IntegratedScienceAssessmentfor

OxidesofNitrogen—HealthCriteria

Annexes

UnitedStates

EnvironmentalProtection

Agency

EPA/600/R-08/072

July2008
July 2008 

EPA/600/R-08/072 

Integrated Science Assessment 

for Oxides of Nitrogen – Health Criteria 

Annexes 

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.
i

Table of Contents 

List of Tables _________________________________________________________________________________________ i

List of Figures _______________________________________________________________________________________ iii

Abbreviations and Acronyms____________________________________________________________________________ v

Annex 1. Framework for Review________________________________________________________________________ 1-1

AX1.1. Literature Selection and Retrieval __________________________________________________________________________ 1-1

AX1.1.1. General Criteria for Study Selection ____________________________________________________________________ 1-3

AX1.1.2. Criteria for Selecting Epidemiology Studies ______________________________________________________________ 1-3

AX1.1.3. Criteria for Selecting Toxicology Studies ________________________________________________________________ 1-4

AX1.2. Guidelines for the Determination of Causality _________________________________________________________________ 1-4

AX1.2.1. Surgeon General’s Report: The Health Consequences of Smoking ___________________________________________ 1-4

AX1.2.2. The EPA Guidelines for Carcinogen Risk Assessment _____________________________________________________ 1-6

AX1.2.3. Improving Presumptive Disability Decision-Making Process for Veterans _______________________________________ 1-9

AX1.2.4. Formulation of Scientific Findings for Policy Purposes_____________________________________________________ 1-13

AX1.2.5. IARC Cancer Guidelines for Scientific Review and Evaluation ______________________________________________ 1-14

AX1.2.6. National Toxicology Program Criteria __________________________________________________________________ 1-21

Annex 2. Atmospheric Chemistry of NOX ________________________________________________________________ 2-1

AX2.1. Introduction ____________________________________________________________________________________________ 2-1

AX2.2. Chemistry of NOX in the Troposphere________________________________________________________________________ 2-2

AX2.2.1. Basic Chemistry ___________________________________________________________________________________ 2-2

AX2.2.2. NOX Concentrations and O3 Formation__________________________________________________________________ 2-6

AX2.2.3. Multiphase Chemistry Involving NOX ___________________________________________________________________ 2-8

AX2.2.4. Formation of Nitro-PAHs____________________________________________________________________________ 2-10

AX2.2.5. Multiphase Chemical Processes Involving NOX and Halogens ______________________________________________ 2-13

AX2.3. Transport of NOX in the Atmosphere _______________________________________________________________________ 2-16

AX2.3.1. Convective Transport ______________________________________________________________________________ 2-16

AX2.3.2. Observations of the Effects of Convective Transport ______________________________________________________ 2-16

AX2.3.3. Effects on Photolysis Rates and Wet Scavenging ________________________________________________________ 2-17

AX2.4. Sources and Emissions of NOX ___________________________________________________________________________ 2-18

AX2.4.1. Interactions of NOX with the Biosphere _________________________________________________________________ 2-18

AX2.4.1.1. NO2 and HNO3 Flux Data from Harvard Forest ____________________________________________________ 2-21

AX2.4.2. Emissions of NOX _________________________________________________________________________________ 2-27

AX2.4.2.1. Emissions of NO2 from Motor Vehicles __________________________________________________________ 2-27

AX2.4.2.2. NOX Emissions from Natural Sources ___________________________________________________________ 2-29

AX2.4.2.3. Uses of Satellite Data to Derive Emissions _______________________________________________________ 2-30

AX2.4.2.4. Field Studies Evaluating Emissions Inventories____________________________________________________ 2-31

AX2.5. Methods for Calculating NOX Concentrations in the Atmosphere _________________________________________________ 2-32

AX2.5.1. Chemistry-Transport Models_________________________________________________________________________ 2-32

AX2.5.1.1. Regional Scale Chemistry Transport Models______________________________________________________ 2-33

AX2.5.1.2. Intra-urban Scale Dispersion Modeling __________________________________________________________ 2-37

AX2.5.1.3. Global Scale CTMs__________________________________________________________________________ 2-37

AX2.5.1.4. Modeling the Effects of Convection _____________________________________________________________ 2-39

AX2.5.2. CTM Evaluation __________________________________________________________________________________ 2-40

AX2.6. Sampling and Analysis of NOX ____________________________________________________________________________ 2-49

AX2.6.1. Availability and Accuracy of Ambient NOY Measurements __________________________________________________ 2-49

AX2.6.1.1. Measurement of NO _________________________________________________________________________ 2-49

AX2.6.1.2. Measurements of NO2 _______________________________________________________________________ 2-50

AX2.6.1.3. Monitoring for NO2 Compliance Versus Monitoring for O3 Formation ___________________________________ 2-52

AX2.6.2. Summary of Methods for Measuring NO2 _______________________________________________________________ 2-52

AX2.6.3. Measurements of HNO3 ____________________________________________________________________________ 2-52

AX2.6.4. Techniques for Measuring Other NOY Species __________________________________________________________ 2-54

AX2.6.5. Remote Sensing of Tropospheric NO2 Columns for Surface NOX Emissions and Surface NO2 Concentrations ________ 2-54

AX2.7. Policy-relevant Background NOX Concentrations______________________________________________________________ 2-57

AX2.7.1. Analysis of PRB Contribution to U.S. NOX Concentrations and Deposition _____________________________________ 2-57
ii

Annex 3. Ambient Concentrations and Exposures _______________________________________________________ 3-66

AX3.1. Introduction ___________________________________________________________________________________________ 3-66

AX3.1.1. Characterizing Ambient NO2 Concentrations ____________________________________________________________ 3-66

AX3.1.2. Relationship to the 1993 AQCD for NOX _______________________________________________________________ 3-66

AX3.2. Ambient Concentrations of NOX ___________________________________________________________________________ 3-66

AX3.2.1. Temporal Variability in Ambient NOX Concentrations in Urban Areas _________________________________________ 3-67

AX3.2.1.1. Diurnal Variability in NO2 Concentrations_________________________________________________________ 3-67

AX3.2.1.2. Seasonal Variability in NO2 Concentrations _______________________________________________________ 3-67

AX3.2.1.3. Urban Sites________________________________________________________________________________ 3-67

AX3.2.1.4. Regional Background Sites ___________________________________________________________________ 3-68

AX3.2.2. Relationships between NO2 and Other Pollutants ________________________________________________________ 3-76

AX3.2.3. Abundance of NOY Species _________________________________________________________________________ 3-78

AX3.2.3.1. PANs ____________________________________________________________________________________ 3-78

AX3.2.3.2. HONO____________________________________________________________________________________ 3-81

AX3.2.3.3. HNO3 and NO3 _____________________________________________________________________________ 3-81

AX3.2.3.4. Nitro-PAHs ________________________________________________________________________________ 3-83

AX3.3. Measuring Personal and Indoor NO2 Concentrations __________________________________________________________ 3-84

AX3.3.1. Issues in Measuring Personal/Indoor NO2 ______________________________________________________________ 3-84

AX3.3.1.1. Active (Pumped) Sampling____________________________________________________________________ 3-85

AX3.3.1.2. Passive (Diffusive) Sampling __________________________________________________________________ 3-85

AX3.3.1.3. Tube Type Samplers ________________________________________________________________________ 3-87

AX3.3.1.4. Badge-Types Samplers ______________________________________________________________________ 3-88

AX3.3.1.5. Radial Sampler Types _______________________________________________________________________ 3-88

AX3.4. NOX in Indoor Air_______________________________________________________________________________________ 3-89

AX3.4.1. Indoor Sources and Concentrations of NOX _____________________________________________________________ 3-89

AX3.4.1.1. Gas Cooking Appliances _____________________________________________________________________ 3-89

AX3.4.1.2. Other Combustion Sources ___________________________________________________________________ 3-90

AX3.4.1.3. Other Indoor Environments ___________________________________________________________________ 3-92

AX3.4.2. Reactions of NO2 in Indoor Air _______________________________________________________________________ 3-92

AX3.5. Personal Exposure _____________________________________________________________________________________ 3-96

AX3.5.1. Personal Exposure in the Residential Indoor Environment _________________________________________________ 3-96

AX3.5.1.1. School and Office ___________________________________________________________________________ 3-99

AX3.5.1.2. Exposure Reconstruction ____________________________________________________________________ 3-100

AX3.5.2. Factors Affecting Exposure_________________________________________________________________________ 3-100

AX3.5.3. Associations between HONO and NO2________________________________________________________________ 3-101

AX3.6. Modeling Human Exposures to NO2_______________________________________________________________________ 3-102

AX3.6.1. Exposure Models ________________________________________________________________________________ 3-102

AX3.6.1.1. Population Exposure Models _________________________________________________________________ 3-106

AX3.6.1.2. Ambient Concentrations of NO2 and Related Air Pollutants _________________________________________ 3-108

AX3.6.1.3. Characterization of Microenvironmental Concentrations ____________________________________________ 3-109

AX3.6.1.4. Characterization of Outdoor Microenvironments Concentrations _____________________________________ 3-110

AX3.6.1.5. Characterization of Indoor Microenvironments ___________________________________________________ 3-111

AX3.6.1.6. Characterization of Activity Events_____________________________________________________________ 3-113

AX3.6.1.7. Characterization of Inhalation Intake and Uptake _________________________________________________ 3-113

AX3.6.1.8. Issues to be Addressed in Future Exposure Modeling Efforts ________________________________________ 3-114

Annex 4. Toxicological Effects of NOX___________________________________________________________________ 4-1

AX4.1. Pulmonary Effects of NOX_________________________________________________________________________________ 4-1

AX4.1.1. Effects of NO2 on Oxidant and Antioxidant Metabolism _____________________________________________________ 4-1

AX4.1.2. Lipid Metabolism and Content of the Lung _______________________________________________________________ 4-2

AX4.1.3. Lung Host Defense, Lung Permeability and Inflammation, Immune Responses, and Infectious Agents _______________ 4-3

AX4.1.4. Emphysema Following NO2 Exposure __________________________________________________________________ 4-3

AX4.1.5. Lung Function _____________________________________________________________________________________ 4-4

AX4.1.6. Nitrates (NO3

-

)_____________________________________________________________________________________ 4-4

AX4.2. Dosimetry of Inhaled NOX_________________________________________________________________________________ 4-4

AX4.2.1. Mechanisms of NO2 Absorption _______________________________________________________________________ 4-5

AX4.2.2. Regional and Total Respiratory Absorption of NO2 ________________________________________________________ 4-6

AX4.2.2.1. Dosimetry Models____________________________________________________________________________ 4-7

AX4.3. Experimental Studies of NO2 Uptake ________________________________________________________________________ 4-8

AX4.3.1. Upper Respiratory Tract Absorption ____________________________________________________________________ 4-8

AX4.3.2. Lower Respiratory Tract Absorption ____________________________________________________________________ 4-9

AX4.3.3. Total Respiratory Tract Absorption _____________________________________________________________________ 4-9
iii

AX4.4. Metabolism, Distribution and Elimination of NO2 _______________________________________________________________ 4-9

AX4.5. Extra-Pulmonary Effects of NO2 and NO ____________________________________________________________________ 4-10

AX4.5.1. Body Weight, Hepatic, Renal, and Miscellaneous Effects __________________________________________________ 4-10

AX4.5.2. Brain Effects _____________________________________________________________________________________ 4-11

AX4.5.3. NO_____________________________________________________________________________________________ 4-11

AX4.5.4. Effects of Mixtures Containing NO2 ___________________________________________________________________ 4-11

AX4.5.5. Simple Mixtures Containing NO2 _____________________________________________________________________ 4-12

AX4.5.6. Complex Mixtures Containing NO2 ____________________________________________________________________ 4-12

Annex 5. Clinical Studies: Exposure to NOX ______________________________________________________________ 5-1

AX5.1. Introduction ____________________________________________________________________________________________ 5-1

AX5.1.1. Considerations in Controlled Human Clinical Studies ______________________________________________________ 5-2

AX5.2. Effects of NO2 in Healthy Subjects __________________________________________________________________________ 5-2

AX5.3. Effects of NOX Exposure in Sensitive Subjects ________________________________________________________________ 5-3

AX5.4. Effects of Mixtures Containing NOX _________________________________________________________________________ 5-3

Annex 6. Epidemiologic Studies Related to Ambient Exposure to NOX________________________________________ 6-2

AX6.1. Interpretation of Epidemiologic Studies ______________________________________________________________________ 6-2

AX6.1.1. Exposure Assessment and Measurement Error ___________________________________________________________ 6-2

AX6.1.2. NO2 Exposure Indices Used __________________________________________________________________________ 6-4

AX6.1.3. Lag Time: Period between Exposure and Health Effect_____________________________________________________ 6-4

AX6.1.4. Model Specification for Temporal Trends and Meteorological Effects __________________________________________ 6-5

AX6.1.5. Confounding Effects of Copollutants ___________________________________________________________________ 6-6

AX6.1.6. Generalized Estimating Equations _____________________________________________________________________ 6-6

AX6.1.7. Hypothesis Testing and Model Selection ________________________________________________________________ 6-6

AX6.1.8. Generalized Additive Models _________________________________________________________________________ 6-7

AX6.2. Cardiovascular Effects Related to Short-Term NO2 Exposure_____________________________________________________ 6-8

AX6.2.1. Hospital Admissions and ED Visits: All CVD _____________________________________________________________ 6-8

AX6.2.2. Hospital Admissions and ED Visits: Myocardial Infarction (MI) ______________________________________________ 6-13

AX6.2.3. Hospital Admissions and ED Visits: Arrhythmia and Congestive Heart Failure (CHF) ____________________________ 6-13

AX6.2.4. Hospital Admissions and ED Visits: Cerebrovascular Disease ______________________________________________ 6-13

AX6.2.4.1. Vaso-Occlusion in Sickle Cell__________________________________________________________________ 6-13

AX6.2.5. Hospital Admissions and ED Visits: Multipollutant Modeling Results__________________________________________ 6-14

AX6.2.6. Heart Rate Variability ______________________________________________________________________________ 6-16

AX6.2.7. Repolarization Changes ____________________________________________________________________________ 6-17

AX6.2.8. Arrhythmias Recorded on Implantable Cardioverter Defibrilators (ICDs)_______________________________________ 6-17

AX6.2.9. Markers of Cardiovascular Disease ___________________________________________________________________ 6-18

AX6.3. Epidemiologic Studies __________________________________________________________________________________ 6-19
List of Tables 

Table AX2.6-1. Satellite Instruments Used to Retrieve Tropospheric NO2 Columns. _____________________ 2-55

Table AX2.4-1. Emissions of nitrogen oxides and ammonia in the United States in 2002 _________________ 2-61

Table AX3.2-1. NOX and NOY concentrations at regional background sites in the Eastern United States. 

Concentrations are given in ppb.________________________________________________ 3-115

Table AX3.3-1. Passive samplers used in NO2 measurements. ____________________________________ 3-115

Table AX3.3-2. The performance of sampler/sampling method for NO2 measurements in the air.__________ 3-116

Table AX3.4-1. NO2 concentrations (ppb) in homes in Latrobe Valley, Victoria, Australia. ________________ 3-116

Table AX3.4-2. NO2 concentrations (ppb) in homes in Connecticut. _________________________________ 3-117

Table AX3.4-3. NO2 concentrations near indoor sources – short-term averages. _______________________ 3-117

Table 3.5-1a. Indoor/outdoor ratio and the indoor vs. outdoor regression slope. ______________________ 3-118

Table 3.5-1b. Summary of regression models of personal exposure to ambient/outdoor NOR2R __________ 3-120

Table AX3.5-2. NO2 concentrations (ppb) in different rooms. ______________________________________ 3-122

Table AX3.5-3a. Average ambient and nonambient contributions to population exposure _________________ 3-123

Table AX3.5-3b. Indoor and outdoor contributions to indoor concentrations. ___________________________ 3-123

Table AX3.5-4. The association between indoor, outdoor, and personal NO2

. _________________________ 3-124

Table AX3.5-5. Indoor, outdoor, and personal NO2 levels stratified by exposure factors (concentrations 

are in ppb and slopes are dimensionless)_________________________________________ 3-127

Table AX3.5-6. Personal NO2 levels stratified by demographic and socioeconomic factors (concentrations 

are in ppb and slopes are dimensionless). ________________________________________ 3-133

Table AX3.6-1. The essential attributes of the pNEM, HAPEM, APEX, SHEDS, and MENTOR-1A. ________ 3-134

Table AX4.1. Oxidant and antioxidant homeostasis. ____________________________________________ 4-13

Table AX4.2. Lung amino acids, proteins, lipids, and enzymes.____________________________________ 4-15

Table AX4.3. Alveolar macrophages and lung host defense. ______________________________________ 4-18

Table AX4.4. Lung permeability and inflammation.______________________________________________ 4-22

Table AX4.5. Immune responses.___________________________________________________________ 4-25

Table AX4.6. Infectious agents. ____________________________________________________________ 4-28

Table AX4.7. Lung structure._______________________________________________________________ 4-31

Table AX4.8. Pulmonary function.___________________________________________________________ 4-34

Table AX4.9. Hematological parameters. _____________________________________________________ 4-34

Table AX4.10. Iron, enzymes, and nucleic acids. ________________________________________________ 4-35

Table AX4.11. Genotoxicity in vitro and in plants.________________________________________________ 4-36

Table AX4.12. Genoticity in vivo. ____________________________________________________________ 4-37

Table AX4.13. Genotoxicity. ________________________________________________________________ 4-37

Table AX5.2-1. Clinical studies – healthy subjects. ________________________________________________ 5-3

Table AX5.3-1. Subjects with respiratory disease._________________________________________________ 5-6

Table AX5.3-2. Inhaled allergen. ______________________________________________________________ 5-7

Table AX5.4-1. NO2 and other pollutants. _______________________________________________________ 5-8

Table AX6.3-1. Studies examining exposure to indoor NO2 and respiratory symptoms.___________________ 6-19
ii

Table AX6.3-2. Studies examining exposure to ambient NO2 and acute respiratory symptoms using 

generalized estimating equations (GEE) in the analysis method ________________________ 6-22

Table AX6.3-3. Respiratory health effects of oxides of nitrogen: hospital admissions. ____________________ 6-24

Table AX6.3-4. Respiratory Health Effects of Oxides of Nitrogen: Emergency Department Visits ___________ 6-56

Table AX6.3-5. Respiratory Health Effects of Oxides of Nitrogen: General Practitioner/Clinic Visits _________ 6-73

Table AX6.3-6. Human Health Effects of Oxides of Nitrogen: CVD Hospital Admissions and Visits: United 

States and Canada ___________________________________________________________ 6-78

Table AX6.3-7. Human Health Effects of Oxides of Nitrogen: CVD Hospital Admissions and Visits: 

Australia and New Zealand _____________________________________________________ 6-88

Table AX6.3-8. Human health effects of oxides of nitrogen: cvd hospital admissions and visits: Europe ______ 6-92

Table AX6.3-9. Human health effects of oxides of nitrogen: cvd hospital admissions and visits: Asia _______ 6-100

Table AX6.3-10. Studies examining exposure to ambient NO2 and heart rate variability as measured by 

standard deviation of normal-to-normal intervals (SDNN). ____________________________ 6-104

Table AX6.3-11. Studies examining exposure to ambient NO2 and heart rate variability as measured by 

variables recorded on implantable cardioverter defibrillators (ICDs). ____________________ 6-105

Table AX6.3-12. Birth weight and long-term NO2 exposure studies __________________________________ 6-106

Table AX6.3-13. Preterm delivery and long-term NO2 exposure studies_______________________________ 6-109

Table AX6.3-14. Fetal growth and long-term NO2 exposure studies __________________________________ 6-111

Table AX6.3-15. Lung function and long-term NO2 exposure. _______________________________________ 6-112

Table AX6.3-16. Asthma and long-term NO2 exposure. ___________________________________________ 6-114

Table AX6.3-17. Respiratory symptoms and long-term NO2 exposure.________________________________ 6-118

Table AX6.3-18. Lung cancer. _______________________________________________________________ 6-124

Table AX6.3-19. Effects of acute NOX exposure on mortality. Risk estimates are standardized for per 

20 ppb 24-h avg NO2 increment.________________________________________________ 6-125
iii

List of Figures 

Figure AX1.1-1. Selection process for studies included in ISA. _______________________________________ 1-2

Figure AX1.2-1. Focusing on unmeasured confounders/covariates, or other sources of spurious 

association from bias. _________________________________________________________ 1-11

Figure AX1.2-2. Example posterior distribution for the determination of Sufficient. _______________________ 1-12

Figure AX1.2-3. Example posterior distribution for the determination of Equipoise and Above.______________ 1-12

Figure AX1.2-4. Example posterior distribution for the determination of Against._________________________ 1-12

Figure AX2.2-1. Schematic diagram of the cycle of reactive nitrogen species in the atmosphere._____________ 2-2

Figure AX2.2-2. Measured values of O3 and NOZ (NOY – NOX)._______________________________________ 2-8

Figure AX2.2-3. Structures of same nitro-polycyclic aromatic hydrocarbons.____________________________ 2-10

Figure AX2.2-4. Formation of 2-nitropyrene (2NP) from the reaction of OH with gaseous pyrene (PY). _______ 2-11

Figure AX2.4-1. Diel cycles of median concentrations (upper panels) and fluxes (lower panels). ____________ 2-22

Figure AX2.4-2. Simple NOX photochemical canopy model outputs. __________________________________ 2-23

Figure AX2.4-3. Hourly (dots) and median nightly (pluses) NO2 flux vs. concentration. ____________________ 2-24

Figure AX2.4-4. Averaged profiles at Harvard Forest. _____________________________________________ 2-25

Figure AX2.4-5. Summer (June-August) 2000 median concentrations (upper panels), fractions of NOY

(middle panels), and fluxes (lower panels) of NOY and component species. _______________ 2-26

Figure AX2.5-1. Time series for measured gas-phase species in comparison with results from 

aphotochemical model. ________________________________________________________ 2-41

Figure AX2.5-2. Time series for measured gas-phase species in comparison with results from a 

photochemical model. _________________________________________________________ 2-42

Figure AX2.5-3. Correlations for O3 versus NOZ (NOY–NOX) in ppb from chemical transport models for the 

northeast corridor, Lake Michigan, Nashville, the San Joaquin Valley, and Los Angeles. _____ 2-44

Figure AX.2.5-4. Evaluation of model versus measured O3 versus NOY for two model scenarios for Atlanta. ___ 2-45

Figure AX2.5-5. Evaluation of model versus: (a) measured O3 versus NOZ and (b) O3 versus the sum 

2H2O2 + NOZ for Nashville, TN.__________________________________________________ 2-46

Figure AX2.5-6. Time series of concentrations of RO2, HO2, and OH radicals, local O3 photochemical 

production rate and concentrations of NOX. ________________________________________ 2-48

Figure AX2.6-1. Tropospheric NO2 columns (molecules NO2/cm2) retrieved from the SCIAMACHY satellite 

instrument for 2004-2005. ______________________________________________________ 2-56

Figure AX2.7-1. Annual mean concentrations of NO2 (ppb) in surface air over the United States in the 

present-day (upper panel) and policy relevant background (middle panel) MOZART-2 

simulations. _________________________________________________________________ 2-58

The bottom panel shows the percentage contribution of the background to the present-day concentrations. 

Please see text for details. _____________________________________________________ 2-58

Figure AX2.7-2. Same as for Figure AX2.7-1 but for wet and dry deposition of HNO3, NH4NO3, NOX, 

HO2NO2, and organic nitrates (mg N m-2/y). ________________________________________ 2-59

Figure AX2.7-3. July mean soil NO emissions (upper panels; 1 Η 10 9 molecules cm-2 s1) and surface PRB 

NOX concentrations (lower panels; ppt). ___________________________________________ 2-60

Figure AX3.2-1. Time series of 24-h avg NO2 concentrations at individual sites in New York City from 2003 

through 2006. _______________________________________________________________ 3-69

Figure AX3.2-2. Time series of 24-h average NO2 concentrations at individual sites in Chicago, IL from 

2003 through 2005. ___________________________________________________________ 3-70
iv

Figure AX3.2-3. Time series of 24-h avg NO2 concentrations at individual sites in Baton Rouge, LA from 

2003 through 2005. ___________________________________________________________ 3-71

Figure AX3.2-4. Time series of 24-h avg NO2 concentrations at individual sites in Houston, TX from 2003 

through 2005. _______________________________________________________________ 3-72

Figure AX3.2-5. Time series of 24-h avg NO2 concentrations at individual sites in Los Angeles, CA from 

2003 through 2005. ___________________________________________________________ 3-73

Figure AX3.2-5. (Continued) Time series of 24-h avg NO2 concentrations at individual sites in Los Angeles, 

CA from 2003 through 2006. ____________________________________________________ 3-74

Figure AX3.2-6. Time series of 24-h avg NO2 concentrations at individual sites in Riverside, CA from 2003 

through 2006. _______________________________________________________________ 3-75

Figure AX3.2-6. (Continued) Time series of 24-h avg NO2 concentrations at individual sites in Riverside, 

CA from 2003 through 2006. ____________________________________________________ 3-76

Figure AX3.2-7. Relationship between O3, NO, and NO2 as a function of NOX concentration. ______________ 3-77

Figure AX3.2-8. Variation of odd oxygen (= O3 + NO2) with NOX._____________________________________ 3-78

Figure AX3.2-9. Measured O3 (ppb) versus PAN (ppt) in Tennessee, including (a) aircraft measurements, 

and (b, c, and d) suburban sites near Nashville._____________________________________ 3-79

Figure AX3.2-10. Relationship between benzene and NOY at a measurement site in Boulder, CO. ___________ 3-80

Figure AX3.2-11. Ratios of PAN to NO2 observed at Silwood Park, Ascot, Berkshire, U.K. from July 24 to 

August 12 1999. _____________________________________________________________ 3-82

Figure AX3.2-12. Ratios of HONO to NO2 observed in a street canyon (Marylebone Road) in London, U.K. 

from 11 a.m. to midnight during October 1999. Data points reflect 15-min avg 

concentrations of HONO and NO2. _______________________________________________ 3-82

Figure AX3.2-13. Concentrations of particulate nitrate measures as part of the EPA’s speciation network. _____ 3-84

Figure AX3.6-1. Schematic description of a general framework identifying the processes (steps or 

components) involved in assessing inhalation exposures and doses for individuals and 

populations. ________________________________________________________________ 3-104

Figure AX6.2-1. Relative risks (95% CI) for associations of 24-h NO2 (per 20 ppb) and daily 1-h max NO2* 

with hospitalizations or emergency department visits for all cardiovascular diseases 

(CVD). ______________________________________________________________________ 6-9

Figure AX6.2-2. Relative risks (95% CI) for associations of 24-h NO2 (per 20 ppb) and daily 1-h max NO2* 

(per 30 ppb) with hospitalizations for Ischemic Heart Disease (IHD)._____________________ 6-10

Figure AX6.2-3. Relative risks (95% CI) for associations between 24-h avg NO2 (per 20 ppb) and 

hospitalizations for myocardial infarction (MI). ______________________________________ 6-11

Figure AX6.2-4. Relative risks (95% CI) for associations of 24-h avg NO2 (per 20 ppb) and 1-h max NO2* 

with hospitalizations for congestive heart failure (CHF). _______________________________ 6-12

Figure AX6.2-5. Relative risks (95% CI) for associations of 24-h avg NO2 exposure (per 20 ppb) and daily 

1-h max NO2* (per 30 ppb) with hospitalizations or emergency department visits for CVD. ___ 6-15
v

Abbreviations and Acronyms 

8-OHdG 8-hydroxy-2Ν-deoxyguanosine 

5-HETE 5-hydroxyeicosatebrachoic 

AA arachidonic acid 

ACP accumulation mode particle 

AHH aryl hydrocarbon hydroxylase 

ALT alanine-amino-transferase 

AM alveolar macrophages 

AMMN N-nitroso-acetoxymethylmethylamine 

AP alkaline phosphatase 

ARIC Atherosclerosis Risk in Communities (Study) 

AST aspartate-amino-transferase 

B[a]P benzo[a]pyrene 

BAL bronchoalveolar lavage 

BALF bronchoalveolar lavage fluid 

BC black carbon 

BHPN N-bis(2-hydroxypropyl) nitrosamine 

BLF bronchial lavage fluid 

bpm beats per minute 

bw body weight 

C carbon or carbon black particles 

CA chromosome aberrations 

CAT catalase 

CHD coronary heart disease 

CHF congestive heart failure 

Chol cholesterol 

CMD count median diameter 

CO carbon monoxide 

COPD Chronic obstructive pulmonary disease 

CRP C-reactive protein 

CVD cardiovascular disease 

CYP Cytochrome P450 

Dae aerodynamic diameter 

DEcCBP DEP extract coated carbon black particles 

DEN diethylnitrosamine 

DEP diesel exhaust particles 

DEP+C diesel exhaust particle extract adsorbed to C 

dG 2Ν-deoxyguanosine 

DMBA 7, 12-dimethylbenzanthracene 

DMSO dimethyl sulfoxide 

EC elemental carbon 

ED emergency department 
vi

ELF epithelial lining fluid 

FHLC fetal hamster lung cells 

GAM generalized additive models 

γGCS γ-glutamylcysteine synthetase 

GPx glutathione peroxidase 

GPx Se-dependent glutathione peroxidase 

GPx glutathione peroxidase 

GRed glutathione reductase 

GS glutathione synthetase 

GSD geometric standard deviation 

GSH glutathione 

GSSG glutathione disulfide 

GSSO3H glutathione S-sulfonate 

GST glutathione-S-transferase 

γGT γ-glutamyl transpeptidase 

HF heart failure 

HNO2 nitrous acid 

HNO3 nitric acid 

HP hydrolyzed protein 

HR heart rate 

HRV heart rate variability 

GT γ-glutamyl transpeptidase 

HVA-ICa high-voltage activated calcium currents 

ICAM-1 intercellular adhesion molecule 

ICD implantable cardioverter defibrillators 

IgG immunoglobulin 

IHD ischemic heart disease 

IQR interquartile range 

LDH lactate dehydrogenase 

LOESS locally estimated smoothing splines 

LTB4 leukotrine B4 

MAD median aerodynamic diameter 

MI myocardial infraction 

MMAD mass median aerodynamic density 

MMD mass median diameter 

MN micronuclei 

MNPCE micronucleated PCE 

Mo molybdenum 

NADPH nicotinamide adenie dinuclotide phosphate 

NDMA nitrosodimethylamine 

NHANES III Third National Health and Nutrition Examination Survey 

NHLBI National Heart, Lung, and Blood Institute 

NMBzA N-nitrosomethylbenzylamine 

NO nitric oxide 
vii

NO2 nitrogen dioxide 

NOX oxides of nitrogen; mono-nitrogent oxides (e.g. NO, NO2) 

NOY total reactive nitrogen (all oxided forms of nitrogen) 

[Sum of NOX and NOZ) 

NOZ other oxides of nitrogen (e.g. HNO3 and PAN) 

NR Not Reported 

O3 ozone 

OC organic carbon 

PAF paroxysmal atrial fibrillation 

PAH polycyclic aromatic hydrocarbons 

PAR proximal alveolar region 

PCE polychromatic erythrocytes 

PEC pulmonary endocrine cells 

PKA cyclic AMP-dependent protein kinase A 

PKI synthetic peptide inhibitor of PKA 

PL phospholipids 

PM2.5 particulate matter particles equal to or smaller than 2.5 μm 

PM10 particulate matter particles between 2.5 μm and 10 μm 

PNC particle number concentration 

ppb parts per billion 

ppm parts per million 

RBC red blood cell or erythrocyte 

RH relative humidity 

r-MSSD root mean square successive difference (in heart period series) a time 

domain measurement of heart period variability 

SCE sister chromatid exchanges 

SDNN standard deviation of all normal-to-normal R-R intervals 

SEPs somatosensory-evoked potentials 

SO2 sodium dioxide 

SOD superoxide dismutase 

SP-A surfactant protein A 

SPF specific pathogen free 

SPM suspended particulate matter extract 

SQCA squamous cell carcinoma 

SSO seabuckthorn seed oil 

SV40 simian virus 40 

TBA thiobarbituric acid 

TBARS thiobarbituric acid-reactive substance 

TOC potassium channel transient outward currents 

TTX tetrodotoxin 

TTX-R tetrodotoxin-resistant 

TTX-S tetrodotoxin-sensitive 

TxB2 thromboxane B2 

UFP ultafine particles 

μm micon, micrometer: 10-6 meter or (1/1000 of a millimeter) 
viii

 ̇ VE ventilation rate 

VEPs visual-evoked potentials 

VT tidal volume 

VWF von Willibrand factor 

W tungsten 

WBC white blood cell 
1-1

Annex 1. Framework for Review 

This Integrated Science Assessment (ISA) presents a concise synthesis of the most policy-relevant 

science to form the scientific foundation for the review of the primary (health-based) National Ambient 

Air Quality Standards (NAAQS) for nitrogen dioxide (NO2). The Annexes: (1) provide more details of 

the most pertinent scientific literature relative to the review of the NO2 NAAQS in the areas of 

atmospheric sciences, air quality analyses, exposure assessment, dosimetry, controlled human exposure 

studies, toxicology, and epidemiology; and (2) focus on the key policy relevant questions and studies 

published since the last NAAQS review. 

Annex AX1 details the methods used to identify and select studies; and frameworks for evaluating 

scientific evidence relative to causality determination. The overarching framework for evaluation of 

evidence for causality is outlined in Chapter 1 of this ISA, and this Annex provides supporting 

information for that framework, including excerpts from decision frameworks or criteria developed by 

other organizations. 

Annex AX2 presents evidence related to the physical and chemical processes controlling the 

production, destruction, and levels of reactive nitrogen compounds in the atmosphere, including both 

oxidized and reduced species. Annex AX3 presents information on environmental concentrations, 

patterns, and human exposure to ambient oxides of nitrogen; however, most information relates to NO2. 

Annex AX4 presents results from toxicological studies as well as information on dosimetry of oxides of 

nitrogen. Annex AX5 discusses results from controlled human exposure studies, and Annex AX6 presents 

evidence from epidemiologic studies. These Annexes include more detailed information on health or 

exposure studies that is summarized in tabular form, as well as more extensive discussion of atmospheric 

chemistry, source, exposure, and dosimetry information. Annex tables for health studies are generally 

organized to include information about (1) concentrations of oxides of nitrogen levels or doses and 

exposure times, (2) description of study methods employed, (3) results and comments, and (4) 

quantitative outcomes for oxides of nitrogen measures. 

AX1.1. Literature Selection and Retrieval 

Literature searches were conducted routinely to identify studies published since the last review. The 

review included publications from 1-2 years prior to the publication of 1993 AQCD for Oxides of 

Nitrogen (U.S. Environmental Protection Agency, 1993). Search strategies were iteratively modified in an 

effort to optimize the identification of pertinent publications. Additional papers were identified for 

inclusion in several ways: review of pre-publication tables of contents for journals in which relevant 

papers may be published; independent identification of relevant literature by expert authors; and. 

identification by public and CASAC during the external review process. Generally, only information that 

had undergone scientific peer review and had been published or accepted for publication was considered. 

The following sections briefly summarize criteria for selection of studies for this ISA. 

The selection process for studies included in this ISA is shown in Figure AX1.1-1. Studies were 

evaluated by EPA staff and outside experts to determine if the studies merited inclusion. Criteria used for 

study selection are summarized below. 
1-2

Figure AX1.1-1. Selection process for studies included in ISA. 
1-3

AX1.1.1. General Criteria for Study Selection 

In assessing the scientific quality and relevance of epidemiological and human or animal 

toxicological studies, the following considerations have been taken into account. 

 Are the study populations adequately selected and are they sufficiently well defined to allow for 

meaningful comparisons between study groups? 

 Are the statistical analyses appropriate, properly performed, and properly interpreted? 

 Are likely covariates (i.e., potential confounders or effect modifiers) adequately controlled or 

taken into account in the study design and statistical analysis? 

 Are the reported findings internally consistent, biologically plausible, and coherent in terms of 

consistency with other known facts? 

 To what extent are the aerometric data, exposure, or dose metrics of adequate quality and 

sufficiently representative to serve as indicators of exposure to ambient NOX? 

Consideration of these issues informs the judgments on the relative quality of individual studies and 

allows the most pertinent studies to be the focus of the assessment. 

AX1.1.2. Criteria for Selecting Epidemiology Studies 

In selecting epidemiological studies, EPA considered whether a given study contained information 

on (1) associations with measured oxides of nitrogen concentrations using short- or long-term exposures 

at or near ambient levels of oxides of nitrogen, (2) health effects of specific oxides of nitrogen species or 

indicators related to oxides of nitrogen sources (e.g., motor vehicle emissions, combustion-related 

particles), (3) health endpoints and populations not previously extensively researched, (4) multiple 

pollutant analyses and other approaches to address issues related to potential confounding and 

modification of effects, and/or (5) important methodology issues (e.g., lag of effects, model 

specifications, thresholds, mortality displacement) related to interpretation of the health evidence. Among 

the epidemiological studies, particular emphasis was placed on those studies most relevant to the review 

of the NAAQS. Specifically, studies conducted in the United States or Canada were discussed in more 

detail than those from other geographical regions. Particular emphasis was placed on: (1) recent multicity 

studies that employ standardized analyses methods for evaluating effects of oxides of nitrogen and that 

provide overall estimates for effects based on combined analyses of information pooled across multiple 

cities, (2) new studies that provide quantitative effect estimates for populations of interest, and (3) studies 

that consider oxides of nitrogen as a component of a complex mixture of air pollutants. 

Not all studies are accorded equal weight in the overall interpretive assessment of evidence 

regarding NO2-associated health effects. Among well-conducted studies with adequate control for 

confounding, increasing scientific weight is accorded in proportion to the precision of their effect 

estimates. Small-scale studies without a wide range of exposures generally produced less precise 

estimates compared to larger studies with a broad exposure gradient. For time-series studies, the size of 

the study, as indicated by the length of the study period and total number of events, and the variability of 

NO2 exposures are important components to determine the precision of the health effect estimates. In 

evaluating the epidemiologic evidence in this chapter, more weight is accorded to estimates from studies 

with narrow confidence bands. 

The goal was to perform a balanced and objective evaluation that summarized, interpreted, and 

synthesized the most important studies and issues in the epidemiologic database pertaining to oxides of 

nitrogen exposure. For each study presented, the quality of the exposure and outcome data, as well as the 

quality of the statistical analysis methodology were discussed. The framework for evaluation of evidence 

is further described below. 
1-4

AX1.1.3. Criteria for Selecting Toxicology Studies 

Criteria for the selection of research evaluating animal toxicological or controlled human exposure 

studies included a focus on studies conducted within an order of a magnitude of ambient NO2

concentrations and those studies that approximated expected human exposure conditions in terms of 

concentration and duration. Studies that elucidated mechanisms of action and/or susceptibility, 

particularly if the studies were conducted under atmospherically relevant conditions, were emphasized 

whenever possible. 

The selection of research evaluating controlled human exposures to oxides of nitrogen is mainly 

limited to studies in which subjects are exposed to <5 ppm NO2. For these controlled human exposures, 

emphasis is placed on studies that (1) investigate potentially susceptible populations such as asthmatics, 

particularly studies that compare responses in susceptible individuals with those in age-matched healthy 

controls; (2) address issues such as concentration-response or time-course of responses; (3) investigate 

exposure to NO2 separately and in combination with other pollutants such as O3 and SO2; (4) include 

control exposures to filtered air; and (5) have sufficient statistical power to assess findings. 

AX1.2. Guidelines for the Determination of Causality 

The following sections include excerpts from several reports that have documented approaches for 

the determination of causality, or related decision-making processes. These sections provide 

supplementary documentation of approaches that are similar in nature to EPA’s framework for evaluation 

of health evidence. 

AX1.2.1. Surgeon General’s Report: The Health Consequences of 

Smoking 

The Surgeon General’s Report (CDC, 2004) evaluates the health effects of smoking; it builds upon 

the first Surgeon General’s report published in 1964 (USDEHW, 1964). It also updates the methodology 

for evaluating evidence that was first presented in the 1964 report. The 2004 report acknowledges the 

effectiveness of the previous methodology, but attempts to standardize the language surrounding causality 

of associations. 

The Surgeon General’s Reports on Smoking played a central role in the translation of scientific 

evidence into policy. As such, it was important that scientific evidence was presented in a manner that 

conveyed most succinctly the link between smoking and a health effect. Specifically, the report stated: 

The statement that an exposure “causes” a disease in humans represents a serious 

claim, but one that carries with it the possibility of prevention. Causal 

determinations may also carry substantial economic implications for society and 

for those who might be held responsible for the exposure or for achieving its 

prevention. 

To address the issue of identifying causality, the 2004 report provided the following summary of 

the earlier 1964 report: 

When a relationship or an association between smoking...and some condition in 

the host was noted, the significance of the association was assessed. 
1-5

The characterization of the assessment called for a specific term. ...The word 

cause is the one in general usage in connection with matters considered in this 

study, and it is capable of conveying the notion of a significant, effectual 

relationship between an agent and an associated disorder or disease in the host. 

No member was so naive as to insist upon mono-etiology in pathological 

processes or in vital phenomena. All were thoroughly aware... that the end 

results are the net effect of many actions and counteractions. 

Granted that these complexities were recognized, it is to be noted clearly that the 

Committee’s considered decision to use the words “a cause,” or “a major cause,” 

or “a significant cause,” or “a causal association” in certain conclusions about 

smoking and health affirms their conviction (USDHEW, 1964, p. 21). 

This 2004 report created uniformly labeled conclusions that were used throughout the document. 

The following excerpts from the report included a description of the methodology and the judgments used 

to reach a conclusion: 

Terminology of Conclusions and Causal Claims 

The first step in introducing this revised approach is to outline the language that 

will be used for summary conclusions regarding causality, which follows 

hierarchical language used by Institute of Medicine committees (Institute of 

Medicine, 1999) to couch causal conclusions, and by IARC to classify 

carcinogenic substances (IARC, 1986). These entities use a four-level hierarchy 

for classifying the strength of causal inferences based on available evidence as 

follows: 

Evidence is sufficient to infer a causal relationship. 

Evidence is suggestive but not sufficient to infer a causal relationship. 

Evidence is inadequate to infer the presence or absence of a causal relationship 

(which encompasses evidence that is sparse, of poor quality, or conflicting). 

Evidence is suggestive of no causal relationship. 

For this report, the summary conclusions regarding causality are expressed in this 

four-level classification. Use of these classifications should not constrain the 

process of causal inference, but rather bring consistency across chapters and 

reports, and greater clarity as to what the final conclusions are actually saying. 

As shown in Table 1.1 [see original document], without a uniform classification 

the precise nature of the final judgment may not always be obvious, particularly 

when the judgment is that the evidence falls below the “sufficient” category. 

Experience has shown that the “suggestive” category is often an uncomfortable 

one for scientists, since scientific culture is such that any evidence that falls short 

of causal proof is typically deemed inadequate to make a causal determination. 

However, it is very useful to distinguish between evidence that is truly 

inadequate versus that which just falls short of sufficiency. 
1-6

There is no category beyond “suggestive of no causal relationship” as it is 

extraordinarily difficult to prove the complete absence of a causal association. At 

best, “negative” evidence is suggestive, either strongly or weakly. In instances 

where this category is used, the strength of evidence for no relationship will be 

indicated in the body of the text. In this new framework, conclusions regarding 

causality will be followed by a section on implications. This section will separate 

the issue of causal inference from recommendations for research, policies, or 

other actions that might arise from the causal conclusions. This section will 

assume a public health perspective, focusing on the population consequences of 

using or not using tobacco and also a scientific perspective, proposing further 

research directions. The proportion of cases in the population as a result of 

exposure (the population attributable risk), along with the total prevalence and 

seriousness of a disease, are more relevant for deciding on actions than the 

relative risk estimates typically used for etiologic determinations. In past reports, 

the failure to sharply separate issues of inference from policy issues resulted in 

inferential statements that were sometimes qualified with terms for action. For 

example, based on the evidence available in 1964, the first Surgeon General’s 

report on smoking and health contained the following statement about the 

relationship between cardiovascular diseases and smoking: 

It is established that male cigarette smokers have a higher death rate 

from coronary artery disease than non-smoking males. Although the 

causative role of cigarette smoking in deaths from coronary disease 

is not proven, the Committee considers it more prudent from the 

public health viewpoint to assume that the established association 

has causative meaning, than to suspend judgment until no 

uncertainty remains (USDHEW, 1964, p. 32). 

Using this framework, this conclusion would now be expressed differently, 

probably placing it in the “suggestive” category and making it clear that although 

it falls short of proving causation, this evidence still makes causation more likely 

than not. The original statement makes it clear that the 1964 committee judged 

that the evidence fell short of proving causality but was sufficient to justify 

public health action. In this report, the rationale and recommendations for action 

will be placed in the implications section, separate from the causal conclusions. 

This separation of inferential from action-related statements clarifies the degree 

to which policy recommendations are driven by the strength of the evidence and 

by the public health consequences acting to reduce exposure. In addition, this 

separation appropriately reflects the differences between the processes and goals 

of causal inference and decision making. 

AX1.2.2. The EPA Guidelines for Carcinogen Risk Assessment 

The EPA Guidelines for Carcinogen Risk Assessment, published in 2005 (U.S. Environmental 

Protection Agency, 2005), was an update to the previous risk assessment document published in 1986. 

This document served to guide EPA staff and public about the Agency’s risk assessment development and 

methodology. In the 1986 Guidelines, a step-wise approach was used to evaluate the scientific findings. 

However, this newer document was similar to the Surgeon General’s Report on Smoking in that it used a 

single integrative step after assessing all of the individual lines of evidence. Five standard descriptors are 

used to evaluate the weight of evidence: 
1-7

1. Carcinogenic to Humans 

2. Likely to Be Carcinogenic to Humans 

3. Suggestive Evidence of Carcinogenic Potential 

4. Inadequate Information to Assess Carcinogenic Potential 

5. Not Likely to Be Carcinogenic to Humans. 

The 2005 Guidelines recommended that a separate narrative be prepared on the weight of evidence and 

the descriptor. The Guidelines further recommended that the descriptors should only be used in the 

context of a weight of evidence discussion. 

The following excerpt describes how a weight of evidence narrative should be developed and a how 

a descriptor should be selected (U.S. Environmental Protection Agency, 2005): 

The weight of the evidence should be presented as a narrative laying out the 

complexity of information that is essential to understanding the hazard and its 

dependence on the quality, quantity, and type(s) of data available, as well as the 

circumstances of exposure or the traits of an exposed population that may be 

required for expression of cancer. For example, the narrative can clearly state to 

what extent the determination was based on data from human exposure, from 

animal experiments, from some combination of the two, or from other data. 

Similarly, information on mode of action can specify to what extent the data are 

from in vivo or in vitro exposures or based on similarities to other chemicals. The 

extent to which an agent’s mode of action occurs only on reaching a minimum 

dose or a minimum duration should also be presented. A hazard might also be 

expressed disproportionately in individuals possessing a specific gene; such 

characterizations may follow from a better understanding of the human genome. 

Furthermore, route of exposure should be used to qualify a hazard if, for 

example, an agent is not absorbed by some routes. Similarly, a hazard can be 

attributable to exposures during a susceptible lifestage on the basis of our 

understanding of human development. 

The weight of evidence narrative should highlight: 

 the quality and quantity of the data; 

 all key decisions and the basis for these major decisions; and 

 any data, analyses, or assumptions that are unusual for or new to EPA. 

To capture this complexity, a weight of evidence narrative generally includes 

 conclusions about human carcinogenic potential (choice of descriptor(s), described 

below) 

 a summary of the key evidence supporting these conclusions (for each descriptor used), 

including information on the type(s) of data (human and/or animal, in vivo and/or in 

vitro) used to support the conclusion(s) 

 available information on the epidemiologic or experimental conditions that characterize 

expression of carcinogenicity (e.g., if carcinogenicity is possible only by one exposure 

route or only above a certain human exposure level), 

 a summary of potential modes of action and how they reinforce the conclusions, 
1-8

 indications of any susceptible populations or lifestages, when available, and 

 a summary of the key default options invoked when the available information is 

inconclusive. 

To provide some measure of clarity and consistency in an otherwise free-form 

narrative, the weight of evidence descriptors are included in the first sentence of 

the narrative. Choosing a descriptor is a matter of judgment and cannot be 

reduced to a formula. Each descriptor may be applicable to a wide variety of 

potential data sets and weights of evidence. These descriptors and narratives are 

intended to permit sufficient flexibility to accommodate new scientific 

understanding and new testing methods as they are developed and accepted by 

the scientific community and the public. Descriptors represent points along a 

continuum of evidence; consequently, there are gradations and borderline cases 

that are clarified by the full narrative. Descriptors, as well as an introductory 

paragraph, are a short summary of the complete narrative that preserves the 

complexity that is an essential part of the hazard characterization. Users of these 

cancer guidelines and of the risk assessments that result from the use of 

these cancer guidelines should consider the entire range of information 

included in the narrative rather than focusing simply on the descriptor. 

In borderline cases, the narrative explains the case for choosing one descriptor 

and discusses the arguments for considering but not choosing another. For 

example, between “suggestive” and “likely” or between “suggestive” and 

“inadequate,” the explanation clearly communicates the information needed to 

consider appropriately the agent's carcinogenic potential in subsequent decisions. 

Multiple descriptors can be used for a single agent, for example, when 

carcinogenesis is dose- or route-dependent. For example, if an agent causes 

point-of-contact tumors by one exposure route but adequate testing is negative by 

another route, then the agent could be described as likely to be carcinogenic by 

the first route but not likely to be carcinogenic by the second. Another example is 

when the mode of action is sufficiently understood to conclude that a key event 

in tumor development would not occur below a certain dose range. In this case, 

the agent could be described as likely to be carcinogenic above a certain dose 

range but not likely to be carcinogenic below that range. 

Descriptors can be selected for an agent that has not been tested in a cancer 

bioassay if sufficient other information, e.g., toxicokinetic and mode of action 

information, is available to make a strong, convincing, and logical case through 

scientific inference. For example, if an agent is one of a well-defined class of 

agents that are understood to operate through a common mode of action and if 

that agent has the same mode of action, then in the narrative the untested agent 

would have the same descriptor as the class. Another example is when an 

untested agent's effects are understood to be caused by a human metabolite, in 

which case in the narrative the untested agent could have the same descriptor as 

the metabolite. As new testing methods are developed and used, assessments may 

increasingly be based on inferences from toxicokinetic and mode of action 

information in the absence of tumor studies in animals or humans. 

When a well-studied agent produces tumors only at a point of initial contact, the 

descriptor generally applies only to the exposure route producing tumors unless 
1-9

the mode of action is relevant to other routes. The rationale for this conclusion 

would be explained in the narrative. 

When tumors occur at a site other than the point of initial contact, the descriptor 

generally applies to all exposure routes that have not been adequately tested at 

sufficient doses. An exception occurs when there is convincing information, e.g., 

toxicokinetic data that absorption does not occur by another route. 

When the response differs qualitatively as well as quantitatively with dose, this 

information should be part of the characterization of the hazard. In some cases 

reaching a certain dose range can be a precondition for effects to occur, as when 

cancer is secondary to another toxic effect that appears only above a certain dose. 

In other cases exposure duration can be a precondition for hazard if effects occur 

only after exposure is sustained for a certain duration. These considerations differ 

from the issues of relative absorption or potency at different dose levels because 

they may represent a discontinuity in a dose-response function. 

When multiple bioassays are inconclusive, mode of action data are likely to hold 

the key to resolution of the more appropriate descriptor. When bioassays are few, 

further bioassays to replicate a study’s results or to investigate the potential for 

effects in another sex, strain, or species may be useful. 

When there are few pertinent data, the descriptor makes a statement about the 

database, for example, “Inadequate Information to Assess Carcinogenic 

Potential,” or a database that provides “Suggestive Evidence of Carcinogenic 

Potential.” With more information, the descriptor expresses a conclusion about 

the agent’s carcinogenic potential to humans. If the conclusion is positive, the 

agent could be described as “Likely to Be Carcinogenic to Humans” or, with 

strong evidence, “Carcinogenic to Humans.” If the conclusion is negative, the 

agent could be described as “Not Likely to Be Carcinogenic to Humans.” 

Although the term “likely” can have a probabilistic connotation in other contexts, its use as a 

weight of evidence descriptor does not correspond to a quantifiable probability of whether the chemical is 

carcinogenic. This is because the data that support cancer assessments generally are not suitable for 

numerical calculations of the probability that an agent is a carcinogen. Other health agencies have 

expressed a comparable weight of evidence using terms such as “Reasonably Anticipated to Be a Human 

Carcinogen” (NTP) or “Probably Carcinogenic to Humans” (International Agency for Research on 

Cancer). 

AX1.2.3. Improving Presumptive Disability Decision-Making Process 

for Veterans 

A recent publication by the Institute of Medicine (IOM) also provided the foundation for the 

causality framework adapted in this ISA (IOM, 2007). The Committee on Evaluation of the Presumptive 

Disability Decision-Making Process for Veterans was charged by the Veterans Association to describe 

how presumptive decisions are made for veterans with health conditions arising from military service 

currently, as well as recommendations for how such decisions could made in the future. The committee 

proposed a multiple-element approach that included a quantification of the extent of disease attributable 

to an exposure. This process involved a review of all relevant data to decide the strength of evidence for 

causation, using one of four categories: 
1-10

 Sufficient: the evidence is sufficient to conclude that a causal relationship exists. 

 Equipoise and Above: the evidence is sufficient to conclude that a causal relationship is at least as 

likely as not, but not sufficient to conclude that a causal relationship exists. 

 Below Equipoise: the evidence is not sufficient to conclude that a causal relationship is at least as 

likely as not, or is not sufficient to make a scientifically informed judgment. 

 Against: the evidence suggests the lack of a causal relationship. 

The following is an excerpt from this report and describes these four categories in detail: 

In light of the categorizations used by other health organizations and agencies as 

well as considering the particular challenges of the presumptive disability 

decision-making process, we propose a four-level categorization of the strength 

of the overall evidence for or against a causal relationship from exposure to 

disease. 

We use the term “equipoise” to refer to the point at which the evidence is in 

balance between favoring and not favoring causation. The term “equipoise” is 

widely used in the biomedical literature, is a concept familiar to those concerned 

with evidence-based decision-making and is used in VA processes for rating 

purposes as well as being a familiar term in the veterans’ community. 

Below we elaborate on the four-level categorization which the Committee 

recommends. 

Sufficient 

If the overall evidence for a causal relationship is categorized as Sufficient, then 

it should be scientifically compelling. It might include: 

 replicated and consistent evidence of a causal association: that is, evidence of an 

association from several high-quality epidemiologic studies that cannot be explained 

by plausible noncausal alternatives (e.g., chance, bias, or confounding) 

 evidence of causation from animal studies and mechanistic knowledge 

 compelling evidence from animal studies and strong mechanistic evidence from 

studies in exposed humans, consistent with (i.e., not contradicted by) the 

epidemiologic evidence. 

Using the Bayesian framework to illustrate the evidential support and the 

resulting state of communal scientific opinion needed for reaching the Sufficient 

category (and the lower categories that follow), consider again the causal 

diagram in Figure AX1.2-1. In this model, used to help clarify matters 

conceptually, the observed association between exposure and health is the result 

of: (1) measured confounding, parameterized by α; (2) the causal relation, 

parameterized by β; and (3) other, unmeasured sources such as bias or 

unmeasured confounding, parameterized by γ. The belief of interest, after all the 

evidence has been weighed, is in the size of the causal parameter β. Thus, for 

decision making, what matters is how strongly the evidence supports the 

proposition that β is above 0. As it is extremely unlikely that the types of 

exposures considered for presumptions reduce the risk of developing disease, we 
1-11

exclude values of β below 0. If we consider the evidence as supporting degrees of 

belief about the size of β, and we have a posterior distribution over the possible 

size of β, then a posterior like Figure AX1.2-2 illustrates a belief state that might 

result when the evidence for causation is considered Sufficient. 

Source: IOM (2007). 

Figure AX1.2-1. Focusing on unmeasured confounders/covariates, or other sources of spurious association 

from bias. 

Equipoise and Above 

To be categorized as Equipoise and Above, the scientific community should 

categorize the overall evidence as making it more confident in the existence of a 

causal relationship than in the non-existence of a causal relationship, but not 

sufficient to conclude causation. 

For example, if there are several high-quality epidemiologic studies, the 

preponderance of which show evidence of an association that cannot readily be 

explained by plausible noncausal alternatives (e.g., chance, bias, or 

confounding), and the causal relationship is consistent with the animal evidence 

and biological knowledge, then the overall evidence might be categorized as 

Equipoise and Above. Alternatively, if there is strong evidence from animal 

studies or mechanistic evidence, not contradicted by human or other evidence, 

then the overall evidence might be categorized as Equipoise and Above. 

Equipoise is a common term employed by VA and the courts in deciding 

disability claims. 

Again, using the Bayesian model to illustrate the idea of Equipoise and Above, 

Figure AX1.2-3 shows a posterior probability distribution that is an example of 

belief compatible with the category Equipoise and Above. 
1-12

Figure AX1.2-2. Example posterior distribution for the determination of Sufficient. 

Source: IOM (2007). 

Figure AX1.2-3. Example posterior distribution for the determination of Equipoise and Above. 

Source: IOM (2007). 

Figure AX1.2-4. Example posterior distribution for the determination of Against. 

In this figure, unlike the one for evidence classified as Sufficient, there is 

considerable mass over zero, which means that the scientific community has 

considerable uncertainty as to whether exposure causes disease at all; that is, 

whether β is greater than zero. At least half of the mass is to the right of the zero, 
1-13

however, so the community judges causation to be at least as likely as not, after 

they have seen and combined all the evidence available. 

Below Equipoise 

To be categorized as Below Equipoise, the overall evidence for a causal 

relationship should either be judged not to make causation at least as likely as 

not, or not sufficient to make a scientifically informed judgment. 

This might occur: 

 when the human evidence is consistent in showing an association, but the evidence is 

limited by the inability to rule out chance, bias, or confounding with confidence, and 

animal or mechanistic evidence is weak 

 when animal evidence suggests a causal relationship, but human and mechanistic 

 evidence is weak or inconsistent 

 when mechanistic evidence is suggestive but animal and human evidence is weak or 

inconsistent 

 when the evidence base is very thin. 

Against 

To be categorized as Against, the overall evidence should favor belief that there 

is no causal relationship from exposure to disease. For example, if there is human 

evidence from multiple studies covering the full range of exposures encountered 

by humans that are consistent in showing no causal association, or there are is 

animal or mechanistic evidence supporting the lack of a causal relationship, and 

combining all of the evidence results in a posterior resembling Figure AX1.2-4 

then the scientific community should categorize the evidence as Against

causation. 

AX1.2.4. Formulation of Scientific Findings for Policy Purposes 

The following guidelines in the form of questions were developed and published in 1991 by the 

National Acid Precipitation Assessment Program (NAPAP) Oversight Review Board for NAPAP to assist 

scientists in formulating presentations of research results to be used in policy decision processes. 

Is the statement sound? Have the central issues been clearly identified? Does each statement 

contain the distilled essence of present scientific and technical understanding of the phenomenon 

or process to which it applies? Is the statement consistent with all relevant evidence – evidence 

developed either through NAPAP research or through analysis of research conducted outside of 

NAPAP? Is the statement contradicted by any important evidence developed through research 

inside or outside of NAPAP? Have apparent contradictions or interpretations of available 

evidence been considered in formulating the statement of principal findings? 

Is the statement directional and, where appropriate, quantitative? Does the statement correctly 

quantify both the direction and magnitude of trends and relationships in the phenomenon or 

process to which the statement is relevant? When possible, is a range of uncertainty given for 

each quantitative result? Have various sources of uncertainty been identified and quantified, for 
1-14

example, does the statement include or acknowledge errors in actual measurements, standard 

errors of estimate, possible biases in the availability of data, extrapolation of results beyond the 

mathematical, geographical, or temporal relevancy of available information, etc. In short, are 

there numbers in the statement? Are the numbers correct? Are the numbers relevant to the general 

meaning of the statement? 

Is the degree of certainty or uncertainty of the statement indicated clearly? Have appropriate 

statistical tests been applied to the data used in drawing the conclusion set forth in the statement? 

If the statement is based on a mathematical or novel conceptual model, has the model or concept 

been validated? Does the statement describe the model or concept on which it is based and the 

degree of validity of that model or concept? 

Is the statement correct without qualification? Are there limitations of time, space, or other 

special circumstances in which the statement is true? If the statement is true only in some 

circumstances, are these limitations described adequately and briefly? 

Is the statement clear and unambiguous? Are the words and phrases used in the statement 

understandable by the decision makers of our society? Is the statement free of specialized jargon? 

Will too many people misunderstand its meaning? 

Is the statement as concise as it can be made without risk of misunderstanding? Are there any 

excess words, phrases, or ideas in the statement which are not necessary to communicate the 

meaning of the statement? Are there so many caveats in the statement that the statement itself is 

trivial, confusing, or ambiguous? 

Is the statement free of scientific or other biases or implications of societal value judgments? Is 

the statement free of influence by specific schools of scientific thought? Is the statement also free 

of words, phrases, or concepts that have political, economic, ideological, religious, moral, or 

other personal-, agency-, or organization-specific values, overtones, or implications? Does the 

choice of how the statement is expressed rather than its specific words suggest underlying biases 

or value judgments? Is the tone impartial and free of special pleading? If societal value judgments 

have been discussed, have these judgments been identified as such and described both clearly and 

objectively? 

Have societal implications been described objectively? Consideration of alternative courses of 

action and their consequences inherently involves judgments of their feasibility and the 

importance of effects. For this reason, it is important to ask if a reasonable range of alternative 

policies or courses of action have been evaluated? Have societal implications of alternative 

courses of action been stated in the following general form? 

“If this [particular option] were adopted then that [particular outcome] would be 

expected.” 

Have the professional biases of authors and reviewers been described openly? Acknowledgment 

of potential sources of bias is important so that readers can judge for themselves the credibility of 

reports and assessments. 

AX1.2.5. IARC Cancer Guidelines for Scientific Review and Evaluation 

The following is excerpted from the International Agency for Research on Cancer (IARC) 

Monographs on the evaluation of carcinogenic risks to humans (IARC, 2006). 
1-15

The available studies are summarized by the Working Group, with particular 

regard to the qualitative aspects discussed below. In general, numerical findings 

are indicated as they appear in the original report; units are converted when 

necessary for easier comparison. The Working Group may conduct additional 

analyses of the published data and use them in their assessment of the evidence; 

the results of such supplementary analyses are given in square brackets. When an 

important aspect of a study that directly impinges on its interpretation should be 

brought to the attention of the reader, a Working Group comment is given in 

square brackets. 

The scope of the IARC Monographs programme has expanded beyond chemicals 

to include complex mixtures, occupational exposures, physical and biological 

agents, lifestyle factors and other potentially carcinogenic exposures. Over time, 

the structure of a Monograph has evolved to include the following sections: 

1. Exposure data 

2. Studies of cancer in humans 

3. Studies of cancer in experimental animals 

4. Mechanistic and other relevant data 

5. Summary 

6. Evaluation and rationale 

In addition, a section of General Remarks at the front of the volume discusses the 

reasons the agents were scheduled for evaluation and some key issues the 

Working Group encountered during the meeting. 

This part of the Preamble discusses the types of evidence considered and 

summarized in each section of a Monograph, followed by the scientific criteria 

that guide the evaluations. 

Evaluation and rationale 

Evaluations of the strength of the evidence for carcinogenicity arising from 

human and experimental animal data are made, using standard terms. The 

strength of the mechanistic evidence is also characterized. 

It is recognized that the criteria for these evaluations, described below, cannot 

encompass all of the factors that may be relevant to an evaluation of 

carcinogenicity. In considering all of the relevant scientific data, the Working 

Group may assign the agent to a higher or lower category than a strict 

interpretation of these criteria would indicate. 

These categories refer only to the strength of the evidence that an exposure is 

carcinogenic and not to the extent of its carcinogenic activity (potency). A 

classification may change as new information becomes available. 
1-16

An evaluation of the degree of evidence is limited to the materials tested, as 

defined physically, chemically or biologically. When the agents evaluated are 

considered by the Working Group to be sufficiently closely related, they may be 

grouped together for the purpose of a single evaluation of the degree of evidence. 

(a) Carcinogenicity in humans 

The evidence relevant to carcinogenicity from studies in humans is classified into 

one of the following categories: 

Sufficient evidence of carcinogenicity: The Working Group considers that a 

causal relationship has been established between exposure to the agent and 

human cancer. That is, a positive relationship has been observed between the 

exposure and cancer in studies in which chance, bias and confounding could be 

ruled out with reasonable confidence. A statement that there is sufficient evidence

is followed by a separate sentence that identifies the target organ(s) or tissue(s) 

where an increased risk of cancer was observed in humans. Identification of a 

specific target organ or tissue does not preclude the possibility that the agent may 

cause cancer at other sites. 

Limited evidence of carcinogenicity: A positive association has been observed 

between exposure to the agent and cancer for which a causal interpretation is 

considered by the Working Group to be credible, but chance, bias or confounding 

could not be ruled out with reasonable confidence. 

Inadequate evidence of carcinogenicity: The available studies are of insufficient 

quality, consistency or statistical power to permit a conclusion regarding the 

presence or absence of a causal association between exposure and cancer, or no 

data on cancer in humans are available. 

Evidence suggesting lack of carcinogenicity: There are several adequate studies 

covering the full range of levels of exposure that humans are known to encounter, 

which are mutually consistent in not showing a positive association between 

exposure to the agent and any studied cancer at any observed level of exposure. 

The results from these studies alone or combined should have narrow confidence 

intervals with an upper limit close to the null value (e.g. a relative risk of 1.0). 

Bias and confounding should be ruled out with reasonable confidence, and the 

studies should have an adequate length of follow-up. A conclusion of evidence 

suggesting lack of carcinogenicity is inevitably limited to the cancer sites, 

conditions and levels of exposure, and length of observation covered by the 

available studies. In addition, the possibility of a very small risk at the levels of 

exposure studied can never be excluded. 

In some instances, the above categories may be used to classify the degree of 

evidence related to carcinogenicity in specific organs or tissues. 

When the available epidemiological studies pertain to a mixture, process, 

occupation or industry, the Working Group seeks to identify the specific agent 

considered most likely to be responsible for any excess risk. The evaluation is 

focused as narrowly as the available data on exposure and other aspects permit. 
1-17

(b) Carcinogenicity in experimental animals 

Carcinogenicity in experimental animals can be evaluated using conventional 

bioassays, bioassays that employ genetically modified animals, and other in-vivo 

bioassays that focus on one or more of the critical stages of carcinogenesis. In the 

absence of data from conventional long-term bioassays or from assays with 

neoplasia as the end-point, consistently positive results in several models that 

address several stages in the multistage process of carcinogenesis should be 

considered in evaluating the degree of evidence of carcinogenicity in 

experimental animals. 

The evidence relevant to carcinogenicity in experimental animals is classified 

into one of the following categories: 

Sufficient evidence of carcinogenicity: The Working Group considers that a 

causal relationship has been established between the agent and an increased 

incidence of malignant neoplasms or of an appropriate combination of benign 

and malignant neoplasms in (a) two or more species of animals or (b) two or 

more independent studies in one species carried out at different times or in 

different laboratories or under different protocols. An increased incidence of 

tumours in both sexes of a single species in a well-conducted study, ideally 

conducted under Good Laboratory Practices, can also provide sufficient evidence. 

A single study in one species and sex might be considered to provide sufficient 

evidence of carcinogenicity when malignant neoplasms occur to an unusual 

degree with regard to incidence, site, type of tumour or age at onset, or when 

there are strong findings of tumours at multiple sites. 

Limited evidence of carcinogenicity: The data suggest a carcinogenic effect but 

are limited for making a definitive evaluation because, e.g. (a) the evidence of 

carcinogenicity is restricted to a single experiment; (b) there are unresolved 

questions regarding the adequacy of the design, conduct or interpretation of the 

studies; (c) the agent increases the incidence only of benign neoplasms or lesions 

of uncertain neoplastic potential; or (d) the evidence of carcinogenicity is 

restricted to studies that demonstrate only promoting activity in a narrow range of 

tissues or organs. 

Inadequate evidence of carcinogenicity: The studies cannot be interpreted as 

showing either the presence or absence of a carcinogenic effect because of major 

qualitative or quantitative limitations, or no data on cancer in experimental 

animals are available. 

Evidence suggesting lack of carcinogenicity: Adequate studies involving at least 

two species are available which show that, within the limits of the tests used, the 

agent is not carcinogenic. A conclusion of evidence suggesting lack of 

carcinogenicity is inevitably limited to the species, tumour sites, age at exposure, 

and conditions and levels of exposure studied. 
1-18

(c) Mechanistic and other relevant data 

Mechanistic and other evidence judged to be relevant to an evaluation of 

carcinogenicity and of sufficient importance to affect the overall evaluation is 

highlighted. This may include data on preneoplastic lesions, tumour pathology, 

genetic and related effects, structure–activity relationships, metabolism and 

toxicokinetics, physicochemical parameters and analogous biological agents. 

The strength of the evidence that any carcinogenic effect observed is due to a 

particular mechanism is evaluated, using terms such as ‘weak’, ‘moderate’ or 

‘strong’. The Working Group then assesses whether that particular mechanism is 

likely to be operative in humans. The strongest indications that a particular 

mechanism operates in humans derive from data on humans or biological 

specimens obtained from exposed humans. The data may be considered to be 

especially relevant if they show that the agent in question has caused changes in 

exposed humans that are on the causal pathway to carcinogenesis. Such data 

may, however, never become available, because it is at least conceivable that 

certain compounds may be kept from human use solely on the basis of evidence 

of their toxicity and/or carcinogenicity in experimental systems. 

The conclusion that a mechanism operates in experimental animals is 

strengthened by findings of consistent results in different experimental systems, 

by the demonstration of biological plausibility and by coherence of the overall 

database. Strong support can be obtained from studies that challenge the 

hypothesized mechanism experimentally, by demonstrating that the suppression 

of key mechanistic processes leads to the suppression of tumour development. 

The Working Group considers whether multiple mechanisms might contribute to 

tumour development, whether different mechanisms might operate in different 

dose ranges, whether separate mechanisms might operate in humans and 

experimental animals and whether a unique mechanism might operate in a 

susceptible group. The possible contribution of alternative mechanisms must be 

considered before concluding that tumours observed in experimental animals are 

not relevant to humans. An uneven level of experimental support for different 

mechanisms may reflect that disproportionate resources have been focused on 

investigating a favoured mechanism. 

For complex exposures, including occupational and industrial exposures, the 

chemical composition and the potential contribution of carcinogens known to be 

present are considered by the Working Group in its overall evaluation of human 

carcinogenicity. The Working Group also determines the extent to which the 

materials tested in experimental systems are related to those to which humans are 

exposed. 

(d) Overall evaluation 

Finally, the body of evidence is considered as a whole, in order to reach an 

overall evaluation of the carcinogenicity of the agent to humans. 

An evaluation may be made for a group of agents that have been evaluated by the 

Working Group. In addition, when supporting data indicate that other related 

agents, for which there is no direct evidence of their capacity to induce cancer in 
1-19

humans or in animals, may also be carcinogenic, a statement describing the 

rationale for this conclusion is added to the evaluation narrative; an additional 

evaluation may be made for this broader group of agents if the strength of the 

evidence warrants it. 

The agent is described according to the wording of one of the following 

categories, and the designated group is given. The categorization of an agent is a 

matter of scientific judgement that reflects the strength of the evidence derived 

from studies in humans and in experimental animals and from mechanistic and 

other relevant data. 

Group 1: The agent is carcinogenic to humans. 

This category is used when there is sufficient evidence of carcinogenicity in 

humans. Exceptionally, an agent may be placed in this category when evidence 

of carcinogenicity in humans is less than sufficient but there is sufficient evidence 

of carcinogenicity in experimental animals and strong evidence in exposed 

humans that the agent acts through a relevant mechanism of carcinogenicity. 

Group 2. 

This category includes agents for which, at one extreme, the degree of evidence 

of carcinogenicity in humans is almost sufficient, as well as those for which, at 

the other extreme, there are no human data but for which there is evidence of 

carcinogenicity in experimental animals. Agents are assigned to either Group 2A 

(probably carcinogenic to humans) or Group 2B (possibly carcinogenic to 

humans) on the basis of epidemiological and experimental evidence of 

carcinogenicity and mechanistic and other relevant data. The terms probably 

carcinogenic and possibly carcinogenic have no quantitative significance and are 

used simply as descriptors of different levels of evidence of human 

carcinogenicity, with probably carcinogenic signifying a higher level of evidence 

than possibly carcinogenic. 

Group 2A: The agent is probably carcinogenic to humans. 

This category is used when there is limited evidence of carcinogenicity in humans 

and sufficient evidence of carcinogenicity in experimental animals. In some 

cases, an agent may be classified in this category when there is inadequate 

evidence of carcinogenicity in humans and sufficient evidence of carcinogenicity

in experimental animals and strong evidence that the carcinogenesis is mediated 

by a mechanism that also operates in humans. Exceptionally, an agent may be 

classified in this category solely on the basis of limited evidence of 

carcinogenicity in humans. An agent may be assigned to this category if it clearly 

belongs, based on mechanistic considerations, to a class of agents for which one 

or more members have been classified in Group 1 or Group 2A. 

Group 2B: The agent is possibly carcinogenic to humans. 

This category is used for agents for which there is limited evidence of 

carcinogenicity in humans and less than sufficient evidence of carcinogenicity in 

experimental animals. It may also be used when there is inadequate evidence of 
1-20

carcinogenicity in humans but there is sufficient evidence of carcinogenicity in 

experimental animals. In some instances, an agent for which there is inadequate 

evidence of carcinogenicity in humans and less than sufficient evidence of 

carcinogenicity in experimental animals together with supporting evidence from 

mechanistic and other relevant data may be placed in this group. An agent may 

be classified in this category solely on the basis of strong evidence from 

mechanistic and other relevant data. 

Group 3: The agent is not classifiable as to its carcinogenicity to humans. 

This category is used most commonly for agents for which the evidence of 

carcinogenicity is inadequate in humans and inadequate or limited in 

experimental animals. 

Exceptionally, agents for which the evidence of carcinogenicity is inadequate in 

humans but sufficient in experimental animals may be placed in this category 

when there is strong evidence that the mechanism of carcinogenicity in 

experimental animals does not operate in humans. 

Agents that do not fall into any other group are also placed in this category. 

An evaluation in Group 3 is not a determination of non-carcinogenicity or overall 

safety. It often means that further research is needed, especially when exposures 

are widespread or the cancer data are consistent with differing interpretations. 

Group 4: The agent is probably not carcinogenic to humans. 

This category is used for agents for which there is evidence suggesting lack of 

carcinogenicity in humans and in experimental animals. In some instances, 

agents for which there is inadequate evidence of carcinogenicity in humans but 

evidence suggesting lack of carcinogenicity in experimental animals, 

consistently and strongly supported by a broad range of mechanistic and other 

relevant data, may be classified in this group. 

(e) Rationale 

The reasoning that the Working Group used to reach its evaluation is presented 

and discussed. This section integrates the major findings from studies of cancer 

in humans, studies of cancer in experimental animals, and mechanistic and other 

relevant data. It includes concise statements of the principal line(s) of argument 

that emerged, the conclusions of the Working Group on the strength of the 

evidence for each group of studies, citations to indicate which studies were 

pivotal to these conclusions, and an explanation of the reasoning of the Working 

Group in weighing data and making evaluations. When there are significant 

differences of scientific interpretation among Working Group Members, a brief 

summary of the alternative interpretations is provided, together with their 

scientific rationale and an indication of the relative degree of support for each 

alternative. 
1-21

AX1.2.6. National Toxicology Program Criteria 

The criteria for listing an agent, substance, mixture, or exposure circumstance in the National 

Toxicology Program’s Report on Carcinogens (NTP, 2005) are as follows: 

Known To Be Human Carcinogen: 

There is sufficient evidence of carcinogenicity from studies in humans*, which 

indicates a causal relationship between exposure to the agent, substance, or 

mixture, and human cancer. 

Reasonably Anticipated To Be Human Carcinogen: 

There is limited evidence of carcinogenicity from studies in humans*, which 

indicates that causal interpretation is credible, but that alternative explanations, 

such as chance, bias, or confounding factors, could not adequately be excluded, 

or 

there is sufficient evidence of carcinogenicity from studies in experimental 

animals, which indicates there is an increased incidence of malignant and/or a 

combination of malignant and benign tumors (1) in multiple species or at 

multiple tissue sites, or (2) by multiple routes of exposure, or (3) to an unusual 

degree with regard to incidence, site, or type of tumor, or age at onset, 

or 

there is less than sufficient evidence of carcinogenicity in humans or laboratory 

animals; however, the agent, substance, or mixture belongs to a well-defined, 

structurally related class of substances whose members are listed in a previous 

Report on Carcinogens as either known to be a human carcinogen or reasonably 

anticipated to be a human carcinogen, or there is convincing relevant information 

that the agent acts through mechanisms indicating it would likely cause cancer in 

humans. 

Conclusions regarding carcinogenicity in humans or experimental animals are 

based on scientific judgment, with consideration given to all relevant 

information. Relevant information includes, but is not limited to, dose response, 

route of exposure, chemical structure, metabolism, pharmacokinetics, sensitive 

sub-populations, genetic effects, or other data relating to mechanism of action or 

factors that may be unique to a given substance. For example, there may be 

substances for which there is evidence of carcinogenicity in laboratory animals, 

but there are compelling data indicating that the agent acts through mechanisms 

which do not operate in humans and would therefore not reasonably be 

anticipated to cause cancer in humans. 

*This evidence can include traditional cancer epidemiology studies, data from clinical studies, 

and/or data derived from the study of tissues or cells from humans exposed to the substance in question 

that can be useful for evaluating whether a relevant cancer mechanism is operating in people. 
2-1

Annex 2. Atmospheric Chemistry of NOX

AX2.1. Introduction 

Nitrogen oxides (NOX) along with volatile organic compounds (VOCs) including anthropogenic 

and biogenic hydrocarbons, aldehydes, etc. and carbon monoxide (CO) serve as precursors in the 

formation of ozone (O3) and other elements of photochemical smog. Nitrogen dioxide is an oxidant and 

can further react to form other photochemical oxidants, in particular the organic nitrates, including peroxy 

acetyl nitrates (PAN) and higher PAN analogues. It can also react with toxic compounds such as 

polycyclic aromatic hydrocarbons (PAHs) to form nitro-PAHs, which may be even more toxic than the 

precursors. 

The role of NOX in O3 formation was reviewed in Chapter 2 (Section 2.2) of the 2006 AQCD for 

Ozone and Other Photochemical Oxidants (U.S. Environmental Protection Agency, 2006), and in 

numerous texts (e.g., Seinfeld and Pandis, 1998; Jacob, 2000; Jacobson, 2002). Mechanisms for 

transporting O3 precursors, the factors controlling the efficiency of O3 production from NOX, methods for 

calculating O3 from its precursors, and methods for measuring NOX were all reviewed in Section 2.6 of 

the 2006 O3 AQCD. The main points from those discussions in the 2006 O3 AQCD and updates, based on 

new materials will be presented here. 

The overall chemistry of reactive nitrogen compounds in the atmosphere is summarized in Figure 

AX2.2-1 and is described in greater detail in the following sections. Nitrogen oxides are emitted primarily 

as NO with smaller quantities of NO2. Emissions of NOX are spatially distributed vertically with some 

occurring at or near ground level (e.g., mobile sources) and others aloft (e.g., electric generating utility 

(EGUs) stacks) as indicated in Figure AX2.2-1. Because of atmospheric chemical reactions, the relative 

abundance of different compounds contributed by different sources varies with location. Both 

anthropogenic and natural (biogenic) processes emit NOX. In addition to gas phase reactions, multiphase 

processes are important for forming aerosol-phase pollutants, including aerosol NO3

-

. 
2-2

Figure AX2.2-1. Schematic diagram of the cycle of reactive nitrogen species in the atmosphere. MPP refers 

to multi-phase process; h< to a photon of solar energy. 

AX2.2. Chemistry of NOX in the Troposphere 

AX2.2.1. Basic Chemistry 

There is a rapid photochemical cycle in the troposphere that involves photolysis of NO2 by solar 

UV-A radiation to yield NO and a ground-state oxygen atom, O(3

P) 

 (AX2.2-1) 

This ground-state oxygen atom can then combine with molecular oxygen (O2) to form O3; and, 

colliding with any molecule from the surrounding air (M = N2, O2, etc.), the newly formed O3 molecule 

transfers excess energy and is stabilized 

 (AX2.2-2) 

where M = N2, O2. Reaction AX2.2-2 is the only significant reaction forming O3 in the troposphere. 

NO and O3 react to reform NO2

 (AX2.2-3) 
2-3

Reaction AX2.2-3 is responsible for O3 decreases and NO2 increases found near sources of NO 

(e.g., highways). The falloff of NO2 from a road depends on wind speed and direction and the local 

structure of turbulent mixing, temperature (through the temperature dependence of Reaction AX2.2-3) 

and the amount of sunlight (through Reaction AX2.2-1). Oxidation of reactive VOCs leads to formation 

of reactive radical species that allow the conversion of NO to NO2 without participation of O3 as in 

Reaction AX2.2-3 

 (AX2.2-4) 

O3, therefore, can accumulate as NO2 photolyzes as in Reaction AX2.2-1, followed by Reaction 

AX2.2-2. Specific mechanisms for the oxidation of a number of VOCs were discussed in the O3 AQCD 

(U.S. Environmental Protection Agency, 2006). 

It is often convenient to speak about families of chemical species defined in terms of members that 

interconvert rapidly among themselves on time scales that are shorter than those for formation or 

destruction of the family as a whole. For example, an “odd oxygen” (OX) family can be defined as 

 (AX2.2-5) 

In much the same way, NOX is sometimes referred to as “odd nitrogen”. Hence, we see that 

production of OX occurs by the schematic Reaction AX2.2-5, and that the sequence of reactions given by 

reactions AX2.2-1 through AX2.2-3 represents no net production of OX. Definitions of species families 

and methods for constructing families are discussed in Jacobson (1999) and references therein. Other 

families that include nitrogen-containing species, and which will be referred to later in this chapter, are: 

 (AX2.2-6) 

In this equation, NOZ refers to the sum of the oxidation products of NOX. 

The reaction of NO2 with O3 leads to the formation of NO3

-

 radical 

 (AX2.2-7) 

However, because the NO3 radical photolyzes rapidly (lifetime of ~5 s during the photochemically 

most active period of the day around local solar noon (Atkinson et al., 1992), 

 (AX2.2-8a) 

 (AX2.2-8b) 
2-4

its concentration remains low during daylight hours, but can increase after sunset to nighttime 

concentrations of <5 × 107

 to 1 × 1010 molecules/cm-3 (<2 to 430 parts per trillion (ppt)) over continental 

areas influenced by anthropogenic emissions of NOX (Atkinson et al., 1986). At night, NO3, rather than 

the hydroxyl radical (OH), is the primary oxidant in the system. 

Nitrate radicals can combine with NO2 to form dinitrogen pentoxide (N2O5) 

 (AX2.2-9) 

and N2O5 both photolyzes and thermally decomposes back to NO2 and NO3 during the day; however, 

N2O5 concentrations can accumulate during the night to parts per billion (ppb) levels in polluted urban 

atmospheres. 

The tropospheric chemical removal processes for NOX include reaction of NO2 with the OH radical 

and hydrolysis of N2O5 in aqueous aerosol solutions if there is no organic coating. Both of these reactions 

produce HNO3 

 (AX2.2-10) 

 (AX2.2-11) 

The gas-phase reaction of OH radical with NO2 (Reaction AX2.2-11) initiates one of the major and 

ultimate removal processes for NOX in the troposphere. This reaction removes OH and NO2 radicals and 

competes with hydrocarbons for OH radicals in areas characterized by high NOX concentrations, such as 

urban centers (see Section AX2.2.2). The timescale (τ) for conversion of NOX to HNO3 in the planetary 

boundary layer at 40 N latitude ranges from about 4 h in July to about 16 h in January. The corresponding 

range in τ at 25 N latitude is between 4 and 5 h, while at 50 N latitude, HNO3 τ ranges from about 4 to 20 

h (Martin et al., 2003). In addition to gas-phase HNO3, Golden and Smith (2000) have shown on the basis 

of theoretical studies that pernitrous acid (HOONO) is also produced by the reaction of NO2 and OH 

radicals. However, this channel of production most likely represents a minor yield approximately 15% at 

the surface (Jet Propulsion Laboratory, 2003). 

Pernitrous acid will thermally decompose and can photolyze. Gas-phase HNO3 formed from 

Reactions AX2.2-10 and AX2.2-11 undergoes wet and dry deposition to the surface, and uptake by 

ambient aerosol particles. Reaction AX2.2-10 limits NOX τ to a range of hours to days. Geyer and Platt 

(2002) concluded that Reaction AX2.2-10 constituted about 10% of the removal of NOX at a site near 

Berlin, Germany during spring and summer. However, other studies found a larger contribution to HNO3

production from Reaction AX2.2-10. Dentener and Crutzen (1993) estimated 20% in summer and 80% of 

HNO3 production in winter is from Reaction AX2.2-10. Tonnesen and Dennis (2000) found 16 to 31% of 

summer HNO3 production was from Reaction AX2.2-10. The contribution of Reaction AX2.2-11 to 

HNO3 formation is highly uncertain during both winter and summer. The importance of Reaction AX2.2-

11 could be much higher during winter than during summer because of the much lower concentration of 

OH radicals and the enhanced stability of N2O5 due to lower temperatures and less sunlight. Note that 

Reaction AX2.2-11 proceeds as a heterogeneous reaction. Recent work in the northeastern United States 

indicates that this reaction proceeds at a faster rate in power plant plumes than in urban plumes (Brown 

et al., 2006a,b; Frost et al., 2006). 

In addition to the uptake of HNO3 on particles and in cloud drops, it photolyzes and reacts with OH 

radicals via 

 (AX2.2-12) 
2-5

and 

 (AX2.2-13) 

The lifetime of HNO3 with respect to these two reactions is long enough for HNO3 to act as a 

reservoir species for NOX during long-range transport. 

OH radicals also can react with NO to produce nitrous acid (HONO or HNO2) 

 (AX2.2-14) 

In the daytime, HNO2 is rapidly photolyzed back to the original reactants. 

 (AX2.2-15) 

Reaction AX2.2-15 is, however, a negligible source of HONO, which is formed mainly by 

multiphase processes (see Section AX2.2.3). At night, heterogeneous reactions of NO2 in aerosols or at 

the earth’s surface result in accumulation of HONO (Lammel and Cape, 1996; Jacob, 2000; Sakamaki 

et al., 1983; Pitts et al., 1984; Svensson et al., 1987; Jenkin et al., 1988; Lammel and Perner, 1988; 

Notholt et al., 1992a,b). Harris et al. (1982) suggested that photolysis of this HNO2 at sunrise could 

provide an important early-morning source of OH radicals to drive O3 formation. 

Hydroperoxy (HO2) radicals can react with NO2 to produce pernitric acid (HNO4) 

 (AX2.2-16) 

which then can thermally decompose and photolyze back to its original reactants. The acids formed in 

these gas-phase reactions are all water soluble. Hence, they can be incorporated into cloud drops and in 

the aqueous phase of particles. 

Although the lifetimes of HNO4 and N2O5 are short (minutes to hours) during typical summer 

conditions, they can be much longer at the lower temperatures and darkness found during the polar night. 

Under these conditions, species such as PAN, HNO3, HNO4, and N2O5 serve as NOX reservoirs that can 

liberate NO2 upon the return of sunlight during the polar spring. 

A broad range of organic nitrogen compounds can be directly emitted by combustion sources or 

formed in the atmosphere from NOX emissions. Organic nitrogen compounds include the PANs, 

nitrosamines, nitro-PAHs, and the more recently identified nitrated organics in the quinone family. 

Oxidation of VOCs produces organic peroxy radicals (RO2), as discussed in the 2006 AQCD for Ozone. 

Reaction of RO2 radicals with NO and NO2 produces organic nitrates (RONO2) and peroxynitrates 

(RO2NO2) 

 (AX2.2-17) 

 (AX2.2-18) 

Reaction (AX2.2-17) is a minor branch for the reaction of RO2 with NO; the major branch involves 

NO2 as in Reaction AX2.2-18. Note, however, that organic nitrate yields increase with carbon number 

(Atkinson, 2000). 

The most important of these organic nitrates is PAN, the dominant member of the broader family of 

peroxyacylnitrates which includes peroxypropionyl nitrate (PPN) of anthropogenic origin and 
2-6

peroxymethacrylic nitrate (MPAN) produced from isoprene oxidation. The PANs are formed by the 

combination reaction of acetyl peroxy radicals with NO2

 (AX2.2-19) 

where the acetyl peroxy radicals are formed mainly during the oxidation of ethane (C2H6). Acetaldehyde 

(CH3CHO) is formed as an intermediate species during the oxidation of ethane. Acetaldehyde can be 

photolyzed or react with OH radicals to yield acetyl radicals. 

 (AX2.2-20) 

 (AX2.2-21) 

Acetyl radicals then react with O2 to yield acetyl peroxy radicals. 

 (AX2.2-22) 

However, acetyl peroxy radicals will react with NO in areas of high NO concentrations 

 (AX2.2-23) 

and the acetyl-oxy radicals will then decompose 

 (AX2.2-24) 

Thus, the formation of PAN is favored at conditions of high ratios of NO2 to NO which are most 

typically found under low NOX conditions. The PANs both thermally decompose and photolyze back to 

their reactants on timescales of a few hours during warm sunlit conditions, having lifetimes with respect 

to thermal decomposition ranging from ~1 hour at 298 K to ~2.5 days at 273 K, up to several weeks at 

250 K. Thus, they can provide an effective sink of NOX at cold temperatures and high solar zenith angles, 

allowing release of NO2 as air masses warm, in particular by subsidence. The PANs are also removed by 

uptake to vegetation (Sparks et al., 2003; Teklemariam and Sparks, 2004). 

The organic nitrates may react further, depending on the functionality of the R group, but they will 

typically not return NOX and can therefore be viewed mainly as a permanent sink for NOX as alkyl 

nitrates are sparingly soluble and will photolyze. This sink is usually small compared to HNO3 formation 

although the formation of isoprene nitrates may be a significant sink for NOX in the United States in 

summer (Liang et al., 1998). 

The peroxynitrates produced by AX2.2-19 are thermally unstable and most have very short 

lifetimes (less than a few minutes) owing to thermal decomposition back to the original reactants. They 

are thus not effective sinks of NOX. 

AX2.2.2. NOX Concentrations and O3 Formation 

Ozone is unlike some other species whose rates of formation vary directly with the emissions of 

their precursors in that O3 production (P(O3)) changes nonlinearly with the concentrations of its 

precursors. At the low NOX concentrations found in most environments ranging from remote continental 

areas to rural and suburban areas downwind of urban centers, the net production of O3 increases with 
2-7

increasing NOX. At the high NOX concentrations found in downtown metropolitan areas especially near 

busy streets and roadways and in power plant plumes, there is net destruction of O3 by (titration) reaction 

with NO. Between these two regimes is a transition stage in which O3 shows only a weak dependence on 

NOX concentrations. In the high NOX regime, NO2 scavenges OH radicals which would otherwise oxidize 

VOCs to produce peroxy radicals, which in turn would oxidize NO to NO2. In the low NOX regime, VOC 

oxidation generates, or at least does not consume, free radicals, and O3 production varies directly with 

NOX. Sometimes the terms ‘VOC-limited’ and ‘NOX-limited’ are used to describe these two regimes; 

also, the terms NOX-limited and NOX-saturated are used, e.g., by Jaeglé et al., 2001. The chemistry of OH 

radicals, which are responsible for initiating the oxidation of hydrocarbons, shows behavior similar to that 

for O3 with respect to NOX concentrations (Hameed et al., 1979; Pinto et al., 1993; Poppe et al., 1993; 

Zimmerman and Poppe, 1993). These considerations introduce a high degree of uncertainty into attempts 

to relate changes in O3 concentrations to emissions of precursors. It should also be noted at the outset that 

in a NOX-limited (or NOX-sensitive) regime, O3 formation is not insensitive to radical production or the 

flux of solar UV photons, just that O3 formation is more sensitive to NOX. For example, global 

tropospheric O3 is sensitive to the concentration of CH4 even though the troposphere is predominantly 

NOX-limited. 

Various analytical techniques have been proposed that use ambient NOX and VOC measurements to 

derive information about O3 production and O3-NOX-VOC sensitivity. Previously (e.g., National Research 

Council, 1991), it was suggested that O3 formation in individual urban areas could be understood in terms 

of measurements of ambient NOX and VOC concentrations during the early morning. In this approach, the 

ratio of summed VOC to NOX concentrations (unweighted by chemical reactivity) is used to determine 

whether conditions are NOX-sensitive or VOC sensitive. This technique is inadequate to characterize O3

formation because it omits many factors recognized as important for P(O3), including: the effect of 

biogenic VOCs (which are not present in urban centers during early morning); important individual 

differences in the ability of VOCs to generate free radicals, rather than just from total VOC concentration 

and other differences in O3-forming potential for individual VOCs (Carter, 1995); the effect of multiday 

transport; and general changes in photochemistry as air moves downwind from urban areas (Milford 

et al., 1994). 

Jacob et al. (1995) used a combination of field measurements and a chemical transport model 

(CTM) to show that the formation of O3 changed from NOX-limited to NOX-saturated as the season 

changed from summer to fall at a monitoring site in Shenandoah National Park, VA. Photochemical 

production of O3 generally occurs together with production of other species including HNO3, organic 

nitrates, and hydrogen peroxide (H2O2). The relative rates of P(O3) and the production of other species 

varies depending on photochemical conditions and can be used to provide information about O3-precursor 

sensitivity. 

There are no hard and fast rules governing the levels of NOX at which the transition from NOXlimited to NOX-saturated conditions occurs. The transition between these two regimes is highly spatially 

and temporally dependent. In the upper troposphere, responses to NOX additions from commercial aircraft 

have been found which are very similar to these in the lower troposphere (Brühl et al., 2000). Brühl et al. 

(2000) found that the NOX levels for O3 production versus loss are highly sensitive to the radical sources 

included in model calculations. They found that inclusion of only CH4 and CO oxidation leads to a 

decrease in net O3 production in the North Atlantic flight corridor due to NO emissions from aircraft. 

However, the additional inclusion of acetone photolysis was found to shift the maximum in O3 production 

to higher NOX mixing ratios, thereby reducing or eliminating areas in which O3 production rates 

decreased due to aircraft emissions. 

Trainer et al. (1993) suggested that the slope of the regression line between O3 and NOZ can be used 

to estimate the rate of P(O3) per NOX ,also known as the O3 production efficiency or OPE. Ryerson et al. 

(1998, 2001) used measured correlations between O3 and NOZ to identify different rates of O3 production 

in plumes from large point sources. 

Sillman (1995) and Sillman and He (2002) identified several secondary reaction products that show 

different correlation patterns for NOX-limited conditions and NOX-saturated conditions. The most 
2-8

important correlations are for O3 versus NOY, O3 versus NOZ, O3 versus HNO3, and H2O2 versus HNO3. 

The correlations between O3 and NOY, and O3 and NOZ are especially important because measurements 

of NOY and NOX are widely available. Measured O3 versus NOZ (Figure AX2.2-2) shows distinctly 

different patterns in different locations. In rural areas and in urban areas such as Nashville, TN, O3 shows 

a strong correlation with NOZ and a relatively steep slope to the regression line. By contrast, in Los 

Angeles O3 also increases with NOZ, but the rate of increase of O3 with NOZ is lower and the O3

concentrations for a given NOZ value are generally lower. 

Figure AX2.2-2. Measured values of O3 and NOZ (NOY – NOX). Measured during the afternoon at rural sites in 

the eastern United States (gray circle) and in urban areas and urban plumes associated with 

Nashville, TN (gray dash), Paris, FR (black diamond) and Los Angeles, CA (X) 

The difference between NOX-limited and NOX-saturated regimes is also reflected in measurements 

of H2O2. Formation of H2O2 takes place by self-reaction of photochemically generated HO2 radicals, so 

that there is large seasonal variation of H2O2 concentrations, and values in excess of 1 ppb are mainly 

limited to the summer months when photochemistry is more active (Kleinman, 1991). Hydrogen peroxide 

is produced in abundance only when O3 is produced under NOX-limited conditions. When the 

photochemistry is NOX-saturated, much less H2O2 is produced. In addition, increasing NOX tends to slow 

the formation of H2O2 under NOX-limited conditions. Differences between these two regimes are also 

related to the preferential formation of sulfate during summer and to the inhibition of sulfate and 

hydrogen peroxide during winter (Stein and Lamb, 2003). Measurements in the rural eastern United 

States (Jacob et al., 1995), at Nashville (Sillman et al., 1998), and at Los Angeles (Sakugawa and Kaplan, 

1989) show large differences in H2O2 concentrations likely due to differences in NOX availability at these 

locations. 

AX2.2.3. Multiphase Chemistry Involving NOX

Recent laboratory studies on sulfate and organic aerosols indicate that the reaction probability 

γN2O5 is in the range of 0.01 to 0.05 (Kane et al., 2001; Hallquist et al., 2003; Thornton et al., 2003). Tie 
2-9

et al. (2003) found that a value of 0.04 in their global model gave the best simulation of observed NOX

concentrations over the Arctic in winter. 

Using aircraft measurements over the northeastern United States, Brown et al. (2006b) found that 

the uptake coefficient for N2O5, γN2O5, on the surfaces of particles depends strongly on their sulfate 

content. They found that γN2O5 was highest (0.017) in regions where the aerosol sulfate concentration 

was highest and lower elsewhere (<0.0016). This result contrasts with that of Dentener and Crutzen 

(1993) who concluded that γN2O5 would be independent of aerosol composition, based on a value for 

γN2O5 of 0.1, implying that the heterogeneous hydrolysis of N2O5 would be saturated for typical ambient 

aerosol surface areas. The importance of this reaction to tropospheric chemistry depends on the value of 

γN2O5. If it is 0.01 or lower, there may be difficulty explaining the loss of NOY and the formation of 

aerosol nitrate, especially during winter. A decrease in N2O5 slows down removal of NOX by leaving 

more NO2 available for reaction and thus increases O3 production. Based on the consistency between 

measurements of NOY partitioning and gas-phase models, Jacob (2000) considered it unlikely that HNO3

is recycled to NOX in the lower troposphere in significant concentrations. However, only one of the 

reviewed studies (Schultz et al., 2000) was conducted in the marine troposphere and none was conducted 

in the MBL. An investigation over the equatorial Pacific reported discrepancies between observations and 

theory (Singh et al., 1996) which might be explained by HNO3 recycling. It is important to recognize that 

both Schultz et al. (2000) and Singh et al. (1996) involved aircraft sampling at altitude which, in the 

MBL, can significantly under-represent sea salt aerosols and thus most total NO3 (defined to be HNO3 + 

NO3

-

) and large fractions of NOY in marine air (see Huebert et al., 1996). Consequently, some caution is 

warranted when interpreting constituent ratios and NOY budgets based on such data. 

Recent work in the Arctic has quantified significant photochemical recycling of NO3

-

 to NOX and 

attendant perturbations of OH chemistry in snow (Honrath et al., 2000; Dibb et al., 2002; Dominé and 

Shepson, 2002) which suggest the possibility that similar multiphase pathways could occur in aerosols. 

As mentioned above, NO3

-

 is photolytically reduced to NO2

-

 (Zafiriou and True, 1979) in acidic sea salt 

solutions (Anastasio et al., 1999). Further photolytic reduction of NO2

-

 to NO (Zafiriou and True, 1979) 

could provide a possible mechanism for HNO3 recycling. Early experiments reported production of NOX

during the irradiation of artificial seawater concentrates containing NO3

-

 (Petriconi and Papee, 1972). 

Based on the above, HNO3 recycling in sea salt aerosols is potentially important and warrants further 

investigation. Other possible recycling pathways involving highly acidic aerosol solutions and soot are 

reviewed by Jacob (2000). 

Stemmler et al. (2006) studied the photosensitized reduction of NO2 to HONO on humic acid films 

using radiation in the UV-A through the visible spectral regions. They also found evidence for reduction 

occurring in the dark, reactions which may occur involving surfaces containing partly oxidized aromatic 

structures. For example, Simpson et al. (2006) found that aromatic compounds constituted ~20% of 

organic films coating windows in downtown Toronto. They calculated production rates of HONO that are 

compatible with observations of high HONO levels in a variety of environments. The photolysis of 

HONO formed this way could account for up to 60% of the integrated source of OH radicals in the inner 

planetary boundary layer. A combination of high NO2 levels and surfaces of soil and buildings and other 

man-made structures exposed to diesel exhaust would then be conducive to HONO formation and, hence, 

to high OH concentrations. 

Ammann et al. (1998) reported the efficient conversion of NO2 to HONO on fresh soot particles in 

the presence of water. They suggested that interaction between NO2 and soot particles may account for 

high concentrations of HONO observed in urban environments. Conversion of NO2 to HONO and the 

subsequent photolysis of HONO to NO + OH would constitute a NOX-catalyzed O3 sink involving snow. 

High concentrations of HONO can lead to the rapid growth in OH concentrations shortly after sunrise, 

giving a “jump start” to photochemical smog formation. Prolonged exposure to ambient oxidizing agents 

appears to deactivate this process. Bröske et al. (2003) studied the interaction of NO2 on secondary 

organic aerosols and concluded that the uptake coefficients were too low for this reaction to be an 

important source of HONO in the troposphere. 
2-10

Choi and Leu (1998) evaluated the interactions of HNO3 on model black carbon soot (FW2), 

graphite, hexane, and kerosene soot. They found that HNO3 decomposed to NO2 and H2O at higher HNO3

surface coverages, i.e., P(HNO3) ≥10−4

 Torr. None of the soot models used were reactive at low HNO3

coverages, at P(HNO3) = 5 × 10−7

 Torr or at temperatures below 220 K. They concluded that it is unlikely 

that aircraft soot in the upper troposphere/lower stratosphere reduces HNO3. 

Heterogeneous production on soot at night is believed to be the mechanism by which HONO 

accumulates to provide an early morning source of HOX in high NOX environments (Harrison et al., 1996; 

Jacob, 2000). HONO has been frequently observed to accumulate to levels of several ppb overnight, and 

this has been attributed to soot chemistry (Harris et al., 1982; Calvert et al., 1994; Jacob, 2000). 

Longfellow et al. (1999) observed the formation of HONO when methane, propane, hexane, and 

kerosene soots were exposed to NO2. They suggested that this reaction may account for some part of the 

unexplained high levels of HONO observed in urban areas. They commented that without details about 

the surface area, porosity, and amount of soot available for this reaction, reactive uptake values cannot be 

estimated reliably. They further commented that soot and NO2 are produced in close proximity during 

combustion, and that large quantities of HONO have been observed in aircraft plumes. 

Saathoff et al. (2001) studied the heterogeneous loss of NO2, HNO3, NO3/N2O5, HO2/HO2NO2 on 

soot aerosol using a large aerosol chamber. Reaction periods of up to several days were monitored and 

results used to fit a detailed model. Saathoff et al. derived reaction probabilities at 294 K and 50% relative 

humidity (RH) for NO2, NO3, HO2, and HO2NO2 deposition to soot; HNO3 reduction to NO2; and N2O5

hydrolysis. When these probabilities were included in photochemical box model calculations of a 4-day 

smog event, the only noteworthy influence of soot was a 10% reduction in the second day O3 maximum, 

for a soot loading of 20 μg/m3

, i.e., roughly a factor of 10 times observed black carbon loadings seen in 

U.S. urban areas, even during air pollution episodes. 

Muñoz and Rossi (2002) conducted Knudsen cell studies of HNO3 uptake on black and grey decane 

soot produced in lean and rich flames, respectively. They observed HONO as the main species released 

following HNO3 uptake on grey soot, and NO and traces of NO2 from black soot. They concluded that 

these reactions would only have relevance in special situations in urban settings where soot and HNO3 are 

present in high concentrations simultaneously. 

AX2.2.4. Formation of Nitro-PAHs 

Nitro-polycyclic aromatic hydrocarbons (nitro-PAHs) (see Figure AX2.2-3 for some example nitroPAHs) are generated from incomplete combustion processes through electrophilic reactions of polycyclic 

aromatic hydrocarbons (PAHs) in the presence of NO2 (International Agency for Research on Cancer 

[IARC], 1989; World Health Organization [WHO], 2003). Among combustion sources, diesel emissions 

have been identified as the major source of nitro-PAHs in ambient air (Bezabeh et al., 2003; Gibson, 

1983; Schuetzle, 1983; Tokiwa and Ohnishi, 1986). Direct emissions of nitro-PAHs in PM vary with type 

of fuel, vehicle maintenance, and ambient conditions (Zielinska et al., 2004). 

Figure AX2.2-3. Structures of same nitro-polycyclic aromatic hydrocarbons. 
2-11

In addition to being directly emitted, nitro-PAHs can also be formed from both gaseous and 

heterogeneous reactions of PAHs with gaseous nitrogenous pollutants in the atmosphere (Arey et al., 

1986, 1989, Arey, 1998; Perrini, 2005; Pitts, 1987; Sasaki et al., 1997; Zielinska et al., 1989). Different 

isomers of nitro-PAHs are formed through different formation processes. For example, the most abundant 

nitro-PAH in diesel particles is 1-nitropyene (1NP), followed by 3-nitrofluoranthene (3NF) and 8-

nitrofluoranthene (8NF) (Bezabeh et al., 2003; Gibson, 1983; Schuetzle, 1983; Tokiwa and Ohnishi, 

1986). However, in ambient particulate organic matter (POM), 2-nitrofluoranthene (2NF) is the dominant 

compound, followed by 1NP and 2-nitropyrene (2NP) (Arey et al., 1989; Bamford et al., 2003; Reisen 

and Arey, 2005; Zielinska et al., 1989), although 2NF and 2NP are not directly emitted from primary 

combustion emissions. The reaction mechanisms for the different nitro-PAH formation processes have 

been well documented and are presented in Figure AX2.2-4. 

The dominant process for the formation of nitro-PAHs in the atmosphere is gas-phase reaction of 

PAHs with OH radicals in the presence of NOX (Arey et al., 1986, Arey, 1998; Atkinson and Arey, 1994; 

Ramdahl et al., 1986; Sasaki et al., 1997). Hydroxyl radicals can be generated photochemically or at night 

through ozone-alkene reactions, (Finlayson-Pitts and Pitts, 2000). The postulated reaction mechanism of 

OH with PAHs involves the addition of OH at the site of highest electron density of the aromatic ring, for 

example, the 1-position for pyrene (PY) and the 3-position for fluoranthene (FL). This reaction is 

followed by the addition of NO2 to the OH-PAH adduct and elimination of water to form the nitroarenes 

(Figure AX2.2-4) (Arey et al., 1986; Atkinson et al., 1990; Pitts, 1987). After formation, nitro-PAHs with 

low vapor pressures (such as 2NF and 2NP) immediately migrate to particles under ambient conditions 

(Fan et al., 1995; Feilberg et al., 1999). The second order rate-constants for the reactions of OH with most 

PAHs range from 10-10 to 10-12 cm3

/molecule/s at 298 K with the yields ranging from ~0.06 to ~5% 

(Atkinson and Arey, 1994). 2NF and 2NP have been found as the most abundant nitro-PAHs formed via 

reactions of OH with gaseous FL and PY, respectively, in ambient air. 

Figure AX2.2-4. Formation of 2-nitropyrene (2NP) from the reaction of OH with gaseous pyrene (PY). 

The second important process for the formation of nitro-PAHs in the atmosphere is the nitration of 

PAHs by NO3

-

 in the presence of NOX at night (Atkinson et al., 1990; Atkinson and Arey, 1994; Sasaki 

et al., 1997). Nitrate radicals can be generated by reaction of ozone (O3) with NO2 in the atmosphere by 

Reaction AX2.2-25. 
2-12

 (AX2.2-25) 

Similar to the mechanism of OH reactions with PAHs, NO3 initially adds to the PAH ring to form 

an NO3-PAH adduct, followed by loss of HNO3 to form nitro-PAHs (Atkinson et al., 1990; Atkinson and 

Arey, 1994; Sasaki et al., 1997). For example, in the mixture of naphthalene and N2O5-NO3-NO2, the 

major products formed through the NO3 reaction are 1- and 2-nitro-naphthalene (1NN and 2NN) 

(Atkinson et al., 1990; Feilberg et al., 1999; Sasaki et al., 1997). 2NF and 4NP were reported as the 

primary products of the gas-phase reactions of FL and PY with NO3 radical, respectively (Atkinson et al., 

1990; Atkinson and Arey, 1994). 

The reaction with NO3 is of minor importance in the daytime because NO3 radical is not stable in 

sunlight. In addition, given the rapid reactions of NO with NO3 and with O3 in the atmosphere (FinlaysonPitts and Pitts 2000), concentrations of NO3 at ground level are low during daytime. However, at night, 

concentrations of NO3 radicals formed in polluted ambient air are expected to increase. According to 

Atkinson (1991), the average NO3 concentration is about 20 ppt in the lower troposphere at night and can 

be as high as 430 ppt. It is also worth noting that significant NO3 radical concentrations are found at 

elevated altitudes where O3 is high but NO is low (Reissell and Arey, 2001; Stutz et al., 2004a). When 

NO3 reaches high concentrations, the formation of nitro-PAHs by the reaction of gaseous PAHs with NO3

may be of environmental significance. At 10-17 to 10-18 cm3

 /molecule/s, the rate constants of NO3 with 

most PAHs are several orders of magnitude lower than those of OH with the same PAHs; however, the 

yields of nitro-PAHs from NO3 reactions are generally much higher than those of OH reactions. For 

example, the yields of 1-NN and 2NF are 0.3% and 3%, respectively from OH reactions, but the yields 

are 17% and 24% for these two compounds generated from the NO3 radical reactions (Atkinson and Arey, 

1994). Therefore, formation of nitro-PAHs via reactions of NO3 at nighttime under certain circumstances 

can be significant. 

The third process of nitro-PAH formation in the atmosphere is nitration of PAHs by NO2/N2O5 in 

the presence of trace amounts of HNO3 in both gas and particle phases. This mechanism could be 

operative throughout the day and night (Pitts, 1983, 1985a,b; Grosjean et al., 1983; Ramdahl et al., 1984; 

Kamens et al., 1990). The formation of nitro-fluoranthenes was observed when adsorbed FL was exposed 

to gaseous N2O5, and the distribution of product NF isomers was 3- > 8- > 7- > 1- NF (Pitts et al., 

1985a,b). The proposed mechanism for this reaction was an ionic electrophilic nitration by nitronium ion 

(NO2

+

). It was speculated that N2O5 became ionized prior to the reaction with FL (Zielinska et al., 1986). 

Only 1NP was observed for the reaction of PY with N2O5 on filters (Pitts et al., 1985b). Compared to the 

reactions of OH and NO3, nitration of PAHs by NO2/N2O5 is less important. 

Measurements of nitro-PAHs in ambient air provide evidence for the proposed reaction mechanism, 

i.e. the reactions of OH and NO3 radicals with PAHs are the major sources of nitro-PAHs (Bamford and 

Baker, 2003; Reisen and Arey, 2005; and references therein). 2NF is a ubiquitous component of ambient 

POM, much higher than 1NP, itself a marker of combustion sources. Nitro-PAH isomer ratios show 

strong seasonality. For instance, the mean ratios of 2NF/1NP were higher in summer than in winter 

(Bamford et al., 2003; Reisen and Arey, 2005), indicating that reactions of OH and NO3 with FL are the 

major sources of nitro-PAHs in ambient air in summer. The ratio of 2NF/1NP was lower in winter than in 

summer because of lower OH concentrations and, therefore, less production of 2NF via atmospheric 

reactions. A ratio of 1NP/2NF greater than 1 was observed in locations with major contributions from 

vehicle emissions (Dimashki et al., 2000; Feilberg et al., 2001). In addition, the ratio of 2NF/2NP was 

also used to evaluate the contribution of OH and NO3 initiated reactions to the ambient nitro-PAHs 

(Bamford et al., 2003; Reisen and Arey, 2005). 

The concentrations for most nitro-PAHs found in ambient air are much lower than 1 pg/m3

, except 

NNs, 1NP, and 2NF, which can be present at several pg/m3

. These levels are much lower (~2 to ~1000 

times lower) than their parent PAHs. However, nitro-PAHs are much more toxic than PAHs (Durant 

et al., 1996; Grosovsky et al., 1999; Salmeen et al., 1982; Tokiwa et al., 1998; Tokiwa and Ohnishi, 

1986). Moreover, most nitro-PAHs are present in particles with a mass median diameter <0.1 μm. 
2-13

Esteve et al. (2006) examined the reaction of gas-phase NO2 and OH radicals with various PAHs 

adsorbed onto model diesel particulate matter (SRM 1650a, NIST). Using pseudo second order rate 

coefficients, they derived lifetimes for conversion of the particle-bound PAHs to nitro-PAHs of a few 

days (for typical urban NO2 levels of 20 ppb). They also found that the rates of reaction of OH with the 

PAHs were about four orders of magnitude larger than for the reactions involving NO2. However, since 

the concentrations of NO2 used above are more than four orders of magnitude larger than those for OH 

(106

-107/cm3), they concluded that the pathway involving NO2 is expected to be favored over that 

involving OH radicals. Consistent with the importance of the gas-phase formation of NPAHS, both the 

mutagenic potency of PM and the content of NPAHs in PM vary by particle size, and are higher in the 

submicron size range (Xu and Lee, 2000; Kawanaka et al., 2004). 

The major loss process of nitro-PAHs is photodecomposition (Fan et al., 1996; Feilberg et al., 

1999; Feilberg and Nielsen, 2001), with lifetimes on the order of hours. However, lacking direct UV light 

sources indoors, nitro-PAHs are expected have a longer lifetimes (days) indoors than outdoors; and may 

therefore pose increased health risks. Many nitro-PAHs are semi- or nonvolatile organic compounds. As 

stated above, indoor environments have much greater surface areas than outdoors. Thus, it is expected 

that gas/particle distribution of nitro-PAHs indoors will be different from those in ambient air. A 

significant portion of nitro-PAHs will probably be adsorbed by indoor surfaces, such as carpets, leading 

to different potential exposure pathways to nitro-PAHs in indoor environments. The special 

characteristics of indoor environments, which can affect the indoor chemistry and potential exposure 

pathways significantly, should be taken into consideration when conducting exposure studies of nitroPAHs. 

Reaction with OH and NO3 radicals is a major mechanism for removing gas-phase PAHs, with OH 

radical initiated reactions predominating depending on season (Vione et al., 2004; Bamford et al., 2003). 

Particle-bound PAH reactions occur but tend to be slower. Nitronaphthalenes tend to remain in the vapor 

phase, but because phase partitioning depends on ambient temperature, in very cold regions these species 

can condense (Castells et al., 2003) while the higher molecular weight PAHs such as the nitroanthracenes, 

nitrophenantrenes and nitrofluoranthenes condense in and on PM (Ciganek et al., 2004; Cecinato, 2003). 

AX2.2.5. Multiphase Chemical Processes Involving NOX and Halogens 

Four decades of observational data on O3 in the troposphere have revealed numerous anomalies not 

easily explained by gas-phase HOX-NOX photochemistry. The best-known example is the dramatic 

depletion of ground-level O3 during polar sunrise due to multiphase catalytic cycles involving inorganic 

Br and Cl radicals (Barrie et al., 1988; Martinez et al., 1999; Foster et al., 2001). Other examples of 

anomalies in tropospheric O3 at lower latitudes include low levels of O3 (<10 ppb) in the marine boundary 

layer (MBL) and overlying free troposphere (FT) at times over large portions of the tropical Pacific (Kley 

et al., 1996), as well as post-sunrise O3 depletions over the western subtropical Pacific Ocean (Nagao 

et al., 1999), the temperate Southern Ocean (Galbally et al., 2000), and the tropical Indian Ocean 

(Dickerson et al., 1999). The observed O3 depletions in near-surface marine air are generally consistent 

with the model-predicted volatilization of Br2, BrCl, and Cl2 from sea salt aerosols through autocatalytic 

halogen “activation” mechanisms (e.g., Vogt et al., 1996; Von Glasow et al., 2002a) involving these 

aqueous phase reactions. 

 (AX2.2-26) 

 (AX2.2-27) 
2-14

 (AX2.2-28) 

In polluted marine regions at night, the heterogeneous reaction 

 (AX2.2-29) 

may also be important (Finlayson-Pitts et al., 1989; Behnke et al., 1997; Erickson et al., 1999). Diatomic 

bromine, BrCl, Cl2, and ClNO2 volatilize and photolyze in sunlight to produce atomic Br and Cl. The 

acidification of sea salt aerosol via incorporation of HNO3 (and other acids) leads to the volatilization of 

HCl (Erickson et al., 1999), e.g. 

 (AX2.2-30) 

and the corresponding shift in phase partitioning can accelerate the deposition flux to the surface of total 

NO3 (Russell et al., 2003; Fischer et al., 2006). However, Pryor and Sorensen (2000) have shown that the 

dominant form of nitrate deposition is a complex function of wind speed. In polluted coastal regions 

where HCl from Reaction AX2.2-30 often exceeds 1 ppb, significant additional atomic Cl-

 is produced via 

 (AX2.2-31) 

(Singh and Kasting, 1988; Keene et al., 2007). Following production, Br and Cl atoms catalytically 

destroy O3 via 

 (AX2.2-32) 

 (AX2.2-33) 

 (AX2.2-34) 

where (X = Br and Cl). 

Formation of Br and Cl nitrates via 

 (AX2.2-35) 

and the subsequent reaction of XNO3 with sea salt and sulfate aerosols via 

 (AX2.2-36) 

and 

 (AX2.2-37) 
2-15

(where Y = Cl, Br, or I) accelerates the conversion of NOX to particulate NO3

-

 and thereby contributes 

indirectly to net O3 destruction (Sander et al., 1999; Vogt et al., 1999, Pszenny et al., 2004). 

Most XNO3 reacts via Reaction AX2.2-36 on sea salt whereas reaction AX2.2-37 is more important 

on sulfate aerosols. Partitioning of HCl on sulfate aerosols following Henry’s Law provides Cl-

 for 

Reaction AX2.2-37 to form BrCl. Product NO3

-

 from both Reactions AX2.2-36 and AX2.2-37 partitions 

with the gas-phase HNO3 following Henry’s Law. Because most aerosol size fractions in the MBL are 

near equilibrium with respect to HNO3 (Erickson et al., 1999; Keene et al., 2004), both sulfate and sea salt 

aerosol can sustain the catalytic removal of NOX and re-activation of Cl and Br with no detectable change 

in composition. The photolytic reduction of NO3

-

 in sea salt aerosol solutions recycles NOX to the gas 

phase (Pszenny et al., 2004). Halogen chemistry also impacts O3 indirectly by altering OH/HO2 ratios 

through the steps of Reactions AX2.2-33 and AX2.2-34 (e.g., Stutz et al., 1999; Bloss et al., 2005). 

In addition to O3 destruction via Reaction AX2.2-32, atomic Cl oxidizes hydrocarbons (HCs) 

primarily via hydrogen abstraction to form HCl vapor and organic products (Jobson et al., 1994; Pszenny 

et al., 2006). The enhanced supply of odd-H radicals from HC oxidation leads to net O3 production in the 

presence of sufficient NOX (Pszenny et al., 1993). Available evidence suggests that Cl-

 radical chemistry 

may be a significant net source for O3 in polluted coastal/urban air (e.g., Tanaka et al., 2003; Finley and 

Saltzman, 2006). 

An analogous autocatalyic O3 destruction cycle involving multiphase iodine (I) chemistry also 

operates in the marine atmosphere (Alicke et al., 1999, Vogt et al., 1999; McFiggans et al., 2000; 

Ashworth et al., 2002). In this case, the primary source of I is believed to be either photolysis of CH2I2, 

other I-containing gases (Carpenter et al., 1999; Carpenter, 2003), or perhaps I2 (McFiggans et al., 2004; 

Saiz-Lopez and Plane, 2004; McFiggans, 2005) emitted by micro-and macro flora. Sea salt and sulfate 

aerosols provide substrates for multiphase reactions that sustain the catalytic I-IO cycle. The IO radical 

has been measured by long-path (LP) and multi axis (MAX) differential optical absorption spectroscopy 

(DOAS) at Mace Head, Ireland; Tenerife, Canary Islands; Cape Grim, Tasmania; and coastal New 

England, USA. These studies have established that the IO radical has average daytime levels of about 1 

ppt with maxima up to 7 ppt (e.g., Allan et al., 2000; Pikelnaya et al., 2006). Modeling suggests that up to 

13% per day of O3 in marine air may be destroyed via multiphase iodine chemistry (McFiggans et al., 

2000). The reaction of IO with NO2 followed by uptake of INO3 into aerosols (analogous to Reactions 

AX2.2-32 through AX2.2-34) accelerates the conversion of NOX to particulate NO3

-

 and thereby 

contributes to net O3 destruction. The reaction IO + NO → I + NO2 also influences NOX cycling. 

Most of the above studies focused on halogen-radical chemistry and related influences on NOX

cycling in coastal and urban air. However, available evidence suggests that similar chemical 

transformations proceed in other halogen-rich tropospheric regimes. For example, Cl, Br, and/or I oxides 

have been measured at significant concentrations in near-surface air over the Dead Sea, Israel, the Great 

Salt Lake, Utah (e.g., Hebestreit et al., 1999; Stutz et al., 1999, 2002; Zingler and Platt, 2005), and the 

Salar de Uyuni salt pan in the Andes mountains (U. Platt, unpublished data, 2006); high column densities 

of halogenated compounds have also been observed from satellites over the northern Caspian Sea 

(Wagner et al., 2001; Hollwedel et al., 2004). The primary source of reactive halogens in these regions is 

thought to be from activation along the lives of that in Reactions AX2.2-26 through AX2.2-28 involving 

concentrated salt deposits on surface evaporative pans. High concentrations of BrO have also been 

measured in volcanic plumes (Bobrowski et al., 2003, Gerlach, 2004). Although virtually unexplored, the 

substantial emissions of inorganic halogens during biomass burning (Lobert et al., 1999; Keene et al., 

2006) and in association with crustal dust (Keene et al., 1999; Sander et al., 2003) may also support active 

halogen-radical chemistry and related transformations involving NOX downwind of sources. Finally, 

observations from satellites, balloons, and aircraft indicate that BrO is present in the free troposphere at 

levels sufficient to significantly influence photochemistry (e.g., Von Glasow et al., 2004). 
2-16

AX2.3. Transport of NOX in the Atmosphere 

Major episodes of high O3 concentrations in the eastern United Sates and in Europe are associated 

with slow moving high-pressure systems. High-pressure systems during the warmer seasons are 

associated with subsidence, resulting in warm, generally cloudless conditions with light winds. The 

subsidence results in stable conditions near the surface, which inhibit or reduce the vertical mixing of O3

precursors (NOX, VOCs, and CO). Photochemical activity is enhanced because of higher temperatures 

and the availability of sunlight. However, it is becoming increasingly apparent that transport of O3 and 

NOX and VOC from distant sources can provide significant contributions to local [O3] even in areas 

where there is substantial photochemical production. There are a number of transport phenomena 

occurring either in the upper boundary layer or in the free troposphere which can contribute to high O3

values at the surface. These phenomena include stratospheric-tropospheric exchange (STE), deep and 

shallow convection, low-level jets, and the so-called “conveyor belts” that serve to characterize flows 

around frontal systems. 

AX2.3.1. Convective Transport 

Crutzen and Gidel (1983), Gidel (1983), and Chatfield and Crutzen (1984) hypothesized that 

convective clouds played an important role in rapid atmospheric vertical transport of trace species and 

first tested simple parameterizations of convective transport in atmospheric chemical models. At nearly 

the same time, evidence was shown of venting the boundary layer by shallow, fair weather cumulus 

clouds (e.g., Greenhut et al., 1984; Greenhut, 1986). Field experiments were conducted in 1985 which 

resulted in verification of the hypothesis that deep convective clouds are instrumental in atmospheric 

transport of trace constituents (Dickerson et al., 1987). Once pollutants are lofted to the middle and upper 

troposphere, they typically have a much longer chemical lifetime and with the generally stronger winds at 

these altitudes, they can be transported large distances from their source regions. Transport of NOX from 

the boundary layer to the upper troposphere by convection tends to dilute the higher in the boundary layer 

concentrations and extend the NOX lifetime from less than 24 h to several days. Photochemical reactions 

occur during this long-range transport. Pickering et al. (1990) demonstrated that venting of boundary 

layer NOX by convective clouds (both shallow and deep) causes enhanced O3 production in the free 

troposphere. Dilution of NOX at the surface can often increase O3 production efficiency. Therefore, 

convection aids in the transformation of local pollution into a contribution to global atmospheric 

pollution. Downdrafts within thunderstorms tend to bring air with less NOX from the middle troposphere 

into the boundary layer. Lightning produces NO which is directly injected chiefly into the middle and 

upper troposphere. The total global production of NO by lightning remains uncertain, but is on the order 

of 10% of the total. 

AX2.3.2. Observations of the Effects of Convective Transport 

The first unequivocal observations of deep convective transport of boundary layer pollutants to the 

upper troposphere were documented by Dickerson et al. (1987). Instrumentation aboard three research 

aircraft measured CO, O3, NO, NOX, NOY, and hydrocarbons in the vicinity of an active mesoscale 

convective system near the Oklahoma/Arkansas border during the 1985 PRE-STORM experiment. Anvil 

penetrations about 2 h after maturity found greatly enhanced mixing ratios inside the cloud of all of the 

aforementioned species compared with outside it. NO mixing ratios in the anvil averaged 3 to 4 ppb, with 

individual 3-min observations reaching 6 ppb; boundary layer NOX was typically 1.5 ppb or less outside 

the cloud. Therefore, the anvil observations represent a mixture of boundary layer NOX and NOX

contributed by lightning. Luke et al. (1992) summarized the air chemistry data from all 18 flights during 

PRE-STORM by categorizing each case according to synoptic flow patterns. Storms in the maritime 
2-17

tropical flow regime transported large amounts of CO, O3, and NOY into the upper troposphere with the 

midtroposphere remaining relatively clean. During frontal passages a combination of stratiform and 

convective clouds mixed pollutants more uniformly into the middle and upper levels. 

Prather and Jacob (1997) and Jaegle et al. (1997) noted that precursors of HOX are also transported 

to the upper troposphere by deep convection, in addition to primary pollutants (e.g., NOX, CO, VOCs). 

The HOX precursors of most importance are water vapor, HCHO, H2O2, CH3OOH, and acetone. The 

hydroperoxyl radical is critical for oxidizing NO to NO2 in the O3 production process as described above. 

Over remote marine areas, the effects of deep convection on trace gas distributions differ from 

those over moderately polluted continental regions. Chemical measurements taken by the NASA ER-2 

aircraft during the Stratosphere-Troposphere Exchange Project (STEP) off the northern coast of Australia 

show the influence of very deep convective events. Between 14.5 and 16.5 km on the February 2-3, 1987 

flight, chemical profiles that included pronounced maxima in CO, water vapor, and CCN, and minima of 

NOY, and O3 (Pickering et al., 1993). Trajectory analysis showed that these air parcels likely were 

transported from convective cells 800-900 km upstream. Very low marine boundary layer mixing ratios 

of NOY and O3 in this remote region were apparently transported upward in the convection. A similar 

result was noted in Central Equatorial Pacific Experiment (CEPEX) (Kley et al., 1996) and in Indian 

Ocean Experiment (INDOEX) (DeLaat et al., 1999) where a series of ozonesonde ascents showed very 

low upper tropospheric O3 following deep convection. It is likely that similar transport of low-ozone 

tropical marine boundary layer air to the upper troposphere occurs in thunderstorms along the east coast 

of Florida. Deep convection occurs frequently over the tropical Pacific. Low-ozone and low-NOX

convective outflow likely will descend in the subsidence region of the subtropical eastern Pacific, leading 

to some of the cleanest air that arrives at the west coast of the United States. 

The discussion above relates to the effects of specific convective events. Observations have also 

been conducted by NASA aircraft in survey mode, in which the regional effects of many convective 

events can be measured. The Subsonic Assessment Ozone and Nitrogen Oxides Experiment (SONEX) 

field program in 1997 conducted primarily upper tropospheric measurements over the North Atlantic. The 

regional effects of convection over North America and the Western Atlantic on upper tropospheric NOX

were pronounced (Crawford et al., 2000; Allen et al., 2000). A discussion of the results of model 

calculations of convection and its effects can be found in Section AX2.7. 

