with aggravation of asthma effects that include
symptoms, medication use, and lung function. Effects of NO2 on asthma were most evident with
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cumulative lag of 2 to 6 days, rather than same-day levels of NO2. Time-series studies also demonstrated
a relationship in children between hospital admissions or ED visits for asthma and NO2 exposure. In
many of these studies, there were high correlations between ambient measures of NO2 and CO and PM;
however, the effect estimates for NO2 were robust after the inclusion of CO and PM in multipollutant
models. Recent epidemiologic studies provided somewhat inconsistent evidence on short-term exposure
to NO2 and inflammatory responses in the airways, as well as for associations with lung function
decrements. The epidemiologic evidence for these effects can be characterized as consistent, in that
associations are reported in studies conducted in numerous locations with a variety of methodological
approaches. While the individual risk estimates were small in magnitude, and thus not considered strong
individually, the body of epidemiologic evidence had strength in that fairly precise and robust risk
estimates were reported from multicity studies.
Important evidence also was available from epidemiologic studies of indoor NO2 exposures. A
number of recent studies showed associations with wheeze, chest tightness, and length of symptoms
(Belanger et al., 2006); respiratory symptom rates (Nitschke et al., 2006); school absences (Pilotto et al,
1997a); respiratory symptoms, likelihood of chest tightness, and asthma attacks (Smith et al., 2000); and
severity of virus-induced asthma (Chauhan et al., 2003). A particular intervention study (Pilotto et al.,
2004) provided strong evidence of a detrimental effect of exposure to NO2. Considering this large body of
epidemiologic studies alone, the findings are coherent in the sense that the studies report associations with
respiratory health outcomes that are logically linked together.
Experimental evidence offered some coherence and plausibility for the observed epidemiologic
associations. Toxicological studies have also shown that lung host defenses, including mucociliary
clearance and AM and other immune cell functions, are sensitive to NO2 exposure, with effects observed
at concentrations of less than 1 ppm (see Annex Tables AX4.3 and AX4.5). The limited evidence from
human studies indicated that NO2 may increase susceptibility to injury by subsequent viral challenge.
Devlin et al. (1999) found reduced AM phagocytic capacity after NO2 exposure, which indicated a
reduced ability to clear inhaled bacteria or other infectious agents. Frampton et al. (2002) found enhanced
epithelial cell injury in response to RSV infection after NO2 exposure. Taken together with the
epidemiologic evidence described above linking NO2 exposure with viral illnesses, there was coherent
and consistent evidence that NO2 exposure can result in lung host defense or immune system effects. This
group of outcomes provided some plausibility for other respiratory system effects as well. For example,
effects on ciliary action (clearance) or on macrophage function (i.e. phagocytosis, cytokine production)
can lead to the type of outcomes assessed in epidemiologic studies, such as respiratory illness or
symptoms.
Human clinical studies provided evidence for airway hyperresponsiveness i.e., a heightened
bronchoconstrictive response to a challenge agent, following short-term exposure to NO2. In acute
exacerbations of asthma, bronchial smooth muscle contraction (bronchoconstriction) occurs quickly to
narrow the airways in response to exposure to various stimuli including allergens or irritants.
Bronchoconstriction is the dominant physiological event leading to clinical symptoms and interference
with airflow (National Heart, Lung, and Blood Institute, 2007). Recent studies involving allergen
challenge in asthmatics showed that NO2 may enhance the sensitivity to allergen-induced decrements in
lung function and affect allergen-induced inflammatory responses following exposures as low as
0.26 ppm NO2 for 30 min during rest. Nonspecific responsiveness also was increased following 30-min
exposures of resting asthmatic subjects to 0.2- to 0.3-ppm NO2 and following 1-h exposures to
0.1-ppmNO2.
The few recent epidemiologic studies reported associations between ambient NO2 exposure and
airway inflammation. These studies were indicative of effects in children, but offered more limited
evidence for effects in adults. Human clinical studies provided consistent evidence for airway
inflammation at a NO2 concentration of 2.0 ppm (one study found airway inflammation at a concentration
of 1.5 ppm); the onset of inflammatory responses in healthy subjects appeared to be between 100 and
200 ppm-min, i.e., 1 ppm for 2 to 3 h. Biological markers of inflammation were reported in antioxidant-
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deficient laboratory animals with exposures to 0.4 ppm NO2, though healthy animals did not respond until
exposed to much higher levels, i.e., 5 ppm NO2. The biochemical effects observed in the respiratory tract
following exposure to NO2 included chemical alteration of lipids, amino acids, proteins, enzymes, and
changes in oxidant/antioxidant homeostasis, with membrane polyunsaturated fatty acids and thiol groups
as the main biochemical targets for NO2 exposure. However, the biological implications of such
alterations are unclear. Potential mechanisms for effects on the respiratory system included membrane
damage from increases in reactive oxygen species, lipid and protein pertubations, and recruitment of
inflammatory cells from epithelial cell injury by reactive oxygen species.
In evaluating the potential relationships between short-term exposure to NO2 and respiratory
effects, it is important to note the interrelationships between NO2 and other pollutants, and the potential
for NO2 to serve as a marker for a pollutant mixture, particularly traffic-related pollution. As outlined in
the preface to this ISA, this included consideration of potential pathways, such as the direct causal
pathway for effects, mediation of effects, the pollutant acting as a surrogate for a pollutant mixture, or
confounding between pollutants. As observed above, associations with NO2 were often robust to
adjustment for traffic-related pollutants (e.g., PM and CO), even in locations where the correlations
between pollutants were substantial. The epidemiologic evidence has thus been found to be consistent and
coherent for respiratory symptoms and respiratory hospitalization and ED visits. In addition, toxicological
and clinical studies reported effects of exposure to gaseous NO2, as discussed previously, for outcomes
related to lung host defense and immune system changes. The experimental studies indicated that NO2 is
solely responsible for the effects reported. The findings of direct effects of NO2 in toxicological or human
clinical studies, in combination with robust associations reported in epidemiologic studies, supported a
conclusion that NO2 is independently responsible for some respiratory effects. There was little available
evidence to evaluate the potential for NO2 effects to be modified by other pollutants or exposures; further,
clinical and epidemiologic study findings did not appear to support that coexposure with another pollutant
is required to observe NO2-related effects.
The evidence summarized here supports the conclusion that there is a likely causal relationship
between short-term exposure to NO2 and effects on the respiratory system. However, the challenge
remains in considering the potential for NO2 to serve as an indicator for a mixture of combustion-related
pollutants. Most studies examined showed that personal NO2 exposures were significantly correlated
either with ambient or personal level PM2 5, or other combustion-generated products (e.g., CO and EC).
As discussed in Chapter 2, ambient NO2 measurements can provide a valid estimate of personal exposure
to ambient NO2 as used in most epidemiology studies. Although the evidence indicated that NO2 exposure
is independently associated with some respiratory health effects, there remains the possibility that NO2
also serves as a marker for combustion-related emissions, particularly from traffic, for some health
outcomes. Although this complicates efforts to disentangle specific NO2-related health effects, the
evidence indicates that NO2 associations generally remain robust in multipollutant models and supports a
direct effect of short-term NO2 exposure on respiratory morbidity at current ambient concentrations.
3.2. Cardiovascular Effects Related to Short-Term Exposure
This ISA includes approximately 40 studies published since 1993 characterizing the effect of short-
term NOX exposure on hospitalizations or ED visits for CVD. These studies form a new body of literature
that was unavailable in 1993, when the previous AQCD was published.
3.2.1. Heart Rate Variability
Heart rate variability (HRV), a measure of the beat-to-beat change in heart rate, is a reflection of the
overall autonomic control of the heart. It is hypothesized that increased air pollution levels may stimulate
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the autonomic nervous system and lead to an imbalance of cardiac autonomic control characterized by
sympathetic activation unopposed by parasympathetic control (Liao et al., 2004; Brook et al, 2004). Such
an imbalance of cardiac autonomic control may predispose susceptible people to greater risk of
ventricular arrhythmias and consequent cardiac deaths (Liao et al., 2004; Brook et al., 2004). Findings
from studies of ambient NO2 and HRV were mixed with some studies reporting an adverse effect
(reduction in variability) (Liao et al., 2004; Chan et al., 2005; Wheeler et al., 2006), while other studies
reported no significant change (Luttman-Gibson et al., 2006; Holguin et al., 2003; Schwartz et al. 2005).
In some studies reporting reductions in HRV, reductions for PM were similar to those observed for NO2
(Liao et al., 2004; Wheeler et al. 2006). See Annex Table AX6.3-10 for a detailed discussion of HRV
studies.
3.2.2. Arrhythmias Recorded on Implanted Defibrillators
Results from studies directly measuring ventricular arrhythmias were inconsistent and potentially
confounded by PM (Peters et al., 2000; Dockery et al., 2005; Rich et al., 2005, 2006a; Metzger et al,.
2007). Among the ambient air pollutants, the strongest association with arrhythmias was observed for
PM, which was highly correlated to NO2 concentrations in these studies (Dockery et al., 2005; Rich et al.,
2005; Metzger et al., 2007). Rich et al. (2006b) did not observe an association between NO2 level and
paroxysmal atrial fibrillation (PAF). See Annex Table AX6.3-11 for detailed discussion of defibrillator
studies.
3.2.3. Repolarization Changes
In addition to the role played by the autonomic nervous system in arrhythmogenic conditions,
myocardial vulnerability and repolarization abnormalities are believed to be key factors contributing to
the mechanism of such diseases. Henneberger et al. (2005) reported that NO2 and NO were not associated
with repolarization abnormalities.
3.2.4. Markers of Cardiovascular Disease Risk
Several investigators have explored potential mechanisms by which air pollution could cause CVD.
In particular, markers of inflammation, cell adhesion, coagulation, and thrombosis have been evaluated in
epidemiologic studies. Pekkanen et al. (2000) reported a significant increase in fibrinogen associated with
short-term NO2 exposure while Steinvil et al. (2007) reported significant decreases in fibrinogen
associated with NO2. Schwartz (2001) reported increases in fibrinogen and platelet count associated with
NO2 level in single-pollutant models, which changed direction in multipollutant models also containing
PM10. Liao et al. (2005) did not observe differences in white blood cell (WBC) count, Factor VIII-C,
fibrinogen, von Willibrand Factor (VWF), or albumin associated with 24-h avg NO2 levels. However,
PMio was associated with factor VIII-C in the cohort examined. Ruckerl et al. (2006) observed a
significant association of NO2 (lagged 2-6 days) with C-reactive protein (CRP) greater than the 90th
percentile but the strongest effect on CRP was observed for UFP. Baccarelli et al. (2007) reported a
shorter prothrombin time (PT) with increasing NO2 levels but, a similar decrease in PT was observed for
PM10.
Collectively, associations reported for NO2 and markers of cardiovascular risk in epidemiologic
studies appeared to be potentially confounded by PM and other traffic-related pollutants. Several authors
proposed that these biomarker studies provide evidence for biological plausibility of the effect of PM on
cardiovascular health rather than NO2 (Schwartz 2001; Seaton and Dennekamp, 2003).
A few human clinical studies which indicated effects of NO2 exposure on cardiac output, blood
pressure, and circulating red blood cells at concentrations of less than 2.0 ppm (Drechsler-Parks, 1995;
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Linn et al., 1985a; Posin et al., 1978; (Frampton et al., 2002) require confirmation. Drechsler-Parks
(1995) observed a lower mean stroke volume for NO2+O3 than for air and speculated that chemical
interactions between O3 and NO2 at the level of the epithelial lining fluid led to the production of nitrite,
leading to vasodilatation, with reduced cardiac preload and cardiac output. Linn et al. (1985a) reported
small but statistically significant reductions in blood pressure after exposure to 4 ppm NO2 for 75 min, a
finding consistent with systemic vasodilatation in response to the exposure; this finding has not been
repeated. Frampton et al. (2002) reported a concentration-related reduction in hematocrit and hemoglobin
in both males and females, among health subjects exposed to NO2, confirming the findings of an earlier
study conducted by Posin et al. (1978). See Annex AX5 for a detailed discussion of these studies.
The results on the effect of NO2 on various hematological parameters in animals were inconsistent
and, thus, provided little biological plausibility for the epidemiology findings. There have also been
reported changes in the red blood cell membranes of experimental animals following NO2 exposure. Red
blood cell D-2,3-diphosphoglycerate was reportedly increased in guinea pigs following exposure to
0.36 ppm NO2 for 1 week (Mersch et al., 1973). An increase in red blood cell sialic acid, indicative of a
younger population of red blood cells, was reported in rats exposed to 4.0 ppm NO2 continuously for 1 to
10 days (Kunimoto et al., 1984). However, in another study, exposure to the same concentration of NO2
resulted in a decrease in red blood cell number (Mochitate and Miura, 1984). A more recent study
(Takano et al., 2004) using an obese rat strain found changes in blood triglycerides, high-density
lipoprotein cholesterol (HDL), and HDL/total cholesterol ratios with a 24-week exposure to 0.16 ppm
NO2. In the only study conducted with an exposure of less than 5 ppm NO2 that evaluated methemoglobin
formation, Nakajima and Kusumoto (1968) reported that, in mice exposed to 0.8 ppm NO2 for 5 days, the
amount of methemoglobin was not increased. This is in contrast to some (but not all) in vitro and high-
concentration NO2 in vivo studies, which have found methemoglobin effects (U.S. Environmental
Protection Agency, 1993).
3.2.5. Toxicology of Inhaled Nitric Oxide
Nitric oxide is used in humans therapeutically as a pulmonary vasodilator, and has shown little
evidence for adverse respiratory effects. The literature on therapeutic uses of nitric oxide provides the
strongest evidence for its lack of toxicity. Infants and adults with acute respiratory failure and refractory
hypoxemia, as well as pulmonary hypertension, are sometimes considered candidates for inhaled NO.
Inhaled NO acts as a selective pulmonary vasodilator, causing vascular smooth muscle relaxation and
increased perfusion in ventilated lung regions. Beneficial effects in patients with respiratory failure
include reduced pulmonary artery pressures and improved ventilation-perfusion matching. Nitric oxide is
used clinically at concentrations ranging from 5 ppm to as high as 80 ppm. There has been little or no
toxicity reported, even when used in premature infants with respiratory failure. In a recently published
multicenter study (Kinsella et al., 2006), 793 premature infants with respiratory failure were randomized
to therapy with inhaled NO or air. NO therapy was associated with a reduced risk of brain injury, and in a
reduced risk of bronchopulmonary dysplasia, a chronic lung condition resulting from lung injury in
infancy, in infants weighing at least 1000 gm. NO can cause methemoglobinemia, and this was seen
transiently in only 2 infants. NO can inhibit activation of blood leukocytes and platelets (Gianetti et al.
2002); however there was no evidence for increased susceptibility to infection or bleeding. One of the
concerns about NO therapy is the potential for NO to be oxidized to NO2, so administration systems are
designed to avoid this.
3.2.6. Hospital Admissions and ED Visits for CVD
Cases of CVD are typically identified using ICD codes, which were recorded on hospital discharge
records in these studies. However, counts of hospital or ED admissions were used in some studies.
Studies of ED visits may include cases that are less severe than those included in hospital admission
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studies. Hospital admission studies are distinguished from ED visit studies in Annex Tables AX6.3-6
through AX6.3-9. Many studies grouped all CVD diagnoses (ICD9 codes 390-459), evaluating cardiac
diseases (ICD9 codes 390-429), and cerebrovascular disease (ICD9 430-448) together. Other studies
evaluated cardiac and cerebrovascular diseases separately or further distinguished ischemic heart disease
(IHD: ICD9 410-414), myocardial infarction (MI: ICD9 410), congestive heart failure (CHF: ICD9 428),
cardiac arrhythmia (ICD9 427), angina pectoris (ICD9 413), or stroke (ICD9 430-438).
Numerous studies have shown a positive association between both 24-h avg and 1-h max NO2
levels and hospital admissions or ED visits for all CVD, in single-pollutant models (Linn et al., 2000;
Metzger et al., 2004; Tolbert et al., 2007; Ballester et al., 2001, 2006; Anderson et al., 2007a; Atkinson et
al., 1999a,b; Poloniecki et al., 1997; Barnett et al., 2006; Hinwood et al., 2006; Jalaludin et al., 2006;
Chang et al., 2005; Wong et al., 1999; Yang et al., 2004b). A discussion of results from studies reporting
associations between NO2 and all CVD are found in Annex section AX6.2.1.
3.2.7. Cardiac Disease
Findings from studies examining the association of NO2 with cardiac disease are found in Figure
3.2-1. Most investigators who distinguished cardiac disease from all CVD reported significant positive
associations in single-pollutant models. Increased risks were observed in Canadian populations (Burnett
et al., 1997b; Fung et al., 2005). The average daily 1-h maxNO2 level was approximately 39 ppb in
metropolitan Toronto, ON, where these studies were conducted. Estimates from two Australian multeity
studies (Barnett et al., 2006; Simpson et al., 2005a) were also significantly increased. The 24-h NO2 level
in the Australian cities studied by Barnett et al. (2006) was 7 to 11.5 ppb. The range of 1-h max NO2 level
in cities studied by Simpson et al. (2005a) was 16 to 24 ppb. Von Klot et al. (2005) observed a
statistically significant association between readmission for cardiac disease among MI survivors, a
potentially susceptible subpopulation and NO2 concentrations in five European cities. The range in 24-h
NO2 level was 15.8 to 26 ppb in the five cities studied. Two single-city Australian studies and one single-
city Taiwanese study also reported positive single-pollutant model results (Jalaludin et al., 2006; Morgan
et al., 1998a; Chang et al., 2005). Studies of the association of 24-h avg and 1-h max NO2 level with IHD,
MI, CHF and arrhythmia are less consistently positive and significant. Results from these studies are
described in Annex section AX6.2-1.
Most investigators reporting results from multipollutant models observed diminished effect
estimates for NO2 and hospital admissions or ED visits for CVDs. In two U.S. studies conducted in Los
Angeles, investigators 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 an Atlanta study, Metzger et al. (2004) and Tolbert et al. (2007)
also observed a diminished effect of NO2 on visits for CVD when CO was modeled with NO2, while the
effect of CO remained robust. Tolbert et al. (2007) discussed the limitations of multipollutant models and
concluded that these models might help researchers identify the strongest predictor of disease, but might
not isolate the independent effect of each pollutant. NO2 was not robust to adjustment for other pollutants
in several non-U.S. studies (Jalaludin et al., 2006; Ballester et al., 2006; Simpson et al., 2005a; Poloniecki
et al., 1997; Barnett et al., 2006; Llorca et al., 2005). However, in other studies, investigators reported that
the effect of NO2 was robust in multipollutant models (Von Klot et al., 2005; Yang et al., 2004b; Chang et
al., 2005; Morgan et al., 1998a; Burnett et al., 1997a, 1999). See Annex section AX6.2.1.6 for a detailed
description of results from multipollutant models.
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Simpson et al. (2005a)*
Simpson etal. (2005a)*
Jaludin et al. (2006)*
Jaludin et al. (2006)*
Jaludin et al. (2006)*
Morgan etal. (1998a)*
Morgan etal. (1998a)*
Morgan etal. (1998a)*
Chang et al. (2005)
Chang et al. (2005)
Location Season
Ontario
Ontario
Ontario
Europe
Valencia
Spain, Multicity
Australia, NZ
Australia, NZ
Australia, Multicity
Australia, Multicity
Australia, Multicity
Sydney
Sydney
Sydney
Sydney
Sydney
Sydney
Taipei Warm
Taipei Cool
Age
65+
65+
65+
Ml Survivors 35+
All ages
All ages
65+
15-64
All
15-64
65+
65+
65+
65+
All
65+
0-65
All ages
All ages
Lag
0
0-1
0-2
0
2
0-1
0-1
0-1
0-1
0
0-1
0
1
0-1
0
0
0
0-2
0-2
Figure 3.2-1. Relative risks (95% Cl) for associations of 24-h N02 (per 20 ppb) and daily 1-h max* N02 (per
30 ppb) with hospitalizations or emergency department visits for cardiac diseases. Primary
author and year of publication, city, stratification variable(s), and lag are listed. Results for
lags 0 or 1 are presented as available.
3.2.8. Hospital Admissions for Stroke and Cerebrovascular Disease
Studies of the association between all cerebrovascular disease and ambient NO2 concentration are
summarized in Figure 3.2-2. Results from these studies were generally inconsistent. Metzger et al. (2004)
reported a significant increase in cerebrovascular disease emergency visits in Atlanta. However, Peel et al.
(2007) did not find associations between cerebrovascular disease visits and NO2 concentrations among
those with hypertension and diabetes in the same city. The daily 1-h max NO2 level in Atlanta during the
study period ranged from 26 to 45.9 ppb (Metzger et al., 2004; Peel et al., 2007). Ballester et al. (2001)
reported a relatively large increased risk in cerebrovascular admissions in the Spanish city of Valencia at
lag 4, while Poloniecki et al. (1997) and Ponka and Virtanen (1996) did not observe associations in
London and Helsinki. Two Asian studies report positive but nonsignificant associations of
cerebrovascular disease with 24-h avg NO2 (Chan et al., 2006; Wong et al., 1999). The 24-h avg NO2
levels reported for Taipei and Hong Kong were approximately 30 ppb and 27 ppb, respectively (Chan
et al., 2006; Wong et al., 1999).
3-47
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Reference Location Age Lag
Linn et al. (2000) Metro LA All Ages 0
Metzger etal, (2004)* Atlanta
Peel etal. (2007)*
Peel etal. (2007)*
Atlanta
Atlanta
Ballester etal. (2001)* Valencia
Poloneiki et al. (1997) London
Chan etal. (2006) Taipei
Wong etal. (1999)
Hong Kong
All Ages 3 d moving .
All Ages 0-2
All Ages 0-2
All Ages
All Ages
50+
All Ages 0-1
\
.9
I I I
1.1 1.3 1.5
Relative risk
Figure 3.2-2. 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 all cerebrovascular disease. Primary author and year of
publication, city, stratification variable(s), and lag are listed. Results for lags 0 or 1 are
presented as available.
Studies of hospital admissions or ED visits for specific cerebrovascular diseases provided little
evidence for a NO2 effect. In a large study, conducted in metropolitan Los Angeles where the mean 24-h
NO2 level ranged from 28 to 41 ppb depending on the season, no association was observed for all
cerebrovascular disease (Linn et al., 2000). However, authors reported an increase in hospitalizations of
4.0% (95% CI: 2.0, 6.0) for occlusive stroke per 20 ppb increase in NO2.
Wellenius et al. (2005) found a 5% increase in ischemic stroke (IS) admissions per 20-ppb increase
in 24-h avg NO2 level. A study of all-stroke in Ontario reported null findings for 24-h avg NO2 at lags 0
and 1 (Ito et al. 2004). Villeneuve et al. (2006) reported an association between NO2 exposure and IS
during the winter months among the elderly (OR=1.41 [95% CI: 1.13, 1.75], per 20 ppb, lag 3 day
average). Villeneuve et al. (2006) also reported positive but nonsignificant associations for hemorrhagic
stroke (HS) (OR=1.25 95% CI: 0.91, 1.71 per 20-ppb increase in NO2). No associations between air
pollutants and stroke were reported in a multicity study conducted in Australia and New Zealand (Barnett
et al., 2006). An increase in 24-h avg NO2 resulted in increased risk of hospitalization for primary
intracerebral hemorrhage (PIH) (OR: 1.68 [95% CI: 1.39, 2.04] lag 0 to 2 per 20 ppb increase), and
ischemic stroke (IS) (OR: 1.67 95% CI: 1.49 1.88, lag 0-2) during the warm season in Taiwan (Tsai et al.,
2003).
Several investigators presented estimates for the association of NO2 with cerebrovascular outcomes
from multipollutant models. The association of NO2 with stroke was not robust to adjustment for CO in a
3-48
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Canadian study (Villeneuve et al., 2006). Although results from a Taiwanese study indicated the effect of
NO2 on stroke admissions was robust in two-pollutant models, the authors noted that the association of
NO2 with stroke might not be causal if NO2 is a surrogate for other components of the air pollution
mixture (Tsai et al., 2003).
Summary of Cardiovascular Effects Related to Short-Term Exposure
The available evidence on the effect of short-term exposure to NO2 on cardiovascular health effects
was inadequate to infer the presence or absence of a causal relationship at this time. Evidence from
epidemiologic studies of HRV, repolarization changes, and cardiac rhythm disorders among heart patients
with implanted cardioverter defibrilators are inconsistent. In most studies, observed associations with PM
were similar or stronger than associations with NO2. Generally positive associations between ambient
NO2 concentrations and hospital admissions or ED visits for CVD have been reported in single-pollutant
models; however, most of the effect estimates were diminished in multipollutant models also containing
CO and PM indices. Mechanistic evidence of a role for NO2 in the development of CVDs from studies of
biomarkers of inflammation, cell adhesion, coagulation, and thrombosis was lacking. Furthermore, the
effects of NO2 on various hematological parameters in animals are inconsistent and, thus, provide little
biological plausibility for effects of NO2 on the cardiovascular system. However, there was limited
evidence from human clinical studies which showed a reduction in hemoglobin with NO2 exposure at
concentrations of 1.0 to 2.0 ppm (with 3 h exposures) that requires confirmation.
3.3. Mortality Related to Short-Term Exposure
There was no epidemiologic study reviewed in the 1993 AQCD that examined the mortality effects
of ambient NO2. Since the 1993 AQCD, a number of studies, mostly using time-series analyses, reported
short-term mortality risk estimates for NO2 (see Annex Table AX6.3-19). However, since most of these
studies' original focus or hypothesis was on PM, a quantitative interpretation of the NO2 mortality risk
estimates requires caution. Risk estimates were summarized across studies after reviewing individual
multicity studies.
3.3.1. Multicity Studies and Meta-Analyses
In reviewing the range of mortality risk estimates, multicity studies provided the most useful
information because they analyzed multiple cities data in a consistent method, avoiding potential
publication bias. Risk estimates from multicity studies usually are reported for consistent lag days, further
reducing potential bias caused by choosing the "best" lag in individual studies. There have been several
multicity studies from the U.S., Canada, and Europe. Meta-analysis studies also provided useful
information on describing heterogeneity of risk estimates across studies, but unlike multicity studies, the
heterogeneity of risk estimates seen in meta-analysis may also reflect the variation in analytical
approaches across studies. Thus, we focused our review mainly on the results from multicity studies, and
effect estimates from these studies were summarized. Discussion focused on the studies that were not
affected by generalized additive models (GAMs) with convergence issues (Dominici et al., 2002; Ramsay
et al., 2003) unless otherwise noted when the studies raised relevant issues.
