1 March 2009 

2 EPA/600/R-09/019 

Integrated Science Assessment for 

Carbon Monoxide – 

First External Review Draft 

National Center for Environmental Assessment-RTP Division 

Office of Research and Development 

U.S. Environmental Protection Agency 

Research Triangle Park, NC 

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Disclaimer 

This document is the first external review draft for review purposes only and does not constitute 

U.S. Environmental Protection Agency policy. Mention of trade names or commercial products does not 

constitute endorsement or recommendation for use. 

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Table of Contents 

Table of Contents__________________________________________________________________________________________________ v

List of Figures ___________________________________________________________________________________________________ xi

Acronyms and Abbreviations______________________________________________________________________________________ xxii

Chapter 1. Introduction ___________________________________________________________________________________________ 1-1

1.1. Legislative Requirements ____________________________________________________________________________ 1-2

1.2. History of the NAAQS for CO _________________________________________________________________________ 1-4

1.3. Document Development _____________________________________________________________________________ 1-5

1.4. Document Organization______________________________________________________________________________ 1-5

1.5. Document Scope ___________________________________________________________________________________ 1-6

1.6. EPA Framework for Causal Determination _______________________________________________________________ 1-7

1.6.1. Scientific Evidence Used in Establishing Causality____________________________________________________ 1-8

1.6.2. Association and Causation ______________________________________________________________________ 1-8

1.6.3. Evaluating Evidence for Inferring Causation _________________________________________________________ 1-9

1.6.4. Application of Framework for Causal Determination __________________________________________________ 1-13

1.6.5. Determination of Causality _____________________________________________________________________ 1-14

1.6.6. Concepts in Evaluating Adversity of Health Effects __________________________________________________ 1-18

1.7. Summary ________________________________________________________________________________________ 1-18

Chapter 2. Integrative Health Effects Overview _______________________________________________________________________ 2-1

2.1. Ambient Concentrations, Sources, and Exposure__________________________________________________________ 2-2

2.2. Dosimetry, Pharmacokinetics, and Mode of Action_________________________________________________________ 2-4

2.2.1. Dosimetry and Pharmacokinetics _________________________________________________________________ 2-4

2.2.2. Mode of Action________________________________________________________________________________ 2-4

2.3. Health Effects _____________________________________________________________________________________ 2-6

2.3.1. Cardiovascular Morbidity ________________________________________________________________________ 2-6

2.3.2. Central Nervous System Effects __________________________________________________________________ 2-7

2.3.3. Birth Outcomes and Developmental Effects _________________________________________________________ 2-9

2.3.4. Respiratory Morbidity__________________________________________________________________________ 2-10

2.3.5. Mortality ____________________________________________________________________________________ 2-11

2.4. Public Health Impacts ______________________________________________________________________________ 2-13

2.4.1. Concentration-Response Relationship ____________________________________________________________ 2-13

2.4.2. Potentially Susceptible and Vulnerable Subpopulations _______________________________________________ 2-14

Chapter 3. Source to Exposure ____________________________________________________________________________________ 3-1

3.1. Introduction _______________________________________________________________________________________ 3-1

3.2. Sources and Emissions of CO_________________________________________________________________________ 3-1

3.3. Physics and Chemistry of Atmospheric CO_______________________________________________________________ 3-8

3.4. Ambient Measurements______________________________________________________________________________ 3-9

3.4.1. Ambient Measurement Instruments_______________________________________________________________ 3-10

3.4.2. Ambient Sampling Network Design _______________________________________________________________ 3-12

3.4.2.1. Monitor Siting Requirements _____________________________________________________________ 3-12

3.4.2.2. Spatial and Temporal Coverage __________________________________________________________ 3-12

3.5. Environmental Concentrations________________________________________________________________________ 3-20

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3.5.1. Spatial Variability _____________________________________________________________________________ 3-20

3.5.1.1. National Scale ________________________________________________________________________ 3-20

3.5.1.2. Urban Scale __________________________________________________________________________ 3-28

3.5.1.3. Neighborhood Scale____________________________________________________________________ 3-39

3.5.2. Temporal Variability___________________________________________________________________________ 3-44

3.5.2.1. Multi-year Trends ______________________________________________________________________ 3-44

3.5.2.2. Hourly Variation _______________________________________________________________________ 3-46

3.5.3. Associations with Copollutants __________________________________________________________________ 3-50

3.5.4. Policy-Relevant Background ____________________________________________________________________ 3-52

3.6. Issues in Exposure Assessment ______________________________________________________________________ 3-54

3.6.1. Summary of Findings from 2000 CO AQCD ________________________________________________________ 3-54

3.6.2. General Exposure Concepts ____________________________________________________________________ 3-55

3.6.3. Monitoring Issues Associated with Exposure Assessment _____________________________________________ 3-58

3.6.3.1. Exposure Assessment using Community-Based Ambient Monitors _______________________________ 3-58

3.6.3.2. Personal Exposure Monitors _____________________________________________________________ 3-58

3.6.4. Indoor/Outdoor Relationships and Infiltration _______________________________________________________ 3-59

3.6.5. Personal/Ambient Relationships _________________________________________________________________ 3-60

3.6.5.1. Panel and Population Exposure Studies ____________________________________________________ 3-60

3.6.5.2. Commuting Time CO Exposure Studies ____________________________________________________ 3-62

3.6.5.3. CO Exposure Assessment Variability and Error ______________________________________________ 3-65

3.6.6. Multi-pollutant Exposures ______________________________________________________________________ 3-67

3.6.7. Exposure Modeling ___________________________________________________________________________ 3-68

3.7. Summary and Conclusions __________________________________________________________________________ 3-71

3.7.1. Sources of CO _______________________________________________________________________________ 3-71

3.7.2. Physics and Chemistry of Atmospheric CO_________________________________________________________ 3-71

3.7.3. Ambient CO Measurements ____________________________________________________________________ 3-72

3.7.4. Environmental CO Concentrations _______________________________________________________________ 3-72

3.7.5. Exposure Assessment and Implications for Epidemiology _____________________________________________ 3-73

Chapter 4. Dosimetry and Pharmacokinetics of Carbon Monoxide _______________________________________________________ 4-1

4.1. Introduction _______________________________________________________________________________________ 4-1

4.2. Carboxyhemoglobin Formation ________________________________________________________________________ 4-2

4.2.1. The Coburn-Forster-Kane and Other Models ________________________________________________________ 4-2

4.2.2. Multicompartment Model ________________________________________________________________________ 4-5

4.2.3. Mathematical Model Usage ______________________________________________________________________ 4-6

4.3. Absorption, Distribution, and Elimination_________________________________________________________________ 4-7

4.3.1. Pulmonary Absorption __________________________________________________________________________ 4-7

4.3.1.1. Mass Transfer of Carbon Monoxide_________________________________________________________ 4-8

4.3.1.2. Lung Diffusion of Carbon Monoxide_________________________________________________________ 4-9

4.3.2. Tissue Uptake _______________________________________________________________________________ 4-10

4.3.2.1. The Respiratory Tract___________________________________________________________________ 4-10

4.3.2.2. The Blood ____________________________________________________________________________ 4-10

4.3.2.3. Heart and Skeletal Muscle _______________________________________________________________ 4-12

4.3.2.4. Other Tissues _________________________________________________________________________ 4-13

4.3.3. Pulmonary and Tissue Elimination _______________________________________________________________ 4-13

4.4. Conditions Affecting Uptake and Elimination_____________________________________________________________ 4-15

4.4.1. Environment and Activity _______________________________________________________________________ 4-15

4.4.2. Altitude_____________________________________________________________________________________ 4-16

4.4.3. Physical Characteristics________________________________________________________________________ 4-17

4.4.4. Health Status ________________________________________________________________________________ 4-18

4.5. Endogenous CO Production and Metabolism ____________________________________________________________ 4-18

4.6. Summary and Conclusions __________________________________________________________________________ 4-20

Chapter 5. Integrated Health Effects ________________________________________________________________________________ 5-1

5.1. Mode of Action of CO Toxicity _________________________________________________________________________ 5-1

5.1.1. Introduction __________________________________________________________________________________ 5-1

5.1.2. Hypoxic Mechanisms___________________________________________________________________________ 5-1

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5.1.3. Non-Hypoxic Mechanisms_______________________________________________________________________ 5-2

5.1.3.1. Non-Hypoxic Mechanisms Reviewed in the 2000 CO AQCD _____________________________________ 5-2

5.1.3.2. Recent Studies of Non-Hypoxic Mechanisms _________________________________________________ 5-4

5.1.3.3. Implications of Non-Hypoxic Mechamisms __________________________________________________ 5-11

5.2. Cardiovascular Effects______________________________________________________________________________ 5-13

5.2.1. Epidemiologic Studies _________________________________________________________________________ 5-13

5.2.1.1. Epidemiologic Studies with Short-Term Exposure_____________________________________________ 5-14

5.2.1.2. Epidemiologic Studies with Long-Term Exposure _____________________________________________ 5-44

5.2.1.3. Summary of Epidemiologic Studies of Exposure to CO and Cardiovascular Effects __________________ 5-44

5.2.2. Controlled Human Exposure Studies _____________________________________________________________ 5-45

5.2.3. Toxicological Studies__________________________________________________________________________ 5-48

5.2.3.1. Endothelial Dysfunction _________________________________________________________________ 5-48

5.2.3.2. Cardiac Remodeling Effects______________________________________________________________ 5-50

5.2.3.3. Electrocardiographic Effects _____________________________________________________________ 5-52

5.2.3.4. Summary of Cardiovascular Toxicology_____________________________________________________ 5-52

5.2.4. Summary of Cardiovascular Effects ______________________________________________________________ 5-53

5.3. Central Nervous System Effects ______________________________________________________________________ 5-54

5.3.1. Controlled Human Exposure Studies _____________________________________________________________ 5-54

5.3.2. Toxicological Studies__________________________________________________________________________ 5-55

5.3.3. Summary of Central Nervous System Effects _______________________________________________________ 5-55

5.4. Birth Outcomes and Developmental Effects _____________________________________________________________ 5-56

5.4.1. Epidemiologic Studies _________________________________________________________________________ 5-56

5.4.1.1. Preterm Birth _________________________________________________________________________ 5-57

5.4.1.2. Birth Weight, Low Birth Weight, and Intrauterine Growth Restriction/Small for Gestational Age _________ 5-61

5.4.1.3. Congenital Anomalies __________________________________________________________________ 5-71

5.4.1.4. Neonatal and Post-Neonatal Mortality ______________________________________________________ 5-73

5.4.1.5. Summary of Epidemiologic Studies of Birth Outcomes and Developmental Effects ___________________ 5-75

5.4.2. Toxicological Studies of Birth Outcomes and Developmental Effects_____________________________________ 5-75

5.4.2.1. Birth Outcomes________________________________________________________________________ 5-75

5.4.2.2. Developmental Effects __________________________________________________________________ 5-83

5.4.3. Summary of Birth Outcomes and Developmental Effects ______________________________________________ 5-94

5.5. Respiratory Effects ________________________________________________________________________________ 5-95

5.5.1. Epidemiologic Studies with Short-Term Exposure ___________________________________________________ 5-95

5.5.1.1. Pulmonary Function, Respiratory Symptoms, and Medication Use________________________________ 5-95

5.5.1.2. Respiratory Hospital Admissions, ED Visits and Physician Visits ________________________________ 5-103

5.5.2. Epidemiologic Studies with Long-Term Exposure___________________________________________________ 5-113

5.5.2.1. Pulmonary Function ___________________________________________________________________ 5-113

5.5.2.2. Asthma and Asthma Symptoms__________________________________________________________ 5-114

5.5.2.3. Allergic Rhinitis_______________________________________________________________________ 5-115

5.5.2.4. Summary of Associations between Long-Term Exposure to CO and Respiratory Morbidity ___________ 5-115

5.5.3. Controlled Human Exposure Studies ____________________________________________________________ 5-115

5.5.4. Toxicological Studies_________________________________________________________________________ 5-116

5.5.5. Summary of Respiratory Health Effects __________________________________________________________ 5-117

5.5.5.1. Short-Term Exposure to CO ____________________________________________________________ 5-117

5.5.5.2. Long-Term Exposure to CO _____________________________________________________________ 5-118

5.6. Mortality ________________________________________________________________________________________ 5-119

5.6.1. Epidemiologic Studies with Short-Term Exposure to CO _____________________________________________ 5-119

5.6.1.1. Summary of Findings from 2000 CO AQCD ________________________________________________ 5-119

5.6.1.2. Multicity Studies ______________________________________________________________________ 5-120

5.6.1.3. Meta-Analysis of All Criteria Pollutants ____________________________________________________ 5-127

5.6.1.4. Single-City Studies____________________________________________________________________ 5-128

5.6.1.5. Summary of Mortality and Short-Term Exposure to CO _______________________________________ 5-131

5.6.2. Epidemiologic Studies with Long-Term Exposure to CO _____________________________________________ 5-132

5.6.2.1. U.S. Cohort Studies ___________________________________________________________________ 5-133

5.6.2.2. U.S. Cross-Sectional Analysis ___________________________________________________________ 5-137

5.6.2.3. Summary of Mortality and Long-Term Exposure to CO________________________________________ 5-138

5.7. Public Health Impacts _____________________________________________________________________________ 5-138

5.7.1. Concentration-Response Relationship ___________________________________________________________ 5-139

5.7.2. Potentially Susceptible and Vulnerable Populations _________________________________________________ 5-140

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5.7.2.1. Susceptibility Characteristics ____________________________________________________________ 5-141

5.7.2.2. Vulnerability Characteristics_____________________________________________________________ 5-144

Annex A. Atmospheric Science ____________________________________________________________________________________ A-1

Annex B. Dosimetry Studies______________________________________________________________________________________ B-51

Annex C. Epidemiology Studies____________________________________________________________________________________ C-1

Annex D. Controlled Human Exposure Studies _______________________________________________________________________ D-1

Annex E. Toxicological Studies ____________________________________________________________________________________ E-1

References _______________________________________________________________________________________________________ 1

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List of Tables 

Table 1-1. Aspects to aid in judging causality. ____________________________________________________________________ 1-13

Table 1-2. Weight of evidence for causal determination. ____________________________________________________________ 1-15

Table 2-1. Causal determinations for health effects outcomes. ________________________________________________________ 2-6

Table 3-1. Proximity to CO monitors for the total population by city. ___________________________________________________ 3-19

Table 3-2. Proximity to CO monitors for adults aged 65 and older by city. ______________________________________________ 3-19

Table 3-3. Distribution of 1-h avg CO concentration (ppm) derived from AQS data. _______________________________________ 3-23

Table 3-4. Distribution of 24-h avg CO concentration (ppm) derived from AQS data. ______________________________________ 3-25

Table 3-5. Distribution of 1-h daily max CO concentration (ppm) derived from AQS data. __________________________________ 3-26

Table 3-6. Distribution of 8-h daily max CO concentration (ppm) derived from AQS data. __________________________________ 3-27

Table 3-7. Table of inter-sampler comparison statistics, as defined in the text, including Pearson r, P90 (ppm), COD and 

d (km) for each pair of hourly CO monitors reporting to AQS for 2005-2007 in Phoenix, AZ. _______________________ 3-31

Table 3-8. Table of inter-sampler comparison statistics, as defined in the text, including Pearson r, P90 (ppm), COD and 

d (km) for each pair of hourly CO monitors reporting to AQS for 2005-2007 in Pittsburgh, PA.______________________ 3-34

Table 3-9. Percentage of time exposed to ambient CO (adjusted to reflect the absence of non-ambient CO from ETS and gas 

cooking), average CO exposures, and percentage of exposure estimated for the population. ______________________ 3-61

Table 4-1a. CO concentration in pmol/100 g ww tissue – human. _______________________________________________________ 4-8

Table 4-1b. CO concentration in pmol/mg fresh weight – adult mouse.___________________________________________________ 4-9

Table 5-1. Responses to low and moderate CO exposures. __________________________________________________________ 5-9

Table 5-2. Tissue concentration of CO following inhalation exposure.__________________________________________________ 5-12

Table 5-3. Tissue concentration of CO following increased endogenous production. ______________________________________ 5-12

Table 5-4. Summary of studies investigating the effect of CO exposure on HRV parameters. _______________________________ 5-17

Table 5.5. Summary of studies investigating the effect of CO exposure on cardiac arrhythmias. _____________________________ 5-20

Table 5-6. Summary of studies investigating the effect of CO exposure on blood markers of coagulation and inflammation. _______ 5-24

Table 5-7. Summary of IHD hospital admission studies.1____________________________________________________________ 5-29

Table 5-8. Summary of stroke hospital admission studies.1 __________________________________________________________ 5-34

Table 5-9. Summary of HF hospital admission studies. _____________________________________________________________ 5-37

Table 5-10. Association of ambient air pollution levels and cardiovascular morbidity in visits with and without specific 

secondary conditions. ______________________________________________________________________________ 5-38

Table 5-11. Summary of non-specific CVD hospital admission studies. _________________________________________________ 5-41

Table 5-12. Brief summary of PTB studies. _______________________________________________________________________ 5-61

Table 5-13. Brief summary of birth weight studies.__________________________________________________________________ 5-70

Table 5-14. Range of CO concentrations reported in key respiratory morbidity studies that examined effects associated with 

short-term exposure to CO. __________________________________________________________________________ 5-96

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Table 5-15. Range of CO concentrations reported in key respiratory hospital admission and ED visit studies that examine 

effects associated with short-term exposure to CO. ______________________________________________________ 5-104

Table 5-16. Range of CO concentrations reported in multi-city studies that examine mortality effects associated with short-term 

exposure to CO.__________________________________________________________________________________ 5-120

Table 5-17. Range of CO concentrations reported in single-city studies that examine mortality effects associated with shortterm exposure to CO. _____________________________________________________________________________ 5-128

Table 5-18. Characteristics of susceptible subpopulations. __________________________________________________________ 5-140

Table 5-19. Characteristics of vulnerable subpopulations. ___________________________________________________________ 5-141

Table A-1. Table of inter-sampler comparison statistics, including Pearson r, P90 (ppm), COD, and d (km), as defined in the 

text, for each pair of hourly CO monitors reporting to AQS in Anchorage, AK.___________________________________ A-17

Table A-2. Table of inter-sampler comparison statistics, including Pearson r, P90 (ppm), COD, and d (km), as defined in the 

text, for each pair of hourly CO monitors reporting to AQS in Atlanta, GA.______________________________________ A-19

Table A-3. Table of inter-sampler comparison statistics, including Pearson r, P90 (ppm), COD, and d (km), as defined in the 

text, for each pair of hourly CO monitors reporting to AQS in Boston, MA. _____________________________________ A-22

Table A-4. Table of inter-sampler comparison statistics, including Pearson r, P90 (ppm), COD, and d (km), as defined in the 

text, for each pair of hourly CO monitors reporting to AQS in Denver, CO. _____________________________________ A-25

Table A-5. Table of inter-sampler comparison statistics, including Pearson r, P90 (ppm), COD, and d (km), as defined in the 

text, for each pair of hourly CO monitors reporting to AQS in Houston, TX._____________________________________ A-28

Table A-6. Table of inter-sampler comparison statistics, including Pearson r, P90 (ppm), COD, and d (km), as defined in the 

text, for each pair of hourly CO monitors reporting to AQS in Los Angeles, CA. _________________________________ A-31

Table A-7. Table of inter-sampler comparison statistics, including Pearson r, P90 (ppm), COD, and d (km), as defined in the 

text, for each pair of hourly CO monitors reporting to AQS in New York City, NY. ________________________________ A-37

Table A-8. Table of inter-sampler comparison statistics, including Pearson r, P90 (ppm), COD, and d (km), as defined in the 

text, for each pair of hourly CO monitors reporting to AQS in St. Louis, MO. ____________________________________ A-41

Table B-1. Recent studies related to CO dosimetry and pharmacokinetics.______________________________________________ B-51

Table C-1. Studies of CO exposure and cardiovascular morbidity.______________________________________________________ C-1

Table C-2. Studies of CO exposure and cardiovascular hospital admissions and ED visits. __________________________________ C-8

Table C-3. Studies of CO exposure and neonatal and postneonatal outcomes. __________________________________________ C-15

Table C-4. Studies of short-term CO exposure and respiratory morbidity _______________________________________________ C-22

Table C-5. Studies of short-term CO exposure and respiratory hospital admissions and ED visits. ___________________________ C-28

Table C-6. Studies of long-term CO exposure and respiratory morbidity. _______________________________________________ C-48

Table C-7. Studies of short-term CO exposure and mortality. ________________________________________________________ C-51

Table C-8. Studies of long-term CO exposure and mortality. _________________________________________________________ C-72

Table D-1. Controlled human exposure studies. ____________________________________________________________________ D-1

Table E-1. Human and animal studies. ___________________________________________________________________________ E-1

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List of Figures 

Figure 3-1. CO emissions (MT) in the U.S. by source sector in 2002. ___________________________________________________ 3-2

Figure 3-2. Trends in anthropogenic CO emissions (MT) in the U.S. by source category for 1990 and 1996-2002. ________________ 3-3

Figure 3-3. CO concentrations measured by satellite at the 700 hectoPascal level (~10,000 feet above sea level) from 

MOPITT for the period 15-23 July 2004 during intense wildfires in Alaska and Yukon. _____________________________ 3-6

Figure 3-4. Trends in sub-national CO emissions in the 10 U.S. EPA Regions for 1990 and 1996 to 2002. ______________________ 3-6

Figure 3-5. CO emissions density map and distribution for the state of Colorado, and for selected counties in Colorado. ___________ 3-7

Figure 3-6. Map of ~385 CO monitor locations in the U.S. in 2007. ____________________________________________________ 3-14

Figure 3-7. Map of CO monitor locations with respect to population density in the Phoenix, AZ CBSA, total population. ___________ 3-15

Figure 3-8. Map of CO monitor locations with respect to population density in the Phoenix, AZ CBSA, age 65 and older. __________ 3-16

Figure 3-9. Map of CO monitor locations with respect to population density in the Pittsburgh, PA CSA, total population.___________ 3-17

Figure 3-10. Map of CO monitor locations with respect to population density in the Pittsburgh, PA CSA, age 65 and older. _________ 3-18

Figure 3-11. County-level map of second-highest 1-h avg CO concentrations in the U.S. in 2007. _____________________________ 3-21

Figure 3-12. County-level map of second-highest 8-h avg CO concentrations in the U.S. in 2007. _____________________________ 3-22

Figure 3-13. Map of CO monitor locations with AQS Site IDs for Phoenix, AZ. ____________________________________________ 3-29

Figure 3-14. Box plots illustrating the seasonal distribution of 2005-2007 hourly CO concentrations in Phoenix, AZ. Note: 1 = 

winter, 2 = spring, 3 = summer, and 4 = fall on the x-axis. __________________________________________________ 3-30

Figure 3-15. Map of CO monitor locations with AQS Site IDs for Pittsburgh, PA. ___________________________________________ 3-32

Figure 3-16. Box plots illustrating the seasonal distribution of 2005-2007 hourly CO concentrations in Pittsburgh, PA. _____________ 3-33

Figure 3-17. Aerial view of the location of CO monitor C. _____________________________________________________________ 3-36

Figure 3-18. Aerial view of the location of CO monitor E. _____________________________________________________________ 3-37

Figure 3-19. Aerial view of the location of CO monitors A, B, and C. ____________________________________________________ 3-38

Figure 3-20. Aerial view of the location of CO monitor F. _____________________________________________________________ 3-39

Figure 3-21. Relative concentrations of CO and copollutants at various distances from the 710 freeway in Los Angeles. ___________ 3-41

Figure 3-22. Dimensionless tracer gas concentration as a function of elevation at windward and leeward locations and street 

canyon aspect ratios (H/W). _________________________________________________________________________ 3-42

Figure 3-23. Inter-sampler correlations as a function of distance between CO monitors for samplers located within 4 km 

(neighborhood scale) for Boston, Denver, Phoenix, and Pittsburgh. __________________________________________ 3-43

Figure 3-24. (Top) Trends in ambient CO in the U.S., 1980-2006, reported as the annual second highest 8-h concentrations 

(ppm) for the mean, median, 10% and 90% values. (Bottom) Trends in ambient CO in the U.S., 1980-2006, 

reported as the number of trend sites (y-axis) with annual second-highest 8-h concentrations above the level of the 

NAAQS (9 ppm). __________________________________________________________________________________ 3-44

Figure 3-25. Trends in ambient CO in the U.S., 1980-2005, reported as the annual second highest 8-h concentrations (ppm) for 

the EPA Regions 1 through 10, along with a depiction of the geographic extent of those Regions ___________________ 3-46

Figure 3-26. Diel plot generated from weekday hourly CO data (ppm) for the eleven CSAs and CBSAs 2005-2007. _______________ 3-48

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Figure 3-27. Diel plot generated from weekend hourly CO data (ppm) for the eleven CSAs and CBSAs 2005-2007._______________ 3-49

Figure 3-28. Seasonal plots of nationwide correlations between hourly CO concentration with hourly (1) SO2, (2) NO2, (3) O3, 

(4) PM10, and (5) PM2.5 concentrations._________________________________________________________________ 3-50

Figure 3-29. Map of the baseline monitor sites used in this assessment to compute policy-relevant background concentrations. _____ 3-53

Figures 3-30. Monthly (circles) and annual (squares) average CO concentrations (ppb), 2005-2007 . ___________________________ 3-54

Figure 3-31. Distribution of time that a sample population spends in various environments, from the National Human Activity 

Pattern Survey. ___________________________________________________________________________________ 3-58

Figure 3-32. Comparison of in-vehicle (solid line) and engine (dotted line) results for (left) driving with windows closed and air 

conditioner in recirculating air mode, and (right) driving with windows closed and air conditioner in fresh air mode. _____ 3-63

Figure 3-33. Hourly personal vs. ambient CO concentrations obtained in Baltimore, MD. ____________________________________ 3-66

Figure 3-34. Box plots of the ratio of personal to ambient concentrations obtained in Baltimore, MD. ___________________________ 3-66

Figure 4-1. Plot of fractional sensitivities of selected variables versus time of exposure. _____________________________________ 4-4

Figure 4-2. Overall structure of the multicompartment model of storage and transport of CO._________________________________ 4-6

Figure 4-3. Predicted COHb increments over endogenous levels resulting from 1, 8, 12, and 24 h CO exposures in a modeled 

healthy human at rest. _______________________________________________________________________________ 4-7

Figure 4-4. Diagrammatic presentation of CO uptake and elimination pathways and CO body stores. __________________________ 4-9

Figure 4-5. O2Hb dissociation curve of normal human blood, of blood containing 50% COHb, and of blood with only 50% Hb 

because of anemia. ________________________________________________________________________________ 4-12

Figure 4-6. Changes in blood COHb after short-term and long-term exposure to CO, representing the biphasic nature of CO 

elimination._______________________________________________________________________________________ 4-15

Figure 5-1. Summary of effect estimates (95% confidence intervals) associated with hospital admissions for various froms of 

IHD. ____________________________________________________________________________________________ 5-28

Figure 5-2. Summary of effect estimates (95% confidence intervals) associated with ED visits and hospital admissions for 

stroke. __________________________________________________________________________________________ 5-33

Figure 5.3. Summary of effect estimates (95% confidence intervals) associated with hospital admissions for CHF._______________ 5-36

Figure 5-4. Summary of effect estimates (95% confidence intervals) associated with hospital admissions for CVD. ______________ 5-41

Figure 5-5. Effect estimates from studies of ED visits and hospital admissions for CVD outcomes other than stroke from single 

pollutant (CO only, closed circles) and copollutant (CO plus PM, NO2, SO2 and O3, open circles) models. ____________ 5-43

Figure 5-6. Summary of effect estimates (95% confidence intervals) for PTB associated with maternal exposure to ambient 

CO._____________________________________________________________________________________________ 5-60

Figure 5-7. Summary of change in birth weight (95% confidence intervals) associated with maternal exposure to ambient CO. _____ 5-67

Figure 5-8. Summary of effect estimates (95% confidence intervals) for LBW associated with maternal exposure to ambient 

CO._____________________________________________________________________________________________ 5-68

Figure 5-9. Summary of effect estimates (95% confidence intervals) for SGA associated with maternal exposure to ambient 

CO._____________________________________________________________________________________________ 5-69

Figure 5-10. Estimates for FEV1 change expressed per SD change of the individual pollutants _______________________________ 5-98

Figure 5-11. Asthma symptoms, respiratory symptoms and medication use in asthmatic individuals associated with short-term 

exposure to CO.__________________________________________________________________________________ 5-102

Figure 5-12. Summary of associations between short-term exposure to CO and respiratory hospital admissions.,________________ 5-109

Figure 5-13. Summary of associations between short-term exposure to CO and respiratory ED visits._________________________ 5-112

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Figure 5-14. Posterior means and 95% posterior intervals of national average estimates for CO effects on total (non-accidental) 

mortality at lags 0, 1, and 2 within sets of the 90 U.S. cities with available pollutant data._________________________ 5-122

Figure 5-15. Summary of mortality risk estimates for short-term exposure to CO from multi-city studies. _______________________ 5-127

Figure 5-16. Summary of mortality risk estimates for long-term exposure to CO.__________________________________________ 5-136

Figure A-1. CO emissions density map and distribution for the state of Alaska and for Yukon-Koyukuk County in Alaska.___________ A-1

Figure A-2. CO emissions density map and distribution for the state of Utah and for selected counties in Utah.___________________ A-2

Figure A-3. CO emissions density map and distribution for the state of Massachusetts and for selected counties in 

Massachusetts. ____________________________________________________________________________________ A-3

Figure A-4a. CO emissions density map and distribution for the state of Georgia and for selected counties in Georgia (1 of 2) ________ A-4

Figure A-4b. CO emissions distribution for selected counties in Georgia (2 of 2) ____________________________________________ A-5

Figure A-5. CO emissions density map and distribution for the state of California and for selected counties in California. ___________ A-6

Figure A-6. CO emissions density map and distribution for the state of Alabama and for Jefferson County in Alabama. ____________ A-7

Figure A-7. Map of CO monitor locations with respect to population density in the Anchorage CBSA, total population. _____________ A-8

Figure A-8. Map of CO monitor locations with respect to population density in the Anchorage CBSA, ages 65 and older. ___________ A-8

Figure A-9. Map of CO monitor locations with respect to population density in the Atlanta CSA, total population.__________________ A-9

Figure A-10. Map of CO monitor locations with respect to population density in the Atlanta CSA, ages 65 and older. _______________ A-9

Figure A-11. Map of CO monitor locations with respect to population density in the Boston CSA, total population._________________ A-10

Figure A-12. Map of CO monitor locations with respect to population density in the Boston CSA, ages 65 and older. ______________ A-10

Figure A-13. Map of CO monitor locations with respect to population density in the Denver CSA, total population. ________________ A-11

Figure A-14. Map of CO monitor locations with respect to population density in the Denver CSA, ages 65 and older. ______________ A-11

Figure A-15. Map of CO monitor locations with respect to population density in the Houston CSA, total population. _______________ A-12

Figure A-16. Map of CO monitor locations with respect to population density in the Houston CSA, ages 65 and older. _____________ A-12

Figure A-17. Map of CO monitor locations with respect to population density in the Los Angeles CSA, total population. ____________ A-13

Figure A-18. Map of CO monitor locations with respect to population density in the Los Angeles CSA, ages 65 and older. __________ A-13

Figure A-19. Map of CO monitor locations with respect to population density in the New York City CSA, total population.___________ A-14

Figure A-20. Map of CO monitor locations with respect to population density in the New York City CSA, ages 65 and older. ________ A-14

Figure A-21. Map of CO monitor locations with respect to population density in the Seattle CSA, total population._________________ A-15

Figure A-22. Map of CO monitor locations with respect to population density in the Seattle CSA, ages 65 and older. ______________ A-15

Figure A-23. Map of CO monitor locations with respect to population density in the St. Louis CSA, total population. _______________ A-16

Figure A-24. Map of CO monitor locations with respect to population density in the St. Louis CSA, ages 65 and older. _____________ A-16

Figure A-25. Map of CO monitor locations with AQS Site IDs for Anchorage, AK. __________________________________________ A-17

Figure A-26. Box plots illustrating the seasonal distribution of hourly CO concentrations in Anchorage, AK. Note: 1 = winter, 2, = 

spring, 3 = summer, and 4 = fall on the x-axis. ___________________________________________________________ A-18

Figure A-27. Map of CO monitor locations with AQS Site IDs for Atlanta, GA. _____________________________________________ A-19

March 2009 xiii DRAFT—DO NOT QUOTE OR CITE 
Figure A-28. Box plots illustrating the seasonal distribution of hourly CO concentrations in Atlanta, GA. Note: 1 = winter, 2, = 

spring, 3 = summer, and 4 = fall on the x-axis. ___________________________________________________________ A-20

Figure A-29. Map of CO monitor locations with AQS Site IDs for Boston, MA. _____________________________________________ A-21

Figure A-30. Box plots illustrating the seasonal distribution of hourly CO concentrations in Boston, MA. Note: 1 = winter, 2, = 

spring, 3 = summer, and 4 = fall on the x-axis. ___________________________________________________________ A-23

Figure A-31. Map of CO monitor locations with AQS Site IDs for Denver, CO. _____________________________________________ A-24

Figure A-32. Box plots illustrating the seasonal distribution of hourly CO concentrations in Denver, CO. Note: 1 = winter, 2, = 

spring, 3 = summer, and 4 = fall on the x-axis. ___________________________________________________________ A-26

Figure A-33. Map of CO monitor locations with AQS Site IDs for Houston, TX. ____________________________________________ A-27

Figure A-34. Box plots illustrating the seasonal distribution of hourly CO concentrations in Houston, TX. Note: 1 = winter, 2, = 

spring, 3 = summer, and 4 = fall on the x-axis. ___________________________________________________________ A-29

Figure A-35. Map of CO monitor locations with AQS Site IDs for Los Angeles, CA. _________________________________________ A-30

Figure A-36a. Box plots illustrating the seasonal distribution of hourly CO concentrations in Los Angeles, CA. Note: 1 = winter, 2, 

= spring, 3 = summer, and 4 = fall on the x-axis. _________________________________________________________ A-33

Figure A-36b. Box plots illustrating the seasonal distribution of hourly CO concentrations in Los Angeles, CA. Note: 1 = winter, 2, 

= spring, 3 = summer, and 4 = fall on the x-axis. _________________________________________________________ A-34

Figure A-36c. Box plots illustrating the seasonal distribution of hourly CO concentrations in Los Angeles, CA. Note: 1 = winter, 2, 

= spring, 3 = summer, and 4 = fall on the x-axis. _________________________________________________________ A-35

Figure A-37. Map of CO monitor locations with AQS Site IDs for New York City, NY. _______________________________________ A-36

Figure A-38. Box plots illustrating the seasonal distribution of hourly CO concentrations in New York City, NY. Note: 1 = winter, 

2, = spring, 3 = summer, and 4 = fall on the x-axis. _______________________________________________________ A-38

Figure A-39. Map of CO monitor locations with AQS Site IDs for Seattle, WA. _____________________________________________ A-39

Figure A-40. Box plots illustrating the seasonal distribution of hourly CO concentrations in Seattle, WA. Note: 1 = winter, 2, = 

spring, 3 = summer, and 4 = fall on the x-axis. ___________________________________________________________ A-40

Figure A-41. Map of CO monitor locaitons with AQS Site IDs for St. Louis, MO. ___________________________________________ A-41

Figure A-42. Box plots illustrating the seasonal distribution of hourly CO concentrations in St. Louis, MO. Note: 1 = winter, 2, = 

spring, 3 = summer, and 4 = fall on the x-axis. ___________________________________________________________ A-42

Figure A-43. Seasonal plots of correlations between hourly CO concentration with hourly (1) SO2, (2) NO2, (3) O3, (4) PM10, and 

(5) PM2.5 concentrations for Anchorage, AK. Also shown are correlations between 24-h average CO concentration 

with (6) daily max 1-h and (7) daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h 

CO concentrations. (Refer the numbers in this caption to those on the y-axis of each seasonal plot.) Note that the 

data are not obtained for Anchorage during the summer, and so are not presented here. _________________________ A-43

Figure A-44. Seasonal plots of correlations between hourly CO concentration with hourly (1) SO2, (2) NO2, (3) O3, (4) PM10, and 

(5) PM2.5 concentrations for Atlanta, GA. Also shown are correlations between 24-h average CO concentration with 

(6) daily max 1-h and (7) daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h CO 

concentrations. (Refer the numbers in this caption to those on the y-axis of each seasonal plot.) ___________________ A-44

Figure A-45. Seasonal plots of correlations between hourly CO concentration with hourly (1) SO2, (2) NO2, (3) O3, (4) PM10, and 

(5) PM2.5 concentrations for Boston, MA. Also shown are correlations between 24-h average CO concentration with 

(6) daily max 1-h and (7) daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h CO 

concentrations. (Refer the numbers in this caption to those on the y-axis of each seasonal plot.) Red bars denote 

the median, and green stars denote the arithmetic mean. __________________________________________________ A-45

March 2009 xiv DRAFT—DO NOT QUOTE OR CITE 
Figure A-46. Seasonal plots of correlations between hourly CO concentration with hourly (1) SO2, (2) NO2, (3) O3, (4) PM10, and 

(5) PM2.5 concentrations for Denver, CO. Also shown are correlations between 24-h average CO concentration 

with (6) daily max 1-h and (7) daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h 

CO concentrations. (Refer the numbers in this caption to those on the y-axis of each seasonal plot.) Red bars 

denote the median, and green stars denote the arithmetic mean. ____________________________________________ A-46

Figure A-47. Seasonal plots of correlations between hourly CO concentration with hourly (1) SO2, (2) NO2, (3) O3, (4) PM10, and 

(5) PM2.5 concentrations for Los Angeles, CA. Also shown are correlations between 24-h average CO 

concentration with (6) daily max 1-h and (7) daily max 8-h CO concentrations and (8) between daily max 1-h and 

daily max 8-h CO concentrations. (Refer the numbers in this caption to those on the y-axis of each seasonal plot.) _____ A-47

Figure A-48. Seasonal plots of correlations between hourly CO concentration with hourly (1) SO2, (2) NO2, (3) O3, (4) PM10, and 

(5) PM2.5 concentrations for New York City, NY. Also shown are correlations between 24-h average CO 

concentration with (6) daily max 1-h and (7) daily max 8-h CO concentrations and (8) between daily max 1-h and 

daily max 8-h CO concentrations. (Refer the numbers in this caption to those on the y-axis of each seasonal plot.) _____ A-48

Figure A-49. Seasonal plots of correlations between hourly CO concentration with hourly (1) SO2, (2) NO2, (3) O3, (4) PM10, and 

(5) PM2.5 concentrations for Phoenix, AZ. Also shown are correlations between 24-h average CO concentration 

with (6) daily max 1-h and (7) daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h 

CO concentrations. (Refer the numbers in this caption to those on the y-axis of each seasonal plot.) ________________ A-49

Figure A-50.

________________ A-50

Seasonal plots of correlations between hourly CO concentration with hourly (1) SO2, (2) NO2, (3) O3, (4) PM10, and 

(5) PM2.5 concentrations for Seattle, WA. Also shown are correlations between 24-h average CO concentration 

with (6) daily max 1-h and (7) daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h 

CO concentrations. (Refer the numbers in this caption to those on the y-axis of each seasonal plot.)

March 2009 xv DRAFT—DO NOT QUOTE OR CITE 
Authors and Contributors 

Authors 

Dr. Thomas Long (CO Team Leader)—National Center for Environmental Assessment, 

U.S. Environmental Protection Agency, Research Triangle Park, NC 

Dr. Jeffrey Arnold—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Barbara Buckley—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Mr. Allen Davis—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Steven J. Dutton—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Craig Hansen— Oak Ridge Institute for Science and Education, Postdoctoral Research Fellow to 

National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research 

Triangle Park, NC 

Dr. Erin Hines—National Center for Environmental Assessment, U.S. Environmental Protection Agency, 

Research Triangle Park, NC 

Dr. Douglas Johns—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Thomas Luben—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Elizabeth Oesterling Owens— National Center for Environmental Assessment, U.S. Environmental 

Protection Agency, Research Triangle Park, NC 

Dr. Jennifer Richmond-Bryant—National Center for Environmental Assessment, U.S. Environmental 

Protection Agency, Research Triangle Park, NC 

Dr. Mary Ross—National Center for Environmental Assessment, U.S. Environmental Protection Agency, 

Research Triangle Park, NC 

Mr. Jason Sacks—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Matthew Campen—Lovelace Respiratory Research Institute, Albuquerque, NM 

Dr. Kazuhiko Ito—Department of Environmental Medicine, New York University School of Medicine, 

Tuxedo, NY 

Dr. Jennifer Peel—Department of Environmental and Radiological Health Sciences, Colorado State 

University, Fort Collins, CO 

March 2009 xvi DRAFT—DO NOT QUOTE OR CITE 
Contributors 

Dr. Richard Baldauf—National Risk Management Research Laboratory, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Vernon Benignus—National Health and Environmental Effects Laboratory, U.S. Environmental 

Protection Agency, Research Triangle Park, NC 

Mr. Lance McCluney—Office of Air Quality Planning and Standards, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Kris Novak—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Adam Reff—Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, 

Research Triangle Park, NC 

Mr. Mark Schmidt—Office of Air Quality Planning and Standards, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Ms. Rhonda Thompson—Office of Air Quality Planning and Standards, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Reviewers 

Dr. Richard Baldauf—National Risk Management Research Laboratory, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Vernon Benignus—National Health and Environmental Effects Laboratory, U.S. Environmental 

Protection Agency, Research Triangle Park, NC 

Dr. Souad Benromdhane—Office of Air Quality Planning and Standards, Office of Air and Radiation, 

U.S. Environmental Protection Agency, Research Triangle Park, NC 

Dr. Philip Bromberg—School of Medicine, University of North Carolina, Chapel Hill, NC 

Dr. Matthew Campen—Lovelace Respiratory Research Institute, Albuquerque, NM 

Dr. Daniel Costa—National Program Director for Air, U.S. Environmental Protection Agency, Research 

Triangle Park, NC 

Dr. Andrew Ghio—National Health and Environmental Effects Laboratory, U.S. Environmental 

Protection Agency, Research Triangle Park, NC 

Dr. Kazuhiko Ito—Department of Environmental Medicine, New York University School of Medicine, 

Tuxedo, NY 

Dr. Petros Koutrakis—Harvard School of Public Health, Harvard University, Cambridge, MA 

Mr. John Langstaff—Office of Air Quality Planning and Standards, Office of Air and Radiation, 

U.S. Environmental Protection Agency, Research Triangle Park, NC 

Dr. Barry Lefer—Department of Geosciences, University of Houston, Houston, TX 

Dr. Karen Martin—Office of Air Quality Planning and Standards, Office of Air and Radiation, 

U.S. Environmental Protection Agency, Research Triangle Park, NC 

March 2009 xvii DRAFT—DO NOT QUOTE OR CITE 
Dr. Dave McKee—Office of Air Quality Planning and Standards, Office of Air and Radiation, 

U.S. Environmental Protection Agency, Research Triangle Park, NC 

Ms. Connie Meacham—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Ines Pagan—Office of Air Quality Planning and Standards, Office of Air and Radiation, U.S. 

Environmental Protection Agency, Research Triangle Park, NC 

Dr. Jennifer Parker—National Center for Health Statistics, Centers for Disease Control, Atlanta, GA 

Dr. Jennifer Peel—Department of Environmental and Radiological Health Sciences, Colorado State 

University, Fort Collins, CO 

Dr. Pradeep Rajan—Office of Air Quality Planning and Standards, Office of Air and Radiation, U.S. 

Environmental Protection Agency, Research Triangle Park, NC 

Mr. Harvey Richmond—Office of Air Quality Planning and Standards, Office of Air and Radiation, U.S. 

Environmental Protection Agency, Research Triangle Park, NC 

Dr. Joseph Somers—Office of Transportation and Air Quality Planning and Standards, Office of Air and 

Radiation, U.S. Environmental Protection Agency, Ann Arbor, MI 

Dr. John Vandenberg—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Alan Vette—National Exposure Research Laboratory, U.S. Environmental Protection Agency, 

Research Triangle Park, NC 

Dr. William Vizuete—Department of Environmental Sciences and Engineering, University of North 

Carolina, Chapel Hill, NC 

Ms. Debra Walsh—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Lin Weaver—Department of Internal Medicine, LDS Hospital, Salt Lake City, UT 

Dr. Lewis Weinstock—Office of Air Quality Planning and Standards, Office of Air and Radiation, U.S. 

Environmental Protection Agency, Research Triangle Park, NC 

Mr. Ron Williams—National Exposure Research Laboratory, U.S. Environmental Protection Agency, 

Research Triangle Park, NC 

CO Project Team 

Executive Direction 

Dr. John Vandenberg (Director)—National Center for Environmental Assessment-RTP Division, U.S. 

Environmental Protection Agency, Research Triangle Park, NC 

Ms. Debra Walsh (Deputy Director)—National Center for Environmental Assessment-RTP Division, 

U.S. Environmental Protection Agency, Research Triangle Park, NC 

Dr. Mary Ross (Branch Chief)—National Center for Environmental Assessment, U.S. Environmental 

Protection Agency, Research Triangle Park, NC 

March 2009 xviii DRAFT—DO NOT QUOTE OR CITE 
Scientific Staff 

Dr. Thomas Long (CO Team Leader)—National Center for Environmental Assessment, U.S. 

Environmental Protection Agency, Research Triangle Park, NC 

Dr. Jeffrey Arnold—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Barbara Buckley—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Mr. Allen Davis—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Steven J. Dutton—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Craig Hansen— Oak Ridge Institute for Science and Education, Postdoctoral Research Fellow to 

National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research 

Triangle Park, NC 

Dr. Erin Hines—National Center for Environmental Assessment, U.S. Environmental Protection Agency, 

Research Triangle Park, NC 

Dr. Douglas Johns—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Thomas Luben—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Elizabeth Oesterling Owens— National Center for Environmental Assessment, U.S. Environmental 

Protection Agency, Research Triangle Park, NC 

Dr. Jennifer Richmond-Bryant—National Center for Environmental Assessment, U.S. Environmental 

Protection Agency, Research Triangle Park, NC 

Mr. Jason Sacks—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Technical Support Staff 

Ms. Ellen Lorang— National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Ms. Connie Meacham—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Ms. Deborah Wales—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

March 2009 xix DRAFT—DO NOT QUOTE OR CITE 
Clean Air Scientific Advisory Committee 

CO NAAQS Review Panel 

Chairperson 

Dr. Joseph Brain*, Department of Environmnetal Health, Harvard School of Public Health, Harvard 

University, Boston, MA 

Members 

Dr. James Crapo*, Department of Medicine, National Jewish Medical and Research Center, Denver, CO

Dr. Douglas Crawford-Brown, Department of Environmental Sciences and Engineering, University of 

North Carolina, Chapel Hill, NC 

Dr. Thomas Dahms, Department of Anesthesiology Research and Critical Care, St. Louis University 

School of Medicine, St. Louis, MO 

Dr. Russell R. Dickerson, Department of Meteorology, University of Maryland, College Park, MD 

Dr. Laurence Fechter, Research Service, Department of Veterans Affairs, Loma Linda VA Medical 

Center, Loma Linda, CA 

Dr. H. Christopher Frey*, College of Engineering, Department of Civil, Construction, and Environmental 

Engineering, North Carolina State University, Raleigh, NC

Dr. Milan Hazucha, Department of Medicine, Center for Environmental Medicine, Asthma and Lung 

Biology, University of North Carolina, Chapel Hill, NC 

Dr. Michael T. Kleinman, Department of Community & Environmental Medicine, University of 

California-Irvine, Irvine, CA 

Dr. Arthur Penn, Department of Comparative Biomedical Sciences, Louisiana State University School of 

Veterinary Medicine, Baton Rouge, LA 

Dr. Beate Ritz, School of Public Health, Epidemiology, University of California at Los Angeles, Los 

Angeles, CA 

Dr. Paul Roberts, Sonoma Technology, Inc., Petaluma, CA 

Dr. Armistead (Ted) Russell*, Department of Civil and Environmental Engineering, Georgia Institute of 

Technology, Atlanta, GA 

Dr. Stephen R. Thom, Institute for Environmental Medicine, University of Pennsylvania, Philadelphia, 

PA 

* Members of the statutory Clean Air Scientific Advisory Committee (CASAC) appointed by the EPA 

Administrator 

March 2009 xx DRAFT—DO NOT QUOTE OR CITE 
Science Advisory Board Staff 

Dr. Ellen Rubin, Designated Federal Officer 

1200 Pennsylvania Avenue, N.W. 

Washington, DC, 20460 

Phone: 202-343-9975 

Fax: 202-233-0643 

Email: rubin.ellen@epa.gov

Physical/Courier/FedEx Address: 

Dr. Ellen Rubin, EPA Science Advisory Board Staff Office 

Mail Code 1400F 

Woodies Building, Room 3610E 

1025 F Street, N.W. 

Washington, DC 20004 

March 2009 xxi DRAFT—DO NOT QUOTE OR CITE 
Acronyms and Abbreviations 

α alpha, ambient exposure factor (varies between 0 and 1) 

a air exchange rate of the microenvironment 

AA abdominal aorta(s) 

ABR auditory brainstem response 

ACS American Cancer Society 

ACS-CPS-II ACS Cancer Prevention Study II 

ADP adenosine diphosphate 

AEFV area under the expiratory flow-volume curve 

AGL above ground level 

Akt Akt cell signaling pathway - protein family (of protein kinases B [PKB[) 

AM, a.m. morning hours 

AMI acute myocardial infarction 

AMP adenosine monophosphate 

ANOVA analysis of variance 

APO E apolipoprotein E 

APO E-/- mouse strain used as a model of atherosclerosis 

ARI acute respiratory infection 

AP action potential 

APD action potential duration 

APEX Air Pollution Exposure (population-based model) 

APHEA Air Pollution and Health: A European Approach (multi-cities analyses) 

APHEA2 extended analysis of APHEA 

APTT activated partial thromboplastin time, (blood coagulability endpoint) 

AQ air quality 

AQCD Air Quality Criteria Document 

AQS (EPA) Air Quality System (database) 

AR gastronomy reared with artificial feeding system (milk substitutes using gastronomyfeeding) 

ARCO gastronomy reared + CO exposure 

ARIC Atherosclerosis Risk in Communities (study) 

ARID gastronomy reared with iron deficient diet 

ARIDCO gastronomy reared with iron deficient diet + CO exposure 

ATP adenosine triphosphate 

ATS American Thoracic Society 

avg average 

AVP aortic valve prosthesis (surgically implantable biological prosthesis) 

March 2009 xxii DRAFT—DO NOT QUOTE OR CITE 
β beta (beta coefficient, slope) 

B lymphocytes bursa-dependent lymphocytes 

BALF bronchoalveolar lavage fluid 

BC black carbon 

BEAS-2B human bronchial epithelial cell line 

BEIS Biogenic Emissions Inventory System 

BELD Biogenic Emissions Landcover Database 

BHR bronchial hyper-responsiveness 

BKCa voltage and Ca2+-activated K+

 channel(s) 

BP blood pressure 

BQ-123 a selective endothelin A (ETA) receptor antagonist 

BS black smoke 

BSP black smoke particles 

C carbon 

Ca ambient concentration 

CA cardiac arrhythmia 

Ca2+ calcium ion 

CAA Clean Air Act 

CAD coronary artery disease 

CALINE California Line Source Dispersion Model 

CALPUFF Gaussian puff modeling system to simulate air quality dispersion and accesses long range 

transport of pollutants. Distributed by TRC Solutions. 

CAMP Childhood Asthma Management Program (study) 

cAMP cyclic AMP 

CAP(s) compound action potential(s) 

CASAC Clean Air Scientific Advisory Committee 

CASN Cooperative Air Sampling Network 

CAth cardiac atherosclerosis 

CBSA Core-Based Statistical Area (containing at least one urban area of 10,000 people [as 

determined by the 2000 census]; replaces the older MSA definition from the 1990 

census). 

CCGG Carbon Cycle Greenhouse Gases Group (within ESRL) 

CD cardiac dysrhthmias 

Cd cadmium 

CD-1 mouse strain 

CDC Centers for Disease Control and Prevention 

CdCl2 cadmium chloride 

CFK Coburn-Forster-Kane (equation or modeling) 

CFR Code of Federal Regulations 

cGMP cyclic GMP 

CH2O formaldehyde 

CH2O2 formic acid 

March 2009 xxiii DRAFT—DO NOT QUOTE OR CITE 
CH3 methyl groups 

CH3CHO acetaldehyde 

CH3CO acetyl radical(s) 

CH3CO3NO2 PAN, peroxyacetyl nitrate 

CH3O2 methyl peroxy radical 

CH3OOH methyl hydroperoxide 

CH4 methane 

ChAT choline acetyl-transferase 

CHD coronary heart disease 

CHF congestive heart failure 

CI confidence interval(s) 

CIS cerebral ischemic stroke 

Cj airborne concentration at location j 

CL/P cleft lip with or without palate 

CNS central nervous system 

CO carbon monoxide 

CO2 carbon dioxide 

COD coefficient of divergence 

CoH, COH coefficient of haze (a measurement of visibility interference in the atmosphere, as the 

quantity of dust and smoke in a theoretical 1,000 linear feet of air). 

COHb carboxyhemoglobin (% concentration measured in (mL CO/mL blood)) 

[COHb]0 carboxyhemoglobin concentration at time zero (0) 

[COHb]t carboxyhemoglobin concentration at time t 

COMb carboxymyoglobin 

Complex IV mitochondrial cytochrome c oxidase complex assembly, respiratory chain complex IV in 

the mitochondrial inner membrane. 

Cong U.S. Congress 

CONUS contiguous U.S. (United States of America) 

COPD chronic obstructive pulmonary disease 

CPS II Cancer Prevention Study II 

C-R concentration-response (relationship) 

CRC Coordinating Research Council 

CrMP collapsin response mediator protein 

CRP C-reactive protein 

CSA Combined Statistical Area (an aggregate of adjacent CBSAs tied by specific commuting 

behaviors), as defined by the 2000 census 

Cu copper (element) 

CVD cardiovascular disease 

Δ delta, change, difference 

d straight-line distance between monitor pairs 

Dahl/Rapp salt-sensitive model of hypertension (substrain of Sprague Dawley rat) 

D.C. District of Columbia 

March 2009 xxiv DRAFT—DO NOT QUOTE OR CITE 
df degrees of freedom 

df/yr degrees of freedom per year 

DL lung diffusing capacity 

DLCO lung diffusing capacity of CO ( in mL/min/mmHg) 

DmCO capacity for diffusion of CO into the muscle 

DMT-1 divalent metal transporter-1 protein (transport and detoxification of metals) 

DMV dorsal motor nucleus of the vagus nerve 

DNA deoxyribonucleic acid 

DOCA Deoxycorticosterone acetate 

dP/dtLV left ventricular maximal and minimal first derived pressure (+dP/dtLV, -dP/dtLV) 

dP/dtRV right ventricular maximal and minimal first derived pressure (+dP/dtRV, -dP/dtRV) 

DSA deletion/substitution/addition 

dt time spent in each location 

Σ sigma, sum of a series 

E exposure over some duration 

Ea exposure to pollutant of ambient origin 

E/A mitral ratio of peak early to late diastolic filling velocity (E/A). 

EC elemental carbon 

ED emergency department 

EKG, ECG electrocardiogram; electrical activity of the heart over time, measured by an 

electrocardiograph 

Ena exposure to pollutant of non-ambient origin 

eNOS endothelial nitric oxide synthase 

EPA U.S. Environmental Protection Agency 

EPO erythropoietin (stimulates production of new red blood cells) 

EPR Electron Paramagnetic Resonance (spectroscopy), aka Electron Spin Resonance 

(spectroscopy) 

EPRI Electric Power Research Institute 

ERK1/2 ERK-1 (MAPK p42) and ERK-2 (MAPK p44) (extracellular signal-regulated kinases [in 

cell signaling pathway]) 

ESRL Earth System Research Laboratory (within GMD of NOAA) 

ET-1 endothelin-1; vasoconstrictor that mediates regulation of vascular tone 

ETA endothelin A (ETA) receptor subtype 

ETS environmental tobacco smoke 

EXPOLIS six-city European air pollution study 

Factor VII FVII, a vitamin K-dependent serine protease glycoprotein (also known as stable factor or 

proconvertin), initiates the process of coagulation. 

Factor VIIIC blood-coagulation factor VIII. (antihemophilic factor, part of factor VIII/von Willebrand 

factor complex; acts in the intrinsic pathway of blood coagulation 

FAS apoptosis stimulating fragment 

FC interference filter 

Fe iron 

March 2009 xxv DRAFT—DO NOT QUOTE OR CITE 
Fe2+ ferrous iron 

Fe3+ ferric iron 

FEF forced expiratory flow (Liters/second) 

FEF25-75 determined from the time in seconds at which 25% and 75% of the vital capacity is 

reached. The volume of air exhaled between these two times is the FEF 25–75. This value 

reflects the status of medium and small sized airways. 

FEM Federal equivalent method 

FEV1 forced expiratory volume in 1 second (volume of air exhaled into the spirometer 

mouthpiece in one second, using maximum effort) 

fi fraction of time spent indoors 

FICO fractional concentration of CO in ambient air (in ppm) 

Finf infiltration factor 

fo fraction of time spent outdoors 

FR Federal Register 

FGR fetal growth restriction(s) 

FRM Federal reference method 

FSH follicle stimulating hormone 

FVC forced vital capacity (volume of air that is expelled into the spirometer following a 

maximum inhalation effort) 

FVII Factor VII, a vitamin K-dependent serine protease glycoprotein (also known as stable 

factor or proconvertin), initiates the process of coagulation 

FW fresh weight 

G0/G1 phase Phase of Cell Cycle in which cell commits to mitosis (division) 

g, mg, μg gram(s), milligram(s), microgram(s) 

g/mole unit of molar mass (grams/mole) 

GAM generalized additive model(s) 

GD gestational day 

GEE generalized estimating equations 

GEM gas extraction monitor 

GFAP glial fibrillary acidic protein 

GFC gas filter correlation 

GLM generalized linear models 

GLMM generalized linear mixed models 

GMD Global Monitoring Division (of NOAA) 

GMP guanosine monophosphate 

GSH glutathione 

GSSG oxidized glutathione 

GTP guanosine triphosphate 

GWP(s) global warming potential(s) 

H atomic hydrogen, hydrogen radical, height 

h hour 

H2 molecular hydrogen 

March 2009 xxvi DRAFT—DO NOT QUOTE OR CITE 
H2O water 

H2O2 hydrogen peroxide 

H9c2 rat embryonic cardiomyocytes (cell line) 

Hb hemoglobin 

HC(s) hydrocarbon(s) 

HCFC(s) hydrochlorofluorocarbon(s) 

HCO formyl radical 

HEAPSS Health Effects of Air Pollution among Susceptible Subpopulations (study) 

hectoPascal atmospheric level measured by atmospheric pressure (e.g., 700 hectoPascals ≈10,000 feet 

above sea level) 

HEK293 human embryonic kidney cells (experimentally transformed cell line) 

Hem hemorrhagic (stroke) 

Hep3B Human hepatocarcinoma cell line 

HF heart failure 

HF high energy frequency (HRV parameter) 

HFLFR high energy frequency versus low energy frequency ratio (HRV parameter) 

HH hypobaric hypoxia (measured in torr) 

HIF-1α oxygen-responsive component of the hypoxia-inducible factor (HIF) 1 complex 

his-Fe2+-his iron binding scheme in neuroglobin (Nb); hexacoordinated deoxy ferrous (Fe2+) form of 

Nb, bound to histidine 

HO heme oxygenase (microsomal enzyme that degrades heme protein to form endogenous 

CO and iron (Fe2+)) 

HO2 hydroperoxy radical, hydrogen dioxide 

HO-1 inducible isoform of heme oxygenase (induced by stressors) 

HO-2 constitutively expressed isoform of heme-oxygenase 

HO/CO heme oxygenase/carbon monoxide system (signaling pathway) 

HR heart rate (beats per minute), hazard ratio 

H/R hypoxia followed by reoxygenation 

HRV heart rate variability (beat-to-beat alterations in the heart determined by analyses of time 

and frequency domains in ECG(s). 

HUVEC(s) human umbilical vein endothelial cell(s) 

hν photon 

H/W height to width ratio 

I electrical current 

IARC International Agency for Research on Cancer 

IC inferior colliculus (an auditory integrative section of the midbrain) 

Ica,L electrical current (I) through the L-type Ca2+ channel 

ICAM-1 intercellular adhesion molecule 

ICD implantable cardioverter defibrillator(s) 

ICR substrain of CD1 mouse, Institute for Cancer Research 

IDW inverse-distance-weighted 

IHD ischemic heart disease 

March 2009 xxvii DRAFT—DO NOT QUOTE OR CITE 
IL-6 interleukin-6 

IL-8 interleukin-8 

INDAIR Indoor Air Model 

I/O indoor to outdoor concentration ratio 

IOM Institute of Medicine 

i.p. intraperitoneal injection 

IQR interquartile range 

IR immunoreactivity 

IS ischemic stroke 

ISA Integrated Science Assessment 

Isch ischemic (stroke) 

ITA internal thoracic artery of the heart (coronary artery bypass surgery graft) 

Ito transient outward current 

i.v. intravenous (injection) 

IUGR intrauterine growth restriction 

K+

 potassium ion 

k dissociation rate, rate of CO loss from the microenvironment 

kCO dissociation rate of carbon monoxide from hemoglobin 

kHz kilohertz (1,000 cycles per second; frequency of an electrical signal) 

Km Michaelis Constant; the substrate concentration which gives 1⁄2 Vmax (in the MichaelisMenten equation of enzyme kinetics) 

km kilometer(s) 

Km/Vmax slope of Michaelis-Menten equation; measures the efficency of an enzyme 

kO2 Dissociation rate of oxygen from hemoglobin 

L, dL, mL, μL Liter, deciLiter, milliLiter, microLiter 

lag 0 same day as the hospital, ED, clinic, physician visit 

lag 0-3 the three previous days before the hospital, ED, clinic, physician visit 

LBW low birth weight (<2,500 grams, (≈5lbs, 8 oz)) 

LCA+ leucocyte common antigen cells 

LD lactational day 

LDH lactate dehydrogenase (release of LDH is an indicator of cell membrane damage) 

LDL low-density lipoprotein 

leeward downwind 

LF low energy frequency (HRV parameter) 

LF/HF low energy frequency to high energy frequency ratio (HRV parameter) 

LH luetenizing hormone 

LOAEL lowest observed adverse effect level 

LOD limit of detection 

LOESS locally weighted scatterplot smoothing (modeling method); aka LOWESS 

LPS lipopolysaccharide 

LTP long-term potentiation (hippocampal) 

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LUR land use regression (model[s]) 

LV left ventricle (of heart) 

LV+S left ventricular plus septum 

LVDP left ventricular developed pressure 

LVESP left ventricular end diastolic pressure 

LVSF left ventricular shortening fraction 

LVW left ventricular work 

m, cm, μm, nm meter(s), centimeter, micrometer(s) = micron(s), nanometer(s) 

M, mM, μM, nM, pM Molar, milliMolar, microMolar, nanoMolar, picoMolar 

M Haldane coefficient representing the CO chemical affinity for Hb [or Mb]), Reaction 

mediator. 

ma moving average 

MAPK mitogen-activated protein kinase(s), MAP kinase(s) 

MAO-A monoamine oxidase A (mitochondrial enzyme; deaminates amine-neurotransmitters) 

max maximum 

Mb myoglobin 

MC ultrafine particle mass concentration 

METs metabolic equivalent unit(s) 

mg/m3 unit of chemical concentration in air (milligrams per cubic meter) 

MHC major histocompatibility complex 

MI myocardial infarction, “heart attack” 

Miller PEE Miller equation prediction equation estimates 

min minute(s) 

MIP-2 macrophage inflammatory protein-2 

mitral E to A ratio mitral ratio of peak early to late diastolic filling velocity (E/A). 

MMEF maximal midexpiratory flow 

mmHg millimeters of mercury (unit of pressure) 

MMP matrix metalloproteinase 

MOA(s) mode(s) of Action 

MOBILE6 (EPA) Mobile source emission factor model 

MODIS Moderate Resolution Imaging Spectroradiometer 

mol, nmol mole, nanomole 

MONICA Monitoring of Trends and Determinants in Cardiovascular Disease (study) 

MOPITT Measurement of Pollution in the Troposphere (instrument) 

MPO myeloperoxidase (indicative of neutrophil degranulation) 

MPT mitochondrial permeability transition 

MR maternally reared; rodent pups reared with their respective dams 

mRNA messenger RNA 

ms millisecond(s) 

MSA Metropolitan Statistical Area 

MSNA muscle sympathetic nerve activity 

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MT million tons 

MVO2 myocardial oxygen consumption 

n, N sample size 

N number of monitor days 

N2 nitrogen gas 

NAAQS National Ambient Air Quality Standards 

NADPH nicotinamide adenine dinucleotide phosphate 

NADH-TR nicotinamide adenine dinucleotide - tetrazolium reductase; histochemical stain of muscle 

tissue - the space between the myofibrils. 

NAPAP National Acid Precipitation Assessment Program 

NARSTO (formerly) North American Research Strategy for Tropospheric Ozone 

NAS National Academy of Sciences 

NASA National Aeronautics and Space Administration 

Nb neuroglobin 

NC ultrafine particle number concentration 

NDIR nondispersive infrared (detection) 

NE norepinephrine 

NEI (EPA) National Emissions Inventory 

NF-κB nuclear factor kappa B 

Ni nickel (element) 

NIHL noise-induced hearing loss 

NMDA N-methyl-D-aspartate 

NMHC(s) nonmethane hydrocarbon(s) 

NMMAPS National Morbidity, Mortality, and Air Pollution Study 

nmol nanomole 

NN normal-to-normal (NN or RR) time interval between each QRS complex in the EKG 

nNOS neuronal nitric oxide synthase (NOS) 

NO nitric oxide 

NO• nitric oxide free radicals, free radical species of nitric oxide 

NO2 nitrogen dioxide 

NOAA National Oceanic and Atmospheric Administration 

NOAEL no observed adverse effect level 

NO•-Hb nitrosyl bound Hb 

NO•-Mb nitrosyl bound Mb 

NOX nitrogen oxides, oxides of nitrogen 

NRC National Research Council 

NTS nucleus of the solitary tract (in brainstem) 

O2 oxygen 

O3 ozone 

O2Hb oxyhemoglobin (% concentration in mL O2 / mL blood) 

O2Mb oxymyoglobin 

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OAE otoacoustic emissions (testing) 

OAQPS Office of Air Quality Planning and Standards 

OC organic carbon 

OH, OH OH• hydroxyl group, hydroxyl radical(s) 

OR odds ratio(s) 

OS occlusive stroke 

OSPM Operational Street Pollution Model 

P penetration (of pollutant) 

P, p probability 

P number of paired hourly observations 

P1CO CO partial pressure in inhaled air (in mmHg) 

P13K phosphoinositide 3-kinase 

p21Waf1/Cip1 inhibitor of CDK(s) (cyclin-dependent kinases) - regulates cell cycle arrest following 

DNA damage. 

p38 p38 MAP kinase 

P90 90th percentile of the absolute difference in concentrations 

P450 cytochrome P450 

PA alveolar pressure 

PA pulmonary artery (myocytes) 

PACF partial auto-correlation functions 

PACO alveolar pressure for carbon monoxide 

PAF platelet activating factor 

PAH polycyclic aromatic hydrocarbon 

PAHT pulmonary artery hypertension 

PAN peroxyacetyl nitrate (CH3CO3NO2) 

PAO2 alveolar pressure for oxygen 

PaO2 arterial oxygen pressure 

PARP poly(ADP-ribose) polymerase; (involved in DNA repair; cleaved by caspases during 

early apoptosis) 

Pb lead 

PB barometric pressure (in mmHg) 

PBN the free radical inhibitors PBN (a spin trap) or N-tert-butyl-alpha-phenylnitrone (free 

radical inhibitor) an organic spin trap agent designed specifically to form "stable" adducts 

with free radicals in electron spin resonance studies. 

CP average partial pressure in lung capillaries 

pCO partial pressure of CO in lung capillaries (in mmHg) 

PCO partial pressure of CO 

COP 2 average partial pressure of O2 in lung capillaries (mmHg) 

PDGF platelet derived growth factor 

PEE prediction equation estimates 

PEF peak expiratory flow (L/min) 

PEFD(s) Personal Exposures Frequency Distributions 

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PEM(s) personal exposure monitor(s) 

% percent 

PHD pulmonary heart disease 

PI partial pressure of inhaled air 

Pi inorganic phosphate 

PIH primary intracerebral hemorrhage 

PKB protein kinases B 

PM particulate matter 

p.m. afternoon/evening hours 

PM2.5 fine particulate matter, particles with a nominal mean aerodynamic diameter less than or 

equal to 2.5 μm 

PM10 particles with a mean aerodynamic diameter less than or equal to 10 μm 

PM10-2.5 coarse particulate matter, coarse fraction of PM10 (referred to as thoracic coarse particles 

or coarse-fraction particles; generally including particles with a nominal mean 

aerodynamic diameter greater than 2.5 μm and less than or equal to 10 μm). 

PMN polymorphonuclear leukocytes 

pmol picomole 

PNC particle number concentration / count 

PND post natal day 

pNEM/CO probabilistic NAAQS Exposure Model for CO (preceded APEX) 

PNN proportion of interval differences of successive normal-beat intervals in EKG 

PNN50 proportion of interval differences of successive normal-beat intervals greater than 50 ms 

in EKG 

PNS peripheral nervous system 

pO2 partial pressure of oxygen in lung capillaries (in mmHg) 

ppb parts per billion 

ppm parts per million 

PRB policy-relevant background 

PT prothrombin time (blood coagulability endpoint) 

PTB preterm birth (birth after the 20th week, but before the 38th week of human pregnancy) 

PVCD peripheral vascular and cerebrovascular disease 

PvO2 venous oxygen tension 

PVO2 peak oxygen consumption •

•

•

Q cardiac output 

QCP Quantitative Circulatory Physiology (model) 

QM blood flow to muscle 

QOT blood flow to other tissues 

r correlation coefficient 

R2 coefficient of determination 

R State R state of hemoglobin; structural shape of protein when binding oxygen (oxy state) 

RA radial artery of the heart 

RAW 264.7 mouse macrophage cell line 

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RBC red blood cell 

redox status ratio of interconvertible reduced/oxidized forms of a molecule 

rho(0) rho(0) cells (cells lacking mitochondrial DNA) 

rMSSD mean squared differences of successive difference normal-beat to normal-beat (NN or 

RR) time intervals between each QRS complex in the EKG 

RNA ribonucleic acid 

ROE Report on the Environment 

ROFA residual oil fly ash (particles) 

ROS reactive oxygen species 

RR normal-to-normal (NN or RR) time interval between each QRS complex in the EKG 

RR risk ratio(s) 

RUPERT Reducing Urban Pollution Exposure from Road Transport (study) 

RV right ventricle (of heart) 

RVEDP right ventricular end diastolic pressure 

RVESP right ventricular end-systolic pressure 

RVSF right ventricular shortening fraction 

RVW right ventricular work 

S second(s) 

SA sphinganine 

SAA serum amyloid A 

SAB (EPA) Science Advisory Board 

SBP systolic blood pressure 

SDNN standard deviation normal-to-normal (NN or RR) time interval between each QRS 

complex in the EKG 

Se selenium 

SEM standard error of mean 

sEng soluble endoglin 

SES socioeconomic status 

Sess Session (of Congress) 

SF6 sulfur hexafluoride (tracer gas) 

sFlt soluble Fms-like tyrosine kinase-1 

SGA small for gestational age 

sGC soluble guanylate cyclase 

SHEDS (EPA) Stochastic Human Exposure and Dose Simulation (model) 

SHR Spontaneously hypertensive rat strain 

SIDS sudden infant death syndrome 

SIPs State Implementation Plan(s) 

siRNA small inhibitory RNA (silencing RNA) 

SLAMS State and Local Air Monitoring Stations 

SMC smooth muscle cell(s) 

Sn tin 

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snMP tin-(IV)-mesoporphyrin (HO inhibitor) 

SNP single-nucleotide polymorphism 

SnPP-IX tin protoporphyrin IX, (HO inhibitor) 

SO sphingosine 

SO2 sulfur dioxide 

SO4

2- sulfate 

SOD superoxide dismutase 

SOD1 cytoplasmic superoxide dismutase 

SOD2 mitochondrial superoxide dismutase 

SOPHIA Study of Particles and Health in Atlanta 

ST-segment segment of EKG between QRS complex, and T wave. ST-segment elevation may indicate 

myocardial infarction 

STAT1 / STAT3 signal transducers and activators of transcription (transcription factors involved in cell 

signaling) 

STEMS Space-Time Exposure Modeling System 

STN (EPA) Speciation Trends Network 

STPD standard temperature and pressure, dry 

SV stroke volume 

SVEB supraventricular (atrium or atrioventricular node) ectopic beats 

τ photochemical lifetime 

T trial 

T lymphocytes thymus-dependent lymphocytes 

T State T state of hemoglobin; structural shape of protein when not binding oxygen (deoxy state) 

TBARS thiobarbituric acid reactive substances 

TC total carbon 

TFAM mitochondrial transcription factor A 

Tg teragram(s) 

TH tyrosine hydroxylase 

THb total blood concentration of hemoglobin (in g Hb/mL blood) 

THP-1 human monocyte-derived cell line, (can differentiate into macrophages) 

TIA transient ischemic attack (mini-stroke) 

TNF-α tissue necrosis factor alpha (WBC protein boosts immune system, too much can cause 

inflammation) 

torr unit of pressure, equal to 0.001316 (1/760) atmosphere 

TPM total particulate matter 

TSP total suspended particles 

UFP ultrafine particle(s) 

U.K. United Kingdom 

ULTRA (Exposure and Risk Assessment for Fine and )Ultrafine Particles in Ambeint Air (Study) 

URI upper respiratory infection 

U.S. United States of America 

USC U.S. Code 

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UV ultraviolet 

V vanadium 

•

V minute ventilation 

V A

•

 alveolar ventilation (in mL/min at STPD) 

VA alveolar volume, a mesuremnt of lung size 

Vb blood volume ( in mL) 

VCO CO uptake rate (the product of DLCO and the mean PACO). •

V CO endogenous CO production rate (in mL/min at STPD) 

VD Dead space volume 

VE ventilation rate 

VEGF vascular endothelial growth factor 

VLF very low energy frequency (HRV parameter) 

Vmax maximum velocity (catalyzed by a fixed enzyme concentration, in the Michaelis-Menten 

equation of enzyme kinetics) 

VO2 max maximum volume per time, of oxygen (maximal oxygen consumption, maximal oxygen 

uptake or aerobic capacity) 

VOC(s) volatile organic compound(s) 

vol volume 

VPB ventricular premature beat 

V/Q ventilation-perfusion ratio 

vWF von Willebrand factor (part of factor VIII/von Willebrand factor complex; acts in the 

intrinsic pathway of blood coagulation) 

W width 

WBC white blood cell 

WHI Women’s Health Initiative (Study) 

windward upwind 

WKY Wistar-Kyoto rat strain 

ww wet weight 

Xij, Xik observed hourly concentrations for time period i at sites j and k

yr year 

Z/H elevation of the measurement (Z) scaled by height (H) 

ZIP (code) Zone Improvement Plan (system of postal codes used in the U.S.) 

Zn zinc 

ZnPP IX Zn protoporphyrin IX, HO inhibitor 

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Chapter 1. Introduction 

The Integrated Science Assessment (ISA) is a concise evaluation and synthesis of the most policyrelevant science for reviewing the national ambient air quality standards (NAAQS). Because the ISA 

communicates critical science judgments relevant to the NAAQS review, it forms the scientific 

foundation for the review of the NAAQS for carbon monoxide (CO). The existing primary CO standards 

include a 1-hour (h) average (avg) standard set at 35 parts per million (ppm), and an 8-h avg standard set 

at 9 ppm, neither to be exceeded more than once per year. There is currently no secondary standard for 

CO. 

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The ISA accurately reflects “the latest scientific knowledge useful in indicating the kind and extent 

of identifiable effects on public health which may be expected from the presence of [a] pollutant in 

ambient air” (U.S. Code, 1970). Key information and judgments formerly contained in the Air Quality 

Criteria Document (AQCD) for CO are incorporated in this assessment. Additional details of the pertinent 

scientific literature published since the last review, as well as selected older studies of particular interest, 

are included in a series of annexes. This first external draft ISA thus serves to update and revise the 

information available at the time of the previous AQCD for CO in 2000. 

The Integrated Plan for Review of the NAAQS for CO (U.S. EPA, 2008b) identifies key policyrelevant questions that provide a framework for this review of the scientific evidence. These questions 

frame the entire review of the NAAQS and thus are informed by both science and policy considerations. 

The ISA organizes and presents the scientific evidence such that it, when considered along with findings 

from risk analyses and policy considerations, will help the U.S. Environmental Protection Agency (EPA) 

address these questions during the NAAQS review: 

 Has new information altered the scientific support for the occurrence of health effects 

following short- and/or long-term exposure to levels of CO found in the ambient air? 

 To what extent is key evidence becoming available that could inform our understanding of 

human subpopulations that are particularly sensitive to CO exposures? Is there new or 

emerging evidence on health effects beyond cardiovascular and respiratory endpoints 

(e.g., systemic effects, developmental effects, birth outcomes) that suggest additional 

sensitive subpopulations should be given increased focus in this review (e.g., neonates)? 

 What do recent studies focused on the near-roadway environment, including bus stops and 

intersections, tell us about high-exposure human subpopulations and the health effects of CO? 

What information is available on elevated exposures due to other transportation sources, such 

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as shipping, port operations, and recreational vehicles? What is the effect of altitude on CO 

sources and health effects? 

 At what levels of CO exposure do health effects of concern occur? 

 To what extent is key scientific evidence becoming available to improve our understanding of 

the health effects associated with various time periods of CO exposures, including not only 

daily, but also chronic (months to years) exposures? To what extent is critical research 

becoming available that could improve our understanding of the relationship between various 

health endpoints and different lag periods (e.g., single day, multi-day distributed lags)? 

 To what extent does the evidence suggest that alternate dose indicators other than 

carboxyhemoglobin (COHb) levels (e.g., tissue oxygenation) should be evaluated to 

characterize the biological effect? 

 Has new information altered conclusions from previous reviews regarding the plausibility of 

adverse health effects caused by CO exposure? 

 To what extent have important uncertainties identified in the last review been reduced and/or 

have new uncertainties emerged? 

 Have new information or scientific insights altered the scientific conclusions regarding the 

occurrence of direct (or indirect) welfare effects associated with levels of CO found in the 

ambient air? 

1.1. Legislative Requirements 

Two sections of the Clean Air Act (CAA) govern the establishment and revision of the NAAQS. 

Section 108 (42 U.S.C. 7408) directs the Administrator to identify and list “air pollutants” that “in [her] 

judgment, may reasonably be anticipated to endanger public health and welfare” and whose “presence ... 

in the ambient air results from numerous or diverse mobile or stationary sources” and to issue air quality 

criteria for those that are listed (42 U.S.C. 7408). Air quality criteria are intended to “accurately reflect the 

latest scientific knowledge useful in indicating the kind and extent of identifiable effects on public health 

or welfare which may be expected from the presence of [a] pollutant in ambient air...” 42 U.S.C. 7408(b). 

Section 109 (42 U.S.C. 7409) of the Clean Air Act directs the EPA Administrator to propose and 

promulgate “primary” and “secondary” National Ambient Air Quality Standards (NAAQS) for pollutants 

listed under section 108. Section 109(b)(1) defines a primary standard as one “the attainment and 

maintenance of which in the judgment of the Administrator, based on such criteria and allowing an 

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adequate margin of safety, are requisite to protect the public health.”1

 A secondary standard, as defined in 

section 109(b)(2), must “specify a level of air quality the attainment and maintenance of which, in the 

judgment of the U.S. EPA Administrator, based on such criteria, is required to protect the public welfare 

from any known or anticipated adverse effects associated with the presence of [the] pollutant in the 

ambient air.”

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 The requirement that primary standards include an adequate margin of safety was intended 

to address uncertainties associated with inconclusive scientific and technical information available at the 

time of standard setting. It was also intended to provide a reasonable degree of protection against hazards 

that research has not yet identified. See Lead Industries Association v. EPA, 647 F.2d 1130, 1154 (D.C. 

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

1186 (D.C. Cir. 1981) cert. denied, 455 U.S. 1034 (1982). The aforementioned uncertainties are 

components of the risk associated with pollution at levels below those at which human health effects can 

be said to occur with reasonable scientific certainty. Thus, in selecting primary standards that include an 

adequate margin of safety, the Administrator is seeking not only to prevent pollution levels that have been 

demonstrated to be harmful but also to prevent lower pollutant levels that may pose an unacceptable risk 

of harm, even if the risk is not precisely identified as to nature or degree. 

In selecting a margin of safety, the EPA considers such factors as the nature and severity of the 

health effects involved, the size of sensitive population(s) at risk, and the kind and degree of the 

uncertainties that must be addressed. The selection of any particular approach to providing an adequate 

margin of safety is a policy choice left specifically to the Administrator’s judgment. See Lead Industries 

Association v. EPA, supra, 647 F.2d at 1161-62. 

In setting standards that are “requisite” to protect public health and welfare, as provided in Section 

109(b), EPA’s task is to establish standards that are neither more nor less stringent than necessary for 

these purposes. In so doing, EPA may not consider the costs of implementing the standards. See Whitman 

v. American Trucking Associations, 531 U.S. 457, 465-472, 475-76 (D.C. Cir. 2001). 

Section 109(d)(1) requires that “not later than December 31, 1980, and at 5-year intervals 

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

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

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

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

primary and secondary ambient air quality standards...and shall recommend to the Administrator any 

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level . . . which will protect the health of any [sensitive] group of the population,” and that for this purpose “reference should be made to a 

representative sample of persons comprising the sensitive group rather than to a single person in such a group” [S. Rep. No. 91-1196, 91st 

Cong., 2d Sess. 10 (1970)]. 

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as effects on economic values and on personal comfort and well-being.” 

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new...standards and revisions of existing criteria and standards as may be appropriate...” Since the early 

1980s, this independent review function has been performed by the Clean Air Scientific Advisory 

Committee (CASAC) of EPA’s Science Advisory Board (SAB). 

1.2. History of the NAAQS for CO 

On April 30, 1971, EPA promulgated identical primary and secondary NAAQS forCO, under 

section 109 of the Clean Air Act, set at 9 ppm, 8-h avg and 35 ppm, 1-h avg, neither to be exceeded more 

than once per year (36 FR 8186). In 1979, EPA published the Air Quality Criteria Document for Carbon 

Monoxide (U.S. EPA, 1979a), which updated the scientific criteria upon which the initialCO standards 

were based. A Staff Paper (U.S. EPA, 1979b) was prepared and, along with the AQCD, served as the basis 

for development of proposed rulemaking (45 FR 55066) published on August 18, 1980. Delays due to 

uncertainties regarding the scientific basis for the final decision resulted in EPA announcing a second 

public comment period (47 FR 26407). Following substantial reexamination of the scientific data, EPA 

prepared an Addendum to the 1979 AQCD (U.S. EPA, 1984b) and an updated Staff Paper (U.S. EPA, 

1984a). Following review by CASAC, EPA announced its final decision (50 FR 37484) not to revise the 

existing primary standard and to revoke the secondary standard for CO on September 13, 1985, due to a 

lack of evidence of direct effects on public welfare at ambient concentrations. 

In 1987, EPA initiated action to revise the criteria for CO and released a revised AQCD for CASAC 

and public review. In a “closure letter” (McClellan, 1991) sent to the Administrator, the CASAC 

concluded that the AQCD (U.S. EPA, 2000) “. . . provides a scientifically balanced and defensible 

summary of current knowledge of the effects of this pollutant and provides an adequate basis for the EPA 

to make a decision as to the appropriate primary NAAQS for CO.” A revised Staff Paper subsequently 

was reviewed by CASAC and the public, and in a “closure letter” (McClellan, 1992) sent to the 

Administrator, CASAC stated “. . . that a standard of the present form and with a numerical value similar 

to that of the present standard would be supported by the present scientific data on health effects of 

exposure to carbon monoxide.” Based on the revised AQCD (U.S. EPA, 2000) and staff conclusions and 

recommendations contained in the revised Staff Paper (U.S. EPA, 1992), the Administrator announced the 

final decision (59 FR 38906) on August 1, 1994, that revision of the primary NAAQS for CO was not 

appropriate at that time. 

In 1997, revisions to the AQCD were initiated. A workshop was held in September 1998 to review 

and discuss material contained in the revised AQCD. On June 9, 1999, CASAC held a public meeting to 

review the draft AQCD and a draft exposure analysis methodology document. Comments from CASAC 

and the public were considered in a second draft AQCD, which was reviewed at a CASAC meeting, held 

on November 18, 1999. After revision of the second draft AQCD, the final AQCD (U.S. EPA, 2000) was 

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released in August 2000. EPA put the review on hold when Congress called on the National Research 

Council (NRC) to conduct a review of the impact of meteorology and topography on ambient CO 

concentrations in high altitude and extreme cold regions of the U.S. In response, the NRC convened the 

committee on Carbon Monoxide Episodes in Meteorological and Topographical Problem Areas, which 

focused on Fairbanks, Alaska as a case study in an interim report, which was completed in 2002. A final 

report, Managing Carbon Monoxide Pollution in Meteorological and Topographical Problem Areas, was 

published in 2003 (NRC, 2003) and offered a wide range of recommendations on management of CO air 

pollution, cold start emissions standards, oxygenated fuels, and CO monitoring. EPA did not complete the 

review which started in 1997. 

1.3. Document Development 

EPA initiated the current review of the NAAQS for CO on September 13, 2007 with a call for 

information from the public (72 FR 52369). In addition to the call for information, publications were 

identified through an ongoing literature search process that includes extensive computer database mining 

on specific topics. Additional publications were identified by EPA scientists in a variety of disciplines by 

combing through peer-reviewed scientific literature obtained through these ongoing literature searches, 

reviewing previous EPA reports, and a review of reference lists from important publications. All relevant 

epidemiologic, human clinical, and animal toxicological studies, including those related to exposureresponse relationships, mode(s) of action (MOA), or susceptible subpopulations published since the last 

review were considered. Added to the body of research were EPA’s analyses of air quality and emissions 

data, studies on atmospheric chemistry, transport, and fate of these emissions, as well as issues related to 

exposure to CO. A literature search conducted on the ecological effects of ambient CO did not identify 

any relevant information. Further information was acquired from consultation with scientific experts and 

the public. 

1.4. Document Organization 

The ISA is composed of five chapters. This introductory chapter presents background information, 

and provides an overview of EPA’s framework for making causal judgments. Chapter 2 is an integrated 

summary of key findings and conclusions regarding the source to dose paradigm, MOA, and important 

health effects of CO, including cardiovascular, nervous system, perinatal/developmental, respiratory, and 

mortality outcomes. Chapter 3 highlights key concepts and evidence relevant to understanding the 

sources, ambient concentrations, atmospheric behavior, and exposure to ambient CO. Chapter 4 describes 

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the dosimetry and pharmacokinetics of CO, including formation and fate of carboxyhemoglobin (COHb). 

Chapter 5 presents a discussion of the MOA of CO and evaluates and integrates epidemiologic, human 

clinical, and animal toxicological information on the health effects of CO, including cardiovascular and 

systemic effects, central nervous system (CNS) effects, birth outcomes and developmental effects, 

respiratory effects, and mortality. 

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A series of annexes supplement this ISA. The annexes provide tables summarizing additional 

details of the pertinent literature published since the last review, as well as selected older studies of 

particular interest. These annexes contain information on: 

 atmospheric chemistry of CO, sampling and analytic methods for measurement of CO 

concentrations, emissions, sources and human exposure to CO (Annex A) 

 studies on the dosimetry and pharmacokinetics of CO (Annex B) 

 epidemiologic studies of health effects from short- and long-term exposure to CO (Annex C) 

 controlled human exposure studies of health effects related to exposure to CO (Annex D); 

and 

 toxicological studies of health effects in laboratory animals (Annex E) 

Within the Annexes, detailed information about methods and results of health studies is 

17 summarized in tabular format, and generally includes information about concentrations of CO and 

18 averaging times, study methods employed, results and comments, and quantitative results for 

19 relationships between effects and exposure to CO. 

1.5. Document Scope 

For the current review of the primary CO standards, relevant scientific information on human 

exposures and health effects associated with exposure to ambient CO has been assessed. Health effects 

resulting from accidental exposures to very high concentrations of non-ambient CO (i.e., carbon 

monoxide poisoning) are not directly relevant to ambient exposures, and as such, a discussion of these 

effects has deliberately been excluded from this document. For a detailed review of the effects of high 

level exposures to CO, the reader is referred to the extensive body of literature related to carbon 

monoxide poisoning (Ernst and Zibrak, 1998; Penney, 2007; Raub et al., 2000). The possible influence of 

other atmospheric pollutants on the interpretation of the role of CO in health effects studies is considered. 

This includes other pollutants with the potential to co-occur in the environment (e.g., nitrogen dioxide 

[NO2], sulfur dioxide [SO2], ozone [O3], and particulate matter [PM]). The review also assesses relevant 

scientific information associated with known or anticipated public welfare effects that may be identified. 

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As discussed in Section 1.3, a literature review of the ecological effects of ambient CO identified no 

relevant information, and thus these effects are not assessed. With regard to climate effects this review 

includes updates and additional data available since the 2000 CO AQCD on the interaction of largely 

urban CO emissions and hydroxyl radical concentrations, and on background concentrations in the U.S. 

1.6. EPA Framework for Causal Determination 

The EPA has developed a consistent and transparent basis to evaluate the causal nature of air 

pollution-induced health or environmental effects. The framework described below establishes uniform 

language concerning causality and brings more specificity to the findings. This standardized language was 

drawn from across the federal government and wider scientific community, especially from the recent 

National Academy of Sciences (NAS) Institute of Medicine (IOM) document, Improving the Presumptive 

Disability Decision-Making Process for Veterans (IOM, 2008), the most recent comprehensive work on 

evaluating causality. 

This introductory section focuses on the evaluation of health effects evidence. While focusing on 

human health outcomes, the concepts are also generally relevant to causality determination for welfare 

effects. This section: 

 describes the kinds of scientific evidence used in establishing a general causal relationship 

between exposure and health effects; 

 defines cause, in contrast to statistical association; 

 discusses the sources of evidence necessary to reach a conclusion about the existence of a 

causal relationship; 

 highlights the issue of multifactorial causation; 

 identifies issues and approaches related to uncertainty; and 

 provides a framework for classifying and characterizing the weight of evidence in support of 

a general causal relationship. 

Approaches to assessing the separate and combined lines of evidence (e.g., epidemiologic, human 

25 clinical, and animal toxicological studies) have been formulated by a number of regulatory and science 

26 agencies, including the IOM of the NAS (IOM, 2008), International Agency for Research on Cancer 

27 (IARC, 2006), EPA Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005), Centers for Disease 

28 Control and Prevention (CDC, 2004), and National Acid Precipitation Assessment Program (NAPAP, 

29 1991). These formalized approaches offer guidance for assessing causality. The frameworks are similar in 

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1 nature, although adapted to different purposes, and have proven effective in providing a uniform structure 

2 and language for causal determinations. Moreover, these frameworks have supported decision-making 

3 under conditions of uncertainty. 

1.6.1. Scientific Evidence Used in Establishing Causality 

Causality determinations are based on the evaluation and synthesis of evidence from across 

scientific disciplines; the type of evidence that is most important for such determinations will vary by 

pollutant or assessment. The most compelling evidence of a causal relationship between pollutant 

exposures and human health effects comes from human clinical studies. This type of study experimentally 

evaluates the health effects of administered exposures in human volunteers under highly-controlled 

laboratory conditions. 

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In epidemiologic or observational studies of humans, the investigator does not control exposures or 

intervene with the study population. Broadly, observational studies can describe associations between 

exposures and effects. These studies fall into several categories: cross-sectional, prospective cohort, and 

time-series studies. “Natural experiments” offer the opportunity to investigate changes in health with a 

change in exposure; these include comparisons of health effects before and after a change in population 

exposures, such as closure of a pollution source. 

Experimental animal data complement the clinical and observational data; these studies can help 

characterize effects of concern, exposure-response relationships, susceptible subpopulations and MOAs. 

In the absence of clinical or epidemiologic data, animal data alone may be sufficient to support a likely 

causal determination, assuming that humans respond similarly to the experimental species. 

1.6.2. Association and Causation 

“Cause” is a significant, effectual relationship between an agent and an effect on health or public 

welfare. “Association” is the statistical dependence among events, characteristics, or other variables. An 

association is prima facie evidence for causation; alone, however, it is insufficient proof of a causal 

relationship between exposure and disease. Unlike an association, a causal claim supports the creation of 

counterfactual claims; that is, a claim about what the world would have been like under different or 

changed circumstances (IOM, 2008). Much of the newly available health information evaluated in this 

ISA comes from epidemiologic studies that report a statistical association between ambient exposure and 

health outcome. 

Many of the health and environmental outcomes reported in these studies have complex etiologies. 

Diseases such as asthma, coronary heart disease (CHD) or cancer are typically initiated by multiple 

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agents. Outcomes depend on a variety of factors, such as age, genetic susceptibility, nutritional status, 

immune competence, and social factors (Gee and Payne-Sturges, 2004; IOM, 2008). Effects on 

ecosystems are often also multifactorial with a complex web of causation. Further, exposure to a 

combination of agents could cause synergistic or antagonistic effects. Thus, the observed risk represents 

the net effect of many actions and counteractions. 

1.6.3. Evaluating Evidence for Inferring Causation 

Moving from association to causation involves elimination of alternative explanations for the 

association. In estimating the causal influence of an exposure on health or environmental effects, it is 

recognized that scientific findings incorporate uncertainty. Uncertainty can be defined as a state of having 

limited knowledge where it is impossible to exactly describe an existing state or future outcome; e.g., the 

lack of knowledge about the correct value for a specific measure or estimate. Uncertainty characterization 

and uncertainty assessment are two activities that lead to different degrees of sophistication in describing 

uncertainty. Uncertainty characterization generally involves a qualitative discussion of the thought 

processes that lead to the selection and rejection of specific data, estimates, scenarios, etc. The uncertainty 

assessment is more quantitative. The process begins with simpler measures (e.g., ranges) and simpler 

analytical techniques and progresses, to the extent needed to support the decision for which the 

assessment is conducted, to more complex measures and techniques. Data will not be available for all 

aspects of an assessment and those data that are available may be of questionable or unknown quality. In 

these situations, evaluation of uncertainty can include professional judgment or inferences based on 

analogy with similar situations. The net result is that the assessments will be based on a number of 

assumptions with varying degrees of uncertainty. 

Uncertainties commonly encountered in evaluating health evidence for the criteria air pollutants are 

outlined below for epidemiologic and experimental studies. Various approaches to characterizing 

uncertainty include classical statistical methods, sensitivity analysis, or probabilistic uncertainty analysis, 

in order of increasing complexity and data requirements. The ISA generally evaluates uncertainties 

qualitatively in assessing the evidence from across studies; in some situations quantitative analysis 

approaches, such as meta-regression may be used. 

Controlled human exposure studies evaluate the effects of exposures to a variety of pollutants in a 

highly controlled laboratory setting. Also referred to as human clinical studies, these experiments allow 

investigators to expose subjects to known concentrations of air pollutants under carefully regulated 

environmental conditions and activity levels. In some instances, controlled human exposure studies can 

also be used to characterize concentration-response relationships at pollutant concentrations relevant to 

ambient conditions. Controlled human exposures are typically conducted using a randomized crossover 

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design with subjects exposed both to CO and a clean air control. In this way, subjects serve as their own 

controls, effectively controlling for many potential confounders. However, human clinical studies are 

limited by a number of factors including a small sample size and short exposure time. The repetitive 

nature of ambient CO exposures at levels that can vary widely may lead to cumulative health effects, but 

this type of exposure is not practical to replicate in a laboratory setting. In addition, although subjects do 

serve as their own controls, personal exposure to pollutants in the hours and days preceding the controlled 

exposures may vary significantly between and within individuals. Finally, human clinical studies require 

investigators to adhere to stringent health criteria for a subject to be included in the study, and therefore 

the results cannot necessarily be generalized to an entire population. Although some human clinical 

studies have included health-compromised individuals such as asthmatics or individuals with chronic 

obstructive pulmonary disease (COPD) or coronary artery disease (CAD), these individuals must also be 

relatively healthy and do not represent the most sensitive individuals in the population. Thus, a lack of 

observation of effects from human clinical studies does not necessarily mean that a causal relationship 

does not exist. While human clinical studies provide important information on the biological plausibility 

of associations observed between air pollutant exposure and health outcomes in epidemiologic studies, 

observed effects in these studies may underestimate the response in certain subpopulations. 

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Epidemiologic studies provide important information on the associations between health effects 

and exposure of human populations to ambient air pollution. These studies also help to identify 

susceptible or vulnerable subgroups and associated risk factors. There are important methodological 

issues to be considered in evaluating results from air pollution epidemiologic studies, especially the 

potential for confounding and/or effect modification; and exposure measurement error. 

Scientific judgment is needed regarding sources and magnitude of potential confounding by 

covariates, together with judgment about how well the existing constellation of study designs, results, and 

analyses address this potential threat to inferential validity. One key consideration is evaluation of the 

potential contribution of CO to health effects when it is a component of a complex air pollutant mixture. 

There are multiple ways by which CO might cause or be associated with adverse health effects. First, the 

reported CO effect estimates in epidemiologic studies may reflect independent CO effects on health. 

Second, ambient CO may be serving as an indicator of complex ambient air pollution mixtures that share 

the same source as CO (e.g., motor vehicle emissions). Finally, copollutants may mediate the effects of 

CO, or CO may influence the toxicity of copollutants. 

Epidemiologists use the term “interaction” or “effect modification” to denote the departure from 

the observed joint risk from what might be expected based on the separate effects of the factors. In 

addition, confounding can result in the production of an association between adverse health effects and 

CO that is actually attributable to another factor that is associated with CO in a particular study. 

Multivariate models are the most widely used strategy to address confounding in epidemiologic studies, 

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but such models are not always easily interpreted when assessing effects of covarying pollutants such as 

fine particulate matter (PM2.5) and NO2. 

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Inferring causation requires consideration of potential confounders. In confounding, the apparent 

effect of the exposure of interest is distorted because the effect of an extraneous factor is mistaken for or 

mixed with the actual exposure effect, which may be null. When associations are found in epidemiologic 

studies, one approach to remove spurious associations from possible confounders is to control for 

characteristics that may differ between exposed and unexposed persons; this is frequently termed 

“adjustment.” Multivariable regression models constitute one tool for estimating the association between 

exposure and outcome after adjusting for characteristics of participants that might confound the results. 

The use of multipollutant regression models has been the prevailing approach for controlling potential 

confounding by copollutants in air pollution health effects studies. Finding the likely causal pollutant 

from multipollutant regression models is made difficult by the possibility that one or more air pollutants 

may be acting as a surrogate for an unmeasured or poorly-measured pollutant or for a particular mixture 

of pollutants. Further, the correlation between the air pollutant of interest and various copollutants may 

show temporal and spatial discongruities that can influence exposures and health effects. Thus, results of 

models that attempt to distinguish gaseous and particle effects must be interpreted with caution. Despite 

these limitations, the use of multipollutant models is still the prevailing approach employed in most air 

pollution epidemiologic studies and may provide some insight into the potential for confounding or 

interaction among pollutants. 

Another way to adjust for potential confounding is through stratified analysis, i.e., examining the 

association within homogeneous groups with respect to the confounding variable. Stratified analysis can 

also be used to examine potential effect modification. The use of stratified analyses has an additional 

benefit: it allows examination of effect modification through comparison of the effect estimates across 

different groups. If investigators successfully measured characteristics that distort the results, adjustment 

of these factors help separate a spurious from a true causal association. Appropriate statistical adjustment 

for confounders requires identifying and measuring all reasonably expected confounders. Deciding which 

variables to control for in a statistical analysis of the association between exposure and disease depends 

on knowledge about possible mechanisms and the distributions of these factors in the population under 

study. Identifying these mechanisms makes it possible to control for potential sources that may result in a 

spurious association. 

Measurement error is another problem encountered when adjusting for spurious associations. 

Controlling for confounders, whether by adjustment or stratification, is only successful when the 

confounder is well-measured. Considered together, the effects of a well-measured covariate may be 

overestimated in contrast to a covariate measured with greater error. There are several components that 

contribute to exposure measurement error in these studies, including the difference between true and 

measured ambient concentrations, the difference between average personal exposure to ambient pollutants 

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and ambient concentrations at central monitoring sites, and the use of average population exposure rather 

than individual exposure estimates. Previous assessments have examined the role of measurement error in 

time-series epidemiologic studies using simulated data and mathematical analyses and suggested that 

“transfer of effects” would only occur under unusual circumstances (e.g., “true” predictors having high 

positive or negative correlation; substantial measurement error; or extremely negatively correlated 

measurement errors) (U.S. EPA, 2004). 

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Confidence that unmeasured confounders are not producing the findings is increased when multiple 

studies are conducted in various settings using different subjects or exposures; each of which might 

eliminate another source of confounding from consideration. Thus, multi-city studies which use a 

consistent method to analyze data from across locations with different levels of covariates can provide 

insight on potential confounding in associations. The number and degree of diversity of covariates, as 

well as their relevance to the potential confounders, remain matters of scientific judgment. Intervention 

studies, because of their experimental nature, can be particularly useful in characterizing causation. 

In addition to clinical and epidemiologic studies, the tools of experimental biology have been 

valuable for developing insights into human physiology and pathology. Laboratory tools have been 

extended to explore the effects of putative toxicants on human health, especially through the study of 

model systems in other species. Background knowledge of the biological mechanisms by which an 

exposure might or might not cause disease can prove crucial in establishing, or negating, a causal claim. 

Testable hypotheses about the causal nature of proposed mechanisms or modes of action are central to 

utilizing experimental data in causal determinations. 

Interpretations of experimental studies of air pollution effects in animals, as in the case of 

environmental comparative toxicology studies, are affected by limitations associated with extrapolation 

models. The differences between humans and rodents with regard to pollutant absorption and distribution 

profiles based on metabolism, hormonal regulation, breathing pattern, exposure dose, and differences in 

lung structure and anatomy all have to be taken into consideration. Also, in spite of a high degree of 

homology and the existence of a high percentage of orthologous genes across humans and rodents 

(particularly mice), extrapolation of molecular alterations at the gene level is complicated by speciesspecific differences in transcriptional regulation. Given these molecular differences, there are 

uncertainties associated with quantitative extrapolations at this time between laboratory animals and 

humans of observed pollutant-induced pathophysiological alterations under the control of widely varying 

biochemical, endocrine, and neuronal factors. 

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1.6.4. Application of Framework for Causal Determination 

EPA uses a two-step approach to evaluate the scientific evidence on health or environmental effects 

of criteria pollutants. The first step determines the weight of evidence in support of causation and 

characterizes the strength of any resulting causal classification. The second step includes further 

evaluation of the quantitative evidence regarding the concentration-response relationships and the loads or 

levels, duration and pattern of exposures at which effects are observed. 

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To aid judgment, various “aspects”1 of causality have been discussed by many philosophers and 

scientists. The most widely cited aspects of causality in epidemiology, and public health, in general, were 

articulated by Sir Austin Bradford Hill in 1965 and have been widely used (CDC, 2004; IARC, 2006; 

IOM, 2008; U.S. EPA, 2005, 2008d). Several adaptations of the Hill aspects have been used in aiding 

causality judgments in the ecological sciences (Adams, 2003; Buck et al., 2000; Collier, 2003; Fox, 1991; 

Gerritsen et al., 1998). These aspects (Hill, 1965) have been modified (Table 1-1) for use in causal 

determinations specific to health and welfare effects or pollutant exposures (U.S. EPA, 2008f).2

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aspects are more likely than others to be relevant for evaluating evidence on the health or environmental 

effects of criteria air pollutants. For example, the analogy aspect does not always apply, especially for the 

gaseous criteria pollutants, and specificity would not be expected for multi-etiologic health outcomes, 

such as asthma or cardiovascular disease, or ecological effects related to acidification. Aspects that 

usually play a larger role in determination of causality are consistency of results across studies, coherence 

of effects observed in different study types or disciplines, biological plausibility, exposure-response 

relationship, and evidence from “natural” experiments. 

Table 1-1. Aspects to aid in judging causality. 

Consistency of the 

observed association 

An inference of causality is strengthened when a pattern of elevated risks is observed across several independent studies. The 

reproducibility of findings constitutes one of the strongest arguments for causality. If there are discordant results among 

investigations, possible reasons such as differences in exposure, confounding factors, and the power of the study are considered.

Coherence An inference of causality from epidemiologic associations may be strengthened by other lines of evidence (e.g., clinical and animal 

studies) that support a cause-and-effect interpretation of the association. Evidence on ecological or welfare effects may be drawn 

from a variety of experimental approaches (e.g., greenhouse, laboratory, and field) and subdisciplines of ecology (e.g., community 

ecology, biogeochemistry and paleological/historical reconstructions). The coherence of evidence from various fields greatly adds 

to the strength of an inference of causality. The absence of other lines of evidence, however, is not a reason to reject causality.

Biological plausibility. An inference of causality tends to be strengthened by consistency with data from experimental studies or other sources 

demonstrating plausible biological mechanisms. A proposed mechanistic linking between an effect and exposure to the agent is an

important source of support for causality, especially when data establishing the existence and functioning of those mechanistic

links are available. A lack of biologic understanding, however, is not a reason to reject causality. 

 

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 The “aspects” described by Hill (1965) have become, in the subsequent literature, more commonly described as “criteria.” The original term 

“aspects” is used here to avoid confusion with ‘criteria’ as it is used, with different meaning, in the Clean Air Act. 

2

 The Hill aspects were developed for interpretation of epidemiologic results. They have been modified here for use with a broader array of data, 

i.e., epidemiologic, controlled human exposure, and animal toxicological studies, as well as in vitro data, and to be more consistent with EPA’s 

Guidelines for Carcinogen Risk Assessment. 

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Strength of the observed 

association 

The finding of large, precise risks increases confidence that the association is not likely due to chance, bias, or other factors. 

However, given a truly causal agent, a small magnitude in the effect could follow from a lower level of exposure, a lower potency, 

or the prevalence of other agents causing similar effects. While large effects support causality, modest effects therefore do not 

preclude it. 

Biological gradient 

(exposure-response 

relationship) 

A clear exposure-response relationship (e.g., increasing effects associated with greater exposure) strongly suggests cause and 

effect, especially when such relationships are also observed for duration of exposure (e.g., increasing effects observed following 

longer exposure times). There are, however, many possible reasons that a study may fail to detect an exposure-response 

relationship. Thus, although the presence of a biologic gradient may support causality, the absence of an exposure-response 

relationship does not exclude a causal relationship. 

Experimental evidence. The strongest evidence for causality can be provided when a change in exposure brings about a change in occurrence or 

frequency of health or welfare effects.

Temporal relationship of 

the observed association 

Evidence of a temporal sequence between the introduction of an agent, and appearance of the effect, constitutes another 

argument in favor of causality. 

Analogy Structure activity relationships and information on the agent’s structural analogs can provide insight into whether an association is 

causal. Similarly, information on mode of action for a chemical, as one of many structural analogs, can inform decisions regarding 

likely causality. 

While these aspects provide a framework for assessing the evidence, they do not lend themselves to 

being considered in terms of simple formulas or fixed rules of evidence leading to conclusions about 

causality (Hill, 1965). For example, one cannot simply count the number of studies reporting statistically 

significant results or statistically nonsignificant results and reach credible conclusions about the relative 

weight of the evidence and the likelihood of causality. Rather, these important considerations are taken 

into account with the goal of producing an objective appraisal of the evidence, informed by peer and 

public comment and advice, which includes weighing alternative views on controversial issues. 

Additionally, it is important to note that the aspects in Table 1-1 cannot be used as a strict checklist, but 

rather to determine the weight of the evidence for inferring causality. In particular, not meeting one or 

more of the principles does not automatically preclude a determination of causality (e.g., see discussion in 

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1.6.5. Determination of Causality 

In the ISA, EPA assesses the results of recent relevant publications, building upon evidence 

available during the previous NAAQS review, to draw conclusions on the causal relationships between 

relevant pollutant exposures and health or environmental effects. This ISA uses a five-level hierarchy that 

classifies the weight of evidence for causation, not just association1

; that is, whether the weight of 

scientific evidence makes causation at least as likely as not, in the judgment of the reviewing group. In 

developing this hierarchy, EPA has drawn on the work of previous evaluations, most prominently the 

IOM’s Improving the Presumptive Disability Decision-Making Process for Veterans (IOM, 2008), EPA’s 

Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005), and the U.S. Surgeon General’s smoking 

reports (CDC, 2004). In the ISA, EPA uses a series of five descriptors to characterize the weight of 

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 It should be noted that the CDC and IOM frameworks use a four-category hierarchy for the strength of the evidence. A five-level hierarchy is 

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evidence for causality (U.S. EPA, 2008d, f). This weight of evidence evaluation is based on various lines 

of evidence from across the health and environmental effects disciplines. These separate judgments are 

integrated into a qualitative statement about the overall weight of the evidence and causality. The five 

descriptors for causal determination are described in Table 1-2. 

Table 1-2. Weight of evidence for causal determination. 

Health Effects Ecological and Welfare Effects 

Causal 

relationship 

Evidence is sufficient to conclude that there is a causal relationship 

between relevant pollutant exposures and the health outcome. That is, a 

positive association has been observed between the pollutant and the 

outcome in studies in which chance, bias, and confounding could be 

ruled out with reasonable confidence. Evidence includes, for example, 

controlled human exposure studies; or observational studies that cannot 

be explained by plausible alternatives or are supported by other lines of 

evidence (e.g., animal studies or mode of action information). Evidence 

includes replicated and consistent high-quality studies by multiple 

investigators. 

Evidence is sufficient to conclude that there is a causal relationship 

between relevant pollutant exposure and the outcome. Causality is 

supported when an association has been observed between the 

pollutant and the outcome in studies in which chance, bias, and 

confounding could be ruled out with reasonable confidence. Controlled 

exposure (laboratory or small- to medium-scale field studies) provides 

the strongest evidence for causality, but the scope of inference may be 

limited. Generally, determination is based on multiple studies 

conducted by multiple research groups, and evidence that is 

considered sufficient to infer a causal relationship is usually obtained 

from the joint consideration of many lines of evidence that reinforce 

each other. 

Likely to be a 

causal 

relationship 

Evidence is sufficient to conclude that a causal relationship is likely to 

exist between relevant pollutant exposures and health outcome but 

important uncertainties remain. That is, a positive association has been 

observed between the pollutant and the outcome in studies in which 

chance and bias can be ruled out with reasonable confidence but 

potential issues remain. For example: a) observational studies show 

positive associations but copollutant exposures are difficult to address 

and/or other lines of evidence (controlled human exposure, animal, or 

mode of action information) are limited or inconsistent; or b) animal 

evidence from multiple studies, sex, or species is positive but limited or 

no human data are available. Evidence generally includes replicated and 

high-quality studies by multiple investigators. 

Evidence is sufficient to conclude that there is a likely causal 

association between relevant pollutant exposures and the outcome. 

That is, an association has been observed between the pollutant and 

the outcome in studies in which chance, bias and confounding are 

minimized, but uncertainties remain. For example, field studies show a 

relationship, but suspected interacting factors cannot be controlled, 

and other lines of evidence are limited or inconsistent. Generally, 

determination is based on multiple studies in multiple research groups.

Suggestive of 

a causal 

relationship 

Evidence is suggestive of a causal relationship between relevant 

pollutant exposures and the health outcome, but is limited because 

chance, bias and confounding cannot be ruled out. For example, at least 

one high-quality study shows a positive association but the results of 

other studies are inconsistent. 

Evidence is suggestive of an association between relevant pollutant 

exposures and the outcome, but chance, bias and confounding cannot 

be ruled out. For example, at least one high-quality study shows an 

association, but the results of other studies are inconsistent. 

Inadequate to 

infer a causal 

relationship 

Evidence is inadequate to determine that a causal relationship exists 

between relevant pollutant exposures and health outcome. The available 

studies are of insufficient quantity, quality, consistency or statistical 

power to permit a conclusion regarding the presence or absence of an 

association between relevant pollutant exposure and the outcome. 

The available studies are of insufficient quality, consistency or 

statistical power to permit a conclusion regarding the presence or 

absence of an association between relevant pollutant exposure and 

the outcome. 

Suggestive of 

no causal 

relationship 

Evidence is suggestive of no causal relationship between relevant 

pollutant exposures and health outcome. Several adequate studies, 

covering the full range of levels of exposure that human beings are 

known to encounter and considering susceptible or vulnerable 

subpopulations, are mutually consistent in not showing a positive 

association between exposure and the outcome at any level of 

exposure. 

Several adequate studies, examining relationships between relevant 

exposures and outcomes, are consistent in failing to show an 

association between exposure and the outcome at any level of 

exposure. 

Source: U.S. EPA (2008f) 

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For the CO ISA, determination of causality involved the evaluation of evidence for different types 

of health effects associated with short- and long-term exposure periods. In making determinations of 

causality for CO, evidence was evaluated for health outcome categories, such as cardiovascular effects, 

and then conclusions were drawn based upon the integration of evidence from across disciplines 

(e.g., epidemiology, clinical studies and toxicology) and also across the suite of related individual health 

outcomes. To accomplish this integration, evidence from multiple and various types of studies was 

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considered. Response was evaluated over a range of observations which was determined by the type of 

study and methods of exposure or dose and response measurements. Results from different protocols were 

compared and contrasted. 

In drawing judgments regarding causality for the criteria air pollutants, EPA focuses on evidence of 

effects at relevant pollutant exposures. To best inform reviews of the NAAQS, these evaluations go 

beyond a determination of causality at any dose or concentration to emphasize the relationship apparent at 

relevant pollutant exposures. Concentrations generally within an order of magnitude or two of ambient 

pollutant measurements are considered to be relevant for this determination. Building upon the 

determination of causality are questions relevant to quantifying health or environmental risks based on 

our understanding of the quantitative relationships between pollutant exposures and health or welfare 

effects. While the causality determination is based primarily on evaluation of health or environmental 

effects evidence, EPA also evaluates evidence related to the doses or levels at which effects are observed. 

Considerations relevant to evaluation of quantitative relationships for health and environmental effects are 

summarized below. 

Effects on Human Populations 

Important questions regarding quantitative relationships include: 

 What is the concentration-response or dose-response relationship in the human population? 

 What is the interrelationship between incidence and severity of effect? 

 What exposure conditions (dose or exposure, duration and pattern) are important? 

 What subpopulations appear to be differentially affected i.e., more susceptible or vulnerable 

to effects? 

Addressing these questions requires evaluating the entirety of policy-relevant quantitative evidence 

regarding the concentration-response relationships including levels of pollutant and exposure durations at 

which effects were observed, and subpopulations that differ in susceptibility or vulnerability from the 

general population. This integration of evidence resulted in identification of a study or set of studies that 

best approximated the concentration response relationship for the U.S. population, given the current state 

of knowledge and the uncertainties that surrounded these estimates. 

An important consideration in characterizing the public health impacts associated with exposure to 

a pollutant is whether the concentration-response relationship is linear across the full concentration range 

encountered, or if nonlinear relationships exist along any part of this range. Of particular interest is the 

shape of the concentration-response curve at and below the level of the current standards. The shape of 

the concentration-response curve varies, depending on the type of health outcome, underlying biological 

mechanisms and dose. At the human population level, however, various sources of variability and 

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uncertainty (such as the low data density in the lower concentration range, possible influence of 

measurement error, and individual differences in susceptibility to air pollution health effects) tend to 

smooth and “linearize” the concentration-response function. Additionally, many chemicals and agents 

may act by perturbing naturally occurring background processes that lead to disease, which also linearizes 

population concentration-response relationships (Clewell and Crump, 2005; Crump et al., 1976; Hoel, 

1980). These attributes of population dose-response may explain why the available human data at ambient 

concentrations for some environmental pollutants (e.g., PM, O3, lead [Pb], environmental tobacco smoke 

[ETS], radiation) do not exhibit evident thresholds for cancer or noncancer health effects, even though 

likely mechanisms include nonlinear processes for some key events. These attributes of human population 

dose-response relationships have been extensively discussed in the broader epidemiologic literature 

(Rothman and Greenland, 1998). 

Effects on Ecosystems or Public Welfare 

Key questions for understanding the quantitative relationships between exposure (or concentration 

or deposition) to a pollutant and risk to ecosystems or the public welfare include: 

 What elements of the ecosystem (e.g., types, regions, taxonomic groups, populations, 

functions, etc.) appear to be affected, or are more sensitive to effects? 

 Under what exposure conditions (amount deposited or concentration, duration and pattern) 

are effects seen? 

 What is the shape of the concentration-response or exposure-response relationship? 

Evaluations of causality typically characterize how the probability of ecological and welfare effects 

change in response to exposure. A challenge to the quantification of exposure-response relationships for 

ecological effects is the variability across ecosystems. Ecological responses are evaluated within the range 

of observations, so a quantitative relationship may be determined for a given ecological system and scale. 

There is great regional and local variability in ecosystems, thus an exposure-response relationship 

generally cannot be determined at the larger national or even regional scale. Quantitative relationships 

therefore are available site by site. For example, an ecological response to deposition of a given pollutant 

can differ greatly between ecosystems. Where results from greenhouse or animal ecotoxicological studies 

are available, they may be used to aid in characterizing exposure-response relations, particularly relative 

to mechanisms of action, and characteristics of sensitive biota. 

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1.6.6. Concepts in Evaluating Adversity of Health Effects 

In evaluating the health evidence, a number of factors can be considered in determining the extent 

to which health effects are “adverse” for health outcomes such as changes in lung function or in 

cardiovascular health measures. Some health outcome events, such as hospitalization for respiratory or 

cardiovascular diseases, are clearly considered adverse; what is more difficult is determining the extent of 

change in the more subtle health measures that is adverse. What constitutes an adverse health effect may 

vary between populations. Some changes in healthy individuals may not be considered adverse while 

those of a similar type and magnitude are potentially adverse in more susceptible individuals. 

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For example, the extent to which changes in lung function are adverse has been discussed by the 

American Thoracic Society (ATS) in an official statement titled What Constitutes an Adverse Health 

Effect of Air Pollution? (American Thoracic Society, 2000). This statement updated the guidance for 

defining adverse respiratory health effects that had been published 15 years earlier 

(American Thoracic Society, 1985), taking into account new investigative approaches used to identify the 

effects of air pollution and reflecting concern for impacts of air pollution on specific susceptible groups. 

In the 2000 update, there was an increased focus on quality of life measures as indicators of adversity and 

a more specific consideration of population risk. Exposure to air pollution that increases the risk of an 

adverse effect to the entire population is viewed as adverse, even though it may not increase the risk of 

any identifiable individual to an unacceptable level. For example, a population of asthmatics could have a 

distribution of lung function such that no identifiable individual has a level associated with significant 

impairment. Exposure to air pollution could shift the distribution such that no identifiable individual 

experiences clinically relevant effects; this shift toward decreased lung function, however, would be 

considered adverse because individuals within the population would have diminished reserve function 

and, therefore, would be at increased risk to further environmental insult. 

It is important to recognize that the more subtle health outcomes may be linked to health events 

that are clearly adverse. For example, air pollution has been shown to affect markers of transient 

myocardial ischemia such as ST-segment abnormalities and onset of exertional angina. In some cases, 

these effects are silent yet may still increase the risk of a number of cardiac events, including myocardial 

infarction and sudden death. 

1.7. Summary 

This first external review draft ISA is a concise evaluation and synthesis of the most 

policy-relevant science for reviewing the NAAQS, and it communicates critical science judgments 

relevant to the NAAQS review. It reviews the most policy-relevant evidence from health and 

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environmental effects studies, including mechanistic evidence from basic biological science. Annexes to 

the ISA provide additional details of the literature published since the last review. A framework for 

making critical judgments concerning causality was presented in this chapter. It relies on a widely 

accepted set of principles and standardized language to express evaluation of the evidence. This approach 

can bring rigor and clarity to the current and future assessments. This ISA should assist EPA and others, 

now and in the future, to accurately represent what is presently known—and what remains unknown—

concerning the effects of CO on human health and public welfare. 

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Chapter 2. Integrative Health Effects 

Overview 

The subsequent chapters of this ISA present the most policy-relevant information related to the 

review of the NAAQS for CO, including a synthesis of the evidence presented in the 2000 CO AQCD 

with the results of more recent studies. This chapter integrates important findings from the disciplines 

evaluated in the assessment of the CO scientific literature, which include atmospheric sciences, ambient 

air data analyses, exposure assessment, dosimetry, and health effects research (animal toxicology, 

controlled human exposure, and epidemiologic studies). The EPA framework for causal determinations 

described in Chapter 1 has been applied to the body of evidence evaluated in this assessment in order to 

characterize the relationship between exposure to CO at relevant pollutant exposures and health effects. 

The EPA framework applied here employs a five-level hierarchy for causal determination: 

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 Causal relationship 

 Likely to be a causal relationship 

 Suggestive of a causal relationship 

 Inadequate to infer a causal relationship 

 Suggestive of no causal relationship 

This evaluation led to causal determinations for a range of health outcomes, and characterization of 

16 the magnitude of these responses, including responses in susceptible or vulnerable subpopulations, over a 

17 range of relevant concentrations. This integration of evidence also provides a basis for characterizing the 

18 concentration-response relationships of CO and adverse health outcomes for the U.S. population, given 

19 the current state of knowledge. 

This chapter summarizes and integrates the recently available scientific evidence along with key 

findings and conclusions from the 2000 CO AQCD that best informs consideration of the policy-relevant 

questions that frame this assessment as presented in Chapter 1. Section 2.1 discusses the trends in ambient 

concentrations and sources of CO and provides a brief summary of factors influencing personal exposure 

to ambient CO. Section 2.2 summarizes CO dosimetry and pharmacokinetics and describes what is 

known regarding the hypoxic and non-hypoxic modes of action of CO. Section 2.3 integrates the evidence 

for studies that examined health effects related to short- and long-term exposure to CO and discusses 

important uncertainties identified in the interpretation of the scientific evidence. Finally, Section 2.4 

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presents the public health impacts associated with exposure to CO, and includes evidence of effects in 

potentially susceptible and vulnerable populations from CO exposure. 

2.1. Ambient Concentrations, Sources, and Exposure 

CO is formed by incomplete combustion of carbon-containing fuels and by photochemical 

reactions in the atmosphere. Nationally, on-road mobile sources constituted more than half of total CO 

emissions in 2002, or ~63 of ~109 million tons (MT) of total CO emissions, based on the most recent 

publicly available data from EPA’s National Emissions Inventory (NEI). On-road mobile source emissions 

decreased by 16% since 1997, which is the most recent year covered in the 2000 CO AQCD. In 

metropolitan areas in the U.S., as much as 70-75% of all CO emissions result from on-road vehicle 

exhaust. The majority of these on-road CO emissions are derived from gasoline-powered vehicles since 

diesel vehicles emit relatively little CO. When emissions from incomplete combustion of fuels powering 

non-road mobile sources, such as farm and construction equipment, lawnmowers, boats, ships, 

snowmobiles, and aircraft are included, all mobile sources accounted for ~80% of total CO emissions in 

the U.S. in 2002. Other sources of CO include wildfires, controlled vegetation burning, residential 

biomass combustion, and industrial processes. While CO emissions from non-road mobile sources, fire, 

and industry have remained fairly constant, on-road mobile source CO emissions have decreased by 

roughly 5% per year since the early 1990s. 

Significant reductions in ambient CO concentrations and in the number of NAAQS exceedances 

have been observed over the past 25 years, a continuation of trends documented in the 2000 CO AQCD. 

Nationwide ambient CO data from the EPA Air Quality System (AQS), for the years 2005-2007, show 

that the median 1-h daily maximum (max) concentration across the U.S. was 0.7 ppm; the mean was 

0.9 ppm; the 95th percentile was 2.4 ppm; and the 99th percentile was 3.8 ppm. The median 8-h daily 

max ambient CO concentration for the years 2005-2007 was 0.5 ppm; the mean was 0.7 ppm; the 95th 

percentile was 1.7 ppm; and the 99th percentile was 2.6 ppm. The current CO NAAQS are 35 ppm (1-h 

avg) and 9 ppm (8-h avg), not to be exceeded more than once per year (yr). During the years 2005-2007, 

1-h and 8-h CO concentrations did not exceed the NAAQS level more than once per year at any 

monitoring site. Moreover, in these 3 years, a 1-h avg concentration in excess of 35 ppm was reported 

only once, in 2007, in Ogden, UT (39 ppm), and there were only 7 reported 8-h avg values nationwide in 

excess of 9 ppm in all 3 years. Seasonally divided box plots of data from 2005-2007 compiled for 

spatially diverse urban metropolitan areas illustrate the tendency for higher median CO concentrations 

and wider variations in concentrations in the winter and fall compared with the spring and summer. 

Policy-relevant background (PRB) concentrations of CO were computed for this assessment using 

data for the years 2005-2007 collected at 12 remote sites in the U.S. which are part of the National 

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Oceanic and Atmospheric Administration’s (NOAA) Global Monitoring Division (GMD) and are not part 

of the EPA national regulatory network. The 3-year avg CO PRB averaged ~0.13 ppm in Alaska, 

~0.10 ppm in Hawaii, and ~0.13 ppm over the contiguous U.S. (CONUS). (Note that the analysis for 

North American PRB in this assessment was made by segregating the three Alaska sites based on their 

high latitude and the two Hawaii sites based on their distance from the continent and then treating the 

remaining seven sites as representative of the CONUS PRB.) 

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A person’s total personal exposure to CO is a combination of ambient and non-ambient exposures. 

Ambient exposure can be further broken down into direct exposure while outdoors and exposure to 

ambient CO which has infiltrated into buildings and vehicles. Several studies have shown that in the 

absence of indoor sources such as ETS or gas stoves, CO concentrations are highest in automobiles and 

near highly traveled roadways. Specific concentrations vary as a function of vehicle ventilation rate and 

CO emissions source strength and are related to, for example, on-road vehicle numbers and speed. Invehicle CO concentrations are typically reported to be between 2 and 5 times higher than ambient 

concentrations measured at the roadside, but have been reported to be as much as 25 times higher. A 

portion of these high concentrations comes from the vehicle’s own engine emissions entrained with the 

ambient concentrations from the roadway, although the exact fraction of this self-pollution in the invehicle total varies with body integrity of the vehicle and is not known with certainty. Concentrations of 

CO, like those of other on-road vehicle pollutants, often display an inverse relationship with distance 

from the roadway. For CO, this fall-off with distance can be very sharp up to ~70 meters (m), beyond 

which the concentration increment from roadway emissions is largely indistinguishable from local 

ambient concentrations. The siting and location instructions for federal regulatory CO monitors explicitly 

recognize the need to measure at distances close to mobile sources with a requirement for monitored areas 

that at least one monitor be sited to measure max concentration. This is often accomplished with a 

monitor situated at the CFR-defined microscale of 2-10 m from the roadway; these microscale monitors 

also have sample inlets mounted at 3 ± 0.5 m above ground level (AGL), unlike those at larger scale 

distances whose inlet heights can vary between 2 and 15 m AGL. In 2007, there were at least 70 CO 

monitors described as microscale reporting to AQS. 

Although the correlations across CO monitors sited to sample at different scales can be greater than 

0.8 in some areas, they also can vary widely from within and between cities across the U.S. as a function 

of natural and urban topography, meteorology, and strength and proximity to sources. At the same time, 

personal exposure monitoring captures both ambient and non-ambient CO concentrations. Because 

non-ambient CO exposure is not expected to be correlated with ambient CO concentrations at the 

monitor, the non-ambient contribution to total personal CO exposure complicates interpretation of health 

effects relationships observed in epidemiologic studies. For the general U.S. population, exposure error 

analysis for epidemiologic studies, as summarized in Chapter 3, indicates that fixed-site measured 

ambient CO concentration is generally a good indicator of ambient exposure to CO. 

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2.2. Dosimetry, Pharmacokinetics, and Mode of Action 

2.2.1. Dosimetry and Pharmacokinetics 

CO elicits various health effects by binding to and altering the function of a number of hemecontaining molecules, mainly hemoglobin (Hb). The formation of COHb reduces the oxygen 

(O2)-carrying capacity of blood and impairs the release of O2 from oxyhemoglobin (O2Hb) to the tissues. 

The 2000 CO AQCD has a detailed description of the well-established Coburn-Forster-Kane (CFK) 

equation, which has been used for many years to model COHb formation. More recent work since then 

has developed models that include myoglobin (Mb) and extravascular storage compartments, as well as 

other dynamics of physiology relevant to CO uptake and elimination. These models have indicated that 

CO has a biphasic elimination curve, due to initial washout from the blood followed by a slower flux 

from the tissues. The flow of CO between the blood and alveolar air or tissues is controlled by diffusion 

down the CO concentration gradient. The uptake of CO is governed not only by this CO pressure 

differential, but also by physiological parameters, such as minute ventilation and lung diffusing capacity, 

that can, in turn, be affected by factors such as exercise, age, and medical conditions (e.g., obstructive 

lung disease). Susceptible populations, including health compromised individuals and developing fetuses, 

are at a greater risk from COHb induced health effects due to altered CO kinetics, compromised 

cardiopulmonary processes, and increased baseline hypoxia levels. Altitude also may have a substantial 

effect on the kinetics of COHb formation, especially for visitors to high altitude areas. Compensatory 

mechanisms, such as increased cardiac output, combat the decrease in barometric pressure. Altitude also 

increases the endogenous production of CO through upregulation of heme oxygenase (HO). CO is 

considered a second messenger and is endogenously produced from the catabolism of heme proteins by 

enzymes such as HO-1 (the inducible form of heme oxygenase) and through endogenous lipid 

peroxidation. Finally, CO is removed from the body by expiration and oxidation to CO2. 

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2.2.2. Mode of Action 

The diverse effects of CO are dependent upon dose, duration of exposure, and the cell types and 

tissues involved. Responses to CO are not necessarily due to a single process and may instead be 

mediated by a combination of effects including COHb-mediated hypoxic stress and mechanisms unrelated 

to tissue hypoxia including free radical production and the initiation of cell signaling. However binding of 

CO to reduced iron in heme proteins with subsequent alteration of heme protein function is the underlying 

mechanism for both the hypoxic and non-hypoxic biological responses to CO. 

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As discussed in the 2000 CO AQCD, the most well-known pathophysiological effect of CO is 

tissue hypoxia caused by binding of CO to Hb. Not only does the formation of COHb reduce the 

O2-carrying capacity of blood, but it also impairs the release of O2 from O2Hb. Compensatory alterations 

in hemodynamics, such as vasodilation and increased cardiac output, protect from tissue hypoxia. At 

ambient CO concentrations, these compensatory changes are slight and likely tolerable in people with a 

healthy cardiovascular system. However, people with cardiovascular detriments may be unable to endure 

these small changes in hemodynamics which may lead to the presentation of health effects as described in 

Sections 5.2.1 and 5.2.2. Binding of CO to Mb, as discussed in the 2000 CO AQCD and in Section 

4.3.2.3, can also impair the delivery of O2 to tissues. Mb has a high affinity for CO; however, 

physiological effects are seen only after high dose exposures to CO, resulting in COMb concentrations far 

above baseline levels. 

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Non-hypoxic mechanisms underlying the biological effects of CO have been the subject of a 

substantial amount of recent research since the 2000 CO AQCD. Most of these mechanisms are related to 

CO’s ability to bind heme-containing proteins other than Hb and Mb. These mechanisms, which may be 

interrelated, include alteration in nitric oxide (NO) signaling, inhibition of cytochrome c oxidase, heme 

loss from proteins, disruption of iron homeostasis, and alteration in cellular redox status. CO is a 

ubiquitous cell signaling molecule and the physiological functions of HO-derived CO are numerous. The 

endogenous generation and release of CO from HO-1 and HO-2 is tightly controlled, as is any 

homeostatic process. Thus, exogenously-applied CO has the capacity to disrupt myriad heme-based 

signaling pathways due to its nonspecific nature. Only a limited amount of information is available 

regarding the impact of exogenous CO on tissue and cellular levels of CO. However recent animal studies 

demonstrated increased tissue CO levels and biological responses following exposure to 50 ppm CO. 

Whether or not environmentally relevant exposures to CO can affect endogenous CO signaling pathways 

and lead to adverse health effects is an open question for which there are no definitive answers at this 

time. 

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2.3. Health Effects 

Table 2-1. Causal determinations for health effects outcomes. 

Outcome Category Exposure Period Causality Determination 

Cardiovascular morbidity Short-term Likely to be a causal relationship 

Central nervous system effects Short- and long-term Suggestive of a causal relationship 

Birth outcomes and Developmental effects Long-term Suggestive of a causal relationship 

Short-term Suggestive of a causal relationship Respiratory morbidity Long-term Inadequate to infer a causal relationship 

Short-term Suggestive of a causal relationship Mortality Long-term Suggestive of no causal relationship 

2.3.1. Cardiovascular Morbidity 

The most compelling evidence of a CO-induced effect on the cardiovascular system at COHb 

levels relevant to the current NAAQS comes from a series of controlled human exposure studies among 

individuals with CAD (see Section 5.2). These studies, described in the 1991 and 2000 CO AQCDs, 

demonstrate consistent decreases in the time to onset of exercise-induced angina and ST-segment changes 

following CO exposures resulting in COHb levels of 3-6%, with one multicenter study reporting similar 

effects at COHb levels as low as 2.4%. No human clinical studies have evaluated the effect of controlled 

exposures to CO resulting in COHb levels lower than 2.4%. Human clinical studies published since the 

2000 CO AQCD have reported no association between CO and ST-segment changes or arrhythmia; 

however, none of these studies included individuals with diagnoses of heart disease. 

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While the exact physiological significance of the observed ST-segment changes among individuals 

with CAD is unclear, ST-segment depression is a known indicator of myocardial ischemia. It is also 

important to note that the individuals with CAD who participated in these controlled exposure studies 

were not representative of the most sensitive individuals in the population. In fact, the most sensitive 

individuals may respond to levels of COHb lower than 2.4%. Variability in activity patterns and severity 

of disease among individuals with CAD is likely to influence the critical level of COHb which leads to 

adverse cardiovascular effects. 

The degree of ambient CO exposure which leads to attainment of critical levels of COHb will also 

vary between individuals. First of all, endogenous CO production varies as described in Section 4.5, but 

generally results in less than 1% COHb. Secondly, nonambient exposures to CO, such as exposure to 

ETS, can increase COHb above baseline levels. Ambient exposures will result in an additive increase in 

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COHb. Using mathematical modeling to predict changes in COHb in healthy inactive adults (Quantitative 

Circulatory Physiology [QCP] model, Section 4.2.3), it can be estimated that exposure to 35 ppm CO for 

1 h results in an increase of 0.6% COHb over baseline and exposure to 9 ppm CO for 8 h results in an 

increase of 0.8% COHb over baseline. Furthermore, 24-h exposure to 3 ppm CO results in an increase of 

0.4% COHb above baseline which can also be obtained following 1-h exposure to 30 ppm CO. 

Consequently, exposure to CO at concentrations relevant to the NAAQS has the potential to increase 

COHb to levels associated with adverse cardiovascular health effects in some individuals. 

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Findings of controlled human exposure studies are coherent with findings of recent epidemiologic 

studies conducted since the 2000 CO AQCD, which observed associations between ambient CO 

concentration and emergency department (ED) visits and hospital admissions for ischemic heart disease 

(IHD), congestive heart failure (CHF) and all-cause cardiovascular disease (CVD). All but one of these 

epidemiologic studies were conducted in locations where the entire distribution of CO concentrations 

were at or below the level of the current NAAQS, with mean 24-h avg concentrations ranging from 

0.5 ppm (Montreal, Canada) to 9.4 ppm (Tehran, Iran) (Table 5-7). A single study reported a negative 

association between CO concentration and hospital admissions and ED visits for IHD among all ages; all 

other associations were positive, with increases in hospital admissions and ED visits for IHD between 

0.2% and 19.8% per standardized increase in CO concentration (Figure 5-1). These recent studies build 

upon the conclusions of the 2000 CO AQCD that short-term variations in ambient CO concentrations are 

associated with daily hospital admissions for heart disease. 

These health outcomes are consistent with a role for CO in limiting O2 availability (i.e., hypoxic 

mechanisms) in individuals with CAD. However, recent toxicological studies suggested that CO may also 

act through non-hypoxic mechanisms by disrupting cellular signaling. Studies in healthy animals 

demonstrated oxidative injury and inflammation in response to 50-100 ppm CO while studies in disease 

models demonstrate effects on heart rhythm and exacerbation of cardiomyopathy and vascular remodeling 

in response to 35-50 ppm CO. Furthermore, in utero exposure to 150 ppm CO alters postnatal 

elecrophysiological maturation in rat cardiomyocytes. Further investigations will be useful in determining 

the importance of non-hypoxic mechanisms following environmentally-relevant CO exposures. Taken 

together, the evidence from epidemiologic, human clinical, and toxicological studies is sufficient to 

conclude that a causal relationship is likely to exist between relevant short-term CO exposures and 

cardiovascular morbidity. 

2.3.2. Central Nervous System Effects 

Exposure to high levels of CO has long been known to adversely affect CNS function, with 

symptoms following acute CO poisoning including headache, dizziness, cognitive difficulties, 

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disorientation, and coma. However, the relationship between ambient levels of CO and neurological 

function is less clear and has not been evaluated in epidemiologic studies. Studies of controlled human 

exposures to CO discussed in the 2000 CO AQCD reported inconsistent neural and behavioral effects 

following exposures resulting in COHb levels of 5-20%. No new human clinical studies have evaluated 

central nervous system or behavioral effects of exposure to CO. At ambient-level exposures, healthy 

adults may be protected against CO-induced neurological impairment owing to compensatory responses 

including increased cardiac output and cerebral blood flow. However, these compensatory mechanisms 

are likely impaired among certain potentially susceptible groups including individuals with reduced 

cardiovascular function. 

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Toxicological studies that were not discussed in the 2000 CO AQCD employed rodent models to 

show that low level CO exposure during the in utero period can adversely affect adult outcomes including 

behavior, neuronal myelination, neurotransmitter levels or function, and the auditory system (discussed in 

Section 5.3). In utero CO exposure, including both intermittent and continuous exposure, has been shown 

to impair multiple behavioral outcomes in offspring including active avoidance behavior (150 ppm CO), 

non-spatial memory (75 and 150 ppm CO), spatial learning (endogenous CO inhibition), homing behavior 

(150 ppm CO), locomotor movement (150 ppm CO), and negative geotaxis (125 and 150 ppm). In two 

separate studies, in utero CO exposure (75 and 150 ppm) was associated with significant myelination 

decrements without associated changes in motor activity in adult animals. Multiple studies demonstrated 

that in utero CO exposure affected glutamatergic, cholinergic, catecholaminergic, and dopaminergic 

neurotransmitter levels or transmission in exposed male rodents. Possible or demonstrated adverse 

outcomes from the CO-mediated aberrant neurotransmitter levels or transmission include respiratory 

dysfunction (200 ppm CO), impaired sexual behavior (150 ppm CO), and an adverse response to 

hyperthermic insults resulting in neuronal damage (200 ppm). Finally, perinatal CO exposure has been 

shown to affect the developing auditory system of rodents, inducing permanent changes into adulthood. 

This is manifested with atrophy of cochlear cells innervating the inner hair cells (25 ppm CO), decreased 

immunostaining associated with impaired neuronal activation (12.5 ppm CO), impaired myelination of 

auditory associated nerves (25 ppm CO), decreased energy production in the sensory cell organ of the 

inner ear or the organ of corti (25 ppm CO), some of which is mechanistically proposed to be mediated by 

reactive oxygen species (ROS) (25 ppm CO). Functional tests of the auditory system of neonatally, low 

level CO-exposed rodents, using otoacoustic emissions (OAE) testing (50 ppm CO) and amplitude 

measurements of the 8th cranial nerve action potential (12, 25, 50, 100 ppm CO), revealed decrements in 

auditory function at PND22 and permanent changes into adulthood using AP testing (50 ppm CO). 

Together, these animal studies demonstrate that in utero exposure to CO can adversely affect adult 

behavior, neuronal myelination, neurotransmission, and the auditory system in adult male rodents. 

Considering the combined evidence from controlled human exposure and toxicological studies, the 

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evidence is suggestive of a causal relationship between relevant short- and long-term CO exposures 

and central nervous system effects.

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2.3.3. Birth Outcomes and Developmental Effects 

The most compelling evidence for a CO-induced effect on birth and developmental outcomes is for 

preterm birth (PTB) and cardiac birth defects. These outcomes were not addressed in the 2000 CO 

AQCD, which included only two studies that examined the effect of ambient CO on low birth weight 

(LBW). Since then, a number of studies have been conducted looking at varied outcomes, including PTB, 

birth defects, fetal growth (including LBW), and infant mortality. 

There is limited epidemiologic evidence that CO during early pregnancy (e.g., first month and first 

trimester) is associated with an increased risk of PTB. The only U.S. studies to investigate the PTB 

outcome were conducted in California, and these reported consistent results whereby all studies reported a 

significant association with CO exposure during early pregnancy, and exposures were assigned from 

monitors within close proximity of the mother’s residential address. Additional studies conducted outside 

of the U.S. provide supportive, though less consistent, evidence of an association between CO 

concentration and PTB. 

Very few epidemiologic studies have examined the effects of CO on birth defects. Two of these 

studies found maternal exposure to CO to be associated with an increased risk of cardiac birth defects. 

This insult to the heart is coherent with results of human clinical studies demonstrating the heart as a 

target for CO effects (Section 5.2). Animal toxicological studies provide additional evidence for such an 

insult to the heart, and reported transient cardiomegaly at birth after continuous in utero CO exposure (60, 

125, 250 and 500 ppm CO), delayed myocardial electrophysiolgical maturation (150 ppm CO), or 

systemic splenic immunocompromise (75 or 150 ppm CO). Toxicological studies have also shown that 

exogenous continuous in utero CO exposure (250 ppm) induced teratogenicity in rodent offspring in a 

dose-dependent manner that was further exacerbated by dietary protein restriction (65 ppm CO) or zinc 

depletion (500 ppm CO). Toxicological studies of exogenous CO exposure over the duration of gestation 

have shown skeletal alterations (7 h/day, CO 250 ppm) or limb deformities (24 h/day, CO 180 ppm) in 

prenatally exposed offspring. 

There is evidence of ambient CO exposure during pregnancy having a negative effect on fetal 

growth in epidemiologic studies. In general, the reviewed studies, summarized in Figures 5-7 through 5-9, 

reported small reductions in birth weight (ranging ~5-20 g). Several studies examined various 

combinations of birth weight, LBW, and small for gestational age (SGA)/intrauterine growth restriction 

(IUGR) and inconsistent results are reported across these metrics. It should be noted that having a 

measurable, even if small, change in a population is different than having an effect on a subset of 

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susceptible births and increasing the risk of IUGR/LBW/SGA. It is difficult to conclude if CO is related 

to a small change in birth weight in all births across the population, or a marked effect in some subset of 

births. 

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In general, there is limited epidemiologic evidence that CO is associated with an increased risk of 

infant mortality during the neonatal or post-neonatal periods. In support of this limited evidence, animal 

toxicological studies do provide some evidence that exogenous CO exposure to pups in utero significantly 

increased postnatal mortality (7 h/day and 24 h/day, 250 ppm CO; 24 h/day, 90 or 180 ppm CO) and 

prenatal mortality (7 h/day, 250 ppm CO). 

Evidence exists for additional developmental outcomes which have been examined in toxicological 

studies, but not epidemiologic or human clinical studies, including behavioral abnormalities, learning and 

memory deficits, locomotor effects, neurotransmitter changes, and changes in the auditory system. 

Structural aberrations of the cochlea involving neuronal activation (12.5, 25 and 50 ppm CO) and 

auditory related nerves (25 ppm CO) were seen in pups after in neonatal CO exposure. Auditory 

functional testing using OAE (50 ppm CO) and ABR (12, 25, 50, 100 ppm CO) on rodents exposed 

perinataly to CO showed that CO-exposed neonates had auditory decrements at PND22 (OAE and ABR) 

and permanent changes into adulthood with ABR (50 ppm CO). 

Overall, there is limited, though positive, epidemiologic evidence for a CO-induced effect on PTB 

and birth defects, and weak evidence for a decrease in birth weight, other measures of fetal growth, and 

infant mortality. Animal toxicological studies provide support and coherence for these effects. Both 

hypoxic and non-hypoxic mechanisms that could lead to such effects have been proposed in the 

toxicological literature (Section 5.1), though a clear understanding of the mechanisms underlying 

reproductive and developmental effects is still lacking. Taking into consideration the positive evidence for 

some birth and developmental outcomes from epidemiologic studies and the resulting coherence for these 

associations in animal toxicological studies, the evidence is suggestive of a causal relationship 

between long-term exposures to relevant CO concentrations and developmental effects and birth 

outcomes.

2.3.4. Respiratory Morbidity 

New epidemiologic studies, supported by the body of literature summarized in the 2000 CO 

AQCD, provide evidence of positive associations between short-term exposure to CO and respiratoryrelated outcomes including pulmonary function, respiratory symptoms, medication use, hospital 

admissions, and ED visits (discussed in Section 5.5). However, the interpretation of the results from 

epidemiologic studies is difficult due to the lack of extensive copollutant analyses along with the 

moderate to high correlation between CO and other combustion/traffic generated pollutants. To date the 

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majority of the literature has not extensively examined the association between CO and respiratory 

morbidity due to studies focusing primarily on effects associated with exposure to other criteria 

pollutants, namely PM and O3. This has contributed to the inability to disentangle the effects attributed to 

CO from the larger complex air pollution mix. In addition, uncertainty as to a biological mechanism to 

explain the respiratory-related effects observed in the epidemiologic literature further complicates the 

interpretation of these results, especially considering the low ambient CO concentrations reported (24-h 

avg: 0.35–2.1 ppm). However, animal toxicological studies do provide some evidence that short-term 

exposure to CO (50-100 ppm) can cause oxidative injury and inflammation and alter pulmonary vascular 

remodeling. Human clinical studies have not extensively examined the effect of short-term exposure to 

CO on respiratory morbidity, specifically pulmonary function. The limited number of clinical studies that 

have been conducted prior to and since the 2000 CO AQCD provide very little evidence of any adverse 

effect of CO on the respiratory system at COHb levels <10%. Although human clinical studies have not 

provided evidence to support CO-related respiratory health effects, the epidemiologic studies that 

examined the effects of short-term exposure to CO and lung-related outcomes show positive associations 

and animal toxicological studies demonstrate the potential for an underlying biological mechanism, which 

together provide evidence that is suggestive of a causal relationship between short-term exposure to 

relevant CO concentrations and respiratory morbidity. 

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Currently, only a few studies have been conducted that examine the association between long-term 

exposure to CO and respiratory morbidity. Although some studies did observe associations between longterm exposure to CO and respiratory health outcomes key uncertainties still exist. These uncertainties 

include: the lack of replication and validation studies to evaluate new methodologies (i.e., 

Deletion/Substitution/Addition (DSA) algorithm) that have been used to examine the association between 

long-term exposure to CO and respiratory health effects; whether the respiratory health effects observed 

in response to long-term exposure to CO can be explained by the proposed biological mechanisms; and 

the lack of co-pollutant analyses to disentangle the respiratory effects associated with CO due to its high 

correlation with NO2 and other combustion-related pollutants. Overall, the evidence available is

inadequate to conclude that a causal relationship exists between long-term exposure to relevant 

CO concentrations and respiratory morbidity.

2.3.5. Mortality 

Among the gaseous pollutants examined in time-series mortality studies, CO is the least frequently 

studied criteria air pollutant. Because CO was mostly treated as a potential confounder in these studies, 

the information available regarding the nature of the association between short-term exposure to CO and 

mortality is limited compared to the other pollutants. However, the recently available multi-city studies, 

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which consist of larger sample sizes, and single-city studies generally confirmed the findings reported in 

the 2000 CO AQCD (see Section 5.6). 

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The multi-city studies report comparable CO mortality risk estimates for total (non-accidental) 

mortality with the APHEA2 European multi-city study showing slightly higher estimates for 

cardiovascular mortality in single-pollutant models. However, when examining potential confounding by 

copollutants these studies consistently showed that CO mortality risk estimates were reduced when NO2

was included in the model, but this observation may not be “confounding” in the usual sense in that NO2

may also be a surrogate marker of other pollutants or pollution sources (i.e., traffic). 

Only one of the multi-city studies focused specifically on the CO-mortality association (the 

APHEA study), examining: (1) model sensitivity; (2) the CO-mortality concentration-response (C-R) 

relationship; and (3) potential effect modifiers of CO mortality risk estimates. The APHEA2 study 

performed a sensitivity analysis, which indicated an approximate 50 - 80% difference in CO risk 

estimates from a reasonable range of alternative models. In addition, the study examined the CO-mortality 

C-R relationship through a grid search of varying threshold points, and found only weak evidence of a CO 

threshold at 0.5 mg/m3

 (0.43 ppm), but this result is complicated by the lowest 10% of the CO distribution 

for seven of the 19 cities examined being at or above 2 mg/m3

 (1.74 ppm). The examination of a variety 

of city-specific variables to identify potential effect modifiers of the CO-mortality relationship found that 

geographic region explained most of the heterogeneity in CO mortality risk estimates with the 

CO-mortality associations being stronger in western and southern European cities than eastern cities. A 

similar pattern has been reported for black smoke (BS) and SO2 in previous APHEA studies, but the 

geographic variability observed does not provide specific information which could be used to evaluate the 

CO-mortality association. 

 The results from the single-city studies evaluated are generally consistent with the multi-city 

studies in that some evidence of a positive association was found for mortality upon short-term exposure 

to CO. However, the CO-mortality associations were often, but not always, attenuated when other 

copollutants were included in the regression models. In addition, limited evidence was available to 

identify cause-specific mortality outcomes (e.g., cardiovascular causes of death) associated with shortterm exposure to CO. 

The new multi- and single-city studies evaluated provide evidence that an association between 

short-term exposure to CO and mortality exists, but limited evidence is available to evaluate causespecific mortality outcomes associated with CO exposure and it is unclear if CO is acting alone or as a 

surrogate for other combustion-related pollutants. In addition, the results also underscore the limitation of 

current analytical methods to disentangle the health effects associated with one pollutant in the complex 

air pollution mixture. Overall, the epidemiologic evidence is suggestive of a causal relationship 

between short-term exposure to relevant CO concentrations and mortality.

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The evaluation of new epidemiologic studies conducted since the 2000 CO AQCD that investigated 

the association between long-term exposure to CO and mortality consistently found null or negative 

mortality risk estimates. No such studies were discussed in the 2000 CO AQCD. The re-analysis of the 

American Cancer Society (ACS) data by Jerrett et al. (2003) found no association between long-term 

exposure to CO and mortality. Similar results were obtained in an updated analysis conducted by the 

original ACS investigators when using earlier (1980) CO data, but negative associations were found when 

using more recent (1982-1998) data. The Women’s Health Initiative (WHI) Study also found no 

association between CO and CVD events (including mortality) using the data from recent years 

(1994-1998), while the series of Veterans Cohort studies found no association or a negative association 

between mean annual 95th percentile of hourly CO values and mortality. In addition, a cross-sectional 

study of U.S. counties reported results that are generally consistent with the cohort studies: positive 

associations between long-term exposure to PM2.5 and sulfate (SO4

2-) and mortality, and generally 

negative associations with CO. Overall, the consistent null and negative associations observed across 

epidemiologic studies which included cohort populations encompassing potentially susceptible 

subpopulations (i.e., post-menopausal women and hypertensive men) combined with (1) the lack of 

evidence for respiratory and cardiovascular morbidity outcomes following long-term exposure to CO; and 

(2) the absence of a proposed mechanism to explain the progression to mortality following long-term 

exposure to CO provide supportive evidence that is suggestive of no causal relationship between longterm exposure to relevant CO concentrations and mortality. 

2.4. Public Health Impacts 

2.4.1. Concentration-Response Relationship 

Currently, very limited information is available in the human clinical and epidemiologic literature 

regarding the CO C-R relationship and the potential presence of a CO threshold. Two human clinical 

studies described in the 2000 CO AQCD have evaluated the C-R relationship between CO and onset of 

exercise-induced angina among individuals with CAD, but at the high end of CO concentrations (i.e., CO 

levels above the current NAAQS). Anderson et al. (1973) exposed 10 adult men with stable angina for 4 h 

to CO concentrations of 50 and 100 ppm, which resulted in average COHb levels of 2.9% and 4.5%, 

respectively. Both exposures significantly decreased the time to onset of exercise-induced angina relative 

to room air control (1.6% COHb). However, there was no difference in response between the two 

exposure concentrations of CO. In a much larger study, 63 adults with stable angina were exposed for 1 h 

to two concentrations of CO (average exposure concentrations of 117 and 253 ppm) resulting in average 

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pre-exercise COHb levels of 2.4% and 4.7% (Allred et al., 1989). Relative to control (average COHb 

0.7%), COHb levels of 2.4% and 4.7% were observed to decrease the time to onset of angina by 4.2% (p 

= 0.054) and 7.1% (p = 0.004), respectively. In addition, these investigators reported a statistically 

significant decrease in the time to exercise-induced ST-segment depression with increasing COHb levels. 

These findings provide some evidence of a significant C-R relationship at COHb concentrations between 

2.4 and 4.7%. However, the human clinical literature has yet to evaluate the C-R relationship at lower CO 

concentrations or COHb levels. 

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One study in the epidemiologic literature attempted to examine the C-R relationship at the low end 

of CO concentrations through a threshold analysis. Samoli et al. (2007) in their examination of the 

association between short-term exposure to CO and mortality conducted an ancillary analysis to examine 

the potential presence of a CO threshold. In this analysis the authors compared city-specific models to the 

threshold model, which consisted of thresholds at 0.5 mg/m3

 (0.43 ppm) increments. Samoli et al. (2007) 

then computed the deviance between the two models and summed the deviances for a given threshold 

over all cities. While the minimum deviance suggested a potential threshold of 0.43 ppm (the lowest 

threshold examined), the comparison with the linear no-threshold model indicated weak evidence (pvalue >0.9) for a threshold. However, determining the presence of a threshold at the very low range of CO 

concentrations (i.e., at 0.43 ppm) in this data set is challenging, because, in seven of the 19 European 

cities examined, the lowest 10% of the CO distribution was at or above 2 mg/m3

 (1.74 ppm). By only 

using the 12 cities in the analysis that had minimum CO concentrations approaching 0.5 mg/m3

(0.43 ppm), a limited number of observations were examined around the threshold of interest, which 

subsequently contributed to the inability to draw conclusions regarding the potential presence of a 

threshold with any certainty. 

2.4.2. Potentially Susceptible and Vulnerable Subpopulations 

The examination of both susceptible and vulnerable subpopulations to CO exposure allows for the 

NAAQS to provide an adequate margin of safety for both the general population and sensitive 

subpopulations (see Section 5.7 for a more detailed discussion). During the evaluation of the CO 

literature, numerous studies were identified that examined whether underlying factors increased the 

susceptibility or vulnerability of an individual to CO-related health effects. In this ISA, a susceptible 

subpopulation is defined as those individuals with intrinsic biological characteristics that might exhibit an 

adverse health effect to a pollutant at concentrations lower than those needed to elicit the same response 

in the general population or those individuals that might elicit a more adverse health effect at the same 

concentration. A vulnerable subpopulation is defined as those individuals with external, nonbiological 

factors that increase the risk of adverse health effects, such as differential exposure or living at altitude. 

