IntegratedScienceAssessmentfor

OxidesofNitrogen—HealthCriteria

UnitedStates

EnvironmentalProtection

Agency

EPA/600/R-08/071

July2008
July 2008 

EPA/600/R-08/071 

Integrated Science Assessment 

for Oxides of Nitrogen – Health Criteria 

National Center for Environmental Assessment-RTP Division 

Office of Research and Development 

U.S. Environmental Protection Agency 

Research Triangle Park, NC 
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. 

 
iii 

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

1.1. Document Development ____________________________________________________________________________________ 1-1

1.2. Document Organization_____________________________________________________________________________________ 1-2

1.3. EPA Framework for Causal Determinations _____________________________________________________________________ 1-2

1.3.1. Scientific Evidence Used in Establishing Causality ___________________________________________________________ 1-3

1.3.2. Moving from Association to Causation_____________________________________________________________________ 1-3

1.3.3. Multifactorial Causation ________________________________________________________________________________ 1-5

1.3.4. Uncertainty__________________________________________________________________________________________ 1-6

1.3.5. Application of Framework ______________________________________________________________________________ 1-7

1.3.6. First Step—Determination of Causality ____________________________________________________________________ 1-8

1.3.7. Second Step—Evaluation of Population Response___________________________________________________________ 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. O3 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. NO2 Concentrations __________________________________________________________________________________ 2-22

2.4.3. Seasonal Variability in NO2 at Urban Sites ________________________________________________________________ 2-23

2.4.4. Diurnal Variability in NO2 Concentrations _________________________________________________________________ 2-25

2.4.5. Concentrations of NOZ Species _________________________________________________________________________ 2-26

2.4.6. Policy-Relevant Background Concentrations of NO2_________________________________________________________ 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 NO2 ____________________________________ 2-28

2.5. Exposure Issues _________________________________________________________________________________________ 2-28

2.5.1. Introduction ________________________________________________________________________________________ 2-28

2.5.2. Personal Sampling of NO2 _____________________________________________________________________________ 2-31

2.5.3. Spatial Variability in NO2 Concentrations__________________________________________________________________ 2-32

2.5.3.1. Variability of NO2 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. NO2 On or Near Roads _______________________________________________________________________________ 2-36

2.5.5. Indoor Sources and Sinks of NO2 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. NO2 as a Component and Indicator of Pollutant Mixtures _____________________________________________________ 2-53
iv 

2.5.7.1. Associations between Ambient NO2 and Ambient Copollutants __________________________________________ 2-53

2.5.7.2. Associations among NO2 and Other Pollutants in Indoor Environments____________________________________ 2-54

2.5.7.3. Personal and Ambient Associations between NO2 and Copollutants ______________________________________ 2-56

2.5.7.4. NO2 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 NO2 _________________________________________________________ 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 NO2 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 NO2 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
v 

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 N-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
vii 

List of Tables 

Table 1.3-1. Aspects to aid judging causality. __________________________________________________ 1-7

Table 1.3-2. Weight of evidence for causal determination (adapted from Institute of Medicine, 2007). ______ 1-9

Table 2.2-1. Annual 2002 average anthropogenic NOX emissions in the U.S. (million metric tons). ________ 2-2

Table 2.5-1. Spatial variability of NO2 in selected U.S. urban areas. _______________________________ 2-33

Table 2.5-2. NO2 concentration near indoor sources: minute to hour averages. ______________________ 2-39

Table 2.5-3. NO2 concentration near indoor sources: 24-h to 2 week averages. ______________________ 2-39

Table 2.5-4. Association between personal exposure and ambient concentration (longitudinal 

correlation coefficients).________________________________________________________ 2-42

Table 2.5-5. Association between personal exposure and ambient concentration (pooled correlation 

coefficients). ________________________________________________________________ 2-43

Table 2.5-6. Association between personal exposure and outdoor concentration. _____________________ 2-44

Table 2.5-7. Pearson correlation coefficient between ambient NO2 and ambient copollutants. ___________ 2-54

Table 2.5-8. Pearson correlation coefficient between ambient NO2 and personal copollutants.___________ 2-56

Table 2.5-9. Pearson correlation coefficient between personal NO2 and ambient copollutants.___________ 2-56

Table 2.5-10. Pearson correlation coefficient between personal NO2 and personal copollutants. __________ 2-57

Table 2.5-11. Pearson correlation coefficient between NOX and traffic-generated pollutants. _____________ 2-57

Table 3.1-1. Proposed mechanisms whereby NO2 and respiratory virus infections may exacerbate 

upper and lower airway symptoms. ________________________________________________ 3-3

Table 3.1-2. Changes in airway responsiveness associated with NO2 exposure.______________________ 3-15

Table 3.1-3. Fraction of NO2-exposed asthmatics with increased non-specific airway 

hyperresponsiveness. _________________________________________________________ 3-16

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._____________________ 3-19

Table 4.1-1. Gradation of individual responses to short-term NO2 exposure in persons with impaired 

respiratory systems. ___________________________________________________________ 4-2

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). ________ 4-10

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. _______________ 5-5

Table 5.3-2. Key studies and effects of exposure to NO2 from clinical studies. ________________________ 5-9

Table 5.3-3. Summary of toxicological effects in rats from NO2 exposure. ___________________________ 5-10

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. ___________________ 5-17
ix 

List of Figures 

Figure 1.3-1. Exposure–disease–stress model for environmental health disparities._____________________ 1-4

Figure 1.3-2. Potential relationships of NOX with adverse health effects.______________________________ 1-6

Figure 2.1-1. A generalized conceptual model for integrating research on NOX pollution and human 

health effects. ________________________________________________________________ 2-1

Figure 2.2-1. Schematic diagram of the cycle of reactive oxidized N species in the atmosphere.___________ 2-3

Figure 2.4-1. Location of ambient NO2 monitors in the U.S. as of November 5, 2007. ___________________ 2-9

Figure 2.4-2. 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. ______________________________________________ 2-10

Figure 2.4-3. 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. ______________________________________________ 2-11

Figure 2.4-4. 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. ______________________________________________ 2-12

Figure 2.4-5. 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. ______________________________________________ 2-13

Figure 2.4-6. 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. ______________________________________________ 2-14

Figure 2.4-7. 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. _____________ 2-15

Figure 2.4-8. 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.___________________________ 2-16

Figure 2.4-9. 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.____________________________________________________________ 2-17

Figure 2.4-10. 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. ______________________________________________ 2-18

Figure 2.4-11. 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. _________________________________ 2-19

Figure 2.4-12. 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. ____________________________________________________________________ 2-20

Figure 2.4-13. Ambient concentrations of NO2 measured at all monitoring sites located within MSAs in 

the U.S. from 2003 through 2005. ________________________________________________ 2-21

Figure 2.4-14. Monthly average NO2 concentrations in ppb for January 2002 (left panel) and July 2002 

(right panel) calculated by CMAQ ________________________________________________ 2-22

Figure 2.4-15. Nationwide trend in NO2 concentrations.___________________________________________ 2-23

Figure 2.4-16. Time series of 24-h avg NO2 concentrations at individual sites in Atlanta, GA from 2003 

through 2005. _______________________________________________________________ 2-24
x 

Figure 2.4-17. Mean hourly NO2 concentrations on weekdays and weekends measured at two sites in 

Atlanta, GA. _________________________________________________________________ 2-25

Figure 2.4-18. Upper panel: Annual mean NO2 concentrations (in ppb) in the U.S. Middle panel: Annual 

mean PRB concentrations (in ppb) for NO2 in the U.S.________________________________ 2-27

Figure 2.5-1. Percentage of time people spend in different environments in the U.S. ___________________ 2-29

Figure 2.5-2. NO2 and NOX concentrations normalized to ambient values, plotted as a function of 

downwind distance from the freeway. _____________________________________________ 2-34

Figure 2.5-3. NO2 concentrations measured at 4 m (Van) and at 15 m at NY Department of 

Environmental Conservation ambient monitoring sites ________________________________ 2-35

Figure 2.5-4. Distribution of correlation coefficients (U.S. studies) between personal NO2 exposure and 

ambient NO2 concentrations based on Fisher’s Z transform. ___________________________ 2-47

Figure 2.5-5. Distribution of correlation coefficients (European studies) between personal NO2 exposure 

and ambient NO2 concentrations based on Fisher’s Z transform.________________________ 2-48

Figure 2.5-6. Correlations of NO2 to O3 versus correlations of NO2 to CO for Los Angeles, CA 

(2001-2005). ________________________________________________________________ 2-55

Figure 2.5-7. Composite, diurnal variability in 1-h avg NO2 in urban areas.___________________________ 2-58

Figure 3.1-1. Studies of airway inflammatory responses in relation to the total exposure to NO2, 

expressed as ppm-minutes. _____________________________________________________ 3-7

Figure 3.1-2. Airway responsiveness to allergen challenge in asthmatic subjects following a single 

exposure to NO2. _____________________________________________________________ 3-11

Figure 3.1-3. Geometric mean symptom rates (95% CI) for cough with phlegm (panel A) and proportions 

(95% CI) of children absent from school ___________________________________________ 3-20

Figure 3.1-4. 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. __________________________________________________________________ 3-22

Figure 3.1-5. Odds ratios (95%CI) for daily asthma symptoms (panel A) and rate ratios (95% CI) for 

daily rescue inhaler use________________________________________________________ 3-24

Figure 3.1-6. Odds ratios (95% CI) for associations between asthma symptoms in children and 24-h 

average NO2 concentrations (per 20 ppb). _________________________________________ 3-26

Figure 3.1-7. Odds ratios and 95% CI for associations between asthma symptoms and 24-h average 

NO2 concentrations (per 20 ppb) from multipollutant models.___________________________ 3-27

Figure 3.1-8. Relative risks (95% CI) for hospital admissions or ED visits for all respiratory disease 

stratified by all ages or children. _________________________________________________ 3-32

Figure 3.1-9. Relative Risks (95% CI) for hospital admissions or ED visits for all respiratory disease 

stratified by adults or older adults (≥ 65 years).______________________________________ 3-33

Figure 3.1-10. Relative risks (95% CI) for hospital admissions or emergency department visits for all 

respiratory causes, standardized from two-pollutant models adjusted for particle 

concentration. _______________________________________________________________ 3-34

Figure 3.1-11. Relative risks (95% CI) 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% CI) for hospital admissions or emergency department visits for 

asthma stratified by all ages or children. ___________________________________________ 3-36

Figure 3.1-13. Relative risks (95% CI) 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% CI) 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% CI) 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
xi 

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 (FEV1) associated with a 20-ppb increase in 

NO2 (A) and a 20-μg/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-μg/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 FEV1 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 FEV1, 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
xiii 