AX2.3.3. Effects on Photolysis Rates and Wet Scavenging 

Thunderstorm clouds are optically very thick, and, therefore, have major effects on radiative fluxes 

and photolysis rates. Madronich (1987) provided modeling estimates of the effects of clouds of various 

optical depths on photolysis rates. In the upper portion of a thunderstorm anvil, photolysis is likely to be 

enhanced by a factor of 2 or more due to multiple reflections off the ice crystals. In the lower portion and 

beneath the cloud, photolysis is substantially decreased. With enhanced photolysis rates, the NO/NO2

ratio in the upper troposphere is driven to larger values than under clear-sky conditions. 

Thunderstorm updraft regions, which contain copious amounts of water, are regions where efficient 

scavenging of soluble species can occur (Balkanski et al., 1993). NO2 itself is not very soluble and 

therefore wet scavenging is not a major removal process for it. However, a major NOX reservoir species, 

HNO3 is extremely soluble. Very few direct field measurements of the amounts of specific trace gases 

that are scavenged in storms are available. Pickering et al. (2001) used a combination of model estimates 

of soluble species that did not include wet scavenging and observations of these species from the upper 

tropospheric outflow region of a major line of convection observed near Fiji. Over 90% of the NOX in the 

outflow air appeared to have been removed by the storm; about 50% of CH3OOH and about 80% of 

HCHO had been lost. 

Convective processes and small-scale turbulence transport pollutants both upward and downward 

throughout the planetary boundary layer and the free troposphere. Ozone and its precursors (NOX, CO, 

and VOCs) can be transported vertically by convection into upper part of the mixed layer on one day, 
2-18

then transported overnight as a layer of elevated mixing ratios, perhaps by a nocturnal low-level jet, and 

then entrained into a growing convective boundary layer downwind and brought back to the surface. 

Because NO and NO2 are only slightly soluble, they can be transported over longer distances in the 

gas phase than can more soluble species which can be depleted by deposition to moist surfaces, or taken 

up more readily on aqueous surfaces of particles. During transport, they can be transformed into reservoir 

species such as HNO3, PANs, and N2O5. These species can then contribute to local NOX concentrations in 

remote areas. For example, it is now well established that PAN decomposition provides a major source of 

NOX in the remote troposphere (Staudt et al., 2003). PAN decomposition in subsiding air masses from 

Asia over the eastern Pacific could make an important contribution to O3 and NOX enhancement in the 

United States (Kotchenruther et al., 2001; Hudman et al., 2004). Further details about mechanisms for 

transporting ozone and its precursors were described at length in the 2006 AQCD for Ozone. 

AX2.4. Sources and Emissions of NOX

NOX has natural and anthropogenic sources. In Section AX2.4.1, interactions of NOX with the 

terrestrial biosphere are discussed. Because of the tight coupling between processes linking emissions and 

deposition, they are discussed together. Additional sources are described in Section AX2.4.2. Field 

studies evaluating emissions inventories are discussed in Section AX2.4.2.4. 

AX2.4.1. Interactions of NOX with the Biosphere 

NOX affect vegetated ecosystems, and in turn the atmospheric chemistry of NOX is influenced by 

vegetation. Extensive research on nitrogen inputs from the atmosphere to forests was conducted in the 

1980s as part of the Integrated Forest Study, and is summarized by Johnson and Lindberg (1992). The 

following sections discuss sources of NOX from soil, deposition of NOX to foliage, reactions with 

biogenic hydrocarbons, and ecological effects of nitrogen deposition. 

NO from soil metabolism is the dominant but not exclusive source of NOX from the biosphere to 

the atmosphere. As noted below, our understanding of NO2 exchange with vegetation suggests that there 

should be emission of NO2 from foliage when ambient concentrations are less than about 1 ppb. However, 

Lerdau et al. (2000) have pointed out that present understanding of the global distribution of NOX is not 

consistent with a large source that would be expected in remote forests if NO2 emission was important 

when atmospheric concentrations were below the compensation point. 

The pathways for nitrification and denitrification include two gas-phase intermediates, NO and 

N2O, some of which can escape. While N2O is of interest for its greenhouse gas potential and role in 

stratospheric chemistry it is not considered among the reactive NOX species important for urban and 

regional air quality and will not be discussed further. Temperature and soil moisture are critical factors 

that control the rates of reaction and importantly the partitioning between NO and N2O which depend on 

oxygen levels: in flooded soils where oxygen levels are low, N2O is the dominant soil nitrogen gas; as soil 

dries, allowing more O2 to diffuse, NO emissions increase. In very dry soils, microbial activity is 

inhibited and emissions of both N2O and NO decrease. Nitrogen metabolism in soil is strongly dependent 

on the substrate concentrations. Where nitrogen is limiting, nitrogen is efficiently retained and little 

gaseous nitrogen is released. Where nitrogen is in excess of demand, gaseous nitrogen emissions increase; 

consequently, soil NO emissions are highest in fertilized agriculture and tropical soils (Davidson and 

Kingerlee, 1997; Williams et al., 1992). 

Several reactive nitrogen are species are deposited to vegetation, among them, HNO3, NO2, PAN, 

and organic nitrates. Deposition of HNO3 appears to be relatively simple. Field observations based on 

concentration gradients and recently using eddy covariance demonstrate rapid deposition that approaches 

the aerodynamic limit (as constrained by atmospheric turbulence) in the Wesely (1989) formulation based 
2-19

on analogy to resistance. Surface resistance for HNO3 uptake by vegetation is negligible. Deposition rates 

are independent of leaf area or stomatal conductance, implying that deposition occurs to branches, soil, 

and leaf cuticle as well as internal leaf surfaces. 

Deposition velocities (Vd) typically exceed 1 cm/s and exhibit a daily pattern controlled by 

turbulence characteristics such that midday maximum and lower values at night when there is stable 

boundary layer. 

Nitrogen dioxide interaction with vegetation is more complex. Application of 15N-labeled NO2

demonstrates that NO2 is absorbed and metabolized by foliage (Siegwolf et al., 2001; Möcker et al., 1998; 

Segschneider et al., 1995; Weber, et al., 1995). Exposure to NO2 induces nitrate reductase (Weber et al., 

1995, 1998), a necessary enzyme for assimilating oxidized nitrogen. Understanding NO2 interactions with 

foliage is largely based on leaf cuvette and growth chamber studies, which expose foliage or whole plants 

to controlled levels of NO2 and measure the fraction of NO2 removed from the chamber air. A key finding 

is that the fit of NO2 flux to NO2 concentration, has a non-zero intercept, implying a compensation point 

or internal concentration. In studies at very low NO2 concentrations emission from foliage is observed 

(Teklemariam and Sparks, 2006). Evidence for a compensation point is not solely based on the fitted 

intercept. NO2 uptake rate to foliage is clearly related to stomatal conductance. Internal resistance is 

variable, and may be associated with concentrations of reactive species such as ascorbate in the plant 

tissue that react with NO2 (Teklemariam and Sparks, 2006). Foliar NO2 emissions show some dependence 

on nitrogen content (Teklemariam and Sparks, 2006). Internal NO2 appears to derive from plant nitrogen 

metabolism. 

Two approaches to modeling NO2 uptake by vegetation are the resistance-in-series analogy which 

considers flux (F) as the product of concentration (C) and Vd, where is related to the sum of aerodynamic, 

boundary layer, and internal resistances (Ra, Rb, and Rc; positive fluxes are from atmosphere to foliage) 

 (AX2.4-1) 

 (AX2.4-2) 

Ra and Rb and controlled by turbulence in the mixed layer; Rc is dependent on characteristics of the 

foliage and other elements of the soil, and may be viewed as 2 combination of resistance internal to the 

foliage and external on the cuticle, soils, and bark. This approach is amenable to predicting deposition in 

regional air quality models (Wesely, 1989). Typically, the NO2, Vd is less than that for O3, due to the 

surface’s generally higher resistance to NO2 uptake, consistent with NO2’s lower reactivity. 

Alternatively, NO2 exchange with foliage can be modeled from a physiological viewpoint where 

the flux from the leaf is related to the stomatal conductance and a concentration gradient between the 

ambient air and interstitial air in the leaf. This approach best describes results for exchange with 

individual foliage elements, and is expressed per unit leaf (needle) area. While this approach provides 

linkage to leaf physiology, it is not straightforward to scale up from the leaf to ecosystem scale 

 (AX2.4-3) 

This model implicitly associates the compensation point with a finite internal concentration. 

Typically observed compensation points are around 1 ppb. Finite values of internal NO2 concentration are 

consistent with metabolic pathways that include oxides of nitrogen. In this formulation, the uptake will be 

linear with NO2 concentration, which is typically observed with foliar chamber studies. 

Several studies have shown the UV dependence of NO2 emission, which implies some photoinduced surface reactions that release NO2. Rather than model this as a UV-dependent internal 

concentration, it would be more realistic to add an additional term to account for emission that is 

dependent on light levels and other surface characteristics 
2-20

 (AX2.4-4) 

The mechanisms for surface emission are discussed below. Measurement of NO2 flux is 

confounded by the rapid interconversion of NO, NO2, and O3 (Gao et al., 1991). 

PAN is phytotoxic, absorbed largely at the leaf. Observations based on inference from 

concentration gradients and rates of decline at night (Shepson et al., 1992; Schrimpf et al., 1996) and leaf 

chamber studies (Teklemariam and Sparks, 2004) have indicated that PAN uptake is slower than that of 

O3; however, recent work in coniferous canopy with direct eddy covariance PAN flux measurements 

indicated a Vd more similar to that of O3. Uptake of PAN is under stomatal control, has a non-zero 

deposition at night, and is influenced by leaf wetness (Turnipseed et al., 2006). On the other hand, flux 

measurements determined by gradient methods over a grass surface showed a Vd closer to 0.1 cm/s, with 

large uncertainty (Doskey et al., 2004). Whether the discrepancies are methodological or indicate intrinsic 

differences between different vegetation is unknown. Uptake of PAN is smaller than its thermal 

decomposition in all cases. 

The biosphere also interacts with NOX through hydrocarbon emissions and their subsequent 

reactions to form multi-functional organic nitrates. Isoprene nitrates are an important class of these. 

Isoprene reacts with OH to form a radical that adds NO2 to form a hydroxyalkyl nitrate. The combination 

of hydroxyl and nitrate functional group makes these compounds especially soluble with low vapor 

pressures; they likely deposit rapidly (Shepson et al., 1996; Treves et al., 2000). Many other unsaturated 

hydrocarbons react by analogous routes. Observations at Harvard Forest show a substantial fraction of 

total NOY not accounted for by NO, NO2 and PAN, which is attributed to the organic nitrates (Horii et al., 

2006, Munger et al., 1998). Furthermore, the total NOY flux exceeds the sum of HNO3, NOX, and PAN, 

which implies that the organic nitrates are a substantial fraction of nitrogen deposition. Other observations 

that show evidence of hydoxyalkyl nitrates include those of Grossenbacher et al. (2001) and Day et al. 

(2003). 

Formation of the hydroxyalkyl nitrates occurs following OH attack on VOCs. In some sense, this 

mechanism is just an alternate pathway for OH to react with NOX to form a rapidly depositing species, 

because if VOC were not present, OH would be available to react with any NO2 present to form HNO3. 

HNO2 formation on vegetative surfaces at night has long been observed based on measurements of 

positive gradients (Harrison and Kitto, 1994). Surface reactions of NO2 enhanced by moisture were 

proposed to explain these results. Production was evident at sites with high ambient NO2; at low 

concentration, uptake of HONO exceeded the source. Daytime observations of HONO when rapid 

photolysis is expected to deplete ambient concentrations to very low levels implies a substantial source of 

photo-induced HONO formation at a variety of forested sites where measurements have been made. 

Estimated source strengths are 200-1800/ppt·h in the surface layer (Zhou et al., 2002a, 2003), which is 

about 20 times faster than all nighttime sources. HNO2 sources could be important to OH/HO2 budgets as 

HONO is rapidly photolyzed by sunlight to OH and NO. Additional evidence of light-dependent reactions 

to produce HONO comes from discovery of a HONO artifact in pyrex sample inlet lines exposed to 

ambient light. Either covering the inlet or washing it eliminated the HONO formation (Zhou et al., 

2002b). Similar reactions might serve to explain observations of UV-dependent production of NOX in 

empty foliar cuvettes that had been exposed to ambient air (Hari et al., 2003; Raivonen et al., 2003). 

Production of HONO in the dark is currently believed to occur via a heterogeneous reaction 

involving NO2 on wet surfaces (Jenkin et al., 1988; Pitts et al., 1984; He et al., 2006; Sakamaki et al., 

1983), and it is proposed that the mechanism has first-order dependence in both NO2 and H2O (Kleffmann 

et al., 1998; Svensson et al., 1987) despite the stoichiometry. However, the molecular pathway of the 

mechanism is still under debate. Jenkin et al. (1988) postulated a H2O·NO2 water complex reacting with 

gas phase NO2 to produce HONO, which is inconsistent with the formation of an N2O4 intermediate 

leading to HONO as proposed by Finlayson-Pitts et al. (2003). Another uncertainty is whether the 

reaction forming HONO is dependent on water vapor (Svensson et al., 1987; Stutz et al., 2004b) or water 

adsorbed on surfaces (Kleffmann et al., 1998). Furthermore, the composition of the surface and the 
2-21

available amount of surface or surface-to-volume ratio can significantly influence the HONO production 

rates (Kaiser and Wu, 1977; Kleffmann et al., 1998; Svensson et al., 1987), which may explain the 

difference in the rates observed between laboratory and atmospheric measurements. 

There is no consensus on a chemical mechanism for photo-induced HONO production. Photolysis 

of HNO3 or NO3

-

 absorbed on ice or in surface water films has been proposed (Honrath et al., 2002; 

Ramazan et al., 2004; Zhou et al., 2001, 2003); but alternative pathways include NO2 interaction with 

organic surfaces such as humic substances (George et al., 2005; Stemmler et al., 2006). Note that either 

NO3

-

 photolysis or heterogeneous reaction of NO2 are routes for recycling deposited NOX back to the 

atmosphere in an active form. NO3 photolysis would return nitrogen that heretofore was considered 

irreversibly deposited, surface reactions between NO2 and water films or organic molecules would 

decrease the effectiveness of observed NO2 deposition if the HONO were re-emitted. 

AX2.4.1.1. NO2 and HNO3 Flux Data from Harvard Forest 

Harvard Forest is a rural site in central Massachusetts, where ambient NOX, NOY, and other 

pollutant concentrations and fluxes of total NOY have been measured since 1990 (Munger et al., 1996). 

An intensive study in 2000 utilized a Tunable Diode Laser Absorption Spectrometer (TDLAS) to measure 

NO2 and HNO3. TDLAS has an inherently fast response, and for species such as NO2 and HNO3 with 

well-characterized spectra it provides an absolute and specific measurement. Absolute concentrations of 

HNO3 were measured, and the flux inferred based on the dry deposition inferential method that uses 

momentum flux measurements to compute a deposition velocity and derives an inferred flux (Wesely and 

Hicks, 1977; Hicks et al., 1987). Direct eddy covariance calculations for HNO3 were not possible because 

the atmospheric variations were attenuated by interaction with the inlet walls despite very short residence 

time and use of fluorinated silane coatings to make the inlet walls more hydrophobic. Nitrogen oxide 

response was adequate to allow both concentration and eddy covariance flux determination. 

Simultaneously, NO and NOY eddy covariance fluxes were determined with two separate O3

chemiluminescence detectors, one equipped with a H2-gold catalyst at the inlet to convert all reactive 

nitrogen compounds to NO. Additionally, the measurements include concentration gradients for NO, 

NO2, and O3 over several annual cycles to examine their vertical profiles in the forest canopy. 

Overall, the results show typical NO2 concentrations of 1 ppb under clean-air conditions and mean 

concentrations up to 3 ppb at night and 1 ppb during daytime for polluted conditions. Net positive fluxes 

(emission) of NO2 were evident in the daytime and negative fluxes (deposition) were observed at night 

(Figure AX2.4-1). Nitric oxide fluxes were negative during the daytime and near zero at night. 
2-22

Source: Horii et al. (2004). 

Figure AX2.4-1. Diel cycles of median concentrations (upper panels) and fluxes (lower panels). These are 

for the Northwest (clean sector, left panels) and Southwest (polluted sector, right panels) 

wind sectors at Harvard Forest, April-November, 2000, for NO, NO2, and O3/10. NO and O3

were sampled at a height of 29 m, and NO2 at 22 m. Vertical bars indicate 25th and 27th 

quartiles for NO and NO2 measurements. NO2 concentration and nighttime deposition are 

enhanced under southwesterly conditions, as are O3 and the morning NO maximum. 

In part the opposite NO and NO2 fluxes are simply consequences of variable NO/NO2 distributions 

responding to vertical gradients in light intensity and O3 concentration, which resulted in no net flux of 

NOX (Gao et al., 1993). In the Harvard Forest situation, the NO and NO2 measurements were not at the 

same height above the canopy, and the resulting differences derive at least in part from the gradient in 

flux magnitude between the two inlets (Figure AX2.4-2). 
2-23

Source: Horii (2002). 

Figure AX2.4-2. Simple NOX photochemical canopy model outputs. Left panel, concentrations of NO 

(dashed) and NO2 (solid); right, fluxes of NO (dashed) and NO2 (solid). Symbols indicate 

measurement heights for NO (29m) and NO2 (22m) at Harvard Forest. The model solves the 

continuity equation for NO concentration at 200 levels, d/dz(-Kc(dNO/dz)) = PNO - LNO, 

where PNO = [NO]/t1, LNO = [NO]/t2, and zero net deposition or emission of NOX is allowed. 

NOX (NO + NO2) is normalized to 1ppb. t1 = 70s in this example. Due to the measurement 

height difference, observed upward NO2 flux due to photochemical cycling alone should be 

substantially larger than observed downward NO flux attributable to the same process. 

At night, when NO concentrations are near 0 due to titration by ambient O3 there is not a flux of 

NO to offset NO2 fluxes. Nighttime data consistently show NO2 deposition (Figure AX2.4-3), which 

increases with increasing NO2 concentrations. Concentrations above 10 ppb were rare at this site, but the 

few high NO2 observations suggest a nonlinear dependence on concentration. The data fit a model with 

Vd of −0.08 plus an enhancement term that was second order in NO2 concentration. The second order 
2-24

term implies that NO2 deposition rates to vegetation in polluted urban sites would be considerably larger 

than what was observed at this rural site. 

Source: Horii et al. (2004). 

Figure AX2.4-3. Hourly (dots) and median nightly (pluses) NO2 flux vs. concentration. The results of leastsquares fit on the hourly data (curve). The flux is expressed in units of concentration times 

velocity (nmol/mol·cm·s) in order to simplify the interpretation of the coefficients in the 

least-squares fit. Pressure and temperature corrections have been taken into account in the 

conversion from density to mixing ratio. 

After accounting for the NO-NO2 null cycle the net NOX flux could be derived. Overall, there was a 

net deposition of NOX during the night and essentially zero flux in the day, with large variability in the 

magnitude and sign of individual flux observations. For the periods with [NO2] > 2 ppb, deposition was 

always observed. These canopy-scale field observations are consistent with a finite compensation point 

for NO2 in the canopy that offsets foliar uptake or even reverses it when concentrations are especially 

low. At concentrations above the compensation point, NOX is absorbed by the canopy. Examination of 

concentration profiles corroborates the flux measurements (Figure AX2.4-4). During daytime for lowNOX conditions, there is a local maximum in the concentration profile near the top of the canopy where 

O3 has a local minimum, which is consistent with foliar emission or light-dependent production of NOX in 

the upper canopy. Depletion is evident for both NOX and O3 near the forest floor. Air reaching the ground 
2-25

has passed through the canopy where uptake is efficient and the vertical exchange rates near the ground 

are slow. At night, the profiles generally decrease with decreasing height above the ground, showing only 

uptake. At higher concentrations, the daytime NOX concentrations are nearly constant through the canopy; 

no emission is evident from the sunlit leaves. 

Source: Horii et al. (2004). 

Figure AX2.4-4. Averaged profiles at Harvard Forest. These give some evidence of some NO2 input near the 

canopy top from light-mediated ambient reactions, or emission from open stomates. 
2-26

Source: Horii et al. (2006). 

Figure AX2.4-5. Summer (June-August) 2000 median concentrations (upper panels), fractions of NOY

(middle panels), and fluxes (lower panels) of NOY and component species. These are 

separated by wind direction (Northwest on the left and Southwest on the right). Vertical 

lines in the flux panels show 25th and 75th quartiles of F(NOY) and F(HNO3); negative fluxes 

represent deposition; F(NOX) is derived from eddy covariance F(NO) and F(NO2) 

measurements (corrected for photochemical cycling), F(HNO3) is inferred, and F(NOY) was 

measured by eddy covariance. The sum of NOX, HNO3, and PAN accounts for all of the NOY

concentration and flux for Northwesterly (unpolluted background) flows, whereas up to 50% 

of NOY and F(NOY) under Southwesterly flows are in the form of reactive nitrogen species 

whose fluxes are not measured or estimated here. 

Figure AX2.4-5 compares observed fluxes of all the observed species. The measured NOX and 

estimated PAN fluxes are small relative to the observed total NOY flux. In clean air, HNO3 accounts for 

nearly all the NOY flux and the sum of all measured species is about equal to the NOY concentration. 

However, in polluted conditions, unmeasured species are up to 25% of the NOY, and HNO3 fluxes cannot 
2-27

account for all the total NOY flux observed. Likely these unmeasured NOY species are hydroxyalkyl 

nitrates and similar compounds and are rapidly deposited. Although NO2 uptake may be important to the 

plant, because it is an input directly to the interior of foliage that can be used immediately in plant 

metabolism, it is evidently not a significant part of overall nitrogen deposition to rural sites. The 

deposition of HNO3 and multifunctional organic nitrates are the largest elements of the nitrogen dry 

deposition budget. Two key areas of remaining uncertainty are the production of HONO over vegetation 

and the role of very reactive biogenic VOCs. HONO is important because its photolysis is a source of OH 

radicals, and its formation may represent an unrecognized mechanism to regenerate photochemically 

active NOX from nitrate that had been considered terminally removed from the atmosphere. 

AX2.4.2. Emissions of NOX

Estimated annual emissions of NOX for 2002 (U.S. Environmental Protection Agency, 2006) are 

shown at end of Annex AX2, in Table AX2.4-1. Information relevant for estimating emissions of criteria 

pollutants is given in U.S. Environmental Protection Agency (1999). Discussions of uncertainties in 

current emissions inventories and strategies for improving them can be found in NARSTO (2005). 

As can be seen from the table, combustion by stationary sources, such as electrical utilities and 

various industries, accounts for roughly half of total anthropogenic emissions of NOX. Mobile sources 

account for the other half, with highway vehicles representing the major mobile source component. 

Approximately half the mobile source emissions are contributed by diesel engines, the remainder are 

emitted by gasoline-fueled vehicles and other sources. 

Emissions of NOX associated with combustion arise from contributions from both fuel nitrogen and 

atmospheric nitrogen. Combustion zone temperatures greater than about 1300 K are required to fix 

atmospheric N2

 (AX2.4-5) 

Otherwise, NO can be formed from fuel N according to this reaction 

 (AX2.4-6) 

In addition to NO formation by the schematic reactions given above, some NO2 and CO are also 

formed depending on temperatures, concentrations of OH and HO2 radicals and O2 levels. Fuel nitrogen is 

highly variable in fossil fuels, ranging from 0.5 to 2.0 percent by weight (wt %) in coal to 0.05% in light 

distillates (e.g., diesel fuel), to 1.5 wt % in heavy fuel oils (UK AQEG, 2004). 

AX2.4.2.1. Emissions of NO2 from Motor Vehicles 

NO2 in exhaust gasoline-powered engines generally comprises <1-3% of total NOX (Heeb et al., 

2008; Hilliard and Wheeler, 1979), according to dynamometer studies. Some European studies have 

reported NO2/NOX ratios of 15% or higher from gasoline vehicles based on integrated measurements from 

Tedlar bags (Lenner, 1987; Soltic and Weilenmann, 2003). However, subsequent studies suggesting that 

NO-to-NO2 conversion can occur within bag sample of a diluted exhaust have led the groups performing 

these measurement to revise their measurement techniques and avoid the use of Tedlar bag samples 

(Alvarez et al., 2007; Heeb et al., 2008). As a result, dynamometer-based measurements generally 

indicate that in the absence of post-tailpipe transformation, NO2 comprises, at most, a few percent of total 

NOX in gasoline exhaust. 
2-28

Emission control devices for diesel engines can increase the fraction of exhaust NOX emitted as 

NO2. The two major types that can increase NO2/NOX ratios include diesel oxidation catalysts (DOCs) 

and catalyzed diesel particle filters (CDPFs). Both are considered “after treatment” devices, in that they 

affect exhaust after it leaves an engine. In both DOCs and CDPFs, the effect on NO2/NOX ratios depends 

on the composition of the catalytic washcoats. 

DOCs are flow-through devices consisting of porous ceramic with a catalytic washcoat. Catalysis 

within the DOC reduces exhaust concentrations of organic material, including PM and hydrocarbons. In 

doing so, DOCs convert NO to NO2, the excess of which may be emitted through the tailpipe. 

Like DOCs, CDPFs also may be a part of the emission control system for diesel vehicles. CDPFs 

can reduce PM emissions from a diesel vehicle by ≥ 90%. They commonly consist of a DOC and a 

particle trapping filter or a particle trap with a catalytic wash coat, generally containing noble metals. As 

exhaust gases pass through a CDPF, soot and organic material in the exhaust becomes trapped in the 

filter. The DOC or the filter’s catalytic washcoat converts exhaust NO to NO2 to use in oxidizing the 

particulate matter trapped by the filter. Excess NO2 is emitted through the tailpipe. 

 Widespread use of CDPFs in European cities has been identified as a cause for increasing 

NO2/NOX ratios at urban air quality monitors. Carslaw et al. (2007a) report upward trends in roadside 

concentrations of NO2 between 2002 and 2006 at Marylebone Road, site on the edge of central London, 

UK. Using ambient data analysis methods developed in earlier publications (Carslaw et al., 2005a), 

Carslaw et al. (2007b) estimated the NO2/NOX emission ratio from the London vehicle fleet over time. 

NO2/NOX emission ratios increased significantly along Marylebone Road between 2002 and 2006, from 

approximately 10% by volume to over 20% by volume. Using multivariate regression, Carslaw et al. 

(2005b) estimated that the largest contributor to the increasing NO2/NOX emission ratios was the retrofit 

of London transit buses with catalyzed diesel particle filters (CDPF). 

 Kessler et al. (2006) employed the methods developed by Carslaw et al. (2005a) to estimate the 

NO2/NOX ratio from traffic in Baden-Württemberg, Germany between 1995 and 2005. The estimates 

increase from approximately 5% in 1995 to over 20% in 2005. The investigators attributed the increase to 

an increase in the use of oxidation catalysts in diesel-fueled passenger cars. 

 Ayala et al. (2002) compared NO2/NOX ratios in a bus before and after installation of a CDPF. 

NO2 emissions before retrofit were 0.92-2.14 g/mi across different test cycles, comprising 3-9% of total 

NOX. After retrofit, NO2 emissions were 9.0-24.0 g/mi, comprising 39-49% of total NOX. 

 Two U.S. studies where investigators sampled the plumes of heavy-duty diesel vehicles found 

that, vehicles retrofitted with CDPFs had lower PM emissions and higher NO2/NOX ratios compared to 

diesel vehicles without CDPFs. Shorter et al. (2005) conducted a “chase” experiment of vehicles in New 

York City, including metropolitan transit buses equipped with CDPFs. Overall, while following CDPFequipped buses, approximately one-third of exhaust NOX was NO2. In contrast, less than 10% of NOX was 

NO2 behind city buses lacking a CDPF. Kittleson et al. (2006) used an on-road laboratory to sample the 

exhaust plumes of a truck under highway cruise conditions, equipped with one of two different CDPFs. 

NO2/NOX ratios for CDPF-treated exhaust under highway cruise conditions ranged from 59-70%. The 

comparison of these two studies illustrates the role that temperature can play in increasing CDPF catalytic 

activity. 

The ratio of NO2 to NOX in primary emissions ranges from 3 to 5 % from gasoline engines, 5 to 

12% from heavy-duty diesel trucks, 5 to 10% from vehicles fueled by compressed natural gas and from 5 

to 10% from stationary sources. In addition to NOX, motor vehicles also emit HONO, with ratios of 

HONO to NOX ranging from 0.3% in the Caldecott Tunnel, San Francisco Bay (Kirchstetter and Harley, 

1996) to 0.5 to 1.0% in studies in the United Kingdom (UK AQEG, 2004). 

The NO2 to NOX ratios in emissions from turbine jet engines are as high as 32 to 35 % during taxi 

and takeoff (1993 AQCD for NOX). Sawyer et al. (2000) have reviewed the factors associated with NOX

emissions by mobile sources. Marine transport represents a minor source of NOX, but constitutes a larger 

source in the EU where it is expected to represent about two-thirds of land-based sources (UK AQEG, 

2004). 
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AX2.4.2.2. NOX Emissions from Natural Sources 

Soil 

Emission rates of NO from cultivated soil depend mainly on fertilization levels and soil 

temperature. About 60% of the total NOX emitted by soils occurs in the central corn belt of the United 

States. The oxidation of NH3, emitted mainly by livestock and soils, leads to the formation of NO, as do 

NH4

+

 and NO3

-

 fertilizers on soils. Estimates of emissions from natural sources are less certain than those 

from anthropogenic sources. On a global scale, the contribution of soil emissions to the oxidized nitrogen 

budget is on the order of 10% (Van Aardenne et al., 2001; Finlayson-Pitts and Pitts, 2000; Seinfeld and 

Pandis, 1998), but NOX emissions from fertilized fields are highly variable. Soil NO emissions can be 

estimated from the fraction of the applied fertilizer nitrogen emitted as NOX, but the flux varies strongly 

with land use and temperature. Estimated globally averaged fractional applied nitrogen loss as NO varies 

from 0.3% (Skiba et al., 1997) to 2.5% (Yienger and Levy, 1995). Variability within biomes to which 

fertilizer is applied, such as shortgrass versus tallgrass prairie, accounts for a factor of three in uncertainty 

(Williams et al., 1992; Yienger and Levy, 1995; Davidson and Kingerlee, 1997). 

The local contribution can be much greater than the global average, particularly in summer and 

especially where corn is grown extensively. Williams et al. (1992) estimated that contributions to NO 

budgets from soils in Illinois are about 26% of the emissions from industrial and commercial processes in 

that State. In Iowa, Kansas, Minnesota, Nebraska, and South Dakota, all states with smaller human 

populations, soil emissions may dominate the NO budget. Conversion of NH3 to NO3 (nitrification) in 

aerobic soils appears to be the dominant pathway to NO. The mass and chemical form of nitrogen 

(reduced or oxidized) applied to soils, the vegetative cover, temperature, soil moisture, and agricultural 

practices such as tillage all influence the amount of fertilizer nitrogen released as NO. 

Emissions of NO from soils peak in summer when O3 formation is also at a maximum. An NRC 

panel report (NRC, 2002) outlined the role of agriculture in emissions of air pollutants including NO and 

NH3. That report recommends immediate implementation of best management practices to control these 

emissions, and further research to quantify the magnitude of emissions and the impact of agriculture on 

air quality. Civerolo and Dickerson (1998) report that use of the no-till cultivation technique on a 

fertilized cornfield in Maryland reduced NO emissions by a factor of seven. 

NOX from Biomass Burning 

During biomass burning, nitrogen is derived mainly from fuel N and not from atmospheric N2, 

since temperatures required to fix atmospheric N2 are likely to be found only in the flaming crowns of the 

most intense boreal forest fires. Nitrogen is present mainly in plants as amino (NH2) groups in amino 

acids. During combustion, nitrogen is released mainly in unidentified forms, presumably as N2, with very 

little remaining in fuel ash. Apart from N2, the most abundant species in biomass burning plumes is NO. 

Emissions of NO account for only about 10 to 20% relative to fuel N (Lobert et al., 1991). Other species 

such as NO2, nitriles, ammonia, and other nitrogen compounds account for a similar amount. Emissions 

of NOX are about 0.2 to 0.3% relative to total biomass burned (e.g., Andreae, 1991; Radke et al., 1991). 

Westerling et al. (2006) have noted that the frequency and intensity of wildfires in the western United 

States have increased substantially since 1970. 

Lightning Production of NO 

Annual global production of NO by lightning is the most uncertain source of reactive nitrogen. In 

the last decade, literature values of the global average production rate range from 2 to 20 Tg N per year. 

However, the most likely range is from 3 to 8 Tg N per year, because the majority of the recent estimates 

fall in this range. The large uncertainty stems from several factors: (1) a large range of NO production 

rates per meter of flash length (as much as two orders of magnitude); (2) the open question of whether 
2-30

cloud-to-ground (CG) flashes and intracloud flashes (IC) produce substantially different amounts of NO; 

(3) the global flash rate; and (4) the ratio of the number of IC flashes to the number of CG flashes. 

Estimates of the amount of NO produced per flash have been made based on theoretical considerations 

(e.g., Price et al., 1997), laboratory experiments (e.g., Wang et al., 1998); field experiments (e.g., Stith 

et al., 1999; Huntrieser et al., 2002, 2007); and through a combination of cloud-resolving model 

simulations, observed lightning flash rates, and anvil measurements of NO (e.g., DeCaria et al., 2000, 

2005; Ott et al., 2007). The latter method was also used by Pickering et al. (1998), who showed that only 

~5 to 20% of the total NO produced by lightning in a given storm exists in the boundary layer at the end 

of a thunderstorm. Therefore, the direct contribution to boundary layer O3 production by lightning NO is 

thought to be small. However, lightning NO production can contribute substantially to O3 production in 

the middle and upper troposphere. DeCaria et al. (2005) estimated that up to 10 ppb of O3 was produced 

in the upper troposphere in the first 24 h following a Colorado thunderstorm due to the injection of 

lightning NO. A series of midlatitude and subtropical thunderstorm events have been simulated with the 

model of DeCaria et al. (2005), and the derived NO production per CG flash averaged 500 moles/flash 

while average production per IC flash was 425 moles/flash (Ott et al., 2006). 

A major uncertainty in mesoscale and global chemical transport models is the parameterization of 

lightning flash rates. Model variables such as cloud top height, convective precipitation rate, and upward 

cloud mass flux have been used to estimate flash rates. Allen and Pickering (2002) have evaluated these 

methods against observed flash rates from satellite, and examined the effects on O3 production using each 

method. 

AX2.4.2.3. Uses of Satellite Data to Derive Emissions 

Satellite data have been shown to be useful for optimizing estimates of emissions of NO2 (Leue 

et al., 2001; Martin et al., 2003; Jaeglé et al., 2005). Satellite-borne instruments such as Global Ozone 

Monitoring Experiment (GOME) (Martin et al., 2003; and references therein) and Scanning Imaging 

Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) (Bovensmann et al., 1999) 

retrieve tropospheric columns of NO2, which can then be combined with model-derived chemical 

lifetimes of NOX to yield emissions of NOX. 

Top-down inference of the NOX emission inventory from the satellite observations of NO2 columns 

by mass balance requires at minimum three pieces of information: the retrieved tropospheric NO2 column, 

the ratio of tropospheric NOX to NO2 columns, and the NOX lifetime against loss to stable reservoirs. A 

photochemical model has been used to provide information on the latter two pieces of information. The 

method is generally applied exclusively to land surface emissions, excluding lightning. Tropospheric NO2

columns are insensitive to lightning NOX emissions since most of the lightning NOX in the upper 

troposphere is present as NO at the local time of the satellite measurements (Ridley et al., 1996), owing to 

the slower reactions of NO with O3 there. 

Jaeglé et al. (2005) applied additional information on the spatial distribution of emissions and on 

fire activity to partition NOX emissions into sources from fossil fuel combustion, soils, and biomass 

burning. Global a posteriori estimates of soil NOX emissions are 68% larger than the a priori estimates. 

Large increases are found for the agricultural region of the western United States during summer, 

increasing total U.S. soil NOX emissions by a factor of 2 to 0.9 Tg N/yr. Bertram et al. (2005) found clear 

signals in the SCIAMACHY observations of short intense NOX pulses following springtime fertilizer 

application and subsequent precipitation over agricultural regions of the western United States. For the 

agricultural region in North-Central Montana, they calculate a yearly SCIAMACHY top-down estimate 

that is 60% higher than a commonly used model of soil NOX emissions by Yienger and Levy (1995). 

Martin et al. (2006) retrieved tropospheric NO2 columns for May 2004 to April 2005 from the 

SCIAMACHY satellite instrument to derive top-down NOX emissions estimates via inverse modeling 

with a global chemical transport model (GEOS-Chem). The top-down emissions were combined with a 

priori information from a bottom-up emission inventory with error weighting to achieve an improved a 
2-31

posteriori estimate of the global distribution of surface NOX emissions. Their a posteriori inventory 

improves the GEOS-Chem simulation of NOX, PAN, and HNO3 with respect to airborne in situ 

measurements over and downwind of New York City. Their a posteriori inventory shows lower NOX

emissions from the Ohio River valley during summer than during winter, reflecting recent controls on 

NOX emissions from electric utilities. Their a posteriori inventory is highly consistent (R2

 = 0.82, bias = 

3%) with the NEI99 inventory for the United States. In contrast, their a posteriori inventory is 68% larger 

than a recent inventory by Streets et al. (2003) for East Asia for the year 2000. 

AX2.4.2.4. Field Studies Evaluating Emissions Inventories 

Comparisons of emissions model predictions with observations have been performed in a number 

of environments. A number of studies of ratios of concentrations of CO to NOX and NMOC to NOX

during the early 1990s in tunnels and ambient air (summarized in Air Quality Criteria for Carbon 

Monoxide (U.S. Environmental Protection Agency, 2000)) indicated that emissions of CO and NMOC 

were systematically underestimated in emissions inventories. However, the results of more recent studies 

have been mixed in this regard, with many studies showing agreement to within ±50% (U.S. 

Environmental Protection Agency, 2000). Improvements in many areas have resulted from the process of 

emissions model development, evaluation, and further refinement. It should be remembered that the 

conclusions from these reconciliation studies depend on the assumption that NOX emissions are predicted 

correctly by emissions factor models. Roadside remote sensing data indicate that > 50% of NMHC and 

CO emissions are produced by less than about 10% of the vehicles (Stedman et al., 1991). These “superemitters” are typically poorly maintained vehicles. Vehicles of any age engaged in off-cycle operations 

(e.g., rapid accelerations) emit much more than if operated in normal driving modes. Bishop and Stedman 

(1996) found that the most important variables governing CO emissions are fleet age and owner 

maintenance. 

Emissions inventories for North America can be evaluated by comparison to measured long-term 

trends and or ratios of pollutants in ambient air. A decadal field study of ambient CO at a rural site in the 

eastern United States (Hallock-Waters et al., 1999) indicates a downward trend consistent with the 

downward trend in estimated emissions over the period 1988 to 1999 (U.S. Environmental Protection 

Agency, 1997), even when a global downward trend is accounted for. Measurements at two urban areas in 

the United States confirmed the decrease in CO emissions (Parrish et al., 2002). That study also indicated 

that the ratio of CO to NOX emissions decreased by a factor of almost three over 12 years. (Such a 

downward trend was noted in the 1996 O3 AQCD). Emissions estimates (U.S. Environmental Protection 

Agency, 1997) indicate a much smaller decrease in this ratio, suggesting that NOX emissions from mobile 

sources may be underestimated and/or increasing. Parrish et al. (2002) conclude that O3 photochemistry in 

U.S. urban areas may have become more NOX-limited over the past decade. 

Pokharel et al. (2002) employed remotely sensed emissions from on-road vehicles and fuel use data 

to estimate emissions in Denver. Their calculations indicate a continual decrease in CO, HC, and NO 

emissions from mobile sources over the 6-year study period. Inventories based on the ambient data were 

30 to 70% lower for CO, 40% higher for HC, and 40 to 80% lower for NO than those predicted by the 

MOBILE6 model. 

Stehr et al. (2000) reported simultaneous measurements of CO, SO2, and NOY at an East Coast site. 

By taking advantage of the nature of mobile sources (they emit NOX and CO but little SO2) and power 

plants (they emit NOX and SO2 but little CO), the authors evaluated emissions estimates for the eastern 

United States. Results indicated that coal combustion contributes 25 to 35% of the total NOX emissions in 

rough agreement with emissions inventories (U.S. Environmental Protection Agency, 1997). 

Parrish et al. (1998) and Parrish and Fehsenfeld (2000) proposed methods to derive emission rates 

by examining measured ambient ratios among individual VOC, NOX and NOY. There is typically a strong 

correlation among measured values for these species because emission sources are geographically 

collocated, even when individual sources are different. Correlations can be used to derive emissions ratios 
2-32

between species, including adjustments for the impact of photochemical aging. Investigations of this type 

include correlations between CO and NOY (e.g., Parrish et al., 1991), between individual VOC species 

and NOY (Goldan et al., 1995, 1997, 2000) and between various individual VOC (Goldan et al., 1995, 

1997; McKeen and Liu, 1993; McKeen et al., 1996). Buhr et al. (1992) derived emission estimates from 

principal component analysis (PCA) and other statistical methods. Many of these studies are summarized 

in Trainer et al. (2000), Parrish et al. (1998), and Parrish and Fehsenfeld (2000). Goldstein and Schade 

(2000) also used species correlations to identify the relative impacts of anthropogenic and biogenic 

emissions. Chang et al. (1996, 1997) and Mendoza-Dominguez and Russell (2000, 2001) used the more 

quantitative technique of inverse modeling to derive emission rates, in conjunction with results from 

chemistry-transport models. 

AX2.5. Methods for Calculating NOX Concentrations in the 

Atmosphere 

Atmospheric chemistry and transport models are the major tools used to calculate the relations 

among O3, other oxidants, and their precursors, the transport and transformation of air toxics, the 

production of secondary organic aerosol, the evolution of the particle size distribution, and the production 

and deposition of pollutants affecting ecosystems. Chemical transport models are driven by emissions 

inventories for primary species such as the precursors for O3 and PM and by meteorological fields 

produced by other numerical models. Emissions of precursor compounds can be divided into 

anthropogenic and natural source categories. Natural sources can be further divided into biotic 

(vegetation, microbes, animals) and abiotic (biomass burning, lightning) categories. However, the 

distinction between natural sources and anthropogenic sources is often difficult to make because human 

activities can affect directly or indirectly emissions from what would have been considered natural 

sources during the preindustrial era. Moreover, emissions from plants and animals used in agriculture 

have been referred to as anthropogenic or natural in different applications. Wildfire emissions may be 

considered to be natural, except that forest management practices may have led to the buildup of fuels on 

the forest floor, thereby altering the frequency and severity of forest fires. Needed meteorological 

quantities such as winds and temperatures are taken from operational analyses, reanalyses, or circulation 

models. In most cases, these are off-line analyses, i.e., they are not modified by radiatively active species 

such as O3 and particles generated by the model. 

A brief overview of atmospheric chemistry-transport models is given in Section AX2.5.1. 

A discussion of emissions inventories of precursors used by these models is given in Section AX2.5. 

Uncertainties in emissions estimates have also been discussed in the AQCD for PM (U.S. Environmental 

Protection Agency, 2004). Chemistry-transport model evaluation and an evaluation of the reliability of 

emissions inventories are presented in Section AX2.5.4. 

AX2.5.1. Chemistry-Transport Models 

Atmospheric CTMs have been developed for application over a wide range of spatial scales ranging 

from neighborhood to global. Regional scale CTMs are used chiefly for four purposes: (1) to obtain better 

understanding of the processes controlling the formation, transport, and destruction of gas-and particlephase criteria and hazardous air pollutants; (2) to understand the relations between O3 concentrations and 

concentrations of its precursors such as NOX and VOCs, the factors leading to acid deposition, and hence 

to possible damage to ecosystems; (3) to understand relations among the concentration patterns of various 

pollutants that may exert adverse health effects; (4) for determining control strategies for O3 precursors. 

However, this last application has met with varying degrees of success because of the highly nonlinear 
2-33

relations between O3 and emissions of its precursors, and uncertainties in emissions, parameterizations of 

transport, and chemical production and loss terms. Uncertainties in meteorological variables and 

emissions can be large enough to lead to significant errors in developing control strategies (e.g., Russell 

and Dennis, 2000; Sillman et al., 1995). 

Global scale CTMs are used to address issues associated with climate change, stratospheric ozone 

depletion, and to provide boundary conditions for regional scale models. CTMs include mathematical 

(and often simplified) descriptions of atmospheric transport, the transfer of solar radiation through the 

atmosphere, chemical reactions, and removal to the surface by turbulent motions and precipitation for 

pollutants emitted into the model domain. Their upper boundaries extend anywhere from the top of the 

mixing layer to the mesopause (about 80 km in height), to obtain more realistic boundary conditions for 

problems involving stratospheric dynamics. There is a trade-off between the size of the modeling domain 

and the grid resolution used in the CTM that is imposed by computational resources. 

There are two major formulations of CTMs in current use. The first approach, grid-based or 

Eulerian air quality models subdivide the region to be modeled (the modeling domain) into a threedimensional array of grid cells. Spatial derivatives in the species continuity equations are cast in finitedifference form over this grid and a system of equations for the concentrations of all the chemical species 

in the model are solved numerically at each grid point. Finite element Eulerian models also exist and have 

been applied, but less frequently. Time dependent continuity (mass conservation) equations are solved for 

each species including terms for transport, chemical production and destruction, and emissions and 

deposition (if relevant), in each cell. Chemical processes are simulated with ordinary differential 

equations, and transport processes are simulated with partial differential equations. Because of a number 

of factors such as the different time scales inherent in different processes, the coupled, nonlinear nature of 

the chemical process terms, and computer storage limitations, all of the terms in the equations are not 

solved simultaneously in three dimensions. Instead, operator splitting, in which terms in the continuity 

equation involving individual processes are solved sequentially, is used. In the second CTM formulation, 

trajectory or Lagrangian models, a number of hypothetical air parcels are specified as following wind 

trajectories. In these models, the original system of partial differential equations is transformed into a 

system of ordinary differential equations. 

A less common approach is to use a hybrid Lagrangian/Eulerian model, in which certain aspects of 

atmospheric chemistry and transport are treated with a Lagrangian approach and others are treated in an 

Eulerian manner (e.g., Stein et al., 2000). Each approach has its advantages and disadvantages. The 

Eulerian approach is more general in that it includes processes that mix air parcels and allows integrations 

to be carried out for long periods during which individual air parcels lose their identity. There are, 

however, techniques for including the effects of mixing in Lagrangian models such as FLEXPART (e.g., 

Zanis et al., 2003), ATTILA (Reithmeier and Sausen, 2002), and CLaMS (McKenna et al., 2002). 

AX2.5.1.1. Regional Scale Chemistry Transport Models 

Major modeling efforts within the U.S. Environmental Protection Agency center on the Community 

Multiscale Air Quality modeling system (CMAQ) (Byun and Ching, 1999; Byun and Schere, 2006). A 

number of other modeling platforms using Lagrangian and Eulerian frameworks have been reviewed in 

the 96 AQCD for O3 (U.S. Environmental Protection Agency, 1997), and in Russell and Dennis (2000). 

The capabilities of a number of CTMs designed to study local- and regional-scale air pollution problems 

were summarized by Russell and Dennis (2000). Evaluations of the performance of CMAQ are given in 

Arnold et al. (2003), Eder and Yu (2005), Appel et al. (2005), and Fuentes and Raftery (2005). The 

domain of CMAQ and other Eulerian CTMs can extend from several hundred km to the entire 

hemisphere. In addition, both of these classes of models allow resolution of the calculations over 

specified areas to vary. CMAQ is most often driven by the MM5 mesoscale meteorological model 

(Seaman, 2000), though it may be driven by other meteorological models including RAMS. Simulations 

of pollution episodes over regional domains have been performed with a horizontal resolution as low as 1 
2-34

km, and smaller calculations over limited domains have been accomplished at even finer scales. However, 

simulations at such high resolutions require better parameterizations of meteorological processes such as 

boundary layer fluxes, deep convection and clouds (Seaman, 2000), as well as finer-scale emissions. Finer 

spatial resolution is necessary to resolve features such as urban heat island circulation; sea, bay, and land 

breezes; mountain and valley breezes, and the nocturnal low-level jet, all of which can affect pollutant 

concentrations. 

The most common approach to setting up the horizontal domain is to nest a finer grid within a 

larger domain of coarser resolution. However, there are other strategies such as the stretched grid (e.g., 

Fox-Rabinovitz et al., 2002) and the adaptive grid. In a stretched grid, the grid’s resolution continuously 

varies throughout the domain, thereby eliminating any potential problems with the sudden change from 

one resolution to another at the boundary. Caution should be exercised in using such a formulation, 

because certain parameterizations (such as for convection) valid on a relatively coarse grid scale may not 

be valid on finer scales. Adaptive grids are not fixed at the start of the simulation, but instead adapt to the 

needs of the simulation as it evolves (e.g., Hansen et al., 1994). They have the advantage that they can 

resolve processes at relevant spatial scales. However, they can be very slow if the situation to be modeled 

is complex. Additionally, if adaptive grids are used for separate meteorological, emissions, and 

photochemical models, there is no reason a priori why the resolution of each grid should match, and the 

gains realized from increased resolution in one model will be wasted in the transition to another model. 

The use of finer horizontal resolution in CTMs will necessitate finer-scale inventories of land use and 

better knowledge of the exact paths of roads, locations of factories, and, in general, better methods for 

locating sources and estimating their emissions. 

The vertical resolution of these CTMs is variable, and usually configured to have higher resolution 

near the surface and decreasing aloft. Because the height of the boundary layer is of critical importance in 

simulations of air quality, improved resolution of the boundary layer height would likely improve air 

quality simulations. Additionally, current CTMs do not adequately resolve fine scale features such as the 

nocturnal low-level jet in part because little is known about the nighttime boundary layer. 

CTMs require time-dependent, three-dimensional wind fields for the period of simulation. The 

winds may be generated either by a model using initial fields alone or with four-dimensional data 

assimilation to improve the model’s performance; (i.e., model equations can be updated periodically 

“nudged” to bring results into agreement with observations). Modeling efforts typically focus on 

simulations of several days’ duration, the typical time scale for individual O3 episodes; but longer term 

modeling series of several months or multiple seasons of the year are now common. The current trend in 

modeling applications is towards annual simulations. This trend is driven in part by the need to better 

understand observations of periods of high wintertime PM (e.g., Blanchard et al., 2002) and the need to 

simulate O3 episodes occurring outside of summer. 

Chemical kinetics mechanisms (a set of chemical reactions) representing the important reactions 

occurring in the atmosphere are used in CTMs to estimate the rates of chemical formation and destruction 

of each pollutant simulated as a function of time. Unfortunately, chemical mechanisms that explicitly treat 

the reactions of individual reactive species are too computationally demanding to be incorporated into 

CTMs for regulatory use. So, for example, one very extensive “master mechanism” (Derwent et al., 2001) 

includes approximately 10,500 reactions involving 3603 chemical species (Derwent et al., 2001), but 

“lumped” mechanisms that group compounds of similar chemistry together may be used. The chemical 

mechanisms used in existing photochemical O3 models contain significant uncertainties that may limit the 

accuracy of their predictions; the accuracy of each of these mechanisms is also limited by missing 

chemistry. Because of different approaches to the lumping of organic compounds into surrogate groups, 

chemical mechanisms can produce somewhat different results under similar conditions. The CB-IV 

chemical mechanism (Gery et al., 1989), the RADM II mechanism (Stockwell et al., 1990), the SAPRC 

(e.g., Wang et al., 2000a,b; Carter, 1990) and the RACM mechanisms can be used in CMAQ. Jimenez 

et al. (2003) provide brief descriptions of the features of the main mechanisms in use and they compared 

concentrations of several key species predicted by seven chemical mechanisms in a box model simulation 

over 24 h. The average deviation from the average of all mechanism predictions for O3 and NO over the 
2-35

daylight period was less than 20%, and was 10% for NO2 for all mechanisms. However, much larger 

deviations were found for HNO3, PAN, HO2, H2O2, ethylene (C2H4), and isoprene (C5H8). An analysis for 

OH radicals was not presented. The large deviations shown for most species imply differences between 

the calculated lifetimes of atmospheric species and the assignment of model simulations to either NOXlimited or radical quantity limited regimes between mechanisms. Gross and Stockwell (2003) found small 

differences between mechanisms for clean conditions, with differences becoming more significant for 

polluted conditions, especially for NO2 and organic peroxy radicals. Faraji et al. (2005) found differences 

of 40% in peak 1-h O3 in the Houston-Galveston-Brazoria area between simulations using SAPRAC and 

CB-IV. They attributed differences in predicted O3 concentrations to differences in the mechanisms of 

oxidation of aromatic hydrocarbons. 

CMAQ and other CTMs (e.g., PM-CAMx) incorporate processes and interactions of aerosol-phase 

chemistry (Mebust et al., 2003). There have also been several attempts to study the feedbacks of 

chemistry on atmospheric dynamics using meteorological models, like MM5 (e.g., Grell et al., 2000; Liu 

et al., 2001a; Lu et al., 1997; Park et al., 2001). This coupling is necessary to simulate accurately 

feedbacks such as may be caused by the heavy aerosol loading found in forest fire plumes (Lu et al., 

1997; Park et al., 2001), or in heavily polluted areas. Photolysis rates in CMAQ can now be calculated 

interactively with model produced O3, NO2, and aerosol fields (Binkowski et al., 2007). 

Spatial and temporal characterizations of anthropogenic and biogenic precursor emissions must be 

specified as inputs to a CTM. Emissions inventories have been compiled on grids of varying resolution 

for many hydrocarbons, aldehydes, ketones, CO, NH3, and NOX. Emissions inventories for many species 

require the application of algorithms for calculating the dependence of emissions on physical variables 

such as temperature and to convert the inventories into formatted emission files which can be used by a 

CTM. For example, preprocessing of emissions data for CMAQ is done by the Spare-Matrix Operator 

Kernel Emissions (SMOKE) system. For many species, information concerning the temporal variability 

of emissions is lacking, so long-term (e.g., annual or O3-season) averages are used in short-term, episodic 

simulations. Annual emissions estimates are often modified by the emissions model to produce emissions 

more characteristic of the time of day and season. Significant errors in emissions can occur if an 

inappropriate time dependence or a default profile is used. Additional complexity arises in model 

calculations because different chemical mechanisms can include different species, and inventories 

constructed for use with another mechanism must be adjusted to reflect these differences. This problem 

also complicates comparisons of the outputs of these models because one chemical mechanism may 

produce some species not present in another mechanism yet neither prediction may agree with the 

measurements. 

In addition to wet deposition, dry deposition (the removal of chemical species from the atmosphere 

by interaction with ground-level surfaces) is an important removal process for pollutants on both urban 

and regional scales and must be included in CTMs. The general approach used in most models is the 

resistance in series method, in which where dry deposition is parameterized with a deposition velocity 

(Vd), which is represented as 

vd = (ra + rb + rc)

-1 AX2.5-1 

where ra, rb, and rc represent the resistance due to atmospheric turbulence, transport in the fluid sublayer 

very near the elements of surface such as leaves or soil, and the resistance to uptake of the surface itself. 

This approach works for a range of substances, although it is inappropriate for species with substantial 

emissions from the surface or for species whose deposition to the surface depends on its concentration at 

the surface itself. The approach is also modified somewhat for aerosols: the terms rb and rc are replaced 

with a surface Vd to account for gravitational settling. In their review, Wesely and Hicks (2000) pointed 

out several shortcomings of current knowledge of dry deposition. Among those shortcomings are 

difficulties in representing dry deposition over varying terrain where horizontal advection plays a 
2-36

significant role in determining the magnitude of ra and difficulties in adequately determining a Vd for 

extremely stable conditions such as those occurring at night (e.g., Mahrt, 1998). Under the best of 

conditions, when a model is exercised over a relatively small area where dry deposition measurements 

have been made, uncertainties as large as ±30% (e.g., Massman et al., 1994; Brook et al., 1996; Padro, 

1996) persist. Wesely and Hicks (2000) stated that an important result of these comparisons is that the 

current level of sophistication of most dry deposition models is relatively low, and that deposition 

estimates therefore must rely heavily on empirical data. Still larger uncertainties exist when the surface 

features in the built environment are not well known or when the surface comprises a patchwork of 

different surface types, as is common in the eastern U.S. 

The initial conditions, i.e., the starting concentration fields of all species computed by a model, and 

the boundary conditions, i.e., the concentrations of species along the horizontal and upper boundaries of 

the model domain throughout the simulation must be specified at the beginning of the simulation. Both 

initial and boundary conditions can be estimated from models or data or, more generally, model-data 

hybrids. Because data for vertical profiles of most species of interest are sparse, results of model 

simulations over larger, usually global, domains are often used. As may be expected, the influence of 

boundary conditions depends on the lifetime of the species under consideration and the time scales for 

transport from the boundaries to the interior of the model domain (Liu et al., 2001b). 

Each of the model components described above has associated uncertainties and the relative 

importance of these uncertainties varies with the modeling application. The largest errors in 

photochemical modeling are still thought to arise from the meteorological and emissions inputs to the 

model (Russell and Dennis, 2000). Within the model itself, horizontal advection algorithms are still 

thought to be significant source of uncertainty (e.g., Chock and Winkler, 1994), though more recently, 

those errors are thought to have been reduced (e.g., Odman and Ingram, 1996). There are also indications 

that problems with mass conservation continue to be present in photochemical and meteorological models 

(e.g., Odman and Russell, 1999), and can result in significant simulation errors. The effects of errors in 

initial conditions can be minimized by including several days “spin-up” time in a simulation to allow the 

model to be driven by emitted species before the simulation of the period of interest begins. 

While the effects of poorly specified boundary conditions propagate through the model’s domain, 

the effects of these errors remain undetermined. Because many meteorological processes occur on spatial 

scales which are smaller than the model grid spacing (either horizontally or vertically) and thus are not 

calculated explicitly, parameterizations of these processes must be used and these introduce additional 

uncertainty. 

Uncertainty also arises in modeling the chemistry of O3 formation because it is highly nonlinear 

with respect to NOX concentrations. Thus, the volume of the grid cell into which emissions are injected is 

important because the nature of O3 chemistry (i.e., O3 production or titration) depends in a complicated 

way on the concentrations of the precursors and the OH radical as noted earlier. The use of ever-finer grid 

spacing allows regions of O3 titration to be more clearly separated from regions of O3 production. The use 

of grid spacing fine enough to resolve the chemistry in individual power-plant plumes is too demanding 

of computer resources for this to be attempted in most simulations. Instead, parameterizations of the 

effects of sub-grid-scale processes such as these must be developed, else serious errors can result if 

emissions are allowed to mix through an excessively large grid volume before the chemistry step in a 

model calculation is performed. In light of the significant differences between atmospheric chemistry 

taking place within and without of a power plant plume (e.g., Ryerson et al., 1998 and Sillman, 2000), 

inclusion of a separate, meteorological module for treating large, tight plumes can be useful. Because the 

photochemistry of O3 and many other atmospheric species is nonlinear, emissions correctly modeled in a 

tight plume may be incorrectly modeled in a more dilute plume. Fortunately, it appears that the chemical 

mechanism used to follow a plume’s development need not be as detailed as that used to simulate the rest 

of the domain, as the inorganic reactions are the most important in the plume see (e.g., Kumar and 

Russell, 1996). The need to include explicit plume-in-grid chemistry only down to the level of the 

smallest grid disappears if one uses the adaptive grid approach mentioned previously, though such grids 
2-37

are more computationally intensive. These differences in simulations may be significant for calculated 

sensitivity of O3 to its precursors (e.g., Sillman et al., 1995). 

Because the chemical production and loss terms in the continuity equations for individual species 

are coupled, the chemical calculations must be performed iteratively until calculated concentrations 

converge to within some preset criterion. The number of iterations and the convergence criteria chosen 

also can introduce error. 

AX2.5.1.2. Intra-urban Scale Dispersion Modeling 

The grid spacing in regional chemistry transport models between 1 and 12 km2, is usually too 

coarse to resolve spatial variations on the neighborhood scale. The interface between regional scale 

models and models of personal exposure described in Annex 3, Section AX3.5 is provided by smaller 

scale dispersion models. Several models could be used to simulate concentration fields near roads, each 

with its own set of strengths and weaknesses. For example, AERMOD 

(http://www.epa.gov/scram001/dispersion_prefrec.htm) is a steady-state plume model that was 

formulated as a replacement to the ISC3 dispersion model. In the stable boundary layer (SBL), it assumes 

the concentration distribution to be Gaussian in both the vertical and horizontal. In the convective 

boundary layer, the horizontal distribution is also assumed to be Gaussian, but the vertical distribution is 

described with a bi-Gaussian probability density function (pdf). AERMOD has provisions to be applied to 

flat and complex terrain, and multiple source types (including, point, area and volume sources) in both 

urban and rural areas. It incorporates air dispersion based on planetary boundary layer turbulence 

structure and scaling concepts, and is meant to treat both surface and elevated sources and simple and 

complex terrain in rural and urban areas. The dispersion of emissions from line sources like highways is 

treated as the sum of emissions from a number of point sources placed side by side. However, emissions 

are usually not in steady state and there are different functional relationships between buoyant plume rise 

in point and line sources. It should be remembered that NO2 is largely secondary in nature as it is 

produced by Reaction AX 2.2-3. However, AERMOD does not have provision for including secondary 

sources. The more appropriate use of AERMOD would be to simulate the total of NO and NO2, or NOX. 

There are models that are non-steady state and can incorporate plume rise explicitly from different 

types of sources. For example, CALPUFF (http://www.src.com/calpuff/calpuff1.htm) is a non-steadystate puff dispersion model that simulates the effects of time- and space-varying meteorological 

conditions on pollution transport, transformation, and removal and has provisions for calculating 

dispersion from surface sources. However, it should be noted that neither model was designed to treat the 

dispersion of emissions from roads or to include secondary sources. In using either model, the user would 

have to specify dispersion parameters that are specific to traffic. The distinction between a steady–state 

and time varying model might not be important for long time scales; however for short time scales, the 

temporal variability in traffic emissions could result in underestimation of peak concentration and 

exposures. 

AX2.5.1.3. Global Scale CTMs 

The importance of global transport of O3 and O3 precursors and their contribution to regional O3

levels in the United States is now apparent. There are at present on the order of 20 three-dimensional 

global models developed by various groups to address problems in tropospheric chemistry. These models 

resolve synoptic meteorology, O3-NOX-CO-hydrocarbon photochemistry, have parameterizations for wet 

and dry deposition, and parameterize sub-grid scale vertical mixing processes such as convection. Global 

models have proven useful for testing and advancing scientific understanding beyond what is possible 

with observations alone. For example, they can calculate quantities of interest that cannot be measured 
2-38

directly, such as the export of pollution from one continent to the global atmosphere or the response of the 

atmosphere to future perturbations to anthropogenic emissions. 

Global simulations are typically conducted at a horizontal resolution of 200 km2

 or more. 

Simulations of the effects of transport from long-range transport link multiple horizontal resolutions from 

the global to the local scale. Finer resolution will only improve scientific understanding to the extent that 

the governing processes are more accurately described at that scale. Consequently, there is a critical need 

for observations at the appropriate scales to evaluate the scientific understanding represented by the 

models. 

During the recent IPCC-AR4 tropospheric chemistry study coordinated by the European Union 

Atmospheric Composition Change: the European Network of excellence (ACCENT), 26 atmospheric 

CTMs were used to estimate the impacts of three emissions scenarios on global atmospheric composition, 

climate, and air quality in 2030 (Dentener et al., 2006a). All models were required to use anthropogenic 

emissions developed at IIASA (Dentener et al., 2005) and GFED version 1 biomass burning emissions 

(Van der Werf et al., 2003) as described in Stevenson et al. (2006). The base simulations from these 

models were evaluated against a suite of present-day observations. Most relevant to this assessment report 

are the evaluations with ozone, NO2, and nitrogen deposition (Stevenson et al., 2006; Van Noije et al., 

2006; Dentener et al., 2006a), which are summarized briefly below. 

An analysis of the standard deviation of zonal mean and tropospheric column O3 reveals large intermodel variability in the tropopause region and throughout the polar troposphere, likely reflecting 

differences in model tropopause levels and the associated stratospheric injection of O3 to the troposphere 

(Stevenson et al., 2006). Ozone distributions in the tropics also exhibit large standard deviations (~30%), 

particularly as compared to the mid-latitudes (~20%), indicating larger uncertainties in the processes that 

influence ozone in the tropics: deep tropical convection, lightning NOX, isoprene emissions and 

chemistry, and biomass burning emissions (Stevenson et al., 2006). 

Stevenson et al., (2006) found that the model ensemble mean (MEM) typically captures the 

observed seasonal cycles to within one standard deviation. The largest discrepancies between the MEM 

and observations include: (1) an underestimate of the amplitude of the seasonal cycle at 30°-90°N with a 

10 ppb overestimate of winter ozone, possibly due to the lack of a seasonal cycle in anthropogenic 

emissions or to shortcomings in the stratospheric influx of O3, and (2) an overestimate of O3 throughout 

the northern tropics. However, the MEM was found to capture the observed seasonal cycles in the 

southern hemisphere, suggesting that the models adequately represent biomass burning and natural 

emissions. 

The mean present-day global ozone budget across the current generation of CTMs differs 

substantially from that reported in the IPCC Third Assessment Report (TAR), with a 50% increase in the 

mean chemical production (to 5100 Tg O3/yr), a 30% increase in the chemical and deposition loss terms 

(to 4650 and 1000 Tg O3/yr, respectively) and a 30% decrease in the mean stratospheric input flux (to 550 

Tg O3/yr) (Stevenson et al., 2006). The larger chemical terms as compared to the IPCC TAR are 

attributed mainly to higher NOX (as well as an equatorward shift in distribution) and isoprene emissions, 

although more detailed schemes and/or improved representations of photolysis, convection, and 

stratospheric-tropospheric exchange may also contribute (Stevenson et al., 2006). 

A subset of 17 of the 26 models used in the Stevenson et al. (2006) study was used to compare with 

three retrievals of NO2 columns from the GOME instrument (van Noije et al., 2006) for the year 2000. 

The higher resolution models reproduce the observed patterns better, and the correlation among simulated 

and retrieved columns improved for all models when simulated values are smoothed to a 5° Η 5° grid, 

implying that the models do not accurately reproduce the small-scale features of NO2 (Van Noije et al., 

2006). Van Noije et al. (2006) suggest that variability in simulated NO2 columns may reflect model 

differences in OH distributions and the resulting NOX lifetimes, as well as differences in vertical mixing 

which strongly affect partitioning between NO and NO2. Overall, the models tend to underestimate 

concentrations in the retrievals in industrial regions (including the eastern United States) and overestimate 

them in biomass burning regions (Van Noije et al., 2006). 
2-39

Over the eastern United States and in industrial regions more generally, the spread in absolute 

column abundances is generally larger among the retrievals than among the models, with the discrepancy 

among the retrievals particularly pronounced in winter (Van Noije et al., 2006), suggesting that the 

models are biased low, or that the European retrievals may be biased high as the Dalhousie/SAO retrieval 

is closer to the model estimates. The lack of seasonal variability in fossil fuel combustion emissions may 

contribute to a wintertime model underestimate (Van Noije et al., 2006) manifested most strongly over 

Asia. In biomass burning regions, the models generally reproduce the timing of the seasonal cycle of the 

retrievals, but tend to overestimate the seasonal cycle amplitude, partly due to lower values in the wet 

season, which may reflect an underestimate in wet season soil NO emissions (Van Noije et al., 2006, 

Jaeglé et al., 2004, 2005). 

AX2.5.1.4. Modeling the Effects of Convection 

The effects of deep convection can be simulated using cloud-resolving models, or in regional or 

global models in which the convection is parameterized. The Goddard Cumulus Ensemble (GCE) model 

(Tao and Simpson, 1993) has been used by Pickering et al. (1991, 1992a,b, 1993, 1996), Scala et al. 

(1990), and Stenchikov et al. (1996) in the analysis of convective transport of trace gases. The cloud 

model is nonhydrostatic and contains a detailed representation of cloud microphysical processes. Twoand three-dimensional versions of the model have been applied in transport analyses. The initial 

conditions for the model are usually from a sounding of temperature, water vapor and winds 

representative of the region of storm development. Model-generated wind fields can be used to perform 

air parcel trajectory analyses and tracer advection calculations. 

Such methods were used by Pickering et al. (1992b) to examine transport of urban plumes by deep 

convection. Transport of an Oklahoma City plume by the 10-11 June 1985 PRE-STORM squall line was 

simulated with the 2-D GCE model. This major squall line passed over the Oklahoma City metropolitan 

area, as well as more rural areas to the north. Chemical observations ahead of the squall line were 

conducted by the PRE-STORM aircraft. In this event, forward trajectories from the boundary layer at the 

leading edge of the storm showed that almost 75% of the low level inflow was transported to altitudes 

exceeding 8 km. Over 35% of the air parcels reached altitudes over 12 km. Tracer transport calculations 

were performed for CO, NOX, O3, and hydrocarbons. Rural boundary layer NOX was only 0.9 ppb, 

whereas the urban plume contained about 3 ppb. In the rural case, mixing ratios of 0.6 ppb were 

transported up to 11 km. Cleaner air descended at the rear of the storm lowering NOX at the surface from 

0.9 to 0.5 ppb. In the urban plume, mixing ratios in the updraft core reached 1 ppb between 14 and 15 km. 

At the surface, the main downdraft lowered the NOX mixing ratios from 3 to 0.7 ppb. 

Regional chemical transport models have been used for applications such as simulations of 

photochemical O3 production, acid deposition, and fine PM. Walcek et al. (1990) included a 

parameterization of cloud-scale aqueous chemistry, scavenging, and vertical mixing in the chemistry 

model of Chang et al. (1987). The vertical distribution of cloud microphysical properties and the amount 

of sub-cloud-layer air lifted to each cloud layer are determined using a simple entrainment hypothesis 

(Walcek and Taylor, 1986). Vertically integrated O3 formation rates over the northeast U. S. were 

enhanced by ~50% when the in-cloud vertical motions were included in the model. 

Wang et al. (1996) simulated the 10-11 June 1985 PRE-STORM squall line with the NCAR/Penn 

State Mesoscale Model (MM5) (Grell et al., 1994; Dudhia, 1993). Convection was parameterized as a 

sub-grid-scale process in MM5 using the Kain and Fritsch (1993) scheme. Mass fluxes and detrainment 

profiles from the convective parameterization were used along with the 3-D wind fields in CO tracer 

transport calculations for this convective event. 

Convective transport in global chemistry and transport models is treated as a sub-grid-scale process 

that is parameterized typically using cloud mass flux information from a general circulation model or 

global data assimilation system. While GCMs can provide data only for a “typical” year, data assimilation 

systems can provide “real” day-by-day meteorological conditions, such that CTM output can be compared 
2-40

directly with observations of trace gases. The NASA Goddard Earth Observing System Data Assimilation 

System (GEOS-1 DAS and successor systems; Schubert et al., 1993; Bloom et al., 1996; Bloom et al., 

2005) provides archived global data sets for the period 1980 to present, at 2Ε Η 2.5Ε or better resolution 

with 20 layers or more in the vertical. Deep convection is parameterized with the Relaxed 

Arakawa-Schubert scheme (Moorthi and Suarez, 1992) in GEOS-1 and GEOS-3 and with the Zhang and 

McFarlane (1995) scheme in GEOS-4. Pickering et al. (1995) showed that the cloud mass fluxes from 

GEOS-1 DAS are reasonable for the 10-11 June 1985 PRE-STORM squall line based on comparisons 

with the GCE model (cloud-resolving model) simulations of the same storm. In addition, the GEOS-1 

DAS cloud mass fluxes compared favorably with the regional estimates of convective transport for the 

central United States presented by Thompson et al. (1994). However, Allen et al. (1997) have shown that 

the GEOS-1 DAS overestimates the amount and frequency of convection in the tropics and 

underestimates the convective activity over midlatitude marine storm tracks. 

Global models with parameterized convection and lightning have been run to examine the roles of 

these processes over North America. Lightning contributed 23% of upper tropospheric NOY over the 

SONEX region according to the UMD-CTM modeling analysis of Allen et al. (2000). During the summer 

of 2004 the NASA Intercontinental Chemical Transport Experiment - North America (INTEX-NA) was 

conducted primarily over the eastern two-thirds of the United States, as a part of the International 

Consortium for Atmospheric Research on Transport and Transformation (ICARTT). Deep convection 

was prevalent over this region during the experimental period. Cooper et al. (2006) used a particle 

dispersion model simulation for NOX to show that 69-84% of the upper tropospheric O3 enhancement 

over the region in Summer 2004 was due to lightning NOX. The remainder of the enhancement was due to 

convective transport of O3 from the boundary layer or other sources of NOX. Hudman et al. (2007) used a 

GEOS-Chem model simulation to show that lightning was the dominant source of upper tropospheric 

NOX over this region during this period. Approximately 15% of North American boundary layer NOX

emissions were shown to have been vented to the free troposphere over this region based on both the 

observations and the model. 

AX2.5.2. CTM Evaluation 

The comparison of model predictions with ambient measurements represents a critical task for 

establishing the accuracy of photochemical models and evaluating their ability to serve as the basis for 

making effective control strategy decisions. The evaluation of a model’s performance, or its adequacy to 

perform the tasks for which it was designed can only be conducted within the context of measurement 

errors and artifacts. Not only are there analytical problems, but there are also problems in assessing the 

representativeness of monitors at ground level for comparison with model values which represent 

typically an average over the volume of a grid box. 

Global-scale CTMs have generally been evaluated by comparison with measurements for a wide 

array of species, rather than just for O3 (e.g., Wang et al., 1998; Emmons et al., 2000; Bey et al., 2001; 

Hess, 2001; Fiore et al., 2002). These have included evaluation of major primary species (NOX, CO, and 

selected VOCs) and an array of secondary species (HNO3, PAN, H2O2) that are often formed concurrently 

with O3. Models for urban and regional O3 have also been evaluated against a broader ensemble of 

measurements in a few cases, often associated with measurement intensives (e.g., Jacobson et al., 1996; 

Lu et al., 1997; Sillman et al., 1998). The results of a comparison between observed and computed 

concentrations from Jacobson et al. (1996) for the Los Angeles Basin are shown in Figures AX2.5-1 and 

AX2.5-2. 

The highest concentrations of primary species usually occur in close proximity to emission sources 

(typically in urban centers) and at times when dispersion rates are low. The diurnal cycle includes high 

concentrations at night, with maxima during the morning rush hour, and low concentrations during the 

afternoon (Figure AX2.5-1). The afternoon minima are driven by the much greater rate of vertical mixing 

at that time. Primary species also show a seasonal maximum during winter, and are often high during fog 
2-41

episodes in winter when vertical mixing, is suppressed. By contrast, secondary species such as O3 are 

typically highest during the afternoon (the time of greatest photochemical activity), on sunny days and 

during summer. 

During these conditions, concentrations of primary species may be relatively low. Strong 

correlations between primary and secondary species are generally observed only in downwind rural areas 

where all anthropogenic species are simultaneously elevated. The difference in the diurnal cycles of 

primary species (CO, NOX and ethane) and secondary species (O3, PAN, and HCHO) is evident in Figure 

AX2.5-2. 

Source: Jacobson et al. (1996) 

Figure AX2.5-1. Time series for measured gas-phase species in comparison with results from 

aphotochemical model. The dashed lines represent measurements, and solid lines 

represent model predictions (in parts per million, ppmv) for August 26–28, 1988 at sites in 

southern California. The horizontal axis represents hours past midnight, August 25. Results 

represent O3 and NOX at Reseda, and CO at Riverside. 
2-42

Source: Jacobson et al. (1996). 

Figure AX2.5-2. Time series for measured gas-phase species in comparison with results from a 

photochemical model. The circles represent measurements, and solid lines represent 

model predictions (in parts per million, ppmv) for August 26–28, 1988 at sites in southern 

California. The horizontal axis represents hours past midnight, August 25. Results represent 

ethane and formaldehyde at Claremont, and PAN at Los Angeles. 

Models for urban and regional chemistry have been evaluated less extensively than global-scale 

models in part because the urban/regional context presents a number of difficult challenges. Global-scale 

models typically represent continental-scale events and can be evaluated effectively against a sparse 

network of measurements. By contrast, urban/regional models are critically dependent on the accuracy of 

local emission inventories and event-specific meteorology, and must be evaluated separately for each 

urban area that is represented. 

The evaluation of urban/regional models is also limited by the availability of data. Measured NOX

and speciated VOC concentrations are widely available through the regulatory networks of the U.S. EPA. 

Evaluation of urban/regional models versus measurements has generally relied on results from a limited 

number of field studies in the United States. Short-term, research-grade measurements for species 

relevant to O3 formation, including VOCs, NOX, PAN, HNO3, and H2O2 are also available at selected 
2-43

rural and remote sites (e.g., Daum et al., 1990, 1996; Martin et al., 1997; Young et al., 1997; Thompson 

et al., 2000; Hoell et al., 1997, 1999; Fehsenfeld et al., 1996; Emmons et al., 2000; Hess, 2001; Carroll 

et al., 2001). The equivalent measurements are available for some polluted rural sites in the eastern 

United States, but only at a few urban locations (Meagher et al., 1998; Hübler et al., 1998; Kleinman 

et al., 2000, 2001; Fast et al., 2002; Lu et al., 1997). Extensive measurements have also been made in 

Vancouver (Steyn et al., 1997) and in several European cities (Staffelbach et al., 1997; Prévôt et al., 1997, 

Dommen et al., 1999; Geyer et al., 2001; Thielman et al., 2001; Martilli et al., 2002; Vautard et al., 2002). 

The results of straightforward comparisons between observed and predicted concentrations of O3

can be misleading because of compensating errors, although this possibility is diminished when a number 

of species are compared. Ideally, each of the main modules of a CTM system (for example, the 

meteorological model and the chemistry and radiative transfer routines) should be evaluated separately. 

However, this is rarely done in practice. To better indicate how well physical and chemical processes are 

being represented in the model, comparisons of relations between concentrations measured in the field 

and concentrations predicted by the model can be made. These comparisons could involve ratios and 

correlations between species. For example, correlation coefficients could be calculated between primary 

species as a means of evaluating the accuracy of emission inventories or between secondary species as a 

means of evaluating the treatment of photochemistry in the model. In addition, spatial relations involving 

individual species (correlations, gradients) can also be used as a means of evaluating the accuracy of 

transport parameterizations. Sillman and He (2002) examined differences in correlation patterns between 

O3 and NOZ in Los Angeles, CA, Nashville, TN, and various sites in the rural United States. Model 

calculations (Figure AX2.5-3) show differences in correlation patterns associated with differences in the 

sensitivity of O3 to NOX and VOCs. Primarily NOX-sensitive (NOX-limited) areas in models show a 

strong correlation between O3 and NOZ with a relatively steep slope, while primarily VOC-sensitive 

(NOX-saturated) areas in models show lower O3 for a given NOZ and a lower O3-NOZ slope. They found 

that differences found in measured data ensembles were matched by predictions from chemical transport 

models. Measurements in rural areas in the eastern United States show differences in the pattern of 

correlations for O3 versus NOZ between summer and autumn (Jacob et al., 1995; Hirsch et al., 1996), 

corresponding to the transition from NOX-limited to NOX-saturated patterns, a feature which is also 

matched by CTMs. 
2-44

Source: Sillman and He (2002). 

Figure AX2.5-3. Correlations for O3 versus NOZ (NOY–NOX) in ppb from chemical transport models for the 

northeast corridor, Lake Michigan, Nashville, the San Joaquin Valley, and Los Angeles. 

Each location is classified as NOX-limited or NOX-sensitive (circles), NOX-saturated or VOCsensitive (crosses), mixed or with near-zero sensitivity (squares), and dominated by NOX

titration (asterisks) based on the model response to reduced NOX and VOC. 

The difference in correlations between secondary species in NOX-limited to NOX-saturated environments 

can also be used to evaluate the accuracy of model predictions in individual applications. Figures 

AX2.5-4, show results for two different model scenarios for Atlanta. As shown in the figures, the first 

model scenario predicts an urban plume with high NOY and O3 formation apparently suppressed by high 

NOY. Measurements show much lower NOY in the Atlanta plume. This error was especially significant 

because the model locations sensitive to NOX. The second model scenario (with primarily NOX sensitive 

conditions) shows much better agreement with measured values. Figure AX2.5-5 shows modelmeasurement comparison for secondary species in Nashville, showing better agreement with measured 

conditions. Greater confidence in the predictions made by CTMs will be gained by the application of 

techniques such as these on a more routine basis. 
2-45

Source: Sillman et al. (1997). 

Figure AX.2.5-4. Evaluation of model versus measured O3 versus NOY for two model scenarios for Atlanta. 

The model values are classified as NOX-limited (circles), NOX-saturated (crosses), or mixed 

or with low sensitivity to NOX (squares). Diamonds represent aircraft measurements. 
2-46

Source: Sillman et al. (1998). 

Figure AX2.5-5. Evaluation of model versus: (a) measured O3 versus NOZ and (b) O3 versus the sum 2H2O2 + 

NOZ for Nashville, TN. The model values are classified as NOX-limited (gray circles), NOXsaturated (X’s), mixed or near-zero sensitivity (squares), or dominated by NOX titration (filled 

circles). Diamonds represent aircraft measurements. 

The ability of chemical mechanisms to calculate the concentrations of free radicals under 

atmospheric conditions was tested in the Berlin Ozone Experiment, BERLIOZ (Volz-Thomas et al., 2003) 

during July and early August at a site located about 50 km NW of Berlin. (This location was chosen 

because O3 episodes in central Europe are often associated with SE winds.) 

Concentrations of major compounds such as O3, hydrocarbons, etc., were fixed at observed values. 

Figure AX2.5-6 compares the concentrations of RO2, HO2, and OH radicals predicted by RACM and 
2-47

MCM with observations made by the laser-induced fluorescence (LIF) technique and by matrix isolation 

ESR spectroscopy (MIESR). Also shown are the production rates of O3 calculated using radical 

concentrations predicted by the mechanisms and those obtained by measurements, and measurements of 

NOX concentrations. As can be seen, there is good agreement between measurements of RO2, HO2, OH, 

radicals with values predicted by both mechanisms at high concentrations of NOX (>10 ppb). However, at 

lower NOX concentrations, both mechanisms substantially overestimate OH concentrations and 

moderately overestimate HO2 concentrations. Agreement between models and measurements is generally 

better for organic peroxy radicals, although the MCM appears to overestimate their concentrations 

somewhat. In general, the mechanisms reproduced the HO2 to OH and RO2 to OH ratios better than the 

individual measurements. The production of O3 was found to increase linearly with NO (for NO < 

0.3 ppb) and to decrease with NO (for NO > 0.5 ppb). 

OH and HO2 concentrations measured during the PM2.5 Technology Assessment and 

Characterization Study conducted at Queens College in New York City in the summer of 2001 were also 

compared with those predicted by RACM (Ren et al., 2003). The ratio of observed to predicted HO2

concentrations over a diurnal cycle was 1.24 and the ratio of observed to predicted OH concentrations 

was about 1.10 during the day, but the mechanism significantly underestimated OH concentrations during 

the night. 
2-48

Source: Volz-Thomas et al. (2003). 

Figure AX2.5-6. Time series of concentrations of RO2, HO2, and OH radicals, local O3 photochemical 

production rate and concentrations of NOX. These are from measurements made during 

BERLIOZ. Also shown are comparisons with results of photochemical box model 

calculations using the RACM and MCM chemical mechanisms. 
2-49

AX2.6. Sampling and Analysis of NOX

AX2.6.1. Availability and Accuracy of Ambient NOY Measurements 

Sections AX2.6.1-AX2.6.4 focus on current methods and on promising new technologies, but no 

attempt is made here to cover the extensive development of these methods or of methods such as wet 

chemical techniques no longer in widespread use. More detailed discussions of these methods may be 

found elsewhere (U.S. Environmental Protection Agency, 1993, 1996). McClenny (2000), Parrish and 

Fehsenfeld (2000), and Clemitshaw (2004) reviewed methods for measuring NOX and NOY compounds. 

Discussions in Sections 2.6.1-2.6.4 center on chemiluminescence and optical Federal Reference and 

Equivalent Methods (FRM and FEM, respectively). 

The use of methods such as observationally based methods or source apportionment models, either 

as stand-alone methods or as a basis for evaluating chemical transport models, is often limited by the 

availability and accuracy of measurements. Measured NOX and speciated VOC concentrations are widely 

available in the United States through the Photochemical Assessment Monitoring Stations (PAMS) 

network. The PAMs network currently includes measured NO and NOX. However, Cardelino and 

Chameides (2000) reported that measured NO during the afternoon was frequently at or below the 

detection limit of the instruments (1 ppb), even in large metropolitan regions (Washington, DC; Houston, 

TX; New York, NY). NO2 measurements are made with commercial chemiluminescent detectors with hot 

molybdenum converters. However, these measurements typically include a wide variety of other reactive 

N species, such as organic nitrates in addition to NOX, and cannot be interpreted as a “pure” NOX

measurement (see summary in Parrish and Fehsenfeld, 2000). 

Total reactive nitrogen (NOY) is included in the PAMS network only at a few sites. The possible 

expansion of PAMS to include more widespread NOY measurements has been suggested (McClenny, 

2000). NOY measurements are also planned for inclusion in the NCore network (U.S. Environmental 

Protection Agency, 2005). A major issue to be considered when measuring NOX and NOY is the 

possibility that HNO3, a major component of NOY, is sometimes lost in inlet tubes and not measured 

(Luke et al., 1998; Parrish and Fehsenfeld, 2000). This problem is especially critical if measured NOY is 

used to identify NOX-limited versus NOX-saturated conditions. The problem is substantially alleviated 

although not necessarily completely solved by using much shorter inlets on NOY monitors than on NOX

monitors and by the use of surfaces less likely to take up HNO3. The correlation between O3 and NOY

differs for NOX-limited versus NOX-saturated locations, but this difference is driven primarily by 

differences in the ratio of O3 to HNO3. If HNO3 were omitted from the NOY measurements, then the 

measurements would represent a biased estimate and their use would be problematic. 

AX2.6.1.1. Measurement of NO 

Gas-phase Chemiluminescence (CL) Methods 

Nitric oxide can be measured reliably using the principle of gas-phase chemiluminescence induced 

by the reaction of NO with O3 at low pressure. Modern commercial NOX analyzers have sufficient 

sensitivity and specificity for adequate measurement in urban and many rural locations (U.S. 

Environmental Protection Agency, 1993, 1996, 2006). Research grade CL instruments have been 

compared under realistic field conditions to spectroscopic instruments, and the results indicate that both 

methods are reliable (at concentrations relevant to smog studies) to better than 15 percent with 95 percent 

confidence. Response times are on the order of 1 minute. For measurements meaningful for understanding 

O3 formation, emissions modeling, and N deposition, special care must be taken to zero and calibrate the 

instrument frequently. A chemical zero, obtained by reacting the NO up-stream and out of view of the 

photomultiplier tube, is preferred because it accounts for interferences such as light emitting reactions 
2-50

with unsaturated hydrocarbons. Standard additions of NO at the inlet will account for NO loss or 

conversion to NO2 in the lines. In summary, CL methods, when operated carefully in an appropriate 

manner, can be suitable for measuring or monitoring NO (e.g., Crosley, 1996). 

Spectroscopic Methods for NOX

NO has also been successfully measured in ambient air with direct spectroscopic methods; these 

include two-photon laser-induced fluorescence (TPLIF), tunable diode laser absorption spectroscopy 

(TDLAS), and two-tone frequency-modulated spectroscopy (TTFMS). These were reviewed thoroughly 

in the previous AQCD and will be only briefly summarized here. The spectroscopic methods demonstrate 

excellent sensitivity and selectivity for NO with detection limits on the order of 10 parts per trillion (ppt) 

for integration times of 1 min. Spectroscopic methods compare well with the CL method for NO in 

controlled laboratory air, ambient air, and heavily polluted air (e.g., Walega et al., 1984; Gregory et al., 

1990; Kireev et al., 1999). These spectroscopic methods remain in the research arena due to their 

complexity, size, and cost, but are essential for demonstrating that CL methods are reliable for monitoring 

NO concentrations involved in O3 formation, from around 20 ppt to several hundred of ppb. 

Atmospheric pressure laser ionization followed by mass spectroscopy has also been deployed for 

detection of NO and NO2. Garnica et al. (2000) describe a technique involving selective excitation at one 

wavelength followed by ionization at a second wavelength. They report good selectivity and detection 

limits well below 1 ppb. The practicality of the instrument for ambient monitoring, however, has yet to be 

demonstrated. 

AX2.6.1.2. Measurements of NO2

Gas-Phase Chemiluminescence Methods 

Reduction of NO2 to NO on the surface of a heated (to 300 to 400°C) molybdenum oxide 

(MoOx)substrate followed by detection of the chemiluminescence produced during the reaction of NO 

with O3 at low pressure as described earlier for measurement of NO serves as the basis of the FRM for 

measurement of ambient NO2. However, the substrate used in the reduction of NO2 to NO is not specific 

to NO2; hence the chemiluminescence analyzers are subject to interference from nitrogen oxides other 

than NO2 produced by oxidized NOY compounds or NOZ. Thus, this technique will overestimate NO2

concentrations particularly in areas downwind of sources of NO and NO2 as NOX is oxidized to NOZ in 

the form of PANs and other organic nitrates, and HNO3 and HNO4. Many of these compounds are 

reduced at the catalyst with nearly the same efficiency as NO2. Interferences have also been found from a 

wide range of other compounds as described in the latest AQCD for NO2. 

Other Methods 

NO2 can be selectively converted to NO by photolysis. For example, Ryerson et al. (2000) 

developed a gas-phase chemiluminescence method using a photolytic converter based on a Hg lamp with 

increased radiant intensity in the region of peak NO2 photolysis (350 to 400 nm) and producing 

conversion efficiencies of 70% or more in less than 1 s. Metal halide lamps with conversion efficiency of 

about 50% and accuracy on the order of 20% (Nakamura et al., 2003) have been used. Because the 

converter produces little radiation at wavelengths less than 350 nm, interferences from HNO3 and PAN 

are minimal. Alternative methods to photolytic reduction followed by CL are desirable to test the 

reliability of this widely used technique. Any method based on a conversion to measured species presents 

potential for interference a problem. Several atmospheric species, PAN and HO2NO2 for example, 

dissociate to NO2 at higher temperatures. 
2-51

Laser induced fluorescence for NO2 detection involves excitation of atmospheric NO2 with laser 

light emitted at wavelengths too long to induce photolysis. The resulting excited molecules relax in a 

photoemissive mode and the fluorescing photons are counted. Because collisions would rapidly quench 

the electronically excited NO2, the reactions are conducted at low pressure. Matsumi et al. (2001) describe 

a comparison of LIF with a photofragmentation chemiluminescence instrument. The LIF system involves 

excitation at 440 nm with a multiple laser system. They report sensitivity of 30 ppt in 10 s and good 

agreement between the two methods under laboratory conditions at mixing ratios up to 1.0 ppb. This 

high-sensitivity LIF system has yet to undergo long-term field tests. Cleary et al. (2002) describe field 

tests of a system that uses continuous, supersonic expansion followed by excitation at 640 nm with a 

commercial tunable diode laser. More recently, LIF has been successfully used to detect NO2 with 

accuracy of about 15% and detections limits well below 1 ppb. When coupled with thermal dissociation, 

the technique also measures peroxy nitrates such as PAN, alkyl nitrates, HNO4 and HNO3 (Cohen, 1999; 

Day et al., 2002; Farmer et al., 2006; Pérez et al., 2007; Thornton et al., 2003). This instrument can have 

very fast sampling rates (>1 Hz) and shows good correlation with chemiluminescent techniques but 

remains a research-grade device. 

NO2 can be detected by differential optical absorption spectroscopy (DOAS) in an open, long-path 

system by measuring narrow band absorption features over a background of broad band extinction (e.g., 

Stutz et al., 2000; Kim and Kim, 2001). A DOAS system manufactured by OPSIS is designated as a 

Federal Equivalent Method for measuring NO2. DOAS systems can also be configured to measure NO, 

HONO, and NO3 radicals. Typical detection limits are 0.2 to 0.3 ppb for NO, 0.05 to 0.1 ppb for NO2, 

0.05 to 0.1 ppb for HONO, and 0.001 to 0.002 ppb for NO3, at path lengths of 0.2, 5, 5, and 10 km, 

respectively. The obvious advantage compared to fixed point measurements is that concentrations 

relevant to a much larger area are obtained, especially if multiple targets are used. At the same time, any 

microenvironmental artifacts are minimized over the long path integration. However, comparisons to 

other measurements made at point not a real location are difficult. A major limitation in this technique 

had involved inadequate knowledge of absorption cross sections. Harder et al. (1997) conducted an 

experiment in rural Colorado involving simultaneous measurements of NO2 by DOAS and by photolysis 

followed by chemiluminescence. They found differences of as much as 110% in clean air from the west, 

but for NO2 mixing ratios in excess of 300 ppt, the two methods agreed to better than 10%. Stutz et al. 

(2000) cites two intercomparisons of note. NO was measured by DOAS, by photolysis of NO2 followed 

by chemiluminescence, and by LIF during July 1999 as part of the SOS in Nashville, TN. On average, the 

three methods agreed to within 2%, with some larger differences likely caused by spatial variability over 

the DOAS path. In another study in Europe, and a multi-reflection set-up over a 15 km path, negated the 

problem of spatial averaging here agreement with the chemiluminescence detector following photolytic 

conversion was excellent (slope = 1.006 ± 0.005; intercept = 0.036 ± 0.019; r = 0.99) over a concentration 

range from about 0.2 to 20 ppb. 

NO2 can also be detected from space with DOAS-like UV spectroscopy techniques (Kim et al., 

2006; Ma et al., 2006). These measurements appear to track well with emissions estimates and can be a 

useful indicator of column content as well as for identifying hot spots in sources. See also Richter et al., 

2005. Leigh (2006) report on a DOAS method that uses the sun as a light source and compares well with 

an in situ chemiluminescence detector in an urban environment. 

Chemiluminescence on the surface of liquid luminol has also been used for measurement of NO2

(Gaffney et al., 1998; Kelly et al., 1990; Marley et al., 2004; Nikitas et al., 1997; Wendel et al., 1983). 

This technique is sensitive and linear and more specific than hot MoOX. Luminol does not emit light 

when exposed to HNO3 or alkyl nitrates, but does react with PAN. This interference can be removed by 

chromatographic separation prior to detection and the resulting measurement compares well with more 

specific techniques for moderate to high (≥1 ppb) mixing ratios of NO2. 

Several tunable diode laser spectroscopy techniques have been used successfully for NO2 detection 

(Eisele et al., 2003; Osthoff et al., 2006). These devices remain research grade instruments, not yet 

practical for urban monitoring. 
2-52

Measurements of NOY 

Gold catalyzed CO or H2 reduction for conversion on hot MoOx catalyst has been used to reduce 

NOY to NO before then detection by chemiluminescence (Fehsenfeld et al., 1987; Crosley, 1996). Both 

techniques offer generally reliable measurements, with response times on the order of 60 s and a linear 

dynamic range demonstrated in field intercomparisons from about 10 ppt to 10s of ppb. Under certain 

conditions, HCN, NH3, RNO2, and CH3CN can be converted to NO, but at normal concentrations and 

humidity these are minor interferences. Thermal decomposition followed by LIF has also been used for 

NOY detection, as described above. In field comparisons, instruments based on these two principles 

generally showed good agreement (Day et al., 2002). The experimental uncertainty is estimated to be of 

15-30%. 

AX2.6.1.3. Monitoring for NO2 Compliance Versus Monitoring for O3 Formation 

Regulatory measurements of NO2 have been focused on demonstrating compliance with the 

NAAQS for NO2. Today, few locations violate that standard, but NO2 and related NOY compounds 

remain among the most important atmospheric trace gases to measure and understand. Unfortunately, 

with an internal MoOx converter for NOX to NO conversion, the instruments may not give a faithful 

indication of total NOY either; reactive species such as HNO3 will adhere to the walls of the inlet system. 

Most recently, commercial vendors such as Thermo Environmental (Franklin, MA) have offered NO/NOY

detectors with external MoOx converters. If such instruments are calibrated through the inlet with a 

reactive nitrogen species such as propyl nitrate, they give accurate measurements of total NOY suitable for 

evaluation of photochemical models. (Crosley, 1996; Fehsenfeld et al., 1987; Nunnermacker et al., 1998; 

Rodgers and Davis, 1989). Under conditions of fresh emissions, such as in urban areas during the rush 

hour, NOY ~ NOX and these monitors can be used for testing emissions inventories (Dickerson, et al., 

1995; Parrish, 2006). 

AX2.6.2. Summary of Methods for Measuring NO2

A variety of techniques exist for reliable monitoring of atmospheric NO2 and related reactive 

nitrogen species. For demonstration of compliance with the NAAQS for NO2, commercial 

chemiluminescence instruments are adequate. For certain conditions, luminol chemiluminescence is 

adequate. Precise measurements of NO2 can be made with research grade instruments such as LIF and 

TDLS. For path-integrated concentration determinations UV spectroscopic methods provide useful 

information. Commercial NOX instruments are sensitive to other NOY species, but do not measure NOY

quantitatively. NOY instruments with external converters offer measurements more useful for comparison 

to chemical transport model calculations. 

AX2.6.3. Measurements of HNO3

Accurate measurement of HNO3, has presented a long-standing analytical challenge to the 

atmospheric chemistry community. In this context, it is useful to consider the major factors that control 

HNO3 partitioning between the gas and deliquesced-particulate phases in ambient air. In equation form, 

 (AX2.6-1) 

where KH is the Henry’s Law constant in M atm−1

 and Ka is the acid dissociation constant in M. 
2-53

Thus, the primary controls on HNO3 phase partitioning are its thermodynamic properties (KH, Ka, 

and associated temperature corrections), aerosol liquid water content (LWC), solution pH, and kinetics. 

Aerosol LWC and pH are controlled by the relative mix of different acids and bases in the system, 

hygroscopic properties of condensed compounds, and meteorological conditions such as RH, temperature, 

and pressure. It is evident from relationship AX2.6-1 that, in the presence of chemically distinct aerosols 

of varying acidities (e.g., supermicron predominantly sea salt and submicron predominantly S aerosol), 

HNO3 will partition preferentially with the less-acidic particles; and this is consistent with observations 

(e.g., Huebert et al., 1996; Keene and Savoie, 1998; Keene et al., 2002). Kinetics are controlled by 

atmospheric concentrations of HNO3 vapor and particulate NO3

-

 and the size distribution and 

corresponding atmospheric lifetimes of particles against deposition. Submicron diameter aerosols 

typically equilibrate with the gas phase in seconds to minutes while supermicron aerosols require hours to 

a day or more (e.g., Meng and Seinfeld, 1996; Erickson et al., 1999). Consequently, smaller aerosol size 

fractions are typically close to thermodynamic equilibrium with respect to HNO3 whereas larger size 

fractions (for which atmospheric lifetimes against deposition range from hours to a few days) are often 

undersaturated (e.g., Erickson et al., 1999; Keene and Savioe, 1998). 

Many sampling techniques for HNO3 (e.g., annular denuder, standard filterpack and mist-chamber 

samplers) employ upstream prefilters to remove particulate species from sample air. However, when 

chemically distinct aerosols with different pHs (e.g., sea salt and S aerosols) mix together on a bulk filter, 

the acidity of the bulk mixture will be greater than that of the less acidic aerosols with which most NO3

-

 is 

associated. This change in pH may cause the bulk mix to be supersaturated with respect to HNO3 leading 

to volatilization and, thus, positive measurement bias in HNO3 sampled downstream. Alternatively, when 

undersaturated supermicron size fractions (e.g., sea salt) accumulate on a bulk filter and chemically 

interact over time with HNO3 in the sample air stream, scavenging may lead to negative bias in HNO3

sampled downstream. Because the magnitude of both effects will vary as functions of the overall 

composition and thermodynamic state of the multiphase system, the combined influence can cause net 

positive or net negative measurement bias in resulting data. Pressure drops across particle filters can also 

lead to artifact volatilization and associated positive bias in HNO3 measured downstream. 

Widely used methods for measuring HNO3 include standard filterpacks configured with nylon or 

alkaline-impregnated filters (e.g., Goldan et al., 1983; Bardwell et al., 1990), annular denuders (EPA 

Method IP-9), and standard mist chambers (Talbot et al., 1990). Samples from these instruments are 

typically analyzed by ion chromatography. Intercomparisons of these measurement techniques (e.g., 

Hering et al., 1988; Tanner et al., 1989; Talbot et al., 1990) report differences on the order of a factor of 

two or more. 

More recently, sensitive HNO3 measurements based on the principle of Chemical Ionization Mass 

Spectroscopy (CIMS) have been reported (e.g., Huey et al., 1998; Mauldin et al., 1998; Furutani and 

Akimoto, 2002; Neuman et al., 2002). CIMS relies on selective formation of ions such as SiF5

-

≅HNO3 or 

HSO4

-

≅HNO3 followed by detection via mass spectroscopy. Two CIMS techniques and a filter pack 

technique were intercompared in Boulder, CO (Fehsenfeld et al., 1998). Results indicated agreement to 

within 15% between the two CIMS instruments and between the CIMS and filterpack methods under 

relatively clean conditions with HNO3 mixing ratios between 50 and 400 ppt. In more polluted air, the 

filterpack technique generally yielded higher values than the CIMS suggesting that interactions between 

chemically distinct particles on bulk filters is a more important source of bias in polluted continental air. 

Differences were also greater at lower temperature when particulate NO3

−

 corresponded to relatively 

greater fractions of total NO3

-

. 

Three semi-continuous methods for detecting nitric acid (HNO3) were tested against the annular 

denuder filter pack (ADS) integrated collection technique at the Tampa Bay Regional Atmospheric 

Chemistry Experiment (BRACE) Sydney research station ~20 km downwind of the Tampa, Florida, 

urban core. The semi-continuous instruments included: two slightly differing implementations of the NOY

-- NOY* (total oxides of nitrogen minus that total denuded of HNO3) denuder difference technique, one 

from the NOAA Air Resources Lab (ARL), and one from Atmospheric Research and Analysis, Inc. 

(ARA); the parallel plate wet diffusion scrubber online ion chromatography technique from Texas Tech 
2-54

University (TTU); and the chemical ionization mass spectrometer from the Georgia Institute of 

Technology (GIT). Twelve hour ADS samples were collected by the University of South Florida (USF). 

Results for 10 min samples computed from the various higher sampling frequencies of each semicontinuous instrument showed good agreement (R2

>0.7)for afternoon periods of the highest production 

and accumulation of HNO3. Further, agreement was within ± 30% for these instruments even at HNO3

concentrations 0.30 ppb. The USF ADS results were biased low, however, by 44%, on average, compared 

to the corporate 12-h aggregated means from the semi-continuous methods, and by 460% for the 

nighttime samples; ADS results were below the corporate mean maximum HNO3 concentration by 430% 

as well. The four instruments using semi-continuous methods, by contrast, were all within 10% of each 

other’s 12-h mean mixing ratios. While only ARA employed a formal minimum detection limit at 

0.050 ppb, error analysis with the other techniques established that at the same level of precision, TTU’s 

effective limit was approximately the same as ARA’s and that ARL’s limit was 0.030 ppb; analysis for 

GIT showed no apparent effective limit at the levels of HNO3 encountered in this field study. The 

importance of sample inlet height for HNO3measurements was indirectly shown through comparison to 

previous field work at this site when sample inlet heights ranged from 1.5–10 m and produced systematic 

discrepancies in HNO3 concentrations correlated with height of more than a factor of 2 (Arnold et al., 

2007). 

AX2.6.4. Techniques for Measuring Other NOY Species 

Methods for sampling and analysis of alkyl nitrates in the atmosphere have been reviewed by 

Parrish and Fehsenfeld (2000). Peroxyacetylnitrate, PPN, and MPAN are typically measured using a 

chromatograph followed by electron capture detectors or GC/ECD (e.g., Gaffney et al., 1998), although 

other techniques such as FTIR could also be used. Field measurements are made using GC/ECD with a 

total uncertainty of ± 5 ppt + 15% (Roberts et al., 1998). 

In the IMPROVE network and in the EPA Chemical Speciation Network (CSN), particulate nitrate 

in the PM2.5 size range is typically collected on nylon filters downstream of annular denuders coated with 

a basic solution capable of removing acidic gases such as HNO3, HNO2, and SO2. Filter extracts are then 

analyzed by ion chromatography (IC) for nitrate, sulfate, and chloride. Nitrite ions are also measured by 

this technique but their concentrations are almost always beneath detection limits. However, both of these 

networks measure nitrate only in the PM2.5 fraction. Because of interactions with more highly acidic 

components on filter surfaces, there could be volatilization of nitrate in PM10 samples. These effects are 

minimized if separate aerosol size fractions are collected, i.e., the more acidic PM2.5 and the more alkaline 

PM10-2.5 as in a dichotomous sampler or multistage impactor. 

AX2.6.5. Remote Sensing of Tropospheric NO2 Columns for Surface 

NOX Emissions and Surface NO2 Concentrations 

Table AX2.6-1 contains an overview of the three satellite instruments that are used retrieve 

tropospheric NO2 columns from measurements of solar backscatter. All three instruments are in polar 

sun-synchronous orbits with global measurements in the late morning and early afternoon. The spatial 

resolution of the measurement from SCIAMACHY is 7 times better than that from Ozone Monitoring 

Instrument (GOME), and that from Ozone Monitoring Instrument (OMI) is 40 times better than that from 

GOME. 
2-55

Table AX2.6-1. Satellite Instruments Used to Retrieve Tropospheric NO2 Columns. 

INSTRUMENT COVERAGE TYPICAL U.S. 

MEASUREMENT TIME 

TYPICAL 

RESOLUTION (KM) 

RETURN TIME 

(DAYS)1

INSTRUMENT 

OVERVIEW 

GOME 1995-2002 10:30-11:30 AM 320 Η 40 3 Burrows et al. (1999) 

SCIAMACHY 2002- 10:00-11:00 AM 30 Η 60 6 Bovensmann et al. 

(1999) 

OMI 2004- 12:45-1:45 PM 13 Η 24 1 Levelt et al. (2006) 

1

 Return time is reported here for cloud free conditions. Note that due to precession of the satellite’s orbit, return measurements are 

close to but not made over the same location. In practice, clouds decrease observation frequency by a factor of 2. 

Figure AX2.6-1 shows tropospheric NO2 columns retrieved from SCIAMACHY. Pronounced 

enhancements are evident over major urban and industrial emissions. The high degree of spatial 

heterogeneity over the southwestern United States provides empirical evidence that most of the 

tropospheric NO2 column is concentrated in the lower troposphere. Tropospheric NO2 columns are more 

sensitive to NOX in the lower troposphere than in the upper troposphere (Martin et al., 2002). This 

sensitivity to NOX in the lower troposphere is due to the factor of 25 decrease in the NO2/NO ratio from 

the surface to the upper troposphere (Bradshaw et al., 1999) that is driven by the temperature dependence 

of the NO + O3 reaction. Martin et al. (2004a) integrated in situ airborne measurements of NO2 and found 

that during summer the lower mixed layer contains 75% of the tropospheric NO2 column over Houston 

and Nashville. However, it should be noted that these measurements are also sensitive to surface albedo 

and aerosol loading. 
2-56

Source: Martin et al. (2006). 

Figure AX2.6-1. Tropospheric NO2 columns (molecules NO2/cm2) retrieved from the SCIAMACHY satellite 

instrument for 2004-2005. 

The retrieval involves three steps: (1) determining total NO2 line-of-sight (slant) columns by 

spectral fitting of solar backscatter measurements, (2) removing the stratospheric columns by using data 

from remote regions where the tropospheric contribution to the column is small, and (3) applying an air 

mass factor (AMF) for the scattering atmosphere to convert tropospheric slant columns into vertical 

columns. The retrieval uncertainty is determined by (1) and (2) over remote regions where there is little 

tropospheric NO2, and by (3) over regions in regions of elevated tropospheric NO2 (Martin et al., 2002; 

Boersma et al., 2004). 

The paucity of in situ NO2 measurements motivates the inference of surface NO2 concentrations 

from satellite measurements of tropospheric NO2 columns. This prospect would take advantage of the 

greater sensitivity of tropospheric NO2 columns to NOX in the lower troposphere than in the upper 

troposphere as discussed earlier. Tropospheric NO2 columns show a strong correlation with in situ NO2

measurements in northern Italy (Ordóñez et al., 2006). 

Quantitative calculation of surface NO2 concentrations from a tropospheric NO2 column would 

require information on the relative vertical profile. Comparison of vertical profiles of NO2 in a chemical 

transport model (GEOS-Chem) versus in situ measurements over and downwind of North America shows 

a high degree of consistency (Martin et al., 2004b, 2006), suggesting that chemical transport models could 

be used to infer the relationship between surface NO2 concentrations and satellite observations of the 

tropospheric NO2 column. 

However, the satellites carrying the spectrometer (GOME/SCIAMACHY/OMI) are in near polar, 

sun-synchronous orbits. As a result, these measurements are made only once per day, typically between 
2-57

about 10:00 to 11:00 a.m. or 1 p.m. local time, during a brief overflight. Thus the utility of these 

measurements is limited as they would likely miss short-term features. 

AX2.7. Policy-relevant Background NOX Concentrations 

Background concentrations of nitrogen oxides used for purposes of informing decisions about 

NAAQS are referred to as Policy-relevant Background (PRB) concentrations. Policy Relevant 

Background concentrations are those concentrations that would occur in the United States in the absence 

of anthropogenic emissions in continental North America (defined here as the United States, Canada, and 

Mexico). Concentrations include contributions from natural sources everywhere in the world and from 

anthropogenic sources outside these three countries. Background levels so defined facilitate separation of 

pollution levels that can be controlled by U.S. regulations (or through international agreements with 

neighboring countries) from levels that are generally uncontrollable by the United States. EPA assesses 

risks to human health and environmental effects from NO2 levels in excess of PRB concentrations. 

Contributions to PRB concentrations include natural emissions of NO2 and photochemical reactions 

involving natural emissions of reduced nitrogen compounds, as well as their long-range transport from 

outside North America. Natural sources of NO2 and its precursors include biogenic emissions, wildfires, 

lightning, and the stratosphere. Natural sources of reduced nitrogen compounds, mainly NH3, include 

biogenic emissions and wildfires. Biogenic emissions from agricultural activities are not considered in the 

formation of PRB concentrations. Discussions of the sources and estimates of emissions are given in 

Section AX2.4.2. 

AX2.7.1. Analysis of PRB Contribution to U.S. NOX Concentrations and 

Deposition 

The MOZART-2 global model of tropospheric chemistry (Horowitz et al., 2003) was used to 

diagnose the PRB contribution to nitrogen oxide concentrations, as well as to total (wet plus dry) 

deposition. The model setup for the present-day simulation has been published in a series of papers from a 

recent model intercomparison (Dentener et al., 2006a,b; Shindell et al., 2006; Stevenson et al., 2006; Van 

Noije et al., 2006). MOZART-2 is driven by National Center for Environmental Prediction 

meteorological fields and IIASA 2000 emissions at a resolution of 1.9° Η 1.9° with 28 sigma levels in the 

vertical, and it includes gas- and aerosol phase chemistry. Results shown in Figures AX2.7-1 to AX2.7-3 

are for the meteorological year 2001. Note that color images are available on the web. An additional PRB 

simulation was conducted in which continental North American anthropogenic emissions were set to zero. 

We first examine the role of PRB in contributing to NO2 concentrations in surface air. Figure 

AX2.7-1 shows the annual mean NO2 concentrations in surface air in the base case simulation (top panel) 

and the PRB simulation (middle panel), along with the percentage contribution of the background to the 

total base case NO2 (bottom panel). Maximum concentrations in the base case simulation occur along the 

Ohio River Valley and in the Los Angeles basin. While present-day concentrations are often above 5 ppb, 

PRB is less than 300 ppt over most of the continental United States, and less than 100 ppt in the eastern 

United States. The distribution of PRB (middle panel of Figure AX2.7-1) largely reflects the distribution 

of soil NO emissions, with some local enhancements due to biomass burning such as is seen in western 

Montana. In the northeastern United States, where present-day NO2 concentrations are highest, PRB 

contributes <1% to the total. 
2-58

Figure AX2.7-1. Annual mean concentrations of NO2 (ppb) in surface air over the United States in the 

present-day (upper panel) and policy relevant background (middle panel) MOZART-2 

simulations. The bottom panel shows the percentage contribution of the background to the 

present-day concentrations. Please see text for details. 
2-59

Figure AX2.7-2. Same as for Figure AX2.7-1 but for wet and dry deposition of HNO3, NH4NO3, NOX, HO2NO2, 

and organic nitrates (mg N m-2/y). 
2-60

Figure AX2.7-3. July mean soil NO emissions (upper panels; 1 Η 10 9 molecules cm-2 s1) and surface PRB 

NOX concentrations (lower panels; ppt). These are over the United States from MOZART-2 

(left) and GEOS-Chem (right) model simulations in which anthropogenic O3 precursor 

emissions were set to zero in North America. 

The spatial pattern of NOY (defined here as HNO3, NH4NO3, NOX, HO2NO2, and organic nitrates) 

wet and dry deposition is shown in Figure AX2.7-2. Figure AX2.7-2 (upper panel) shows that highest 

values are found in the eastern United States in and downwind of the Ohio River Valley. The pattern of 

nitrogen deposition in the PRB simulation (Figure AX2.7-2, middle panel), however, shows maximum 

deposition centered over Texas and in the Gulf Coast region, reflecting a combination of nitrogen 

emissions from lightning in the Gulf region, biomass burning in the Southeast, and from microbial 

activity in soils (maximum in central Texas and Oklahoma). The bottom panel of Figure AX2.7-2 shows 

that the PRB contribution to nitrogen deposition is less than 20% over the eastern United States, and 

typically less than 50% in the western United States where NOY deposition is low (25-50 mg N/m2

/yr). 

Thus far, the discussion has focused on results from the MOZART-2 tropospheric chemistry model. 

In Figure AX2.7-3, results from MOZART-2 are compared with those from another tropospheric 

chemistry model, GEOS-Chem (Bey et al., 2001), which was previously used to diagnose PRB O3 (Fiore 

et al., 2003; U.S. Environmental Protection Agency, 2006). In both models, the surface PRB NOX

concentrations tend to mirror the distribution of soil NO emissions, which are highest in the Midwest. The 

higher soil NO emissions in GEOS-Chem (by nearly a factor of 2) as compared to MOZART-2 reflect 

different assumptions regarding the contribution to soil NO emissions largely through fertilizer, since 

GEOS-Chem total soil NO emissions are actually higher than MOZART-2 (0.07 versus 0.11 Tg N) over 
2-61

the United States in July. Even with the larger PRB soil NO emissions, surface NOX concentrations in 

GEOS-Chem are typically below 500 ppt. 

Table AX2.4-1. Emissions of nitrogen oxides and ammonia in the United States in 2002 

2002 EMISSIONS (TG/YR) NOX

1 NH3

Source Category 

TOTAL ALL SOURCES 23.19 4.08 

FUEL COMBUSTION TOTAL 9.11 0.02 

FUEL COMB. ELEC. UTIL 5.16 <0.01 

Coal 4.50 <0.01 

 Bituminous 2.90 

 Subituminous 1.42 

 Anthracite & lignite 0.18 

 Other <0.01 

Oil 0.14 <0.01 

 Residual 0.13 

 Distillate 0.01 

Gas 0.30 <0.01 

 Natural 0.29 

 Process 0.01 

 Other 0.05 <0.01 

Internal Combustion 0.17 <0.01 

FUEL COMBUSTION INDUSTRIAL 3.15 <0.01 

Coal 0.49 <0.01 

 Bituminous 0.25 

 Subbituminous 0.07 

 Anthracite & Lignite 0.04 

 Other 0.13 

Oil 0.19 <0.01 

 Residual 0.09 

 Distillate 0.09 

 Other 0.01 

Gas 1.16 <0.01 

 Natural 0.92 

 Process 0.24 

 Other <0.01 

Other 0.16 <0.01 
2-62

2002 EMISSIONS (TG/YR) NOX

1 NH3

 Wood/bark waste 0.11 

 Liquid waste 0.01 

 Other 0.04 

Internal Combustion 1.15 <0.01 

FUEL COMB. OTHER 0.80 <0.01 

Commercial/Institutional Coal 0.04 <0.01 

Commercial/Institutional Oil 0.08 <0.01 

Commercial/Institutional Gas 0.25 <0.01 

Misc. Fuel Comb. (Except Residential) 0.03 <0.01 

Residential Wood 0.03 

Residential Other 0.36 

Distillate oil 0.06 

Bituminous/subituminous 0.26 

Other 0.04 

INDUSTRIAL PROCESS TOTAL 1.10 0.21 

CHEMICAL & ALLIED PRODUCT MFG 0.12 0.02 

Organic Chemical Mfg 0.02 <0.01 

Inorganic Chemical Mfg 0.01 <0.01 

Sulfur compounds 

Other 

Polymer & Resin Mfg <0.01 <0.01 

Agricultural Chemical Mfg 0.05 0.02 

Ammonium nitrate/urea mfg. <0.01 

Other 0.02 

Paint, Varnish, Lacquer, Enamel Mfg 0.00 

Pharmaceutical Mfg 0.00 

Other Chemical Mfg 0.03 <0.01 

METALS PROCESSING 0.09 <0.01 

Non-Ferrous Metals Processing 0.01 <0.01 

 Copper 

 Lead 

 Zinc 

 Other 

Ferrous Metals Processing 0.07 <0.01 

Metals Processing 0.01 <0.01 

PETROLEUM & RELATED INDUSTRIES 0.16 <0.01 

Oil & Gas Production 0.07 <0.01 
2-63

2002 EMISSIONS (TG/YR) NOX

1 NH3

 Natural gas 

 Other 

Petroleum Refineries & Related Industries 0.05 <0.01 

 Fluid catalytic cracking units <0.01 

 Other <0.01 

Asphalt Manufacturing 0.04 

OTHER INDUSTRIAL PROCESSES 0.54 0.05 

Agriculture, Food, & Kindred Products 0.01 <0.01 

Textiles, Leather, & Apparel Products <0.01 <0.01 

Wood, Pulp & Paper, & Publishing Products 0.09 <0.01 

Rubber & Miscellaneous Plastic Products <0.01 <0.01 

Mineral Products 0.42 <0.01 

Cement mfg 0.24 

Glass mfg 0.01 

Other 0.10 

Machinery Products <0.01 <0.01 

Electronic Equipment <0.01 <0.01 

Transportation Equipment <0.01 

Miscellaneous Industrial Processes 0.01 0.05 

SOLVENT UTILIZATION 0.01 <0.01 

Degreasing <0.01 <0.01 

Graphic Arts <0.01 <0.01 

Dry Cleaning <0.01 <0.01 

Surface Coating <0.01 <0.01 

Other Industrial <0.01 <0.01 

Nonindustrial <0.01 

Solvent Utilization NEC <0.01 

STORAGE & TRANSPORT <0.01 <0.01 

Bulk Terminals & Plants <0.01 <0.01 

Petroleum & Petroleum Product Storage <0.01 <0.01 

Petroleum & Petroleum Product Transport <0.01 <0.01 

Service Stations: Stage II <0.01 

Organic Chemical Storage <0.01 <0.01 

Organic Chemical Transport 0.01 

Inorganic Chemical Storage <0.01 <0.01 

Inorganic Chemical Transport <0.01 

Bulk Materials Storage 0.01 <0.01 
2-64

2002 EMISSIONS (TG/YR) NOX

1 NH3

WASTE DISPOSAL & RECYCLING 0.17 0.14 

Incineration 0.06 <0.01 

Industrial 

Other 

 Open Burning 0.10 <0.01 

 Industrial 

 Land clearing debris 

 Other 

Public Operating Treatment Works <0.01 0.14 

Industrial Waste Water <0.01 <0.01 

Treatment, Storage, and Disposal Facility <0.01 <0.01 

 Landfills <0.01 <0.01 

 Industrial 

 Other 

 Other <0.01 <0.01 

TRANSPORTATION TOTAL 12.58 0.32 

HIGHWAY VEHICLES 8.09 0.32 

Light-Duty Gas Vehicles & Motorcycles 2.38 0.20 

 light-duty gas vehicles 2.36 

 Motorcycles 0.02 

 Light-Duty Gas Trucks 1.54 0.10 

 Light-duty gas trucks 1 1.07 

 Light-duty gas trucks 2 0.47 

Heavy-Duty Gas Vehicles 0.44 <0.01 

 Diesels 3.73 <0.01 

 Heavy-duty diesel vehicles 3.71 

 Light-duty diesel trucks 0.01 

Light-duty diesel vehicles 0.01 

OFF-HIGHWAY 4.49 <0.01 

Non-Road Gasoline 0.23 <0.01 

 Recreational 0.01 

 Construction 0.01 

 Industrial 0.01 

 Lawn & garden 0.10 

 Farm 0.01 

 Light commercial 0.04 

 Logging <0.01 
2-65

2002 EMISSIONS (TG/YR) NOX

1 NH3

 Airport service <0.01 

 Railway maintenance <0.01 

 Recreational marine vessels 0.05 

Non-Road Diesel 1.76 <0.01 

 Recreational 0.00 

 Construction 0.84 

 Industrial 0.15 

 Lawn & garden 0.05 

 Farm 0.57 

 Light commercial 0.08 

 Logging 0.02 

 Airport service 0.01 

 Railway maintenance <0.01 

 Recreational marine vessels 0.03 

 Aircraft 0.09 

 Marine Vessels 1.11 

 Diesel 1.11 

 Residual oil 

 Other 

 Railroads 0.98 

Other 0.32 <0.01 

 Liquefied petroleum gas 0.29 

 Compressed natural gas 0.04 

MISCELLANEOUS 0.39 3.53 

Agriculture & Forestry <0.01 3.45 

 Agricultural crops <0.01 

 Agricultural livestock 2.66 

Other Combustion 0.08 

 Health Services 

 Cooling Towers 

 Fugitive Dust 

 Other 

Natural Sources 3.10 0.03 

 
3-66

Annex 3. Ambient Concentrations 

and Exposures 

AX3.1. Introduction 

Topics discussed in this chapter include the characterization of ambient air quality for nitrogen 

dioxide (NO2), the uses of these data in assessing human exposures to NO2; concentrations and sources of 

NO2 in different microenvironments, and personal exposures to NO2. The NO2 data contained in this 

chapter are taken mainly from the U.S. Environmental Protection Agency’s Air Quality System (AQS) 

database (formerly the AIRS database) (U.S. Environmental Protection Agency, 2007). 

AX3.1.1. Characterizing Ambient NO2 Concentrations 

The “concentration” of a specific air pollutant is typically defined as the amount (mass) of that 

material per unit volume of air. However, most of the data presented in this chapter are expressed as 

“mixing ratios” in terms of a volume-to-volume ratio (e.g., parts per million [ppm] or parts per billion 

[ppb]. Data expressed this way are often referred to as concentrations, both in the literature and in the 

text, following common usage. Human exposures are expressed in units of mixing ratio times time. 

AX3.1.2. Relationship to the 1993 AQCD for NOX

The 1993 AQCD for Oxides of Nitrogen emphasized NO2 indoor sources (gas stoves) and the 

relationship between personal total exposure and indoor or outdoor NO2 concentrations 

(U.S. Environmental Protection Agency, 1993). At that time, only few personal exposure studies had been 

conducted with an emphasis on residential indoor NO2 sources and concentrations. Although the concept 

of microenvironment had been introduced in the document, NO2 concentrations were seldom reported for 

microenvironments other than residences. Exposure measurements at that time relied on Palmes tubes and 

Yanagisawa badges; and exposure-modeling techniques were limited mainly to simple linear regression. 

In the 1993 AQCD, NO2 was treated as an independent risk factor, and confounding issues were not 

mentioned in the human environmental exposure chapters. 

The current chapter summarizes and discusses the state-of-the-science and technology regarding 

NO2 human exposures since 1993. Since then, numerous human exposure studies have been conducted 

with new measurement and modeling techniques. Microenvironmental measurements were not limited to 

residential indoor environments; NO2 concentrations were also measured in vehicles, schools and offices, 

and microenvironments close to traffic. More indoor sources have been identified and more NO2

formation and transformation mechanisms in the indoor environment have been reported. 

AX3.2. Ambient Concentrations of NOX

As discussed in Chapter 2 of the ISA, most measurements of NOX are made by instruments that 

convert NO2 to NO, which is then measured by chemiluminescence. However, the surface converters that 

reduce NO2 to NO also reduce other reactive NOY species. As indicated in Chapter 2, NOY compounds 

consist of NOX, gas phase inorganic nitrates, such as chlorine nitrate (ClNO3); organic nitrates, such as 
3-67

PANs; inorganic acids, given by the formulas HNOY (Y = 2 to 4); and particulate nitrate. In urban areas 

or in rural areas where there are large local sources, NO and NO2 are expected to be the major forms of 

NOY. Thus, interference from PANs and other NOY species near sources are expected to minor; in most 

rural and remote areas, interference may be substantial as concentrations of other NOY species may be 

much larger than those for NO and NO2 (National Research Council, 1991). 

Because of their short lifetime with respect to oxidation to PANs and HNO3, NOX concentrations 

are highly spatially and temporally variable. Average concentrations range from tens of ppt in remote 

areas of the globe to tens of ppb in urban cores, i.e., by three orders of magnitude. Median NO, NOX, and 

NOY concentrations at the surface are typically below 0.01, 0.05, and 0.3 ppb, respectively, in remote 

areas such as Alaska, northern Canada, and the eastern Pacific; median NOY concentrations range from 

about 0.7 to about 4.3 ppb at regional background sites in the eastern United States (Emmons et al., 

1997). Note that the last two values, especially, contain a substantial contribution from pollution. 

Maximum short-term average (1-h) NOX concentrations near heavy traffic (e.g., in Los Angeles, CA) 

approach 1 ppm, but these levels decrease rapidly away from sources. Even at sites where such high 

hourly values are found, 24-h avg concentrations are much lower. For example, the maximum 24-h 

average NOX concentration at any site in Los Angeles in 2004 was 82 ppb. 

AX3.2.1. Temporal Variability in Ambient NOX Concentrations in Urban 

Areas 

AX3.2.1.1. Diurnal Variability in NO2 Concentrations 

As might be expected from a pollutant having a major traffic source, the diurnal cycle of NO2 in 

typical urban areas is characterized by traffic emissions, with peaks in emissions occurring during 

morning and evening rush hour traffic. Motor vehicle emissions consist mainly of NO, with only about 

10% of primary emissions in the form of NO2. The diurnal pattern of NO and NO2 concentrations is also 

strongly influenced by the diurnal variation in the mixing layer height. Thus, during the morning rush 

hour when mixing layer heights are still low, traffic produces a peak in NO and NO2 concentrations. As 

the mixing layer height increases during the day, dilution of emissions occurs. During the afternoon rush 

hour, mixing layer heights are at or are near their daily maximum values resulting in dilution of traffic 

emissions through a larger volume than in the morning. Starting near sunset, the mixing layer height 

drops and conversion of NO to NO2 occurs without photolysis of NO2 recycling NO. 

The composite diurnal variability of NO2 in selected urban areas with multiple sites (New York, 

NY; Atlanta, GA; Baton Rouge, LA; Chicago. IL; Houston, TX; Riverside, CA; and Los Angeles, CA) 

shows that lowest hourly median concentrations are typically found at around midday and that highest 

hourly median concentrations are found either in the early morning or in mid-evening. Median values 

range by about a factor of two from about 13 ppb to about 25 ppb (Figures AX3.2-1 to AX3.2-6). 

However, individual hourly concentrations can be considerably higher than these typical median values, 

and hourly NO2 concentrations > 0.10 ppm can be found at any time of day. 

AX3.2.1.2. Seasonal Variability in NO2 Concentrations 

AX3.2.1.3. Urban Sites 

As might be expected from an atmospheric species that behaves essentially like a primary pollutant 

emitted from surface sources, there is strong seasonal variability in NOX and NO2 concentrations. Highest 

concentrations are found during winter, consistent with lowest mixing layer heights found during the year. 
3-68

Mean and peak concentrations in winter can be up to a factor of two larger than in the summer at several 

sites in Los Angeles County. 