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The most important susceptibility characteristic for increased risk due to CO exposure is CAD. 

Individuals with heart disease may be at a greater risk from CO exposure since they may already have 

compromised O2 delivery. CO is notable among air pollutants because it is especially harmful in 

individuals with impaired cardiovascular systems. Persons with a normal cardiovascular system can 

tolerate substantial concentrations of CO, if they vasodilate in response to the hypoxemia produced by 

CO. In contrast, individuals unable to vasodilate in response to CO exposure may show evidence of 

ischemia at low concentrations of COHb. Many of the controlled human exposure studies have focused 

on individuals with IHD. Other medical conditions that confer increased susceptibility include obstructive 

lung disease, which impairs airflow to the lungs, and conditions such as anemia that alter the blood O2

carrying capacity or content and result in a greater risk from COHb induced hypoxia and decreased tissue 

O2 delivery. Age is important for susceptibility in older adults, who have an increased elimination time for 

COHb. Newborns can also be considered a susceptible subpopulation due to critical phases of 

development and differences between fetal and maternal CO pharmacokinetics in utero. 

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Subpopulations considered vulnerable to CO-related health effects include people who spend a 

considerable amount of time in or near traffic, live at high altitude, exercise and/or use medications which 

may increase endogenous CO production. Individuals that spend a substantial amount of time on or near 

heavily traveled roadways, such as commuters and those living or working near freeways, are likely to 

experience elevated CO concentrations and therefore constitute a potentially vulnerable subpopulation 

due to differential exposure. Vulnerable subpopulations also include individuals at high altitude, who 

undergo physiologic changes that favor increased CO uptake and COHb formation. Exercising 

individuals are also considered vulnerable because exercise facilitates CO uptake and transport by 

increasing gas exchange efficiency and the COHb elimination rate decreases with physical activity. 

Individuals who use certain medications which may increase endogenous CO production and the baseline 

level of COHb are also considered vulnerable to CO-related health effects. 

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Chapter 3. Source to Exposure 

3.1. Introduction 

This chapter contains basic information about concepts and findings in atmospheric sciences and 

exposure assessment pertaining to CO levels relevant for establishing a foundation for the detailed 

discussions of health effects data in subsequent chapters. Section 3.2 provides an overview of the sources 

of CO and examples of their spatial distribution. Atmospheric chemistry involved in the production and 

removal of CO by oxidation processes is discussed in Section 3.3. Descriptions of CO measurement 

methods and monitor siting requirements and locations are presented in Section 3.4. Data for ambient CO 

concentrations are characterized in Section 3.5. Policy relevant background concentrations of CO, 

i.e., those concentrations defined to result from uncontrollable emissions, are also presented in Section 

3.5. Finally, factors related to human exposure to CO are discussed in Section 3.6. 

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3.2. Sources and Emissions of CO 

CO is a colorless, odorless, tasteless gas consisting of one carbon (C) atom covalently bonded to 

one O2 atom; its molar mass is 28.0101 g/mol. CO is formed primarily by incomplete combustion of 

carbon-containing fuels and photochemical reactions in the atmosphere. In general, any increase in fuel 

O2 content, burn temperature, or mixing time in the combustion zone will tend to decrease production of 

CO relative to CO2; hence CO emissions from large fossil-fueled power plants are typically very low 

since the boilers at these plants are tuned for highly efficient combustion with the lowest possible fuel 

consumption. Internal combustion engines used in mobile sources, by contrast, have widely varying 

operating conditions and, thus, inherently higher and varying CO formation. 

Figure 3-1 lists CO emissions totals in tons segregated by individual source sectors in the U.S. for 

2002, which is the most recent publicly available data. In the U.S., CO emissions data are tracked in the 

National Emissions Inventory (U.S. EPA, 2006a), a composite of data from various sources including 

industries and state, tribal, and local air agencies. NEI data are collected for all states, the District of 

Columbia, the U.S. territories of Puerto Rico and Virgin Islands, and some of the territories of federally 

recognized American Indian nations. Different data sources use different data collection methods, most of 

which are based on engineering calculations and estimates rather than measurements. Most fuel 

combustion and industrial sources, for example, estimate their CO emissions using EPA-approved 

emission factors, as do on-road and non-road mobile source emitters (U.S. EPA, 2007). Although these 

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estimates are generated using well-established approaches, uncertainties are inherent in the emission 

factors and models used to represent sources for which emissions have not been directly measured. 

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Nationally, on-road mobile sources in the NEI constituted more than half of total CO emissions in 

2002, or ~63 MT of ~109 MT total. For this reason, high concentrations of CO can often occur in areas of 

heavy traffic. In metropolitan areas in the U.S., for example, as much as 70-75% of all CO emissions 

came from on-road vehicle exhaust in the 2002 NEI (U.S. EPA, 2006a). The majority of these on-road CO 

emissions derive from gasoline-powered vehicles since the O2 content, pressure, and temperature required 

for diesel fuel ignition result in much less CO production. When the emissions from incomplete 

combustion of fuels powering non-road mobile sources were included, all mobile sources accounted for 

~80% of total CO emissions in the U.S. in 2002; see Figure 3-1. 

Nationwide Emissions 2002

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Solvent Use

Miscellaneous

Waste Disposal

Industrial Processes

Electricity Generation

Fossil Fuel Combustion

Residential Wood

Combustion

Non-Road Equipment

Fires

On-Road Vehicles

Emissions (MT)

Source: U.S. EPA (2006a) 

Figure 3-1. CO emissions (MT) in the U.S. by source sector in 2002. 

Figure 3-2 shows present and historical CO emissions from the traditionally inventoried 

anthropogenic source categories: (1) fuel combustion, which includes emissions from coal-, gas-, and oilfired power plants and industrial, commercial, and institutional sources, as well as residential heaters 

(e.g., wood-burning stoves) and boilers; (2) industrial processes, which includes chemical production, 

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petroleum refining, metals production, and industrial processes other than fuel combustion; (3) on-road 

vehicles, which includes cars, trucks, buses, and motorcycles; and (4) non-road vehicles and engines, such 

as farm and construction equipment, lawnmowers, chainsaws, boats, ships, snowmobiles, aircraft, 

locomotive, and others. Using these NEI data, trends in the national CO emissions can be computed and 

compared over time. So, for example, the national-scale estimated anthropogenic CO emissions decreased 

35% between 1990 and 2002; see Figure 3-2. The trend plot in Figure 3-2 demonstrates that controls in 

the on-road vehicle sector have produced nearly all the national-level CO reductions since 1990. Data are 

presented here for 1990 and from 1996 to 2002 because only the 1990 data has been updated to be 

comparable to the more recent inventories made since 1996. 

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Source: U.S. EPA (2006a, 2008c) 

Figure 3-2. Trends in anthropogenic CO emissions (MT) in the U.S. by source category for 1990 

and 1996-2002. 

With the exception of this downward trend resulting from emissions controls, anthropogenic CO 

emissions demonstrate less interannual variability than biogenic emissions (Bergamaschi et al., 2000). 

Several recent reports using both ambient concentrations and fuel-based emissions estimates have 

explored this annual-to-decadal emissions decrease in anthropogenic CO in finer detail. They included 

Harley et al. (2001; 2005), Parrish et al. (2002; 2006), Pollack et al. (2004), and Mobley et al. (2005). The 

consistent conclusion from those investigations has been that annual average U.S. on-road vehicle CO 

emissions decreased at a rate of ~5% per year since the early 1990s. Additional analyses by Harley et al. 

(2005), Parrish et al. (2002) and Parrish (2006) were also consistent with the suggestion in Pollack et al. 

(2004) that the EPA MOBILE6 vehicle emissions model (

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Biogenic emissions can vary widely from year to year (Bergamaschi et al., 2000). CO isotopes, 

most notably 14CO, have been especially useful in helping refine emissions estimates and partition them 

between combustion and other sources. Weinstock and Niki (1972) analyzed measurements of the 14C 

content of CO in Tonawanda, NY (a suburb of Buffalo) obtained by MacKay et al. (1963) during the 

winter of 1960 and found at that time that a large proprotion of the CO was produced by sources 

containing modern carbon and not fossil carbon. The main source of this modern carbon CO was 

conjectured by Weinstock and Niki (1972) to have been from photochemical oxidation of CH4; however, 

CH4 has a long atmospheric life time (about 8 years) but does oxidize to compounds like methyl radical 

and also is a contributor to O3 formation. Other work suggested that CO sources such as isoprene 

oxidation and biomass burning were also important. For example, Conny (1998) reviewed studies using 

measurements of 14CO to characterize winter sources of CO in Las Vegas, NV, and Albuquerque, NM. 

These studies concluded that the contribution from residential wood burning could have been as high as 

30% in the samples collected. 

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Estimates of non-anthropogenic CO emissions are made using the Biogenic Emissions Inventory 

System (BEIS) model with data from the Biogenic Emissions Landcover Database (BELD) and annual 

meteorological data; see http://www.epa.gov/ttnchie1/emch/biogenic. National biogenic emissions, 

excluding fires, were estimated to contribute ~5% of total CO emissions from all sources in 2002; fires in 

2002 added another 13%, or ~14.5 MT, to the national CO emissions total. Geogenic emissions of CO 

also included in this inventory, include volcanic gases released from molten rock in the earth’s mantle. 

Mixing ratios of dissolved CO in this rock vary in a range from 0.01 to 2% as a function of the rock 

stratum surrounding the volcano and other geologic conditions. This high variability and infrequent 

though often violent release mean geogenic CO measurements are very difficult to make with precision. 

On a global scale, however, the magnitude of their contribution is small relative to all anthropogenic 

sources. Photodecomposition of organic matter in oceans, rivers, lakes, and other surface waters, and 

from soil surfaces also releases CO (Goldstein and Galbally, 2007). However, soils can act as a CO source 

or a sink depending on soil moisture, UV flux reaching the soil surface, and soil temperature (Conrad and 

Seiler, 1985). Soil uptake of CO is driven by anaerobic bacteria (Inman et al., 1971). Emissions of CO 

from soils appear to occur by abiotic processes, such as thermodecomposition or photodecomposition of 

organic matter. In general, warm and moist conditions found in most soils favor CO uptake, whereas hot 

and dry conditions found in deserts and some savannas favor the release of CO (King, 1999). 

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Biomass burning consists of wildfires and the intentional burning of vegetation to clear new land 

for agriculture and population resettlement; to control the growth of unwanted plants on pasture land; to 

manage forest resources with prescribed burning; to dispose of agricultural and domestic waste; and as 

fuel for cooking, heating, and water sterilization. Globally, most wildfires may be ignited directly as the 

result of human activities leaving only 10 to 30% initiated by lightning (Andreae, 1991). However, 

because fire management practices suppress natural wildfires, the buildup of fire fuels increases the 

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susceptibility of forests to more severe but less frequent fires in the future. There is considerable 

uncertainty in attributing the fraction of wildfire emissions to human activities because the emissions 

from naturally occurring fires that would have been present in the absence of fire suppression practices 

are not known. 

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Biomass burning also exhibits strong seasonality and interannual variability (van der Werf et al., 

2006), with most biomass burned during the local dry season. This is true for both prescribed burns and 

wildfire. The unusually warm and dry weather in central Alaska and western Yukon in the summer of 

2004, for example, contributed to the burning of 11 million acres there. These fires, the largest on record 

for this region, produced CO emissions easily tracked by the Measurement of Pollution in the 

Troposphere (MOPITT) instrument on NASA’s Terra satellite; see Figure 3-3. The high CO concentration 

measured by MOPITT coincided with the surface location of fires using aerosol plumes identified by the 

Moderate Resolution Imaging Spectroradiometer (MODIS) also on Terra. Subsequent modeling by Pfister 

et al. (2005) showed that the CO contribution from these fires in July 2004 was 30 (±5) teragrams (Tg) 

that summer, or in the range of the total U.S. anthropogenic CO emissions during the same time. 

The smoldering phase of combustion yields higher CO emissions than the flaming phase. Using 

controlled combustion chamber experiments Lobert et al. (1991) found that with a wide variety of 

vegetation types, on average, 84% of the CO from biomass fires was produced during the smoldering 

phase and 16% during the flaming phase of combustion. 

CO emissions data for EPA’s ten administrative Regions in the U.S. depicted in Figure 3-4 show a 

more nuanced view of the national concentrations and trends described just above. Net anthropogenic CO 

emissions were estimated to have declined in all EPA Regions between 1990 and 2002 with the largest 

decrease (10.8 MT) occurring in Region 9. 

On still finer scales, CO emissions from on-road mobile sources or from fires can dominate in 

different places across the U.S. Figure 3-5 illustrates this variability with CO state-level emissions total 

and selected county totals in 2002 for Colorado. (Annex A includes analogous data for Alaska, Utah, 

Massachusetts, Georgia, California, and Alabama.) In Colorado, emissions from fires and on-road 

vehicles were nearly equal: ~0.9 MT from fires and ~1.1 MT from on-road vehicles, though emissions 

varied strongly across counties with urban Denver County dominated by on-road vehicle emissions at 

71% and rural Garfield County dominated by fire emissions at 67%. 

March 2009 3-5 DRAFT – DO NOT CITE OR QUOTE 
Source: Fishman et al. (2008) 

Figure 3-3. CO concentrations measured by satellite at the 700 hectoPascal level (~10,000 feet 

above sea level) from MOPITT for the period 15-23 July 2004 during intense wildfires 

in Alaska and Yukon. 

Source: U.S. EPA (2006a, 2008c) 

Figure 3-4. Trends in sub-national CO emissions in the 10 U.S. EPA Regions for 1990 and 1996 

to 2002. 

March 2009 3-6 DRAFT – DO NOT CITE OR QUOTE 
Denver 

County

Garfield 

County

Colorado State Emissions 2002

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334

527

6,838

7,290

25,743

78,308

385,460

966,816

1,110,980

0 1,400,000

Solvent Use

Miscellaneous

Waste Disposal

Industrial Processes

Electricity Generation

Fossil Fuel Combustion

Residential Wood Combustion

Non-Road Equipment

Fires

On-Road Vehicles

Emissions (Tons)

Denver County Emissions 2002

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1,864

48,658

0

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0 200,000

Solvent Use

Miscellaneous

Waste Disposal

Industrial Processes

Electricity Generation

Fossil Fuel Combustion

Residential Wood Combustion

Non-Road Equipment

Fires

On-Road Vehicles

Emissions (Tons)

Garfield County Emissions 2002

0

3

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135

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2,239

3,121

5,607

64,816

20,309

0 200,000

Solvent Use

Miscellaneous

Waste Disposal

Industrial Processes

Electricity Generation

Fossil Fuel Combustion

Residential Wood Combustion

Non-Road Equipment

Fires

On-Road Vehicles

Emissions (Tons)

Figure 3-5. CO emissions density map and distribution for the state of Colorado, and for 

selected counties in Colorado. 

March 2009 3-7 DRAFT – DO NOT CITE OR QUOTE 
3.3. Physics and Chemistry of Atmospheric CO 

In addition to being emitted directly by combustion sources, CO is produced by photooxidation of 

methane (CH4) and other VOCs including nonmethane hydrocarbons (NMHCs) in the atmosphere, and of 

organic molecules in surface waters and soils. CH4 oxidation is summarized in the following reaction 

sequence: 

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2 2

2 2

2

2 2

3 2 2 2

23 3 2

23 23

4 23

)( )(

HOCOOHCO

OHOCHor OHHCO

HHCOhOCHor

COHhOCH

HOOCHOOCH

NOOCHNOOCH

MOCHMOCH

OHCHOHCH

+→+

+→+

+→+

+→+

+→+

+→+

→++ +

+ → +

ν

ν

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where M is a reaction mediator providing collisional energy but is neither created nor destroyed. 

Photolysis of formaldehyde (CH2O) proceeds by two pathways. The first produces molecular 

hydrogen (H2) and CO with a reaction yield of 55% in conditions of clear skies and low zenith angles; the 

second yields a hydrogen radical (H) and the formyl radical (HCO). HCO then reacts with O2 to form 

hydroperoxy radical (HO2) and CO. Reaction of methyl peroxy radical (CH3O2) with HO2 radicals 

(reaction not shown) to form methyl hydroperoxide (CH3OOH) is also operative, especially in low oxides 

of nitrogen (NOX) conditions. Heterogeneous removal of the water-soluble intermediate products 

CH3OOH, CH2O, and radicals will decrease CO yields from CH4 oxidation. 

While oxidation of CH2O nearly always produces CO and some small quantities of formic acid 

(CH2O2) in the reaction of CH2O with HO2 (not shown here), oxidation of acetaldehyde (CH3CHO) does 

not always yield two CO molecules. Reaction of CH3CHO with OH can yield acetyl radicals (CH3CO) 

which then will participate with O2 in a termolecular recombination reaction to form peroxyacyl radicals, 

which then can react with nitric oxide (NO) to form CH3 and CO2; or the peroxyacyl radicals can react 

with NO2 to form peroxyacetyl nitrate (PAN), CH3CO3NO2. In this way, one carbon atom is oxidized 

directly to CO2 without passing through CO. The yield of CO from these pathways depends on the OH 

concentration and the photolysis rate of CH3CHO, as well as on the abundance of NO, since peroxyacyl 

radicals also will react with other odd hydrogen radicals like OH. 

Estimating the CO yield from oxidation of hydrocarbons (HCs) larger than CH4 requires computing 

the yields of CH2O, CH3CHO, CH3CO, and analogous radicals from oxidation of the parent molecules. 

Moreover, the extent of heterogeneous removal of soluble intermediate products also affects oxidation of 

more complex HCs. However, the detailed gas-phase kinetics for many HCs with more than a few 

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carbons is still unknown, and this is especially the case for several important classes of VOCs including 

the aromatics, biogenic HCs including isoprene, and their intermediate oxidation products like epoxides, 

nitrates, and carbonyls. It has long been known that as much as 30% of the carbon in HCs in many urban 

areas is in the form of aromatics largely from mobile sources since gasoline contains significant quantities 

of aromatics (Grosjean and Fung, 1984; Seila et al., 1989). Yet mass balance analyses performed on 

irradiated smog chamber mixtures of aromatic HCs indicate that only about one-half of the carbon is in 

the form of compounds that can be identified. In addition, reactions like the oxidation of terpenes that 

produce condensable products are also significant because these reactions produce secondary organic 

aerosols, thereby reducing the potential yield of CO. The CO yield from oxidation of CH4, for example, is 

~0.9 on a per carbon basis (Kanakidou and Crutzen, 1999). Yields from other compounds range from less 

than 0.1 for anthropogenic alkanes (Altshuller, 1991) to ~0.7 for some other non-CH4 HCs; see 

Kanakidou and Crutzen (1999) for CO yields from other HCs. 

The major pathway for removal of CO from the atmosphere is reaction with OH to produce CO2

and H radicals that rapidly combine with O2 to form HO2 radicals with a rate constant at 1 atm in air of 

~2.4 × 10-13 cm3

/molecule/s (Finlayson-Pitts and Pitts Jr). The mean tropospheric photochemical lifetime 

(τ) of CO in the northern hemisphere is ~57 days (Khalil and Rasmussen, 1990; Thompson and Cicerone, 

1986). Owing to variation in atmospheric water vapor, OH concentration, and insolation, shorter τ are 

found nearer the tropics and longer ones at higher latitudes. During winter at high latitudes CO has nearly 

no photochemical reactivity on urban and regional scales. The CO τ is shorter than the characteristic time 

scale for mixing between the hemispheres of ~1 year; hence a large gradient in concentrations can exist 

between the hemispheres. In addition, the CO τ at high latitudes is long enough to result in much smaller 

gradients between 30° latitude and the pole of either hemisphere. The typical residence times of CO in 

urban areas when assuming a diel-average OH concentration of 3 × 106

/cm3 in urban areas is ~16 days, so 

CO will not be destroyed in urban areas where it is emitted and will likely be mixed on continental and 

larger scales. OH concentrations are orders of magnitude lower in indoor environments and so CO will 

generally not be destroyed by indoor air reactions. 

Recent data do not alter the current well-established understanding of the role of urban and regional 

CO in continental and global-scale chemistry outlined in the 2000 CO AQCD (U.S. EPA, 2000), and 

subsequently confirmed in the global assessments of climate change by the Intergovernmental Panel on 

Climate Change (IPCC, 2001, 2007). CO is a weak contributor to greenhouse warming because its 

fundamental absorption band near 4.63 μm is far from the spectral maximum of earth’s longwave 

radiation at ~10 μm. However, because reaction with CO is also the major sink for OH, changes in CO 

concentrations can lead to changes in the concentrations of other trace gases whose loss processes involve 

OH attack. Some of these trace gases, CH4, for example, absorb infrared radiation from the earth’s surface 

and contribute to the greenhouse effect directly; others, including the hydrochlorofluorocarbons (HCFCs) 

March 2009 3-9 DRAFT – DO NOT CITE OR QUOTE 
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and methyl chloride and methyl bromide, can deplete stratospheric O3, increasing the surface-incident UV 

flux. 

This indirect effect of CO on stratospheric O3 concentrations is opposite in sign to the effect of CO 

on O3 in the troposphere where CO reacts in a manner similar to other VOCs in the presence of NOX and 

UV to create O3. (See the detailed description of O3 formation from VOCs and NOX in the 2008 NOX ISA 

(U.S. EPA, 2008e). The long chemical lifetime and one-to-one stoichiometry of CO oxidation (whereby 

one molecule of CO converts only one molecule of NO to NO2) means that CO has significantly lower O3

forming potential than other VOCs in the troposphere. Carter (1998) computed a maximum incremental 

reactivity for CO of 0.07 g O3 for 1 g CO, as compared to reactivities of total on-road vehicle exhaust 

emissions in the range of 3 to 4. However, because the total mass of CO emissions is substantially greater 

than those of the other VOCs with higher carbon numbers and faster reactivities, CO can contribute 

greatly to O3 formation even though its photochemical processing is slow. Using data from instrumented 

models including that of Jeffries (1995), NRC (1999) estimated, for example, that CO can contribute 15-

25% of the total O3 forming potential of gasoline exhaust emissions; this figure varies from region to 

region. The contribution of CO to urban and regional O3 concentration is often less than 10% owing to its 

very slow reactivity on these scales and to locally variable radical concentration ratios. 

Because the greenhouse warming effects from CO are nearly completely indirect, and because CO 

concentrations are spatially heterogeneous, neither the IPCC nor EPA computes global warming potentials 

(GWPs) for CO, just as they do not for tropospheric O3, NO, NO2, or VOCs (U.S. EPA, 2008a). 

Additionally, urban and regional-scale oxidation of CO to CO2 under current atmospheric conditions 

proceeds very slowly and IPCC considers production of CO2 through this pathway to be double counting 

of CO effects (IPCC, 2007). 

3.4. Ambient Measurements 

3.4.1. Ambient Measurement Instruments 

To promote uniform enforcement of the air quality standards set forth under the Clean Air Act, EPA 

has established provisions in the Code of Federal Regulations (CFR) under which analytical methods can 

be designated as federal reference methods or federal equivalent methods (FRM or FEM, respectively). 

Measurements for determinations of NAAQS compliance must be made with FRMs or FEMs. As of 

December 2008, 19 automated FRMs and no FEMs had been approved for CO 

28 (http://www.epa.gov/ttn/amtic/criteria.html). 

March 2009 3-10 DRAFT – DO NOT CITE OR QUOTE 
All EPA FRMs for CO operate on the principle of nondispersive infrared (NDIR) detection and can 

include the gas filter correlation (GFC) methodology. NDIR is an automated and continuous method 

based on the specific absorption of infrared radiation by the CO molecule. Most commercially available 

analyzers incorporate a gas filter to minimize interferences from other gases and operate near atmospheric 

pressure. The most sensitive trace-level versions of these instruments can detect minimum CO 

concentrations of ~0.04 ppm; the required lower detection limit for FRMs in the EPA network is 1.0 ppm 

(40 CFR 53.20 Table B-1). 

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NDIR is based on the physics of CO’s characteristic infrared absorption near 4.63 μm. NDIR 

methods have several practical advantages over other techniques for CO detection in that they are not 

sensitive to flow rate changes, require no wet chemicals, are reasonably independent of ambient air 

temperature changes, are sensitive over wide concentration ranges, and have fast response times. Earlier 

concerns over zero-drift and nonlinear span responses have been addressed. An extensive and 

comprehensive review of NDIR, GFC, and alternative, non-FRM techniques for CO detection including 

tunable diode laser spectroscopy, gas chromatography, mercury liberation, and resonance fluorescence 

was made for the 2000 CO AQCD (U.S. EPA, 2000), and the reader is directed there for additional 

information. The description here is limited to a brief outline of the FRM NDIR and GFC techniques. 

GFC spectroscopy analyzers are used most frequently now in documenting compliance with 

ambient air standards. A GFC monitor has all of the advantages of an NDIR instrument and the additional 

advantages of smaller size, no interference from CO2, and very small interference from water vapor. 

During operation, air flows continuously through a sample cell. Radiation from the infrared source is 

directed by optical transfer elements through two main optical subsystems: (1) the rotating gas filter and 

(2) the optical multipass (sample) cell. The beam exits the sample cell through an interference filter (FC), 

which limits the spectral passband to a few of the strongest CO absorption lines. Detection of the 

transmitted radiation occurs at the infrared detector. The gas correlation cell is constructed with two 

compartments, one filled with 0.5 atm CO, and a second with pure N2. Radiation transmitted through the 

CO is completely attenuated at the wavelengths where CO absorbs strongly. The radiation transmitted 

through the nitrogen gas (N2) is reduced by coating the exit window of the cell with a neutral attenuator 

so that the amounts of radiation transmitted by the two cells are made approximately equal in the 

passband that reaches the detector. In operation, radiation passes alternately through the two cells as they 

are rotated to establish a signal modulation frequency. If CO is present in the sample, the radiation 

transmitted through the CO is not appreciably changed, whereas that through the N2 cell is changed. This 

imbalance is linearly related to CO concentrations in ambient air. 

March 2009 3-11 DRAFT – DO NOT CITE OR QUOTE 
3.4.2. Ambient Sampling Network Design 

3.4.2.1. Monitor Siting Requirements 

CO monitoring is included at all active NCore sites and State and Local Air Monitoring Stations 

(SLAMS) where continued measurements of CO using FRM are required until discontinuation is 

approved by the EPA Regional Administrator. Where SLAMS CO monitoring is required, at least one of 

the sites must be a max concentration site for that specific area. In 2007, there were ~385 CO monitors 

reporting values to the EPA Air Quality System (AQS) database. Where CO monitoring is ongoing, 40 

CFR Part 58 requires at least one CO monitor to capture maximum levels in a given region. This is often 

done with a monitor situated at the CFR-defined microscale distance (2-10 m) from the side of a roadway 

for CO. Microscale monitor locations also have sample inlets mounted at 3 ± 0.5 m above ground level, 

unlike the monitors sampling for larger scales, whose inlet heights can vary between 2 and 15 m. For the 

CFR-defined middle (up to 500 m) and neighborhood (~500 m-4 km) scale monitoring, the minimum 

monitor distance from a major roadway is inversely related to the average daily traffic counts on that 

roadway to ensure that measurements are not substantially influenced by any one roadway. For example, 

the minimum distance of a middle scale CO monitor from a roadway with an average daily traffic count 

of 50,000 vehicles per day is 135 m. More detail on siting requirements can be found in 40 CFR Part 58 

Appendices D through E. 

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3.4.2.2. Spatial and Temporal Coverage 

Figure 3-6 depicts the distribution of the ~385 regulatory CO monitors operating in the U.S. in 

2007. Data from 285 of the ~385 CO monitors operating year-round in the years 2005-2007 met the 75% 

data completeness criterion for inclusion in the multi-year ambient data analyses for this assessment. The 

greatest density of monitors is in the CSAs for Los Angeles, CA and San Francisco, CA, and along the 

Mid-Atlantic sea board. Monitors are also located in regions where biomass burning is more prevalent, 

such as Anchorage, AK, but not all of these monitors report values from all seasons of all years. 

Eleven metropolitan regions were chosen for closer investigation of monitor siting based on their 

relevance to the health studies assessed in subsequent chapters of this ISA and to demonstrate specific 

points about geospatial distributions of CO emissions and concentrations. These regions were: Anchorage, 

AK; Atlanta, GA; Boston, MA; Denver, CO; Houston, TX; Los Angeles, CA; New York City, NY; 

Phoenix, AZ; Pittsburgh, PA; Seattle, WA; and St. Louis, MO. Core-Based Statistical Areas (CBSAs) and 

Combined Statistical Areas (CSAs), as defined by the U.S. Census Bureau (http://www.census/gov/), 

were used to determine which counties, and hence which monitors, to include for each metropolitan 

27 

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March 2009 3-12 DRAFT – DO NOT CITE OR QUOTE 
region.1

 As an example, Figures 3-7 through 3-10 display CO monitor density with respect to population 

density (for total population and elderly adults aged 65 and over) for Phoenix and Pittsburgh. (Annex A 

includes analogous plots for the other nine metropolitan regions.) 

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Although ambient monitors for CO and other criteria pollutants are explicitly not located in order 

to monitor population exposures, it is instructive to test the utility of the current network for 

characterizing this exposure. Tables 3-1 and 3-2 show the population density around CO monitors for the 

total population and for elderly adults aged 65 and over for each CSA/CBSA. The percentage of 

population within specific radii of the monitors for each city was, for the most part, similar between the 

total and elderly populations. In the cases of Anchorage, Denver, Phoenix, and St. Louis however, the 

percentage of the elderly population within given radii of the monitors was considerably different 

compared with the total population. Between-city disparities in population density were larger. Los 

Angeles, with 85%, and Denver, with 68%, had the largest proportion of the total population within 15 

km of a monitor. Seattle, with 18%, had the lowest population coverage in large part because ambient CO 

concentrations there require only a single CO monitor. As concerns the elderly population, Los Angeles, 

at 83%, Anchorage, at 73%, and Denver, at 70%, had the greatest population coverage within 15 km of a 

monitor, whereas Seattle, at 18%, again, had the lowest coverage. Proximity to monitoring stations is 

considered further in sections 3.5 and 3.6 regarding spatial variability within cities. Figures 3-7 through 3-

10 show that multiple CO monitors in Phoenix and Pittsburgh were in the city center, and that Pittsburgh 

also had monitors in outlying areas of moderate to low population density. The CO monitors in Phoenix 

appear to provide good coverage of areas of the highest total population density, while areas of high 

elderly population density were located at greater distances from the monitors. CO monitors in Pittsburgh 

appear to sample areas of high population density well for both the total and elderly populations. 

Figures 3-13 and 3-15 in section 3.5 and additional figures in Annex A show the locations of CO 

monitors for the 11 CSAs/CBSAs in relation to major roadways, including Interstate highways, U.S. 

highways, state highways, and other major roadways required for traffic network connectivity. In most 

cases, the monitors were concentrated near the center of the CSA/CBSA. Regional background sites were 

not included on the maps unless they lay within the CSA/CBSA. 

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 A CBSA represents a county-based region surrounding an urban center of at least 10,000 people determined using 2000 census data and 

replaces the older Metropolitan Statistical Area (MSA) definition from 1990. The CSA represents an aggregate of adjacent CBSAs tied by 

specific commuting behaviors. The broader CSA definition was used when selecting monitors for the cities listed above with the exception of 

Anchorage and Phoenix, which are not contained within a CSA. Therefore, the smaller CBSA definition was used for these metropolitan areas. 

March 2009 3-13 DRAFT – DO NOT CITE OR QUOTE 
Figure 3-6. Map of ~385 CO monitor locations in the U.S. in 2007. Locations are indicated with 

triangles: filled triangles show locations of the 285 monitors used in data analysis 

for this assessment; open triangles are at locations with monitors which did not 

meet the data completeness requirements for analysis; blue lines mark the 

boundaries of the 11 CSAs/CBSAs used in the data analysis for this assessment. 

March 2009 3-14 DRAFT – DO NOT CITE OR QUOTE 
Figure 3-7. Map of CO monitor locations with respect to population density in the Phoenix, AZ 

CBSA, total population. 

March 2009 3-15 DRAFT – DO NOT CITE OR QUOTE 
Figure 3-8. Map of CO monitor locations with respect to population density in the Phoenix, AZ 

CBSA, age 65 and older. 

March 2009 3-16 DRAFT – DO NOT CITE OR QUOTE 
Figure 3-9. Map of CO monitor locations with respect to population density in the Pittsburgh, 

PA CSA, total population. 

March 2009 3-17 DRAFT – DO NOT CITE OR QUOTE 
Figure 3-10. Map of CO monitor locations with respect to population density in the Pittsburgh, 

PA CSA, age 65 and older. 

March 2009 3-18 DRAFT – DO NOT CITE OR QUOTE 
Table 3-1. Proximity to CO monitors for the total population by city. 

Region Total CSA/ 

CBSA ≤ 1 km ≤ 5 km ≤ 10 km ≤ 15 km 

N N % N % N % N % 

Anchorage, AK 352,225 5,391 1.53 131,608 37.36 212,834 60.43 239,842 68.09 

Atlanta, GA 5,316,742 5,480 0.10 149,772 2.82 672,701 12.65 1,444,986 27.18 

Boston, MA 7,502,707 95,732 1.28 1,180,054 15.73 2,432,846 32.43 3,418,353 45.56 

Denver, CO 2,952,039 26,096 0.88 497,598 16.86 1,091,444 36.97 1,720,360 58.28 

Houston, TX 5,503,320 2,9068 0.53 599,796 10.90 1,669,117 30.33 2,506,830 45.55 

Los Angeles, CA 17,655,319 202,340 1.15 4,064,309 23.02 11,928,427 67.56 15,074,972 85.38 

New York, NY 22,050,940 201,350 0.91 3,711,369 16.83 8,385,801 38.03 12,454,837 56.48 

Phoenix, AZ 3,818,147 47,478 1.24 503,433 13.19 1,033,102 27.06 1,581,887 41.43 

Pittsburgh, PA 2,515,383 29,136 1.16 369,965 14.71 895,252 35.59 1,359,596 54.05 

Seattle, WA 3,962,434 4,814 0.12 94,649 2.39 279,976 7.07 699,490 17.65 

St. Louis, MO 2,869,955 16,638 0.58 255,499 8.90 886,412 30.89 1,303,636 45.42 

Table 3-2. Proximity to CO monitors for adults aged 65 and older by city. 

Region Total CSA/ 

CBSA ≤ 1 km ≤ 5 km ≤ 10 km ≤ 15 km 

N N % N % N % N % 

Anchorage, AK 17,742 361 2.03 8,986 50.65 12,038 67.85 12,990 73.22 

Atlanta, GA 362,201 423 0.12 12,758 3.52 54,148 14.95 111,232 30.71 

Boston, MA 945,790 8,272 0.87 131,198 13.87 297,392 31.44 430,502 45.52 

Denver, CO 232,974 2,541 1.09 42,760 18.35 102,783 44.12 163,682 70.26 

Houston, TX 377,586 1,703 0.45 42,312 11.21 130,567 34.58 182,049 48.21 

Los Angeles, CA 1,626,663 17,974 1.10 380,079 23.37 1,069,188 65.73 1,355,461 83.33 

New York, NY 2,710,675 29,534 1.09 427,601 15.77 940,121 34.68 1,429,215 52.73 

Phoenix, AZ 388,150 2,877 0.74 35,839 9.23 77,244 19.90 125,300 32.28 

Pittsburgh, PA 449,544 5,383 1.20 66,967 14.90 166,440 37.02 255,220 56.77 

Seattle, WA 390,372 556 0.14 12,142 3.11 3,1036 7.95 69,858 17.90 

St. Louis, MO 358,747 3,203 0.89 42,890 11.96 127,274 35.48 184,491 51.43 

March 2009 3-19 DRAFT – DO NOT CITE OR QUOTE 
3.5. Environmental Concentrations 

3.5.1. Spatial Variability 

3.5.1.1. National Scale 

The current NAAQS designates that the level of the NAAQS can only be exceeded once per year at 

a given location. Figures 3-11 and 3-12 show the second-highest 1-h and second-highest 8-h countyaverage CO concentrations, respectively, over the U.S. along with estimates of the fraction of U.S. total 

population exposed to those concentrations. Although 93% of the U.S. counties are not represented in 

AQS reporting, based on their population densities and proximity to sources, those counties are not 

expected to have higher concentrations than the ones analyzed here in the absence of extreme events such 

as wildfires. Continuous hourly averages are reported from U.S. monitoring stations. 1-h and 8-h CO data 

were available for 243 counties and autonomous cities or municipalities (e.g., Anchorage, AK, 

Washington, DC) where CO monitors met the 75% data completeness criteria used in this analysis for the 

years 2005-2007. In 2007, no monitored location reported a second-highest 1-h CO concentration above 

35 ppm, the level of the current 1-h NAAQS; see Figure 3-11. Moreover, only two monitored locations, 

one in Weber Co., UT and the other in Jefferson Co., AL (including Birmingham, AL), reported secondhighest 1-h CO concentrations above 18 ppm, or approximately one-half the level of the 1-h standard. 

Figure 3-12 shows that no counties reported second-highest 8-h CO concentrations above 9 ppm, the level 

of the 8-h NAAQS. Only nine counties reported second-highest 8-h CO concentrations above 4.5 ppm: 

Jefferson Co., AL, Kay Co., OK, Imperial Co., CA, Weber Co., UT, Philadelphia Co., PA, Anchorage 

Municipality, AK, Los Angeles Co., CA, Jefferson Co., OH, and San Diego Co., CA. 

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Figure 3-11. County-level map of second-highest 1-h avg CO concentrations in the U.S. in 2007. 

The bar on the left shows the total U.S. population who live in counties with CO 

concentrations in the range indicated. Note that approximately 150 million people 

live in counties with no CO monitors. 

March 2009 3-21 DRAFT – DO NOT CITE OR QUOTE 
Figure 3-12. County-level map of second-highest 8-h avg CO concentrations in the U.S. in 2007. 

The bar on the left shows the total U.S. population who live in counties with CO 

concentrations in the range indicated. Note that approximately 150 million people 

live in counties with no CO monitors. 

March 2009 3-22 DRAFT – DO NOT CITE OR QUOTE 
Table 3-3. Distribution of 1-h avg CO concentration (ppm) derived from AQS data. 

Percentiles 

 n Mean Min 1 5 10 25 50 75 90 95 99 Max 

NATIONWIDE STATISTICS (N = NUMBER OF OBSERVATIONS) 

2005-2007 7,180,700 0.5 0.0 0.0 0.0 0.1 0.2 0.4 0.6 0.9 1.2 2.1 39.0 

2005 2,391,962 0.5 0.0 0.0 0.0 0.1 0.2 0.4 0.6 1.0 1.3 2.3 22.3 

2006 2,402,153 0.5 0.0 0.0 0.0 0.1 0.2 0.4 0.6 0.9 1.2 2.1 35.3 

2007 2,386,585 0.4 0.0 0.0 0.0 0.1 0.2 0.3 0.5 0.8 1.1 1.9 39.0 

Winter (December - February) 1,752,340 0.6 0.0 0.0 0.0 0.1 0.3 0.4 0.7 1.2 1.6 2.7 20.0 

Spring (March - May) 1,826,167 0.4 0.0 0.0 0.0 0.1 0.2 0.3 0.5 0.8 1.0 1.7 35.3 

Summer (June - August) 1,811,082 0.4 0.0 0.0 0.0 0.0 0.2 0.3 0.5 0.7 0.9 1.5 39.0 

Fall (September - November) 1,791,111 0.5 0.0 0.0 0.0 0.1 0.2 0.4 0.6 1.0 1.3 2.2 24.1 

NATIONWIDE STATISTICS, POOLED BY SITE (N = NUMBER OF SITES) 

2005-2007 285 0.5 0.0 0.0 0.1 0.2 0.3 0.4 0.6 0.7 0.8 1.0 1.5 

2005 285 0.5 0.0 0.0 0.1 0.2 0.4 0.5 0.6 0.8 0.9 1.3 1.6 

2006 285 0.5 0.0 0.0 0.1 0.2 0.3 0.4 0.6 0.7 0.8 1.2 1.4 

2007 285 0.4 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.7 0.7 1.1 1.5 

Winter (December - February) 285 0.6 0.0 0.0 0.2 0.2 0.4 0.5 0.7 0.9 1.1 1.5 1.6 

Spring (March - May) 285 0.4 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.7 0.7 1.0 1.6 

Summer (June - August) 285 0.4 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 1.1 1.5 

Fall (September - November) 285 0.5 0.0 0.0 0.1 0.2 0.4 0.4 0.6 0.8 0.9 1.1 1.5 

STATISTICS FOR INDIVIDUAL CSAS/CBSAS (2005-2007 ) (N = NUMBER OF OBSERVATIONS) 

Anchoragea 25,672 1.1 0.0 0.1 0.2 0.3 0.5 0.7 1.3 2.3 3.1 5.0 13.1 

Atlanta 76,683 0.5 0.0 0.0 0.2 0.2 0.3 0.4 0.6 0.8 1.1 1.6 10.8 

Boston 171,975 0.4 0.0 0.0 0.0 0.1 0.2 0.4 0.5 0.7 0.9 1.4 10.0 

Denver 129,038 0.5 0.0 0.0 0.1 0.2 0.3 0.4 0.6 1.0 1.3 2.2 9.3 

Houston 123,925 0.3 0.0 0.0 0.0 0.0 0.2 0.3 0.4 0.6 0.8 1.4 4.6 

Los Angeles 592,960 0.5 0.0 0.0 0.0 0.1 0.2 0.3 0.6 1.0 1.4 2.3 8.4 

New York 226,673 0.5 0.0 0.0 0.1 0.1 0.3 0.5 0.6 0.9 1.1 1.6 5.8 

Phoenix 127,477 0.8 0.0 0.0 0.1 0.2 0.3 0.5 1.0 1.9 2.5 3.6 7.8 

Pittsburgh 179,758 0.3 0.0 0.0 0.0 0.0 0.1 0.2 0.4 0.6 0.8 1.2 6.7 

Seattle 25,818 0.8 0.0 0.1 0.2 0.3 0.4 0.6 0.9 1.3 1.6 2.5 5.9 

St. Louis 77,142 0.4 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.7 0.9 1.4 5.7 

Not in the 11 cities 5,449,251 0.5 0.0 0.0 0.0 0.1 0.2 0.4 0.6 0.9 1.2 2.1 39.0 

aCO monitoring is only available for quarters 1 and 4; since monitoring data is not available year-round, Anchorage is not included in the nationwide statistics shown in this table. 

1 

2 

3 

4 

5 

6 

Table 3-3 contains the distribution of hourly CO measurements reported to AQS for 2005-2007. All 

monitoring locations meeting the 75% data completeness criteria have been included in this table. 

Anchorage did not meet the data completeness criteria since its monitoring sites were required to report 

CO measurements during the first and fourth quarters of each year. Anchorage was included in the table, 

however, for an approximate comparison with the other CSAs and CBSAs reporting year-round 

measurements to AQS. (Anchorage was not, however, included in the national averages shown in the 

March 2009 3-23 DRAFT – DO NOT CITE OR QUOTE 
1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

table.) The nationwide mean, median, and interquartile range for 1-h measurements reported for 

2005-2007 were 0.5, 0.4 and 0.4 ppm, respectively, and these statistics did not change by more than 

0.1 ppm over the 3-year period. The largest recorded second-highest 1-h concentration, 26.3 ppm, for this 

period was reported in 2006 in Birmingham, AL (AQS site ID: 0107360004). The absolute highest 1-h 

concentration, 39 ppm, between 2005 and 2007, was reported in Ogden, UT (AQS site ID: 490570006). 

Concentrations are generally highest in the winter (December-February) and fall (September-November) 

and decrease on average during the spring (March-May) and summer (June-August). 

Nationwide statistics pooled by site are listed in the center of Table 3-3 and illustrate the 

distribution of the site average CO concentrations recorded at the 285 monitoring sites for 2005-2007 (see 

Figure 3-6 for these sites). The site reporting the highest 3-year pooled 1-h avg CO concentration, 

1.5 ppm, was located in San Juan, Puerto Rico (AQS site ID: 721270003). The eleven individual 

CSAs/CBSAs discussed earlier are included in the table, none of which reported concentrations above the 

value of the 1-h NAAQS. Four of the eleven cities (Boston, Houston, Pittsburgh and St. Louis) had 95th 

percentile 1-h CO concentrations below 1 ppm; the 95th percentile concentrations for the remaining cities 

were below 3.1 ppm. Lack of year-round monitoring in Anchorage prevented a direct comparison with the 

other metropolitan regions. However, Anchorage exhibited a 1-h CO distribution shifted higher in 

concentration when compared to the U.S. average during fall or winter. 

March 2009 3-24 DRAFT – DO NOT CITE OR QUOTE 
Table 3-4. Distribution of 24-h avg CO concentration (ppm) derived from AQS data. 

Percentiles 

 n Mean Min 1 5 10 25 50 75 90 95 99 Max 

NATIONWIDE STATISTICS (N = NUMBER OF OBSERVATIONS) 

2005-2007 303,843 0.5 0.0 0.0 0.0 0.1 0.3 0.4 0.6 0.9 1.1 1.7 7.0 

2005 101,184 0.5 0.0 0.0 0.0 0.1 0.3 0.4 0.6 0.9 1.1 1.8 5.8 

2006 101,652 0.5 0.0 0.0 0.0 0.1 0.3 0.4 0.6 0.9 1.1 1.6 7.0 

2007 101,007 0.4 0.0 0.0 0.0 0.1 0.2 0.4 0.5 0.8 1.0 1.6 6.9 

Winter (December - February) 74,144 0.6 0.0 0.0 0.1 0.2 0.3 0.5 0.7 1.1 1.3 2.0 7.0 

Spring (March - May) 77,317 0.4 0.0 0.0 0.0 0.1 0.2 0.4 0.5 0.7 0.9 1.4 6.4 

Summer (June - August) 76,562 0.4 0.0 0.0 0.0 0.1 0.2 0.3 0.5 0.7 0.8 1.3 6.9 

Fall (September - November) 75,820 0.5 0.0 0.0 0.0 0.1 0.3 0.4 0.6 0.9 1.1 1.7 5.8 

NATIONWIDE STATISTICS, POOLED BY SITE (N = NUMBER OF SITES) 

2005-2007 285 0.5 0.0 0.0 0.1 0.2 0.3 0.4 0.6 0.7 0.8 1.0 1.5 

2005 285 0.5 0.0 0.0 0.1 0.2 0.4 0.5 0.6 0.8 0.9 1.3 1.6 

2006 285 0.5 0.0 0.0 0.1 0.2 0.3 0.4 0.6 0.7 0.8 1.2 1.4 

2007 285 0.4 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.7 0.7 1.1 1.5 

Winter (December - February) 285 0.6 0.0 0.0 0.2 0.2 0.4 0.5 0.7 0.9 1.1 1.5 1.6 

Spring (March - May) 285 0.4 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.7 0.7 1.0 1.6 

Summer (June - August) 285 0.4 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 1.1 1.5 

Fall (September - November) 285 0.5 0.0 0.0 0.1 0.2 0.4 0.4 0.6 0.8 0.9 1.1 1.5 

STATISTICS FOR INDIVIDUAL CSAS/CBSAS (2005-2007 ) (N = NUMBER OF OBSERVATIONS) 

Anchoragea 1,074 1.1 0.0 0.2 0.2 0.4 0.6 0.9 1.4 1.9 2.4 3.3 4.6 

Atlanta 3,229 0.5 0.0 0.1 0.2 0.2 0.3 0.4 0.6 0.8 0.9 1.2 1.6 

Boston 7,446 0.4 0.0 0.0 0.1 0.1 0.3 0.4 0.5 0.7 0.8 1.1 2.2 

Denver 5,363 0.5 0.0 0.1 0.2 0.2 0.3 0.5 0.6 0.9 1.1 1.5 2.3 

Houston 5,188 0.3 0.0 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.9 1.9 

Los Angeles 25,803 0.5 0.0 0.0 0.1 0.1 0.2 0.4 0.6 1.0 1.2 1.7 3.8 

New York 9,513 5.1 0.0 0.0 0.1 0.2 0.4 0.5 0.6 0.8 1.0 1.3 2.5 

Phoenix 5,348 0.8 0.0 0.1 0.2 0.3 0.4 0.6 1.1 1.6 1.9 2.5 3.4 

Pittsburgh 7,497 0.3 0.0 0.0 0.0 0.0 0.1 0.2 0.4 0.6 0.7 1.0 1.9 

Seattle 1,079 0.8 0.1 0.2 0.3 0.4 0.5 0.7 0.9 1.2 1.4 1.8 2.4 

St. Louis 3,216 0.4 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.7 0.8 1.0 1.9 

Not in the 11 cities 230,161 0.5 0.0 0.0 0.0 0.1 0.2 0.4 0.6 0.8 1.1 1.6 7.0 

*CO monitoring is only available for quarters 1 and 4; since monitoring data is not available year-round, Anchorage is not included in the nationwide statistics shown in this table. 

1 

2 

3 

4 

5 

Table 3-4 contains the distribution of 24-h avg CO concentrations derived from the 1-h 

concentrations reported to AQS and summarized in Table 3-3. The nationwide mean, median, and 

interquartile range for 24-h avg for 2005-2007 were 0.5, 0.4 and 0.3 ppm, respectively. These were 

similar to those for the 1-h values. The maximum 24-h avg concentration in these years, 7 ppm, was 

reported in Birmingham, AL (AQS site ID: 010736004). 

March 2009 3-25 DRAFT – DO NOT CITE OR QUOTE 
Table 3-5. Distribution of 1-h daily max CO concentration (ppm) derived from AQS data. 

Percentiles 

 n Mean Min 1 5 10 25 50 75 90 95 99 Max 

NATIONWIDE STATISTICS (N = NUMBER OF OBSERVATIONS) 

2005-2007 303,843 0.9 0.0 0.0 0.1 0.3 0.4 0.7 1.2 1.8 2.4 3.8 39.0 

2005 101,184 1.0 0.0 0.0 0.2 0.3 0.5 0.8 1.3 2.0 2.6 4.1 22.3 

2006 101,652 0.9 0.0 0.0 0.1 0.3 0.4 0.7 1.2 1.9 2.4 3.9 35.3 

2007 101,007 0.8 0.0 0.0 0.1 0.2 0.4 0.7 1.1 1.7 2.1 3.4 39.0 

Winter (December - February) 74,144 1.2 0.0 0.0 0.2 0.3 0.5 0.9 1.6 2.5 3.1 4.7 20.0 

Spring (March - May) 77,317 0.8 0.0 0.0 0.1 0.3 0.4 0.7 1.0 1.6 2.0 3.0 35.3 

Summer (June - August) 76,562 0.7 0.0 0.0 0.1 0.2 0.4 0.6 0.9 1.3 1.6 2.5 39.0 

Fall (September - November) 75,820 1.0 0.0 0.0 0.2 0.3 0.5 0.8 1.3 2.0 2.5 3.8 24.1 

NATIONWIDE STATISTICS, POOLED BY SITE (N = NUMBER OF SITES) 

2005-2007 285 0.9 0.1 0.1 0.3 0.5 0.6 0.8 1.1 1.5 1.7 2.3 3.9 

2005 285 1.0 0.1 0.1 0.4 0.5 0.7 0.9 1.2 1.6 2.0 2.5 3.7 

2006 285 0.9 0.1 0.1 0.3 0.5 0.6 0.9 1.1 1.6 1.8 2.3 4.8 

2007 285 0.8 0.1 0.1 0.3 0.4 0.6 0.8 1.0 1.4 1.6 2.0 3.1 

Winter (December - February) 285 1.2 0.0 0.1 0.4 0.6 0.8 1.0 1.5 2.1 2.5 3.4 4.1 

Spring (March - May) 285 0.8 0.1 0.1 0.3 0.4 0.6 0.8 1.0 1.3 1.5 2.1 4.0 

Summer (June - August) 285 0.7 0.0 0.1 0.2 0.3 0.5 0.6 0.8 1.1 1.3 2.2 3.3 

Fall (September - November) 285 1.0 0.1 0.1 0.3 0.5 0.7 0.9 1.2 1.7 2.0 2.4 4.1 

STATISTICS FOR INDIVIDUAL CSAS/CBSAS (2005-2007 ) (N = NUMBER OF OBSERVATIONS) 

Anchoragea 1,074 2.6 0.0 0.3 0.6 0.8 1.3 2.2 3.5 5.0 6.1 7.6 13.1 

Atlanta 3,229 0.8 0.0 0.2 0.3 0.3 0.4 0.7 1.1 1.4 1.7 2.2 10.8 

Boston 7,446 0.7 0.0 0.1 0.2 0.3 0.4 0.6 0.9 1.2 1.6 2.6 10.0 

Denver 5,363 1.2 0.1 0.2 0.4 0.5 0.7 1.0 1.5 2.2 2.7 3.9 9.3 

Houston 5,188 0.7 0.0 0.0 0.1 0.2 0.4 0.6 0.8 1.3 1.7 2.6 4.6 

Los Angeles 25,803 1.0 0.0 0.1 0.2 0.3 0.5 0.8 1.3 2.0 2.6 4.0 8.4 

New York 9,513 0.9 0.0 0.1 0.2 0.4 0.6 0.8 1.1 1.5 1.8 2.5 5.8 

Phoenix 5,348 1.9 0.0 0.3 0.5 0.6 0.9 1.6 2.5 3.5 4.1 5.3 7.8 

Pittsburgh 7,497 0.6 0.0 0.0 0.0 0.1 0.2 0.5 0.8 1.1 1.4 2.0 6.7 

Seattle 1,079 1.5 0.2 0.4 0.5 0.7 0.9 1.3 1.8 2.4 2.9 4.3 5.9 

St. Louis 3,216 0.8 0.0 0.1 0.3 0.4 0.5 0.6 0.9 1.3 1.7 2.7 5.7 

Not in the 11 cities 230,161 0.9 0.0 0.0 0.1 0.2 0.4 0.7 1.2 1.8 2.4 3.8 39.0 

*CO monitoring is only available for quarters 1 and 4; since monitoring data is not available year-round, Anchorage is not included in the nationwide statistics shown in this table. 

1 

2 

3 

4 

Table 3-5 contains the distribution of 1-h daily max CO concentrations derived from 1-h values 

reported to AQS for all monitors meeting the inclusion criteria described earlier. The nationwide mean, 

median, and interquartile range for 1-h daily max concentrations reported for 2005-2007 were 0.9, 0.7 and 

0.8 ppm, respectively. 

March 2009 3-26 DRAFT – DO NOT CITE OR QUOTE 
Table 3-6. Distribution of 8-h daily max CO concentration (ppm) derived from AQS data. 

Percentiles 

 n Mean Min 1 5 10 25 50 75 90 95 99 Max 

NATIONWIDE STATISTICS (N = NUMBER OF OBSERVATIONS) 

2005-2007 303,843 0.7 0.0 0.3 0.3 0.3 0.3 0.5 0.8 1.3 1.7 2.6 10.9 

2005 101,184 0.7 0.0 0.3 0.3 0.3 0.3 0.6 0.9 1.4 1.8 2.8 9.7 

2006 101,652 0.7 0.0 0.3 0.3 0.3 0.3 0.5 0.8 1.3 1.7 2.6 9.8 

2007 101,007 0.6 0.0 0.3 0.3 0.3 0.3 0.5 0.8 1.2 1.5 2.3 10.9 

Winter (December - February) 74,144 0.9 0.0 0.3 0.3 0.3 0.4 0.7 1.1 1.7 2.1 3.2 9.8 

Spring (March - May) 77,317 0.6 0.0 0.3 0.3 0.3 0.3 0.5 0.7 1.1 1.3 2.0 9.6 

Summer (June - August) 76,562 0.5 0.0 0.3 0.3 0.3 0.3 0.4 0.6 0.9 1.1 1.7 10.9 

Fall (September - November) 75,820 0.7 0.0 0.3 0.3 0.3 0.3 0.6 0.9 1.4 1.8 2.7 9.0 

NATIONWIDE STATISTICS, POOLED BY SITE (N = NUMBER OF SITES) 

2005-2007 285 0.7 0.2 0.3 0.3 0.4 0.5 0.6 0.8 1.0 1.2 1.7 2.1 

2005 285 0.7 0.3 0.3 0.3 0.4 0.5 0.6 0.9 1.1 1.4 1.9 2.2 

2006 285 0.7 0.2 0.3 0.3 0.4 0.5 0.6 0.8 1.1 1.2 1.8 2.4 

2007 285 0.6 0.2 0.3 0.3 0.4 0.5 0.6 0.7 1.0 1.1 1.6 2.0 

Winter (December - February) 285 0.9 0.2 0.3 0.4 0.4 0.6 0.8 1.1 1.4 1.7 2.4 2.6 

Spring (March - May) 285 0.6 0.2 0.3 0.3 0.4 0.4 0.5 0.7 0.9 1.1 1.6 2.2 

Summer (June - August) 285 0.5 0.2 0.3 0.3 0.3 0.4 0.5 0.6 0.8 0.9 1.5 2.0 

Fall (September - November) 285 0.7 0.2 0.3 0.3 0.4 0.5 0.6 0.9 1.2 1.3 1.8 2.2 

STATISTICS FOR INDIVIDUAL CSAS/CBSAS (2005-2007 ) (N = NUMBER OF OBSERVATIONS) 

Anchoragea 1,074 1.7 0.3 0.3 0.4 0.6 0.9 1.5 2.3 3.3 3.9 5.0 6.5 

Atlanta 3,229 0.6 0.0 0.2 0.2 0.3 0.4 0.5 0.8 1.1 1.3 1.7 2.5 

Boston 7,446 0.6 0.3 0.3 0.3 0.3 0.3 0.5 0.7 0.9 1.1 1.8 5.8 

Denver 5,363 0.8 0.3 0.3 0.3 0.3 0.5 0.7 1.0 1.4 1.8 2.4 3.4 

Houston 5,188 0.5 0.3 0.3 0.3 0.3 0.3 0.4 0.6 0.9 1.1 1.7 3.3 

Los Angeles 25,803 0.7 0.3 0.3 0.3 0.3 0.3 0.6 0.9 1.5 1.8 2.7 6.2 

New York 9,513 0.7 0.3 0.3 0.3 0.3 0.4 0.6 0.9 1.2 1.4 1.8 3.0 

Phoenix 5,348 1.3 0.3 0.3 0.3 0.4 0.6 1.0 1.8 2.5 3.0 3.8 5.8 

Pittsburgh 7,497 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.6 0.9 1.0 1.5 3.7 

Seattle 1,079 1.1 0.3 0.3 0.4 0.5 0.7 1.0 1.4 1.8 2.2 3.2 4.0 

St. Louis 3,216 0.6 0.3 0.3 0.3 0.3 0.3 0.5 0.7 0.9 1.2 1.9 4.2 

Not in the 11 cities 230,161 0.7 0.0 0.3 0.3 0.3 0.3 0.5 0.8 1.3 1.6 2.5 10.9 

aCO monitoring is only available for quarters 1 and 4; since monitoring data is not available year-round, Anchorage is not included in the nationwide statistics shown in this table. 

1 

2 

3 

4 

5 

Table 3-6 contains the distribution of 8-h daily max concentrations derived from the 1-h CO 

concentrations reported to AQS. This was done by first calculating the average concentration for each 

successive 8-h period, thereby producing 24 8-h avg per day. The maximum of these values for a given 

monitor within a given day (midnight-to-midnight) was used as the 8-h daily max statistic for that 

monitor and day. The nationwide mean, median, and interquartile range for 8-h daily max concentrations 

March 2009 3-27 DRAFT – DO NOT CITE OR QUOTE 
1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

reported for 2005-2007 were 0.7, 0.5, and 0.5 ppm, respectively. The highest 8-h daily max concentration, 

10.9 ppm, was recorded at a monitor located five miles north of Newkirk, OK (AQS site ID: 400719010). 

3.5.1.2. Urban Scale 

This section covers urban variability in CO concentrations reported to AQS at the individual 

CSA/CBSA level. Phoenix, AZ and Pittsburgh, PA were selected for this assessment to illustrate the 

variability in CO concentrations measured across contrasting metropolitan regions. Information on the 

other nine cities evaluated for this assessment is included in Appendix A. Maps of the Phoenix CBSA and 

Pittsburgh CSA shown in Figures 3-13 and 3-15, respectively, illustrate the location of all CO monitors 

included in the analyses described above. Letters on the maps identify the individual monitor locations 

and correspond with the letters provided in the accompanying box plots (Figures 3-14 and 3-16) and pairwise comparison tables (Tables 3-7 and 3-8). The box plots for each monitor include the hourly CO 

concentration median and interquartile range with whiskers extending from the 5th to the 95th percentile. 

Data from 2005-2007 were used to generate the box plots which are stratified by season as follows: 

1 = winter (December-February), 2 = spring (March-May), 3 = summer (June-August), and 4 = fall 

(September-November). The comparison tables include the Pearson correlation coefficient (r), the 90th 

percentile of the absolute difference in concentrations (P90) in ppm, the coefficient of divergence (COD) 

and the straight-line distance between monitor pairs (d) in km. The COD provides an indication of the 

variability across the monitoring sites within each CSA/CBSA and is defined as follows: 

2

1

1

∑= ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+

− =

p

i ikij

ikij

jk XX

XX

p

COD

Equation 3-1 

18 

19 

20 

21 

22 

where Xij and Xik represent the observed hourly concentrations for time period i at sites j and k, and p is 

the number of paired hourly observations. A COD of 0 indicates there are no differences between 

concentrations at paired sites (spatial homogeneity), while a COD approaching 1 indicates extreme spatial 

heterogeneity. Similar maps, box plots and comparison tables for the nine remaining CSAs/CBSAs 

described earlier in this chapter along with Phoenix and Pittsburgh are included in Annex A. 

March 2009 3-28 DRAFT – DO NOT CITE OR QUOTE 
Figure 3-13. Map of CO monitor locations with AQS Site IDs for Phoenix, AZ. 

March 2009 3-29 DRAFT – DO NOT CITE OR QUOTE 
A B C D E 

Mean 0.93 0.84 0.58 0.76 0.79 

Obs 25382 25589 25657 25414 25435 

SD 0.95 0.88 0.64 0.72 0.64 

Figure 3-14. Box plots illustrating the seasonal distribution of 2005-2007 hourly CO 

concentrations in Phoenix, AZ. Note: 1 = winter, 2 = spring, 3 = summer, and 4 = fall 

on the x-axis. The box plots for each monitor include the hourly CO concentration 

median and interquartile range with whiskers extending from the 5th to the 95th 

percentile. 

March 2009 3-30 DRAFT – DO NOT CITE OR QUOTE 
Table 3-7. Table of inter-sampler comparison statistics, as defined in the text, including 

Pearson r, P90 (ppm), COD and d (km) for each pair of hourly CO monitors reporting to 

AQS for 2005-2007 in Phoenix, AZ. 

Phoenix 

A B C D E 

A 1.00 0.89 0.80 0.86 0.84 

0.0 0.7 1.1 0.8 0.9 

0.0 0.37 0.43 0.39 0.37 

0.0 1.6 8.9 3.9 3.5 

B 1.00 0.81 0.88 0.89 

0.0 0.9 0.6 0.7 

0.0 0.38 0.34 0.24 

 0.0 9.4 3.4 4.9 

C 1.00 0.81 0.85 

0.0 0.7 0.6 

0.0 0.41 0.36 

 0.0 6.6 6.8 

D 1.00 0.83 

0.0 0.6 

0.0 0.33 

Legend 0.0 5.2 

E r 1.00 

P90 0.0 

COD 0.0 

d 0.0 

March 2009 3-31 DRAFT – DO NOT CITE OR QUOTE 
Figure 3-15. Map of CO monitor locations with AQS Site IDs for Pittsburgh, PA. 

March 2009 3-32 DRAFT – DO NOT CITE OR QUOTE 
A B C D E F G 

Mean 0.28 0.32 0.47 0.28 0.32 0.21 0.07 

Obs 25655 25936 25818 25500 25745 25319 25785 

SD 0.32 0.26 0.33 0.27 0.26 0.23 0.15 

Figure 3-16. Box plots illustrating the seasonal distribution of 2005-2007 hourly CO 

concentrations in Pittsburgh, PA. Note: 1 = winter, 2 = spring, 3 = summer, and 

 4 = fall on the x-axis. Median interquartile range is shown with whiskers extending 

from the 5th to the 95th percentile. 

March 2009 3-33 DRAFT – DO NOT CITE OR QUOTE 
Table 3-8. Table of inter-sampler comparison statistics, as defined in the text, including 

Pearson r, P90 (ppm), COD and d (km) for each pair of hourly CO monitors reporting to 

AQS for 2005-2007 in Pittsburgh, PA. 

Pittsburgh 

A B C D E F G 

A 1.00 0.52 0.43 0.05 0.11 0.42 0.18 

0.0 0.5 0.6 0.7 0.6 0.5 0.7 

0.0 0.69 0.68 0.74 0.72 0.73 0.88 

0.0 2.2 1.8 41.9 66.8 34.4 45.8 

B 1.00 0.73 0.30 0.42 0.29 0.35 

0.00 0.40 0.50 0.40 0.50 0.50 

0.00 0.39 0.54 0.51 0.62 0.86 

 0.00 0.70 43.40 68.00 33.60 43.70 

C 1.00 0.20 0.39 0.25 0.30 

0.0 0.7 0.6 0.7 0.8 

0.0 0.56 0.51 0.65 0.88 

 0.0 43.4 68.2 33.3 44.1 

D 1.00 0.16 0.09 0.02 

0.0 0.6 0.6 0.7 

0.0 0.57 0.69 0.87 

 0.0 27.5 75.0 84.1 

E 1.00 0.26 0.21 

0.0 0.5 0.6 

0.0 0.68 0.87 

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G r 1.00 

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The Phoenix CBSA in Figure 3-13 incorporates an area of 37,786 km2

 with the CO monitors 

densely packed near the urban center. The maximum straight-line distance between CO monitors in the 

Phoenix CBSA is 9.4 km. By contrast, the Pittsburgh CSA in Figure 3-15 is less than half the size, having 

an area of 14,627 km2

, but the monitors are farther apart with a maximum straight-line distance between 

them of 104.1 km. Of the eleven CSAs/CBSAs investigated, Phoenix has among the highest correlations 

across monitors, while Pittsburgh has among the lowest. This discrepancy is partly due to the close 

proximity of the monitors as noted here, but horizontal distance alone does not explain the range of 

correlations observed between monitors. For example, the three monitors located in the downtown urban 

core of Pittsburgh (sites A, B and C; AQS site IDs 420030010, 420030031 and 420030038; maximum 

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distance = 2.2 km) exhibited considerably lower correlations (0.43 ≤ r ≤ 0.73) than any of the monitor 

pairs in Phoenix (0.80 ≤ r ≤ 0.89), located up to 9.4 km apart. 

The correlation structures for measurements at the monitors in each of the eleven CSAs/CBSAs 

included in this analysis reveal a wide range of response between monitors in each city and among the 

cities judged against each other. While this wide range is produced by the interactions of many physical 

and chemical elements, the location of each monitor and the uniqueness of its immediate surroundings 

can often explain much of the agreement or lack thereof. Compare, for example, Figures 3-17 and 3-18, 

the aerial views of monitors C (AQS site ID 040133002) and E (AQS site ID 040139997), respectively, in 

Phoenix. Monitor C has a correlation value with monitor E of 0.85 even though C is less than 1 km from 

the intersection of two major highways and E is surrounded by neighborhood residences. Because all 

other correlations of monitors in Phoenix are also >0.8, the network of CO monitors there appears to have 

captured the behavior of emitted and transported CO with high fidelity. In contrast, monitors in Pittsburgh 

show a low degree of correlation, with only two values >0.5 and eight values ≤ 0.2. The topography of 

Pittsburgh is mountainous and therefore more complex than that of Phoenix, and this could account for 

some of the disparity across space. But in addition, several CO monitors in Pittsburgh are sited at 

distances far from the urban core, making them less likely to correlate with those monitors in the highdensity urban core. Figure 3-19 depicts the three monitors in the Pittsburgh urban core (monitor A: AQS 

site ID 420030010, monitor B: AQS site ID 420030031, and monitor C: AQS site ID 420030038), which 

demonstrated the highest correlation values in this CSA for 2005-2007. Figure 3-20 shows the location of 

a more distant monitor, monitor F (AQS site ID 421250005), which does not correlate well with the three 

monitors located downtown, very likely because it is located at a great distance from any strong CO 

sources in the downtown urban core. This analysis demonstrates that agreement between monitors on an 

urban scale is a complex function of monitor siting, location to sources, geography, and 

micrometeorology. 

In addition to high correlations, Phoenix exhibits similar average concentration levels across 

monitors with 0.24 ≤ COD ≤ 0.43; see Table 3-7. The seasonal patterns shown in Figure 3-14 also 

demonstrate homogeneity across monitors for Phoenix. By contrast, Pittsburgh had more variability 

between monitored values at the sites shown in Figure 3-16 with the range in COD shifted upward 

(0.39 ≤ COD ≤ 0.90); see Table 3-8. For the three sites located in the downtown urban core of Pittsburgh 

(sites A, B and C), the COD is as high as 0.69, indicating greater spatial variability in CO concentrations 

in Pittsburgh, even for these more proximal monitors. 

The information in Table 3-8 should be used with some caution since many of the reported 

concentrations for the years 2005-2007 are very near or even well below the monitors’ stated lowest 

detection limits. Because ambient concentrations are now in large part very near to the FRM detection 

limit of 1.0 ppm and the coarsely reported measurement resolution is 0.1 ppm, the comparison statistics 

shown in these tables might be biased to show a specious heterogeneity in the box plots. These cautions 

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are especially important for interpreting data from site G in Pittsburgh and selected other sites in other 

metropolitan regions where one or more monitors consistently reported values below the stated detection 

limit. Such monitors include Houston site D, AQS site ID 482011035, and New York site I, AQS site ID 

361030009, included in Annex A. 

Figure 3-17. Aerial view of the location of CO monitor C. AQS ID 040133002 (marked by the 

yellow pin) in Phoenix, AZ, depicting its proximity to major roadways and 

neighborhood residences. 

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Figure 3-18. Aerial view of the location of CO monitor E. AQS ID 040139997 (marked by the 

yellow pin) in Phoenix, AZ, depicting its proximity to neighborhood residences and 

secondary surface roads. 

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Figure 3-19. Aerial view of the location of CO monitors A, B, and C. AQS IDs 420030010, 

420030031, and 420030038 (marked by yellow pins) in Pittsburgh, PA, depicting their 

proximity to major roadways and areas of high commercial density. 

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Figure 3-20. Aerial view of the location of CO monitor F. AQS ID 421250005 (marked by the yellow 

pin) in the Pittsburgh, PA CSA, depicting its proximity to secondary surface roads 

and water ways. 

3.5.1.3. Neighborhood Scale 

Roadway density is an important determinant of the spatial distribution of CO concentrations in 

urban areas. Mobile sources are the largest single source of CO, and their abundance and density affect 

the magnitude of CO production. Urban topography around roadways also affects CO transport and 

dispersion (e.g., Mfula et al., 2005; Rigby and Toumi, 2008). CO concentration gradients have been 

observed in near-road environments (Baldauf et al., 2008; Pirjola et al., 2006; Zhu et al., 2002) such that 

CO concentrations are often higher on or near roadways compared with upwind sites, and can decrease 

with increasing distance from the road. For example, Zhu et al. (2002) found that on-road CO 

concentrations were approximately 10 times higher than at an upwind monitoring site, as shown in Figure 

3-21. At 30 m downwind from the road, relative CO concentrations were decreased to 7 times above 

upwind levels, and were approximately 2.5 times the upwind levels at 100 m. Concentrations continued to 

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decrease and were still somewhat higher than upwind levels at the final monitoring site 300 m away. 

Other traffic-related pollutants (BC, particle number) followed a similar pattern, although with different 

downwind:upwind ratios. This indicates that near-road and on-road exposures are important in CO 

exposure assessment. 

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Field measurements, computational modeling, and wind tunnel experiments have shown that 

roadway design and roadside features can affect CO and other pollutant concentrations near roadways. 

Field measurements reported by Baldauf et al. (2008) indicated that noise barriers could reduce near-road 

pollutant concentrations by as much as 50 percent, although this effect was highly dependent on 

meteorological conditons. This study also showed that the presence of mature vegetation, where the noise 

barrier further reduced concentrations and flattened the concentration gradient away from the road. Urban 

dispersion and wind-field modeling by Bowker et al. (2007) also demonstrated the influence of noise 

barriers and vegetation on the concentrations and spatial variability of inert pollutants emitted from traffic 

sources. Wind tunnel work reported by Baldauf et al. (In press) demonstrated the effects of noise barriers 

as well as roadway design characteristics, such as the presence of cut and elevated roadway segments. 

These results indicated that cut sections reduced concentrations and altered the gradient away from the 

road for inert gases emitted from traffic sources such as CO. These results showed similar concentrations 

as Zhu et al. (2002) for roadway segments at-grade with no obstructions to air flow as well as elevated 

roadway segments with fill conditions. 

The geometry of urban street canyons has a profound effect on the distribution of CO 

concentrations on a micro-scale. A number of studies have performed computational and wind tunnel 

modeling of street canyons using nonreactive tracers and demonstrated the potential variability in 

concentration within a canyon (e.g., Borrego et al., 2006; Chang and Meroney, 2003; Kastner-Klein and 

Plate, 1999; So et al., 2005; Xiaomin et al., 2006). Because CO is a pollutant with very low reactivity on 

urban and regional scales, results from these models are directly relevant to CO concentration 

distributions in street canyons. Influential parameters include canyon height to width ratio (H/W), source 

positioning, wind speed and direction, building shape, and upstream configuration of buildings. 

Figure 3-22 shows dimensionless concentrations obtained from wind tunnel and computational fluid 

dynamics simulations of tracer gas transport and dispersion in an infinitely long street canyon with a line 

source centered at the bottom of the canyon (Xiaomin et al., 2006). When the canyon height was equal to 

the street width (typical of moderate density suburban or urban fringe residential neighborhoods) and 

lower background wind speed existed, concentrations on the leeward (downwind) canyon wall were four 

times those of the windward (upwind) wall near ground level. When the canyon height was twice the 

street width (typical of higher-density cities) and background winds were somewhat higher, near groundlevel concentrations on the windward canyon wall were roughly three times higher than those measured at 

the leeward wall. These results suggest that the magnitude of microscale CO concentrations may vary by 

factors of three or four times at different locations within a street canyon. 

March 2009 3-40 DRAFT – DO NOT CITE OR QUOTE 
Source: Zhu et al. (2002) 

Figure 3-21. Relative concentrations of CO and copollutants at various distances from the 710 

freeway in Los Angeles. 

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In a multi-site survey of curbside CO concentration in London, U.K., Croxford and Penn (1998) 

observed differences in concentration related to the side of the street on which the monitor was positioned 

relative to the wind direction. Bogo et al. (2001) measured CO concentrations in a street canyon with 

building height-street width ratio of 1 in Buenos Aires, Argentina using a continuous CO monitor. Similar 

to the Xiaomin et al. (2006) simulation results for a height-width ratio of 1, Bogo et al. (2001) observed 

slightly higher leeward concentrations than windward concentrations within the canyon, where 

recirculating airflow inside the canyon causes pollutants to collect in higher concentration on one side. 

For part of this study, they estimated aggregated emissions from several fixed sources, including thermal 

power stations and home heating, and from traffic. Mobile source emissions were estimated to be more 

than 800 times higher than fixed source emissions given assumptions regarding vehicle fleet. Moreover, 

they estimated that up to 30% of the measured CO concentrations came from within the local street 

canyon in which measurements were made. 

March 2009 3-41 DRAFT – DO NOT CITE OR QUOTE 
Source: Xiaomin et al. (2006) 

Figure 3-22. Dimensionless tracer gas concentration as a function of elevation at windward and 

leeward locations and street canyon aspect ratios (H/W). Z/H is the elevation of the 

measurement (Z) scaled by building height (H). (a) Dimensionless concentration on 

the windward and leeward sides of the canyon when H/W = 1 and wind speed = 3 

m/s. (b) Dimensionless concentration on the windward and leeward sides of the 

canyon when H/W = 2 and wind speed = 5 m/s. Computational fluid dynamics 

modeling was performed, and measurements were obtained in wind tunnel 

simulations. 

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Identification of neighborhood-scale variability is important for interpreting data from ambient CO 

monitors. Figure 3-23 shows a scatter plot of CO correlation as a function of distance (Pearson r) for 

monitors sited within 4 km of each other in four of the eleven CSAs and CBSAs. (The other seven CSAs 

or CBSAs were excluded from this analysis because their monitors were spaced farther than 4 km from 

each other.) The purpose of this plot is not to show a simple relationship between inter-sampler 

correlation and distance but to illustrate how this relationship varies between cities at the neighborhood 

scale. High correlation is seen for the monitors in the Phoenix, AZ CBSA, while those in the Pittsburgh, 

PA, CSA have much lower inter-sampler correlations with steeper slope (Δr/Δd = -0.04 for Phoenix and 

Δr/Δd = -0.27 for Pittsburgh). The inter-sampler correlation observed for the Boston, MA CSA is also 

March 2009 3-42 DRAFT – DO NOT CITE OR QUOTE 
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substantially lower than those observed for Phoenix. As discussed in section 3.5.1.2, these differences 

could be due to a variety of factors, including but not limited to natural and urban topography, different 

traffic conditions on roads located near monitors, and proximity of the monitors to the roadway. 

Figure 3-23. Inter-sampler correlations as a function of distance between CO monitors for 

samplers located within 4 km (neighborhood scale) for Boston, Denver, Phoenix, 

and Pittsburgh. 

March 2009 3-43 DRAFT – DO NOT CITE OR QUOTE 
3.5.2. Temporal Variability 

3.5.2.1. Multi-year Trends 

Figures 3-24 (top) shows ambient CO concentrations in ppm from 1980 to 2006 based on 

continuous measurements averaged over 8-h time segments. The 8-h NAAQS is indicative of exposures 

occurring over a sustained period of time, an outdoor worker’s exposure over the course of a day, for 

example. Figure 3-24 (bottom) depicts trends in the annual second-highest 8-h CO concentrations for 144 

sites in 102 counties nationwide having data either in the State and Local Air Monitoring Stations 

(SLAMS) network or from other special purpose monitors. 

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Source: U.S. EPA (2008c) 

Figure 3-24. (Top) Trends in ambient CO in the U.S., 1980-2006, reported as the annual second 

highest 8-h concentrations (ppm) for the mean, median, 10% and 90% values. 

(Bottom) Trends in ambient CO in the U.S., 1980-2006, reported as the number of 

trend sites (y-axis) with annual second-highest 8-h concentrations above the level of 

the NAAQS (9 ppm). 

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The 2006 annual second highest 8-h CO concentration averaged across 144 monitoring sites 

nationwide was 75% below that for 1980, and is the lowest concentration recorded during the past 27 

years; see Figure 3-24 (top). Since 1992, more than 90% of all these sites have reported CO 

concentrations below the 8-h NAAQS of 9 ppm; see Figure 3-24. The mean annual second highest 8-h 

ambient CO concentration has been below 5 ppm since 2004. The downward trend in CO concentrations 

in the 1990s parallels the downward trend observed in CO emissions, attributed largely to decreased 

mobile source emissions; see Figure 3-2. In addition, of the 144 sites used to determine this trend, from a 

total of 375 monitoring sites operating in 2006, the number reporting second-highest CO concentrations 

above the level of the CO NAAQS declined to zero over the same period; see Figure 3-24 (bottom). 

Consistent with the nationwide trends in emissions and concentrations, CO concentrations in all ten 

EPA Regions have steadily decreased since 1980, with reductions over this period ranging from 68% in 

Region 7 to 85% in Region 1; see Figure 3-25. This is also consistent with declining emissions seen in 

many regions of the U.S., shown in Figure 3-4. Reductions in anthropogenic CO emissions in the U.S. 

and elsewhere in the extra-tropical northern hemisphere are a major cause for the observed decrease in 

hemispheric and global-average CO concentrations observed since 1991 (Bakwin et al., 1994), although 

~30% of the decline between 1991 and 2001 was attributed to decreases in CO following the eruption of 

Mt. Pinatubo (Novelli et al., 2003). 

March 2009 3-45 DRAFT – DO NOT CITE OR QUOTE 
Source: U.S. EPA (2008c) 

 

Figure 3-25. Trends in ambient CO in the U.S., 1980-2005, reported as the annual second highest 

8-h concentrations (ppm) for the EPA Regions 1 through 10, along with a depiction 

of the geographic extent of those Regions 

3.5.2.2. Hourly Variation 

Weekday and weekend diel variation for the mean, median, 5th, 10th, 90th, and 95th percentiles of 

hourly CO concentration over 2005-2007 are shown in Figures 3-26 and 3-27, respectively among the 

eleven CSAs and CBSAs examined in this assessment. The weekday data showed that the Anchorage 

mean, median, 5th and 10th percentile CO concentration curves exhibit more pronounced morning and 

evening rush hour peak CO levels. Boston, Denver, Houston, Los Angeles, Phoenix, Pittsburgh, and St. 

Louis all exhibited similar trends, although the magnitude of the concentrations shown was roughly twice 

as high for Anchorage as the other cities. The curves had less overall variability for Boston, Pittsburgh, 

and St. Louis. The Atlanta plot shows that the median concentration was fairly constant throughout the 

24-h period, with a slightly elevated mean during the morning hours. The 90th and 95th percentile curves 

exhibit stronger morning and evening CO concentration peaks. New York City shows fairly constant CO 

mean and median concentration throughout the day with slight elevations throughout the morning rush 

hour and a slight trough between 1:00 and 5:00 AM. The Seattle plot shows a daytime plateau beginning 

around 5:00 AM and lasting until roughly 10:00 PM, with higher concentrations during morning and 

afternoon rush hour. Differences in hourly variation among the eleven CSAs and CBSAs reflect city-to1 

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city variation in source characteristics and meteorology. For instance, the rush hour peaks in many cities 

likely correspond to increased mobile source emissions during those periods. Local meteorology and 

topography, which influence mixing heights, can also affect hourly variation in CO concentration. 

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Figure 3-27 illustrates weekend diel trends for the eleven CSAs and CBSAs considered in this 

assessment. For Anchorage during the period 2005-2007, the mean and median concentration curves 

peaked during the morning and evening hours. A daytime concentration trough is evident. The 90th and 

95th percentiles of concentration were similar but more pronounced. The shape of this plot is also 

characteristic of Atlanta, Boston, Denver, Houston, Los Angeles, Phoenix, Pittsburgh, Seattle, and St. 

Louis, although the Anchorage CO concentrations are nearly 100% higher than concentrations in the other 

cities. The weekend diel plot for New York shows that the mean and median CO concentrations remain 

fairly constant throughout the day, with a slight reduction between 2:00 and 7:00 AM. The 90th and 95th 

percentile curves illustrate more diel variation. 

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Figure 3-26. Diel plot generated from weekday hourly CO data (ppm) for the eleven CSAs and 

CBSAs 2005-2007. Included are the number of monitor days (N) and the median, 

mean, 5th, 10th, 90th and 95th percentiles of CO concentration. Note that the y-axis 

of the Anchorage CBSA plot is scaled to 5.2 ppm while the other plots are scaled to 

2.8 ppm. 

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Figure 3-27. Diel plot generated from weekend hourly CO data (ppm) for the eleven CSAs and 

CBSAs 2005-2007. Included are the number of monitor days (N) and the median, 

mean, 5th, 10th, 90th and 95th percentiles of CO concentration. Note that the y-axis 

of the Anchorage CBSA plot is scaled to 5.2 ppm while the other plots are scaled to 

2.8 ppm. 

March 2009 3-49 DRAFT – DO NOT CITE OR QUOTE 
3.5.3. Associations with Copollutants 

Associations between hourly CO and other copollutants, including SO2, NO2, O3, PM10, and PM2.5

are provided in box plots for all U.S. monitors in Figure 3-28. The figure also shows the correlation of the 

24-h avg CO concentration with the daily max 1-h and daily max 8-h CO concentrations as well as the 

correlation between the daily max 1-h and daily max 8-h concentrations. AQS data were obtained from all 

available co-located monitors across the U.S. after application of 75% completeness criteria. Pearson 

correlation coefficients (r) were calculated using 2005-2007 data. Correlation plots analogous to Figure 3-

28 for select individual cities are provided in Annex A. 

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Figure 3-28. Seasonal plots of nationwide correlations between hourly CO concentration with 

hourly (1) SO2, (2) NO2, (3) O3, (4) PM10, and (5) PM2.5 concentrations. Also shown 

are correlations between 24-h avg CO concentration with (6) daily max 1-h and (7) 

daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h 

CO concentrations. (The numbers in this caption refer to those on the y-axis of each 

seasonal plot.) Red bars denote the median, and green stars denote the arithmetic 

mean. 

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The nationwide mean and median of the correlation with CO were positive for NO2, PM10, and 

PM2.5; near zero for SO2

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; and negative for O3. These findings might reflect common combustion sources 

for CO, NO2, and PM2.5. In all cases, a wide range of correlation values were reported. There were 

significant positive correlations for all CO-CO (24-h avg, daily max 1-h, daily max 8-h) comparisons 

rendered. Among those copollutants with positive associations, NO2 had the highest mean and median 

correlations, followed by PM2.5 and PM10 (correlations vary by season). The correlations of CO with SO2

and PM10 were not significantly different from zero for any season; SO2 would not be expected to 

correlate well with CO because SO2 emanates primarily from industrial sources. Correlations between CO 

and NO2 were significant and positive for winter, spring, and fall. Correlations between CO and PM2.5

were significant and positively correlated for winter and fall. Correlations between CO and O3 were 

significant and negative for winter only, when CO emissions tend to be high and O3 formation is low. The 

copollutant correlation plots for individual cities shown in Annex A illustrate higher and more statistically 

significant correlations of CO with both PM10 and PM2.5 in several but not all of the select cities 

displayed. It is widely believed that sources of CO and PM2.5 are highly correlated because they are both 

emitted directly during incomplete combustion and because secondary nitrate PM comes from NOX, 

which is largely produced from mobile sources. The wide confidence intervals displayed in the 

nationwide plots reflect the large pool of data in addition to the micrometeorological factors in each city. 

Additionally, CO monitors tend to be located apart from other pollutant monitors based on the criteria 

from the CFR of siting at least one monitor near the highest source of CO. Lack of co-location can affect 

the correlation of reported CO with PM10 and PM2.5 concentrations; this limitation also would inhibit the 

significance of correlations between CO and PM10-2.5. 

Several studies reported correlations between ambient CO and copollutants. Reported relationships 

were generally consistent with the correlation data reported by the AQS. Sarnat et al. (2001) reported 

significant positive Spearman’s correlations of CO with NO2 (r = 0.76) and PM2.5 (r = 0.69) and 

significant negative correlations of CO with O3 (r = -0.67) in Baltimore. Correlation of CO with SO2 was 

insignificant (r = -0.12). The Sarnat et al. (2001) study focused on correlations of ambient and personal 

PM2.5 with gaseous copollutants, so seasonal information is only available for the correlation between 

PM2.5 and CO. High correlation of ambient CO with NO2 is expected given that both are closely related to 

mobile source combustion emissions. Sarnat et al (2005) also reported significant year-round association 

between CO and PM2.5 and significant associations between CO and SO4

2- aerosols. Tolbert et al. (2007) 

reported correlations between multiple pollutants in Atlanta and also showed the highest Spearman’s 

correlation for CO with NO2 (r = 0.70). CO was also reported to have fairly high correlation with PM2.5

elemental carbon (EC) (r = 0.66), PM2.5 organic carbon (OC) (r = 0.59), and PM2.5 total carbon (TC) 

(r = 0.63). Correlations were reported to be much lower for CO with O3 (r = 0.27) and PM2.5 SO4

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(r = 0.14). Kim et al. (2006) measured CO, NO2, and PM2.5 at ambient fixed sites in Toronto, Canada and 

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found associations, averaged over monitoring stations, of CO with PM2.5 (Spearman’s r = 0.38, nonsignificant) and of CO with NO2 (r = 0.72, significant). 

3.5.4. Policy-Relevant Background 

Background concentrations of pollutants used for informing policy decisions about national 

standards in the U.S. are commonly referred to as policy-relevant background (PRB) concentrations. PRB 

concentrations are those that are exclusive of anthropogenic emissions in the U.S., Canada and Mexico 

(North America), and consist of world-wide biogenic emissions (including North America) and 

anthropogenic emissions elsewhere in the world. 

 PRB concentrations of CO can best be determined from the extensive and long-running network 

of remote-site baseline CO measurements conducted by NOAA’s Earth System Research Laboratory 

(ESRL), Global Monitoring Division (GMD), as part of their Carbon Cycle Greenhouse Gases Group 

(CCGG) Cooperative Air Sampling Network (CASN) (http://www.esrl.noaa.gov/gmd/ccgg/iadv). CO data 

through December 2007 are available with extensive quality assurance and control information from a 

worldwide network of 72 nodes active in December 2008. ESRL GMD uses the highly sensitive gas 

chromatography-mercury liberation photometric detection technique with precision to 1 parts per billion 

(ppb) in 50 ppb (2 ppb in 200 ppb) and accuracy to 1.5 ppb in 500 ppb (2 ppb in 200 ppb) (Novelli et al., 

2008). 

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Figure 3-29. Map of the baseline monitor sites used in this assessment to compute policyrelevant background concentrations. 

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This assessment used data from 2005 through 2007 at 12 remote sites in the U.S. to determine PRB 

(see the map in Figure 3-29): Cold Bay, AK; Barrow, AK; Shemya Island, AK; Cape Kumukahi, HI; 

Mauna Loa, HI; Trinidad Head, CA; Point Arena, CA; Wendover, UT; Niwot Ridge, CO; Park Falls, WI; 

Southern Great Plains, OK; and Key Biscayne, FL. Average concentrations for each month and for each 

year, 2005-2007 are shown for each site in Figure 3- 30. All sites demonstrate the well-known seasonality 

in background CO with minima in the summer and fall and maxima in the winter and spring. Summertime minima may be related to photochemical reaction of CO with OH, as described in section 3.3. 

Analysis for North American PRB is made here by segregating the three Alaska sites (based on their high 

latitude) and the two Hawaii sites (based on their distance from the continent) and treating the remaining 

seven sites as representative of the CONUS background. The 3-year avg CO PRB in Alaska ranged from 

127 to 135 ppb with an average of 130 ppb; in Hawaii from 95.3 to 103.1 ppb with an average of 99.2 

ppb; and over the CONUS from 118 to 146 ppb with an average of 132 ppb. 

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Figures 3-30. Monthly (circles) and annual (squares) average CO concentrations (ppb), 2005-2007 . 

Cold Bay, AK; Barrow, AK; Shemya Island, AK; Cape Kumukahi, HI; Wendover, UT; 

Niwot Ridge, CO; Mauna Loa, HI; Trinidad Head, CA; Point Arena, CA; Park Falls, WI; 

Southern Great Plains, OK; and Key Biscayne, FL. 

3.6. Issues in Exposure Assessment 

3.6.1. Summary of Findings from 2000 CO AQCD 

The 2000 CO AQCD (U.S. EPA, 2000) describes the results of studies completed prior to 1999 on 

personal exposures and microenvironmental concentrations of CO. Although these studies may no longer 

be representative of current exposure levels due to declining ambient CO concentrations, the personalmicroenvironmental-ambient relationships are still instructive. Time spent commuting, particularly in 

cars, was a major contributor to personal CO exposures. Many studies measured in-vehicle concentrations 

of CO and found elevated concentrations compared to fixed-site monitors. Roadside CO monitors were 

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elevated compared to ambient levels, and equal to or lower than in-vehicle levels (e.g., Ott, 1994; Rodes 

et al., 1999). A small portion of the CO concentrations inside a vehicle cabin comes from the vehicle 

itself, while a substantial fraction comes from roadway traffic emissions entering the cabin via air 

exchange. Studies summarized in the 2000 CO AQCD found that in-vehicle CO concentrations were 

generally two to five times higher than ambient CO concentrations. High traffic volumes contributed to 

increased in-vehicle concentrations. 

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Prior to the 2000 CO AQCD, it was well-known that CO levels in residences may be elevated 

above ambient due to non-ambient indoor sources, such as cooking, space heating, and smoking. 

Separation of indoor CO into ambient and non-ambient components is important for determining the 

effect of ambient CO concentrations, although this has not been done successfully in previous studies. 

Two large studies done in Denver, CO and Washington, DC in the early 1980s found that fixed-site 

monitor concentrations were higher than personal exposures for those with low-level exposures, while 

fixed site monitor concentrations were lower than exposures for those with high-level exposures (Akland, 

1985; Johnson, 1984). Non-ambient sources contributing to high total exposures likely obscured this 

relationship. In Denver, gas stove operation, passive smoking, and attached garages increased residential 

indoor exposure by 2.6, 1.6, and 0.4 ppm respectively compared to individuals without those sources 

present. Categorical analyses found significantly higher personal exposures on high ambient 

concentration days than on low ambient concentration days, suggesting that personal exposures are 

related to ambient levels. Non-ambient exposures tend to obscure the relationship between ambient CO 

concentrations and personal exposure. 

3.6.2. General Exposure Concepts 

A general framework for human exposure modeling was described in the 2008 Draft PM ISA, 2008 

NOX ISA, and 2008 SOX ISA (U.S. EPA, 2008e, f). A brief conceptual model of human exposure is 

provided here with respect to CO for the readers’ convenience. An individual’s daily exposure to CO can 

be described based on a compartmentalization of the person’s activities over a time period of interest: 

∫ = jdtCE

Equation 3-2 

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where E = exposure over some duration, Cj = CO concentration at location j, and dt = time spent in 

location j. This basic equation can be broken down into a microenvironmental model that accounts for 

exposure to CO of ambient (Ea) and non-ambient (Ena) origin of the form: 

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= + EEE naa

Equation 3-3 

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This assessment focuses on the ambient component of exposure because the non-ambient portion of 

exposure is subject to individual behavior. Daily Ea can be expressed in terms of the fraction of time spent 

outdoors and indoors being exposed to ambient CO concentration, Ca (Wilson et al., 2000): 

( ) oa CFffE ainfi ∑ i +=

Equation 3-4 

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where f = fraction of time, subscript o = outdoor, subscript i = indoor, and Finf = infiltration factor. Finf 

quantifies the equilibrium fraction of the CO concentration outside the microenvironment that penetrates 

inside the microenvironment and remains in mixture. It is a function of the building air exchange 

characteristics and the properties of the gas. Assuming steady state ventilation conditions, the infiltration 

factor is a function of the penetration (P) of CO, the air exchange rate (a) of the microenvironment, and 

the rate of CO loss (k) in the microenvironment; Finf = Pa/(a+k). Given that k Æ 0 for CO, Finf reduces to 

P. Equation 3-4 is subject to the constraint fo + Σfi = 1 to reflect the total exposure over a specified time 

period. The indoor term has a summation because indoor exposure can occur in various 

microenvironments throughout a time period of interest. “Outdoor” exposure can occur in parks, yards, 

sidewalks, and on bicycles or motorcycles. “Indoor” refers to being inside any aspect of the built 

environment, e.g., home, office buildings, enclosed vehicles (automobiles, trains, buses), and/or 

recreational facilities (movies, restaurants, bars). The complex human activity patterns that dictate 

exposure across the population (all ages) are illustrated in Figure 3-31 (Klepeis et al., 2001). This figure 

illustrates the diversity of daily activities among the population as well as the proportion of time spent in 

each microenvironment. Although activities in Klepeis et al. (2001) are presented over a day, information 

from this figure can be extracted to deduce variation over a 1-h or 8-h period. 

As seen in equation 3-4, ambient exposure is a linear function of the ambient CO concentration. 

The ambient exposure factor, α, is the ratio between the personal exposure to ambient CO and the ambient 

concentration of CO (or the ambient exposure factor); i.e., α = Ea/Ca. α is therefore the proportionality 

factor in equation 3-4, i.e., α = fo + Σfi (α varies between 0 and 1). If a person’s exposure occurs in a 

single microenvironment, the ambient component of a microenvironmental CO concentration can be 

represented as the product of the ambient concentration and Finf. If there are no significant local outdoor 

sources and sinks of CO, then Ca can be approximated by the concentration measured by an ambient 

monitor. If local sources and sinks exist and are significant, then the ambient component of outdoor air 

must be estimated using dispersion models, land use regression models, receptor models, fine scale 

chemistry-transport models or some combination of these techniques. 

Finfi

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A variety of approaches can be used to estimate exposure to ambient CO. In some cases, individual 

personal exposures are measured with personal exposure monitors (PEMs), where personal samples are 

taken to estimate population exposure. In other cases, ambient concentrations are used as an exposure 

indicator. The ambient concentration may be based on measurements made at a single ambient monitor or 

as the average of several ambient monitors. 

Very few recent exposure assessment studies utilized measurements of CO concentration and none 

of the recent exposure error studies used measurements of personal and ambient CO exposure data, 

although Sheppard et al. (2005) presented a conceptual model for nonreactive pollutants that could 

include CO. Many recent studies analyzing exposure error used PM data (e.g., Sheppard et al., 2005; 

Wilson and Brauer, 2006; Zeger et al., 2000). The lack of recent CO data contributes to uncertainty 

regarding personal-ambient relationships. However, review of exposure error studies for PM, under the 

assumption that it is nonreactive, is instructive for discerning the relative influence of ambient and nonambient exposures if consideration is made for differences between CO and PM with respect to indoor 

source variability and infiltration. 

Wilson and Brauer (2006) showed significantly stronger associations between health effects and 

ambient exposure than between health effects and total personal exposure. The use of personal exposure 

in population exposure assessment studies could cause various errors in the health effect estimate because 

the correlation between personal exposure and ambient concentration may be different for each subject in 

a panel and may not be statistically significant (Wilson et al., 2007). The correlation between ambient 

concentration and personal exposure may be high for some subjects, in which case the exposure error 

caused by using ambient concentration instead of personal exposure may be small. In other subjects, the 

correlation may be low or negative (and not statistically significant). In this case, the exposure error will 

be high and may obscure relationships between ambient exposure and health effects. Differences in 

correlation observed in these studies occurred in part because the ambient exposure factor, α, was 

different for each subject. The relationships between ambient concentration and the ambient component 

of personal exposure were statistically significant for all subjects. The analyses of Wilson and Brauer 

(2006) and Wilson et al. (2007) focused on PM, which is subject to indoor-outdoor differences in size 

distribution and chemical composition related to differential infiltration. However, this issue is still 

relevant for CO because non-ambient CO exposure is uncorrelated with ambient CO exposure and 

therefore could obscure health effects relationships observed in epidemiologic studies. Therefore, ambient 

concentration is better than total personal exposure as an indicator of ambient CO exposure. 

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Source: Klepeis et al. (2001)) 

Figure 3-31. Distribution of time that a sample population spends in various environments, from 

the National Human Activity Pattern Survey. 

3.6.3. Monitoring Issues Associated with Exposure Assessment 

3.6.3.1. Exposure Assessment using Community-Based Ambient Monitors 

Instrumentation and associated monitoring errors are described in section 3.4; exposure error 

related to instrumental measurement error in ambient monitors is described here. Because there will likely 

be some random component to instrumental measurement error, the correlation of the measured CO 

concentration with the true CO concentration will likely be less than 1. Sheppard et al. (2005) indicated 

that instrument error in the individual or daily average concentrations have “the effect of attenuating the 

estimate of α.” However, Zeger et al. (2000) stated that the “instrument measurement error in the ambient 

levels...is close to the Berkson type” and in order for this error to cause substantial bias in later estimation 

of the health outcome, the error term (the difference between the true concentrations and the measured 

concentrations) must be strongly correlated with the measured concentrations. Zeger et al. (2000) 

suggested that, “Further investigations of this correlation in cities with many monitors are warranted.” 

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3.6.3.2. Personal Exposure Monitors 

Portable monitors for measuring personal CO exposure include the Langan and Draeger monitors, 

both of which use electrochemical oxidation-reduction techniques (Langan, 1992). These monitors 

continuously log CO concentrations, making them suitable for use in personal monitoring studies. More 

detail on personal CO monitoring is provided in the 2000 CO AQCD (U.S. EPA, 2000). 

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Measurement Error in Personal Exposure Monitors 

Personal electrochemical CO monitors are subject to interference and drift, and have a relatively 

high detection limit (approximately 1 ppm) relative to current ambient concentrations. Previous studies in 

the 1980’s and 1990’s, when ambient levels were higher, were able to successfully deploy these monitors, 

but more recent exposure studies have avoided personal CO measurements due to the high percentage of 

non-detects. The lack of a suitable personal monitor for measuring low-level exposures (<1 ppm) has 

hampered field studies assessing personal exposure to ambient CO. Chang et al. (2001) evaluated the 

Langan CO monitor as part of an air quality sampling manifold. At high (0.4-3.0 ppm) CO 

concentrations, the instrument correlated well (R2

 = 0.93) with a reference CO monitor with the Langan 

underestimating the CO concentration by 41%. When ambient levels fell consistently below that level, 

coefficient of determination (R2

) between the Langan and reference monitor fell to R2 = 0.4 in summer 

and R2

 = 0.59 in winter with the arithmetic average concentration underestimated by 47% in summer and 

by 63% in winter. Chang et al. (2001) pointed out the need for frequent instrument zeroing to minimize 

instrument drift. Abi Esber et al. (2007a) evaluated a similar personal electrochemical CO sensor, the 

GEMTM 2000, by comparing measured concentrations with those obtained through co-located grab bag 

sampling in a vehicle cabin. Differences between the GEMTM 2000 and the reference samples were fairly 

low during weekday driving (differences = 2.1-10.6%). Differences on Sundays, when traffic was 

significantly lower than during weekdays, were dependent on vehicle ventilation conditions, with better 

agreement when vehicle ventilation allowed for higher cabin CO concentrations (differences = 3.4-5.6%), 

but the electrochemical sensor did not compare well with reference values when concentrations were low 

(differences = 20-71%). In general, it is difficult to separate the large instrumental measurement error 

seen at concentrations below instrument LOD from variation in non-ambient exposures. This large 

variation in personal measurements can result in high levels of classical measurement error (Sheppard et 

al., 2005). 

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3.6.4. Indoor/Outdoor Relationships and Infiltration 

CO is a relatively inert gas, making the indoor decay rate negligible compared to typical air 

exchange rates (~1/h). In the absence of indoor sources, this would lead to an indoor-outdoor 

concentration ratio (I/O) of approximately 1. For this reason, few studies have measured I/O for CO. 

Polidori et al. (2007) measured I/O of 0.94-1.21 for two retirement communities in the Los Angeles area. 

The authors suggested that similarity between I/O for CO and NOX can be attributed to lack of indoor 

sources of either gas. Chaloulakou and Mavroidis (2002) reported I/O for CO measurements in the 

absence of indoor sources in a school building in Athens, Greece and found that I/O varies with season. 

During the summer, median I/O was reported to be 0.57 on weekdays, 0.91 on Saturdays, and 0.81 on 

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Sundays. In winter, median I/O was reported to be 0.82 during weekdays, 0.90 on Saturdays, and 0.74 on 

Sundays. The authors attributed the lower weekday I/O values to traffic-based peaks in outdoor CO 

concentrations that were not translated to indoors. In a related work, Chaloulakou et al. (2003) reported 

the median I/O over all days as 0.8 for the same school and 0.9 for an Athens office building with no ETS 

(the presence of other sources was not clearly stated but assumed zero). However, observed indoor values 

are often greater than outdoor concentrations in the presence of indoor sources. A recent study in the U.K. 

reported I/O of 3.9-4.3 in homes with gas cookers (Dimitroulopoulou et al., 2006), which is consistent 

with previous studies. A multipollutant study conducted in 2000-2001 attempted to measure I/O for CO 

and calculated residential infiltration factors, but low CO concentrations resulted in a large number of 

non-detects (Williams et al., 2003). 

3.6.5. Personal/Ambient Relationships 

3.6.5.1. Panel and Population Exposure Studies 

Although several multi-pollutant exposure studies have been conducted recently in the U.S., (e.g., 

Sarnat, 2006), most have not included CO in the suite of pollutants, possibly due to limitations in personal 

monitoring techniques. A few studies conducted in Europe and Canada measured personal-ambient 

relationships for CO. 

The EXPOLIS study (Georgoulis et al., 2002) found that 48-h personal exposures were 

significantly correlated with ambient concentrations in each of five European cities (Athens, Basel, 

Helsinki, Milan, and Prague). Controlling for source terms, including ETS, traffic, and natural gas 

appliances, regression coefficients between personal exposure and ambient concentration ranged from 

0.28 in Milan to 1.99 in Helsinki. Regression coefficients greater than 1 may indicate location of a fixed 

site monitor away from local sources (e.g., roadways), resulting in a lower ambient value than 

experienced in the urban core. As part of this study, personal CO exposure was measured for a panel of 50 

office workers in Milan (Bruinen de Bruin et al., 2004). Average measured 1-h personal exposures were 

7.3 ppm in comparison with 5.0 ppm for fixed site 1-h measurements. Average 8-h (3.3 ppm) and 24-h 

(2.1 ppm) CO concentrations were the same for personal and fixed site measurements. Percentage of time 

exposed, exposures, and percentage of exposure from the Bruinen de Bruin et al. (2004) study, in the 

absence of non-ambient CO from ETS and gas cooking, are shown in Table 3-9. The largest percentage of 

CO exposure was attributed to home indoor exposure in the absence of indoor sources, while the highest 

exposure levels were observed during transit. Scotto di Marco et al. (2005) found similar results. Bruinen 

de Bruin et al. (2004) and Scotto di Marco et al. (2005) found that mobile source emissions were 

important contributors to personal exposure, as described in the following subsection. 

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Table 3-9. Percentage of time exposed to ambient CO (adjusted to reflect the absence of 

non-ambient CO from ETS and gas cooking), average CO exposures, and percentage 

of exposure estimated for the population. 

Percent of time exposed (%) Exposure (ppm) Percent of exposure (%) 

INDOORS 89.6 81.1 

Home 56.5 1.8 49.4 

Work 29.1 1.9 26.8 

Other 4.1 2.5 4.9 

OUTDOORS 1.8 2.1 

Home 0.2 2.3 0.2 

Work 0.6 2.1 0.6 

Other 1.0 2.6 1.2 

IN-TRANSIT 8.5 16.8 

Walking 3.0 3.0 4.4 

Train/metro 0.7 3.0 1.0 

Bus/tram 2.0 3.8 3.7 

Motorbike 0.2 4.5 0.4 

Car/taxi 2.6 5.7 7.2 

Source: Bruinen de Bruin et al. (2004). 

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EXPOLIS also looked at the special case of children’s exposure to CO because children generally 

do not produce CO in their daily activities and have no occupational exposures. Alm et al. (2000; 2001) 

reported higher personal exposures than ambient concentrations for children aged 3-6 years old in 

Helsinki. Their mean daily max 1-h exposure was 5.2 ppm, compared to 1.4 ppm measured at a fixed-site 

monitor. For daily max 8-h and 24-h avg concentrations, the corresponding values were 2.9 ppm and 

2.1 ppm for personal exposure and 0.8 and 0.6 ppm, respectively, for fixed site measurements. The 

Spearman rank correlation, although statistically significant, was relatively low (r = 0.15) between 

individual 24-h avg exposure and the ambient monitor. The correlation improved when the average 

exposure of children measured on the same day (r = 0.33, 3-6 children) or the same week (r = 0.55, 10-23 

children) was compared to the monitor data. A regression model using questionnaire data found that 

parental smoking status, parental education, and presence of a gas stove explained 12% of the variability 

in the 8-h max exposures, indicating that other factors, such as time spent outdoors and proximity to 

roadways are likely to be important in determining personal exposure. 

Kim et al. (2006) reported mean CO concentrations of 1.4 ppm for a panel of 28 cardiaccompromised individuals in Toronto, Canada. Corresponding fixed-site monitor mean concentrations 

ranged from 0.5-1.4 ppm, with an overall mean of 1.0 ppm. The observed higher personal exposures may 

have been due to both indoor sources and proximity to roadways when outdoors. Personal-ambient 

Spearman correlations ranged from -0.65 to 0.93, with a median of r = 0.31, indicating that while 

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moderate correlations are observed overall, inter-individual differences based on time spent in different 

microenvironments have a strong influence on the observed correlation. 

Lai et al. (2004) measured relationships between personal CO exposure and microenvironmental 

(home indoor, home outdoor, and work indoor) concentrations in Oxford, U.K.. The highest personal 

exposures were associated with smoking, cooking, and transportation while low correlations were 

observed between personal and indoor residential concentrations, further indicating the importance of 

indoor sources and the need to separate ambient contributions to personal exposure from total personal 

exposure. 

3.6.5.2. Commuting Time CO Exposure Studies 

A number of studies focused on transit-time CO exposure, which can occur in a vehicle or while 

walking or cycling. Kaur (2005) found that transit time exposures in London, U.K. were significantly 

higher than measurements made at a fixed site background monitor away from traffic (0.3 ± 0.1 ppm) for 

car riders (1.3 ± 0.2 ppm), taxi riders (1.1 ± 0.1 ppm), bicyclers (1.1 ± 0.2 ppm), walkers (0.9 ± 0.2 ppm), 

and bus riders (0.8 ± 0.1 ppm). Curbside measurements (1.5 ± 0.7 ppm) in this study were slightly higher 

than car riders’ exposures. Duci et al. (2003) found that average in-transit concentrations were highest for 

cars (winter: 21.4 ± 4 ppm), followed by pedestrians (winter: 11.5 ± 2.6 ppm; summer: 10.1 ± 1.7 ppm), 

buses (winter: 10.4 ± 2.9 ppm; summer: 9.4 ± 3.6 ppm), trolley (winter: 9.6 ± 1.9 ppm; summer: 8.2 ± 

3 ppm), and rail (winter: 4 ± 0.6 ppm; summer: 3.4 ± 0.7 ppm). Duci et al. (2003) did not provide fixed 

site CO concentrations but stated that in-transit exposures were higher in each case. Additional analyses 

from the EXPOLIS study indicated that on-road mobile source emissions were the most important source 

of CO exposure for non-ETS-exposed subjects (Bruinen de Bruin et al., 2004; Scotto di Marco et al., 

2005). Scotto di Marco et al. (2005) found that, for a panel of 201 adult Helsinki residents (aged 25-55 

years), subjects spent 8.1% of their time in transport, which accounted for 12.6% of their total exposure 

(range of means = 0.96 ppm on a train – 2.8 ppm in a car). Similarly, in a panel study of 50 office 

workers, Bruinen de Bruin et al. (2004) found that, in the absence of non-ambient sources, the subjects 

spent 8.5% of their time in transit, which accounted for 16.8% of their total exposure, with 2.6% of time 

spent in a car or taxi accounting for 7.2% of exposure (mean = 5.7 ppm). Commuting time was an 

important predictor of exposure, such that subjects living in low CO concentration suburban areas and 

commuting to work experienced higher levels than urban residents with short commute times. 

Gómez-Perales et al. (2004; 2007) measured CO exposures on buses, mini-buses, and metro cars in 

Mexico City, Mexico to be 12 ppm, 15 ppm, and 7 ppm, respectively. These values are higher than 

CONUS measurements and those presented by Kaur et al. (2005), but the relative difference between the 

minibus and bus exposures in the Gómez-Perales et al. (2004; 2007) study are similar to those seen for the 

taxi-to-bus or car-to-bus comparisons in Kaur et al. (2005). Alm et al. (1999) reported in-vehicle CO 

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concentrations of 5.7 ppm in the morning and 3.1 ppm in the afternoon commute for Kuopio, Finland. 

Abi Esber et al. (2007b) report results from CO concentration measurements taken within an automobile 

in Beirut, Lebanon during the morning commute period of 7:30 to 9:30 a.m. Weekday trip CO levels 

ranged from 10.8 ppm with the windows open and vents closed to 37.4 ppm when driving with windows 

and vents closed. Mean and standard deviation for ambient CO concentrations, obtained using a roadside 

monitor in Beirut during the periods September-December 2003, August-September 2004, and 

May-August 2005 were 1.4 ± 0.7, 1.6 ± 0.4, and 1.1 ± 0.7 ppm, respectively. 

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Abi Esber and El-Fadel (2008) compared the amount of CO produced by an automobile, driving 

the same route of Beirut described in Abi Esber et al. (Abi Esber et al., 2007b) above, by sampling CO 

directly from the engine of the vehicle and separately from the cabin of the car under three different 

ventilation conditions. For the case when one window was half-open and vents were closed, engine CO 

concentrations averaged 12.6 ppm while in-vehicle concentrations averaged 17.7 ppm, which was a 

40.5% increase. With windows closed and the air conditioner operating on “recirculating air” mode, CO 

concentrations averaged 13 ppm from the engine and 30.2 ppm in the vehicle cabin, a 132% increase. 

With windows closed and the air conditioner on “fresh air” mode, engine CO concentrations averaged 

18.3 ppm while in-vehicle concentrations were 20.5 ppm, which was only a 12% increase. Figure 3-32 

shows that the time series for the cabin and engine CO samples are very similar for the fresh air scenario, 

but for the recirculating air ventilation the concentration increases as a logarithmic-type function as CO 

builds up in the cabin of the vehicle. 

Source: Abi Esber and El Fadel (2008) 

Figure 3-32. Comparison of in-vehicle (solid line) and engine (dotted line) results for (left) driving 

with windows closed and air conditioner in recirculating air mode, and (right) driving 

with windows closed and air conditioner in fresh air mode. 

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Riedeker et al. (2003) measured CO concentrations inside patrol cars during shifts. Troopers 

recorded in a time-activity diary the ventilation settings of their cars and exit/entry from the vehicle, and 

the air conditioning was typically set to recirculation mode during the shifts. Riedeker et al. (2003) found 

that CO concentrations (mean, SD: 2.6 ± 1.1 ppm) were higher than ambient monitor concentrations 

(0.8 ± 0.3 ppm). They were also higher than roadside CO concentrations (1.1 ± 0.3 ppm), indicating that 

either the vehicle itself contributes to in-cabin CO, or on-road concentrations are higher than roadside 

concentrations, or both. Riedeker et al. (2003) noted that within-shift variability was higher than betweenshift variability, which underscores the variability in police officers’ activities during a given shift. No 

data was segregated by ventilation settings. Chang et al. (2000) measured CO concentrations during a 

scripted activity study in Baltimore, MD in 1998 and 1999. Mean 1-h CO concentrations were near the 

1 ppm detection limit of the Langan CO monitor. Microenvironmental CO concentrations were 

significantly correlated with concentrations measured at a fixed-site ambient monitor for residential, other 

indoor, in-vehicle, and outdoor near-road microenvironments during the winter. Significant correlations 

were observed only for residential microenvironments during the summer. The location of the ambient 

monitor near a roadway may have contributed to the lack of correlation with concentrations measured at 

outdoor locations away from roadways. Microenvironmental concentrations inside vehicles were 

significantly higher than those for other microenvironments. 

Several factors may affect commuters’ exposures. Van Wijnen et al. (1995) observed that while 

bicycling, cyclists inhale 2.3 times as much air as pedestrians and drivers so that this group may, in fact, 

inhale the most CO. Likewise, in their review of roadway exposures to CO and PM, Kaur et al. (2007) 

listed a number of uncontrolled factors that may influence exposure. Vertical CO concentration gradients 

have been documented with concentrations decreasing with height; lower breathing zone height among 

children may make them more likely to be exposed to higher CO concentrations. With respect to 

transportation, Kaur et al. (2007) suggested that vehicle ventilation, speed, position in traffic, and 

start/stop activity influence in-vehicle exposures. Gómez-Perales et al. (2007) also noted that meteorology 

can impact in-vehicle exposures, with evening increases in wind speed causing a 50% reduction in CO 

exposures among bus and minibus commuters. Alm et al. (1999) made a similar observation in a study of 

urban commuters’ exposure within a vehicle. 

Studies of vehicle self-pollution are instructive for considering potential for ambient CO infiltration 

in vehicles. Behrentz et al. (2004) used sulfur hexafluoride (SF6) tracer gas emitted from school bus 

engines to determine the proportion of in-vehicle pollution related to self-pollution. Based on the SF6

concentration, they calculated that 0.04-0.29% of the bus cabin air contained exhaust for high emitting 

diesel engines, 0.01-0.03% for “regular” diesel buses, 0.02-0.04% for buses fitted with a particle trap, and 

0.03-0.04% for buses running on compressed natural gas. SF6 concentrations were higher when bus 

windows were closed. 

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3.6.5.3. CO Exposure Assessment Variability and Error 

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

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

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

ambient concentrations. If there are no non-ambient sources of a pollutant, the total personal exposure is 

equal to the ambient personal exposure. However, non-ambient sources could significantly affect personal 

exposures to CO. Unlike PM, CO has no chemical identifiers that can be used to apportion ambient and 

non-ambient sources of CO (other than radioactive isotopes, whose detection is rare). For the general U.S. 

population, exposure error analysis for epidemiologic studies indicates that fixed-site measured ambient 

CO concentration is generally a good indicator of ambient exposure to CO, as discussed in more detail 

below. 

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Figure 3-33 shows hourly vs. personal CO concentration data obtained by Chang et al. (2000) for a 

1998-1999 multi-pollutant sampling campaign in Baltimore, MD. Personal exposures were obtained in 

five separate microenvironments in this study. A high degree of scatter is evident in this figure, which 

suggests that these personal exposures are influenced by both ambient and non-ambient sources of CO. 

Figure 3-34 is a box plot of the personal-to-ambient CO concentration ratio for the same five 

microenvironments. Figure 3-34 shows that personal exposures in vehicles were on average 2.8 times 

higher than ambient during the summer and 4.1 times higher than ambient in the winter. For the other four 

microenvironments tested, the average ratio was around 1. Wide variability is seen in these plots, 

particularly during the summer. Much of that variability could be due to the influence of non-ambient 

sources, which would then result in poor correlation between total personal exposure and ambient 

concentration. 

Zeger et al. (2000) stated that there are three error terms in the estimate of a person’s exposure: 1) 

error from using pooled population data in lieu of individual data, 2) error between total exposure and 

ambient concentration, and 3) error between the actual and measured ambient concentration. Zeger et al. 

(2000) also described that errors related to individual variability and measurement (as stated above for the 

latter) are of the Berkson type and therefore not expected to bias estimates of exposure or health effects. 

Moreover, Sheppard et al. (2005) simulated ambient and non-ambient exposures to a non-reactive 

pollutant and observed that non-ambient CO exposure has no effect on the association between ambient 

CO exposure and health outcomes for the case where ambient and non-ambient sources were 

independent. 

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Source: Chang et al. (2000) 

Figure 3-33. Hourly personal vs. ambient CO concentrations obtained in Baltimore, MD. Summer 

of 1998 in five settings: indoor residence, indoor other, outdoor near road, outdoor 

away from road, in vehicle. 

Source: Adapted from Chang et al. (2000) 

Figure 3-34. Box plots of the ratio of personal to ambient concentrations obtained in Baltimore, 

MD. Summer of 1998 and winter of 1999 in five settings: indoor residence, indoor 

other, outdoor near road, outdoor away from road, in vehicle. The dotted line shows 

the mean, and the solid line shows the median. S = summer; W = winter. 

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For community time-series epidemiology, the community averaged concentration, not the 

concentration at each fixed monitoring site, is the concentration variable of concern (Zeger et al., 2000). 

The correlation between the concentration at a central community ambient monitor and the community 

averaged concentration depends on at least the following three factors. First, the distribution of monitors 

across space: CO emissions from traffic might show spatial heterogeneity near roadway sources but have 

a more homogeneous distribution farther downwind. It follows that use of a large number of samplers will 

dampen inter-sampler variability. Second, the relationship between the measurement at the ambient 

monitoring site and the community average concentration: if the site is selected to measure a “hot spot” or 

pollution from a nearby source, estimates of community exposure could be skewed upwards. Third, 

terrain features or source location: different terrain features or source locations across sub-communities 

may differ in the temporal pattern of pollution. Intra-urban spatial heterogeneity was discussed in detail in 

Section 3.5.1.2. Community exposure may not be well-represented when monitors cover large areas with 

several sub-communities having different sources and topographies. For example, with the exception of 

two closely located samplers, there is poor inter-sampler correlation among the Pittsburgh, PA CO 

samplers. This variability reflects the wide topographical differences throughout the Pittsburgh CSA as 

well as variation in traffic usage and distance to near-road sources at each point in contrast to the Phoenix 

CBSA, where inter-sampler correlation was high. 

Epidemiologic studies of long-term exposure rely on differences among communities in long-term 

average ambient concentrations. If exposure errors are different in the different communities, the 

differences in long-term ambient concentrations among communities may not represent the differences in 

long-term average exposures. For example, there may be community to community differences in 

measurement error, in the average ambient exposure factor (α) or the average non-ambient exposure. This 

could happen if exposure to newly formed pollutants generated by vehicular traffic or pollutants from 

other localized sources differed among the spatial areas. Thus, in a regression of health effects against 

average concentration (as an indicator for average exposure) there could be a different amount of error 

(either positive or negative) in the exposure indicator for each spatial area. This could add error and bias 

the slope up or down. Geostatistical tools enable the use of concentration fields that include spatial 

variations in concentration. However, it has not yet been possible to include individual or small-area 

variations in the personal exposure to ambient concentrations or variations in personal exposures to 

indoor-generated pollutants in long-term studies of the associations of pollutants with health effects. 

3.6.6. Multi-pollutant Exposures 

Since incomplete combustion is the primary source of ambient CO, exposure to ambient CO is 

accompanied by exposure to other combustion-related pollutants, such as NOX and PM. Thus, ambient 

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CO is often considered a surrogate for exposure to traffic-generated pollutants. However, the specific mix 

of CO with NOX and PM depends on the source; for example, the mixture generated by gasoline engines 

differs from that produced by gas combustion. Correlations between ambient CO and ambient PM2.5, 

PM10, NO2, SO2, and O3 from AQS data and the peer-reviewed literature were presented in section 3.5.3. 

Nationwide ambient CO was most highly correlated with ambient NO2 followed by PM2.5 and PM10. 

Correlations between CO and PM2.5 were not always statistically significant on a national basis; 

correlations were insignificant for ambient CO with ambient SO2 and ambient PM10, and ambient CO was 

negatively correlated with ambient O3. Correlations of ambient CO with PM2.5 and PM10, were much 

higher and more statistically significant at some of the select cities, as shown in Annex A. 

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Sarnat et al. (2001) analyzed the relationship between personal exposure to PM2.5 and ambient CO 

concentrations and found significant associations during winter of ambient CO with personal exposure to 

total PM2.5 (slope = 3.99) and to ambient PM2.5 (slope = 6.30). Ambient CO was not significantly 

associated with personal PM2.5 exposure during summer. Sarnat et al. (2005) also reported significant 

association between ambient CO concentrations and personal PM2.5 exposure for winter (three 12-day 

sessions in 2000, dates not specified) only and significant year-round associations between ambient CO 

concentrations and personal exposure to SO4

2- aerosols. 

Relationships between personal CO exposures and copollutants were reported less frequently in the 

literature, but results from these studies were consistent with the findings cited above. In a study of 

personal exposures to CO, PM2.5, and ultrafine PM in a street canyon, Kaur et al. (2005) found low 

Pearson’s correlation of personal CO exposure with personal PM2.5 exposure (r = 0.23). Personal CO 

exposure had much better correlation with personal ultrafine PM exposure (r = 0.68). Chang et al. (2000) 

reported correlations of personal CO exposure with personal PM2.5, personal toluene, and personal 

benzene exposures in Baltimore, MD at five locations, labeled indoor residential, indoor nonresidential, 

outdoors near roadway, outdoors away from road, and in vehicle. Much variability was observed in the 

correlations for different locations and seasons (winter vs. summer). In general, the correlations tended to 

be stronger and more significant in the winter. Chang et al. (2000) pointed out that indoor air exchange 

rates tend to be lower in winter, which could cause these correlations to be more sensitive to non-ambient 

sources. Significant associations of CO with benzene and toluene were observed in-vehicle. Because CO 

exposures most often occur together with exposure to other combustion-related pollutants, interpretation 

of health studies using ambient CO data can be a challenge, as discussed further in Chapter 5. 

3.6.7. Exposure Modeling 

A number of modeling techniques to describe air pollutant exposures at the individual and 

population level have been published since 2002. Ambient CO exposure models often assume that 

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vehicular traffic is the sole source of emissions given that roughly 90% of urban CO is estimated to come 

from traffic (Gulliver and Briggs, 2005). Individual-level models can predict personal exposure based on 

a person’s path of travel and on conditions of the surrounding environment using traffic and meteorology 

data. One such example is the Space-Time Exposure Modeling System (STEMS) (Gulliver and Briggs, 

2005). Population-based methods, such as the Air Pollution Exposure (APEX) model 

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www.epa.gov/ttn/fera/human_apex.html), Stochastic Human Exposure and Dose Simulation (SHEDS) 

(Burke et al., 2001), and EXPOLIS models (Kruize et al., 2003), involve stochastic treatment of the 

model input factors. Another approach is to predict location-based exposures using a deterministic model 

such as the CMAQ model, California Line Source Dispersion Model (CALINE), CALPUFF (long-range 

plume transport model), or Operational Street Pollution Model (OSPM) for determination of street-level 

pollution coupled with infiltration models to represent indoor exposure to ambient levels (Appel et al., 

2008; Gilliam et al.; Hering, 2007; Mensink and Cosemans, 2008; Wilson and Zawar-Reza, 2006). 

Stochastic and deterministic methods are often combined, as described below. Land use regression (LUR) 

models have also been developed to describe pollution levels as a function of geographic source behavior 

(Gilliland et al., 2005; Ryan and LeMasters, 2007; Veen et al., 1997). Similarly, other spatial interpolation 

techniques have been used to determine geographic distributions of exposure (Marshall et al., 2008). 

Compartmental models, such as the Indoor Air Model (INDAIR), can be used to assess exposure to 

infiltrated ambient air pollutants in a deterministic or probabilistic framework (Dimitroulopoulou et al., 

2001; 2006). A detailed description of various modeling techniques is provided in the 2008 NOX ISA and 

2008 SOX ISA annexes (2008f, g), and an explanation of associated modeling errors is provided in the 

2008 Draft PM ISA (U.S. EPA, 2008f). Applications of these models to assessment of CO exposure are 

described in the following paragraphs. 

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The STEMS model maps exposures based on inputs for traffic levels, atmospheric dispersion, 

background concentrations, and geography. Gulliver and Briggs (2005) tested the STEMS model for CO 

and observed some correlation between modeled and measured CO concentrations (R2

 = 0.41), which was 

consistent with results for PM10 and NOX. Exposures were estimated from the predicted ambient CO 

concentration using a term similar to α that varied depending on whether the individual was walking or in 

a vehicle. Gulliver and Briggs (2005) noted that a limitation to modeling CO is the scarcity of background 

CO data obtained at rural sites. For this reason, they assumed a constant value obtained from estimates 

made over the North Atlantic Ocean. Although the authors only presented detailed results for a model of 

PM10 based on traffic and meteorology in Northampton, U.K., they found that the majority of variation on 

a given day in modeled exposure among school children was due to differences in travel routes. Variation 

across days was also influenced by background and meteorological conditions. Similar results can be 

expected for CO based on the tendency for variation of the CO concentration profile on the neighborhood 

and micro-scales (Jerrett et al., 2004). Flachsbart (Flachsbart) tested numerous meteorological, traffic, and 

background CO input variables in a regression approach to predicting CO exposure among individuals 

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while traveling in a vehicle. This work showed travel time and average speed of on-road vehicles to be 

important determinants of CO exposure in a vehicle. Results from individual models of this nature can be 

pooled to develop a distribution for examination of population effects or for comparison with population 

exposure models. 

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Bruinen de Bruin et al. (2004) utilized the EXPOLIS model to predict CO population exposures in 

Milan, Italy. The simulation results showed that the U.S. 8-h NAAQS level was exceeded in one case out 

of 1,000. The model also showed that exposures exceeded 20 ppm in one case out of 100,000. The results 

were not shown to be very sensitive to the number of microenvironments (e.g., outdoors, indoors, in 

vehicle) included in the model. The probabilistic NAAQS Exposure Model for CO (pNEM/CO) was used 

in the 1992 and 2000 CO AQCDs (U.S. EPA, 1992, 2000) to estimate personal CO exposures among 

adult populations. Given reductions in ambient CO levels, details, and results from those efforts are 

provided here. The current version of this model, now known as APEX, was used for exposure 

assessment in the O3 NAAQS review (U.S. EPA, 2006b). 

To examine indoor concentrations of ambient CO, Dimitroulopoulou et al. (2006) used the 

probabilistic formulation of the INDAIR model to examine indoor exposure to ambient CO, along with 

NOX and PM for a given distribution of background CO levels, meteorology, residential air exchange rate, 

and residential room dimensions. They found that 24-h avg CO concentration increased from 1.86 ppm 

outdoors to 1.90-1.93 ppm indoors in the absence of non-ambient sources, and that indoor 24-h avg CO 

concentration could increase to 1.93-2.00 ppm in the presence of smoking and to 1.98-2.32 ppm in the 

presence of gas cooking. Similarity between the outdoor and non-source indoor concentrations was 

attributed to the lack of CO loss mechanisms. With maximum CO concentrations modeled at 3.3 ppm in 

the absence of sources, the probability density function of indoor exposure to ambient fell well below the 

8-h NAAQS. In the Reducing Urban Pollution Exposure from Road Transport (RUPERT) study, Bell et 

al. (2004) presented methodology to use the probabilistic form of INDAIR for development of personal 

exposure frequency distributions of CO, NOX, and PM based on time spent in residential, transportation, 

school, office, and recreational environments with inputs from transportation source categories (Chen et 

al., 2008). 

Another set of approaches to improve exposure estimates in urban areas involves construction of a 

concentration surface over the geographic area. This does not estimate exposure directly because it does 

not account for activity patterns or concentrations in different microenvironments. It provides an 

improved estimate of the expected local outdoor concentration near residences, schools or workplaces, 

and roadways across the area. There are two main types of approaches: spatial interpolation of measured 

concentrations, and regression models using land use, roadway characteristics, and other variables to 

predict concentrations at receptors in the domain. Rigorous first-principles models, such as dispersion 

models and chemical transport models, can also be used for this type of application, but are less suitable 

because they have intensive resource requirements and are typically applied over larger domains. 

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Marshall et al. (2008) compared four spatial interpolation techniques for estimation of CO 

concentrations in Vancouver, BC. The investigators assigned a daily average CO concentration to each of 

the 51,560 postal code centroids using one of the following techniques: (1) the concentration from the 

nearest monitor within 10 km, (2) the average of all monitors within 10 km, (3) the inverse-distanceweighted (IDW) average of all monitors in the area, and (4) the IDW average of the three closest monitors 

within 50 km. Method 1 (the nearest-monitor approach) and Method 4 (IDW-50 km) had similar mean 

and median estimated annual average concentrations, although the 10th-90th percentile range was smaller 

for IDW-50. This is consistent with the averaging of extreme values inherent in IDW methods. The 

Pearson correlation coefficient between the two methods was 0.88. Methods 2 and 3 were considered suboptimal and were excluded from further analysis. In the case of method 2, a single downtown highconcentration monitor skewed the results in the vicinity, partially as a result of the asymmetric layout of 

the coastal city of Vancouver. Method 3 was too spatially homogenous, because it assigned most locations 

a concentration near the regional average, except for locations immediately adjacent to a monitoring site. 

LUR results were also reported in this study for NO and NO2, and indicated that LUR’s higher spatial 

precision reflects neighborhood-scale effects from nearby land use, but may not account for urban-scale 

variation. These results highlight the variation in local concentration estimates with choice of estimation 

technique. 

3.7. Summary and Conclusions 

3.7.1. Sources of CO 

In the U.S., on-road mobile sources constituted more than half of total CO emissions in 2002, or 

~63 MT of ~109 MT total. In metropolitan areas in the U.S., for example, as much as 70-75% of all CO 

emissions can come from on-road vehicle exhaust (U.S. EPA, 2006a). The majority of these on-road CO 

emissions derive from gasoline-powered vehicles since the O2 content, pressure, and temperature 

required for diesel fuel ignition do not produce large quantities of CO. Anthropogenic CO emissions are 

estimated to have decreased 35% between 1990 and 2002. On-road vehicle sector emissions controls have 

produced nearly all these national-level CO reductions. Nationally, biogenic emissions, excluding fires, 

were estimated to contribute ~5% of total CO emissions from all sources in 2002, and fires in 2002 added 

another 13%, or ~14.5 MT, to the national CO emissions total. 

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3.7.2. Physics and Chemistry of Atmospheric CO 

CO is produced by photooxidation of CH4 and other VOCs in the atmosphere. Estimating the CO 

yield from oxidation of HCs larger than CH4 requires computing the yields of several intermediate 

products and reactants from oxidation of the parent molecules. The major pathway for removal of CO 

from the atmosphere is reaction with OH to produce CO2 and HO2. The mean photochemical lifetime (τ) 

of CO in the northern hemisphere is ~57 days. During winter at high latitudes, CO has nearly no 

photochemical reactivity on urban and regional scales. 

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3.7.3. Ambient CO Measurements 

As of March 2008, 19 automated FRMs and no FEMs had been approved for CO. All EPA FRMs 

for CO operate on the principle of nondispersive infrared (NDIR) detection and can include gas filter 

correlation (GFC). The required lower detection limit for FRMs in the EPA network is 1.0 ppm. In 2007, 

there were 285 CO monitors meeting the 75% completeness requirements and reporting values yearround to the AQS in the 50 states, plus the District of Columbia, Puerto Rico, and the Virgin Islands. At 

least 70 monitors across the U.S. have been positioned at microscale within 10 m of a road to capture 

near-road concentrations. At larger scales, monitor distance from a road is inversely related to the road’s 

average daily traffic count to capture community averages. 

3.7.4. Environmental CO Concentrations 

CO concentration data for 1-h and 8-h intervals were available for 243 counties and autonomous 

cities or municipalities that maintained active CO monitoring stations meeting the 75% completeness 

criteria for the years 2005-2007. There were no violations of the 1-h or 8-h NAAQS in those years. The 

nationwide mean, median, and interquartile range for 1-h measurements reported between 2005-2007 

were 0.5, 0.4, and 0.4 ppm, respectively, and these statistics did not change by more than 0.1 ppm for 

each year of the 3-year period. The nationwide mean, median, and interquartile range for 8-h daily max 

concentrations reported between 2005-2007 were 0.7, 0.5, and 0.5 ppm, respectively. The 2006 annual 

second highest 8-h CO concentration averaged across 144 monitoring sites nationwide was 75% below 

that for 1980 and is the lowest concentration recorded during the past 27 years. The mean annual second 

highest 8-h ambient CO concentration has been below 5 ppm since 2004. The downward trend in CO 

concentrations in the 1990s parallels the downward trend observed in CO emissions and can be attributed 

largely to decreased mobile source emissions. 

The correlation structures for measurements at the monitors in each of the 11 CSAs/CBSAs 

examined for this assessment reveal a wide range of response between monitors in each city and among 

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the cities judged against each other. While this wide range is produced by the interactions of many 

physical and chemical elements, the location of each monitor and the uniqueness of its immediate 

surroundings can often explain much of the agreement or lack thereof. CO concentrations can be elevated 

near roadways and decrease with increasing distance from the road. Likewise, micro- and neighborhoodscale variation related to urban topography or microenvironmental source distribution may have a 

significant effect on ambient CO concentrations with respect to street canyon concentrations. Anchorage, 

AK had concentrations roughly twice those of the other metropolitan areas. Most of the CSAs/CBSAs 

examined here had diel concentration curves with pronounced morning and evening rush hour peak CO 

levels, although diel CO concentrations had less variability for New York City, Atlanta, and Seattle than 

for the other eight cities. For most metropolitan areas examined here, concentrations were generally 

highest in the winter (December-February) and fall (September-November) and decreased, on average, 

during the spring (March-May) and summer (June-August). Measurements near or below the required 

FRM lower detection limit of 1.0 ppm coupled with the coarsely reported measurement resolution 

(0.1 ppm) can artificially influence the comparison statistics shown in the tables and result in apparent 

heterogeneity in the box plots (Figures 3-13 through Figures 3-16). 

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For nationwide copollutant correlation data, ambient CO had the highest mean and median 

correlations with ambient NO2, followed by PM2.5 (correlations vary by season). The SO2 and PM10

correlations did not appear to be statistically significantly different from zero for any season, and 

statistical significance was only observed in winter and fall for PM2.5. Correlations between CO and NO2

were significant for all seasons but summer. Correlations between CO and O3 were significant and 

negatively correlated for winter only. The copollutant correlation plots shown in Annex A illustrate higher 

and more statistically significant correlations between CO and PM in several but not all of the select cities 

displayed. 

This assessment has used data from 2005-2007 at 12 remote sites as part of the international CCGG 

CASN in the CONUS, Alaska, and Hawaii to determine PRB. All sites demonstrate the well-known 

seasonality in background CO with minima in the summer and fall and maxima in the winter and spring. 

The 3-year avg CO PRB in Alaska was 130 ppb; in Hawaii it was 99 ppb; and over the CONUS it was 

132 ppb. 

3.7.5. Exposure Assessment and Implications for Epidemiology 

Very few recent exposure assessment studies involve CO concentration data. The studies of 

personal exposure to CO presented here generally found that the largest fraction of an individual’s 

exposure to ambient CO occurs indoors but the highest CO exposure levels occur in transit. Among 

commuters, exposures were highest for those traveling in automobiles in comparison with those traveling 

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in buses and motorbikes and with those cycling or walking. A portion of that exposure is thought to come 

from the vehicle in which the exposed person travels. Commuting time was an important predictor of 

exposure, such that subjects living in low-CO concentration suburban areas and commuting to work 

experienced higher CO levels than residents of higher-CO concentration urban areas with short commute 

times. Additional analyses indicated that on-road mobile emissions were the most important source of CO 

exposure for non-ETS-exposed subjects. Results have also indicated that time spent outdoors and 

proximity to roadways may have been important in determining personal exposure in children. 

Uncontrolled factors that may influence exposure include vertical CO concentration gradients; vehicle 

ventilation, speed, position in traffic, and start/stop activity; and meteorology. 

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For the general U.S. population, exposure error analysis for epidemiologic studies indicates that 

fixed-site measured ambient CO concentration is generally a good indicator of ambient exposure to CO. 

Errors associated with exposure to non-ambient CO sources, for example smoking or gas cooking, are of 

the Berkson type and therefore not expected to bias estimates of exposure or health effects. Simulations of 

ambient and non-ambient exposures to a non-reactive generic pollutant demonstrated that non-ambient 

exposure had no effect on health effects outcomes for the case where ambient and non-ambient sources 

were independent. Likewise for community time-series epidemiology, selected monitoring sites are 

thought to be representative of community averages. Measurement at a “hot spot” could skew community 

exposure estimates upward. Topographical or source variability could produce differences in the temporal 

pattern of pollution. Differences such as those produced by variation in local sources among communities 

in long-term exposure studies may also produce error in estimation of the ambient exposure factor. Such 

differences could add error, and therefore influence the slope of health effects estimate regressions up or 

down. 

Several studies have examined multi-pollutant exposure. Since incomplete combustion is the 

primary source of ambient CO, exposure to ambient CO is accompanied by exposure to other 

combustion-related pollutants, such as NOX and PM. High correlations of ambient CO with NO2 and 

PM2.5 have been observed in the peer-reviewed literature and AQS data. Thus, ambient CO is often 

considered a surrogate for exposure to traffic-generated pollutants. Because CO exposures most often 

occur together with exposure to other combustion-related pollutants, interpretation of health studies using 

ambient CO data can be a challenge, as discussed further in Chapter 5. 

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Chapter 4. Dosimetry and 

Pharmacokinetics of Carbon Monoxide 

4.1. Introduction 

Inhaled ambient CO elicits various health effects by binding with and altering the function of a 

number of heme-containing molecules, mainly Hb. Traditional concepts for CO pathophysiology have 

been based on the high affinity of CO for deoxyhemoglobin, resulting in COHb formation and consequent 

reduction in O2-carrying capacity of blood and impaired O2 delivery to tissues. Research on the basics of 

CO pharmacokinetics date back to the 1890s, but since the late 1970s has become limited. Current 

literature primarily focuses on endogenous CO produced by the metabolic degradation of heme by heme 

oxygenase (HO) and its role as a gaseous messenger. This chapter reviews the physiology and 

pharmacokinetics of CO. The chapter draws heavily from Chapter 5 of the previous AQCD (U.S. EPA, 

2000). Relevant new data are included when available. New explanations of recent models of Hb binding 

are included, as well as discussion on tissue CO concentrations using new methods of extraction. 

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CO binds with a number of heme-containing molecules including Mb and cytochromes, but none 

have been studied as extensively as Hb. The primary focus of this chapter is placed on the models and 

kinetics of such binding and the factors influencing this event. The chapter discusses effects at ambient or 

near ambient levels of CO leading to low COHb levels (≤ 5%); however few studies are available at 

ambient CO concentrations. Both human and animal studies using higher CO exposure concentrations, 

resulting in moderate to high COHb levels (<20%), are discussed where needed to understand CO 

kinetics, pathophysiologic processes, and mechanisms of cytotoxicity. Where human studies could not 

experimentally test certain hypotheses or were unavailable, animal experiments were used as surrogates. 

CO uptake and elimination has been shown to be inversely proportional to body mass over 

environmentally relevant exposure levels, meaning the smaller the animal, the faster the rate of absorption 

and elimination (Klimisch et al., 1975; Tyuma et al., 1981). However, the basic mechanisms of CO 

toxicity between experimental animals and humans are similar and are thus extrapolated from animals to 

humans in this chapter, keeping in mind a number of interspecies differences. 

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4.2. Carboxyhemoglobin Formation 

4.2.1. The Coburn-Forster-Kane and Other Models 

Investigators have modeled the effect of CO binding to Hb in a number of ways. Empirical and 

mechanistic models are two distinct approaches that have been taken to model in vivo COHb formation 

after CO exposure. First, empirical models were used to predict COHb by regressing concentration and 

duration of exogenous CO exposure with COHb. These methods were reviewed in depth in the previous 

AQCD (U.S. EPA, 2000). These models are limited to estimating COHb in the exact conditions on which 

the models were based. These simple models include those by Peterson and Stewart (1970) and Ott and 

Mage (1978), as well as various others (Selvakumar et al., 1992; Singh et al., 1991). Using these simple 

models, it was shown that the presence of brief ambient CO concentration spikes averaged over hourly 

intervals may lead to underestimating the COHb concentration by as much as 21% of the true value. To 

avoid this problem, it was suggested that ambient CO measurements be monitored and averaged over 10–

15 minute periods (Ott and Mage, 1978). 

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Secondly, mechanistic models use physical and physiological processes and an understanding of 

biological processes to predict COHb production. The most commonly used mechanistic method for 

predicting levels of blood COHb after CO inhalation is the Coburn-Forster-Kane equation or CFK model 

developed in 1965 (Coburn et al., 1965). This differential equation was developed to examine endogenous 

CO production, using the major physiological and physical variables influencing this value. Since then, it 

has been shown to provide a good approximation to the COHb level at a steady level of inhaled 

exogenous CO (Peterson and Stewart, 1975; Stewart et al., 1973b). The CFK model describes a fourelement, physical system containing an exogenous CO source, a transfer interface, an endogenous CO 

source, and a storage compartment. The linear CFK model assumes O2Hb concentration is constant and is 

as follows in Equation 4-1: 

[ ] [ ]

[ ]

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⎛

− +

−=

• •

•

A

B

L

I

A

B

L

2

0 C 2

CO t

b

V

47P

COD

1

COP

V

47P

COD

1

1

MHbO

OPCOHb

V

dt

COHbd V

Equation 4-1 

where Vb is blood volume in milliliters (mL); [COHb]t is the COHb concentration at time t in mL CO/mL 

blood, at standard temperature and pressure, dry (STPD); 

22 

VCO

•

 is the endogenous CO production rate in 

mL/min, STPD; [COHb]0 is the COHb concentration at time zero in mL CO/mL blood, STPD; [O2Hb] is 

the O2Hb concentration in mL O2/mL blood, STPD; M is the Haldane coefficient representing the CO 

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March 2009 4-2 DRAFT – DO NOT CITE OR QUOTE 
chemical affinity for Hb; COP 2 is the average partial pressure of O2 in lung capillaries in mmHg; VA

•

 is 

the alveolar ventilation in mL/min, STPD; DLCO is the lung diffusing capacity of CO in mL/min/mmHg, 

STPD; PB is the barometric pressure in mmHg; and PICO is the CO partial pressure in inhaled air in 

mmHg. 

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This model assumes instant equilibration of COHb concentration between venous and arterial 

blood, gases in the lung, and COHb concentrations between blood and extravascular tissues, which is not 

physiologically representative. The nonlinear CFK equation incorporates the interdependence of COHb 

and O2Hb levels since they are derived from the same pool of blood Hb. The nonlinear equation is more 

physiologically accurate; however the linear CFK equation gives a good approximation to the nonlinear 

solution over a large range of values during CO uptake and during low levels of CO elimination (Smith, 

1990). The linear equation prediction of COHb concentration at or below 6% will only differ ± 0.5% from 

the nonlinear equation prediction. Sensitivity analysis of the CFK equations has shown that alterations in 

each variable of the equation will affect the outcome variably at different times of exposure (McCartney, 

1990). Figure 4-1 illustrates the temporal changes in fractional sensitivities of the principal physiological 

determinants of CO uptake for the linear form of the CFK equation, where THb is the total blood 

concentration of Hb in g Hb/mL blood and FICO is the fractional concentration of CO in ambient air 

in ppm. The fractional sensitivity of unity means that, for example, a 5% error in the selected variable 

induces a 5% error in the predicted COHb value by the nonlinear model. As Figure 4-1 demonstrates, a 

constant or given percent error in one variable of the model does not generally produce the same error in 

the calculated blood COHb, and the error is time dependent. Thus, each variable influencing CO uptake 

and elimination will exert its maximal influence at different times of exposure. 

March 2009 4-3 DRAFT – DO NOT CITE OR QUOTE 
Source: modified from McCartney (1990) 

Figure 4-1. Plot of fractional sensitivities of selected variables versus time of exposure. 

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The mechanistic CFK model contains a number of assumptions under which the model is solely 

applicable, including 1) ventilation is a continuous process, 2) equilibrium between plasma CO 

concentration and COHb concentration is obtained in the pulmonary system, 3) percent COHb can exceed 

100% saturation in the linear model, and 4) it does not account for the shape of the O2 or CO saturation 

versus pO2 or pCO relation (McCartney, 1990). Estimations outside of these assumptions have been 

attempted but with less predictive agreement. For example, transient exposures such as those that would 

simulate everyday conditions would violate the assumption of a single, well-mixed vascular compartment. 

COHb levels during exposure of subjects exposed to frequent but brief high CO exposures (667 to 

7,500 ppm for 75 s to 5 min) were not accurately predicted by CFK modeling (Benignus et al., 1994; 

Tikuisis et al., 1987a; Tikuisis et al., 1987b). Consistently, the predicted COHb value overpredicted 

venous COHb (0.8 to 6%) and underpredicted arterial COHb (1.5 to 6.1%) and this disparity increased 

after exercise. Individual differences between arterial and venous COHb varied from 2.3 to 12.1% 

(Benignus et al., 1994). These inaccuracies between measured and predicted COHb values disappeared 

after simulated mixing of arterial and venous blood and thus are likely due to delays in mixing of arterial 

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and venous blood and differences in cardiac output and lung wash-in. A modified CFK was created to 

adjust for these issues and produce a more accurate COHb prediction (Smith et al., 1994). 

4.2.2. Multicompartment Model 

In addition to the limitations discussed above, the CFK model does not account for extravascular 

storage sites for CO, such as muscle Mb. CO will undergo reversible muscle Mb binding, similar to Hb, 

as well as uptake into other extravascular tissues (Vreman et al., 2006). A five compartment model has 

been proposed to predict CO uptake and distribution from acute inhalation exposure and contains 

components for lung, arterial blood, venous blood, muscle tissue, and nonmuscle tissue (Bruce and Bruce, 

2003; Bruce et al., 2008; 2006). This model structure is illustrated in Figure 4-2. This model includes the 

dynamics of CO storage in the lung and its dependence on ventilation and CO pressure of mixed venous 

blood, relaxes the assumption Hb is saturated by including the role of CO in altering the O2 dissociation 

curve, includes a subcompartmentalized muscle tissue compartment, accounts for dissolved CO in blood 

and tissue, and predicts COHb based on age and body dimensions. This multicompartment model is 

limited by its exclusion of cellular metabolism or Mb diffusion, simplification of within tissue bed spatial 

variability, and assumes ventilation and PAO2 are constant. This model better predicts COHb levels when 

inspired CO levels change rapidly or when incomplete blood mixing has occurred, and better predicts the 

CO washout time course compared to the CFK equation. Bruce and Bruce (2003) compared the two 

models and found similar results for long term exposure settings (1,000 min), however, the 

multicompartment model predicted somewhat lower COHb levels compared to the CFK model over 

transient CO uptake conditions when using data taken from Peterson and Stewart (1970). 

March 2009 4-5 DRAFT – DO NOT CITE OR QUOTE 
Source: Modified from Bruce et al. (2008) 

Figure 4-2. Overall structure of the multicompartment model of storage and transport of CO. 

Includes compartments for lung, arterial blood, venous blood, muscle tissue, and 

nonmuscle tissue. The muscle compartment is divided into two subcompartments 

for diffusion of gases within the tissue. 

4.2.3. Mathematical Model Usage 

Since measurements of COHb in the population are not readily available, mathematical models are 

used to predict the resulting COHb levels from various CO exposure scenarios. Figure 4-3 illustrates the 

predictions of COHb after 1, 8, 12, or 24 h of CO exposure at a range of concentrations in a healthy, 

inactive, adult human. The Quantitative Circulatory Physiology (QCP) model, which integrates human 

physiology using over 4,000 variables and equations based on published biological interactions, was used 

to predict these values (Abram et al., 2007; Benignus et al., 1987; 2006). This dynamic model uses the 

nonlinear CFK equation with modifications presented in Smith et al. (1994). The data in Figure 4-3 are 

presented as the change in percent COHb from endogenous due to CO exposure to NAAQS relevant 

levels from 0-35 ppm (A) and relevant ambient levels from 0-6 ppm (B). Endogenous CO production 

varies as described in Section 4.5 but generally results in less than 1% COHb, with a QCP modeled value 

of 0.39% at time zero. Figure 4-3 illustrates that 35 ppm CO for 1 h results in an increase of 0.56% COHb 

over endogenous levels and 9 ppm CO for 8 h results in a 0.83% increase over endogenous levels. Also, 

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March 2009 4-6 DRAFT – DO NOT CITE OR QUOTE 
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these graphs show that long term, low concentration CO exposure results in equivalent COHb levels to 

higher concentration, acute exposure. For example, COHb resulting from 35 ppm for 1 h (0.56%) is 

approximately equivalent to 6 ppm for 8 h (0.55%) or 4 ppm for 24 h (0.57%). 

Figure 4-3. Predicted COHb increments over endogenous levels resulting from 1, 8, 12, and 24 h 

CO exposures in a modeled healthy human at rest. The QCP model used a dynamic 

nonlinear CFK with affinity constant M = 230. The data are presented as the change 

in percent COHb from endogenous levels due to CO exposure to 0-6 ppm (A) and 

0-35 ppm (B). Note: Graph A is a subset of Graph B. 

4.3. Absorption, Distribution, and Elimination 

4.3.1. Pulmonary Absorption 

Pulmonary uptake of CO accounts for all environmental CO absorption and occurs at the 

respiratory bronchioles and alveolar ducts and sacs. CO and O2 share various physico-chemical 

properties, thus allowing for the extension of the knowledge about O2 kinetics to those of CO despite the 

differences in the reactivity of the gases. The exchange of CO between the air and the body depends on a 

number of physical (e.g., mass transfer and diffusion), as well as physiological factors (e.g., alveolar 

ventilation and cardiac output), which are controlled by environmental conditions, physical exertion, and 

other processes discussed in Section 4.4. The ability of the lung to take up inhaled CO is measured by 

DLCO, and CO uptake (VCO) representing the product of DLCO and the mean alveolar pressure (PACO). 

The importance of dead space volume, gas mixing and homogeneity, and ventilation/perfusion matching 

were discussed in depth in the 2000 AQCD (U.S. EPA, 2000). 

0

1

2

3

4

5

6

0 5 10 15 20 25 30 35

CO (ppm)

COHb (percent over endogenous)

24 h

12 h

8 h

1 h

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0123456

CO (ppm)

COHb (percent over 

endogenous)

24 h

12 h

8 h

1 h

A B

March 2009 4-7 DRAFT – DO NOT CITE OR QUOTE 
4.3.1.1. Mass Transfer of Carbon Monoxide 

Mass transfer refers to the molecular and convective transport of CO molecules within the body 

stores, driven by random molecule motion from high to low concentrations. CO enters through the airway 

opening (mouth and nose) and transfers in a gas phase to the alveoli. CO transport is due to convective 

flow, the mechanical action of the respiratory system, and diffusion in the acinar zone of the lung (Engel, 

1983). Then, CO diffuses in a “liquid” phase across the air-blood interface, binding red blood cell (RBC) 

Hb. At environmental CO levels, CO uptake into RBC is limited by the reaction rate of binding of CO to 

O2Hb forming COHb. Pulmonary capillary RBC CO diffusion is rapidly achieved (Chakraborty et al., 

2004; Gibson and Roughton, 1955; Reeves and Park, 1992; Roughton and Forster, 1957). The rate and 

level of COHb depends upon the pCO, pO2 in the air, time of exposure, and the ventilation rate (Roughton 

and Forster, 1957). Most of the body CO is bound to Hb; however, 10-15% of the total body CO is 

located in extravascular tissues primarily bound to other heme proteins (Coburn, 1970a). Considerable 

concentrations of CO have been measured in spleen, lung, kidney, liver, muscle, and heart (Vreman et al., 

2005; Vreman et al., 2006), whereas less CO is localized to fatty tissues, such as adipose and brain (Table 

4-1). The transfer of CO occurs by a partitioning of CO between Hb and tissue. Less than 1% of the total 

body CO stores appear as dissolved in body fluids, due to the insolubility and small tissue partial pressure 

of CO (Coburn, 1970a). Transport pathways and body stores of CO are shown in Figure 4-4. 

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Table 4-1a. CO concentration in pmol/100 g ww tissue – human. 

Exposure Adipose Brain Muscle Heart Kidney Lung Spleen Blood % COHb 

Background 2 ± 1 3 ± 3 15 ± 9 31 ± 23 23 ± 18 57 ± 59 79 ± 75 165 ± 143 1.5 ± 1.2 

Fire 5 ± 4 7 ± 5 24 ± 16 54 ± 33 27 ± 11 131 ± 127 95 ± 69 286 ± 127 3.8 ± 3.2 

Fire + CO 18 ± 29 17 ± 14 168 ± 172 128 ± 63 721 ± 427 1097 ± 697 2290 ± 1409 3623 ± 1975 40.7 ± 28.8 

CO asphyxiation 25 ± 27 72 ± 38 265 ± 157 527 ± 249 885 ± 271 2694 ± 1730 3455 ± 1347 5196 ± 2625 56.4 ± 28.9 

Source: Vreman et al. (2006)

March 2009 4-8 DRAFT – DO NOT CITE OR QUOTE 
Table 4-1b. CO concentration in pmol/mg fresh weight – adult mouse. 