Acronyms and Abbreviations 

1NP 1-nitropyrene 

2NF 2-nitrofluoranthene 

2NP 2-nitropyrene 

α Alpha 

ACS American Cancer Society 

ai air exchange rate (for microenvironment i) 

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 

β beta; the calculated health effect parameter 

BAL bronchoalveolar lavage 

BALF bronchoalveolar lavage fluid 

BHPN N-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 
xiv 

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 

FEV0.5 forced expiratory volume in 0.5 seconds 

FEV1 forced expiratory volume in 1 second 

Finfi

 infiltration factor (for microenvironment i) 

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 
xv 

HS hemorrhagic stroke 

H2S hydrogen sulfide 

HSO4

–

 bisulfate ion 

H2SO4 sulfuric acid 

hv solar ultraviolet photon 

i microenvironment 

IARC International Agency for Research on Cancer 

ICAM-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 

ki 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 
xvi 

NDMA N-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 N-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 FEV1

PD100 provocative dose that produces a 100% increase in sRAW 

PEACE Pollution Effects on Asthmatic Children in Europe (study) 

PEF peak expiratory flow 

Pi penetration coefficient (for microenvironment i) 

PIH primary intracerebral hemorrhage 

PM particulate matter 
xvii 

PM2.5 particulate matter with 50% upper cut point aerodynamic diameter of 

2.5 μm for sample collection; surrogate for fine PM 

PM10 particulate matter with 50% upper cut point aerodynamic diameter of 

10 μm for sample collection 

PM10-2.5 particulate matter with 10 μm as upper cut point aerodynamic diameter 

and 2.5 μm as lower cut point for sample collection; surrogate for 

thoracic coarse PM (does not include fine PM) 

PMN(s) polymorphonuclear leukocyte(s) 

PNO3

-

 particulate nitrate 

POM particulate 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 

RTP Research Triangle Park 

SAPALDIA 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 polymorphism35S sulfur-35 radionuclide 

SLAMS State and Local Air Monitoring Stations 

SO sulfur monoxide 

SO2 sulfur dioxide 

SO4

2– sulfate ion 

SOX sulfur oxides 

sRaw specific airway resistance 

STN Speciation Trends Network 

τ tau; atmospheric lifetime 

TEA triethanolamine 

TNF tumor necrosis factor (e.g., TNF-α) 

TSP total suspended particles 

UFP Ultrafine particles (<100 nm) 

URI upper respiratory infections 
xviii 

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 
xix 

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 
xx 

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 
xxi 

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 
xxii 

Dr. Halûk A. Özkaynak—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 
xxiii 

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 (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. 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 
xxiv 

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 
xxv 

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 
xxvi 

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 
xxvii 

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)(1) defines a primary 

standard as one “the attainment and maintenance of which in the judgment of the Administrator, based on 
xxviii 

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 (D.C. 

Cir 1980), cert. denied, 449 U.S. 1042 (1980); American Petroleum Institute v. Costle, 665 F.2d 1176, 

1186 (D.C. Cir. 1981), cert. denied, 455 U.S. 1034 (1982). Both kinds of uncertainties are components of 

the risk associated with pollution at levels below those at which human health effects can be said to occur 

with reasonable scientific certainty. Thus, in selecting primary standards that include an adequate margin 

of safety, the Administrator 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)(1) requires that “not later than December 31, 1980, and at 5-year intervals 

thereafter, the Administrator shall complete a thorough review of the criteria published under Section 108 

and the national ambient air quality standards and shall make such revisions in such criteria and standards 

and promulgate such new standards as may be appropriate...” Section 109(d)(2) requires that an 

independent scientific review committee “shall complete a review of the criteria...and the national 

primary and 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 NO2

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.” 
xxix 

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. 
1-1 

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).1

 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, 

 

1

The 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. 
1-2 

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 
1-3 

 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 
1-4 

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. 

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. 
1-5 

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 well-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. 
1-6 

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. 

Figure 1.3-2. Potential relationships of NOX with adverse health effects. 
1-7 

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. 

 

1

The “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. 
1-8 

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 

IOM’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. 
1-9 

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. 
1-10 

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). 
2-1 

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. 

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 (N) compounds, including 

NO, NO2, and all other oxidized N-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 

polycyclic 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. 
2-2 

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 N 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 EMISSIONS 

On-Road Mobile Sources 7.4 

Electricity-Generating Units 4.3 

Non-Road Mobile Sources 4.0 

Industrial/Commercial/Residential Fuels 2.3 

Industrial Processes 1.1 

Prescribed Fires 0.19 

Waste Disposal 0.10 

Residential Wood 0.04 

Fertilizers and Livestock 0.019 

Miscellaneous 0.011 

Road Dust <0.01 

Solvent Use 0.0082 

TOTAL 19.4 

Source: NEI 2002 Emissions Booklet (U.S. Environmental Protection Agency, 2006b) 
2-3 

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; Hilliard 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. 

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 NO2 (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, hν to a solar photon and R 

to an organic radical. Particle-phase RONO2 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 nitrogencontaining compounds inside the large dashed-line box, the same group generally termed 

“NOY” by atmospheric scientists. 
2-4 

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 N species in the outer box are often collectively termed NOZ; thus, 

NOX+NO-Z=NOY. 

The concentrations and atmospheric lifetimes (τ) 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 N 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). 
2-5 

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 N 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 N-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 μm. 

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. 
2-6 

2.2.3. O3 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 N 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 
2-7 

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 
2-8 

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

convertor 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 N 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 N 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 N-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 

N compounds in the U.S.; it also provides estimates of Policy-Relevant Background (PRB) concentrations, 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. 
2-9 

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 NO2 monitors in the U.S. as of November 5, 2007. Shaded states have 

NO2 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.) 
2-10 

Figure 2.4-2. NO2 monitor locations in the Atlanta, GA CMSA shown in relation to major roadways, pointsource electric generating units, and population densities for total population, and fractions ≤

17 years and ≥ 65 years. 
2-11 

Figure 2.4-3. NO2 monitor locations in the Boston, MA CMSA shown in relation to major roadways, pointsource electric generating units, and population densities for total population, and fractions ≤ 

17 years and ≥ 65 years. 
2-12 

Figure 2.4-4. NO2 monitor locations in the Chicago, IL CMSA shown in relation to major roadways, pointsource electric generating units, and population densities for total population, and fractions ≤ 

17 years and ≥ 65 years. 
2-13 

Figure 2.4-5. NO2 monitor locations in the Houston, TX CMSA shown in relation to major roadways, pointsource electric generating units, and population densities for total population, and fractions ≤ 

17 years and ≥ 65 years. 
2-14 

Figure 2.4-6. 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. 
2-15 

Figure 2.4-7. 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. 
2-16 

Figure 2.4-8. 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. 
2-17 

Figure 2.4-9. 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. 
2-18 

Figure 2.4-10. 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. 
2-19 

Figure 2.4-11. 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. 
2-20 

Figure 2.4-12. 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. 
2-21 

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): 1010xxxx 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. 

Figure 2.4-13. Ambient concentrations of NO2 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 
2-22 

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 max NO2 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 of ten 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-h avgs. 

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 τ 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. 

Figure 2.4-14. Monthly average NO2 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. NO2 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 
2-23 

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. 

Figure 2.4-15. Nationwide trend in NO2 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 NO2 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. 
2-24 

Figure 2.4-16. Time series of 24-h avg NO2 concentrations at individual sites in Atlanta, GA from 2003 

through 2005. A natural spline function (with 9 degrees of freedom) was fit and overlaid to the 

data (dark solid line).
2-25 

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 NO2 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. 

Figure 2.4-17. Mean hourly NO2 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. 
2-26 

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 N 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 NO2

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. 
2-27 

Figure 2.4-18. Upper panel: Annual mean NO2 concentrations (in ppb) in the U.S. Middle panel: Annual mean 

PRB concentrations (in ppb) for NO2 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 NO2 concentrations shown in the upper panel. See text in 

Annex section AX2.7 for details. 
2-28 

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 NO2

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. PolicyRelevant 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, Ci is the average concentration, and ti is the time spent in the ith 

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. 
2-29 

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. 

Source: Klepeis et al. (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: 

(2.5-2) 

subject to the constraint, 

 (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; yo is the fraction of time people spend outdoors and yi is the 

fraction of time they spend in microenvironment i; Finfi

, Pi, ai, and ki are the infiltration factor, penetration 

coefficient, air exchange rate, and decay rate for microenvironment i. 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: 

 (2.5-4) 

where y is the fraction of time persons spend outdoors, and α 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 
2-30 

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: 

 (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 V is 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, ai

 The pollutant specific penetration coefficient, Pi

 The pollutant specific decay rate, ki

 The fraction of time an individual spends in the microenvironment, yi

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 (Finf or α if people spend 100% of their time indoors). In 
2-31 

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 crosscommunity 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 NO2

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 
2-32 

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 chemiluminescent monitors, remain the only approach for estimating 

short-term, peak exposures. 

2.5.3. Spatial Variability in NO2 Concentrations 

2.5.3.1. Variability of NO2 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 

 (2.5-6) 

where Xij and Xik represent observed concentrations averaged over some measurement averaging period 

(hourly, daily, etc.), for measurement period i at site j and site k, and p 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. 
2-33 

Table 2.5-1. Spatial variability of NO2 in selected U.S. urban areas. 

URBAN AREA 

(# OF MONITORS) 

3-YEAR MEAN 

CONCENTRATION 

(RANGE) 

PEARSON 

CORRELATION 

COEFFICIENT 

90TH PERCENTILE 

DIFFERENCE 

BETWEEN 

MONITORS 

COEFFICIENT OF 

DIVERGENCE 

New York, NY (5) 29 ppb (25–37) 0.77–0.90 7–19 0.08–0.23 

Atlanta, GA (5) 11 ppb (5–16) 0.22–0.89 7–24 0.15–0.59 

Chicago, IL (7) 22 ppb (6–30) -0.05–0.83 10–39 0.13–0.66 

Houston, TX (7) 13 ppb (7–18) 0.31–0.80 6–20 0.13–0.47 

Los Angeles, CA (14) 25 ppb (14–33) 0.01–0.90 8–32 0.08–0.51 

Riverside, CA (9) 21 ppb (5–32) 0.03–0.84 10–40 0.14–0.70 

 

The same statistics shown in Table 2.5-1 have been used to describe the spatial variability of PM2.5

(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). 
2-34 

Source: Singer et al. (2004). 

Figure 2.5-2. NO2 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 

of NO2. 

2.5.3.3. Small-Scale Vertical Variability 

Inlets to instruments for monitoring gas-phase criteria pollutants can be located from 3 to 15 m 

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 
2-35 

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. 