The month-to-month variability in NO2 at individual sites in selected urban areas is illustrated in 

Figures AX3.2-1 to AX3.2-6. Seasonal patterns can be found at some sites but not in others. There 

appears to be a somewhat regular pattern for the southern cities with winter maxima and summer minima. 

Monthly maxima tend to be found from late winter to early spring in Chicago and New York with minima 

occurring from summer through the fall. However, in Los Angeles and Riverside, monthly maxima tend 

to occur from autumn through early winter with minima occurring from spring through early summer. 

AX3.2.1.4. Regional Background Sites 

Surface NOX and NOY data obtained in Shenandoah National Park, VA from 1988 to 1989 show 

wintertime maxima and summertime minima (Doddridge et al., 1991, 1992; Poulida et al., 1991). NOX

and NOY data collected in Harvard Forest, MA from 1990 to 1993 show a similar seasonal pattern 

(Munger et al., 1996). In addition the within-season variability was found to be smaller in the summer 

than in the winter as shown in Table AX3.2-1. 
3-69

Source: U.S. EPA AQS, 2007

Figure AX3.2-1. Time series of 24-h avg NO2 concentrations at individual sites in New York City from 2003 

through 2006. A natural spline function (with 9 degrees of freedom) was fit and overlaid to 

the data (dark solid line). 
3-70

Figure AX3.2-2. Time series of 24-h average NO2 concentrations at individual sites in Chicago, IL from 2003 

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

the data (dark solid line). 
3-71

Figure AX3.2-3. Time series of 24-h avg NO2 concentrations at individual sites in Baton Rouge, LA from 2003 

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

the data (dark solid line). 
3-72

Figure AX3.2-4. Time series of 24-h avg NO2 concentrations at individual sites in Houston, TX from 2003 

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

the data (dark solid line). 
3-73

Figure AX3.2-5. Time series of 24-h avg NO2 concentrations at individual sites in Los Angeles, CA from 2003 

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

the data (dark solid line). 
3-74

Figure AX3.2-5. (Continued) Time series of 24-h avg NO2 concentrations at individual sites in Los Angeles, 

CA from 2003 through 2006. A natural spline function (with 9 degrees of freedom) was fit 

and overlaid to the data (dark solid line). 
3-75

Figure AX3.2-6. Time series of 24-h avg NO2 concentrations at individual sites in Riverside, CA from 2003 

through 2006. A natural spline function (with 9 degrees of freedom) was fit and overlaid to 

the data (dark solid line). 
3-76

Figure AX3.2-6. (Continued) Time series of 24-h avg NO2 concentrations at individual sites in Riverside, CA 

from 2003 through 2006. A natural spline function (with 9 degrees of freedom) was fit and 

overlaid to the data (dark solid line). 

Source: U.S. Environmental Protection Agency (2003) 

 

AX3.2.2. Relationships between NO2 and Other Pollutants 

Relationships between O3, NO, and NO2 are shown in Figures AX3.2-7 and AX3.2-8. Figure 

AX3.2-7 shows daylight average concentrations based on data collected from November 1998 and 1999 

at several sites in the United Kingdom representing a wide range of pollution conditions (open symbols). 

The solid lines represent calculations of photostationary state values subject to the constraint that OX = 
3-77

31.1 + 0.104 (NOX), where OX = O3 + NO2. Note that OX is defined in the UK AQG report as oxidant, as 

used in this document, and in the latest AQCD for Ozone and other Photochemical Oxidants (U.S. 

Environmental Protection Agency, 2006a) it is taken to refer to “odd oxygen” as defined in Section 2.2. 

The reason is that oxidants also include PANs, peroxides, and reactive oxygen species in particles etc., in 

addition to O3 and NO2. The concentrations of NO2 (an oxidant and a component of odd oxygen) varying 

linearly with emissions of NOX, especially after NO has reacted with O3 to form NO2 as shown in Figure 

AX3.2-7. Thus the concentration of OX (and not O3, as is often stated) can be taken to be the sum of 

regional and local contributions. 

Figure AX3.2-8 shows that primary emissions from motor vehicles are major sources of oxidant in 

the form of NO2, as evidenced by the high values of OX at elevated NOX. 

Source: Clapp and Jenkin (2001). 

Figure AX3.2-7. Relationship between O3, NO, and NO2 as a function of NOX concentration. Open circles 

represent data collected at a number of sites in the United Kingdom. Lines represent 

calculated relationships based on photostationary state. 
3-78

Figure AX3.2-8. Variation of odd oxygen (= O3 + NO2) with NOX. The figure shows the “regional” and the 

“local” contributions. Note that OX refers to odd oxygen in the document and the latest O3

AQCD. 

AX3.2.3. Abundance of NOY Species 

Data for individual NOY species are much less abundant than for either oxides of nitrogen or for 

total NOY. Data for several NOY species are collected typically only as part of research field studies, e.g., 

the Southern Oxidant Study (SOS), Texas Air Quality Study (TexAQS I and TexAQS II) in the United 

States. As a result, this information is simply not available for a large number of areas in the United 

States. 

AX3.2.3.1. PANs 

Organic nitrates consist of PAN, a number of higher-order species with photochemistry similar to 

PAN (e.g., PPN), and species such as alkyl nitrates with somewhat different photochemistry. These 

species are produced by a photochemical process very similar to that of O3. Photochemical production is 

initiated by the reaction of primary and secondary VOCs with OH radicals, the resulting organic radicals 

subsequently react with NO2 (producing Source: Clapp and Jenkin (2001). PAN and analogous species) 

or with NO (producing alkyl nitrates). The same sequence (with organic radicals reacting with NO) leads 

to the formation of O3. 

In addition, at warm temperatures, the concentration of PAN forms a photochemical steady state 

with its radical precursors on a timescale of roughly 30 min. This steady state value increases with the 

ambient concentration of O3 (Sillman et al., 1990). O3 and PAN may show different seasonal cycles, 

because they are affected differently by temperature. Ambient O3 increases with temperature, driven in 

part by the photochemistry of PAN (see description in Chapter 2). The atmospheric lifetime of PAN 

decreases rapidly with increasing temperature due to thermal decomposition. Based on the above, the 
3-79

ratio of O3 to PAN is expected to show seasonal changes, with highest ratios in summer, although there is 

no evidence from measurements. Measured ambient concentrations (Figure AX3.2-9) show a strong 

nonlinear association between O3 and PAN, and between O3 and other organic nitrates (Pippin et al., 

2001; Roberts et al., 1998). Moreover, uncertainty in the relationship between O3 and PAN grows as the 

level of PAN increases. Individual primary VOCs are generally highly correlated with each other and 

with NOX (Figure AX3.2-10). 

Source: Roberts et al. (1998). 

Figure AX3.2-9. Measured O3 (ppb) versus PAN (ppt) in Tennessee, including (a) aircraft measurements, and 

(b, c, and d) suburban sites near Nashville. 
3-80

Source: Goldan et al. (1995). 

Figure AX3.2-10. Relationship between benzene and NOY at a measurement site in Boulder, CO. Instances 

with SO2 >10 ppb are identified separately (open circles), because these may reflect 

different emission sources. 

Measurements and models show that PAN in the United States includes major contributions from 

both anthropogenic and biogenic VOC precursors (Horowitz et al., 1998; Roberts et al., 1998). 

Measurements in Nashville during the 1999 summertime Southern Oxidants Study (SOS) showed PPN 

and MPAN amounting to 14% and 25% of PANs, respectively (Roberts et al., 2002). Measurements 

during the TexAQS 2000 study in Houston indicated PAN concentrations of up to 6.5 ppb (Roberts et al., 

2003). PAN measurements in southern California during the SCOS97-NARSTO study indicated peak 

concentrations of 5-10 ppb, which can be contrasted to values of 60-70 ppb measured back in 1960 

(Grosjean, 2003). Vertical profiles measured from aircraft over the United States and off the Pacific 

coasts typically show PAN concentrations above the boundary layer of only a few hundred ppt, although 

there are significant enhancements associated with long-range transport of pollution plumes from Asia 

(Kotchenruther et al., 2001; Roberts et al., 2004). 

Observed ratios of PAN to NO2 as a function of NOX at a site at Silwood Park, Ascot, Berkshire, 

UK are shown in Figure AX3.2-11 United Kingdom Air Quality Expert Group (U.K. AQEG, 2004). As 

can be seen there is a very strong inverse relation between the ratio and the NOX concentration, indicating 

photochemical oxidation of NOX has occurred in aged air masses and that PAN can make a significant 

contribution to measurements of NO2 especially at low levels of NO2 (cf. ISA, Section 2-3). It should be 

noted that these ratios will likely differ from those found in the United States because of differences in the 

composition of precursor emissions, the higher solar zenith angles found in the UK compared to the 

United States., and different climactic conditions. 

Nevertheless, these results indicate the potential importance of interference from NOY compounds 

in measurements of NO2. 
3-81

AX3.2.3.2. HONO 

The ratio of HONO to NO2 as a function of NOX measured at a curbside site in a street canyon in 

London, UK is shown in Figure AX3.2-12 (U.K. AQEG, 2004). The ratio is highly variable, ranging from 

about 0.01 to 0.1, with a mean ~0.05. As NO2 constitutes several percent of motor vehicle emissions of 

NOX, the above implies that emissions of HONO represent a few tenths of a percent of mobile NOX

emissions. A similar range of ratios have been observed at other urban sites in the United Kingdom 

(Lammel and Cape, 1996). The ratios of HONO to NO2 shown in Figure AX3.2-12 indicate that 

HONO can make a measurable contribution to measurements of NO2 (cf. ISA, Section 2-3). However, 

similar arguments about extrapolating the use of UK data to the United States can be made for HONO as 

for PAN. 

AX3.2.3.3. HNO3 and NO3

Elevated O3 is generally accompanied by elevated HNO3, although the correlation is not as strong 

as between O3 and organic nitrates. O3 is often associated with HNO3 because they have the same 

precursor NOX. However, HNO3 can be produced in significant quantities in winter, even when O3 is low. 

The ratio between O3 and HNO3 also shows great variation in air pollution events, with NOX-saturated 

environments having much lower ratios of O3 to HNO3 (Ryerson et al., 2001). Aerosol nitrate is formed 

primarily by the combination of nitrate (supplied by HNO3) with ammonia, and may be limited by the 

availability of either nitrate or ammonia. Nitrate is expected to correlate loosely with O3 (see above), 

whereas ammonia is not expected to correlate with O3. Concentrations of particulate nitrate measured as 

part of the Environmental Protection Agency’s speciation network at several locations are shown in 

Figure AX3.2-13. Concentrations shown are annual averages for 2003. Also shown are the estimated 

contributions from regional and local sources. A concentration of 1 μg/m3

 corresponds to ~0.40 ppb 

equivalent gas phase concentration for NO3

-

. 
3-82

Source: UK AQEG (2004). 

Figure AX3.2-11. Ratios of PAN to NO2 observed at Silwood Park, Ascot, Berkshire, U.K. from July 24 to 

August 12 1999. Each data point represents a measurement averaged over 30 minutes. 

Source: UK AQEG (2004). 

Figure AX3.2-12. Ratios of HONO to NO2 observed in a street canyon (Marylebone Road) in London, U.K. from 

11 a.m. to midnight during October 1999. Data points reflect 15-min avg concentrations of 

HONO and NO2. 

Thus, annual average particulate nitrate can account for several ppb of NOY, with the higher values 

in the West. There is a strong seasonal variation, which is especially pronounced in western areas where 
3-83

there is extensive wood burning in the winter resulting in a larger fractional contribution of local sources. 

Areas in the East where there are topographic barriers might be expected to show higher fractional 

contributions from local sources than other eastern areas that are influenced by regionally dispersed 

sources. 

However, depending on the acidity of the particles, which in turn depends strongly on their sulfate 

and ammonium contents, higher nitrate concentrations could be found in coarse mode particles PM10-2.5

than in PM2.5 samples. The average nitrate content of PM2.5 and PM10 is typically about a percent in the 

eastern United States; and 15.7% and 4.5% in the western United States (U.S. Environmental Protection 

Agency, 1996). These values suggest that most of the nitrate was in the PM2.5 size fraction in the studies 

conducted in the western United States, but nitrate in the studies in the eastern United States was mainly 

in the PM10-2.5 size fraction. 

AX3.2.3.4. Nitro-PAHs 

Nitro-PAHs are widespread and found even in high altitude, relatively unpolluted environments 

(Schauer et al., 2004) but there are differences in composition and concentration profiles both within and 

between sites (rural vs. urban) as well as between and within urban areas (Albinet et al., 2006; Söderström 

et al., 2005; Naumova et al., 2002, 2003), with some differences in relative abundances of nitro- and oxoPAHs also reported. Source attribution has remained largely qualitative with respect to concentrations or 

mutagenicity (Eide et al., 2002). The spatial and temporal concentration pattern for the NPAHs may differ 

from that of the parent compounds (PAHs) because concentrations of the latter are dominated by direct 

emission from local combustion sources. These emissions results in higher concentrations during 

atmospheric conditions more typical of wintertime when mixing heights tend to be low. The 

concentrations of secondary Nitro-PAHs are elevated under conditions that favor hydroxyl and nitrate 

radical formation, i.e., during conditions more typical of summertime, and are enhanced downwind of 

areas of high emission density of parent PAHs and show diurnal variation (Fraser et al., 1998; Reisen and 

Arey, 2005; Kameda et al., 2004). Nitro-napthalene concentrations in Los Angeles, CA varied between 

about 0.15 to almost 0.30 ng/m3

 compared to 760 to 1500 ng/m3 for napthalene. Corresponding values for 

Riverside, CA were 0.012 to more than 0.30 ng/m3

 for nitro-napthalene and 100 to 500 ng/m3 for 

napthalene. Nitro-pyrene concentrations in LA varied between approximately 0.020 to 0.060 ng/m3

compared to 3.3 to 6.9 ng/m3

 pyrene, whereas corresponding values for Riverside were 0.012 to 0.025 

ng/m3

 and 0.9 to 2.7 ng/m3. 
3-84

Source: U.S. Environmental Protection Agency (2004). 

Figure AX3.2-13. Concentrations of particulate nitrate measures as part of the EPA’s speciation network. 1 

μg/m3 ~0.45 ppb equivalent gas phase concentration for NO3-. (Note: Regional 

concentrations are derived from the rural IMPROVE monitoring network, 

http://vista.cira.colostate.edu/improve. 

AX3.3. Measuring Personal and Indoor NO2 Concentrations 

AX3.3.1. Issues in Measuring Personal/Indoor NO2

Nitrogen dioxide has been sampled in ambient and indoor air using active pumped systems both for 

continuous monitoring and collection onto adsorbents, and by diffusive samplers of various designs, 

including badges and tubes. Nitrogen dioxide concentrations in personal air have been typically measured 

using diffusive samplers because they are: (1) small in size and light-weight, (2) unobtrusive and thus 

more readily used by study participants, (3) comparatively easier to use and handle in field studies 

because they do not require power (e.g., battery or extra electrical sources), (4) cost-effective, and (5) 

usable not only for residential indoor and outdoor air sampling but also personal monitoring. However, 

diffusive samplers usually have lower equivalent sampling rates than active methods and so require 

relatively long sampling times (24-h or longer). Consequently, diffusive samplers including those used for 

NO2 monitoring provide integrated but not short-term concentration measurements. 

Both active and passive sampling methods can collect other gas-phase nitrogen oxide species. 

However, semivolatile nitrogen oxide compounds require separation of the gas- and particle-bound 

phases. This selective separation of gases from gas-particle matrices is commonly done by means of 

diffusion denuders (Vogel, 2005), an approach also useful for measuring other gas phase airborne 

contaminants such as SO2 (Rosman et al., 2001). Application of denuder sampling to personal exposure or 

indoor air monitoring has been relatively limited. 

Active air sampling with a pump can collect larger volumes of air and thus detect the lower 

concentrations found in community environments within relatively short time periods. Automated active 
3-85

sampling methods have been the preferred method used to monitor NO2 continuously at ambient sites for 

environmental regulation compliance purposes. However, practical considerations impede the use of these 

continuous monitors in residential air and exposure monitoring studies. Small, low flow active samplers 

using battery-operated pumps have been used instead, however, there are only a few such studies. 

The first passive sampling devices for NO2 were intended for occupational exposure monitoring, 

but were later adapted for environmental monitoring purposes. Since this sampler, the Palmes tubes 

(Palmes et al., 1976), was first developed, other tube, badge-type (Yanagisawa and Nishimura, 1982) and 

radial (Cocheo et al., 1996) diffusive samplers have been employed as monitors in exposure studies 

worldwide. The theories behind and applications of Palmes tubes and Yanagisawa badges have been 

described in the last AQCD for Oxides of Nitrogen (U.S. Environmental Protection Agency, 1993). There 

are currently several commercially available samplers (e.g., Ogawa, 1998; Radiello, 2006) which are 

modifications of the original Palmes tube design. Most modifications are directed at reducing effects 

related to meteorological conditions (e.g., insufficient or too high a wind speed, humidity, temperature), 

increasing the sampling uptake rate, and improving analytical sensitivity. 

AX3.3.1.1. Active (Pumped) Sampling 

Nitrogen dioxide measurement by active pumping systems as part of continuous monitors has been 

widely employed for ambient air monitoring as these instruments require relatively little maintenance; 

however they have been used less frequently for indoor sampling. Devices needing a pump to draw air 

can measure average concentrations of pollutants over short time periods, but are not generally suitable 

for measuring personal exposures because they are heavy and large. Some exposure studies employed this 

approach for active sampling with stationary chemiluminescent analyzers or portable monitors to measure 

nitrogen dioxide levels in residential indoor air (Mourgeon et al., 1997; Levesque et al., 2000; Chau et al., 

2002). Recently, Staimer and his colleagues (2005) evaluated a miniaturized active sampler, suitable for 

personal exposure monitoring, to estimate the daily exposure of pediatric asthmatics to nitrogen dioxide, 

and reported that this small active sampling system is useful for this purpose in exposure studies where 

daily measurements are desired. 

AX3.3.1.2. Passive (Diffusive) Sampling 

Passive samplers are based on the well known diffusion principle described by Fick’s law (Krupa 

and Legge, 2000). A convenient formulation of this law that can be easily related to sampler design 

considerations is: 

 (AX3.3-1) 

where: 

J = flux (mg/s) 

D = diffusion coefficient in air (cm2

/s) 

A = diffusion cross-sectional area of the sampler (cm2) 

L = diffusion path length from the inlet to sorbent (cm), 

Cair = concentration of analyte in air (mg/cm3

) 

Csor = concentration of analyte at the sorbent (mg/cm3

) 
3-86

The term D(A/L) can be related to the uptake or sampling rate (cm3/s) which is conceptually 

analogous to the sampling rate in an active monitor. Once the amount of analyte in the passive sampler 

sorbent is determined, the concentration in air (Cair) can be calculated as: 

 (AX3.3-2) 

where: 

M = mass of analyte collected in the sorbent 

t = sampling time 

Fick’s law strictly applies only under ideal, steady state conditions assuming that the sorbent is a 

perfect sink. However, there can be deviations between the theoretical sampling rate for a given analyte 

and the actual rate depending on sampling conditions. It is also clear that sampling rate can be optimized 

by modifying the geometry of the diffusive sampler, either by reducing L, increasing A or a suitable 

combination. However, the impact of deviations from ideality on actual sampling rate due to geometry 

also poses a limit to the extent of possible modifications. Thus, passive samplers, either diffusive or 

permeation, are prepared as tubes or badges. These two main designs are the basis for all further 

modifications which, as indicated above, have been made in order to improve efficiency, reduce 

sensitivity to wind turbulence of the samplers, and to simplify analyte desorption. Tube-type samplers are 

characterized by a long, axial diffusion length, and a low cross-sectional area; this results in relatively low 

sampling rates (Namieśnik et al., 2005). Badge-type samplers have a shorter diffusion path length and a 

greater cross-sectional area which results in uptake rates that are typically higher than diffusion tubes 

(Namieśnik et al., 2005) but the sampling rate may be more variable because it is more affected by 

turbulence. Physical characteristics of these two fundamental passive sampler types, tube-type and badgetype, are summarized and provided in Table AX3.3-1. Performance characteristics are presented in Table 

AX3.3-2. 

The sorbent can be either physically sorptive or chemisorptive; passive samplers for NO2 are 

chemisorptive, that is, a reagent coated on a support (e.g., metal mesh, filter) reacts with the NO2. The 

sorbent is extracted and analyzed for one or more reactive derivatives; the mass of NO2 collected is 

derived from the concentration of the derivative(s) based on the stoichiometry of the reaction. Thus, an 

additional approach to reducing detection limits associated with passive samplers is to modify the 

chemisorptive reaction and the extraction and analysis methods to increase analytical sensitivity. 

However, although chemisorption is less prone to the back diffusion phenomenon of sorptive-only 

methods, analyte losses could occur due to interferences from other pollutants that also react with the 

sorbent or the derivatives. The most commonly used NO2 passive samplers rely on the classical reaction 

with triethanolamine (TEA). TEA requires hydration for quantitative NO2 sampling (i.e., 1:1 conversion 

to nitrite) and the reaction products have been subject to a number of investigations and several have been 

reported, including TEA-nitrate and nitrite, triethanolammonium nitrate, nitrosodiethanolamine, and 

triethanolamine N-oxide (Glasius et al., 1999). Known interferences include HONO, PAN, and nitric acid 

(Gair et al., 1991.). 

The tube-type passive samplers (Palmes tubes) require week-long sampling periods and have been 

extensively used for residential indoor/outdoor measurements, mostly for exploring the relationship 

between indoor and outdoor levels (Cyrys et al., 2000; Raw et al., 2004; Simoni et al., 2004; Janssen 

et al., 2001). Passive diffusion tubes have also been widely used for measurements of NO2 in ambient air 

(Gonzales et al., 2005; Gauderman et al., 2005; Da Silva et al., 2006; Lewné et al., 2004; Stevenson et al., 

2001; Glasius et al., 1999). Personal exposure studies have also been conducted using the Palmes tubes 

(Mukala et al., 1996; Kousa et al., 2001). Some of these studies evaluated passive sampler performance 

by collocating them with chemiluminescence analyzers during at least some portion of the field studies 

(Gair et al., 1991; Gair and Penkett, 1995; Plaisance et al., 2004; Kirby et al., 2001). The majority of these 

studies indicate that these samplers have very good precision (generally within 5%) but tend to 
3-87

overestimate NO2 by 10 to 30%. However, there has not been a methodical evaluation of variables 

contributing to variance for the range of samplers available when used in field conditions. Thus, it is not 

clear if the bias is due to deviations from ideal sampling conditions that can affect actual sampling rates, 

contributions from co-reacting contaminants or, most probably, a combination of these variables. 

A badge-type sampler was introduced by Yanagisawa and Nishimura (1982) to overcome the long 

sampling time required by Palmes tubes. Since then, these sensitive NO2 short path length samplers (Toyo 

Roshi Ltd) have been optimized and evaluated for indoor air and for personal monitoring (Lee et al., 

1993a,b). They have been used extensively for personal exposure studies (Ramirez-Aguilar et al., 2002; 

Yanagisawa et al., 1986; Berglund et al., 1994, Lee et al., 2004) and indoor air measurements (Kodama 

et al., 2002; Bae et al., 2004; Algar et al., 2004; Shima and Adachi, 2000; Smedje, et al., 1997) and to a 

more limited amount for ambient monitoring (Tashiro and Taniyama, 2002; Levy et al., 2006; Norris and 

Larson, 1999). Due to the greater uptake rate resulting from the larger cross sectional area of the badges 

and shorter diffusion length compared to the tube-type samplers, sampling times can be decreased from 

one-week to one-day for typical environmental air concentrations. This makes diffusive filter-badges 

more suitable for shorter-term sampling while long-term ambient monitoring can still be conducted using 

the Palmes-tubes. 

AX3.3.1.3. Tube Type Samplers 

Gradko Sampler (http://www.gradko.co.uk) 

The Gradko sampler is based on the Palmes tube design (Gerboles et al., 2006a). It collects O3 or 

NO2 by molecular diffusion along an inert tube by chemisorption. A stable complex is formed with 

triethanolamine coated on a stainless steel screen in the tube. The complex is spectroscopically analyzed 

by adding an azo dye (Chao and Law, 2000). The sampler has a detection limit of 0.5 ppb for NO/NO2

and the precision of ± 6% above 5 ppb levels when used for two weeks (Table AX3.3-2). This sampler 

has been used to measure personal exposures, concentrations of residential air indoors such as in the 

kitchen and bedroom, and concentrations of outdoor air (Chao and Law, 2000; Gallelli et al., 2002; Lai 

et al., 2004). It has been used to measure ambient NO2 levels in Southern California as a marker of trafficrelated pollution in San Diego County (Ross et al., 2006). 

Passam Sampler (http://www.passam.ch) 

This sampler is also based on the design of the Palmes tube (Palmes et al., 1976). It collects NO2 by 

molecular diffusion along an inert polypropylene tube to an absorbent, triethanolamine. The collected 

NO2 is determined spectrophotometrically by the well-established Saltzmann method. When used 

outdoors the samplers are placed in a special shelter to protect them from rain and minimize wind 

turbulence effects. The Passam sampler is sold in two different models, one for long-term and one for 

short-term sampling. 

AnalystTM Sampler (http://www.monitoreurope.com) 

The AnalystTM sampler is also a modification of the open-Palmes-tube design and was developed by 

the Italian National Research Council (CNR – Instituto Inquinamento Atmosferico) in 2000 (Bertoni 

et al., 2001). The AnalystTM consists of a glass vessel, which contains a reactant supported on a stainless 

steel grid. It is suitable for long-term monitoring (typically one month) of oxides of nitrogen, sulfur 

dioxide, and volatile organic compounds in ambient air. The target compound is analyzed by gas 

chromatography with minimum detection limit of 0.1 mg/m3

 (~52 ppb) for a twelve-week sample 

duration, and has relatively high precision. The AnalystTM method development (De Santis et al., 1997, 

2002) and actual field application (De Santis et al., 2004) have been described. The primary use for 

AnalystTM is as a reliable tool for long-term determination of concentration in indoor as well as outdoor 

environments (Bertoni et al., 2001) and as a screening tool for ambient monitoring to identify pollution 

“hot spots” (De Santis et al., 2004). 
3-88

AX3.3.1.4. Badge-Types Samplers 

Ogawa Passive Sampler (http://www.ogawausa.com) 

This sampler is a double face badge that can monitor NO, NOX, and NO2. The design can be used 

also for the determination of SO2, O3, and NH3 levels in air. The manufacturer-reported detection limits 

for nitrogen oxides are 2.3 ppb and 0.32 ppb for 24-h and 168-h sampling, respectively. Reported actual 

sampling rates for NO2 are two to three times higher than the manufacturer’s values. The normal 

operation ranges are 0 to 25 ppm for 24-h exposure and 0 to 3.6 ppm for 168-h exposure. The 

manufacturer recommends a sampling height of 2.5 meters and storage time of up to 1 year when kept 

frozen. Ogawa passive samplers have been extensively used for human exposure studies to measure 

personal air concentrations and (or) indoor/outdoor levels for residents in a number of locations, including 

adults of Richmond, Virginia (Zipprich et al., 2002), children of Santiago, Chile (Rojas-Bracho et al., 

2002), office workers of Paris, France (Mosqueron et al., 2002), and cardiac compromised individuals of 

Toronto, Canada (Kim et al., 2006). The samplers have been used also in air monitoring networks to 

assess traffic-related pollutant exposure (Singer et al., 2004), as well as to evaluate spatial variability of 

nitrogen dioxide ambient concentrations in Montreal, Canada (Gilbert et al., 2005). 

IVL Sampler (http//www.ivl.se/en/business/monitoring/diffusive_samplers.asp) 

The IVL method development has been described in detail by Ferm and Svanberg (1998). It was 

developed by Swedish Environmental Research Institute in the mid of 1980s (Sjödin et al., 1996), is 

designed to minimize turbulent wind effects outdoors as well as “starvation effects” indoors (i.e., very 

low face velocities), interferences from within sampling tube chemistry, temperature and humidity 

effects, and artifacts and losses during post-sampling storage. Manufacturer-reported detection limits for 

this sampler with sampling times of ~1 month are 0.1 μg/m3

 (0.05 ppb) for NO2, and 0.5 μg/m3 (0.42 ppb) 

for NO, respectively. Due to its long sampling time, this sampler has been extensively used for NO2

background monitoring in ambient air in rural or urban areas (Fagundez et al., 2001; Sjödin et al., 1996; 

Pleijel et al., 2004). 

Willems Badge Sampler 

The Willems badge, a short-term diffusion sampler, was developed at the University of 

Wageningen, Netherlands, originally for airborne ammonia measurements and later for measuring NO2

(Hagenbjörk-Gustafsson et al., 1996). It consists of a cylinder of polystyrene with a Whatman GF-A glass 

fiber filter impregnated with triethanolamine at its based held in place by a 6 mm distance ring. A Teflon 

filter is placed on the 6 mm polystyrene ring, which is secured with a polystyrene ring of 3 mm 

(Hagenbjörk-Gustafsson et al., 1996). The badge is closed by a polyethylene cap to limit influences by air 

turbulence. The diffusion length in the badge is 6 mm. This sampler was evaluated for ambient air 

measurements in laboratory and field tests (Hagenbjörk-Gustafsson et al., 1999). It has a manufacturer’s 

reported detection limit of 2 μg/m3

 (~1 ppb) for 48-h sampling duration. When used for personal 

sampling in an occupational setting with a minimum wind velocity of 0.3 m/s, detection limits of 18 

(~9.4 ppb) and 2 μg/m3

 (~1 ppb) for 1-h and 8-h sampling, respectively, have been reported (HagenbjörkGustafsson et al., 2002, Glas et al., 2004). 

AX3.3.1.5. Radial Sampler Types 

Radiello® -the radial diffusive sampler (http://www.radiello.com) 

Radiello® samplers use radial diffusion over a microporous cylinder into an absorbing inner 

cylinder, instead of axial diffusion, which increases the uptake rate by a factor of about 100 (Hertel et al., 

2001). Nitrogen dioxide is chemiadsorbed onto triethanolamine as nitrite, which is quantified by visible 

spectrometry. Sample collection of up to 15 days is feasible but relative humidity higher than 70% can 

cause interferences when used for extended periods of more than 7 days. The manufacturer-reported 

typical sampling rate for nitrogen dioxide sampling is 75 ± 3.72 ml/min at temperatures between –10 and 

40 °C. The rate can vary with humidity in the range of 15 to 90% and wind speed between 0.1 and 10 m/s 
3-89

(Radiello® Manual, 2006). A Danish study (Sørensen et al., 2005) recruited 30 subjects during each of 

four seasons in Copenhagen, and measured the subjects’ personal exposures, home indoor/front door air 

concentrations during 2-day periods with this sampler. 

EMD (Ecole des Mines de Douai) Sampler 

A new high-uptake rate diffusive sampler has been recently developed by the Ecole des Mines de 

Douai (EMD) laboratory (Piechocki-Minguy et al., 2003) and evaluated in the laboratory and field for 

measurement of NO2 levels in ambient air. It is composed of a porous cartridge impregnated with 

triethanolamine and fitted in a cylindrical protective box equipped with caps at its extremities (PiechockiMinguy et al., 2006). The large sampling area (cartridge surface) and the two circular openings provide a 

high uptake rate (exceeding 50 cm3

/min). The sampling rate was reported to be on average 0.89 cm3/s for 

indoor sampling and 1.00 cm3

/s for outdoor sampling. Detection limits were determined to be 11 μg/m3

(~5.8 ppb) for 1-h measurement. The sampling rate was not significantly influenced by wind at speeds 

higher than 0.3 m/s (Piechocki-Minguy et al., 2003). This sampler has been used in France to assess 

personal exposures in a series of microenvironments (home, other indoor places, transport and outdoor) 

for two 24-h time periods (weekday and weekend) (Piechocki-Minguy et al., 2006). 

AX3.4. NOX in Indoor Air 

AX3.4.1. Indoor Sources and Concentrations of NOX

Penetration of outdoor NO2 and combustion in various forms are the major sources of NO2 to 

indoor environments. These environments include homes, schools, restaurants, theaters etc. As might be 

expected, indoor concentrations of NO2 in the absence of combustion sources are determined by the 

infiltration of outdoor NO2 (Spengler et al., 1994; Weschler et al., 1994; Levy et al., 1998a), with a much 

smaller contribution from chemical reactions in indoor air. Indoor sources of nitrogen oxides have been 

characterized in several reviews, namely the last AQCD for Oxides of Nitrogen (U.S. Environmental 

Protection Agency, 1993); the Review of the Health Risks Associated with Nitrogen Dioxide and Sulfur 

Dioxide in Indoor Air for Health Canada (Brauer et al., 2002); and the Staff Recommendations for 

revision of the NO2 Standard in California (CARB, 2007). Mechanisms by which nitrogen oxides are 

produced in the combustion zones of indoor sources were reviewed in the last AQCD for Oxides of 

Nitrogen (U.S. Environmental Protection Agency, 1993) and will not be repeated here. Sources of 

ambient NO2 are reviewed in Chapter 2 of this document. It should also be noted that indoor sources can 

affect ambient NO2 levels, particularly in areas in which atmospheric mixing is limited. 

Ideally, exposure to NO2 should be cumulated over all indoor environments in which an individual 

spends time. These indoor environments may include homes, schools, offices, restaurants, theaters, ice 

skating rinks, stores, etc. However, in a study by Leaderer et al. that used two-week integrated measures, 

concentrations of NO2 inside the home accounted for 80% of the variance in total personal exposure, 

indicating that home concentrations are a reasonable proxy for personal exposure (Leaderer et al., 1986). 

AX3.4.1.1. Gas Cooking Appliances 

A large number of studies, as described in the reviews cited above, have all noted the importance of 

gas cooking appliances as sources of NO2 emissions. Depending on geographical location, season, other 

sources, length of monitoring period, and household characteristics, homes with gas cooking appliances 

have approximately 50% to over 400% higher NO2 concentrations than homes with electric cooking 

appliances (Gilbert et al., 2006; Lee et al., 2000, 2002; García-Algar et al., 2004; Raw et al., 2004; 

Leaderer et al., 1986; García-Algar, 2003). Gas cooking appliances remain significantly associated with 
3-90

indoor NO2 concentrations after adjusting for several potential confounders including season, type of 

community, socioeconomic status, use of extractor fans, household smoking, and type of heating 

(García-Algar et al., 2004; Garrett et al., 1999). 

Gas appliances with pilot lights emit more NO2 than gas appliances with electronic ignition. 

Spengler et al. (1994) found that NO2 concentrations in bedrooms of homes with a gas range without a 

pilot light averaged 4 ppb higher than in homes with an electric range, but were 15 ppb higher in homes 

with gas ranges with pilot lights. Lee et al. (1998) found somewhat larger differences in NO2

concentrations in homes in the Boston area, with minor seasonal variation. Homes with gas stoves 

without pilot lights averaged between 11 ppb (summer) and 18 ppb (fall) higher than homes with electric 

stoves, while those with pilot lights averaged between 19 ppb (summer) and 27 ppb (fall) higher than 

electric stove homes. 

Use of extractor fans reduces NO2 concentrations in homes with gas cooking appliances (Gallelli et 

al., 2002; García-Algar et al., 2003), although absolute NO2 levels tend to remain higher than in homes 

with electric stoves. In a multivariate analysis, García-Algar et al. (2004) found that having a gas cooker 

remained significantly increased NO2 concentrations even after adjusting for extractor fan use. Raw et al. 

(2004) found only a small effect of extraction fan use on NO2 levels in the bedroom in gas cooker homes. 

Among homes with gas cooking, geometric mean bedroom NO2 levels were 1.7 ppb lower in homes with 

an extractor fan than in homes without one. As expected, among homes with no fossil fuel cooking, there 

were no differences in mean bedroom levels of NO2 in homes with and without extractor fans. 

AX3.4.1.2. Other Combustion Sources 

Secondary heating appliances are additional sources of NO2 in indoor environments, particularly if 

they are unvented or inadequately vented. As heating costs increase, the use of these secondary heating 

appliances tends to increase. From 1988 to 1994, an estimated 13.7 million homes used unvented heating 

appliances, with disproportionately higher usage rates among southern, rural, low-income, and AfricanAmerican homes (Slack and Heumann, 1997). Of the 83.1 million households using gas stoves or ovens 

for cooking, 7.7 million (9.3%) also used the stove for heating (Slack and Heumann, 1997). 

Gas heaters, particularly when unvented or inadequately vented, produce high levels of NO2. 

Kodama et al. (2002) examined the associations between secondary heating sources and NO2

concentrations measured over a 48-h exposure period in the living rooms of homes in Tokyo, Japan. They 

found much higher NO2 concentrations during February 1998 and January 1999 in homes with kerosene 

heaters in both southern (152.6 ppb and 139.7 ppb for 1998 and 1999, respectively) and northern (102.4 

and 93.1 ppb for 1998 and 1999, respectively) areas of Tokyo compared to homes with electric heaters 

(30.8 and 31.1 for the southern and 37.2 and 31.6 for northern areas, 1998 and 1999, respectively). 

In a study by Garrett et al. (1999) of 78 homes in Latrobe Valley, Australia, the two highest indoor 

NO2 levels recorded in the study were 129 ppb for the only home with an unvented gas heater and 69 ppb 

for a home with a vented gas heater. Levels of NO2 in the kitchens and living rooms of homes with a 

vented gas heater (mean = 6.9 ppb in living room, 7.3 ppb in kitchen, n = 15) were comparable to homes 

with gas stoves (mean = 6.7 ppb in living room, 8.0 ppb in kitchen, n = 15) (Table AX3.4-1). These 

concentrations include results from all seasons combined, so the levels are somewhat lower than those 

found by Triche et al. (2005) for winter monitoring periods only. 

Triche et al. (2005) also found high levels of NO2 in homes with gas space heaters, although 

information on whether the appliance was vented or unvented was not available. Data from this study 

were analyzed in more detail and are shown in Table AX3.4-2. The median NO2 concentration in the 6 

homes with gas space heater use during monitoring periods with no gas stove use was 15.3 ppb; a similar 

incremental increase in total NO2 levels was noted for homes with gas space heater use during periods 

when gas stoves were also used (Median = 36.6 ppb) compared to homes where gas stoves were used but 

no secondary heating sources were present (Median = 22.7 ppb) (Table AX3.4-2). 
3-91

Shima and Adachi (1998) examined associations between household characteristics, outdoor NO2, 

and indoor NO2 in 950 homes during the heating season (640 with unvented and 310 vented heaters) and 

905 homes during the non-heating season in urban, suburban, and rural areas of Japan. While no 

information is provided on gas stove use, the authors note that nearly all homes in Japan have gas stoves, 

though relatively few have pilot lights. During the heating season, geometric mean NO2 levels in homes 

with unvented heaters (66.4 ppb) are about three times higher than in homes with vented heaters 

(20.6 ppb). In the non-heating season, the mean levels were lower at only 13.8 ppb, suggesting a 

contribution from vented heaters as well. 

In multivariate analyses, Gilbert et al. (2006) found that gas and mixed/other heating systems were 

significantly associated with NO2 levels, adjusting for presence of gas stoves and air exchange rates in 96 

homes in Quebec City, Canada during the winter/early spring period. Many homes with gas space heaters 

also have gas stoves, and the contribution from multiple sources is much higher than from any single 

source alone (Garrett et al., 1999). In the Garrett et al. (1999) study, homes were classified into five 

categories: no indoor source (n = 15), gas stove only (n = 15), gas heater only (n = 14), smoker in the 

household only (n = 7), and multiple sources (n = 29). Homes with multiple sources had much higher 

NO2 concentrations homes with either a gas stove only or gas heater only Table 3.4-1. 

Kerosene heaters are also important contributors to indoor NO2 levels. Leaderer et al. (1986) 

enrolled a cohort of kerosene heater users identified from local kerosene dealers and a cohort of controls 

systematically chosen from the same neighborhoods with each matched pair treated as a sampling unit 

(i.e., sampled at the same randomly assigned time period). A total of 302 homes were monitored for at 

least one two-week period. While outdoor concentrations never exceeded 100 μg/m3

 (53 ppb), 

approximately 5% of homes with either no gas but 1 kerosene heater or gas but no kerosene heater had 

levels exceeding 53 ppb. Between 17%-33% of homes with both gas and kerosene heater(s) exceeded this 

limit, while nearly one quarter of homes with no gas, but two or more kerosene heaters had these levels. 

Data from Triche et al. (2005) (Table AX3.4-2) also indicated increased levels of NO2 for kerosene 

heater homes during monitoring periods with no gas stove use (Median = 18.9 ppb) compared to homes 

with no sources (Median = 6.3 ppb), which is similar to levels found in homes using gas space heaters 

(Median = 15.3 ppb). However, these NO2 concentrations are of the same magnitude as those in homes 

with gas stove use (Median = 17.2 ppb). 

Data are available for unvented gas hot water heaters from a number of studies conducted in the 

Netherlands. Results summarized by Brauer et al. (2002) indicate that concentrations of NO2 in homes 

with unvented gas hot water heaters were 10 to 21 ppb higher than in homes with vented heaters, which in 

turn, had NO2 concentrations 7.5 to 38 ppb higher than homes without gas hot water heaters. 

The contribution from combustion of biomass fuels has not been studied as extensively as that from 

gas. A main conclusion from the previous AQCD was that properly vented wood stoves and fireplaces 

would make only minor contributions to indoor NO2 levels. Several studies conclude that use of wood 

burning appliances does not increase indoor NO2 concentrations. Levesque et al. (2001) examined the 

effects of wood-burning appliances on indoor NO2 concentrations in 49 homes in Quebec City, Canada. 

The homes, which had no other combustion source, were sampled for 24 h while the wood-burning 

appliance was being used. No significant differences in mean NO2 levels were found in homes with (6.6 + 

3.6 ppb) and without (8.8 + 1.9 ppb) a wood-burning appliance. Data from Triche et al. (2005) confirm 

these findings (Table AX3.4-2). Homes with wood burning sources had comparable NO2 concentrations 

to homes without other secondary heating sources, with (Median = 5.9 ppb) and without (Median = 

16.7 ppb) gas stove use. 

Data are available for unvented gas hot water heaters from a number of studies conducted in the 

Netherlands. Results summarized by Brauer et al. (2002) indicate that concentrations of NO2 in homes 

with unvented gas hot water heaters were 10 to 21 ppb higher than in homes with vented heaters, which in 

turn, had NO2 concentrations 7.5 to 38 ppb higher than homes without gas hot water heaters. 

As can be seen from the tables, shorter-term average concentrations tend to be much higher than 

longer term averages. However, as Triche et al. (2005) point out, the 90th percentile concentrations can be 
3-92

substantially greater than the medians, even for two week long samples. This finding illustrates the high 

variability found among homes. 

In 10% of homes with fireplaces studied by Triche et al. (2005), NO2 concentrations were greater 

than or equal to 80 ppb, or about twice the level found in homes with no indoor combustion source (see 

Table AX3.4-2). In a study of students living in Copenhagen, Sørensen et al. (2005) found that personal 

exposures to NO2 were significantly associated with time exposed to burning candles in addition to other 

sources. However, they did not provide data for concentrations in spaces in which candles were burned. 

Results of studies relating NO2 concentrations and exposures to environmental tobacco smoke (ETS) have 

been mixed. Several studies found positive associations between NO2 levels and ETS (e.g., Linaker et al., 

1996); Farrow et al., 1997; Alm et al., 1998; Levy et al., 1998a; Monn et al., 1998; Cyrys et al., 2000; Lee 

et al., 2000; García-Algar et al., 2004) whereas others have not (e.g., Hackney et al., 1992; Kawamoto 

et al., 1993). In a study of 57 homes in Brisbane, Australia (Lee et al., 2000), levels of NO2 were higher in 

homes with smokers present (14.9 ± 7.7 ppb) than without smokers (9.9 ± 5.0 ppb). However, these 

concentrations did not account for presence of a gas range (n = 18 of 57 homes had a gas range). Garrett 

et al. (1999) found that smoking in the home increased levels of NO2 in the winter, but not in the summer 

when windows tended to be opened. In a study of students living in Copenhagen, Sørensen et al. (2005) 

did not find a significant association between ETS and personal exposures to NO2. However, they found 

that burning candles was a significant prediction of bedroom levels of NO2. 

AX3.4.1.3. Other Indoor Environments 

Indoor ice skating rinks have been cited as environments containing high levels of NO2 when fuel 

powered ice resurfacing machines are used especially without ventilation. As part of a three year study, 

Levy et al. (1998b) measured NO2 concentrations at 2 locations at the outside of the ice surface in 19 

skating rinks in the Boston area over 3 winters. Although different passive samplers were used in the first 

year (Palmes tubes, 7 day sampling time) and in years 2 and 3 (Yanagisawa badges, 1 day working hours) 

of the study, consistently high mean NO2 concentrations were associated with the use of propane fueled 

resurfacers (248 ppb in the first year and 206 ppb in the following years) and gasoline fueled resurfacers 

(54 ppb in the first year and 132 ppb in the following years) than with electric resurfacers (30 ppb in the 

first year and 37 ppb in the following years). During all three years of the study peak NO2 concentrations 

were several times higher in the rinks with propane and gasoline fueled resurfacers than the values given 

above. A number of earlier studies have also indicated NO2 concentrations of this order and even higher 

(Paulozzi et al., 1993; Berglund et al., 1994; Lee et al., 1994; Brauer et al., 1997). In these studies peak 

averages were in the range of a few ppm. 

AX3.4.2. Reactions of NO2 in Indoor Air 

Chemistry in indoor settings can be both a source and a sink for NO2 (Weschler and Shields, 1997). 

NO2 is produced by reactions of NO with ozone or peroxy radicals, while NO2 is removed by gas phase 

reactions with ozone and assorted free radicals and by surface promoted hydrolysis and reduction 

reactions. The concentration of indoor NO2 also affects the decomposition of peroxyacyl nitrates. Each of 

these processes is discussed in the following paragraphs. They are important not only because they 

influence the indoor NO2 concentrations to which humans are exposed, but also because certain products 

of indoor chemistry may confound attempts to examine associations between NO2 and health. 
3-93

Indoor NO can be oxidized to NO2 by reaction with ozone or peroxy radicals; the latter are 

generated by indoor air chemistry involving O3 and unsaturated hydrocarbons such as terpenes found in 

air fresheners and other household products (Sarwar et al., 2002a,b; Nazaroff and Weschler, 2004; 

Carslaw, 2007). The rate coefficient for the reaction 

 (AX3.4-1) 

at room temperature (298 K) is 1.9 × 10−14 cm3

/molec-sec or 4.67 Η 10−4 ppb−1 s−1 (Jet Propulsion 

Laboratory, 2006). At an indoor O3 concentration of 10 ppb and an indoor NO concentration that is 

significantly less than that of O3, the half-life of NO is 2.5 min. This reaction is sufficiently fast to 

compete with even relatively fast air exchange rates. Hence, the amount of NO2 produced from NO tends 

to be limited by the amount of O3 available. The indoor concentrations of NO and O3 are negatively 

correlated; significant concentrations of NO can only accumulate when small amounts of O3 are present 

and vice versa (Weschler et al., 1994). 

The rapid reaction between NO and O3 also means that humans, themselves, can be indirect sources 

of NO2 in the rooms they occupy. Exhaled human breath contains NO that is generated endogenously 

(Gustafsson et al., 1991). For a typical adult male, the average nasal NO output is 325 nL min−1

 or 23.9 

μg h−1

 (Imada et al., 1996). If ozone is present in the indoor air, some or all of these exhaled NO 

molecules will be oxidized to NO2. To put this source in perspective, consider the example of an adult 

male in a 30 m3

 room ventilated at 1 air change per hour (h-1) with outdoor air. The steady-state 

concentration of NO in the room as a consequence of NO in exhaled breath is 0.80 μg/m3

 or 0.65 ppb if 

none of the NO were to be oxidized. However, assuming a meaningful concentration of ozone in the 

ventilation air (>5 ppb), most of this NO is oxidized to NO2 before it is exhausted from the room. In this 

scenario, the single human occupant is indirectly a source for 0.65 ppb of NO2 in the surrounding air. At 

higher occupant densities, lower air exchange rates and elevated concentrations of O3 in the ventilation 

air, human exhaled breath could contribute as much as 5 ppb to the total concentration of indoor NO2. 

The reaction of NO2 with ozone produces nitrate radicals (NO3): 

 (AX3.4-2) 

The second order rate-constant for this reaction at room temperature (298 K) is 3.2 × 10−17 cm3

/molec-sec 

or 7.9 x 10−7

 ppb−1 s−1 (Jet Propulsion Labatory, 2006). For indoor concentrations of 20 ppb and 30 ppb 

for O3 and NO2, respectively, the production rate of NO3 radicals is 1.7 ppb h−1

. This reaction is strongly 

temperature dependent, an important consideration given the variability of indoor temperatures with time 

of day and season. The nitrate radical is photolytically unstable (Finlayson-Pitts and Pitts, 2000). As a 

consequence, it rapidly decomposes outdoors during daylight hours. Indoors, absent direct sunlight, 

nitrate radical concentrations may approach those measured during nighttime hours outdoors. To date 

there have been no indoor measurements of the concentration of nitrate radicals in indoor settings. 

Modeling studies by Nazaroff and Cass (1986), Weschler et al. (1992), Sarwar et al. (2002b), and Carslaw 

(2007) estimate indoor nitrate radical concentrations in the range of 0.01 to 5 ppt, depending on the 

indoor levels of O3 and NO2. 

The nitrate radical and NO2 are in equilibrium with dinitrogen pentoxide (N2O5): 

 (AX3.4-3) 

Dinitrogen pentoxide reacts with water to form nitric acid. The gas phase reaction with water is too 

slow (Sverdrup et al., 1987) to compete with air exchange rates in most indoor environments. Due to 

mass transport limits on the rate at which N2O5 is transported to indoor surfaces, reactions of N2O5 with 
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water sorbed to indoor surfaces are much slower than gas phase reactions between nitrate radicals and 

commonly occurring indoor alkenes. 

Once formed, NO3 radicals can oxidize organic compounds by either adding to an unsaturated 

carbon bond or abstracting a hydrogen atom (Wayne et al., 1991). In certain indoor settings, the nitrate 

radical may be a more important indoor oxidant than either ozone or the hydroxyl radical. Table 8 in 

Nazaroff and Weschler (2004) illustrates this point. Assuming indoor concentrations of 20 ppb, 

5 x 10-6 ppb, and 0.001 ppb for O3, OH, and NO3, respectively, the pseudo first-order rate constants for 

reactions of most terpenoids are larger for reactions with NO3 than for reactions with either O3 or OH. For 

example, for the stated conditions, the half-lives of d-limonene and α-pinene are roughly three times 

shorter as a consequence of reaction with NO3 versus reaction with O3. The products of reactions between 

NO3 and various organic compounds include nitric acid, aldehydes, ketones, organic acids and organic 

nitrates; these have been summarized by Wayne et al. (1991). Nitrate radicals and the products of nitrate 

radical chemistry may be meaningful confounders in NO2 exposure studies. 

Reactions between NO2 and various free radicals can be an indoor source of organo-nitrates, 

analogous to the chain-terminating reactions observed in photochemical smog (Weschler and Shields, 

1997). Additionally, based on laboratory measurements and measurements in outdoor air (Finlayson-Pitts 

and Pitts, 2000), one would anticipate that NO2, in the presence of trace amounts of HNO3, can react with 

PAHs sorbed on indoor surfaces to produce mono- and dinitro-PAHs. 

As noted in the ISA, Section 2.2, HONO occurs in the atmosphere mainly via multiphase processes 

involving NO2. HONO is observed to form on surfaces containing partially oxidized aromatic structures 

(Stemmler et al., 2006) and on soot (Ammann et al., 1998). Indoors, surface-to-volume ratios are much 

larger than outdoors, and the surface mediated hydrolysis of NO2 is a major indoor source of HONO 

(Brauer et al., 1990; Febo and Perrino, 1991; Spicer et al., 1993; Brauer et al., 1993; Spengler et al., 1993; 

Wainman et al., 2001; Lee et al., 2002). Spicer et al. (1993) made measurements in a test house that 

demonstrated HONO formation as a consequence of NO2 surface reactions and postulated the following 

mechanism to explain their observations. 

 (AX3.4-4) 

 (AX3.4-5) 

In a series of chamber studies, Brauer et al. (1993) reported HONO formation as a consequence of 

NO2 surface reactions and further reported that HONO production increased with increasing relative 

humidity. Wainman et al. (2001) confirmed Brauer’s findings regarding the influence of relative 

humidity. They also found that NO2 removal and concomitant HONO production was greater on synthetic 

carpet surfaces compared to Teflon surfaces, and that the affinity of a surface for water influences 

HONO’s desorption from that surface. Lee et al. (2002) measured HONO and NO2 concentrations in 119 

Southern California homes. Average indoor HONO levels were about 6 times larger than outdoors 

(4.6 ppb versus 0.8 ppb). Indoor HONO concentrations averaged 17% of indoor NO2 concentrations, and 

the two were strongly correlated. Indoor HONO levels were higher in homes with humidifiers compared 

to homes without humidifiers (5.9 ppb versus 2.6 ppb). This last observation is consistent with the studies 

of Brauer et al. (1993) and Wainman et al. (2001) indicating that the production rate of HONO from 

NO2/surface reactions is larger at higher relative humidities. Based on detailed laboratory studies, the 

hydrolysis mechanism, Equations AX3.4-4 and AX3.4-5, have been refined. Finlayson-Pitts et al. (2003) 

hypothesize that the symmetric form of the NO2 dimer is sorbed on surfaces, isomerizes to the 

asymmetric dimer which auto ionizes to NO+

NO3

-

; the latter then reacts with water to form HONO and 

surface adsorbed HNO3. FTIR-based analyses indicate that the surface adsorbed HNO3 exists as both 

undissociated nitric acid-water complexes, (HNO3)X(H2O)Y, and nitrate ion-water complexes, 
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(NO3

-

)X(H2O)Y (Dubowski et al., 2004, Ramazan et al., 2006). Such adsorbed species may serve as 

oxidizing agents for organic compounds sorbed to these same surfaces (Ramazan et al., 2006). 

HONO and much smaller amounts of HNO3 are also emitted directly by combustion by gas 

appliances and can infiltrate from outdoors. Spicer et al. (1993) compared the measured increase in 

HONO in a test house resulting from direct emissions of HONO from a gas range and from production by 

surface reactions of NO2. They found that emissions from the gas range could account for about 84% of 

the measured increase in HONO and surface reactions for 11% in an experiment that lasted several hours. 

An equilibrium between adsorption of HONO from the gas range (or other indoor combustion sources) 

and HONO produced by surface reactions (see Equation AX3.4-5) also determines the relative 

importance of these processes in producing HONO in indoor air. In a study of Southern CA homes (Lee 

et al., 2002), indoor levels of NO2 and HONO were positively associated with the presence of gas ranges. 

It is known that the photolysis of HONO (g) in the atmosphere (outdoors) is a major source of the 

hydroxyl radical (OH). Given high indoor HONO concentrations and the presence of lighting (sun light 

penetrating windows, incandescent lights, fluorescent lights), the photolysis of indoor HONO may be a 

meaningful source of indoor hydroxyl radical, under favorable reaction conditions. Given the large suite 

of man-made chemicals present indoors at elevated concentrations, indoor free radicals (e.g., OH and 

NO3) can initiate and drive a complex series of indoor chemical reactions. 

NO2 can also be reduced on certain surfaces, forming NO. Spicer et al. (1989) found that as much 

as 15% of the NO2 removed on the surfaces of masonite, ceiling tile, plywood, plasterboard, bricks, 

polyester carpet, wool carpet, acrylic carpet and oak paneling was re-emitted as NO. Weschler and 

Shields (1996) found that the amount of NO2 removed by charcoal building filters were almost equally 

matched by the amount of NO subsequently emitted by these same filters. 

Spicer et al. (1993) determined the 1st order rate constants for removal of several NOY components 

by reaction with indoor surfaces. They found lifetimes (e-folding times) of about half an hour for HNO3, 

an hour for NO2, and hours for NO and HONO. Thus the latter two components, if generated indoors are 

more likely to be lost to the indoor environment through exchange with outside air than by removal on 

indoor surfaces. However, HONO is in equilibrium with the nitrite ion (NO2

−

) in aqueous surface films 

 (AX3.4-6) 

Ozone oxidation of nitrite ions in such films is a potential sink for indoor HONO (Lee et al., 2002). 

Jakobi and Fabian (1997) measured indoor and outdoor concentrations of ozone and peroxyacetyl nitrate 

(PAN) in several offices, private residences, a classroom, a gymnasium and a car. They found that indoor 

levels of PAN were 70% to 90% outdoor levels, and that PAN’s indoor half-life ranged from 0.5 to 1 h. 

The primary indoor removal process is thermal decomposition. 

 (AX3.4-7) 

As is indicated by Equation AX3.4-7, PAN is in equilibrium with the peroxylacetyl radical and 

NO2. Hence, the indoor concentration of NO2 affects the thermal decomposition of PAN and, 

analogously, other peroxyacyl nitrates. Peroxylalkyl radicals rapidly oxidize NO to NO2, so the indoor 

concentration of NO also influences the thermal decomposition of PAN type species (Finlayson-Pitts and 

Pitts, 2000). 

Reactions between hydroxyl radicals and aldehydes in the presence of NO2 can lead to the 

formation of peroxyacyl nitrates. Weschler and Shields (1997) have speculated that such chemistry may 

sometimes occur indoors. For example, the requisite conditions for the formation of the highly irritating 

compound peroxybenzoyl nitrate may occur when ozone, certain terpenes, styrene and NO2 are present 

simultaneously at low air exchange rates. This relatively common indoor mixture of pollutants produces 

hydroxyl radicals and benzaldehyde, which can subsequently react as noted above. In her detailed model 
3-96

of indoor chemistry, Carslaw (2007) explores the indoor formation of PAN-type species (see Figure 2 in 

the cited reference). 

Recent work indicates that indoor NO2 also can affect the formation of secondary organic aerosols 

(SOA) resulting from the reaction of O3 with terpenes such as d-limonene and α-pinene (Nøjgaard et al., 

2006). At concentrations of 50 ppb for O3 and the terpenes, NO2 decreased the formation of SOA 

compared to the levels formed in the absence of NO2. The effect was more pronounced for SOA derived 

from α-pinene than d-limonene, and at lower NO2 concentrations, appears to be explained by the O3 loss 

resulting from its reaction with NO2. The resultant nitrate radicals apparently are not as efficient at 

producing SOA as the lost O3. 

Nitro-PAHs have been found in indoor environments (Mumford et al., 1991; Wilson et al., 1991). 

The major indoor sources of nitro-PAHs include cooking, wood burning, and the use of kerosene heater 

(World Health Organization (WHO), 2003). It is also likely that nitro-PAHs outdoors can infiltrate 

indoors. One of the potential sources of nitro-PAHs indoors, which has not been characterized, is 

reactions via indoor chemistry. The reactions of PAHs with OH and NO3 may occur in indoor 

environments. Although no direct measurements of OH or NO3 in indoor environments, OH and NO3 can 

be formed via indoor chemistry and may present at significant levels indoors (Nazaroff and Cass 1986, 

Sarwar et al., 2002a; Carslaw, 2007). Concentrations of ~10−6

 ppb for OH and 0.01-5 ppt of NO3 have 

been predicted through indoor chemical reactions (Nazaroff and Cass 1986, Sarwar et al., 2002a, Carslaw, 

2007), depending on the indoor levels of O3, alkenes, and NO2. Observation of secondary organic aerosols 

(SOA) formation in a simulated indoor environment also suggested that ~10−5

 ppb steady-state OH 

radicals were generated from the reactions of O3 with terpenes (Fan et al., 2003). PAHs are common 

indoor air pollutants (Chuang et al., 1991; Naumova et al., 2002), and the concentrations of some PAHs 

indoors are often higher than outdoors (Naumova et al., 2002). Therefore, the reactions of OH and NO3

with PAHs may occur at rates comparable to air exchange rates to form nitro-PAHs indoors. In addition, 

the reactions of NO3 with PAHs may be more significant indoors than outdoors because indoor NO3 is 

more stable due to the low uv in indoor environments. Given the high surface areas available indoors, the 

formation of nitro-PAHs via surface reactions of PAHs with nitrating species may be more important 

compared to heterogeneous reactions outdoors. 

In summary, indoor chemistry can meaningfully alter the indoor concentration of NO2. Indoor 

exposure to NO2 may be accompanied by indoor exposures to nitrate radicals, organic nitrates, and nitroPAHs. 

AX3.5. Personal Exposure 

AX3.5.1. Personal Exposure in the Residential Indoor Environment 

People spend most of their daily time in a residential indoor environment (Klepeis et al., 2001). 

NO2 found in an indoor environment originates both indoor and outdoors; and therefore, people in an 

indoor environment are exposed to both indoor and outdoor generated NO2. In a residential indoor 

environment, personal exposure concentration equals the residential indoor concentration (if there is no 

personal cloud) which can be broken down into two parts: indoor generation and ambient contribution. 

The relationship between personal NO2 exposure and ambient NO2 can be modified by the indoor 

environment in the following ways: (1) during the infiltration processes, ambient NO2 can be lost through 

penetration and decay (chemical and physical processes) in the indoor environment, and therefore, the 

concentration of indoor NO2 of ambient origin is not the ambient NO2 concentration but the product of the 

ambient NO2 concentration and the infiltration factor (Finf, or α if people spend 100% of their time 

indoors); (2) in an indoor environment, people are exposed to not only ambient generated NO2 but also 

indoor generated NO2, and therefore, the relative contribution of ambient and nonambient NO2 to 
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personal exposure depends not only on the ambient NO2 concentration but also on the infiltration factor 

(attenuation factor) and the indoor source contribution; (3) the strength of the association between 

personal exposure to NO2 of ambient origin and ambient NO2 concentration is determined by the temporal 

and spatial variation in the infiltration factor; and (4) the strength of the association between personal total 

exposure and ambient NO2 is determined by the variation in the indoor source contribution and the 

variation in the infiltration factor. Below, factors affecting infiltration factor and the indoor source 

contribution will be evaluated, and the key issues, such as those mentioned above, related to ambient 

contribution to personal NO2 exposure will be addressed. 

Due to the lack of specific P, k, and a for study homes or a study population, instead of using P, k, 

and a, alternative approaches to obtain the infiltration factor are the ratio of indoor/outdoor NO2 and the 

regression based RCS model. The basic rationale of the RCS model has been introduced in the previous 

section. Without indoor sources, the ratio between indoor NO2 and outdoor NO2 should be always less 

than or equal to 1. If the indoor to outdoor ratio is larger than 1 (after adjusting for measurement error), 

we can surely say that indoor sources exist. However, if an indoor/outdoor ratio is less than one, we 

cannot exclude the effect of indoor sources; otherwise, the infiltration factor would be overestimated. In 

order to use an indoor/outdoor ratio as the infiltration factor, study designs and questionnaires must be 

carefully read, and only the ratio for homes without identified indoor sources can be used as an indicator 

of infiltration factor. The population averaged infiltration factor is the slope of the regression line of 

indoor concentration vs. outdoor concentration. The reliability of the regression slope is dependent upon 

the sample size and how to deal with the outlier effects. Indoor/outdoor ratios and the regression slopes 

are summarized in Table AX3.5-1a. Most of the infiltration factors range from 0.4 to 0.7. Similarly, α

ranges from 0.3 to 0.61 (Table AX3.5-1b). Theoretically, infiltration factor is a function of air exchange 

rate, which has been indicated by season in some studies. However, most studies do not report the 

infiltration factor by season, and therefore, a seasonal trend of infiltration factor could not be observed in 

Table AX3.5-1a. 

As mentioned before, personal NO2 exposure is not only affected by air infiltrating from outdoors 

but also by indoor sources. The NO2 residential indoor sources reported are gas cooking, gas heating, 

kerosene heating, smoking and burning candles (Schwab et al., 1994; Spengler et al., 1994; Nakai et al., 

1995; Lee et al., 1996; Linaker et al., 1996; Cotterill and Kingham, 1997; Farrow et al., 1997; Kawamoto 

et al., 1997; Lee, 1997; Raaschou-Nielsen et al., 1997; Alm et al., 1998; Levy et al., 1998a; Monn et al., 

1998; Garrett et al., 1999; Chao, 2001; Dennekamp et al., 2001; Dutton et al., 2001; Emenius et al., 2003; 

Kodama et al., 2002; Lee et al., 2002; Mosqueron et al., 2002; García-Algar et al., 2003; García-Algar 

et al., 2004; Lai et al., 2004; Lee et al., 2004; Yang et al., 2004; Zota et al., 2005; Sørensen et al., 2005; 

Lai et al., 2006). Spengler et al. (1994) reported that personal exposures in homes with gas range with 

pilot light were 15 ppb higher than those in homes with electric range, and it was 5 ppb higher in homes 

with gas range without pilot light than homes with electric ranges. Schwab et al. (1994) reported that 

homes with gas stove with pilot light had higher indoor NO2 concentrations (peak concentrations ranging 

from 30 to 35 ppb), followed by homes with gas stove without a pilot light (peak concentrations ranging 

from 15 to 20 ppb) and then homes with electric stoves (peak concentrations ranging from 5 to 10 ppb). In 

an international study, Levy et al., (1998a) reported that the use of a gas stove in the home was the 

dominant activity influencing NO2 concentrations with a 67% increase in mean personal NO2 exposure 

and an increase in indoor-outdoor ratios from 0.7 to 1.2. Smoking was found to be another significant 

factor elevating personal and indoor NO2 exposure. Monn et al. (1998) reported that during 1-week 

integrated measurement, smoking contributed 1 ppb more NO2 exposure. Alm et al. (1998) reported that 

one-week integrated personal NO2 exposure for smokers and nonsmokers were 12.9 ppb and 10.7 ppb, 

respectively. Zota et al. (2005) observed that smoking was not a significant indoor source. However, the 

authors pointed out that the effect of smoking might have been overwhelmed by the presence of the gas 

stove. Sørensen et al. (2005) found that burning candles were significantly associated with the elevation 

of indoor NO2 (p = 0.02). NO2 concentration in an indoor environment affected by the indoor sources is 

not homogeneously distributed: NO2 concentration is usually the highest in the kitchen, lowest in the 

bedroom and the concentration in a living room is in between as shown in Table AX3.5-2. The 
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concentration differences between a bedroom and a kitchen ranged from 1 ppb to 28 ppb, and largest 

difference occurred in homes with gas stoves. 

The concentration differences in indoor microenvironments reflect the differences in personal 

exposure in those microenvironments, which is related to personal activities and behaviors. People who 

spend more time in a kitchen are expected to have higher NO2 exposures. Also, in most exposure studies, 

integrated indoor and personal exposures were measured from 2 days to 2 weeks with passive samplers. 

Therefore, the peak exposure concentration could be even higher. 

Indoor source contributions to indoor and personal exposure are determined by indoor source 

strength (S), house volume (V), air exchange rate (a) and the NO2 decay rate (k) in an indoor environment, 

through the equation Cnona = S/[V(a + k)]. Indoor source strength has been summarized in a previous 

section (Indoor sources and concentrations of nitrogen oxides). With a mass balance approach, Yang et al. 

(2004) reported that the source strength for electric range was 3.5 ppb/h, 11.5 ppb/h for gas range in 

Brisbane, and 23.4 ppb/h for gas range in Seoul. The age of house and the house type are associated with 

ventilation, indoor sources, and house volume. As mentioned before, Lee et al. (1996) reported that the 

building type was significantly associated with volume of dwelling unit, and air exchange rate. Garrett 

et al. (1999) reported that older houses were associated with higher nitrogen dioxide levels, possibly as a 

result of older and less efficient appliances in older homes or due to smaller rooms. 

In theory, personal exposure of ambient origin should be at least as much as the indoor NO2 of 

ambient origin in that people spend time in either an indoor or an outdoor environment. However, it was 

shown in the previous part (Table AX3.5-3a and Table AX3.5-3b) that the ambient contribution to 

population exposure ranged from 20% to 50% based on four studies (Rojas-Bracho et al., 2002; Monn 

et al., 1998; Levy et al., 1998a; Spengler et al., 1994); and results here show that the ambient contribution 

to indoor NO2 is around 70% with a wide range from 40 to 90% based on another four studies 

(Mosqueron et al., 2002; Yang et al., 2004; Kulkarni et al., 2002; Monn et al., 1998). It is not clear at 

present why the indoor NO2 of ambient origin is larger than the personal NO2 exposure of ambient origin. 

The strength of the indoor, outdoor and personal NO2 associations (rp: Pearson correlation 

coefficient; rs: Spearman correlation coefficient; and R2

: coefficient of determination) are summarized in 

Table AX3.5-4. The strength of the associations are determined by the variation in Finf (P, k, and a) and 

indoor source contributions from home to home and from day to day. In general, the correlation between 

indoor and outdoor NO2 ranges from poor to good (rp: 0.06 to 0.86). When we break down the correlation 

coefficient by season and indoor sources, it is obvious that the association between indoor and outdoor 

NO2 is stronger during spring and summer but weaker during wintertime, and the association is stronger 

for homes without indoor sources but weaker for homes with strong indoor sources. Mukala et al. (2000) 

reported an rp of 0.86 for the indoor and outdoor NO2 association during the spring and it reduced to 0.54 

during the winter. Spengler et al. (1994) reported that the associations were 0.66 and 0.75 (rp) for homes 

with and without air conditioning system, respectively. Emenius et al. (2003) reported that the association 

between indoor and outdoor NO2 was 0.69 (rp) for homes without smoker and without gas stove using, 

but the association was not significant for homes with gas stove or smokers. Yang et al. (2004) reported 

that the indoor and outdoor NO2 association was 0.70 (R2

) for homes with electric ranges, and was 0.57 

(R2

) for homes with gas ranges. In other words, personal exposure to ambient NO2 in a residential indoor 

environment will be modified the least when the air exchange rate is high and the indoor source 

contribution is not significant. Considering the large spatial variation in ambient NO2 concentrations and 

the relative sparseness of ambient NO2 monitors, the associations between indoor and outdoor 

concentrations are usually stronger than the associations between indoor and ambient concentrations. As 

shown in Table AX3.5-4, a stronger personal vs. residential indoor relationship than the personal vs. 

outdoor relationship has been reported by most studies (Lai et al., 2004; Monn et al., 1998, Levy et al., 

1998a; Spengler et al., 1994; Kousa et al., 2001; Linaker et al., 1996), which is a reminder that personal 

exposure to ambient NO2 mostly happens in the residential indoor environment. It should be pointed out 

that the association between indoor, outdoor and personal NO2 and the relative contributions of indoor 

and outdoor NO2 to indoor and personal exposures were calculated based on time integrated indoor, 

outdoor and personal NO2 measurement with passive samplers and an average measurement time of a 
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couple of days to two weeks. In most studies, an equilibrium condition was assumed and the effects of 

dynamics on the indoor, outdoor, and personal association were not evaluated, which could result in 

missing the peak exposure and obscuring the real short-term outdoor contribution to indoor and personal 

exposure. For example, the NO2 concentrations at locations close to busy streets in urban environments 

may vary drastically with time. If the measurement is carried out during a non-steady-state period, the 

indoor/outdoor concentration ratio may indicate either a too low relative importance of indoor sources (if 

the outdoor concentration is in an increasing phase) or a too high relative importance of indoor resources 

(if the outdoor concentration is in a decreasing phase). The lower the air exchange rate, the greater the 

error due to the effects of transients (Ekberg, 1996). 

AX3.5.1.1. School and Office 

Workplaces (schools and offices) are the places where people spend most of their time after homes 

in an urban area. The location, indoor sources as well as the ventilation pattern of schools and offices 

could be different from people’s homes. Therefore, personal exposure patterns in schools and offices 

could be different from exposure patterns at home. However, NO2 concentrations in schools and offices 

have only been measured in only a few exposure studies. 

Most studies reported the personal exposure levels were lower than or equal to office NO2 levels. 

Lai et al. (2004) reported that a cohort in Oxford spent 17.5% of their daily time in offices, and mean 

personal total NO2 exposure was 15 ppb and 16.8 ppb for mean office concentrations. Mosqueron et al. 

(2002) reported Paris office worker exposure levels and no significant difference was found between 

personal total exposure (22.8 ppb) and NO2 concentrations in office (23.5 ppb). Personal exposures in 

schools were studied in Helsinki, Southampton and Southern California. Alm et al. (1998) and Mukala 

et al. (2000) reported the personal exposure levels in Helsinki for pre-school children. They reported that 

median personal exposures were lower than the median NO2 concentrations measured inside the day care 

center (13.1 ppb for personal exposure versus 18.8 ppb for inside day-care center for downtown winter; 

14.7 ppb versus 24.1 ppb for downtown spring; 8.9 ppb versus 15.2 ppb for suburban winter; and 8.9 ppb 

versus 13.1 ppb for suburban spring). Linaker et al. (1996) found that the geometric mean of school 

children exposures (18.8 ppb) was higher than geometric means of the NO2 concentrations in classrooms 

(8.4 to 14.1 ppb) in a study of children’s exposures to NO2 in Southampton, UK. A similar exposure 

pattern was found by Linn et al. (1996) during the Southern California school children exposure study. 

During the study, personal exposure (22 ppb) was higher than the NO2 concentration inside school 

(16 ppb). NO2 concentration in school/office is determined by ambient NO2 level, local traffic sources, 

floor height and building ventilation pattern. Partti-Pellinen et al. (2000) studied the effect of ventilation 

and air filtration systems on indoor air quality in a children’s day-car center in Finland. Without filtration, 

NOX and PM generated by nearby motor traffic penetrated readily indoors. With chemical filtration, 50 to 

70% of nitrogen oxides could be removed. The authors suggested that the possible adverse health effects 

of nitrogen oxides and particles indoors could be countered by efficient filtration. Mosqueron et al. (2002) 

reported 24% of variations in in-office NO2 concentrations could be explained by outdoor NO2 levels 

(18%), and floor height (6%) and an inverse relation was observed between in-office concentration and 

floor height. Alm et al. (1998) attributed the high NO2 concentration in the day-care center to its close to 

major roads. Obviously, the relative scale of personal exposure and school concentration also depends on 

personal activities outside schools and workplaces. 

Significant associations between personal exposure and workplace concentrations were reported by 

most studies. Mosqueron et al. (2002) reported office NO2 was a significant predictor of personal 

exposure and 15% of the personal exposure was explained by time weighted office NO2 concentrations. 

Alm et al. (1998) reported population NO2 exposures were highly correlated with the NO2 levels inside 

the day-care centers (R2

 = 0.88). However, Lai et al. (2004) reported a nonsignificant Pearson correlation 

coefficient (0.15) between personal exposure and workplace indoor concentration and the authors 

suggested that the strong residential indoor sources and long time indoors obscured the personal versus 
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office relationship. Personal total exposure is a function of NO2 concentrations in different indoor and 

outdoor microenvironments and how long a person stays in that microenvironment. The large variation of 

NO2 exposure in some microenvironments could obscure the association between personal exposure and 

NO2 concentrations in other microenvironments. 

AX3.5.1.2. Exposure Reconstruction 

Personal exposure has been evaluated in each major microenvironment, where either the NO2

concentration is high or people spend most of their daily time. Personal exposure could be reasonably 

reconstructed if we know the NO2 concentration in each microenvironment and the duration of personal 

exposure in each microenvironment. Levy et al., (1998a) reconstructed personal exposures measured in an 

international study with a time-weighted average exposure model. The personal exposure was 

reconstructed based on the measured NO2 concentrations in residential indoor, residential outdoor, and 

workplace microenvironments, and the time people spent in those environments. The mean measured 

personal NO2 exposure was 28.8 ppb and a mean of estimated NO2 exposure was 27.2 ppb. The Spearman 

correlation coefficient between personal measured exposure and reconstructed exposure was 0.81. The 

same approach was applied by Kousa et al. (2001) to reconstruct the personal exposures in the EXPOLIS 

study. A correlation coefficient of 0.86 was observed for the association between measured NO2 exposure 

and reconstructed NO2 exposure (data were log-transformed), and the slope and the intercept were 0.90 

and 0.22 respectively for the reconstructed exposure vs. measured exposure. In the two studies mentioned 

above, NO2 exposure during commuting was not measured. Probably that is part of the reason why 

reconstructed NO2 exposure was lower than the measured NO2 exposure. 

AX3.5.2. Factors Affecting Exposure 

Physically, personal exposure levels are determined by the time people spend in each 

microenvironment and the NO2 concentrations in each microenvironment, which is determined by source 

emission strength, air exchange rate, penetration coefficient, the NO2 decay rate and the volume of the 

microenvironment. Any factors that can influence the above physical parameters can modify the level of 

personal exposure. The indoor, outdoor and personal NO2 levels on each stratum of those factors will be 

summarized. 

Those factors can be classified in to the following categories: (1) factors associated with 

environmental conditions, such as weather and season; (2) factors associated with dwelling conditions, 

such as the location of the house and ventilation system; (3) factors associated with indoor sources, such 

as the type of range and the fuel type; (4) factors associated with personal activities, such as the time 

spent on cooking or commuting; (5) socioeconomic status, such as the level of education and the income 

level; and (6) demographic factors, such as age and gender. 

Most studies addressed the influences of dwelling condition and indoor sources on indoor and 

personal exposures. A few studies explored the impacts of environmental factors and personal activities 

on personal exposures. Indoor and personal exposures have rarely been stratified by socioeconomic and 

demographic factors. Indoor, outdoor, and personal exposure levels are presented in Table AX3.5-5, 

stratified by environmental factors, dwelling conditions, indoor sources, and personal activities factors. 

The effects of socioeconomic and demographic factors on the indoor, outdoor, and personal levels are 

summarized in Table AX3.5-6. 

Season is an environmental factor affecting both indoor and outdoor levels, and thus personal NO2

levels. During the wintertime, the mixing height is usually lower than during the summer, and therefore 

concentrations of many primary pollutants are higher than in the summer. Wintertime is also a heating 

season, which usually leads to higher indoor source emissions and lower air exchange rates. Therefore, a 

higher indoor NO2 concentration can be expected during the winter. For most cases, the differences of 

indoor or personal NO2 exposure between the heating and non-heating season are within several ppb, but 
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sometimes the difference could be close to 20 ppb (Zota et al., 2005). Other environmental factors include 

day of the week (weekday versus weekend), and the wind direction. 

The dwelling conditions are also associated with indoor, outdoor, and personal NO2 levels. A house 

located in an urban center or close to a major road is expected to have higher outdoor and indoor NO2

levels, and the differences in NO2 exposures are often within 20 ppb based on passive sampler 

monitoring. The age of the house, house type, and window type can affect the ventilation of dwelling 

units, and sometimes the type of heating and cooking appliances in a house. Range and fuel type are the 

indoor source factors discussed the most in the literature. It is common to see differences larger than 

10 ppb in indoor and personal NO2 exposures between a gas range home (especially gas range with pilot 

light) and an electric range home. Sometimes the differences could be as high as 40 ppb. For peak shortterm exposures, the difference could reach 100 ppb. 

The level of personal exposure is dependent upon the time a person spends in each 

microenvironment. Kawamoto et al. (1997), Levy et al. (1998a), and Chao and Law (2000) clearly 

showed that personal NO2 exposure increases with time spent cooking or commuting. 

There are inconsistencies in the literature. For example, smoking is found to be a significant factor 

in some studies but not in others, and the same can be said for proximity to a major road. For another 

example, a higher indoor NO2 level could be found in a rural home rather than in an urban home, 

although most studies found the opposite. Part of the reason is that exposure indicators function together, 

as a multidimensional parameter space, on indoor and personal exposures. They are not independent of 

each other. Unfortunately, studies have rarely been conducted to understand the associations between 

these exposure indicators and to use the study findings to explain indoor and personal NO2 exposures. 

More effort put on exposure indicator studies should help in finding better surrogate measurements 

for personal exposures. Although misclassifying exposures in epidemiological studies is almost 

inevitable, and it is unlikely that the personal exposures of all subjects will be measured, a better 

knowledge of the effects of exposure indicators on personal exposure will help reduce exposure errors in 

exposure and epidemiological studies and help interpret those study results. 

AX3.5.3. Associations between HONO and NO2

Spicer et al. (1993) and Wainman et al. (2000) suggested the presence of a strong indoor source of 

HONO from heterogeneous reactions involving NO2 and water films on indoor surfaces. Hence, 

combustion appliances are sources for exposures to both NO2 exposure and HNO2. Epidemiological 

studies of NO2 health effects should consequently consider the potential confounding effects of NO2 and 

vice versa. 

Jarvis et al. (2005) reported the indoor nitrous acid and lung function in adults as part of European 

Community Respiratory Health Survey (ECRHS). Indoor HONO and indoor and outdoor NO2 were 

measured. Indoor NO2 were correlated with HONO (rp = 0.77) but no significant association of indoor 

NO2 with symptoms or lung function was observed. 

Lee et al. (2002) studied the nitrous acid, nitrogen dioxide, and ozone concentrations in residential 

environments. The authors found that indoor NO2 was significantly correlated with HONO (rp = 0.511). 

As shown above, very few studies showed the relationship between personal NO2 exposure and 

other pollutant exposures. In general, personal NO2 was moderately correlated with PM2.5 and CO. Due to 

the lack of personal HONO exposure data, indoor HONO was used as an indicator for personal exposure, 

and current studies showed that indoor HONO was correlated with indoor NO2 with high correlation 

coefficients, which suggested that the collect ion of HONO exposure data would help interpret adverse 

health outcome in the NO2 health risk assessment. 
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AX3.6. Modeling Human Exposures to NO2 

AX3.6.1. Exposure Models 

Predictive (or prognostic) exposure modeling studies1, specifically focusing on NO2, could not be 

identified in the literature, though, often, statistical (diagnostic) analyses have been reported using data 

obtained in various field exposure studies. However, existing prognostic modeling systems for the 

assessment of inhalation exposures can in principle be directly applied to, or adapted for, NO2 studies; 

specifically, such systems include APEX, SHEDS, and MENTOR-1A, to be discussed in the following 

sections. Nevertheless, it should be mentioned that such applications will be constrained by data 

limitations, such as the degree of ambient concentration characterization (e.g., concentrations at the local 

level) and quantitative information on indoor sources and sinks. 

Predictive models of human exposure to ambient air pollutants such as NO2 can be classified and 

differentiated based upon a variety of attributes. For example, exposure models can be classified as: 

 models of potential (typically maximum) outdoor exposure versus models of actual exposures 

(the latter including locally modified microenvironmental exposures, both outdoor and indoor), 

 Population Based Exposure Models (PBEM) versus Individual Based Exposure Models (IBEM), 

 deterministic versus probabilistic (or statistical) exposure models, 

 observation-driven versus mechanistic air quality models. 

Some points should be made regarding terminology and essential concepts in exposure modeling, 

before proceeding to the overview of specific developments reported in the current research literature: 

First, it must be understood that there is significant variation in the definitions of many of the terms 

used in the exposure modeling literature; indeed, the science of exposure modeling is a rapidly evolving 

field and the development of a standard and commonly accepted terminology is an ongoing process (see, 

e.g., WHO, 2004). 

Second, it should also be mentioned that, very often, procedures that are called exposure modeling, 

exposure estimation, etc. in the scientific literature, may in fact refer to only a sub-set of the complete set 

of steps or components required for a comprehensive exposure assessment. For example, certain selfidentified exposure modeling studies focus solely on refining the sub-regional or local spatio-temporal 

dynamics of pollutant concentrations (starting from raw data representing monitor observations or 

regional grid-based model estimates). Though not exposure studies per se, such efforts have value and are 

included in the discussion of the next sub-section, as they provide potentially useful tools that can be used 

in a complete exposure assessment. On the other hand, formulations that are self-identified as exposure 

models but actually focus only on ambient air quality predictions, such as chemistry-transport models, are 

not included in the discussion that follows. 

Third, the process of modeling human exposures to photochemical pollutants (traditionally focused 

on ozone) is very often identified explicitly with population-based modeling, while models describing the 

specific mechanisms affecting the exposure of an actual individual (at specific locations) to an air 

contaminant (or to a group of co-occurring gas and/or aerosol phase pollutants) are usually associated 

with studies focusing specifically on indoor air chemistry modeling. 

Finally, fourth, the concept of microenvironments, introduced in earlier sections of this document, 

should be clarified further, as it is critical in developing procedures for exposure modeling. In the past, 

microenvironments have typically been defined as individual or aggregate locations (and sometimes even 

as activities taking place within a location) where a homogeneous concentration of the pollutant is 

 

1

 i.e. Assessments that start from emissions and demographic information and explicitly consider the physical and chemical processes of 

environmental and microenvironmental transport and fate, in conjunction with human activities, to estimate inhalation intake and uptake. 
3-103

encountered. Thus a microenvironment has often been identified with an ideal (i.e. perfectly mixed) 

compartment of classical compartmental modeling. More recent and general definitions view the 

microenvironment as a control volume, either indoors or outdoors, that can be fully characterized by a set 

of either mechanistic or phenomenological governing equations, when appropriate parameters are 

available, given necessary initial and boundary conditions. The boundary conditions typically would 

reflect interactions with ambient air and with other microenvironments. The parameterizations of the 

governing equations generally include the information on attributes of sources and sinks within each 

microenvironment. This type of general definition allows for the concentration within a 

microenvironment to be non-homogeneous (non-uniform), provided its spatial profile and mixing 

properties can be fully predicted or characterized. By adopting this definition, the number of 

microenvironments used in a study is kept manageable, but variability in concentrations in each of the 

microenvironments can still be taken into account. Microenvironments typically used to determine 

exposure include indoor residential microenvironments, other indoor locations (typically occupational 

microenvironments), outdoors near roadways, other outdoor locations, and in-vehicles. Outdoor locations 

near roadways are segregated from other outdoor locations (and can be further classified into street 

canyons, vicinities of intersections, etc.) because emissions from automobiles alter local concentrations 

significantly compared to background outdoor levels. Indoor residential microenvironments (kitchen, 

bedroom, living room, etc. or aggregate home microenvironment) are typically separated from other 

indoor locations because of the time spent there and potential differences between the residential 

environment and the work/public environment. 

Once the actual individual and relevant activities and locations (for Individual Based Modeling), or 

the sample population and associated spatial (geographical) domain (for Population Based Modeling) 

have been defined along with the temporal framework of the analysis (time period and resolution), the 

comprehensive modeling of individual/population exposure to NO2 (and related pollutants) will in general 

require seven steps (or components, as some of them do not have to be performed in sequence) that are 

listed below. This list represents a composite based on approaches and frameworks described in the 

literature over the last twenty-five years (Ott, 1982; Ott, 1985; Lioy, 1990; U.S. Environmental Protection 

Agency, 1992; Georgopoulos and Lioy, 1994; U.S. Environmental Protection Agency, 1997; Buck et al., 

2003; Price et al., 2003; Georgopoulos et al., 2005; WHO, 2005; U.S. Environmental Protection Agency, 

2006a; Georgopoulos and Lioy, 2006) as well on the structure of various inhalation exposure models 

(NEM/pNEM, HAPEM, SHEDS, REHEX, EDMAS, MENTOR, ORAMUS, APEX, AIRPEX, 

AIRQUIS, etc., to be discussed in the following section) that have been used in the past or in current 

studies to specifically assess inhalation exposures. Figure AX3.6-1, adapted from Georgopoulos et al. 

(2005), schematically depicts the sequence of steps involved that are summarized here (and further 

discussed in the following sub-sections). 
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Source: Figure adapted with modifications from 

Georgopoulos et al. (2005). 

Figure AX3.6-1. Schematic description of a general framework identifying the processes (steps or 

components) involved in assessing inhalation exposures and doses for individuals and 

populations. In general terms, existing comprehensive exposure modeling systems such 

as SHEDS, APEX, and MENTOR-1A follow this framework. 

6. Estimation of the background d or ambient levels of both NO2 and related photochemical 

pollutants. This is done through either (or a combination of): 

a. multivariate spatio-temporal analysis of fixed monitor data, or 

b. emissions-based, photochemical, air quality modeling (typically with a regional, 

grid-based model such as Models-3/CMAQ or CAMx) applied in a coarse 

resolution mode. 

7. Estimation of local outdoor pollutant levels of both NO2 and related photochemical 

pollutants. These levels could typically characterize the ambient air of either an administrative 

unit (such as a census tract, a municipality, a county, etc.) or a conveniently defined grid cell of 

an urban scale air quality model. Again, this may involve either (or a combination of): 

a. spatio-temporal statistical analysis of monitor data, or 

b. application of an urban multi-scale, grid based model (such as CMAQ or CAMx) 

at its highest resolution (typically around 2-4 km), or 

c. correction of the estimates of the regional model using some scheme that adjusts 

for observations and/or for subgrid chemistry and mixing processes. 
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8. Characterization of relevant attributes of the individuals or populations under study 

(residence and work locations, occupation, housing data, income, education, age, gender, race, 

weight, and other physiological characteristics). For Population Based Exposure Modeling 

(PBEM) one can either: 

a. select a fixed-size sample population of virtual individuals in a way that 

statistically reproduces essential demographics (age, gender, race, occupation, 

income, education) of the administrative population unit used in the assessment 

(e.g., a sample of 500 people is typically used to represent the demographics of a 

given census tract, whereas a sample of about 10,000 may be needed to represent 

the demographics of a county), or 

b. divide the population-of interest into a set of cohorts representing selected 

subpopulations where the cohort is defined by characteristics known to influence 

exposure. 

9. Development of activity event (or exposure event) sequences for each member of the sample 

population (actual or virtual) or for each cohort for the exposure period. This could utilize: 

a. study-specific information, if available 

b. existing databases based on composites of questionnaire information from past 

studies 

c. time-activity databases, typically in a format compatible with U.S. 

Environmental Protection Agency’s Consolidated Human Activity Database 

(CHAD - McCurdy et al., 2000) 

10. Estimation of levels and temporal profiles of both NO2 and related photochemical pollutants 

in various outdoor and indoor microenvironments such as street canyons, roadway intersections, 

parks, residences, offices, restaurants, vehicles, etc. This is done through either: 

a. linear regression of available observational data sets, 

b. simple mass balance models (with linear transformation and sinks) over the 

volume (or a portion of the volume) of the microenvironment, 

c. lumped (nonlinear) gas or gas/aerosol chemistry models, or 

d. detailed combined chemistry and Computational Fluid Dynamics modeling. 

11. Calculation of appropriate inhalation rates for the members of the sample population, 

combining the physiological attributes of the (actual or virtual) study subjects and the activities 

pursued during the individual exposure events. 

12. Calculation of target tissue dose through biologically based modeling estimation (specifically, 

respiratory dosimetry modeling in the case of NO2 and related reactive photochemical pollutants) 

if sufficient information is available. 

Implementation of the above framework for comprehensive exposure modeling has benefited 

significantly from recent advances and expanded availability of computational technologies such as 

Relational Database Management Systems (RDBMS) and Geographic Information Systems (GIS) 

(Purushothaman and Georgopoulos, 1997, 1999a,b; Georgopoulos et al., 2005). 

In fact, only relatively recently comprehensive, predictive, inhalation exposure modeling studies for 

ozone, PM, and various air toxics, have attempted to address/incorporate all the components of the 

general framework described here. In practice, the majority of past exposure modeling studies have either 

incorporated only subsets of these components or treated some of them in a simplified manner, often 

focusing on the importance of specific factors affecting exposure. Of course, depending on the objective 
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of a particular modeling study, implementation of only a limited number of steps may be necessary. For 

example, in a regulatory setting, when comparing the relative effectiveness of emission control strategies, 

the focus can be on expected changes in ambient levels (corresponding to those observed at NAAQS 

monitors) in relation to the density of nearby populations. The outdoor levels of pollutants, in conjunction 

with basic demographic information, can thus be used to calculate upper bounds of population exposures 

associated with ambient air (as opposed to total exposures that would include contributions from indoor 

sources) useful in comparing alternative control strategies. Though the metrics derived would not be 

quantitative indicators of actual human exposures, they can serve as surrogates of population exposures 

associated with outdoor air, and thus aid in regulatory decision making concerning pollutant standards 

and in studying the efficacy of emission control strategies. This approach has been used in studies 

performing comparative evaluations of regional and local emissions reduction strategies in the eastern 

United States (e.g., Purushothaman and Georgopoulos, 1997; Georgopoulos et al., 1997a; Foley et al., 

2003). 

AX3.6.1.1. Population Exposure Models 

Existing comprehensive inhalation exposure models consider the trajectories of individual human 

subjects (actual or virtual), or of appropriately defined cohorts, in space and time as sequences of 

exposure events. In these sequences each event is defined by time, a geographic location, a 

microenvironment, and the activity of the subject. U.S. Environmental Protection Agency offices 

(OAQPS and NERL) have supported the most comprehensive efforts in developing models implementing 

this general concept (see, e.g., Johnson, 2002), and these efforts have resulted in the NEM/pNEM 

(National Exposure Model and Probabilistic National Exposure Model - Whitfield et al., 1997), HAPEM 

(Hazardous Air Pollutant Exposure Model - Rosenbaum, 2005), SHEDS (Simulation of Human Exposure 

and Dose System - Burke et al., 2001), APEX (Air Pollutants Exposure model – U.S. Environmental 

Protection Agency, 2006b,c), and MENTOR (Modeling Environment for Total Risk studies - 

Georgopoulos et al., 2005; Georgopoulos and Lioy, 2006) families of models. European efforts have 

produced some formulations with similar general attributes as the above U.S. models but, generally, 

involving simplifications in some of their components. Examples of European models addressing 

exposures to photochemical oxidants (specifically ozone) include the AirPEx (Air Pollution Exposure) 

model (Freijer et al., 1998), which basically replicates the pNEM approach and has been applied to the 

Netherlands, and the AirQUIS (Air Quality Information System) model (Clench-Aas et al., 1999). 

The NEM/pNEM, SHEDS, APEX, and MENTOR-1A (MENTOR for One-Atmosphere studies) 

families of models provide exposure estimates defined by concentration and breathing rate for each 

individual exposure event, and then average these estimates over periods typically ranging from one hour 

to one year. These models allow simulation of certain aspects of the variability and uncertainty in the 

principal factors affecting exposure. An alternative approach is taken by the HAPEM family of models 

that typically provide annual average exposure estimates based on the quantity of time spent per year in 

each combination of geographic locations and microenvironments. The NEM, SHEDS, APEX, and 

MENTOR-type models are therefore expected to be more appropriate for pollutants with complex 

chemistry such as NO2, and could provide useful information for enhancing related health assessments. 

More specifically, regarding the consideration of population demographics and activity patterns: 

1. pNEM divides the population of interest into representative cohorts based on the 

combinations of demographic characteristics (age, gender, and employment), home/work 

district, residential cooking fuel and replicate number, and then assigns activity diary record 

from CHAD (Consolidated Human Activities Database) to each cohort according to 

demographic characteristic, season, day-type (weekday/weekend) and temperature. 

2. HAPEM6 divides the population of interest into demographic groups based on age, gender 

and race, and then for each demographic group/day-type (weekday/weekend) combination, 
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select multiple activity patterns randomly (with replacement) from CHAD and combine 

them to find the averaged annual time allocations for group members in each census tract 

for different day types. 

3. SHEDS, APEX, and MENTOR-1A generate population demographic files, which contain a 

user-defined number of person records for each census tract of the population based on 

proportions of characteristic variables (age, gender, employment, and housing) obtained 

from the population of interest, and then assign a matching activity diary record from 

CHAD to each individual record of the population based on the characteristic variables. 

It should be mentioned that, in the formulations of these models, workers may commute 

from one census tract to another census tract for work. So, with the specification of 

commuting patterns, the variation of exposure concentrations due to commuting between 

different census tracts can be captured. 

The essential attributes of the pNEM, HAPEM, APEX, SHEDS, and MENTOR-1A models are 

summarized in Table AX3.6-1. 

The conceptual approach originated by the SHEDS models was modified and expanded for use in 

the development of MENTOR-1A (Modeling Environment for Total Risk – One Atmosphere). Flexibility 

was incorporated into this modeling system, such as the option of including detailed indoor chemistry of 

the O3-NOX system and other relevant microenvironmental processes, and providing interactive linking 

with CHAD for consistent definition of population characteristics and activity events (Georgopoulos et 

al., 2005). 

NEM/pNEM implementations have been extensively applied to ozone studies in the 1980s and 

1990s. The historical evolution of the pNEM family of models of OAQPS started with the introduction of 

the first NEM model in the 1980’s (Biller et al., 1981). The first such implementations of pNEM/O3 in the 

1980’s used a regression-based relationship to estimate indoor ozone concentrations from outdoor 

concentrations. The second generation of pNEM/O3 was developed in 1992 and included a simple mass 

balance model to estimate indoor ozone concentrations. A report by Johnson et al. (2000) describes this 

version of pNEM/O3 and summarizes the results of an initial application of the model to 10 cities. 

Subsequent enhancements to pNEM/O3 and its input databases included revisions to the methods used to 

estimate equivalent ventilation rates, to determine commuting patterns, and to adjust ambient ozone levels 

to simulate attainment of proposed NAAQS. During the mid-1990's, Environmental Protection Agency 

applied updated versions of pNEM/O3 to three different population groups in selected cities: (1) the 

general population of urban residents, (2) outdoor workers, and (3) children who tend to spend more time 

outdoors than the average child. This version of pNEM/O3 used a revised probabilistic mass balance 

model to determine ozone concentrations over one-h periods in indoor and in-vehicle microenvironments 

(Johnson, 2001). 

In recent years, pNEM has been replaced by (or “evolved to”) the Air Pollution Exposure Model 

(APEX). APEX differs from earlier pNEM models in that the probabilistic features of the model are 

incorporated into a Monte Carlo framework (Langstaff, 2007; U.S. Environmental Protection Agency, 

2006b,c). Like SHEDS and MENTOR-1A, instead of dividing the population-of-interest into a set of 

cohorts, APEX generates individuals as if they were being randomly sampled from the population. APEX 

provides each generated individual with a demographic profile that specifies values for all parameters 

required by the model. The values are selected from distributions and databases that are specific to the 

age, gender, and other specifications stated in the demographic profile. Environmental Protection Agency 

has applied APEX to the study of exposures to ozone and other criteria pollutants; APEX can be modified 

and used for the estimation of NO2 exposures, if required. 

Reconfiguration of APEX for use with NO2 or other pollutants would require significant literature 

review, data analysis, and modeling efforts. Necessary steps include determining spatial scope and 

resolution of the model; generating input files for activity data, air quality and temperature data; and 

developing definitions for microenvironments and pollutant-microenvironment modeling parameters 

(penetration and proximity factors, indoor source emissions rates, decay rates, etc.) (ICF Consulting 
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2005). To take full advantage of the probablistic capabilities of APEX, distributions of model input 

parameters should be used wherever possible. 

AX3.6.1.2. Ambient Concentrations of NO2 and Related Air Pollutants 

As mentioned earlier, background and regional outdoor concentrations of pollutants, over a study 

domain, may be estimated either through emissions-based mechanistic modeling, through ambient-databased modeling, or through a combination of both. Emissions-based models calculate the spatio-temporal 

fields of the pollutant concentrations using precursor emissions and meteorological conditions as inputs. 

The ambient-data-based models typically calculate spatial or spatio-temporal distributions of the pollutant 

through the use of interpolation schemes, based on either deterministic or stochastic models for allocating 

monitor station observations to the nodes of a virtual regular grid covering the region of interest. The 

geostatistical technique of kriging provides various standard procedures for generating an interpolated 

spatial distribution for a given time, from data at a set of discrete points. Kriging approaches were 

evaluated by Georgopoulos et al. (Georgopoulos et al., 1997b) in relation to the calculation of local 

ambient ozone concentrations for exposure assessment purposes, using either monitor observations or 

regional/urban photochemical model outputs. It was found that kriging is severely limited by the 

nonstationary character of the concentration patterns of reactive pollutants; so the advantages this method 

has in other fields of geophysics do not apply here. The above study showed that the appropriate 

semivariograms had to be hour-specific, complicating the automated reapplication of any purely spatial 

interpolation over an extended time period. 

Spatio-temporal distributions of pollutant concentrations, such as ozone, PM, and various air toxics 

have alternatively been obtained using methods of the Spatio-Temporal Random Field (STRF) theory 

(Christakos and Vyas, 1998a,b). The STRF approach interpolates monitor data in both space and time 

simultaneously. This method can thus analyze information on temporal trends, which cannot be 

incorporated directly in purely spatial interpolation methods such as standard kriging. Furthermore, the 

STRF method can optimize the use of data that are not uniformly sampled in either space or time. STRF 

was further extended within the Bayesian Maximum Entropy (BME) framework and applied to ozone 

interpolation studies (Christakos and Hristopulos, 1998; Christakos and Kolovos, 1999; Christakos, 

2000). It should be noted that these studies formulate an over-arching scheme for linking air quality with 

population dose and health effects; however they are limited by the fact that they do not include any 

microenvironmental effects. MENTOR has incorporated STRF/BME methods as one of the steps for 

performing a comprehensive analysis of exposure to ozone and PM (Georgopoulos et al., 2005). 

Subgrid spatial variability is a major issue with respect to characterizing local concentrations of 

NO2. Indeed, the fast rates of the reactions involving the O3-NOX system result in significant 

concentration gradients in the vicinity of sources of NOX. These gradients are not resolved directly by 

currently operational grid photochemical air quality simulation models (PAQSMs) such as CMAQ and 

CAMx. However, both these models include a plume-in-grid. (PinG) option (AER, 2004; Emery and 

Yarwood, 2005; Gillani and Godowitch, 1999; U.S. Environmental Protection Agency, 2006d) that can 

be used for large point NOX sources (such as smokestacks). Nevertheless, PinG formulations typically 

will resolve gradients in upper atmospheric layers and thus are not necessarily relevant to human 

exposure calculations, which are affected by gradients caused by a multiplicity of smaller ground level or 

near ground level combustion sources such as motor vehicles. 

Currently PAQSMs are typically applied with horizontal resolutions of 36 km, 12 km, and 4 km 

and a surface layer thickness that is typically of the order of 30 m. Though computationally it is possible 

to increase the resolution of these simulations, there are critical limits that reflect assumptions inherent in 

the governing equations for both (a) the fluid mechanical processes embodied in the meteorological 

models (e.g., typically MM5 and RAMS) that provide the inputs for the PAQSMs, and (b) the dispersion 

processes which become more complex at fine scales (see, e.g., Georgopoulos and Seinfeld, 1989) and 
3-109

thus cannot be described by simple formulations (such as constant dispersion coefficients) when the 

horizontal resolutions is 2 km or finer. 

Application of PAQSMs to urban domains is further complicated by urban topography, the urban 

heat island, etc. It is beyond the scope, however, of the present discussion, to overview the various issues 

relevant to urban fluid dynamics and related transport/fate processes of contaminants. However, the issue 

of modeling subgrid atmospheric dispersion phenomena within complex urban areas in a consistent 

manner is a very active research area. Reviews of relevant issues and of available approaches for 

modeling urban fluid mechanics and dispersion can be found in, e.g., Fernando et al. (2001) and Britter 

and Hanna (2003). 

The issue of subgrid variability (SGV) from the perspective of interpreting and evaluating the 

outcomes of grid-based, multiscale, PAQSMs is discussed in Ching et al. (2006), who suggest a 

framework that can provide for qualitative judgments on model performance based on comparing 

observations to the grid predictions and its SGV distribution. From the perspective of Population 

Exposure Modeling, the most feasible/practical approach for treating subgrid variability of local 

concentrations is probably through (1) the identification and proper characterization of an adequate 

number of outdoor microenvironments (potentially related to different types of land use within the urban 

area as well as to proximity to different types of roadways) and (2) then, concentrations in these 

microenvironments will have to be adjusted from the corresponding local background ambient 

concentrations through either regression of empirical data or various types of local atmospheric 

dispersion/transformation models. This is discussed further in the next subsection. 

AX3.6.1.3. Characterization of Microenvironmental Concentrations 

Concentrations in microenvironments that can represent either outdoor or indoor settings when 

individuals come in contact with the contaminant of concern (e.g., NO2) can be characterized. This 

process can involve modeling of various local sources and sinks, and interrelationships between ambient 

and microenvironmental concentration levels. Three general approaches have been used in the past to 

model microenvironmental concentrations: 

 Empirical (typically linear regression) fitting of data from studies relating ambient/local and 

microenvironmental concentration levels to develop analytical relationships. 

 Parameterized mass balance modeling over, or within, the volume of the microenvironment. This 

type of modeling has ranged from very simple formulations, i.e. from models assuming ideal 

(homogeneous) mixing within the microenvironment (or specified portions of it) and only linear 

physicochemical transformations (including sources and sinks), to models incorporating 

analytical solutions of idealized dispersion formulations (such as Gaussian plumes), to models 

that take into account aspects of complex multiphase chemical and physical interactions and 

nonidealities in mixing. 

 Detailed Computational Fluid Dynamics (CFD) modeling of the outdoor or indoor 

microenvironment, employing either a Direct Numerical Simulation (DNS) approach, a Reynolds 

Averaged Numerical Simulation (RANS) approach, or a Large Eddy Simulation (LES) approach, 

the latter typically for outdoor situations (see, e.g., Milner et al., 2005; Chang and Meroney, 2003; 

Chang, 2006). 

Parameterized mass balance modeling is the approach currently preferred for exposure modeling 

for populations. As discussed earlier, the simplest microenvironmental setting corresponds to a 

homogeneously mixed compartment, in contact with possibly both outdoor/local environments as well as 

other microenvironments. The air quality of this idealized microenvironment is affected mainly by the 

following processes: 
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a. Transport processes: These can include advection/convection and dispersion that are 

affected by local processes and obstacles such as vehicle induced turbulence, street canyons, 

building structures, etc. 

b. Sources and sinks: These can include local outdoor emissions, indoor emissions, surface 

deposition, etc. 

c. Transformation processes: These can include local outdoor as well as indoor gas and aerosol 

phase chemistry, such as formation of secondary organic and inorganic aerosols. 

Examples of the above are discussed next, specifically for outdoor and for indoor 

microenvironments. 

AX3.6.1.4. Characterization of Outdoor Microenvironments Concentrations 

Empirical regression analyses have been used in some studies to relate specific outdoor locations - 

that can be interpreted as generalized types of exposure microenvironments - to spatial variability of NO2

concentrations. For example, Gilbert et al. (2005) in May 2003 measured NO2 for 14 consecutive days at 

67 sites across Montreal, Canada. Concentrations ranged from 4.9 to 21.2 ppb (median 11.8 ppb), and 

they used linear regression analysis to assess the association between logarithmic values of NO2

concentrations and land-use variables via a geographic information system. In univariate analyses, NO2

was negatively associated with the area of open space and positively associated with traffic count on 

nearest highway, the length of highways within any radius from 100 to 750 m, the length of major roads 

within 750 m, and population density within 2000 m. Industrial land-use and the length of minor roads 

showed no association with NO2. In multiple regression analyses, distance from the nearest highway, 

traffic count on the nearest highway, length of highways and major roads within 100 m, and population 

density showed significant associations with NO2. The authors of that study point out the value of using 

land-use regression modeling to assign exposures in large-scale epidemiologic studies. Similar analyses 

have been performed in a predictive setting by Sahsuvaroglu et al. (2006) for Hamilton, Ontario, Canada. 

The category of parameterized mass balance models for outdoor microenvironments includes 

various local roadway, intersection, and street canyon models. For example, Fraigneau et al. (1995) 

developed a simple model to account for fast nitrogen oxide – ozone reaction/dispersion in the vicinity of 

a motorway. Venegas and Mazzeo (2004) applied a combination of simple point and area source 

analytical plume models to characterize NO2 concentration patterns in Buenos Aires, Argentina, which 

they used for a simplified (potential) population exposure study. ROADWAY-2 (Rao, 2002), is another 

near-highway pollutant dispersion model that incorporates vehicle wake parameterizations derived from 

canopy flow theory and wind tunnel measurements. The atmospheric velocity and turbulence fields are 

adjusted to account for velocity-deficit and turbulence production in vehicle wakes and a turbulent kinetic 

energy closure model of the atmospheric boundary layer is used to derive the mean velocity, temperature, 

and turbulence profiles from input meteorological data. 

In parameterized street canyon models, typically, concentrations of exhaust gases are calculated 

using a combination of a plume model for the direct contribution and a box model for the recirculating 

part of the pollutants in the street. Parameterization of flow and dispersion conditions in these models is 

usually deduced from analysis of experimental data and model tests that considered different street 

configurations and various meteorological conditions. An example of a current model that belongs in the 

parameterized mass balance category is the Danish Operational Street Pollution Model (OSPM) 

(Berkowicz, 2002), which updates earlier formulations of street canyon models such as STREET of 

Johnson et al. (1973) and CPBM (Canyon Plume-Box Model) of Yamartino and Weigand (1986). A 

variation of this simple approach is the model of Proyou et al. (1998), which uses a three-layer 

photochemical box model to represent a street canyon. 

A variety of CFD based street canyon models have been developed in recent years (see, e.g., the 

series of International Conferences on Harmonization - http://www.harmo.org), employing various 
3-111

alternatives for closure of the turbulent transport equations. A review and intercomparison of five of these 

models (CHENSI, CHENSI-2, MIMO, MISKAM, TASCflow) vis-a-vis field data from a street canyon in 

Hannover, Germany can be found in the articles by Sahm et al. (2002) and by Ketzel et al. (2002). 

These complex localized models could be useful for improving population exposure model 

estimates by calculating pollutant concentrations at the microenvironmental level. Lack of input 

parameter data and parameter variation across the modeling domain (spatial and temporal) contributes to 

uncertainty in microenvironmental concentrations calculated by exposure models. In such cases, 

parameterized mass balance models could provide outdoor concentration values for estimating exposure. 

If infiltration factors are known, these concentrations could also be used to estimate indoor exposures. 

AX3.6.1.5. Characterization of Indoor Microenvironments 

Numerous indoor air quality modeling studies have been reported in the literature; however, 

depending on the modeling scenario, only few of them address (and typically only a limited subset of) 

physical and chemical processes that affect photochemical oxidants indoors (Nazaroff and Cass, 1986; 

Hayes, 1989, 1991; Freijer and Bloemen, 2000). 

It is beyond the scope of the present discussion to review in detail the current status of indoor air 

modeling. Existing indoor air concentration models indeed are available as a wide range of (a) empirical 

regression relationships, (b) parameterized mass balance models (that can be either single-zone—that is, 

single well-mixed room—or multi-zone models), and (c) CFD formulations. Recent overviews of this 

area can be found in Milner et al. (2005), who focus, in particular, on the issue of entrainment from 

outdoor sources, and in Teshome and Haghighat, (2004), who focus on different formulations of zonal 

models and on how they compare with more complex CFD models. 

Few indoor air models have considered detailed nonlinear chemistry, which, however, can have a 

significant effect on the indoor air quality, especially in the presence of strong indoor sources (e.g., gas 

stores and kerosene heaters, in the case of NO2). Indeed, the need for more comprehensive models that 

can take into account the complex, multiphase processes that affect indoor concentrations of interacting 

gas phase pollutants and particulate matter has been recognized and a number of formulations have 

appeared in recent years. For example, the Exposure and Dose Modeling and Analysis System (EDMAS) 

(Georgopoulos et al., 1997c) included an indoor model with detailed gas-phase atmospheric chemistry to 

estimate indoor concentrations resulting from penetration and reaction of ambient pollutants. This indoor 

model was dynamically coupled with (a) the outdoor photochemical air quality models UAM-IV and 

UAM-V, which provided the gas-phase composition of influent air; and (b) with a physiologically based 

uptake and dosimetry model. Subsequent work (Isukapalli et al., 1999) expanded the approach of the 

EDMAS model to incorporate alternative representations of gas-phase chemistry as well as multiphase 

photochemistry and gas/aerosol interactions. The microenvironmental model corresponding to this more 

general formulation is mathematically represented by the following equation, when an assumption of 

uniform mixing is used for each component (e.g., individual room) of the indoor environment. Sarwar et 

al. (2001) presented a more comprehensive modeling study of the gas phase aspects of ozone indoor 

chemistry focusing on the impact of different factors (such as outdoor ozone, indoor emissions, 

ventilation rates, etc.) on the levels of indoor hydroxyl radicals (OH), which in turn are expected to 

control the rate of formation of secondary toxicants indoors. 

(AX3.6-1) 

where, 

Vi = volume of compartment (m3

) 

Ci = concentration of species in compartment (mol/m3) 
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Kij = mass transfer coefficient from compartment (m/h) 

aij = interfacial air exchange area between compartments (m2) 

Cij = concentration in compartment i in equilibrium with concentration in j (mol/m3) 

Qij = volumetric flow rate from compartment i to j (m3/h) 

Ri = rate of formation of species in compartment i (gmol/h) 

and, 

 (AX3.6 -2) 

More recent work (Sørensen and Weschler, 2002) has coupled CFD calculations with gas-phase 

atmospheric chemistry mechanisms to account for the impact of nonideal flow mixing (and associated 

concentration gradients) within a room on the indoor spatial distribution of ozone and other secondary 

pollutants. This work has identified potential limitations associated with the assumption of uniform 

mixing in indoor microenvironments when calculating personal exposures. 

A recent indoor air model that specifically focuses on NO2 (along with CO, PM10, and PM2.5 is 

INDAIR (Dimitroulopoulou et al., 2006). The INDAIR model considers three interconnected residential 

microenvironments: kitchen, lounge, and bedroom. Removal processes are lumped together and 

quantified via an apparent deposition velocity. Specifically, a loss rate of 0.99 ± 0.19/h (Yamanaka, 

1984), is used in this model corresponding to a mean deposition velocity of 1.2 Η 10-4 m/s. The sources of 

NO2 considered in INDAIR are from gas stove cooking and from cigarette smoking, but only the former 

contributes significantly to indoor NO2 levels, based on available model parameterizations. 

Estimation of NO2 emission rates from gas cooking utilized the following empirical information: 

(a) NOX emission rate equal to 0.125 g kWh-1 (Wooders, 1994); (b) an assumption that NO2 represents 

25% of the total NOX emissions and (c) gas consumption per household in cooking equal to 5–7 

kWh/day, assuming 1 h cooking per day. By multiplying the estimates in (a), (b), and (c) together, NO2

gas cooking emission rates were calculated to be in the range 0.16 to 0.22 g/h, with a uniform 

distribution. 

In a range of simulations performed with INDAIR for houses in the UK, it was found that the 

predicted maximum 1-h mean concentrations in the kitchen were increased, compared to no-source 

simulations, by a factor of 10 for NO2 (30 for PM10 and 15 for PM2.5) and were higher in winter than in 

summer. Cooking activity in the kitchen resulted in significantly elevated 24-h mean concentrations of 

NO2, PM10, and PM2.5 in the lounge, as well as the kitchen, while there was a relatively small effect in the 

bedroom, which was not connected directly to the kitchen in the model structure (i.e., the direct internal 

air exchange rate was zero). 

A very wide range of predictions was derived from the INDAIR simulations. The 95th percentile 

concentrations were typically 50% higher than mean concentrations during periods of average 

concentration, and up to 100% higher than mean concentrations during concentration peaks, which were 

associated with cooking emissions. There was approximately a factor of 2 variation in concentrations, and 

all modeled concentrations were below those outdoors. The effect of cooking was to shift the distribution 

to the right, but the degree of variation was not greatly increased. This may reflect the fact that for the 

fixed emission scenarios that were used, the additional variation in emission rates was small compared to 

that of other factors such as deposition rate and air exchange rate. In this scenario, modeled 

concentrations in the lounge all remained below those outdoors, but a proportion of kitchens (16%) had 

modeled values above the outdoor concentration. For the gas-cooking scenario, indoor/outdoor ratios for 

NO2 ranged from 0.5 to 0.8 for the bedroom, 0.7 to 1.6 for the lounge and 0.9 to 3.6 for the kitchen. 

According to Dimitrolopoulou et al. (2006), these results were broadly consistent with indoor/outdoor 
3-113

ratios reported for the UK. Modeled peak concentrations associated with gas cooking, of about 300 ppb in 

the kitchen and 100 ppb in the lounge, were also consistent with results from UK studies. 

AX3.6.1.6. Characterization of Activity Events 

An important development in inhalation exposure modeling has been the consolidation of existing 

information on activity event sequences in the Consolidated Human Activity Database (CHAD) 

(McCurdy, 2000; McCurdy et al., 2000). Indeed, most recent exposure models are designed (or have been 

re-designed) to obtain such information from CHAD which incorporates 24-h time/activity data 

developed from numerous surveys. The surveys include probability-based recall studies conducted by 

Environmental Protection Agency and the California Air Resources Board, as well as real-time diary 

studies conducted in individual U.S. metropolitan areas using both probability-based and volunteer 

subject panels. All ages of both genders are represented in CHAD. The data for each subject consist of 

one or more days of sequential activities, in which each activity is defined by start time, duration, activity 

type (140 categories), and microenvironment classification (110 categories). Activities vary from one min 

to one h in duration, with longer activities being subdivided into clock-hour durations to facilitate 

exposure modeling. A distribution of values for the ratio of oxygen uptake rate to body mass (referred to 

as metabolic equivalents or METs) is provided for each activity type listed in CHAD. The forms and 

parameters of these distributions were determined through an extensive review of the exercise and 

nutrition literature. The primary source of distributional data was Ainsworth et al. (1993), a compendium 

developed specifically to facilitate the coding of physical activities and to promote comparability across 

studies. 

AX3.6.1.7. Characterization of Inhalation Intake and Uptake 

Use of the information in CHAD provides a rational way for incorporating realistic intakes into 

exposure models by linking inhalation rates to activity information. As mentioned earlier, each cohort of 

the pNEM-type models, or each (virtual or actual) individual of the SHEDS, MENTOR, APEX, and 

HAPEM4 models, is assigned an exposure event sequence derived from activity diary data. Each 

exposure event is typically defined by a start time, a duration, assignments to a geographic location and 

microenvironment, and an indication of activity level. The most recent versions of the above models have 

defined activity levels using the activity classification coding scheme incorporated into CHAD. A 

probabilistic module within these models converts the activity classification code of each exposure event 

to an energy expenditure rate, which in turn is converted into an estimate of oxygen uptake rate. The 

oxygen uptake rate is then converted into an estimate of total ventilation rate (VE), expressed in liters/min. 

Johnson (2001) reviewed briefly the physiological principles incorporated into the algorithms used in 

pNEM to convert each activity classification code to an oxygen uptake rate and describes the additional 

steps required to convert oxygen uptake to VE. 

McCurdy (1997a,b, 2000) has recommended that the ventilation rate should be estimated as a 

function of energy expenditure rate. The energy expended by an individual during a particular activity can 

be expressed as EE = (MET)(RMR) in which EE is the average energy expenditure rate (kcal/min) during 

the activity and RMR is the resting metabolic rate of the individual expressed in terms of number of 

energy units expended per unit of time (kcal/min). MET (the metabolic equivalent of tasks) is a ratio 

specific to the activity and is dimensionless. If RMR is specified for an individual, then the above 

equation requires only an activity-specific estimate of MET to produce an estimate of the energy 

expenditure rate for a given activity. McCurdy et al. (2000) developed distributions of MET for the 

activity classifications appearing in the CHAD database. 

Finally, in order to relate intake to dose delivered to the lungs, it is important to take into account 

the processes affecting uptake following inhalation intake of NO2, in a biologically based dosimetry 
3-114

modeling framework. As a reactive gas, NO2 participates in transformation reactions in the lung epithelial 

lining fluid, and products of these reactions are thought to be responsible for toxic effects (Postlethwait 

et., 1991), although kinetic modeling of these reactions has not been performed. Dosimetry models 

indicate that deposition varies spatially within the lung and that this spatial variation is dependent on 

ventilation rate (Miller et al., 1982; Overton and Graham, 1995). Controlled exposure studies found that 

fractional uptake of NO2 increases with exercises and ventilation rate (e.g., Bauer et al., 1986), making 

activities with high MET values important for quantifying total NO2 exposure. Further discussion of NO2

dosimetry modeling is provided in Section 4.2. 

AX3.6.1.8. Issues to be Addressed in Future Exposure Modeling Efforts 

An issue that should be mentioned in closing is that of evaluating comprehensive prognostic 

exposure modeling studies, for either individuals or populations, with field data. Although databases that 

would be adequate for performing a comprehensive evaluation are not expected to be available any time 

soon, there have been a number of studies, reviewed in earlier sections of this Chapter, that can be used to 

start building the necessary information base. Some of these studies report field observations of personal, 

indoor, and outdoor ozone levels and have also developed simple semi-empirical personal exposure 

models that were parameterized using the observational data and regression techniques. 

In conclusion, though existing inhalation exposure modeling systems have evolved considerably in 

recent years, limitations of available modeling methods and data, in relation to potential NO2 studies that 

include the following, should be taken into account and be addressed by future research efforts: 

 Ambient photochemical modeling systems are not optimized for estimating NO2 at a local scale. 

 Subgrid scale modeling (LES, RANS, DNS) is needed to properly characterize effects of 

nonhomogeneous mixing (i.e., of spatial subgrid variability) on fast nonlinear chemical 

transformations; the outcomes of this characterization then should be incorporated in simpler 

models, appropriate for use in conjunction with exposure modeling systems. 

 Microenvironmental modeling efforts need to balance mechanistic detail and usability by 

developing: 

 — A simplified but adequate indoor chemistry mechanism for NO2 and related 

oxidants, 

 — Databases of realistic distributions of indoor NO2 source magnitudes and 

activities, 

 — Flexible, multi-zonal models of indoor residential and occupational 

microenvironments. 

Existing prognostic modeling systems for inhalation exposure can in principle be directly applied 

to, or adapted for, NO2 studies; APEX, SHEDS, and MENTOR-1A are candidates. However, such 

applications would be constrained by data limitations such as ambient characterization at the local scale 

and by lack of quantitative information for indoor sources and sinks. 
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Table AX3.2-1. NOX and NOY concentrations at regional background sites in the Eastern United 

States. Concentrations are given in ppb. 

SHENANDOAH NP, VA HARVARD FOREST, MA 

NO 

Winter 0.39-2.2 1 — 

Summer 0.12-0.28 — 

NOX — — 

Winter — 1-15 

Summer — 0.4-1.2 

NOY — — 

Winter 2.7-8.6 4.4 2

Summer 2.3-5.7 2.7 2

1 Ranges represent 1σ limits. 2 Values represent medians. 

Table AX3.3-1. Passive samplers used in NO2 measurements. 

SAMPLING RATE PASSIVE 

SAMPLER 

DIMENSION 

(DIFFUSION LENGTH × 

CROSS-SECTIONAL 

AREA)

ABSORBENT ANALYTICAL 

METHOD MANUFACTURER EXPERIMENT 

REFERENCE 

Palmes tube 7.1cm × 0.71cm2 Triethanolamine Spectrophotometry N.A. 0.92 cm3/min Palmes et al. (1976) 

Gradko sampler 7.1cm × 0.93cm2 Triethanolamine Spectrophotometry 1.2 cm3/min 1.212 cm3/min Plaisance et al.(2004)

Gradko (2007) 

Short 15.5 cm3 Passam /min N.A. 

sampler 

Long 

0.74cm × 0.75cm2 Triethanolamine Spectrophotometry 

0.854 cm3

/min 0.833 cm3/min 

Passam (2007) 

AnalystTM 2.54cm × 3.27cm2 Active charcoal Gas 

chromatography 

N.A. 12.3 cm3/min De Santis et al. 

(2002) 

Yanagisawa 

badge 

1.0cm × 20cm2 Triethanolamine Spectrophotometry N.A. N.R. Yanagisawa and 

Nishimura (1982) 

Ogawa sampler 0.6cm × 0.79cm2 Triethanolamine Spectrophotometry N.A. 16.2 cm3/min Ogawa & Company 

(1998a) Gerboles 

et al. (2006a) 

IVL sampler 1.0cm × 3.14cm2 Potassium iodide & 

sodium arsenite 

Spectrophotometry N.A. 29 cm3/min Ferm and Svanberg 

(1998) 

Willems badge 0.6cm × 5.31cm2 Triethanolamineacetone Spectrophotometry N.A. 46 cm3/min HagenbjörkGustafsson et al. 

(2002) 

Radiello® 1.8cm × 2.0cm2 Triethanolamine Spectrophotometry 75 cm3/min N.R. Radiello® (2006) 

EMD sampler N.A. Triethanolamine Ion chromatography N.A. 53.4 cm3/min Piechocki-Minguy 

et al. (2006) 

*N.A.: not available; 
3-116

Table AX3.3-2. The performance of sampler/sampling method for NO2 measurements in the air. 

TYPE SAMPLER OPTIMAL DURATION OF 

SAMPLING 

CONCENTRATION 

RANGE 

DETECTION 

LIMIT COMMENT 

Impinger method 2-24 h 10 – 400 ppb 0.2 ppb 

Chemiluminescence Continuous 0.5 – 1000 ppb 0.05 ppb SD < 5% 

Active 

sampling 

Personal monitor Real-time 0.1 – 50 ppm 0.1 ppm Accuracy ± 5% 

Palmes tube 1-4 wks 10 - 100 ppb 10 ppb 

Gradko sampler 2-4 wks 1.0 – 10,000 ppb 0.5 ppb Precision ± 5% above 

5 ppb 

Short 8-48 h 5 – 240 μg/m3 2-5 μg/m3 Uncertainty ~ 27% at 80 

μg/m3

Passam 

sampler 

Long 1-4 wks 1 – 200 μg/m3

 0.64 μg/m3 Uncertainty ~ 25% at 20-

40 μg/m3

AnalystTM 1-3 mos 24 – 1,237 μg/m3 100 μg/m3 Accuracy ± 5%; Precision 

within 3% 

Yanagisawa badge 1-14 days NR 3.0 ppb 

Ogawa sampler 24-168 h 0 – 3,600 ppb 2.3 ppb 

IVL sampler 1 mo + 0.1 – 400 μg/m3 0.1 μg/m3 SD ~ 4% 

Willems badge 2-8 h & 1-7 days 2.0 – 150 μg/m3 2 μg/m3 Uncertainty ~ 24%; RSD 

22% 

Radiello® 1-24 h & 1-7 days 1.0 – 496 ppb 1.0 ppb Uncertainty ~ 12% 

Passive 

sampling 

EMD sampler 1-24 h NR 11 μg/m3

 Uncertainty ~ 28% 

Table AX3.4-1. NO2 concentrations (ppb) in homes in Latrobe Valley, Victoria, Australia. 

LIVING ROOM KITCHEN 

 Mean ppb Min ppb Max ppb Mean ppb Min ppb Max ppb 

No source 3.77 < 0.37 9.27 3.82 < 0.37 8.17 

Gas stove only 6.70 1.57 18.32 8.01 2.62 24.14 

Gas heater only 6.86 2.20 18.06 7.33 2.88 26.23 

Smoking only 6.02 0.94 14.61 6.60 1.83 16.44 

Multiple sources 14.50 2.25 114.66 10.73 2.62 128.80 

Source: Garrett et al. (1999). 
3-117

Table AX3.4-2. NO2 concentrations (ppb) in homes in Connecticut. 

NO GAS STOVE USED IN MONITORING PERIOD YES GAS STOVE USED IN MONITORING PERIOD

Secondary Heating Source N 10th 25th Median 75th 90th N 10th 25th Median 75th 90th 

None 1018 1.7 3.5 6.3 12.3 28.2 564 8.4 14.5 22.7 33.8 48.1 

Gas space heater 6 0.1 9.2 15.3 68 69.6 6 19.5 34.6 36.6 54.8 147.2 

Wood burning source 200 1.8 3.6 5.9 12.2 28.2 78 6 9.5 16.7 31.4 58.6 

Kerosene heater 159 3.3 7.1 18.9 42.7 88.3 14 0 9.6 17.2 33.6 46.1 

GSH + Wood 3 12.6 12.6 80.6 81.9 81.9 5 36.2 44.8 57.1 114.2 156.6 

GSH + KH 0 -- -- -- -- -- 1 n/a n/a 147.7 n/a n/a 

Wood + KH 73 1.9 8.2 16.4 35.2 66.8 5 8.9 12.7 17.3 23.5 72.9 

GSH + Wood + KH 0 -- -- -- -- -- 1 n/a n/a 107.8 n/a n/a 

Source: Triche et al. (2005). 

Table AX3.4-3. NO2 concentrations near indoor sources – short-term averages. 

AVERAGE 

CONCENTRATION 

(ppb) 

PEAK 

CONCENTRATION 

(ppb) 

COMMENT REFERENCE 

191 kitchen 

195 living room 

184 bedroom 

375 kitchen 

401 living room 

421 bedroom 

Cooked full meal with use of gas stove and range for 2 h, 20 min; 

avg conc. is time-weighted over 7 h. 