Exposure Brain Muscle Heart Kidney Lung Spleen Liver Intestine Testes Blood % COHb 

Background 2 ± 0 10 ± 1 6 ± 1 7 ± 2 3 ± 1 6 ± 1 5 ± 1 4 ± 2 2 ± 1 45 ± 5 0.5 

500 ppm CO 18 ± 4 14 ± 1 100 ± 18 120 ± 12 250 ± 2 229 ± 55 115 ± 31 9 ± 7 6 ± 3 2648 ± 400 28 

30 μM heme 2 ± 0 7 ± 1 14 ± 3 7 ± 2 8 ± 3 11 ± 1 8 ± 3 3 ± 1 2 ± 0 88 ± 10 0.9 

Source: Vreman et al. (2005)

Source: Adapted from Coburn (1967) 

Figure 4-4. Diagrammatic presentation of CO uptake and elimination pathways and CO body 

stores. 

4.3.1.2. Lung Diffusion of Carbon Monoxide 

Lung diffusion of CO is an entirely passive process of gas diffusion across the alveolo-capillary 

membrane, through the plasma, across the RBC membrane and into the RBC stroma, where CO binding 

to Hb rapidly occurs. Membrane and blood phase transfer are governed by physico-chemical laws, 

including Fick’s first law of diffusion. The diffusing capacity of the lung for CO, represented as DLCO, is 

a measurement of the partial pressure difference between inspired and expired CO. Due to the rapid 

binding of CO to Hb, a high pressure differential between air and blood exists when CO air levels are 

increased. Inhalation of CO-free air reverses the pressure differential (higher CO pressure on the blood 

side than the alveolar side), and then CO is released into the alveolar air. Since CO is also produced 

endogenously, CO release will also be affected by this production pressure. However, the air-blood 

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March 2009 4-9 DRAFT – DO NOT CITE OR QUOTE 
gradient for CO is usually higher than the blood-air gradient; therefore, CO uptake will be a 

proportionately faster process than CO elimination. 

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3 A number of factors have been found to affect DLCO including Hb concentration, cardiac output 

( •

Q ), erythrocyte flow, COHb concentration, PACO2, body position, exercise, time of day, age, etc. 

(Forster, 1966; Hsia, 2002). DLCO consistently decreases after intense bouts of exercise, likely due to the 

redistribution of blood volume to the periphery (Hanel et al., 1997; Manier et al., 1991). However, in 

going from rest to exercise DLCO can increase linearly from: lung expansion leading to unfolding and 

distension of alveolar septa, opening and/or distension of capillaries as 

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Q increases, increased capillary 

hematocrit, and more homogeneous distribution of capillary erythrocytes (Hsia, 2002). DLCO is less 

dependent upon lung volume at mid-range vital capacity, but at extreme volumes the diffusion rate is 

varied, higher than average at total lung capacity and lower at residual volume (McClean et al., 1981). 

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4.3.2. Tissue Uptake 

4.3.2.1. The Respiratory Tract 

The upper respiratory tract contributes little to the overall COHb uptake. The lung has nearly 

constant exposure to CO; however, relatively little CO diffuses into the tissue except for at the alveolar 

region en route to the circulation. No detectable uptake of CO was observed in the human nasal cavity or 

upper airway (Guyatt et al., 1981) or in the monkey oronasal cavity after high CO exposure (Schoenfisch 

et al., 1980). 

4.3.2.2. The Blood 

The blood is the largest reservoir for CO, where it reversibly binds to Hb. The chemical affinity of 

CO for adult human Hb is approximately 218 times greater than that of O2, meaning one part CO and 218 

(210-250) parts O2 would form equal parts of O2Hb and COHb (Engel et al., 1969; Rodkey et al., 1969; 

Roughton, 1970). This would happen when breathing air containing 21% O2 and 960 ppm CO. This 

concept was presented by Haldane and Smith (1898) and later represented as the Haldane constant M 

(210-250) in the Haldane equation by Douglas, Haldane, and Haldane (1912). M is relatively unaffected 

by changes in physiological pH, CO2, temperature, or 2,3-diphosphoglycerate: 

March 2009 4-10 DRAFT – DO NOT CITE OR QUOTE 
( ) 2 2 ÷ = × ÷ pOpCOMHbOCOHb

Equation 4-2 

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The Hb association rate for CO is 10% slower than O2 and occurs in a cooperative manner 

(Chakraborty et al., 2004; Sharma et al., 1976). Hb is composed of four globin chains each containing a 

heme group capable of binding CO or O2. The associative reaction rates become faster with successive 

heme binding, attributed to interactions within the protein and to strains imposed on the heme and its 

ligands (Alcantara et al., 2007). More simply, the greater the number of heme sites bound to CO, the 

greater the affinity of free heme sites for O2, thus causing Hb to bind and retain O2 that would normally be 

released to tissues. Cooperativity is greatly reduced in CO dissociation, but the rate of dissociation of CO 

from Hb is orders of magnitude slower than O2 (kCO = 4 × 10-4 kO2), which accounts for the high affinity 

values (Chakraborty et al., 2004). The half-time of dissociation reaction is about 11 seconds at 37 °C 

(Holland, 1970). In general, CO uptake to COHb equilibrium is slower in humans and large animals, 

requiring 8-24 h, than in smaller species such as rats, which will equilibrate in 1-2 h (Penney et al., 1988). 

CO binding to Hb also has effects on the O2 dissociation curve of the remaining Hb by shifting the 

curve progressively to the left and altering the normal S-shaped curve to become more hyperbolic due to 

increased cooperative O2 binding (Roughton, 1970). This is referred to as the “Haldane effect” and causes 

tissues to have more trouble obtaining O2 from the blood, even compared to the same extent of reduced 

Hb resulting from anemia. For example, Figure 4-5 (as explained in the 2000 AQCD) illustrates that an 

acute anemia patient (50% of Hb) at a venous pO2 of 26 mmHg (v’1), 5 vol % of O2 (50% saturation) was 

extracted from the blood. In contrast, a CO poisoned person with 50% COHb, the venous pO2 will have to 

drop to 16 mmHg (v’2) to release the same 5 vol % O2. This more severe effect on O2 pressure may lead 

to brain O2 depletion and loss of consciousness if any higher demand of O2 is needed (e.g., exercise). 

March 2009 4-11 DRAFT – DO NOT CITE OR QUOTE 
Source: U.S. EPA (1991) 

Figure 4-5. O2Hb dissociation curve of normal human blood, of blood containing 50% COHb, 

and of blood with only 50% Hb because of anemia. 

4.3.2.3. Heart and Skeletal Muscle 

Mb is a globular heme protein that facilitates O2 diffusion from the muscle sarcoplasm to 

mitochondria, acting as an O2 supply buffer to maintain adequate pO2 for mitochondria when the O2

supply changes, as in exercise. O2 has a greater affinity for Mb than Hb, which allows small changes in 

tissue pO2 to release large amounts of O2 from O2Mb (Wittenberg et al., 1975). Small reductions in O2

storage capacity of Mb, due to CO binding, may have a profound effect on the supply of O2 to the tissue. 

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Like Hb, Mb will undergo reversible CO binding, however the affinity constant is approximately 

eight-times lower than Hb (M = 20-40 versus 218, respectively) (Haab, 1990). The association rate 

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constant of CO and Mb is approximately 27 times lower than O2, however the dissociation rate constant is 

approximately 630 times lower than O2 (Gibson et al., 1986) causing CO to be retained and possibly 

stored in the muscle. CO levels have been measured in human muscle and heart tissues with less than 2% 

COHb concentrations at background levels averaging 15 and 31 picomole (pmol) CO/100 grams ww, 

respectively (Vreman et al., 2006). Under conditions of CO asphyxiation, tissue concentrations increased 

to 265 and 527 pmol CO/100 grams ww muscle and heart tissue, respectively; however, heart tissue 

concentrations varied widely between individuals (See Table 4-1a). The capacity for diffusion of CO into 

the muscle is represented by the coefficient DmCO and is generally larger in males than in females, likely 

due to the differences in muscle mass and capillary density (Bruce and Bruce, 2003). COMb 

concentrations in the heart and skeletal muscle increase with work load, causing an increase in 

COMb/COHb that is not as greatly seen at rest (Sokal et al., 1984). Subjects with 2% COHb, but not 

those with 20% COHb levels, showed a significant uptake of CO from the blood to the muscle with 

increasing work intensity of the quadriceps muscle (Richardson et al., 2002). 

4.3.2.4. Other Tissues 

CO binds with other hemoproteins, such as cytochrome P450, cytochrome c oxidase, catalase, and 

peroxidase, but the possibility of this binding influencing CO-O2 kinetics has not been established. CO 

transfers between COHb and tissue, the extent of which varies between organs. Blood to tissue flux 

causes less CO to be expired following CO exposure than what is lost from the blood in terms of COHb 

(Roughton and Root, 1945). This value is estimated to be 0.3-0.4% min-1 or 0.24 mL/min (Bruce and 

Bruce, 2003; Prommer and Schmidt, 2007). The equilibration rate from blood to tissue is controversial. 

Newly modeled CO trafficking kinetics shows that CO continues to be taken up by the muscle and 

extravascular tissues well beyond the end of exposure because of a less than instant equilibration (Bruce 

and Bruce, 2006). Tables 4-1a and 4-1b contain tissue CO concentrations from human and mouse under 

different CO exposure conditions. The distribution of CO between the different organs was shown to 

follow the same pattern versus percent of the blood CO concentration, irrespective of the level of blood 

CO (Vreman et al., 2006). These results are in conflict with older papers suggesting that negligible 

retention of CO occurs in the liver or brain (Sokal et al., 1984; Topping, 1975). 

4.3.3. Pulmonary and Tissue Elimination 

Blood COHb concentrations are generally considered to have a monotonically decreasing, secondorder (logarithmic or exponential) elimination rate from equilibrium. However, more recent reports have 

presented evidence for a biphasic washout curve, especially after short-term CO exposure (Figure 4-6) 

(Bruce and Bruce, 2006; Shimazu et al., 2000; Wagner et al., 1975). This event is modeled by a twoMarch 2009 4-13 DRAFT – DO NOT CITE OR QUOTE 
compartment system where the initial rapid decrease is the washout rate from the blood, followed by a 

slower phase due to CO flux from the muscle and extravascular compartments back to the blood. Tissue 

elimination rates have been reported as slower than those for blood (Landaw, 1973). The biphasic curve is 

more obvious after short-term CO exposure (less than 1 h), whereas long-term CO exposure (5 h or more) 

results in a virtually monoexponential elimination, which could account for the historical findings. 

However, this elimination curve also follows a biphasic curve with a slightly higher rate of elimination 

initially (Shimazu et al., 2000). Differences in elimination kinetics could also be a result of the variation 

in CO exposure duration (Weaver et al., 2000). 

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The elimination of COHb is affected by a number of factors, including duration of exposure, PaO2, 

minute ventilation, the time post-exposure for analysis due to extravascular stores, as well as interindividual variability (Bruce and Bruce, 2006; Landaw, 1973; Shimazu, 2001). The elimination rate does 

not seem to be dependent upon the CO exposure source (e.g., fire, non-fire CO exposure) (Levasseur et 

al., 1996). In addition, in a series of poisoned patients, the COHb elimination half-life was not influenced 

by gender, age, smoke inhalation, history of loss of consciousness, concurrent tobacco smoking, degree of 

initial metabolic acidosis (base excess), or the initial COHb level (Weaver et al., 2000). COHb 

elimination half-life falls as the fractional inspired O2 concentration increases. While breathing air at sea 

level pressure, the expected half-life in adult males is approximately 285 minutes, but may be shorter in 

adult females. With inhalation of normobaric 40% O2, the half-life falls to 75 minutes and futher to 21 

minutes when breathing 100% O2 because of greater competition for Hb by O2 (Landaw, 1973). Another 

study reports the half-life falls to 74 minutes (mean) after breathing 100% O2, although the range in this 

particular study was 26 to 148 minutes (Weaver et al., 2000). In addition, COHb half-life will fall further 

after normocapnic hyperoxic hyperpnea (i.e., hyperventilation while maintaining normal CO2 pressure in 

high O2) (Takeuchi et al., 2000). 

March 2009 4-14 DRAFT – DO NOT CITE OR QUOTE 
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10

100

0 30 60 90 120 150 180

Time (min)

COHb (%)

Short-term

Long-term

Adapted from Shimazu et al. (2000) 

Figure 4-6. Changes in blood COHb after short-term and long-term exposure to CO, 

representing the biphasic nature of CO elimination. Note: y-axis is log-scale. 

4.4. Conditions Affecting Uptake and Elimination 

4.4.1. Environment and Activity 

Elevated CO exposure and COHb levels are dependent upon the changes in CO concentration in 

the local environment. Pedestrians are exposed to high levels of CO for short time periods from vehicle 

exhaust at busy intersections (see also Chapter 3.6). Higher exposure can also result from riding in an 

automobile or stopping at busy intersections (Ott, 1994). Indoor exposure occurs from ETS and unvented 

combustion appliances, such as natural gas cooking stoves, attached garages, and gas fireplaces, which 

can accumulate to over 100 ppm (Dutton et al., 2001). Recreational exposure at levels exceeding 200 ppm 

and peaks of 1,600 ppm could occur in indoor ice rinks using fossil fuel powered ice resurfacers and 

coliseums housing malfunctioning equipment or poor ventilation (Levesque et al., 2000; Pelham et al., 

2002). Certain occupations provide instances and conditions for transient moderate-to-high CO levels, 

including fire fighters and machinery operators. Such transient exposures have the ability to increase 

COHb levels. For example, exposure for 5 minutes or less of a resting individual to 6,600 ppm CO will 

result in up to 20% COHb (Benignus et al., 1994). 

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Exercise is an important determinant of CO kinetics and toxicity due to the extensive increase in 

gas exchange. O2 consumption can increase more than 10 fold during exercise. Similarly, ventilation, 

March 2009 4-15 DRAFT – DO NOT CITE OR QUOTE 
membrane and lung diffusing capacity, pulmonary capillary blood volume, and cardiac output increase 

proportional to work load. The majority of these changes facilitate CO uptake and transport, by increasing 

gas exchange efficiency. 

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The COHb elimination rate decreases with physical activity (Joumard et al., 1981). Healthy 

subjects exposed to CO and achieving COHb levels of approximately 4-5% observed a significant 

detriment to exercise duration and maximal effort capability (measured by metabolic equivalent units) 

(Adir et al., 1999). It is possible that CO lowers the anaerobic threshold, allowing earlier fatigue of the 

skeletal muscles and decreased maximal effort capability. 

4.4.2. Altitude 

Increased altitude changes a number of factors that contribute to the uptake and elimination of CO. 

The relationship between altitude and CO exposure has been discussed in depth in the 2000 AQCD and 

other documents (U.S. EPA, 1978). In an effort to maintain proper O2 transport and supply, physiological 

changes occur as compensatory mechanisms to combat the decreased barometric pressure and resulting 

altitude induced hyperbaric hypoxia (HH). HH, unlike CO hypoxia, causes humans to hyperventilate, 

which reduces arterial blood CO2 (hypocapnia) and increases alveolar partial pressure of O2. Hypocapnia 

will lead to difficulty of O2 dissociation and decreased blood flow, thus reducing tissue O2 supply. HH 

increases blood pressure (BP) and cardiac output and leads to redistribution of blood from skin to organs 

and from blood vessels to extravascular compartments. Generally these changes will favor increased CO 

uptake and COHb formation, as well as CO elimination. In hypoxic conditions both CO and O2 bind 

reduced Hb through a competitive-parallel reaction (Chakraborty et al., 2004). Breathing CO (9 ppm) at 

rest at altitude produced higher COHb compared to sea level (McGrath et al., 1993), whereas high altitude 

exposure with exercise caused a decrease in COHb levels versus similar exposure at sea level (Horvath et 

al., 1988). This decrease could be a shift in CO storage or suppression of COHb formation, or both. 

Altitude also increases the baseline COHb levels by inducing endogenous CO production. Initial HH 

increased lung HO-1 protein and activity, whereas chronic HH induced endogenous CO production in 

nonpulmonary sites (see Section 4.5) (Carraway et al., 2000). 

As the length of stay increases at high altitude, acclimatization occurs, inducing hyperventilation, 

polycythemia or increased red blood cell count, and increased tissue capillarity and Mb content in skeletal 

muscle, which could also favor increased CO uptake. Most of the early adaptive changes gradually revert 

to sea level values. However, differences in people raised at high altitude persist even after 

reacclimatization to sea level (Hsia, 2002). 

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4.4.3. Physical Characteristics 

Certain physical characteristics (e.g., age, sex, pregnancy) can alter the variables that influence the 

uptake, distribution, and elimination of CO. Values of CO uptake and elimination change with age. Young 

children eliminate COHb more rapidly than adults after CO exposure (Joumard et al., 1981; Klasner et al., 

1998). After infancy, the COHb half-life increases with age, nearly doubling between 2 and 70 years 

(Joumard et al., 1981). The rate of this increase in CO elimination is very rapid in the growing years (2 to 

16 years of age), but slows beyond adolescence. Alveolar volume and DLCO increase with increasing 

body length of infants and toddlers (Castillo et al., 2006), suggesting a further degree of lung 

development and faster CO uptake. After infancy, increasing age decreases DLCO and increases VA/Q 

mismatch, causing it to take longer to both load and eliminate CO from the blood (Neas and Schwartz, 

1996). 

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COHb concentrations are generally lower in female subjects than in male subjects (Horvath et al., 

1988) and the COHb half-life may be longer in healthy men than in women of the same age, which may 

be partially explained by differences in muscle mass or the slight correlation between COHb half-life and 

increased height (Joumard et al., 1981). The rate of decline of DLCO with age is lower in middle-aged 

women than in men; however, it evens out towards older age (Neas and Schwartz, 1996). Women also 

tended to be more resistant to altitude hypoxia (Horvath et al., 1988). 

Fetal CO pharmacokinetics do not follow the same kinetics as maternal CO exposure, making it 

difficult to estimate fetal COHb based on maternal levels. Human fetal Hb has a higher affinity for CO 

than adult Hb (Di Cera et al., 1989). Maternal and fetal COHb concentrations have been modeled as a 

function of time using a modified CFK equation (Hill et al., 1977). At steady-state conditions, the fetal 

COHb is up to 10% higher than the maternal COHb levels, for example, exposure to 30 ppm CO results 

in a maternal COHb of 5% and a fetal COHb of 5.5%. The fetal CO uptake lags behind the maternal for 

the first few hours but later may overtake the maternal values. Similarly, during washout, the fetal COHb 

levels are maintained for longer, with a half-life of around 7.5 hours versus the maternal half-life of 

around 4 hours. 

Ethnicity does alter physiological variables that determine CO uptake and kinetics. Lung volumes 

are 10-15% less in both Asian and African-American populations when compared to Caucasians. This 

causes a reduced alveolar surface area (20% less than estimated values) for gas exchange, leading to a 

13% difference in diffusion capacity, DLCO (Pesola et al., 2004; Pesola et al., 2006). Certain factors such 

as socioeconomic status (SES) were not controlled for in these studies. SES has been shown to affect 

pulmonary function, including decreasing DLCO (Hegewald and Crapo, 2007). 

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4.4.4. Health Status 

Health status can influence the toxicity involved with CO exposure by influencing the severity of 

hypoxia resulting from CO exposure. Any condition that would alter the blood O2 carrying capacity or 

content will result in a greater risk from COHb induced hypoxia and decreased tissue O2 delivery. The 

severity of this effect depends upon the initial level of hypoxia. 

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Anemias are a group of diseases that result in insufficient blood O2 or hypoxia due to Hb 

deficiency through hemolysis, hemorrhage, or reduced hematopoiesis. Anemia may result from pathologic 

conditions characterized by chronic inflammation such as malignant tumors or chronic infections 

(Cavallin-Stahl et al., 1976a, b). The bodies of people with anemia compensate causing cardiac output to 

increase as both heart rate and stroke volume increase. The endogenous production of CO, thus COHb, is 

increased in patients with hemolytic anemia due to increased heme catabolism, causing an increased 

baseline COHb concentration. One of the most prevalent anemias arises from a single-point mutation of 

Hb, causing sickle cell diseases. The Hb affinity for O2 and O2 carrying capacity is reduced causing a shift 

to the right in the O2 dissociation curve. It is well documented that African-American populations have a 

higher incidence of sickle cell anemia, which may be a risk factor for CO hypoxia. 

Chronic obstructive pulmonary disease (COPD) is often accompanied by a number of changes in 

gas exchange, including increased deadspace volume (VD) and ventilation-perfusion ratio (V/Q) 

inequality (Marthan et al., 1985), which could slow both CO uptake and elimination. Patients with 

pulmonary sarcoidosis may have a decrease in lung volumes, a loss of DLCO, and gas exchange 

abnormalities during exercise, including decreased arterial oxygen pressure (PaO2) and increased alveolararterial oxygen pressure difference (Lamberto et al., 2004). 

Individuals with heart disease may be at a greater risk from CO exposure since they may already 

have compromised O2 delivery. Time to onset of angina was reduced after exposure to 100 ppm carbon 

monoxide, compared to clean air (Kleinman et al., 1998). Hyperlipidemic patients may have decreased 

CO diffusion capacity, a loss of V/Q gradient, and a decrease in PaO2 (Enzi et al., 1976). 

4.5. Endogenous CO Production and Metabolism 

Humans breathing air containing no environmental sources of CO will still have a low measurable 

level of circulating COHb. This is due to endogenous CO production from heme protein catabolism 

among other sources. In the natural degradation of RBC Hb, the porphyrin ring of heme is broken at the 

methene bridge and catabolized in an O2 dependent manner by HO complexed with NADPH-cytochrome 

P450 reductase and biliverdin reductase to biliverdin, Fe, and CO. Biliverdin is then further broken down 

into bilirubin, a powerful endogenous antioxidant. Two main HO isoforms exist, HO-1 and HO-2. 

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Expression of HO-1 is inducible, whereas HO-2 is constitutively expressed. The major site of heme 

catabolism, thus the major organ of CO production is the liver, followed by the spleen, brain, and 

erythropoietic system (Berk et al., 1976). These rates of CO formation may be due to higher levels of HO 

activity in these tissues. The whole body production rate of CO is approximately 16.4 μmol/h (0.42 mL/h) 

and produces between 400-500 μmol CO per day (Coburn et al., 1964; Coburn et al., 1966; Coburn, 

1970b). The endogenous rate of CO formation has been shown to vary little between different tissues, 

ranging from 0.029 nmol/mg protein/h in chorionic villi of term human placenta to 0.28 nmol/mg 

protein/h in rat olfactory receptor neurons in culture and in rat liver perfusate (Marks et al., 2002), 

however these estimations are questionable since CO is quickly scavenged in the cytosol of living cells. 

CO is endogenously produced in the nose and paranasal sinus which may contribute to exhaled CO 

concentrations (Andersson et al., 2000). 

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HO mediated metabolism functions as the rate-limiting enzyme step in heme degradation and 

endogenous CO production (Wu and Wang, 2005). Three isoforms of HO exist, but HO-1 is the only 

inducible form (Maines and Kappas, 1974; Maines et al., 1986; McCoubrey et al., 1997). Endogenous CO 

production can be increased by the up-regulation of HO-1 expression and activity by inducers such as 

oxidative stress, hypoxia, heavy metals, sodium arsenite, heme and heme derivatives, various cytokines, 

and also exogenous CO (Wu and Wang, 2005). High levels of CO (2,500 ppm) have been shown to 

increase HO-1 activity in the brain of rats, as well as liberate intracellular heme to further stimulate 

endogenous CO production (Cronje et al., 2004). 

Not all endogenous CO production is derived from Hb breakdown. Other hemoproteins, such as 

Mb, cytochromes, peroxidases, and catalase, contribute 20-25% to the total amount of endogenous CO 

(Berk et al., 1976). All of these sources result in a normal blood COHb concentration between 0.4 and 

0.7% (Coburn et al., 1965). This baseline level of endogenous production can be altered by drugs or a 

number of physiological conditions that alter RBC destruction, other hemoprotein breakdown, or bilirubin 

production. Because of this sensitivity, ranges of endogenous COHb levels in the population are 

uncertain. Nicotinic acid (Lundh et al., 1975), allyl-containing compounds (acetamids and barbiturates) 

(Mercke et al., 1975b), diphenylhydantoin (Coburn, 1970b), progesterone (Delivoria-Papadopoulos et al., 

1974), and contraceptives (Mercke et al., 1975a) will increase CO production. Compounds such as carbon 

disulfide and sulfur-containing chemicals (parathion and phenyltiourea) will increase CO by acting on 

P450 system moieties (Landaw et al., 1970). The P450 system may also cause large increases in CO 

produced from the metabolic degradation of dihalomethanes leading to very high (>10%) COHb levels 

(Manno et al., 1992), which can be further enhanced by prior exposure to HCs or ethanol (Pankow et al., 

1991; Wirkner and Poelchen, 1996). HO can catalyze the release of CO from the auto-oxidation of 

phenols, photo-oxidation of organic compounds, and lipid peroxidation of cell membrane lipids (Rodgers 

et al., 1994). 

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Any disturbance in RBC hemostasis by acceleration of destruction of hemoproteins will lead to 

increased production of CO. Pathologic conditions such as anemias, hematomas, thalassemia, Gilbert’s 

syndrome with hemolysis, and other hematological diseases and illness will accelerate CO production 

(Berk et al., 1974; Hampson, 2007; Meyer et al., 1998; Solanki et al., 1988). Patients with hemolytic 

anemia exhibit COHb levels 2- to 3-times higher than healthy individuals and CO production rates 2- to 

8-times higher (Coburn et al., 1966). Endogenous CO production rate varied from 0.70 to 3.44 mL/h in 

anemic patients (Coburn et al., 1965). Women experience fluctuating COHb levels during pregnancy as 

well as through the menstrual cycle when endogenous CO production doubles in the progesterone phase 

(Delivoria-Papadopoulos et al., 1974; Mercke and Lundh, 1976). 

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Altitude has been shown to be positively associated with baseline COHb concentrations (McGrath, 

1992; 1993). This increase in COHb with altitude induced hypoxia has also been associated with 

increases in the mRNA, protein, and activity of HO-1 in rats and cells leading to enhanced endogenous 

CO production (Carraway et al., 2002; Lee et al., 1997). Whether other variables such as an accelerated 

metabolism or a greater pool of Hb, transient shifts in body stores, or a change in the elimination rate of 

CO play a role has not been explored. 

Endogenous CO is removed from the body mainly by expiration and oxidation. CO will diffuse 

across the alveolar-capillary membrane and then is exhaled. This event has been used as a noninvasive 

measurement of endogenous CO and CO body load (Stevenson et al., 1979). CO can also be oxidized to 

CO2 by cytochrome c oxidase in the mitochondria (Fenn, 1970; Young and Caughey, 1986). However, the 

rates of CO metabolism are much slower than the rates of endogenous CO production, with the rate of 

consumption representing only 10% of the rate of CO production in dogs (Luomanmaki and Coburn, 

1969). 

4.6. Summary and Conclusions 

CO elicits various health effects by binding with and altering the function of a number of hemecontaining molecules, mainly Hb. The formation of COHb reduces the O2-carrying capacity of blood and 

impairs the release of O2 from O2Hb to the tissues. COHb formation has been modeled mainly by the 

CFK equation, but more recent models have included Mb and extravascular storage compartments, as 

well as other dynamics of CO physiology. These models have indicated that CO has a biphasic 

elimination curve, due to initial washout from the blood followed by a slower flux from the tissues. The 

flow of CO between the blood and alveolar air or tissues is controlled by diffusion down the CO 

concentration gradient. The uptake of CO is governed not only by this CO pressure differential, but also 

by physiological factors, such as minute ventilation and lung diffusing capacity, that can, in turn, be 

affected by conditions such as exercise, age, and health. Susceptible populations, including health 

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compromised individuals and developing fetuses, are at a greater risk from COHb induced health effects 

due to altered CO kinetics, compromised cardiopulmonary processes, and increased baseline hypoxia 

levels. Altitude may also significantly affect the kinetics of COHb formation. Compensatory mechanisms, 

such as increased cardiac output, combat the decrease in barometric pressure. Altitude also increases the 

endogenous production of CO through upregulation of HO-1. CO is considered a second messenger and 

is endogenously produced from the catabolism of heme proteins by enzymes such as HO-1. Finally, CO is 

removed from the body by expiration and oxidation to CO2. 

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Chapter 5. Integrated Health Effects 

5.1. Mode of Action of CO Toxicity 

5.1.1. Introduction 

The diverse effects of CO are dependent upon concentration and duration of exposure as well as on 

the cell types and tissues involved. Responses to CO are not necessarily due to a single process and may 

instead be mediated by a combination of effects including COHb-mediated hypoxic stress and 

mechanisms unrelated to tissue hypoxia including free radical production and the initiation of cell 

signaling. However, binding of CO to reduced iron in heme proteins with subsequent alteration of heme 

protein function is the common mechanism underlying the biological responses to CO. 

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5.1.2. Hypoxic Mechanisms 

As discussed in the 2000 CO AQCD, the most well-known pathophysiological effect of CO is 

tissue hypoxia caused by binding of CO to Hb. Not only does the formation of COHb reduce the O2-

carrying capacity of blood, but it also impairs the release of O2 from O2Hb. Compensatory alterations in 

hemodynamics, such as vasodilation and increased cardiac output, protect from tissue hypoxia. At 

ambient CO concentrations, these compensatory changes are slight and likely tolerable in people with a 

healthy cardiovascular system. However, people with cardiovascular detriments may be unable to endure 

these small changes in hemodynamics which may lead to the presentation of health effects as described in 

Sections 5.2.1 and 5.2.2. 

The 2000 CO AQCD reported changes in vasodilation due to CO levels between 500-2000 ppm 

(Kanten et al., 1983; MacMillan, 1975). Vasodilation can be dependent on or independent of perturbations 

in O2 supply (Koehler et al., 1982). For example, cerebral blood flow elevations that were independent of 

O2 availability were blocked by the inhibition of nitric oxide synthase (NOS) indicating a role for the free 

radical species nitric oxide (NO•) in mediating vasodilation (Meilin et al., 1996). 

Increased cardiac ouput was also discussed in the 2000 CO AQCD as a compensatory response to 

CO-mediated tissue hypoxia. Findings of studies measuring hemodynamic alterations following CO 

exposure were equivocal and sometimes contradictory (Penney, 1988). While most studies reported a 

positive correlation between COHb and cardiac output at COHb levels above 20%, one study 

demonstrated increased cardiac output in humans following acute exposure to 5% CO resulting in COHb 

levels around 9% (Ayres et al., 1973). There was no increase in cardiac output following acute exposure 

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to 0.1% CO in this latter study. Increased heart rate and stroke volume (SV) were also observed in 

response to CO exposure (Stewart et al., 1973b); however, some experiments found no change in SV in 

humans with 18-20% COHb (Vogel and Gleser, 1972) or 12.5% COHb (Klausen et al., 1968). The 2000 

CO AQCD reported that BP was generally unchanged in human CO exposure studies, while a number of 

animal studies demonstrated CO-induced hypotension (Penney et al., 1988). No changes in forearm blood 

flow, BP, or heart rate were reported in humans experiencing approximately 8% COHb (Hausberg and 

Somers, 1997). However, high concentration animal exposures (3,000 to 10,000 ppm) showed diminished 

organ blood flow (Brown and Piantadosi, 1992). In depth discussion of hemodynamic changes resulting 

from CO exposure in recent human clinical studies can be found in Section 5.2.2. 

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Binding of CO to Mb, as discussed in the 2000 CO AQCD and in Section 4.3.2.3, can also impair 

the delivery of O2 to tissues. Mb has a high affinity for CO; however, physiological effects are seen only 

after high dose exposures to CO, resulting in COMb concentrations far above baseline levels. High 

energy phosphate production in cardiac myocytes was inhibited when COMb concentrations exceeded 

40%, corresponding to an estimated COHb level between 20-40% (Wittenberg and Wittenberg, 1993). 

Conversely, rat hearts perfused with 6% CO (60,000 ppm) exhibited no change in high energy phosphate 

production, respiration rate, or contractile function (Chung et al., 2006; Glabe et al., 1998). These 

conflicting studies employed CO levels that are not relevant for ambient exposure to CO. 

5.1.3. Non-Hypoxic Mechanisms 

 Non-hypoxic mechanisms underlying the biological effects of CO were discussed in the 2000 CO 

AQCD and are summarized below. Most of these mechanisms are related to CO’s ability to bind hemecontaining proteins other than Hb and Mb (Raub and Benignus, 2002). Since then, additional experiments 

have confirmed and extended these findings. While the majority of the older studies utilized 

concentrations of CO far higher than ambient levels, many of the newer studies have employed more 

environmentally relevant levels of CO. 

5.1.3.1. Non-Hypoxic Mechanisms Reviewed in the 2000 CO AQCD 

Inhibition of heme-containing proteins such as cytochrome c oxidase and cytochrome P450 

oxidases may alter cellular function. CO interacts with the ferrous heme a3 of the terminal enzyme of the 

electron transport chain, cytochrome c oxidase (Petersen, 1977). Cytochrome c oxidase inhibition not 

only interrupts cellular respiration and energy production, but can also enhance reactive oxygen species 

(ROS) production. In vivo studies observed CO binding to cytochrome c oxidase under conditions where 

COHb concentrations are above 50% (Brown and Piantadosi, 1992). It is unlikely that this could arise 

under physiological conditions or under conditions relevant to ambient exposures. 

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Studies indicated that CO exposure produces a pro-oxidant cellular environment by a second 

mechanism. CO exposure at concentrations of 10-20 ppm and above caused platelets of laboratory rats, as 

well as cultured bovine pulmonary endothelial cells, to release NO• (Thom and Ischiropoulos, 1997). An 

increase in available NO· was also seen in the lung and brain of CO-exposed rats (Ischiropoulos et al., 

1996; Thom et al., 1999b). NO• combines with superoxide to form the highly active oxidant species, 

peroxynitrite (Thom et al., 1997), which can lead to the activation and sequestration of leukocytes in brain 

vessels (Thom et al., 2001b) and aorta, oxidation of plasma lipoproteins (Thom et al., 1999a), and the 

formation of protein nitrotyrosine (Ischiropoulos et al., 1996; Thom et al., 1999a; Thom et al., 1999b). 

The mechanism leading to NO• release is likely the displacement of NO· from nitrosyl bound Mb (NO•-

Mb) or Hb (NO·-Hb) by CO. The rate of this event is slow; however it occurs at environmentally relevant 

concentrations of CO (Thom et al., 1997). 

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CO exposure also increases the production of other pro-oxidant species, including hydrogen 

peroxide (H2O2) and hydroxyl radical (OH ̇). High level CO exposure (2,500 ppm) increases OH• in rat 

brain and this response is distinct from tissue hypoxia (Piantadosi et al., 1997). The mechanism for 

enhanced H2O2 production is unclear. The release of H2O2 in the lung of CO-exposed rats was dependent 

upon the production of NO•, as it was inhibited by the pretreatment with a NOS inhibitor (Thom et al., 

1999b). It is possible that peroxynitrite formed after CO exposure inhibited electron transport at 

complexes I through III, or that cytochrome c oxidase interference led to mitochondrial dysfunction and 

ROS production. 

It has also been suggested that CO leads to vasorelaxation by three different mechanisms. First, CO 

may inhibit the P450-dependent synthesis of vasoconstrictors (Wang, 1998). Vasodilation has been 

demonstrated via this P450-mediated mechanism with high concentrations of CO (approximately 

90,000 ppm) (Coceani et al., 1988). In the case of cytochrome P450 enzymes, tissue CO levels may have 

to be abnormally increased to elicit a physiological response since the in vitro Warburg binding 

coefficients, the ratio of CO to O2 at which half the reactive sites are occupied by CO, for cytochrome 

P450s range from 0.1-12 (Piantadosi, 2002). P450 inhibition may also reduce the hypoxia-induced 

expression of mitogens such as erythropoietin (EPO), vascular endothelial growth factor (VEGF), 

endothelin-1 (ET-1), and platelet derived growth factor (PDGF) which may decrease smooth muscle 

proliferation in response to hypoxia (Wang, 1998). CO may also interfere with the metabolism of 

barbiturates and other drugs; however, this is probably due to the hypoxic actions of CO rather than to 

P450 inhibition (Roth and Rubin, 1976a, b). 

Secondly, CO has been shown to play a physiological role in vasomotor control and in signal 

transduction by activation of soluble guanylate cyclase (sGC), causing a conversion of GTP to cyclic 

GMP (cGMP). CO interacts with sGC and forms an unstable complex with the heme core of the protein. 

The resulting porphyrin IX intermediate triggers cGMP production (Ndisang et al., 2004). CO causes 

vascular relaxation, independent of the endothelium, in human arterial rings (Achouh et al., 2008), rat tail 

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artery (Wang et al., 1997), and rat thoracic aorta (Lin and McGrath, 1988), but not cerebral vessels 

(Andresen et al., 2006; Brian et al., 1994). Activation of sGC by CO has been linked to 

neurotransmission, vasodilation, bronchodilation, inhibition of platelet aggregation, and inhibiton of 

smooth muscle proliferation (Brune and Ullrich, 1987; Cardell et al., 1998; Morita et al., 1997; Verma et 

al., 1993). 

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CO-mediated vasorelaxation can also be caused by activation of voltage-activated potassium (K+ ) 

channels or calcium (Ca2+)-activated K+

 channels, which leads to membrane hyperpolarization, voltagedependent Ca2+ channel closing, reduction of resting Ca2+ concentration, and then vascular tissue 

relaxation (Farrugia et al., 1993; Wang et al., 1997). This effect may be linked to sGC activity; however it 

has also been reported to occur independently (Dubuis et al., 2003; Naik and Walker, 2003). 

Developmental stage and tissue type will determine whether K+

 channels or the sGC/cGMP pathway 

plays more of a role in vasorelaxation (Ndisang et al., 2004). 

Collectively, these older studies demonstrated that exposures to high concentrations of CO resulted 

in altered functions of heme proteins other than Hb and Mb. Decreased cellular respiration and energy 

production and increased pro-oxidant status following cytochrome c oxidase inhibition would likely 

predispose towards cellular injury and death. The increase in free NO• following its release from 

sequestered stores could also contribute to the pro-oxidant status. Increased ROS and reactive nitrogen 

species are known to promote cell signaling events leading to inflammation and endothelial dysfunction. 

An inappropriate increase in vasorelaxation due to inhibition of vasoconstrictor production or to 

activation of vasodilatory pathways (sGC and ion channels) could potentially limit compensatory 

alterations in hemodynamics. Alternatively, CO-binding to sGC could result in decreased vasorelaxation 

by interfering with the binding of NO• to sGC. NO• can also activate sGC, and with a 30-fold greater 

affinity than CO is one-thousand fold more potent with respect to vasodilation and sGC activation (Stone 

and Marletta, 1994). CO could further contribute to endothelial dysfunction by this mechanism. Although 

the 2000 CO AQCD made no definitive links between these non-hypoxic mechanisms of CO and 

CO-mediated health effects, it did establish the potential for CO to interfere with basic cellular and 

molecular processes which could lead to dysfunction and/or disease. 

5.1.3.2. Recent Studies of Non-Hypoxic Mechanisms 

Since the 2000 CO AQCD, new studies have provided additional evidence for non-hypoxic 

mechanisms of CO which involve the binding of CO to reduced iron in heme proteins. These 

mechanisms, which may be inter-related, are described below and include: 

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 Alteration in NO• 1 signaling 

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 Inhibition of cytochrome c oxidase 

 Heme loss from protein 

 Disruption of iron homeostasis 

 Alteration in cellular redox status 

This assessment evaluates these non-hypoxic mechanisms in terms of their potential to contribute 

7 to health effects associated with environmentally-relevant CO exposures. As discussed above, CO at high 

8 concentrations may promote oxidative stress, cell injury and death, inflammation and endothelial 

9 dysfunction. Whether lower CO concentrations trigger these same processes is of key interest since these 

10 can potentially contribute to adverse health effects. 

In addition, a large number of studies published since the 2000 CO AQCD has focused on the role 

of COs as an endogenous signaling molecule and the potential therapeutic effects of exogenous CO 

administered at high concentrations. This assessment addresses these topics only briefly, as they pertain to 

the evaluation of health effects associated with environmentally-relevant CO exposures. 

Alteration in NO• Signaling 

Work by Thorup et al. (1999) demonstrated altered NO• signaling in isolated rat renal resistance 

arteries. In one set of experiments, rapid release of NO• was observed in response to a supra-physiologic 

dose of CO (100 nM). These findings are similar to those of Thom and colleagues (Ischiropoulos et al., 

1996; Thom et al., 1994; Thom and Ischiropoulos, 1997; Thom et al., 1997; Thom et al., 1999a; Thom et 

al., 1999b; Thom et al., 2000; Thom et al., 2006) who demonstrated NO• release, presumably from 

sequestered stores, in platelets, endothelial cells, aorta and lung in response to CO (see above). 

Furthermore in a second set of experiments, Thorup et al. (1999) demonstrated inhibition of NOS in 

isolated rat renal resistance arteries. Here rapid NOS-dependent release of NO• following carbachol 

stimulation was blocked by pretreatment with 100 nM NO•. Both sets of studies illustrate the potential of 

CO to alter processes dependent on endogenous NO•. This could be critical in the case of cGMPmediated vasodilation since, as discussed above, NO• activates sGC to a greater extent than CO. Thus in 

the presence of excess CO, NO•-dependent vasodilation may be significantly less. 

Inhibition of Cytochrome c Oxidase 

High concentrations of CO are known to inhibit cytochrome c oxidase, the terminal enzyme in the 

mitochondrial electron transport chain, resulting in inhibition of cellular respiration and the formation of 

superoxide from mitochondrial substrates. Several recent studies demonstrated CO-mediated decreases in 

cytochrome c oxidase activity in model systems ranging from isolated mitochondria to whole animals. In 

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a study by Alonso et al. (2003), exposure of isolated mitochondria from human skeletal muscle to 50-

500 ppm CO for 5 min decreased cytochrome c oxidase activity. Similarly, exposure of cultured 

macrophages to 250 ppm CO for 1 h inhibited cytochrome c oxidase (Zuckerbraun et al., 2007). In this 

study, increased ROS were observed following exposure to 250 ppm, as well as to CO concentrations as 

low as 50 ppm, for 1 h. Animal studies demonstrated that exposure of rats to 250 ppm CO for 90 min 

inhibited cytochrome c oxidase activity in myocardial fibers (Favory et al., 2006a) and exposure of mice 

to 1,000 ppm CO for 3 h decreased cytochrome c oxidase activity in heart mitochondria (Iheagwara et al., 

2007). COHb levels were reported to be 61% in the latter study. 

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Heme Content Loss from Proteins 

In addition to decreasing the activity of cyctochrome c oxidase, exposure of mice to 1,000 ppm CO 

for 3 h resulted in decreased protein levels and heme content of cytochrome c oxidase (Iheagwara et al., 

2007). CO-mediated heme release was also seen in a study by Cronje et al. (2004), and was followed by 

increased endogenous CO production through the activation of HO-2 and the induction of HO-1. Loss of 

heme from proteins leads to loss of protein function and often to protein degradation. 

Disruption of Iron Homeostasis 

Exposure of rats to 50 ppm CO for 24 h increased levels of iron and ferritin in the bronchoalveolar 

lavage fluid (BALF), decreased lung non-heme iron and increased liver non-heme iron (Ghio et al., 

2008). Furthermore in this same study, exposure of respiratory epithelial cells to 10-100 ppm CO for 24 h 

caused a dose-dependent decrease in cellular non-heme iron and ferritin. Heme loss observed in other 

studies (Cronje et al., 2004; Iheagwara et al., 2007) might be expected to result in disruption of iron 

homeostasis. Iron homeostasis is critical for the sequestration of free iron and the prevention of ironmediated redox cycling which can lead to ROS generation and lipid peroxidation. 

Alteration in Cellular Redox Status 

Recent studies demonstrated that exposure to low, moderate and high levels of CO increased 

cellular oxidant stress in cultured cells (Kim et al., 2008; Zuckerbraun et al., 2007). A dose-dependent 

increase in dichlorofluoroscein (DCF) fluorescence (an indicator of ROS) occurred following 1-h 

exposure to 50-500 ppm CO in macrophages and following 1-h exposure to 250 ppm CO in hepatocytes. 

NOS inhibition had no effect on the increase in DCF fluorescence in CO-treated macrophages indicating 

that the effects were not due to an interaction of CO and NO• (Zuckerbraun et al., 2007). Mitochondria 

were identified as the source of the increased ROS since mitochondria-impaired cells (rho zero cells and 

treatment with antimycin A) did not respond to CO with an increase in DCF fluorescence. Furthermore, 

1-h exposure to 250 ppm CO inhibited mitochondrial cytochrome c oxidase enzymatic activity in 

macrophages (Zuckerbraun et al., 2007). Recently, inhibition of cytochrome c oxidase was demonstrated 

March 2009 5-6 DRAFT – DO NOT CITE OR QUOTE 
in HEK-293 cells transfected with HO-1 and in macrophages with induced HO-1, and this effect was 

attributed to endogenously produced CO (D'Amico et al., 2006). However inhibition of cytochrome c

oxidase at CO concentrations below 250 ppm has not been definitively demonstrated. In hepatocytes, 

exposure to 250 ppm CO for 1 h resulted in Akt phosphorylation and nuclear translocation of nuclear 

factor kappa B (NF-κB), effects which were blocked by antioxidants (Kim et al., 2008). Significant 

increases in apoptosis were also observed in this model. Thus, CO appeared to uncouple mitochondrial 

respiration leading to ROS-induced programmed cell death. 

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Further evidence for cellular redox stress is provided by studies in which glutathione stores were 

altered following CO exposure in vitro (Kim et al., 2008; Patel et al., 2003). In addition, mitochondrial 

redox stress was observed in livers of rats exposed to 50 ppm CO (Piantadosi et al., 2006). Furthermore, 

an adaptive increase in intracellular antioxidant defenses (i.e., superoxide dismutase) was observed in 

endothelial cells exposed to 10 ppm CO for 40 min (Thom et al., 2000) and mitochondrial biogenesis was 

observed in hearts of mice exposed to 50 and 250 ppm CO for 1 h (Suliman et al., 2007). 

Several mechanisms could contribute to the cellular redox stress elicited by CO exposure. As 

discussed above, inhibition of cytochrome c oxidase results in the formation of superoxide from 

mitochondrial substrates. However, interactions of CO with heme proteins can lead to the release of heme 

and free iron which could also lead to the generation of ROS. As mentioned above, increased ROS 

generation has been linked to cellular injury and death, inflammation, and endothelial dysfunction. 

 Two of the above-mentioned studies demonstrated that CO-mediated mechanisms were unrelated 

to hypoxia by showing that hypoxic conditions failed to mimic the results obtained with CO. Hence the 

mitochondrial redox stress and mitochondrial pore transition observed in livers from rats exposed to CO 

(Piantadosi et al., 2006) and the cardiac mitochondrial biogenesis observed in mice exposed to CO 

(Suliman et al., 2007) could be attributed specifically to non-hypoxic mechanisms of CO. 

Recent studies also demonstrated non-hypoxic mechanisms of CO which do not directly involve 

heme protein interactions. These mechanisms are described below and include: 

 Alteration in ion channel activity 

 Modulation of protein kinase pathways 

Alteration in Ion Channel Activity 

Work by Dubuis et al. (2002) demonstrated increased current through Ca2+-activated K+ channels in 

smooth muscle cells from pulmonary arteries of rats exposed to 530 ppm CO for 3 weeks. These findings 

provide further evidence for non cGMP-dependent vasodilatory actions of CO. 

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Modulation of Protein Kinase Pathways 

Endogenously produced CO is a gaseous second messenger molecule in the cell. Work from 

numerous laboratories has demonstrated the potential for CO to be used as a therapeutic gas with 

numerous possible clinical applications, since it can produce anti-inflammatory, anti-apoptotic, and antiproliferative effects (Ryter et al., 2006). These studies generally involved pretreatment with CO followed 

by exposure to another agent 18-24 h later. There is extensive literature on this topic as reviewed by Ryter 

et al. (2006) and others. A number of these processes are mediated through cGMP while others involve 

redox-sensitive kinase pathways, possibly secondary to CO-dependent generation of ROS. For example, 

250 ppm CO inhibited growth of airway smooth muscle cells by attenuating the activation of the 

extracellular signal-regulated kinase 1/2 (ERK 1/2) pathway, independent of sGC and other MAP kinases 

(Song et al., 2002). A second example is provided by the study of Kim et al. (2005) where 250 ppm CO 

inhibited PDGF- induced smooth muscle cell proliferation by upregulating p21Waf1/Cip1 and caveolin-1, and 

down-regulating cyclin A expression. These events were dependent upon cGMP and the p38 MAPK 

pathway (Kim et al., 2005). Thirdly, rat endothelial cells exposed to 15 ppm CO escaped anoxiareoxygenation mediated apoptosis via modulation of the signaling pathways involving phosphoinositide 

3-kinase (PI3K), Akt, p38 MAP kinase, Signal Transducers and Activators of Transcription (STAT-1) and 

STAT-3 (Zhang et al., 2005). In a fourth study, Akt was found to be responsible for the CO-induced 

activation of NF-κB, protecting against hepatocyte cell death (Kim et al., 2008). While research focusing 

on therapeutic applications of CO generally involves high level, short-term exposure to CO (250-

1,000 ppm for up to 24 h), some studies found effects below 20 ppm (Zhang et al., 2005). Few if any 

studies on the therapeutic effects of CO have explored the dose-response relationship between CO and 

pathway activation/deactivation, so it remains unclear how these effects may be related to 

environmentally-relevant exposures. 

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Concentration-Response Relationships 

In many cases the concentrations of exogenous CO required for these non-hypoxic mechanisms is 

much higher than what would be expected to result from exposures at ambient levels (Alonso et al., 2003; 

Favory et al., 2006b; Iheagwara et al., 2007; Thorup et al., 1999). However in some studies the effects are 

mimicked by upregulation of HO-1 which would result in increased local production of CO as well as of 

iron and biliverdin (D'Amico et al., 2006; Imai et al., 2001; Thorup et al., 1999). For example, HO-1 

overexpression attenuated carbachol-mediated NO• release and NO•-mediated vasodilation, similar to the 

effects of exogenous CO in these same models (Imai et al., 2001; Thorup et al., 1999). In the study by 

D’Amico et al. (2006), overexpression of HO-1 in cells inhibited cellular respiration by 12% and 

decreased cytochrome c oxdase activity by 23%. It is not clear how comparable these conditions 

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involving increased intracellular concentrations of endogenous CO are to increased intracellular 

concentrations of CO resulting from ambient CO exposures. 

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There is a growing appreciation that non-hypoxic mechanisms may contribute to the effects 

associated with CO poisoning (Ischiropoulos et al., 1996; Thom et al., 1994; Weaver et al., 2007). On the 

other hand, recent studies suggest that exogenous CO at low concentrations may have beneficial antiinflammatory, anti-proliferative and cytoprotective effects under certain circumstances (Ryter et al., 

2006). Since the focus of this assessment is on mechanisms which are relevant to ambient exposures it is 

important to understand which mechanisms may occur at “low” (50 ppm and less) and “moderate” 

(50-500 ppm CO) concentrations of CO. Hence, both recent animal studies and relevant older ones which 

add to the understanding of mechanisms in this range of CO concentrations are briefly summarized in 

Table 5-1. It should be noted that most of the above-mentioned non-hypoxic mechanisms are 

demonstrated at CO concentrations of 50 ppm and less. 

Table 5-1. Responses to low and moderate CO exposures. 

Reference Model System CO Exposure Response Notes 

IN VITRO 

Alonso et al. 

(2003) 

Human muscle 

mitochondria 

50, 100, 500 ppm 

5 min 

Decreased cytochrome c oxidase activity 

Thom and 

Ischiropoulos 

(1997) 

Rat 

platelets 

10 ppm Increased free NO• 

Thom et al. 

(1997) 

Bovine pulmonary artery 

endothelial cells 

20 ppm 

30-60 min 

Increased free NO• and peroxynitrite Reported to correspond to 7% COHb 

Thom et al. 

(2000) 

Bovine pulmonary artery 

endothelial cells 

10 ppm 

40 min 

Increased MnSOD and protection against toxic 

effects of 100 ppm CO 

Adaptive responses 

Song et al. 

(2002) 

Human aortic smooth 

muscle cells 

50-500 ppm 

24 h 

Inhibition of cellular proliferation Blocked activation of ERK1/2 

pathway, independent of sGC and 

other MAP kinases 

Kim et al. (2005) Rat pulmonary artery 

smooth muscle cells 

250 ppm 

1 h 

Inhibited PDGF- induced smooth muscle cell 

proliferation 

Upregulated p21Waf1/Cip1 and caveolin1, and down-regulated cyclin A 

expression. 

Kim et al. (2008) Rat hepatocytes 250 ppm 

1 h 

2x per day 

250 ppm 

1 h 

Blocked spontaneous apoptosis 

Increased mitochondrial ROS generation, increased 

mitochondrial glutathione oxidation, and decreased 

cellular ascorbic acid 

CO induced Akt phosphorylation via 

ROS production 

CO activated NFκB 

Zhang et al. 

(2005) 

Rat pulmonary artery 

endothelial cells 

15 ppm 

0.5-24 h 

Blocked anoxia-reoxygenation mediated apoptosis Modulation of PI3K/Akt/p38 MAP 

kinase and STAT-1 and STAT-3 

Zuckerbraun et 

al. (2007) 

Mouse macrophages 50 and 250 ppm 

1 h 

Increased ROS generation (dose dependent 

response for 50-500 ppm CO) 

Mitochondrial derived ROS and 

cytochrome c oxidase inhibition 

demonstrated for 250 ppm 

March 2009 5-9 DRAFT – DO NOT CITE OR QUOTE 
Reference Model System CO Exposure Response Notes 

Ghio et al. (2008) Human bronchial 

epithelial cells 

10-100 ppm 

24 h 

Dose-dependent decrease in cellular non-heme iron 

(effect at 10 ppm was signficant, effect at 50 ppm 

maximal) 

Dose-dependent decrease in cellular ferritin at 50-

100 ppm 

50 ppm blocked iron uptake by cells 

50 ppm increased iron release from cells 

Compare with in vivo experiment in 

same paper 

IN VIVO 

Ghio et al. (2008) Rats 50 ppm 

24 h 

Mild neutrophil accumulation in BALF 

Increased lavage MIP-2, protein, LDH 

Lavage iron and ferritin were increased by CO 

Lung non-heme iron was decreased by CO 

Liver non-heme iron was increased by CO 

Thom et al., 

(1999b) 

Rats 50 ppm 

1 h 

100 ppm 

1 h 

Increased nitrotyrosine in aorta 

Leukocyte sequestration in aorta after 18 h 

Albumin efflux from skeletal muscle 

microvasculature 3 h after CO 

LDL oxidation 

Effects blocked by NOS inhibitor 

Thom et al., 

(1999a) 

Rats 100 ppm 

1 h 

50 ppm 

1 h 

Elevated free NO• during CO exposure (EPR) 

Elevated nitrotyrosine in lung homogenates 

Lung capillary leakage 18 h after exposure 

Inhibition of NOS abrogated CO 

effects 

Sorhaug et al. 

(2006) 

Rats 200 ppm 

72 weeks 

No changes in lung morphology 

No pulmonary hypertension 

No atherosclerotic lesions in systemic vessels 

Ventricular hypertrophy 

Loennechen et 

al. (1999) 

Rats 100 and 200 ppm 

1-2 weeks 

Increased ET-1 mRNA in the heart ventricles, 

increased right and left ventricular weight 

12 and 23% COHb 

Favory et al. 

(2006b) 

Rats 250 ppm 

90 min 

Complex IV inhibition in myocardial fibers 

Inhibition of vasodilatory response to acetylcholine 

and SNP, Increased coronary perfusion pressure 

and contractility 

11% COHb 

Piantadosi et al. 

(2006) 

Rats 50 ppm CO or HH 

for 1, 3, or 7 days 

Liver mitochondrial oxidative and nitrosative stress, 

altered mitochondrial permeability pore transition 

sensitivity 

CO effects not mimicked by HH 

Suliman et al. 

(2007) 

Mice 50 and 250 ppm 

1 h 

Cardiac mitochondrial biogenesis Activation of GC involved. No role for 

NOS. Increased mitochondrial H2O2

and activation of Akt proposed 

Wellenius et al. 

(2004) 

Rats 

Model of MI 

35 ppm 

1 h 

Decreased delayed ventricular beat frequency Altered arrhythmogenesis 

Wellenius et al. 

(2006a) 

Rats 

Model of MI 

35 ppm 

1 h 

Decreased supraventricular ectopic beats Altered arrhythmogenesis 

Carraway et al. 

(2002) 

Rats 

Model of hypoxic 

pulmonary vascular 

remodeling 

Hypobaric hypoxia 

± 50 ppm CO 

3 weeks 

CO promoted remodeling and increased pulmonary 

vascular resistance 

Gautier et al. 

(2007) 

Rats 

Model of right ventricle 

hypertrophy secondary to 

chronic hypoxia 

3 weeks of HH with 

50 ppm CO during 

last week 

Rats with pulmonary hypertension were more 

sensitive to CO which altered the right ventricular 

adaptive response to pulmonary hypertension 

leading to ischemic lesions 

Melin et al. 

(2005) 

Rats 

Model of right ventricle 

hypertrophy secondary to 

chronic hypoxia 

50 ppm 

10 wk 

CO increased cardiac dilation and decreased left 

ventricular function 

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Reference Model System CO Exposure Response Notes 

Melin et al. 

(2002) 

Rats 

Model of right ventricle 

hypertrophy secondary to 

chronic hypoxia 

50 ppm 

10 wk 

CO increased right ventricular hypertrophy, 

decreased right ventricular diastolic function and 

increased left ventricular weights 

5.1.3.3. Implications of Non-Hypoxic Mechamisms 

A key issue in understanding the biological effects of environmentally-relevant exposures to CO is 

whether the resulting partial pressures of CO in cells and tissues can initiate cell signaling or perturb 

signaling which normally is mediated by endogenously generated CO. 

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Several aspects need to be considered. One is that, for most cells and tissues, Hb acts as a buffer to 

limit the availability of free CO. At the same time, COHb delivers CO to cells and tissues. CO 

dissociation from Hb occurs as a function of CO diffusion down a concentration gradient. Hence, greater 

release of CO from COHb will occur under conditions of low cell/tissue CO concentration. 

A second consideration is the role played by O2 in competing with CO for binding to intracellular 

heme protein targets. In general, heme proteins (e.g., cytochrome c oxidase) are more sensitive to CO 

when O2 is limited. Hence hypoxic conditions would be expected to enhance the effects of CO. This 

concept is demonstrated in the study by D’Amico et al. (2006). 

A third consideration is whether certain cell types serve as primary targets for the effects of CO. 

Besides the RBC, the first cells encountering CO which dissociates from Hb will be the endothelial cells 

lining blood vessels. White blood cells in the circulation may also be first-line targets of Hb-dissociated 

CO. An exception to this situation is in the lungs where epithelial and inflammatory cells found in 

airways and alveoli are exposed to free CO prior to CO binding to Hb. These lung cells may also serve as 

unique targets for CO. Processes such as endothelial dysfunction, inflammatory cell activation and 

respiratory epithelial injury may ensue as a result of preferential targeting of these cell types. 

Fourth, it should be considered that adaptation to chronic exogenous CO exposure might occur and 

that intermittent exogenous CO exposure might have unexpected effects. 

Since there is potential for exogenous CO to affect endogenous pools of CO, it is important to 

know the concentrations of CO in cells and tissues before and after exogenous exposures. Table 5-2 

summarizes findings from 4 recent studies relevant to this issue. It should be noted that exposure to 

50 ppm CO resulted in a 3-5 fold increased in tissue CO concentration. 

March 2009 5-11 DRAFT – DO NOT CITE OR QUOTE 
Table 5-2. Tissue concentration of CO following inhalation exposure. 

Reference CO Exposure Tissue CO Concentrations COHb Notes 

Cronje, et al. (2004) Rat 

2,500 ppm 

45 min 

Blood: 27,500 (800) pmol/mg 

Heart: 800 (300) pmol/mg 

Muscle: 90 (80) pmol/mg 

Brain: 60 (40) pmol/mg 

Control levels in parentheses 

66-72% CO concentration increased in the heart but not in brain or 

skeletal muscle after CO exposure 

A later report stated that these tissue CO values were too 

high due to a computational error (Piantadosi et al., 2006) 

Vreman, et al. (2005) Mice 

500 ppm 

30 min 

Blood: 2648 ± 400 (45) pmol/mg 

Heart: 100 ± 18 (6) pmol/mg 

Muscle: 14 ± 1 (10) pmol/mg 

Brain: 18 ± 4 (2) pmol/mg 

Kidney: 120 ± 12 (7) pmol/mg 

Spleen: 229 ± 55 (6) pmol/mg 

Liver: 115 ± 31 (5) pmol/mg 

Lung: 250 ± 2 (3) pmol/mg 

Intestine: 9 ± (4) pmol/mg 

Testes: 6 ± 3 (2) pmol/mg 

Control levels in parentheses 

28% CO concentration relative to 100% blood: 

Lung: 9.4% 

Spleen: 8.6% 

Kidney: 4.5% 

Liver: 4.3% 

Heart: 3.8% 

Brain: 0.7% 

Muscle: 0.5% 

Intestine: 0.3%, 

Testes: 0.2% 

Piantadosi et al. (2006) Rats 

50 ppm 

1-7 days 

Liver: 30-40 pmol/mg 

Control liver 10 pmol/mg 

4-5% 

Control 1% 

CO concentration plateaued after 1 day 

Suliman et al. (2007) Mice 

50, 250 and 

1250 ppm 

1 h 

Heart (left ventricle) 

50 ppm: 50 pmol/mg 

250 ppm: 95 pmol/mg 

1250 ppm: 160 pmol/mg 

Control heart: 9 pmol/mg 

No mention of COHb% but exposures were similar to 

those in Cronje et al. (2004) 

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Furthermore, endogenous CO production is increased during inflammation, hypoxia, increased 

heme availability and other conditions where HO-1 or HO-2 activity is increased. A few studies reported 

increased COHb levels and/or cell and tissue concentrations of CO resulting from enhanced endogenous 

CO production. Table 5-3 summarizes these findings. 

Table 5-3. Tissue concentration of CO following increased endogenous production. 

Reference Exposure Tissue CO COHb Notes 

Carraway,et al. (2000) Rats 

Hypobaric Hypoxia for 21 days 

1.5-2.8% 

Control 0.5% 

COHb highest after days 1 and 21 

at 3-4 fold higher than controls 

Piantadosi et al. (2006) Rats 

Hypobaric Hypoxia 

1-7 days 

Liver: 5-12 pmol/mg 

Control liver 10 pmol/mg 

1-1.25% 

Control 1% 

CO concentration plateaued after 1 day 

Vreman et al. (2005) Mice 

30 μM heme 

Blood: 88 ± 10 (45) pmol/mg 

Heart: 14 ± 3 (6) pmol/mg 

Muscle: 7 ± 1 (10) pmol/mg 

Brain: 2 ± 0 (2) pmol/mg 

Kidney: 7 ± 2 (7) pmol/mg 

Spleen: 11 ± 1 (6) pmol/mg 

Liver: 8 ± 3 (5) pmol/mg 

Lung: 8 ± 3 (3) pmol/mg 

Intestine: 3 ± 1 (4) pmol/mg 

Testes: 2 ± 0 (2) pmol/mg 

Control levels in parentheses 

0.9% CO concentration relative to 100% blood: 

Heart: 16% 

Spleen: 13% 

Lung: 9% 

Liver: 9% 

Kidney: 8% 

Muscle: 8% 

Intestine: 3% 

Brain: 2% 

Testes: 2% 

 

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It should be noted that increased cellular and tissue concentrations of biliverdin and iron 

accompany the increased endogenous production of CO by HO-1 and HO-2. Biliverdin and iron have 

known biological effects, with biliverdin exhibiting antioxidant properties and iron exhibiting pro-oxidant 

properties (Piantadosi, 2008), which could impact interpretation of results from studies in which HO-1 

and HO-2 activities are increased. Hence the situations of increased endogenous CO production and that 

of exogenous CO exposure are not equivalent. Nonetheless, additional measurements of CO levels in cells 

and tissues following increased endogenous production and following inhalation of exogenous CO is 

essential for a better understanding of the relationship between the CO tissue dose and the response. 

In summary, CO is a ubiquitous cell signaling molecule and the physiological functions of HOderived CO are numerous. The endogenous generation and release of CO from HO-1 and HO-2 is tightly 

controlled, as is any homeostatic process. Thus, exogenously-applied CO has the capacity to disrupt 

myriad heme-based signaling pathways due to its nonspecific nature. Only a limited amount of 

information is available regarding the impact of exogenous CO on tissue and cellular levels of CO. 

However recent animal studies demonstrated increased tissue CO levels and biological responses 

following exposure to 50 ppm CO. Whether or not environmentally relevant exposures to CO can affect 

endogenous CO signaling pathways and lead to adverse health effects is an open question for which there 

are no definitive answers at this time. 

5.2. Cardiovascular Effects 

This section characterizes the evidence from epidemiologic, controlled human exposure and animal 

toxicological studies on the cardiovascular effects of CO. While epidemiologic studies evaluated the 

effects of ambient exposures, experimental studies employed higher than ambient concentrations of CO 

but not levels of exposure associated with poisoning. 

5.2.1. Epidemiologic Studies 

The 2000 CO AQCD concluded that epidemiologic studies provided evidence that short-term 

variations in ambient CO concentrations were associated with daily hospital admissions for heart disease. 

The following section reviews the literature since the 2000 CO AQCD, including new studies on 

physiological cardiac endpoints and biomarkers and additional studies of daily hospital admissions for 

heart disease that support the evidence in the 2000 CO AQCD. 

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5.2.1.1. Epidemiologic Studies with Short-Term Exposure 

Heart Rate and Heart Rate Variability 

Heart rate variability (HRV) refers to the beat-to-beat alterations in the heart and is generally 

determined by analyses of time and frequency domains measured by electrocardiograms (ECG). The time 

domains often analyzed are (a) normal-to-normal (NN or RR) time interval between each QRS complex, 

(b) standard deviation of the normal-to-normal interval (SDNN), and (c) mean squared differences of 

successive difference normal-beat to normal-beat intervals (rMSSD), shorter time domain variables 

results in lower HRV. The frequency domains often analyzed are a) the ratio of low energy frequency (LF) 

to high energy frequency (HF) and b) the proportion of interval differences of successive normal-beat 

intervals greater than 50 ms (PNN50), reflecting autonomic balance. Decreased HRV is associated with a 

variety of adverse cardiac outcomes such as arrhythmia, myocardial infarction (MI), and heart failure (De 

Jong and Randall, 2005; Deedwania et al., 2005; Huikuri et al., 1999; Rajendra Acharya et al., 2006). 

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Two studies investigated the association between ambient air pollution, including CO, and HRV in 

Boston, MA and reported inconsistent results. The earlier of these studies recruited twenty-one 53-to 

87-year old active residents and performed up to 12 ECG assessments on each subject over a period of 4 

months (during summer 1997). Particles (PM10, PM2.5) and several gaseous pollutants (O3, NO2, and SO2) 

were monitored at fixed sites (up to 4.8 miles from the study site) while CO was monitored 0.25 miles 

from each participants’ residence. Lag periods for the preceding 1-h, 4-h, and 24-h before each subject’s 

HRV assessment were analyzed and results showed that only PM2.5 and O3 were associated with HRV 

parameters (Gold et al., 2000). 

A similar study by the same group of researchers two years later involved 28 older subjects (aged 

61-89) who were living at or near an apartment complex located on the same street as the Harvard School 

of Public Health. The subjects were seen once a week for up to 12 weeks and HRV parameters (SDNN, 

r-MSSD, PNN50, LF/HF ratio) were measured for 30 minutes each session. Data for PM2.5, black carbon 

(BC), and CO were recorded at the Harvard School of Public Health (<1 km from the residence) while 

data for NO2, O3, and SO2 were collected from government regulatory monitoring sites. There were 

moderate correlations between CO and PM2.5 (r = 0.61) and NO2 (r = 0.55), but not with SO2 (r = 0.18) or 

O3 (r = 0.21). Similarly PM2.5 was associated with HRV, whereas in contrast to the previous study, CO 

was associated1

 with a negative change in SDNN (% change: -13 [95% CI: -24.06 to -1.88]), r-MSSD 

(% change: -31.88 [95% CI: -38 to -7.5]), and PNN50 (% change: -46.25 [95% CI -103.95 to -9.38] per 

0.5 ppm increase in 24-h avg CO concentration) (Schwartz et al., 2005). 

1 The effect estimates from epidemiologic studies have been standardized to a 1 ppm increase in ambient CO for 1-h max CO 

concentrations, 0.75 ppm for 8-h max CO concentrations, and 0.5 ppm for 24-h avg CO concentrations throughout this section (text, tables, and 

figures).

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A later Boston, MA study examined HRV parameters (SDNN, LF, HF, LF/HF ratio) among 603 

persons from the Normative Aging Study, a longitudinal study that originally recruited 2,280 men in the 

greater Boston area during 1963. The cohort members were examined (November 2000 to October 2003) 

and the ECG data were linked to air pollution data for PM2.5, particle number concentration, BC, O3, NO2, 

SO2, and CO. Lagged pollutant effects for a 4-h, 24-h, and 48-h moving avg were used. Since previous 

studies established variable CO results, the main pollutant effects were with PM2.5 and O3 while CO was 

not associated with HRV (Park et al., 2005b). 

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A study in Mexico City selected 30 subjects from the outpatient clinic at the National Institute of 

Cardiology and followed them for ~10 hours (starting at 0900 hrs) (Riojas-Rodriguez et al., 2006). Each 

subject was connected to a Holter ECG monitor (e.g., a portable ECG monitor) and also given personal 

PM2.5 and CO monitors. The subjects went about their usual daily activities and the personal PM2.5 and 

CO data were linked to various ECG parameters (heart rate [HR], R-R, LF, HF) at various lags. In 

copollutant models (PM2.5 and CO) personal CO exposure for the same 5-min period was significantly 

associated with a decrease in LF and very low energy frequency (VLF) parameters with coefficients equal 

to -0.024 (95% CI: -0.041 to -0.007) and -0.034 (95% CI: -0.061 to -0.007) respectively for a 1 ppm 

increase in 1-h CO concentration. 

In Mexico City, 34 residents from a nursing home underwent HRV analysis every other day for 3 

months (Holguin et al., 2003). Exposure assessment for ambient PM2.5 was based on data recorded at a 

monitor on the roof of the nursing home while exposures to ambient O3, NO2, SO2, and CO were derived 

from data recorded at a fixed site 3 km from the nursing home. Exposures for the same day and 1-day lags 

were analyzed and only O3 and PM2.5 were positively associated with HRV. 

Wheeler et al. (2006) examined 18 individuals with COPD and 12 individuals with recent MI 

living in Atlanta, GA. Morning ECG readings were collected by a Holter system by a field technician in 

the subjects’ homes. Ambient air pollution exposures for PM2.5, O3, NO2, SO2 and CO were derived from 

data recorded at fixed sites throughout metropolitan Atlanta. Three exposure periods were analyzed: the 

hour of the ECG reading, 4-h mean and 24-h mean before the reading. While positive effects were 

reported for NO2 and PM2.5, no quantitative results were reported for CO. 

After reviewing 2,000 patient charts, Dales (2004) recruited 36 subjects with CAD from the 

Toronto Western Hospital’s noninvasive cardiac diagnostic unit. HR and HRV (SDNN, N-N, HF, LF, 

HF/LH ratio) were assessed 1 day each week for up to 10 weeks by a Holter monitoring system. Personal 

air sampling for PM2.5 and CO was carried out for the same 24-h period whereby subjects went about 

their usual daily activities for that period. Stratified results showed that among those not on beta-receptorblockers, personal CO exposure was positively associated with SDNN (p = 0.02). However, in the group 

taking beta blockers there was a negative association (p = 0.06). Personal exposure to PM2.5 was not 

associated with HRV. 

March 2009 5-15 DRAFT – DO NOT CITE OR QUOTE 
HR was examined among a sub-sample of the Monitoring of Trends and Determinants in 

Cardiovascular Disease (MONICA) study (n = 2,681) in Augsburg, Germany by Peters and colleagues 

(1999a). Total suspended particles (TSP), SO2, and CO data were collected from a single monitoring 

station located in the center of the city and linked to each subject to estimate exposures on the same day 

and 5 days prior. A 0.5 ppm change in 24-h CO concentration was associated with an increase in HR of 

approximately 1 beat per minute, whereas CO based on a 5-day exposure had no effect on HR. 

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Liao et al (2004) investigated men and women aged 45-64 years from the Atherosclerosis Risk in 

Communities (ARIC) study (Washington County, MD; Forsyth County, NC; and selected suburbs of 

Minneapolis, MN). The sample sizes were 4,899, 5,431, 6,232, 4,390 and 6,784 for analyses involving 

PM10, O3, CO, NO2, and SO2 respectively. County level exposure estimates for 24 h CO were calculated 

for 1, 2, and 3 days prior to clinical examination. A 0.5 ppm increase in 24-h CO concentration (at lag 1) 

was associated with an increase in HR (beats/minute) (β = 0.357, p <0.05). CO was not significantly 

associated with changes in SDNN. 

The Exposure and Risk Assessment for Fine and Ultrafine Particles in Ambient Air (ULTRA) study 

was carried out in three European cities: Amsterdam, the Netherlands, Erfurt, Germany, and Helsinki, 

Finland, whereby a panel of subjects with CAD was followed for 6 months with biweekly clinical visits, 

which included an ECG reading to assess HRV (Timonen et al., 2006). The time domain measures of 

HRV (SDNN and rMSSD) were analyzed along with frequency domain measures, which included power 

spectrum densities for LF and HF. Exposures to ambient air pollution (PM2.5, PM10, NO2, CO) were 

derived from data recorded at fixed monitoring site networks within each city. Correlation coefficients for 

NO2 and CO ranged from 0.32 to 0.86 in the three cities. CO was moderately correlated with PM10 in 

Helsinki (r = 0.40) and with PM2.5 in Amsterdam (r = 0.58) and more highly correlelated with PM10 in 

Erfurt (r = 0.77). Various lag periods were examined including lag 0 (24 h prior to the clinical visit) 

through a 0-2-day avg lag and a 0-4-day avg lag. In total there were 1,266 ECG recordings used in the 

final analyses. In the pooled analyses (e.g., across cities) a 0.5 ppm increase in 24-h CO concentration 

was associated with a decrease in LF/HF ratio at lag 1-day (β -16.4 [95% CI: -29.9 to -0.3]), and a 

decrease in SDNN and HF at lag 2-day (β -3.4 [95% CI: -6.1 to -0.4]; β= -17.6 [95% CI: -34.4 to -0.9], 

respectively). However, the same study reported no effect for CO on BP and HR (Ibald-Mulli et al., 

2004). 

A small panel study in Kuopio, Finland, which was designed as the pilot study for the ULTRA 

study examined simultaneous ambulatory ECG and personally monitored CO readings among 6 male 

patients with CAD (Tarkiainen et al., 2003). The patients were asked to follow their usual daily activities, 

but data were recorded only three times with 1-week intervals. The CO exposures were divided into low 

(≤ 2.7 ppm) and high (>2.7 ppm) and during the high CO exposure r-MSSD increased on average by 2.4 

ms. However, there was no effect on RR or SDNN. 

March 2009 5-16 DRAFT – DO NOT CITE OR QUOTE 
A study in Taiwan recruited 83 patients (aged 40-75) from the National Taiwan University 

Hospital, Taipei and conducted ambulatory ECG readings using a Holter system (Chan et al., 2005). 

Ambient air pollution exposures for PM10, NO2, SO2, and CO were derived from 12 fixed site monitoring 

stations across Taipei. Lag periods of 1 h to 8 h prior to the ECG reading were analyzed and only NO2

was associated with HRV parameters (SDNN and LF). CO was not associated with HRV. 

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The ST-segment of an ECG represents the period of slow repolarization of the ventricles and 

ST-segment depression can be associated with adverse cardiac outcomes. Gold et al. (2005) recruited a 

panel of 28 older adults living at or near an apartment complex located within 1 km of a monitoring site in 

Boston, MA. Each subject underwent weekly ECGs for 12 weeks in summer 1999 with the main outcome 

of interest being the ST-segment. Air pollution data in the form of PM2.5, black carbon (BC), and CO were 

collected from a central site within 0.5 km of the residences of the subjects and averaged over various lag 

periods (1-24 h, 12 h and 24 h moving average [ma] ) before the ECG. The final analyses included 24 

subjects with 269 observations and results showed consistent negative associations of ST-segment level 

with increased BC with the strongest association with the 5-h lag. CO during the same lag period also 

showed a negative association with ST-segment depression, however only BC remained significant in 

multipollutant models. 

In summary, few studies have examined the effect of CO on HR and while two of the three studies 

reported a positive association, further research is warranted to corroborate the current results. Similarly, 

while a larger number of studies have examined the effect of CO on various HRV parameters, mixed 

results have been reported throughout these studies. Furthermore, with several HRV parameters often 

examined, there are mixed results across the studies as to the HRV parameters that are positively 

associated with CO exposure. Table 5-4 shows a summary of the reviewed studies. 

Table 5-4. Summary of studies investigating the effect of CO exposure on HRV parameters. 

Study Location (Sample Size) Cardiac Endpoint Exposure 

Assessment 

Mean CO Level 

(ppm) Copollutants 

Gold et al. (2000) Boston, MA 

(n = 21) 

HR, SDNN, 

r-MSSD 

Ambient Mean: 0.47(24 h) 

Range: 0.12-0.82 

PM10, PM2.5, O3, NO2, 

SO2

Schwartz et al. 

(2005) 

Boston, MA 

(n = 28) 

SDNN, 

r-MSSD, PNN, LF/HF 

Ambient 25th, 50th, 75th 

percentiles: 

0.38, 0.45, 0.54 

PM2.5, BC, NO2, O3

Park et al. 

(2005b) 

Boston, MA 

(n = 4 97) 

SDNN, LF, HF, LF/HF Ambient Mean: 0.50 (24 h) 

Range: 0.13-1.8 

PM2.5, BC, O3, NO2, SO2

Riojas-Rodriguez 

et al. (2006) 

Mexico City, Mexico 

(n = 30) 

HF, LF, VLF, HR, R-R Personal Mean: 2.9 (11 h) 

Range: 0.1-18 

PM2.5

Holguin et al. 

(2003) 

Mexico City, Mexico (n = 34) HF,Double check 

LF, LF/HF 

Ambient Mean: 3.3(24 h) 

Range: 1.8-4.8 

PM2.5, O3, NO2, SO2

March 2009 5-17 DRAFT – DO NOT CITE OR QUOTE 
Study Location (Sample Size) Cardiac Endpoint Exposure 

Assessment 

Mean CO Level 

(ppm) Copollutants 

Wheeler et al. 

(2006) 

Atlanta, GA 

(n = 30) 

SDNN, r-MSSD, PNN, LF, 

HF, LF/HF 

Ambient Mean: 362 ppb (4h) 

25th, 50th, 75th 

percentiles: 

221.5, 304.3, 398.1 

PM2.5, O3, NO2, SO2

Dales et al. 

(2004) 

Toronto, Canada 

(n = 36) 

SDNN, HF, LF, LF/HF, N-N Personal Mean: 2.4* 

Range: 0.4-16.5 

PM2.5

Peters et al. 

(1999a) 

Augsburg, Germany (n = 2681) HR Ambient Mean: 3.6 

Range: 1.5-7.1 

TSP, SO2

Liao et al. (2004) Maryland, North Carolina, 

Minnesota, 

(n = 4899-6784) 

HR, SDNN, LF, HF Ambient Mean: 0.65 (24 h) PM10, O3, NO2, SO2

Timonen et al. 

(2006) 

Amsterdam, the Netherlands; 

Erfurt, Germany; Helsinki, Finland 

(n = 131) 

SDNN, HF, LF/HF Ambient Mean: 0.35-0.52 

Range: 0.09-2.17 

PM2.5, PM10, NO2

Ibald-Mulli et al. 

(2004) 

Amsterdam, the Netherlands; 

Erfurt, Germany; Helsinki, Finland 

(n = 131) 

BP, HR Ambient Mean: 0.35-0.52 

Range: 0.09-2.17 

UFP, PM10, PM2.5, NO2, SO2

Tarkiainen et al. 

(2003) 

Kuopio, Finland 

(n = 6) 

PNN, SDNN, r-MSSD Personal Mean: 4.6 

Range: 0.5-27.4 

None 

Chan et al. (2005) Taipei, Taiwan 

(n = 83) 

SDNN, 

r-MSSD, LF 

Ambient Mean: 1.1 

Range: 0.1-7.7 

PM10, NO2, SO2

Gold et al. (2005) Boston, MA 

(n = 28) 

ST-segment Ambient Median: 0.56 (12 h) 

Maximum: 1.04 

PM2.5, BC 

*95th percentile of 24-h levels 

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Cardiac arrhythmia refers to a broad group of conditions where there is irregular electrical activity 

in the heart. The main types of arrhythmias are fibrillation, tachycardia, and bradycardia, all of which can 

be associated with the upper (atria) and lower (ventricle) chambers of the heart. Briefly, fibrillation refers 

to when a chamber of the heart quivers chaotically rather than pumps in an orderly fashion, tachycardia 

refers to a rapid heart beat (e.g., >100 beats/minute) while bradycardia refers to a slow heart beat 

(e.g., <60 beats/minute). A few air pollution panel studies have examined the occurrence of cardiac 

arrhythmias by analyzing data recorded by implantable cardioverter defibrillators (ICDs) among cardiac 

patients. The majority of these studies were conducted in North America with the main outcome 

investigated being tachycardia. Results of these studies provide little evidence for an association between 

cardiac arrhythmia and ambient CO. 

For example, Dockery and colleagues (2005) analyzed the relationship between ambient air 

pollution and the daily incidence of ventricular tachyarrhythmia among 203 patients with ICDs in Boston, 

MA. An hourly city average for the Boston metropolitan area was calculated for CO, O3, NO2, SO2, SO4

2-, 

BC, and PM2.5. Although positive associations between ventricular arrhythmic episode days were found 

for all mean pollutant levels on the same day and previous days, none of these associations approached 

statistical significance. However, when the analyses were stratified by patients who had a previous 

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incidence of ventricular arrhythmia within 3 days, or greater than 3 days to the day of interest, a 0.5 ppm 

increase in 24-h CO concentration was positively associated with incidence of ventricular arrhythmia 

(OR: 1.68 [95% CI: 1.18-2.41) among those who had a ventricular arrhythmia within the last 3 days. 

A similar study in eastern Massachusetts examined cardiac arrhythmia by analyzing defibrillator 

discharges precipitated by either ventricular tachycardia or fibrillation among 100 cardiac patients (Peters 

et al., 2000b). Exposure to ambient CO was estimated for the same day, 1-day, 2-day, 3-day, and a 5-day 

mean lag period. Co was moderately correlated with PM10 (r = 0.51) and PM2.5 (r = 0.56) and more highly 

correlated with NO2 (r = 0.71). When analyzing patients who had at least one defibrillator discharge 

(n = 33) there was no association with CO. However, when analyzing patients who had at least 10 

discharges (n = 6), a 0.5 ppm increase in 24-h CO concentration (lag 0-4) was associated with an 

increased odds of a defibrillator discharge (OR: 1.66 [95% CI: 1.01-2.76]). 

In contrast, other air pollution panel studies conducted in St Louis, MO (among 56 subjects) (Rich 

et al., 2006b), Atlanta, GA (among 518 subjects) (Metzger et al., 2007), Boston, MA (among 

203 subjects) (Rich et al., 2005), and Vancouver, Canada (Rich et al., 2004; Vedal et al., 2004) (among 34 

and 50 subjects respectively) did not find an association between short term changes in ambient CO and 

occurrence of cardiac arrhythmia in patients with implantable defibrillators. The study in Boston also 

examined atrial fibrillation episodes among the same group of subjects and also did not find an 

association with ambient CO (Rich et al., 2005). 

An alternative method used to assess the relationship between cardiac arrhythmia and ambient air 

pollution is to analyze cardiac data recorded via ECG. Two studies have employed this method and 

reported inconsistent results. A study in Steubenville, OH, which is located in an industrial area, examined 

weekly ECG data among 32 non-smoking older adults for 24 weeks during summer and fall (Sarnat et al., 

2006). Ambient exposures for up to 5 days prior to the health assessment (based on a 5-day moving 

average) were calculated for PM2.5, SO4

2-, elemental carbon (EC), O3, NO2, SO2, and CO from data 

recorded at one central monitoring site. Increases in ambient CO were not associated with increased odds 

of having at least one arrhythmia during the study period. 

In contrast, a study in Germany examined the relationship between ambient air pollution and the 

occurrence of supraventricular (atria) and ventricular tachycardia recorded via monthly 24-h ECGs among 

57 subjects over a 6 month period (Berger et al., 2006). Exposure estimates were calculated for ambient 

ultrafine particles, PM2.5, CO, NO, NO2, and SO2 for various lag periods (0-23 h, 24-47 h, 48-71 h, 

72-95 h, and 5-day avg) prior to the ECG. Results showed that a 0.5 ppm increase in ambient 24-h CO 

concentration (lag 0-4 days prior to ECG) was positively associated with the occurrence of 

supraventricular tachycardia (OR: 1.36 [95% CI: 1.08-1.74]). However, ambient CO was not associated 

with ventricular tachycardia. 

In summary, few studies have examined associations between CO and the occurrence of cardiac 

arrhythmias, and these studies provided little evidence of a CO effect on cardiac arrhythmias. While more 

March 2009 5-19 DRAFT – DO NOT CITE OR QUOTE 
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studies analyzed data from ICDs, very few reported significant associations. This was similar for the 

mixed results from the two studies that analyzed ECG data to evaluate cardiac arrhythmias in association 

with CO exposures. Table 5.5 summarizes the reviewed studies. 

Table 5.5. Summary of studies investigating the effect of CO exposure on cardiac arrhythmias. 

Study Location, 

Sample Size Cardiac Endpoint Exposure 

Assessment 

Mean CO Level 

(ppm) Copollutants 

ARRHYTHMIAS (AMONG PATIENTS WITH ICDS) 

Dockery et al. 

(2005) 

Boston, MA 

(n = 203) 

Ventricular 

Tachycardia 

Ambient 25th, 50th, 75th, 95th, percentiles: 

0.53, 0.80, 1.02, 1.37 (2-day) 

PM2.5, BC, O3, NO2, SO2, SO4

2-

Peters et al. 

(2000b) 

Massachusetts, 

(n = 100) 

Ventricular fibrillation or 

tachycardia 

Ambient Mean: 0.58 (24 h) 

Max: 1.66 

PM2.5, PM10, BC, O3, NO2, SO2, 

SO4

2-

Rich et al. (2006b) Boston, MA 

(n = 56) 

Ventricular arrhythmia Ambient 25th, 50th, 75th percentiles: 

0.4, 0.5, 0.6 (24 h) 

PM2.5, EC, O3, NO2, SO2

Metzger et al. 

(2007) 

Atlanta, GA 

(n = 518) 

Ventricular 

 Tachycardia 

Ambient Mean: 1.7 (1 h) 

Range: 0.1-7.7 

PM10, PM2.5, O3, NO2, SO2

Rich et al. (2005) Boston, MA 

(n = 203) 

Atrial fibrillation Ambient 25th, 50th, 75th, 95th, percentiles: 

0.53, 0.80, 1.02, 1.37 (2-day) 

PM2.5, BC, O3, NO2, SO2

Rich et al. (2004) Vancouver, 

Canada 

(n = 34) 

ICD discharge due to 

arrhythmia 

Ambient Mean: 0.55 (24 h) 

IQR: 0.16 

PM2.5, PM10, EC, O3, NO2, SO2, 

SO4

Vedal et al. (2004) Vancouver, 

Canada 

(n = 50) 

ICD discharge due to 

arrhythmia 

Ambient Mean: 0.6 (24 h) 

Range: 0.3-1.6 

PM10, O3, NO2, SO2

ARRHYTHMIAS (VIA ECG) 

Sarnat et al. (2006) Steubenville, OH 

(n = 32) 

Atrial or ventricular 

tachycardia 

Ambient Mean: 0.2 (24 h) 

Range: 0.1, 1.5 

PM2.5, O3, NO2, SO2, SO4, EC 

Berger et al. (2006) Erfurt, Germany 

(n = 57) 

Atrial or ventricular 

tachycardia 

Ambient Mean: 0.45 (24 h) 

Min, Med, Max 

0.10, 0.38, 1.68 

PM10, PM2.5, NO2, NO, SO2, UF 

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Cardiac arrest refers to the abrupt loss of heart function due to failure of the heart to contract 

effectively during systole, which can lead to sudden cardiac death if not treated immediately. Very few 

studies have investigated the association between ambient CO exposure and the risk of cardiac arrest and 

none reported a significant link between increased CO exposure and the occurrence of cardiac arrest. 

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Two similar studies were conducted in Seattle, WA, and both did not report an association between 

ambient CO and cardiac arrest. Both studies employed a case-crossover design and examined air pollution 

exposures for black smoke particles (BSP), PM10, SO2, and CO. The correlation coefficient for PM10 and 

CO was 0.81. The first of these studies examined paramedic-attended out-of-hospital primary cardiac 

arrests among 362 cases (between 1998-1994) in Seattle and King County, WA whereby lags of 0-5 days 

were analyzed (Levy et al., 2001). The second of these studies examined out-of-hospital primary cardiac 

arrest for a ten year period (1985-1994) among subjects within a health organization database (the Group 

Health Cooperative of Puget Sound) whereby 0-day through 2-day lags were analyzed (Sullivan et al., 

2003). 

Myocardial Infarction 

As previously stated, MI is commonly referred to as ‘heart attack’ and is another cardiac outcome 

that has received limited attention within the area of air pollution research. Only one study has 

investigated the association between short-term changes in ambient CO and the onset of MI. Peters and 

colleagues (2001) employed a case-crossover study design to analyze short term exposures (0-5 h and 0-

5 days before the onset of MI) to particles (PM10, PM2.5, PM10-2.5, BC) and gases (CO, O3, NO2, SO2) 

among 772 patients with MI in the greater Boston area. While all pollutants showed positive associations 

with the onset of MI, only PM2.5 reached statistical significance with the main exposure period being 2 h 

before the onset. 

Blood Pressure 

Only two studies have investigated whether short-term ambient CO influences BP. The earlier of 

these two studies examined BP among 2607 men and women aged 25-64 who participated in the 

Augsburg, Germany MONICA study (Ibald-Mulli et al., 2001). Exposures to ambient TSP, SO2 and CO 

(from one monitor in the center of the city) during the same day as the BP reading and an average over the 

5 days prior were examined. Results showed that ambient CO had no association with BP. 

Similarly, the second of these studies extracted baseline and repeated-measures of cardiac 

rehabilitation data from a Boston, MA hospital for 62 subjects with 631 visits and analyzed ambient air 

pollution exposures (with particular focus on PM2.5) averaged over various periods up to 5 days before the 

visit (Zanobetti et al., 2004b). While results showed significant associations between increased BP and 

ambient PM2.5, SO2, O3, and BC, there was no significant effect for CO. 

Blood Markers of Coagulation and Inflammation 

Several studies have investigated the association between ambient CO and various blood markers 

related to coagulation and inflammation. The main endpoints analyzed have been plasma fibrinogen, 

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Factor VII, C-reactive protein (CRP), prothrombin, intercellular adhesion molecule (ICAM-1), and white 

blood cell count (WBC). 

Pekkanen et al. (2000) examined the association between daily concentrations of air pollution and 

concentrations of plasma fibrinogen measured among 4982 male and 2223 female office workers in 

Whitehall, London, U.K. between September 1991 and May 1993. Plasma fibrinogen data were linked to 

ambient exposure to BS, PM10, O3, NO2, SO2, and CO, where the exposures were derived from data 

recorded at 5 fixed sites across London. There was a high correlation between levels of CO and NO2

(r = 0.81) and more moderate correlations with PM10 (r = 0.57) and SO2 (r = 0.61). The pollution data on 

the same day when the blood sampling was done (lag 0) and on the 3 previous days (lags 1-3) were 

analyzed. Results showed that ambient CO at all lags was significantly associated with an increase in 

plasma fibrinogen. Results were similar for NO2 while all other pollutants were not associated with an 

increase in plasma fibrinogen. 

Liao et al. (2005) examined associations between various air pollutants and hemostatic and 

inflammatory markers (fibrinogen, factor VIII-C, von Willebrand factor, serum albumin, WBC) among 

10,208 middle-aged males and females from the ARIC study. Exposure estimates for ambient PM10, NO2, 

SO2, O3 and CO were calculated for days 1-3 prior to the blood sampling. A 0.5 ppm increment in 24-h 

CO concentration was significantly associated with 0.015 g/dL decrease in serum albumin among persons 

with a history of CVD. CO was not associated with other hemostatic or inflammatory factors. 

In Israel, Steinvil et al. (2008) examined WBC, fibrinogen, and CRP among 3,659 study subjects 

enrolled in the Tel-Aviv Sourasky Medical Center inflammation survey, in which subjects lived <11km 

from an ambient air pollution monitor. Air pollution data in the form of PM10, NO2, SO2, O3, and CO were 

derived from data recorded at fixed sites. The correlations coefficients were high between CO and NO2

(r = 0.86) and PM10 (r = 0.75). Exposures for lag days 1-7 were analyzed and ambient CO had a 

significant negative effect on fibrinogen only among males. Significant associations were reported for lag 

0 (e.g., same day) and lags 2-5 with the decrease in fibrinogen ranging from -5.5 mg/dL to -9.8 mg/dL per 

0.5 ppm increase in 24-h CO concentration. A similar negative effect for CO was observed on WBC 

among males only. The average CO exposure over the week prior to the sampling yielded the largest 

reduction in WBC (-263 cells/μL). 

In a German study, Rückerl and colleagues (2006) recruited 57 non-smoking male patients with 

CHD who were scheduled for 12 subsequent clinical visits where samples of blood were collected. The 

authors tested the primary hypothesis that CRP would increase in association with a rise in air pollution 

levels. CRP is an acute phase protein that increases during inflammatory processes in the body. Other 

markers of inflammation (serum amyloid A [SAA]), cell adhesion (E-selectin, von Willebrand factor 

antigen [vWF], ICAM-1), and coagulation (fibrinogen, factor VII [FVII], prothrombin fragment 1+2) 

were also examined. Ambient air pollution in the form of PM10, ultrafine particles (UFP), EC, NO2, and 

CO was monitored at one central site and a 24-h avg immediately preceding the clinic visit (lag 0) and up 

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to 5 days (lags 1-4) was calculated for each patient. For CRP, the odds of observing CRP concentrations 

above the 90th percentile were 2.41 (95% CI: 1.23-5.02) in association with a 0.5 ppm increase in 24-h 

CO concentration (lag 2). CO concentration during lags 1 and 2 was associated with observing ICAM-1 

concentrations above the 90th percentile (OR: 2.41 [95% CI: 1.49-4.04]; OR: 3.17 [95% CI: 1.77-6.11], 

respectively). CO concentration during lags 0-3 was associated with a decrease in FVII. 

A similar study by Ruckerl and colleagues (2007) was conducted among 1,003 MI survivors across 

6 European cities (Athens, Greece; Augsburg, Germany; Barcelona, Spain; Helsinki, Finland; Rome, 

Italy; Stockholm, Sweden). The study compared repeated measurements of interleukin-6 (IL-6), CRP and 

fibrinogen with concurrent ambient levels of air pollution (particle number count [PNC], PM10, PM2.5, 

NO, NO2, O3, SO2, CO) from fixed sites across each city. Lags 0-1 and the 5-day mean prior to the blood 

sampling were analyzed and ambient CO was not associated with any of the inflammatory endpoints. 

Baccarelli et al. (2007) recruited 1,218 healthy individuals from the Lombardia region in Italy and 

assessed whether blood coagulability is associated with ambient air pollution. The main blood 

coagulability endpoints of interest were prothrombin time (PT) and activated partial thromboplastin time 

(APTT), which are measures of the quality of the coagulation pathways, assuming that, if shortened these 

measures would reflect hypercoagulability. Air pollution data (PM10, O3, NO2, and CO) were obtained 

from 53 fixed stations across the Lombardia region, which was divided into nine different study areas and 

a network average for each pollutant was calculated across the available monitors within each of the nine 

study areas. The analyses examined air pollution at the time of the blood sampling as well as averages for 

the 7 days prior and 30 days prior. Results showed that ambient CO at the time of blood sampling was 

associated with a decrease in PT (coefficient = -0.11 [95% CI: -0.18 to -0.05, p <0.001), indicating 

hypercoagulability. However, PM10 and NO2 at the time of blood sampling were also associated with a 

decrease in PT and results from multipollutant models were not reported. Acute phase reactants such as 

fibrinogen, and naturally occurring anticoagulants such as antithrombin, protein C and protein S were 

examined and none were associated with ambient air pollution. 

In summary, despite the small number of studies, there was some evidence of a significant link 

between CO exposure and blood markers of coagulation and inflammation. Table 5.6 summarizes the 

reviewed studies. 

March 2009 5-23 DRAFT – DO NOT CITE OR QUOTE 
Table 5-6. Summary of studies investigating the effect of CO exposure on blood markers of 

coagulation and inflammation. 

Study Location, 

Sample Size Cardiac Endpoint Exposure 

Assessment Mean CO Level (ppm) Copollutants 

Pekkanen et al (2000) London, U.K. 

(n = 7205) 

Plasma fibrinogen Ambient Mean: 1.22 (24 h) 

10th, 50th, 90th, Max: 

0.61, 1.04, 2.0, 8.61 

PM10, BS, O3, NO2, SO2

Liao et al (2005) USA 

(n = 10.208) 

Fibrinogen, VII-C, WBC, 

albumin, vWF 

Ambient Mean: 1.4 (24 h) PM10, O3, NO2, SO2

Steinvil et al (2008) Tel-Aviv, Israel 

(n = 3659) 

CRP, fibrinogen, 

WBC 

Ambient Mean: 0.8 

25th, 50th, 75th percentiles: 

0.7, 0.8, 1.0 

PM10, O3, NO2, SO2

Ruckerl et al (2006) Erfurt, Germany 

(n = 57) 

CRP, SAA, cell 

adhesions and 

coagulation 

Ambient Mean: 0.45 (24 h) 

Range: 0.10, 1.68 

PM10, PM2.5, UFP, EC, NO2

Rukerl et al (2007) Six European cities 

(n = 1003) 

IL-6, CRP, fibrinogen Ambient Mean (24 h): 0.29-1.48 PM10, PM2.5, O3, NO2, SO2

Baccarelli et al (2007) Lombardia region, Italy 

(n = 1218) 

PT, APTT, fibrinogen, 

anticoagulants 

Ambient Mean: 1.14-3.11 

Max: 5.52-11.43 

PM10, O3, NO2, SO2

Hospital Admissions and Emergency Department Visits 

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Since the 2000 CO AQCD there have been a number of studies investigating the effect of ambient 

CO on hospital admissions and ED visits for cardiovascular diseases. Some of these studies have focused 

solely on one specific CVD outcome, and these studies are discussed first. This is followed by a 

discussion of studies that investigated admissions for all CVD outcomes (e.g., non-specific) or a variety 

of specific CVD outcomes. 

Ischemic Heart Disease, Myocardial Infarction, and Angina Pectoris 

Ischemic heart disease (IHD), also known as coronary heart disease (CHD), is caused by 

inadequate circulation of the blood to the heart muscle, which is a result of the heart arteries being 

blocked by cholesterol deposits. IHD can lead to sudden episodes such as MI (“heart attack”) or death, as 

well as chronic conditions such as angina pectoris (chest pain). 

Ischemic Heart Disease. Very few studies have focused directly on hospitalizations for IHD. 

There is a lot of variation among these studies with regard to methods employed and results reported. It 

should be noted that within these studies IHD included MI and angina pectoris (ICD-9 codes 410-414; 

ICD-10 codes 120, 121-123, 124). Mann and colleagues (2002) investigated the modifying effect of 

secondary diagnosis of arrhythmia and congestive heart failure (CHF) on the relationship between 

hospital admissions for IHD (ICD-9: 410-414) and ambient air pollutants for the period of 1988 to 1995 

in southern California. There were 54,863 visits analyzed and a 0.75 ppm increase in 8-h max CO 

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concentration was associated with a 2.69% (95% CI: 1.21-4.19) increase in same-day IHD admissions 

among persons with a secondary diagnosis of CHF, a 2.23% (95% CI: 1.35-3.13) increase among persons 

with a secondary diagnosis of arrhythmia, and a 1.21% (95% CI: 0.49-1.94) among persons without either 

secondary diagnosis. Of all pollutants examined (PM10, NO2, O3, CO), only NO2 showed similar positive 

effects to CO and no multipollutant models were analyzed. The correlation coefficients between CO and 

NO2 ranged from 0.64 to 0.86 across the seven regions. This study indicated that people with IHD and 

accompanying CHF and /or arrhythmia are a sensitive group in relation to the effects of ambient air 

pollution. 

By using a time-series approach, ED visits for IHD (ICD-9: 410-414) in Montreal, Canada 

(1997-2002) were examined in relation to ambient CO concentrations (lags 0 and 1) (Szyszkowicz, 2007). 

A total of 4,979 visits were analyzed and results showed significant positive effects with a 0.5 ppm 

increase in 24-h CO concentration (lag 0) attributing to a 14.1% (95% CI: 5.8-20.6) increase in daily ED 

visits among all patients. Stratified analyses showed that this effect was mostly among male patients 

(19.8% [95% CI: 9.2-31.6]). NO2 was the only other pollutant examined, and it too showed significant 

positive associations with ED visits for IHD for same-day exposure; however, no multipollutant models 

were examined. 

Lee and colleagues (2003b) examined daily counts of hospital admissions for IHD in Seoul, Korea 

for the period from December 1997 to December 1999. Single-day lags 0 to 5 were analyzed and the lag 

period with the strongest association for each pollutant was chosen. For CO, lag 5 showed the strongest 

effect with a 1 ppm increase in 1-h maximum (max) CO concentration associated with a daily increase in 

the number of hospital admissions for IHD; however, this was only among patients 64+ years of age (RR: 

1.07 [95% CI: 1.01-1.13]). All other pollutants (PM10, O3, NO2) except SO2 showed similar significant 

effects and in a two-pollutant model with PM10 the CO effect attenuated toward the null. 

Other studies have examined hospital admissions for IHD while investigating a broad group of 

CVD outcomes. A study was conducted in Atlanta, GA, where over 4 million ED visits from 31 hospitals 

for the period 1993 to 2000 were analyzed (Study of Particles and Health in Atlanta [SOPHIA]). Several 

articles have been published from this research with two examining cardiovascular admissions in relation 

to CO concentrations. The first of these (Metzger et al., 2004a) used a time-series design and analyzed a 

3-day moving average over single-day lags 0-2 as the a priori lag structure. Although of borderline 

statistical significance, CO was positively associated with an increase in ED visits for IHD (RR 1.016 

[95% CI: 0.999-1.034] per 1 ppm increase in 1-h max CO concentration). 

The second of these reports (Peel et al., 2007) examined the association of ambient air pollution 

levels and cardiovascular morbidity in visits with and without specific secondary conditions (e.g., comorbidity). Within a time-stratified case-crossover design using the same lag structure already mentioned, 

the main results showed that a 1 ppm increase in 1-h max CO concentration was associated with an 

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increase in IHD among those without diabetes (OR: 1.023 [95% CI: 1.004-1.042]), and without CHF 

(OR: 1.024 [95% CI: 1.006-1.042]). 

Two Australian studies have also examined associations between ambient CO concentrations and 

increased hospital admissions for various CVD outcomes. The first of these studies (Barnett et al., 2006) 

analyzed data from 5 of the largest cities in Australia (Brisbane, Canberra, Melbourne, Perth, Sydney) and 

two New Zealand cities (Auckland, Christchurch) for the period 1998-2001. A time-stratified casecrossover design was employed and the age groups of 15-64 years and ≥ 65 years were analyzed for the 

0-1 lag period. The pooled estimates across all cities showed that a 0.75 ppm increase in 8-h max CO 

concentration was associated with a 1.9% (95% CI: 0.7-3.2) increase in admissions for IHD, but only 

among the elderly group (≥ 65 years). 

The second of the Australian studies (Jalaludin et al., 2006) examined ED visits for CVD outcomes 

in the elderly (65+ years) in Sydney for the period 1997 to 2001. Using a time-series approach, single-day 

lags of 0, 1, 2, 3 and an average over lags 0 and 1 were examined. A 0.75 ppm increase in 8-h max CO 

concentration (lag 0) was associated with increases in IHD emergency department visits of 3.1% 

(95% CI: 1.3-4.9). 

Myocardial Infarction. Linn et al. (2000) examined the association between ambient air pollution 

and hospital admissions for cardiopulmonary illnesses in metropolitan Los Angeles for the years 

1992-1995. Using a time-series approach, a 0.5 ppm increase in same-day 24-h avg CO concentration was 

associated with a 2.0% increase in MI hospital admissions among people aged >30 years. When the 

analyses were stratified by season, no significant effects were observed (No quantitative seasonal effects 

reported). A time-series study in Denver, Colorado, investigated daily hospital admissions for various 

CVD outcomes among older adults (>65 years) across 11 hospitals (Koken et al., 2003). Data between 

July and August for the period 1993-1997 were analyzed. Single-day lags 0 to 4 were examined and CO 

showed no association with hospital admissions for MI (quantitative results were not reported). 

As part of the HEAPSS (Health Effects of Air Pollution among Susceptible Subpopulations) study, 

Lanki et al. (2006) investigated the association between traffic-related exposure to air pollutants and 

hospitalization for first acute myocardial infarction (AMI). Data were collected from five European cities 

with either AMI registers (Augsburg, Barcelona), or hospital discharge registers (Helsinki, Rome, 

Stockholm). Correlation coefficients between CO and NO2 ranged from 0.43 to 0.75 across the five cities, 

and for PM10 the range was 0.21 to 0.56. A total of 26,854 hospitalizations were analyzed and pooled 

estimates from all 5 cities showed that there was a weak positive association with AMI hospitalizations 

and 24-h avg CO concentrations at lag 0 (RR: 1.014 [95% CI: 1.000-1.029] per 0.5 ppm increase), but 

more so when only using data from the three cites (Helsinki, Rome, Stockholm) with hospital discharge 

registers (RR: 1.020 [95% CI: 1.003-1.035] per 0.5 ppm increase). When analyses were stratified by 

fatality and age, results showed that the CO effect was significantly associated with fatal AMI among the 

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<75-year age group (RR: 1.080 [95% CI: 1.017-1.144), and with non-fatal AMI in the ≥ 75-year age 

group (RR: 1.044 [95% CI: 1.011-1.076). 

Further analyses within the HEAPSS cohort was conducted where the event of cardiac readmission 

among the first MI survivors (n = 22, 006) was linked to ambient air pollution (von Klot et al., 2005). The 

readmissions of interest were those with primary diagnosis of AMI, angina pectoris, dysrhythmia, and 

heart failure that occurred at least 29 days after the index event. Single-day lags 0 to 3 were examined and 

pooled estimates from all 5 cities showed that a 0.5 ppm increase in same-day (lag 0) CO was associated 

with an increase in cardiac (e.g., any of the diagnoses) readmissions (RR: 1.041 [95% CI: 1.003-1.076]) 

and this persisted in two-pollutant models that included either PM10 or O3. Correlation coefficients with 

CO ranged from 0.21 to 0.57 for PM10 and 0.44 to 0.75 for NO2. 

A study in Rome, Italy, also found an association between ambient CO and hospitalizations for first 

episode MI among 6,531 subjects (January 1995-June 1997) (D'Ippoliti et al., 2003). A case-crossover 

design with stratification of time into separate months was used to select referent days as the days falling 

on the same day of the week within the same month as the index day. CO concentration was positively 

associated for lag 2 (OR: 1.019 [95% CI: 1.001-1.037]). The other pollutants analyzed were NO2 and TSP, 

both of which exhibited a significant positive effect at lag 0. TSP also showed a significant positive effect 

at lag 0-2 and when entered into a model with CO, the CO effect did not persist. 

The previously mentioned Australian and New Zealand study that analyzed data from seven cities 

(Brisbane, Canberra, Melbourne, Perth, Sydney, Auckland, and Christchurch) for the period 1998-2001 

also reported an association between CO and MI hospitalization (Barnett et al., 2006). The pooled 

estimates across all cities showed that a 0.75 ppm increase in 8-h max CO concentration was associated 

with a 2.4% (95% CI: 0.6-4.1) increase in admissions for MI, but only among older adults (≥ 65 years). 

Angina Pectoris. In the current literature, only one study was identified that focused solely on 

angina pectoris as an endpoint. Admissions data for angina pectoris were collected from 25 academic 

hospitals in Tehran, Iran, and linked to ambient air pollution for the period of 1996 to 2001 (Hosseinpoor 

et al., 2005). Using a time-series approach, single-day lags of 0 to 3 were analyzed and a 0.5 ppm increase 

in 24-h avg CO concentration at lag 1 was associated with increased hospital admissions for angina (OR: 

1.005 [95% CI: 1.003-1.007). This result persisted in a multipollutant model that also included NO2, 

PM10, and O3 with CO being the only significant pollutant (OR: 1.005 [95% CI: 1.001-1.008]).Figure 5-1 

shows the effect estimates associated with daily admissions for various forms of IHD from selected 

studies. Table 5.7 shows a summary of the IHD hospital admission studies that examined CO exposures. 

In summary, the majority of studies reported significant increases in the daily number of 

admissions for IHD and MI in relation to CO exposures. In studies that stratified by age groups and/or 

sex, the effects were larger among the elderly and males. Among the different lag periods being 

examined, the associations were more commonly observed with same day CO (lag 0) or an average over 

the same day and previous day (lag 0-1). 

March 2009 5-27 DRAFT – DO NOT CITE OR QUOTE 
Study Location Lag 

Mann et al. (2002) California, 0-3 CHF IHD

Mann et al. (2002) California, 0-3 ARR 

Szyszkowicz (2007) Montreal, Canada 0 All Ages 

Szyszkowicz (2007) Montreal, Canada 0 All Ages, Males 

Szyszkowicz (2007) Montreal, Canada 0 All Ages, Females 

Szyszkowicz (2007) Montreal, Canada 0 >64 years 

Szyszkowicz (2007) Montreal, Canada 0 >64 years, Males

Szyszkowicz (2007) Montreal, Canada 0 >64 years, Females 

Lee et al. (2003b) Seoul, Korea 5 All Ages 

Lee et al. (2003b) Seoul, Korea 5 >64 years 

Metzger et al. (2004a) Atlanta, GA 0-2 

Peel et al. (2007) Atlanta, GA2002) 0-2 

Barnett et al. (2006) Australia, New Zealand 0-1 15-64 years 

Barnett et al. (2006) Australia, New Zealand 0-1 >64 years 

Jalaludin et al. (2006) Sydney, Australia 0-1 

 

Barnett et al. (2006) Australia, New Zealand 0-1 15-64 years MI

Barnett et al. (2006) Australia, New Zealand 0-1 >64 years 

Linn et al. (2000) Los Angeles, CA 0 

Lanki et al. (2006) Multi-city, Europe 0 >34 years, All cities 

Lanki et al. (2006) Multi-city, Europe 0 <75 years, Non-fatal 

Lanki et al. (2006) Multi-city, Europe 0 <75 years, Fatal 

Lanki et al. (2006) Multi-city, Europe 0 75+ years, Non-fatal 

Lanki et al. (2006) Multi-city, Europe 0 Fatal 

von Klot et al. (2005) Multi-city, Europe 0 

D'Ippoliti et al. (2003) Rome, Italy 0-2 18+ years 

D'Ippoliti et al. (2003) Rome, Italy 0-2 18-64 years 

D'Ippoliti et al. (2003) Rome, Italy 0-2 65-74 years 

D'Ippoliti et al. (2003) Rome, Italy 0-2 75+ years 

 

von Klot et al. (2005) Multi-city, Europe 0 Angina 

Hosseinpoor et al. (2005) Tehran, Iran 1 

 

 

 Effect Estimate 

Figure 5-1. Summary of effect estimates (95% confidence intervals) associated with hospital 

admissions for various froms of IHD. Effect estimates have been standardized to a 

1 ppm increase in ambient CO for 1-h max CO concentrations, 0.75 ppm for 8-h max 

CO concentrations, and 0.5 ppm for 24-h avg CO concentrations. 

March 2009 5-28 DRAFT – DO NOT CITE OR QUOTE 
Table 5-7. Summary of IHD hospital admission studies.1

Study Location Endpoints Examined Copollutants Lags Examined CO Levels (ppm) 

STUDIES THAT FOCUSED SOLELY ON IHD, MI, OR ANGINA 

Mann et al. (2002) Southern California 

(1988-1995) 

IHD PM10, NO2, O3 0,1,2, 2-4ma Mean: 2.07 (8h) 

Szyszkowicz (2007) Montreal, Canada 

(1997-2002) 

IHD NO2 0,1 Mean: 0.5 (24 h) 

Lee et al. (2003b) Seoul, Korea 

(1997-1999) 

IHD PM10, NO2, SO2, O3 0,1,2,3,4,5 Mean: 1.8 

Lanki et al. (2006)2 5 European cities 

(1992-2000) 

MI (first acute) PM10, NO2, O3, PNC 0,1,2,3 Highest city was Rome. 

25th = 1.5 

75th = 2.9 mg/m3

von Klot et al. (2005)2 5 European cities 

(1992-2001) 

MI, Angina, Cardiac* PM10, NO2, O3, PNC 0,1,2,3 Mean: highest city was Rome: 

1.9 (24 h) 

D’Ippoliti et al. (2003)2 Rome, Italy 

(1995-1997) 

MI TSP, NO2, SO2 0,1,2,3,4, 0-2 Mean: 3.8 (24 h) 

Hosseinpoor et al. 

(2005)2

Tehran, Iran 

(1996-2001) 

Angina PM10, NO2, SO2, O3 0,1,2,3 Mean: 9.4 (24 h) 

STUDIES THAT EXAMINED IHD, MI, OR ANGINA AMONG OTHER CVDS 

Metzger et al. (2004a) Atlanta, GA 

(1993-2000) 

IHD, All CVD, CD, CHF, 

PVCD 

PM10, NO2, SO2, O3 0-2ma Mean: 1.5 (1 h) 

Peel et al. (2007) Atlanta, GA 

(1993-2000) 

IHD, All CVD, CD, CHF, 

PVCD 

PM10, NO2, SO2, O3 0-2ma Mean: 1.5 (1 h) 

Barnett et al. (2006) Australia and New 

Zealand 

(1998-2001) 

IHD, MI, All CVD, CA, 

Stroke 

PM10, NO2, O3 Lag 0-1 Mean: (8 h) 

0.5- 2.1 

Jalaludin et al. (2006) Sydney, Australia 

(1997-2001) 

IHD, All CVD, Stroke, 

Cardiac 

PM10, NO2, SO2, O3 0,1,2,3, 0-1 Mean: 0.82 (8 h) 

Linn et al. (2000) Los Angeles, CA 

(1992-1995) 

MI, All CVD, CHF, CA, OS PM10, NO2, O3 0 Mean: (24 h) 

Winter 1.7, Spring 1.0, 

Summer 1.2, Fall 2.1 

Koken et al. (2003) Denver, CO 

(1993-1997) 

MI, CAth, PHD, CD, CHF PM10, NO2, SO2, O3 0,1,2,3,4 Mean: 0.9 ppm (24 h) 

1Cardiac = AMI, angina, dysrhythmia, or HF; CA = Cardiac arrhythmia; CAth = Cardiac atherosclerosis; CD = cardiac dysrhthmias; CHF = Congestive heart failure; PHD = Pulmonary 

heart disease; OS = Occlusive stroke; PVCD = peripheral vascular and cerebrovascular disease, ma = moving average. 2 These studies presented CO concentrations in the units mg/m3. The concentrations were converted to ppm using the conversion factor 1 ppm = 1.15 mg/m3, which assumes standard 

atmosphere and tempterature. 

March 2009 5-29 DRAFT – DO NOT CITE OR QUOTE 
Stroke 

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A stroke is the result of either the blood supply to the brain being blocked (e.g., embolism), which 

refers to an ischemic stroke (80% of strokes), or the occurrence of a burst blood vessel or hemorrhaging, 

referred to as a hemorrhagic stroke. Hemorrhagic stroke has two main groupings; intracerebral 

hemorrhagic stroke (10% of strokes), which is when a blood vessel in the brain leaks, and subarachnoid 

hemorrhage (3% of strokes), which is bleeding under the outer membranes of the brain. The third type of 

stroke is a transient ischemic attack (TIA), or mini–stroke, which has the same early symptoms as a 

normal stroke but the symptoms disappear within 24 hours, leaving no apparent deficits. 

A small number of air pollution studies have investigated hospital admissions for the three main 

forms of stroke with the majority reporting positive associations with ambient CO and lag periods 

between 0 and 3 days. 

A U.S. study across 9 cities investigated hospital admissions for ischemic and hemorrhagic stroke 

among Medicare beneficiaries aged 65+ years of age (155,503 ischemic and 19,314 hemorrhagic 

admissions from the ED) (Wellenius et al., 2005a). Single-day lags 0 to 2 were examined and based on a 

pooled estimate, same-day CO (lag 0) was associated with an increase in admissions of 1.98% 

(95% CI: 0.86-3.12) per 0.5 ppm increase in 24-h CO concentration) for ischemic stroke admissions but 

not hemorrhagic stroke admissions (-1.14%, 95% CI: -3.40 to 1.18). All other pollutants examined (PM10, 

NO2, SO2) were associated with an increase in ischemic stroke admissions, but not hemorrhagic stroke 

admissions. 

Villeneuve and colleagues (2006a) studied ED visits for hemorrhagic strokes, acute ischemic 

strokes and transient ischemic attacks among individuals 65+ years of age at 5 hospitals within the 

Edmonton area in Canada between April 1992 and March 2002 (12,422 visits). Within a time-stratified 

case-crossover design the analyses were stratified by two seasonal groups (October-March and AprilSeptember) and CO only had an effect on ischemic stroke during April-September. A 0.5 ppm increase the 

CO concentration for a 3-day avg across lags 0-2 was associated with a 32% increase in risk (OR: 1.32 

[95% CI 1.09-1.60]). CO had no effect on any other stroke subtype. In two-pollutant models the CO effect 

on ischemic stroke persisted after controlling for PM10, PM2.5, SO2, and O3. When all seasons and all 

strokes were combined there was no statistically significant association between all the pollutants 

examined and increased admissions for stroke. 

In Kaohsiung City, Taiwan, CO averaged over lags 0-2 was associated with increased admissions 

for stroke across 63 hospitals (Tsai et al., 2003b). From 1997 to 2000 a total of 23,179 admissions were 

analyzed and on warm days (≥ 20°C) the odds ratios for primary intracerebral hemorrhage and ischemic 

stroke were 1.39 (95% CI: 1.16-1.66) and 1.39 (95% CI: 1.25-1.53) respectively for a 0.5 ppm increase in 

24-h CO concentration. For the same increase in CO on cool days (<20°C) the odds ratios were 1.33 

(95% CI: 0.38-2.55) for intracerebral hemorrhage and 2.68 (95% CI: 1.59-4.49) for ischemic stroke. 

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These results persisted in two-pollutant models that included PM10, SO2, and O3, but did not persist when 

controlling for NO2. 

Earlier research conducted in metropolitan Los Angeles examined hospital admissions for 

cardiopulmonary illnesses from 1992-1995 (Linn et al., 2000). Using a time-series approach, a 0.5 ppm 

increase in 24-h CO concentration (lag 0) was associated with a 2.18% (95% CI: 1.73-2.62) increase in 

occlusive (ischemic) stroke hospital admissions among people aged >30 years. When the analyses were 

stratified by season there was a 1.8% increase during winter, a 4.55% increase during summer, and a 1.6% 

increase during fall (results for spring were not reported). 

A study in Taipei, Taiwan analyzed 8,582 emergency admissions for cerebrovascular diseases, 

hemorrhagic stroke, ischemic stroke, and all strokes during 1997 to 2002 (Chan et al., 2006). Single-day 

lags 0 to 3 were analyzed and a 0.75 ppm increase in 8-h max CO concentration (lag 2) was associated 

with an increase in cerebrovascular diseases (OR: 1.03 [95% CI: 1.01-1.05]) and all strokes (OR: 1.03 

[95% CI: 1.01-1.05]). These results persisted in two- and three-pollutant models that included O3 and 

PM10. There was no association with individual ischemic or hemorrhagic stroke. CO was moderately 

correlated with PM10 (r = 0.47) and PM2.5 (r = 0.44), and the correlation was higher with NO2 (r = 0.77). 

The only time-series study that focused specifically on stroke hospital admissions that did not 

report a significant association with ambient CO was conducted in Dijon, France (Henrotin et al., 2007). 

Hospital admissions for different types of first-ever stroke (e.g., ischemic, hemorrhagic) among subjects 

over 40 years of age were analyzed for the period of 1994 to 2004. A bi-directional case-crossover study 

design was employed where single-day lags of 0 to 3 were examined and CO had no significant 

association across all lags. This was also the case when the analyses were stratified by gender and types 

of ischemic stroke (large arteries, lacunar, cardioembolic, transient). Of all pollutants examined (PM10, 

NOX, O3, SO2, CO) only O3 showed a significant effect. 

Two Australian studies examined associations between ambient CO and hospital admissions for 

various CVDs. The first of these studies analyzed data from five of the largest cities in Australia 

(Brisbane, Canberra, Melbourne, Perth, Sydney) and two New Zealand cities (Auckland, Christchurch) 

for the period 1998-2001 (Barnett et al., 2006). A time-stratified case-crossover design was employed and 

the age groups of 15-64 years and ≥ 65 years were analyzed for the 0-1 lag period (average over lag 0 and 

1). The pooled estimates across all cities showed that CO had no effect on stroke admissions (quantitative 

results not reported). 

The second of the Australian studies examined ED visits for CVDs in older adults (65+ years) in 

Sydney for the period from 1997 to 2001 (Jalaludin et al., 2006). Using a time-series approach, single-day 

lags of 0 to 3 and an average over lags 0 and 1 (e.g., lag 0-1) were examined and CO showed no effect on 

stroke ED visits. When the analyses were stratified by cool and warm periods a 0.75 ppm increase in 8-h 

max CO concentration during the cool period was associated with a 3.8% (95% CI: 0.76-6.94) increase in 

stroke ED visits. 

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Figure 5-2 shows the effect estimates associated with daily admissions for stroke from selected 

studies. Table 5-8 shows a summary of the stroke hospital admission studies that examined CO exposures. 

In summary, there was some evidence that increased ambient CO concentrations were associated 

with an increase in the number of hospital admissions for stroke. The largest positive effects came from 

the Taiwan study in Kaohsiung (Tsai et al., 2003b) with slightly larger effects during the warmer period 

(>20°C). Similarly, in the Canadian study by Villeneuve and colleagues (2006a) there was a stronger 

effect during the warmer period (April-September). 

March 2009 5-32 DRAFT – DO NOT CITE OR QUOTE 
Study Location Lag 

Wellenius et al. (2005a) Multi-city, U.S. 0-2 65+ years Ischemic Stroke 

Villeneuve et al. (2006a) Edmonton, Canada 0-2 IS, 65+ years, all seasons 

Villeneuve et al. (2006a) Edmonton, Canada 0-2 IS, 65+ years, Apr-Sep 

Villeneuve et al. (2006a) Edmonton, Canada 0-2 IS, 65+ years, Oct-Mar 

Villeneuve et al. (2006a) Edmonton, Canada 0-2 CIS, 65+ years, all seasons 

Villeneuve et al. (2006a) Edmonton, Canada 0-2 CIS, 65+ years, Apr-Sep 

Villeneuve et al. (2006a) Edmonton, Canada 0-2 CIS, 65+ years, Oct-Mar 

Tsai et al. (2003b) Kaohsiung, Taiwan 0-2 < 20°C Temp 

Tsai et al. (2003b) Kaohsiung, Taiwan 0-2 >20°C Temp 

Linn et al. (2000) Los Angeles, CA 0 

Chan et al. (2006) Taipei, Taiwan 1 

Henrotin et al. (2007) Dijon, France 3 

Wellenius et al. (2005a) Multi-city, U.S. 0-2 65+ years Hemorrhagic Stroke 

Villeneuve et al. (2006a) Edmonton, Canada 0-2 65+ years, all seasons 

Villeneuve et al. (2006a) Edmonton, Canada 0-2 65+ years, Apr-Sep 

Villeneuve et al. (2006a) Edmonton, Canada 0-2 65+ years, Oct-Mar 

Tsai et al. (2003b) Kaohsiung, Taiwan 0-2 <20°C Temp 

Tsai et al. (2003b) Kaohsiung, Taiwan 0-2 >20°C Temp 

Chan et al. (2006) Taipei, Taiwan 1 

Henrotin et al. (2007) Dijon, France 1 

Chan et al. (2006) Taipei, Taiwan 2 Stroke (Non-Specific) 

Jalaludin et al. (2006) Sydney, Australia 0-1 

IS= Ischemic Stroke, CIS=Cerebral Ischemic Stroke Effect Estimate 

Figure 5-2. Summary of effect estimates (95% confidence intervals) associated with ED visits 

and hospital admissions for stroke. Effect estimates have been standardized to a 

1 ppm increase in ambient CO for 1-h max CO concentrations, 0.75 ppm for 8-h max 

CO concentrations, and 0.5 ppm for 24-h avg CO concentrations. 

March 2009 5-33 DRAFT – DO NOT CITE OR QUOTE 
Table 5-8. Summary of stroke hospital admission studies.1

Study Location Type Of Stroke 

Examined Copollutants Lags Examined CO Levels (ppm) 

STUDIES THAT FOCUSED SOLELY ON STROKE 

Wellenius et al. (2005a) 9 cities, USA 

(1993-1999) 

Isch, Hem PM10, NO2, SO2 0,1, 2 25th, 50th, 75th percentiles:

0.73, 1.02, 1.44 

Villeneuve et al. 

(2006a) 

Edmonton, Canada (1992-

2002) 

Isch, Hem, TIA NO2, SO2, O3 0,1, 0-2 Mean: 0.8 (24 h) 

Tsai et al. (2003b) Kaohsiung, Taiwan 

(1997-2000) 

Isch, Hem PM10, NO2, SO2, O3 0-2 Mean: 0.79 (24 h) 

Chan et al. (2006) Taipei, Taiwan 

(1997-2002) 

All, Isch, Hem PM10, NO2, SO2, O3 0,1,2,3 Mean: 1.7 (8h) 

Henrotin et al. (2007)2 Dijon, France 

(1994-2004) 

Isch, Hem PM10, NOX, SO2, O3 0,1,2,3 Mean: 0.59 (24 h) 

STUDIES THAT EXAMINED STROKE AMONG OTHER CVDS 

Linn et al. (2000) Los Angeles, CA 

(1992-1995) 

Isch PM10, NO2, O3 Lag 0 Mean: (24 h) 

Winter 1.7, Spring 1.0, 

Summer 1.2, Fall 2.1 

Barnett et al. (2006) Australia and New Zealand 

(1998-2001) 

All PM10, NO2, O3 Lag 0-1 Mean: (8h) 

0.5-2.1 

Jalaludin et al. (2006) Sydney, Australia 

(1997-2001) 

All PM10, NO2, SO2, O3 0,1,2,3, 0-1 Mean: 0.82 (8h) 

1Isch = Ischemic; Hem = Hemorrhagic; TIA = transient ischemic attack 

2 These studies presented CO concentrations in the units mg/m3. The concentrations were converted to ppm using the conversion factor 1 ppm = 1.15 mg/m3, which assumes standard 

atmosphere and tempterature. 

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Heart failure (HF) is a condition in which the heart is unable to adequately pump blood to the rest 

of the body. It does not refer to the cessation of the heart, but more to the inability of the heart to operate 

at an optimal capacity. HF is often called congestive heart failure (CHF), which refers to when the 

inadequate pumping leads to a buildup of fluid in the tissues. The underlying causes of CHF are 

hypertension, CAD, MI, and diabetes. 

Wellenius and colleagues (2005b) examined the rate of hospitalization for CHF among 55,019 

Medicare recipients (aged ≥ 65 years) residing in Allegheny County, PA, during 1987-1999. A timestratified case-crossover design was employed and single-day lags of 0 to 3 were analyzed and a 1 ppm 

increase in 1-h max CO concentration on the same-day (lag 0) was associated with a 9.31% 

(95% CI: 6.77-11.92) increase in the rate of hospitalization for CHF. This result persisted in two-pollutant 

models that included PM10, NO2, O3, and SO2. CO was moderately correlated with SO2 (r = 0.54) and 

PM10 (r = 0.57) and more highly correlated with NO2 (r = 0.70). 

Another U.S. study recruited 125 patients diagnosed with CHF who were admitted to Johns 

Hopkins Bayview Medical Center in Baltimore, MD (Symons et al., 2006). The patients were interviewed 

after admission through the ED during their stays in overnight wards. The interview was designed to 

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collect information about symptom onset, health conditions, and factors related to air pollution exposure. 

Various lag periods (single day and cumulative days 0 to 3) prior to the onset of symptoms were analyzed 

and although the focus of this study was exposure to PM2.5, of all the pollutants examined (PM2.5, CO, 

NO2, O3) only 8-h max CO concentration at lag 2 was significantly associated with the onset of CHF 

symptoms (OR: 1.68 [95% CI: 1.28- 2.80]). 

Earlier research conducted in metropolitan Los Angeles, CA examined hospital admissions for 

cardiopulmonary illnesses 1992-1995 (2000). Using a time-series approach, a 0.5 ppm increase in sameday 24-h avg CO concentration was associated with a 1.25% increase in CHF hospital admissions among 

people aged >30 years. When the analyses were stratified by seasons only summer showed a significant 

increase (3.7%); however, the study did not report the results for the other seasons. 

A time-series study in Denver, Colorado, investigated daily admissions for various CVDs among 

older adults (>65 years) across 11 hospitals (Koken et al., 2003). Single-day lags 0 to 4 were examined 

and an increase of 0.5 ppm in 24-h avg CO concentration for lag 3 was associated with an 18% 

(95% CI: 0.2-39.3) increase in risk of hospitalization for CHF. 

As stated earlier, a study was conducted in Atlanta, GA, where over 4 million ED visits from 

31 hospitals for the period 1993 to 2000 were analyzed (Metzger et al., 2004a). A time-series design was 

used and a 3-day moving average over single-day lags 0-2 as the a priori lag structure was analyzed. 

Results showed that 1-h max CO concentration was not associated with an increase in ED visits for CHF 

(RR: 1.010 [95% CI: 0.988-1.032] per 1 ppm increase). When the analyses examined the same CVDs 

among those with and without specific secondary conditions (e.g., co-morbidity) 1-h max CO 

concentration was associated with an increase in ED visits for CHF only among those with COPD (OR: 

1.058 [95% CI: 1.003-1.115] per 1 ppm increase) (Peel et al., 2007). 

In Kaohsiung city, Taiwan, a study analyzed 13,475 admissions for CHF across 63 hosptials for the 

period 1996 through 2004 (Lee et al., 2007a). A 0.5 ppm increase in 24-h avg CO concentration averaged 

over lag days 0-2 was positively associated with CHF hospital admissions on cool days (<25°C) (OR: 

1.70 [95% CI: 1.43-2.01) with a slightly weaker effect on warm days (>25o

C) (OR: 1.32 [95% CI: 1.15-

1.55]). These results persisted in two-pollutant models that included PM10, SO2, O3, and models with NO2

only on warmer days, not with NO2 on cooler days. 

Figure 5-3 shows the effect estimates for associations between CO and daily admissions for HF 

from selected studies. Table 5-9 summarizes the HF hospital admission studies that examined CO 

exposures. 

In summary, many of the studies that examined associations between ambient CO concentrations 

and daily hospital admissions for CHF reported significant associations at lags of 0 to 3 days. 

March 2009 5-35 DRAFT – DO NOT CITE OR QUOTE 
Study Location Lag 

Wellenius et al. (2005b) Pittsburgh, PA 0 

Symons et al. (2006) Baltimore, MD 0-3 

Linn et al. (2000) Los Angeles, CA 0 

Koken et al. (2003) Denver, CO 3 

Metzger et al. (2004a) Atlanta, GA 0-2 

Peel et al. (2007) Atlanta, GA 0-2 

Lee et al. (2007a) Kaohsiung, Taiwan 0-2 <25 °C 

Lee et al. (2007a) Kaohsiung, Taiwan 0-2 >25 °C 

Effect Estimate 

Figure 5.3. Summary of effect estimates (95% confidence intervals) associated with hospital 

admissions for CHF. Effect estimates have been standardized to a 1 ppm increase 

in ambient CO for 1-h max CO concentrations, 0.75 ppm for 8-h max CO 

concentrations, and 0.5 ppm for 24-h avg CO concentrations. 

March 2009 5-36 DRAFT – DO NOT CITE OR QUOTE 
Table 5-9. Summary of HF hospital admission studies. 

Study Location Endpoints Examined Copollutants Lags Examined CO Levels (ppm) 

STUDIES THAT FOCUSED SOLELY ON HF 

Wellenius et al. (2005b) Pittsburgh, PA (1987-1999) CHF PM10, NO2, SO2, O3 0,1,2,3 Mean: 1.03 (24 h) 

Symons et al. (2006) Baltimore, MD (2002) CHF PM2.5, NO2, O3 0,1,2,3 Mean: 0.4 (24 h) 

Lee et al. (2007a) Kaohsiung, Taiwan 

(1996-2004) 

CHF PM10, NO2, SO2, O3 0-2 Mean: 0.76 (24 h) 

STUDIES THAT EXAMINED HF AMONG OTHER CVDS 

Linn et al. (2000) Los Angeles, CA (1992-1995) CHF, MI, All CVD, CA, OS PM10, NO2, O3 0 

Mean: (24 h) 

Winter 1.7; Spring 1.0

Summer 1.2; Fall 2.1 

Koken et al. (2003) Denver, CO (1993-1997) CHF, MI, CAth, PHD, CD PM10, NO2, SO2, O3 0,1,2,3 Mean: 0.9 (24 h) 

Metzger et al. (2004a) Atlanta, GA (1993-2000) CHF, IHD, All CVD, CD, PVCD PM10, NO2, SO2, O3 0-2ma Mean 1.5 (1 h) 

Peel et al. (2007) Atlanta, GA (1993-2000) CHF, IHD, All CVD, CD, PVCD PM10, NO2, SO2, O3 0-2ma Mean 1.5 (1 h) 

*Cardiac = AMI, angina, dysrhythmia, or HF; CA = Cardiac arrhythmia; CAth = Cardiac atherosclerosis; CD = cardiac dysrhthmias; CHF = Congestive heart failure; PHD = Pulmonary 

heart disease; OS = Occlusive stroke; PVCD = peripheral vascular and cerebrovascular disease, ma = moving average. 

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The following section reviews studies that have investigated the effect of CO on ED visits and 

hospital admissions for all CVD outcomes (e.g., non-specific). Several of these studies also examined 

specific CVDs and were briefly discussed in previous sections. 

As discussed earlier, a study was conducted in Atlanta, GA where over 4 million ED visits from 

31 hospitals for the period 1993 to 2000 were analyzed (SOPHIA). Several articles have been published 

from this research with three examining cardiovascular admissions in relation to CO exposures. The first 

of these used a time-series design and analyzed a 3-day moving average over single-day lags 0-2 as the a 

priori lag structure (Metzger et al., 2004a). Results showed that a 1 ppm increase in 1-h max CO 

concentration was associated with an increase in daily ED visits for all CVDs (RR: 1.017 

[95% CI: 1.008-1.027]). This persisted in two-pollutant models that included NO2 and PM2.5. 

The second of these publications examined the association of ambient air pollution levels and 

cardiovascular morbidity in visits with and without specific secondary conditions (Peel et al., 2007). 

Within a time-stratified case-crossover design, a 3-day moving average over single-day lags 0-2 was used 

as the a priori lag structure. Results from the case-crossover analyses on all cardiovascular and peripheral 

vascular and cerebrovascular disease were similar to the time-series results presented earlier. Results from 

the various co-morbidity analyses are presented in Table 5-10. Similar to the results from the earlier 

publication, CO was mostly associated with peripheral vascular and cerebrovascular disease (PVCD) 

among those with and without the co-morbidities, except among those with CHF. Overall, there is limited, 

if any, evidence of susceptibility to the effects of CO concentration for those with comorbid conditions. 

March 2009 5-37 DRAFT – DO NOT CITE OR QUOTE 
Table 5-10. Association of ambient air pollution levels and cardiovascular morbidity in visits with 

and without specific secondary conditions. 

Co-morbidity IHD Dysryhthmias PVCD CHF 

HYPERTENSION 

- With 1.007 (0.978-1.037) 1.065 (1.015-1.118) 1.038 (1.004-1.074) 1.037 (0.997-1.079) 

- Without 1.022 (1.000-1.043) 1.008 (0.988-1.029) 1.027 (1.002-1.054) 1.010 (0.985-1.037) 

DIABETES 

- With 0.985 (0.945-1.027) 1.058 (0.976-1.146) 1.065 (1.012-1.121) 1.020 (0.975-1.067) 

- Without 1.023 (1.004-1.042) 1.014 (0.995-1.034) 1.025 (1.003-1.048) 1.018 (0.993-1.044) 

COPD 

- With 0.996 (0.938-1.057) 0.972 (0.878-1.077) 1.113 (1.027-1.205) 1.058 (1.003-1.115) 

- Without 1.018 (1.000-1.036) 1.018 (0.999-1.038) 1.026 (1.004-1.047) 1.011 (0.987-1.036) 

CHF 

- With 0.956 (0.907-1.007) 1.065 (0.968-1.173) 1.072 (0.981-1.172) - 

- Without 1.024 (1.006-1.042) 1.015 (0.996-1.034) 1.029 (1.008-1.051) - 

DYSRYHTHMIAS 

- With 1.028 (0.985-1.072) - 1.072 (1.011-1.138) 1.004 (0.960-1.051) 

- Without 1.014 (0.995-1.033) - 1.026 (1.004-1.048) 1.023 (0.998-1.049) 

PVCD - peripheral vascular and cerebrovascular disease, IHD = ischemic heart disease, CHF = congestive heart failure.

Source: Peel et al. (2007) 

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The third study utilizing the SOPHIA data extended the time period to include 1993 through 2004 

(Tolbert et al., 2007) and focused on two large outcome groups: a respiratory diseases group and a 

cardiovascular diseases group. The combined cardiovascular case group included the following groups of 

primary ICD-9 diagnostic codes: IHD (410-414), cardiac dysrhythmias (427), CHF (428), and peripheral 

vascular and cerebrovascular disease (433-437, 440, 443-445, 451-453). Results showed that a 1 ppm 

increase in 1-h max CO concentration was associated with an increase in daily ED visits for all CVDs 

(RR: 1.016 [95% CI: 1.008-1.024]). CO was the strongest predictor of CVD effects in models with twopollutant combinations of NO2, CO and TC, as well as in a model including all three pollutants. 

Earlier research conducted in Los Angeles, CA, showed that a 0.5 ppm increase in same-day 24-h 

avg CO concentration was associated with a 1.6% increase in CVD hospital admissions among people 

aged >30 years (2000). When the analyses were stratified by season the significant CO effect was 

strongest during winter (1.9% increase) followed by summer (1.8%) and fall (1.4%) with no effect in 

spring. 

In contrast to other North American studies, a study in Spokane, WA, did not find an association 

between CO (lags of 1 to 3 days) and an increase in the number of daily cardiac hospital admissions 

(quantitative results not reported) (Slaughter et al., 2005). Similarly, a time-series study in Windsor, 

Ontario, did not find an association between ambient CO and daily hospital admissions for CVDs 

(defined as HF, IHD, or dysrhythmias) (Fung et al., 2005). A total of 11,632 cardiac admissions were 

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analyzed for the period of 1995 to 2000. The lag periods analyzed in this study were lag 0 (same-day), a 

2-day avg (lag 0-1), and a 3-day avg (lag 0-2). For a 1 ppm increase in 1-h max CO concentration the 

mean percent change in daily admissions for the <65 age group (lag 0) was -2.6 (95% CI: -6.2 to 3.3); and 

for the 65+ age group, 0.4 (95% CI: -1.9 to 2.7). The authors reported moderate to low correlations with 

NO2 (r = 0.38), PM10 (r = 0.21) and SO2 (r = 0.16). 

Two case-crossover studies in Taiwan reported an association between ambient CO and hospital 

admissions for CVDs. In Taipei, a total of 74,509 CVD admissions from 47 hospitals for the period of 

1997-2001 were analyzed (Chang et al., 2005). An increase of 0.5 ppm in 24-h avg CO concentration 

(average over lags 0-2) during warmer periods (≥ 20°C) was associated with an increase in daily hospital 

admissions (OR: 1.09 [95% CI: 1.065-1.121) but not cooler periods (<20°C) (OR: 0.98 

[95% CI: 0.93-1.004]). These results persisted after controlling for PM10, SO2, or O3 in two-pollutant 

models. An identical study in Kaohsiung analyzed 29,661 CVD admissions for the period 1997-2000 

(Yang et al., 2004b). Results showed that a 0.5 ppm increase in 24-h avg CO concentration was associated 

with an increase in CVD hospital admissions during both the warmer periods (OR: 1.50 

[95% CI: 1.38-1.63) and cooler periods (OR: 1.89 [95% CI: 1.69-2.12]). 

Similarly, two Australian studies also reported associations between ambient CO concentrations 

and increased hospital admissions among older adults. The first of these studies analyzed data from five 

of the largest cities in Australia (Brisbane, Canberra, Melbourne, Perth, Sydney) and two New Zealand 

cities (Auckland, Christchurch) for the period 1998-2001 (Barnett et al., 2006). The combined estimates 

showed that an increase of 0.75 ppm in the average 8-h max CO concentration over the current and 

previous day (lag 0-1) was associated with a 1.8% (95% CI: 0.7-2.8) increase in all CVD admissions 

among those aged 65+ years. Among those aged 15-64 years there was a smaller increase in CVD 

admissions (1.0% [95% CI: 0.2-1.7]). The second of the Australian studies examined ED visits for CVDs 

in older adults (65+ years) in Sydney for the period 1997 to 2001 (Jalaludin et al., 2006). A 0.75 ppm 

increase in 8-h max CO concentration for single-day lags 0 and 1 was associated with increases in 

admissions of 2.5% (95% CI: 1.6-3.5) and 1.4% (95% CI: 0.5-2.4) respectively. Based on an average over 

lags 0 and 1 (e.g., lag 0-1) there was an increase of 2.6% (95% CI: 1.5-3.6). There were positive increases 

of approximately 3% in CVD ED visits during the cool (May-October) period, but not the warm period 

(November-April). 

Very few studies investigating the association between CO and cardiovascular hospital admissions 

have been conducted in European cities. Ballester et al. (2001) analyzed emergency hospital admissions 

in Valencia, Spain for the period 1994 to 1996. The mean daily number of CVD admissions was 7 and 

when using a time-series approach there was no association between CO and admissions for all CVDs 

(RR: 1.009 [95% CI: 0.99-1.016] per 1 ppm increase in 1-h max CO concentration), heart diseases (RR: 

1.010 [95% CI: 0.993-1.028] per 1 ppm increase), and cerebrovascular diseases (RR: 0.985 

[95% CI: 0.959-1.012] per 1 ppm increase). When the analyses were stratified by hot and cold seasons, 

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only CO concentrations during the hot season were associated with an increase in all cardiovascular 

admissions (RR: 1.033 [95% CI: 1.006-1.064] per 1 ppm increase), heart disease admissions (RR: 1.033 

[95% CI: 1.000-1.067] per 1 ppm increase), and cerebrovascular admissions (RR: 1.074 

[95% CI: 1.007-1.113] per 1 ppm increase). 

Ballester et al. (2006) extended this research to include data from 14 Spanish cities for the period 

of 1995 to 1999. An average exposure period over lags 0-1 was analyzed and for the combined estimates 

a 0.75 ppm increase in 8-h max CO concentration was associated with a 1.77% (95% CI: 0.56-2.99) 

increase in all cardiovascular emergency hospital admissions and a larger increase of 3.57% 

(95% CI: 1.12-6.08) for heart disease admissions. These results persisted in two-pollutant models that 

included NO2, O3 and SO2. 

Table 5.11 summarizes the non-specific CVD hospital admission studies that examined CO 

exposures. Figure 5-4 shows the effect estimates associated with daily admissions for non-specific CVD 

hospital admissions from selected studies. 

In summary, many of the studies that examined associations between ambient CO concentrations 

and ED visits and daily hospital admissions for CVD reported significant associations at short (0-1 day) 

lags. Among studies that conducted stratified analyses, there were slightly stronger effects among older 

adults and possibly during warmer periods.

March 2009 5-40 DRAFT – DO NOT CITE OR QUOTE 
Study Location Lag 

Tolbert et al. (2007)* Atlanta, GA 0-2 

 

Linn et al. (2000) Los Angeles, CA 0 All year 

Linn et al. (2000) Los Angeles, CA 0 Spring 

Linn et al. (2000) Los Angeles, CA 0 Summer 

Linn et al. (2000) Los Angeles, CA 0 Fall 

Linn et al. (2000) Los Angeles, CA 0 Winter 

 

Fung et al. (2005) Windsor, Canada 0-2 <65 years 

Fung et al. (2005) Windsor, Canada 0-2 65+ years 

 

Chang et al. (2005) Taipei, Taiwan 0-2 > 20°C Temp 

Chang et al. (2005) Taipei, Taiwan 0-2 < 20°C Temp 

 

Yang et al. (2004a) Kaohsiung, Taiwan 0-2 < 20°C Temp 

Yang et al. (2004a) Kaohsiung, Taiwan 0-2 > 20°C Temp 

 

Barnett et al. (2006) Australia, New Zealand 0-1 15-64 years 

Barnett et al. (2006) Australia, New Zealand 0-1 65+ years 

 

Jalaludin et al. (2006) Sydney, Australia 0-1 65+ years 

 

Ballester et al. (2006) Multi-city, Spain 0-1 

 

 Effect Estimate 

*Represents the results reported by Metzger et al. (2004b) and Peel et al. (2007)

Figure 5-4. Summary of effect estimates (95% confidence intervals) associated with hospital 

admissions for CVD. Effect estimates have been standardized to a 1 ppm increase in 

ambient CO for 1-h max CO concentrations, 0.75 ppm for 8-h max CO 

concentrations, and 0.5 ppm for 24-h avg CO concentrations. 

March 2009 5-41 DRAFT – DO NOT CITE OR QUOTE 
Table 5-11. Summary of non-specific CVD hospital admission studies. 

Study Location CVD Codes Copollutants Lags Examined CO Levels (ppm) 

Metzger et al. 

(2004a) 

Atlanta, GA 

(1993-2000) 

All CVD PM10, NO2, SO2, O3 0-2ma Mean: 1.5 (1 h) 

Peel et al. (2007) Atlanta, GA 

(1993-2000) 

All CVD PM10, NO2, SO2, O3 0-2ma Mean 1.5 (1 h) 

Tolbert et al. 

(2007) 

Atlanta, GA 

(1993-2004) 

All CVD PM10, NO2, SO2, O3 0-2ma Mean 1.6 (1 h) 

Linn et al. (2000) Los Angeles, CA 

(1992-1995) 

All CVD PM10, NO2, O3 0 Mean: (24 h) 

Winter 1.7; Spring 1.0; 

Summer 1.2; Fall 2.1 

Slaughter et al. 

(2005) 

Spokane, WA 

(1995-2001) 

All CVD 

(ICD9: 390-459) 

PM10, PM2.5, CO 1,2,3 Mean: range across 5 monitors 0.42-

1.82 (24 h) 

Fung et al. (2005) Windsor, Canada 

(1995-2000) 

All CVD (HF, IHF, or 

Dysrhythmia) 

PM10, NO2, SO2, O3 0, 0-1, 0-2 Mean: 1.3 (24 h) 

Chang et al. (2005) Taipei, Taiwan 

(1997-2001) 

All CVD 

(ICD9: 410-429) 

PM10, NO2, SO2, O3 0-2 Mean: 1.37 (24 h) 

Yang et al. (2004b) Kaohsiung, Taiwan 

(1997-2000) 

All CVD 

(ICD9: 410-429) 

PM10, NO2, SO2, O3 0-2 Mean: 0.79 (24 h) 

Australia and New 

Zealand 

(1998-2001) 

All CVD 

(ICD9: 390-459) 

PM10, NO2, O3 0-1 Mean: (8h) 

0.5-2.1 

Barnett et al. 

(2006) 

Jalaludin et al. 

(2006) 

Sydney, Australia 

(1997-2001) 

All CVD 

(ICD9: 390-459) 

PM10, NO2, SO2, O3 0,1,2,3, 0-1 Mean: 0.82 (8h) 

Ballester et al. 

(2001)1

Valencia, Spain 

(1994-1996) 

All CVD 

(ICD9: 390-459) 

BS, NO2, SO2, O3 1,2,3,4,5 Mean: 0.54 (24 h) 

Ballester et al. 

(2006)1

Multi-city, Spain 

(1995-1999) 

All CVD 

(ICD9: 390-459) 

BS, PM10, TSP, NO2, SO2, 

O3

0-1 Mean: range across 14 cities 

0.12-0.24 (8h) 

1 These studies presented CO concentrations in the units mg/m3. The concentrations were converted to ppm using the conversion factor 1 ppm = 1.15 mg/m3, which assumes standard 

atmosphere and tempterature. 

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Figure 5-5 summarizes the effects of CO concentration on ED visits and hospital admissions for all 

CVD outcomes other than stroke from studies that presented the results from two-pollutant models. 

Generally, the CO effect estimates from these studies are robust to the inclusion of copollutants, including 

PM10, PM2.5, NO2, SO2, and O3. In all but one instance (Lee et al., 2007a) (<25°C adjusted for NO2) when 

the single pollutant effect estimate was positive for CO, it remained positive after the addition of any of 

the copollutants investigated. 

March 2009 5-42 DRAFT – DO NOT CITE OR QUOTE 
March 2009 5-43 DRAFT—DO NOT QUOTE OR CITE 

Study Outcome Lag Copollutant 

 

Wellenius et al. (2005b) CHF 0 CO alone PM10

 CO+PM10 

Chan et al. (2006) CD 2 CO alone 

 CO+PM10 

Lee et al. (2007a) CHF 0 CO alone >25°C 

 CO+PM10 >25°C 

D’Ippoliti et al. (2003) HA 0-2 CO alone 

 CO+TSP 

Chang et al. (2005) CVD 0 CO alone ≥ 20°C 

 CO+PM10 ≥ 20°C 

Chang et al. (2005) CVD 0 CO alone <20°C 

 CO+PM10 20°C 

Lee et al. (2003b) IHD 0 CO alone 64+ years 

 CO+PM10 64+ years 

Ballester et al. (2006) CVD 0-1 CO alone 

 CO+PM10 

von Klot et al. (2005) Cardiac 0 CO alone 

 CO+PM10 

Lee et al. (2007a) CHF 0 CO alone <25°C 

 CO+PM10 <25°C 

 

Chan et al. (2006) CD 2 CO alone PM2.5

CO+PM2.5

 

Wellenius et al. (2005b) CHF 0 CO alone NO2

 CO+NO2 

Ballester et al. (2006) CVD 0-1 CO alone 

 CO+NO2 

Chang et al. (2005) CVD 0 CO alone ≥ 20°C 

 CO+NO2 ≥ 20°C 

Chang et al. (2005) CVD 0 CO alone <20°C 

 CO+NO2 <20°C 

Lee et al. (2007a) CHF 0 CO alone >25°C 

 CO+NO2 >25°C 

Lee et al. (2007a) CHF 0 CO alone <25°C 

 CO+NO2 <25°C 

 

Wellenius et al. (2005b) CHF 0 CO alone SO2

CO+SO2

Ballester et al. (2006) CVD 0-1 CO alone 

CO+SO2

Chang et al. (2005) CVD 0 CO alone ≥ 20°C 

CO+SO2 ≥ 20°C 

Chang et al. (2005) CVD 0 CO alone <20°C 

CO+SO2 <20°C 

Lee et al. (2007a) CHF 0 CO alone >25°C 

CO+SO2 >25°C 

Lee et al. (2007a) CHF 0 CO alone <25°C 

CO+SO2 <25°C 

 

Wellenius et al. (2005b) CHF 0 CO alone O3

 CO+O3 

Chan et al. (2006) CD 2 CO alone 

 CO+O3 

Ballester et al. (2006) CVD 0-1 CO alone 

 CO+O3 

Chang et al. (2005) CVD 0 CO alone ≥ 20°C 

 CO+O3 ≥ 20°C 

Chang et al. (2005) CVD 0 CO alone < 20°C 

 CO+O3 <20°C 

von Klot et al. (2005) Cardiac 0 CO alone 

 CO+O3 

Lee et al. (2007a) CHF 0 CO alone >25°C 

 CO+O3 >25°C 

Lee et al. (2007a) CHF 0 CO alone <25°C 

 CO+O3 <25°C 

 

 Effect Estimate 

Figure 5-5. Effect estimates from studies of ED visits and hospital admissions for CVD outcomes other than stroke from single pollutant (CO only, closed circles) and copollutant (CO plus PM, NO2, SO2 and O3, open circles) models. Effect estimates are 

standardized to 1 ppm increase in ambient CO for 1-h max CO concentrations, 

0.75 ppm for 8-h max CO levels, and 0.5 ppm for 24-h avg CO concentrations. 
5.2.1.2. Epidemiologic Studies with Long-Term Exposure 

Only two studies examined CVD outcomes in association with long-term exposure to CO. 

2 Rosenlund et al. (2006) investigated long-term exposure (30 years) to urban air pollution and the risk of 

3 MI in Sweden. The study included 2,246 cases and 3,206 controls aged between 45 to 70 years and 

4 residing in Stockholm County during 1992 to 1993. A detailed postal questionnaire was completed by 

5 4067 subjects and all addresses inhabited during more than 2 years since 1960 were geocoded. The 

6 exposures were then derived from dispersion calculations based on emissions data for each decade since 

7 1960. These calculations were estimates of annual mean levels of traffic-generated NOX, NO2, CO, PM10, 

8 and PM2.5, with the addition of SO2 from heating sources. The analyses were stratified by all cases, 

9 nonfatal cases, fatal cases, in-hospital death, and out-of-hospital death. Based on a 30 year avg exposure 

10 all pollutants were not associated with overall MI incidence. However, increased CO was associated with 

11 out-of-hospital death from MI (OR: 1.81 [95% CI: 1.02-3.23] per 0.5 ppm increase in 30-year avg CO 

12 concentration). Similar results were reported for NO2. The correlation between the 30-year NO2 and CO 

13 exposures was reasonably strong (r = 0.74) and multipollutant models with both these pollutants included 

14 (NO2, CO) were not examined. No other pollutants were significantly associated with all other MI 

15 outcomes. 

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A small-area ecologic study analyzed mortality and hospital admissions for stroke across 1,030 

census districts in Sheffield, U.K. (Maheswaran et al., 2005b). Stroke counts within each census district 

were linked to modeled air pollution data which was then grouped into quintiles of exposure. For stroke 

hospital admissions, when the analyses were adjusted for only sex and age demographics there was an 

exposure-response pattern exhibited across the quintiles of CO exposure with all levels reaching 

significance (RR: 1.37 [95% CI: 1.24-1.52] for the highest exposure group compared to the lowest 

group). However, this result did not persist when also adjusting for a deprivation index and smoking rates 

across the districts (RR: 1.11 [95% CI: 0.99-1.25]). 

5.2.1.3. Summary of Epidemiologic Studies of Exposure to CO and Cardiovascular 

Effects 

A substantial number of epidemiologic studies have examined the potential association between 

25 exposure to CO and various physiological cardiac endpoints or biomarkers. Overall, despite some mixed 

26 results reported among panel and retrospective cohort studies, there was evidence that exposure to CO has 

27 an effect on HR, various HRV parameters, and blood markers of coagulation and inflammation. 