Source: Restrepo et al. (2004). 

Figure 2.5-3. NO2 concentrations measured at 4 m (Van) and at 15 m at NY Department of Environmental 

Conservation ambient monitoring sites(DEC709406 and DEC709407). 
2-36 

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 PM2.5 for their respective 

exposures. This conclusion is based on a comparison of metrics of spatial variability for O3 or PM2.5 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. NO2 On or Near Roads 

Lee et al. (2000) reported that NO2 concentration in heavy traffic (~60 ppb) can be more than double 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 Francisco 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. Lewné 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 
2-37 

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 exposure 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 concentration 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; Lal 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 (Alm 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 decreasing 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 exposure is also influenced by indoor sources. Alm 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 μm (PM10), 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 NO2 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. 
2-38 

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 last NOX 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 
2-39 

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 AVERAGE 

CONCENTRATION (ppb) 

PEAK 

CONCENTRATION (ppb) COMMENT 

Fortmann 

et al. (2001) 

191 kitchen 

195 living room 

184 bedroom 

375 kitchen 

401 living room 

421 bedroom 

Cooked full meal with gas range for 2 h, 20 min; 7 h TWA. 

Fortmann 

et al. (2001) 

400 kitchen 

living room 

bedroom 

673 bedroom Self-cleaning gas range. Avgs are over the entire cycle. 

Dutton et al. 

(2001) 

90 (low setting) 

350 (med setting) 

360 (high setting) 

NR Natural gas unvented fireplace, 0.5 h TWA in main living area of 

house (177 m3

). 

Girman et al. 

(1982) 

NR 1000 Room concentration with kerosene heater operating for 46 min. 

Girman et al. 

(1982) 

NR 1500 Room concentration with gas heater operating for 10 min. 

Girman et al. 

(1982) 

180 to 650 NR 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; 1Unvented 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 AVERAGE CONCENTRATION COMMENT 

Lee et al. (1998) 30 to 33 ppb 

22 ppb 

6 to 11 ppb 

Gas stoves with pilot lights 

Gas stoves without pilot lights 

Electric ranges 

Study conducted in 517 homes in Boston. Values represent 2-wk avgs 

Triche et al. (2005) 55 (Median) 

41 (90th percentile) 

80 (90th percentile) 

84 (90th percentile) 

147 (90th percentile) 

52 (90th percentile) 

Gas space heaters 

No indoor combustion sources 

Fireplaces 

Kerosene heaters 

Gas space heaters 

Wood stoves 

All values represent 2-wk avgs in living rooms 

Zipprich et al. (2002) 18 ppb 

19 ppb 

15 ppb 

Bedrooms 

Living rooms 

Outdoors 

Almost all homes had gas stoves. Values represent 2-wk avgs 

 
2-40 

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 (Lévesque 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, Sørensen 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., Alm 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 

et al., 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. 
2-41 

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. 
2-42 

Table 2.5-4. Association between personal exposure and ambient concentration (longitudinal 

correlation coefficients). 

STUDY METHODS MEAN 

CONCENTRATION 

ASSOCIATION 

VARIABLE LOCATION SEASON rp, rs, or R2

Linaker et al. 

(2000) 

Location: 

Southampton, 

Hampshire, UK 

Time period: 

Oct 1994 to Dec 

1995 

Type: Longitudinal 

Subjects: 114 asthmatic 

children, aged 7-12 

Method: at least 16 consecutive 

samples (1-wk avgs) for each 

child (mean duration of followup: 32 wks). 

Ambient: 6.5 ppb 

Personal: 8.9 ppb 

Personal vs. central 

(overall measurements across 

children and time) 

Personal vs. central 

(subject-wise) 

Pooled, urban, 

no major 

indoor sources 

By person 

Pooled Not significant 

(n=NR) 

-0.77 to 0.68 

and median 

-0.02 (rp) 

(n=NR) 

Kim et al. 

(2006) 

Location: 

Toronto, 

Canada 

Time period: 

Aug 1999 to 

Nov 2001 

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. 

Ambient: 24 ppb 

Personal: 14 ppb 

Personal vs. central 

(subject-wise) 

Urban Pooled -0.36 to 0.94 (rs) 

with a median of 

0.57 (15 

subjects) 

Sarnat et al. 

(2001) 

Koutrakis et al. 

(2005) 

Location: 

Baltimore, MD 

Time period: 

summer of 1998 

and winter of 

1999 

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. 

Ambient: 20–25 ppb 

Personal: 10–15 ppb 

Personal vs. central 

(subject-wise) 

Urban Summer 

Winter 

-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) 

Sarnat et al. 

(2005) 

Koutrakis et al. 

(2005) 

Location: 

Boston, MA 

Time period: 

summer of 

1999; winter of 

2000 

Type: Longitudinal 

Subjects: 43 seniors and 

schoolchildren 

Method: Similar study design as 

Sarnat et al. (2001). 

Ambient: 21.1– 

32.6 ppb 

Personal: 10.6–

29.6 ppb 

Personal vs. central 

(subject wise) 

Urban Summer 

Winter 

-0.25 to 0.5 (rs) 

with a median of 

0.3* (n=NR) 

Slope=0.19 

(95% CI, 0.08-

0.30) 

-0.5 to 0.9 (rs) 

with a median of 

0.4* (n=NR) 

Slope=-0.03 

(95% CI, -0.21-

0.15) 

* Values were estimated from figures in the original paper. ** NR: Not Reported. 
2-43 

Table 2.5-5. Association between personal exposure and ambient concentration (pooled 

correlation coefficients). 

STUDY STUDY DESIGN MEAN 

CONCENTRATION 

ASSOCIATION 

VARIABLE LOCATION SEASON rp, rs, or R2

Linn et al. (1996) 

Location: 

Southern 

California 

Time period: fall, 

winter, spring, 

1992-1994 

Type: Longitudinal 

Subjects: 269 school children 

Method: 24-h avg, 1-wk consecutive 

measurement for each season for 

each child. 

Ambient: 37 ppb 

Personal: 22 ppb 

Personal vs. 

central 

Pooled Pooled 0.63 (rp) 

(n=107) 

Personal vs. 

central 

Downtown Spring 0.64 (rp) 

p<0.001 

(n=NR**) 

Personal vs. 

central 

Suburban Spring 0.78 (rp) 

p<0.001 

(n=NR) 

Personal vs. 

central 

Downtown Winter -0.06 (rp) 

p >0.05 

(n=NR) 

Personal vs. 

central 

Suburban Winter 0.32 (rp) p 

>0.05 

(n=NR) 

Personal vs. 

central 

Downtown 

(electric stove 

home) 

Pooled 0.42 (rp) 

p<0.01 

(n=NR) 

Personal vs. 

central 

Downtown 

(gas stove 

home) 

Pooled 0.16 (rp) p 

>0.01 

(n=NR) 

Personal vs. 

central 

Suburban 

(electric stove 

home) 

Pooled 0.55 (rp) 

p<0.001 

(n=NR) 

Personal vs. 

central 

Downtown 

(non-smoking 

home) 

Pooled 0.47 (rp) 

p<0.001 

(n=NR) 

Personal vs. 

central 

Downtown 

(smoking 

home) 

Pooled 0.23 (rp) p 

>0.01 

(n=NR) 

Personal vs. 

central 

Suburban 

(non-smoking 

home) 

Pooled 0.53 (rp) 

p<0.001 

(n=NR) 

Personal vs. 

central 

Suburban 

(smoking 

home) 

Pooled 0.52 (rp) 

p<0.001 

(n=NR) 

Alm et al. (1998) 

Location: Helsinki, 

Finland 

Time period: 

winter and spring, 

1991 

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. 

Ambient: 16.8–

26.3 ppb 

Personal: 9–16.6 ppb 

Personal vs. 

central 

Pooled Pooled 0.37 (R2

) 

(n=24) 

Adults vs. central Urban Summer 0.41 (R2) 

p<0.0001 

(n=NR) 

Liard et al. (1999) 

Location: Paris, 

France 

Time period: MayJune 1996 

Type: Daily avg/cross-sectional 

Subjects: 55 adults and 39 children 

Method: Three 4-day avg measurements for each person, during each 

measurement session, all subjects 

measured at same time. 

Ambient: 26.3–

36.8 ppb 

Personal: 15.8–

26.3 ppb Children vs. central Urban Summer 0.17 (R2

) 

p=0.0004 

(n=NR) 
2-44 

STUDY STUDY DESIGN MEAN 

CONCENTRATION 

ASSOCIATION 

VARIABLE LOCATION SEASON rp, rs, or R2

Personal vs. 

central (Grenoble) 

Urban Pooled 0.01 (R2) 

(n=NR) 

Personal vs. 

central (Toulouse) 

Urban Pooled 0.04 (R2) 

(n=NR) 

Gauvin et al. 

(2001) 

Location: 3 French 

metropolitan areas 

Time period: AprJune 1998 in Grenoble; May-June 

1998 in Toulouse; 

June-Oct 1998 in 

Paris 

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. 

Ambient: 10.2–

25.7 ppb 

Personal: 13.2–17 ppb 

Personal vs. 

central (Paris) 

Urban Pooled 0.02 (R2

) 

(n=NR) 

Personal 

(exposure at 

home) vs. central 

Urban Pooled 0.09 (R2

) 

p=0.0101 

(n=NR) 

Piechocki-Minguy 

et al. (2006) 

Location: Lille 

(northern France) 

Time period: 

winter 2001 (first 

campaign); 

summer 2002 

(second 

campaign) 

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). 

Ambient: 15.8–

57.9 ppb 

Personal: 8.9–

20.0 ppb Personal 

(exposure at 

home) vs. central 

Urban 

(electric stove 

and electric 

heater home) 

Summer 0.61 (R2

) 

p=0.0001 

(n=NR) 

Summer 0.14 (R2) 

(n=122) 

p<0.05 

Sarnat et al. 

(2006) 

Location: 

Steubenville, OH 

Time period: 

summer and fall of 

2000 

Type: Longitudinal 

Subjects: 15 senior subjects 

Method: two consecutive 24-h 

samples were collected for each 

subject for each wk, 23 wks total 

Ambient: 9.5–11.3 ppb 

Personal: 9.9–

12.1 ppb 

Personal vs. 

central 

Urban 

Fall 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 METHODS ASSOCIATION 

VARIABLE LOCATION SEASON rp, rs, or R2

Personal vs. 

outdoor 

Krämer et al. Pooled Pooled 0.37 (rp) (n=281) 

(2000) 

Location: 

Germany 

Time period: 

Mar and Sep 

1996 

Subjects: 191 children 

Method: two 1-wk averaged measurements for 

each 

child in each mo. Personal vs. 

outdoor 

Urban Pooled 0.06 (rp) (n=182) 

Rojas-Bracho 

et al. (2002) 

Location: 

Santiago, Chile 

Time period: 

winters of 1998 

and 1999 

Subjects: 20 children 

Method: five 24-h avg samples for 5 consecutive 

days for each child. 