Fortmann et al. 

(2001) 

400 kitchen, 

living room, 

bedroom 

673 bedroom Automatic oven cleaning of gas stove. Avgs are over the entire 

cycle. 

Fortmann et al. 

(2001) 

90 (low setting) 

350 (med setting) 

360 (high setting) 

NR Natural gas unvented fireplace,1 2-h-time-weighted avg in main 

living area of house (177 m3

). 

Dutton et al. 

(2001) 

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 heaters operating in a 1400 ft2

 house, 1.0 ach. 

Girman et al. 

(1982) 

1 Unvented fireplaces are not permitted in many areas such as California. 
3-118

Table 3.5-1a. Indoor/outdoor ratio and the indoor vs. outdoor regression slope. 

STUDY DESCRIPTION SEASON

REGRESSION 

FORMAT OR 

RATIO 

INDOOR 

CHARACTERISTICS FINF COMMENTS 

Baxter 

et al. 

(2007a) 

Location: Boston, MA 

Subjects: 43 homes (a 

lower social-economic 

status population) 

Time period: May-October 

(non-heating season), and 

Dec-Mar (heating season), 

2003-2005 

Method: indoor and outdoor 

3- to 4-day samples of NO2

were collected 

simultaneously at each 

home in both seasons; 

when possible, 2 

consecutive measurements 

were collected. 

Overall 

study 

seasons 

Residential indoor 

vs. ambient and 

indoor source and 

proximity to traffic 

Gas stove usage 0.66–0.79 The overall R2

 was 0.20–

0.25. 

Overall homes 0.48 

Homes with high ventilation 

rate 

0.56 

Residential indoor 

vs. residential 

outdoor 

Homes with low ventilation 

rate 

0.47 

Home with an 

indoor/outdoor sulfur 

ratio larger than 0.76 (the 

median) was defined as 

a high ventilation home; 

Home with an 

indoor/outdoor sulfur 

ratio less than 0.76 (the 

median) was defined as 

a low ventilation home. 

Baxter 

et al. 

(2007b) 

Location: Boston, MA 

Subjects: 43 homes (a 

lower social-economic 

status population) 

Time period: May-Oct (nonheating season), and DecMar (heating season), 2003-

2005 

Method: indoor and outdoor 

3- to 4-day samples of NO2

were collected 

simultaneously at each 

home in both seasons; 

when possible, 2 

consecutive measurements 

were collected. 

Overall 

study 

seasons 

Residential indoor 

vs. residential 

outdoor and indoor 

sources 

Overall homes 0.53 The overall R2

 was 0.16. 

Residential indoor 

vs. ambient and 

using gas cooking 

Cooking 0.26 (n = 

62) 

The overall R2 was 0.14, 

and ambient NOR2

R and 

indoor cooking account 

accounted for 0.07 each. 

Mosqueron 

et al. 

(2002) 

Location: Paris, France 

Subjects: 62 office workers 

Time period: Dec 1999 to 

Sept 2000 

Method: 48-h residential 

indoor, workplace, outdoor, 

and personal exposure 

were measured. 

Overall 

study 

seasons 

Office indoor vs. 

ambient and floor 

height 

None 0.56 (n = 

62) 

The overall R2

 was 0.24, 

partial R2

 for ambient and 

floor height were 0.18 

and 0.06, respectively. 

Lee 

et al. 

(1999) 

Location: Hong Kong, China 

Subjects: 14 public places 

with mechanical ventilation 

systems, 

Time period: Oct 1996 to 

Mar 1997 

Method: Teflon bags were 

used to collect indoor and 

outdoor NO and NOR2R 

ring peak hours. 

Overall 

study 

seasons 

Indoor vs. outdoor — 0.59 (n = 

14) 

R2

 was 0.59. The slopes 

for NO and NOX were 

1.11 and 1.04 

respectively. 

Monn 

et al. 

(1997) 

Location: Switzerland 

Subjects: 17 homes across 

Switzerland 

Time period: winter 1994 to 

summer 1995 

Method: 48- to 72-h indoor, 

outdoor, and personal 

NOR2R were measured. 

Overall 

study 

seasons 

Indoor/outdoor ratio Without gas cooking 0.4, -0.7 (n 

= 26) 

— 
3-119

STUDY DESCRIPTION SEASON

REGRESSION 

FORMAT OR 

RATIO 

INDOOR 

CHARACTERISTICS FINF COMMENTS 

Lee 

et al. 

(1995) 

Location: Boston area, MA 

Subjects: 517 residential 

homes 

Time period: Nov 1984 to 

Oct 1986 

Method: 2-wk averaged 

indoor (kitchen, living room, 

and bedroom) and outdoor 

NO2 were measured. 

Summer Indoor/outdoor ratio Electric stove homes 0.77 

(bedroom) 

(Sample 

size was 

not 

reported) 

Homes with gas stove 

and gas stove with pilot 

light have an I/O ratio 

> 1, but the values were 

not reported. 

Garrett 

et al. 

(1999) 

Location: Latrobe Valley, 

Victoria, Australia 

Subjects: 80 homes 

Time period: Mar-Apr 1994, 

and Jan-Feb 1995 

Method: 4-day averaged 

indoor (bedroom, living 

room, and kitchen) and 

outdoor NO2 was monitored. 

Overall 

study 

seasons 

Indoor/outdoor ratio No major indoor sources 

(major sources were gas 

stove, vented gas heater, 

and smoking) 

0.8 (n = 

15) 

The ratio increased to 

1.3, to 1.8, and to 2.2 for 

homes with one, two and 

three major indoor 

sources. 

All homes 0.47 (n = 

1544) 

R2 Monn was 0.37. 

et al. 

(1998) 

Location: Geneva, Basle, 

Lugano, Aarau, Wald, 

Payerne, Montana, and 

Davos (SAPALDIA study, 

Switzerland) 

Subjects: 140 subjects 

Time period: Dec 1993 to 

Dec 1994 

Method: each home was 

monitored for 3 periods of 1 

mo; in the 1st wk of each 

period, personal, indoor and 

outdoor levels were 

measured; for the next 3 

wks, only outdoor levels 

were measured (1-wk 

averaged measurement). 

Overall 

study 

seasons 

Residential indoor 

vs. residential 

outdoor 

Homes without smokers 

and gas-cooking 

0.40 (n = 

968) 

R2

 was 0.33. 

Gas range with pilot light 0.49 (n = 

314) 

R2 was 0.44. 

Gas range without pilot light 0.4 (n = 

148) 

R2 was 0.39. 

Spengler 

et al. 

(1994) 

Location: Los Angeles 

Basin, CA 

Subjects: probability-based 

sample, 70 subjects 

Time period: May 1987 to 

May 1988 

Method: 48-h averaged, in 

the micro-environmental 

component, each participant 

was monitored during each 

of 8 sampling cycles 

throughout the yr. 

Overall 

study 

seasons 

Residential indoor 

vs. residential 

outdoor 

Electric stove 0.4 (n = 

170) 

R2

 was 0.41. 

 
3-120

Table 3.5-1b. Summary of regression models of personal exposure to ambient/outdoor NOR

2R 

STUDY LOCATION SEASON MODEL TYPE SLOPE 

(SE) 

INTERCEPT 

/ ppb R2

RojasBracho et al. 

(2002) 

Location: Santiago, Chile 

Subjects: 20 children 

Time period: winters of 1998 and 1999 

Method: five, 24-h avg samples on consecutive 

days for each child. 

Winter Personal vs. 

outdoor 

(n = 87) 

0.33 

(0.05) 

7.2 0.27 

Alm et al. 

(1998) 

Location: Helsinki, Finland 

Subjects: 246 children aged 3-6 yrs 

Time period: winter and spring of 1991 

Method: 1-wk averaged sample for each person, 

6 consecutive wks in the winter and 7 consecutive wks 

in the spring. 

Winter + Spring Population vs. 

outdoor 

(n = 23) 

0.4 4.7 0.86 

Personal (all 

subjects) vs. 

outdoor 

(n = 1,494) 

Monn et al. 0.45 7.2 0.33 

(1998) 

Location: Geneva, Basle, Lugano, Aarau, Wald, 

Payerne, Montana, and Davos (SAPALDIA study, 

Switzerland) 

Subjects: 140 subjects 

Time period: Dec 1993 to Dec 1994 

Method: each home was monitored for 3 periods of 

1 mo; in the 1st wk of each period, personal, indoor rand 

outdoor levels were measured, and in the next 

3 consecutive wks, only outdoor levels were measured 

(1-wk averaged measurement). 

All 

Personal (no 

smokers and gas 

cooking) vs. 

outdoor (n = 943) 

0.38 7.2 0.27 

Levy et al. 

(1998b) 

Location: 18 cities across 15 countries 

Subjects: 568 adults 

Time period: Feb or Mar 1996 

Method: One, 48-h avg measurement for each person, 

all people were measured on the same day. 

Winter Personal vs. 

outdoor 

(n = 546) 

0.49 14.5 — 

Spengler 

et al. (1994) 

Location: Los Angeles Basin 

Subjects: probability-based sample, 70 subjects 

Time period: May 1987 to May 1988 

Method: in the microenvironmental component of the 

study, each participant was monitored for 48 h during 

each of 8 sampling cycles throughout the yr. 

All Personal vs. 

outdoor 

0.56 15.8 0.51 

All Personal vs. 

outdoor 

(n = 73) 

0.60 

(0.07) 

— — 

(>8 oC) Personal vs. 

outdoor 

(n = 35) 

0.68 

(0.09) 

— — 

Sørensen 

et al. (2005) 

Location: Copenhagen, Denmark 

Subjects: 30 subjects (20-33 yrs old) in each 

measurement campaign 

Time period: fall 1999, and winter, spring and summer of 

2000 

Method: four measurement campaigns in 1 yr; each 

campaign lasted 5 wks with 6 subjects each wk; one 48-

h avg NOR2R measurement for each subject. (<8 oC) Personal vs. 

outdoor 

(n = 38) 

0.32 

(0.13) 

— — 

Piechocki- Location: Pooled, Lille (northern France) All Personal vs. 0.13 6.0 0.09 
3-121

STUDY LOCATION SEASON MODEL TYPE SLOPE 

(SE) 

INTERCEPT 

/ ppb R2

Minguy et al. 

(2006) 

Subjects: 13 participants in the first campaign, and 31 

participants in the second campaign 

Time period: winter 2001 (first campaign), and summer 

2002 (second campaign) 

Method: two 24-h sampling periods (one during the 

workdays and the other during the weekends) for each 

subject in each campaign; during each sampling period, 

each subject received four samplers to measure 

personal exposure in four different microenvironments 

(home, other indoor environment, transport, and 

outdoors). 

Summer 

(homes with no 

major indoor 

NOR2R 

sources) 

central 

(Assuming 

people stayed 

indoors all the 

time) 

0.86 !9.7 0.61 

Sørensen 

et al. 

(2005) 

Location: Copenhagen 

Subjects: 30 subjects (20-33 yrs old) in each 

measurement campaign 

Time period: fall 1999, and winter, spring and summer of 

2000 

Method: four measurement campaigns in 1 yr; each 

campaign lasted 5 wks with 6 subjects each wk; one 48-

h avg NOR2R measurement for each subject. 

All Personal vs. 

central 

(n = 66) 

0.56 

(0.09) 

— — 

Alm et al. 

(1998) 

Location: Helsinki, Finland 

Subjects: 246 children aged 3-6 yrs 

Time period: winter and spring of 1991 

Method: 1-wk averaged sample for each person, 

6 consecutive wks in the winter and 7 consecutive wks 

in the spring. 

Winter + Spring Population vs. 

central (n = 24) 

0.3 5.0 0.37 

Summer Personal vs. 

central 

(n = 225 for 24 

subjects) 

Sarnat et al. 0.04* 9.5 — 

(2001) 

Location: Baltimore, MD 

Subjects: 56 seniors, Schoolchildren, and people with 

COPD 

Time period: summer of 1998 and winter of 1999 

Method: 14 of 56 subjects participated in both sampling 

seasons; all subjects were monitored for 12 consecutive 

days (24-h avg sample) in each of the one or two 

seasons, with the exception of children who were 

measured for 8 consecutive days during the summer. 

Winter Personal vs. 

central 

(n = 487 for 45 

subjects) 

!0.05* 18.2 — 

Summer Personal vs. 

central 

(n = 341) 

Sarnat et al. 0.19 — — 

(2005) 

Location: Boston, MA 

Subjects: 43 seniors and schoolchildren 

Time period: summer of 1999 and winter of 2000 

Method: Similar study design as Sarnat et al., 2001. Winter Personal vs. 

central 

(n = 298) 

!0.03* — — 

Sarnat et al. 

(2006) Location: Steubenville 

Subjects: 15 senior subjects 

Time period: summer and fall of 2000 

Method: two consecutive 24-h samples were collected 

for each subject for each wk, 23 wks total. 

Summer Personal vs. 

central 

(n = 122) 

0.25 

(0.06) 

— 0.14

 
3-122

Table AX3.5-2. NO2 concentrations (ppb) in different rooms. 

STUDY

 

CONDITIONS OUTDOOR KITCHEN LIVING 

ROOM BEDROOM COMMENTS 

Topp et al. First visit 12.4 — 7.8 7.2 

(2004) 

Second visit 12.5 — 8.0 7.6 

Indoor and outdoor NO2 concentrations for 777 residential 

homes in five study areas were measured: Erfurt, Hamburg, 

Zerbst, Bitterfeld and Hettstedt during two visits (from June 

1995 to May 1997, and from April 1996 to Sept 1998). In 

the study, one-week averaged NO2 were measured by 

Palmes tube. 

No identified 

indoor sources 

4.7 3.8 3.8 3.0 

Gas stove homes 4.7 8.0 6.7 6.3 

Gas heater homes 4.7 7.3 6.9 5.0 

Smoking homes 4.7 6.6 6.0 5.7 

Garrett 

et al. 

(1999) 

Homes with 

multiple sources 

4.7 10.7 14.5 11.2 

Garrett (1999) investigated the levels and sources of NO2 in 

Australian homes. During the study, four-day averaged NO2

was monitored using Yanagisawa passive samplers in 80 

homes in the Latrobe Valley, Victoria in March-April 1994, 

and Jan-Feb 1995. 

Gas Stove homes 20.9 35.6 17.3 11.5 

Electric cooker 

homes 

20.9 9.9 8.9 7.3 

Gas cooker home 

with single glazing 

window 

20.9 31.4 16.8 11.0 

Cotterill 

and 

Kingham 

(1997) 

Gas cooker home 

with double 

glazing window 

20.9 39.8 18.3 12.0 

Three consecutive two-week averaged outdoor, kitchen, 

living room, and bedroom NO2 were measured using 

Palmes tubes in 40 houses in Huddersfield, UK in late 

1994. Half the houses were located close to a busy main 

road and half on residential roads set back and parallel to 

the main road. The sample was split so that half had gas 

cookers and half had electric cookers. These subsets were 

split again so that half had double glazing and half had 

single glazed windows. 

Overall 19 43 36 — 

Heating season 21 50 43 — 

Zota et al. 

(2005) 

Non-heating 

season 

17 33 26 — 

The indoor and outdoor NO2 concentrations for low-income, 

urban neighborhoods were measured, where asthma 

prevalence is high. NO2 was measured in 77 homes within 

three Boston public housing developments, using Palmes 

tubes (two-wk integrated sample) placed in the kitchen, 

living room, and outdoors. Air exchange rate for each home 

was also measured. 

Overall study — 24.6 — 13.0 

With vent — 18.1 — — 

Gallelli 

et al. 

(2002) 

Without vent — 30.9 — — 

During the study, one-wk integrated indoor (kitchen and 

bedroom) and personal NO2 were measured in Genoa, 

Italy, for 89 subjects with Palmes samplers. Study 

volunteers included students, workers, and housewives 

living in three areas of Genoa differing by street traffic and 

industrial plant location. 

Linaker 

et al. 

(1996) 

Overall study — 27.2 20.9 — During the study, one-wk integrated personal, indoor 

(kitchen, living room), classroom, and playground NO2 were 

measured using Palmes tubes for school children in 

Southampton. 

Feb 1998 40, 31.3 81.8 73.5 55.2 

June 1998 38, 28 33.2 28.8 24 

July 1998 29, 26.7 24.8 21.9 17.4 

Oct 1998 40, 35 23.5 24.7 18.2 

Kodama 

et al. 

(2002) 

Jan 1999 49, 50 70.9 65.8 50.7 

The first number in outdoor column was the ambient 

concentration in the South Area; and the second number is 

the ambient concentration in the North Area. During the 

study, personal, indoor (kitchen, living room, bedroom and 

study room), and outdoor NO2 were measured for 150 

junior high school students with Yanagisawa badges in 

Tokyo. The investigation was conducted five times 

seasonally, 3 days each, from February 1998 to January 

1999. 

Chao and 

Law 

(2000) 

Overall study 37.6 31.9 28.2 26.4 Personal and indoor exposures were monitored with 

passive sampler in Hong Kong for 60 subjects. Twelve of 

the subjects were selected to conduct more detailed study 

to examine the behavioral and microenvironmental effects 

on personal exposure to NO2. 

 
3-123

Table AX3.5-3a. Average ambient and nonambient contributions to population exposure 

STUDY MODEL 

TYPE 

SLOPE 

(SE) 

INTERCEPT 

/ ppb 

MEAN OF 

PERSONAL 

TOTAL 

EXPOSURE 

/ ppb 

MEAN AMBIENT 

CONTRIBUTION 

/ ppb 

PERCENT 

AMBIENT 

CONTRIBUTION 

% 

PERCENT 

NONAMBIENT 

CONTRIBUTION %

RojasBracho 

et al. 

(2002) 

Personal vs. 

outdoor 

0.33 

(0.05) 

7.2 36.4 7.2 19.8 80.2 

Personal vs. 

central 

Alm et al. 0.3 5.0 — 5.0 — — 

(1998) 

Personal vs. 

outdoor 

0.4 4.7 — 4.7 — — 

Personal (all 

subjects) vs. 

outdoor 

Monn 0.45 7.2 14.1 7.2 51.1 48.9 

et al. 

(1998) 

Personal (no 

smokers and 

gas cooking) 

vs. outdoor 

0.38 7.2 — 7.2 — — 

Levy et al. 

(1998a) 

Personal vs. 

outdoor 

0.49 14.5 28.8 14.5 50.3 49.7 

Spengler 

et al. 

(1994) 

Personal vs. 

outdoor 

0.56 15.8 37.6 15.8 42.0 58.0 

Table AX3.5-3b. Indoor and outdoor contributions to indoor concentrations. 

STUDY CONDITION SLOPE INTERCEPT

MEAN 

INDOOR 

CONCENTRATION 

MEAN 

OUTDOOR 

CONCENTRATION 

PERCENT 

OUTDOOR 

CONTRIBUTION 

PERCENT 

INDOOR 

CONTRIBUTION 

INDOOR 

SOURCE 

STRENGTH

COMMENTS

Mosqueron 

et al. 

(2002) 

Overall study 0.258 — 18.4 31.5 44.2 55.8 — — 

Brisbane, 

electric range 

0.65 0.8 10.3 — 92.4 7.6 3.5 ppb/h — 

Brisbane, gas 

range 

0.56 3.0 18.3 — 83.5 16.5 11.5 ppb/ h — 

Yang et al. 

(2004) 

Seoul, gas 

range 

0.58 4.8 33.4 40.4 85.7 14.3 23.4 ppb/ h — 

Monn et al. Overall study 0.47 3.2 11.0 16.2 70.5 29.5 — — 

(1998) 

Homes 

without 

smokers and 

gas cooking 

0.40 3.2 6.8 16.2 53.1 46.9 — Mean indoor 

was estimated 

based on the 

text 

description. 
3-124

STUDY CONDITION SLOPE INTERCEPT

MEAN 

INDOOR 

CONCENTRATION 

MEAN 

OUTDOOR 

CONCENTRATION 

PERCENT 

OUTDOOR 

CONTRIBUTION 

PERCENT 

INDOOR 

CONTRIBUTION 

INDOOR 

SOURCE 

STRENGTH

COMMENTS

Gas range 

with pilot light 

0.49 — 30 37 60.4 39.6 — Mean indoor 

and mean 

outdoor are 

estimated from 

Figure 2 in 

Spengler et al. 

(1994). 

Gas range 

without pilot 

light 

0.4 — 22 33 60.0 40.0 — Mean indoor 

and mean 

outdoor are 

estimated from 

Figure 2 in 

Spengler et al. 

(1994). 

Electric stove 0.4 — 17 33 77.6 22.4 — Mean indoor 

and mean 

outdoor are 

estimated from 

Figure 2 in 

Spengler et al. 

(1994). 

Spengler 

et al. 

(1994) 

Overall 0.49 8.64 27.2 38.3 68.2 31.8 — — 

 

Table AX3.5-4. The association between indoor, outdoor, and personal NO2

. 

STUDY SUMMARY CONDITION 

INDOOR 

VS. 

OUTDOOR 

PERSONAL 

VS. 

INDOOR 

PERSONAL 

VS. 

OUTDOOR 

COMMENTS 

Mosqueron 

et al. (2002) 

Simultaneous personal, indoor, and in-office 

48-h averaged NO2 concentrations were 

measured with Ogawa badges for 62 people, 

and ambient concentrations were provided 

by local air monitoring network. 

Overall study 0.07 

(partial R2

) 

— — Gas cooking 

interpreted 

another 7% of 

indoor NO2

variation 

Without smoker 

and gas stove 

was not used 

0.69 (rp) — — p < 0.001 

With gas stove 

and with 

smoker 

0.13 (rp) — — p = 0.43 

Emenius 

et al. (2003) 

Palmes tubes were used to measure indoor 

(in the main living room) and outdoor 

(outside the window of this room) NO2

concentrations during a four-wk period 

(mean 28 days, range 26-31) in the first 

winter season following recruitment in the 

case-control study. 

With gas stove 

but without 

smoker 

0.06 (rp) — — p = 0.75 

Lee et al. 

(1999) 

Indoor and outdoor air quality of 14 public 

places with mechanical ventilation systems 

in Hong Kong were measured from Oct 1996 

to March 1997. Traffic peak h NO, NO2 was 

sampled using Teflon bags and then shipped 

back to the laboratory for further analysis. 

Overall study 0.59 (R2

) — — 0.92 for NO and 

0.92 for NOX. 
3-125

STUDY SUMMARY CONDITION 

INDOOR 

VS. 

OUTDOOR 

PERSONAL 

VS. 

INDOOR 

PERSONAL 

VS. 

OUTDOOR 

COMMENTS 

GarcíaAlgar et al. 

(2003) 

Yanagisawa passive filter badges were used 

to measure indoor NO2 concentrations for 

7~15 days for 340 homes in Barcelona, 

Spain during 1996~1999. Outdoor NO2

concentrations were obtained from the fixed 

monitoring stations by the method of CL. 

Overall study 0.15 (rp) — — p = 0.007 

Lai et al. 

(2006) 

The study was conducted between 1996 and 

2000 in six EU cities: Athens, Basel, Helsinki, 

Milan, Oxford, and Prague. 48-h averaged 

indoor and outdoor NO2 were collected each 

home using diffusion tubes for 302 homes. 

Overall study 0.13 (partial 

R2) 

— — The overall R2 for 

the multiple linear 

regression was 

0.67 

Lee et al. 

(2002) 

Six-day integrated indoor and outdoor 

concentrations of NO2 were measured in two 

communities in Southern California using 

Yanagisawa badges for 119 homes in April 

and May 1996. 

Overall study 0.60 (rp) — — — 

Spring 0.86 (rp) — — — 

Winter 0.54 (rp) — — — 

Spring (ambient 

vs. indoor) 

0.45 (rp) — — — 

Mukala 

et al. (2000) 

The one-week averaged indoor (day-care 

center), outdoor (outside day care center) 

and personal NO2 for 162 children aged 3-6 

years old nitrogen dioxide exposure were 

measured by Palmes tube in Helsinki, in 

1991. 

Winter (ambient 

vs. indoor) 

0.36 (rp) — — — 

Garrett 

et al. (1999) 

Four-day averaged NO2 was monitored using 

Yanagisawa passive samplers in 80 homes 

in the Latrobe Valley, Victoria, Australia in 

March-April 1994, and Jan-Feb 1995. 

Overall study 0.28 (R2

) — — Log10 

transformed data 

Cotterill and 

Kingham 

(1997) 

Three consecutive two-week averaged 

outdoor, kitchen, living room, and bedroom 

NO2 were measured using Palme’s tubes in 

40 houses in Huddersfield, UK in late 1994. 

Half the houses were located close to a busy 

main road and half on residential roads set 

back and parallel to the main road. The 

sample was split so that half had gas 

cookers and half had electric cookers. These 

subsets were split again so that half had 

double glazing and half had single glazed 

windows. 

Overall study 0.59 (rp) — — — 

Brisbane, 

electric range 

house 

0.70 (R2) — — — 

Brisbane, gas 

range house 

0.57 (R2) — — — 

Yang et al. 

(2004) 

Daily indoor and outdoor NO2 concentrations 

were measured for 30 consecutive days in 

28 house in Brisbane (between April and 

May in 1999), and for 21 consecutive days in 

37 houses in Seoul (between June and Aug 

in 2000) using Yanagisawa badges. 

Seoul, gas 

range house 

0.52 (R2

) — — — 

Lai et al. 

(2004) 

During the study, 48-averaged personal, 

residential indoor, residential outdoor, and 

workplace indoor pollutants were measured 

for 50 adults between 1998 and 2000 in 

Oxford, once per person. NO2 were 

measured using passive sampling badges. 

Overall study 0.29 (rp) (not 

significant) 

0.47 (rp) (p < 

0.01) 

-0.41 (rp) 

(p < 0.05) 

Data were logtransformed 

Overall study 0.37 (R2) 0.51 (R2) 0.33 (R2 Monn et al. ) — 

(1998) 

During the study, one-wk integrated indoor, 

outdoor and personal samples were 

collected for a subpopulation (n = 140) of 

SAPALDIA study using Pamles tube between 

Dec 1993 and Dec 1994 at eight study 

centers in Switzerland. 

Homes without 

smoker and 

without gascooking 

0.34 (R2

) 0.47 (R2) 0.27 (R2) — 
3-126

STUDY SUMMARY CONDITION 

INDOOR 

VS. 

OUTDOOR 

PERSONAL 

VS. 

INDOOR 

PERSONAL 

VS. 

OUTDOOR 

COMMENTS 

Levy et al., 

(1998a) 

48-h averaged indoor, outdoor and personal 

NO2 were measured in 18 cities in 15 

countries around the world with passive filter 

badges in Feb or March, 1996. 

Overall study — 0.75 (rs) 0.57 (rs) — 

Overall study 0.4 (R2) 0.6 (R2) 0.51 (R2) — 

Electric range 0.41 (R2) — — 

Gas range 

without pilot 

light 

0.39 (R2) — 

0.52 (R2

) 

— 

Gas range with 

pilot light 

0.44 (R2) — 0.44 (R2) — 

With air 

conditioning 

0.66 (rp) — — — 

Without air 

conditioning 

0.75 (rp) — — — 

High ambient 

concentration 

— — 0.47 (R2) — 

Spengler 

et al. (1994) 

Probability based population, Los Angeles 

Basin, 48-h averaged indoor, outdoor and 

personal NO2 were measured 

(microenvironmental component of the 

study), from May 1987 to May 1988 

Low ambient 

concentration 

— — 0.33 (R2

) — 

Overall study 0.44 (R2) 0.53 (R2) 0.37 (R2) Data were logtransformed Kousa et al. 

(2001) 

The indoor, outdoor, and personal NO2

relationship in three EXPOLIS centers 

(Basel, Helsinki, and Prague) were reported. 

During the study, 48-averaged indoor, 

outdoor, and personal NO2 were measured 

with Palmes tubes during 1996-1997. 

Helsinki — 0.45 (R2

) 0.40 (R2) Data were logtransformed 

Linaker 

et al. (1996) 

During the study, one-wk integrated 

personal, indoor (kitchen, living room), 

classroom and playground NO2 were 

measured using Palmes tubes for 46 school 

children aged 9-11 in Southampton, UK. 

Overall study — 0.53-0.76 (rp) 0.61-0.65 (rp) Data were logtransformed 

Overall study — 0.88 (R2) 0.86 (R2) 0.37 (R2) for 

personal vs. 

ambient 

Winter — — 0.04 (partial 

R2) 

p = 0.01; log 

transformed data 

Spring — — 0.50 (partial 

R2) 

p = 0.0001; log 

transformed data 

Winter 

downtown 

0.44 (rp) 0.32 (rp) 0.46 (rp) Personal vs. 

indoor was not 

significant (daycare center, not 

residential 

indoor). 

Spring 

downtown 

0.84 (rp) 0.75 (rp) 0.80 (rp) 

Winter 

suburban 

0.22 (rp) 0.04 (rp) 0.49 (rp) Personal vs. 

indoor, and indoor 

vs. outdoor were 

not significant 

Spring 

suburban 

0.46 (rp) 0.75 (rp) 0.82 (rp) — 

Alm et al. 

(1998) 

During the study, weekly personal, indoor 

(day care center), outdoor (day care center), 

and ambient site NO2 exposures of 246 

children aged 3-6 yrs were measured with 

Palmes tubes during 13 wks in winter and 

spring in 1991 in Helsinki. 

Downtown 

electric stove 

— 0.67 (rp) 0.55 (rp) — 
3-127

STUDY SUMMARY CONDITION 

INDOOR 

VS. 

OUTDOOR 

PERSONAL 

VS. 

INDOOR 

PERSONAL 

VS. 

OUTDOOR 

COMMENTS 

Downtown gas 

stove 

— 0.50 (rp) 0.59 (rp) — 

Downtown nonsmoking — 0.67 (rp) 0.73 (rp) — 

Downtown 

smoking 

— 0.47 (rp) 0.51 (rp) — 

Suburban 

electric stove 

— 0.55 (rp) 0.63 (rp) — 

Suburban gas 

stove 

— — 

Suburban nonsmoking — 0.50 (rp) 0.59 (rp) — 

Suburban 

smoking 

— 0.48 (rp) 0.46 (rp) — 

Kodama Summer — 0.31 (rp) 0.24 (rp) — 

et al. (2002) 

During the study, personal, indoor (kitchen, 

living room, bedroom, and study room), and 

outdoor NO2 were measured for 150 junior 

high school students with Yanagisawa 

badges in Tokyo. The investigation was 

conducted five times seasonally, 3 days 

each, from Feb 1998 to Jan 1999. 

Winter — 0.57 (rp) 0.08 (rp) — 

 

Table AX3.5-5. Indoor, outdoor, and personal NO2 levels stratified by exposure factors 

(concentrations are in ppb and slopes are dimensionless) 

REFERENCE FACTOR 

NAME 

FACTOR 

LEVEL 

AMBIENT 

NO2

LEVEL 

AMBIENT 

SLOPE 

INDOOR 

NO2

LEVEL 

INDOOR 

SLOPE 

PERSONAL 

NO2 LEVEL

PERSONAL 

SLOPE COMMENTS 

Environmental conditions 

Upwind of 

freeway 

20.5 — — — — — — 

Downwind 

and close to 

freeway 

26.5 — — — — — — 

Singer et al. 

(2004) 

Wind 

Direction 

Downward 

and far from 

freeway 

21 — — — — — — 

Zota et al. Heating 21 — 43 — — — — 

(2005) 

Season 

Non-Heating 17 — 26 — — — — 

Sørensen et al. < 8C 14.6 — 8.9 — 11.4 — — 

(2005) 

Season 

> 8C 7.8 — 6.6 — 9.2 — — 

Alm et al. 

(1998) 

Season Winter 

downtown 

smoker 

— — — — 13.5 — — 
3-128

REFERENCE FACTOR 

NAME 

FACTOR 

LEVEL 

AMBIENT 

NO2

LEVEL 

AMBIENT 

SLOPE 

INDOOR 

NO2

LEVEL 

INDOOR 

SLOPE 

PERSONAL 

NO2 LEVEL

PERSONAL 

SLOPE COMMENTS 

Spring 

downtown 

smoker 

— — — — 15.4 — — 

Winter 

downtown 

nonsmoker 

— — — — 13.0 — — 

Spring 

downtown 

nonsmoker 

— — — — 14.1 — — 

Winter 

suburban 

smoker 

— — — — 11.2 — — 

Spring 

suburban 

smoker 

— — — — 10.7 — — 

Winter 

suburban 

nonsmoker 

— — — — 9.2 — — 

Spring 

suburban 

nonsmoker 

— — — — 8.7 — — 

Zota et al. 

(2005) 

Heating 

season 

— — 3.87 — 17.3 — — — 

Vukovich (2000) Day Weekday — — — — — 39% more than 

weekend 

Weekday — — — — — — The effect of 

weekday/weekend is clear but 

the paper didn't 

give a value to 

cite 

Lee (1997) Day 

Weekend — — — — — — — 

Dwelling conditions 

Levy et al. With — — — — 30 — — 

(1998a) 

Window open 

Without — — — — 26.7 — — 

Single 

Glazing 

— — 9.4 — — — — 

Double 

Glazing 

— — 9.4 — — — — 

Single 

Glazing 

— — 11.0 — — — Gas cooker 

homes 

Cotterill and 

Kingham (1997) 

Window 

Double 

Glazing 

— — 12.0 — — — Gas cooker 

homes 

Mechanical 

filter 

Partti-Pellinen 12.3 — 9.6 — — — — 

et al. (2000) 

Type of 

Filtration 

Mechanical 

intake and 

mechanical 

filter 

11.5 — 12.5 — — — — 
3-129

REFERENCE FACTOR 

NAME 

FACTOR 

LEVEL 

AMBIENT 

NO2

LEVEL 

AMBIENT 

SLOPE 

INDOOR 

NO2

LEVEL 

INDOOR 

SLOPE 

PERSONAL 

NO2 LEVEL

PERSONAL 

SLOPE COMMENTS 

Mechanical 

intake and 

mechanical 

and chemical 

filter 

12.4 — 6.5 — — — — 

Yamanaka 

(1984) 

Surface type — — — — — — Affect decay rate 

Zota et al. 

(2005) 

Occupancy — — — — 3.2 — — — 

Levy et al. 1 — — — — 25.9 — — 

(1998a) 

Occupancy 

2 — — — — 30.8 — — 

Urban 16.5 — 9.6 — — — — 

Semi-urban 11.3 — 6.4 — — — — 

Emenius et al. 

(2003) 

Location 

Suburban 7.2 — 4.2 — — — — 

On Main 

Road 

— — 7.9 — — — Electric cooker 

homes 

Cotterill and 

Kingham (1997) 

Location 

50-85m from 

Main Road 

— — 6.8 — — — Electric cooker 

homes 

Zota et al. 

(2005) 

Location — — -0.0093 — — — — — 

Lee et al. Industrial — — — — 34.9 — — 

(2004) 

Location 

Residential — — — — 27.8 — — 

Liard et al. Main Road — — — — 28.1 — — 

(1999) 

Location 

Side Road — — — — 24.3 — — 

 < 20 m 42.4 — 43.8 — 43.1 — Recalculated 

based published 

data 

20-150 m 34.9 — 38.4 — 35.9 — Recalculated 

based published 

data 

Nakai et al. 

(1995) 

 

Location 

> 150 m 20.3 — 36.4 — 30.1 — Recalculated 

based published 

data 

Downtown 

smoker 

— — — — 14.6 — — 

Suburban 

smoker 

— — — — 10.9 — — 

Downtown 

nonsmoker 

— — — — 13.6 — — 

Alm et al. 

(1998) 

Location 

Suburban 

nonsmoker 

— — — — 9.0 — — 

Single DU 17 — 17 — — — Winter 

Small multiDU 23 — 28.9 — — — Winter 

Lee et al. 

(1996) 

House 

structure 

Large multiDU 23.6 — 26.8 — — — Winter 
3-130

REFERENCE FACTOR 

NAME 

FACTOR 

LEVEL 

AMBIENT 

NO2

LEVEL 

AMBIENT 

SLOPE 

INDOOR 

NO2

LEVEL 

INDOOR 

SLOPE 

PERSONAL 

NO2 LEVEL

PERSONAL 

SLOPE COMMENTS 

Single DU 18.4 — 17.8 — — — Fall 

Small multiDU 25.1 — 30.2 — — — Fall 

Large multiDU 25.1 — 25.4 — — — Fall 

Single DU 15.9 — 17.3 — — — Summer 

Small multiDU 23.7 — 27.8 — — — Summer 

Large multiDU 24.5 — 29.1 — — — Summer 

Heating Individual — — 13.7 — — — Bedroom data 

system 

 Central — — 12.5 — — — Bedroom data 

Metal — — 12.6 — — — Bedroom data 

Gallelli et al. 

(2002) 

Frames 

Wood — — 15.0 — — — Bedroom data 

Zota et al. 

(2005) 

Floor level — — 2 — — — — — 

Mosqueron 

et al. (2002) 

Floor level — — — — −1.78 — — — 

Liard et al. Without — — — — 27.5 — — 

(1999) 

Extractor fan 

over cooker 

With — — — — 24.8 — — 

Gallelli et al. With vent — — 18.1 — — — Kitchen data 

(2002) 

Chimney 

Without vent — — 30.9 — — — Kitchen data 

Yang et al. With — — 17.3 — — — — 

(2004) 

Attached 

garage 

Without — — 11.4 — — — — 

Garrett et al. 

(1999) 

Age of house — — — — 0.5 — — — 

Indoor sources 

Zota et al. 

(2005) 

Supplemental 

Heating with 

stove 

— — — — 7.84 — — — 

Lai et al. (2004) Smoking Smoking — — 10.9 — 10.8 — — 

Nonsmoking — — 11.5 — 14.1 — — 

Levy et al. With — — — — 34.8 — — 

(1998a) 

Smokers 

present 

Without — — — — 26.8 — — 

Belanger et al. Electric — — 8.6 — — — — 

(2006) 

Ranges 

Gas — — 25.9 — — — — 

Gas — — 35.6 — — — Kitchen 

Electric — — 9.9 — — — Kitchen 

Gas — — 11.5 — — — Bedroom 

Cotterill and 

Kingham (1997) 

Ranges 

Electric — — 7.3 — — — Bedroom 

Yang et al. Ranges Gas — — 18.3 — — — — 
3-131

REFERENCE FACTOR 

NAME 

FACTOR 

LEVEL 

AMBIENT 

NO2

LEVEL 

AMBIENT 

SLOPE 

INDOOR 

NO2

LEVEL 

INDOOR 

SLOPE 

PERSONAL 

NO2 LEVEL

PERSONAL 

SLOPE COMMENTS 

(2004) Not Gas — — 10.3 — — — — 

Gas with pilot 

light 

— — 20.3 — — — Summer 1998 

data 

Gas without 

pilot light 

— — 11.7 — — — Summer 1998 

data 

Schwab et al. 

(1994) 

Ranges 

Electric — — 8 — — — Summer 1998 

data 

Gas Geneva — — 20.9 — 23.6 — — 

Electric 

Geneva 

— — 16.8 — 19.9 — — 

Gas Basle — — 15.2 — 18.3 — — 

Electric Basle — — 12.6 — 16.2 — — 

Gas Lugano — — 18.8 — 20.9 — — 

Monn et al. 

(1998) 

Ranges 

Electric 

Lugano 

— — 15.7 — 18.3 — — 

Spengler et al. 

(1994) 

Ranges — — — — — — Gas with pilot was 

15 ppb higher 

than electric; gas 

without pilot was 

4 ppb higher than 

electric 

Alm et al. 

(1998) 

Ranges Electric 

smoker 

— — — — 13.0 — — 

RaaschouNielsen et al. 

(1997) 

Near fire — — — — — 0.052 — 

Oil fan heater — — — — — 2.59 — 

Kerosene 

heater 

— — — — — 1.17 — 

Kawamoto et al. 

(1997) 

Heating time 

Clean heater — — — — — — — 

Coal 

briquette 

Lee et al. — — — — 22.2 — — 

(2004) 

Heating fuel 

Petroleum — — — — 33.1 — — 

Liard et al. Gas — — — — 27.9 — — 

(1999) 

Heating 

appliance 

Other — — — — 25.2 — — 

Kerosene 

heater 

— — 152.6 — — — Sourth area, Feb 

1998 

Gas stove — — 77.5 — — — Sourth area, Feb 

1998 

Kodama et al. 

(2002) 

Heater 

Electric 

heater 

— — 30.8 — — — Sourth area, Feb 

1998 

Yang et al. With — — 18.1 — — — — 

(2004) 

Gas water 

heater 

Without — — 11.9 — — — — 

With — — — — 30.5 — — 

Without — — — — 28.2 — — 

Levy et al. 

(1998a) 

Gas water 

heater 

With — — — — 36.4 — — 
3-132

REFERENCE FACTOR 

NAME 

FACTOR 

LEVEL 

AMBIENT 

NO2

LEVEL 

AMBIENT 

SLOPE 

INDOOR 

NO2

LEVEL 

INDOOR 

SLOPE 

PERSONAL 

NO2 LEVEL

PERSONAL 

SLOPE COMMENTS 

Without — — — — 28.5 — — 

Gas range With — — — — 34.8 — — 

Without — — — — 20.5 — — 

Monn et al. With — — — — — — I/O > 1.2 

(1997) 

Gas cooking 

Without — — — — — — I/O ~ 0.4 – 0.7 

Mosqueron 

et al. (2002) 

Gas cooking — — — 0.068 — — — 

RaaschouNielsen et al. 

(1997) 

Gas 

appliances at 

home 

 — — — — — 0.202 — 

None — — 3.0 — — — I/O ratio increase 

from 0.8 to 1.3 to 

1.8 to 2.2 in 

houses with no, 

one, two, or three 

major indoors 

sources 

Gas stove — — 6.3 — — — — 

Gas heater — — 5.0 — — — — 

Smoking — — 5.7 — — — — 

Garrett et al. 

(1999) 

Gas and 

smoking 

Multiple — — 11.2 — — — — 

Low — — 90 — — — — 

Middle — — 350 — — — — 

Dutton et al. 

(2001) 

Fireplace 

setting 

High — — 360 — — — — 

Sørensen et al. 

(2005) 

Exposure to 

burning 

candle 

— — — — — — 0.031 — 

Liard et al. With — — — — 25.1 — — 

(1999) 

Exposure to 

ETS 

Without — — — — 26.3 — — 

RaaschouNielsen et al. 

(1997) 

Exposure to 

ETS 

 — — — — — 0.056 — 

Petroleum — — — — 26.1 — — 

Gas — — — — 33.1 — — 

Lee et al. 

(2004) 

Cooking fuel 

Coal 

briquette 

— — — — 20.6 — — 

Gas — — — — 25.8 — — 

Electric — — — — 25.5 — — 

Liard et al. 

(1999) 

Cooking 

appliance 

 

1 ring — — 437 — — — The max 5 min 

concentrations 

2 rings — — 310 — — — The max 5 min 

concentrations 

Dennekamp 

et al. (2001) 

Cooking 

3 rings — — 584 — — — The max 5 min 

concentrations 
3-133

REFERENCE FACTOR 

NAME 

FACTOR 

LEVEL 

AMBIENT 

NO2

LEVEL 

AMBIENT 

SLOPE 

INDOOR 

NO2

LEVEL 

INDOOR 

SLOPE 

PERSONAL 

NO2 LEVEL

PERSONAL 

SLOPE COMMENTS 

4 rings — — 996 — — — The max 5 min 

concentrations 

Boil water — — 184 — — — The max 5 min 

concentrations 

Stir fry — — 92 — — — The max 5 min 

concentrations 

Fry bacon — — 104 — — — The max 5 min 

concentrations 

Bake cake — — 230 — — — The max 5 min 

concentrations 

Roast meat — — 296 — — — The max 5 min 

concentrations 

Bake 

potatoes 

— — 373 — — — The max 5 min 

concentrations 

Personal activities 

Commuting 

less than 1 h 

Levy et al. — — — — 29.9 — — 

(1998a) 

Commute 

Without 

commuting 

— — — — 27.9 — — 

< 1 h — — — — 21.7 — — 

1-2 h — — — — 24.7 — — 

2-3 h — — — — 24.6 — — 

3-4 h — — — — 20.1 — — 

Commute 

4-6 h — — — — 27.9 — — 

Chao and Law 

(2000) 

Cooking to 

stay home h 

ratio 

— — — — — — 55.4 — 

Kawamoto et al. 

(1997) 

Cooking time — — — — — — 1.61 — 

 

Table AX3.5-6. Personal NO2 levels stratified by demographic and socioeconomic factors 

(concentrations are in ppb and slopes are dimensionless). 

REFERENCES FACTOR TYPE FACTOR NAME FACTOR LEVELS PERSONAL NO2 LEVEL PERSONAL SLOPE

Rotko et al. (2001) Demography Age 25-34 13.1 

Rotko et al. (2001) Demography Age 35-55 13.1 

Raaschou-Nielsen (1997) Demography Age 0.056 

Lee et al., (2004) Demography Gender Female 33 

Lee et al., (2004) Demography Gender Male 29 

Rotko et al. (2001) Demography Gender Female 12.9 
3-134

REFERENCES FACTOR TYPE FACTOR NAME FACTOR LEVELS PERSONAL NO2 LEVEL PERSONAL SLOPE

Rotko et al. (2001) Demography Gender Male 13.4 

Raaschou-Nielsen (1997) Demography Gender 0.267 

Rotko et al. (2001) Socioeconomic Education years <14 years 13.8 

Rotko et al. (2001) Socioeconomic Education years ≥14 years 12.8 

Rotko et al. (2001) Socioeconomic Employment Employed 13.3 

Rotko et al. (2001) Socioeconomic Employment Not employed 11.5 

Rotko et al. (2001) Socioeconomic Occupational status Non white collar 13.4 

Rotko et al. (2001) Socioeconomic Occupational status White collar 13.0 

Algar et al. (2004) Socioeconomic Employment Managerial, technical 

and professional 

(Barcelona) 

12.2 

Algar et al. (2004) Socioeconomic Employment Skilled (manual and 

non-manual) 

(Barcelona) 

12.3 

Algar et al. (2004) Socioeconomic Employment Unskilled and partly 

skilled (Barcelona) 

12.1 

 

Table AX3.6-1. The essential attributes of the pNEM, HAPEM, APEX, SHEDS, and MENTOR-1A. 

PNEM HAPEM APEX SHEDS MENTOR-1A 

Exposure Estimate Hourly averaged Annual averaged Hourly averaged Activity event based Activity event based 

Characterization of 

the High-End 

Exposures 

Yes No Yes Yes Yes 

Typical Spatial 

Scale/Resolution 

Urban areas/Census 

tract level 

Ranging from 

urban to 

national/ Census 

tract level 

Urban area/Census 

tract level 

Urban areas/Census tract 

level 

Multiscale/ Census tract 

level 

Temporal 

Scale/Resolution 

A yr/one h A yr/one h A yr/one h A yr/event based A yr/activity event based 

time step 

Population Activity 

Patterns Assembly 

Top-down approach Top-down 

approach 

Bottom-up “personoriented” approach Bottom-up “personoriented” approach Bottom-up “person-oriented” 

approach 

Microenvironment 

Concentration 

Estimation 

Non-steady-state and 

steady-state mass 

balance equations 

(hard-coded) 

Linear 

relationship 

method (hardcoded) 

Non-steady-state mass 

balance and linear 

regression (flexibility of 

selecting algorithms) 

Steady-state mass 

balance equation 

(residential) and linear 

regression (nonresidential) (hard-coded) 

Non-steady-state mass 

balance equation with 

indoor air chemistry module 

or regression methods 

(flexibility of selecting 

algorithms) 

Microenviron-mental 

(ME) Factors 

Random samples 

from probability 

distributions 

Random 

samples from 

probability 

distributions 

Random samples from 

probability distributions 

Random samples from 

probability distributions 

Random samples from 

probability distributions 

Specification of 

Indoor Source 

Emissions 

Yes (gas-stove, 

tobacco smoking) 

Available; set to 

zero in HAPEM6 

Yes (multiple sources 

defined by the user) 

Yes (gas-stove, tobacco 

smoking, other sources) 

Yes (multiple sources 

defined by the user) 

Commuting Patterns Yes Yes Yes Yes Yes 
3-135

PNEM HAPEM APEX SHEDS MENTOR-1A 

Exposure Routes Inhalation Inhalation Inhalation Inhalation Multiple (optional) 

Potential Dose 

Calculation 

Yes No Yes Yes Yes 

Physiologically 

Based Dose 

No No No Yes Yes 

Variability/ 

Uncertainty 

Yes No Yes Yes Yes (Various “Tools”) 
4-1

Annex 4. Toxicological Effects of NOX

AX4.1. Pulmonary Effects of NOX

AX4.1.1. Effects of NO2 on Oxidant and Antioxidant Metabolism 

NO2 is an oxidant; lipid peroxidation is believed to be a major molecular event responsible for its 

toxicity. As a result, there has been considerable attention paid to the effect of NO2 on the antioxidant 

defense system in the epithelial lining fluid and in pulmonary cells. Repeated exposure to NO2 at 

concentrations ranging from 0.04 to 33 ppm has been shown to alter low molecular weight antioxidants 

such as glutathione, vitamin E, and vitamin C, as well as some enzymes involved in cell oxidant 

homeostasis. 

A number of studies have investigated the hypothesis, originally proposed by Menzel (1970), that 

antioxidants might protect the lung from NO2 damage by inhibiting lipid peroxidation (see Table AX4.1). 

Changes in the activity of enzymes in the lungs of NO2-exposed animals that regulate levels of 

glutathione (GSH) have been reported in response to relatively low exposure concentrations. Sagai et al. 

(1984) studied the effects of prolonged (9 and 18 months) exposure to 0.04, 0.4, and 4.0 ppm NO2 on rats. 

After exposure duration, non-protein sulfhydryl levels were increased at 0.4 ppm or greater, and exposure 

to 4.0 ppm decreased the activity of GSH peroxidase but increased glucose-6-phosphate dehydrogenase 

activity. Glutathione peroxidase activity was also decreased in rats exposed to 0.4 ppm NO2 for 18 

months. Three GSH S-transferases were also studied, two of which (aryl S-transferase and aralkyl Stransferase) exhibited decreased activities after 18 months of exposure to 0.4 ppm or greater NO2. No 

effects were observed on the activities of 6-phosphogluconate dehydrogenase, superoxide dismutase, or 

disulfide reductase. Effects followed a concentration- and exposure-duration response function. The 

decreases in glutathione-related enzyme activities were inversely related to the apparent formation of lipid 

peroxides (see lipid peroxidation subsection). Shorter exposures (4 months) to NO2 between 0.4 and 

4.0 ppm also caused concentration- and duration-dependent effects on antioxidant enzyme activities 

(Ichinose and Sagai, 1982). For example, glucose-6-phosphate dehydrogenase increased, reaching a peak 

at 1 month, and then decreased towards the control value. Shorter (2-week) exposure to 0.4 ppm NO2

caused no such effects in rats or guinea pigs (Ichinose and Sagai, 1989). 

The activities of GSH reductase and glucose-6-phosphate dehydrogenase were significantly 

increased during exposure to 6.2 ppm NO2 for 4 days; GSH peroxidase activity was not affected (Chow et 

al., 1974). The possible role of edema and cellular inflammation in these findings was not examined. 

Since NO2 had no significant effect on lung GSH peroxidase activity in this study, but did significantly 

increase the activities of GSH reductase and glucose-6-phosphate dehydrogenase, the authors concluded 

that NO2 attacks mainly GSH and NADPH. 

Newer studies also identified effects on GSH. Changes in GSH status in the blood and lung 

(bronchoalveolar lavage (BAL) fluid) occurred in rats exposed to 5 ppm and 10 ppm NO2 continuously 

for 24 h, but not for 7 days (Pagani et al., 1994). Total glutathione – total of reduced (GSH) and oxidized 

(GSSG) form – was significantly increased in blood but not in BAL fluid; however, GSSG was elevated 

in BAL fluid only. A decreased GSH/GSSG ratio was observed in the blood and BAL fluid, but not in 

lung type II cells, of rats continuously exposed to 10 ppm NO2 for 3 or 20 days (Hochscheid et al., 2005). 

Interestingly, lipid peroxidation was decreased in type II cells at 3 days, but was similar to controls at 20 

days. Gene expression, as measured by mRNA levels of the enzymes involved in the biosynthesis of 

glutathione – gamma-glutamylcysteine synthetase (γGCS) and glutathione synthetase (GS), decreased at 

both time points, but gamma-glutamyltranspeptidase (γGT) mRNA expression increased. No GSH 
4-2

peroxidase activity (important for hydroperoxide reduction of complex lipids) was detected at 3 days, and 

was barely detected at 20 days. 

Malnutrition of animals can drastically affect their response to toxicants, including NO2. 

Experimental interest in this area has mainly focused on dietary lipids, vitamin E and other lipid-soluble 

antioxidants, and vitamin C and other water-soluble antioxidants. Ayaz and Csallany (1978) exposed 

vitamin E-deficient and vitamin E-supplemented (30 or 300 mg/kg of diet) weanling mice continuously 

for 17 months to 0.5 or 1.0 ppm NO2 and assayed blood, lung, and liver tissues for GSH peroxidase 

activity. Exposure to 1.0 ppm NO2 alone or combined with vitamin E deficiency decreased the enzyme 

activity in the blood and lungs. Neither vitamin E deficiency nor NO2 exposure affected liver GSH 

peroxidase activity. However, in vitamin E-supplemented mice, GSH peroxidase activity increased at 

0.5 ppm and 1.0 ppm NO2. 

AX4.1.2. Lipid Metabolism and Content of the Lung 

Lipid peroxidation is an important mechanism of cell damage arising from changes in cell 

membrane structure and function. The ability of NO2 exposure to induce lipid peroxidation in the 

respiratory tract has been demonstrated; as measured by increased ethane exhalation in the breath, as 

thiobarbituric acid (TBA) reactive substances in tissues, and as the content of conjugated dienes in tissue 

homogenates. 

A number of studies investigated the effects of NO2 exposure on lipid metabolism and lipid content 

of the lung. Lipid peroxidation induced by NO2 exposure has been detected at exposure concentrations as 

low as 0.04 ppm. Increased ethane exhalation was observed in rats exposed to 0.04 or 0.12 ppm after 9 

and 18 months of exposure (Sagai et al., 1984). Exposure to 0.4 ppm NO2 for 9 months or longer and to 

4.0 ppm for 6 months resulted in increased TBA reactants (Ichinose et al., 1983). NO2 exposure for 

shorter durations also increased lipid peroxidation in rats. For example, NO2 exposure of 1.2 ppm or 

greater for 1 week (Ichinose and Sagai, 1982; Ichinose et al., 1983) increased ethane exhalation in rats, 

while exposure of pregnant rats to 0.53 or 5.3 ppm NO2 for 5 h/day for 21 days resulted in increased lung 

lipid peroxidation products (Balabaeva and Tabakova, 1985). These results indicate at least some degree 

of duration-dependence in the formation of lipid peroxidation, with lower effect thresholds identified with 

longer durations of exposure. 

Lipid peroxidation results in altered phospholipid composition, which in turn may adversely affect 

membrane fluidity and thus, membrane function. Significant depression of lipid content and total content 

of saturated fatty acids such as phosphatidyl-ethanolamine, lecithin (phosphatidylcholine), 

phosphatidylinositol, and phosphatidylserine were measured in rats exposed to 2.9 ppm NO2 for 24 h/day, 

5 days/week for 9 months (Arner and Rhoades, 1973). Exposure of rabbits to 1.0 ppm NO2 for 2 weeks 

also caused depression of lecithin synthesis after one week of exposure (Seto et al., 1975), while exposure 

of rats to 5.5 ppm NO2 for 3 h/day for 7 or 14 days elicited only few changes in lipid metabolism 

(Yokoyama et al., 1980). In beagle dogs, the amount of unsaturated fatty acids in the phospholipids from 

the lungs was increased after exposure to concentrations ranging from 5 to 16 ppm, but not to 3 ppm 

(Dowell et al., 1971). Exposure of either mice or guinea pigs to 0.4 ppm for a week resulted in a 

decreased concentration of phosphatidylethanolamine and a relative increase in the phosphatidylcholine 

concentration (Sagai et al., 1987). Concentration- and exposure duration-dependent increases were 

reported in phospholipid components in BAL fluid, when rats were exposed to 10 ppm NO2 continuously 

for 1 day or 3 days (Müller et. al., 1994). 

Functional studies conducted on surfactant phospholipid extracts indicated that NO2 exposures of 

5 ppm or greater, directly impaired surface tension, although the structure of the surfactant protein A (SPA) was not altered by NO2 exposure. Changes in the phospholipid composition of membranes may result 

in disruption of the cell membrane barrier. Müller et al. (2003) found that uptake of liposomes by type II 

lung cells occurred more easily from animals exposed to 10 ppm NO2 for 3 to 28 days, possibly as a result 

of increased demand of phosphatidylcholine during lung injury. 
4-3

Lipid peroxidation can also activate phospholipases. Activation of phospholipase A1 in cultured 

endothelial cells occurred at 5 ppm after 40 h of exposure and was speculated to depend on a specific 

NO2-induced increase in phosphatidyl serine in the plasma membranes (Sekharam et al., 1991). 

One function of phospholipases is the release of arachidonic acid (AA), which serves as a mediator 

of inflammatory response. NO2 exposure affects the release and metabolism of arachidonic acid both in 

vivo and in vitro. The products of arachidonic acid metabolism, such as prostaglandins, prostacyclin, 

thromboxanes, and leukotrienes play an important role (such as recruitment of neutrophils to sights of 

local irritation) in modulating inflammatory response. Schlesinger et al. (1990) reported elevated 

concentrations of thromboxane B2 (TxB2) following NO2 exposures of 1.0 ppm for 2 h, depressed 

concentrations at 3.0 ppm, and significant depression 24-h postexposure at 10 ppm NO2. The same 

investigators also reported depressed level of 6-keto-prostaglandin F1α at 1.0 ppm NO2, but exposure to 

NO2 did not affect prostaglandins E2 and F2 and leukotriene B4 (LTB4) levels. 

Changes in activation of arachidonate metabolism were also reported in rat alveolar macrophages 

(AMs) when these animals were exposed to 0.5 ppm NO2 for 0.5, 1, 5, and 10 days (Robison et al., 1993). 

Unstimulated AM synthesis of LTB4 was depressed after 0.5 days and again after 5 days of exposure to 

NO2. Alveolar macrophage production of TxB2, LTB4, and 5-hydroxyeicosatetraenoic acid (5-HETE) in 

response to stimulation with the calcium ionophore, A23187, was depressed after 0.5 days of exposure 

and recovered to air-control values with longer exposure periods. 5-HETE levels were increased after 10 

days of exposure. However, AM production of LTB4 in response to zymosan-activated rat serum was 

depressed only after 5 days of exposure. 

The effects of NO2 on structural proteins of the lungs have been of concern because elastic recoil is 

lost after exposure. Collagen synthesis rates were increased in rats exposed to NO2 concentrations as low 

as 5.0 ppm NO2. It has been assumed that increased collagen synthesis reflect increases in total lung 

collagen which, if sufficient, could result in pulmonary fibrosis after longer periods of exposure. Such 

correlation has yet to be confirmed by in vivo studies involving NO2 exposure. 

Alterations in xenobiotic metabolism pathways following NO2 exposure are also summarized in 

Table AX4.2, in addition to changes in phase I enzymes (such as cytochrome P450s) and phase II 

enzymes (GST as described earlier). While these changes are not necessarily toxic manifestations of NO2

per se, such changes may impact the metabolism and toxicity of other chemicals. Glycolytic pathways 

may also be affected. For example, glycolytic metabolism was increased by NO2 exposure, possibly due 

to a concurrent increase in type II cells (Mochitate et al., 1985). 

AX4.1.3. Lung Host Defense, Lung Permeability and Inflammation, 

Immune Responses, and Infectious Agents 

Impaired lung host-defenses, increased risk of susceptibility to both viral and bacterial agents, as 

well as increased lung permeability and inflammation, provide some important evidence for mechanisms 

of action potentially underlying the health effects observed in epidemiology studies. These effects are 

discussed in Chapter 3 but study details are provided in Tables AX 4.3, 4.4, 4.5, and 4.6. 

AX4.1.4. Emphysema Following NO2 Exposure 

Emphysema as a result of chronic exposure to NO2 has been reported in animal studies. The 

definition of emphysema has changed over time; thus, it is important to compare the findings of studies 

using the current definition of emphysema. U.S. Environmental Protection Agency (1993) evaluated 

animal studies reporting emphysema caused by or in response to chronic exposure to NO2 based upon the 

most recent definition of emphysema from the report of the National Heart, Lung and Blood Institute 

(NHLBI), Division of Lung Diseases Workshop (Snider et al., 1985), and U.S. Environmental Protection 

Agency (1993). Because the focus of this document is an extrapolation from NO2 exposure to potential 
4-4

hazard for humans, only those studies reporting emphysema of the type seen in human lungs were 

included AQCD (1993). 

Humanlike emphysema linked to > 5 ppm NO2 exposure was reported by Haydon et al. (1967) in 

rabbits and rats (Freeman et al., 1972; Barth et al., 1995). See Annex Table AX4.7 for more recent studies 

linking NO2 exposure and lung structure changes. 

AX4.1.5. Lung Function 

Lung function is discussed extensively in Section 3.1.5 of the ISA. The limited animal toxicology 

data is presented in Table AX4.8. 

AX4.1.6. Nitrates (NO3

-

) 

Busch et al. (1986) exposed rats and guinea pigs with either normal lungs or elastase-induced 

emphysema to ammonium nitrate aerosols at 1 mg/m3

 for 6 h/day, 5 days/week for 4 weeks. Using light 

and electron microscopy, the investigators concluded that there were no significant effects of exposure on 

lung structure. 

AX4.2. Dosimetry of Inhaled NOX

This section provides an overview of NO2 dosimetry and updates information provided in the 1993 

AQCD for NOX. Dosimetry of NO2 refers to the measurement or estimation of the amount of NO2 or its 

reaction products reaching and persisting at specific sites in the respiratory tract following an exposure. 

NO2, classified as a reactive gas, interacts with surfactants, antioxidants, and other compounds in the 

epithelial lining fluid (ELF). The compounds thought responsible for adverse pulmonary effects of 

inhaled NO2 are the reaction products themselves or the metabolites of these products in the ELF. At the 

time of the 1993 NOX AQCD, it was thought that inhaled NO2 probably reacted with the water molecules 

in the ELF to form nitrous acid (HNO2) and nitric acid (HNO3). However, some limited data suggested 

that the absorption of NO2 was linked to reactive substrates in the ELF and subsequent nitrite production. 

Since then, the reactive absorption of NO2 has been examined in a number of studies (see Section 4.2.2). 

These studies have characterized the absorption kinetics and reactive substrates for NO2 delivered to 

various sites in the respiratory tract. Researchers have attempted to obtain a greater understanding of how 

these complex interactions affect NO2 absorption and NO2-induced injury. 

With respect to quantifying absolute NO2 absorption, the following were reported in the 1993 NOX

AQCD. The principles of O3 uptake were generally assumed applicable for NO2 modeling studies. The 

results indicated that NO2 is absorbed throughout the lower respiratory tract, but the major delivery site is 

the centriacinar region, i.e., the junction between the conducting and respiratory airways in humans and 

animals. Experimental studies have found that the total respiratory tract uptake in humans ranges from 72 

to 92% depending on the study and the breathing conditions. The percent total uptake increases with 

increasing exercise level. In laboratory animals, upper respiratory tract uptakes ranged from as low as 

25% to as high as 94% depending on the study, species, air flow rate, and mode of breathing (nasal or 

oral). Upper respiratory tract uptake of NO2 was found to decrease with increasing ventilation. Uptake 

during nasal breathing was determined to be significantly greater than during oral breathing. 
4-5

AX4.2.1. Mechanisms of NO2 Absorption 

The ELF is the initial barrier against NO2 delivery to the underlying epithelial cells. Postlethwait 

and Bidani (1990) suggested that acute NO2 uptake in the lower respiratory tract was rate limited by 

chemical reactions of NO2 with ELF constituents rather than by gas solubility in the ELF. Subsequently, 

Postlethwait et al. (1991) reported that inhaled NO2 (10 ppm) did not penetrate the ELF to reach 

underlying sites and suggested that cytotoxicity may be due to NO2 reactants formed in the ELF. Since 

then, the reactive absorption of NO2 has been examined in a number studies that have sought to identify 

reactive substrates for NO2 and quantify the absorption kinetics of NO2 in the respiratory tract. 

Postlethwait and Bidani (1994) concluded that the reaction between NO2 and water does not 

significantly contribute to the absorption of inhaled NO2. Uptake is a first-order process for NO2

concentrations less than 10 ppm, is aqueous substrate-dependent, and is saturable. The absorption of 

inhaled NO2 is thought to be coupled with free radical-mediated hydrogen abstraction to form HNO2 and 

an organic radical (Postlethwait and Bidani, 1989, 1994). At physiologic pH, the HNO2 subsequently 

dissociates to H+ and nitrite (NO2

-

). The concentration of the resulting nitrite is thought insufficient to be 

toxic, so effects are thought to be due to the organic radical and/or the proton load. Nitrite may enter the 

underlying epithelial cells and blood. In the presence of red blood cells, nitrite is oxidized to nitrate 

(NO3

-

) (Postlethwait and Mustafa, 1981). Beyond cell susceptibility and the concentration of NO2 in the 

lumen, site-specific injury was proposed to depend on rate of ‘toxic’ reaction product formation and the 

quenching of these products within the ELF. Related to the balance between reaction product formation 

and removal, it was further suggested that cellular responses may be nonlinear with greater responses 

being possible at low levels of NO2 uptake versus higher levels of uptake. Since the ELF may vary 

throughout the respiratory tract, the uptake of inhaled NO2 and reaction with constituents of the 

pulmonary ELF may be related to the heterogeneous distribution of epithelial injury observed from NO2

exposure. 

Postlethwait et al. (1995) sought to determine the absorption substrates for NO2 in the ELF lavaged 

from male Sprague-Dawley rats. Since the bronchoalveolar lavage fluid (BALF) collected from the rats 

may be diluted up to 100-fold relative to the native ELF, the effect of concentrating the BAL fluid on NO2

absorption was investigated. A linear association was found between the first-order rate constant for NO2

absorption and the concentration of the BALF. This suggested that concentration of the reactive substrates 

in the ELF determines the rate of NO2 absorption. The absorption due to specific ELF constituents was 

also examined in chemically pure solutions. Albumin, cysteine, reduced GSH, ascorbic acid, and uric acid 

were hydrophilic moieties found to be active substrates for NO2 absorption. Unsaturated fatty acids (such 

as oleic, linoleic, and linolenic) were also identified as active absorption substrates and thought to account 

for up to 20% of NO2 absorption. Vitamins A and E exhibited the greatest reactivity of the substrates that 

were examined. However, the low concentrations of uric acid and vitamins A and E were thought to 

preclude them from being appreciable substrates in vivo. The authors concluded that ascorbate and GSH 

were the primary NO2 absorption substrates in rat ELF. Postlethwait et al. (1995) also found that the 

pulmonary surfactant, dipalmitoyl phosphatidylcholine, was not an effective substrate for NO2 absorption. 

Later, Connor et al. (2001) suggested that dipalmitoyl phosphatidylcholine may actually inhibit NO2

absorption. 

In Vitro 

In a subsequent study, Velsor and Postlethwait (1997) investigated the mechanisms of acute 

epithelial injury from NO2 exposure. The impetus for this work was to evaluate the supposition that NO2

reaction products rather than NO2 itself caused epithelial injury. Red blood cell membranes were 

immobilized to the bottom of Petri dishes, covered with a variety of well characterized aqueous layers, 

and exposed to gaseous NO2 (10 ppm for 20 min). The study focused on the potential roles of GSH and 

ascorbic acid reaction products in mediating cellular injury. Based on negligible membrane oxidation 

when covered with only an aqueous phosphate buffer, the diffusive/reactive resistance of a thin aqueous 
4-6

layer clearly prevented direct interaction between NO2 and the underlying membrane. The presence of 

unsaturated fatty acids was not observed to affect NO2 absorption, but a sufficiently thin liquid layer was 

required for membrane oxidation to occur. Interestingly, membrane oxidation was not a simple monotonic 

function of GSH and ascorbic acid levels. The maximal levels of membrane oxidation were observed at 

low antioxidant levels versus null or high antioxidant levels. Glutathione and ascorbic acid related 

membrane oxidation were superoxide and hydrogen peroxide dependent, respectively. The authors 

suggested that at the higher antioxidant concentrations, there was increased absorption of NO2, but little 

secondary oxidation of the membrane because the reactive species (e.g., superoxide and hydrogen 

peroxide) generated during absorption were quenched. At the low antioxidant concentrations, there was a 

lower rate of NO2 absorption, but oxidants were not quenched and so were available to interact with the 

cell membrane. 

Humans (In Vivo) 

Kelly et al. (1996a) examined the effect of a 4-h NO2 (2 ppm) exposure on antioxidant levels in 

bronchial lavage fluid (BLF) and BALF of 44 healthy nonsmoking adults (19-45 year, median 24 years). 

Subjects were randomly assigned to three groups and lavaged at either 1.5-h (n = 15), 6-h (n = 15), or 

24-h (n = 14) after the NO2 exposure. The baseline concentrations of uric acid and ascorbic acid were 

strongly correlated between the BLF and BALF within individuals (r = 0.88, p < 0.001; r = 0.78, p = 

0.001; respectively), whereas the concentrations of GSH in the BLF and BALF were not correlated. Uric 

acid levels in both lavage fractions were significantly reduced at 1.5-h (p < 0.04), significantly increased 

at 6-h (p < 0.05), and back to baseline at 24-h postexposure. A statistically significant loss of ascorbic 

acid was also found in both lavage fractions at 1.5-h (p < 0.05). At 6 and 24-h postexposure, the ascorbic 

acid levels had returned to baseline. In contrast, GSH levels were significantly increased at both 1.5-h 

(p < 0.01) and 6-h (p < 0.03) in BLF. At 24 h postexposure, the GSH levels in BLF returned to baseline. 

Although GSH in BLF increased at 1.5 and 6 h postexposure, oxidized GSH levels remained similar to 

baseline in both BLF and BALF. No changes in BALF levels of GSH were observed at any time point. 

Humans (Ex Vivo) 

The depletion of uric acid and ascorbic acid, but not GSH has also been observed with ex vivo 

exposure of human BALF to NO2. Kelly et al. (1996b) collected BALF from male lung cancer patients (n 

= 16) and exposed the BALF ex vivo at 37°C to NO2 (0.05 to 2.0 ppm; 4 h) or O3 (0.05 to 1.0 ppm; 4 h). 

Kelly and Tetley (1997) also collected BALF from lung cancer patients (n = 12, 54 ± 16 years) and 

exposed the BALF ex vivo to NO2 (0.05 to 1.0 ppm; 4 h). Both studies found that NO2 depletes uric acid 

and ascorbic acid, but not GSH from BALF. Kelly et al. (1996b) noted a differential consumption of the 

antioxidants with uric acid loss being greater than that of ascorbic acid which was lost at a much greater 

rate than GSH. Kelly and Tetley (1997) found that the rates of uric acid and ascorbic acid consumption 

were correlated with their initial concentrations in the BAL fluid, such that higher initial antioxidant 

concentrations were associated with a greater rate of antioxidant depletion. Illustrating the complex 

interaction of antioxidants, these studies also suggest that GSH oxidized by NO2 may be again reduced by 

uric acid and/or ascorbic acid. 

AX4.2.2. Regional and Total Respiratory Absorption of NO2

There has been very limited work related to the quantification of NO2 uptake since the 1993 NOX

AQCD. As a result, there is an abbreviated discussion here of some papers that were previously reviewed. 
4-7

AX4.2.2.1. Dosimetry Models 

There is a paucity of theoretical studies investigating NO2 dosimetry. Like O3, NO2 is highly 

reactive in ELF and is not very soluble. An O3 model has been utilized to predict the uptake of NO2 in the 

lower respiratory tract of humans, rats, guinea pigs, and rabbits (Miller et al., 1982; Overton, 1984). In 

this model, there was a strong distinction between uptake and dose. Uptake referred to the amount of NO2

being removed from gas phase per lung surface area (μg/cm2

), whereas, dose referred to the amount of 

NO2 per lung surface area (μg/cm2

) that diffused through the ELF and reached the underlying tissues. 

These investigators assessed NO2 uptake and dose on a breath by breath basis. Miller et al. (1988) 

provided uptake and dose rates (μg/cm2

-min) for O3 in the same species. 

Miller et al. (1982) and subsequently Overton (1984) did not attempt to predict the amount of 

reactants in the ELF or the transport of reactants to the tissues. Rather, they focused mainly on the 

sensitivity of NO2 tissue dose on NO2 reaction rates in the ELF and the Henry’s law constant. Reaction 

rates of NO2 in the ELF were varied from zero, 50%, and 100% of the reaction rate for O3 in ELF. The 

Henry’s law constant was varied from half to double the Henry’s law constant for NO2 in water at 37 °C. 

Effects of species, lung morphology, and tidal volume (VT) were also examined. In general, the model 

predicted that NO2 is taken up throughout the lower respiratory tract. In humans, NO2 uptake was fairly 

constant from the trachea to the terminal bronchioles, beyond which uptake decreased with distal 

progression. This pattern of NO2 uptake predicted for humans is very similar to the pattern of O3 uptake 

per unit time predicted for humans, rats, rabbits, and guinea pigs by Miller et al. (1988). Thus, it is 

reasonable to expect that the pattern NO2 uptake per unit time will also be similar between these species. 

The NO2 tissue dose was highly dependent on the Henry’s law constant and reaction rate in the ELF. In 

the conducting airways, the NO2 tissue dose decreased as the Henry’s law constant increased (i.e., 

decreased gas solubility), whereas the NO2 tissue dose in the alveolar region increased. The site of 

maximal NO2 tissue dose was fairly similar between species, ranging from the first generation of 

respiratory bronchioles in humans to the alveolar ducts in rats. In guinea pigs and rabbits, the maximal 

NO2 tissue dose was predicted to occur in the last generation of respiratory bronchioles. Based on Miller 

et al. (1988), the dose rate of NO2 is also expected to be similar between species. The simulations also 

showed that exercise increases the NO2 tissue dose in the pulmonary region relative to rest. Miller et al. 

(1982) also reported that increasing the NO2 reaction rate decreased NO2 tissue dose in the conducting 

airways, but had no effect on the dose delivered to the pulmonary region. 

Simultaneously occurring diffusion and chemical reactions in the ELF have been suggested as the 

limiting factors in O3 (Santiago et al., 2001) and NO2 uptake (Postlethwait and Bidani, 1990). Hence, 

Miller et al. (1982) should have found an increase in the uptake of NO2 in the conducting airways with 

increasing the rate of chemical reactions in the ELF. This increase in NO2 uptake in the conducting 

airways would then lead to a reduction in the amount of NO2 reaching and taken up in the pulmonary 

region. The Miller et al. (1982) model considered reactions of NO2 with constituents in the ELF as 

protective in that these reactions reduced the flux of NO2 to the tissues. Others have postulated that NO2

-

reactants formed in the ELF, rather than NO2 itself, could actually cause adverse responses (Overton, 

1984; Postlethwait and Bidani, 1994; Velsor and Postlethwait, 1997). 

Overton and Graham (1995) examined NO2 uptake in an asymmetric anatomic model of the rat 

lung. The multiple path model of Overton and Graham (1995) allowed for variable path lengths from the 

trachea to the terminal bronchioles, whereas Miller et al. (1982) used a single or typical path model of the 

conducting airways. The terms “dose” and “uptake” were used synonymously to describe the amount of 

NO2 gas lost from the gas phase in a particular lung region or generation by Overton and Graham (1995). 

Reactions of NO2 in the ELF were not explicitly considered. Their simulations were conducted for rats 

breathing at 2 mL VT at a frequency of 150 breaths per minute. The mass transfer coefficients of 0.173, 

0.026, and 0.137 cm/sec were assumed for the upper respiratory tract, the tracheobronchial airways, and 

the pulmonary region, respectively. Uptake was predicted to decrease with distal progression into the 

lung. In general, the modeled NO2 dose varied among anatomically equivalent ventilatory units as a 
4-8

function of path length from the trachea with shorter paths showing greater dose. A sudden increase in 

NO2 uptake was predicted in the proximal alveolar region (PAR) which was due to the increase in the 

assumed mass transfer coefficient relative to the adjacent terminal bronchiole. Overton et al. (1996) 

showed that increasing the mass transfer coefficient of the tracheobronchial airways decreased the dose to 

the PAR and vice versa. Additionally, the PAR dose would also be reduced by the more realistic 

modeling of tracheobronchial airways expansion during inspiration versus the static condition employed 

by Overton and Graham (1995). 

More recently, two studies examined the influence of age on reactive gas dosimetry in humans 

(Ginsberg et al., 2005; Sarangapani et al., 2003). Both studies specifically considered the dosimetry O3

during light activity (on average) in their analysis. It is assumed here that their general findings should 

also be applicable to NO2. Sarangapani et al. (2003) used a physiologically based pharmacokinetic model 

and found that regional uptake of O3 is relatively insensitive to age (range: infants to elderly). Ozone 

uptake per unit surface area was 2- to 8-fold higher in infants compared to adults. However, this finding 

(i.e., uptake per unit surface area) is a less informative expression of dose than the rate of uptake per unit 

surface area. The rate of uptake, obtained by multiplying by the ventilation rate, adjusts for the greater 

rate of gas intake by adults relative to children. Ginsberg et al. (2005) utilized the U.S. EPA (1994) 

reference concentration methodology and found no effect of age (infants vs. adults) on the uptake rate of 

O3 per unit surface area. 

In summary, these modeling studies predict that the net NO2 dose (NO2 flux to air-liquid interface) 

is relatively constant from the trachea to the terminal bronchioles and then rapidly decreases in the 

pulmonary region. The pattern of net NO2 dose rate or uptake rate is expected to be similar between 

species and unaffected by age in humans. The predicted tissue dose and dose rate of NO2 (NO2 flux to 

liquid-tissue interface) is low in the trachea, increases to a maximum in the terminal bronchioles and the 

first generation of the pulmonary region, and then decreases rapidly with distal progression. The site of 

maximal NO2 tissue dose is predicted to be fairly similar between species, ranging from the first 

generation of respiratory bronchioles in humans to the alveolar ducts in rats. The production of toxic 

NO2-reactants in the ELF and the movement of the reactants to the tissues have not been modeled. 

AX4.3. Experimental Studies of NO2 Uptake 

AX4.3.1. Upper Respiratory Tract Absorption 

The nasal uptake of NO2 has been experimentally measured in dogs, rabbits, and rats under 

conditions of unidirectional flow. Yokoyama (1968) reported 42.1 ± 14.9% (Mean ± SD) uptake of NO2

in the isolated nasal passages of two dogs (3.5 L/min) and three rabbits (0.75 L/min) exposed to 4 and 

41 ppm NO2. Uptake did not appear to depend on the exposure concentration and was relatively constant 

over a 10 to 15 min period. Cavanagh and Morris (1987) measured uptakes of 28% and 25% uptake of 

NO2 (40.4 ppm) in the noses of four naive and four previously exposed rats (0.10 L/min), respectively. 

Uptake was not affected by a 4-h prior exposure (naive versus previously exposed rats) to 40.4 ppm NO2

and was constant over the 24-min period during which uptake was determined. 

Kleinman and Mautz (1991) measured the penetration of NO2 through the upper airways during 

inhalation in six tracheotomized dogs exposed to 1.0 or 5.0 ppm NO2. Uptake in the nasal passages was 

significantly greater at 1.0 ppm than at 5.0 ppm, although the magnitude of this difference was not 

reported. The mean uptake of NO2 (1.0 ppm) in the nasal passages decreased from 55% to 40% as the 

ventilation rate increased from about 2 to 8 L/min. During oral breathing, uptake was not dependent on 

concentration. The mean oral uptake of NO2 (1.0 and 5.0 ppm) decreased from 65% to 30% as the 

ventilation rate increased from 2 to 8 L/min. 
4-9

AX4.3.2. Lower Respiratory Tract Absorption 

Postlethwait and Mustafa (1989) investigated the effect of exposure concentration and breathing 

frequency on the uptake of NO2 in isolated perfused rat lungs. To evaluate the effect of exposure 

concentration, the lungs were exposed to NO2 (4 to 20 ppm) while ventilated at 50 breaths/min with a VT

of 2.0 mL. To examine the effect of breathing frequency, the lungs were exposed to NO2 (5 ppm) while 

ventilated at 30-90 breaths/min with a VT of 1.5 mL. All exposures were for 90 min. The uptake of NO2

ranged from 59 to 72% with an average of 65% and was not affected by exposure concentration or 

breathing frequency. A combined regression showed a linear relationship between NO2 uptake and total 

inspired dose (25 to 330 μg NO2). Illustrating variability in NO2 uptake measurements, Postlethwait and 

Mustafa (1989) observed 59% NO2 uptake in lungs ventilated at 30 breaths/min with a VT of 1.5 mL, 

whereas, Postlethwait and Mustafa (1981) measured 35% NO2 uptake for the same breathing condition. In 

another study, 73% uptake of NO2 was reported for rat lungs ventilated 50 breaths/min with a VT of 2.3 

mL (Postlethwait et al., 1992). It should be noted that typical breathing frequencies are around 80, 100, 

and 160 breaths/min for rats during sleep, rest, and light exercise, respectively (Winter-Sorkina and 

Cassee, 2002). Hence, the breathing frequencies at which NO2 uptake has been measured are lower than 

for rats breathing normally. 

In addition to measuring upper respiratory tract uptakes, Kleinman and Mautz (1991) also measured 

NO2 uptake in the dog lung. In general, there was about 90% NO2 uptake in the lungs which was 

independent of ventilation rates from 3 to 16 L/min. 

AX4.3.3. Total Respiratory Tract Absorption 

Bauer et al. (1986) measured the uptake of NO2 (0.3 ppm) in 15 adult asthmatics exposed for 30 

min (20 min at rest, then 10 min exercising on a bicycle ergometer) via a mouthpiece during rest and 

exercise. There was a statistically significant increase in uptake from 72% during rest to 87% during 

exercise. The minute ventilation also increased from 8.1 L/min during rest to 30.4 L/min during exercise. 

Hence, exercise increased the uptake exposure rate of NO2 by 5-fold in these subjects. In an earlier study 

of seven healthy adults in which subjects were exposed to a nitric oxide (NO)/NO2 mixture containing 

0.29 to 7.2 ppm NO2 for brief (but unspecified) periods, Wagner (1970) reported that NO2 uptake 

increased from 80% during normal respiration (VT, 0.4 L) to 90% during maximal respiration (VT, 2 to 

4 L). 

Kleinman and Mautz (1991) also measured the total respiratory tract uptake of NO2 (5 ppm) in 

female beagle dogs while standing at rest or exercising on a treadmill. The dogs breathed through a small 

face mask. Total respiratory tract uptake of NO2 was 78% during rest and increased to 94% during 

exercise. In large part, this increase in uptake may be due to the increase in VT from 0.18 L during rest to 

0.27 L during exercise. Coupled with an increase in minute ventilation from 3.8 L/min during rest to 10.5 

L/min during exercise, the uptake rate of NO2 was 3-fold greater for the dogs during exercise than rest. 

AX4.4. Metabolism, Distribution and Elimination of NO2 

As stated earlier, NO2 absorption is coupled with nitrous acid (HNO2) formation, which 

subsequently dissociates to H+

 and nitrite (NO2

-

). Nitrite enters the underlying epithelial cells and 

subsequently the blood. In the presence of red blood cells and possibly involving oxyhemoglobin, nitrite 

is oxidized to nitrate (NO3

-

) (Postlethwait and Mustafa, 1981). Nitrate may subsequently be excreted in 

the urine. There has been concern that inhaled NO2 may lead to N-nitrosamine production, many of which 

are carcinogenic, since NO2 can produce nitrite and nitrate (in blood). Nitrate can be converted to nitrite 

by bacterial reduction in saliva, the gastrointestinal tract, and the urinary bladder. Nitrite has been found 
4-10

to react with secondary amines to form N-nitrosamines. This remains speculative since nitrosamines are 

not detected in tissues of animals exposed by inhalation to NO2 unless precursors to nitrosamines and/or 

inhibitors of nitrosamine metabolism are co-administered. Rubenchik et al. (1995) could not detect Nnitrosodimethylamine (NDMA) in tissues of mice exposed to 4 to 4.5 ppm NO2 for 1 h. However, NDMA 

was found in tissues if mice were simultaneously given oral doses of amidopyrine and 4-methylpyrazole, 

an inhibitor of NDMA metabolism. Nevertheless, the main source of NO2 in the body is formed 

endogenously, and food is also a contributing source of nitrite from the conversion of nitrates. Thus, the 

relative importance of inhaled NO2

-

 to N-nitrosamine formation has yet to be demonstrated. 

Metabolism of inhaled NO2 may also transform other chemicals that may be present in the body, in 

some cases into mutagens and carcinogens. Van Stee et al. (1983) reported N-nitrosomorpholine 

(NMOR), production in mice gavaged with 1 g of morpholine/kg body weight per day and then exposed 

(5-6 h daily for 5 days) to 16.5 to 20.5 ppm NO2. N-nitrosomorpholine is a nitrosamine that is a potent 

animal carcinogen. The single site containing the greatest amount of NMOR was the gastrointestinal tract. 

Later, Van Stee et al. (1995) exposed mice to approximately 20 ppm 15NO2 and to 1 g/kg morpholine 

simultaneously. N-nitrosomorpholine was found in the body of the exposed mice. Ninety-eight point four 

percent was labeled with 15N that was derived from the inhaled 15NO2 and 1.6% was derived presumably 

from endogenous sources. 

Inhaled NO2 may also be involved in the production of mutagenic (and carcinogenic) nitro 

derivatives of other co-exposed compounds, such as PAHs, via nitration reactions. Miyanishi et al. (1996) 

co-exposed rats, mice, guinea pigs and hamsters to 20 ppm NO2 with various PAHs (pyrene, 

fluoranthene, fluorene, anthracene, or chrysene). Nitro derivatives of these PAHs were excreted in the 

urine of these animals, which were found to be highly mutagenic in the Ames/S. typhimurium assay. 

Specifically, the nitrated metabolite of pyrene (1-nitro-6/8-hydroxypyrene and 1-nitro-3hydroxypyrene) 

was detected in the urine. Further studies indicated that these metabolites are nitrated by an ionic reaction 

in vivo after the hydroxylation of pyrene in the liver. 

AX4.5. Extra-Pulmonary Effects of NO2 and NO 

Exposure to NO2 produces a wide array of health effects beyond the confines of the lung. Thus, 

NO2 and/or some of its reactive products penetrate the lung or nasal epithelial and endothelial layers to 

enter the blood and produce alteration in blood and various other organs. Effects on the systemic immune 

system were discussed above and the summary of other systemic effects is quite brief because the 

literature suggests that effects on the respiratory tract and immune response are of greatest concern. A 

more detailed discussion of extrapulmonary responses can be found in U.S. Environmental Protection 

Agency (1993). 

AX4.5.1. Body Weight, Hepatic, Renal, and Miscellaneous Effects 

Conflicting results have been reported on whether NO2 affects body weight gain in experimental 

animals as a general indicator of toxicity (U.S. Environmental Protection Agency, 1993). More recent 

subchronic studies show no significant effects on body weight in rats, guinea pigs, and rabbits exposed up 

to 4 ppm NO2 (Tepper et al., 1993; Douglas et al., 1994; Fujimaki and Nohara, 1994). 

Effects on the liver, such as changes in serum chemistry and xenobiotic metabolism, have been 

reported by various investigators to result from exposure to NO2 (U.S. Environmental Protection Agency 

1993). Drozdz et al. (1976) found decreased total liver protein and sialic acid, but increased protein-bound 

hexoses in guinea pigs exposed to 1 ppm NO2, 8 h/day for 180 days. Liver alanine and aspartate 

aminotransferase activity was increased in the mitochondrial fraction but decreased in the cytoplasmic 
4-11

fraction of the liver. Electron micrographs of the liver showed intracellular edema and inflammatory and 

parenchymal degenerative changes. 

No new studies on liver effects were located in the literature since the 1993 NOX AQCD. Several 

older studies have shown changes in kidney function and xenobiotic metabolism in animals following 

NO2, although no histopathological changes were reported. 

Increases in urinary protein and specific gravity of the urine were reported by Sherwin and Layfield 

(1974) in guinea pigs exposed continuously to 0.5 ppm NO2 for 14 days. Proteinuria (albumin and alpha-, 

beta-, and gamma-globulins) was found in another group of animals exposed to 0.4 ppm NO2 for 4 h/day. 

However, differences in water consumption or in the histology of the kidney were not found. No new 

studies were located in the literature since the 1993 NOX AQCD. 

Four studies (Table AX4.9) consider hematologic parameters. Several additional studies report on 

iron, enzymes and nucleic acid (Table AX4.10). 

AX4.5.2. Brain Effects 

There are several studies suggesting that NO2 affects the brain. Decreased activity of protein 

metabolizing enzymes, increased glycolytic enzymes, changes in neurotransmitter levels (5-HT and 

noradrenaline), and increased lipid peroxidation, accompanied by lipid profile and antioxidant changes, 

have been reported (Farahani and Hasan, 1990, 1991, 1992; Sherwin et al., 1986; Drozdz et al., 1975). 

The U.S. Environmental Protection Agency (1993) concluded that “none of these effects have been 

replicated and all reports lack sufficient methodological rigor; thus, the implications of these findings, 

albeit important, are not clear and require further investigation”. 

A developmental neurotoxicity study by Tabacova et al.(1985) suggest that in utero exposure to 

NO2 may result in postnatal neurobehavioral development changes as described in the section on 

reproductive and developmental toxicology. 

AX4.5.3. NO 

The genotoxicity of NO has been studied both in vitro and in vivo (Arroyo et al., 1992; Nguyen 

et al., 1992) (see Tables AX4.11-4.13). Overall, the synthesis of these older studies suggests that NO has 

some genotoxic potential; however, the effect is slight and to a lesser extent when compared to NO2. 

AX4.5.4. Effects of Mixtures Containing NO2

Humans are generally exposed to NO2 in a mixture with other air pollutants. A limitation of animal 

toxicity studies is the extrapolation of concentration-response data from controlled exposures to NO2

alone, to air pollutant mixtures that are typically found in the environment. It is difficult to predict the 

effects of NO2 in a mixture based on the effects of NO2 alone. In order to understand how NO2 is affected 

by mixtures of other air pollutants, studies are typically conducted with mixtures containing NO2 and one 

or two other air pollutants, such as O3 and/or H2SO4. The result of exposure to two or more pollutants 

may be simply the sum of the responses to individual pollutants (additivity), may be greater than the sum 

of the individual responses, suggesting some type of interaction or augmentation of the response 

(synergism) or may be less than additive (antagonism). 

Animal toxicity studies have shown an array of interactions, including no interaction, additivity or 

synergism. Because no clear understanding of NO2 interactions has yet emerged from this database, only 

a brief overview is provided here. A more substantive review can be found in U.S. Environmental 

Protection Agency (1993). There were animal studies, which studied the effects of ambient air mixtures 

containing NO2 or gasoline or diesel combustion exhausts containing NOX. Generally these studies 

provided useful information on the mixtures, but lacked NO2-only groups, making it impossible to discern 
4-12

the influence of NO2. Therefore, this class of research is not described here, but is reviewed elsewhere 

(U.S. Environmental Protection Agency, 1993). 

AX4.5.5. Simple Mixtures Containing NO2

Most of the interaction studies involved NO2 and O3. After subchronic exposure, lung morphology 

studies did not show any interaction of NO2 with O3 (Freeman et al., 1974) or with SO2 (Azoulay et al., 

1980). Some biochemical responses to NO2 plus O3 display no positive interaction or synergism. For 

example, Mustafa et al. (1984) found synergism for some endpoints (e.g., increased activities of O2

consumption and antioxidant enzymes), but no interaction for others (e.g., DNA or protein content) in rats 

exposed for 7 days. Ichinose and Sagai (1989) observed a species dependence in regard to the interaction 

of O3 (0.4 ppm) and NO2 (0.4 ppm) after 2 weeks of exposure. Guinea pigs, but not rats, had a synergistic 

increase in lung lipid peroxides. Rats, but not guinea pigs, had synergistic increases in antioxidant factors 

(e.g., non-protein thiols, vitamin C, glucose-6-phosphate dehydrogenase, GSH peroxidase). Duration of 

exposure can have an impact. Schlesinger et al. (1990) observed a synergistic increase in prostaglandin E2

and F2α in the lung lavage of rabbits exposed acutely for 2 h to 3.0 ppm NO2 plus 0.3 ppm O3; the 

response appeared to have been driven by O3. However, with 7 or 14 days of repeated 2-h exposures, only 

prostaglandin E2 was decreased and appeared to have been driven by NO2; there was no synergism 

(Schlesinger et al., 1991). 

Using an infectivity model, Ehrlich et al. (1977) found additivity after acute exposure to mixtures 

of NO2 and O3 and synergism after subchronic exposures. Exposure scenarios involving NO2 and O3 have 

also been performed using a continuous baseline exposure to one concentration or mixture, with 

superimposed short-term peaks to a higher level (Ehrlich et al., 1979; Gardner, 1980, 1982; Graham et al., 

1987). Differences in the pattern and concentrations of the exposure are responsible for the increased 

susceptibility to pulmonary infection, without indicating clearly the mechanism controlling the 

interaction. 

Some aerosols may potentiate response to NO2 by producing local changes in the lungs that 

enhance the toxic action of co-inhaled NO2. The impacts of NO2 and H2SO4 on lung host defenses have 

been examined by Schlesinger and Gearhart (1987) and Schlesinger (1987). In the former study, rabbits 

were exposed for 2 h/day for 14 days to either 0.3 ppm or 1.0 ppm NO2, or 500 μg/m3

 H2SO4 alone, or to 

mixtures of the low and high NO2 concentrations with H2SO4. Exposure to either concentration of NO2

accelerated alveolar clearance, whereas H2SO4 alone retarded clearance. Exposure to either concentration 

of NO2 with H2SO4 resulted in retardation of clearance in a similar manner to that seen with H2SO4 alone. 

Using a similar exposure design but different endpoints, exposure of rabbits to 1.0 ppm NO2 increased the 

numbers of PMNs in lavage fluid at all time points (not seen with either pollutant alone), and increased 

phagocytic capacity of AMs after two or six exposures (Schlesinger et al., 1987). Exposure to 0.3 ppm 

NO2 with acid, however, resulted in depressed phagocytic capacity and mobility. The NO2/H2SO4 mixture 

was generally either additive or synergistic, depending on the specific cellular endpoint being examined. 

Exposure to high levels of NO2 (#5.0 ppm) with very high concentrations of H2SO4 (1 mg/m3

) 

caused a synergistic increase in collagen synthesis rate and protein content of the lavage fluid of rats (Last 

and Warren, 1987; Last, 1989). 

AX4.5.6. Complex Mixtures Containing NO2

Although many studies have examined the response to NO2 with only one additional pollutant, the 

atmosphere in most environments is a complex mixture of more than two materials. A number of studies 

have attempted to examine the effects of multi-component atmospheres containing NO2, but as mentioned 

before, in many cases the exact role of NO2 in the observed responses is not always clear. One study by 

Stara et al. (1980) deserves mention because pulmonary function changes appeared to progress after 

exposure ceased. 
4-13

In the study by Stara et al. (1980), dogs were exposed for 68 months (16 h/day) to raw or 

photochemically reactive vehicle exhaust which included mixtures of NOX: one with a high NO2 level and 

a low NO level (0.64 ppm, NO2; 0.25 ppm, NO), and one with a low NO2 level and a high NO level 

(0.14 ppm, NO2; 1.67 ppm, NO). At the end of exposure, the animals were maintained for about 3 years 

in normal indoor air. Numerous pulmonary functions, hematological and histological endpoints were 

examined at various times during and after exposure. The lack of an NO2-only or NO-only group 

precludes determination of the nature of the interaction. Nevertheless, the main findings are of interest. 

Pulmonary function changes appeared to progress after exposure ceased. Dogs in the high NO2 group had 

morphological changes considered to be analogous to human centrilobular emphysema. Because these 

morphological measurements were made after a 2.5- to 3-year holding period in clean air, it cannot be 

determined with certainty whether these disease processes abated or progressed during this time. This 

study suggests progression of damage after exposure ends. 

Table AX4.1. Oxidant and antioxidant homeostasis. 

ppm EXPOSURE GENDER AGE SPECIES 

(STRAIN) EFFECTS REFERENCES 

0.04 

0.4 

4.0 

Continuous, 

9 and 18 mos 

M 8 wks Rat (Wistar) NPSHs increased at ≥0.4 ppm after 9 or 18 mos; 

GSH peroxidase activity increased after a 9-mo 

exposure to 4.0 ppm; G-6-P dehydrogenase was 

increased after a 9- and 18-mo exposure to 

4.0 ppm; no effects on 6-P-G dehydrogenase , SOD 

disulfide reductase; some GSH S-transferase had 

decreased activities after 18-mo exposure to 

≥0.4 ppm. 

Sagai et al. (1984) 

Ichinose et al. 

(1983) 

0.4 2 wks NR NR Rat 

Guinea Pig 

No effect on TBA reactants, antioxidants, or 

antioxidant enzyme activities. 

Ichinose and 

Sagai (1989) 

0.4 

1.2 

4.0 

Continuous, 

4 mos 

M 13 wks Rat (Wistar) Duration dependent pattern for increase in activities 

of antioxidant enzymes; increase, peaking at wk 4 

and then decreasing. Concentration-dependent 

effects. 

Ichinose and 

Sagai 

(1982) 

0.4-

0.5 

Continuous, 

1.5 yrs 

F NR Mouse 

(NR) 

Growth reduced; Vitamin E (30 or 300 mg/kg diet) 

improved growth. 

Csallany (1975) 

0.5 

1.0 

Continuous, 

17 mos 

F 4 wks Mouse (C57B1/6J) At 1 ppm, GSH-peroxidase activity decreased in 

vitamin E-deficient mice and increased in Vitamin Esupplemented mice. 

Ayaz and Csallany 

(1978) 

1.0 4 h/day, 

6 days 

NR NR Rat 

(Sprague-Dawley) 

Vitamin E-supplement reduced lipid peroxidation. Thomas et al. 

(1967) 

1.0 

2.3 

6.2 

Continuous, 

4 days 

M 8 wks Rat 

(Sprague-Dawley) 

Activities of GSH reductase and G-6-P 

dehydrogenase increased at 6.2 ppm proportional to 

duration of exposure; plasma lysozyme and GSH 

peroxidase not affected at 6.2 ppm; no effects at 1.0 

or 2.3 ppm. 

Chow et al. (1974) 

1.2 

1.8 

Continuous, 

3 days 

M 12 wks Rat 

(Sprague-Dawley) 

Increases in G-6-P dehydrogenase, isocitrate 

dehydrogenase, disulfide reductase, and NADPH 

cytochrome c reductase activities at 1.8 ppm only. 

Lee et al. (1989, 

1990) 

2.0 

10.0 

3 days M/F 5->60 days Rat (Wistar) Guinea 

pig (Dunkin Hartley) 

Decreased SOD activity in 21-day-old animals. Azoulay-Dupuis 

et al. (1983) 

2.0 

4.0 

10.0 

14 days 

10 days 

7 days 

M 12-24 wks Rat (Wistar) G-6-P dehydrogenase increased at ≥2 ppm; at 

2 ppm, 14 days of exposure needed 

Mochitate et al. 

(1985) 
4-14

ppm EXPOSURE GENDER AGE SPECIES 

(STRAIN) EFFECTS REFERENCES 

3.0 7 days M/F 1 day to 

>8 wks 

Rat 

(Sprague-Dawley) 

Increased lipid peroxidation (TBA-reactive 

substances) with vitamin E deficiency. 

Sevanian et al. 

(1982) 

9.5 7 h/day, 

5 days/wks, 

6 mos 

M In utero and 

6 mos 

Rat (Fischer 344) Increase in GSH reductase activity in younger rats 

and SDH peroxidase activity in older rats. 

Mauderly et al. 

(1987) 

3.0 

7.0 

10 

15 

4 days 

4 days 

4 days 

1-7 days 

M NR Rat (SpragueDawley) No effects on parameters tested. 

Increase in lung weight, G-6-P dehydrogenase, 

GSH reductase, and GSH peroxidase activities. 

Increased lung weight, G-6-P dehydrogenase; and 

GSH reductase activities. 

Increase in lung weight, DNA content, G-6-P 

dehydrogenase, 6-P-G dehydrogenase, GSH 

reductase, disulfide reductase, GSH peroxidase, 

disulfide reductase, succinate oxidase, and 

cytochrome oxidase activities; no effect on lung 

protein 

Mustafa et al. 

(1979) 

4.0 3 h M/F 21-33 yrs Human Decreased elastase inhibitory capacity and 

increased lipid peroxidation products in BAL of 

subjects not administered supplement of vitamin C 

and E prior to NO2 exposure. 

Mohsenin (1991) 

5.0 

10.0 

Continuous, 

24 h 7 days 

M NR Rats (CD Cobs) Changes in the GSH levels in blood and lung 

occurred in rats exposed for 24 h, but returned to 

normal after 7 days. 

Pagani et al. 

(1994) 

6.0 

15 

28 

4 h/day, 

30 days 

7 days 

F NR Mouse 

(NR) 

Increase in GSH reductase and G-6-P 

dehydrogenase activities. 

Increase in GSH levels, G-6-P dehydrogenase, and 

GSH peroxidase activities. 

Csallany (1975) 

9.5 7 h/day, 

5 days/wk, 

24 mos 

M 18 wks Rat (Fischer 344) Increase in GSH reductase activity in BAL. Mauderly et al., 

(1990) 

10.0 Continuous 

3 days, 

20 days 

NR NR Rat (Fischer 344) Decreased GSH/GSSG ratio in blood and BAL fluid, 

but not in lung type II cells. Lipid peroxidation was 

decreased in type II cells at 3 days, but was similar 

to controls at 20 days. mRNA expression of the 

enzymes involved in the biosynthesis ((GCS and 

GS) was decreased at both time points. (GT (redox 

of GSH) mRNA expression was increased. 

Hochscheid et al. 

(2005) 

14.0 NR NR NR Human Rapid depletion of vitamin C, glutathione and 

vitamin E 

Halliwell et al. 

(1992) 

M = Male 

NPSHs = Nonprotein sulfhydryls 

G-6-P dehydrogenase = Glucose-6-phosphate dehydrogenase 

G6-P-G dehydrogenase = 6-phosphosgluconate dehydrogenase 

SOD = superoxide dismutase 

F = Female 

NR = Not Reported 

NADP = Nicotinamide-adenine dinucleotide phosphate (reduced form) 

TBA = Thiobarbituric acid 

BAL = Bronchoalveolar lavage 

GR = Glutathione reductase 

GS = Glutathione synthetase 

GSH = Reduced glutathione 

GSSG = Oxidized glutathione 

(-GCS - (-Glutamyl-cystein synthetase 

(-GT - (-Glutamyltranspeptidase 
4-15

Table AX4.2. Lung amino acids, proteins, lipids, and enzymes. 

ppm EXPOSURE GENDER AGE SPECIES 

(STRAIN) EFFECTS REFERENCES 

0.4 

1.0 

3.0 

5.0 

5.0 

0.4 

72 h 

3 h 

Continuous, 1 wk 

M NR Guinea Pig 

(Hartley) 

No effect at 0.4 ppm; increase in BAL protein 

in vitamin C-depleted, but not normal, animals 

at 1.0 ppm and above. 

Increased BAL protein in vitamin C-depleted 

guinea pigs 15 h postexposure. 

No effect on BAL protein. 

Selgrade et al. 

(1981) 

0.4 Continuous, 1 wk M NR Guinea Pig Increased protein content of BAL from 

vitamin-C-deficient guinea-pigs. 

Sherwin and 

Carlson (1973) 

0.4 

1.2 

4.0 

1-14 wks M 22-24 wks Rat (Wistar) Complex concentration and duration 

dependence of effects. Example: at 0.4 ppm, 

cytochrome P-450 levels decreased at 2 wks, 

returned to control level by 5 wks. At 1.2 ppm, 

cytochrome P-450 levels decreased initially, 

increased at 5 wks, and decreased at 10 wks. 

Effects on succinate-cytochrome c reductase 

also. 

Takahashi et al. 

(1986) 

0.5 

1.0 

6 h/day, 

5 days/wk, 

4 wks 

M NR Rat (Fischer 344) 0.5 ppm; increase in urinary hydroxylysine 

output starting during wk 1; BAL 

hydroxylysine level, angiotensin-converting 

enzyme level, and BAL protein content 

unchanged. 

1.0 ppm: gradual increase in urinary 

hydoxylysine output, becoming significant the 

week after exposure ended; BAL 

hydroxylysine level lower following exposure 

and 4 wks postexposure; andiotensinconverting enzyme level increased. 

Evans et al. (1989) 

1.0 

7.5 

15 

25 

30 

6 h/day, 2 days M NR Rat (Fischer 344) Concentration dependent increase in urinary 

hydroxylysine output and BAL hydroxyxlysine 

content, but only significant at ≥7.5 ppm and 

15 ppm, respectively; angiotensin-converting 

enzyme levels and BAL protein increased in 

highest-exposed groups. 

Evans et al. (1989) 

1.0 

5.0 

7 h/day, 

5 days/wk, 

up to 15 wks 

M/F 14-16 wks Rat (Fischer 344) Change in BAL and tissue levels of enzymes 

early in exposure, resolved by 15 wks. 

Gregory et al. 

(1983) 

1.2 

1.2 

4.0 

7 days M 10 wks Rat (Wistar) Decrease in levels of cytochrome P-450 at 

1.2 ppm. 

Mochitate et al. 

(1984) 

2.0 1, 2, or 3 wks M NR Guinea pig Increased lactate dehydrogenase (LDH) 

content of the lower lobes of the lung. 

Sherwin and 

Carlson (1973) 

0.8 

5 

10 

1 or 3 days M NR Rat 

(Sprague-Dawley) 

BAL protein content significantly increased in 

a concentration- and exposure durationdependent manner, with the change 

becoming significant at 5 ppm for 3 days and 

at 10 ppm for ≥1 day of exposure. 

Müller et al. (1994) 

2.0 

4.0 

10 

14 days 

10 days 

7 days 

M 12-24 wks Rats (Wistar) Increase activity of lung glycolytic enzymes. Mochitate et al. 

(1985) 

3.0 7 days M/F 8 wks Rat 

(Sprague-Dawley) 

Various changes in lung homogenate protein 

and DNA content and enzyme activities, 

changes more severe in vitamin E-deficient 

rats. 

Elsayed and 

Mustafa (1982) 
4-16

ppm EXPOSURE GENDER AGE SPECIES 

(STRAIN) EFFECTS REFERENCES 

3.6 

7.2 

10.8 

14.4 

24 h 

12 h 

8 h 

6 h 

M 10-12 wks Rat 

(Sprague-Dawley) 

Increased BAL protein ≥7.2 ppm. Gelzleichter et al. 

(1992) 

4.0 

10 

10 days 

7 days 

M 21-24 wks Rat (Wistar) Initial decrease in lung protein content 

followed by an increase; changes on 

microsomal enzyme activities. 

Mochitate et al. 

(1984) 

4.0 

10 

25 

6 h/day 

5 days/wk, 

7, 14, and 21 days 

M NR Rat (Wistar) Increased gamma-glutamyl transferase on 

days 14 and 21; no consistent effect on 

alkaline phosphatase, LDH, or total protein. 

Hooftman et al. 

(1988) 

4.5 16 hrs M/F NR Guinea pig 

(Hartley) 

Increased lung wet weight, alterations in lung 

antioxidant levels in Vitamin C- deficient 

animals. 

Hatch et al. (1986) 

4.8 3 h M NR Guinea pig 

(Hartley) 

Increased lung lavage fluid protein content in 

vitamin C-deficient animals. 

Hatch et al. (1986) 

4.8 8 h/day, 

7 days 

M 8 wks Mouse (Swiss 

Webster) 

No significant changes in lung homogenate 

parameters. 

Mustafa et al. 

(1984) 

5.0 14-72 h F NR Mouse (NR) Increase in lung protein (14 to 58 h) by 

radioactive label incorporation. 

Csallany (1975) 

5.0 2 wks M 5 wks Rat 

(Fischer 344) 

Increased amounts of the tryptophan 

metabolites and xanthurenic and kynurenic 

acids excreted in urine during wk 2 of 

exposure, but had returned to normal levels 

by wk 4. 

Suzuki et al. 

(1988) 

5.0 6 h/day, 

6 days 

NR NR Mice Modest increase in albumin in BAL; no effect 

on LDH or lysosomal enzyme peroxidase.\ 

Rose et al. (1989) 

5.0-

25.0 

Continuous, 

7 days 

M 10-11 wks Rat (SpragueDawley) Concentration-related increase in collagen 

synthesis rate; 125% increase in rats exposed 

to 5.0 ppm. 

Last et al. (1983) 

5.0 

20.0 

50.0 

3 h NR NR Rabbit 

(New Zealand) 

Benzo [a] pyrene hydroxylase activity of 

tracheal mucosa not affected. 

Palmer et al. 

(1972) 

5.0 Continuous, 

1, 3, or 7 days 

M NR Rat 

(Sprague-Dawley) 

Increased BAL protein at 3 days (day 7 not 

measured); increased (120% collagen 

synthesis at 7 days (not measured other 

days). 

Last & Warren 

(1987) 

8.0 Continuous, 14 days F NR Mouse (NR) Increase in lung protein. Csallany (1975) 

9.5 7 h/day, 5 days/wk, 

6 mos 

M In utero 

and 6 mos 

Rat 

(Fischer 344) 

Increase in BAL alkaline phosphatase, acid 

phosphatase, and LDH in older rats only. 

Mauderly et al. 

(1987) 

9.5 7 h/day, 5 days/wk, 

24 mos 

M 18 wks Rat 

(Fischer 344) 

Increase in BAL levels of LDH and alkaline 

phosphatase activities and in collagenous 

peptides. 

Mauderly et al. 

(1990) 

10 24 h or 7 days M NR Rat (CD cobs) Protein content of BALF increased 

significantly in rats after only 24 h. BALF 

elastase activity was not affected. 

Concentration-dependent increase in α1 proteinase inhibitor content after 24 h of 

exposure, but not with longer exposures. 

Pagani et al. 

(1994) 

10 Continuous, 14 days M 8 wks Rat (Wistar) Changes in several enzymes in whole lung 

homogenates. 

Sagai et al. (1982) 
4-17

ppm EXPOSURE GENDER AGE SPECIES 

(STRAIN) EFFECTS REFERENCES 

10 

20 

30 

40 

4 h M NR Rat (Long Evans) Increased activities of various enzymes, sialic 

acid, and BAL protein; attenuation by high 

dietary levels of vitamin E. 

Guth and Mavis 

(1985, 1986) 

10 24 h/day, 0 (control), 

3 days or 20 days 

NR NR Rat (Fischer 344) LPO decreased in type II pneumocytes after 3 

days compared to controls but remained 

comparable to controls after 20 days. Authors 

stated that LPO is a very early reaction during 

oxidative stress and that the decrease after 3-

day exposure suggests an adaptation 

mechanism. 

Exposure duration-dependent, statistically 

significant increase in GPx and GR enzyme 

activities after 3 and 20 days over control 

values. Changes in mRNA expression of GSH 

synthesizing enzymes in type II pneumocytes 

were also observed. 

Hochscheid et al. 

(2005) 

10 24 h/day, for 0, 3, 

20, or 28 days 

M NR Rat (SpragueDawley) Uptake of surfactant-like liposomes by type II 

pneumocytes in the presence or absence of 

SP-A was faster and significantly higher in 

cells from all NO2 exposed groups than in 

control cells. No difference in the uptake 

kinetics between cells from exposed groups of 

different duration. Increase in liposome 

uptake suggests NO2 exposure likely 

disrupted cell membranes to allow liposomes 

to enter the cells easily. Lipid uptake 

associated with duration-dependent increase 

in internalization of label found in PC fraction. 

Suggests increased demand of PC in lung 

injury. 

Muller et al. (2003) 

0.8 

5.0 

10 

Presumably 

continuous, 1 day or 

3 days 

M NR Rat (SpragueDawley) Phospholipid component in BAL increased in 

a concentration- and exposure durationdependent manner, with significance only at 

10 ppm, but not at 5 ppm or below, for ≥1 day 

of exposure. PC content in BAL of exposed 

animal did not change significantly compared 

to controls. At 10 ppm, but not at 5 ppm or 

below, percentage of saturated PC decreased 

and that of unsaturated PC increased 

statistically significant, while significant 

decreases in palmitic acid (16:0) and 

increases in arachidonic acid (20:4) contained 

in PC were observed. Sphingomyelin – as a 

marker for an influx of serum into the alveolar 

airspace – was also not changed by NO2

exposure. 

Functional studies on surfactant phospholipid 

extracts indicated increased values for the 

surface tension at equilibrium, and for the 

maximal and minimal surface tension of 

animals exposed to ≥5 ppm, but not to 

0.8 ppm. Suggests NO2 directly impaired 

surface tension at ≥5 ppm. Structure of the 

SP-A not altered by NO2 exposure. 

Authors suggested exposure to NO2 impaired 

surfactant components may be used as 

markers of altered surfactant metabolism. 

Muller et al. (1994) 
4-18

ppm EXPOSURE GENDER AGE SPECIES 

(STRAIN) EFFECTS REFERENCES 

5 

10 

24 h or 24 h/day for 

7 days 

M NR Rat (CD Cobs) Concentration-dependent increase in α-1 PI 

content. Exposure to 5 and 10 ppm NO2

significantly increased α-1 PI content only 

after 24 h and returned to control values after 

7 days at each exposure concentration. Since 

blood GSH content was also increased 

together with α-1 PI content at 24 h, authors 

suggested the increase in these parameters 

can be considered a prompt protective 

response resulting in no further increase of α1 PI. 

Pagani et al. 

(1994) 

LPO = Lipid peroxidation 

PC = Phosphatidylcholine 

SP-A = Surfactant protein-AGPx = Glutathione peroxidase 

GPx = Glutathione peroxidase 

GR = Glutathione reductase 

GSH = Glutathione 

α-1 PI = α-1 proteinase inhibitor 

Table AX4.3. Alveolar macrophages and lung host defense. 

ppm EXPOSURE GENDER AGE SPECIES 

(STRAIN) EFFECTS REFERENCE 

0.05 

base + 

2.0 

peaks 

3 h base + three 

15-min peaks 

NR NR Human No effects at 0.05 ppm NO2 with peaks; trend 

(p < 0.07) towards AMs losing ability to inactivate 

influenza virus at 0.6 ppm. 

Frampton et al. 

(1989) 

0.6 3 h NR NR Human No effects at 0.05 ppm NO2 with peaks; trend 

(p < 0.07) towards AMs losing ability to inactivate 

influenza virus at 0.6 ppm. 

Frampton et al. 

(1989) 

0.1 

1.0 

5.0 

20 

1 h NR NR Rat 

(Sprague-Dawley) 

(in vitro) 

At 5.0 ppm: increase in LTB4; concentration-related 

decrease in SOD production in AMs at ≥1.0 ppm; 

increase in LDH in AMS at 5.0 and 20 ppm 

Robinson et al. 

(1990) 

0.2 

0.5 

2.0 

 Gestation 

12 wks 

Rat (BrownNorway) Reactive oxygen species generation from alveolar 

macrophages was significantly suppressed in NO2

exposed weanling animals; no changes in reactive 

oxygen generating capability in the embryonic 

exposed animals. 

Kumae and 

Arakawa (2006) 

0.5 

0.1 base 

+ 1.0 

peak 

2.0 

0.5 base 

+ 2.0 

peak 

Continuous, 

24 wks 

Continuous 

base + 3-h peak, 

5 days/wk, 

24 wks 

Continuous, 

33 wks 

Continuous 

base + 1-h peak, 

5 days/wk, 

33 wks 

NR NR Mouse No effects on AM morphology at 0.5 ppm 

continuous or 0.1 ppm base + peak. 

After 21 wks of exposure to 2.0 ppm continuous or 

0.5 ppm base + peak, morphological changes were 

identified, such a loss of surface processes, 

appearance of fenestrae, bleb formation, and 

denuded surface areas. 

Aranyi et al. 

(1976) 

0.3 

1.0 

2 h/day 

2, 6, 13 days 

M NR Rabbit 

(New Zealand) 

Decreased phagocytic ability of AMs at 0.3 ppm 

after 2 days of exposure; increased at 1.0 ppm 

after 2 days of exposure; no effect on cell number 

or viability; random mobility reduced at 0.3 ppm 

only; no effects after 6 days of exposure. 

Schlesinger 

(1987) 

0.3 

1.0 

2 h/day up to 

14 days 

M NR Rabbit 

(New Zealand) 

Increase in alveolar clearance. Schlesinger and 

Gearhart (1987) 
4-19

ppm EXPOSURE GENDER AGE SPECIES 

(STRAIN) EFFECTS REFERENCE 

0.3 

1.0 

3.0 

10 

1.0 

10 

2 h 

2 h/day, 14 days 

M NR Rabbit 

(New Zealand) 

Concentration-related acceleration in clearance of 

particles from lung with the greatest increase at two 

lowest concentrations, effects from repeated 

exposures similar to those seen after acute 

exposures to same concentrations. 

Vollmuth et al. 

(1986) 

0.5 0.5, 1, 5 and 

10 days 

exposure 

NR NR Rat (NR) Superoxide production in alveolar macrophages 

from BALF, stimulated by phorbol myrisate acetate 

(PMA), was decreased after 0.5 days of exposure, 

and continued to be depressed after 1, 5, and 

10 days. 

Robinson et al. 

(1993) 

0.5 base 

+ 1.5 

peak 

2.0 base 

+6.0 

peak 

Base 22 h/day, 

7 days/wk + two 

1-h peaks, 5 

days/wk, 6 wks 

M 1 day and 

6 wks 

Rat (Fischer 344) Trend towards increase in number of AMs and cell 

volume in younger animals; increase in number of 

AMs and cell volume in older rats. 

Crapo et al. 

(1984) 

Chang et al. 

(1986) 

0.5 

1.3 

2.7 

Continuous, 

28 days 

M 6 wks Rat (Wistar) Increase in AMs in highest exposed group; no 

effects noted in 2 lowest exposure groups. 

Rombout et al. 

(1986) 

1.0 

2.0 

4.0 

24 h/day, 12 wks Guinea pig (NR) 

Rat (NR) 

IgE-mediated histamine release from lung mast 

cells was enhanced in guinea pigs, but not rats 

exposed to 4.0 ppm. No effect observed at lower 

concentrations. 

Fujimaki and 

Nohara (1994) 

1.0 

5.0 

15 

6 h/day, 2 days NR 4-6 wks Mouse (CD1) Exposure-related decrease in AM phagocytosis 

from 1.0-5.0 ppm, decrease was not further 

affected by 15 ppm. 

Rose et al. 

(1989) 

1.0 

2.0 

4.0 

24 h/day, 12 wks Guinea pig (NR) 

Rat (NR) 

IgE-mediated histamine release from lung mast 

cells was enhanced in guinea pigs, but not rats 

exposed to 4.0 ppm. No effect observed at lower 

concentrations. 

Fujimaki and 

Nohara, (1994) 

1.0 + 

0.9 ppm 

No 

15 

24 

7 h/day, 

5 days/wks for 

11 or 22 

exposures 

NR NR Rat (Long Evans) Stimulated clearance of particles from lung at 

lowest concentration, but decreased clearance rate 

at two highest concentrations. 

Ferin and Leach 

(1977) 

1.0 

5.0 

base + 

5.0 

peaks 

7 h/day, 

5 days/wks 

Base 7 h/day, 

5 days/wks; 

two 1.5-h 

peaks/day; 

15 wks 

M/F 14-16 wks Rat (Fischer 344) Accumulation of AMs. Superimposed peak 

exposures produced changes that may persist with 

continued exposures. 

Gregory et al. 

(1983) 

1.3-17 NR (“acute”) F NR Rat 

(Sprague-Dawley) 

Decreased production of superoxide anion radical. Amoruso et al. 

(1981) 

2.0 

10 

3 days M/F 5, 10, 21, 

45, 55, 60, 

and 

>60 days 

Guinea pig 

(Dunkin Hartley) 

Rat (Wistar) 

Newborns were less affected than adults when 

AMs were tested for SOD levels. 

Azoulay-Dupuis 

et al. (1983) 

2.0 8 h/day, 5 

days/wk, 6 mo 

M/F 3-4 yrs Baboon Impaired AM responsiveness to migration inhibitory 

factor. 

Green and 

Schneider 

(1978) 
4-20

ppm EXPOSURE GENDER AGE SPECIES 

(STRAIN) EFFECTS REFERENCE 

2.0 4 h NR NR Human Decreased phagocytosis and superoxide anion 

release. 

Devlin et al. 

(1992) 

2.7 24 h M 6 wks Rat (Wistar) Increase in number of AMs. Rombout et al. 

(1986) 

3-6 3 h NR NR Dog (Beagle) Enhanced swelling of AMs. Dowell et al. 

(1971) 

3.6 

12.1 

1 h 

2 h 

F NR Rat (SpragueDawley) (in vitro) Enhanced macrophage agglutination with 

concanavalin A at both concentrations tested. 

Goldstein et al. 

(1977) 

4 

10 

25 

6 h/day, 7, 14, or 

21 days 

M NR Rat (Wistar) Changes in morphology at all concentrations; 

increase in number of AMs at ≥10 ppm; phagocytic 

capacity reduced after 14 and 21 days of exposure 

to 25 ppm. 

Hooftman et al. 

(1988) 

4.0 10 days 19-23 wks Increase in number of AMs; no increase in PMNs; 

increased metabolic activity, protein, and DNA 

synthesis; all responses peaked on day 4 and 

returned to normal on day 10. 

Mochitate et al. 

(1986) 

4.0 

8.0 

Up to 10 days NR NR Rat (Fischer 344) Increase in number of AMs at both concentrations, 

reaching a peak on day 3 and 5; no increase in 

number of PMNs; decrease in AM viability 

throughout exposure period. Suppression of 

phagocytic activity after 7 days of exposure to 

4 ppm and after 5 days of exposure to 8 ppm; 

returned to normal value at 10 days. Decrease in 

superoxide radical production, but at 4 ppm, the 

effect became significant on days 3, 5, and 10; at 

8 ppm, the effect was significant at all time periods 

tested. 

Suzuki et al. 

(1986) 

5.0 7 days F NR Mouse (CD-1) No effect on phagocytic activity. Lefkowitz et al. 

(1986) 

5 

15 

3 h after infection 

with 

parainfluenza 3 

virus 

NR NR Rabbit (New 

Zealand) 

AMs lost resistance to challenge with rabbit pox 

virus after exposure to 15 ppm. 

Acton and 

Myrvik (1972) 

5 

10 

15 

3 h M 

Fb 

NR Humans (in vitro 

exposure) 

No change in cell viability, release of neutrophil 

chemotactic factor, or 

interleukin-1. 

Pinkston et al. 

(1988) 

5-60 3 h NR NR Rabbit (New 

Zealand) 

Inhibition of phagocytic activity. Gardner et al. 

(1969) 

Acton and 

Myrvik (1972) 

7.0 24 h NR NR Rabbit Increased rosette formation in AMs treated with 

lipase. 

Hadley et al. 

(1977) 

9.5 7 h/day; 5 

days/wk; 

18-22 mo 

M 18 wks Rat (Fischer 344) No effect on long-term clearance of radiolabeled 

tracer particles. 

Mauderly et al. 

(1990) 

10 Continuous 7 

days 

NR NR Rat (NR) High influx of PMNs in the lung (BALF) after 24 h of 

exposure, reversed for macrophages; no change in 

the lymphocyte population. 

Pagani et al. 

(1994) 

10 35 days NR NR Guinea pig 63% increase in epithelial cells positive for 

macrophage congregation. 

Sherwin et al. 

(1968) 

10 4 h F NR Mouse (Swiss) Increase in total pulmonary cells in animals infected 

with some species of bacteria. 

Jakab (1988) 

10 

25 

24 h M 12-13 wks Rat (SpragueDawley) Decreased phagocytosis at 25 ppm only. Katz and Laskin 

(1976) 
4-21

ppm EXPOSURE GENDER AGE SPECIES 

(STRAIN) EFFECTS REFERENCE 

Clearance 

3 

9 

6 h/day, 

6 days/wk, for 2 

wks 

F NR Guinea Pig Significant, dose-dependent decrease in ciliary 

activity, significant at 3 ppm (12%) and 9 ppm 

(30%), and increase in eosinophil accumulation on 

epithelium and submucosal layer. 

Ohashi et al. 

(1994) 

10 

20 

14 h/day, for 15 

days, 20 or 25 

days 

M NR Mouse (C57BL/5) 20 ppm NO2 induced an increased mucus 

production due to goblet cell hyperplasia in the 

central airways. 

Wegman and 

Herz (2002) 

Alveolar Macrophage Endpoints 

0.5 8 h/day, 

5 days/wk, for 

0.5, 1, 5, or 10 

days 

M NR Rat (SpragueDawley) Acute depression of pulmonary arachidonate 

metabolism observed. Unstimulated AM synthesis 

of LTB4 depressed within 1 day and also on day 5 

of exposure. Acute depression of AM synthesis of 

TxB2, LBT4, and 5-HETE on stimulation by the 

calcium ionophore, A23187, within 1 day of 

exposure but not with longer exposure, while 5-

HETE increased significantly at 10 days of 

exposure only. Suggests rapid depression of 

cyclooxygenase and 5-lipoxygenase activities. 

ZAS-stimulated LTB4 production delayed until 

5 days and remained lower at 10 days. AM 

superoxide production stimulated by PMA was 

rapidly and continuously depressed throughout the 

study. BAL fluid levels of LTB4 and TxB2 paralleled 

ex vivo depression of AM production. 

Robinson et al. 

(1993) 

0.2 

0.5 

2.0 

Continuous, 

presumably 

7 days/wk, up to 

12 wks 

F Neonates 

or 5 wks 

old 

Rat (BrownNorway) Animals were exposed during embryonic or 

weanling (5-wks old) period. ROS generation was 

significantly suppressed at 0.5 and 2.0 ppm NO2 in 

animals exposed during the weanling period. 

Cytokine level measurement in AM culture 

mediums indicated that inflammatory reactions 

(significant increases in TFNα and IFNγ) were 

initiated at 8 wks and terminated at 12 wks in 

animals exposed during the embryonic period while 

inflammatory reactions (significantly increased 

TFNα level) were not initiated at 8 wks but take 

place at 12 wks in animals exposed as weanlings. 

Results suggest that NO2 exposure from the 

weanling period has stronger effects on AM activity. 

Kumae and 

Arakawa (2006) 

 
4-22

Table AX4.4. Lung permeability and inflammation. 

ppm EXPOSURE GENDER AGE SPECIES 

(STRAIN) EFFECTS REFERENCE 

0.8 

5 

10 

Presumably 

continuous, 1 day 

or 3 days 

M NR Rat 

(SpragueDawley) 

BAL protein content significantly increased in a 

concentration- and exposure duration-dependent manner, 

with the change becoming significant at 5 ppm for 3 days 

and 10 ppm for ≥1 day of exposure. 

Muller et al. 

(1994) 

5 

10 

24 h or 24 h/day 

for 7 days 

M NR Rat (CD 

Cobs) 

Exposure induced inflammatory response in the lungs. At 

10 ppm, influx of PMN, maximal at 24 h, but no influx 

observed after 7 days of exposure, a trend that was also 

observed with protein content in BAL fluid. In contrast, no 

influx of macrophages was observed, but the influx was 

maximal after 7 days of exposure. No significant changes in 

lymphocyte counts at any exposure concentration and 

protein content in BAL fluid not significantly affected at 

5 ppm. 

Protein content of BAL fluid increased significantly only after 

24 h of exposure to 10 ppm NO2. 

Pagani et al. 

(1994) 

5 

25 

6 h/day for 1, 3, or 

5 days 

NR NR Mouse 

(C57BL/6) 

Exposure to 5 ppm NO2 did not cause any lung inflammation 

or injury. Exposure to 25 ppm NO2 induced acute lung injury 

(characterized by increases in protein, LDH, macrophages, 

and neutrophils recovered by BAL) that peaked after 3 days, 

lesions within terminal bronchioles, and AHR. OVAsensitized animals exposed to 25 ppm, but not 5 ppm, NO2

showed augmentation of eosinophilic inflammation and 

terminal bronchiolar lesions, which extended significantly 

into the alveoli. No increased expression of mucus cellassociated gene products in non-sensitized or OVAsensitized animals exposed at any concentration. 