28 Conversely, based on results from panel studies there was little evidence of a link between CO and 

29 cardiac arrhythmia, cardiac arrest, the occurrence of myocardial infarction, and increased BP. 

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Studies of ED visits and hospital admissions provide evidence that CO is associated with various 

forms of CVD with lag periods ranging from 0 to 3 days. There is little evidence that ambient CO is 

associated with an increase in hospital admissions for ischemic stroke. Studies of hospital admissions and 

ED visits for IHD and CHF provide the strongest evidence of ambient CO being associated with adverse 

CVD outcomes. It is difficult to determine from this group of studies the extent to which CO is 

independently associated with CVD outcomes or if CO is a marker for the effects of another trafficrelated pollutant or mix of pollutants. On-road vehicle exhaust emissions are a nearly ubiquitous source of 

combustion pollutant mixtures that include CO and can be an important contributor to CO in near-road 

locations. Although this complicates the efforts to disentangle specific CO-related health effects, the 

evidence indicates that CO associations generally remain robust in copollutant models, are coherent with 

the effects demonstrated by controlled human exposure and animal toxicological studies, and supports a 

direct effect of short-term CO exposure on CVD morbidity at ambient concentrations below the current 

NAAQS level. Such direct effects are plausible considering that long-term, low concentration CO 

exposure (See Section 4.2.3) could result in a COHB level approaching those used in controlled human 

exposure studies (See Section 5.2.2). 

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5.2.2. Controlled Human Exposure Studies 

Controlled human exposure studies provide valuable information related to the health effects of 

17 exposure to air pollutants following short term exposures. These types of experiments are often referred to 

18 as human clinical studies and are conducted in a laboratory setting under carefully regulated exposure 

19 concentrations, environmental conditions, and subject activity levels. Human clinical studies are typically 

20 conducted using a randomized crossover study design with subjects exposed to both the pollutant(s) of 

21 interest and a clean air control. Results of controlled human exposure studies can be used to provide 

22 coherence with the evidence from epidemiologic studies by expanding the understanding of potential 

23 mechanisms for the observed health outcomes. However, they may also provide information that can be 

24 used directly in quantitatively characterizing the exposure concentration-health response relationships at 

25 ambient or near-ambient concentrations. Human clinical studies are limited by a number of factors 

26 including a small sample size and relatively short exposure time. In addition, although health27 compromised individuals have been included in human clinical studies, all subjects participating must be 

28 relatively healthy and do not represent the most sensitive individuals in the population. 

Several human clinical studies cited in the 2000 CO AQCD observed changes in measures of 

cardiovascular function among individuals with CAD following short term exposures to CO. In a multilaboratory study of men with stable angina, Allred et al. (1989, 1991) evaluated the effect of CO exposure 

on exercise-induced angina and ST-segment changes indicative of myocardial ischemia. Relative to clean 

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air exposure (COHb ≈ 0.6%), exposures to CO resulting in COHb concentrations of 2.4% and 4.7% were 

shown to decrease the time required to induce ST-segment changes by 5.1% (p = 0.02) and 12.1% 

(p <0.001), respectively. These changes were well correlated with the onset of exercise-induced angina. A 

number of other studies involving individuals with stable angina have also demonstrated a CO-induced 

decrease in time to onset of angina as well as reduction in duration of exercise at COHb concentrations 

between 3 and 6% (Adams et al., 1988; Anderson et al., 1973) (Kleinman et al., 1989; Kleinman et al., 

1998). However, Sheps et al. (1987) observed no change in time to onset of angina or maximal exercise 

time following a 1-h exposure to 100 ppm CO (targeted COHb of 4%) among a group of 30 patients with 

CAD. In a subsequent study conducted by the same laboratory, a significant increase in number of 

ventricular arrhythmias during exercise was observed relative to room air among individuals with CAD 

following a 1-h exposure to 200 ppm CO (targeted COHb of 6%), but not following a 1-h exposure to 

100 ppm CO (targeted COHb of 4%) (Sheps et al., 1990). It should be noted that although the subjects 

evaluated in the studies described above are not necessarily representative of the most sensitive 

population, the level of disease in these individuals was relatively severe, with the majority either having 

a history of MI or having ≥ 70% occlusion of one or more of the coronary arteries. 

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The 2000 CO AQCD presented very little evidence of CO-induced changes in cardiovascular 

function in healthy adults. Davies and Smith (1980) exposed healthy young adults continuously for 7 days 

to CO concentrations of 0, 15, or 50 ppm. In this study, a marked ST-segment depression was 

demonstrated in only 1 out of 16 subjects following exposure to 15 ppm CO (2.4% COHb) or 50 ppm CO 

(7.2% COHb). Since the publication of the 2000 CO AQCD, no new human clinical studies have been 

published involving controlled CO exposures among subjects with CAD. However, a number of new 

studies have evaluated changes in various measures of cardiovascular and systemic responses following 

controlled exposures to CO in healthy adults. Adir et al. (1999) exposed 15 young healthy adult males to 

room air or CO for approximately 4 min, using a CO exposure concentration which had been shown to 

produce the targeted COHb level of 4-6%. Following each exposure, subjects performed an exercise 

treadmill test at their maximal capacity. Exposure to CO was not observed to cause arrhythmias, 

ST-segment changes, or changes in myocardial perfusion (thallium scintigraphy) during post-exposure 

exercise. However, CO was demonstrated to decrease the post-exposure duration of exercise by 

approximately 10% (p = 0.0012). In addition, the authors reported significant CO-induced decreases in 

metabolic equivalent units (p <0.001), which is a relative measure of O2 consumption. These results 

support the findings of several studies cited in the 2000 CO AQCD which observed decreases in exercise 

duration and maximal aerobic capacity among healthy adults at COHb levels ≥ 3% (Drinkwater et al., 

1974; Ekblom and Huot, 1972; Horvath et al., 1975; Raven et al., 1974). While these decreases in 

exercise duration were relatively small and only likely to be noticed by competing athletes, the findings 

are nonetheless important in providing coherence with the observed effects of CO on exercise-induced 

myocardial ischemia among patients with CAD. 

March 2009 5-46 DRAFT – DO NOT CITE OR QUOTE 
Kizakevich et al. (2000) evaluated the cardiovascular effects of increasing CO concentration in 

healthy adults engaged in upper and lower body exercise. Subjects were initially exposed for 4-6 min to 

CO concentrations between 1,000 and 3,000 ppm, followed by continued exposure to 27, 55, 83, and 

100 ppm to maintain COHb levels of 5, 10, 15, and 20%, respectively. Relative to room air control, CO 

exposure was not observed to cause ST-segment changes or affect cardiac rhythm at any concentration 

during either upper or lower body exercise. Compensation mechanisms for reduced O2 carrying capacity 

during CO exposure were demonstrated, with statistically significant increases in heart rate occurring at 

COHb levels ≥ 5%, and statistically significant increases in cardiac output and cardiac contractility 

observed at COHb levels ≥ 10%. In a human clinical study designed to evaluate the contribution of CO to 

cardiovascular morbidity associated with cigarette smoking, Zevin et al. (2001) exposed 12 healthy male 

smokers for 7 consecutive days to clean air, CO, or cigarette smoke, with each subject serving as his own 

control. The COHb levels were similar between the exposures to cigarette smoke and CO, with average 

concentrations of 6% and 5%, respectively. Cigarette smoke, but not CO, was observed to significantly 

increase plasma levels of CRP and plasma platelet factor 4 relative to the air control arm of the study. 

Neither cigarette smoke nor CO was shown to affect BP. Hanada et al. (2003) observed an increase in leg 

muscle sympathetic nerve activity (MSNA) following controlled exposures to CO (COHb ≈20%) under 

normoxic or hyperoxic conditions. Although an increase in the magnitude of sympathetic activation is 

typically associated with regional vasoconstriction, no CO-induced changes in femoral venous blood flow 

were observed in this study. These findings are in agreement with those of Hausberg et al. (1997) who 

observed no change in forearm blood flow or BP in a study of 10 healthy men and women following a 

controlled exposure to CO (COHb ≈ 8%). Interestingly, one recent study did observe an increase in retinal 

blood flow, retinal vessel diameter, and choroidal blood flow following controlled exposures to CO at a 

concentration of 500 ppm (Resch et al., 2005). This protocol resulted in COHb concentrations of 5.6% 

and 9.4% following exposures of 30 and 60 min, respectively, with statistically significant increases in 

retinal and choroidal blood flow observed at both time points relative to synthetic air control. This 

CO-induced change in ocular hemodynamics may have been due to local tissue hypoxia; however, the 

clinical significance of this finding is unclear. Exposures to CO have also been shown to affect skeletal 

muscle function, with one recent human clinical study reporting a decrease in muscle fatigue resistance in 

healthy adult males using both voluntary and electrically-induced contraction protocols following 

controlled exposures to CO resulting in an average COHb level of 6% (Morse et al., 2008). 

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In summary, controlled human exposures to CO among individuals with CAD have been shown to 

consistently increase markers of myocardial ischemia at COHb levels between 3 and 6%, with one study 

reporting similar effects following CO exposures resulting in COHb concentrations of 2.4%. No such 

effects have been observed in healthy adults following controlled exposures to CO. Although some 

studies have reported CO-induced hemodynamic changes among healthy adults at COHb concentrations 

of as low as 5%, this effect has not been consistent across studies. 

March 2009 5-47 DRAFT – DO NOT CITE OR QUOTE 
5.2.3. Toxicological Studies 

While novel toxicological research on environmental levels of CO was limited in the 2000 CO 

2 AQCD, several sections reported potential relationships between CO exposure and cardiovascular effects. 

3 Conflicting experimental data relating to the role of CO in promoting atherosclerotic vessel disease was 

4 discussed. While some animal studies have linked chronic CO exposure with atherosclerosis development 

5 resulting from increased fatty streaking and cellular lipid loading (Davies et al., 1976; Thomsen, 1974; 

6 Turner et al., 1979), other studies have failed to see this association (Penn et al., 1992; Stupfel and 

7 Bouley, 1970). Ventricular hypertrophy has also been shown after chronic CO exposure (Penney et al., 

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The following sections describe recent studies dealing with toxicity of relatively low levels of CO. 

There has been little new research with the overt purpose of examining environmentally-relevant levels of 

CO. For the most part, studies were designed to mimic exposures related to cigarette smoke, either sidestream or mainstream, accidental CO poisoning, or for the purposes of therapeutic application. Thus, few 

studies examined levels of CO within the current 1 h (35 ppm) or 8 h (9 ppm) NAAQS levels, and fewer 

still examined concentration response curves to delineate no effects levels. However, it is apparent that 

CO, at low to moderate levels (35-250 ppm), has pathophysiological effects on the cardiovascular system 

and on relatively ubiquitous cellular pathways. 

CO exposure at environmentally-relevant levels is unlikely to overwhelm normal physiology in a 

healthy cell; however, susceptibility may be rendered by disease or early development. A common theme 

appears to be the vulnerability of vascular cells, especially the endothelium, which could be considered 

the first organ of contact once taken up into the circulation. While relatively little research has been 

conducted since the 2000 CO AQCD, several key studies conducted at near-environmental CO levels 

provide important clues to the potential public health implications of ambient CO exposure. 

5.2.3.1. Endothelial Dysfunction 

While the preferential binding to heme and effective displacement of O2 by CO has been well 

24 established for over a century, new information from various fields of study are beginning to elucidate 

25 non-hypoxic mechanisms that may lead to cardiovascular abnormalities associated with CO exposure. 

26 Research by Thom, Ischiropoulos, and colleagues (Ischiropoulos et al., 1996; Thom et al., 1994; Thom 

27 and Ischiropoulos, 1997; Thom et al., 1997; Thom et al., 1999a; Thom et al., 1999b; Thom et al., 2000; 

Thom et al., 2006) has focused on CO-mediated displacement of NO• 28 from heme-binding sites. Some of 

29 this work demonstrates a specific pathway by which severe CO poisoning can lead to the release NO•

30 from platelets with subsequent neutrophil activation and vascular injury (Ischiropoulos et al., 1996; Thom 

et al., 2006). The steps include (1) peroxynitrite generation from the reaction of NO• 31 from platelets with 

March 2009 5-48 DRAFT – DO NOT CITE OR QUOTE 
1 neutrophil-derived superoxide followed by (2) stimulation of intravascular neutrophil degranulation that 

2 can result in (3) myleoperoxidase deposition along the vascular lining. Products from myeloperoxidase3 mediated reactions can cause endothelial cell activation (Thom et al., 2006) and can lead to endothelial 

4 dysfunction. The concentrations used in these studies are greatly in excess of the NAAQS levels, but 

5 certainly within the range of accidental or occupational exposures. Research by these same investigators 

6 at more environmentally-relevant CO levels was partially reviewed in the 2000 CO AQCD. The release of 

free NO• 7 was noted in isolated rat platelets exposed to 10-20 ppm CO (Thom and Ischiropoulos, 1997). 

8 Increased nitrotyrosine content of the aorta was observed in rats exposed to 50 ppm CO for 1 h (1999a; 

9 Thom et al., 1999b). Furthermore in this same study, a 1-h exposure to 100 ppm CO led to albumin efflux 

10 from skeletal muscle microvasculature at 3 h and leukocyte sequestration in the aorta at 18 h. LDL 

11 oxidation was also reported. These effects were dependent on NOS but not on neutrophils or platelets. A 

12 second study demonstrated NO-dependent effects of 50-100 ppm CO in lungs and is described in Section 

13 5.5.4 (Thom et al., 1999b). Studies in cultured endothelial cells were also conducted using buffer 

14 saturated with 10-100 ppm CO (Thom et al., 1997). These experiments were designed to mimic 

15 conditions where blood COHb levels were between 3.8 and 28% resulting in exposure of endothelial cells 

to 11-110 nM CO. CO-stimulated release of NO• 16 from endothelial cells along with peroxynitrite 

17 formation and delayed cell death was observed at CO concentrations of 22 nM and higher (Thom et al., 

18 1997). A more recent study demonstrated adaptive responses in endothelial cells exposed to this same 

19 range of CO concentrations (Thom et al., 2000). Specifically, 1-h exposure to 11 nM CO resulted in 

20 MnSOD and HO-1 induction and resistance to the apoptotic effects of 110 nM CO. These protective 

effects of CO were mediated by NO• 21 , as demonstrated using an inhibitor of NOS and a scavenger of 

22 peroxynitrite. Collectively, these experiments demonstrated altered oxidative stress, the initiation of 

23 inflammation, increased microvascular permeability and altered cell signaling in animals and isolated 

24 cells following exposure to 10-100 ppm CO. 

CO is an endogenous regulator of vasomotor tone through vasodilatory effects mediated by 

activation of soluble guanylate cyclase and activation of large conductance Ca2+ activated K+ channels. 

However, CO does not cause vasodilation in every vascular bed. For example, 5, 100, 500 and 2,500 ppm 

CO administered by inhalation to near-term fetal lambs did not induce pulmonary vasodilation and the 

HO inhibitor zinc protoporphyrin IX failed to affect baseline vascular tone (Grover et al., 2000). In some 

cases CO promotes vasoconstriction, which is thought to be mediated by inhibition of endothelial NOS 

(Johnson et al., 2003; Thorup et al., 1999) or decreased NO• bioavailability. An interesting series of 

studies has also suggested that endogenous CO derived from HO-1 which is induced in a variety of 

disease models (salt-sensitive forms of hypertension, metabolic syndrome in obese rats) is responsible for 

skeletal muscle arterial endothelial dysfunction (Johnson et al., 2003, 2004; Teran et al., 2005). Additional 

studies will be useful in determining whether environmentally-relevant concentrations of CO have 

detrimental effect on pre-existing conditions such as hypertension, metabolic syndrome or pregnancy. 

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Several recent animal studies examined the vascular effects of controlled exposures to complex 

combustion mixtures containing CO. Vascular dilatation was decreased following exposure to diesel (4 h 

at 4 ppm) (Knuckles et al., 2008) and gasoline engine emissions (6 h/day x 1, 3, and 7 day at 80 ppm) 

(Lund et al., 2009). Furthermore, evidence of vascular ROS following gasoline emissions has been shown 

in certain animal models (6 h/day x 50 day at 8-80 ppm) (Lund et al., 2007). While none of these studies 

examined the potential role of CO, alone or in combination, it is clearly a common factor in the various 

combustion atmospheres, and future work will be needed to reveal its importance on vascular health. 

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5.2.3.2. Cardiac Remodeling Effects 

Cardiomyopathy, or abnormal growth of the cardiac muscle, can manifest in different ways, 

9 depending on the nature of the insult. The adverse effects of cardiac hypertrophy are due to reduction of 

10 ventricular chamber volume and a diminishing efficiency of the heart. Such concentric hypertrophy 

11 typically occurs in response to chronic increases in load, as occurs with hypertension. Ischemia of the 

12 cardiac tissue can also lead to cardiac remodeling and myopathy. During and after an acute infarction or 

13 obstruction of major coronary vessels, downstream tissues can suffer severe regional ischemia that leads 

14 to significant necrosis. Such regions will lose the ability to contract, and surrounding tissue will show 

15 deficits in contractility. Decreased contractility is often a result of structural thinning of the ventricular 

16 wall, as well as metabolic impairments. Chronic ischemia, such as may result from CAD, may similarly 

17 impair cardiomyocyte function and cause decreased contractility and remodeling. However, ultimately 

18 cardiomyopathies are of a complex origin involving mismanagement of fluid balance, abnormal hormonal 

19 influences (epinephrine, angiotensin), and insufficient perfusion/nutrition. Assessing the role of 

20 exogenous CO in altering pathways leading to cardiomyopathy is a relatively new endeavor and several 

21 new findings are of great interest. 

The heart is a known target for CO toxicity, potentially due to its high rate of O2 consumption. 

Direct effects of CO on the healthy heart have only been observed at relatively high concentrations. For 

example, a recent study by Sorhaug et al. (2006) demonstrated cardiac hypertrophy in rats exposed for 72 

weeks to 200 ppm CO. COHb levels were reported to be 14.7%. Neither structural signs of hypertension 

in the pulmonary arteries or atherosclerotic lesions in the systemic arteries were observed. Cardiac 

hypertrophy was also demonstrated in rats exposed to 100-200 ppm CO for 1-2 weeks (Loennechen et al., 

1999). This response was accompanied by an increase in endothelin-1 expression. COHb levels were 

reported to be 12-23% in this study. 

Direct effects of CO on the healthy heart have also been demonstrated following short-term 

exposures. In a study by Favory et al. (2006b), rats were exposed to 90 min of 250 ppm CO, which led to 

peak COHb values of roughly 11%; recovery of 96 h was needed for COHb levels to return to baseline. 

The authors noted that within the first 24 hours of recovery, while COHb values decreased from 11% to 

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5%, the coronary vascular perfusion pressure and the left ventricular developed pressure were 

significantly increased compared to baseline. Concomitantly, the ratio of cGMP to cAMP decreased and 

the sensitivity of the coronary vascular bed to both acetylcholine and a NO• donor were reduced by CO 

exposure. The authors concluded that the discordant alterations in contractility (increased) and perfusion 

(decreased) may place the heart at risk of O2 limitations following this exposure to CO. 

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Several studies examined the impact of lower levels (50 ppm) on pre-existing or concurrent cardiac 

pathologies. In one such study, CO exacerbated the effects of a hypoxia-based model of right ventricular 

remodeling and failure (Gautier et al., 2007). In controlled laboratory settings, chronic hypobaric hypoxia 

(HH) caused right ventricular hypertrophy as a result of pulmonary arterial vasoconstriction and increased 

pulmonary resistance. Using such a model (Wistar rats exposed for 3 weeks to hypoxia), CO (50 ppm 

during the last week of hypoxia, continuous) only increased COHb from 0.5% to 2.4% in the hypoxia 

model, yet had significant effects on blocking compensatory functional responses to hypoxia, such as 

increased fractional shortening and contractility. Also, while right ventricular weight was increased by 

hypoxia alone, significant pathology related to necrosis was observed in the hypoxia + CO-exposed rats. 

The reduced coronary perfusion of the right ventricle in hypoxia + CO-exposed rats may help explain the 

histopathological findings. The authors cited previous work demonstrating that exogenous CO can inhibit 

NOS (Thorup et al., 1999), which is essential for coronary dilation and angiogenesis. Thus, this study 

provided evidence that exogenous CO may interrupt or downregulate pathways that endogenous CO may 

activate. 

In two studies by Melin et al. (2002; 2005), Dark Agouti rats were exposed for 10 weeks to either 

HH, 50 ppm CO or HH plus 50 ppm CO. CO exposure amplified the right ventricular cardiac hypertrophy 

and decreased the right ventricular diastolic function which occurred in response to HH. In addition, the 

combined exposure led to effects on left ventricular morphology and function which were not seen with 

either exposure alone. Changes in HRV were also reported. Results from both of these studies combined 

with results of Gautier and colleagues (2007) indicated that CO may interfere with normal homeostatic 

responses to hypoxia. This could occur by blocking HIF-1α-responsive elements (vascular endothelial 

growth factor, erythropoietin) or other cell signaling pathways. 

In a similar study, Carraway et al. (2002) exposed rats to HH (380 torr) with or without coexposure to CO (50 ppm). These exposures were continuous for up to 21 days and focused on pulmonary 

vascular remodeling. While the addition of CO to HH did not alter the thickness or diameter of vessels in 

the lung, there was a significant increase in the number of small (<50 μm) diameter vessels compared to 

control, HH only, and CO-only exposures. Despite the greater number of vessels, the overall pulmonary 

vascular resistance was increased in the combined CO + hypoxic exposure, which the authors attributed 

to enhancement of muscular arterioles and β-actin. Results of this study taken together with results from 

the Gautier et al. (2007) and Melin studies (2002; 2005) suggested that the combined effect of low levels 

of CO with hypoxia is an enhanced right ventricle workload and an exacerbated cardiomyopathy related 

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to pulmonary hypertension. The population at risk of primary pulmonary hypertension is low, but 

secondary pulmonary hypertension is a frequent complication of COPD and certain forms of heart failure. 

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5.2.3.3. Electrocardiographic Effects 

In two related studies, Wellenius et al. (2004; 2006b) examined the effect of CO on a rat model of 

4 ischemia-related arrhythmia that was previously shown to produce significant results with exposures to 

5 PM (Wellenius et al., 2002). ECG changes were observed during exposure to residual oil fly ash (ROFA) 

6 particles in a rat model of MI. Thus, using an anesthetized model of post-infarction myocardial sensitivity, 

7 Wellenius and colleagues tested the effects of 35 ppm CO (1-h exposure) on the induction of spontaneous 

8 arrhythmias in Sprague Dawley rats (Wellenius et al., 2004). CO exposure caused a statistically 

9 significant decrease (60.4%) in ventricular premature beat (VPB) frequency during the exposure period in 

10 rats with a high number of pre-exposure VPB. No interaction was observed with co-exposure to carbon 

11 concentrated particles, which independently reduced VPB frequency during the post-exposure period 

12 when administered alone. In a follow-up publication, results from the analysis of supraventricular ectopic 

13 beats (SVEB) were provided (Wellenius et al., 2006b). A decrease in the number of SVEB was observed 

14 with CO (average concentration 37.9 ppm) compared to filtered air. While the authors concluded that CO 

15 exposure did not increase risk of SVEB in this particular rodent model of coronary occlusion, the fact that 

16 cardiac electrophysiological dynamics are significantly altered by short-term exposure to low level CO 

17 may be of concern for other models of susceptibility. 

5.2.3.4. Summary of Cardiovascular Toxicology 

Recent studies demonstrated that short-term exposure to 50-100 ppm CO resulted in aortic injury 

19 as measured by increased nitrotyrosine and the sequestration of activated leukocytes in healthy rats. In 

20 addition, skeletal muscle microvascular permeability was increased. Short term-exposure to 35 ppm CO 

21 altered cardiac electrophysiology in a rat model of myocardial ischemia. Furthermore short-term exposure 

22 to 50 ppm CO exacerbated cardiac pathology and impaired function in animal models of hypertrophic 

23 cardiomyopathy and/or pulmonary hypertension. Ventricular hypertrophy was observed in healthy rats in 

24 response to chronic exposures of 100-200 ppm CO. These studies provide a strong basis for the 

25 development of adverse health effects resulting from exposures to CO at environmentally-relevant 

26 concentrations. 

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5.2.4. Summary of Cardiovascular Effects 

The most compelling evidence of a CO-induced effect on the cardiovascular system at COHb 

2 levels relevant to the current NAAQS comes from a series of controlled human exposure studies among 

3 individuals with CAD (see Section 5.2). These studies, described in the 1991 and 2000 CO AQCDs, 

4 demonstrate consistent decreases in the time to onset of exercise-induced angina and ST-segment changes 

5 following CO exposures resulting in COHb levels of 3-6%, with one multicenter study reporting similar 

6 effects at COHb levels as low as 2.4%. No human clinical studies have evaluated the effect of controlled 

7 exposures to CO resulting in COHb levels lower than 2.4%. Human clinical studies published since the 

8 2000 CO AQCD have reported no association between CO and ST-segment changes or arrhythmia; 

9 however, none of these studies included individuals with diagnoses of heart disease. 

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While the exact physiological significance of the observed ST-segment changes among individuals 

with CAD is unclear, ST-segment depression is a known indicator of myocardial ischemia. It is also 

important to note that the individuals with CAD who participated in these controlled exposure studies 

were not representative of the most sensitive individuals in the population. In fact, the most sensitive 

individuals may respond to levels of COHb lower than 2.4%. Variability in activity patterns and severity 

of disease among individuals with CAD is likely to influence the critical level of COHb which leads to 

adverse cardiovascular effects. 

The degree of ambient CO exposure which leads to attainment of critical levels of COHb will also 

vary between individuals. First of all, endogenous CO production varies as described in Section 4.5, but 

generally results in less than 1% COHb. Secondly, nonambient exposures to CO, such as exposure to 

ETS, can increase COHb above baseline levels. Ambient exposures will result in an additive increase in 

COHb. Using mathematical modeling to predict changes in COHb in healthy inactive adults (Quantitative 

Circulatory Physiology [QCP] model, Section 4.2.3), it can be estimated that exposure to 35 ppm CO for 

1 h results in an increase of 0.6% COHb over baseline and exposure to 9 ppm CO for 8 h results in an 

increase of 0.8% COHb over baseline. Furthermore, 24 h exposure to 3 ppm CO results in an increase of 

0.4% COHb above baseline which can also be obtained following 1-h exposure to 30 CO ppm. 

Consequently, exposure to CO at concentrations relevant to the NAAQS has the potential to increase 

COHb to levels associated with adverse cardiovascular health effects in some individuals. 

Findings of controlled human exposure studies are coherent with findings of recent epidemiologic 

studies conducted since the 2000 CO AQCD, which observed associations between ambient CO 

concentration and ED visits and hospital admissions for IHD, CHF and all-cause cardiovascular disease. 

All but one of these epidemiologic studies were conducted in locations where the entire distribution of 

CO concentrations were at or below the level of the current NAAQS, with mean 24-h avg concentrations 

ranging from 0.5 ppm (Montreal, Canada) to 9.4 ppm (Tehran, Iran) (Table 5-7). A single study reported a 

negative association between CO concentration and hospital admissions and ED visits for IHD among all 

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ages; all other associations were positive, with increases in hospital admissions and ED visits for IHD 

between 0.2% and 19.8% per standardized increase in CO concentration (Figure 5-1). These recent 

studies build upon the conclusions of the 2000 CO AQCD that short-term variations in ambient CO 

concentrations are associated with daily hospital admissions for heart disease. 

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These health outcomes are consistent with a role for CO in limiting O2 availability (i.e., hypoxic 

mechanisms) in individuals with CAD. However, recent toxicological studies suggested that CO may also 

act through non-hypoxic mechanisms by disrupting cellular signaling. Studies in healthy animals 

demonstrated oxidative injury and inflammation in response to 50-100 ppm CO while studies in disease 

models demonstrate effects on heart rhythm and exacerbation of cardiomyopathy and vascular remodeling 

in response to 35-50 ppm CO. Furthermore, in utero exposure to 150 ppm CO alters postnatal 

elecrophysiological maturation in rat cardiomyocytes. Further investigations will be useful in determining 

the importance of non-hypoxic mechanisms following environmentally-relevant CO exposures. Taken 

together, the evidence from epidemiologic, human clinical and toxicological studies is sufficient to 

conclude that a causal relationship is likely to exist between relevant short-term CO exposures and 

cardiovascular morbidity. 

5.3. Central Nervous System Effects 

5.3.1. Controlled Human Exposure Studies 

The behavioral effects of controlled human exposures to CO have been examined by several 

17 laboratories, and these studies were summarized in the 2000 CO AQCD. Briefly, decreases in visual 

18 tracking as well as visual and auditory vigilance were observed following exposures to CO resulting in 

19 COHb levels between 5% and 20% (Benignus et al., 1987; Fodor and Winneke, 1972; Horvath et al., 

20 1971; Putz et al., 1979). One study reported similar behavioral effects (time discrimination) among a 

21 group of healthy volunteers with COHb levels <3% (Beard and Wertheim, 1967); however, subsequent 

22 studies were unable to replicate these findings at such low exposure concentrations (Otto et al., 1979; 

23 Stewart et al., 1973a). These outcomes represent a potentially important adverse effect of CO exposure 

24 resulting in COHb levels ≥ 5%, although it is important to note that these findings have not been 

25 consistent across studies. Similarly, some studies demonstrated decreases in reaction time as well as 

26 decrements in cognitive function and fine motor skills following controlled exposures to CO; however, 

27 these studies were not typically conducted using double-blind procedures, which may significantly affect 

28 the outcome of behavioral studies (Benignus, 1993). It should be noted that all behavioral studies of 

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1 controlled CO exposure were conducted in normal, healthy adults. No new human clinical studies have 

2 evaluated CNS or behavioral effects of exposure to CO. 

5.3.2. Toxicological Studies 

The evidence for toxicological effects of CO exposure in laboratory animal models comes from in 

4 utero or perinatal exposure to relatively low levels of CO (25 to 750 ppm). Affected endpoints from this 

5 early, developmental CO exposure include behavior, memory, learning, locomotor ability, peripheral 

6 nervous system myelination, auditory decrements, and neurotransmitter changes. These data are 

7 addressed in detail in the birth outcomes section of the ISA (Section 5.4.2). 

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5.3.3. Summary of Central Nervous System Effects 

Exposure to high levels of CO has long been known to adversely affect CNS function, with 

9 symptoms following acute CO poisoning including headache, dizziness, cognitive difficulties, 

10 disorientation, and coma. However, the relationship between ambient levels of CO and neurological 

11 function is less clear and has not been evaluated in epidemiologic studies. Studies of controlled human 

12 exposures to CO discussed in the 2000 CO AQCD reported inconsistent neural and behavioral effects 

13 following exposures resulting in COHb levels of 5-20%. No new human clinical studies have evaluated 

14 central nervous system or behavioral effects of exposure to CO. At ambient-level exposures, healthy 

15 adults may be protected against CO-induced neurological impairment owing to compensatory responses 

16 including increased cardiac output and cerebral blood flow. However, these compensatory mechanisms 

17 are likely impaired among certain potentially susceptible groups, including individuals with reduced 

18 cardiovascular function. 

Toxicological studies that were not discussed in the 2000 CO AQCD employed rodent models to 

show that low level CO exposure during the in utero or perinatal period can adversely affect adult 

outcomes including behavior, neuronal myelination, neurotransmitter levels or function, and the auditory 

system (discussed in Section 5.3). In utero CO exposure, including both intermittent and continuous 

exposure, has been shown to impair multiple behavioral outcomes in offspring including active avoidance 

behavior (150 ppm CO), non-spatial memory (75 and 150 ppm CO), spatial learning (endogenous CO 

inhibition), homing behavior (150 ppm CO), locomotor movement (150 ppm CO), and negative geotaxis 

(125 and 150 ppm). In two separate studies, in utero CO exposure (75 and 150 ppm) was associated with 

significant myelination decrements without associated changes in motor activity in adult animals. 

Multiple studies demonstrated that in utero CO exposure affected glutamatergic, cholinergic, 

catecholaminergic, and dopaminergic neurotransmitter levels or transmission in exposed male rodents. 

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Possible or demonstrated adverse outcomes from the CO-mediated aberrant neurotransmitter levels or 

transmission include respiratory dysfunction (200 ppm CO), impaired sexual behavior (150 ppm CO), and 

an adverse response to hyperthermic insults resulting in neuronal damage (200 ppm). Finally, perinatal 

CO exposure has been shown to affect the developing auditory system of rodents, inducing permanent 

changes into adulthood. This is manifested with atrophy of cochlear cells innervating the inner hair cells 

(25 ppm CO), decreased immunostaining associated with impaired neuronal activation (12.5 ppm CO), 

impaired myelination of auditory associated nerves (25 ppm CO), decreased energy production in the 

sensory cell organ of the inner ear or the organ of corti (25 ppm CO), some of which is mechanistically 

proposed to be mediated by ROS (25 ppm CO). Functional tests of the auditory system of neonatally, low 

level CO-exposed rodents, using OAE testing (50 ppm) and amplitude measurements of the 8th cranial 

nerve action potential (12, 25, 50, 100 ppm), revealed decrements in auditory function at PND22 and 

permanent changes into adulthood using action potential (AP) testing (50 ppm). Together, these animal 

studies demonstrated that in utero or perinatal exposure to CO can adversely affect adult behavior, 

neuronal myelination, neurotransmission, and the auditory system in adult male rodents. Considering the 

combined evidence from controlled human exposure and toxicological studies, the evidence is 

suggestive of a causal relationship between relevant short- and long-term CO exposures and 

central nervous system effects. 

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5.4. Birth Outcomes and Developmental Effects 

5.4.1. Epidemiologic Studies 

Although the body of literature is growing, the research focusing on adverse birth outcomes is 

19 limited when compared to the numerous studies that have examined the more well-established health 

20 effects of air pollution.Various dichotomized measures of birth weight, such as LBW, SGA, and IUGR, 

21 have been the most examined outcomes in air pollution research while preterm birth (PTB), congenital 

22 malformations, and infant mortality are less studied. 

In the 2000 CO AQCD only two studies were cited that examined the effect of ambient air 

pollution on adverse birth outcomes and both of these studies investigated LBW as an endpoint 

(Alderman et al., 1987; Ritz and Yu, 1999). At that time this area of research was in its infancy and since 

then there has been increasing interest. 

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5.4.1.1. Preterm Birth 

A small number of air pollution-birth outcome studies have investigated the possible association 

2 between PTB and maternal exposure to CO with the majority of U.S. studies conducted in southern 

3 California. The earliest of these studies examined exposures to ambient CO during the first month of 

4 pregnancy and the last 6 weeks prior to birth among a cohort of 97,158 births in southern California 

5 between 1989 and 1993 (Ritz et al., 2000). The exposure assessment within this study was based on data 

6 from fixed site monitors that fell within a 2-mile radius of the mother’s ZIP code area. The crude relative 

7 risks for PTB associated with a 1 ppm increase in 3-h avg CO concentration (6:00 to 9:00 a.m.) during the 

8 last 6 weeks prior to birth and the first month of pregnancy were 1.04 (95% CI: 1.03-1.5) and 1.01 

9 (95% CI: 1.00-1.03) respectively. However, when the authors controlled for other risk factors, only the 

10 effect associated with CO during the last 6 weeks prior to birth persisted (RR: 1.02 [95% CI: 1.01-1.03]). 

11 Furthermore, when the analyses included variables for either season or other pollutants the CO effect 

12 estimates generally were reduced. 

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Expanding on this research, Wilhelm and Ritz (2005) examined PTB among a cohort of 106,483 

births in Los Angeles County, CA between 1994 and 2000. Based on data recorded at monitoring stations 

of varying proximities to the mother’s residence, the main exposure windows examined were the first 

trimester and the last 6 weeks prior to birth. Among women living within a 1-mile radius of a CO 

monitoring station, a 0.5 ppm increase in 24-h avg CO concentration during the first trimester was 

associated with a 3% (RR: 1.03 [95% CI: 1.00-1.06]) increased risk of PTB. This result persisted after 

simultaneously adjusting for NO2 and O3 (RR: 1.05 [95% CI: 1.00-1.10]), but not with the inclusion of 

PM10 into the regression model (RR: 0.99 [95% CI: 0.91-1.09]). The result from the single pollutant 

model for CO exposures averaged over the 6 weeks prior to birth was similar in magnitude but failed to 

reach statistical significance (RR: 1.02 [95% CI: 0.99-1.04]). 

A limitation of many air pollution-birth outcome studies is the limited availability of detailed 

information on maternal lifestyle factors and time-activity patterns during pregnancy. To assess possible 

residual confounding due to these factors, Ritz and colleagues (Ritz et al., 2007) were able to analyze 

detailed maternal information from a survey of 2,543 of 6,374 women sampled from a cohort of 58,316 

eligible births in 2003 in Los Angeles County. Based on data from the closest monitor to the mother’s ZIP 

code area, exposures to CO, NO2, O3, and PM2.5 during the first trimester and last 6 weeks prior to 

delivery were examined and results from the overall cohort (n = 58,316) with limited maternal 

information were compared to the more detailed nested case-control cohort (n = 2,543). Within the overall 

cohort, CO during the first trimester was associated with an increased risk of 25% (OR: 1.25 

[95% CI: 1.12-1.38]; highest exposure group >1.25 ppm vs. lowest ≤ 0.58 ppm). This result persisted 

within the nested case-control cohort (OR: 1.21 [95% CI: 0.88-1.65]) where factors such as passive 

smoking and alcohol use during pregnancy were included in the model; however, the confidence intervals 

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were wider due to the smaller sample. Any possible association between CO and PTB was less evident 

during the last 6 weeks prior to birth. A strength of this study was that it also highlighted how there was 

little change in the air pollution effect estimates when controlling for more detailed maternal information 

(e.g., smoking, alcohol use), as opposed to only controlling for more limited maternal information that is 

routinely collected on birth registry forms. 

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In contrast to the Los Angeles studies, a case-control study of PTB across California for the period 

1999 through 2000 found a positive, though not statistically significant association, with 24-h CO 

concentration during the entire pregnancy (OR: 1.03 [95% CI: 0.98-1.09] per 0.5 ppm increase), the first 

month of gestation (OR: 1.05 [95% CI: 0.99-1.10] per 0.5 ppm increase), and the last 2 weeks of gestation 

(OR: 1.00 [95% CI: 0.96-1.04] per 0.5 ppm increase) (Huynh et al., 2006). Although there was an 

indication of an effect during early pregnancy, the small sample size (when compared to other studies) 

may have influenced the lack of statistical significance. Furthermore, exposures within this study were 

assigned based on a county-level average which may explain the lack of effect, given the poor level of 

exposure assessment. 

Studies outside of the U.S. have been conducted in Canada, Australia, and Korea with mixed 

results reported. In Vancouver, Canada, based on a city-wide average across available monitoring sites, 

24-h avg CO concentration during the last month of pregnancy was associated with a 4% (OR: 1.04 

[95% CI: 1.00-1.07]) increased risk of PTB per 0.5 ppm increase while there was no association found 

during the first month of pregnancy (OR: 0.98 [95% CI: 0.94-1.00]) (Liu et al., 2003). This study 

investigated maternal exposures to ambient gaseous pollutants (CO, NO2, SO2, O3) averaged over the first 

and last month of pregnancy among a cohort of 229,085 births between 1985 and 1998. 

In a cohort of 52,113 births in Incheon, Korea between 2001-2002, CO concentrations during the 

first trimester was associated with a 26% (RR: 1.26 [95% CI: 1.11-1.44]) increased risk of PTB for the 

highest quartile of exposure when compared to the lowest quartile (Leem et al., 2006). There was also a 

strong significant trend exhibited across the quartiles. A similar result was found for 24-h avg CO 

concentration during the last trimester although the effect was less pronounced (RR: 1.16 

[95% CI: 1.01-1.24]). To assign the maternal exposures to CO, this study used a kriging technique, which 

is a statistical mapping technique that allows the prediction of an average concentration over a spatial 

region from data collected at specific points. The spatial average CO concentrations were then linked to 

each study subject’s residential address. 

Conversely, a study in Sydney, Australia, examined maternal exposure to ambient air pollution 

during the first and last month, and the first and last trimester of pregnancy among a cohort of 123,840 

births between 1998-2000 and found no association between PTB and CO (Jalaludin et al., 2007). 

Maternal exposure estimates in this study were based on a city-wide average of available monitoring sites 

and also based on data from fixed sites within 5 km of the mother’s postcode area. The odds ratios for 

PTB associated with 8-h avg CO concentrations during the first trimester and last three months of 

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gestation were 1.18 (95% CI: 0.85-1.63) and 1.08 (95% CI: 0.95-1.22), respectively, when including 

births within 5 km of a monitor. Interestingly, when all births were included in the analyses and the 

exposure was based on a city-wide average, these effects had become protective for the first trimester 

(OR: 0.82 [95% CI: 0.77-0.87]) and last three months of gestation (OR: 0.99 [95% CI: 0.92-1.07]). This 

suggests that exposures based on data from fixed sites closer to the mother’s address are more likely to 

detect an effect than a city-wide average. 

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Figure 5-6 shows the risk ratios for the risk of delivering a preterm infant from the reviewed 

studies. Table 5-12 provides a brief overview of the PTB studies. In summary there are mixed results 

across the studies. Although these studies are difficult to compare directly due to the different exposure 

assessment methods employed, there is some evidence that CO during early pregnancy (e.g., first month 

and trimester) is associated with an increased risk of PTB. The most consistency is exhibited within the 

studies conducted around Los Angeles, CA and surrounding areas whereby all studies reported a 

significant association with CO exposure during early pregnancy, and exposures were assigned from 

monitors within close proximity of the mother’s residential address (Ritz et al., 2000; Ritz et al., 2007; 

Wilhelm and Ritz, 2005). It should also be noted that the mixed results when analyzing different cohorts 

that resided within varying proximities to a monitor may be attributable to analyzing different 

populations. 

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Location Exposure 

Period 

Exposure 

Study Area 

Ritz et al (2000) California Month 1 <2 miles of monitor 

Ritz et al (2000) California Last 6 weeks <2 miles of monitor 

Los Angeles, CA First trimester ZIP code level Wilhelm & Ritz 

(2005) 

Los Angeles, CA Last 6 weeks ZIP code level Wilhelm & Ritz 

(2005) 

Huynh et al (2006) California First month County level 

Huynh et al (2006) California Last two weeks County level 

Huynh et al (2006) California Entire pregnancy County level 

Liu et al. (2003) Vancouver, Canada First month City wide 

Liu et al. (2003) Vancouver, Canada Last month City wide 

Jalaludin et al. 

(2007) Sydney, Australia First month City wide 

Jalaludin et al. 

(2007) Sydney, Australia First trimester City wide 

Jalaludin et al. 

(2007) Sydney, Australia Last month City wide 

Effect Estimate 

Figure 5-6. Summary of effect estimates (95% confidence intervals) for PTB associated with 

maternal exposure to ambient CO. Effect estimates have been standardized to a 

1 ppm increase in ambient CO for 1-h max CO concentrations, 0.75 ppm for 8-h max 

CO concentrations, and 0.5 ppm for 24-h avg CO concentrations. 

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Table 5-12. Brief summary of PTB studies. 

Study Location (Sample Size) Mean CO (ppm) Exposure Assessment Exposure Window 

Ritz et al. (2000) California 

(n = 97,158) 

2.7 (6-9 a.m.) <2 miles of monitor Month 1 

Last 6 weeks 

Wilhelm and Ritz (2005) Los Angeles, CA 

(n = 106,483) 

1.4 (24 h) Varying distances to monitor Month 1 

Last 6 weeks 

Ritz et al. (2007) Los Angeles, CA 

(n = 58,316) 

0.87 (24 h) Nearest monitor to ZIP code Entire pregnancy 

Trimester 1 

Last 6 weeks 

Huynh et al. (2006) California 

(n = 42,692) 

0.8 (24 h) County level Entire pregnancy 

Month 1 

Last 2 weeks 

Liu et al. (2003) Vancouver, Canada (n = 229,085) 1.0 (24 h) City wide average Month 1 

Last month 

Leem et al. (2006) Incheon, Korea 

(n = 52,113) 

0.9 (24 h) Residential address within Dong-based on 

Kriging 

First trimester 

Last trimester 

Jalaludin et al. (2007) Sydney, Australia 

(n = 123,840) 

0.9 (8 h) City wide average and 

<5 km from monitor 

First month 

First trimester 

Last trimester 

Last month 

5.4.1.2. Birth Weight, Low Birth Weight, and Intrauterine Growth Restriction/Small for 

Gestational Age 

With birth weight routinely collected in vital statistics and a powerful predictor of infant mortality, 

2 it is the most studied outcome within air pollution-birth outcome research. Air pollution researchers have 

3 analyzed birth weight as a continuous variable, and/or as a dichotomized variable in the forms of low 

4 birth weight (LBW) (<2,500g [5 lbs, 8 oz]) and SGA. 

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It should be noted that the terms small for gestational age (SGA), which is defined as a birth weight 

<10th percentile for gestational age (and often sex), and intrauterine growth restriction (IUGR) are used 

interchangeably. However, this definition of SGA does have limitations. For example, using this 

definition of IUGR may overestimate the percentage of ‘growth-restricted’ neonates as it is unlikely that 

10% of neonates have growth restriction (Wollmann, 1998). On the other hand, when the 10th percentile 

is based on the distribution of live births at a population level the percentage of SGA among preterm 

births is most likely underestimated (Hutcheon and Platt, 2008). 

Nevertheless, the terms SGA and IUGR are often used interchangeably and it therefore should be 

noted that SGA represents a statistical description of a small neonate, whereas the term IUGR is reserved 

for those with clinical evidence of abnormal growth. Meaning that all IUGR neonates will be SGA, but 

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not all SGA neonates will be IUGR (Wollmann, 1998). In the following sections the terms SGA and 

IUGR are referred to as each cited study used the terms. 

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Over the past decade a number of studies examined various metrics of birth weight in relation to 

maternal exposure to CO with the majority conducted in the U.S. Given that most studies examined 

multiple birth weight metrics, in order to avoid overlap of the studies the following section focuses on 

each study only once and presents results for each metric within that study. 

Most of the U.S. studies have been conducted in southern California with inconsistent results 

reported with regard to gestational timing of the CO effects. The first of these studies was reviewed in the 

2000 CO AQCD and is briefly summarized here. Ritz and Yu (1999) examined the effect of ambient CO 

during the last trimester on LBW among 125,573 births in Los Angeles between 1989 and 1993. When 

compared to neonates born to women in the lowest CO exposure group (<2.2 ppm), neonates born to 

women in the highest exposure group (5.5 ppm - 95th percentile) had a 22% (OR: 1.22 [95% CI: 1.03-

1.44]) increased risk of being born as LBW. 

Building upon this research, Wilhelm and Ritz (2005) reported similar results when extending this 

study to include 136,134 births for the period of 1994–2000. Exposure to ambient CO during each 

trimester was based on data recorded at monitoring stations of varying proximities to the mother’s 

residence. For women residing within 1 mile of a station, there was 36% (OR: 1.36 [95% CI: 1.04-1.76]) 

increased risk of having a term LBW baby for women in the highest CO exposure category 

(1.84 ppm-75th percentile) for the third-trimester. There was also an increased risk of term LBW (OR: 

1.28 [95% CI: 1.12-1.47]) among women in the highest exposure group when the analyses included 

women within a 5 mile radius of a station. However, when the analyses included women within a 1-2 

mile, or 2-4 mile radius of a station, the CO effects failed to reach statistical significance and there was no 

evidence of an exposure-response pattern exhibited across the varying distances to a station. Furthermore, 

none of the significant CO results persisted after controlling for other pollutants. Although standard errors 

were certainly increased after controlling for the other pollutants leading to non-significant results, some 

of the effect sizes were similar, providing some consistency. It is interesting to note, however, that 

maternal exposure to CO during trimesters one and two was not associated with LBW (results not 

reported). 

Further validation in association with exposure times was observed in an analysis using a susbset of 

participants in the Children’s Health Study. Salam and colleagues (2005) found that CO only during the 

first trimester was associated with reduced fetal growth. Their research examined birth weight, LBW, and 

IUGR among a subset of participants in the Children’s Health Study (Peters et al., 1999b) who were born 

in California between 1975-1987 (n = 3901). The study examined term births with a gestational age 

between 37-44 weeks. Exposures in this study were based on CO data from up to the three nearest 

monitoring sites within 50 km of the centroid of the mother’s ZIP code. Exposures for the entire 

pregnancy and each trimester were analyzed and a 0.5 ppm increase in 24-h CO concentration during the 

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first trimester was associated with a 7.8 g (95% CI: 15.1-0.4) decrease in birth weight, which also 

translated to a 6.7% (OR: 1.07 [95% CI: 1.00-1.13]) increased risk of IUGR; however, there was no 

significant association with LBW (OR: 1.00 [95% CI: 0.88-1.16]). 

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In contrast to the previous studies, another California study of 18,247 singleton births born at 40 

weeks gestation during 2000 found no association between ambient 24-h CO concentration and reduced 

birth weight or SGA where the highest quartile of exposure was 0.98 ppm. Based on data from fixed sites 

within 5 miles of the mother’s residence, exposures to CO and PM2.5 during the entire pregnancy and each 

trimester were analyzed. Although CO during the entire pregnancy was associated with a 20 g 

(95% CI: 40.1-0.8) reduction in birth weight, this did not persist after controlling for PM2.5. PM2.5 was 

found to have a strong effect on birth weight within each trimester (Parker et al., 2005). 

Two similar studies were conducted in the Northeast of the U.S. with inconsistent results. A study 

of 89,557 singleton term births in Boston, MA, Hartford, CT, Philadelphia, PA, Pittsburgh, PA, and 

Washington, DC between 1994-1996 found that exposure to ambient CO during the third trimester was 

associated with an increased risk of LBW (OR: 1.14 [95% CI: 1.03-1.27] per 0.5 ppm increase) (Maisonet 

et al., 2001). When stratified by race this effect was only significant among African Americans for the 

first and third trimesters (First OR: 1.32 [95% CI: 1.22-1.43]; Third OR: 1.20 [95% CI: 1.09-1.32]). 

Exposures to PM10 and SO2 were examined and there was no strong evidence that these pollutants were 

associated with LBW. Exposures for this study were based on a city-wide average of monitors within the 

mother’s city of residence. The second study examined 358,504 births at 32-44 weeks gestation between 

1999-2002 in Connecticut and Massachusetts (Bell et al., 2007). 24-h CO exposures were estimated from 

fixed sites within each mother’s county of residence (e.g., county level). CO averaged over the entire 

pregnancy was associated with a reduction in birth weight of 27.0 g (95% CI: 21.0-32.8). This result 

persisted after controlling for each additional pollutant (PM10, PM2.5, NO2, and SO2) in two-pollutant 

models. However, this reduction in birth weight did not translate to an increased risk of LBW (OR: 1.05 

[95% CI: 0.97-1.12]). When controlling for exposure during each trimester, the reduction in birth weight 

associated with a 0.5 ppm increase in 24-h CO concentration during the first trimester ranged from 18.8 to 

16.5 g while the reductions associated with third trimester exposure ranged between 23.3 and 27.2 g. It is 

interesting to note that, although the exposures were based on data averaged at the county level, CO was 

associated with a reduction in birth weight. Whereas, in a previously cited California study by Huynh and 

colleagues (Huynh et al., 2006) exposures were also at the county level yet there was no association with 

PTB. This difference may be due to the counties being smaller in New England than in California, 

resulting in more precise exposure estimates. 

Two studies in Canada investigated the effects of ambient air pollution on fetal growth with 

exposures derived from a city-wide average across the available monitoring sites. The first of these 

studies was among a cohort of 229,085 singleton term births (37-42 weeks gestation) in Vancouver, BC 

with monthly and trimester exposures to CO investigated in relation to LBW and IUGR (Liu et al., 2003). 

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For a 0.5 ppm increase in 24-h CO concentration during the first month of pregnancy there was an 

increased risk of IUGR (OR: 1.03 [95% CI: 1.00-1.05]) and this was of borderline significance when CO 

was averaged over the first trimester (OR: 1.02 [95% CI: 1.00-1.05]). This result persisted after 

controlling for other gaseous pollutants. Conversely, maternal exposure to CO was not associated with 

LBW. The more recent of these 2 studies examined 386,202 singleton term births (37-42 weeks gestation) 

in Calgary, Edmonton and Montreal between 1986 and 2000 (Liu et al., 2007). The study examined 

monthly and trimester exposures to CO with IUGR being the only endpoint. A 0.5 ppm increase in 24-h 

CO concentration was associated with an increased risk of IUGR in the first (OR: 1.09 

[95% CI: 1.07-1.11]), second (OR: 1.07 [95% CI: 1.05-1.09]), and third trimesters (OR: 1.09 

[95% CI: 1.07-1.11]) of pregnancy. This result translated to CO exposure having a positive effect on 

IUGR within each individual month of pregnancy with the highest effect during the first and last months. 

This result persisted after controlling for concurrent NO2 and PM2.5. 

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Two studies in Sao Paulo, Brazil, a city with notably high levels of air pollution (mean CO 

3.7 ppm) investigated associations between maternal exposures to CO in relation to reduced birth weight 

and LBW within two consecutive time periods and found similar results. In both studies the exposures 

were derived from a city-wide average across the available monitoring sites. The first study examined 

179,460 singleton term births during 1997 and found that a 0.75 ppm increase in 8-h CO concentration 

averaged over the first trimester was associated with a 17.3 g (95% CI: 31.0-3.7) reduction in birth weight 

(Gouveia et al., 2004). The second of these studies examined 311,735 singleton births (37-41 weeks 

gestation) between 1998 and 2000 and reported a 6.0 g reduction in birth weight associated with a 

0.5 ppm increase in 24-h CO concentration averaged over the first trimester (Medeiros and Gouveia, 

2005). It is important to note that neither of these studies found an association between CO exposure and 

an increased risk of LBW. Therefore, despite CO during the first trimester being associated with reduced 

birth weight, it was not associated with LBW. 

Similar to the two studies in Sao Paulo, Brazil, researchers in Seoul, South Korea conducted two 

studies using data from two consecutive time periods. Both of these studies based the exposure estimates 

on a city-wide average from all available fixed sites and as would be expected, the results pertaining to 

CO were similar for both studies. For example, Ha and colleagues (2001) examined maternal exposures to 

CO during the first and third trimesters among 276,763 singleton term births in Seoul between 1996 and 

1997. Exposure to CO during the first trimester was associated with a decrease in birth weight of 13.3 g, 

which also translated into an increased risk of LBW (RR: 1.10 [95% CI: 1.05-1.14] per 0.5 ppm increase 

in 24-h CO concentration). When Lee and colleagues (2003a) extended this study to include singleton 

term births (37-44 weeks gestation) for the period of 1996 to 1998 with 24-h CO concentrations averaged 

over each month of pregnancy and trimester, CO exposure during the first trimester was associated with 

an increased risk of LBW (OR: 1.04 [95% CI: 1.01-1.07] per 0.5 ppm increase). No associations were 

March 2009 5-64 DRAFT – DO NOT CITE OR QUOTE 
found in the third trimester for any of the pollutants. Monthly-specific exposures showed that the risk of 

LBW tended to increase with CO exposure between months 2-5 of pregnancy. 

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In contrast to other studies reporting that early and late pregnancy are the critical periods for CO 

exposure, a Sydney, Australia study of 138,056 singleton births between 1998-2000 reported a reduction 

in birth weight of 21.7 g (95% CI: 38.2-5.1) and 17.2 g (95% CI: 33.4-0.9) associated with a 0.75 ppm 

increase in maternal exposure to 8-h CO averaged over the second and third trimesters respectively 

(Mannes et al., 2005). However, this result did not persist after controlling for other pollutants (PM10, 

NO2) and was only significant when including births where the mother resided within 5 km of a monitor. 

Furthermore, this result did not translate to an increased risk of SGA, which was defined as a birth weight 

two standard deviations below the mean. The odds ratios for SGA for CO exposures during the first, 

second and third trimesters were 0.96 (95% CI: 0.91-1.03), 0.99 (95% CI: 0.92-1.07), and 1.01 

(95% CI: 0.93-1.08) respectively. While the majority of studies restrict the analyses to term births as a 

method of controlling for gestational age, it is important to note that the Sydney study used all births and 

controlled for gestational age in the birth weight analyses and SGA was derived from each gestational age 

group. 

Of all studies reviewed, only two failed to find an association between maternal exposure to CO 

and adverse birth outcomes. In northern Nevada, Chen and colleagues (2002) examined CO, PM10, and O3

exposures among a cohort of 39,338 term births (37-44 weeks gestation) between 1991 and 1999 and 

found no association between CO exposure during the entire pregnancy (and each trimester) and a 

reduction in birth weigh or an increased risk of LBW. For a 0.75 ppm increase in 8-h CO concentration 

averaged over the entire pregnancy there was a reduction in birth weight of 6 g, however it failed to reach 

statistical significance. Exposures for this study were based on data from all monitoring sites across 

Washoe County, Nevada. 

In a retrospective cohort study among 92,288 singleton term births (37-44 weeks gestation) in 

Taipei and Kaoshiung, Taiwan between 1995-1997, maternal exposures to CO, SO2, O3, NO2, and PM10 in 

each trimester of pregnancy were examined and only SO2 during the third trimester showed evidence of 

attributing to LBW. Exposure assessment was based on data from the monitor closest to the centroid of 

the mother’s residential district and the final analyses only included mothers whose district centroid was 

within 3 km of a monitor. CO exposures were grouped into low (~1.1 ppm), medium (~1.2-15.0 ppm), 

and high (>15.0 ppm) and when compared to the lowest exposure group, the odds ratio for LBW in the 

highest exposure group was 0.90 (95% CI: 0.75-1.09) for the first trimester, 1.00 (95% CI: 0.82-1.22) for 

the second trimester, and 0.86 (95% CI: 0.71-1.03) for the third trimester (Lin et al., 2004a). 

Table 5-13 provides a brief overview of the birth weight studies. In summary, there is evidence of 

ambient CO during pregnancy having a negative effect on fetal growth. From the reviewed studies 

Figure 5-7 shows the change in birth weight (grams), Figure 5-8 shows the effect estimates for LBW, and 

Figure 5-9 shows the effect estimates for SGA. In general the reported reductions in birth weight are 

March 2009 5-65 DRAFT – DO NOT CITE OR QUOTE 
small (ranging ~10-20g). It is difficult to conclude whether CO is related to a small change in birth weight 

in all births across the population, or a marked effect in some subset of births. 

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Furthermore, there is a large degree of inconsistency across these studies. This may be due to 

several factors such as inconsistent exposure assessment and statistical methods employed, different CO 

concentrations, and/or different demographics of the birth cohorts analyzed. The main inconsistency 

among these findings is the gestational timing of the CO effect. Although the majority of studies reported 

significant effects during either the first or third trimester, other studies failed to find a significant effect 

during these periods. Several studies found an association with exposure during the entire pregnancy, 

providing evidence for a possible accumulative effect; however, these results are inconclusive and this 

may be the result of correlated exposure periods. 

Several studies examined various combinations of birth weight, LBW, and SGA/IUGR and 

inconsistent results are reported across these metrics. For example, several studies reported an association 

between maternal exposure to CO and decreased birth weight yet the decrease in birth weight did not 

translate to an increased risk of LBW or SGA. However, it needs to be noted that a measureable change, 

even if only a small one, on a population is different than an effect on a subset of susceptible births which 

may increase the risk of IUGR/LBW/SGA. 

The possibility exists that the small reductions in birth weight associated with maternal CO 

exposures are the result of residual confounding associated with other factors (e.g., other pollutants, 

temperature, and spatial/temporal variation in maternal factors) or other correlated pollutants. For 

example, in some studies the CO effect did not persist after controlling for other pollutants (Mannes et al., 

2005; Parker et al., 2005; Wilhelm and Ritz, 2005) while in some studies it did persist (Bell et al., 2007; 

Gouveia et al., 2004; Liu et al., 2003, 2007), and other studies did not report results from multipollutant 

models (Ha et al., 2001; Lee et al., 2003a; Maisonet et al., 2001; Medeiros and Gouveia, 2005). In 

addition, various methods have been employed to control for seasonality and trends (e.g., month of birth, 

season of birth, year of birth, smoothed function of time), which may explain some of the mixed results. 

The two U.S. studies conducted in the Northeast compared results from analyses stratified by race. 

The earlier of these studies found an association between CO and LBW among African Americans but not 

among whites and hispanics (Maisonet et al., 2001). In contrast, despite reporting an 11g reduction in 

birth weight among African-Americans and a 17 g reduction among whites, the more recent of the two 

studies found no significant difference between these reductions by race (Bell et al., 2007). Parker and 

colleagues (2005) also tested for interactions between race and found no significant association. 

March 2009 5-66 DRAFT – DO NOT CITE OR QUOTE 
Location Exposure Period Exposure 

Area Study 

Connecticut & 

Massachusetts Bell et al (2007) Entire pregnancy County level 

Salam et al. (2005) California First trimester ZIP code level 

Salam et al. (2005) California Second trimester ZIP code level 

Salam et al. (2005) California Third trimester ZIP code level 

Salam et al. (2005) California Entire pregnancy ZIP code level 

Chen et al. (2002) Northern NV First trimester County level 

Chen et al. (2002) Northern NV Second trimester County level 

Chen et al. (2002) Northern NV Third trimester County level 

Chen et al. (2002) Northern NV Entire pregnancy County level 

Mannes et al. (2005) Sydney, Australia First trimester City level 

Mannes et al. (2005) Sydney, Australia Second trimester City level 

Mannes et al. (2005) Sydney, Australia Third trimester City level 

Mannes et al. (2005) Sydney, Australia Last month City level 

Gouveia et al. (2004) Sao Paulo, Brazil First trimester City level 

Gouveia et al. (2004) Sao Paulo, Brazil Second trimester City level 

Gouveia et al. (2004) Sao Paulo, Brazil Third trimester City level 

Medeiros et al. (2005) Sao Paulo, Brazil First trimester City level 

Medeiros et al. (2005) Sao Paulo, Brazil Second trimester City level 

Medeiros et al. (2005) Sao Paulo, Brazil Third trimester City level 

Change in Birth weight (g)

Figure 5-7. Summary of change in birth weight (95% confidence intervals) associated with 

maternal exposure to ambient CO. Effect estimates have been standardized to a 

1 ppm increase in ambient CO for 1-h max CO concentrations, 0.75 ppm for 8-h max 

CO concentrations, and 0.5 ppm for 24-h avg CO concentrations. 

March 2009 5-67 DRAFT – DO NOT CITE OR QUOTE 
Location Exposure Period Exposure 

Area Author 

Connecticut & 

Massachusetts Entire pregnancy County level Bell et al (2007) 

Maisonet et al. (2001) Northeastern USA First trimester City level 

Maisonet et al. (2001) Northeastern USA Second trimester City level 

Maisonet et al. (2001) Northeastern USA Third trimester City level 

Wilhelm and Ritz (2005) Los Angeles County, CA Third trimester ZIP code level 

Salam et al. (2005) California First trimester ZIP code level 

Salam et al. (2005) California Second trimester ZIP code level 

Salam et al. (2005) California Third trimester ZIP code level 

Salam et al. (2005) California Entire pregnancy ZIP code level 

Liu et al. (2003) Vancouver, Canada First month City level 

Liu et al. (2003) Vancouver, Canada Second month City level 

Ha et al (2001) Seoul, Korea First trimester City level 

Ha et al (2001) Seoul, Korea Third trimester City level 

Lee et al. (2003a) Seoul, Korea First trimester City level 

Lee et al. (2003a) Seoul, Korea Second trimester City level 

Lee et al. (2003a) Seoul, Korea Third trimester City level 

Lee et al. (2003a) Seoul, Korea Entire pregnancy City level 

Effect Estimate 

Figure 5-8. Summary of effect estimates (95% confidence intervals) for LBW associated with 

maternal exposure to ambient CO. Effect estimates have been standardized to a 

1 ppm increase in ambient CO for 1-h max CO concentrations, 0.75 ppm for 8-h max 

CO concentrations, and 0.5 ppm for 24-h avg CO concentrations. 

March 2009 5-68 DRAFT – DO NOT CITE OR QUOTE 
Location Exposure 

Period 

Exposure 

Area Study 

Salam et al. (2005) California First trimester ZIP code level 

Salam et al. (2005) California Second trimester ZIP code level 

Salam et al. (2005) California Third trimester ZIP code level 

Salam et al. (2005) California Entire pregnancy ZIP code level 

Liu et al. (2003) Vancouver, Canada First month City level 

Liu et al. (2003) Vancouver, Canada First trimester City level 

Liu et al. (2003) Vancouver, Canada Second trimester City level 

Liu et al. (2003) Vancouver, Canada Third trimester City level 

Liu et al. (2003) Vancouver, Canada Last month City level 

Liu et al. (2007) 3 Cities, Canada First trimester City level 

Liu et al. (2007) 3 Cities, Canada Second trimester City level 

Liu et al. (2007) 3 Cities, Canada Third trimester City level 

Mannes et al. (2005) Sydney, Australia First trimester City level 

Mannes et al. (2005) Sydney, Australia Second trimester City level 

Mannes et al. (2005) Sydney, Australia Third trimester City level 

Mannes et al. (2005) Sydney, Australia Last month City level 

Effect Estimate 

Figure 5-9. Summary of effect estimates (95% confidence intervals) for SGA associated with 

maternal exposure to ambient CO. Effect estimates have been standardized to a 

1 ppm increase in ambient CO for 1-h max CO concentrations, 0.75 ppm for 8-h max 

CO concentrations, and 0.5 ppm for 24-h avg CO concentrations. 

March 2009 5-69 DRAFT – DO NOT CITE OR QUOTE 
Table 5-13. Brief summary of birth weight studies. 

Outcomes 

Examined Study Location (Sample Size) Mean CO (ppm) Exposure Assessment Exposure Windows 

UNITED STATES 

Ritz and Yu (1999) LBW Los Angeles, CA 

(n = 125, 573) 

2.6 (6-9 a.m.) <2 miles of monitor Trimester 3 

Wilhelm and Ritz 

(2005) 

LBW Los Angeles County, CA 

(n = 136, 134) 

1.4 (24 h) Varying distances from monitor Trimesters 1, 2, 3 

Salam et al. (2005) Birth weight 

LBW 

IUGR 

California 

(n = 3901) 

1.8 (24 h) ZIP code level Entire pregnancy 

 Trimesters 1, 2, 3 

Parker et al. (2005) Birth weight 

SGA 

California 

(n = 18,247) 

0.75 (8 h) <5 miles from monitor Entire pregnancy 

 Trimesters 1, 2, 3 

 

Maisonet et al. (2001) LBW Boston, MA; Hartford, CT; 

Philadelphia & Pittsburg, PA; 

Washington DC 

(n = 103,465) 

1.1 (24 h) City wide average Trimesters 1, 2, 3 

Bell et al. (2007) Birth weight 

LBW 

Connecticut and 

Massachusetts, 

(n = 358,504) 

0.6 (24 h) County level average Entire pregnancy 

 Trimesters 1, 3 

Chen et al. (2002) Birth weight 

LBW 

Northern Nevada, 

(n = 36,305) 

0.9 (8 h) County level Trimesters 1, 2, 3 

CANADA 

Liu et al. (2003) LBW 

IUGR 

Vancouver, Canada 

(n = 229,085) 

1.0 (24 h) City wide average Trimester 1 

Liu et al. (2007) IUGR Calgary, Edmonton, Montreal, 

Canada 

(n = 386,202) 

1.1 (24 h) City wide average Trimesters 1, 2, 3 

SOUTH AMERICA 

Gouveia et al. (2004) Birth weight 

LBW 

Sao Paulo, Brazil 

(n = 179,460) 

3.7 (8 h) City wide average Trimesters 1, 2, 3 

Medeiros et al. (2005) Birth weight 

LBW 

Sao Paulo, Brazil 

(n = 311,735) 

3.0 (24 h) 

(Presented in 

graph) 

City wide average Trimesters 1, 2, 3 

AUSTRALASIA 

Ha et al. (2001) Birth weight 

LBW 

Seoul, Korea 

(n = 276,763) 

1.2 (24 h) City wide average Trimesters 1 and 3 

Lee et al. (2003a) LBW Seoul, Korea 

(n = ?) 

1.2 (24 h) City wide average Entire pregnancy 

Trimesters 1, 2, 3 

Mannes et al. (2005) Birth weight 

SGA 

Sydney, Australia 

(n = 138,056) 

0.8 (8 h) City wide average and 

<5 km from monitor 

Trimesters 1, 2, 3 

Last 30 days 

Lin et al. (2004b) LBW Taipei, Kaoshiung, Taiwan 

(n = 92,288) 

Taipei 1.1, 

Kaoshiung 8.1 

<3 km of monitor Entire pregnancy 

Trimesters 1, 2, 3 

 

March 2009 5-70 DRAFT – DO NOT CITE OR QUOTE 
5.4.1.3. Congenital Anomalies 

Despite the growing evidence of an association between ambient air pollution and various adverse 

2 birth outcomes, only three studies investigated the effect of temporal variations in ambient air pollution 

3 on congenital anomalies. Given the higher prevalence and associated mortality, heart defects have been 

4 the main focus of two of these three recent air pollution studies. The other study’s focus was cleft 

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The first of these studies was conducted in southern California (Ritz et al., 2002). Exposure to 

ambient CO, NO2, O3 and PM10 during each of the first three months of pregnancy was examined among 

births during 1987-1993. Maternal exposure estimates were based on data from the fixed site closest to 

the mother’s ZIP code area and when using a case-control design where cases were matched to 10 

randomly selected controls, results showed that CO during the second month of pregnancy was associated 

with cardiac ventricular septal defects. The CO exposures were grouped by quartiles (25th = 1.14, 

50th = 1.57, 75th = 2.39 ppm) and when compared to those in the lowest quartile exposure group 

(<1.14 ppm), the odds ratios for ventricular septal defects across the 3 exposure groups were 1.62 

(95% CI: 1.05-2.48), 2.09 (95% CI: 1.19-3.67), and 2.95 (95% CI: 1.44-6.05) respectively. In a 

multipollutant model a similar exposure-response pattern was exhibited across the quartiles with the 

highest quartile of exposure reaching statistical significance (OR: 2.84 [95% CI: 1.15-6.99]). The only 

other pollutant associated with a defect was O3 during the second month of pregnancy, which was 

associated with aortic artery and valve defects. 

The second study was conducted in Texas (Gilboa et al., 2005), where exposure to ambient CO, 

NO2, SO2, O3 and PM10 during the 3rd to 8th week of gestation was examined among births between 

1997-2000. Maternal exposure estimates were calculated by assigning the data from the closest monitor to 

the mother’s residential address. If data were missing on a particular day then data from the next closest 

site were used. The median distances from a monitor ranged from 8.6-14.2 km with maximum distances 

ranging from 35.5-54.5 km. The main results showed that CO was associated with multiple conotruncal 

defects and Tetralogy of Fallot. CO exposures were grouped into quartiles of much lower concentrations 

(25th = 0.4, 50th = 0.5, 75th = 0.7 ppm) than the California study and when compared to the lowest 

quartile, the odds ratios for conotruncal defects across the 3 CO exposure groups were 1.38 

(95% CI: 0.97-1.97), 1.17 (95% CI: 0.81-1.70), and 1.46 (1.03-2.08) respectively without a significant 

test for trend (p for trend = 0.0870). Whereas, a strong exposure-response pattern was exhibited across 

the quartiles of CO exposure for Tetralogy of Fallot (25th OR: 0.82 [95% CI: 0.52-1.62]; 50th OR: 1.27 

[95% CI: 0.75-2.14]; 75th OR: 2.04 [95% CI: 1.26-3.29]; p for trend = 0.0017). The only significant 

associations found with other pollutants were between PM10 and isolated atrial septal defects, and SO2

and ventricular septal defects. 

March 2009 5-71 DRAFT – DO NOT CITE OR QUOTE 
The last of these three studies was a case-control study that examined maternal exposure to various 

air pollutants during the first three months of pregnancy and the risk of delivering an infant with an oral 

cleft, namely cleft lip with or without palate (CL/P). Birth data from the Taiwanese birth registry from 

2001 to 2003 was linked to air pollutant data that were spatially interpolated from all fixed monitoring 

sites across Taiwan. Based on data at the center of the townships or districts, exposure estimates for PM10, 

SO2, NOX, O3, and CO were averaged over each of the first three months of pregnancy. Interestingly, of 

all the pollutants examined, only O3 during the first two months of pregnancy was significantly associated 

with an increased risk of CL/P. In multipollutant models CO was not associated with CL/P (Hwang and 

Jaakkola, 2008). 

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The main results from the southern California study showed that CO was associated with an 

increased risk of ventricular septal defects and this was exhibited by an exposure-response pattern across 

the quartiles of exposure, yet there was no indication that ambient CO concentration in Texas was 

associated with ventricular septal defects. Conversely, ambient CO concentration in Texas was associated 

with an increased risk of conotruncal defects, yet there was no indication that CO in southern California 

was associated with conotruncal defects, and on the contrary, reported results of a protective effect. 

Interestingly, similar inconsistencies were also found for PM10 between these studies. For example, 

PM10 in Texas was associated with an increased risk of atrial septal defects, yet there was no indication of 

such an effect in southern California where PM10 concentrations were markedly higher. 

The authors of the Texas study (Gilboa et al., 2005) provide little discussion toward the 

inconsistent results with the southern California study. One suggestion is the different CO concentrations 

across the studies with the 75th quartile in southern California being 2.39 ppm while in Texas it was much 

lower at 0.7 ppm. However, this suggests that different defects are associated with different 

concentrations of CO, yet it still does not explain why particular associations were reported in Texas and 

not southern California where concentrations were higher. Similarly, the authors of the Texas study 

(Gilboa et al., 2005) also suggested the inconsistency was due to different exposure periods. In Texas the 

exposures were averaged over the 3rd to 8th week while in southern California the exposures were 

averaged over the second month of pregnancy. However, there was no reason provided as to why this 

small difference in the examined exposure period would explain the inconsistent results. 

Overall, there is little evidence that maternal exposure to CO is associated with an increased risk of 

congenital anomalies, namely heart defects and cleft lip and palate. Further research is required to 

corroborate these findings. 

March 2009 5-72 DRAFT – DO NOT CITE OR QUOTE 
5.4.1.4. Neonatal and Post-Neonatal Mortality 

A handful of studies examined the effect of ambient air pollution on neonatal and post-neonatal 

2 mortality with the former the least studied. These studies varied somewhat with regard to the outcomes 

3 and exposure periods examined, and study designs employed. 

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Neonatal 

In Sao Paulo, Brazil, a time-series study examined daily counts of neonatal (up to 28 days after 

birth) deaths for the period of 1998-2000 in association with concurrent day exposure to SO2, CO, O3, 

PM10. Moving averages from 2 to 7 days were examined. The mean city-wide CO concentration was 

2.8 ppm and there was no association between daily ambient CO and neonatal deaths. Despite CO being 

correlated with PM10 (r = 0.71) and SO2 (r = 0.55), only PM10 and SO2 were associated with an increase in 

the daily rate of neonatal deaths (Lin et al., 2004a). 

Post-Neonatal 

Two studies in the U.S. examined the potential association between ambient CO and post-neonatal 

(from 28 days to 1 year after birth) mortality and inconsistent results were reported. These studies, 

however, varied somewhat in study design. 

The first of these studies employed a case-control design and examined all infant deaths during the 

first year of life among infants born alive during 1989-2000 within 16 km from a monitoring site within 

the South Coast Air Basin of California. Exposures for 2-week, 1-month, 2-month, and 6-month periods 

before death were linked to each individual death. Extensive analyses were conducted for all-cause infant 

deaths, respiratory causes of death, and sudden infant death syndrome (SIDS). Given the long time period 

of the data analyzed, in order to alleviate the confounding trends in infant mortality and CO levels this 

study was able to match by year (Ritz et al., 2006). Ambient 1-h max CO concentrations averaged over 

the 2 months before death were associated with an 11% (OR: 1.11 [95% CI: 1.06-1.16]) increase in risk of 

all-cause post-neonatal death (per 1 ppm increase) and a 19% (OR: 1.19 [95% CI: 1.10-1.28]) increase in 

risk of SIDS. In the multipollutant models (including PM10, NO2, O3) the positive CO mortality effect 

decreased by around 50% and failed to reach statistical significance. Based on exposure from 2 weeks 

before death, CO was associated with an increased risk of respiratory related post-neonatal deaths 

occurring 28 days to 1 year after birth (OR: 1.14 [95% CI: 1.03-1.25] per 1 ppm increase) and 28 days to 

3 months after birth (OR: 1.20 [95% CI: 1.02-1.40]), but no effect was observed for respiratory related 

deaths occurring 4-12 months after birth. These results persisted in the multipollutant models and 

exposure-response patterns were exhibited across the exposures groupings of 1.02 to <2.08, and 

≥ 2.08 ppm. To control for gestational age and birth weight the analyses were stratified by ‘term/normalweight infants’ and ‘preterm and/or LBW infants.’ When these two strata were analyzed, CO was 

March 2009 5-73 DRAFT – DO NOT CITE OR QUOTE 
associated with an increased risk of all-cause death and SIDS within both strata (ORs ranged from 1.12 to 

1.46). However, these effects did not persist in multipollutant models (Ritz et al., 2006). 

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The second of these 2 studies examined 3,583,495 births, including 6,639 post-neonatal deaths 

occurring in 96 counties throughout the U.S. (in counties with more than 250,000 residents) between 

1989 and 2000 (Woodruff et al., 2008). Only exposure during the first two months of life was examined 

and these were based on an average of CO concentrations recorded across all available monitors within 

the mother’s county of residence. In contrast to the other postnatal mortality study in California, CO 

averaged over the first two months of life was not associated with all-cause death (OR: 1.01 

[95% CI: 0.94-1.09] per 0.5 ppm increase in 24-h CO concentration), or with respiratory related deaths 

(OR: 1.08 [95% CI: 0.91-1.54] per 0.5 ppm increase in 24-h CO concentration), SIDS (OR 0.85 

[95% CI: 0.70-1.04] per 0.5 ppm increase in 24-h CO concentration), or other causes of post-neonatal 

mortality (OR: 1.03 [95% CI: 0.96-1.09] per 0.5 ppm increase in 24-h CO concentration). These null 

findings may be due to higher error of the exposure assessment at the county-level as opposed to using 

data from monitors within close proximity to the residence. 

The only other postnatal mortality studies have been conducted throughout Asia. Two identical 

studies in Taiwan failed to find an association between daily counts of post-neonatal deaths and ambient 

air pollutants, including CO. The data analyzed were from the cities of Taipei (Yang et al., 2006) and 

Kaohsiung (Tsai et al., 2006b) with ambient CO concentrations being 1.6 ppm and 0.8 ppm respectively. 

Both studies examined deaths for the period of 1994-2000 and employed a case-crossover design that 

compared air pollution levels 1 week before and after each infant’s death. 

Similarly, another study in South Korea examined post-neonatal mortality for the period of 1995-

1999 using a time-series design. Same-day CO was not associated with all-cause death (RR: 1.02 

[95% CI: 0.97-1.06] per 0.5 ppm increase). However, same-day CO was associated with post-neonatal 

mortality when the analyses were restricted to respiratory mortality (RR: 1.33 [95% CI: 1.01-1.76] per 

0.5 ppm increase) (Ha et al., 2003). 

In general, the inconsistent exposure periods examined among these studies allows for limited 

direct comparison and interpretation. Nevertheless, there is limited evidence that CO is associated with an 

increased risk of infant mortality during the post-neonatal period. The exposure periods examined varied 

from the same-day CO to lag periods up to a 6 month period prior to birth with one study alternatively 

exploring exposures averaged over the first two months of life. Furthermore, given that birth weight and 

gestational age are strong predictors of infant mortality, in all of the reviewed studies these factors have 

not been considered at either the design or analysis stage. Hence, the link between fetal exposures, 

neonatal exposures, and post-neonatal exposures, and the possible interaction that birth weight and 

gestational age may have on the results yielded from these examined exposure periods, needs further 

attention within this field of research. 

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5.4.1.5. Summary of Epidemiologic Studies of Birth Outcomes and Developmental 

Effects 

There is some evidence that CO during early pregnancy (e.g., first month and first trimester) is 

2 associated with an increased risk of PTB. Only two studies examined the effects of CO on birth defects. 

3 Both studies found maternal exposure to CO to be associated with an increased risk of cardiac birth 

4 defects. This insult to the heart is coherent with the CO effects on the heart characterized in Section 5.2. 

5 In general, there is limited evidence that CO is associated with an increased risk of infant mortality during 

6 the post-neonatal period. 

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There is evidence of ambient CO during pregnancy having a negative effect on fetal growth. In 

general, the reviewed studies (Figures 5.7 through 5.9) reported small reductions in birth weight (ranging 

~10-20 g). Although the majority of studies reported significant effects during either the first or third 

trimester, other studies failed to find a significant effect during these periods. Several studies examined 

various combinations of birth weight, LBW, and SGA/IUGR and inconsistent results are reported across 

these metrics. For example, six studies reported an association between maternal exposure to CO and 

decreased birth weight yet the decrease in birth weight did not translate to an increased risk of LBW or 

SGA. It should be noted that having a measurable, even if small, change in a population is different than 

having an effect on a subset of susceptible births, which may increase the risk of IUGR/LBW/SGA. It is 

difficult to conclude if CO is related to a small change in birth weight in all births across the population, 

or a marked effect in some subset of births. 

5.4.2. Toxicological Studies of Birth Outcomes and Developmental 

Effects 

5.4.2.1. Birth Outcomes 

Decreased Birth Weight 

Multiple reports have been published associating low level CO exposure in laboratory animals and 

decrements in birth weight; some of these studies noted reduced growth rates persisting in the neonatal 

period. Prigge et al. (1977) saw significant decreases in near-term fetal body weight (GD21) after 21 days 

of continuous exposure of pregnant Wistar rats to CO (125, 250, or 500 ppm). Fechter and Annau (1977) 

exposed pregnant rats to 150 ppm CO continuously during gestation via inhalation and found 5% 

significantly decreased birth weights at PND1 in gestationally exposed pups versus control animals with 

weight decrements persisting to weaning; lactational cross fostering did not ameliorate the reduced 

growth rates. Dams exposed to CO during gestation had COHb over gestation of 15% with control dams 

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having less than 1%. Decreased birth weight and pre-weaning weight were seen in CO-exposed pups 

despite a lack of weight decrement in CO-exposed dams versus air-exposed control dams. 

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Penney et al. (1983) exposed pregnant rats to CO (157, 166, and 200 ppm) over GD6-GD19 and 

found significant decreases in near term fetal rat weight at GD20; gestation in rats is ~ 22 days. Carmines 

et al. (2008) exposed Sprague-Dawley rats to ~600 ppm CO via nose-only inhalation (levels similar to 

those seen in cigarette smoke) during GD6-GD19 of gestation for 2 h/day and found significant decreases 

in birth weight (0.5 g or 13%) of exposed pups versus controls. Dam COHb was 30% immediately after 

exposure. The half life in rats at this exposure level is ~ 42 minutes. Maternal body weight was 

unchanged during gestation, but corrected terminal body weight (body weight minus uterine weight) was 

significantly elevated in CO-exposed dams at term. There were no external malformations (teratogenicity) 

seen. Singh et al. (1992; 2006) found significant decreases in birth weight in gestationally CO-exposed 

mouse pups (65, 125, 250 or 500 ppm) in two studies. In the 2006 study, mice were exposed to CO for 6 

h/day for the first 2 weeks of pregnancy, and in the 1992 sutdy, dams were exposed to CO (GD0-GD18) 

longer with fetuses collected at GD18. Astrup et al. (1972) found significant decreases (11 and 20%, 

respectivelty) in birth weight of rabbits exposed to either 90 or 180 ppm CO continuously over the 

duration of gestation. Fechter and Annau (1977) reported decreased birth weight, albeit not significant, of 

Long-Evans rats exposed in utero to 150 ppm CO continuously throughout gestation (dam COHb 15%), 

but by PND4 and through pre-weaning to PND21, exposed pups weighed significantly less than controls. 

Tolcos et al. (2000a) found significant decreases in body weight and crown to rump length in guinea pig 

fetuses after being exposed to 200 ppm CO for 10h/day from GD23-GD25 until GD61-GD63, at which 

time the fetuses were collected (term ranges from GD68 to GD72). However, in other studies, Tolcos 

found no significant differences in birth weight of guinea pig pups after a similar exposure (GD23-GD25 

to term). During pregnancy, fetal and maternal COHb levels were 13% and 8.5%, respectively. 

Pregnancy Loss and Perinatal Death 

Schwetz et al. (1979) exposed CF-1 mice or New Zealand rabbits to 250 ppm CO for either 7 h/day 

or 24 h/day over GD6-GD15 (mice) or GD6-GD18 (rabbits), yielding 4 exposure paradigms. The fetuses 

were then collected by C-section at the termination of exposure, which was near term. Maternal COHb in 

the 7 h/day exposure groups was approximately 10-15% COHb in rabbits and mice; COHb was not 

followed in the 24 h exposure groups. The mice exposed to 7 h/day CO had a significant increase in the 

number of resorbed pups (not seen in 24 h/day CO exposure). Fetal mouse weight was significantly 

greater than control in the 7 h exposures and significantly less in the 24 h exposure groups with 

corresponding significant differences in crown to rump length in the two groups. Rabbits seemed to be 

less affected by CO exposure manifesting with no significant perinatal death or pregnancy loss. 

Astrup et al. (1972) studied the effect of CO on fetal development after continuous CO exposure 

(90 or 180 ppm CO) over the duration of gestation in rabbits. COHb was 16-18% and 8-9% in the 180 

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and 90 ppm exposure groups, respectively. In the immediate neonatal period, 24 h postpartum, 35% 

(180 ppm) and 9.9% (90 ppm) of CO-exposed animals died. In the postpartum period after the first 24 

hours and extending out to PND21, 90 ppm CO-exposed pups experienced 25% mortality versus 13% in 

controls; there was no difference from control at the 180 ppm CO exposure level. Gestation length was 

unchanged with CO exposure. 

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Fechter and Annau (1977) exposed Long-Evans rats in utero to 150 ppm CO continuously through 

gestation (dam COHb 15%) and saw no effects of CO on litter mortality or pup number at PND1. 

Maternal Diet 

Maternal Protein Intake and Neonatal Mouse Mortality

Pregnant CD-1 mice were exposed intermittently (6 h/day for first two weeks of pregnancy) to CO 

(0, 65, or 125 ppm) in combination with protein modified diets [27% (supplemental protein), 16% 

(control), 8% (low), or 4% (very low protein)] to assess the role of CO exposure coupled with low-protein 

diet on neonatal mortality at 1 week of age (Singh, 2006). Litter size was not affected by CO exposure. 

Pup weight was inversely related to CO exposure and directly related to dam diet protein content during 

pregnancy. Pup mortality at birth was directly related to CO exposure in certain protein groups 

(supplemental, and 4% protein) and inversely related to the dam’s dietary protein content. At 1 week of 

age, pup mortality was significantly increased by CO-exposure. Dietary protein restriction also induced 

pup mortality at 1 week of age; all pups in the 4% protein diet died by 1 week of age. CO exposure 

(65 ppm only) combined with a normal protein diet significantly increased pup mortality at 1 week. 

Animals receiving supplemental protein diets with CO exposure (65 and 125 ppm) had pups that had 

significant increases in mortality at 1 week versus control air pups. Control protein diet pups had 

significantly increased pup mortality at 1 week with CO exposure (65 ppm only) versus control air 

animals (0 ppm CO). Contrary to other findings, low protein diet (8%) combined with CO (125 ppm) led 

to a slight yet significant decrease in pup mortality at 1 week of age versus control (0 ppm CO). In 

summary, these data show that in utero CO exposure induced increased neonatal mouse deaths at 1 week 

in supplemental protein and normal protein diet exposure groups. 

Maternal Low Protein Diet and CO-Dependent Teratogenicity

The role of diet as a contributor to teratogenicity of CO (0, 65, 125, or 250 ppm CO) in CD-1 mice 

given a various protein diets (27%, 16%, 8% or 4% protein) during pregnancy was explored by Singh et 

al. (1992). Timed pregnant CD-1 mice were exposed continuously to CO from GD8-GD18 of pregnancy 

at which point animals were sacrificed and fetuses collected. Subsequent work by this group has shown 

that low protein diets plus CO exposure act in an additive fashion to increase placental COHb in mice 

(Singh et al., 1992; 2003). As expected, all levels of CO and the lowest protein diet (8 or 4% protein) 

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given to the dams during gestation resulted in significantly decreased fetal weight of normal fetuses at 

GD18. CO exposure did not produce maternal toxicity except for a significant decrease in maternal 

weight at GD18 with 4 and 8% protein diets versus control diet in non-CO-exposed animals. Dam dietary 

protein levels were inversely related to gross malformations including jaw changes. All concentrations of 

CO exposure significantly increased the percentage of litters with malformations. There was a CO dosedependent increase in percent of litters with malformations within each maternal dietary protein level. 

Skeletal malformations were present in offspring with the percent of litters affected inversely related to 

dietary protein levels. CO exposure concomitant with a low protein diet exacerbated the percent of 

skeletal malformations in offspring. The percent of dead, resorbed, or grossly malformed fetuses was 

directly related to CO concentration and inversely related to maternal dietary protein levels. CO and 

maternal dietary protein restriction had a synergistic effect on mouse offspring mortality and an additive 

effect on malformations. 

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Zinc-Supplemented Protein-Deficient Maternal Diet and Mortality and Teratogenicity

Further studies by Neggers and Singh (2006) explored how teratogenicity and fetal mortality were 

affected by zinc (Zn) modulation in CO-exposed pregnant dams (CD-1 mouse) given protein insufficient 

diets. Developmental toxicitiy of CO was attenuated by protein supplementation, i.e., protein 

supplemented animals (27%) showed a significantly lower incidence of fetal mortality versus 8% and 

16% protein groups. Further, dietary restriction of both protein and Zn with co-exposure to CO during 

gestation increased the incidence of pup mortality and malformations including gastroschisis. 

Earlier studies by Schwetz et al. (1979) found fetal skeletal alterations in lumbar ribs or spurs were 

significantly increased in the 7 h/day and 24 h/day gestationally CO-exposed fetal CF-1 mice collected 

near term after CO exposure (250 ppm), over GD6-GD15 (dam gestational COHb 10-15% for 7 h/day 

exposure, 24 h/day dam COHb not measured); these changes were not seen in similarly exposed fetal 

rabbits (1979). 

Astrup et al. (1972) studied the effect of CO exposure on fetal rabbit development via continuous 

CO exposure (90 or 180 ppm with gestational dam COHb of 9% and 17%, respectively) over the duration 

of gestation. Three pups (n = 123) in the 180 ppm CO group had deformities in their extremities at birth, 

whereas no control and no 90 ppm CO-exposed animals manifested with this malformation. 

Skeletal Abnormalities 

Earlier studies by Schwetz et al. (1979) found fetal skeletal alterations in lumbar ribs or spurs were 

significantly increased in the 7 h/day and 24 h/day gestationally CO-exposed fetal CF-1 mice collected 

near term after CO exposure (250 ppm), over GD6-GD15 (dam gestational COHb 10-15% for 7h/day 

exposure, 24 h/day dam COHb not measured); these changes were not seen in similarly exposed fetal 

rabbits. 

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Astrup et al. (1972) studied the effect of CO exposure on fetal rabbit development via continuous 

CO exposure (90 or 180 ppm with gestational dam COHb of 9 and 17%, respectively) over the duration 

of gestation. Three pups (n = 123) in the 180 ppm CO group had deformities in their extremities at birth, 

whereas no control and no 90 ppm CO-exposed animals manifested with this malformation. 

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Endogenous CO 

Spontaneous Abortions 

Idiopathic spontaneous abortions are more frequent in women with HO-1 polymorphisms in their 

genome (Denschlag et al., 2004). To evaluate the role of HO-1 in spontaneous abortion, a mouse model 

that spontaneously undergoes abortion (CBA/J x DBA/2J mice) (Zenclussen et al., 2006) was used with 

and without HO adenovirus treatment to see if pregnancy outcome could be modulated by changing HO 

concentration. HO-1 is known to protect organs from rejection (Kotsch et al., 2006) and thus, HO activity 

may protect the developing fetus from rejection by the non-self maternal immune system. Pregnancy 

outcome was significantly better (abortion rate significantly decreased) in mice overexpressing HO due to 

adenovirus transfer. Thus, in this model, it appears that upregulation of the HO/CO system is able to 

protect the developing fetus from spontaneous abortion. 

Depressed Vascular Reactivity during Pregnancy and CO 

CO through the production of soluble guanylate cyclase is able to stimulate the relaxation of 

vascular smooth muscle (Villamor et al., 2000). Further, the role of HO expression is important in the 

maintenance and outcome of pregnancy and lactation. During pregnancy, there is increased blood volume 

without a concurrent increase in systemic BP; this is accomplished by a decrease in total peripheral 

vascular resistance, to which CO contributes (Zhao et al., 2008). In humans, genetic polymorphisms in 

HO-1 (microsatellite polymorphisms associated with altered HO-1 transcription) are linked to idiopathic 

recurrent miscarriages (Denschlag et al., 2004) and administering HO-inhibitors to pregnant rodents 

induced total litter loss (Alexandreanu and Lawson, 2002). Various pathologies of pregnancy including 

IUGR and pre-eclampsia are associated with significant decreases in placental HO activity (Denschlag et 

al., 2004; McLaughlin et al., 2003). Thus, the HO/CO system appears to be crucial in maintaining 

pregnancy. 

CO and Vasular Relaxation during Pregnancy 

Isolated rat aortic rings and tail artery rings from pregnant dams (Sprague-Dawley rats) can be 

relaxed by submersion in exogenous CO solutions (Longo et al., 1999). Further, the administration of the 

HO inhibitor SnMP induced increased maternal BP (systolic, diastolic, and mean arterial pressure) during 

pregnancy in FVB mice (Zhao et al., 2008). Zhao also showed pregnancy induced increased total body 

CO excretion as measured in inhalation chambers, and that this increased CO production could be 

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significantly decreased by SnMP administration. Pregnant dam abdominal aortas (AA) are significantly 

dilated with pregnancy and SnMP treatment leads to AA vasoconstriction to levels similar to nonpregnant mice. Zhao looked at blood flow changes using Doppler technology and observed a significant 

increase in uterine artery blood flow velocity following SnMP administration. Thus, it appears that the 

increased CO production during pregnancy may partially account for the decreased peripheral vascular 

resistance seen in pregnancy that prevents the increased blood volume of pregnancy from affecting BP. 

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Placenta 

Women living at high altitude (chronic hypoxia exposure), women with pre-eclampsia, or women 

who had pregnancies with fetal growth restrictions (FGR) produced term placenta with significant 

decreases in HO-2 versus women living at lower altitude with uncomplicated pregnancies (Barber et al., 

2001; Lyall et al., 2000). FGR and pre-eclampsia HO-2 changes were detected in endothelial cells; HO-1 

immunostaining was very low in the placenta. Women living at high altitude have an increased risk of 

adverse pregnancy outcomes versus women living at lower altitudes (Zamudio et al., 1995). Oxygenation 

is important in early pregnancy and triggers trophoblast invasion of the spiral arteries (Kingdom and 

Kaufmann, 1997). Isolated human placenta exposed to solutions containing CO demonstrated a 

concentration-dependent decrease in perfusion pressure (Bainbridge et al., 2002) further demonstrating 

the role of CO in maintaining basal vasculature tone. 

The endogenous generation of CO in the placenta has been demonstrated in chorionic villi of term 

placenta (McLaughlin et al., 2001) with HO activity highest in the placenta near term (McLean et al., 

2000). CO can be generated from multiple endogenous sources; the source of CO in term human placental 

chorionic villi was found to be the catalysis of heme by HO and not endogenous lipid peroxidation 

(Ahmed et al., 2005). Term human placental cell types including syncytiotrophoblasts and 

cytotrophoblasts were grown in cell culture under basal and hypoxic conditions to explore changes in HO 

expression (Newby et al., 2005). HO-1 was expressed at significantly lower amounts in 

syncytiotrophoblasts versus cytrotrophoblasts at normoxic conditions. HO-1 was unchanged in 

cytotrophoblasts under hypoxia, but HO-1 was significantly decreased in hypoxic syncytiotrophoblasts. 

HO-2 was unchanged in either cell type with hypoxia. These cell culture data can give insight into what 

cell types might be responsive to hypoxia through the HO/CO system in the human placenta. Nonetheless, 

these data should be interpreted remembering that isolated cell culture models lack the interaction with 

the intact placenta and may lead to different physiological outcomes. 

Uterus at Parturition 

The addition of exogenous CO to isolated human and rat uterine tissue during pregnancy failed to 

induce relaxation of uterine tissue (Longo et al., 1999). Thus, exogenous CO failed to quiet the 

spontaneous contractility of rat or human myometrium (uterine smooth muscle). CO is not able to relax 

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all types of vascular smooth muscle (Brian et al., 1994), and pregnancy appears to modulate the response 

of tissues to CO (Katoue et al., 2005). 

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HO-1 induction and HO concentration have been shown to be regulated by estrogen in the rat 

(Sprague Dawley) uterus (Cella et al., 2006) during pregnancy and in non-gravid rats. This agrees with 

work by Tschugguel et al. (2001) in which CO was generated by primary endothelial cells from human 

umbilical veins and uterine arteries after exogenous 17-β estradiol administration. 

Ovaries 

Ovarian Follicular Atresia. As a part of normal follicular maturation in the ovaries, the majority of 

follicles undergo artresia via apoptosis prior to ovulation. Harada et al. (2004) harvested porcine 

granulosa cells from ovaries and found that cells naturally undergoing atresia or cell death more strongly 

expressed HO-1 than did successful follicles. Addition of the HO substrate hemin or the HO inhibitor Zn 

protoporphyrin IX (ZnPP IX) significantly induced or inhibited granulosa cell apoptosis, respectively. In 

this porcine model, HO was able to augment granulosa cell apoptosis allowing for proper follicular 

maturation. 

Ovarian Steroidogenesis. HO-1 and HO-2 are localized in the ovaries in rats (Sprague Dawley) 

and treatment of rat ovaries in vitro with CrMP, an inhibitor of HO, or with hemin, a substrate for HO 

induced steroidogenic changes in the ovaries (Alexandreanu and Lawson, 2002). CrMP significantly 

decreased ovarian production of gonadotrophin-induced androstenedione and progesterone without 

affecting estradiol levels. Hemin treatment caused androstenedione and estradiol production from rat 

ovaries in vitro. Thus, the HO/CO pathway may play an important role in rat ovarian steroidogenesis. 

Anterior Pituitary and Heme Oxygenase 

HO-1 and HO-2 are expressed in rat (Sprague Dawley) anterior pituitary and the secretion of 

gonadotropins and prolactin is affected by HO inhibitors and HO substrates (Alexandreanu and Lawson, 

2003a). The estrogen-induced afternoon surge of LH was advanced forward in time by chronic 

administration of the HO inhibitor CrMP and this advance could be reversed by concomitant 

administration of hemin, a HO substrate. The serum FSH surge was unaffected by CrMP or hemin but in 

vitro treatment of GnRH-stimulated pituitaries with hemin led to a significant increase in FSH release. 

The estrogen-dependent afternoon prolactin surge was inhibited or delayed by CrMP and CrMP+hemin 

and CrMP significantly decreased prolactin release. In vitro studies using pituitary explants showed that 

LH release was significantly increased by CrMP administration and unaffected by hemin. Modulation of 

the HO/CO system in the anterior pituitary of the female rat led to altered secretion of gonadotropins and 

prolactin. 

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Lactation and Pregnancy and HO Modulation 

The role of the HO/CO system in estrous cyclicity, pregnancy, and lactation were monitored using 

a Sprague Dawley rat model (Alexandreanu and Lawson, 2002). Pregnancy outcomes used dams that 

were injected daily (GD5-GD16) with hemin, a HO substrate, or CrMP, a HO inhibitor that were followed 

until 2-days post-term at which point they were euthanized and uteri examined for implantation sites and 

fetuses. Another group of female rats were monitored for estrous cycle changes after receiving daily 

hemin (10 or 11 days of injections) or CrMP (11 or 14 days of injections). A third group of animals was 

monitored during lactation after receiving either CrMP or hemin exclusively during lactation (lactation 

days LD5-LD15); the lactational effect was monitored by weighing pups out to weaning (LD21). Hemin 

had no effect on any of the outcomes in this study, except a slight effect on litter weight gain during 

lactational exposure. CrMP decreased time in estrous in a dose-dependent manner. CrMP exposure 

significantly increased uterine fetal resorptions and led to no live births, possibly due to vasoconstriction 

and associated ischemia of the placental vascular bed. CrMP induced decreased litter weight gain during 

lactation, which the authors attributed to decreased maternal milk production or ejection problems as 

cross-fostered pups regained weight lost during nursing on CrMP dams. The lactational effects seen in 

this model may be explained by changes in prolactin as previously reported (Alexandreanu and Lawson, 

2002). These data indicate that the HO/CO pathway is important in estrous cyclicity, parturition, and 

lactation. Nonetheless, CrMP may also inhibit NO• production, a mechanism that is distinct from 

CO-dependent effects. 

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Summary of Toxicological Studies on Birth Outcomes 

Endogenous CO production by various organs and the vascular system during pregnancy is 

important for maintaining pregnancy and for the proper development of the fetus and/or offspring 

postnatally. Exogenous CO addition has been shown to alter perinatal effects and birth outcomes in 

various toxicological studies. Endogenous CO production is required for maintenance of a pregnancy as 

has been shown by use of HO inhibitors; further, genetic polymorphisms in HO-1, which are associated 

with increased HO expression upon stress, are associated with idiopathic recurrent miscarriages in a study 

of a Caucasian population. A rodent study of spontaneous abortion demonstrated better pregnancy 

outcome in animals in which HO-1 levels were increased by adenovirus transfer. 

Rodent studies have explored the role of exogenous CO exposure on birth outcomes. Exogenous 

CO exposure to rodent fetuses in utero significantly increased postnatal mortality. CO exposure in utero 

induced teratogenicity in offspring in a dose-dependent manner that was further exacerbated by dietary 

protein restriction or zinc depletion. Two studies noted skeletal abnormalities following in utero CO 

exposure (180-250 ppm). 

CO induces cGMP production by vascular smooth muscle beds, which can regulate vascular tone. 

Thus, endogenous CO production is associated with maintenance of decreased peripheral vascular 

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resistance during pregnancy that prevents the increased blood volume of pregnancy from affecting BP. 

This has been demonstrated in the human placenta and cells from the placenta with exogenous CO 

addition further decreasing perfusion pressure. However, exogenous CO addition to human and rat uterine 

tissue during pregnancy was unable to relax spontaneous myometrial contraction. 

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Estrogens have been shown to upregulate HO-1 levels in the uterus in non-gravid and pregnant rat 

uteri; other studies have shown isolated cell culture systems administered 17-beta estradiol can generate 

CO. In the female reproductive tract, the endogenous HO/CO system is involved in proper follicular 

maturation, ovarian steroidogenesis, the secretion of gonadotropin and prolactin by the anterior pituitary, 

in maintenance of lactation, and estrous cyclicity in rodent studies. 

5.4.2.2. Developmental Effects 

CNS Developmental Effects 

Behavioral 

Active Avoidance Behavior. To assess behavioral changes after in utero exposure, pregnant Wistar 

rats were exposed to CO (0, 75 or 150 ppm) continuously over GD0-GD20 (De Salvia et al., 1995). Male 

pups from exposed dams were evaluated for active avoidance behavior (mild shock avoidance) during 

acquisition and reacquisition. This work was designed to expand on the studies of Mactutus and Fechter 

(1985), who showed delayed acquisition (120 days of age) of an active avoidance task and disruption of 

retention at a later test date (360 days) after continuous in utero CO exposure (150 ppm CO, dam COHb 

concentrations of 15.6 ± 1.1%), and to determine if these behavioral changes were permanent. De Salvia 

et al. (1995) found there were no significant behavioral impairments in the low dose animals (75 ppm). 

Animals exposed to the higher dose of CO (150 ppm) in utero had significantly impaired acquisition (at 

3 and 18 months of age) and reacquisition (at 18 months of age) of conditioned avoidance behavior. The 

authors speculated that this CO-dependent behavioral change may be mediated through neurotransmitter 

signaling, specifically changes in dopamine in the neostriatum or nucleus accumbens. These studies 

demonstrate that low level CO exposure in utero can lead to permanent behavioral changes in male 

offspring. 

Mactutus and Fechter (1984) also found that acquisition in a two-way conditioned avoidance 

(flashing light warnings followed by mild footshock) test failed to improve with age of in utero 

CO-exposed (150 ppm, dam COHb 15%) Long-Evans rats (male and female offspring) in contrast to 

air-exposed controls who improved with age/maturation, indicating a failure in the associative process of 

learning. They also found impairments in reacquisition performance, an index of retention, in PND31 rats 

that had received continuous in utero CO exposure. Prenatal CO exposure induced learning and memory 

deficits in male and female offspring. 

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Habituation and Non-Spatial Working Memory. Investigators have used animal models to study 

how low dose CO exposure (75 or 150 ppm) during gestation can affect behavioral outcomes 

(habituation, exploration of novel objects, and motor activity) in offspring as adults. Giustino et al. (1999) 

exposed primparious pregnant Wistar rats to CO (0, 75 or 150 ppm) by inhalation from GD0-GD20. 

Blood COHb concentrations (mean % ± SEM) on GD20 have been reported (0 ppm: 1.6 ± 0.1; CO 

75 ppm: 7.36 ± 0.2; CO 150 ppm: 16.1 ± 0.9). At age 40 weeks, male offspring were given two trials. In 

the first trial (T1), two similar objects were presented. In the second trial (T2), one object from the first 

trial was presented as well as one novel object. Exploration time was defined as time exploring objects 

during each trial. Global habituation was quantified as a comparison of the time spent exploring the two 

objects in T1 to time spent exploring objects in T2. Discrimination between new and familiar objects was 

measured in T2 by contrasting the time spent exploring the familiar object to time spent exploring the 

new object. This object recognition tests for the preference that rats have for investigating novel objects in 

lieu of familiar objects and is a measurement of non-spatial working memory. The results of this study 

showed gestationally CO (75 and 150 ppm) exposed animals at 40 days of age have a significantly 

decreased T1 during exploration of novel objects. Global habituation of control and 75 ppm CO rats 

showed exploratory times with T2 <T1. However, T1 and T2 were not significantly different in the 

150 ppm CO group, showing a lack of habituation after the second exposure to a previously viewed 

object. Also, the decreased time spent with a familiar object by control rats was not seen in CO-exposed 

animals (75 or 150 ppm). Further, the authors speculated that the mesolimbic dopaminergic system may 

be responsible for these changes, possibly involving the nucleus accumbens. The human literature shows 

a possible connection with these CO-dependent rodent effects; infants whose mothers smoked during 

pregnancy manifest with habituation defects. Nonentheless, CO is just one of many consitutients of 

cigarette smoke. The results from these animal toxicology studies showed that in utero exposure to CO 

affects non-spatial working memory in young adult male rats. 

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Long-Term Potentiation and Spatial Learning. The above study agreed with earlier work showing 

that hippocampal long-term potentiation (LTP) (Zhuo et al., 1993) of brain sections was significantly 

affected by CO exposure with ZnPPIX, a HO inhibitor, blocking hippocampal LTP, but was in contrast to 

Bing et al. (1995) who showed enhanced spatial learning in the Morris water maze in rodents exposed to 

the HO inhibitor tin protoporphyrin (Sn-PP). Thus, distinct types of learning may be differentially 

regulated by CO exposure, and endogenous CO, as modulated with the above inhibitors, may manifest 

with different outcomes versus exogenous CO. 

Homing Behavior. Development of homing behavior, orientation by the rat toward its home cage, 

was significantly delayed in rats prenataly exposed continuously to 150 ppm CO (150 ppm) (Fechter and 

Annau, 1980). 

Locomotor Effects. Fechter and Annau (1977) exposed Long-Evans rats in utero to 150 ppm CO 

continuously through gestation (dam COHb 15%) and saw significant effects of CO on pup locomotor 

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activity at PND1 and PND4 (both after subcutaneous L-DOPA administration to induce movement) and at 

PND14, but not at PND21. CO-exposed pups showed significantly less activity than air-exposed controls 

through the pre-weaning window. 

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Fechter and Annau (1980) exposed rats to CO (150 ppm) continuously throughout gestation and 

found that exposed offspring manifested with poorer than normal performance on the negative geotaxis, a 

reflexive response that results in a directional movement with or against gravity. In these studies, negative 

geotaxis was defined as performing a 180° turn to face the top of an inclined plane. Continuous prenatal 

CO exposure (125 ppm, GD7-GD18) in CD-1 mice impaired negative geotaxis at PND10 in a study by 

Singh (1986). The standardization and use of geotaxis as a vestibular, motor or postural metric in infant 

rodents has been debated in the literature (Kreider and Blumberg, 2005). 

Prenatal exposure to CO (125 ppm, GD7-GD18) significantly affected the righting reflex (the 

turning of an animal from its supine position to its feet) in exposed CD-1 mice on PND1. Also, the aerial 

righting score, or turning 180° and landing on the feet when dropped from the supine position at a height, 

was significantly decreased in pups exposed to CO in utero (65 and 125 ppm) (Singh, 1986) at PND14. 

These behavioral tests indicated neuromuscular, vestibular or postural effects in the CO-exposed neonate. 

Earlier studies by Fechter and Annau (1977) identified an early window of sensitivity for CO-dependent 

motor activity defecits of PND1-PND4, with recovery by PND21. Carratu’s (2000) PND40 and PND90 

monitoring of motor activity of in utero CO-exposed animals may be too late to detect CO-dependent 

changes. 

Neuronal 

Sciatic Nerve Myelination. In utero exposure (GD0-GD20) to low levels of CO (0, 75 or 100 ppm) 

and its effect on sciatic nerve myelination in male offspring was studied in Wistar rats (Carratù et al., 

2000). The dam CO blood concentration expressed as %COHb was determined for 0 ppm (GD10: 0.97 ± 

0.02; GD20: 1.62 ± 0.1.), 75 ppm (GD10: 7.20 ± 0.12; GD20: 7.43 ± 0.62), 150 ppm (GD10: 14.42 ± 

0.52; GD20: 16.08 ± 0.88). There were significant increases in %COHb in all CO-exposed animals. The 

myelin sheath thickness of the nerve fibers was significantly decreased in CO-exposed animals (75 and 

150 ppm). Axon diameter was not affected by CO exposure. Even though CO affected myelination, it did 

not significantly affect motor activity of CO-exposed mice at 40 and 90 days. In conclusion, in utero 

exposure of male rats to CO induces decreased myelination of the sciatic nerve without concomitant 

decreases in motor activity. 

Brain and Peripheral Nervous System Myelination. The effect of in utero exposure (GD0-GD20) 

of Wistar rats to CO (0 or 150 ppm) on pup neuronal myelination was studied in the peripheral nervous 

system and the brain of male offspring at 90 days of age (Carratù et al., 2000). CO exposure during 

development induces hypoxia, and hypoxia can induce sphingomyelin changes which could lead to 

impaired myelination and motor activity decrements. This study reported maternal COHb (mean % ± 

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SEM) as 1.9 ± 0.04 and 16.02 ± 0.98 in control and 150 ppm CO-exposed animals, respectively. Prenatal 

CO exposure had no effect on brain sphinganine (SA) or sphingosine (SO) levels in male offspring at 

90 days of age. However, the sciatic nerve had significant increases in SO after CO exposure, no changes 

in SA and a significant decrease in the SA/SO ratio at 90 days of age. Motor activity, which could be 

affected by changes in myelination, showed no differences between CO and control animals at 90 days of 

age. SO is an intermediate in sphingolipid turnover and SA is an intermediate of de novo sphingolipid 

biosynthesis. These results demonstrate that sphingolipid homeostasis in the PNS but not CNS is 

interrupted in offspring exposed in utero to CO without a manifestation of changes in motor activity. 

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Neurotransmitter Changes 

Medullar Cholinergic and Catecholaminergic Changes after In Utero CO Exposure and SIDS. 

One theory related to Sudden Infant Death Syndrome (SIDS) relates to aberrant development of brain 

stem nuclei controlling respiratory, cardiovascular, and arousal activity. To address these changes in an 

animal model, Tolcos et al. (2000a) exposed pregnant guinea pigs to CO (0 or 200 ppm) for 10 hours per 

day over the last 60% of gestation leading to fetal and maternal COHb levels of 13% (versus control of 

0.25%) and 8.5% (versus control of 1.6%), respectively. Guinea pigs are a good model because they 

display a similar time course of CNS development to humans with the majority of development occurring 

in utero. Pups and sows were collected near term and CO-exposed pups were found to have significant 

decrements in body, brain, and liver weights as well as decreased crown to rump length when compared 

to control pups. Medullar volume was also significantly decreased in CO-exposed pups. Neurotransmitter 

systems were affected after CO exposure. Specifically, the catecholaminergic system of the brainstem 

displayed significant decreases in immunoreactivity for tyrosine hydroxylase (TH), which is likely due to 

decreased cell number in specific medullar regions. This was consistent with earlier work showing 

aberrant respiratory responses (to asphyxia and CO2) of offspring with prenatal CO exposure (McGregor 

et al., 1998). The cholinergic system is also affected by prenatal CO exposure with significant increases in 

choline acetyl-transferase (ChAT) immunoreactivity of the medulla and no changes in muscarinic 

acetylcholine receptor. This is in contrast to human infants with SIDS that show decreased brainstem 

muscarinic receptor binding (Kinney et al., 1995). ChAT changes in this study (Tolcos et al., 2000a) were 

from areas of the medulla associated with tongue innervation, which is crucial to swallowing and which 

may be impaired in SIDS infants. 

Mesolimbic Dopaminergic Effects and Impaired Sexual Behavior. Exposure (GD0-GD20) of 

pregnant Wistar rats to CO (0, 75 or 150 ppm) and its effects on adult (5 and 10 months of age) male 

offspring sexual behavior and mesolimbic dopaminergic function was accessed by Cagiano et al. (1998). 

Maternal COHb at GD10 was 1, 7, and 15% (0, 75 and 150 pm CO, respectively) and at GD20 was 1.5, 7, 

and 16% (0, 75, 150 ppm CO, respectively). Using the aforementioned parameters, only animals at the 

150 ppm CO exposure were significantly affected by CO exposure. At 5 months of age, CO-exposed male 

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offspring showed decrements in sexual behavior including an increase in mount to intromission latency, a 

decrease in mount to intromission frequency, and a decrease in ejaculation frequency. Further, 

administration of amphetamine, which stimulates copulatory activity, did not change CO-induced 

increases in mount to intromission latency or decreases in mount to intromission frequency. Basal 

extracellular dopamine concentration in the nucleus accumbens was unchanged after CO exposure. 

However, when stimulated with amphetamine administration, control rats had increased release of 

dopamine that was absent with CO-exposed rats. This demasculination of the CO-exposed offspring 

paralleled earlier studies of mice exposed gestationally to hypoxia (Hermans et al., 1993). When rats were 

followed at ten months of age, there were no significant changes in copulatory activity or neurochemical 

parameters of the CO-exposed male offspring showing a recovery from earlier decrements. In summary, 

in utero exposure to CO induced impairment of copulatory sexual behavior in male offspring with 

accompanying changes in the mesolimbic dopaminergic system at five months of age. By ten months of 

age, these changes were no longer detectable in CO-exposed males. 

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Dopamine in the Neostriatum Is Affected by In Utero Plus Perinatal CO Exposure. Exposure of 

Long Evans rat dams and pups continuously to CO (75, 150, or 300 ppm with maternal COHb of 11, 19, 

and 27%, respectively) from conception to PND10 induced significant elevations in dopamine in the 

striatum at PND21 in CO-exposed offspring versus air exposed controls (Fechter et al., 1987). 

Cerebellum Weight and Neurotransmitter Changes after In Utero and Perinatal CO Exposure. 

Long Evans dams and pups exposed to CO (75, 150, or 300 ppm with COHb 9%, 15% and 24%) over the 

duration of gestation and out to PND10, during the period of cerebellular development in the rat which is 

equivalent to the in utero cerebellar development in the human, yielded a dose-dependent reduction in 

cerebellum wet weight (significant at 150 and 300 ppm) at PND21 with increases in norepinephrine (NE) 

concentration in the extrinsic noradrenergic system (a system that terminates in the cerebellum) in 

CO-exposed animals monitored from PND14 to PND42, which is in contrast to a lack of NE change seen 

in the cerebral cortex with in utero only CO exposure (Storm and Fechter, 1985a, b). With the same 

exposures as Storm and Fechter, CO-exposed (300 ppm only) pups at PND21 had significant decreases in 

cerebellar GABA content, decreased uptake of exogenous radio-labeled GABA, decreased fissures in the 

cerebellum and decreased cerebellum size (Storm and Fechter, 1985b). Thus, in these studies, it appeared 

that the cerebellum and neurotransmitters of the cerebellum were significantly affected by CO exposure 

during cerebellar development. 

Neonatal Hyperthermia Effects on Neurotransmitters and Neuroglia. To explore the interaction 

of hyperthemia and hypoxia (CO-induced), two risk factors for SIDS, pregnant guinea pigs were exposed 

to CO (0 or 200 ppm) for 10 h/day for the last 60% of gestation. At PND4 male pups were exposed to 

hyperthermia or ambient temperature as a control. Brains were then collected at 1 and 8 weeks of age. CO 

exposure had no effect on litter size or litter birth weight. In utero CO exposure sensitized some areas of 

the brain to future hyperthermic insults. Specifically, CO plus hyperthermia induced significant increases 

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in serotonin in multiple brain regions (NTS, DMV, and hypoglossal nucleus) at 1 week of age; this change 

was no longer evident at 8 weeks of age. Hyperthermia exposure alone induced decreased met-enkephalin 

neurotransmitter immunoreactivity at 1 week of age that was absent at 8 weeks and absent in CO plus 

hyperthermia exposed animals. Brain stem neurotransmitter (met-enkephalin, serotonin, TH, substance P) 

immunohistochemical differences were not apparent with CO treatment alone. At 8 weeks of age, CO 

plus hyperthermia exposure induced glial aggregations and gliosis surrounding infarct or necrotic areas in 

the brain and the medulla lesions stained positive for glial fibrillary acidic protein (GFAP). GFAP 

upregulation is classically seen with neuronal diseases or following neurodegeneration. Gross structural 

observations revealed no differences in the medulla or cerebellum following in utero CO exposure alone. 

Together, these data showed that CO exposure in utero sensitizes the brain to future hyperthermic insults 

leading to generation of necrotic lesions in the brain and changes in neurotransmitter levels. 

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Glutamatergic Transmission Impairment by In Utero CO Exposure. Pregnant Wistar rats were 

exposed continuously to CO (75 ppm) during gestation (GD5-GD20) (Antonelli et al., 2006). Primary cell 

cultures obtained from the cerebral cortex of exposed offspring (PND1) had decreased extracellular 

glutamate (basal and K+

-evoked) levels versus air exposed controls, which may functionally impair 

cortical glutamatergic transmission in CO-exposed offspring, possibly affecting learning and memory. 

Electrophysiological Changes 

Gestational exposure of pregnant Wistar rats to continuous CO (0, 75 or 150 ppm with dam COHb 

for 150 ppm CO of 15%) for the entirety of gestation yielded electrophysiological changes in the 

peripheral nervous system with reversible changes (present at PND40 and absent at PND270) in sodium 

channel inactivation kinetics and irreversible changes in the sodium equilibrium potential (Carratu et al., 

1993) in CO-exposed pups (75 and 150 ppm) versus control pups. These voltage clamp studies showed 

that in utero CO exposure had both reversible and irreversible effects on sodium channels, which are 

essential for proper electrophysiological function of the PNS. 

The Developing Auditory System 

The developing auditory system of rodents has recently been recognized to be a target of CO 

exposure. Low concentrations of CO exposure via inhalation (0, 12.5, 25, or 50 ppm) over PND8-PND22 

during the period of extensive auditory development and synaptogenesis was studied by Webber , who 

looked specifically at the inferior colliculus (IC), an auditory integrative section of the midbrain. Rodent 

pups were allowed to be maternally reared (MR) or were removed from their respective dams and 

nutritionally supported with gastronomy-reared nutrition (AR), an artificial feeding system or received 

AR plus CO exposure (ARCO). AR reared pups were fed a milk substitute comparable to natural rat milk 

via intragastric cannulation. AR allowed nursing pups to be exposed to CO without possible 

CO-dependent developmental changes being confounded by materal CO co-exposure or lactational 

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exposures. Half of the pups were collected at PND27 and half were collected at PND75-PND77. Brains 

were sectioned and stained for c-Fos, a marker of neuronal activation in the nervous system. AR and MR 

exposed animals were found to have no significant differences in c-FOS staining. Further, c-Fos 

immunoreactivity in the central IC was significantly decreased in the ARCO-exposed animals at both 

PND27 and PND75-PND77 over all dose groups (12.5, 25, or 50 ppm CO); immnunostaining of other 

subregions of the IC were not affected by CO. These studies showed exposure to low concentrations of 

CO during development can lead to permanent changes in the auditory system of mice that persist into 

adulthood. 

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Others have shown that mild CO exposure early in development impacts cochlear development 

(Lopez et al., 2003). Sprague Dawley rats were reared from PND6 to weaning (PND19-PND20) on milk 

substitutes using gastronomy-feeding (AR) or maternally reared (MR). These pups were exposed to low 

levels of CO (12 or 25 ppm, ARCO) via inhalation from PND6-PND27. At PND27, the animals were 

sacrificed and examinations of the cochlea were performed to determine the effect of mild CO exposure. 

In ARCO animals, there was no evidence of damage to the inner or outer hair cells. After CO exposure 

(25 ppm), there was atrophy or vacuolization of the nerve cells that innervate the inner (not outer) hair 

cells. Also, fibers of the 8th cranial nerve at the level of the internal auditory canal of the ARCO animals 

exposed to 25 ppm CO had distorted myelination and vacuolization of the axoplasm. Energy production 

markers in the organ of corti and spiral ganglion neurons including cytochrome oxidase and NADH-TR 

were significantly decreased in 25 but not 12 ppm CO exposure groups versus control (AR and MR). 

Expression of the calcium-mediated myosin ATPase in the organ of corti and spiral ganglion neurons was 

significantly decreased in the 25 ppm CO exposure group when compared to controls. Together, these 

findings show that specific areas of the cochlea were affected after low level developmental CO exposure. 

Two non-invasive measurements of auditory function, otoacoustic emissions testing (OAE) and 

auditory brainstem response (ABR) are routinely performed on the majority of human newborns in 

hospitals throughout the U.S. to detect early hearing loss. OAE requires the insertion of a microphone and 

earphone into a sleeping newborn’s ear; when a sound is generated, an echo is recorded on the 

microphone in a functional ear and absent or reduced in an affected ear. The ABR measures brainwaves 

generated after exposure to a sound. Otherwise healthy, term human infants born to smoking mothers 

have impaired cochlear development, albeit mild, with decreased amplitudes of transient evoked 

otoacoustic emissions versus newborns born to non-smokers (Korres et al., 2007); CO is one of many 

potential affective components of cigarette smoke. 

Stockard-Sullivan et al. (2003) used these two functional tests in Sprague-Dawley rat pups 

receiving ARCO (12, 25, or 50 ppm CO) to determine how continuous (22 h/day) perinatal CO exposure 

(PND6-PND22) functionally affected hearing in the developing rat. OAEs were measured in two ARCO 

groups (25 and 50 ppm) at PND22. At 50 ppm, significant reductions in OAE amplitude were detected at 

specific frequencies (7.13 and 8.01 kHz). ABR conduction time was not affected in CO-exposed animals 

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(12, 25, 50, 100 ppm). Using another otoacoustic test revealed significant attenuation of the action 

potential of the 8th cranial nerve with ARCO exposure (12, 25, and 50 ppm CO) versus AR controls at 

PND22, but this is complicated by the finding that AR control animals had significant attenuation of the 

8th cranial nerve AP versus MR control animals, implying that artificial diet contributes to AP changes 

before CO was introduced. Nonetheless, the ARCO-dependent attenuation of the 8th cranial nerve AP 

(versus AR control) was permanent, persisting until adulthood in the 50 ppm CO exposure group (the 

only CO group monitored); OAE was not measured in adult animals. These functional tests reported that 

neonatal exposure to low concentrations of CO can induce auditory functional changes in rodents. 

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Multiple high dose CO studies have shown a role for ROS in CO-dependent ototoxicity. Studies 

using high dose CO (i.p. injection of pure CO [35 mL/kg] inducing 40% COHb) have shown 

CO-dependent otoxoxicity in adult guinea pigs as measured by loss of threshold of cochlear compound 

action potentials (CAP) could be attenuated using the free radical inhibitors PBN (a spin trap) or 

allopurinol (a superoxide scavenger), implicating ROS in high dose CO dependent cochlear damage 

(Fechter et al., 1997). Fechter et al. (2002) found that noise-induced hearing loss (NIHL) was potentiated 

by CO (500 ppm) coexposure. In the same manuscript, Fechter et al. (2002) showed potentiation of NIHL 

post-CO exposure (1,200 ppm for 30 minutes in adult rodents) with significant elevations seen in free 

radical production above control animals only in the CO+NIHL group; an interesting finding of this study 

was that the acute high dose CO exposure did not induce significant increases in free radical generation 

with this high dose acute CO exposure alone. A possible mechanism for this high-dose CO cochlear 

damage is via glutamate release. Use of an NMDA inhibitor attenuated acute CO-dependent (1,200 ppm 

or i.p. injection of pure CO [35 mL/kg]) CAP threshold disruption at 15 minutes post CO-exposure. 

Glutamate is a known ROS generator (Liu and Fechter, 1995) . The glutamate receptor blockade did not 

afford long-term protection (1 h or 4 weeks post-CO exposure) from high doses of CO (1,200 ppm or i.p. 

injection of 35ml/kg pure CO) (Chen et al., 2001; Liu and Fechter, 1995) 

Cognizant of the ROS-dependent contribution to the auditory changes seen with high dose 

CO-exposure in animal toxicology models, Webber et al. investigated whether limiting iron availability 

through dietary iron restriction could confer protection against low dose perinatal CO-dependent auditory 

decrements. Briefly, rat pups were reared from PND6 through the duration of lacation with gastronomyreared nutrition with adequate iron (AR) or AR with iron-deficient diets (ARID) or pups were reared with 

their respective dams (MR). Animals exposed to CO were denoted as ARCO and ARIDCO and received 

either 25 or 100 ppm CO from PND9 to PND24 with all animals collected on PND27. ARID, ARIDCO, 

and ARCO groups were compared to controls (MR and AR) to determine differences in the auditory 

system after low iron exposure and/or CO exposure. Coincidentally, ARIDCO mice became anemic but 

ARID mice were not. Cochlea were stained for transferrin, an iron-transport protein, and low iron 

exposure (ARID and ARIDCO) induced increased transferrin. Neurofilament loss from the spiral 

gangilian neurons and somas after ARCO treatment was rescued (no detectable neurofilament loss) with 

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low iron (ARIDCO); ARID treatment induced no change in neurofilaments. CuZn superoxide dismutase 

(SOD1) was significantly increased with CO exposure (ARCO) and rescued in ARIDCO animals; SOD1 

was unchanged in low iron only animals (ARID). Low iron treatment or CO exposure alone led to 

significant decreases in c-fos positive cell numbers of the central IC, but c-fos levels were unchanged 

after low iron diet concomitant with CO exposure (ARIDCO). These authors postulated that ROS 

generated via the interaction of peroxide and iron (via the Fenton reaction or Haber Weiss chemistry) can 

lead to an oxidative stress which leads to decreased c-fos expression in the IC and decreased numbers of 

neurofilaments; decreasing the available iron to the developing animal decreases the total pool available 

for ROS generation. Further, the attenuation of the elevated SOD levels with iron restriction post 

CO-exposure gives credence to this model. The changes in c-fos are not completely explained as ARID 

mice also show significant decreases in c-fos IC staining. 

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Summary of Toxicological Studies on Developmental Central Nervous System Effects 

Toxicological studies employing rodent models have shown that low level CO exposure during the 

in utero period can adversely affect adult outcomes including behavior, neuronal myelination, 

neurotransmitter levels or function, and the auditory system. In utero CO exposure has been shown to 

impair active avoidance behavior in male offspring after in utero CO exposure. Other studies have shown 

that after in utero CO exposure, adult male offspring manifest with altered behavior including a lack of 

habituation and an absence of familiarity with previously viewed objects (non-spatial memory). In two 

separate studies, in utero CO exposure (75 and 150 ppm) was associated with significant PNS 

myelination decrements without associated changes in motor activity in adult animals. With in utero CO 

exposure peripheral myelination was decreased and the sphingolipid homeostasis (150 ppm CO) was 

disrupted; CNS myelination was not affected. Multiple studies demonstrated that in utero CO exposure 

affected glutamatergic (75 ppm), cholinergic (200 ppm), catecholaminergic (200 ppm), and dopaminergic 

(75 ppm) neurotransmitter levels or transmission in exposed male rodents. Possible or demonstrated 

adverse outcomes from the CO-mediated aberrant neurotransmitter levels or transmission include 

respiratory dysfunction (150 ppm), impaired sexual behavior (150 ppm), and an adverse response to 

hyperthermic insults resulting in neuronal damage (200 ppm). Finally, in utero CO exposure has been 

shown to affect the developing auditory system of rodents, inducing permanent changes into adulthood. 

The IC showed decreased c-Fos staining indicating decreased neuronal activation post-perinatal CO 

exposure (12 and 25 ppm CO). The cochlea also showed adverse effects after in utero CO exposure with 

atrophy of nerve cells innervating the inner hair cells (25 ppm CO), impaired myelination of the 8th 

cranial nerve (25 ppm CO), and decreased energy production in the organ of corti (25 ppm CO). Auditory 

functional testing using OAE (50 ppm CO) and attenuation of the 8th cranial nerve AP (12, 25, 50, 

100 ppm CO) on rodents exposed perinataly to CO showed that CO-exposed nenonates had auditory 

decrements at PND22 (OAE and AP) and into adulthood with AP decrements. Further studies limiting 

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iron consumption in the diet protected CO-exposed mice from cochlear damage at the spiral ganglion, 

possibly through a diminuation of ROS. Together, these animal studies demonstrate that in utero or 

perinatal exposure to CO can adversely affect adult behavior, neuronal myelination, neurotransmission, 

and the auditory system in rodents. 

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Cardiovascular and Systemic Developmental Effects 

Myocardial Electrophysiological Maturation 

A rat model of in utero exposure was employed to study CO effects on the development of cardiac 

myocytes. Results demonstrated that in utero CO exposure (150 ppm) alters postnatal cellular 

electrophysiological maturation in the rat heart (Sartiani et al., 2004). Specifically, at 4 weeks of age, the 

action potential duration (APD) of isolated cardiac myocytes from CO-exposed animals failed to shorten 

or mature as did the APD of control animals. Further, the two ion conduction channels Ito (transient 

outward current, K+

-mediated) and ICa,L (L-type Ca2+ current), which largely control the rat APD, were 

significantly different from control animals after in utero CO exposure, at 4 weeks of age. These 

CO-dependent changes resolved by 8 weeks of age, reflecting a delayed maturation. The authors noted 

that human SIDS is increased in infants whose mothers are smokers and cigarette smoke is composed of 

approximately 4% CO. Further, these authors postulated that a CO-dependent delay in 

electrophysiological maturation of the cardiac myocyte (lack of APD shortening) could lead to 

arrhythmias and thus could be associated with SIDS deaths. However, no SIDS-like cardiac abberations 

were followed in intact Holter-monitored rats in this study. 

Heart Morphological changes after In Utero or Perinatal CO Exposure 

Multiple authors have reported cardiomegaly following in utero low level CO exposure. Prigge and 

Hochrainer (1977) reported increased fetal Wistar rat heart wet weight or cardiomegaly following 

continuous in utero CO (60, 125, 250, and 500 ppm) exposure with no decreases in near term fetal 

hematocrit or Hb levels seen at exposures below 250 ppm. Fechter et al (1980) found that prenatal 

exposure to CO affected cardiac development in exposed offspring. Long Evans rats that were exposed to 

CO continuously (150 ppm) during gestation manifested with significant elevations in wet heart weight at 

PND1 as well as heart weight in relation to body weight; body weight was significantly decreased in 

CO-exposed pups. At no other age measured (PND4, PND14, or PND21) did the CO-exposed pups show 

increased heart weight (absolute or relative wet or dry weight) versus control. At PND14, control pups 

that had significantly increased body weight, also had significantly increased absolute heart weights (dry 

and wet) versus CO-exposed offspring. Dry to wet weight ratios revealed that the increased heart weight 

of CO-exposed pups at birth was due to edema or water content. Penney et al. (1982) studied 

CO-dependent (500 ppm) cardiomegaly in neonates (continuous CO exposure for 32 days starting at 

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PND1). Other studies of adult male Charles River derived rats exposed to CO for 6 weeks (at 400 or 

500 ppm CO) as adults only developed CO-dependent cardiomegaly during exposure that significantly 

regressed within a couple of months after termination of CO exposure (Styka and Penney, 1978). 

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Systemic Immune Toxicology after In Utero CO Exposure 

In utero exposure (GD0-GD20) of male Wistar rats to relatively low CO (0, 75, or 150 ppm) 

concentrations induced reversible changes in macrophage function (Giustino et al., 1993). The killing of 

Candida albicans (yeast) by splenic macrophages was significantly decreased at PND15 in in utero 

CO-exposed male offspring (75 and 150 ppm) but recovered and maintained function by PND21 and 

PND60. Macrophage phagocytosis of Candida albicans was significantly reduced at PND15 and PND21 

in CO-exposed males (150 ppm only) and recovery was seen at PND60. Superoxide production by the 

splenic macrophage respiratory burst was significantly decreased at PND15 and PND21 after in utero CO 

exposure (150 ppm only) with recovery to control levels at PND60. In summary, CO exposure in utero 

leads to a reversible and dose dependent loss of function of splenic macrophages with decreased killing 

ability, decreased phagocytosis, and decreased ROS production during the macrophage respiratory burst. 

Further studies by the same laboratory showed that in utero exposure of male Wistar rats to CO 

exposure induced changes in the frequency of splenic immunocompotent cells (Giustino et al., 1994). 

Specifically, there was a significant decrease in the number of leucocyte common antigen cells (LCA+) 

cells in PND21 male rats exposed during gestation to 150 ppm CO; other cell subpopulations including 

macrophages, Major Histocompatibility (MHC) II cells, T and B lymphocytes did not show significant 

decreases in cell numbers with with CO exposure. 

Summary of Toxicological Studies of Cardiovascular and Systemic Development 

In utero CO exposure is associated with various adverse, albeit non-persistent, cardiac aberrations. 

Exposure to 150 ppm induced a delayed maturation of the cardiac action potential in CO-exposed 

offspring. Specifically, APD failed to shorten at 4 weeks of age but shortened to control duration by 8 

weeks of age. Also, ion channels in CO-exposed animals, which are related to APD, were significantly 

different from controls at 4 weeks but matured to control levels by 8 weeks of age. In other studies, 

continuous in utero CO exposure (60, 125, 250, and 500 ppm) induced cardiomegaly at PND1 which was 

transient and regressed by PND4. Systemic immunocompromise was displayed in two studies focusing on 

splenic cells. In the first study, a reversible and dose dependent loss of function of splenic macrophages 

with decreased killing ability, decreased phagocytosis, and decreased ROS production during the 

macrophage respiratory burst was observed (150 ppm but not 75 ppm CO). In the second study, the 

distribution of splenic immunocompotent cells was skewed because of a decrease in the number of LCA+ 

cells in PND21 male rats exposed during gestation to 150 ppm CO. In conclusion, in utero exposure to 

low doses of CO (60, 125, 150, 250, or 500 ppm) is able to induce transient changes in cardiac 

March 2009 5-93 DRAFT – DO NOT CITE OR QUOTE 
morphology, cardiac action potentials, and systemic immunity that may make the immediate neonatal 

period a time for a CO-exposed animal more susceptible to other outside stressors. 

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5.4.3. Summary of Birth Outcomes and Developmental Effects 

The most compelling evidence for a CO-induced effect on birth and developmental outcomes is for 

4 PTB and cardiac birth defects. These outcomes were not addressed in the 2000 CO AQCD, which 

5 included only two studies that examined the effect of ambient CO on LBW. Since then, a number of 

6 studies have been conducted looking at varied outcomes, including PTB, birth defects, fetal growth 

7 (including LBW), and infant mortality. 

There is limited epidemiologic evidence that CO during early pregnancy (e.g., first month and first 

trimester) is associated with an increased risk of PTB. The only U.S. studies to investigate the PTB 

outcome were conducted in California, and these reported consistent results whereby all studies reported a 

significant association with CO exposure during early pregnancy, and exposures were assigned from 

monitors within close proximity of the mother’s residential address. Additional studies conducted outside 

of the U.S. provide supportive, though less consistent, evidence of an association between CO 

concentration and PTB. 

Very few epidemiologic studies have examined the effects of CO on birth defects. Two of these 

studies found maternal exposure to CO to be associated with an increased risk of cardiac birth defects. 

This insult to the heart is coherent with results of human clinical studies demonstrating the heart as a 

target for CO effects (Section 5.2). Animal toxicological studies provide additional evidence for such an 

insult to the heart, and reported transient cardiomegaly at birth after continuous in utero CO exposure (60, 

125, 250 and 500 ppm CO), delayed myocardial electrophysiolgical maturation (150 ppm CO), or 

systemic splenic immunocompromise (75 or 150 ppm CO). Toxicological studies have also shown that 

exogenous continuous in utero CO exposure (250 ppm) induced teratogenicity in rodent offspring in a 

dose-dependent manner that was further exacerbated by dietary protein restriction (65 ppm CO) or zinc 

depletion (500 ppm CO). Toxicological studies of exogenous CO exposure over the duration of gestation 

have shown skeletal alterations (7 h/day, CO 250 ppm) or limb deformities (24 h/day, CO 180 ppm) in 

prenatally exposed offspring. 

There is evidence of ambient CO exposure during pregnancy having a negative effect on fetal 

growth in epidemiologic studies. In general, the reviewed studies, summarized in Figures 5-7 through 5-9, 

reported small reductions in birth weight (ranging ~5-20 g). Several studies examined various 

combinations of birth weight, LBW, and SGA/IUGR and inconsistent results are reported across these 

metrics. It should be noted that having a measurable, even if small, change in a population is different 

than having an effect on a subset of susceptible births and increasing the risk of IUGR/LBW/SGA. It is 

March 2009 5-94 DRAFT – DO NOT CITE OR QUOTE 
difficult to conclude if CO is related to a small change in birth weight in all births across the population, 

or a marked effect in some subset of births. 

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In general, there is limited epidemiologic evidence that CO is associated with an increased risk of 

infant mortality during the neonatal or post-neonatal periods. In support of this limited evidence, animal 

toxicological studies do provide some evidence that exogenous CO exposure to pups in utero significantly 

increased postnatal mortality (7 h/day and 24 h/day, 250 ppm CO; 24 h/day, 90 or 180 ppm CO) and 

prenatal mortality (7 h/day, 250 ppm CO). 

Evidence exists for additional developmental outcomes which have been examined in toxicological 

studies, but not epidemiologic or human clinical studies, including behavioral abnormalities, learning and 

memory deficits, locomotor effects, neurotransmitter changes, and changes in the auditory system. 

Structural abberations of the cochlea involving neuronal activation (12.5, 25 and 50 ppm CO) and 

auditory related nerves (25 ppm CO) were seen in pups after neonatal CO exposure. Auditory functional 

testing using OAE (50 ppm CO) and 8th cranial nerve AP amplitude measurements (12, 25, 50, 100 ppm 

CO) on rodents exposed perinataly to CO showed that CO-exposed nenonates had auditory decrements at 

PND22 (OAE and AP) and permanent changes into adulthood with AP (50 ppm CO). 

Overall, there is limited, though positive, epidemiologic evidence for a CO-induced effect on PTB 

and birth defects, and weak evidence for a decrease in birth weight, other measures of fetal growth, and 

infant mortality. Animal toxicological studies provide support and coherence for these effects. Both 

hypoxic and non-hypoxic mechanisms that could lead to such effects have been proposed in the 

toxicological literature (Section 5.1), though a clear understanding of the mechanisms underlying 

reproductive and developmental effects is still lacking. Taking into consideration the positive evidence for 

some birth and developmental outcomes from epidemiologic studies and the resulting coherence for these 

associations in animal toxicological studies, the evidence is suggestive of a causal relationship 

between long-term exposures to relevant CO concentrations and developmental effects and birth 

outcomes. 

5.5. Respiratory Effects 

5.5.1. Epidemiologic Studies with Short-Term Exposure 

5.5.1.1. Pulmonary Function, Respiratory Symptoms, and Medication Use 

The 2000 CO AQCD briefly discussed the potential acute respiratory health effects associated with 

27 short-term exposure to CO. An evaluation of the epidemiologic literature at the time did not find any 

28 evidence of an association between short-term exposure to CO and lung function, respiratory symptoms, 

March 2009 5-95 DRAFT – DO NOT CITE OR QUOTE 
1 or respiratory disease. As a result, the 2000 CO AQCD did not conclude that a causal association exists 

2 between short-term exposure to CO and respiratory health effects. The following section evaluates the 

3 current literature that examines the potential association between short-term exposure to CO and 

4 respiratory health effects. Table 5-14 lists the studies evaluated in this section along with the respiratory 

5 health outcomes examined and CO concentrations reported. 

Table 5-14. Range of CO concentrations reported in key respiratory morbidity studies that 

examined effects associated with short-term exposure to CO. 

Location Years Health Outcome Metric 

Mean 

Concentration 

(ppm) 

Middle/Upper Percentile 

Concentrations (ppm) Author 

Rabinovitch et al. 

(2004) 

Denver, CO 

Year 1: n = 41 

Year 2: n = 63 

Year 3: n = 43 

11/1999-3/2000; 

11/2000-3/2001; 

11/2001-3/2002 

Pulmonary function; 

Medication use 

24-h avg 1.0 50th: 0.9 

75th: 1.2 

Maximum: 3.5 

Silkoff et al. 

(2005) 

Denver, CO 

Year 1: n = 16 

Year 2: n = 18 

1999-2000 (winter); 

2000-2001 (winter) 

Pulmonary function; 

Medication use 

24-h avg 1999-2000: 1.2 

2000-2001: 1.1 

1999-2000 

50th: 1.10 

75th: 1.43 

Maximum: 3.79 

2000-2001 

50th: 0.975 

75th: 1.34 

Maximum: 2.81 

Fischer et al. 

(2002)1

The Netherlands 

n = 68 

March - April2 Pulmonary function 24-h avg 0.80 Max: 1.34 

Ranzi et al. 

(2004)1

Emilia-Romagna 

Region, Italy 

n = 120 

2/1999-5/1999 Pulmonary function; 

Respiratory 

symptoms; 

Medication use 

24-h avg Urban: 1.34 

Rural: 1.06 

NR 

Lagorio et al. 

(2006)1

Rome, Italy 

(n = 29) 

5/1999-6/1999; 

11/1999-12/1999 

Pulmonary 

Function 

24-h avg Spring: 1.83 

Winter: 10.7 

Overall: 6.4 

Overall 

Max: 25.1 

Penttinen et al. 

(2001)1

Helsinki, Finland 

n = 57 

11/1996-4/1997 Pulmonary function 24-h avg NR 50th: 0.35 

75th: 0.43 

Maximum: 0.96 

Kuopio, Finland 

n = 33 

Timonen et al. 2/1994-4/1994 Pulmonary function 24-h avg 0.52 Maximum: 2.43 

(2002)1

Chen et al. 

(1999) 

Taiwan 

n = 941 

5/1995-1/1996 Pulmonary function 1-h max; 

24-h avg 

NR 1-h max 

Maximum: 3.6 

Delfino et al. 

(2003) 

Los Angeles, CA 

n = 22 

11/1999-1/2000 Asthma symptoms 1-h max; 

8-h max 

1-h max: 7.7 

8-h max: 5.0 

1-h max 

90th: 12.0 

Maximum: 17 

8-h max 

90th: 7.9 

Maximum: 10 

Slaughter et al. 

(2003) 

Seattle, WA 

n = 133 

12/1993-8/1995 Asthma symptoms; 

Medication use 

24-h avg NR 50th: 1.47 

75th: 1.87 

Yu et al. (2000) Seattle, WA 

n = 133 

11/1993-8/1995 Asthma symptoms 24-h avg 1.6 50th: 1.47 

Maximum: 4.18 

Schildcrout et al. 

(2006) 

8 North American 

cities 

n = 990 

11/1993-9/1995 Asthma symptoms; 

Medication use 

24-h avg NR 50th: 0.63-1.49 

75th: 0.77-1.90 

90th: 0.95-2.40 

March 2009 5-96 DRAFT – DO NOT CITE OR QUOTE 
Author Location Years Health Outcome Metric 

Mean 

Concentration 

(ppm) 

Middle/Upper Percentile 

Concentrations (ppm) 

von Klot et al. 

(2002)1

Erfurt, Germany 

n = 53 

10/1996-3/1997 Asthma symptoms; 

Medication use 

24-h avg 0.78 50th: 0.70 

75th: 1.04 

Maximum: 2.60 

Park et al. 

(2005a) 

Incheon, Korea 

n = 64 

3/2002-6/2002 Asthma symptoms; 

Medication use 

24-h avg Control days: 0.64 

Dust days: 0.65 

NR 

Rodriguez et al. 

(2007) 

Perth, Australia 

n = 263 

6/1996-7/1998 Symptoms 

associated with 

respiratory illness 

8-h max 1.41 Maximum: 8.03 

de Hartog et al. 

(2003)1

Amsterdam, the 

Netherlands 

n = 37 

Erfurt, Germany 

n = 47 

Helsinki, Finland 

n = 47 

1998-1999 (winter) Respiratory 

symptoms 

24-h avg Amsterdam: 0.52 

Erfurt: 0.35 

Helsinki: 0.35 

Maximum: 

Amsterdam: 1.39 

Erfurt: 2.17 

Helsinki: 0.87 

1 These studies presented CO concentrations in the units mg/m3. The concentrations were converted to ppm using the conversion factor 1 ppm = 1.15 mg/m3, which assumes 

standard atmosphere and ambient tempterature. 

2 These studies did not provide the year(s) in which air quality data was collected. 

Pulmonary Function 

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Rabinovitch et al. (2004) examined the association between exposure to ambient air pollutants and 

asthma exacerbation in a panel of urban minority children, 6-12 years old, with moderate to severe asthma 

over three winters in Denver, CO. The investigators examined pulmonary function by measuring forced 

expiratory volume in 1 second (FEV1) and peak expiratory flow (PEF) in the morning on school days, and 

also at night on weekends or other nonschool days. Using a 3-day moving average (lag 0-2) for all 

pollutants, Rabinovitch et al. (2004) did not find an association between CO and either lung function 

parameter during the morning or at night. 

Silkoff et al. (2005) also examined lung function in Denver during the winter months, but in a 

panel of former smokers that were at least 40 years old and had been diagnosed with COPD. In this study 

CO concentrations were similar to those reported in Rabinovitch et al. (2004). The authors examined the 

association between exposure to air pollutants and lung function (i.e., FEV1 and PEF) in both the morning 

and the evening. Silkoff et al. (2005) found contradictory results when examining the effects of CO for 

each of the winter periods separately, 1999-2000 and 2000-2001. During the analysis of the first winter 

(i.e., 1999-2000) CO was not found to be associated with lung function decrements at any lag, but an 

increase in FEV1 during the morning was observed at lag 1. For the second winter (i.e., 2000-2001) the 

authors found a significant negative association between CO exposure and FEV1 in the evening at lag 2 

(Figure 5-10). CO was not found to be associated with PEF at any lag during either winter period. Silkoff 

et al. (2005) postulated that the difference in results for the two study periods could be due to higher 

pollution concentrations along with somewhat lower temperatures and higher humidity in 2000-2001. 

However, mean CO levels remained relatively constant between the first and second winters, whereas, 

PM10, PM2.5, and NO2 concentrations all increased. The decrements in FEV1 observed in the second 

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winter, therefore, may have been due to the slightly worse, although not significantly different, baseline 

lung function of the panel of subjects used during the second winter (Silkoff et al., 2005). 

Source: Silkoff et al. (2005) 

Figure 5-10. Estimates for FEV1 change expressed per SD change of the individual pollutants 

PM2.5, PM10, CO, and NO2 for the 1999-2000 (top) and 2000-2001 (bottom) winters at 

lags 0, 1, and 2. The asterisk indicates a significant association (p <0.05). 

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In the recent literature, the majority of studies that examined the association between short-term 

exposure to CO and lung function have been conducted in Europe. The results from these studies 

contradict those reported in the U.S.-based studies previously discussed. Negative associations between 

short-term exposure to CO and lung function were observed primarily in individuals with underlying 

respiratory conditions; however, some evidence also exists for effects in children that live in urban 

environments. Penttinen et al. (2001) examined the association between CO and lung function in a panel 

consisting of 57 non-smoking adult asthmatics during the winter and spring in Helsinki, Finland. The 

authors observed negative associations with PEF (L/min) for a 0.5 ppm increase in 24-h avg CO 

concentrations in the morning at lag 1 (β = -0.54, SE = 0.084), and in the afternoon (β = -1.52, SE = 0.29) 

and evening (β = -1.81, SE = 0.27) for a 5-day average. In two-pollutant models with daily mean particle 

number concentration (PNC), CO effects on PEF in the morning were attenuated at lag 1, but remained 

negative. In addition, negative associations with PEF persisted in the afternoon and evening in a twopollutant model at lag 0. In this study, high correlations between UFP and other traffic generated 

pollutants (e.g., CO and NOX) make it difficult to attribute the observed respiratory effects to a specific 

pollutant. 

Lagorio et al. (2006) also conducted a study that examined the association between CO and lung 

function in adults. In this study, 3 panels of subjects that resided in Rome, Italy, were selected with 

underlying asthma, COPD, or IHD. The ages of the subjects varied depending on the panel, but overall 

the subjects ranged from 18-80 years old. In single-pollutant models CO was found to be negatively 

associated with both FVC (forced vital capacity) and FEV1 at most of the lags examined (i.e., 0, 0-1, and 

0-2) for both the COPD and asthma panels. No association was observed between CO and FVC or FEV1

in the IHD panel. Lagorio et al. (2006) did observe high correlations between CO and PM2.5, but not NO2. 

Unfortunately, copollutant models were not conducted in this analysis to identify whether the CO 

associations observed are confounded by other pollutants. 

Timonen et al. (2002) examined the effect of CO on bronchial responsiveness and pulmonary 

function (i.e., FVC, FEV1, MMEF, and AEFV) in a panel of children 7-12 years old with chronic 

respiratory symptoms during the winter in Kuopio, Finland. The authors found that CO was significantly 

associated with decrements in baseline lung function for FVC (mL) at lags 2 (-17.5 mL), 3 (-24.8 mL), 

and 4-day avg (-52.5 mL), and for FEV1 (mL) at lag 3 (-20.9 mL) for a 0.5 ppm increase in 24-h avg CO 

concentration. However, CO was not associated with exercise induced changes in lung function. Overall, 

Timonen et al. (2002) found that high concentrations of combustion byproducts (i.e., BS, PM10, particle 

numbers, NO2, and CO) were associated with impairment in baseline lung function. These associations, 

along with the high correlation between pollutants, contributed to the inability of the authors to conclude 

that the lung function effects observed were due to biological changes in lung pathology specific to CO 

exposure. 

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Chen et al. (1999) examined the effect of CO on lung function in a panel of 8-13 year old asthmatic 

children in Taiwan. The authors observed an association between short-term exposure to CO and 

decrements in FVC (mL) at a 2-day lag when using daytime average CO concentrations (from 0800 to 

1800) in a single-pollutant model. In addition, the authors found a high correlation between CO and NO2

concentrations (r = 0.86-0.98), but did not conduct multipollutant analyses to examine the effect of each 

pollutant. 

One additional study, Fischer et al. (2002), examined the association between CO and respiratory 

health, specifically lung function in a non-selected cohort study of 68 children ages 10-11 that live in an 

urban environment (i.e., Utrecht, the Netherlands). In this study, the authors examined whether eNO was 

a more sensitive measure of lung damage than the traditional pulmonary function measurements 

(i.e., FVC, FEV1, PEF, and MMEF). Fischer et al. (2002) found negative associations between CO and 

FEV1, PEF, and MMEF at both lags 1 and 2. Additonally, the authors found an association between CO 

and an increase in eNO at lag 1. However, the study did not present the correlations between pollutants or 

examine copollutant models. 

Respiratory Symptoms in Asthmatic Individuals 

Upon evaluating the literature that examined the association between short-term exposure to CO 

and respiratory symptoms in asthmatic individuals, consistent positive results were observed across 

studies. Studies consisting of children enrolled in the Childhood Asthma Management Program (CAMP) 

study found that CO was positively associated with asthma symptoms. Yu et al. (2000) found a 14% 

increase in asthma symptoms ([95% CI: 5-23] per 0.5 ppm increase in 24-h avg CO concentrations at lag 

1) in a population of 5-13 year old children (n = 133) with asthma in Seattle, WA. These effects persisted 

when controlling for previous day’s asthma symptoms (12% [95% CI: 5-19] at lag 1). Using the same 

population of children, Slaughter et al. (2003) found a significant association between short-term 

exposure to CO at lag 1 and asthma severity both with and without controlling for the previous day’s 

asthma severity, (RR = 1.04 [95% CI: 1.01-1.08]) and (RR = 1.03 [95% CI: 1.00-1.05]), respectively. 

Schildcrout et al. (2006) examined the association between air pollutants and asthma symptoms in 990 

children ages 5-12 in 8 North American cities. The authors found a positive association between shortterm exposure to CO and asthma symptoms at lag 0 (OR = 1.04 [95% CI: 1.00-1.07] per 0.5 ppm increase 

in 24-h avg CO), but similar effects were also observed at lag 1, 2, and the 3-day moving sum. The CO 

effects observed persisted when NO2, PM10, and SO2 where included in joint pollutant models. 

Two additional U.S. studies also found positive associations between CO and asthma symptoms, 

Rabinovitch et al. (2004) and Delfino et al. (2003). Rabinovitch et al. (2004) found a positive association 

between 24-h avg CO concentrations for a 3-day moving average (lag 0-2) and asthma exacerbtions (OR 

= 1.02 [95% CI: 0.89-1.16] per 0.5 ppm increase in 24-h avg CO) in a population of urban poor children 

with moderate to severe asthma in Denver, CO. Delfino et al. (2003) also observed positive associations 

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between CO and asthma symptoms in a population of Hispanic children with asthma in a Los Angeles, 

CA, community. However, positive associations were found only when using the previous day’s 

maximum 8-h avg CO concentration as the exposure metric. These results are in contrast to those studies 

reported above which found positive associations when using 24-h avg CO concentrations as the exposure 

metric. It should be noted that in comparison to Rabinovitch et al. (2004) and the other studies discussed 

above, the mean ambient concentrations for 1-h maximum and maximum 8-h avg reported by Delfino et 

al. (2003) were 7.7 ppm and 5.0 ppm, respectively, both of which are approximately 3.5 times higher than 

the corresponding 24-h avg concentrations reported in the other studies. 

In a panel study consisting of 53 adults with asthma or asthma symptoms in Germany, von Klot 

et al. (2002) observed a marginal association between CO concentration and the prevalence of wheezing 

at lag 0 (OR = 1.03 [95% CI: 0.97-1.08] per 0.5 ppm increase in 24-h avg CO), and a positive association 

for a 5-day mean concentration (OR = 1.12 [95% CI: 1.05-1.21] per 0.5 ppm increase in 24-h avg CO). 