Personal vs. 

outdoor 

Urban Winter 0.27 (R2

) (n=87) 
2-45 

STUDY METHODS ASSOCIATION 

VARIABLE LOCATION SEASON rp, rs, or R2

Personal vs. 

outdoor 

Urban Pooled 0.15 (R2 Raaschou- ) (n=97) 

Nielsen et al. 

(1997) 

Location: 

Copenhagen, 

Denmark and 

rural areas 

Time period: 

Oct 1994, Apr, 

May, and June 

1995 

Subjects: 204 children 

Method: two 1-wk avg measurements for each child 

in each mo. 

Personal vs. 

outdoor 

Rural Pooled 0.35 (R2

) (n=99) 

Personal vs. 

outdoor 

Downtown Winter 0.46 (rp) (n=NR) 

Personal vs. 

outdoor 

Suburban Winter 0.49 (rp) (n=NR) 

Personal vs. 

outdoor 

Downtown Spring 0.80 (rp) (n=NR) 

Personal vs. 

outdoor 

Suburban Spring 0.82 (rp) (n=NR) 

Personal vs. 

outdoor 

Downtown 

(electric stove 

home) 

Pooled 0.55 (rp) (n=NR) 

Personal vs. 

outdoor 

Downtown (gas 

stove home) 

Pooled 0.59 (rp) (n=NR) 

Personal vs. 

outdoor 

Suburban 

(electric stove 

home) 

Pooled 0.63 (rp) (n=NR) 

Personal vs. 

outdoor 

Downtown (nonsmoking home) Pooled 0.73 (rp) (n=NR) 

Personal vs. 

outdoor 

Downtown 

(smoking home) 

Pooled 0.51 (rp) (n=NR) 

Personal vs. 

outdoor 

Suburban (nonsmoking home) Pooled 0.59 (rp) (n=NR) 

Personal vs. 

outdoor 

Suburban 

(smoking home) 

Pooled 0.46 (rp) (n=NR) 

Alm et al. 

(1998) 

Location: 

Helsinki, 

Finland 

Time period: 

winter and 

spring of 1991 

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. 

Personal vs. 

outdoor 

Pooled Pooled 0.86 (R2

) (n=23) 

Monn et al. 

(1998) 

Location: Geneva, Basel, 

Lugano, Aarau, 

Wald, Payerne, 

Montana, and 

Davos (SAPALDIA study, 

Switzerland) 

Time period: 

Dec 1993 to 

Dec 1994 

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). 

Personal vs. 

outdoor 

Pooled Pooled 0.33 (R2

) (n=1,494) 
2-46 

STUDY METHODS ASSOCIATION 

VARIABLE LOCATION SEASON rp, rs, or R2

Levy et al. 

(1998a) 

Location: 18 

cities across 

15 countries 

Time period: 

Feb or Mar 

1996 

Subjects: 568 adults 

Method: one 2-day avg measurement for each 

person, all people were measured on the same 

winter day. 

Personal vs. 

outdoor 

Urban Winter 0.57 (rs) (n=546) 

Personal vs. 

outdoor 

Kodama et al. Urban Summer 0.24 (rp) (n=NR) 

(2002) 

Location: 

Tokyo, Japan 

Time period: 

Feb 24-26, 

June 2-4, July 

13-15, and Oct 

14-16 in 1998 

and Jan 26-28 

in 1999 

Subjects: 150 junior-high school students and their 

family members 

Method: 3-day avg, personal exposures were 

monitored on the same day. Personal vs. 

outdoor 

Urban Winter 0.08 (rp) (n=NR) 

Spengler et al. 

(1994) 

Location: Los 

Angeles Basin, 

CA 

Time period: 

May 1987 to 

May 1988 

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. 

Personal vs. 

outdoor 

Pooled Pooled 0.48 (R2

) (n=NR) 

Lai et al. 

(2004b) 

Location: 

Oxford, 

England 

Time period: 

Dec 1998 to 

Feb 2000 

Subjects: 50 adults 

Method: one 48-h avg measurement per person. 

Personal vs. 

outdoor 

Urban Pooled 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=0.5ln [(1+r)/(1 - r)]), 

where r is the originally reported and Z is the transformed correlation coefficient (Fisher, 1925). The 

variance of Z is expressed as 1/(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. 
2-47 

Figure 2.5-4. Distribution of correlation coefficients (U.S. studies) between personal NO2 exposure and 

ambient NO2 concentrations based on Fisher’s Z transform. 

Two main aspects of these analyses are discussed below: (1) the meanings of the correlation 

coefficients in the context of exposure assessments in epidemiologic studies, and (2) factors affecting the 

strength of the association between personal NO2 exposure and ambient NO2 concentrations. 

In the context of determining the effects of ambient pollutants on human health, the association 

between the ambient component of personal exposures and ambient concentrations is more relevant than 

the association between personal total exposures (ambient component+nonambient component) and 

ambient concentrations. As described in Equations 2.5-2 and 2.5-4, personal total exposure can be 

decomposed into two parts; an ambient and a nonambient component. Usually, the ambient component of 

personal exposure is not directly measurable, but it can be estimated by exposure models, or the personal 

total exposure can be regarded as the personal exposure of ambient origin if there are no indoor or 

nonambient sources. Personal exposures were clearly stratified by indoor sources in only four studies 

among the studies examined for NO2 (Alm et al., 1998; Linaker et al., 2000; Piechocki-Minguy et al., 

2006; Sarnat et al., 2006) and only two studies (Alm et al., 1998; Piechocki-Minguy et al., 2006) 

compared the association between personal total exposures and ambient concentrations and the 

association between the ambient component of personal exposures and ambient concentrations. A 

stronger association was observed between the ambient component of personal exposures and the ambient 

concentrations (Alm et al., 1998; Piechocki-Minguy et al., 2006). It is expected that the association 

between ambient concentrations and the ambient component of personal exposures would be stronger 

than the association between ambient concentrations and personal total exposures as long as the ambient 

and nonambient component of personal total exposure are independent. The correlation coefficients 

between personal ambient NO2 exposures and ambient NO2 concentrations in different types of exposure 

studies are relevant to different types of epidemiologic studies. 

There are three types of correlations generated from the different study designs as listed in Tables 

2.5-4 and 2.5-5: longitudinal, “pooled,” and daily-average correlations (U.S. Environmental Protection 

Agency, 2004). 
2-48 

Figure 2.5-5. Distribution of correlation coefficients (European studies) between personal NO2 exposure 

and ambient NO2 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. 

 

1

( )( )

( ) x a

j

ij i j

ax n s s

x x a a

r

i

i −1

− −

=

∑ 

where “r” is the longitudinal correlation coefficient between personal exposure and ambient concentration, “a” represents the ambient concentration, 

“x” represents exposure, “i” represents the ith subject, “j” 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 aj

 could be measured by one ambient monitor or the average of several ambient monitors. 
2-49 

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., Alm 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 fivepanel 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 

 

1

( )( )

( ) x a

i j

ij j

ax n s s

x x a a

r

1

,

−

− −

=

∑ 

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. 

2

( )( )

( ) x a

j

j j

a x n s s

x x a a

r

j

−1

− −

=

∑ , 

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. 
2-50 

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. Alm 

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. Alm 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 (Alm 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 Alm 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 
2-51 

(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 (Finf) and alpha (α) are the keys to evaluate 

personal NO2 exposure of ambient origin. As defined in Equations 2.5-2 through 2.5-5, the Finf of 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 (α) is a function of Finf and the fraction of time people spend outdoors (y), and the physical 

meaning of α is the ratio of personal ambient exposure concentration to ambient concentration, (i.e., in the 

absence of exposures to nonambient sources (i.e., when Ena=0). 

The values for α and Finf can be calculated physically using Equations 2.5-2 through 2.5-5, if P, k, 

a, and y are known. However, the values of P 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; Sørensen et al., 2005; Zota et al., 2005). As a result, using a fixed 

value, as mentioned above, would either over- or underestimate the true α 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 of P, 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; 

Sørensen et al., 2005; Zota et al., 2005). García-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 doubleglazed 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. 
2-52 

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 α 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 α 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 α was 

overestimated by this ratio because of indoor sources. 

The random component superposition (RCS) model is an alternative way to calculate Finf or α 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 AX3.5-1a, Finf ranges from 0.4 to 0.7. Similarly, as 

shown in Annex Table AX3.5-1b, α, 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 α 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, α is also determined by the 

selection of the predictor. Using residential outdoor NO2 concentrations as the model predictor might give 

a different estimate of α 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. 
2-53 

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. NO2 as a Component and Indicator of Pollutant Mixtures 

2.5.7.1. Associations between Ambient NO2 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 pollutionhealth 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 (CH4) 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 PM2.5 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 N 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. 
2-54 

Table 2.5-7. Pearson correlation coefficient between ambient NO2 and ambient copollutants. 

STUDY LOCATION PM2.5 CO O3 SO2

AQS (2007)* Los Angeles, CA 0.49 (u2) 

0.56 (s) 

0.59 (u) 

0.64 (s) 

–0.29 (u) 

–0.11 (s) 

NR 

AQS (2007)* Riverside, CA NR 0.43 (u) 

0.41 (s) 

0.15 (r) 

0.045 (u) 

0.10 (s) 

–0.31 (r) 

NR 

AQS (2007)* Chicago, IL 0.49 (s) 0.53 (u) 

0.46 (s) 

–0.20 (u) NR 

AQS (2007)* New York, NY 0.58 (u) 0.46 (u) –0.06 (u) NR 

Kim et al. (2006) Toronto, Canada 0.44 0.72 NR NR 

Sarnat et al. (2006) Steubenville, OH (autumn) 0.78 (0.70 for sulfate; 0.82 for EC) NR NR NR 

Sarnat et al. (2006) Steubenville, OH (summer) 0.00 (0.1 for sulfate; 0.24 for EC) NR NR NR 

Connell et al. (2005) Steubenville, OH 0.50 NR NR NR 

Kim et al. (2005) St. Louis, MO (RAPS) NR 0.644 NR NR 

Sarnat et al. (2001)1 Baltimore, MD (summer) 0.37 0.75 0.02 (not significant) NR 

Sarnat et al. (2001) Baltimore, MD (winter) 0.75 0.76 –0.71 –0.17 

Hochadel et al. (2006) Ruhr area, Germany 0.41 (0.93 for EC3) NR NR NR 

Hazenkamp-von Arx et al. (2004) 21 European cities 0.75 NR NR NR 

Cyrys et al. (2003) Erfurt, Germany 0.50 0.74 NR NR 

Brook et al. (2007) 10 Canadian cities 0.54 NR NR NR 

Mosqueron et al. (2002) Paris, France 0.69 NR NR NR 

Rojas-Bracho et al. (2002) Santiago, Chile 0.77 NR NR NR 

1

 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/ttn/airs/airsaqs/ 

2.5.7.2. Associations among NO2 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 

ultrafine-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 
2-55 

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. 