Poynter et al. 

(2006) 

5 

20 

3 h M NR Mouse 

(BALB/c) 

Exposure of OVA-challenged animals to 20 ppm produced 

BHR and caused significant increase in neutrophils and 

fibronectin concentration, significant reduction in eosinophil 

count, and exudation and release of IL-5 in BAL fluid 24 h 

and/or 72 h after exposure. Exposure to 5 ppm did not 

modify BHR, but significantly reduced pulmonary eosiniphilic 

inflammation (reduced esinophilic count and eosinophil 

peroxidase activity) and the production of IL-5 in the BAL 

fluid. Exposure to NO2 did not cause any significant changes 

in IgE anti-OVA antibody in exposed animals, while IgG1 

titers were significantly increased in animals exposed only to 

5 ppm NO2, compared to controls. There was no 

development of mucosal metaplasia in any NO2 exposed 

group compared to controls. 

Proust et al. 

(2002) 

5 

10 

20 

24 h/day, for 3 or 

25 days 

M NR Rat 

(SpragueDawley) 

Exposure to NO2 exhibited concentration- and exposure 

duration-dependent, and tissue localization-specific 

differences in Clara cell proliferation. Increased proliferation 

(measured as BrdU-LI) in both bronchial and bronchiolar 

epithelium of all exposed groups, with significance starting at 

exposure to 5 ppm for 3 or 25 days. Exposure to 5 and 

10 ppm NO2 for 3 days showed significantly higher 

proliferative activity in bronchiolar epithelium than in the 

bronchial epithelium for corresponding exposure groups, 

while the difference in proliferation between proximal and 

distal airways diminished after 25 days of exposure. Clara 

cell proliferation was not accompanied by a change in Clara 

cell numbers in any of exposure groups, a finding the 

authors explained by the assumption that the process of 

proliferation takes place at the same rate as Clara cell 

differentiates into other cells, a transition that leads to the 

loss of phenotypic Clara properties in differentiating cells. 

Barth and Muller 

(1999) 

1.2 24 h/day, for 

3 days 

M NR Rat 

(SpragueDawley) 

No significant differences in cell viability and percentages of 

pulmonary AMs or PMNs between animals exposed to 

1.2 ppm NO2 and controls. 

Bermudez 

(2001) 
4-23

ppm EXPOSURE GENDER AGE SPECIES 

(STRAIN) EFFECTS REFERENCE 

10 

20 

14 h/day, for 

15 days, 20 or 25 

days 

M NR Mouse 

(C57BL/5) 

Exposure to 10 ppm for 15 days – the only exposure 

duration used for this concentration – did not significantly 

affect influx of inflammatory cells (leukocyte subpopulations 

and macrophages). 20 ppm NO2 induced airway and 

parenchymal inflammation – dominated by a significant influx 

of macrophages and neutrophils –, peaking at 15 days of 

exposure and declining at day 25. 

Wegman and 

Herz (2002) 

3.6 

7.2 

10.8 

14.4 

24 h, 12h, 8h, and 

6 h, respectively, 

for 3 days, giving a 

C Η T of 86.4 ppmh 

M NR Rat 

(SpragueDawley) 

Significant cell proliferation (increased labeling index) in 

peripheral airways compared to controls, regardless of 

concentration or exposure duration. Suggests Haber’s law (c 

Η t = k) was not followed over the concentration ranges 

studied. No proliferative response was observed in alveolar 

epithelium. 

Rajini et al. 

(1993) 

0.5 8 h/day, 5 days/wk, 

for 0.5, 1, 5, or 10 

days 

M NR Rat 

(SpragueDawley) 

No effect on weight gain. No effects on neutrophil, 

lymphocyte macrophage/monocyte levels or cell population 

percentages in BAL. Suggests no significant influx of 

inflammatory cells into lung airways and alveolar spaces. 

Robinson et al. 

(1993) 

5 

10 

20 

24 h/day, for 3 or 

25 days 

M NR Rat 

(SpragueDawley) 

Compared to controls, proliferative activity significantly 

increased, but with no concentration-dependence in 

respiratory bronchiolar epithelium at 5 ppm and above after 

3-day exposure, but increase was concentration-dependent, 

with significance at ≥10 ppm following 25-day exposure. 

Proliferative activity increased in a concentration-dependent 

manner in bronchial epithelium, with significance only at 

20 ppm after 3 days of exposure and at ≥10 ppm following 

25 days of exposure. 

Concentration-dependent thickening of alveolar septa and 

bronchiolar walls, significant only at ≥10 ppm after 3- and 25-

day exposure, but 5 ppm caused significant thickening only 

in bronchiolar walls after 25 days, but not after 3 days, of 

exposure compared to controls. 

Statistically significant reduction in alveoli surface density at 

≥10 ppm after 25 days of exposure and at 20 ppm only after 

3 days of exposure, while alveolar circumference increased 

statistically significantly at ≥10 ppm after 25 days of 

exposure and at 20 ppm following 3 days of exposure. 

5 ppm did not significantly affect these endpoints after 3 or 

25 days of exposure compared to controls. Data suggest 

concentration- and exposure duration-dependent 

development of emphysema, significant only at ≥10 ppm, but 

not at 5 ppm, after 3 or 25 days of exposure. Alveolar duct 

length increased significantly at ≥10 ppm after 25 days and 

at 20 ppm after 3 days of exposure; no effect at 5 ppm. 

Radial alveolar count values decreased significantly at 

20 ppm after 3 and 25 days and at 10 ppm after 25 days 

exposure; no effect at 5 ppm. Increase in avg medial 

thickness at 10 ppm with significance only after 25 days of 

exposure and at 20 ppm with significance after 3 and 25 

days of exposure. In contrast, 5 ppm caused significant 

increase at both 3 and 25 days of exposure. Authors 

suggested effect at 5 ppm was due a reduction of pulmonary 

arterial mass and not as a result of vasodilation induced by 

nitrogen oxide. 

Barth et al. 

(1994b) 
4-24

ppm EXPOSURE GENDER AGE SPECIES 

(STRAIN) EFFECTS REFERENCE 

0, 

1.0, 

2.0, 

4.0 

24 h/day, for 12 

wks 

M 10 wks Rat (Wistar) 

Guinea Pig 

(Hartley) 

No change in body weight or absolute lung weight in any 

exposed group compared to controls, but relative lung 

weight was significantly increased in animals exposed to 

4.0 ppm NO2. Number of lung cells from animals exposed to 

≥2.0 ppm significantly reduced (but extent of reduction at 

4.0 ppm < 2.0 ppm), whereas number of mast cells not 

significantly different in exposed animals compared to 

controls. IgE-mediated histamine release from lung mast 

cells significantly reduced at 2.0 ppm NO2 but not at 4.0 ppm 

or 1.0 ppm, but no difference observed in A23187-stimulated 

histamine release in lung mast cells in any exposed group 

compared to controls. Results from histamine release 

suggest that NO2 exposure to rat lung mast cells did not 

induce histamine-releasing activity. 

No change in body weight or absolute or relative lung weight 

in any exposed group compared to controls. Number of lung 

cells or mast cells was not significantly different in exposed 

animals compared to controls. Increasing trend observed in 

IgE-mediated histamine release from lung mast cells, the 

change becoming significant only at 4.0 ppm. No significant 

change in ionophore A2318-stimulated histamine release in 

exposed groups compared to controls. Data suggest NO2

exposure in guinea pigs enhanced histamine-releasing 

activity in lung mast cells. 

Fujimaki and 

Nohara (1994) 

0.2 

0.5 

2 

Continuous, 

presumably 

7 days/wk, up to 

12 wks 

F Neonates 

or 5 wks 

old 

Rat (BrownNorway) Rats were exposed from embryonic or weanlings (5 wks old) 

period up to 12 wks of age. Significantly decreased levels of 

AM + Mo in weanling animals exposed to 0.5 and 2.0 ppm, 

and significantly increased levels of neutrophils in animals 

exposed to 2.0 ppm at 12 wks. Levels of AM + Mo 

significantly increased in animals exposed to 0.5 ppm during 

embryonic period while levels of neutrophil population 

significantly decreased, compared to controls, at 12 wks. 

[Changes in cell populations in BAL fluid not investigated in 

animals exposed to 2.0 ppm during the embryonic period 

due to loss of sample.] Mean level of lymphocytes 

significantly increased in the embryonic group exposed to 

0.2 ppm and in the weanling group exposed to 0.5 and 

2.0 ppm, but increase not concentration-dependent in the 

weanling group. Except for exposure to 0.2 ppm, NO2

exposure appeared to improve allergic conditions in the BAL 

fluid of the embryonic group, but caused inflammatory 

changes in the BAL fluid of the weanling group. 

Data suggest NO2 exposure from the weanling period has 

stronger effects on AM activity. 

Kumae and 

Arakawa (2006) 

AHR = Airway hyperresponsiveness 

AM = Alveolar macrophage 

BAL = Bronchoalveolar lavage 

BHR = Bronchopulmonary hyperreactivity 

BrdU-LI = Bromodeoxyuridine-laebling index 

IgE = Immunoglobulin E 

IgG = Immunoglobulin G 

IL-5 = Interleukin-5 

LDH = Lactate dehydrogenase 

Mo = Monocytes 

OVA = Ovalbumin 

PMN = Polynorphonuclear neutrophil 
4-25

Table AX4.5. Immune responses. 

ppm EXPOSURE GENDER AGE SPECIES 

(STRAIN) EFFECTS REFERENCES 

0.5 

0.1 

base 

+ 

0.25, 

0.5, or 

1.0 

peak 

Continuous 

Continuous base + 

3 h/day, 5 days/wk 

peak for 1, 3, 6, 9, 

12 mos 

NR NR Mouse Suppression of splenic T and B cell responsiveness to 

mitogens variable and not related to concentration or 

duration, except for the 940 μg/m3

 continuous group, which 

had a linear decrease in PHA-induced mitogenesis with 

NO2 duration. 

Maigetter et al. 

(1978) 

0.25 7 h/day, 

5 days/wk, 7wks 

F 6 wks Mouse 

(AKR/cum) 

Reduced percentage of total T-cell population and trend 

towards reduced percentage of certain T-cell 

subpopulations; no reduction of mature T cells or natural 

killer cells. 

Richters and 

Damji (1988) 

0.25 7 h/day, 

5 days/ wk, 36 wks 

F 5 wks Mouse 

(AKR/cum) 

Reduced percentage of total T-cell population and 

percentages of T helper/inducer cells on days 37 and 181. 

Richters and 

Damji (1990) 

0.35 7 h/day, 

5 days/ wk, 12 wks 

M 6 wks Mouse 

(C57BL/6J) 

Trend towards suppression in total percentage of T-cells. 

No effects on percentages of other T-cell subpopulations. 

Richters and 

Damji (1988) 

0.4 

1.6 

24 h/day 

4 wks 

M 7 wks Mouse 

(BALB/c) 

Decrease in primary PFC response at ≥ 752 μg/m3. 

Increase in secondary PFC response at 3010 μg/m3

. 

Fujimaki et al. 

(1982) 

0.5 

base 

+ 1.5 

peak 

22 h/day, 7 days/wk 

base + 6 h/day, 

5 days/wk peak for 

1, 3, 13, 52, 78 wks 

M 10 wks Rat 

(Fischer 

344) 

No effect on splenic or circulatory B or T cell response to 

mitogens. After 3 weeks of exposure only, decrease in 

splenic natural killer cell activity. No histological changes in 

lymphoid tissues. 

Selgrade et al. 

(1991) 

0.5 

base 

+ 2.0 

peak 

24 h/day, 5 days/wk 

base + 1 h/day, 

5 days/wk peak for 

3 mos 

M 6 wks Mouse 

(CD-1) 

Vaccination with influenza A2/Taiwan virus after exposure. 

Decrease in serum neutralizing antibody; hemagglutination 

inhibition antibody titers unchanged. Before virus challenge, 

NO2 exposure decreased serum IgA and increased IgG1, 

IgM, and IgG2; after virus, serum IgA unchanged and IgM 

increased. 

Ehrlich et al. 

(1975) 

0.5 8 h/day, 5 days/wk, 

for 0.5, 1, 5, or 

10 days 

M NR Rat 

(SpragueDawley) 

Levels of TxB2, LTB4, and PGE2 in BAL fluid were 

depressed within 4 h of exposure. Suggests acute 

depression of pulmonary arachidonate metabolism 

observed in BAL fluid. Unstimulated AM synthesis of LTB4 

was depressed within 1 day and also on day 5 of exposure. 

The AM synthesis of TxB2, LTB4, and 5-HETE on 

stimulation by the calcium ionophore, A23187, was acutely 

depressed within 1 day of exposure but not with longer 

exposure, while 5-HETE was significantly increased at 10 

days. Suggests rapid depression of cyclooxygenase and 5-

lipoxygenase activities. ZAS-stimulated LTB4 production 

was delayed until 5 days and remained lower at 10 days. 

BAL fluid levels of LTB4 and TxB2 paralleled ex vivo 

depression of AM production. AM superoxide production 

stimulated by PMA was rapidly and continuously depressed 

throughout the study. 

Robinson et al. 

(1993) 
4-26

ppm EXPOSURE GENDER AGE SPECIES 

(STRAIN) EFFECTS REFERENCES 

5 3 h F 7 wks Rat (BrownNorway) Rats were immunized intraperitoneally and challenged 

intratracheally with house mite dust. Animals were exposed 

to NO2 after sensitization or challenge or after sensitization 

and challenge (double exposure). A single exposure after 

sensitization or challenge caused a significant decrease in 

antigen-specific IgG in BAL fluid, single exposure after 

sensitization caused significant increase in serum IgE, 

while exposure only after challenge caused significant 

decrease in antigen-specific IgA levels in BAL fluid. Double 

NO2 exposure caused significantly higher levels of antigenspecific serum IgE and local IgA, IgG, and IgE antibody, 

and significant increase in lymphocyte responsiveness to 

antigen in the spleen and mediastinal lymph nodes. Double 

exposure also significantly increased the ratio of 

inflammatory cells to alveolar macrophages without 

affecting the total number of lavageable cells. 

Data suggest a 3-h exposure to 5 ppm NO2 after 

intraperitoneal sensitization and pulmonary challenge with 

house dust mite allergen was necessary to enhance 

specific immune responses to the allergen and increased 

the number of inflammatory cells in the lungs. Additionally, 

data suggest an upregulation of specific immune responses 

and subsequent immune-mediated pulmonary 

inflammation. 

Gilmour et al. 

(1996) 

5 

20 

3 h M NR Mouse 

(BALB/c) 

Animals were sensitized and challenged with the antigen 

OVA to generate airway inflammation before being exposed 

to NO2. 

Exposure of OVA-challenged animals to 20 ppm produced 

BHR and caused significant increase in neutrophils and 

fibronectin concentration, significant reduction in eosinophil 

count, and exudation and release of IL-5 in broncholaveolar 

fluid 24 h and/or 72 h after exposure. Exposure to 5 ppm 

did not modify BHR, but significantly reduced pulmonary 

eosiniphilic inflammation (reduced esinophilic count and 

eosinophil peroxidase activity) and the production of IL-5 in 

the BAL fluid. Authors suggested that potentiation of BHR 

by 20 ppm NO2 in allergic mice may be accounted for by an 

increased vascular/epithelial permeability, facilitating the 

allergen availability and accelerating the inflammatory 

process. 

Exposure to NO2 did not cause any significant changes in 

IgE anti-OVA antibody in exposed animals, while IgG1 titers 

were significantly increased in animals exposed only to 

5 ppm NO2, compared to controls. There was no 

development of mucosal metaplasia in any NO2 exposed 

group compared to controls. 

Proust et al. 

(2002) 

4 2 h/day M and F From 

birth 

until 3 

mos of 

age 

Rabbit 

(NZW) 

No effect on mortality, health, behavior, body weight, or 

basal pulmonary function (lung resistance, dynamic 

compliance, respiration rates, tidal volume, and minute 

volumes) in animals immunized against house mite dust 

compared to littermates exposed to air. Immune parameters 

were not evaluated. 

Douglas et al. 

(1994) 

4.76 4 h/day, 5 days/wk, 

for 6 wks 

(30 exposures, 

total) 

M NR Guinea Pig 

(Hartley) 

Animals were intraperitoneally sensitized twice and then 

challenged pulmonarily with C. albicans. Animals were 

exposed, from the first day of sensitization, and throughout 

the study period. Exposure to NO2 resulted in significantly 

increased respiratory rate (tachypnea) (2.3-2.7 times/s) 15 

h after antigen challenge compared to controls, but did not 

significantly affect the expiration/inspiration ratio. Authors 

indicated that delayed-type dyspneic symptoms in this 

study were increased by exposure to NO2. 

Kitabatake et al. 

(1995) 
4-27

ppm EXPOSURE GENDER AGE SPECIES 

(STRAIN) EFFECTS REFERENCES 

0.06 

0.5 

1 

2 

4 

24 h/day, for 6 or 

12 wks 

M NR Guinea Pig 

(Hartley) 

Concentration- and exposure duration-dependent increases 

in airway responsiveness to inhaled histamine aerosol, 

significant at 1.0 ppm and above in animals exposed for 

12 wks and at 2 ppm and above in those exposed for 6 

wks. No significant increase in specific airway resistance 

(sRaw) values at any NO2 concentration at 6 wks of 

exposure, but there was a concentration-dependent 

increase in this parameter at 12 wks of exposure, with 

significance at 2.0 ppm and above. Authors concluded NO2

could be a potent risk factor for alteration of pulmonary 

function and airway responsiveness. 

Kobayashi and 

Miura (1995) 

1 

2 

4 

24 h/day, for 12 

wks 

M 

M 

10 wks 

10 wks 

Rat (Wistar) 

Guinea Pig 

(Hartley) 

No change in body weight or absolute lung weight in any 

exposed group compared to controls, but relative lung 

weight significantly increased in animals exposed to 

4.0 ppm NO2. Number of lung cells from animals exposed 

to ≥ 2.0 ppm significantly reduced (but extent of reduction 

at 4.0 ppm < 2.0 ppm), but number of mast cells not 

significantly different in exposed animals compared to 

controls. IgE-mediated histamine release from lung mast 

cells significantly reduced at 2.0 ppm NO2 but not at 

4.0 ppm or 1.0 ppm, but no difference observed in A23187-

stimulated histamine release in lung mast cells in any 

exposed group compared to controls. Results from 

histamine release suggest that NO2 exposure to rat lung 

mast cells did not induce histamine-releasing activity in 

rats. 

No change in body weight or absolute or relative lung 

weight in any exposed group compared to controls. Number 

of lung cells or mast cells not significantly different in 

exposed animals compared to controls. Increasing trend 

observed in IgE-mediated histamine release from lung mast 

cells, the change becoming significant only at 4.0 ppm. No 

significant change in ionophore A2318-stimulated histamine 

release in exposed groups compared to controls. Data 

suggest NO2 exposure in guinea pigs enhanced histaminereleasing activity in lung mast cells. Thus, species 

differences exist between the rat and guinea pig in 

response to induction of histamine-releasing activity 

following NO2 exposure. 

Fujimaki and 

Nohara (1994) 

5 

25 

6 h/day for 1, 3, or 

5 days 

NR NR Mouse 

(C57BL/6) 

Exposure to 25 ppm, but not 5 ppm, NO2 induced acute 

lung injury (characterized by increases in protein, LDH, 

macrophages, and neutrophils recovered by 

bronchoalveolar lavage) that peaked after 3 days, lesions 

within terminal bronchioles, and AHR. OVA-sensitized 

animals exposed to 25 ppm, but not 5 ppm, NO2 showed 

augmentation of eosinophilic inflammation and terminal 

bronchiolar lesions, which extended significantly into the 

alveoli. There was no increased expression of mucus cellassociated gene products in non-sensitized or OVAsensitized animals exposed at any concentration. 

Poynter et al. 

(2006) 

5-HETE = 5-Hydroxyeicosatrtraenoate 

AM = Alveolar macrophage 

AHR = AHR = Airway hyperresponsiveness 

BAL = Bronchoalveolar lavage 

BHR = Bronchopulmonary hyperreactivity 

IFNγ = Interferon γ

IgA = Immunoglobulin A 

IgE = Immunoglobulin E 

IgG = Immunoglobulin G 

IL-5 = Interleukin-5 

LDH = Lactate dehydrogenase 

LTB4 = Leukotriene B4 

OVA = Ovalbumin 

PGE2 = Prostaglandin E2 

PMA = Phorbol myristate acetate 

ROS = Reactive oxygen species 

SRawo = Specific airway resistance 

TNFα = Tumor necrosis factor α

TxB2 = Thromboxane B2 

ZAS = Zymosan-activated rat serum 
4-28

Table AX4.6. Infectious agents. 

ppm EXPOSURE GENDER AGE SPECIES 

(STRAIN) 

INFECTIVE 

AGENT EFFECTS REFERENCES 

0.05 

base 

+ 0.1 

peak 

No effect. 

0.5 

+ 

peak 

Increased mortality 

1.2 

base 

+ 

2.5 

peak 

Continuous, base 

+ twice/day 1-h 

peaks, 5 days/wk 

for 15 days 

F NR Mouse 

(CD-1) 

Streptococcus sp. 

Increased mortality 

Gardner (1980, 

1982) 

Graham et al. 

(1987) 

0.2 

base 

+ 0.8 

peak 

23 h/day, 7 

days/wk base+ 

twice daily 1-h 

peaks, 5 days/wk 

for 1 yr 

F 6-8 

wks 

Mouse 

(CD-1) 

Streptococcus sp. Peak plus baseline caused significantly 

greater mortality than baseline. 

Miller et al. (1987) 

0.3-

0.5 

Continuous, 3 

mos 

Continuous, 6 

mos 

F 4 wks Mouse 

(ICR:JCL) 

A/PR/8 virus High incidence of adenomatous 

proliferation peripheral and bronchial 

epithelial cells; NO2 alone and virus alone 

caused less severe alterations. 

No enhancement of effect of NO2 and 

virus. 

Motomiya et al. 

(1973) 

0.5 Intermittent, 6 or 

18 h/ day, up to 

12 mos 

Continuous, 

90 days 

F NR Mouse 

(Swiss) 

K. pneumoniae Increased mortality after 6 mos 

intermittent exposure or after 3, 6, 9, or 

12 mos continuous exposure, increased 

mortality was significant only in 

continuously exposed mice. 

Ehrlich and Henry 

(1968) 

0.5-

1.0 

10 

Continuous, 

39 days 

2 h/day, 1, 3, and 

5 days 

F NR Mouse (ICR, 

dd) 

A/PR/8 

virus 

Increased susceptibility to infection. Ito (1971) 

0.5-28 Varied F NR Mouse (CD1) Streptococcus sp. Increase mortality with increased time 

and concentration; concentrations is 

more important than time. 

Gardner et al. 

(1977 a,b) 

Coffin et al. (1977)

0.5 3 h/day, 3 mos F 6-8 

wks 

Mouse (CD2 

F1, CD-1) 

Streptococcus sp. Increase in mortality with reduction in 

mean survival time. 

Ehrlich et al. 

(1979) 

0.5 

1.0 

1.5 

5.0 

24 h/day, 

7 days/wk, 

3 mos 

3 days 

F NR Mouse (CF1) K. pneumoniae Significant increase in mortality after 3-

day exposure to 5.0 ppm; no effect at 

other concentrations, but control mortality 

very high. 

McGrath and 

Oyervides (1985) 

0.5 

1.0 

2.0 

5.0 

4 h M/F 8-10 

wks 

Mouse 

(C57BL/6N) 

Mycoplasma 

pulmonis 

Decrease in intrapulmonary killing only at 

5.0 ppm. 

Davis et al. (1991, 

1992) 
4-29

ppm EXPOSURE GENDER AGE SPECIES 

(STRAIN) 

INFECTIVE 

AGENT EFFECTS REFERENCES 

1.0 

2.3 

6.6 

17 h M NR Mouse 

(Swiss) 

S. aureus after 

exposure 

No difference in number of bacteria 

deposited, but at the two highest 

concentrations, there was a decrease in 

pulmonary bactericidal activity of 6 and 

35%, respectively; no effect at 1.0 ppm 

Goldstein et al. 

(1974) 

1.0 

2.5 

5.0 

10.0 

4 h F NR Mouse 

(Swiss) 

S. aureus Injection with corticosteroids increased 

NO2-induced impairment of bactericidal 

activity at ≥2.5 ppm. 

Jakab (1988) 

1.0 48 h M NR Mouse 

(Swiss 

Webster) 

Streptococcus sp. 

S. aureus 

Increased proliferation of Streptococcus 

in lung of exposed mice but no effect with 

S. aureus. 

Sherwood et al. 

(1981) 

1.0 

3.0 

3 h F 5-6 

wks 

Mouse (CD1) Streptococcus sp. Exercise on continuously moving wheels 

during exposure increased mortality at 

3.0 ppm. 

Illing et al. (1980) 

1.0 

2.5 

5.0 

6 h/day, 6 days NR 4-6 

wks 

Mouse 

(CD-1) 

Cytomegalovirus Increase in virus susceptibility at 5.0 ppm 

only. 

Rose et al. (1988, 

1989) 

1.5- 

50 

2 h NR NR Mouse (NR) 

Hamster 

(NR) 

Monkey 

(Squirrel) 

K. pneumoniae Increased mortality in mice, hamsters, 

and monkeys at ≥3.5, ≥35, and 50 ppm 

NO2, respectively 

Ehrlich (1980) 

1.5 After 1 wk, mortality with continuous 

exposure was greater than that for 

intermittent after 2 wks, no significant 

difference between continuous and 

intermittent exposure. 

3.5 

Continuous or 

intermittent, 7 

h/day, 7 days/wk, 

up to 15 days 

F NR Mouse 

(CD-1) 

Streptococcus sp. 

Increased mortality with increased 

duration of exposure; no significant 

difference between continuous and 

intermittent exposure; with data adjusted 

for total difference in C Η T, mortality 

essentially the same. 

Gardner et al. 

(1979) 

Coffin et al. (1977)

1.5 

base 

+ 4.5 

peak 

Mortality increased with 3.5- and 7-h 

single peak when bacterial challenge was 

after an 18 h baseline exposure. 

4.5 

Continuous 64 h, 

then peak for 1, 

3.5, or 7 h, then 

continuous 18 h 

base 

1, 3.5, or 7 h 

F NR Mouse (CD1) Streptococcus sp. 

Mortality proportional to duration when 

bacterial challenge was immediate, but 

not 18 h postexposure. 

Gardner (1980) 

Gardner (1982) 

Graham et al. 

(1987) 

1.5 7 h/day, 4, 5, and 

7 days 

NR NR Mouse (NR) Streptococcus sp. Elevated temperature (32°C) increased 

mortality after 7 days. 

Gardner (1982) 

1.9 

3.8 

7.0 

9.2 

14.8 

4 h M NR Mouse (NR) S. aureus Physical removal of bacteria unchanged 

by exposure. Bactericidal activity 

decreased by 7, 14, and 50%, 

respectively, in three highest NO2-

exposed groups. 

Goldstein et al. 

(1973) 

1.5- 

5.0 

3 h F 6-10 

wks 

Mouse (CF1, CD2F1) Streptococcus sp. Increased mortality in mice exposed to 

≥2.0 ppm 

Ehrlich et al. 

(1977) 

Ehrlich (1980) 
4-30

ppm EXPOSURE GENDER AGE SPECIES 

(STRAIN) 

INFECTIVE 

AGENT EFFECTS REFERENCES 

1.5 

2.5 

3.5 

5.0 

10 

15 

2 h NR 6-8 

wks 

Mouse 

(Swiss 

Webster) 

K. pneumoniae No effect at 1.5 or 2.5 ppm; increased 

mortality at 3.5 ppm and above. Increase 

in mortality when K. pneumoniae 

challenge 1 and 6 h after 5 or 10 pm NO2

exposure; when K. pneumoniae 

challenge 27 h following NO2 exposure, 

effect only at 15 ppm. 

Purvis and Ehrlich 

(1963) 

Ehrlich (1979) 

2.0 1.5 h/day, 

5 days/wk for 1, 

2, and 3 wks 

NR 2 wks Hamster 

(Golden 

Syrian) 

(in vitro) 

A/PR/8/34 

influenza virus 

Peak virus production in tracheal 

explants occurred earlier. 

Schiff (1977) 

2.5 

4.0 

5.0 

10 

15 

4 h F NR Mouse 

(Swiss) 

S. aureus, Proteus 

mirabilis, 

Pasteurella 

pneumotropica 

Concentration-related decrease in 

bactericidal activity at ≥ 4.0 ppm with S. 

aureus when NO2 exposure after 

bacterial challenge; when NO2 exposure 

was before challenge, effect at 10 ppm; 

NO2 concentrations >5.0 ppm required to 

affect bactericidal activity for other tested 

microorganisms. 

Jakab (1987, 

1988) 

5.0 

10 

Continuous, 

2 mos 

Continuous, 1 mo 

M NR Monkey 

(Squirrel) 

K. pneumoniae or 

A/PR/8 influenza 

virus 

Increased viral-induced mortality (1/3). 

Increase in Klebsiella-induced mortality 

(2/7); no control deaths. 

Increased virus-induced mortality (6/6) 

within 2-3 days after infection; no control 

deaths. Increase in Klebsiella-induced 

mortality (1/4), no control deaths. 

Henry et al. 

(1970) 

5.0 

10 

4 h M/F 6-10 

wks 

Mouse 

(C57BL6N, 

C3H/HeN) 

Mycoplasma 

pulmonis 

NO2 increased incidence and severity of 

pneumonia lesions and decreased the 

number of organisms needed to induce 

pneumonia; no effect on physical 

clearance; decreased mycoplasmal killing 

and increased growth; no effect on 

specific IgM in serum; C57B1/6N mice 

generally more sensitive than C3H/HeN 

mice. At 10 ppm, one strain (C57B1/6N) 

of mice had increased mortality. 

Parker et al. 

(1989) 

10 

15 

35 

50 

2 h M/F NR Monkey 

(Squirrel) 

K. pneumoniae Clearance of bacteria from lungs of 10-, 

15-, and 35-ppm groups delayed or 

prevented. All three animals in highest 

exposed group died. 

Henry et al. 

(1969) 

5 NR NR NR Mice (NR) Parainfluenza 

(murine sendei 

virus) 

Altered the severity but not the course of 

the infection 

Jakab (1988) 

Source: Modified from U.S. Environmental Protection Agency (1993). 
4-31

Table AX4.7. Lung structure. 

ppm EXPOSURE GENDER AGE SPECIES 

(STRAIN) EFFECTS REFERENCE 

3 

9 

6 h/day, 6 days/wk, 

for 2 wks 

F NR Guinea Pig Morphological changes observed at both concentrations. 

Ciliated cells displayed pathological changes such as 

cytoplasmic vacuolation and protrusion at 3 ppm, but 

eosinophils displayed normal morphology. Minor to major 

changes in epithelial cells (decreased number of specific 

granules, cytoplasmic vacuolization, and morpholigcal changes 

in specific granules) and ciliated cells showed compound cilia, 

cytoplasmic vacuolization, and sloughing) at 9 ppm. 

Ohashi et al. 

(1994) 

5 

25 

6 h/day for 1, 3, or 5 

days 

NR NR Mouse 

(C57BL/6) 

No lung inflammation or injury observed at 5 ppm at any time, 

compared to controls. 25 ppm NO2 induced acute lung injury 

(characterized by increases in protein, LDH, macrophages, and 

neutrophils recovered by BAL) at 25 ppm that peaked after 

3 days, lesions within terminal bronchioles, and AHR. Another 

group of mice exposed for 5 days to 25 ppm and allowed to 

recover in room air for 20 days demonstrated resolution of the 

pattern of acute lung injury in these animals. 

Poynter et al. 

(2006) 

5 

10 

20 

24 h/day, for 3 days M NR Rat 

(SpragueDawley) 

Significant alteration in morphology of Clara cells (loss of apical 

intra-luminal projects and damaged epithelium covered by a 

layer of CC10-reactive material) at ≥5 ppm. 

Barth and Muller 

(1999) 

5 

10 

20 

24 h/day, for 25 

days 

M NR Rat 

(SpragueDawley) 

No significant alteration of morphology of Clara cells compared 

to controls. 

Barth and Muller 

(1999) 

5 

10 

20 

24 h/day, for 25 

days 

M NR Rat 

(SpragueDawley) 

Exposure to 5 ppm showed no significant qualitative changes of 

the lung tissue, but animals exhibited slight fibrosis of the 

centroacinar alveolar septa, respiratory bronchioli, and 

interstitium, and irregularly shaped alveolar spaces at ≥10 ppm. 

Morphometric analysis showed significantly diminished alveolar 

surface density at ≥10 ppm. Suggests development of 

emphysema at ≥10 ppm. The avg medial thickness of the 

pulmonary artery was significantly increased at ≥10 ppm, but at 

5 ppm, this parameter was significantly decreased, compared 

to controls. Authors reported negative correlation between avg 

medial thickness and alveolar surface density. 

Barth et al. 

(1995) 

5 

10 

20 

24 h/day, for 3 days M NR Rat 

(SpragueDawley) 

Histopathology revealed structural alterations extending from 

slight interstitial edema after exposure to 5 ppm, to epithelial 

necrosis and interstitial inflammatory infiltration after exposure 

to 10 ppm, and an additional intra-alveolar edema after 20 ppm. 

Light microscopic examination did not confirm the qualitative 

histological changes, particularly muscularization of intra-acinar 

vessels. Exposure to ≥10 ppm for 25 days caused emphysema 

and slight centrilobular interstitial fibrosis. 

Morphometric analysis showed significantly diminished alveolar 

surface density at 10 ppm after 25 days of exposure and at 

20 ppm after 3 and 25 days of exposure. Avg medial thickness 

of the pulmonary artery significantly increased at ≥10 ppm, but 

at 5 ppm, this parameter was significantly decreased both 

during after 3-day and 25-day exposures, compared to controls. 

Authors regarded the decrease in the medial thickness at 

5 ppm as reflecting a reduction of pulmonary arterial mass and 

not as a result of vasodilation. Avg medial thickness and 

alveolar surface density were negatively correlated. Study 

indicates exposures to as low as 5 ppm is not likely to induce 

structural changes in the lung of rats. Effect on the 

morphometry of the alveolar region appeared to be timedependent, since significant changes were seen at 10 ppm 

after 25 days, but only at 20 ppm in the 3-day exposure study. 

Barth et al. 

(1995) 
4-32

ppm EXPOSURE GENDER AGE SPECIES 

(STRAIN) EFFECTS REFERENCE 

10 

20 

14 h/day, for 15 

days, 20 or 25 days 

M NR Mouse 

(C57/BL/5) 

Initial dose response experiment identified 20 ppm NO2 as 

concentration causing lung injury and air inflammation (marked 

influx of inflammatory cells in the airways, predominated by 

macrophages and neutrophils and to a lesser extent by 

lymphocytes) for exposure that lasted 15 days, whereas 

10 ppm did not induce significant increase in leukocyte amount 

in BAL fluid. Actual study using 20 ppm NO2 observed induction 

of air space enlargement (evidenced by a significant increase in 

mass-specific lung volume and volume-weighted alveolar 

volume). No significant changes in total alveolar surface area. 

Wegman and 

Herz (2002) 

0.8 

5 

10 

24 h/day, for 1 or 3 

days 

M NR Rat 

(SpragueDawley) 

Significant increase in Type II cell proliferation (evidenced by 

increases in AgNOR-number and BrdU-LI) after exposure to 

5 ppm NO2 for 3 days and 10 ppm for 1 and 3 days. Significant 

increase in bronchiolar epithelial proliferation (increases in 

AgNOR-number and BrdU-LI) at ≥0.8 ppm for 1 and 3 days. In 

the bronchial epithelium, statistically significant increase in 

proliferation as increase in AgNOR-number at 10 ppm only after 

3 days of exposure and as increase in BrdU-LI after exposure 

to 5 and 10 ppm for ≥1 day. Results showed highest rate of 

epithelial proliferation in the bronchiolar epithelium compared to 

bronchial epithelium and Type II cells. Study indicates cell 

proliferation changes beginning at concentrations as low 

0.8 ppm NO2 following a single day of exposure. 

Barth et al. 

(1994a) 

5 

10 

20 

24 h/day, for 3 or 25 

days 

M NR Rat 

(SpragueDawley) 

Compared to controls, proliferative activity (evidenced by 

increase in AgNOR-number) significantly increased, but with no 

concentration-dependence in respiratory bronchiolar epithelium 

at 5 ppm and above after 3-day exposure, but the increase was 

concentration-dependent, with significance at ≥10 ppm 

following 25-day exposure. Activity was increased in a 

concentration-dependent manner in bronchial epithelium with 

significance only at 20 ppm after 3 days of exposure and at 

≥10 ppm following 25 days of exposure. 

Concentration-dependent thickening of alveolar septa and 

bronchiolar walls, significant only at ≥10 ppm after 3- and 25-

day exposure, but 5 ppm caused significant thickening only in 

bronchiolar walls after 25 days, but not 3 days, of exposure 

compared to controls. 

Statistically significant reduction in alveoli surface density at 

≥10 ppm after 25 days of exposure and at 20 ppm only after 3 

days of exposure, while alveolar circumference increased 

statistically significantly at ≥10 ppm after 25 days of exposure 

and at 20 ppm following 3 days of exposure. 5 ppm did not 

significantly affect these endpoints after 3 or 25 days of 

exposure compared to controls. Data suggest concentrationand exposure duration-dependent development of emphysema, 

significant only at ≥10 ppm, but not at 5 ppm, after 3 or 25 days 

of exposure. 

Alveolar duct length increased significantly at ≥10 ppm after 25 

days and at 20 ppm after 3 days of exposure; no effect at 

5 ppm. 

Radial alveolar count values decreased significantly at 20 ppm 

after 3 and 25 days and at 10 ppm after 25 days exposure; no 

effect at 5 ppm. 

Increase in avg medial thickness at 10 ppm with significance 

only after 25 days of exposure and at 20 ppm with significance 

after 3 and 25 days of exposure. However, 5 ppm caused 

significant decrease at both 3 and 25 days of exposure. Authors 

suggested effect at 5 ppm was due to vasodilation induced by 

nitrogen oxide. 

Barth et al. 

(1994b) 
4-33

ppm EXPOSURE GENDER AGE SPECIES 

(STRAIN) EFFECTS REFERENCE 

25 

50 

75 

100 

150 

200 

250 

5, 15, or 30 min 

2, 5, 15, or 30 min 

2, 5, or 15 min 

M NR Rat 

(Fischer344) 

Animals exposed to ≥200 ppm NO2 for 30 min died within 24 h 

after exposure. 

NO2 exposure induced proportional increases in LWW, 

indicative of pulmonary edematous responses, over the same 

exposure period after the 15-min exposures. Animals exposed 

to 25 ppm NO2 did not produce observable lung injury (i.e., 

occurrence of alveolar fibrin and Type II cell hyperplasia) on 

exposure for 5 min. Animals exposed to all exposure 

concentrations for 15 min showed alveolar fibrin, while Type II 

hyperplasia occurred at 50 ppm and its level of expression 

correlated with exposure concentration. After 30 min of 

exposure, the occurrence of fibrin increased as a function of 

exposure concentration, while over a concentration range of 

25-150 ppm NO2, the Type II cell hyperplastic response 

increased with increasing exposure concentration. Data 

suggest exposure concentration was evidently more important 

than exposure time in terms of causing lung injury when high 

concentrations of NO2 are inhaled. 

Lehnert et al. 

(1994) 

3.6 

7.2 

10.8 

14.4 

24 h, 12 h, 8 h, and 

6 h, respectively, 

for 3 days, giving a 

C Η T of 86.4 ppm h 

M NR Rat 

(SpragueDawley) 

Short-term exposure was not sufficient to produce significant 

type I alveolar cell necrosis or a significant migration of 

inflammatory cells across the interstitium and alveolar 

epithelium. 

Rajini et al. 

(1993) 

0.5 

base + 

1.5 

peak 

Base presumably 

continuous, two 1-h 

peaks/day, for 9 wks 

M 7 

wks 

Rat 

(Fischer344) 

No significant differences in thickness of the alveolar septal 

components, between controls and exposed group. 

Analysis of parenchymal cell populations showed no significant 

differences in the avg volumes of different cell types or in their 

surface areas. Total number of fenestrae in the lungs of NO2-

exposed animals occurred at a greater frequency than in 

controls, but no significant alterations were found in the 

connective tissue matrix or interstitial cell population, 

suggesting that connective tissue matrix and interstitial cells of 

the lung parenchyma did not undergo significant degeneration 

on exposure to the low level of NO2 used in this study. 

Mercer et al. 

(1995) 

AgNOR = Silver-stainable nucleolar organizer regions 

AHR = Airway hyperresponsiveness 

BAL = Bronchoalveolar lavage 

BrdU-LI = Bromodeoxyuridine-laebling index 

LDH = Lactate dehydrogenase 

LWW = Lung wet weight 
4-34

Table AX4.8. Pulmonary function. 

ppm EXPOSURE GENDER AGE SPECIES 

(STRAIN) EFFECTS REFERENCE 

0.5 

1.5 

0.5 ppm background 

level for 16 h, a 6-h 

exposure spike, and a 2-

h downtime; profile was 

run each day for 1, 3, 

13, 52 or 78 wks 

M 67 

days 

Rat 

(Fischer344) 

No NO2 related-effects on body weight or pulmonary 

function (total lung capacity, vital capacity, residual volume 

(difference between total lung capacity and vital capacity), 

respiratory system compliance, single-breath diffusing 

capacity of carbon monoxide, slope of nitrogen wash-out 

curve, and end-expiratory volume) observed following 13-, 

52-, or 78-wk exposure. NO2-related effects on small 

airway function [FVC, peak flow, flow at 50% (FEF 50%), 

25% (FEF 25%) and 10% (FEF 10%) of FVC] evaluated at 

52- or 78-wk exposure or at 26- and 17-wks postexposure 

were not significantly different from controls. However, 

breathing patterns and mechanics (tidal volume, expiratory 

resistance, inspiratory and expiratory time) were generally 

greater and FOB was significantly slower in NO2-exposed 

animals compared to controls at all of the time points 

evaluated. 

Tepper et al. 

(1993) 

0.5 

base 

+ 1.5 

peak 

Base presumably 

continuous, two 1-h 

peaks/day, for 9 wks 

M 7 wks Rat 

(Fischer344) 

No significant differences in lung volume, total air volume 

of the lungs, total lung tissue volume, surface area, body 

weight, or thickness of the alveolar septal components, 

between controls and exposed group. 

Analysis of parenchymal cell populations showed no 

significant differences in the avg volumes of different cell 

types or in their surface areas. Total number of fenestrae in 

the lungs of NO2-exposed animals occurred at a greater 

frequency than in baseline controls, but no significant 

alterations were found in the connective tissue matrix or 

interstitial cell population, suggesting that connective tissue 

matrix and interstitial cells of the lung parenchyma did not 

undergo significant degeneration on exposure to the low 

level of NO2 used in this study. 

Mercer et al. 

(1995) 

10 

20 

14 h/day, for 15 days, 20 

or 25 days 

M NR Mouse 

(C57/BL/6) 

20 ppm NO2 induced development of progressive airflow 

obstruction (evidenced by decreases in midexpiratory 

airflow, breathing frequency and tidal volume, with 

statistical significance only at day 25 of exposure). 

Wegman and 

Herz (2002) 

FVC = Forced vital capacity 

FOB = Frequency of breathing 

Table AX4.9. Hematological parameters. 

ppm EXPOSURE GENDER AGE SPECIES 

(STRAIN) EFFECTS REFERENCES 

0.05 Continuous 90 days NR NR Rat No effect on blood hemoglobin or RBCs. Shalamberidze 

(1969) 

0.36 1 wk NR NR Guinea Pig Increase of red blood cell D-2,3-diphosphoglycerate Mersch et al. (1973) 

0.5-

0.8 + 

Continuous 1 to 

1.5 mos 

M/F 4 wks Mouse 

(ICR:JCL) 

Addition of 50 ppm CO to NO2 failed to affect 

carboxyhemoglobin. 

Nakajima and 

Kusumoto (1970) 

0.8 Continuous, 5 days M 7 wks Mouse (ICR) No effect on methemoglobin. Nakajima and 

Kusumoto (1968) 

1.0 Continuous, 

16 mos 

M NR Monkey 

(Squirrel) 

No effect on hematocrit or hemoglobin with NO2 and 

influenza exposure. 

Fenters et al. (1973) 

1.0 

5.0 

Continuous, 

18 mos 

M NR Dog (Mongrel) No changes in hemoglobin or hematocrit. Wagner et al. (1965) 
4-35

ppm EXPOSURE GENDER AGE SPECIES 

(STRAIN) EFFECTS REFERENCES 

1-30 18 h NR NR Mouse (NR) Concentration-related increase in methemoglobin and 

nitrosylhemoglobin 

Case et al. (1979) 

1.3-

3.0 

2 h/day, 15 and 

17 wks 

NR NR Rabbit (NR) Decreased RBCs. Mitina (1962) 

2.0 Continuous, 

14 mos 

M/F 

M 

NR Monkey 

(Macaca 

speciosa) 

Rat (SpragueDawley) 

With or without NaCl (330 μg/m3): polycythemia with 

reduced mean corpuscular volume and normal mean 

corpuscular hemoglobin. 

Furiosi et al. (1973) 

2.0 Continuous, up to 

6 wks 

M 8 wks Rat 

(Wistar) 

No effect on hemoglobin, hematocrit or RBC count; no 

methemoglobin was observed. 

Azoulay et al. (1978) 

4.0 1-10 days NR NR Rat 

(NR) 

Increase in RBC sialic acid. Kunimoto et al. 

(1984) 

4.0 NR NR NR NR Decrease in RBCs. Mochitate and Miura 

(1984) 

5-40 1 h F 4 mos Mouse 

(JCL:ICR) 

No increase in methemoglobin. Increased nitrite and 

especially nitrate. 

Oda et al. (1981) 

10 2 h/day, 5 days/wk, 

up to 30 wks 

F 6-8 

wks 

Mouse 

(BALB/c) 

Small decrease in hemoglobin and mean corpuscular 

hemoglobin concentration. 

Holt et al. (1979) 

Source: Modified from U.S. Environmental Protection Agency (1993). 

Table AX4.10. Iron, enzymes, and nucleic acids. 

EFFECT REFERENCE 

Sodium nitroprusside (NO donor) mobilizes iron from ferritin Reif and Simmons (1990) 

Modulation of arachidonic acid metabolism via interference with iron Kanner et al. (1991, 1992) 

Inhibition of aconitase (an enzyme in the Krebs cycle, and also complex 1 and 2 of the respiratory chain) Hibbs et al. (1988) 

Persson et al. (1990) 

Stadler et al. (1991) 

Permanent modification of hemoglobin, possibly via deamination Moriguchi et al. (1992) 

Deamination of DNA Wink et al. (1991) 

DNA strand breaks Nguyen et al. (1992) 

Inhibition of DNA polymerase and ribonucleotide reductase Lepoivre et al. (1991) 

Kwon et al. (1991) 

Antimitogenic; inhibition of T cell proliferation in rat spleen cells Fu & Blankenhorn (1992) 

Inhibition of DNA synthesis, cell proliferation, and mitogenesis in vascular tissue Nakaki et al. (1990) 

Inhibition of mitogenesis and cell proliferation (vascular smooth muscle cells) Garg and Hassid (1989) 

Adenosine diphosphate ribosylation is stimulated by NO-generating agents Nakaki et al. (1990) 
4-36

Table AX4.11. Genotoxicity in vitro and in plants. 

TEST 

ORGANISM ENDPOINT EXPOSURE COMMENTS RESULTS REFERENCE 

Salmonella TA100 Mutations 6-10 ppm, 40 mins + Isomura et al. 

(1984) 

Salmonella TA100 Mutations 10-15 ppm, 6 h Concentrations >10 ppm were bacteriotoxic + Victorin and 

Ståhlberg (1988) 

Salmonella TA100 

and TA102 

Mutations Bubbling of 10-90 ppm 

through bact. susp., 

30 mins as above 

 - Kosaka et al. 

(1985) 

Salmonella TA100 SOS repair Bubbling of 10-90 ppm 

through bact. susp., 

30 mins 

Effect not considered solely attributed to 

nitrite in suspension. No effect seen with NO 

gas. 

+ Kosaka et al. 

(1985) 

E. coli, WP2 Mutations Bubbling of 10-90 ppm 

through bact. susp., 

30 mins 

 + Kosaka et al. 

(1986, 1987) 

E. coli SOS repair Bubbling of 10-90 ppm 

through bact. susp., 

30 mins 

 + Kosaka et al. 

(1986, 1987) 

Bacillus subtilis 

spores 

Mutations 500 ppm, 2-3 h + Sasaki et al. 

(1980) 

V79 hamster cells Chromatid-type 

aberrations, SCE 

10-100 ppm, 10 mins Effect shown not to be solely due to nitric 

acid or nitrite. No effect if cells not washed 

with Hank’s salt solution prior to exposure 

+ Tsuda et al. (1981) 

V79 hamster cells SCE 2-3 ppm, 10 mins + Shiraishi and 

Bandow (1985) 

Don hamster cells Mutations (8-

G resistance) 

2-3 ppm, 10 mins Slight response - Isomura et al. 

(1984) 

V79 hamster cells DNA single-strand 

breaks 

10 ppm, 20 mins Effect not due to formation of nitrite + Görsdorf et al. 

(1990) 

Tradescantia Micronuclei in 

pollen 

5 ppm, 24 h + Ma et al. (1982) 

Tradescantia Mutations in 

stamen hair 

50 ppm, 6 h + Schairer et al. 

(1979) 

Source: Victorin (1994). 
4-37

Table AX4.12. Genoticity in vivo. 

TEST ORGANISM ENDPOINT EXPOSURE RESULT REFERENCE 

Drosophila Recessive lethals 500-7000 ppm, 1 h - Inoue et al. (1981) 

Drosophila Somatic mutations (wing spot test) 50-280 ppm, 2 days - Victorin et al. (1990) 

Rats Mutations in lung cells (oubain res.) 50-560 ppm, >12 days + Isomura et al. (1984) 

Rats Chromosome aberrations in lung cells 27 ppm, 3 h + Isomura et al. (1984) 

Mice Chromosome aberrations in lymphocytes and spermatocytes 0.1-10 ppm, 6 h - Gooch et al. (1977) 

Mice Micronuclei in bone marrow 20 ppm, 23 h - Victorin et al. (1990) 

Source: Victorin (1994). 

Table AX4.13. Genotoxicity. 

TEST 

ORGANISM ENDPOINT EXPOSURE RESULT REFERENCE 

Salmonella TA100 Mutations 25-30 ppm, 40 min + Isomura et al. (1984) 

Salmonella SOS repair Bubbling of 10-90 ppm - Kosaka et al. (1985) 

Don hamster cells Mutations (8-AG resistance) 2-3 ppm, 10 min + Isomura et al. (1984) 

V79 hamster cells DNA single-strand breaks 500 ppm, 30 min - Görsdorf et al. 

(1990) 

TK 6 human cells Mutations, DNA single-strand 

breaks 

Injection of 0.12-0.38 ml NO gas/ml of culture medium, 

1 h 

+ Nguyen et al. (1992) 

Salmonella TA1535 Mutations 30 min to 5-90 ppm + Arroyo et al. (1992) 

Rats Mutations in lung cells (oubain 

res.) 

27 ppm, 3 h - Isomura et al. (1984) 

Source: Victorin (1994); Arroyo et al. (1992) added. 
5-1 

Annex 5. Clinical Studies: 

Exposure to NOX

AX5.1. Introduction 

This annex summarizes the effects of NOX on human volunteers exposed under controlled 

conditions. The goal is to review the scientific literature on human clinical studies of NOX exposure 

published since the 1993 NOX Air Quality Criteria Document (AQCD) (EPA 1993). The primary focus 

was on NO2 because it is the most abundant NOX species in the atmosphere and there are few human 

studies of exposure to other NOX species. 

Clinical summary conclusions from the 1993 AQCD are provided below: 

 NO2 causes decrements in lung function, particularly increased airway resistance in healthy 

subjects at concentrations exceeding 2.0 ppm for 2 h. 

 NO2 exposure results in increased airway responsiveness in healthy, nonsmoking subjects 

exposed to concentrations exceeding 1.0 ppm for 1 hour or longer. 

 NO2 exposure at levels above 1.5 ppm may alter numbers and types of inflammatory cells in the 

distal airways or alveoli, but these responses depend upon exposure concentration, duration, and 

frequency. NO2 may alter function of cells within the lung and production of mediators that may 

be important in lung host defenses. 

 NO2 exposure of asthmatics causes, in some subjects, increased airway responsiveness to a 

variety of provocative mediators, including cholinergic and histaminergic chemicals, SO2 and 

cold air. However, the presence of these responses appears to be influenced by the exposure 

protocol, particularly whether or not the exposure includes exercise. 

 Modest decrements in spirometric measures of lung function (3 to 8%) may occur in some 

asthmatics and COPD patients under certain NO2 exposure conditions. 

 Nitric acid levels in the range of 50 to 200 ppb may cause some pulmonary function responses in 

adolescent asthmatics, but not in healthy adults. Other commonly occurring NOX species do not 

appear to cause any pulmonary function responses at concentrations expected in the ambient 

environment, even at higher levels than in worst-case scenarios. However, not all NOX acid 

species have been studied sufficiently. 

 No association between lung function responses and respiratory symptom responses were 

observed. Furthermore, there is little evidence of a concentration-response relationship for 

changes in lung function, airway responsiveness, or symptoms at the NO2 levels that are reviewed 

here. 

In the summary and integration chapter of the 1993 NOX criteria document, one of the key health 

effects at near ambient concentrations of NO2 was increased airway responsiveness or 

hyperresponsiveness in asthmatic individuals after short-term exposures. The 1993 AQCD notes the 

absence of a concentration-response relationship for NO2 exposure and airway responsiveness in 

asthmatics. For example, most responses to NO2 that had been observed in asthmatics occurred at 

concentrations between 0.2 and 0.5 ppm. However, other studies showed an absence of effects on airway 

responsiveness or hyperresponsiveness at much higher concentrations, up to 4 ppm. Since 1993, 

additional studies have suggested that exposure to low concentrations of NO2, either alone or in 
5-2 

combination with other pollutants such as SO2, may enhance allergen responsiveness in asthmatic 

subjects. 

In the years since the preparation of the 1993 AQCD, many studies from a variety of disciplines 

have convincingly demonstrated that exposure to particulate air pollution increases the risk for 

cardiovascular events. In addition, a number of epidemiological studies have shown associations between 

ambient NO2 levels and adverse cardiovascular outcomes, at concentrations well below those shown to 

cause respiratory effects. However, to date there remain very few clinical studies of NO2 that include 

endpoints relevant to cardiovascular disease. 

AX5.1.1. Considerations in Controlled Human Clinical Studies 

Human clinical studies attempt to engineer laboratory atmospheric conditions relevant to ambient 

pollutant atmospheres, with careful control of concentrations, duration, timing, and other conditions 

which may impact responses. These studies provide the opportunity to measure symptoms and 

physiological markers of health effects that result from breathing the atmospheres. The carefully 

controlled environment allows investigators to identify responses to individual pollutants, to characterize 

exposure-response relationships, to examine interactions among pollutants, and to study the effects of 

other variables such as exercise, humidity, or temperature. Susceptible populations can participate, 

including individuals with acute and chronic respiratory and cardiovascular diseases, with appropriate 

limitations based on subject comfort and protection from risk. Endpoint assessment traditionally has 

included symptoms and pulmonary function, but more recently a variety of markers of pulmonary, 

systemic, and cardiovascular function have been used to assess pollutant effects. 

Human clinical studies have limitations. For practical and ethical reasons, studies must be limited to 

relatively small groups, to short durations of exposure, and to pollutant concentrations that are expected to 

produce only mild and transient responses. Findings from the short-term exposures in clinical studies may 

provide limited insight into the health effects of chronic or repeated exposures. 

Specific issues of protocol design in human clinical studies have been reviewed (Frampton, 

Pietropaoli et al. 2006), and will not be considered further here, except in the context of specific studies of 

NO2 exposure described in the following pages. 

In clinical studies, humans are the species of interest, so findings have particular relevance to risk 

assessment. However, the utility of clinical studies in risk assessment is tempered by the obvious need to 

avoid adverse health effects of the study itself. This usually means selecting subjects that are not the most 

susceptible to the pollutant being studied. Furthermore, clinical studies depend on outcome markers with 

variable relevance or validation as markers of true health effects. The statement from the American 

Thoracic Society, “What constitutes an adverse health effect?” (American Thoracic Society 2000) 

addresses issues relevant to selection and interpretation of outcome markers in clinical studies. 

The 1993 NOX AQCD included a description of key outcome measures that had been in use up to 

that date. These included primarily respiratory outcomes, including pulmonary function tests such as 

spirometry, lung volumes, and airway resistance, and tests of pulmonary clearance of inhaled aerosols. A 

brief description of bronchoalveolar lavage was also included, which had come into use prior to 1993 to 

assess airway inflammation and changes in the epithelial lining fluid in response to NO2 exposure. 

AX5.2. Effects of NO2 in Healthy Subjects 

Table AX5.2-1 summarizes the key clinical studies of NO2 exposure in healthy subjects since 1993, 

with a few key studies included prior to that date. 
5-3 

AX5.3. Effects of NOX Exposure in Sensitive Subjects 

Table AX5.3-1 summarizes studies of potentially sensitive subjects. The potential for NO2 exposure 

to enhance responsiveness to allergen challenge in asthmatics deserves special mention. Several recent 

studies, summarized in Table AX5.3-2, have reported that low-level exposures to NO2, both at rest and 

with exercise, enhance the response to specific allergen challenge in mild asthmatics. 

These recent studies involving allergen challenge suggest that NO2 may enhance the sensitivity to 

allergen-induced decrements in lung function, and increase the allergen-induced airway inflammatory 

response. 

AX5.4. Effects of Mixtures Containing NOX

Table AX5.4-1 summarizes human clinical studies of NO2-containing mixtures or sequential 

exposures that are most relevant to ambient exposure scenarios. 

Table AX5.2-1. Clinical studies – healthy subjects. 

STUDY LOCATION PARTICIPANTS METHODS FINDINGS COMMENTS 

Avissar et al. 

(2000) 

Rochester, 

NY, USA 

21 healthy nonsmokers Measurements of 

extracellular glutathione 

peroxidase (eGPx) 

activity and protein levels 

in epithelial lining fluid 

from NO2 exposure study 

described in Frampton 

et al. (2002) (see below). 

No effects of NO2 exposure on 

eGPx activity and protein 

concentrations. (O3 exposure 

decreased eGPx activity and 

protein concentrations.) 

NO2 up to 1.5 ppm for 

3 h did not deplete this 

mode of antioxidant 

defense in the epithelial 

lining fluid. 

Azadniv et al. 

(1998) 

Rochester, 

NY, USA 

2 studies, 12 healthy 

nonsmokers in each 

Air vs. 2 ppm NO2 for 6 h 

with intermittent 

exercise. 

Phase 1: BAL 18 h after 

exposure; 

Phase 2: BAL 

immediately after 

exposure. 

Increased BAL neutrophils, 

decreased blood CD8+ and null 

T lymphocytes 18 h after 

exposure. No effects on 

symptoms or lung function. 

2 ppm NO2 for 6 h 

caused mild 

inflammation. 

Blomberg et al. 

(1997) 

Sweden 30 healthy nonsmokers Air vs. 2 ppm NO2 for 4 

h, with intermittent 

exercise. 

Increased neutrophils and 

interleukin-8 in bronchial wash. 

Increases in specific lymphocyte 

subsets in BAL fluid. 

Symptoms/lung function not 

reported. 

2 ppm NO2 for 4 h 

caused airway 

inflammation. 

Blomberg et al. 

(1999) 

Sweden 12 healthy nonsmokers Air vs. 2 ppm NO2 for 4 h 

on 4 days, with 

intermittent exercise. 

After 4 days of NO2, increased 

neutrophils in bronchial wash but 

decreased neutrophils in 

bronchial biopsy. 2% decrease in 

FEV1 after first exposure to NO2, 

attenuated with repeated 

exposure. Symptoms not 

reported. 

Decreased lung 

function, not confirmed 

in other studies at this 

concentration. 

Conflicting information 

on airway inflammation. 
5-4 

STUDY LOCATION PARTICIPANTS METHODS FINDINGS COMMENTS 

Devlin et al. 

(1999; 

Frampton, 

Boscia et al. 

2002) 

Chapel Hill, 

NC, USA 

8 healthy nonsmokers Air and 2.0 ppm NO2 for 

4 h with intermittent 

exercise. 

Increased bronchial lavage 

neutrophils, IL-6, IL-8, alpha1-

antitrypsin, and tissue 

plasminogen activator. 

Decreased alveolar macrophage 

phagocytosis and superoxide 

production. No effects on 

pulmonary function. Symptoms 

not reported. 

2 ppm NO2 for 4 h 

caused airway 

inflammation. 

Drechsler-Parks 

(1995) 

Santa 

Barbara, CA, 

USA 

8 older healthy 

nonsmokers 

4 2-h exposures with 

intermittent exercise: air, 

0.60 ppm NO2, 0.45 ppm 

O3, and 0.60 ppm NO2 + 

0.45 ppm O3. 

Significant reduction in cardiac 

output during exercise, estimated 

using noninvasive impedance 

cardiography, with NO2 + O3. 

Symptoms and pulmonary 

function not reported. 

Suggests cardiac effects 

of NO2 + O3. Small 

number of subjects 

limits statistical power, 

has not been replicated. 

Frampton et al. 

(1991) 

Rochester, 

NY, USA 

39 healthy nonsmokers 3 protocols, all for 3 h 

with control air exposure: 

(1) continuous 0.06 ppm 

NO2, (2) baseline 

0.05 ppm NO2 with 

peaks of 2.0 ppm, and 

(3) continuous 1.5 ppm 

NO2. 

No symptoms or direct effects on 

pulmonary function. Increased 

airways responsiveness to 

carbachol after 1.5 ppm NO2. 

Evidence for increased 

nonspecific airways 

responsiveness with 

NO2 as low as 1.5 ppm 

for 3 h. 

Frampton 

et al.(2002) 

Rochester, 

NY, USA 

21 healthy nonsmokers Exposure to air, 0.6, 

1.5 ppm NO2 for 3 h with 

intermittent exercise. 

Dose-related decrease in 

hematocrit, hemoglobin, blood 

lymphocytes, and T lymphocytes. 

Mild increase in neutrophils 

recovered in bronchial portion of 

BAL fluid. In vitro viral challenge 

of bronchial epithelial cells 

showed increased cytotoxicity 

after NO2. No effects on 

symptoms or pulmonary function. 

Indicates NO2 causes 

airway inflammation. 

Suggest subtle effects 

on red blood cells, 

possibly RBC 

destruction (hemolysis). 

Gong et al. 

(2005) 

Downey, CA, 

USA 

6 healthy nonsmokers 

and 18 ex-smokers with 

COPD 

2 h exposures with 

intermittent exercise to: 

(1) air, 

(2) 0.4 ppm NO2, 

(3) 200 μg/m3

concentrated ambient 

particulate matter 

(CAPs), (4) NO2 + CAPs. 

Reduced maximum midexpiratory flow rate and oxygen 

saturation with CAPs exposures; 

no effects of NO2 alone or 

additive effect with CAPs. 

Exposures not fully 

randomized. Small 

number of healthy 

subjects limits 

interpretation for healthy 

group. 

Helleday et al. 

(1994) 

Sweden 8 healthy smokers, 8 

healthy nonsmokers 

3.5 ppm NO2 for 20 min 

with 15 min exercise. 

BAL 24 h after exposure 

compared with nonexposure control BAL. 

Different inflammatory cell 

increases in smokers and 

nonsmokers. No effects on 

symptoms. Pulmonary function 

not reported. 

Lack of control air 

exposure with exercise 

is problematic. 

Helleday et al. 

(1995) 

Sweden 24 healthy nonsmokers, 

8 in each of 3 groups 

Bronchoscopic 

assessment of 

mucociliary activity: 

(1) 45 min after 1.5 ppm 

NO2 for 20 min, 

(2) 45 min after 3.5 ppm 

NO2 for 20 min, and 

(3) 24 h after 3.5 ppm 

NO2 for 4 h. 

Complete abolition of mucociliary 

activity 20 min after NO2; 

increased activity 24 h after NO2. 

Symptoms/ pulmonary function 

not reported. 

No true air control 

exposure, order of 

procedures not 

randomized, subjects 

not blinded. 

Jörres et al. 

(1995) 

Germany 8 healthy nonsmokers & 

12 mild asthmatics 

Air or 1 ppm NO2

exposure for 3 h with 

intermittent exercise. 

In asthmatics, 2.5% decrease 

FEV1 after NO2 vs. 1.3% 

decrease after air, p = 0.01. 

FEV1 decreased 20% in 

1 subject after NO2. No 

significant lung function effect in 

healthy subjects. Changes in 

eicosanoids (more pronounced in 

asthmatics), but not inflammatory 

cells, in BAL fluid. 

Lung function effects 

consistent with other 

studies, suggesting 

some asthmatics 

susceptible. Evidence 

for mild airway 

inflammation. 
5-5 

STUDY LOCATION PARTICIPANTS METHODS FINDINGS COMMENTS 

Kim et al. (1991) Seattle, WA, 

USA 

9 healthy athletes Air, 0.18, and 0.30 ppm 

NO2 for 30 min with 

exercise. 

No effects on pulmonary function. 

Symptoms not reported. 

Small number of 

subjects limits 

conclusions. 

Morrow et al. 

(1992) 

Rochester, 

NY, USA 

20 COPD subjects (14 

current smokers) and 

20 elderly healthy (13 

never-smokers, 4 former 

smokers, 3 current 

smokers) 

Air vs. 0.3 ppm NO2 for 

4 h with intermittent 

exercise. 

COPD: small declines in FVC 

and FEV1 with NO2. Healthy: No 

symptoms or pulmonary function 

effects for group as a whole. 

Healthy smokers showed a 2.3% 

decline in FEV1 with NO2, and 

differed from nonsmokers. 

Mild lung function 

effects of 0.3 ppm for 4 

h in exercising patients 

with COPD. Small 

number of healthy 

smoking subjects limits 

conclusions regarding 

this group. 

Pathmanathan 

et al. (2003) 

United 

Kingdom 

12 healthy nonsmokers Air vs. 2 ppm NO2 for 4 h 

on 4 days, with 

intermittent exercise. 

Bronchoscopy and 

biopsy 1 h after 

exposure. 

Epithelial expression of IL-5, IL10, IL-13, and ICAM-1 increased 

following NO2 exposure. No data 

on inflammatory cells in BAL 

fluid. 

Supportive evidence for 

pro-allergic airway 

inflammation favoring 

following NO2 exposure. 

Posin et al. 

(1978) 

Downey, CA, 

USA 

10 healthy nonsmokers 3 daily exposures for 2.5 

h. 

1st day: air; 

2nd and 3rd days: 1 or 

2 ppm NO2. Intermittent 

exercise. Subsequent 

control series of 3 daily 

air exposures. 

Reduced hemoglobin and 

hematocrit, and red blood cell 

acetyl cholinesterase. 

Suggests red blood cell 

effects of NO2 (see 

Frampton et al., 2002). 

Exposures not 

randomized. 

Rasmussen 

et al. (1992) 

Denmark 14 healthy nonsmokers Air vs. 2.3 ppm NO2 for 

5 h. 

Small increases in FVC and 

FEV1. Reduced lung 

permeability and blood 

glutathione peroxidase after 

exposure. 

Only 1 wk between 

exposures may have 

confounded results. 

Rigas et al. 

(1997) 

State 

College, PA, 

USA 

12 healthy nonsmokers 2 h of 0.36 ppm NO2, 

0.75 ppm NO2, 0.36 ppm 

SO2, or 0.36 ppm O3. 

Boluses of O3 every 30 

min to measure O3

absorption. 

NO2 and SO2 increased O3

absorption by increasing 

biochemical substrates. 

Suggests breathing 

mixtures of NO2 and O3

would increase O3 dose 

to airways. 

Sandström et al. 

(1990) 

Sweden 32 healthy nonsmokers, 

4 groups of 8 subjects 

4 ppm NO2 for 20 min 

with 15 min exercise. 

BAL 4, 8, 24, 72 h after 

exposure, compared with 

non-exposure control 

BAL. 

Increase in BAL mast cells and 

lymphocytes 4-24 h after 

exposure. 

Study weakened by lack 

of control air exposure. 

Sandström et al. 

(1991) 

Sweden 18 healthy nonsmokers 2.25, 4.0, 5.5 ppm NO2

for 20 min with light 

exercise. BAL 24 h after 

exposure, compared with 

non-exposure control 

BAL. 

Increase in BAL mast cells (all 

concentrations) and lymphocytes 

(4.0 and 5.5 ppm). 

Study weakened by lack 

of control air exposure. 

Sandström et al. 

(1992a) 

Sweden 10 healthy nonsmoking 

men 

4 daily exposures to 

4 ppm NO2 for 20 min 

with 15 min exercise. 

BAL 24 h after exposure, 

compared with nonexposure control BAL. 

Reduction in alveolar 

macrophages, NK cells, and CD8 

lymphocytes in BAL; reduction in 

total lymphocytes in blood. 

Study weakened by lack 

of control air exposure. 

Sandström et al. 

(1992b) 

Sweden 8 healthy nonsmokers 1.5 ppm NO2 for 20 min 

with 15 min exercise, 

every 2nd day Η 6. BAL 

24 h after exposure 

compared with nonexposure control BAL. 

Reduced CD8+ T lymphocytes 

and NK cells in BAL fluid. 

Study weakened by lack 

of control air exposure. 
5-6 

STUDY LOCATION PARTICIPANTS METHODS FINDINGS COMMENTS 

Solomon 

et al.(2000) 

San 

Francisco, 

CA, USA 

15 healthy nonsmokers Air or 2.0 ppm NO2 with 

intermittent exercise, for 

4 h daily Η 4. BAL 18 h 

after exposure. 

Increased neutrophils in 

bronchial lavage decreased 

CD4+ T lymphocytes in BAL. No 

changes in blood. 

Airway inflammation 

with 2 ppm NO2 for 4 

daily 4 h exposures. 

Vagaggini et al. 

(1996) 

Italy 7 healthy nonsmokers Air vs. 0.3 ppm NO2 for 1 

h with intermittent 

exercise. 

Mild increase in symptoms. No 

effects on lung function, nasal 

lavage, or induced sputum. 

Small number of 

subjects limits statistical 

power. 

 

Table AX5.3-1. Subjects with respiratory disease. 

REFERENCE LOCATION PARTICIPANTS APPROACH & 

METHODS FINDINGS COMMENTS 

Gong et al. 

(2005) 

Downey, CA, 

USA 

6 healthy 

nonsmokers and 

18 ex-smokers with 

COPD 

2 h exposures with 

intermittent exercise to: 

(1) air, 

(2) 0.4 ppm NO2, 

(3) 200 μg/m3

 concentrated 

ambient particulate matter 

(CAPs), 

(4) NO2 + CAPs. 

Reduced maximum midexpiratory flow rate and 

oxygen saturation with 

CAPs exposures; no effects 

of NO2 alone or additive 

effect with CAPs. 

Exposures not fully 

randomized. Small number 

of subjects limits 

interpretation for healthy 

group. 

Hackney et al. 

(1992) 

Downey, CA, 

USA 

26 smokers with 

symptoms and 

reduced FEV1 

Personal monitoring and 

chamber exposure to air and 

0.3 ppm NO2 for 4 h with 

intermittent exercise. 

No significant effects on 

lung function. 

Jörres and 

Magnussen 

(1991) 

Germany 11 mild asthmatics Air vs. 0.25 ppm NO2 for 

30 min with 10 min exercise. 

No effects on lung function 

or airways responsiveness 

to methacholine. 

Jörres et al. 

(1995) 

Germany 8 healthy 

nonsmokers & 

12 mild asthmatics 

Air or 1 ppm NO2 exposure 

for 3 h with intermittent 

exercise. 

In asthmatics, 2.5% 

decrease FEV1 after NO2

vs. 1.3% decrease after air, 

p = 0.01. FEV1 decreased 

20% in 1 subject after NO2. 

No significant lung function 

effect in healthy subjects. 

Changes in eicosanoids 

(more pronounced in 

asthmatics), but not 

inflammatory cells, in BAL 

fluid. 

Lung function effects 

consistent with other studies, 

suggesting some asthmatics 

susceptible. Evidence for 

mild airway inflammation. 

Small number of healthy 

subjects limits statistical 

power. 

Morrow et al. 

(1992) 

Rochester, 

NY, USA 

20 COPD, 20 healthy 

elderly 

Air vs. 0.3 ppm NO2 for 4 h 

with intermittent exercise. 

Equivocal reduction in FVC 

with COPD patients, but not 

healthy subjects. 

Strand et al. 

(1997) 

Sweden 19 mild asthmatics Air vs. 0.26 ppm NO2 for 

30 min with intermittent 

exercise. 

Increased airway 

responsiveness to 

histamine 5 h after 

exposure. No effects on 

lung function. 

Suggests increased 

nonspecific airways 

responsiveness at much 

lower concentration than 

healthy subjects. Differs from 

findings in Jörres and 

Magnussen (1991). 

Vagaggini et al. 

(1996) 

Italy 8 mild asthmatics, 

7 COPD 

Air vs. 0.3 ppm NO2 for 1 h 

with intermittent exercise. 

Mild decrease in FEV1 in 

COPD subjects in 

comparison with air 

exposure, but not with 

baseline. No effects on 

nasal lavage or induced 

sputum. 

No convincing effect of NO2

in this study. Small number 

of subjects limits statistical 

power. 

 
5-7 

Table AX5.3-2. Inhaled allergen. 

REFERENCE LOCATION PARTICIPANTS APPROACH & METHODS FINDINGS COMMENTS 

Barck et al. 

(2002) 

Sweden 13 mild asthmatics, 

4 ex-smokers 

30 min exposures to air and 

0.26 ppm NO2 (at rest?), 

allergen challenge 4 h and BAL 

19 h after exposure. 

Randomized, crossover, 

double blind. 

Increased PMN in bronchial 

wash and BAL fluid, increased 

eosinophil cationic protein in 

bronchial wash, and reduced cell 

viability and BAL volume with 

NO2 + allergen. No effects on 

lung function response to 

allergen. 

Key study suggesting 

that NO2 enhances 

inflammatory 

response to allergen 

in mild asthmatics. 

Barck et al. 

(2005) 

Sweden 18 mild asthmatics, 

4 ex-smokers 

Day 1: 15 min exposures, 

Day 2: 2 15-min exposures to 

air and 0.26 ppm NO2

separated by 1 h, at rest. 

Allergen challenge 4 h after 

exposure on day 1 and 3 h 

after exposure on day 2. 

Sputum induction before 

exposure on days 1 & 2, and 

morning of day 3. Randomized, 

crossover, single blind. 

Increased eosinophilic cationic 

protein in sputum and blood, and 

increased myeloperoxidase in 

blood with NO2 + allergen. No 

differences in lung function or 

sputum cells. 

Provides supporting 

evidence that NO2

enhances the airway 

inflammatory 

response to allergen. 

Barck et al. 

(2005) 

Sweden 16 mild asthmatics 

with rhinitis 

30 min exposures to air and 

0.26 ppm NO2 at rest, nasal 

allergen challenge 4 h after 

exposure. Nasal lavage before 

and at intervals after exposure 

and challenge. 

No significant differences 

between air and NO2 exposure. 

0.26 ppm NO2 did not 

enhance nasal 

inflammatory 

response to allergen 

challenge. 

Devalia et al. 

(1994) 

United 

Kingdom 

8 mild asthmatics 6 h exposures to combination 

of 0.4 ppm NO2 and 0.2 ppm 

SO2. 

Increased allergen 

responsiveness 10 min after 

exposure to combination of NO2

and SO2, but not to individual 

gases. 

Small number of 

subjects limits 

statistical power. 

Jenkins et al. 

(1999) 

United 

Kingdom 

11 mild asthmatics (1) 6-h exposures to air, 

0.1 ppm O3, 0.2 ppm NO2, and 

combination followed by 

allergen challenge; 

(2) 3-h exposures to air, 

0.2 ppm O3, 0.4 ppm NO2, and 

combination; 

All exposures with intermittent 

exercise. 

All of the second exposure 

scenarios (O3, NO2, and 

combination), but none of the 

first exposure scenarios, resulted 

in reduced concentration of 

allergen causing a 20% decline 

in FEV1. Authors conclude that 

concentration more important 

than total inhaled pollutant. 

Suggests 0.4 ppm for 

3 h with intermittent 

exercise increases 

allergen 

responsiveness. 

Rusznak et al. 

(1996) 

United 

Kingdom 

13 mild asthmatics 6 h exposures to combination 

of 0.4 ppm NO2 and 0.2 ppm 

SO2. 

Increased allergen 

responsiveness to combination 

of NO2 and SO2, 10 min, 24, and 

48 h after exposure. 

Confirms findings of 

Devalia et al. (1994), 

that NO2 + SO2 for 6 h 

increases allergen 

responsiveness. 

Strand et al. 

(Strand, Rak et 

al. 1997) 

Sweden 18 patients with mild 

asthma, age 18-50 

yrs 

Exposure to 0.26 ppm NO2 for 

30 min at rest, allergen 

challenge 4 h after exposure. 

Late phase, but not early phase, 

response to allergen enhanced 

by NO2. 

Suggests 0.26 ppm 

NO2 for 30 min at rest 

increases late 

response. 

Strand et al. 

(1998) 

Sweden 16 patients with mild 

to moderate asthma, 

age 21-52 yrs 

4 daily repeated exposures to 

0.26 ppm NO2 for 30 min at 

rest. 

Significant increases in both 

early and late phase response to 

allergen after 4th day of 

exposure. 

Suggests repeated 

0.26 ppm NO2 at rest 

increases allergen 

response. 

Tunnicliffe et al. 

(1994) 

United 

Kingdom 

10 nonsmoking mild 

asthmatics age 

16-60 yrs. 

8 subjects 

completed. 

Exposure to air, 0.1 ppm, and 

0.4 ppm NO2 for 1 h at rest, 

separated by at least 1 wk, 

followed by allergen challenge. 

Post-challenge reduction in 

FEV1 after 0.4 ppm NO2 was 

greater than after air, for both the 

early (p < 0.009) and late (p < 

0.02) responses. No difference in 

nonspecific airway 

responsiveness. 

Suggests threshold 

for allergen 

responsiveness effect 

is between 0.1 and 

0.4 ppm for 1 h 

resting exposure. 
5-8 

REFERENCE LOCATION PARTICIPANTS APPROACH & METHODS FINDINGS COMMENTS 

Wang et al. 

(1995) 

United 

Kingdom 

2 groups of 

8 subjects with 

allergic rhinitis 

Exposure to 0.4 ppm NO2 (at 

rest?) for 6 h followed by nasal 

allergen challenge and nasal 

lavage. 

Increase in myeloperoxidase and 

eosinophil cationic protein in 

nasal lavage fluid following 

allergen challenge. 

Suggests enhanced 

nasal inflammatory 

response to allergen 

with 0.4 ppm. 

Wang et al. 

(1999) 

United 

Kingdom 

16 subjects with 

allergic rhinitis 

Treatment with nasal 

fluticasone or placebo for 4 

wks followed by exposure to 

0.4 ppm NO2 for 6 h, allergen 

challenge, and nasal lavage. 

Fluticasone suppressed the NO2

and allergen–induced increase in 

eosinophil cationic protein in 

nasal lavage fluid. 

Confirms earlier 

findings of this group 

that 0.4 ppm NO2

enhances nasal 

allergen response. 

Table AX5.4-1. NO2 and other pollutants. 

STUDY LOCATION PARTICIPANTS METHODS FINDINGS COMMENTS 

Devalia 

et al.(1994) 

United 

Kingdom 

8 mild asthmatics 6 h exposures to 

combination of 0.4 ppm NO2

and 0.2 ppm SO2. 

Increased allergen 

responsiveness 10 min after 

exposure to combination of NO2

and SO2, but not to individual 

gases. 

Small number of subjects 

limits statistical power. 

DrechslerParks (1995) Santa 

Barbara, CA, 

USA 

8 older healthy 

nonsmokers 

4 2-h exposures with 

intermittent exercise: air, 

0.60 ppm NO2, 0.45 ppm O3, 

and 0.60 ppm NO2 + 

0.45 ppm O3. 

Significant reduction in cardiac 

output during exercise, 

estimated using noninvasive 

impedance cardiography, with 

NO2 + O3. Symptoms and 

pulmonary function not reported. 

Suggests cardiac effects 

of NO2 + O3. Small 

number of subjects limits 

statistical power, has not 

been replicated. 

Gong et al. 

(2005) 

Downey, CA, 

USA 

6 healthy nonsmokers 

and 18 ex-smokers 

with COPD 

2 h exposures with 

intermittent exercise to: 

(1) air, 

(2) 0.4 ppm NO2, 

(3) 200 μg/m3

 concentrated 

ambient particulate matter 

(CAPs), 

(4) NO2 + CAPs. 

Reduced maximum midexpiratory flow rate and oxygen 

saturation with CAPs exposures; 

no effects of NO2 alone or 

additive effect with CAPs. 

Exposures not fully 

randomized. Small 

number of healthy 

subjects limits 

interpretation for healthy 

group. 

Hazucha 

et al. (1994) 

Chapel Hill, 

NC, USA 

21 healthy female 

nonsmokers 

2 h exposure to air or 

0.6 ppm NO2 followed 3 h 

later by exposure to 0.3 ppm 

O3, with intermittent exercise.

NO2 enhanced spirometric 

responses and airways 

responsiveness following 

subsequent O3 exposure. 

0.6 ppm NO2 enhanced 

ozone responses. 

Jörres and 

Magnussen 

(1990) 

Germany 14 nonsmoking mild 

asthmatics 

30 min exposures to air, 

0.25 ppm NO2, or 0.5 ppm 

SO2 at rest followed 15 min 

later by 0.75 ppm SO2

hyperventilation challenge. 

NO2 but not SO2 increased 

airways responsiveness to SO2

challenge. 

Findings contrast with 

Rubenstein et al. (1990). 

Koenig et al. 

(1994) 

Seattle, WA, 

USA 

28 asthmatic 

adolescents; 6 

subjects did not 

complete. 

Exposure for 90 min with 

intermittent exercise to: 

(1) 0.12 ppm ozone + 

0.3 ppm NO2, (2) 0.12 ppm 

ozone + 0.3 ppm NO2 + 

68 μg/m3

 H2SO4, or 

(3) 0.12 ppm ozone + 

0.3 ppm NO2 + 0.05 ppm 

nitric acid. 

No effects on pulmonary 

function. 

Absence of lung function 

effects of 0.3 ppm NO2

consistent with other 

studies; no effects of 

mixtures. 

Rubenstein 

et al. (1990) 

San 

Francisco, 

CA, USA 

9 stable asthmatics 30 min exposures to air or 

0.3 ppm NO2 with 20 min 

exercise, followed 1 h later 

by SO2 inhalation challenge. 

No effects on pulmonary function 

or SO2 responsiveness. 

Findings contrast with 

Jörres and Magnussen 

et al. (1990). 
5-9 

STUDY LOCATION PARTICIPANTS METHODS FINDINGS COMMENTS 

Rudell et al. 

(1999) 

Sweden 10 healthy 

nonsmokers 

Air and diesel exhaust for 1 

h, with and without particle 

trap. NO2 concentration 1.2-

1.3 ppm. BAL 24 h after 

exposures. 

Increased neutrophils in BAL 

fluid, no significant reduction in 

effect with particle trap. 

Filter only partially 

trapped particles. Unable 

to draw conclusions about 

role of NO2 in causing 

effects. 

Rusznak et 

al. (1996) 

United 

Kingdom 

13 mild asthmatics 6 h exposures to 

combination of 0.4 ppm NO2

and 0.2 ppm SO2. 

Increased allergen 

responsiveness to combination 

of NO2 and SO2, 10 min, 24, and 

48 h after exposure. 

Confirms findings of 

Devalia et al. (1994), that 

NO2 + SO2 for 6 h 

increases allergen 

responsiveness. 

 
6-2 

Annex 6. Epidemiologic Studies Related to 

Ambient Exposure to NOX

This annex provides supplemental information on various epidemiologic methods and studies that 

are referenced in the 2008 NOX ISA. The first section describes considerations in the interpretation of 

epidemiologic studies. This is followed by a section on cardiovascular effects that are related to shortterm exposure to NO2. This topic is discussed in the ISA, but more detail is provided in this annex due to 

inconsistency with supporting studies. The second section of this annex presents tables detailing the 

epidemiologic studies presented in the ISA. In general, these tables are divided into sections based on the 

endpoint of concern. Tables AX6.3-1 through AX6.3-5 cover respiratory endpoints, while tables AX6.3-6 

to AX6.3-9 address cardiovascular disease. The two tables, AX6.3-10 and AX6.3-11 summarize heart rate 

variability. Tables AX6.3-12 through AX6.3-14 address birth outcomes: birthweight, pre-term births, and 

lung growth, respectively, and tables AX6.3-15 through AX6.3-19 look at the long-term effects 

associated with NO2 exposure (i.e., lung function, asthma, respiratory symptoms, lung cancer, and 

mortality). Lastly, table AX6.3-20 summarizes the effects of NO2 on asthmatics. 

AX6.1. Interpretation of Epidemiologic Studies 

This section mainly focuses on the topics of exposure assessment and model specification in air 

pollution epidemiologic studies. The initial discussion addresses potential biases that may result from 

NO2 exposure measurement error and from the choice of exposure index and lag period. The remaining 

discussion highlights model specification issues and potential confounding by temporal factors, 

meteorological effects, seasonal trends, and copollutants. 

AX6.1.1. Exposure Assessment and Measurement Error 

In many air pollution epidemiologic studies, especially time-series studies that use administrative 

data on mortality and hospitalization outcomes, air pollution data from central ambient monitoring sites 

are used as the estimate of exposure. Personal exposures of individual study subjects, usually, are not 

directly measured in epidemiologic studies. The relationship between NO2 concentrations from ambient 

monitors and personal NO2 exposures was discussed previously (Annex AX3). Routinely collected 

ambient monitor data, though readily available and convenient, may not represent true personal exposure, 

which includes both ambient and nonambient (i.e., indoor) source exposures. Also, personal exposure 

measurements and ambient measurements are subject to different types of artifacts and measurement 

error. Therefore, they may not be measuring the same quantities. 

As discussed thoroughly in the 2004 PM AQCD (Section 8.4.5), the resulting exposure 

measurement error and its effect on the estimates of relative risk is an important consideration for 

interpreting epidemiologic study results. In theory, there are three components to exposure measurement 

error in time-series studies as described by Zeger et al. (2000): (1) the use of average population rather 

than individual exposure data; (2) the difference between average personal ambient exposure and ambient 

concentrations at central monitoring sites; and (3) the difference between true and measured ambient 

concentrations. The first error component, having aggregate rather than individual exposure data, is a 

Berksonian measurement error, which in a simple linear model increases the standard error, but does not 

bias the risk estimate. The second error component, which results from the difference between community 

average personal ambient exposure and outdoor ambient concentration level, has the greatest potential to 
6-3 

introduce bias. If the error is of a fixed amount (i.e., absolute differences do not change with increasing 

concentrations), there is no bias. However, if the error is not a fixed difference, this error will likely 

attenuate the NO2 risk estimate as personal NO2 exposures are generally lower than ambient NO2

concentrations in homes without sources, while they are higher in homes with sources. The third error 

component, the instrument measurement error in the ambient levels, is referred to as nondifferential 

measurement error and is unlikely to cause substantial bias, although it can lead to a bias toward the null. 

The impact of exposure measurement error on NO2 effect estimates was demonstrated in a study by 

Kim et al. (2006), which is a longitudinal study that investigated personal exposures to NO2, PM2.5, and 

CO for cardiac compromised individuals in Toronto, Canada. The mean (SD) personal exposure for NO2

was 14 ppb (6). NO2 personal exposures were less than central-fixed-site ambient measurements. 

Ambient NO2 was correlated with the personal exposure to NO2 (median Spearman’s correlation 

coefficient of 0.57). Personal exposures to PM2.5 were correlated with the personal exposure to NO2

(median Spearman’s correlation coefficient of 0.43). This study suggests that central-fixed-site 

measurements of PM2.5 and NO2 may be treated as surrogates for both exposure to PM2.5 and NO2 in timeseries epidemiology studies, and that NO2 is a potential confounder of PM2.5 and vice versa. As described 

in Chapter 2, Nerriere et al. (2005) provided additional data from European cities, noting that season, city, 

and land use dependence were important factors affecting the relationship between personal exposure to 

ambient NO2 and corresponding ambient monitoring site concentrations, and recommended a site-specific 

analysis for a specific study. Zidek (1997) noted that a statistical analysis must balance bias and 

imprecision (error variance). Ignoring measurement error in air pollution epidemiologic studies often 

results in underestimated risk estimates and standard errors. 

In addition, the use of ambient NO2 concentrations may obscure the presence of thresholds in 

epidemiologic studies at the population level due to the overestimation of exposure and the resulting 

underestimation of effects. Using PM2.5 as an example, Brauer et al. (2002) examined the relationship 

between ambient concentrations and mortality risk in a simulated population with specified common 

individual threshold levels. They found that no population threshold was detectable when a low threshold 

level was specified. Even at high-specified individual threshold levels, the apparent threshold at the 

population level was much lower than the specified threshold. Brauer et al. (2002) concluded that 

surrogate measures of exposure (i.e., those from centrally-located ambient monitors) that were not highly 

correlated with personal exposures obscured the presence of thresholds in epidemiologic studies at the 

population level, even if a common threshold exists for individuals within the population. 

As discussed in Chapter 2, NO2 concentrations measured at central ambient monitors may explain, 

at least partially, the variance of individual personal exposures; however, this relationship is influenced by 

factors such as air exchange rates in housing and time spent outdoors, which may vary by city. Other 

studies conducted in various cities observed that the daily averaged personal NO2 exposures from the 

population were well correlated with monitored ambient NO2 concentrations, although substantial 

variability existed among the personal measurements. Thus, there is supportive evidence that ambient 

NO2 concentrations from central monitors may serve as valid surrogate measures for mean personal NO2

exposures experienced by the population, which is of most relevance to time-series studies (See 

Chapter 2). Respiratory hospital visit and admission studies are influenced by the visits and admission of 

asthmatics. In children, for whom asthma is more prevalent, ambient monitors may correlate, to some 

extent, with personal exposure to NO2 of ambient origin because children spend more time outdoors in 

the warm season and have an increased potential for exposure due to traffic. However, of some concern 

for mortality and hospitalization time-series studies is the extent to which ambient NO2 concentrations are 

representative of personal NO2 exposures in another particularly susceptible group of individuals, the 

debilitated elderly. To date, the correlation between the two measurements has not been examined in this 

population. A better understanding of the relationship between ambient concentrations and personal 

exposures, as well as the factors that affect the relationship will improve the interpretation of the ambient 

concentration-population health response associations observed. 

Existing epidemiologic models may not fully take into consideration all of the biologically relevant 

exposure history or reflect the complexities of all the underlying biological processes. Using ambient 
6-4 

concentrations to determine exposure may overestimate true personal NO2 exposures (depending on 

indoor sources), resulting in biased descriptions of underlying concentration-response relationships (i.e., 

in attenuated risk estimates). The implication is that the effects being estimated occur at exposures that 

are uncertain and the potency of NO2 is different than these effect estimates indicate. As very few studies 

evaluating NO2 health effects with personal NO2 exposure measurements exist in the literature, effect 

estimates determined from ambient NO2 concentrations must be evaluated and used with caution to assess 

the health risks of NO2. 

The ultimate goal of the NO2 NAAQS is to set a standard for the ambient level, not personal 

exposure level, of NO2. Confidence in the use of ambient concentrations in epidemiologic studies is 

greatly strengthened if they are shown to be associated with personal exposures. However, until more 

information on personal NO2 exposure becomes available, the use of routinely monitored ambient NO2

concentrations as a surrogate for personal exposures is not generally expected to change the principal 

conclusions from NO2 epidemiologic studies. 

AX6.1.2. NO2 Exposure Indices Used 

The NO2-related effect estimates for mortality and morbidity health outcomes are usually presented 

in this document as relative risk, i.e., the risk rate relative to a baseline mortality or morbidity rate. 

Relative risks are based on an incremental change in exposure. To enhance comparability between 

studies, presenting these relative risks by a uniform exposure increment is needed. However, determining 

a standard increment is complicated by the use of different NO2 exposure indices in the existing health 

studies. The daily NO2 exposure indices that most often appear in the literature are the maximum 1-h 

average within a 24-h period (1-h max) and 24-h avg (24-h avg) concentrations. As levels are lower and 

less variable for the longer averaging times, relative risk of adverse health outcomes for a specific 

numeric concentration range are not directly comparable across metrics. Using the nationwide 

distributional data for NO2 monitors in U.S. Metropolitan Statistical Areas, increments representative of a 

low-to-high change in NO2 concentrations were approximated on the basis of annual mean to 95th 

percentile differences (Langstaff, 2006): 

Daily Exposure Index

Exposure Increment ppb

1-h avg NO2 30 

24-h avg NO2 20 

2-wk avg NO2 20 

Efforts were made to standardize the NO2 risk estimates using these increments throughout the ISA, 

except as noted. The specified incremental change for each daily NO2 exposure index ensures that risk 

estimates are comparable across the different metrics. The different increments for each NO2 exposure 

index do not represent inconsistencies; rather, they are appropriately scaled to facilitate comparisons 

between the various studies that used different indices. Note that in the Chapter 6 Annex Tables (see 

Annex Section AX6.3), effect estimates are not standardized; there, the results are presented in the tables 

as reported in the published papers. 

AX6.1.3. Lag Time: Period between Exposure and Health Effect 

Exposure lags may reflect the distribution of effects across time in a population and the potential 

mechanisms of effects. The choice of lag days for the relationship between exposure and health effects 

depends on the hypothesis being tested and the mechanism involved in the expression of the outcome. 
6-5 

Effects can occur acutely with exposure on the same or previous day, cumulatively over several days, or 

after a delayed period of a few days. With knowledge of the mechanism of effect, the choice of lag days 

can be determined prior to analysis. As one example, one would expect cough to occur acutely after 

exposure with a lag of 0 or 1 day, given that NO2 can act as a short-term irritant. However, an NO2-related 

inflammatory response may not lead to asthma exacerbation until several days later. An asthmatic may be 

impacted by NO2 on the first day of exposure, have further effects triggered on the second day, and then 

report to the emergency room for an asthmatic attack three days after exposure. Further, within a 

population of asthmatics, exacerbation of asthma symptoms may be observed over a period of several 

days, since each asthmatic may have varying individual aspects of the disease and may be affected by the 

exposure differently depending on his/her sensitivity and disease severity. The results from controlled 

human studies may be useful in assessing the adequacy of lags for some respiratory health outcomes. 

The concepts of lags are well discussed in the O3 AQCD (2006) and are only briefly reviewed here, 

as the concept for O3 pertains to NO2 as well. Selection of lag periods should depend on the hypothesis of 

the study and the potential mechanism of the effect. When the mechanism of the health effect is unknown, 

investigating the association between outcome and exposure using cumulative distributed lag models may 

be informative. Analyzing a large number of lags and simply choosing the largest and most significant 

results may bias the air pollution risk estimates towards or away from the null. Most studies have shown 

that NO2 has a fairly consistent, immediate effect on health outcomes, including respiratory 

hospitalizations and mortality. Several studies also observed significant NO2 effects over longer 

cumulative lag periods, suggesting that in addition to single-day lags, multiday lags should be 

investigated to fully capture a delayed NO2 effect on health outcomes. In this document, discussion 

largely focuses on effect estimates from 0- and 1-day lags, with some consideration of cumulative, 

multiday lag effects. It is not straightforward to compare and contrast results from single-day versus 

multiday lag models, because the parameters estimated from these models are not the same. These 

complications need to be taken into consideration when interpreting results from various lag models. 

AX6.1.4. Model Specification for Temporal Trends and Meteorological 

Effects 

Several challenges are encountered with respect to designing and interpreting time-series studies. 

The principal challenge facing the analyst in the daily time-series context is avoiding bias due to 

confounding by short-term temporal factors operating over time scales from days to seasons, thus 

adjusting for long-term trends in the evaluation of acute or short-term associations. In the current 

regression models used to estimate short-term effects of air pollution, two major potential confounders 

generally need to be considered: (1) seasonal trend and other “long-wave” temporal trends; and (2) 

weather effects. Both of these variables tend to predict a significant fraction of fluctuations in time-series 

analyses. 

Current weather models used in time-series analyses can be classified by their use of: (1) quantile 

(e.g., quartile, quintile) indicators; (2) parametric functional forms such as V- or U-shape functions; and 

(3) parametric (e.g., natural splines) or nonparametric (e.g., locally estimated smoothing splines 

[LOESS]) smoothing functions. More recent studies tend to use smoothing functions. While these 

methods provide flexible ways to fit health outcomes as a function of temperature and other weather 

variables, there are two major issues that need further examination to enable more meaningful 

interpretation of NO2 morbidity and mortality effects. 

The first issue is the interpretation of weather or temperature effects. Most researchers agree that 

extreme temperatures (i.e., heat waves or cold spells) contribute to morbidity and mortality effects. 

However, as extreme hot or cold temperatures, by definition, happen rarely, much of the health effects 

occur in the mild or moderate temperature range. Given the significant correlation between NO2 and 

temperature, ascribing the association between temperature and health outcomes solely to temperature 

effects may underestimate the effect of NO2. The second issue deals with the fact that weather model 
6-6 

specifications are fitted for year-round data in most studies. Such models will ignore the correlation 

structure that can change across seasons, resulting in inefficiency and model mis-specification. This is 

particularly important for NO2, which appears to change its relationship with temperature as well as with 

other pollutants across seasons. 

This changing relationship between NO2 and temperature, as well as between NO2 and other 

pollutants across seasons, and its potential implications for health effects modeling have not been 

examined thoroughly in the time-series literature. Even the flexible smoother-based adjustments for 

seasonal and other time-varying variables cannot fully take into account these complex relationships. One 

way to alleviate or avoid this complication is to analyze data by season. While this practice reduces 

sample size, the reduction would not be as serious as for PM (which is collected only every sixth day in 

most locations) because NO2 is collected daily. An alternative approach is to include separate NO2

concentration variables for each season (e.g., by multiplying NO2 concentrations by a season indicator 

variable). However, this approach assumes that all effects in the model that are not indicated to be seasonspecific do not vary seasonally. 

In locations where seasonal variability may be a factor, NO2 effect estimates calculated using yearround data can be misleading, as the changing relationship between NO2, temperature, and other 

pollutants across seasons may have a significant influence on the estimates. Analyses have indicated that 

confounding from seasonal variability may be controlled effectively by stratifying the data by season. 

AX6.1.5. Confounding Effects of Copollutants 

Extensive discussion of issues related to confounding effects among air pollutants in time-series 

studies are provided in Section 8.4.3 of the 2004 PM AQCD (U.S. Environmental Protection Agency, 

2004). Since the general issues discussed there are applicable to all pollutants, such discussions are not 

repeated here. 

AX6.1.6. Generalized Estimating Equations 

Since the publication of the 1993 NO2 AQCD (U.S. Environmental Protection Agency, 1993), 

methods to analyze panel and longitudinal studies have improved. The general mixed model method of 

Stiratelli et al. (1984) was an improvement over the method of Korn and Whittemore (1979) in that all the 

data could be used, including that from subjects with insufficient data to permit fitting of a separate 

logistical regression model. Generalized Estimating Equations, Liang and Zeger (1986) is an extension of 

generalized linear models. The joint distribution of the subject's observations does not have to be 

specified to derive the estimating equations. This is avoided by assuming a marginal distribution at each 

time. However, a covariate that is constant for a subject cannot be included in this model. Besides 

Gaussian outcome variables, the method can also be used for binomial or Poisson variables. 

AX6.1.7. Hypothesis Testing and Model Selection 

Epidemiologic studies investigated the association between various measures of NO2 (e.g., multiple 

lags, different metrics, etc.) and various health outcomes using different model specifications. Statistically 

testing a null hypothesis (i.e., there is no effect of NO2) requires one to calculate the value of a test 

statistic (i.e., the t-value). If the observed test statistic exceeds a critical value (oftentimes the 95th 

percentile) or is outside a range of values, the null hypothesis is rejected. However, when multiple testing 

is done using a critical value determined for a single test, the chance that at least one of the hypotheses is 

significant may be greater than the specified error rate. Procedures are available to ensure that the 

rejection error rate does not exceed the expected error rate (usually 5%) when conducting multiple 
6-7 

hypothesis testing. However, often the multiple hypotheses being tested are not statistically independent, 

thus some corrections, such as the Bonferroni adjustment, may be overly conservative. 

Multiple hypothesis testing and model selection also contribute to publication bias. Publication bias 

is the tendency of investigators to submit manuscripts or editors to accept manuscripts for publication 

based on the strength of the study findings. Although publication bias commonly exists for many topics of 

research, it may be present to a lesser degree in the air pollution literature. Several air pollutants often are 

examined in a single study, leading to the publication of significant, as well as nonsignificant, individual 

pollutant results. For example, many air pollution papers with a focus on PM health effects also published 

NO2 results. NO2 was largely considered a potentially confounding copollutant of PM; thus, NO2 effect 

estimates were often presented regardless of the statistical significance of the results. Another aspect of 

publication bias is only selecting the largest or statistically strongest effect estimate to report and not the 

array of models evaluated. See a full discussion in the O3 AQCD (U.S. Environmental Protection Agency, 

2006). 

The summary of health effects in this ISA was vulnerable to the errors of publication bias and 

multiple testing. Efforts have been made to reduce the impact of multiple testing errors on the conclusions 

in this document. To address multiple hypothesis testing, emphasis was placed in this document on a 

priori hypotheses. As identifying a priori hypotheses is difficult in the majority of the studies, the most 

common hypotheses were considered. For example, although many studies examined multiple single-day 

lag models, priority was given to effects observed at 0- or 1-day lags rather than at longer lags. Both 

single- and multiple-pollutant models that include NO2 were considered and examined for robustness of 

results. Analyses of multiple model specifications for adjustment of temporal or meteorological trends 

were considered sensitivity analyses. Sensitivity analyses were not granted the same inferential weight as 

the original hypothesis-driven analysis; however, these analyses are discussed in this document as 

appropriate given their valuable insights that may lead scientific knowledge in new directions. 

AX6.1.8. Generalized Additive Models 

Generalized Additive Models (GAM) have been widely utilized for epidemiologic analysis of the 

health effects attributable to air pollution. The impact of the GAM convergence issue was thoroughly 

discussed in Section 8.4.2 of the 2004 PM AQCD. Reports have indicated that using the default 

convergence criteria in the Splus software package for the GAM function can lead to biased regression 

estimates for PM and an underestimation of the standard error of the effect estimate (Dominici et al., 

2002; Ramsay et al., 2003). The GAM default convergence criterion in the Splus software package is 10 

with a maximum number of 10 iterations. The user can specify convergence criteria, that is orders of 

magnitude smaller than the default value and can also allow for many more iterations before terminating 

the program. The use of the default convergence criterion was found to be a problem when the estimated 

relative risks were small and two or more nonparametric smoothing curves were included in the GAM 

(Dominici et al., 2002). The magnitude and direction of the bias depend in part on the concurvity of the 

independent variables in the GAM and the magnitude of the risk estimate. Recent focus has been on the 

influence of the GAM function on effect estimates for PM. 

The GAM convergence problem appears to vary depending on data sets, and likely depends upon 

the intercorrelation among covariates and the magnitude of the risk estimate; thus, its impact on the 

results of individual studies cannot be known without a reanalysis. Consistent with the approach used in 

the 2004 PM AQCD, the results from studies that analyzed data using GAM with the default convergence 

criterion and at least two nonparametric smoothing terms are generally not considered in this chapter, 

with some exceptions as noted. 
6-8 

AX6.2. Cardiovascular Effects Related to Short-Term NO2

Exposure 

AX6.2.1. Hospital Admissions and ED Visits: All CVD 

Results of studies of short-term NO2 exposure and hospitalization or Emergency Department visits 

for cardiovascular disease are summarized in Figure AX6.2-1. Studies of both 1-h max NO2 level and 

24-h avg NO2 level are included. With the exception of lag 1 results reported by Jalaludin et al. (2006), 

most point estimates are positive with confidence limits excluding the null value. Jalaludin et al. report a 

lag 0 cool season relative risk (not pictured) of 1.09 (95%CI: 1.05, 1.13) per 30 ppb increase in 1-h max 

NO2 level (Jalaludin et al., 2006). (Note that the IQR reported by Jalaludin et al. is 9.3 ppb so the 30-ppp 

increase into which the results were standardized may be unlikely in Sydney, where the study was 

conducted.) Although results for cardiovascular diseases were not tabulated for a reanalysis of GAM 

impacted study of Los Angeles and Cook County hospital admissions, authors note that they observed an 

association of NO2 with CVD hospital admissions in the reanalyses (Moolgavkar, 2003). The association 

was diminished with the use of increasingly stringent convergence criteria, however (Moolgavkar, 2003). 

Ischemic Heart Disease 

Studies that further narrowed the cardiac disease grouping to evaluate Ischemic Heart Diseases (IHD) are 

summarized in Figure AX6.2-2. Several US studies examined the association of ambient NO2 level with 

IHD (Ito, 2004; Mann et al. 2002; Metzger et al. 2004; Peel et al. 2007). Ito (2004) reported a null 

association of 24-h avg NO2 level with IHD admissions in Ontario where the mean ambient level was 

21.3 ppb. Mann et al. (2002) examined the association of 24-h avg NO2 level with IHD and secondary 

diagnoses of arrhythmia, IHD and secondary diagnosis of CHF, IHD and no secondary diagnosis, and all 

IHD regardless of secondary diagnosis in single-pollutant models. The authors noted that the strongest 

effect observed (IHD with secondary diagnosis of CHF) may have been driven by the MI primary 

diagnoses. The 24-h avg NO2 level in the South Coast Air Basin of California, where this study was 

conducted was approximately 37 ppb. This study was novel in that exposure level was assigned based on 

the zip code of the health insurance participant and proximity to the monitoring station (Mann et al., 

2002). A non-significant increased risk of ED visit for IHD was observed in single-pollutant models, 

among those with hypertension but not diabetes in a study conducted in Atlanta where the daily 1-h max 

NO2 level was approximately 46 ppb (Peel et al., 2007). 

Two studies of IHD and hospital admissions conducted in Europe have produced conflicting results 

(Atkinson et al., 1999a; Poloniecki et al., 1997). Atkinson et al. (1999a) reported a significant increase in 

IHD admission in a study in London. A study conducted in Helsinki reported an association of NO with 

both hospitalization and ED visits for IHD while no association with NO2 was observed (Pönkä and 

Virtanen, 1996). In addition, Several Australian studies, including two multicity studies, supported an 

association of hospital admissions and emergency visits for IHD and ambient NO2 level among older 

adults in single-pollutant models (Jalaludin et al., 2006; Barnett et al., 2006; Simpson et al., 2005a,b). 

One study conducted in Hong Kong reports slightly elevated non-significant association of IHD with 24-h 

avg NO2 level (Wong et al., 1999). In addition, Lee et al. (2003a) reported an increase in IHD admissions 

associated with 24-h avg NO2 level at lag 5. 
6-9 

Figure AX6.2-1. Relative risks (95% CI) for associations of 24-h NO2 (per 20 ppb) and daily 1-h max NO2* with 

hospitalizations or emergency department visits for all cardiovascular diseases (CVD). 

Primary author and year of publication, city, stratification variable(s), and lag are listed. 

Results for lags 0 or 1 are presented, as available. *NO2 1 h max; all others 24 h avg. 
6-10 

Figure AX6.2-2. 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 Ischemic Heart Disease (IHD). Primary author and year of 

publication, city, stratification variable(s), and lag are listed. Results for lags 0 or 1 are 

presented, as available. *NO2 1 h max; all others 24 h avg. 
6-11 

Figure AX6.2-3. Relative risks (95% CI) for associations between 24-h avg NO2 (per 20 ppb) and 

hospitalizations for myocardial infarction (MI). Primary author and year of publication, city, 

stratification variable(s), and lag are listed. Results for lags 0 or 1 are presented as 

available. *NO2 1 h max; all others 24 h avg. 
6-12 

Figure AX6.2-4. Relative risks (95% CI) for associations of 24-h avg NO2 (per 20 ppb) and 1-h max NO2* with 

hospitalizations for congestive heart failure (CHF). Primary author and year of publication, 

city, stratification variable(s), and lag are listed. Results for lags 0 or 1 are presented as 

available. 
6-13 

AX6.2.2. Hospital Admissions and ED Visits: Myocardial Infarction (MI) 

Studies of hospital admissions for MI are summarized in Figure AX6.2-3. In the United States, 

positive single-pollutant associations for emergency admissions for MI and increases in ambient NO2

level were reported in Boston (Zanobetti and Schwartz, 2006) and California (Linn et al., 2000; Mann 

et al., 2002). 

Pooled results from two European multicity studies are inconsistent. Von Klot et al. (2005) reported an 

increase in MI readmissions at lag 0 while Lanki et al. (2006) reported a null effect at lag 1. The NO2

levels were similar in the cities studied with Lanki et al. (2006) reporting the 24-h avg level of 23 ppb and 

Von Klot et al. (2005) reporting a range in 24-h avg across the cities studied of 12-37 ppb. A single-city 

study in Italy (D’Ippoliti et al., 2003) found positive significant associations between 24-h avg NO2 level 

and admission for the first episode of MI. The 24-h avg NO2 level reported by D’Ippoliti was 

approximately 45 ppb. A study conducted in London reported a positive association of ED visit for MI 

with 24-h avg NO2 where the 24-h avg reported was approximately 35 ppb. Finally, positive associations 

were reported for MI in a multicity study conducted in Australia and New Zealand among older adults 

and adults from 15 to 64 years old (Barnett et al., 2006). The 24-h avg NO2 level ranged from 

approximately 7-12 ppb in the Australian cities studied (Barnett et al., 2006). 

AX6.2.3. Hospital Admissions and ED Visits: Arrhythmia and 

Congestive Heart Failure (CHF) 

Hospital or ED admissions for arrhythmia were inconsistently associated with increases in ambient 

NO2 level. Some studies reported positive associations (Rich et al., 2006a; Mann et al., 2002; Barnett 

et al., 2006) while others reported null associations (Metzger et al., 2004; Lippmann et al., 2000; 

reanalysis Ito, 2004). Single-pollutant models of hospital admissions and ED visits for CHF have also 

produced mixed results (Figure AX6.2-4). A seven city study conducted in the U.S. among the elderly 

found positive associations in Los Angeles (RR: = 1.52 [1.35, 1.71]), Chicago (RR: = 1.60 [1.24, 2.07]) 

and New York (RR: = 1.23 [1.05, 1.43]) per 30-ppb increase in NO2 (Morris et al., 1995). Estimates were 

close to the null value in Philadelphia, Detroit, Houston, and Milwaukee and only the estimate for New 

York remained significant in multi-pollutant models (Morris et al., 1995). The 1-h max NO2 level in the 

cities studied ranged from 40 ppb in Milwaukee to 77 ppb in Los Angeles (Morris et al., 1995). Ito, 2004 

reported null associations for CHF and NO2 in Ontario where the 24-h avg NO2 level is approximately 

21 ppb (Ito, 2004). Elevated but non-significant associations were reported in Atlanta (Metzger et al., 

2004; Peel et al., 2007) and elevated significant associations were reported in Pittsburgh (Wellenius et al., 

2005a). Null associations were reported in London (Poloniecki et al., 1997) while positive significant 

associations were reported in a multicity study in Australia and New Zealand (Barnett et al., 2006) and in 

Hong Kong (Wong et al., 1999). 

AX6.2.4. Hospital Admissions and ED Visits: Cerebrovascular Disease 

AX6.2.4.1. Vaso-Occlusion in Sickle Cell 

A recent study evaluated the association of pain in Sickle Cell patients, which is thought to be 

caused by vaso-occlusion, with air pollution (Yallop et al., 2007). A time series analysis was performed to 

link daily hospital admissions for acute pain among sickle cell patients with daily air pollution levels in 

London using the cross correlation function. No association was reported for NO2. However, Yallop et al. 

(2007) observed an association (CCF = -0.063, lag 0) for NO, CO, and O3. 
6-14 

AX6.2.5. Hospital Admissions and ED Visits: Multipollutant Modeling 

Results 

Multipollutant models may have limited utility to distinguish the independent effects of specific 

pollutants if model assumptions are not met. However, these models are widely used in air pollution 

research and results for CVD hospital admissions and ED visits are summarized in Figure AX6.2-5. This 

figure includes only those studies that present two-pollutant results in tabular form. Studies with 

qualitative descriptions or figures summarizing two-pollutant results are discussed in the text that follows 

(Linn et al., 2000; Mann et al., 2002; Metzger et al., 2004; Tolbert et al., 2007; Zanobetti and Schwartz, 

2006; Jalaludin et al., 2006; Von Klot et al., 2005; Ballester et al., 2006; Wong et al., 1999). In addition, 

we included text discussion of studies that simultaneous adjust for several pollutants (Morris et al., 1995; 

Llorca et al., 2005) and several cerebrovascular disease studies that report multipollutant results (Ballester 

et al., 2001; Villeneuve et al., 2006; Tsai et al., 2003a; Chan et al., 2006). 

A U.S. study showed a diminishment of the relative risk for CHF in two-pollutant models 

(Wellenius et al., 2005a). Morris et al. (1995) also observed a similar diminishment of the CHF 

association in multipollutant models containing SO2, CO, and Ozone (Morris et al., 1995). The 

association of cardiac disease admissions with NO2 in several non-U.S. studies was not robust in twopollutant models (Simpson et al., 2005a,b; Poloniecki et al., 1997; Barnett et al., 2006; Ballester et al., 

2006). Llorca et al. (2005) reported a similar lack of robustness in models containing NO2, TSP, H2S, NO, 

and SO2. Estimates from studies conducted in Taiwan reporting relatively high associations of NO2 with 

CVD in single-pollutant models remained robust in two-pollutant models during the cool (Yang et al., 

2004a) or warm (Chang et al., 2005) seasons only. In an Australian study of older adults (65+ years), the 

effect estimate for NO2 was robust to simultaneous adjustment for O3 and particles (Morgan et al., 1998a). 
6-15 

Figure AX6.2-5. Relative risks (95% CI) for associations of 24-h avg NO2 exposure (per 20 ppb) and daily 1-h 

max NO2* (per 30 ppb) with hospitalizations or emergency department visits for CVD. 

Studies with 2 pollutant model results. Primary author and year of publication, city, 

stratification variable(s), and lag are listed. Results for lags 0 or 1 are presented as 

available. 
6-16 

In two additional U.S. studies, investigators provided text descriptions of multipollutant model 

results and indicated that their analyses were unable to distinguish the effects of NO2 from PM, CO, and 

other traffic pollutants (Linn et al., 2000; Mann et al., 2002.) In both studies, CO was more highly 

correlated with NO2 than PM. In a Canadian study in which default GAM procedures were used, the 

significant association of NO2 with ED visits for cardiac disease was reduced and non-significant in 

multipollutant models (Stieb et al., 2000). Further, in a study of emergency department visits to Atlanta 

hospitals, Metzger et al., 2004 observed a diminishment of the effect of NO2 on visits for cardiovascular 

disease when CO was modeled with NO2, while the effect of CO remained robust. This finding was 

repeated in an analysis that included several additional years of data (Tolbert et al., 2007). In this paper, 

Tolbert et al. (2007) discussed the limitations of multipollutant models in detail and conclude that these 

models may help researchers identify the strongest predictor of disease but may not isolate the 

independent effect of each pollutant. In an Australia study (Jalaludin et al., 2006) and a Spanish multicity 

study (Ballester et al., 2006) presenting multipollutant results, the association of NO2 with cardiac disease 

was not robust to adjustment for other pollutants (CO, SO2, particles). However, in a European multicity 

study investigators reported that the effect of NO2 on cardiac readmissions among MI survivors was not 

diminished in multipollutant models (Von Klot et al., 2005). 

Burnett et al. (1997a) reported robust estimates for cardiac disease hospital admissions and NO2, 

whereas the observed association for cardiac hospitalizations and PM were explained by gaseous 

pollutants. In another multicity study conducted in the same area, associations of NO2 with cardiac 

disease were not attenuated when CO, SO2, and PM variables were included in the models (Burnett et al., 

1999). 

The association of NO2 with stroke was not robust to adjustment for CO in a Canadian study 

(Villeneuve et al., 2006). The association of NO2 with all cerebrovascular disease was not robust to 

adjustment for BS and SO2 in a Spanish single city study (Ballester et al., 2001). Although results from a 

Taiwanese study indicated that the effect of NO2 on stroke admissions is not diminished in two-pollutant 

models, the authors noted that the association of NO2 with stroke may not be causal if NO2 is a surrogate 

other components of the air pollution mixture (Tsai et al., 2003a). 

Studies using alternative methods to investigate the influence of copollutants on observed 

associations of NO2 with cardiovascular disease are few in number. In an study of emergency admissions 

for MI and ambient pollution in Boston investigators attempt to distinguish traffic from non-traffic related 

pollutants through their definition of an exposure metric for non-traffic PM (residuals in model of PM2.5

regressed against BC) but found NO2, PM2.5 and non-traffic PM each may trigger MI during the warm 

season (Zanobetti and Schwartz, 2006). In a study conducted in Hong Kong, investigators looked at the 

association of NO2 with CVD during high PM10 and high ozone days (Wong et al., 1999). An interaction 

between NO2 and ozone was observed (in the single-pollutant model NO2 associated with heart failure, 

RR: 1.18 95% CI: 1.10, 1.26 per 20 ppb, lag 0-3). 

AX6.2.6. Heart Rate Variability 

Liao et al. (2004) investigated short-term associations between ambient pollutants and cardiac 

autonomic control from the fourth cohort examination (1996 to 1998) of the population-based 

Atherosclerosis Risk in Communities (ARIC) Study. PM10, NO2, and other gaseous air pollutants were 

examined in this study. PM10 (24-h avg) and NO2 (24-h avg) 1 day prior to the randomly allocated 

examination date were used. The mean ± SD NO2 level was 21 ± 8 ppb. PM10 concentrations measured 1 

day prior to the HRV measurements were inversely associated with both frequency- and time-domain 

HRV indices. Ambient NO2 concentrations were inversely associated with high-frequency power and 

SDNN. In single-pollutant models, a 20-ppb increase in ambient NO2 was associated with a 5% reduction 

(95% CI: 0.7, 9.2), in mean SDNN. Consistently more pronounced associations were suggested between 

PM10 and HRV among persons with a history of hypertension. 
6-17 

Various measures of HRV have been examined in relation to daily levels of ambient air pollution in 

other studies (Chan et al., 2005; Wheeler et al., 2006; Holguín et al., 2003; Luttmann-Gibson et al., 2006; 

Schwartz et al., 2005). Chan et al. (2005) recruited 83 patients from the cardiology section of a hospital in 

Taiwan. Patients included 39 with coronary heart disease (CHD) and 44 with more than one risk factor for 

CHD. The authors reported finding significant associations between increases in NO2 and decline in 

SDNN (NO2 lagged 4 to 8 h) and LF (NO2 lagged 5 or 7 h) (see Annex Table AX6.3-10 for quantitative 

results). There were no significant associations for r-MSSD or HF and NO2. None of the other pollutants 

tested (PM10, CO, SO2, O3) were significantly associated with any of the HRV measured. Wheeler et al. 

(2006) examined HRV and ambient air pollution in Atlanta in 12 patients who had an MI from 3 to 12 

months prior to enrollment and 18 COPD patients. The results in the two patient groups were quite 

different: increasing concentration of NO2 in the previous 4-h significantly reduced SDNN in MI patients 

and significantly increased SDNN in COPD patients (see Annex Table AX6.10). Similar significant 

associations were seen with increases in 4-h ambient PM2.5. The PM2.5 concentrations were moderately 

correlated with NO2 levels (r = 0.4). 

In contrast, Holguín et al. (2003) found PM2.5 concentrations were moderately correlated with NO2

levels (v = 0.04) in 34 elderly adults in Mexico City and found no significant associations with increases 

in NO2, but did find significant effects of PM2.5 on HF, particularly among hypertensive subjects. 

Similarly, Luttmann-Gibson et al. (2006) also found significant effects of PM2.5 and SO4 on HRV 

measured in a panel of 32 senior adults in Steubenville, OH, but observed no effect of increasing NO2. 

Likewise, Schwartz et al. (2005) found significant effects of increases in PM2.5 on measures of HRV, 

while no associations with NO2 were observed. A population-based study of air pollutants and HRV was 

conducted in Boston, MA on 497 men from the VA Normative Aging Study (NAS) (Park et al., 2005). 

The mean ± SD 24-h avg NO2 concentration was 22.7 ± 6.2 ppb. Associations with HRV outcomes were 

observed with a 4-h moving avg of O3 and PM2.5 concentrations, but not with NO2. 

AX6.2.7. Repolarization Changes 

A prospective panel study, conducted in East Germany, analyzed 12 repeat ECG recordings for 

56 males with IHD (Henneberger et al., 2005). Ambient air pollutants measured at fixed monitoring sites 

were used to assign individual exposures for 0 to 5, 5 to 11, 12 to 17, 18 to 23, 0 to 23 h and for 2 to 5 

days prior to the EEG. Pollutants considered were ultrafine particles (UFP), accumulation mode particle 

(ACP), PM2.5, elemental carbon (EC), organic carbon (OC), SO2, NO2, NO, and CO. Associations were 

observed between (1) QT duration and EC and OC; (2) T-wave amplitude and UFP, ACP and PM2.5; and 

(3) T-wave complexity and PM10, EC, and OC. NO (r = 0.83) and NO2 (0.76) were highly correlated with 

UFP but were not associated with repolarization abnormalities. 

AX6.2.8. Arrhythmias Recorded on Implantable Cardioverter 

Defibrilators (ICDs) 

In a pilot study, Peters et al. (2000a) abstracted device records for 3 years for each of 100 patients 

with ICDs. Defibrillator discharge events were positively associated with the previous day and 5-day 

mean NO2 concentrations: each 20-ppb increase in the previous day’s NO2 level was associated with an 

increased risk of a discharge event (OR = 1.55 [95% CI: 1.05, 2.29]) (see Annex Table AX6.3-1 for the 

increase associated with a 20-ppb increase in NO2). 

Three papers by the same team of investigators examined the association between air pollution and 

the incidence of ventricular arrhythmias (Dockery et al., 2005; Rich et al., 2005) and PAF episode (Rich 

et al., 2006b) in Boston. A total of 203 patients with ICDs who lived within 25 miles of the ambient 

monitoring site were monitored. Data included a total of 635 person-years of follow-up or an average of 

3.1 years per subject. The median (IQR) 48-h avg NO2 concentration was 22.7 (7.7) ppb. In the study by 
6-18 

Dockery et al. (2005), significant positive associations were observed between ventricular arrhythmias 

within 3 days of a prior event, and a 2-day mean exposure to several air pollutants including PM2.5, BC, 

NO2, CO, and SO2. Rich et al. (2005) examined associations between ambient air pollution levels less 

than 24 h before the occurrence of a ventricular arrhythmia to make use of the precise time definition 

available from the implantable cardioverter defibrillator (Rich et al., 2005). In single-pollutant models, 

each 20-ppb increase in the mean NO2 level over the previous 2 days was associated with an increased 

likelihood of ventricular arrhythmia, OR = 1.54 (95% CI: 1.11, 2.18). The association with NO2 was not 

significant in two-pollutant models with PM2.5, but remained marginally significant in models with O3

(2.0-ppb increase in 24-h moving avg NO2 was associated with an OR = 1.36 [95% CI: 1.00, 1.80]). 

There was a strong association between an increase of NO2 (by 20 ppb) and ventricular arrhythmia in the 

presence of ventricular arrhythmia within the previous 72 h (OR = 2.09 [95% CI: 1.26, 3.51]). Increased 

but non-significant associations were observed in this population between NO2 levels and PAF, as well as 

fine particles and black carbon (Rich et al., 2006b). 

A study conducted in St. Louis, which also examined the association of air pollutant level within 24 

h of a ventricular arrhythmia, reports non-significant increases for NO2 and elemental carbon, while SO2

was significantly associated with increased occurrence of arrhythmia (Rich et al., 2006a). Metzger et al. 

(2007) examined the association of ventricular tachyarrhythmias with air pollutants in the largest study to 

date (N = 518), which was conducted in Atlanta. These investigators reported “suggestive” findings for 

course particulate but generally no evidence of an association of NO2 and other pollutants with 

tachyarrhythmias (Metzger et al., 2007). 

AX6.2.9. Markers of Cardiovascular Disease 

In a large cross-sectional study of 7,205 office workers in London, Pekkanen et al. (2000) collected 

blood samples and analyzed the association between plasma fibrinogen, a risk factor for CVD, and 

ambient levels of air pollution. In models adjusting for weather, demographic, and socioeconomic factors, 

there was an increased likelihood of blood levels of fibrinogen >3.19 g/l (90th percentile) for each 20-ppb 

increase in NO2 lagged by 3 days (OR = 1.14 [95% CI: 1.03, 1.25]). The correlation between daily NO2

and other traffic-related pollutants were high: daily levels of black smoke (r = 0.75), PM10 (r = 0.76), SO2

(r = 0.62), CO (r = 0.81). The authors proposed that the increased concentrations of fibrinogen, a mediator 

of cardiovascular morbidity and mortality, may be an indicator of inflammatory reactions caused by air 

pollution. 

Schwartz (2001) examined the association between fibrinogen, platelet count, and white blood cell 

(WBC) count in the Third National Health and Nutrition Examination Survey (NHANES III). In singlepollutant models NO2 was associated with platelet counts and fibrinogen. However, in a two-pollutant 

model with PM10 these associations became negative. 

Pekkanen et al. (2002) enrolled a panel of 45 adults with coronary heart disease in order to examine 

associations between heart function as measured by risk of ST-segment depression and particulate 

pollution. Level of particulate and gaseous pollutants, including NO2, lagged by 2 days was found to have 

the strongest effect on risk of ST-segment depression during mild exercise tests (OR = 14.1 [95% CI: 3.0, 

65.4] for ST-segment depression of >0.1mV with a 20-ppb increase in NO2 lagged by 2 days). A large 

(n = 863) cross-sectional study of resting heart rate (HR) of adults in France found significant 

associations between elevated levels of NO2 within 8-h of measurement and resting HR of ≥75 beats per 

minute (bpm) (OR = 2.7 [95% CI: 1.2, 5.4] for resting HR >75 bpm for each 20-ppb increase in NO2) 

(Ruidavets et al., 2005). 

In a population based study of participants in the Atherosclerosis Risk in Communities (ARIC) 

study, Liao et al., 2005 did not observe differences in White Blood Cell (WBC) count, Factor VIII C, 

fibrinogen, Von Willibrand Factor (VWF) or albumin depending on 24-h avg NO2 level lagged 1 to 3 

days prior to the examination date. However, PM10 was associated with factor VIII-C in this cohort. An 
6-19 

association between PM10 and serum albumin was observed only among persons with a history of CVD 

(Liao et al., 2005). 

Ruckerl et al. (2006) examined several markers of inflammation, cell adhesion, and coagulation 

among a panel of 57 male patients with CHD. These authors primary hypothesis was that C-reactive 

protein (CRP) would be increased with increases in air pollution. They also investigated the effect of air 

pollution on other markers including serum amyloid A (SAA), E-selectin, von Wildebrand factor antigen, 

intercellular adhesion molecule 1 (ICAM-1), fibrinogen, factor VII, prothrombin fragment 1+2, and Ddimer. A significant association was observed for NO2 with CRP greater than the 90th percentile but the 

strongest effect on CRP was observed for ultrafine particles. 

Steinvil et al. (2007) investigated the association of air pollutants with several markers of 

inflammation (fibrinogen, CRP, and WBC). Significant decreases in fibrinogen associated with increases 

of 13 ppb in ambient NO2 were reported among men (all lags 0-7 and 7 day avg) and women (lag 0, 7 day 

avg). The absolute change in fibrinogen ranged from 7.9 to 16.7 mg/dL (Steinvil et al., 2007). The mean 

NO2 level was 19.5 ppb (Steinvil et al., 2007). PM10 was significantly associated with increased 

fibrinogen only at day 7. No correlations with CRP and WBC were observed (Steinvil et al., 2007). 