However, the authors found CO to be highly correlated with UFP, making it unclear if the effect observed 

was due to CO alone. Additionally, Park et al. (2005a) in a panel study of individuals 16-75 years old in 

Incheon, Korea with bronchial asthma did not find an association between CO and nighttime asthma 

symptoms or cough. Figure 5-11 summarizes the results from studies that provided usable quantitative 

results and examined the association between short-term exposure to CO and asthma or respiratory 

symptoms in asthmatic individuals. 

To further examine the effect of CO on asthma and asthma symptoms some studies also analyzed 

medication use in asthmatic individuals in response to an increase in air pollution levels. The majority of 

U.S.-based studies (i.e., Rabinovitch (2004), Slaughter et al. (2003), and Schildcrout et al. (2006)) 

focused on rescue inhaler use in children with ages ranging from 5-13 years old. Rabinovitch et al. (2004) 

found a weak association (OR = 1.08 [95% CI: 1.00-1.17] per 0.5 ppm increase in 24-h avg CO) between 

rescue inhaler use in a population of 6-12 year old urban minority children with moderate to severe 

asthma in the winter in Denver, CO, which is evident by the large confidence intervals surrounding the 

estimate. Slaughter et al. (2003) in a population of 5-12 year old children with asthma in Seattle, WA 

found a positive and significant association with rescue inhaler use both with and without taking into 

consideration the previous day’s asthma severity, (RR: 1.04 [95% CI: 1.01-1.08] per 0.5 ppm increase in 

24-h avg CO) and (RR: 1.03 [95% CI: 1.00-1.05] per 0.5 ppm increase in 24-h avg CO), respectively. 

Similar results were observed in a multi-city study conducted by Schildcrout et al. (2006), which analyzed 

rescue inhaler use in 990 children ages 5-13 with asthma in eight North American cities. Schildcrout et al. 

(2006) found that short-term exposure to CO was positively associated with rescue inhaler use at lags of 

0, 2, and a 3-day moving sum, and that the association was fairly robust to an increase in other pollutants 

(i.e., NO2, PM10, and SO2) when included in joint models with CO. However, both Slaughter et al. (2003) 

and Schildcrout et al. (2006) attributed the associations observed to other combustion byproducts. 

Additional studies (Park et al., 2005a; Silkoff et al., 2005; von Klot et al., 2002) conducted in Denver, 

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CO; Erfurt, Germany; and Incheon, Korea, respectively, found results that are consistent with those 

previously reported, but in populations with combined ages ranging from 16-77. Figure 5-11 presents the 

risk estimates from studies that examined the association between short-term exposure to CO and 

medication use in asthmatic individuals. 

 

Reference Location Population Age Lag 

Schildcrout et al. 

(2006) 

8 U.S. cities Asthmatics (n = 990) 5-12 0-2a Asthma Symptoms Children

------- Respiratory Symptoms--------- 

Yu et al. 

(2000) 

Seattle, WA Mild-to-moderate 

asthmatics (n = 133)

5-13 1 Asthma Symptoms 

Rabinovitch et al. 

(2004) 

Denver, CO Asthmatics (n = 147) 6-12 0-2 Asthma Exacerbation 

Los 

Angeles, CA 

Hispanic children 

with asthma (n = 22)

Delfino et al. 10-16 1 Symptom Score >2 (1-h max) 

(2003) 

Los 

Angeles, CA 

Hispanic children 

with asthma (n = 22)

Delfino et al. 10-16 1 Symptom Score >2 (max 8-h avg) 

(2003) 

(von Klot et al., 

2002) 

Erfurt, 

Germany 

Asthma/asthma 

symptoms (n = 53) 

37-77 0-5 Wheeze Adults

 

------------------ Medication Use ---------------- 

Schildcrout et al. 

(2006) 

8 U.S. cities Asthmatics (n = 990) 5-12 0-2a Inhaler Use Children

Slaughter et al. 

(2003) 

Seattle, WA Mild-to-moderate 

asthmatics n = 133) 

5-13 1 Inhaler Useb

Slaughter et al. 

(2003) 

Seattle, WA Mild-to-moderate 

asthmatics (n = 133)

5-13 1 Inhaler Usec

Rabinovitch et al. 

(2004) 

Denver, CO Asthmatics (n = 147) 6-12 0-2 Inhaler Use 

 

(von Klot et al., 

2002) 

Erfurt, 

Germany 

Asthma/asthma 

symptoms (n = 53) 

37-77 0-5 Inhaler Use (β2-agonist) Adults

 

(von Klot et al., 

2002) 

Erfurt, 

Germany 

Asthma/asthma 

symptoms (n = 53) 

37-77 0-5 Inhaler Use (Corticosteroid) 

Odds Ratios a Sum of 0-, 1-, and 2-day lags. 

b Estimate from model that did not control for previous day’s asthma severity. b Estimate from model that controlled for previous day’s asthma severity. 

Figure 5-11. Asthma symptoms, respiratory symptoms and medication use in asthmatic 

individuals associated with short-term exposure to CO1. Effect estimates were 

standardized depending on the averaging time used in the study: 0.5 ppm for 24-h 

avg, 0.75 ppm for max 8-h avg, and 1.0 ppm for 1-h max. 

Respiratory Symptoms in Non-Asthmatic Individuals 

In addition to examining the association between short-term exposure to CO and respiratory 

symptoms (e.g., cough, wheeze, shortness of breath, etc.) in asthmatic populations some studies examined 

these respiratory effects in individuals classified as non-asthmatics. Rodriguez et al. (2007) and de Hartog 

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et al. (2003) examined the effect of CO on respiratory symptoms in a panel of 263 children 0-5 years old 

at high risk for developing asthma in Perth, Australia, and a 3-city panel of individuals ≥ 50 years of age 

with CHD during the winter in Amsterdam, the Netherlands, Erfurt, Germany, and Helsinki, Finland, 

respectively. Rodriguez et al., (2007) found CO concentrations to be positively associated with 

wheeze/rattle chest and runny/blocked nose at both a 5-day lag and a 0-5-day lag in Perth, Australia. It is 

unclear which pollutant is driving the effect observed by Rodriguez et al. (2007) because multipollutant 

models were not examined and additional analyses were not conducted to further characterize the 

associations observed. 

In a panel of elderly individuals with CHF in three European locations, de Hartog et al. (2003) 

observed some marginal associations, specifically between CO concentration and the prevalence of the 

respiratory symptom shortness of breath, at lag 3 and 0-5-days. Although a marginal association was 

observed, the authors found that the associations between air pollution exposure and respiratory 

symptoms were stronger for PM2.5 than for gaseous air pollutants. 

Summary of Associations between Short-Term Exposure to CO and Pulmonary 

Function, Respiratory Symptoms, and Medication Use 

A limited body of evidence is available that examined the effect of short-term exposure to CO on 

various respiratory health outcomes. Among asthmatics, the studies reviewed generally find positive 

associations between short-term exposure to CO and respiratory-related health effects (i.e., decrements in 

lung function/lung function growth, respiratory symptoms, and medication use). It is difficult to 

determine from this group of studies if CO is independently associated with respiratory outcomes or if 

CO is an indicator for other traffic-related pollutants. On-road vehicle exhaust emissions are a nearly 

ubiquitous source of combustion pollutant mixtures that include CO and can be an important contributor 

to CO-related health effects in near-road locations. A lack of copollutant analyses among this group of 

studies complicates the efforts to disentangle the health effects attributed to CO from the larger trafficrelated pollutant mix. Additional uncertainty exists as to a biologically plausible mechanism which could 

explain the effect of CO on respiratory health. 

5.5.1.2. Respiratory Hospital Admissions, ED Visits and Physician Visits 

The 2000 CO AQCD (U.S. EPA, 2000) evaluated a limited amount of literature that examined the 

26 association between short-term exposure to CO and respiratory hospital admissions, ED visits, and 

27 physician visits in the U.S. (i.e., Seattle, WA, Reno, NV, and Anchorage, AK) and Europe (i.e., Barcelona, 

28 Spain). From these studies, the 2000 CO AQCD concluded that positive associations were observed for 

29 short-term exposure to CO with several respiratory outcomes, including asthma and COPD. However, the 

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1 lack of a biologically plausible mechanism for CO-induced respiratory morbidity at that time brought into 

2 question the validity of the results observed. 

Since the 2000 CO AQCD, the number of studies that examined the association between short-term 

exposure to CO and respiratory morbidity has increased; however, the total number of studies published is 

still considerably less than the number that examine the health effects associated with exposure to other 

criteria air pollutants. This section focuses primarily on those studies conducted in the U.S. and Canada 

which examined the potential respiratory health effects associated with CO at concentrations at or similar 

to those found in the U.S. Unlike previous sections, which also evaluated studies conducted outside of the 

U.S., the expansive U.S.-based respiratory hospital admission and ED visits literature provides adequate 

evidence to examine the association between short-term exposure to CO and respiratory HA and ED 

visits. Collectively, the studies conducted outside of the U.S. observed associations that are consistent 

with those observed in the U.S.-based studies evaluated below (see Annex C). 

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Overall, this section focuses on respiratory-related hospital admissions because the majority of the 

literature examines hospital admissions as opposed to ED visits or physician visits (Table 5-15 presents 

the studies evaluated in this section along with the range of CO concentrations measured in each study). It 

must be noted that when examining the association between short-term exposure to CO and health 

outcomes that require medical attention, it is important to distinguish between hospital admissions, ED 

visits, and physician visits for respiratory outcomes (more so than for cardiovascular outcomes). This is 

because it is likely that a small percentage of respiratory ED visits will be admitted to the hospital and, 

therefore, may represent potentially less serious, but more common outcomes. To adequately distinguish 

between the results presented in hospital admission, ED visit, and physician visit studies, each outcome is 

evaluated in individual sections. In addition, each section presents results separately for respiratory health 

outcomes which includes all respiratory diagnoses (ICD-9: 460-519) or selected diseases (e.g., asthma, 

COPD, pneumonia and other respiratory infections) in order to evaluate the potential effect of short-term 

exposure to CO on each outcome. 

Table 5-15. Range of CO concentrations reported in key respiratory hospital admission and ED 

visit studies that examine effects associated with short-term exposure to CO. 

Location Type of Visit (ICD9) Metric Mean Concentration (ppm) Middle/Upper Percentile 

Concentrations (ppm) Author 

Cakmak et al. 

(2006a) 

10 Canadian 

cities 

Hospital Admissions: Respiratory 

disease (i.e., Acute bronchitis and 

bronchiolitis; Pneumonia; Chronic and 

unspecific bronchitis; Emphysema; 

Asthma; Bronchiectasis; Chronic 

airway obstruction) 

24-h avg 0.8 Maximum: 6.5 

Linn et al. (2000) Los Angeles, 

CA 

Hospital Admissions: Pulmonary; 

Asthma; COPD 

24-h avg Winter: 1.7; Spring: 1.0; 

Summer: 1.2; Fall: 2.1 

Maximum: 

Winter: 5.3; Spring: 2.2; 

Summer: 2.7; Fall: 4.3; 

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Location Type of Visit (ICD9) Metric Mean Concentration (ppm) Middle/Upper Percentile 

Concentrations (ppm) Author 

Slaughter et al. 

(2005) 

Spokane, WA ED Visits and Hospital Admissions: 

Respiratory; Asthma; COPD; 

Pneumonia; Acute Respiratory 

Infection 

24-h avg Hamilton St.: 1.73 

Backdoor Tavern: 1.29 

Spokane Club: 1.41 

Third and Washington: 1.82 

Rockwood: 0.42 

95th: 3.05 

Burnett et al. 

(2001) 

Toronto, ON, 

Canada 

Hospital Admissions: Respiratory 

disease (i.e., Asthma; Acute 

bronchitis/bronchiolitis; Croup; 

Pneumonia) 

1-h max 1.9 50th: 1.8; 75th: 2.3; 

95th: 3.3; 99th: 4.0 

Maximum: 6.0 

Yang et al. (2003) Vancouver, BC, 

Canada 

Hospital Admissions: Respiratory 

diseases 

24-h avg 0.98 50th: 0.82; 75th: 1.16 

Maximum: 4.90 

Lin et al. (2003) Toronto, ON, 

Canada 

Hospital Admissions: Asthma 24-h avg 1.18 50th: 1.10; 75th: 1.40 

Maximum: 6.10 

Lin et al. (2004c) Vancouver, BC, 

Canada 

Hospital Admissions: Asthma 24-h avg 0.96 50th: 0.80; 75th: 1.12 

Maximum: 4.90 

Cook County, 

IL; Los Angeles 

County, CA 

Hospital Admissions: COPD 24-h avg NR Cook: 

50th: .99; 75th: 1.25 

Maximum: 3.91 

Los Angeles: 

50th: 1.35; 75th: 2.16 

Maximum: 5.96 

Moolgavkar 

(2003a) (reanalysis of 

Moolgavkar 

2000a) 

Yang et al. (2005) Vancouver, BC, 

Canada 

Hospital Admissions: COPD 24-h avg 0.71 50th: 0.64 

Maximum: 2.48 

South Coast Air 

Basin, CA 

Hospital Admissions: Acute 

bronchiolitis 

24-h avg Lag 1:

Index: 1.730 

Referrent: 1.750 

Lag 4:

Index: 1.760 

Referrent: 1.790 

Lag 1: 

Index: 

50th: 1.52; 75th: 2.26; 

90th: 3.16 

Maximum: 9.60 

Referrent: 

50th: 1.51; 75th: 2.29; 

90th: 3.23 

Maximum: 9.60 

Lag 4: 

Index: 

50th: 1.54; 75th: 2.31; 

90th: 3.23 

Maximum: 8.71 

Referrent: 

50th: 1.55; 75th: 2.35; 

90th: 3.30 

Maximum: 9.60 

Karr et al. (2006) 

South Coast Air 

Basin, CA 

Hospital Admissions: Acute 

bronchiolitis 

24-h avg; 

Monthly avg 

24-h avg: 1.720 

Monthly: 1.770 

24-h avg: 

50th: 1.61; 75th: 2.08; 

90th: 2.75 

Maximum: 5.07 

Monthly avg: 

50th: 1.63; 75th: 2.13; 

90th: 2.88 

Maximum: 8.30 

Karr et al. (2007) 

Zanobetti and 

Schwartz (2006) 

Boston, MA Hospital Admissions: Pneumonia 24-h avg NR 50th: 0.48; 75th: 0.60; 

95th: 0.88 

Lin et al. (2005) Toronto, ON, 

Canada 

Hospital Admissions: Respiratory 

infections 

24-h avg 1.16 50th: 1.05; 75th: 1.37 

Maximum: 2.45 

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Location Type of Visit (ICD9) Metric Mean Concentration (ppm) Middle/Upper Percentile 

Concentrations (ppm) Author 

Peel et al. (2005) Atlanta, GA ED Visits: All respiratory; Asthma; 

COPD; URI; Pneumonia 

1-h max 1.8 90th: 3.4 

Tolbert et al. 

(2007) 

Atlanta, GA ED Visits: Respiratory diseases 

(i.e., Asthma; COPD; URI; Pneumonia; 

Bronchiolitis) 

1-h max 1.6 50th: 1.3; 75th: 2.0; 90th: 3.0 

Maximum: 7.7 

Ito et al. (2007) New York, NY ED Visits: Asthma 8-h max 1.31 50th: 1.23; 75th: 1.52; 

95th: 2.11 

Villeneuve et al. 

(2006b) 

Toronto, ON, 

Canada 

Physicians Visits: 

Allergic rhinitis 

24-h avg 1.1 Maximum: 2.2 

Hospital Admissions 

Respiratory Disease 

All but two of the published hospital admission studies that examined the association between 

short-term exposure to CO and all respiratory diseases from North America were conducted in Canada, 

and only one study presented results from a combined analysis of multiple cities (Cakmak et al., 2006b). 

In a study of 10 of the largest Canadian cities, Cakmak et al. (2006b) examined respiratory hospital 

admissions (ICD-9: 466, 480-486, 490-494, 496) in relation to ambient gaseous pollutant concentrations 

for the time period 1993-2000. This study reported a 0.37% (95% CI: 0.12-0.50) increase in respiratory 

hospital admissions for all ages for a 0.5 ppm increase in 24-h avg CO (lag 2.8 days averaged over the 10 

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). U.S.-based studies (Los Angeles and Spokane) reported similarly weak or null associations for 

respiratory disease hospital admissions (Linn et al., 2000; Slaughter et al., 2005). In a study conducted in 

Toronto, Canada for the time period 1980-1994, Burnett et al. (2001) reported a relatively strong 

association between 1-h max CO and respiratory disease hospital admissions in children less than two 

years of age, for the diagnoses of asthma (493), acute bronchitis/bronchiolitis (466), croup (464.4), and 

pneumonia (480-486). The authors found a 9.7% (95% CI: 4.1-15.5) increase in hospital admissions for a 

2-day avg (lag 0-1) per 1 ppm increase in 1-h max CO. In the two-pollutant model analysis, the estimates 

for both CO and O3 remained elevated. Yang et al. (2003) reported similar results (OR = 1.04 

[95% CI: 1.01-1.06] at lag 1 per 0.5 ppm increase in 24-h avg CO) for pediatric (<3 years of age) 

respiratory disease (ICD-9: 460-519) admissions in Vancouver for the time period 1986-1998. Yang et al. 

(2003) also reported elevated associations with 24-h avg CO and respiratory hospital admissions (ICD-9: 

codes 460-519) for ages 65 and over in Vancouver, Canada (OR = 1.02 [95% CI: 1.00-1.04]) at lag 1 for a 

0.5 ppm increase in 24-h avg CO. The authors found that the risk estimate remained elevated when O3

was included in the model. 

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 To determine the lag for the combined estimate across all 10 cities, Cakmak et al. averaged the strongest associations from lags 0-5 days from 

each city. 

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Asthma 

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Some studies that examined the effect of short-term exposure to CO on asthma hospital admissions 

conducted all age and age-stratified analyses, specifically to examine effects in children. In two of these 

hospital admission studies conducted in Canada, evidence was observed for increased pediatric (ages 

6-12) asthma hospital admissions (ICD-9: 493) in boys, but not girls (Lin et al., 2003; 2004c); however, a 

biological explanation was not provided which could explain this difference. Lin et al. (2003) used a bidirectional case-crossover analysis in Toronto, Canada for the years 1981–1993 reported an OR of 1.05 

(95% CI: 1.00-1.11) per 0.5 ppm increase in 24-h avg CO for a 1-day lag for boys with similar results 

being reported when averaging CO concentrations up to 7 days prior to hospitalization. Risk estimates for 

girls did not provide evidence of an association using the same lag structure that was used in the boys’ 

analysis (OR = 1.00 [95% CI: 0.93-1.06]); lag 1). In a copollutant analysis, the estimates for boys were 

essentially unchanged when adjusting for all PM indices (Lin et al., 2003). It should be noted that this 

study used a bi-directional case-crossover analysis, which may be biased (Levy et al., 2001). Studies that 

examined the various referent selection strategies for the case-crossover study design have concluded that 

the preferred control selection strategy is the time-stratified framework (Levy et al., 2001). In an 

additional analysis conducted by Lin et al. (2004c), the authors found less consistent evidence for a 

greater effect in boys versus girls in Vancouver during the years 1987-1998 using a time-series study 

design that stratified results by socioeconomic status (SES). In one additional study that examined asthma 

hospital admissions for all ages and genders combined, Slaughter et al. (2005) observed some evidence of 

an increase in asthma hospital admissions (ICD-9 493) in Spokane (1995-2000) for CO at lag 2 (RR = 

1.03 [ 95% CI: 0.98-1.08]) for a 0.5 ppm increase in 24--h avg, but not for the other two lags examined 

(lag 1 and lag 3). 

Chronic Obstructive Pulmonary Disease 

A few of the studies examined the effect of short-term exposure to CO on COPD, or obstructive 

lung disease, and hospital admissions. Moolgavkar (2003a) (a reanalysis of (Moolgavkar, 2000a) 

examined hospital admissions for COPD plus “allied diseases” (ICD-9 490-496) in two U.S. counties 

(Cook County, IL and Los Angeles County, CA) for the years 1987-1995 using Poisson generalized linear 

models (GLMs) or generalized additive models (GAM) with the more stringent convergence criteria. 

Overall, the results from both models were similar. Using the GAM models the study reported percent 

increases of 0.53-1.20% for all ages in Los Angeles County, and 0.17-1.41% for ages 65 and older in 

Cook County, for a 0.5 ppm increase in 24-h avg CO and lags ranging from 0 to 5 days. Yang et al. (2005) 

reported similar results for COPD hospital admissions (ICD-9 490-492, 494, 496) in Vancouver for ages 

65 and older for the years 1994-1998 for a moving average of 0-6 day lags (RR = 1.14 [95% CI: 1.03-

1.23] per 0.5 ppm increase in 24-h avg CO). However, Slaughter et al. (2005) found no association 

between short-term exposure to CO and COPD hospital admissions (ICD-9 491, 492, 494, 496) in 

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Spokane, WA at lag 1-day (RR = 0.97 [95% CI: 0.93-1.01] per 0.5 ppm increase in 24-h avg CO) with 

similar results being reported for 2- and 3-day lags. 

Acute Bronchiolitis in Infants 

Two studies (Karr et al., 2006; 2007) from the South Coast Air Basin in California examined both 

short term (lag 0 or 1) and longer term levels of CO in relation to acute bronchiolitis (ICD-9: 466) 

hospital admissions during the first year of life from 1995-2000. Karr et al. (2006) found no evidence of a 

short-term association between ambient CO concentrations and hospital admissions for acute bronchiolitis 

at lag 1 day (OR= 0.99 [95%CI: 0.98-1.01] per 0.5 ppm increase in 24-h avg CO). In addition, Karr et al. 

(2007), which examined longer term exposures (average in the month prior to hospitalization and lifetime 

average) in a matched case-control study, also did not provide any evidence of an association with CO. 

Pneumonia and Other Respiratory Infections 

In addition to examining the effect of short-term exposure to CO on health outcomes that can limit 

the function of the respiratory system, some studies examined the effect of CO on individuals with 

pneumonia (ICD-9: 480-486) separately or in combination with other respiratory infections. Zanobetti 

and Schwartz (2006) examined pneumonia hospital admissions (ICD-9 480-487) in Boston, MA, for the 

years 1995-1999 for ages 65 and older using a time-stratified case-crossover analysis. The authors 

reported an increase in pneumonia hospital admissions at lag 0 of 5.4% (95% CI: 1.2-10.0) per 0.5 ppm 

increase in 24-h avg CO. While Zanobetti and Schwartz (2006) did not report multipollutant results, they 

suggested that CO was most likely acting as a marker for traffic-related pollutants because CO was highly 

correlated with both BC (r = 0.80) and NO2 (r = 0.67), and moderately correlated with PM2.5 (r = 0.52). 

Instead of examining the effect of CO on pneumonia hospital admissions separately as was done by 

Zanobetti and Schwartz (2006), Lin et al. (2005) presented results for the overall effect of CO on 

respiratory infection hospital admissions (ICD-9: 464, 466, 480-487). In this analysis, Lin et al. (2005) 

examined the potential increase in respiratory hospital admissions in children less than 15 years of age in 

Toronto, Canada for 1998-2001 using a bi-directional case-crossover approach. The authors reported 

elevated estimates for boys (OR = 1.15 [95% CI: 1.02-1.29] per 0.5 ppm increase in 24-h avg CO for a 6-

day ma) while the estimate for girls was weaker and with wider confidence intervals (OR = 1.06 [95%CI: 

0.92-1.21]). Lin et al. (2005) did not provide an explanation as to why the estimates are stronger for boys 

than for girls. It should be noted that this study used a bi-directional case-crossover analysis, which may 

be biased (Levy et al., 2001). Studies that have examined the various referent selection strategies for the 

case-crossover study design have concluded that the preferred control selection strategy is the timestratified framework (Levy et al., 2001). 

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Endpoint Reference Location Age Lag 

Burnett et al. (2001) Toronto, CAN <2 0-1 

Yang et al. (2003) Vancouver, CAN <3 1 

Linn et al. (2000) Los Angeles, CA 30+ 0 All 

Respiratory Yang et al. (2003) Vancouver, CAN 65+ 1 

Cakmak et al. (2006b) 10 Canadian Cities All 2.8 

Slaughter et al. (2005) Spokane, WA All 2 

Lin et al. (2003) Toronto, CAN 6-12 1 Boys 

Asthma Lin et al. (2003) Toronto, CAN 6-12 1 Girls 

Slaughter et al. (2005) Spokane, WA All 2 

Slaughter et al. (2005) Spokane, WA 15+ 2 

COPD 

Yang et al. (2003) Vancouver, CAN 65+ 0-6 

Pneumonia Zanobetti and Schwartz 

(2006) Boston, MA 65+ 0 

Karr et al. (2006) SQAB, CA <1 1 Acute 

Bronchiolitis 

Lin et al. (2005) Toronto, CAN <15 0-5 Boys Respiratory 

Infection Lin et al. (2005) Toronto, CAN <15 0-5 Girls 

Effect Estimates 

Figure 5-12. Summary of associations between short-term exposure to CO and respiratory 

hospital admissions.1,2 Effect estimates were standardized depending on the 

averaging time used in the study: 0.5 ppm for 24-h avg, 0.75 ppm for max 8-h avg, 

and 1.0 ppm for 1-h max. 

Emergency Department Visits 

Respiratory Disease 

Peel et al. (2005) conducted a large single-city ED study in Atlanta, GA, which included data from 

31 hospitals for the time period 1993–2000. In this study, results were reported for various respiratory1 

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 Risk estimates from Moolgavkar (2003a) were not included in this figure because the study presented a range of effect estimates using different 

statistical models. The results from this study were more adequately highlighted in the evaluation of the study in the COPD section. 

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 Risk estimates from Lin et al. (2004c) were not included in the figure because the results were stratified by SES and therefore could not be 

readily compared to effect estimates from Lin et al..(2003). 

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related visits (ICD-9 460-466, 477, 480-486, 491-493, 496, 786.09). In an all ages analysis, the authors 

found a RR of 1.011 (95% CI: 1.004-1.019) for a 3-day avg (lag 0-2) per 1 ppm increase in 1-h max CO 

concentration for all respiratory diseases. Tolbert et al. (2007) expanded the time period used in the Peel 

et al. (2005) study to include ED visits through 2004, and reported similar results for respiratory ED visits 

(RR: 1.013 [95% CI: 1.007-1.018] per 1 ppm increase in 1-h max CO). The CO risk estimates from the 

Atlanta, GA, ED visits studies were attenuated when O3, NO2, or PM were added to the model (). In 

addition, Tolbert et al. (2007) reported high correlations between CO and NO2 (r = 0.70) and EC 

(r = 0.66); and a moderate correlation with PM2.5 (r = 0.51). One additional ED visits study that also 

examined respiratory disease (Slaughter et al., 2005) presented essentially null results at lag 1 and 2, but 

found similar results to Peel et al. (2005) and Tolbert et al. (2007) at lag 3 (RR = 1.02 [95% CI: 1.00-1.03] 

per 0.5 ppm increase in 24-h avg CO). 

Asthma 

The association between short-term exposure to CO and asthma ED visits (ICD-9 493, 786.09) was 

also examined in Atlanta, GA by Peel et al. (2005). In this study the authors reported results from 

distributed lag models including lags 0-13 in addition to a moving average of lags 0, 1, and 2 (lag 0-2) for 

specific respiratory outcomes (e.g., asthma). Effect estimates from the distributed lag models were 

stronger than those produced from models that used 3-day moving average CO concentrations (RR = 

1.010 [95% CI: 0.999-1.022] for lags 0-2 compared to RR = 1.076 [95% CI: 1.047-1.105] for an 

unconstrained distributed lag of 0-13 for a 1 ppm increase in 1-h max CO). These results demonstrated 

the potential effect of CO exposures up to 13 days prior to an asthma ED visit. Estimates were stronger 

for pediatric ED visits (ages 2-18 years) (RR = 1.019 [95% CI: 1.004-1.035] per 1 ppm increase in 1-h 

max CO) for a 3-day avg (lag 0-2) compared to all ages (Peel et al., 2005). Slaughter et al. (2005), which 

also examined ED visits for Spokane (1995-2001), reported an increase in asthma ED visits for all ages 

for CO at lag 3 (RR = 1.03 [95% CI: 1.00-1.05] per 0.5 ppm increase in 24-h avg CO), but not for the 

other two lags examined (lags 1 and 2). The results from Ito et al. (2007) also provide evidence of 

increased ED visits for asthma (ICD-9 493) for all ages in New York City for 1999-2002, but quantitative 

results were not provided. In addition, Ito et al. (2007) stated that increased CO effect estimates were 

attenuated when NO2 was included in the model, but effect estimates remained elevated in two-pollutant 

models with either PM2.5 or O3. 

Chronic Obstructive Pulmonary Disease 

In the examination of the effect of short-term exposure to CO on COPD ED visits (ICD-9 491, 492, 

496), Peel et al. (2005) reported elevated estimates for Atlanta, GA for 1993-2000 (RR = 1.03 [95%CI: 

1.00-1.05] per 1 ppm increase in 1-h max CO for a moving average of lag 0-2) with similar results for the 

distributed lag model (RR = 1.03 [95% CI: 0.98-1.09). However, results from Slaughter et al. (2005) from 

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Spokane were consistent with a null or slightly protective association at lag 1 (RR = 0.96 

[95% CI: 0.92-1.00] per 0.5 ppm increase in 24-h avg CO at lag 1) with similar results for lags 2 and 3. 

Pneumonia and Other Respiratory Infections 

Similar to the hospital admission analysis conducted by Zanobetti and Schwartz (2006) discussed 

above, Peel et al. (2005) examined the effect of CO on pneumonia separately (ICD-9: 480-486), but also 

included an analysis of upper respiratory infection (ICD-9: 460-466, 477) ED visits for all ages in Atlanta, 

GA during the years 1993-2000. The authors reported a weak estimate for pneumonia for the three-day 

moving average (lag 0-2) (RR = 1.01 [95% CI: 0.996-1.021] per 1 ppm increase in 1-h max CO). 

However, when using an unconstrained distributed lag (days 0-13), Peel et al. (2005) observed evidence 

of an association (RR = 1.045 [95% CI: 1.01-1.08]). An examination of URI ED visits, the largest of the 

respiratory ED groups, found slightly increased risk estimates for both the three-day moving average 

(lag 0-2) (RR = 1.01 [95% CI: 1.00-1.02]) and the unconstrained distributed lag for days 0-13 (RR = 1.07 

[ 95% CI: 1.05-1.09]) per 1 ppm increase in 1-h max CO. In copollutant models, CO risk estimates were 

largely attenuated when PM10, O3, or NO2 were included in the model. Upon conducting an age-stratifed 

analysis, Peel et al. (2005) also found that infant (less than one year of age) and pediatric (ages 2-18) URI 

ED visit CO risk estimates were substantially stronger than all age risk estimates. 

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Reference Location Age Endpoint Lag 

Slaughter et al. (2005) Spokane, WA All 3 

Peel et al. (2005) Atlanta, GA All 0-2 All 

Respiratory

Tolbert et al. (2007) Atlanta, GA All 0-2 

Peel et al. (2005) Atlanta, GA 2-18 0-2 

Peel et al. (2005) Atlanta, GA All 0-2 

Peel et al. (2005) Atlanta, GA All 0-13*

Asthma 

Slaughter et al. (2005) Spokane, WA All 3 

Slaughter et al. (2005) Spokane, WA 15+ 1 

Peel et al. (2005) Atlanta, GA All 0-2 COPD 

Peel et al. (2005) Atlanta, GA All 0-13*

Peel et al. (2005) Atlanta, GA All 0-2 

Pneumonia 

Peel et al. (2005) Atlanta, GA All 0-13*

Peel et al. (2005) Atlanta, GA All 0-2 Respiratory

Infection Peel et al. (2005) Atlanta, GA All 0-13*

* Unconstrained distributed lag 

Effect Estimates 

Figure 5-13. Summary of associations between short-term exposure to CO and respiratory ED 

visits. Effect estimates were standardized depending on the averaging time used in 

the study: 0.5 ppm for 24-h avg, 0.75 ppm for max 8-h avg, and 1.0 ppm for 1-h max. 

Physician Visits 

Although hospital admissions and ED visits are the two most well studied measures of morbidity, a 

few studies also examined the effect of CO on unscheduled physician visits. In a time-series study, 

Villeneuve et al. (2006b) examined the effect of CO on physician visits for allergic rhinitis in individuals 

65 and older in Toronto, Canada. Although quantitative results were only presented in figures, upon 

observation it was evident that estimates were consistent with a null association for lags 0-6 (Villeneuve 

et al., 2006b). In an additional study, Sinclair et al. (2004) reported results for urgent care visits for asthma 

and respiratory infections in a health maintenance organization in Atlanta, GA; however, the study only 

reported statistically significant results, of which none were for CO. 

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March 2009 5-112 DRAFT – DO NOT CITE OR QUOTE 
Summary of Associations between Short-Term Exposure to CO and Respiratory 

Hospital Admissions, ED Visits, and Physicians Visits 

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Relatively few studies evaluated the association between short-term exposure to ambient CO and 

hospital admissions and ED visits for various respiratory outcomes compared to other criteria air 

pollutants (e.g., O3 and PM). Although evidence for consistent positive associations (See Figures 5-12 and 

5-13) has been found across these studies, various issues surrounding the association between short-term 

exposure to CO and respiratory-related health effects have not been addressed due to: the lack of studies 

that examined potential confounders of the CO-respiratory hospital admission and ED visits relationship; 

and uncertainty as to a biologically plausible mechanism which could explain the association between CO 

exposure and respiratory-related health effects. Some of the studies evaluated suggest that CO acts as an 

indicator of combustion (e.g., traffic), which is supported by the moderate to high correlation between CO 

and other traffic-related pollutants such as NO2, PM2.5, EC, or BC and in addition complicates the results 

presented. Only two studies examined potential confounding of CO risk estimates by other pollutants 

through copollutant models, and found that CO risk estimates were robust or attenuated but remained 

positive in two-pollutant models with O3, NO2, or PM indices. 

5.5.2. Epidemiologic Studies with Long-Term Exposure 

The 2000 CO AQCD did not evaluate any studies that examined the effect of long-term exposure to 

15 CO on respiratory health. The following section discusses those studies that analyze the effect of long16 term exposure to CO on pulmonary function, asthma/asthma symptoms, and allergic rhinitis. 

5.5.2.1. Pulmonary Function 

Mortimer et al. (2008) examined the effect of prenatal and lifetime exposures to air pollutants on 

18 pulmonary function in 232 asthmatic children that resided in the San Joaquin Valley of California. The 

19 strong temporal correlation between pollutants and pollutant metrics for different time periods in the 

20 study area contributed to the inability to draw conclusions about the effect of individual pollutant metrics 

21 on pulmonary function (Mortimer et al., 2008). In an attempt to remedy this problem the authors used a 

22 newly developed Deletion/Substitution/Addition (DSA) algorithm “to identify which pollutant metrics 

23 were most predictive of pulmonary function” (Mortimer et al., 2008). This methodology uses an 

24 exploratory process to identify the best predictive model for each outcome of interest. Using this 

25 approach, Mortimer et al. (2008) found that exposure to CO early in life (ages 0-3) was negatively 

26 associated with FEV1/FVC and FEF25-75/FVC resulting in an effect size of -2.5% and -4.8%, respectively, 

March 2009 5-113 DRAFT – DO NOT CITE OR QUOTE 
per IQR increase in CO.1 1 Additional negative associations were observed between exposure to CO during 

2 the first 6 years of life and FEF25 (-6.7%), and prenatal, not trimester-specific exposure, and FEF25-75 (% 

3 reduction not reported). Overall, Mortimer et al. (2008) found that the effects were limited to subgroups, 

4 including African Americans, individuals diagnosed with asthma before the age of 2 years, and 

5 individuals exposed to maternal smoking during pregnancy. It must be noted that research still needs to be 

6 conducted to validate the aforementioned results obtained using the DSA algorithm and the subsequent 

7 calculation of effect estimates using GEE because the current model could underestimate the uncertainty 

8 surrounding the associations reported (Mortimer et al., 2008). Although the authors did find associations 

9 between long-term exposure to CO and decrements in pulmonary function, they also observed high 

10 correlations between CO and NO2, which together are markers for pollutants generated by urban 

11 combustion sources (e.g., mobile sources) (Mortimer et al., 2008). 

5.5.2.2. Asthma and Asthma Symptoms 

All of the studies that examined the association between long-term exposure to CO and asthma 

13 and/or asthma symptoms presented consistent, positive results, which is similar to what was observed 

14 when evaluating the effects attributed to short-term exposure to CO. Two U.S.-based studies, Goss et al. 

15 (2004) and Meng et al. (2007), were evaluated which examined the effect of long-term exposure to CO 

16 on: (1) pulmonary exacerbations in a cohort of individuals with cystic fibrosis >6 years of age, and (2) 

17 poorly controlled asthma in a population of asthmatics ≥ 18 years old that resided close to cross-street 

18 intersections in Los Angeles County and San Diego County, respectively. Of these two studies only Goss 

19 et al. (2004) observed a positive association between a respiratory outcome (i.e., pulmonary 

20 exacerbations) and long-term exposure to CO. However, it is unclear if the effects observed are due to CO 

21 alone because the authors did not conduct co-pollutant analyses. 

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When evaluating studies conducted in other countries, Hirsch et al. (1999), in a study conducted in 

Germany, and Guo et al. (1999), Wang et al. (1999), and Hwang et al. (2005), in studies conducted in 

Taiwan, all found positive associations between long-term exposure to CO and asthma or asthma 

symptoms in populations ranging from 6–16 years old. In these studies, the authors addressed the 

observed associations differently. Guo et al. (1999) and Hwang et al. (2005) both concluded that it is 

unlikely CO directly affects the respiratory system; Hirsch et al. (1999) attributed the increase in the 

prevalence of cough and bronchitis to exposure to traffic-related air pollutants (i.e., NO2, CO, and 

benzene); and Wang et al. (1999) did not interpret the association between long-term exposure to CO and 

adolescent asthma. Only Hwang et al. (2005) conducted a co-pollutant analysis and found that the asthma 

effects observed were robust to the inclusion of PM10, SO2 and O3 in the model. This is of note because 

1

 The study did not present the IQR for CO; therefore, the effect estimates presented were not standardized using the approach mentioned 

previously in this ISA. 

March 2009 5-114 DRAFT – DO NOT CITE OR QUOTE 
inclusion of the traffic-related pollutants NOX or NO2 in some of the aforementioned studies complicated 

the overall results because NOX and NO2 are highly correlated with CO, which makes it difficult to 

separate the effects attributed to each pollutant. 

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5.5.2.3. Allergic Rhinitis 

Hwang et al. (2006) and Lee et al. (2003c) both examined the effect of long-term exposure to air 

5 pollutants on the prevalence of allergic rhinitis in a population of schoolchildren in Taiwan. Both studies 

6 found an association between allergic rhinitis prevalence and CO, but they also observed an association 

7 with NOX. As a result, although Hwang et al. (2006) and Lee et al. (2003c) observed an increase in the 

8 prevalence of allergic rhinitis in response to an increase in long-term CO levels, they concluded that the 

9 combination of an association being observed for both CO and NOX can be attributed to the complex 

10 mixture of traffic-related pollutants and not necessarily CO alone. 

5.5.2.4. Summary of Associations between Long-Term Exposure to CO and 

Respiratory Morbidity 

To date, a limited number of studies have examined the potential association between long-term 

12 exposure to CO and respiratory morbidity. Although studies have reported positive associations for 

13 various respiratory outcomes, the limited evidence available, the new analytical methods employed, and 

14 the lack of studies that examined potential confounders of the CO-respiratory morbidity relationship, 

15 especially due to the high correlation between CO and other traffic-related pollutants, brings into question 

16 the validity of the associations observed. 

5.5.3. Controlled Human Exposure Studies 

Human clinical studies provide very little and inconsistent evidence of changes in pulmonary 

18 function following exposure to CO. In one older study, Chevalier et al. (1966) observed a significant 

19 decrease in total lung capacity following a short term exposure to 5,000 ppm resulting in a COHb level of 

20 4%. However, a similar study conducted at a higher CO concentration resulting in COHb levels of 

21 17-19% found no CO-induced changes in lung volume or mechanics (Fisher et al., 1969). The 2000 CO 

22 AQCD reported no evidence of CO-induced changes in exercise ventilation at COHb levels <15% during 

23 submaximal exercise (Koike et al., 1991). In two recent human clinical studies, exposure to CO (COHb ≈

24 10%) was not found to significantly affect resting pulmonary ventilation compared with exposure to clean 

25 air under either hypoxic or hyperoxic exposure conditions (Ren et al., 2001; Vesely et al., 2004)`. The 

26 results of these studies demonstrate that the hypoxia- and CO2-induced increases in pulmonary ventilation 

March 2009 5-115 DRAFT – DO NOT CITE OR QUOTE 
1 are not affected by CO. One recent study evaluated the potential anti-inflammatory effects of controlled 

2 exposures to CO in the airways of 19 individuals with COPD (Bathoorn et al., 2007). Subjects were 

3 exposed to both CO at concentrations of 100-125 ppm as well as room air for 2 h on each of four 

4 consecutive days. The authors reported a small decrease in sputum eosinophils, as well as a slight increase 

5 in the provocative concentration of methacholine required to cause a 20% reduction in FEV1 following 

6 exposure to CO. Although this study appears to demonstrate some evidence of an anti-inflammatory 

7 effect of CO among subjects with COPD, it must be noted that two of these patients experienced 

8 exacerbations of COPD during or following CO exposure. A similar study found no evidence of systemic 

9 anti-inflammatory effects following exposure to higher CO concentrations (500 ppm for 1 h) in a group of 

10 healthy adults (Mayr et al., 2005). 

5.5.4. Toxicological Studies 

As discussed in Section 5.2.3., the work of Thom, Ischiropoulos and colleagues (Ischiropoulos et 

12 al., 1996; Thom and Ischiropoulos, 1997; Thom et al., 1997; Thom et al., 1999a; Thom et al., 1999b) 

focused on CO-mediated displacement of NO• 13 from heme-binding sites. Although the concentrations of 

14 CO used in many of their studies were far higher than ambient levels, some of this research involved 

15 more environmentally-relevant CO levels. In one study, 1-h exposure of rats to 50 ppm CO resulted in 

16 increased lung capillary leakage 18 h later (Thom et al., 1999a). Increased NO• was observed in the lungs 

17 by electron paramagnetic resonance during 1-h exposure to 100 ppm CO and was accompanied by 

18 increases in H2O2 and nitrotyrosine. All of these effects were blocked by inhibition of NOS. These results, 

19 which were partially discussed in the 2000 CO AQCD, demonstrate the potential for exogenous CO to 

interact with NO• 20 -mediated pathways and to lead to pathophysiological effects in the lung. 

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32 

Recent work by Ghio et al. (2008) showed a disruption of cellular iron homeostasis following 

exposure to a low level of CO (50 ppm x 24 h) in rats. In lungs of inhalation-exposed rats, non-heme iron 

was significantly reduced, while lavagable iron was increased dramatically, suggesting an active removal 

of cellular iron. Lavagable ferritin was also increased following the CO exposure. Concurrently, liver iron 

levels increased, implying that the anatomical distribution of iron stores may significantly shift 

during/after CO exposures. These investigators were able to replicate the effect of loss of cellular iron in 

an in vitro model of cultured BEAS-2B cells and reported statistically significant effects at 10 ppm CO 

and an apparent maximal effect at 50 ppm CO (concentrations up to 500 ppm did not significantly 

enhance the iron loss beyond 50 ppm). Similar responses were observed for cellular ferritin. Both 

enhancement of iron removal and diminished iron uptake were noted in CO-exposed cells. Furthermore, 

decreased oxidative stress, mediator release and proliferation were noted in respiratory cells. These effects 

were reversible with a recovery period in fresh air. Interestingly, the in vivo exposure to CO induced mild, 

March 2009 5-116 DRAFT – DO NOT CITE OR QUOTE 
but significant neutrophilia in the lungs compared to air-exposed rats. This finding is contrary to the 

concept that CO acts as an anti-inflammatory agent; however, with alterations in iron handling several 

potential pathways could be initiated to recruit inflammatory cells into airways. The authors pointed out 

that while CO derived from HO activity may have an important role in iron regulation, the non-specific 

application of exogenous CO will have little capacity to discriminate between excessive and/or 

inappropriate iron which catalyzes oxidative stress and iron which may be required for normal 

homeostasis. 

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A chronic inhalation study by Sorhaug et al. (2006) demonstrated no alterations in lung 

morphology in Wistar rats exposed to 200 ppm CO for 72 weeks. COHb levels were reported to be 14.7% 

and morphological changes were noted in the heart as described in Section 5.2.3. 

A recent study by Carraway et al. (2002) involved continuous exposure of rats to HH (380 torr) 

with or without co-exposure to CO (50 ppm) for up to 21 days. The focus of this study was on remodeling 

of the pulmonary vasculature. While the addition of CO to HH did not alter the thickness or diameter of 

vessels in the lung, there was a significant increase in the number of small (<50 μm) diameter vessels 

compared to control, HH only, and CO-only exposures. Despite the greater number of vessels, the overall 

pulmonary vascular resistance was increased in the combined CO + HH exposure, which the authors 

attribute to enhancement of muscular arterioles and β-actin. 

In summary, one older study (Thom et al., 1999a) and two new studies (Carraway et al., 2002; Ghio 

et al., 2008) demonstrated effects of 50-100 ppm CO on the lung. Responses included an increase in 

alveolar capillary permeability, disrupted iron homeostasis, mild pulmonary inflammation and an 

exacerbation of pulmonary vascular remodeling elicited by HH. These results should be considered in 

view of the potential for inhaled CO to interact directly with lung epithelial cells and resident 

macrophages. However, a chronic study involving 200 ppm CO demonstrated no changes in pulmonary 

morphology (Sorhaug et al., 2006). 

5.5.5. Summary of Respiratory Health Effects 

5.5.5.1. Short-Term Exposure to CO 

New epidemiologic studies, supported by the body of literature summarized in the 2000 CO 

26 AQCD, provide evidence of positive associations between short-term exposure to CO and respiratory27 related outcomes including pulmonary function, respiratory symptoms, medication use, hospital 

28 admissions, and ED visits. However, the interpretation of the results from epidemiologic studies is 

29 difficult due to the lack of extensive copollutant analyses along with the moderate to high correlation 

30 between CO and other combustion/traffic generated pollutants. To date the majority of the literature has 

31 not extensively examined the association between CO and respiratory morbidity due to studies focusing 

March 2009 5-117 DRAFT – DO NOT CITE OR QUOTE 
1 primarily on effects associated with exposure to other criteria pollutants, namely PM and O3. This has 

2 contributed to the inability to disentangle the effects attributed to CO from the larger complex air 

3 pollution mix (particularly motor vehicle emissions). In addition, uncertainty as to a biological 

4 mechanism to explain the respiratory-related effects observed in the epidemiologic literature further 

5 complicates the interpretation of these results, especially considering the low ambient CO concentrations 

6 reported (24-h avg: 0.35-2.1 ppm). However, animal toxicological studies do provide some evidence that 

7 short-term exposure to CO (50-100 ppm) can cause oxidative injury and inflammation and alter 

8 pulmonary vascular remodeling. Human clinical studies have not extensively examined the effect of 

9 short-term exposure on respiratory morbidity, specifically pulmonary function. The limited number of 

10 human clinical studies that have been conducted prior to and since the 2000 CO AQCD provide very little 

11 evidence of any adverse effect of CO on the respiratory system at COHb levels <10%. Although human 

12 clinical studies have not provided evidence to support CO-related respiratory health effects, the 

13 epidemiologic studies that examined the effects of short-term exposure to CO and lung-related outcomes 

14 show positive associations and animal toxicological studies demonstrate the potential for an underlying 

15 biological mechanism, which together provide evidence that is suggestive of a causal relationship 

16 between short-term exposure to relevant CO concentrations and respiratory morbidity. 

5.5.5.2. Long-Term Exposure to CO 

Currently, only a few studies have been conducted that examine the association between long-term 

18 exposure to CO and respiratory morbidity. Although some studies did observe associations between long19 term exposure to CO and respiratory health outcomes key uncertainties still exist. These uncertainties 

20 include: the lack of replication and validation studies to evaluate new methodologies (i.e., 

21 Deletion/Substitution/Addition (DSA) algorithm) that have been used to examine the association between 

22 long-term exposure to CO and respiratory health effects; whether the respiratory health effects observed 

23 in response to long-term exposure to CO can be explained by the proposed biological mechanisms; and 

24 the lack of co-pollutant analyses to disentangle the respiratory effects associated with CO due to its high 

25 correlation with NO2 and other combustion-related pollutants. Overall, the evidence available is 

26 inadequate to conclude that a causal relationship exists between long-term exposure to relevant 

27 CO concentrations and respiratory morbidity. 

17 

March 2009 5-118 DRAFT – DO NOT CITE OR QUOTE 
5.6. Mortality 

5.6.1. Epidemiologic Studies with Short-Term Exposure to CO 

The relationship between short-term exposure to CO and mortality has not been extensively 

2 examined over the years due to the majority of epidemiologic studies focusing on mortality effects 

3 associated with exposure to PM and O3. As a result, a clear understanding of the association between 

4 short-term exposure to CO and mortality has yet to be developed. This section summarizes the main 

5 findings of the 2000 CO AQCD, and evaluates the newly available information on the relationship 

6 between short-term exposure to CO and daily mortality in an effort to disentangle the CO-mortality effect 

7 from those effects attributed to other criteria air pollutants. 

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5.6.1.1. Summary of Findings from 2000 CO AQCD 

The 2000 CO AQCD examined the association between short-term exposure to CO and mortality 

9 through the analysis of twelve single-city time-series studies, and one multi-city study, which included 11 

10 Canadian cities. While the results presented by these studies did provide suggestive evidence that an 

11 association exists between CO and mortality the AQCD concluded that inadequate evidence existed to 

12 infer a causal association between mortality and short-term exposure to ambient concentrations of CO. 

13 The reasons for this conclusion, which are shared with those studies that examined the effect of short14 term exposure to CO on morbidity, were due to: internal inconsistencies and lack of coherence of the 

15 reported results within and across studies; the representativeness of the average ambient CO levels of 

16 spatially heterogeneous ambient CO values derived from fixed monitoring sites or of personal exposures 

17 that often include nonambient CO; and the lack of biological plausibility for any harmful effects 

18 occurring with the very small changes in COHb levels (from near 0 up to 1.0%) over typical baseline 

19 levels (about 0.5%) that would be expected with the low average ambient CO levels (<5.0 ppm, 1-h daily 

20 max) reported in the epidemiologic studies (U.S. EPA, 2000). Additionally, some epidemiologic studies 

21 have also suggested that CO is acting as an indicator for other combustion-related pollutants, which has 

22 led to investigators questioning the CO-mortality relationship even when associations have been 

23 observed. 

To date the aforementioned issues have not been addressed primarily due to the majority of the 

recent time-series mortality studies focusing on the effects of only PM and O3. As such, CO has usually 

been considered one of the potential confounding copollutants in air pollution epidemiologic studies. As a 

result, the available CO information from these PM and O3 studies most frequently consists of risk 

estimates from single- and multipollutant models. Given the limitation that most of these studies were not 

March 2009 5-119 DRAFT – DO NOT CITE OR QUOTE 
conducted to examine CO, the goal of this review is to evaluate the CO-mortality association, and 

specifically the: magnitude of associations; evidence of confounding; and evidence of effect modification. 

1 

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3 

5.6.1.2. Multi-City Studies 

The following sections evaluate the recent literature that examined the association between short4 term exposure to CO and mortality, and in addition discuss newly available information with regard to the 

5 issues specific to CO mentioned above. This evaluation focuses primarily on multi-city studies because 

6 they provide: a more representative sample of potential CO-related mortality effects; and especially useful 

7 information by analyzing data from multiple cities using a consistent method, and thus avoiding potential 

publication bias.1 8 Table 5-16 lists the multi-city studies evaluated along with the mean CO concentrations 

9 reported in each study. 

Table 5-16. Range of CO concentrations reported in multi-city studies that examine mortality 

effects associated with short-term exposure to CO. 

Location Years Averaging Time Mean Concentration 

(ppm) 

Range of Mean 

Concentrations Across 

Cities (ppm) 

Author 

82 U.S. cities1 

(NMMAPS) 

1987-1994 24-h avg 1.02 Baton Rouge = 0.43 

Spokane = 2.19 

Dominici et al. (2003b; 

2005a) (reanalysis of 

Samet et al., 2000b) 

Burnett et al. (2004) 12 Canadian cities 1981-1999 24-h avg 1.02 Winnipeg = 0.58 

Toronto = 1.31 

Samoli et al. (2007)2 19 European cities 

(APHEA2) 

1990-19973 8-h max 2.12 Basel = 0.52 

Athens = 5.3 

1 The study actually consisted of 90 U.S. cities, but only 82 had CO data. 

2 This study presented CO concentrations in the units mg/m3. The concentrations were converted to ppm using the conversion factor 1 ppm = 1.15 mg/m3

, which assumes standard 

atmosphere and temperature. 

3 The study period varied from city to city. These years represent the total years in which data was collected across all cities. 

National Morbidity, Mortality, and Air Pollution Study of 90 U.S. Cities 

The time-series analysis of the largest 90 U.S. cities (82 cities for CO) in the National Morbidity, 

Mortality, and Air Pollution Study (NMMAPS) Dominici et al. (2003b; 2005a) (reanalysis of Samet et al., 

2000b) is by far the largest multi-city study conducted to date to investigate the mortality effects of air 

pollution, but the study primarily focused on PM10. The range in 24-h avg CO concentrations in the 

largest 20 cities (by population size) was 0.66 ppm (Detroit, MI) to 2.04 ppm (New York City). The 

analysis in the original report used GAM with default convergence criteria. In response to the bias 

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1

 To compare studies in this section that used different averaging times, effects estimates were standardized to the following: 0.5 ppm for studies 

that used 24-h avg concentrations and 0.75 ppm for studies that used max 8-h avg concentrations. These standardized values represent the range 

of current mean ambient concentrations across the U.S. 

March 2009 5-120 DRAFT – DO NOT CITE OR QUOTE 
observed in the estimates generated using GAM models with default convergence criteria (Dominici et 

al., 2002), Dominici et al. (2003b; 2005a) (reanalysis of Samet et al., 2000b) used the data using GAM 

with stringent convergence criteria as well as GLM. 

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Focusing on the results obtained using GLM, PM10 and O3 (in summer) appeared to be more 

strongly associated with mortality than the other gaseous pollutants. The authors stated that the results did 

not indicate associations of CO, SO2, or NO2, with total (non-accidental) mortality. However, as with 

PM10, the gaseous pollutants CO, SO2, and NO2 each showed the strongest association at a 1-day lag (for 

O3, a 0-day lag). Figure 5-14 presents the total mortality risk estimates for CO from Dominici et al. 

(2003b). The authors found a mortality risk estimate of 0.23% (95% PI: 0.09, 0.36) per 0.5 ppm increase 

in 24-h avg CO for a 1-day lag in a single-pollutant model. The inclusion of PM10 or PM10 and O3 in the 

model did not reduce CO risk estimates. However, the confidence bands were wider in the multipollutant 

models, but this could be attributed to: (1) PM10 data in many of the cities being collected every 6th day, 

as opposed to daily data for gaseous pollutants; and (2) O3 being collected in some cities only during 

warm months. The addition of NO2 (along with PM10) to the model resulted in a reduced CO risk 

estimate. Some caution is required when interpreting this apparent reduction because a smaller number of 

cities (57 cities1

) were available for the CO multipollutant analysis with PM10 and NO2 compared to the 

single-pollutant CO analysis (82 cities). However, most of the cities that did not have NO2 data (26 out of 

32), and subsequently were not included in the multipollutant analysis, were some of the least populated 

cities. Thus, the difference in the number of cities in the multi- and single-pollutant analyses is unlikely to 

be the underlying cause for the reduction in the CO risk estimate in the CO multipollutant analysis with 

PM10 and NO2. In comparison to the PM10 risk estimates, which were not reduced in multipollutant 

models, the CO risk estimates from multipollutant models indicate less consistent associations with 

mortality. 

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 One city was excluded from the multipollutant analysis because it contained NO2 data, but did not contain CO data. 

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Source: Dominici et al. (2003b) 

Figure 5-14. Posterior means and 95% posterior intervals of national average estimates for CO 

effects on total (non-accidental) mortality at lags 0, 1, and 2 within sets of the 90 U.S. 

cities with available pollutant data. Models A = CO alone; B = CO + PM10; C = CO + 

PM10 + O3; D = CO + PM10 + NO2; E = CO + PM10 + SO2. 

Canadian Multi-City Studies 

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Since the 2000 CO AQCD two Canadian multi-city studies have been published that examined the 

association between mortality and short-term exposure to air pollutants: (1) an analysis of PM10, PM2.5, 

PM10-2.5, and gaseous pollutants in 8 cities from 1986 to 1996 (Burnett et al., 2000); and (2) an analysis of 

PM10, PM2.5, PM10-2.5, and gaseous pollutants in 12 cities from 1981 to 1999 (Burnett et al., 2004). The 

2000 study utilized GAM with default convergence criteria, and upon reanalysis only examined PM 

indices (Burnett and Goldberg, 2003). 

Burnett et al. (2004) is the most extensive Canadian multi-city study conducted to date, both in 

terms of the length of the study and the number of cities covered. Although the study focused on NO2

because it was the best predictor of short-term mortality fluctuations among the pollutants examined 

(NO2, O3, SO2, CO, PM2.5, and PM10-2.5), it did present single- and copollutant results for all pollutants 

included in the analysis. The mean CO concentrations reported by Burnett et al. (2004) are similar to 

those reported in NMMAPS (see Table 5-14). 

Burnett et al. (2004) examined the effect of short-term exposure to CO on total (non-accidental) 

mortality. The authors found the strongest mortality association at lag 1-day for CO, SO2, PM2.5, PM10-2.5, 

PM10 (arithmetic addition of PM2.5 and PM10-2.5), and CoH, whereas for NO2, it was the 3-day moving 

average (i.e., average of 0-, 1-, and 2-day lags), and for O3, it was the 2-day moving average. In this study, 

Burnett et al. (2004) used 24-h avg pollutant concentrations because these values showed stronger 

associations than the daily 1-h max values for all of the gaseous pollutants and CoH, but not for O3. In a 

single-pollutant model the CO risk estimate for total mortality was 0.33% (95% CI: 0.12-0.54) per 

March 2009 5-122 DRAFT – DO NOT CITE OR QUOTE 
0.5 ppm increase in 24-h avg CO with a 1-day lag. After adjusting for NO2, the CO risk estimate was 

reduced to 0.04% (95% CI: -0.19 to 0.26), while the NO2 risk estimate was only slightly affected 

(increased from 2.25% to 2.35%) when including CO in the model. In this analysis, a copollutant model 

including both CO and PM was not presented. The similarity between the results presented in this 

Canadian multi-city and NMMAPS is that, in both analyses, CO risk estimates appeared to be sensitive to 

the addition of NO2 in the regression model. However, interpretation of these results requires some 

caution because: (1) NO2 tends to have a more spatially uniform distribution within a city compared to 

CO; (2) CO and NO2 share common sources (e.g., traffic); and (3) CO and NO2 are often moderately to 

highly correlated. 

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Air Pollution and Health: A European Approach 

Most of the Air Pollution and Health: A European Approach (APHEA) analyses have focused on 

the mortality effects of PM (PM10 and BS), SO2, NO2, and O3, but not CO. In addition, some of the 

analyses have not even considered CO as a potential confounder, such as the extended analysis 

(APHEA2) of PM (Katsouyanni et al., 2001), and NO2. One study, Gryparis et al. (Gryparis et al., 2004) 

did consider CO as a potential confounder in an analysis of O3, and found that the addition of CO 

increased O3 mortality risk estimates both in the summer and winter although the number of cities 

included in the copollutant model were reduced from 21 to 19. However, the study did not present CO 

risk estimates. Unlike other APHEA studies (or the NMMAPS and Canadian multi-city studies), the 

Samoli et al. (2007) analysis focused specifically on CO. 

Samoli et al. (2007) investigated the effect of short-term exposure to CO on total (non-accidental) 

and cardiovascular mortality in 19 European cities participating in the APHEA2 project by using a twostage analysis to examine city-specific effects and potential sources of heterogeneity in CO-mortality risk 

estimates. The mean levels of the max 8-h avg CO concentration in this study ranged from 0.52 ppm 

(Basel, Switzerland, and the Netherlands) to 5.3 ppm (Athens, Greece). The max 8-h avg CO 

concentration for the APHEA2 study of 2.12 ppm is higher than the estimated max 8-h avg CO 

concentrations reported in U.S. (Dominici et al., 2003b, 2005b) and Canadian (Burnett et al., 2004) cities 

of 1.53 ppm.1

 In APHEA cities, the correlation between CO and BS (r = 0.67-0.82) was higher than the 

correlation between CO and PM10 (r = 0.16-0.70) or the correlation between CO and 1-h max NO2

(r = 0.03-0.68). 

To examine the CO-mortality relationship, Samoli et al. (2007) conducted a time-series analysis of 

individual cities following the revised APHEA2 protocol.2

 The primary results presented by the authors 

are from a sensitivity analysis that used two alternative methods to select the extent of adjustment for 

1

 The max 8-h avg concentration for the Dominici et al. (2003b) and Burnett et al. (2004) studies was calculated using the conversion factor of 2:3 

to convert 24-h avg concentrations to max 8-h avg concentrations. 

2

 The APHEA2 protocol used a Poisson GAM model with penalized splines as implemented in the statistical package R. 

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temporal confounding. These methods consisted of: (1) confining the extent of smoothing to 8 degrees of 

freedom per year (df/yr); and (2) selecting the appropriate extent of smoothing through minimization of 

the absolute value of the sum of partial auto-correlation functions (PACF) of the residuals, which resulted 

in the analysis using on average 5 df/yr for total mortality and 4 df/yr for cardiovascular mortality. The 

authors also conducted copollutant analyses using PM10, BS, SO2, NO2, or O3 (1 h). In the second stage 

model Samoli et al. (2007) examined heterogeneity in CO risk estimates between cities by regressing risk 

estimates from individual cities on potential effect modifiers including: a) the air pollution level and mix 

in each city (i.e., mean levels of pollutants, ratio PM10/NO2); b) the exposure (number of CO monitors, 

correlation between monitors’ measurements); c) variables describing the health status of the population 

(e.g., crude mortality rate); d) the geographic area (northern, western, and central-eastern European 

cities); and e) the climatic conditions (mean temperature and relative humidity levels). 

Samoli et al. (2007) found that CO was associated with total and cardiovascular mortality. The 

primary results represent the combined random effects estimate for a 0.75 ppm increase in max 8-h avg 

CO concentrations for the average of 0- and 1-day lag for total mortality (1.03% [95% CI: 0.55-1.53]) and 

for cardiovascular mortality (1.08% [95% CI: 0.25-1.90]). These results were obtained using PACF to 

choose the extent of adjustment for temporal trends. Although the results obtained using PACF are 

insightful, the use of 8 df/yr would have been more consistent with the NMMAPS model (7 df/yr), and 

would have allowed for a more accurate comparison of the results between APHEA2 and NMMAPS. The 

corresponding risk estimates obtained using the 8 df/yr model are: 0.57% (95% CI: 0.23-0.91) for total 

mortality and 0.70% (95% CI: 0.31-1.09) for cardiovascular mortality. In the sensitivity analysis, Samoli 

et al. (2007) used 8 or 12 df/yr to adjust for temporal confounding. Both approaches led to similar risk 

estimates, but using PACF to choose the extent of smoothing generally resulted in larger CO risk 

estimates (by ~50 to 80%). 

During the examination of results obtained from the copollutant models, the authors noted that 

there was indication of confounding of CO risk estimates by BS and NO2, but not PM10. These results are 

consistent with CO, BS, and NO2 being part of the traffic pollution mixture and PM10 likely including 

secondary aerosols that do not correlate well with traffic-derived pollution. The risk estimates from the 

model using 8 df/yr that included NO2 were: 0.26% (-0.09 to 0.61) for total mortality and 0.37% (-0.05 to 

0.80) for cardiovascular mortality. Thus, the inclusion of NO2 in the model nearly halved the CO risk 

estimates (whereas the NO2 risk estimate was not sensitive to the inclusion of CO in the model). A similar 

magnitude of reduction in the CO risk estimates was also observed when including BS in the model. 

Overall, the sensitivity of CO risk estimates to the inclusion of NO2 in the model is consistent with the 

results presented in NMMAPS and the Canadian multi-city studies. 

In the second stage model, Samoli et al. (2007) found that geographic region was the most 

significant effect modifier, while the other effect modifiers (mentioned above) did not result in strong 

associations. Effects were primarily found in western and southern European cities, and were larger in 

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cities where the standardized mortality rate was lower. Earlier APHEA studies also reported a regional 

pattern of air pollution associations for BS and SO2, and found that western cities showed stronger 

associations than eastern cities. However, the heterogeneity in CO risk estimates by geographic region 

does not provide specific information to evaluate the CO-mortality association. 

An ancillary analysis conducted by Samoli et al. (2007) examined the possible presence of a CO 

threshold. The authors compared city-specific models to the threshold model, which consisted of 

thresholds at 0.5 mg/m3

 (0.43 ppm) increments. Samoli et al. (2007) then computed the deviance between 

the two models and summed the deviances for a given threshold over all cities. While the minimum 

deviance suggested a potential threshold of 0.43 ppm (the lowest threshold examined), the comparison 

with the linear no-threshold model indicated weak evidence (p-value >0.9) for a threshold. However, 

determining the presence of a threshold at the very low range of CO concentrations (i.e., at 0.43 ppm) in 

this data set is challenging, because, in seven of the 19 European cities examined, the lowest 10% of the 

CO distribution was at or above 2 mg/m3

 (1.74 ppm). Thus, the interpretation of the suggestive indication 

of a threshold is limited. 

In summary, the APHEA2 analysis of CO in 19 cities found an association between CO and total 

and cardiovascular mortality in single-pollutant models, but the associations were substantially reduced 

when NO2 or BS was included in copollutant models. The evidence for potential confounding of CO risk 

estimates by NO2 is consistent with the findings from the NMMAPS and Canadian 12 cities studies. In 

addition, Samoli et al. (2007) found that geographic region was a potential effect modifier, but such 

geographic heterogeneity is not specific to CO, based on previously conducted APHEA studies. Finally, 

examination of the CO concentration-response relationship found weak evidence of a CO threshold. 

Other European Multi-City Studies 

An additional European multi-city study was conducted by Biggeri et al. (2005) in eight Italian 

cities. The authors examined the effect of short-term exposure to CO on mortality in single-pollutant 

models using a time-series approach. In this analysis, because all of the pollutants showed positive 

associations with the mortality endpoints examined and the correlations among the pollutants were not 

presented, it is unclear if the observed associations are shared or confounded. 

Summary of Multi-City Studies 

In summary, the mortality risk estimates from single-pollutant models are comparable for the 

NMMAPS and Canadian 12-city studies, 0.23 and 0.33, respectively, with the estimate from the APHEA2 

study being slightly larger (0.57%) (Figure 5-15). In both the NMMAPS and Canadian studies, a 1-day 

lag showed the strongest association; but the APHEA2 study used an a priori exposure window, the 

average of 0- and 1-day lags, which has been found to be the exposure window most strongly associated 

with mortality in PM analyses. 

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The APHEA2 risk estimates presented in Figure 5-15 are from a model that used a fixed amount of 

smoothing to adjust for temporal confounding (8 df/yr), which is similar to that used in the NMMAPS 

study (7 df/yr). However, the APHEA2 sensitivity analysis suggested an approximate 50 to 80% 

difference in CO risk estimates between the models that used 8 or 12 df/yr, and the models that used 

minimization of the absolute value of the sum of PACF of the residuals as a criterion to choose the 

smoothing parameters. Thus, some model uncertainty likely influences the range of CO risk estimates 

obtained from the studies evaluated. 

The CO risk estimates from the aforementioned studies are also consistently sensitive to the 

inclusion of NO2 in a copollutant model (0.11, 0.03, and 0.26%, for the NMMAPS, Canadian 12-cities 

study, and APHEA2, respectively). Thus, these results suggest confounding by NO2. However, this 

interpretation is further complicated because as with CO, NO2 itself may be an indicator of combustion 

sources, such as traffic. Because CO measurements tend to reflect more local impacts, due to the location 

of monitors, than NO2 (which is a secondary pollutant and therefore more spatially uniform) it is also 

possible that CO, the less precisely measured pollutant in terms of spatial distribution, may “lose” in the 

multipollutant model. Thus, it may not be accurate to interpret these results as evidence of “confounding 

by NO2.” 

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 Dominici et al. (2003a; 2003b) 

 (re-analysis by 2000a) 

 NMMAPS, lag 1 

 CO only (82 cities) 

 + PM10 (82 cities) 

 + PM10 and NO2 (57 cities) 

 Burnett et al. (2004) 

 12 Canadian cities, lag 1 

 CO only 

 + NO2

a

 Samoli et al. (2007) 

 APHEA2 (19 cities), lag 0-1 

 CO only 

 + PM10

 + BS 

 + NO2

aNO2 is the average of 0, 1, and 2 day lags. 

% Increase 

Figure 5-15. Summary of mortality risk estimates for short-term exposure to CO from multi-city 

studies. Estimates were standardized to 0.5 ppm and 0.75 ppm for studies that used 

24-h avg CO and max 8-h avg CO exposure metrics, respectively. 

5.6.1.3. Meta-Analysis of All Criteria Pollutants 

Stieb et al. (2002) reviewed the time-series mortality studies published between 1985 and 2000, 

2 and conducted a meta-analysis to estimate combined effects for PM10, CO, NO2, O3, and SO2. Because 

3 many of the studies reviewed in the 2000 analysis used GAM with default convergence criteria, 

4 Stieb et al. (2003) updated the estimates from the meta-analysis by separating the GAM versus non-GAM 

5 studies. In this meta-analysis the authors also presented separate combined estimates for single- and 

6 multipollutant models. Overall, there were more GAM estimates than non-GAM estimates for all of the 

1 

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1 pollutants except SO2. For CO, 4 single-pollutant model risk estimates were identified, resulting in a 

2 combined estimate of 3.18% (95% CI: 0.76-5.66) per 0.5 ppm increase in 24-h avg CO, and only 

3 1 multipollutant model risk estimate (0.00% [95% CI: -1.71 to 1.74]) from the non-GAM studies. Thus, 

4 for CO, this study did not provide useful meta-estimates because the number of studies that contributed to 

5 the combined estimates for CO was rather small. 

5.6.1.4. Single-City Studies 

In addition to the multi-city studies discussed above, there have also been several single-city U.S.- 

7 and Canadian-based time-series mortality studies that examined CO. The single-city studies, similar to the 

8 multi-city studies, often focused on the PM-mortality association, but also provided additional 

9 information that is not available in the multi-city studies. Because the sample size used in each single-city 

10 study is small, and subsequently results in wide confidence bands, a quantitative comparison of the results 

11 from single- and multi-city studies is difficult. In addition, some studies do not present CO results 

12 quantitatively adding to the inability to adequately compare studies. Table 5-17 lists the single-city studies 

13 evaluated along with the mean CO concentrations reported in each study. 

6 

Table 5-17. Range of CO concentrations reported in single-city studies that examine mortality 

effects associated with short-term exposure to CO. 

Location Years Averaging 

Time 

Mean Concentration 

(ppm) 

Upper Percentile 

Concentrations (ppm) Study 

De Leon et al. (2003) New York, NY 1985-1994 24-h avg 2.45 95th: 4.04 

Klemm et al. (2004) Atlanta, GA 1998-2000 1-h max 1.31 Max: 7.40 

75th: 1.66 

Vedal et al. (2003) Vancouver, BC, Canada 1994-1996 24-h avg 0.6 Max: 1.9 

90th: 0.9 

Villeneuve et al. (2003) Vancouver, BC, Canada 1986-1999 24-h avg 1.0 Max: 4.9 

90th: 1.6 

Goldberg et al. (2003) Montreal, Quebec, Canada 1984-1993 24-h avg 0.8 Max: 5.1 

75th: 1.0 

Hoek et al. (2000; 2001; 

Hoek, 2003) 

The Netherlands 1986-1994 24-h avg Entire Country: 0.46 

Four Major Cities: 0.59 

Max. 

Entire Country: 2.6 

Four Major Cities: 4.6 

1 Study reported median CO concentrations. 

Single-City Studies Conducted in the United States 

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De Leon et al. (2003) focused on the role of contributing respiratory diseases on the association 

between air pollution (i.e., PM10, O3, NO2, SO2, and CO) and primary non-respiratory mortality 

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(circulatory and cancer) in New York City, NY during the period 1985-1994. This study only presented 

risk estimates graphically for each of the pollutants analyzed, except PM10. In single-pollutant models, 

PM10, CO, SO2, and NO2 all showed the same pattern of association with circulatory mortality for 

individuals ≥ 75, indicating a larger risk of death in individuals with contributing respiratory diseases 

compared to those without. In two-pollutant models, PM10 and CO risk estimates were reduced, but each 

remained significantly positive. 

Klemm et al. (2004) analyzed 15 air pollutants for their associations with mortality in Atlanta, GA, 

for a two-year period starting in August 1998. These pollutants included PM2.5, PM10-2.5, UFP surface area 

and counts, aerosol acidity, EC, OC, SO4

2-, O3, CO, SO2, and NO2. This study presented risk estimates 

using three levels of smoothing (quarterly, monthly, and biweekly knots) for temporal trend adjustment, 

and suggested that the risk estimates were rather sensitive to the extent of smoothing. It should be noted 

that temporal smoothing using biweekly knots is a more aggressive modeling approach than the degrees 

of freedom approach used by most studies. In the single-pollutant models for all-cause mortality, the 

strongest association, which was also statistically significant, was found for PM2.5. CO, SO4

2-, and PM10-2.5

also showed positive associations with all-cause mortality, but they were not significant (CO: Quarterly 

knots and Monthly Knots β = 0.00002 [SE = 0.00001]; Biweekly knots β = 0.00001 [SE = 0.00002]). 

However, CO was significantly associated with circulatory mortality in older adults (≥ 65), and these 

associations remained when PM2.5 was included in the model (results were presented graphically). 

Single-City Studies Conducted in Canada 

Vedal et al. (2003) examined the association between short-term exposure to “low levels” of air 

pollution (i.e., PM10, O3, NO2, SO2, and CO) and daily morality in Vancouver, British Columbia, Canada 

for the years 1994-1996. In this analysis, all of the risk estimates were presented graphically; however, the 

results suggested that O3 in the summer and NO2 in the winter showed the strongest associations with 

mortality. Vedal et al. (2003) found that CO was positively, but not significantly associated with mortality. 

Additionally, the association between short-term exposure to NO2 and mortality was found to be 

consistent with the results from the Canadian multi-city study conducted by Burnett et al. (2004). 

Villeneuve et al. (2003) also conducted an analysis using data from Vancouver, Canada, using a 

cohort of 550,000 individuals whose vital status was ascertained between 1986 and 1999. In this study, 

PM2.5, PM10–2.5, TSP, CoH, PM10, SO4

2-, O3, CO, SO2, and NO2 were examined for their associations with 

all-cause, cardiovascular, and respiratory mortality. When examining the association between gaseous 

pollutants and all-cause mortality in this data set, NO2 and SO2 showed the strongest associations, while 

the association between CO and all-cause mortality were generally weaker than those for NO2 and SO2. 

For cardiovascular mortality, SO2 risk estimates were smaller than those for NO2 or CO, while for 

respiratory mortality, SO2 showed the strongest associations. However, the wider confidence bands of 

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these categories and the smaller daily counts make it difficult to assess CO associations with causespecific mortality outcomes. 

Goldberg et al. (2003) contrasted associations between air pollution and mortality in individuals 

with underlying CHF vs. mortality in individuals who were identified as having CHF one year prior to 

death based on information from the universal health insurance plan in Montreal, Quebec, Canada, during 

the period 1984-1993. In this study, Goldberg et al. (2003) examined associations between PM2.5, CoH, 

SO4

2-, O3, CO, SO2, and NO2, and mortality. The authors found no association between any of the air 

pollutants and mortality with underlying CHF. However, Goldberg et al. (2003) found positive 

associations between air pollution and mortality in individuals diagnosed with CHF one year prior to 

death. Of the air pollutants examined, CoH, NO2, and SO2 were most consistently associated with 

mortality for ages 65 and older, while CO showed positive but weaker associations compared to these 

three pollutants. 

Single-City Studies Conducted in Other Countries 

 Of the epidemiologic studies conducted in other countries that examine the association between 

short-term exposure to CO and mortality only those studies conducted in European countries that have 

CO levels comparable to the U.S. were evaluated. However, because Samoli et al. (2007) conducted a 

multi-city study of European cities that focused on short-term exposure to CO, there are only a few 

single-city studies that provide additional information, specifically those studies conducted in the 

Netherlands. The Netherland studies were evaluated because they provide risk estimates for multiple 

pollutants and cause-specific mortality, and consisted of relatively large sample sizes (i.e., the mortality 

time-series of the entire country was analyzed). 

Hoek et al. (2000) re-analyzed by (Hoek, 2003) examined associations between air pollution and 

all-cause, cardiovascular, COPD, and pneumonia deaths in the entire Netherlands, the four major cities 

combined, and the entire country minus the four major cities for the period 1986 to 1994. The air 

pollutants analyzed included BS, PM10, O3, NO2, SO2, CO, SO4

2- and NO3-

. In the single-pollutant 

models, all of the pollutants were significantly associated with all-cause mortality at lag 1-day and 0-

6 days when using the entire Netherlands data set. In the two-pollutant model, CO risk estimates were 

reduced to null when PM10, BS, SO4

2- and NO3-

 were included in the model while the risk estimates for 

these copollutants remained significantly positive. BS, CO, and NO2 were highly correlated (r >0.85) in 

this data set, and the authors noted “all these pollutants should be interpreted as indicators for motorized 

traffic emissions” (Hoek et al., 2000). The authors found that O3 showed the most consistent and 

independent associations with mortality and that the risk estimates for all of the pollutants were 

substantially higher in the summer months than in the winter months. Pneumonia deaths showed the 

largest risk estimates for most pollutants including CO. The result from the Hoek et al. (2000) study is 

somewhat in contrast to the result from the Samoli et al. (2007) multi-city study in that, in the Hoek et al. 

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(2000) analysis, CO was more sensitive to the addition of PM indices in copollutant models. This may be 

due to the high correlation between CO and PM indices in the Netherlands. 

Hoek et al. (2001) reanalysis by (Hoek, 2003) analyzed the Netherlands data using more specific 

cardiovascular causes of death: MI and other IHD, arrhythmia, heart failure, cerebrovascular mortality, 

and embolism/thrombosis. In this analysis, the authors analyzed O3, BS, PM10, CO, SO2, and NO2 in only 

single-pollutant models. For all of the pollutants, risk estimates were larger for arrhythmia, heart failure, 

and cerebrovascular mortality than for the combined cardiovascular mortality outcome. While the results 

suggested larger impacts of air pollution on more specific cardiovascular causes, the authors did not 

provide evidence of an association that was specific to any particular pollutant which would aid in 

addressing the question of confounding. 

Summary of Single-City Studies 

Overall, it is difficult to identify a clear pattern of CO-mortality associations from the single-city 

studies evaluated because of the relatively small sample sizes, expected variation in the air pollution mix 

across cities, and differences in the analytical approaches used across studies. However, in all of these 

studies, CO showed, at least to some extent, positive associations with all-cause or circulatory/ 

cardiovascular deaths. In addition the one study that examined specific cardiovascular causes of death 

(Hoek et al., 2001)reanalysis by (Hoek, 2003) did not find an association that is specific to CO. Although 

the extent of sensitivity of CO-mortality associations varied across studies, it could be attributed to the 

likely variation in the correlation between CO and copollutants. 

5.6.1.5. Summary of Mortality and Short-Term Exposure to CO 

Among the gaseous pollutants examined in time-series mortality studies, CO is the least frequently 

20 studied criteria air pollutant. Because CO was mostly treated as a potential confounder in these studies, 

21 the information available regarding the nature of the association between short-term exposure to CO and 

22 mortality is limited compared to the other pollutants. However, the recently available multi-city studies, 

23 which consist of larger sample sizes, and single-city studies generally confirmed the findings reported in 

24 the 2000 CO AQCD. 

The multi-city studies which were evaluated, reported comparable CO mortality risk estimates for 

total (non-accidental) mortality with the APHEA2 European multi-city study (Samoli et al., 2007) 

showing slightly higher estimates for cardiovascular mortality in single-pollutant models. However, when 

examining potential confounding by copollutants these studies consistently showed that CO mortality risk 

estimates were reduced when NO2 was included in the model, but this observation may not be 

“confounding” in the usual sense in that NO2 may also be an indicator of other pollutants or pollution 

sources (i.e., traffic). 

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Only one of the multi-city studies focused specifically on the CO-mortality association (Samoli et 

al., 2007), the APHEA2 study), and in the process examined: (1) model sensitivity; (2) the CO-mortality 

C-R relationship; and (3) potential effect modifiers of CO mortality risk estimates. The APHEA study 

performed a sensitivity analysis, which indicated an approximate 50 - 80% difference in CO risk 

estimates from a reasonable range of alternative models. In addition, the study examined the CO-mortality 

concentration-response relationship through a grid search of varying threshold points, and found only 

weak evidence of a CO threshold at 0.5 mg/m3

 (0.43 ppm), but this result was complicated by the lowest 

10% of the CO distribution for seven of the 19 cities examined being at or above 2 mg/m3

 (1.74 ppm). 

The examination of a variety of city-specific variables to indentify potential effect modifiers of the 

CO-mortality relationship found that geographic region explained most of the heterogeneity in CO 

mortality risk estimates with the CO-mortality associations being stronger in western and southern 

European cities than eastern cities (Samoli et al., 2007). A similar pattern has been reported for black 

smoke (BS) and SO2 in previous APHEA studies, but the geographic variability observed does not 

provide specific information, which could be used to evaluate the CO-mortality association. 

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 The results from the single-city studies are generally consistent with the multi-city studies in that 

some evidence of a positive association was found for mortality upon short-term exposure to CO. 

However, the CO-mortality associations were often, but not always, attenuated when other copollutants 

were included in the regression models. In addition, limited evidence was available to identify causespecific mortality outcomes (e.g., cardiovascular causes of death) associated with short-term exposure to 

CO. 

The evidence from the recent multi- and single-city studies suggests that an association between 

short-term exposure to CO and mortality exists, but limited evidence is available to evaluate causespecific mortality outcomes associated with CO exposure; and it is unclear if CO is acting alone or as an 

indicator for other combustion-related pollutants. In addition, the results underscore the limitation of 

current analytical methods to disentangle the health effects associated with one pollutant in the complex 

air pollution mixture. Overall, the epidemiologic evidence is suggestive of a causal relationship 

between short-term exposure to relevant CO concentrations and mortality. 

5.6.2. Epidemiologic Studies with Long-Term Exposure to CO 

The 2000 CO AQCD did not evaluate the association between long-term exposure to CO and 

29 mortality because there were no studies at the time that examined this relationship. Since then there have 

30 been several new studies that examined the association between long-term exposure to CO and mortality, 

31 but it should be noted that these studies primarily focused on PM, and CO was only considered in these 

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1 studies as a potential confounder. Therefore, the information available from these new long-term exposure 

2 studies is somewhat limited, especially in comparison to that for PM. 

5.6.2.1. U.S. Cohort Studies 

American Cancer Society Cohort Studies 

Pope et al. (1995) investigated associations between long-term exposure to PM and mortality 

outcomes in the ACS cohort. In this study, ambient air pollution data from 151 U.S. metropolitan areas in 

1981 were linked with individual risk factors in 552,138 adults who resided in these areas when enrolled 

in the prospective study in 1982. Death outcomes were ascertained through 1989. PM2.5 and SO4

2- were 

associated with total (non-accidental), cardiopulmonary, and lung cancer mortality, but not with mortality 

for all other causes (i.e., non-accidental minus cardiopulmonary and lung cancer). Gaseous pollutants 

were not analyzed in Pope et al. (1995). Jerrett et al. (2003) (using data from (Krewski et al., 2000) 

conducted an extensive sensitivity analysis of the Pope et al. (1995). ACS data, augmented with 

additional gaseous pollutants data. Due to the smaller number of CO monitors available compared to 

SO4

2-, the number of metropolitan statistical areas (MSAs) included in the CO analysis were reduced 

(from 151 with SO4

2-) to 107. The mean annual CO concentrations in these MSAs ranged from 0.19 to 

3.95 ppm. CO was weakly negatively correlated with SO4

2- (r = -0.07). Among the gaseous pollutants 

examined (CO, NO2, O3, SO2), only SO2 showed positive associations with mortality, and in addition was 

the only copollutant that reduced SO4

2- risk estimates. For CO, the relative risk estimates for total (nonaccidental) mortality in single and copollutant models with SO4

2- was 0.99 (95% CI: 0.96-1.01) and 0.98 

(95% CI: 0.96-1.01), respectively, per 0.5 ppm increase in mean annual average CO concentrations. 

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Pope et al. (2002) conducted an extended analysis of the ACS cohort with double the follow-up 

time (to 1998) and triple the number of deaths compared to the original Pope et al. (1995) study. In 

addition to PM2.5, data for all of the gaseous pollutants were retrieved for the extended period and 

analyzed for their associations with mortality-specific outcomes. As in the 1995 analysis, the air pollution 

exposure estimates were based on the MSA-level averages. The authors found that PM2.5 and SO4

2- were 

both associated with total, cardiopulmonary, and lung cancer mortality.1

 In this study, the CO analysis 

used two different data sets. The first data set consisted of 1980 data and 113 MSAs; while the second 

data set used averages of the years 1982-1998 and 122 MSAs. The authors found, when using the 1980 

data, that CO was not associated (risk estimates ~ 1) (See Figure 5-16) with all-cause, cardiopulmonary, 

lung cancer, or mortality for all other causes. However, the analysis of the 1982-1998 data found that CO 

was negatively (and significantly) associated with all-cause, cardio-pulmonary, and lung cancer mortality. 

It is unclear why significant negative associations were observed when analyzing the 1982-1998 data, but 

1

 These results were presented graphically in Pope et al. (2002) and were estimated for Figure 5-16. 

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evidence from other mortality studies that examined the association between long-term exposure to CO 

and mortality does not suggest that CO elicits a protective effect. 

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Women’s Health Initiative Cohort Study 

Miller et al. (2007) studied 65,893 postmenopausal women between the ages of 50 and 79 years 

without previous CVD in 36 U.S. metropolitan areas from 1994 to 1998. They examined the association 

between one or more fatal or nonfatal cardiovascular events and air pollutant concentrations. Exposures to 

air pollution were estimated by assigning the annual mean levels of air pollutants measured at the nearest 

monitor to the location of residence of each subject on the basis of its five-digit ZIP code centroid, which 

allowed estimation of effects due to both within-city and between-city variation of air pollution. The 

investigators excluded monitors whose measurement objective focused on a single point source or those 

with “small measurement scale (0 to 100 meters).” Thus, presumably these criteria reduced some of the 

exposure measurement error associated with monitors that are highly impacted by local sources. 

During the course of the study, a total of 1,816 women had one or more fatal or nonfatal 

cardiovascular events, including 261 cardiovascular-related deaths. Hazard ratios for the initial 

cardiovascular event were estimated. The following results are for models that only included subjects 

with non-missing exposure data for all pollutants (n = 28,402 subjects, resulting in 879 CVD events). In 

the single-pollutant models, PM2.5 showed the strongest associations with CVD events among all 

pollutants (HR = 1.24 [95% CI: 1.04-1.48] per 10-μg/m3

 increase in annual average), followed by SO2

(HR = 1.07 [95% CI: 0.95-1.20] per 5-ppb increase in the annual average). For CO the single-pollutant 

risk estimate was slightly (but not significantly) negative (HR = 0.96 [95%CI: 0.84-1.10]). In the 

multipollutant model, which included all pollutants (i.e., PM2.5, PM10-2.5, SO2, NO2, and O3), the CO risk 

estimate was similar to the one presented in the single-pollutant model (HR = 0.96 [95% CI: 0.82-1.14]). 

In addition, CO was not associated with CVD events in a single pollutant model (HR = 1.00 [95%CI: 

0.90-1.10] per 0.5 ppm increase in mean annual average CO concentration) that used all available 

observations. This study did not examine the correlations among pollutants and, therefore, the extent of 

confounding could not be examined, but PM2.5 was clearly the best predictor of cardiovascular events. 

The Washington University-EPRI Veterans’ Cohort Mortality Studies 

Lipfert et al. (2000a) conducted an analysis of a national cohort of ~70,000 male U.S. military 

veterans who were diagnosed as hypertensive in the mid 1970s and were followed for approximately 21 

years (up to 1996). Demographically, 35% of the cohort consisted of African American men and 57% of 

the cohort was defined as current smokers; however, 81% of the cohort had been smokers at one time in 

their life. The study examined mortality effects in response to long-term exposure to multiple pollutants 

including, PM2.5, PM10, PM10-2.5, TSP, SO4

2-, CO, O3, NO2, SO2, and Pb. Lipfert et al. (2000a) estimated 

exposures by indentifying the county of residence at the time of entry to the study. Four exposure periods 

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(1960-1974, 1975-1981, 1982-1988, and 1989-1996) were defined, and deaths during each of the three 

most recent exposure periods were considered. The mean annual 95th percentile of hourly CO values 

during these periods declined from 10.8 ppm to 2.4 ppm. The authors noted that the pollution risk 

estimates were sensitive to the regression model specification, exposure periods, and the inclusion of 

ecological and individual variables. Lipfert et al. (2000a) reported that indications of concurrent mortality 

risks (i.e., associations between mortality and air quality for the same period) were found for NO2 and 

peak O3. The estimated CO mortality risks were all negative, but not significant. 

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Lipfert et al. (2006b) examined associations between traffic density and mortality in the same 

Veterans’ Cohort, but in this analysis the follow-up period was extended to 2001. As in their 2000 study, 

four exposure periods were considered but more recent years were included in the 2006 analysis. The 

authors used the mean annual average of the 95th percentile of 24-h avg CO in each of the exposure 

periods as the averaging metric. The traffic density variable was the most significant predictor of 

mortality in their analysis, remaining so in two- and three pollutant models with other air pollutants 

(i.e., CO, NO2, O3. PM2.5, SO4

2-, non-SO42- PM2.5, and PM10-2.5). In the multipollutant models, mortality 

risk estimates were not statistically significant for any of the other pollutants, except O3. The natural log 

of the traffic density variable (VKTA = vehicle-km traveled per year) was not correlated with CO 

(r = -0.06), but moderately correlated with PM2.5 (r = 0.50) in this data set. For the 1989-1996 data period, 

the estimated mortality relative risk was 1.02 (95% CI: 0.98-1.06) per 1 ppm increase in the mean annual 

95th percentile of hourly CO concentration in a single-pollutant model. The two-pollutant model, which 

included the traffic density variable, resulted in a relative risk of 1.00 (95% CI: 0.96-1.04). Lipfert et al. 

(2006b) note that the low risk estimates for CO in this study were consistent with those observed in other 

long-term exposure studies, and may have been due to the localized nature of CO, which can lead to 

exposure errors when data from centralized monitors is used to represent an entire county. Interestingly, 

as Lipfert et al. (2006b) pointed out, the risk estimates due to traffic density did not vary appreciably 

across these four periods even though regulated tailpipe emissions declined during the study period. The 

authors speculated that some combination of other environmental factors such as road dust, psychological 

stress, and noise (all of which constitute the environmental effects of vehicular traffic) along with spatial 

gradients in SES might contribute to the non-negative effects observed. 

Lipfert et al. (2006a) extended the analysis of the Veterans Cohort data to include the EPA’s 

Speciation Trends Network (STN) data, which collected chemical components of PM2.5. The authors 

analyzed the STN data for the year 2002, and again used county-level averages. In addition, they analyzed 

PM2.5 and gaseous pollutants data for 1999 through 2001. As in the other Lipfert et al. (2006b) study, 

traffic density was the most important predictor of mortality, but associations were also observed for EC, 

vanadium (V), nickel (Ni), and NO3

-

. Ozone, NO2, and PM10 also showed positive, but weaker 

associations. The authors found no association between the mean annual 95th percentile of hourly CO 

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values and mortality (RR = 0.995 [95% CI: 0.988-1.001] per 1 ppm increase in CO concentration) in a 

single-pollutant model. The study did not present multipollutant model results for CO. 

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Endpoint Reference Cohort 

Years of 

Mortality 

Data 

Year(s) of AQ

Data 

Pope et al. (2002)a ACS 1982-1998 1980 

 

 

Pope et al. (2002) 1982-1998 1982-1998 a ACS 

Lipfert et al. (2006b) Veterans 1976-2001 1976-2001 + ln(VKTA)d

All-cause Lipfert et al. (2006b) Veterans 1976-2001 1976-2001 

Lipfert et al. (2006a) Veterans 1997-2001 1999-2001 

ACS 1982-1998 1982-c Jerrett et al. (2003) 

Jerrett et al. (2003) ACS 1982-1998 1982-c + SO4

2-

Pope et al. (2002)a ACS 1982-1998 1980 

Cardio-pulmonary 

Pope et al. (2002)a ACS 1982-1998 1982-1998 

Miller et al. (2007)b WHI 1994-1998 2000 

Cardiovascular 

WHI 1994-1998 2000 

 

 + PM2.5, PM10-2.5, SO2, NO2, O3 Miller et al. (2007)b

a The study does not present CO results quantitatively. This effect estimate and 

95% confidence interval were estimated from Figure 5 in Pope et al. (2002). 

b Effect estimate is only for subjects with non-missing exposure data for all pollutants 

c The study did not report the range of years of CO data used; however, it does specify 

that air quality data was obtained from pollution monitoring stations operating in 1982. 

d Natural log of vehicle-km traveled per year (VKTA).

Effect Estimates 

Figure 5-16. Summary of mortality risk estimates for long-term exposure to CO. Estimates were 

standardized to 0.5 ppm and 1.0 ppm for studies that used mean annual average CO 

and the mean annual 95th percentile of hourly CO values exposure metrics, 

respectively. 

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5.6.2.2. U.S. Cross-Sectional Analysis 

An ecological cross-sectional analysis involves regressing county- (or city) average health outcome 

2 values on county-average explanatory variables such as air pollution and census statistics. Unlike the 

3 cohort studies described above, to the extent that individual level confounders are not adjusted for, the 

4 cross-sectional study design is considered to be subject to ecologic confounding. However, all of the 

5 cohort studies described above are also semi-ecologic in that the air pollution exposure variables are 

6 ecologic (Kuenzli and Tager, 1997). In this sense, cross-sectional studies may be useful in evaluating the 

7 correlation among exposure variables. 

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Lipfert and Morris (2002) conducted ecological cross-sectional regressions for U.S. counties 

(except Alaska) during five periods: 1960-1969, 1970-1974, 1979-1981, 1989-1991, and 1995-1997. They 

regressed age-specific (15-44, 45-64, 65-74, 76-84, and 85+) all-cause (excluding AIDS and trauma) 

mortality on air pollution, demography, climate, SES, lifestyle, and diet. The authors analyzed TSP, PM10, 

PM2.5, SO4

2-, SO2, CO, NO2, and O3. However, air pollution data was only available for limited periods of 

time depending on the pollutant: TSP up to 1991; PM10 between 1995 and 1999; and PM2.5 between 

1979-1984 and 1999. In response to the varying number of counties with valid air pollution data by 

pollutant and time, Lipfert and Morris (2002) employed a staged regression approach. In the first stage, a 

national model was developed for each dependent variable, excluding air pollution variables. In the 

second stage, regressions were performed with the residuals on concurrent and previous periods’ air 

pollution variables to identify the pollutants of interest. Overall, there were too many results to summarize 

because of the large number of age groups, lagged exposure time windows, and mortality study periods 

examined in the study, but similar to the ACS cohort studies, PM2.5 and SO4

2- were found to be 

consistently and positively associated with mortality. Lipfert and Morris (2002) generally found the 

strongest associations in the earlier time periods, and when mortality and air quality were measured in 

different periods (e.g., mortality data 1995-1997 and CO data 1970-1974). Also, consistent with the 

Lipfert et al. (2000a) and the Pope et al. (2002) cohort studies, CO was frequently negatively (and often 

significantly) associated with mortality in older age groups, especially when mortality was matched with 

CO levels in more recent time periods. The younger age group (15-44) often showed a positive 

association with CO, but considering the small number of deaths attributed to this age group (less than 

1% of total deaths), the association was not informative. Overall, this study highlighted that the 

CO-mortality associations presented in purely ecologic study designs are generally consistent with those 

found in semi-individual cohort studies. 

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5.6.2.3. Summary of Mortality and Long-Term Exposure to CO 

The evaluation of new epidemiologic studies conducted since the 2000 CO AQCD (2000) that 

2 investigated the association between long-term exposure to CO and mortality consistently found null or 

3 negative mortality risk estimates. No such studies were discussed in the 2000 CO AQCD. The re-analysis 

4 of the ACS data (Pope et al., 1995) by Jerrett et al. (2003) found no association between long-term 

5 exposure to CO and mortality. Similar results were obtained in an updated analysis conducted by Pope 

6 et al. (2002) when using earlier (1980) CO data, but negative associations were found when using more 

7 recent (1982-1998) data. The Women’s Health Initiative (WHI) Study also found no association between 

8 CO and CVD events (including mortality) using the data from recent years (1994-1998) (Miller et al., 

9 2007), while the series of Veterans Cohort studies found no association or a negative association between 

10 mean annual 95th percentile of hourly CO values and mortality (Lipfert et al., 2006a; Lipfert et al., 

11 2006b). One additional study was identified that used a cross-sectional study design, Lipfert and Morris 

12 (2002), which reported results for a study of U.S. counties that are generally consistent with the cohort 

studies: positive associations between long-term exposure to PM2.5 and SO4

2- 13 and mortality, and generally 

14 negative associations with CO. Overall, the consistent null and negative associations observed across 

15 epidemiologic studies which included cohort populations encompassing potentially susceptible 

16 subpopulations (i.e., post-menopausal women and hypertensive men) combined with the lack of evidence 

17 for respiratory and cardiovascular morbidity outcomes following long-term exposure to CO; and the 

18 absence of a proposed mechanism to explain the progression to mortality following long-term exposure to 

19 CO provide supportive evidence that is suggestive of no causal relationship between long-term 

20 exposure to CO and mortality. 

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5.7. Public Health Impacts 

This section addresses several issues relating to the broader public health impact from exposure to 

22 ambient CO through a discussion on: (1) the shape of the concentration-response (C-R) relationship for 

23 CO, which is based primarily on controlled human exposure studies; and (2) the identification of 

24 subpopulations which may experience increased risks from CO exposures, through either enhanced 

25 susceptibility (e.g., as a result of pre-existing disease, genetic factors, age) and/or vulnerability associated 

26 with external factors or differential exposure (e.g., altitude, close proximity to sources, activity patterns). 

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5.7.1. Concentration-Response Relationship 

 Currently, very limited information is available in the human clinical and epidemiologic literature 

2 regarding the CO C-R relationship and the potential presence of a CO threshold. Two human clinical 

3 studies described in the 2000 CO AQCD had evaluated the C-R relationship between CO and onset of 

4 exercise-induced angina among individuals with CAD, but at the high end of CO concentrations (i.e., CO 

5 levels above the current NAAQS). Anderson et al. (1973) exposed 10 adult men with stable angina for 4 h 

6 to CO concentrations of 50 and 100 ppm, which resulted in average COHb levels of 2.9% and 4.5%, 

7 respectively. Both exposures significantly decreased the time to onset of exercise-induced angina relative 

8 to room air control (1.6% COHb). However, there was no difference in response between the two 

9 exposure concentrations of CO. In a much larger study, 63 adults with stable angina were exposed for 1 h 

10 to two concentrations of CO (average exposure concentrations of 117 and 253 ppm) resulting in average 

11 pre-exercise COHb levels of 2.4% and 4.7% (Allred et al., 1989). Relative to control (average COHb 

12 0.7%), COHb levels of 2.4% and 4.7% were observed to decrease the time to onset of angina by 4.2% 

13 (p = 0.054) and 7.1% (p = 0.004), respectively. In addition, these investigators reported a statistically 

14 significant decrease in the time to exercise-induced ST-segment depression with increasing COHb levels. 

15 These findings provide some evidence of a significant C-R relationship at COHb concentrations between 

16 2.4 and 4.7%. However, the human clinical literature has yet to evaluate the C-R relationship at lower CO 

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One study in the epidemiologic literature attempted to examine the C-R relationship at the low end 

of CO concentrations through a threshold analysis. Samoli et al. (2007) in their examination of the 

association between short-term exposure to CO and mortality conducted an ancillary analysis to examine 

the potential presence of a CO threshold. In this analysis the authors compared city-specific models to the 

threshold model, which consisted of thresholds at 0.5 mg/m3

 (0.43 ppm) increments. Samoli et al. (2007) 

then computed the deviance between the two models and summed the deviances for a given threshold 

over all cities. While the minimum deviance suggested a potential threshold of 0.43 ppm (the lowest 

threshold examined), the comparison with the linear no-threshold model indicated weak evidence 

(p-value >0.9) for a threshold. However, determining the presence of a threshold at the very low range of 

CO concentrations (i.e., at 0.43 ppm) in this data set is challenging, because, in seven of the 19 European 

cities examined, the lowest 10% of the CO distribution was at or above 2 mg/m3

 (1.74 ppm). By only 

using the 12 cities in the analysis that had minimum CO concentrations approaching 0.5 mg/m3

(0.43 ppm), a limited number of observations were examined around the threshold of interest, which 

subsequently contributed to the inability to draw conclusions regarding the potential presence of a 

threshold with any certainty. 

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5.7.2. Potentially Susceptible and Vulnerable Populations 

Interindividual variation in human responses to air pollutants indicates that not all individuals 

2 exposed to pollutants respond similarly. That is, some subpopulations are at increased risk to the 

3 detrimental effects of pollutant exposure. The NAAQS are intended to provide an adequate margin of 

4 safety for both general populations and susceptible and vulnerable subpopulations, or those subgroups 

5 potentially at increased risk for ambient air pollution health effects. For the purposes of this document, a 

6 susceptible subpopulation is defined as one with an intrinsic biological characteristic (e.g., disease) that 

7 might result in an adverse health effect at pollutant concentrations below those needed to elicit the same 

8 response in the general population, or result in a more adverse effect at the same concentration. A 

9 vulnerable subpopulation is one with a non-biological characteristic (e.g., differential exposure, living at 

10 altitude) that results in increased incidence of health effects from an ambient pollutant relative to the 

11 general population. The previous review of the CO NAAQS identified certain groups within the 

12 population that may be more susceptible to the effects of CO exposure, including individuals (particularly 

13 older adults) with CAD and other vascular diseases, anemia patients, patients with obstructive lung 

14 disease, and infants. Other subgroups considered to be somewhat vulnerable in the previous review 

15 include individuals living at altitude and those using certain medications that can increase endogenous CO 

16 production. Tables 5-18 and 5-19 provide an overview of the characteristics that contribute to susceptible 

17 and vulnerable subpopulations, respectively, which have been observed in the examination of the NAAQS 

18 for all criteria pollutants. Those characteristics of susceptible/vulnerable subpopulations exposed 

19 specifically to CO, as mentioned in the literature that encompasses this ISA, are discussed below. 

1 

Table 5-18. Characteristics of susceptible subpopulations. 

Susceptibility Characteristics1

Pre-existing disease: Cardiovascular diseases, Anemia, Obesity, Diabetes, Respiratory diseases (e.g., asthma, obstructive lung disease) 

Age: Children, Older Adults (65+) 

Infants: Premature, Low Birth Weight 

Gender 

Pregnancy 

Birth Defects 

Race/Ethnicity 

Genetic Factors 

Nutritional status 

1 Susceptible (i.e., intrinsic) refers to biological characteristics of an individual, which can include life stage, genetics, and pre-existing disease. 

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Table 5-19. Characteristics of vulnerable subpopulations. 

Vulnerability Characteristics1

Altitude 

Level of Exercise 

Proximity to Roadways 

Medication Use 

Geographic Location 

Work Environment (e.g., outdoor workers) 

Air Conditioning Use 

SES 

Education Level 

Nutritional status 

1 Vulnerable (i.e., extrinsic) refers to non-biological variables associated with an individual that can result in a health effect. 

5.7.2.1. Susceptibility Characteristics 

Cardiovascular Disease 

Individuals with heart disease may be at a greater risk from CO exposure since they may already 

have compromised O2 delivery. CO is notable among air pollutants because it is especially harmful in 

individuals with impaired cardiovascular systems. Persons with a normal cardiovascular system can 

tolerate substantial concentrations of CO, if they vasodilate in response to the hypoxemia produced by 

CO. In contrast, individuals unable to vasodilate in response to CO exposure may show evidence of 

ischemia at low concentrations of COHb. Many of the controlled human exposure studies have focused 

on individuals with IHD. 

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An estimated 81 million American adults (1 in 3) have one or more type of cardiovascular disease 

(CVD), with an estimated 47% of these being 60 or more years of age. CVD is the leading cause of death 

in the U.S. with nearly 2,400 deaths each day-an average of one death every 37 seconds (Rosamond et al., 

2008). For the major diseases within the category of total CVD, about 73 million Americans have high 

BP, 16 million have CHD, 5 million have heart failure, 5 million have stroke, and the estimated 

prevalence of congenital cardiovascular defects is estimated to be between 650,000 to 1.3 million 

(Rosamond et al., 2008). In the U.S., IHD is the largest major killer, causing 1 in 5 deaths. Because the 

numbers of affected people are so high, even relatively small percent increases in cardiovascular mortality 

or morbidity in the population could have a large impact on public health. 

Each year in the U.S. approximately 780,000 people experience a new or recurrent stroke, with the 

majority of these being a first stroke (77%). On average this equates to someone in the U.S. having a 

stroke every 40 seconds with a death occurring every 3 to 4 minutes. When considered separately from 

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other CVDs, stroke ranks third among all causes of death, behind diseases of the heart and cancer 

(Rosamond et al., 2008). While epidemiologic evidence is weak regarding associations between ambient 

CO and stroke, diseased individuals could exhibit increased sensitivity to CO exposure. 

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Obstructive Lung Disease 

Patients with obstructive lung disease, such as COPD, were identified as a susceptible 

subpopulation in the 2000 CO AQCD. COPD is a progressive disease resulting in decreased air flow to 

the lungs, and is especially prevalent among smokers. A majority of COPD patients have exercise 

limitations as demonstrated by a decrease in O2 saturation during mild to moderate exercise. This makes 

individuals with hypoxia resulting from COPD particularly sensitive to CO during submaximal exercise 

typical of normal daily activity. COPD patients who are smokers may have baseline COHb levels of 

4-8%, leaving little reserve for increases in COHb due to ambient exposure. COPD is often accompanied 

by a number of changes in gas exchange, including increased VD and V/Q inequality (Marthan et al., 

1985), which could slow both CO uptake and elimination. Patients with pulmonary sarcoidosis may have 

a decrease in lung volumes, a loss of DLCO, and gas exchange abnormalities during exercise, including 

decreased PaO2 and increased alveolar-arterial oxygen pressure difference (Lamberto et al., 2004). A 

controlled human exposure study on 19 individuals with COPD (Bathoorn et al., 2007) found that two of 

the patients experienced COPD exacerbation during or following CO exposure at 100-125 ppm for 2 h, 

although a slight anti-inflammatory effect was also observed. The few epidemiologic studies that 

evaluated the relationship between ambient CO and increased hospital admissions or ED visits for COPD 

show weak positive associations. Epidemiologic results were similar for asthmatics, who can also 

experience exercise-induced airflow limitation. 

Anemia 

Health status can influence the toxicity involved with CO exposure by influencing the severity of 

hypoxia resulting from CO exposure. Any condition that would alter the blood O2 carrying capacity or 

content will result in a greater risk from COHb induced hypoxia and decreased tissue O2 delivery. The 

severity of this effect depends upon the initial level of hypoxia. Anemias are a group of diseases that 

result in insufficient blood O2 or hypoxia due to Hb deficiency through hemolysis, hemorrhage, or 

reduced hematopoiesis. Anemia may result from pathologic conditions characterized by chronic 

inflammation such as malignant tumors or chronic infections (Cavallin-Stahl et al., 1976a, b). The bodies 

of people with anemia compensate causing cardiac output to increase as both heart rate and stroke volume 

increase. The endogenous production of CO, thus COHb, is increased in patients with hemolytic anemia 

due to increased heme catabolism, causing an increased baseline COHb concentration. One of the most 

prevalent anemias arises from a single-point mutation of Hb, causing sickle cell diseases. The Hb affinity 

for O2 and O2 carrying capacity is reduced causing a shift to the right in the O2 dissociation curve. It is 

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well documented that African-American populations have a higher incidence of sickle cell anemia, which 

may be a risk factor for CO hypoxia. 

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Any disturbance in RBC hemostasis by acceleration of destruction of hemoproteins will lead to 

increased production of CO. Pathologic conditions such as anemias, hematomas, thalassemia, Gilbert’s 

syndrome with hemolysis, and other hematological diseases and illness will accelerate CO production 

(Berk et al., 1974; Hampson, 2007; Meyer et al., 1998; Solanki et al., 1988). Patients with hemolytic 

anemia exhibit COHb levels 2- to 3-times higher than healthy individuals and CO production rates 2- to 

8-times higher (Coburn et al., 1966). 

Age and Gender 

Age and gender alter the variables that influence the uptake, distribution, and elimination of CO. 

COHb levels decline more rapidly in young children than adults after CO exposure (Joumard et al., 1981; 

Klasner et al., 1998). After infancy, the COHb half-life increases with age, practically doubling between 2 

and 70 years (Joumard et al., 1981). The rate of this decrease in CO elimination is very rapid in the 

growing years (2 to 16 years of age), but slows beyond adolescence. Alveolar volume and DLCO 

increased with increasing body length of infants and toddlers (Castillo et al., 2006), suggesting a further 

degree of lung development and faster CO uptake. After infancy, increasing age decreases DLCO and 

increases VA/Q mismatch, causing it to take longer to both load and eliminate CO from the blood (Neas 

and Schwartz, 1996). 

COHb concentrations are generally lower in female subjects than in male subjects (Horvath et al., 

1988) and the COHb half-life is longer in healthy men than in women of the same age, which may be 

partially explained by differences in muscle mass or the slight correlation between COHb half-life and 

increased height (Joumard et al., 1981). The rate of decline of DLCO with age is lower in middle-aged 

women than in men; however, it evens out towards older age (Neas and Schwartz, 1996). Women also 

tended to be more resistant to altitude hypoxia (Horvath et al., 1988). 

Newborns and Young Infants 

Fetal CO pharmacokinetics do not follow the same kinetics as maternal CO exposure, making it 

difficult to estimate fetal COHb based on maternal levels. Human fetal Hb has a higher affinity for CO 

than adult Hb (Di Cera et al., 1989). Maternal and fetal COHb concentrations have been modeled as a 

function of time using a modified CFK equation (Hill et al., 1977). At steady-state conditions, the fetal 

COHb is up to 10% higher than the maternal COHb levels, for example, exposure to 30 ppm CO results 

in a maternal COHb of 5% and a fetal COHb of 5.5%. The fetal CO uptake lags behind the maternal for 

the first few hours but later may overtake the maternal values. Similarly, during washout, the fetal COHb 

levels are maintained for longer, with a half-life of around 7.5 hours versus the maternal half-life of 

around 4 hours. In addition, women experience fluctuating COHb levels during pregnancy as well as 

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through the menstrual cycle when endogenous CO production doubles in the progesterone phase 

(Delivoria-Papadopoulos et al., 1974; Mercke and Lundh, 1976). 

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Epidemiologic studies of birth outcomes have examined well-established clinical metrics of infant 

health. Preterm birth (PTB, <37 weeks gestation) and low birth weight (LBW, birth weight <2,500 g) 

have been established as strong predictors of infant mortality and morbidity (Barker et al., 2002; 

Berkowitz and Papiernik, 1993; Li et al., 2003; McIntire et al., 1999). In 2004, 36.5 percent of all infant 

deaths in the U.S. were preterm-related (MacDorman et al., 2007). Vital statistics for the year 2005 in the 

U.S. showed that the rate for PTB was 12.7%, which has risen 20% since 1990, and the rate for LBW was 

8.2%, which has risen 17% since 1990 (Martin et al.). 

Congenital anomalies remain the leading cause of infant death in the U.S. (Martin et al.). In 2004, 

for every 1,000 live births in the U.S. the infant (up to 1 year of age) mortality rate was 6.8 deaths, while 

the neonatal (under 28 days of age) mortality rate was 4.5 deaths, and the postneonatal (between 28 days 

and 1 year of age) mortality rate was 2.4 deaths (National Center for Health Statistics, 2007). From 1968 

to 1995, the proportion of infant mortality attributable to birth defects increased from 14.5% to 22.2 % 

(Centers for Disease Control and Prevention, 1998). Limited epidemiologic research has been conducted 

on congenital anomalies because of their rare occurrence, which makes it difficult to detect small effects 

within defined exposure periods. For example, in 2005 the rates for spina bifida and anencephaly in the 

U.S. were 18.0 and 11.3 per 100,000 births respectively (Martin et al., 2007) and as a result of folic acid 

fortification the prevalence of these defects declined considerably from 1995 to 2002 (Williams et al., 

2005). Survival rate among infants with spina bifida had also improved due to folic acid fortification (Bol 

et al., 2006). The rate of cleft lip/palate is higher at approximately 79.1 per 100,000 births (Martin et al., 

2007). 

The rate of cardiovascular defects is much higher. Data from the Metropolitan Atlanta Congenital 

Defects Program (MACDP), which is one of the most comprehensive birth defect registries in the U.S., 

showed that the prevalence of congenital heart defects had increased between 1968 and 1997. During 

1995-1997 the rate was 90.2 per 10,000 births (0.9%) and this had increased from 58.7 per 10,000 births 

since 1986-1972 (Botto et al., 2001). Cardiovascular defects are the single largest contributor to infant 

mortality attributable to birth defects (Centers for Disease Control and Prevention, 1998). Between 1979 

and 1997, 1 in 10 infant deaths (9.8%) was associated with a congenital heart defect, and 1 in 13 infant 

deaths (7.4%) was due to a congenital heart defect (Boneva et al., 2001). 

5.7.2.2. Vulnerability Characteristics 

Altitude

Increased altitude changes a number of factors that contribute to the uptake and elimination of CO. 

The relationship between altitude and CO exposure has been discussed in depth in the 2000 CO AQCD 

March 2009 5-144 DRAFT – DO NOT CITE OR QUOTE 
and other documents (U.S. EPA, 1978). In an effort to maintain proper O2 transport and supply, 

physiological changes occur as compensatory mechanisms to combat the decreased barometric pressure 

and resulting altitude induced HH (HH). HH, unlike CO hypoxia, causes humans to hyperventilate, which 

reduces arterial blood CO2 (hypocapnia) and increases alveolar partial pressure of O2. Hypocapnia will 

lead to difficulty of O2 dissociation and decreased blood flow, thus reducing tissue O2 supply. HH 

increases BP and cardiac output and leads to redistribution of blood from skin to organs and from blood to 

extravascular compartments. Generally these changes will favor increased CO uptake and COHb 

formation, as well as CO elimination. In hypoxic conditions both CO and O2 bind reduced Hb through a 

competitive-parallel reaction (Chakraborty et al., 2004). Breathing CO (9 ppm) at rest at altitude produced 

higher COHb compared to sea level (McGrath et al., 1993), whereas high altitude exposure with exercise 

caused a decrease in COHb levels versus similar exposure at sea level (Horvath et al., 1988). This 

decrease could be a shift in CO storage or suppression of COHb formation, or both. Altitude also 

increases the baseline COHb levels by inducing endogenous CO production. Early HH increased lung 

HO-1 protein and activity, whereas chronic HH induced endogenous CO production in nonpulmonary 

sites (see Section 4.5) (Carraway et al., 2000). 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

24 

25 

26 

27 

28 

29 

30 

31 

32 

33 

34 

As the length of stay increases at high altitude, acclimatization occurs, inducing hyperventilation, 

polycythemia or increased red blood cell count, and increased tissue capillarity and Mb content in skeletal 

muscle, which could also favor increased CO uptake. Most of the initial adaptive changes gradually revert 

to sea level values. However, differences in people raised at high altitude persist even after 

reacclimatization to sea level (Hsia, 2002). 

Altitude has been shown to be positively associated with baseline COHb concentrations (McGrath, 

1992; McGrath et al., 1993). This increase in COHb with altitude induced hypoxia has also been 

associated with increases in the mRNA, protein, and activity of HO-1 in rats and cells leading to 

enhanced endogenous CO production (Carraway et al., 2002; Chin et al., 2007). Whether other variables 

(such as an accelerated metabolism or a greater pool of Hb, transient shifts in body stores, or a change in 

the elimination rate of CO) play a role has not been explored. 

Activity Patterns 

Exercise is an important determinant of CO kinetics and toxicity due to the extensive increase in 

gas exchange. O2 consumption can increase more than 10 fold during exercise. Similarly, ventilation, 

membrane and lung diffusing capacity, pulmonary capillary blood volume, and cardiac output increase 

proportional to work load. The majority of these changes facilitate CO uptake and transport, by increasing 

gas exchange efficiency. 

The COHb elimination rate decreases with physical activity (Joumard et al., 1981). Healthy 

subjects exposed to CO and achieving COHb levels of approximately 4-5% observed a significant 

detriment to exercise duration and maximal effort capability (measured by metabolic equivalent units) 

March 2009 5-145 DRAFT – DO NOT CITE OR QUOTE 
(Adir et al., 1999). It is possible that CO lowers the anaerobic threshold, allowing earlier fatigue of the 

skeletal muscles and decreased maximal effort capability. 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

Proximity to Roadways 

Individuals that spend a substantial amount of time on or near heavily traveled roadways, such as 

commuters and those living or working near freeways, are likely to experience elevated CO 

concentrations, as discussed in Chapter 3, and therefore constitute a potentially vulnerable subpopulation 

due to differential exposure. Studies of commuters have shown that commuting time is an important 

predictor of CO exposure for those traveling by car, cycling, and walking, and that on-road CO 

concentrations are typically two to five times higher than concentrations measured at the roadside. 

Medication Use 

Not all endogenous CO production is derived from Hb breakdown. Other hemoproteins, such as 

Mb, cytochromes, peroxidases, and catalase, contribute 20-25% to the total amount of endogenous CO 

(Berk et al., 1976). All of these sources result in a blood COHb concentration between 0.4 and 0.7% 

(Coburn et al., 1965). This baseline level of endogenous production can be altered by drugs or a number 

of physiological conditions that alter RBC destruction, other hemoprotein breakdown, or bilirubin 

production. Nicotinic acid (Lundh et al., 1975), allyl-containing compounds (acetamids and barbiturates) 

(Mercke et al., 1975b), diphenylhydantoin (Coburn, 1970b), progesterone (Delivoria-Papadopoulos et al., 

1974), and contraceptives (Mercke et al., 1975a) will increase CO production. Compounds such as carbon 

disulfide and sulfur-containing chemicals (parathion and phenyltiourea) will increase CO by acting on 

P450 system moieties (Landaw et al., 1970). The P450 system may also cause large increases in CO 

produced from the metabolic degradation of dihalomethanes leading to very high (>10%) COHb levels 

(Manno et al., 1992), which can be further enhanced by prior exposure to HCs or ethanol (Pankow et al., 

1991; Wirkner and Poelchen, 1996). HO can catalyze the release of CO from the auto-oxidation of 

phenols, photo-oxidation of organic compounds, and lipid peroxidation of cell membrane lipids (Rodgers 

et al., 1994). 

March 2009 5-146 DRAFT – DO NOT CITE OR QUOTE 
Annex A. Atmospheric Science 

YukonKoyukuk

Alaska State Emissions 2002

0

40

1,698

4,256

6,060

27,954

12,746

84,990

8,403,218

145,340

0 12,000,000

Solvent Use

Miscellaneous

Waste Disposal

Industrial Processes

Electricity Generation

Fossil Fuel Combustion

Residential Wood Combustion

Non-Road Equipment

Fires

On-Road Vehicles

Emissions (Tons)

Yukon-Koyukuk County Emissions 2002

0

0

52

0

0

8

398

3,192

8,095,363

2,494

0 12,000,000

Solvent Use

Miscellaneous

Waste Disposal

Industrial Processes

Electricity Generation

Fossil Fuel Combustion

Residential Wood Combustion

Non-Road Equipment

Fires

On-Road Vehicles

Emissions (Tons)

Figure A-1. CO emissions density map and distribution for the state of Alaska and for YukonKoyukuk County in Alaska. 

March 2009 A-1 DRAFT—DO NOT QUOTE OR CITE 
Weber

Utah

Grand

Utah State Emissions 2002

17

277

152

42,466

4,234

32,365

12,031

203,475

257,199

768,164

0 1,000,000

Solvent Use

Miscellaneous

Waste Disposal

Industrial Processes

Electricity Generation

Fossil Fuel Combustion

Residential Wood Combustion

Non-Road Equipment

Fires

On-Road Vehicles

Emissions (Tons)

Weber County Emissions 2002

3

24

0

2,079

56

497

1,046

12,491

916

46,841

0 150,000

Solvent Use

Miscellaneous

Waste Disposal

Industrial Processes

Electricity Generation

Fossil Fuel Combustion

Residential Wood Combustion

Non-Road Equipment

Fires

On-Road Vehicles

Emissions (Tons)

Utah County Emissions 2002

0

44

1

1,346

1

1,919

2,048

20,195

2,439

89,535

0 150,000

Solvent Use

Miscellaneous

Waste Disposal

Industrial Processes

Electricity Generation

Fossil Fuel Combustion

Residential Wood Combustion

Non-Road Equipment

Fires

On-Road Vehicles

Emissions (Tons)

Grand County Emissions 2002

0

2

1

32

0

298

44

1,771

123,963

13,242

0 150,000

Solvent Use

Miscellaneous

Waste Disposal

Industrial Processes

Electricity Generation

Fossil Fuel Combustion

Residential Wood Combustion

Non-Road Equipment

Fires

On-Road Vehicles

Emissions (Tons)

Figure A-2. CO emissions density map and distribution for the state of Utah and for selected 

counties in Utah. 

March 2009 A-2 DRAFT—DO NOT QUOTE OR CITE 
Suffolk

Middlesex

Norfolk

Massachusetts State Emissions 2002

77

482

3,877

1,948

11,569

28,111

104,462

490,378

0

926,079

0 1,200,000

Solvent Use

Miscellaneous

Waste Disposal

Industrial Processes

Electricity Generation

Fossil Fuel Combustion

Residential Wood Combustion

Non-Road Equipment

Fires

On-Road Vehicles

Emissions (Tons)

Middlesex County Emissions 2002

7

77

323

287

492

7,063

492

101,247

0

214,556

0 260,000

Solvent Use

Miscellaneous

Waste Disposal

Industrial Processes

Electricity Generation

Fossil Fuel Combustion

Residential Wood Combustion

Non-Road Equipment

Fires

On-Road Vehicles

Emissions (Tons)

Norfolk County Emissions 2002

8

24

285

208

189

2,728

8,803

44,873

0

154,098

0 260,000

Solvent Use

Miscellaneous

Waste Disposal

Industrial Processes

Electricity Generation

Fossil Fuel Combustion

Residential Wood Combustion

Non-Road Equipment

Fires

On-Road Vehicles

Emissions (Tons)

Suffolk County Emissions 2002

0

110

1

143

225

1,689

2,326

63,447

0

64,900

0 260,000

Solvent Use

Miscellaneous

Waste Disposal

Industrial Processes

Electricity Generation

Fossil Fuel Combustion

Residential Wood Combustion

Non-Road Equipment

Fires

On-Road Vehicles

Emissions (Tons)

Figure A-3. CO emissions density map and distribution for the state of Massachusetts and for 

selected counties in Massachusetts. 

March 2009 A-3 DRAFT—DO NOT QUOTE OR CITE 
Fulton

Dekalb

Chatham

Liberty

Glynn

Georgia State Emissions 2002

16

500

175,000

49,802

9,824

54,857

47,461

720,812

53,412

2,604,609

0 3,500,000

Solvent Use

Miscellaneous

Waste Disposal

Industrial Processes

Electricity Generation

Fossil Fuel Combustion

Residential Wood Combustion

Non-Road Equipment

Fires

On-Road Vehicles

Emissions (Tons)

Fulton County Emissions 2002

0

92

279

275

0

2,850

2,262

80,197

0

223,633

0 300,000

Solvent Use

Miscellaneous

Waste Disposal

Industrial Processes

Electricity Generation

Fossil Fuel Combustion

Residential Wood Combustion

Non-Road Equipment

Fires

On-Road Vehicles

Emissions (Tons)

Dekalb County Emissions 2002

7

5

254

104

0

1,907

2,010

68,502

0

142,648

0 300,000

Solvent Use

Miscellaneous

Waste Disposal

Industrial Processes

Electricity Generation

Fossil Fuel Combustion

Residential Wood Combustion

Non-Road Equipment

Fires

On-Road Vehicles

Emissions (Tons)

Figure A-4a. CO emissions density map and distribution for the state of Georgia and for selected 

counties in Georgia (1 of 2). 

March 2009 A-4 DRAFT—DO NOT QUOTE OR CITE 
Liberty County Emissions 2002

0

4

55,427

385

0

234

75

4,832

0

19,711

0 300,000

Solvent Use

Miscellaneous

Waste Disposal

Industrial Processes

Electricity Generation

Fossil Fuel Combustion

Residential Wood Combustion

Non-Road Equipment

Fires

On-Road Vehicles

Emissions (Tons) ` 

Chatham County Emissions 2002

0

15

73

1,849

189

2,388

414

25,904

0

67,821

0 300,000

Solvent Use

Miscellaneous

Waste Disposal

Industrial Processes

Electricity Generation

Fossil Fuel Combustion

Residential Wood Combustion

Non-Road Equipment

Fires

On-Road Vehicles

Emissions (Tons)

Glynn County Emissions 2002

0

4

1,129

6,619

377

3,056

134

15,467

0

27,024

0 3

Solvent Use

Miscellaneous

Waste Disposal

Industrial Processes

Electricity Generation

Fossil Fuel Combustion

00,000

Residential Wood Combustion

Non-Road Equipment

Fires

On-Road Vehicles

Emissions (Tons)

Figure A-4b. CO emissions distribution for selected counties in Georgia (2 of 2). 

March 2009 A-5 DRAFT—DO NOT QUOTE OR CITE 
Los 

Angeles

San 

Diego

Orange

California State Emissions 2002

379

3,548

22,308

34,908

28,471

98,990

287,944

1,191,454

733,553

3,436,059

0 4,500,000

Solvent Use

Miscellaneous

Waste Disposal

Industrial Processes

Electricity Generation

Fossil Fuel Combustion

Residential Wood Combustion

Non-Road Equipment

Fires

On-Road Vehicles

Emissions (Tons)

Los Angeles County Emissions 2002

27

674

143

6,960

9,814

10,795

7,220

226,349

62,576

824,676

0 1,000,000

Solvent Use

Miscellaneous

Waste Disposal

Industrial Processes

Electricity Generation

Fossil Fuel Combustion

Residential Wood Combustion

Non-Road Equipment

Fires

On-Road Vehicles

Emissions (Tons)

San Diego County Emissions 2002

0

226

162

231

895

14,855

20,821

101,515

62,192

283,597

0 1,000,000

Solvent Use

Miscellaneous

Waste Disposal

Industrial Processes

Electricity Generation

Fossil Fuel Combustion

Residential Wood Combustion

Non-Road Equipment

Fires

On-Road Vehicles

Emissions (Tons)

Orange County Emissions 2002

8

184

107

134

655

2,871

1,660

105,638

978

220,477

0 1,000,000

Solvent Use

Miscellaneous

Waste Disposal

Industrial Processes

Electricity Generation

Fossil Fuel Combustion

Residential Wood Combustion

Non-Road Equipment

Fires

On-Road Vehicles

Emissions (Tons)

Figure A-5. CO emissions density map and distribution for the state of California and for 

selected counties in California. 

March 2009 A-6 DRAFT—DO NOT QUOTE OR CITE 
Jefferson

Alabama State Emissions 2002

228

539

135,164

106,529

11,212

76,631

60,654

375,495

277,465

1,275,969

0 1,600,000

Solvent Use

Miscellaneous

Waste Disposal

Industrial Processes

Electricity Generation

Fossil Fuel Combustion

Residential Wood Combustion

Non-Road Equipment

Fires

On-Road Vehicles

Emissions (Tons)

Jefferson County Emissions 2002

14

115

2,645

40,891

3,360

39,488

1,344

54,092

34

193,274

0 250,000

Solvent Use

Miscellaneous

Waste Disposal

Industrial Processes

Electricity Generation

Fossil Fuel Combustion

Residential Wood Combustion

Non-Road Equipment

Fires

On-Road Vehicles

Emissions (Tons)

Figure A-6. CO emissions density map and distribution for the state of Alabama and for 

Jefferson County in Alabama. 

March 2009 A-7 DRAFT—DO NOT QUOTE OR CITE 
Figure A-7. Map of CO monitor locations with respect to population density in the Anchorage 

CBSA, total population. 

Figure A-8. Map of CO monitor locations with respect to population density in the Anchorage 

CBSA, ages 65 and older. 

March 2009 A-8 DRAFT—DO NOT QUOTE OR CITE 
Figure A-9. Map of CO monitor locations with respect to population density in the Atlanta CSA, 

total population. 

Figure A-10. Map of CO monitor locations with respect to population density in the Atlanta CSA, 

ages 65 and older. 

March 2009 A-9 DRAFT—DO NOT QUOTE OR CITE 
Figure A-11. Map of CO monitor locations with respect to population density in the Boston CSA, 

total population. 

Figure A-12. Map of CO monitor locations with respect to population density in the Boston CSA, 

ages 65 and older. 

March 2009 A-10 DRAFT—DO NOT QUOTE OR CITE 
Figure A-13. Map of CO monitor locations with respect to population density in the Denver CSA, 

total population. 

Figure A-14. Map of CO monitor locations with respect to population density in the Denver CSA, 

ages 65 and older. 

March 2009 A-11 DRAFT—DO NOT QUOTE OR CITE 
Figure A-15. Map of CO monitor locations with respect to population density in the Houston CSA, 

total population. 

Figure A-16. Map of CO monitor locations with respect to population density in the Houston CSA, 

ages 65 and older. 

March 2009 A-12 DRAFT—DO NOT QUOTE OR CITE 
Figure A-17. Map of CO monitor locations with respect to population density in the Los Angeles 

CSA, total population. 

Figure A-18. Map of CO monitor locations with respect to population density in the Los Angeles 

CSA, ages 65 and older. 

March 2009 A-13 DRAFT—DO NOT QUOTE OR CITE 
Figure A-19. Map of CO monitor locations with respect to population density in the New York City 

CSA, total population. 

Figure A-20. Map of CO monitor locations with respect to population density in the New York City 

CSA, ages 65 and older. 

March 2009 A-14 DRAFT—DO NOT QUOTE OR CITE 
Figure A-21. Map of CO monitor locations with respect to population density in the Seattle CSA, 

total population. 

Figure A-22. Map of CO monitor locations with respect to population density in the Seattle CSA, 

ages 65 and older. 

March 2009 A-15 DRAFT—DO NOT QUOTE OR CITE 
Figure A-23. Map of CO monitor locations with respect to population density in the St. Louis 

CSA, total population. 

Figure A-24. Map of CO monitor locations with respect to population density in the St. Louis 

CSA, ages 65 and older. 

March 2009 A-16 DRAFT—DO NOT QUOTE OR CITE 
Figure A-25. Map of CO monitor locations with AQS Site IDs for Anchorage, AK. 

Table A-1. Table of inter-sampler comparison statistics, including Pearson r, P90 (ppm), COD, 

and d (km), as defined in the text, for each pair of hourly CO monitors reporting to 

AQS in Anchorage, AK. 

Anchorage 

A B 

A 1.00 0.73 

0.0 1.1 

0.00 0.32 

0.0 9.0 

Legend 

B r 1.00 

P90 0.0 

COD 0.00 

d 0.0 

March 2009 A-17 DRAFT—DO NOT QUOTE OR CITE 
A B 

Mean 1.04 1.10 

Obs 12969 12703 

SD 0.94 1.04 

Figure A-26. Box plots illustrating the seasonal distribution of hourly CO concentrations in 

Anchorage, AK. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis. 

March 2009 A-18 DRAFT—DO NOT QUOTE OR CITE 
Figure A-27. Map of CO monitor locations with AQS Site IDs for Atlanta, GA. 

Table A-2. Table of inter-sampler comparison statistics, including Pearson r, P90 (ppm), COD, 

and d (km), as defined in the text, for each pair of hourly CO monitors reporting to 

AQS in Atlanta, GA. 

Atlanta 

A B C 

A 1.00 0.60 0.12 

0.0 0.5 0.7 

0.00 0.27 0.37 

0.0 22.5 74.7 

B 1.00 0.10 

0.0 0.7 

0.00 0.38 

 Legend 0.0 61.7 

C r 1.00 

P90 0.0 

COD 0.00 

d 0.0 

March 2009 A-19 DRAFT—DO NOT QUOTE OR CITE 
 A B C 

Mean 0.53 0.58 0.30 

Obs 25531 25440 25712 

SD 0.35 0.30 0.13 

Figure A-28. Box plots illustrating the seasonal distribution of hourly CO concentrations in 

Atlanta, GA. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis. 

March 2009 A-20 DRAFT—DO NOT QUOTE OR CITE 
Figure A-29. Map of CO monitor locations with AQS Site IDs for Boston, MA. 

March 2009 A-21 DRAFT—DO NOT QUOTE OR CITE 
Table A-3. Table of inter-sampler comparison statistics, including Pearson r, P90 (ppm), COD, 

and d (km), as defined in the text, for each pair of hourly CO monitors reporting to 

AQS in Boston, MA. 

Boston 

A B C D E F G 

A 1.00 0.35 0.50 0.49 0.41 0.50 0.40 

0.0 0.4 0.4 0.5 0.4 0.6 0.4 

0.00 0.58 0.48 0.42 0.41 0.44 0.40 

0.0 37.2 39.7 57.9 41.3 18.3 89.1 

B 1.00 0.52 0.34 0.27 0.35 0.34 

0.0 0.4 0.6 0.5 0.7 0.4 

0.00 0.56 0.59 0.58 0.60 0.55 

 0.0 2.5 58.0 78.2 55.1 60.2 

C 1.00 0.37 0.26 0.38 0.36 

0.0 0.5 0.5 0.6 0.4 

0.00 0.45 0.45 0.46 0.47 

 0.0 58.9 80.7 57.5 58.7 

D 1.00 0.40 0.46 0.34 

0.0 0.4 0.5 0.5 

0.00 0.28 0.25 0.39 

 0.0 85.8 61.5 58.9 

E 1.00 0.49 0.29 

0.0 0.5 0.4 

0.00 0.30 0.37 

 0.0 26.1 128.6 

F 1.00 0.43 

0.0 0.6 

0.00 0.39 

 Legend 0.0 102.6 

G r 1.00 

P90 0.0 

COD 0.00 

d 0.0 

March 2009 A-22 DRAFT—DO NOT QUOTE OR CITE 
A B C D E F G 

Mean 0.33 0.26 0.36 0.53 0.45 0.60 0.34 

Obs 24362 24134 24260 24446 25197 25869 23707 

SD 0.22 0.24 0.26 0.23 0.27 0.37 0.22 

Figure A-30. Box plots illustrating the seasonal distribution of hourly CO concentrations in 

Boston, MA. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis. 

March 2009 A-23 DRAFT—DO NOT QUOTE OR CITE 
Figure A-31. Map of CO monitor locations with AQS Site IDs for Denver, CO. 

March 2009 A-24 DRAFT—DO NOT QUOTE OR CITE 
Table A-4. Table of inter-sampler comparison statistics, including Pearson r, P90 (ppm), COD, 

and d (km), as defined in the text, for each pair of hourly CO monitors reporting to 

AQS in Denver, CO. 

Denver 

A B C D E 

A 1.00 0.53 0.59 0.64 0.52 

0.0 0.6 0.6 0.5 0.6 

0.00 0.43 0.29 0.37 0.34 

0.0 38.5 10.1 10.9 68.2 

B 1.00 0.46 0.49 0.54 

0.0 0.7 0.7 0.6 

0.00 0.44 0.47 0.43 

 0.0 46.9 47.0 44.6 

C 1.00 0.76 0.45 

0.0 0.5 0.7 

0.00 0.34 0.36 

 0.0 1.3 78.3 

D 1.00 0.46 

0.0 0.7 

0.00 0.42 

Legend 0.0 79.0 

E r 1.00 

P90 0.0 

COD 0.00 

d 0.0 

March 2009 A-25 DRAFT—DO NOT QUOTE OR CITE 
A B C D E 

Mean 0.52 0.42 0.65 0.52 0.55 

Obs 25920 25559 25959 25552 26048 

SD 0.36 0.38 0.42 0.46 0.46 

Figure A-32. Box plots illustrating the seasonal distribution of hourly CO concentrations in 

Denver, CO. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis. 

March 2009 A-26 DRAFT—DO NOT QUOTE OR CITE 
Figure A-33. Map of CO monitor locations with AQS Site IDs for Houston, TX. 

March 2009 A-27 DRAFT—DO NOT QUOTE OR CITE 
Table A-5. Table of inter-sampler comparison statistics, including Pearson r, P90 (ppm), COD, 

and d (km), as defined in the text, for each pair of hourly CO monitors reporting to 

AQS in Houston, TX. 

Houston 

A B C D E 

A 1.00 0.72 0.45 0.56 0.68 

0.0 0.3 0.4 0.5 0.3 

0.00 0.29 0.47 0.73 0.24 

0.0 17.5 16.7 19.8 32.2 

B 1.00 0.56 0.65 0.63 

0.0 0.4 0.5 0.4 

0.00 0.47 0.73 0.29 

 0.0 16.3 25.2 39.7 

C 1.00 0.53 0.43 

0.0 0.5 0.4 

0.00 0.74 0.47 

 0.0 9.3 23.5 

D 1.00 0.57 

0.0 0.4 

0.00 0.72 

 Legend 0.0 14.5 

E r 1.00 

P90 0.0 

COD 0.00 

d 0.0 

March 2009 A-28 DRAFT—DO NOT QUOTE OR CITE 
A B C D E 

Mean 0.42 0.39 0.35 0.14 0.33 

Obs 23997 25241 24922 25285 24480 

SD 0.27 0.33 0.26 0.22 0.16 

Figure A-34. Box plots illustrating the seasonal distribution of hourly CO concentrations in 

Houston, TX. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis. 

March 2009 A-29 DRAFT—DO NOT QUOTE OR CITE 
Figure A-35. Map of CO monitor locations with AQS Site IDs for Los Angeles, CA. 

March 2009 A-30 DRAFT—DO NOT QUOTE OR CITE 
Table A-6. Table of inter-sampler comparison statistics, including Pearson r, P90 (ppm), COD, 

and d (km), as defined in the text, for each pair of hourly CO monitors reporting to 

AQS in Los Angeles, CA. 

Los Angeles

A B C D E F G H I J K L M N O P Q R S T U V W X 

A 1.00 0.45 0.43 0.46 0.44 0.21 0.57 0.41 0.17 0.19 0.43 0.27 0.29 0.14 0.42 0.34 0.54 0.33 0.47 0.56 0.17 0.24 0.61 0.48 

0.0 0.5 1.0 0.8 0.7 1.5 0.8 0.8 1.1 0.7 0.5 0.6 0.6 0.7 0.5 0.8 0.7 0.5 0.8 0.4 0.6 0.6 0.5 0.6 

0.00 0.38 0.35 0.38 0.33 0.44 0.33 0.43 0.47 0.72 0.47 0.62 0.43 0.64 0.52 0.33 0.33 0.36 0.43 0.38 0.62 0.50 0.30 0.35 

0 50.0 36.5 29.0 56.5 35.0 17.7 18.7 42.4 51.0 61.9 62.2 33.9 51.2 60.6 23.6 52.6 131.4 49.2 74.8 117.7 68.4 27.4 59.9 

B 1.00 0.69 0.71 0.64 0.50 0.63 0.49 0.43 0.59 0.36 0.42 0.59 0.47 0.57 0.59 0.57 0.35 0.55 0.55 0.25 0.41 0.50 0.53 

0.0 0.8 0.6 0.7 1.3 0.7 0.8 0.9 0.6 0.6 0.7 0.5 0.7 0.6 0.6 0.7 0.6 0.7 0.5 0.7 0.6 0.6 0.6 

0.00 0.38 0.38 0.34 0.50 0.41 0.49 0.48 0.69 0.50 0.60 0.39 0.59 0.50 0.37 0.42 0.38 0.46 0.39 0.62 0.50 0.40 0.41 

 0 18.9 21.2 17.8 26.4 65.0 31.6 35.2 11.4 37.4 74.9 53.7 64.4 85.9 48.5 98.1 178.1 96.1 112.0 161.1 115.3 76.4 109.1 

C 1.00 0.84 0.74 0.70 0.78 0.67 0.62 0.64 0.39 0.59 0.72 0.70 0.69 0.75 0.70 0.40 0.52 0.58 0.35 0.58 0.59 0.65 

0.0 0.5 0.6 0.9 0.5 0.7 0.8 1.0 1.1 1.1 0.7 0.9 1.0 0.6 0.6 1.2 0.8 1.0 1.2 1.0 0.7 0.7 

0.00 0.29 0.28 0.35 0.26 0.39 0.41 0.72 0.52 0.65 0.41 0.63 0.56 0.26 0.29 0.46 0.42 0.46 0.67 0.53 0.29 0.35 

 0 14.7 20.0 29.1 53.5 18.1 40.8 27.0 30.1 57.4 51.8 66.3 84.6 43.6 88.2 167.7 85.3 106.5 142.7 97.8 63.8 96.3 

D 1.00 0.64 0.74 0.77 0.67 0.60 0.59 0.34 0.53 0.70 0.58 0.63 0.74 0.62 0.32 0.49 0.53 0.34 0.51 0.57 0.58 

0.0 0.7 1.0 0.5 0.7 0.8 0.8 0.9 0.9 0.6 0.8 0.8 0.5 0.6 1.0 0.8 0.8 1.0 0.8 0.6 0.6 

0.00 0.34 0.41 0.33 0.44 0.45 0.73 0.52 0.63 0.40 0.63 0.55 0.31 0.37 0.46 0.45 0.46 0.66 0.53 0.36 0.39 

 0 31.8 15.3 43.8 11.7 27.1 22.7 44.7 67.5 37.3 51.6 70.2 29.8 77.4 157.4 75.1 93.4 143.4 95.9 55.2 87.9 

E 1.00 0.49 0.70 0.54 0.44 0.52 0.53 0.53 0.61 0.57 0.55 0.61 0.73 0.42 0.62 0.66 0.31 0.55 0.58 0.71 

0.0 1.2 0.6 0.8 0.9 0.9 0.8 0.9 0.7 0.8 0.8 0.7 0.6 0.9 0.7 0.8 1.0 0.8 0.6 0.6 

0.00 0.41 0.30 0.42 0.44 0.72 0.49 0.62 0.41 0.62 0.54 0.29 0.29 0.40 0.40 0.40 0.64 0.50 0.30 0.32 

 0 42.1 73.4 38.0 52.3 29.1 20.4 64.0 68.3 80.7 101.2 61.5 108.0 187.6 105.2 125.1 158.2 115.6 83.8 116.3 

F 1.00 0.65 0.53 0.70 0.63 0.23 0.55 0.71 0.67 0.57 0.78 0.54 0.28 0.35 0.39 0.33 0.51 0.39 0.44 

0.0 1.0 1.0 1.0 1.5 1.6 1.6 1.1 1.3 1.5 0.9 1.1 1.7 1.2 1.6 1.7 1.5 1.3 1.3 

0.00 0.29 0.39 0.39 0.76 0.60 0.72 0.51 0.70 0.65 0.30 0.30 0.56 0.45 0.56 0.73 0.61 0.35 0.42 

 0 45.0 23.8 11.8 20.4 58.2 82.5 27.4 38.6 59.5 23.8 74.8 154.5 73.7 86.0 152.6 103.4 57.0 88.6 

G 1.00 0.65 0.52 0.51 0.47 0.61 0.69 0.60 0.70 0.77 0.78 0.47 0.60 0.69 0.40 0.58 0.78 0.68 

0.0 0.6 0.8 1.0 0.9 0.9 0.7 0.9 0.9 0.5 0.4 1.0 0.7 0.8 1.1 0.9 0.5 0.6 

0.00 0.34 0.38 0.73 0.53 0.66 0.44 0.66 0.58 0.20 0.19 0.47 0.38 0.46 0.68 0.54 0.18 0.31 

 0 35.4 48.6 63.9 79.6 75.4 31.4 46.3 48.9 24.3 35.0 114.2 31.8 58.1 113.3 62.4 12.0 44.2 

H 1.00 0.41 0.40 0.32 0.45 0.54 0.50 0.55 0.58 0.53 0.39 0.39 0.47 0.31 0.46 0.53 0.52 

0.0 0.9 1.0 1.0 1.0 0.8 0.9 0.9 0.7 0.7 1.0 0.8 0.9 1.1 0.9 0.7 0.7 

0.00 0.46 0.75 0.58 0.70 0.52 0.68 0.62 0.37 0.35 0.54 0.46 0.53 0.71 0.59 0.35 0.41 

 0 34.7 34.4 46.2 59.6 37.7 54.0 69.5 28.1 70.1 149.6 67.2 89.2 131.7 84.3 46.0 78.6 

I 1.00 0.59 0.15 0.43 0.63 0.64 0.48 0.62 0.46 0.24 0.31 0.36 0.24 0.43 0.28 0.39 

0.0 1.0 1.2 1.1 0.8 0.9 1.1 0.7 0.8 1.2 1.0 1.1 1.3 1.1 0.9 0.9 

0.00 0.75 0.60 0.69 0.48 0.64 0.58 0.37 0.39 0.53 0.48 0.54 0.70 0.59 0.41 0.46 

 0 26.4 69.4 94.0 23.2 29.5 52.1 24.6 74.1 152.4 74.0 81.1 159.7 109.6 60.3 90.1 

J 1.00 0.24 0.39 0.58 0.60 0.42 0.59 0.45 0.26 0.43 0.43 0.18 0.41 0.27 0.43 

0.0 0.6 0.6 0.6 0.5 0.5 0.8 0.9 0.5 0.9 0.5 0.6 0.6 0.9 0.8 

0.00 0.73 0.74 0.71 0.69 0.73 0.72 0.73 0.70 0.74 0.70 0.75 0.72 0.73 0.72 

 0 48.7 84.4 47.4 55.8 78.3 44.2 95.0 174.8 93.7 106.1 166.0 118.6 75.8 108.0 

K 1.00 0.40 0.29 0.25 0.28 0.34 0.54 0.40 0.46 0.56 0.25 0.39 0.53 0.50 

0.0 0.5 0.7 0.7 0.5 0.9 0.8 0.4 0.9 0.3 0.5 0.5 0.6 0.6 

0.00 0.62 0.53 0.67 0.59 0.52 0.53 0.45 0.56 0.47 0.62 0.54 0.51 0.51 

 0 48.4 81.8 96.2 114.6 73.4 114.5 192.2 110.8 135.3 148.8 110.7 88.3 119.3 

L 1.00 0.56 0.55 0.51 0.63 0.56 0.41 0.41 0.50 0.45 0.67 0.49 0.50 

0.0 0.7 0.6 0.4 0.9 0.9 0.4 0.9 0.4 0.4 0.4 0.7 0.7 

0.00 0.60 0.66 0.61 0.64 0.67 0.56 0.67 0.57 0.62 0.57 0.65 0.64 

 0 94.7 112.0 122.7 84.2 104.6 171.9 99.2 132.5 104.0 75.4 77.9 100.4 

M 1.00 0.75 0.67 0.81 0.62 0.37 0.49 0.53 0.31 0.54 0.47 0.55 

0.0 0.5 0.5 0.5 0.7 0.7 0.8 0.6 0.8 0.6 0.7 0.6 

0.00 0.55 0.46 0.38 0.46 0.41 0.49 0.42 0.62 0.50 0.45 0.46 

 0 17.3 32.9 10.5 51.3 129.2 51.7 58.7 144.7 93.7 41.6 68.5 

March 2009 A-31 DRAFT—DO NOT QUOTE OR CITE 
N 1.00 0.62 0.72 0.56 0.34 0.40 0.46 0.28 0.55 0.36 0.50 

0.0 0.5 0.7 0.8 0.6 0.9 0.5 0.7 0.6 0.8 0.7 

0.00 0.56 0.62 0.66 0.59 0.67 0.59 0.69 0.62 0.65 0.64 

 0 23.7 27.9 57.1 129.6 59.3 55.1 158.5 107.5 54.9 76.9 

O 1.00 0.67 0.61 0.44 0.43 0.51 0.33 0.45 0.57 0.51 

0.0 0.8 0.8 0.3 0.9 0.3 0.4 0.4 0.7 0.6 

0.00 0.54 0.58 0.47 0.59 0.50 0.63 0.56 0.56 0.56 

 0 41.5 43.3 107.9 47.5 32.4 152.3 102.5 52.6 64.6 

P 1.00 0.66 0.40 0.50 0.55 0.34 0.58 0.54 0.55 

0.0 0.6 1.0 0.8 0.8 1.0 0.8 0.6 0.7 

0.00 0.24 0.42 0.38 0.43 0.66 0.51 0.25 0.33 

 0 51.0 130.6 50.2 63.7 137.1 86.4 35.8 65.7 

Q 1.00 0.48 0.67 0.72 0.35 0.55 0.70 0.73 

0.0 0.9 0.6 0.7 1.0 0.8 0.5 0.5 

0.00 0.47 0.37 0.46 0.68 0.54 0.20 0.30 

 0 80.1 6.1 30.5 110.6 62.8 27.4 21.3 

R 1.00 0.40 0.49 0.29 0.38 0.47 0.46 

0.0 0.9 0.3 0.3 0.4 0.7 0.7 

0.00 0.50 0.32 0.55 0.44 0.43 0.43 

0 82.4 75.7 123.6 102.8 104.2 73.3 

S 1.00 0.66 0.19 0.38 0.54 0.64 

0.0 0.8 1.1 0.9 0.6 0.6 

0.00 0.49 0.69 0.58 0.37 0.41 

 0 36.64 105.4 57.05 22.78 17.67 

T 1.00 0.30 0.49 0.68 0.69 

0.0 0.4 0.4 0.6 0.6 

0.00 0.58 0.47 0.43 0.42 

 0 137.8 92.3 54.8 48.0 

U 1.00 0.43 0.36 0.31 

0.0 0.4 0.8 0.8 

0.00 0.59 0.66 0.65 

 0 51.0 103.7 90.2 

V 1.00 0.47 0.51 

0.0 0.7 0.6 

0.00 0.52 0.52 

 0 52.7 44.9 

W 1.00 0.64 

0.0 0.5 

0.00 0.29 

 Legend 0 32.7 

X r 1.00 

P90 0.0 

COD 0.00 

d 0 

March 2009 A-32 DRAFT—DO NOT QUOTE OR CITE 
A B C D E F G H 

Mean 0.42 0.41 0.66 0.56 0.57 0.98 0.69 0.72 

Obs 2,5001 24916 24892 24645 24281 24825 24912 24804 

SD 0.27 0.36 0.59 0.50 0.54 0.89 0.45 0.48 

Figure A-36a. Box plots illustrating the seasonal distribution of hourly CO concentrations in Los 

Angeles, CA. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis. 

March 2009 A-33 DRAFT—DO NOT QUOTE OR CITE 
I J K L M N O P 

Mean 0.69 0.24 0.30 0.23 0.42 0.31 0.26 0.62 

Obs 24259 24965 24860 24135 24264 24760 24831 24705 

SD 0.56 0.37 0.25 0.29 0.46 0.47 0.25 0.55 

Figure A-36b. Box plots illustrating the seasonal distribution of hourly CO concentrations in Los 

Angeles, CA. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis. 

March 2009 A-34 DRAFT—DO NOT QUOTE OR CITE 
Q R S T U V W X 

Mean 0.67 0.25 0.60 0.29 0.17 0.30 0.59 0.53 

Obs 24885 24938 24778 24792 24105 24796 24767 24844 

SD 0.42 0.14 0.46 0.20 0.17 0.28 0.32 0.38 

Figure A-36c. Box plots illustrating the seasonal distribution of hourly CO concentrations in Los 

Angeles, CA. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis. 

March 2009 A-35 DRAFT—DO NOT QUOTE OR CITE 
Figure A-37. Map of CO monitor locations with AQS Site IDs for New York City, NY. 

March 2009 A-36 DRAFT—DO NOT QUOTE OR CITE 
Table A-7. Table of inter-sampler comparison statistics, including Pearson r, P90 (ppm), COD, 

and d (km), as defined in the text, for each pair of hourly CO monitors reporting to 

AQS in New York City, NY. 

New York 

A B C D E F G H I 

A 1.00 0.58 0.65 0.55 0.40 0.56 0.56 0.41 0.30 

0.0 0.4 0.7 0.4 0.4 0.4 0.4 0.5 0.8 

0.00 0.22 0.28 0.25 0.25 0.24 0.23 0.28 0.75 

0.0 7.0 15.9 45.8 70.6 43.7 10.5 17.8 76.5 

B 1.00 0.64 0.55 0.35 0.54 0.54 0.59 0.49 

0.0 0.8 0.4 0.5 0.4 0.4 0.4 0.7 

0.00 0.29 0.23 0.26 0.23 0.23 0.23 0.74 

 0.0 16.8 45.4 71.5 38.1 15.0 24.5 82.9 

C 1.00 0.54 0.32 0.48 0.52 0.43 0.31 

0.0 0.9 0.9 0.9 0.7 0.9 1.3 

0.00 0.35 0.34 0.34 0.24 0.35 0.81 

 0.0 29.9 55.0 35.7 8.9 20.5 85.5 

D 1.00 0.50 0.57 0.41 0.46 0.33 

0.0 0.4 0.4 0.4 0.4 0.7 

0.00 0.24 0.23 0.28 0.27 0.72 

 0.0 27.5 36.7 37.5 45.1 107.8 

E 1.00 0.47 0.33 0.33 0.32 

0.0 0.4 0.4 0.4 0.6 

0.00 0.23 0.25 0.27 0.73 

 0.0 61.9 61.0 65.0 120.3 

F 1.00 0.41 0.34 0.31 

 0.0 0.4 0.4 0.7 

 0.00 0.26 0.27 0.72 

 0.0 43.6 55.8 119.7 

G 1.00 0.46 0.29 

0.0 0.4 0.7 

0.00 0.26 0.77 

 0.0 12.3 76.8 

H 1.00 0.43 

 0.0 0.6 

 0.00 0.73 

 Legend 0.0 65.1 

I r 1.00 

P90 0.0 

COD 0.00 

d 0.0 

March 2009 A-37 DRAFT—DO NOT QUOTE OR CITE 
A B C D E F G H I 

Mean 0.55 0.52 0.85 0.48 0.50 0.49 0.62 0.47 0.12 

Obs 23113 25150 25646 25028 25727 25691 25547 25022 25749 

SD 0.27 0.30 0.43 0.27 0.24 0.25 0.21 0.23 0.17 

Figure A-38. Box plots illustrating the seasonal distribution of hourly CO concentrations in New 

York City, NY. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis. 