3-49
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3.3.1.1. National Morbidity, Mortality, and Air Pollution Study (NMMAPS)
The time-series analysis of the largest 90 U.S. cities (Samet et al., 2000; reanalysis Dominici et al.,
2003) in the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) was by far the largest
multicity study conducted to date to investigate the mortality effects of air pollution, but its primary
interest was PM (i.e., PMi0), and NO2 was not measured in 32 of the 90 cities. This study's model
adjustment for weather effects employed more terms than other time-series studies in the literature,
showing that the model adjusted for potential confounders more aggressively than the models in other
studies. PMio and O3 (in summer) appeared to be more strongly associated with mortality than the other
gaseous pollutants. Regarding NO2, SO2, and CO, the authors stated, "The results did not indicate
associations of these pollutants with total mortality." PMi0, NO2, SO2, and CO showed the strongest
association at lag 1 day (for O3, it was lag 0 day), and the addition of other copollutants in the model at
lag 1 day hardly affected the mortality risk estimates for PMi0 or the gaseous pollutants. Figure 3.3-1
shows the total mortality risk estimates for NO2 from Dominici et al. (2003). The NO2 risk estimates in
the multipollutant models were about the same or larger. Thus, these results do not indicate that the
NO2-mortality association was confounded by PMi0 or other pollutants (and vice versa).
Q. 1.0
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Source: Dominici etal. (2003).
Figure 3.3-1. Posterior means and 95% posterior intervals of national average estimates for N02 effects on
total mortality from nonexternal causes at lags 0, 1, and 2 within sets of the 90 cities with
pollutant data available. Models A=N02 alone; B=N02+PMi0; C=N02+PMio+03;
D=N02+PMio+S02; E=S02+PMio+CO.
3.3.1.2. Canadian Multicity Studies
There have been four Canadian multicity studies conducted by the same group of investigators
(Burnett et al., 1998, 2000, 2004; Brook et al., 2007). This section focuses on Burnett et al. (2004) and
Brook et al. (2007), as these studies are most extensive both in terms of the length and coverage of cities.
Total (nonaccidental), cardiovascular, and respiratory mortality were analyzed in the Burnett et al.
(2004) study of 12 Canadian cities from 1981 to 1999. Daily 24-h avg as well as 1-h max values were
analyzed for all the gaseous pollutants and coefficient of haze (CoH). For PM2 5, coarse PM (PMi0.2 5),
o, CoH, SO2, and CO, the strongest mortality association was found at lag 1, whereas for NO2, it was
3-50
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the 3-day moving average (i.e., average of 0-, 1-, and 2-day lags), and for O3, it was the 2-day moving
average. Of the single-day lag estimates forNO2,
Lag 1 day showed the strongest associations, which was consistent with the NMMAPS result, but
its risk estimate was more than 4 times larger than that for the NMMAPS study. The 24-h avg values
showed stronger associations than the 1-h max values for all the gaseous pollutants and CoH except for
O3. The pooled NO2 mortality risk estimate in a single-pollutant model (for all available days) was 2.0%
(95% CI: 1.1, 2.9) per 20-ppb increase in the 3-day moving average of NO2. The magnitudes of the effect
estimates were similar for total, cardiovascular, and respiratory mortality. Larger risk estimates were
observed for warmer months. NO2 was most strongly correlated with CoH (r=0.60), followed by PM2 5
(r=0.48). The NO2-mortality association was not sensitive to adjustment for these or any of other
pollutants in the two-pollutant models. However, Burnett et al. (2004) noted that simultaneous inclusion
of daily PM25 data (available for 1998 and 2000; sample size comparable to the main analysis [every 6th
day from 1981 to 1999]) and NO2 in the model resulted in a considerable reduction of the NO2 risk
estimates. Authors discussed that reducing combustion would result in public health benefits because NO2
or its products originate from combustion sources, but cautioned that they could not implicate NO2 as a
specific causal pollutant.
Brook et al. (2007) further examined data from 10 Canadian cities with a special focus on NO2 and
the role of other traffic-related air pollutants. Again, NO2 showed the strongest associations with mortality
among the pollutants examined including NO, and none of the other pollutants substantially reduced NO2
risk estimates in multipollutant models. The analysis also confirmed the Burnett et al. (2004) study results
that NO2 risk estimate was larger in the warm season. Generally, NO was more strongly correlated with
the primary VOCs (e.g., benzene, toluene, xylenes) than NO2 or PM2 5. NO2 was more strongly correlated
with the organic compounds than it was with the PM mass indices or trace metals in PM2 5. Brook et al.
(2007) concluded that the strong NO2 effects seen in Canadian cities could be a result of it being the best
indicator, among the pollutants monitored, of fresh combustion as well as photochemically processed
urban air.
In summarizing the Canadian multicity studies, NO2 was most consistently associated with
mortality among the air pollutants examined, especially in the warm season. Adjustments for PM indices
and its components generally did not reduce NO2 risk estimates. NO2 also was shown to be associated
with volatile organic compounds that are indicative of combustion products (traffic-related air pollution)
and photochemical reactions.
3.3.1.3. Air Pollution and Health: A European Approach (APHEA) Studies
The APHEA project was a European multicity effort, which analyzed data from multiple studies
using a standardized methodology. This section focuses on the more recent APHEA2 studies which
included 29 European cities.
Samoli et al. (2006) analyzed 29 APHEA2 cities to estimate NO2 associations for total,
cardiovascular, and respiratory deaths. The average of lags 0-1 days was chosen a priori to avoid potential
bias with the "best" lag approach. In addition, the association of total mortality with NO2 over 6 days
(lags 0-5) were summarized over all cities using a cubic polynomial distributed lag model. Results from
this model showed multiday effects, with the strongest association shown at lag 1 day, which was
consistent with the results from NMMAPS and Canadian multicity studies. The risk estimates for total,
cardiovascular, and respiratory causes were comparable. In the two-pollutant models with black smoke,
PMio, SO2, and O3, the risk estimates for total and cardiovascular mortality were not affected. The
second-stage analysis examined possible effect modifiers. For total and cardiovascular mortality, the
geographical area (defined as western, southern, and central eastern European cities) was the most
important effect modifier (estimates were lower in eastern cities), followed by smoking prevalence (NO2
risk estimates were higher in cities with a lower prevalence of smoking). The authors concluded that the
3-51
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results showed effects of NO2 on mortality, but that the role of NO2 as a surrogate of other unmeasured
pollutants could not be completely ruled out.
In an earlier study, Katsouyanni et al. (2001; reanalysis, 2003) analyzed data from 29 European
cities and reported risk estimates for PMi0 and not for NO2, but found that the cities with higher NO2
levels tended to have larger PMi0 risk estimates. Furthermore, simultaneous inclusion of PMi0 and NO2
reduced the PMi0 risk estimate by half. An analysis of the elderly mortality in 28 of the same cities (Aga
et al., 2003) also found a similar effect modification of PM by NO2. Thus, PM and NO2 risk estimates in
these European cities may be reflecting the health effects of the same air pollution source and/or act as
effect modifiers of each other.
3.3.1.4. The Netherlands Study
While the Netherlands studies for the 1986 to 1994 data (Hoek et al., 2000, 2001; reanalysis in
Hoek, 2003) are not multicity studies and the Netherlands data were also analyzed as part of APHEA2
(Samoli et al., 2006), the results from the reanalysis (Hoek, 2003) are discussed here, because the
database comes from a large population (14.8 million for the entire country) and a more extensive
analysis was conducted than in the multicity studies. PMi0, black smoke, O3, NO2, SO2, CO, sulfate
(SO42), and nitrate (NO3 ) were analyzed at lags 0, 1, and 2 days and the average of lags 0-6 days. All the
pollutants were associated with total mortality, and for single-day models, lag 1 day showed strongest
associations for all the pollutants. NO2 was most highly correlated with black smoke (r=0.87), and the
simultaneous inclusion of NO2 and black smoke reduced both pollutants' risk estimates (the NO2 estimate
was reduced by more than 50%). PMi0 was less correlated with NO2 (r=0.62), and the simultaneous
inclusion of these pollutants resulted in an increase in the NO2 risk estimate.
3.3.1.5. Other Multicity Studies
Other European multicity studies, conducted in eight Italian cities (Biggeri et al., 2005), nine
French cities (Le Tertre et al., 2002) and seven Spanish cities (Saez et al., 2002) provide evidence for a
short-term NO2 effect on mortality. An additional multicity study was conducted in Australian cities
(Simpson et al., 2005b). The studies by Biggeri et al. (2005) and Simpson et al. (2005b) are summarized
in this section. The studies by Le Tertre et al. (2002) and Saez et al. (2002), conducted using GAM
methods with the default convergence setting, are presented in Annex Table AX6.3-19.
Biggeri et al. (2005) analyzed eight Italian cities (Turin, Milan, Verona, Ravenna, Bologna,
Florence, Rome, and Palermo) from 1990 to 1999. Only single-pollutant models were examined in this
study. Statistically significant positive associations were observed between NO2 and total, cardiovascular,
and respiratory mortality, with the largest effect estimate observed for respiratory mortality. Since all the
pollutants showed positive association and the correlations among the pollutants were not presented, it
was not clear how much of the observed associations are shared or confounded. The mortality risk
estimates were not heterogeneous across cities for all the gaseous pollutants.
Simpson et al. (2005b) analyzed data from four Australian cities (Brisbane, Melbourne, Perth, and
Sydney) using methods similar to the APHEA2 approach. They also examined sensitivity of results to
alternative regression models. Associations between mortality and NO2, O3, and nephelometer readings (a
measure of PM) were examined at single-day lag 0, 1,2, and 3 days and using the average of 0- and 1-day
lags. Among the three pollutants, correlation was strongest between NO2 and nephelometer readings,
ranging from (r -0.62 among the four cities). Of the three pollutants, NO2 showed the largest mortality
risk estimates per interquartile range. Similar to the study by Biggeri et al. (2005), the strongest
association was observed between NO2 and respiratory mortality, compared to total or cardiovascular
mortality. The three alternative regression models yielded similar results. The NO2 risk estimates were not
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sensitive to the addition of nephelometer readings in the two-pollutant models for total mortality, but the
nephelometer risk estimate was greatly reduced in the model with NO2.
3.3.1.6. M eta-Analyses of N02 Mortality Studies
Stieb et al. (2002) reviewed time-series mortality studies published between 1985 and 2000, and
conducted a meta-analysis to estimate combined effects for each of PMi0, CO, NO2, O3, and SO2. Since
many of the studies reviewed in that analysis were affected by the GAM convergence issue, Stieb et al.
(2003) updated the estimates by separating the GAM versus non-GAM studies and by single- versus
multipollutant models. There were more GAM estimates than non-GAM estimates for all the pollutants
except SO2. For NO2, there were 11 estimates from single-pollutant models and only 3 estimates from
multipollutant models. The lags and multiday averaging used in these estimates varied. The combined
estimate for total mortality was 0.8% (95% CI: 0.2, 1.5) per 20-ppb increase in the daily average NO2
from the single-pollutant models and 0.4% (95% CI: -0.2, 1.1) per 20-ppb increase in the 24-h average
from the multipollutant models. Note that, although the estimate from the multipollutant models was
smaller than that from the single-pollutant models, the number of the studies for the multipollutant models
was small (3), also, the data extraction procedure of this meta-analysis for the multipollutant models was
to extract from each study the multipollutant model that resulted in the greatest reduction in risk estimate
compared with that observed in single-pollutant models. It should be noted that all the multicity studies
whose combined estimates have been discussed above were published after this meta-analysis.
3.3.2. Summary of Mortality Related to Short-Term Exposure
The epidemiologic evidence on the effect of short-term exposure to NO2 on total nonaccidental and
cardiopulmonary mortality was suggestive but not sufficient to infer a causal relationship. The
epidemiologic studies were generally consistent in reporting positive associations. However, there was
little evidence available to evaluate coherence and plausibility for the observed associations, particularly
for cardiovascular and total mortality.
In the short-term exposure studies, the range of NO2 total mortality risk estimates is 0.5 to 3.6% per
20-ppb increase in the 24-h average NO2 or 30-ppb increase in daily 1-h max (Figure 3.3-2). The use of
various lag periods, averaging days, and distributed lags did not appear to alter the estimates substantially.
The heterogeneity of estimates in these studies may be due to several factors, including the differences in
(1) model specification, (2) NO2 levels, and (3) effect modifying factors. Interestingly, the Canadian 12-
city study showed combined risk estimates (average of 0-1 day or single 1-day lag) about 4 times larger
than that for the U.S. estimate, despite the fact that the range of Canadian NO2 concentrations (10 to 26
ppb) was somewhat lower than that for the U.S. data (9 to 39 ppb for the 10%-trimmed data). In fact, the
NMMAPS estimate is the smallest among the multicity studies, a pattern appearing in PMi0 mortality risk
estimates (U.S. Environmental Protection Agency, 2004). Thus, it is possible that this may be due to the
difference in model specifications.
Several multicity studies provided risk estimates for broad cause-specific categories (typically all-
cause, cardiovascular, and respiratory) using consistent lags/averaging for broad causes (cardiovascular
and respiratory), but the patterns were not always consistent. This inconsistency was likely due to smaller
sample size, or the lags reported not being consistent across the specific causes examined (Figure 3.3-3).
While the smaller multicity studies (the Italian and Australian studies) reported larger risk estimates for
respiratory mortality, the larger Canadian and APHEA2 studies reported comparable risk estimates among
the broad specific causes of deaths. In addition, since other pollutants also showed similar associations
with these causes or categories, it is difficult to discuss consistency with causal inference that is specific
to NO2. The multipollutant models in these studies generally did not alter NO2 risk estimates, except for
the Netherlands study in which NO2 was highly correlated with the copollutant black smoke. While the
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multipollutant results generally indicated a lack of confounding, it was difficult to attribute the observed
excess mortality risk estimates to NO2 alone.
U.S. 90 cities study (Dominici et al., 2003}
24-hr average, lag 1 day
with PMioandSOa
Canadian 12 cities study (Burnett et al., 2004)
24-hr average, average of lag 0-2 days
withO3
24-hr average, lag 1 day (every-6th-day)
with PM2.5
European 30 cities study (Samoll et al., 2006)
1-hr daily max.average of lag 0-1 days
with SO2
Italian 8 cities study (Biggerl et al., 2005)
24-hr average, aver age of lag 0-1 days
The Netherlands study (Hock. 2003)
24-hr average, lag 1 day
average of lag 0-6 days
with black smoke
Australian 4 cities study (Simpson et al., 2005)
1 -hr daily max.average of lag 0-1 days
with fine particles by nephelometer
Meta-analysis (Stieb et al.. 2003}
24-hr average, lag and multi-day averages mixed
with co-pollutants that showed largest reduction
Percent excess mortality
0246
Figure 3.3-2. Combined NOz mortality risk estimates from multicity and meta-analysis studies. Risk
estimates are computed per 20-ppb increase for 24-h average or 30-ppb increase for 1-h daily
max NC-2 concentrations. For multipollutant models, results from the models that resulted in
the greatest reduction in NO? risk estimates are shown.
While the multicity studies examining the relationship between short-term NO2 exposure and
mortality observed statistically significant associations for total, cardiovascular, and respiratory causes,
the issue of surrogacy of the role of NO2 and possible interactions with PM and other pollutants remained
unresolved. As reviewed in earlier sections, human clinical studies, by necessity, were restricted to acute,
fully reversible functional and/or symptomatic responses in healthy or mildly asthmatic subjects. A
number of animal studies (described in Section 3.1.3) have shown biochemical, lung host defense,
permeability, and inflammation effects with acute exposures and may provide weak biological plausibility
for mortality in susceptible individuals. A 5 ppm NO2 exposure for 24 h in rats caused increases in blood
and lung total GSH and a similar exposure resulted in impairment of alveolar surface tension of surfactant
3-54
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phospholipids due to altered fatty acid content. A fairly large body of literature described the effects of
NO2 on lung host defenses at low exposures. However, most of these effects were seen only with
subchronic or chronic exposure and, therefore, do not correlate well with the short lag times evidenced in
the epidemiologic studies. The results from several large U.S. and European multicity studies and a meta-
analysis study observed positive associations between ambient NO2 concentrations and risk of all-cause
(nonaccidental) mortality, with effect estimates ranging from 0.5 to 3.6% excess risk in mortality per
standardized increment (Section 3.3.2, Figure 3.3-2). In general, the NO2 effect estimates were robust to
adjustment for copollutants. Both cardiovascular and respiratory mortality have been associated with
increased NO2 concentrations in epidemiologic studies (Figure 3.3-3); however, similar associations were
observed for other pollutants, including PM and SO2. The range of mortality excess risk estimates was
generally smaller than that for other pollutants such as PM.
Canadian 12 cities study _
(Burnett et al., 2004) -
Avg. 0-2 days -
European 30 cities study
(Samoll et al., 2006)
Avg. 0-1 days
Italian 8 cities study
(Bigger! et al., 2005)
Avg. 0-1 days
Australian 4 cities study -
(Simpson et al., 2005b) -
Avg. 0-1 days -
Percent Excess Mortality
0 2 4 6 8 10
_| _ | _ | _ | _ | _ |
12
-x-
• Total
^ Cardiovascular
X Respiratory
Figure 3.3-3. Combined N02 mortality risk estimates for broad cause-specific categories from multicity
studies. Risk estimates are computed per 20-ppb increase for 24-h avg or 30-ppb increase for
1-h daily max N02 concentrations.
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3.4. Respiratory Effects Related to Long-Term Exposure
There was no epidemiologic evidence available in the 1993 AQCD on the respiratory effects of
long-term exposure (>2 weeks) to ambient NO2. The 1993 AQCD reported that chronic exposure to high
NO2 levels (>8 ppm) caused emphysema in several animal species. Since the 1993 AQCD, a number of
studies reported associations between long-term NO2 exposure and respiratory effects (see Annex Tables
AX6.3-15, AX6.3-16, and AX6.3-17).
While NO2 exposure, alone or in conjunction with other pollutants, may contribute to increased
mortality, evaluation of the biological plausibility of this effect was difficult. Clinical studies showing
hematologic effects and animal toxicological studies showing biochemical, lung host defense,
permeability, and inflammation changes with short-term exposures to NO2 provided limited evidence of
plausible pathways by which risks of morbidity and, potentially, mortality may be increased, but no
coherent picture is evident at this time.
3.4.1. Lung Function Growth
Studies of lung function demonstrate some of the strongest effects of long-term exposure to NO2.
Recent cohort studies have examined the effect of long-term exposure to NO2 in both children and adults
(see Annex Table AX6.3-15). Forest plots of the results for FEVi and FVC from three major children's
cohort studies (Gauderman et al., 2004; Rojas-Martinez et al., 2007a,b; Oftedal et al., 2008) are presented
in Figures 3.4-1 and 3.4-2.
The Children's Health Study (CHS) in southern California was a longitudinal cohort study
designed to investigate the effect of chronic exposure to several air contaminates (including NO2) on
respiratory health in children. Twelve California communities were selected based on historical data
indicating different levels of specific pollutants. In each community, monitoring sites were set up to
measure hourly O3, NO2, and PM10 and 2-week averages of PM25, and acid vapor. Children in grades 4, 7,
and 10 were recruited though local schools. The study followed children for 10 years, with annual
questionnaires and lung function measurement. The study had several important characteristics: it was
prospective and exposure and outcome data were collected in a consistent manner over the duration of the
study, and confounding by SES was controlled in the models by selecting communities similar in
demographic characteristics at the outset.
Peters et al. (1999) reported the initial results from the CHS: a cross-sectional analysis of lung
function tests conducted on 3,293 children in the first year of the study. Both NO2 and PM10 were
associated with decreases in FVC, FEVi, and MMEF. Avol et al. (2001) then studied the effect of
relocating to areas of differing air pollution levels in 110 children 10 years of age who were participating
in the CHS. As a group, subjects who had moved to areas of lower NO2 showed increased growth in lung
function, but the effects did not reach statistical significance. In general, the authors focused on
associations with PM, where larger and statistically significant effects were observed.
In 2004, Gauderman et al. reported results for an 8-year follow up of the children enrolled in
grade 4 (n=l,759). Exposure to NO2 was significantly associated with deficits in lung growth over the
8-year period. The difference in FVC for children exposed to the lowest versus the highest levels of NO2
(34.6 ppb) was -95.0 mL (95% CI: -189.4 to -0.6). For FEVj, the difference was -101.4 mL (95% CI:
-165.5 to -38.4), and for MMEF, -221.0 mL/s (95% CI: -377.6, -44.4). Results were similar for boys and
girls and among children without a history of asthma. These deficits in growth of lung function resulted in
clinically significant differences in FEVi at age 18. In addition, the NO2 concentration associated with
deficits in lung function growth was 34.6 ppb (range of means across communities: 4.4-39.0 ppb), a level
below the current standard. Similar results were reported for acid vapor (resulting primarily from
photochemical conversions of NOX to HNO3). These results are depicted in Figure 3.4-3. The authors
3-56
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concluded that the effects of NO2 could not be distinguished from the effects of particles (PM2 5 and
) as NO2 was strongly correlated with these contaminants (0.79, and 0.67, respectively).
Study
Gaudermann
(2004)
Oftedal (2008)
Oftedal (2008)
Oftedal (2008)
Oftedal (2008)
Oftedal (2008)
Oftedal (2008)
Rojas Martinez
(2007a,b)
Rojas Martinez
(2007a,b)
Location
Southern
California
Oslo
Oslo
Oslo
Oslo
Oslo
Oslo
Mexico City
Mexico City
Gender Period Baseline age
Both 1993-2001 8 1759-
Both 1991-1992 Istyroflife 1847 -
Boys 1991-1992 Istyroflife 938-
Girls 1991-1992 Istyroflife 909'
Both 1992-2002 9-10 1847 -
Boys 1991-2002 9-10
Girls 1991-2002 9-10
Boys 1996-1999 10
Girls 1996-1999 10
938-
909-
1103-
1115 -
I I I I I
-25 -20 -15 -10 -5 0 5 10
FEV, (ml_) per 20 ppb N02 per year
Study
Gaudermann
(2004)
Oftedal (2008)
Oftedal (2008)
Oftedal (2008)
Oftedal (2008)
Oftedal (2008)
Oftedal (2008)
Rojas Martinez
(2007a,b)
Rojas Martinez
(2007a,b)
Location
Southern
California
Oslo
Oslo
Oslo
Oslo
Oslo
Oslo
Mexico City
Mexico City
Gender
Both
Both
Boys
Girls
Both
Boys
Girls
Boys
Girls
Period
1993-2001
1991-1992
1991-1992
1991-1992
1992-2002
1992-2002
1992-2002
1996-1999
1996-1999
Baseline age
8
Istyroflife
Istyroflife
Istyroflife
9-10
9-10
9-10
10
10
N
1759-
1847 -
938 -
909 -
1847-
938-
909-
1103-
1115 -
J HI
4
I
ff
1
-100 -75 -50 -25 0 25 50 75 100
FEV|(mL)per20Mg/mLofPM10 per year
Source: Gauderman et al. (2004); Oftedal et al. (2008), Rojas-Martinez et al. (2007a,b).
Figure 3.4-1. Decrements in forced expiratory volume in 1 s (FEVi) associated with a 20-ppb increase in
N02 (A) and a 20-|jg/m3 increase in PM™ (B) in children, standardized per year of follow-up.
Results from three major children's long-term cohort studies are presented.
3-57
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Study Location
Gauderrnann (2004) Southern
California
Oftedal (2008) Oslo
Oftedal (2008) Oslo
Oftedal (2008) Oslo
Oftedal (2008) Oslo
Oftedal (2008) Oslo
Oftedal (2008) Oslo
Rojas Martinez (2007a,b) Mexico City
Rojas Martinez (2007a,b) Mexico City
Gaudermann (2004) Southern
California
Oftedal (2008) Oslo
Oftedal (2008) Oslo
Oftedal (2008) Oslo
Oftedal (2008) Oslo
Oftedal (2008) Oslo
Oftedal (2008) Oslo
Rojas Martinez (2007a,b) Mexico City
Rojas Martinez (2007a,b) Mexico City
Gender
Both
Both
Boys
Girls
Both
Boys
Girls
Boys
Girls
Both
Both
Boys
Girls
Both
Boys
Girls
Boys
Girls
Period Baseline N
age
1993-2001 8 1759 -
1991-1992 1st yr of life 1847 -
1991-1992 1st yr of life 938 -
1991-1992 1st yr of life 909 -
1992-2002 9-10 1847 -
1991-2002 9-10 938 -
1991-2002 9-10 909 -
1996-1999 10 1103 -
1996-1999 10 1115 •
-3
H
f
ft
}
-•-
^_
i i i i i i i i i i
5 -30 -25 -20 -15 -10 -5 0 5 10 15 20
FVC (ml) per 20 ppb N02 per year
1993-2001 8 1759 -
1991-1992 Istyroflife 1847 -
1991-1992 Istyroflife 938 -
1991-1992 Istyroflife 909 -
1992-2002 9-10 1847 -
1992-2002 9-10 938 -
1992-2002 9-10 909 -
1996-1999 10 1103 -
1996-1999 10 1115 '
-4 H
1
J
:-
fl
1
-100 -75 -50 -25 0 25 50 75 100
FVC (mL) per 20 (jg/mL of PM10 per year
Source: Gauderman et al. (2004); Oftedal et al. (2008), Rojas-Martinez et al. (2007a,b).
Figure 3.4-2. Decrements in FVC associated with a 20-ppb increase in NC-2 (A) and a 20-|jg/m3 increase in
PMio (B) in children, standardized per year of follow-up. Results from three major children's
long-term cohort studies are presented.
3-58
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£8
•a
£
£
0.
° o
o 2
oo
i°
LU
R = 0,04
P = 0.89
»UP
SD
*SM
*AT
LM
LN
25 35 45 55 65 75
O3 from 10 a.m. to 6 p.m. (ppb)
N02 (ppb)
*UP
468
Acid Vapor (ppb)
10
12
10
8
6
4
2
R = 0.79
P = 0.002
SM
*I_E
LN
10
15 20
PM2.5(Mg/m3)
25
30
0
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4
Elemental Carbon (|jg/m:*)
Source: Derived from Gauderman et al. (2004).
Figure 3.4-3. Proportion of 18-year olds with a FEVi below 80% of the predicted value plotted against the
average levels of pollutants from 1994 through 2000 in the 12 southern California
communities of the Children's Health Study.
AL=Alpine; AT=Atascadero; LA=Lake Arrowhead; LB=Long Beach; LE=Lake Elsinore; LM=Lompoc; LN=Lancaster;
ML=Mira Loma; RV=Riverside; SD=San Dimas; SM=Santa Maria; UP=Upland
3-59
-------
O, (Girls)
3.2
2.9
Six Month mean concentrations
64.3,69.3, 75.7 ppb
56.42, 67.63,92.22 (Jg/m3
28.92, 34.57 40.85 ppb
- « p25
-• pSO
• A - - - p75
3.2
fc 2.9
03 (Boys)
p25
pSO
p75
Adjust with PMIO and NO2
Adjust with PM10 and NO2
PM10 (Girls)
PM10 (Boys)
Adjust with O3 and NO2
Adjust with O3 and NO2
NO, (Girls)
NO, (Boys)
_ .»_ . P25
P50
*- - p75
Adjust with O3 and PM|0
34567
Phase
Adjust with O3 and PM10
Source: Derived from Rojas-Martinez et al. (2007a,b).