Baccarelli et al. (2007) investigated the effect of ambient NO2 with prothrombin time (PT) and 

activated partial thromboplastin time (APTT) in 1218 normal subjects in Italy. Both NO2 (coefficient = -

0.08 95%CI: -0.15, 0.00) and PM10 (coefficient = -0.08 95% CI: -0.14, -0.01) on the same day and the 

average for 30 days prior to the examination were negatively correlated with PT (e.g., PT became shorter 

indicating hypercoagulability), while no effect on APTT was reported (Baccarelli et al., 2007). 

AX6.3. Epidemiologic Studies 

Table AX6.3-1. Studies examining exposure to indoor NO2 and respiratory symptoms. 

AUTHOR, 

YEAR, 

LOCATION 

STUDY DESIGN EXPOSURE 

TIME MEAN (SD) RANGE (PPB) 

OUTCOME & ESTIMATE 

(95% CI) 

Pilotto et al. (2004) 

Australia 

Subjects: 118 asthmatic 

children 

Analysis: negative 

binomial 

Monitoring Device: 

passive diffusion badges 

6 h intervention: 16 

(7) 

control: 47 (27) 

intervention: 7, 38 

control: 12, 116 

difficulty breathing, day

RR 2.44 (1.02, 14.29)*

chest tightness, day

RR 2.22 (1.23, 4.00)*

asthma attacks, day

RR 2.56 (1.08, 5.88)*

difficulty breathing, night

RR 3.12 (1.45, 7.14)* 

Pilotto et al. (1997) 

Australia 

Subjects: 388 children 

Analysis: generalized 

linear mixed models 

Monitoring Device: 

passive diffusion badges 

6 h overall: 4, 132 wheeze (>40 ppb vs. ≤ 40 ppb) 

OR 1.41 (0.63, 3.15) 

Dry cough (>40 ppb vs. ≤ 40 ppb) 

OR 1.08 (0.62, 1.90) 
6-20 

AUTHOR, 

YEAR, 

LOCATION 

STUDY DESIGN EXPOSURE 

TIME MEAN (SD) RANGE (PPB) 

OUTCOME & ESTIMATE 

(95% CI) 

Nitschke et al. 

(2006) 

Australia 

Subjects: 174 asthmatic 

children 

Analysis: negative 

binomial 

Monitoring Device: 

passive diffusion badges 

6 h home: 20 (22) 

school: 34 (28) 

 Wheeze

Day: school RR 1.01 (0.97,1.06) 

 home RR 0.98 (0.92, 1.04) 

Night: school RR 0.99 (0.93, 1.06) 

 home RR 1.00 (0.90, 1.11) 

difficulty breathing

Day: school RR 1.09 (1.03, 1.15) 

 home RR 1.00 (0.98, 1.03) 

Night: school RR 1.11 (1.05, 1.18) 

 home RR 1.03 (1.01, 1.05) 

chest tightness

Day: school RR 1.08 (0.99, 1.19) 

 home RR 0.97 (0.89, 1.06) 

Night: school RR 1.12 (1.07, 1.17) 

 home RR 1.02 (0.95, 1.09) 

Cough

Day: school RR 1.01 (0.99, 1.03) 

 home: RR 1.01 (0.97, 1.05) 

Night: school RR 1.01 (0.98, 1.04) 

 home RR 0.99 (0.96, 1.02) 

asthma attacks

Day: school RR 1.03 (0.98, 1.08) 

 home: RR 1.00 (0.95, 1.05) 

Night: school RR 1.00 (0.93, 1.08) 

 home RR 1.04 (1.00, 1.07) 

results given for 10-ppb increase in NO2

Garrett et al. 

(1998) 

Australia 

Subjects: 148 children 

(age 7-14) 

Analysis: multiple logistic 

regression 

Monitoring Device: 

passive monitors 

4 days med 6 Cough 

Gas stove OR 2.25 (1.13, 4.49) 

Bedroom OR 1.47 (0.99, 2.18) 

Shortness of Breath

Gas stove OR 1.49 (0.72, 3.08) 

Bedroom OR 1.23 (0.92, 1.64) 

wheeze 

Gas stove OR 1.79 (0.80, 3.99) 

Bedroom OR 1.15 (0.85, 1.54) 

asthma attack 

Gas stove OR 1.73 (0.77, 3.90) 

Bedroom OR 1.06 (0.77, 1.46) 

chest tightness

Gas stove OR 3.11 (1.07, 9.05) 

Bedroom OR 1.12 (0.81, 1.56) 
6-21 

AUTHOR, 

YEAR, 

LOCATION 

STUDY DESIGN EXPOSURE 

TIME MEAN (SD) RANGE (PPB) 

OUTCOME & ESTIMATE 

(95% CI) 

Smith et al. (2000) 

Australia 

Subjects: 125 asthmatic 

adults/children 

Analysis: GEE 

Monitoring Device: 

passive diffusion badges 

4.5 h overall: 4, 147 children (n = 48, age 0-14) 

wheeze 

OR 1.04 (0.89, 1.12) 

breathlessness 

OR 0.95 (0.70, 1.31) 

chest tightness 

OR 1.29 (1.16, 1.43) 

Cough

OR 1.07 (0.89, 1.29) 

Asthma attack, day

OR 1.13 (1.02, 1.26) 

Asthma stack, night

OR 1.16 (1.03, 1.30) 

results given for 1 SD (43.8-ppb) 

increase in NO2 

Belanger et al. 

(2006) 

Northeast U.S. 

Subjects: 728 asthmatic 

children 

Analysis: logistic, Poisson 

regression 

Monitoring Device: 

Palmes tubes 

2 wks gas home: 26 

(18) 

elect home: 9 

(9) 

 multifamily housing

wheeze RR 1.33 (1.05, 1.68) 

persistent cough RR 1.07 (0.84, 1.35) 

breathlessness RR 1.23 (0.95, 1.59) 

chest tightness RR 1.51 (1.18, 1.91) 

single-family housing

wheeze RR 0.98 (0.78, 1.22) 

persistent cough RR 0.91 (0.69, 1.20) 

breathlessness RR 0.86 (0.63, 1.18) 

chest tightness RR 0.92 (0.68, 1.25) 

Chauhan et al. 

(2003) 

Southampton, U.K. 

Subjects: 114 asthmatic 

children 

Monitoring Device: 

Palmes diffusion tubes 

7 d Exposure 

tertiles: 

<7.5; 7.5-14; 

>14 

 Increased symptom score for all virus 

types

3rd vs. 1st tertile: 0.6 (0.01, 1.18) 

Increased symptom score for RSV only

3rd vs. 1st tertile: 2.1 (0.52, 3.81) 

Van Strien et al. 

(2004) 

Northeast U.S. 

Subjects: 762 infants 

Analysis: Poisson 

regression 

 med: 10 wheeze

<5.1 ppb: RR 1.0 

5.1, 9.9 ppb: RR 1.15 (0.79, 1.67) 

9.9, 17.4 ppb: RR 1.03 (0.69, 1.53) 

>17.4 ppb: RR 1.45 (0.92, 2.27) 

persistent cough

<5.1 ppb: RR 1.0 

5.1, 9.9 ppb: RR 0.96 (0.69, 1.36) 

9.9, 17.4 ppb: RR 1.33 (0.94, 1.88) 

>17.4 ppb: RR 1.52 (1.00, 2.31) 

shortness of breath

<5.1 ppb: RR 1.0 

5.1, 9.9 ppb: RR 1.59 (0.96, 2.62) 

9.9, 17.4 ppb: RR 1.95 (1.17, 3.27) 

>17.4 ppb: RR 2.38 (1.31, 4.34) 

Unless otherwise noted, results given for 20-ppb increase in NO2. 

*For purpose of comparison, RRs from Pilotto et al. (2004) are shown here as risk of symptoms given greater exposure to NO2, i.e., control (unflued gas heater) vs intervention 

(flued or electric replacement heater). 

RRs reported by Pilotto et al. (2004) as protective effects for intervention vs. control. 
6-22 

Table AX6.3-2. Studies examining exposure to ambient NO2 and acute respiratory 

symptoms using generalized estimating equations (GEE) in the analysis method 

AUTHOR, 

YEAR, 

LOCATION 

SUBJECTS AVG 

TIME 

MIDRANGE 

(ppb) 

RANGE (ppb) COPOLLUTANTS & 

CORRELATIONS OUTCOME OR (95% CI) 

Children: Multi-City Studies 

Schwartz 

et al. (1994) 

6 U.S. Cities 

1844 children 24 h med 13 p10-p90, 5, 24 PM2.5: r = 0.35 

PM10: r = 0.36 

O3: r = -0.28 

SO2: r = 0.51 

cough, incidence: 

 lag 1-4 mean 

1.61 (1.08, 2.43) 

Mortimer 

et al. (2002) 

U.S., 

NCICAS 

864 asthmatic 

children 

4 h med 25 7, 90 O3: r =0.27 asthma symptoms: 

lag 1-6 mean 

1.48 (1.02, 2.16) 

Schildcrout 

et al. (2006) 

North 

America 

CAMP 

990 asthmatic 

children 

24 h med 23 min p10 to max 

p90, 10, 37 

PM10: r = 0.26, 0.64 

O3: r = 0.04, 0.47 

SO2: r = 0.23, 0.68 

CO: r = 0.63, 0.92 

asthma symptoms: 

 lag 0 1.06 (1.00, 1.13) 

 lag 1 1.04 (0.97, 1.10) 

 lag 2 1.09 (1.03, 1.15) 

 3-day moving sum 1.04 (1.01, 1.07) 

Ward et al. 

(2002) 

Birmingham 

and 

Sandwell, 

UK 

162 children 24 h Winter 

med 

18.0 

Summer 

med 

13.3 

Winter: 4-35 

Summer 2-33 

O3, PM10, PM2.5, SO2, 

SO4

2-

ΔPEF, morning , lag 0 -0.81 (-3.66, 2.01)( 

 ΔPEF, afternoon , lag 

1 

-1.76 (-4.61, 0.96) 

Children: Single-City Studies 

Pino et al. 

(2004) 

Chile 

504 infants 24 h mean 

(sd) 41 

(19) 

p5-p95, 20, 81 wheezy bronchitis: 

6 day lag 

1.14 (1.04, 1.30) 

Ostro et al. 

(2001) 

Southern 

California 

138 asthmatic 

children, 

African 

American 

1 h mean 

(sd) 80 

(4) 

20, 220 PM2.5: r = 0.34 

PM10: r = 0.63 

O3: r = 0.48 

cough, incidence: 

lag 3 

1.07 (1.00, 1.14) 

 wheeze, incidence: 

lag 3 

1.05 (1.01, 1.09) 

Delfino et al. 

(2002) 

Southern 

California 

22 asthmatic 

children 

8 h mean 

(sd) 15 

(7) 

6, 34 PM10: r = 0.55 

O3: r = 0.26 

asthma symptoms: 

lag 0 

1.91 (1.07, 3.39) 
6-23 

AUTHOR, 

YEAR, 

LOCATION 

SUBJECTS AVG 

TIME 

MIDRANGE 

(ppb) 

RANGE (ppb) COPOLLUTANTS & 

CORRELATIONS OUTCOME OR (95% CI) 

Ségala et al. 

(1998) 

Paris 

84 asthmatic 

children 

24 h mean 

(sd) 30 

(8) 

13, 65 PM2.5: r = (0.61)* 

PM10: r = 0.55 

SO2: r = 0.54 

asthma symptoms: 

incidence: lag 0 

1.89 (1.13, 3.17) 

 lag 1 1.36 (0.70, 2.64) 

 lag 4 1.80 (1.07, 3.01) 

 nocturnal cough: 

incidence: lag 3 

1.44 (0.99, 2.08) 

 lag 4 1.74 (1.20, 2.52) 

Delfino et al. 

(2003a) 

24 asthmatic 

children 

1 h mean 

(sd) 7 (2) 

3, 13 O3

CO 

SO2

lag 0 

asthma symptoms 

NO2:12.0 (1.98, 72.7) 

NO2+Benzene: 3.58 

(0.49, 26.2) 

NO2+Toluene: 5.97 

(0.83, 43.1) 

NO2+xylene: 6.33 

(0.72, 55.4) 

Linn et al. 

(1996) 

269 children 24 h 

1 h 

32-42 

175-195 

 PM10: r = 0.48 

O3: r = 0.61 

FVC. a.m. 

FEV1, a.m. 

FVC, p.m. 

FEV1, p.m. 

∆FVC, p.m.-a.m. 

∆FEV1, p.m.-a.m. 

Total score, a.m. 

Total score, p.m. 

-0.40 (0.16) 

-0.11 (0.18) 

-0.18 (0.21) 

-0.26 (0.19) 

-0.11 (0.18) 

-0.39 (0.16) 

-0.01 (0.022) 

-0.028 (0.025) 

Just et al. 

(2002) 

Paris 

82 asthmatic 

children 

24 h mean 

(sd) 29 

(9) 

12, 59 PM2.5: r = 0.92* 

PM10: r = 0.54 

O3: r = 0.09 

SO2: r = 0.69 

nocturnal cough: 

incidence: lag 0 

2.11 (1.20, 3.74) 

 lag 0-2 1.80 (0.89, 3.84) 

 lag 0-4 1.58 (0.73, 3.54) 

Jalaludin 

et al. (2004) 

Austrailia 

148 children 

with wheeze 

history 

15 h mean 

(sd) 15 

(6) 

3, 79 PM10: r = 0.26 

O3: r = -0.31 

Wet cough: lag 0 1.13 (1.00, 1.26) 

Adults 

Ségala et al. 

(2004) Paris 

46 nonsmoking 

adults 

24 h mean 

(sd) 30 

(9) 

12, 71 PM2.5: r = 0.82* 

PM10: r = 0.83 

sore throat, cough: 

lag 0-4 

4.05 (1.20, 13.60) 
6-24 

AUTHOR, 

YEAR, 

LOCATION 

SUBJECTS AVG 

TIME 

MIDRANGE 

(ppb) 

RANGE (ppb) COPOLLUTANTS & 

CORRELATIONS OUTCOME OR (95% CI) 

Von Klot 

et al. (2002) 

Germany 

53 asthmatic 

adults 

24 h med 24 4, 63 PM10: r = 0.74 

SO2: r = 0.36 

CO: r = 0.82 

wheeze, prev: 

5-day mean 

1.15 (1.02, 1.31) 

 phlegm, prev: 

5-day mean 

1.22 (1.10, 1.39) 

 cough, prev: 

5-day mean 

1.15 (1.00, 1.31) 

 breathing prob in 

a.m.: 5-day mean 

1.25 (1.10, 1.39) 

Odds ratios (OR) given for 20 ppb increase in NO2 with 24-h averaging time, or 30 ppb for 1-h averaging time. (20 ppb increases also used for times between 1 and 24 h.) *BS 

Table AX6.3-3. Respiratory health effects of oxides of nitrogen: hospital admissions. 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

UNITED STATES 

Moolgavkar 

(2000a,b,c) 

Moolgavkar (2003) 

Multi-city, United 

States: Chicago, 

Los Angeles, 

Maricopa County, 

(Phoenix). 

Period of Study: 

1987-1995 

Outcomes (ICD 9 codes): COPD 

including asthma (490-496) 

Age groups analyzed: 0-19, 20-64, 

65+ (LA only) 

Study Design: Time-series 

Statistical Analyses: Poisson 

regression, GAM 

Covariates: Day of wk, temporal 

trends, temperature, relative humidity 

Lag: 0-5 days 

Chicago 

Median: 25 ppb 

IQR: 10 ppb 

Los Angeles 

Median: 38 ppb 

IQR: 18 ppb 

Maricopa 

Median: 19 ppb 

IQR: 12 ppb 

Chicago: 

PM10; r = 0.49 

CO; r = 0.63 

SO2; r = 0.44 

O3; r = 0.02 

LA: 

PM2.5; r = 0.73 

PM10; r = 0.70 

CO; r = 0.80 

SO2; r = 0.74 

O3; r = -0.10 

Maricopa: 

PM10; r = 0.22 

CO; r = 0.66 

SO2; r = 0.02 

O3; r = -0.23 

Increment: 10 ppb 

COPD, >65 yrs 

Chicago 1.7% [CI 0.36, 3.05] lag 

0 - GAM default 

Chicago 2.04% [t = 2.99] lag 0 - 

GAM-100 

Los Angeles 2.5% [CI 1.85, 3.15] 

lag 0 - GAM default 

Los Angeles 2.84% [t = 13.32] lag 

0 - GAM - 30 

Los Angeles 1.80% [t = 9.60] lag 

0 - GAM - 100 

Los Angeles 1.78% [t = 7.72] lag 

0 - NR-100 

Phoenix 4.4% [CI 1.07, 7.84] lag 

5 

Chronic Respiratory Disease 

Los Angeles 

0-19 yrs 4.9% [CI 4.1, 5.7] lag 2 

20-64 yrs 1.7% [CI 0.9, 2.1] lag 2 

Multi-pollutant model 

NO2 and PM10: 1.72% [t = 3.18] 

lag 0 - GAM-100 

NO2 and PM2.5: 1.51% [t = 2.07] 

lag 0 - GAM-100 
6-25 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Moolgavkar*et al. 

(1997) 

United States: 

Minneapolis-St. 

Paul 

Period of Study: 

1986-1991 

Outcomes (ICD 9 codes): COPD 

including asthma (490-496), 

Pneumonia (480-487) 

Age groups analyzed: 65+ 

Study Design: Time-series 

Statistical Analyses: Semi-parametric 

Poisson regression, GAM 

Covariates: Day of wk, season, 

temporal trends, temperature 

Statistical Package: S Plus 

Lag: 0-3 days 

NO2 24-h avg (ppb) 

16.3 ppb 

IQR: 9.5 ppb 

PM10; r = 0.31 

SO2; r = 0.09 

CO; r = 0.58 

Increment: 10 ppb 

Sum of Pneumonia and COPD 

2.2% [0.2, 4.2] lag 1 

Pneumonia Only 

3.1% [0.6, 5.6] lag 1, 20 df 

1.7% [-0.8, 4.2] lag 1, 130 df 

Neidell (2004) 

California 

Period of Study: 

1992-1998 

Outcomes (ICD 9 codes): Asthma 

Age groups analyzed: <18; 0-1; 1-3; 

3-6; 

6-12; 12-18 

Study Design: Time-series 

Statistical Analyses: NR 

Covariates: Temperature, 

precipitation, influenza epidemic 

Seasons: Nov-Mar only 

Lag: 0-4 days 

NO2 (ppb) 

Mean: 45.947 

SD = 17.171 

O3

CO 

PM10 

Increment: NR 

Age 0-1 

Fixed effects: 0.009 (0.014) 

Controlled for avoidance 

behavior: 0.009 (0.014) 

Single-pollutant: 0.001 (0.011) 

Adjusted for SES: 0.021 (0.017) 

Interaction with Low SES: -0.017 

(0.029) 

Age: 1-3 

Fixed effects: 0.002 (0.016) 

Controlled for avoidance 

behavior: 0.002 (0.016) 

Single-pollutant: 0.009 (0.013) 

Adjusted for SES: -0.001 (0.020) 

Interaction with Low SES: -0.004 

(0.032) 

Age 3-6 

Fixed effects: 0.006 (0.016) 

Controlled for avoidance 

behavior: 0.006 (0.016) 

Single-pollutant: 0.028 (0.013) 

Adjusted for SES: 0.020 (0.020) 

Interaction with Low SES: -0.037 

(0.033) 

Age 6-12 

Fixed effects: 0.041 (0.015) 

Controlled for avoidance 

behavior: 0.042 (0.015) 

Single-pollutant: 0.047 (0.012) 

Adjusted for SES: 0.040 (0.018) 

Interaction with Low SES: -0.016 

(0.031) 

Age: 12-18 

Fixed effects: 0.005 (0.013) 

Controlled for avoidance 

behavior: 0.005 (0.013) 

Single-pollutant: 0.015 (0.010) 

Adjusted for SES: 0.013 (0.017) 

Interaction with Low SES: -0.020 

(0.026) 
6-26 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Karr et al. (2006) 

Southern LA 

County, CA, United 

States 

Period of Study: 

1995-2000 

Outcomes (ICD 9 codes): Acute 

bronchiolitis (466.1) 

Age groups analyzed: 0-1 yr 

Study Design: Case-crossover 

N: 19,109 

Statistical Analyses: Conditional 

logistic regression 

Covariates: Day of wk, temperature, 

humidity 

Seasons: Nov-Mar only 

Lag: 0-4 days 

1-h max NO2 (ppb) 

Mean: 59 ppb 

IQR: 26 ppb 

Number of Stations: 34 

CO 

PM2.5 

Increment: 26 ppb (IQR) 

Acute bronchiolitis 

OR 0.96 [0.94, 0.99] lag 4 

OR 0.97 [0.95, 0.99] lag 1 

Stratified by Gestational Age at 

Birth: 

37-44 wks 

0.98 [0.95, 1.00] lag 1; 0.97 [0.94, 

0.99] lag 4 

34-36 wks 

0.90 [0.84, 0.97] lag 1; 0.94 [0.88, 

1.02] lag 4 

29-33 wks 

1.01 [0.91, 1.13] lag 1; 0.90 [0.80, 

1.01] lag 4 

25-28 wks 

0.94 [0.78, 1.13] lag 1; 0.90 [0.73, 

1.11] lag 4 

Linn et al. (2000) 

Los Angeles, 

United States 

Period of Study: 

1992-1995 

Outcomes (ICD 9 codes): Asthma 

(493), COPD (APR-DRG 88), 

Pulmonary diagnoses 

(APR-DRG 75-101) 

Age groups analyzed: >30 

Study Design: Time-series 

N: 302,600 

Statistical Analyses: Poisson 

regression, GAM, OLS regression 

Covariates: day of wk, holiday, max 

temperature, min temperature, rain 

days, mean temperature, barometric 

pressure, season 

Seasons: Winter (Jan-Mar), 

Spring (Apr-Jun), Summer (Jul-Sep), 

Fall (Oct-Dec) 

Statistical Package: SPSS and SAS 

Lag: 0, 1 days 

All concentrations are in 

pphm. 

Winter: 3.4 ± 1.3 

Spring: 2.8 ± 0.9 

Summer: 3.4 ± 1.0 

Autumn: 4.1 ± 1.4 

Overall: 3.4 ± 1.3 

Winter 

CO; r = 0.89 

PM10; r = 0.88 

O3; r = -0.23 

Spring 

CO; r = 0.92 

PM10; r = 0.67 

O3; r = 0.35 

Summer 

CO; r = 0.94 

PM10; r = 0.80 

O3; r = 0.11 

Winter 

CO; r = 0.84 

PM10; r = 0.80 

O3; r = -0.00 

Increment: 10 ppb 

All pulmonary 

All seasons: 0.7% ± 0.3% 

Winter: 1.1% ± 0.5% 

Spring: 0.7% ± 0.1% 

Summer: 0.4% ± 0.8% 

Autumn: 1.2% ± 0.4% 

Asthma 

All season: 1.4% ± 0.5% 

Winter: 2.8% ± 0.1% 

Spring: NR 

Summer: NR 

Autumn: 1.9% ± 0.8% 

COPD 

All season: 0.8% ± 0.4% 

Winter: NR 

Spring: NR 

Summer: NR 

Autumn: 1.6% ± 0.6% 

Magas et al. 

(2007) 

Oklahoma City, OK 

Period of Study: 

2001-2003 

Outcomes (ICD 9 codes): Asthma 

(493) 

Age groups analyzed: 0-14 

Study Design: Time-series 

N: 1,270 

Statistical Analyses: negative 

binomial regression 

Covariates: gender, day of wk, 

holiday, 

Lag: 

24-h avg: 11.7 ppb 

Number of monitors: 10 

O3

PM2.5 

Qualitative results: ambient 

concentrations of NO2 increased 

pediatric asthma hospitalizations 
6-27 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Gwynn*et al. 

(2000) 

Buffalo, NY 

United States 

Period of Study: 

1988-1990 

Days: 1,090 

Outcomes (ICD 9 codes): Respiratory 

admissions: Acute 

bronchitis/bronchiolitis (466); 

Pneumonia (480-4860); COPD and 

Asthma (490-493, 496) 

Age groups analyzed: 6 

Study Design: Time-series 

N: 24, 

Statistical Analyses: Poisson 

regression with GLM and GAM 

Covariates: Season, day of wk, 

holiday, temperature, relative 

humidity 

Lag: 0-3 days 

24-h avg NO2 (ppb): 

Min: 4.0 

25th: 15.5 

Mean: 20.5 

75th: 24.5 

Max: 47.5 

H+; r = 0.22 

SO42-; r = 0.36 

PM10; r = 0.44 

O3; r = 0.06 

SO2; r = 0.36 

CO; r = 0.65 

COH; r = 0.72 

Increment: 27 9 ppb (Max-Mean; 

IQR) 

NO2 alone: 

Max-Mean RR 1.033 (t = 1.32) 

lag 1 

IQR RR 1.01 (t = 1.32) lag 1 

Zanobetti and 

Schwartz (2006) 

Boston, MA, 

United States 

Period of Study: 

1995-1999 

Outcomes (ICD 9 codes): Pneumonia 

(480-7) 

Age groups analyzed: 65+ 

Special Population: Medicare patients 

only 

Study Design: Case-crossover 

N: 24,857 

Statistical Analyses: Conditional 

logisitic regression 

Covariates: Apparent temperature, 

day of wk 

Seasons: Warm (Apr-Sep), Cool 

(Oct-Mar) 

Statistical Package: SAS 

Lag: 0, 1 days, 0-1 avg 

NO2 median 23.20 ppb; 90-

10%: 20.41 ppb; 

For lag 0-1 2 day avg 90-

10% = 16.8 ppb; 

IQR = 10.83 

Number of Stations: 5 

PM2.5; r = 0.55 

BC; r = 0.70 

CO; r = 0.67 

O3; r = -0.14 

Increment: 20.41 ppb (90-10%) 

Pneumonia 

-0.16% [-4.73, 4.42] lag 0 

Increment: 16.78 ppb (90-10%) 

Pneumonia 

2.26% [-2.55, 7.01] lag 0-1 

CANADA 

Burnett et al. 

(1997a) 

16 cities 

Canada 

Period of Study: 

4/1981-12/1991 

Days: 3,927 

Outcomes (ICD 9 codes): All 

respiratory admissions 

(466, 480-6, 490-4, 496) 

Study Design: Time-series 

N: 720,519 

# of hospitals: 134 

Statistical Analyses: Random effects 

relative risk regression model 

Covariates: Long-term trend, season, 

day of wk, hospital, 

Statistical Package: NR 

Lag: 0, 1, 2 day 

1-h max NO2 (ppb) 

Mean: 35.5 

SD = 16.5 

25th: 25 

50th: 33 

75th: 43 

95th: 62 

99th: 87 

O3; r = 0.20 

CO 

SO2

COH 

Increment: 10 ppb 

Single-pollutant 

NO2 and respiratory admissions, 

p = 0.772 

Multipollutant model (adjusted for 

CO, O3, SO2, COH, dew point): 

RR 0.999 [0.9922, 1.0059] lag 0 
6-28 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Yang et al. (2003) 

Vancouver, 

Canada 

Period of Study: 

1986-1998 

Days: 4748 

Outcomes (ICD 9 codes): All 

respiratory admissions (460-519) 

Study Design: Case-crossover 

Age groups analyzed: <3, ≥65 

Statistical Analyses: Conditional 

logistic regression 

Statistical Package: NR 

Lag: 0-5 days 

24-h avg NO2 (ppb): 

Mean: 18.74 

SD = 5.66 

5th: 11.35 

25th: 14.88 

50th: 17.80 

75th: 21.45 

100th: 49.00 

IQR: 5.57 

Number of stations: 30 

CO 

SO2

O3; r = -0.32 

COH 

Increment: 5.57 ppb (IQR) 

All Respiratory Admissions <3 

yrs: 

NO2 alone: OR 1.05 [1.02, 1.09] 

lag 1 

NO2 + O3: OR 1.05 [1.02, 1.09] 

lag 1 

NO2 + O3 + CO + COH + SO2: 

OR 1.05 

[0.99, 1.11] lag 1 

All Respiratory Admissions ≥65 

yrs: 

NO2 alone: OR 1.05 [1.03, 1.07] 

lag 1 

NO2 + O3: OR 1.04 [1.02, 1.07] 

lag 1 

NO2 + O3 + CO + COH + SO2: 

OR 1.05 

 [1.01, 1.08] lag 1 

Fung et al. (2006) 

Vancouver, BC, 

Canada 

Period of Study: 

6/1/95-3/31/99 

Outcomes (ICD 9 codes): All 

respiratory hospitalizations (460-519) 

Age groups analyzed: 65+ 

Study Design: (1) Time-series, (2) 

Case-crossover, (3) DM-models 

(Dewanji and Moolgavkar 2000, 

2002) 

N: 40,974 

Statistical Analyses: (1) Poisson, 

(2) conditional logistic regression, (3) 

DM method – analyze recurrent data 

in which the occurrence of events at 

the individual level over time is 

available 

Covariates: Day of wk 

Statistical Package: S-Plus and R 

Lag: Current day, 3 and 5 day lag 

NO2 24-h avg: 

Mean: 16.83 ppb, 

SD = 4.34; 

IQR: 5.43 ppb; 

Range: 7.22, 33.89 

CO; r = 0.74 

COH; r = 0.72 

SO2; r = 0.57 

PM10; r = 0.54 

PM2.5; r = 0.35 

PM10-2.5; r = 0.52 

O3; r = -0.32 

Increment: 5.43 ppb. (IQR) 

NO2 Time-series 

RR 1.018 [1.003, 1.034] lag 0 

RR 1.024 [1.004, 1.044] lag 0-3 

RR 1.025 [1.000, 1.050] lag 0-5 

RR 1.027 [0.998, 1.058] lag 0-7 

NO2 Case-crossover 

RR 1.028 [1.010, 1.047] lag 0 

RR 1.035 [1.012, 1.059] lag 0-3 

RR 1.032 [1.006, 1.060] lag 0-5 

RR 1.028 [0.997, 1.060] lag 0-7 

NO2 DM model 

RR 1.012 [0.997, 1.027] lag 0 

RR 1.018 [1.000, 1.037] lag 0-3 

RR 1.007 [0.988, 1.026] lag 0-5 

RR 1.002 [0.981, 1.023] lag 0-7 

DM method produced slightly 

higher RR estimates on O3, SO2, 

and PM2.5 compared to timeseries and case-crossover, and 

slightly lower RR estimates on 

COH, NO2, and PM10, though the 

results were not significantly 

different from one another. 
6-29 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Yang (2005) 

Vancouver, BC, 

Canada 

Period of Study: 

1994-1998 

Days: 1826 

Outcomes (ICD 9 codes): COPD 

excluding asthma (490-2, 494, 496) 

Age groups analyzed: 65+ 

Study Design: Time-series 

N: 6,027 

Statistical Analyses: Poisson 

regression with GAM (with more 

stringent criteria) 

Covariates: Temperature, relative 

humidity, day of wk, temporal trends, 

season 

Statistical Package: S-Plus 

Lag: 0-6 days, moving avgs 

24-h avg: 17.03 ppb, SD = 

4.48; 

IQR: 5.47 ppb; 

Range: 4.28, 33.89 

Winter: 19.20 (4.86) 

Spring: 15.36 (3.72) 

Summer: 16.33 (4.57) 

Fall: 17.27 (3.77) 

Number of Stations: 31 

PM10; r = 0.61 

SO2; r = 0.61 

CO; r = 0.73 

O3; r = -0.10 

Increment: 5.5 ppb (IQR) 

COPD 

>65 yrs, yr round 

RR 1.05 [1.01, 1.09] lag 0 

RR 1.04 [1.00, 1.10] lag 0-1 

RR 1.07 [1.01, 1.13] lag 0-2 

RR 1.08 [1.02, 1.15] lag 0-3 

RR 1.10 [1.03, 1.17] lag 0-4 

RR 1.11 [1.04, 1.19] lag 0-5 

RR 1.11 [1.04, 1.20] lag 0-6 

Two-pollutant model 

PM10: RR 1.03 [0.90, 1.17] lag 0 

CO: RR 1.07 [0.96, 1.20] lag 0-6 

O3: RR 1.12 [1.04, 1.20] lag 0-6 

Multipollutant models 

NO2, CO, SO2, O3, PM10: RR 

1.01 [0.88, 1.16] 

NO2, CO, SO2, O3: RR 1.06 [0.95, 

1.19] 

NO2 was strongest predictor of 

hospital admission for COPD 

among all gaseous pollutants in 

single-pollutant models. 
6-30 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Lin* et al. (2004) 

Vancouver, BC 

Canada 

Period of Study: 

1987-1991 

Outcomes (ICD 9 codes): Asthma 

(493) 

Age groups analyzed: 6-12 

Study Design: Time-series 

N: 3,754 (2,331 male, 1,423 female) 

Statistical Analyses: Semi-parametric 

Poisson regression with GAM (with 

default and more stringent criteria) 

Covariates: Trend, day of wk 

Statistical package: S-Plus 

Lag: Cumulative 1-7 day 

24-h avg NO2 (ppb) 

Mean: 18.65 

SD = 5.59 

Min: 4.28 

25th: 14.82 

50th: 17.75 

75th: 21.36 

Max: 45.36 

Number of stations: 30 

CO; r = 0.73 

SO2; r = 0.67 

O3; r = -0.03 

PM2.5; r = 0.37 

PM10; r = 0.55 

Increment: 6.54 ppb (IQR) 

Boys 6-12 yrs by SES status: 

Low; High 

Lag 1 RR 1.13 [1.04, 1.23]; 1.04 

[0.95, 1.14] 

Lag 2 RR 1.13 [1.02, 1.24]; 1.06 

[0.95, 1.18] 

Lag 3 RR 1.14 [1.02, 1.27]; 1.06 

[0.94, 1.19] 

Lag 4 RR 1.14 [1.02, 1.28]; 1.05 

[0.92, 1.19] 

Lag 5 RR 1.12 [0.99, 1.27]; 1.10 

[0.96, 1.26] 

Lag 6 RR 1.12 [0.98, 1.28]; 1.07 

[0.93, 1.23] 

Lag 7 RR 1.11 [0.97, 1.28]; 1.09 

[0.94, 1.27] 

Girls 6-12 yrs by SES status: 

Low; High 

Lag 1 RR 1.07 [0.96, 1.19]; 1.01 

[0.90, 1.13] 

Lag 2 RR 1.03 [0.91, 1.17]; 0.98 

[0.85, 1.12] 

Lag 3 RR 1.04 [0.91, 1.20]; 0.98 

[0.84, 1.13] 

Lag 4 RR 1.11 [0.95, 1.29]; 1.01 

[0.86, 1.19] 

Lag 5 RR 1.11 [0.94, 1.30]; 0.99 

[0.83, 1.17] 

Lag 6 RR 1.08 [0.91, 1.28]; 1.03 

[0.86, 1.24] 

Lag 7 RR 1.07 [0.90, 1.28]; 1.09 

[0.90, 1.32] 

Multipollutant model (adjusted for 

SO2) 

Boys, Low SES: 

1.16 [1.06, 1.28] lag 1 

1.18 [1.03, 1.34] lag 4 

Results presented are default 

GAM, but authors state that use 

of natural cubic splines with a 

more stringent convergence rate 

produced similar results. 

Chen et al. (2005) 

Vancouver, BC 

Period of Study: 

6/1995-3/1999 

Outcomes (ICD 9 codes): All 

Respiratory (460-519) 

Age groups analyzed: 65+ 

Study Design: Time-series 

N: 12,869 overall admissions 

Statistical Analyses: Poisson 

regression with GLM 

Covariates: Trend, day of wk, weather

Statistical package: S-Plus 

Lag: 1-7 

24-h avg: 16.8 (4.3) ppb 

Range: 7.2-33.9 

IQR: 5.4 

PM10; r = 0.54 

PM10-2.5; r = 0.54 

PM2.5; r = 0.36 

CO; r = 0.74 

SO2; r = 0.57 

O3; r = -0.32 

No analyses for NO2
6-31 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Lin et al. (2003) 

Toronto, ON 

Period of Study: 

1981-1993 

Outcomes (ICD 9 codes): Asthma 

(493) 

Age groups analyzed: 6-12 

Study Design: Bi-directional casecrossover 

N: 7,319 

Statistical Analyses: Conditional 

logistic regression 

Covariates: Daily maximum and 

minimum temperatures and avg 

relative humidity 

Lag: Cumulative lag of 1-7 days 

NO2 24-h avg: 25.24 ppb, SD 

= 9.04; 

IQR: 11 ppb; 

Range: 3.00, 82.00 

Number of Stations: 4 

CO; r = 0.55 

SO2; r = 0.54 

PM10; r = 0.52 

O3; r = 0.03 

PM2.5; r = 0.50 

PM10-2.5; r = 0.38 

Increment: 11 ppb (IQR) 

Boys 6-12 yrs; Girls 6-12 yrs 

Lag 0: OR 1.04 [0.99, 1.10]; 0.99 

[0.92, 1.06] 

Lag 0-1: OR 1.07 [1.00, 1.14]; 

1.03 [0.94, 1.12] 

Lag 0-2: OR 1.09 [1.01, 1.17]; 

1.07 [0.96, 1.18] 

Lag 0-3: OR 1.10 [1.01, 1.20]; 

1.09 [0.97, 1.21] 

Lag 0-4: OR 1.10 [1.00, 1.20]; 

1.14 [1.02, 1.28] 

Lag 0-5: OR 1.12 [1.01, 1.23]; 

1.16 [1.02, 1.31] 

Lag 0-6: OR 1.11 [1.00, 1.24]; 

1.16 [1.02, 1.32] 

Burnett et al. 

(1997b) 

Toronto, Canada 

Period of Study: 

1992-1994 

Outcomes (ICD 9 codes): Respiratory 

tracheobronchitis (480-6), COPD 

(491-4, 496) 

Study Design: Time-series 

Statistical Analyses: Poisson 

regression, GEE, GAM 

Covariates: Temperature, dew point 

temperature, long-term trend, 

season, influenza, day of wk 

Seasons: summers only 

Lag: 0,1,2,3,4 days 

Mean NO2: 38.5 ppb 

IQR NO2: 5.75 ppb 

Range: 12, 81 

Number of Stations: 

6-11 

PM10; r = 0.61 

CO; r = 0.25 

H+; r = 0.25 

SO4; r = 0.34 

TP; r = 0.61 

FP; r = 0.45 

CP; r = 0.57 

COH; r = 0.61 

O3; r = 0.07 

SO2; r = 0.46 

Increment: 5.75 ppb (IQR) 

Respiratory - Percent increase 

4.4% [CI 2.4, 6.4], lag 0 

Copollutant and multipollutant 

models RR 

(t-statistic): 

NO2, COH: 1.018 (1.36) 

NO2, H+: 1.037 (3.61) 

NO2, SO4: 1.033 (3.05) 

NO2, PM10: 1.039 (2.85) 

NO2, PM2.5: 1.037 (3.13) 

NO2, PM10-2.5: 1.037 (2.96) 

NO2, O3, SO2: 1.028 (2.45) 

NO2, O3, SO2, COH: 1.010 (0.71) 

NO2, O3, SO2, H+: 1.027 (2.39) 

NO2, O3, SO2, SO4: 1.027 (2.36) 

NO2, O3, SO2, PM10: 1.028 (1.77)

NO2, O3, SO2, PM2.5: 1.028 

(2.26) 

NO2, O3, SO2, PM10-2.5: 1.022 

(1.71) 
6-32 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Burnett et al. 

(1999) 

Metro Toronto, 

Canada 

Period of Study: 

1980-1994 

Outcomes (ICD 9 codes): Asthma 

(493); Obstructive lung disease (490-

2, 496); Respiratory Infection (464, 

466, 480-7, 494) 

Study Design: Time-series 

Statistical Analyses: Poisson 

regression model with stepwise 

analysis 

Covariates: Long-term trends, 

season, day of wk, daily maximum 

temperature, daily minimum 

temperature, daily avg dew point 

temperature, daily avg relative 

humidity 

Statistical Package: S-Plus, SAS 

Lag: 0,1,2 days, cumulative 

24-h mean: 25.2 ppb, SD = 

9.1, 

CV: 36; 

IQR: 23 

Number of stations: 4 

COH; r = NR 

PM2.5; r = 0.50 

PM10-2.5; r = 0.38 

PM10; r = 0.52 

CO; r = 0.55 

SO2; r = 0.54 

O3; r = -0.03 

Increment: 25.2 ppb (Mean) 

7.72 excess daily admissions due 

to pollution of all sorts. 40.4% 

increase; or 3 excess daily 

admissions traced to NO2. 

Single-pollutant model percent 

increase (t statistic) 

Asthma: 3.33% (2.37) lag 0 

OLD 2.21% (1.07) lag 1 

Respiratory infection: 6.89% 

(5.53), lag 2 

Multipollutant model percent 

increase (SE) 

Respiratory infection: 

NO2 alone: 4.64 (SE ≥3) 

NO2 + SO2 + O3 + PM2.5: 4.04 

(SE ≥2) 

NO2 + SO2 + O3 + PM10-2.5: 4.56 

(SE ≥3) 

NO2 + SO2 + O3 + PM10: 4.16 

(SE ≥3) 

NO2 + O3 + PM2.5: 4.44 (SE ≥2) 

Lin et al. (2005) 

Toronto, Canada 

Period of Study: 

1998-2001 

Outcomes (ICD 9 codes): Respiratory 

infections (464, 466, 480-487) 

Age groups analyzed: 0-14 

N: 6,782 

Study Design: Bidirectional casecrossover 

Statistical Analyses: Conditional 

logistic regression 

Covariates: Temperature, dew point 

temperature 

Statistical Package: SAS v 8.2 

Lag: 1-7 day exposure averages 

24-h avg: 24.54 (7.56) ppb 

Range: 9.2-53.75 

25th: 18.75 

50th: 24.00 

75th: 29.33 

Number of monitors: 7 

CO; r = 0.20 

SO2; r = 0.61 

O3; r = 0 

PM10; r = 0.54 

PM2.5; r = 0.48 

PM10-2.5; r = 0.40 

Increment: 10.6 ppb (IQR) 

All children: 

NO2 alone: 1.20 [1.08, 1.34] lag 

0-5 

NO2 + PM2.5 + PM10-2.5: 1.13 

[0.97, 1.31] lag 0-5 

Boys: 

NO2 alone: 1.13 [0.98, 1.29] lag 

0-5 

NO2 + PM2.5 + PM10-2.5: 1.00 

[0.83, 1.21] lag 0-5 

Girls: 

NO2 alone: 1.28 [1.09-1.50] lag 0-

5 

NO2 + PM2.5 + PM10-2.5: 1.31 

[1.05, 1.63] lag 0-5 

Burnett* et al. 

(2001) 

Toronto, Canada 

Period of Study: 

1980-1994 

Outcomes (ICD 9 codes): Croup 

(464.4), pneumonia (480-486), 

asthma (493), acute 

bronchitis/bronchiolitis (466) 

Age groups analyzed: <2 yrs 

Study Design: Time-series 

Statistical Analyses: Poisson 

regression with GAM 

Covariates: temporal trend, day of 

wk, temperature, relative humidity 

Statistical Package: S-Plus 

Lag: 0-5 days 

1-h max NO2 (ppb) 

Mean: 44.1 

CV: 33 

5th: 25 

25th: 35 

50th: 42 

75th: 52 

95th: 70 

99th: 86 

100th: 146 

Number of stations: 4 

O3; r = 0.52 

SO2

CO 

PM2.5 

PM10-2.5 

Increment: NR 

All respiratory admissions: 

Single-pollutant: 

Percent increase: 20.2 (t = 3.43) 

lag 0-1 

Multipollutant (adjusted for O3): 

Percent increase: 7.1 (t = 1.09) 

lag 0-1 
6-33 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Fung et al. (2007) 

Ontario, Canada 

Period of Study: 

1996-2000 

Outcomes (ICD 9 codes): All 

respiratory (460-519) 

Age groups analyzed: 0-4, 5-14, 15-

19, 20-24, 25-54, 55-64, 65-74, 75+ 

Study Design: 

Statistical Analyses: 

Covariates: 

Statistical Package: 

Lag: 

London 

Mean: 18.10 ppb (7.86) 

Range: 0-53 

Windsor 

Mean: 23.50 ppb (7.59) 

Range: 6-50 

Sarnia 

Mean: 16.85 ppb (8.13) 

Range: 0-52 

SO2

O3

CO 

Not reported 

Luginaah et al. 

(2005) 

Windsor, ON, 

Canada 

Period of Study: 

4/1/95-12/31/00 

Outcomes (ICD 9 codes): Respiratory 

admissions (460-519) 

Age groups analyzed: 0-14, 15-64, 

65+, all ages 

Study Design: (1) Time-series and (2) 

case-crossover 

N: 4,214 

# of Hospitals: 4 

Statistical Analyses: (1) Poisson 

regression, GAM with natural splines 

(stricter criteria), (2) conditional 

logistic regression with Cox 

proportional hazards model 

Covariates: Temperature, humidity, 

change in barometric pressure, day 

of wk 

Statistical Package: S-Plus 

Lag: 1,2,3 days 

NO2 mean 1-h max: 

38.9 ppb, 

SD = 12.3; 

IQR: 16 

Number of stations: 4 

SO2; r = 0.22 

CO; r = 0.38 

PM10; r = 0.33 

COH; r = 0.49 

O3; r = 0.26 

TRS; r = 0.06 

Increment: 16 ppb (IQR) 

Time-series, females; males 

All ages, lag 1 1.035 [0.971, 

1.104]; 0.944 

[0.886, 1.006] 

0-14 yrs, lag 2 1.114 [0.994, 

1.248]; 0.955 

[0.866, 1.004] 

15-65 yr, lag 3 1.121 [0.978, 

1.285], 1.012 

[0.841, 1.216] 

65+ yr, lag 1 1.020 [0.930, 1.119]; 

0.9196 

[0.832, 1.016] 

Case-crossover, females; males 

All ages, lag 1 1.078 [0.995, 

1.168]; 0.957 

[0.883, 1.036] 

0-14 yrs, lag 2 1.189 [1.002, 

1.411]; 0.933 

[0.810, 1.074] 

15-65 yr, lag 3 1.114 [0.915, 

1.356]; 0.972 

[0.744, 1.268] 

65+ yr, lag 1 1.081 [0.964, 1.212]; 

0.915 

[0.810, 1.034] 
6-34 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

AUSTRALIA/NEW ZEALAND 

Simpson et al. 

(2005a) 

Multi-city study, 

Australia (Sydney, 

Melbourne, 

Brisbane, Perth) 

Period of Study: 

1996-1999 

Outcomes (ICD 9/ICD 10): All 

respiratory (460-519/J00-J99 

excluding J95.4-J95.9, RO9.1, 

RO9.8), asthma (493/J45, J46, 

J44.8), COPD (490-492, 494-

496/J40-J44, J47, J67), pneumonia 

with bronchitis (466, 480-486/J12-17, 

J18.0, J18.1, J18.8, J18.9, J20, J21) 

Age groups analyzed: 15-64 

(asthma), 65+ (all respiratory, COPD, 

asthma, pneumonia with bronchitis) 

Study Design: Time-series 

Statistical Analyses: Followed 

APHEA2 protocol: (1) Single city: (a) 

GAM with default and more stringent 

criteria, (b) GLM with default and 

more stringent criteria, (c) penalized 

spline models. (2) Multicity meta 

analysis: random effects metaanalysis 

Covariates: Temperature, relative 

humidity, day of wk, holiday, influenza 

epidemic, brushfire/controlled burn 

Statistical Package: S-Plus, R 

Lag: 0,1,2 days 

1 h max NO2 ppb (range) 

Brisbane: 24.1 

(2.1, 63.3) 

Sydney: 23.7 

(6.5, 59.4) 

Melbourne: 23.7 

(4.4, 66.7) 

Perth: 16.3 (1.9, 41.0) 

Brisbane: 

O3; r = 0.15 

BSP; r = 0.50 

Melbourne: 

O3; r = 0.30 

BSP; r = 0.29 

Sidney: 

O3; r = 0.24 

BSP; r = 0.54 

Perth: 

O3; r = 0.28 

BSP; r = 0.62 

Increment: 1 ppb 

Respiratory 

≥65 yrs 1.0027 [1.0015, 1.0039] 

lag 0-1 

COPD and Asthma 

>65 yrs 1.0020 [1.0003, 1.0037] 

lag 0-1 

Pneumonia and Acute Bronchitis 

>65 yrs 1.0030 [1.0011, 1.0048] 

lag 0-1 

Multipollutant Model 

Respiratory ≥65 yrs 

NO2 Alone: 1.0027 

[1.0015,1.0039] lag0-1 

NO2 + BSP: 1.0023 [1.0009, 

1.0038] lag 0-1 

NO2 + O3: 1.0028 [1.0016, 

1.0040] lag 0-1 

GAM results from S-Plus and R 

similar to one another, but 

different than results from GLM. 

GAM results from S-Plus 

presented. 
6-35 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Barnett et al. 

(2005) 

Multicity, Australia/ 

New Zealand; 

(Auckland, 

Brisbane, 

Canberra, 

Christchurch, 

Melbourne, Perth, 

Sydney) 

Period of Study: 

1998-2001 

Outcomes (ICD 9/ICD 10): All 

respiratory (460-519/J00-J99 

excluding J95.4-J95.9, RO9.1, 

RO9.8), asthma (493/J45, J46, 

J44.8), COPD (490-492, 494-

496/J40-J44, J47, J67), pneumonia 

with bronchitis (466, 480-486/J12-17, 

J18.0, J18.1, J18.8, J18.9, J20, J21) 

Age groups analyzed: 0, 1-4, 5-14 

Study Design: Case-crossover 

Statistical Analyses: Conditional 

logistic regression, random effects 

meta-analysis 

Covariates: Temperature, currentprevious day temperature, relative 

humidity, pressure, extremes of hot 

and cold, day of wk, holiday, day after 

holiday 

Season: Cool, May-Oct; Warm, NovApr 

Statistical Package: SAS 

Lag: 0-1 days 

24-h avg (ppb) (range): 

Auckland 10.2 

(1.7, 28.9) 

Brisbane 7.6 (1.4, 19.1) 

Canberra 7.0 (0, 22.5) 

Christchurch 7.1 

(0.2, 24.5) 

Melbourne 11.7 (2, 29.5) 

Perth 9.0 (2, 23.3) 

Sydney 11.5 (2.5, 24.5) 

IQR: 5.1 ppb 

Daily 1-h max (range): 

Auckland 19.1 

(4.2, 86.3) 

Brisbane 17.3 (4, 44.1) 

Canberra 17.9 (0, 53.7) 

Christchurch 15.7 

(1.2, 54.6) 

Melbourne 23.2 

(4.4, 62.5) 

Perth 21.3 (4.4, 48) 

Sydney 22.6 (5.2, 51.4) 

IQR: 9.0 ppb 

BS; r = 0.39, 0.63 

PM2.5; r = 0.34, 

0.68 

PM10; r = 0.21, 0.57 

CO; r = 0.53, 0.73 

SO2; r = 0.15, 0.58 

O3; r = -0.15, 0.28 

Increment: 5.1 ppb (24 h) or per 

9 ppb 

(1-h max). (IQR) 

24-h avg NO2 (5.1 ppb change) 

Pneumonia and acute bronchitis 

0 yrs 3.2% [-1.8, 8.4] lag 0-1 

1-4 yrs 4.8% [-1.0, 11.0] lag 0-1 

5-14 yrs (sample size too small) 

Respiratory 

0 yrs 3.1% [-1.0, 7.3] lag 0-1 

1-4 yrs 2.4% [-0.8, 5.7] lag 0-1 

5-14 yrs 5.8% [1.7, 10.1] lag 0-1 

Asthma 

0 yrs No analysis (poor 

diagnosis) 

1-4 yrs 2.6% [-1.3, 6.6] lag 0-1 

5-14 yrs 6.0% [0.2, 12.1] lag 0-1 

1 h NO2 max (9.0 ppb change) 

Pneumonia and acute bronchitis 

0 yrs 2..8% [-1.8, 7.7] lag 0-1 

1-4 yrs 4.1% [-2.4, 11.0] lag 0-1 

5-14 yrs (sample size too small) 

Respiratory 

0 yrs 2.2% [-1.6, 6.1] lag 0-1 

1-4 yrs 2.8% [0.7, 4.9] lag 0-1 

5-14 yrs 4.7% [1.6, 7.9] lag 0-1 

Asthma 

0 yrs No analysis (poor 

diagnosis) 

1-4 yrs 2.5% [-0.2, 5.2] lag 0-1 

5-14 yrs 2.6% [-2.2, 7.6] lag 0-1 

Erbas et al. (2005) 

Melbourne, 

Australia 

Period of Study : 

2000-2001 

Outcomes (ICD 10): Asthma (J45, 

J46) 

Age groups analyzed: 1-15 

Study Design: Time-series 

N: 8,955 

# of Hospitals: 6 

Statistical Analyses: Poisson 

regression, GAM and GEE 

Covariates: Day of wk 

Dose-response investigated?: Yes 

Statistical Package: NR 

Lag: 0,1,2 days 

1-h mean NO2: 16.80 ppb, 

SD = 8.61; 

Range: 2.43, 63.00 

PM10 

O3

Increment: 90th-10th percentile 

Inner Melbourne; increment = 

25.54 ppb 

RR 0.83 [0.68, 0.98] lag 0 

Western Melbourne; increment = 

28.86 ppb 

RR 1.15 [1.03, 1.27] lag 2 

Eastern Melbourne; increment = 

17.67 ppb 

RR 1.07 [0.93, 1.22] lag 0 

South/Southeastern; increment = 

17.74 ppb 

RR 0.98 [0.79, 1.18] lag 1 
6-36 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Hinwood et al. 

(2006) 

Perth, Australia 

Period of Study: 

1992-1998 

Outcomes (ICD 9): COPD (490-496, 

excluding 493); Pneumonia (480-

489.99); Asthma (493) 

Age groups analyzed: <15, 65+, all 

ages 

Study Design: Case-crossover, timestratified 

Statistical Analyses: Conditional 

logistic regression 

Covariates: Temperature, change in 

temperature, maximum humidity, 

holiday, day of wk 

Statistical Package: NR 

Lag: 0,1,2,3 days or cumulative 0-2 

and 

0-3 days 

24-h mean [Std. Dev] 

(10th and 90th percentile) 

All yr 10.3 [5.0] 

(4.4, 17.1) 

Summer 9.6 [4.8] 

(4.3, 15.7) 

Winter 11.1 [5.1] 

(4.8, 18.0) 

Daily 1-h max 

Mean [Std. Dev] 

All yr 24.8 [10.1] (13.3, 37.5) 

Summer 24.9 [8.9] 

(12.4, 39.2) 

Winter 24.7 [11.1] 

(14.4, 35.7) 

Number of stations: 3 

O3; r = -0.06 

CO; r = 0.57 

BS; r = 0.39 

PM10 

PM2.5 

Increment: 1 ppb (all values were 

estimated from the graphs) 

All respiratory NO2 (24 h) 

≥65 yrs OR: 1.005 [1.001, 1.011] 

lag 1 

All ages OR: 1.002 [0.998, 1.004] 

lag 1 

Pneumonia NO2 (24 h) 

≥65 yrs OR: 1.006 [0.999, 1.014] 

lag 1 

All ages OR: 1.002 [0.998, 1.010] 

lag 1 

COPD NO2 (24 h) 

≥65 yrs OR: 1.004 [0.990, 1.012] 

lag 2 

All ages OR: 1.001 [0.995, 1.010] 

lag 2 

Asthma NO2 (24 h) 

0-14 yrs OR: 1.002 [0.998, 1.004] 

lag 0 

≥65 yrs OR: 0.996 [0.988, 1.002] 

lag 0 

All ages OR: 1.001 [0.999, 1.003] 

lag 0 

Morgan et al. 

(1998a) 

Sydney, Australia 

Period of Study: 

1990-1994 

Outcomes (ICD 9): COPD (490-492, 

494, 496); Asthma (493) 

Age groups analyzed: 1-14, 15-64, 

65+, all ages 

Study Design: Time-series 

# of hospitals: 27 

Statistical Analyses: APHEA protocol, 

Poisson regression, GEE 

Covariates: Long-term trend, 

temperature, dew point, day of wk, 

holiday 

Statistical Package: SAS 

Lag: 0,1,2 days and cumulative 

24-h daily mean: 15 ppb, 

SD = 6, Range: 0, 52, 

IQR: 11, 90-10th 

percentile: 17 

Mean daily 1-h max: 29 ppb, 

SD = 3, 

Range: 0, 139, 

IQR: 15, 90-10th 

percentile: 29 

# of stations: 3-14, r = 0.52 

24-h avg NO2: 

PM(24 h); r = 0.53 

PM (1 h); r = 0.51 

O3; r = -0.9 

1-h max NO2: 

PM(24 h); r = 0.45 

PM (1 h); r = 0.44 

O3; r = 0.13 

Increment: 90-10th percentile 

24-h avg (17 ppb) 

Asthma: 1-14 yrs 3.28% [-1.72, 

8.54] lag 0 

 15-64 yrs 2.29% [-

2.97, 7.83] lag 0 

COPD: >65 yrs 4.30% [-0.75, 

9.61] lag 1 

Daily 1-h max (29 ppb) 

Asthma: 1-14 yrs 5.29% [1.07, 

9.68] lag 0 

 15-64 yrs. 3.18% [-

1.53, 8.11] lag 0 

COPD: 65+ yrs. 4.60% [-0.17, 

9.61] lag 1 

Multipollutant model (29 ppb) 

Asthma: 1-14 yrs. 5.95% [1.11, 

11.02] lag 0 

COPD: 65+ yrs. 3.70% [-1.03, 

8.66] lag 1 
6-37 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Petroeschevsky 

et al. (2001) 

Brisbane, Australia 

Period of Study: 

1987-1994 

Days: 2922 

Outcomes (ICD 9): All respiratory 

(460-519); Asthma (493) 

Age groups analyzed: 0-4, 5-14, 15-

64, 65+, all ages 

Study Design: Timeseries 

N: 33,710 (13,246 = asthma) 

Statistical Analyses: APHEA protocol, 

Poisson regression, GEE 

Covariates: Temperature, humidity, 

season, infectious disease, day of 

wk, holiday 

Season: Summer, Autumn, Winter, 

Spring, All yr 

Dose-response investigated?: Yes 

Statistical Package: SAS 

Lag: Single: 1,2,3 day 

Mean (range) 24-h avg: 

Cumulative: 0-2, 0-4 

Overall: 139 (12, 497) 

Summer: 97 (20, 331) 

Autumn: 129 (33, 319) 

Winter: 179 (12, 454) 

Spring: 153 (35, 497) 

Mean (range) 1-h max 

Overall: 282 (35, 1558) 

Summer: 206 (35, 580) 

Autumn: 256 (70, 585) 

Winter: 354 (35, 805) 

Spring: 321 (35, 1558) 

# of stations: 3, 

r = 0.43, 0.53 

BSP 

O3

SO2

Increment: 10 ppb 

Respiratory (1-h max): 

0-4 yrs 1.015 [0.996, 1.035] lag 3 

5-14 yrs 0.985 [0.950, 1.021] lag 

0 

All ages 0.989 [0.977, 1.002] lag 

1 

Respiratory (24-h avg): 

15-64 yrs 1.027 [0.984, 1.071] lag 

0 

>65 yrs 0.903 [0.851, 0.959] lag 5 

Asthma (1-h max): 

0-4 yrs 0.975 [0.947, 1.004] lag0 

5-64 yrs 0.983 [0.949, 1.018] lag 

1 

All ages 0.962 [0.936, 0.989] lag 

0-2 

EUROPE 

Anderson et al. 

(1997) 

Multicity, Europe 

(Amsterdam, 

Barcelona, 

London, Paris, 

Rotterdam) 

Period of study: 

1977-1989 for 

Amsterdam and 

Rotterdam 

1986-1992 for 

Barcelona 

1987-1991 for 

London 

1980-1989 for 

Milan 

1987-1992 for 

Paris 

Outcomes (ICD 9): COPD - 

unspecified bronchitis (490), chronic 

bronchitis (491), emphysema (492), 

chronic airways obstruction (496) 

Study Design: Time-series 

Statistical Analyses: APHEA protocol, 

Poisson regression, meta-analysis 

Covariates: Trend, season, day of wk, 

holiday, influenza, temperature, 

humidity 

Season: Cool, Oct-Mar; Warm, AprSep 

Statistical Package: NR 

Lag: 0,1,2 days and 0-3 cumulative 

24-h all yr avg: (μg/m3

) 

Amsterdam: 50 

Barcelona: 53 

London: 67 

Paris: 42 

Rotterdam: 52 

1-h max: 

Amsterdam: 75 

Barcelona: 93 

London: 67 

Paris: 64 

Rotterdam: 78 

SO2

BS 

TSP 

O3

Increment: 50 μg/m3 

Meta-analytic results - Weighted 

mean values from 6 cities 

COPD-Warm season 

24 h 1.03 [1.00, 1.06] lag 1 

1 h 1.02 [1.00, 1.05] lag 1 

COPD-Cool season 

24 h 1.01 [0.99, 1.03] 

1 h 1.02 [0.99, 1.05] 

COPD-All Year 

24 hr 1.019 [1.002, 1.047] lag 1 

24 hr 1.026 [1.004, 1.036] lag 0-

3, cumulative 

1 hr 1.013 [1.003, 1.022] lag 1 

1 hr 1.014 [0.976, 1.054] lag 0-3, 

cumulative 

Atkinson et al. 

(2001) 

Multicity, Europe 

(Barcelona, 

Birmingham, 

London, Milan, 

Netherlands, Paris, 

Rome, Stockholm) 

Period of study: 

1998-1997 

Outcomes (ICD 9): Asthma (493), 

COPD (490-496), All respiratory (460-

519) 

Study Design: Time-series 

Statistical Analyses: APHEA protocol, 

Poisson regression, meta-analysis 

Covariates: Season, temperature, 

humidity, holiday, influenza 

Statistical Package: NR 

Lag: NR 

1-h max of NO2 (μg/m3

) 

Barcelona: 94.4 

Birmingham: 75.8 

London: 95.9 

Milan: 147.0 

Netherlands: 50.1 

Paris: 87.2 

Rome: 139.7 

Stockholm: 35.6 

SO2, O3, CO, BS 

PM10; r = 

Barcelona: 0.48 

B’gham: 0.68 

London: 0.70 

Milan: 0.72 

Netherlands: 0.64 

Paris: 0.44 

Rome: 0.32 

Stockholm: 0.30 

Increment: 10 μg/m3

 for PM10; 

change in NO2 not described. 

Asthma, 0 to 14 yrs: 

For PM10: 1.2% [0.2, 2.3] 

For PM10 + NO2: 0.1 [-0.8, 1.0] 

Asthma, 15 to 64 yrs: 

For PM10: 1.1% [0.3, 1.8] 

For PM10 + NO2: 0.4 [-0.5, 1.3] 

COPD + Asthma, ≥65 yrs 

For PM10: 1.0% [0.4, 1.5] 

For PM10 + NO2: 0.8 [-0.6, 2.1] 

All Respiratory, ≥65 yrs of age 

For PM10: 0.9% [0.6, 1.3] 

For PM10 + NO2: 0.7 [-0.3, 1.7] 
6-38 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Sunyer et al. 

(1997) 

Multicity, Europe 

(Barcelona, 

Helsinki, Paris, 

London) 

Period of Study: 

1986-1992 

Outcomes (ICD 9): Asthma (493) 

Age groups analyzed: <15, 15-64 

Study Design: Time-series 

Statistical Analyses: APHEA protocol, 

Poisson regression, GEE; metaanalysis 

Covariates: Humidity, temperature, 

influenza, soybean, long-term trend, 

season, day of wk 

Season: Cool, Oct-Mar; Warm: 

Apr-Sep 

Statistical Package: NR 

Lag: 0,1,2,3 and cumulative 1-3 

24-h median (range) (μg/m3

) 

Barcelona: 53 (5, 142) 

Helsinki: 35 (9, 78) 

London: 69 (27, 347) 

Paris: 42 (12, 157) 

# of stations: 

Barcelona: 3 

London: 2 

Paris: 4 

Helsinki: 8 

SO2

black smoke 

O3

Increment: 50 μg/m3

 of 24-h avg 

for all cities combined 

Asthma 

15-64 yrs 

1.029 [1.003, 1.055] lag 0-1 

1.038 [1.008-1.068] lag 0-3, 

cumulative 

<15 yrs 

1.026 [1.006, 1.049] lag 2 

1.037 [1.004, 1.067] lag 0-3, 

cumulative 

1.080 [1.025, 1.140] – Winter only

Two-pollutant models: 

NO2/Black smoke 

15-64 yrs 

1.055 [1.005, 1.109] lag 0-1 

15-64 yrs 1.088 [1.025, 1.155] 

cumulative 

0-3 

<15 yrs 

1.036 [0.956, 1.122] 

NO2/SO2

<15 yrs 

1.034 [0.988, 1.082] 

Schouten et al. 

(1996) 

Multicity, The 

Netherlands 

(Amsterdam, 

Rotterdam) 

Period of Study: 

04/01/77-09/30/89 

Outcomes (ICD 9): All respiratory 

(460-519), COPD (490-2, 494, 496), 

Asthma (493) 

Age groups analyzed: 15-64, 65+, all 

ages 

Study Design: Time-series 

Statistical Analyses: APHEA protocol, 

Poisson regression 

Covariates: Long-term trend, season, 

influenza, day of wk, holiday, 

temperature, humidity 

Season: Cool, Nov-Apr; Warm: MayOct 

Statistical Package: NR 

Lag: 0,1,2 days; and cumulative 0-1 

and 

0-3 day lags 

24-h avg NO2

Amsterdam 

Mean/Med: 50/50 μg/m3

Rotterdam 

Mean: 54/52 μg/m3

Daily max 1 h 

Amsterdam 

Mean/Med: 75/75 μg/m3

Rotterdam 

Mean/Med: 82/78 μg/m3

# of stations: 1 per city 

SO2

BS 

O3

Increment: 100 μg/m3

 increment 

All respiratory, Amsterdam 24 h 

mean; 1-h max 

15-64 yrs RR 0.890 [0.783, 

1.012]; 0.894 [0.821, 0.973] lag 1 

>65 yrs RR 1.023 [0.907, 1.154]; 

0.996 [0.918, 1.080] lag 2 

All respiratory, Rotterdam 24 h 

mean; 1-h max (1985-89) 

15-64 yrs RR 0.965 [0.833, 

1.118]; 1.036 [0.951, 1.129] lag 1 

>65 yrs RR 1.172 [0.990, 1.387]; 

1.073 [0.970, 1.186] lag 0 

COPD, Amsterdam, 24-h mean, 

All ages RR 0.937 [0.818, 1.079] 

lag 1 

Asthma Amsterdam, 24-h mean, 

All ages RR 1.062 [0.887, 1.271] 

lag 2 

COPD, Rotterdam 24-h mean 

All ages RR 1.051 [0.903, 1.223] 

lag 2 
6-39 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Ponce de Leon 

et al. (1996) 

London, England 

Period of Study: 

04/1987-1988; 

1991-02/1992 

Outcomes (ICD 9): All respiratory 

(460-519) 

Age groups analyzed: 0-14, 15-64, 

65+, all ages 

Study Design: Timeseries 

N: 19,901 

Statistical Analyses: APHEA protocol, 

Poisson regression GAM 

Covariates: Long-term trend, season, 

influenza, day of wk, holiday, 

temperature, humidity 

Season: Cool, Oct-Mar; Warm: 

Apr-Sep 

Dose-Response Investigated: Yes 

Statistical Package: SAS 

Lag: 0,1,2 days, 0-3 cumulative avg 

NO2 24-h avg: 37.3 ppb, 

Med: 35 

SD = 13.8 

IQR: 14 ppb 

1-h max: 57.4 ppb, 

Med: 51 

SD = 26.4 

IQR: 21 ppb 

# of stations: 2 

SO2; r = 0.45 

BS; r = 0.44 

O3

Increment: 90th-10th percentile 

(24-h avg: 27 ppb) 

All yr 

All ages 1.0114 [1.006, 1.0222] 

lag 2 

0-14 yrs 1.0104 [0.9943, 1.0267] 

lag2 

15-64 yr 1.0113 [0.9920, 1.0309] 

lag 1 

≥65 yr 1.0216 [1.0049, 1.0386] 

lag 2 

Warm season 

All ages 1.0276 [1.0042, 1.0515] 

lag 2 

0-14 yrs 1.038 [1.0009, 1.0765] 

lag 2 

15-64 yr 1.0040 [0.9651, 1.0445] 

lag 1 

>65 yr 1.0326 [0.9965, 1.0699] 

lag 2 

Cool season 

All ages 1.0060 [0.9943, 10177] 

lag2 

0-14 yrs 1.0027 [0.9855, 1.0202] 

lag2 

15-64 yr 1.0136 [0.9920, 1.0357] 

lag 1 

>65 yr 1.0174 [0.9994, 1.0358] 

lag 2 

Atkinson et al. 

(1999a) 

London, England 

Period of Study: 

1992 to 1994 

Days: 1096 

Outcomes (ICD 9): All respiratory 

(460-519), Asthma (493), Asthma + 

COPD (490-6), Lower respiratory 

disease (466, 480-6) 

Age groups analyzed: 0-14, 15-64, 

65+, all ages 

Study Design: Time-series 

N: 165,032 

Statistical Analyses: APHEA protocol, 

Poisson regression 

Covariates: Long-term trend, season, 

influenza, day of wk, holiday, 

temperature, humidity 

Season: Cool, Oct-Mar; Warm: AprSep 

Dose-Response Investigated?: Yes 

Statistical Package: SAS 

Lag: 0,1,2 days, 0-1, 0-2, 0-3 cum. 

avg. 

NO2 1-h mean: 50.3 ppb, 

SD = 17.0, 

Range: 22.0, 224.3 ppb, 

10th percentile: 34.3, 

90th percentile: 70.3 

# of stations: 3; r = 0.7, 0.96 

O3, 

CO, 

PM10, 

BS, 

SO2

Increment: 36 ppb (90th-10th 

centile) 

All ages 

Respiratory 1.64% [0.14, 3.15] 

lag 1 

Asthma 

1.80% [-0.77, 4.44] lag 0 

0-14 yrs 

Respiratory 1.94% [-0.39, 4.32] 

lag 2 

Asthma 

1% [-1.42, 5.77] lag 3 

15-64 yrs 

Respiratory 1.61% [-0.82, 4.09] 

lag 1 

Asthma 

5.08% [0.81, 9.53] lag 1 

65+ yrs 

Respiratory 2.53% [0.58, 4.52] 

lag 3 

Asthma 4.53% [-2.36, 11.91] lag 3

COPD 3.53% [0.64, 6.50] lag 3 

Lower Resp. 3.47% [0.08, 6.97] 

lag 3 
6-40 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Spix et al. (1998) 

Multi-city (London, 

Amsterdam, 

Rotterdam, Paris), 

Europe 

Period of Study: 

1977 and 1991 

Hospital Admissions 

Outcomes (ICD 9 codes): All 

respiratory 

(460-519); Asthma (493) 

Age groups analyzed: 15-64, 65+ 

Study Design: Time-series 

# of Hospitals: 

Statistical Analyses: Poisson 

regression following APHEA protocol. 

Pooled meta-analysis adjusted for 

heterogeneity 

Covariates: Trend, seasonality, day of 

wk, holiday, temperature, humidity, 

unusual events (strikes, etc.) 

Statistical Package: 

Lag: 1 to 3 days 

NO2 daily mean (μg/m3

) 

London 35 

Amsterdam 50 

Rotterdam 53 

Paris 42 

SO2, O3, BS, TSP Increment: 50 μg/m3. 

All cities, yr round 

15-64 yrs RR 1.010 [0.985, 

1.036] 

 Warm RR 1.00 [0.96, 

1.04] 

 Cold RR 1.01 [0.98, 

1.04] 

≥65 yrs RR 1.019 [0.982, 1.060] 

 Warm RR 1.02 [0.99, 

1.06] 

 Cold RR 1.00 [0.98, 

1.03] 

Wong* et al. 

(2002) 

London England 

and Hong Kong 

Period of Study: 

London: 1992-

1994 

Hong Kong: 

1995-1997 

Days: 1,096 

Outcomes (ICD 9): All respiratory 

admissions (460-519); asthma (493) 

Age groups analyzed: 15-64, 65+, all 

ages 

Study Design: Time-series 

Statistical Analyses: APHEA protocol, 

Poisson regression with GAM 

Covariates: Long-term trend, season, 

influenza, day of wk, holiday, 

temperature, humidity, thunderstorms 

Season: Cool, Oct-Mar; Warm: AprSep 

Dose-Response Investigated?: Yes 

Statistical Package: S-Plus 

Lag: 0,1,2,3,4 days, 0-1 cum. avg 

24 h NO2 μg/m3

Hong Kong 

Mean: 55.9 

Warm: 48.1 

Cool: 63.8 

SD = 19.4 

Range: 15.3, 151.5 

10th: 31.8 

50th: 53.5 

90th: 81.8 

London 

Mean: 64.3 

Warm: 62.6 

Cool: 66.1 

SD = 20.4 

Range: 23.7, 255.8 

10th: 42.3 

50th: 61.2 

90th: 88.8 

# of stations: 

Hong Kong: 7; r = 0.65, 0.90 

London: 3; r = 0.80 

Hong Kong 

PM10; r = 0.82 

SO2; r = 0.37 

O3; r = 0.43 

London 

PM10; r = 0.68 

SO2; r = 0.71 

O3; r = -0.29 

Increment: 10 μg/m3

Asthma, 15-64 yrs 

Hong Kong 

ER -0.6 [-2.1, 1.0] lag 0-1 

ER -1.3 [-2.6, 0.1] lag 1 

Warm: ER -0.5 [-2.7, 1.6] lag 0-1 

Cool: ER -0.6 [-2.8, 1.6] lag 0-1 

London 

ER 1.0 [0.0, 2.1] lag 0-1 

ER 1.1 [0.2, 2.0] lag 2 

Warm: ER 0.6 [-0.8, 2.0] lag 0-1 

Cool: ER 1.3 [-0.1, 2.8] lag 0-1 

Respiratory 65+ yrs 

Hong Kong 

ER 1.8 [1.2, 2.4] lag 0-1 

ER 1.3 [0.8, 1.8] lag 0 

Warm: ER 0.8 [0.1, 1.6] lag 0-1 

Cool: ER 3.0 [2.1, 3.9] lag 0-1 

+O3: ER 1.6 [1.0, 2.3] lag 0-1 

+PM10: ER 1.7 [0.8, 2.7] lag 0-1 

+SO2: ER 1.6 [0.8, 2.4] lag 0-1 

London 

ER -0.1 [-0.6, 0.5] lag 0-1 

ER 0.9 [0.5, 1.3] lag 3 

Warm: ER 0.6 [-0.2, 1.4] lag 0-1 

Cool: ER -0.7 [-1.4, 0.0] lag 0-1 

+O3: ER -0.1 [-0.5, 0.6] lag 0-1 

+PM10: ER -0.4 [-1.2, 0.4] lag 0-1 

+SO2: ER -0.2 [-0.9, 0.5] lag 0-1 
6-41 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Anderson et al. 

(1998) 

London, England 

Period of Study: 

Apr 1987-Feb 

1992 

Days: 1,782 

Outcomes (ICD 9): Asthma (493) 

Age groups analyzed: <15, 15-64, 

65+ 

Study Design: Time-series 

N: 16 

Statistical Analyses: APHEA protocol, 

Poisson regression 

Covariates: Time trends, seasonal 

cycles, day of wk, public holidays, 

influenza epidemics, temperature, 

humidity 

Season: Cool: Oct-Mar; Warm: AprSep 

Dose-Response Investigated?: Yes 

Statistical Package: S 

Lag: 0,1,2 days 

24-h avg NO2 (ppb) 

Mean: 37.2 

SD = 12.3 

Range: 14, 182 

5th: 22 

10th: 25 

25th: 30 

50th: 36 

75th: 42 

90th: 50 

95th: 58 

1-h max NO2 (ppb) 

Mean: 57.2 

SD = 23.0 

Range: 21, 370 

5th: 35 

10th: 38 

25th: 44 

50th: 52 

75th: 64 

90th: 81 

95th: 98 

Number of stations: 2 

O3

SO2

BS 

Increment: 10 ppb in 24-h NO2

0-14 yrs 

Whole yr RR 1.25 [0.3, 2.2] lag 2; 

RR 1.77 [0.39, 3.18] lag 0-3 

+ O3 RR 1.13 [-0.10, 2.36] 

lag 2 

+ SO2 RR 0.97 [-0.05, 1.99] 

lag 2 

+ BS RR 2.26 [0.83, 3.71] 

lag 2 

Warm season RR 1.42 [-0.3, 

3.17] lag 2; RR 3.01 [3.8, 5.72] 

lag 0-3 

Cool season RR 1.18 [0.02, 2.35] 

lag 2; RR 1.22 [-0.48, 2.96] lag 0-

3 

15-64 yrs 

Whole yr RR 0.95 [-0.26, 2.17] 

lag 0; RR 0.99 [-0.36, 3.36] lag 0-

1 

Warm RR 0.46 [-1.70, 2.67] lag 0; 

RR 0.05[-2.45, 2.61] lag 0-1 

Cool season RR 1.21 [-0.22, 2.5] 

lag 0; RR 1.43 

[-0.18, 3.06] lag 0-1 

65+ yrs 

Whole yr RR 2.96 [0.67, 5.31] lag 

2; RR 3.14 

[-0.04, 6.42] lag 0-3 

+ O3 RR 4.51 [1.43, 7.69] 

lag 2 

+ SO2 RR 2.49 [-0.25, 5.31] 

lag 2 

+ BS RR 1.88 [-1.49, 5.36] 

lag 2 

Warm RR 1.89 [-2.41, 6.38] 

lag 2; 

RR -1.76 [-7.27, 4.07] lag 0-3 

Cool season RR 3.52 [0.81, 6.30] 

lag 2; RR 5.57 [1.85, 9.43] lag 0-3
6-42 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Anderson et al. 

(1998) (cont’d) 

 + O3 RR 5.14 [0.69, 9.79] 

lag 2 

+ SO2 RR 2.10 [-1.08, 5.39] 

lag 2 

+ BS RR 4.47 [-0.04, 9.19] 

lag 2 

All ages 

Whole yr RR 1.25 [0.49, 2.02] lag 

2; RR 2.05 [0.96, 3.15] lag 0-3 

+ O3 RR 1.08 [0.12, 2.05] 

lag 2 

+ SO2 RR 0.99 [0.18, 1.81] 

lag 2 

+ BS RR 1.23 [0.47, 2.00] 

lag 2 

Warm RR 1.15 [-0.25, 2.57] 

lag 2; RR 1.54 [-0.54, 3.67] lag 0-

3 

Cool season RR 1.30 [0.38, 2.23] 

lag 2; RR 2.26 [0.94, 3.59] lag 0-3

+ O3 RR 0.50 [-0.79, 1.81] 

lag 2 

+ SO2 RR 1.10 [0.12, 2.08] 

lag 2 

+ BS RR 1.29 [0.37, 2.22] 

lag 2 

Anderson et al. 

(2001) 

West Midlands 

conurbation, 

United Kingdom 

Period of Study: 

10/1994-12/1996 

Hospital Admissions: 

Outcomes (ICD 9 codes): All 

respiratory (460-519), Asthma (493), 

COPD (490-496, excluding 493) 

Age groups analyzed: 0-14, 15-64, 

65+ 

Study Design: Time-series 

Statistical Analyses: Followed APHEA 

2 protocol, GAM 

Covariates: Season, temperature, 

humidity, epidemics, day of wk, 

holidays 

Statistical Package: S-Plus 4.5 Pro 

Lag: 0,1,2,3, 0-1, 0-2, 0-3 

1-h max avg: 37.2 ppb, 

15.1 (SD) 

Min: 10.7 ppb 

Max: 176.1 ppb 

10th: 22.9 ppb 

90th: 51.7 ppb 

# of monitors: 5 

PM10; r = 0.62 

PM2.5; r = 0.61 

PM2.5-10; r = 0.25 

BS; r = 0.65 

SO4; r = 0.30 

SO2; r = 0.52 

O3; r = 0.08 

CO; r = 0.73 

Increment: 25.5 ppb (90th – 10th) 

All respiratory 

All ages 1.7% [-0.2, 3.7] lag 0-1 

0-14 yrs 2.3% [-0.6, 5.3] lag 0-1 

15-64 yrs 0.0% [-3.7, 3.8] lag 0-1 

≥65 yrs 1.0% [-1.8, 3.9] lag 0-1 

COPD with asthma 

0-14 yrs 4.0% [-2.0, 10.2] lag 0-1 

15-64 yrs -3.3% [-10.4, 4.4] lag 0-

1 

≥65 yrs 2.5% [-2.1, 7.3] lag 0-1 

Prescott et al. 

(1998) 

Edinburgh, United 

Kingdom 

Period of Study: 

10/92-6/95 

Outcomes (ICD 9): Pneumonia (480-

7), COPD + Asthma (490-496) 

Age groups analyzed: <65, 65+ 

Study Design: Time-series 

Statistical Analyses: Poisson log 

linear regression 

Covariates: Trend, seasonal and wkly 

variation, temperature, wind speed, 

day of wk 

Lag: 0,1 or 3 day rolling avg 

NO2: 26.4 ± 7.0 ppb 

Min: 9 ppb 

Max: 58 ppb 

IQR: 10 ppb 

# of Stations: 1 

CO 

PM10 

SO2

O3

BS 

Increment: 10 ppb 

Respiratory admissions 

>65 yrs 

3.1 [-4.6, 11.5] rolling 3-day avg 

<65 yrs 

-0.2% [-7.5, 7.7] rolling 3-day avg 
6-43 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Thompson et al. 

(2001) 

Belfast, Northern 

Ireland 

Period of Study: 

1993-1995 

Outcomes: Asthma 

ICD9: NR 

Age groups analyzed: 0-14 

Study Design: Time-series 

N: 1,095 

Number of hospitals: 1 

Statistical Analyses: Poisson 

regression 

Covariates: Season, long-term trend, 

temperature, day of wk, holidays 

Season: Warm (May-Oct), Cold 

(Nov-Apr) 

Statistical Package: Stata 

Lag: 0,1,2,3 days 

24-h mean: 

Warm: 19.2 (7.9) ppb; 

Range: 13-23 

Cold: 23.3 (9.0) ppb; 

Range: 18-28 

SO2; r = 0.82 

PM10; r = 0.77 

CO; r = 0.69 

O3; r = -0.62 

NOX; r = 0.93 

log (NO); r = 0.84 

log (CO); r = 0.69 

Increment: 10 ppb 

All seasons 

RR 1.08 [1.03, 1.13] lag 0 

RR 1.11 [1.05, 1.17] lag 0-1 

RR 1.10 [1.04, 1.17] lag 0-2 

RR 1.12 [1.03, 1.02] lag 0-3 

Warm season 

RR 1.14 [1.04, 1.26] lag 0-1 

Cold season 

RR 1.10 [1.03, 1.17] lag 0-1 

NO2 + Benzene 

RR 0.99 [0.87, 1.13] lag 0-1 

*Model made no allowance for 

possible autocorrelation in the 

data or for extra-Poisson 

variation. 

Hagen et al. 

(2000) 

Drammen, Norway 

Period of Study: 

1994-1997 

Outcomes (ICD 9): All respiratory 

admissions (460-519) 

Age groups analyzed: All ages 

Study Design: Time-series 

Number of hospitals: 1 

Statistical Analyses: Poisson 

regression with GAM (adhered to HEI 

phase 1.B report) 

Covariates: Time trends, day of wk, 

holiday, influenza, temperature, 

humidity 

Lag: 0,1,2,3 days 

NO2 24-h avg (μg/m3

): 36.15, 

SD = 16 

IQR: 16.92 μg/m3

# of Stations: 2 

PM10; r = 0.61 

SO2; r = 0.58 

Benzene; r = 0.31 

NO; r = 0.70 

O3; r = -0.47 

Formaldehyde; 

r = 0.68 

Toluene; r = 0.65 

Increment: NO2: 16.92 μg/m3

(IQR); 

NO: 29μg/m3

 (IQR) 

Single-pollutant model 

Respiratory disease only 

NO2: RR 1.058 [0.994, 1.127] 

NO: 1.048 [1.013, 1.084] 

All disease 

NO2: RR 1.011 [0.988, 1.035] 

Two-pollutant model with PM10 

NO2: 1.044 [0.966, 1.127] 

NO: 1.045 [1.007, 1.084] 

Three-pollutant model with PM10 

+ Benzene 

NO2: 1.015 [0.939, 1.097] 

NO: 1.031 [0.986, 1.077] 

Oftedal et al. 

(2003) 

Drammen, Norway 

Period of Study: 

1994-2000 

Outcomes (ICD 10): All respiratory 

admissions (J00-J99) 

Age groups analyzed: All ages 

Study Design: Time-series 

Statistical Analyses: Semi-parametric 

Poisson regression, GAM with more 

stringent criteria 

Covariates: Temperature, humidity, 

influenza 

Lag: 2,3 days 

Mean: 33.8 μg/m3

SD = 16.2 

IQR: 20.8 μg/m3

PM10 

SO2

O3

Benzene 

Formaldehyde 

Toluene 

Increment: 20.8 μg/m3

 (IQR) 

All respiratory disease 

Single-pollutant model 

RR 1.060 [1.017, 1.105] lag 3 

Two-pollutant model 

Adjusted for PM10 

RR 1.063 [1.008, 1.120] 

Adjusted for benzene 

RR 1.046 [1.002, 1.091] 
6-44 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Pönkä (1991) 

Helsinki, Finland 

Period of Study: 

1987-1989 

Outcomes (ICD 9 codes): Asthma 

(493) 

Age groups analyzed: 0-14; 15-64; 

≥65 yrs 

Study Design: Time-series 

N: 4,209 

Statistical Analyses: Correlations and 

partial correlations 

Covariates: Minimum temperature 

Statistical Package: 

Lag: 0-1 

24-h avg: 38.6 (16.3) μg/m3

Range: 4.0-169.6 

Number of Monitors: 4 

SO2; r = 0.4516 

NO; r = 0.6664 

O3; r = -0.2582 

TSP; r = 0.1962 

CO 

Correlations between hospital 

admissions (HA) for asthma and 

pollutants and temperature by 

ages. 

0-14 yrs 

HA: -0.0166 

Emergency HA: 0.0061 

15-64 yrs 

HA: 0.1648 p < 0.0001 

Emergency HA: 0.1189 p < 

0.0001 

≥65 yrs 

HA: 0.1501 p < 0.0001 

Emergency HA: 0.1392 p < 

0.0001 

Partial correlations between 

admissions for asthma and SO2

were standardized for 

temperature. 

HA: 0.1830 p < 0.0001 

Emergency HA: 0.1137 p = 

0.0004 

Pönkä and 

Virtanen (1994) 

Helsinki, Finland 

Period of Study: 

1987-1989 

Days: 1096 

Outcomes (ICD 9): Chronic bronchitis 

and emphysema (491-492) 

Age groups analyzed: <65, ≥65 

Study Design: Time-series 

Statistical Analyses: Poisson 

regression 

Covariates: Season, day of wk, yr, 

influenza, humidity, temperature 

Season: Summer (Jun-Aug), Autumn 

(Sep-Nov), Winter (Dec-Feb), Spring 

(Mar-May) 

Lag: 0-7 days 

24-h mean: 39 μg/m3

SD = 16.2; 

Range: 4, 170 

# of stations: 2 

SO2

O3

TSP 

Increment: NR 

Chronic bronchitis and 

emphysema 

≥65 yrs 

RR 0.87 [0.71, 1.07] lag 0 

RR 1.07 [0.86, 1.33] lag 1 

RR 1.16 [0.93, 1.46] lag 2 

RR 1.08 [0.86, 1.35] lag 3 

RR 0.94 [0.76, 1.18] lag 4 

RR 0.90 [0.72, 1.12] lag 5 

RR 1.31 [1.03, 1.66] lag 6 

RR 0.82 [0.67, 1.01] lag 7 

<65 yrs 

NR 

Pönkä and 

Virtanen, (1996) 

Helsinki, Finland 

Period of study: 

1987-1989 

Hospital Admissions 

Outcomes (ICD 9 codes): Asthma 

(493) 

Age groups analyzed: 0-14, 15-64, 

65+ 

Study Design: Time-series 

Statistical Analyses: 

Covariates: Long-term trend, season, 

epidemics, day of wk, holidays, 

temperature, relative humidity 

Statistical Package: 

Lag: 0-2 

24-h avg (μg/m3): 

Winter: 38 

Spring: 44 

Summer: 39 

Fall: 34 

SO2

O3

TSP 

No results presented for NO2

because they were not 

statistically significant 
6-45 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Rossi et al. (1993) 

Oulu, Finland 

Period of Study: 

10/1/1985-

9/30/1986 

ED Visits 

Outcome(s) (ICD 9): Asthma (493) 

Age groups analyzed: 15-85 

Study Design: Time-series 

N: 232 

Statistical Analyses: Pearson’s and 

partial correlation coefficients and 

multiple regression with stepwise 

discriminate analysis 

Covariates: Temperature, humidity 

Statistical Package: BMDP software 

Lag: 0,1,2,3 

24-h mean: 13.4 μg/m3

Range: 0-69 

1-hr max: 

38.5 μg/m3

Range: 0-154 

# of Monitoring Stations: 4 

NO2; r = 0.48 TSP; 

H2S 

Pearson correlation coefficients 

ED asthma visits and same day 

SO2: r = 0.20 p < 0.001 lag 0 

Weekly ED asthma visits and 

same wk SO2: r = 0.42 p < 0.001 

Weekly ED asthma visits and 

previous wk SO2: r = 0.58 p < 

0.001 

Multi-pollutant (NO2; TSP; H2S) 

Regression coefficient: 

All yr: ≥ = 0.209, p = 0.034 

Winter: ≥ = 0.201, p = 0.014 

Summer: ≥ = 0.041, p = 0.714 

Andersen et al. 

(2007a) 

Copenhagen, 

Denmark 

Period of Study: 

1999-2004 

Outcomes (ICD 10): chronic 

bronchitis (J41-42), emphysema 

(J43), COPD (J44), asthma (J45), 

status asthmaticus (J46) 

Age groups analyzed: 5-18, 65+ 

Number of hospitals: 9 

Study Design: Time-series 

Statistical Analyses: Poisson 

regression with GAM 

Covariates: Temperature, long-term 

trend, seasonality, influenza, day of 

wk, public holidays, school holidays 

Lag: 0, 1, 2, 3, 4, 5, 0-4, 0-5 days 

24-h avg: 12 (5) ppb 

Statistical package: R 

IQR: 7 

25th: 8 

75th: 15 

PM10; r = 0.42 

PM10–biomass; r = 

0.41 

PM10–Secondary; r 

= 0.43 

PM10–Oil; r = 0.42 

PM10–Crustal; r = 

0.24 

PM10–Sea salt; r = -

0.19 

PM10–Vehicle; r = 

0.65 

CO; r = 0.74 

Increment: 7 ppb (IQR): 

All respiratory disease (65+): 

NO2: 1.040 [1.009, 1.072] lag 5 

day moving avg 

NO2 + PM10: 1.014 [0.978, 

1.051] lag 5 day ma 

Asthma (5-18 yrs): 

NO2: 1.128 [1.029, 1.235] lag 6 

day ma 

NO2+PM10: 1.032 [0.917-1.162] 

lag 6 day ma 

Andersen et al. 

(2007b) 

Copenhagen, 

Denmark 

Period of Study: 

5/15/2001-

12/31/2004 

Outcomes (ICD 10): chronic 

bronchitis 

(J41-42), emphysema (J43), COPD 

(J44), asthma (J45), status 

asthmaticus (J46) 

Age groups analyzed: 5-18, 65+ 

Number of hospitals: 9 

Study Design: Time-series 

Statistical Analyses: Poisson 

regression with GAM 

Covariates: Temperature, long-term 

trend, seasonality, influenza, day of 

wk, public holidays, school holidays 

Lag: 0, 1, 2, 3, 4, 5, 0-4, 0-5 days 

24-h avg: 11 (5) ppb 

Statistical package: R 

IQR: 6 

25th: 8 

50th: 11 

75th: 14 

99th: 28 

PM10 

PM2.5 

CO 

O3

Increment: 6 ppb (IQR): 

All respiratory disease (65+): 

NO2: 1.06 [1.01, 1.12] lag 0-4 

moving avg 

NO2 + NCtot: 1.06 [0.99, 1.13] lag 

0-4 ma 

Asthma (5-18 yrs): 

NO2: 1.04 [0.92, 1.18] lag 0-5 ma 

NO2+NCtot: 0.97 [0.83-1.14] lag 

0-5 ma 
6-46 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Dab* et al. (1996) 

Paris, France 

Period of Study: 

1/1/87-9/30/92 

Outcomes (ICD 9): All respiratory 

(460-519), Asthma (493), COPD 

(490-496) 

Age groups analyzed: All ages 

Study Design: Time-series 

Number of hospitals: 27 

Statistical Analyses: Poisson 

regression, followed APHEA protocol 

Covariates: Temperature, relative 

humidity, influenza, long-term trend, 

season, holiday, medical worker 

strike 

Lag: 0,1,2 days, 0-3 cumulative 

NO2 24-h avg: 45 μg/m3

5th: 22, 99th: 108.3 

Daily maximum 1-h 

Concentration: 73.8 μg/m3

5th: 37.5, 99th: 202.7 

SO2

O3

PM13 

BS 

Increment: 100 μg/m3 

All respiratory (1987-1990) 

24-h avg NO2: RR 1.043 [0.997, 

1.090] lag 0 

1-h max NO2: RR 1.015 [0.993, 

1.037] lag 0 

Asthma (1987-1992) 

24-h avg: RR 1.175 [1.059, 

1.304] lag 0-1 

1-h max: RR 1.081 [1.019, 1.148] 

lag 0-1 

COPD 

24-h avg: RR 0.974 [0.898, 

1.058] lag 2 

1-h max: RR 0.961 [0.919, 1.014] 

lag 2 

Linares et al. 

(2006) 

Madrid, Spain 

Period of Study: 

1995-2000 

Outcomes (ICD 9): All respiratory 

(460-519), bronchitis (460-496), 

pneumonia (480-487) 

Age groups analyzed: <10 

Study Design: Time-series 

Number of hospitals: 1 

Statistical Analyses: Poisson 

regression 

Covariates: Temperature, pressure, 

relative humidity 

Statistical Package: S Plus 2000 

Lag: 

24-h avg: 64.8 (17.1) ug/m3

Range: 23-144 

Number of monitors: 24 

PM10; r = 0.71 

O3; r = -0.41 

SO2; r = 0.63 

Qualitative results suggest linear 

relationship without threshold for 

NO2 concentration and 

respiratory hospital admissions. 

Llorca et al. (2005) 

Torrelavega, Spain 

Period of Study: 

1992-1995 

Days: 1,461 

Outcomes (ICD 9): All respiratory 

admissions (460-519) 

Age groups analyzed: All ages 

Study Design: Time-series 

Number of hospitals: 1 

Statistical Analyses: Poisson 

regression 

Covariates: Short and long-term 

trends 

Statistical Package: Stata 

Lag: NR 

24-h avg NO2: 21.3 μg/m3

, 

SD = 16.5 

24-h avg NO: 12.2 μg/m3

, SD 

= 15.2 

# of Stations: 3 

SO2; r = 0.588 

NO; r = 0.855 

TSP; r = -0.12 

SH2; r = 0.545 

Increment: 100 μg/m3

Single-pollutant model 

All cardio-respiratory admissions 

NO2: RR 1.37 [1.26, 1.49] 

NO: RR 1.33 [1.22, 1.46] 

Respiratory admissions 

NO2: RR 1.54 [1.34, 1.76] 

NO: RR 1.35 [1.17, 1.56] 

5-pollutant model 

All cardio-respiratory admissions 

NO2: RR 1.20 [1.05, 1.39] 

NO: RR 0.93 [0.79, 1.09] 

Respiratory admissions 

NO2: RR 1.69 [1.34, 2.13] 

NO: RR 0.87 [0.67, 1.13] 
6-47 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Migliaretti and 

Cavallo (2004) 

Turin, Italy 

Period of Study: 

1997-1999 

Outcome(s) (ICD 9): Asthma (493) 

Age groups analyzed: <4, 4-15 

Study Design: Case-control 

Controls: Age matched with other 

respiratory disease (ICD9: 460-7, 

490-2, 494-6, 500-19) 

N: Cases = 734, controls = 25,523 

Statistical Analyses: Logistic 

regression 

Covariates: Seasonality, temperature, 

humidity, solar radiation 

Seasons: Cold: Oct-Mar; Warm: AprSep 

Statistical Package: SPSS 

Lag: 0-3 days and cumulative 

Controls: 

Mean: 113.3 μg/m3

, 

SD = 30.5 

Cases: 

Mean: 117.4 μg/m3

, 

SD = 29.7 

TSP Increment: 10 μg/m3

 

<4 yrs 2.8% [0.03, 5.03] lag 1-3 

cumulative 

4-15 yrs 2.7% [-0.01, 6.06] lag 1-

3 cumulative 

All ages 2.8% [0.07, 4.09] lag 1-3 

cumulative 

Two-pollutant model adjusted for 

TSP 

NO2 2.1% [-0.1, 5.6] 

Farchi et al. (2006) 

Rome, Italy 

Period of Study: 

11/94-2/95 

Outcome(s) (ICD 9): All respiratory 

conditions (381-382, 460-466, 480-

493); acute upper respiratory tract 

infections (380-382, 460-465); lower 

respiratory tract conditions including 

asthma (466, 480-493) 

Age groups analyzed: 6-7 

Study Design: Cohort (SIDRIA) 

N: 2,947 

Statistical Analyses: Cox regression 

models, GAM 

Covariates: Gender, paternal 

education, paternal smoking 

Statistical Package: STATA 8.0 

Mean: 46.9 μg/m3

 (10.2) 

IQR: 17 

Range: 24-66 

Traffic Increment: 10 μg/m3

All respiratory conditions: 

HR: 1.28 [0.98-1.68] 

1st Quartile (24-35 μg/m3

): 1.00 

2nd Quartile (35-47 μg/m3): 1.06 

[0.45-2.53] 

3rd Quartile (47-52 μg/m3

): 1.57 

[0.59-4.13] 

4th quartile (52-66 μg/m3

): 1.95 

[0.81-4.71] 

Acute URT infections: 

HR: 1.56 [0.96-2.56] 

1st Quartile (24-35 μg/m3

): 1.00 

2nd Quartile (35-47 μg/m3

): 0.55 

[0.08-3.61] 

3rd Quartile (47-52 μg/m3

): 1.25 

[0.25-6.24] 

4th quartile (52-66 μg/m3

): 3.04 

[0.67-13.79] 

Acute LRT infections and asthma: 

HR: 1.10 [0.80-1.51] 

1st Quartile (24-35 μg/m3

): 1.00 

2nd Quartile (35-47 μg/m3

): 1.34 

[0.51-3.21] 

3rd Quartile (47-52 μg/m3

): 1.58 

[0.35-4.10] 

4th quartile (52-66 μg/m3

): 1.24 

[0.64-3.08] 
6-48 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Fusco* et al. 

(2001) 

Rome, Italy 

Period of Study: 

1/1/95-10/31/97 

Outcomes (ICD 9): All respiratory 

(460-519 excluding 470-478), Asthma 

(493), COPD (490-492, 494-496), 

Respiratory infections (460-466, 480-

486) 

Age groups analyzed: 0-14, all ages 

Study Design: Time-series 

Statistical Analyses: Semi-parametric 

Poisson regression with GAM 

Covariates: Influenza, day, 

temperature, humidity, day of wk, 

holiday 

Season: Warm (Apr-Sep), Cold (OctMar) 

Statistical Package: S-Plus 4 

Lag: 0-4 days 

NO2 24-h avg (μg/m3

): 86.7, 

SD = 16.2 

IQR: 22.3 μg/m3

# of stations: 5; r = 0.66-0.79 

PM10: 

All yr; r = 0.35 

Cold; r = 0.50 

Warm; r = 0.25 

SO2: 

All yr; r = 0.33 

Cold; r = 0.40 

Warm; r = 0.68 

CO: 

All yr; r = 0.31 

Cold; r = 0.41 

Warm; r = 0.59 

O3: 

All yr; r = 0.19 

Cold; r = 0.19 

Warm; r = 0.13 

Increment: 22.3 μg/m3

 (IQR) 

All respiratory 

All ages: 2.5% [0.9, 4.2] lag 0 

0-14 yrs: 4.0% [0.6, 7.5] lag 0 

Respiratory infections 

All ages: 4.0% [1.6, 6.5] lag 0 

0-14 yrs: 4.0% [0.2, 8.0] lag 0 

Asthma 

All ages: 4.6% [-0.5, 10.0] lag 0 

0-14 yrs: 10.7% [3.0, 19.0] lag 1 

COPD 

≥65 yrs: 2.2% [-0.7, 5.2] lag 0 

Multipollutant models 

All respiratory (NO2 + CO) 

All ages: 0.9% [-0.8, 2.8] lag 0 

0-14 yrs: 3.3% [-0.2, 6.9] lag 0 

Acute infections (NO2 + CO) 

All ages: 3.9% [1.3, 6.7] lag 0 

0-14 yrs: 2.9% [-1.0, 7.0] lag 0 

Asthma (NO2 + CO) 

All ages: 1.4% [-3.9, 7.1] lag 0 

0-14 yrs: 8.3% [-0.1, 17.4] lag 1 

COPD (NO2 + CO) 

≥65 yrs: -1.0%[-4.1, 2.2] lag 0 

Pantazopoulou 

et al. (1995) 

Athens, Greece 

Period of Study: 

1988 

Outcomes: All respiratory admissions 

ICD9: NR 

Age groups analyzed: All ages 

Study Design: Time-series 

N: 15,236 

Number of hospitals: 14 

Statistical Analyses: Multiple linear 

regression 

Covariates: Season, day of wk, 

holiday, temperature, relative 

humidity 

Season: Warm (3/22-9/21), Cold (1/1-

3/21 and 9/22-12/31) 

Lag: NR 

NO2 24-h avg 

Winter: 94 μg/m3, 

SD = 25 

5th: 59, 50th: 93, 95th: 135 

Summer: 111 μg/m3

, SD = 32 

5th: 65, 50th: 108, 95th: 173 

# of stations: 2 

CO 

BS 

Increment: 76 μg/m3

 in winter and 

108 μg/m3 in summer (95th-5th) 

Respiratory disease admissions 

Winter: Percent increase: 24% 

[6.4, 43.5] 

Summer: Percent increase: 9.3% 

[-14.1, 24.4] 
6-49 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

LATIN AMERICA 

Gouveia and 

Fletcher, (2000a) 

São Paulo, Brazil 

Period of Study: 

11/92-9/94 

Outcomes (ICD 9): All respiratory; 

Pneumonia (480-486); asthma or 

bronchitis (466, 490, 491, 493) 

Age groups analyzed: <1; <5 yrs 

Study Design: Time-series 

Statistical Analyses: Poisson 

regression Covariates: Long-term 

trend, season, temperature, relative 

humidity, day of wk, holiday, strikes in 

public transport or health services 

Season: Cool (May-Oct), Warm (NovApr) 

Statistical Package: SAS 

Lag: 0, 1, 2 days 

1-h max NO2 (μg/m3

) 

Mean: 174.3 

SD = 101.3 

Range: 26.0, 692.9 

5th: 62.0 

25th: 108.8 

50th: 151.7 

75th: 210.0 

95th: 388.0 

# of stations: 4 

SO2; r = 0.37 

PM10; r = 0.40 

CO; r = 0.35 

O3; r = 0.25 

Increment: 319.4 μg/m3

 (90th10th) 

All Respiratory 

<5 yrs: RR 1.063 [0.999, 1.132] 

lag 0 

<5 yrs + O3: RR 1.050 [0.985, 

1.120] 

<5 yrs + PM10: RR 1.043 [0.972, 

1.119] 

<5 yrs + O3+ PM10: RR 1.035 

[0.963, 1.113] 

<5 yrs Cool: RR 1.04 [0.96, 1.11] 

(estimated from graph) 

<5 yrs Warm: RR 1.09 [1.01, 

1.16] (estimated from graph) 

Pneumonia 

<5 yrs: RR 1.093 [1.016, 1.177] 

lag 0 

<1 yr: RR 1.091 [0.996, 1.193] lag 

0 

Asthma 

<5 yrs: RR 1.107 [0.940, 1.300] 

lag 2 

Braga* et al. 

(1999) 

Sao Paulo, Brazil 

Period of Study: 

10/1992-10/1993 

Hospital Admissions 

Outcomes (ICD 9 codes): All 

respiratory (466,480-486,491-

492,496) 

Age groups analyzed: <13 yrs 

Study Design: Time-series 

N: 68,918 

# of Hospitals: 112 

Statistical Analyses: Multiple linear 

regression models (least squares). 

Also used Poisson regression 

techniques. GLM and GAM using 

LOESS for smoothing. 

Covariates: Season, temperature, 

humidity, day of wk 

Statistical Package: SPSS, S-Plus 

Lag: 1,2,3,4,5,6,7 moving avgs 

24-h avg 174.84 

(101.38) μg/m3

Min: 26.0 

Max: 668.3 

# of monitors: 13 

PM10; r = 0.53 

CO; r = 0.42 

SO2; r = 0.53 

O3; r = 

Due to problems with NO2

monitors, this pollutant could not 

be included in the analysis. 

Braga* et al. 

(2001) 

São Paulo, Brazil 

Period of Study: 

1/93-11/97 

Outcomes (ICD 9): All respiratory 

admissions (460-519) 

Age groups analyzed: 0-19, #2, 3-5, 

6-13, 14-19 

Study Design: Time-series 

Statistical Analyses: Poisson 

regression with GAM 

Covariates: Long-term trend, season, 

temperature, relative humidity, day of 

wk, holiday 

Statistical Package: S-Plus 4.5 

Lag: 0-6 moving avg 

NO2 mean: 141.4 μg/m3

, 

SD = 71.2 

IQR: 80.5 μg/m3

Range: 25, 652.1 

# of stations: 5-6 

PM10; r = 0.62 

SO2; r = 0.54 

CO; r = 0.58 

O3; r = 0.34 

Increment: 80.5 μg/m3

 (IQR) 

All Respiratory admissions 

<2 yrs 9.4% [6.2, 12.6] lag 5 

3-5 yrs 1.6% [-6.4, 9.6] 

6-13 yrs 2.3% [-5.9, 10.4] 

14-19 yrs -3.0% [-15.7, 9.7] 

All ages 6.5% [3.3, 9.7] 
6-50 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Braga* et al. 

(2001) 

São Paulo, Brazil 

Period of Study: 

1/93-11/97 

Outcomes (ICD 9): All respiratory 

admissions (460-519) 

Age groups analyzed: 0-19, #2, 3-5, 

6-13, 14-19 

Study Design: Time-series 

Statistical Analyses: Poisson 

regression with GAM 

Covariates: Long-term trend, season, 

temperature, relative humidity, day of 

wk, holiday 

Statistical Package: S-Plus 4.5 

Lag: 0-6 moving avg 

NO2 mean: 141.4 μg/m3

, 

SD = 71.2 

IQR: 80.5 μg/m3

Range: 25, 652.1 

# of stations: 5-6 

PM10; r = 0.62 

SO2; r = 0.54 

CO; r = 0.58 

O3; r = 0.34 

Increment: 80.5 μg/m3

 (IQR) 

All Respiratory admissions 

<2 yrs 9.4% [6.2, 12.6] lag 5 

3-5 yrs 1.6% [-6.4, 9.6] 

6-13 yrs 2.3% [-5.9, 10.4] 

14-19 yrs -3.0% [-15.7, 9.7] 

All ages 6.5% [3.3, 9.7] 

Farhat* et al. 

(2005) 

São Paulo, Brazil 

Period of Study: 

8/96-8/97 

Days: 396 

Outcomes (ICD 9): 

Pneumonia/bronchiopheumonia (480-

6), asthma (493), bronchiolitis (466), 

Obstructive disease 493, 466) 

Age groups analyzed: <13 

Study Design: Time-series 

N: 1,021 

Number of hospitals: 1 

Statistical Analyses: Poisson 

regression with GAM 

Covariates: Time, temperature, 

humidity, day of wk, season 

Statistical package: S-Plus 

Lag: 0-7 days, 2,3,4 day moving avg 

Mean: 125.3 μg/m3

SD = 51.7 

IQR: 65.04 μg/m3

Range: 42.5, 369.5 

PM10; r = 0.83 

SO2; r = 0.66 

CO; r = 0.59 

O3; r = 0.47 

Increment: 65.04 μg/m3

 (IQR) 

Single-pollutant models 

(estimated from graphs) 

Lower respiratory tract disease: 

NO2 alone: −18% [13, 24] lag 0-3 

NO2 + PM10 16.1% [5.4, 26.8] 

lag 0-2 

NO2 + SO2 24.7% [18.2, 31.3] lag 

0-2 

NO2 + CO 19.2% [11.8, 26.6] lag 

0-2 

NO2 + O3 16.1% [9.5, 22.7] lag 0-

2 

Multipollutant model (PM10, SO2, 

CO, O3) 18.4% [3.4, 33.5] 2 day 

avg 

Pneumonia: 

NO2 alone: −17.5% [3, 32.5] lag 

0-2 

NO2 + PM10 8.1% [-11.4, 27.6] 

lag 0-2 

NO2 + SO2 13.1% [-3.4, 29.7] lag 

0-2 

NO2 + CO 14.6% [-4.9, 34.1] lag 

0-2 

NO2 + O3 12.4% [-5.6, 30.4] lag 

0-2 

Multipollutant model (PM10, SO2, 

CO, O3) 

1.8% [-23.9, 27.6] 2 day avg 

Asthma or Bronchiolitis 

NO2 alone: 30.5% [9, 56] lag 0-1 

NO2 + PM10 47.7% [1.15, 94.2] 

lag 0-2 

NO2 + SO2 33.1% [5.7, 60.5] lag 

0-2 

NO2 + CO 28.8% [-0.2, 57.9] lag 

0-2 

NO2 + O3 28.0% [-1.0, 57.0] lag 

0-2 

Multipollutant model (PM10, SO2, 

CO, O3) 

39.3% [-14.9, 93.5] 2 day avg 
6-51 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Arbex et al., 2007 

Araraquara, Brazil 

Period of Study: 

3/2003-7/2004 

Outcomes (ICD 10): asthma (J15) 

Age groups analyzed: <13 

Study Design: Ecological Time-series 

N: 1,021 

Number of hospitals: 1 

Statistical Analyses: Poisson 

regression with GLM 

Covariates: Long-term trend, weather 

Statistical package: S-Plus 

Lag: 0-7 days, 2,3,4 day moving avg 

TSP (μg/m3

) Increment: 10 μg/m3

Asthma hospital admissions: 

11.6% [5.4, 17.7] lag 1-5 

ASIA 

Lee et al. (2006) 

Hong Kong, China 

Period of Study: 

1997-2002 

Days: 2,191 

Outcomes (ICD 9): Asthma (493) 

Age groups analyzed: #18 

Study Design: Time-series 

N: 26,663 

Statistical Analyses: Semi-parametric 

Poisson regression with GAM (similar 

to APHEA 2) 

Covariates: Long-term trend, 

temperature, relative humidity, 

influenza, day of wk, holiday 

Statistical package: SAS 8.02 

Lag: 0-5 days 

NO2 24-h mean: 64.7 μg/m3

, 

SD = 20.9 

IQR: 27.1 μg/m3

25th: 49.7, 75th: 76.8 

# of stations: 9-10, r = 0.53, 

0.94, 

Mean: 0.78 

PM10; r = 0.78 

PM2.5; r = 0.75 

SO2; r = 0.49 

O3; r = 0.35 

Increment: 27.1 μg/m3 (IQR) 

Asthma 

Single-pollutant model 

4.37% [2.51, 6.27] lag 0 

5.88% [4.00, 7.70] lag 1 

7.19% [5.37, 9.04] lag 2 

9.08% [7.26, 10.93] lag 3 

7.64% [5.84, 9.48] lag 4 

6.40% [4.60, 8.22] lag 5 

Multipollutant model – including 

PM, SO2, and O3

5.64% [3.21, 8.14] lag 3 

Other lags NR 

Chew et al. (1999) 

Singapore 

Period of Study: 

1990-1994 

Outcome(s) (ICD 9): Asthma (493) 

Age groups analyzed: 3-12, 13-21 

Study Design: Time-series 

N: 23,000 

# of Hospitals: 2 

Statistical Analyses: Linear 

regression, GLM 

Covariates: Variables that were 

significantly associated with ER visits 

were retained in the model 

Statistical Package: SAS/STAT, 

SAS/ETS 6.08 

Lag: 1,2 days avgs 

24-h avg: 18.9 μg/m3

, 

SD = 15.0, 

Max < 40 

# of Stations: 15 

SO2; r = -0.22 

O3; r = 0.17 

TSP; r = 0.23 

Categorical analysis (via ANOVA) 

p-value and Pearson correlation 

coefficient (r) using continuous 

data comparing daily air pollutant 

levels and daily number of 

hospital admissions. 

Age Group: 3-12 13-21 

Lag 0 r = 0.13 r = 0.05 

 p = 0.013 p < 0.18 

Lag 1 r = 0.13 r = 0.02 

 P = 0.02 p = 0.75 

Lag 2 r = 0.13 r = 0.07 

 p = 0.35 p = 0.012 
6-52 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Tsai et al. (2006) 

Kaohsiung, Taiwan 

Period of Study: 

1996-2003 

Days: 2922 

Outcomes (ICD 9): Asthma (493) 

Study Design: Case-crossover 

N: 17,682 

Statistical Analyses: Conditional 

logistic regression 

Covariates: Temperature, humidity 

Season: Warm (≥25oC); Cool (<25oC) 

Statistical package: SAS 

Lag: 0-2 days cumulative 

NO2 24-h mean: 27.20 ppb 

IQR: 17 ppb 

Range: 4.83, 63.40 

# of stations: 6 

PM10 

SO2

O3

CO 

Increment: 17 ppb (IQR) 

Seasonality 

Single-pollutant model 

>25°C 1.259 [1.111, 1.427] lag 0-

2 

<25°C 2.119 [1.875, 2.394] lag 0-

2 

Dual-pollutant model 

Adjusted for PM10 

>25°C 1.082 [0.913, 1.283] lag 0-

2 

<25°C 2.105 [1.791, 2.474] lag 0-

2 

Adjusted for CO 

>25°C 0.949 [0.792, 1.137] lag 0-

2 

<25°C 2.30 [1.915, 2.762] lag 0-2 

Adjusted for SO2

>25°C 1.294 [1.128, 1.485] lag 0-

2 

<25°C 2.627 [2.256, 3.058] lag 0-

2 

Adjusted for O3

>25°C 1.081 [0.945, 1.238] lag 0-

2 

<25°C 2.096 [1.851, 2.373] lag 0-

2 

Chen et al. (2006) 

Taiwan 

Period of Study: 

1/1998-12/2001 

Outcomes (ICD 9): Asthma (493) 

Age Groups: 0-4, 5-14, 15-44, 45-64, 

65+ 

Study Design: Time-series 

N: 126,671 

Statistical Analyses: Spearman Rank 

Correlations 

Covariates: 

Season: 

Statistical package: SPSS 

Lag: 

Mean monthly NO2 averaged 

across 55 monitors: 37.64 

(4.89) ppb 

Min: 29.52 

25th: 33.72 

50th: 37.07 

75th: 40.63 

Max: 47.65 

PM10; r = 

SO2; r = 

CO; r = 

O3; r = 

Spearman rank correlations show 

that seasonal variations in adult 

asthma admissions are 

significantly correlated with levels 

of NO2 (r = 0.423, p = 0.003). 

Lee* et al. (2002) 

Seoul, Korea 

Period of Study: 

12/1/97-12/31/99 

Days: 822 

Outcomes (ICD 10): Asthma (J45 – 

J46) 

Age groups analyzed: <15 

Study Design: Time-series 

N: 6,436 

Statistical Analyses: Poisson 

regression, log link with GAM 

Covariates: Time, day of wk, 

temperature, humidity 

Season: Spring (Mar-May), Summer 

(Jun-Aug), Fall (Sep-Nov), Winter 

(Dec-Feb) 

Statistical package: NR 

Lag: 0-2 days cumulative 

24-h NO2 (ppb) 

Mean: 31.5 

SD = 10.3 

5th: 16.0 

25th: 23.7 

50th: 30.7 

75th: 38.3 

95th: 48.6 

# of stations: 27 

SO2; r = 0.72 

O3; r = -0.07 

CO; r = 0.79 

PM10; r = 0.74 

Increment: 14.6 ppb (IQR) 

Asthma 

NO2: RR 1.15 [1.10, 1.20] lag 0-2 

NO2 + PM10: RR 1.13 [1.07, 

1.19] lag 0-2 

NO2 + SO2: RR 1.20 [1.11, 1.29] 

lag 0-2 

NO2 + O3: RR 1.14 [1.09, 1.20] 

lag 0-2 

NO2 + CO: RR 1.12 [1.03, 1.22] 

lag 0-2 

NO2 + O3 + CO + PM10 + SO2: 

RR 1.098 [1.002, 1.202] 
6-53 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Yang et al. (2007) 

Taipei, Taiwan 

Period of Study: 

1996-2003 

Outcomes (ICD 9): Asthma (493) 

Age groups analyzed: All 

Study Design: Case-crossover 

N: 25,602 

Number of hospitals: 47 

Statistical Analyses: Conditional 

logistic regression 

Covariates: Temperature, humidity 

Statistical package: SAS 

Lag: 0-2 

24-h avg: 30.77 ppb 

Range: 3.84-77.97 

25th: 25.55 

50th: 30.31 

75th: 35.60 

Number of monitors: 6 

SO2

PM10 

CO 

O3

Increment: 10.05 ppb (IQR) 

NO2 alone: 

≥25 ΕC: 1.178 [1.113, 1.247] lag 

0-2 

<25 ΕC: 1.128, 1.076, 1.182] lag 

0-2 

NO2 + PM10: 

≥25 ΕC: 1.328 [1.224, 1.441] lag 

0-2 

<25 ΕC: 1.144 [1.077, 1.215] lag 

0-2 

NO2 + SO2: 

≥25 ΕC: 1.224 [1.140, 1.314] lag 

0-2 

<25 ΕC: 1.219 [1.150, 1.291] lag 

0-2 

NO2 + CO: 

≥25 ΕC: 1.084 [0.999, 1.176] lag 

0-2 

<25 ΕC: 1.198 [1.111, 1.291] lag 

0-2 

NO2 + O3: 

≥25 ΕC: 1.219 [1.142, 1.301] lag 

0-2 

<25 ΕC: 1.156 [1.102, 1.212] lag 

0-2 

Yang and Chen 

(2007) 

Taipei, Taiwan 

Period of Study: 

1996-2003 

Outcomes (ICD 9): COPD (493) 

Age groups analyzed: 

Study Design: Case-crossover 

N: 25,602 

Number of hospitals: 47 

Statistical Analyses: Conditional 

logistic regression 

Covariates: Temperature, humidity 

Statistical package: SAS 

Lag: 0-2 

24-h avg: 30.77 ppb 

Range: 3.84-77.97 

25th: 25.55 

50th: 30.31 

75th: 35.60 

Number of monitors: 6 

SO2

PM10 

CO 

O3

Increment: 10.05 ppb (IQR) 

NO2 alone: 

≥20 ΕC: 1.193 [1.158, 1.230] lag 

0-2 

<20 ΕC: 0.972 [0.922, 1.024] lag 

0-2 

NO2 + PM10: 

≥20 ΕC: 1.183 [1.137, 1.231] lag 

0-2 

<20 ΕC: 0.920 [0.862, 0.982] lag 

0-2 

NO2 + SO2: 

≥20 ΕC: 1.302 [1.254, 1.351] lag 

0-2 

<20 ΕC: 0.895 [0.837, 0.956] lag 

0-2 

NO2 + CO: 

≥20Ε C: 1.154 [1.102, 1.208] lag 

0-2 

<20 ΕC: 0.972 [0.892, 1.059] lag 

0-2 

NO2 + O3: 

≥20 ΕC: 1.163 [1.126, 1.200] lag 

0-2 

<20 ΕC: 0.952 [0.901, 1.006] lag 

0-2 
6-54 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Ko et al. (2007a) 

Hong Kong 

Period of Study 

Outcomes (ICD 9): 

Age groups analyzed: 

# of hospitals: 

Study Design: 

Statistical Analyses: 

Covariates: 

Statistical package: 

Lag: 

 

Lee et al. (2006) 

Hong Kong, China 

Period of Study: 

1997-2002 

Outcomes (ICD 9): asthma (493) 

Age groups analyzed: <18 

N: 26,663 

Study Design: Time-series 

Statistical Analyses: Poisson 

regression with GAM 

Covariates: Temperature, humidity, 

influenza, day of wk, holidays 

Statistical package: SAS v 8.02 

Lag: 0, 1, 2, 3, 4, 5 

24-h avg: 64.7 (20.9) μg/m3

25th: 49.7 

50th: 63.5 

75th: 76.8 

IQR: 27.1 

Number of monitors: 10 

SO2; r = 0.49 

PM10; r = 0.78 

PM2.5; r = 0.75 

O3; r = 0.35 

Increment: 27.1 μg/m3 (IQR) 

Lag 0: 4.37% [2.51, 6.27] 

Lag 1: 5.88% [4.00, 7.70] 

Lag 2: 7.19% [5.37, 9.04] 

Lag 3: 9.08% [7.26, 10.93] 

Lag 4: 7.64% [5.84, 9.48] 

Lag 5: 6.40% [4.60, 8.22] 

NO2 alone: 9.08% [7.26, 10.93] 

lag 3 

NO2 + SO2 + PM10 + PM2.5 + 

O3: 5.64% [3.21, 8.14] lag 3 

Lee et al. (2007) 

Kaohsiung, Taiwan 

Period of Study: 

1996-2003 

Outcomes (ICD 9): COPD (490-492, 

494, 496) 

Age groups analyzed: All 

# of hospitals: 63 

N: 25,108 

Study Design: Case-crossover 

Statistical Analyses: Conditional 

logistic regression 

Covariates: Temperature, humidity 

Season: Warm: >25 ΕC, cool: <25 ΕC 

Statistical package: SAS v 8.2 

Lag: 0-2 cumulative avg 

24-h avg: 27.2 ppb 

Range: 4.83-63.40 

25th: 18.4 

50th: 27.17 

75th: 35.40 

# of monitors: 6 

SO2

PM10 

CO 

O3

Increment : 17 ppb (IQR) 

NO2 alone: 

≥25 C: 1.241 [1.117, 1.379] lag 0-

2 

<25 C: 1.975 [1.785, 2.186] lag 0-

2 

NO2 + PM10: 

≥25 C: 1.083 [0.939, 1.249] lag 0-

2 

<25 C: 1.957 [1.709, 2.241] lag 0-

2 

NO2 + SO2: 

≥25 C: 1.264 [1.127, 1.418] lag 0-

2 

<25 C: 2.378 [2.095, 2.700] lag 0-

2 

NO2 + CO: 

≥25 C: 0.984 [0.848, 1.141] lag 0-

2 

<25 C: 2.035 [1.746, 2.373] lag 0-

2 

NO2 + O3: 

≥25 C: 1.076 [0.961, 1.205] lag 0-

2 

<25 C: 1.946 [1.755, 2.157] lag 0-

2 
6-55 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

& 

CORRELATIONS

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE 

& CONFIDENCE 

INTERVALS (95%) 

Wong et al. (1999) 

Hong Kong, China 

Period of Study: 

1994-1995 

Outcomes (ICD 9): All respiratory 

admissions (460-6, 471-8, 480-7, 490-

6); Asthma (493), COPD (490-496), 

Pneumonia (480-7) 

Age groups analyzed: 0-4, 5-64, ≥65, 

all ages 

# of hospitals: 12 

Study Design: Time-series 

Statistical Analyses: Poisson 

regression (followed APHEA protocol) 

Covariates: Trend, season, day of wk, 

holiday, temperature, humidity 

Statistical package: SAS 8.02 

Lag: days 0-3 cumulative 

Median 24-h NO2: 51.39 

μg/m3

Range: 16.41, 122.44 

25th: 39.93, 75th: 66.50 

# of stations: 7, 

r = 0.68, 0.89 

O3

SO2

PM10; r = 0.79 

Increment = 10 μg/m3

Overall increase in admissions: 

1.020 [1.013, 1.028] lag 0-3 

Respiratory Relative Risks (RR) 

0-4 yrs: 1.020 [1.010, 1.030] lag 

0-3 

5-64yrs: 1.023 [1.011, 1.034] lag 

0-3 

>65 yrs: 1.024 [1.014, 1.035] lag 

0-3 

Cold Season: 1.004 [0.988, 

1.020] 

NO2 + high PM10: 1.009 [0.993, 

1.025] 

NO2 + high O3: 1.013 [0.999, 

1.026] 

Asthma: 1.026 [1.01, 1.042] lag 

0-3 

COPD: 1.029 [1.019, 1.040] lag 

0-3 

Pneumonia: 1.028 [1.015, 1.041] 

lag 0-3 

Wong et al. 

(2001a) 

Hong Kong, China 

Period of Study: 

1993-1994 

Outcomes (ICD 9): Asthma (493) 

Age groups analyzed: #15 

N: 1,217 

# of hospitals: 1 

Study Design: Time-series 

Statistical Analyses: Poisson 

regression (followed APHEA protocol) 

Covariates: Season, temperature, 

humidity 

Season: Summer (Jun-Aug), 

Autumn (Sep-Nov), Winter (Dec-Feb), 

Spring (Mar-May) 

Lag: 0, 1, 2, 3, 4, 5 days; and 

cumulative 0-2 and 0-3 days 

24-h avg 

NO2 mean: 43.3 μg/m3

, 

SD = 16.6 

Range: 9, 106 μg/m3

Autumn: 51.7 (17.6) 

Winter: 46.6 (15.5) 

Spring: 40.7 (11.8) 

Summer: 32.6 (13.7) 

# of stations: 9 

PM10 

SO2

Increment: 10 μg/m3

Asthma 

All yr: 1.08 p = 0.001 

Autumn: 1.08 p = 0.017 

Winter: NR 

Spring: NR 

Summer: NR 

*Default GAM 

+Did not report correction for over-dispersion 

APHEA: Air Pollution and Health: A European Approach 
6-56 

Table AX6.3-4. Respiratory Health Effects of Oxides of Nitrogen: Emergency Department Visits 

STUDY METHODS 

MEAN LEVELS OF 

NO2 & 

MONITORING 

STATIONS 

COPOLLUTANT 

CORRELATIONS 

EFFECTS AND INTERPRETATION: 

RELATIVE RISK & CONFIDENCE 

INTERVALS (95%) 

UNITED STATES 

Jaffe et al. 

(2003) 

3 cities, Ohio, 

(Cleveland, 

Columbus, 

Cincinnati) 

Period of Study: 

7/91-6/96 

Outcome (ICD-9): Asthma 

(493) 

Age Groups Analyzed: 5-34 

Study Design: Time-series 

N: 4,416 

Statistical Analyses: Poisson 

regression using a standard 

GAM approach 

Covariates: City, day of wk, wk, 

yr, minimum temperature, 

overall trend, dispersion 

parameter 

Season: Jun to Aug only 

Dose-response investigated: 

Yes 

Statistical Package: NR 

Lag: 0-3 days 

Cincinnati: 

24-h avg: 50 ppb, SD 

= 15 

Cleveland: 

24-h avg: 48 ppb, SD 

= 16 

NO2 was not 

monitored in 

Columbus due to 

relatively low levels 

Cincinnati: 

PM10; r = 0.36 SO2; r = 

0.07 

O3; r = 0.60 

Cleveland: 

PM10; 0.34 

SO2; r = 0.28 

O3; r = 0.42 

No multipollutant 

models were utilized. 

Increment: 10 ppb 

Cincinnati: 6% [-1.0, 13] lag 1 

Cleveland: 4% [-1, 8] lag 1 

All cities: 3% [-1.0, 7] 

Attributable risk from NO2 increment: 

Cincinnati 0.72 (RR 1.06) 

Cleveland 0.44 (RR 1.04) 

Regression diagnostics for Cincinnati showed 

significant linear trend during entire study 

period and for each wk (6/1-8/31). No trends 

observed for Cleveland. 