March 2009 A-38 DRAFT—DO NOT QUOTE OR CITE 
Figure A-39. Map of CO monitor locations with AQS Site IDs for Seattle, WA. 

March 2009 A-39 DRAFT—DO NOT QUOTE OR CITE 
 A 

Mean 0.75 

Obs 25818 

SD 0.49 

Figure A-40. Box plots illustrating the seasonal distribution of hourly CO concentrations in 

Seattle, WA. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis. 

March 2009 A-40 DRAFT—DO NOT QUOTE OR CITE 
Figure A-41. Map of CO monitor locaitons with AQS Site IDs for St. Louis, MO. 

Table A-8. Table of inter-sampler comparison statistics, including Pearson r, P90 (ppm), COD, 

and d (km), as defined in the text, for each pair of hourly CO monitors reporting to 

AQS in St. Louis, MO. 

St. Louis 

A B C 

A 1.00 0.19 0.60 

0.0 0.5 0.3 

0.00 0.40 0.24 

0.0 21.2 9.5 

B 1.00 0.19 

0.0 0.5 

0.00 0.42 

Legend 0.0 19.8 

C r 1.00 

P90 0.0 

COD 0.00 

d 0.0 

March 2009 A-41 DRAFT—DO NOT QUOTE OR CITE 
 A B C 

Mean 0.44 0.43 0.42 

Obs 25325 25879 25938 

SD 0.25 0.25 0.29 

Figure A-42. Box plots illustrating the seasonal distribution of hourly CO concentrations in St. 

Louis, MO. Note: 1 = winter, 2, = spring, 3 = summer, and 4 = fall on the x-axis. 

March 2009 A-42 DRAFT—DO NOT QUOTE OR CITE 
Figure A-43. Seasonal plots of correlations between hourly CO concentration with hourly (1) SO2, 

(2) NO2, (3) O3, (4) PM10, and (5) PM2.5 concentrations for Anchorage, AK. Also shown 

are correlations between 24-h average CO concentration with (6) daily max 1-h and 

(7) daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h 

CO concentrations. (Refer the numbers in this caption to those on the y-axis of each 

seasonal plot.) Note that the data are not obtained for Anchorage during the 

summer, and so are not presented here. 

March 2009 A-43 DRAFT—DO NOT QUOTE OR CITE 
Figure A-44. Seasonal plots of correlations between hourly CO concentration with hourly (1) SO2, 

(2) NO2, (3) O3, (4) PM10, and (5) PM2.5 concentrations for Atlanta, GA. Also shown are 

correlations between 24-h average CO concentration with (6) daily max 1-h and (7) 

daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h 

CO concentrations. (Refer the numbers in this caption to those on the y-axis of each 

seasonal plot.) 

March 2009 A-44 DRAFT—DO NOT QUOTE OR CITE 
Figure A-45. Seasonal plots of correlations between hourly CO concentration with hourly (1) SO2, 

(2) NO2, (3) O3, (4) PM10, and (5) PM2.5 concentrations for Boston, MA. Also shown are 

correlations between 24-h average CO concentration with (6) daily max 1-h and (7) 

daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h 

CO concentrations. (Refer the numbers in this caption to those on the y-axis of each 

seasonal plot.) Red bars denote the median, and green stars denote the arithmetic 

mean. 

March 2009 A-45 DRAFT—DO NOT QUOTE OR CITE 
Figure A-46. Seasonal plots of correlations between hourly CO concentration with hourly (1) SO2, 

(2) NO2, (3) O3, (4) PM10, and (5) PM2.5 concentrations for Denver, CO. Also shown are 

correlations between 24-h average CO concentration with (6) daily max 1-h and (7) 

daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h 

CO concentrations. (Refer the numbers in this caption to those on the y-axis of each 

seasonal plot.) Red bars denote the median, and green stars denote the arithmetic 

mean. 

March 2009 A-46 DRAFT—DO NOT QUOTE OR CITE 
Figure A-47. Seasonal plots of correlations between hourly CO concentration with hourly (1) SO2, 

(2) NO2, (3) O3, (4) PM10, and (5) PM2.5 concentrations for Los Angeles, CA. Also 

shown are correlations between 24-h average CO concentration with (6) daily max 1-

h and (7) daily max 8-h CO concentrations and (8) between daily max 1-h and daily 

max 8-h CO concentrations. (Refer the numbers in this caption to those on the yaxis of each seasonal plot.) 

March 2009 A-47 DRAFT—DO NOT QUOTE OR CITE 
Figure A-48. Seasonal plots of correlations between hourly CO concentration with hourly (1) SO2, 

(2) NO2, (3) O3, (4) PM10, and (5) PM2.5 concentrations for New York City, NY. Also 

shown are correlations between 24-h average CO concentration with (6) daily max 1-

h and (7) daily max 8-h CO concentrations and (8) between daily max 1-h and daily 

max 8-h CO concentrations. (Refer the numbers in this caption to those on the yaxis of each seasonal plot.) 

March 2009 A-48 DRAFT—DO NOT QUOTE OR CITE 
Figure A-49. Seasonal plots of correlations between hourly CO concentration with hourly (1) SO2, 

(2) NO2, (3) O3, (4) PM10, and (5) PM2.5 concentrations for Phoenix, AZ. Also shown 

are correlations between 24-h average CO concentration with (6) daily max 1-h and 

(7) daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h 

CO concentrations. (Refer the numbers in this caption to those on the y-axis of each 

seasonal plot.) 

March 2009 A-49 DRAFT—DO NOT QUOTE OR CITE 
Figure A-50. Seasonal plots of correlations between hourly CO concentration with hourly (1) SO2, 

(2) NO2, (3) O3, (4) PM10, and (5) PM2.5 concentrations for Seattle, WA. Also shown are 

correlations between 24-h average CO concentration with (6) daily max 1-h and (7) 

daily max 8-h CO concentrations and (8) between daily max 1-h and daily max 8-h 

CO concentrations. (Refer the numbers in this caption to those on the y-axis of each 

seasonal plot.) 

1 

March 2009 A-50 DRAFT—DO NOT QUOTE OR CITE 
Annex B. Dosimetry Studies 

Table B-1. Recent studies related to CO dosimetry and pharmacokinetics. 

Reference Purpose Findings 

Alcantara et al. (2007) To use a quantum mechanics/molecular mechanics 

approach to understand the cooperativity of Hb ligand 

binding and differences in energy between T and R 

Hb functional states. 

The ligand binding energies between R and T states differ due to 

strain induced in the heme and its ligands and in protein contacts in 

the α and β chains. 

Adir et al. (1999) To determine if low concentrations of CO would affect 

exercise performance and myocardial perfusion in 

young healthy men. 

Men with COHb levels between 4-6% had decreased exercise 

performance measured by decreased mean duration of exercise (1.52 

min) and maximal effort described by metabolic equivalent units 

(2.04). No changes were seen in lactate/pyruvate ratio, arrhythmias, 

or myocardial perfusion. 

Andersson et al. (2000) To investigate if CO could be endogenously produced 

in the nose and paranasal sinuses. 

Both nose and paranasal sinuses contained HO-like immunoreactivity, 

mostly in the respiratory epithelium, indicating local CO production in 

the upper respiratory airways. 

Bruce and Bruce (2003) To create a mathematical model to predict uptake and 

distribution of CO in both vascular and tissue 

compartments during constant or variable inhalation 

levels of CO. 

This model contains 5 compartments: lung, arterial blood, venous 

blood, muscle tissue, and nonmuscle tissue. It was constructed to 

include tissue compartment flux and difference between venous and 

arterial COHb for short exposures which is not possible with the CFK 

model. 

Bruce and Bruce (2006) To use their mathematical multicompartment model 

along with experimental data to predict the factors that 

influence the washout rates of CO, along with 

predicting the rates of CO uptake, distribution in 

vascular and extravascular (muscle and non-muscle 

tissue) compartments, and washout over a range of 

exposure and conditions. 

Rates of CO washout follow a biphasic elimination where washout 

was faster immediately post exposure. The difference in rates is likely 

due to slow equilibration between vascular and extravascular 

compartments. Important factors contributing to washout kinetics 

include: peak COHb level, exposure duration and concentration, time 

after exposure samples were obtained, and individual variability. 

Bruce et al. (2008) To develop a mathematical model able to integrate a 

large body of indirect experimental findings on the 

uptake and distribution of CO by accounting for 

arteriole to venule shunting via intra-tissue pathways 

and diffusion of blood gases into tissues from precapillary vessels like arterioles. 

The former model of Bruce and Bruce (2006) was altered by adding a 

mass balance equation for O2 so pO2 is directly calculated in the 

compartments and the muscle compartment is divided into two subcompartments of muscle and non-muscle tissue. CO uptake from 

blood by muscle is much slower than O2, thus COHb% will fall rapidly 

while COMb% could remain high. 

Carraway et al. (2000) To test the hypothesis that HO-1 gene expression and 

protein are upregulated in the lungs of rats during 

chronic hypoxia. 

Rats were exposed to HH (17,000 ft) for 1-21 days. COHb increased 

after 1 day and progressively after 14 days. HO-1 protein and activity 

were upregulated during early chronic hypoxia. This HO-1 was 

localized to inflammatory cells and then to newly muscularized 

arterioles. 

To describe a new method for measurement of CO 

diffusing capacity (DLCO) and alveolar volume (VA) in 

sleeping infants (6-22 months old), using a single 

4-sec breath-hold technique. 

VA30 and DLCO increased with increasing body length and the method 

could be used as a measurement of lung development and growth. 

Castillo et al. (2006) 

To present an analytical expression for diffusing 

capacity of CO, NO, CO2, and O2 to the red blood cell 

in terms of optimum size and shape of the RBC, 

thickness of the unstirred plasma layer surrounding 

the RBC, diffusivities and solubilities of the gas in 

RBC and boundary layer, hematocrit, and the slope of 

the dissociation curve. 

Results indicate the discoidal shape of the RBC is optimal for O2

uptake and reaction velocity is limited by mass transfer resistance in 

surrounding stagnant plasma layer. The paper overviews rate 

constants and reaction kinetics for CO binding to Hb. CO diffusing 

capacity is shown to be reaction rate limited at low PCO under 

normoxic and hyperoxic conditions, but diffusion rate limited under 

hypoxic and high PCO conditions. 

Chakraborty et al. (2004) 

Cronje et al. (2004) To analyze CO uptake and elimination in the brain, 

muscle, heart, and blood of rats, with the intent of 

testing the Warburg hypothesis that CO partitioning is 

directly proportional to the CO/O2 ratio. 

Results indicate that tissue and blood [CO] dissociate during CO 

inhalation, but [CO] does not follow blood [CO] or 1/pO2 as in the 

Warburg theory during intake or elimination. Tissue [CO] increases 

later during the resolution period and varies significantly among 

animals and tissues. The deviation from the predicted values in the 

brain is likely due to the release of heme and increase in NADPH 

stimulating endogenous CO production by HO. 

March 2009 B-51 DRAFT—DO NOT QUOTE OR CITE 
Reference Purpose Findings 

Dutton et al. (2001) To monitor CO, NO2, and PAH emissions during the 

operation of unvented natural gas fireplaces in two 

residences in Boulder, CO, at various times between 

1997 and 2000. 

Results showed significant accumulation of CO, NO2, and PAH 

indoors when the fireplaces were used. CO concentrations could 

exceed 100 ppm. NO2 concentrations average 0.36 ppm over 4 hours. 

PAH 4-h time average reached 35 ng/m3. 

Hampson (2007) To present a case study of a man with drug-induced 

hemolytic anemia and hepatic failure. 

The man had elevated endogenous CO production resulting in levels 

of COHb as high as 9.7%. 

Hsia (2002) To review the current concepts and practical 

relevance of the diffusing capacity/cardiac output 

interaction, in hopes of aiding in the interpretation of 

diffusing capacity, membrane diffusing capacity, and 

capillary blood volume. 

This review helped to understand the determinants of changes in 

diffusing capacity, including hematocrit, erythrocyte distribution, blood 

volume, lung volume, cardiac output, etc. 

Lamberto et al. (2004) To evaluate which component, alveolar membrane 

diffusing capacity (Dm) and pulmonary capillary blood 

volume (Vc), is responsible for decreased resting 

DLCO in sarcoidosis patients and which component is 

the best predictor of gas exchange abnormalities. 

Patients with pulmonary sarcoidosis had decreased lung volumes, a 

loss in DLCO, and gas exchange abnormalities during exercise 

including decreased PaO2 and increased alveolar-arterial oxygen 

pressure difference. Dm accounted for the majority of the decrease in 

DLCO and was predictive for gas exchange abnormalities. 

Levesque et al. (2000) To describe the results of air quality monitoring in an 

indoor ice skating rink during Monster Truck and car 

demolition exhibitions. 

Maximum time-weighted average levels of CO were 100 ppm with 

several peaks exceeding 200 ppm (maximum: 1,600 ppm). 

Marks et al. (2002) To review the analytical methods for measurement of 

endogenous formation of CO in a variety of tissues. 

A variety of methods have been used to measure endogenous CO. 

The rate of formation varies over a narrow range from 0.029 nmol/mg 

protein/h to 0.28 nmol/mg protein/h depending on tissue. Brain and 

liver regions tend to have the highest rates of CO formation likely due 

to high levels of HO activity in these tissues. 

Merx et al. (2001) To investigate the effect of CO inactivation of Mb in 

wild-type and myo-/- mice on hemodynamics and 

oxygen dynamics. 

Fully oxygenated Mb treated with 20% CO had no change in left 

ventricular developed pressure or coronary venous PO2. Partially 

O2-saturated Mb (87% O2Mb) exposed to 20% CO had significantly 

decreased LVDP (12%) and PvO2 (30%) in wild-type but not myo-/-

hearts. 

Pelham et al. (2002) To review the literature on exposure and effects of 

mainly CO and NO2 in enclosed ice rinks. 

CO levels as high as 300 ppm were recorded after episodes of 

malfunctioning ice resurfacing equipment or inadequate ventilation. 

Pesola et al. (2004) To determine if healthy African Americans may be 

misdiagnosed has having respiratory deficient due to 

comparison using Caucasian-derived prediction 

equation estimates of DLCO. 

The lung volume of African American individuals is 10-15% lower than 

Caucasians. The measured DLCO was consistently significantly lower 

in African Americans than what would be predicted, thus the authors 

suggest a race correction reduction of the Miller PEE for diffusion of 

12%. 

Pesola et al. (2006) To determine if healthy Asians may be misdiagnosed 

has having respiratory deficient due to comparison 

using Caucasian-derived prediction equation 

estimates of DLCO. 

The lung volume of Asian individuals is 10-15% lower than 

Caucasians, thus a Chinese derived prediction for DLCO should be 

used. 

Prommer and Schmidt (2007) To determine the error in total Hb mass 

measurements using the optimized CO-rebreathing 

method due to loss of CO to Mb 

Optimal blood mixing (when venous and arterial blood COHb% are 

equivalent) was determined to be after 6 min. A small volume of 

administered CO leaves the vascular space (0.32% per min). 2.3% 

increase in total Hb mass would be found if CO diffusion was not 

included. 

Richardson et al. (2002) To combine invasive vascular measures of arterial 

and venous blood and muscle blood flow with 

noninvasive magnetic spectroscopy of deoxymyoglobin and high energy phosphates to determine 

the effects of mild CO poisoning (20% COHb) in 

humans during muscular work. 

Five humans were analyzed under normoxia, hypoxia, normoxia + CO 

(20% COHb), and 100% O2 + CO. Maximum works rates and maximal 

oxygen uptake were reduced in H, COnorm, and COhyper. CO and H 

caused elevated blood flow. Net muscle CO uptake from blood was 

less during 20% COHb trials than during normoxia and hypoxia (1-2%) 

trials. 

Shimazu et al. (2000) To study the effects of short-term (minutes) or longterm (several hours) CO exposure on COHb 

elimination and developing a mathematical model to 

simulate this event. 

COHb exhibited an initial rapid decrease followed by a slower phase 

which is compatible with a 2-compartment model and biphasic 

elimination. Both exposures fit the 2-compartment, single central outlet 

mathematical model. 

Shimazu (2001) To discuss the findings of Weaver et al. (2000) on 

COHb t1/2. 

The authors discuss that CO elimination is biphasic and is heavily 

affected by duration of exposure which was not taken into account in 

the Weaver, et al. (2000) paper. 

Takeuchi et al. (2000) To examine the relationship between minute 

ventilation and rate of COHb reduction during 

breathing 100% O2 and during normocapnic hyperoxic 

hyperpnea. 

Patients were exposed to 400 to 1,000 ppm CO, resulting in 10-12% 

COHb. The half-time of COHb reduction was 78 ± 24 min during 

100% O2 treatment and 31 ± 6 min during normocapnic hyperpnea 

with O2 treatment. 

Vreman et al. (2005) To develop a sensitive and reproducible method of 

CO quantification in rodent (mouse and rat) tissue 

pre- and post-exposure in hopes of understanding 

endogenous CO production. 

Tissues were sonicated mixed with sulfosalicylic acid for 30 min at 

0°C and then liberated CO was analyzed by gas chromatograph. 

Blood contained the highest CO concentration. Lowest concentrations 

were found in brain, testes, intestine, and lung (endogenously). 

March 2009 B-52 DRAFT—DO NOT QUOTE OR CITE 
Reference Purpose Findings 

Vreman et al. (2006) To test a method of CO quantification in frozen 

postmortem human tissues from 3 determined 

categories of fatalities: trauma with no suspected CO 

exposure (controls), fire-related, and CO asphyxiation. 

CO levels were analyzed in adipose, brain, muscle, heart, kidney, 

lung, spleen, and blood (ordered from approximate low to high tissue 

concentration). It was suggested that blood, muscle, brain, lung, and 

kidney are suitable for diagnosing death due to lethal CO exposure 

due to regression analysis against COHb values. 

Weaver et al. (2000) To determine in COHb half-life is influenced by CO 

poisoning vs. experimental CO exposure, loss of 

consciousness, concurrent tobacco smoking, or PaO2. 

COHb t1/2 determined was 74 ± 25 min with a range from 26 to 148 

min by a single exponential decrease function. This is shorter than 

most clinical studies and was inversely proportionate to PaO2, however 

not influenced by age, gender, smoke inhalation, loss of 

consciousness, tobacco smoking, or method of O2 treatment. 

Wu and Wang (2005) To review the endogenous production of CO through 

HO, as well as discuss physiological roles for CO both 

toxic and therapeutic. 

CO is produced endogenously by HO-1 and -2 and acts as a 

gasotransmitter, inducing cell signaling cascades. The review 

discusses possible roles for CO in the various organ systems. Also, it 

discusses the potential pharmacological and therapeutic applications 

for CO. 

March 2009 B-53 DRAFT—DO NOT QUOTE OR CITE 
Annex C. Epidemiology Studies 

Table C-1. Studies of CO exposure and cardiovascular morbidity. 

Study Design Concentrations CO Effect Estimates (95% CI) 

CHANGES IN HEART RATE AND HEART RATE VARIABILITY 

Author: Chan et al. (2005) 

Period of Study: 

December 2001 – 

February 2002 

Location: 

Taipei, Taiwan 

Health Outcome: Various measures of 

HRV via ambulatory ECG (Holter system) 

Study Design: Panel 

Statistical Analyses: Linear regression 

(mixed effects) 

Age Groups Analyzed: 

40 – 75 years 

Sample Description: 

83 patients from the National Taiwan 

University Hospital 

Averaging Time: 

1-h ma 

Mean (SD) unit: 

1.1 ppm 

Range (Min, Max): 

0.1, 7.7 

Copollutant: NR 

 

Increment: NR 

RR Estimate [Lower CI, Upper CI] 

Lags examined (-h ma): 1, 2, 3, 4, 5, 6, 7, 8 

CO had no statistically significant effect on SDNN, rMSSD, 

LF, HF. 

Author: Dales et al. (2004) 

Period of Study: NR

Location: 

Toronto, Canada 

Health Outcome: Various measures of 

HRV via Holter system 

Study Design: Panel 

Statistical Analyses: Linear regression 

(mixed effects) 

Age Groups Analyzed: 

51 – 88 years (mean 65 years) 

Sample Description: 36 subjects with 

pre-existing CAD 

Averaging Time: 24-h 

Mean (SD) unit:

2.40 ppm (95th percentile) 

Personal monitoring 

Range (Min, Max):0.4, 

16.5 

Copollutant: correlation 

PM2.5: r = 0.17 

 

Increment: NR 

Regression co-efficient [Lower CI, Upper CI] 

Lags examined : NR 

CO had no statistically significant effect on LF, HF, HFLFR, 

SDNN among those taking Beta-blockers. Whereas CO 

had a positive effect on SDNN among those not taking 

Beta-blockers. Slope = 0.0111 (0.002-0.020, p=0.02) 

Author: Gold et al. (2000) 

Period of Study: June – September 1997 

Location: 

Boston, MA 

Health Outcome (ICD9 or ICD10): Heart 

Rate and various measures of HRV via 

Holter system 

Study Design: Panel/Cohort 

Statistical Analyses: Linear regression 

(fixed effects/random effects) 

Age Groups Analyzed: 

53 – 87 years 

Sample Description: 21 active Boston 

residents observed up to 12 times. 

Averaging Time: 24-h 

Mean (SD) unit: 

0.47 ppm 

Range (Min, Max): 0.12, 

0.82 

Copollutant: NR 

 

Increment: 0.6 ppm 

% Change [Lower CI, Upper CI] 

Lags examined : 24-h 

No significant effect with CO (no results recorded) 

Author: Gold et al. (2005) 

Period of Study: 

June – September 1999 

Location: 

Boston, MA 

Health Outcome: ST- segment. 

Study Design: Panel 

Statistical Analyses: Linear regression 

(mixed models) 

Age Groups Analyzed: 

61 – 88 years 

Sample Description: 24 Active Boston 

residents - each observed up to 12 times. 

Averaging Time: 

1-h, 24-h 

Mean (SD) unit: NR 

Range (Min, Max): (ppm) 

(personal monitoring) 

10th = 0.20 

90th = 1.08 

Copollutant: NR 

Increment: NR 

RR Estimate [Lower CI, Upper CI] 

Lags examined : 1-24 h 

Although CO was associated with ST-segment depression 

in single pollutant models, this result did not persist in 

multiple pollutant models. 

Author: Holguin et al. 

(2003) 

Period of Study: February – April 2000 

Location: 

Mexico City, Mexico 

Health Outcome: Various measures of 

HRV via ECG 

Study Design: Panel 

Statistical Analyses: GEE 

Age Groups Analyzed: 

60-96 years (mean age 79 years) 

Sample Description: 

34 patients who were permanent residents 

of a nursing home in the Northeast 

metropolitan area. 

Averaging Time: 24-h 

Mean (SD) unit: 3.3 ppm 

Range (Min, Max): 1.8, 

4.8 

Copollutant: NR 

 

Increment: 10 ppm 

Regression Coefficients [Lower CI, Upper CI] 

Lags examined : 0 

Lag 0 : 

HF : 0.003 (-0.004 to 0.001) 

LF : 0.001 (-0.006 to 0.008) 

LF/HF : 0.001 (-0.005 to 0.002) 

March 2009 C-1 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Liao et al. (2004) 

Period of Study: 

1996 - 1998 

Location: 

Forsyth County, NC; 

Selected suburbs of 

Minneapolis, MN; 

Jackson, MI 

Health Outcome: Heart Rate & various 

rates of HRV. 

Study Design: Cohort 

Statistical Analyses: Linear regression 

Age Groups Analyzed: 45 – 64 years (mean 62 years) 

Sample Description: 

6784 study subjects from the 

atherosclerosis risk in communities study 

Averaging Time: 24-h 

Mean (SD) unit: 

0.65 ppm (0.44) 

Range (Min, Max): NR 

Copollutant: NR 

 

Increment: 0.44 ppm 

Regression coefficients Lags examined : 1 

Lag 1 : 

HF (log transformed) : -0.033 

LF (log transformed) : 0.006 

SDNN : -0.274 

Heart Rate (bpm) : 0.404* 

Confidence Intervals not recorded 

*p < 0.05 

Author: Park et al. (2005b) 

Period of Study: 

2000 - 2003 

Location: 

Boston, MA 

Health Outcome: Various measures of 

HRV via ECG 

Study Design: Panel/Cohort 

Statistical Analyses: Linear regression 

Age Groups Analyzed: 

21 – 81 years 

Sample Description: 

497 men from the Normative aging study 

in Greater Boston 

Averaging Time: 24-h 

Mean (SD) unit:

0.50 ppm 

Range (Min, Max): 

0.13, 1.8 

Copollutant: NR 

 

Increment: 0.24 ppm 

% Change in HRV [Lower CI, Upper CI] 

Lags examined: 4-h ma, 24-h ma, 48-h ma 

Lag 4-h ma:

SDNN (Log10): 2.0 (-2.9 to 7.3) 

HF (Log10): 8.8 (-4.6 to 24.1) 

LF(Log10) : 3.2 (-7.0 to 14.6) 

LF :HF(Log10) : -5.1 (-13.5 to 4.1) 

Lag 24-h ma: 

SDNN (Log10): -2.2 (-7.7 to 3.6) 

HF (Log10): -13.2 (-25.4 to 1.0) 

LF(Log10) : -0.6 (-11.9 to 12.1) 

LF :HF(Log10) : 14.5 (2.9-27.5) 

Lag 48-h ma: SDNN(Log10): -3.4 (-10.2 to 3.9) 

HF (Log10): -13.8 (-.28.9 to 4.4) 

LF (Log10): -2.4 (-16.2 to 13.6) 

LF :HF (Log10): 13.2 (-1.1 to 29.6) 

Author: Peters et al. 

(1999a) 

Period of Study: 

1984 - 1985 

Location: 

Augsburg, Germany 

Health Outcome: Heart Rate 

Study Design: Cohort 

Statistical Analyses: Linear regression 

(GEE) 

Age Groups Analyzed: 

25 – 64 years 

Sample Description: 

2681 men & women who participated in 

the MONICA study 

Averaging Time: 24-h 

Mean (SD) unit: 

During air pollution 

episode: 4.54 mg/m3

Outside air pollution 

episode: 4.51 mg/m3

Range (Min, Max): 

During air pollution 

episode: 2.39, 6.85 

Outside air pollution 

episode: 0.91, 11.51 

Respectively 

Copollutant: NR 

Increment: 6.6 mg/m3

Mean Change in Heart Rate (beats/min) [Lower CI, 

Upper CI] 

Lags examined: 0, 5-day avg 

All

Lag 0 : 0.97 (0.02-1.91) 

Lag 5-day avg : 0.70 (-0.09 to 1.48) 

Men 

Lag 0 : 0.95 (-0.37 to 2.27) 

Lag 5-day avg : 0.91 (-0.25 to 2.07) 

Women 

Lag 0 : 0.98 (-0.37 to 2.34) 

Lag 5-day avg : 0.52 (-0.55 to 1.59) 

Author: Riojas-Rodriguez 

et al. (2006) 

Period of Study:

December 2001 – April 

2002 

Location: 

Mexico City, Mexico 

Health Outcome: Various measures of 

HRV via Holter system 

Study Design: Panel 

Statistical Analyses: Linear regression 

(mixed effects models) 

Age Groups Analyzed: 25 – 76 years (mean 55 years) 

Sample Description: 

30 patients from the Outpatient clinic of 

the National Institute of Cardiology of 

Mexico 

Averaging Time: 24-h 

Mean (SD) unit: 2.9 ppm 

(personal monitor) 

Range (Min, Max): 0.1, 

18.0 

Copollutant: NR 

Increment: 1 ppm 

Regression Coefficients [Lower CI, Upper CI] 

Lags examined (per minutes) : 5, 10 

Lag 5 minutes : 

HF : -0.006 (-0.023 to 0.010) 

LF : -0.024 (-0.041 to -0.007) 

VLF : -0.034 (-0.061 to -0.007) 

Notes: VLF = Very low frequency 

Author: Schwartz et al. 

(2005) 

Period of Study: 1999 

Location: 

Boston, MA 

Health Outcome: Measures of HRV via 

Holter system 

Study Design: Panel 

Statistical Analyses: Linear regression 

(hierarchical model) 

Age Groups Analyzed: 

 61 – 89 years 

Sample Description: 

28 subjects living at or near an apartment 

complex located on the same street at the 

Harvard School of Public Health 

Averaging Time: 24-h 

Mean (SD) unit: NR 

Range (Min, Max): ppm 

25th = 0.38; 75th = 0.54 

Copollutant: correlation 

PM2.5: r = 0.61 

NO2: r = 0.55 

SO2: r = -0.18 

O3: r = 0.21 

 

Increment: 0.16 ppm 

% Change in HRV [Lower CI, Upper CI] 

Lags examined : 24-h, 1-h 

Lag 1-h: 

SDNN : -2.6 (-5.6 to 0.5); rMSSD : -3.9 (-10.6 to 3.3); 

PNN50 : -3.5 (-13.7 to 8.0); LF :HF : 4.5 (-1.2 to 10.5) 

Lag 24-h: 

SDNN : -4.2 (-0.6 to -7.7); rMSSD : -10.2 (-2.4 to -17.4); 

PNN50 : -14.8 (-3.0 to -25.2); LF :HF : 6.2 (-0.6 to 13.4) 

March 2009 C-2 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Tarkiainen et al. 

(2003) 

Period of Study: 

October 1997 – May 1998 

Location: 

Kuopio, Finland 

Health Outcome: Various measures of 

HRV via Ambulatory ECG (Holter system) 

Study Design: Panel 

Statistical Analyses: ANOVA for 

repeated errors (GLM) 

Age Groups Analyzed: 

Age 55 – 68 years 

Sample Description: 6 male patients with 

angiographically verified CAD 

Averaging Time: 24-h 

Mean (SD) unit: 

4.6 ppm (maximum of CO 

episode) (personal 

monitoring) 

Range (Min, Max): 0.5, 

27.4 (maximum of CO 

episode) 

Copollutant: NR 

 

Increment: NR 

RR Estimate [Lower CI, Upper CI] 

Lags examined : 5 minute prior to CO episode, 5 minute 

during CO episode 

CO had no statically significant effect on NN, SDNN or 

rMSSD. However, during high CO exposure (>2.7 ppm) CO 

was associated with an increase in rMSSD of 2.4ms 

(p=0.034). 

Author: Timonen et al. 

(2006) 

Period of Study: 

1998 - 1999 

Location: 

3 Cities in Europe: 

Amsterdam, Netherlands; 

Erfert, Germany; Helsinki, 

Finland 

Health Outcome: 

Stable CAD: Various measures of HRV via 

ambulatory ECG (Holter system) 

Study Design: Panel 

Statistical Analyses: Linear regression 

(mixed model) 

Age Groups Analyzed: Mean age across 

3 cities; 64 – 71 years. 

Sample Description: 

131 subjects with Stable CAD followed for 

6 months with bi-weekly clinical visits. 

Averaging Time: 24-h 

Mean (SD) unit: 

Amsterdam: 0.6 mg/m3

Erfert: 0.4 mg/m3

Helsinki: 0.4 mg/m3

Range (Min, Max): 

Amsterdam: 0.4, 1.6 

Erfert: 0.1, 2.5 

Helsinki: 0.1, 1.0 

Copollutant: correlation 

Amsterdam: 

PM2.5: r = 0.58 

NO2: r = 0.76 

Erfert: 

PM10: r = 0.77 

NO2: r = 0.86 

Helsinki: 

PM10: r = 0.40 

NO2: r = 0.32 

Increment: 1 mg/m3

Regression co-efficient [Lower CI, Upper CI] 

Lags examined (days): 0, 1, 2, 3, 5-day avg 

SDNN: 

Lag 0 : -1.21 (-4.44 to 2.03); Lag 1 : -1.71 (-6.05 to 2.63); 

Lag 2 : -5.69 (-10.7 to -0.72); Lag 3 : 0.66 (-3.83 to 5.15); 

5-day avg: -3.60 (-9.88 to 2.68) 

HF: 

Lag 0 : 5.0 (-15.1 to 25.1); Lag 1 : -2.0 (-37.1 to 33.1); 

Lag 2 : -30.7 (-59.8 to -1.5); Lag 3 : -9.3 (-35.8 to -17.3); 

5-day avg: -15.2 (-53.0 to 22.6) 

LF/HF:

Lag 0 : -3.6 (-21.8 to 14.5); Lag 1 : -28.6 (-52.0 to -5.3); 

Lag 2 : -10.1 (-36.9 to 16.7); Lag 3 : 7.7 (-16.5 to 31.9); 

5-day avg: -16.9 (-51.2 to 17.3) 

Author: Wheeler et al. 

(2006) 

Period of Study: 

1999 - 2000 

Location: 

Atlanta, GA 

Health Outcome: Various measures of 

HRV via Holter system 

Study Design: Panel 

Statistical Analyses: Linear regression 

(mixed effects models) 

Age Groups Analyzed: 

Mean 65 years – IQR 55- 73 years. 

Sample Description: 

18 subjects with COPD and 12 subjects 

with recent MI. 

Averaging Time: 1-h 

Mean (SD) unit: 

362.0 ppb 

Range (Min, Max): 

25th = 221.5; 75th = 398.1

Copollutant: correlation 

PM2.5: r = 0.43 

 

Increment: NR 

RR Estimate [Lower CI, Upper CI] ; lag : 

Lags examined (h ma): 1, 4, 24 

No CO results reported. 

ONSET OF CARDIAC ARRHYTHMIA 

Author: Berger et al. 

(2006) 

Period of Study: 

October 2000 – April 2001 

Location: 

Erfurt, Germany 

Health Outcome: 

Runs of supraventricular and ventricular 

tachycardia recorded via 24-h ECG. 

Study Design: Panel 

Statistical Analyses: 

Poisson regression (GAM) Linear 

regression 

Age Groups Analyzed: 52 – 76 years (mean 76years) 

Sample Description:

57 men with CHD 

Averaging Time: 24-h 

Mean (SD) unit: 0.52 

mg/m3

Range (Min, Max): 0.11, 

1.93 

Copollutant: correlation 

NR 

 

Increment: 

All: 0.27 mg/m3

5-day avg : 0.22 mg/m3

RR Estimate [Lower CI, Upper CI] 

Lags examined (h): 0, 0-23, 24-47, 48-71, 72-95, 5-day avg

Supraventricular extrasystoles: 

Lag 0 : 1.18 (1.00-1.38) Lag 0-23 : 1.16 (1.02-1.31); Lag 

24-47 : 1.13 (1.00-1.28); Lag 48-71 : 1.18 (1.03-1.36); 

Lag 72-95 : 1.08 (0.98-1.20); 5-day avg: 1.18 (1.04-1.35) 

Mean % Change [Lower CI, Upper CI] 

Hourly Lags examined: 

0, 0-23, 24-47, 48-71, 72-95, 5-day avg 

Ventricular extrasystoles: 

Lag 0 : 0.0 (-4.1 to 4.4); Lag 0-23 : 1.1 (-3.3 to 5.7); 

Lag 24-47 : 1.9 (-2.6 to 6.6); Lag 48-71 : 4.2 (-0.3 to 8.9); 

Lag 72-95 : 2.7 (-1.3 to 6.9); 5-day avg: 3.0 (-1.8 to 8.0) 

March 2009 C-3 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Dockery et al. 

(2005) 

Period of Study: 

1995 - 2002 

Location: 

Boston, MA 

Health Outcome: 

Tachyarrhythmias: 

Study Design: Panel 

Statistical Analyses: Logistic regression 

(GEE) 

Age Groups Analyzed: 19 – 90 years; mean age 64 years 

Sample Description: 

203 cardiac patients with ICDs within 

40km of air monitoring site at Harvard 

School of Public Health, Boston 

Averaging Time: 24-h 

Mean (SD) unit: NR 

Range (Min, Max): 

25th = 0.53; 75th = 1.02 

Copollutant: NR 

Increment: 0.48 ppm 

OR for Ventricular Arrhythmia [Lower CI, Upper CI] 

Lags examined (days): 0, 1, 2, 3 

Lag 2day ma: 1.14 (0.95-1.29) 

Among those who had an Arrhythmia – 

within 3 days : 1.65 (1.17-2.33) 

later than 3 days : 1.04 (0.83-1.29) 

Author: Metzger et al. 

(2007) 

Period of Study: 

1993 - 2002 

Location: 

Atlanta, GA 

Health Outcome: 

Cardiac Arrhythmia, ICD, 

Ventricular tachyarrhythmia 

Study Design: Panel

Statistical Analyses: 

Logistic regression (GEE) 

Age Groups Analyzed: 

15 – 88 years 

Sample Description: 

518 patients with ICDs with at least one 

ventricular tachyarrhythmic event 

Averaging Time: 1-h 

Mean (SD) unit: 1.7 ppm 

Range (Min, Max): 0.1, 

7.7 

Copollutant: NR 

 

Increment: 1 ppm 

OR for Tachyarrhythmic event [Lower CI, Upper CI] 

Lags examined (days) : 0 

Results for All events 

Lag 0 : 0.999 (0.970-1.028) 

Events resulting in cardiac pacing or defibrillation

Lag 0 : 1.008 (0.964-1.054) 

Events resulting defibrillation

Lag 0 : 1.012 (0.925-1.10.7) 

Author: Peters et al. 

(2000b) 

Period of Study: 

1995 - 1997 

Location: 

Eastern Massachusetts 

Health Outcome: 

Defibrillated discharges for ventricular 

tachycardia or fibrillation 

Study Design: Panel 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: 

Mean age of 62 years 

Sample Description:

100 patients with ICDs 

Averaging Time: 24-h 

Mean (SD) unit:

0.58 ppm 

Range (Min, Max): 

25th = 0.43; 75th = 0.66 

Copollutant: correlation 

PM10: r = 0.51 

PM2.5: r = 0.56 

NO2: r = 0.71 

SO2: r = 0.41 

O3: r = -0.40 

Increment: 

0.65 ppm (Lags 0, 1, 2, 3); 0.42 ppm (Lag 5-day mean) 

OR for Defibrillated Discharge [Lower CI, Upper CI] 

Lags examined (days): 0, 1, 2 ,3, 5-day mean 

At least one discharge: 

Lag 0: 1.07 (0.62-1.86); Lag 1 : 1.06 (0.61-1.85); 

Lag 2: 1.05 (0.62-1.77); Lag 3 : 0.09 (0.65-1.83); 

Lag 5-day mean : 1.23 (0.71- 2.12) 

At least 10 discharges:

Lag 0: 1.12 (0.54-2.32); Lag 1 : 1.13 (0.54-2.33); 

Lag 2: 1.62 (0.85-3.09); Lag 3 : 1.98 (1.05-3.72); 

Lag 5-day mean : 1.94 (1.01-.75) 

Author: Rich et al. (Rich et 

al.) 

Period of Study: 

February – December 

2000 

Location: 

Vancouver, Canada 

Health Outcome: Cardiac arrhythmia via 

patients ICD 

Study Design: 

Case-crossover 

Statistical Analyses: 

Conditional Logistic regression 

Age Groups Analyzed: 

15 – 85 years 

Sample Description:

34 patients who experienced at least 1 

ICD discharge (8201 person days) 

Averaging Time: 24-h 

Mean (SD) unit: 

553.8 ppb 

Range (Min, Max): 

IQR: 162.7 

Copollutant: correlation 

PM10: r = 0.40 

SO2: r =0.75 

NO2: r =0.68 

O3: r = -0.56 

Increment: NR 

RR Estimate [Lower CI, Upper CI] 

Lags examined (days): 0, 1, 2, 3 

No significant effect (results not reported in table). 

Author: Rich et al. (2005) 

Period of Study: 1995 - 1999 

Location: 

Boston, MA 

Health Outcome: Ventricular arrhythmias 

via ICD 

Study Design: Panel/Case-crossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: All 

Sample Description: 

203 patients with implanted ICD at the 

New England Medical Center 

Averaging Time: 1-h & 24-h 

Mean (SD) unit: NR 

Range (percentiles): 

1-h: 

25th = 0.46 

75th = 1.04 

24-h: 

25th = 0.52 

75th = 1.03 

Copollutant: NR 

Increment: 0.56 ppm; 0.54; 0.51; 0.49 respectively for 

results shown below 

OR Estimate [Lower CI, Upper CI] 

Ventricular Arrythmia 

Hours prior to event : 

0-2 : 1.01 (0.87-1.18) 

0-6 : 1.00 (0.85-1.17) 

0-23 : 1.03 (0.84-1.25) 

0-47 : 1.11 (0.88-1.40) 

March 2009 C-4 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Rich et al. (2006b) 

Period of Study: 

2001 & 2002 

Location:

St. Louis, MO 

Health Outcome: Ventricular arrhythmia 

Study Design: Case-crossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: All 

Sample Description:

60 subjects with at least 1 ICD recorded 

arrhythmia who lived within 40 km of 

St. Louis – Midwest supersite. 

Averaging Time: 24-h 

Mean (SD) unit: NR 

Range (Min, Max): 

25th = 0.4; 75th = 0.6 

Copollutant: NR 

 

Increment: 0.2 ppm 

OR for Ventricular Arrhythmia [Lower CI, Upper CI] 

Lags examined : 0-23 h-ma 

0-23h-ma : 0.99 (0.80-1.21) 

Author: Rich et al. (2006a) 

Period of Study: 

1995 - 1999 

Location: 

Boston, MA 

Health Outcome: ICD Episode of Atrial 

fibrillation 

Study Design: Panel/case-crossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: All 

Sample Description:

203 patients with ICDs at the New 

England Medical Center 

Averaging Time: 1-h & 

24-h 

Mean (SD) unit: NR 

Range (Min, Max): 

1-h: 

25th = 0.46; 75th = 1.04 

24-h: 

25th = 0.52; 75th = 1.03 

Copollutant: NR 

Increment: 

Lag (hrs) 0 : 0.58 ppm 

Lag (hrs) 0-23 : 0.51 ppm 

OR for Episode of Atrial Fibrillation [Lower CI, Upper 

CI] 

Lags (h) : 0, 0-23 

Lag 0 : 0.87 (0.56-1.37) 

Lag 0-23 : 0.71 (0.39-1.28) 

Author: Sarnat et al. 

(2006) 

Period of Study: 

24 weeks during the 

Summer and Fall of 2000 

Location:

Steubenville, OH 

Health Outcome: Arrhythmia via ECG 

measurements 

Study Design: Panel 

Statistical Analyses: 

Logistic regression 

Age Groups Analyzed: 

53-90 years (mean age 71) 

Sample Description:

32 non-smoking older adults 

Averaging Time: 24-h 

Mean (SD) unit:

0.02 ppm 

Range (Min, Max): -0.1, 

1.5 

Copollutant: correlation 

PM2.5: r = 0.45 

SO2: r = 0.62 

NO2: r = 0.66 

O3: r = -0.37 

Increment: 0.2 ppm 

RR Estimate [Lower CI, Upper CI] ; lag : 

Lags examined (days): 1, 2, 3, 4, 5, 5-day ma 

Lag 5-day ma : 

Supraventricular Ectopy 

SVE : 0.99 (0.76-1.29) 

Ventricular Ectopy 

VE : 1.05 (0.75-1.46) 

Author: Vedal et al. (2004) 

Period of Study: 

1997 - 2000 

Location: 

Vancouver, Canada 

Health Outcome: Cardiac arrythmia via 

patients with ICD 

Study Design: Panel 

Statistical Analyses: 

Logistic regression (GEE) 

Age Groups Analyzed: Range from 

12-77 (mean age 53) 

Sample Description:

50 patients who experienced 1 or more 

arrhythmia event days during the four 

years 

Averaging Time: 24-h 

Mean (SD) unit: 0.6 ppm 

Range (Min, Max): 

0.3, 1.6 

Copollutant: correlation 

PM10: r = 0.43 

SO2: r = 0.62 

NO2: r = 0.74 

O3: r = -0.52 

Increment: 0.2 ppm 

RR Estimate [Lower CI, Upper CI] 

Lags examined (days) : 0, 1, 2, 3 

No significant effect for CO (results shown in plots) 

CARDIAC ARREST 

Author: Levy et al. (2001) 

Period of Study: 1988 - 1994 

Location: 

Seattle, WA 

Health Outcome: Out of hospital primary 

cardiac arrest 

Study Design: Case-crossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: 

25-75 years 

Sample Description: 

 362 cases 

Averaging Time: 24-h 

Mean (SD) unit:

1.79 ppm 

Range (Min, Max): 

0.52, 5.92 

Copollutant: correlation 

PM10: r = 0.81 

SO2: r = 0.29 

Increment: NR 

RR Estimate [Lower CI, Upper CI] 

Lags examined (days): 0, 1 

Lag 1 : 0.99 (0.83-1.18) 

Author: Sullivan et al. 

(2003) 

Period of Study: 

1985 - 1994 

Location: 

Washington State 

Health Outcome: 

Out of Hospital Cardiac Arrest. 

Study Design: Case-crossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: All 

Sample Description:

1,542 members of a large health 

maintenance organization 

Averaging Time: 24-h 

Mean (SD) unit: 

1.92 ppm 

Range (Min, Max): 

0.52, 7.21 

Copollutant: NR 

Increment: 1.02 ppm 

OR Estimate [Lower CI, Upper CI] 

Lags examined (days) : 0, 1, 2 

Lag 0 : 0.95 (0.85-1.05) 

Lag 1 : 0.97 (0.87-1.08) 

Lag 2 : 0.99 (0.89-1.11) 

March 2009 C-5 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations CO Effect Estimates (95% CI) 

MYOCARDIAL INFARCTION 

Author: Peters et al. 

(2001) 

Period of Study: 

1995 - 1996 

Location:

Boston, MA 

Health Outcome: Onset of MI: 

Study Design: Case-crossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: All 

Sample Description: 

772 participants 

Averaging Time: 24-h 

Mean (SD) unit: 1.09 

Range 

(percentiles): ppm 

5th = 0.49 

95th = 1.78 

Copollutant: NR 

Increment: 2 H – 1 ppm; 24 h – 0.6 ppm 

OR Estimate [Lower CI, Upper CI] 

Onset of MI: 

2 h prior : 1.22 (0.89-1.67) 

24 h prior : 0.98 (0.70-1.36) 

Author: Rosenlund et al. 

(2006) 

Period of Study: 

1992 - 1994 

Location:

Stockholm, Sweden 

Health Outcome: MI 

Study Design: Case-control 

Statistical Analyses: Logistic regression 

Age Groups Analyzed: 

45 – 70 years 

Sample Description:

1,397 cases, 1,870 controls 

Averaging Time: 

Mean (SD) unit: 

66.8 μg/m3 

(est. 30yr residential 

exposure) 

Range (percentiles):

5th = 13.9; 95th = 295.7 

Copollutant: NR 

Increment: 300 μg/m3

OR Estimate [Lower CI, Upper CI] ; lag : 

Estimated 30 yr avg. exposure 

All cases : 1.04 (0.89-1.21) 

Non-fatal cases : 0.98 (0.82-1.16) 

Fatal cases : 1.22 (0.98-1.52) 

In-hospital death : 1.16 (0.89-1.51) 

Out-of-hospital death : 1.36 (1.01-1.84) 

CHANGES IN BLOOD PRESSURE 

Author: 

Ibalde-Mulli et al. (2001) 

Period of Study: 

1984 - 1985 

Location: 

Augsburg, Germany 

Health Outcome: BP - SPB 

Study Design: Cohort 

Statistical Analyses: 

Gaussian regression for repeated 

measures 

Age Groups Analyzed: 25-64 years 

Sample Description: 

2,607 men & women aged 25-64 years 

Averaging Time: 24-h 

Mean (SD) unit: 

4.1 mg/m3

Range (Min, Max): 

1.7, 8.2 

Copollutant: NR 

Increment: Lag 0 : 5.6 mg/m3

5-day prior avg. 

Mean Change [Lower CI, Upper CI] 

SPB mmHg 

Lag 0 (days): 

All : 0.53 (-0.66 to 1.72); Men : 0.68 (-0.94 to 2.31); 

Women : 0.51 (-1.31 to 2.19) 

5-day prior avg: 

All : 1.06 (-0.17 to 2.29); Men : 0.92 (-0.87 to 2.70); 

Women : 0.91 (-0.87 to 2.70) 

Author: Zanobetti et al. 

(2004a) 

Period of Study: 

1999 - 2001 

Location: 

Boston, MA 

Health Outcome: BP 

Study Design: Cohort/Panel 

Statistical Analyses: Random effects 

Age Groups Analyzed: 39 – 90 years 

Sample Description:

62 subjects with 631 total visits 

Averaging Time: 

1-h & 120-h avg 

Mean (SD) unit: 

Same Hr: 0.81 ppm 

120 Hr av: 0.66 ppm 

Range (Min, Max): 

Same h: 

10th = 0.48; 90th = 1.22 

120-h av: 

10th = 0.48; 90th = 0.86 

Copollutant: NR 

Increment: NR 

RR Estimate [Lower CI, Upper CI] 

CO had no significant effect on BP 

CHANGES IN BLOOD MARKERS OF COAGULATION AND INFLAMMATION 

Author: Baccarelli et al. 

(2007) 

Period of Study: 1995 - 2005 

Location: 

Milan, Italy 

Health Outcome: Prothrombin time (PT) 

and Activated partial thromboplastin time 

(APTT) 

Study Design: Panel 

Statistical Analyses: GAMS 

Age Groups Analyzed: 

11-84 years (mean 43years) 

Sample Description:

1,218 healthy individuals who were 

partners or friends of patients with 

thrombosis who attended the thrombosis 

center of the University of Milan. 

Averaging Time: 1-h 

Mean (SD) unit: NR 

Range (percentiles): 

Sept-Nov: 

25th = 1.36; 75th = 3.52 

Dec-Feb: 

25th = 2.00; 75th = 4.31 

Mar-May: 

25th = 1.03; 75th = 2.14 

Jun-Aug: 

25th = 0.73; 75th = 1.58 

Copollutant: NR 

Increment: NR 

Regression co-efficient [Lower CI, Upper CI] 

Lags examined (time of blood sampling – avg): 0, 7, 30 

PT: 

Lag 0 : -0.11 (-0.18 to -0.05); Lag 7 : -0.07 (-0.14 to 0.01); 

Lag 30 : -0.05 (-0.13 to 0.02) 

APTT: 

Lag 0 : 0.03 (-0.04 to 0.10); Lag 7 : 0.04 (-0.04 to 0.11); Lag 

30 : 0.06 (-0.01 to 0.14) 

Notes: CO had no effect on fibrinogen, functional 

antithrombin, functional protein C, protein C antigen, 

functional protein S, free protein S for all lag periods. 

Author: Liao et al. (2005) 

Period of Study: 1996 to 1998 

Location:

Forsyth County, NC; 

Selected suburbs of 

Minneapolis, MN; 

Jackson, MI 

Health Outcome: Various measures of 

hemostasis/ inflammation 

Study Design: Cohort 

Statistical Analyses: 

Linear regression 

Age Groups Analyzed: 45 – 64 years 

Sample Description:

10208 subjects from the Atherosclerosis 

Risk in Communities Study 

Averaging Time: 24-h 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutant: NR 

Increment: 0.6 ppm 

Regression coefficients [SE] 

Lags examined (days): 1 

Lag 1: 

Fibrinogen (mg/dL) : -0.16 (0.67) 

Factor VIII –C (%) : 0.45 (0.42) 

vWF % : -0.29 (0.50) 

WBC (x 103/mm3) : 0.003 (0.017) 

Albumin (g/dL) : -0.018 (0.003)** 

** p < 0.01 

March 2009 C-6 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Pekkanen et al. 

(2000) 

Period of Study: 

1991 - 1993 

Location: 

London, England 

Health Outcome: Fibrinogen 

Study Design: Cohort 

Statistical Analyses: Logistic regression 

Age Groups Analyzed: 

35 – 55 years 

Sample Description:

7,205 office workers 

Averaging Time: 8-h 

Mean (SD) unit: 

1.4 mg/m3

Range (Min, Max): 

Min = NR, Max = 9.9 

Copollutant correlation: 

PM10: r = 0.57 

NO2: r = 0.81 

SO2: r = 0.61 

O3: r = -0.45 

Increment: 1.6 mg/m3

% Change in Fibrinogen Concentration [p value] ; 

Lags examined : 0, 1, 2, 3 

Lag 0 : 1.43 (<0.01); Lag 1 : 1.49 (<0.01); 

Lag 2 : 1.59 (<0.01); Lag 3 : 1.26 (<0.01) 

OR for having Fibrinogen above 3.19 g/l [p value] 

Lags examined : 0, 1, 2, 3 

Lag 0 : 1.17 (0.05); Lag 1 : 1.09 (0.31); 

Lag 2 : 1.14 (0.11); Lag 3 : 1.22 (<0.01) 

Author: Ruckerl et al. 

(2006) 

Period of Study: 

2000 - 2001 

Location: 

Erfert, Germany 

Health Outcome: Blood markers of 

inflammation and coagulation 

Study Design: Panel 

Statistical Analyses: Linear and logistic regression (fixed 

effects) 

Age Groups Analyzed: 

51-76 years (mean age 66 years) 

Sample Description:

57 male patients with CHD 

Averaging Time: 24-h 

Mean (SD) unit: 

0.52 mg/m3

Range (Min, Max): 

0.11, 1.93 

Copollutant correlation: 

NO2: r = 0.82 

 

Increment: 0.27 mg/m3

OR Estimate for blood marker >90th percentile [Lower 

CI, Upper CI] 

Lags examined (h): 0-23, 24-47, 48-71, 

5-day avg. 

CRP (C-reactive protein)

0-23 : 0.9 (0.7-1.2); 24-47 : 1.0 (0.7-1.5); 

48-71 : 1.5 (1.1-2.1); 5-day avg 1.1 (0.8-1.6) 

ICAM-1 (Intercellular adhesion molecule 1) 

0-23 : 0.8 (0.6-1.0); 24-47 : 1.5 (1.2-1.9); 

48-71 : 1.7 (1.3-2.3); 5-day avg 1.2 (1.0-1.6) 

% of change from the mean of blood marker 

vWF (von Willebrand factor antigen)

0-23 : 4.4 (1.4- 7.5); 24-47 : 2.7 (-0.8 to 6.1); 

48-71 : 2.0 (-1.7 to 5.8); 5-day avg : 4.9 (1.0-8.8) 

FVII (Factor VII) 

0-23 : -1.4 (-3.8 to 1.1); 24-47 : -2.6 (-4.8 to 0.3); 

48-71 : -2.8 (-5.1 to -0.4); 5-day avg : -3.0 (-5.5 to -0.4) 

Author: Ruckerl et al. 

(2007) 

Period of Study: 

May 2003-July 2004 

Location:

6 cities across Europe: 

Athens, Greece; 

Augsburg, Germany; 

Barcelona, Spain; 

Helsinki, Finland; 

Rome, Italy; 

Stockholm, Sweden 

Health Outcome: Interleukin-6, 

C-reactive protein, Fibrinogen 

Study Design: Panel/Cohort 

Statistical Analyses: 

Linear regression (mixed effects) 

Age Groups Analyzed: 

37-81 years 

Sample Description:

1,003 MI survivors who had at least 2 valid 

repeated blood samples 

Averaging Time: 24-h 

Mean (SD) unit: 

 Athens: 1.48 mg/m3

Augsburg: 0.58 mg/m3

Barcelona: 0.59 mg/m3

Helsinki: 0.31 mg/m3

Rome: 1.40 mg/m3

Stockholm: 0.29 mg/m3

Range (Min, Max): NR 

Copollutant: NR 

 

Increment: 0.34 mg/m3

% Change in mean [Lower CI, Upper CI] 

Lags examined: 0, 1, 2, 5-day avg 

(Pooled estimates) 

Interleukin-6

Lag 0: 0.57 (-0.63 to 1.79); Lag 1 : 0.44 (-0.79 to 1.68); 

Lag 2: -2.36 (-4.82 to 0.17); 5-day avg: -0.28 (-2.53 to 2.02)

C-reactive protein

Lag 0 : -0.01 (-1.72 to 1.73); Lag 1 : -1.51 (-3.30 to 0.32); 

Lag 2 : -2.35 (-6.84 to 2.36); 

5-day avg : -0.85 (-.5.37 to 3.90) 

Fibrinogen

Lag 0 : 0.24 (-0.54 to 0.92); Lag 1 : 0.32 (-0.35 to 1.00); 

Lag 2 : -0.44 (-1.11 to 0.23); 5-day avg : 0.12 (-0.81 to 1.05)

Author: Steinvil et al. 

(2008) 

Period of Study: 

2003 - 2006 

Location: 

Tel Aviv, Israel 

Health Outcome: Various measures of 

inflammation sensitive biomarkers 

Study Design: Cohort 

Statistical Analyses: Linear regression 

Age Groups Analyzed: 

 mean age 46 years 

Sample Description:

3659 subjects living within 11 km of 

monitoring site 

Averaging Time: 24-h 

Mean (SD) unit: 0.8 ppm 

Range (percentiles): 

25th = 0.7; 75th = 1.0 

Copollutant: correlation 

PM10: r = 0.75 

NO2: r = 0.857 

SO2: r = 0.671 

O3: r = -0.656 

 

Increment: 0.3 ppm 

Regression co-efficient [Lower CI, Upper CI] 

Lags examined (days): 0, 1, 2, 3, 4, 5, 6, 7, last week avg 

Fibrinogen – Men

Lag 0 : -3.3 (-6.1 to -0.6); Lag 1 : -2.6 (-5.5 to 0.4); 

Lag 2 : -3.4 (-6.6 to -0.3); Lag 3 : -3.4 (-6.5 to -0.2); 

Lag 4 : -5.9 (-8.9 to -2.9); Lag 5 : -4.7 (-7.8 to -1.6); 

Lag 6 : -2.0 (-5.1 to 1.0); Lag 7 : -2.7 (-5.7 to 0.2); 

Last week avg: -7.7 (-12.1 to -3.3) 

Notes: No effect on fibrinogen among women. CO had no 

effect on CRP among men and no effect on CRP and WBC 

among women for all Lag times examined. 

March 2009 C-7 DRAFT—DO NOT QUOTE OR CITE 
Table C-2. Studies of CO exposure and cardiovascular hospital admissions and ED visits. 

Study Design Concentrations CO Effect Estimates (95% CI) 

STROKE 

Author: Chan et al. (2006) 

Period of Study: 1997 - 2002 

Location: 

Taipei, Taiwan 

ED Visits 

Health Outcome (ICD9): 

Cerebrovascular disease 

(430-437); Strokes (430-434); 

Hemorrhagic stroke (430-432); Ischemic 

stroke (433-434) 

Study Design: Time-series 

Statistical Analyses: GAM 

Age Groups Analyzed: All 

Sample Description: NR 

Averaging Time: 8-h 

Mean (SD) unit: 

1.7 ppm 

Range (Min, Max): 0.6, 4.4 

Copollutant: correlation 

O3: r = 0.30 

SO2:r = 0.63 

NO2: r = 0.77 

PM2.5: r = 0.44 

PM10: r = 0.47 

Increment: 0.8 ppm 

OR Estimate [Lower CI, Upper CI] 

Lags (days) examined 0, 1, 2, 3 

Cerebrovascular disease: Lag 2, 1.03 (1.01, 

1.06) 

Stroke : Lag 2, 1.03 (1.01, 1.05) 

Ischemic and Hemorrhagic stroke : not 

significant. 

Cerebrovascular 2 pollutant model: 

CO + O3 : Lag 2, 1.03 (1.01-1.05) 

CO + PM2.5 : Lag 2, 1.02 (1.00-1.04) 

CO + PM10 : Lag 2, 1.03 (1.01-1.05) 

Author: Henrotin et al. (2007) 

Period of Study: 1994 - 2004 

Location: 

Dijon, France 

Health Outcome (ICD9 or ICD10): Stroke (Ischemic & Hemorrhagic) 

Study Design: Bi-directional Casecrossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: ≥ 40 

Sample Description: NR 

Averaging Time: 24-h 

Mean (SD) unit: 683 μg/m3 

Range (Min, Max): 0, 4014 

Copollutant: NR 

Increment: 10 μg/m3

OR Estimate [Lower CI, Upper CI] 

Lags (days) examined: 0, 1, 2, 3. 

Ischemic:

Lag 0 : 0.999 (0.997-1.001) 

Lag 1 : 0.998 (0.997-1.001) 

Lag 2 : 0.999 (0.998-1.001) 

Lag 3 : 1.000 (0.998-1.001) 

Hemorrhagic:

Lag 0 : 1.000 (0.996-1.004) 

Lag 1 : 1.001 (0.997-1.005) 

Lag 2 : 0.999 (0.995-1.004) 

Lag 3 : 0.998 (0.994-1.002) 

Also not significant when stratified by sex. 

Author: Maheswaran et al. 

(2005b) 

Period of Study: 1994 - 1998 

Location: 

Sheffield, UK 

Health Outcome (ICD9 or ICD10): 

Stroke deaths (ICD9: 430-438); Stroke 

Hospital admissions (ICD10: I60-I69) 

Study Design: Ecological 

Statistical Analyses: 

Poisson regression 

Age Groups Analyzed: ≥ 45 years 

Sample Description: 1,030 census districts 

Averaging Time: NR 

Mean (SD) unit: Quintiles 

Range (Min, Max): NR 

Copollutant: NR 

Increment: 

NR – Quintiles of exposure 

RR Estimate [Lower CI, Upper CI] 

Adjusted for sex, age, deprevation, smoking.

Quintiles: 

2nd : 1.04 (0.94-1.16) 

3rd : 1.01 (0.91-1.13) 

4th : 1.10 (0.99-1.23) 

5th : 1.11 (0.99-1.25) 

Adjusted for sex, age: 

2nd : 1.11 (1.01-1.22) 

3rd : 1.15 (1.04-1.27) 

4th : 1.29 (1.17-1.42) 

5th : 1.37 (1.24-1.52) 

Author: Tsai et al. (2003b)

Period of Study: 

1997-2000 

Location: 

Kaohsiung, Taiwan 

Study Design: Case-crossover 

Health Outcome (ICD9 or ICD10): 

Cerebrovascular diseases: ICD9: 430 to 

438 (Subarachnoid hemorrhagic stroke 

430, Primary intracerebral hemorrhage 

(PIH): 431-432, Ischemic stroke (IS): 433-

435). 

Statistical Analyses: NR 

Age Groups Analyzed: All 

Sample Description: NR 

Averaging Time: 24-h 

Mean (SD) unit: 0.79 ppm 

Range (Min, Max): 0.24, 1.72 

Copollutant: NR 

Increment: 0.8 ppm (IQR) 

RR Estimate [Lower CI, Upper CI] 

Lag (days): 0-2 

>20oC 

PIH : OR 1.21 (1.09-1.34) 

IS : OR 1.21 (1.14-1.28) 

<20oC 

PIH : OR 1.18 (0.80-0.72) 

IS : OR 1.77 (1.31-2.39) 

Notes: 

2-pollutant models: 

PIH results persisted when adjusting for SO2

and O3

IS results persisted when controlling for PM10, 

SO2 and O3

March 2009 C-8 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Villeneuve et al. 

(2006a) 

Period of Study: 1992-2002 

Location: 

Edmonton, Canada 

ED Visits (within 5 hospitals) 

Health Outcome (ICD9): Stroke (430-

438); Ischemic (434-436) Hemorrhagic 

(430-432); Transient Ischemic Attack 

(435) 

Study Design: Case-crossover 

Statistical Analyses: Conditional logistic 

regression 

Age Groups Analyzed: 65+ 

Sample Description: 

12,422 visits 

Averaging Time: 24-h 

Mean (SD) unit: 0.8 ppm 

Range (percentiles): 

25th = 0.5; 75th = 1.0 

Copollutant correlation : 

O3: r = -0.54 

PM2.5: r = 0.43 

PM10: r = 0.30 

Increment: 0.5 ppm 

OR Estimate [Lower CI, Upper CI] 

Lags (days) examined : 0, 1 & 0-2 

Ischemic (April-Sept) 

Lag 0 : 1.16 (1.00, 1.33) 

Lag 1 : 1.17 (1.01, 1.36) 

Lag 0-2 : 1.32 (1.09, 1.60) 

Notes:

- Not significant for all seasons or Oct-Mar. 

- Hemorrhagic : Not significant for all seasons 

or Oct-Mar, Apr-Sept. 

- Transient Ischemic Attack : Not significant 

for all seasons or Oct-Mar, Apr-Sept. 

Author: Wellenius et al. (2005a) 

Period of Study: NR 

Location: 

9 U.S. cities: Chicago, Detroit, 

Pittsburgh, Cleveland, 

Birmingham, New Haven, 

Seattle, Minneapolis, Salt Lake 

City 

ED Visits 

Health Outcome: 

Stroke among Medicare beneficiaries: 

(Ischemic, hemorrhagic) 

Study Design: Time-series 

Statistical Analyses: 

Logistic regression 

Age Groups Analyzed: ≥ 65 years 

Sample Description: 155,503 visits 

Averaging Time: NR 

Mean (SD) unit: NR 

Range (percentiles): 

25th = 0.73; 50th = 1.02; 

75th = 1.44 (ppm) 

Copollutant: correlation 

PM10: r = 0.43 

Increment: 0.71 ppm 

% Change [Lower CI, Upper CI] 

Lag : 0 

Ischemic : 2.83 (1.23-4.46) 

Hemorrhagic : -1.61 (-4.79 to 1.68) 

ISCHEMIC HEART DISEASE 

Author: D’Ippoliti et al. (2003) 

Period of Study: 1995 - 1997 

Location: 

Rome, Italy 

Hospital Admissions 

Health Outcome (ICD9): MI (410) 

Study Design: Case-crossover 

Statistical Analyses: Conditional logistic 

regression 

Age Groups Analyzed: 18+ 

Sample Description: 

6,531 patients. 

Averaging Time: 24-h 

Mean (SD) unit: 4.4 mg/m3

Range (percentiles): 

25th = 2.8; 75th = 4.3 

Copollutant: correlation 

TSP: r = 0.35 

SO2: r = 0.56 

NO2: r = 0.31 

Increment: 1 mg/m3

OR Estimate [Lower CI, Upper CI] ; lag : 

Lags examined (days): 0, 1, 2, 3, 4, 0-2 

Acute MI 

Lag 0 : 1.021 (0.988-1.054) 

Lag 1 : 1.020 (0.988-1.054) 

Lag 2 : 1.033 (1.001-1.066) 

Lag 3 : 1.010 (0.982-1.040) 

Lag 4 : 1.025 (0.996-1.055) 

Lag 0-2 : 1.044 (1.000- .089 

Author: Hosseinpoor et al. 

(2005) 

Period of Study:

1996 - 2001 

Location: 

Tehran, Iran 

Health Outcome: Angina Pectoris (ICD9: 413; ICD10: I20) 

Study Design: Time-series 

Statistical Analyses: 

Poisson regression 

Age Groups Analyzed: All 

Sample Description: NR 

Averaging Time: 24-h 

Mean (SD) unit: 10.8 mg/m3

Range (Min, Max): 1.6, 57.8 

Copollutant: NR 

Increment: 1 mg/m3

RR Estimate [Lower CI, Upper CI] 

Lags examined (days): 0, 1, 2, 3 

Lag 1 : 1.00957 (1.00600-1.01315) 

March 2009 C-9 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Lanki et al. (2006) 

Period of Study: 1994 - 2000 

Location: 

5 European cities: 

Augsburg, Germany 

Barcelona, Spain 

Helsinki, Finland 

Rome, Italy 

Stockholm, Sweden 

Health Outcome: 

First AMI (ICD9: 410; ICD10: I21, I22) 

Study Design: Time-series 

Statistical Analyses: Poisson regression (GAM) 

Age Groups Analyzed: 35+ years 

Sample Description: 

26,854 Hospital Admissions 

Averaging Time: 24-h 

Mean (SD) unit: NR 

Unit: mg/m3

Range (percentiles): 

Augsburg, Germany 

25th = 0.7; 75th = 1.1 

Barcelona, Spain 

25th = 0.6; 75th = 1.4 

Helsinki, Finland 

25th = 0.3; 75th = 0.5 

Rome, Italy 

25th = 1.7; 75th = 2.9 

Stockholm, Sweden 

25th = 0.3; 75th = 0.5 

Copollutant: correlation 

PM10: r = 0.21 – 0.56 

NO2: r = 0.43 – 0.75 

O3 : r = -.023 – 020 

Increment: 0.2 mg/m3

RR Estimate [Lower CI, Upper CI] ; lag : 

Lags examined : 0, 1, 2, 3 

All 5 cities: 

Lag 0 : 1.005 (1.000-1.010) 

Lag 1 : 1.002 (0.996-1.007) 

Lag 2 : 1.002 (0.997-1.007) 

Lag 3 : 0.998 (0.992-1.003) 

3 cities with Hospital Discharge 

Register(HDR): 

Lag 0 : 1.007 (1.001-1.012) 

Lag 1 : 1.002 (0.996-1.008) 

Lag 2 : 1.003 (0.998-1.009) 

Lag 3 : 1.004 (0.988-1.020) 

3 cities with HDR – ≤ 75years 

Fatal: 

Lag 0 : 1.027 (1.006-1.048) 

Lag 1 : 1.021 (1.000-1.042) 

Lag 2 : 1.018 (0.997-1.039) 

Lag 3 : 1.015 (0.994-1.037) 

Non-Fatal: 

Lag 0 : 1.001 (0.995-1.008) 

Lag 1 : 1.000 (0.994-1.007) 

Lag 2 : 1.004 (0.998-1.011) 

Lag 3 : 0.999 (0.992-1.006) 

3 cities with HDR – ≥ 75years 

Fatal: 

Lag 0 : 1.009 (0.992-1.006) 

Lag 1 : 1.001 (0.985-1.018) 

Lag 2 : 1.006 (0.990-1.023) 

Lag 3 : 1.000 (0.983-1.017) 

Non-Fatal: 

Lag 0 : 1.015 (1.004-1.086) 

Lag 1 : 1.006 (0.995-1.017) 

Lag 2 : 0.995 (0.983-1.006) 

Lag 3 : 0.998 (0.987-1.009) 

Author: Lee et al. (2003b) 

Period of Study: 1997 - 1999 

Location: 

Seoul, Korea 

Study Design: Time-series 

Health Outcome (ICD9 or ICD10): 

Angina: ICD10: 120 

AMI: ICD10: I21-I23 

Other Acute IHDs: ICD10: I24 

Statistical Analyses: 

Poisson regression, GAM 

Age Groups Analyzed: 64 + 

Sample Description: 822 days 

Averaging Time: 

Daily max 

Mean (SD) unit:

1.8 ppm 

Range (percentiles): 

25th = 1.2 

75th = 2.2 

Copollutant: correlation 

PM20: 0.60 

SO2: 0.81 

NO2: 0.79 

O3: -0.39 

Increment: 1 ppm (IQR) 

RR Estimate [Lower CI, Upper CI] 

Lags examined (days) : 0, 1, 2, 3, 4, 5, 6 

All year: 

Lag 5 : All ages : 0.94 (0.91 0.98) 

Lag 5 : 64+ age : 1.07 (1.01-1.13) 

Summer: 

Lag 5 : All ages : 1.19 (1.02-1.38) 

Lag 5 : 64+ age : 1.60 (1.27-2.03) 

2-pollutant model: 

Lag 5 : 64+ age : 

CO + PM10 : 1.04 (0.98-1.11) 

Author: Maheswaran et al. 

(2005b)

Period of Study: 

1994 - 1998 

Location: 

Sheffield, UK 

Emergency Hospital Admission 

Health Outcome (ICD9): 

CHD (410-414) 

Study Design: Ecological 

Statistical Analyses: Poisson regression 

Age Groups Analyzed: 45+ years 

Sample Description: 

11,407 Emergency Hospital Admissions 

for CHD in patients 45+ years (within 

1,030 census districts) 

Averaging Time: NR 

Mean (SD) unit: Quintiles 

Range (Min, Max): NR 

Copollutant: NR 

Increment: NA 

RR Estimate [Lower CI, Upper CI] 

Lowest quintile reference category 

Adjusted for sex, age, deprivation, smoking: 

2nd : 0.97 (0.89-1.07) 

3rd : 0.94 (0.86-1.04) 

4th : 0.96 (0.97-1.06) 

5th : 0.88 (0.79- 0.98) 

Adjusted for sex, age: 

2nd : 1.09 (1.00-1.19) 

3rd : 1.15 (1.05-1.26) 

4th : 1.19 (1.09-1.30) 

5th : 1.20 (1.09-1.32) 

March 2009 C-10 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Mann et al. (2002) 

Period of Study: 1988 - 1995 

Location: 

Southern California 

Health Outcome (ICD9): IHD (IHD) (410-

414); Myocardial Infarction (MI) (410) 

Study Design: Time-series 

Statistical Analyses: Poisson regression, GAM 

Age Groups Analyzed: All 

Sample Description: 

54,863 IHD admissions among Southern 

California Kaiser- Permanente members 

(within 20km of monitor) 

Averaging Time: 8-h 

Mean (SD) unit: 2.07 ppm 

Range (Min, Max): 0.30, 11.8 

Copollutant: correlation 

Ranging across 7 regions: 

NO2: r = 0.64, 0.86 

O3: r = -0.37, 0.28 

PM10: r = 0.15, 0.40 

 

Increment: 1 ppm 

% Change [Lower CI, Upper CI] 

Lags examined (days) : 0, 1, 2, 2 ma, 3 ma, 4 

ma 

With arrythmia:

Lag 0 : 2.99 (1.80-4.99) 

Lag 1 : 1.51 (0.37-2.66) 

Lag 2 : 1.26 (0.15-2.38) 

2 ma : 2.66 (1.40-3.94) 

3 ma : 2.59 (1.27-3.92) 

4ma : 2.25 (0.90-3.63) 

With CHF:

Lag 0 : 3.60 (1.620-5.63) 

Lag 1 : 3.34 (1.48-5.22) 

Lag 2 : 1.90 (0.11-3.72) 

2 ma : 4.23 (2.13-6.37) 

3 ma : 4.14 (1.96-6.37) 

4 ma : 4.07 (1.81-6.38) 

Without secondary diagnosis: 

Lag 0 : 1.62 (0.65-2.59) 

Lag 1 : 1.45 (0.54-2.37) 

Lag 2 : 0.92 (0.04-1.82) 

2 ma : 1.83 (0.80-2.86) 

3 ma : 1.79 (0.72-2.87) 

4 ma : 1.82 (0.71-2.94) 

Author: Szyszkowicz (2007)

Period of Study: 1997 - 2003

Location: Montreal, Canada 

Study Design: Time-series 

Health Outcome (ICD9 or ICD10): 

ED Visits. 

IHD: ICD9: 410-414 

Statistical Analyses: 

Poisson regression (GLMM) 

Age Groups Analyzed: All 

Sample Description: 4979 ED Visits 

Averaging Time: 24-h 

Mean (SD) unit: 0.5 ppm 

Range (Min, Max): 0.1, 3.1 

Copollutant: NR 

 

Increment: 0.2 ppm 

% Change [Lower CI, Upper CI] ; lag : 

Lags examined (days): 0, 1 

All Patients: Lag 0 : 5.4 (2.3-8.5) 

Males: Lag 0 : 7.5 (3.6-11.6) 

Females: Lag 0 : 2.7 (-2.0 to 7.6) 

Ages ≥ 64 

All Patients: Lag 0 : 4.9 (1.3-8.7) 

Males: Lag 0 : 7.5 (2.6-12.6) 

Females: Lag 0 : 2.4 (-3.0 to.0) 

Lag 1 not significant for all results 

Author: von Klot et al. (2005) 

Period of Study: 1992 - 2001 

Location: 

5 European cities: 

Augsburg, Germany 

Barcelona, Spain 

Helsinki, Finland 

Rome, Italy 

Stockholm, Sweden 

Health Outcome: Hospital Cardiac 

(Myocardial Infacrtion (MI), Angina, 

Dysrythmia, Heart Failure) Re-admissions 

Study Design: 

Prospective Cohort 

Statistical Analyses: Poisson regression 

Age Groups Analyzed: All 

Sample Description:22006 survivors of 

first MI 

Averaging Time: 24-h 

Unit: mg/m3

Mean (SD) unit: 

Augsburg, Germany: 0.93 

Barcelona, Spain: 1.00 

Helsinki, Finland: 0.42 

Rome, Italy: 2.21 

Stockholm, Sweden: 0.43 

Range (Min, Max): NR 

Copollutant: correlation 

PM10: r = 0.21 – 0.57 

NO2: r = 0.44 – 0.75 

O3 : r = -.027 – 0.47 

Increment: 0.2 mg/m3 (0.172 ppm) 

RR Estimate [Lower CI, Upper CI] 

Lags examined (days): 0, 1, 2, 3 

Lag 0: 

MI :1.022 (0.998-.047) 

Angina : 1.009 (0.992-.02) 

Cardiac : 1.014 (1.001-.026) 

HEART FAILURE 

Author: Lee et al. (2007a) 

Period of Study: 1996 - 2004 

Location: 

Kaohsiung City, Taiwan 

Hospital Admissions 

Health Outcome (ICD9): CHF (428) 

Study Design: Case-crossover 

Statistical Analyses: Conditional logistic 

regression 

Age Groups Analyzed: All 

Sample Description: 

13475 Hospital Admissions (63 Hospitals) 

Averaging Time: 24-h 

Mean (SD) unit: 0.76 ppm 

Range (Min, Max): 0.14, 1.72 

Copollutant: NR 

 

Increment: 0.31 ppm 

OR Estimate [Lower CI, Upper CI] 

Lag examined (days): 0-2 

≥ 25°C: 1.19 (1.09-1.31) 

<25°C: 1.39 (1.24-1.54) 

Adjusted for PM10: 

≥ 25°C; 1.15 (1.04-1.27) 

<25°C; 1.21 (1.206-1.38) 

Adjusted for SO2:

≥ 25°C: 1.23 (1.11-1.36) 

<25°C: 1.39 (1.24-1.55) 

Adjusted for NO2:

≥ 25°C: 1.22 (1.08-1.39) 

<25°C: 0.94 (0.81-1.10) 

Adjusted for O3: 

≥ 25°C: 1.17 (1.07-1.28) 

<25°C: 1.36 (1.22-1.51) 

March 2009 C-11 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Symons et al. (2006) 

Period of Study: 2002 (April – 

November) 

Location: 

Johns Hopkins Bayview Medical 

Center, Baltimore, MD 

Hospital Admissions 

Health Outcome: NR 

Study Design: Case-crossover 

Statistical Analyses: Conditional logistic regression 

Age Groups Analyzed: All 

Sample Description: 

398 Hospital Admissions for CHF 

Averaging Time: 24-h 

Mean (SD) unit: 0.4 ppm 

Range (Min, Max): 0.1, 1.0 

Copollutant: NR 

Increment: 0.2 ppm 

OR Estimate [Lower CI, Upper CI] 

Lags examined (days): 

0, 1, 2, 3, cum 1, cum 2, cum 3 

Lag 0 : 0.86 (0.67-1.11) 

Lag 1 : 0.90 (0.70-1.17) 

Lag 2 : 0.96 (0.73-1.26) 

Lag 3 : 0.88 (0.67-1.16) 

Cum. Lag1 : 0.82 (0.60-1.13) 

Cum. Lag2 : 0.80 (0.54-1.17) 

Cum. Lag3 : 0.27 (0.46-1.14) 

Author: Wellenius et al. (2005b) 

Period of Study: 1987 - 1999 

Location: 

Pennsylvania, PA 

Hospital Admissions 

Health Outcome (ICD9): CHF 

(428, 428.1) 

Study Design: Case-crossover 

Statistical Analyses: Conditional logistic 

regression 

Age Groups Analyzed: 65+ 

Sample Description: 

54019 Hospital Admissions among 

Medicare beneficiaries 

Averaging Time: 24-h 

Mean (SD) unit: 1.03 ppm 

Range (percentiles): 

25th = 0.68; 75th = 1.23 

Copollutant: correlation 

PM10: r = 0.57 

NO2: r = 0.70 

O3: r = -0.25 

SO2: r = 0.54 

Increment: 0.55 ppm 

% Change [Lower CI, Upper CI] 

Lags examined (days): 0, 1, 2, 3 

Lag 0:

Single pollutant model: 4.55 (3.33-5.79) 

Adjusted for PM10 : 5.18 (3.49-6.89) 

Adjusted for NO2 : 4.84 (3.06-6.66) 

Adjusted for O3 : 4.35 (3.08-5.64) 

Adjusted for SO2 : 4.51 (3.15-5.90) 

CARDIOVASCULAR DISEASES – NON-SPECIFIC 

Author: Ballester et al. (2001) 

Period of Study: 1994 - 1996 

Location: Valencia, Spain 

ED Visits 

Health Outcome (ICD9: CVD (390-459); 

Heart Diseases (410-414, 427, 428); 

Cerebrovascular Disease (430 – 438) 

Study Design: Time-series 

Statistical Analyses: Poisson regression 

Age Groups Analyzed: All 

Sample Description: NR 

Averaging Time: 24-h 

Mean (SD) unit: 6.2 mg/m3

Range (Min, Max): 0.6, 17.8 

Copollutant: correlation 

BS: r = 0.64 

NO2: r = 0.03 

SO2: r = 0.74 

O3: r = -0.26 

Increment: 1 mg/m3

RR Estimate [Lower CI, Upper CI] ; lag: 

Lags examined (days): 0, 1, 2, 3, 4, 5 

All cardiovascular: 

Lag 2 : 1.0077 (0.9912-1.0138) 

Heart Disease: 

Lag 1 : 1.0092 (0.9945-1.0242) 

Cerebrovascular Disease: 

Lag 1 : 0.9874 (0.9646-1.0107) 

Author: Ballester et al. (2006) 

Period of Study: 1995 - 1999 

Location: 14 Cities in Spain 

Health Outcome (ICD9: 

All CVD (390-459);Heart Diseases (410-

414, 427, 428) 

Study Design: Time-series 

Statistical Analyses: GAM 

Age Groups Analyzed: All 

Sample Description: NR 

Averaging Time: 8-h 

Mean (SD) unit: 

Range across 14 cities, 

1.4-2.8 mg/m3

Range (percentiles): 

10th = 0.4-1.7; 90th = 2.0-3.9 

Copollutant: NR 

Increment: 1 mg/m3

% Change [Lower CI, Upper CI] 

Lags examined (days): 0-1 

All CVD: Lag 0-1 : 2.06 (0.65-3.48) 

Heart Disease: Lag 0-1 : 4.15 (1.31-7.08) 

Author: Barnett et al. (2006) 

Period of Study: 1998 – 2001 

Location: 

Brisbane, Canberra, Melbourne, 

Perth, Sydney Australia 

Auckland & Christchurch, New 

Zealand 

Hospital Admissions with 

Cardiovascular Diseases 

Health Outcome (ICD9: Arrythmia (247); 

Cardiac Disease (390 – 429); Cardiac 

Failure (428); IHD (410-413); MI (410); 

Total CVD (390-459) 

Study Design: Case-crossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: 

15-64 years & ≥ 65 years 

Sample Description: NR 

Averaging Time: 8-h 

Mean (SD) unit: ppm

Brisbane: 1.7 

Canberra: 0.9 

Melbourne: 1.0 

Perth: 1.0 

Sydney: 0.8 

Auckland: 2.1 

Christchurch: 0.5 

Range (Min, Max): ppm 

Brisbane: 0.0, 7.0 

Canberra: 0.0, 5.8 

Melbourne: 0.1, 8.0 

Perth: 0.1, 4.0 

Sydney: 0.0, 4.5 

Auckland: 0.2, 7.9 

Christchurch: 0.0, 5.4 

Copollutant: NR 

Increment: 0.9 ppm 

% Change [Lower CI, Upper CI] 

Lags examined (days): 0-1 

15-64 years 

Arrythmia: 2.5 (0.1-4.9) 

Cardiac: 1.7 (0.5-2.9) 

Cardiac Failure: 4.2 (0.6-7.8) 

IHD: 1.6 (-0.6 to 3.9) 

MI: 1.8 (-0.7 to 4.3) 

Total CVD: 1.2 (0.3-2.1) 

≥ 65 years

Arrythmia: 0.1 (-1.8 to 2.1) 

Cardiac: 2.8 (1.3-4.4) 

Cardiac Failure: 6.0 (3.5-8.5) 

IHD: 2.3 (0.9-3.8) 

MI: 2.9 (0.8-4.9) 

Total CVD: 2.2 (0.9-3.4) 

March 2009 C-12 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Chang et al. (2005) 

Period of Study: 1997 - 2001 

Location: 

Taipei, Taiwan 

Health Outcome (ICD9: CVD Hospital 

Admissions 

(410 – 429) 

Study Design: Case-crossover 

Statistical Analyses: Conditional logistic regression 

Age Groups Analyzed: All 

Sample Description: 

74,509 CVD hospital admissions 

(47 Hospitals) 

Averaging Time: 24-h 

Mean (SD) unit: 1.37 ppm 

Range (Min, Max): 0.37, 3.66 

Copollutant: NR 

Increment: 0.49 ppm 

OR Estimate [Lower CI, Upper CI] 

Lag examined (days) : 0-2 

≥ 20°C: 1.090 (1.064-1.118) 

<20°C: 0.984 (0.927-1.044) 

Adjusted for PM10: 

≥ 20°C: 1.171 (1.132-1.211) 

<20°C: 0.946 (0.892-1.003) 

Adjusted for SO2: 

≥ 20°C: 1.232 (1.194-1.272) 

<20°C: 1.098 (1.034-1.165) 

Adjusted for NO2: 

≥ 20°C: 1.048 (1.003-1.095 

<20°C: 0.983 (0.914-1.058) 

Adjusted for O3: 

≥ 20°C: 1.196 (1.161-1.232) 

<20°C: 1.092 (1.031-1.157) 

Author: Fung et al. (2005) 

Period of Study: 1995 - 2000 

Location: 

Windsor, Ontario, 

Canada 

Hospital Admissions of 

Cardiovascular Diseases 

Health Outcome (ICD9): CHF (428); 

IHD (410 – 414); Dysrythmias (427) 

Study Design: Time-series 

Statistical Analyses: GLM 

Age Groups Analyzed: All 

Sample Description: 

11,632 Cardiac hospital admissions 

Averaging Time: 24-h 

Mean (SD) unit: 1.3 ppm 

Range (Min, Max): 0.0, 11.8 

Copollutant: correlation 

PM10: r = 0.21 

NO2: r = 0.38 

SO2: r = 0.16 

O3: r = 0.10 

Increment: 1.2 ppm 

% Change [Lower CI, Upper CI] 

Lags examined (days): 0, 0-1, 0-2 

<65 years

Lag 0 : -3.1 (-7.4 to 1.4) 

Lag 0-1 : -2.7 (-8.1 to 3.0) 

Lag 0-2 : -0.5 (-6.7 to 6.0) 

≥ 65 years

Lag 0 : 0.5 (-2.2 to 3.3) 

Lag 0-1 : 2.3 (-1.1 to 5.9) 

Lag 0-2 : 2.8 (-1.1 to 7.0) 

Author: Jalaludin et al. (2006) 

Period of Study: 

1997 - 2001 

Location: 

Sydney, Australia 

ED Visits 

Health Outcome (ICD9): 

All Cardiovascular (390-459); Cardiac 

Disease (390-429); IHD (410-413); 

Cerebrovascular or Stroke (430-438) 

Study Design: Time-series 

Statistical Analyses: 

GLM & GAM 

Age Groups Analyzed: 

65+ years 

Sample Description: NR 

Averaging Time: 8-h 

Mean (SD) unit: 0.82 ppm 

Range (Min, Max): 0.02, 4.63 

Copollutant: correlation 

PM10: r = 0.31 

NO2: r = 0.71 

SO2: r = 0.51 

O3: r = 0.19 

Increment: 0.69 ppm 

% Change [Lower CI, Upper CI] 

Lags examined (days): 0, 1, 2, 3, 0-1 

All Cardiovascular:

Lag 0 : 2.32 (1.45-3.19) 

Lag 1 : 1.33 (0.47-2.20) 

Lag 0-1 : 2.35 (1.39-3.32) 

Cardiac Disease: 

Lag 0 : 2.52 (1.50-3.54) 

Lag 1 : 1.85 (0.83-2.88) 

Lag 2 : 1.11 (0.0-2.15) 

Lag 0-1 : 2.85 (1.71-4.01) 

IHD:

Lag 0 : 2.83 (1.22-4.48) 

Lag 1 : 1.58 (0.01-3.19) 

Lag 0-1 : 2.86 (1.07-4.68) 

Stroke: No results were significant for 

Stroke. 

All Cardiovascular Disease: 

Cool period: Lag 0 : 3.26 (2.00-4.53) 

Cardiac Disease:

Cool period: Lag 0 : 3.43 (1.95-4.93) 

IHD: 

Cool period: Lag 0 : 3.64 (1.28-6.06) 

Warm period: Lag 0 : 2.29 (0.01-4.62) 

Stroke: 

Cool period: Lag 0 : 3.54 (0.78-6.37) 

Notes: 

Cool : May to October 

Warm : November to April 

Author: Koken et al. (2003) 

Period of Study: 1993 - 1997 

Location: 

Denver, CO 

Hospital Admissions for 

Cardiovascular Disease 

Health Outcome (ICD9: MI (410-410.92); 

Coronary Atherosclerosis (414 – 414.05); 

Pulmonary Heart Disease (416 - 416.9); 

Cardiac Dysrythmia (427 – 427.9); 

CHF (428) 

Study Design: Time-series 

Statistical Analyses: GLM 

Age Groups Analyzed: >65 years 

Sample Description: NR 

Averaging Time: 24-h 

Mean (SD) unit: 0.9 ppm 

Range (Min, Max): 0.3, 1.6 

Copollutant: correlation 

PM10: r = 0.25 

NO2: r = 0.73 

SO2: r = 0.21 

O3: r = -0.40 

Increment: 0.3 ppm 

% Change [Lower CI, Upper CI] 

Lags examined (days): 1, 2, 3, 4 

CHF: Lag 3 : 10.5 (0.1-22.0) 

CO not significantly associated with other Lag 

periods. 

March 2009 C-13 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Linn et al. (2000) 

Period of Study: 1992 - 1995 

Location: 

Los Angeles, CA 

Health Outcome: 

Hospital Admissions for Cardiovascular, 

Cerebrovascular, Pulmonary. 

Study Design: Time-series 

Statistical Analyses: Ordinary least squares regression; 

Poisson regression 

Age Groups Analyzed: >30 years 

Sample Description: NR 

Averaging Time: 24-h 

Mean (SD) unit: 

Winter: 1.7; Spring: 1.0; 

Summer: 1.2; Fall: 2.1 

Range (Min, Max): 

Winter: 0.5, 5.3; Spring: 0.4, 2.2; 

Summer: 0.3, 2.7; Fall: 0.2, 4.3 

Copollutant: correlation 

Winter:

PM10: r = 0.78; NO2: r = 0.89; 

O3: r = -0.43; 

Spring:

PM10: r = 0.54; NO2: r = 0.92; 

O3: 0.29 

Summer:

PM10: r = 0.72; NO2: r = 0.94; 

O3: 0.03 

Fall: 

PM10: r = 0.58; NO2: r = 0.84; 

O3: r = -0.36 

Increment: 1 ppm 

Co-efficient [SE] 

Lags examined (lags) : 0, 1 

Lag 0: 

Cardiovascular 

All: 0.032 (0.003)* (e.g. 3.2% increase) 

Winter: 0.038 (0.006)* 

Spring: 0.010 (0.015) 

Summer: 0.035 (0.014)* 

Fall: 0.027 (0.006)* 

Cerebrovascular 

All : 0.009 (0.007) 

Winter: -0.008 (0.014) 

Spring: 0.107 (0.033)* 

Summer: 0.030 (0.033) 

Fall: 0.008 (0.012) 

Myocardial Infarction 

All : 0.040 (0.009) * 

CHF 

All : 0.025 (0.009)* 

Cardiac Arrythmia 

All : 0.023 (0.009)* 

Stroke 

All : 0.044 (0.009)* 

Notes:* p < 0.05 

Author: Metzger et al. (2004a) 

Period of Study: 1993 - 2000 

Location: 

Atlanta, GA 

ED Visits (from 31 hospitals) 

Health Outcome (ICD9: Cardiovascular:

IHD (410 – 414); Acute MI (410); 

Dysrythmia (427); Cardiac Arrest (427.5); 

CHF (428); Peripheral Vascular & 

Cereberovascular Disease (PVCD) (433 – 

437, 440, 443, 444, 451-453); 

Atherosclerosis (440); Stroke (436) 

Study Design: Case-crossover 

Statistical Analyses: 

Poisson regression (GLM) 

Age Groups Analyzed: All 

Sample Description: 4,407,535 visits 

Averaging Time: 1-h 

Median (SD) unit: 1.5 ppm 

Range (percentiles): 

10th = 0.5; 90th = 3.4 

Copollutant: correlation 

PM10: r = 0.47 

NO2: r =0.68 

SO2: r =0.26 

O3: r =0.20 

 

Increment: 1 ppm 

RR Estimate [Lower CI, Upper CI] 

Lags examined (days): 0-2ma 

All CVD : 1.017 (1.008-1.027) 

Dysrythmia : 1.012 (0.993-1.031) 

CHF : 1.010 (0.988-1.032) 

IHD : 1.016 (0.999-1.034) 

PVCD : 1.031 (1.010-1.052) 

Author: Peel et al. (2007) 

Period of Study: 1993 - 2000 

Location: 

Atlanta, GA 

ED Visits (from 31 hospitals) 

Health Outcome (ICD9: Cardiovascular:

IHD (410 – 414); Dysrythmia (427); CHF 

(428); PVCD (433 – 437, 440, 443, 444, 

451-453) 

Study Design: Case-crossover 

Statistical Analyses: Conditional logistic 

regression 

Age Groups Analyzed: All 

Sample Description: 4,407,535 visits 

Averaging Time: 1-h 

Mean (SD) unit: 1.8 ppm 

Range (SD): SD: 1.2 

Copollutant: NR 

Increment: 1.2 ppm 

OR Estimate [Lower CI, Upper CI] 

Lags examined (days): 0-2ma 

IHD:

Without Diabetes : 1.023 (1.004-1.420) 

Without CHF: 1.024 (1.006-1.042) 

Dysrythmias:

With Hypertension : 1.065 (1.015-1.118) 

PVCD:

With Hypertension : 1.038 (1.004-1.074) 

Without Hypertension: 1.027 (1.002-1.054) 

With Diabetes: 1.065 (1.012-1.121) 

Without Diabetes: 1.025 (1.003-1.048) 

With COPD: 1.113 (1.027-1.205) 

Without COPD: 1.026 (1.004-1.047) 

Without CHF : 1.029 (1.008-1.051) 

With Dysrythmias: 1.072 (1.011-1.138) 

Without Dysrythmias : 1.026 (1.004-1.048) 

CHF: With COPD : 1.058 (1.003-1.115) 

Author: Slaughter et al. (2005) 

Period of Study: 1995 - 2001 

Location:

Spokane, WA 

Health Outcome (ICD9: Cardiac Hospital 

Admissions: (390-459) 

Study Design: Time-series 

Statistical Analyses: 

Poisson regression (GLM & GAM) 

Age Groups Analyzed: All 

Sample Description: NR 

Averaging Time: 24-h 

Mean (SD) unit: 0.42-1.82 

Range (Min, Max): NR 

Copollutant correlation : 

PM10: r = 0.32 

PM2.5: r = 0.62 

Increment: NR 

RR Estimate [Lower CI, Upper CI] ; lag : 

Lags examined (days): 1, 2, 3 

No significant association. Results not 

reported. 

March 2009 C-14 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Yang et al. (2004a) 

Period of Study: 1997 - 2000 

Location: 

Kaohsiung City, Taiwan 

Health Outcome (ICD9: 

Cardiovascular diseases (410-429) 

Study Design: Case-crossover 

Statistical Analyses: Conditional logistic 

regression 

Age Groups Analyzed: All 

Sample Description: 

29,661 Cardiovascular hospital 

admissions (63 hospitals) 

Averaging Time: 24-h 

Mean (SD) unit: 0.79 ppm 

Range (Min, Max): 0.24, 1.72 

Copollutant: NR 

Increment: 0.28 ppm 

OR Estimate [Lower CI, Upper CI] 

Lag examined (days): 0-2 

≥ 25°C: 1.264 (1.205-1.326) 

<25°C: 1.448 (1.357-1.545) 

Adjusted for PM10: 

≥ 25°C: 1.206 (1.146-1.270) 

<25°C: 1.314 (1.213-1.423) 

Adjusted for SO2:

≥ 25°C: 1.406 (1.327-1.489) 

<25°C: 1.3450 (1.352-1.555) 

Adjusted for NO2:

≥ 25°C: 1.246 (1.166-1.332) 

<25°C: 0.905 (0.819-0.999) 

Adjusted for O3:

≥ 25°C: 1.250 (1.191-1.311) 

<25°C: 1.447 (1.356-1.545) 

Table C-3. Studies of CO exposure and neonatal and postneonatal outcomes. 

Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Bell et al. 

(2007) 

Period of Study: 

1999 - 2002 

Location: 

Connecticut and 

Massachusetts 

Health Outcome: 

Birth weight and LBW 

Study Design: 

Retrospective cohort 

Statistical Analyses: 

Linear and logistic regression 

Age Groups Analyzed: NA 

Sample Description:

358,504 full term live singleton 

births (32-44 weeks) 

Averaging Time: 24-h 

Mean (SD) unit: 

0.65 ppm (0.18) 

Range (Min, Max): NR 

Copollutant: NR 

Increment: Interquartile range – 0.30 ppm 

Regression co-efficient for birth weight (g) [Lower CI, Upper CI] 

Entire pregnancy: -16.2 (-19.7 to -12.6) 

Stratified by race. 

Black mother : -10.9 (-20.2 to -1.6) 

White mother : -17.5 (-21.3 to -13.7) 

OR for LBW [Lower CI, Upper CI] 

Entire pregnancy : 1.028 (0.983-1.074) 

Author: Brauer et 

al. (2008) 

Period of Study:

1999 - 2004 

Location: 

Vancouver, Canada 

Health Outcome: LBW, PTB 

and SGA 

Study Design: 

Retrospective cohort 

Statistical Analyses: 

Logistic regression 

Age Groups Analyzed: NA 

Sample Description:

70249 live singleton births 

Averaging Time: 

Land use regression model 

Mean (SD) unit: 633 μg/m3

Range (Min, Max): 

124, 1409 

Copollutant: correlation: PM10: r = 0.73 

NO2: r = 0.75 

SO2: r = 0.82 

O3: r = -0.39 

Increment: 100 μg/m3

OR for SGA [Lower CI, Upper CI] ; 

Entire pregnancy : 1.06 (1.03-1.08) 

OR for term LBW [Lower CI, Upper CI] ; 

Entire pregnancy : 1.02 (0.96-1.09) 

OR PTB [Lower CI, Upper CI] ; 

Entire pregnancy : 1.16 (1.01-1.33) 

Author: Chen et al. 

(2002) 

Period of Study:

1991 - 1999 

Location: 

Northern Nevada 

Health Outcome:

Birth weight & LBW 

Study Design: 

Retrospective cohort 

Statistical Analyses: 

Linear and logistic regression 

Age Groups Analyzed: NA 

Sample Description:

39,338 full term live singleton 

births (37-44 weeks) 

Averaging Time: 8-h 

Mean (SD) unit: 0.98 ppm 

Range (Min, Max): 0.25, 4.87 

Copollutant: NR 

Increment: NR 

Regression co-efficient for birth weight (g) [SE] 

Trimesters: 

First : -1.02 (6.68) 

Second : -0.07 (6.58) 

Third : -3.95 (6.76) 

Entire pregnancy : -8.28 (14.9) 

Notes: CO not associated with LBW 

Author: Conceicao 

et al. (2001) 

Period of Study:

1994 - 1997 

Location:

Sao Paulo, Brazil 

Health Outcome: Child 

mortality, under 5 years of age 

Study Design: Time-series 

Statistical Analyses: 

Poisson regression (GAM) 

Age Groups Analyzed: NA 

Sample Description: NR 

Averaging Time: 24-h 

Mean (SD) unit: 

4.4 ppm (2.2) 

Range (Min, Max): NR 

Copollutant: NR 

 

Increment: NR 

Regression co-efficient for Child mortality – under 5 years of age 

[SE] ; 

Lags examined : 0, 1, 2, 3 

Lag 2 : 0.0306 (0.0076) (p < 0.01) 

Lag chosen for best fitting model 

March 2009 C-15 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Gilboa et 

al. (2005) 

Period of Study:

1997 - 2000 

Location: 

Texas 

Health Outcome: 

Birth defects (heart defects & 

orofacial clefts) 

Study Design: Case-control 

Statistical Analyses: 

Conditional Logistic regression 

Age Groups Analyzed: NA 

Sample Description: NR 

Averaging Time: NR 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutant: NR 

Increment: Exposure categories (ppm):<0.4; 0.4 – 0.5; 0.5 – 0.7; >0.7 

OR for Birth Defects [Lower CI, Upper CI] ; 

Exposure period : weeks 3 – 8 of pregnancy 

Conotruncal defects:

1.00; 1.38 (0.97-1.97); 1.17 (0.81-1.70); 1.46 (1.03-2.08) 

Tetralogy of Fallot: 

1.00; 0.92 (0.52-1.62); 1.27 (0.75-2.14); 2.04 (1.26-3.29) 

Notes: CO was not associated with the following defects: Aortic artery & 

valve, atrial septal, pulmonary artery & valve, ventricular septal, 

endocardial cushion & mitral valve , cleft lip, cleft palate, aortic valve 

stenosis, coarctation of the aorta, ostium secundum. 

Author: Gouveia et 

al. (2004) 

Period of Study:

1997 

Location: 

Sao Paulo, Brazil 

Health Outcome:

Birth weight & LBW 

Study Design:

Retrospective cohort 

Statistical Analyses: 

Linear and logistic regression 

Age Groups Analyzed: NA 

Sample Description:

179,460 live singleton term 

births (>37 weeks) 

Averaging Time: 8-h 

Mean (SD) unit: 3.7 ppm 

Range (Min, Max): 

1.1, 11.4 

Copollutant: NR 

 

Increment: 1 ppm 

Regression co-efficient for birth weight (g) [Lower CI, Upper CI] 

Trimesters: 

First : -23.1 (-41.3 to -4.9); Second : 3.2 (-18.2 to 24.5); 

Third : 1.9 (-18.2 to 22.0) 

OR for LBW ) [Lower CI, Upper CI] 

4th quartile exposure (compared to lowest quartile): 

First : 1.02 (0.82-1.27); Second : 1.07 (0.88-1.30); 

Third : 0.93 (0.76-1.12) 

Author: Ha et al. 

(2001) 

Period of Study:

1996 - 1997 

Location: 

Seoul, South Korea 

Health Outcome: 

LBW 

Study Design: 

Retrospective cohort 

Statistical Analyses: 

Logistic regression (GAM) 

Age Groups Analyzed: NA 

Sample Description:

276 763 full term live singleton 

births (>37 weeks) 

Averaging Time: 24-h 

Mean (SD) unit: NR 

Range (Min, Max): 

Percentiles: 

25th: 0.99 ppm 

75th: 1.41 ppm 

Copollutant correlation: TSP: 

r = 0.73 

NO2: r = 0.75 

SO2: r = 0.82 

O3: r = -0.39 

Increment: 0.42 ppm 

RR for LBW [Lower CI, Upper CI] 

Trimesters: 

First : 1.08 (1.04, 1.12) 

Third : 0.91 (0.87, 0.96) 

Author: Ha et al. 

(2003) 

Period of Study:

1995 - 1999 

Location: 

Seoul, South Korea 

Health Outcome: 

Post-neonatal mortality 

(1 month – 1 yr) (also looked at 

older age groups) 

Study Design: Time-series 

Statistical Analyses: 

Poisson regression (GAM) 

Age Groups Analyzed: NA 

Sample Description: NR 

Averaging Time: 24-h 

Mean (SD) unit: 1.2 ppm 

Range (Min, Max): 

0.39, 3.38 

Copollutant correlation: 

PM10: r = 0.63 

NO2: r = 0.72 

SO2: r = 0.75 

O3: r = -0.46 

Increment: 0.57 ppm 

RR for Post–neonatal mortality (1 month – 1 yr) 

[Lower CI, Upper CI] 

Lags examined : 0 

Total mortality: 

Lag 0 : 1.020 (0.976-1.067) 

Respiratory mortality: 

Lag 0 : 1.388 (1.009-1.911) 

Author: Huynh et 

al. (2006) 

Period of Study:

1999 - 2000 

Location: 

California 

Health Outcome:

PTB (24-36 weeks gestation) 

Study Design: Case-control 

Statistical Analyses: 

Conditional Logistic regression 

Age Groups Analyzed: 

Cases = 24-36 weeks gestation; 

Controls = 39 – 44 weeks 

Sample Description:

10,673 PTBs (cases); 32,119 

term births (controls) 

Averaging Time: NR 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutant: NR 

Increment: 1 ppm 

Exposure level – Quartiles of exposure for first month and last two 

weeks of gestation (mg/m3) 

First : <0.61; Second : 0.61 – 0.82; Third : 0.82 – 1.07; 

Fourth : >1.07 

Quartiles for entire pregnancy and last two weeks of pregnancy were 

similar. 

OR for PTB [Lower CI, Upper CI] 

First month of gestation:

Per 1 ppm increase : 1.10 (0.99-1.20) 

Second quartile : 0.94 (0.88-1.01) 

Third quartile : 1.04 (0.97-1.11) 

Fourth quartile : 1.05 (0.96-1.14) 

Last two weeks of gestation:

Per 1 ppm increase : 1.00 (0.93-1.09) 

Second quartile : 1.03 (0.97-1.10) 

Third quartile : 1.04 (0.97-1.12) 

Fourth quartile : 0.99 (0.91-1.08) 

Entire pregnancy: 

Per 1 ppm increase : 1.06 (0.95-1.18) 

Second quartile : 0.97 (0.91-1.04) 

Third quartile : 0.99 (0.92-1.05) 

Fourth quartile : 1.02 (0.94-1.09) 

Lowest quartile used as reference group

March 2009 C-16 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Hwang 

and Jaakkola 

(2008) 

Period of Study:

2001-2003 

Location: Taiwan 

Health Outcome: Oral clefts 

(with or without palate) 

Study Design: Case-control 

Statistical Analyses: 

Logistic regression 

Age Groups Analyzed: NA 

Sample Description:

6,530 cases from 721,289 

newborns 

Averaging Time: 8-h 

Mean (SD) unit: 0.69 (0.4) 

Range (Min, Max): 0.25, 2.7 

Copollutant correlation: 

PM10: r = -0.19 

NOX: r = 0.82 

SO2: r = 0.24 

O3: r = -0.19 

Increment: 100 ppb 

RR for oral cleft [Lower CI, Upper CI] 

Month 1 : 1.00 (0.96-1.04) 

Month 2 : 1.00 (0.96-1.03) 

Month 3 : 1.00 (0.96-1.03) 

Author: Jalaludin 

et al. (2007) 

Period of Study:

1998 - 2000 

Location: 

Sydney, Australia 

Health Outcome: PTB 

Study Design:

Retrospective cohort 

Statistical Analyses: 

Logistic regression 

Age Groups Analyzed: NA 

Sample Description:

123,840 full term live singleton 

births (<42 weeks) 

Averaging Time: 8-h 

Mean (SD) unit: 

0.9 ppm (0.68) 

Range (Min, Max): NR 

Copollutant correlation: 

PM10: r = 0.28 

NO2: r = 0.60 

SO2: r = 0.24 

O3: r = -0.21 

Increment: 1 ppm 

RR for PTB [Lower CI, Upper CI] 

First month: 

All of Sydney : 0.89 (0.84-0.95) 

Within 5km of site : 1.03 (0.68-1.54) 

First trimester: 

All of Sydney : 0.77 (0.71-0.83) 

Within 5km of site : 1.24 (0.81-1.91) 

1 month prior to birth:

All of Sydney : 0.96 (0.88-1.04) 

Within 5km of site : 1.00 (0.86-1.15) 

3 months prior to birth: 

All of Sydney : 0.99 (0.90-1.09) 

Within 5km of site : 1.11 (0.94-1.31) 

Author: Lee et al. 

(2003a) 

Period of Study: 

1996 - 1998 

Location: 

Seoul, South Korea 

Health Outcome: 

LBW 

Study Design: 

Retrospective cohort 

Statistical Analyses: 

Logistic regression 

Age Groups Analyzed: NA 

Sample Description:

388,105 full term live singleton 

births (37-44 weeks) 

Averaging Time: 24-h 

Mean (SD) unit: 1.2 ppm 

Range (Min, Max): 0.4, 3.4 

Copollutant correlation: 

PM10: r = 0.47 

NO2: r = 0.77 

SO2: r = 0.79 

Increment: 0.5 ppm 

OR for LBW [Lower CI, Upper CI] 

First : 1.04 (1.01-1.07) 

Second : 1.03 (1.00-1.06) 

Third : 0.96 (0.93-0.99) 

Entire pregnancy : 1.05 (1.01-1.09) 

Author: Leem et 

al. (2006) 

Period of Study:

2001 - 2002 

Location: 

Incheon, Korea 

Health Outcome: 

PTB 

Study Design: 

Retrospective cohort 

Statistical Analyses: 

Logistic regression 

Age Groups Analyzed: NA 

Sample Description:

52,113 live singleton births 

Averaging Time: 

Kriging was used to estimate 

exposure 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutant correlation: 

PM10: r = 0.27 

NO2: r = 0.63 

SO2: r = 0.31 

 

Increment: 

Exposure level – Quartiles of exposure for first trimester (mg/m3) 

First : 0.47-0.63; Second : 0.6 -0.77; 

Third : 0.78-0.90; Fourth : 0.91-1.27 

- exposure groups for third trimester was similar 

OR for PTB [Lower CI, Upper CI] 

First Trimester: 

Second quartile : 0.92 (0.81-1.05) 

Third quartile : 1.14 (1.01-1.29) 

Fourth quartile : 1.26 (1.11-1.44) 

Third Trimester: 

Second quartile : 1.07 (0.95-1.21) 

Third quartile : 1.07 (0.94-1.22) 

Fourth quartile : 1.16 (1.01-1.34) 

Lowest quartile used as reference group.

Author: Lin et al. 

(2004a) 

Period of Study:

1998 - 2000 

Location:

Sao Paulo, Brazil 

Health Outcome:

Neonatal death (within first 

28 days of life) 

Study Design: Time-series 

Statistical Analyses: 

Poisson regression (GAM) 

Age Groups Analyzed: NA 

Sample Description: NR 

Averaging Time: 24-h 

Mean (SD) unit: 2.83 ppm 

Range (Min, Max): 0.54, 10.25 

Copollutant correlation: 

PM10: r = 0.71 

NO2: r = 0.67 

SO2: r = 0.55 

O3: r = 0.03 

Increment: NR 

Regression coefficent for neonatal death [SE] 

Lags examined : 0 

Lag 0 : 0.0061 (0.0110) 

March 2009 C-17 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Lin et al. 

(2004b) 

Period of Study:

1995 - 1997 

Location: 

Taipei & 

Kaoshiung, Taiwan 

Health Outcome:

LBW 

Study Design: 

Retrospective cohort 

Statistical Analyses: 

Logistic regression 

Age Groups Analyzed: NA 

Sample Description:

92288 full term live singleton 

births (>37 weeks) within 3km of 

monitoring site. 

Averaging Time: 24-h 

Mean (SD) unit: 

Taipei (avg over 5 sites) 

0.84 – 1.31 

Kaohsiung (avg over 5 sites) 

5.56 – 10.05 

Range (Min, Max): NR 

Copollutant: NR 

Increment: Exposure groups 

M = Median exposure 1.1-14.2 ppm 

H = High exposure >14.2 ppm 

OR for LBW [Lower CI, Upper CI] 

Trimesters: 

First : M 1.01 (0.89, 1.16), H 0.90 (0.75, 1.09) 

Second : M 1.02 (0.90, 1.16), H 1.00 (0.82, 1.22) 

Third : M 0.88 (0.77, 1.00), H 0.86 (0.71, 1.03) 

Entire pregnancy : 

M 0.89 (0.77, 1.01), H 0.77 (0.63, 0.94) 

Notes: Cut off for exposures groups for second and third trimester were 

similar to those presented above. 

Author: Liu et al. 

(2003) 

Period of Study:

1985 - 1998 

Location: 

Vancouver, 

BC, Canada 

Health Outcome:

PTB, IUGR, LBW 

Study Design: 

Retrospective cohort 

Statistical Analyses: 

Logistic regression 

Age Groups Analyzed: NA 

Sample Description:

229,085 live singleton births 

Averaging Time: 24-h 

Mean (SD) unit: 1.0 ppm 

Range (Min, Max): 

25th: 0.7; 75th: 1.2 

Copollutant: NR 

 

Increment: 1.0 ppm 

OR for LBW [Lower CI, Upper CI] 

Month of pregnancy: 

First month: 1.01 (0.93-1.09) 

Last month: 0.96 (0.88-1.04) 

OR for PTB [Lower CI, Upper CI] 

First month : 0.95 (0.89-1.01) 

Last month : 1.08 (1.01-1.15) 

OR for IUGR [Lower CI- Upper CI] 

First month : 1.06 (1.01-1.10) 

Last month : 0.98 (0.94-1.03) 

Trimester 1 : 1.05 (1.00-1.10) 

Trimester 2 : 0.97 (0.92-1.01) 

Trimester 3 : 0.97 (0.93-1.02) 

Author: Liu et al. 

(2007) 

Period of Study:

1995 - 2000 

Location: 

Calgary, Edmonton, 

and Montreal 

Canada 

Health Outcome: IUGR 

Study Design: 

Retrospective cohort 

Statistical Analyses: 

Logistic regression 

Age Groups Analyzed: NA 

Sample Description:

386,202 live singleton births 

Averaging Time: 24-h 

Mean (SD) unit: 1.1 ppm 

Range (Min, Max): 

25th: 0.6; 75th: 1.3 

Copollutant correlation: 

PM2.5: r = 0.31 

NO2: r = 0.71 

SO2: r = 0.21 

O3: r = -0.42 

Increment: 1 ppm 

RR for LBW [Lower CI, Upper CI] 

Notes: CO was associated with an increased risk of IUGR of 

approximately 16% and 23% in the first and nine month of pregnancy. 

(All results presented in Figures) 

Author: Maisonet 

et al. (2001) 

Period of Study:

1994 - 1996 

Location: 

Northeastern USA 

Health Outcome: 

Live birth weight 

Study Design: 

Retrospective cohort 

Statistical Analyses: 

Logistic regression 

Age Groups Analyzed: NA 

Sample Description:

89,557 live singleton term births 

(37-44 weeks) 

Averaging Time: 24-h 

Mean (SD) unit: NR 

Range (Min, Max): 

Percentiles: 

25th: 0.93 ppm; 75th: 1.23 ppm

Copollutant: NR 

Increment: 1 ppm 

OR for LBW [Lower CI, Upper CI] 

Trimester:

First : 1.08 (0.91-1.28); Second : 1.14 (0.83-1.58); 

Third : 1.31 (1.06-1.62) 

Stratified results among African-Americans:

First : 1.43 (1.18-1.74); Second : 1.27 (0.87-1.86); 

Third : 1.75 (1.50-2.04) 

Notes: CO had no effect on whites or Hispanics 

March 2009 C-18 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Mannes 

et al. (2005) 

Period of Study:

1998 - 2000 

Location: 

Sydney, Australia 

Health Outcome:

Birth weight and SGA 

Study Design:

Retrospective cohort 

Statistical Analyses: 

Linear and logistic regression 

Age Groups Analyzed: NA 

Sample Description:

138,056 full term all singleton 

births (including stillbirths) (at 

least 20 weeks gestation) 

Averaging Time: 8-h 

Mean (SD) unit: 0.8 ppm 

Range (Min, Max): 0.0, 4.6 

Copollutant: correlation 

PM10: r = 0.26 

NO2: r = 0.57 

O3: r = -0.20 

 

Increment: 1 ppm 

Regression coefficients for birth weight (g) [Lower CI, Upper CI] 

All births:

First trimester : 1.86 (-8.31 to 12.03) 

Second trimester : -10.72 (-23.09 to 1.65) 

Third trimester : -6.63 (-18.57 to 5.31) 

One month prior to birth : -15.28 (-25.59 to -4.97) 

Births within 5 km of monitor:

First trimester : -8.56 (-28.60 to 10.68) 

Second trimester : -28.87 (-50.98 to -6.76) 

Third trimester : -22.88 (-44.58 to -1.18) 

One month prior to birth : -10.41 (-30.03 to 9.21) 

OR for SGA [Lower CI, Upper CI] 

All births:

First trimester : 0.95 (0.88-1.04) 

Second trimester : 0.99 (0.90-1.10) 

Third trimester : 1.01 (0.91-1.11) 

One month prior to birth : 1.06 (0.98-1.16) 

Births within 5km of monitor:

First trimester : 0.99 (0.86-1.14) 

Second trimester : 1.06 (0.90-1.25) 

Third trimester : 1.05 (0.90-1.23) 

One month prior to birth : 1.10 (0.96-1.27) 

Author: Medeiros 

et al. (2005) 

Period of Study:

1998 - 2000 

Location: 

Sao Paulo, Brazil 

Health Outcome: Birth weight 

and LBW 

Study Design: 

Retrospective cohort 

Statistical Analyses: 

Linear and logistic regression 

Age Groups Analyzed: NA 

Sample Description:

311,735 full term live singleton 

births (37-41 weeks) 

Averaging Time: 24-h 

Mean (SD) unit:

Daily mean shown in Figure 

(see paper) 

Range (Min, Max): NR 

Copollutant: NR 

Increment: 1 ppm 

Regression co-efficient for birth weight (g) [Lower CI, Upper CI] 

Trimesters: 

First : -11.9 (-15.5 to -8.2); Second : 4.9 (0.5-9.3); 

Third : 12.1 (7.6-16.6) 

OR for LBW [Lower CI, Upper CI] 

4th quartile exposure (compared to lowest quartile) 

First : 0.98 (0.91-1.06); Second : 0.97 (0.90-1.05); 

Third : 1.03 (0.96-1.11) 

Author: Parker et 

al. (2005) 

Period of Study:

2000 

Location: 

California 

Health Outcome:

Birth weight & SGA 

Study Design: 

Retrospective cohort 

Statistical Analyses: 

Linear and logistic regression 

Age Groups Analyzed: NA 

Sample Description:

18,247 full term live singleton 

births (40 weeks) within 5 miles 

of a monitor 

Averaging Time: 24-h 

Mean (SD) unit: 0.78 ppm 

Range (Min, Max): NR 

Copollutant: NR 

 

Increment: Quartiles of exposure for first trimester 

First : <0.57; Second : 0.57-0.76 ; 

Third : 0.76- 0.93; Fourth : >0.93 

- exposure groups for other trimesters were similar 

Regression co-efficient for birth weight (g) [Lower CI, Upper CI] 

Trimesters: 

4th quartile exposure (compared to lowest quartile) 

First : -7.3 (-29.7 to 15.0); Second : 14.2 (-8.9 to 37.3); 

Third : -8.4 (-32.2 to 15.3); 

Entire pregnancy: -20.5 (-40.1 to -0.8) 

OR for SGA [Lower CI, Upper CI] 

4th quartile exposure (compared to lowest quartile) 

First: 0.91 (0.76-1.09); Second: 0.80 (0.66-0.97); 

Third: 0.90 (0.75-1.10); 

Entire pregnancy: 0.95 (0.81-1.12) 

Author: Ritz et al. 

(2000) 

Period of Study:

1989 - 1993 

Location: 

Southern California 

Health Outcome: PTB 

Study Design: 

Retrospective Cohort 

Statistical Analyses: 

Logistic regression 

Age Groups Analyzed: Eligible study subjects were 

singletons born at 26-44 weeks 

gestation 

Sample Description:

97,518 neonates born in 

Southern California 

Averaging Time: 

6-9 a.m. 

Mean (SD) unit: 2.70 ppm 

Range (Min, Max): 0.36, 9.12 

Copollutant correlation: 

PM10: r = 0.37 

NO2: r = 0.60 

O3: r = -0.44 

Increment: 3 ppm 

RR for PTB [Lower CI, Upper CI] 

Adjusted for various risk factors and season of birth and 

conception

6 weeks prior to birth : 1.04 (0.99-1.10) 

1st month of pregnancy : 1.04 (0.99-1.09) 

Adjusted for various risk factors

6 weeks prior to birth : 1.06 (1.02-1.10) 

1st month of pregnancy : 1.01 (0.97-1.04) 

March 2009 C-19 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Ritz et al. 

(2002) 

Period of Study:

1987 - 1993 

Location: 

Southern California 

Health Outcome: 

Birth defects (heart defects & 

orofacial clefts) 

Study Design: Case-control 

Statistical Analyses: 

Logistic regression 

Age Groups Analyzed: NA 

Sample Description: NR 

Averaging Time: NR 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutant: NR 

Increment: Exposure categories: ppm 

<1.14; 1.14-1.57; 1.57- 2.39; >2.39 

OR for Birth defects [Lower CI, Upper CI]: 

Period of exposure – Second month of pregnancy. 

Aortic artery & valve defects: 

1.00 (ref group); 1.10 (0.73-1.66); 1.25 (0.74-2.13); 

0.93 (0.47-1.85) 

Pulmonary artery & valve anomalies:

1.00 (ref group); 1.09 (0.69-1.73); 0.92 (0.50-1.70); 

1.00 (0.46-2.17) 

Ventricular septal defects:

1.00 (ref group); 1.62 (1.05-2.48); 2.09 (1.19-3.67); 

2.95 (1.44-6.05) 

Conotruncal defects:

1.00 (ref group); 0.79 (0.47-1.32); 0.73 (0.36-1.47); 

0.95 (0.38-2.38) 

Notes: Results also presented for more specific defects, however CO 

showed no association (see paper Table 3.). CO not associated with 

orofacial clefts) 

Author: Ritz et al. 

(2006) 

Period of Study:

1989 - 2000 

Location: 

Southern California 

Health Outcome: Post-neonatal 

mortality (28 days to 1 yr); All 

causes; SIDS 

Study Design: Case-control 

Statistical Analyses: 

Conditional Logistic regression 

Sample Description:

Mothers residing within 16 km of 

monitoring site 

Averaging Time: 24-h 

Mean (SD) unit: 1.63 ppm 

Range (Min, Max): 

0.38, 3.44 

Copollutant: correlation 

PM10: r = 0.33 

NO2: r = 0.72 

O3: r = -0.57 

Increment: 1 ppm 

OR for Post-neonatal death [Lower CI, Upper CI] 

Exposure period : 2 weeks prior to death, 1 month prior to death, 

2 months prior to death, 6 months prior to death 

All causes: 

2 weeks prior to death : 1.14 (1.03-1.25) 

2 months prior to death : 1.11 (1.06-1.16) 

SIDS:

2 months prior to death : 1.19 (1.10-1.28) 

Term/normal weight births 

2 months prior to death:

All causes: 1.12 (1.05-1.19) 

SIDS : 1.17 (1.07-1.29) 

Respiratory : 1.14 (0.95-1.36) 

Preterm &/or LBW births 

2 months prior to death:

All causes: 1.12 (1.01-1.25) 

SIDS : 1.46 (1.09-1.94) 

Respiratory : 1.03 (0.83-1.27) 

Notes: These results did not persist in multipollutant models (CO, NO2, 

PM10, O3) 

Author: Ritz et al. 

(2007) 

Period of Study: 

January – 

December 2003 

Location: 

Los Angeles, CA 

Health Outcome: PTB 

Study Design: 

Nested case-control 

Statistical Analyses: 

Logistic regression 

Age Groups Analyzed: NA 

Sample Description:

A survey of 2,543 of 6,374 

women sampled from a cohort of 

58,316 eligible births in Los 

Angeles county. 

Averaging Time: 24-h 

Mean (SD) unit: NR 

Copollutant correlation: 

TSP: r = 0.73 

NO2: r = 0.75 

SO2: r = 0.82 

O3: r = -0.39 

Increment: Exposure categories (ppm): 

Less than 0.58: 0.59-0.91; 0.92-1.25; >1.25 

RR for LBW [Lower CI, Upper CI] 

First trimester: 

1.00 (Ref group); 1.17 (1.08-1.26); 1.15 (1.05-1.26); 1.25 (1.12-1.38) 

6 weeks prior to birth 

1.00 (Ref group); 1.00 (0.93-1.08); 1.08 (0.98-1.20); 1.03 (0.93-1.14) 

Entire pregnancy: 

1.00 (Ref group); 0.76 (0.70-0.82); 0.84 (0.77-0.91); 1.03 (0.91-1.17) 

Author: Salam et 

al. (2005) 

Period of Study:

1975 - 1987 

Location: 

California 

Health Outcome: Birth weight, 

LBW , IUGR 

Study Design: 

Retrospective cohort 

Statistical Analyses: 

Linear and logistic regression 

Age Groups Analyzed: NA 

Sample Description:

3,901 infants from the California 

Children’s Health Study 

Averaging Time: 24-h 

Mean (SD) unit:

1.8 ppm (0.9) 

(Entire pregnancy) 

Range: NR 

Copollutant: correlation 

PM10: r = 0.41 

NO2: r = 0.69 

O3: r = -0.27 

 

Increment: Entire pregnancy 1.2 ppm 

Trimesters: First : 1.4 ppm; Second : 1.4 ppm; Third : 1.3 ppm 

Regression co-efficient for birth weight (g) [Lower CI, Upper CI] 

Trimesters: 

First: -21.7 (-42.3 to -1.1); Second: 11.3 (-9.7 to 32.3); 

Third : 11.8 (-8.4 to 32.1); 

Entire pregnancy: 2.2 (-20.1 to 24.4) 

OR for LBW [Lower CI, Upper CI] 

Trimesters: 

First: 1.0 (0.7-1.5); Second: 0.9 (0.6-1.3); 

Third: 0.7 (0.5-1.1); Entire pregnancy: 0.8 (0.6-1.3) 

OR for IUGR [Lower CI, Upper CI] 

Trimesters: 

First: 1.2 (1.0-1.4); Second: 1.0 (0.9-1.1); 

Third: 1.0 (0.8-1.1); Entire pregnancy: 1.0 (0.9-1.2) 

March 2009 C-20 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Tsai et al. 

(2006b) 

Period of Study:

1994 - 2000 

Location: 

Kaoshiung, Taiwan 

Health Outcome: 

Postneonatal death 

(27 days-1 yr old) 

Study Design: Case- crossover 

Statistical Analyses: 

Poisson regression 

Age Groups Analyzed: NA 

Sample Description: NR 

Averaging Time: 24-h 

Mean (SD) unit: 

8.27 ppm x10 

Range (Min, Max): 

2.26, 17.7 

Copollutant: NR 

Increment: Interquartile range : 0.31 ppm 

OR for Post-neonatal mortality [Lower CI, Upper CI] 

Lag examined : 0-2 

Lag 0-2: 1.051 (0.304-3.630) 

Author: Wilhelm et 

al. (2005) 

Period of Study:

1994 - 2000 

Location: 

Los Angeles, CA 

Health Outcome: 

Term LBW and PTB 

Study Design: Retrospective cohort 

Statistical Analyses: 

Logistic regression 

Age Groups Analyzed: NA 

Sample Description:

518,254 births within 4 miles of 

a monitoring station. Varied 

according to analyses. 

Averaging Time: 24-h 

Mean (SD) unit: 

Trimester 1: 1.42 ppm 

Results for third trimester and 6 

weeks prior to birth were 

similar to first trimester 

Range (Min, Max): 

0.26, 2.82 

Copollutant correlation: 

First Trimester: 

PM10: r = 0.12 

PM2.5: r = 0.57 

NO2: r = 0.81 

SO2: r = -0.31 

 

Increment: 1 ppm 

RR for PTB [Lower CI, Upper CI] 

First trimester:

Less than 1 mile: 1.06 (1.00-1.12) 

1-2 miles: 1.06 (1.03-1.10) 

2-4 miles: 1.08 (1.06-1.09) 

ZIP code level: 1.04 (1.01-1.07) 

6 weeks prior to birth: 

Less than 1 mile: 1.04 (0.98-1.09) 

1-2 miles: .04 (1.01-1.08) 

2-4 miles: 1.01 (0.99-1.02) 

ZIP code level: 1.03 (1.00-1.06) 

Notes: All results above did not persist in multipollutant model (CO, 

NO2, O3, PM10) 

OR for term LBW [Lower CI, Upper CI] 

Third trimester:

Less than 1 mile: 1.10 (0.98-1.23) 

1-2 miles: 1.05 (0.99-1.13) 

2-4 miles: 1.06 (1.02-1.10) 

ZIP code level: 1.12 (1.05-1.19) 

Notes: All results above did not persist in multipollutant model (CO, 

NO2, O3, PM10) 

See paper for results based on exposure category groupings. 

Author: Woodruff 

et al. (2008) 

Period of Study:

1999 - 2002 

Location: 

U.S. counties with 

>250,000 residents 

Health Outcome:

Post-neonatal deaths 

All causes; respiratory; SIDS; illdefined + SIDS; other causes. 

Study Design: 

Retrospective cohort 

Statistical Analyses: 

Logistic regression (GEE) 

Age Groups Analyzed: NA 

Sample Description: NR 

Averaging Time: 24-h 

Mean (SD) unit:

All causes: 0.70 ppm 

Range (Min, Max): 

Percentiles: 

25th: 0.48; 75th: 0.87 

Copollutant correlation: 

PM10: r = 0.18 

SO2: r = 0.27 

O3: r = -0.46 

Increment: 0.39 ppm 

OR for Post-neonatal mortality [Lower CI, Upper CI] 

Avg exposure over the first 2 months of life: 

All causes: 1.01 (0.95-1.07) 

Respiratory: 1.14 (0.93-1.40) 

SIDS: 0.88 (0.76-1.03) 

Ill-defined + SIDS: 0.93 (0.84-1.02) 

Other causes: 1.02 (0.97-1.07) 

Author: Yang et al. 

(2004b) 

Period of Study:

1994 - 2000 

Location:

Taipei, Taiwan 

Health Outcome: 

Post-neonatal mortality 

(27 days-1 yr old) 

Study Design: Case-crossover 

Statistical Analyses: 

Poisson regression 

Age Groups Analyzed: NA 

Sample Description: NR 

Averaging Time: 24-h 

Mean (SD) unit: 

15.8 ppm x10 

Range (Min, Max):

3.20, 48.4 

Copollutant: NR 

 

Increment: Interquartile range: 0.56 ppm 

OR for Post-neonatal mortality [Lower CI, Upper CI] 

Lag examined : 0-2 

Lag 0-2: 1.038 (0.663-1.624) 

March 2009 C-21 DRAFT—DO NOT QUOTE OR CITE 
Table C-4. Studies of short-term CO exposure and respiratory morbidity 

Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Chen et al. (1999) 

Period of Study: 5/1995 – 

1/1996 

Location: 

3 Taiwan communities 

Health Outcome: Lung function (FVC, 

FEV1, FEV1/FVC, FEF25-75%, PEF) 

Study Design: Cross-sectional survey 

Statistical Analyses: 

Multivariate linear model 

Population: 

941 children (Boys: 453; Girls: 488) 

Age Groups Analyzed: 8-13 

Pollutant: CO 

Averaging Time: 

1-h maximum; 24-h avg 

Mean (SD) unit: NR 

Range (Min, Max): 

1-h maximum: (0.4, 3.6) 

Copollutant correlation: 

NO2: r = 0.86 – 0.98 

Note: To represent the 

schoolchildren’s exposure the daytime 

avg and peak concentrations were 

measured from 0800 to 1800. 

Increment: NR 

β Coefficient (SE); lag: 

FVC (mL) 

24-h avg

-66.6 (40.73); 1 

-147.71 (64.48); 2 

2.2 (48.13); 7 

1-h maximum

-33.25 (20.74); 1 

-16.48 (19.67); 2 

-5.18 (16.48); 7 

FEV1 (mL) 

24-h avg

 20.55 (38.24); 1 

-82.42 (60.95); 2 

48.23 (45.58); 7 

1-h maximum

1.2 (19.48); 1 

-1.44 (18.57); 2 

20.96 (15.67); 7 

Author: Chen et al. (2000) 

Period of Study: 

8/1996 – 6/1998 

Location: 

Washoe County, NV 

Health Outcome: School absenteeism 

Study Design: Time-series 

Statistical Analyses: Maximum 

likelihood 

Population: 1st to 6th grade children: 

27,793 

Age Groups Analyzed: 1st to 6th grade 

children 

Pollutant: CO 

Averaging Time: 1-h maximum 

Mean (SD) unit: 

2.73 (1.154) ppm 

Range (Min, Max): (0.65, 2.73) 

Copollutant correlation: 

PM10: r = 0.721 

O3: r = -0.204 

Increment: 1.0 ppm 

% Increase (Lower CI, Upper CI); lag: 

3.79% (1.04-6.55); 0 

Author: de Hartog et al. 

(2003) 

Period of Study: 1998 – 1999 

Location: 

Amsterdam, Netherlands; 

Erfurt, Germany; 

Helsinki, Finland 

Health Outcome: Respiratory 

symptoms (shortness of breath, being 

awakened by breathing problems, 

phlegm, wheezing, tripping heart) 

Study Design: Time-series 

Statistical Analyses: 

Logistic regression 

Population: 

Non-smoking individuals with CHD: 

Amsterdam: 37 

Erfurt: 47 

Helsinki: 47 

Age Groups Analyzed: 

≥ 50 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 

Amsterdam: 0.6 mg/m3

Erfurt: 0.4 mg/m3

Helsinki: 0.4 mg/m3

Range (Min, Max): 

Amsterdam: (0.4, 1.6) 

Erfurt: (0.1, 2.5) 

Helsinki: (0.1, 1.0) 

Copollutant: 

PM2.5; 

NO2

Increment: 0.25 mg/m3

Odds Ratio (Lower CI, Upper CI); lag: 

Incidence of symptoms 

Shortness of breath 

1 (0.92-1.1); 0 

0.96 (0.88-1.05); 1 

1 (0.92-1.09); 2 

1.07 (0.98-1.16); 3 

1.03 (0.9-1.18); 0-4 

Being awakened by breathing problems 

1.02 (0.92-1.14); 1 

1.03 (0.93-1.15); 2 

1.11 (1-1.22); 3 

1.16 (0.98-1.37); 0-4 

Phlegm 

1.05 (0.93-1.19); 0 

1.02 (0.91-1.14); 1 

1.08 (0.96-1.22); 2 

1.09 (0.97-1.22); 3 

1.13 (0.94-1.35); 0-4 

Prevalence of symptoms 

Shortness of breath

1 (0.94-1.06); 0 

0.99 (0.94-1.05); 1 

0.99 (0.93-1.05); 2 

1.01 (0.95-1.07); 3 

0.98 (0.9-1.07); 0-4 

Being awakened by breathing problems 

1.01 (0.93-1.1); 1 

0.99 (0.91-1.08); 2 

1.1 (1.02-1.19); 3 

1.13 (1-1.29); 0-4 

March 2009 C-22 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Delfino et al. 

(2003) 

Period of Study: 

11/1999 – 1/2000 

Location: 

Los Angeles, CA 

Health Outcome: 

Asthma symptoms (Cough, wheeze, 

sputum production, shortness of breath, 

chest tightness) (symptom scores >1, 

symptoms scores >2); Lung function 

(PEF) 

Study Design: Panel study 

Statistical Analyses: 

Asthma symptoms: GEE 

Lung function: Generalized linear mixed 

model 

Population: 

22 asthmatic Hispanic children 

Age Groups Analyzed: 10-15 

Pollutant: CO 

Averaging Time: 

1-h maximum; 8-h maximum 

Mean (SD) unit: 

1-h maximum: 7.7 (3.1) ppb 

8-h maximum: 5.0 (2.0) ppb 

Range (Min, Max): 

1-h maximum: (2, 17) 

8-h maximum: (1, 10) 

Copollutant correlation: 

NO2: r = 0.65; O3: r = -0.17; 

Acetaldehyde: r = 0.51; 

Acetone: r = 0.28; 

Formaldehyde: r = 0.41; 

Benzene: r = 0.50; 

Ethylbenzene: r = 0.62; 

Tetrachloroethylene: r = 0.63; 

Toluene: r = 0.71; 

m,p - Xylene: r = 0.72; 

PM10: r = 0.50; 

EC: r = 0.60; 

OC: r = 0.55; 

SO2: r = 0.69 

Increment: 5.0 ppb & 3.0 ppb 

Odds Ratio (Lower CI, Upper CI); lag: 

1-maximum

Increment: 5.0 ppb 

Symptom scores >1

0.95 (0.52-1.75); 0 

1.11 (0.75-1.65); 1 

Symptom scores >2 

0.48 (0.07-3.53); 0 

.28 (0.53-3.12); 1 

8-h maximum

Increment: 3.0 ppb 

Symptom scores >1

0.95 (0.55-1.62); 0 

1.2 (0.77-1.86); 1 

Symptom scores >2

0.53 (0.10-2.92); 0 

1.43 (0.41-5.00); 1 

Author: Estrella et al. 

(2005) 

Period of Study: 1/2000 – 

4/2000 

Location: 

Quito, Ecuador 

Health Outcome: 

Acute respiratory infection 

Study Design: Prospective study 

Statistical Analyses: Logistic regression; Poisson 

Population: 960 children 

Age Groups Analyzed: 6-11 

Pollutant: CO 

Averaging Time: NR 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutant: NR 

Increment: NR 

Odds Ratio (Lower CI, Upper CI); lag: 

Acute respiratory infection 

ARI in children COHb >2.5% vs. COHb <2.5%: 

Adjusted Logistic Regression Model 

3.25 (1.65-6.38) 

ARI in children COHb >2.5% vs. COHb <2.5%: 

Crude Logistic Regression Model 

2.06 (1.30-3.20) 

Log-Linear Model (Each Percent Increase in 

COHb above 2.5%) 

1.15 (1.03-1.28) 

Author: Fischer et al. 

(2002) 

Period of Study: NR 

Location: 

Utrecht, Netherlands 

Health Outcome: 

Lung function (FVC, FEV1, PEF, MMEF) 

Study Design: Panel study 

Statistical Analyses: 

Restricted maximum likelihood linear 

model 

Population: 68 children 

Age Groups Analyzed: 10-11 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 921 μg/m3

Range (Min, Max): (319, 1540) 

Copollutant: 

PM10; BS; NO2; NO 

Increment: 100 μg/m3

mL (SE); lag: 

FVC: 0.5 (0.4); 1; 0.1 (0.2); 2 

FEV1: -0.4 (0.5); 1; -0.2 (0.2); 2 

m/s (SE); lag: PEF: -1.1 (2.8); 1; -0.6 (1.1); 2 

MMEF: -0.5 (1.4); 1; -0.3 (0.6); 2 

March 2009 C-23 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Lagorio et al. 

(2006) 

Period of Study: 

5/1999 – 6/1999; 

11/1999 – 12/1999 

Location: 

Rome, Italy 

Health Outcome: 

Lung function (FVC, FEV1) 

Study Design: 

Time-series panel study 

Statistical Analyses: Generalized estimating equations (GEE)

Population: 

COPD panel: 11 

Asthma panel: 11 

IHD panel: 7 

Age Groups Analyzed: COPD panel: 50-80 

Asthma panel: 18-64 

IHD panel: 40-64 

Notes: Asthma panel was restricted to 

never smokers, while COPD and IHD 

panels include former smokers if 

smoking cessation occurred at least 1 

year prior to enrollment. 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 

Overall: 7.4 (6.2) mg/m3 Spring: 2.1 

(0.3) mg/m3 Winter: 12.3 (4.9) mg/m3

Range (Min, Max): 

Overall: (1.6, 28.9) 

Copollutant correlation: 

PM2.5: r = 0.67 

PM10-2.5: r = -0.09 

PM10: r = 0.55 

NO2: r = 0.05 

O3: r = -0.87 

SO2: r = 0.65 

Increment: 1 mg/m3

β Coefficient (SE); lag: 

COPD panel 

FVC (% of predicted) 

-0.14 (0.15);0 

-0.13 (0.18); 0-1 

0.15 (0.23); 0-2 

FEV1 (% of predicted) 

-0.05 (0.13); 0 

-0.12 (0.16); 0-1 

-0.03 (0.2); 0-2 

Asthma panel

FVC (% predicted) 

0.02 (0.12); 0 

-0.001 (0.13); 0-1 

-0.06 (0.16); 0-2 

FEV1 (% predicted)

-0.05 (0.14); 0 

-0.16 (0.15); 0-1 

-0.28 (0.18); 0-2 

IHD panel

FVC (% of predicted) 

0.176 (0.101); 0 

0.132 (0.120); 0-1/ 

0.132 (0.165); 0-2 

FEV1 (% of predicted) 

0.204 (0.120); 0 

0.114 (0.142); 0-1 

0.159 (0.194); 0-2 

Author: Park et al. (2002) 

Period of Study: 

3/1996 – 12/1999 

Location: 

Seoul, Korea 

Health Outcome: School absenteeism 

Study Design: Time-series 

Statistical Analyses: 

Poisson GAM, LOESS 

Population: 

~ 1,264 children (671 Boys, 593 girls) 

Age Groups Analyzed: 1st through 6th grade students 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 1.11 (0.40) ppm 

Range (Min, Max): (0.39, 2.97) 

Copollutant correlation: 

PM10: r = 0.56; 

NO2: r = 0.70; 

SO2: r = 0.67; 

O3: r = -0.46 

Increment: 0.52 ppm 

Relative Risk (Lower CI, Upper CI); lag: 

Total Absences: 

0.95 (0.94-0.97); 0 

Non-Illness Related Absences: 

0.99 (0.96-1.02); 0 

Illness-Related Absences: 

0.96 (0.94-0.98); 0 

Author: Park et al. (2005a) 

Period of Study: 3/2002 – 6/2002 

Location: 

Incheon, Korea 

Health Outcome: 

Lung function (PEF variability (>20%), 

Mean PEF); Respiratory symptoms 

(night respiratory symptoms, cough, 

inhaler use) 

Study Design: Panel study 

Statistical Analyses: 

GEE; Poisson GAM 

Population: 64 bronchial asthmatics 

Age Groups Analyzed: 16-75 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 

Control days: 0.6368 (0.1522) ppm 

Dust days: 0.6462 (0.0945) ppm 

Range (Min, Max): NR 

Copollutant: NR 

Increment: NR 

Relative Risk (Lower CI, Upper CI); lag: 

PEF variability (>20%): 1.43 (0.54-3.75) 

Night respiratory symptoms: 

0.98 (0.51-1.86) 

β Coefficient (SE); lag: 

PEF variability (>20%): 0.9737 (0.3187) 

Mean PEF (L/min): -10.103 (2.7146) 

Night respiratory symptoms: 

 -0.018 (0.3654) 

Cough: 0.0855 (0.1826) 

Inhaler Use: 0.0796 (0.1733) 

March 2009 C-24 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Penttinen et al. 

(2001) 

Period of Study: 11/1996 – 

4/1997 

Location: 

Helsinki, Finland 

Health Outcome: 

Lung function (PEF) 

Study Design: Panel study 

Statistical Analyses: First order autoregressive linear model 

Population: 57 non-smoking adult asthmatics 

Age Groups Analyzed: NR 

Pollutant: CO 

Averaging Time: 24-h avg 

Median unit: 0.4 mg/m3

Range (Min, Max): 

(0.1, 1.1) mg/m3

Copollutant correlation: 

PM10: r = -0.03 

PM10-2.5: r = -0.30 

PM2.5: r = 0.32 

PM1: r = 0.39 

PNC: r = 0.44 

NC0.01-0.1: r = 0.43 

NC0.1-1: r = 0.47 

NO: r = 0.60 

NO2: r = 0.44 

Increment: 0.2 mg/m3

β Coefficient (SE); lag: 

PEF Deviations (L/min) 

Morning 

0.27 (0.38); 0 

-1.08 (0.36); 1 

0.23 (0.38); 2 

-1.11 (1.19); 5-day avg 

Afternoon 

-0.4 (0.43); 0 

-0.13 (0.41); 1 

-0.71 (0.41); 2 

-3.03 (1.06); 5-day avg 

Evening 

-0.7 (0.45); 0; 

-0.31 (0.44); 1

0.3 (0.44); 2 

-3.62 (1.19); 5-day avg 

Co-pollutant models with PNC

Morning: -0.67 (0.64); 1 

Afternoon: -0.46 (0.69); 0 

Evening: -0.46 (0.73); 0 

Author: Rabinovitch et al. 

(2004) 

Period of Study: 11/1999 – 

3/2000; 11/2000 – 3/2001; 

11/2001 – 3/2002 

Location: 

Denver, CO 

Health Outcome: 

Lung function (FEV1); asthma 

exacerbation; bronchodilator use 

Study Design: Panel study 

Statistical Analyses: 

Pulmonary function: Mixed effects 

model; Asthma exacerbation and 

medication use: GLM 

Population: 

Urban poor asthmatic children: 

1999-2000: 41 

2000-2001: 63 

2001-2002: 43 

Age Groups Analyzed: 6-12 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 1.0 (0.4) ppm 

Range (Min, Max): (0.3, 3.5) 

Copollutant: 

PM2.5; PM10; NO2; SO2; O3

Increment: 0.4 ppm 

β Coefficient (SE); lag: FEV1

AM: -0.001 (0.008); 3-day ma 

PM: 0.015 (0.01); 3-day ma 

Odds Ratio (Lower CI, Upper CI); lag: 

Asthma exacerbation: 

1.012 (0.913-1.123); 3-day ma 

Bronchodilator use: 1.065 (1.001-1.133); 3-day ma 

Author: Ranzi et al. (2004) 

Period of Study: 

2/1999 – 5/1999 

Location: 

Emilia-Romagna, Italy 

Health Outcome: 

Lung function; respiratory symptoms, 

medication use 

Study Design: Panel study 

Statistical Analyses: GLM 

Population: 

120 “asthma-like” school children 

Age Groups Analyzed: 6-11 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 

Urban area: 1.54 mg 

Rural area: 1.22 mg 

Range (Min, Max): NR 

Copollutant: NO2; TSP; PM2.5

The study did not present quantitative 

results for CO. 

Author: Rodriguez et al. 

(2007) 

Period of Study: 

1996 – 2003 

Location: 

Perth, Australia 

Health Outcome: 

Respiratory symptoms (body 

temperature, cough, wheeze/rattle 

chest, runny/blocked nose) 

Study Design: Panel study 

Statistical Analyses: 

Logistic regression, GEE 

Population: 

263 children at high risk of developing 

asthma 

Age Groups Analyzed: 0-5 

Pollutant: CO 

Averaging Time: 8-h avg 

Mean (SD) unit: 1.408 ppm 

Range (Min, Max): (0.012, 8.031) 

Copollutant: NR 

Increment: NR 

Odds Ratio (Lower CI, Upper CI); lag: 

Body Temperature

1.024 (0.911-1.151); 0 

1.056 (0.943-1.184); 5 

0.991 (0.962-1.021); 0-5 

Cough

1.001 (0.996-1.005); 0 

1.064 (0.941-1.02); 5 

1.028 (0.996-1.061); 0-5 

Wheeze/Rattle Chest

1.089 (0.968-1.226); 0 

1.136 (1.016-1.26); 5 

1.035 (1.005-1.066); 0-5 

Runny/Blocked Nose

1.094 (0.824-1.453); 0 

1.38 (1.028-1.853); 5 

1.101 (1.025-1.183); 0-5 

March 2009 C-25 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Schildcrout et al. 

(2006) 

Period of Study: 

11/1993 – 9/1995 

Location: 

8 North American cities: 

Albuquerque, NM 

Baltimore, MD 

Boston, MA 

Denver, CO 

San Diego, CA 

Seattle, WA 

St. Louis, MO 

Toronto, ON, Canada 

Health Outcome: 

Asthma symptoms; rescue inhaler use 

Study Design: Panel study 

Statistical Analyses: Asthma symptoms: Logistic regression; 

Rescue Inhaler Use: Poisson regression

Population: 

990 asthmatic children 

Age Groups Analyzed: 5-12 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutant: 

NO2; O3; PM10; SO2

Increment: 1.0 ppm 

Odds Ratio (Lower CI, Upper CI); lag: 

Asthma Symptoms 

1.08 (1.01-1.14); 0 

1.07 (0.99-1.16); 1 

1.08 (1.02-1.15); 2 

1.05 (1.01-1.09); 0-2 

Asthma Symptoms 

+ 20 ppb increase in NO2

1.07 (1-1.14); 0 

1.04 (0.96-1.11); 1 

1.09 (1.02-1.16); 2 

1.04 (1-1.08); 0-2 

+ 25 μg/m3 increase in PM10

1.08 (1.01-1.15); 0 

1.06 (0.99-1.14); 1 

1.08 (1.02-1.14); 2 

1.05 (1.01-1.08); 0-2 

+ 10 ppb increase in SO2

1.07 (0.99-1.16); 0 

1.06 (0.96-1.19); 1 

1.1 (1.02-1.18); 2 

1.05 (1-1.09); 0-2 

Rescue Inhaler Use 

1.07 (1.01-1.13); 0 

1.05 (0.99-1.1); 1 

1.06 (1.01-1.1); 2 

1.04 (1.01-1.07); 0-2 

Rescue Inhaler Use 

+ 20 ppb increase in NO2

1.05 (0.99-1.12); 0 

1.04 (0.98-1.11); 1 

1.07 (1.02-1.12); 2 

1.04 (1-1.07); 0-2 

+ 25 μg/m3 increase in PM10

1.06 (0.99-1.13); 0 

1.05 (0.99-1.11); 1 

1.05 (1.01-1.09); 2 

1.03 (1-1.07); 0-2 

+ 10 ppb increase in SO2

1.04 (0.96-1.12); 0 

1.04 (0.97-1.1); 1 

1.08 (1.03-1.13); 2 

1.04 (1-1.08); 0-2 

Author: Silkoff et al. (2005) 

Period of Study:

11/11/1999 – 3/31/2000; 

11/1/2000 – 3/16/2001 

Location: 

Denver, CO 

Health Outcome: 

Lung function (FEV1, PEF); recorded 

symptoms; rescue medication use 

Study Design: Panel study 

Statistical Analyses: 

Rescue medication use and total 

symptom score: GEE; 

Lung function: Mixed effects model 

Population: 

1st winter: 16 with a history of more than 

10 pack years of tobacco use, airflow 

limitation with FEV1 of less than 70% of 

predicted value, and FEV1/ FVC ratio of 

less than 60% 

2nd winter: 18 with a history of more 

than 10 pack years of tobacco use, 

airflow limitation with FEV1 of less than 

70% of predicted value, and FEV1/ FVC 

ratio of less than 60% 

Age Groups Analyzed: ≥ 40 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 

1999-2000: 1.2 (0.555) ppm 

2000-2001: 1.1 (0.5) ppm 

Range (Min, Max): 

1999-2000: (0.340, 3.790) 

2000-2001: (0.360, 2.810) 

Copollutant: NR 

The study did not present quantitative 

results for CO. 

March 2009 C-26 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: Slaughter et al. 

(2003) 

Period of Study: 

12/1994 – 8/1995 

Location: 

Seattle, WA 

Health Outcome: 

Asthma severity; medication use 

Study Design: Panel study 

Statistical Analyses: Asthma severity: Ordinal logistic 

regression; Medication use: Poisson 

Population: 133 mild-to-moderate 

asthmatic children 

Age Groups Analyzed: 5-13 

Pollutant: CO 

Averaging Time: 24-h avg 

Median unit: 1.47 ppm 

IQR (25th, 75th): (0.23, 1.87) 

Copollutant: NR 

Increment: 

Increased asthma attack severity: 0.67 ppm 

Increased rescue inhaler use: 1.0 ppm 

Odds Ratio (Lower CI, Upper CI); lag: 

Increased asthma attack severity: 

Without transition: 1.21; 1 

With transition: 1.17; 1 

Increased rescue inhaler use: 

Without transition: 1.09 (1.03-1.16); 1 

With transition: 1.06 (1.01-1.1); 1 

Author: Steerenberg et al. 

(2001) 

Period of Study: NR 

Location: 

Bilthoven and Utrecht, 

the Netherlands 

Health Outcome: 

Lung function (PEF); exhaled nitric 

oxide; inflammatory nasal markers 

Study Design: Panel study 

Statistical Analyses: 

Restricted maximum likelihood linear 

model 

Population: 126 children 

Age Groups Analyzed: 8-13 

Notes: The study was only conducted 

for a two month period: February and 

March. 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 

Utrecht: 0.8 mg/m3

Bilthoven: 0.5 mg/m3

Range (Min, Max): 

Utrecht: (0.3, 2.3) 

Bilthoven: (0.3, 0.9) 

Copollutant: NR 

The study did not present quantitative 

results for CO. 

Increment: 0.32 mg/m3

β Coefficient (SE); lag: 

Exercise induced responsiveness

Author: Timonen et al. 

(2002) 

Period of Study: 2/1994 – 

4/1994 

Location: 

Kuopio, Finland 

Health Outcome: 

Exercise induced bronchial 

responsiveness; Lung function (FVC, 

FEV1, MMEF, AEFV) 

Study Design: Panel study 

Statistical Analyses: Linear regression 

Population: 33 children with chronic respiratory 

symptoms 

Age Groups Analyzed: 7-12 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 0.6 mg/m3

Range (Min, Max): (0.1, 2.8) 

Copollutant correlation: 

PM10: r = 0.52 

BS: r = 0.80 

PNC0.01-0.03: r = 0.81 

PNC0.03-0.1: r = 0.87 

PNC0.1-0.3: r = 0.71 

PNC0.3-1.0: r = 0.60 

PNC1.0-3.2: r = 0.84 

PNC3.2-10: r = 0.79 

NO2: r = 0.85 

ΔFEV1 (%) 

-0.081 (0.647); 0 

0.03 (0.262); 1 

0.087 (0.26); 2 

-0.091 (0.275); 3 

0.19 (0.599); 0-3 

ΔMMEF (%) 

0.442 (1.79); 0 

0.52 (0.723); 1 

0.313 (0.719); 2 

-0.616 (0.75); 3 

0.096 (1.64); 0-3 

ΔAEFV (%) 

0.287 (1.19); 0 

0.281 (0.482); 1 

0.904 (0.474); 2 

0.15 (0.483); 3 

1.6 (1.05); 0-3 

FVC (mL) 

0.064 (10.9); 0 

-4.79 (4.51); 1 

-9.78 (4.24); 2 

-13.9 (4.7); 3 

-29.4 (10.1); 0-3 

FEV1 (mL) 

19.2 (13.2); 0 

-9.04 (5.45); 1 

-9.15 (5.21); 2 

-11.7 (5.77); 3 

-17.5 (12.5); 0-3 

MMEF (mL/s) 

22.2 (36.9); 0 

-23 (15.2); 1 

-4.63 (14.7); 2 

-30.9 (16); 3 

 -24.9 (34.8); 0-3 

AEFV (L2/s) 

-0.093 (0.088); 0 

-0.068 (0.036); 1 

 -0.06 (0.035); 2 

-0.05 (0.039); 3 

-0.076 (0.083); 0-3

March 2009 C-27 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations CO Effect Estimates (95% CI) 

Author: von Klot et al. 