Figure 3.4-4.
Estimated annual growth in FEVi, of Os, PMio, and NOa in girls and boys. Mexico City, 1996 to
1999 (multipollutant models). Adjusted for age, body mass index, height, height by age,
weekday time spent in outdoor activities, environmental tobacco smoke exposure, pervious-
day mean air pollutant concentration, and study phase of every six months.
More recently, Gauderman et al. (2007) has reported results of an 8-year follow-up on 3,677
children who participated in the CHS. Children living <500 m from a freeway (n=440) had significant
deficits in lung function growth over the 8-year follow-up compared to children who lived at least 1500 m
from a freeway. The difference in FVC was -63 mL (-131 to 5); the difference in FEVi -81 mL (-143 to
-18); and the difference in MMEF -127 mL/s (-243 to -11). This study did not attempt to measure specific
pollutants near freeways or to estimate exposure to specific pollutants for study subjects. Thus, while the
3-60
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study presented important findings with respect to traffic pollution and respiratory health in children, it
did not provide evidence that NO2 was responsible for these deficits in lung function growth.
Further evaluation of exposure estimation was done in this cohort of schoolchildren (Molitor et al.,
2007). Several models of interurban air pollution exposure were used to classify and predict FVC in an
integrated Bayesian modeling framework using three interurban predictors: distance to a freeway, traffic
density, and predicted average NO2 exposure from the California line source dispersion (CALINE4)
model. Results indicated that the inclusion of residual spatial terms can reduce uncertainty in the
prediction of exposures and associated health effects.
In Mexico City, Rojas-Martinez et al. (2007a,b) evaluated the association between long-term
exposure to PMi0, O3, and NO2 and lung function growth in a cohort of 3,170 children aged 8 years at
baseline in 31 schools from April 1996 through May 1999. Ten air-quality monitoring stations within
2 km of the schools provided exposure data, cthe results for FEVi, by gender and pollutant with
adjustments noted for copollutants. The results of this 3-year study supported the hypothesis that long-
term exposure to ambient air pollutants is associated with deficit in lung function growth in children. The
results were, in part, consistent with previous results from the CHS. Similar to the CHS, the high
correlation among the three pollutants studied did not allow independent effects to be accurately
estimated in this long-term exposure study.
Another cohort study in Oslo, Norway, examined short- and long-term NO2 and other pollutant
exposure effects on lung function (PEF, forced expiratory flow at 25% of forced vital capacity [FEF25],
forced expiratory flow at 50% of forced vital capacity [FEF50]) in 2,307 nine- and ten-year-old children
(Oftedal et al., 2008). The EPISODE dispersion model (Slordal et al., 2003) was used for the exposure
estimate and evaluation concluded that the modeled NO2 and PM levels represent the long- and short-term
exposure reasonably well. An incremental change equal to the IQR of lifetime exposure to NO2, PMi0,
and PM25 was associated with changes in adjusted peak flow of-79 mL/s (95% CI: -128,-31), -66 mL/s
(95% CI: -110, -23), and -58 mL/s (95% CI: -94, -21), respectively. Examining short- and long-term NO2
exposures simultaneously yielded only the long-term effects. Adjusting for a contextual socioeconomic
factor diminished the association. The association between long-term exposure to NO2 and decreased PEF
was comparable to that found in the CHS, but associations with forced volumes were considerably
weaker.
In another European study, Moseler et al. (1994) measured NO2 outside the homes of 467 children,
including 106 who had physician-diagnosed asthma, in Freiburg, Germany. Five of six lung function
parameters were reduced among asthmatic children exposed to NO2 at concentrations of >21 ppb. No
significant reductions in lung function were detected among children without asthma.
To examine the effect of lifetime exposure to air pollutants in young adults, lung function in
students attending the University of California (Berkeley) who had been lifelong residents of the Los
Angeles or San Francisco areas was assessed (Tager et al, 2005). Using geocoded address histories, a
lifetime exposure to air pollution was constructed for each student. Increasing lifetime exposure to NO2
was associated with decreased FEF75 and FEF25_75. In models including O3 and PMi0 as well as NO2, the
effect of NO2 diminished significantly while the O3 effect remained robust.
The SAPALDIA (Study of Air Pollution and Lung Diseases in Adults) study (Ackermann-Liebrich
et al., 1997) compared 9,651 adults (age 18 to 60) in eight different regions in Switzerland. Significant
associations of NO2, SO2, and PMi0 with FEVi and FVC were found with a 10-(ig/m3 (5.2 ppb) increase
in annual average exposure. Due to the high correlations between NO2 and the other pollutants (SO2:
r=0.86; PMi0: r=0.91), it was difficult to assess the effect of a specific pollutant. A random subsample of
560 adults from SAPALDIA recorded personal measurements of NO2 and measurements of NO2 outside
their homes (Schindler et al., 1998). Using the personal and home measurements of NO2, similar
associations were reported between NO2 with FEVi and FVC. Downs et al. (2007) reported the
relationship in this group of long-term reduced exposure to PMi0 and age-related decline in lung function,
but they did not examine NO2 or other pollutants.
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Goss et al. (2004) examined the relationship of ambient pollutants on individuals with cystic
fibrosis using the Cystic Fibrosis Foundation National Patient Registry in 1999 and 2000. Exposure was
assessed by linking air pollution values from the Aerometric Information Retrieval System with the
patient's home ZIP code. Associations were reported between PM and exacerbations or lung function
changes, but no clear associations were found for O3, SO2, NO2, or CO. The odds of patients with cystic
fibrosis having two or more pulmonary exacerbations per 10-ppb NO2 was 0.98 (95% CI: 0.91, 1.01) for
the year 2000.
A number of epidemiologic studies examined the effects of long-term exposure to NO2 and
observed associations with decrements in lung function and partially irreversible decrements in lung
function growth. Decreases in FEVi ranged from 1 to 17.5 per 20 ppb increase in annual NO2
concentration. Results from the Southern California Children's Health Study indicated that decrements
were similar for boys compared to girls, and among children who did not have a history of asthma
(Gauderman et al., 2004). The mean NO2 concentrations in these studies range from 21.5 to 34.6 ppb;
thus, all have been conducted in areas where mean NO2 levels are below the level of the NAAQS. The
epidemiologic studies of long-term exposure to NO2, however, may be confounded by other ambient
copollutants. In particular, similar associations have also been found for PM and proximity to traffic
(<500 m). The results of the CHS study support an association between decreased lung function growth
and a mixture of traffic-related pollutants, but as observed by the authors, it is difficult to distinguish
effects for NO2 and other individual pollutants within these mixtures (Gauderman et al., 2004).
3.4.2. Asthma Prevalence and Incidence
Several publications from the CHS in southern California reported results on the associations of
NO2 exposure with asthma prevalence and incidence. Gauderman et al. (2005) conducted a study of
children randomly selected from the CHS with exposure measured at children's homes. Although only
208 were enrolled, exposure to NO2 was strongly associated with both lifetime history of asthma and
asthma medications use. Gauderman et al. (2005) measured ambient NO2 with Palmes tubes attached to
the subjects' homes at the roofline eaves, signposts, or rain gutters at an approximate height of 2 m above
the ground. Samplers were deployed for 2-week periods in both summer and fall. Traffic-related
pollutants were characterized by three metrics: (1) proximity of home to freeway, (2) average number of
vehicles within 150 meters, and (3) model-based estimates. Yearly average NO2 levels within the
10 communities ranged from 12.9 to 51.5 ppb. The average NO2 concentration measured at home was
associated with asthma prevalence (OR=8.33 [95% CI: 1.15, 59.87] per 20-ppb increase) with similar
results by season and when taking into account several potential confounders. In each community studied,
NO2 was more strongly correlated with estimates of freeway-related pollution than with non-freeway-
related pollution. In a related CHS study, McConnell et al. (2006) studied the relationship of proximity to
major roads and asthma and also found a positive relationship.
Islam et al. (2007) studied whether lung function is associated with new onset asthma and whether
this relationship varies by exposure to ambient air pollutants by examining a cohort of 2,057 fourth-grade
children who were asthma- and wheeze-free at the start of the CHS and following them for 8 years. A
hierarchal model was used to evaluate the effect of individual air pollutants (NO2, PMi0, PM2 5, and acid
vapor, NO2, EC, and OC) on the association of lung function with asthma. This study showed that better
airflow, characterized by higher FEF25_75 and FEVi during childhood was associated with decreased risk
of new-onset asthma during adolescence. However, exposure to high levels of ambient pollutants (NO2
and others) attenuated this protective association of lung function on asthma occurrence.
Millstein et al. (2004) studied the effects of ambient air pollutants on asthma medication use and
wheezing among 2,034 fourth-grade schoolchildren from the CHS. Included in the pollutants examined
were NO2 and HNO3. They observed that monthly average pollutant levels produced primarily by
photochemistry (i.e., HNO3, acetic acid), but not NO2, were indicative of a positive association with
asthma medication use among children with asthma—especially among children who spent more than the
3-62
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calculated median time outdoors. The March-August OR for HNO3 (IQR 1.64 ppb) was 1.62 (95% CI:
0.94, 2.80) and forNO2 (IQR 5.74 ppb), 0.96 (95% CI: 0.68, 1.37).
Kim et al. (2004a) reported associations with both NO2 and NOX for girls in the San Francisco bay
area. They studied 1,109 students (grades 3 to 5) at 10 school sites for bronchitis symptoms and asthma in
relation to ambient pollutant levels to include NO, NO2, and NOX measured at the school site. Mean
levels ranged for schools from 33 to 69 ppb for NOX; 19 to 31 for NO2; and 11 to 38 ppb for NO. NOX
and NO2 measurements at school sites away from traffic were similar to levels measured at the regional
site. They found associations between traffic-related pollutants and asthma and bronchitis symptoms,
which was consistent with previous reports of traffic and respiratory outcomes. The higher effect
estimates with black carbon, NOX, and NO compared with NO2 and PM2 5 indicated that primary or fresh
traffic emissions may play an etiologic role in these relationships and that, while NOX and NO may serve
as indicators of traffic exposures, they also may act as etiologic agents themselves.
Brauer et al. (2007) assessed the development of asthmatic/allergic symptoms and respiratory
infections during the first 4 years of life in a birth cohort study in the Netherlands (n=4,000, but the
number of participants decreased over the study to -3,500). Air pollution concentrations at the home
address at birth were calculated by a validated model combining air pollution measurements with a
Geographic Information System (GIS). Wheeze, physician-diagnosed asthma, and flu and serious colds
were associated with air pollutants (considered traffic-related: NO2, PM2 5, soot) after adjusting for other
potential confounding variables; for example, NO2 was associated with physician-diagnosed asthma
(OR=1.28 [95% CI: 1.04, 1.56]) as a cumulative lifetime indicator. In comments to this study, Jerrett
(2007) observed that the effects were larger and more consistent than in participants of the same study at
age 2 and that these effects showed that onset and persistence of respiratory disease formation begins at
an early age and continues. He further noted that the more sophisticated method for exposure assessment
based on spatially and temporally representative field measurements and land use regression was capable
of capturing small area variations in traffic pollutants.
Other studies (see Annex Table AX6.3-16) also have investigated asthma prevalence and incidence
in children associated with NO2 exposure. Although several of these studies have reported positive
associations, the large number of comparisons made and the limited number of positive results do not
support a strong relationship between long-term NO2 exposure and asthma. Several studies used the
International Study of Asthma and Allergies in Children (ISAAC) protocol. Children were interviewed in
school and results of the questionnaire were compared with air pollution measurements in their
communities. These studies included thousands of children in several European countries and Taiwan,
and the results in all but one study were nonsignificant. Exposure in these studies varied, but medians
were often greater than 20 ppb. Most of the studies did not report correlations of NO2 exposure with other
air pollutants; therefore, it is not possible to determine whether some of these associations were related to
other air contaminants.
Overall, results from the available epidemiologic evidence investigating the association between
long-term exposure to NO2 and increases in asthma prevalence and incidence are inconsistent. Two major
cohort studies, the Children's Health Study in southern California (Gauderman et al., 2005) and a birth
cohort study in the Netherlands (Brauer et al., 2007) observed significant associations; however, several
other studies did not find consistent associations between long-term NO2 exposure and asthma outcomes.
3.4.3. Respiratory Symptoms
Annex Table AX6.3-17 lists studies examining the association between long-term exposure to NO2
and respiratory symptoms. Most of the studies reported some positive associations with NO2 exposure
and symptoms, but all reported a large number of negative results. Only one of these studies (Peters et al.,
1999b) reported an association of NO2 exposure with wheeze, and in boys. This was despite the fact that
wheeze was investigated in a large number of studies, including several studies that included thousands of
children.
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McConnell et al. (2003) studied the relationship between bronchitis symptoms and air pollutants in
the CHS. Symptoms assessed yearly by questionnaire from 1996 to 1999 were associated with the yearly
variability for the pollutants forNO2 (OR= 1.071 [95% CI: 1.02, 1.13). In two-pollutant models, the
effects of yearly variation in NO2 were only modestly reduced by adjusting for other pollutants except for
the model containing both OC and NO2 (Figure 3.4-5). McConnell et al. (2006) further evaluated whether
the association of exposure to air pollution with annual prevalence of chronic cough, phlegm production,
or bronchitis was modified by dog or cat ownership indicators or allergen and endotoxin exposure.
Subjects consisted of 475 children from the CHS. Among children owning a dog, there was a strong
association between bronchitis symptoms and all pollutants studied. The odds ratio for NO2 was 1.49
(95% CI: 1.14, 1.95), indicating that dog ownership may worsen the relationship between air pollution
and respiratory symptoms in asthmatic children.
1.5 -i
.a
Q.
2 1 2 H
*J I .£.
re
TJ
o 1.0
0.9
Risk of Bronchitic Symptoms as a Function
of Yearly Deviation in NOa
Adjustment Air Pollutants
Source: McConnell etal. (2003).
Figure 3.4-5. Odds ratios for within-community bronchitis symptoms associations with N02, adjusted for
other pollutants in two-pollutant models for the 12 communities of the Children's Health
Study.
In the Netherlands (Brauer et al., 2002), the same protocol was used to estimate NO2 exposure in a
birth cohort of 3,730 infants. However, these study subjects lived in many different communities from
rural areas to large cities in northern, central, and western parts of the Netherlands. Forty sites were
selected to represent different exposures and measurements were taken as in the Gehring et al. (2002)
study. In this study, ear, nose, and throat infections (OR=1.16 [95% CI: 1.00, 1.34]) and physician-
diagnosed flu (OR=1.11 [95% CI: 1.00, 1.23]) were marginally significant. The association of NO2 with
dry cough at night observed in the German study could not be replicated, nor was NO2 associated with
asthma, wheeze, bronchitis, or eczema.
In both of these studies, the 40 monitoring sites set up to measure NO2 also measured PM2 5 with
Harvard Impactors. Estimates of NO2 and PM2 5 were highly correlated in Brauer et al. (r=0.97). The
correlation was not reported in Gehring et al. (2002); however, the similarity of odds ratios for each
pollutant showed that the estimated exposures were also highly correlated. Thus, a restricting factor of
these studies was the inability to distinguish the effects of different pollutants.
3-64
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In a study of 3,946 Munich schoolchildren, Nicolai et al. (2003) assessed traffic exposure using two
different methods. First, all street segments within 50 m of each child's home were identified and the
average daily traffic counts were totaled. Second, a model was constructed based on measurement of NO2
at 34 sites throughout the city using traffic counts and street characteristics (R2=0.77). The model was
then used to estimate NO2 exposure at each child's home address. When traffic counts of < 50m were
used as an exposure variable, a significant association was found with current asthma (OR=1.79 [95% CI:
1.05, 3.05]), wheeze (OR=1.66 [95% CI: 1.07, 2.57]), and cough (OR=1.62 [95% CI: 1.16, 2.27]). Similar
results were found when modeled NO2 exposure was substituted as the exposure variable (current asthma
OR=1.65 [95% CI: 0.94, 2.90], wheeze OR=1.58 [95% CI: 1.05, 2.48], cough OR=1.60 [95% CI: 1.14,
2.23]). Asthma, wheeze, and cough were also associated with estimated exposures to soot and benzene
derived from models, indicating that some component of traffic pollution was increasing risk of
respiratory conditions in children, but making it difficult to determine whether NO2 was the cause of these
conditions.
In summary, epidemiologic studies conducted in both the U.S. and Europe have observed
inconsistent results regarding an association between long-term exposure to NO2 and respiratory
symptoms. While some positive associations were noted, a large number of symptom outcomes were
examined and the results across specific outcomes were inconsistent.
3.4.4. Respiratory Morphology
Animal toxicology studies demonstrated morphological changes to the respiratory tract from
exposure to NO2 that may provide further biological plausibility for the decrements in lung function
growth observed in epidemiologic studies discussed above. Several investigators have studied the
temporal progression of early events due to NO2 exposure in the rat (e.g., Freeman et al., 1966, 1968,
1972; Stephens etal., 1971, 1972; Evans etal., 1972, 1973a,b, 1974, 1975, 1976, 1977; Cabral-Anderson
et al., 1977; Rombout et al., 1986) and guinea-pig (Sherwin and Carlson, 1973). The results of these
studies were summarized in the 1993 AQCD. Overall, animal toxicological studies demonstrated that NO2
exposure resulted in permanent alterations resembling emphysema-like disease, morphological changes in
the centriacinar region of the lung and in bronchiolar epithelial proliferation, which might provide
biological plausibility for the observed epidemiologic associations between long-term exposure to NO2
and respiratory morbidity.
3.4.5. Summary of Respiratory Effects Related to Long-Term Exposure
Overall, the epidemiologic and experimental evidence was suggestive but not sufficient to infer a
causal relationship between long-term NO2 exposure and respiratory morbidity. The available database
evaluating the relationship between respiratory illness in children associated with long-term exposures to
NO2 has increased. Three recent studies in large cohorts in three countries have examined this
relationship. The California-based CHS, examining NO2 exposure in children over an 8-year period,
demonstrated deficits in lung function growth (Gauderman et al., 2004). This has been observed also in
Mexico City, Mexico (Rojas-Martinez et al., 2007a,b), and in Oslo, Norway (Oftedal et al., 2008), with
decrements ranging from 1 to 17.5 ml per 20- ppb increase in annual NO2 concentration.
Deficit in lung function growth is a known risk factor for chronic respiratory disease and possibly
for premature mortality in later life stages. Lung growth continues from early development through early
adulthood, reaches a plateau, and then eventually declines with advancing age. Dockery and Brunekreef
(1996) hypothesized that the risk for chronic respiratory disease is associated with maximum lung size,
the length of time the lung size has been at the plateau, and the rate of decline of lung function. Therefore,
exposures to NO2 and other air pollutants in childhood may reduce maximum lung size by limiting lung
growth and subsequently increase the risk in adulthood for chronic respiratory disease.
3-65
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Models and/or mechanisms of action for decrements in lung function growth and other respiratory
effects from long-term exposure to air pollution are not clearly established. Figure 3.4-6 was adapted
from an earlier model discussed by Gilliland et al. (1999), reflective of efforts of the CHS research.
Gilliland et al. (1999) proposed that respiratory effects in children from exposure to gaseous and
particulate air pollutants result from chronically increased oxidative stress, alterations in immune
regulation, and repeated pathologic inflammatory responses that overcome lung defenses to disrupt the
normal regulatory and repair processes. Rojas-Martinez et al. (2007a,b) noted that oxidative stress
resulting from increased exposure to oxidized compounds (O3, NO2, and particle components) has been
identified as a major feature underlying the toxic effects of air pollutants (Kelly et al., 2003; Saxon and
Diaz-Sanchez, 2005; Cross et al., 2002). They further noted that the resulting increased expression of
enhanced proinflammatory cytokines leads to enhanced inflammatory response (Saxon and Diaz-Sanchez,
2005) and potential chronic lung damage. If this results in permanent loss, it is not clear whether repeated
versus average exposure is the major factor. Current data and the nonlinear pattern of childhood lung
function growth (Perez-Padilla et al., 2003) were noted by Rojas-Martinez et al. (2007a,b) as limitations
on estimating the impact on lung function attained in early adulthood.
Ambient NO,
I
Total
personal
exposure
to NO,
I
Indoor NO,
Dietary
antioxidants
Antioxidant
enzymes
Total
personal
dose
Oxidative/radical
damage
MPO
Asthma
AtopyTNFa
* V
Dietary PUFA Physical activity
->• Neutrophilic
inflammation
Tissue
damage
Lung function
growth
t Asthma
Source: Adapted from Gilliland et al. (1999).
Figure 3.4-6. Biological pathways of long-term N02 exposure on morbidity.
MPO=myeloperoxidase; PUFA=polyunsaturated fatty acids; TNF-a=tumor necrosis factor-alpha.
Other important biochemical mechanisms examined in animals may provide biological plausibility
for the chronic effects of NO2 observed in epidemiologic studies. The main biochemical targets of NO2
exposure appear to be antioxidants, membrane polyunsaturated fatty acids, and thiol groups. Reactions of
NO2 with these species in the extracellular lining fluid of the lung leads to the formation of nitrite (NO2~)
and hydrogen (FT1") ions. NO2 effects include changes in oxidant/antioxidant homeostasis and chemical
alterations of lipids and proteins. Lipid peroxidation has been observed at NO2 exposures as low as
0.04 ppm for 9 months and at exposures of 1.2 ppm for 1 week, indicating lower effect thresholds with
longer durations of exposure. Other studies showed decreases in formation of key arachidonic acid
metabolites in AMs following NO2 exposures of 0.5 ppm. NO2 has been shown to increase collagen
synthesis rates at concentrations as low as 0.5 ppm. This could indicate increased total lung collagen,
which is associated with pulmonary fibrosis, or increased collagen turnover, which is associated with
remodeling of lung connective tissue. Morphological effects following chronic NO2 exposures have been
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identified in animal studies that link to these increases in collagen synthesis and may provide plausibility
for the deficits in lung function growth described in epidemiologic studies.
An alternative explanation for the decrease in lung function growth observed in the CHS needs to
be considered. Since this response was associated with both NO2 and HNO3 exposure, ambient levels of
NO may also have been involved. Three groups have reported emphysematous changes in animal studies
following prolonged exposure to NO. In the Mercer study (1995), a decreased number of interstitial cells
and thinning of the alveolar septa was observed. Other studies in vitro and in animal models have
demonstrated that NO inhibits protein synthesis and cellular proliferation. Whether NO plays a role in
maintaining the alveolar interstitial compartment requires further investigation. Furthermore, the
formation of NO or NO-related species may have occurred following complex reactions of NO2 and
HNO3 with components of the extracellular lining fluid. The role of NO2", FT1", NO and other metabolites
in modulating responses to NO2 and/or HNO3 is unknown.
In regard to asthma prevalence incidence associated with NO2 long-term exposure, the results are
inconsistent. In two major cohort studies, the CHS in southern California and a birth cohort study in the
Netherlands, significant associations were reported; however, several other studies did not find consistent
associations between long-term NO2 exposure and asthma prevalence.
3.5. Other Morbidity Effects Related to Long-Term Exposure
This ISA includes a number of studies published since 1993 characterizing the effect of long-term
NOX exposure on cancer, CVD, reproductive, and developmental morbidity. These studies form a new
body of literature that was unavailable in 1993, when the previous AQCD was published.
3.5.1. Cancer Incidence
Two studies (see Annex Table AX6.3-18) investigated the relationship between NO2 exposure and
lung cancer and reported positive associations. Although this ISA concentrated on studies that measured
exposure to NO2, modeled exposures were considered for cancer studies. This was necessary because the
relevant exposure period for lung cancer may be 30 years or more.
Nyberg et al. (2000) reported results of a case control study of 1,043 men age 40 to 75 years with
lung cancer and 2,364 controls in Stockholm County. They mapped residence addresses to a GIS database
indicating 4,300 traffic-related line sources and 500 point sources of NO2 exposure. Exposure was
derived from a model validated by comparison to actual measurements of NO2 at six sites. Exposure to
NO2 at 10 (ig/m3 (5.2 ppb) was associated with an OR of 1.10 (95% CI: 0.97, 1.23) for lung cancer.
Exposure to the 90th percentile (> 29.26 (ig/m3 [15.32 ppb]) of NO2 was associated with an OR of 1.44
(95% CI: 1.05, 1.99).
Very similar results were reported in a Norwegian study (Nafstad et al., 2003). The study
population was a cohort of 16,209 men who enrolled in a study of CVD in 1972. The Norwegian cancer
registry identified 422 incident cases of lung cancer. Exposure data was modeled based on residence,
estimating exposure for each person in each year from 1974 to 1998. Exposure to 10 (ig/m3 (5.2 ppb) of
NO2 was associated with an OR of 1.08 (95% CI: 1.02, 1.15) for lung cancer; exposure of > 30 (ig/m3
(15.7 ppb) was associated with an OR of 1.36 (95% CI: 1.01, 1.83). However, controlling for SO2
exposure did appreciably change the effect estimates for NO2.
What is particularly striking in these two studies is the similarity in the estimate of effect. Despite
the fact that these two studies were conducted by different investigators, in different countries, using
different study designs and different methods for modeling exposure, the odds ratios and CI for exposure
per 10 (ig/m3 (5.2 ppb) and above 30 (ig/m3 (15.7 ppb) are virtually identical. It is possible that NO2 may
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be acting as an indicator of traffic-related carcinogens, and thus the observed increased cancer incidence
may be related to exposure of these carcinogens, such as PAHs.
3.5.1.1. Animal and In Vitro Carcinogenicity and Genotoxicity Studies
There is no clear evidence that NO2 or gaseous nitrogen oxides act as a complete carcinogen. No
studies were found on NO2 using classical carcinogenesis whole-animal bioassays. Of the existing studies
that evaluated the carcinogenic and cocarcinogenic potential of NO2, results were often unclear or
conflicting. Witschi (1988) critically reviewed some of the important theoretical issues in interpreting
these types of studies. NO2 appeared to act as a tumor promoter at the site of contact (i.e., in the
respiratory tract from inhalation exposure), possibly due to its ability to produce cellular damage and,
thus, promote regenerative cell proliferation. This hypothesis was supported by observed hyperplasia of
the lung epithelium from NO2 exposure (see Lung Morphology section, NOX AQCD, EPA, 1993), which
is a common response to lung injury, and enhancement of endogenous retrovirus expression (Roy-
Burman et al., 1982). However, these findings were considered by EPA (1993) to be inconclusive.