Figure 2.5-6. Correlations of NO2 to O3 versus correlations of NO2 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 
2-56 

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 NO2 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, PM10, 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 PM2.5 or other combustion-generated pollutants, e.g., CO, EC. 

Table 2.5-8. Pearson correlation coefficient between ambient NO2 and personal copollutants. 

STUDY LOCATION PM2.5 SULFATE EC UFP 

Sarnat et al. (2006) Steubenville, OH 

Fall 

0.71 0.52 0.70 — 

Sarnat et al. (2006) Steubenville, OH Summer 0.00 0.1 not significant 0.26 — 

Vinzents et al. (2005) Copenhagen, Denmark — — — 0.49 (R2) explained by ambient NO2 and ambient temperature 

 

Table 2.5-9. Pearson correlation coefficient between personal NO2 and ambient copollutants. 

STUDY LOCATION PM2.5 SULFATE EC PM10 CO 

Sarnat et al. (2006) Steubenville, OH 

Fall 

0.46 0.35 0.57 — — 

Sarnat et al. (2006) Steubenville, OH 

Summer 

0.00 0.1 (not significant) 0.17 — — 

Kim et al. (2006) Toronto, Canada 0.30 — — — 0.20 

Rojas-Bracho et al. (2002) Santiago, Chile 0.65 — — 0.39 — 

 

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. 
2-57 

Table 2.5-10. Pearson correlation coefficient between personal NO2 and personal copollutants. 

STUDY LOCATION PM2.5 CO VOCs HONO 

Kim et al. (2006) Toronto, Canada 0.41 0.12 — 

Modig et al. (2004) Umea, Sweden — — 0.06 for 1,3-butadiene;

0.10 for benzene 

— 

Mosqueron et al. 

(2002) 

Paris, France 0.12 but not 

significant 

— — — 

Jarvis et al. (2005) 21 European cities — — — 0.77 for indoor NO2 and 

indoor HONO 

Lee et al. (2002) — — — — 0.51 for indoor NO2 and 

indoor HONO 

Lai et al. (2004a) Oxford, England –0.1 0.3 –0.11 for total VOCs — 

 

Table 2.5-11. Pearson correlation coefficient between NOX and traffic-generated pollutants. 

SPECIES ALL SITES WITHOUT UPWIND OR BACKGROUND SITE 

NOX: PM2.5 (motor vehicle component) 0.48<r<0.751 0.48<r<0.752

NOX: CO 0.30<r<0.771 0.54<r<0.772

NOX: Pb 0.42<r<0.761 0.48<r<0.762

NOX: Br 0.55<r<0.731 0.58<r<0.732

NO2: EC 0.933 — 

NO2: EC 0.82 autumn, 0.24 summer4 — 

1

St. Louis RAPS (Kim et al., 2006), all sites 2St. Louis RAPS (Kim et al., 2006), all sites with upwind background site removed 3

Ruhr Valley (Hochadel et al., 2006) 4Steubenville, OH (Sarnat et al., 2006) 

2.5.7.4. NO2 as an Indicator of the Mixture of Traffic Pollutants 

In the Restrepo et al. (2004) study, NO2 behaved as if traffic were its main source, since NO2

behaved similarly to CO and PM2.5; i.e., their concentrations decreased with height. O3 showed the 

opposite vertical gradient; i.e., its concentration increased with height. 

UFPs have long been associated with motor vehicle traffic (e.g., PM AQCD, 2004). Seaton and 

Dennekamp (2003) proposed that NO2 may be a surrogate for UFPs, in particular for particle number 

concentrations. The results from the measurements made at a background site in Aberdeen, Scotland, over 

the course of 6 months showed very high correlation between the number concentration of particles <100 

nm in diameter and NO2. The correlation between NO2 and the particle number concentration (r=0.89) 

was much higher than that between NO2 and PM2.5 (r=0.55) and that between NO2 and PM10 (r=0.45). A 

time-series mortality study (re-analysis by Stölzel et al., 2003; Wichmann et al., 2000) conducted in 

Erfurt, Germany, measured, and analyzed UFP number and mass concentrations as well as NO2. Unlike 

Seaton and Dennekamp’s data, in this data set, the correlation between NO2 and various number 

concentration indices were not much stronger than those between PM2.5 and number concentration indices 
2-58 

or those between PM10 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, PM2.5, and 

PM10 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. 

Figure 2.5-7. Composite, diurnal variability in 1-h avg NO2 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. 
2-59 

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, PM10, and NO2 in trafficdominated, 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, β, 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 β 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 α (Sheppard, 2005) and is unlikely to greatly affect β, 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 
2-60 

local sources, monitor siting, meteorology, and topography. Low correlations between the ambient 

concentration and the community avg concentration quantitatively reduce β if the single pollutant model 

is the true model. Similarly, β 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, Xt

A

, with daily community avg 

concentration, Ct, that is important, not the correlation of each individual’s exposure Xit

A

 with Ct. Thus, 

the low correlation of Xit

A

 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 PM10 and showed that the correlation of Xt

A

 with Ct was much greater than the correlation of 

Xit

A

 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 αit around the daily community avg αt tends to produce Berkson 

error, which will not change the point estimate of β, 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 α, 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 

over time 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 over time 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 β, nor will variations in the 

daily average α 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 α, or long-term averages of nonambient exposure differ 
2-61 

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 β. 

2.5.9. Summary of Issues in Assessing Exposures to NO2

 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 relevant 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 β, and therefore are not expected to change the principal 

conclusions from NO2 epidemiologic studies. 
2-62 

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, 
2-63 

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. 
3-1 

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 
3-2 

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 (Sandström 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 

μg/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 
3-3 

of 2.1 [95% CI: 0.52, 3.81]). A significant reduction in peak expiratory flow (PEF) was associated with 

exposure greater than 14 μg/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 μg/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 ↓ Ciliary beat frequency 

↑ Epithelial permeability 

Lower Airway 

 Epithelium (as in upper airway) 

 Cytokines ↓ Epithelial-derived IL-8, GM-CSF, TNF-α

↑ Macrophage-derived IL-1b, IL-6, IL-8, TNF-α

↑ Mast cell tryptase 

↑ Neutrophils 

↑ Total lymphocytes 

↑ NK lymphocytes 

 Inflammatory cells 

↓ T-helper/T-cytotoxic cell ratio 

 Inflammatory mediators ↑ Free radicals, proteases, TXA2, TXB2, LTB4 

↑ Penetrance due to ciliostasis 

↓ PD20-FEV1

↑ Antigen-specific IgE 

 Allergens 

↑ Epithelial permeability 

Peripheral Blood 

↓ B and NK lymphocytes 

↓ Total lymphocytes 

Source: Adapted from Chauhan et al. (1998). 

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; Sandström 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 
3-4 

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. Sandström 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 (Sandström 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 

1.5 ppm NO2 for 3 h. 

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 μg/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 
3-5 

(Ohashi et al., 1994). These concentration-dependent decreases were accompanied by a concentrationdependent 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 [IL]-8, urea, uric acid, 

albumin) but not greater levels of exhaled NO. However, within the urban group, a statistically significant 
3-6 

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 μg/m3

]), those in the high-exposure group had significantly lower plasma 

β-carotene levels. This difference was even greater when the analysis was stratified by dietary β-carotene 

intake: exposure to >21.3 ppb (40-μg/m3

) NO2 had the largest effect on plasma β-carotene level among 

subjects whose diet contained <4 mg/day β-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. 
3-7 

Figure 3.1-1. Studies of airway inflammatory responses in relation to the total exposure to NO2, 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 CD11b 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 
3-8 

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. Jörres 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 at NO2 levels of as low as 1880 μg/m3

 (1.0 ppm) after a 72-h exposure, but a 1-week exposure to 752 

μg/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-μg/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 
3-9 

in Annex Table AX4.4), such as Müller 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 antiinflammatory agents, while the fixed component is less reversible and does not respond to antiinflammatory 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 
3-10 

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 

(FEV1/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 (FEV1). 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 
3-11 

“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. 

Figure 3.1-2. Airway responsiveness to allergen challenge in asthmatic subjects following a single 

exposure to NO2. 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 NO2 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 
3-12 

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 FEV1 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 FEV1 (PD20-FEV1) 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 that NO2 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 μg/m3

 (range, 203 to 462), or 0.166 ppm, (range, 

0.106 to 0.242). PM10 levels were 170 μg/m3

 (range, 103 to 613), and PM2.5 levels were 95 μg/m3 (range, 

61 to 128). Median NO2 levels outside the hotel were 11 μg/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 
3-13 

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. 

Toxicological Studies 

Acute exposures of Brown Norway rats to NO2 at a concentration of 5 ppm for 3 h resulted in increased 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 

et al., 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 
3-14 

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. 
3-15 

Table 3.1-2. Changes in airway responsiveness associated with NO2 exposure. 