(2002) 

Period of Study: 

9/1996 – 3/1997 

Location: 

Erfurt, Germany 

Health Outcome: 

Asthma symptoms; medication use 

Study Design: Panel study 

Statistical Analyses: Logistic regression 

Population: 53 adults with asthma or asthma 

symptoms 

Age Groups Analyzed: 37-77 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 0.9 mg/m3

Range (Min, Max): (0.3, 3.0) 

Copollutant correlation: 

NC0.01-0.1: r = 0.66 

NC0.1-0.5: r = 0.79 

NC0.5-2.5: r = 0.46 

MC0.1-0.5: r = 0.66 

MC0.01-2.5: r = 0.65 

PM2.5-10: r = 0.42 

PM10: r = 0.69 

NO2: r = 0.82 

SO2: r = 0.32 

Increment: 

0 and 5-day avg lag: 0.6 mg/m3

14-day avg lag: 0.54 mg/m3 

Odds Ratio (Lower CI, Upper CI); lag: 

Prevalence: Inhaled β2-agonist use 

0.98 (0.93-1.03); 0 

1.04 (0.97-1.12); 0-4 

0.93 (0.86-1.01); 0-13 

Prevalence: Inhaled corticosteroid use 

1.05 (1-1.11); 0 

1.25 (1.17-1.34); 0-4 

1.06 (0.97-1.15); 0-13 

Prevalence: Wheezing 

1.03 (0.97-1.08); 0 

1.13 (1.05-1.22); 0-4 

1.14 (1.05-1.25); 0-13 

Co-pollutant models 

Inhaled β2-agonist use

CO+MC0.01-2.5: 1 (0.91-1.11); 0-4 

CO+NC00.01-0.1: 

1.01 (0.91-1.11); 0-4 

Inhaled corticosteroid use

CO+MC0.01-2.5: 

0.89 (0.81-0.98); 0-13 

CO+NC: 0.01-0. 

1: 0.81 (0.72-0.91); 0-13 

Wheezing

CO+MC0.01-2.5: 

1.15 (1.04-1.27); 0-4 

CO+NC0.01-0.1: 

1.09 (0.98-1.22); 0-4 

Author: Yu et al. (2000) 

Period of Study: 

11/1993 – 8/1995 

Location: 

Seattle, Washington 

Health Outcome: 

Asthma symptoms (Wheezing, 

coughing, chest tightness, shortness of 

breath) 

Study Design: Panel study 

Statistical Analyses: 

Repeated measures logistic regression 

models (GEE) 

Population: 

133 mild-to-moderate asthmatics 

Age Groups Analyzed: 5-13 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 1.6 ppm 

Range (Min, Max): (0.65, 4.18) 

Copollutant correlation: 

PM1.0: r = 0.82 

PM10: r = 0.86 

SO2: r = 0.31 

Increment: 1.0 ppm 

Odds Ratio (Lower CI, Upper CI); lag: 

Marginal GEE

1.22 (1.03-1.45); 0 

1.3 (1.11-1.52); 1 

1.26 (1.09-1.46); 2 

Transition GEE

1.18 (1.02-1.37); 0 

1.25 (1.1-1.42); 1 

1.18 (1.04-1.33); 2 

Table C-5. Studies of short-term CO exposure and respiratory hospital admissions and ED visits. 

Study Design Concentrations Effect Estimates (95% CI) 

Author: Anderson et al. 

(2001) 

Period of Study: 

10/1994-12/1996 

Location: 

West Midlands; U.K. 

Hospital Admission 

Health Outcome (ICD9): 

Respiratory Diseases 

Asthma (493) 

COPD (490-492, 494-496) 

Study Design: Time-series 

Statistical Analyses: 

Regression with quasi-likelihood 

approach and GAM 

Age Groups Analyzed: 

All ages 

0-14 

15-64 

≥ 65 

Pollutant: CO 

Averaging Time: 

Maximum 8-h avg 

Mean (SD) unit: 0.8 (0.7) ppm 

Range (Min, Max): (0.2, 10) 

Copollutant: correlation 

PM10: r = 0.55; 

PM2.5: r = 0.54; 

PM2.5-10: r = 0.10; 

BS: r = 0.77; 

SO4

2-: r = 0.17; 

NO2: r = 0.73; 

O3: r = -0.29; 

SO2: r = 0.49 

Increment: 1.0 ppm 

% Increase (Lower CI, Upper CI); lag: 

Respiratory Diseases 

Age Group 

All ages: 0.3% (-1.10 to 1.70); 0-1 

0-14: 1.50% (-0.60 to 3.60); 0-1 

15-64: -0.70% (-3.60 to 2.30); 0-1 

≥ 65: 0.00% (-2.10 to 2.10); 0-1 

Asthma 

Age Group 

0-14: 3.90% (-0.50 to 8.50); 0-1 

15-64: -4.90% (-10.60 to 1.10); 0-1 

COPD 

Age Group 

≥ 65: 1.00% (-2.50 to 4.60); 0-1 

March 2009 C-28 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Andersen et al. 

(2007) 

Period of Study:

1/1999-12/2004 

Location: 

Copenhagen, 

Denmark 

Hospital Admission 

Health Outcome (ICD10):

Respiratory diseases: Chronic 

bronchitis (J41-42), Emphysema 

(J43), COPD (J44), Asthma 

(J45), Status asthmaticus (J46), 

Pediatric asthma (J45), 

Pediatric asthmaticus (J46) 

Study Design: Time-series 

Statistical Analyses: Poisson 

GAM 

Age Groups Analyzed: 5-18; 

≥ 65 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 0.3 (0.1) ppm 

IQR (25th, 75th): (0.22, 0.34) 

Copollutant; correlation: 

PM10: r = 0.45 

Increment: 0.12 ppm 

Relative Risk (Lower CI, Upper CI); lag: 

Respiratory Disease

Age Group: ≥ 65

CO: 1.024 (0.997-1.053); 0-4 

CO, PM10: 1.001 (0.961-1.042); 0-4 

Asthma

Age Group: 5-18

CO: 1.104 (1.018-1.198); 0-5 

CO, PM10: 1.023 (0.911-1.149); 0-5 

Author: Atkinson et al. 

(1999) 

Period of Study:

1/1992-12/1994 

Location: 

London, 

U.K. 

ED Visits 

Health Outcome (ICD9): 

Respiratory complaints: 

wheezing, inhaler request, chest 

infection, chronic obstructive 

lung disease (COLD), difficulty 

breathing, cough, other 

respiratory complaints. 

e.g., croup, pleurisy, noisy 

breathing; Asthma (493) 

Study Design: Time-series 

Statistical Analyses: Poisson 

Age Groups Analyzed: 

All ages 

0-14 

15-64 

≥ 65 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 0.8 (0.4) ppm 

Range (Min, Max): (0.2, 5.6) 

Copollutant; correlation: 

NO2

O3

SO2

PM10

BS 

Increment: 0.8 ppm 

% Increase (Lower CI, Upper CI); lag: 

Respiratory complaints 

Age Group

All ages: 0.76% (-0.83, 2.38); 1 

0-14: 2.92% (0.60, 5.30); 1 

15-64: 2.15% (-0.27, 4.63); 1 

≥ 65: 4.29% (1.15, 7.54); 0 

Asthma visits: 

Single-pollutant model

Age Group: 

All ages: 3.32% (0.56, 6.16); 1 

0-14: 4.13% (-0.11, 8.54); 0 

15-64: 4.41% (0.46, 8.52); 1 

Multi-pollutant model 

Age Group: 

0-14

CO, NO2: 2.05% (-2.25, 6.54); 0 

CO, O3: 4.48% (0, 9.16); 0 

CO, SO2: 2.34% (-1.94, 6.81); 0 

CO, PM10: 2.93% (-1.53, 7.58); 0 

CO, BS: 4.19% (-0.04, 8.60); 0 

Author: Bedeschi et al. 

(2007) 

Period of Study: 

1/2001-3/2002 

Location: Reggio Emilia, 

Italy 

ED Visits 

Health Outcome (ICD9): 

Asthma (493); Asthma-like 

disorders, i.e., asthma, 

bronchiolitis, dyspnea/shortness 

of breath; Other respiratory 

disorders (i.e., upper and lower 

respiratory illness including 

sinusitis, bronchitis, and 

pneumonia) 

Study Design: Time-series 

Statistical Analyses: 

Poisson GAM, penalized splines 

Age Groups Analyzed: <15 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 1.4 (0.7) mg/m3

Range (Min, Max): (0.4, 4.6) 

Copollutant; correlation: 

PM10: r = 0.61 

TSP: r = 0.61 

SO2: r = 0.71 

NO2: r = 0.77 

The study did not provide quantitative results for CO. 

March 2009 C-29 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Bell et al. 

(2008) 

Period of Study:

1/1995-12/2002 

Location: Taipei, Taiwan 

Hospital Admissions 

Health Outcome (ICD9):

Pneumonia (486); Asthma (493) 

Study Design: Time-series 

Statistical Analyses: Poisson 

Age Groups Analyzed:

All ages 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SE) unit: 0.9 ppm 

Range (Min, Max): (0.3, 3.6) 

Copollutant: NR 

Increment: 0.5 ppm 

% Increase (Lower CI, Upper CI); lag 

Asthma (avg correlation between monitor pairs = 0.75 

(13 monitors)) 3.29% (-0.74 to 7.49); 0 

.49% (-4.25 to 3.41); 1 

-0.84% (-4.43 to 2.88); 2 

0.48% (-4.02 to 3.18); 3 

0.74% (-4.62 to 6.4); 0-3 

Pneumonia (avg correlation between monitor pairs = 

0.75 (13 monitors)) 

1.91% (-1.97 to 5.95); 0 

0.03% (-3.65 to 3.85); 1 

0.36% (-3.2 to 4.04); 2 

-1.29% (-4.77 to 2.32); 3 

0.21% (-5.03 to 5.73); 0-3 

Asthma (avg correlation between monitor pairs = 0.88 (5 

monitors)) 

1.68% (-1.68 to 5.15); 0 

-1.19% (-4.29 to 2.01); 1 

-0.83% (-3.83 to 2.26); 2 

-0.35% (-3.32 to 2.71); 3 

-0.31% (-4.9 to 4.5); 0-3 

Pneumonia (avg correlation between monitor pairs = 

0.88 (5 monitors)) 

1.24% (-2.02 to 4.6); 0 

-0.01% (-3.06 to 3.13); 1 

0.57% (-2.4 to 3.62); 2 

-0.85% (-3.78 to 2.16); 3 

0.31% (-4.23 to 5.06); 0-3 

Asthma (monitors with ≥ 0.75 between monitor 

correlations (11 monitors), avg correlation between 

monitor pairs = 0.81) 

2.87% (-0.91 to 6.79); 0 

-0.71% (-4.2 to 2.91); 1 

-0.73% (-4.08 to 2.73); 2 

-0.41% (-3.72 to 3.01); 3 

0.51% (-4.6 to 5.89); 0-3 

Pneumonia (monitors with ≥ 0.75 between monitor 

correlations (11 monitors) to avg correlation between 

monitor pairs = 0.81) 

0.98% (-1.68 to 5.76); 0 

-0.12% (-3.54 to 3.42); 1 

0.37% (-2.95 to 3.8); 2 

-1.08% (-4.34 to 2.3); 3 

0.3% (-4.71 to 5.57); 0-3 

Author: Bellini et al. 

(2007) 

Period of Study:

1996-2002 

Location: 

15 Italian cities 

Hospital Admissions 

Health Outcome: 

Respiratory Conditions 

Study Design: 

Time-series; Meta-analysis 

Statistical Analyses: 

1. GLM for city-specific 

estimates 

2. Bayesian random-effects for 

meta analysis 

Age Groups Analyzed: All 

ages 

Pollutant: CO 

Averaging Time: NR 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutant: correlation NR 

Increment: 1 mg/m3

% Increase (Lower CI, Upper CI); Lag 

Respiratory conditions 

All ages: 

Season: 

Winter: 0.58%; 0-1 

Summer: 3.47%; 0-1 

All Season: 1.25%; 0-3 

Note: Estimates from Biggeri et al. (2004) 

Author: Braga et al. 

(2001) 

Period of Study:

1/1993-11/1997 

Location: 

Sao Paulo, Brazil 

Hospital Admissions 

Health Outcome (ICD9): 

Respiratory (460-519) 

Study Design: Time-series 

Statistical Analyses: 

Poisson GAM, LOESS 

Age Groups Analyzed: 

≤ 2 

3-5 

6-13 

14-19 

0-19 

Pollutant: CO 

Averaging Time: 

Maximum 8-h avg 

Mean (SD) unit: 4.8 (2.3) ppm 

Range (Min, Max): (0.6, 19.1) 

Copollutant: 

correlationCopollutant: correlation 

PM10: r = 0.60 

O3: r = -0.07 

SO2: r = 0.47 

Increment: 3 ppm 

% Increase (Lower CI, Upper CI); lag: 

Respiratory 

Age Group: 

≤ 2: 5.00% (3.30-6.80); 0-6 

3-5: 4.90% (1.40-8.50); 0-6 

6-13: 1.00% (-2.50 to4.60); 0-6 

14-19: 11.30% (5.90-16.80); 0-6 

0-19: 4.90% (3.50-6.40); 0-6 

March 2009 C-30 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Burnett et al. 

(1999) 

Period of Study: 

1/1980-12/1994 

Location: 

Toronto, ON, 

Canada 

Hospital Admissions 

Health Outcome (ICD9): 

Asthma (493); COPD (490-492, 

496); 

Respiratory infection 

(464, 466, 480-487, 494) 

Study Design: Time-series 

Statistical Analyses: Poisson 

GAM, LOESS 

Age Groups Analyzed: All 

ages 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 1.18 ppm 

IQR (25th, 75th): (0.9, 1.4) 

Copollutant: correlation 

PM2.5: r = 0.49 

PM10-2.5: r = 0.20 

PM10: r = 0.43 

NO2: r = 0.55 

SO2: r = 0.37 

O3: r = -0.23 

Increment: 1.18 ppm 

% Increase (t-value); lag: 

Asthma: 5.35% (3.92); 0 

COPD: 2.93% (1.48); 0 

Respiratory Infection: 5.00% (4.25); 0 

Asthma: 

Multi-pollutant model

CO, SO2, O3: 5.15% 

CO, PM2.5, SO2, O3: 4.63% 

CO, PM10-2.5, SO2, O3: 5.25% 

CO, PM10, SO2, O3: 4.80% 

CO, PM10-2.5, O3: 4.00% 

COPD: 

Multi-pollutant model

CO, SO2, O3: 3.02% 

CO, PM2.5, SO2, O3: 2.46% 

CO, PM10-2.5, SO2, O3: 3.00% 

CO, PM10, SO2, O3: 2.75% 

CO, PM10-2.5, O3: 3.00% 

Author: Burnett et al. 

(2001) 

Period of Study: 

1/1980-12/1994 

Location: 

Toronto, ON, 

Canada 

Hospital Admissions 

Health Outcome (ICD9): 

Asthma (493); Acute bronchitis/ 

bronchiolitis (466); Croup 

(464.4) ; Pneumonia (480-486) 

Study Design: Time-series 

Statistical Analyses: 

Poisson GAM 

Age Groups Analyzed: <2 

Pollutant: CO 

Averaging Time: 1-h avg 

Mean (SD) unit: 1.9 ppm 

IQR (25th, 75th): (1.3, 2.3) 

Copollutant: correlation

O3: r = 0.24 

Increment: 1.9 ppm 

% Increase (Lower CI, Upper CI); lag 

Respiratory problems 

CO: 19.20%; 0-1 

CO, O3: 14.30%; 0-1 

Author: Cakmak et al. 

(2006b) 

Period of Study: 

4/1993-3/2000 

Location: 

10 Canadian cities 

Hospital Admissions 

Health Outcome (ICD9): 

Actue bronchitis/bronchiolitis 

(466); Pneumonia (480-486); 

Chronic/ unspecified bronchitis 

(490, 491); Emphysema (492); 

Asthma (493); Bronchiectasis 

(494); Chronic airway 

obstruction (496) 

Study Design: Time-series 

Statistical Analyses: 

1. Poisson 

2. Restricted Maximum 

Likelihood Method 

Age Groups Analyzed: All 

ages 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 0.8 ppm 

Range (Min, Max): (0.0, 6.5) 

Copollutant: correlation 

SO2

NO2

O3

Increment: 0.8 ppm 

% Increase (Lower CI, Upper CI); lag: 

Respiratory disease 

CO: 0.60% (0.20, 1); 2.8 

CO, SO2, NO2, O3: -0.20% (-0.70- 0.30); 2.8 

Author: Cheng et al. 

(2007) 

Period of Study: 1996-2004 

Location: 

Kaohsiung, Taiwan 

Hospital Admissions 

Health Outcome (ICD9): 

Pneumonia (480-486) 

Study Design: Bi-directional case-crossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: All 

ages 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 0.76 ppm 

Range (Min, Max): (0.14, 1.72) 

Copollutant: correlation

PM10

SO2

NO2

O3

Increment: 0.31 ppm 

Odds Ratio (Lower CI, Upper CI); lag: 

OR for pneumonia and exposure to various pollutants 

for all ages in areas ≥ 25°C or <25°C 

Pollutant and Temperature

CO, ≥ 25 oC: 1.18 (1.14-1.23); 0-2 

CO, <25 oC: 1.47 (1.41-1.53); 0-2 

CO, PM10, ≥ 25 oC: 1.15 (1.11-1.2); 0-2 

CO, PM10, <25 oC: 1.28 (1.21-1.35); 0-2 

CO, SO2, ≥ 25 oC: 1.22 (1.17-1.27); 0-2 

CO, SO2, <25 oC: 1.49 (1.42-1.56); 0-2 

CO, NO2, ≥ 25 oC: 1.2 (1.15-1.27); 0-2 

CO, NO2, <25 oC: 1.01 (0.95-1.08); 0-2 

CO, O3, ≥ 25 oC: 1.16 (1.12-1.2); 0-2 

CO, O3, <25 oC: 1.44 (1.38-1.5); 0-2 

March 2009 C-31 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Cho et al. 

(2000) 

Period of Study: 

1/1996-12/1996 

Location: 

3 South Korea cities: 

Hospital Admissions 

Health Outcome (ICD9): 

Bronchial asthma; COPD; 

Bronchitis 

Study Design: Time-series 

Statistical Analyses: Poisson 

GAM, LOESS 

Age Groups Analyzed: All 

Ages 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 

Daejeon: 1.424 (0.611) ppm 

Ulsan: 0.950 (0.211) ppm 

Suwon: 1.270 (0.549) ppm 

Range (Min, Max): 

Daejeon: (.364, 3.504) 

Ulsan: (.380, 1.675) 

Suwon: (.250, 3.616) 

Copollutant: correlation 

Daejeon

SO2: r = 0.280; NO2: r = 0.041; 

TSP: r = 0.193; O3: r = -0.101; 

O3 Max: r = -0.069 

Ulsan

SO2: r = 0.108; NO2: r = 0.446; 

TSP: r = 0.286; O3: r = -0.195; 

O3 Max: r = -0.107 

Suwon

SO2: r = 0.556; NO2: r = 0.291; 

TSP: r = 0.496; O3: r = -0.371; 

O3 Max: r = -0.365 

Increment: 1,000 ppm 

Relative Risk (Lower CI, Upper CI); lag: 

Estimates obtained using dummy variables to apply 

environmental indicators to the model 

Daejeon

CO: 1.26 (1.08-1.47) 

TSP, SO2, NO2, O3: 1.21 (1.02-1.44) 

Ulsan

CO: 3.55 (1.65-7.63) 

TSP, SO2, NO2, O3: 2.51 (1.06-5.93) 

Suwon

CO: 1.24 (0.97-1.59) 

TSP, SO2, NO2, O3: 1.19 (0.88-1.61) 

Estimates obtained using actual measured integrated 

environmental pollution indicator values 

Daejeon

CO: 1.34 (1.14-1.58) 

Ulsan

CO: 1.27 (0.94-1.71) 

Suwon

CO: 3.55 (1.27-9.93) 

Author: Farhat et al. 

(2005) 

Period of Study: 

8/1996-8/1997 

Location: 

Sao Paulo, Brazil 

Hospital Visits & ED Visits 

Health Outcome (ICD9): 

Pneumonia/bronchopneumonia 

(480-486); Asthma (493); 

Bronchiolitis (466) 

Study Design: Time-series 

Statistical Analyses: 

Poisson GAM, LOESS 

Age Groups Analyzed: All 

ages 

Pollutant: CO 

Averaging Time: 

Maximum 8-h avg 

Mean (SD) unit: 3.8 (1.6) ppm 

Range (Min, Max): (1.1, 11.4) 

Copollutant: correlation 

PM10: r = 0.72; 

SO2: r = 0.49; 

NO2: r = 0.59; 

O3: r = -0.8 

Increment: 1.8 ppm 

% Increase (Lower CI, Upper CI); lag: 

Lower Respiratory Tract Disease ED Visits

CO, PM10: -0.10% (-5.60 to 5.30); 0-2 

CO, NO2: -1.20% (-6.70 to 4.20); 0-2 

CO, SO2: 3.70% (-1.00 to 8.40); 0-2 

CO, O3: 4.80% (0.50-9.10); 0-2 

CO, PM10, NO2, SO2, O3:- 0.64% (-6.90 to 5.60); 0-2 

Pneumonia/ Bronchopneumonia Hospital Admissions 

CO, PM10: 4.40% (-7.90 to 16.70); 0-2 

CO, NO2: 4.40% (-88.70 to 17.50); 0-2 

CO, SO2: 7.80% (-2.50 to 18.20); 0-2 

CO, O3: 9.60% (-0.50 to 19.70); 0-2 

CO, PM10 to NO2, SO2, O3:5.10% (-9.60 to 19.70); 0-2 

Asthma/ Bronchiolitis Hospital Admissions

CO, PM10: 6.10% (-14.90 to 27.10); 0-2 

CO, NO2: 2.40% (-16.90 to 21.70); 0-2 

CO, SO2: 10.60% (-6.60 to 27.80); 0-2 

CO, O3: 12.40% (-3.60 to 28.40); 0-2 

CO, PM10 to NO2, SO2, O3: 8.80% (-15.60 to 33.30); 0-2 

Author: Fung et al. 

(2006) 

Period of Study: 

6/1995-3/1999 

Location: 

Vancouver, 

Canada 

Hospital Admissions 

Health Outcome (ICD9): 

Respiratory Illness 

Study Design: 

1. Dewanji and Moolgavkar 

2. Time-series 

3. Bi-directional case-crossover 

Statistical Analyses: 

1. Dewanji and Moolgavkar 

2. Poisson 

3. Conditional logistic regression 

Age Groups Analyzed: ≥ 65 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 0.69 0.25) ppm 

Range (Min, Max): (0.28, 2.03) 

Copollutant: correlation 

CoH: r = 0.85; O3: r = -0.53; 

NO2: r = 0.74; SO2: r = 0.61; 

PM10: r = 0.46; PM2.5: r = 0.23; 

PM10-2.5: r = 0.51 

Increment: 0.24 ppm 

Relative Risk (Lower CI, Upper CI); lag 

Dewanji and Moolgavkar

1.008 (0.997-1.02); 0 

1.012 (0.999-1.025); 0-2 

1.010 (0.995-1.025); 0-4 

1.009 (0.991-1.026); 0-6 

Time-series

1.012 (1.000-1.023); 0 

1.017 (1.003-1.032); 0-2 

1.017 (1.001-1.035); 0-4 

1.016 (0.996-1.036); 0-6 

Bi-directional case-crossover

1.010 (0.006-1.023); 0 

1.012 (0.996-1.027); 0-2 

1.012 (0.995-1.03); 0-4 

1.010 (0.991 1.031; 0-6 

March 2009 C-32 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Fusco et al. 

(2001) 

Period of Study: 

1/1995 10/1997 

Location: 

Rome, 

Italy 

Hospital Admissions 

Health Outcome (ICD9): 

Respiratory conditions (460-

519, 

excluding 470-478); Acute 

respiratory infections plus 

pneumonia (460-466, 480-486); 

COPD (490-492, 494-496) 

Asthma (493) 

Study Design: Time-series 

Statistical Analyses: Poisson 

GAM 

Age Groups Analyzed: 

All ages

0-14 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 

3.6 (1.2) mg/m3

IQR (25th, 75th): (2.8, 4.3) 

Copollutant: correlation

All Year

SO2: r = 0.56

NO2: r = 0.31 

O3: r = -0.57 

Cold Season

SO2: r = 0.37 

NO2: r = 0.41 

O3: r = -0.44 

Warm Season

SO2: r = 0.44

NO2: r = 0.59 

O3: r = -0.38 

Increment: 1.5 mg/m3

% Increase (Lower CI, Upper CI); lag: 

Age Group: All Ages

Respiratory conditions

2.80% (1.30-4.30); 0 

1.80% (0.20-3.30); 1 

0.20% (-1.30 to 1.80); 2 

0.50% (-2.00 to 1.10); 3 

0.70% (-0.80 to 2.20); 4 

CO, NO2: 2.30% (0.60-4.00); 0 

Acute Respiratory Infections plus Pneumonia 

2.20% (0.00-4.40); 0 

2.10% (-0.10 to 4.40); 0 

1.70% (-0.50 to 4.00); 2 

-0.90% (-3.00 to 1.30); 3 

1.50% (-0.70 to 3.70); 4 

CO, NO2: 0.00% (-2.30 to 2.40); 0 

Asthma

5.50% (0.90-10.40); 0 

0.80% (-3.80 to 5.70); 1 

-1.30% (-5.90 to 3.50); 2 

-3.00% (-7.40 to 1.60); 3 

0.60% (-4.00 to 5.30); 4 

CO, NO2: 4.80% (0.30-9.50); 0 

COPD

4.30% (1.60-7.10); 0 

-0.20% (-2.90 to 2.50); 1 

-0.20% (-2.90 to 2.60); 2 

-0.30% (-3.00 to 2.40); 3 

-0.10% (-2.80 to 2.60); 4 

CO, NO2: 4.80% (0.90-7.90); 0 

Warm Season

Respiratory Conditions: 

10.80% (6.70-14.80); 0 

Acute respiratory infections plus pneumonia: 

8.60% (2.90-14.60); 0 

COPD: 

13.90% (6.80-21.50); 0 

Age Group: 0-14

Respiratory conditions

2.50 (-0.30 to 5.50); 0 

0.80 (-2.10 to 3.80); 1; 

0.20 (-2.70 to 3.10); 2 

-1.00 (-3.70 to 1.90); 3 

3.20 (0.40- 6.20); 4 

CO, NO2: 4.10 (-1.20 to 9.80); 1 

Acute Respiratory Infections plus Pneumonia

2.50 (-0.80 to 5.80); 0 

-0.10 (-3.40 to 3.20); 1 

0.90 (-2.30 to 4.30); 2 

-2.00 (-5.10 to 1.20); 3 

3.20 (0.00-6.60); 4 

CO, NO2: 6.90 (0.80-13.40); 1 

Asthma

6.30 (-0.50 to 13.50); 0 

8.20 (1.10-15.70); 1; 

-0.70 (-7.30 to 6.30); 2 

3.50 (-3.20 to 10.60); 3; 

4.80 (-1.90 to 12.00); 4 

CO, NO2: 3.30 (-4.20 to 11.30); 1 

Author: Gouveia and 

Fletcher (2000a) 

Period of Study: 

11/1992-9/1994 

Location: 

Sao Paulo, 

Brazil 

Hospital Admissions 

Health Outcome (ICD9): All 

respiratory diseases Pneumonia 

(480-486); Asthma (493); 

Bronchitis (466, 490, 491) 

Study Design: Time-series 

Statistical Analyses: Poisson 

Age Groups Analyzed: <1

<5 

Pollutant: CO 

Averaging Time: 

Maximum 8-h avg 

Mean (SD) unit: 5.8 (2.4) ppm 

Range (Min, Max): (1.3, 22.8) 

Copollutant: correlation 

PM10: r = 0.63

SO2: r = 0.65

NO2: r = 0.35 

Increment: 6.9 ppm 

Relative Risk (Lower CI, Upper CI); lag: 

All respiratory diseases

Age Group: <5: 1.017 (0.971-1.065); 0 

Pneumonia

Age Group: 

<5: 1.015 (0.961-1.071); 0; 

<1: 1.035 (0.975-1.099); 2 

Asthma

Age Group: 

<5: 1.081 (0.98-1.192); 0 

March 2009 C-33 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Hajat et al. 

(1999) 

Period of Study: 

1/1992-12/1994 

Location: 

London, 

U.K. 

General Practitioner Visits 

Health Outcome (ICD9): 

Asthma (493); Lower 

Respiratory Diseases (464, 466, 

476, 480-483, 485-487, 490-

492, 494-496, 500, 501, 503-

505, 510-515, 518, 519, 786) 

Study Design: Time-series 

Statistical Analyses: Poisson 

Age Groups Analyzed: 

All ages

0-14

15-64

≥ 65 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 

All year: 0.8 (0.4) ppm 

Warm Season 

(April-September): 0.7 (0.3) ppm 

Cool Season 

(October-March): 1.0 (0.5) ppm 

Range (10th, 90th): 

All Year: (0.5, 1.3) 

Warm Season: (0.4, 1.0) 

Cool Season: (0.5, 1.6) 

Copollutant: correlation

All Year

NO2: r = 0.72; 

SO2: r = 0.51; 

BS: r = 0.85; 

O3: r = -0.40; 

PM10: r = 0.56 

Warm Season

NO2: r = 0.70; 

SO2: r = 0.32; 

BS: r = 0.65; 

O3: r = -0.12; 

PM10: r = 0.58 

Cool Season

NO2: r = 0.84; 

SO2: r = 0.58; 

BS: r = 0.87 

Increment: 0.8 & 0.7 ppm 

% Increase (Lower CI, Upper CI); Lag 

All Year: 

Asthma – Single Day Lags

Increment: 0.8 ppm 

Age Group

0-14: 4.10% (-0.10 to 8.40); 2 

15-64: 0.90% (-2.10 to 4.10); 0 

≥ 65: 7.50% (0.50-14.90); 2 

All ages: 1.60% (-1.20 to 4.60); 2 

Asthma – Cumulative exposure

Increment: 0.7 ppm 

Age Group

0-14: 6.90% (1.30-12.90); 0-3 

15-64: 1.00% (-3.20 to 5.40); 0-2 

≥ 65: 8.20% (0.40-16.60); 0-2 

All ages: 1.80% (-1.50 to 5.20); 0-2 

Lower Respiratory Diseases – Single Day Lags

Increment: 0.8 ppm 

Age Group

0-14: 4.40 (1.70-7.10); 2 

15-64: 1.10 (-0.70 to 3.00); 2 

≥ 65: -2.60 (-4.80 to -0.30); 3 

All ages: 2.00 (0.50-3.40); 2 

Lower Respiratory Diseases – Cumulative exposure

Increment: 

0.7 ppm for 0-2 and 0-3; 0.8 for 0-1 

Age Group

0-14: 3.00% (-1.00 to 7.20); 0-3 

15-64: -0.70% (-2.90 to 1.50); 0-1 

≥ 65: -1.60% (-5.10 to 2.00); 0-3 

All ages: 1.80% (0.10-3.60); 0-2 

Warm or Cold Seasons: 

Asthma, Increment: 0.8 ppm 

Age Group & Season

0-14 & Warm Season: 11.40% (3.30-20.00); 2 

0-14 & Cold Season: 2.90% (-3.20 to 9.40); 2 

15-64 & Warm Season: 4.80% (-0.60 to 10.60); 0 

15-64 & Cold Season: -0.30% (-4.80 to 4.50); 0 

≥ 65 & Warm Season: 15.60% (3.10-29.60); 2 

≥ 65 & Cold Season: 4.20% (-6.00 to 15.60); 2 

Lower Respiratory Diseases, Increment: 0.8 ppm 

Age Group & Season

0-14 & Warm Season: 2.70% (-2.90 to 8.60); 2 

0-14 & Cold Season: 6.20% (2.30-10.20); 2 

15-64 & Warm Season: 6.20% (2.30-10.20); 2 

15-64 & Cold Season: 2.40% (-1.20 to 6.10); 2 

≥ 65 & Warm Season: 1.00% (-1.60 to 3.80); 2 

≥ 65 & Cold Season: -2.20% (-6.50 to 2.40); 3 

Author: 

Hajat et al. (2002) 

Period of Study: 

1/1992-12/1994 

Location: 

London, U.K. 

General Practitioner Visits 

Health Outcome (ICD9): 

Upper Respiratory Diseases 

(URD) 

Study Design: Time-series 

Statistical Analyses: 

Poisson, GAM, LOESS 

Age Groups Analyzed: 

0-14

15-64

≥ 65 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 

All year: 0.8 (0.4) ppm 

Warm Season (April-September):

0.7 (0.3) ppm 

Cool Season (October-March):

1.0 (0.5) ppm 

Range (10th, 90th): 

All Year: (0.5, 1.3) 

Warm Season: (0.4, 1.0) 

Cool Season: (0.5, 1.6) 

Copollutant: NR 

Increment: 0.6 ppm, 0.8 ppm, & 1.1 ppm 

% Increase (Lower CI, Upper CI); lag: 

Warm Season, Increment: 0.6 ppm 

Age Group

0-14: 2.90% (-0.60 to 6.40); 1 

14-64: 7.90% (4.80-11.10); 1 

≥ 65: 4.90% (-1.80 to 12.10); 3 

Cold Season, Increment: 1.1 ppm 

Age Group

0-14: -2.50% (-4.90 to 0.10); 1 

14-64: 0.60% (-1.60 to 2.90); 1 

≥ 65: 5.60% (0.90-10.60); 3 

All Year, Increment: 0.8 ppm 

Age Group

0-14: -2.20% (-4.00 to -0.30); 1 

14-64: 2.70% (0.10-5.50); 1 

≥ 65: 5.80% (2.40 to 9.30); 3 

March 2009 C-34 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Hapcioglu et 

al. (2006) 

Period of Study: 

1/1997-12/2001 

Location: 

Istanbul, 

Turkey 

Hospital Admissions 

Health Outcome (ICD9): 

COPD (490-492, 494-496) 

Study Design: Cross-sectional 

Statistical Analyses: 

Pearson Correlation Coefficient 

Age Groups Analyzed: All 

ages 

Pollutant: CO 

Averaging Time: Monthly 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutant: NR 

Correlation Coefficient: 

Between CO exposure and COPD: 0.57 

Between CO exposure and COPD when controlling for 

temperature: 0.25 

Author: Hinwood et al. 

(2006) 

Period of Study: 

1/1992-12/1998 

Location: 

Perth, 

Australia 

Hospital Admissions 

Health Outcome (ICD9): 

COPD (490.00-496.99 

excluding asthma) 

Pneumonia/influenza (480.00-

489.99); Asthma (493) 

Study Design: Case-crossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: All 

ages 

Pollutant: CO 

Averaging Time: Maximum 8-h avg 

Mean (SD) unit: 

All Year: 2.3 (1.3) ppm; 

November-April: 2.2 (1.3) ppm; 

May-October: 2.4 (1.2) ppm 

Range (10th, 90th): 

All Year: (0.9, 4.2) 

November-April: (0.8, 4.2) 

May-October: (1.1, 4.2) 

Copollutant: correlation

All Year: 

NO2: r = 0.57 

O3: r = 0.00 

November-April: NO2: r = 0.55 

O3: r = 0.00 

May-October: NO2: r = 0.57 

O3: r = 0.16 

Increment: 2.3 ppm 

Odds Ratio (Lower CI, Upper CI); Lag 

Pneumonia

0.99999 (0.9737-1.0268); 0 

1.00650 (0.9806-1.0331); 1 

1.00351 (0.9779-1.0298); 2 

1.00424 (0.9790-1.0301); 3 

1.00581 (0.9752-1.0374); 0-1 

1.01005 (0.9755-1.0458); 0-2 

1.00805 (0.9701-1.0474); 0-3 

COPD

0.99915 (0.9693-1.0297); 0 

1.00205 (0.9727-1.0323); 1 

0.98630 (0.9577-1.0158); 2 

0.98970 (0.9619-1.0182); 3 

0.99960 (0.9647-1.0357); 0-1 

0.99260 (0.9538-1.0329); 0-2 

0.99160 (0.9493-1.0357); 0-3 

Author: Hwang and 

Chan (2002) 

Period of Study: 

1998 

Location: 

50 communities in 

Taiwan 

Clinic Visits 

Health Outcome (ICD9): 

Lower respiratory tract 

infections (466, 480-486) 

Study Design: Time-series 

Statistical Analyses: 

1. General linear regression 

2. Bayesian hierarchical 

modeling 

Age Groups Analyzed: 

All Ages 

0-14 

15-64 

≥ 65 

Pollutant: CO 

Averaging Time: 

Maximum 8-h avg 

Mean (SD) unit: 

1.00 (0.30) ppm 

Range (Min, Max): (0.51, 1.71) 

Copollutant: NR 

Increment: 0.1 ppm 

% Increase (Lower CI, Upper CI); Lag 

Age Group: All Ages

0.80% (0.60-1.00); 0 

0.10% (-0.10 to 0.30); 1 

0.10% (-0.10 to 0.30); 2 

Age Group: 0-14

0.70% (0.50-1.00); 0 

0.10% (-0.20 to 0.30); 1 

0.20% (-0.10 to 0.40); 2 

Age Group: 15-64

0.90% (0.60-1.10); 0 

0.20% (0.00-0.50); 1 

0.20% (-0.10 to 0.40); 2 

Age Group: ≥ 65

1.10% (0.80-1.50); 0 

0.60% (0.30-1.00); 1 

0.40% (0.10-0.80); 2 

Author: Ito et al. (2007) 

Period of Study: 

1999-2002 

Location: 

New York City, NY 

ED Visits 

Health Outcome (ICD9): 

Asthma (493) 

Study Design: Time-series 

Statistical Analyses: Poisson 

GLM 

Age Groups Analyzed: All 

ages 

Pollutant: CO 

Averaging Time: Maximum 8-h avg 

Mean (SD) unit: 

All Season: 1.31 (0.43) ppm 

Warm Months (April-September):

1.22 (0.32) ppm 

Cold Months (October-March): 1.41 

(0.5) ppm 

Range (5th, 95th): 

All season: (0.77, 2.11) 

Warm Months (April-September):

(0.75, 1.82) 

Cold Months (October-March):

(0.78, 2.33) 

Copollutant: NR 

Increment: 1.3 ppm 

Relative Risk (Lower CI, Upper CI); Lag 

Warm months: 1.15 (1.07-1.25); 0-1 

March 2009 C-35 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Karr et al. 

(2007) 

Period of Study: 

1995-2000 

Location: 

South Coast Air Basin, 

CA 

Hospital Admissions 

Health Outcome (ICD9): 

Acute bronchiolitis (466.1) 

Study Design: Matched casecontrol 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: 

Infants: 3 weeks -1 yr 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 

Chronic: 1,770 ppb 

Subchronic: 1,720 ppb 

Range (Min, Max): 

Chronic: (120, 8300) 

Subchronic: (130, 5070) 

Copollutant: NR 

Increment: 910 ppb, 960 ppb 

Odds Ratio (Lower CI, Upper CI); lag: 

Increment: 910 ppb 

Subchronic broncholitis: 1 (0.97-1.03) 

Increment: 960 ppb 

Chronic broncholitis: 1 (0.97-1.03) 

Author: Karr et al. 

(2006) 

Period of Study: 

1995-2000 

Location: 

South Coast Air Basin, 

CA 

Hospital Admissions 

Health Outcome (ICD9): 

Acute bronchiolitis (466.1) 

Study Design: Case-Crossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: 

Infants: 3 weeks – 1 year 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 

1-day lag: 

Index*: 1,730 ppb 

Referent*: 1,750 ppb 

 4-day lag: 

Index*: 1,760 ppb 

Referent*: 1,790 ppb 

Range (Min, Max): 

Lag 1: 

Index*: (4, 9600) Referent*: (4, 9600) 

Lag 4: 

Index* (4, 8710) Referent* (4, 9600) 

Copollutant: NR 

* Index days: days lagged in reference 

to date of hospitalization of a case. 

Referent days: are for each case and 

includes all days that are the same 

day of week and in the same month as 

the index day for that case for CO. 

Increment: 1361, 1400 ppb 

Odds Ratio (Lower CI, Upper CI); Lag 

Increment: 1361 ppb 

Age Group: 

Overall: 0.99 (0.96-1.02); 1 

25-29 weeks: 0.86 (0.68-1.1); 1 

29 1/7 – 34 weeks: 1 (0.86-1.15); 1 

34 1/7 – 37 weeks: 0.95 (0.87-1.04); 1 

37 1/7 – 44 weeks: 1 (0.97-1.03); 1 

Increment: 1400 ppb 

Age Group: 

Overall: 0.97 (0.94-1); 4 

25-29 weeks: 0.93 (0.72-1.2); 4 

29 1/7 – 34 weeks: 0.89 (0.77-1.03); 4 

34 1/7 – 37 weeks: 0.98 (0.90-1.08); 4 

37 1/7 – 44 weeks: 0.97 (0.94-1); 4 

Author: Kim et al. 

(2007) 

Period of Study: 

2002 

Location: 

Seoul, 

Korea 

Hospital Admissions 

Health Outcome (ICD10): 

Asthma (J45 and J46) 

Study Design: 

Bi-directional case-crossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: 

All Ages 

Pollutant: CO 

Averaging Time: 

Maximum 8-h avg 

Mean (SD) unit: 

Daily Concentration: 8.6 (4.6) ppm 

Relevant Concentration: 

2.8 (2.8) ppm 

Range (Min, Max): 

Daily Concentration: (0.8, 44.0) 

Relevant Concentration: (0.0, 30.4) 

Copollutant: NR 

Relative Risk (Lower CI, Upper CI); lag: 

Individual Level SEP

Quintile 1: 1.06 (1.02-1.09); 1-3 ma 

Quintile 2: 1.05 (1.02-1.09); 1-3 ma 

Quintile 3: 1.05 (1.01-1.08); 1-3 ma 

Quintile 4: 1.07 (1.03-1.11); 1-3 ma 

Quintile 5: 1.05 (1.00-1.09); 1-3 ma 

Regional Level SEP

Quintile 1: 0.99 (0.92-1.07); 1-3 ma 

Quintile 2: 1.06 (1.02-1.11); 1-3 ma 

Quintile 3: 1.04 (1.02-1.07); 1-3 ma 

Quintile 4: 1.10 (1.06-1.15); 1-3 ma 

Quintile 5: 1.06 (1.03-1.09); 1-3 ma 

Overall: 1.06 (1.04-1.07); 1-3 ma 

Relative Effect Modification for SES 

Individual Level SEP

Quintile 1: 1 

Quintile 2: 1 (0.95.1.04); 1-3 ma 

Quintile 3: 0.99 (0.94-1.03); 1-3 ma 

Quintile 4: 1.02 (0.97-1.06); 1-3 ma 

Quintile 5: 0.99 (0.94-1.04); 1-3 ma 

Regional Level SEP

Quintile 1: 1 

Quintile 2: 1.05 (0.97-1.14); 1-3 ma 

Quintile 3: 1.03 (0.96-1.11); 1-3 ma 

Quintile 4: 1.08 (1-1.16); 1-3 ma 

Quintile 5: 1.05 (0.97-1.13); 1-3 ma 

March 2009 C-36 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Kontos et al. 

(1999) 

Period of Study: 

1/1987-12/1992 

Location: 

Piraeus, Greece 

Hospital Admissions 

Health Outcome (ICD9): 

Respiratory conditions 

(laryngitis, bronchiolitis, 

tonsillitis, acute rhinopharyngitis, 

otitis, bronchopneumonia, 

pneumonia, asthma) 

Study Design: Time-series 

Statistical Analyses: 

Stochastic dynamical system 

approach 

Age Groups Analyzed: 0-14 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean Range (SD) unit: 

1987: 4.2 mg/m3 

1992: 3.6 mg/m3

Range (Min, Max): NR 

Copollutant: correlation

1987-1989

Smoke: r = 0.2979; 

SO2: r = 0.2166; 

NO2: r = 0.1913 

1990-1992

Smoke: r = 0.5383; 

SO2: r = 0.43283; 

NO2: 0.5223 

This study did not present quantitative results for CO. 

Author: Lee et al. 

(2002) 

Period of Study: 

12/1997-12/1999 

Location: 

Seoul, Korea 

Hospital Admissions 

Health Outcome (ICD10): 

Asthma (J45, J46) 

Study Design: Time-series 

Statistical Analyses: 

Poisson GAM, LOESS 

Age Groups Analyzed: <15 

Pollutant: CO 

Averaging Time: 1-h maximum 

Mean Range (SD) unit: 

1.8 (0.7) ppm 

IQR (25th, 75th): (1.2, 2.2) 

Copollutant: correlation

PM10: r = 0.598 

SO2: r = 0.812 

NO2: r = 0.785 

O3: r = -0.388 

Increment: 1.0 ppm 

Relative Risk (Lower CI, Upper CI); lag: 

RR for asthma and exposure to various pollutants for 

children under 15 years old 

Pollutant: 

CO: 1.16 (1.10-1.22); 2-3 avg 

CO, PM10: 1.13 (1.07-1.20); 2-3 avg 

CO, SO2: 1.17 (1.08-1.27); 2-3 avg 

CO, NO2: 1.04 (0.95-1.14); 2-3 avg 

CO, O3: 1.16 (1.11-1.22); 2-3 avg 

CO, O3, PM10: 1.148 (1.084-1.217); 2-3 avg 

CO, O3, PM10, SO2: 1.168 (1.075-1.269); 2-3 avg 

CO, O3, PM10, SO2, NO2: 1.098 (0.994-1.214); 2-3 avg 

Author: Lee et al. 

(2006) 

Period of Study: 

1/2002-12/2002 

Location: 

Seoul, 

Korea 

Hospital Admissions 

Health Outcome (ICD10): 

Asthma (J45-46) 

Study Design: Time-series 

Statistical Analyses: 

GAM with stringent parameters 

Age Groups Analyzed: <15 

Pollutant: CO 

Averaging Time: 

Maximum 2-h avg 

Mean (SD) unit: 

High SES: 6.08 (2.10) ppb 

Moderate SES: 6.35 (2.44) ppb 

Low SES: 6.67 (2.59) ppb 

Range (Min, Max): NR 

Copollutant: correlation

NO2: r = 0.55 

SO2: r = 0.72 

PM10: r = 0.28

O3: r = -0.36 

Increment: 3.01 ppb, 0.26 ppb, 4.52 ppb, 3.68 ppb 

Relative Risk (Lower CI, Upper CI); lag: 

Increment: 3.01 ppb 

Overall: 1.07 (0.96-1.20); 0 

Increment: 0.26 ppb 

High SES: 1.06 (0.96-1.17); 0 

Increment: 4.52 ppb 

Moderate SES: 0.96 (0.84-1.10); 0 

Increment: 3.68 ppb 

Low SES: 1.02 (0.85-1.24); 0 

Author: Lee et al. 

(2007b) 

Period of Study: 

1996-2003 

Location: 

Kaohsiung, Taiwan 

Hospital Admissions 

Health Outcome (ICD9): 

COPD (490-492, 494, 496) 

Study Design: Bi-directional 

case-crossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: All 

ages 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 0.77 ppm 

Range (Min, Max): (0.23, 1.72) 

Copollutant: 

PM10

SO2

NO2

O3

Increment: 0.29 ppm 

Odds Ratio (Lower CI, Upper CI); lag: 

CO 

<25°C : 1.398 (1.306-1.496); 0-2 

≥ 25°C : 1.189 (1.123-1.259); 0-2 

CO. PM10

<25°C : 1.257 (1.152-1.371); 0-2 

≥ 25°C : 1.149 (1.079-1.224); 0-2 

CO, SO2

<25°C : 1.396 (1.295-1.504); 0-2 

≥ 25°C : 1.241 (1.161-1.326); 0-2 

CO, NO2

<25°C : 0.973 (0.877-1.080); 0-2 

≥ 25°C : 1.196 (1.104-1.297); 0-2 

CO, O3

<25°C : 1.378 (1.286-1.477); 0-2 

≥ 25°C : 1.170 (1.105-1.239); 0-2 

March 2009 C-37 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Lin et al. 

(1999) 

Period of Study: 

5/1991-4/1993 

Location: 

Sao Paulo, Brazil 

ED Visits 

Health Outcome (ICD9): 

Respiratory illness (lower 

respiratory illness, upper 

respiratory illness, wheezing) 

Study Design: Time-series 

Statistical Analyses: Poisson 

Age Groups Analyzed: <13 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 5 ppm 

Range (Min, Max): (1, 12) 

Copollutant: correlation 

PM10: r = 0.50

NO2: r = 0.35

SO2: r = 0.56

O3: r = 0.04 

Increment: NR 

Relative Risk (Lower CI, Upper CI); lag: 

Overall Respiratory Illnesses

CO: 1.206 (1.066-1.364); 0-5 

CO, PM10, O3, SO2, NO2: 0.945 (0.808-1.105); 0-5 

Lower Respiratory Illness

CO: 1.203 (0.867-1.669); 0-5 

CO, PM10, O3, SO2, NO2: 0.971 (0.641-1.472); 0-5 

Upper Respiratory Illness

CO: 1.237 (1.072-1.428); 0-5 

CO, PM10, O3, SO2, NO2: 0.944 (0.785-1.135); 0-5 

Wheezing

CO: 0.813 (0.606-1.091); 0-5 

CO, PM10, NO2, SO2, O3: 0.74 (0.505-1.085); 0-5 

Author: Lin et al. 

(2003) 

Period of Study: 

1/1981-12/1993 

Location: 

Toronto, ON, 

Canada 

Hospital Admissions 

Health Outcome (ICD9): 

Asthma (493) 

Study Design: Case-crossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: 6-12 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 1.18 (0.50) ppm 

Range (Min, Max): (0, 6.10) 

Copollutant: correlation 

SO2: r = 0.37

NO2: r = 0.55

O3: r = -0.16

PM2.5: r = 0.45

PM10-2.5: r = 0.17

PM10: r = 0.38 

Increment: 0.5 ppm 

Odds Ratio (Lower CI, Upper CI); lag: 

Boys: 

Adjusting for Daily Weather Variables

1.05 (1-1.11); 1 / 1.07 (1.01-1.14); 2 

1.08 (1.01-1.16); 3 / 1.08 (1-1.17); 4 

1.07 (0.99-1.16); 5 / 1.07 (0.98-1.17); 6 

1.07 (0.98-1.17); 7 

Adjusting for PM and Daily Weather Variables

1.05 (0.99-1.11); 1 / 1.08 (1.01-1.16); 2 

1.09 (1.01-1.18); 3 / 1.10 (1.02-1.20); 4 

1.09 (1.00-1.18); 5 / 1.09 (0.99-1.19); 6 

1.09 (0.99-1.20); 7 

Girls: 

Adjusting for Daily Weather Variables

1.00 (0.93-1.06); 1 / 1.01 (0.94-1.10); 2 

1.00 (0.91-1.09); 3 / 0.98 (0.89-1.09); 4 

1.01 (0.91-1.13); 5 / 1.03 (0.92-1.16); 6 

1.04 (0.93-1.17); 7 

Adjusting for PM and Daily Weather Variables

1.00 (0.93-1.07); 1 / 1.01 (0.92-1.10); 2 

0.99 (0.90-1.09); 3 / 0.97 (0.87-1.08); 4 

0.99 (0.89-1.11); 5 / 1.02 (0.90-1.15); 6 

1.05 (0.93-1.20); 7 

Author: Lin et al. 

(2004c) 

Period of Study: 

1/1987-12/1998 

Location: 

Vancouver, BC 

Canada 

Hospital Admissions 

Health Outcome (ICD9): 

Asthma (493) 

Study Design: Time-series 

Statistical Analyses: 

GAM, LOESS 

Age Groups Analyzed: 6-12 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 0.96 (0.52) ppm 

Range (Min, Max): (0.23, 4.90) 

Copollutant: correlation 

SO2: r = 0.67

NO2: r = 0.73

O3: r = -0.35 

Increment: 0.5 ppm 

Relative Risk (Lower CI, Upper CI); lag: 

Boys 

High SES: 

1.06 (0.98-1.14); 1 / 1.06 (0.97-1.15); 2 

1.07 (0.97-1.17); 3 / 1.03 (0.93-1.14); 4 

1.01 (0.91-1.12); 5 / 1.01 (0.91-1.13); 6 

1.06 (0.94-1.18); 7 

Low SES: 

1.06 (0.99-1.14); 1 / 1.03 (0.95-1.12); 2 

1.01 (0.93-1.11); 3 / 0.99 (0.90-1.09); 4 

0.96 (0.87-1.06); 5 / 0.98 (0.88-1.08); 6 

0.98 (0.88-1.09); 7 

Girls 

High SES:

1.05 (0.94-1.16); 1 / 1.02 (0.90-1.15); 2 

0.97 (0.85-1.11); 3 / 0.95 (0.83-1.10); 4 

0.93 (0.80-1.08); 5 / 0.95 (0.82-1.11); 6 

1.01 (0.87-1.19); 7 

Low SES:

1.01 (0.92-1.11); 1 / 0.98 (0.89-1.10); 2 

0.99 (0.88-1.11); 3 / 1.05 (0.93-1.19); 4 

1.07 (0.94-1.21); 5 / 1.07 (0.94-1.23); 6 

1.04 (0.91-1.20); 7 

March 2009 C-38 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Lin et al. 

(2005) 

Period of Study: 

1998-2001 

Location: 

Toronto, 

Canada 

Hospital Admissions 

Health Outcome (ICD9): 

Respiratory Infections (464, 

466, and 480-487) 

Study Design: 

Bi-directional case-crossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: <15 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 1.16 (0.38) ppm 

Range (Min, Max): (0.38, 2.45) 

Copollutant: correlation 

PM2.5: r = 0.10

PM10-2.5: r = 0.06

PM10: r = 0.10

SO2: r = 0.12

NO2: r = 0.20

O3: r = -0.11 

Increment: 0.44 ppm 

Odds Ratio (Lower CI, Upper CI); Lag 

Boys 

No adjustment: 

1.11 (1.01-1.22); 0-3 / 1.10 (1.00-1.22); 0-5 

Adjustment for weather variables: 

1.13 (1.03-1.24); 0-3 / 1.13 (1.02-1.25); 0-5 

Adjustment for weather variables and PM: 

1.08 (0.98-1.20); 0-3 / 1.08 (0.97-1.20); 0-5 

Girls

No adjustment: 

0.99 (0.89-1.10); 0-3 / 1.00 (0.89-1.13); 0-5 

Adjustment for weather variables: 1.02 (0.92-1.14); 0-3: / 1.05 (0.93-1.18); 0-5 

Adjustment for weather variables and PM: 

1.01 (0.90-1.13); 0-3 / 1.02 (0.90-1.15); 0-5 

Total

No adjustment: 

1.06 (0.98-1.14); 0-3 / 1.06 (0.98-1.15); 0-5 

Adjustment for weather variables: 

1.09 (1.01-1.17); 0-3 / 1.10 (1.01-1.19); 0-5 

Adjustment for weather variables and PM: 

1.05 (0.97-1.14); 0-3 / 1.06 (0.97-1.15); 0-5 

Author: Linn et al. 

(2000) 

Period of Study: 

1992-1995 

Location: 

Los Angeles, CA 

Hospital Admissions 

Health Outcome (ICD9): 

APR-DRG Codes: Pulmonary 

(75-101); COPD (88) 

ICD9 Codes: Asthma (493) 

Study Design: Time-series 

Statistical Analyses: Poisson 

Age Groups Analyzed: 

0-29, 

≥ 30 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 

Winter 1.7 (0.8) ppm 

Spring 1.0 (0.3) ppm 

Summer 1.2 (0.4) ppm 

Fall 2.1 (0.8) ppm 

Range (Min, Max): 

Winter: (0.5, 5.3) 

Spring: (0.4, 2.2) 

Summer: (0.3,2.7) 

Fall: (0.6, 4.3) 

Copollutant: correlation

Winter

NO2: r = 0.89; PM10: r = 0.78; 

O3: r = -0.43 

Spring

NO2: r = 0.92; PM10: r = 0.54; 

O3: r = 0.29 

Summer

NO2: r = 0.94; PM10: r = 0.72; 

O3: r = 0.03 

Fall

NO2: r = 0.84; PM10: r = 0.58; 

O3: r = -0.36 

Increment: 1.0 ppm 

β (SE); lag: 

Pulmonary 

Age Group: ≥ 30

All Year: 0.007 

Winter: 0.016 

Spring: 0.014 

Summer: 0.020 

Fall: 0.020 

Asthma 

Age Group 0-29

All Year: 0.036 

Asthma 

Age Group: ≥ 30; All Year: 0.028 

Winter: 0.045 

Fall: 0.039 

COPD 

Age Group: ≥ 30

All Year: 0.019 

Winter: 0.035 

Fall: 0.029 

March 2009 C-39 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Luginaah et al. 

(2005) 

Period of Study: 

4/1995-12/2000 

Location: 

Windsor, ON, Canada 

Hospital Admissions 

Health Outcome (ICD9): 

Respiratory illness (460-519) 

Study Design: Time-series and Case-crossover 

Statistical Analyses: 

1. Time-series: Poisson 

2. Case-crossover: conditional 

logistic regression 

Age Groups Analyzed: All ages 

0-14 

15-64 

≥ 65 

Pollutant: CO 

Averaging Time: 1-h maximum 

Mean (SD) unit: 1.3 (1.0) ppm 

Range (Min, Max): (0, 11.82) 

Copollutant: correlation

NO2: r = 0.38

SO2: r = 0.16

O3: r = 0.10

CoH: r = 0.31

PM10: r = 0.21 

Increment: 1.17 ppm 

Relative Risk (Lower CI, Upper CI); Lag 

Females and Case-crossover study design 

Age Group: All ages: 

1.037 (0.968-1.111); 1 

1.063 (0.976-1.158); 2 

1.087 (0.982-1.203); 3 

Age Group: 0-14: 

1.147 (1.006-1.307); 1 

1.186 (1.020-1.379); 2 

1.221 (1.022-1.459); 3 

Age Group: 15-64: 

1.005 (0.884-1.141); 1 

1.007 (0.859-1.181); 2 

1.032 (0.858-1.240); 3 

Age Group: ≥ 65: 

1.014 (0.922-1.116); 1 

1.024 (0.907-1.156); 2 

1.035 (0.893-1.200); 3 

Males and Case-crossover study design 

Age Group: All Ages:

0.950 (0.884-1.020); 1 

0.945 (0.862-1.036); 2 

0.965 (0.866-1.075); 3 

Age Group: 0-14: 1.003 (0.904-1.113); 1 

0.997 (0.871-1.141); 2 

0.970 (0.824-1.141); 3 

Age Group: 15-64: 

1.036 (0.870-1.233); 1 

1.033 (0.821-1.299); 2 

0.991 (0.760-1.293); 3 

Age Group: ≥ 65: 

0.867 (0.775-0.970); 1 

0.865 (0.752-0.994); 2 

0.946 (0.807-1.109); 3 

Female and Time-series study design 

Age Group: All Ages:

1.049 (0.993-1.108); 1 

1.032 (0.993-1.188); 2 

1.051 (0.993-1.112); 3 

Age Group: 0-14: 

1.077 (0.979-1.184); 1 

1.068 (1.001-1.139); 2 

1.100 (0.997-1.213); 3 

Age Group: 15-64: 1.072 (0.962-1.195); 1 

1.025 ( 0.944-1.112); 2 

1.081 (0.963-1.213); 3 

Age Group: ≥ 65: 

1.029 (0.957-1.118); 1 

1.030 (0.928-1.144); 2 

1.013 (0.899-1.142); 3 

Male and Time-series study design 

Age Group: All Ages:

0.989 (0.932-1.049); 1 

0.986 (0.946-1.029); 2 

0.987 (0.929-1.048); 3 

Age Group: 0-14: 

1.034 (0.949-1.126); 1 

0.996 (0.933-1.062); 2 

0.968 (0.881-1.064); 3 

Age Group: 15-64: 

0.994 (0.854-1.157); 1 

0.988 (0.884-1.104); 2 

0.951 (0.806-1.121); 3 

Age Group: ≥ 65: 

0.901 (0.817-0.994); 1 

0.904 (0.803-1.019); 2 

0.963 (0.845-1.098); 3 

March 2009 C-40 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Martins et al. 

(2002) 

Period of Study: 

5/1996-9/1998 

Location: 

Sao Paulo, Brazil 

ED Visits 

Health Outcome (ICD10): 

Chronic Lower Respiratory 

Disease (CLRD: J40-47) for 

chronic bronchitis, emphysema, 

other COPD, asthma, and 

bronchiectasia 

Study Design: Time-series 

Statistical Analyses: 

Poisson GAM, LOESS 

Age Groups Analyzed: >64 

Pollutant: CO 

Averaging Time: 

Maximum 8-h avg 

Mean (SD) unit: 3.7 (1.7) ppm 

Range (Min, Max): (1.0, 12.6) 

Copollutant: correlation

NO2: r = 0.62; 

SO2: r = 0.51; 

PM10: r = 0.73; 

O3: r = 0.07 

Increment: 1.63 ppm 

β (SE); lag: 

Chronic Lower Respiratory Diseases

Age Group

>64: 0.0489 (0.0274); 2 

Author: Masjedi et al. 

(2003) 

Period of Study: 

9/1997-2/1998 

Location: 

Tehran, Iran 

ED Visits 

Health Outcome (ICD9): 

Total acute respiratory 

conditions; Asthma (493); 

COPD (490-492, 494, 496) 

Study Design: Time-series 

Statistical Analyses: 

Multiple stepwise regression 

Age Groups Analyzed: Adults 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 8.85 ppm 

Range (Min, Max): (2.15, 23.8) 

Copollutant: NR 

Increment: NR 

β (p-value); lag; 

Asthma: -0.779 (0.12) 

COPD: 0.012 (0.71) 

Acute Respiratory conditions: -0.086 (0.400) 

Correlation coefficients: 

Mean 3-day CO levels and asthma: -0.300 (0.149) 

Mean weekly CO level and asthma: -0.14 (0.2) 

Mean 10-day CO levels and asthma: -0.05 (0.43) 

Author: McGowan et 

al. (2002) 

Period of Study: 

6/1988- 12/1998 

Location: 

Christchurch, 

New Zealand 

Hospital Admissions 

Health Outcome (ICD9): 

Pneumonia (480-487); Acute 

respiratory infections (460-466); 

Chronic lung Diseases (491-

492, 494-496); Asthma (493) 

Study Design: Time-series 

Statistical Analyses: 

Generalized Additive Model 

Age Groups Analyzed: 

<15; >64 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 1.16 (1.51) mg/m3

Range (Min, Max): (0, 15.7) 

Copollutant: NR 

This study did not provide quantitative results for CO. 

Author: Migliaretti et al. 

(2007) 

Period of Study: 

1/1997 - 12/1999 

Location: 

Turin, Italy 

Hospital Admissions 

Health Outcome (ICD9): 

Respiratory Illness (chronic 

bronchitis, emphysema, and 

other COPD) 

(490-496) 

Study Design: Case-control 

Statistical Analyses: 

Multiple logistic regression 

Age Groups Analyzed: 

≥ 15 

15-64 

>64 

Pollutant: CO 

Averaging Time: 8-h median 

Median (SD) unit: 

3.36 (1.57) mg/m3

Range (Min, Max): NR 

Copollutant: correlation TSP 

Increment: 1 mg/m3

Odds Ratio (Lower CI, Upper CI); lag: 

CO 

Age Group

≥ 15: 1.053 (1.030-1.070) 

15-64: 1.040 (0.987-1.085) 

>64: 1.054 (1.027-1.083) 

CO , TSP

Age Group

≥ 15: 1.058 (1.024-1.096) 

15-64: 1.062 (0.993-1.135) 

>64: 1.054 (1.011-1.099) 

March 2009 C-41 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Moolgavkar 

(2000a) 

Period of Study: 

1987 – 1995 

Location: 

3 U.S. counties: 

Los Angeles County,CA 

Cook County, IL 

Maricopa County, AZ 

Hospital Admissions 

Health Outcome (ICD9): 

COPD plus asthma (490-496) 

Study Design: Time-series 

Statistical Analyses: 

Poisson GAM 

Age Groups Analyzed: All Ages 

0-19 

20-64 

≥ 65 

Pollutant: CO 

Averaging Time: 24-h median 

Median unit: 

Cook: 993 ppb 

LA: 1347 ppb 

Maricopa: 1240 ppb 

Range (Min, Max): 

Cook: (224, 3912) 

LA:(237,5955) 

Maricopa: (269, 4777) 

Copollutant: correlation

Cook County: 

NO2: r = 0.63; SO2: r = 0.35; 

O3: r = -0.28 

LA County: 

NO2: r = 0.80; SO2: r = 0.78; 

O3: r = -0.52 

Maricopa County: 

NO2: r = 0.66; SO2: r = 0.53; 

O3: r = -0.61 

Increment: 1.0 ppm 

% Increase (t-statistic); lag: 

Age Group: ≥ 65 

Cook County

CO: 

2.60 (1.9); 0; / 3.00 (2.2); 1; / 1.30 (1.0); 2; 

1.40 (1.1); 3; / 1.10 (0.8); 4; / 2.30 (1.8); 5 

Los Angeles County

CO: 

5.40 (11.3); 0; / 4.90 (10.1); 1; / 5.00 (10.2); 2; 

4.90 (10.1); 3; / 4.00 (8.3); 4; / 4.30 (8.6); 5; 

CO, PM10: 

4.30 (3.3); 0; / 5.30 (4.2); 1; / 5.10 (4.0); 2; 

6.80 (5.6); 3; / 6.90 (5.4); 4; / 6.30 (4.7); 5; 

CO, PM2.5: 

3.00 (1.9); 0; / 3.90 (2.5); 1; / 4.20 (2.6); 2; 

6.50 (4.4); 3; / 5.80 (3.8); 4; / 5.10 (3.1); 5 

Maricopa County

CO: 

1.40 (1.0); 0; / 0.80 (0.6); 1; / 1.20 (0.9); 2; 

1.20 (0.9); 3; / 1.50 (1.1); 4; / 4.90 (3.8); 5 

Age Group: 0-19

Los Angeles County

CO: 

8.20 (14.4); 0; / 9.00 (15.9); 1; / 9.20 (16.4); 2; 

8.50 (15.0); 3; / 7.00 (12.1); 4; / 4.80 (8.1); 5; 

CO, PM10: 

7.50 (14.4); 0; /: 7.40 (5.2); 1; / 6.40 (4.3); 2; 

8.00 (5.5); 3; / 6.30 (4.0); 4; / 5.30 (3.5); 5; 

CO, PM10-2.5: 

5.70 (3.4); 0; / 7.50 (4.9); 1; / 5.60 (3.3); 2; 

5.40 (3.5); 3; / 4.40 (2.7); 4; / 1.80 (1.1); 5 

Age Group: 20-64 

Los Angeles County

CO: 

3.70 (8.6); 0; / 3.90 (9.1); 1; / 4.50 (10.6); 2; 

3.50 (8.3); 3; / 3.40 (7.9); 4; / 3.50 (7.9); 5; 

CO, PM10: 

5.00 (4.6); 0; / 3.00 (2.7); 1; / 3.10 (2.8); 2; 

5.20 (4.7); 3; / 5.90 (5.1); 4; / 4.90 (4.4); 5; 

CO, PM2.5: 

3.50 (2.5); 0; / 0.60 (0.4); 1; / 1.10 (0.8); 2; 

5.70 (4.1); 3; / 4.70 (3.3); 4; / 3.90 (2.8); 5; 

CO, PM10-2.5: 

2.80 (2.2); 0; / 2.50 (2.0); 1;/ 0.60 (0.5); 2; 

3.90 (3.2); 3; / 3.40 (2.8); 4; / 4.00 (3.4); 5 

Author: Moolgavkar 

(2003b) 

Period of Study: 

1987 – 1995 

Location: 

2 U.S. counties: 

Los Angeles County, 

CA, and Cook County, 

IL 

Hospital Admissions 

Health Outcome (ICD9): 

COPD plus asthma (490-496) 

Study Design: Time-series 

Statistical Analyses: 

Poisson GAM, Poisson GLM 

with natural splines 

Age Groups Analyzed: 

All Ages; 

≥ 65 

Pollutant: CO 

Averaging Time: 24-h median 

Median unit: 

Cook: 993 ppb 

LA: 1347 ppb 

Maricopa: 1240 ppb 

Range (Min, Max): Cook: (224, 3912) 

LA: (237,5955) 

Copollutant: correlation

Cook County: NO2: r = 0.63; SO2: r = 0.35; 

O3: r = -0.28 

Los Angeles County: NO2: r = 0.80; SO2: r = 0.78; 

O3: r = -0.52 

Increment: 1 ppm 

% Increase (t-statistic); lag: 

COPD - Los Angeles County

CO -

GAM-30 (10-8): 

5.48 (17.67); 0; / 5.67 (18.22); 1; / 5.90 (19.01); 2; 

5.28 (16.94); 3; / 4.59 (14.50); 4; / 4.10 (12.80); 5 

GAM-100 (10-8): 

2.37 (8.67); 0; / 2.41 (8.73); 1; / 2.41 (8.76); 2; 

1.81 (6.58); 3; / 1.38 (4.94); 4; / 1.07 (3.82); 5 

NS-100: 2.28 (5.65); 0; / 2.29 (5.50); 1; / 2.32 (5.33); 2; 

1.74 (4.10); 3; / 1.30 (3.16); 4; / 1.00 (2.46); 5 

COPD - Cook County

CO - 

GAM-100 (10-8): 

2.11 (1.62); 0; / 2.85 (2.16); 1; / 1.14 (0.86); 2; 

1.05 (0.79); 3; / 0.43 (0.33); 4; / 0.34 (0.26); 5 

March 2009 C-42 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Neidell et al. 

(2004) 

Period of Study: 

1992-1998 

Location: 

California 

Hospital Admissions 

Health Outcome (ICD9): 

Asthma (493) 

Study Design: Time-series 

Statistical Analyses: 

Linear Regression 

Age Groups Analyzed: 0-1 

1-3 

3-6 

6-12 

12-18 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 1.777 (1.037) ppm 

Range (Min, Max): NR 

Copollutant: correlation

O3

PM10

NO2

Increment: NR 

β (SE); lag; 

Single-pollutant model 

Age Group 

0-1: -0.007 (0.009); 

1-3: 0.027 (0.009); 

3-6: 0.053 (0.010); 

6-12: 0.047 (0.009); 

12-18: 0.025 (0.008) 

Fixed effect controlling for O3, PM10, and NO2

Age Group 

0-1: -0.01 (0.01); 

1-3: 0.024 (0.011); 

3-6: 0.049 (0.011); 

6-12: 0.023 (0.011); 

12-18: 0.021 (0.009) 

Fixed effect controlling for O3, PM10, NO2 and Avoidance 

Behavior

Age Group 

0-1: -0.010 (0.010); 

1-3: 0.027 (0.011); 

3-6: 0.051 (0.011); 

6-12: 0.025 (0.011); 

12-18: 0.021 (0.009) 

Author: Norris et al. 

(1999) 

Period of Study: 

9/1995- 12/1996 

Location: 

Seattle, WA 

ED Visits 

Health Outcome (ICD9): 

Asthma (493) 

Study Design: Time-series 

Statistical Analyses: 

Semiparametric Poisson GAM 

Age Groups Analyzed: <18 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 1.6 (0.5) ppm 

Range (Min, Max): (0.6, 4.1) 

Copollutant: correlation 

PM10: r = 0.74 

NO2 (1-h max): r = 0.47 

NO2 (24-h avg.): r = 0.66 

SO2 (1-h max): r = 0.15 

SO2 (24-h avg.): r = 0.32 

Increment: 0.6 ppm 

Relative Risk (Lower CI, Upper CI); Lag 

High Utilization: 1.04 (0.93-1.16); 1 

Low Utilization: 1.15 (1.05-1.28); 1 

All: 1.10 (1.02-1.19); 1 

Author: Peel et al. 

(2005) 

Period of Study: 

1/1993- 8/2000 

Location: 

Atlanta, GA 

ED Visits 

Health Outcome (ICD9): 

Asthma (493, 786.09); COPD 

(491, 492, 496); URI (460-466, 

477); Pneumonia (480-486) 

Study Design: Time-series 

Statistical Analyses: 

1. Poisson GEE or asthma, URI, 

all respiratory 

2. Poisson GLM for pneumonia 

and COPD 

Age Groups Analyzed: Primary 

Analysis: All Ages Secondary 

Analysis: 2-18 

Pollutant: CO 

Averaging Time: 1-h maximum 

Mean (SD) unit: 1.8 (1.2) ppm 

Range (10th, 90th): (0.5, 3.4) 

Copollutant: NR 

Increment: 1.0 ppm 

Relative Risk (Lower CI, Upper CI); Lag 

Health Condition

All respiratory illnesses: 1.011 (1.004-1.019); 0-2 

URI: 

1.012 (1.003-1.021); 0-2 / 1.066 (1.045-1.087); 0-13 

Asthma: 

1.010 (0.999-1.022); 0-2 

1.076 (1.047-1.105); 0-13 

Pneumonia: 

1.009 (0.996-1.021); 0-2 

1.045 (1.011-1.080); 0-13 

COPD: 

1.026 (1.004-1.048); 0-2 

1.032 (0.975-1.092); 0-13 

RR for asthma and exposure to CO for children age 2-18:

1.019 (1.004-1.035); 0-2 

RR for all respiratory illnesses and CO exposure for all 

ages 

AQS (1/1/93- 8/31/00): 1.011 (1.004-1.019); 0-2 

AQS (8/1/98- 8/31/00): 1.010 (1.000-1.021); 0-2 

ARIES (8/1/98- 8/31/00): 1.018 (1.003-1.033); 0-2 

Author: Sheppard et 

al. (1999) 

Period of Study: 

1987-1994 

Location: 

Seattle, WA 

Hospital Admissions 

Health Outcome (ICD9): 

Asthma (493) 

Study Design: Time-series 

Statistical Analyses: Poisson 

Age Groups Analyzed: <65 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 1831 ppb 

IQR (25th, 75th): (1277, 2201) 

Copollutant: correlation

PM10: r = 0.83; PM2.5: r = 0.78; 

PM10-2.5: r = 0.56; O3: r = -0.18; 

SO2: r = 0.24 

Increment: 924 ppb 

% Increase (Lower CI, Upper CI); Lag 

CO: 6% (3, 9); 3 

CO, PM2.5: 5% (1, 8); 3 

March 2009 C-43 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Slaughter et al. 

(2005) 

Period of Study: 

1/1995 – 6/2001 

Location: 

Spokane, WA 

Hospital Admissions & ED 

Visits 

Health Outcome (ICD9): 

Respiratory causes (460-519) 

Asthma (493); COPD (491, 492, 

494, 496) Acute respiratory tract 

infections not including colds 

and sinusitis (464-466, 490) 

Study Design: Time-series 

Statistical Analyses: 

Poisson GLM, Natural Splines 

Age Groups Analyzed: 

All ages, 

Adults 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: NR 

Range (5th, 95th): (1.25, 3.05) 

Copollutant: correlation

PM1: r = 0.63 

PM2.5: r = 0.62 

PM10: r = 0.32 

PM10-2.5: r = 0.32 

Increment: 1.0 ppm 

Relative Risk (Lower CI, Upper CI); lag: 

ED Visits

All Respiratory Illnesses 

Age Group: All Ages: 

0.99 (0.96-1.02); 1 / 1.01 (0.98-1.04); 2 

1.03 (1.00-1.06); 3 

Asthma 

Age Group: All Ages: 

1.00 (0.95-1.06); 1 / 1.01 (0.96-1.07); 2 

1.06 (1.00-1.11); 3 

COPD 

Age Group: Adults:

0.92 (0.85-1.00); 1 / 0.99 (0.91-1.08); 2 

1.01 (0.93-1.10); 3 

Hospital Admissions: 

All Respiratory Illnesses 

Age Group: All Ages: 

0.99 (0.95-1.02); 1 / 1.00 (0.96-1.04); 2 

0.99 (0.96-1.03); 3 

Asthma 

Age Group: All Ages:

1.02 (0.92-1.13); 1 / 1.06 (0.96-1.17); 2 

1.00 (0.91-1.11); 3 

COPD 

Age Group: Adults:

0.94 (0.86-1.03); 1 / 1.04 (0.95-1.13); 2 

0.97 (0.88-1.06); 3 

Author: Steib et al. 

(2007) 

Period of Study: 

7/1992- 3/1996 

Location: 

Saint John, 

Canada 

ED Visits 

Health Outcome (ICD9): 

Asthma; COPD; Respiratory 

infections; All respiratory 

illnesses 

Study Design: Time-series 

Statistical Analyses: 

Poisson GAM, LOESS 

Age Groups Analyzed: All 

ages 

Pollutant: CO 

Averaging Time: 24-h avg 

1-h maximum 

Mean (SD) unit: 

All year: 0.5 (0.3) ppm 

May-September: 0.6 (0.3) ppm 

All year: 1.6 (1.1) ppm, 

May-September: 1.7 (0.9) ppm 

Range (Min, Max): NR 

Copollutant: correlation

H2S: r = -0.10; NO2: r = 0.68; 

O3: r = -0.05; SO2: r = 0.31; 

TRS: r = 0.07; PM10: r = 0.28; 

PM2.5: r = 0.27; H+: r = 0.23; 

SO4

2-: r = 0.27; CoH: r = 0.55 

Increment: 0.5 & 1.7 ppm 

% Increase (Lower CI, Upper CI); lag: 

All Respiratory Illnesses 

Increment: 0.5 ppm 

All Year: -3.40; 7 

Increment: 1.7 ppm 

May- September: -5.70 

Author: Sun et al. 

(2006) 

Period of Study: 

1/2004- 12/2004 

Location: 

Taiwan 

ED Visits 

Health Outcome (ICD9): 

Asthma (493) 

Study Design: Cross-sectional 

Statistical Analyses: 

Pearson correlation analysis 

Age Groups Analyzed: 

<16; 16-55 

Pollutant: CO 

Averaging Time: Monthly 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutant: NR 

Increment: NR 

Correlation Coefficient:

Asthma 

Age Group: 

<16: 0.653 

16-55: 0.425 

March 2009 C-44 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Tenias et al. 

(2002) 

Period of Study: 

1/1994- 12/1995 

Location: 

Valencia, Spain 

ED Visits 

Health Outcome (ICD9): 

COPD (491, 492, 494, 496) 

Study Design: Time-series 

Statistical Analyses: 

1. Poisson autoregressive 

2. Sensitivity: GAM, LOESS 

Age Groups Analyzed: >14 

Pollutant: CO 

Averaging Time: 

24-h avg 

1-h maximum 

Mean (SD) unit: 

24-h avg 

All year: 3.1 mg/m3

Warm Months: 2.5 mg/m3 Cold Months: 3.7 mg/m3 

1-h avg 

All year: 6.7 mg/m3 

Warm Months: 5.4 mg/m3 

Cold Months: 8.0 mg/m3

Range (Min, Max): 

24-h avg: (0.9, 7.1) 

1-h maximum: (1.6, 17.2) 

Copollutant: correlation

SO2: r = 0.734; NO2: r = 0.180; 

O3: r = -0.517 

Increment: 1 mg/m3

Relative Risk (Lower CI, Upper CI); Lag 

24-h avg 

All Year: 1.074 (0.998- 1156); 1 

Cold Months: 1.070 (0.991-1.156); 1 

Warm Months: 1.129 (0.960-1.329); 1 

1-h maximum 

All Year: 1.039 (1.014-1.066); 1 

Cold Months: 1.037 (1.010-1.064); 1 

Warm Months: 1.058 (0.994-1.127); 1 

All Year: sinusoidal terms: 

1.039 (1.010-1.066); 1 

All Year: humidity and temperature variables: 

1.040 (1.014-1.067); 1 

All Year: GAM, LOESS: 

1.042 (1.019-1.066); 1 

Author: Thompson et 

al. (2001) 

Period of Study: 

1/1993- 12/1995 

Location: 

Belfast, 

Northern Ireland 

ED Visits 

Health Outcome (ICD9): 

Asthma (493) 

Study Design: Time-series 

Statistical Analyses: Poisson 

Age Groups Analyzed:

Children 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 

Warm Season: 0.57 (0.41) ppm 

Cold Season: 0.74 (0.73) ppm 

IQR (25th, 75th): 

Warm Season: (0.3, 0.7) 

Cold Season: (0.4, 0.8) 

Copollutant: correlation

SO2 (log): r = 0.64; 

PM10 (log): r = 0.57; 

O3: r = -0.52; NOx (log): r = 0.74; 

NO (log): r = 0.71; NO2: r = 0.69 

Increment: NR 

Relative Risk (Lower CI, Upper CI); lag: 

Temperature included in the model: 

1.04 (1.00-1.09); 0 / 1.07 (1.02-1.12); 0-1 

1.06 (1.00-1.12); 0-2 / 1.07 (1.00-1.14); 0-3 

Warm Season: 1.06 (0.98-1.16); NR 

Cold Season: 1.07 (1.01-1.14); NR 

Adjusted for benzene level: 

0.92 (0.83-.02); 0-1 avg. 

Note: The increment the study uses to calculate effect 

estimates is a doubling in CO levels, but The study did not 

provide this value. 

Author: Tolbert et al. 

(2007) 

Period of Study: 

1/1993- 12/2004 

Location: 

Atlanta, GA 

ED Visits 

Health Outcome (ICD9): 

Respiratory Disease: Asthma 

(493, 786.07, 786.09); COPD 

(491, 492, 496); URI (460-465, 

460.0, 477); Pneumonia (480-

496); Bronchiolitis 

(466.1, 466.11, 466.19)) 

Study Design: Time-series 

Statistical Analyses: 

Poisson GLM 

Age Groups Analyzed: All 

ages 

Pollutant: CO 

Averaging Time: 1-h maximum 

Mean (SD) unit: 1.6 ppm 

Range (Min, Max): (0.1, 7.7) 

Copollutant: correlation

PM10: r = 0.51; O3: r = 0.27; 

NO2: r = 0.70; SO2: r = 0.28; 

Coarse PM: r = 0.38; PM2.5: r = 0.47; 

SO4: r = 0.14;EC: r = 0.66; 

OC: r = 0.59; TC: r = 0.63; 

OHC: r = 0.29 

Increment: 1.22 ppm 

Relative Risk (Lower CI, Upper CI); lag: 

Respiratory Diseases: 1.016 (1.009-1.022); 3 

Note: The study only provides results of the multi-pollutant 

models in figures, not quantitatively. 

Author: Trapasso and 

Keith (1999) 

Period of Study: 

1/1994- 12/1994 

Location: 

Bowling Green, KY 

Hospital Admissions 

Health Outcome (ICD9): 

Asthma (493) 

Study Design: Time-series 

Statistical Analyses: 

Spearman Rank Correlation 

Coefficient 

Age Groups Analyzed: All 

ages 

Pollutant: CO 

Averaging Time: NR 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutant: NR 

Increment: NR 

Correlation Coefficient (lag) 

CO Mean: r = 0.19;0 

CO Mean: r = 0.27; 1 

CO Mean: r = 0.21; 2 

CO Max: r = 0.26; 0 

CO Max: r = 0.36; 1 

CO Max: r = 0.24; 2 

March 2009 C-45 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Tsai et al. 

(2006a) 

Period of Study: 

1996 - 2003 

Location: 

Kaohsiung, Taiwan 

Hospital Admissions 

Health Outcome (ICD9): 

Asthma (493) 

Study Design: Case-crossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: All 

ages 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 0.77 ppm 

Range (Min, Max): (0.23, 1.72) 

Copollutant: 

PM10

SO2

NO2

O3

Increment: 0.29 ppm 

Odds Ratio (Lower CI, Upper CI); Lag 

OR for getting asthma and exposure to various pollutants for 

all ages at either <25°C or ≥ 25°C

CO 

<25°C : 1.414 (1.300-1.537); 0-2 

≥ 25°C : 1.222 (1.138-1.312); 0-2 

CO, PM10

<25°C : 1.251 (1.125-1.393); 0-2 

≥ 25°C : 1.178 (1.088-1.274); 0-2 

CO, SO2

<25°C : 1.207 (1.076-1.354); 0-2 

≥ 25°C : 1.290 (1.188-1.400); 0-2 

CO, NO2

<25°C : 0.916 (0.807-1.039); 0-2 

≥ 25°C : 1.249 (1.127-1.384); 0-2 

CO, O3

<25°C : 1.396 (1.282-1.520); 0-2 

≥ 25°C : 1.195 (1.113-1.284); 0-2 

Author: Vigotti et al. 

(2007) 

Period of Study: 

1/2000- 12/2000 

Location: 

Pisa, Italy 

ED Visits 

Health Outcome (ICD9): 

Respiratory Disease: Asthma 

(493); Dry cough (468); Acute 

bronchitis (466) 

Study Design: Time-series 

Statistical Analyses: 

Poisson GAM, LOESS 

Age Groups Analyzed: <10; 

>65 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 1.5 (0.7) ug/m3

Range (Min, Max): (0.3, 3.5) 

Copollutant: correlation 

NO2: r = 0.62 

PM10: r = 0.70 

Increment: 1mg/m3

% Increase (Lower CI, Upper CI); Lag 

Age Group

<10: 18.60% (-6.90 to 51.10); 1 

>65: 26.50% (3.40-54.80); 4 

Author: Villeneuve et 

al. (2006b) 

Period of Study: 

1995-2000 

Location: 

Toronto, ON, 

Canada 

Physician Visits 

Health Outcome (ICD9): 

Allergic rhinitis (177) 

Study Design: Time-series 

Statistical Analyses: Poisson 

GLM 

Age Groups Analyzed: >65 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 1.1 (0.4) ppm 

Range (Min, Max): (0.0, 2.2) 

Copollutant: 

PM2.5

PM10

PM10-2.5

SO2

NO2

O3

Increment: 0.4 ppm 

Odds Ratio (Lower CI, Upper CI); Lag 

The study did not present quantitative results for CO. 

Author: Xirasagar et al. 

(2006) 

Period of Study: 

1998- 2001 

Location: 

Taiwan 

Hospital Admissions 

Health Outcome (ICD9): 

Asthma (493) 

Study Design: Cross-sectional 

Statistical Analyses: 

Spearman Rank Correlations 

Age Groups Analyzed: 

0-14; <2; 2-5; >5 

Pollutant: CO 

Averaging Time: Monthly 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutant: NR 

Increment: NR 

Correlation Coefficient (Lag) 

Age Group: 

<2: r = -0.208 

2-5: r = -0.281 

>5: r = -0.134 

Author: Yang et al. 

(2007) 

Period of Study: 

1996 - 2003 

Location: 

Taipei, 

Taiwan 

Hospital Admissions 

Health Outcome (ICD9): 

Asthma (493) 

Study Design: Case-crossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: 

All ages 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 1.33 ppm 

Range (Min, Max): (0.32, 3.62) 

Copollutant: 

PM10

SO2

NO2

O3

Increment: 0.53 ppm 

Odds Ratio (Lower CI, Upper CI); Lag 

CO 

<25°C : 1.076 (1.019-1.136); 0-2 

≥ 25°C : 1.277 (1.179-1.383); 0-2 

CO, PM10

<25°C : 1.050 (0.983-1.122); 0-2 

≥ 25°C : 1.332 (1.216-1.459); 0-2 

CO, SO2

<25°C : 1.131 (1.059-1.207); 0-2 

≥ 25°C : 1.278 (1.174-1.392); 0-2 

CO, NO2

<25°C : 0.915 (0.839-0.997); 0-2 

≥ 25°C : 1.177 (1.049-1.320); 0-2 

CO, O3

<25°C : 1.169 (1.102-1.240); 0-2 

≥ 25°C : 1.275 (1.177-1.382); 0-2 

March 2009 C-46 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Yang et al. 

(2007) 

Period of Study: 

1996-2003 

Location: 

Taipei, Taiwan 

Hospital Admissions 

Health Outcome (ICD9): 

COPD: (490-492, 494, 496) 

Study Design: Case-crossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: All 

ages 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 1.33 ppm 

Range (Min, Max): 

(0.32, 3.66) ppm 

Copollutant: 

PM10

SO2

NO2

O3

Increment: 0.53 ppm 

Odds Ratio (Lower CI, Upper CI); Lag 

CO 

<20°C: 0.975 (0.921,1.033); 0-2 

≥ 20°C: 1.227 (1.178-1.277); 0-2 

CO, PM10

<20°C: 0.925 (0.863-0.992); 0-2 

≥ 20°C: 1.177 (1.123-1.235); 0-2 

CO, SO2 

<20°C: 0.895 (0.832-0.962); 0-2 

≥ 20°C: 1.274 (1.219-1.331); 0-2 

CO, NO2

<20°C: 1.000 (0.910-1.099); 0-2 

≥ 20°C: 1.061 (0.998-1.129); 0-2 

CO, O3

<20°C: 0.935 (0.875-0.999); 0-2 

≥ 20°C: 1.234 (1.185-1.285); 0-2 

Author: Yang et al. 

(2005) 

Period of Study: 

1/1994- 12/1998 

Location: 

Vancouver, 

Canada 

Hospital Admissions 

Health Outcome (ICD9): 

COPD (490-492, 494, 496) 

Study Design: Time-series 

Statistical Analyses: Poisson 

Age Groups Analyzed:

≥ 65 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: .71 (0.28) ppm 

Range (Min, Max): (0.30, 2.48) 

Copollutant: correlation

O3: r = -0.56 

NO2: r = 0.73 

SO2: r = 0.67 

PM10: r = 0.50 

Increment: 0.3 ppm 

Relative Risk (Lower CI, Upper CI); Lag 

CO 

1.03 (1.00-1.06); 0 / 1.04 (1.01-1.08); 0-1 

1.05 (1.01-1.09); 0-2 / 1.05 (1.00-1.10); 0-3 

1.06 (1.01-1.11); 0-4 / 1.07 (1.02-1.12); 0-5 

1.08 (1.02-1.13); 0-6 

Multipollutant: CO, O3: 1.11 (1.04-1.18); 0-6 

CO, NO2: 1.04 (0.95-1.14); 0-6 

CO, SO2: 1.11 (1.01-1.22); 0-6 

CO, PM10: 1.02 (0.93-1.12); 0-6 

CO, PM10, O3, NO2, SO2: 1.08 (0.96-1.22); 0-6 

CO, O3, NO2, SO2: 1.10 (0.98-1.23); 0-6 

Author: Yang et al. 

(2003) 

Period of Study: 

1/1986- 12/1998 

Location: 

Vancouver, BC, 

Canada 

Hospital Admissions 

Health Outcome (ICD9): 

Respiratory diseases (460-519) 

Study Design: Case-crossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: 

<3; 

≥ 65 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 0.98 (0.54) ppm 

IQR (25th, 75th): (0.62, 1.16) 

Copollutant: correlation

O3: r = -0.52 

CoH 

NO2

SO2

Increment: 0.54 ppm 

Odds Ratio (Lower CI, Upper CI); Lag 

OR for respiratory diseases and exposure to various 

pollutants for people <3 and ≥ 65 

Age Group: <3

CO alone: 1.04 (1.01-1.07); 1 

CO, O3: 1.04 (1.01-1.07); 1 

CO, O3, CoH, NO2, SO2: 1.02 (0.96-1.08); 1 

Age Group: ≥ 65

CO alone: 1.02 (1.00-1.04); 1 

CO, O3: 1.02 (1.00-1.04); 1 

CO, O3, CoH, NO2, SO2: 0.96 (0.93-1.00); 1 

Author: Yang et al. 

(2004b) 

Period of Study: 

6/1/1995 – 3/31/1999 

Location: 

Vancouver, 

Canada 

Hospital Admissions 

Health Outcome (ICD9): 

Respiratory diseases (460-519); 

Pneumonia (480-486); Asthma 

(493) 

Study Design: Case-control 

Statistical Analyses: 

Pearson’s correlation coefficient 

Age Groups Analyzed: <3 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 0.70 (0.30) ppm 

IQR (25th, 75th): (0.50, 0.80) 

Copollutant: correlation

PM10: r = 0.46; PM2.5: r = 0.24; 

PM10-2.5: r = 0.33; O3: r = -0.53; 

NO2: r = 0.74; SO2: r = 0.61 

This study did not present quantitative results for CO. 

Author: Zanobetti and 

Schwartz (2006) 

Period of Study: 

1995-1999 

Location: 

Boston, MA 

Hospital Admissions 

Health Outcome (ICD9): 

Pneumonia (480-487) 

Study Design: Case-crossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: All 

ages 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: NR 

IQR (25th, 75th): (0.39, 0.60) 

Copollutant: correlation

PM2.5: r = 0.52; BC: r = 0.82; 

NO2: r = 0.67; O3: r = -0.30 

Increment: 0.475 ppm 

% Increase (Lower CI, Upper CI); lag: 

5.45 (1.10, 9.51); 0 

5.12 (0.83, 9.16); 0-1 

March 2009 C-47 DRAFT—DO NOT QUOTE OR CITE 
Table C-6. Studies of long-term CO exposure and respiratory morbidity. 

Study Design Concentrations Effect Estimates (95% CI) 

Author: Goss et al. 

(2004) 

Period of Study: 

1999 – 2000 

Location: 

U.S. 

Health Outcome: Lung function 

(FEV1, Cystic fibrosis pulmonary 

exacerbation) 

Study Design: Cohort 

Statistical Analyses: Logistic regression 

Population: 

11,484 cystic fibrosis patients 

Age Groups Analyzed: >6 

Pollutant: CO 

Averaging Time: 

Annual avg 

Mean (SD) unit: 

0.692 (0.295) ppm 

IQR (25th, 75th): (0.48, 0.83) 

Copollutant: NR 

Increment: 1.0 ppm 

Odds Ratio (Lower CI, Upper CI); lag: 

Two or More Pulmonary Exacerbations During 2000 

1.02 (0.85-1.22) 

Author: Guo et al. 

(1999) 

Period of Study:

10/1995 – 5/1996 

Location: 

Taiwan 

Health Outcome: Asthma 

Study Design: Cohort 

Statistical Analyses: 

Logistic regression 

Population: 

331,686 non-smoking children 

Age Groups Analyzed: 

Middle-school children 

(mean age: 13.8) 

Pollutant: CO 

Averaging Time: Annual avg 

Mean (SD) unit: 853 (277) ppb 

Range (Min, Max): (381, 1610) 

Copollutant: NR 

Increment: 326 ppb 

% Increase (Lower CI, Upper CI); lag: 

Boys

Physician-diagnosed asthma: 

1.17% (0.63-1.72) 

Questionnaire-diagnosed asthma: 

1.10% (0.45-1.75) 

Girls

Physician-diagnosed asthma: 

0.84% (0.45-1.22) 

Questionnaire-diagnosed asthma: 

1% (0.44-1.56) 

Author: Hirsch et al. 

(1999) 

Period of Study:

Population: 

9/1995-6/1996 

Air: 

4/1994 – 4/1995 

Location: 

Dresden, Germany 

Health Outcome: 

Asthma symptoms in the past 12 

months (wheeze, morning cough); 

Doctor's diagnosis (asthma, 

bronchitis); Lung function (bronchial 

hyperresponsiveness (BHR), FEV1

<85% pred., FEF25-75% <70% pred.) 

Study Design: Cross-sectional 

Statistical Analyses: 

Multiple logistic regression 

Population: 

5-7: 2,796; 9-11: 2,625 

Age Groups Analyzed: 

5-7 and 9-11 

Pollutant: CO 

Averaging Time: 

Annual avg 

Mean (SD) unit: 0.69 mg/m3

Range (Min, Max): (0.32, 1.54) 

Copollutant: NR 

Increment: 0.2 μg/m3

Prevalence Odds Ratio (Lower CI, Upper CI); lag: 

Symptoms in the past 12 months: Wheeze

Home Exposure 

Age Groups: 5-7; 9-11: 1.05 (0.93-1.18) 

Home/School Exposure 

Age Groups: 9-11: 1.02 (0.85-1.22) 

Morning Cough 

Home Exposure 

Age Groups: 5-7; 9-11: 1.12 (1.01-1.23) 

Age Group: 9-11: 1.13 (0.98-1.3) 

Doctor’s diagnosis: Asthma

Home Exposure 

Age Groups: 5-7; 9-11: 1.07 (0.94-1.21) 

Age Groups: 9-11: 1.16 (0.97-1.38) 

Doctor’s diagnosis: Bronchitis

Age Groups: 5-7; 9-11: 1.19 (1.11-1.27) 

Age Group: 9-11: 1.24 (1.12-1.38) 

Lung function: BHR

Age Groups: 5-7; 9-11: 0.79 (0.63-0.99) 

Age Group: 9-11: 0.77 (0.6-0.99) 

Lung function: FEV1 <85% pred.

Age Groups: 5-7; 9-11: 1.09 (0.81-1.47) 

Age Group: 9-11: 1.01 (0.73-1.41) 

Lung function: FEV25-75% <70% pred. 

Age Groups: 5-7; 9-11: 1.15 (0.94-1.39) 

Age Group: 9-11: 1.07 (0.86-1.34) 

Symptoms in the past 12 months: Wheeze

Age Groups: 5-7; 9-11

Atopic children: 1 (0.81-1.24) 

Nonatopic children: 1.05 (0.83-1.31) 

Morning cough 

Age Groups: 5-7; 9-11

Atopic children: 1.03 (0.82-1.29) 

Nonatopic children: 1.22 (1.05-1.41) 

Doctor’s diagnosis: Asthma

Atopic children: 1.05 (0.83-1.32) 

Nonatopic children: 1.29 (1.05-1.59) 

Doctor’s diagnosis: Bronchitis 

Age Groups: 5-7; 9-11

Atopic children: 1 (0.86-1.16) 

Nonatopic children: 1.21 (1.1-1.33) 

Notes: Atopic Children were defined as those children 

with specific IgE to aeroallergens >0.7 kU-L-1; Nonatopic 

March 2009 C-48 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Children were defined as those children with specific IgE 

to aeroallergens ≤ 0.7 kU-L-1. 

Author: Hwang et al. 

(2006) 

Period of Study: 

2001 

Location: 

Taiwan 

Health Outcome: 

Allergic rhinitis 

Study Design: Cross-sectional 

Statistical Analyses: Two-stage hierarchical model 

(logistic and linear regression) 

Population: 

32,143 Taiwanese school children 

Age Groups Analyzed: 6-15 

Pollutant: CO 

Averaging Time: 

Annual avg 

Mean (SD) unit: 664 (153) ppb 

Range (Min, Max): (416, 964) 

Copollutant: correlation 

NOx: r = 0.88 

O3: r = -0.37 

PM10: r = 0.27 

SO2: r = 0.40 

Increment: 100 ppb 

Adjusted Odds Ratio (Lower CI, Upper CI); lag: 

Physician-diagnosed allergic rhinitis

1.05 (1.04-1.07) 

CO, SO2: 1.04 (1.02-1.06) 

CO, PM10: 1.05 (1.03-1.07) 

CO, O3: 1.07 (1.05-1.09) 

Male: 1.06 (1.03-1.08); Female: 1.05 (1.02-1.08) 

Parental atopy: Yes: 1.05 (1.02-1.08) 

Parental atopy: No: 1.06 (1.03-1.08) 

Parental Education: <6: 1 (0.91-1.09) 

Parental Education: 6-8: 1.07 (1.02-.12) 

Parental Education: 9-11: 1.05 (1.02-1.08) 

Parental Education: ≥ 12: 1.06 (1.03-1.09) 

ETS: Yes: 1.06 (1.03-1.08); ETS: No: 1.05 (1.02-1.08) 

Visible Mold: Yes: 1.07 (1.03-1.11) 

Visible Mold: No: 1.05 (1.03-1.07) 

Author: Hwang et al. 

(2005) 

Period of Study: 

2001 

Location: 

Taiwan 

Health Outcome: Asthma 

Study Design: Cross-sectional 

Statistical Analyses: 

Two-stage hierarchical model 

(logistic and linear regression) 

Population: 

32,672 Taiwanese school children 

Age Groups Analyzed: 6-15 

Pollutant: CO 

Averaging Time: Annual avg 

Mean (SD) unit: 664 (153) ppb 

Range (Min, Max): (416, 964) 

Copollutant: correlation 

NOx: r = 0.88 

O3: r = -0.37 

PM10: r = 0.27 

SO2: r = 0.40 

Increment: 100 ppb 

Adjusted Odds Ratio (Lower CI, Upper CI); lag: 

Physician-diagnosed asthma: 1.045 (1.017-1.074) 

CO, SO2: 1.066 (1.034-1.099) 

CO, PM10: 1.079 (1.047-1.112) 

CO, O3: 1.063 (1.1-1.474) 

CO, SO2, O3: 1.111 (1.074-1.15) 

CO, PM10, O3: 1.119 (1.084-1.155) 

Male: 1.49 (1.37-1.63); Female: 1 

Parental atopy: Yes: 1 

Parental atopy: No: 2.72 (2.5-2.97) 

Parental Education: <6: 1 

Parental Education: 6-8: 1.17 (0.9-1.52) 

Parental Education: 9-11: 1.61 (1.26-2.05) 

Parental Education: ≥ 12: 2.43 (1.9-3.09) 

ETS: Yes: 0.85 (0.78-0.92); ETS: No: 1 

Visible Mold: Yes: 1.27 (1.16-1.4); Visible Mold: No: 1 

Maternal smoking during pregnancy: 

Yes: 1.18 (0.89-1.56) 

Maternal smoking during pregnancy: No: 1 

Cockroaches noted monthly: 

Yes: 1.15 (1.03-1.29) 

Cockroaches noted monthly: No: 1 

Water damage: Yes: 0.96 (0.81-1.12) 

Water damage: No: 1 

Author: Lee et al. 

(2003c) 

Period of Study:

10/1995-5/1996 

Location: 

Taiwan 

Health Outcome: 

Allergic rhinitis 

Study Design: Cohort 

Statistical Analyses: 

Multiple logistic regression 

Population: 

331,686 non-smoking children 

Age Groups Analyzed: 12-14 

Pollutant: CO 

Averaging Time: 

Annual avg 

Mean (SD) unit: 853 (277) ppb 

Range (Min, Max): (381, 1610) 

Copollutant: NR 

The study did not present quantitative results for CO. 

Author: Meng et al. 

(2007) 

Period of Study:

11/2000 – 9/2001 

Location: 

Los Angeles County 

and San Diego County, 

California 

Health Outcome: Asthma 

Study Design: Cohort 

Statistical Analyses: 

Logistic regression 

Population: 

1,609 physician-diagnosed 

asthmatics 

Age Groups Analyzed: ≥ 18 

Pollutant: CO 

Averaging Time: 

Annual avg 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutant: correlation 

Traffic: r = -0.04; O3: r = -0.55; 

PM10: r = 0.42; PM2.5: r = 0.52; 

NO2: r = 0.55 

The study did not present quantitative results for CO. 

March 2009 C-49 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Mortimer et al. 

(2008) 

Period of Study: 

1989 – 2000 

Location: 

San Joaquin Valley, 

CA 

Health Outcome: Lung function 

(FVC, FEV1, PEF, FEF25-75, 

FEV1/FVC, FEF25-75/FVC, FEF25, 

FEF75) 

Study Design: Cohort 

Statistical Analyses: 

1. DSA algorithm 

2. GEE 

Population: 232 asthmatic children 

Age Groups Analyzed: 6-11 

Pollutant: CO 

Averaging Time: 

8-h maximum monthly mean 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutant; correlation: 

Lifetime

NO2 (24-h avg): r = 0.68 

O3 (8-h maximum): r = -0.40 

PM10 (24-h avg): r = 0.05 

Prenatal

CO (8-h maximum): r = 0.52 

NO2 (24-h avg): r = 0.37 

O3 (8-h maximum): r = -0.16 

PM10 (24-h avg): r = -0.05 

Increment: NR 

Effect Size per IQR Increase in Pollutant (SE): 

FEF25-75: 

24-h avg CO exposure during 1st trimester 

0.90% (0.0113) 

FEV1/FVC

Daily maximum CO exposure during ages 0 to 3 

-2.50% (0.0016) 

FEF25-75/FVC

24-h avg CO exposure during ages 0 to 6 and diagnosed 

with asthma <2 years old 

-4.80% (0.0446) 

FEF25

24-h avg CO exposure during ages 0 to 6 and diagnosed 

with asthma <2 years old plus 24-h avg PM10 exposure 

during 2nd trimester and mother smoked when pregnant 

-6.70% (0.015) 

Coefficient (SE): 

FVC 

24-h avg CO exposure during 2nd trimester 

-0.0878 (0.0415) 

FEF25-75

Lifetime 24-h avg CO exposure 

-0.94454 (0.3975) 

FEF25-75/FVC

-0.1090 (0.0303) 

FEV1/FVC

Prenatal 8-h maximum CO exposure: 0.1711 (0.0653) 

Lifetime 1-h maximum CO exposure: -0.3242 (0.0919) 

24-h avg CO exposure during ages 0-3 and diagnosed 

with asthma <2 years old: -0.1814 (0.0599) 

FEF25

24-h avg CO exposure during ages 0-6 and diagnosed 

with asthma <2 years old: -1.0460 (0.1953) 

FEF75

Lifetime 8-h maximum CO exposure: -0.4214 (0.1423) 

Author: Singh et al. 

(2003) 

Period of Study: NR 

Location: 

Jaipur, India 

Health Outcome: Lung function 

Study Design: Panel study 

Statistical Analyses: Parametric 

statistical methods 

Population: 

Campus panel: 142 

Commuter panel: 158 

Age Groups Analyzed: ~20 

Pollutant: CO 

Averaging Time: 

Annual avg 

Mean (SD) unit: 

Roadside: 3,175 μg/m3

Campus: 2,150 μg/m3

Range (Min, Max): NR 

Copollutant: NR 

The study did not present quantitative results for CO. 

Author: Wang et al. 

(1999) 

Period of Study:

10/1995-6/1996 

Location: 

Kaohsiung and 

Pintong, Taiwan 

Health Outcome: Asthma 

Study Design: Cross-sectional 

Statistical Analyses: 

Multiple logistic regression 

Population: 165,173 high school students 

Age Groups Analyzed: 11-16 

Pollutant: CO 

Averaging Time: Annual median 

Median (SD) unit: 0.80 ppm 

Range (Min, Max): NR 

Copollutant: NR 

Increment: NR 

Adjusted Odds Ratio (Lower CI, Upper CI); lag: 

CO Concentrations: <0.80 ppm: 1.0 

CO Concentrations ≥ 0.80 ppm: 1.23 (1.19-1.28) 

Multivariate analysis with variables for exercise, smoking, 

alcohol, incense use, ETS: 1.15 (1.1-1.2) 

March 2009 C-50 DRAFT—DO NOT QUOTE OR CITE 
Table C-7. Studies of short-term CO exposure and mortality. 

Study Design Concentrations Effect Estimates (95% CI) 

Author: Anderson et al. 

(2001) 

Period of Study: 10/1994 – 

12/1996 

Location: 

West Midlands, 

United Kingdom 

Health Outcome (ICD9): 

Mortality: All-cause (nonaccidental) (<800); 

Cardiovascular (390-459); 

Respiratory (460-519) 

Study Design: Time-series 

Statistical Analyses: 

Poisson GAM 

Age Groups Analyzed: 

All ages 

Pollutant: CO 

Averaging Time: 

Maximum 8-h moving avg 

Mean (SD) unit: 0.8 (0.7) ppm 

Range (Min, Max): (0.2, 10.0) 

Copollutant correlation: 

PM10: r = 0.55; PM2.5: r = 0.54; 

PM10-2.5: r = 0.10; BS: r = 0.77; 

SO4

2-: r = 0.17; NO2: r = 0.73; 

O3: r = -0.29; SO2: r = 0.49 

Increment: 1.0 ppm 

% Increase (Lower CI, Upper CI); lag: 

All-cause 

0.8% (-0.6 to 2.2); 0-1 

Cardiovascular 

2.5% (0.4-4.6); 0-1 

Respiratory 

1.2% (-2.1 to 4.6); 0-1 

Author: Bellini et al. (2007) 

Period of Study: 1996 – 2002

Location: 

15 Italian cities 

Health Outcome (ICD9): 

Mortality: All-cause (nonaccidental) (<800); 

Cardiovascular (390-459); 

Respiratory (460-519) 

Study Design: Meta-analysis 

Statistical Analyses: 

Poisson GLM 

Age Groups Analyzed: All ages

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutant: 

SO2

NO2

O3

PM10

Increment: 1 mg/m3

% Increase (Lower CI, Upper CI); lag: 

All-cause 

1.19% (0.61-1.72); 0-1 

Respiratory 

0.66% (-1.46 to 2.88); 0-1 

Cardiovascular 

0.93% (-0.10 to 1.77); 0-1 

Author: Biggeri et al. (2005) 

Period of Study: 1990-1999 

Location: 

8 Italian Cities (Turin, Milan, 

Verona, Bologna, Ravenna, 

Florence, Rome, and 

Palermo) 

Health Outcome (ICD9): 

Mortality: All-cause (nonaccidental) (<800); 

Cardiovascular (390-459); 

Respiratory (460-519); Cardiorespiratory 

Study Design: Meta-analysis 

Statistical Analyses: Poisson 

GLM, cubic splines 

Age Groups Analyzed: All ages

Pollutant: CO 

Averaging Time: 

Maximum 8-h moving average 

Mean (SD) unit: 

Turin, 1991-1994: 5.8 mg/m3

Turin, 1995-1998: 4.0 mg/m3

Milan, 1990-1994: 5.9 mg/m3

Milan, 1995-1997: 4.0 mg/m3

Verona, 1995-1999: 2.5 mg/m3

Ravenna, 1991-1995: 1.8 mg/m3

Bologna, 1996-1998: 2.4 mg/m3

Florence, 1996-1998: 2.7 mg/m3

Rome, 1992-1994: 6.5 mg/m3

Rome, 1995-1997: 5.4 mg/m3

Palermo, 1997- 1999: 2.1 mg/m3

Range (Min, Max): 

Turin, 1991-1994: (NR, 24.7) 

Turin, 1995-1998: (NR, 19.8) 

Milan, 1990-1994: (NR, 26.5) 

Milan, 1995-1997: (NR, 12.3) 

Verona, 1995-1999: (NR, 10.2) 

Ravenna, 1991-1995: (NR, 7.0) 

Bologna, 1996-1998: (NR, 11.1) 

Florence, 1996-1998: (NR, 8.7) 

Rome, 1992-1994: (NR, 22.3) 

Rome, 1995-1997: (NR, 18.5) 

Palermo, 1997- 1999: (NR, 8.0) 

Copollutant: NR 

Increment: 1.0 mg/m3

% Increase (Lower CI, Upper CI); lag: 

Non-accidental 

Fixed: 0.93 (0.50-1.36); 0-1 

Random: 0.93 (0.50-1.36); 0-1 

Cardiovascular 

Fixed: 1.29 (0.62-1.96); 0-1 

Random: 1.29 (0.62-1.96); 0-1 

Respiratory 

Fixed: 2.44 (0.74-4.17); 0-1 

Random: 2.47 (0.14-4.85); 0-1 

Author: Botter et al. (2002) 

Period of Study: 1991-1993 

Location: São Paulo, Brazil 

Health Outcome (ICD9): 

Mortality 

Study Design: 

Longitudinal study 

Statistical Analyses: 

State space model 

Age Groups Analyzed: ≥ 65 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutant: TSP; NO2; O3; SO2

Increment: NR 

β (SE): 

Model 1: 0.0053 (0.0036) 

Model 2: 0.0046 (0.0028) 

Model 3: 0.0040 (0.0028) 

Model 4: 0.0032 (0.0028) 

March 2009 C-51 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Bremner et al. (1999) 

Period of Study: 

1/1992–12/1994 

Location: London, U.K. 

Health Outcome (ICD9): 

Mortality: All-cause (nonaccidental) (<800); 

Cardiovascular (390-459); 

Respiratory (460-519) 

Study Design: Time-series 

Statistical Analyses: Poisson, 

cubic splines 

Age Groups Analyzed: 

All ages 

0-64 

≥ 65 

65-74 

≥ 75 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 0.8 (0.4) ppm 

Range (Min, Max): (0.2, 5.6) 

Copollutant: 

NO2; 

O3; 

SO2; 

PM10; 

BS 

Increment: 0.8 ppm 

% Increase (Lower CI, Upper CI); lag: 

All-cause

Age Group: 

All ages: 0.9% (-0.2 to 2.0); 1 

0-64: 1.2% (-1.0 to 3.5); 1 

≥ 65: 0.8% (-0.4 to 1.9); 2 

65-74: 0.8% (-1.2 to 2.8); 3 

≥ 75: 0.9% (-0.4 to 2.2); 2 

Respiratory 

Age Group:

All ages: 2.0% (-0.3 to 4.5); 3 

0-64: 7.8% (0.2-15.9); 3 

≥ 65: 0.7% (-1.7 to 3.2); 3 

65-74: 7.5% (2.1-13.2); 3 

≥ 75: 2.3% (-0.5 to 5.3); 0 

Multipollutant : 

CO, SO2: 1.90% (0.18-3.64); 3 

CO, PM10: 1.25% (0.04-2.47); 3 

CO, BS: 2.41% (-0.65 to 5.57); 3 

Cardiovascular 

Age Group: 

All ages: 1.4% (-0.1 to 3.0); 1 

0-64: 2.1% (-1.7 to 6.0); 2 

≥ 65: 1.1% (-0.4 to 2.8); 2 

65-74: 2.4% (-0.6 to 5.5); 2 

≥ 75: 1.9% (0.0-3.9); 2 

Multipollutant: 

CO, NO2: 2.55% (0.40-4.75); 1 

CO, O3: 3.98% (0.85-7.21); 1 

CO, PM10: 0.62% (-0.59 to 1.85); 1 

CO, BS: 1.29% (-1.53 to 4.19); 1 

Author: Burnett et al. (2000) 

Period of Study: 1986-1996 

Location: 

8 Canadian cities 

Health Outcome (ICD9): 

Mortality: All-cause (nonaccidental) (<800) 

Study Design: Time-series 

Statistical Analyses: 

1. Single-pollutant models: 

Poisson GAM, LOESS 

2. Multi-pollutant models: 

Principal component regression 

analysis 

Age Groups Analyzed: 

All ages 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 0.9 ppm 

Range (Max): 7.2 ppm 

Copollutant: correlation 

O3: r = -0.05 

SO2: r = 0.42 

PM2.5: r = 0.44 

PM10-2.5: r = 0.29 

PM10: r = 0.45 

Increment: 0.9 ppm 

% Increase (t-value); lag: 

Temporally filtered daily non-accidental mortality (days 

in which PM10 data available) 

CO: 0.4 (0.4); 0; 2.0 (2.3); 1 

CO, PM2.5: -0.7 (-0.7); 0; 1.1 (1.1); 1 

CO, PM10-2.5: 0.1 (0.2); 0; 1.8 (2.1); 1 

CO, PM10: -0.5 (-0.6); 0; 1.2 (1.3); 1 

Daily filtered non-accidental mortality 

Single-pollutant model: 2.1 (2.1) 

Multi-pollutant models: 

Model 1: CO, PM2.5, PM10-2.5, O3, NO2, SO2: 0.7 (1.9) 

Model 2: CO, SO4, Ni, Fe, Zn, O3, NO2: 0.7 (1.7) 

Author: Burnett et al. (2004) 

Period of Study: 1981 – 1999

Location: 

12 Canadian cities 

Health Outcome (ICD9): 

Mortality: All-cause (nonaccidental) (<800) 

Study Design: Time-series 

Statistical Analyses: 

1. Poisson, natural splines 

2. Random effects regression 

model 

Age Groups Analyzed: 

All ages 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 1.02 ppm 

Range (Min, Max): NR 

Copollutant: 

NO2; 

O3; 

SO2; 

PM2.5; 

PM10-2.5

Increment: 1.02 ppm 

% Increase (t-value); lag: 

0.68% (3.12); 1 

CO, NO2: 0.07% (0.30); 1 

March 2009 C-52 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Cakmak et al. (2007) 

Period of Study: 

1/1997 – 12/2003 

Location: 

Chile – 7 cities 

Health Outcome (ICD9): 

Mortality: All-cause (nonaccidental) (<800); 

Cardiovascular diseases (390-

459); Respiratory diseases (460-

519) 

Study Design: Time-series 

Statistical Analyses: Poisson; 

Random effects regression 

model 

Age Groups Analyzed: 

All ages 

≤ 64 

65 – 74 

75 – 84 

≥ 85 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 1.29 ppm 

Range (Min, Max): NR 

Copollutant correlation: 

O3: r = -0.55 to -0.01 

SO2: r = 0.31 to 0.67 

PM10: r = 0.49 to 0.82 

Note: Correlations are between 

pollutants for seven monitoring 

stations. 

Increment: 1.29 ppm 

% Increase (t-value); lag: 

Non-accidental: 

5.88% (6.42); 1; 9.39% (6.89); 0-5 

CO+PM10+O3+SO2: 6.13% (4.34); 1 

Age Group: ≤ 64

4.10% (2.52); 1; / 4.76% (2.19); 0-5 

Age Group: 65-74

6.24% (3.17); 1; / 8.12% (3.88); 0-5 

Age Group: 75-84

8.64% (4.82); 1; / 13.12% (5.12); 0-5 

Age Group: ≥ 85 

8.58% (4.45); 1; / 13.20% (4.82); 0-5 

April-September 

7.09% (4.02); 1; / 9.65% (4.50); 0-5 

October-March 

5.45% (1.14); 1; / 7.80% (1.89); 0-5 

Cardiac 

7.79% (4.56); 1; / 11.22% (4.8); 0-5 

Respiratory 

12.93% (5.78); 1; / 21.31% (6.34); 0-5 

March 2009 C-53 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Chock et al. (2000) 

Period of Study: 

1989 – 1991 

Location: 

Pittsburgh, PA 

Health Outcome (ICD9): 

Mortality: Respiratory (480-486, 

490-496, 507); Cardiovascular 

(390-448); Influenza (487) 

Study Design: Time-series 

Statistical Analyses: 

Poisson GAM; Cubic B-spline 

basis functions 

Age Groups Analyzed: 

All ages 

<75 

>75 

Pollutant: CO 

Averaging Time: 1-h avg 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutant: 

PM10; 

PM2.5; 

O3; 

SO2; 

NO2

Increment: NR 

β (SE); lag: 

Age Group: <75 

CO alone: 0.0080 (1.56); 0 

PM10, CO: 0.0030 (0.48); 0 

PM10, NO2, CO: 0.0079 (1.14); 0 

PM10, O3, SO2, NO2, CO: 0.072 (1.02); 0 

CO 

-0.00738 (-1.42); -3; / 0.00133 (0.23); -2; 

-0.00219 (-0.38); -1; / 0.00809 (1.48); 0; 

-0.00129 (-0.22); 1; / 0.00512 (0.90); 2; 

-0.00974 (-1.87); 3 

CO, PM10, O3, SO2, NO2

-0.01103 (-1.48); -3; / -0.00097 (-0.13); -2; 

0.00514 (0.67); -1; / 0.00853 (1.15); 0; 

-0.00404 (-0.52); 1; / -0.00296 (-0.39); 2; 

-0.00346 (-0.46); 3 

Season 

CO 

Winter: 0.00539 (0.78); 0 

Spring: 0.01655 (1.90); 0 

Summer: 0.00155 (0.14); 0 

Fall: 0.00797 (1.14); 0 

CO, PM10

Winter: -0.00563 (-0.50); 0 

Spring: 0.01233 (0.99); 0 

Summer: -0.00712 (-0.48); 0 

Fall: 0.00661 (0.73); 0 

CO, PM10, O3, SO2, NO2

Winter: -0.01326 (-0.95); 0 

Spring: 0.02501 (1.54); 0 

Summer: 0.01874 (0.92); 0 

Fall: 0.01011 (0.88); 0 

Age Group:>75 

CO Alone: -0.0035 (-0.67); 0 

CO, PM10: -0.0104 (-1.67); 0 

CO, PM10, NO2: -0.0128 (-1.80); 0 

CO, PM10, O3, SO2, NO2: -0.0144 (-1.99); 0 

CO 

-0.00025 (-0.05); -3; / -0.00242 (-0.42); -2; 

-0.00238 (-0.41); -1; / -0.00302 (-0.54); 0; 

-0.00116 (-0.20); 1; / -0.00508 (-0.88); 2; 

-0.00251 (-0.48); 3 

CO, PM10, O3, SO2, NO2 

-0.00123 (-0.17); -3; / -0.00876 (-1.13); -2; 

-0.00682 (-0.88); -1; / -0.01248 (-1.66); 0; 

-0.00672 (-0.86); 1; / -0.00181 (-0.23); 2; 

-0.00515 (-0.69); 3 

Season 

CO 

Winter: -0.00304 (-0.43); 0 

Spring: 0.00482 (0.54); 0 

Summer: 0.01178 (1.07); 0 

Fall: -0.01011 (-1.43); 0 

CO, PM10

Winter: -0.02303 (-2.03); 0 

Spring: -0.00517 (-0.40); 0 

Summer: 0.00735 (0.50); 0 

Fall: -0.01042 (-1.14); 0 

CO, PM10, O3, SO2, NO2

Winter: -0.03370 (-2.41); 0 

Spring: -0.00652 (-0.39); 0 

Summer: 0.01258 (0.61); 0 

Fall: -0.01250 (-1.07); 0 

March 2009 C-54 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Cifuentes et al. 

(2000) 

Period of Study: 

1988 – 1996 

Location: 

Santiago, Chile 

Health Outcome (ICD9): 

Mortality: All causes (nonaccidental) (<800) 

Study Design: Time-series 

Statistical Analyses: Poisson 

GAM, GAM with filtered variables 

& GLM 

Age Groups Analyzed: All ages 

Pollutant: CO 

Averaging Time: 1-h avg 

Mean (SD) unit: 2.5 ppb 

Range (5th, 95th): (0.6, 6.2) 

Copollutant correlation: 

PM2.5: r = 0.80 

PM10-2.5: r = 0.47 

SO2: r = 0.62 

NO2: r = 0.65 

O3: r = -0.01 

Increment: 

All year: 2.5 ppm 

Winter: 3.6 ppm 

Summer: 1.3 ppm 

Relative Risk (t-ratio); Lag 

All Year 

CO: 1.041 (7.2); 0-1 

CO, PM2.5: 1.025 (3.5); 0-1 

CO, PM10-2.5: 1.035 (4.9); 0-1 

CO, SO2: 1.038 (6.0); 0-1 

CO, NO2: 1.026 (3.9); 0-1 

CO, O3: 1.036 (4.8); 0-1 

Winter 

CO: 1.052 (5.9); 0-1 

CO, PM2.5: 1.025 (2.1); 0-1 

CO, PM10-2.5: 1.049 (4.3); 0-1 

CO, SO2: 1.049 (5.0); 0-1 

CO, NO2: 1.027 (2.6); 0-1 

CO, O3: 1.051 (4.4); 0-1 

Summer 

CO: 1.053 (6.0); 0-1 

CO, PM2.5: 1.053 (5.3); 0-1 

CO, PM10-2.5: 1.053 (5.3); 0-1 

CO, SO2: 1.050 (5.2); 0-1 

CO, NO2: 1.047 (5.2); 0-1 

CO, O3: 1.042 (3.6); 0-1 

All Year 

GAM model 

CO: 1.041 (7.2); 0-1 

CO, PM2.5, PM10-2.5, SO2, NO2, O3: 

1.032 (4.6); 0-1 

GAM Filtered Variables 

CO: 1.030 (4.3); 0-1 

CO, PM2.5, PM10-2.5, SO2, NO2, O3: 

1.022 (2.4); 0-1 

GLM 

CO: 1.023 (2.4); 0-1 

CO, PM2.5, PM10-2.5, SO2, NO2, O3: 

1.013 (1.1); 0-1 

Author: Conceicao et al. 

(2001) 

Period of Study: 

1994 – 1997 

Location: 

Sao Paulo, Brazil 

Health Outcome (ICD9): 

Mortality: Respiratory diseases 

(460-519) 

Study Design: Time-series 

Statistical Analyses: Poisson 

GAM 

Age Groups Analyzed:

<5 

Pollutant: CO 

Averaging Time: 

Maximum 8-h moving avg 

Mean (SD) unit: 

Total: 4.4 (2.2) ppm 

1994: 5.1 (2.4) ppm 

1995: 5.1 (2.4) ppm 

1996: 3.9 (2.0) ppm 

1997: 3.7 (1.6) ppm 

Range (Min, Max): NR 

Copollutant: 

PM10; SO2; O3

Increment: NR 

β (SE); lag: 

CO: 0.0306 (0.0076); 2 

CO, SO2, PM10, O3: 0.0259 (0.0116); 2 

Model 1: Pollutant concentration: 

0.0827 (0.0077); 2 

Model 2: 1+loess(time): 

0.0285 (0.0074); 2 

Model 3: 2+loess(temperature)+humidity: 

0.0309 (0.0076); 2 

Model 4: 3+nonrespiratory counts: 

0.0306 (0.0076); 2 

Model 5: 4+autoregressive parameters: 

0.0292 (0.0118); 2 

Author: De Leon et al. (2003) 

Period of Study: 

1/1985 – 12/1994 

Location: 

New York, NY 

Health Outcome (ICD9): 

Mortality: Circulatory (390-459); 

Cancer (140-239) 

Study Design: Time-series 

Statistical Analyses: 

Poisson GAM 

Age Groups Analyzed: 

All ages 

<75 

>75 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 2.45 ppm 

IQR (25th, 75th): (1.80, 2.97) 

Copollutant: 

PM10; 

O3; 

SO2; 

NO2

The study did not present quantitative results for 

CO. 

March 2009 C-55 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Dominici et al. 

(2003b) 

Period of Study: 

1987 – 1994 

Location: 

90 U.S. cities (NMMAPS) 

Health Outcome (ICD9): 

Mortality: All-cause (nonaccidental); Cardiovascular; 

Respiratory 

Study Design: Time-series 

Statistical Analyses: 

1. GAM with S-PLUS default 

convergence criteria 

2. GAM with more stringent 

convergence criteria 

3. Poisson GLM with natural 

cubic splines 

Age Groups Analyzed: All ages 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutant: 

O3; NO2; SO2; CO 

Increment: 1 ppm 

% Increase (Lower CI, Upper CI); Lag 

CO 

0.08% (-0.18 to 0.34); 0 

0.46% (0.18-0.73); 1 

0.16% (-0.12 to 0.45); 2 

Author: Fairley et al. (1999) 

Period of Study: 

1989 – 1996 

Location: 

Santa Clara, CA 

Health Outcome (ICD9): 

Mortality: Respiratory; 

Cardiovascular 

Study Design: Time-series 

Statistical Analyses: Poisson 

GAM 

Age Groups Analyzed: 

All ages 

Pollutant: CO 

Averaging Time: 

24-h avg; Maximum 8-h avg 

Median (SD) unit: 

24-h avg: 1.4 (1.0) ppm 

Maximum 8-h avg: 2.1 (1.6) ppm 

Range (Min, Max): 

24-h avg: (0.0, 7.6) 

Maximum 8-h avg: (0.2, 2.5) 

Copollutant: correlation 

PM10: r = 0.609; 

PM2.5: r = 0.435; 

PM10-2.5: r = 0.326; 

COH: r = 0.736; 

NO3: r = 0.270; 

SO4: r = 0.146; O3: r = -0.215 

Increment: 2.2 ppm 

Relative Risk (Lower CI, Upper CI); lag: 

1980-1986 

CO: 1.04; 0; 

CO: 1.05; 1; 

CO, COH: 1.00; 1; 

CO, NO3: 1.03; 

CO, NO3, O3, COH: 1.00 

1989-1996 

CO: 1.02; 0; 

CO: 1.04; 1; 

CO, PM2.5: 0.98; 

CO, NO3: 1.01; 

CO, NO2, O3, NO3: 1.06 

Respiratory mortality: CO: 1.08; 1 

Cardiovascular mortality: CO: 1.04; 1 

Author: Fischer et al. (2003) 

Period of Study: 

1986 – 1994 

Location: The Netherlands 

Health Outcome (ICD9): 

Mortality: Non-accidental 

(<800); Pneumonia 

(480-486); COPD (490-496); 

Cardiovascular (390-448) 

Study Design: Time-series 

Statistical Analyses: 

Poisson GAM, LOESS 

Age Groups Analyzed: 

<45 

45-64 

65-74 

≥ 75 

Pollutant: CO 

Averaging Time: 24-h avg 

Median (SD) unit: 406 μg/m3

Range (Min, Max): (174, 2620) 

Copollutant: 

PM10; BS; O3; NO2; SO2

Increment: 1,200 μg/m3

Relative Risk (Lower CI, Upper CI); lag: 

Cardiovascular 

Age Group: 

<45: 0.965 (0.750-1.240); 0-6 

45-64: 1.029 (0.941-1.125); 0-6 

65-74: 1.038 (0.972-1.108); 0-6 

≥ 75: 1.024 (0.984-1.065); 0-6 

COPD 

Age Group:

<45: 1.710 (0.852-3.435); 0-6 

45-64: 1.181 (0.850-1.640); 0-6 

65-74: 1.377 (1.147-1.654); 0-6 

≥ 75: 1.072 (0.963-1.193); 0-6 

Pneumonia 

Age Group:

<45: 0.927 (0.463-1.856); 0-6 

45-64: 2.691 (1.509-4.800); 0-6 

65-74: 1.118 (0.743-1.683); 0-6 

≥ 75: 1.230 (1.090-1.389); 0-6 

Author: Forastiere et al. 

(2005) 

Period of Study: 

1998 – 2000 

Location: 

Rome, Italy 

Health Outcome (ICD9): 

Mortality: IHD (410-414) 

Study Design: Time-stratified case-crossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: >35 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 2.4 (1.0) mg/m3

IQR (25th, 75th): (1.7, 2.9) 

Copollutant correlation: 

PNC: r = 0.89; PM10: r = 0.34; 

NO2: r = 0.54; SO2: r = 0.52; 

O3: r = 0.01 

Increment: 1.2 mg/m3

% Increase (Lower CI, Upper CI); lag: 

6.5% (1.0-12.3); 0 

4.7% (-0.9 to 10.7); 1 

2.6% (-3.0 to 8.5); 2 

-0.1% (-5.5 to 5.5); 3 

7.0% (0.8-13.7); 0-1 

March 2009 C-56 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Forastiere et al. 

(2007) 

Period of Study: 

1998 – 2001 

Location: Rome, Italy 

Health Outcome (ICD9): 

Mortality: Malignant Neoplasms 

(140-208); Diabetes Mellitus 

(250); Hypertensive (401-405); 

Previous AMI (410, 412); IHD 

(410-414); Conduction disorders 

of the heart (426); Dysrhythmia 

(427); Heart Failure (428); 

Cerebrovascular (430-438); 

Peripherical Artery disease (440-

448); COPD (490-496) 

Study Design: Time-stratified 

case-crossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: >35 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: NR 

IQR (25th, 75th): NR 

Copollutant: 

PM10; PM2.5; NOX; Benzene 

This study did not present quantitative results for 

CO. 

Author: Goldberg et al. 

(2001) 

Period of Study: 

1984 – 1993 

Location: Montreal, Quebec, 

Canada 

Health Outcome (ICD9): 

Mortality: Upper respiratory 

diseases (472-478); Acute Upper 

respiratory diseases (460-465); 

Acute Lower Respiratory (466, 

480-487, 512, 513, 518, 519) 

Study Design: Time-series 

Statistical Analyses: Poisson 

GAM; LOESS 

Age Groups Analyzed: 

<65; ≥ 65 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 0.8 (0.5) ppm 

Range (Min, Max): (0.1, 5.1) 

Copollutant: 

TSP; PM10; PM2.5; Sulfates; COH; SO2; 

NO2; NO; O3

The study did not present quantitative results for 

CO. 

Author: Goldberg et al. 

(2003) 

Period of Study: 1984 – 1993

Location: 

Montreal, Quebec, Canada 

Health Outcome (ICD9): 

Mortality: CHF (428) 

Study Design: Time-series 

Statistical Analyses: Poisson 

GLM, natural splines 

Age Groups Analyzed:

≥ 65 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 0.8 (0.5) ppm 

Range (Min, Max): (0.1, 5.1) 

Copollutant: 

PM2.5; Sulfate; SO2; NO2; O3

Increment: 0.50 ppm 

% Increase (Lower CI, Upper CI); lag: 

Daily mortality from CHF

-0.99% (-6.31 to 4.63); 0 

0.12% (-5.29 to 5.84); 1 

-1.38% (-8.81 to 6.66); 0-2 

Daily mortality among persons classified as having 

CHF before death 

2.10% (-0.24 to 4.49); 0 

2.28% (-0.09 to 4.72); 1 

2.86% (-0.46 to 6.29); 0-2 

Author: Goldberg et al. 

(2006) 

Period of Study: 

1984 – 1993 

Location: 

Montreal, Quebec, Canada 

Health Outcome (ICD9): 

Mortality: Diabetes (250) 

Study Design: Time-series 

Statistical Analyses: Poisson, 

natural splines 

Age Groups Analyzed: ≥ 65 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 0.8 (0.5) ppm 

Range (Min, Max): (0.1, 5.1) 

Copollutant: 

PM2.5; 

Sulfate; 

SO2; 

NO2; 

O3

Increment: 0.50 ppm 

% Increase (Lower CI, Upper CI); lag: 

Daily mortality from diabetes

2.64% (-2.56 to 8.12); 0 

6.54% (1.31-12.03); 1 

8.08% (1.03-15.62); 0-2 

Daily mortality among persons classified as having 

diabetes before death

1.15% (-1.69 to 4.07) ; 0 

1.30% (-1.58 to 4.27) ; 1 

2.63% (-1.42 to 6.85) ; 0-2 

Author: Gouveia et al. 

(2000b) 

Period of Study: 

1991 – 1993 

Location: 

Sao Paulo, Brazil 

Health Outcome (ICD9): 

Mortality: Respiratory; 

Cardiovascular; All other causes 

Study Design: Time-series 

Statistical Analyses: Poisson 

Age Groups Analyzed: 

All ages 

>65 

<5 

Pollutant: CO 

Averaging Time: 

Maximum 8-h moving avg 

Mean (SD) unit: 5.8 (2.1) ppm 

Range (Min, Max): (1.3, 16.2) 

Copollutant: 

PM10; SO2; NO2; O3

Increment: 5.1 ppm 

Relative Risk (Lower CI, Upper CI); lag: 

Age Group: All ages: All-causes 1.012 (0.994-1.031); 0 

Age Group: >65 

All-causes: 1.020 (0.996-1.046); 0 

Respiratory: 0.981 (0.927-1.037); 2 

CVD: 1.041 (1.007-1.076); 0 

Age Group: <5 

Respiratory: 1.086 (0.950-1.238); 0 

Pneumonia: 1.141 (0.962-1.321); 2 

March 2009 C-57 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Gwynn et al. (2000) 

Period of Study: 

5/1988 – 10/1990 

Location: 

Buffalo, NY 

Health Outcome (ICD9): 

Mortality: Respiratory (466, 480-

486); Circulatory (401-405, 410-

414, 415-417); All non-accidental 

causes (<800) 

Study Design: Time-series 

Statistical Analyses: 

Poisson GLM 

Age Groups Analyzed: All ages

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutant correlation:

H+: r =0.15; SO4

2-: r = 0.24; 

O3: r = -0.23; SO2: r = 0.11; 

NO2: r = 0.65 

Increment: NR 

β (SE); lag: 

Respiratory mortality: 0.032466 (0.053802); 0 

Circulatory mortality: 0.039216 (0.026544); 3 

Total mortality: 0.040214 (0.015205); 3 

Author: Hoek et al. (2001) 

Period of Study: 

1986-1994 

Location: The Netherlands 

Health Outcome (ICD9): 

Mortality: Heart Failure (428); 

Arrhythmia (427); 

Cerebrovascular (430-436); 

Thrombocytic (433, 434, 444, 

452, 453); Cardiovascular (390-

448) 

Study Design: Time-series 

Statistical Analyses: 

Poisson GAM 

Age Groups Analyzed: All ages

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutant: 

O3; BS; PM10; SO2; NO2

Increment: 120 μg/m3

Relative Risk (Lower CI, Upper CI); Lag 

Total CVD mortality: 1.026 (0.993-1.060); 0-6 

MI and other IHD mortality: 

1.050 (1.004-1.099); 0-6 

Arrhythmia: 1.062 (0.937-1.203); 0-6 

Heart failure mortality: 1.109 (1.012-1.216); 0-6 

Cerebrovascular mortality: 

1.066 (1.029-1.104); 0-6 

Embolism, thrombosis: 1.065 (0.926-1.224); 0-6 

Author: Hoek et al. (2000) 

Period of Study: 

1986 – 1994 

Location: The Netherlands 

Health Outcome (ICD9): 

Mortality: Pneumonia 

(480-486); COPD (490-496); 

Cardiovascular diseases (CVD) 

(390-448) 

Study Design: Time-series 

Statistical Analyses: 

Poisson GAM, LOESS 

Age Groups Analyzed: All ages

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 

Netherlands: 457 μg/m3 

Four Major Cities: 589 μg/m3

Range (Min, Max): 

Netherlands: (174, 2620) 

Four Major Cities: (202, 4621) 

Copollutant correlation: 

PM10: r = 0.64; BS: r = 0.89; 

O3: r = -0.48; NO2: r = 0.89; 

SO2: r = 0.65; SO4

2-: r = 0.55; 

NO3

-

: r = 0.58 

Increment: Single-day lag (1): 1,500 μg/m3

Weekly avg (0-6): 1200 μg/m3

Relative Risk (Lower CI, Upper CI); Lag 

CO

Four Major Cities: 1.022 (0.995-1.050); 1 

Four Major Cities: 1.044 (1.008-1.082); 0-6 

Netherlands w/o Major Cities: 1.040 (1.020-1.060); 1 

Netherlands w/o Major Cities: 

1.051 (1.026-1.076); 0-6 avg 

Entire Netherlands: 1.035 (1.018-1.052); 1 

Entire Netherlands: 1.046 (1.025-1.068); 0-6 

CVD: 1.044 (1.012-1.077); 0-6 

COPD: 1.194 (1.099-1.298); 0-6 

Pneumonia: 1.276 (1.143-1.426); 0-6 

Winter: 1.038 (1.013-1.063); 0-6 

Summer: 1.199 (1.108-1.296); 0-6 

Multi-pollutant model

CO, PM10

Total mortality: 0.969 (0.914-1.028); 0-6 

CVD: 1.005 (0.918-1.101); 0-6 

BS, CO 

Total mortality: 0.980 (0.933-1.030); 0-6 

CVD: 0.927 (0.860-0.999); 0-6 

CO, SO4

2-

Total mortality: 0.990 (0.951-1.030); 0-6 

CVD: 0.999 (0.939-1.063); 0-6 

Author: Honda et al. (2003) 

Period of Study: 

1976-1990 

Location: 

Tokyo, Japan 

Health Outcome (ICD9): 

Mortality: 

Total (non-accidental) (<800) 

Study Design: Time-series 

Statistical Analyses: Poisson 

Age Groups Analyzed: 

≥ 65 

Pollutant: CO 

Averaging Time: 24-h avg 

Median (SD) unit: 1.6 ppm 

Range (Min, Max): (0, 6.8) 

Copollutant correlation: 

NO: r = 0.403; NO2: r = 0.415; 

Oxidant: r = 0.396; SO2: r = 0.675 

Increment: NR 

Rate Ratio (Lower CI, Upper CI); lag: 

CO concentration 

<1.1 ppm: 1.00 (reference category) 

1.1-1.6 ppm: 1.017 (1.009, 1.026) 

1.6-2.2 ppm: 1.031 (1.020, 1.041) 

>2.2 ppm: 1.051 (1.039, 1.063) 

Author: Hong et al. (2002b) 

Period of Study: 

1/1991-12/1997 

Location: 

Seoul, Korea 

Health Outcome (ICD9): 

Mortality: Hemorrhagic and 

ischemic stroke (431-434) 

Study Design: Time-series 

Statistical Analyses: 

Poisson GAM, LOESS 

Age Groups Analyzed: 

All ages 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 1.44 (0.70) ppm 

Range (Min, Max): (0.430, 5.14) 

Copollutant: 

TSP; SO2; NO2; O3

Increment: 0.76 ppm 

Relative Risk (Lower CI, Upper CI); lag: 

1.06 (1.02, 1.09); 1 

Multipollutant: 

CO, TSP: 1.07 (1.03, 1.11); 1 

CO, NO2: 1.06 (1.00, 1.11); 1 

CO, SO2: 1.05 (1.01, 1.10); 1 

CO, O3: 1.09 (1.05, 1.13); 1 

March 2009 C-58 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Hong et al. (1999a) 

Period of Study: 

1/1995 - 12/1995 

Location: 

Inchon, Korea 

Health Outcome (ICD9): 

Mortality: Cardiovascular (400-

440); Respiratory 

(460-519); Non-accidental 

causes (<800) 

Study Design: Time-series 

Statistical Analyses: 

Poisson GAM, LOESS 

Age Groups Analyzed: 

All ages 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 1.7 (0.8) ppm 

Range (Min, Max): (0.3, 5.1) 

Copollutant: 

SO2; NO2; O3

Increment: 1 ppm 

Relative Risk (Lower CI, Upper CI); lag: 

Total mortality: 

0.993 (0.950, 1.037); 0-4 

Cardiovascular mortality: 

0.965 (0.892, 1.044); 0-4 

Author: Hong et al. (2002a) 

Period of Study: 

1/1995 - 12/1998 

Location: 

Seoul, Korea 

Health Outcome (ICD9): 

Mortality: Stroke (160-169) 

Study Design: Time-series 

Statistical Analyses: 

Poisson GAM 

Age Groups Analyzed: 

All ages 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 1.2 (0.5) ppm 

Range (Min, Max): (0.4, 3.4) 

Copollutant: correlation 

PM10: r = 0.22; NO2: r = 0.64; 

SO2: r = 0.90; O3: r = -0.35 

Increment: 0.3 ppm 

% Increase (Lower CI, Upper CI); lag: 

CO: 2.2% (0.4, 4.1); 2 

CO (stratified by PM10 concentration): 

<median concentration of PM10: 1.1; 2 

≥ median concentration of PM10: 3.6; 2 

Author: Hong et al. (1999b) 

Period of Study: 

1/1995 - 8/1996 

Location: 

Inchon, South Korea 

Health Outcome (ICD9): 

Mortality: Total (non-accidental) 

(<800); Respiratory; 

Cardiovascular 

Study Design: Time-series 

Statistical Analyses: Poisson 

GAM; LOESS 

Age Groups Analyzed: 

All ages 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 15.2 (7.1) ppb 

Range (Min, Max): (2.9, 51.2) 

Copollutant: 

PM10; NO2; SO2; O3

Increment: 100 ppb 

β (SE); lag: 

Total Mortality 

CO 

0.0019 (0.0015); 1 

0.0024 (0.0041); 0-4 

CO, PM10, NO2, SO2, O3

-0.0009 (0.0019); 1 

-0.0018 (0.0043); 0-4 

Cardiovascular Mortality 

CO 

0.0019 (0.0073); 1 

-0.0008 (0.0028); 0-4 

CO, PM10, NO2, SO2, O3

-0.0053 (0.0078); 1 

-0.0037 (0.0033); 0-4 

Respiratory Mortality 

CO 

0.0148 (0.0065); 1 

0.0063 (0.0171); 0-4 

CO, PM10, NO2, SO2, O3

0.0121 (0.0079); 1 

-0.0034 (0.0183); 0-4 

Author: Keatinge et al. (2001) 

Period of Study: 

1976-1995 

Location: 

London, England 

Health Outcome (ICD9): 

Mortality: Non-accidental causes 

(<800) 

Study Design: Time-series 

Statistical Analyses: Single and 

multiple delay regression 

Age Groups Analyzed: All ages 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutant: 

SO2; PM10

The study did not present quantitative results for CO. 

Author: Kettunen et al. (2007) 

Period of Study: 1998-2004 

Location: 

Helsinki, Finland 

Health Outcome (ICD10): 

Mortality: Stroke (I60-I61, 

I63-I64) 

Study Design: Time-series 

Statistical Analyses: 

Poisson GAM, penalized thinplate splines 

Age Groups Analyzed: ≥ 65 

Pollutant: CO 

Averaging Time: 

Maximum 8-h moving avg 

Median (SD) unit: 

Cold Season: 0.5 mg/m3

Warm Season: 0.4 mg/m3

Range (Min, Max): 

Cold Season: (0.1, 2.4) 

Warm Season: (0.1, 1.1) 

Copollutant: correlation 

Cold Season: 

PM2.5: r = 0.32; UFP: r = 0.47 

Warm Season: PM2.5: r = 0.24; UFP: r = 0.39 

Increment: 0.2 mg/m3

% Increase (Lower CI, Upper CI); lag: 

Cold Season

0.47 (-3.29 to 4.39); 0; / -0.63 (-4.39 to 3.28); 1; 

-2.69 (-6.46 to 1.24); 2; / -0.19 (-3.93 to 3.69); 3 

Warm Season 

3.95 (-3.78 to 12.30); 0; / 8.33 (0.63 to 16.63); 1; 

6.97 (-0.59 to 15.11); 2; / 7.54 (-0.05 to 15.71); 3 

March 2009 C-59 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Klemm et al. (2004) 

Period of Study: 

8/1998-7/2000 

Location: 

Fulton County and DeKalb 

County, GA (ARIES) 

Health Outcome (ICD9): 

Mortality: Non-accidental 

(<800); Cardiovascular 

(390-459); Respiratory 

(460-519); Cancer (140-239) 

Study Design: Time-series 

Statistical Analyses: 

Poisson GLM, natural cubic 

splines 

Age Groups Analyzed: 

<65; ≥ 65 

Pollutant: CO 

Averaging Time: 1-h maximum 

Median (SD) unit: 

1,310 (939.13) ppb 

Range (Min, Max): 

(303.58, 7400) 

Copollutant: 

PM2.5; PM10-2.5; O3; NO2; SO2; Acid; EC; 

OC; SO4; 

Oxygenated HCs; 

NMHCs; NO3

Increment: NR 

β (SE); lag: 

Quarterly Knots: 0.00002 (0.00001); 0-1 

Monthly Knots: 0.00002 (0.00001); 0-1 

Biweekly Knots: 0.00001 (0.00002); 0-1 

Author: Kwon et al. (2001) 

Period of Study: 

1994-1998 

Location: 

Seoul, Korea 

Health Outcome (ICD9): 

Mortality: CHF (428); 

Cardiovascular (390-459) 

Study Design: 

1. Time-series 

2. Bi-directional case-crossover 

Statistical Analyses: 

1. Poisson GLM, LOESS 

2. Conditional logistic regression 

Age Groups Analyzed: 

<55 

55-64 

65-74 

75-84 

≥ 85 

Pollutant: CO 

Averaging Time: 1-h avg 

Mean (SD) unit: 12.4 ppb 

Range (Min, Max): (4.1, 38.0) 

Copollutant correlation: 

PM10: r = 0.713; NO2: r = 0.744; 

SO2: r = 0.843; O3: r = -0.367 

Increment: 0.59 ppm 

Odds Ratio (Lower CI, Upper CI); lag: 

From GAM approach 

CHF patients: 1.054 (0.991-1.121); 0; 0 

General Population: 1.022 (1.017- 1.029); 0 

From case-crossover design 

CHF patients: 1.033 (0.946-1.127); 0 

General Population: 1.007 (0.997- .016); 0 

Modifiers and CHF patients (case-crossover 

design)

Gender 

Male: 1.025 (0.890-1.180); 0 

Female: 1.035 (0.925-1.157); 0 

Age Group: 

<75: 0.948 (0.890-1.180); 0 

≥ 75: 1.116 (0.989-1.258); 0 

Time from admission to death 

4 or less weeks: 1.088 (0.907-1.306); 0 

>4 weeks: 1.017 (0.920-1.124); 0 

Total mortality: 1.033 (0.946-1.127); 0 

Cardiovascular mortality: 1.033 (0.920-1.160); 0 

Cardiac death: 1.052 (0.919-1.204); 0 

Two-pollutant model in CHF patients (casecrossover design) 

CO alone: 1.054 (0.991-1.121); 0 

CO, PM10: 1.096 (0.981-1.224); 0 

CO, NO2: 1.022 (0.932-1.122); 0 

CO, SO2: 1.014 (0.909-1.131); 0 

CO, O3: 1.056 (0.992-1.124); 0 

Author: Lee et al. (2007c) 

Period of Study: 

1/2000-12/2004 

Location: 

Seoul, Korea 

Health Outcome (ICD10): 

Mortality: Non-accidental (A00-

R99) 

Study Design: Time-series 

Statistical Analyses: Poisson 

GAM 

Age Groups Analyzed: All ages

Pollutant: CO 

Averaging Time: 

Maximum 8-h moving avg 

Mean (SD) unit: 

w/ Asian dust days: 0.92 (0.42) ppm 

w/o Asian dust days:0.92 (0.41) ppm 

Asian dust days only: 1.00 (0.47) ppm 

Range (Min, Max): NR 

Copollutant: PM10; NO2; SO2; O3

Increment: 0.54 ppm 

% Increase (Lower CI, Upper CI); lag: 

Model with Asian Dust Days: 3.3% (2.5-4.1); 1 

Model without Asian dust days: 3.3% (2.5-4.2); 1 

March 2009 C-60 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Lipfert et al. (2000b) 

Period of Study: 

5/1992 - 9/1995 

Location: 

Philadelphia, PA, three nearby 

suburban Pennsylvania 

counties, and three nearby 

New Jersey counties 

Health Outcome (ICD9): 

Mortality: Respiratory 

(460-519); Cardiac (390-448); 

Cancer; Other causes (<800) 

Study Design: Time-series 

Statistical Analyses: 

Stepwise regression 

Age Groups Analyzed: 

<65 

≥ 65 

Pollutant: CO 

Averaging Time: 

24-h avg; 1-h maximum 

Mean (SD) unit: 

Camden: 

24-h avg: 0.75 (0.40) ppm 

Philadelphia: 

24-h avg: 0.63 (0.40) ppm 

1-h maximum: 1.44 (1.04) 

Range (Min, Max): 

Camden: (0.10, 3.8) 

Philadelphia: 

24-h avg: (0.10, 3.3) 

1-h maximum: (0.0, 7.8) 

Copollutant: 

NO; NO2; O3; SO2; SO4

2-; 

PM10; PM2.5

Increment: NR 

Attributable Risk; lag: 

Peak CO 

All-cause 

Philadelphia: 0.0054; 0-1 

4 Pennsylvania Counties: 0.0081; 0-1 

Pennsylvania + NJ: 0.0085; 0-1 

CO 

All seven counties in Pennsylvania and New Jersey 

All ages

Respirator y: -0.0067; Cardiac: 0.0131; 

Other: 0.0078 

All-cause: 

<65: 0.0148; 0-1; ≥ 65: 0.0054; 0-1 

Joint model with CO 

Philadelphia: 0.0059; 0-1 

4 Pennsylvania Counties: 0.0089; 0-1 

Pennsylvania + NJ: 0.0096; 0-1 

Cardiac: 0.0135; 0-1; 

Other causes: 0.0084 

<65: 0.0154; 0-1; 

≥ 65: 0.0060; 0-1 

Author: Lippmann et al. 

(2000) 

Period of Study: 

1985-1990 

1992-1994 

Location: 

Detroit, MI and Windsor, ON 

Health Outcome (ICD9): 

Mortality: Total (non-accidental) 

(<800); Circulatory (390-459); 

Respiratory (460-519) 

Study Design: Time-series 

Statistical Analyses: 

Poisson GLM 

Age Groups Analyzed: 

≥ 65 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 

1985-1990: 0.9 ppm 

1992-1994: 0.72 ppm 

Range (5th, 95th): 

1985-1990: (.46, 1.61) 

1992-1994: (0.36, 1.2) 

Copollutant correlation: 

1985-1990

PM10: r = 0.35; TSP: r = 0.28; 

TSP-PM10: r = 0.02; 

TSP-SO4

2-: r = 0.18; 

O3: r = -0.22; SO2: r = 0.36; 

NO2: r = 0.58 

1992-1994

PM10: r = 0.38; PM2.5: r = 0.38; 

PM10-2.5: r = 0.24; 

H+: r = 0.16; SO4

2-:r = 0.32; 

O3: r = 0.16; SO2: r = 0.42; 

NO2: r = 0.68 

Increment: 

1985-1990: 11.5 ppm; 1992-1994: 8.4 ppm 

Relative Risk (Lower CI, Upper CI); lag: 

1985-1990 

Total Mortality: 

0.9842 (0.9667-1.002); 0 

1.0103 (0.9926-1.0284); 1 

1.0075 (0.9898-1.0254); 2 

1.0145 (0.9967-1.0326); 3 

0.9968 (0.9789-1.0151); 0-1 

1.0105 (0.9925-1.0288); 1-2 

1.0134 (0.9954-1.0317); 2-3 

1.0003 (0.9823-1.0187); 0-2 

1.0152 (0.9971-1.0336); 1-3 

1.0053 (0.9873-1.0236); 0-3 

Circulatory Mortality: 

0.9818 (0.9574-1.0068); 0 

0.9991 (0.9745-1.0243); 1 

0.9980 (0.9735-1.0232); 2 

1.0088 (0.9841-1.0341); 3 

0.9888 (0.9640-1.0144); 0-1 

0.9981 (0.9732-1.0237); 1-2 

1.0042 (0.9792-1.0298); 2-3 

0.9900 (0.9650-1.0157); 0-2 

1.0029 (0.9777-1.0287); 1-3 

0.9944 (0.9692-1.0202); 0-3 

Respiratory Mortality; 

0.9644 (0.9042-1.0287); 0 

1.0142 (0.9518-1.0808); 1 

1.0483 (0.9845-1.1164); 2 

1.0468 (0.9828-1.1149); 3 

0.9868 (0.9248-1.053); 0-1 

1.0372 (0.9730-1.1056); 1-2 

1.0554 (0.9904-1.1246); 2-3 

1.0088 (0.9457-1.0762); 0-2 

1.0466 (0.9817-1.1158); 1-3 

1.0205 (0.9569-1.0884); 0-3 

Total minus respiratory and circulatory mortality: 

0.9939 (0.9668-1.0217); 0 

1.0278 (1.0001-1.0562); 1 

1.0178 (0.9902-1.0461); 2 

1.0227 (0.9948-1.0514); 3 

1.0127 (0.9860-1.0412); 0-1 

1.0269 (0.9989-1.0556); 1-2 

1.0249 (0.9968-1.0538); 2-3 

1.0172 (0.9893-1.0458); 0-2 

1.0322 (1.0041-1.0612); 1-3 

1.0229 (0.9950-1.0516); 0-3 

1992-1994 

Total Mortality 

0.9933 (0.9636-1.024); 0 

March 2009 C-61 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

1.0162 (0.9860-1.0473); 1 

1.0116 (0.9816-1.0426); 2 

0.9947 (0.9648-1.0254); 3 

1.0056 (0.9756-1.0366); 0-1 

1.0165 (0.9864-1.0476); 1-2 

1.0038 (0.9739-1.0476); 2-3 

1.0098 (0.9796-1.0409); 0-2 

1.0104 (0.9862-1.0414); 1-3 

1.0064 (0.9755-1.0382); 0-3 

Circulatory Mortality 

1.0076 (0.9640-1.0531); 0 

1.0307 (0.9865-1.0768); 1 

1.0142 (0.9705-1.0598); 2 

0.9523 (0.9102-0.9964); 3 

1.0229 (0.9788-1.0688); 0-1 

1.0267 (0.9827-1.0727); 1-2 

0.9802 (0.9375-1.0248); 2-3 

1.0243 (0.9801-1.0726); 0-2 

0.9987 (0.9553-1.0441); 1-3 

1.0019 (0.9573-1.0487); 0-3 

Respiratory Mortality 

0.9894 (0.8912-1.0984); 0 

0.9474 (0.8521-1.0533); 1 

0.9652 (0.8682-1.0732); 2 

0.9931 (0.8934-1.1040); 3 

0.9626 (0.8668-1.0691); 0-1 

0.9485 (0.8535-1.0541); 1-2 

0.9752 (0.8775-1.0838); 2-3 

0.9555 (0.8802-1.0615); 0-2 

0.9567 (0.8607-1.0635); 1-3 

0.9584 (0.9604-1.0675); 0-3 

Total minus respiratory and circulatory mortality: 

0.9769 (0.9332-1.0227); 0 

1.0135 (0.9682-1.0609); 1 

1.0195 (0.9747-1.0664); 2 

1.0429 (0.9974-1.0905); 3 

0.9940 (0.9494-1.0406); 0-1 

1.0197 (0.9746-1.0670); 1-2 

1.0371 (0.9918-1.0845); 2-3 

1.0045 (0.9596-1.0515); 0-2 

1.0353 (0.9896-1.0831); 1-3 

1.0215 (0.9749-1.0702); 0-3 

Author: Maheswaran et al. 