When studied using in vivo assays, no inductions of recessive lethal mutations were observed in
Drosophila exposed to NO2 (Inoue et al., 1981; Victorin et al., 1990). NO2 did not increase chromosomal
aberrations in lymphocytes and spermatocytes or micronuclei in bone marrow cells (Gooch et al., 1977;
Victorin et al., 1990). No increased stimulation of poly (ADP-ribose) synthetase activity (an indicator of
DNA repair, and possible DNA damage) was reported in AMs recovered from BAL of rats continuously
exposed to 1.2 ppm NO2 for 3 days (Bermudez, 2001).
NO2 has been shown to be positive when tested for genotoxicity in in vitro assays. NO2 is
mutagenic in bacteria and in plants. In cell cultures, three studies showed chromosomal aberrations, sister
chromatid exchanges (SCEs), and DNA single-strand breaks. However, a fourth study (Isomura et al.,
1984) concluded that NO, but not NO2, was mutagenic in hamster cells (see Annex Tables AX4.11,4.12,
and 4.13).
3.5.1.2. lexicological Studies of Coexposure with Known Carcinogens
Rats were injected with 7V-bis (2-hydroxy-propyl) nitrosamine (BHPN) and continuously exposed
to 0.04, 0.4, or 4 ppm NO2 for 17 months. Although the data indicated 5 times as many lung adenomas or
adenocarcinomas in the rats injected with BHPN and exposed to 4 ppm NO2 (5/40 compared to 1/10), the
results failed to achieve statistical significance (Ichinose et al., 1991). In a later study, Ichinose and Sagai
(1992) reported increased lung tumors in rats injected with BHPN, followed the next day by either clean
air (0%), 0.05 ppm NO2 (8.3%), 0.05 ppm NO2+0.4 ppm O3 (13.9%), or 0.4 ppm O3+l mg/m3 H2SO4
aerosol (8.3%) for 13 months, and then maintained for another 11 months until study termination.
Exposure to NO2 was continuous, while the exposures to O3 and H2SO4-aerosol were intermittent
(exposure for 10 h/day). The increased lung tumors from combined exposure of NO2 and O3 were
statistically significant.
Ohyama et al. (1999) coexposed rats to diesel exhaust particle extract-coated carbon black particles
(DEPcCBP) once a week for 4 weeks by intratracheal instillation and to either 6 ppm NO2, 4 ppm SO2, or
6 ppm NO2+4 ppm SO2 16 h/day for 8 months, and thereafter exposed to clean air for 8 months. Alveolar
adenomas were increased in animals exposed to DEPcCBP and either NO2 and/or SO2 compared to
animals in the DEPcCBP-only group and to controls. The incidences of lung tumors for the NO2, SO2,
and NO2 and/or SO2 groups were 6/24 (25%), 4/30 (13%), and 3/28 (11%), respectively. No alveolar
adenomas were observed in animals exposed to DEPcCBP alone or in the controls. Increased alveolar
hyperplasia was elevated in all groups compared to controls. In addition, DNA adducts, as determined by
32P postlabelling, were observed in the animals exposed to both DEPcCBP and either NO2 and/or SO2,
but not in animals exposed to DEPcCBP alone or controls. The authors concluded that the cellular
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damage induced by NO2 and/or SO2 may have resulted in increased cellular permeability of the
DEPcCBP particles into the cells.
3.5.1.3. Studies in Animals with Spontaneously High Tumor Rates
The frequency and incidence of spontaneously occurring pulmonary adenomas was increased in
strain A/J mice (with spontaneously high tumor rates) after exposure to 10-ppm NO2 for 6 h/day,
5 days/week for 6 months (Adkins et al., 1986). These small, but statistically significant, increases were
only detectable when the control response from nine groups (n=400) were pooled. Exposure to 1 and
5 ppm NO2 had no effect. In contrast, Richters and Damji (1990) found that an intermittent exposure to
0.25 ppm NO2 for up to 26 weeks decreased the progression of a spontaneous T cell lymphoma in
AKR/cum mice and increased survival rates. The investigators attributed this effect to an NO2-induced
decrease in the proliferation of T lymphocyte subpopulation in the spleen (especially T-helper/inducer
CD+ lymphocytes) that produces growth factors for the lymphoma. A study by Wagner et al. (1965)
showed that NO2 may accelerate the production of tumors in CAFl/Jax mice (a strain that has
spontaneously high pulmonary tumor rates) after continuous exposure to 5 ppm NO2. After 12 months of
exposure, 7/10 mice in the exposed group had tumors, compared to 4/10 in the controls. No differences in
tumor production were observed after 14 and 16 months of exposure. A statistical evaluation of the data
was not presented.
3.5.1.4. Facilitation of Metastases
Whether NO2 facilitates metastases has been the subject of several experiments by Richters and
Kuraitis (1981, 1983), Richters and Richters (1983), and Richters et al. (1985). Mice were exposed to
several concentrations and durations of NO2 and were injected intravenously with a cultured-derived
melanoma cell line (B16) after exposure, and subsequent tumors in the lung were counted. Although
some of the experiments showed an increased number of lung tumors, statistical methods were
inappropriate. Furthermore, the experimental technique used in these studies probably did not evaluate
metastases formation, as the term is generally understood, but more correctly, colonization of the lung by
tumor cells.
3.5.1.5. Production of N-Nitroso Compounds and other Nitro Derivatives
Because of evidence that NO2 could produce NO2" and NO3" in the blood and the fact that NO2" is
known to react with amines to produce animal carcinogens (nitrosamines), the possibility that NO2 could
produce cancer via nitrosamine formation has been investigated. Iqbal et al. (1980) were the first to
demonstrate a linear time- and concentration-dependent relationship between the amount of
TV-nitrosomorpholine (NMOR, an animal carcinogen) found in whole-mouse homogenates after the mice
were gavaged with 2 mg of morpholine (an exogenous amine that is rapidly nitrosated) and exposure to
15 to 50 ppm NO2 for between 1 and 4 h. In a follow-up study at more environmentally relevant
exposures, Iqbal et al. (1981) used dimethylamine (DMA), an amine that is slowly nitrosated to
dimethylnitrosamine (DMN). They reported a concentration-related increase in biosynthesis of DMN at
NO2 concentrations of as low as 0.1 ppm; however, the rate was significantly greater at concentrations
above 10 ppm NO2. Increased length of exposure also increased DMN formation between 0.5 and 2 h, but
synthesis of DMN was less after 3 or 4 h of exposure than after 0.5 h.
Mirvish et al. (1981) concluded that the results of Iqbal et al. (1980) were technically flawed, but
they found that in vivo exposure to NO2 could produce a nitrosating agent (NSA) that would nitrosate
morpholine only when morpholine was added in vitro. Further experiments showed that the NSA was
localized in the skin (Mirvish et al., 1983) and that mouse skin cholesterol was a likely NSA (Mirvish
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et al., 1986). It has also been reported that only very lipid-soluble amines, which can penetrate the skin,
would be available to the NSA. Compounds such as morpholine, which are not lipid-soluble, could only
react with NO2 when painted directly on the skin (Mirvish et al., 1988). Iqbal (1984), responded to the
Mirvish et al. (1981) criticisms, verifing their earlier (Iqbal et al., 1980) studies.
The relative contribution of NO2" from NO2 compared with other NO2" sources such as food,
tobacco, and nitrate-reducing oral bacteria is uncertain. Nitrosamines have not been detected in tissues of
animals exposed by inhalation to NO2 unless precursors to nitrosamines and/or inhibitors of nitrosamine
metabolism are coadministered. Rubenchik et al. (1995) could not detect TV-nitrosodimethylamine
(NDMA) in tissues of mice exposed to 7.5- to 8.5-mg/m3 NO2 for 1 h. NDMA was found in tissues,
however, 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 endogenously formed, and
food is also a contributing source of nitrite (from nitrate conversion).
3.5.1.6. Summary of Cancer Incidence Related to Long-Term Exposure
In summary, two epidemiologic studies conducted in Europe showed an association between long-
term NO2 exposure, used as an indicator of traffic exposure, and incidence of cancer (Nyberg et al., 2000;
Nafstad et al., 2003); however, the animal toxicological studies have provided no clear evidence that NO2
directly acts as a carcinogen, though it does appear to act as a tumor promoter at the site of contact
(Section 3.5.1). There are no in vivo studies that support that NO2 causes teratogenesis or malignant
tumors. Only very high exposure studies, i.e., levels not relevant to ambient NO2 levels, demonstrated
increased chromosomal aberrations and mutations in in vitro studies. A more likely pathway for NO2
involvement in cancer induction is through secondary formation of nitro-PAHs, as nitro-PAHs are known
to be more mutagenic than their parent compounds. The evidence for a causal relationship between NO2
and increased cancer risk is inadequate to infer the presence or absence of a causal relationship at this
time.
The information presented in this section is relevant to potential mechanisms by which exposure to
products formed by reaction of gaseous nitrogen oxides with organic compounds can be carcinogenic. As
discussed previously in Section 2.2, nitro-PAHs and other nitrated organic compounds can be produced
through reactions of NO2 or NO with organic compounds in the atmosphere. Nitro-PAHs are largely
found on particles, and they can also be including in direct emissions of particles, such as diesel exhaust
particles. Effects of particulate nitrogen compounds have been considered in previous reviews of the PM
NAAQS. In addition, it is possible that the products of NO2 (NO2~ and NO3") could produce carcinogens
(e.g., jV-nitrosomorpholine) through exposure from an environmentally occurring precursor compound
(e.g., morpholine) within the body. The studies demonstrated that this is a possible mechanism; however,
it should be pointed out that (1) that these studies used only a single precursor compound whereas humans
would be exposed to multiple precursor compounds thus producing an array of nitrosamines and other
nitrated compounds, (2) the level of nitrosamines per se produced in this fashion would be small
compared to the nitrosamines that come from cigarette smoke, smoked meats, and other food sources and
from the atmospheric transformation of products in the ambient air, and (3) a wide array of nitrated
products are produced in the ambient air with a number of these products known to be carcinogens and/or
mutagens.
Cardiovascular Effects
One epidemiologic study examined the association of cardiovascular effects with long-term
exposure to NO2. Miller et al. (2007) studied 65,893 postmenopausal women between the ages of 50 and
79 years without previous CVD in 36 U.S. metropolitan areas from 1994 to 1998. They examined the
association between one or more fatal or nonfatal cardiovascular events and the women's exposure to air
pollutants. Subject's exposures to air pollution were estimated by assigning the annual mean levels of air
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pollutants in 2000 measured at the monitor nearest the residence based on its five-digit ZIP Code
centroid, which resulted in a more spatially resolved exposure estimate. A total of 1,816 women had one
or more fatal or nonfatal cardiovascular events, including 261 deaths from cardiovascular causes. The
main focus of the study was PM2 5, but the overall CVD events (but not results for death events only)
using all the copollutants (PMi0, PMi0-2.5, SO2, NO2, CO, and O3) in both single- and multipollutant
models were presented. The results for the models only including subjects with non-missing exposure
data (n=28,402 subjects resulting in 879 CVD events) are described here. In the single-pollutant model
results, PM2 5 showed the strongest associations with the CVD events by far among the pollutants,
followed by SO2. NO2 did not show any association with the overall CVD events (heart rate [HR]=0.98
[95% CI: 0.89, 1.08] per 10-ppb increase in the annual average). In the multipollutant model, which
included all the pollutants, the association of PM2 5 and SO2 with overall CVD events became even
stronger. NO2 became negatively associated with the overall CVD events (HR=0.82 [95% CI: 0.70,
0.95]). Correlations among these pollutants were not described; therefore, it was not possible to estimate
the extent of confounding among these pollutants in these associations, but it is clear that PM25 was the
best predictor of the CVD events.
Limited toxicology data exist on the effect of NO2 on the heart. Alterations in vagal responses have
been shown to occur in rats exposed to 10 ppm NO2 for 24 h; however, exposure to 0.4 ppm NO2 for
4 weeks revealed no change (Tsubone and Suzuki, 1984). NO2-induced effects on cardiac performance
were indicated by a significant reduction in the pressure of oxygen in arterial blood (PaO2) in rats exposed
to 4.0 ppm NO2 for 3 months. When exposure was decreased to 0.4-ppm NO2 over the same exposure
period, PaO2 was not affected (Suzuki et al., 1981). In addition, a reduction in HR has been reported in
mice exposed to both 1.2 and 4.0 ppm NO2 for 1 month (Suzuki et al., 1984). Whether these effects were
the direct result of NO2 exposure or secondary responses to lung edema and changes in blood hemoglobin
content was not known (U.S. Environmental Protection Agency, 1993). A more recent study (Takano
et al., 2004) using an obese rat strain found changes in blood triglycerides, HDL, and HDL/total
cholesterol ratios in response to a 24-week exposure of 0.16 ppm NO2.No effects on hematocrit and
hemoglobin have been reported in squirrel monkeys exposed to 1.0 ppm NO2 for 16 months (Fenters
et al., 1973) or in dogs exposed to < 5.0 ppm NO2 for 18 months (Wagner et al., 1965). There were,
however, polycythemia and an increased ratio of PMNs to lymphocytes in rats exposed to 2.0 and
1.0 ppm NO2 for 14 months (Furiosi et al., 1973).
The few available epidemiologic and toxicological evidence did not support that long-term
exposure to NO2 has cardiovascular effects. The U.S. Women's Health Initiative study (Miller et al.,
2007) did not find any associations between long-term NO2 exposure and cardiovascular events. The
toxicological studies observed some effects of NO2 on cardiac performance and heart rate, but only at
exposure levels of as high as 4 ppm. Overall, these data are inadequate to infer the presence or absence of
a causal relationship.
3.5.2. Reproductive and Developmental Effects
The effects of maternal exposure during pregnancy to air pollution have been examined by several
investigators in recent years (2000 through 2006). These outcomes were not evaluated in the 1993
AQCD. The most common endpoints studied were low birth weight, preterm delivery, and measures of
intrauterine growth (e.g., small for gestational age). Generally, these studies used routinely collected air
pollution data and birth certificates from a given area for their analysis.
While most studies analyzed average NO2 exposure for the whole pregnancy, many also considered
exposure during specific trimesters or other time periods. Fetal growth, for example, is much more
variable during the third trimester. Thus, studies of fetal growth might anticipate that exposure during the
third trimester would have the greatest likelihood of an association, as is true for the effect of maternal
smoking during pregnancy. However, growth can also be affected through placentation, which occurs in
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the first trimester. Similarly, preterm delivery might be expected to be related to exposure early in
pregnancy affecting placentation, or through acute effects occurring just before delivery.
Of three studies conducted in the U.S., one (Bell et al., 2007) reported a significant decrease in
birthweight associated with exposure to NO2 among mothers in Connecticut and Massachusetts. The two
studies conducted in California did not find associations between NO2 exposure and any adverse birth
outcome (Ritz et al., 2000; Salam et al., 2005). Differences in these studies that may have contributed to
the differences in results included the following: sample size, exposure assessment methods, average NO2
concentration, and different pollution mixtures. The results reported by Bell et al. (2007) had the largest
sample size and, therefore, greater power to assess small increases in risk. The two California studies
reported higher mean concentrations of NO, but also strong correlations of NO2 exposure with PM mass
and CO.
Annex Table AX6.3-12 lists seven studies that investigated the relationship of ambient NO2
exposure with birth weight. Since low birth weight may result from either inadequate growth in utero or
delivery before the usual 40 weeks of gestation, three of the authors only considered low birth weight
(<2500 g [5 Ibs, 8 oz]) in full-term deliveries (>37 weeks); the other four controlled for gestational age in
the analysis. When correlations with other pollutants were reported in these studies, they ranged from 0.5
to 0.8. All of these studies reported strong effects for other pollutants.
Lee et al. (2003) reported a significant association between NO2 and low birth weight, and the
association was only for exposure in the second trimester. It is difficult to hypothesize any biological
mechanism relating NO2 exposure and fetal growth specifically in the second trimester. Bell et al. (2007)
reported an increased risk of low birth weight with NO2 exposure averaged over pregnancy (OR= 1.027
[95% CI: 1.002, 1.051]) and a deficit in birthweight specific to the first trimester. In addition, the deficit
in birthweight appeared to be greater among black mothers (-12.7 g per IQR increase in NO2 [95% CI:
-18.0, -7.5]) than for white mothers (-8.3 g per IQR increase inNO2 [95% CI: -10.4, -6.3]).
Six studies investigated NO2 exposure related to preterm delivery (Annex Table AX6.3-13). Three
reported positive associations (Bobak, 2000; Maroziene and Grazuleviciene, 2002; Leem et al., 2006) and
three reported no association (Liu et al., 2003; Ritz et al., 2000; Hansen et al., 2006). Among the studies
reporting an association, two (Bobak, 2000; Leem et al., 2006) reported significant associations for both
the first trimester and the third trimester of pregnancy. The third (Maroziene and Grazuleviciene, 2002)
reported significant increases in risk for exposure in the first trimester and averaged over all of pregnancy.
In two (Bobak, 2000; Leem et al., 2006) of the positive studies, NO2 exposure was correlated with SO2
exposure (r=0.54, 0.61 for the two studies); the third study did not report correlations.
Three studies (see Annex Table AX6.3-14) specifically investigated fetal growth by comparing
birth weight for gestational age with national standards. Two of these studies reported associations
between small for gestational age and NO2 exposure. Mannes et al. (2005) determined increased risk for
exposure in trimesters 2 and 3, while Liu et al. (2003) reported risks associated only with NO2 exposure
in the first month of pregnancy. In all three studies, NO2 exposure was correlated with CO exposure
(r=0.69, 0.57, 0.72 in the three studies) (Salam et al., 2005; Mannes et al., 2004; Liu et al., 2003).
Two additional epidemiologic studies found that NO2 concentrations were associated with
hospitalization for respiratory disease in the neonatal period (Dales et al., 2006) and sudden infant death
syndrome (SIDS) (Dales et al, 2004).
Only a few studies have investigated the effects of NO2 on reproduction and development
toxicology. Exposure to 1 ppm NO2 for 7 h/day, 5 days/week for 21 days resulted in no alterations in
spermatogenesis, germinal cells, or interstitial cells of the testes of 6 rats (Kripke and Sherwin, 1984).
Similarly, breeding studies by Shalamberidze and Tsereteli (1971) found that long-term NO2 exposure
had no effect on fertility. However, there was a statistically significant decrease in litter size and neonatal
weight when male and female rats exposed to 1.3 ppm NO2, 12 h/day for 3 months were bred. In utero
death due to NO2 exposure resulted in smaller litter sizes, but no direct teratogenic effects were observed
in the offspring. In fact, after several weeks, NO2-exposed litters approached weights similar to those of
controls.
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Following inhalation exposure of pregnant Wistar rats to 0.5 and 5.3 ppm NO2 for 6 h/day
throughout gestation (21 days), maternal toxic effects and developmental disturbances in the progeny
were reported (Tabacova et al., 1985; Balabaeva and Tabacova, 1985; Tabacova and Balabaeva, 1988).
Maternal weight gain during gestation was significantly reduced at 5.3 ppm, with findings of pathological
changes, e.g., desquamative bronchitis and bronchiolitis in the lung, mild parenchymal dystrophy and
reduction of glycogen in the liver, and blood stasis and inflammatory reaction in the placenta. At gross
examination, the placentas of the high-dose dams were smaller in size than those of control rats. A
marked increase of lipid peroxides was found in maternal lungs and particularly in the placenta at both
exposure levels by the end of gestation (Balabaeva and Tabacova, 1985). Disturbances in the prenatal
development of the progeny were reported, such as 2- to 4-fold increase in late post-implantation lethality
at 0.5 and 5.3 ppm, respectively, as well as reduced fetal weight at term and stunted growth at 5.3 ppm.
These effects were significantly related to the content of lipid peroxides in the placenta, which was
indicative of a pathogenetic role of placental damage. Teratogenic effects were not observed, but dose-
dependent morphological signs of embryotoxicity and retarded intrauterine development, such as
generalized edema, subcutaneous hematoma, retarded ossification, and skeletal aberrations, were found at
both exposure levels.
In a developmental neurotoxicity study, Wistar rats were exposed by inhalation to 0, 0.025, 0.05,
0.5, or 5.3-ppm NO2 during gestational days 0 through 21. Maternal toxicity was not reported. Viability
and physical development (i.e., incisor eruption and eye opening) were significantly affected only in the
high dose group. There was a concentration-dependent change in neurobehavioral endpoints such as
disturbances in early neuromotor development, including coordination deficits, retarded locomotor
development, and decreased activity and reactivity. Statistical significance was observed in some or all of
the developmental endpoints at the time point(s) measured in the 0.05, 0.5, and 5.3 ppm exposure groups.
Di Giovanni et al. (1994) investigated whether in utero exposure of rats to NO2 changed ultrasonic
vocalization, a behavioral response indicator of the development of emotionality. Pregnant Wistar female
rats were exposed by inhalation to 0, 1.5, and 3 ppm NO2 from day 0 to 20 of gestation. Dam weight gain,
pregnancy length, litter size at birth, number of dams giving birth, and postnatal mortality were
unaffected by NO2. There was a significant decrease in the duration of ultrasonic signals elicited by the
removal of the pups from the nest in the 10-day and 15-day-old male pups in the 3 ppm NO2 group. No
other parameters of offspring ultrasonic emission, or of motor activity, were significantly affected. Since
prenatal exposure to NO2 did not significantly influence the rate of calling, the authors concluded that the
decrease in the duration of ultrasounds did not necessarily indicate altered emotionality, and the
biological application of these findings remains to be determined.
3.5.2.1. Summary of Reproductive and Developmental Effects Related to Long-Term
Exposure
In summary, the epidemiologic evidence did not consistently report associations between NO2
exposure and intrauterine growth retardation; however, some evidence is accumulating for effects on
preterm delivery. Similarly, scant animal evidence supported a weak association between NO2 exposure
and adverse birth outcomes and provided little mechanistic information or biological plausibility for an
association between long-term NO2 exposure and reproductive or developmental effects.
3.5.3. Summary of Other Morbidity Effects Related to Long-Term
Exposure
Epidemiologic and toxicological studies evaluating limited evidence of cancer incidence,
cardiovascular effects, and reproductive and developmental effects linked to long-term NO2 exposure
were presented. The epidemiologic studies report some associations between long-term NO2 exposure on
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adverse birth outcomes and cancer incidence; however, NO2 is specifically used as an indicator of traffic
exposure in the studies of cancer incidence. Animal studies did not provide mechanistic information to
support these observational findings. Some toxicological studies demonstrated an effect of NO2 exposure
on cardiovascular endpoints. However, whether these effects are the direct result of NO2 exposure or
secondary responses to lung edema and changes in blood hemoglobin content are not known. Similar
findings were reported in the epidemiologic literature for short-term exposures only. Thus, while some
individual associations may be reported for these effects, the findings are inconsistent and there is no
evidence supporting coherence or plausibility in the data. Overall, these data are inadequate to infer the
presence or absence of a causal relationship.
3.6. Mortality Related to Long-Term Exposure
No studies of mortality associated with long-term NO2 exposure were evaluated in the 1993 AQCD.
More recently, there have been several studies that examined mortality associations with long-term
exposure to air pollution, including NO2, using Cox proportional hazards regression models with
adjustment for potential confounders. The U.S. studies tended to focus on effects of PM, while the
European studies tended to investigate the influence of traffic-related air pollution.
3.6.1. U.S. Studies on Mortality Related to Long-Term Exposure
Dockery et al. (1993) conducted a prospective cohort study to examine the effects of air pollution,
focusing on PM components, in six U.S. cities, which were chosen based on the levels of air pollution
(with Portage, WI being the least polluted and Steubenville, OH, the most polluted). In this study, a 14-to-
16-year mortality follow-up of 8,111 adults in the six cities was conducted. Fine particles were the
strongest predictor of mortality; NO2 was not analyzed in this study. Krewski et al. (2000) conducted a
sensitivity analysis of the Harvard Six Cities study and examined associations between gaseous pollutants
(i.e., O3, NO2, SO2, CO) and mortality. NO2 showed risk estimates similar to those for PM2 5 per "low to
high" range increment with total (1.15 [95% CI: 1.04, 1.27] per 10-ppb increase), cardiopulmonary (1.17
[95% CI: 1.02, 1.34]), and lung cancer (1.09 [95% CI: 0.76, 1.57]) deaths; however, in this datasetNO2
was highly correlated with PM2 5 (r=0.78), SO42 (r=0.78), and SO2 (r=0.84).
Pope et al. (1995) examined PM effects on mortality using the American Cancer Society (ACS)
cohort. Air pollution data from 151 U.S. metropolitan areas in 1980 were linked with individual risk
factors in 552,138 adults who resided in these areas when enrolled in the study in 1982. Mortality was
followed up until 1989. As with the Harvard Six Cities Study, the main hypothesis of this study was
focused on fine particles and SO42 , and gaseous pollutants were not analyzed. Krewski et al. (2000)
examined association between gaseous pollutants (means by season) and mortality in the Pope et al.
(1995) study data set. NO2 showed weak but negative associations with total and cardiopulmonary deaths
using either seasonal means. An extended study of the ACS cohort doubled the follow-up time (to 1998)
and tripled the number of deaths compared to the original study (Pope et al., 2002). In addition to PM2 5,
all the gaseous pollutants were examined. SO2 was associated with all the mortality outcomes (including
all other cause of deaths), butNO2 showed no associations with the mortality outcomes (RR=1.00 [95%
CI: 0.98, 1.02] per 10-ppb increase in multiyear average NO2).
Lipfert et al. (2000a) conducted an analysis of a national cohort of-70,000 male U.S. military
veterans who were diagnosed as hypertensive in the mid 1970s and were followed up for about 21 years
(up to 1996). This cohort was 35% black and 81% had been smokers at one time. Thus, unlike other
cohort studies described in this section, this hypertensive cohort with a very high smoking rate is not
representative of the U.S. population. Total suspended particulates (TSP), PMi0, CO, O3, NO2, SO2, SO42,
PM2 5, and PM10-2.5 were considered. The county of residence at the time of entry to the study was used
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to estimate exposures. Four exposure periods (1960-1974, 1975-1981, 1982-1988, and 1989-1996) were
defined, and deaths during each of the three most recent exposure periods were considered. Lipfert et al.