CHANGE IN AHR AVERAGE 

PD ± SD STUDY N NO2

(ppm)

EXP 

(min)

CHALLENGE 

TYPE 

END 

POINT 

TIME 

POST

EXP 

(min)

EXER

INC DEC AIR NO2

NOTES

Ahmed et al. (1983b) 20 0.1 60 CARB sGAW — N 13 7 6.0 2.7 

Ahmed et al. (1983a) 19 0.1 60 RAG sGAW — N 10 8 9.0 ± 25 3.4 ± 4.6 

Hazucha et al. (1983) 15 0.1 60 METH sRaw 20 N 6 7 1.9 ± 0.4 2.0 ± 1.0 

Orehek et al. (1976) 20 0.1 60 CARB sRaw 0 N 14 3 0.6 0.4 

Rasmussen et al. (1990) 20 0.1 120 METH FEV1 0 Y — — (AIR-NO2=0.00) 

Orehek et al. (1981) 7 0.11 60 GRASS sRaw 0 N — — 1.2 ± 0.3 1.3 ± 0.3 

4 allergic

3 asthmatic 

Bylin et al. (1988) 20 0.14 30 HIST sRaw 25 N 14 6 — — 

Roger et al. (1990) 19 0.15 80 METH sRaw 60 Y 10 7 3.3 ± 0.7 3.1 ± 0.7 

Kleinman et al. (1983) 31 0.20 120 METH FEV1 0 Y 20 7 8.6 ± 16 3.0 ± 6.2 

Rasmussen et al. (1990) 20 0.20 120 METH FEV1 0 Y — — (AIR-NO2=0.02) 

Jörres and Magnussen 

(1990) 14 0.25 30 SO2 sRaw 27 N 11 2 47 ± 5.1 37.7 ± 3.5 

Jörres and Magnussen 

(1991) 11 0.25 30 METH sRaw 60 Y 7 4 0.4 ± 1.6 0.4 ± 1.6 

Bylin et al. (1988) 20 0.27 30 HIST sRaw 25 N 14 6 — — 

Avol et al. (1988) 37 0.30 120 COLD FEV1 60 Y 11 16 -8.4 ± 11 -10.7 ± 12 Delta 

FEV1

Avol et al. (1989) 34 0.30 30 COLD FEV1 60 Y 12 21 -5.3 ± 12 -4.7 ± 13 Delta 

FEV1

Bauer et al. (1986a) 12 0.30 30 COLD FEV1 60 Y 9 3 0.8 ± 0.4 0.5 ± 0.3 PD10RHE

Linn et al. (1986) 21 0.30 120 COLD FEV1 0 Y -11.4 -12.1 Delta 

FEV1

Morrow and Utell (1989) 20 0.30 225 CARB sGaw — Y — — — — 

Roger et al. (1990) 19 0.30 80 METH sRaw 60 Y 8 9 3.3 ± 0.7 3.3 ± 0.8 

Rubinstein et al. (1990) 9 0.30 30 SO2 sRaw 60 Y 4 5 1.3 ± 0.7 1.3 ± 0.8 

Bylin et al. (1985) 8 0.48 20 HIST sRaw 20 N 5 — — — 

Mohsenin (1987b) 10 0.50 60 METH sGaw 0 N 7 2 9.2 ± 1.5 4.6 ± 8.0 

Bylin et al. (1988) 20 0.53 30 HIST sRaw 25 N 12 7 — — 

Avol et al. (1988) 37 0.60 120 COLD FEV1 60 Y 13 16 -8.4 ± 11 -10.4 ± 14 Delta 

FEV1

Roger et al. (1990) 19 0.60 80 METH sRaw 60 Y 11 8 3.3 ± 0.7 3.7 ± 1.1 

Rasmussen et al. (1990) 20 0.80 120 METH FEV1 0 Y — — (AIR-NO2=-0.06) 

Linn et al. (1986) 21 1.00 120 COLD FEV1 0 Y — — -11.4 -11.2 Delta 

FEV1

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 FEV1
3-16 

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. 

Jörres 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. Jörres 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 ALL EXPOSURES EXPOSURE WITH EXERCISE EXPOSURE AT REST 

0.1 0.66 (50)B — 0.66 (50)B

0.1 - 0.15 0.66 (87)C 0.59 (17) 0.67 (70)C

0.2 - 0.3 0.58 (187)B 0.52 (136) 0.75 (51)C

>0.3 0.59 (81) 0.49 (48) 0.73 (33)B

0.1 - 0.6 0.60 (355)C 0.52 (201) 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. 
3-17 

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). 
3-18 

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. 
3-19 

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 MEAN RATE INTERVENTION 

(N=45)

MEAN RATE CONTROL 

(N=69) RR (95% CI) 

Wheeze during the day 4.9 (15.2) 5.1 (10.5) 0.95 (0.45, 2.01) 

Wheeze during the night 2.2 (5.6) 2.3 (5.5) 0.94 (0.36, 2.50) 

Difficulty breathing during the day 2.2 (3.7) 5.4 (12.1) 0.41 (0.07, 0.98) 

Difficulty breathing during the night 0.8 (2.2) 2.6 (6.9) 0.32 (0.14, 0.69) 

Chest tightness during the day 2.3 (4.3) 5.1 (9.9) 0.45 (0.25, 0.81) 

Chest tightness during the night 1.5 (3.3) 2.5 (6.2) 0.59 (0.28, 1.29) 

Cough during the day 17.5 (21.5) 13.7 (13.7) 1.27 (0.81, 2.00) 

Cough during the night 10.7 (16.6) 11.6 (12.4) 0.92 (0.49, 1.73) 

Difficulty breathing after exercise 3.8 (7.4) 6.4 (13.9) 0.59 (0.31, 1.13) 

Asthma attacks during the day 1.1 (2.3) 2.7 (5.3) 0.39 (0.17, 0.93) 

Asthma attacks during the night 0.7 (2.1) 1.8 (3.8) 0.38 (0.13, 1.07) 

Missed school due to asthma 1.6 (2.0) 1.2 (2.8) 1.34 (0.68, 2.60) 

Visit to health care facilities due to asthma 0.5 (0.8) 0.8 (1.2) 0.60 (0.35, 1.03) 

Taking any asthma medication 26.9 (36.7) 34.6 (37.1) 0.77 (0.49, 1.21) 

Taking any reliever 13.8 (23.2) 22.4 (28.8) 0.62 (0.31, 1.25) 

Taking any preventer 26.2 (40.1) 29.9 (42.2) 0.87 (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% CI: 0.19, 1.08), while the reduction in asthma attacks during the night reached statistical significance (RR=0.33; 95% CI: 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. 
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Source: Adapted from Pilotto et al. (1997a) 

Figure 3.1-3. Geometric mean symptom rates (95% CI) for cough with phlegm (panel A) and proportions 

(95% CI) of children absent from school for at least 1 day (panel B) during the winter heating 

period grouped by estimated NO2 exposure at home and at school (n=number of children at 

that NO2 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% CI: 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% CI: 0.76, 2.15]). Exposure-response relation-
3-21 

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 FEV1/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 μg/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] for NO2 at levels above 27.5 μg/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=1.52 [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, medication 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 
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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). 

Source: Adapted from van Strien et al. (2004). 

Figure 3.1-4. 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. 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 that NO2 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. 
3-23 

3.1.4.2. Ambient NO2 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., “betweenstudy 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 μm (PM10) (OR for NO2=1.45 [95% CI: 0.63, 3.34]). 
3-24 

Source: Schildcrout et al. (2006). 

Figure 3.1-5. Odds ratios (95% CI) for daily asthma symptoms (panel A) and rate ratios (95% CI) for daily 

rescue inhaler use (panel B) associated with shifts in within-subject concentrations of NO2 for 

single- and joint (with PM10)-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. 
3-25 

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, PM10, 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 PM10. 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 PM10, 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. 
3-26 

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). 

Figure 3.1-6. Odds ratios (95% CI) for associations between asthma symptoms in children and 24-h 

average NO2 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 PM10. 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. 
3-27 

Figure 3.1-7. Odds ratios and 95% CI for associations between asthma symptoms and 24-h average NO2

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, FEV1, 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 FEV1 (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 
3-28 

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 FEV1 (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 μg/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 FEV1, 

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 FEV1 (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 FEV1 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 
3-29 

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, FEV1 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 FEV1

during exposure, and their responses were significantly different from the 7 nonsmokers (percent change 

in FEV1 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 μg/m3

 concentrated ambient 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 spirometry, 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. 
3-30 

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 FEV1. 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 μg/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. Jörres 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 FEV1 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 μg/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 FEV1 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 FEV1 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. 
3-31 

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 max NO2

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. 
3-32 

Figure 3.1-8. Relative risks (95% CI) 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; ^ indicates 1-h max averaging times, 

all others are 24-h mean averaging times). 
3-33 

Figure 3.1-9. Relative Risks (95% CI) 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; ^ indicates 1-h max 

averaging times, all others are 24-h mean averaging times). 
3-34 

Figure 3.1-10. Relative risks (95% CI) 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). 
3-35 

Figure 3.1-11. Relative risks (95% CI) 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). 
3-36 

Figure 3.1-12. Relative Risks (95% CI) 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; ^ indicates 1-h max averaging times, 

all others are 24-h mean averaging times). 
3-37 

Figure 3.1-13. Relative risks (95% CI) 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; ^ 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 casecrossover 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 to14 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). 
3-38 

In a multicity study of all hospitalizations for respiratory disease for adults ages ≥ 65 years, Simpson 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 presented 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 twopollutant 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. 
3-39 

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 
3-40 

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 (Tenías et al., 1998; Galán 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; Pönkä, 1991; Pönkä 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, and AX6.3-5. 

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 

(Dab et al., 1996). 
3-41 

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; Pönkä 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-ppm NO2. 

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-
3-43 

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 shortterm 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 
3-44 

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, 

PM10 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; 
3-45 

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 highconcentration 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 
3-46 

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 max NO2 level was approximately 39 ppb in 

metropolitan Toronto, ON, where these studies were conducted. Estimates from two Australian multicity 

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 singlecity 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. 
3-47 

Figure 3.2-1. Relative risks (95% CI) 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. 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 Pönkä 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). 
3-48 

Figure 3.2-2. Relative risks (95% CI) for associations of 24-h NO2 (per 20 ppb) and daily 1-h max NO2* (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 
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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. 
3-50 

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., PM10), 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. PM10 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.” PM10, 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 PM10 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 PM10 or other pollutants (and vice versa). 

Source: Dominici et al. (2003). 

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. Models A=NO2 alone; B=NO2+PM10; C=NO2+PM10+O3; 

D=NO2+PM10+SO2; E=SO2+PM10+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 (PM10-2.5), 

PM10, CoH, SO2, and CO, the strongest mortality association was found at lag 1, whereas for NO2, it was 
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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 for NO2, 

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 PM2.5 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, 

PM10, 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 
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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 PM10 and not for NO2, but found that the cities with higher NO2

levels tended to have larger PM10 risk estimates. Furthermore, simultaneous inclusion of PM10 and NO2

reduced the PM10 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. PM10, black smoke, O3, NO2, SO2, CO, sulfate 

(SO4

2–), 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%). PM10 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. Meta-Analyses of NO2 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 PM10, 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 PM10 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 allcause, 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. 

Figure 3.3-2. Combined NO2 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 NO2 concentrations. For multipollutant models, results from the models that resulted in 

the greatest reduction in NO2 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 
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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 metaanalysis 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. 

Figure 3.3-3. Combined NO2 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 NO2 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 FEV1 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 PM2.5, 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, FEV1, 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=1,759). 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 FEV1, 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 FEV1 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 
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concluded that the effects of NO2 could not be distinguished from the effects of particles (PM2.5 and 

PM10) as NO2 was strongly correlated with these contaminants (0.79, and 0.67, respectively). 