Regression Models assessing exposure 

thresholds showed a possible dose-response 

for NO2 (percent increase after 40 ppb). No 

increased risk until minimum concentration of 

40 ppb was reached. 

Ito et al. (2007) 

New York, NY 

Outcome (ICD-9): 

Asthma (493) 

Age Groups Analyzed: All ages 

Study Design: Time-series 

N: 1,460 

Statistical Analyses: 

Poisson’s Generalized Linear 

Model 

Covariates: temporal trends, 

day of wk, weather, overdispersion 

Season: all year; warm (April to 

September), cold (October to 

March) 

Statistical Package: NR 

Lag: 0-1 day 

All year 

24-h avg: 31.1 

SD = 8.7 

Warm season 

24-h avg: 30.4 

SD = 8.8 

Cold season 

24-h avg: 31.8 

SD = 31.8 

PM2.5: r = 0.91 

O3: r = 0.89 

SO2: r = 0.74 

CO: r = 0.60 

All Year: Increment : 24 ppb 

24-h avg, lag 0 

RR 1.14 (1.09, 1.19) 

Warm Months: Increment : 25 ppb 

24-h avg, lag 0 

RR 1.32 (1.23, 1.42) 
6-57 

STUDY METHODS 

MEAN LEVELS OF 

NO2 & 

MONITORING 

STATIONS 

COPOLLUTANT 

CORRELATIONS 

EFFECTS AND INTERPRETATION: 

RELATIVE RISK & CONFIDENCE 

INTERVALS (95%) 

NYDOH (2006) 

Bronx and 

Manhattan, NY 

Period of Study: 

1/1999-11/2000 

Outcome (ICD-9): 

Asthma (493) 

Age Groups Analyzed: 

All ages 

Study Design: Time-series 

Statistical Analyses: 

Poisson regression 

Covariates: temporal cycles, 

temperature, day of wk 

Season: All year 

Dose-response investigated: 

Yes 

Statistical Package: NR 

Lag: 0-4 days 

24-h avg: 34 ppb PM2.5

SO2

O3

Increment : 20 ppb 

lag 0-4 

24-h avg 

Bronx 

NO2 : 1.06 (1.01, 1.10) 

NO2 + O3 : 1.05 (1.00, 1.10) 

NO2 + FRM PM2.5 : 1.03 (0.98, 1.08) 

NO2 + PM2.5 max : 1.02 (0.98, 1.08) 

NO2 + SO2 : 1.01 (0.96, 1.07) 

Manhattan 

NO2 : 0.97 (0.82, 1.14) 

NO2 + O3 : 0.95 (0.80, 1.12) 

NO2 + FRM PM2.5 : 0.90 (0.41, 1.10) 

NO2 + PM2.5 max : 0.90 (0.41, 1.11) 

NO2 + SO2 : 0.97 (0.80, 1.17) 

Norris* et al. 

(1999) 

Seattle, WA, 

United States 

Period of Study: 

1995-1996 

Outcome (ICD-9): Asthma 

(493) 

Age groups analyzed: <18 yrs 

Study Design: Time-series 

N: 900 ER visits 

Statistical Analyses: Semi 

parametric Poisson regression 

using GAM 

Covariates: day of wk, time 

trends, temperature, dew point 

temperature 

Dose-response investigated: 

Yes 

Statistical Package: NR 

Lag: 0,2 days 

24 h: 20.2 ppb, SD = 

7.1 

IQR: 9 ppb 

1-h max: 34.0 ppb, 

SD = 11.3 

IQR: 12 ppb 

CO; r = 0.66 

PM; r = 0.66 

SO2; r = 0.25 

Increment: IQR 

24-h avg (9-ppb increment) 

RR 0.99 [0.90, 1.08] lag 2 

1-h max (12-ppb increment) 

RR 1.05 [0.99, 1.12] lag 0 

Age and hospital utilization (high and low) 

segregation (<5, 5-11, and 12-17 yrs) did not 

figure significantly in the association between 

emergency room visits and asthma. 

Lipsett et al. 

(1997) 

Santa Clara 

County, 

California, 

United States 

Period of Study: 

1988-1992 

Outcome(s): Asthma 

ICD-9 Code(s): NR 

Age groups analyzed: All 

Study Design: Time-series 

Statistical Analyses: Poisson 

Regression; GEE repeated 

with GAM 

Covariates: Minimum 

temperature, day of study, 

precipitation, hospital, day of 

wk, yr, overdispersion 

parameter 

Season: Winters only 

Statistical Package: SAS, S 

Plus, Stata 

Lag: 0-5 days 

NO2 1-h mean: 

69 ppb, 

SD = 28 

Range: 29, 150 ppb 

PM10; r = 0.82 

COH; r = 0.8 

No multipollutant model 

due to high correlation 

between pollutants 

Same day NO2 was associated with ER visits 

for asthma (≥ = 0.013, p = 0.024) 

Absence of association between lagged or 

multiday specifications of NO2 and asthma 

ER visits (data not shown) suggest that same 

day association may be artifact of covariation 

with PM10. 
6-58 

STUDY METHODS 

MEAN LEVELS OF 

NO2 & 

MONITORING 

STATIONS 

COPOLLUTANT 

CORRELATIONS 

EFFECTS AND INTERPRETATION: 

RELATIVE RISK & CONFIDENCE 

INTERVALS (95%) 

Peel et al. (2005) 

Atlanta, GA, 

United States 

Period of Study: 

1/93-8/2000 

Outcome(s) (ICD-9): All 

respiratory (460-6, 477, 480-6, 

480-6, 490-3, 496); Asthma 

(493); COPD (491-2, 496); 

Pneumonia (480-486); Upper 

Respiratory Infection (460-6, 

477) 

Age groups analyzed: All, 2-18 

Study Design: Time-series 

N: 484,830 

# of Hospitals: 31 

Statistical Analyses: Poisson 

Regression, GEE, GLM, and 

GAM (data not shown for 

GAM) Covariates: day of wk, 

hospital entry/exit, holidays, 

time trend; season, 

temperature, dew point 

temperature 

Statistical Package: SAS, SPlus 

Lag: 0 to 7 days. 3-day moving 

avgs 

1-h max: 45.9 ppb, 

SD = 17.3 

O3; r = 0.42 

SO2; r = 0.34 

CO; r = 0.68 

PM10; r = 0.46 

Evaluated multipollutant 

models (data not 

shown) 

Increment: 20 ppb 

All respiratory 

RR 1.016 [1.006, 1.027] lag 0-2, 3-day 

moving avg 

Upper Respiratory Infection (URI) 

RR 1.019 [1.006, 1.031] lag 0-2, 3-day 

moving avg 

Asthma 

All: 1.014 [0.997, 1.030] lag 0-2, 3-day 

moving avg 

2-18: 1.027 [1.005, 1.050] lag 0-2, 3-day 

moving avg 

Pneumonia 

RR 1.000 [0.983, 1.019] lag 0-2, 3-day 

moving avg 

COPD 

RR 1.035 [1.006, 1.065] lag 0-2, 3-day 

moving avg 

Tolbert et al. 

(2000) 

Atlanta, GA, 

United States 

Period of Study: 

1993-1995 

Outcome(s) (ICD-9): Asthma 

(493), wheezing (786.09), 

Reactive airways disease 

(RADS) (519.1) 

Age groups analyzed: 0-16; 2-

5, 6 10, 11-16 

Study Design: Case-Control 

N: 5,934 

Statistical Analyses: Ecological 

GEE analysis (Poisson model 

with logit link) and logistic 

regression 

Covariates: Day of wk, day of 

summer, yr, interaction of day 

of summer and yr 

Season: Summers only 

Statistical Package: SAS 

Lag: 1 day (a priori) 

NOX 1-h max 

continuous 

Mean: 81.7 ppb, 

SD = 53.8 

Range = 5.35, 306 

Number of stations: 2 

PM10; r = 0.44 

O3; r = 0.51 

Increment: 50 ppb 

Age 0-16: 

RR 1.012 [0.987, 1.039] lag 1 

Tolbert et al. 

(2007) 

Atlanta, GA 

Period of Study: 

1993-2004 

Outcome(s) (ICD-9): Combined 

respiratory diseases (493, 

786.07, 786.09, 491, 492, 496, 

460-465, 477, 480-486, 466.1, 

466.11, 466.19) 

Age groups analyzed: All 

Study Design: Time-series 

N: 1,072,429 

Number of hospitals: 41 

Statistical Analyses: Poisson 

regression with GLM 

Covariates: Day of wk, season, 

hospital, holiday, temperature, 

dew point 

Statistical Package: SAS vs. 

9.1 

Lag: 0-2 (a priori) 

1-h max: 43.2 ppb 

Range: 1.0-181.0 

10th: 22.0 

25th: 31.0 

50th: 41.0 

75th: 54.0 

90th: 66.0 

PM10; r = 0.53 

O3; r = 0.44 

SO2; r = 0.36 

CO; r = 0.70 

PM2.5; r = 0.47 

PM10-2.5; r = 0.48 

PM2.5sulfate; r = 0.14 

PM2.5EC; r = 0.64 

PM2.5OC; r = 0.62 

OHC; r = 0.24 

Increment: 23 ppb (IQR) 

RR 1.015 [1.004, 1.025] lag 0-2 
6-59 

STUDY METHODS 

MEAN LEVELS OF 

NO2 & 

MONITORING 

STATIONS 

COPOLLUTANT 

CORRELATIONS 

EFFECTS AND INTERPRETATION: 

RELATIVE RISK & CONFIDENCE 

INTERVALS (95%) 

Cassino* et al. 

(1999) 

New York City, 

NY 

United States 

Period of Study: 

1/1989-12/1993 

Outcome(s) (ICD-9): Asthma 

(493); COPD (496), bronchitis 

(490), emphysema (492), 

bronchiectasis (494) 

Study Design: Time-series 

N: 1,115 

# of Hospitals: 11 

Statistical Analyses: Timeseries regression, Poisson 

regression with GLM and GAM; 

Linear regression, Logistic 

regression with GEE 

Covariates: Season, trend, day 

of wk, temperature, humidity 

Statistical Package: S Plus and 

SAS 

Lag: 0-3 days 

24-h avg NO2: 

Mean: 45.0 ppb 

Median: 43 ppb 

10% 31 ppb 

25% 37 ppb 

75% 53 ppb 

90% 63 ppb 

O3

CO 

SO2

Increment: 15 ppb (IQR) 

RR 0.97 [0.85, 1.09] lag 0 

RR 1.04 [0.92, 1.18] lag 1 

RR 1.06 [0.94, 1.2] lag 2 

RR 0.97 [0.86, 1.09] lag 3 
6-60 

STUDY METHODS 

MEAN LEVELS OF 

NO2 & 

MONITORING 

STATIONS 

COPOLLUTANT 

CORRELATIONS 

EFFECTS AND INTERPRETATION: 

RELATIVE RISK & CONFIDENCE 

INTERVALS (95%) 

CANADA 

Bates et al. 

(1990) 

Vancouver 

Region, 

BC, Canada 

Period of Study: 

7/1/1984-

10/31/1986 

Outcome(s) (ICD 9): Asthma 

(493); Pneumonia (480-486); 

Chronic bronchitis (491,492,49

6); 

Other respiratory (466) 

Age groups analyzed: All; 15-

60 

Study Design: 

# of Hospitals: 9 

Statistical Analyses: Pearson 

correlation coefficients were 

calculated between asthma 

visits and environmental 

variables 

Season: Warm (May-Oct); 

Cool (Nov-Apr) 

Covariates: NR 

Statistical Package: NR 

Lag: 0, 1, 2 

May-Oct 

SO2 1-h max: 

Range: 0.0337-

0.0458 ppm 

Nov-Apr 

Range: 0.0364-

0.0455 ppm 

Number of stations: 11

May-Oct. 

O3; r = 0.35 

SO2; r = 0.67 

CoH; r = 0.53 

SO4; r = 0.50 

Nov-Apr 

O3; r = 0.31 

SO2; r = 0.61 

CoH; r = 0.69 

SO4; r = 0.49 

Correlation Coefficients: 

Warm Season (May-Oct) 

Asthma (1-14 yrs) 

NR lag 0 

NR lag 1 

NR lag 2 

Respiratory (1-14) 

NR lag 0 

NR lag 1 

NR lag 2 

Total (1-14) 

NR lag 0 

NR lag 1 

NR lag 2 

Asthma (15-60 yrs) 

NR lag 0 

NR lag 1 

NR lag 2 

Respiratory (15-60 yrs) 

r = 0.120 lag 0 p < 0.01 

NR lag 1 

NR lag 2 

Total (15-60 yrs) 

NR lag 0 

NR lag 1 

NR lag 2 

Asthma (61+ yrs) 

NR lag 0 

NR lag 1 

NR lag 2 

Respiratory (61+ yrs) 

NR lag 0 

NR lag 1 

NR lag 2 
6-61 

STUDY METHODS 

MEAN LEVELS OF 

NO2 & 

MONITORING 

STATIONS 

COPOLLUTANT 

CORRELATIONS 

EFFECTS AND INTERPRETATION: 

RELATIVE RISK & CONFIDENCE 

INTERVALS (95%) 

Bates et al. 

(1990) (cont’d) 

 Total (61+ yrs) 

NR lag 0 

NR lag 1 

NR lag 2 

Cool Season (Nov - Apr) 

Asthma (1-14 yrs) 

NR lag 0 

NR lag 1 

NR lag 2 

Respiratory (1-14) 

NR lag 0 

NR lag 1 

NR lag 2 

Total (1-14) 

NR lag 0 

NR lag 1 

NR lag 2 

Asthma (15-60 yrs) 

NR lag 0 

NR lag 1 

NR lag 2 

Respiratory (15-60 yrs) 

r = 0.120 lag 0 p < 0.01 

NR lag 1 

NR lag 2 

Total (15-60 yrs) 

NR lag 0 

NR lag 1 

NR lag 2 

Asthma (61+ yrs) 

NR lag 0 

Bates et al. 

(1990) 

(cont’d) 

 NR lag 1 

NR lag 2 

Respiratory (61+ yrs) 

r = 0.132 lag 0 p < 0.01 

r = 0.176 lag 1 p < 0.001 

r = 0.178 lag 2 p < 0.001 

Total (61+ yrs) 

NR lag 0 

NR lag 1 

NR lag 2 
6-62 

STUDY METHODS 

MEAN LEVELS OF 

NO2 & 

MONITORING 

STATIONS 

COPOLLUTANT 

CORRELATIONS 

EFFECTS AND INTERPRETATION: 

RELATIVE RISK & CONFIDENCE 

INTERVALS (95%) 

Kesten et al. 

(1995) 

Toronto, ON 

Period of Study: 

7/1/1991-

6/30/1992 

Outcome(s): Asthma 

Age groups analyzed: All 

Study Design: Time-series 

N: 854 

# of Hospitals: 1 

Statistical analysis: 

Autoregressive technique 

Statistical Package: SAS v 6.04 

Lag: 0,1 

24-h avg NO2: 

Range: 2.20-3.75 Η

0.01 ppm 

SO2

O3

Lag 0: “No statistically discernible regression 

coefficients” 

Lag 0-6: “No statistically discernible 

regression coefficients” 

Mean weekly indices lagged 1 wk behind 

weekly mean number of visits: p = 0.005 

Stieb et al. 

(1996) 

St. John, New 

Brunswick, 

Canada 

Period of Study: 

1984-1992 

Outcome(s): Asthma 

(May-Sept only) 

ICD-9 Codes: NR 

Age groups analyzed: 0-15, 

>15 

Study Design: Time-series 

N: 1,163 

# of Hospitals: 2 

Statistical Analyses: SAS NLIN 

(Equivalent to Poisson GEE) 

Covariates: day of wk, longterm trends 

Season: Summers only (MaySep) 

Dose-response investigated: 

Yes 

Statistical Package: SAS 

Lag: 0-3 days 

1-h max NO2 (ppb) 

Mean: 25.2 

Range: 0, 120 

95th: 60 

O3; r = 0.16 

SO2; r = -0.03 

SO42-; r = 0.16 

TSP; r = 0.15 

Increment: NR 

NO2 + O3: ≥ = -0.0037 (0.0023) lag 2 

Stieb* et al. 

(2000) 

Saint John, New 

Brunswick, 

Canada 

Period of Study: 

Retrospective: 

7/92-6/94 

Prospective: 

7/94-3/96 

Outcome(s): Asthma; COPD; 

Respiratory infection 

(bronchitis, bronchiolitis, croup, 

pneumonia); 

All respiratory 

ICD-9 Codes: NR 

Age groups analyzed: All 

Study Design: Time-series 

N: 19,821 

Statistical Analyses: Poisson 

regression, GAM 

Covariates: Day of wk, 

selected weather variables in 

each model 

Seasons: All yr, summer only 

Dose-response investigated: 

Yes 

Statistical Package: S-Plus 

Lag: all yr = 0; summer 

only = 8 

Annual mean: 8.9 ppb 

Spring/fall mean: 

10.0 ppb Max: 82 

O3; r = -0.02 

SO2; r = 0.41 

TRS; r = 0.16 

PM10; r = 0.35 

PM2.5; r = 0.35 

H+; r = 0.25 

SO42-; r = 0.33 

COH; r = 0.49 

Assessed multipollutant 

models 

Increment: 8.9 ppb (IQR) 

Respiratory visits: -3.8%, p = 0.070 lag 0 

May to Sept: 11.5%, p = 0.17 lag 8 

Multipollutant model (NO2, O3, SO2) 

-3.6% [-7.5, 0.5] lag 0 

Multipollutant model (ln(NO2), O3, SO2 COH) 

May to Sep: 4.7% [0.8 to 8.6] lag 8 

Non-linear effect of NO2 on summertime 

respiratory visits observed and log 

transformation strengthened the association. 
6-63 

STUDY METHODS 

MEAN LEVELS OF 

NO2 & 

MONITORING 

STATIONS 

COPOLLUTANT 

CORRELATIONS 

EFFECTS AND INTERPRETATION: 

RELATIVE RISK & CONFIDENCE 

INTERVALS (95%) 

EUROPE and MIDDLE-EAST 

Sunyer et al. 

(1997) 

Multi-city, 

Europe 

(Barcelona, 

Helsinki, Paris, 

London) 

Period of Study: 

1986-1992 

Outcomes (ICD-9): Asthma 

(493) 

Age groups analyzed: <15, 15-

64 

Study Design: Time-series 

Statistical Analyses: APHEA 

protocol, Poisson regression, 

GEE; meta-analysis 

Covariates: Humidity, 

temperature, influenza, 

soybean, long-term trend, 

season, day of wk 

Season: Cool, Oct-Mar; Warm: 

Apr-Sep 

Statistical Package: NR 

Lag: 0,1,2,3 and cumulative 1-

3 

24-h median (range) 

(μg/m3

) 

Barcelona: 53 (5, 142) 

Helsinki: 35 (9, 78) 

London: 69 (27, 347) 

Paris: 42 (12, 157) 

# of stations: 

Barcelona: 3 

London: 2 

Paris: 4 

Helsinki: 8 

SO2

black smoke 

O3

Increment: 50 μg/m3

 of 24-h avg for all cities 

combined 

Asthma 

15-64 yrs 

1.029 [1.003, 1.055] lag 0-1 

1.038 [1.008, 1.068] lag 0-3, cumulative 

<15 yrs 

1.026 [1.006, 1.049] lag 2 

1.037 [1.004, 1.067] lag 0-3, cumulative 

1.080 [1.025, 1.140] - Winter only 

Two-pollutant models: 

NO2/Black smoke 

15-64 yrs 1.055 [1.005, 1.109] lag 0-1 

15-64 yrs 1.088 [1.025, 1.155] cumulative 0-

3 

<15 yrs 1.036 [0.956, 1.122] 

NO2/SO2

<15 yrs 1.034 [0.988, 1.082] 

Atkinson et al. 

(1999b) 

London, United 

Kingdom 

Period of Study: 

1/92-1294 

Outcome(s) (ICD-9): 

Respiratory ailments (490-

496), including asthma, 

wheezing, inhaler request, 

chest infection, COPD, 

difficulty in breathing, cough, 

croup, pleurisy, noisy breathing 

Age groups analyzed: 0-14; 15-

64; ≥65; All ages 

Study Design: Time-series 

N: 98,685 

# of Hospitals: 12 

Statistical Analyses: Poisson 

regression, APHEA protocol 

Covariates: Long-term trend, 

season, day of wk, influenza, 

temperature, humidity 

Statistical Package: SAS 

Lag: 0,1,0-2, and 0-3 days 

1-h max: 50.3 ppb, 

SD = 17.0 

# of Stations: 3; 

r = 0.70, 0.96 

NO2, O3

(8 h), SO2

(24 h), CO (24 h), 

PM10 (24 h), BS 

Increment: 36 ppb in 1-h max 

Single-pollutant model 

Asthma Only 

0-14 yrs 8.97% [4.39, 13.74] lag 1 

15-64 yrs 4.44% [0.14, 8.92] lag 1 

All ages 4.37% [1.32, 7.52] lag 0 

All Respiratory 

0-14 yrs 2.17% [-0.49, 4.91] lag 1 

15-64 yrs 1.87% [-0.69, 4.49] lag 2 

≥65 yrs 3.97% [0.51, 7.55] lag 0 

All Ages 1.20% [-0.57, 3.00] 

Two-pollutant model Asthma Only 0-14 yrs: 

SO2: 5.75% [0.39, 11.40] lag 1 

CO: 8.34% [3.61, 13.29] lag 0 

PM10: 6.95% [1.96, 12.19] lag 2 

BS: 8.32% [3.56, 13.30] lag 2 

O3: 9.68% [5.02, 14.54] lag 0 
6-64 

STUDY METHODS 

MEAN LEVELS OF 

NO2 & 

MONITORING 

STATIONS 

COPOLLUTANT 

CORRELATIONS 

EFFECTS AND INTERPRETATION: 

RELATIVE RISK & CONFIDENCE 

INTERVALS (95%) 

Buchdahl et al. 

(1996) 

London, United 

Kingdom 

Period of Study: 

3/1/92-2/28/93 

Outcomes: Daily acute wheezy 

episodes 

ICD-9: NR 

Age groups analyzed: ≥16 

Study Design: Case-control 

N: 1,025 cases, 4,285 controls 

Number of hospitals: 1 

Statistical Analyses: Poisson 

regression 

Covariates: Season, 

temperature, wind speed 

Season: Spring (Apr-Jun), 

Summer 

(Jul-Sep), Autumn (Oct Dec), 

Winter (Jan-Mar) 

Statistical Package: Stata 

Lag: 0-7 days 

NO2 24-h yr round 

mean: 60 μg/m3, 

SD = 17 

IQR: 17 μg/m3

Spring: 59 (19) 

Summer: 55 (18) 

Fall: 66 (13) 

Winter: 61 (17) 

SO2 r = 0.62 

O3 r = -0.18 

Increment: 17 μg/m3 (IQR) 

No adjustments to model 

RR 1.07 [1.01, 1.14] lag not specified 

Adjusted for temperature and season. 

RR 1.02 [0.96, 1.09] lag not specified 

Thompson et al. 

(2001) 

Belfast, Northern 

Ireland 

Period of Study: 

1993-1995 

Outcome(s): Asthma 

ICD-9 Code(s): NR 

Age groups analyzed: Children 

Study Design: Time-series 

N: 1,044 

Statistical Analyses: Followed 

APHEA protocol, Poisson 

regression analysis 

Covariates: Season, long-term 

trend, temperature, day of wk, 

holiday 

Season: Warm (May-Oct); 

Cold (Nov-Apr) 

Statistical Package: Stata 

Lag: 0-3 

Warm Season 

NO2 (ppb): Mean: 

19.20; 

SD = 7.90; 

IQR: 13.0, 23.0 

NOX (ppb): Mean: 

35.50; 

SD = 25.50; 

IQR: 21.0, 40.0 

NO (ppb): Mean: 16.4; 

SD = 19.70; 

IQR: 7.0, 17.0 

Cold Season 

NO2 (ppb): Mean: 

23.30; 

SD = 9.00; 

IQR: 18.0, 28.0 

NOX (ppb): Mean: 

50.50; 

SD = 50.50; 

IQR: 26.0, 56.0 

NO (ppb): Mean: 

27.30; 

SD = 43.10; 

IQR: 9.0, 28.0 

NO2: 

PM10; r = 0.77 

SO2; r = 0.82 

NOX; r = 0.93 

NO; r = 0.84 

O3; r = -0.62 

CO; r = 0.69 

Benzene; r = 0.83 

NOX: 

PM10; r = 0.73 

SO2; r = 0.83 

NO2; r = 0.92 

NO; r = 0.97 

O3; r = -0.73 

CO; r = 0.74 

Benzene; r = 0.86 

NO: 

PM10; r = 0.65 

SO2; r = 0.76 

NOX; r = 0.97 

NO2; r = 0.84 

O3; r = -0.76 

CO; r = 0.71 

Benzene; r = 0.82 

NO2 Increment: 10 ppb 

NOX Increment: per doubling 

NO Increment: per doubling 

NO2

Lag 0 RR 1.08 [1.03, 1.13] 

Lag 0-1 RR 1.11 [1.05, 1.17] 

Lag 0-2 RR 1.10 [1.04, 1.17] 

Lag 0-3 RR 1.12 [1.03, 1.20] 

Warm only Lag 0-1 RR 1.14 [1.04, 1.26] 

Cold only Lag 0-1 RR1.10 [1.03, 1.17] 

Adjusted for Benzene Lag 0-1 RR 0.99 

[0.87, 1.13] 

NOX

Lag 0 RR 1.07 [1.02, 1.12] 

Lag 0-1 RR 1.10 [1.05, 1.16] 

Lag 0-2 RR 1.10 [1.03, 1.17] 

Lag 0-3 RR 1.11 [1.04, 1.20] 

Warm only Lag 0-1 RR 1.13 [1.03, 1.24] 

Cold only Lag 0-1 RR 1.09 [1.02, 1.16] 

Adjusted for Benzene Lag 0-1 RR 0.89 

[0.77, 1.03] 

NO 

Lag 0 RR 1.04 [1.01, 1.07] 

Lag 0-1 RR 1.07 [1.03, 1.11] 

Lag 0-2 RR 1.06 [1.02, 1.11] 

Lag 0-3 RR 1.08 [1.02, 1.14] 

Warm only Lag 0-1 RR 1.08 [1.01, 1.16] 

Cold only Lag 0-1 RR 1.06 [1.01, 1.11] 

Adjusted for Benzene Lag 0-1 RR 0.93 

[0.85, 1.01] 
6-65 

STUDY METHODS 

MEAN LEVELS OF 

NO2 & 

MONITORING 

STATIONS 

COPOLLUTANT 

CORRELATIONS 

EFFECTS AND INTERPRETATION: 

RELATIVE RISK & CONFIDENCE 

INTERVALS (95%) 

Boutin-Forzano 

et al. (2004) 

Marseille, 

France 

Period of Study: 

4/97-3/98 

Outcome(s): Asthma 

ICD-9 Code(s): NR 

Age groups analyzed: 3-49 

Study Design: Case-crossover 

N: 549 

Statistical Analyses: Logistic 

regression 

Covariates: Minimal daily 

temperature, maximum daily 

temperature, minimum daily 

relative humidity, maximum 

daily relative humidity, day of 

wk 

Statistical Package: NR 

Lag: 0-4 days 

Mean NO2: 34.9 μg/m3

Range: 3.0, 85 

SO2; r = 0.56 

O3; r = 0.58 

Increment: 10 μg/m3

Increased ER visits 

OR 1.0067 [0.9960, 1.0176] lag 0 

Castellsague 

et al. (1995) 

Barcelona, Spain 

Period of Study: 

1986-1989 

Outcome(s): Asthma 

ICD-9 Code(s): NR 

Age groups analyzed: 15-64 

Study Design: Time-series 

# of Hospitals: 4 

Statistical Analyses: Poisson 

regression 

Covariates: long-time trend, 

day of wk, temperature, relative 

humidity, dew point 

temperature 

Seasons: Winter: Jan-Mar; 

Summer: Jul-Sep 

Dose-Response investigated: 

Yes 

Statistical Package: NR 

Lag: 0, 1-5 days and 

cumulative 

Summer: lag 2 days 

Winter: lag 1 day 

Mean NO2 (μg/m3

) 

Summer: 104.0 

Winter: 100.8 

IQR (μg/m3

): 

Summer: 48 

Winter: 37 

# of Stations: 15 

manual, 3 automatic 

SO2; r = NR 

O3; r = NR 

Increment: 25 μg/m3 

Seasonal differences 

Summer: 

1.071 [1.101, 1.130] lag 0-5 cumulative 

1.045 [1.009, 1.081] lag 0 

Winter: 

1.072 [1.010, 1.137] lag 0-2 cumulative 

1.056 [1.011, 1.104] lag 0 

Asthma visits increased across quartiles of 

NO2 in summer; a positive but less consistent 

increase across quartiles was observed in 

winter. 

Tobías et al. 

(1999) 

Barcelona, Spain 

Period of Study: 

1986-1989 

Outcome(s): Asthma 

ICD-9: NR 

Age groups analyzed: >14 

Study Design: Time-series 

Statistical Analyses: Poisson 

regression, followed APHEA 

protocol 

Covariates: temperature, 

humidity, long-term trend, 

season, day of wk 

Statistical Package: NR 

Lag: NR 

24-h avg NO2 μg/m3

Non-epidemic days: 

54.7 (20.8) 

Epidemic days: 

58.9 (26.7) 

BS 

SO2

O3

≥ Η 104 (SE Η 104 ) using Std Poisson 

Without modeling asthma epidemics: 

11.25 (11.79) p > 0.1 

Modeling epidemics with 1 dummy variable: 

1.18 (7.59) p > 0.1 

Modeling epidemics with 6 dummy variables: 

13.60 (7.79) p < 0.1 

Modeling each epidemic with dummy 

variable: 14.40 (7.44) p < 0.1 

≥ Η 104 (SE Η 104) using Autoregressive 

Poisson 

Without modeling asthma epidemics: 

13.65 (11.81) p > 0.1 

Modeling epidemics with 1 dummy variable: 

3.28 (7.77) p > 0.1 

Modeling epidemics with 6 dummy variables: 

16.49 (8.01) p < 0.05 

Modeling each epidemic with dummy 

variable: 18.18 (8.01) p < 0.1 
6-66 

STUDY METHODS 

MEAN LEVELS OF 

NO2 & 

MONITORING 

STATIONS 

COPOLLUTANT 

CORRELATIONS 

EFFECTS AND INTERPRETATION: 

RELATIVE RISK & CONFIDENCE 

INTERVALS (95%) 

Galán et al. 

(2003) 

Madrid, Spain 

Period of Study: 

1995-1998 

Outcome(s) (ICD-9): Asthma 

(493) 

Age groups analyzed: All 

Study Design: Time-series 

N: 4,827 

Statistical Analyses: Poisson 

regression, (1) classic APHEA 

protocol and (2) GAM with 

stringent criteria 

Covariates: trend, yr, season, 

day of wk, holidays, 

temperature, humidity, 

influenza, acute respiratory 

infections, pollen 

Statistical Package: NR 

Lag: 0-4 days 

24-h mean: 67.1 

μg/m3

SD = 18.0 

IQR: 20.5 

Max: 147.5 

# of Stations: 15 

PM10; r = 0.717 

SO2; r = 0.610 

O3; r = -0.209 

Increment: 10 μg/m3

Asthma: 

RR 1.013 [0.991, 1.035] lag 0 

RR 1.011 [0.989, 1.032] lag 1 

RR 1.013 [0.992, 1.034] lag 2 

RR 1.033 [1.013, 1.054] lag 3 

RR 1.026 [1.006, 1.047] lag 4 

Multipollutant model: 

NO2/SO2 1.031 [1.004, 1.059] lag 3 

NO2/PM10 1.001 [0.971, 1.031] lag 3 

NO2/Pollen 1.024 [1.004, 1.044] lag 3 

NO2/Pollen/O3 1.024 [1.005, 1.045] Poisson 

NO2/Pollen/O3 1.022 [1.005, 1.040] GAM 

Tenías et al. 

(1998) 

Valencia, Spain 

Period of Study: 

1993-1995 

Seasons: 

Cold: Nov-Apr 

Warm: May-Oct 

Outcome(s): Asthma 

ICD-9 Code(s): NR 

Age groups analyzed: >14 

Study Design: Time-series 

N: 734 

Statistical Analyses: Poisson 

regression, APHEA protocol 

Covariates: Seasonality, 

temperature, humidity, longterm trend, day of wk, holidays, 

influenza 

Seasons: Cold: Nov-Apr; 

Warm: May-Oct 

Dose-Response Investigated: 

Yes 

Statistical Package: NR 

Lag: 0-3 days 

24 h: 

57.7 μg/m3

Cold: 55.9 

Warm: 59.4 

1-h max: 

101.1 μg/m3

Cold: 97.3 

Warm: 102.8 

# of Stations: 2 

24 h: 

O3; r = -0.304 

SO2 (24 h); r = 0.265 

SO2 (1 h); r = 0.261 

1 h: 

O3; r = -0.192 

SO2 (24 h); r = 0.199 

SO2 (1 h); r = 0.201 

Increment: 10 μg/m3

NO2 24-h avg 

All yr 1.076 [1.020, 1.134] lag 0 

Cold: 1.083 [1.022, 1.148] lag 0 

Warm: 1.066 [0.989, 1.149] lag 0 

NO2 1-h max 

All yr 1.037 [1.008, 1.066] lag 0 

Cold: 1.034 [1.004, 1.066] lag 0 

Warm: 1.044 [1.002, 1.088] lag 0 

Tenías et al. 

(2002) 

Valencia, Spain 

Period of Study: 

1994-1995 

Outcome(s): COPD 

ICD-9 Code(s): NR 

Age groups analyzed: >14 

Study Design: Time-series 

N: 1,298 

# of Hospitals: 1 

Statistical Analyses: Poisson 

regression, APHEA protocol; 

basal models and GAM 

Covariates: Seasonality, 

annual cycles, temperature, 

humidity, day of wk, feast days 

Seasons: Cold: Nov-Apr; 

Warm: May-Oct 

Dose-Response Investigated: 

Yes 

Statistical Package: NR 

Lag: 0-3 days 

NO2 24-h avg: 

7.7 μg/m3

; Range: 12, 

135 

1-h max: 100.1 μg/m3

; 

Range: 31, 305 

# of Stations: 6 

manual and 5 

automatic; r = 0.87 

BS; r = 0.246 

SO2; r = 0.194 

CO; r = 0.180 

O3; r = -0.192 

Increment: 10 μg/m3

24-h avg NO2

All yr RR 0.979 [0.943, 1.042] lag 0 

Cold: 24-h avg: RR 0.991 [0.953, 1.030] lag 0 

Warm: 24-h avg: RR 0.961 [0.900, 1.023] lag 

0 

1-h max NO2

All yr RR 0.986 [0.966, 1.007] lag 0 

Cold: 24-h avg: RR 0.996 [0.975, 1.018] lag 0 

Warm: 24-h avg: RR 0.968 [0.935, 1.003] lag 

0 

Possibility of a linear relationship between 

pollution and risk of emergency cases could 

not be ruled out. 
6-67 

STUDY METHODS 

MEAN LEVELS OF 

NO2 & 

MONITORING 

STATIONS 

COPOLLUTANT 

CORRELATIONS 

EFFECTS AND INTERPRETATION: 

RELATIVE RISK & CONFIDENCE 

INTERVALS (95%) 

Migliaretti et al. 

(2005) 

Turin, Italy 

Period of Study: 

1997-1999 

Outcome (ICD-9): Asthma 

(493) 

Age groups analyzed: 

<15, 15-64, >64 

Study Design: Case-Control 

Controls: age matched with 

other respiratory disease (ICD9: 460 487, 490-2, 494-6, 500-

19) or heart disease 

(ICD-9: 390-405, 410-429) 

N: cases = 1,401 

controls = 201,071 

Statistical Analyses: Logistic 

regression 

Covariates: Seasonality, 

temperature, humidity, solar 

radiation, wind velocity, day of 

wk, holiday, gender, age, 

education level 

Seasons: Cold: Oct-Mar; 

Warm: Apr-Sep 

Statistical Package: NR 

Lag: 0-3 days and cumulative 

All Participants: 

24-h mean: 112.7 

μg/m3

, 

SD = 30.2, 

Median: 107.7 

Cases: 

24-h mean: 117.1 

μg/m3

, 

SD = 30.0, 

Median: 113.0 

Controls: 

24-h mean: 112.7 

μg/m3

, 

SD = 30.2, 

Median: 107.7 

# of Stations: 10; r = 

0.79 

TSP; r = 0.8 

Two-pollutant model 

adjusted for TSP 

Increment: 10 μg/m3

 

Single-pollutant (NO2): 

<15 yrs 2.3% [0.3, 4.40] 

15-64 yrs 3.10% [-0.01, 7.70] 

>64 yrs 7.70% [0.20, 15.20] 

All ages 2.40% [0.5, 4.30] 

Copollutant (NO2 and TSP) 

<15 yrs 1.71% [-0.02, 5.00] 

15-64 yrs 1.20% [-0.06, 6.50] 

>64 yrs 0.91% [-0.08, 5.91] 

All ages 1.10% [-0.02, 3.82] 

Bedeschi et al. 

(2007) 

Reggio Emilia, 

Italy 

Period of Study: 

03/2001-

03/2002 

Outcome (ICD-9): Respiratory 

disorders, asthma or asthmalike disorders, other respiratory 

disorders 

Age groups analyzed: <15 

Study Design: Time-series 

N: 854 children, 1051 visits 

Statistical Analyses: Poisson 

regression with GAM 

Covariates: Weekday, festivity 

day, humidity, precipitation, 

temperature, flu epidemic, 

pollen concentrations 

Statistical Package: R software 

Lag: 0-5 days and cumulative 

24-h avg: NO2 (μg/m3

) 

Mean: 49 (13.8) 

Range: 21.6-107.5 

Median: 47.5 

SO2; r = 0.56 

CO;r = 0.77 

TSP; r = 0.58 

PM10; r = 0.57 

O3; r = -0.50 

Increment: 10 μg/m3

All Respiratory Disorders: 

Italians: 9% [1.0, 17.6] lag 4 

Foreigners: 17.6% [3.9, 33.0] lag 4 

All: 11.0% [3.6, 18.8] lag 4 

Vigotti et al. 

(2007) 

Pisa, Italy 

Period of Study: 

2000 

Outcome (ICD-9): Respiratory 

complaints (493, 468, 466) 

Age groups analyzed: <10, >65 

Study Design: Ecologic 

N: 966 

Number of hospitals: 1 

Statistical Analyses: Robust 

poisson regression in GAM 

model 

Covariates: Day of study, 

temperature, humidity, rain, 

influenza epidemics, day of wk, 

holidays 

Statistical Package: NR 

Lag: up to 5 d 

24-h avg: 45.6 (11.0) 

μg/m3

Range: 21.3-74.0 

50th: 44.8 

Number of monitors: 3 

PM10; r = 0.58 

CO; r = 0.62 

Increment: 10 μg/m3

Children: 1.118 [1.014, 1.233] lag 0-2 

65+: 1.06 [0.967, 1.162] lag 0-2 
6-68 

STUDY METHODS 

MEAN LEVELS OF 

NO2 & 

MONITORING 

STATIONS 

COPOLLUTANT 

CORRELATIONS 

EFFECTS AND INTERPRETATION: 

RELATIVE RISK & CONFIDENCE 

INTERVALS (95%) 

Pantazopoulou 

et al. (1995) 

Athens, Greece 

Period of Study : 

1988 

Outcomes: All respiratory visits 

ICD-9: NR 

Age groups analyzed: All ages 

Study Design: Time-series 

N: 213,316 

Number of hospitals: 14 

Statistical Analyses: Multiple 

linear regression 

Covariates: Season, day of wk, 

holiday, temperature, relative 

humidity 

Season: Warm (3/22-9/21), 

Cold (1/1-3/21 and 9/22-12/31) 

Lag: NR 

NO2 24-h avg 

Winter: 94 μg/m3

, 

SD = 25 

5th: 59, 50th: 93, 

95th: 135 

Summer: 111 μg/m3

, 

SD = 32 

5th: 65, 50th: 108, 

95th: 173 

# of stations: 2 

CO 

BS 

Increment: 76 μg/m3

 in winter and 108 μg/m3

in summer (95th-5th) 

Respiratory disease admissions 

Winter: Percent increase: ≥ = 66.8 [19.6, 

113.9] 

Summer: Percent increase: ≥ = 21.2 [-35.1, 

77.5] 

Garty et al. 

(1998) 

Tel Aviv, Israel 

1993 

Outcome(s): Asthma 

ICD-9 Code(s): NR 

Age groups analyzed: 1-18 

Study Design: Descriptive study 

with correlations 

N: 1,076 

Statistical Analyses: Pearson 

correlation and partial 

correlation coefficients 

Covariates: Maximum and 

minimum ambient 

temperatures, relative humidity, 

and barometric pressure 

Statistical Package: Statistix 

24-h mean of NOX

(estimated from 

histogram): 60 μg/m3

; 

Range: 50, 250 

 Correlation between NOX and ER visits for 

asthma: 

All Yr: 

Daily data r = 0.30 

Running mean for 7 days r = 0.62 

Excluding Sept: 

Daily data r = 0.37 

Running mean for 7 days r = 0.74 

38% of variance in number of ER visits 

explained by fluctuations in NOX. Increases to 

55% when Sept. is omitted from analyses. 

LATIN AMERICA 

Farhat* et al. 

(2005) 

São Paulo, 

Brazil 

Period of Study: 

1996-1997 

Outcome(s) (ICD-9): Lower 

Respiratory Disease (466, 480-

5) 

Age groups analyzed: <13 

Study Design: Time-series 

N: 4,534 

# of Hospitals: 1 

Statistical Analyses: (1) Poisson 

regression and (2) GAM-no 

mention of more stringent 

criteria 

Covariates: Long-term trends, 

seasonality, temperature, 

humidity 

Statistical Package: S-Plus 

Lag: 0-7 days, 2,3,4 day moving 

avg 

Mean: 125.3 μg/m3

SD = 51.7 

IQR: 65.04 μg/m3

# of Stations: 6 

PM10; r = 0.83 

SO2; r = 0.66 

CO; r = 0.59 

Increment: IQR of 65.04 μg/m3

Single-pollutant models (estimated from 

graphs): 

LRD ~17.5% [12.5, 24] 

Multipollutant models: 

Adjusted for: 

PM10 16.1% [5.4, 26.8] 4 day avg 

SO2 24.7% [18.2, 31.3] 4 day avg 

CO 19.2% [11.8, 26.6] 4 day avg 

Multipollutant model 

18.4% [3.4, 33.5] 4 day avg 
6-69 

STUDY METHODS 

MEAN LEVELS OF 

NO2 & 

MONITORING 

STATIONS 

COPOLLUTANT 

CORRELATIONS 

EFFECTS AND INTERPRETATION: 

RELATIVE RISK & CONFIDENCE 

INTERVALS (95%) 

Martins* et al. 

(2002) 

São Paulo, 

Brazil 

Period of Study: 

5/96-9/98 

Outcome(s) (ICD-10): Chronic 

Lower Respiratory Disease 

(CLRD) (J40-J47); includes 

chronic bronchitis, emphysema, 

other COPDs, asthma, 

bronchiectasia 

Age groups analyzed: >64 

Study Design: Time-series 

N: 712 

# of Hospitals: 1 

Catchment area: 13,163 total 

ER visits 

Statistical Analyses: Poisson 

regression and GAM - no 

mention of more stringent 

criteria 

Covariates: Weekdays, time, 

minimum temperature, relative 

humidity, daily number of nonrespiratory emergency room 

visits made by elderly 

Statistical Package: S-Plus 

Lag: 2-7 days and 3 day moving 

avgs 

NO2 max 1-h avg 

(μg/m3

): 

117.6, 

SD = 53.0, 

Range: 32.1, 421.6 

IQR: 62.2 μg/m3

# of Stations: 4 

O3; r = 0.44 

SO2; r = 0.67 

PM10; r = 0.83 

CO; r = 0.62 

Increment: IQR of 62.2 μg/m3 

Percent increase: 4.5% [-6.5, 15] lag 3 day 

moving avg (estimated from graph) 
6-70 

STUDY METHODS 

MEAN LEVELS OF 

NO2 & 

MONITORING 

STATIONS 

COPOLLUTANT 

CORRELATIONS 

EFFECTS AND INTERPRETATION: 

RELATIVE RISK & CONFIDENCE 

INTERVALS (95%) 

Ilabaca et al. 

(1999) 

Santiago, Chile 

Period of Study: 

2/1/95 – 8/31/96 

Outcome(s) (ICD-9): Upper 

respiratory illness (460 465, 

487); 

Days: 578 

Lower respiratory illness 

(466, 480-486, 490-494, 496, 

519.1, 033.9); 

Pneumonia (480 486) 

Age groups analyzed: <15 

Study Design: Time-series 

# of Hospitals: 1 

Statistical Analyses: Poisson 

regression 

Covariates: Long-term trend, 

season, day of wk, temperature, 

humidity, influenza epidemic 

Season: Warm (Sep-Apr), 

Cool (May-Aug) 

Statistical Package: NR 

Lag: 0-3 days 

24-h avg NO2: 

Warm: 

Mean: 97.0 

Median: 91.5 

SD = 34.6 

Range: 37.2, 246 

5th: 54.3 

95th: 163.0 

Cool: 

Mean: 160.2 

Median: 154.4 

SD = 59.5 

Range: 60.1, 397.5 

5th: 74.4 

95th: 266.0 

# of stations: 4, r = 

0.70, 0.88 

Warm: 

SO2; r = 0.66 

O3; r = 0.15 

PM10; r = 0.71 

PM2.5; r = 0.70 

Cool: 

SO2; r = 0.74 

O3; r = 0.22 

PM10; r = 0.82 

PM2.5; r = 0.80 

Increment: IQR 

All respiratory 

Cool 

Lag 2 IQR: 56.4 RR 1.0378 [1.0211, 1.0549] 

Lag 3 IQR: 56.4 RR 1.0294 [1.0131, 1.0460] 

Lag avg 7 IQR: 33.84 RR 1.0161 [1.0000, 

1.0325] 

Warm 

Lag 2 IQR: 30.08 RR 1.0208 [0.9992, 1.0428] 

Lag 3 IQR: 30.08 RR 1.0395 [1.0181, 1.0612] 

Lag avg 7 IQR: 22.56 RR 1.0251 [0.9964, 

1.0548] 

Upper respiratory 

Cool 

Lag 2 IQR: 56.4 RR 1.0569 [1.0339, 1.0803] 

Lag 3 IQR: 56.4 RR 1.0318 [1.0095, 1.0545] 

Lag avg 7 IQR: 33.84 RR 1.0177 [0.9960, 

1.0399] 

Warm 

Lag 2 IQR: 30.08 RR 1.0150 [0.9881, 1.0426] 

Lag 3 IQR: 30.08 RR 1.0425 [1.0157, 1.0699] 

Lag avg 7 IQR: 22.56 RR 0.9944 [0.9591, 

1.0311] 

Pneumonia 

Cool 

Lag 2 IQR: 56.4 RR 1.0824 [1.0300, 1.1374] 

Lag 3 IQR: 56.4 RR 1.0768 [1.0273, 1.1287] 

Lag avg 7 IQR: 33.84 RR 1.0564 [1.0062, 

1.1092] 

Warm 

Lag 2 IQR: 30.08 RR 1.1232 [1.0450, 1.2072] 

Lag 3 IQR: 30.08 RR 1.0029 [0.9332, 1.0779] 

Lag avg 7 IQR: 22.56 RR 1.1084 [1.0071, 

1.2200] 
6-71 

STUDY METHODS 

MEAN LEVELS OF 

NO2 & 

MONITORING 

STATIONS 

COPOLLUTANT 

CORRELATIONS 

EFFECTS AND INTERPRETATION: 

RELATIVE RISK & CONFIDENCE 

INTERVALS (95%) 

Lin et al. (1999) 

São Paulo, 

Brazil 

Period of Study: 

May 1991 - Apr 

1993 

Days: 621 

Outcome(s): Respiratory 

disease, Upper respiratory 

illness, Lower respiratory 

illness, Wheezing 

ICD-9 Code(s): NR 

Age groups analyzed: <13 

Study Design: Time-series 

# of Hospitals: 1 

Statistical Analyses: Gaussian 

and Poisson regression 

Covariates: Long-term trend, 

seasonality, day of wk, 

temperature, humidity 

Statistical Package: NR 

Lag: 5-day lagged moving avgs 

NO2 μg/m3

: 

Mean: 163 

SD = 85 

Range: 2, 688 

Number of stations: 3 

SO2; r = 0.38 

CO; r = 0.35 

PM10; r = 0.40 

O3; r = 0.15 

Increment: NR 

All respiratory illness 

NO2 alone: RR 1.003 [1.001, 1.005] 5-day 

moving avg 

NO2 + PM10 + O3 + SO2 + CO: RR 0.996 

[0.994, 0.998] 

Lower respiratory illness 

NO2 alone: RR 0.999 [0.991, 1.007] 5-day 

moving avg 

NO2 + PM10 + O3 + SO2 + CO: RR 0.990 

[0.982, 0.998] 

Upper respiratory illness 

NO2 alone: RR 1.003 [0.999, 1.007] 5-day 

moving avg 

NO2 + PM10 + O3 + SO2 + CO: RR 0.996 

[0.992, 1.000] 

Wheezing 

NO2 alone: RR 0.996 [0.990, 1.002] 5-day 

moving avg 

NO2 + PM10 + O3 + SO2 + CO: RR 0.991 

[0.983, 0.999] 

ASIA 

Kim et al. (2007) 

Seoul, Korea 

Period of Study: 

2002 

Outcome(s) (ICD-10): Asthma 

(J45, J46) 

Age groups analyzed: All 

Study Design: Case-crossover 

N: 92,535 

Statistical Analyses: Conditional 

logistic regression 

Covariates: Time trend, 

weather conditions, seasonality 

Statistical Package: NR 

Lag: 0, 1, 2, 3, 4, 2-4 ma 

24-h avg: 36.0 

(14.7) ppb 

Range: 2.3-108.0 

50th: 34.3 

IQR: 20.1 

PM10 

CO 

SO2

O3

Increment: 20.1 ppb 

Stratified by individual SES: 

Highest SES quintile: 1.06 [1.02, 1.10] lag 2-4 

2nd Quintile: 1.06 [1.02, 1.09] lag 2-4 

3rd Quintile: 1.03 [0.99, 1.06] lag 2-4 

4th Quintile: 1.06 [1.02, 1.10] lag 2-4 

Lowest SES quintile: 1.05 [1.00, 1.10] lag 2-4 

Stratified by Regional SES: 

Highest SES quintile: 0.96 [0.90, 1.02] lag 2-4 

2nd Quintile: 1.08 [1.04, 1.13] lag 2-4 

3rd Quintile: 1.03 [1.00, 1.07] lag 2-4 

4th Quintile: 1.06 [1.02, 1.10] lag 2-4 

Lowest SES quintile: 1.06 [1.02, 1.09] lag 2-4 

Overall: 1.05 [1.03, 1.06] 

Relative Effect Modification for Interaction 

Terms: 

Stratified by individual SES Η air pollution: 

Highest SES quintile: 1.00 [referent] 

2nd Quintile: 0.99 [0.95, 1.04] 

3rd Quintile: 0.96 [0.92, 1.01] 

4th Quintile: 1.00 [0.95, 1.05] 

Lowest SES quintile: 0.99 [0.93, 1.05] 

Stratified by Regional SES Η air pollution: 

Kim et al. (2007) 

(cont’d) 

 Highest SES quintile: 1.00 [referent] 

2nd Quintile: 1.11 [1.03, 1.20] 

3rd Quintile: 1.07 [1.00, 1.15] 

4th Quintile: 1.09 [1.02, 1.17] 

Lowest SES quintile: 1.09 [1.02, 1.16] 
6-72 

STUDY METHODS 

MEAN LEVELS OF 

NO2 & 

MONITORING 

STATIONS 

COPOLLUTANT 

CORRELATIONS 

EFFECTS AND INTERPRETATION: 

RELATIVE RISK & CONFIDENCE 

INTERVALS (95%) 

Sun et al. (2006) 

Central Taiwan 

Period of Study: 

2004 

Outcome(s) (ICD-9): asthma 

(493) 

Age groups analyzed: <16, 16-

55 

Study Design: Cross-sectional 

Number of hospitals: 4 

Statistical Analyses: Multiple 

correlation coefficients/multiple 

regression analysis 

Covariates: 

Statistical Package: SPSS v 

12.0 

Lag: 

Number of monitors: 

11 

SO2

O3

CO 

PM10 

Children: 

r = 0.72, p = 0.004 

Adults: 

r = 0.428, p = 0.083 

Emergency visits for asthma increased with 

increased levels of NO2 for children but not 

for adults. 

Chew et al. 

(1999) 

Singapore 

Period of Study: 

1990-1994 

Outcome(s) (ICD-9): Asthma 

(493) 

Age groups analyzed: 3-12, 13-

21 

Study Design: Time-series 

N: 23,000 

# of Hospitals: 2 

Statistical Analyses: Linear 

regression, GLM 

Covariates: Variables that were 

significantly associated with ER 

visits were retained in the 

model 

Statistical Package: SAS/STAT, 

SAS/ETS 6.08 

Lag: 1,2 days avgs 

24-h avg: 18.9 μg/m3

, 

SD = 15.0, Max < 40 

# of Stations: 15 

SO2; r = -0.22 

O3; r = 0.17 

TSP; r = 0.23 

Categorical analysis (via ANOVA) p-value 

and Pearson correlation coefficient (r) using 

continuous data comparing daily air pollutant 

levels and daily number of ER visits 

Age Group:3-12 13-21 

Lag 0 r = 0.10 r = 0.09 

 p = 0.0019 p < 0.001 

Lag 1 r = 0.12 r = 0.04 

 p < 0.001 p = 0.0014 

Lag 2 r = 0.14 r = 0.03 

 p < 0.001 p = 0.0066 

Hwang and 

Chan (2002) 

Taiwan 

Period of Study: 

1998 

Outcome(s) (ICD-9): Lower 

Respiratory Disease (LRD) 

(466, 480-6) including acute 

bronchitis, acute bronchiolits, 

pneumonia 

Age groups analyzed: 0-14, 15-

64, ≥65, all ages 

Study Design: Time-series 

Catchment area: Clinic records 

from 50 communities 

Statistical Analyses: Linear 

regression, GLM 

Covariates: Temperature, dew 

point temperature, season, day 

of wk, holiday 

Statistical Package: NR 

Lag: 0,1,2 days and avgs 

24-hr avg: 23.6 ppb, 

SD = 5.4, Range: 

13.0, 34.1 

SO2

PM10 

O3

CO 

No correlations for 

individual pollutants 

Colinearity of pollutants 

prevented use of 

multipollutant models 

Increment: 10% change in NO2 (natural avg) 

which is equivalent to 2.4 ppb. NOTE: The 

percent change is for the rate of clinic use 

NOT for relative risk for adverse effect. 

Increased clinic visits for lower respiratory 

disease (LRD) by age group 

0-14 yrs 

1.3% [1.0, 1.6] lag 0 

15-64 yrs 

1.5% [1.3, 1.8] lag 0 

≥65 yrs 

1.8% [1.4, 2.2] lag 0 

All ages 

1.4% [1.2, 1.6] lag 0 
6-73 

STUDY METHODS 

MEAN LEVELS OF 

NO2 & 

MONITORING 

STATIONS 

COPOLLUTANT 

CORRELATIONS 

EFFECTS AND INTERPRETATION: 

RELATIVE RISK & CONFIDENCE 

INTERVALS (95%) 

Tanaka et al. 

(1998) 

Kushiro, Japan 

Period of Study: 

1992-1993 

Outcome(s): Asthma 

ICD-9 Code(s): NR 

Age groups analyzed: 15-79 

Study Design: Time-series 

N: 102 

# of Hospitals: 1 

Statistical Analyses: Poisson 

regression 

Covariates: Temperature, vapor 

pressure, barometric pressure, 

relative humidity, wind velocity, 

wind direction at maximal 

velocity 

Statistical Package: NR 

NO2 24-h avg 

9.2 ± 4.6 ppb in fog 

11.5 ± 5.7 in fog free 

days 

Max NO2 24-h avg 

<30 ppb 

NO2; r = NR 

SO2; r = NR 

SPM (TSP); 

r = 0.70 

O3; r = NR 

Increment: 15 ppb 

Nonatopic 

OR 0.62 [0.45, 0.84] 

Atopic 

OR 0.81 [0.67, 0.97] 

*Default GAM 

+Did not report correction for over-dispersion 

NR: Not Reported 

APHEA: Air Pollution and Health: a European Approach 

Table AX6.3-5. Respiratory Health Effects of Oxides of Nitrogen: General Practitioner/Clinic 

Visits 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS 

& MONITORING 

STATIONS 

COPOLLUTANTS 

CORRELATIONS

EFFECTS: 

RELATIVE RISK & CONFIDENCE 

INTERVALS (95%) 

NORTH AMERICA 

Sinclair and 

Tolsma (2004) 

Atlanta, GA 

Period of Study: 

8/1998-8/2000 

Outcome(s) (ICD9): Asthma 

(493), URI (460-466, 477), LRI 

(466.1, 480-486) 

Age groups analyzed: <18, >18 

Study Design: Time-series 

N: 232,350 

Statistical Analyses: Poisson 

regression with GLM 

Covariates: Season, day of wk, 

federal holiday, study month, 

long-term trend 

Statistical Package: SAS v 8.02 

Lag: 0-2, 3-5, 6-8 

1-h max: 51.22 

(18.54) ppb 

Monitors: 1 ARIES 

monitor in 

downtown Atlanta 

PM2.5, 

PM10-2.5 

PM10 

PM2.5 components 

PMuf 

Polar VOCs 

O3

SO2

No NO2 results presented because they were not 

statistically significant for any lag periods 

examined. 
6-74 

REFERENCE, 

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LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS 

& MONITORING 

STATIONS 

COPOLLUTANTS 

CORRELATIONS

EFFECTS: 

RELATIVE RISK & CONFIDENCE 

INTERVALS (95%) 

HernándezGarduño et al. 

(1997) 

Mexico City, 

Mexico 

Period of Study: 

May 15, 1992 - 

January 31, 

1993 

Outcome(s): Respiratory illness 

ICD9: NR 

Age groups analyzed: <15, 15+, 

all ages (0-92) 

Study Design: Time-series 

N: 24,113 

Number of Clinics: 5 

Statistical Analyses: Crosscorrelation, linear regression 

and autoregressive error model 

analyses 

Covariates: Long-term trend, 

day of wk, temperature, 

humidity 

Statistical Package: SAS 

Lag: 0-6 

Number of Stations: 

5 

SO2

O3

CO 

NOX

Increment: Max NO2 concentration of all daysMean NO2 concentration of all days 

<14 yrs: 

NO2 lag 0: RR 1.29 ± 0.09 (p < 0.01) 

NO2 lag 6: RR 1.18 ± 0.09 (p > 0.05) 

15+ yrs: 

NO2 lag 0: RR 1.14 ± 0.07 (p < 0.05) 

NO2 lag 6: RR 1.10 ± 0.06 (p > 0.05) 

All ages: 

NO2 lag 0: RR 1.43 ± 0.15 (p < 0.01) 

NO2 lag 6: RR 1.29 ± 0.15 (p > 0.05) 

Villeneuve et al. 

(2006) 

Toronto, ON, 

Canada 

Period of Study: 

1995-2000 

Days: 2,190 

Outcome(s) (ICD9): Allergic 

Rhinitis (177) 

Age groups analyzed: ≥65 

Study Design: Time-series 

N: 52,691 

Statistical Analyses: GLM, using 

natural splines (more stringent 

criteria than default) 

Covariates: Day of wk, holiday, 

temperature, relative humidity, 

aero-allergens 

Season: All yr; Warm, May-Oct; 

Cool, Nov-Apr 

Statistical Package: S-Plus 

Lag: 0-6 

24-h avg: 25.4 ppb, 

SD = 7.7 

IQR: 10.3 ppb, 

Range 9.2, 71.7 

Number of stations: 

9 

SO2

O3

CO 

PM2.5

PM10-2.5 

PM10 

Increment: 10.3 ppb (IQR) 

All results estimated from Stick Graph: 

All Yr: 

Mean Increase: 1.9% [-0.2, 3.8] lag 0 

Warm: 

Mean Increase: 0.1% [-3.2, 3.8] lag 0 

Cool: 

Mean Increase: 1.4% [0.0, 5.9] lag 0 
6-75 

REFERENCE, 

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LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS 

& MONITORING 

STATIONS 

COPOLLUTANTS 

CORRELATIONS

EFFECTS: 

RELATIVE RISK & CONFIDENCE 

INTERVALS (95%) 

EUROPE 

Hajat et al. 

(1999) 

London, United 

Kingdom 

Period of Study: 

1992-1994 

Outcome (ICD9): Asthma (493); 

Lower respiratory disease (464, 

466, 476, 480-3, 490-2, 485-7, 

4994-6, 500, 503-5, 510-5) 

Age groups analyzed: 0-14; 15-

64; 65+; all ages 

Study Design: Time-series 

analysis 

Statistical Analysis: Poisson 

regression, APHEA protocol 

Covariates: Long-term trends, 

seasonality, day of wk, 

temperature, humidity 

Seasons: Warm, Apr-Sep; Cool, 

Oct-Mar; All yr 

Dose-response investigated?: 

Yes 

Statistical package: SAS 

Lag: 0-3 days, cumulative 

All yr 24-h avg: 

33.6 ppb, 

SD = 10.5 

Warm: 32.8 (19.8) 

Cool: 34.5 (10.1) 

10th-90th all yr 

percentile: 24 ppb 

SO2; r = 0.61 

BS; r = 0.70 

CO; r = 0.72 

PM10; r = 0.73 

O3; r = -0.10 

Increment: 24 ppb (90th-10th percentile) 

Asthma 

All ages 2.1% [-0.7, 4.9] lag 0; 3.1% 

[-0.4, 6.7] lag 0-1 

0-14 yrs 6.1% [1.2, 11.3] lag 1; 6.9% 

[1.7, 12.4] lag 0-1 

Warm: 13.2% [5.6, 21.3] lag 1 

Cool: -0.1% [-6.3, 6.5] lag 1 

15-64 yrs 3.0% [-0.7, 6.7] lag 0; 3.1% 

[-1.6, 7.9] lag 0-3 

Warm: 3.3% [-2.0, 8.9] lag 0 

Cool: 2.6% [-2.3, 7.7] lag 0 

65+ yrs 9.9% [1.6, 18.7] lag 2; 5.3% 

[-3, 14.3] lag 0-3 

Warm: 18.6% [6.3, 32.4] lag 2 

Cool: -0.5% [-9.6, 11.8] lag 2 

Lower Respiratory disease 

All ages 1.3% [-0.4, 3.0] lag 1; 1.2% 

[-0.7, 3.1] lag 0-2 

0-14 yrs 4.8% [1.3, 8.3] lag 2; 4.5% 

[0.4, 8.7] lag 0-3 

Warm: 1.4% [-3.8, 6.9] lag 2 

Cool: 7.2% [2.8, 11.6] lag 2 

15-64 yrs 1.1% [-1.1, 3.4] lag 2; 0.8% [-1.8, 3.5] 

lag 0-2 

Warm: 2.3% [-1.2, 5.9] lag 2 

Cool: 0.2% [-2.6, 3.1] lag 2 

65+ -1.7% [-4.3, 1.1] lag 0 

Warm: -1.7% [-5.9, 2.6] lag 0 

Cool: -1.6% [-4.8, 1.8] lag 0 

Two-pollutant model-Asthma 

NO2 alone: 5.2% [0.8, 9.8] 

NO2/O3: 6.7% [2.2, 11.4] 

NO2/SO2: 3.9% [-1.2, 9.2] 

NO2/PM10: 5.3% [-0.6, 11.6] 

Hajat et al. 

(1999) 

(cont’d) 

 Two-pollutant model - Lower Respiratory disease 

NO2 alone 4.2% [1.1, 7.3] 

NO2/O3 4.9% [1.8, 8.2] 

NO2/SO2 2.5% [-1.1, 6.2] 

NO2/PM10 3.5% [0.1, 6.9] 
6-76 

REFERENCE, 

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LOCATION, & 

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METHODS 

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& MONITORING 

STATIONS 

COPOLLUTANTS 

CORRELATIONS

EFFECTS: 

RELATIVE RISK & CONFIDENCE 

INTERVALS (95%) 

Hajat* et al. 

(2001) 

London, United 

Kingdom 

Period of Study: 

1992-1994 

Outcome (ICD9): Allergic 

Rhinitis (477) 

Age groups analyzed: 0-14; 15-

64; 65+; all ages 

Study Design: Time-series 

analysis 

N: 4,214 

Statistical Analysis: Poisson 

regression, GAM 

Covariates: Long-term trends, 

seasonality, day of wk, 

temperature, humidity, variation 

in practice population, counts 

for lagged allergic pollen 

measures, daily number of 

consultations for influenza 

Dose-response investigated? 

Yes 

Statistical package: S-Plus 

Lag: 0-6 days, cumulative 

NO2 24-h avg: 

33.6 ppb, 

SD = 10.5 

# of Stations: 3, 

r = 0.7-0.96 

SO2; r = 0.61 

BS; r = 0.70 

CO; r = 0.72 

PM10; r = 0.73 

O3; r = -0.10 

Increment: 24 ppb (90th-10th percentile) 

Single-pollutant model 

<1 to 14 yrs 

11.0% [3.8, 18.8] lag 4 

12.6% [4.6, 21.3] lag 0-4 

15 to 64 yrs 

5.5% [2.0, 9.1] lag 6 

11.1% [6.8, 15.6] lag 0-6 

>64 yrs - too small for analysis 

Two-pollutant models 

<1 to 14 yrs 

NO2 & O3: 7.9% [0.6, 15.8] 

NO2 & SO2: -3.8% [11.8, 5.0] 

NO2 & PM10: 10.8% [0.1, 22.7] 

15 to 64 yrs 

NO2 & O3: 4.8% [1.0, 8.8] 

NO2 & SO2: 1.0% [-3.7, 5.8] 

NO2 & PM10: 0.5% [-4.9, 6.3] 

Hajat* et al. 

(2002) 

London, United 

Kingdom 

Period of Study: 

1992-1994 

Outcome (ICD9): Upper 

Respiratory Disease, excluding 

Rhinitis (460-3, 465, 470-5, 

478) 

Age groups analyzed: 0-14; 15-

64; 65+; all ages 

Study Design: Time-series 

analysis 

Statistical Analysis: Poisson 

regression, GAM 

Covariates: Long-term trends, 

seasonality, day of wk, holidays, 

temperature, humidity, variation 

in practice population, counts 

for lagged allergic pollen 

measures, daily number of 

consultations for influenza 

Seasons: Warm: Apr-Sep; 

Cool: Oct-Mar 

Dose-response investigated?: 

Yes 

Statistical package: S-Plus 

Lag: 0,1,2,3 days 

NO2 24-h avg: 

33.6 ppb, 

SD = 10.5 

Warm: (Apr-Sep) 

Mean: 32.8 ppb, 

SD = 10.1 

Cool: (Oct-Mar) 

Mean: 34.5 ppb, 

SD = 10.1 

# of Stations: 3 

SO2; r = 0.61 

BS; r = 0.70 

CO; r = 0.72 

PM10; r = 0.73 

O3; r = -0.10 

Increment (90th-10th percentile): All yr: 24 ppb; 

Warm season: 25.8 ppb; 

Cool season: 22.1 ppb 

Single-pollutant model 

All yr 

0-14 yr 2.0% [-0.3, 4.3] lag 3 

15-64 yrs 5.1% [2.0, 8.3] lag 2 

>65 yrs 8.7% [3.8, 13.8] lag 2 

Warm 

0-14 yrs 2.5% [-0.9, 6.1] lag 3 

15-64 yrs 6.7% [3.7, 9.8] lag 2 

≥65 yrs 6.6% [-1.1, 14.9] lag 2 

Cool 

0-14 yrs 1.7% [-1.1, 4.6] lag 3 

15-64 yrs 1.2% [-1.3, 3.9] lag 2 

>65 yrs 9.4% [2.8, 16.4] lag 2 

Two-pollutant models 

0-14 yrs 

NO2 & O3: 1.7% [-0.6, 3.9] 

NO2 & SO2: 2.2% [-0.4, 5.0] 

NO2 & PM10: 1.5% [-1.7, 4.8] 

For 15-64 yrs 

NO2 & O3: 4.4% [2.2, 6.8] 

NO2 & SO2: 4.4% [1.6, 7.2] 

NO2 & PM10: 2.7% [-0.5, 5.9] 

For >65 yrs 

NO2 & O3: 8.1% [3.0, 13.6] 

NO2 & SO2: 8.6% [2.1, 15.4] 

NO2 & PM10: 4.3% [-2.8, 11.8] 
6-77 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS 

& MONITORING 

STATIONS 

COPOLLUTANTS 

CORRELATIONS

EFFECTS: 

RELATIVE RISK & CONFIDENCE 

INTERVALS (95%) 

Chardon et al. 

(2007) 

Greater Paris, 

France 

Period of Study: 

2000-2003 

Outcome (ICD9): Asthma, URD, 

LRD 

Age groups analyzed: 0-14; 15-

64; 65+; all ages 

Study Design: Time-series 

analysis 

Statistical Analysis: Poisson 

regression, GAM 

Covariates: Long-term trends, 

seasonality, day of wk, holidays, 

temperature, humidity, counts 

for lagged allergic pollen 

measures, daily number of 

consultations for influenza 

Seasons: Warm: Apr-Sep; 

Cool: Oct-Mar 

Dose-response investigated?: 

Yes 

Statistical package: R software 

Lag: 0, 1, 2, 3, 0-1, 0-2, 0-3 

days 

24-h avg: 44.4 

(14.92) μg/m3

Median: 43.6 

IQR: 33.7-53.2 

Range: 12.3-132.8 

Number of 

monitors: 12-15 

PM10; r = 0.68 

PM2.5; r = 0.68 

Increment: 10 μg/m3

URD: 0.7% [-0.9, 2.3] lag 0-3 

LRD: 1.1% [-0.7, 2.9], lag 0-3 

Asthma: -0.3% [-3.3, 2.7], lag 0-3 

* Default GAM 

+ Did not report correction for over-dispersion 

APHEA: Air Pollution and Health: a European Approach 
6-78 

Table AX6.3-6. Human Health Effects of Oxides of Nitrogen: CVD Hospital Admissions 

and Visits: United States and Canada 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS 

& MONITORING 

STATIONS 

COPOLLUTANTS 

(CORRELATIONS)

EFFECTS: RELATIVE RISK OR 

PERCENT CHANGE & 

CONFIDENCE INTERVALS ([95% 

LOWER, UPPER]) 

Burnett et al. 

(1997a) 

Metropolitan Toronto 

(Toronto, North 

York, East York, 

Etobicoke, 

Scarborough, York), 

Canada 

Study period: 

1992-1994, 388 

days, summers only 

Outcome(s) (ICD9): IHD 

410-414; Cardiac Dysrhythmias 

427; Heart failure 428. All 

Cardiac 410-414, 427, 428. 

Obtained from hospital discharge 

data. 

Population: 2.6 Million residents 

Study design: Time-series 

Age groups analyzed: all 

# Hospitals: NR 

Statistical analysis: Relative risk 

regression models, GEEs. 

Covariates: Adjusted for longterm trends, seasonal and 

subseasonal variation, day of the 

wk, temperature, dew point 

Seasons: Summer only 

Dose response: Figures 

presented 

Statistical package: NR 

Lag: 1-4 days 

NO2 daily 1-h max 

(ppb): 

Mean: 38.5 

CV: 29 

Min: 12 

25th percentile: 31 

50th percentile: 38 

75th percentile: 45 

Max: 81 

# of Stations: 6-11 

(Results are reported 

for additional metrics 

including 24-h avg and 

daytime avg (day)) 

H+ (0.25) 

SO4 (0.34) 

TP (0.61) 

FP (0.45) 

CP (0.61) 

COH (0.61) 

O3 (0.07) 

SO2 (0.46) 

CO (0.25) 

Results reported for RR for an IQR 

increment increase in NO2. T ratio in 

parentheses. 

All Cardiac Disease 

Single-pollutant model 

1.049 (3.13), daily avg over 4 days, lag 0 

Multipollutant model 

1.30 (1.68), w/ NO2, O3, SO2, 

Objective of study was to evaluate the 

role of particle size and chemistry on 

cardio and respiratory diseases. NO2

attenuated the effect of particulate in this 

study. 
6-79 

REFERENCE, 

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LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS 

& MONITORING 

STATIONS 

COPOLLUTANTS 

(CORRELATIONS)

EFFECTS: RELATIVE RISK OR 

PERCENT CHANGE & 

CONFIDENCE INTERVALS ([95% 

LOWER, UPPER]) 

Burnett et al. (1999) 

* 

Metropolitan Toronto 

(Toronto, North 

York, East York, 

Etobicoke, 

Scarborough, York), 

Canada 

Study Period: 

1980-1995, 15 yr 

Outcome(s) (ICD9): IHD 

410-414; Cardiac Dysrhythmias 

427; Heart failure 428; All 

Cardiac 410-414, 427, 428; 

Cerebrovascular Disease 

Obtained from hospital discharge 

data 430-438; Peripheral 

Circulation Disease 440-459. 

Population: 2.13-2.42 million 

residents 

Study Design: Time-series 

Statistical Analysis: GAMs with 

LOESS smoothers to remove 

temporal and weather related 

trends, stepwise regression used 

to select minimum number of air 

pollutants in multipollutant 

models. 

Covariates: Long-term trends, 

seasonal variation, day of wk, 

temperature, and humidity. 

Statistical Package: SPLUS, SAS 

Lag(s): 0-2 day 

NO2 daily avg (ppb) 

Mean: 25.2 

5th percentile: 13 

25th percentile: 19 

50th percentile: 24 

75th percentile: 30 

95th percentile: 42 

Max: 82 

Multiple day avgs used 

in models 

PM2.5 (0.50) 

PM10-2.5 (0.38) 

PM10 (0.52) 

CO (0.55) 

SO2 (0.55) 

O3 (-0.04) 

Results reported for % increase in 

hospital admissions for an increment 

increase in NO2 equal to the mean 

value. 

Single-Pollutant Models: 

Dysrhythmias: 5.33 (1.73) 3-day avg, lag 

0 

Heart Failure: 9.48 (6.33), 1 day, lag 0 

IHD: 9.73 (8.4) 2-day avg, lag 0 

Cerebrovascular disease: 1.98 (1.34), 1 

day, lag 0 

Peripheral circulation: 3.57 (1.78), 1-day, 

lag 0 

Multipollutant Models: 

Heart failure 

6.89 (w/ CO) 

6.68 (w/ CO, PM2.5) 

6.33 (w/ CO, PM2.5, PM10-2.5) 

6.45 (w/ CO, PM2.5, PM10-2.5, PM10) 

IHD 

8.34 (w/ CO, SO2) 

7.76 (w/ CO, SO2, PM2.5) 

8.41 (w/ CO, SO2, PM2.5, PM10-2.5) 

8.52 (w/ CO, SO2, PM2.5, PM10-2.5, 

PM10) 

In multipollutant models, gaseous 

pollutants were selected by stepwise 

regression. PM variables were then 

added to the model. 

Morris et al. (1995) 

US (Chicago, 

Detroit, LA, 

Milwaukee, NYC, 

Philadelphia) 

Study Period: 

1986-1989, 4 yr 

Outcome(s) (ICD9): CHF 428. 

Daily Medicare hospital 

admission records. 

Study Design: Time-series 

Statistical Analyses: GLM, 

negative binomial distribution 

Age groups analyzed: ≥65 yrs 

Covariates: Temperature, 

indicator variables for mo to 

adjust for weather effects and 

seasonal trends, day of wk, yr 

Statistical Software: S-PLUS 

Lag(s): 0-7 day 

NO2 1 h-max (ppm) 

Mean: (SD) 

LA: 0.077 (0.028) 

Chicago: 0.045 (0.013) 

Philadelphia: 0.054 

(0.017) 

New York: 0.064 

(0.022) 

Detroit: 0.041 (0.015) 

Houston: 0.041 (0.017) 

Milwaukee: 0.040 

(0.014) 

SO2 1-h max 

O3 1-h max 

CO 1-h max 

Correlations of NO2

with other pollutants 

strong. 

Multipollutant models 

run. 

Results reported for RR of admission for 

CHF associated with an incremental 

increase in NO2 of 10 ppb. 

CHF: 

LA: 1.15 (1.10, 1.19) 

Chicago: 1.17 (1.07, 1.27) 

Philadelphia: 1.03 (0.95, 1.12) 

New York: 1.07 (1.02, 1.13) 

Detroit: 1.04 (0.92, 1.18) 

Houston: 0.99 (0.88, 1.10) 

Milwaukee: 1.05 (0.89, 1.23) 

RR diminished in multipollutant models 

(4 copollutants) for all cities with the 

exception of New York. 
6-80 

REFERENCE, 

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METHODS 

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& MONITORING 

STATIONS 

COPOLLUTANTS 

(CORRELATIONS)

EFFECTS: RELATIVE RISK OR 

PERCENT CHANGE & 

CONFIDENCE INTERVALS ([95% 

LOWER, UPPER]) 

Dales et al (2006) 

Canada (11 largest 

cities) 

Study period: 

January 1, 1986- 

December 31, 2000. 

Outcomes (ICD-9): Asphyxia 

(799.0), respiratory failure 

(799.1), dyspnea and respiratory 

abnormalities (786.0), respiratory 

distress syndrome (769), 

unspecificed birth asphyxia in 

live-born infant (768.9), other 

respiratory problems after birth 

(770.8) and pneumonia (486) 

Data from: Canaidian Institutue 

for Health Information: 

9542 records for patients aged 

birth to 27 days 

Study Design: Time series 

Statistical analysis: Randomeffects regression model 

Statitsitical Software: S-PLUS 

NO2 24-hour mean 

levels (ppb): 

Calgary: 25.6 

Edmonton: 24.6 

Halifax: 15.1 

Hamilton: 20.8 

London: 20.0 

Ottawa: 21.2 

Saint John: 9.2 

Toronto: 25.1 

Vancouver: 19.0 

Windsor: 24.9 

Winnipeg: 15.2 

Population weighted 

avg: 21.8 

Range of Pearson 

pairwise correlations 

PM10: -0.26 to 0.69 

O3: -0.55 to 0.05 

SO2: 0.20 to 0.67 

CO: 0.13 to 0.76 

Pooled estimate of % increase in 

neonatal resipartory hospital admissions 

(95% CI): 

Interquartile range: 10.0 

Single-pollutant model: 2.94 (1.93 to 

3.95) 

Multi pollutant model: 2.85 (1.68 to 4.02) 

Multipollutant model restricted to days 

with PM10 measures: 2.48 (1.18 to 3.80) 

Wellenius et al. 

(2005b) 

Birmingham, 

Chicago, Cleveland, 

Detroit, Minneapolis, 

New Haven, 

Pittsburgh, Seattle 

Study Period: 

Jan 1986-Nov 1999 

(varies slightly 

depending on city) 

Outcome(s) IS, primary diagnosis 

of acute but ill-defined 

cerebrovascular disease or 

occlusion of the cerebral arteries; 

HS, primary diagnosis of 

intracerebral hemorrhage. ICD 

codes not provided. Hospital 

admissions ascertained from the 

Centers for Medicare and 

Medicaid Services. Cases 

determined from discharge data 

were admitted from the ER to the 

hospital. 

N IS: 155,503 

N HS: 19,314 

Study Design: Time-stratified 

case-crossover. Control days 

chosen such that they fell in 

same mo and same day of wk. 

Design controls for seasonality, 

time trends, chronic and other 

slowly varying potential 

confounders. 

Statistical Analysis: 2-stage 

hierarchical model (random 

effects), conditional logistic 

regression for city effects in the 

first stage. 

Software package: SAS 

Covariates: 

Lag(s): 0-2, unconstrained 

distributed lags 

NO2 24-h (ppb) 

10th: 13.71 

25th: 18.05 

Median: 23.54 

75th: 29.98 

90th: 36.54 

NO2 data not available 

for Birmingham, Salt 

Lake, and Seattle. 

PM10 (0.53) 

CO, SO2

Correlation only 

provided for PM 

because study 

hypothesis involves 

PM 

Results reported for percent increase in 

stroke admissions for an incremental 

increase in NO2 equivalent to one IQR 

(11.93). 

Ischemic Stroke: 2.94 (1.78, 4.12), lag 0 

Hemorrhagic Stroke: 0.38 (-2.66, 3.51), 

lag 0 

Multipollutant models not run. 
6-81 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS 

& MONITORING 

STATIONS 

COPOLLUTANTS 

(CORRELATIONS)

EFFECTS: RELATIVE RISK OR 

PERCENT CHANGE & 

CONFIDENCE INTERVALS ([95% 

LOWER, UPPER]) 

Fung et al. (2005) 

Windsor, Ontario, 

Canada 

Study Period: 

Apr 1995-Jan 2000 

Outcome(s) (ICD9): CHF 428; 

IHD 410-414; dysrhythmias 427. 

Hospital admissions from Ontario 

Health Insurance Plan records. 

Study Design: Time-series 

Statistical Analysis: GLM 

N: 11,632 cardiac admission, 

4.4/day for 65+ age group 

Age groups analyzed: 65+, <65 

yr 

Statistical Software: SPLUS 

Lag(s): lag 0, 2, 3 day avg 

NO2 1-h max (ppb): 

Mean (SD): 38.9 (12.3) 

Min: 0 

Max: 117 

SO2 (0.22) 

CO (0.38) 

O3 (0.26) 

COH (0.39) 

PM10 (0.33) 

Results expressed as percent change 

associated with an incremental increase 

in NO2 equivalent to the IQR (16 ppb) 

Cardiac: 

<65 age group: 

0.7 (-5.5, 6.6) 

2.1 (-3.7, 8.2) 

3.7 (-2.9, 10.7) 

65+ age group: 

0.8 (-2.2, 3.9), lag 0 

0.9 (-2.7, 4.6), 2-day avg (lag 0-1) 

0.8 (-3.3, 5.0), 3-day avg (lag 0-2) 

Effect for NO2 not observed in these 

data. Association of SO2 with cardiac 

admissions observed. 

Linn et al. (2000) 

Metropolitan Los 

Angeles, USA 

Study Period: 

1992-1995 

Outcome(s): All Patient Refined 

Diagnosis Related Groups 

(based on medicare diagnosis 

related groups). CVD APR-DRG 

103-144; Cerebrovascular APRDRG 14-17 and 22; CHF APRDRG 127; MI APR-DRG 111, 

115, 121; cardiac ARR APR-DRG 

138; Occlusive Stroke APR-DRG 

14. Hospital admission records 

used to ascertain cases. 

Study Design: Time-series 

Statistical Analyses: Poisson 

regression, cubic spline smooth 

on time, indicator variables to 

adjust for temperature and rain. 

Covariates: Day of wk, holidays, 

long-term trend, seasonal 

variation, temperature, humidity 

Lag(s): 0-1 

Seasons: Winter, Spring, 

Summer, Autumn 

Statistical Software: SPSS, SAS 

NO2 24-h (ppm) 

Winter 

Mean: (SD) 3.4 (1.3) 

Range: 1.1, 9.1 

Spring 

Mean (SD): 2.8 (0.9) 

Range: 1.1, 6.1 

Summer 

Mean (SD): 3.4 (1.0) 

Range: 0.7, 6.7 

Autumn 

Mean (SD): 4.1 (1.4) 

Range: 1.6, 8.4 

CO (0.84, 0.92) 

O3 (-0.23, 0.11) 

PM10 (-0.67, 0.8) 

Range in correlations 

depends on the 

season, independent 

effects of pollutants 

could not be 

distinguished. 

# Stations: 6+ 

Results reported as increase % increase 

in admission for a 10 ppb increase in 

NO2. SD in parentheses. 

CVD, lag 0 

All Seasons: 1.4 (0.2) 

Winter: 1.6 (0.4) 

Spring: 0.1 (0.6) 

Summer: 1.1 (0.5) 

Autumn: 1.4 (0.3) 

Cerebrovascular, lag 0 

All Seasons: 0.4 (0.4) 

Winter: -1.3 (0.7) 

Spring: 4.2 (1.2) 

Summer: 0.9 (1.2) 

Autumn: 0.7 (0.6) 

MI, lag 0 (yr round) 

1.1 (0.5) 

CHF, lag 0 (yr round) 

1.0 (0.5), winter 1.9 (0.9) 

Cardiac Arrhythmia, lag 0 

(yr round) 

0.6 (0.5) 

Occlusive stroke, lag 0 

(yr round) 

2.0 (0.5), winter 2.7 (1.0), autumn 0.1 

(0.05) 

Significant effects observed in fall for 

occlusive stroke and winter for CHF. 
6-82 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS 

& MONITORING 

STATIONS 

COPOLLUTANTS 

(CORRELATIONS)

EFFECTS: RELATIVE RISK OR 

PERCENT CHANGE & 

CONFIDENCE INTERVALS ([95% 

LOWER, UPPER]) 

Lippmann et al. 

(2000*; reanalysis 

Ito, 2003, 2004) 

Windsor Ontario 

(near Detroit MI) 

Study period: 

1992-1994 (hospital 

admissions – 

mortality study 

spanned longer 

period) 

Outcome(s): IHD 410-414; 

dysrhythmias 427; heart failure 

428; stroke 431-437. 

Study Design: Time-series 

Statistical Analysis: Poisson 

regression GAM. Results of 

reanalysis by Ito 2003, 2004 with 

GLM are presented. 

Lag(s): 0-3 day 

NO2 24-h avg (ppb) 

5th %: 11 

25th %: 16 

50th %: 21 

75th %: 26 

95th %: 36 

Mean: 21.3 

PM10 (0.49) 

PM2.5 (0.48) 

PM10-2.5 (0.32) 

H+ (0.14) 

SO4 (0.35) 

O3 (0.14) 

SO2 (0.53) 

CO (0.68) 

Results reported for RR for incremental 

increase in NO2 of 5th to 95th percentile 

(28 ppb). 

IHD 

(0.94, 1.10), lag 0 

0.98 (0.90, 1.06), lag 1 

Dysrhythmias 

0.98 (0.86, 1.12), lag 0 

1.03 (0.90, 1.06), lag 1 

Heart Failure 

1.00 (0.91, 1.09), lag 0 

1.07 (0.98, 1.17), lag 1 

Stroke 

0.99 (0.90, 1.09), lag 0 

0.98 (0.89, 1.08), lag 1 

Mann et al. (2002) 

South coast air 

basin of CA, United 

States 

Study Period: 

1988-1995, 8 yr 

Outcome(s) IHD 410-414; or IHD 

with accompanying diagnosis of 

CHF 428; or Arrhythmia 426, 

427; Ascertained through health 

insurance records. 

Study Design: Time-series 

N: 54,863 IHD admissions 

Age groups analyzed: #40; 40-

59; ≥60. Statistical Analysis: 

Poisson regression, GAM with 

cubic splines, results pooled 

across air basins using inverse 

variance weighting as no 

evidence of heterogeneity was 

observed 

Covariates: Study day, 

temperature, relative humidity, 

day of wk 

Lag(s): 0-2, 2-4 day moving avg 

Software: SPLUS 

Seasons: Some analyses 

restricted to Apr-Oct 

NO2 24-h avg (ppb): 

Exposure assigned for 

each air basin based on 

health insurance 

participant’s zip code. 

Mean (SD): 37.2 (15.7) 

Range: 3.69, 138 

Median: 34.8 

# Stations: 25-35 

O3 8 h-max 

(-0.16, 0.54) 

CO 8-h max (0.64, 

0.86) 

PM10 24-h avg (0.36, 

0.60) 

Range depends on air 

basin. 

No multipollutant 

models run. Traffic 

pollution generally 

implicated in findings. 

Results reported for percent increase in 

admissions for a 10-ppb incremental 

increase in NO2. 

All IHD: 1.68 (1.08, 2.28), lag 0 

MI: 1.04 (1.05, 3.02), lag 0 

Other acute IHD: 1.75 (0.72, 2.78), lag 0 

IHD w/ secondary diagnosis of 

Arrhythmia: 

1.81 (0.78, 2.85), lag 0 

IHD w/ secondary diagnosis of CHF: 

2.32 (0.69, 3.98), lag 0 

IHD w/ no secondary diagnosis: 

0.46 (-0.81, 1.74), lag 0 

Effect of secondary diagnosis strongest 

in the 40-59 age group. 

Group with secondary CHF may be 

sensitive subpopulation or their 

vulnerability may be due to greater 

prevalence of MI as the primary 

diagnosis. 
6-83 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS 

& MONITORING 

STATIONS 

COPOLLUTANTS 

(CORRELATIONS)

EFFECTS: RELATIVE RISK OR 

PERCENT CHANGE & 

CONFIDENCE INTERVALS ([95% 

LOWER, UPPER]) 

Metzger et al. 

(2004) 

Atlanta, GA 

Period of Study: Jan 

1993-Aug 31 2000, 

4 yr 

Outcome(s): IHD 410-414; AMI 

410; Dysrhythmias 427; cardiac 

arrest 427.5; congestive heart 

failure 428; peripheral and 

cerebrovascular disease 433-

437, 440, 443-444, 451-453; 

atherosclerosis 440; stroke 436. 

ED visits from billing records. 

N: 4,407,535 visits, 37 CVD 

visits/day 

# Hospitals: 31 

Age groups analyzed: Adults 

≥19, elderly 56+ 

Statistical Analysis: Poisson 

regression, GLM. Sensitivity 

analyses using GEE and GAM 

(strict convergence criteria) 

Covariates: long-term trends, 

mean and dew point temp, 

relative humidity (cubic splines) 

Statistical Software: SAS 

Season: Warm: Apr 15-Oct 14; 

Cool: Oct 15-Apr 14 

Lag(s): 0-3 day 

NO2 1-h max (ppb): 

Median: 26.3 

10th-90th percentile 

Range: 25, 68 

PM10 24 h (0.49) 

O3 8-h max (0.42) 

SO2 (0.34) 

CO 1 h (0.68) 

1998-2000 Only 

PM2.5 (0.46) 

Course PM (.46) 

Ultrafine PM (.26) 

Water-soluble metals 

(.32) 

Sulfates (.17) 

OC (0.63) 

EC (.37) 

OHC (0.3) 

Multipollutant models 

used. All models 

specified a priori. 

Results presented for RR of an 

incremental increase in 1-h max NO2

equivalent to 20 ppb (1 SD). 

All CVD: 1.025 (1.012, 1.039) 

Dysrhythmia: 1.019 (0.994, 1.044) 

CHF: 1.010 (0.981, 1.040) 

IHD: 1.029 (1.005, 1.053) 

PERI: 1.041 (1.013, 1.069) 

Finger wounds: 1.010 (0.993, 1.027) 

Lag is 3-day moving avg for results 

above. 

NO2 effect was generally attenuated in 

two-pollutant models. The attenuation 

was strongest in the period after 1998. 

Moolgavkar 

(2000a,b,c)* 

Cook County, IL, 

Los Angeles County, 

CA, 

Maricopa County, 

AZ 

1987-1995 

Outcome(s) (ICD9): CVD 

390-429; Cerebrovascular 

disease 

430-448. Hospital admissions 

from CA department of health 

database. 

Age groups analyzed: 20-64, 65+ 

yrs 

Study Design: Time-series 

N: 118 CVD admissions/day 

# Hospitals: NR 

Statistical Analysis: Poisson 

regression, GAM 

Covariates: Adjustment for day of 

wk, long-term temporal trends, 

relative humidity, temperature 

Statistical Package: SPLUS 

Lag: 0-5 days 

NO2 24-h avg (ppb) 

Cook County: 

Min: 7 

Q1: 20 

Median: 25 

Q3: 30 

Max: 58 

NO2 24-h avg (ppb) LA 

County: 

Min: 10 

Q1: 30 

Median: 38 

Q3: 48 

Max: 102 

NO2 24-h avg (ppb) 

Maricopa County: 

Min: 2 

Q1: 14 

Median: 19 

Q3: 26 

Max: 56 

PM10 (0.22-0.70) 

PM2.5 (0.73) (LA only) 

CO (0.63-0.80) 

SO2 (0.02- 0.74) 

O3 (-0.23-0.02) 

Two-pollutant models 

(see results) 

Results reported for percent change in 

hospital admissions per 10-ppb increase 

in 24-h avg NO2. T statistic in 

parentheses. 

CVD, 65+: 

Cook County 

2.9 (10.2), lag 0 

2.3 (6.7), lag 0, two-pollutant model 

(PM10) 

2.9 (8.1), lag 0, two-pollutant model (CO)

2.8 (8.8), lag 0, two-pollutant model 

(SO2) 

LA County 

2.3 (16.7), lag 0 

-0.1 (-0.5), lag 0, two-pollutant model 

(CO) 

1.7 (8.0), lag 0, two-pollutant model 

(SO2) 

Maricopa County 

2.9 (4.1), lag 0 

-0.3 (-0.3), lag 0, two-pollutant model 

(CO) 

2.6 (3.6), lag 0, two-pollutant model 

(SO2) 

Cerebrovascular Disease, 65+: 

Cook County 

1.6 (3.6) 

LA County 

1.1 (5.7) 

Effect size generally diminished with 

increasing lag time. Increase in hospital 

admissions (1.3 for CVD and 1.9 for 

cerebrovascular) also observed for the 

20-64 age group. 
6-84 

REFERENCE, 

STUDY 

LOCATION, & 

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METHODS 

MEAN LEVELS 

& MONITORING 

STATIONS 

COPOLLUTANTS 

(CORRELATIONS)

EFFECTS: RELATIVE RISK OR 

PERCENT CHANGE & 

CONFIDENCE INTERVALS ([95% 

LOWER, UPPER]) 

Moolgavkar (2003)* 

Cook County, IL, 

Los Angeles County, 

CA, 

Maricopa County, 

AZ 

1987-1995 

Outcome(s) (ICD9): CVD 

390-429; Cerebrovascular 

disease 

430-448 was not considered in 

the reanalysis. Hospital 

admissions from CA department 

of health database. 

Age groups analyzed: 20-64, 65+ 

yrs 

Study Design: Time-series 

N: 118 CVD admissions/day 

# Hospitals: NR 

Statistical Analysis: Poisson 

regression, GAM with strict 

convergence criteria (10-8), GLM 

using natural splines 

Covariates: Adjustment for day of 

wk, long-term temporal trends, 

relative humidity, temperature 

Statistical Package: SPLUS 

Lag: 0-5 days 

NO2 24-h avg (ppb) 

Cook County: 

Min: 7 

Q1: 20 

Median: 25 

Q3: 30 

Max: 58 

NO2 24-h avg (ppb) 

LA County: 

Min: 10 

Q1: 30 

Median: 38 

Q3: 48 

Max: 102 

NO2 24-h avg (ppb) 

Maricopa County: 

Min: 2 

Q1: 14 

Median: 19 

Q3: 26 

Max: 56 

PM10 (0.22-0.70) 

PM2.5 (0.73) (LA only) 

CO (0.63-0.80) 

SO2 (0.02-0.74) 

O3 (-0.23-0.02) 

Two-pollutant models 

(see results) 

Results for CVD not shown but use of 

stringent criteria in GAM did not alter 

results substantially. However, increased 

smoothing of temporal trends attenuated 

results for all gases. 

Peel et al. (2007) 

Atlanta, GA 

Study Period: 

Jan 1993-Aug 2000 

Outcome(s) (ICD9): IHD 410-

414; dysrhythmia 427; CHF 428; 

peripheral vascular and 

cerebrovascular disease 433-

437, 440, 443, 444, 451-453. 

Computerized billing records for 

ED visits. 

Comorbid conditions: 

Hypertension 401-405; diabetes 

250; dysrhythmia 427, CHF 428; 

atherosclerosis 440; COPD 491, 

492, 496; pneumonia 480-486; 

upper respiratory infection 460-

465, 466.0; asthma 493, 786.09. 

# Hospitals: 31 

N: 4,407,535 visits 

Study Design: Case-crossover. 

CVD outcomes among 

susceptible groups with 

Comorbid conditions. 

Statistical Analyses: Conditional 

logistic regression. 

Covariates: Cubic splines for 

temperature and humidity 

included in models. Time 

independent variables controlled 

through design. 

Statistical Software: SAS 

Lag(s): 3-day avg, lagged 0-2 

day 

NO2 1-h max (ppb): 

Mean (SD): 45.9 (17.3) 

10th: 25.0 

90th: 68.0 

PM10 24-h avg 

O3 8-h max 

SO2 1-h max 

CO 1-h max 

Correlations not 

reported 

Results expressed as OR for association 

of CVD admissions with a 20-ppb 

incremental increase in NO2. 

Comorbid Hypertension: 

IHD: 1.036 (0.997, 1.076) 

Dysrhythmia: 1.095 (1.030, 1.165) 

PERI: 1.031 (0.987, 1.076) 

CHF: 1.037 (0.985, 1.090) 

Comorbid Diabetes: 

IHD: 1.003 (0.95, 1.059) 

Dysrhythmia: 1.158 (1.046, 1.282) 

PERI: 1.012 (0.947, 1.082) 

CHF: 1.017 (0.959, 1.078) 
6-85 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS 

& MONITORING 

STATIONS 

COPOLLUTANTS 

(CORRELATIONS)

EFFECTS: RELATIVE RISK OR 

PERCENT CHANGE & 

CONFIDENCE INTERVALS ([95% 

LOWER, UPPER]) 

Schwartz, (1997) * 

Tucson, AZ 

Study Period: 

Jan 1988-Dec 1990 

Outcome(s) (ICD9): CVD 390-

429. Ascertained from hospital 

discharge records. 

Study Design: Time-series 

Statistical Analysis: Poisson 

regression, GAM 

Age groups analyzed: 65+ 

Covariates: Long-term and 

seasonal trends, day of the wk, 

temperature, dew point 

Statistical Software: SPLUS 

NO2 24-h avg (ppb): 

Mean: 19.3 

10th: 9.9 

25th: 13.2 

50th: 19 

75th: 24.6 

90th: 29.8 

PM10 (0.326) 

O3 (-0.456) 

SO2 (0.482) 

CO (0.673) 

Results reported as a percent increase in 

admission for an increment in NO2

equivalent to the IQR (11.4 ppb). 

CVD 0.69% (-2.3, 3.8) 

Tucson selected to minimize correlations 

between pollutants. Since there was no 

association between NO2 and 

admissions, author suggests results for 

CO not confounded by NO2. 

Stieb et al. (2000) * 

Saint John, New 

Brunswick Canada 

Study Period: 

July 1992-Mar 1996 

Outcome(s): Angina pectoris; MI; 

dysrhythmia/conduction 

disturbance; CHF; All Cardiac. 

ED Visits collected prospectively. 

Study Design: Time-series 

Statistical Analyses: Poisson 

regression, GAM, LOESS 

smooth for temporal and weather 

related variables 

N: 19,821 ER visits 

# Hospitals: 2 

Lag(s): 1-8 days 

NO2 24-h avg (ppb) 

Mean (SD): 8.9 (5.5) 

95th: 19 

Max: 35 

NO2 max (ppb) 

Mean (SD): 20.2 

95th: 39 

Max: 82 

CO (0.68) 

H2S (-0.07) 

O3 (-0.02) 

SO2 (0.41) 

PM10 (0.35) 

PM2.5 (0.35) 

H+ (-0.25) 

SO4 (0.33) 

COH (0.49) 

Results reported for percent change in 

admissions based on a single-pollutant 

model for incremental increase in NO2

equivalent to 1 IQR (8.9 ppb) 

Cardiac visits: 

-3.9, p-value = 0.136, lag 2, all yr 

10.1, p-value = 0.051, lag 5, May-Sept 

For specific CVD diagnoses, ARR and 

CHF approached significance. NO2 was 

not a focus of this paper. 

Tolbert et al. (2007) 

Atlanta, GA 

Study Period: 

1993-2004 

Outcome(s) (ICD9): All CVD 

including IHD 410-414; cardiac 

dysrhythmias 427; CHF 428; 

peripheral vascular and 

cerebrovascular disease 433-

437, 440 443-445, 451-453. 

Emergency visits primary and 

secondary diagnostic codes. 

Study Design: Time-series 

Statistical Analysis: GLM, cubic 

splines with monthly knots, 

indicators for season, day of wk, 

holiday, excluded days with 

missing exposure data 

Statistical Software: SAS 

Lag(s): 3 day moving avg (0-2 d) 

NO2 1-h max (ppb): 

Mean: 43.2 

Min: 1.0 

10th: 22 

25th: 31 

Median: 41 

75th: 54 

90th: 66 

Max: 181 

PM10 (0.53) 

O3 (0.44) 

CO (0.70) 

SO2 (0.36) 

Course PM (0.70) 

PM2.5 (0.47) 

PM2.5 SO4 (0.14) 

PM2.5 EC (0.64) 

PM2.5 OC (0.62) 

PM2.5 TC (0.65) 

PM2.5 water sol 

metals (0.32) 

OHC (0.24) 

Results reported for RR based on 

incremental increase of NO2 equivalent 

to 1 IQR (23 ppb): 

Single-pollutant model results: 

CVD 1.015 (1.004, 1.025), lag 0-2 

NO2 effect diminished in multipollutant 

models containing CO and PM2.5TC 

(shown in figure). 
6-86 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS 

& MONITORING 

STATIONS 

COPOLLUTANTS 

(CORRELATIONS)

EFFECTS: RELATIVE RISK OR 

PERCENT CHANGE & 

CONFIDENCE INTERVALS ([95% 

LOWER, UPPER]) 

Villeneuve et al. 

(2006) 

Edmonton, Canada 

Study Period: 

Apr 1992-Mar 2002 

Outcome(s) (ICD9): Acute 

ischemic stroke 434, 436; 

hemorrhagic stroke 430, 432; 

transient ischemic attack (TIA) 

435; Other 433, 437, 438. ED 

visits supplied by Capital Health. 

N: 12,422 Stroke Visits 

Catchment area: 1.5 million 

people 

Study Design: Case-crossover, 

exposure index time compared to 

referent time. Time-independent 

variables controlled in the 

design. Index and referent day 

matched by day of wk. 

Statistical Analysis: Conditional 

logistic regression, stratified by 

season and gender. 

Covariates: Temperature and 

humidity 

Statistical Software: SAS 

Season: Warm: Apr-Sep; 

Cool: Oct-Mar 

Lag(s): 0, 1, 3-day avg 

NO2 24 h ppb: 

All yr 

Mean (SD): 24 (9.8) 

Median: 22.0 

25th: 16.5 

75th: 30.0 

IQR: 13.5 

Summer 

Mean (SD): 18.6 (6.4) 

Median: 17.5 

25th: 14.0 

75th: 22.0 

IQR: 8 

Winter 

Mean (SD): 29.4 (9.6) 

Median: 28.5 

25th: 22.5 

75th: 35.5 

IQR: 13.0 

O3 24-h max (-0.33) 

O3 24-h avg (-0.51) 

SO2 25-h avg (0.42) 

CO 24-h avg (0.74) 

PM10 24-h avg (0.34) 

PM2.5 24-h avg (0.41) 

All yr correlations 

summarized. 

Results reported for an incremental 

increase in NO2 equivalent to one IQR 

NO2. 

Ischemic Stroke, Summer 

1.17 (1.05, 1.31), lag 0 

1.18 (1.05, 1.31), lag 1 

1.26 (1.09, 1.46), 3-day avg 

Hemorrhagic stroke, Summer 

1.16 (0.99, 1.37) 

1.14 (0.97, 1.35) 

1.18 (0.95, 1.46) 

TIA not associated with increase in NO2. 

Above results are strongest effects, 

which were observed during summer. 

Authors attribute NO2 effect to vehicular 

traffic since NO2 and CO are highly 

correlated. 

Wellenius et al. 

(2005a) 

Allegheny County, 

PA (near Pittsburgh) 

Study Period: 

Jan 1987-Nov 1999 

Outcome(s): CHF 428. Cases 

are Medicare patients admitted 

from ER with discharge of CHF. 

Study Design: Case-crossover, 

control exposures same mo and 

day of wk, controlling for season 

by design. 

Statistical Analysis: Conditional 

logistic regression 

N: 55,019 admissions, including 

repeat admissions, 86% 

admitted #5 times 

Age groups analyzed: 65+ yrs 

(Medicare recipients) 

Covariates: Temperature and 

pressure. Effect modification by 

age, gender, secondary 

diagnosis arrhythmias, atrial 

fibrillation, COPD, hypertension, 

type 2 diabetes, AMI within 30 

days, angina pectoris, IHD, acute 

respiratory infection. 

Statistical Software: SAS 

Lag(s): 0-3 

NO2 24-h avg (ppb): 

Mean (SD): 26.48 

(8.02) 

5th: 15.10 

25th: 20.61 

Median: 25.70 

75th: 31.30 

95th: 4102 

# Stations: 2 

PM10 (0.64) 

CO (0.70) 

O3 (-0.04) 

SO2 (0.52) 

Results reported for the percent increase 

in admissions for an increment of NO2

equivalent to one IQR (11 ppb). 

CHF, single-pollutant model 

4.22 (2.61, 5.85), lag 0 

CHF, two-pollutant model 

4.05 (1.83, 6.31), adjusted for PM10 

-0.37 (-2.59, 1.89), adjusted for CO 

3.73 (2.10, 5.39), adjusted for O3

3.79 (1.93, 5.67), adjusted for SO2

CHF admission was 3x higher among 

those with history of MI. 
6-87 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS 

& MONITORING 

STATIONS 

COPOLLUTANTS 

(CORRELATIONS)

EFFECTS: RELATIVE RISK OR 

PERCENT CHANGE & 

CONFIDENCE INTERVALS ([95% 

LOWER, UPPER]) 

Zanobetti and 

Schwartz (2006) 

Boston, MA 

1995-1999 

Outcome(s) (ICD9): MI 410. 

Admissions through the 

emergency room from Medicare 

claims. 

Age group analyzed: 65+ yrs 

Study Design: Case-crossover, 

control days matched yr, mos 

and temperature 

Statistical Analysis: Conditional 

logistic regression 

N: 15,578 

Covariates: Temperature 

(regression spline), day of wk 

Seasons: Hot (Apr-Sept) and 

cold 

Software: SAS 

Lags: 0, 0-1 previous day avg 

NO2 24-h avg ppb 

5th: 12.59 

25th: 18.30 

Median: 23.20 

75th: 29.13 

95th: 

90th-10th: 20.41 

# Stations: 4 

O3 (-0.14) 

BC (0.70) 

CO (0.67) 

PM2.5 (0.55) 

PM non-traffic (0.14) 

(residuals from model 

of PM2.5 regressed on 

BC) 

Results reported for percent increase in 

admissions for incremental increase in 

NO2 equivalent to the 90th-10th 

percentiles (20.41 or 16.80 for 0-1, 

previous day avg). 

MI 

10.21 (3.82, 15.61), lag 0 

12.67 (5.82, 18.04), lag 0-1, previous day 

avg 

Results suggest traffic exposure is 

responsible for the observed effect. 

Effects more pronounced in the summer 

season. 

*Default GAM 

AMI Acute Myocardial Infarction 

ARR Arrhythmia 

BC Black Carbon 

COH coefficient of haze 

CP Course Particulate 

CVD Cardiovascular Disease 

EC Elemental Carbon 

FP Fine Particulate 

HS Hemorrhagic Stroke 

ICD9 International Classification of Disease, 9th Revision 

IHD Ischemic Heart Disease 

IS ischemic stroke 

MI Myocardial Infarction 

OC Organic Carbon 

OHC Oxygenated Hydrocarbons 

PERI Peripheral Vascular and Cerebrovascular Disease 

PM Particulate Matter 

PIH primary intracerebral hemorrhage 

PNC Particle Number Concentration 

SHS Subarachnoid hemorrhagic stroke 

TP Total Particulate 

UBRE Unbiased Risk Estimator 
6-88 

Table AX6.3-7. Human Health Effects of Oxides of Nitrogen: CVD Hospital Admissions 

and Visits: Australia and New Zealand 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

(CORRELATIONS) 

EFFECTS: RELATIVE RISK OR 

PERCENT CHANGE & CONFIDENCE 

INTERVALS ([95% LOWER, UPPER]) 

Barnett et al. 

(2006) 

Australia and New 

Zealand: 

Brisbane, 

Canberra, 

Melbourne, Perth, 

Sydney 

Period of Study: 

1998-2001 

Outcome(s) (ICD9): All CVD 

390-459; ARR 427; Cardiac 

disease 390-429; Cardiac 

failure 428; IHD 410-413; MI 

410; Stroke 430-438. 

Ages groups analyzed: 15-64 

yrs, ≥65yrs 

Study Design: Time stratified, 

case-crossover, multicity study 

# of Hospitals: All ER 

admissions from state 

government health 

departments 

Statistical Analyses: Random 

effects meta analysis, 

heterogeneity assessed using 

I2 statistic. 

Covariates: Matched analysis 

controlling for long-term trend, 

seasonal variation, and 

respiratory epidemics. 

Temperature (current-previous 

day) and relative humidity, 

pressure, extremes of hot and 

cold, days of wk, holidays, day 

after holiday, rainfall in some 

models. Matched on 

copollutants. 

Statistical Package: SAS 

Lag: 0-3 

NO2 (ppb), Mean 

(range) 

Auckland 

1-h max: 19.1 (4.2-

86.3) 

24-h avg: 10.2 (1.7-

28.9) 

Brisbane 

1-h max: 17.3 (4-44.1) 

24-h avg: 7.6 (1.4-

19.1) 

Canberra 

1-h max: 17.9 (0-53.7) 

24-h avg: 7.0 (0-22.5) 

Christchurch 

1-h max: 15.7 (1.2-

54.6) 

24-h avg: 7.1 (0.2-

24.5) 

Melbourne 

1-h max: 23.2 (4.4-48) 

24-h avg: 11.7 (2-29.5) 

Perth 

1-h max: 21.3 (4.4-48) 

24-h avg: 9.0 (2-23.3) 

Sydney 

1-h max: 22.6 (5.2-

51.4) 

24-h avg: 11.5 (2.5-

24.5) 

24 h avg IQR: 5.1 

# of Stations: 1-13 

depending on the city 

PM10 24 h 

CO 24 h 

SO2 24 h 

O3 8 h 

BS 24 h 

Matched analysis 

conducted to control 

for copollutants 

Results reported for % change in hospital 

admissions associated with one IQR 

increase in 24-h avg NO2, lag 0-1. 

Arrhythmia 

≥65: 0.4 (-1.8, 2.6) 

15-64: 5.1 (2.2, 8.1) 

Cardiac 

≥65: 3.4 (1.9, 4.9) 

15-64: 2.2 (0.9, 3.4) 

Cardiac failure 

≥65: 6.9 (2.2, 11.8) 

15-64: 4.6 (0.1, 6.1) 

IHD 

≥65: 2.5 (1.0, 4.1) 

15-64: 0.7 (-1.0, 2.4) 

MI 

≥65: 4.4 (1.0, 8.0) 

15-64: 1.7 (-1.1, 2.4) 

All CVD 

≥65: 3.0 (2.1, 3.9) 

15-64: 1.7 (0.6, 2.8) 

NO2 association became smaller when 

matched with CO. Authors hypothesize that 

NO2 is a good surrogate for PM, which may 

explain these associations. 
6-89 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

(CORRELATIONS) 

EFFECTS: RELATIVE RISK OR 

PERCENT CHANGE & CONFIDENCE 

INTERVALS ([95% LOWER, UPPER]) 

Simpson et al. 

(2005a) 

Australia 

(Brisbane, 

Melbourne, Perth, 

Sydney). 

Study Period: 

Jan 1996-Dec 

1999 

Outcome(s) (ICD9): Cardiac 

disease 390-429; IHD 410-413; 

stroke 430-438. 

Study Design: Time-series. 

Statistical analysis: APHEA2 

protocol, GAM (did not indicate 

use of stringent convergence 

criteria), GLM with natural 

splines, penalized splines. 

Random effects meta-analysis 

with tests for homogeneity. 

Age groups analyzed: All, 15-

64, 65+ 

Covariates: long-term trend, 

temperature, humidity, day of 

wk, holidays, influenza 

epidemics 

Software package: SPLUS, R 

Lag(s): 1-3 days 

NO 1-h max (ppb): 

Mean (range): 

Brisbane: 21.4 (2.1, 

63.3) 

Sydney: 23.7 (6.5, 

59.4) 

Melbourne: 23.7 (4.4, 

66.7) 

Perth: 16.3 (1.9, 41.0) 

PM10 24 h 

PM2.5

BS 24 h (0.29, 0.62) 

O3 1 h 

CO 8 h 

Not all correlations 

reported. NO2 affect 

attenuated slightly 

when modeled with BS 

but not with O3. 

May be confounding of 

NO2 effect by 

particulate. 

Single-city results reported for percent 

increase for an increment in 1-h max NO2

equivalent to one IQR. Pooled results 

reported for an increment of 1 ppb NO2. 

Cardiac 

All ages: 1.0023 (1.0016, 1.0030), lag 0-1 

15-64: 1.0015 (1.0006, 1.0025), lag 0 

≥65: 1.0018 (1.0011, 1.0025), lag 0-1 

IHD 

All ages: 1.0019 (1.0010, 1.0027) 

≥65: 1.0017 (1.0007, 1.0027) 

No effect observed/reported for stroke. 

Multipollutant results (65+ age group): 

Cardiac: 

1.0032 (1.0006, 1.0022), w/ BS, lag 0-1 

1.0032 (1.0024, 1.0039), w/ O3, lag 0-1 

Heterogeneity in CVD results among cities, 

probably due to different pollutant mixtures, 

may have affected the results. 

Hinwood et al. 

(2006) 

Perth, Australia 

Study Period: 

1992-1998 

Outcome(s): All CVD 

unscheduled admissions. 

Obtained from discharge 

records using ICD9 Codes. 

Age groups analyzed: All ages, 

65+ 

Study design: Case-crossover, 

time-stratified with 3-4 controls 

within same mo 

Statistical Analysis: conditional 

logistic regression 

N: 26.5 daily CVD admissions 

Seasons: Nov-Apr, May-Oct 

NO2 24-h (ppb) 

Mean: 10.3 

SD = 5 

10th percentile: 4.4 

90th percentile: 17.1 

NO2 1-h max (ppb) 

Mean: 24.8 

SD = 10.1 

10th percentile: 13.3 

90th percentile: 37.5 

# of Stations: 3 

O3 1 h, 8 h (-.06) 

CO 8 h (.57) 

BSP 24 h (.39) 

Results reported for OR per incremental 

increase of 1 ppb NO2. 

All CVD (estimated from graph) 

NO2 24 h 65+: 1.005 (1.001, 1.006), lag 1 

NO2 24 h all ages: 1.003 (1.001, 1.007), lag 

1 
6-90 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

(CORRELATIONS) 

EFFECTS: RELATIVE RISK OR 

PERCENT CHANGE & CONFIDENCE 

INTERVALS ([95% LOWER, UPPER]) 

alaludin et al. 

(2006) 

Sydney, Australia 

Period of Study: 

Jan 1997-Dec 

2001 

Outcome(s) (ICD9): All CVD 

390-459; cardiac disease 390-

429; IHD 410-413; and 

cerebrovascular disease or 

stroke 430-438; Emergency 

room attendances obtained 

from health department data. 

Age groups included: 65+ 

Study Design: Time-series, 

multicity APHEA2 Protocol. 

Statistical Analysis: GAM (with 

appropriate convergence 

criteria) and GLM Models. Only 

GLM presented. 

Lag: 0-3 

Covariates: Daily avg 

temperature and daily relative, 

humidity, long-term trends, 

seasonality, weather, day of 

wk, public school holidays, 

outliers and influenza 

epidemics. 

Dose response: Quartile 

analysis 

Season: Separate analyses for 

warm (Nov-Apr) and cool 

periods (May-Oct). 

NO2 daily 1-h avg 

Mean: 32.2 

SD = 7.4 

Min: 5.2 

Q1: 18.2 

Median: 23 

Q3: 27.5 

Max: 59.4 

# of Stations: 14 

BS 24-h avg (0.35) 

PM10 24-h avg (0.44) 

PM2.5 24-h avg (0.45) 

CO 8-h avg (0.55) 

O3 1-h avg (0.45) 

SO2 24-h avg (0.56) 

Two-pollutant models 

to adjust for 

copollutants 

Results reported for % change in hospital 

admissions associated with one IQR 

increase in 1-h avg NO2. 

All CVD 

2.32 (1.45, 3.19), lag 0 

0.45 (-0.52, 1.42), lag 1 

1.31 (0.28, 2.35), lag 0-1 

Cardiac Disease 

2.00 (0.81, 3.20), lag 0 

0.91 (-0.26, 2.09), lag 1 

1.78 (0.54, 3.04), lag 0-1 

IHD 

2.11 (0.34, 3.91), lag 0 

0.76 (-0.97, 2.52), lag 1 

1.73 (-0.10, 3.59), lag 0-1 

Stroke 

-1.66 (-3.80, 0.51) lag 0 

-1.11 (-3.19, 1.02), lag 1 

-1.68 (-3.90, 0.60), lag 0-1 

Effect of NO2 attenuated when CO was 

included in the model. NO2 effect most 

prominent during the cool season. 

Morgan et al. 

(1998a) 

Sydney, Australia 

Study Period: 

Jan 1990-Dec 

1994 

Outcome(s) (ICD9): Heart 

Disease 410, 413, 427, 428. 

Inpatient statistics database for 

New South Wales Health 

Department. 

Study Design: Time-series 

Statistical Analysis: Poisson 

regression, GEE 

# Hospitals: 27 

Covariates: Daily mean 

temperature, dew point 

temperature 

Lag(s): 0-2 days, cumulative 

Statistical Software: SAS 

NO2 24-h avg (ppb): 

Mean (SD): 15 (6) 

IQR: 11 ppb 

10th-90th: 17 

NO2 1-h max (ppb): 

Mean (SD): 29 (3) 

10-90th: 29 ppb 

NO2 24-h max: 52 

NO2 1-h max: 139 

# Stations: 3-14 

(1990-1994) 

O3 1-h max (-0.086) 

PM (0.533, 0.506) 

Correlations for 

24-h avg NO2

concentrations 

Multipollutant models 

Results reported as percent increase in 

admissions associated with an incremental 

increase in 1-h max NO2 and 24-h avg 

equivalent to the 10th-90th percentile. 

Heart Disease: 

24-h avg, lag 0 

All ages: 7.52 (5.21, 9.88) 

65+: 8.39 (5.41, 11.46) 

0-64: 5.81 (1.63, 10.17) 

1-h max, lag 0 

All ages: 6.08 (3.63, 8.59) 

65+: 6.71 (4.25, 9.23) 

0-64: 4.79 (1.18, 8.53) 

65+: 6.68 (3.61, 9.84) Particulate, O3

Results lost precision but did not change 

substantially when stratified by age or when 

24-h averaging time was used. 
6-91 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

(CORRELATIONS) 

EFFECTS: RELATIVE RISK OR 

PERCENT CHANGE & CONFIDENCE 

INTERVALS ([95% LOWER, UPPER]) 

Petroeschevsky 

et al. (2001) 

Brisbane, 

Australia 

Study Period: 

Jan 1987-Dec 

1994, 

2,922 days 

Outcome(s) (ICD9): CVD 390-

459. Hospital admissions, nonresidents excluded. 

Study Design: Time-series 

Statistical Analyses: Poisson 

regression, APHEA protocol, 

linear regression and GEEs 

Age groups analyzed: 15-64, 

65+ 

Covariates: Temperature, 

humidity, rainfall. Long-term 

trends, season, flu, day of wk, 

holidays. 

Statistical Software: SAS 

Lag(s): lag 0-4, 3-day avg, 5 

day avg 

NO2 1-h max (pphm) 

Summer 

Mean: 206 

Min: 0.35 

Max: 5.8 

Fall 

Mean: 2.56 

Min: 0.70 

Max: 5.85 

Winter 

Mean: 3.54 

Min: 0.35 

Max: 8.05 

Spring 

Mean: 3.12 

Min: 0.55 

Max: 15.58 

Overall 

Mean: 2.82 

Min: 0.35 

Max: 15.58 

BSP 

O3

SO2

Correlation between 

pollutants not reported. 

Results reported for RR for CVD emergency 

admissions associated with a one-unit 

increase in NO2 1-h max. 

CVD 15-64 yrs 

0.986 (0.968, 1.005), lag 3 

CVD 65+ yrs 

0.990 (0.977, 1.003) 

CVD all ages 

0.987 (0.976, 0.998) 

*Default GAM 

AMI Acute Myocardial Infarction 

ARR Arrhythmia 

BC Black Carbon 

COH coefficient of haze 

CP Course Particulate 

CVD Cardiovascular Disease 

EC Elemental Carbon 

FP Fine Particulate 

HS Hemorrhagic Stroke 

ICD9 International Classification of Disease, 9th Revision 

IHD Ischemic Heart Disease 

IS ischemic stroke 

MI Myocardial Infarction 

OC Organic Carbon 

OHC Oxygenated Hydrocarbons 

PERI Peripheral Vascular and Cerebrovascular Disease 

PM Particulate Matter 

PIH primary intracerebral hemorrhage 

PNC Particle Number Concentration 

SHS Subarachnoid hemorrhagic stroke 

TP Total Particulate 

UBRE Unbiased Risk Estimator 
6-92 

Table AX6.3-8. Human health effects of oxides of nitrogen: cvd hospital admissions and visits: 

Europe 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN 

LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

(CORRELATIONS) 

EFFECTS: RELATIVE RISK OR 

PERCENT CHANGE & 

CONFIDENCE INTERVALS ([95% 

LOWER, UPPER]) 

Ballester et al. 

(2006) 

Multicity, Spain: 

Barcelona, Bilbao, 

Castellon, Gijon, 

Huelva, Madrid, 

Granada, Oviedo, 

Seville, Valencia, 

Zaragoza 

Period of Study: 

1995/1996-1999, 

N = 1,096 day 

Outcome(s) (ICD9): All CVD 390-

459; Heart diseases 410-

414,427,428. Emergency admission 

from hospital records. Discharge 

data used. 

Study Design: Time-series, metaanalysis to pool cities 

N: daily mean admissions reported 

by city 

Statistical Analyses: Poisson 

regression and GAM, with stringent 

convergence criteria, meta-analysis 

with fixed effect model. Tested 

linearity by modeling pollutant in 

linear and non-linear way (spline 

smoothing). Linear model provided 

best results 55% of time but used in 

all cases to facilitate comparability. 

Covariates: temperature, humidity 

and influenza, day of wk unusual 

events, seasonal variation and trend 

of the series 

Seasons: Hot: May to Oct; 

Cold: Nov to Apr 

Statistical Package: SPLUS 

Lag: 0-3 

NO2 24-h avg 

(μg/m2): 

Mean: 51.5 

10th percentile: 

29.5 

90th percentile: 

74.4 

# of Stations: 

Depends on the 

city 

Correlation 

among stations: 

NR 

CO 8-h max (0.58) 

O3 8-h max (-0.03) 

SO2 24 h (0.46) 

BS 24 h (0.48) 

TSP 24 h (0.48) 

PM10 24 h (0.40) 

Two-pollutant models 

used to adjust for 

copollutants. 

Results reported for % change in hospital 

admissions associated with 10 μg/m2 

increase in NO2. 

All CVD 

0.38% (0.07%, 0.69%), lag 0-1 

Heart Diseases: 

0.86% (0.44%, 1.28%) 

Effect of NO2 was diminished in twopollutant models. 
6-93 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN 

LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

(CORRELATIONS) 

EFFECTS: RELATIVE RISK OR 

PERCENT CHANGE & 

CONFIDENCE INTERVALS ([95% 

LOWER, UPPER]) 

Lanki et al. (2006) 

Europe (Augsburg, 

Helsinki, Rome, 

Stockholm) 

Study period: 

1992-2000 

Outcome(s) (ICD9): AMI 410. 

Ascertained from discharge records 

or AMI registry data depending on 

the city. 

Study Design: Time-series 

Statistical Analysis: Poisson 

regression, for non-linear 

confounders – penalized splines in 

GAM chosen to minimize UBRE 

score. Random-effects model for 

pooled estimates. 

N: 26,854 hospitalizations 

Statistical Software: R package 

Covariates: Barometric pressure, 

temperature, humidity. 

Lag(s): 0-3 day 

NO2 (μg/m3

) 

Augsburg 

25th: 40.2 

50th: 49.2 

75th: 58.9 

98th: 88.7 

Barcelona 

25th: 34.8 

50th: 45.0 

75th: 60.0 

98th: 86.0 

Helsinki 

25th: 21.8 

50th: 28.7 

75th: 37.6 

98th: 64.7 

Rome 

25th: 61.9 

50th: 70.6 

75th: 80.4 

98th: 102.5 

Stockholm 

25th: 16.3 

50th: 22.2 

75th: 28.6 

98th: 45.9 

PM10 (0.29, 0.64) 

CO (0.43, 0.75) 

O3 (0.17, 0.38) 

Range in correlations 

depends on the city. 

Two-pollutant models 

for PNC with O3 and 

PM10 only. 

Results reported as RR associated with an 

incremental increase in NO2 equivalent to 

the IQR (8 μg/m2). 

Pooled results for 5 Cities: 

First MI: 

0.996 (0.988, 1.015), lag 0 

0.998 (0.986, 1.010), lag 1 

1.003 (0.994, 1.011), lag 2 

1.001 (0.989, 1.014), lag 3 

No significant results observed for analyses 

stratified by age or season for lag 0/1. 
6-94 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN 

LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

(CORRELATIONS) 

EFFECTS: RELATIVE RISK OR 

PERCENT CHANGE & 

CONFIDENCE INTERVALS ([95% 

LOWER, UPPER]) 

Von Klot et al. 

(2005) 

Europe (Augsburg, 

Barcelona, 

Helsinki, Rome, 

Stockholm) 

Study Period: 

1992-2000 

Outcome(s) (ICD9): Re-admission for 

AMI 410; angina pectoris 411 and 

413; Cardiac diseases including AMI 

angina pectoris, dysrhythmia (427), 

heart failure (428). Hospital 

admissions database used to identify 

cases. 

Population: Incident cases of MI 

during 1992-2000 among those ≥35 

yrs old. 

N Augsburg: 1560 

N Barcelona: 1134 

N Helsinki: 4026 

N Rome: 7384 

N Stockholm: 7902 

Study Design: Prospective Cohort 

Statistical Analyses: Poisson 

regression, at risk period from the 

29th day after the index event until 

the event of interest, death, 

migration, or loss to follow-up. GLM 

models, penalized spline functions 

for continuous confounders. City 

results pooled using random-effects 

model. Heterogeneity assessed. 

Sensitivity analyses conducted 

varying the smooth functions, 

convergence criteria, and how 

confounders were specified. 

Statistical Software: R package 

Covariates: Daily mean temperature, 

dew point temperature, barometric 

pressure, relative humidity, vacations 

or holidays. 

Lag: 0-3 days 

NO2 24-h avg 

(μg/m2): 

Augsburg 

Mean: 49.6 

5th: 30 

25th: 39.7 

75th: 57.2 

95th: 75.3 

Barcelona 

Mean: 47.7 

5th: 18 

25th: 34.0 

75th: 60 

95th: 83 

Helsinki 

Mean: 30.1 

5th: 13 

25th: 21.2 

75th: 36.7 

95th: 52.9 

Rome 

Mean: 15.8 

5th: 5.4 

25th: 10.1 

75th: 21.7 

95th: 25.9 

Stockholm 

Mean: 22.8 

5th: 10.3 

25th: 16 

75th: 28 

95th: 39.4 

# Stations: 1-5 

CO 24 h 

(0.44, 0.75) 

O3 8 h 

(-0.2, -0.13) 

PM10 (.29, .66) 

PNC (.44, .83) 

Two-pollutant models 

but NO2, CO, and PNC 

not modeled together 

because they were too 

highly correlated. 