(2000a) noted that the pollution risk estimates were sensitive to the regression model specification,
exposure periods, and the inclusion of ecological and individual variables. The authors reported that
indications of concurrent mortality risks were found for NO2 (the estimate was not given with confidence
bands) and peak O3. Their subsequent analysis (Lipfert et al., 2003) reported that the air pollution-
mortality associations were not sensitive to the adjustment for blood pressure. Lipfert et al. (2006a) also
examined associations between traffic density and mortality in the same cohort, whose follow-up period
was extended to 2001. They reported that traffic density was a better predictor of mortality than the
ambient air pollution variables, with the possible exception of O3. The log-transformed traffic density
variable was moderately correlated with NO2 (r=0.48) and PM2 5 (r=0.50) in this data set. For the 1989 to
1996 data period (the period that generally showed the strongest associations with exposure variables
among the four periods), the estimated mortality relative risk for NO2 was 1.025 (95% CI: 0.983, 1.068)
per 10-ppb increase in a single-pollutant model. The two-pollutant model with the traffic density variable
reduced NO2 risk estimates to 0.996 (95% CI: 0.954, 1.040). Interestingly, as the investigators pointed
out, the risk estimates due to traffic density did not vary appreciably across these four periods. They
speculated that other environmental factors such as particles from tire, traffic noise, spatial gradients in
socioeconomic status might have been involved. Lipfert et al. (2006b) further extended analysis of the
veteran's cohort data to include one year of the EPA's Speciation Trends Network (STN) data, which
collected chemical components of PM2 5. As in the previous Lipfert et al. (2006a) study, traffic density
was the most important predictor of mortality, but associations were also seen for EC, vanadium, NO3-,
and nickel. NO2, O3, and PMi0 also showed positive but weaker associations. The risk estimate for NO2
was 1.043 (95% CI: 0.967, 1.125) per 10-ppb increase in a single-pollutant model. Multipollutant model
results were not presented for NO2 in this updated analysis. The results from the series of studies by
Lipfert et al. are indicative of a traffic-related air pollution effect on mortality, but the study population
(hypertensive with very high smoking rate) was not representative of the general U.S. population.
Abbey et al. (1999) investigated associations between long-term ambient concentrations of PMi0,
O3, NO2, SO2, and CO (1973 to 1992) and mortality (1977 to 1992) in a cohort of 6,338 nonsmoking
California Seventh-day Adventists. Monthly indices of ambient air pollutant concentrations at 348
monitoring stations throughout California were interpolated to ZIP code centroids according to home or
work location histories of study participants, cumulated, and then averaged overtime. They reported
associations between PMi0 and total mortality for males and nonmalignant respiratory mortality for both
sexes. NO2 was not associated with all-cause, cardiopulmonary, or respiratory mortality for either sex.
Lung cancer mortality showed large risk estimates for most of the pollutants in either or both sexes, but
the number of lung cancer deaths in this cohort was very small (12 for female and 18 for male); therefore,
it was difficult to interpret these estimates.
When comparing the results of the U.S. studies mentioned above, differences in study population
characteristics and geographic unit of averaging for pollution exposure estimates need to be considered.
Most of the U.S. studies used a "semi-individual" study design, in which information on health outcomes
and potential confounders were collected and adjusted for on an individual basis, but community-level air
pollution exposure estimates were used. It is not clear to what extent exposure error affects these types of
studies. Unlike regional air pollutants (e.g., SO42 , PM2 5) in the eastern U.S. whose levels are generally
uniform within the scale of the metropolitan area, the within-city variation for more locally-impacted
pollutants such as NO2, SO2, and CO are likely to be larger and, therefore, are more likely to have larger
exposure errors in the semi-individual studies. The smaller number of monitors available for NO2 in the
U.S. may make the relative error worse for NO2 compared to other pollutants. Exposure error in these
long-term exposure studies likely contributes to the inconsistencies observed across studies. For example,
the ACS study found no associations with NO2; however, NO2 was among the pollutants that showed
associations with mortality in the veterans' study, with traffic density showing the strongest association.
The geographic resolution of air pollution exposure estimation varied in these studies: MSA-level
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averaging in the ACS study and county-level averaging in the veterans' study. Traffic density and other
pollutants that showed mortality associations in the veterans study, including EC and NO2, were more
localized pollutants; therefore, using county-level aggregation, rather than MSA-level, may have resulted
in smaller exposure misclassification.
3.6.2. European Studies on Mortality Related to Long-Term Exposure
In contrast to the U.S. studies described above, the European studies described below, have more
spatially-resolved exposure estimates, because their hypotheses or study designs were evaluating effects
of related air pollution. Only one study from France (Filleul et al., 2005) used a design similar to the
Harvard Six Cities study or ACS in that it did not study traffic-related air pollution and the exposure
estimate was not done on an individual basis.
Hoek et al. (2002) investigated a random sample of 5,000 subjects from the Netherlands Cohort
Study on Diet and Cancer (NLCS) ages 55 to 69 from 1986 to 1994. Long-term exposure to traffic-related
air pollutants (black smoke and NO2) was estimated using 1986 home addresses. Exposure was estimated
with the measured regional and urban background concentration and an indicator variable for living near
major roads. Cardiopulmonary mortality was associated with living near a major road (RR=1.95 [95% CI:
1.09, 3.52]) and less strongly with the estimated air pollution levels (e.g., forNO2, RR=1.32 [95% CI:
0.88, 1.98] per 10-ppb increase). The risk estimate for living near a major road was 1.41 (95% CI: 0.94,
2.12) for total mortality. For estimated NO2 (incorporating both background and local impact), the RR
was 1.15 (95% CI: 0.60, 2.23) per 10-ppb increase). Because the NO2 exposure estimates were modeled,
interpretation of their risk estimates was not straightforward. However, these results did support that NO2,
as a marker of traffic-related air pollution, was associated with these mortality outcomes.
Filleul et al. (2005) investigated long-term effects of air pollution on mortality in 14,284 adults who
resided in 24 areas from seven French cities when enrolled in the PAARC survey (for air pollution and
chronic respiratory diseases) in 1974. Models were run before and after exclusion of six area monitors
influenced by local traffic as determined by the NO/NO2 ratio of >3. Before exclusion of the six areas,
none of the air pollutants were associated with mortality outcomes. After exclusion of these areas,
analyses showed associations between total mortality and TSP, black smoke, NO2, and NO. The estimated
NO2 risks were 1.28 (95% CI: 1.07, 1.55), 1.58 (95% CI: 1.07, 2.33), and 2.12 (95% CI: 1.11, 4.03) per
10-ppb increase in NO2 mean over the study period for total, cardiopulmonary, and lung cancer mortality,
respectively. From these results, the authors noted that inclusion of air monitoring data from stations
directly influenced by local traffic could overestimate the mean population exposure and bias the results.
This point raised a concern for NO2 exposure estimates used in other studies (e.g., ACS) in which the
average of available monitors was used to represent the exposure of each city's entire population.
Nafstad et al. (2004) investigated the association between mortality and long-term air pollution
exposure in a cohort of 16,209 Norwegian men followed from 1972/1973 through 1998. PM was not
considered in this study because measurement methods changed during the study period. NOX, rather than
NO2, was used. Exposure estimates for NOX and SO2 were constructed using models based on subjects'
addresses and emission data for industry, heating, and traffic measured concentrations. Addresses linked
to 50 of the busiest streets were given an additional exposure based on estimates of annual average daily
traffic. The adjusted risk estimate for total mortality was 1.16 [95% CI: 1.12, 1.22] for a 10-ppb increase
in the estimated exposure to NOX. Corresponding mortality risk estimates for respiratory causes other
than lung cancer was 1.16 (95% CI: 1.06, 1.26); for lung cancer, 1.11 (95% CI: 1.03, 1.19); and for
ischemic heart diseases, 1.08 (95% CI: 1.03, 1.12). SO2 did not show similar associations. The risk
estimates presented for categorical levels of these pollutants showed mostly monotonic exposure-response
relationships for NOX. These results are indicative of the effects of traffic-related air pollution on long-
term mortality, butNOx likely represented the combined effects of that source, possibly including PM,
which could not be analyzed in this study. A case-control study of 1,043 men aged 40 to 75 with lung
cancer and 2,364 controls in Stockholm County (Nyberg et al., 2000) reported similar results to this
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study. They mapped residence addresses to a GIS database indicating 4,300 traffic-related line sources
and 500 point sources of NO2 exposure. Exposure was derived from a model validated by comparison to
actual measurements of NO2 at six sites. Exposure to 10 ppb NO2 was associated with an OR of 1.20
(95% CI: 0.94 1.49). Exposure to the 90th percentile (> 29.26 (ig/m3 915.5 ppb]) of NO2 was associated
with an OR of 1.44 (95% CI: 1.05, 1.99).
Gehring et al. (2006) investigated the relationship between long-term exposure to air pollution
originating from traffic and industrial sources, and total and cause-specific mortality in a cohort of women
living in North Rhine-Westphalia, Germany. The area includes the Ruhr region, one of Europe's largest
industrial areas. Approximately 4,800 women (age 50 to 59 years) were followed for vital status and
migration. Exposure to air pollution was estimated by GIS models using the distance to major roads, NO2,
and PMio (estimated from 0.71 X TSP, based on available PMi0 and TSP data in the area) concentrations
from air monitoring station data. Cardiopulmonary mortality was associated with living within a 50-m
radius of a major road (RR=1.70 [95% CI: 1.02, 2.81]) andNO2 (RR=1.72 [95% CI: 1.28, 2.29] per
10-ppb increase in annual average). Exposure to NO2 was also associated with all-cause mortality (1.21
[95% CI: 1.03, 1.42] per 10 ppb increase). NO2 was generally more strongly associated with mortality
than the indicator for living near a major road (within versus beyond a 50-m radius) or PMi0.
Nsess et al. (2007) investigated the concentration-response relationships between air pollution (i.e.,
NO2, PMio, PM25) and cause-specific mortality using all the inhabitants of Oslo, Norway, aged 51 to 90
years on January 1, 1992 (n=143,842), with follow-up of deaths from 1992 to 1998. An air dispersion
model was used to estimate the air pollution levels for 1992 through 1995 in all 470 administrative
neighborhoods. Correlations among these pollutants were high (range 0.88 to 0.95). All causes of deaths,
cardiovascular causes, lung cancer, and COPD were associated with all indicators of air pollution for both
sexes and both age groups. The investigators reported that the effects appeared to increase at NO2 levels
higher than 40 (ig/m3 (21 ppb) in the younger age (51 to 70 years) group and with a linear effect in the
interval of 20 to 60 (ig/m3 (10 to 31 ppb) for the older age group (see Figure 3.6-1). However, they also
noted that a similar pattern was found for both PM2 5 and PMi0. Thus, the apparent threshold effect was
not unique to NO2. NO2 risk estimates for all-cause mortality were presented only in a figure. The
findings are generally consistent with those from the Nafstad et al. (2003, 2004) studies, in which a
smaller number of male-only subjects were analyzed. While NO2 effects were demonstrated, the high
correlation among the PM indices and NO2 or NOX made it difficult to confidently ascribe these
associations to NO2/NOX alone.
Most of the European cohort studies estimated an individual subject's exposure based on spatial
modeling using emission and concentration data. These studies may have provided more accurate
exposure estimates than the community-level air pollution estimates typically used in the U.S. studies.
However, because they generally involved modeling with such information as traffic volume and other
emission estimates in addition to monitored concentrations, additional uncertainties may have been
introduced. Thus, validity and comparability of various methods may need to be examined. In addition,
because the relationship between the concentration measured at the community monitors and the health
effects was ultimately of interest in this ISA, interpreting the risk estimates based on individual-level
exposures will require an additional step to translate the difference. Finally, a more accurate exposure
estimate does not solve the problem of the surrogate role thatNO2 may play. Most of these studies did
acknowledge this issue and generally treated NO2 as a surrogate marker, but the extent of such surrogacy
and confounding with other traffic- or combustion-related pollutant was not clear at this point. In the
Hoek et al. study (2002), the indicator of living near a major road was a better predictor of mortality than
the estimated NO2 exposures. In the Gehring et al. (2006) study, the estimated NO2 exposure was a better
predictor of total and cardiopulmonary mortality than the indicator of living near a major road.
Comparing the results for the indicators of living near a major road and the estimated NO2 or NOX
exposures is not straightforward, but it is possible that, depending on the presence of other combustion
sources (e.g., the North Rhine-Westphalia area included highly industrial areas), NO2 may represent more
than traffic-related pollution.
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Ages 51-70 years
0.6
0.4-
0.0 -
0.2 -
All causes
0.7
0.2
Ages 71-90 years
0.4
20 40 60
Nitrogen dioxide (ng/m3)
20 40 60
Nitrogen dioxide (ng/m*)
Source: Nasssetal. (2007).
Figure 3.6-1. Age-adjusted, nonparametric smoothed relationship between N02 and mortality from all
causes in Oslo, Norway, 1992 through 1995.
3.6.3. Summary of Mortality Related to Long-Term Exposure
Figure 3.6-2 summarizes the NO2 relative risk estimates for total mortality from the studies
reviewed in the previous sections. The relative risk estimates are grouped by those that used community -
or ecologic-level exposure estimates and those that used individual-level exposure estimates, but because
of the small number of studies listed, no systematic pattern could be elucidated. The relative risk
estimates for total mortality ranged from 1.0 to 1.28 per 10-ppb increase in annual or longer averages
ofNO2.
Potential confounding by copollutants needs to be considered in the interpretation of the NO2 risk
estimates. Not all of the studies presented correlations between NO2 and other pollutants, but those that
did indicated generally moderate to high correlations. For example, in the Harvard Six Cities study
(Krewski et al, 2000), the French study (Filleul et al., 2005), and the German study (Gehring et al., 2006),
the correlation between NO2 and PM indices ranged from 0.72 to 0.8. The high correlations between NO2
and PM indicated possible confounding between these pollutants. Further, the results from the
Netherlands study (Hoek et al., 2002), that living near major roads was more strongly associated with
mortality than NO2, supported a possible surrogate role of NO2 as a marker of traffic-related pollution.
However, this does not preclude the possibility of NO2 playing a role in interactions among the traffic-
related pollutants. Essentially no information was available on the possible effect modification of
apparent NO2-mortalty associations.
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Relative risk per 10 ppb N02
05 1.0 1.5 2.0 25
Seventh-day Adventist (Abbey et al,, 1999)
Male
Female
Harvard six cities (Krewski et al,, 2000)
ACS (Popeetal., 2002)
Veterans' cohort study (Lipfert et al., 200%)
French PAAC survey (Filleul et al,, 2005)
The Netherlands NLCS (Hoetc et al., 2002) -
North Mine-Westphalia, Germany; female
(Gehrinn et al., 2006)
Studies wilh ecologic exposure estimates
Studies with individual exposure estimates
Figure 3.6-2. Total mortality relative risk estimates from long-term studies. The original estimate for the
Norwegian study was estimated for NOx. Conversion of NC-2=0.35 X NOx was used.
Available information on risk estimates for more specific causes of death with long-term exposure
to NO2 was sparse. Among the studies with larger number of subjects, the ACS study (Pope et al., 2002)
examined cardiopulmonary and lung cancer deaths, but as with the all-cause deaths, they were not
associated with NO2. In the Nsess et al. (2007) analysis of all inhabitants of Oslo, Norway, NO2 relative
risk estimates for COPD were higher than those for other causes, but the same pattern was seen for PM2 5
and PMio. In the German study by Gehring et al. (2006), NO2 relative risk estimates for cardiopulmonary
mortality were larger than those for all-cause mortality, but, again, the same pattern was seen for PMi0.
Thus, higher risk estimates seen for specific causes of deaths were not specific to NO2 in these studies.
In long-term exposure studies, different geographic scales were used to estimate air pollution
exposure estimates across studies. Since the relative strength of association with health outcomes among
various air pollutant indices may have been affected by the spatial distribution of the pollutants (i.e.,
regional versus local), the numbers of monitors available, and the scale of aggregation in the study design,
it was not clear how these factors affected the apparent difference in results.
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In the U.S. and European cohort studies examining the relationship between long-term exposure to
NO2 and mortality, results were generally not consistent. Further, when associations were suggestive, they
were not specific to NO2, also implicating PM and other traffic indicators. The relatively high correlations
reported between NO2 and PM indices (r -0.8) and the unresolved issue of surrogacy and interactions
make it difficult to interpret the observed associations; thus, these data are inadequate to infer the
presence or absence of a causal relationship.
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Chapter 4. Public Health Impact
This chapter discusses several issues relating to the broader public health impact of exposure to
NOX. First, concepts related to defining adverse health effects are discussed. Second, the concentration-
response relationship for NO2 and evidence for thresholds (the concentration of NO2 that must be
exceeded to elicit a health response) are discussed, with consideration of the limited evidence available to
assess individual and population threshold values for health effects. The next section identifies
characteristics of subpopulations that may experience increased risks from NO2 exposures, through either
enhanced susceptibility (i.e., as a result of an intrinsic condition such as pre-existing disease, genetic
factors, age) and/or differential vulnerability associated with increased exposure (e.g. residing close to
roadways). In the final section the size of the potentially susceptible and vulnerable populations in the
U.S. is discussed.
4.1. Defining Adverse Health Effects
A recent American Thoracic Society (ATS) statement (ATS, 2000b) updated the guidance for
defining adverse respiratory health effects published 15 years earlier (ATS, 1985), taking into account
new investigative approaches used to identify the effects of air pollution and reflecting concern for
impacts of air pollution on specific susceptible groups. In the 2000 update, there was an increased focus
on quality-of-life measures as indicators of adversity and a more specific consideration of population risk.
An increased risk to the entire population was identified as adverse, even though it may not increase the
risk of any identifiable individual to an unacceptable level (ATS, 2000b). For example, a population of
asthmatics could have a distribution of lung function such that no individual demonstrates impairment.
Exposure to air pollution could adversely shift the distribution,without demonstrable clinical effects. This
distribution shift would be considered adverse because individuals would have diminished reserve
function, putting them at increased risk if affected by another agent or as a result of aging.
The 2006 Ozone AQCD (U.S. Environmental Protection Agency, 2006a) provided information
useful in helping to define adverse health effects associated with ambient O3 exposure by describing the
gradation of severity of respiratory effects. The definitions that relate to responses in impaired persons are
presented in Table 4.1-1. The severity of effects described in the table is valid and reasonable in the
context of the new ATS (2000b) statement, and can be applied to NO2 exposure.
As assessed in detail in Chapter 3, Section 3.1, exposures to a range of NO2 concentrations have
been reported to be associated with increased severity of health effects, such as respiratory symptoms, ED
visits and hospital admission for respiratory causes. The adverse effects associated with NO2 exposure are
anticipated to lead to a shift in the population distribution of reserve capacity for exposed individuals,
and/or increase the proportion of severe responses across a broad spectrum of respiratory outcomes.
These adverse outcomes have the potential to impair the quality of life among those affected.
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Table 4.1-1. Gradation of individual responses to short-term NO2 exposure in persons with
impaired respiratory systems.
SYMPTOMATIC
RESPONSE
Wheeze
Cough
Chest pain
Duration of response
FUNCTIONAL
RESPONSE
FEVi change
Bronchial responsiveness
Specific airway resistance
(sRaw)
Duration of response
IMPACT OF
RESPONSES
Interference with
normal activity
Medical treatment
NORMAL
None
Infrequent Cough
None
None
NONE
Decrements of <3%
Within normal range
Within normal range
(± 20%)
None
NORMAL
None
No change
MILD
With otherwise normal
breathing
Cough with deep breath
Discomfort just noticeable
on exercise or deep breath
<4h
SMALL
Decrements of 3 -1 0%
Increases of <100%
sRaw increased <100%
<4h
MILD
Few persons choose
to limit activity
Normal medication
as needed
MODERATE
With shortness of breath
Frequent spontaneous
cough
Marked discomfort
on exercise or deep breath
4-24h
MODERATE
Decrements of 10-20%
Increases of 100-300%
sRaw increased 100-200%
or up to 15 cm H2O-s
4-24h
MODERATE
Many persons choose
to limit activity
Increased frequency
of medication use or
additional medication
SEVERE
Persistent with
shortness of breath
Persistent
uncontrollable cough
Severe discomfort on
exercise or deep breath
>24h
LARGE
Decrements of > 20%
Increases of >300%
sRaw increased >200% or
more than 15 cm H2O-s
>24h
SEVERE
Most persons choose
to limit activity
Physician or emergency
department visit
An increase in bronchial responsiveness of 100% is equivalent to a 50% decrease in provocative dose that produces a 20% decrease in FEV-i (PD20) or provocative dose that
produces a 100% increase in sRaw(PDIOO). Source: This table is adapted from the 2006 O3 AQCD (Table 8-3, page 8-68) (U.S. Environmental Protection Agency, 2006a).
4.2. Concentration-Response Functions and Potential
Thresholds
An important consideration in characterizing the public health impacts associated with NO2
exposure is whether the concentration-response relationship is linear across the full concentration range
encountered or if nonlinear departures exist along any part of this range. Of particular interest is the shape
of the concentration-response curve at and below the level of the current annual average standard of
0.053 ppm (53 ppb).
Human clinical studies typically provide individual-level response data in relation to different
levels of NO2. The percentage of individuals showing responses across a range of NO2 exposures, the
interindividual variability in response and the concentration at which an individual begins to respond can
often be determined based on these studies. The previous assessment concluded that human clinical
studies provided evidence that some asthmatics are more susceptible to the effects of NO2 but a
concentration-response relationship was not evident (U.S. Environmental Protection Agency, 1993).
Findings from recent human clinical studies do not provide evidence that would change the previous
conclusion (see Chapter 3). Inconsistencies in clinical findings could be due to a sensitive subpopulation
4-2
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not well represented in human clinical studies or peculiarities in protocols necessary to elicit a response.
Such peculiarities might include an unanticipated time course for a response not well captured in human
clinical studies or other factors that influence a response that may not be measured in all human clinical
studies (i.e., prior allergen sensitization by allergen or infectious agent).
Epidemiologic studies evaluate population-level responses, rather than individual responses. Low
data density in the lower concentration range, response measurement error, exposure measurement error,
and a shallow slope are some of the factors that complicated the ability to determine the shape of the
concentration-response curve. A sigmoidal or S-shaped curve with a shallow slope may approximate
linearity over a wide range of concentrations, making it difficult to characterize the exact nature of the
concentration-response relationship. Biological characteristics that tend to linerarize concentration-
response relationships include inter-individual variability, additivity of NO2" induced effects to a naturally
occurring background of disease, and additivity to effects induced by other pollutant exposures.
Epidemiologic studies are generally consistent with a linear or log linear relationship between
ambient NO2 concentration and the health outcome; however the shape of the NO2 concentration-response
relationship has only been explored in several studies. To examine the shape of the concentration-
response relationship between NO2 and daily physician consultations for asthma and lower respiratory
disease in children, Hajat et al. (1999) used bubble plots to examine residuals of significant models
plotted against moving averages of NO2 concentration. They noted a weak trend for asthma and 0-1 day
moving average lag of NO2 and proposed that effects are weaker at lower concentrations and stronger at
higher concentrations than predicted by the linear model. These departures were in accord with the shape
of the sigmoidal dose-response model below the median effective dosage.
Several studies of ED visits or hospitalizations for cardiac or respiratory disease examined the
shape of the concentration-response curve. Burnett et al. (1997a) used the locally estimated smoothing
splines (LOESS) to describe the concentration-response for respiratory and cardiac hospitalizations for
the summers of 1992-94 in Toronto. Both graphs showed a smaller slope at lower concentrations, but a
chi-square test detected no significant difference between the LOESS and a linear effect. In another study
of respiratory hospitalizations in 16 Canadian cities, Burnett et al. (1997b) were unsuccessful in
identifying the shape of the concentration-response function, i.e. a linear effect was not significant nor did
inclusion of a quadratic term improve the fit of the model. In another study of hospitalizations for CHF in
10 Canadian cities, Burnett et al. (1997c) found that a logarithmic concentration-response model, which
has a steeper slope at lower concentrations, provided the best fit for the data compared to the other forms
of the model examined. In a study among Medicaid-enrolled asthmatics in two urban cities in Ohio, Jaffe
et al. (2003) found that when a concentration-response relationship was examined by quintile of NO2
concentration, no consistent pattern was found. Tenias et al. (1998) also reported no consistent pattern in
their study of the association between ambient NO2 and ED visits in Valencia's Hospital Clinic
Universitari from 1994 to 1995. Castellsague et al. (1995) found a small but significant association of
NO2 and ED visits due to asthma in Barcelona. Specifically, the adjusted risk estimates of asthma visits
for each quartile of NO2 showed increased risks in each quartile for the summer months, but not the
winter months. Together these studies indicate some disagreement in the trend of the concentration-
response curve below 50 ppb 24 h NO2.
Samoli et al. (2003) examined the relationship between mortality and NO2 in a subset of 9
European cities out of 30 APHEA cities. The cities were selected to have overlapping NO2 ranges to
improve the detection of nonlinearity. They found the linear assumption to be adequate for these cities.
Kim et al. (2004b) investigated non-linearity in relationships between air pollutants and mortality in
Seoul, Korea, by analyzing data using a log-linear Generalized Additive Model (GAM; linear model), a
cubic natural spline model (nonlinear model), and a B-mode splined model (threshold model). They did
not detect a nonlinear association for NO2 with mortality.
In conclusion, of the epidemiology studies that attempted to look at the shape of the concentration-
response below 50 ppb, one indicated that effects were weaker at lower levels (Hajat et al. 1999), and one
showed a steeper log-linear relationship at lower doses (Burnett et al. 1997c). The remainder found that a
4-3
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linear function best described the data (Burnett et al. 1997a,b; Jaffe et al. 2003; Tenias et al., 1998;
Castellsague et al., 1995). These results do not provide adequate evidence to suggest that nonlinear
departures exist along any part of this range of NO2 exposure concentrations. Evidence from human
clinical studies has not helped to clarify understanding of the concentration-response function of NO2 (see
chapters).
4.3. Susceptible and Vulnerable Populations
The NAAQS are intended to provide an adequate margin of safety for both general populations and
sensitive subpopulations, or those subgroups potentially at increased risk for ambient air pollution health
effects. The term susceptibility generally encompasses innate or acquired factors that make individuals
more likely to experience effects with exposure to pollutants. Genetic or developmental factors can lead
to innate susceptibility, while acquired susceptibility may result from age, disease, or personal risk factors
such as smoking, diet, or exercise. In addition, new attention has been paid to the concept of some
population groups having increased vulnerability to pollution-related effects due to extrinsic factors
including socioeconomic status (e.g., reduced access to health care) or particularly elevated exposure
levels. Potentially susceptible and/or vulnerable groups comprise a large fraction of the U.S. population.
Given the likely heterogeneity of individual responses to air pollution, the severity of health effects
experienced by a susceptible subgroup may be much greater than that experienced by the population at
large (Zanobetti et al., 2000).
Many factors such as genetic (Kleeberger et al., 2005) and social (Gee and Payne-Sturges, 2006)
determinants of disease may contribute to interindividual variability and heightened susceptibility to NO2.
The previous NOX AQCD (U.S. Environmental Protection Agency, 1993) identified certain groups within
the population that may be more susceptible to the effects of NO2 exposure, including persons with
preexisting respiratory disease, children, and older adults. Findings from recent studies supported the
conclusions from the previous assessment with regard to susceptibility.
4.3.1. Preexisting Disease as a Potential Risk Factor
A recent report of the National Research Council (NRC) emphasized the need to evaluate the effect
of air pollution on susceptible groups including those with respiratory illnesses and cardiovascular disease
(CVD) (NRC, 2004). Generally, chronic obstructive pulmonary disease (COPD), conduction disorders,
CHF, diabetes, and MI are conditions believed to put persons at greater risk for adverse events associated
with air pollution. In addition, epidemiologic evidence indicates persons with airway hyperresponsiveness
as determined by methacholine provocation may be at greater risk for symptoms such as phlegm and
lower respiratory symptoms than subjects without airway hyperresponsiveness (Boezen et al., 1998).