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 (FEV1) associated with a 20-ppb increase in 

NO2 (A) and a 20-μg/m3 increase in PM10 (B) in children, standardized per year of follow-up. 

Results from three major children’s long-term cohort studies are presented. 
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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 NO2 (A) and a 20-μg/m3 increase in 

PM10 (B) in children, standardized per year of follow-up. Results from three major children’s 

long-term cohort studies are presented. 
3-59 

Source: Derived from Gauderman et al. (2004). 

Figure 3.4-3. Proportion of 18-year olds with a FEV1 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 
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Source: Derived from Rojas-Martinez et al. (2007a,b). 

Figure 3.4-4. Estimated annual growth in FEV1, of O3, PM10, and NO2 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, perviousday 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 FEV1 -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 
3-61 

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 PM10, 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 FEV1, by gender and pollutant with 

adjustments noted for copollutants. The results of this 3-year study supported the hypothesis that longterm 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, PM10, 

and PM2.5 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 PM10 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 PM10 with FEV1 and FVC were found with a 10-μg/m3

 (5.2 ppb) increase 

in annual average exposure. Due to the high correlations between NO2 and the other pollutants (SO2: 

r=0.86; PM10: 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 FEV1 and FVC. Downs et al. (2007) reported the 

relationship in this group of long-term reduced exposure to PM10 and age-related decline in lung function, 

but they did not examine NO2 or other pollutants. 
3-62 

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 FEV1 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-freewayrelated 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, PM10, 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 FEV1 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 
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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 for NO2 (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. 
3-64 

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 for NO2 (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. 

Source: McConnell et al. (2003). 

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. 

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 physiciandiagnosed 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. 
3-65 

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 et al., 1971, 1972; Evans et al., 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. 
3-66 

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 (Pérez-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. 

Source: Adapted from Gilliland et al. (1999). 

Figure 3.4-6. Biological pathways of long-term NO2 exposure on morbidity. 

MPO=myeloperoxidase; PUFA=polyunsaturated fatty acids; TNF-α=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 (H+

) 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 
3-67 

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

-

, H+, 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 μg/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 μg/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 μg/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 μg/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 μg/m3

 (5.2 ppb) and above 30 μg/m3 (15.7 ppb) are virtually identical. It is possible that NO2 may 
3-68 

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 (RoyBurman 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. Toxicological Studies of Coexposure with Known Carcinogens 

Rats were injected with N-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+1 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 
3-69 

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 CAF1/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 

N-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 
3-70 

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 N-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 longterm 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., N-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 (PM10, PM10-2.5, 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 PM2.5 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 
3-72 

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 lbs, 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 in NO2 [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 dosedependent 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 
3-74 

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-to16-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 dataset NO2

was highly correlated with PM2.5 (r=0.78), SO4

2– (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 SO4

2–, 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), but NO2 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), PM10, CO, O3, NO2, SO2, SO4

2

, 

PM2.5, and PM10-2.5 were considered. The county of residence at the time of entry to the study was used 
3-75 

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 pollutionmortality 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 PM10 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 PM10, 

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 over time. They reported 

associations between PM10 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., SO4

2–, 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., for NO2, 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 longterm mortality, but NOX 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 μg/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 PM10 (estimated from 0.71 X TSP, based on available PM10 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]) and NO2 (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 PM10. 

Næss et al. (2007) investigated the concentration-response relationships between air pollution (i.e., 

NO2, PM10, PM2.5) 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 μg/m3

 (21 ppb) in the younger age (51 to 70 years) group and with a linear effect in the 

interval of 20 to 60 μg/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 PM10. 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 that NO2 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. 
3-78 

Source: Næss et al. (2007). 

Figure 3.6-1. Age-adjusted, nonparametric smoothed relationship between NO2 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 communityor 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 

of NO2. 

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 trafficrelated pollutants. Essentially no information was available on the possible effect modification of 

apparent NO2-mortalty associations. 
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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 NO2=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 Næss 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 PM10. 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 PM10. 

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. 
3-80 

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. 
4-1 

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 concentrationresponse 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. 
4-2 

Table 4.1-1. Gradation of individual responses to short-term NO2 exposure in persons with 

impaired respiratory systems. 

SYMPTOMATIC 

RESPONSE NORMAL MILD MODERATE SEVERE 

Wheeze None With otherwise normal 

breathing With shortness of breath Persistent with 

shortness of breath 

Cough Infrequent Cough Cough with deep breath Frequent spontaneous 

cough 

Persistent 

uncontrollable cough 

Chest pain None Discomfort just noticeable 

on exercise or deep breath 

Marked discomfort 

on exercise or deep breath 

Severe discomfort on 

exercise or deep breath 

Duration of response None <4 h 4 – 24 h >24 h 

FUNCTIONAL 

RESPONSE NONE SMALL MODERATE LARGE 

FEV1 change Decrements of <3% Decrements of 3 -10% Decrements of 10-20% Decrements of ≥ 20% 

Bronchial responsiveness Within normal range Increases of <100% Increases of 100-300% Increases of >300% 

Specific airway resistance 

(sRaw) 

Within normal range 

(± 20%) sRaw increased <100% sRaw increased 100-200% 

or up to 15 cm H2Oxs 

sRaw increased >200% or 

more than 15 cm H2Oxs 

Duration of response None <4 h 4 – 24 h >24 h 

IMPACT OF 

RESPONSES NORMAL MILD MODERATE SEVERE 

Interference with 

normal activity None Few persons choose 

to limit activity 

Many persons choose 

to limit activity 

Most persons choose 

to limit activity 

Medical treatment No change Normal medication 

as needed 

Increased frequency 

of medication use or 

additional medication 

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 FEV1 (PD20) or provocative dose that 

produces a 100% increase in sRaw (PD100). 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 
4-3 

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 concentrationresponse 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 concentrationresponse 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. Tenías et al. (1998) also reported no consistent pattern in 

their study of the association between ambient NO2 and ED visits in Valencia’s Hospital Clìnic 

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 concentrationresponse 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 concentrationresponse 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 
4-4 

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 

chapter 3). 

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 
4-5 

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 PM10. Modification of the association between NO2 and 

MI by conduction disorders but not diabetes or hypertension was observed by D’Ippoliti et al. (2003). 
4-6 

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-yearsof-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., 
4-7 

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 FEV1 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 Ile/Ile- and Ile/Val-genotype children were more likely to experience cough and bronchodilator 

use, specifically in response to NO2 than GSTM1-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. 
4-8 

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 

 Age, Gender 

 Adverse birth outcomes: e.g., preterm birth, low birth 

weight, growth restriction, birth defects 

 Race/ethnicity 

 Genetic factors 

 Pre-existing disease, e.g., diabetes 

 Obesity 

 Respiratory diseases, e.g., asthma, COPD 

 Cardiovascular diseases 

Vulnerability Factors 

 Socioeconomic status 

 Education level 

 Air conditioning Use 

 Proximity to Roadways 

 Geographic Location (West 

vs. East) 

 Level of Exercise 

 Work Environment (e.g., 

outdoor workers) 

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 atrisk 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 pollutioninduced 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 
4-9 

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; Künzli 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 
4-10 

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).

AGE (YEARS) REGION 

ALL ADULTS 18-44 45-64 65-74 75+ NORTHEAST 

MIDWEST SOUTH WEST CHRONIC 

CONDITION/DISEASE 

ADULTS (18+ YEARS) 

CASES 

(× 106) % % % % % % % % % 

Asthma 14.4 6.7 6.4 7.0 7.5 6.6 6.8 6.8 6.0 7.5 

COPD: Chronic Bronchitis 8.6 4.2 3.2 4.9 6.1 6.3 4.0 4.7 4.4 3.5 

COPD: Emphysema 3.5 1.7 0.3 2 4.9 6.0 1.5 1.7 2.0 1.1 

ALL CHILDREN 0-4 5-11 12-17 NORTHEAST 

MIDWEST SOUTH WEST CHRONIC 

CONDITION/DISEASE 

CHILDREN (<18 YEARS) CASES 

(× 106) % % % % % % % % 

Respiratory Conditions 6.5 8.9 6.8 9.9 9.6 10.1 8.5 9.3 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. 
4-11 

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. 
4-12 

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 populationlevel 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. 
5-1 

Chapter 5. Summary and Conclusions 

5.1. Introduction 

The Integrated Plan for the Primary NAAQS for NO2 (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. 
5-2 

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 N-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 N-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. 
5-3 

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 (PM2.5). 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 (α), 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. 
5-4 

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., 
5-5 

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 NO2

Respiratory Morbidity No Overall Conclusion “sufficient to infer a likely causal relationship” 

Lung Host Defense 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. 

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. 

Airway Inflammation No 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. 

Airway Hyperresponsiveness An increase in responsiveness to 

bronchoconstrictors was found in 

asthmatics and healthy individuals 

exposed to NO2 at rest. 

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). 

Respiratory Symptoms 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. 

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. 

Lung Function 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. 

The association between ambient NO2 concentrations and lung function in 

epidemiologic studies were generally inconsistent. Recent clinical evidence 

generally confirms prior findings. 

ED Visits / Hospital 

Admissions 

No Studies 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. 

Cardiovascular Morbidity No Studies “inadequate to infer the presence or absence of a causal relationship” 

Cardiovascular Effects No Studies Evidence from epidemiologic studies of heart rate variability, repolarization 

changes, and cardiac rhythm disorders among heart patients with ischemic 

cardiac disease are inconsistent. 

ED Visits / Hospital 

Admissions 

No Studies 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. 

Mortality No Studies “suggestive but not sufficient to infer a causal relationship” 

All Cause and 

Cardiopulmonary Mortality 

No Studies Positive and generally robust associations between ambient NO2

concentrations and risk of nonaccidental and cardiopulmonary mortality. 
5-6 

HEALTH OUTCOME CONCLUSION FROM 

PREVIOUS NAAQS REVIEW CONCLUSION FROM 2008 ISA 

LONG-TERM EXPOSURE TO NO2

Respiratory Morbidity No Overall Conclusion “suggestive but not sufficient to infer a causal relationship” 

Respiratory Effects NO2 can cause emphysema 

(meeting the human definition 

criteria) in animals at high 

concentrations of NO2. 

Epidemiologic studies observed decrements in lung function growth 

associated with long-term exposure to NO2. 

Other Morbidity No Studies “inadequate to infer the presence or absence of a causal relationship” 

Cancer No Studies 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. 

Cardiovascular Effects No Studies Very limited epidemiologic and toxicological evidence does not suggest that 

long-term exposure to NO2 has cardiovascular effects. 

Birth Outcomes No Studies 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. 