(2005a) 

Period of Study: 

1994-1998 

Location: 

Sheffield, United Kingdom 

Health Outcome (ICD9): 

Mortality: CHD (410-414) 

Study Design: Ecological 

Statistical Analyses: Poisson 

Age Groups Analyzed: 

≥ 45 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutant: 

NOX; 

PM10

Notes: Quintiles represent the 

following mean CO concentrations and 

category limits: 

5: 482 μg/m3 (≥ 455) 

4: 443 μg/m3 (≥ 433 to <455) 

3: 426 μg/m3 (≥ 419 to <433) 

2: 405 μg/m3 (≥ 387 to <419) 

1: 360 μg/m3 (<387) 

Increment: NR 

Rate Ratios (Lower CI, Upper CI): 

CO 

Adjusted for sex and age

Quintile: 

5 (highest): 1.24 (1.14, 1.36) 

4: 1.30 (1.19, 1.41) 

3: 1.15 (1.05, 1.25) 

2: 1.08 (0.99, 1.17) 

1: (lowest): 1.00 

CO 

Adjusted for sex, age, deprivation, and smoking 

Quintile:

5 (highest): 1.05 (0.95, 1.16); 

4: 1.16 (1.06, 1.28); 

3: 1.04 (0.95, 1.14); 

2: 1.03 (0.94, 1.13); 

1 (lowest): 1.00 

CO 

Adjusted for sex, age, deprivation, and smoking 

(spatially smoothed using a 1 km radius) 

Quintile: 

5 (highest): 1.07 (0.96, 1.18); 

4: 1.13 (1.03, 1.24); 

3: 1.04 (0.95, 1.14); 

2: 1.01 (0.92, 1.10); 

1 (lowest): 1.00 

March 2009 C-62 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Maheswaran et al. 

(2005b) 

Period of Study: 

1994-1998 

Location: 

Sheffield, United Kingdom 

Health Outcome (ICD9): 

Mortality: Stroke deaths 

(430-438) 

Study Design: Ecological 

Statistical Analyses: Poisson 

Age Groups Analyzed: ≥ 45 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 

Quintile: 

5: 482 μg/m3; 

4: 443 μg/m3; 

3: 426 μg/m3; 

2: 405 μg/m3; 

1: 360 μg/m3

Range (Min, Max): NR 

Copollutant correlation : 

PM10: r = 0.88; NOX: r = 0.87 

Notes: Quintiles represent the 

following mean CO concentrations and 

category limits: 

5: 482 μg/m3 (≥ 455) 

4: 443 μg/m3 (≥ 433 to <455) 

3: 426 μg/m3 (≥ 419 to <433) 

2: 405 μg/m3 (≥ 387 to <419) 

1: 360 μg/m3 (<387) 

Increment: NR 

Rate Ratios (Lower CI, Upper CI); lag: 

RR for mortality and CO modeled outdoor air 

pollution 

Adjusted for sex and age 

Quintile: 

5 (highest): 1.35 (1.19, 1.53); 

4: 1.40 (1.24, 1.58); 

3: 1.08 (0.95, 1.23); 

2: 1.10 (0.97, 1.24); 

1 (lowest): 1.00 

Adjusted for sex, age, deprivation, and smoking 

Quintile: 

5 (highest): 1.26 (1.10, 1.46); 

4: 1.32 (1.15, 1.50); 

3: 1.07 (0.93, 1.22); 

2: 1.12 (0.99, 1.28); 

1 (lowest): 1.00 

Not spatially smoothed CO outdoor air pollution 

Quintile: 

5 (highest): 1.26 (1.10, 1.46); 

4: 1.32 (1.15, 1.50); 

3: 1.07 (0.93, 1.22); 

2: 1.12 (0.99, 1.28); 

1 (lowest): 1.00 

Spatially smoothed using a 1-km radius 

Quintile: 

5 (highest): 1.16 (1.01, 1.34); 

4: 1.22 (1.07, 1.39); 

3: 0.95 (0.83, 1.09); 

2: 0.97 (0.85, 1.11); 

1 (lowest): 1.00 

Author: Mar et al. (2000) 

Period of Study: 

1995-1997 

Location: 

Phoenix, AZ 

Health Outcome (ICD9): 

Mortality: Total (non-accidental) 

(<800); Cardiovascular (390-449)

Study Design: Time-series 

Statistical Analyses: Poisson 

Age Groups Analyzed: 

>65 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 1.5 (0.8) ppm 

Range (Min, Max): 

1995: (0.5, 4.0) ppm 

1996: (0.3, 4.0) ppm 

1997: (0.3, 3.7) ppm 

Copollutant correlation: 

PM2.5: r = 0.85; 

PM10: r = 0.53; 

PM10-2.5: r = 0.34; 

NO2: r = 0.87; 

O3: r =-0.40; 

SO2: r =0.53 

Increment: 1.19 ppm 

Relative Risk (Lower CI, Upper CI); lag: 

Total Mortality (CO exposure): 

1.06 (1.02, 1.09); 0; 

1.05 (1.01, 1.09); 1 

Cardiovascular Mortality (CO exposure): 

1.05 (1.00, 1.11); 0; 

1.10 (1.04, 1.15); 1; 

1.07 (1.02, 1.12); 2; 

1.07 (1.02, 1.12); 3; 

1.08 (1.03, 1.13); 4 

Author: Moolgavkar et al. 

(2000b) 

Period of Study: 

1987-1995 

Location: 

Cook County, IL 

Los Angeles County, CA 

Maricopa County, AZ 

Health Outcome (ICD9): 

Mortality: 

Circulatory (390-448); 

Cardiovascular (390-429); 

Cerebrovascular (430-448); 

COPD (490-496); Asthma (493) 

Study Design: Time-series 

Statistical Analyses: Poisson 

GAM, spline smoother 

Age Groups Analyzed: All ages 

Pollutant: CO 

Averaging Time: 24-h avg 

Median unit: 

Cook county: 993 ppb 

Los Angeles: 1347 ppb 

Maricopa: 1240 ppb 

Range (Min, Max): 

Cook county: (224, 3912) 

Los Angeles: (237, 5955) 

Maricopa: (269, 4777) 

Copollutant correlation :

PM10: 

Cook: r = 0.30; 

LA: r = 0.45; 

Maricopa: r = 0.20 

NO2: 

Cook: r = 0.63; 

LA: r = 0.80; 

Maricopa: r = 0.66 

SO2: 

Cook: r = 0.35; 

LA: r = 0.78; 

Maricopa: r = 0.53 

O3: 

Increment: 1 ppm 

% Change (Lower CI, Upper CI); lag: 

CVD Mortality

Cook County

CO 

-1.07 (-2.67, 0.54); 0; / 1.25 (-0.36, 2.87); 1; 

1.49 (-0.09, 3.07); 2; / 1.90 (0.32, 3.48); 3; 

1.44 (-0.16, 3.03); 4; / 0.72 (-0.89, 2.32); 5 

Los Angeles County

CO 

3.47 (2.94, 4.00); 0; / 3.93 (3.41, 4.46); 1; 

4.08 (3.56, 4.60); 2; / 3.76 (3.24, 4.28); 3; 

2.91 (2.37, 3.44); 4; / 2.63 (2.09, 3.17); 5 

CO, PM10

2.27 (0.88, 3.66); 0; / 4.33 (2.96, 5.69); 1; 

4.72 (3.38, 6.05); 2; / 4.26 (2.90, 5.63); 3; 

2.49 (1.10, 3.88); 4; / 5.93 (4.60, 7.27); 5 

CO and PM2.5

0.43 (-1.35, 2.20); 0; / 2.88 (1.16, 4.60); 1; 

4.65 (2.93, 6.37); 2; / 5.93 (4.20, 7.65); 3; 

3.88 (2.13, 5.63); 4; / 5.85 (4.12, 7.58); 5 

Maricopa County 

CO 

0.81 (-0.79, 2.39); 0; / 2.20 (0.61, 3.79); 1; 

March 2009 C-63 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Cook: r = -0.28; 

LA: r = -0.52; 

Maricopa: r = -0.61 

3.05 (1.49, 4.61); 2; / 3.78 (2.27, 5.28); 3; 

3.73 (2.27, 5.19); 4; / 2.25 (0.76, 3.72); 5 

COPD Mortality 

Cook County

CO 

-2.65 (-7.05, 1.75); 0; / 2.80 (-1.60, 7.19); 1; 

0.98 (-3.34, 5.31); 2; / 2.20 (-2.12, 6.53); 3; 

1.31 (-3.06, 5.68); 4; / 1.59 (-2.78, 5.97); 5 

Los Angeles County 

CO 

3.78 (2.31, 5.25); 0; / 5.23 (3.78, 6.69); 1; 

5.71 (4.26, 7.17); 2; / 5.42 (3.95, 6.89); 3; 

4.01 (2.51, 5.50); 4; / 3.82 (2.31, 5.33); 5 

Maricopa County

CO 

1.29 (-2.19, 4.76); 0; / 4.63 (1.17, 8.09); 1; 

0.07 (-3.36, 3.50); 2; / 3.00 (-0.30, 6.30); 3; 

6.21 (3.02, 9.40); 4; / 3.27 (0.04, 6.50); 5 

Cerebrovascular Disease Mortality 

Cook County 

-0.41 (-3.30, 2.47); 0; / 3.13 (0.23, 6.02); 1; 

2.12 (-0.73, 4.97); 2; / 1.00 (-1.85, 3.86); 3; 

2.50 (-0.36, 5.37); 4; / 1.88 (-1.00, 4.76); 5 

Los Angeles County 

3.31 (2.32, 4.31); 0; / 3.88 (2.89, 4.87); 1; 

3.23 (2.25, 4.22); 2; / 2.65 (1.66, 3.65); 3; 

2.11 (1.11, 3.12); 4; / 2.04 (1.02, 3.06); 5 

Maricopa County 

0.26 (-2.65, 3.16); 0; / 3.50 (0.60, 6.41); 1; 

3.52 (0.66, 6.38); 2; / 4.61 (1.85, 7.37); 3; 

4.78 (2.10, 7.46); 4; / 5.15 (2.45, 7.84); 5 

Notes: Total Mortality effect estimates were not 

presented quantitatively. 

Author: Moolgavkar et al. 

(2003b) 

Period of Study: 1987-1995 

Location: 

Cook County, Illinois & Los 

Angeles County, California 

Health Outcome (ICD9): 

Mortality: Total (non-accidental) 

(<800); Circulatory (390-448) 

Study Design: Time-series 

Statistical Analyses: Poisson 

GAM 

Age Groups Analyzed: 

All Ages 

Pollutant: CO 

Averaging Time: 24-h avg 

Median unit: 

Cook County: 993 ppb 

LA County: 1347 ppb 

Range (Min, Max): 

Cook County: (224, 3912) ppb 

LA County: (237, 5955) ppb 

Copollutant correlation: 

Cook County:

NO2: r = 0.63; 

O3: r = -0.22; 

SO2: r = 0.35; 

PM10: r = 0.30 

LA County: 

NO2: r = 0.80; 

O3: r = -0.52; 

SO2: r = 0.78; 

PM10: r = 0.45; 

PM2.5: r = 0.58 

Increment: 1 ppm 

% Increase (t-statistic); lag 

Total Mortality Cook County 

CO:

0.6% (1.2); 0; / 2.5% (5.4); 1; / 1.2% (2.6); 2; 

1.5% (3.2); 3; / 1.1% (2.5); 4; / 0.6% (1.3); 5 

CO, PM10:

-0.5% (-1.0); 0; / 2.2% (4.3); 1; / 1.1% (2.2); 2; 

1.0% (1.9); 3; / 1.1% (2.1); 4; / 1.4% (2.7); 5 

Total Mortality Los Angeles County 

CO: 

1.3% (7.4); 0; / 1.9% (10.5); 1; / 1.6% (8.9); 2; 

1.4% (8.1); 3; / 1.0% (5.9); 4; / 0.7% (4.1); 5 

CO, PM10: 

0% (0); 0; / 2.2% (4.8); 1; / 1.4% (3.1); 2; 

0.8% (1.8); 3; / 0.7% (1.6); 4; / 1.3% (3.0); 5 

CO, PM2.5: 

-0.1% (-1.5); 0; / 1.5% (2.5); 1; / 2.4% (3.8); 2; 

0.3% (0.5); 3; / 1.6% (2.8); 4; / 1.5% (2.6); 5 

Total Mortality (Season-specific) Cook County

Spring (CO): 

0.8% (0.9); 0; / 2.4% (2.9); 1; / 0% (0); 2; 

1.2% (1.5); 3; / 0.8% (1.0); 4; / -0.1% (-0.2); 5 

Summer (CO): 

1.2% (1.0); 0; / 3.6% (3.0); 1; / 4.2% (3.6); 2; 

-0.3% (-0.2); 3; / -1.1% (-1.0); 4; /-0.7% (-0.6); 5 

Fall (CO): 

1.2% (1.5); 0; / 2.1% (2.7); 1; / 0% (0); 2; 

0% (0); 3; /-0.5% (-0.6); 4; / -0.7% (-0.9); 5 

Winter (CO): 

-0.7% (-1.0); 0; / 1.8% (2.3); 1; / -0.2% (-0.3); 2; 

0.5% (0.6); 3; / 1.2% (1.5); 4; / 1.0% (1.3); 5 

Los Angeles County

Total Mortality (Season-specific) 

Spring (CO): 

3.6% (6.3); 0; / 3.5% (6.2); 1; / 1.9% (3.4); 2; 

0.6% (1.0); 3; / -0.5% (-0.8); 4; / -0.7% (-1.2); 5 

March 2009 C-64 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Summer (CO):

3.0% (3.0); 0; / 4.7% (4.6); 1; / 5.2% (5.1); 2; 

4.1% (3.8); 3; / 1.9% (1.8); 4; / 1.4% (1.3); 5 

Fall (CO): 

1.8% (4.6); 0; / 2.0% (5.1); 1; / 1.0% (2.6); 2; 

0.6% (1.5); 3; / 0.4% (1.2); 4; / 0.2% (0.6); 5 

Winter (CO): 

0% (0); 0; / 0.8% (2.5); 1; / 0.9% (3.1); 2; 

1.0% (3.4); 3; / 0.5% (1.7); 4; / 0.5% (1.6); 5 

CVD Mortality Cook County

CO: 

-1.1% (-1.5); 0; / 1.8% (2.5); 1; / 1.5% (2.2); 2; 

1.6% (2.4); 3; / 1.4% (2.1); 4; / 0.7% (1.0); 5 

CO, PM10:

-2.1% (-2.6); 0; / 1.5% (1.8); 1; / 1.4% (1.7); 2; 

0.1% (1.1); 3; / 1.4% (1.9); 4; / 1.6% (2.1); 5 

CVD Mortality Los Angeles County 

CO: 

1.6% (6.3); 0; / 1.9% (7.6); 1; / 1.6% (6.6); 2; 

1.9% (8.2); 3; / 1.6% (7.1); 4; / 1.4% (6.1); 5 

CO, PM10:

-0.8% (-1.2); 0; / 1.9% (3.0); 1; / 2.7% (4.3); 2; 

1.3% (2.2); 3; / 0.5% (0.9); 4; / 2.8% (4.7); 5 

CO, PM2.5: -2.2% (-2.7); 0; / 1.5% (1.8); 1; / 1.9% (2.0); 2; 

1.9% (2.2); 3; / 2.1% (2.6); 4; / 3.7%(4.5); 5 

CVD Mortality (Season Specifid) Cook County

Spring (CO): 

0.7% (0.5); 0; / 1.4% (1.1); 1; / 0.3% (0.3); 2; 

1.1% (0.9); 3; / 0.4% (3.1); 4; / 0.1% (0.6); 5 

Summer (CO): 

-2.6% (-1.4); 0; / 2.5% (1.4); 1; / 6.5% (3.7); 2; 

0.9% (0.5); 3; / -1.9% (-1.1); 4; / -1.0% (-0.6); 5 

Fall (CO): 

0% (0); 0; / 2.9% (2.5); / 1; 0% (0); 2; 

0% (0); 3; /-0.8% (-0.7); / 4; 0% (0); 5 

Winter (CO):

-2.5% (-2.2); 0; / 0.7% (0.6); 1; / 0% (0); 2; 

1.3% (1.1); 3; / 0.8% (0.7); 4; / 0.4% (0.4); 5 

Los Angeles County 

CVD Mortality (Season-specific) 

Spring (CO): 

3.0% (3.7); 0; / 3.3% (4.1); 1; / 2.3% (2.9); 2; 

0.7% (0.9); 3; / -1.2% (-1.6); 4; / 0% (0); 5 

Summer (CO):

4.0% (2.8); 0; / 5.2% (3.5); 1; / 6.3% (4.3); 2; 

5.0% (3.3); 3; / 3.1% (2.0); 4; / 3.6% (2.3); 5 

Fall (CO): 

2.3% (4.2); 0; / 2.1% (3.7); 1; / 1.1% (1.9); 2; 

1.2% (2.2); 3; / 1.5% (2.9); 4; / 1.0% (1.8); 5 

Winter (CO): 

0.3% (0.8); / 0; 0.7% (1.7); 1; / 0.8% (2.0); 2; 

1.4% (3.4); 3; / 1.0% (2.3); 4; / 1.1% (2.5); 5 

Author: Ostro et al. (1999) 

Period of Study: 

1989-1992 

Location: 

Coachella Valley, California 

Health Outcome (ICD9): 

Mortality: Total (non-accidental) 

(<800); Respiratory (460-519); 

Cardiovascular (393-440) 

Study Design: Time-series 

Statistical Analyses: Poisson 

GAM; LOESS 

Age Groups Analyzed: >50 

Pollutant: CO 

Averaging Time: 1-h maximum 

Mean (SD) unit: 1.35 ppm 

Range (Min, Max): (0, 6.0) 

Copollutant correlation: 

PM10: r = -0.18; O3: r = -0.47; NO2: r = 

0.65 

Increment: NR 

β (SE); lag: 

CO: 0.0371 (0.0157); 2 

CO, PM10: 0.0300 (0.0194); 2 

March 2009 C-65 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Penttinen et al. 

(2004) 

Period of Study: 

1988-1996 

Location: 

Helsinki, Finland 

Health Outcome (ICD9): 

Mortality: Total (non-accidental) 

(<800); Respiratory (460-519); 

Cardiovascular (393-440) 

Study Design: Time-series 

Statistical Analyses: Poisson 

GAM, LOESS 

Age Groups Analyzed: All ages 

15-64 years 

65-74 years 

≥ 75 

Pollutant: CO 

Averaging Time: 

Maximum 8-h avg 

Median unit: 1.2 mg/m3

Range (Min, Max): (0, 12.4) 

Copollutant correlation: 

O3: r = -0.46; NO2: r = 0.59; 

SO2: r = 0.55; PM10: r = 0.45; TSP: r = 

0.26; 

TSP Blackness: r = 0.26 

Increment: 1 mg/m3

% Increase (Lower CI, Upper CI); lag: 

Total Mortality 

-1.50% (-2.78, -0.22); 0 

0.15% (-1.09, 1.39); 1 

-1.00% (-2.80, 0.81); 0-3 

Cardiovascular Mortality 

-2.48% (-4.30, -0.66); 0 

-0.84% (-2.61, 0.93); 1 

-1.87% (-4.43, 0.69); 0- 

Respiratory Morality 

-0.48% (-4.84, 3.87); 0 

-0.14% (-4.43, 4.15); 1 

-1.49% (-7.73, 4.74); 0-3 

Author: Peters et al. (2000a) 

Period of Study: 

1982-1994 

Location: 

Northern Bavaria (Rural 

Germany) and the Coal Basin 

of the Czech Republic 

Health Outcome (ICD9): 

Mortality: Total (non-accidental) 

(<800); Cardiovascular (390-

459); Respiratory (460-519); 

Cancer (140-239) 

Study Design: Time-series 

Statistical Analyses: 

(1) Poisson Regression Models 

by logistic regression analyses 

with a cubic function; 

(2) Poisson GAM, natural splines

Age Groups Analyzed: 

All Ages 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 

Coal Basin: 0.58 (0.39) mg/m3

Northeast Bavaria: 

0.88 (0.69) mg/m3

Range (Min, Max): 

Coal Basin: (-0.1, 2.88) 

Northeast Bavaria: (0.1, 6.2) 

Copollutant correlation: 

SO2: r = 0.37; TSP: r = 0.37; NO2: r = 

0.32; O3: r = -0.57; PM10: r = 0.44; 

PM2.5: r = 0.42 

Increment: 1 mg/m3

Relative Risk (Lower CI, Upper CI); lag: 

Coal Basin of the Czech Republic

Total Mortality: 

1.016 (0.998, 1.035); 0; / 1.016 (0.998, 1.034); 1; 1.013 

(0.996, 1.030); 2; / 1.012 (0.995, 1.028); 3 

Northeast Bavaria

Total Mortality: 

1.014 (0.994, 1.034); 0; / 1.023 (1.005, 1.041); 1; 1.013 

(0.995, 1.031); 2; / 1.003 (0.985, 1.021); 3 

Cardiovascular Disease Mortality: 

1.018 (0.994, 1.044); 0; / 1.012 (0.987, 1.038); 1; 1.016 

(0.991, 1.041); 2; / 1.004 (0.980, 1.029); 3 

Author: Rainham et al. (2003) 

Period of Study: 

1980-1996 

Location: 

Toronto, ON, Canada 

Health Outcome (ICD9): 

Mortality: Cardiac (390-459); 

Respiratory (480-519); Total 

(non-accidental) (<800) 

Study Design: Time-series 

Statistical Analyses:Poisson 

GAM, natural cubic splines 

Age Groups Analyzed: 

<65 

≥ 65 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 1.0 (0.4) ppm 

Range (Min, Max): (0.0, 4.0) 

Copollutant: O3; NO2; SO2

The study did not present quantitative results for 

CO. 

Author: Roemer et al. (2001) 

Period of Study: 

1/1987 - 11/1998 

Location: Amsterdam 

Health Outcome (ICD9): 

Mortality: Total (non-accidental) 

(<800) 

Study Design: Time-series 

Statistical Analyses: 

Poisson GAM 

Age Groups Analyzed: 

All ages 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 

Air pollution background: 

836 μg/m3

Air pollution traffic: 1805 μg/m3

Range (10th, 90th): 

Air pollution background: 

(448, 1315) μg/m3

Air pollution traffic: 

(727, 3192) μg/m3

Copollutant: 

BS; PM10; SO2; NO2; NO; O3

Increment: 

Lag 1 and 2: 100 μg/m3

Lag 0-6: 50 μg/m3

Relative Risk (Lower CI, Upper CI); lag: 

Total Population using Background sites 

1.002 (1.000-1.004); 1; 

1.001 (0.999-1.003); 2; 

1.001 (1.000-1.003); 0-6 

Traffic Population using Background Sites 

1.003 (0.997-1.008); 1; 

1.008 (1.003-1.013); 2; 

1.003 (0.999-1.007); 0-6 

Total population using Traffic Sites 

1.000 (1.000-1.001); 1; 

1.000 (0.999-1.001); 2; 

1.000 (1.000-1.001); 0-6 

March 2009 C-66 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Samet et al. (2000b) 

Period of Study: 

1987-1994 

Location: 

20 U.S. Cities: Los Angeles, 

CA; New York, NY; Chicago, 

IL; Dallas, TX; Houston, TX; 

San Diego, CA; Anaheim, CA; 

Phoenix, AZ; Detroit, MI; 

Miami, FL; Philadelphia, PA; 

Minneapolis, MN; Seattle, WA; 

San Jose, CA; Cleveland, OH; 

San Bernardino, CA; 

Pittsburgh, PA; Oakland, CA; 

Atlanta, GA; San Antonio, TX 

Health Outcome (ICD9): 

Mortality: Cardiovascular (390-

459); Respiratory 

(460-519); Other (non-accidental)

(<800) 

Study Design: Time-series 

Statistical Analyses: 

Two-stage log linear regression 

model 

Age Groups Analyzed: <65 

65-74 

≥ 75 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 

Los Angeles: 15.1 ppm 

New York: 20.4 ppm 

Chicago: 7.9 ppm 

Dallas: 7.4 ppm 

Houston: 8.9 ppm 

San Diego: 11.0 ppm 

Anaheim: 12.3 ppm 

Phoenix: 12.6 ppm 

Detroit: 6.6 ppm 

Miami: 10.6 ppm 

Philadelphia: 11.8 ppm 

Minneapolis: 11.8 ppm 

Seattle: 17.8 ppm 

San Jose: 9.4 ppm 

Cleveland: 8.5 ppm 

San Bernardino: 10.3 ppm 

Pittsburgh: 12.2 ppm 

Oakland: 9.1 ppm 

Atlanta: 8.0 ppm 

San Antonio: 10.1 ppm 

Range (10th, 90th): 

Los Angeles: (5.9, 28.3) 

New York: (14.8, 27.6) 

Chicago: (4.5, 11.9) 

Dallas: (3.6, 12.0) 

Houston: (4.0, 14.2) 

San Diego: (4.5, 20.5) 

Anaheim: (3.7, 25.2) 

Phoenix: (5.4, 22.6) 

Detroit: (3.2, 11.1) 

Miami: (6.5, 15.9) 

Philadelphia: (7.0, 17.2) 

Minneapolis: (7.0, 17.0) 

Seattle: (10.5, 26.4) 

San Jose: (1.7, 21.3) 

Cleveland: (3.7, 13.8) 

San Bernardino: (4.0, 17.5) 

Pittsburgh: (6.1, 19.8) 

Oakland: (2.9, 17.0) 

Atlanta: (3.2, 14.3) 

San Antonio: (4.1, 17.3) 

Copollutant correlation: 

PM10: r = 0.45; O3: r = -0.19; 

NO2: r = 0.64; SO2: r = 0.41 

This study did not provide quantitative results for 

CO. 

Author: Samoli et al. (2007) 

Period of Study: 

1990-1997 

Location: 

19 European Cities 

(APHEA2) 

Health Outcome (ICD9): 

Mortality: Total (non-accidental) 

(<800); Cardiovascular (390-459)

Study Design: Time-series 

Statistical Analyses: 

Poisson and two-stage 

hierarchical model 

Age Groups Analyzed: 

All ages 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean Range (unit - mg/m3): 

Athens: 6.1; Barcelona: 0.9; Basel: 0.6; 

Birmingham: 1.0; Budapest: 5.1; 

Geneva: 1.5; Helsinki: 1.2; Ljubljana: 

1.6; London: 1.4; Lyon: 3.8; 

Milano: 5.4; Netherlands: 0.6; Prague: 

0.9; Rome: 4.1; Stockholm: 0.8; 

Teplice: 0.7; Torino: 5.5; Valencia: 4.1; 

Zurich: 1.2 

Range (10th, 90th): 

Athens: (3.5, 9.2) 

Barcelona: (0.4, 1.7) 

Basel: (0.4, 1.1) 

Birmingham: (0.5, 1.6) 

Budapest: (3.3, 7.4) 

Geneva: (0.8, 2.6) 

Helsinki: (0.7, 1.9) 

Ljubljana: (0.6, 3.0) 

London: (0.7, 2.2) 

Lyon: (2.0, 6.0) 

Milano: (2.9, 8.7) 

Netherlands: (0.4, 1.2) 

Prague: (0.5, 1.5) 

Rome: (2.5, 5.9) 

Stockholm: (0.5, 1.2) 

Increment: 1 mg/m3

% Increase (Lower CI, Upper CI); lag: 

Non-accidental mortality 

8 Degrees of Freedom per year

Fixed Effects: 

CO: 0.59% (0.41-0.78); 0-1 

CO, BS: 0.35% (-0.03 to 0.72); 0-1 

CO, PM10: 0.48% (0.24-0.72); 0-1 

CO, SO2: 0.44% (0.21-0.67); 0-1 

CO, O3: 0.66% (0.46-0.86); 0-1 

CO, NO2: 0.27% (0.03-0.51); 0-1 

Random Effects: 

CO: 0.66% (0.27-1.05); 0-1 

CO, BS: 0.45% (-0.01 to 0.92); 0-1 

CO, PM10: 0.58% (0.12-1.04); 0-1 

CO, SO2: 0.46% (0.07-0.85); 0-1 

CO, O3: 0.76% (0.45-1.06); 0-1 

CO, NO2: 0.30% (-0.11 to 0.71); 0-1 

PACF: (Partial Autocorrelation Function) Plot Fixed 

Effects: 

CO: 1.00% (0.83-1.18); 0-1 

CO, BS: 0.67% (0.30-1.04); 0-1 

CO, PM10: 0.78% (0.55-1.00); 0-1 

CO, SO2: 0.68% (0.47-0.90); 0-1 

CO, O3: 1.12% (0.93-1.31); 0-1 

CO, NO2: 0.72% (0.50-0.95); 0-1 

March 2009 C-67 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Teplice: (0.3, 1.2) 

Torino: (2.8, 9.1) 

Valencia: (2.4, 5.9) 

Zurich: (0.7, 2.0) 

Copollutant correlation: 

PM10: r = 0.16 to 0.70 

BS: r = 0.67 to 0.82 

SO

2: r = 0.35 to 0.82 

NO

2: r = 0.03 to 0.68 

O

3: r = -0.25 to -0.65 

Random Effects:

CO: 1.20% (0.63-1.77); 0-1 

CO, BS: 0.77% (0.28-1.26); 0-1 

CO, PM10: 1.09% (0.36-1.83); 0-1 

CO, SO

2: 0.75% (0.26-1.26); 0-1 

CO, O

3: 1.37% (0.81-1.95); 0-1 

CO, NO

2: 0.88% (0.22-1.55); 0-1 

Cardiovascular Mortality 

8 Degrees of Freedom per Year 

Fixed Effects:

CO: 0.80% (0.53-1.07); 0-1 

CO, BS: 0.49% (-0.04 to 1.02); 0-1 

CO, PM10: 0.73% (0.39-1.07); 0-1 

CO, SO

2: 0.72% (0.39-1.04); 0-1 

CO, O

3: 0.91% (0.62-1.20); 0-1 

CO, NO

2: 0.44% (0.10-0.79); 0-1 

Random Effects: 

CO: 0.81% (0.36-1.26); 0-1 

CO, BS: 0.49% (-0.04 to 1.02); 0-1 

CO, PM10: 0.73% (0.39-1.07); 0-1 

CO, SO

2: 0.68% (-0.03 to 1.40); 0-1 

CO, O

3: 1.02% (0.58-1.46); 0-1 

CO, NO

2: 0.43% (-0.06 to 0.93); 0-1 

PACF (Partial Autocorrelation Function) Fixed 

Effects:

CO: 1.06% (0.80-1.32); 0-1 

CO, BS: 0.83% (0.31-1.35); 0-1 

CO, PM10: 0.95% (0.62-1.27); 0-1 

CO, SO

2: 0.91% (0.59-1.22); 0-1 

CO, O

3: 1.28% (1.01-1.56); 0-1 

CO, NO

2: 0.68% (0.35-1.00); 0-1 

Random Effects:

CO: 1.25% (0.30-2.21); 0-1 

CO, BS: 0.83% (0.31-1.35); 0-1 

CO, PM10: 1.13% (0.60-1.67); 0-1 

CO, SO

2: 0.86% (0.06-1.66); 0-1 

CO, O

3: 1.62% (0.72-2.52); 0-1 

CO, NO

2: 0.84% (-0.03 to 1.71); 0-1 

Effect Modifiers 

Non-accidental Mortality 

8 Degrees of Freedom per Year 

Number of CO monitors: 

25th Percentile: 0.71% (0.48-0.94); 0-1 

75th Percentile: 0.54% (0.34-0.74); 0-1 

Mean PM10 Levels: 

25th Percentile: 0.37% (0.08-0.66); 0-1 

75th Percentile: 0.49% (0.28-0.69); 0-1 

Standardized Mortality Rate: 

25th Percentile: 0.79% (0.55-1.03); 0-1 

75th Percentile: 0.44% (0.22-0.66); 0-1 

Western cities: 0.75% (0.47-1.03); 0-1 

Southern cities: 0.61% (0.32-0.91); 0-1 

Eastern cities: 0.03% (-0.47 to 0.53); 0-1 

PACF (Partial Autocorrelation Function) 

Number of CO monitors: 

25th Percentile: 1.18% (0.96-1.39); 0-1 

75th Percentile: 0.92% (0.73-1.11); 0-1 

Mean PM10 Levels: 

25th Percentile: 0.74% (0.46-1.02); 0-1 

75th Percentile: 1.07% (0.87-1.27); 0-1 

Standardized Mortality Rate: 

25th Percentile: 1.29% (1.06-1.52); 0-1 

75th Percentile: 0.77% (0.56-0.98); 0-1 

Western cities: 1.15% (0.90-1.40); 0-1 

Southern cities: 1.08% (0.79-1.38); 0-1 

Eastern cities: 0.27% (-0.20 to 0.74); 0-1 

Cardiovascular Mortality 

8 Degrees of Freedom per Year 

Mean O

3: 

25th Percentile: 1.04% (0.67-1.41); 0-1 

75th Percentile: 0.82% (0.55-1.10); 0-1 

Standardized Mortality Rate:

25th Percentile: 1.06% (0.71-1.42); 0-1 

75th Percentile: 0.61% (0.30-0.93); 0-1 

Population >75 years of age (%):

25th Percentile: 0.58% (0.25-0.92); 0-1 

75th Percentile: 0.94% (0.64-1.24); 0-1 

March 2009 C-68 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Western cities: 1.06% (0.67-1.46); 0-1 

Southern cities: 0.70% (0.26-1.14); 0-1 

Eastern cities: 0.21% (-0.48 to 0.90); 0-1 

PACF (Partial Autocorrelation Function) 

Mean O3: 

25th Percentile: 1.32% (0.96-1.68); 0-1 

75th Percentile: 1.09% (0.83-1.14); 0-1 

Standardized Mortality Rate:

25th Percentile: 1.40% (1.06-1.75); 0-1 

75th Percentile: 0.85% (0.55-1.14); 0-1 

Population >75 years of age (%):

25th Percentile: 0.74% (0.41-1.06); 0-1 

75th Percentile: 1.25% (0.96-1.54); 0-1 

Western cities: 1.38% (1.00-1.76); 0-1 

Southern cities: 0.90% (0.47-1.33); 0-1 

Eastern cities: 0.48% (-0.14 to 1.11); 0-1 

Author: Schwartz et al. 

(1999) 

Period of Study: 

1989-1995 

Location: 

Spokane, WA 

Health Outcome (ICD9): 

Mortality: Total (non-accidental) 

(<800) 

Study Design: Time-series 

Statistical Analyses: Poisson 

GAM 

Age Groups Analyzed: All ages 

Pollutant: CO 

Averaging Time: 1-h avg 

Mean (SD) unit: 

Dust Storm Days: 

09/08/1990: 6.37 ppm 

09/12/1990: 3.40 ppm 

10/04/1990: 3.15 ppm 

11/09/1990: 2.45 ppm 

11/23/1990: 2.50 ppm 

09/13/1991: 4.60 ppm 

10/16/1991: 2.10 ppm 

10/21/1991: 2.20 ppm 

09/04/1992: 3.43 ppm 

09/12/1992: 1.80 ppm 

09/13/1992: 1.65 ppm 

09/25/1992: 2.95 ppm 

09/26/1992: 4.30 ppm 

10/08/1992: 3.85 ppm 

09/11/1993: 1.88 ppm 

11/3/1993: 5.33 ppm 

07/24/1994: 2.10 ppm 

08/30/1996: 2.85 ppm 

Range (Min, Max): NR 

Copollutant: PM10

The study did not present quantitative results for 

CO. 

Author: Sharovsky et al. 

(2004) 

Period of Study: 

1996-1998 

Location: 

Sao Paulo, Brazil 

Health Outcome (ICD10): 

Mortality: Myocardial Infarction 

(I.21) 

Study Design: Time-series 

Statistical Analyses: 

Poisson GAM, LOESS 

Age Groups Analyzed: 35-109 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 3.7 (1.6) ppm 

Range (Min, Max): (1.0, 11.8) 

Copollutant: correlation 

SO2: r = 0.73; PM10: r = 0.51 

Increment: NR 

β x 100 (SE); lag: 

CO: 1.42 (1.01) 

CO, SO2, PM10: 0.97 (1.27) 

Notes: The study did not present the lag used for CO. 

Author: Slaughter et al. 

(2005) 

Period of Study: 

1/1995 - 6/2001 

Location: 

Spokane, WA 

Health Outcome (ICD9): 

Mortality: Total (non-accidental) 

(<800); Respiratory (460-519); 

Asthma (493); COPD (491, 492, 

494, 496); Pneumonia (480-487); 

Acute Upper Respirator y Tract 

Infections (464-466, 490); 

Cardiac Outcomes (390-459) 

Study Design: Time-series 

Statistical Analyses: 

Log-linear Poisson GLM, natural 

splines for calendar time 

Age Groups Analyzed: All ages

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 

Areas in Spokane 

Hamilton St: 1.73 (0.46) ppm 

Backdoor Tavern: 

1.29 (0.23) ppm 

Spokane Club: 1.41 (0.32) ppm 

Third and Washington: 

1.82 (0.33) ppm 

Rockwood: 0.42 (0.15) ppm 

Range (Min, Max): NR 

Copollutant correlation : 

PM1: r = 0.63; PM2.5: r = 0.62; 

PM10: r = 0.32; 

PM10-2.5: r = 0.32 

The study did not present quantitative results for 

CO. 

March 2009 C-69 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Stieb et al. (2003) 

Period of Study: 

1985-2000 

Location: All locations 

Health Outcome (ICD9): 

Mortality: Non-accidental 

Study Design: Meta-analysis 

Statistical Analyses: NR 

Age Groups Analyzed: All ages

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: NR 

IQR (25th, 75th): NR 

Copollutant: NR 

Increment: 1.1 ppm 

% Excess Mortality (Lower CI, Upper CI); lag: 

Non-GAM: 

Single-pollutant model (4 studies): 4.7% (1.1-8.4) 

Multi-pollutant model (1 study): 0.0% (-3.8 to 3.8) 

GAM:

Single-pollutant model (18 studies): 1.6% (1.1-2.1) 

Multi-pollutant model (11 studies): 0.7% (-0.1 to 1.5) 

Author: Stölzel et al. (2006) 

Period of Study: 

9/1995-8/2001 

Location: 

Erfurt, Germany 

Health Outcome (ICD9): 

Mortality: Total (non-accidental) 

(<800); 

Cardio-respiratory (390-459, 460-

519, 785, 786) 

Study Design: Time-series 

Statistical Analyses: Poisson 

GAM 

Age Groups Analyzed: 

All ages 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 

0.47 (0.39) mg/m3

IQR (25th, 75th): (0.23, 0.57) 

Copollutant correlation: 

MC0.1-0.5: r = 0.58; 

MC0.01-2.5: r = 0.57; 

PM10: r = 0.50; NO: r = 0.70; 

NO2: r = 0.71 

Increment: 0.34 mg/m3

Relative Risk (Lower CI, Upper CI); lag: 

Total (non-accidental) 

1.000 (0.977-1.023); 0; 

1.002 (0.980-1.024); 1; 

1.013 (0.991-1.035); 2; 

1.007 (0.986-1.029); 3; 

1.012 (0.990-1.034); 4; 

0.995 (0.974-1.017); 5 

Author: Sunyer et al. (2001) 

Period of Study: 

1990-1995 

Location: 

Barcelona, Spain 

Health Outcome (ICD9): 

Mortality: COPD 

(491, 492, 494, 496) 

Study Design: 

Bi-directional case-crossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed:>35 

Pollutant: CO 

Averaging Time: 8-h avg 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutant: PM10; NO2; O3

Increment: 4.5 μg/m3

Odds Ratio (Lower CI, Upper CI); lag: 

CO: 1.052 (0.990-1.117); 0-2 

CO, PM10: 1.017 (0.947-1.091); 0-2 

Author: Sunyer et al. (2002) 

Period of Study: 

1985-1995 

Location: 

Barcelona, Spain 

Health Outcome (ICD9): 

Mortality: Respiratory mortality 

Study Design: Case-crossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed:>14 

Study population: 

Asthmatic individuals: 5,610 

Pollutant: CO 

Averaging Time: 24-h avg 

Median (SD) unit: 7.7 μg/m3

Range (Min, Max): (0.6, 66.0) 

Copollutant: 

PM10; BS; NO2; O3; SO2

Increment: 7.2 μg/m3

Odds Ratio (Lower CI, Upper CI); lag: 

Asthmatic individuals with 1 ED visit 

1.127 (0.895-1.418); 0-2 

Asthmatic individuals with >1 ED visit 

1.125 (0.773-1.638); 0-2 

Asthma/COPD individuals with >1 ED visit 

0.815 (0.614-1.082); 0-2 

Author: Tsai et al. (2003a) 

Period of Study: 

1994-2000 

Location: 

Kaohsiung, Taiwan 

Health Outcome (ICD9): 

Mortality: Total (non-accidental) 

(<800); Respiratory (460-519); 

Circulatory (390-459) 

Study Design: 

Bidirectional case-crossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: All ages

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 0.827 ppm 

Range (Min, Max): (0.226, 1.770) 

Copollutant: 

PM10; SO2; NO2; O3

Increment: 0.313 ppm 

Odds Ratio (Lower CI, Upper CI); lag: 

Total (non-accidental): 1.003 (0.968-1.039); 0-2 

Respiratory: 1.011 (0.883-1.159); 0-2 

Circulatory: 0.986 (0.914-1.063); 0-2 

Author: Tsai et al. (2006b) 

Period of Study: 

1994-2000 

Location: 

Kaohsiung, Taiwan 

Health Outcome (ICD9): 

Mortality: Total (non-accidental) 

(<800) 

Study Design: Case-crossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: 

27 days old to <1 yr of age 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 8.27 ppm 

Range (Min, Max): (2.26, 17.70) 

Copollutant: 

PM10; SO2; O3; NO2

Increment: 0.31 ppm 

Odds Ratio (Lower CI, Upper CI); lag: 

Postneonatal Mortality 

1.051 (0.304-3.630); 0-2 

Author: Vedal et al. (2003) 

Period of Study: 

1/1994-12/1996 

Location: 

Vancouver, BC, Canada 

Health Outcome (ICD9): 

Mortality: Total (non-accidental) 

(<800); Respiratory (460-519); 

Cardiovascular (390-459) 

Study Design: Time-series 

Statistical Analyses: 

Poisson GAM, LOESS 

Age Groups Analyzed: All ages

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 0.6 (0.2) ppm 

Range (Min, Max): (0.3, 1.9) 

Copollutant correlation: 

Summer: 

PM10:r = 0.71; O3: r = 0.12; 

NO2: r = 0.81; SO2: r = 0.67 

Winter: PM10: r = 0.76; O3: r = -0.65; 

NO2: r = 0.78; SO2: r = 0.83 

The study did not present quantitative results for 

CO. 

March 2009 C-70 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Villeneuve et al. 

(2003) 

Period of Study: 

1986-1999 

Location: 

Vancouver, BC, Canada 

Health Outcome (ICD9): 

Mortality: Non-accidental 

(<800); Cardiovascular 

(401-440); Respiratory 

(460-519); Cancer (140-239) 

Study Design: Time-series 

Statistical Analyses: 

Poisson, natural splines 

Age Groups Analyzed: ≥ 65 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 1.0 ppm 

Range (Min, Max): (0.2, 4.9) 

Copollutant: 

PM2.5; PM10; PM10-2.5; TSP; 

SO4; CO; COH; O3; NO2; SO2

Increment: 1.1 ppb 

% Increase (Lower CI, Upper CI); lag: 

Non-accidental

0.5% (-1.9 to 2.9); 0-2; / -0.3% (-2.2 to 1.7); 0; 

0.6% (-1.3 to 2.6); 1; / 0.5% (-1.4 to 2.5); 2 

Cardiovascular

2.3% (-1.6 to 6.3); 0-2; / 1.6% (-1.5 to 4.7); 0; 

1.2% (-2.0 to 4.5); 1; / 1.5% (-1.5 to 4.4); 2 

Respiratory 

-1.0% (-7.3 to 5.8); 0-2; / 1.3% (-4.4 to 7.3); 0; 

-0.1% (-5.3 to 5.4); 1; -/ 2.8% (-7.8 to 2.6); 2 

Cancer 

-2.8% (-7.6 to 2.4); 0-2; / -3.0% (-6.9 to 1.1); 0; 

-1.6% (-5.6 to 2.4); 1; / -0.5% (-4.7 to 3.8); 2 

Author: Wichmann et al. 

(2000) 

Period of Study: 

9/1995 - 12/1998 

Location: 

Erfurt, Germany 

Health Outcome (ICD9): 

Mortality: Non-accidental 

(<800); Cardiovascular 

(401-440); Respiratory 

(460-519) 

Study Design: Time-series 

Statistical Analyses: 

Poisson GAM, LOESS 

Age Groups Analyzed: 

<70 

70-79 

≥ 80 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 0.6 (0.5) mg/m3

Range (Min, Max): (0.10, 2.50) 

Copollutant correlation: 

PM2.5: r = 0.62; PM10: r = 0.58; 

TSP: r = 0.57; SO2: r = 0.59; 

NO2: r = 0.71 

Increment: 0.5 ppm 

Relative Risk (Lower CI, Upper CI); lag: 

Single-Day Lag 

CO: 1.055 (1.003-1.110); 4 

Polynomial Distributed Lag

Multi-pollutant model: 1.076 (1.017-1.138); 4 

Total Mortality 

CO: 1.012 (0.977-1.049); 0 

Log-transformed: 1.016 (0.962-1.073); 0 

1.004 (0.969-1.040); 1 

Log-transformed: 1.027 (0.973-1.083); 1 

1.020 (0.984-1.057); 2 

Log-transformed: 1.024 (0.970-1.081); 2 

1.019 (0.984-1.055); 3 

Log-transformed: 1.037 (0.984-1.093); 3 

1.029 (0.995-1.063); 4 

Log-transformed: 1.055 (1.003-1.110); 4 

0.997 (0.965-1.031); 5 

Log-transformed: 1.014 (0.966-1.065); 5 

Total Mortality (Season-specific): 

Log-transformed 

Winter: 1.002 (0.922-1.088); 4 

Spring: 1.019 (0.942-1.102); 4 

Summer: 1.085 (1.018-1.156); 4 

Fall: 1.111 (1.039-1.188); 4 

Winter-specific: Log-transformed 

10/95-3/96: 1.046 (0.949-1.153); 4 

10/96-3/97: 1.091 (0.998-1.193); 4 

10/97-3/98: 1.028 (0.966-1.095); 4 

One-pollutant Model: Log-transformed 

CO: 1.055 (1.003-1.110); 4 

Author: Yang et al. (2004a) 

Period of Study: 

1994-1998 

Location: 

Taipei, Taiwan 

Health Outcome (ICD9): 

Mortality: 

Non-accidental (<800); 

Circulatory (390-459); 

Respiratory (460-519) 

Study Design: 

Bi-directional case-crossover 

Statistical Analyses: 

Conditional logistic regression 

Age Groups Analyzed: All ages

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 1.16 ppm 

Range (Min, Max): (0.24, 4.42) 

Copollutant: 

PM10; SO2; NO2; O3

Increment: 0.52 ppm 

Odds Ratio (Lower CI, Upper CI); lag: 

Non-accidental: 1.005 (0.980-1.031); 0-2 

Respiratory: 1.014 (0.925-1.110); 0-2 

Circulatory: 0.996 (0.948-1.046); 0-2 

March 2009 C-71 DRAFT—DO NOT QUOTE OR CITE 
Table C-8. Studies of long-term CO exposure and mortality. 

Study Design Concentrations Effect Estimates (95% CI) 

Author: Lipfert et al. 

(2000b) 

Period of Study: 

1975-1996 

Location: 

32 Veterans Hospitals, 

USA 

Mortality 

Health Outcome (ICD9): 

Non-accidental 

Study Design: Cohort 

Study Population:

~90,000 hypertensive male U.S. 

veterans 

Statistical Analyses: 

Staged regression 

Age Groups Analyzed: NR 

Pollutant: CO 

Averaging Time: 

95th Percentile Annual avg 

Mean (SD) unit: 

1960-1974: 10.82 (5.15) ppm 

1975-1981: 7.64 (2.94) ppm 

1982-1988: 3.42 (0.95) ppm 

1989-1996: 2.36 (0.67) ppm 

Range (Min, Max): 

1960-1974: (0.94, 35.30) 

1975-1981: (0.43, 22.38) 

1982-1988: (0.30, 15.20) 

1989-1996: (0.30, 7.10) 

Copollutants; correlation: 

1960-1974: 

O3: r = 0.004; 

NO2: r = 0.690; 

SO4

2-: r = 0.469 

1975-1981: O3: r = 0.109; 

NO2: r = 0.249; 

SO4

2-: r = -0.155; 

IP SO4

2-: r = 0.356; 

PM2.5: r = 0.634; 

PM10-2.5: r = 0.498; 

PM15: r = 0.626 

1982-1988

O3: r = 0.158; NO2: r = 0.413; SO4

2-: 

r = -0.518; 

IP SO4

2-: r = 0.075; 

PM2.5: r = 0.296; 

PM10-2.5: r = 0.135 

PM15: r = 0.284 

1989-1996

O3: r = 0.397; 

NO2: r = 0.492; 

SO4

2-: r = -0.551 

Increment: NR 

Coefficient: 

Baseline Model

Exposure Period: up to 1975

Single Period: -0.000 

Deaths, 1976-81: 0.0043 

Deaths, 1982-88: -0.0002 

Deaths after 1988: -0.0041 

Exposure Period: 1975-81 

Single Period: -0.013 

Deaths, 1976-81: -0.0170 

Deaths, 1982-88: -0.0217 

Deaths after 1988: -0.0240 

Exposure Period: 1982-88 

Single Period: -0.028 

Deaths, 1976-81: -0.0294 

Deaths, 1982-88: -0.0484 

Deaths after 1988: -0.0424 

Exposure Period: 1989-96 

Single Period: -0.046 

Deaths, 1976-81: -0.0590 

Deaths, 1982-88: -0.0581 

Deaths after 1988: -0.0536 

Final Model w/ Ecological Variables 

Exposure Period: up to 1975 

Single Period: -0.001 

Deaths, 1976-81: 0.0013 

Deaths, 1982-88: -0.0022 

Deaths after 1988: -0.0061 

Exposure Period: 1975-81

Single Period: -0.008 

Deaths, 1976-81: -0.0128 

Deaths, 1982-88: -0.0186 

Deaths after 1988: -0.0203 

Exposure Period: 1982-88

Single Period: -0.009 

Deaths, 1976-81: -0.0007 

Deaths, 1982-88: -0.0246 

Deaths after 1988: -0.0216 

Exposure Period: 1989-96

Single Period: -0.009 

Deaths, 1976-81: -0.0106 

Deaths, 1982-88: -0.0136 

Deaths after 1988: -0.0078 

Notes: Mortality risks based on mean concentrations 

of pollutants less estimated background weighted by 

the number of subjects in each county, but The study 

did not present this value for each pollutant. 

Author: Lipfert and 

Morris (2002) 

Period of Study: 

1960-1997 

Location: 

U.S. counties 

Mortality 

Health Outcome (ICD9): 

Non-accidental 

Study Design: 

Ecological/ cross-sectional 

Statistical Analyses: 

Staged regression 

Age Groups Analyzed: 

15-44 

45-64 

65-74 

75-84 

≥ 85 

Pollutant: CO 

Averaging Time: Annual avg 

Mean (SD) unit: 

1960-1969: 13.81 (8.47) ppm 

1970-1974:9.64 (5.63) ppm 

1979-1981:5.90 (3.54) ppm 

1989-1991:2.69 (1.22) ppm 

1995-1997:1.72 (0.76) ppm 

Range (Min, Max): NR 

Copollutant: 

TSP 

SO4

2- 

SO2

NO2

O3

Increment: NR 

Attributable risk (SE): 

Attributable Risks of mortality (1960-4) 

Peak CO 1960-1964, All locations

Ages 15-44: 0.1299 (0.0341) 

Ages 45-64: 0.0340 (0.0280) 

Ages 65-74: -0.0058 (0.0220) 

Ages 75-84: 0.0121 (0.0188) 

Ages ≥ 85: 0.0374 (0.0225) 

Log Mean: 0.0365 (0.0149) 

Attributable Risks of mortality (1970-4) 

Peak CO 1970-1974, All locations

Ages 15-44: 0.0553 (0.0240) 

Ages 45-64: 0.0181 (0.0148) 

Ages 65-74: -0.0146 (0.0134) 

Ages 75-84: -0.0128 (0.0098) 

Ages ≥ 85: -0.0151 (0.0093) 

Log Mean: 0.0038 (0.0086) 

March 2009 C-72 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Attributable Risks of mortality (1979-81) 

Peak CO 1979-1981, All locations

Ages 15-44: 0.0054 (0.0174) 

Ages 45-64: -0.0060 (0.0141) 

Ages 65-74: -0.0251 (0.0105) 

Ages 75-84: -0.0331 (0.0086) 

Ages ≥ 85: -0.0123 (0.0079) 

Log Mean: -0.0183 (0.0077) 

Peak CO 1970-1974, All locations

Ages 15-44: 0.0218 (0.0200) 

Ages 45-64: 0.0327 (0.0161) 

Ages 65-74: -0.0136 (0.0119) 

Ages 75-84: -0.0250 (0.0105) 

Ages ≥ 85: -0.0202 (0.0085) 

Log Mean: -0.0048 (0.0077) 

Peak CO 1960-1969, All locations

Ages 15-44: 0.0506 (0.0478) 

Ages 45-64: 0.0704 (0.0337) 

Ages 65-74: 0.0100 (0.0211) 

Ages 75-84: -0.0124 (0.0143) 

Ages ≥ 85: 0.0187 (0.0135) 

Log Mean: 0.0084 (0.0149) 

Peak CO 1979-1981, CO 1970-1974 

Ages 15-44: 0.0244 (0.0209) 

Ages 45-64: 0.0016 (0.0181) 

Ages 65-74: -0.0183 (0.0128) 

Ages 75-84: -0.0382 (0.0108) 

Ages ≥ 85: -0.0201 (0.0089) 

Log Mean: -0.0165 (0.0089) 

Peak CO 1979-1981, CO 1960-1969 

Ages 15-44: 0.0748 (0.0679) 

Ages 45-64: 0.0844 (0.0496) 

Ages 65-74: 0.0144 (0.0259) 

Ages 75-84: -0.0158 (0.0168) 

Ages ≥ 85: -0.0073 (0.0170) 

Log Mean: 0.0109 (0.0218) 

Peak CO 1979-1981, CO 1960-1969

Ages 15-44: 0.1191 (0.0709) 

Ages 45-64: 0.1163 (0.0491) 

Ages 65-74: 0.0177 (0.0310) 

Ages 75-84: -0.0120 (0.0212) 

Ages ≥ 85: -0.0040 (0.0202) 

Log Mean: 0.0211 (0.0231) 

Attributable Risks of mortality (1989-91)

Peak CO 1989-1991, All locations 

Ages 15-44: 0.0404 (0.0322) 

Ages 45-64: -0.0262 (0.0162) 

Ages 65-74: -0.0397 (0.0115) 

Ages 75-84: -0.0464 (0.0097) 

Ages ≥ 85: -0.0209 (0.0073) 

Log Mean: -0.0178 (0.0098) 

Peak CO 1979-1981, All locations

Ages 15-44: 0.0522 (0.0227) 

Ages 45-64: -0.0047 (0.0121) 

Ages 65-74: -0.0165 (0.0078) 

Ages 75-84: -0.0268 (0.0068) 

Ages ≥ 85: -0.0027 (0.0055) 

Log Mean: -0.0020 (0.0065) 

Peak CO 1970-1974, All locations

Ages 15-44: 0.0685 (0.0274) 

Ages 45-64: 0.0022 (0.0148) 

Ages 65-74: -0.0051 (0.0091) 

Ages 75-84: -0.0158 (0.0079) 

Ages ≥ 85: -0.0069 (0.0060) 

Log Mean: 0.0038 (0.0077) 

Peak CO 1960-1969, All locations

Ages 15-44: 0.0578 (0.0713) 

Ages 45-64: 0.0583 (0.0347) 

Ages 65-74: 0.0007 (0.0174) 

Ages 75-84: -0.0245 (0.0130) 

Ages ≥ 85: -0.0138 (0.0113) 

Log Mean: 0.0041 (0.0176) 

Attributable Risks of mortality (1995-97) 

Peak CO 1995-1997, All locations

March 2009 C-73 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Ages 15-44: 0.0344 (0.0256) 

Ages 45-64: -0.0203 (0.0198) 

Ages 65-74: -0.0346 (0.0146) 

Ages 75-84: -0.0378 (0.0161) 

Ages ≥ 85: -0.0283 (0.0119) 

Log Mean: -0.0188 (0.0103) 

Peak CO 1989-1991, All locations

Ages 15-44: 0.0289 (0.0248) 

Ages 45-64: -0.0192 (0.0192) 

Ages 65-74: -0.0466 (0.0140) 

Ages 75-84: -0.0497 (0.0147) 

Ages ≥ 85: -0.0301 (0.0108) 

Log Mean: -0.0240 (0.0096) 

Peak CO 1979-1981, All locations

Ages 15-44: 0.0336 (0.0176) 

Ages 45-64: -0.0037 (0.0135) 

Ages 65-74: -0.0298 (0.0096) 

Ages 75-84: -0.0301 (0.0105) 

Ages ≥ 85: -0.0087 (0.0078) 

Log Mean: -0.0094 (0.0071) 

Peak CO 1970-1974, All locations

Ages 15-44: 0.0464 (0.0202) 

Ages 45-64: 0.0202 (0.0155) 

Ages 65-74: -0.0032 (0.0112) 

Ages 75-84: -0.0157 (0.0122) 

Ages ≥ 85: -0.0142 (0.0084) 

Log Mean: 0.0007 (0.0077) 

Peak CO 1960-1969, All locations

Ages 15-44: 0.0679 (0.0441) 

Ages 45-64: 0.0772 (0.0405) 

Ages 65-74: 0.0059 (0.0173) 

Ages 75-84: -0.0085 (0.0213) 

Ages ≥ 85: -0.0158 (0.0162) 

Log Mean: 0.0162 (0.0149) 

Author: Lipfert et al. 

(2006b) 

Period of Study: 

1976-2001 

Location: 

32 Veterans Hospitals, 

USA 

Mortality 

Health Outcome (ICD9): 

Non-accidental 

Study Design: Cohort 

Study Population:

 ~70,000 hypertensive male U.S. 

veterans 

Statistical Analyses: 

Cox proportional-hazards model 

Age Groups Analyzed: NR 

Pollutant: CO 

Averaging Time: 

95th Percentile Annual avg 

Mean (SD) unit: 

1976-1981: 7.6 (2.9) ppm 

1982-1988: 3.4 (9.5) ppm 

1989-1996: 2.4 (0.67) ppm 

1997-2001: 1.6 (5.6) ppm 

Range (Min, Max): NR 

Copollutants correlation: 

ln(VKTA): r = -0.06

Avg NO2: r = 0.43 

Peak O3: r = 0.08 

Peak SO2: r = -0.05 

PM2.5: r = 0.08 

SO4

2-: r = -0.16 

Note: VKTA=annual vehicle-km 

traveled/km2 

Increment: 2 ppm 

Relative risk (Lower CI, Upper CI): 

CO: 1.032 (0.954-1.117) 

CO, lnVKTA: 0.999 (0.923-1.081) 

CO, lnVKTA, NO2: 1.012 (0.923-1.110) 

CO, lnVKTA, NO2+O3: 1.023 (0.939-1.115) 

March 2009 C-74 DRAFT—DO NOT QUOTE OR CITE 
Study Design Concentrations Effect Estimates (95% CI) 

Author: Lipfert et al. 

(2006a) 

Period of Study: 

1997-2002 

Location: 

32 Veterans Hospitals, 

USA 

Mortality 

Health Outcome (ICD9): 

Non-accidental 

Study Design: Cohort 

Study Population:

 ~18,000 hypertensive male U.S. 

veterans 

Statistical Analyses:

Cox proportional-hazards model 

Age Groups Analyzed: NR 

Pollutant: CO 

Averaging Time: 

95th Percentile Annual avg 

Mean (SD) unit: 

1999-2001: 1.63 (0.84) ppm 

1999-2001 (STN sites only): 1.73 

(0.77) 

Range (Min, Max): 

1999-2001: (0.40, 6.7) 

1999-2001 (STN sites only): (0.47, 4.2)

Copollutants correlation: 

ln(traffic density): r = -0.199 

PM2.5: r = 0.040; As: r = 0.148

Cr: r = 0.448; Cu: r = 0.177 

Fe: r = -0.138; Pb: r = 0.420 

Mn: r = 0.357; Ni: r = 0.090 

Se: r = -0.110; V: r = 0.230 

Zn: r = 0.472; OC: r = 0.470 

EC: r = 0.234; SO4

2-: r = -0.123 

NO3-: r = -0.088 

∑PM2.5 comp.: r = 0.133 

NO2: r = 0.418 

Peak O3: r = 0.172 

Peak SO2: r = 0.405 

Increment: NR 

β coefficient (SE); t-statistic: 

-0.00000536 (0.0000324); -0.165 

Author: Jerrett et al. 

(2003) 

Period of Study: 

1982-1989 

Location: 

107 U.S. cities 

Mortality 

Health Outcome (ICD9): 

Cardiovascular; CHD; 

Cerebrovascular disease 

Study Design: Cohort 

Study Population: 65, 893 postmenopausal women 

without previous cardiovascular 

disease 

Statistical Analyses: 

Cox proportional-hazards model 

Age Groups Analyzed: ≥ 30 

Pollutant: CO 

Averaging Time: Annual avg 

Mean (SD) unit: 1.56 ppm 

Range (Min, Max): (0.19, 3.95) 

Copollutants correlation: 

Sulfates: r = -0.07 

NO2

O3

SO2

Increment: 1 ppm 

Relative risk (Lower CI, Upper CI): 

CO: 0.98 (0.92-1.03) 

CO, Sulfates: 0.97 (0.92-1.03) 

Author: Miller et al. 

(2007) 

Period of Study: 

1994-1998 

Location: 

36 U.S. cities 

Mortality 

Health Outcome (ICD9): 

Cardiovascular; CHD; 

Cerebrovascular disease 

Study Design: Cohort 

Study Population: 65, 893 postmenopausal women 

without previous cardiovascular 

disease 

Statistical Analyses: 

Cox proportional-hazards model 

Age Groups Analyzed: 50-79 

Pollutant: CO 

Averaging Time: Annual avg 

Mean (SD) unit: NR 

Range (Min, Max): NR 

Copollutants: 

PM2.5

PM10-2.5

SO2

NO2

O3

Increment: 1 ppm 

Hazard ratio (Lower CI, Upper CI): 

All subjects 

CO: 1.0 (0.81-1.22) 

Only subjects with non-missing exposure data 

CO: 0.92 (0.71-1.21) 

CO, PM2.5, PM10-2.5, SO2, NO2, O3: 0.93 (0.67, 1.30) 

Author: Pope et al. 

(2002) 

Period of Study: 

1980-1998 

Location: 

All 50 States, Washington 

DC, and Puerto Rico 

(ACS-CPS-II) 

Mortality 

Health Outcome (ICD9): 

Total (non-accidental) (<800); Lung 

Cancer (162); Cardiopulmonary (401-

440, 460-519) 

Study Design: 

Prospective cohort 

Statistical Analyses: 

Cox proportional hazards model 

Age Groups Analyzed: ≥ 30 

Pollutant: CO 

Averaging Time: 24-h avg 

Mean (SD) unit: 

1980: 1.7 (0.7) ppm 

1982-1998: 1.1 (0.4) ppm 

Range (Min, Max): NR 

Copollutant: 

PM2.5; PM10; TSP; SO2; NO2; O3

The study presents results for CO graphically. 

March 2009 C-75 DRAFT—DO NOT QUOTE OR CITE 
Annex D. Controlled Human 

Exposure Studies 

Table D-1. Controlled human exposure studies. 

Study Subjects Exposure Findings 

Adir et al. 

(1999) 

15 healthy nonsmoking males; 

22-34 years old 

Subjects exposed to both CO and room air for 3 min 45 sec. 

Actual inhaled CO concentration not provided. Subjects 

exposed to CO concentration required to produce a venous 

COHb of 4-6%. Exposures were separated by 1 month with 

the order of exposure randomly assigned. 

Exposure to CO resulted in a decrease in post-exposure 

exercise duration (Bruce protocol) relative to clean air 

exposure in 13 out of 15 subjects (p=0.0012). Statistically 

significant decreases in metabolic equivalent units (METs) 

were also reported following CO exposure (p = 0.0001). No 

CO-induced changes in heart rate (HR), BP, ECG 

parameters, or myocardial perfusion were observed. 

Bathoorn et al. 

(2007) 

19 former smokers 

with COPD; 

18 males/1 female; 

66-70 years old 

Exposures to both CO and room air conducted for 2 h on 

each of four consecutive days in a randomized crossover 

design. 9 subjects were exposed to 100 ppm and 

10 subjects were exposed to 125 ppm. CO and room air 

exposures were separated by at least 1 week. 

Following the fourth day of exposure, CO inhalation reduced 

sputum eosinophils relative to room air and also increased 

the provocative concentration of methacholine required to 

cause a 20% reduction in FEV1. Neither of these effects 

were shown to reach statistical significance. No changes in 

sputum neutrophils, white blood cell counts or serum Creactive protein (CRP) were observed. Although this study 

appears to demonstrate some evidence of an antiinflammatory effect of CO among subjects with COPD, it 

must be noted that two of these patients experienced 

exacerbations of COPD during or following CO exposure, 

with one patient requiring hospitalization two months after 

exposure (initial symptoms first experienced 1-week postexposure). 

Hanada et al. 

(2003) 

20 healthy males; 

26 ± 1 years old 

15 subjects exposed for 20 min (10 min rest, 5 min handgrip 

exercise, 2 min post-exercise ischemia, 3 min recovery) 

under the following four conditions: (1) normoxia (inspiratory 

O2 fraction 21.4%), (2) hypoxia (inspiratory O2 fraction 

10.3%), (3) CO + normoxia, (4) CO + hyperoxia (inspiratory 

O2 fraction 95.9%). Inhaled CO administered to achieve 

~20% COHb in venous blood. Each of the four conditions 

were separated by 20 min of rest. 5 subjects served as 

controls (four consecutive 20 min periods of normoxia). 

Blood oxygenation, BP, HR and respiratory rate were 

measured during exposure. Muscle sympathetic nerve 

activity (MSNA) and leg hemodynamics were evaluated in 

two subsets of the study group (n = 8 and 7, respectively). 

Arterial oxygen saturation (pulse oximetry) was significantly 

lower, and resting HR and ventilation significantly higher 

during the period of hypoxia compared to the other periods; 

none of these measures were affected by exposure to CO. 

MSNA was shown to increase during hypoxia and CO 

exposure relative to normoxia. Neither hypoxia nor CO was 

found to affect leg blood flow or vasoconstriction. 

16 healthy nonsmoking males; 

18-29 years old 

Subjects exposed on 4 separate days to increasing CO 

concentrations during either upper-body exercise (handcrank) or lower-body exercise (treadmill). Targeted COHb 

levels were initially attained using short term (4-6 min) 

exposures to CO at concentrations between 1,000 and 

3,000 ppm. Chamber exposures were then conducted at CO 

concentrations required to maintain COHb levels of <2% 

(room air), 5% (27 ppm), 10% (55 ppm), 15% (83 ppm), and 

20% (100 ppm). 

At all levels of upper- and lower-body exercise, exposures to 

CO resulted in increases in HR, cardiac output, and cardiac 

contractility relative to clean air exposures. Increases in HR 

reached statistical significance at COHb concentrations ≥

5%, and increases in both cardiac output and cardiac 

contractility reached statistical significance at COHb 

concentrations ≥ 10%. CO exposure during exercise was 

not observed to cause ventricular arrhythmias or affect ECG 

wave shape (no evidence of ST-segment depression) at 

COHb concentrations ≤ 20%. 

Kizakevich 

et al. (2000) 

Mayr et al. 

(2005) 

13 healthy nonsmoking males; 

18-38 years old 

Subjects exposed to both 500 ppm CO and clean air for 1 h, 

with exposures separated by a 6-week period. Immediately 

following exposure, subjects were administered an 

intravenous bolus dose (2 ng/kg) of lipopolysaccharide 

(LPS). 

The average COHb concentration was 7% following the 1 h 

CO exposure. Infusion of LPS significantly increased plasma 

concentrations of TNF-α, CRP, IL-6, and IL-8, with no 

difference in the inflammatory response between clean air 

and CO exposures. 

Morse et al. 

(2008) 

12 healthy nonsmoking males; 

25 ± 2.9 years old 

Exposures conducted on two separate occasions to both 

room air (6 min) and CO. Subjects were exposed to 

3,000 ppm CO until COHb reached 6% (3-8 min exposures).

Leg strength and muscle fatigue were evaluated immediately 

following exposure. CO exposure did not affect muscle 

strength (maximal voluntary isometric contraction), but did 

cause a statistically significant increase in muscle fatigue (p 

<0.05) 

March 2009 D-1 DRAFT—DO NOT QUOTE OR CITE 
Study Subjects Exposure Findings 

Ren et al. 

(2001) 

12 healthy subjects; 

11 nonsmokers; 

9 males / 3 females; 

20-32 years old 

Each subject underwent four different 8 h experimental 

protocols: (1) isocapnic hypoxia (end-tidal PO2 held at 

55 mmHg), (2) withdrawal of 500 mL of venous blood at the 

start of an 8 h period, (3) CO exposure at a concentration 

required to maintain a COHb level of 10%, and (4) a control 

exposure where subjects breathed room air with no 

intervention. 

A statistically significant increase in ventilation was observed 

following hypoxia, but no such increase was found following 

any of the other 3 protocols, including exposure to CO. One 

subject felt faint during the blood withdrawal protocol and did 

not complete the study. 

Resch et al. 

(2005) 

15 healthy nonsmoking males; 

27 ± 4 years old 

Subjects exposed to 500 ppm CO and synthetic air for 1 h at 

rest in a randomized crossover study design. Exposures 

were separated by a period of at least 1 week. 

COHb levels averaged 5.6% after 30 min and 9.4% after 60 

min of exposure. Statistically significant increases in retinal 

blood flow, retinal vessel diameter, and choroidal blood flow 

were observed with CO exposure relative to synthetic air at 

both time points. Exposure to CO did not affect oxygen 

saturation of arterial blood. 

Vesely et al. 

(2004). 

10 healthy nonsmoking males; 

22-52 years old 

Each subject was exposed to CO for 30-45 min to achieve a 

COHb level of 10%. Prior to and following exposure, 

subjects performed hypoxic and hyperoxic rebreathing tests. 

Four subjects were exposed to hypoxic conditions first, 

while six subjects were exposed to hyperoxic conditions 

first, both prior to and following CO exposure. 

Ventilation rate was observed to significantly increase during 

hypoxic rebreathing relative to hyperoxic rebreathing. 

However, exposure to CO had no effect on ventilation under 

either hypoxic or hyperoxic conditions. The authors 

concluded that exposure to low levels of CO does not 

significantly affect chemoreflex sensitivity of the CO2-

induced stimulation of ventilation. 

Zevin et al. 

(2001) 

12 healthy male 

smokers; 

27-47 years old 

Exposures were conducted over 21 consecutive days under 

three different protocols, with each protocol lasting 7 days. 

In one protocol, subjects smoked 20 cigarettes per day, one 

every 45 min. In the other two protocols, every 45 min (20 

times per day) subjects breathed either air or CO 

(~1200 ppm) from a 1 liter bag once per min for 10 min at a 

time. Subjects completed all three protocols, with six 

subjects exposed sequentially to CO, smoking, then air, and 

the other six exposed sequentially to air, smoking, then CO. 

COHb levels were similar during smoking and exposure to 

CO, with average concentrations of 6% and 5%, 

respectively. Blood was drawn on day 4 of each exposure 

and analyzed for CRP, plasma platelet factor 4, and white 

blood cell count. Plasma levels of CRP and platelet factor 4 

were significantly elevated with smoking, but not with CO 

exposure, relative to air control. HR and BP were evaluated 

on day 3 of each protocol. Cigarette smoke, but not CO, was 

observed to significantly increase HR, while no difference in 

BP was observed between any of the three exposures. 

March 2009 D-2 DRAFT—DO NOT QUOTE OR CITE 
Annex E. Toxicological Studies 

Table E-1. Human and animal studies. 

Species / Model Exposure 

Duration Reference CO Concentration Findings 

Acevedo and Ahmed 

(1998) 

Human HO-1 and HO-2 (mRNA and protein) were upregulated in 

pregnant myometrium when compared to non-pregnant 

myometrium. The HO activator hemin inhibited spontaneous 

and oxytocin-induced contractility of the myometrium. 

Progesterone induced HO-1 and HO-2 mRNA expression. 

Human arteries Until equilibrium Approximately 30 μM CO induced endothelium- and NO•-independent relaxation of 

precontracted human ITA and RA graft by partially stimulating 

cGMP production. The mechanism and extent of relaxation 

depended upon the tissue. 

Achouh et al. (2008) 

Ahmed et al. (2000) Human Placental HO-1 was significantly higher at term. HO-1 

significantly attenuated TNFα-dependent cellular damage in 

placental explants. HO-1 was significantly attenuated in preeclampsia pregnancies versus non-pre-eclamptic pregnancies. 

Placental arteries exposed to the HO activator hemin 

demonstrated reduced vascular tension (i.e., placental blood 

vessel relaxation). 

Ahmed et al. (2005) Human The source of CO in term human placental chorionic villi was 

found to be the catalysis of heme by HO and not endogenous 

lipid peroxidation. 

Alexander et al. (2007) Rat 

(Sprague Dawley) 

Adult female 

. Modulation of the HO/CO system in the anterior pituitary of the 

female rat led to altered secretion of gonadotropins and 

prolactin. 

Alexandreanu et al. 

(2002) 

Rat 

(Sprague Dawley) 

The role of the HO/CO system in estrous cyclicity, pregnancy 

and lactation was evaluated using HO inhibitors and stubstrates. 

The HO inhibitor CrMP decreased time in estrous. Administering 

HO-inhibitors to pregnant rodents induced total litter loss. CrMP 

induced decreased litter weight gain during lactation, which the 

authors attribute to maternal milk production or ejection 

problems as cross-fostered pups regained weight lost during 

nursing on CrMP dams. 

Alexandreanu and 

Lawson (2003a) 

Rat 

(Sprague Dawley) 

Adult female 

Modulation of the HO/CO system in the anterior pituitary of the 

female rat led to altered secretion of gonadotropins and 

prolactin. 

Alexandreanu and 

Lawson (2003b) 

Rat 

(Sprague Dawley) 

Adult female ovary 

HO-1 and HO-2 were localized in the ovaries in rats and 

treatment of rat ovaries in vitro with CrMP, an inhibitor of HO, or 

with hemin, a substrate for HO induced steroidogenic changes 

in the ovaries. 

Alonso et al. (2003) Human muscle tissue 

mitochondria 

5 min 50-500 ppm CO significantly reduced muscle mitochondrial cytochrome c

oxidase activity by 20%, 42%, and 55% after treatment with 50, 

100, and 500 ppm CO respectively but did not change the 

activity of three other electron transport proteins. 

Andresen et al. (2006) Rat 

(Long Evans) 

Male 

Mouse 

(C57BL/6J) 

Male 

Cerebral vessels 

1-100 μM CO did not dilate rat or mouse cerebral arteries until 100 μM, 

which is not a physiological concentration. Also, the HO 

inhibitors constricted vessels in a nonspecific manner. 

Antonelli et al. (2006) Rat 

(Wistar) 

GD5-GD20 75 ppm Pups exposed to CO in utero had significant impairment of 

cortical neuronal glutamanergic transmission at PND1 in both 

neurons at rest and in neurons stimulated with depolarization. 

Appleton and Marks 

(2002) 

Human placenta Endogenous CO production by HO in the human placenta was 

regulated by O2 availability. Placental HO activity was directly 

dependent on O2 availability; this does not vary between preeclamptic and normotensive placentas. 

March 2009 E-1 DRAFT—DO NOT QUOTE OR CITE 
Species / Model Exposure 

Duration Reference CO Concentration Findings 

Ashfaq et al. (2003) Human plancenta Placentas were collected from smokers and nonsmokers who 

gave birth to male infants. Premature aging and a statistically 

significant increase in apoptotic cells were seen in placentas 

from smokers vs nonsmokers. 

Astrup (1972) Rabbit 

(strain not identified) 

Continuous CO 

exposure over 

gestation 

90 or 180 ppm Skeletal abnormalities: Three pups (from n = 123) in the 180 

ppm CO group had deformities in their extremities at birth, 

whereas no control and no 90 ppm CO-exposed animals 

manifested with this malformation. 

Bainbridge et al. 

(2002) 

Human placenta Isolated human placenta exposed to solutions containing CO 

demonstrated a concentration-dependent decrease in perfusion 

pressure further demonstrating the role of CO in maintaining 

basal vasculature tone. 

Bainbridge et al. 

(2006) 

Human placenta 6 h Starting concentrations 

of CO: 3.9 μM CO in cell 

culture media (control) 

and CO-exposed groups: 

116 μM, 145 μM, 

181 μM. 

After 3 hours, the CO in 

the culture media was 

3.7 μM (control), and 

CO-exposed cells 

10.2, 12, and 15.9 μM. 

C-section placentas were collected from healthy term 

pregnancies.Villous explants of placentas were cultured under 

hypoxia followed by reoxygenation (H/R). H/R + CO-exposed 

placental tissue had decreased apoptosis and decreased PARP 

(a protein marker of apoptosis) versus control H/R exposed 

cells. Secondary necrosis of the placental tissue post H/R was 

inhibited by CO treatment. 

Bainbridge and Smith 

(2005) 

Human placenta The role of HO in the placenta and during pregnancy are 

reviewed in this article. The conflicting data on the activity, 

localization, and expression of HO in the placentas of preeclamptic women are presented. 

Bamberger et al. 

(2001) 

Human placenta Expression and tissue localization of soluble guanylyl cyclase in 

human placenta using antibody localization were characterized. 

These tools can be used in future studies to elucidate the 

NO•/CO/cGMP pathway. 

Barber et al. (1999) Human HO and NOS did not maintain human uterine quiescence during 

pregnancy. 

Barber et al. (2001) Human placenta Women who had pregnancies with fetal growth restrictions 

(FGR) produced term placenta with significant decreases in HO2 versus healthy pregnancies. 

Baum et al. (2000) Human End-tidal CO measurements in women with pregnancy-induced 

hypertension and pre-eclampsia were significantly lower than in 

normotensive pregnant women. 

Bergeron et al. (1998) Rat 

Brain 

To address the developmental changes of HO staining in the 

brain, immunohistochemical staining for HO-1 was performed 

on the developing rat brain at PND7, PND14, and PND21. HO-1 

staining was most intense at PND7 and by PND21 reached its 

adult pattern of staining localizing to the hippocampus, thalamic 

and hypothalamic nuclei, with virtually no staining of 

endothelium, white matter and cortex. HO-2 is the dominant HO 

isoform in the brain. 

Bing et al. (1995) Rodent . Spatial learning in the Morris water maze was enhanced in 

rodents exposed to the HO inhibitor tin protoporphyrin (Sn-PP). 

Burmester et al. (2000) Human 

and 

Mouse 

Nb had a high oxygen affinity similar to Mb, thus may increase 

the availabilty of O2 to brain tissue. 

Bye et al. (2008) Rat 

(Wistar) 

Female 

100 h/wk for 18 

mo 200 ppm CO-exposed (11-14.7% COHb) rats experienced a 24% decrease in aerobic capacity evidenced by VO2 max deficits. 

Left ventricular cardiomyocytes were longer and wider, had 

increased expression of growth-related proteins, and had 

impaired contraction-relaxation cycles. CO increased cGMP and 

impaired cardiomyocyte Ca2+ handling. No change in BP was 

observed. 

Cagiano et al. (1998) Rat GD0-GD20 75 or 150 ppm At 5 months of age, CO-exposed male offspring showed 

decrements in sexual behavior including an increase in mount to 

intromission latency, a decrease in mount to intromission 

frequency, and a decrease in ejaculation frequency. Basal 

extracellular dopamine concentration in the nucleus accumbens 

was unchanged after CO-exposure. However, when stimulated 

with amphetamine administration, control rats had increased 

release of dopamine that is absent with CO-exposed rats. 

March 2009 E-2 DRAFT—DO NOT QUOTE OR CITE 
Species / Model Exposure 

Duration Reference CO Concentration Findings 

Carmines and 

Rajendran (2008) 

Rat 

(Sprague Dawley) 

GD6-GD19 of 

gestation for 

2 h/day 

600 ppm Significant decreases in birth weight were reported after CO 

exposure. Maternal body weight was unchanged during 

gestation, but corrected terminal body weight (body weight 

minus uterine weight) was significantly elevated in CO-exposed 

dams at term. 

Carratu et al. (1995) Rat 

(Wistar) 

150 ppm Sphingoipid homeostasis was disrupted in male offspring of 

prenatally exposed rats, without a disruption in motor function. 

Carratu et al. (2000) Rat 

(Wistar) 

GD0-GD20 150 ppm Maternal COHb (mean % ± SEM) was 1.9 ±0.04 and 16.02 ± 

0.98 in control and 150 ppm CO-exposed animals, respectively. 

Prenatal CO exposure had no effect on brain sphinganine (SA) 

or sphingosine (SO) levels in male offspring at 90 days of age. 

However, the sciatic nerve had significant increases in SO after 

CO exposure, no changes in SA at 90 days of age. Motor 

activity, which could be affected by changes in myelination, 

showed no differences between CO and control animals at 

90 days of age. 

Carratu et al. (2000) Rat 

(Wistar) 

GD0-GD20 75 or 100 ppm The myelin sheath thickness of the nerve fibers was significantly 

decreased in CO-exposed animals (75 and 150 ppm). Axon 

diameter was not affected by CO exposure. Even though CO 

affected myelination, it did not significantly affect motor activity 

of CO-exposed rats at 40 and 90 days. 

Carraway et al. (2002) Rat model of hypoxic 

pulmonary vascular 

remodeling 

Strain of rat not stated 

3 weeks Hypobaric hypoxia ± 

50 ppm 

CO promoted remodeling and increased pulmonary vascular 

resistance in response to HH. The number of small muscular 

vessels was increased compared with HH alone. Changes in 

cell proliferation, apoptosis, actin and HO-1 gene and protein 

expression correlated with structural changes. COHb levels 

were <0.5% in controls, 1.5-2.8% in the HH treatment group and 

3.5-3.9% in the HH + CO treatment group. 

Cella et al. (2006) Rat 

(Sprague Dawley) 

HO-1 production and HO concentration were shown to be 

regulated by estrogen in the rat uterus. 

Chung et al. (2006) Rat 

(Sprague Dawley) 

Male 

3-6% CO inactivation of Mb does not induce any change in the 

respiration rate, contractile function, or high-energy phosphate 

levels in perfused rat hearts. 

Cronje et al. (2004) Rat 

(Sprague Dawley) 

Male 

240-325 g 

45 min 2,500 ppm Results indicate that tissue and blood [CO] (66-72% COHb) 

dissociate during CO inhalation, but tissue [CO] does not follow 

blood [CO] or 1/pO2 as in the Warburg theory during intake or 

elimination. Tissue [CO] increases later during the resolution 

period and varies significantly among animals and tissues. The 

deviation from the predicted values in the brain is likely due to 

the release of heme and increase in NADPH stimulating 

endogenous CO production by HO. Immediately following exposure, tissue CO concentrations were found to be: 

Blood: 27,500 (800) pmol/mg 

Heart: 800 (300) pmol/mg 

Muscle: 90 (80) pmol/mg 

Brain: 60 (40) pmol/mg 

These values are estimates taken from a graph, with control 

levels in parentheses 

A later report stated that these tissue CO values were too high 

due to a computational error (Piantadosi et al., 2006) 

Cudmore et al. (2007) Human placenta 

Human (HUVEC) 

Mouse 

(HO-1 deficient mouse 

on 129/SV x C57BL/6 

background) 

Pig 

(Porcine aortic 

endothelial cells) 

HUVEC cells, porcine aortic endothelial cells, HO-1 null mice 

and placental villous explants (normotensive and pre-eclamptic 

pregnancies) were used in this study. The HO-1/CO system 

inhibited sFlt-1 and sEng release, two factors upregulated in 

pre-eclampsia. 

D'Amico et al. (2006) Human embryonic 

kidney (HEK293) cells 

0-30 min 20 μM Exogenous CO inhibited respiration in HEK293 cells under 

ambient O2 concentration (21%). Inhibition was enhanced under 

hypoxic conditions. Increased endogenous CO resulting from 

HO-1 overexpression inhibited respiration by 12% and 

cytochrome c oxidase activity by 23%. This effect was enhanced 

under hypoxic conditions. 

Dani et al. (2007) Human 

(neonatal blood) 

CO was lower at birth and 48-72 hours postpartum in infants 

born by elective C-section and higher in vaginally born infants. 

March 2009 E-3 DRAFT—DO NOT QUOTE OR CITE 
Species / Model Exposure 

Duration Reference CO Concentration Findings 

De Salvia et al. (1995) Rat 

(Wistar) 

 GD0-GD20 75 or 150 ppm Animals exposed to the higher dose of CO (150 ppm) in utero 

had significantly impaired acquisition (at 3 and 18 months) and 

reacquisition (at 18 months) of conditioned avoidance behavior. 

Denschlag et al. 

(2004) 

Human Genetic polymorphisms in human HO-1 are linked to idiopathic 

recurrent miscarriages. 

Dewilde et al. (2001) Nb exists as a reversibly hexacoordinated Hb type with a HisFe2+-His binding scheme. Dissociation of the internal ligand by 

O2 or CO is the rate limiting step. 

Dubuis et al. (2002) Rat 

(Wistar) 

Adult female 

250 g 

3 weeks 530 ppm Intrapulmonary resistance artery smooth muscle cells were 

isolated from control and exposed rats. Electrophysiological recordings provided evidence of increased Ca2+-activated K+ 

current consequent to chronic CO exposure. The authors 

speculated that this could in part explain the vasodilatory effect 

of CO in the pulmonary circulation. 

Dubuis et al. (2005) Rat 

(Wistar) 

Male 

21 days 50 ppm CO attenuated PAHT by activating BKCa channels in PA 

myocytes and reduced hemodynamic changes of PAHT. 

Dubuis et al. (2003) Rat 

(Wistar) 

Male 

21 days 50 ppm CO induced relaxation of pulmonary artery rings in normoxic, 

hypoxic, and hypoxic-CO rats and it was not endothelium 

dependent. Chronic hypoxia decreased acute CO sensitivity, 

while CO-hypoxia increased it. K+ channel blocker reduced this 

effect while sGC blocker did not. 

Favory et al. (2006b) Rat 

250-300 g 

Strain not stated 

90 min 250 ppm CO inhibited myocardial permeabilized fiber respiration 

(complex IV), increased coronary perfusion pressure and left 

ventricular developed pressure (LVDP) first derivative and 

decreased the cGMP/cAMP ratio in the heart. These changes 

were maintained over 24-48 h of recovery in air. Cardiac 

function and vasodilatory responses were evaluated at 3-h 

recovery in air. β-adrenergic blockade had no effect on coronary 

perfusion pressure or LVDP first derivative. Total inhibition of 

vasodilator response to acetylcholine and partial inhibition of 

vasodilator response to nitroprusside were observed. An 

increase in myofilament calcium sensitivity was also observed. 

Thus CO promotes abnormalities in mitochondrial respiration, 

coronary vascular relaxation and myocardial contractility. The 

authors speculated that CO may have a detrimental effect on 

heart O2 supply-to-utilization which could potentially lead to 

myocardial hypoxia because of the increased O2 demand 

resulting from increased contractility, the inhibited mitochondrial 

respiration and the reduced coronary blood-flow reserve 

resulting from the decreased vasodilatory capacity. 

COHb was found to be 11% immediately after exposure. COHb 

levels gradually returned to baseline (1.5%) over the next 96 h. 

Fechter and Annau 

(1977) 

Rat 

(Long Evans) 

Continuous CO 

exposure 

throughout 

pregnancy 

150 ppm CO The authors found a 5% significantly decreased birth weights at 

PND1 in gestationally CO-exposed pups versus control animals 

with weight decrements persisting to weaning; lactational cross 

fostering did not ameliorate the CO-dependent reduced growth 

rates. Dams exposed to CO during gestation had COHb over 

gestation of 15% with control dams having less than 1%. 

Decreased birth weight and pre-weaning weight were seen in 

CO-exposed pups despite a lack of weight decrement in 

CO-exposed dams versus air-exposed control dams. 

Fechter et al. (1980) Rat 

(Long Evans) 

Continous CO 

exposure 

throughout 

pregnancy 

150 ppm CO-exposed animals had cardiomegaly at birth (wet heart 

weight) that dissipated by PND4. 

Fechter and Annau 

(1980) 

Rat Continous CO 

exposure 

throughout 

pregnancy 

150 ppm CO-exposed animals had decreased birth weight, impaired 

righting reflexes, impaired negative geotaxis, and delayed 

homing behavior. 

Fechter et al. (1987) Rat 

(Long Evans) 

Continuous CO 

exposure 

throughout 

pregnancy or from 

GD0 to PND10 

75, 150, or 300 ppm The neostriatum of PND21 rat brains were collected and 

showed disrupted development following CO exposure (GD0-

PND10 group, 300 ppm CO). Dopamine levels were also 

significantly elevated in CO-exposed animals (GD0-PND10, 150 

and 300 ppm CO). 

March 2009 E-4 DRAFT—DO NOT QUOTE OR CITE 
Species / Model Exposure 

Duration Reference CO Concentration Findings 

Garofolo et al. (2002) Human infants 

Rat Rat: PND2-PND5 

Human infants who die from SIDS show decreased brainstem 

muscarinic receptor binding versus infants dying from other 

causes. ß-adrenergic modulation of muscarinic receptors in 

developing heart was observed. 

Rodent ß-adrenergic agonists at PND2-PND5 induced 

muscarinic receptor decrement in adenylyl cyclase. 

Rat 

(Wistar) 

Adult male 

Model of right 

ventricular hypertrophy 

secondary to chronic 

hypoxia 

3 weeks of HH ± 

CO in final week 

Or 1 week of CO 

50 ppm CO altered the right ventricular adaptive response to pulmonary 

hypertension which occurs secondarily to chronic hypoxia. Right 

ventricular end-systolic pressure (RVESP) and right ventricular 

shortening fraction (RVSF) were smaller in rats treated with 

CO+HH compared with rats treated with HH alone. CO alone 

had no effect on these measures. Hypobaric hypoxia had no 

effect on left ventricular function while CO+ HH led to an increased left ventricular shortening fraction (LVSF). CO alone led 

to a decrease in LVSF and the mitral E-to-A ratio, indicative of a 

LV filling impairment. Hypobaric hypoxia decreased the relative 

RV perfusion and increased the relative LV perfusion. These 

effects were prevented with concomitant exposure to CO 

although exposure to CO alone had no effects on myocardial 

perfusion. Morphologic and histologic analysis demonstrated RV 

hypertrophy in both the HH group and the CO+HH group and 

fibrotic lesions in the CO+HH group. The authors concluded that 

the 1-week exposure to 50 ppm CO had a deleterious effect on 

RV myocardial perfusion adaptation to chronic hypoxia and 

pressure overload. Although the reduced RV pressure overload 

was beneficial it was counterbalanced by impaired RV perfusion 

and redistribution of perfusion toward the LV. 

Gautier et al. (2007) 

Gaworski et al. (2004) Rat 

(Sprague Dawley) 

2 h/day, 

7 days/week by 

nose-only 

inhalation 

Males: 4 weeks 

prior to and during 

mating; and 

Females: 2 weeks 

prior to mating, 

during mating, 

and through 

weaning to 

PND21 

Cigarette smoke: 

150, 300, or 600 mg/m3 

Total Particulate Matter 

(TPM) 

Maternal exposure to high concentrations of cigarette smoke 

during gestation and lactation reduced pup birth weight and 

retarded neonatal pup growth. Developmental and 

neurobehavioral testing of neonates did not show any 

behavioral effects following parental smoke exposure. 

Rat 

(Sprague Dawley) 

Adult male 

24 h 50 ppm Mild neutrophil accumulation was observed in BALF 

accompanied by increases in BALF MIP-2, protein and LDH. 

Iron status was altered since CO exposure led to an increase in 

BALF iron and ferritin, a decrease in lung non-heme iron and an 

increase in liver non-heme iron. 

Ghio et al. (2008) 

Human bronchial 

epithelial cells 

(BEAS-2B) 

2-24 h 10-100 ppm CO exposure for 24 h led to a dose-dependent decrease in 

cellular non-heme iron, with the effect at 10 ppm statistically 

significant and the effect at 50 ppm maximal. This effect was 

reversible since removing the cells after 2 h of CO and incubating them in air restored non-heme iron concentrations at 24 

h. A dose-dependent decrease in cellular ferritin was observed 

following exposure for 24 h to 50-500 ppm CO. In addition, 

exposure to 50 ppm CO for 20 h blocked iron uptake by cells 

while exposure to 50 ppm CO for 2 h increased iron release 

from cells. Increased protein expression of the iron transporter 

DMT-1 was also noted after 24 h exposure to 50 ppm CO. 

Oxidative stress, mediator release and cell proliferation were 

also decreased by exposure to 50 ppm for 24 h. This effect was 

also reversible upon removal to air. Effects of CO on cell 

proliferation indices were mimicked by with the iron-depleting 

agent deferoxamine. The authors concluded that CO exposure 

altered lung iron homeostasis possibly by initially causing heme 

release from proteins. 

Giustino et al. (1999) Rat 

(Wistar) 

Male and 

Pregnant female 

From GD0-GD20 

of pregnancy 

75 or 150 ppm This study showed that CO (75 and 150 ppm) exposed male 

animals at 40 days of age had a significantly decreased time of 

exploration of novel objects. The 150 ppm CO group showed a 

lack of habituation after the second exposure to a previously 

viewed object. Blood COHb concentrations (mean % ± SEM) on 

GD20 were reported (0 ppm: 1.6 ± 0.1; CO 75 ppm: 7.36 ± 0.2; 

CO 150 ppm: 16.1 ± 0.9). 

March 2009 E-5 DRAFT—DO NOT QUOTE OR CITE 
Species / Model Exposure 

Duration Reference CO Concentration Findings 

Giustino et al. (1993) Rat 

(Wistar) 

GD0-20 75, or 150 ppm CO exposure in utero led to a reversible and dose dependent 

loss of function of splenic macrophages with decreased killing 

ability, decreased phagocytosis, and decreased ROS production 

during the macrophage respiratory burst. 

Glabe et al. (1998) Rat 

(Sprague Dawley) 

Male, 

Myocardium 

PCO = 0 - 107 Torr Increased PCO and increased COMb saturation did not alter high 

energy phosphate signals (ATP, phosphocreatine, Pi). MVO2

began to decline at 87.6% COMb and is likely not due to 

cytochrome c oxidase inhibition. 

Grover et al. (2000) Fetal lamb 

(mixed breed) 

10 min 500 ppm Fetal methoxyhemoglobin (COHb%) ranged from 3.8 ± 0.2 to 

8.1 ± 2.0 at 0 and 500 ppm CO, respectively. Inhaled 0-

500 ppm CO administered to near-term fetal lambs did not 

induce pulmonary vasodilation (main pulmonary artery, left 

pulmonary artery, aorta and left atrium) and the HO inhibitor zinc 

protoporphyrin IX failed to affect baseline vascular tone. 

Hara et al. (2002) Rat 

(Sprague Dawley) 

Male 

40 min 1,000-3,000 ppm CO exposure increased extracellular dopamine levels and 

decreased its major metabolites in a Na+-dependent pathway. 

CO withdrawal and reoxygenation caused levels to return to 

control or overshoot which may suggest an increase in oxidative 

metabolism of CO, mediated by MAO-A. 

Harada et al. (2004) Pig 

(Granulosa cells) 

In this porcine model, HO was able to augment granulosa cell 

apoptosis allowing for proper follicular maturation. 

Hendler and Baum 

(2004) 

Human End-tidal breath CO measurements in pregnant women with 

contractions (term and pre-term) were lower than those 

measurements in non-contracting women. 

Hofmann and Brittain 

(1998) 

Human Partitioning of O2 and CO in the human embryonic Hb is discussed. 

Iheagwara et al. 

(2007) 

Mouse 

(C57Bl6) 

Male 

3 h 1,000 ppm CO significantly reduced cytochrome c oxidase activity and Vmax

but not Km in myocardial mitochondria. Cytochrome c oxidase 

protein levels and heme content were significantly decreased. 

The average COHb level was 61% but no tissue hypoxia was 

observed in the heart. 

Imai et al. (2001) HO-1 transgenic mice 

which specifically overexpress HO-1 in 

smooth muscle 

Transgenic mice had a significant increase in arterial pressure 

and impaired nitrovasodilatory aortic responses. The mice had 

enhanced NO• production and impaired sGC activity. The 

authors speculated that the effect of HO-1 overexpression was 

to suppress vasodilatory responses to NO• in vascular smooth 

muscle. 

Rat 

(Wistar) 

Male 

200-290 g 

60 min 

40-60 min 

1,000-3,000 ppm 

1,000 ppm 

CO poisoning resulted in free NO• in brains as measured by 

electron paramagnetic resonance spectroscopy and in a 10-fold 

increase in nitrotyrosine as measured by immunohistochemical 

staining. These responses were blocked by pretreatment with a 

NOS inhibitor but not by neutrophil depletion. 

Brain nitrotyrosine formation was blocked by platelet depletion 

following 40 min but not 60 min exposure to 1,000 ppm CO. 

Following CO poisoning, myeloperoxidase activity, a measure of 

leukocyte sequestration, was increased in brain microvessels. 

This response was blocked by NOS inhibition but not by platelet 

depletion. Similar effects were noted for xanthine oxidase 

activation. 

The authors concluded that perivascular reactions mediated by 

peroxynitrite are key to CO poisoning effects in brain. 

Ischiropoulos et al. 

(1996) 

Johnson and Johnson 

(2003) 

Rat 

(Sprague Dawley) 

Male 

250-300 g 

0-100 μM CO produced a concentration dependent, endotheliumdependent vasoconstriction in isolated gracilis muscle arterioles, 

evident at 1 μM CO. Pre-treatment with a NOS substrate 

prevented this response while pretreatment with a NOS inhibitor 

converted this response to a vasodilation. The authors 

concluded that exogenous CO was acting through NOS 

inhibition. 

March 2009 E-6 DRAFT—DO NOT QUOTE OR CITE 
Species / Model Exposure 

Duration Reference CO Concentration Findings 

Johnson et al. (2003) Rat 

(Dahl/Rapp saltresistant and saltsensitive model) 

Male 

 High-salt diet increased COHb, BP, and aortic HO-1 protein 

levels in salt-sensitive Dahl rats. Enhanced immunostaining was 

observed for HO-1 but not HO-2 in isolated gracilis muscle 

arterioles. Compared with the low-salt diet, the high-salt diet 

resulted in a smaller vasoconstrictor response when NOS was 

inhibited. Vasoconstriction was exacerbated in arterioles from 

both low salt- and high salt-treated rats using both NOS and HO 

inhibitors. Acetylcholine-induced vasodilation was diminished in 

the high-salt diet group compared with the low-salt diet group. 

This effect was not seen using the HO inhibitor. The high-salt 

diet did not alter endothelium-independent vasodilation. The 

authors concluded that HO-derived CO caused dysfunction of 

the NO• system in salt-sensitive rats treated with a high-salt 

diet. 

Rat 

(Sprague Dawley) 

Male 

Deoxycorticosterone 

acetate (DOCA)-salt 

hypertension model 

WKY rats 

Spontaneously 

hypertensive rats 

(SHR) 

 Salt-sensitive DOCA rats, but not SHR, had elevated aortic HO1 expression and blood COHb levels. Both had elevated mean 

arterial BP compared with controls. Acetylcholine-mediated 

vasodilation of isolated gracilis muscle arterioles was attenuated 

in DOCA rats but not SHR. Pretreatment with a HO inhibitor 

restored the response in DOCA rats. The authors concluded 

that HO-1-derived CO contributes to endothelial dysfunction in 

DOCA but not SHR. 

Johnson et al. (2004) 

Johnson et al. (2006) Rat 

(Zucker) 

Lean and obese 

Male 

 100 μM CO The obese rats had increased CO expiration and mean arterial 

pressure which was decreased by pretreatment with a HO 

inhibitor. No difference was observed in HO-1 protein between 

lean and obese rats. Acetylcholine- and flow-mediated vasodilation of isolated gracilis muscle arterioles was attenuated in 

obese but not lean rats. Pretreatment with a HO inhibitor 

restored the response in obese rats. Exogenous CO prevented 

the restoration of flow-induced dilation by the HO inhibitor. The 

authors concluded that HO-derived CO contributes to 

endothelial dysfunction in this model of metabolic syndrome. 

Katoue et al. (2005) Rat 

(Wistar) 

HO activity in the aorta is significantly increased during 

pregnancy but aortic AVP-dependent vasoconstriction appears 

to be HO/CO independent. 

Katoue et al. (2006) Rat 

(Wistar) 

Pregnancy-induced modulation of calcium mobilization and 

down-regulation of Rho-kinase expression contributed to 

attenuated vasopressin-induced contraction of the rat aorta. 

Khan et al. (2006) Nb overexpressing 

BDF × CD1 mice 

Cerebral and myocardial infarcts were decreased in neuroglobin 

overexpressing mice, decreasing ischemic injury. 

Kim et al. (2005) Primary rat pulmonary 

artery smooth muscle 

cells 

Inbred LEW rat 

(Sprague Dawley) 

200-250 g 

 24 h or 

pretreatment for 

1-2 h followed by 

24 h 

posttreatment 

250 ppm Exposure of cells in culture to 250 ppm CO for 24-h inhibited 

serum-stimulated cell proliferation, increased expression of 

p21Waf1/Cip1 and decreased expression of cyclin A. CO also 

inhibited PDGF-stimulated cell proliferation and reversed the 

inhibitory effect of PDGF on caveolin-1 expression. Genetic 

silencing of caveolin-1 using siRNA, prevented the 

antiproliferative effect of CO. Endogenous CO derived from HO1 in an overexpression system was found to upregulate 

caveolin-1 expression. Effects of CO on caveolin-1 were found 

to be mediated by p38 MAPK and cGMP. Experiments in 

fibroblasts deficient in p38 confirmed a role for p38 in 

CO-mediated inhibition of cellular proliferation via effects on 

p21Waf1/Cip1, cyclin A and caveolin-1. Experiments in fibroblasts 

deficient in caveolin-1 confirmed the role of caveolin-1 in the 

anti-proliferative effects of CO. 

In a model of neointimal injuries induced by balloon injuries in 

intact animals, exposure to CO inhibited neointimal formation 

and increased caveolin-1 expression in the intima and media. 

March 2009 E-7 DRAFT—DO NOT QUOTE OR CITE 
Species / Model Exposure 

Duration Reference CO Concentration Findings 

Kim et al. (2008) Primary rat 

hepatocytes 

Primary mouse 

hepatocytes 

Respiration-deficient 

human Hep3B cells 

10-60 min 250 ppm Exposure of cells in culture to 250 CO for 1 h twice a day 

prevented spontaneous hepatocyte death over 6 days in culture. 

CO also decreased caspase-3 activity. Cell death was determined to be partly due to apoptosis. CO also increased ROS as 

measured by dichlorofluorescein fluorescence in rat 

hepatocytes, mouse hepatocytes and Hep3B cells but not in 

respiration-deficient Hep3B cells indicating that ROS were 

mitochondrial in origin. An increase in mitochondrial oxidized 

glutathione was noted in rat hepatocytes treated with CO for 30 

min. Increased Akt phosphorylation occurred following 10-30 

min CO and was diminished by treatment with antioxidants. CO 

was found to activate NFκB through a PI3K and oxidantdependent pathway. CO mediated spontaneous cell death was 

found to be dependent on ROS and Akt phosphorylation. The 

authors concluded that CO prevents hepatocyte apoptosis 

through redox mechanisms leading to cytoprotection. 

Kinobe et al. (2006) Sheep 

(gravid and non-gravid 

sheep and their near 

term fetuses) 

There were no significant differences in hypoxic adult and 

hypoxic fetal sheep when compared to their normoxic controls. 

Knuckles et al. (2008) Mouse 4 h Diesel emissions: 350 

μg/m3

Diesel exhaust enhanced vasoconstriction in veins but not 

arteries. It was suggested that this is through the uncoupling of 

eNOS. 

Kreiser et al. (2004) Human End tidal CO concentrations were lower in pregnant women with 

gestational hypertension and pre-eclampsia than normotensive women. 

Lash et al. (2003) Human 

Term placental 

chroionic villi from 

healthy or preeclamptic placentas 

Infarcted areas of placenta had decreased HO expression (in 

pre-eclamptic placenta only). 

Li et al. (2008) Mouse 

(ICR [CD-1]) 

Pregnant 

The effect of maternal LPS exposure on fetal liver HO was 

measured. HO-1 was upregulated in fetal livers post-LPS 

exposure and this HO-1 upregulation was attenuated with the 

spin trap agent PBN, pointing to a ROS dependent HO-1 

upregualtion post maternal LPS treatment. 

Rat 

(Sprague Dawley) 

Female 

220-240g 

1 week 

1 week 100 ppm 

and 

1 week 200 pm 

100 ppm 

100-200 ppm 

Endothelin-1 expression increased by 53% and 54% in the left 

and right ventricle respectively during the 2 week exposure and 

by 43% and 12% in the left and right ventricle respectively 

during the 1 week exposure. Right ventricular to body weight 

ratio was increased by 18% and 16% in the 2 week and 1 week 

exposure groups respectively. COHb levels were 23% and 12% 

in the 2 week and 1 week exposure groups respectively. 

Loennechen et al. 

(1999)

Longo et al. (1999) Rat 

(Sprague Dawley) 

Human 

The addition of exogenous CO to isolated human and rat uterine 

tissue failed to induce relaxation of uterine tissue. Isolated rat 

aortic rings and tail artery rings from pregnant dams can be 

relaxed by submersion in exogenous CO solutions. 

Rat 

(Sprague-Dawley) 

Pregnant rats 

exposed to CO 

GD5-GD20 

(Group A) or 

 GD5-GD20 plus 

PND5-PND20 

(Group B); 

Group C (control 

air exposure). 

10 - 18 h/day 

25 ppm CO exposure induced damage to the spiral ganglia neurons and 

inner hair cells with oxidative stress seen in cochlear blood 

vessels. At PND20 groups A and B show vacuolization of 

afferent terminals at the base of the cochlea. At PND3, group A 

shows decreased synapsin-1 staining of the efferent nerve 

terminals. At PND20, groups A and B show decreased neurofilament-IR (staining) in type I spiral ganglia neurons and afferent 

nerve fibers. At PND12 and PND20, group B shows increased 

HO-1 and SOD-1-IR in blood vessels of the stria vasularis; 

group A is similar to controls. From PND3-PND20, there is 

increased iNOS and increased nitrotyrosine-IR in blood vessels 

of the cochlea. 

Lopez et al. (2008) 

Lopez et al. (2003) Rat 

(Sprague-Dawley) 

PND6 to weaning 

(PND19-PND20) 

12 or 25 ppm In the cochlea, atrophy or vacuolization of the nerve cells that 

innervate the inner (not outer) hair cells was seen. Fibers of the 

8th cranial nerve (internal auditory canal of the ARCO animals, 

25 ppm) had distorted myelination and vacuolization of the 

axoplasm. In the organ of corti and spiral ganglion neurons, 

cytochrome c oxidase and NADH-TR were significantly 

decreased in 25 ppm exposure group versus control. 

Expression of the calcium-mediated myosin ATPase in the 

organ of corti and spiral ganglion neurons was significantly decreased in the 25 ppm CO exposure group versus controls. 

March 2009 E-8 DRAFT—DO NOT QUOTE OR CITE 
Species / Model Exposure 

Duration Reference CO Concentration Findings 

Mouse 

(ApoE-/-) 

Male 

High fat diet 

6 h/day, 

7days/week, 

7 weeks 

8, 40, or 60 μg/m3 PM 

whole gasoline exhaust; 

or filtered exhaust with 

gases matching the 60 

μg/m3 concentration. CO 

concentrations were 9, 

50, and 80 ppm 

corresponding to the 8, 

40, and 60 μg/m3 PM 

whole exhaust 

exposures 

Both whole and filtered exhaust increased aortic mRNA 

expression of matrix metalloproteinase-3 (MMP-3), MMP-7, and 

MMP-9, tissue inhibitor of metalloproteinases-2, endothelin-1 

and HO-1 at 60 μg/m3. Aortas also showed increased 

immunostaining for MMP-9 and nitrotyrosine in 60 μg/m3 PM 

whole exhaust and PM-filtered exhaust exposed groups. Aortic 

TBARS, a measure of lipid peroxidation, was also increased in 

all treatment groups. 

Lund et al. (2007) 

Mouse 

(ApoE-/-) 

Male 

High fat diet 

6 h/day, 1 or 

7 days 

Gasoline engine exhaust 

containing 60 μg/m3 PM 

and 80 ppm CO 

Gasoline exhaust exposure increased aortic MMP-2/9 activity at 

1 and 7 days. Protein levels of aortic MMP-9, MMP-2, TMP-2 

and plasma MMP-9 were also increased after 7 days. Lipid 

peroxidation in aorta resulting from gasoline exhaust exposure 

was inhibited by treatment with the antioxidant Tempol, while 

increases in mRNA for ET-1 and MMP-9 in aortas were inhibited 

by treatment with BQ-123, an antagonist of ETA receptor. 

Treatment with BQ-123 also reduced aortic MMP-2/9 activity in 

aortas following gasoline exhaust exposure. The authors 

concluded that ETA receptor pathway is a key mediator of 

gasoline engine exhaust effects in the vasculature. 

Lund et al. (2009) 

Lyall and Myatt (2002) Human Women with pre-eclampsia, produced term placenta with 

significant decreases in HO-2 versus women with healthy 

pregnancies. 

Lyall et al. (2000) Human 

(placentas from 8-19 

weeks pregnancy and 

term placentas) 

The use of a HO inhibitor, ZnPP, increased placental perfusion 

pressure. HO-1 and HO-2 were expressed in the placenta and 

placental bed and vary in expression over the course of 

pregnancy. HO may thus be involved in trophoblast invasion, 

placental function and perfusion pressure. 

Mactutus and Fechter 

(1984) 

Rat 

(Long Evans) 

Continous 

exposure 

 to CO over 

gestation 

150 ppm Acquisition as measured in a two-way conditioned avoidance 

(flashing light warnings followed by mild footshock) test failed to 

improve with age of in utero CO-exposed (150 ppm, dam COHb 

15%) rats (male and female offspring) in contrast to air-exposed 

controls who improved with age/maturation, indicating a failure 

in the associative process of learning. The authors also found 

impairments in reacquisition performance, an index of retention, 

in PND31 rats that had received continuous in utero CO 

exposure. Prenatal CO exposure induced learning and memory 

deficits in male and female offspring. 

McGregor and 

Westcott (1998) 

Guinea pig GD23-GD25 until 

term 

(approximately 

68 days) 

10 h/day 

200 ppm Aberrant respiratory responses (to asphyxia and CO2) of 

offspring with prenatal CO exposure. The authors hypothesized 

this may be related to changes in the brainstem. COHb in 

maternal (8.53 ± 0.6% versus 0.25 ± 0.1%) and fetal blood 

(13.0 ± 0.4% versus 1.6 ± 0.1%) from CO-treated versus 

controls. 

McLaughlin et al. 

(2001) 

Human Various pathologies of pregnancy including IUGR and preeclampsia are associated with significant decreases in placental 

HO activity. The endogenous generation of CO in the placenta 

has been demonstrated in chroionic villi of term placenta. 

McLaughlin et al. 

(2000) 

Human placenta Placental regional localization of HO was explored. The 

chorionic plate, chorionic villi, basal plate and choorio-decidua 

had significantly higher HO activity than the amnion. 

McLaughlin et al. 

(2003) 

Human placenta HO expression in various regions of term placentas was 

explored. Microsomal HO-2 protein content was not different 

between normotensive and milk pre-eclamptic pregnancies. 

There was increased expression of microsomal HO-1 protein in 

chorionic villi and fetal membranes from pre-eclamptic 

pregnancies versus normotensive pregnancies. 

McLean et al. (2000) Human placenta HO activity was highest in the placenta near term. 

March 2009 E-9 DRAFT—DO NOT QUOTE OR CITE 
Species / Model Exposure 

Duration Reference CO Concentration Findings 

Rat 

(Dark Agouti) 

Male 

Model of right ventricle 

hypertrophy secondary 

to chronic hypoxia 

(HH 10 weeks) 

10 weeks 50 ppm alone 

or concomitant with HH 

Hb and hematocrit levels were increased above controls in HH 

rats, CO rats and HH+CO rats, with the increase due to the 

combined treatment significantly higher than the increase due to 

HH. COHb levels were 1.1% in controls, 1.3% in HH rats, 4.7% 

in CO rats and 9.1% in HH plus CO rats. HH treatment 

significantly increased right ventricular (RV) heart weight above 

controls while CO treatment had no effect on any postmortem 

heart weights. Combined treatment with HH+CO resulted in a 

significant increase in left ventricular plus septum (LV+S) weight 

and RV weight compared with HH treatment alone. Echocardiographic left ventricular morphology and mass also showed 

the greatest changes in the HH+CO group. Hemodynamic 

measurements of LV function demonstrated significant effects in 

the HH+CO group for left ventricular end diastolic pressure 

(LVESP), left ventricular maximal first derived pressure 

(+dP/dtLV), and left ventricular work (LVW) compared with 

controls. Hemodynamic measurements of RV function 

demonstrated significant effects in the HH group for right 

ventricular end systolic and diastolic pressure (RVESP, 

RVEDP), right ventricular maximal and minimal first derived 

pressure (+dP/dtRV, , -dP/dtRV) and right ventricular work (RVW). 

CO significantly enhanced the effects of HH on RVEDP and 

significantly diminished the effects of HH on dP/dtRV and RVW. 

The authors concluded that CO intensified the HH-induce RV 

hypertrophy, increased LV weight and induced severe 

hematological responses that could hamper adaptation. 

Melin et al. (2002)

Melin et al. (2005) Rat 

(Dark Agouti) 

Male and female 

Model of right ventricle 

hypertrophy secondary 

to chronic hypoxia (HH, 

10 weeks) 

Half of the animals 

were exercise- trained 

to induce LV 

hypertrophy 

10 weeks 50 ppm alone 

or concomitant with HH 

In untrained animals, combined treatment with HH+CO led to 

increased LV+S and RV weights compared with HH treatment 

alone. HH+CO led to several changes in measured 

echocardiographic parameters including increased anterior and 

posterior wall thickness in diastole (AWTd, PWTd) and to 

increased fraction of shortening. These effects were not seen 

with HH alone. In addition RVEDP was enhanced in HH+CO 

compared with HH alone. HRV components were altered by 

HH+CO but not by CO alone. 

Mereu et al (2000) Rat 

(Wistar) 

GD0-GD20 

continuous CO 

exposure 

150 ppm In utero exposure to CO disrupted hippocampal LTP with 

concomitant HO-2 and nNOS reductions. The authors surmised 

that these changes may be related to the memory deficits seen 

in animals exposed to CO in utero. 

Middendorff et al. 

(2000) 

Human 

Adult males aged 65-

75. Testicular tissue 

from orchiectomy 

Zn protoporphryin (ZnPP) and Hb both significantly reduced 

seminiferous tubular cGMP generation, suggesting a role for CO 

in human testicular tissue. 

Naik and Walker 

(2003) 

Rat 

(Sprague-Dawley) 

Male 

210 μL of CO/100 mL 

of physiological 

saline solution 

Endogenous CO mediated vasorelaxation involved cGMPindependent activation of vascular smooth muscle largeconductance Ca2+-activated K+ channels. However exogenous 

CO vasodilation was cGMP dependent. 

Ndisang et al. (2004) Review of CO and hypertension. CO is a vasorelaxant due to 

activation of the big conductance calcium-activated potassium 

channels and soluble guanylate cyclase/cGMP pathway. 

Developmental stage and tissue type will determine which of 

these pathways plays more of a role in vasorelaxation. 

Neggers and Singh 

(2006) 

Mouse 

(CD-1) 

GD7-GD18 500 ppm Developmental toxicitiy of CO was attenuated by protein 

supplementation, i.e., protein supplemented animals (27%) 

showed a significantly lower incidence of fetal mortality versus 

8% and 16% protein groups. Further, dietary restriction of both 

protein and zinc with CO-exposure to CO during gestation 

increased the incidence of pup mortality and malformations 

including gastroschisis. Zinc supplementation to protein 

deficient diet in CO-exposed mice decreased fetal mortality and 

malformation. 

March 2009 E-10 DRAFT—DO NOT QUOTE OR CITE 
Species / Model Exposure 

Duration Reference CO Concentration Findings 

Newby et al. (2005) Human 

(placental cells 

in culture) 

Term human placental cells were grown in cell culture under 

basal and hypoxic conditions to explore changes in HO 

expression. HO-1 was unchanged in cytotrophoblasts under 

hypoxia, but HO-1 was significantly decreased in hypoxic 

syncytiotrophoblasts. HO-2 was unchanged in either cell type 

with hypoxia. These cell culture data can give insight into what 

cell types might be responsive to hypoxia through the HO/CO 

system in the human placenta. 

Odrcich et al. (1998) Guinea pig Immunohistochemical localization of HO in guinea pig placentae 

showed that HO-1 staining was highest near term (PND62) and 

lesser at term or earlier in pregnancy. HO-1 was localized in the 

advential layer of fetal blood vessels. 

Ozawa et al. (2002) Rat 

(Wistar) 

Adult male 

The role of HO-1 in spermatogenesis was explored. CdCl2

induced testicular HO-1 and reduced HO-2 protein in rats. 

Pretreatment with ZnPPIX attenuated CdCl2-dependent 

apoptosis. Leydig cells use HO-1 derived CO to tirgger 

apoptosis of pre-meiotic germ cells and modulate spermatogenesis under CdCl2 dependent oxidative stress. 

Rat 

(Sprague Dawley) 

Male 

262 ± 30 g 

Isolated hearts 

30 min Buffer saturated with 

0.01 and 0.05% CO 

The ventricular glutathione content, both reduced and oxidized, 

decreased by 76% and 84% 90 min post-exposure to 0.01% 

and 0.05% CO, respectively. Treatment with antioxidants 

partially blocked the decreases in glutathione. Increased 

creatine kinase activity was observed in heart perfusate during 

and after treatment. 

Patel et al. (2003) 

Penney et al. (1983) Rat 

(strain not reported) 

GD17 - GD22 157, 166 or 200 ppm In utero CO exposure induced decreased fetal body weight, 

decreased placental weight, increased wet heart weight at birth, 

and altered cardiac enzymes at birth. 

Piantadosi (2002) Reviews the biochemical activities of CO, including various 

heme protein binding. The review stresses the importance of the 

CO/O2 ratio in determining the physiological effects of CO. 

Piantadosi (2008) Reviews the physiologic responses to exogenous and 

endogenous CO and biochemical effects including the binding 

to heme proteins, the generation of reactive O2 species and 

activation related signaling pathways. 

Piantadosi et al. 

(2006) 

Rat 

(Sprague Dawley) 

Adult male 

1, 3, or 7 days 50 ppm or HH COHb produced COHb levels of 4-5% (controls approximately 

1%) and liver CO concentration of 30-40 pmol/mg wet weight 

(controls approximately 10 pmol/mg wet weight). Both CO and 

HH led to increased expression of hypoxia-sensitive proteins 

HO-1 and HIF-1α and mitochondrial antioxidant protein SOD-2. 

CO caused a greater change in mitochondrial GSH/GSSG than 

HH. Only CO increased mitochondrial 3-nitrotyrosine and 

protein mixed disulfides. Mitochondria isolated from 

CO-exposed rats, but not from HH-exposed rats, showed an 

increase in the calcium sensitivity of the mitochondrial 

permeability transition (MPT). Exposure to CO or HH resulted in 

a loss of the ability of adenine nucleotides to protect mitochondria from MPT. This effect was restored in the presence of a 

strong reductant. The authors conclude that CO causes 

mitochondrial pore stress independently of its hypoxic effects 

Prigge and Hochrainer 

(1977) 

Rat 

(Wistar, SPF) 

GD0-GD20 60, 100, 250, 500 ppm Fetuses were collected by C-section after 21-days exposure. 

Significant increases in fetal heart weight were seen in fetuses 

exposed to CO in all dose groups. Fetal body weight was significantly decreased (NOAEL 125 ppm CO). 

Raub and Benignus 

(2002) 

Reviews the physiology of CO and the effects on the nervous 

system. It is estimated that COHb would have to rise to 15-20% 

before a 10% reduction in any behavioral or visual 

measurement could be observed. 

Richardson et al. 

(2002) 

Human 

Male 

20% COHb 20% COHb did not influence O2Mb binding indicated by 

unaltered deoxy-myoblobin signal. Resting skeletal muscle 

metabolic rate was unaffected by 20% COHb. VO2 max was decreased. No decrement in intracellular PO2 was found. 20% 

COHb altered exercising bioenergetics, pH, PCr, and ATP 

levels. 

Ryter et al. (2006) Reviews the basic science of exogenous and endogenous CO 

including HO-1 regulation. It also reviews some therapeutic 

applications for CO. 

March 2009 E-11 DRAFT—DO NOT QUOTE OR CITE 
Species / Model Exposure 

Duration Reference CO Concentration Findings 

Sartiani et al. (2004) Rat 

(Wistar) 

In utero inhalation

exposure 

150 ppm At 4 weeks of age, the action potential duration APD of isolated 

cardiac myocytes from CO-exposed animals failed to shorten or 

mature as did the APD of control animals. Further, the two ion 

conduction channels Ito (transient outward current, K+-mediated) 

and ICa,L (L-type Ca2+ current), which largely control the rat APD, 

were significantly different from control animals after CO 

exposure at 4 weeks of age. All of these CO-dependent 

changes were no longer different from controls at 8 weeks of 

age, showing a delayed maturation. 

Mouse 

(CF-1) 

Rabbit 

(New Zealand) 

7 or 24-h/day 

GD6-GD15 (Mice)

GD6-GD18 

(Rabbits) 

250 ppm In mice there was a significant increase in number of skeletal 

abnormalities in CO-exposed mice. Decreased birth weight in 

mice exposed to 24 h/day CO versus control. Increased birth 

weight in mice exposed to 7 h/day CO versus controls. No 

similar effects were seen in rabbits. 

Schwetz et al. (1979) 

Singh et al. (1992) Mouse 

(CD-1) 

GD8-GD18 65, 125, or 250 ppm CO exposure concomitant with a low protein diet exacerbated 

the percent of skeletal malformations in offspring. The percent of 

dead, resorbed, or grossly malformed fetuses was directly 

related to CO concentration and inversely related to maternal 

dietary protein levels. CO and maternal dietary protein 

restriction have a synergistic effect on offspring survival and an 

additive effect on malformations. 

Singh (2006) Mouse 

(CD-1) 

6 h/day during 

the first 

2 weeks 

of pregnancy 

65 or 125 ppm Modulating dam protein intake during in utero CO exposure 

altered pup mortality. 

Sitdikova et al. (2007) Frog neuro-muscular 

junctions 

20 min 96 μM CO induced acetylcholine release, without effects on the presynaptic action potential or functional properties of post-synaptic 

receptors in frog neuro-muscular preparations. 

Song et al. (2002) Human 

Primary human aortic 

smooth muscle cells 

0-48 h 10-250 ppm CO inhibited SMC proliferation at concentrations from 50-

500 ppm. The cell cycle arrest occurred at the G0/G1 phase of 

the cell cycle. CO increased expression of the cell cycle inhibitor 

p21Cip1 at 1 h and decreased expression of cyclin D1 over 24-48 

h. The antiproliferative actions of CO were found to be 

independent of sGC, but instead exerted through the inhibition 

of ERK MAPK activation since 15 min exposure to 250 ppm CO 

blocked serum-mediated ERK phosphorylation. 

Sorhaug et al. (2006) Rat 

(Wistar) 

Female 

169 ± 4.5 g 

20 h/day, 

x 5 days/week, 

x 72 weeks 

200 ppm COHb was 14.7% in CO-exposed animals and 0.3% in controls. 

Total Hb was also increased in following CO exposure. CO 

caused no changes in lung morphology or pulmonary 

hypertenstion. No atherosclerotic lesions were found in aorta or 

femoral artery. Weight increases of 20% and 14% were 

observed in the right ventricle and left ventricle plus septum, 

respectively, indicative of ventricular hypertrophy following 

chronic CO exposure. 

Stockard-Sullivan et al. 

(2003) 

Rat 

(Sprague-Dawley) 

22 h/day, PND6-

PND22 

12, 25, 50, or 100 ppm Using functional OAE testing and ABR showed that with 

perinatal CO exposure (50 and 100 ppm CO), there were 

significant decrements in OAE in CO-exposed animals. ABR 

showed no functional deficits with CO exposure. Using another 

otoacoustic test revealed significant attenuation of the AP of the 

8th cranial nerve with CO exposure (12, 25, and 50 ppm CO) 

versus controls at PND22. 

March 2009 E-12 DRAFT—DO NOT QUOTE OR CITE 
Species / Model Exposure 

Duration Reference CO Concentration Findings 

Suliman et al. (2007) Mouse 

(C57BL/6 wild-type and 

eNOS deficient) 

Male 

Rat 

(embryonic 

cardiomyocytes H9c2 

cells) 

1 h 50-1,250 ppm 

Or HH 

Or 100 mM 

dichloromethane 

1-h exposure of mice to 1,250 ppm CO increased cardiac 

mitochondrial content of all 5 respiratory complexes 24 h later. 

The volume density of interfibrillar mitochondria was increased 

by 30% after 24 h demonstrating that CO caused cardiac 

mitochondrial biogenesis. The CO concentration in heart 

increased from 9 pmol/mg to 50-150 pmol/mg in mice exposed 

to 50-1,250 ppm CO for 1 h. These levels declined to baseline 

by 6 h. Peroxisome proliferator-activated receptor gamma 

coactivator 1 alpha (PGC-1α) expression was increased 6 h 

following exposure to 50-1,250 ppm CO. Expression of DNA 

polymerase and mitochondrial transcription factor A (TFAM) was 

increased 6 and 24 h after exposure, while mitochondrial DNA 

was increased 2-3 fold 24 h after exposure. CO activated gene 

expression of these proteins involved in cardiac mitochondrial 

biogenesis beginning at 2 h post exposure for PGC-1α, nuclear 

respiratory factors 1 and 2 (NRF-1 and -2) and at 6 h 

postexposure for TFAM. These effects were independent of 

NOS and not seen with HH. CO exposure resulted in 

phosphorylation of p38 MAPK and Akt at 2 and 6 h postexposure to 1,250 ppm CO for 1 h. Inhibition of p38 activation 

failed to inhibit the CO-mediated increase in cardiac 

mitochondrial biogenesis. 

In cell culture experiments, CO derived from dichloromethane 

metabolism resulted in increased cGMP, protein levels of SOD2, 

TFAM, NRF-1, NRF-2, PGC-1,mitochondrial ROS, Akt 

phosphorylation, and mitochondrial DNA. Inhibition of GC or 

PI3K/Akt, but not p38, blocked the responses to CO. A role for 

mitochondrial H2O2 in Akt regulation was demonstrated. 

Mitochondrial H2O2 and the PI3K/Akt pathway were important 

mediators of TFAM expression. 

The authors concluded that CO exposure increased 

mitochondrial ROS which promoted mitochondrial biogenesis in 

the heart. 

Sun et al. (2001) Mouse 

Neuronal cultures 

prepared from the 

cerebral hemispheres 

of 16-day Charles River 

CD1 mouse embryos 

Nb expression was increased by neuronal hypoxia in vitro and 

focal cerebral ischemia in vivo. Inhibiting Nb reduced neuronal 

survival after hypoxia whereas Nb overexpression enhanced 

neuronal survival. 

Tattoli et al. (1999) Rat 

(Wistar) 

Male and 

Pregnant female 

PND1-PND10 75 and 150 ppm Cognitive function was assessed in rats after postnatal CO 

exposure at 3 and 18 months of age. Postnatal CO exposure 

did not affect the acquisition and reacquisition of an active 

avoidance task. This is different from previous findings by the 

same laboratory indicating that in utero exposure to CO (75 and 

150 ppm) induced long-lasting learning and memory deficits. 

Human 

Myometrium tissue 

obtained from gravid 

[pre-term (25-34 weeks 

gestation), term not in 

labor or term in labor] 

and non-gravid women 

cGMP was monitored in various myometrial tissues. cGMP was 

significantly higher than that from nonpregnant tissue and 

decreased at term, especially in tissue from laboring women. 

Telfer et al. (2001) 

Teran et al. (2005) Rat 

(Dahl/Rapp 

salt-sensitive rats) 

Male 

 100 μM A high salt diet for 1-4 weeks resulted in increased aortic HO-1 

protein expression, an increase in mean arterial pressure and 

time-dependent inhibition of flow- and acetylcholine-mediated 

vasodilation in isolated gracilis muscle arterioles. A smaller 

degree of inhibition of acetylcholine-mediated vasodilation was 

observed with a low salt diet for 1-4 weeks. Pretreatment with a 

HO inhibitor restored these responses but this effect was 

reversed in the presence of exogenous CO. Mean arterial 

pressure was decreased in intact animals fed a high salt diet for 

4 weeks and then treated with a HO inhibitor. The authors 

concluded that the HO-derived CO contributed to the 

development of hypertension and the impairment of 

endothelium-dependent vasodilator responses in this model. 

March 2009 E-13 DRAFT—DO NOT QUOTE OR CITE 
Species / Model Exposure 

Duration Reference CO Concentration Findings 

Rat 

(Wistar) 

Male 

Isolated blood cells 

1 h 

OR 

>1 h 

30 min 

1,000 ppm 

OR 

1,000-3,000 and 

higher ppm 

0.5 mL of pure CO 

CO poisoning inhibited B2 integrin-dependent PMN adherence 

in heparinized blood obtained from rats immediately after exposure. Adherence was restored when platelet number was 

decreased. Adherence was also decreased when PMN from 

control animals were incubated with platelets from poisoned 

animals. Adherence of activated PMN was reduced in the 

presence of SOD and enhanced by NOS inhibition. Platelet 

production of NO• was significantly greater, while platelet NOS 

activity was significantly inhibited after poisoning. 

When whole blood or platelet-rich plasma was incubated with 

CO, PMN adherence was inhibited. 

The authors concluded that PMN B2 integrin activity was 

inhibited by CO-dependent release of NO• from the platelets 

into the blood. 

Thom et al. (1994) 

Thom and 

Ischiropoulos (1997) 

Rat 

(Wistar) 

Male 

200-290 g 

Platelet-rich plasma 

from rats was used as 

the source of platelets 

Bovine pulmonary 

artery endothelial cells 

1 h 

30 min or 2 h 

1 h 

20-1,000 ppm 

10-20 ppm 

10-100 ppm 

Platelets isolated from rats exposed to 20-1,000 ppm CO for 1-h 

released NO• in a dose-dependent manner. COHb levels were 

0.7% in controls, and 3.2%, 7.8% and 51.0% in 20, 100 and 

1,000 ppm exposure groups respectively. 

Isolated platelets released NO• when incubated for 30 min with 

20-100 ppm CO. NOS activity was not enhanced by 100 ppm 

CO. Platelets released NO• in response to 10-100 ppm CO after 

30 min pretreatment with a NOS inhibitor, suggesting that CO 

displaces NO• from heme-binding sites. Longer incubations (2 

h) with the NOS inhibitor led to a diminished response to 

100 ppm CO. There appears to be a discrepancy in the results 

depending on how NO• was measured (electrode versus Greiss 

reaction). 

Endothelial cells released NO• in response to 20-100 ppm CO. 

NOS inhibition blocked the response to 100 ppm CO. CO was 

found not to affect arginine transport or NOS activity in 

endothelial cells. Exposure to 40-100 ppm CO resulted in the 

release of short-lived oxidants. This response was blocked by 

NOS inhibition. Lysates from cells exposed to 50 and 100 ppm 

CO had increased nitrotyrosine content. This response was 

blocked by NOS inhibition. Cellular reduced sulfhydryls were not 

decreased by 100 ppm CO. Dihydrorhodamine 123 oxidation, a 

measure of peroxynitrite formation, was increased by exposure 

to 100 ppm CO. This effect was blocked by NOS inhibition. 

Cytotoxicity of CO was evaluated by the release of 51chromium. 

Cytotoxicity was evident 4 h following a 2-h incubation with 

100 ppm CO, but not immediately after exposure. This response 

was not blocked by NOS inhibition, although NOS inhibition had 

protective effects under conditions of continuous CO exposure 

of 4 h. Exposure to 20 and 100 ppm CO for 2 h led to the loss of 

membrane integrity, measured by ethidium homodimer-1 

staining, 18 h later. 

Results demonstrate that 10-20 ppm CO released NO• from 

platelets and endothelial cells in vitro. Platelets from rats that inhaled 20 ppm CO also released NO• in vitro. The authors 

suggested that CO-mediated NO• release from platelets and 

endothelial cells resulted from disrupted intracellular scavenging 

for NO•. They also suggested that peroxynitrite may have been 

generated in response to CO. 

March 2009 E-14 DRAFT—DO NOT QUOTE OR CITE 
Species / Model Exposure 

Duration Reference CO Concentration Findings 

Thom et al. (1997) Bovine pulmonary 

artery endothelial cells 

30 min-4 h 10-100 ppm (11-110 nM) 1-h exposure to 111-110 nM CO led to a dose-dependent 

increase in NO• release, as measured by nitrite+nitrate. Significance was achieved at 22 nM (corresponding to an interstitial 

partial pressure of 20 ppm and a blood COHb level of 7%). NOS 

inhibition blocked the response to 110 nM CO. A dosedependent increase in cellular nitrotyrosine was also observed 

following a 2-h exposure to CO, with significance achieved at 55 

nM CO. NOS inhibition blacked the response to 110 nM. CO 

exposure failed to decrease the concentration of reduced 

sulfhydryls, but did result in the extracellular release of a shortlived oxidant species which was blocked by NOS inhibition. 

Dihydrorhodamine oxidation, a measure of peroxynitrite 

formation, occurred in response to 110 nM CO, an effect which 

blocked by NOS inhibition. Cytotoxicity of CO was evaluated by 

the release of 51chromium. Cytotoxicity was evident 4 h following 

a 2-h incubation with 110 nM CO, but not immediately after 

exposure. This response was not blocked by NOS inhibition, although NOS inhibition had protective effects under conditions of 

continuous CO exposure of 4 h. Exposure to 110 nM CO for 2 h 

led to the loss of membrane integrity, measured by ethidium 

homodimer-1 staining, 18 h later. This response was blocked by 

NOS inhibition. Exposure to 110 nM CO had no effect on O2

consumption, production of intracellular H2O2 or cellular redox 

activity. Exposure to 110 nM did not alter arginine transport or 

NOS activity. NO• release from cells which had been pre-treated 

with a NOS inhibitor and then exposed briefly to 5% CO was 

measured using a NO-selective electrode suggesting that CO 

competed with intracellular binding sites of NO•. 

The authors concluded that endothelial cells release NO• and 

NO•-derived oxidants in response to CO. A delayed cell death 

occurred following exposures to 22 nM and higher 

concentrations of CO.

Thom, et al. (1999b) Rat 

(Wistar) 

Male 

200-290 g 

Some rats fed a high 

cholesterol diet 

1 h 50-1,000 ppm Nitrotyrosine immunoreactivity was found in aortic intima in rats 

exposed to CO for 1 h but not in controls. Nitrotyrosine content 

was quantitated and found to be increased in a dose-dependent 

manner following 1-h exposure to 50-1,000 ppm CO. The effect 

was significant at 50 ppm but the COHb content measured 

immediately after exposure was not different than controls. 

Platelet and neutrophil depletion did not alter nitrotyrosine 

content following CO exposure. Leukocyte adherence to the 

aorta occurred 18 h, but not immediately, after a 1-h exposure to 

100 ppm CO. This effect was blocked by NOS inhibition. The 

influx of albumin from the microvasculature into skeletal muscle 

increased during the 3 h after exposure to 100 ppm CO but was 

not seen 18 h later. This effect was blocked by NOS inhibition. 

Rats fed a high cholesterol diet and exposed to 100 ppm CO for 

1 h had increased aortic nitrotyrosine content which was not 

different than that in CO-exposed rats fed the standard diet. 

However, rats on the high cholesterol diet had a 6-fold increase 

in LDL oxidation immediately after 1-h exposure to 100 ppm CO. 

This effect was not blocked by NOS inhibition. 

The authors concluded that CO can alter vascular status by 

several mechanisms linked to NO•-derived oxidants. 

March 2009 E-15 DRAFT—DO NOT QUOTE OR CITE 
Species / Model Exposure 

Duration Reference CO Concentration Findings 

Thom et al. (1999a) Rat 

(Wistar) 

Male 

200-290 g 

 1 h 50-1,000 ppm Leakage of albumin into lung parenchyma occurred 18 h, but 

not at earlier time points, after rats were exposed to 100 ppm 

CO for 1 h. This response was also observed using 50 and 

1,000 ppm but not 20 ppm CO. Leakage resolved by 48 h. 

Furthermore no leakage occurred when rats which were 

exposed to 100 ppm CO were pretreated with a NOS inhibitor. 

COHb levels were 0.9% in controls and 4.8%, 10.6% and 53.7% 

following 1-h exposure to 50, 100 and 1,000 ppm CO, 

respectively. Elevated free NO•, determined by EPR, was 

observed in lungs of rats exposed to 100 ppm CO for 1 h. This 

effect was blocked when rats were pretreated with a NOS 

inhibitor. Lung H2O2 was elevated by exposure to 100 ppm CO 

for 1 h and this effect was blocked when rats were pretreated 

with a NOS inhibitor. Elevated nitrotyrosine content was 

observed in lung homogenates 2-4 h following 1-h exposure of 

rats to 100 ppm CO. This effect was also blocked by 

pretreatment with a NOS inhibitor. No leukocyte sequestration 

was observed in lungs18 h following exposure to 100 ppm CO. 

CO-induced lung leak was not affected by neutrophil depletion. 

The authors concluded that CO causes lung vascular injury 

which is dependent on NO•. 

Thom et al. (2000) Bovine pulmonary 

artery endothelial cells 

40 min-2 h 11-110 nM 

(10-100 ppm) 

Increased uptake of ethidium homodimer-1, a measure of 

decreased membrane integrity and cell death, was observed in 

endothelial cells 18 h after exposure to 110 nM for 60-120 min. 

Exposures of 20-40 nM were ineffective in this regard. Ethidium 

uptake was also increased by 2-h exposure to 88 nM CO. 

Preincubation for 2 h with an inhibitor of eNOS, an antioxidant, 

and an inhibitor of peroxynitrite reactions blocked the 

CO-mediated cell death. Morphological changes in cells were 

observed 2 h following a 2-h exposure to 110 nM CO. Cell death 

induced by 110 nM CO was also blocked by inhibition of protein 

synthesis and inhibition of caspase-1 but of caspase-3. 

Caspase-1 activity was increased following 2-h exposure to 110 

nM CO; this effect was blocked by inhibiting eNOS. Preexposure of cells to 11 nM CO for 40 min followed by a 3-h 

incubation period resulted in an increased level of MnSOD and 

protection against cell death 18 h following a 2-h exposure to 

110 nM CO. The authors concluded that exposure to 11 nM CO 

led to an adaptive response which protected cells from injury 

and apoptosis resulting from NO•-derived oxidants. 

Thom et al. (2001a) Rat Until lost 

consciousness 

1,000-3,000 ppm Neutrophils sequestration was observed in the brain vessels of 

rats exposed to high dose CO. CO also led to increased 

nitrotyrosine formation in the brain vessels. These events were 

blocked by pretreatment with a peroxynitrite scavenger or a PAF 

receptor antagonist. 

Thom et al. (2006) Human 

Rat (Wistar), male 

Mouse (C57B6J, 

MPO-deficient) 

Blood samples and 

brain tissue 

1 h Humans: acute CO 

poisoning 

Rats and mice: 

1,000-3,000 ppm 

In humans, COHb was 20-30.5%. Increased cell surface 

expression of CD18 and PAC1 was observed in neutrophils 

from people with CO poisoning. Increased surface-bound 

myeloperoxidase (MPO, indicative of neutrophil degranulation), 

increased plasma MPO and more numerous platelet-neutrophil 

aggregates were also observed. 

Similar changes were observed in blood of CO-poisoned rats. 

Platelet depletion, inhibition of NOS and inhibition of platelet 

integrin-dependent adhesion blocked these responses. Brains 

from poisoned rats had significant elevations in MPO which 

could reflect either an increase number of neutrophils or an 

increase in neutrophil degranulation. Perivascular MPO and 

nitrotyrosine were CO-localized in brain. CO poisoning also 

resulted in altered brain myelin basic protein. 

Similar changes were observed in blood of CO-poisoned mice. 

MPO deficiency blocked the CO-mediated alteration in brain 

myelin basic protein. 

The authors concluded that exposure to CO triggers 

intravascular interactions between platelets and neutrophils that 

lead to neutrophil degranulation in experimental animals and 

people with CO poisoning. 

March 2009 E-16 DRAFT—DO NOT QUOTE OR CITE 
Species / Model Exposure 

Duration Reference CO Concentration Findings 

Thorup et al. (1999) Rat 

(Sprague Dawley) 

Males 

200-250 g 

0.01-10 μM Perfusion of Isolated rat renal resistance arteries with 

CO-containing buffer (0.001-10 μM) led to the biphasic release 

of NO•, peaking at 100 nM and declining to undetectable 

responses at 10 μM. Sequential pulses of 100 nM resulted in a 

blunting of NO• release with consecutive pulses, consistent with 

a depletion of intracellular NO• stores. NO• release was 

dependent on arginine concentrations and was inhibited by 

pretreatment with a NOS inhibitor. Perfusion with 100 nM CO 

blocked carbachol-dependent NO• release from vessels. 

Rats were treated with a HO-1 inducer and renal resistance 

arteries were isolated 12 h later. Carbachol-induced NO•

release was smaller in the HO-1 induced rats compared with 

controls suggesting that endogenous CO has a similar effect as 

100 nM exogenous CO. This effect was reversed in the 

presence of excess arginine. 

Vasodilation was measured in blood-perfused afferent arterioles 

perfused with CO in solution. A biphasic vasodilatory response 

was observed as well as a blunted muscarinic vasorelaxation. 

CO (0.1-10 μM) suppressed the release of NO• from purified 

recombinant eNOS in solution. 

The authors concluded that low levels of CO may release NO•

and elicit vasorelaxation and modulate basal vascular tone while 

higher levels of CO may inhibit eNOS and NO• generation. 

Tolcos et al. (2000b) Guinea pig 10 h/day over the 

last 60% of 

gestation 

200 ppm Fetal and maternal COHb were 13% and 8.5% respectively. 

Neurotransmitter systems were affected after CO exposure. The 

catecholaminergic system of the brainstem displayed significant 

decreases in immunoreactivity for tyrosine hydroxylase (TH), 

which was likely due to decreased cell number in specific 

medullar regions. The cholinergic system was also affected by 

prenatal CO exposure with significant increases in ChAT 

immunoreactivity of the medulla and no changes in muscarinic 

acetylcholine receptor. 

Tolcos et al. (2000a) Guinea pig 10 h/day for the 

last 60% of 

gestation 

200 ppm Brains were collected at 1 and 8 weeks of age. These data 

showed that CO exposure in utero sensitized the brain to hyperthermia at PND4 leading to generation of necrotic lesions in the 

brain and changes in neurotransmitter levels. 

Tschugguel et al. 

(2001) 

Human 

HUVEC 

CO was generated by primary endothelial cells from human 

umbilical veins and uterine arteries after exogenous 17-ß estradiol administration. 

Vallone et al. (2004) Mouse protein The authors present the X-ray structure of CO-bound ferrous 

murine Nb. When CO binds, the heme group slides deeper into 

the protein crevice. 

Vreman et al. (2000) Human umbilical cord 

(artery and vein) 

Rat 

Aorta, vena cavae, liver 

and heart 

HO activity was quantified in human umbilical cord and in the rat 

vasculature (aorta and vena cavae). Human umbilical artery and 

vein HO activity were equal. The rat aorta and vena cavae 

produced equal amounts of HO activity (wet weight/g tissue) but 

generated 3x greater HO than the heart and 0.2x of the liver. 

HO activity in rat vasculature was 3x that of the human cord 

tissues. Use of the HO inhibitor CrMP effectively blocked HO 

activity in the rat liver and heart but was less effective at 

blocking HO activity in the human umbilical cord or the rat 

vasulature (only 50% effective). The activity of HO in the 

umbilical vessels may provide a role for CO in control of 

vasculature tone during pregnancy. 

March 2009 E-17 DRAFT—DO NOT QUOTE OR CITE 
Species / Model Exposure 

Duration Reference CO Concentration Findings 

Vreman et al. (2005) Mouse 

(BALB/c) 

30 min 500 ppm 

OR 

Heme arginate 

30 μmol/kg body weight 

i.v. 

Following CO exposure, COHb levels were 28%. Tissue 

concentrations of CO were as follows with control levels in 

parenthesis. 

Blood: 2648 ± 400 (45) pmol/mg 

Heart: 100 ±18 (6) pmol/mg 

Muscle: 14 ± 1 (10) pmol/mg 

Brain: 18 ± 4 (2) pmol/mg 

Kidney: 120 ± 12 (7) pmol/mg 

Spleen: 229 ± 55 (6) pmol/mg 

Liver: 115 ± 31 (5) pmol/mg 

Lung: 250 ± 2 (3) pmol/mg 

Intestine: 9 ± 7 (4) pmol/mg 

Testes: 6 ± 3 (2) pmol/mg 

CO concentration relative to 100% blood: 

Lung: 9.4%, 

Spleen: 8.6% 

Kidney: 4.5%, 

Liver: 4.3%, 

Heart: 3.8%, 

Brain: 0.7%, 

Muscle: 0.5%, 

Intestine: 0.3%, 

Testes: 0.2% 

Injection of heme arginate resulted in a 3-fold increase in CO 

excretion reaching a maximaum at 60 min. Animals were sacrificed at 90 min. COHb levels were 0.9%. Tissue concentrations 

of CO were as follows with control levels in parenthesis. 

Blood: 88 ±10 (45) pmol/mg 

Heart: 14 ± 3 (6) pmol/mg 

Muscle: 7 ± 1 (10) pmol/mg 

Brain: 2 ± 0 (2) pmol/mg 

Kidney: 7 ± 2 (7) pmol/mg 

Spleen: 11 ± 1 (6) pmol/mg 

Liver: 8 ± 3 (5) pmol/mg 

Lung: 8 ± 3 (3) pmol/mg 

Intestine: 3 ± 1 (4) pmol/mg 

Testes: 2 ± 0 (2) pmol/mg 

CO concentration relative to 100% blood: 

Heart: 16% 

Spleen: 13% 

Lung: 9% 

Liver: 9% 

Kidney: 8% 

Muscle: 8% 

Intestine: 3% 

Brain: 2% 

Testes: 2% 

Weaver et al.(2007) Human Acute CO poisoning Mean COHb in humans with acute CO poisoning was 35%. 

Hyperbaric O2 reduces cognitive sequelae in a randomized 

clinical trial of CO-poisoned patients. Risk factors for cognitive 

sequelae without hyperbaric O2 included older age and longer 

CO exposures. Patients with loss of consciousness or high 

initial COHb levels should also be treated with hyperbaric O2. 

Webber et al. (2003) Rat 

Strain not stated 

 PND8-PND22 12.5, 25, or 50 ppm Immunostating of c-Fos, a marker of neuronal activation in the 

nervous system was followed. C-Fos immunoreactivity in the 

central IC was significantly decreased in the CO-exposed 

animals at both PND27 and PND75-PND77 over all dose 

groups of CO; immnunostaining of other subregions of the IC 

were not affected by CO. These studies show exposure to CO 

during development can lead to permanent changes in the 

auditory system of rats that persist into adulthood. 

Webber et al. (2005) Rat 

Strain not stated 

PND9-PND24 25 or 100 ppm Neurofilament loss from the spiral gangilian neurons and somas 

after ARCO treatment was rescued (no detectable neurofilament 

loss) with low iron+CO (ARIDCO); ARID (Iow iron) treatment 

induced no change in neurofilaments. CuZn superoxide 

dismutase (SOD1) was significantly increased with CO 

exposure (ARCO) and rescued in ARIDCO animals; SOD1 was 

unchanged in low iron only animals (ARID). Low iron treatment 

or CO exposure alone led to significant decreases in c-fos 

positive cell numbers of the central IC, but c-fos levels were unchanged after low iron diet concomitant with CO exposure 

(ARIDCO). 

March 2009 E-18 DRAFT—DO NOT QUOTE OR CITE 
Species / Model Exposure 

Duration Reference CO Concentration Findings 

Wellenius et al. (2004) Rat 

(Sprague Dawley) 

250 g 

Diazepam-sedated 

Model of acute MI 

induced by 

thermocoagulation 

1 h, 12-18 h after 

surgery 

35 ppm CO exposure decreased ventricular premature beat frequency 

by 60.4% during the exposure period compared to controls. 1-h 

exposure to CAPs (318 μg/m3) decreased ventricular premature 

beat frequency in specific subgroups. Neither CAPs nor CO had 

an effect on heart rate. There were no significant interactions 

between their effects when rats were exposed to both CO and 

CAPs. 

Rat 

(Sprague Dawley) 

250 g 

Diazepam sedated 

Model of acute MI 

induced by 

thermocoagulation 

1 h, 12-18 h after 

surgery 

35 ppm Exposure to CO failed to increase the probability of observing 

supraventricular ectopic beats (SVEB). Exposure to CAPs (646 

μg/m3) for 1 h decreased the frequency of SVEB. There were no 

significant effects observed when rats were exposed to both CO 

and CAPs. Among a subset of rats with one or more SVEB at 

baseline, a significant decrease in number of SVEB during the 

exposure period was observed with either CO or CAPs 

exposure compared with controls. 

Wellenius et al. 

(2006b) 

Yoshiki et al. (2001) Human HO localization in human endometrium and its changes in 

expression over the menstral cycle were explored in this study. 

HO-1 was constitutively expressed throughout the menstral 

cycle and HO-2 was greater in the secretory than the 

proliferative phase of the menstral cycle. HO-1 was localized to 

the epithelial cells and macrophages. HO-2 was found in 

endothelial cells and smooth muscle cells of endometrial blood 

vessels. 

Zamudio et al. (1995) Human Women living at high altitude had an increased risk of adverse 

pregnancy outcomes versus women living at lower altitudes. 

Zenclussen et al. 

(2006) 

Mouse 

(CBA/J x DBA/2J) 

To evaluate the role of HO-1 in spontaneous abortion, a mouse 

model that spontaneously undergoes abortion (CBA/J x DBA/2J 

mice) was used with and without HO adenovirus treatment to 

see if pregnancy outcome could be modulated by changing HO 

concentration. Pregnancy outcome was significantly better 

(abortion rate significantly decreased) in mice overexpressing 

HO due to adenovirus transfer. 

Zhang et al. (2005) Rat 

(pulmonary artery 

endothelial cells) 

8-28 h 15 ppm Exposure to 15 ppm CO during anoxia resulted in decreased 

phosphorylation of STAT1 and increased phosphorylation of 

STAT3 at 8-24 h. Similar responses were observed when 24 h 

anoxia was followed by a period of reoxygenation (0.5-4 h). 

DNA binding of STAT1 was decreased while that of STAT3 was 

enhanced by CO treatment during anoxia/reoxygenation. 

Exposure to 15 ppm during 8-24 h anoxia or 24 h anoxia 

followed by 0.5-4 h reoxygenation resulted in increased 

phosphorylation of Akt and p38 MAPK. Inhibitor studies 

demonstrated that activation of the PI3K pathway by CO was 

upstream of p38 MAPK activation during anoxia/reoxygenation. 

Similarly, the PI3K and p38 MAPK pathways were found to be 

upstream of STAT modulation. The anti-apoptotic effects of 

15 ppm CO during anoxia-reoxygenation involved decreased 

FAS expression and decreased caspase 3 acvitiry. These 

effects were dependent on activation of the PI3K, p38 MAPK 

and STAT3 pathways. 

The authors concluded that CO blocks anoxia-reoxygenation 

mediated apoptosis through modulation of PI3K/Akt/p38 MAPK 

and STAT1 and STAT3. 

Zhang et al. (2007) Mouse A single dose of LPS administered to pregnant mice induced upregulation of HO-1 but not HO-2 in the mouse placenta 12-48 h 

post-LPS treatment. Pre-treatment of mice with the spin trap 

agent PBN or the TNFα inhibitor pentoxifylline prevented the 

LPS-dependent HO-1 upregulation. Thus ROS may mediate the 

LPS-dependent upregulation of HO-1. 

Zhao et al. (2008) Mouse 

(FVB) 

With pregnancy, there was an increased blood volume without a 

concurrent increase in systemic BP; this was accomplished by a 

decrease in total vascular resistance, to which CO contributed 

as determined by using HO inhibitors. 

Zhuo et al. (1993) Rodent Hippocampal LTP of brain sections is significantly affected by 

CO exposure with ZnPP IX, a HO inhibitor, blocking 

hippocampal LTP. 

March 2009 E-19 DRAFT—DO NOT QUOTE OR CITE 
Species / Model Exposure 

Duration Reference CO Concentration Findings 

Zuckerbraun et al. 

(2007) 

Macrophages 

RAW 264.7 

THP-1 cells, wild-type 

and respirationdeficient 

10 min - 24 h 50 – 500 ppm Exposure of RAW macrophages to 250 ppm CO for 10-60 min 

increased ROS generation, measured as dichlorofluorescein 

(DCF) fluorescence. ROS generation at 1 h was dosedependent with significant effects observed at 50, 250 and 

500 ppm CO. This response was not blocked with a NOS 

inhibitor. A 1-h exposure to 250 ppm resulted in decreased 

intracellular glutathione levels. CO treatment was found to block 

TNFα production and to enhance p38 MAPK phosphorylation in 

LPS-stimulated cells. These effects were diminished by 

pretreatment with antioxidants. The source of CO-derived 

oxidants was determined to be mitochondrial since respirationdeficient THP-1 macrophages, unlike wild-type cells, failed to 

generate ROS in response to 250 ppm CO. Furthermore, 

treatment of RAW cells with the mitochondrial complex III 

inhibitor antimycin C, blocked ROS generation in response to 

250 ppm CO. Exposure of RAW cells to 250 ppm CO for 1 h 

inhibited cytochrome c oxidase activity by 50%. Exposure to 

250 ppm CO for 6 h had no effect on cellular ATP levels or 

mitochondrial membrane potential. Antimycin C treatment was 

found to reverse the effects of CO on LPS-mediated responses 

(TNFα and p38 MAPK), suggesting that mitochondrial-derived 

ROS mediated the effects of CO. The authors concluded that 

CO increased the generation of mitochondrial-derived ROS. 

March 2009 E-20 DRAFT—DO NOT QUOTE OR CITE 
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