Several researchers have investigated the effect of air pollution among potentially sensitive groups with
preexisting medical conditions.
4.3.1.1. Asthmatics
Evidence from epidemiologic studies shows an association between NO2 exposure and children's
hospital admissions, ED visits, and calls to doctors for asthma. This evidence came from large
longitudinal studies, panel studies, and time-series studies. NO2 exposure was associated with aggravation
of asthma effects that include symptoms, medication use, and lung function. Effects of NO2 on asthma
were most evident with a cumulative lag of 2 to 6 days, rather than same-day levels of NO2. Time-series
studies also demonstrated a relationship in children between hospital admissions or ED visits for asthma
4-4
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and NO2 exposure, even after adjusting for copollutants such as PM and CO. Important evidence was also
available from epidemiologic studies of indoor NO2 exposures. A number of recent studies showed
associations with wheeze, chest tightness, and length of symptoms (Belanger et al., 2006); respiratory
symptom rates (Nitschke et al., 2006); school absences (Pilotto et al., 1997a); respiratory symptoms,
likelihood of chest tightness, and asthma attacks (Smith et al., 2000); and severity of virus-induced
asthma (Chauhan et al., 2003). However, several studies (Mukala et al., 1999, 2000; (Farrow et al., 1997)
evaluating younger children found no association between indoor NO2 and respiratory symptoms.
Airway hyperresponsiveness in asthmatics to both nonspecific chemical and physical stimuli and to
specific allergens appeared to be the most sensitive indicator of response to NO2 (U.S. Environmental
Protection Agency, 1993). Responsiveness is determined using a challenge agent, which causes an
abnormal degree of constriction of the airways as a result of smooth muscle contraction. This response
ranges from mild to severe (spanning orders of magnitude) and is often accompanied by production of
sputum, cough, wheezing, shortness of breath, and chest tightness. Though some asthmatics do not have
this bronchoconstrictor response (Pattemore et al., 1990), increased airway hyperresponsiveness is
correlated with asthma symptoms and increased asthma medication usage. Clinical studies reported
increased airway hyperresponsiveness to allergen challenge in asthmatics following exposure to 0.26-ppm
NO2 for 30 min during rest (Barck et al., 2002; et al.; Strand et al., 1997; 1998).
Toxicological studies provided biological plausibility that asthmatics are likely susceptible to the
effects of NO2 exposure. Numerous animal studies provide evidence that NO2 can produce inflammation
and lung permeability changes. These studies provided evidence for several mechanisms by which NO2
exposure can induce effects, including reduced mucociliary clearance, and alveolar macrophage function
such as depressed phagocytic activity and altered humoral- and cell-mediated immunity. Chauhan et al.
(2003) reviewed potential mechanisms by which NO2 exacerbates asthma in the presence of viral
infections. These mechanisms included "direct effects on the upper and lower airway by ciliary
dyskinesis, epithelial damage, increases in pro-inflammatory mediators and cytokines, rises in IgE
concentration, and interactions with allergens, or indirectly through impairment of bronchial immunity."
These are all mechanisms that can provide biological plausibility for the NO2 effects in asthmatic children
observed in epidemiologic studies. However, it must be noted that the experimental animal studies that
looked at effects on markers of inflammation, such as BAL fluid levels of total protein and lactate
dehydrogenase and recruitment or proliferation of leukocytes, occured only at exposure levels of > 5 ppm.
Studies conducted at these higher exposure concentrations may elicit mechanisms of action and effects
that do not occur at near-ambient levels of NO2.
4.3.1.2. Cardiopulmonary Disease and Diabetes
While less evidence was available for these conditions, preexisting cardiovascular-related
conditions may lead to heightened susceptibility to the effects of NO2 exposure. Recent epidemiologic
studies reported that persons with preexisting conditions may be at increased risk for adverse cardiac
health events associated with ambient NO2 concentrations (Peel et al., 2007; Mann et al., 2002; D'Ippoliti
et al., 2003; von Klot et al., 2005). Peel et al. (2007) reported evidence of effect modification by
comorbid hypertension and diabetes on the association between ED visits for arrhythmia and NO2
exposure. In another study, a statistically significant positive relationship was reported between NO2
concentrations and hospitalizations for IHD among those with prior diagnoses of CHF and arrhythmia
(Mann et al., 2002). However, Mann et al. (2002) noted the vulnerability in the secondary CHF group
could be due to increased prevalence of MI as the primary diagnosis in this group. In addition, these
authors stated they were unable to distinguish the effects of NO2 from other traffic pollutants (Mann et al.,
2002). Von Klot et al. (2005) reported cardiac readmission among MI survivors was associated with NO2
and this association was robust to adjustment for PMi0. Modification of the association between NO2 and
MI by conduction disorders but not diabetes or hypertension was observed by D'Ippoliti et al. (2003).
4-5
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Park et al. (2005b) examined the relationship of NO2 and HRV among those with IHD, hypertension and
diabetes but did not find an association.
There was limited evidence from clinical or toxicological studies on potential susceptibility to NO2
in persons with CVD; however, the limited epidemiologic evidence indicated that these individuals may
be more sensitive to effects of NO2 exposure or air pollution in general. Reductions in blood hemoglobin
(-10%) have been reported in healthy subjects following exposure to NO2 (1 to 2 ppm) for a few hours
during intermittent exercise (Frampton et al., 2002). The clinical importance of hemoglobin reduction in
persons with significant underlying lung disease, heart disease, or anemia has not been evaluated, but the
reductions could lead to adverse cardiovascular consequences. These consequences would be exacerbated
by concomitant exposure to CO, a combustion copollutant of NO2 that binds to hemoglobin and reduces
oxygen availability to tissues and organs.
4.3.2. Age as a Potential Risk Factor
Children and older adults (65+ years) are often considered at increased risk from air pollution
compared to the general population. The American Academy of Pediatrics (2004) concluded that children
and infants are among the most susceptible to many air pollutants, including NO2. Because 80% of alveoli
are formed postnatally and changes in the lung continue through adolescence, the developing lung is
highly susceptible to damage from exposure to environmental toxicants (Dietert et al., 2000). In addition
to children, older adults frequently are classified as being particularly susceptible to air pollution. The
basis of the increased sensitivity in the elderly is not known, but one hypothesis is that it may be related
to changes in the respiratory tract lining's fluid antioxidant defense network and/or to a decline in
immune system surveillance or response (Kelly et al., 2003). The generally declining health status of
many older adults may also increase their risks to air pollution-induced effects.
Evidence showed that associations of NO2 with both respiratory ED visits and hospitalizations were
stronger among children (Peel et al., 2005; Atkinson et al., 1999b; Fusco et al., 2001; Hinwood et al.,
2006; Anderson et al., 1998) and older adults (Migliaretti et al., 2005; Atkinson et al., 1999b; Schouten
et al., 1996; Ponce de Leon et al., 1996; Prescott et al., 1998). However, two studies (Sunyer et al., 1997;
Migliaretti et al., 2005) found no difference in the rates of ED visits associated with NO2 concentrations
for children (<15 years) and adults (15 to 64 years). Luginaah et al. (2005) and Wong et al. (1999) found
no statistically significant difference in the elderly and adult age groups.
Many field studies focused on the effect of NO2 on the respiratory health of children, while fewer
field studies have compared the effect of NO2 in adults and other age groups. In general, children and
adults experienced decrements in lung function associated with short-term ambient NO2 exposures (see
Section 3.1.5). Importantly, a number of long-term exposure studies indicated that effects in children that
include impaired lung function growth, increased respiratory symptoms and infections, and onset of
asthma (see Section 3.4).
In elderly populations, associations between NO2 and hospitalizations or ED visits for CVD,
including stroke, have been observed in several studies (Anderson et al., 2007a; Atkinson et al., 1999b;
Jalaludin et al., 2006; Hinwood et al., 2005; Wong et al., 1999; Barnett et al., 2006; Zanobetti and
Schwartz, 2006; Simpson et al., 2005a; Wellenius et al., 2005b; Morgan et al., 1998a; Morris et al.,
1995). However, some results were inconsistent across cities (Morris et al., 1995), and investigators could
not distinguish the effect of NO2 from the effect of other traffic-related pollutants such as PM and CO
(Simpson et al., 2005a; Barnett et al., 2006; Morgan et al., 1998b; Jalaludin et al., 2006; Zanobetti and
Schwartz, 2006).
Several mortality studies investigated age-related differences in NO2 effects. Among the studies
that observed positive associations between NO2 and mortality, a comparison of all-age- or < 64-years-
of-age-group NO2-mortality risk estimates to that of the > 65-years-of-age group indicated that, in
general, the elderly population was more susceptible to NO2 effects (Biggeri et al., 2005; Burnett et al.,
4-6
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2004). One study (Simpson et al., 2005a) found no difference in increases in CVD mortality associated
with NO2 concentrations between all ages and those participants of > 65 years of age.
4.3.3. Gender as a Potential Risk Factor
A limited number of studies stratified results by gender. Lugninaah et al. (2005) found increases in
hospital admissions associated with NO2 among females but not males. In a study of children in Toronto,
Canada, NO2 was positively associated with asthma admissions among both boys and girls (Lin et al.,
2005). However, in a study of asthma admissions among children in Vancouver, NO2 was significantly
and positively associated with asthma hospitalization only for boys in the low socioeconomic group (Lin
et al., 2004). An increased association with asthma with exposure to traffic pollutants was observed for
girls (Kim et al., 2004a). Decrements in FVC and FEVi growth associated with NO2 were reported in
male and female children in Mexico (Rojas-Martinez et al., 2007a,b).
4.3.4. Genetic Factors for Oxidant and Inflammatory Damage
A consensus now exists among epidemiologists that genetic factors related to health outcomes and
ambient pollutant exposures merit serious consideration (Kauffmann et al., 2004; Gilliland et al., 1999;
ATS 2000b). Interindividual variation in human responses to air pollutants may indicate that that some
subpopulations are at increased risk of detrimental effects from pollutant exposure, and it has become
clear that genetic background is an important susceptibility factor (Kleeberger, 2005). Several criteria
must be satisfied in selecting and establishing useful links between polymorphisms in candidate genes and
adverse respiratory effects. First, the product of the candidate gene must be instrumentally involved in the
pathogenesis of the adverse effect of interest, often a complex trait with many determinants. Second,
polymorphisms in the gene must produce a functional change in either the protein product or in the level
of expression of the protein. Third, in epidemiologic studies, the issue of confounding by other
environmental exposures must be carefully considered. In general, work has focused on genes involved in
oxidant and inflammation damage.
Several glutathione S-transferase (GST) genes have common, functionally important polymorphic
alleles that affect host defense function in the lung (e.g., homozygosity for the null allele at the GSTM1
and GSTT1 loci, homozygosity for the A105G allele at the GSTP1 locus). GST genes are inducible by
oxidative stress. Exposure to radicals and oxidants in air pollution induces decreased GSH that increases
transcription of GSTs. Individuals with genotypes that result in enzymes with reduced or absent peroxide
activity are likely to have reduced oxidant defenses and potentially increased susceptibility to inhaled
oxidants and radicals.
Studies of genotype, respiratory health, and air pollution in general have been conducted (Lee et al.,
2004; Gilliland et al., 2002; Gauderman et al., 2007). NO2-related genetic effects have been presented
primarily by Romieu et al. (2006) and indicated that asthmatic children with GSTM1 null and GSTP1
Val/Val genotypes appear to be more susceptible to developing respiratory symptoms related to O3, but
not NO2, concentrations. Though, it was hypothesized that ambient NO2 concentrations may affect
breathing in general in children regardless of their GSTM1 or GSTP1 genotypes, GSTM1-positive and
GSTP1 lie/lie- and He/Val-genotype children were more likely to experience cough and bronchodilator
use, specifically in response to NO2 than GSTMl-null and GSTP1-Val/Val children. Contrary to
expectations, a 20-ppb increase in ambient NO2 concentrations was associated with a decrease in
bronchodilator use among GSTP1 Val/Val -genotype children. It remains plausible that there are genetic
factors that can influence health responses to NO2, though the few available studies did not provide
specific support for genetic susceptibility to NO2 exposure.
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4.3.5. Other Potentially Susceptible Populations
Although data specific to NO2 exposures was lacking for many of the susceptibility factors listed
below, several potentially susceptible groups deserve specific mention in this document. These include
individuals in a chronic pro-inflammatory state (e.g., diabetics), obesity, and children born prematurely or
with low birth weight.
Factors that may influence susceptibility or vulnerability are:
Susceptibility Factors Vulnerability Factors
, .-, , • Socioeconomic status
• Age, Gender
, , , . ., , . ., , , . ., • Education level
• Adverse birth outcomes: e.g., preterm birth, low birth
weight, growth restriction, birth defects • Air conditioning Use
Race/ethnicity • Proximity to Roadways
Genetic factors
Pre-existing disease, e.g., diabetes
Genetic factors • Geographic Location (West
vs. East)
^ .. • Level of Exercise
• Obesity
D • + A- +u r-f\nr\ * Work Environment (e.g.,
• Respiratory diseases, e.g., asthma, COPD , , x
outdoor workers)
• Cardiovascular diseases
Chronic inflammation appears to enhance susceptibility for air pollution-related cardiovascular
events in older individuals and persons with diabetes, coronary artery disease, obesity, and past
myocardial infarctions (Bateson and Schwartz 2004, Goldberg et al., 2001; Zanobetti and Schwartz, 2002;
Peel et al. 2007). Dubowsky et al. (2006) reported that individuals with conditions associated with both
chronic inflammation and increased cardiac risk were more vulnerable to the short-term pro-inflammatory
effects of air pollution. This included individuals with diabetes; obesity; and concurrent diabetes, obesity
and hypertension. Zanobetti and Schwartz (2001) reported more than twice the risk for hospital
admissions for heart disease in persons with diabetes than in persons without diabetes associated with
exposure to ambient air pollution, indicating that persons with diabetes are an important at-risk group.
Data from the Third National Health and Nutrition Examination Survey indicated that 5.1% of the U.S.
population older than 20 years of age has diagnosed diabetes and an additional 2.7% has undiagnosed
diabetes (Harris et al., 1998). Moreover, another study found that subjects with impaired glucose
tolerance without type II diabetes also had reduced HRV (Schwartz, 2001). This may indicate that the at-
risk population may be even larger.
Mortimer et al. (2000) reported that among asthmatic children, birth characteristics continue to be
associated with increased susceptibility to air pollution later in life, demonstrating that air pollution-
induced asthma symptoms were more severe in children born prematurely or of low birth weight.
Specifically, the authors revealed asthmatic children born more than three weeks prematurely or weighing
less than 2,500 grams (5.5 pounds) had a six-fold decrease in breathing capacity associated with air
pollution compared to full-weight, full-term children. The low birth weight and premature children also
reported a five-fold greater incidence of symptoms like wheezing, coughing and tightness in the chest.
4.3.6. Increased Vulnerability Associated with Increased Exposure
Certain groups may experience relatively high exposure to NO2, thus forming a potentially
vulnerable population. Many studies found that indoor, personal, and outdoor NO2 levels are strongly
4-8
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associated with proximity to traffic or traffic density (see Section 2.5.4). NO2 concentrations in heavy
traffic or on freeways, have been observed in the range of 40 to 70 ppb and can be more than twice the
residential outdoor or residential/arterial road level (Lee et al., 2000; Westerdahl et al., 2005). Due to high
air exchange rates, NO2 concentrations inside a vehicle could rapidly approach levels outside the vehicle
during commuting; the mean in-vehicle NO2 concentration has been observed to be between 2 to 3 times
non-traffic ambient levels (see Section 2.5.4). Those with occupations that require them to be in or close
to traffic or roadways (e.g., bus and taxi drivers, highway patrol officers, toll collectors) or those with
long commutes could be exposed to relatively high levels of NO2 compared to ambient levels.
SES is a known determinant of health, and there is evidence that SES modifies the effects of air
pollution (O'Neill et al. 2003; Makri and Stilianakis, 2008). Higher exposures to air pollution and greater
susceptibility to its effects may contribute to a complex pattern of risk among those with lower SES.
Conceptual frameworks have been proposed to explain the relationship between SES, susceptibility, and
exposure to air pollution. Common to these frameworks is the consideration of the broader social context
in which persons live, and its effect on health in general (O'Neill et al., 2003; Gee and Payne-Sturges,
2004), as well as on maternal and child health (Morello-Frosch and Shenassa, 2006) and asthma (Wright
and Subramanian, 2007) specifically. Multilevel modeling approaches that allow parameterization of
community-level stressors such as increased life stress as well as individual risk factors were considered
by these authors. In addition, statistical methods that allow for temporal and spatial variability in exposure
and susceptibility have been discussed in the recent literature (Jerrett and Finkelstein, 2005; Kiinzli et al.,
2005).
Many recent studies examined modification by SES indicators on the association between mortality
and PM (O'Neill et al., 2003; Martins et al., 2004; Jerrett et al., 2004; Finkelstein et al., 2003; Romieu et
al., 2004a) or other indices such as traffic density, distance to roadway or a general air pollution index
(Ponce et al., 2005; Woodruff et al., 2003; Finkelstein et al., 2004). SES modification of NO2 associations
has been examined in fewer studies. For example, in a study conducted in Seoul, Korea, community-level
SES indicators modified the association of air pollution with ED visits for asthma; of the five criteria air
pollutants evaluated, NO2 showed the strongest association in lower SES districts compared to high SES
districts (Kim et al., 2007.) In addition, Clougherty et al. (2007) evaluated exposure to violence (a chronic
stressor) as a modifier of the effect of traffic-related air pollutants, including NO2, on childhood asthma.
The authors reported an elevated risk of asthma with a 4.3-ppb increase in NO2 exposure solely among
children with above-median exposure to violence in their neighborhoods.
4.4. At-Risk Susceptible Population Estimates
Although NO2-related health risk estimates may appear to be small, they may well be important
from an overall public health perspective owing to the large numbers of persons in the potential risk
groups. Several population groups have been identified as possibly having increased susceptibility or
vulnerability to adverse health effects from NO2, including children, older adults, and persons with
preexisting pulmonary diseases. One consideration in the assessment of potential public health impacts is
the size of various population groups that may be at increased risk for health effects associated with
NO2-related air pollution exposure. Table 4.4.1 summarizes information on the prevalence of chronic
respiratory conditions in the U.S. population in 2004 and 2005 (National Center for Health Statistics,
2006a,b). Individuals with preexisting cardiopulmonary disease constitute a fairly large proportion of the
population, with tens of millions of persons included in each disease category. Of most concern are those
persons with preexisting respiratory conditions, with approximately 10% of adults and 13% of children
having been diagnosed with asthma and 6% of adults with COPD (chronic bronchitis and/or emphysema).
There are approximately 2.5 million deaths from all causes per year in the U.S. population, with
about 100,000 deaths from chronic lower respiratory diseases (Kochanek et al., 2004) and 4,000 from
4-9
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asthma (NCHS, 2006c). For respiratory health diseases, there are nearly 4 million hospital discharges per
year (DeFrances et al, 2005), 14 million ED visits (McCaig and Burt, 2005), 112 million ambulatory care
visits (Woodwell and Cherry, 2004), and an estimated 700 million restricted-activity days per year due to
respiratory conditions (Adams et al., 1999). Of the total number of visits for respiratory disease, 1.8
million annual ED visits were reported for asthma, including more than 750,000 visits by children. In
addition, nearly 500,000 annual hospitalizations for asthma were reported (NCHS, 2006c).
Centers for Disease Control and Prevention (CDC) analyses have shown that the burden of asthma
has increased over the past two decades (NCHS, 2006c). In 2005, approximately 22.2 million people
(7.7% of the population) had asthma. The incidence was higher among children (8.9% of children)
compared to adults (7.2%) (Note: 2004 data is shown in Table 4.4-1, with a prevalence of 6.7%). In
addition, prevalence and severity is higher among certain ethnic or racial groups such as Puerto Ricans,
American Indians, Alaskan Natives, and African Americans. The asthma hospitalization rate for black
persons was 240% higher than for white persons. Puerto Ricans were reported to have the highest asthma
death rate (360% higher than non-Hispanic white persons) and non-Hispanic black persons had an asthma
death rate that was 200% higher than non-Hispanic white persons. Furthermore, a higher prevalence of
asthma among persons of lower SES and an excess burden of asthma hospitalizations and mortality in
minority and inner-city communities have been observed in several studies (Wright and Subramanian,
2007). Gender and age are also determinants of prevalence and severity: adult females had a 40% higher
prevalence than adult males; and boys, a 30% higher prevalence than girls. Overall, females had a
hospitalization rate about 35% higher than males.
Table 4.4-1. Prevalence of selected respiratory disorders by age group and by geographic
region in the U.S.(2004 [U.S. Adults] and 2005 [U.S. Children] National
Health Interview Survey).
CHRONIC
CONDITION/DISEASE
ADULTS (18+ YEARS)
Asthma
COPD: Chronic Bronchitis
COPD: Emphysema
CHRONIC
POMniTIOM/niQPAQP
CHILDREN (<18 YEARS)
Respiratory Conditions
AGE (YEARS)
ALL ADULTS
CASES
(x 106)
14.4
8.6
3.5
%
6.7
4.2
1.7
ALL CHILDREN
CASES
(x 106)
6.5
8.9
18-44
%
6.4
3.2
0.3
0-4
6.8
45-64
%
7.0
4.9
2
5-11
9.9
65-74
%
7.5
6.1
4.9
12-17
9.6
75+
%
6.6
6.3
6.0
REGION
NORTH-
EAST
%
6.8
4.0
1.5
NORTH-
EAST
10.1
MID-
WEST
%
6.8
4.7
1.7
MID-
WEST
8.5
SOUTH
%
6.0
4.4
2.0
SOUTH
9.3
WEST
%
7.5
3.5
1.1
WEST
7.9
Source: National Center for Health Statistics (2006a,b)
In addition, population groups based on age group also comprise substantial segments of the
population that may be potentially at risk for NO2-related health impacts. Based on U.S. census data from
2000, about 72.3 million (26%) of the U.S. population are under 18 years of age, 18.3 million (7.4%) are
under 5 years of age, and 35 million (12%) are 65 years of age or older. Hence, large proportions of the
U.S. population are in age groups that are likely to have increased susceptibility and vulnerability for
health effects from ambient NO2 exposure.
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£0
«
E"1
0
90 -
80 -
70 -
60 -
5Q -
40 -
30 -
20 '
10 -
n
Population Fraction
* HAPEM6(2007) .
• Garshick et al. (2003)
A McConnell et al. (2006)
• American Housing Survey (2005)
Black Carbon
o Zhuetal. (2002)
•
A
»
1
m
' A
0
0
o
0
- 90
- 80
- 70
' 60
- 50
- 40
- 30
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c
0)
0
o
o
c
O
.a
re
0
0
re
DO
50 100 150 200 250 300 350
Distance from Roadway, meters
400 450
Figure 4.4-1. Fraction of the study populations living within a specified distance from roadways. For
comparison, concentrations of the traffic pollutant black carbon are plotted as a function of
distance from the roadway.
Based on data from the American Housing Survey, approximately 36 million persons live within
300 feet (~90 meters) of a four-lane highway, railroad, or airport and 12.6% of U.S. housing units are
located within this distance (U.S. Census Bureau, 2006). Furthermore, several exposure studies offer
insight into differential exposures to NO2 from traffic in childhood. In California, 2.3% of schools, grades
K-12, with a total enrollment of more than 150,000 students were located within -500 feet (150 m) of
high-traffic roads, and a higher proportion of nonwhite and economically disadvantaged students attended
schools within close proximity to these high-traffic roadways (Green et al., 2004). Similar findings were
reported for Detroit schoolchildren (Wu and Batterman, 2006). Figure 4.4-1 shows the proportion of
study populations in Boston, MA (Garshick et al. 2003) and Los Angeles, CA (McConnell et al. 2006),
the entire U.S. (American Housing Survey, 2005), and from population exposure models (HAPEM6,
2007) living within a certain distance from major roadways. It also presents results of air quality
measurements showing the decrease in concentration of black carbon, a traffic-related pollutant, with
increasing distance from the roadway. The considerable size of the population groups at risk indicate that
exposure to ambient NO2 could have an impact on public health in the U.S.
4-11
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4.5. Summary of Public Health Issues
In the few studies that specifically examined concentration-response relationships between NO2 and
health outcomes, there was little evidence of an effect threshold. However, various factors, such as
interindividual variation in response, additivity to background of effect and/or exposure, and
measurement error tend to linearize the concentration-response relationship and obscure any population-
level thresholds that might exist.
Persons with preexisting respiratory disease, children, and older adults may be more susceptible to
the effects of NO2 exposure. Individuals in sensitive groups may be affected by lower levels of NO2 than
the general population or experience a greater impact with the same level of exposure. A number of
factors may increase susceptibility to the effects of NO2. Studies generally reported a positive excess risk
for asthmatics, and there was emerging evidence that CVD may cause persons to be more susceptible,
though it is difficult to distinguish the effect of NO2 from other traffic pollutants. Children and older
adults (65+ years) may be more susceptible than adults, possibly due to physiological changes occurring
among these age groups.
In addition to intrinsically susceptible groups, a portion of the population may be at increased
vulnerability due to higher exposures, generally people living and working near roadways. A considerable
fraction of the population resides, works, or attends school near major roadways. Of this population, those
with physiological susceptibility will have even greater risks of health effects related to NO2.
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Chapter 5. Summary and Conclusions
5.1. Introduction
The Integrated Plan for the Primary NAAQS forNO2 (U.S. Environmental Protection Agency,
2007) presents a series of policy-relevant questions to structure this assessment of the scientific evidence
(Section 1.1). This ISA focuses on evaluating recent scientific evidence while incorporating information
from the last review to best inform consideration of these policy-relevant questions. The purpose of this
ISA is to form the scientific basis for regulatory decision making as it pertains to retaining or revising the
current primary NAAQS for NO2 (0.053 ppm, annual average). The previous chapters present the most
policy-relevant science. This chapter first summarizes key findings and then draws conclusions about
health effects associated with exposure to NOX. These conclusions are based on explicit guidelines
(Section 1.3) derived from the Hill aspects (Hill, 1965) and modeled after other pertinent frameworks.
The framework for evaluation of evidence regarding causality is described in Chapter 1. The
framework and language draw from similar efforts across the Federal government and the wider scientific
community, especially from the recent NAS Institute of Medicine document, Improving the Presumptive
Disability Decision-Making Process for Veterans (Institute of Medicine, 2007). A five-level hierarchy is
used to be consistent with the Guidelines for Carcinogen Risk Assessment (U.S. Environmental
Protection Agency, 2005). Conclusions concerning causality of association were placed into one of five
categories with regard to weight of the evidence based on the framework. The five descriptors are:
• Sufficient to infer a causal relationship;
• Sufficient to infer a likely causal relationship (i.e. more likely than not);
• Suggestive but not sufficient to infer a causal relationship;
• Inadequate to infer the presence or absence of a causal relationship; and
• Suggestive of no causal relationship.