Mortality No Studies “inadequate to infer the presence or absence of a causal relationship” 

All Cause and 

Cardiopulmonary Mortality 

No Studies The results of epidemiologic studies examining the association between longterm 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 shortterm 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. 
5-7 

Figure 5.3-1. Summary of epidemiologic studies examining short-term exposures to ambient NO2 and respiratory outcomes. Effect estimates for studies conducted in the U.S. or Canada are presented in black. Circles represent effect estimates. Lines represent 95% CI. Legend to figure on following page. 
5-8 

Legend to Figure 5.3-1. 

Respiratory Symptoms 

1 Schwartz et al. (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 et al. 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 et al., (1999a) 

26 Atkinson et al., (1999b) 

27 Ponce de Leon et al. (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 et al. (1999b) 

40 Ponce de Leon et al. (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 et al. (1999) 

48 Lin et al. (1999) 

49 Gouveia and Fletcher (2000a) 

Respiratory Disease – Adults 

50 Luginaah et al. (2005) 

51 Luginaah et al. (2005) 

52 Spix et al. (1998) 

53 Anderson et al. (2001) 

54 Atkinson et al. (1999a) 

55 Atkinson et al. (1999b) 

56 Ponce de Leon et al. (1996) 

57 Schouten et al. (1996) 

58 Schouten et al. (1996) 

59 Petroeschevsky et al. (2001) 

60 Wong et al. (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 et al. (1998) 

66 Anderson et al. (2001) 

67 Atkinson et al. (1999a) 

68 Atkinson et al. (1999b) 

69 Ponce de Leon et al. (1996) 

70 Andersen et al. (2007b) 

71 Andersen et al. (2007a) 

72 Schouten et al. (1996) 

73 Schouten et al. (1996) 

74 Simpson et al. (2005a) 

75 Hinwood et al. (2006) 

76 Petroeschevsky et al. (2001) 

77 Wong et al. (1999) 

Asthma – All Ages 

78 Peel et al. (2005) 

79 Ito et al. (2007)* 

80 Burnett et al. (1999) 

81 Anderson et al. (1998) 

82 Atkinson et al. (1999a) 

83 Atkinson et al. (1999b) 

84 Galan et al. (2003) 

85 Chardon et al. (2007) 

86 Schouten et al. (1996) 

87 Migliaretti et al. (2005) 

88 Migliaretti and Cavallo (2004) 

89 Hinwood et al. (2006) 

90 Petroeschevsky et al. (2001) 

91 Wong et al., (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 Sunyer et al. (1997) 

101 Anderson et al. (1998) 

102 Atkinson et al. (1999a) 

103 Atkinson et al. (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 et al. (2005) 

111 Barnett et al. (2005) 

112 Hinwood et al. (2006) 

113 Petroeschevsky et al. (2001) 

114 Petroeschevsky et al. (2001) 

115 Morgan et al. (1998a) 

116 Ko et al. (2007) 

117 Lee et al. (2006) 

118 Gouveia and Fletcher (2000a) 

119 Jaffe et al. (2003) 

120 Jaffe et al. (2003) 

121 Linn et al. (2000) 

Asthma – Adults 

122 Sunyer et al. (1997) 

123 Anderson et al. (1998) 

124 Atkinson et al. (1999a) 

125 Atkinson et al. (1999b) 

126 Boutin-Forzano et al. (2004) 

127 Tenias et al. (1998) 

128 Castellsague et al. (1995) 

129 Migliaretti et al. (2005) 

130 Morgan et al. (1998a) 

131 Ko et al. (2007) 

132 Anderson et al. (1998) 

133 Atkinson et al. (1999a) 

134 Migliaretti et al. (2005) 

135 Hinwood et al. (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 et al. (2004) 

142 Villeneuve et al. (2003) 

143 Samoli et al. (2006) 

144 Zmirou et al. (1998) 

145 Biggeri et al. (2005) 

146 Anderson et al. (1996) 

147 Bremner et al. (1999) 

148 Anderson et al. (2001) 

149 Le Tertre et al. (2002) 

150 Dab et al. (1996) 

151 Zmirou et al. (1996) 

152 Hoek et al. (2000); 

 (Reanalysis, Hoek (2003) 

153 Hoek et al. (2000); 

 (Reanalysis, Hoek (2003) 

154 Saez et al. (2002) 

155 Garcia-Aymerich et al. (2000) 

156 Saez et al. (1999) 

157 Sunyer et al. (1996) 

158 Borja-Aburto et al. (1998) 

159 Gouveia and Fletcher (2000b) 

160 Simpson et al. (2005a,b) 

161 Simpson et al. (2000) 

162 Tsai et al. (2003) 

163 Yang et al. (2004b) 

164 Wong et al. (2001) 

165 Wong et al. (2002) 
5-9 

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 et al. (1992) 

Vagaggini et al. (1996) 

0.3-0.4 2-4 Inconsistent effects on FVC and FEV1 in COPD patients with mild exercise. 

Azadniv et al. (1998) 

Blomberg et al. (1997; 1999) 

Devlin et al. (1999) 

Frampton et al. (2002) 

Jörres et al. (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 FEV1 reported for asthmatics. 

Beil and Ulmer (1976) 

Nieding et al. (1979) 

Nieding and Wagner (1977) 

Nieding et al. (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 
5-10 

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 toxicological effects in rats from NO2 exposure. 

STUDY NO2 (ppm) EXPOSURE DURATION OBSERVED EFFECTS 

Kumae and 

Arakawa (2006) 

0.2 From conception to 12 wks 

post delivery 

Increase in BALF lymphocytes (indicative of inflammation) 

Kumae and 

Arakawa (2006) 

0.5 Weanling period (from 5 wks 

old to 12 wks) 

Suppression of ROS (indicative of lung host defense impairment) 

Robison et al. 

(1993) 

0.5 0.5-10 days Depressed activation of arachidonic acid metabolism and superoxide production 

(indicative of lung host defense impairment) 

Mercer et al. (1995) 0.5 spikes 

of 1.5 

9 wks Increase in the number of fenestrae in the lungs (morphological effects) 

Barth et al. (1994) 0.8 1 or 3 days 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 allergeninduced 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 
5-11 

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 PM10 (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 
5-12 

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 multicity 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 

 

1

Excess 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. 
5-13 

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 trafficrelated 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 relationship 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 
5-14 

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 
5-15 

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 longterm 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 
5-16 

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. 
5-17 

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 STANDARDIZEDa

 % EXCESS RISK 

(95% CI) 

Respiratory Symptoms 

Schwartz et al. (1994) 6 cities, U.S. 24-h avg 13 ppb (NR) Max: 44 61.3% (8.2, 143.4) Cough Incidence 

Mortimer et al. (2002) 8 urban areas, U.S. 4-h avg 32 ppb (NR) −7, 96 48% (2, 116) Morning Asthma Symptoms 

Schildcrout et al. (2006) 8 North American Cities 24-h avg 17-26 ppb (NR) NR 4.0% (1.0, 7.0) Asthma Symptoms 

Ostro et al. (2001) LA and Pasadena, CA 1-h max 68-80 ppb (NR) 20, 220 7.0% (1.0, 13.8) Cough Onset 

Delfino et al. (2002) Alpine, CA 1-h max 24 ppb (10) 8, 53 34.6% (-17.9, 122.1) Asthma Symptoms 

Delfino et al. (2003) East LA County, CA 1-h max 7.2 ppb (2.1) 3, 14 120% (-46, 2,038) Asthma Symp. Scores >1

Linn et al. (1996) Los Angeles, CA 24-h avg 33 ppb (22) 1, 96 -18.2% (-47.3, 27.1) Morning Symptom 

Score 

Emergency Department Visits – All Respiratory 

Peel et al. (2005) Atlanta, GA 1-h max 45.9 ppb (17.3) Max: 256 2.4% (0.9, 4.1) 

Tolbert et al. (2007) Atlanta, GA 1-h max 43.2 ppb (NR) 1.0-181 2% (0.5, 3.3) 

Emergency Department Visits – Asthma 

Jaffe et al. (2003) 2 cities, OH, (Clev, Cinc) 24-h avg Cinc:50 ppb (15) 

Clev:48 ppb (15) NR 6.1% (-2.0, 14.0) 

Ito et al. (2007) New York, NY 24-h avg 31.1 ppb (8.7) NR 12% (7, 16) 

New York State Department of 

Health (2006) Bronx and Manhattan, NY 24-h avg 34 ppb (NR) NR 6% (1, 10) Bronx. -3% (-18, 14) Manhattan 

Peel et al. (2005) Atlanta, GA 1-h max 45.9 ppb (17.3) NR 2.1% (-0.4, 4.5) All Ages. 4.1% (0.8, 7.6) 2-

18 yrs 

Tolbert et al. (2000) Atlanta, GA 1-h max 81.7 ppb (53.8) 5.4, 306 0.7% (-0.8, 2.3) 

Hospital Admissions – All Respiratory 

Burnett et al. (1997a) 16 Canadian Cities 1-h max 35.5 ppb (16.5) NR -0.3% (-2.4, 1.8) adjusted for CO, O3, SO2, 

CoH 

Yang et al. (2003) Vancouver, BC 24-h avg 18.7 ppb (5.7) NR 19.1% (7.4, 36.3)<3 yrs. 19.1% (11.2, 36.3) 

>65 yrs 

Fung et al. (2006) Vancouver, BC 24-h avg 16.8 ppb (4.3) 7.2, 33.9 9.1% (1.5, 17.2) 

Burnett et al. (2001) Toronto, ON 1-h max 44.1 ppb (NR) Max: 146 13.3% (5.3, 22.0) 

Luginaah et al. (2005) Windsor, ON, 1-h max 38.9 ppb (12.3) NR 6.7% (-5.4, 20.4) female. -10.3% (-20.3, 1.1) 

male 

Hospital Admissions – Asthma 

Linn et al. (2000) Los Angeles, CA 24-h avg 3.4 ppb (1.3) NR 2.8% ± 1.0% 

Lin et al. (2004) Vancouver, BC 24-h avg 18.7 ppb (5.6) 4.3, 5.4 45.3% (12.7, 88.3) Boys. 23.0% (-11.7, 70.2) 

Girls 

Lin et al. (2003) Toronto, ON 24-h avg 25.2 ppb (9.04) 3.0, 82.0 18.9% (1.8, 39.3) Boys. 17.0% (-5.4, 41.4) 

Girls 

Burnett et al. (1999) Toronto, ON 24-h avg 25.2 ppb (9.1) NR 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; 

Norris et al. 1999; Stieb et al. 2000) or did not present sufficient quantitative risk estimates (Lipsett et al., 1997; Sinclair and Tolsma 2004). a

24-h avg effect estimates standardized to 20 ppb increment; 1-h max effect estimates standardized to 30 ppb increment. 
1 

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