Results reported for RR for incremental 

increases in same day NO2 equivalent to 

the mean of the city specific IQR’s multiplied 

by 0.05 (8 μg/m3

). Pooled results are below: 

MI 

1.028 (0.997, 1.060), lag 0 

Angina Pectoris 

1.032 (1.006, 1.058), lag 0 

Cardiac Diseases 

1.032 (1.014, 1.051), lag 0 

Two-pollutant models show that the effect of 

NO2 independent of PM10 and O3. Traffic 

exhaust may be associated with cardiac 

re-admission. 
6-95 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN 

LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

(CORRELATIONS) 

EFFECTS: RELATIVE RISK OR 

PERCENT CHANGE & 

CONFIDENCE INTERVALS ([95% 

LOWER, UPPER]) 

Andersen et al. 

(2007a) 

Copenhagen, 

Denmark 

Study Period: 

1999-2004 

Outcome(s) (ICD10): angina pectoris 

I20; acute and subsequent MI I21-

22; other acute IHD I24; chronic IHD 

I25; pulmonary embolism I26; 

cardiac arrest I46; cardiac 

arrhythmias I48-49; hear failure I50. 

Hospital admissions from Danish 

Hospital Register. 

# Hospitals: 9 (within 15 km of 

monitoring station) 

Study Population: 65 + 

Catchment area: 1.5 million 

Study Design: Time-series 

Statistical Analysis: Principal 

components analysis for source 

apportionment. Poisson GAM, 

smoothing splines for weather, longterm trends/seasonality, indicator 

variables for influenza, holidays 

Software: mgcv package, R 

Lag(s): 0-5 d, 0-3 d avg 

24-h avg NO2

(ppb) 

Mean (SD): 12 

(5) 

25th: 8 

75th: 15 

IQR: 7 

PM10 (0.45) 

CO (0.74) 

Source Specific PM10 

Biomass (0.41) 

Secondary (0.43) 

Oil (0.42) 

Crustal (0.24) 

Sea Salt (-0.19) 

Vehicle (0.65) 

Results reported for RR associated with an 

incremental increase in NO2 equivalent to 

one IQR (7 ppb). 

Single-pollutant 

1.013 (0.993, 1.033), lag 0-3 avg 

2-pollutant, NO2 with PM10 

1.000 (0.975, 1.026) 

Andersen et al. 

(2007b) 

Copenhagen, 

Denmark 

Study Period: 

2001-2004 

Outcome(s) (ICD10): Angina pectoris 

I20; acute and subsequent MI I21-

22; other acute IHD I24; chronic IHD 

I25; pulmonary embolism I26; 

cardiac arrest I46; cardiac 

arrhythmias I48-49; heart failure I50. 

Hospital admissions from Danish 

Hospital Register. 

# Hospitals: 9 (within 15 km of 

monitoring station) 

Study Population: 65 + 

Catchment area: 1.5 million 

Study Design: Time-series 

Statistical Analysis: Poisson GAM, 

smoothing splines for weather, longterm trends/seasonality, indicator 

variables for influenza, holidays 

Software: mgcv package, R 

Lag(s): 0-5 d, 0-3 d avg 

24-h avg NO2

(ppb) 

Mean (SD): 11 

(5) 

25th: 8 

50th: 11 

75th: 14 

99th: 28 

IQR: 6 

24-h avg NOX

(ppb) 

Mean (SD): 15 

(8) 

25th: 9 

50th: 12 

75th: 18 

99th: 46 

IQR: 9 

24-h avg NOX

curbside (ppb) 

Mean (SD): 83 

(36) 

25th: 58 

50th: 76 

75th: 103 

99th: 207 

IQR: 45 

NCtot w/ NO2 (0.68) 

NCtot w/ NOX (0.66) 

NCa57 w/ NO2 (0.57) 

Results reported for associations of a 6-ppb 

increase equivalent to one IQR of NO2 with 

all CVD. 

One-pollutant model: 

1.0 (0.98, 1.03), lag 0-3 

Two-pollutant model with NCtot 

1.0 (0.96, 1.03) 
6-96 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN 

LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

(CORRELATIONS) 

EFFECTS: RELATIVE RISK OR 

PERCENT CHANGE & 

CONFIDENCE INTERVALS ([95% 

LOWER, UPPER]) 

Atkinson et al. 

(1999b) 

London, England 

Period of Study: 

1992-1994, 

N = 1,096 day 

Outcome(s) (ICD9): All CVD 390-

459; IHD 410-414. Emergency 

admissions obtained from the 

Hospital Episode Statistics (HES) 

database. 

Ages groups analyzed: 0-14 yr, 15-

64 yr, 0-64 yr, 65+ yr, 65 74 yr, 75+ 

yr 

Study Design: Time-series, hospital 

admission counts 

N: 189, 109 CVD admissions 

Catchment area: 7 million residing in 

1600 Km2 area of Thames basin. 

Statistical Analyses: APHEA protocol, 

Poisson regression 

Covariates: Adjusted long-term 

seasonal patterns, day of wk, 

influenza, temperature, humidity 

(compared alternative methods for 

modeling meteorological including 

linear, quadratic, piece-wise, spline) 

Seasons: Warm season Apr-Sep, 

cool season remaining mos, 

interactions between season 

investigated 

Dose response investigated: Yes, 

bubble charts presented 

Statistical Package: SAS 

Lag: 0-3 

1-h max (ppb) 

Mean: 50.3 

SD = 17.0 

Min: 22.0 

Max: 224.3 

10th-90th 

percentile: 36 

# of Stations: 3, 

results averaged 

across stations 

Correlation 

among stations: 

0.7-0.96 

PM10 24 h 

CO 24 h 

SO2 24 h 

O3 8 h 

BS 24 h 

Correlations of NO2

with CO, SO2, O3, BS 

ranged from 0.6-0.7 

Correlation of NO2 with 

O3 negative 

Two-pollutant models 

used adjust for 

copollutants 

Results reported for % change in hospital 

admissions associated with 10th-90th 

percentile increase in NO2 (36 ppb) 

All CVD 

Ages 0-64: 1.20% (-0.62%, 3.05%), lag 0 

Ages 65+: 1.68% (0.32%, 3.06%), lag 0 

IHD 

Ages 0-64: 1.53% (-1.22%, 4.37%), lag 0 

Ages 65+: 3.03% (0.87%, 5.24), lag 0 

NO2 was associated with increased CVD 

admissions for all ages but this association 

was stronger among those 65+ yrs old. 

Similar increase associated with IHD among 

those 65+ yrs old. 

Monitors close to roadways were not used in 

the study. Correlations for NO2 between 

urban monitoring sites were high. Authors 

suggest that the pollution levels are uniform 

across the study area. Authors did not 

investigate the interaction between 

meteorological variables and air pollution. In 

two-pollutant models, O3 had little impact on 

NO2. BS moderated the association of NO2

with CVD among the 65+ age group. 

Suggestion that NO2 associations were nonlinear. 

Ballester et al. 

(2001) 

Valencia, Spain 

Period of Study: 

1992-1996 

Outcome(s) (ICD9): All CVD 390-

459; heart diseases 390-459; 

cerebrovascular diseases 430-438. 

Admissions from city registry – 

discharge codes used. 

Study Design: Time-series 

N: 1080 CVD admissions 

# of Hospitals: 2 

Catchment area: 376,681 inhabitants 

of Urban Valencia 

Statistical Analyses: Poisson 

regression, GAM with parametric 

smoothers, APHEA/ Spanish 

EMECAM protocol. Both Linear and 

non parametric model, including a 

loess term was fitted, departure from 

linearity assess by comparing 

deviance of both models. 

Covariates: Long-term trend and 

seasonality, temperature and 

humidity, wk days, flu, special 

events, air pollution. 

Seasons: Hot season May to Oct; 

Cold season Nov to Apr 

Statistical Package: SAS 

Lag: 0-4 

1-h max (μg/m2) 

Mean: 116.1 

SD = NR 

Min: 21.1 

Max: 469.0 

Median: 113.2 

# of Stations: 14 

manual, 

5 automatic 

Correlation 

among stations: 

0.3-0.62 for BS, 

0.46-0.78 for 

gaseous 

pollutants 

CO 24 h (0.03) 

SO2 24 h (0.33) 

O3 8 h (-0.26) 

BS (0.33) 

Two-pollutant models 

used to adjust for 

copollutants. 

Results reported for RR corresponding to a 

10 μg/m2 increase in NO2 

All CVD 

1.0302 (1.0042, 1.0568), lag 0 

Heart Disease 

1.0085 (0.9984, 1.0188), lag 2 

Cerebrovascular Disease 

1.0362 (1.0066, 1.0667), lag 4 

Clear association of NO2 with 

cerebrovascular disease observed. 

Association persisted after Inclusion of BS 

and SO2 in two-pollutant models with NO2. 

Cases of digestive disorders served as a 

control group - null association with NO2

observed. 
6-97 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN 

LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

(CORRELATIONS) 

EFFECTS: RELATIVE RISK OR 

PERCENT CHANGE & 

CONFIDENCE INTERVALS ([95% 

LOWER, UPPER]) 

D’Ippoliti et al. 

(2003) 

Rome, Italy 

Study Period: Jan 

1995- Jun 1997 

Outcome(s) (ICD): AMI 410 (first 

episode). Computerized hospital 

admission data. 

Study Design: Case-crossover, timestratified, control days within same 

mo falling on the same day. 

Statistical Analyses: Conditional 

logistic regression, examined 

homogeneity across co-morbidity 

categories 

N: 6531 cases 

Age groups analyzed: 18-64 yrs, 

65-74 yrs, ≥75 

Season: Cool: Oct-Mar; 

Warm: Apr-Sep 

Lag(s): 0-4 day, 0-2 day cum avg 

Dose Response: OR for increasing 

quartiles presented and p-value for 

trend. 

NO2 24 h 

(μg/m3

) 

Mean (SD): 86.4 

(15.8) 

25th: 74.9 

50th: 86.0 

75th: 96.9 

IQR: 22 

# Stations: 5 

TSP 24 h (0.37) 

SO2 24 h (0.31) 

CO 24 h (0.03) 

No multipollutant 

models 

Results presented for OR associated with 

incremental increase in NO2 equivalent to 

one IQR. 

AMI 

1.026 (1.002, 1.052), lag 0 

1.026 (0.997, 1.057), lag 0-2 

Association observed for NO2 but TSP 

association more consistent. Authors think 

that TSP, CO, and NO2 cannot be 

distinguished from traffic-related pollution in 

general. 

Llorca et al. (2005) 

Torrelavega, Spain 

Study period: 

1992-1995 

Outcome(s) (ICD): CVD (called 

cardiac in paper) 390-459. 

Emergency admissions, excluding 

non residents. Obtained admissions 

records from hospital admin office. 

Study design: Time-series 

Statistical analyses: Poisson 

regression, APHEA protocol 

Covariates: rainfall, temperature, 

wind speed direction 

N: 18,137 admissions 

Statistical software: STATA 

Lag(s): not reported 

NO2 24 h μg/m3

Mean (SD): 21.3 

(16.5) 

TSP (-0.12) 

SO2 (0.588) 

SH2 (0.545) 

NO (0.855) 

Multipollutant models 

Results reported for RR of hospital 

admissions for 100 μg/m3

 increase in NO2. 

CVD admissions: 

1.27 (1.14, 1.42), 1-pollutant model 

1.10 (0.92, 1.32), 5-pollutant model 

Effect of NO2 diminished in multipollutant 

model. 

Pantazopoulou 

et al. (1995) 

Athens, Greece 

Study Period: 

1988 (Winter and 

Summer) 

Outcome(s): Cardiac Disease ICD 

codes not provided. Cases 

ascertained from National Center for 

Emergency Service database. Cases 

diagnosed at time of admission so 

they are ED visits and were not 

necessarily admitted to the hospital. 

Study design: Time-series 

Statistical Analyses: Linear 

regression (not well described) 

Covariates: Dummy variables for 

winter mos with Jan as referent. 

Dummy variables for summer mos 

with Apr as referent. Day of the wk, 

holidays, temperature, relative 

humidity, 

N: 25,027 cardiac admissions. 

Lag(s): NR 

NO2 1-h max 

(μg/m3): Winter 

Mean (SD): 94 

(25) 

5th: 59 

50th: 93 

95th: 135 

Summer 

Mean (SD): 111 

(32) 

5th: 65 

50th: 108 

95th: 173 

# Stations: 2 

CO, BS 

No correlations 

provided 

Results reported for regression coefficients 

based on an incremental increase in NO2 of 

76 μg/m3 in winter and 108 μg/m3 in summer 

(5th to 95th percentile). 

Winter (regression coefficient) 

11.2 (3.3, 19.2) 

Summer (regression coefficient) 

-0.06 (-6.6, 6.5) 
6-98 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN 

LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

(CORRELATIONS) 

EFFECTS: RELATIVE RISK OR 

PERCENT CHANGE & 

CONFIDENCE INTERVALS ([95% 

LOWER, UPPER]) 

Poloniecki et al. 

(1997) 

London, UK 

Study Period: 

Apr 1987-Mar 

1994, 7 yrs 

Outcome(s): All CVD 390-459; MI 

410; Angina pectoris 413; other IHD 

414; ARR 427; congestive heart 

failure 428; cerebrovascular disease 

430-438. Hospital Episode Statistics 

(HES) data on emergency hospital 

admissions. 

Study Design: Time-series 

N: 373, 556 CVD admissions 

Statistical Analyses: Poisson 

regression, linear and quadratic 

terms to adjust for long-term trends. 

APHEA protocol 

Covariates: long-term trends, 

seasonal variation, day of wk, 

influenza, temperature and humidity. 

Season: Warm: Apr-Sep; 

Cool: Oct-Mar 

Lag: 0-1 day 

NO2 24 h ppb: 

Min: 8 

10%: 23 

Median: 35 

90%: 53 

Max: 198 

Black Smoke 

CO 24 h 

SO2 24 h 

O3 8 h 

Correlations between 

pollutants high but not 

specified. 

Results expressed as a relative rate (RR) for 

an incremental increase of NO2 equivalent to 

30 ppb (10th-90th percentile). 

AMI: 1.0274 (1.0084, 1.0479) 

Angina Pectoris: 1.0212 (0.9950, 1.0457) 

Other IHD: 0.99 (0.0067, 1.0289) 

Cardiac ARR: 1.0274 (1.0006, 1.0984) 

Heart Failure: 0.9970 (0.9769, 1.0194) 

Cerebrovascular Disease: 0.9851 

(0.9684, 1.0045) 

Other Circulatory: 1.0182 (1.0000, 1.0398) 

All CVD: 1.0243 (1.0054, 1.0448) 

No attenuation of NO2 association with MI in 

two-pollutant model (cool season). 

Pönkä and 

Virtanen (1996) 

Helsinki, Finland 

Study Period: 

1987-1989, 3 yrs 

Outcome(s) (ICD9): IHD 410-414; MI 

410; TIA 411; Cerebrovascular 

diseases 430-438; Cerebral 

ischemia due to occlusion of 

extracerebral vessels 433; Cerebral 

ischemia due to occlusion of cerebral 

vessels 434; Transient ischemic 

cerebral attack 435. Case 

ascertainment was for both 

emergency admission and hospital 

admissions – done via registry 

system. 

Study Design: Time-series 

Statistical Analyses: Poisson 

regression, pollutant concentrations 

log transformed 

N: 12,664 all IHD admissions; 7005 

IHD ED admissions; 7232 

cerebrovascular hospital admissions; 

3737 cerebrovascular ED 

admissions. 

Covariates: Weather, day of wk, 

long-term trends, influenza 

Lag(s): 1-7 days 

NO2 8 h (μg/m3

) 

Mean (SD): 39 

(16.2) 

Range: 4, 170 

NO 8 h μg/m3

Mean (SD): 91 

(61) 

Range: 7, 467 

# Stations: 2 

SO2 8 h 

NO 8 h 

TSP 8 h 

O3 8 h 

NO2 highly correlated 

with SO2 and TSP. 

Results reported are regression coefficients 

and standard errors (SE). 

NO2 with ED admissions for transient short 

term ischemic attack 

-0.056 (0.105), p = 0.59, lag 1 

NO2 with ED admissions for cerebrovascular 

disease 

-0.025 (0.057), p = 0.657, lag 1 

NO with IHD, all admissions 

0.097 (0.023), p < 0.001, lag 1 

NO with IHD, ED admissions 

0.111 (0.030), p < 0.001, lag 1 

Significant increase in admissions for 

transient short-term ischemic attack and 

cerebrovascular diseases for lag 6 

associated with NO2 exposure. 

Prescott et al. 

(1998) * 

Edinburgh, UK 

Study period: 

Oct 1992-June 

1995 

Outcome(s) (ICD9): Cardiac and 

cerebral ischemia 410-414, 426-429, 

434-440. Extracted from Scottish 

record linkage system. 

Study Design: Time-series 

Statistical Analysis: Poisson, log 

linear regression models 

Age groups analyzed: <65, 65+yrs 

Covariates: Seasonal and wkday 

variation, temperature, and wind 

speed. 

Lag(s): 0, 1, 3 day moving avg 

NO2 24 h (ppb) 

Mean (SD): 26.4 

(7.0) 

Range: 9, 58 

IQR: 10 ppb 

O3, 24 h 

PM, 24 h 

SO2, 24 h 

CO, 24 h 

Correlations not 

reported. 

Results reported for percent change in 

admissions based on an incremental 

increase in NO2 equivalent to the IQR of 

10 ppb. 

<65 yrs, CVD admissions 

-0.05 (-5.2, 4.5), 3 day moving avg 

65+ yrs, CVD admissions 

-0.9 (-8.2, 7.0), 3 day moving avg 

Data for lag 1 not presented. 
6-99 

REFERENCE, 

STUDY 

LOCATION, & 

PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN 

LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

(CORRELATIONS) 

EFFECTS: RELATIVE RISK OR 

PERCENT CHANGE & 

CONFIDENCE INTERVALS ([95% 

LOWER, UPPER]) 

Yallop et al. (2007) 

London, England 

Study Period: 

Jan. 1988-Oct. 

2001, >1400 days 

Outcome(s): Acute pain in Sickle Cell 

Disease (HbSS, HbSC, HbS/≥0, 

thalassaemia, HbS/≥+). Admitted to 

hospital for at least one night. 

Study Design: Time-series 

Statistical Analyses: Crosscorrelation function 

N: 1047 admissions 

Covariates: No adjustment made in 

analysis, discussion includes 

statement that the effects of weather 

variables and copollutants are interrelated. 

Statistical Package: SPSS 

Lag(s): 0-2 days 

Dose response: Quartile analysis, 

graphs presented, ANOVA 

comparing means across quartiles. 

NR O3, CO, NO, NO2, 

PM10: 

daily avg used for all 

copollutants 

High O3 levels 

correlate with low NO, 

low CO, increased 

wind speeds and low 

humidity and each 

was associated with 

admission for pain. 

Not possible to 

distinguish 

associations in 

analysis. 

Results reported are cross-correlation 

coefficients. NO inversely correlated with 

admission for acute pain in SCD. 

CFF: -0.063, lag 0 

*Default GAM 

AMI Acute Myocardial Infarction 

ARR Arrhythmia 

BC Black Carbon 

COH coefficient of haze 

CP Course Particulate 

CVD Cardiovascular Disease 

EC Elemental Carbon 

FP Fine Particulate 

HS Hemorrhagic Stroke 

ICD9 International Classification of Disease, 9th Revision 

IHD Ischemic Heart Disease 

IS ischemic stroke 

MI Myocardial Infarction 

OC Organic Carbon 

OHC Oxygenated Hydrocarbons 

PERI Peripheral Vascular and Cerebrovascular Disease 

PM Particulate Matter 

PIH primary intracerebral hemorrhage 

PNC Particle Number Concentration 

SHS Subarachnoid hemorrhagic stroke 

TP Total Particulate 

UBRE Unbiased Risk Estimator 
6-100 

Table AX6.3-9. Human health effects of oxides of nitrogen: cvd hospital admissions and visits: 

Asia 

REFERENCE, 

STUDY LOCATION, 

& PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

(CORRELATIONS)

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE & 

CONFIDENCE INTERVALS 

([95% LOWER, UPPER]) 

Chan et al. (2006) 

Taipai, Taiwan 

Period of Study: 

Apr 1997-Dec 2002, 2090 

days 

Outcome(s) (ICD9): 

Cerebrovascular disease 430-437; 

stroke 430-434; hemorrhagic 

stroke 430-432; ischemic stroke 

433-434. Emergency admission 

data collected from National 

Taiwan University Hospital. 

Ages groups analyzed: Age >50 

included in study 

Study Design: Time-series 

N: 7341 Cerebrovascular 

admissions among those >50 yrs 

old 

# of Hospitals: 

Catchment area: 

Statistical Analyses: Poisson 

regression, GAMs used to adjust 

for non-linear relation between 

confounders and ER admissions. 

Covariates: Time-trend variables: 

yr, mo, and day of wk, daily 

temperature difference, and dew 

point temperature. 

Linearity: Investigated graphically 

by using the LOESS smoother. 

Statistical Package: NR 

Lag: 0-3, cumulative lag up to 3 

days 

NO2 24-h avg (ppb): 

Mean: 29.9 

SD = 8.4 

Min: 8.3 

Max: 77.1 

IQR: 9.6 ppb 

# of Stations: 16 

Correlation among 

stations: NR 

PM10 24 h; r = 0.50 

PM2.5 24 h; r = 0.64 

CO 8-h avg; r = 0.77 

SO2 24 h; r = 0.64 

O3 1-h max; r = 0.43 

Two-pollutant models 

to adjust for 

copollutants. 

Results reported for OR for 

association of emergency 

department admissions with an 

IQR increase in NO2 (9.3 ppb) 

Cerebrovascular: 

1.032 (0.991, 1.074), lag 0 

Stroke: 

0.994 (0.914, 1.074), lag 0 

Ischemic stroke: 

1.025 (0.956, 1.094), lag 0 

Hemorrhagic stroke: 

0.963 (0.884, 1.042), lag 0 

No significant associations for NO2

reported. Lag 0 shown but similar 

null results were obtained for lags 

1-3. NO2 highly correlated with PM 

and CO. 

Chang et al. (2005) 

Taipei, Taiwan 

Study Period: 

1997-2001, 5 yrs 

Outcome(s) (ICD9): CVD 410-429. 

Daily clinic visits or hospital 

admission from computerized 

records of National Health 

Insurance. Discharge data. 

Source Population: 2.64 Million 

N: 40.8 admissions/day, 74,509/5 

yrs 

# Hospitals: 41 

Study Design: Case-crossover, 

referent day 1 wk before or after 

index day 

Statistical Analyses: Conditional 

logistic regression. 

Covariates: Same day 

temperature and humidity. 

Season: Warm/cool (stratified by 

temperature cutpoint of 20 oC) 

Lag(s): 0-2 days 

NO2 24-h avg (ppb): 

Mean: 31.54 

Min: 8.13 

25th: 26.27 

50th: 31.03 

75th: 36.22 

Max: 77.97 

# of Stations: 6 

CO 24-h avg 

O3 24-h avg 

SO2 24-h avg 

PM10 24-h avg 

Correlations not 

reported. 

Two-pollutant models 

to adjust for 

copollutants 

OR for the association of CVD 

admissions with an incremental 

increase in 24-h avg NO2

equivalent to one IQR, 9.95 ppb. 

Warm (≥20 oC) 

1.177 (1.150, 1.205), lag 0-2 

Cool (<20 oC) 

1.112 (1.058, 1.168), lag 0-2 

NO2 effect remained in all warm 

season two-pollutant models. 

Effect remained in cool season 

two-pollutant models with the 

exception of the model that 

included PM10. 
6-101 

REFERENCE, 

STUDY LOCATION, 

& PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

(CORRELATIONS)

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE & 

CONFIDENCE INTERVALS 

([95% LOWER, UPPER]) 

Hosseinpoor et al. (2005) 

Tehran, Iran 

Study period: 

Mar 1996-Mar 2001, 

5 yrs 

Outcome(s) (ICD9): Angina 

pectoris 413. Primary discharge 

diagnosis from registry databases 

or records. 

Study Design: Time-series 

Statistical Methods: Poisson 

regression 

# Hospitals: 25 

Covariates: Long-term trends, 

seasonality, temperature, humidity, 

holiday, post-holiday, day of wk. 

Lag(s): 0-3 

NO2 24-h avg (μg/m3) 

Mean (SD): 60.01 

(39.69) 

Min: 0.30 

25th: 29.39 

Median: 47.42 

75th: 84.55 

Max: 324.78 

NO2 CO O3 PM10 

Correlations not 

reported 

Results reported for relative risk in 

hospital admissions per increment 

of 10 μg/m3

 SO2. 

Angina 

1.00618 (1.00261, 1.00976), lag 1 

In a multipollutant model only CO 

(lag 1) was significantly associated 

with angina pectoris related 

hospital admissions. 

Lee et al. (2003a)* 

Seoul, Korea 

Study period: 

Dec 1997-Dec 1999, 

822 days, 184 days in 

summer 

Outcome(s) (ICD10): IHD: Angina 

pectoris 120; Acute or subsequent 

MI 121-123; other acute IHD 124. 

Electronic medical insurance data 

used. 

Study Design: Time-series 

Statistical Methods: Poisson 

regression, GAM with default 

convergence criteria. 

Age groups analyzed: all ages, 

64+ 

Covariates: long-term trends 

LOESS smooth, temperature, 

humidity, day of wk. 

Season: Presented results for 

summer (June, July, Aug) and 

entire period. 

Lag(s): 0-6 

NO2 24 h (ppb): 

5th: 16 

10th: 23.7 

Median: 30.7 

75th: 38.3 

95th: 48.6 

Mean (SD): 31.5 (10.3) 

IQR: 14.6 

PM10; r = 0.73, 0.74 

SO2; r = 0.72, 0.79 

O3; r = -0.07, 0.63 

CO; r = 0.67, 0.79 

Range depends on 

summer vs. entire 

period. 

Two-pollutant models 

Results reported for RR of IHD 

hospital admission for an 

incremental increase in NO2

equivalent to one IQR. 

64+, entire study period: 

1.08 (1.03, 1.14), lag 5 

64+, summer only: 

1.25 (1.11, 1.41), lag 5 

Results for lag 5 presented above. 

Lag 0 or 1 results largely null - 

presented graphically. Confounding 

by PM10 was not observed in 

these data using two-pollutant 

models. 

Tsai et al. (2003a) 

Kaohsiung, Taiwan 

Study period: 

1997-2000 

Outcome(s) (ICD9): All 

cerebrovascular 430-438; SHS 

430; PIH 431-432; IS 433-435; 

Other 436-438. Ascertained from 

National Health Insurance 

Program computerized admissions 

records. 

Study Design: Case-crossover 

Statistical Analysis: Conditional 

logistic regression. 

Statistical Software: SAS 

Seasons: ≥20 oC; <20 oC. 

N: 23,179 stroke admissions 

# Hospitals: 63 

Lag(s): 0-2, cumulative lag up to 2 

previous days 

24-h avg NO2 (ppb) 

Min: 6.25 

25th: 19.25 

Median: 28.67 

75th: 36.33 

Max: 63.40 

Mean: 28.17 

PM10 

SO2

CO 

O3

Results reported as OR for the 

association of admissions with an 

incremental increase of NO2

equivalent to the IQR of 17.1 ppb 

PIH admissions 

Warm: 1.56 (1.32, 1.84), lag 0-2 

Cool: 0.81 (0.0, 1.31), lag 0-2 

IS admissions: 

Warm: 1.55 (1.40, 1.71), lag 0-2 

Cool: 1.16 (0.81, 1.68), lag 0-2 

Effects persisted after adjustment 

for PM10, SO2, CO, and O3. 

PIH: 

1.31 (1.03, 1.66) NO2 w/ PM10 

1.66 (1.38, 2.00), NO2 w/ SO2

1.60 (1.25, 2.05) NO2 w/ CO 

1.51 (1.26, 1.80) NO2 w/ O3

IS: 

1.39 (1.20, 1.60) NO2 w/ PM10 

1.62 (1.45, 1.81), NO2 w/ SO2

1.54 (1.33, 1.79), NO2 w/ CO 

1.53 (1.37, 1.71), NO2 w/ O3
6-102 

REFERENCE, 

STUDY LOCATION, 

& PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

(CORRELATIONS)

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE & 

CONFIDENCE INTERVALS 

([95% LOWER, UPPER]) 

Wong et al. (1999) 

Hong Kong, China 

Study Period: 

1994-1995 

Outcome(s) (ICD9): CVD: 410-

417, 420-438, 440-444; CHF 428; 

IHD 410-414; Cerebrovascular 

Disease 430-438. Hospital 

admissions through ER 

departments via Hospital Authority 

(discharge data). 

Study Design: Time-series 

Statistical Analyses: Poisson 

regression, linear and quadradic 

terms for long-term trends, APHEA 

protocol 

# Hospitals: 12 

Covariates: Daily temperature, 

relative humidity day of wk, 

holidays, influenza, long-term 

trends (yr and seasonality 

variables). Interaction of pollutants 

with cold season examined. 

Season: Cold (Dec-Mar) 

Lag(s): 0-3 days 

NO2 24-h avg (μg/m3

) 

Min: 16.41 

25th: 39.93 

Median: 51.39 

75th: 51.39 

Max: 122.44 

PM10; r = 0.79 

SO2

O3

Range for other 

pollutants: r = 0.68, 

0.89. 

Results reported for RR 

associated with incremental 

increase in NO2 equal to 10 μg/m3

. 

CVD 

5-64 yrs: 1.008 (0.998, 1.018), lag 

0-1 

65+ yrs: 1.016 (1.009, 1.023), lag 

0-1 

All ages: 1.013 (1.007, 1.020), lag 

0-1 

CHF 

1.044 (1.25, 1.063), lag 0-3 

IHD 

1.010 (0.999, 1.020), lag 0-1 

Cerebrovascular Disease 

1.008 (0.998, 1.018), lag 0-1 

Interaction of NO2 with O3

observed 

Wong et al. (2002)* 

Hong Kong 

London 

Study Period: 

1995-1997 

Outcome(s) (ICD9): Cardiac 

disease 396-429; IHD 410-414. 

Admissions through the 

emergency department, general 

outpatient, or direct to inpatient 

wards. 

Study design: 

Statistical analysis: Poisson 

regression, GAMs, nonparametric 

smooth functions (LOESS) 

Covariates: 

Statistical Software: SPlus 

24-h avg NO2 

Hong Kong 

Mean (warm/cool): 55.9 

(48.1/63.8) 

Min: 15.3 

10th: 31.8 

50th: 53.5 

90th: 81.8 

Max: 151.5 

Hong Kong 

Mean (warm/cool): 64.3 

(62.6.1/66.1) 

Min: 23.7 

10th: 42.3 

50th: 61.2 

90th: 88.8 

Max: 255.8 

Hong Kong 

SO2; r = 0.37 

PM10; r = 0.82 

O3; r = 0.43 

London 

SO2; r = 0.71 

PM10; r = 0.68 

O3; r = -0.29 

Results reported for excess risk 

associated with a 10 μg/m3

 change 

in mean concentration 

Single-pollutant model. 

Hong Kong: 1.8 (1.2, 2.4), lag 0-1 

London: -0.1 (-0.6, 0.5), lag 0-1 

Multipollutant results 

Hong Kong: 

1.6 (1.0, 1.3), lag 0-1, adjusted for 

Ozone 

1.7 (0.8, 2.7), lag 0-1, adjusted for 

PM10 

1.6 (0.8, 2.4), lag 0-1, adjusted for 

SO2

London: 

0.1 (-0.5, 0.6), lag 0-1, adjusted for 

Ozone 

-0.4 (-1.2, 0.4), lag 0-1, adjusted 

for PM10 

-0.2 (-0.9, 0.5), lag 0-1, adjusted 

for SO2
6-103 

REFERENCE, 

STUDY LOCATION, 

& PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

(CORRELATIONS)

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE & 

CONFIDENCE INTERVALS 

([95% LOWER, UPPER]) 

Yang et al. (2004b) 

Kaohsiung, Taiwan 

Period of Study: 

1997-2000 

Outcome(s) (ICD9): All CVD: 410-

429 *(All CVD typically defined to 

include ICD9 codes 390-459) 

N: 29,661 

Study Design: Case-crossover 

Statistical Analysis: Poisson timeseries regression models, APHEA 

protocol 

# of Hospitals: 63 

Seasons: Authors indicate not 

considered because the 

Taiwanese climate is tropical with 

no apparent seasonal cycle 

Covariates: Stratified by warm 

(≥25o) and cold (<25o) days, 

temperature and humidity 

measurements included in the 

model 

Statistical Package: SAS 

Lag: 0-2 days 

24-h avg (ppb) 

Min: 6.25 

25%: 19.25 

50%: 28.67 

75%: 36.33 

Max: 63.40 

Mean: 28.17 

# of Stations: 6 

Correlation among 

stations: NR 

PM10 

CO 

SO2 

O3 8 

Two-pollutant models 

used to adjust for 

copollutants 

Correlations NR 

OR’s for the association of one 

IQR (17.08 ppb) increase in NO2

with daily counts of CVD hospital 

admissions are reported. 

All CVD (ICD9: 410-429), onepollutant model 

≥25o: 1.380 (1.246, 1.508) 

<25o: 2.215 (2.014, 2.437) 

All CVD (ICD9: 410-429), twopollutant models 

Adjusted for PM10: 

≥25o: 1.380 (1.246, 1.508) 

<25o: 2.215 (2.014, 2.437) 

Adjusted for SO2: 

≥25o: 1.149 (1.017, 1.299) 

<25o: 2.362 (2.081, 2.682) 

Adjusted for CO: 

≥25o: 1.039 (0.919, 1.176) 

<25o: 2.472 (2.138, 2.858) 

Adjusted for O3: 

≥25o: 1.159 (1.051, 1.277) 

<25o: 2.243 (2.037, 2.471) 

Association of CVD admissions 

with NO2 attenuated on warm days 

after adjustment for copollutants. 

Association persisted on cool 

days. Kaohsiung is the center of 

Taiwan’s heavy industry. 

Ye et al. (2001) 

Tokyo, Japan 

Study Period: 

Jul-Aug, 

1980-1995 

Outcome(s): Angina 413; Cardiac 

insufficiency 428; Hypertension 

401-405; MI 410. Diagnosis made 

by attending physician for hospital 

emergency transports. 

Age groups analyzed: 65+ yrs 

male and female 

Statistical analysis: GLM 

Covariates: Maximum 

temperature, confounding by 

season minimal since only 2 

summer mos included in analysis 

Statistical Software: SAS 

Lag(s): 1-4 days 

NO2 24-h avg (ppb) 

Min: 5.3 

Max: 72.2 

Mean (SD): 25.4 (11.4) 

O3; r = 0.183 

PM10; r = 0.643 

SO2; r = 0.333 

CO; r = 0.759 

Results reported for model 

coefficient and 95% CI. 

Angina: 

0.007 (0.004, 0.009) 

Cardiac insufficiency: 

0.006 (0.003, 0.01) 

MI: 

0.006 (0.003, 0.01) 
6-104 

REFERENCE, 

STUDY LOCATION, 

& PERIOD 

OUTCOMES, DESIGN, & 

METHODS 

MEAN LEVELS & 

MONITORING 

STATIONS 

COPOLLUTANTS 

(CORRELATIONS)

EFFECTS: RELATIVE RISK 

OR PERCENT CHANGE & 

CONFIDENCE INTERVALS 

([95% LOWER, UPPER]) 

* Default GAM 

AMI Acute Myocardial Infarction 

ARR Arrhythmia 

BC Black Carbon 

COH coefficient of haze 

CP Course Particulate 

CVD Cardiovascular Disease 

EC Elemental Carbon 

FP Fine Particulate 

HS Hemorrhagic Stroke 

ICD9 International Classification of Disease, 9th Revision 

IHD Ischemic Heart Disease 

IS ischemic stroke 

MI Myocardial Infarction 

OC Organic Carbon 

OHC Oxygenated Hydrocarbons 

PERI Peripheral Vascular and Cerebrovascular Disease 

PM Particulate Matter 

PIH primary intracerebral hemorrhage 

PNC Particle Number Concentration 

SHS Subarachnoid hemorrhagic stroke 

TP Total Particulate 

UBRE Unbiased Risk Estimator

Table AX6.3-10. Studies examining exposure to ambient NO2 and heart rate variability as 

measured by standard deviation of normal-to-normal intervals (SDNN). 

NO2 CONC (PPB) AUTHOR, 

YEAR, 

LOCATION 

STUDY DESIGN AVG 

TIME MEAN 

(SD) RANGE 

COPOLLUTANT 

CORRELATION OUTCOME % CHANGE (95% CI) 

Liao et al. (2004) 

US, ARIC study 

Subjects: 4,390 adults 

Analysis Method: 

multivariable linear 

regression 

24 h 21 (8) none lag 1 -5.0% (-9.2, --

7) 

Subjects: 83 adults 

recruited from 

cardiology 

Analysis Method: linear 

mixed effects 

regression 

1 h 33 (15) 1, 110 PM10: 0.4 

O3: -0.4 

SO2: 0.5 

CO: 0.7 

4-h lag -4.5% (-8.1, --

30) 

Chan et al. (2005) 

Taiwan 

 8-h lag -6.9% (-12.0, -

1.8) 

Subjects: 30 adults (12 

MI + 22 COPD) 

Analysis Method: linear 

mixed models 

4 h 18 (no sd 

given) 

p10-p20, 

7, 30 

PM2.5: 0.4 

CO: 0.5 

MI patients 

[N = 12] 

4-h lag 

-26.0% (-42.1, 

-8.6) 

Wheeler et al. (2006) 

Atlanta 

 COPD 

patients 

[N = 22] 4-h 

lag 

16.6% (0.2, 

34.3) 
6-105 

NO2 CONC (PPB) AUTHOR, 

YEAR, 

LOCATION 

STUDY DESIGN AVG 

TIME MEAN 

(SD) RANGE 

COPOLLUTANT 

CORRELATION OUTCOME % CHANGE (95% CI) 

Luttmann-Gibson et al. 

(2006) 

Steubenville 

Subjects: 32 adults 

(>50 yrs) 

Analysis Method: 

mixed models 

24 h 10 (no sd 

given) 

p25-p75, 

6, 13 

PM2.5: 0.4 

O3: -0.3 

SO2: 0.3 

lag 1 0.3% (-6.0, 

6.6) 

Schwartz et al. (2005) 

Boston 

Subjects: 

28 elderly adults 

Analysis Method: 

hierarchical models 

24 h med 18 p25-p75, 

14, 23 

PM2.5: :0.3 

O3: 0.02 

CO: 0.6 

lag 1 -1.6% (-7.8, 

5.1) 

Table AX6.3-11. Studies examining exposure to ambient NO2 and heart rate variability as 

measured by variables recorded on implantable cardioverter defibrillators (ICDs). 

NO2 CONC (PPB) 

AUTHOR, YEAR, 

LOCATION SUBJECTS ANALYSIS METHOD MEAN 

(SD) RANGE 

COPOLLUTANT 

CORRELATION OUTCOME OR (95% CI) 

100 cardiac 

outpatients 

logistic regression, 

fixed effects 

23 (no sd 

given) 

11, 65 PM2.5: 0.6 

O3: -0.3 

SO2: 0.3 

CO: 0.7 

Risk of ICDrecorded 

ventricular 

arrhythmias 

 lag 1 1.55 (1.05, 

2.29) 

Peters et al. (2000a) 

Eastern MA 

 lag 0-4 1.88 (1.01, 

3.49) 

203 cardiac 

outpatients 

case-crossover med 22 p25-max, 

18, 62 

 All patients lag 

0-1 

1.54 (1.11, 

2.18) 

Rich et al. (2005) 

Boston 

 Patients with 

recent 

arrhythmia 

(<3 days) lag 

0-1 

2.09 (1.26, 

3.51) 

Dockery et al. 

(2005) 

Boston 

307 cardiac 

outpatients 

logistic regression, 

GEE 

med 23 p25-p95, 

19, 34 

PM2.5 > 0.4 

O3 < -0.4 

SO2 > 0.4 

CO: 0.6 

Patients with 

recent 

arrhythmia 

(<3 days) lag 

0-1 

2.14 (1.14, 

4.03) 

Pekkanen et al. 

(2002) 

Finland 

45 cardiac 

patients 

linear regression, 

GAM 

med 16 p25-max, 

12, 36 

PM2.5: 0.4 

CO: 0.3 

lag 2 14.1 (3.0, 65.4) 

Ruidavets et al. 

(2005) 

France 

863 adults polytomous logistic 

regression 

16 (6) 2, 48 O3: -0.3 

SO2: 0.7 

lag 8h 2.7 (1.2, 5.4) 

l results given for 20-ppb increase in NO2 with 24-h averaging time. 
6-106 

Table AX6.3-12. Birth weight and long-term NO2 exposure studies 

CONC RANGE (PPB) AUTHOR, 

YEAR, 

LOCATION 

STUDY DETAILS 

LOW MIDRANGE HIGH

CORRELATION WITH 

OTHER POLLUTANTS OUTCOMES 

ODDS 

RATIO (95% 

CI) 

Subjects: 92,288 birth cert 

Years: 1995-1997 

Group: Term LBW 

Analysis method: Logistic 

regression 

Distance 3 km 

<26.1 26.1, 32.9 >32.9 Pregnancy 

 Medium NO2 1.06 (0.93, 

1.22) 

 High NO2 1.06 (0.89, 

1.26) 

 <24.3 24.3, 34.7 >34.7 Trimester 1 

 Medium NO2 1.10 (0.96, 

1.27) 

 High NO2 1.09 (0.89, 

1.32) 

 <24.0 24.0, 34.4 >34.4 Trimester 2 

 Medium NO2 0.87 (0.76, 

1.00) 

 High NO2 0.93 (0.77, 

1.12) 

 <23.8 23.8, 34.2 >34.2 Trimester 3 

 Medium NO2 1.01 (0.88, 

1.16) 

Lin et al. 

(2004) 

Taiwan 

 High NO2 0.86 (0.71, 

1.03) 

Subjects: 388,105 birth cert 

Years: 1996-1998 

Group: Term LBW model (GAM) , 

Interquartile 

Averaging time: 24h 

Analysis method: Generalized 

additive 

25 31.4 39.7 PM10: 0.66 

SO2: 0.75 

CO: 0.77 

Pregnancy 1.04 (1.00, 

1.08) 

 PM10: 0.81 

SO2: 0.77 

CO: 0.78 

Trimester 1 1.02 (0.99, 

1.04) 

 PM10: 0.8 

SO2: 0.76 

CO: 0.82 

Trimester 2 1.03 (1.01, 

1.06) 

Lee et al. 

(2003b) 

Seoul, Korea 

 Trimester 3 0.98 (0.96, 

1.00) 
6-107 

CONC RANGE (PPB) AUTHOR, 

YEAR, 

LOCATION 

STUDY DETAILS 

LOW MIDRANGE HIGH

CORRELATION WITH 

OTHER POLLUTANTS OUTCOMES 

ODDS 

RATIO (95% 

CI) 

Subjects: 69,935 birth cert 

Year: 1991 

Group: LBW adjusted for GA 

Averaging time: 24 h 

Analysis method: Logistic 

regression, 50 μg increase 

12.2 20 31.1 SO2: 0.53 Trimester 1 0.98 (0.81, 

1.18) 

 SO2: 0.62 Trimester 2 0.99 (0.80, 

1.23) 

Bobak M. 

(2000) 

Czech 

 SO2: 0.63 Trimester 3 0.97 (0.80, 

1.18) 

Subjects: 179,460 live births 

Group: Ministry of Health, Brazil 

Year: 1997 

Analysis method: GAM models 

43.5 117.9 399.6 First Trimester 

 1Q 1 

 2Q 1.060 (0.971-

1.157) 

 3Q 1.197 (0.885-

1.619) 

 4Q 1.126 (0.812-

1.560) 

 Second 

Trimester 

 1Q 1 

 2Q 0.986 (0.902-

1.076) 

 3Q 1.008 (0.871-

1.167 

 4Q 1.034 (0.861-

1.243) 

 Third trimester 

 1Q 1 

 2Q 0.992 (0.913-

1.078) 

 3Q 1.041 (0.927-

1.169 

Gouveia et al. 

(2004) 

Sao Paulo city, 

Brazil 

 4Q 1.046 (0.889-

1.231) 

Subjects: 3,988 birth cert 

Group: LBW adjusted for GA 

Year: 1998 

Analysis method: Logistic 

regression, 10 μg increase 

 Pregnancy 1.28 (0.97, 

1.68) 

 6.2 (5.7) Medium NO2 0.96 (0.47, 

1.96) 

Maroziene 

and 

Grazuleviciene 

(2002) 

Kaunas, 

Lithuania 

 High NO2 1.54 (0.80, 

2.96) 
6-108 

CONC RANGE (PPB) AUTHOR, 

YEAR, 

LOCATION 

STUDY DETAILS 

LOW MIDRANGE HIGH

CORRELATION WITH 

OTHER POLLUTANTS OUTCOMES 

ODDS 

RATIO (95% 

CI) 

 Trimester 1 0.91 (0.53, 

1.56) 

 Trimester 2 0.93 (0.61, 

1.41) 

 Trimester 3 1.34 (0.94, 

1.92) 

Subjects: 229,085 birth cert 

Years: 1986-1998 

Group: LBW adjusted for GA 

Averaging time: 24 h 

Analysis method: Logistic 

regression, 10 ppb increase 

15.1 18.1 22.3 O3: -0.25 

SO2: 0.61 

CO: 0.72 

First mo 0.98 (0.90, 

1.07) 

Liu et al. 

(2003) 

Vancouver 

 Last mo 0.94 (0.85, 

1.04) 

Subjects: 3,901 birth cert 

Group Term LBW, CHS: 

Years: 1975-1987 

Analysis method: Logistic 

regression 

Distance: 5 km or 3 within 50 km, 

within county 

 36.1 (15.4) PM10: 0.55 

O3: -0.1 

CO: 0.41 

Pregnancy 0.8 (0.4, 1.4) 

 IQR 25 Trimester 1 0.9 (0.5, 1.5) 

 Trimester 2 1.0 (0.6, 1.6) 

Salam et al. 

(2005) 

Southern CA 

 Trimester 3 0.6 (0.4, 1.1) 

Subjects: 358,504 birth cert 

Group: LBW adjusted for GA 

Years: 1999-2002 

Analysis method: logistic 

regression, interquartile linear 

regression, difference in gms per 

IQR 

 17.4 

(5.0) 

 PM2.5: 0.64 

PM10: 0.55 

Pregnancy 1.027 (1.002, 

1.051) 

 IQR 4.8 Black mothers -12.7 (-18.0, -

7.5) 

Bell et al. 

(2007) 

CT and MA 

 White mothers -8.3 (-10.4, -

6.3) 

Subjects: 1016 non-premature 

births 

Group: LISA 

Analysis method: Poisson 

Regression 

0.52 0.75 0.90 Adjusted 1Q 1 

 Adjusted 2Q 0.80 (0.52-

1.28) 

 Adjusted 3Q 1.32 (0.86-

2.09) 

 Adjusted 4Q 1.16 (0.71-

1.71) 

Slama et al. 

(2007) 

Munich 

 Continuous 

coding 

1.21 (0.86-

1.68) 
6-109 

CONC RANGE (PPB) AUTHOR, 

YEAR, 

LOCATION 

STUDY DETAILS 

LOW MIDRANGE HIGH

CORRELATION WITH 

OTHER POLLUTANTS OUTCOMES 

ODDS 

RATIO (95% 

CI) 

 

Table AX6.3-13. Preterm delivery and long-term NO2 exposure studies 

CONC RANGE (PPB) AUTHOR, 

YEAR, 

LOCATION 

STUDY DETAILS 

LOW MIDRANGE HIGH 

CORRELATION 

WITH OTHER 

POLLUTANTS 

OUTCOME 

ODDS 

RATIO 

(95% CI) 

Subjects: 69,935 birth cert 

Group: Preterm 

Years: 1991 

Avg time: 24 h 

Analysis Method: Logistic regression, 50 

μg increase 

12.2 20 31.1 SO2: 0.62 Trimester 1 1.10 (1.00, 

1.21) 

 Trimester 2 1.08 (0.98, 

1.19) 

Bobak (2000) 

Czech 

 Trimester 3 1.11 (1.00, 

1.23) 

Subjects: 229,085 birth cert 

Group: Preterm 

Years: 1986-1998 

Avg time: 24 h 

Distance: 13 monitors 

Analysis Method: 10 ppb increase 

15.1 18.1 22.3 O3: -0.25 

SO2: 0.61 

CO: 0.72 

First mo 1.01 (0.94, 

1.07) 

Liu S. et al. (2003) 

Vancouver 

 Last mo 1.08 (0.99, 

1.17) 

Subjects: 3,988 birth cert 

Group: Preterm 

Analysis Method: Logistic regression 

 Pregnancy 1.25 (1.07, 

1.46) 

 6.2 (5.7) Medium NO2 1.14 (0.77, 

1.68) 

 High NO2 1.68 (1.15, 

2.46) 

 Trimester 1 1.67 (1.28, 

2.18) 

 Trimester 2 1.13 (0.90, 

1.40) 

Maroziene and 

Grazuleviciene R. 

(2002) 

Kaunas, Lithuania 

 Trimester 3 1.19 (0.96, 

1.47) 
6-110 

CONC RANGE (PPB) AUTHOR, 

YEAR, 

LOCATION 

STUDY DETAILS 

LOW MIDRANGE HIGH 

CORRELATION 

WITH OTHER 

POLLUTANTS 

OUTCOME 

ODDS 

RATIO 

(95% CI) 

Subjects: 97,158 birth cert 

Group: Preterm 

Years: 1989-1993 

Avg time: 24 h 

Analysis Method: Logistic regression 

Distance: Zip code within 2 miles 

32 40.9 50.4 PM10: 0.74 

O3: -0.12 

CO: 0.64 

Frst mo No effects 

for any 

preg 

period 

Ritz et al. (2000) 

Southern CA 

 6 wks before 

birth 

No effects 

for any 

preg 

period 

Subjects: 52,113 birth cert 

Group: Preterm 

Years: 2001-2002 

Analysis Method: Log binomial regression 

15.78 22.93 29.9 PM10: 0.37 

SO2: 0.54 

CO: 0.63 

Trimester 1 

Q2 

1.13 (0.99, 

1.27) 

 Trimester 1 

Q3 

1.07 (0.94, 

1.21) 

 Trimester 1 

Q4 

1.24 (1.09, 

1.41) 

Trend .02 

 Trimester 3 

Q2 

1.06 (0.93, 

1.20) 

 Trimester 3 

Q3 

1.14 (1.01, 

1.29) 

Leem et al. (2006) 

Inchon, Korea 

 Trimester 3 

Q4 

1.21 (1.07, 

1.37) 

Trend 

<.001 

Subjects: 28,200 birth cert 

Group: Preterm 

Years: 2000-2003 

Avg time: 24 h 

Analysis Method: Logistic regression 

 8.8 (4.1) PM10: 0.32 

O3: 0.13 

Trimester 1 0.93 (0.78, 

1.12) 

Hansen et al. 

(2006) 

Brisbane 

 90 days 

before birth 

1.03 (0.86, 

1.23) 

 
6-111 

Table AX6.3-14. Fetal growth and long-term NO2 exposure studies 

CONC RANGE (PPB) 

AUTHOR, YEAR, 

LOCATION STUDY DETAILS 

LOW MIDRANGE HIGH

CORRELATION WITH 

OTHER POLLUTANTS OUTCOME 

ODDS 

RATIO (95% 

CI) 

Subjects: 3,901 birth cert 

Group: Term SGA, <15% 

of data 

Years: 1975-1987 

Avg time: 24 h 

Analysis Methods: Linear 

mixed model, IQR = 25 

Distance: 5 km or 3 

monitors within 50 km 

 36.1 (15.4) PM10: 0.55 

O3: -0.1 

CO: 0.69 

Pregnancy 1.1 (0.9, 1.3) 

 Trimester 1 1.2 (1.0, 1.4) 

 Trimester 2 1.0 (0.8, 1.2) 

Salam et al. (2005) 

Southern CA, CHS 

 Trimester 3 1.0 (0.8, 1.2) 

Subjects: 51,460 birth cert 

Group: SGA, >2sd below 

national data 

Years: 1998-2000 

Avg time: 1-h max 

Analysis Methods: 

Logistic regression, 1 ppb 

Distance: 5 km 

18 23 27.5 PM2.5: 0.66 

PM10: 0.47 

O3: 0.29 

CO: 0.57 

Trimester 1 1.06 (0.99, 

1.14) 

 23.2 (7.4) Trimester 2 1.14 (1.07, 

1.22) 

 Trimester 3 1.13 (1.05, 

1.21) 

Mannes et al. (2005) 

Sydney 

 1 mo before 

birth 

1.07 (1.00, 

1.14) 

Subjects: 229,085 birth 

cert 

Group: Term SGA, <10% 

national 

Years: 1986-1998 

Avg time: 24 h 

Analysis Methods: 

Logistic regression, 

10 ppb 

Distance: 13 monitors Avg 

15.1 18.1 22.3 SO2: 0.61 

O3: -0.25 

CO: 0.72 

Trimester 1 1.03 (0.98, 

1.10) 

 Trimester 2 0.94 (0.88, 

1.00) 

 Trimester 3 0.98 (0.92, 

1.06) 

 First mo 1.05 (1.01, 

1.10) 

Liu et al. (2003) 

Vancouver 

 Last mo 0.98 (0.92, 

1.03) 

 
6-112 

Table AX6.3-15. Lung function and long-term NO2 exposure. 

CONC RANGE (ppb) AUTHOR, 

YEAR, 

LOCATION 

STUDY DETAILS 

LOW MIDRANGE HIGH

CORRELATION WITH 

OTHER POLLUTANTS OUTCOME 

ODDS 

RATIO 

(95% CI) 

Subjects: 1757 children age 10-

18, CHS 

Group: Lung function, 

Longitudinal 

Avg Time: 24-h annual 

Anlaysis Method: 2-stage linear 

Regression, 34.6 ppb 

Distance: Study monitors in 12 

towns 

 PM2.5: 0.79 

PM10: 0.67 

O3: -0.11 

Difference in 

lung growth 

 FVC -95 (-183.4, 

-0.6) 

 FEV1 -101.4 (-

164.5, -

38.4) 

Gauderman 

(2004) 

Southern CA 

 MMEF -211 (-

377.6, -

44.4) 

Subjects: 467 children age 9-16 

Group: Lung function 

Avg Time: Median wkly 

Anlaysis Method: Linear 

regression, Parameter estimates 

 21.28 

threshold 

 with asthma 

symp 

 FEV1 0.437 

 lnMEF75% -0.011 

 lnMEF50% -0.022 

 lnMEF25% -0.029 

 no asthma 

symp 

 FEV1 -0.049 

 lnMEF75% 0.003 

 lnMEF50% 0.004 

Moseler et al. 

(1994) 

Frieberg, 

Germany 

 lnMEF25% 0.003 

Subjects: 3,115 adults, 3-yr 

residents, nonsmokers, 

SAPALDIA 

Group: Lung function 

Avg Time: 24-h annual 

Anlaysis Method: 2-stage linear 

Regression 

Distance: Monitors in 8 Study 

areas 

 18.9 (8.5) PM10: 0.91 

O3 : -0.78 

SO2: 0.86 

FVC -0.0123 (-

0.0152, -

0.0094) 

AckermannLiebrich et al. 

(1997) 

Switzerland 

 FEV1 -0.0070 (-

0.0099, -

0.0041) 
6-113 

CONC RANGE (ppb) AUTHOR, 

YEAR, 

LOCATION 

STUDY DETAILS 

LOW MIDRANGE HIGH

CORRELATION WITH 

OTHER POLLUTANTS OUTCOME 

ODDS 

RATIO 

(95% CI) 

Subjects: 560 

adults, 3-yr 

residents, 

SAPALDIA 

Group: Lung 

function 

Avg Time: Wkly avg 

Anlaysis Method: 

Linear regression 

Distance: Personal 

and Home monitors 

 FVC home % change -

0.59 

(-1) 

 FVC personal 

 FEV home 

Schindler et al. 

(1998) 

Switzerland 

 FEV personal 

Subjects: 3,293 

children, CHS 

Group: Lung 

function 

Avg Time: 24 h 

Anlaysis Method: 

Linear regression 

Distance: Study 

monitors in 12 

towns 

 FVC all: 1986-1990 -42.6 (13.5) 

 FVC girls: 1986-1990 -58.5 (15.4) 

 FEV1 all: 1986-1990 -23.2 (12.5) 

 FEV1 girls: 1986-

1990 

-39.9 (13.9) 

 FVC all: 1994 -46.2 (16.0) 

 FVC girls: 1994 -56.7 (19.8) 

 FEV1 all: 1994 -22.3 (14.8) 

Peters et al. 

(1999a) 

Southern CA 

 FEV1 girls: 1994 -44.1 (16.1) 

(MEN) 

22 30 40 O3: 0.57 lnFEF75 men -0.029 (0.003) 

(WOMEN) 

Tager et al. (2005) 

Southern & 

Northern CA 

Subjects: 255 

students UC 

Berkeley 

Group: Lung 

function 

Avg Time: 

Anlaysis Method: 

Linear regression 

21 27 40 lnFEF75 women -0.032 (0.002) 

 
6-114 

Table AX6.3-16. Asthma and long-term NO2 exposure. 

CONC RANGE (PPB) AUTHOR, 

YEAR, 

LOCATION 

STUDY 

DESIGN 

ANALYSIS 

METHOD 

CORRELATION 

WITH OTHER 

POLLUTANTS LOW MIDRANGE HIGH 

STUDY 

FACTOR 

ODDS RATIO 

(95% CI) 

Subjects: 148 

children ages 7-

14 

Years: 1994-1995 

Distance: In 

home 

Study Group: 

Asthma, Monash 

Q 

Logistic 

regression 

10 μg 

 6 

 Bedroom NO2 1.01 

(0.75, 1.37) 

 Indoor mean 1.00 

(.075, 1.31) 

 winter 0.99 

(0.84, 1.16) 

Garrett et al. 

(1999) 

Latrobe Valley, 

Australia 

 summer 2.52 

(0.99, 6.42) 

Subjects: 5,421 

children ages 5-7, 

9-11 

Years: 1995-

1996, 12 mo 

residence 

Distance: 4 

monitors within 1 

km 

Study Group: 

Asthma, ISAAC 

Logistic 

regression 

10 μg 

 29.3 33.8 37.8 

 Home address 1.16 

(0.94, 1.42) 

Hirsch et al. 

(1999) 

Dresden, 

Germany 

 Home & school 1.14 

(0.86, 1.51) 

Subjects: 3,676 

children Age 9-16 

Years: 1994 

Avg time: 24 h 

Distance: Study 

monitors in 12 

towns 

Study Groups: 

Asthma, 

Questionnaire 

Logistic 

regression 

IQR = 25 ppb 

 21.5 

mean 

 

 all children 1.21 

(0.850, 1.71) 

 boys 1.25 

(0.90, 1.75) 

Peters et al. 

(1999b) 

Southern CA, 

CHS 

 girls 1.07 

(0.57, 2.02) 
6-115 

CONC RANGE (PPB) AUTHOR, 

YEAR, 

LOCATION 

STUDY 

DESIGN 

ANALYSIS 

METHOD 

CORRELATION 

WITH OTHER 

POLLUTANTS LOW MIDRANGE HIGH 

STUDY 

FACTOR 

ODDS RATIO 

(95% CI) 

Subjects: 2,034 

children age 9-11 

Years: 1995 

Distance: Study 

monitors in 12 

towns 

Study Groups: 

Asthma, 

Medication use 

Mixed effects 

model 

IQR = 

5.74 ppb 

PM2.5: 0.28 

PM10: 0.39 

 Annual 0.94 

(0.71, 1.22) 

 Mar-Aug 0.96 

(0.68, 1.37) 

Millstein et al. 

(2004) 

Southern CA, 

CHS 

 Sep-Feb 0.90 

(0.66, 1.24) 

Subjects: 4,901 

children Age 9-11 

Years: 1999-

2000, 3 yr 

residence 

Avg time: 3 yrs 

Distance: 

monitoring sites, 

school address 

Study Groups: 

Asthma, ISAAC 

Logistic 

regression 

10 μg 

PM10: 0.46 

O3: 0.76 

SO2: 0.35 

8.7, 

16.0 

 16.1, 

25.7 

Lifetime asthma 0.94 

(0.83, 1.07) 

Pénard-Morand 

et al. (2005) 

France 6 towns 

 Current asthma 0.92 

(0.77, 1.10) 

Subjects: 843 

children 

Distance: monitor 

in each 

community 

Avg time: 3 yrs 

Study Group: 

Asthma, ISAAC 

Logistic 

regression 

<.05 

 8.0, 8.7 11.7, 13.3 14.7, 

17.0 

Ever asthma low 1.28 

 Ever asthma 

medium 

2.14 

 Ever asthma 

high 

5.81 

 Current asthma 

low 

1.7 

 Currrent asthma 

medium 

1.47 

Studnicka et al. 

(1997) 

 8 communities, 

Lower Austria 

 Current asthma 

high 

8.78 

Wang et al. 

(1999) 

Taiwan 

Subjects: 

117,080 students 

age 11-16 

Distance: 24 

district monitors 

Study Group: 

Asthma 

Logistic 

regression 

Above/below 

median 

 28 median Current asthma 1.08 

(1.04, 1.13) 
6-116 

CONC RANGE (PPB) AUTHOR, 

YEAR, 

LOCATION 

STUDY 

DESIGN 

ANALYSIS 

METHOD 

CORRELATION 

WITH OTHER 

POLLUTANTS LOW MIDRANGE HIGH 

STUDY 

FACTOR 

ODDS RATIO 

(95% CI) 

Ramadour et al. 

(2000) 

7 communities, 

France 

Subjects: 2,445 

children 

Years: 3-yr 

residence 

Age 13-14 

Distance: 

Monitors in each 

community 

Study Group: 

Asthma, ISAAC 

Logisitic 

regression 

 11-27 

mean 

 Nonsignificant 

Results 

Subjects: 905 

children age 9-10 

Distance: In 

home 

measurements 

Monitors near 

schools 

Study Groups: 

Asthma, 

Prevalence, 

Incidence 

Logistic 

regression 

10-ppb 

increase 

 20-29 30-39 

7-25 mean 

Outdoors 

≥40 Outdoor 4th 

grade girls 

1.14 

(0.65, 2.09) 

 Outdoor 5th 

grade girls 

1.14 

(0.63, 2.13) 

 Outdoor 6th 

grade girls 

0.95 

(0.45, 2.05) 

 Indoor 4th grade 

girls 

1.63 

(1.06, 2.54) 

 Indoor 5th grade 

girls 

1.67 

(1.06, 2.66) 

 Indoor 6th grade 

girls 

1.18 

(0.62, 2.18) 

 Outdoor 2.10 

(1.10, 4.75) 

Shima and 

Adachi et al. 

(2000) 

7 communities, 

Japan 

 Indoor 0.87 

(0.51, 1.43) 
6-117 

CONC RANGE (PPB) AUTHOR, 

YEAR, 

LOCATION 

STUDY 

DESIGN 

ANALYSIS 

METHOD 

CORRELATION 

WITH OTHER 

POLLUTANTS LOW MIDRANGE HIGH 

STUDY 

FACTOR 

ODDS RATIO 

(95% CI) 

Subjects: 1,109 

children Age 9-11 

Distance: 10 

school sites 

Study Group: 

Asthma 

2-stage 

Hierarchical 

model 

IQR = 3.6 NO2

IQR = 14.9 

NOX

PM2.5: “low” 

O3: “low” 

 24 mean All children 1.02 

(0.97, 1.07) 

 All 1 yr residents 1.04 

(0.98, 1.10) 

 1 yr resident girls 1.09 

(1.03, 1.15) 

 1 yr resident 

boys 

1.00 

(0.94, 1.07) 

 All children 1.04 

(0.97, 1.11) 

 All 1 yr residents 1.07 

(1.00, 1.14) 

 1 yr resident girls 1.17 

(1.06, 1.29) 

Kim et al. 

(2004a) 

San Francisco 

Bay area 

 1 yr resident 

boys 

1.02 

(0.93, 1.11) 

Subjects: 208 

children 

Avg time: 4 wk 

Distance: 

Outside home 

Study Group: 

Asthma 

Logistic 

regression 

IQR = 5.7 

 13-51 Lifetime asthma 1.83 

(1.04, 3.21) 

Gauderman 

et al. (2005) 

Southern CA 

CHS 

 Asthma med use 2.19 

(1.20, 4.01) 

Subjects: 32,672 

children 

Distance: 

Schools within 

1 km of monitors 

Study Group: 

Asthma, ISAAC 

2-stage 

Hierarchical 

model 

10 ppb NOX

PM10: 0.34 O3: -

0.39 

SO2: 0.5 

21.5 29.6 33.1 Parental atopy 0.99 

(0.92, 1.07) 

Hwang et al. 

(2005) 

Taiwan, 

National study 

 No parental 

atopy 

1.02 

(0.95, 1.10) 

 
6-118 

Table AX6.3-17. Respiratory symptoms and long-term NO2 exposure. 

CONC RANGE (ppb) 

STUDY LOCATION STUDY 

GROUP SUBJECTS

ODDS 

RATIO 

(95% CI) 

ANALYSIS 

METHOD AVG TIME 

LOW MIDRANGE HIGH 

CORRELATION DISTANCE

Karr et al. 

(2006) 

Southern 

California 

Infant 

Bronchiolitis 

18,595 

cases; 

169,472 

controls ages 

3 wks to 1 yr 

1.03 [0.99, 

1.07] per 

16 ppb 

Conditional 

logistic 

regression 

Chronic 

(lifetime avg of 

1-h daily max) 

(ppb) 

12 58 204 34 monitors 

 

Karr et al. 

(2006) 

Southern 

California 

Infant 

Bronchiolitis 

18,595 

cases; 

169,472 

controls ages 

3 wks to 1 yr 

1.04 [1.00, 

1.08] per 

15 ppb 

Conditional 

logistic 

regression 

Subchronic 

(avg of 1-h 

daily max 1 mo 

prior to 

hospitalization) 

(ppb) 

12 57 152 34 monitors 

in home 

 

Garrett et al. 

(1999) 

Latrobe 

Valley 

Symptoms 148 children Logistic 

regression 

 6 

wheeze Australia Monash Q Age 7-14 1.15 (0.85, 

1.54) 

 

cough 1994-1995 1.47 (0.99, 

2.18) 

10 μg 

short of breath 1.23 (0.92, 

1.64) 

 

chest tightness 1.12 (0.81, 

1.56) 

any symptoms 1.24 (0.91, 

1.68) 

10 μg mean 4 monitors 

any symptoms 1.12 (0.93, 

1.35) 

10 μg winter 

any symptoms 2.71 (1.11, 

6.59) 

10 μg 

summer 

 

 

Hirsch et al. 

(1999) 

Dresden Symptoms 5,421 

children 

 Logistic 

regression 

 29.3 33.8 37.8 Within 1 km 

wheeze home Germany ISAAC Age 5-7, 9 11 1.13 (0.93, 

1.37) 

 

wheeze school 1995-1996 0.95 (0.72, 

1.26) 

10 μg 

cough home 12 mo 

residence 

1.22 (1.94, 

1.44) 

 

cough school 1.21 (0.96, 

1.52) 

cough nonatopic child  1.42 (1.10, 

1.84) 

 

 

Peters et al. 

(1999b) 

Southern CA Symptoms 3,676 

children 

 Logistic 

regression 

24 h 21.5 

mean 

 Study 

monitors 

in 12 towns 
6-119 

CONC RANGE (ppb) 

STUDY LOCATION STUDY 

GROUP SUBJECTS

ODDS 

RATIO 

(95% CI) 

ANALYSIS 

METHOD AVG TIME 

LOW MIDRANGE HIGH 

CORRELATION DISTANCE

wheeze CHS Questionnaire Age 9-16 1.12 (0.86, 

1.45) 

 

cough 1994 1.14 (0.94, 

1.39) 

IQR = 

25 ppb 

 

wheeze boys 1.54 (1.04, -

2.29) 

 

wheeze girls 0.86 (0.57, 

1.29) 

 

Millstein et al. 

(2004) 

Southern CA Symptoms 2,034 

children 

 Mixed 

effects 

model 

Moly PM2.5: 

0.28 

PM10: 

0.39 

Study 

monitors 

in 12 towns 

wheeze CHS Age 9-11 0.93 (0.77, 

1.12) 

 

wheeze 

Mar-Aug 

 1995 0.79 (0.40, 

1.53) 

IQR = 

5.74 ppb 

 

wheeze 

Sep-Feb 

 0.85 (0.64, 

1.14) 

 

 

Pénard-Morand 

et al. (2005) 

France 6 

towns 

Symptoms 4,901 

children 

 Logistic 

regression 

3 yrs 29 

monitoring 

sites, 

school 

address 

 ISSAC Age 9-11 

wheeze past 

12 mos. 

 1999-2000 0.87 (0.75, 

1.01) 

10 μg 8.7, 16.0 16.1, 

25.7 

O3: 0.76 

SO2: 0.35 

PM10: 

0.46 

 3-yr 

residence 

 

 

Roemer et al. 

(1993) 

Wageningen 

and 

Bennekom, 

Netherlands 

Symptoms 

Questionnaire 

73 children 

grades 3-8 

Dec 1990-

Mar 1991 

No 

associations

Time series 

using YuleWalker 

estimation 

method 

24 hr avg 127 PM10: 

0.57 

SO2: 0.26

BS: 0.65 

National Air 

Quality 

Monitoring 

Network 

 

Mukala et al. 

(1999) 

Helsinki Symptoms 163 children GEE Wkly Avg <8.6 8.6, 14.5 >14.5 Palmes 

tubes 

cough Finland Age 3-6 1.23 (0.89, 

1.70) 

2nd tertile On outer 

garment 

cough 1991 1.52 (1.00, 

2.31) 

3rd tertile 

nasal symp 

winter 

 0.99 (0.58, 

1.68) 

2nd tertile 

nasal symp 

winter 

 0.89 (0.44. 

1.82) 

3rd tertile 
6-120 

CONC RANGE (ppb) 

STUDY LOCATION STUDY 

GROUP SUBJECTS

ODDS 

RATIO 

(95% CI) 

ANALYSIS 

METHOD AVG TIME 

LOW MIDRANGE HIGH 

CORRELATION DISTANCE

nasal symp 

spring 

 0.76 (0.56, 

1.02) 

2nd tertile 

nasal symp 

spring 

 0.68 (0.46, 

1.01) 

3rd tertile 

Pikhart et al. 

(2000) 

Prague Symptoms 3,045 

children 

 Multi-level 

model 

 14.8 19 24.1 

wheeze Czech SAVIAH Age 7-10 1.16 (0.95, 

1.42) 

Individual 

covariates 

 

wheeze 1993-1994 1.07 (0.86, 

1.33) 

Ecological 

covariates 

 

wheeze 1.08 (0.86, 

1.36) 

Both 

covariates 

 

 

Setiani (1996) 6 cities in 

Japan 

Hiroshima 

Community 

Health Study 

13,836 adult 

non-smoking 

women aged 

40-59 

Logistic 

regression 

coefficient 

(standard 

error) 

Individual 

multiple 

linear 

regression 

analysis 

24 h graph graph graph SPM: 

0.606 

OX: -

0.337 

Lacrimacy 0.047 

(0.046) 

 

Eye itch 0.036 

(0.046 

 

Runny nose -0.018 

(0.076) 

 

Sore throat 0.059 

(0.042) 

Cough -0.046 

(0.044) 

Plegm -0.088 

(0.049) 

SOB -0.056 

(0.058) 

Sum of cough 

with phlegm 

and SOB 

 -0.035 

(0.030) 

 

 

Van Strien 

(2004) 

CT and MA Symptoms 849 children Poisson 

regression 

10-14 day 5.1 9.9 17.4 In home 

wheeze Age 12 mos 1.15 (0.79, 

1.67) 

Q2 Avg 

wheeze 1.03 (0.69, 

1.53) 

Q3 

wheeze 1.45 (0.92, 

2.27) 

Q4 

cough 0.96 (0.69, 

1.36) 

Q2 

cough 1.33 (0.94, 

1.88) 

Q3 
6-121 

CONC RANGE (ppb) 

STUDY LOCATION STUDY 

GROUP SUBJECTS

ODDS 

RATIO 

(95% CI) 

ANALYSIS 

METHOD AVG TIME 

LOW MIDRANGE HIGH 

CORRELATION DISTANCE

cough 1.52 (1.00, 

2.31) 

Q4 

short of breath 1.59 (0.96, 

2.62) 

Q2 

short of breath 1.95 (1.17. 

3.27) 

Q3 

short of breath 2.38 (1.31, 

4.34) 

Q4 

Nitschke et al. 

(2006) 

Adelaide Symptoms 174 

asthmatic 

 Zero-inflated 

negative 

 School 

34 (28) 

 117 max 9 days in 

class 

Wheeze school Australia Children, age 

5-13 

0.99 (0.93, 

1.06) 

binomial 

regression 

 Home 

20 (22) 

 147 max 3 days at 

home 

Wheeze home 2000 1.00 (0.90, 

1.11) 

 

Cough school 1.01 (0.98, 

1.04) 

10-ppb 

increase 

 

Cough home 0.99 (0.96, 

1.02) 

 

Difficult breath 

school 

 1.11 (1.05, 

1.18) 

 

Difficult breath 

home 

 1.03 (1.01, 

1.05) 

Chest tight 

school 

 1.12 (1.07, 

1.17) 

Chest tight 

home 

 1.02 (0.95, 

1.09) 

 

Hoek and 

Brukekreef 

(1993) 

Wageningen, 

Netherlands 

Primary 

school 

112 children 

grades 4-7 

No 

association

Individual 

linear 

regression 

analysis and 

distribution 

of individual 

regression 

slopes 

24-h 127 PM10: 

0.55 

SO2: 0.28

BS: 0.65 

Delfino et al. 

(2006) 

Southern 

California 

Asthmatic 

children 

45 children 

ages 9-18 

 Linear mixed 

effects 

models 

(Verbeke 

and 

Molenberghs 

2001) 

24-h Personal 

NO2, 

personal 

PM2.5: 

0.33 

Backpack 

monitor, 

active 

sampling 

system, 

central site 

exposure 

Personal NO2 Central 

NO2, 

personal 

PM2.5: 

0.22 

Not taking antiinflammatory 

meds 

 0.80 (-3.01 

to 4.61) 

 Central 

NO2, 

central 

PM2.5: 

0.25 
6-122 

CONC RANGE (ppb) 

STUDY LOCATION STUDY 

GROUP SUBJECTS

ODDS 

RATIO 

(95% CI) 

ANALYSIS 

METHOD AVG TIME 

LOW MIDRANGE HIGH 

CORRELATION DISTANCE

Taking antiinflammatory 

meds 

 1.67 (0.55 

to 2.79) 

 

Inhaled 

corticosteroids 

 1.22 (0.04 

to 2.40 

 

Antileukotrienes 

± inhaled 

corticosteroes 

 1.73 (-0.70 

to 4.16) 

 

Central site 

NO2 

 

Not taking antiinflammatory 

meds 

 0.96 (-1.34 

to 3.26) 

 

Taking antiinflammatory 

meds 

 1.48 (0.47 

to 2.50) 

 

Inhaled 

corticosteroids 

 1.32 (0.33 

to 2.32) 

 

Antileukotrienes 

± inhaled 

corticosteroes 

 -7.5 (-2.83 

to 1.32) 

 

Salome et al. 

(1996) 

Australia asthmatic 20 (9 adults 

and 11 

children) 

 ANOVA 0.02 ppm 1.12 ppm 

Day of 

exposure- room 

air 

 

Change in 

symptom score: 

adult 

 0.01 (0.38) 

Change in 

symptom score: 

Child 

 -0.02 (0.26) 

Wk following 

exposure- room 

air 

 

Severity score: 

adult 

 4.38 (1.5) 

Severity score: 

child 

 4.20 (1.3) 

 

Pattenden et al. 

(2006) 

Russia, 

Austria, Italy, 

Switzerland, 

Netherlands 

PATY 23,955 

children ages 

6-12 

 Logistic 

regression, 

Cochran χ2 

 12.45 50.00 variable 

 1993-1999 

Wheeze 1.01 (0.93-

1.10) 

 

Asthma 1.02 (0.94-

1.09) 

Bronchitis 0.99 (0.88-

1.12) 
6-123 

CONC RANGE (ppb) 

STUDY LOCATION STUDY 

GROUP SUBJECTS

ODDS 

RATIO 

(95% CI) 

ANALYSIS 

METHOD AVG TIME 

LOW MIDRANGE HIGH 

CORRELATION DISTANCE

Phlegm 1.05 (0.95-

1.17) 

 

Nocturnal 

cough 

 1.13 (0.94-

1.35) 

 

Morning cough 1.15 (1.01-

1.30) 

 

Sensitivity to 

inhaled 

allergens 

 1.13 (1.01-

1.26) 

 

Hay fever 1.04 (0.98-

1.11) 

 

Itchy rash 1.05 (0.98-

1.12) 

Woken by 

wheeze 

 1.06 (0.89-

1.26) 

 

Allergy to pets 1.14 (0.99-

1.31) 

 

 

Chen et al. 

(1999) 

Taiwan Study on Air 

Pollution and 

Health 

941 children 

ages 8-13 

Coefficient 

(standard 

error) 

models 9.2 141.6 

Day avg 

1 day lag- FVC -2.66 (1.23) 

1 day lagFEV10  -0.46 (1.16) 

2 day lag- FVC -3.32 (1.53) 

2 day lag 

FEV10 

 -0.93 (1.45) 

7 day lag- FVC 1.39 (1.71) 

7 day lagFEV10  2.52 (1.61) 

Daytime Peak 

1 day lag- FVC -0.59 (0.40) 

1 day lagFEV10  -0.16 (0.37) 

2 day lag- FVC -1.33 (0.72) 

2 day lag 

FEV10 

 -0.36 (0.68) 

7 day lag- FVC -0.13 (0.87) 

7 day lagFEV10  0.43 (0.82 

 
6-124 

Table AX6.3-18. Lung cancer. 

CONC RANGE (PPB) 

AUTHOR, YEAR, 

LOCATION EXPOSURE STUDY SUBJECTS 

LOW MIDRANGE HIGH

ANALYSIS ODDS RATIO 

(95% CI) 

Nyberg et al. (2000) 

Stockholm, Sweeden 

From addresses 

and traffic 

1,042 cases, 2,364 controls 

men age 40-75 

8.1 10.6 13.3 logistic regression 

 30-yr estimated 

exposure 

10 μg 

1.05 (0.93, 1.18) 

 Q2 1.18 (0.93, 1.49) 

 Q3 0.90 (0.71, 1.14) 

 Q4 1.05 (0.79, 1.40) 

 10-yr estimated 

exposure 

10 μg 

1.10 (0.97, 1.23) 

 Q2 1.15 (0.91, 1.46) 

 Q3 1.01 (0.79, 1.29) 

 Q4 1.07 (0.81, 1.42) 

 90th percentile 1.44 (1.05, 1.99) 

 

Nafstad (2004) 

Norway 

Home address 

1972-1974 

16,209 men age 40-49 at entry 

followed 1972-1998 

5.32 10.6 16 Cox proportional 

 lung cancer 

incidence 

10 μg 

1.08 (1.02, 1.15) 

 Q2 0.90 (0.70, 1.15) 

 Q3 1.06 (0.81, 1.38) 

 Q4 1.36 (1.01, 1.83) 

 non-lung cancer 

10 μg 

1.02 (0.99, 1.06 

 Q2 0.98 (0.88, 1.08) 

 Q3 1.05 (0.94, 1.18) 

 Q4 1.04 (0.91, 1.18) 

 
6-125 

Table AX6.3-19. Effects of acute NOX exposure on mortality. Risk estimates are standardized for 

per 20 ppb 24-h avg NO2 increment. 

REFERENCE, 

STUDY 

LOCATION, 

AND PERIOD 

OUTCOME 

MEASURE 

MEAN NO2 

LEVELS 

COPOLLUTANTS 

CONSIDERED 

LAG 

STRUCTURE 

REPORTED 

METHOD/DESIGN EFFECT 

ESTIMATES 

META ANALYSIS 

Stieb et al. 

(2002), 

re-analysis 

(2003) metaanalysis of 

estimates from 

multiple 

countries. 

All cause 24-h avg ranged 

from 13 ppb 

(Brisbane, 

Australia) to 

38 ppb (Santiago, 

Chile). 

“Representative” 

concentration: 

24 ppb 

PM10, O3, SO2, CO The lags and 

multiday 

averaging used 

in these 

estimates varied 

Meta-analysis of Timeseries study results Single-pollutant 

model (11 

estimates): 0.8% 

(95% CI: 0.2, 1.5); 

Multipollutant 

model estimates 

(3 estimates): 

0.4% (95% CI: -

0.2, 1.1) 

UNITED STATES 

Samet et al. 

(2000a,b) 

(reanalysis 

Dominici et al., 

2003) 90 U.S. 

cities (58 U.S. 

cities with NO2 

data) 

1987-1994 

All cause; 

cardiopulmonary 

Ranged from 

9 ppb (Kansas 

City) to 39 ppb 

(Los Angeles), 

24-h avg 

PM10, O3, SO2, 

CO; two-pollutant 

models 

0, 1, 2 Poisson GAM, 

reanalyzed with 

stringent convergence 

criteria; Poisson GLM. 

Time-series study. 

24-h avg NO2 (per 

20 ppb): 

Posterior means: 

All cause: 

Lag 1: 0.50% 

(0.09, 0.90) 

Lag 1 with PM10

and SO2: 0.48% (-

0.54, 1.51) 

Kinney and 

Özkaynak 

(1991) 

Los Angeles 

County, CA 

1970-1979 

All cause; 

respiratory; 

circulatory 

69 ppb, 24-h avg KM (particle optical 

reflectance), NO2, 

SO2, CO; 

multipollutant models 

1 OLS (Ordinary Least 

Squares) on high-pass 

filtered variables. 

Time-series study. 

All cause: 

Exhaustive 

multipollutant 

model: 0.5% (-0.1, 

1.2); 

Two-pollutant with 

OX: 0.7% (0.5, 

1.0) 

Kelsall et al. 

(1997) 

Philadelphia, PA, 

1974-1988 

All cause; 

respiratory; 

cardiovascular, 

39.6 ppb, 24-h 

avg 

TSP, CO, SO2, O3 0 (AIC 

presented for 0 

through 5) 

Poisson GAM All cause: 

Single-pollutant: 

0.3% (-0.6, 1.1); 

With TSP: -1.2% 

(-2.2, -0.2) 

Ostro et al. 

(2000) 

Coachella Valley, 

CA 

1989-1998 

All cause; 

respiratory; 

cardiovascular; 

cancer; other 

20 ppb, 24-h avg PM10, PM2.5, 

PM10-2.5, O3, CO 

0-4 Poisson GAM with 

default convergence 

criteria. Time-series 

study. 

Lag 0 day: 

All cause: 5.5% 

(1.0, 10.3) 

Respiratory: 1.8% 

(-10.3, 15.5) 

Cardiovascular: 

3.7% (-1.7, 9.3) 

Fairley (1999; 

reanalysis 

Fairley, 2003) 

Santa Clara 

County, CA 

1989-1996 

All cause; 

respiratory; 

circulatory 

28 ppb, 24-h avg PM10, PM2.5, 

PM10-2.5, SO42-, 

coefficient of 

haze, NO3-, O3, 

SO2; 

0, 1 Poisson GAM, 

reanalyzed with 

stringent convergence 

criteria; Poisson GLM. 

Time-series study. 

Lag 1: 

All cause: 1.9% 

(0.2, 3.7); 

Cardiovascular: 

1.4% (-1.7, 4.5); 

Respiratory: 4.8% 

(-0.3, 10.2) 
6-126 

REFERENCE, 

STUDY 

LOCATION, 

AND PERIOD 

OUTCOME 

MEASURE 

MEAN NO2 

LEVELS 

COPOLLUTANTS 

CONSIDERED 

LAG 

STRUCTURE 

REPORTED 

METHOD/DESIGN EFFECT 

ESTIMATES 

Gamble (1998) 

Dallas, TX 

1990-1994 

All cause; 

cardiopulmonary 

15 ppb, 24-h avg PM10, O3, SO2, 

CO; two-pollutant 

models 

Avg 4-5 Poisson GLM. Timeseries study. All cause: 4.4% 

(0.0, 9.0) 

Cardiovascular: 

1.9% (-4.6, 9.0) 

Respiratory: 

13.7% (-2.0, 32.0) 

Dockery et al. 

(1992) 

St. Louis, MO 

and Eastern 

Tennessee 

1985-1986 

All cause St. Louis: 20 ppb; 

Eastern 

Tennessee: 

12.6 ppb, 24-h 

avg 

PM10, PM2.5, SO4, 

H+, O3, SO2

Lag 1 Poisson with GEE. 

Time-series study. 

All cause: 

St. Louis, MO: 

0.7% (-3.5, 5.1) 

Eastern 

Tennessee: 3.9% 

(-8.7, 18.2) 

Moolgavkar 

(2003) 

Cook County, IL 

and Los Angeles 

County, CA, 

1987 1995 

All cause; 

cardiovascular 

Cook County: 

25 ppb; Los 

Angeles: 38 ppb, 

24-h avg 

PM2.5, PM10, O3, 

SO2, CO; twopollutant models 

0, 1, 2, 3, 4, 5 Poisson GAM with 

default convergence 

criteria. Time-series 

study. 

All cause: 

Lag 1: 

Cook County: 

Single-pollutant: 

2.2% (1.3, 3.1); 

with PM10: 1.8% 

(0.7, 3.0); 

Los Angeles: 

Single-pollutant: 

2.0% (1.6, 2.5); 

with PM2.5: 1.8% 

(0.1, 3.6). 

Moolgavkar 

(2000a,b,c); 

re-analysis 

(2003). 

Cook County, IL; 

Los Angeles 

County, CA, and 

Maricopa County, 

AZ, 

1987-1995 

Cardiovascular; 

cerebrovascular; 

COPD 

Cook County: 

25 ppb; Los 

Angeles: 38 ppb; 

Maricopa County: 

19 ppb, 24-h avg 

PM2.5, PM10, O3, 

SO2, CO; two- and 

three-pollutant 

models 

0, 1, 2, 3, 4, 5 Poisson GAM with 

default convergence 

criteria in the original 

Moolgavkar (2000); 

GAM with stringent 

convergence criteria 

and GLM with natural 

splines in the 2003 

re-analysis. The 2000 

analysis presented total 

death risk estimates 

only in figures. 

GAM, Lag 1: 

Cardiovascular: 

Cook County: 

1.1% (-0.5, 2.8); 

Los Angeles: 2.8% 

(2.0, 3.6); 

Maricopa Co.: 

4.6% (0.5, 9.0); 

Re-analysis, GLM: 

Total deaths: 2.5% 

(1.5, 3.6) 

Lippmann et al. 

(2000); 

reanalysis Ito, 

(2003, 2004) 

Detroit, MI 

1985-1990 

1992-1994 

All cause; 

respiratory; 

circulatory; 

cause-specific 

1985-1990: 

23.3 ppb, 24-h 

avg 

1992-1994: 

21.3 ppb, 24-h 

avg 

PM10, PM2.5, 

PM10-2.5, SO42-, H+, 

O3, SO2, CO; 

two-pollutant 

models 

0, 1, 2, 3, 0-1, 

0-2, 0-3 

Poisson GAM, 

reanalyzed with 

stringent convergence 

criteria; Poisson GLM. 

Numerical NO2 risk 

estimates were not 

presented in the reanalysis. Time-series 

study. 

Poisson GAM: 

All cause: 

Lag 1: 

1985-1990: 

0.9% (-1.2, 3.0) 

1992-1994: 

1.3% (-1.5, 4.2) 

Lipfert et al. 

(2000) 

Seven counties 

in Philadelphia, 

PA area 

1991-1995 

All cause; 

respiratory; 

cardiovascular; 

all ages; age 

65+ yrs; age 

<65 yrs; various 

subregional 

boundaries 

20.4 ppb, 24-h 

avg 

PM10, PM2.5, 

PM10-2.5, SO4, 

O3, other PM 

indices, NO2, SO2, 

CO; two-pollutant 

models 

0-1 Linear with 19-day 

weighted avg 

Shumway filters. 

Time-series study. 

Numerous results. 

All-cause, avg of 

0- and 1-day lags, 

Philadelphia: 

2.2% (p > 0.05) 
6-127 

REFERENCE, 

STUDY 

LOCATION, 

AND PERIOD 

OUTCOME 

MEASURE 

MEAN NO2 

LEVELS 

COPOLLUTANTS 

CONSIDERED 

LAG 

STRUCTURE 

REPORTED 

METHOD/DESIGN EFFECT 

ESTIMATES 

Chock et al. 

(2000) 

Pittsburgh, PA 

1989-1991 

All cause; age 

<74 yrs; 

age 75+ yrs 

Not reported. PM10, NO2, SO2, 

CO; two-, five-, 

and six-pollutant 

models 

0, plus minus 3 

days 

Poisson GLM. Timeseries study. Numerous 

results 

All cause, lag 0, 

age 

0-74: 0.5% (-2.4, 

3.5); age 75+: 

1.0% (-1.9, 4.0) 

De Leon et al. 

(2003) 

New York City, 

NY 

1985-1994 

Circulatory and 

cancer with and 

without 

contributing 

respiratory causes 

40.6 ppb, 24-h 

avg 

PM10, O3, SO2, 

CO; two-pollutant 

models 

0 or 1 Poisson GAM with 

stringent convergence 

criteria; Poisson GLM. 

Time-series study. 

Gaseous 

pollutants results 

were given only in 

figures. 

Circulatory: 

Age < 75: ~1% 

Age 75+: ~2% 

Klemm and 

Mason (2000); 

Klemm et al. 

(2004) 

Atlanta, GA 

Aug 1998-July 

2000 

All cause; 

respiratory; 

cardiovascular; 

cancer; other; age 

<65 yrs; age 

65+ yrs 

51.3 ppb, max 1-

h. 

PM2.5, PM10-2.5, 

EC, OC, O3, 

SO42-, 

NO3, SO2, CO 

0-1 Poisson GLM using 

quarterly, moly, or 

biweekly knots for 

temporal smoothing. 

Time-series study. 

All cause, age 65+ 

yrs: avg 0-1 days 

Quarterly knots: 

1.0% (-4.2,6.6); 

Moly knots: 

3.1% (-3.0, 9.7); 

Bi-wkly knots: 

0.9% (-5.9, 8.2) 

Gwynn et al. 

(2000) 

Buffalo, NY 

Time-series 

study. 

All cause; 

respiratory; 

circulatory 

24-h avg 21 ppb PM10, CoH, O3, SO2, 

CO, H+, SO42- 

 Poisson GAM with 

Default convergence 

criteria. 

All cause (lag 3): 

2.1% (-0.3, 4.6); 

Circulatory (lag 2): 

1.3% (-2.9, 5.6); 

Respiratory (lag 

1): 6.4% (-2.5, 

16.2) 

CANADA 

Burnett et al. 

(2004) 

12 Canadian 

cities 1981-1999 

All cause 24-h avg ranged 

from 10 (Saint 

John) to 26 

(Calgary) ppb. 

PM2.5, PM10-2.5, O3, 

SO2, CO 

1, 0-2 Poisson GLM. Timeseries study. Lag 0-2, singlepollutant: 2.0% 

(1.1, 2.9); with O3: 

1.8% (0.9, 2.7) 

Days when PM 

indices available, 

lag 1, singlepollutant: 2.4% 

(0.7, 4.1); with 

PM2.5: 3.1% (1.2, 

5.1) 

Burnett et al. 

(2000); reanalysis (2003) 

8 Canadian cities 

1986-1996 

All cause 24-h avg ranged 

from 15 

(Winnipeg) to 26 

(Calgary) ppb. 

PM2.5, PM10, 

PM2.5-10, SO2, 

O3, CO 

0, 1, 0-2 Poisson GAM with 

default convergence 

criteria. Time-series 

study. The 2003 reanalysis did not 

consider gaseous 

pollutants. 

Days when PM 

indices available, 

lag 1, singlepollutant: 3.6% 

(1.6, 5.7); with 

PM2.5: 2.8% (0.5, 

5.2) 

Burnett et al. 

(1998a), 11 

Canadian cities 

1980-1991 

All cause 24-h avg ranged 

from 14 

(Winnipeg) to 28 

(Calgary) ppb. 

SO2, O3, CO 0, 1, 2, 0-1, 0-2 

examined but 

the best 

lag/averaging for 

each city chosen

Poisson GAM with 

default convergence 

criteria. Time-series 

study. 

Single-pollutant: 

4.5% (3.0, 6.0); 

with all gaseous 

pollutants: 3.5% 

(1.7, 5.3) 

Burnett et al. 

(1998b), Toronto, 

1980-1994 

All cause 24-h avg 25 ppb SO2, O3, CO, TSP, 

COH, estimated 

PM10, estimated PM2.5

0, 1, 0-1 Poisson GAM with 

default convergence 

criteria. Time-series 

study. 

Single-pollutant 

(lag 0): 1.7% (0.7, 

2.7); with CO: 

0.4% (-0.6, 1.5) 
6-128 

REFERENCE, 

STUDY 

LOCATION, 

AND PERIOD 

OUTCOME 

MEASURE 

MEAN NO2 

LEVELS 

COPOLLUTANTS 

CONSIDERED 

LAG 

STRUCTURE 

REPORTED 

METHOD/DESIGN EFFECT 

ESTIMATES 

Vedal et al. 

(2003) 

Vancouver, 

British 

Columbia, 

Canada 

1994-1996 

All cause; 

respiratory; 

cardiovascular 

17 ppb, 24-h avg PM10, O3, SO2, 

CO 

0, 1, 2 Poisson GAM with 

stringent convergence 

criteria. Time-series 

study. By season. 

Results presented 

in figures only. 

NO2 showed 

associations in 

winter but not in 

summer. 

Villeneuve et al. 

(2003) 

Vancouver, 

British 

Columbia, 

Canada 

1986-1999 

All cause; 

respiratory; 

cardiovascular; 

cancer; 

socioeconomic 

status 

19 ppb, 24-h avg PM2.5, PM10, 

PM10-2.5, TSP, 

coefficient of 

haze, SO42-, SO2, 

O3, CO 

0, 1, 0-2 Poisson GLM with 

natural splines. 

Time-series study. 

All yr: 

All cause 

Lag 1: 4.0% 

(0.9, 7.2) 

Respiratory: 

Lag 0: 2.1% 

(-3.0, 7.4) 

Cardiovascular: 

Lag 0: 4.3% 

(-4.2, 13.4) 

Goldberg et al. 

(2003) 

Montreal, 

Quebec, Canada 

1984-1993 

Congestive Heart 

Failure (CHF) as 

underlying cause 

of death vs. those 

classified as 

having congestive 

heart failure 1 yr 

prior to death 

22 ppb, 24-h avg PM2.5, coefficient 

of haze, SO42-, 

SO2, O3, CO 

0, 1, 0-2 Poisson GLM with 

natural splines. 

Time-series study. 

CHF as underlying 

cause of death: 

Lag 1: 1.0% 

(-5.1, 7.5) 

Having CHF 1 yr 

prior to death: 

Lag 1: 3.4% 

(0.9, 6.0) 

EUROPE 

Samoli et al. 

(2006) 

30 APHEA2 

cities. Study 

periods vary 

by city, ranging 

from 1990 to 

1997 

All cause, 

respiratory; 

cardiovascular 

1-h max ranged 

from 24 

(Wroclaw) to 81 

(Milan) ppb 

BS, PM10, SO2, O3 01 Poisson model with 

penalized splines. 

All-cause: single: 

1.8% (1.3, 2.2); 

with SO2: 1.5% 

(1.0, 2.0) 

Cardiovascular: 

single: 2.3% 

(1.7, 3.0); with 

SO2: 1.9% (1.1, 

2.7) 

Respiratory: 

single: 2.2% (1.0, 

3.4); with SO2: 

1.1% 

(-0.4, 2.6) 

Samoli et al. 

(2005) 

9 APHEA2 cities. 

Period not 

reported. 

All-cause The selected 

cities had 1-h 

max medians 

above 58 ppb and 

the third quartiles 

above 68. 

None 01 Poisson model with 

either non-parametric or 

cubic spline smooth 

function in each city, 

and combined across 

cities. 

No numeric 

estimate 

presented. The 

concentrationresponse was 

approximately 

linear. 
6-129 

REFERENCE, 

STUDY 

LOCATION, 

AND PERIOD 

OUTCOME 

MEASURE 

MEAN NO2 

LEVELS 

COPOLLUTANTS 

CONSIDERED 

LAG 

STRUCTURE 

REPORTED 

METHOD/DESIGN EFFECT 

ESTIMATES 

Touloumi et al. 

(1997) 

Six European 

cities: 

London, Paris, 

Lyon, 

Barcelona, 

Athens, Koln. 

Study periods 

vary by city, 

ranging from 

1977 to 1992 

All cause Ranged from 37 

(Paris) to 70 

(Athens) ppb, 1-h 

max 

BS, O3; two-pollutant 

models 

0, 1, 2, 3, 0-1, 0-

2, 0-3 (best lag 

selected for 

each city) 

Poisson autoregressive. 

Time-series study. 

All-cause: Singlepollutant model: 

1.0% (0.6, 1.3); 

With BS: 

0.5% (0.0, 0.9). 

Zmirou et al. 

(1998) 

Four European 

cities: London, 

Paris, Lyon, 

Barcelona 

Study periods 

vary by city, 

ranging from 

1985-1992 

Respiratory; 

cardiovascular 

Ranged from 24 

(Paris) to 37 

(Athens) ppb in 

cold season and 

23 (Paris) to 37 

(Athens) ppb in 

warm season, 24-

h avg 

BS, TSP, SO2, O3 0, 1, 2, 3, 0-1, 0-

2, 0-3 (best lag 

selected for 

each city) 

Poisson GLM. Timeseries study. Western Europe: 

Respiratory: 

0.0% (-1.1, 1.1) 

Cardiovascular: 

0.8% (0.0, 1.5) 

Biggeri et al. 

(2005) 

8 Italian cities, 

Period variable 

between 

1990-1999 

All cause; 

respiratory; 

cardiovascular 

24-h avg ranged 

from 30 (Verona) 

to 51 (Rome) ppb 

Only single-pollutant 

models; O3, SO2, CO, 

PM10

0-1 Poisson GLM. Timeseries study. All cause: 3.6% 

(2.3, 5.0) 

Respiratory: 

5.6% (0.2, 11.2) 

Cardiovascular: 

5.1% (3.0, 7.3) 

Anderson et al. 

(1996) London, 

England 

1987-1992 

All cause; 

respiratory; 

cardiovascular 

37 ppb, 24-h avg BS, O3, SO2; 

two-pollutant models 

0, 1 Poisson GLM. Timeseries study. All cause (Lag 1): 

0.6% (-0.1, 1.2); 

Respiratory (lag 

1): 

-0.7% (-2.3, 1.0) 

Cardiovascular: 

0.5% (-0.4, 1.4) 

Bremner et al. 

(1999) London, 

England 

1992-1994 

All cause; 

respiratory; 

cardiovascular; all 

cancer; all others; 

all ages; age 

specific (0-64, 

65+, 65-74, 75+ 

yrs) 

34 ppb, 24-h avg BS, PM10, O3, SO2, 

CO; two-pollutant 

models 

Selected best 

from 

0, 1, 2, 3, (all 

cause); 

0, 1, 2, 3, 0-1, 0-

2, 

0-3 (respiratory, 

cardiovascular) 

Poisson GLM. Timeseries study. All cause (lag 1): 

0.9% (0.0, 1.9) 

Respiratory (lag 

3): 

1.9% (-0.3, 4.2) 

Cardiovascular 

(lag 1): 1.9% (0.6, 

3.2) 

Anderson et al. 

(2001) 

West Midlands 

region, England 

1994-1996 

All cause; 

respiratory; 

cardiovascular. 

37 ppb, 1-h max PM10, PM2.5, 

PM2.5-10, BS, SO42-, 

O3, SO2, CO 

0-1 Poisson GAM with 

default convergence 

criteria. Time-series 

study. 

All cause: 

1.7% (-0.5, 3.8) 

Respiratory: 

3.3% (-1.9, 8.8) 

Cardiovascular: 

3.1% (-0.2, 6.4) 

Prescott et al. 

(1998) 

Edinburgh, 

Scotland 

1992-1995 

All cause; 

respiratory; 

cardiovascular; all 

ages; age <65 

yrs; age ≥ 65 yrs 

26 ppb, 24-h avg BS, PM10, O3, SO2, 

CO; two-pollutant 

models 

0 Poisson GLM. 

Time-series study. 

Results presented 

as figures only. 

Essentially no 

associations in all 

categories. Very 

wide confidence 

intervals. 
6-130 

REFERENCE, 

STUDY 

LOCATION, 

AND PERIOD 

OUTCOME 

MEASURE 

MEAN NO2 

LEVELS 

COPOLLUTANTS 

CONSIDERED 

LAG 

STRUCTURE 

REPORTED 

METHOD/DESIGN EFFECT 

ESTIMATES 

Le Tertre et al. 

(2002) 

Le Havre, Lyon, 

Paris, Rouen, 

Strasbourg, and 

Toulouse, France 

Study periods 

vary by city, 

ranging from 

1990-1995 

All cause; 

respiratory; 

cardiovascular 

Ranged from 

15 (Toulouse) to 

28 (Paris) ppb, 

24-h avg 

BS, O3, SO2 0-1 Poisson GAM with 

default convergence 

criteria. Time-series 

study. 

Six-city pooled 

estimates: 

All cause: 

2.9% (1.6, 4.2) 

Respiratory: 

3.1% (-1.7, 8.0) 

Cardiovascular: 

3.5% (1.1, 5.9) 

Zeghnoun et al. 

(2001) Rouen 

and Le Havre, 

France 1990-

1995 

All cause; 

respiratory; 

cardiovascular 

24-h avg 18 ppb 

in Rouen; 20 ppb 

in Le Havre 

SO2, BS, PM13, O3 0, 1, 2, 3, 0-3, Poisson GAM with 

default convergence 

criteria. Time-series 

study. 

All cause in Rouen 

(lag 1): 5.5% 

(0.2, 11.1) ; in 

Le Havre (lag 1): 

2.4% (-3.4, 8.5) 

Dab et al. (1996) 

Paris, France 

1987-1992 

Respiratory 24 ppb, 24-h avg BS, PM13, O3, SO2, 

CO 

0 Poisson autoregressive. 

Time-series study. 

Lag1: 

2.1% (3.1, 7.7) 

Zmirou et al. 

(1996) 

Lyon, France 

1985-1990 

All cause; 

respiratory; 

cardiovascular; 

digestive 

37 ppb, 24-h avg PM13, SO2, O3 Selected best 

from 

0, 1, 2, 3 

Poisson GLM. 

Time-series study. 

All cause (lag 1): 

1.5% (-1.5, 4.6) 

Respiratory (lag 

2): 

-2.3% (-15.6, 13.0)

Cardiovascular 

(lag 1): 

0.8% (-2.7, 4.3) 

Sartor et al. 

(1995) 

Belgium 

Summer 1994 

All cause; age 

<65 yrs; age 65+ 

yrs 

24-h avg NO2: 

Geometric mean: 

During heat wave 

(42 day period): 

17 ppb 

Before heat wave 

(43 day period): 

15 ppb 

After heat wave 

(39 day period): 

13 ppb 

TSP, NO, O3, SO2 0, 1, 2 Log-linear regression for 

O3 and temperature. 

Time-series study. 

Only correlation 

coefficients 

presented for 

NO2. Unlike O3, 

NO2 was not 

particularly 

elevated during 

the heat wave. 

Hoek et al. 

(2000); 

reanalysis Hoek, 

(2003) 

The Netherlands: 

entire country, 

four urban areas 

1986-1994 

All cause; COPD; 

pneumonia; 

cardiovascular 

24-h avg median: 

17 ppb in the 

Netherlands; 

24 ppb in the four 

major cities 

PM10, BS, SO42-, 

NO3-, O3, SO2, CO; 

two-pollutant models 

1, 0-6 Poisson GAM, 

reanalyzed with 

stringent convergence 

criteria; Poisson GLM. 

Time-series study. 

Poisson GLM: 

All cause: 

Lag 1: 1.9% (1.2, 

2.7) 

Lag 0-6: 2.6% 

(1.2, 4.0); with BS: 

1.3% (-0.9, 3.5); 

Cardiovascular 

(lag 0 6): 2.7% 

(0.7, 4.7). 

COPD (lag 0-6): 

10.4% (4.5, 16.7). 

Pneumonia (lag 0-

6): 

19.9% (11.5, 

29.0). 
6-131 

REFERENCE, 

STUDY 

LOCATION, 

AND PERIOD 

OUTCOME 

MEASURE 

MEAN NO2 

LEVELS 

COPOLLUTANTS 

CONSIDERED 

LAG 

STRUCTURE 

REPORTED 

METHOD/DESIGN EFFECT 

ESTIMATES 

Hoek et al. 

(2001); 

reanalysis Hoek, 

(2003) 

The Netherlands 

1986-1994 

Total 

cardiovascular; 

myocardial 

infarction; 

arrhythmia; heart 

failure; 

cerebrovascular; 

thrombosisrelated 

24-h avg median: 

17 ppb in the 

Netherlands; 

24 ppb in the four 

major cities 

PM10, O3, SO2, CO 1 Poisson GAM, 

reanalyzed with 

stringent convergence 

criteria; Poisson GLM. 

Time-series study. 

Poisson GLM: 

Total 

cardiovascular: 

2.7% (0.7, 4.7) 

Myocardial 

infarction: 

0.3% (-2.6, 3.2) 

Arrhythmia: 

1.7% (-6.6, 10.6) 

Heart failure: 

7.6% (1.4, 14.2) 

Cerebrovascular: 

5.1% (0.9, 9.6) 

Thrombosisrelated: 

-1.2% (-9.6, 8.1) 

Roemer and van 

Wijinen (2001) 

Amsterdam, The 

Netherlands 

1987-1998 

All cause 24-h avg: 

Background sites: 

24 ppb 

Traffic sites: 

34 ppb 

BS, PM10, O3, SO2, 

CO 

1, 2, 0-6 Poisson GAM with 

default convergence 

criteria (only one 

smoother). Time-series 

study. 

Total population 

using background 

sites: Lag 1: 

3.8% (1.7, 5.9); 

Traffic pop. using 

background sites: 

Lag 1: 5.7% (0.6, 

11.0); 

Total pop. using 

traffic sites: Lag 1: 

1.7% (0.4, 3.0) 

Verhoeff et al. 

(1996) 

Amsterdam, The 

Netherlands 

1986-1992 

All cause; all 

ages; age 65+ yrs 

1-h max O3: 

43 μg/m3

Max 301 

PM10, O3, CO; 

multipollutant models 

NO NO2!!! 

0, 1, 2 Poisson. Time-series 

study. 

1-h max O3 (per 

100 μg/m3) 

All ages: 

Lag 0: 1.8% (-3.8, 

7.8) 

Lag 1: 0.1% (-4.7, 

5.1) 

Lag 2: 4.9% (0.1, 

10.0) 

Fischer et al. 

(2003) The 

Netherlands, 

1986-1994 

All-cause, 

cardiovascular, 

COPD, and 

pneumonia in age 

groups <45, 45-

64, 65-74, 75+ 

24-h avg median 

17 ppb 

PM10, BS, O3, SO2, 

CO 

0-6 Poisson GAM with 

default convergence 

criteria. Time-series 

study. 

Cardiovascular: 

Age <45: -1.3% (-

13.0, 12.1): age 

45-64: -0.4% (-4.8, 

4.3); age 65 74: 

4.4% (0.8, 8.0); 

age 75 and up: 

3.5% (1.4, 5.6) 

Spix and 

Wichman (1996) 

Koln, Germany 

1977-1985 

All-cause 24-h avg 24 ppb; 

1-h max 38 ppb 

TSP, PM7, SO2 0, 1, 0-1 Poisson GLM. 

Time-series study. 

Lag 1: 0.4% 

(-0.4, 1.2) 
6-132 

REFERENCE, 

STUDY 

LOCATION, 

AND PERIOD 

OUTCOME 

MEASURE 

MEAN NO2 

LEVELS 

COPOLLUTANTS 

CONSIDERED 

LAG 

STRUCTURE 

REPORTED 

METHOD/DESIGN EFFECT 

ESTIMATES 

Peters et al. 

(2000b) 

NE Bavaria, 

Germany 

1982-1994 

Coal basin in 

Czech Republic 

1993-1994 

All cause; 

respiratory; 

cardiovascular; 

cancer 

24-h avg: 

Czech Republic: 

17.6 ppb 

Bavaria, 

Germany: 

13.2 ppb 

TSP, PM10, O3, SO2, 

CO 

0, 1, 2, 3 Poisson GLM. 

Time-series study. 

Czech Republic: 

All cause: 

Lag 1: 2.1% 

(-1.7, 6.1) 

Bavaria, Germany: 

All cause: 

Lag 1: -0.1% 

(-3.6, 3.6) 

Michelozzi et al. 

(1998) Rome, 

Italy 1992-1995 

All-cause 24-h avg 52 ppb PM13, SO2, O3, CO 0, 1, 2, 3, 4 Poisson GAM with 

default convergence 

criteria. Time-series 

study. 

Lag 2: all-yr: 1.6% 

(0.4, 2.9); 

Cold season 0.3% 

(-1.2, 1.8); 

Warm season: 

4.2% 

(1.8, -6.6) 

Pönkä et al. 

(1998) 

Helsinki, Finland 

1987-1993 

All cause; 

cardiovascular; 

age <65 yrs, age 

65+ yrs 

24-h avg: 

Median 20 ppb 

TSP, PM10, O3, SO2 0, 1, 2, 3, 4, 5, 

6, 7 

Poisson GLM. 

Time-series study. 

No risk estimate 

presented for 

NO2. 

PM10 and O3 were 

reported to have 

stronger 

associations. 

Saez et al. 

(2002) 

Seven Spanish 

cities, variable 

study periods 

between 1991 

and 1996. 

All cause; 

respiratory; 

cardiovascular 

24-h avg mean 

ranged from 

17 ppb in Huelva 

to 35 ppb in 

Valencia. 

O3, PM, SO2, CO 0-3 Poisson GAM with 

default convergence 

criteria. Time-series 

study. 

All cause: 

2.6% (1.6, 3.6); 

with all other poll.: 

1.7% (0.0, 3.3); 

Respiratory: 

7.1% (-14.0, 33.5) 

Cardiovascular: 

4.4% (-0.2, 9.2) 

Garcia-Aymerich 

et al. (2000) 

Barcelona, Spain 

1985-1989 

All cause; 

respiratory; 

cardiovascular; 

general 

population; 

patients with 

COPD 

Levels not 

reported. 

BS, O3, SO2 Selected best 

avg lag 

Poisson GLM. 

Time-series study. 

All cause: 

General 

population: 

Lag 0-3: 3.3% 

(0.8, 5.8) 

COPD patients: 

Lag 0-2: 10.9% 

(0.4, 22.6) 

Respiratory: 

General 

population: 

Lag 0-1: 3.3% (-

2.3, 9.2) 

COPD patients: 

Lag 0-2: 12.1% (-

4.3, 31.4) 

Cardiovascular: 

General 

population: 

Lag 0-3: 2.4% (-

0.9, 5.8) 

COPD patients: 

Lag 0-2: 4.3% (-

13.6, 25.8) 
6-133 

REFERENCE, 

STUDY 

LOCATION, 

AND PERIOD 

OUTCOME 

MEASURE 

MEAN NO2 

LEVELS 

COPOLLUTANTS 

CONSIDERED 

LAG 

STRUCTURE 

REPORTED 

METHOD/DESIGN EFFECT 

ESTIMATES 

Saez et al. 

(1999) 

Barcelona, Spain 

1986-1989 

Asthma mortality; 

age 2-45 yrs 

Levels not 

reported. 

BS, O3, SO2 0-2 Poisson with GEE. 

Time-series study. 

RR = 4.1 (0.5, 

35.0) 

Sunyer et al. 

(1996) 

Barcelona, Spain 

1985-1991 

All cause; 

respiratory; 

cardiovascular; all 

ages; age 70+ yrs 

1-h max: 

Median: 

Summer: 51 ppb 

Winter: 46 ppb 

BS, SO2, O3 Selected best 

single-day lag 

Autoregressive Poisson. 

Time-series study. 

All yr, all ages: 

All cause: 

Lag 1: 1.9% (0.8, 

3.1) 

Respiratory: 

Lag 0: 1.5% (-1.9, 

5.0) 

Cardiovascular: 

Lag 1: 2.2% (0.5, 

3.9) 

Summer risk 

estimates larger 

than winter risk 

estimates. 

Sunyer and 

Basagãna (2001) 

Barcelona, Spain 

1990-1995 

Mortality in a 

cohort of patients 

with COPD 

Mean not 

reported 

IQR 8.9 ppb 24-h 

avg 

PM10, O3, CO 0-2 Conditional logistic 

(case-crossover) 

7.8% (-2.0, 18.6) 

with PM10: 

3.9% (-12.0, 22.5) 

Sunyer et al. 

(2002) 

Barcelona, Spain 

1986-1995 

All cause, 

respiratory, and 

cardiovascular 

mortality in a 

cohort of patients 

with severe 

asthma 

1-h max: median 

47 ppb; 

24-h avg median 

27 ppb 

PM10, BS, SO2, O3, 

CO, pollen 

0-2 Conditional logistic 

(case-crossover) 

Odds Ratio: 

Patients with 1 

asthma admission: 

All cause: 

1.10 (0.80, 1.51) 

Cardiovascular: 

1.70 (0.96, 2.99) 

Patients with more 

than 1 asthma 

admission: 

All cause: 

2.14 (1.10, 4.14) 

Cardiovascular: 

1.53 (0.46, 5.07) 

Díaz et al. (1999) 

Madrid, Spain 

1990-1992 

All cause; 

respiratory; 

cardiovascular 

24-h avg 

Levels not 

reported. 

TSP, O3, SO2, CO 1, 4, 10 Autoregressive linear. 

Time-series study. 

Only significant 

risk estimates 

were shown. For 

NO2, only 

respiratory 

mortality was 

significantly 

(p < 0.05) 

associated with an 

excess percent 

risk 8.5%. 
6-134 

REFERENCE, 

STUDY 

LOCATION, 

AND PERIOD 

OUTCOME 

MEASURE 

MEAN NO2 

LEVELS 

COPOLLUTANTS 

CONSIDERED 

LAG 

STRUCTURE 

REPORTED 

METHOD/DESIGN EFFECT 

ESTIMATES 

LATIN AMERICA 

Borja-Aburto 

et al. (1997) 

Mexico City 

1990-1992 

All cause; 

respiratory; 

cardiovascular; all 

ages; age <5 yrs; 

age >65 yrs 

1-h max O3: 

Median 155 ppb 

8-h max O3: 

Median 94 ppb 

10-h avg O3 

(8 a.m.-6 p.m.): 

Median 87 ppb 

24-h avg O3: 

Median 54 ppb 

TSP, SO2, CO; twopollutant models 

0, 1, 2 Poisson iteratively 

weighted and filtered 

least-squares method. 

Time-series study. 

1-h max O3 (per 

100 ppb): 

All ages: 

Borja-Aburto 

et al. (1998) 

SW Mexico City 

1993-1995 

All cause; 

respiratory; 

cardiovascular; 

other; all ages; 

age >65 yrs 

37.7 ppb, 24-h 

avg 

PM2.5, O3, SO2; twopollutant models 

0, 1, 2, 3, 4, 5, 

and multiday 

avg 

Poisson GAM with 

default convergence 

criteria (only one 

smoother). Time-series 

study. 

Lag 1-5: 

All cause: 2.3% (-

1.0, 5.6); 

Cardiovascular: 

2.8% (-3.2, 9.2); 

Respiratory: 4.7% 

(-5.1, 15.5). 

Loomis et al. 

(1999) 

Mexico City 

1993-1995 

Infant mortality 24-h avg 38 ppb PM2.5, O3 0, 1, 2, 3, 4, 5, 

3-5 

Poisson GAM with 

default convergence 

criteria. Time-series 

study. 

Lag 3-5: 

11.4% (2.2, 21.4); 

with PM2.5: 

2.9% (-10.2, 17.8) 

Gouveia and 

Fletcher (2000b) 

São Paulo, Brazil 

1991-1993 

All ages (all 

cause); age <5 

yrs (all cause, 

respiratory, 

pneumonia); age 

65+ yrs (all cause, 

respiratory, 

cardiovascular) 

1-h max: 84 ppb PM10, O3, SO2, CO 0, 1, 2 Poisson GLM. 

Time-series study. 

All ages: 

All cause: 

Lag 0: -0.1% (-0.7, 

0.4) 

Age 65+: 

All cause: 

Lag 1: 0.4% (-0.2, 

1.1) 

Respiratory: 

Lag 2: 1.0% (-0.6, 

2.5) 

Cardiovascular: 

Lag 1: -0.5% (-0.4, 

1.3) 

Pereira et al. 

(1998) 

São Paulo, Brazil 

1991-1992 

Intrauterine 

mortality 

24-h avg 82 ppb PM10, O3, SO2, CO 0-4 Poisson GLM. 

Time-series study. 

Single-pollutant 

model: 5.1% (2.8, 

7.5); 

With other 

pollutants: 4.7% 

(1.6, 7.9) 

Saldiva et al. 

(1994) 

São Paulo, Brazil 

1990-1991 

Respiratory; age 

<5 yrs 

24-h avg NOX

127 ppb 

PM10, O3, SO2, CO; 

multipollutant models 

0-2 OLS of raw or 

transformed data. Timeseries study. 

NOX slope 

estimate: 

0.007197 

deaths/day/ppb 

(SE 0.003214), p 

= 0.025 
6-135 

REFERENCE, 

STUDY 

LOCATION, 

AND PERIOD 

OUTCOME 

MEASURE 

MEAN NO2 

LEVELS 

COPOLLUTANTS 

CONSIDERED 

LAG 

STRUCTURE 

REPORTED 

METHOD/DESIGN EFFECT 

ESTIMATES 

Saldiva et al. 

(1995) 

São Paulo, Brazil 

1990-1991 

All cause; age 

65+ yrs 

24-h avg NOX

127 ppb 

PM10, O3, SO2, CO; 

two-pollutant models 

0-1 OLS; Poisson with GEE. 

Time-series study. 

NOX slope 

estimate: 

0.0341 

deaths/day/ppb 

(SE 0.0105) 

Cifuentes et al. 

(2000) Santiago, 

Chile 

1988-1966 

All cause 8-h avg 41 ppb PM2.5, PM10-2.5, CO, 

SO2, O3

0, 1, 2, 3, 4, 5, 

1-2, 1 3, 1-4, 1-5

Poisson GAM with 

default convergence 

criteria; Poisson GLM. 

Time-series study. 

GLM model, lag 1-

2: 

Single-pollutant: 

1.7% (0.7, 2.7); 

with other 

pollutants: 1.5% 

(0.3, 2.7) 

(per 25ppb 8-h 

avg) 

Ostro et al. 

(1996) Santiago, 

Chile 1989-1991 

All cause 1-h max 56 ppb PM10, O3, SO2; twopollutant models 

1 OLS, Poisson. 

Time-series study. 

Poisson, lag 1: -

0.5% (-1.1, 0) 

AUSTRALIA 

Simpson Et Al. 

(2005a,B) 

Brisbane, 

Sydney, 

Melbourne, And 

Perth, Australia 

1996-1999 

All Cause, 

Respiratory, And 

Cardiovascular In 

All Ages; 

Cardiovascular In 

Age 65+ Yrs 

1-H Max Ranged 

From 16 To 

24 ppb 

PM10, PM2.5, Bsp 

(Nephelometer), O3, 

Co 

0, 1, 2, 3, 0-1 Poisson Glm, Gam With 

Stringent Convergence 

Criteria. Time-Series 

Study. 

Lag 0-1, Gam, AllCause, 

Single-Pollutant: 

3.4% (1.1, 5.7); 

With Bsp: 3.1% 

(0.3, 5.9); 

Cardiovascular: 

4.3% (0.9, 7.8); 

Respiratory: 

11.4% (3.5, 19.9) 

Simpson et al. 

(2000) 

Brisbane, 

Australia 

1991-1996 

All cause, 

respiratory, and 

cardiovascular in 

all ages; 

cardiovascular in 

age 65+ yrs 

24-h avg: whole 

yr: 12 ppb; cool 

season: 13 ppb; 

warm season 

9 ppb 

PM10, PM2.5, BSP, O3, 

CO 

0, 1, 2, 3, 0-1 Poisson, GAM with 

default convergence 

criteria. Time-series 

study. 

All-cause (lag 1): 

9.7% (4.7, 14.8); 

respiratory: 18.8% 

(1.2, 39.6) 

Morgan et al. 

(1998b) 

Sydney, Australia 

1989-1993 

All cause; 

respiratory; 

cardiovascular 

24-h avg 13 ppb; 

1-h max 26 ppb 

BSP, O3 0-1 Poisson with GEE. 

Time-series study. 

Lag 0-1, singlepollutant, allcause: 3.0% (0.1, 

6.0); 

cardiovascular: 

2.2% (-1.7, 6.4); 

respiratory: 8.6% 

(-0.4, 18.4) 

Simpson et al. 

(1997) 

Brisbane, 

Australia 

1987-1993 

All cause; 

respiratory; 

cardiovascular 

24-h avg 14 ppb; 

1-h max 28 ppb 

PM10, TSP, O3, SO2, 

CO 

0 Autoregressive Poisson 

with GEE. Time-series 

study. 

Lag 0-1, singlepollutant, allcause, all-yr: -

1.0% (-5.2, 3.4); 

summer: -3.6% (-

11.2, 4.7); winter: -

1.2% (-4.0, 6.9) 

ASIA 

Kim et al. 

(2004b) 

Seoul, Korea 

1995-1999 

All cause 24-h avg 33 ppb PM10, O3, SO2, CO; 

two-pollutant models 

1 Poisson GAM with 

stringent convergence 

criteria (linear model); 

GLM with cubic natural 

spline; GLM with B 

mode spline (threshold 

model). Time-series 

study. 

Risk estimates for 

NO2 not reported. 
6-136 

REFERENCE, 

STUDY 

LOCATION, 

AND PERIOD 

OUTCOME 

MEASURE 

MEAN NO2 

LEVELS 

COPOLLUTANTS 

CONSIDERED 

LAG 

STRUCTURE 

REPORTED 

METHOD/DESIGN EFFECT 

ESTIMATES 

Lee et al. (1999) 

Seoul and Ulsan, 

Korea 

1991-1995 

All cause 1-h max O3: 

Seoul: 

32.4 ppb 

10th %-90th % 

14-55 

Ulsan: 

26.0 ppb 

10th %-90th % 

16-39 

TSP, SO2 0 Poisson with GEE. 

Time-series study. 

1-h max O3 (per 

50 ppb): 

Seoul: 

1.5% (0.5, 2.5) 

Ulsan: 

2.0% (-11.1, 17.0) 

Lee and 

Schwartz (1999) 

Seoul, Korea 

1991-1995 

All cause 1-h max O3: 

Seoul: 

32.4 ppb 

10th %-90th % 

14-55 

TSP, SO2 0 Conditional logistic 

regression. Casecrossover with 

bidirectional control 

sampling. 

1-h max O3 

(per 50 ppb): 

Two controls -- 1 

wk: 

1.5% (-1.2, 4.2) 

Four controls -- 2 

wks: 

2.3% (-0.1, 4.8) 

Kwon et al. 

(2001) 

Seoul, Korea 

1994-1998 

Mortality in a 

cohort of patients 

with congestive 

heart failure 

24-h avg 32 ppb PM10, O3, SO2, CO 0 Poisson GAM with 

default convergence 

criteria; case-crossover 

analysis using 

conditional logistic 

regression. 

Odds ratio in 

general 

population: 

1.1% (-0.3, 2.5) 

Congestive heart 

failure cohort: 

15.8% (1.8, 31.7) 

Ha et al. (2003) 

Seoul, Korea 

1995-1999 

All cause; 

respiratory; 

postneonatal (1 

mo to 1 yr); age 2 

64 yrs; age 65+ 

24-h avg 33 ppb PM10, O3, SO2, CO 0 Poisson GAM with 

default convergence 

criteria. Time-series 

study. 

All cause for 

postneonates: 

0.8% (-5.7, 7.7); 

age 65+: 3.8% 

(3.7, 3.9) 

Hong et al. 

(2002) 

Seoul, Korea 

1995-1998 

Acute stroke 

mortality 

24-h avg 33 ppb PM10, O3, SO2, CO 2 Poisson GAM with 

default convergence 

criteria. Time-series 

study. 

4.3% (1.6, 7.0) 

Tsai et al. 

(2003b) 

Kaohsiung, 

Taiwan 

1994-2000 

All cause; 

respiratory; 

cardiovascular; 

tropical area 

24-h avg 29 ppb PM10, SO2, O3, CO 0-2 Conditional logistic 

regression. Casecrossover analysis. 

Odds ratios: 

All cause: 

0.1% (-5.9, 6.6); 

Respiratory: 

-1.0% (-22.2, 

25.9); 

Cardiovascular: 

-1.8% (-14.0, 12.1)

Yang et al. 

(2004b) 

Taipei, Taiwan 

1994-1998 

All cause; 

respiratory; 

cardiovascular; 

subtropical area 

24-h avg 31 ppb PM10, SO2, O3, CO 0-2 Conditional logistic 

regression. Casecrossover analysis. 

Odds ratios: 

All cause: 0.6% (-

3.9, 5.2); 

Respiratory: 2.5% 

(-13.1, 20.8); 

Cardiovascular: 

-1.1% (-9.5, 8.0) 
6-137 

REFERENCE, 

STUDY 

LOCATION, 

AND PERIOD 

OUTCOME 

MEASURE 

MEAN NO2 

LEVELS 

COPOLLUTANTS 

CONSIDERED 

LAG 

STRUCTURE 

REPORTED 

METHOD/DESIGN EFFECT 

ESTIMATES 

Wong et al. 

(2001b) 

Hong Kong 

1995-1997 

All cause; 

respiratory; 

cardiovascular 

24-h avg 25 ppb 

in warm season; 

33 ppb in cold 

season 

PM10, O3, SO2; twopollutant models 0, 1, 2 Poisson GAM with 

default convergence 

criteria. Time-series 

study. 

All cause (lag 1): 

2.6% (0.9, 4.4); 

Respiratory (lag 

0): 

6.1% (-1.8, 10.5); 

Cardiovascular 

(lag 2): 

5.2% (1.8, 8.7) 

Wong et al. 

(2002) 

Hong Kong 

1995-1998 

Respiratory; 

cardiovascular; 

COPD; 

pneumonia and 

influenza; 

ischemic heart 

dis.; 

cerebrovascular 

24-h avg 29 ppb PM10, O3, SO2; twopollutant models 

0, 1, 2, 0-1, 0-2 Poisson GLM. Timeseries study. Respiratory (0-1): 

5.1% (1.6, 8.7); 

Cardiovascular 

(lag 0-2): 

3.1% (-0.2, 6.5) 

Hedley et al. 

(2002) 

Hong Kong 

1985-1995 

Intervention July 

1990 (switch to 

low sulfurcontent fuel) 

All cause; 

cardiovascular; 

respiratory; 

neoplasms and 

other causes; all 

ages; age 15-64 

yrs; age 65+ yrs 

Avg moly NO2: 

Baseline: 29 ppb 

1 yr after 

intervention: 

25 ppb 

2-5 yrs after 

intervention: 

28 ppb 

SO2 (main pollutant of 

interest, 45% 

reduction observed 

5 yrs after 

intervention), PM10, 

SO42-, NO2 

Moly avgs 

considered 

without lags 

Poisson regression of 

moly avgs to estimate 

changes in the increase 

in deaths from warm to 

cool season. Annual 

proportional change in 

death rate before and 

after the intervention 

was also examined. 

Declines observed 

in all cause (2.1%, 

p = 0.001), 

respiratory 

(3.9%, p = 0.001), 

and 

cardiovascular 

(2.0%, p = 0.020) 

mortality after the 

intervention. 

As NO2 levels did 

not change before 

and after the 

intervention, NO2 

likely did not play 

a role in the 

decline in 

observed mortality.

Yang et al. 

(2004b) 

Taipei, Taiwan 

1994-1998 

All cause; 

respiratory; 

cardiovascular; 

subtropical area 

24-h avg 31 ppb PM10, SO2, O3, CO 0-2 Conditional logistic 

regression. Casecrossover analysis. 

Odds ratios: 

All cause: 

0.6% (-3.9, 5.2); 

Respiratory: 

2.5% (-13.1, 20.8); 

Cardiovascular: 

-1.1% (-9.5, 8.0) 

 
1 

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