This integrative discussion begins with some key conclusions from the atmospheric sciences that
are relevant to the interpretation of the health evidence and provide important underpinnings for potential
quantitative assessments, including information about ambient concentrations and monitoring, and
estimation of policy-relevant background. Consideration of exposure measurement error and exposure
assessment issues is an essential component of this review, and provides an overview of the findings that
inform the evaluation of the health evidence. Conclusions regarding causality for different categories of
health outcomes are presented. Highlights of findings that support these conclusions are presented. The
key findings from atmospheric science, exposure assessment, and health effects, including animal
toxicological, human clinical and epidemiologic studies are integrated with regard to levels at which
effects are observed, the time period (or averaging time) over which these effects are observed, and NO2
serving as the indicator for the oxides of nitrogen NAAQS, and presented in Section 5.4.
5-1
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5.2. Key Source to Exposure Findings
5.2.1. Atmospheric Science and Ambient Concentrations
An understanding of atmospheric processes affecting a given pollutant is crucial for understanding
the causal chain linking NOX sources to health effects. NO2 plays a key role in the formation of O3 and
photochemical smog. NO2 is an oxidant and reacts to form other photochemical oxidants, including
organic nitrates like the PANs and inorganic acids like HNO3. NO2 also reacts with toxic compounds such
as PAHs to form nitro-PAHs, which can be more toxic than either reactant alone.
Major anthropogenic sources of NOX include motor vehicles, power plants, and fossil fuel
combustion in general. NOX also is emitted by burning biomass fuels. Natural NOX sources include
wildfires, microbial activity in soils, and lightning. NOX is emitted by all of the above sources mainly as
NO. Atmospheric reactions oxidize NO to NO2. Thus, most NO2 in the atmosphere is the result of the
oxidation of primary NO. As noted in Chapter 2, NO and NO2 interconvert rapidly in the atmosphere, and
so it is often convenient to refer to their sum (NOX) instead of to them individually. The definition of
nitrogen oxides contains a number of TV-containing compounds formed by the oxidation of NO2 as
described in Chapter 2. Aspects of the atmospheric chemistry of NOX most relevant for interpreting the
human exposure and health evidence are:
• The current method of determining ambient NOX and then reporting NO2 concentrations by
subtraction of NO is subject to positive interference by NOX oxidation products, chiefly HNO3
and PAN as well as other oxidized TV-containing compounds. Measurements of these oxidation
products in urban areas are sparse.
• Products are expected to peak in the afternoon because of the continued oxidation to NO2 emitted
during the morning rush hours and during conditions conducive to photochemistry in areas well
downwind of sources, particularly during summer.
• Within urban cores, where many of the ambient monitors are sited close to strong NOX sources
such as motor vehicles on busy streets (i.e., where NO2 concentrations are highest), the positive
artifacts due to NO2 oxidation products are much smaller on a relative basis, generally <10%.
Conversely, the positive artifacts are larger in locations more distant from local NOX sources (i.e.
where NO2 concentrations are lowest) and could exceed 50%. Therefore, variable, positive
artifacts associated with measuring NO2 using the FRM severely hamper its ability to serve as an
accurate and precise indicator of NO2 concentrations at the typical ambient levels generally
encountered outside of urban cores. The result of these positive artifacts when using ambient
monitoring data in health outcome studies depends on whether or not the NO2 oxidation products
exert the same effect as NO2 on the health endpoint being considered.
• Because the dominant urban source is typically on-road vehicle emissions, ambient NO2
generally behaves with the temporal and spatial variability of other traffic-generated pollutants.
• The annual average NO2 concentrations of-15 ppb reported by the regulatory monitoring
networks are well below the level of the current NAAQS (0.053 ppm). However, daily max 1-
h avg concentrations can be greater than 100 ppb in some locations, e.g., areas with heavy traffic.
• Policy-relevant background concentrations of NO2 are much lower than average ambient
concentrations and are typically less than 0.1 ppb over most of the U.S., with the highest
values found in agricultural areas.
5-2
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5.2.2. Exposure Assessment
Personal exposure to ambient and outdoor NO2 is affected by many factors that influence the
contribution of ambient NO2 to personal exposures. Personal activity patterns determine when, where, and
how people are exposed to NO2. The variations of these physical and exposure factors determine the
strength of the association between personal exposure and ambient concentrations in both longitudinal
and cross-sectional studies. The observed strength of the association between personal exposures and
ambient concentrations are not only affected by the variation in physical parameters (e.g., penetration
coefficient (P), mass transfer coefficient (k), air exchange rate (a), and indoor sources) but also affected
by data quality and study design. The collective variability in all of the above parameters, in general,
contributes to exposure error in air pollution-health outcome studies. The errors and uncertainties
associated with the use of ambient NO2 concentrations as a surrogate for personal exposure to
ambient NO2 generally tend to reduce rather than increase effect estimates, and therefore are not
expected to change the principal conclusions from NO2 epidemiologic studies.
The association between the ambient component of personal exposures and ambient concentrations
is most relevant to the interpretation of epidemiologic evidence, but this type of correlation coefficient is
not generally reported. Therefore, the weak association between personal total exposures and ambient
concentrations in some longitudinal studies might not reflect the true association between the ambient
component of personal exposures and ambient concentrations. A number of studies found that personal
NO2 exposure was associated with ambient NO2 exposure, but the strength of the association ranged from
poor to good. Key findings related to assessing NO2 exposures are listed below.
• NO2 concentrations are highly spatially and temporally variable in urban areas. Intersite
correlations for NO2 concentrations range from slightly negative to highly positive in examined
cities. The range of spatial variation in NO2 concentrations is similar to that for O3, but larger than
that of fine particulate matter (PM25). Differences between 24-h avg concentrations at individual
paired sites in a MSA can be larger than the annual means at these sites.
• Elevated and rooftop NO2 measurements, particularly in inner cities, likely underestimate
concentrations and hence personal exposures occurring at lower elevations, closer to motor
vehicle emissions. Pedestrians that spend time walking in street canyons and residents that have
windows opening onto these canyons can therefore experience exposures to high near-road
concentrations that may equal or exceed those on roads in transit.
• Co-located samples showed that passive NO2 samplers generally correlate well with FRM
ambient samplers, and the concentration differences are generally within 10%. However, personal
passive samplers and the ambient samplers were both subject to measurement artifacts.
• In the absence of indoor sources, indoor NO2 levels are about one-half those found outdoors. In
the presence of indoor sources, particularly unvented combustion sources, NO2 levels can be
much higher than reported ambient concentrations.
• Alpha (a), the ratio of personal exposure to NO2 of ambient origin to the ambient NO2
concentration, ranged from -0.3 to -0.6 in studies where it was determined.
• Indoor exposures to NO2 are accompanied by exposures to other products of indoor combustion
and to products of indoor NO2 chemistry, such as HONO.
• The evidence relating ambient levels to personal exposures was inconsistent. Some of the
longitudinal studies examined found that ambient levels of NO2 were reliable proxies of personal
exposures to NO2. However, a number of studies did not find significant associations between
ambient and personal levels of NO2. The differences in results were related in large measure to
differences in study design and in exposure determinants. Measurement artifacts and differences
in analytical measurement capabilities could also have contributed to the inconsistent results.
5-3
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Indeed, in a number of the studies examined, the majority of measurements of personal NO2
concentrations were beneath detection limits, and in all studies some personal measurements were
beneath detection limits.
In two European studies, community averages of personal total exposures were highly correlated
with either ambient or outdoor microenvironment concentrations. However, because of
limitations in these studies, caution should be exercised in using these results to determine
whether ambient concentrations of NO2 can be used as surrogates for long-term community
average exposures in epidemiologic studies.
5.3. Key Health Effects Findings
5.3.1. Findings from the Previous Review
The 1993 NOX AQCD concluded that there were two key health effects of greatest concern at
ambient or near-ambient concentrations of NO2:
• Increases in airway hyperresponsiveness of asthmatic individuals after short-term exposures.
• Increased respiratory illness among children associated with longer-term exposures to NO2.
Evidence also was found for increased risk of emphysema, but this appeared to be of major concern
only with exposures to levels of NO2 that were much higher than current ambient levels of NO2 (U.S.
Environmental Protection Agency, 1993). Qualitative evidence regarding airway hyperresponsiveness and
lung function changes was drawn from human clinical and animal toxicological studies; these did not
elucidate a concentration-response relationship. Epidemiologic studies reported increased respiratory
symptoms with increased indoor NO2 exposures. Animal toxicological findings of lung host defense
system changes with NO2 exposure provided a biologically plausible basis for these results.
Subpopulations considered potentially more susceptible to the effects of NO2 exposure included persons
with preexisting respiratory disease, children, and the elderly. In the 1993 AQCD, the epidemiologic
evidence for respiratory health effects was limited, and no studies had considered effects such as hospital
admissions, ED visits, or mortality.
5.3.2. New Health Effects Findings
Evidence published since the last review generally has confirmed and extended the conclusions
articulated in the 1993 AQCD. The epidemiologic evidence has grown substantially, including new field
or panel studies on respiratory health outcomes, intervention studies, numerous time-series epidemiologic
studies of effects such as hospital admissions, and a substantial number of studies evaluating mortality
risk with short-term NO2 exposures. As noted above, no epidemiologic studies were available in 1993 that
assessed relationships between nitrogen oxides and outcomes such as hospital admissions, ED visits, or
mortality; in contrast, dozens of epidemiologic studies on such outcomes are now included in this
evaluation. Several recent studies also have reported findings from prospective cohort studies on
respiratory health effects with long-term NO2 exposure. In addition, recent evidence characterizing the
responses of susceptible and vulnerable populations has been reported since 1993, particularly concerning
children, asthmatics, and those living or working near roadways. While not as marked as the growth in
the epidemiologic literature, a number of recent toxicological and human clinical studies provide further
insights into relationships between NO2 exposure and health effects.
These new findings allow us to draw several overall conclusions concerning the health effects of
NO2 exposures. These conclusions are integrated across disciplines at the organ-system level (e.g.,
5-4
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respiratory and cardiovascular morbidity, cardiopulmonary mortality). Integration at this level is generally
more meaningful than reporting on separate health effects, which themselves may be serious, but
individually do not fully characterize impacts on health. The conclusions of this ISA are summarized in
Table 5.3-1, along with those of the previous review. Following the table is more discussion of evidence
that supports these conclusions, organized by exposure duration and specific health effect.
Table 5.3-1. Summary of evidence from epidemiologic, human clinical, and animal toxicological
studies on the health effects associated with short- and long-term exposure to
NO2.
HEALTH OUTCOME
CONCLUSION FROM
PREVIOUS NAAQS REVIEW
CONCLUSION FROM 2008 ISA
SHORT-TERM EXPOSURE TO N02
Respiratory Morbidity
Lung Host Defense
Airway Inflammation
Airway Hyperresponsiveness
Respiratory Symptoms
Lung Function
ED Visits /Hospital
Admissions
Cardiovascular Morbidity
Cardiovascular Effects
ED Visits /Hospital
Admissions
Mortality
All Cause and
Cardiopulmonary Mortality
No Overall Conclusion
Human clinical studies suggest NO2
effects; Animal toxicological studies
indicate that alveolar macrophages
and humoral and cell-mediated
immune systems are affected and
show that exposure can impair the
respiratory host defense system
resulting in susceptibility to infection.
No Studies
An increase in responsiveness to
bronchoconstrictors was found in
asthmatics and healthy individuals
exposed to NO2 at rest.
Children living in homes with gas
stoves are at increased risk for
developing respiratory diseases and
illnesses compared to children living
in homes without gas stoves.
Lung function changes in asthmatics
reported at low (0.2 to 0.5 ppm), but
not higher (up to 4 ppm), NO2
concentrations. No convincing
evidence of lung function
decrements in healthy individuals
below 1.0 ppm.
No Studies
No Studies
No Studies
No Studies
No Studies
No Studies
"sufficient to infer a likely causal relationship"
Impaired host-defense systems and increased risk of susceptibility to both
viral and bacterial infections after NO2 exposures have been observed in
epidemiologic, human clinical, and animal toxicological studies.
Human clinical studies report effects of NO2 (1-2 ppm) on airway inflammation
in healthy humans. Animal toxicological studies and limited available
epidemiologic studies on children support these findings.
Human clinical studies of allergen and nonspecific bronchial challenges in
asthmatics observed increased airway hyperresponsiveness at near ambient
concentrations (0.1-0.3 ppm). Increased responsiveness to nonspecific
challenges was also observed in animals at higher NO2 levels (e.g., 0.5 ppm).
Epidemiologic studies provide consistent evidence of an association of
respiratory effects with indoor and personal NO2 exposures in children.
Multicity studies provide further support for associations between ambient
NO2 concentrations (means of 7-70 ppb) and respiratory symptoms in
asthmatic children.
The association between ambient NO2 concentrations and lung function in
epidemiologic studies were generally inconsistent. Recent clinical evidence
generally confirms prior findings.
Positive and generally robust associations observed between ambient NO2
levels (means of 3-50 ppb) and increased ED visits and hospital admissions
for respiratory causes, especially asthma.
"inadequate to infer the presence or absence of a causal relationship"
Evidence from epidemiologic studies of heart rate variability, repolarization
changes, and cardiac rhythm disorders among heart patients with ischemic
cardiac disease are inconsistent.
Generally positive associations between ambient NO2 concentrations and
hospital admissions or ED visits for cardiovascular disease; however, the
effects were not robust to adjustment for copollutants.
"suggestive but not sufficient to infer a causal relationship"
Positive and generally robust associations between ambient NO2
concentrations and risk of nonaccidental and cardiopulmonary mortality.
5-5
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HEALTH OUTCOME
CONCLUSION FROM
PREVIOUS NAAQS REVIEW
CONCLUSION FROM 2008 ISA
LONG-TERM EXPOSURE TO N02
Respiratory Morbidity
Respiratory Effects
Other Morbidity
Cancer
Cardiovascular Effects
Birth Outcomes
Mortality
All Cause and
Cardiopulmonary Mortality
No Overall Conclusion
NO2 can cause emphysema
(meeting the human definition
criteria) in animals at high
concentrations of NO2.
No Studies
No Studies
No Studies
No Studies
No Studies
No Studies
"suggestive but not sufficient to infer a causal relationship"
Epidemiologic studies observed decrements in lung function growth
associated with long-term exposure to NO2.
"inadequate to infer the presence or absence of a causal relationship"
Limited epidemiologic studies observed an association between long-term
NO2 exposure and cancer; animal toxicological studies have not provided
clear evidence that NO2 acts as a carcinogen.
Very limited epidemiologic and toxicological evidence does not suggest that
long-term exposure to NO2 has cardiovascular effects.
The epidemiologic evidence for an association between long-term exposure to
NO2 and birth outcomes is generally inconsistent, with limited support from
animal toxicological studies.
"inadequate to infer the presence or absence of a causal relationship"
The results of epidemiologic studies examining the association between long-
term exposure to NO2 and mortality were generally inconsistent.
5.3.2.1. Respiratory Effects Related to Short-Term Exposure
Taken together, recent studies provided scientific evidence that NO2 is associated with a range of
respiratory effects and provide evidence sufficient to infer a likely causal relationship between short-
term NO2 exposure and adverse effects on the respiratory system. The greatest weight of evidence
comes predominantly from the large body of recent epidemiologic evidence, with supportive evidence
from human and animal experimental studies. The main body of evidence pertaining to causality for
respiratory health outcomes is shown in Figure 5.3-1. The epidemiologic studies generally show positive
associations between NO2 and respiratory symptoms and hospitalization or ED visits, with a number
being statistically significant, particularly the more precise effect estimates. There also is a pattern of
positive associations with respiratory mortality, though most are not statistically significant. A number of
the epidemiologic studies have been conducted in locations where the ambient NO2 levels are well below
the level of the NAAQS of 0.053 ppm (53 ppb) (annual average). The mean ambient concentrations,
associated with the health outcomes reported in Figure 5.3-1 are in the range of 3 to 70 ppb for 24 h avg,
with maximum ambient concentrations as high as 100 to 300 ppb.
The epidemiologic evidence for respiratory effects can be characterized as consistent, in that
associations are reported in studies conducted in numerous locations with a variety of methodological
approaches. The findings are coherent in the sense that the studies report associations with respiratory
health outcomes that are logically linked together.
5-6
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Legend to Figure 5.3-1.
Respiratory Symptoms
1 Schwartz etal. (1994) Cough
2 Mortimer et al. (2002) Asthma symptoms
3 Schildcrout et al. (2006) Asthma symptoms
4 Pino et al. (2004) Wheezy bronchitis
5 Ostro et al. (2001) Wheeze
6 Ostro et al. (2001) Cough
7 Delfino et al. (2002) Asthma symptoms
8 Segala et al. (1998) Asthma symptoms
9 Segala etal. 1998 Cough
10 Just et al. (2002) Cough
11 Jalaludin et al. (2004) Cough
12 Segala et al. (2004) Cough
13 von Klot et al. (2002) Wheeze
14 von Klot et al. (2002) Phlegm
15 von Klot et al. (2002) Cough
16 von Klot et al. (2002) Breathing problems
17 Ward et al (2002) Cough
18 Rodriguez et al. (2007) Cough
19 Boezen et al. (1999) LRS
Respiratory Disease -All Ages
20 Tolbert et al. (2007)
21 Peel et al. (2005)
22 Luginaah et al. (2005)
23 Luginaah et al. (2005)
24 Anderson et al. (2001)
25 Atkinson etal., (1999a)
26 Atkinson etal., (1999b)
27 Ponce de Leon etal. (1996)
28 Llorca et al. (2005)
29 Oftedal et al. (2003)
30 Hagen et al. (2000)
31 Bedeschi et al. (2007)
32 Hinwood et al., (2006)
33 Petroeschevsky et al. (2001)
Respiratory Disease - Children
34 Yang et al. (2003)
35 Luginaah et al. (2005)
36 Luginaah et al. (2005)
37 Anderson et al. (2001)
38 Atkinson et al. (1999a)
39 Atkinson etal. (1999b)
40 Ponce de Leon etal. (1996)
41 Vigotti et al. (2007)
42 Petroeschevsky et al. (2001)
43 Petroeschevsky et al. (2001)
44 Barnett et al. (2005)
45 Barnett et al. (2005)
46 Barnett et al. (2005)
47 Wong etal. (1999)
48 Lin etal. (1999)
49 Gouveia and Fletcher (2000a)
Respiratory Disease -Adults
50 Luginaah et al. (2005)
51 Luginaah et al. (2005)
52 Spix etal. (1998)
53 Anderson et al. (2001)
54 Atkinson etal. (1999a)
55 Atkinson etal. (1999b)
56 Ponce de Leon etal. (1996)
57 Schouten etal. (1996)
58 Schouten etal. (1996)
59 Petroeschevsky et al. (2001)
60 Wong etal. (1999)
Respiratory Disease -
Older Adults (65+)
61 Luginaah et al. (2005)
62 Luginaah et al. (2005)
63 Fung et al. (2006)
64 Yang et al. (2003)
65 Spix etal. (1998)
66 Anderson et al. (2001)
67 Atkinson etal. (1999a)
68 Atkinson etal. (1999b)
69 Ponce de Leon etal. (1996)
70 Andersen et al. (2007b)
71 Andersen et al. (2007a)
72 Schouten etal. (1996)
73 Schouten etal. (1996)
74 Simpson et al. (2005a)
75 Hinwood et al. (2006)
76 Petroeschevsky et al. (2001)
77 Wong etal. (1999)
Asthma-All Ages
78 Peel et al. (2005)
79 Ito et al. (2007)*
80 Burnett etal. (1999)
81 Anderson etal. (1998)
82 Atkinson et al. (1999a)
83 Atkinson et al. (1999b)
84 Galan et al. (2003)
85 Chardon et al. (2007)
86 Schouten etal. (1996)
87 Migliaretti et al. (2005)
88 Migliaretti and Cavallo (2004)
89 Hinwood et al. (2006)
90 Petroeschevsky et al. (2001)
91 Wong etal.,(1999)
92 Tsai et al. (2006)
93 Tsai et al. (2006)
94 Yang et al. (2007)
95 Yang et al. (2007)
Asthma - Children
96 Peel et al. (2005)
97 Tolbert et al. (2000)
98 Lin et al. (2003)
99 Lin et al. (2003)
100 Sunyeretal. (1997)
101 Anderson etal. (1998)
102 Atkinson et al. (1999a)
103 Atkinson etal. (1999b)
104 Thompson et al. (2001)
105 Andersen et al. (2007b)
106 Andersen et al. (2007a)
107 Migliaretti et al. (2005)
108 Migliaretti and Cavallo (2004)
109 Migliaretti and Cavallo (2004)
110 Barnett etal. (2005)
111 Barnett et al. (2005)
112 Hinwood etal. (2006)
113 Petroeschevsky et al. (2001)
114 Petroeschevsky et al. (2001)
115 Morgan etal. (1998a)
116 Ko et al. (2007)
117 Lee etal. (2006)
118 Gouveia and Fletcher (2000a)
119 Jaffe et al. (2003)
120 Jaffe et al. (2003)
121 Linn etal. (2000)
Asthma-Adults
122 Sunyeretal. (1997)
123 Anderson et al. (1998)
124 Atkinson et al. (1999a)
125 Atkinson etal. (1999b)
126 Boutin-Forzano et al. (2004)
127 Ten ias etal. (1998)
128 Castellsague et al. (1995)
129 Migliaretti et al. (2005)
130 Morgan etal. (1998a)
131 Ko et al. (2007)
132 Anderson et al. (1998)
133 Atkinson etal. (1999a)
134 Migliaretti et al. (2005)
135 Hinwood etal. (2006)
136 Ko et al. (2007)
Respiratory Mortality
137 Ostro et al. (2000)
138 Fairley (1999); (Reanalysis 2003)
139 Gamble (1998)
140 Gwynn et al. (2000)
141 Burnett etal. (2004)
142 Villeneuve et al. (2003)
143 Samolietal. (2006)
144 Zmirouetal. (1998)
145 Biggeri et al. (2005)
146 Anderson et al. (1996)
147 Bremneretal. (1999)
148 Anderson et al. (2001)
149 Le Tertre et al. (2002)
150 Dab etal. (1996)
151 Zmirouetal. (1996)
152 Hoeketal. (2000);
(Reanalysis, Hoek (2003)
153 Hoeketal. (2000);
(Reanalysis, Hoek (2003)
154 Saez et al. (2002)
155 Garcia-Aymerich et al. (2000)
156 Saez etal. (1999)
157 Sunyeretal. (1996)
158 Borja-Aburto et al. (1998)
159 Gouveia and Fletcher (2000b)
160 Simpson et al. (2005a,b)
161 Simpson etal. (2000)
162 Tsai etal. (2003)
163 Yang etal. (2004b)
164 Wong etal. (2001)
165 Wong etal. (2002)
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Animal toxicologic and human clinical studies have been conducted within the range of maximum
ambient concentrations observed in epidemiologic studies (100 to 300 ppb) and provide some supporting
evidence for the effects observed in the epidemiologic studies. Generally, exposure durations used in
human clinical studies are more similar to peak exposures than 24-h avg exposures. Tables 5.3-2 and
5.3-3 summarize the health endpoints that have been linked with NO2 exposure in human clinical and
animal toxicologic studies, respectively, along with the lower range of doses or concentrations with which
these effects have been reported. To put the concentration information in perspective, average and
maximum ambient concentrations from earlier years in the United States and elsewhere were substantially
greater than current levels; yet in the 3-year period 2003-2005, 1-h excursions in the United States have
been observed in the range of 100 to 200 ppb (see Chapter 2). The human and animal findings underlying
this causal judgment are summarized below.
Table 5.3-2. Key studies and effects of exposure to NO2 from clinical studies.
STUDY
NO2
(ppm)
EXPOSURE
DURATION
(h)
OBSERVED EFFECTS
Folinsbee(1992)
0.1-0.3
0.5-2.0
Meta-analysis showed increased airway responsiveness following NO2 exposure in
asthmatics. Large variability in protocols and responses. Most studies used
nonspecific airway challenges. Airway responsiveness tended to be greater for resting
(mean 45 min) than exercising (mean 102 min) exposure conditions.
Barck et al. (2002, 2005a)
Strand et al. (1996; 1997;
1998)
0.26
0.5
Asthmatics exposed to NO2 during rest experienced enhanced sensitivity to bronchial
challenge-induced decrements in lung function and increased allergen-induced airway
inflammatory response. Inflammatory response to allergen observed in the absence of
allergen-induced lung function response. No NO2-induced change in lung function.
Gong et al. (2005)
Morrow etal. (1992)
Vagaggini etal. (1996)
0.3-0.4
2-4
Inconsistent effects on FVC and FEV, in COPD patients with mild exercise.
Azadnivetal. (1998)
Blomberg etal. (1997; 1999)
Devlin etal. (1999)
Frampton et al. (2002)
Jorresetal. (1995)
1.0-2.0
2-6
Increased inflammatory response and airway responsiveness to nonspecific challenge
in healthy adults exposed during intermittent exercise. Effects on lung function and
symptoms in healthy subjects not detected by most investigators. Small decrements
in FEVi reported for asthmatics.
Beiland Ulmer(1976)
Niedingetal. (1979)
Nieding and Wagner (1977)
Niedingetal. (1980)
>2.0
1-3
Lung function changes (e.g., increased airway resistance) in healthy subjects. Effects
not found by others at 2-4 ppm.
Lung Host Defenses and Immunity
Impaired host-defense systems and increased risk of susceptibility to both viral and bacterial
infections after NO2 exposures were observed in epidemiologic, human clinical, and animal
toxicological studies (Section 3.1.2). A recent epidemiologic study (Chauhan et al., 2003) provided
evidence that increased personal exposure to NO2 worsened virus-associated symptoms and decreased
lung function in children with asthma. The limited evidence from human clinical studies indicated that
NO2 increases susceptibility to injury by subsequent viral challenge (Frampton et al., 2002). Animal
toxicological studies have shown that lung host defenses are sensitive to NO2 exposure, with several
measures of such effects observed at concentrations of less than 1 ppm. The epidemiologic and
5-9
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experimental evidence indicated coherence for effects of NO2 exposure on host defense (i.e., immune
system effects). This group of outcomes also provided plausibility and potential mechanistic support for
other respiratory effects described subsequently, such as respiratory symptoms or increased ED visits for
respiratory diseases.
Table 5.3-3. Summary of lexicological effects in rats from NO2 exposure.
STUDY
Kumae and
Arakawa (2006)
Kumae and
Arakawa (2006)
Robison et al.
(1993)
Mercer etal. (1995)
Barthetal. (1994)
NO2 (ppm)
0.2
0.5
0.5
0.5 spikes
of 1.5
0.8
EXPOSURE DURATION
From conception to 12 wks
post delivery
Weanling period (from 5 wks
old to 12 wks)
0.5-10 days
9 wks
1 or 3 days
OBSERVED EFFECTS
Increase in BALF lymphocytes (indicative of inflammation)
Suppression of ROS (indicative of lung host defense impairment)
Depressed activation of arachidonic acid metabolism and superoxide production
(indicative of lung host defense impairment)
Increase in the number of fenestrae in the lungs (morphological effects)
Increase in bronchiolar epithelial proliferation (morphological effects)
Note: Lowest-observed-effect level based on category
BALF=Bronchoalveolar lavage fluid ROS=Reactive oxygen species
Airway Inflammation
Together, the findings of human and animal studies provided some evidence for airway
inflammation with NO2 exposure, particularly in the more sensitive subgroups such as children or
asthmatics. The few epidemiologic studies that considered inflammation showed an association between
ambient NO2 concentrations and inflammatory response in the airways in children. The associations were
inconsistent in the adult populations examined (Section 3.1.3). Effects of NO2 on airway inflammation
have been observed in human clinical and animal toxicological studies at higher than ambient levels
(0.4-5 ppm). Human clinical studies shows that airway inflammation is increased at NO2
concentrations of <2.0 ppm; the onset of inflammatory responses in healthy subjects appears to be
between 100 and 200 ppm-min, i.e., 1 ppm for 2 to 3 h (Figure 3.1-1). Increases in biological markers
of inflammation were not observed consistently in healthy animals at levels of less than 5 ppm; however,
increased susceptibility to NO2 concentrations of as low as 0.4 ppm was observed when lung vitamin C
was reduced (by diet) to levels that were <50% of normal. These data provided some evidence for
biological plausibility and one potential mechanism for other respiratory effects, such as exacerbation of
asthma symptoms or increased ED visits for asthma.
Airway Hyperresponsiveness
The evidence from human and animal experimental studies provided some evidence for increased
airway responsiveness to specific allergen challenges following NO2 exposure (Section 3.1.4.1). Recent
human clinical studies involving allergen challenge in asthmatics reported that NO2 exposure may
enhance the sensitivity to allergen-induced decrements in lung function and increase the allergen-
induced airway inflammatory response at exposures as low as 0.26 ppm NO2 for 30 min (Figure 3.1-
2). Increased immune-mediated pulmonary inflammation also was observed in rats exposed to house dust
mite allergen following exposure to 5 ppm NO2 for 3 h. Exposure to NO2 also has been found to enhance
the inherent responsiveness of the airway to subsequent nonspecific challenges in human clinical studies
(Section 3.1.4.2). In general, small but significant increases in nonspecific airway hyperresponsiveness
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were observed in the range of 1.5 to 2.0 ppm for 3 h exposures in healthy adults and between 0.2
and 0.3 ppm NO2 for 30 -min exposures and at 0.1 ppm NO2 for 60-min exposures in asthmatics.
Subchronic exposures (6 to 12 weeks) of animals to NO2 also increased responsiveness to nonspecific
challenges at exposures of 1 to 4 ppm.
Respiratory Symptoms
Consistent evidence has been observed for an association of respiratory effects with indoor and
personal NO2 exposures in children at ambient concentration levels (Section 3.1.5.1). In particular, the
Pilotto et al. (2004) intervention study provided evidence of improvement in respiratory symptoms with
reduced NO2 exposure in asthmatic children. This study linked respiratory effects with exposure to NO2
from an indoor combustion source,(i.e., unflued gas heaters), thus, increased confidence that NO2 is not
solely a marker for an ambient air pollution mixture in observed associations with NO2 from outdoor
sources (in particular traffic emissions) that has infiltrated indoors. The epidemiologic studies using
community ambient monitors also found associations between ambient NO2 concentration and respiratory
symptoms (Section 3.1.4.2, see Figure 3.1-6). The results of recent multicity studies (Schildcrout et al.,
2006; Mortimer et al., 2002) provided further support for associations between respiratory symptoms and
medication use in asthmatic children. Positive associations were observed in cities where the median
range was 18 to 26 (90th percentiles: 34 to 37) ppb for a 24-h avg (Schildcrout et al., 2006) and the
mean NO2 level was 32 (range: 7 to 96) ppb for a 4-h avg (Mortimer et al., 2002). These
concentrations were within the range of 24-h avg concentrations observed in recent years. In the results of
multipollutant models, NO2 associations in these multicity studies were generally robust to adjustment for
copollutants including O3, CO, and PMi0 (Figure 3.1-7).
Most human clinical studies do not report or observe respiratory symptoms with NO2 exposure, and
animal toxicological studies do not measure effects that would be considered symptoms. The
experimental evidence on immune system effects and airway inflammation discussed previously,
however, provide some plausibility and coherence for the observed respiratory symptoms in
epidemiologic studies.
Lung Function
Recent epidemiologic studies that examined the association between ambient NO2 concentrations
and lung function in children and adults generally produced inconsistent results (Section 3.1.5.1). Human
clinical studies generally did not find direct effects of NO2 on lung function in healthy adults at
levels as high as 4.0 ppm (Section 3.1.5.2). For asthmatics, the direct effects of NO2 on lung function
also have been inconsistent at exposure concentrations of less than 1 ppm NO2.
Respiratory ED Visits and Hospitalizations
Epidemiologic evidence exists for positive associations of short-term ambient NO2
concentrations below the current NAAQS level with increased numbers of ED visits and hospital
admissions for respiratory causes, especially asthma (Section 3.1.7). A number of studies were
conducted in locations where mean 24-h concentrations were in the range of 3 to 50 (maxima: 28 to
82) ppb (Figure 5.3-1). These associations are particularly consistent among children and older adults
(65+ years) when all respiratory outcomes are analyzed together (Figures 3.1-8 and 3.1-9), and among
children and subjects of all ages for asthma admissions (Figures 3.1-12 and 3.1-13). When examined with
copollutant models, associations of NO2 with respiratory ED visits and hospital admissions were
generally robust and independent of the effects of copollutants (Figures 3.1-10 and 3.1 -11). In preceding
sections, mechanistic evidence was described related to host defense and immune system changes, airway
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inflammation, and airway responsiveness that provide plausibility and coherence for these observed
effects.
5.3.2.2. Cardiovascular Effects Related to Short-Term Exposure
The available evidence on the effects of short-term exposure to NO2 on cardiovascular health
effects is inadequate to infer the presence or absence of a causal relationship at this time. Evidence
from epidemiologic studies of heart rate variability (HRV), repolarization changes, and cardiac rhythm
disorders among heart patients with ischemic cardiac disease was inconsistent (Section 3.2.1). In most
studies, associations with PM were found to be similar or stronger than associations with NO2. The mean
24-h concentrations generally were in the range of 9 to 39 ppb (Annex Table AX6.3-6). Generally
positive associations between ambient NO2 concentrations and hospital admissions or ED visits for
cardiovascular disease have been reported in single-pollutant models where mean 24-h concentrations
generally were in the range of 20 to 40 ppb (Section 3.2.2); however, most of these effect estimates were
diminished in multipollutant models that also contained CO and PM. Mechanistic evidence of a role for
NO2 in the development of cardiovascular diseases from studies of biomarkers of inflammation, cell
adhesion, coagulation, and thrombosis is lacking (Section 3.2.1.4; Seaton and Dennekamp, 2003).
Furthermore, the effects of NO2 on various hematological parameters in animals are inconsistent and,
thus, provide little biological plausibility for effects of NO2 on the cardiovascular system. However,
evidence from two human clinical studies was indicative of a reduction in hemoglobin with NO2 exposure
at concentrations between 1.0 and 2.0 ppm (with 3 h exposures).
5.3.2.3. Mortality Related to Short-Term Exposure
The epidemiologic evidence is suggestive but not sufficient to infer a causal relationship of
short-term exposure to NO2 with all cause and cardiopulmonary-related mortality. Results from several
large U.S. and European multeity studies and a meta-analysis study indicated positive associations
between ambient NO2 concentrations and the risk of all-cause (nonaccidental) mortality, with effect
estimates ranging from 0.5 to 3.6% excess risk in mortality per standardized increment1 (Section 3.3.1,
Figure 3.3-2). In general, the NO2 effect estimates were robust to adjustment for copollutants. Both
cardiovascular and respiratory mortality were associated with increased NO2 concentrations in
epidemiologic studies (Figure 3.3-3); however, similar associations were observed for other pollutants,
including PM and SO2. The range of risk estimates for excess mortality was generally smaller than that
for other pollutants such as PM. While NO2 exposure, alone or in conjunction with other pollutants, may
contribute to increased mortality, evaluation of the specificity of this effect was difficult. Clinical studies
showing hematologic effects and animal toxicological studies showing biochemical, lung host defense,
permeability, and inflammation changes with short-term exposures to NO2 provide limited evidence of
plausible pathways by which risks of morbidity and, potentially, mortality may be increased, but no
coherent picture is evident at this time.
5.3.2.4. Respiratory Morbidity Related to Long-Term Exposure
The epidemiologic and toxicological evidence examining the effect of long-term exposure to NO2
on respiratory morbidity is suggestive but not sufficient to infer a causal relationship at this time. A
number of epidemiologic studies examined the effects of long-term exposure to NO2 and reported positive
associations with decrements in lung function and partially irreversible decrements in lung function
1Excess risk estimates are standardized to a 20-ppb incremental change in daily 24-h avg NO2 or a 30-ppb incremental change in daily 1-h max NO2.
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growth (Section 3.4.1, Figures 3.4-1 and 3.4-2). Results from the Southern California Children's Health
Study indicated that decrements were similar for boys and girls and among children who had no history of
asthma (Gauderman et al., 2004). The mean NO2 concentrations in these studies range from 21.5 to
34.6 ppb. Similar associations have also been found for PM, O3, and proximity to traffic (<500 m),
though these studies did not report the results of copollutant models. The high correlation among traffic-
related pollutants made it difficult to accurately estimate the independent effects in these long-term
exposure studies. Results from the available epidemiologic evidence investigating the association between
long-term exposure to NO2 and increases in asthma prevalence and incidence were suggestive (Section
3.4.2). Two major cohort studies, the Children's Health Study in southern California (Gauderman et al.,
2005) and a birth cohort study in the Netherlands (Brauer et al., 2007) observed significant associations;
however, several other studies did not find consistent associations between long-term NO2 exposure and
asthma outcomes. Epidemiologic studies conducted in both the U.S. and Europe also have produced
inconsistent results regarding an association between long-term exposure to NO2 and respiratory
symptoms (Section 3.4.3). While some positive associations were noted, a large number of symptom
outcomes were examined and the results across specific outcomes were inconsistent. Animal toxicological
studies demonstrated that NO2 exposure resulted in morphological changes in the centriacinar region of
the lung and in bronchiolar epithelial proliferation (Section 3.4.4), which may provide some biological
plausibility for the observed epidemiologic associations between long-term exposure to NO2 and
respiratory morbidity. Susceptibility to these morphologic effects was found to be influenced by many
factors, such as age, compromised lung function, and acute infections.
5.3.2.5. Other Morbidity Related to Long-Term Exposure
The available epidemiologic and toxicological evidence was inadequate to infer the presence or
absence of a causal relationship for carcinogenic, cardiovascular, and reproductive and developmental
effects related to long-term NO2 exposure. Two epidemiologic studies conducted in Europe showed an
association between long-term NO2 exposure and increased incidence of cancer (Nyberg et al., 2000;
Nafstad et al., 2003). However, the animal toxicological studies provided no clear evidence that NO2 acts
as a carcinogen, though it does appear to act as a tumor promoter at the site of contact (Section 3.5.1).
There were no in vivo studies supporting the hypothesis that NO2 causes teratogenesis or malignant
tumors. A more likely pathway for NO2 involvement in cancer induction is through secondary formation
of nitro-PAHs, as nitro-PAHs are known to be more mutagenic than the parent compounds. The very
limited epidemiologic and toxicological evidence does not indicate that long-term exposure to NO2 has
cardiovascular effects (Section 3.5.2). The U.S. Women's Health Initiative study (Miller et al., 2007) did
not find any associations between long-term NO2 exposure and cardiovascular events. The toxicological
studies found some effects of NO2 on cardiac performance and heart rate, but only at exposure levels of
above 4 ppm. The epidemiologic evidence was not consistent for associations between NO2 exposure and
growth retardation; however, some evidence is accumulating for effects on preterm delivery (Section
3.5.3). Similarly, scant animal evidence supports a weak association between NO2 exposure and adverse
birth outcomes and provides little mechanistic information or biological plausibility for the epidemiologic
findings.
5.3.2.6. Mortality Related to Long-Term Exposure
The epidemiologic evidence was inadequate to infer the presence or absence of a causal rela-
tionship between long-term exposure to NO2 and mortality. In the U.S. and European cohort studies
examining the relationship between long-term exposure to NO2 and mortality, results were generally
inconsistent (Section 3.6, Figure 3.6-2). Further, when associations were noted, they were not specific to
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NO2, but also implicated PM and other traffic indicators. The relatively high correlations reported
between NO2 and PM indices (r -0.8) make it difficult to interpret these associations.
5.3.2.7. Exposure Indices
The available NO2 indices used to indicate short-term ambient NO2 exposure are daily 1-h max;
24-h avg; and 2-week avg NO2 concentrations. New data on short-term exposures have been published
since the 1993 NOX AQCD. Some studies examined only one index, and these studies form an evidence
base for that individual index. A few studies used both 1-h and 24-h data and, thus, allow a comparison of
these averaging periods. These included studies of respiratory symptoms, ED visits for asthma, hospital
admissions for asthma, and mortality. Comparisons of effect estimates for asthma ED visits for the 1-h
and 24-h time periods showed that the effect estimates are not different. Experimental studies in both
animals and humans provided evidence that short-term NO2 exposure (i.e., <1 h to 2-3 h) can result in
respiratory effects such as increased airway responsiveness or inflammation, thereby increasing the
potential for exacerbation of asthma. These findings generally supported epidemiologic evidence on
short-term exposures, but did not provide evidence that distinguishes effects for one short-term averaging
period from another. Differences between daily 1-h max and 24-h avg exposures estimates are unlikely to
be well characterized by the limited monitoring data available. Though an array of studies that examined
short-term (24-h avg and 1-h maximum) NO2 exposures and respiratory morbidity consistently produced
positive associations, it is not possible to discern whether these effects are attributable to average daily (or
multiday) concentrations (24-h avg) or high, peak exposures (1-h max).
5.3.2.8. Susceptible and Vulnerable Populations
Based on both short- and long-term studies of an array of respiratory health effects data, persons
with preexisting pulmonary conditions are likely at greater risk from ambient NO2 exposures than
the general public, with the most extensive evidence available for asthmatics as a potentially susceptible
group. In addition, studies indicated that upper respiratory viral infections can trigger susceptibility to the
effects of exposure to NO2. There was supporting evidence of age-related differences in susceptibility to
NO2 health effects such that the elderly population (>65 years of age) appeared to be at increased risk of
mortality and hospitalizations, and that children (<18 years of age) experienced other potentially adverse
respiratory health outcomes with increased NO2 exposure. People with occupations that require them to
be in or close to traffic or roadways (i.e., bus and taxi drivers, highway patrol officers) may have
enhanced exposure to NO2 compared to the general population, possibly increasing their vulnerability. A
considerable portion of the population resides and/or attends school near major roadways, increasing their
exposure to NO2 and other traffic pollutants. Otherwise susceptible individuals (schoolchildren, older
adults) in this subpopulation may be at increased risk. Recent studies have evaluated the effect of
socioeconomic status (SES) on susceptibility to the effects of NO2 exposure; however, to date, these
studies are too few in number to draw conclusions. Though data are just emerging (Romieu et al., 2006;
Islam et al., 2007), it is believed that a genetic component could be important in characterizing the
association between NO2 exposure and adverse health effects.
5.3.2.9. Concentration-Response Relationships and Thresholds
The conclusions pertaining to respiratory health presented in this ISA are based on numerous
studies, including panel and field, intervention, and multipollutant studies that control for the effects of
other pollutants, and studies conducted in areas where the whole distribution of ambient 24-h avg NO2
concentrations was below the current NAAQS level of 0.053 ppm (annual average). In some cases the
mean exposure in positive epidemiologic studies are <10 ppb; the policy-relevant background
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concentration is <0.01 ppb. In studies that have examined concentration-response relationships between
NO2 and health outcomes, the concentration-response relationship appears linear within the observed
range of data, including at levels below the current standard. There is little evidence of any effect
threshold (Section 4.2). Factors that made it difficult to identify any threshold that may exist included:
interindividual variation; additivity of pollutant-induced effects to the naturally occurring background
disease processes; additivity to health effects due to other environmental insults having a mode of action
similar to that of NO2; exposure error; and response measurement error. Low data density in the lower
concentration range as a result of limited monitoring is a particular problem in terms of measurement
error. Additionally, if the concentration-response relationship was shallow, identification of any threshold
that may exist will be more difficult to discern.
5.4. Conclusions
New evidence confirms previous findings in the 1993 AQCD that short-term NO2 exposure is
associated with increased airway responsiveness, often accompanied by respiratory symptoms,
particularly in children and asthmatics. This ISA concludes that the strongest evidence for an association
between NO2 exposure and adverse human health effects comes from epidemiologic studies of respiratory
symptoms and ED visits and hospital admissions. These new findings were based on numerous studies,
including panel and field studies, multipollutant studies that control for the effects of other pollutants, and
studies conducted in areas where the whole distribution of ambient 24-h avg NO2 concentrations was
below the current NAAQS level of 0.053 ppm (53 ppb) (annual average). The effect estimates from the
U.S. and Canadian studies generally indicate a 2-20% increase in risks for ED visits and hospital
admissions. Risks associated with respiratory symptoms generally were higher. The studies providing this
evidence (summarized in Table 5.4-1) were identified based on criteria for selecting epidemiologic
studies for inclusion in the ISA (Annex Section AX1.3.2 and Annex Figure AX1.3-1). They include U.S.
and Canadian studies conducted at or near ambient concentrations, which were well-designed, properly
implemented, and thoroughly described. All of the U.S. and Canadian studies included in Figure 5.3-1 are
included in Table 5.4-1. Evidence from human clinical studies, especially for airway hyperresponsiveness
in asthmatic individuals, was generally supportive of the epidemiologic evidence (see Table 5.3-2).
These conclusions were supported by some evidence from toxicological and human clinical studies.
These data sets formed a plausible, consistent, and coherent description of a relationship between NO2
exposures and an array of adverse health effects that range from the onset of respiratory symptoms to
hospital admission. Though an array of studies that examined short-term (24-h avg and 1-h max) NO2
exposures and respiratory morbidity consistently produced positive associations, it is not possible to
discern whether these effects are attributable to average daily (or multiday) concentrations (24-h avg) or
high, peak exposures (1-h max). While the evidence supported a direct effect of short-term NO2 exposure
on respiratory morbidity, the available evidence was inadequate to infer the presence or absence of a
causal relationship for morbidity and mortality effects related to long-term NO2 exposure. Further, the
health evidence was inadequate to infer the presence or absence of a causal relationship for carcinogenic,
cardiovascular, and reproductive and developmental effects, or for premature mortality, related to long-
term NO2 exposure.
The available evidence on the effects of short-term exposure to NO2 for cardiovascular health
effects is inadequate to infer the presence or absence of a causal relationship at this time. Though there is
no human clinical or animal toxicological evidence, the epidemiologic evidence is suggestive but not
sufficient to infer a causal relationship of short-term exposure to NO2 with nonaccidental and
cardiopulmonary-related mortality.
It is difficult to determine from these new studies the extent to which NO2 is independently associated
with respiratory effects or if NO2 is a marker for the effects of another traffic-related pollutant or mix of
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pollutants (see Section 5.2.2 for more details on exposure issues). On-road vehicle exhaust emissions are
a nearly ubiquitous source of combustion pollutant mixtures that include NO2 and can be an important
contributor to NO2 levels in near-road locations. Although this complicates the efforts to disentangle
specific NO2-related health effects, the evidence summarized in this assessment indicates that NO2
associations generally remain robust in multipollutant models and supports a direct effect of short-term
NO2 exposure on respiratory morbidity at ambient concentrations below the current NAAQS level. The
robustness of epidemiologic findings to adjustment for copollutants, coupled with data from animal and
human experimental studies, support a determination that the relationship between NO2 and respiratory
morbidity is likely causal, while still recognizing the relationship between NO2 and other traffic related
pollutants. In addition, an intervention study by Pilotto et al. (2004) found that exposure to NO2 from an
indoor combustion source is associated with respiratory effects; in this study NO2 effects would not be
confounded by other motor vehicle emission pollutants, though potential confounding by other pollutants
from gas stove emissions, such as UFP, could occur.
Human clinical and toxicological study findings also provide support for independent effects of
NO2 on respiratory health. Limited evidence from human clinical studies indicated that NO2 may increase
susceptibility to injury by subsequent viral challenge; toxicological studies show that lung host defenses
are sensitive to NO2 exposure. The epidemiologic and experimental evidence together show coherence for
effects of NO2 exposure on host defense or immune system effects providing plausibility and mechanistic
support for respiratory symptoms and ED visits for respiratory disease. Additionally, short-term exposure
to NO2 shows increased airway inflammation in human clinical and animal toxicological studies but at
exposure concentrations higher than ambient levels. Human and animal experimental studies provide
support for increased airways responsiveness to specific and nonspecific challenge following NO2
exposure. Transient increases in airway responsiveness following NO2 exposure have the potential to
increase symptoms and worsen asthma control.
Identification of a concentration-response relationship is an additional uncertainty that must be
considered when describing the association of NO2 and adverse health effects. In studies that have
examined concentration-response relationships between NO2 and health outcomes specifically, there was
little evidence of an effect threshold. Because ambient levels of NO2 are low in many of the
epidemiologic study sites, the concentration-response relationship may be shallow, making it difficult to
identify any threshold.
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Table 5.4-1. Ambient NO2 concentrations and selected effect estimates from studies of respiratory
symptoms, ED visits and hospital admissions in the U.S. and Canada.Complete
results and study details in Annex Tables AX6.3-2 through AX6.3-5.
STUDY
POPULATION
AVG
TIME
MEAN (SD)
RANGE
STANDARDIZED3 % EXCESS RISK
(95% Cl)
Respiratory Symptoms
Schwartz et al. (1994)
Mortimer et al. (2002)
Schildcrout et al. (2006)
Ostroetal. (2001)
Delfino et al. (2002)
Delfino et al. (2003)
Linn etal. (1996)
6 cities, U.S.
8 urban areas, U.S.
8 North American Cities
LA and Pasadena, CA
Alpine, CA
East LA County, CA
Los Angeles, CA
24-h avg
4-h avg
24-h avg
1-h max
1 -h max
1-h max
24-h avg
13ppb(NR)
32 ppb (NR)
17-26ppb(NR)
68-80 ppb (NR)
24 ppb (10)
7.2 ppb (2.1)
33 ppb (22)
Max: 44
-7,96
NR
20, 220
8,53
3, 14
1,96
61.3% (8.2, 143.4) Cough Incidence
48% (2, 116) Morning Asthma Symptoms
4.0% (1.0, 7.0) Asthma Symptoms
7.0% (1.0, 13.8) Cough Onset
34.6% (-17.9, 122.1) Asthma Symptoms
120% (-46, 2,038) Asthma Symp. Scores >1
-18.2% (-47.3, 27.1) Morning Symptom
Score
Emergency Department Visits -All Respiratory
Peel et al. (2005)
Tolbert et al. (2007)
Atlanta, GA
Atlanta, GA
1-h max
1-h max
45.9 ppb (17.3)
43.2 ppb (NR)
Max: 256
1.0-181
2.4% (0.9, 4.1)
2% (0.5, 3.3)
Emergency Department Visits - Asthma
Jaffe et al. (2003)
Ito et al. (2007)
New York State Department of
Health (2006)
Peel et al. (2005)
Tolbert et al. (2000)
2 cities, OH, (Clev, Cine)
New York, NY
Bronx and Manhattan, NY
Atlanta, GA
Atlanta, GA
24-h avg
24-h avg
24-h avg
1-h max
1-h max
Cinc:50 ppb (15)
Clev:48ppb(15)
31.1 ppb (8.7)
34 ppb (NR)
45.9 ppb (17.3)
81. 7 ppb (53.8)
NR
NR
NR
NR
5.4, 306
6.1% (-2.0, 14.0)
12% (7, 16)
6% (1, 10) Bronx. -3% (-18, 14) Manhattan
2.1% (-0.4, 4.5) All Ages. 4.1% (0.8, 7.6) 2-
18yrs
0.7% (-0.8, 2.3)
Hospital Admissions - All Respiratory
Burnett etal. (1997a)
Yang et al. (2003)
Fung et al. (2006)
Burnett etal. (2001)
Luginaah et al. (2005)
16 Canadian Cities
Vancouver, BC
Vancouver, BC
Toronto, ON
Windsor, ON,
1-h max
24-h avg
24-h avg
1-h max
1-h max
35.5 ppb (16.5)
18.7 ppb (5.7)
16.8 ppb (4.3)
44.1 ppb (NR)
38.9 ppb (12.3)
NR
NR
7.2, 33.9
Max: 146
NR
-0.3% (-2.4, 1 .8) adjusted for CO, O3, SO2,
CoH
19.1% (7.4, 36.3)<3yrs. 19.1% (11.2, 36.3)
>65 yrs
9.1% (1.5, 17.2)
13.3% (5.3, 22.0)
6.7% (-5.4, 20.4) female. -10.3% (-20.3, 1.1)
male
Hospital Admissions - Asthma
Linn et al. (2000)
Lin et al. (2004)
Lin et al. (2003)
Burnett etal. (1999)
Los Angeles, CA
Vancouver, BC
Toronto, ON
Toronto, ON
24-h avg
24-h avg
24-h avg
24-h avg
3.4 ppb (1.3)
18.7 ppb (5.6)
25.2 ppb (9.04)
25.2 ppb (9.1)
NR
4.3, 5.4
3.0, 82.0
NR
2. 8% ±1.0%
45.3% (12.7, 88.3) Boys. 23.0% (-11.7, 70.2)
Girls
18.9% (1 .8, 39.3) Boys. 17.0% (-5.4, 41 .4)
Girls
2.60% (0, 5)
Note: Several U.S. and Canadian studies were excluded from Figure 5.3-1 and this table because they were either GAM-impacted (Cassino et al., 1999; Gwynn et al. 2000;
Morris etal. 1999; Stiebetal. 2000) or did not present sufficient quantitative risk estimates (Lipsett et al., 1997; Sinclair and Tolsma 2004).
324-h avg effect estimates standardized to 20 ppb increment; 1-h max effect estimates standardized to 30 ppb increment.
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